Natural Resources Utilization in China: Evaluation, Coordination, and Effects (Contributions to Public Administration and Public Policy) 9819949807, 9789819949809

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Contributions to Public Administration and Public Policy

Malin Song Tao Ding Jiandong Chen

Natural Resources Utilization in China Evaluation, Coordination, and Effects

Contributions to Public Administration and Public Policy

The series Contributions to Public Administration and Public Policy contains publications in all areas of public administration and public policy, such as governance, public finance, public management, organization theory, institutional theory, administrative theory and practice, ethics and others. Publications are primarily monographs and multiple author works containing new research results, but conference and congress reports are also considered. The series covers both theoretical and empirical aspects and is addressed to researchers and policy makers.

Malin Song · Tao Ding · Jiandong Chen

Natural Resources Utilization in China Evaluation, Coordination, and Effects

Malin Song Collaborative Innovation Center for Ecological Economics and Management Anhui University of Finance and Economics Bengbu, Anhui, China

Tao Ding School of Economics Hefei University of Technology Hefei, China

Jiandong Chen School of Public Administration Southwestern University of Finance and Economics Chengdu, China

ISSN 2731-9385 ISSN 2731-9393 (electronic) Contributions to Public Administration and Public Policy ISBN 978-981-99-4980-9 ISBN 978-981-99-4981-6 (eBook) https://doi.org/10.1007/978-981-99-4981-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Preface

Rapid economic development has presented new practical requirements regarding natural resource utilization in China, and the concept of natural resources efficiency utilization has gradually attracted the attention of the academic community. The process of economic development in China requires developing and utilizing all kinds of natural resources, which would inevitably cause serious damage to the ecological balance and hinder the sustainable development of China’s economy and society. With increasing attention being given to ecological protection, many defects have been noted in the current utilization of natural resources. The major problems to be solved in ensuring China’s economic and social development include how to reasonably develop and utilize natural resource assets and realize the optimal allocation and sustainable development of resources. This book focuses on the evaluation, coordination, and effects of China’s natural resources utilization. By adopting both quantitative and qualitative analyses, this study objectively evaluates the spatial distribution characteristics and coupling relationship of China’s natural resources utilization based on the status quo and prominent problems during resource utilization. Moreover, the environmental, economic, and price fluctuation effects of China’s natural resources utilization are discussed. Then, current policy systems for natural resources efficiency utilization in China and abroad are revealed, which suggest a way for China to achieve efficient utilization of natural resources through the appropriate policy mechanism. The book is divided into nine chapters as follows. Chapters 1 and 2 provide background information regarding natural resources and introduce the research methods. In Chaps. 3–8, empirical research is conducted on natural resources utilization in China from the aspects of evaluation, coordination, and effects. Then, Chap. 9 focuses on policy analysis, evaluation, and supervision mechanisms in relation to natural resources efficiency utilization based on the contents of the previous chapters. This book has been compiled by Prof. Malin Song of the Anhui University of Finance and Economics, Dr. Tao Ding of the Hefei University of Technology, and Prof. Jiandong Chen of Southwest University of Finance and Economics and his team. Prof. Malin Song and Dr. Tao Ding are the directors of the National Natural Science Foundation of China 71934001 and 72271080, respectively. Other v

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contributors to this book are Yixiang Zhang, Huali Zhou, Qiqi Zhang, Ximeng Liu, Kexin Wu, and Zhiyong Qian of the Anhui University of Finance and Economics; Dr. Chenyue Liu, Dr. Rui Ke, Jiangyuan Li, Lianxing Tang, Yun Zhang, and Bin Hao of the Hefei University of Technology; and Dr. Jie Qi of Southwest University of Finance and Economics. Specifically, Chap. 1 was compiled by Malin Song and Zhiyong Qian; Chap. 2 by Malin Song and Kexin Wu; Chap. 3 by Malin Song and Yixiang Zhang; Chap. 4 by Malin Song and Ximeng Liu; Chap. 5 by Tao Ding and Jiangyuan Li; Chap. 6 by Tao Ding and Huali Zhou; Chap. 7 by Chenyue Liu and Lianxing Tang; Chap. 8 by Rui Ke and Bin Hao; and Chap. 9 by Tao Ding, Yun Zhang, and Qiqi Zhang. The full-text typesetting, proofreading, logical sorting, literature enrichment, language polishing, and formatting were undertaken by Jiandong Chen and Jie Qi. Despite the authors’ best endeavors, there are, inevitably, limitations in the book owing to the short preparation time and limited writing level. All sincere criticism and suggestions from readers will be warmly appreciated by us. Bengbu, China Hefei, China Chengdu, China April 2023

Malin Song Tao Ding Jiandong Chen

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Status Quo of China’s Natural Resources . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Land Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Forest Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Grassland Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 Mineral Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.5 Energy Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.6 Freshwater Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.7 Ocean Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.8 Biological Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Prominent Problems with Natural Resources . . . . . . . . . . . . . . . . . . . 1.2.1 Problems with Land Resources . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Problems with Forest Resources . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Problems with Grassland Resources . . . . . . . . . . . . . . . . . . . . 1.2.4 Problems with Mineral Resources . . . . . . . . . . . . . . . . . . . . . . 1.2.5 Problems with Energy Resources . . . . . . . . . . . . . . . . . . . . . . . 1.2.6 Problems with Freshwater Resources . . . . . . . . . . . . . . . . . . . 1.2.7 Problems with Marine Resources . . . . . . . . . . . . . . . . . . . . . . . 1.2.8 Problems with Biological Resources . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Index System, Method, and Application of Natural Resources Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Construction Principles of Evaluation Index System . . . . . . . . . . . . . 2.1.1 Significance of Constructing an Evaluation Index System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Principles of Index Construction . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Underlying Idea Behind Index Construction . . . . . . . . . . . . . 2.1.4 Basis for Index Construction of Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 1 2 4 5 7 10 12 15 17 17 19 20 20 21 22 23 24 25 27 27 27 29 29 31

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2.2 Construction of Evaluation Index System for Efficient Utilization of Natural Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Forest Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Water Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Land Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Mineral Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Grassland Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.6 Marine Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.7 Wind Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Evaluation Method for Efficient Utilization of Natural Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Index Screening Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Main Index Evaluation Methods . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Application Scopes, Advantages, and Disadvantages of Index Evaluation Methods with Different Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Application of Evaluation Index System for Natural Resources Efficiency Utilization—Taking China’s Marine Resources as an Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Selection and Description of China’s Marine Resources Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Efficiency Evaluation of Energy and Resource Utilization at the Regional Level in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Current Situation of Energy and Resource Consumption in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Research Progress on Energy Resource Utilization . . . . . . . . . . . . . . 3.3 Evaluation Data and Model of Energy and Resource Utilization Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Data Description and Source . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Model Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional Level in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Economic Benefit of Energy Resources . . . . . . . . . . . . . . . . . 3.4.2 Carbon Emission Efficiency Analysis . . . . . . . . . . . . . . . . . . . 3.4.3 Analysis of Comprehensive Utilization Efficiency of Energy Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . . . 3.5.1 Main Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Spatial Differences in Water–Energy System Coupling Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Domestic and Foreign Research on Water–Energy Correlation . . . . 4.1.1 One-Way Water–Energy Relationship Research . . . . . . . . . . 4.1.2 Water–Energy Synergy Research . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Research on Water–Energy Resource Contradictions . . . . . . 4.1.4 Measurement Methods of Water–Energy Resources . . . . . . . 4.2 Water–Energy Coupling Coordination Evaluation . . . . . . . . . . . . . . . 4.2.1 Variable Selection and Index System Construction . . . . . . . . 4.2.2 Model Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Measurement and Analysis of Efficiencies of Water and Energy Resources Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Measurement Results of Energy Resource Utilization Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Measurement Results of Water Resources Utilization Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Analysis on Coupling Relationship of Water–Energy Correlation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Analysis on Coupling Degree of Water–Energy Correlation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Analysis on the Coupling Coordination Degree of Water–Energy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . . . 4.5.1 Main Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Study on the Economic Effects of Efficient Utilization of Natural Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Research Status of the Relationship Between Natural Resources Efficiency Utilization and Economic Growth . . . . . . . . . . 5.2 Calculation of Comprehensive Index of Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Introduction of Entropy Method . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Construction of Comprehensive Index System of Natural Resources Efficiency Utilization and Data Source Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Calculation Steps and Results of Comprehensive Index of Natural Resources Efficiency Utilization . . . . . . . . . 5.3 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Empirical Analysis of Natural Resources Efficiency Utilization and Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Benchmark Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5.4.3 Heterogeneity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Influence Mechanism Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . . . 5.5.1 Main Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Research on Environmental Effects of Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Analysis of Current Situation of Natural Resources Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Calculation of Comprehensive Index of Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . 6.2 Environmental Effect Evaluation of Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Measurement of Environmental Efficiency in China . . . . . . . 6.2.2 Evaluation of the Effect of Natural Resources Efficiency Utilization on China’s Environment Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Impacts of Natural Resources Efficiency Utilization on the Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Literature Review and Index Construction . . . . . . . . . . . . . . . 6.3.2 Benchmark Regression Results . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Heterogeneity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . . . 6.4.1 Main Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Analysis of Temporal and Spatial Evolution of Natural Resources Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Research Progress on Natural Resources Utilization Efficiency . . . . 7.2 Research Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Entropy Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Coupling Coordination Model . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Current Situation of Natural Resources Utilization Efficiency . . . . . 7.3.1 Analysis of the Current Situation of China’s Economic Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Current Status of Environment in China . . . . . . . . . . . . . . . . . 7.3.3 Analysis of the Current Situation of Chinese Society . . . . . . 7.3.4 Resource Utilization Efficiency Analysis . . . . . . . . . . . . . . . . 7.4 Coupling Analysis of Natural Resources Utilization Efficiency and Economic Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Analysis of the Relationship Between Natural Resources Utilization Efficiency and Economic Utility . . . .

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7.4.2 Economic Utility Evaluation Index System . . . . . . . . . . . . . . 7.4.3 Coupling Degree of Natural Resources Utilization Efficiency and Economic Utility . . . . . . . . . . . . . . . . . . . . . . . . 7.4.4 Analysis of Coupling Coordination Degree of Natural Resources Utilization Efficiency and Economic Utility . . . . 7.5 Coupling Analysis of Natural Resources Utilization Efficiency and Environmental Utility . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Analysis of the Relationship Between Natural Resources Utilization Efficiency and Environmental Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Environmental Utility Evaluation Index System . . . . . . . . . . 7.5.3 Analysis of Coupling Degree Between Natural Resources Utilization Efficiency and Environmental Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.4 Analysis of Coupling Coordination Degree Between Natural Resources Utilization Efficiency and Environmental Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Coupling Analysis of Natural Resources Utilization Efficiency and Social Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 Analysis of the Relationship Between Natural Resources Utilization Efficiency and Social Utility . . . . . . . . 7.6.2 Social Utility Evaluation Index System . . . . . . . . . . . . . . . . . . 7.6.3 Analysis of Coupling Degree Between Natural Resources Utilization Efficiency and Social Utility . . . . . . . . 7.6.4 Analysis of the Coupling Coordination Degree of Natural Resources Utilization Efficiency and Social Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . . . 7.7.1 Main Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.2 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Price Fluctuation of Natural Resources and Its Impacts on Economic Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Progress of Research on Price Fluctuation of Natural Resources and Its Impacts on Economic Development . . . . . . . . . . . 8.1.1 Resource Curse Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Study on Price Fluctuation of Natural Resources . . . . . . . . . . 8.1.3 Research on Volatility Modeling . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 Discussions on the Impacts of Natural Resources Price Fluctuation on Economic Development . . . . . . . . . . . . . 8.1.5 Research Content and Innovation . . . . . . . . . . . . . . . . . . . . . . . 8.2 Study on Natural Resources Price Fluctuation in China . . . . . . . . . . 8.2.1 Model Selection and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Data Selection and Description . . . . . . . . . . . . . . . . . . . . . . . . .

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8.2.3 Model Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Analysis of Price Fluctuation of Natural Resources in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Impact of Natural Resources Price Fluctuation on Economic Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Data Selection and Processing . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Granger Causality Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.3 Model Design and Regression . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.4 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Conclusion and Research Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Main Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Research Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

264 267 270 270 271 273 280 280 280 281 282

9 Research on Policy Support System and Supervision Mechanism of Natural Resources Efficiency Utilization . . . . . . . . . . . . 9.1 Research on Policy, Evaluation, and Supervision System of Natural Resources Efficiency Utilization Abroad . . . . . . . . . . . . . . 9.1.1 Policy Framework of Foreign Natural Resources Laws and Regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.2 The Roles of Subjects Abroad in Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.3 Natural Resources Efficiency Utilization Abroad . . . . . . . . . 9.2 Research on Policy System of Domestic Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Macro-analysis of Domestic Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Roles of Domestic Subjects in Natural Resources Efficiency Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 The Efficient Utilization of Various Natural Resources in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Problems and Suggestions Regarding the Policy Mechanism of Natural Resources Efficiency Utilization in China . . . . . . . . . . . . . 9.3.1 Problems and Suggested Countermeasures of the Policy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Analysis of Current Situation of Evaluation and Supervision Mechanism and Improvement Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

310 315

Correction to: Efficiency Evaluation of Energy and Resource Utilization at the Regional Level in China . . . . . . . . . . . . . . . . . . . . . . . . . . . .

C1

285 285 285 288 295 298 298 301 303 307 307

Chapter 1

Introduction

China has a vast territory and is rich in natural resources. However, feeding its domestic population, which constitutes 22% of the global population, can be a challenge, as the country has only 9% of the world’s arable land, 4% of forests, 6% of water resources, 1.8% of oil, 0.7% of natural gas, 5% of copper, and less than 2% of bauxite. China’s per capita share of most mineral resources is about half the world average, specifically, 55%, 11%, and 4% for coal, oil, and gas, respectively [1]. A large population is a double-edged sword, with a major disadvantage being relatively low levels of per capita resources. China’s natural resources are characterized by the existence of abundant total resources with low per capita resource levels and low utilization efficiency. Therefore, a question worth exploring is how to utilize China’s natural resources efficiently.

1.1 Status Quo of China’s Natural Resources This section examines the status quo of various natural resources in China, discusses the exploitation of natural resources, and compares the increasing demand for natural resources owing to economic development with the existing stock of natural resources.

1.1.1 Land Resources China’s total land area ranks third in the world, while its per capita land resource accounts for only one-third of the world average. Hence, land resource development and utilization efficiency are crucial issues for China. There are many ways to classify land resources, with the most popular methods in China being terrain classification and land-use classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_1

1

2

1 Introduction

(1) According to terrain, land resource can be divided into plateaus, mountains, hills, plains, and basins, demonstrating the natural basis of land use. Generally, forestry and animal husbandry should be developed in mountainous areas, while agriculture should be developed in plains and basins. (2) According to the type of land use, land resource includes common land, cultivated land, forest land, grassland, industrial/mining transportation land, residential land, land suitable for development/use, wasteland, and forest suitable for reclamation. Such classification focuses on land development and utilization from the perspective of society, the economy, and ecology. According to both classification systems, there are great differences between the southeast and northwest regions of China. There is a boundary starting from the Great Khingan Range in the north, passing through the Hetao Plain in the middle of the Ordos Plateau and the Yanchi concentric zone in Ningxia Autonomous Region, and then extending to Jingtai, Yongdeng, and Huangshui river valleys, before turning to the southeast edge of the Qinghai Tibet Plateau. Regions southeast to the boundary are characterized by cultivated land, forest land, freshwater lakes, and runoff systems, among which the proportion of cultivated land accounts for about 90% of all cultivated land in China; regions on the northwest side of the boundary practice animal husbandry, and about 80% of the area consists of grassland with a low reclamation index. The status quo of land resource utilization in China according to land-use type is shown in Table 1.1.

1.1.2 Forest Resource The 9th China Forest Resource Inventory from 2014 to 2018 investigated 415,000 fixed sampling points with an inventory area of 9,756,700 (km2 ). According to the latest survey results, China’s forest resource shows three main development trends: sustainable increase of available forest resource, steady quality improvement of available forest resource, and further growth of ecological functions. This clearly shows that China’s forest resource is improving. According to the survey, China’s forest coverage rate is 22.96%, and the forest area is 220 million hectares (ha), including 79.54 million ha of artificial afforestation; the forest reserve is 17.56 billion cubic meters (m3 ); total biomass of forest vegetation is 18.802 billion tons (t); carbon reserve is 9.186 billion t; annual water saving is 628.95 billion m3 ; annual soil consolidation force is 8.748 billion t; annual dust accumulation is 6.158 billion t; annual air pollutant absorption is 40 million t; annual carbon storage is 433 million t; and annual oxygen release is 1.029 billion t [2]. China’s forest land is mainly divided into the southeast forest area, southwest forest area, south forest area, and north forest area, as shown in Fig. 1.1. Although the implementation of China’s sustainable development strategy has improved in recent years and the forest resource situation has greatly improved,

1.1 Status Quo of China’s Natural Resources

3

Table 1.1 Status quo of China’s land resource utilization Land-use type

Area/108 hm2

Cultivated land

1.33

13.9

Pasture

2.86

29.8

Forest land

1.22

12.7

Land for tea, fruits, and tropical crops

0.02

0.2

Open land, shrubland

0.44

4.6

Inland waters, such as rivers and reservoirs

0.27

2.8

Arable land suitable for farmland

0.35

3.7

Urban roads, industrial and mining land

0.67

6.9

Desert

0.60

6.3

Gobi

0.56

5.8

Desertified land

0.17

1.7

Permanent snow cover and glaciers 0.05

0.6

Bare rock

0.46

4.8

Others

0.59

6.2

Total land of China

9.59

100.0

Proportion of China’s land area (%)

Data source China Land Statistical Yearbook 2020

Fig. 1.1 Overview of China’s forest resources. Data source China Land Statistical Yearbook 2020

4

1 Introduction

there are still many problems pertaining to China’s ownership and utilization of forest resources. China lacks forest and green resources, compared to other countries. At present, although China’s forest coverage rate has increased to 22.96%, it is still lower than the global average of 30.7%. Furthermore, China’s per capita forest area is less than one-third of the world’s, and its per capita forest stock is only one-sixth of the world’s. The ongoing problems of forest resource—that is, low quantity and quality—will continue for a long time in China. To tackle these problems, China must do a good job in ecological construction, and adhere to the strategy of developing China into a “green country.”

1.1.3 Grassland Resource China is rich in grassland resource, with 392.83 million ha of natural grassland, accounting for about 41% of China’s land area and second only to Australia globally. Grasslands are mainly distributed in arid and semi-arid areas with annual precipitation greater than or equal to 400 mm (mm). The grassland area in these regions is larger compared to that in humid, semi-humid, and coastal southern and eastern areas [3]. The natural grassland resource in China is mainly distributed as follows: temperate grassland area in north China, alpine grassland area in the Qinghai Tibet Plateau, and secondary grassland in south and east China. The temperate grassland area in north China is located to the northwest of the 400 mm isohyet line, stretching to the west and southwest along the Great and Lesser Khingan Mountains until the western national border in Xinjiang. Its area accounts for about 41% of the total grassland area in China. It is also the most important grassland for animal husbandry in China. The zonal grasslands are, successively, meadow grassland, typical grassland, desert grassland, grassland desert, and temperate desert. The eastern part of the temperate grassland area comprises the Hulunbuir and Songnen grasslands, mainly composed of Leymus chinensis and Stipa Baikal meadow grasslands; the middle-eastern part is the Xilingol and Horqin grasslands, dominated by Stipa grandis and Stipa Kirschner grasslands; the middle part is the Ulanqab grassland, dominated by Stipa klemenz and Stipa breviflora desert grasslands; and the west part is mainly occupied by mountain grassland, such as the upland meadows in the Qilian and Tianshan mountains, and two pasturing grasslands in Ili and Altai in Northern Xinjiang. The area of the alpine grassland in the Qinghai Tibet Plateau is about 38% of China’s total grassland area. From its southeast to northwest, the grassland successively features alpine meadow, alpine meadow grassland, alpine desert grassland, and alpine desert. In the east and southeast of the region, there are four pasturing areas: Aba grassland, Ganzi grassland, Gannan grassland, and the grassland around Qinghai Lake, which are mainly composed of several small Kobresia, Festuca rubra L., and K. littleidalei C. B. Clarke alpine meadows; the middle part of this region is the alpine grassland, dominated by Stipa purpurea; and the northwest part gradually

1.1 Status Quo of China’s Natural Resources

5

transitions into an alpine desert. The hydrothermal conditions and productivity in the alpine grassland in the Qinghai Tibet Plateau are poor, with 12% of the grassland presenting challenges in utilization. The area of the secondary grassland in south and east China is 12% of China’s total grassland area, mainly comprising the secondary grassland that formed after repeated destruction of forest vegetation. This region is separated by the Qinhuai River line, and its southern part is surrounded by hot grassland. Moreover, some hidden lowland meadows and grasses are distributed around lake banks, rivers, and coastal areas. The current utilization rate of the grassland in this region is still low owing to the scattered grassland distribution and poor grass quality.

1.1.4 Mineral Resource By the end of 2019, reserves of 162 minerals had been discovered in China. Compared with 2018, there were 215 accessory minerals; of these, the reserves in 2019 increased for 106 accessory minerals, decreased for 47, and remained unchanged for 62, accounting for 49%, 22%, and 29%, respectively, of the total reserve of accessory minerals. Thirty-four kinds of mineral resources were added to the main mineral resource availability, while 13 kinds were eliminated, among which the reserve of coal increased by 0.6%, that of technically recoverable oil decreased by 0.5%, that of natural gas increased by 3.0%, and the reserve of shale gas increased by 77.8%. The reserves of non-oil and natural gas mineral resources also increased: Manganese ore increased by 5.6%, lead ore by 6.7%, zinc ore by 6.8%, bauxite by 5.7%, tungsten ore by 4.6%, molybdenum ore by 5.4%, antimony ore by 4.8%, gold ore by 3.6%, magnesite by 12.9%, and graphite by 21.4%. The minerals with significantly reduced reserves were nickel (− 9.4%), fluorite (− 6.3%), and boron (− 4.3%). By the end of 2019, 173 kinds of energy minerals, 59 kinds of metal minerals, 95 kinds of non-metallic minerals, and 6 kinds of water and gas minerals had been found in China. In 2019, China’s reserves of natural gas, shale gas, lead, zinc, bauxite, molybdenum, silver, magnesite, graphite, and other mineral resources increased significantly. Indeed, as one of the major mineral resource countries, China has both a large quantity and wide variety of mineral resources—the confirmed total mineral resource reserves account for 12% of the world’s total. Although China is the third largest country in terms of mineral resources, due to its national conditions, its per capita mineral resources account for only 58% of the world’s per capita mineral resources, ranking 53rd globally [4]. According to the latest mineral resource data, China owns more quantities, compared to other countries, of a wide variety of minerals, including rare Earth, titanium, tungsten, gypsum, tin, bentonite, vanadium, barite, antimony, lithium, graphite, coal, and sulfur, and the minerals with high demand in the global market include pearlite, zinc, kaolin, and barite. Minerals with relatively low reserves in China include iron, copper, gold, uranium, oil, silver, and manganese, while those with insufficient reserves include natural gas, potassium salt, chromium, and diamonds.

6

1 Introduction

In 2019, the proven geological reserves of newly added oil equaled 1.12 billion t, of which 160 million t comprised newly added technically recoverable reserves. Among the main metal ores, the outputs of manganese ore, copper ore, lead ore, zinc ore, tungsten ore, molybdenum ore, and silver ore increased significantly, while those of iron ore, nickel ore, tin ore, and gold ore decreased significantly; among non-metallic minerals, outputs of phosphate ore and graphite witnessed a notable increase (Table 1.2). Generally, China’s minerals are widely distributed and relatively concentrated. Judging from the overall framework, energy minerals, represented by coal, oil, and natural gas, are mainly distributed in northwest, north, and northeast China. The exploitation of energy minerals mainly occurs in these regions, which also determines the utilization of energy resources. Moreover, there are reserves of rare Earth, Table 1.2 Newly added mineral resources by major mineral explorations S. No.

Mineral type

Unit

1

Coal

2

Petroleum

Year 2018

Year 2019

100 million t

556.1

300.1

100 million t

9.6

11.2

m3

Changes in 2019 compared to 2018, % − 46.0 16.7 − 2.7

3

Natural gas

100 million

8311.6

8090.9

4

Coalbed methane

100 million m3

147.1

64.1

5

Shale gas

100 million m3

1246.8

7644.2

6

Iron ore

100 million t of ore

9.9

5.3

− 46.5

7

Manganese ore

100 million t of ore

0.7

1.0

42.9

8

Copper ore

10,000 t of metal

225.1

363.8

61.6

9

Lead ore

1,000 t of metal

371.6

605.2

62.9

10

Zinc ore

10,000 t of metal

575.9

1479.5

156.9

11

Bauxite ore

100 million t of ore

1.2

2.8

133.3

12

Nickel ore

10,000 t of metal

47.2

6.5

− 86.2

13

Tungsten ore

10,000 t of WO2

27.8

48.1

14

Tin ore

10,000 t of metal

16.7

3.9

15

Molybdenum ore

10,000 t of metal

28.2

156.4

16

Antimony ore

10,000 t of metal

18.7

19.3

17

Gold ore

T of metal

719.8

487.7

18

Silver ore

10,000 t of metal

1.2

2.2

19

Pyrite

10,000 t of mineral

14450.2

6862.1

20

Phosphate ore

100 million t of ore

21

Sylvite

10,000 t of KCl

22

Fluorite

23

Graphite

− 56.4 513.1

73.0 − 76.6 454.6 3.2 − 32.2 83.3 − 52.5

2.3

8.8

− 186.7

1452.2

–

10,000 t of mineral

1158.3

1463.2

26.3

10,000 t of mineral

5,497.3

9,216.5

67.7

Data source China Mineral Resources 2019

282.6

1.1 Status Quo of China’s Natural Resources

7

nickel, and other non-metallic minerals in north China. Non-ferrous metal reserves are mainly distributed in the southern part of China, such as the regions to the south of the Yangtze River and southwest China, where copper, tungsten, zinc, titanium, bauxite, and tin are abundant. The distribution of China’s mineral resources has four characteristics. First, the total amount of mineral resources is large while the per capita amount is small, and there are obvious regional differences in the distribution of important mineral resources. Second, the number of super-large deposits is small while that of small and medium-sized deposits is large. Third, most mineral sources in China consist of complex ores that contain symbiotic minerals alongside the main mineral, and the values of these symbiotic minerals are also great. Finally, China’s mineral quality is uneven, and there are few ore sources that can be used for direct smelting.

1.1.5 Energy Resource In China, energy resource is mainly divided into two categories: renewable and non-renewable. The latter mainly includes coal, oil, and natural gas. Among China’s proven energy reserves, coal accounts for 94%, oil for 5.4%, and natural gas for 0.6%. Thus, coal occupies the predominant share in China’s non-renewable energy, and the prevailing situation—in which coal is the primary fuel used for energy production in China—is expected to remain unchanged for the foreseeable future. China is rich in non-renewable energy resources as well but with a low per capita share. Due to increasing energy demand for production and economic development, there is a tense relationship between the supply and demand of energy resources. In China’s current energy mix, the proportion of disposable energy is huge, with few alternative energy sources, with coal providing 75% of China’s industrial fuel and power and 85% of urban civil fuel. China’s proven coal reserves are 900 billion t, accounting for about one-sixth of the proven coal reserves globally; however, the actual coal reserves in China are thought to be far greater. It is believed that there are more than 2 trillion t of coal resources buried within 1000 m (m) under the Earth’s surface, while the reserves within 2000 m are even greater; that is, the coal resource reserves are sufficient in China. However, due to the heavy pollution caused by coal, there is growing emphasis on utilizing more environment-friendly renewable energy resources. The regional distribution of coal resources is very uneven, with nearly 90% of the resources concentrated in north China. Moreover, there are still a lot of coal resources to be mined, and the exploration degree is not high. China’s total proven oil reserves are about 80 billion t. Based on the assumption of an annual output of 100 million t, the recovery period for current oil resource reserves is 30 years. The biggest problem with oil resources is that as a one-time-use energy commodity, any consumption equals reduction in the total reserves by the same amount. Moreover, China’s consumption of oil resources is considerable and depends on imports.

8

1 Introduction

According to the data of the Ministry of Land and Resources, newly proven natural gas reserves in China in 2016 equaled 726.56 billion m3 , exhibiting a growing trend. China’s newly proven geological reserves of natural gas have exceeded 500 billion m3 for 14 consecutive years. Particularly, in 2014, China’s newly proven geological reserves of natural gas, shale gas, and coalbed methane reached a record high of 1.11 trillion m3 for the first time, and the newly proven recoverable reserves were 532.175 billion m3 , an increase of 73.6% compared to the previous year [5] (Fig. 1.2). China’s gas fields are mainly small and medium-sized, and most of them feature complex geological structures, making exploration difficult. At present, the proven natural gas reserves are mainly concentrated in ten basins, followed by Bohai Bay, Sichuan, and Songliao basins (Table 1.3). Reserves (100 million m3) 12000

11107.15 9612.2

10000 8000

7659.54

6772.2

6164.33

6000

8000

7265.6

4000 2000 0

2011

2012

2013

2014

2015

2016

2017

Fig. 1.2 Newly proven geological reserves of natural gas in China from 2011 to 2017. Data source China Mineral Resources 2019

Table 1.3 Regional distribution of natural gas resources in China Amount of natural gas resource (1 trillion m3 )

Region Land

Sea area Overall

Prospective

Geological

Recoverable

East

4.64

2.77

1.47

Central

18.04

10.11

6.37

West

15.85

11.6

7.46

South

1.77

0.76

44

Qinghai and Tibet

2.86

1.69

1.03

Subtotal

43.17

26.93

16.78

Offshore

12.72

8.1

5.25

55.89

35.03

22.03

Data source China Mineral Resources 2019

1.1 Status Quo of China’s Natural Resources

9

Fig. 1.3 Distribution of natural gas geological resources in China. Data source China Mineral Resources 2019

China’s onshore natural gas geological resources equal 69.4 trillion m3 , accounting for 76.85% of the total geological resources; of these, the recoverable resources are 37.9 trillion m3 , accounting for 75.65% of the total recoverable resources. The offshore geological resources are 20.9 trillion m3 , accounting for 23.15% of the total geological resources; of these, the recoverable resources are 12.2 trillion m3 , accounting for 24.35% of the total recoverable resources (Fig. 1.3). Additionally, there are considerable renewable energy resources in China, including hydropower, wind power, solar energy, and biomass energy. Hydropower is the largest renewable energy resource in China. By the end of 2015, China’s total hydropower capacity had reached 319.45 million kilowatts (kW), including 75 million kW of small size of hydropower, 221.51 million kW of large and medium-sized hydropower, and 23.03 million kW of pumped storage, the last of which accounts for 20.9% of China’s total hydropower capacity and ranks first in the world. Regionally, the hydropower capacity in the eastern, central, and western regions was 45.192 million kW, 70.888 million kW, and 203.292 million kW, respectively. Wind energy is the third largest power source in China after coal and hydropower. By the end of 2015, the cumulative installed capacity of the wind power grid in China had reached 129.34 million kW, accounting for 8.6% of China’s total installed capacity and ranking first in the world. In 2015, China’s wind power generation was 186.3 billion kilowatt hours (kWh), accounting for 3.3% of China’s total power generation. Regionally, the combined power generation capacity of northeast, north, and northwest China was 104.48 million kW, that of central China was 14.3 million kW, and that of southern China was 10.55 million kW, accounting for 80.8%, 11.1%, and 8.2% of the total, respectively [6]. By the end of 2015, China’s total installed capacity of photovoltaic power generation had reached 43.18 million kW, accounting for 3% of China’s total installed capacity and ranking first in the world. Centralized solar power plants remained

10

1 Introduction

dominant. In 2015, the newly installed capacity was 13.74 million kW, the cumulative installed capacity was 37.12 million kW, and the solar power generation was 39.2 billion kW, accounting for 0.7% of China’s total power generation. Regionally, the total installed capacity in the eastern, central, and western regions was 11.26 million kW, 4.05 million kW, and 27.86 million kW, respectively accounting for 26.1%, 9.4%, and 64.5% of China’s total installed capacity for solar power generation. China is rich in biomass energy resources, which have significant energy utilization potential. The total annual biomass resources are about 460 million t of standard coal. By 2015, the use of biomass energy was about 35 million t of standard coal, of which commercial biomass energy accounted for 18 million. China’s biomass power generation has reached a certain scale and shows significant momentum. By 2015, China’s total installed capacity of biomass power generation was about 10.3 million kW, accounting for 0.9% of China’s total installed capacity. Of this, power generation from the direct combustion of agricultural and forestry biomass was about 5.3 million kW, that of waste incineration was about 4.7 million kW, and that of biogas was about 300,000 kW, while the all annual power generation was about 52 billion kW.

1.1.6 Freshwater Resource China faces serious drought and water shortage issues. China’s total freshwater resources are 2.8 trillion m3 , accounting for 6% of the global water resources, but lagging behind Brazil, Russia, and Canada. Due to China’s large population, China’s per capita freshwater resources are only 2300 m3 , accounting for only a quarter of that of the world. As one of the countries with the lowest per capita freshwater resources and highest water consumption, China faces a serious imbalance in the supply of and demand for fresh water. China has an average annual rainfall of about 60,000 m3 , equivalent to annual rainfall of 628 mm, that is, 114 mm less than the average annual rainfall in Asia. China’s average annual freshwater resources are about 2.8041 trillion m3 . Refer to Table 1.4 for the specific situation of China’s water resources. The total river runoff in China ranks sixth in the world, after Brazil, the Russia specifically, Indonesia, Canada, and the United States. China’s per capita annual runoff is 2670 m3 , a quarter of the world average. China is located on the west coast of the Pacific Ocean. Due to its vast area, complex terrain, and continental monsoon climate, the distribution of water resources in this region is uneven and changes over time. The precipitation decreases from the southeast coastal area to the northwest inland area, and the region can be divided into five areas based on the annual precipitation amount: rainy, wet, semi-wet, semi-arid, and arid. The uneven distribution of precipitation leads to an imbalance of water and soil resources in China. The cultivated land in the Yangtze River Basin and to the south of the Yangtze River accounts for only 36% of China’s total while the water

1.1 Status Quo of China’s Natural Resources

11

Table 1.4 Regional water resource amounts in China Region

Area Total (10,000 precipitation km2 ) (100 million m3 )

River runoff (100 million m3 )

Groundwater recharge (100 million m3 )

Total Water water production resources coefficient (100 million m3 )

Water production modulus (10,000 m3 )

Heilongjiang River Basin

90.34

4358

1166

431

1334

0.306

14.77

Liaohe River Basin

34.50

1915

487

194

575

0.300

16.67

Hailuanhe River Basin

31.82

1775

288

265

428

0.241

13.45

Yellow River Basin

79.47

3719

662

406

740

0.199

9.32

Huaihe River Basin

32.92

2839

741

393

964

0.340

29.28

Yangtze River Basin

180.85

19,162

9512

2462

9587

0.500

53.01

Pearl River Basin

58.06

8945

4685

1115

4706

0.526

81.05

River basins in Zhejiang, Fujian, and Taiwan

23.98

4342

2,557

613

2591

0.579

108.04

River basins in southwest China

85.14

7846

5853

1540

5853

0.746

68.75

Inland rivers 332.17

4989

1064

786

1161

0.233

3.49

186

100

44

102

0.548

19.25

Six basins in 606.49 north China

19,781

4508

2519

5304

0.268

8.75

Four basins in west

348.04

40,295

22,607 5730

22,737

0.564

65.33

China

954.53

60,076

27,115 8246

28,041

0.467

29.38

Ertis River

5.27

Data source China Water Statistical Yearbook 2019

resources there account for 80% of China’s total; by contrast, the water resources in the Yellow River Basin, Huaihe River Basin, and Haihe River Basin account for only 8% of China’s total while the arable land there accounts for 40% of China’s total. Water resource is greatly different from land resource that precipitation and runoff change greatly over the years. In most parts of China, there is little rainfall in winter and spring, but much rainfall during summer and fall.

12

1 Introduction

1.1.7 Ocean Resource China’s coastline is more than 18,000 km (km) long, with about 3 million km2 of water under the country’s jurisdiction, that is, one-third of China’s land area. There are more than 5000 islands with an area of more than 500 (m2 ). China thus wields great maritime power. Certainly, China is also rich in marine resources, and thus, it is very important to make effective use of these resources and formulate an effective sustainable development strategy. For better understanding, a description of the marine resources has been provided below [7]. (1) Coastal beach and shallow water resource China’s coastal beaches and shallow water areas have a total area of 21,700 km2 with abundant and considerable resources. The annual amount of sediment from China’s oceans is 1.7–2.6 billion t, with an average of about 2 billion t. They accumulate along the coast and form tidal flats. The total area of tidal flats is about 267 million m2 every year, and the tidal flat resources are mainly distributed along the beach coast: 31.3% in the Bohai Sea, 26.8% in the Yellow Sea, 25.6% in the East China Sea, and 16.3% in the South China Sea. Water resources in the continental shelf and shallow areas are also abundant. The shallow sea with a depth of 0–15 m covers an area of 123,800 km2 , accounting for 2.6% of the total offshore area: 31,120 km2 in the Bohai Sea, 30,330 km2 in the Yellow Sea, 38,980 km2 in the East China Sea, and 23,330 km2 in the South China Sea. Moreover, tidal flats and shallow waters are an important foundation for Chinese aquaculture. (2) Port site resource China also has abundant port site resources, which are characterized by narrow beaches, steep slopes, and deep water. Many 5–10-m isobaths are close to the coast, where large and medium-sized ports can be located; the muddy coast is more than 4000 km long, including some protected deep-water sections. The estuary sections of major rivers have relatively stable deep-water channels, which are suitable for constructing large and medium-sized ports. Beaches are mainly composed of sand and grit. There are many types of accumulation landforms that are often accompanied by coastal sand bars, tidal channels, and warm lakes, where small and medium-sized ports can be built based on the water depth and protection conditions. There are more than 160 bays and more than ten large and medium estuaries in China’s coastal areas, with an area of 10 km2 and a total length of more than 400 km. Most areas are not frozen all year around, making them suitable for marine transportation in other seasons. Except for the estuaries adjacent to coasts, most coastal sections have favorable environmental conditions for ports, with little or no sediment deposition. At present, 164 ports have built berths of intermediate and above levels. (3) Island resource Islands represent intersections of land and sea and play an important role in the development of the marine and coastal economy. According to incomplete statistics

1.1 Status Quo of China’s Natural Resources

13

(data of Taiwan, Hong Kong, Macao, and other islands are not included), China has more than 5000 islands, with an area above 500 m2 each and a total area of 80,000 km2 , accounting for 0.8% of China’s total area. The distribution of China’s islands is very uneven. The East China Sea has the largest number of islands, accounting for 58% of the total; the South China Sea has about 28%; and the Yellow Sea and Bohai Sea together have about 14% of the total number of islands. China’s rich island resources are mainly reflected in the following aspects: China’s islands have more than 12.67 billion m2 of farmland and more than 37.33 billion m2 of forest cover, among which Shandong Province has the largest cultivated land area of about 6 billion m2 while Hainan Island has the largest forest area of about 3 billion m2 . China has more than 4.33 billion m2 of tidal flat resources, among which Shandong occupies the largest share of about 1 billion m2 . China’s islands have more than 12 million acres of aquaculture water, among which Fujian has the largest share of 6.2 million acres. China has more than 370 ports, 178 of which are in Zhejiang. There are nearly 300 tourist islands with a unique natural landscape, ecological environment, and cultural relics. Similarly, many islands in China also have rich mineral resources, especially Spratly Islands and its surrounding waters, where petroleum and natural gas are plentiful. (4) Biological resource There are 20,278 species of marine life in China’s coastal waters, mainly temperate species, followed by warm-water species and cold-water species. The high seas in the east and south of the Yellow Sea are semi-closed and surrounded by island chains. Therefore, the distribution of marine species is mostly semi-closed and regional, with most being native species. The main marine plants are algae and seed plants. There are many kinds of marine animals from every genus and species, from protozoa to advanced mammals. There are more than 1500 kinds of planktonic algae and 320 kinds of stemless algae in the seas of China, as well as more than 12,500 kinds of marine animals, including 9000 invertebrates and 3200 vertebrates. Among invertebrates, there are more than 1000 kinds of zooplankton, 2500 kinds of mollusks, and about 2900 kinds of crustaceans. Vertebrates are dominated by about 3000 species of fish, including more than 200 kinds of cartilaginous fish and 2700 kinds of bony fish. China’s coastal waters have an average annual biomass output of 3020 T/km2 . By contrast, that of the South Pacific is 182 T/km2 , China’s offshore 118 T/km2 , the North Sea 4.7 T/km2 , and the East China Sea 3.92 T/km2 [8]. China’s marine fishery resource is in the range of 2.8–3.29 million t, of which 760,000–890,000 t are pelagic fish, accounting for 27% of the total fishery resources; 1.06–1.25 million t are bottom fish and near-bottom fish, accounting for 38%; 110,000,130,000 t are Cephalopods, accounting for 4%; 390,000–460,000 t are shrimp and crab, accounting for 14%; and 480,000–560,000 t are others, accounting for 17%. The best resource catch in the Yellow Sea is 550,000–650,000 t; that in the East China Sea is 1.4–1.7 million t; and that in the South China Sea is 1–1.21 million t.

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(5) Oil and natural gas resource China’s offshore continental shelf has about 24 billion t of oil and 13 trillion m3 of natural gas. According to the preliminary reports of relevant departments, there are about 40 billion t of offshore oil resources and about 1 trillion m3 of natural gas in the Bohai Sea; about 50 billion t and 2 trillion m3 of oil and natural gas, respectively, in the East China Sea; and 5 billion t of oil and 60 billion m3 of natural gas in the South China Sea. China’s deep-sea oil and gas resources have not been fully investigated. By the end of 1990, 63 oil-bearing structures had been discovered and confirmed in China’s offshore regions. Calculating as per the reserves of 39 buildings, the offshore oil reserves equaled 700 million t; the proven reserves were 500 million t, of which the recoverable reserves were 100 million t; and the oil storage area was about 200 km2 . It can be concluded that China’s proven reserves of offshore natural gas exceed 100 billion m3 , the area of gas storage exceeds 80 km2 , and the recoverable reserves exceed 70 billion m3 . (6) Placer resource China is rich in coastal placer resources. There are more than 60 coastal placer deposits in China, mainly ilmenite, zircon, rutile, monazite, magnetite, cassiterite, chromite, niobium (tantalum) iron ore, anatase, quartz sand, and garnet. The proven coastal placer reserves are 1.525 billion t, including 250 million t of coastal metal ores and 1.5 billion t of non-metallic ores. The reserves of metal minerals include ilmenite, zircon, rutile, monazite, and phosphate rock, of which zircon and ilmenite account for more than 90% of the total coastal metal deposits. China’s coastal metal deposits are mainly distributed along its southern coastline. The reserves in Guangdong and Fujian account for more than 90% of China’s coastal metal and non-metallic mineral reserves. There are also zircon, ilmenite, and monazite deposits in Shandong and Liaoning. At present, China has more than 100 major mining areas, 208 deposits, and 106 mines [9]. (7) Chemical resource Seawater also has chemical resources. Globally, seawater contains 400 million t of sodium chloride, and many coastal areas of China have a large amount of salty seawater resources. The average water level of the Paracel Islands and Spratly Islands in the South China Sea is 33–34, the annual average salinity in the southern Bohai Strait is 31, and the annual average salinity in the coastal areas of Fujian and Zhejiang is 28–32. Seawater contains more than 80 elements and various dissolved minerals, which can be used to extract magnesium, potassium, and bromine. It also contains 2 million t of heavy water, which is the raw material for nuclear fusion. In addition, the coastal plains of Bohai Bay and Laizhou Bay have a large number of highly concentrated groundwater resources—about 1567 km2 in the Laizhou Bay, with a total salt water volume of 7.4 billion m3 and 646 million t of salt, including 15 million t of potassium chloride. In the Bohai Bay area, Tianjin alone has a sea water distribution of about 376 km2 , with a salt water reserve of 62.4 billion m3 and a salt content of 27 million t.

1.1 Status Quo of China’s Natural Resources

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These salt water resources are shallow and easy to exploit and are ideal raw materials for the salt and salt chemical industry. (8) Marine tourism resource In terms of marine tourism resources, China’s coastal areas span three major climatic zones: tropical, subtropical, and temperate. A preliminary investigation revealed that China has more than 1500 coastal scenic spots and more than 100 coastal beaches, among which the most important are 16 well-known historical and cultural cities, 25 key scenic spots, 130 key cultural relic protection units, and 5 coastal nature reserves. According to resource type, there are 45 coastal scenic spots, 15 island scenic spots, 19 ecological scenic spots, 5 underwater scenic spots, 62 well-known mountain scenic spots, and 273 cultural scenic spots.

1.1.8 Biological Resource China has rich biodiversity. There are more than 30,000 kinds of higher plants, 6347 vertebrates, and 599 terrestrial ecosystems. Therefore, China can make full use of its considerable biological resources to promote economic development. Rich biological resources are intangible assets with strategic value and provide China with a comparative advantage in terms of intellectual property competition. Their effective utilization will play an important role in China’s economic construction and scientific and technological development. Biological resources have important scientific research value as they provide samples and tools for innovation relating to medicine, agriculture, pharmacy, and other biotechnologies with industrial application. Biological genes can be used in genetic engineering, wild plant strains can be used in breeding, and wild animals and plants or their extracts can be used in biopharmaceuticals to yield significant economic benefits (Tables 1.5 and 1.6). There are distinct differences in the climatic and vegetation conditions in different regions of China, and there is great diversity in the living environment of animals. Therefore, China is rich in animal species, with many native species, accounting Table 1.5 Known species (or genera) and endemic species (or genera) of animals in China Category

Number of known species (or genera)

Number of endemic species (or genera)

Ratio of endemic species (or genera) %

Mammals

581 kinds

110 kinds

18.93

Birds

1244 kinds

98 kinds

7.88

Reptiles

376 kinds

25 kinds

6.65

Amphibians

284 kinds

30 kinds

10.56

Fishes

3862 kinds

404 kinds

10.46

Total

6374 kinds

667 kinds

10.5

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1 Introduction

Table 1.6 Known species (or genera) and endemic species (or genera) of main plants in China Category

Number of known species (or genera)

Number of endemic species (or genera)

Ratio of endemic species (or genera) %

Angiosperm

3123 genera

246 genera

7.5

Gymnosperm

34 genera

10 genera

29.4

Fern

224 genera

6 genera

2.3

Bryophyte

494 genera

13 genera

2.0

Total

3875 genera

275 genera

10.3

Data source Research Report on Biological Resources by Chinese Academy of Sciences 2020

for about 10.72% of the world, some of which are world-class rare animals. The following text focuses on the regional distribution of mammals. First, there are a large number of mammals in the temperate and cold-temperate zones in northeast China, including the reindeer, moose, wolverine, snow rabbit, red deer, weasel, mongoose, lynx, brown bear, wolf, fox, lemming, vole, flying rat, squirrel, roe deer, moose, badger, whisker bat, and big brown bat. Species such as the roe deer, plate antelope, chipmunk, brown rat, hamster, and long-tailed hamster are distributed in the warm temperate zone of north China. Due to its climate, high levels of human occupation, and other reasons, there are few animal species in the agricultural area of the Huang-Huai Plain. Pangolins, macaques, civets, crab-eating lemons, golden cats, yellow deer, hairy crested deer, water deer, hyenas, red-bellied squirrels, bamboo rats, red and white flying rats, and various domestic rats are widely distributed in the hilly plains in the middle and lower reaches of the Yangtze River, with endemic species like the black deer, roe deer, Baiji, and Chinese alligator. Regarding the grassland and desert areas in northwest China, there are mammals on both sides of Tianshan Mountain, such as yellow sheep, Daur pika, Daur yellow mouse, small hairy foot mouse, grassland zokor, Mongolian gerbil, grassland vole, grassland groundhog, red deer, and roe deer. Typical desert species, such as rodents, are mainly distributed in the sandy Gobi areas bordering Mongolia; predators include the tiger weasel, desert cat and sand fox. Wild Bactrian camels, wild donkeys, Mongolian gazelles, and antelopes are distributed in the western deserts. Turning to the southwest mountainous area, giant pandas and lesser pandas are the typical species in this region. Giant pandas are distributed in the middle and north of the Hengduan Mountains, extending eastward to the south slope of the Qinling Mountains, whereas lesser pandas are distributed across the Hengduan Mountains, extending westward to the Himalayas. Similar to the distribution range of the lesser panda, antelopes, musk deer, flying mice, and flying squirrels are mainly distributed along the south of the Himalayas. Thar sheep, Siberian squirrel, leaf monkey long tail, red gazelle, snow leopard, white-lipped deer, Tibetan fox, Tibetan antelope, rock sheep, argali, Himalayan marmot, and gerbil are also widely distributed in this area. The main mammal species in the Qinghai Tibet Plateau are wild yak, Tibetan antelope, and wild donkey. The common species in alpine coniferous forests, alpine

1.2 Prominent Problems with Natural Resources

17

shrubs, and alpine meadows are red deer and plateau vole, among which the narrow skull pika, Western Sichuan Pika, and Muli pika only appear in this area. Finally, the main representative mammals in the tropical and subtropical regions of south China include the gibbon, leaf monkey, bear monkey, Asian elephant, beaver, civet, weasel, finless porpoise, mouse deer, bison, blue-bellied squirrel, giant squirrel, pencil-tailed tree mouse, and long-tailed reptile.

1.2 Prominent Problems with Natural Resources China’s natural resources have recovered somewhat following the implementation of a sustainable development strategy in recent years, but there are still prominent problems, and the utilization efficiency of natural resources needs to be improved.

1.2.1 Problems with Land Resources Table 1.7 shows that the problems with China’s land resources mainly pertain to soil erosion, land desertification, and land salinization. First, soil erosion of cultivated land is very common and constitutes the chief ecological environment problem in China. Generally, there are two causes of soil Table 1.7 Prominent problems with land resources in China Soil erosion

Land desertification

Salinization

Reason

Terrain, soil, climate, vegetation and other natural factors, unreasonable reclamation, and other human factors

Vegetation damage caused by climate warming, drought, wind and sand erosion, indiscriminate reclamation and excavation, overgrazing, and deforestation

Flood irrigation in arid areas with rising groundwater level; groundwater is over-pumped in coastal areas and seawater is poured back

Hazard

Reduction of land productivity and agricultural production

Expansion of desert area, Farmland degradation reduction of cultivated land and agricultural area, wind and sand hazards, production reduction sandstorms, etc.

Countermeasure

Develop ecological agriculture according to local conditions

Plant trees and grasses, return farmland to forests, and practice animal husbandry

Improve the drainage and irrigation system and make use of supporting technologies such as water conservation and biology

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1 Introduction

erosion: One relates to human factors, including neglect of land protection, unreasonable exploitation of land resources, and timber cutting; the other is related to natural factors, such as landform, vegetation types, and vegetation coverage. Soil erosion is a huge problem in that it causes further deterioration of the ecosystem. Generally, soil erosion increases the frequency of drought and flood disasters. In arid areas, drought is aggravated owing to vegetation reduction as well as low rainfall and its uneven distribution, resulting in a reduced use value of land resources. Particularly, because of soil erosion, a large amount of sediment is deposited at the bottom of rivers, which raises the water level and leads to flood disasters. For example, in the Loess Plateau, due to the serious problem of soil erosion, a large amount of sediment is deposited in the downstream riverbed, forming a well-known suspended river on the ground. Moreover, soil erosion brings about natural disasters such as landslides and debris flow in the upper reaches of the Yangtze River in China, which would have a great negative impact on the expansion of surrounding cities and the personal safety of residents. Second, land desertification is also a very prominent problem. The quality of soil determines the survival status of vegetation, while the quality of vegetation determines the value of local land resources. However, natural factors such as climate warming, drought, and wind or sand erosion; the vegetation damage caused by human beings through indiscriminate reclamation and excavation; overgrazing; and deforestation all intensify land desertification. China’s land desertification area has reached 837,000 km2 , accounting for 8.7% of China’s total land area, with this proportion growing at a steady rate. Attention should also be paid to the “rocky desertification” caused by serious soil and water loss in limestone areas in southwest China, such as Guangxi and Guizhou. Owing to the destruction of grassland resources, much grassland is degraded, causing considerable land desertification. China’s land desertification is expressed differently in the north and south, including in terms of the causes and land changes. In northern China, Shaanxi has the greatest degree of land desertification. In southern China, there is serious land desertification in the middle and lower reaches of the Yangtze River due to floods. Finally, regarding the problem of land salinization, in arid areas, planting crops with flood irrigation will raise the groundwater level and affect the land quality. Excessive pumping of groundwater in coastal areas leads to seawater back-irrigation, which is also the main reason for land salinization. The land in various regions of China is suitable for planting different crops, but land salinization will seriously damage the land quality and affect the crop yield. Judging from the general trend in recent years, people have paid attention to water saving during irrigation, in which case the degree of land salinization will decline in step with the area of salinized land. However, the problem of land salinization has also worsened in some parts of China. Therefore, reasonable irrigation and scientific planting are necessary.

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19

1.2.2 Problems with Forest Resources China overall lacks adequate forests and vegetation. Although China has made some advances in returning farmland to forests in recent years, there are still such problems as relatively insufficient attempts at land conversion, uneven distribution, and imperfect forest quality. The main problems can be viewed from the following four aspects. The first problem is that the development of the forest cover has hit a bottleneck. Due to the limitations of terrain, the growth of the forest cover is particularly slow in many areas. Moreover, only 10% of the areas suitable for forest are of good quality, and about 54% are of very poor quality. At this stage, China’s afforestation is becoming increasingly difficult, and more efforts need to be made to consolidate forest resources in the future. The second problem is that there is great pressure to adhere to the forestry ecological red line. In recent years, the forest area occupied by various buildings in violation of laws and regulations has increased, with an average annual area of more than 1.33 billion m2 . In some specific areas, the problems of logging and landfill are still very prominent. Although law enforcement and supervision have been strengthened, local development motives force residents to violate legal provisions, and step over the ecological red line time and again. With the continuous development of China’s urbanization and industrialization, to meet their basic living needs, people will further compress the ecological construction space on the original basis, exerting increasing pressure on staying within the forestry ecological red line and maintaining China’s ecological security. The third problem is that China’s effective supply of forest timber still does not match the growing social demand. China’s dependence on foreign timber is close to 50%, that is, China cannot achieve self-sufficiency in timber supply, which is an alarming situation. Moreover, China’s fine wood and precious wood resources are insufficient, and there are great problems in the supply structure of wood, which also reveals the fragility and incompleteness of China’s forest ecology. The shortage of ecological products is also a great challenge. The fourth problem is the urgent need to strengthen forest management. China’s forest productivity is extremely low, with only 52.76 m3 of plantation per ha, or 69% of the world average. The average diameter of trees at the breast height is only 13.6 cm. The distribution of trees by age is still unreasonable, and the proportion of young and middle-aged forest areas is as high as 65%. The annual average loss of forests has increased by 18%, or 118 million m3 . Therefore, China still needs to increase investment in forest resources, strengthen forest management, improve forest productivity, and enhance ecosystem service functions.

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1 Introduction

1.2.3 Problems with Grassland Resources Considering the distribution and development status of grassland resources in China, the prominent problems in the utilization of grassland resources in China mainly include the following points. The first problem is that the grassland area has decreased as a whole owing to human and natural factors, among which human factors are the main reason. In recent years, many measures have been taken to restore grassland coverage, with some success. The second point concerns the decline in grassland quality and serious grassland degradation. Grassland quality is determined by many factors, such as the animal husbandry capacity, ecological function benefit, and plant yield. Grassland degradation refers to the imbalance between the input and output of the material cycle and energy flow in grassland ecosystems. Owing to the reduction of plant biomass, land problems such as desertification and soil erosion are increasingly emerging in the grassland ecosystem, resulting in a significant decrease in grassland yield. While increasing grassland area, China should also pay attention to overall ecological environment restoration. Only by building a complete ecological chain can China truly implement the sustainable development strategy.

1.2.4 Problems with Mineral Resources China has huge reserves of mineral resources of considerable potential value. However, there are still many problems in exploiting domestic mineral resources. First, China has insufficient per capita mineral resource reserves. China’s total mineral resources rank third in the world after the United States and Russia, but the per capita share is only about half of the world average. With the rapid development of its economy and society, China’s demand for mineral resources is increasing. At present, the investment and development rate of new mineral resources can no longer meet the growing demand for mineral resources. Second, there are prominent problems in China’s mineral resource reserve management. Because of cognition deviation of mineral resources management departments in the development of existing mineral resources, some prominent resource management problems have arisen. For example, there are loopholes in the system of resource statistics, which make it difficult to record the confirmed actual resource reserves, resulting in unreasonable resource allocation and utilization and a waste of resources. In practice, China’s existing policies might not be well implemented due to a poorly developed legal system. Finally, the development and utilization of mineral resources is another tough problem, mainly from the following aspects. First, environmental pollution is the primary problem caused by the development of mineral resources. The environmental damage caused by human-induced mining

1.2 Prominent Problems with Natural Resources

21

cannot be reversed. For example, coal mining not only damages vegetation on the land surface but also generates dust and waste, which pollute the air and water. There are also some development activities that extend beyond the geographical location of the reserves, which destroy the original ecosystem, cause environmental damage, and affect local sustainable development. Second, the illegal exploitation of mineral resources has seriously threatened resource security in China. Despite relevant laws and regulations, illegal mining and indiscriminate development still occur from time to time, especially in some areas where unlicensed mining is still prominent, causing serious safety hazards. Worse, unlicensed miners undertake predatory mining and wanton development without considering local environmental situations, causing great damage to the environment. Third, the waste of mineral resources is another major concern. China’s mineral resources development mode is relatively undeveloped, with a low utilization rate. It is common to observe the phenomena of mining rich ores and abandoning the poor ones, multiple development of one mine, and insufficient development of large mines. Especially in some small villages and towns, resources are seriously wasted owing to outdated equipment and resource grabbing.

1.2.5 Problems with Energy Resources There are also many problems relating to energy resources in China due to the large population and low development and utilization efficiency. The prominent problems are mainly expressed in the following three points. The first problem is that the total energy resources at present do not match the energy demand of the Chinese people, showing a trend of supply exceeding demand. This has three main reasons. (1) People’s living standards have been greatly improved; consequently, demand has increased In recent years, due to the rapid development of China’s economy, Chinese people’s consumption levels and structures have undergone a transformation. Electrical appliances are necessities for every family, and the number of private cars is also increasing rapidly, which means that people’s demand for energy resources will greatly increase. However, China’s per capita share of energy is low. Thus, how to achieve rational development and effective utilization of energy resources is a great challenge for China. (2) Acceleration of industrialization China’s industrial energy consumption accounts for 70% of the country’s energy consumption. According to China’s national conditions, the scale of its industry will continue to increase, as will the industrial energy consumption. China’s industrial energy consumption facilitates the stable growth of China’s GDP. To meet China’s development needs, the effective utilization of energy resources is very important.

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1 Introduction

(3) Increase in energy consumption by the newly added population China’s natural population growth rate is 0.9%. China has a large population base, meaning that the number of annual newly added population is also huge. To tackle the problem of population aging, China has launched the “two-child” policy to encourage fertility, which has also increased the number of new people in recent years. The new population is conducive to promoting the development of urbanization, but according to statistics, if the urbanization rate increases by 1%, China’s final energy consumption will increase by 1.6%. The second problem is that China’s energy consumption level is high, which also suggests that China’s utilization rate of energy resources is relatively low. The level of energy consumption depends on the energy consumption per unit of economic output. At present, China’s economic output mainly depends on industrial energy consumption. Thus, it is necessary to improve the utilization efficiency of energy resources in industrial links. The third problem is that the development and utilization of energy resources impose a great burden on the environment. China relies heavily on coal resources, but coal use is the largest source of pollution in China. Moreover, some resources are non-renewable. Over-exploitation in previous years has generated negative impacts on China’s natural environment. The ecological environment also affects the output of energy resources. Therefore, the exploitation and use of energy resources must go hand in hand with care for the environment.

1.2.6 Problems with Freshwater Resources At present, the total amount of global freshwater resources accounts for only 2.5% of the world’s total water volume. As a country with serious drought and water shortage, China finds it even more difficult to develop and utilize freshwater resources. Considering China’s total freshwater resources, resource distribution, and the temporal and spatial changes of freshwater resources, the prominent problems regarding China’s freshwater resources can be summarized as follows. First, the contradiction between the supply and demand of freshwater resources is very prominent owing to China’s large population base and relative shortage of freshwater resources. China’s annual water shortage is close to 40 billion m3 , meaning that 300 million rural people do not have access to standard drinking water. Due to the uneven distribution of water resources, water transfer projects are necessary. However, to fundamentally solve the problem, it is essential to promote a waterconserving society and establish and improve a water resource management system based on water market theory. The second problem concerns the over-exploitation of freshwater resources. About 50% of China’s water resources have been developed and utilized. In most areas, there is a problem of excessive exploitation of groundwater. Groundwater

1.2 Prominent Problems with Natural Resources

23

accounts for 72% and 66% of the total water resources used in north China and northwest China, respectively. The third problem concerns the waste of water resources. A certain amount of water wastage is inevitable in agriculture, industry, or cities. According to statistics, in the North China Plain, a large amount of water resources is lost due to leakage in the process of farmland water transmission. Israel’s drip irrigation technology perfectly solves the problem of water wastage, and China should learn from it. The fourth problem concerns the pollution of water resources. The seven major water systems in China are all polluted due to the discharge of industrial wastewater and the random disposal of garbage. Moreover, in more than 2800 lakes in China, water eutrophication occurs more or less after receiving urban sewage. More than 80% of China’s total sewage is directly discharged into natural water bodies without any treatment. Therefore, the utilization rate of water resources should be improved to fully realize water conservation and reduce the pollution of water resources.

1.2.7 Problems with Marine Resources Since the 13th Five-Year Plan, China has put forward many measures and policies relating to marine resources and environmental protection, and the construction of a marine ecological civilization has also been advancing steadily. However, for protecting marine resources, we should first fully understand the overall situation of China’s marine resources and the associated environmental problems. The specific problems are as follows. First, China’s marine resources have no significant advantages against foreign resources. The fishing capacity of offshore and open seas marine fisheries is relatively low, with poor productivity. According to the research of the FAO and scholars in China and abroad, China’s annual catch in offshore and open seas is only about 3.5 million t, and the average fishery productivity is 3.18 T/km2 /year, accounting for only 1.16–1.75% of the total allowable catch of marine fisheries in the world. Moreover, China’s offshore oil and gas resources are not dominant. The offshore recoverable oil reserves account for only 3–12% of the world’s reserves. The second problem is that the average value of China’s marine resources is lower than the world average, measured using the indexes of per capita jurisdiction sea area, sea-land area ratio, and coastline coefficient. The data show that China’s per capita sea area is lower than the world average; the ratio of China’s sea area to land area is 0.31:1; finally, although China’s mainland coastline is more than 18,000 km long and the island coastline is more than 14,000 km long, the ratio of coastline area to land area is only 0.0018, which is very low. Third, compared with developed countries, the exploitation and utilization rates of marine resources in China are not high, and there are still many cases of underexploitation. However, in some areas, there are problems of over-exploitation and resource decline. For example, some deep-water ports in China’s coastal areas have not been developed and off-sea fishery resources are rich but not highly utilized; by

24

1 Introduction

contrast, most of China’s offshore areas are over-exploited and some of them are even experiencing the decline and extinction of fishery resources. Fourth, offshore environmental pollution is prominent, and seawater quality problems are still severe. In the process of development and utilization, China has unleashed a series of ecological and environmental problems, and natural and ecological damage has occurred to varying degrees in various sea areas. At present, the marine pollution discharge, marine pollution carrying capacity, and self-purification capacity have exceeded the equilibrium critical values. There are still about 15 million t of various pollutants entering the offshore waters through different channels every year. The fifth problem is that the layout of marine resource industries in some coastal areas is unreasonable and the structure is unbalanced. Some industrial layouts have high pollution, high energy consumption, and high ecological risk around marine ecology, which increases the risk of marine environment pollution and greatly exceeds the carrying capacity of marine resources and the environment.

1.2.8 Problems with Biological Resources The problems concerning China’s biological resources mainly arise from environmental deterioration and the low efficiency of biological resource development and utilization. The prominent problems are as follows. The first problem is the deterioration of the ecological environment. Environmental pollution and food shortage have a great negative impact on the renewability and integrity of biological resources. Thus, it is necessary to control population growth, develop agriculture, and reasonably develop biological resources. The second problem is that the biosafety protection law and biosafety management system are not complete. Domestic unscrupulous traders sell wild animals in the international market, while some foreign countries are eyeing China’s biological genes, posing a threat to China’s biosafety. People’s overall awareness of biological resources protection has improved, but illegal activities continue. The safety of biological resources is related to China’s economic development and the long-term interests of future generations and must be taken seriously. The third problem is that the research, utilization, development, and protection of biological resources necessitates professional talent. Only by cultivating skilled professionals who can master high-end biotechnology can China make more effective use of biological resources.

References

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References 1. Ahmed, Z., Asghar, M.M., Malik, M.N., Nawaz, K.: Moving towards a sustainable environment: the dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China. Resour. Policy 67, 101677 (2020) 2. Li, D.S., Fan, S.H., He, A.J., Yin, F.Q.: Forest resources and environment in China. J. For. Res. 9(4), 307–312 (2004) 3. Wang, D., Wang, L., Liu. J., Zhu, H., Zhong, Z.: Grassland ecology in China: perspectives and challenges. Front. Agric. Sci. Eng. 5(1), 24–43 (2018) 4. Feng, C., Huang, J.B., Wang, M.: The sustainability of China’s metal industries: features, challenges and future focuses. Resour. Policy 60, 215–224 (2019) 5. Wang, Q.: Effects of urbanisation on energy consumption in China. Energy Policy 65, 332–339 (2018) 6. Lin, B.Q., Moubarak, M.: Renewable energy consumption—economic growth nexus for China. Renew. Sustain. Energy Rev. 40, 111–117 7. Fu, X.-M., Zhang, M.-Q., Liu, Y., Shao, C.-L., Hu, Y., Wang, X.-Y., Su, L.-R., Wang, N., Wang, C.-Y.: Protective exploitation of marine bioresources in China. Ocean Coast. Manag. 163, 192–204 (2018) 8. Zhang, Y.-G., Dong, L.-J., Yang, J., Wang, S.-Y., Song, X.-R.: Sustainable development of marine economy in China. Chin. Geograph. Sci. 14(4), 308–313 (2004) 9. Hou, J.C., Zhu, X.W., Liu, P.K.: Current situation and future projection of marine renewable energy in China. Int. J. Energy Res. 43(2), 662–680 (2019)

Chapter 2

Index System, Method, and Application of Natural Resources Evaluation

China’s economic reform and opening up has ushered rapid economic growth in China. In pursuit of economic growth, industrialization, and urbanization, the development scales of land, water, minerals, and energy resources have increased sharply. With the aggravation of environmental pollution and the weakening of ecosystem functions, the upper limit of resource supply, environmental constraints, and ecological security have become the key factors impeding the sustainable development of human society. Given the massive consumption rates of natural resources, to protect the global ecosystem, natural resources must be rationally and efficiently utilized. Particularly, the problems of resource depletion and environmental degradation in China are becoming increasingly severe. Thus, it is necessary to determine different natural resources utilization modes for different regions of China. Although technological progress can reduce the pressure on resources and the environment to a certain extent, most developing countries will eventually destroy the sustainable development of natural resources by sacrificing natural resources and the environment for development motives. Therefore, it is critical to evaluate the utilization of natural resources in China, build an evaluation index system for efficient utilization of natural resources, and formulate a comprehensive resource management strategy.

2.1 Construction Principles of Evaluation Index System 2.1.1 Significance of Constructing an Evaluation Index System To promote the high-quality development of China’s natural resources and ecological environment, the Chinese government continues to promote the construction of an ecological civilization to secure and improve people’s quality of life. In its 2020 Work Report, the Chinese government particularly stressed the need to complete many © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_2

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tasks, such as “optimizing the land spatial pattern” and “promoting the harmonious coexistence of humans and nature,” so as to realize the efficient utilization of natural resources. In recent decades, due to the growth of the global population and economic development, the demand for natural resources has been expanding continuously. At present, life systems are under constant pressure owing to the increased consumption of natural resources that are essential for maintaining life and for socioeconomic development. Therefore, how to make efficient use of natural resources is an urgent issue to solve. The efficient use of natural resources is the ultimate goal for achieving long-term, stable, and sustainable development in China. According to the Principles of Natural Resources (Second Edition), a natural resource is any natural product that human beings can obtain from nature to meet their needs and desires and is the result of human activities. Natural resources refer to the sum of the environmental factors that exist naturally and have use value for human beings at present and in the future. Meanwhile, natural resources are in a limited amount and are non-renewable. Only by cherishing and rationally developing natural resources to promote the highquality and efficient utilization of resources can the high-quality development of cities be achieved. Natural resources are the basis for the survival and development of human society. The rational and effective use of natural resources can enhance the comprehensive national strength and promote China’s economic development. The utilization efficiency of various natural resources can comprehensively reflect the economic development level, resource conditions, and scientific and technological progress of a region. Therefore, based on reviewing and summarizing scholars’ research on the efficiency and level of natural resources utilization, we explore the factors affecting the utilization efficiency of various natural resources. Based on the results, we design evaluation schemes to provide clear ideas and guarantees regarding natural resources efficiency utilization. In addition, China should further promote the organic integration of various natural resources with the ecological environment, social and economic benefits, and the needs for a better life. Many scholars have explored how to achieve an efficient utilization of natural resources from the following perspectives. On the one hand, natural resources can be efficiently utilized through the establishment of long-term and effective incentive mechanisms, supervision mechanisms, and management systems including sustainable development system, clear property right system, and comprehensive supervision system. On the other hand, due to the public good nature of natural resources, the utilization efficiency of natural resources can be improved by clarifying the property rights of natural resources and perfecting the compensation systems for the use of resources. These research results are significant for both deepening the theoretical understanding and improving the degree of natural resources efficiency utilization.

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2.1.2 Principles of Index Construction When constructing the index system, the selected indexes must reflect the proximity and sensitivity of the research environment and follow the principles of scientificity, utilitarianism, and independence. In addition, the following principles should be considered. (1) Normativeness: The selected indexes should comply with Chinese and international standards, follow the scientific basis of natural laws and ecological laws, and measure and evaluate the efficient utilization and realization degree of natural resources in different regions, types, and modes. (2) Guidance: Natural resources are interrelated in various production and living systems, and their interdependent coupling relationship requires that the index system follow strict guidance and hierarchy. The index must accurately reflect the essential characteristics of natural resources and the ecological environment. (3) Completeness: The maximum and minimum restrictions on natural resources efficiency utilization require that the evaluation index system have a wide coverage that can comprehensively reflect the factors affecting natural resources utilization at all levels, from both the horizontal and vertical dimensions. It should also fully reflect the implementation of the measurement standards, such as resource utilization rate, output rate, input–output ratio, socioeconomic benefits, and sustainability of resource utilization. (4) Sustainability: The selected indexes should not only reflect the ecological status quo but also be forward-looking. Natural resources efficiency utilization is a dynamic and continuous change process. On the one hand, it often presents periodicity with seasonal changes. On the other, with continuous improvement of scientific and technological levels, there are also changes in the breadth and depth of natural resources utilization. Therefore, the selected indexes need to reflect the principle of sustainable development. (5) Representativeness: The selected index system should be generally applicable to China and have horizontal comparability. It must reflect social, ecological, economic, and environmental behaviors as accurately as possible. (6) Availability: The selected indexes should be easy to obtain and calculate, investigation can be carried out. When selecting indexes, more representative ones that can be analyzed quantitatively and qualitatively should be considered. Measurement units, such as percentage, unit input–output, and utilization efficiency, may be given preference.

2.1.3 Underlying Idea Behind Index Construction The key to promote green development and realize harmonious coexistence of people and nature is to comprehensively improve the resources utilization efficiency. In addition to taking measures to improve the resources utilization efficiency, the government should also conduct a comprehensive evaluation of the utilization efficiency

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to ensure that the measures can make effective and full use of natural resources. This chapter constructs a comprehensive and objective index evaluation scheme by selecting a series of important indexes regarding natural resource development and combining the existing evaluation index systems of natural resources. Specifically, it includes the following three aspects: (1) Linking efficient utilization and natural resource systems To build an evaluation index system for the efficient utilization of natural resources, we need to measure and consider natural resources efficiency utilization from all dimensions and fully combine emerging concepts, such as green development, sustainable development, and circular development. The development status of natural resources, their management level, and optimized governance can be taken as the horizontal framework while various resource elements can be considered as the vertical framework of the evaluation system. Different types of natural resources should be connected with specific connotations of efficient utilization to realize effective linking between the natural resources evaluation system and resources efficiency utilization. Finally, based on the connotation of efficient utilization, the utilization of different types of natural resources is investigated, and corresponding indexes are designed to characterize the evaluation system of natural resources efficiency utilization and improve the reliability and comparability of the evaluation schemes. (2) Realizing the integrated evaluation of efficient utilization Efficient utilization of natural resources is a natural resources utilization mode integrating the ecological environment, social benefits, economic benefits, and the needs for a better life. Natural resources efficiency utilization would lead to positive or negative externalities to human society and the economy. Therefore, the evaluation system should not only evaluate a single element of natural resources but also realize the integrated evaluation of efficient utilization at different levels and objectively and comprehensively reflect all kinds of natural resource elements. (3) Differentiated adjustment of evaluation system Natural resources exhibit heterogeneity. Moreover, different regional conditions and levels of economic and social development also contribute to the difference. Thus, natural resource evaluation schemes need to focus on individual characteristics besides common problems. When constructing the evaluation system for the efficiency utilization, not only universal indexes suitable for the natural resources efficiency utilization in China but also localized indexes based on typical regional and economic characteristics should be chosen and adopted.

2.1 Construction Principles of Evaluation Index System

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2.1.4 Basis for Index Construction of Natural Resources Efficiency Utilization (1) PSR model for natural resources efficiency utilization In the 1990s, the pressure–state–response (PSR) model was proposed by Organisation for Economic Co-operation and Development (OECD) and United Nations Environment Programme (UNEP). Subsequently, the OECD applied the PSR model to environmental report analysis and evaluated the applicability and effectiveness of the model [1]. Nowadays, the PSR model is widely used in the evaluation of environmental quality, because it can combine human life with the growth environment of natural resources and reflect the relationship and interaction between them. Huang et al. [2] assessed marine biodiversity using the PSR model and revealed the increasing pressure on marine biodiversity. The PSR model has the following three layers. Pressure layer: The natural resource pressure index describes the pressure exerted by human activities and climate change on natural resources and the environment. State layer: The state index of natural resources describes the current situation of the natural environment and ecosystem functions. Response layer: The social response index reflects the degree of social response to the changes and concerns regarding natural resources and the environment. By incorporating the model framework of Yang et al. [3], the PSR model of natural resources efficiency utilization established in this chapter refers to the different degrees of positive or negative externalities to natural resources in the process of human exploration and utilization of natural resources. When natural resources are positively affected by human production and living activities, such positive effects can be brought into full play to maximize the benefits of natural resources efficiency utilization; when natural resources are negatively affected by human activities, the factors causing such negative effects can be determined in order to launch a series of responses to realize efficient utilization. The cyclical mechanism of interaction among the indexes of the PSR model is shown in Fig. 2.1. Natural resources efficiency utilization requires a comprehensive evaluation of the environmental quality of natural resources. The PSR model is mainly used for the evaluation scheme and system of natural resource environment quality. First, human economy, society, and life make use of natural resources—the waste generated as a result is discarded to the natural resource environment, destroying and exerting pressure on natural resources; second, the environmental changes of natural resources affect human society, the economy, production, and life; finally, human beings respond to the sustainable development and efficient utilization of natural resources and take corresponding measures. This constitutes the pressure– state–response relationship between natural resources and human beings. In this chapter, the selection of model indexes is adjusted and improved based on the research of scholars regarding different types of natural resources. When constructing the evaluation index system, in this chapter, we tried to ensure that the data are authentic, reliable, and available, and the evaluation methods are operable.

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Fig. 2.1 PSR model

Meanwhile, attention should be paid to the standardization of the evaluation index system, the data sources, and the supportiveness of the index system to establish a clear evaluation system and guidance for natural resources efficiency utilization. (2) Design features of PSR model indexes First, there is a strong causal relationship in the PSR model, which can be understood from the following logical features: (1) Human society obtains materials from various natural resources to survive and develop, which puts “pressure” on these natural resources and the environment, such as through production sewage discharge, reduction of non-renewable resources, and soil erosion; (2) the economic, social, and ecological benefits generated in the utilization of natural resources cause a “state” of change in natural resources and the environment, such as the output value and contribution rate; (3) to realize the efficient utilization of natural resources, human society takes a series of measures to regulate the exploitation and utilization of natural resources; that is, it offers a “response” to the pollution caused in natural resources and the environment, such as formulating relevant policies and systems. Second, the PSR model is highly comprehensive. It reflects all kinds of problems and phenomena in the interrelationship between human society and natural resources, from the initial activities of human production and life, to the state of natural resources, and then to the measures taken by human beings to expand the positive impacts and reduce the negative impacts.

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Finally, the PSR model is highly flexible. On the one hand, it can be used to evaluate the development, protection, and utilization of natural resources in different periods; on the other hand, it can be adjusted according to the contents of the three layers of pressure, state, and response, and the corresponding indexes can be designed according to different research objectives, such as adding indexes similar to the level of research objectives, or reducing repetitive indexes. By doing that, we can increase the diversity of evaluation programs, the accuracy of evaluation results, and the usability of PSR model.

2.2 Construction of Evaluation Index System for Efficient Utilization of Natural Resources Considering the actual investigation conditions and the calculation operability of various resources, indexes of the three layers of pressure, state, and response are constructed in the following sections.

2.2.1 Forest Resources According to statistics, China’s forest coverage was about 23.04% in 2020, ranking fifth in the world.1 As one part of the ecosystem, forests play a great role in regulating the Earth’s climate, ensuring water and soil resources, and improving the soil environment. As the growth cycle of forest resources is very long, it will take a long time and much energy to protect and repair forest resources. Therefore, it is of great significance to make efficient use of forest resources and form a highly stable forest ecosystem. Efficient utilization of forest resources refers to the sustainable and coordinated development of resources. This section summarizes the economic, ecological, and social information of forest resources and constructs the evaluation index system shown in Table 2.1 in combination with previous scholars’ research on the sustainable utilization of resources, the coordinated development of the economy and society, and the audit and evaluation of forest resource assets. (1) Indexes of the pressure layer (a) Urbanization level: It refers to the ratio of the urban area to the total area in a certain region. Generally, the higher the urbanization level, the less the forest resources available in the region. (b) Proportion of forestry output value in GDP: Forestry output value = forest operation output value + output value of collected forest products + output value of rural harvested bamboo and wood. China’s forestry industry has 1

Data come from the National Forestry and Grassland Administration.

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Table 2.1 Evaluation index system for efficient utilization of forest resources Primary index

Secondary index

Tertiary index

Pressure index

Pressure of economic growth

Urbanization level

Pressure of resource carrying capacity

Ratio of actual forest cutting to the planned forest cutting

Ratio of forestry output value to GDP Percentage of outturn Forest fire damage Disaster rate of forestry pests

State index

State of assets

Forest coverage Living wood growing stock Area suitable for forestry

Response index

State of environment

Proportion of days with air quality index ≥ Grade II

Response of investment and protection

Investment in returning farmland to forest

Response of institutions

Compliance rate of implementation of the Forestry Law and relevant laws and regulations

Response of humanity

Citizens’ awareness of protecting forest resources

Investment in forest resources utilization and conservation Afforestation survival rate Natural landscape preservation rate

the following three advantages: First, forestry is a green industry, and the expansion of domestic demand is accompanied by the improvement of rural purchasing power, so as to further promote the development of green ecology; second, China is rich in wildlife and plant resources, and the potentials worth exploring will be tapped; third, the promotion of collective forest ownership system stimulates the enthusiasm of forestry producers. In 2020, China’s forestry output value was CNY 8.7 trillion.2 The ratio of forestry output value to GDP indicates the contribution of forestry development to economic growth. (c) Ratio of actual cutting to planned cutting: According to the Regulations on the Implementation of the Forestry Law, a quota system is implemented for the annual forest cutting plan, and the protection and utilization of forest resources in a certain area can be investigated according to the ratio of the actual cutting volume to the planned cutting volume. (d) Percentage of outturn: This refers to the ratio of the commercial volume produced by trees of a certain tree species after cutting to the cutting volume. The higher the percentage of outturn, the better the utilization of the forest resource. 2

Data come from the National Forestry and Grassland Administration.

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(e) Forest fire damage: It refers to the ratio of the annual fire area of the forest reserve to the total area of forest land. Relevant departments of each region have established emergency measures, such as fire control to enhance citizens’ awareness of forest fire prevention and control of the fire damage rate within specified ranges. actual disaster area (f) Disaster rate of forestry pests = ExistingAnnual × 100. With forest land area+unforested area regard to reducing the disaster rate, the China Forestry and Grassland Administration has basically prevented and controlled pests through the formulation of pest control objectives. (2) Indexes of the state layer (a) Forest coverage: It refers to the ratio of the forest area to the total land area, indicating the richness of forest resources and ecological balance. In recent years, the Chinese government has achieved remarkable results in afforestation and greening by improving forestry support policies and strengthening forest resource protection, so as to ensure the sustainable growth of forest resources. (b) Living wood growing stock: The calculation methods of living wood stock include whole forest measurement method and standard site investigation method. (c) Forestry suitable area: Forest land is an important natural and strategic resource in China. Forestry suitable area means that the area is suitable for planting trees, has strong production capacity, and can maintain the natural ecological balance. (d) Proportion of days with air quality index ≥ Grade II: It indicates the ecological and environmental benefits of forest resources. The air quality status is determined by the changes in forest resources and can reflect the environmental status of regional forest resources. (3) Indexes of the response layer (a) Investment in returning farmland to forests: Returning farmland to forests is a landmark project in China’s efforts to restore the natural ecosystem. China had returned more than 333,000 km2 of farmland to forest and grassland by 2019, with the investment in the project exceeding CNY 500 billion.3 To prevent the recurrence of problems such as soil erosion and land desertification, relevant departments of national government control land mining strictly to restore the original forest area. (b) Investment in forest resource utilization and conservation: According to the latest measures and requirements of water and soil conservation, the logging team selects appropriate methods, such as stacking, spreading, and burning, and adopts the most appropriate forest clearing method without affecting forest regeneration. Cutting residues are recycled, and the

3

Data are from the National Forestry and Grassland Administration.

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(c)

(d)

(e)

(f)

collection quantity of cutting residues is determined according to surplus materials, processing equipment, and market demand. Afforestation survival rate: It refers to the proportion of surviving trees per unit area in the total number of trees. Relevant departments in China have been improving methods to raise the survival rate of afforestation. Natural landscape preservation rate: With the passage of time, the number of natural landscapes on China’s territory has decreased sharply. In 2019, China invested 10.2 billion yuan in national forest landscape protection; the Chinese government has invested significant sums of money to ensure the sustainable survival of natural forest resources. Rate of compliance with the Forestry Law and relevant laws and regulations: China has updated corresponding laws, regulations, and measures during the implementation of the Forestry Law; for example, the punishment of government departments for violations of forest cutting quota, undocumented cutting, and other related crimes; implementation of the reforestation obligations of relevant departments. Citizens’ awareness of protecting forest resources: The strengthening of citizens’ awareness of the need for forest protection by public departments, schools, and other collective units can effectively prevent forest fires and protect forest resources.

2.2.2 Water Resources Water, as the source of all life, is crucial not only for daily living but also for production and development activities. In 2019, China’s total water resources were 2904.1 billion m3 .4 To protect its water resources, China has vigorously promoted the concept of conserving, protecting, and utilizing water resources. The economic, social, and ecological benefits of water resources are reflected in agricultural irrigation, production and domestic water, urban construction, and many other aspects. As shown in Table 2.2, this section constructs the evaluation index system of efficient utilization of water resources by adopting„ a water-use benefit index, economic benefit index, social benefit index, and ecological environment benefit index. (1) Indexes of the pressure layer (a) Per capita comprehensive water consumption: China’s per capita comprehensive water consumption in 2019 was 432m3 .5 Comprehensive water consumption includes domestic water for residents and water for public buildings. By analyzing the combined water consumption, the relationship of China’s macro-control and market mechanism with the law of water

4 5

Data source: China Water Resource Bulletin 2019. Data source: China Water Resource Bulletin 2019.

2.2 Construction of Evaluation Index System for Efficient Utilization …

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Table 2.2 Evaluation index system for water resources efficiency utilization Primary index

Secondary index

Tertiary index

Pressure index

Pressure of water use

Per capita comprehensive water consumption Industrial water consumption Output value of high water consumption industries

Water circulation pressure

Utilization rate of surface water Utilization rate of underground water

State index

State of water efficiency

Irrigation water utilization coefficient Reuse rate of return water

State of economic benefits

Water productivity Irrigation benefit–cost ratio

State of social benefits

Rate of water-saving irrigation area Water conservation development index Per capita grain share

State of ecological environment benefits

Environmental quality of irrigation area Water safety index Salinization degree of cultivated land

Response index

Response of institutions

Implementation of the Water Law and relevant legislation and regulations

Response of investment

Water efficiency Water-saving project investment

Response of administration

Pollution control Standard rate of water and soil loss control

resource supply and demand can be explored for the reasonable allocation of water resources. (b) Industrial water consumption: It refers to the water used in the process of industrial production and the domestic water consumption of workers. In 2019, China’s total industrial water consumption was 123.7 billion m3 , accounting for 15% of the total water.6 It mainly includes the reuse rate, water consumption per CNY 10,000 output value, water consumption per unit product, indirect water-cooling cycle rate, and other industrial water levels from different angles and ranges, enabling a comprehensive assessment of water resources utilization. (c) Output value of high water consumption industries: At present, China has focused on encouraging the development of products and equipment with

6

Data source: China Water Resource Bulletin 2019.

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high technical content and good water-saving effects. Liu et al. [4] considered that the reduction of water consumption in high water consuming industries would significantly reduce GDP, and the relationship between economic growth and water utilization could be well balanced by moderately increasing the ratio of the tertiary industry. Products with large water consumption in the production process or a large water intake per CNY 10,000 output value should be listed in the restricted development catalog and restricted based on the water-saving quota. Moreover, high water consumption industries, such as textiles, paper, rubber products, and chemicals, should be listed in the prohibited development catalog. (d) Utilization rate of surface water resources: At present, the water supply level after deducting the repeated amount of return water roughly reflects the one-time maximum outer-river water utilization by surface water projects. (e) Utilization rate of underground water: Underground water is an important natural resource and provides a convenient source of domestic water for people. Unlimited extraction of underground water threatens the production and living environment in a region. To raise the utilization rate of underground water, identifying efficient underground water utilization methods and improving technical levels would be greatly helpful. (2) Indexes of the state layer (a) Irrigation water utilization coefficient: It refers to the ratio of the effective water injected into the field to the total water introduced from the channel source, which can comprehensively reflect such factors as irrigation technology, system engineering status, and agricultural planting structure. Irrim Inet i ×Ai gation water utilization coefficient: μirrigation = i=1Itotal , where I is the water needed for irrigation and A is the irrigation area. (b) Reuse rate of return water: Return water refers to the return of water to surface water, such as rivers and lakes, either through the surface or via underground channels. The reuse rate of return water indicates the ratio of the sum of repeated water consumption and return water consumption in the production process to the total water consumption. (c) Water productivity: It refers to the yield or output value obtained per unit of water resources for certain crop varieties and cultivation conditions. Physical water productivity is defined as the ratio of agricultural output quality to water consumption, and economic productivity is defined as the value obtained per unit water consumption. Molden et al. [5] pointed out the need to be cautious when determining the scope and difficulty of improving the water production rate. In high-yield areas, the water production rate of crops is so high that an increase in the unit yield (unit land area) does not necessarily translate into an increase in the water production rate. Although water quality may be affected, water reuse in irrigated areas or watersheds can compensate for water loss on a field scale. In the past, although crop

2.2 Construction of Evaluation Index System for Efficient Utilization …

(d)

(e)

(f)

(g)

(h)

(i)

(j)

7

39

breeding played an important role in improving the water production rate, especially by improving the harvest index, it is difficult to predict such a large harvest in the future. More importantly, there are no favorable conditions for farmers and water managers to improve the water production rate. n ε ×Y ×P Irrigation benefit–cost ratio: R = i=1 i C i i expresses the input–output effect of an irrigated area, where εi is the irrigation benefit sharing coefficient; Yi is the increased yield brought about by irrigation of crop i; Pi is the price of crop i; and C is the irrigation cost. Ratio of water-saving irrigation area: In 2018, China’s water-saving irrigation project area reached about 353 billion m2 .7 Water-saving irrigation refers to saving various water sources through diversified water-saving projects, technologies, and other measures. Water conservation development index: It evaluates the development of water conservation modernization in every region to coordinate and improve the management level of areas with weak of water conservation development. Zhang and Liu [6] evaluated and analyzed water resource guarantee, water resources ecological environment protection, water conservation management, and development. Per capita grain share: China’s Development and Reform Commission announced in 2021 that China’s per capita grain share was 474 kg. The growing demand for food has made China and government departments pay more attention to work related to food security and efficient production. Environmental quality of irrigation area: Sewage irrigation will bring immeasurable harm to the agricultural production environment. Singh et al. [7] analyzed the impact of sewage irrigation on soil properties, crop yield, and environment. Water security index: It quantifies regional water security from the perspective of the regional water resources supply and demand balance. He et al. [8] used the water security index to evaluate  and analyze the utilization of , where P is the regional water regional water resources. WSI = lg flow+P D supply, D is the regional water demand, and flow is the upstream water supply. When WSI > 0, the region has abundant water resources; when WSI < 0, water resources in this region are relatively scarce. Salinization degree of cultivated land: Land salinization refers to the process by which the soluble salt content in the soil layer gradually increases, leading to saline soil. This phenomenon is mainly common in arid and semi-arid areas. For areas with a high degree of salinization, governments need to take comprehensive treatment measures according to local conditions.

Data source: Ministry of Water Resources of People’s Republic of China.

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(3) Indexes of the response layer (a) Implementation of the Water Law and relevant laws and regulations: It strengthens law popularization and does a good job in the development, utilization, and protection of water resources. (b) Water-use efficiency: Jin et al. [9] carried out research from the perspectives of industrial water-use efficiency, technological innovation, and environmental regulation, and put forward the targeted optimization of regional water resource systems to achieve efficient water use. (c) Investment in water-saving projects: According to the statistics of the Ministry of Water Resources, at the end of June 2018, the investment in major water conservation projects under construction in China exceeded CNY 100.11 billion,8 most of which had yielded benefits. Among them, the investment in major agricultural water-saving projects is the most prominent, laying a solid foundation for China’s water conservation infrastructure and providing a strong guarantee for the rapid development of the national economy and the improvement of the ecological environment. (d) Pollution control: The allocation of regional emission rights is uncertain and spans multiple stages. Facing the pollution-receiving constraints of water functional areas, this index is included in related research [10]. (e) Standard rate of water and soil loss control: It refers to the ratio of the surface area of water and soil loss control to the total area of water and soil loss. It reflects the management level of soil erosion control.

2.2.3 Land Resources The development of agriculture, forestry, and animal husbandry is inseparable from that of land resources. China has a vast territory and relatively rich land resources, of which social and economic benefits can be maximized through rational and efficient utilization. However, although China is rich in land and resources, the per capita land occupation is small and it is difficult to develop and utilize land resources owing to geographical conditions. Thus, it is very important to make reasonable arrangements to exploit the limited land resources and to build an evaluation index system for efficient utilization of land resources. This section further expands the evaluation index system for efficient utilization of land resources according to the indexes of sustainable utilization level of regional land resources measured by Kang et al. [11] as shown in Table 2.3. (1) Indexes of the pressure layer (a) Geological conditions: Geological conditions significantly affect the construction of underground infrastructure. In underground engineering, 8

Data source: Ministry of Water Resources of People’s Republic of China.

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Table 2.3 Evaluation index system for efficient utilization of land resources Primary index

Secondary index

Tertiary index

Pressure index

Pressure of physical geography

Geological conditions Geological disasters

Land-use pressure

Population density Cultivated land-carrying capacity Cultivated land pressure index Land utilization rate

Land abandonment pressure

Land desertification rate Idle land

State index

Land natural environment status

Ratio of natural reserves to total area Land reserves

Production and living land status

Per capita construction land Ratio of highway to total area Proportion of prohibited construction areas in the total area

Crop land-use status

Per capita cultivated land area Percentage of basic farmland in cultivated land Areas of fields with high, medium, and low yields

Response index

Investment response

Effective irrigation rate of farmland

Administration response

Land resource utilization, protection, and management Investment in industrial solid waste treatment

geology determines the decision-making on important aspects of underground infrastructure construction such as the feasibility, location and route, constructability, and cost of infrastructure, the practicability of completed structures, and the risk analysis. (b) Geological disasters: Landslide and collapse are the main types of geological disasters. From the perspective of spatial distribution, geological disasters are mainly distributed in the Hunan and Sichuan provinces, where economic losses are the greatest. China faces high risk of landslides and debris flows, which interrupt traffic, block rivers, destroy industries and mines, bury villages and towns, and cause casualties and huge economic losses. Thus, they are considered serious natural disasters. Landslides in China are mostly of large or super-large scale, especially in west China, where the scale of landslides is huge and its influence mechanism is complex. Special geomorphic conditions are the most basic reason for the development of landslides in China.

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(c) Population density: It refers to the population per unit land area. China’s population density is 145 people/km2 .9 By analyzing the relationship between ecological environment quality of land resources and the population, Zhao et al. [12] found that southeast China has the best ecological and environmental quality of land resources in China; it has the largest population density and the highest degree of land resource utilization. Northwest China has poor quality of land resources and ecological environment in China, where the population density is small and the utilization degree of land resources is low. (d) Cultivated land-carrying capacity: It refers to the number of human beings that can be supported in a certain area without causing land degradation. China is the most populous country in the world, and natural resources and environmental security have been the primary concern of researchers and politicians for decades. The rapid economic growth in China since the country’s reform and opening up has driven the process of industrialization, urbanization, and infrastructure construction, causing increasing reduction of cultivated land. The analysis of cultivated land-carrying capacity is of great significance to the coordinated development of the population, social economy, and ecological environment. It is a concept with both natural and social attributes, meaning that it is a complex large-scale system, involving several factors such as population, resource availability, society, economy, and technology, which interact with and restrict each other. (e) Cultivated land pressure index: It refers to the ratio of the minimum per capita cultivated land area to the actual per capita cultivated land area. (f) Land utilization ratio: It refers to the ratio of the used land to the total land area, which can reflect the conditions of land utilization. (g) Land desertification rate: China experiences desertification of a considerable level. Therefore, combating land desertification and protecting the ecological environment are important objectives in the process of China’s sustainable development and construction. (h) Idle land: At present, the situation of idle land in China is relatively severe, which is a great waste of land resources. Idle land requires the relevant governmental departments to formulate corresponding systems and implement effective and feasible measures to improve the effective utilization of resources and coordinate the long-term, stable, and orderly development of the social economy. (2) Indexes of the state layer (a) Ratio of natural reserves to total area: Natural reserves refer to unused land and land that is difficult to use, including wasteland, desert, saline alkali

9

Ranking of population density of countries and regions in the world 2018, “Which Country or Region Has the Largest Population Density—World Population 2019,” World population, July 3, 2018.

2.2 Construction of Evaluation Index System for Efficient Utilization …

(b)

(c) (d) (e)

(f)

(g)

(h)

43

land, and another unused land. The greater the ratio of natural reserves to total area, the lower the degree of land resource availability. Land reserves: The size of land reserves is verified by the local government according to the needs of national economic development. Local governments should store or predevelop land for which land-use rights have been obtained through recovery, acquisition, and requisition, and provide all kinds of construction land to society. The correct verification of land reserves plays a promoting role in optimizing land-use structure and improving the efficiency of urban land allocation. Per capita construction land: It refers to the ratio of the total area of urban planning and construction land to the total urban population. Ratio of roads to the total area: It refers to the ratio of the road area in the built-up area to the total built-up area. Proportion of prohibited construction areas in the total area: Prohibited construction areas refer to the spatial scope of construction land prohibited by the state government within a specified period in order to protect the natural ecological environment. Per capita cultivated land area: China’s total land area is 9.6 million km2 , with an average of 0.54 ha per person.10 China’s land resources are characterized by large absolute quantity, small per capita possession, complexity and diversity, small proportion of cultivated land, and uneven distribution of regional productivity, among others. Percentage of basic farmland in cultivated land: According to China’s newly published Law of the People’s Republic of China on Land Administration, the permanent basic farmland protection system is an upgrade of the cultivated land protection system and provides a strong guarantee for ensuring the quantity and quality of cultivated land in China. Areas of fields with high, medium, and low yields: A high-yield field refers to land with high quality, which can fully meet the requirements of crop growth and obtain high yields; medium- and low-yield land refers to land of low quality that cannot fully meet the growth needs of crops. According to Chinese regulations, based on the local average yield per unit area in the last three years, land with yields 20% higher than the benchmark is considered a high-yield land, land with yields of 80% to 120% of the benchmark comprises medium-yield land, and land with yields more than 20% lower than the benchmark comprises low-yield land.

(3) Indexes of the response layer (a) Farmland effective irrigation rate: It refers to the ratio of the farmland effective irrigation area to the cultivated land area. By calculating the effective irrigation rate, the technical level of regional water-saving irrigation can be determined, so as to build corresponding facilities of automatic lifting 10

The data come from the official website of the Central People’s Government of the People’s Republic of China.

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2 Index System, Method, and Application of Natural Resources Evaluation

water-saving irrigation, micro-sprinkler irrigation, drip irrigation, and other technologies. (b) Agricultural mechanization level: It refers to the production level of agricultural machinery or equipment. It can directly reflect agricultural productivity. (c) Land resource utilization and protection management: The rational utilization and management of land resources are important for realizing the sustainable and efficient utilization of social resources and the effective supervision of land protection and other measures. To realize the efficient and sustainable use of land resources, the supervision and management of land resources is essential. (d) Investment in industrial solid waste treatment: Solid waste refers to the waste residue, dust, and other harmful substances generated in industrial production activities that may be discharged into the natural environment. Increasing investment in the treatment of industrial solid waste can alleviate most environmental damage.

2.2.4 Mineral Resources Mineral resources are the most valuable resources for development and utilization, but most of them are non-renewable. To ensure the rational exploitation and utilization of mineral resources, local governments should strictly control and supervise the mining and exploration of mineral resources. To optimize the allocation of resources, the evaluation scheme of mineral resources efficiency utilization is particularly important. Maximally utilizing limited mineral resources would make an outstanding contribution to the production of human society. By referring to the research on the development and protection of mineral resources in He et al. [13], this section further explores developing an evaluation index system for the efficient utilization of mineral resources from the perspectives of resource mining, analysis of mines’ geological environment, and safe production of mining work as shown in Table 2.4. (1) Indexes of the pressure layer (a) Proportion of mining sales output value in industrial sales output value: Mineral resources are non-renewable. The higher the proportion of their sales output value, the less the remaining available mineral resources. (b) Cumulative untreated land area by mining: Timely treatment is necessary following accidents such as regional land mass collapse after mining. (c) Annual growth rate of land area damaged by mining: The mining process produces a large amount of land that cannot be reused without treatment, that is, abandoned mining areas, leading to increasingly severe problems of ecological environment pollution and resource destruction in mining areas.

2.2 Construction of Evaluation Index System for Efficient Utilization …

45

Table 2.4 Evaluation index system for efficient utilization of mineral resources Primary index

Secondary index

Pressure index

Resource exploitation Mine geological environment Safe production

State index

Resource exploitation

Tertiary index Proportion of mining sales value in industrial sales value Mining GDP contribution index Cumulative untreated land area by mining Annual growth rate of land area damaged by mining Proportion of small mining enterprises Mining employment Rationality of main mineral exploitation Proportion of comprehensive utilization output value of non-oil and gas mineral resources Per capita output of non-oil and gas resources/per capita output value of oil and gas Contribution rate of total mining assets Profit margin of main business income of mining industry Average wage level of mining industry

Mine geological environment Safe production Response index

Resource exploitation

Treatment rate of abandoned land area of legacy mines Control rate of land area damaged by newly added mines Proportion of mine accident deaths in mining employment Mortality per million t of coal Proportion of geological exploration funds in mining output value Proportion of newly transferred exploration rights Drilling efficiency Proportion of newly transferred mining rights Proportion of R&D expenditures in main business income of mining industry Closure rate of illegal cases of mineral resources exploration and mining

Mine geological environment Safe production

Investment intensity of mine environmental treatment funds Deposit intensity of mine geological environment Proportion of large and medium-sized mining enterprises Proportion of fixed asset investment in mining output value

(d) Proportion of small mining enterprises: To ensure safe production, in recent years, the Chinese government has reduced the number of small mining enterprises by 30%11 and increased construction investment in large mining enterprises. 11

Data come from the National Energy Administration of the People’s Republic of China.

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2 Index System, Method, and Application of Natural Resources Evaluation

(2) Indexes of the state layer (a) Rationality of main mineral exploitation: Reasonable exploitation should account for both economic and social ecological benefits as much as possible. The rational development and utilization of mineral resources require high levels of the ore recovery rate, beneficiation recovery rate, and comprehensive utilization rate. (b) Proportion of comprehensive utilization output value of non-oil and gas mineral resources: Non-oil and gas mineral resources are relatively scarce in China, according to relevant data of the National Bureau of Statistics on land and resources. Thus, the importance of their efficient utilization has become gradually valued by the Chinese government. (c) Profit margin of main business income of mining industry: According to data of the China National Bureau of Statistics, the profit of China’s mining industry continues to grow rapidly while the leverage ratio continues to decrease. (d) Proportion of mine accident deaths to mining employment: According to the statistics of China National Mine Safety Administration, there were 434 mine accidents in 2020 and 573 deaths, with a year-on-year decrease of 22%.12 All regions have strengthened the management and control of safe production, vigorously promoted the construction of intelligent information, and continuously improved the supervision efficiency regarding the safety situation of mines. (3) Indexes of the response layer (a) Proportion of geological exploration funds in mining output value: It refers to the exploration right use fee, geological survey, and other expenses incurred in the process of geological exploration. (b) Proportion of newly transferred exploration rights: According to the administrative measures for the transfer of mining rights, optimizing the allocation of mineral resources and standardizing the transfer of mining rights can safeguard the rights and interests of Chinese owners and promote the green, healthy, sustainable, and efficient development of the mining industry. (c) Drilling efficiency: It measures the speed of the drilling process. To improve the utilization of mineral resources, the development of drilling technology to improve drilling efficiency has been the focus of scientific researchers in recent years. (d) Closure rate of illegal cases in mineral resources exploration and mining: Cracking down on illegal acts is of great benefit to maintain the normal order of mineral resources mining, establishing a long-term and effective supervision mechanism, and improving the level of mineral resources development and utilization.

12

Data come from the news release of the National Mine Safety Administration of China.

2.2 Construction of Evaluation Index System for Efficient Utilization …

47

(e) Investment intensity of mine environmental treatment funds: All regions should actively transform abandoned mines into cultivated land, forest, or grassland as much as possible according to the actual conditions of the mines. (f) Proportion of fixed asset investment in mining output value: To maximize production benefits, the investment in fixed assets should be determined according to the scale of mining enterprises and the local conditions based on reliable resource reserves.

2.2.5 Grassland Resource Grassland resource is an important part of the biological cycle. Grassland resource can be used for the development of animal husbandry and other industries. Under reasonable utilization, grassland resource can be regenerated and restored continuously. However, if the utilization exceeds the bearing capacity of grassland, it will have serious consequences, such as desertification. Based on the dynamic evaluation of grassland ecosystem health by Liu et al. [14], this section further expands the analysis and obtains the evaluation index system for grassland resource efficient utilization, as shown in Table 2.5. (1) Indexes of the pressure layer (a) Pest and rat disaster rate: It refers to the ratio of pest and rat-affected areas to the total grassland area in a certain region. As the main component of the terrestrial ecosystem, grasslands can play a role in dust and sand fixation and soil and water conservation. However, the inundation of pests and rats will inevitably reduce the function of grassland resources and have a great impact on animal husbandry. Therefore, it is crucial to prevent and control pest and rat disasters. (b) Average forage yield per mu (1 mu is about 666.67 m2 ): Forage is the food source for livestock; it has strong regeneration ability and is rich in nutritional elements. When planting forage, varieties with relatively high yields that are suited to the local seasonal climate should be selected. (c) Livestock carrying capacity of grassland: It refers to the maximum number of livestock that can be carried in a year’s grazing period under the medium utilization level of grassland. If the livestock is less than the livestock carrying capacity, the grassland still has production and utilization potentials. However, if the livestock carrying capacity is exceeded, there will be bad repercussions, such as grassland degradation. (2) Indexes of the state layer (a) Output value of animal husbandry: In recent years, with the development of modern animal husbandry technology, the total output value of animal

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.5 Evaluation index system for efficient utilization of grassland resources Primary index

Secondary index

Tertiary index

Pressure index

Disaster pressure

Pest and rat disaster rate

Breeding pressure

Supply and demand status of animal husbandry Average yield per mu of forage (1 mu is about 667 m2 ) Grassland livestock carrying capacity

State index

Grassland utilization status

Output value of animal husbandry Artificial grassland area Grassland vegetation coverage

Grassland natural state

Enhanced vegetation index Slope Water network density Natural grassland area

Response index

Investment response

Investment in returning farmland to grassland Grassland improvement investment

Administration response

Annual greening area Desertification rate Grassland grazing prohibition subsidy

husbandry in China had exceeded CNY 300 billion in 2019.13 Due to policy support for development in China’s western region, the continuous maturity of China’s animal husbandry technology, and people’s demand for various animal husbandry products, animal husbandry has gradually developed into an industry that can promote the growth of the national economy. (b) Artificial grassland area: It refers to the grassland with a vegetation coverage of more than 5% in an animal husbandry area and the grassland area cultivated by agricultural technology, which is mainly used for animal husbandry grassland supply. (c) Grassland vegetation coverage: It is an important index that can reflect the grassland ecological environment. Wang et al. [15] studied the vegetation coverage and temporal and spatial distribution characteristics of grassland in Xilin Gol League, providing a basis for analyzing the growth of grassland in the region and allowing the Chinese government and local departments to adopt better corresponding decisions and measures. (d) Enhanced vegetation index (EVI): It is an indicator of vegetation growth state and vegetation coverage. 13

Data come from the China National Bureau of Statistics.

2.2 Construction of Evaluation Index System for Efficient Utilization …

EVI = G ×

ρNIR − ρRed , ρNIR + C1 × ρRed − C2 × ρBlue + L

49

(2.1)

where ρNIR refers to the near-infrared reflectance, ρRed is the red reflectance, ρBlue means the blue reflectance, C1 and C2 represent the aerosol impedance coefficient, and L is the canopy background adjustment factor. (e) Slope: It is the difference between the height difference and the horizontal distance of a certain area. (f) Water network density: It refers to the ratio of the length of regional rivers to the total area of regional land. The larger the index, the richer the water resources and the better the grassland resources. (g) Natural grassland area: China’s natural grassland area accounts for 12% of the world, ranking first globally.14 (3) Indexes of the response layer (a) Investment in returning farmland to grassland: It refers to stopping cultivation of sloping farmland to plant and restore vegetation to prevent and control water and soil loss. (b) Annual greening area: By the beginning of 2020, China had achieved 7.074 million ha of afforestation,15 making outstanding contributions to the grassland ecological security and the construction of an ecological civilization. (c) Desertification rate: The ecological balance of grassland resources has been damaged owing to human over-exploitation and destruction of natural resources. In recent years, government departments have strengthened greening measures to gradually reduce the desertification rate.

2.2.6 Marine Resources China is rich in marine resources, with seabed oil and gas resources of about 2.4 million t, and a mari-culture area of 2.6 million ha.16 China’s marine resources are a huge energy system, which also contains rich mineral and chemical resources. Thus, it is of great significance for China’s economic development to make full use of marine resources. With reference to previous research on a high-quality development index system and evaluation methods of marine economy, this section constructs the evaluation index system for efficient utilization of marine resources, as shown in Table 2.6. 14

Data come from the National Forestry and Grassland Administration of China. Data come from the Bulletin on the Status of China’s Land Greening 2018. 16 Data come from the China Marine Statistical Yearbook 2017. 15

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.6 Evaluation index system for efficient utilization of marine resources Primary index

Secondary index

Tertiary index

Pressure index

Current situation of marine ecology

Occurrence times of red tide Economic loss of marine disaster Storm surge marine aquaculture losses Aquaculture area with storm surge disaster in coastal areas Sea level change

Marine pollution discharge

Sewage caused by pollutant discharge from offshore oil exploration and development into the sea Total industrial wastewater discharge Amount of general industrial solid waste dumped and discarded in coastal areas Marine dredged materials dumping

State index

Marine production capacity Total output value of marine industry Mari-culture area Seawater oil and gas production well Sea salt production capacity Wind power generation capacity Number of travel agencies in coastal areas Marine science and technology

Sea-related employment Employed workers in major marine industries Number of marine institutions Number of employees in marine institutions Fund income of marine institutions Number of research projects of marine institutions Number of research papers of marine institutions Number of patents of marine institutions

Response index

Marine education

Doctoral graduates of marine specialty

Marine protection

Marine area pollution control project Number of marine protected areas

Marine administration and public welfare services

Sea area use right certificate Number of marine administrative inspections Number of observatories in coastal areas

2.2 Construction of Evaluation Index System for Efficient Utilization …

51

(1) Indexes of the pressure layer (a) Occurrence times of red tide: According to a report of the China State Oceanic Administration, there were 24 occurrences of red tides in the waters under China’s jurisdiction in the first half of 2021, compared with the same period of last year, the number of red tides decreased 18 times. It shows that China’s implementation of marine environmental protection measures is effective. (b) Economic loss of marine disasters: In 2020, China’s marine disasters were mainly related to storm surges and waves, and the direct economic loss of marine disasters was the lowest compared to the preceding ten years, indicating that China has carried out much prevention work to improve its disaster response capacity. (c) Storm surge marine aquaculture losses: According to statistics of the China State Oceanic Administration, the direct losses caused by storm surge in marine disasters are the heaviest, accounting for nearly 70%. (d) Aquaculture area with storm surge disaster in coastal areas: If the aquaculture area is too large, marine organisms would be polluted, and the corresponding production chain would be affected. (e) Sea level change: According to the China Sea Level Bulletin 2020, the sea level change along China’s coast is generally fluctuating and rising due to climate warming, among other factors. China has continuously improved its ability to prevent and respond to marine disasters through training and related work. (f) Sewage caused by pollutant discharge from offshore oil exploration and development into the sea: In 2009, the Emission Concentration Limits of Pollutants from Offshore Oil Exploration and Development were jointly issued by the General Administration of Quality Supervision of the People’s Republic of China and China Standardization Administration Committee, effectively alleviating the deterioration of marine pollution. (g) Total discharge of industrial wastewater: This indicator is one of the environmental statistical indicators. China’s annual urban sewage discharge was 44,534 km2 in 2014 and increased to 52.112 billion m2 in 2018.17 (h) Amount of general industrial solid waste dumped and discarded in coastal areas: The disposal methods of general industrial solid waste include the following four categories: comprehensive utilization, storage, disposal, dumping, and discarding. Comprehensive utilization is the main way to treat general industrial solid waste. In recent years, the proportions of comprehensive utilization and disposal of general industrial solid waste in China have decreased, while the proportion of storage has continued to increase. Dumping accounts for less than 0.1%, and thus, can be. largely ignored

17

The data are from the China Business Information Network.

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2 Index System, Method, and Application of Natural Resources Evaluation

(i) Marine dredged materials dumping: The dumping of dredged materials will affect the effective utilization of other marine resources and may cause damage to the marine environment. To make efficient use of marine resources, it is necessary to further develop waste treatment methods. (2) Indexes of the state layer (a) Gross output value of marine industry: It refers to the sum of the value of marine goods and related services of marine production departments in a certain period of time. It can reflect the results of the production and operation activities of the marine industry. (b) Mari-culture area: It refers to the area for marine cultivation of fish, shrimp, and shellfish, among others, in shallow sea areas, such as bays. In 2019, China’s mari-culture area was about 2 million ha.18 (c) Seawater oil and gas production well: The selection of oil production mode is related to the characteristics of the reservoir’s geology, oilfield development, oil well production capacity, and operating environment, which impacts the development and production effect. (d) Sea salt production capacity: China’s coastal areas in the southern part have relatively strong sea salt production capacity as compared with the northern part. The natural salt field is an important reason for China’s high sea salt production capacity. (e) Wind power generation capacity: Generally, offshore wind power is greater than land wind power. As a renewable energy, offshore wind power should be fully used to favor the overall natural resource ecosystem. (f) Number of travel agencies in coastal areas: An abundance of travel agencies in coastal areas shows that the region makes efficient use of marine resources and brings profits through the natural environment. However, massive expansion of tourism represents a burden on natural resources. Therefore, the number of regional tourism projects and travel agencies needs to be controlled at a certain level. (g) Sea-related employment: China’s sea-related employees account comprises a significant part of China’s total labor and employment. With the rapid development of the marine economy, more and more employees are engaged in sea-related work when the marine market is not saturated. (h) Number of marine institutions: Science and technology are the primary productive forces. The Chinese government’s investment in the construction of marine scientific research institutions can promote the efficient output and utilization of marine resources. (i) Number of research papers of marine institutions: Published papers are an important evaluation index for the output effect of scientific research institutions.

18

The data are from the Statistical Bulletin of China’s Marine Economy 2020.

2.2 Construction of Evaluation Index System for Efficient Utilization …

53

(j) Doctoral graduates of marine specialty: It can reflect the importance attached by regions to marine specialty education. (3) Indexes of the response layer (a) Marine pollution control projects: Although China has implemented corresponding measures to control marine pollution, the level of pollution remains serious. This has attracted the attention of the international community. Government departments can contribute to pollution prevention and control by formulating a series of conventions. (b) Sea area use right certificate: According to the measures for the administration of sea area use right certificates, relevant laws and regulations are formulated for marine activities, which can efficiently manage marine activities. (c) Number of observatories in coastal areas: Due to the progress of communication technology, coastal observatories make data transmission faster and more convenient.

2.2.7 Wind Resources China’s wind energy resources mainly come from the southeast coastal areas and the east and northwest regions. Offshore and inland wind energy resources are both abundant. According to the average of measurements from multiple weather stations at an altitude of 100 m, the average wind power density in China is 100 W/m2 . The positive or negative impacts of wind resources depend on the local wind speed and air density. As wind resources are renewable, utilizing them efficiently can greatly alleviate the pressure on the use of non-renewable mineral resources. By referring to the comprehensive evaluation of wind energy utilization level of wind farms by Shi et al. [16], this section designs the evaluation index system for efficient utilization of wind resources, as shown in Table 2.7. (1) Indexes of the pressure layer of power generation equipment × (a) Comprehensive field power rate: = Power consumption Power generation 100%. (b) Equipment failure and maintenance loss: It refers to the shutdown loss caused by sudden equipment failure, including losses related to adjustment, idling, deceleration, processing defect, and commencement. (c) Losses caused by restrictions: for example, wind curtailment and power rationing.

(2) Indexes of the state layer (a) Average wind speed: The sum of wind speeds observed for each time in a given period/observation times. (b) Average air density: The mass of air per unit volume.

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.7 Evaluation index system for efficient utilization of wind resources Primary index

Secondary index

Tertiary index

Pressure index

Wind application pressure

Comprehensive field rate Equipment failure and maintenance loss

State index

Wind curtailment and power rationing pressure

Losses due to restrictions

Natural state of wind

Average wind speed Average air density Hours of effective wind speed

Wind application level

Fan utilization rate Generating capacity Installed capacity

Response index

Administration response

Data management Personnel management

Institutional response

Regulatory system Dynamic reactive power compensation device

Investment protection response

Power quality real-time monitoring device Low voltage crossing capacity

Investigation response

Inspection and maintenance Accident handling Safe operation

(c) Hours of effective wind speed: The cumulative operation time of the fan when its speed is higher than the cut-in wind speed and lower than the cut-out wind speed in the statistical period. A−B × 100%, where A is the number of failure (d) Fan utilization rate: = 1 − 8760−B downtimes, B is the number of downtimes that are not the responsibility of the bidder, and 8760 is the total hours in a year. (e) I Power generation capacity: It refers to the power generated by power generation equipment per unit time, which is usually expressed by installed capacity. (f) Installed capacity: This index is proposed according to China Energy News in 2021. During the 14th Five-Year Plan period, the installed capacity of offshore wind power increased by 5.2 million kW, promoting a rapid growth in the scale of the offshore wind power market.

2.2 Construction of Evaluation Index System for Efficient Utilization …

55

(3) Indexes of the response layer (a) Data management: The development and utilization of wind energy is an important aspect of China’s new energy strategy. Monitoring, management, and analysis of data and systems related to wind power generation equipment can facilitate greater use of wind energy resources. (b) Personnel management: China’s wind power application has entered a large-scale development stage. To adapt to the expanding installed capacity of wind power generation and corresponding maintenance measures, and improve the safe use and operation reliability of wind power equipment, the management of operation and maintenance personnel has become increasingly important. (c) Regulatory system: With the marketization of the wind power equipment manufacturing industry, since 2005, the National Development and Reform Commission and its subordinate Energy Bureau have formulated regulations and policies for the wind power industry and the regulatory system has been implemented. (d) Dynamic reactive power compensation device: Dynamic reactive power compensation can alleviate the power quality problems caused by insufficient system reactive power during the energy supply of wind farms. (e) Power quality real-time monitoring device: It refers to a wind power quality monitoring device that comprehensively grasps the power quality of the wind farm. (f) Low voltage crossing capacity: It is a necessary condition for the power generation system to enter the power grid, which can ensure the normal operation of the new energy power generation system. (g) Inspection and maintenance: To ensure the continuous, safe, and reliable use of wind power equipment, a maintenance plan must be formulated and correct supervision and maintenance methods must be implemented, such as routine inspection and bearing maintenance. (h) Accident handling: The safety production awareness of operators should be strengthened to ensure timely and correct handling of various wind power accidents. (i) Safe operation: The wind power plant should formulate detailed procedures for safe operation to ensure the safety of workers and the stability of wind power equipment.

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2.3 Evaluation Method for Efficient Utilization of Natural Resources 2.3.1 Index Screening Method (1) Frequency statistical method This method is applicable to the statistics of the frequency of a certain index used in the literature related to the evaluation of natural resources efficiency utilization in China and abroad. For indexes with high frequency, the most appropriate indexes are determined after consulting relevant literature and data and conducting preliminary analysis. (2) Theoretical analysis method This method is suitable for a comprehensive comparative analysis of the main factors, connotation, and characteristics of natural resources efficiency utilization, and the selection of important and targeted indexes. (3) Delphi method This method is suitable for asking the opinions of experts on the utilization of natural resources regarding the established initial indexes, with consideration of the influencing factors of efficient utilization of various natural resources, and then making relevant adjustments. (4) Correlation analysis The method establishes an initial database for the selected indexes, statistically analyzes their correlation, and makes screening decisions. Each index is required to be independent of each other to avoid high correlation.

2.3.2 Main Index Evaluation Methods (1) Principal component analysis This method tries to delete the original duplicate variables and recombines them into a new group of independent and representative comprehensive variables; it then selects a smaller set of comprehensive variables from the complex variables to reflect the basic information of the original variables. The steps of principal component analysis are as follows. (a) To centralize data The centralized data A represents the difference between the original vector X and the mean value μ:

2.3 Evaluation Method for Efficient Utilization of Natural Resources

A = X −μ

57

(2.2)

The purpose is to make the center of gravity of the sample set coincide with the origin of coordinates, standardize the indexes with a large order of magnitude difference and obtain the covariance matrix and correlation coefficient matrix. The choice to use one of the two types of matrixes depends on the actual situation. (b) To calculate the covariance matrix of the centralized data matrix The covariance matrix is 

= A AT .

(2.3)

(c) To calculate eigenvalues and eigenvectors Arranging the eigenvalues of the obtained matrix λi (i = 1, 2, …, p), and determining the corresponding eigenvectors αi (i = 1, 2, …, p). (d) To determine the number of principal components The contribution rate of principal component variance after software processing is analyzed. Arrange the proportions of the first m eigenvalues to the sum of all eigenvalues from high to low. It is generally assumed that if the cumulative variance contribution rate reaches 85% or above, then the selected principal components reflect most of the original basic information. (e) To calculate principal components The calculation formula of the first m principal components is as follows: Z m = αmT X,

(2.4)

where αmT is the corresponding eigenvector of the mth eigenvalue. (2) Gray comprehensive evaluation method (a) To determine the reference data sequence and evaluation index To comprehensively reflect the efficient utilization of natural resources, index C0 (n) is selected, and the best value evaluation index is recorded as C0 (k) = {C0 (1), C0 (2), C0 (3), . . . , C0 (n)},

(2.5)

where C0 (k) is the evaluation index reference data sequence (k = 1, 2, …, n), and C0 (n) stands for each index. (b) To normalize the data X i (k) = {X i (1), X i (2), X i (3), . . . , X i (n)},

(2.6)

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2 Index System, Method, and Application of Natural Resources Evaluation

where X i (k) is the normalized sequence of data to be evaluated, and X i (n) is the normalized value of the evaluation data of each index. (c) To calculate correlation coefficient δi (k) =

min min|X 0 (k) − X i (k)| + 0.5 max max|X 0 (k) − X i (k)| i

k

i

k

|X 0 (k) − X i (k)| + 0.5 max max|X 0 (k) − X i (k)| i

,

k

(2.7) where X 0 (k) is the value after the normalization of the evaluation index reference data. (d) To calculate correlation degree Correlation degree Ri =

n 

δi (k)w(k),

k=1

where w(k) is the weight of the evaluation index. (e) To rank the correlation degrees When there are i evaluation data sequences, there are i corresponding correlation degrees. The higher the correlation degree Ri , the closer is the evaluation data sequence to the reference data sequence and the higher is the resource utilization efficiency. (3) Improved analytic hierarchy process In the improved analytic hierarchy process, three scales are used to replace the traditional nine scales to construct a judgment matrix with fast convergence and strong consistency. The specific operation steps are as follows. (a) When constructing the hierarchy, the evaluation objects are divided into target layer, criterion layer, and index layer. (b) To calculate the weight of the criterion layer as follows. (i) To establish a comparison matrix according to the relative importance of the two indexes: ⎡

0 ⎢ a1 ⎢ ⎢ A = ⎢ a2 ⎢ . ⎣ ..

a1 a11 a21 .. .

a2 a12 a22 .. .

⎤ . . . an . . . a1n ⎥ ⎥ . . . a2n ⎥ ⎥ .. ⎥ ... . ⎦

an an1 an2 . . . ann

where a1 ,…, an are indexes of the criterion layer; ai j can be expressed as

2.3 Evaluation Method for Efficient Utilization of Natural Resources

59

⎧ ⎨ 2 ai is more important than a j ai j = 1 ai and a j have the same importance ⎩ 0 a j is more important than ai (ii) To establish a structure judgment matrix B, that is, ⎡

0 ⎢ a1 ⎢ ⎢ B = ⎢ a2 ⎢ . ⎣ ..

a1 b11 b21 .. .

a2 b12 b22 .. .

⎤ . . . an . . . b1n ⎥ ⎥ . . . b2n ⎥ ⎥ .. ⎥ ... . ⎦

an bn1 bn2 . . . bnn

where bi j =

⎧ ⎨ ⎩



ci −c j +1 cmin −1 ci −c j +1 cmin

ci ≥ c j ci ≤ c j

ci =

n 

ai j

j=1

cmin = min{c1 , c2 , . . . , cn }. (iii) To calculate the weight and determine consistency. To calculate matrix B, γmax , and the maximum eigenvalue of the corresponding eigenvector C, that is, the weight vector, γmax , is incorporated into the consistency index I = (γ max − n)/(n − 1). If I is smaller than 0.1, then the matrix is deemed to meet the requirements. Otherwise, the comparison matrix must be recalculated and the consistency checked until the requirements are satisfied. (iv) To calculate the weight of each index. This is obtained by repeating steps (1) to (3) by m experts from the weight vector formed by the judgment matrix. That is,   c(k) = c1k , c2k , . . . , cnk , (k = 1, 2, . . . , m) E=

m 1  (k) c , (k = 1, 2, . . . , m). m k=1

(2.8)

where m is the number of participants, c(k) is the weight vector formed by the k judgment matrix, and E is the expected weight vector. The weight vector P of each index can be obtained by normalizing the weight vector E.

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2 Index System, Method, and Application of Natural Resources Evaluation

(4) Fuzzy comprehensive evaluation method (a) To determine the evaluation level According to China’s statistical data, the utilization of a certain type of resource is divided into five evaluation levels: D = {excellent, good, medium, qualified, and poor}, Dk ∈ [0, 1]. (b) To establish the single factor evaluation matrix To suppose the evaluation index system of the resource utilization level A = {A1, A2, A3, A4, A5, A6}. Set the i value of a single factor as Ri = (ri1 , ri2 , ri3 , ri4 , ri5 ), where rik is the degree of factor i being level k in the evaluation. ⎤ ⎡ ⎤ ⎡ r11 r12 r13 r14 r15 r16 R1 ⎢ R ⎥ ⎢r r r r r r ⎥ ⎢ 2 ⎥ ⎢ 21 14 23 24 25 26 ⎥ ⎥ ⎢ ⎥ ⎢ ⎢ R ⎥ ⎢r r r r r r ⎥ R = ⎢ 3 ⎥ = ⎢ 31 32 33 34 35 36 ⎥ ⎢ R4 ⎥ ⎢ r41 r42 r43 r44 r45 r46 ⎥ ⎥ ⎢ ⎥ ⎢ ⎣ R5 ⎦ ⎣ r51 r52 r53 r54 r55 r56 ⎦ R6 r61 r62 r63 r64 r65 r66 (c) To determine the fuzzy evaluation set The fuzzy comprehensive evaluation set of resource utilization level W represents the product of normalized weight vector P and single factor evaluation matrix R, that is, W = (w1 w2 w3 w4 w5 w6 ) = P · R ⎡ ⎤ r11 r12 r13 r14 r15 ⎢r r r r r ⎥ ⎢ 21 14 23 24 25 ⎥ ⎢ ⎥ ⎢r r r r r ⎥ = ( p1 p2 p3 p4 p5 p6 )⎢ 31 32 33 34 35 ⎥ ⎢ r41 r42 r43 r44 r45 ⎥ ⎢ ⎥ ⎣ r51 r52 r53 r54 r55 ⎦ r61 r62 r63 r64 r65 In this case, the resource utilization level is transformed to the value of W, and wk refers to the degree of the resource utilization level (k) in the evaluation. (d) To determine the grade of the evaluation object according to the evaluation level. To set Q as the evaluation value of the resource utilization level, which can be determined by the weighted average method. That is,   Q = w10 w20 w30 w40 w50 · (q1 q2 q3 q4 q5 )T .

2.3 Evaluation Method for Efficient Utilization of Natural Resources

61

Thus, the comprehensive evaluation of the resource utilization level is transformed into quantitative analysis.

2.3.3 Application Scopes, Advantages, and Disadvantages of Index Evaluation Methods with Different Data Types (1) Application scope Data used by existing evaluation methods can be divided into structured data and unstructured data according to the data attributes, and different evaluation methods can be adopted for different data types. Specifically, the advantages, disadvantages, and applicable data types of each evaluation method are shown in Fig. 2.2 and Table 2.8: (2) Advantages and disadvantages

Fig. 2.2 Evaluation methods with different data types

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.8 Comparison of evaluation methods Methods

Advantages

Disadvantages

Principal component analysis

Using the idea of dimension reduction, the number of original variables is effectively reduced to achieve fast convergence speed

When the principal component factor is positive or negative, the comprehensive evaluation is not clear

Factor analysis

Recombine the original variables, determine the common factors affecting the variables, and make the factor variables more representative by rotation

If OLS is used for factor scoring, this method has no economic significance and easily ignores information

Cluster analysis

Visual

Not applicable to a big sample

Artificial neural network

With self-study ability and associative storage function

Loss of information when converting induction to numerical values; cannot make queries

Bayesian network

Can overcome computational difficulties

Cannot support ring network, high requirements regarding the amount of data, and slow calculation process

Delphi method

Capable of dealing with complex Complex process, and long time task problems

Data envelopment analysis

Facilitates comparison among similar units

Sensitive to abnormal values; cannot accept negative values

Gray comprehensive evaluation

Applicable to problems with accurate and objective indexes

Only judges the pros and cons; does not reflect the absolute level

Fuzzy comprehensive evaluation method

Through accurate digital Complex calculation and processing of fuzzy objects, the subjective actual quantitative evaluation and average price result can be obtained as vector values

Analytic hierarchy process

Systematic and capable of qualitative and quantitative analysis

Unable to provide solutions for decision-making

Entropy method

Objective algorithm

Sensitive to abnormal values and prone to deviation

Expert scoring method

Experts can exchange opinions, which takes a short time

Large randomness and poor scientificity

Gray correlation method

No requirement for sample size and the amount of calculation is small

Imperfect theoretical system

Convolutional neural network

High classification accuracy

The parameters need to be adjusted, and the amount of data and calculation are large

2.4 Application of Evaluation Index System for Natural Resources …

63

2.4 Application of Evaluation Index System for Natural Resources Efficiency Utilization—Taking China’s Marine Resources as an Example 2.4.1 Selection and Description of China’s Marine Resources Indexes According to the previous description as described in Sect. 2.1 of this chapter, the PSR model is used to construct the evaluation indexes for the efficient utilization of China’s marine resources. To realize the efficient and high-quality utilization of natural resources, the Ministry of Natural Resources of China has pointed out that, under the new positioning and tasks of natural resources management in the new era, all departments need to coordinate and constantly improve the management system and mechanism, strengthen the utilization and control of natural resources, and give full play to resources to promote the high-quality economic development of all regions. Specifically, they should first improve planning and ensure the comprehensive and efficient use of natural resources; second, they should optimize management to promote the green and healthy development of natural resources; finally, they should strengthen the prevention and control of natural disasters and ensure the safety of the public. These three arrangements correspond to the “pressure–state– response” attributes of the PSR model. The goal of efficient utilization of marine resources is to realize the organic unity of social benefits, economic benefits, and people’s needs for a better life. This section endeavors to reflect the efficient utilization of China’s marine resources as comprehensively as possible based on previous research on the objectives of sustainable development, development and protection, performance audit and evaluation of marine resources, and the PSR model. Indexes are selected as per the following three aspects, and are analyzed one by one.19 (1) Evaluation of factors influencing the efficient utilization of marine resources Principal components that affect the efficient utilization of China’s marine resources are obtained by using the factor analysis method based on relevant statistical data from the China Marine Statistical Yearbook, Bulletin of China Marine Disasters, and Bulletin of China Sea Level, among other sources, during the period from 2007 to 2017, as shown in Table 2.9. (a) To standardize the data. Five principal components are determined by the SPSS software. The weight A of each principal component is calculated according to the variance contribution and cumulative contribution rate. (b) To calculate the weight of each index B in each principal component. (c) To consider both A and B in calculating the factor weight comprehensive score, as shown in Table 2.10. 19

Due to the lack of data, this section evaluates the efficient utilization of marine resources with different variables and in different coastal areas.

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.9 Analysis of factors influencing efficient utilization of marine resources Primary index

Secondary index

Tertiary index

Pressure index

Current situation of marine ecology

X2 Economic loss of marine disaster (CNY 100 million)

X1 Occurrence times of red tide X3 Storm surge marine aquaculture loss (10,000 ha) X4 Global sea level change (mm) (annual mean sea level is 0)

Marine pollution discharge

X5 Marine dredged materials dumping (10,000 m3 ) X6 Sewage produced by discharging pollutants into the sea from offshore oil exploration and development (10,000 m3 ) X7 Total industrial wastewater discharge (10,000 t) X8 Dumping volume of general industrial solid waste in coastal areas (10,000 t)

State index Marine production capacity

X9 Total output value of marine industry (CNY 100 million) X10 Mari-culture area (ha) X11 Seawater oil and gas production well X12 Sea salt production capacity (10,000 t) X13 Wind power generation capacity (10,000 kW) X14 Number of travel agencies in coastal areas

Marine science and technology

X15 Sea-related employment (10,000 people) X16 Employment in main marine industries (10,000 people) X17 Number of marine institutions X18 Number of employees of marine institutions (person) X19 Fund income of marine institutions (CNY 1000) X20 Number of research subjects of marine institutions X21 Number of papers of marine institutions X22 Number of patents of marine institutions (piece)

Response index

Marine education

X23 Doctoral graduates in marine specialty (person)

Marine protection

X24 Marine area pollution control projects X25 Number of marine protected areas

Marine administration and public welfare services

X26 Sea area use right certificate X27 Number of marine administrative inspections X28 Number of coastal observatories in coastal areas

2.4 Application of Evaluation Index System for Natural Resources …

65

Table 2.10 Comprehensive score of principal component influencing factors Ranking

Serial no

Variable

Comprehensive score

1

X18

Number of employees of marine institutions

0.041563

2

X17

Number of marine institutions

0.040685

3

X8

Dumping volume of general industrial solid waste in coastal areas

0.040597

4

X22

Number of patents of marine institutions

0.04059

5

X4

Global sea level change

0.040392

6

X26

Sea area use right certificate

0.039985

7

X21

Number of papers of marine institutions

0.039817

8

X13

Wind power generation capacity

0.039777

9

X12

Sea salt production capacity

0.039646

10

X23

Doctoral graduates in marine specialty

0.039557

11

X1

Occurrence times of red tide

0.039204

12

X6

Sewage produced by discharging pollutants into the sea from offshore oil exploration and development

0.038541

13

X28

Number of coastal observatories in coastal areas

0.038491

14

X9

Total output value of marine industry

0.038375

15

X11

Seawater oil and gas production well

0.038286

16

X16

Employment in main marine industries

0.037637

17

X15

Sea-related employment

0.037587

18

X14

Number of travel agencies in coastal areas

0.03725

19

X20

Number of research subjects of marine institutions

0.036286

20

X2

Economic loss of marine disaster

0.036215

21

X25

Number of marine protected areas

0.035244

22

X24

Marine area pollution control projects

0.034947

23

X27

Number of marine administrative inspections

0.02986

24

X19

Fund income of marine institutions

0.027518

25

X5

Marine dredged materials dumping

0.024642

26

X10

Mari-culture area

0.023726

27

X3

Storm surge marine aquaculture loss

0.022329

28

X7

Total industrial wastewater discharge

0.021254

According to the results in Table 2.10, the main influencing factors of the highquality development of marine resources are: number of employees of marine institutions (X18), number of marine institutions (X17), dumping volume of general industrial solid waste in coastal areas (X8), number of patents of marine institutions (X22), global sea level change (X4), and sea area use right certificate (X26). The corresponding main influencing factors are marine science and technology, current situation of marine ecology, and marine administration and public welfare services.

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2 Index System, Method, and Application of Natural Resources Evaluation

This shows that marine scientific and technological talents and scientific research institutions play a positive role in the efficient utilization of marine resources, and increasing the number of marine institutions and their employees can promote the efficient utilization of China’s marine resources, which further reflects the importance of attracting and cultivating marine scientific and technological skilled professionals. The total industrial wastewater discharge ranks last, meaning that there is no strong impact of industrial wastewater discharge on the efficient utilization of marine resources, as the inflow of industrial wastewater into the sea has been effectively supervised and controlled in recent years. This also demonstrates that further control measures or corresponding policies need to be taken for the dumping amount of general industrial solid waste. (2) Evaluation of efficient utilization of marine resources in different coastal areas This section statistically analyzes the marine resources in 11 coastal areas20 in China based on the China Marine Statistical Yearbook 2016. Due to missing data missing from some coastal areas, the index system designed above is modified to obtain the evaluation index system for efficient utilization of China’s marine resources, as shown in Table 2.11. SPSS25.0 software is used to conduct principal component analysis on the comprehensive index of natural resources efficiency utilization in 11 coastal areas. The results of the Kaiser–Meyer–Olkin (KMO) test and Bartlett sphericity test show that principal component analysis is suitable for the data matrix. (a) To standardize the data to determine the correlation between each index. According to the correlation coefficient matrix, whether there is strong correlation between variables and whether there are variables with overlapping information can be determined. (b) Five principal components are extracted according to the cumulative variance contribution. We divide the data in the component matrix by the square root of the eigenvalue corresponding to the principal component to obtain the coefficient corresponding to each index in the two principal components, as shown in Table 2.12. The table shows that the variance contribution rate of the first principal component (F1) is 48.13%, mainly related to the storm surge-affected area of aquaculture, proportion of marine GDP to regional GDP, and number of scientific papers published by marine scientific research institutions; we call this the marine disaster and production principal component. The variance contribution rate of the second principal component (F2) is 18.13%, mainly related to mari-culture output, sea salt production in coastal areas, sea salt production capacity in coastal areas, and sea area use right certificate; we call it the marine productivity and protection principal component. The variance contribution rate of the third principal component (F3) is 11.72%, mainly related to fishing ports in coastal areas, number of marine protected areas, and number of coastal observatories; it is called the marine administration and service principal component. 20

The 11 areas are Liaoning, Hebei, Tianjin, Shandong, Shanghai, Zhejiang, Jiangsu, Fujian, Guangdong, Hainan, and Guangxi.

2.4 Application of Evaluation Index System for Natural Resources …

67

Table 2.11 Evaluation index system for efficient utilization of marine resources in China’s coastal areas Primary index

Secondary index

Tertiary index

Pressure index

Marine pollution discharge

Q1 Storm surge-affected area of aquaculture (1000 ha)

State index Marine production capacity

Q2 Gross marine product of coastal areas (CNY 100 million) Q3 Proportion of marine GDP to regional GDP (%) Q4 Marine fishing output (ton) Q5 Mari-culture output (ton) Q6 Offshore crude oil production in coastal areas (10,000 t) Q7 Offshore natural gas production in coastal areas (10,000 m3 ) Q8 Output of sea salt in coastal areas (10,000 t) Q9 Marine cargo transportation volume in coastal areas (10,000 t) Q10 Passenger and cargo throughput of coastal ports (10,000 t/10,000 people) Q11 Fishing ports in coastal areas Q12 Mari-culture area in coastal areas (ha) Q13 Sea salt production capacity in coastal areas (10,000 t) Q14 Number of hotels in coastal areas

Marine science and technology

Q15 Sea-related employment (10,000 people) Q16 Number of marine scientific research institutions Q17 Number of employees in marine scientific research institutions Q18 Fund income of marine scientific research institutions (CNY 1000) Q19 Number of research subjects of marine scientific research institutions Q20 Number of scientific papers published by marine scientific research institutions Q21 Total number of invention patents (PCs.) Q22 R&D expenditure (CNY 1000) (continued)

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.11 (continued) Primary index

Secondary index Marine education

Tertiary index Q23 Doctoral graduates in marine specialty (person) Q24 Added value of marine scientific research, education, and management service industry (CNY 100 million)

Response index

Marine protection

Q25 Number of marine protected areas

Marine administration and public welfare services

Q27 Number of coastal observatories

Q26 Sea area use right certificate

Next, the variance contribution rate of the fourth principal component (F4) is 6.71%, mainly related to the gross marine product of coastal areas, offshore crude oil production in coastal areas, and number of hotels in coastal areas; it is called the marine resource utilization principal component. Finally, the variance contribution rate of the fifth principal component (F5) is 5.62%, mainly related to marine cargo transportation volume in coastal areas, fund income of marine scientific research institutions, R&D expenditure, and doctoral graduates in marine specialty; it is called the marine technology investment and education principal component. (c) To calculate the comprehensive score of each coastal area. By multiplying each principal component by the standardized variable data of each region, the value of each principal component of each region can be obtained. The results are shown in Table 2.13. (d) To calculate the comprehensive score of efficient utilization of marine resources in each coastal area. The coefficient of each principal component in the comprehensive score is first determined, that is, the ratio of contribution rate to the cumulative contribution rate, to obtain the comprehensive score F of each region: F = (48.131/90.304)F1 + (18.132/90.304)F2 + (11.717/90.304)F3 + (6.705/90.304)F4 + (5.618/90.304)F5 The ranking of efficient utilization of marine resources in China’s coastal areas is shown in Table 2.14. According to the ranking of the efficient utilization of marine resources in coastal areas, Shandong Province has the most developed level in terms of the efficient utilization of marine resources. With the help of superior geographical conditions, China’s coastal areas make full use of the advantages of marine resources and vigorously develop the marine industry, so that the marine economy has increasingly become an important part of China’s economic development. In the future, the

2.4 Application of Evaluation Index System for Natural Resources …

69

Table 2.12 Principal component analysis of indexes Variable

F2

F3

Q1

F1 0.267

− 0.044

− 0.023

Q2

0.063

− 0.103

Q3

0.234

− 0.220

Q4

0.127

0.125

Q5

0.197

Q6

0.048

Q7

0.157

Q8 Q9

F4

F5

0.013

0.022

0.092

0.563

0.394

− 0.015

− 0.077

0.046

0.395

− 0.099

0.027

0.213

0.178

0.172

− 0.116

− 0.193

− 0.254

0.309

− 0.357

− 0.305

− 0.137

0.106

− 0.279

0.122

0.352

− 0.170

0.113

− 0.031

0.139

− 0.241

0.163

− 0.253

0.350

Q10

0.222

0.072

− 0.045

− 0.117

− 0.148

Q11

0.170

0.104

0.388

− 0.117

− 0.115

Q12

0.148

0.238

− 0.017

− 0.024

− 0.106

Q13

0.127

0.353

− 0.157

0.107

− 0.015

Q14

0.188

0.063

− 0.037

− 0.506

0.022

Q15

0.262

− 0.067

0.128

0.032

− 0.128

Q16

0.245

0.015

0.073

− 0.042

− 0.027

Q17

0.257

− 0.073

− 0.170

0.031

0.023

Q18

0.211

− 0.006

− 0.262

0.048

0.348

Q19

0.204

− 0.142

− 0.136

− 0.179

− 0.090

Q20

0.252

− 0.088

− 0.158

− 0.048

− 0.098

Q21

0.239

− 0.143

− 0.087

− 0.006

− 0.185

Q22

0.206

− 0.122

− 0.250

0.018

0.311

Q23

0.176

0.279

− 0.163

0.024

0.232

Q24

0.212

0.116

0.242

0.279

− 0.061

Q25

0.159

− 0.205

0.306

0.183

− 0.180

Q26

0.119

0.394

− 0.103

0.019

− 0.079

Q27

0.208

− 0.046

0.250

0.072

0.260

Eigenvalue

12.995

4.896

3.164

1.810

1.517

contribution rate

48.131

18.132

11.717

6.705

5.618

Cumulative contribution rate

48.131

66.263

77.980

84.685

90.304

Eigenvector

Chinese government should prioritize protecting and improving the marine ecological environment, rationally developing marine resources, improving the ability of comprehensive marine management, and providing legal and institutional guarantee for the efficient utilization and rational protection of marine resources in accordance with relevant laws and regulations, and specific policies on marine protection and development. Such legislation includes the Law of the People’s Republic of China on

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2 Index System, Method, and Application of Natural Resources Evaluation

Table 2.13 Analysis of each principal component for each coastal area Area

F1

F2

F3

Tianjin

− 2.42

− 1.54

− 2.36

F4 1.79

F5 − 0.91

Hebei

− 3.34

1.01

− 0.77

− 0.94

− 1.32

Liaoning

0.34

1.48

0.00

− 0.12

0.01

Shanghai

0.15

− 1.93

− 1.92

0.03

3.01

− 0.88

− 0.23

− 0.86

− 2.09

0.13

Zhejiang

0.65

− 0.30

2.39

− 2.07

0.50

Fujian

0.61

0.03

3.51

1.65

0.36

Shandong

5.48

5.01

− 1.25

0.65

0.17

Guangdong

7.16

− 3.78

− 0.08

0.02

− 1.46

Guangxi

− 4.00

0.39

0.13

− 0.36

− 0.90

Hainan

− 3.75

− 0.16

1.21

1.44

0.42

Jiangsu

Table 2.14 Ranking of efficient utilization of marine resources in coastal areas Area

Comprehensive score

Ranking

Area

Comprehensive score

Ranking

Shandong

3.82

1

Jiangsu

− 0.78

7

Guangdong

2.96

2

Hainan

− 1.74

8

Fujian

0.93

3

Tianjin

− 1.83

9

Zhejiang

0.48

4

Hebei

− 1.83

10

Liaoning

0.47

5

Guangxi

− 2.12

11

Shanghai

− 0.37

6

–

–

–

the Administration of the Use of Sea Areas and the Marine Environment Protection Law of the People’s Republic of China. The establishment of an evaluation index system for efficient utilization of natural resources lays a foundation for the government and enterprises to carry out follow-up activities for the protection and efficient utilization of natural resources.

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4. Liu, Y., Bian, J., Li, X., Liu, S., Lageson, D., Yin, Y.: The optimization of regional industrial structure under the water–energy constraint: a case study on Hebei Province in China. Energy Policy 143, 111558 (2020) 5. Molden, D., Oweis, T., Steduto, P., Bindraban, P., Hanjra, M.A., Kijne, J.: Improving agricultural water productivity: between optimism and caution. Agric. Water Manag. 97(4), 528–535 (2010) 6. Zhang, J., Liu, J.: Study on index system and method of modern water resources evaluation in southern Shaanxi. J. China Agric. Resour. Reg. Plann. 41(8), 196–204 (2020) 7. Singh, P.K., Deshbhratar, P.B., Ramteke, D.S.: Effects of sewage wastewater irrigation on soil properties, crop yield and environment. Agric. Water Manag. 103, 100–104 (2012) 8. He, B., Li, S., Zhu, X.: Study on water environment security evaluation and index system in Tianjin City. Water Resour. Protect. 32(1), 125–129 (2020) 9. Jin, W., Zhang, H., Liu, S., Zhang, H.: Technological innovation, environmental regulation, and green total factor efficiency of industrial water resources. J. Clean. Prod. 211, 61–69 (2019) 10. Han, B., Liu, H., Wang, R.: Urban ecological security assessment for cities in the Beijing– Tianjin–Hebei metropolitan region based on fuzzy and entropy methods. Ecol. Modell. 318(15), 217–225 (2015) 11. Kang, P., Chen, W., Hou, Y., Li, Y.: Spatial-temporal risk assessment of urbanization impacts on ecosystem services based on pressure-status—response framework. Sci. Rep. 9, 16806 (2019) 12. Zhao, Q.: Resource and environmental quality changes and adjustment principles for sustainable development in rapidly developing coastal region of southeastern China. Pedosphere 11(4), 289 (2001) 13. He, G., Yu, B., Li, S., Zhu, Y.: Comprehensive evaluation of ecological security in mining area based on PSR-ANP-GRAY. Environ. Technol. 39(23), 3013–3019 (2018) 14. Liu, S.-Y., Ding, J.-L., Zhang, J.-Y., Zhang, Z.-H., Chen, X.-Y., Raxidin, M.: Remote sensing diagnosis of grassland ecosystem environmental health in the Ebinur Lake Basin. Acta Pratacul. Sin. 29(10), 1–13 (2020) 15. Wang, Z.Y., Yu, Q.R., Guo, L.: Quantifying the impact of the Grain-for-Green program on ecosystem health in the typical agro-pastoral ecotone: a case study in the Xilin Gol League, Inner Mongolia. Int. J. Environ. Res. Public Health 17(16), 5631 (2020) 16. Shi, R.-J., Fan, X.-C., He, Y.: Comprehensive evaluation index system for wind power utilization levels in wind farms in China. Renew. Sustain. Energy Rev. 69, 461–471 (2017)

Chapter 3

Efficiency Evaluation of Energy and Resource Utilization at the Regional Level in China

The development of modern economy and society is based on the rapid development of social productive forces. Construction of a material civilization, such as agriculture, industry, transportation, and modern living facilities, is inseparable from the demand for energy. From ancient times, human beings started learning how to use fire, wood, and other natural resources to live, representing the earliest form of energy utilization by human beings. With the rapid development of industrialization in Britain in the eighteenth century, humankind entered the steam age, and the demand for coal increased greatly, resulting in the first energy conversion from charcoal to coal. In the late 1860s, Britain’s Second Industrial Revolution came to an end, triggering the rapid development of global productivity. The emergence of the internal combustion engine caused oil to gradually replace coal in the energy structure, and the second energy conversion from coal to oil was completed. However, the exploitation and utilization of non-renewable energy resources, such as coal and oil, can cause air pollution, and environmental deterioration, and in recent years, the pursuit of economic development has led to the gradual deterioration of the global environment. Therefore, people must take environmental issues into account and adhere to the principles of sustainable development. In the future, low-carbon emission energy sources, such as clean energy and new energy, will inevitably replace the current high-carbon emission energy sources, such as coal and oil, and the third energy conversion will be completed.

The original version of this chapter was revised: the figures and tables have been corrected. The correction to this chapter is available athttps://doi.org/10.1007/978-981-99-4981-6_10 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023, corrected publication 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_3

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3.1 Current Situation of Energy and Resource Consumption in China Energy, also known as energy resources, is an indispensable and important material basis in the process of China’s economic development. It can be divided into primary energy and secondary energy according to the availability of processing. Primary energy is the natural energy that already exists in nature, which can be further divided into renewable energy and non-renewable energy. Secondary energy is the energy source that is transformed from the primary energy directly or indirectly into other types and forms, such as electricity and gasoline. Since secondary energy is processed from primary energy, improving energy conversion efficiency is conducive to increasing the output of secondary energy in China. According to the 2020 China Statistical Yearbook, China’s energy processing and conversion efficiency increased from 69.9% in 1983 to 72.3% in 2018; in 2019, China’s total energy consumption was 4.87 billion t of standard coal, including 57.7% from coal, 18.9% from oil, 8.1% from natural gas, and 15.3% from primary power and other energy. This indicates that the energy consumption in China is mainly the consumption of primary energy, in which coal consumption accounts for the largest share. At present, energy consumption is gradually increasing, and the demand for electricity, oil, and gas is growing faster than that of coal, leading to a contradiction between the supply and demand of oil and gas. According to statistics, the energy consumption in 2019 increased by 3.3% over the previous year, the power consumption increased by 4.4%, and GDP increased by 6.1%. China has a rich variety of energy resources, and its scales of energy development, production, and consumption are all at the global forefront. According to China’s Energy Development in the New Era issued by the State Council in 2020, China has become the largest energy producer in the world, and China’s total primary energy production reached 3.97 billion t of standard coal in 2019. According to the regional distribution of China’s energy resource bases, the number of bases in the west is the largest, followed by the central region. The energy resource base is a key regulatory field for storing various energy minerals, including oil, natural gas, coal, and other minerals. It is also an important guarantee for China’s energy resource security. Establishing and expanding energy resource bases and promoting the scale of China’s mineral resources are conducive to the development efficiency of mineral resources, improving the resource utilization efficiency, and solving the problem of supply and demand of energy resources domestically. Spatially, the distribution of China’s coal, oil, natural gas, and other resources is unbalanced. Moreover, China’s per capita energy holdings rank very low in the world, although the country is at the forefront of the world’s energy consumption and has abundant water resources and coal reserves. As China’s economic development is transitioning from rapid growth to high-quality development, balancing economic development and environmental protection has become increasingly important. China needs to solve the key problems urgently, by realizing high-efficiency energy conversion, energy utilization efficiency improvement, and carbon emission reduction. The first step to improve energy

3.1 Current Situation of Energy and Resource Consumption in China

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efficiency is to fully understand China’s energy consumption (coal, oil, natural gas, renewable energy), whose current situation is discussed in the following subsections: (1) Coal Coal is the most important form of domestic energy consumption; it is a typical nonrenewable fossil energy whose large-scale application in the future is limited. Thus, reducing the proportion of coal in energy consumption has become the only way for development. The fuel obtained from coal chemical production can replace part of oil and natural gas, which can help make up for the shortage of local oil and gas resources and broaden energy channels. China is the world’s largest coal producer, and most of China’s coal resources are distributed in north China and northwest China. For example, the coal resources in Shanxi, Shaanxi, Inner Mongolia, and Xinjiang account for about 73.4% of China’s coal reserves. Coal is the basic energy source for China’s stable energy supply. Since 2012, the annual output of raw coal has remained at 3.41 billion–3.97 billion t. In 2019, coal consumption accounted for 57.7% of the total energy consumption, a decrease of 10.8% over 2012. At present, coal resources are mostly medium and low-sulfur coal and medium ash coal. When coal is burned, the sulfur in it is transformed into sulfur dioxide and other substances, which is the main cause of air pollution and acid precipitation. In the long run, the use of coal will increase environmental costs and is not conducive to China’s environmental protection. According to the opinions issued by the China Energy Administration, the development of coal should be planned uniformly, with special attention being paid to clean and efficient utilization. (2) Natural gas Natural gas refers to a mixed gas mainly composed of hydrocarbons; it exists naturally between strata. It is an important energy resource and the main raw material of urban gas and industrial fuel. In 2019, China’s natural gas reserves increased by 3.0% from 2018, and the proportion of total production in total primary energy production increased to 5.7% from the 2.9% in 1978. The core theory guiding natural gas exploration in China’s oil industry was the “monistic” oil-based gas theory during the 100 years from 1878 to 1978. Due to the immaturity of the guiding theory, the natural gas industry could not develop rapidly at that time. In the late 1970s, the new dualism of the mixture of coal-derived gas and oil-based gas was put forward, which led to a change in China’s oil exploration direction and greatly promoted the development of China’s natural gas industry. China’s natural gas production in 2021 is expected to about 202.5 billion m3 . Compared with the two major energy resources, coal and oil, which occupy important positions in the energy structure, natural gas is considered to be a cleaner and more efficient energy resource with clear development prospects due to its highintensity heat release and low-intensity carbon emission. Natural gas can be divided into conventional natural gas and unconventional natural gas. With the continuous improvement of development technology, it has been found that the reserves of unconventional natural gas are greater than those of conventional natural gas. Among them,

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tight sandstone gas is the most important unconventional natural gas. In the future, tight gas will be the main contributor to China’s natural gas production. (3) Oil The nature of oil developed in different producing areas is different. Most of China’s oil resources are concentrated in the west, and basins are the main places for oil exploitation. At present, most of China’s domestic transportation fuel is oil-based. With the growth in sales of cars and other means of transportation, the domestic demand for oil will also increase. Oil is an indispensable energy resource in China’s energy structure. In 2000, China’s total oil consumption reached about 220 million t. Moreover, with the progress of China’s petroleum exploration technology, 1.12 billion t of geological reserves were newly proven in 2019, among which the newly proven technically recoverable reserves were 160 million t; the newly proven geological reserves of shale gas were 764.42 billion m3 , among which the newly proven technically recoverable reserves were 183.84 billion m3 . The China Energy Administration has suggested that China’s expected oil production in 2021 is about 196 million t. Oil is a non-renewable resource so that people’s exploitation of oil cannot be endless. A sustainable new energy alternative should be found to replace the position of oil in the energy structure in the future. Otherwise, a global energy crisis may ensue. (4) Renewable energy Renewable energy covers solar energy, hydro energy, biomass energy, geothermal energy, and wind energy, among others, which can be obtained all the time. China is extremely rich in solar energy reserves, especially in the western Qinghai Tibet Plateau, Gansu, and Ningxia regions, with long hours of sunshine and high annual radiation. In recent years, China’s photovoltaic technology industry has been continuously developed, and solar power generation technology has been applied to every household. However, solar energy has some defects, such as instability caused by seasonal change and alternation between day and night. To overcome this limitation, research on a solar space stations has determined the direction for the large-scale and stable development of solar energy in the future. The development technology of the solar space station can ensure that the sunlight in space is not affected by the change of seasons or of day into night, to continuously provide energy resources to the ground in the form of infinite energy transmission. China has invested in the development of this technology, and it is believed that China’s utilization of solar energy will be on a new level with the continuous technological progress. Biomass energy is the most important renewable energy resource, referring to a form of biomass stored in organisms as a carrier that can be used and converted into energy. The biggest advantage of biomass energy is that it is composed of only carbon. Biomass energy can obtain convenient and clean fuels by means of direct combustion, thermochemical conversion, and biochemical conversion, in solid, liquid, and gaseous forms. China’s biomass energy, including forest firewood, domestic waste, and animal manure, is an important source of rural energy in China. At present, the

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77

utilization rate of biomass energy in China is still not high owing to late domestic research, immature technology, and high development cost. Wind energy is a kind of kinetic energy generated by the flow of air; it is widely regarded as a source of high efficiency and clean energy, and it is generally converted into electric energy for power generation. China’s utilization of wind energy developed relatively late, but the technology has made rapid progress and becomes basically mature. In 2019, China’s wind power generation reached 357.7 billion kW, with a year-on-year increase of 7%. In addition to the aforementioned energy resources, China is still committed to developing new and available energy sources, such as nuclear energy and hydrogen energy. Nuclear energy is very clean in some respects, with no emissions of sulfur dioxide, carbon dioxide (CO2 ), and other compounds during the use process. It can be used for power generation as well as transportation and industrial heating. The amount of energy created by the fusion reaction of nuclear energy is considerable. If the utilization technology of nuclear energy can be well developed, the nuclear resources on Earth can ensure the long-term energy needs of humankind. The combustion heat of hydrogen is three times that of gasoline, and the combustion product of hydrogen is just water, which shows that hydrogen energy is a very clean new fuel. It is a kind of secondary energy and widely exists in nature, accounting for about 75% of the mass of the universe. China’s hydrogen energy development originated in the early 1960s. It provides important fuel for the development of the aerospace industry for building rockets. In view of the conversion of China’s energy structure, hydrogen energy is the most promising energy resource for replacing other highly polluting energy sources.

3.2 Research Progress on Energy Resource Utilization Scholars have conducted extensive research on energy and resources. Finn [1] considered that the rise of energy prices would lead to the decline of economic activities, by studying the impact of the perfectly competitive market and the rise of energy prices on economic activities; moreover, the long-term impact of energy price shocks on economic activities mainly arise from the relationship between energy utilization and capital services. Jacobson and Delucchi [2] analyzed the feasibility of providing energy by the WWS energy system composed of wind energy, hydropower, and solar energy to the world, and suggested that the system could replace other existing energy resources by 2050 through the analysis of changes in WWS energy, the economy, and policies. Chu and Majumdar [3] showed that clean and stable energy was an important foundation for increasing the prosperity of people’s lives and for the steady rise of the global economy; the authors linked solar energy, hydropower, and biomass energy with transportation and power generation, described the current situation of the world energy pattern, and pointed out multiple opportunities and directions for energy research and development. Coram and Katzner [4] established a dynamic model to describe the dynamic path of zero-emission alternative energy technology,

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to study the best energy production technology to reduce fossil fuel emissions. In addition to exploring the utilization of energy resources from the perspective of social and economic policies, some scholars have explored the issue in terms of the scientific and technological means. Scrosati and Garche [5] found that lithium battery was the preferred power supply in the consumer market of electronic products due to its high specific energy, high efficiency, and long service life, and considered that the design of the electrode and electrolyte materials of a lithium battery would affect the progress of research on power supply from the aspects of energy and cycle performance. Luo et al. [6] considered that the power grid faced great challenges in transmission and distribution, and electric energy storage was a basic technology with great potential to meet these challenges; they undertook a comprehensive analysis of the electric energy storage technology and summarized its operation principle, technical and economic performance, and other characteristics. Zhao et al. [7] discussed how to develop safe and reliable technology for operating the wind power high-permeability power system and described in detail the modern energy storage system and its application in the process of wind power integration. Scholars mainly discuss China’s energy resources research from the following three aspects. (1) They have analyzed the development direction of China’s energy resource utilization by expounding the environment and development of China’s energy resources. Tan et al. [8] analyzed the possibility of forestry biomass resource development from the aspects of policy, resource endowment, market, and technology. Zhu et al. [9] discussed the current situation and prospect of geothermal resource development and application in China. Geng et al. [10] analyzed the current situation, restrictive factors, and reform of China’s new energy development. Zhang et al. [11] evaluated the potential and utilization of biomass resources in Beijing, Tianjin, and Hebei and then discussed the bioenergy development strategy there. Ma et al. [12] discussed the energy revolution in the southwest region, and believed that the measures of the energy revolution to promote the shared development in the southwest region are: promoting the construction of natural gas infrastructure and the efficient development and clean utilization of coal; reasonably formulating the timing of hydropower development; promoting the diversified development and utilization of wind, solar energy, and water; and in terms of follow-up development, improving the hydropower consumption mechanism, breaking regional barriers, implementing targeted fiscal and tax policies, strengthening environmental protection, and noting the construction of high-end think tanks and the introduction of skilled professionals. (2) Scholars have studied the influencing factors of energy resources from the perspective of China’s basic national conditions and people’s living conditions. Dong et al. [13] analyzed China’s carbon emission intensity from 1992 to 2012 through structural decomposition analysis (SDA) and pointed out that the industrial sector was the key consumer of energy; energy efficiency contributes the

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most to the reduction of complete energy intensity (CEI), while the input structure, final demand structure, and final product structure are the factors that hinder the reduction of CEI. Wu et al. [14] studied the relationship between environmental regulation and China’s green energy by using panel data of 30 areas in China from 2005 to 2016, and found that there was a significant Ushaped relationship between environmental regulation and China’s green total factor energy efficiency; owing to deepening environmental decentralization, the independent choice of pollution control by local governments improved, inducing the negative regulatory effect of environmental regulation on green total factor energy efficiency. Huang and Chen [15] discussed the impact of R&D activities on energy intensity and pointed out that domestic technological innovation played a strong role in reducing energy intensity, but this positive impact mainly came from experimental R&D activities rather than basic R&D and application activities. (3) Scholars have studied the effect of energy on other factors from the perspective of the nature and characteristics of energy resources, to find the future development direction of China’s utilization of energy resources. Li et al. [16] analyzed the carbon emission reduction potential of energy in China’s rural areas, and pointed out that, without considering economic and technical constraints, as much as 2.2 times of China’s rural energy consumption could be supplied by 100% carbon-free energy (excluding biomass energy), and 2.4 times by renewable energy based on the energy data in 2008 if the renewable energy could be fully utilized and developed. Huang et al. [17] combined China’s energy system with water resources by establishing a bottom-up model (China time model) to predict the water demand of the power industry; the research showed that the CO2 emission reduction target could help reduce the water consumption of the power sector by promoting the expansion of renewable technologies. Lahiani [18] investigated the asymmetric effect of financial development on China’s CO2 emissions by controlling the effects of economic growth and energy consumption and concluded that there was an asymmetric effect, in that an increase in financial development helped to reduce CO2 emissions.

3.3 Evaluation Data and Model of Energy and Resource Utilization Efficiency 3.3.1 Data Description and Source The energy resource input–output data of 29 provincial and municipal units (Tibet, Hong Kong, Macao, and Taiwan are excluded from the study owing to lack of data; relevant data of Chongqing City are combined with those of Sichuan Province, as it is difficult to estimate the capital stock base period of Chongqing City, which was established after 1996) in China from 2000 to 2017 are chosen for studying the energy resource utilization efficiency in China. The data come from the China Energy

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Statistical Yearbook, China Statistical Yearbook, Economy Prediction System, and China’s provincial CO2 emission data released by the China Emission Accounts and Datasets. Input factors Labor input (symbol: L; unit: 10,000 people). Current-year number of employees in a region is taken as an input index. Capital input (symbol: K; unit: CNY 100 million). As there is a lack of official statistical data on capital stock at present, this chapter estimates the capital stock of the researched areas from 2000 to 2017 by referring to the internationally recognized practice–perpetual inventory method. The formula is K it = K it−1 (1−δ)+ Iit , where K it and K it−1 represent the capital stock in year t and year t − 1, respectively; δ is the depreciation rate, generally 9.6%; and Iit represents fixed asset investment formation. Energy resource input (symbol: E; unit: 10,000 t of standard coal). This index is expressed by the total consumption of energy resources, which mainly depends on the sum of various energies used by various industries in a region. Output factors Desirable output (symbol: G; unit: CNY 100 million). This index is expressed by regional GDP, depending on the sum of the output values of primary, secondary, and tertiary industries in a region. Undesirable output (symbol: C; unit: 10,000 t). The index is expressed by CO2 emissions in each region. An information overview of the above indexes is shown in Table 3.1. Table 3.1 Information of input and output variables

Input factor

Variable

Unit

Maximum value

Minimum value

Mean value

Capital stock (base period 2000)

CNY 100 million

26,717.13

739.00

26,190.21

Labor input

10,000 people

6,962.71

275.50

2,600.27

Energy resource input

10,000 tons of standard coal

40,138.00

479.95

11,385.69

Desirable output

Regional GDP (base period 2000)

CNY 100 million

59,876.80

263.68

10,128.54

Undesirable output

CO2 emissions

10,000 tons

0.80

270.59

1,553.8

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3.3.2 Model Introduction Assume that each region participates in a process of energy resource production and consumption that jointly produce desirable outputs and undesirable outputs; L, K, and E, respectively, represent labor input, capital input, and energy resource input in each region—namely the input factors; G and C, respectively, represent the regional GDP and CO2 emission of each region—that is, the desirable and undesirable outputs. Then, the environmental production technology model with multiple inputs and outputs can be expressed as the following formula: T = {(L , K , E, G, C)|(L .K , E) can produce (G, C)}.

(3.1)

As the above environmental production technology has no specific form, it cannot be directly applied to empirical analysis. In general, it should be formulated using a non-parametric phased linear framework, and then it can be expressed as:  T = (L , K , E, G, C) :

N 

z 1n L 1n ≤L

n=1 N 

z 1n K 1n ≤ K

n=1 N 

z 1n E 1n ≤ E

n=1 N 

z 1n G 1n ≥ G

n=1 N 

z 1n C1n = C

n=1

z 1n ≥ 0, n = 1, 2, . . . , N }.

(3.2)

Based on the research of Fukuyama et al. [19], this chapter defines the following non-radial distance function for the 29 areas in China:  , K , E, G, C; g1 ) = sup{w1T β1 : ((L , K , E, G, C) + g1 · diag(β1 )) ∈ T }, D(L (3.3) where w1 = (w1L , w1K , w1E , w1G , w1C )T represents the normalized weight vector of input and output vectors; g1 = (g1L , g1K , g1E , g1G , g1C )T indicates the direction vector in which the input and output vectors are scaled; and β1 = (β1L , β1K , β1E , β1G , β1C )T ≥ 0 represents the vector of the scaling factor.

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According to the non-radial distance function defined above, it is obvious that input and output are allowed to be adjusted in different proportions in the model.  , K , E, G, C; g1 ) can be solved by using the following data The value of D(L envelopment analysis (DEA) model:  , K , E, G, C; g1 ) = max w1L β1L + w1K β1K + w1E β1E + w1G β1G + w1C β1C D(L N 

s.t.

z 1n L 1n ≤ L + β1L g1L

n=1 N 

z 1n K 1n ≤ K + β1K g1K

n=1 N 

z 1n E 1n ≤ E + β1E g1E

n=1 N 

z 1n G 1n ≥ G + β1G g1G

n=1 N 

z 1n C1n = C + β1C g1C

n=1

z 1n ≥ 0, N = 1, 2, . . . , N , β1L , β1K , β1E , β1G , β1C ≥ 0. (3.4) Different results can be obtained by setting different direction vectors. First, assume that g1 = (0, 0, −E, G, 0), in which case there are only two scaling factors. Thus, the normalized weight vector should be (0, 0, 1/2, 1/2, 0). Under such a DEA model, the input of energy resources should be reduced as much as possible and the economic benefit output should be expanded in different proportions. By solving the formula (3.4), the potential energy resource input and potential economic benefit output of each region can be obtained. The economic benefit index of energy and resource can be defined by the ratio of actual energy efficiency to potential energy efficiency: λ1 =

G/E (G +

∗ β1G G)/(E

−

∗ β1E E)

=

∗ 1 − β1E ∗ , 1 + β1G

(3.5)

∗ ∗ where β1E and β1G represent the optimal solutions of formula (3.4). It is clear that the value range of λ1 is [0, 1], and the higher the value, the more the economic benefit output of energy resource in a region. Similarly, taking labor input and capital input into account and assuming that g1 = (−L , −K , −E, G, 0), the normalized weight vector should be (1/4, 1/4, 1/4, 1/4, 0). The inputs of labor, capital and energy resource should be reduced as much as possible and the economic benefit output should be expanded

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83

in different proportions. By solving formula (3.4), the potential labor input, capital input, energy resource input, and economic benefit output of each region can be obtained. Furthermore, the total factor economic benefit index of labor, capital, and energy resources can be defined as: λ2 =

∗ ∗ ∗ 1 − 13 (β1L + β1K + β1E ) , ∗ 1 + β1G

(3.6)

∗ ∗ ∗ ∗ where β1L , β1K , β1E , and β1G represent the optimal solutions of (3.4). Notably, λ2 and λ1 have the same characteristics. If considering the undesirable output generated by energy resources in production, and assuming g1 = (0, 0, 0, G, −C), the normalized weight vector should ∗ ∗ and β1C are the optimal solutions of (3.4). be (0, 0, 0, 1/2, 1/2). Suppose that β1G Then, the carbon emission efficiency can be defined as the ratio of potential carbon emission intensity to actual carbon emission intensity. From a mathematical point of view, carbon emission efficiency can be expressed as:

λ3 =

∗ ∗ ∗ C)/(G + β1G G) 1 − β1C (C − β1C = ∗ . C/G 1 + β1G

(3.7)

Finally, to consider both economic benefit and environmental benefit at the same time, assume that g1 = (0, 0, −E, G, −C) and g1 = (−L , −K , −E, G, −C). Then, the corresponding normalized weight vector should be (0, 0, 1/3, 1/3, 1/3) ∗ ∗ ∗ and (1/5, 1/5, 1/5, 1/5, 1/5). For easy analysis, further assume that β1L , β1K , β1E , ∗ ∗ β1G and β1C are the optimal solutions of the model (3.4). Similar to the principles of formulae (3.5)–(3.7), the energy carbon emission efficiency index without considering labor input and capital input, λ4 , and the total factor carbon emission efficiency index considering labor input and capital input, λ5 , can be defined as formulae (3.8) and (3.9), respectively: λ4 =

∗ ∗ ∗ ∗ + β1C ) ) + (1 − β1C )) 1 − 1/2(β1E 1/2((1 − β1E = , ∗ ∗ 1 + β1G 1 + β1G

∗ ∗ ∗ ∗ ) + (1 − β1K ) + (1 − β1E ) + (1 − β1C )) 1/4((1 − β1L ∗ 1 + β1G ∗ ∗ ∗ ∗ + β1K + β1E + β1C ) 1 − 1/4(β1L = . ∗ 1 + β1G

(3.8)

λ5 =

(3.9)

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3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional Level in China Through the non-radial distance function and five indexes established in the previous section, the model is solved using Stata software. This section further discusses the energy resource utilization efficiency by dividing China into four major economic regions: (1) the eastern region: Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; (2) central region: Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; (3) western region: Inner Mongolia, Guangxi, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang; (4) northeastern region: Liaoning, Jilin, and Heilongjiang. Moreover, only labor input, capital input, and energy resource input are considered in this section.

3.4.1 Economic Benefit of Energy Resources The economic benefit of energy resources refers to the ability to transform energy resources into economic growth, which is an important material basis in social production activities. The efficient transformation of the economic benefits of energy resources is the key to China’s sustainable economic and social development. At present, there is still much room for improvement in China’s energy consumption per unit regional GDP, and regional differences are significant. Therefore, it is of great significance to measure the economic benefit of energy resources at the regional level in China based on the non-radial distance function. Based on the non-radial distance function constructed by formulae (3.4) and (3.5), this section calculates the energy resource economic benefits of 29 areas and four major economic regions in China from 2000 to 2017 using the Stata software (see Table 3.2). Overall, the change in China’s energy economic benefits showed a steady development trend of first declining and then rising from 2000 to 2017. The economic benefits of energy resources in 2017 were the highest, with an increase of about 11% over 2000. The reason may be closely related to the adjustment of China’s macropolicies. With the development of China’s 12th Five-Year Plan, the rough economic growth model led to low resource utilization efficiencies. Meanwhile, increasingly severe environmental problems also promoted the adjustment of China’s energy consumption structure. In recent years, with the progress of domestic technology and the introduction of foreign advanced technology, the economic benefits of energy resource utilization have developed favorably. With the implementation of the 13th Five-Year Plan, the economic benefits of China’s energy resource utilization have always maintained a growth trend. Spatially, the economic benefits of China’s energy resources in 2017 were high in the east while low in the west, with an obvious gradient downward trend, as shown in Fig. 3.1. Generally, the economic benefits of energy resources have obvious

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

85

Table 3.2 Energy resource economic benefits of China, 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.65

0.66

0.68

0.75

0.74

… 0.79

0.80

0.80

0.80

0.83

Beijing

0.58

0.58

0.56

0.57

0.53

… 0.89

0.92

0.97

0.98

1.00

Fujian

0.83

0.95

0.89

0.92

0.77

… 0.80

0.80

0.87

0.90

0.92

Gansu

0.76

0.80

0.76

0.75

0.73

… 0.55

0.54

0.53

0.55

0.54

Guangdong

1.00

0.98

1.00

1.00

1.00

… 0.93

0.92

0.90

0.92

0.94

Guangxi

0.70

0.73

0.66

0.66

0.65

… 0.55

0.55

0.55

0.57

0.59

Guizhou

0.36

0.32

0.37

0.43

0.43

… 0.52

0.51

0.52

0.52

0.51

Hainan

0.56

0.56

1.00

0.56

0.52

… 0.78

0.80

0.83

0.81

0.80

Hebei

0.57

0.61

0.60

0.58

0.51

… 0.50

0.50

0.49

0.49

0.49

Henan

0.74

0.71

0.74

0.74

0.78

… 0.74

0.72

0.78

0.82

0.83

Heilongjiang

0.60

0.63

0.62

0.66

0.59

… 0.60

0.59

0.63

0.66

0.71

Hubei

1.00

0.71

0.62

0.58

0.54

… 0.56

0.57

0.63

0.65

0.68

Hunan

0.78

0.74

0.75

0.76

0.61

… 0.60

0.60

0.67

0.70

0.73

Jilin

1.00

1.00

0.93

0.61

0.72

… 0.66

0.78

0.86

0.68

1.00

Jiangsu

0.86

0.87

0.90

0.90

0.83

… 0.77

0.79

0.83

0.85

0.88

Jiangxi

0.75

0.84

0.75

0.72

0.74

… 0.69

0.70

0.71

0.73

0.76

Liaoning

0.75

0.76

0.64

0.60

0.58

… 0.59

0.62

0.82

0.89

0.94

Inner Mongolia

0.92

0.85

0.96

1.00

0.91

… 0.61

0.59

0.60

0.60

0.62

Ningxia

0.23

0.23

0.24

0.34

0.43

… 0.32

0.29

0.27

0.27

0.31

Qinghai

0.33

0.36

0.36

0.36

0.30

… 0.25

0.24

0.21

0.21

0.21

Shandong

0.62

0.56

0.56

0.55

0.56

… 0.67

0.84

0.85

0.74

0.80

Shanxi

0.72

0.90

0.70

0.71

0.72

… 0.69

0.71

0.70

0.72

0.76

Shaanxi

0.63

0.64

0.62

0.65

0.69

… 0.91

1.00

1.00

0.98

1.00

Shanghai

0.42

0.38

0.68

1.00

0.88

… 1.00

1.00

0.95

0.87

1.00

Sichuan

0.61

0.58

0.61

0.58

0.55

… 0.57

0.61

0.68

0.68

0.69

Tianjin

0.59

0.60

0.63

0.66

0.64

… 0.69

0.73

0.69

0.84

0.91

Xinjiang

0.47

0.47

0.46

0.47

0.45

… 0.36

0.44

0.40

0.33

0.33

Yunnan

0.51

0.54

0.50

0.52

0.45

… 0.54

0.51

0.51

0.49

0.50

Zhejiang

0.71

0.85

0.75

0.72

0.71

… 0.74

0.76

0.78

0.79

0.81

Eastern region

0.70

0.75

0.77

0.73

0.69

… 0.77

0.79

0.81

0.83

0.85

Central region

0.70

0.66

0.68

0.75

0.68

… 0.71

0.71

0.73

0.73

0.78

Western region

0.56

0.56

0.54

0.53

0.53

… 0.50

0.53

0.54

0.50

0.55

Northeastern region 0.80

0.77

0.78

0.78

0.76

… 0.65

0.64

0.74

0.77

0.80

Average

0.67

0.67

0.67

0.64

… 0.65

0.67

0.69

0.69

0.73

0.66

86

3 Efficiency Evaluation of Energy and Resource Utilization …

Fig. 3.1 Spatial distribution of economic benefits of energy resources in China 2017 (to ensure the integrity of the map, areas with missing data on economic benefits of energy resources are filled with 0, which is the same in following passage)

geographical agglomeration characteristics, especially in the northeastern region and the eastern coastal areas. The economic benefits of energy resources in Beijing, Inner Mongolia, Shanghai, and Shanxi were all 1, meaning that they had the highest energy economic conversion efficiency. Judging from the perspective of the four economic regions (see Fig. 3.2). The economic benefits of energy resources in the eastern region from 2000 to 2017 were very similar to the national average, and the overall level of economic benefits was higher than the national average. From 2000 to 2002, the economic benefits of energy resources in the eastern region showed an obvious upward trend, followed by a slight decline. Until 2012, the economic benefits of energy resources in the eastern region remained stable at about 0.70. Since the 12th Five-Year Plan (2010–2015), the economic benefits of energy resources have greatly improved, with an increase of about 15% during the study period. From 2000 to 2017, the economic benefits of energy resources in the central region changed gently, with a slight decline, and the overall level was higher than the national average. From 2000 to 2002, the economic benefits of energy resources in the central region decreased significantly, and remained relatively stable in subsequent years.

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

87

Fig. 3.2 Change trends of economic benefits of energy resources in four economic regions

After the implementation of the 13th Five-Year Plan (2015–2017), the economic benefits of energy resources in the central region started to improve significantly. From 2000 to 2017, the economic benefits of energy resources in the western region did not change significantly, and the overall level was lower than the national average. From 2000 to 2007, the economic benefits of energy resources in the western region fluctuated slightly until it reached the lowest level in 2007, and then stabilized. On the whole, the fluctuation of economic benefits of energy resources in the western region was smaller than that in the eastern and central regions, showing a stable and gentle trend. From 2000 to 2017, the economic benefits of energy resources in the northeastern region fluctuated greatly, showing a U-trend of decline and rise, and the overall economic benefit level of energy resources was diverged from the national average. From 2000 to 2008, the economic benefits of energy resources in the northeastern region decreased significantly, reaching the lowest point in 2008 with a decline of 27% compared with 2000. The economic benefits of energy resources in the northeastern region changed very gently from 2009 to 2012, and increased with a high rate after 2013, with the region recording the highest economic benefits of energy resources among the four economic regions in 2017 (see Fig. 3.2). The above analysis of the economic benefits of energy resources only considers the energy resources as input. In fact, besides relying on energy resource consumption, economic growth is also affected by factors such as labor input, capital input, and technological progress. Therefore, based on the non-radial distance function and formula (3.6), this study calculates the total factor economic benefits considering all labor input, capital input, and energy resource input. The total factor economic benefit values of 29 areas and the four major economic regions in China from 2000 to 2017 are shown in Table 3.3.

88

3 Efficiency Evaluation of Energy and Resource Utilization …

Table 3.3 Total factor economic benefits in China, 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.62

0.63

0.63

0.67

0.67

… 0.67

0.67

0.66

0.66

0.67

Beijing

0.68

0.66

0.66

0.67

0.63

… 0.81

0.84

0.90

0.95

1.00

Fujian

0.76

0.80

0.79

0.81

0.77

… 0.71

0.71

0.73

0.74

0.75

Gansu

0.74

0.73

0.70

0.71

0.70

… 0.56

0.55

0.54

0.53

0.53

Guangdong

0.92

0.91

1.00

1.00

1.00

… 0.85

0.81

0.80

0.80

0.80

Guangxi

0.63

0.63

0.60

0.61

0.62

… 0.49

0.49

0.48

0.49

0.51

Guizhou

0.44

0.39

0.39

0.45

0.44

… 0.51

0.47

0.46

0.45

0.43

Hainan

0.53

0.53

1.00

0.56

0.54

… 0.66

0.65

0.66

0.65

0.64

Hebei

0.66

0.66

0.67

0.68

0.67

… 0.57

0.56

0.56

0.55

0.54

Henan

0.77

0.77

0.79

0.81

0.85

… 0.73

0.71

0.73

0.75

0.76

Heilongjiang

0.61

0.62

0.62

0.66

0.64

… 0.55

0.53

0.54

0.55

0.57

Hubei

1.00

0.68

0.67

0.65

0.64

… 0.57

0.57

0.59

0.59

0.60

Hunan

0.67

0.66

0.67

0.68

0.63

… 0.59

0.58

0.61

0.62

0.64

Jilin

1.00

1.00

0.92

0.77

0.87

… 0.79

0.83

0.88

0.77

1.00

Jiangsu

0.75

0.75

0.78

0.79

0.78

… 0.75

0.77

0.79

0.81

0.83

Jiangxi

0.67

0.70

0.66

0.65

0.65

… 0.61

0.62

0.62

0.63

0.64

Liaoning

0.76

0.77

0.73

0.73

0.72

… 0.60

0.60

0.65

0.67

0.70

Inner Mongolia

0.94

0.89

0.97

1.00

0.97

… 0.69

0.68

0.70

0.70

0.72

Ningxia

0.49

0.49

0.49

0.42

0.53

… 0.41

0.38

0.35

0.34

0.40

Qinghai

0.37

0.38

0.38

0.38

0.36

… 0.35

0.33

0.29

0.28

0.28

Shandong

0.56

0.53

0.54

0.54

0.55

… 0.66

0.74

0.74

0.70

0.72

Shanxi

0.69

0.76

0.69

0.72

0.73

… 0.68

0.69

0.69

0.71

0.73

Shaanxi

0.68

0.71

0.72

0.78

0.82

… 0.97

1.00

1.00

0.99

1.00

Shanghai

0.59

0.57

0.82

1.00

0.95

… 1.00

1.00

0.96

0.88

1.00

Sichuan

0.60

0.58

0.59

0.59

0.58

… 0.60

0.62

0.64

0.63

0.64

Tianjin

0.67

0.67

0.71

0.73

0.74

… 0.75

0.75

0.71

0.80

0.83

Xinjiang

0.57

0.57

0.56

0.56

0.57

… 0.50

0.57

0.52

0.44

0.43

Yunnan

0.52

0.54

0.52

0.54

0.50

… 0.48

0.46

0.45

0.45

0.46

Zhejiang

0.71

0.76

0.74

0.72

0.71

… 0.69

0.71

0.71

0.72

0.74

Eastern region

0.70

0.72

0.78

0.75

0.74

… 0.74

0.75

0.76

0.77

0.79

Central region

0.69

0.64

0.68

0.72

0.70

… 0.67

0.66

0.66

0.65

0.69

Western region

0.59

0.58

0.57

0.56

0.57

… 0.54

0.54

0.53

0.51

0.54

Northeastern region 0.82

0.81

0.83

0.85

0.85

… 0.68

0.67

0.69

0.71

0.73

Average

0.67

0.69

0.69

0.68

… 0.65

0.65

0.65

0.65

0.67

0.68

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

89

On the whole, after considering labor and capital factors, the fluctuation of total factor economic benefits slowed down. Before 2013, the total factor economic benefits were significantly higher than those of energy resources, and in a state of fluctuation and decline. It was not until the implementation of the 13th Five-Year Plan that the total factor economic benefits increased significantly. This also reflects that the overall factor utilization efficiency level in the process of China’s economic development was not high, and the traditional factor input framework was inadequate for supporting China’s economic development goal at this stage. Figure 3.3 shows the spatial distribution of China’s total factor economic benefits in 2017. It can be seen from the figure that the total factor economic benefits of eastern coastal cities, represented by Shanghai, Jiangsu Province, and Zhejiang Province, were at a high level; those in the central region showed the characteristics of being high in the north while low in the south with geographical agglomeration; the total factor economic benefits in the western region were relatively low. In 2017, the total factor economic benefits of Beijing, Inner Mongolia, Shanghai, and Shanxi Province were all 1, making them the areas with the highest total factor economic transformation efficiency. Judging from the perspective of the four economic regions (see Fig. 3.4).

Fig. 3.3 Spatial distribution of China’s total factor economic benefits in 2017

90

3 Efficiency Evaluation of Energy and Resource Utilization …

Fig. 3.4 Change trends of total factor economic benefits of four major economic regions

The total factor economic benefits in the eastern region from 2000 to 2017 were higher than China’s average, but with consistent change trends, indicating that the eastern region was the key force for China’s total factor economic benefit growth; in 2000, the total factor economic benefit of the eastern region was the lowest, only 0.70; from 2001 to 2017, the total factor economic benefits of the eastern region showed a fluctuating upward trend until it reached the maximum value of 0.79 in 2017, an increase of about 13% from 2000. From 2000 to 2017, the total factor economic benefits of the central region fluctuated greatly. Throughout the study period, the total factor economic benefits of the central region were converged from the national average. From 2000 to 2013, the total factor economic benefits in the central region decreased significantly, the lowest value being 0.60, but rebounded after 2014. The total factor economic benefit of the central region holds constant in 2017 from 2000 levels. From 2000 to 2017, the total factor economic benefits of the western region showed a gentle downward trend. During the whole research period, the total factor economic benefits of the western region were lower than the average level of China. From 2000 to 2010, the total factor economic benefits in the western region were relatively stable, with an obvious downward trend after 2011. The total factor economic benefit of the western region decreased by 8% from 2000 levels. From 2000 to 2017, the total factor economic benefits in the northeastern region fluctuated significantly and higher than national average. From 2000 to 2010, the total factor economic benefits of the northeastern region showed a fluctuating downward trend, with the lowest being 0.67, a decrease of 18% compared with 2000. With the implementation of the 13th Five-Year Plan, the total factor economic benefits in this region were significantly improved.

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

91

3.4.2 Carbon Emission Efficiency Analysis In recent years, with the aggravation of the global climate change problem, China has shown the responsibility of a big country to the world by always responding positively to the problem. With the implementation of emission reduction and emission restriction policies in various regions and the promotion and use of renewable and clean energy, China’s carbon emission pattern has developed favorably to a certain extent. Therefore, based on the non-radial distance function, this section contributes significantly to the measurement of carbon emission efficiency at the regional level in China. Taking energy, the main source of carbon emission, as the input index can make the results more objective and accurate. Based on the non-radial distance function of formulae (3.4) and (3.7), this section calculates the carbon emission efficiencies of 29 areas and the four major economic regions in China from 2000 to 2017 through Stata software (see Table 3.4). Overall, China’s carbon emission efficiency was at a low level from 2000 to 2017. With the implementation of the 12th Five-Year Plan, China’s carbon emission efficiency developed to a certain extent. From 2000 to 2011, China’s carbon emission level showed a downward trend, with the lowest value in 2011, a decrease of about 30% compared with 2000. Although China’s average carbon emission efficiency level rebounded after 2011, it was still lower than the highest level during the study period, and the actual carbon emission intensity was at a high level. Figure 3.5 shows the spatial distribution of China’s carbon emission efficiency in 2017. It can be seen intuitively that the overall level of China’s carbon emission efficiency was not high. In 2017, only Beijing and Shanghai were at high levels, followed by Guangdong and Tianjin, and most areas were at low levels. The spatial distribution pattern is unreasonable and there is no agglomeration effect. Thus, there is a large development room for China’s carbon emission efficiency. Judging from the four major economic regions (see Fig. 3.6). The carbon emission efficiencies in the eastern region from 2000 to 2017 were significantly higher than the average level of China, but displayed a consistent trend, indicating that the eastern region was the key force to improve the overall level of carbon emission efficiency in China. During the study period, the carbon emission efficiency of the eastern region reached the lowest point of 0.41 in 2009, with a decrease of about 21% compared with the highest point, but still higher than the average carbon emission level of China in the same period. After 2014, the carbon emission efficiency of the eastern region has significantly improved. From 2000 to 2017, the carbon emission efficiencies of the central region fluctuated greatly but generally showed a trend of first decreasing and then increasing, and there was a cross-distribution with the average carbon emission level of China. From 2000 to 2001, the carbon emission efficiencies of the central region were relatively above the average level of China. After 2001, the gap between China’s average carbon emission efficiency and that of the central region gradually widened. The low-level development of carbon emission efficiency in the central region restrained the improvement of China’s carbon emission efficiency. In 2011, the carbon emission

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Table 3.4 Carbon emission efficiency in China, 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.31

0.32

0.34

0.34

0.34

… 0.22

0.15

0.18

0.22

0.25

Beijing

0.32

0.36

0.36

0.38

0.53

… 0.74

0.75

0.83

0.92

1.00

Fujian

0.75

0.84

0.76

0.69

0.61

… 0.38

0.35

0.34

0.40

0.41

Gansu

0.20

0.21

0.21

0.21

0.21

… 0.29

0.30

0.31

0.33

0.33

Guangdong

0.79

0.84

1.00

1.00

1.00

… 0.69

0.58

0.62

0.63

0.62

Guangxi

0.49

0.52

0.60

0.55

0.44

… 0.25

0.27

0.29

0.28

0.29

Guizhou

0.22

0.27

0.24

0.16

0.16

… 0.17

0.18

0.18

0.18

0.18

Hainan

0.21

0.22

1.00

0.20

0.42

… 0.23

0.25

0.25

0.28

0.21

Hebei

0.26

0.27

0.28

0.29

0.28

… 0.28

0.19

0.21

0.23

0.29

Henan

0.24

0.28

0.31

0.33

0.33

… 0.19

0.21

0.23

0.24

0.26

Heilongjiang

0.37

0.34

0.35

0.29

0.32

… 0.20

0.24

0.22

0.21

0.21

Hubei

1.00

0.34

0.29

0.25

0.22

… 0.33

0.24

0.27

0.30

0.31

Hunan

0.54

0.52

0.47

0.48

0.48

… 0.28

0.34

0.25

0.27

0.28

Jilin

0.18

0.19

0.19

0.24

0.17

… 0.11

0.10

0.10

0.12

0.12

Jiangsu

0.33

0.35

0.38

0.37

0.36

… 0.38

0.34

0.36

0.37

0.39

Jiangxi

0.43

0.43

0.43

0.35

0.26

… 0.26

0.29

0.33

0.36

0.25

Liaoning

0.27

0.28

0.30

0.30

0.32

… 0.24

0.26

0.21

0.21

0.22

Inner Mongolia

0.21

0.25

0.26

0.27

0.27

… 0.23

0.26

0.28

0.27

0.28

Ningxia

0.59

0.57

0.53

0.15

0.10

… 0.06

0.06

0.06

0.06

0.05

Qinghai

0.28

0.25

0.27

0.27

0.28

… 0.16

0.16

0.21

0.21

0.23

Shandong

0.35

0.37

0.34

0.35

0.32

… 0.17

0.13

0.13

0.15

0.14

Shanxi

0.30

0.25

0.24

0.38

0.35

… 0.23

0.25

0.26

0.27

0.29

Shaanxi

0.27

0.29

0.33

0.35

0.41

… 0.77

1.00

1.00

0.97

1.00

Shanghai

0.28

0.29

0.16

0.12

0.13

… 0.05

0.04

0.05

0.05

0.05

Sichuan

0.32

0.32

0.32

0.28

0.25

… 0.33

0.31

0.29

0.35

0.42

Tianjin

0.31

0.34

0.36

0.21

0.23

… 0.29

0.32

0.69

0.56

0.62

Xinjiang

0.20

0.21

0.24

0.23

0.21

… 0.15

0.10

0.10

0.14

0.13

Yunnan

0.20

0.18

0.17

0.12

0.17

… 0.19

0.23

0.28

0.29

0.28

Zhejiang

0.58

0.39

0.39

0.36

0.28

… 0.38

0.29

0.31

0.32

0.33

Eastern region

0.41

0.42

0.51

0.42

0.45

… 0.44

0.43

0.49

0.50

0.52

Central region

0.49

0.37

0.34

0.31

0.29

… 0.22

0.22

0.22

0.23

0.23

Western region

0.30

0.31

0.31

0.26

0.23

… 0.19

0.18

0.20

0.21

0.22

Northeastern region 0.24

0.27

0.29

0.30

0.30

… 0.22

0.24

0.24

0.24

0.25

Average

0.36

0.38

0.33

0.33

… 0.28

0.28

0.30

0.32

0.33

0.37

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

Fig. 3.5 Spatial distribution of China’s carbon emission efficiency in 2017

Fig. 3.6 Change trends of carbon emission efficiency in four major economic regions

93

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3 Efficiency Evaluation of Energy and Resource Utilization …

efficiency level of the central region reached the lowest value of 0.17, a decrease of about 65% from the highest point. Although the carbon emission efficiency level of the central region rebounded with the implementation of the 12th Five-Year Plan and the 13th Five-Year Plan, it was still at a low level, which is not conducive to the improvement of China’s overall carbon emission efficiency. The carbon emission efficiencies of the western region fluctuated relatively greatly from 2000 to 2017, and there was a downtrend during the study period. Moreover, the carbon emission efficiencies of the western region were lower than the average carbon emission efficiency level of China, which was not conducive to the improvement of China’s carbon emission efficiency level. The level of carbon emission efficiency in the western region reached the lowest value of 0.17 in 2011, a decrease of about 45% compared with the highest point. During the whole study period, the gap between the carbon emission efficiency of the western region and the average carbon emission efficiency of China are relatively stable. From 2000 to 2017, the carbon emission efficiency in Northeast China showed a trend of first increasing and then decreasing. Specially, from 2000 to 2007, the efficiency of carbon emission in Northeast China increased steadily. With the implementation of the 11th Five-Year Plan, the carbon emission efficiencies of the northeastern region decreased greatly and stabilized at a lower level again until 2012. Throughout the study period, the carbon emission efficiencies of the northeastern region were always lower than China’s average carbon emission efficiency level, with a growing gap, which seriously affected the improvement of China’s overall carbon emission level.

3.4.3 Analysis of Comprehensive Utilization Efficiency of Energy Resources In the first three sections, we consider the economic benefit and carbon emission efficiency when energy is used as input. However, there is specificity when energy resources participate in social production activities as factor input. On the one hand, the input of energy resources will promote economic development; on the other hand, energy resources inevitably produce undesirable outputs, represented by CO2 , which inhibits social progress and sustainable development to a certain extent. Therefore, when evaluating the utilization of energy resources, it is necessary to consider both desirable and undesirable outputs generated by the energy factor input simultaneously. 2020 is the closing year of the 13th Five-Year Plan. Over the past 5 years, under the guidance of the new energy security strategy, key tasks were promoted; major projects were constructed; various reform measures were implemented; and remarkable achievements were made in building a clean, low-carbon, safe, and efficient energy system. In the new era, energy development will stand at a new historical starting point and face new historical missions. Based on the non-radial distance function of formulae (3.4) and (3.8), the comprehensive utilization efficiencies of energy resources of 29 areas and the four major

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

95

economic regions in China from 2000 to 2017 are calculated by Stata software (see Table 3.5). Overall, from 2000 to 2017, China’s comprehensive energy utilization efficiency was not high with few changes, showing a decline-rise development trend. From 2000 to 2002, the development of China’s comprehensive energy utilization efficiency was relatively stable; however, from 2003 to 2011, it showed a gentle downward trend and reached the lowest point of 0.33 in 2011; after 2012, China’s comprehensive energy utilization efficiency improved and reached the highest value of 0.43 in 2017, an increase of about 30% compared with 2011. This shows the effectiveness of a series of measures taken by China for the energy industry to ensure macroeconomic sustainability. The conversion efficiency of energy resources has developed to a certain extent. Figure 3.7 shows the spatial distribution of China’s energy resource comprehensive utilization efficiency in 2017, displaying small-scale agglomeration, particularly in the eastern coastal areas represented by Shanghai, Jiangsu Province, and Zhejiang Province, and the central region represented by Anhui Province, Hubei Province, and Hunan Province. The comprehensive utilization levels of energy resources in Beijing, Tianjin, Shanghai, and Guangdong Province were relatively high, and the comprehensive utilization levels of energy resources in eastern China were higher overall than those in the western region. Judging from the four major economic regions (see Fig. 3.8). From 2000 to 2017, the comprehensive utilization efficiencies of energy resources in the eastern region were generally higher than the average national level but displayed a consistent change trend, indicating that during the research period, the comprehensive utilization efficiencies of energy resources in the eastern region were the key driving force behind the average comprehensive utilization efficiency of energy resources in China. In 2002, the comprehensive utilization efficiency of energy resources in the eastern region significantly improved, reaching the highest level during the study period; from 2003 to 2011, the comprehensive utilization efficiencies of energy resources in the eastern region showed a relatively gentle downward trend, with a minimum of 0.48; since 2012, the comprehensive utilization efficiencies of energy resources in the eastern region again showed a benign development trend. From 2000 to 2017, the comprehensive utilization efficiencies of energy resources in the central region were close to that of the whole country, with a U-trend during the study period. From 2000 to 2012, the comprehensive energy utilization efficiencies in the central region decreased year by year, with the lowest being only 0.30, a decline of 42% compared with the highest point; after 2013, the comprehensive energy utilization efficiencies in the central region showed a relatively stable growth in a consistent trend with the average level of China. The change trends of comprehensive energy utilization efficiencies in the western and northeast regions were consistent with the average level of China, but there was an obvious gap between western region and other regions during the study period. From 2000 to 2013, the fluctuation of the comprehensive utilization efficiencies of energy resources in the western region was relatively gentle, while there was an obvious first rise and then down trend in the northeast region; after 2013, the comprehensive utilization efficiencies of energy resources in the western and northeast regions

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3 Efficiency Evaluation of Energy and Resource Utilization …

Table 3.5 Comprehensive utilization efficiency of energy resources in China from 2000 to 2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.31

0.32

0.34

0.37

0.35

… 0.33

0.34

0.35

0.37

0.38

Beijing

0.39

0.42

0.44

0.45

0.55

… 0.81

0.83

0.90

0.95

1.00

Fujian

0.82

0.91

0.85

0.81

0.71

… 0.57

0.54

0.57

0.60

0.62

Gansu

0.25

0.25

0.24

0.24

0.23

… 0.22

0.23

0.25

0.28

0.29

Guangdong

0.88

0.91

1.00

1.00

1.00

… 0.80

0.73

0.76

0.78

0.78

Guangxi

0.56

0.60

0.63

0.60

0.52

… 0.32

0.34

0.37

0.38

0.39

Guizhou

0.21

0.24

0.24

0.19

0.20

… 0.16

0.18

0.20

0.21

0.23

Hainan

0.58

0.61

1.00

0.52

0.68

… 0.36

0.36

0.37

0.39

0.40

Hebei

0.24

0.26

0.26

0.26

0.24

… 0.26

0.28

0.23

0.24

0.27

Henan

0.27

0.30

0.34

0.36

0.37

… 0.32

0.32

0.34

0.35

0.37

Heilongjiang

0.44

0.43

0.44

0.40

0.40

… 0.29

0.31

0.35

0.37

0.40

Hubei

1.00

0.43

0.39

0.35

0.32

… 0.36

0.37

0.41

0.44

0.46

Hunan

0.63

0.60

0.57

0.58

0.52

… 0.38

0.40

0.43

0.44

0.46

Jilin

0.26

0.26

0.25

0.23

0.24

… 0.19

0.20

0.21

0.23

0.23

Jiangsu

0.54

0.56

0.60

0.59

0.54

… 0.47

0.51

0.54

0.55

0.58

Jiangxi

0.54

0.59

0.55

0.48

0.43

… 0.40

0.42

0.43

0.44

0.46

Liaoning

0.30

0.31

0.30

0.29

0.30

… 0.30

0.32

0.40

0.44

0.46

Inner Mongolia

0.27

0.30

0.33

0.35

0.32

… 0.27

0.29

0.31

0.30

0.31

Ningxia

0.41

0.41

0.39

0.16

0.13

… 0.10

0.10

0.10

0.10

0.09

Qinghai

0.14

0.14

0.15

0.15

0.15

… 0.17

0.14

0.18

0.19

0.21

Shandong

0.25

0.24

0.23

0.24

0.22

… 0.26

0.25

0.26

0.28

0.29

Shanxi

0.43

0.46

0.38

0.37

0.35

… 0.32

0.33

0.33

0.35

0.37

Shaanxi

0.37

0.39

0.42

0.44

0.49

… 0.81

1.00

1.00

0.98

1.00

Shanghai

0.21

0.25

0.18

0.17

0.18

… 0.14

0.14

0.14

0.15

0.15

Sichuan

0.41

0.41

0.42

0.38

0.36

… 0.42

0.40

0.47

0.52

0.57

Tianjin

0.24

0.26

0.29

0.32

0.33

… 0.42

0.45

0.67

0.66

0.72

Xinjiang

0.15

0.16

0.17

0.17

0.16

… 0.15

0.16

0.16

0.16

0.15

Yunnan

0.31

0.31

0.29

0.26

0.30

… 0.25

0.27

0.30

0.32

0.33

Zhejiang

0.66

0.59

0.55

0.51

0.44

… 0.45

0.48

0.49

0.51

0.52

Eastern region

0.52

0.54

0.58

0.53

0.53

… 0.53

0.55

0.58

0.60

0.63

Central region

0.52

0.44

0.41

0.39

0.37

… 0.32

0.33

0.35

0.37

0.39

Western region

0.30

0.30

0.30

0.26

0.25

… 0.22

0.23

0.25

0.27

0.28

Northeastern region 0.28

0.30

0.32

0.34

0.33

… 0.30

0.31

0.35

0.36

0.38

Average

0.41

0.42

0.39

0.38

… 0.36

0.37

0.40

0.41

0.43

0.42

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

97

Fig. 3.7 Spatial distribution of comprehensive utilization efficiency of energy resources in China in 2017

Fig. 3.8 Change trends of comprehensive energy utilization efficiency in four economic regions

98

3 Efficiency Evaluation of Energy and Resource Utilization …

showed a benign development trend, and the gap with China’s average level was relatively stable. Similarly, this section takes labor input and capital input as input factors into the model (3.4), and solves the constructed non-radial distance function by using the Stata software. As per formula (3.9), the total factor comprehensive utilization efficiency considering labor input, capital input, and energy resource input is calculated. Table 3.6 shows the total factor comprehensive utilization efficiency values of 29 areas and the four major economic regions in China from 2000 to 2017. Overall, from 2000 to 2017, China’s total factor comprehensive utilization efficiency fluctuated gently, showing a development trend of first decreasing and then increasing during the whole research period. Although the level of China’s total factor comprehensive utilization efficiency increased and fluctuated in a small range from 2000 to 2011, the overall downward trend was obvious, reaching the lowest value of 0.45 in 2011; from 2012 to 2017, China’s total factor comprehensive utilization efficiency developed rapidly, with an increase of about 16% compared with 2011, far less than the increase of the energy comprehensive utilization efficiency in the same period, indicating that the improvement of the energy comprehensive utilization efficiency was a key force in promoting the development of China’s total factor comprehensive utilization level. Figure 3.9 shows the spatial distribution of China’s total factor comprehensive utilization efficiency in 2017. The total factor comprehensive utilization efficiencies of Beijing, Tianjin, Jiangsu, Fujian, Guangdong provinces and Shanghai were at high levels. Generally, there were significant regional differences except in the central region, with no obvious agglomeration characteristics. Judging from the four economic regions (see Fig. 3.10). From 2000 to 2017, the change trend of total factor comprehensive utilization efficiency in the eastern region was very close to that in China, and the overall level of the eastern region was higher than the average level of China, indicating that the total factor comprehensive utilization efficiency in the eastern region was the main force to improve China’s total factor comprehensive utilization efficiency during the research period. From 2000 to 2011, the development of total factor comprehensive utilization efficiency in the eastern region was relatively slow and showed a notable upward trend since 2011. During the whole study period, the level of total factor comprehensive utilization efficiency in the eastern region always maintained a certain gap with the average level in China. The level of total factor comprehensive utilization in the central region was close to that of China overall, showing a decline-and-rise development trend. From 2000 to 2012, the total factor comprehensive utilization efficiency in the central region showed an obvious downward trend and reached the lowest value of 0.41, a decrease of about 31% compared with 2000; from 2013 to 2017, the level of total factor comprehensive utilization efficiency in the central region improved and exhibited a development trend more similar to the national level. From 2000 to 2017, the comprehensive utilization level of total factor in the western region showed a relatively gentle downward trend, and it was always lower than the national average level, which was not conducive to the development of the comprehensive utilization level of total factor in China. Among these, the western

3.4 Evaluation of Energy Resource Utilization Efficiency at the Regional …

99

Table 3.6 Total factor comprehensive utilization efficiency values in China 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.44

0.44

0.45

0.47

0.46

… 0.44

0.45

0.46

0.48

0.49

Beijing

0.53

0.55

0.56

0.57

0.62

… 0.85

0.87

0.92

0.96

1.00

Fujian

0.76

0.81

0.79

0.78

0.73

… 0.63

0.63

0.66

0.69

0.71

Gansu

0.40

0.41

0.41

0.41

0.41

… 0.35

0.35

0.35

0.36

0.36

Guangdong

0.85

0.87

1.00

1.00

1.00

… 0.80

0.74

0.75

0.77

0.78

Guangxi

0.57

0.59

0.60

0.59

0.56

… 0.39

0.40

0.42

0.43

0.44

Guizhou

0.31

0.31

0.29

0.27

0.27

… 0.28

0.28

0.28

0.28

0.29

Hainan

0.57

0.59

1.00

0.54

0.63

… 0.47

0.46

0.46

0.47

0.48

Hebei

0.42

0.42

0.43

0.44

0.43

… 0.38

0.38

0.39

0.40

0.42

Henan

0.45

0.48

0.51

0.54

0.55

… 0.48

0.48

0.50

0.51

0.53

Heilongjiang

0.51

0.51

0.52

0.50

0.51

… 0.39

0.40

0.42

0.43

0.45

Hubei

1.00

0.52

0.51

0.49

0.47

… 0.49

0.50

0.53

0.54

0.56

Hunan

0.61

0.60

0.59

0.60

0.57

… 0.49

0.51

0.54

0.55

0.57

Jilin

0.45

0.46

0.46

0.45

0.41

… 0.35

0.35

0.36

0.38

0.39

Jiangsu

0.59

0.60

0.63

0.64

0.62

… 0.60

0.63

0.66

0.68

0.71

Jiangxi

0.57

0.59

0.57

0.54

0.50

… 0.49

0.51

0.52

0.53

0.55

Liaoning

0.47

0.48

0.47

0.48

0.49

… 0.41

0.43

0.47

0.50

0.52

Inner Mongolia

0.49

0.51

0.55

0.58

0.56

… 0.48

0.48

0.50

0.49

0.51

Ningxia

0.56

0.57

0.57

0.24

0.22

… 0.19

0.18

0.18

0.18

0.17

Qinghai

0.28

0.27

0.27

0.26

0.26

… 0.24

0.23

0.25

0.25

0.26

Shandong

0.37

0.37

0.36

0.36

0.35

… 0.34

0.33

0.34

0.35

0.36

Shanxi

0.53

0.54

0.50

0.50

0.49

… 0.48

0.49

0.49

0.51

0.53

Shaanxi

0.52

0.54

0.56

0.59

0.64

… 0.89

1.00

1.00

0.98

1.00

Shanghai

0.41

0.41

0.39

0.39

0.38

… 0.24

0.24

0.23

0.23

0.24

Sichuan

0.49

0.49

0.50

0.48

0.47

… 0.52

0.51

0.56

0.60

0.64

Tianjin

0.41

0.43

0.46

0.49

0.50

… 0.61

0.64

0.78

0.75

0.77

Xinjiang

0.31

0.31

0.32

0.32

0.32

… 0.29

0.28

0.27

0.28

0.28

Yunnan

0.43

0.42

0.42

0.40

0.42

… 0.36

0.36

0.38

0.38

0.39

Zhejiang

0.67

0.63

0.62

0.59

0.54

… 0.57

0.59

0.62

0.64

0.65

Eastern region

0.58

0.60

0.66

0.61

0.62

… 0.63

0.64

0.67

0.68

0.71

Central region

0.59

0.51

0.50

0.50

0.48

… 0.42

0.43

0.45

0.46

0.48

Western region

0.42

0.42

0.42

0.38

0.37

… 0.33

0.33

0.34

0.35

0.36

Northeastern region 0.47

0.49

0.51

0.53

0.53

… 0.46

0.46

0.49

0.50

0.52

Average

0.51

0.53

0.50

0.50

… 0.47

0.47

0.49

0.50

0.52

0.52

100

3 Efficiency Evaluation of Energy and Resource Utilization …

Fig. 3.9 Spatial distribution of China’s total factor comprehensive utilization efficiency in 2017

Fig. 3.10 Change trends of total factor comprehensive utilization efficiency in four economic regions

3.5 Conclusions and Policy Recommendations

101

region’s comprehensive utilization level of total factors was the lowest in 2014, at 0.33. There was a definite tendency of flat growth after 2015. During the study period, the disparity between the comprehensive utilization level of total factors and the national average level in the western area remained reasonably consistent. Throughout the study period, Northeast China’s comprehensive utilization efficiency of all factors lagged behind the level of the nation as a whole before ultimately converging. In contrast, the comprehensive utilization efficiency of all factors varies widely in Northeast China, and the difference between this region’s comprehensive utilization efficiency and the level of the national average gradually closes. The complete utilization efficiency of all factors in Northeast China has demonstrated a positive development trend since the “12th-Year Plan” was put into effect.

3.5 Conclusions and Policy Recommendations 3.5.1 Main Conclusions The following conclusions can be drawn from the non-radial distance function constructed in Sect. 3.3, based on the measurement and analysis of the economic benefits and comprehensive utilization efficiency of China’s energy resources, carbon emission efficiency, all factor economic benefits, and comprehensive utilization efficiency considering labor input and capital input from 2000 to 2017 using Stata16. (1) Energy resource is an important material basis for the development of modern society, and the efficiency of converting energy resource into economic growth showed a relatively gentle upward trend during the research period, especially after the implementation of the 12th Five-Year Plan. In terms of spatial distribution, the economic benefits of energy resources were high in the east while low in the west, with explicit geographical agglomeration characteristics. From the perspective of the four major economic regions, the economic benefits of energy resources in the eastern region were significantly higher than the average level of China; the economic benefits of energy resources in the central region were higher than the average level of China, but the gap was narrowing; the overall economic benefits of energy resources in the western region were lower than the average level of China, and the gap was gradually widening; the economic benefits of energy and resources in the northeastern region developed vigorously since the 12th Five-Year Plan, gradually exceeding the average level of China and becoming the region second only to eastern region economic benefits of energy resources in 2017. The total factor economic benefits in China were calculated after taking labor input, capital input, and energy resource input into account. Generally, the changes in total factor economic benefits were gentler, with the same distribution of high in the east and low in the west. However, different from the distribution of the economic

102

3 Efficiency Evaluation of Energy and Resource Utilization …

benefits of energy resources, there was a north–south gap in the central region. From the perspective of the four major economic regions, there was little difference in the economic benefits of energy resources, and the total factor economic benefit in the eastern region was still the main force for the improvement of China’s average level. (2) The ratio of potential carbon emissions to actual carbon emissions can reflect the CO2 emission efficiency level of a region. During the study period, the overall level of carbon emission efficiency in China was not high. In 2017, the spatial distribution of China’s carbon emission efficiency did not show an agglomeration effect, and there was still room for further improvement. From the perspective of the four economic regions, the carbon emission efficiency of the eastern region was at a high level, significantly higher than the average level of China during the study period; those in the central, western, and northeastern regions were relatively low, and beneath the average level of China during the study period. (3) Considering both desirable and undesirable outputs generated by the use of energy resources, the comprehensive utilization efficiencies of energy resources in 29 areas in China from 2000 to 2017 are calculated. The study found that, from 2000 to 2017, the comprehensive utilization efficiencies of energy resources in China showed a decline-and-rise development trend. Spatially, there was a small-scale agglomeration of China’s energy resource comprehensive utilization efficiency in 2017, with a distribution feature of high in the east and low in the west. Meanwhile, labor input and capital input were considered to calculate the total factor comprehensive utilization efficiencies of China’s 29 areas. Results showed that the total factor comprehensive utilization efficiencies in China fluctuated significantly between 2000 and 2017, with a decline-and-rise development trend. There were significant differences in the spatial distribution of total factor comprehensive utilization efficiency in 2017. Generally, the eastern, central, and western regions showed a gradient downward trend. Similar to the change trends of energy comprehensive utilization efficiency, except for the eastern and central regions, the total factor comprehensive utilization efficiencies of other regions were lower than the average level of China.

3.5.2 Policy Recommendations Through the analysis of this chapter, it can be seen that the overall levels of economic benefits, carbon emission efficiency, and comprehensive utilization efficiency of China’s energy resources were not high, and there were significant differences and imbalances in spatial distribution. For easy comparison, this chapter calculated the total factor utilization efficiencies with consideration of labor input and capital input. We found that the change in energy resource utilization efficiency was similar to that in total factor utilization efficiency. Therefore, to promote the economic conversion

References

103

efficiency and environmental benefits of energy resources and realize their efficient utilization, this chapter puts forward the following suggestions: (1) Further optimize the energy consumption structure and adjust the spatial distribution pattern of energy efficiency. With the intensification of global climate change, China’s measures to reduce and limit emissions, especially the constraints on total energy consumption, have played a positive role. Since the new era, China has actively explored the path of high-quality development and promoted the use of renewable and clean energy, which is of great significance to the upgrading of energy consumption structure. Tasks for the future include giving further play to the guiding role of government in macroeconomic operation; weakening the leading position of high-carbon emission energy in the energy consumption structure; gradually forming a new energy consumption structure dominated by clean and renewable energy; strengthening interregional cooperation; promoting the circulation of resources, technology and other elements; and accelerating the formation of energy resource-efficient utilization demonstration areas. (2) Give full play to the regional development advantages of the eastern coastal areas, strengthen international exchanges and cooperation, actively introduce advanced energy and resource-efficient utilization technologies, and construct leading demonstration areas to drive the efficient utilization of energy resources in the central and western regions. The central region should maintain the decline of the efficient energy utilization level while developing rapidly. Focus should also be given to developing the level of efficient utilization of energy resources in the western and northeastern regions to promote the overall level of energy resources efficiency utilization in China. (3) Increase the proportion of science and technology expenditures in government financial expenditure and promote technological progress and innovation. Technological progress plays an important role in social production and life, especially the efficient utilization of energy resources. Technological progress can not only improve the ability of converting energy resources into economic benefits but also further reduce the negative impact of energy resources utilization. Therefore, technological progress is of great significance to the efficient utilization of energy resources.

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Chapter 4

Spatial Differences in Water–Energy System Coupling Relationship

Nomenclature J EJ SBM

Joule Energy consumption units (e.g., 1 EJ = 1012 MJ = 1018 J) Slacks-based measure, SBM model is a amelioration of traditional DEA

The Chinese government clearly supports the vigorous promotion of green development and strives to solve outstanding environmental problems when planning future development. For energy resources, China should establish and develop a green low-carbon cyclic economic system; promote the revolution of energy production and consumption, comprehensive conservation, and recycling of resources; and advocate a simple, moderate, green, and low-carbon lifestyle. For water resources, water-saving actions should be taken, and the prevention and control of water pollution should be accelerated. Moreover, pollution discharge standards should be raised, and the environments of river basins and coastal waters should be comprehensively controlled. Water and energy resources are the basic resources for China’s sustainable development; they are interdependent, coordinated, and symbiotic, and together play an extremely important role in China’s production and development. During the rapid development of industrialization and urbanization, it is very important to coordinate effective allocations of water and energy resources to avoid unbalanced and insufficient development. From 2000 to 2014, the proportion of China’s total water consumption in the available amount of water resources increased from 19.97 to 22.35%, exceeding the warning line by 20%; this trend was very likely to lead to a water resources crisis [1]. The Statistical Review of World Energy 2020 showed that the total global energy consumption in 2019 was 583.90 EJ, of which 141.70 EJ were consumed by China. It was predicted that China’s energy consumption would increase by 47% from 2015 to 2035, and the proportion of energy output in consumption would decrease by 2%. China’s energy consumption and effective energy supply would be severe in 2019 [2]. It is becoming increasingly difficult for the existing water resources to meet the growing demand, and the binding relationship between energy © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_4

105

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and water resources has become increasingly apparent. Thus, studying the coupling relationship of the water–energy system is of strategic significance for China’s stable and sustainable development.

4.1 Domestic and Foreign Research on Water–Energy Correlation The research on the relationship between water and energy can be traced back to 1998, which saw the emergence of theoretical analyses on the imbalance between industrial development demand and water resources supply in energy bases, and the one-way relationship between water and energy resources. In the recent five years, with the rapid development of measurement methods, the research on the coordination of water and energy resources has gradually increased, but has rarely focused on the coordination of water and energy at the provincial level. Domestic and foreign scholars’ research on water–energy at present can be divided into one-way relationship research and coordinated two-way research.

4.1.1 One-Way Water–Energy Relationship Research (1) Energy consumption in water resources utilization In the one-way relationship literature, the energy consumption in water resources utilization is mainly divided into five stages: water exploitation–processing–supply– use–treatment, and each stage is accompanied by different degrees of energy consumption. The sources of drinking water mainly include rivers, streams, groundwater, seawater, and wastewater, and the energy consumption in the process of water exploitation is mainly associated with the use of water pumps. The energy consumption of water processing depends on the water quality. The processing of groundwater only needs a small amount of energy consumption due to its excellent water quality, while that of surface water, such as rivers and streams, requires more energy due to discharged wastewater, sewage, and other material impurities in the surface water. A small amount of fresh water can be obtained from seawater and sewage by purification. The energy consumption in the supply process of drinking water is mainly for extraction of local water and pressurization treatment. Water resources usually need to be heated to be utilized, and the generated wastewater needs to be treated by processing technology, with both processes consuming a lot of energy. In the urban water resources system, the terminal water use accounts for about 50% of the total water resources. Urban domestic water includes bathing water, washing water, drinking water, kitchen water, cleaning water, and toilet flushing water, among others. Among them, the energy consumption of bathing water, drinking water, and

4.1 Domestic and Foreign Research on Water–Energy Correlation

107

washing water accounts for the highest proportion of the total water consumption, 30–85%, 6–20%, and 3–20%, respectively [3]. (2) Water consumption in energy utilization Water consumption in energy utilization is mainly divided into four stages: energy mining–refining–production–utilization, and each stage is accompanied by different degrees of water resources consumption. In the energy mining stage, water is mainly used for cooling, wetting, and dust removal. The water consumption in the production and refining process is considerable, and the consumption of new energy is greater than that of traditional energy [4]. In the process of energy utilization, the water consumption represented by cooling is significant. For example, traditional thermal power generation requires a lot of water resources for cooling and dust removal. The cooling methods include direct–flow cooling, circulation cooling, air cooling, seawater cooling, and mixed cooling, for which the water consumption is 0.15–1.2 L/kWh, 1.2–20 L/kWh, 0.2–0.4 L/kWh, 0.3 L/kWh, and 0.8–1 L/kWh, respectively. The water intake and water consumption of different cooling methods vary greatly. The high temperature of water resources after use may have a certain impact on the local ecological environment, and heavy metals and other substances contained in water resources may cause serious damage to water quality. Water resources in China are distributed unevenly, and there are great differences in the demand for different types of water resources. Moreover, there are spatial differences in the distribution of water consumption by energy. For example, the demand for agricultural water in western China is huge; the amount of energy consumed in the extraction process is significant as most of the water resources come from deep groundwater due to terrain and climate [5]. Water consumption in northern China and coastal areas is mainly concentrated in the process of power production [6]: thermal power generation is mainly concentrated in eastern China, such as Jiangsu, Shandong, Inner Mongolia, and Hebei; hydropower generation is mainly concentrated in southern China, such as Hubei, Hunan, Sichuan, and Fujian provinces; raw coal power generation is mainly concentrated in northeast and northern China, such as Inner Mongolia, Shanxi, and Henan; the distribution of crude oil power generation is very consistent with the distribution of oil fields, and is mainly concentrated in Liaoning, Jilin, Heilongjiang, Xinjiang Uygur Autonomous Region, and Tianjin; and natural gas power generation is mainly concentrated in northwest China, such as Qinghai, Inner Mongolia, and Xinjiang [7].

4.1.2 Water–Energy Synergy Research Water–energy synergy includes economic synergy, physical synergy, and management mechanism synergy [1]. Economic synergy refers to the correlation between the use of water resources and the price of energy and energy products. According to the law of supply and demand, when the price of energy or energy products rises, the use

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4 Spatial Differences in Water–Energy System Coupling Relationship

of water resources will decrease, so as to achieve the purpose of water saving. Physical synergy, taking water and electricity consumption as an example, includes water consumption for energy exploitation, water consumption for energy production, and energy consumption for water desalination, at the front end; energy consumption for water collection, transportation, storage, and treatment at the middle end; and finally, interdependent consumption of water and electricity, residential water consumption, wastewater treatment energy consumption, and energy production water consumption. Some scholars also suggest that there is a water–energy management mechanism synergy. Energy exploitation and consumption have no boundary issues, but there are mobility, watershed, and boundary problems associated with water sources. The coordinated management of water and energy involves the problem of future sustainable development. Thus, optimizing the management of two or even more resources requires a more comprehensive and complex management plan [8]. Task allocation at the departmental level in the water resources management control objectives, the upper and lower limits of energy conservation in different proportions are specified according to the energy and water consumption of industries, and a reward and punishment system is implemented to achieve the synergistic benefits of water and energy [9].

4.1.3 Research on Water–Energy Resource Contradictions The continuous increase in energy consumption is in sharp contrast with the safe supply of water resources. Coal resources and water resources in China are inversely distributed. Bounded by the Kunlun-Qinling-Dabie Mountain line, the northern region is extremely rich in coal resources but has extremely scarce water resources; on the contrary, the southern region only has 9.7% of China’s coal resources, but 78.6% of China’s total water resources, which intensifies the contradiction of water–energy supply and demand between regions [10]. Case analysis shows that the historical development factor of water and electricity is the main reason for the water–energy contradiction. The investment in the energy industry will further increase the demand for water resources, and the measures taken by the water industry to deal with drought and climate change may further increase the demand for power, resulting in an incremental supply–demand contradiction of water and energy [8].

4.1.4 Measurement Methods of Water–Energy Resources The calculation of resource utilization efficiency is mainly carried out using the DEA model and Malmquist–Luenberger index. Generally, labor and capital stock are taken as input indexes and total output is taken as the output index to calculate efficiency using the DEA model to reflect the conditions of China’s water resource

4.1 Domestic and Foreign Research on Water–Energy Correlation

109

utilization. By using the DEA model to analyze the water resources utilization efficiencies of 30 areas in China from 1997 to 2002, Hu et al. [11] found that the water resources utilization efficiency in the central region was the lowest, and there was a U-shaped relationship between urban water resources utilization efficiency and per capita average income. Cheng and Shen [12] analyzed the industrial water resource utilization efficiencies of 28 areas in China from 2002 to 2011 except Tibet, Qinghai, Hainan, Hong Kong, Macao, and Taiwan with the cost DEA model, and found that the average annual water resource utilization efficiencies of Beijing, Tianjin, and Shandong were all higher than 0.5 while those of other areas were all lower than 0.3. Zhao et al. [13] calculated the water resources utilization efficiency by using the two-stage SBM model, and found that the water resources utilization efficiency at the first stage was significantly higher than that at the second stage, and the overall efficiencies at the two stages were close, meaning that the efficiency at the second stage determined the overall effectiveness of water resources utilization, and there were significant spatial differences in water resources utilization efficiency. The analysis of water–energy consumption coordination mainly includes decoupling analysis and coupling analysis. Specifically, the total water consumption and water consumption by energy are used for the water–energy decoupling analysis to calculate the relationship between the rate of water consumption by energy and the growth rate of total water consumption. Hong et al. [7] carried out a decoupling analysis of total water–energy consumption in 31 areas in China from 1991 to 2013, and found that: 2005 was a turning point in the total water consumption–thermal power water consumption, total water consumption–hydropower water consumption, and total water consumption–raw coal water consumption relationships, which were in a decoupling state from 1997 to 2005, that is, in a relatively coordinated state. However, after 2005, the decoupling indexes of total water consumption–thermal power water consumption and total water consumption–hydropower water consumption exhibited a decreasing trend, and the growth rate of water consumption by energy continued to exceed the growth rate of total water consumption, implying that the disharmony degree within China had intensified [14]. In summary, current domestic water–energy relationship research mainly focuses on the one-way relationship and the synergy relationship, among which the oneway relationship research includes the theoretical analysis of energy consumption by water resources and water consumption by energy resources, and the synergy research contains qualitative and quantitative analysis and decoupling analysis. Generally, there are few studies on the spatial differences in water–energy synergy using the super-efficiency SBM model considering undesirable outputs. Thus, this chapter builds a super-efficiency SBM model; measures the efficiency with sewage and CO2 emissions as the undesirable outputs of water resources and energy resources, respectively; analyzes the spatial differences; adds the calculation results of the superefficiency SBM into the water–energy coupling index system as a sub-index; and calculates the coupling degrees of 30 areas in China to put forward policy suggestions according to the calculation results.

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4 Spatial Differences in Water–Energy System Coupling Relationship

4.2 Water–Energy Coupling Coordination Evaluation 4.2.1 Variable Selection and Index System Construction (1) Input and output index system There is still no universal framework for the input and output indexes of water and energy resources. Water resources and energy availability are essential main input indexes. From the perspective of the output of economic benefits, labor force is the most basic input factor in production and is an essential factor to be considered in the analysis and evaluation of economic benefits; fixed capital investment is an international comprehensive index that can reflect the amount and quality of asset investment in currency. Therefore, the number of employees and fixed capital investment are chosen as input indexes from the perspective of economic benefits [15, 16]. Considering the economic benefits of the places where resources are generated, the regional GDP, representing the comprehensive economic output, is taken as the desirable output index. To objectively reflect the impact of economic benefits and the social environmental impact of water resources and energy, COD emissions and CO2 emissions are selected as undesirable outputs, respectively. Following the principles of availability and operability, data of 30 provincial areas in China from 2004 to 2018 are selected as the research objects (excluding Tibet for data missing). The CEADs data is only updated to 2017. Thus, to ensure scientificity and rationality, the data of carbon emissions due to fuel combustion (including raw coal, crude oil, and natural gas) in 2017 and 2018 are measured using China-specific emission factors and the IPPC 2006 calculation method; the CEADs data of 2018 are induced as per the growth rate of carbon emissions (Table 4.1). To make the data more objective, the data conversion function is used to process the undesirable outputs of water and energy resources. It is supposed that the undesirable output amount Fit of the production unit I in the tth year: {

Fit > 0(i = 1, 2, 3, 4 . . . ; t = 2004, 2005, 2006 . . .) φ = max{Fit } + C0

where I and t stand for city and time, respectively; C0 is the unconstrained constant term; water resources C0 = 100, and energy resources C0 = 100. Thus, the undesirable output can be converted to Fit∗ = φ– Fit . After data processing, Fit ∗ is positively correlated with the desirable output, and the higher the Fit∗ , the less the undesirable output. (2) Water–energy coupling coordination index system Table 4.2 shows that the water–energy coupling index system. Based on the coupling index systems of Liu et al. [17], Wang et al. [1], Li and Zhang [16] the criterion layer of the water resources system is constructed from five dimensions, including water resources supply and demand, water resources use structure, water resources

4.2 Water–Energy Coupling Coordination Evaluation

111

Table 4.1 Super-efficiency input–output index system Criterion layer

Index system Variable

Year

Water resources

Input index

2004–2018 Statistical yearbooks 2004–2018 of each province

Number of employees Fixed capital investment Water consumption

Data source

2004–2018

Output index GDP (annual real value 2004–2018 measured by GDP index) Energy resources Input index

Sewage discharge

2004–2018

Number of employees

2004–2018 Statistical yearbooks 2004–2018 of each province

Fixed capital investment Energy consumption

2004–2018

Output index GDP (annual real value 2004–2018 measured by GDP index) CO2 emissions

2004–2018 China emission accounts and datasets IPCC standard

sustainability, water resources utilization efficiency, and water resources quality. Water resources supply and demand include per capita water consumption (m3 / person) and per capita water resources use (m3 /person); water resources use structure includes ecological water consumption proportion (%) and industrial water consumption proportion (%); water resources sustainability includes sewage treatment rate (%) and wastewater discharge (100 million t); water resources utilization efficiency includes water consumption per CNY 10,000 GDP (m3 /CNY 10,000) and water resource utilization efficiency; water resource quality includes COD emissions (10,000 t) and ammonia nitrogen emissions (10,000 t). The energy system criterion layer is constructed from four dimensions, including energy supply and demand, energy use structure, energy utilization efficiency, and energy utilization quality. Energy supply and demand include total energy consumption E1 (100 million t of standard coal), total primary energy production (100 million t of standard coal), and per capita energy consumption (E3 t/person); energy use structure includes a negative index, coal consumption proportion (%); energy utilization efficiency includes four indexes: energy industrial investment (CNY 100 million), elasticity coefficient of energy consumption, energy consumption per CNY 10,000 GDP (ton of coal/CNY 10,000), and energy utilization efficiency; and energy utilization quality includes two indexes: total power of agricultural machinery (10,000 kWh) and rural power consumption E9 (10,000 kWh) [18]. Based on the calculation of water and energy resources utilization efficiencies in 30 areas of China in Sect. 4.3, two positive indexes are added to the index layers of water resources and energy resources (Table 4.2).

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4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.2 Water–energy coupling index system Criterion layer

Index layer

Water resources system

Per capita water consumption W 1 (m3 / person) Negative

Index characteristic

Per capita water use W 2 (m3 / person)

Positive

Proportion of ecological water use W 3 /%

Positive

Proportion of industrial water use W 4 /%

Negative

Sewage treatment rate W 5 /%

Positive

Wastewater discharge W 6 (100 million t)

Negative

Water consumption per CNY 10,000 GDP W 7 (m3 /CNY 10,000)

Negative

Energy resource utilization efficiency W 8

Positive

COD emission W 9 (100 million t)

Negative

Ammonia nitrogen emission W 10 (100 million t)

Negative

Energy resources system Total energy consumption E 1 (100 million t) standard coal

Negative

Total primary energy production E 2 (100 million t) standard coal

Positive

Per capita energy consumption E 3 t/ person

Negative

Proportion of coal consumption E 5 %

Negative

Energy industry investment E 4 CNY 100 million

Negative

Elasticity coefficient of energy consumption E 6 Negative Energy resource utilization efficiency

Positive

Energy consumption per CNY 10,000 GDP E 7 Negative (ton of coal/CNY 10,000) Total power of agricultural machinery E 8 (10,000 kWh)

Positive

Rural power consumption E 9 (10,000 kWh)

Negative

4.2.2 Model Setting (1) Super-efficiency SBM model The theoretical basis of DEA is a linear programming model, which is often used to measure the target efficiency of operating units with the same objectives. However, when the measured same-type organization has multiple inputs and outputs and cannot be converted into the same unit, the DEA model will fail. Compared with the traditional DEA model, the super-efficiency SBM model not only combines superefficiency and the SBM model, but also takes slack variables into account to solve the problem that the average efficiency of decision-making units on the efficiency frontier is 1, and allows the average efficiency level of effective decision-making units

4.2 Water–Energy Coupling Coordination Evaluation

113

to be greater than 1, making the efficiency calculation more accurate. By referring to the super-efficiency SBM model construction method of Huang et al. [19], the finite possible production set P(x, y) is set, where x and y are the input vector and output vector, respectively: P/(x0 , y0 ) =

⎧ ⎨ ⎩

x, y|x ≥

n ∑ j=1,/=0

λjxj, y ≤

n ∑

λ j y j , y ≥ 0, λ ≥ 0

j=1,/=0

⎫ ⎬ ⎭

(4.1)

where P/(x0 , y0 ) refers to the production possibility set after excluding the decision˜ 0 , y0 ) is defined: making unit P(x0 , y0 ), based on which a sub-set P/(x ˜ 0 , y0 ) = P/(x0 , y0 ) ∩ {x ≥ x0 , y ≤ y0 } P/(x

(4.2)

˜ 0 , y0 ) will be a non-empty set that refers to the average If X > 0, Y > 0, then P/(x ˜ 0 , y0 ). Thus, the distance function index σ is distance from (x0 , y0 ) to (x, y) ∈ P/(x defined as: δ=

m s 1 ∑ xi 1 ∑ yr / m i=1 xi0 s r =1 yr 0

The denominator of the distance function index δ means the average reduction ˜ 0 , y0 ), and the numerator means the rate of y0 to the midpoint y of (x, y) ∈ P/(x ˜ 0 , y0 ). The smaller the average expansion rate of x0 to the midpoint x of (x, y) ∈ P/(x denominator, the longer the distance from y0 to y. Therefore, the distance function index is defined as the average distance between the decision-making unit and the production frontier in the input–output set space. Based on the above analysis, the super-efficiency SBM model of decision-making units can be expressed as: ⎧ m s 1 ∑ xi 1 ∑ yr ⎪ ∗ ⎪ / ⎪ δ = min δ = m xi0 s yr 0 ⎪ ⎨ r =1 i=1 n n ∑ ∑ st x ≥ λjxj, y ≤ λj yj ⎪ ⎪ ⎪ j=1,/=0 j=1,/=0 ⎪ ⎩ y ≥ 0, λ ≥ 0, x ≥ x0 , y ≤ y0

(4.3)

The solution of the formula (4.3) is the optimal solution (δ ∗ , x ∗ , y ∗ , λ∗ ). (2) Data standardization To ensure that there is no interference of subjectivity in the water–energy coupling coordination index system, the entropy TOPSIS method is adopted to consider the entropy in the data, and the index weights of 30 areas in China (owing to a lack of data in Tibet region, the object of Tibet is deleted) from 2004 to 2018 are determined according to the principle of information entropy. This method overcomes the

114

4 Spatial Differences in Water–Energy System Coupling Relationship

assignment deviation caused by personal subjective factors in the traditional assignment method, and makes the assignment result scientific and accurate. The specific operations are as follows: (3) First, dimensionless processing of data of each area from 2004 to 2018 is carried out to overcome the problems of different dimensions caused by different data units and the differential impact caused by positive and negative indexes: ai j − min(ai j ) × 0.9 + 0.1 max(ai j ) − min(a i j ) max(ai j ) − ai j Negative index: X i j = × 0.9 + 0.1 max(ai j ) − min(a i j ) Positive index: X i j =

(4) Second, index homogenization is performed to calculate the weights of indexes in the hierarchy: Pi j = X i j /

m ∑

Xi j

i=1

(5) Third, the hierarchical index weight is calculated: ) ( ∑m pi j ln pi j 1 − k i=1 ) w j = ∑n ( ∑m j=1 1 − k i=1 pi j ln pi j (6) Fourth, the comprehensive score i of the water–energy coupling index system is calculated: Hi =

m ∑

w j ai j

j=1

(7) Coupling coordination model The system coupling relationship includes two levels: coordination and development. Coordination refers to the close cooperation between the internal elements of two systems. Development refers to the self-development initiative of each element in the system, which can realize the self-transition to a higher level [20]. The two systems in a binary coupling are X and Y, and the deviation coefficient is Cv : / Cv =

1 2(

( f (x)−g(x))2 2

f (x) + g(x))2

(4.4)

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

115

where Cv represents the average deviation degree of the water–energy subsystem. The smaller the value, the smaller the deviation degree of the water–energy subsystem, that is, the stronger the coupling degree of the water–energy system. To make the water–energy system comparable, the above equation Cv is deduced and simplified to: ┌ ( ) | | xy √ Cv = 2 1 − ( x+y )2

(4.5)

2

Cv is a coupling standard model. xy C = ( x+y )2

(4.6)

2

There is an inverse correlation between Cv and C , that is, the smaller the Cv , the higher the system coupling degree, while the higher the C , the higher the system coupling degree. As the value range of C is 0 to 1, considering comparability and intuitiveness, Eq. (4.6) is selected as the measurement method for calculating the water–energy coupling degree. The coupling coordination index of water–energy system is D: D=

√ C∗T

T is the comprehensive coordination index of urban water–energy development, T = α X +βY . Refer to Table 4.3 for the evaluation criteria of coupling coordination degree.

4.3 Measurement and Analysis of Efficiencies of Water and Energy Resources Utilization DEA-Solver 5.0 is used to calculate the non-radial consumption efficiencies of water and energy resources in 30 provincial areas of China (excluding Tibet) with a fixed regression ratio (0.8:1.2).

4.3.1 Measurement Results of Energy Resource Utilization Efficiency According to Table 4.4, from 2004 to 2018, the annual average energy resource utilization efficiencies of Shanghai, Shaanxi, Qinghai, Tianjin, Chongqing, Ningxia,

Extreme disorder 0.0 < D ≤ 0.1

0.5 < D ≤ 0.6

0.1 < D ≤ 0.2

0.2 < D ≤ 0.3

Little coordination

Moderate disorder Severe disorder

0.0 < C ≤ 0.3

0.3 < D ≤ 0.4

0.4 < D ≤ 0.5

D

0.6 < D ≤ 0.7

Low level coupling

Mild disorder

On the verge of disorder

Coordination degree

Basic coordination

0.5 < C ≤ 0.8

Running-in stage

0.3 < C ≤ 0.5

C

0.7 < D ≤ 0.8

0.8 < D ≤ 0.9

Good coordination

Antagonistic stage

Coupling degree

Intermediate coordination

0.9 < D ≤ 1.0

High-quality coordination

0.8 < C ≤ 1.0

High-level coupling

D

Coordination degree

C

Coupling degree

Table 4.3 System coupling coordination level standard

116 4 Spatial Differences in Water–Energy System Coupling Relationship

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

117

Hainan, Beijing, Fujian, Liaoning, Xinjiang, Heilongjiang, Jiangsu, Hebei, and Zhejiang all showed a significant upward trend, with an average annual growth rate of 11.11%, 10.34%, 9.08%, 7.54%, 6.22%, 4.78%, 3.73%, 2.80%, 2.37%, 1.56%, 1.36%, 1.12%, 1.01%, 0.50%, and 0.43%, respectively, indicating that while developing the local economy, the above areas paid attention to the high-quality development of regional energy consumption-related industries and promoted the progress of energy technology. In 2018, Liaoning Province launched a three-year special action to tackle key problems in pollution prevention and control, promoted technologies such as solar hot water and electric heating systems, and made “serving clean energy development” the top priority of energy work. Therefore, the energy resource utilization efficiency of Liaoning Province increased to 1.0712 that year. The utilization efficiency of energy resources in Fujian Province in 2018 decreased significantly. According to the announcement on the responsibility assessment results of the energy-saving objectives of “100” and “1000” key energy users by the Energy Conservation Office of the People’s Government of Fujian Province, only half of the “1000” key enterprises with energy-saving objectives passed the assessment, meaning that Fujian Province still needs to promote the technological revolution of energy production and consumption to build a safe and efficient energy system. From 2004 to 2018, Hainan, Qinghai, and Shanghai ranked the top three in terms of average energy resource utilization efficiency. Overall, the eastern coastal areas had the highest energy resource utilization efficiency, followed by the western areas, the northeastern areas, and finally the central regions. In 2018, the top three areas with high comprehensive energy resource utilization efficiency were Shaanxi, Qinghai, and Shanghai, with efficiency indexes of 1.280, 1.204, and 1.180, respectively; the last three were Shandong, Shanxi, and Inner Mongolia, with efficiency indexes of 0.130, 0.144, and 0.246, respectively. Overall, from 2004 to 2018, China’s comprehensive energy resource utilization efficiency index showed a fluctuating downward trend, reaching the lowest value of 0.5489 in 2015, and then hit the bottom and rebounded gradually to 0.6171 in 2018. It shows that China’s overall energy consumption mode was still in the stage of extensive utilization. The 14th Five-Year Plan clearly pointed out that a green lifestyle should be widely formed, carbon emissions should be stable after reaching the peak, and the ecological environment should be fundamentally improved. Therefore, the transformation from an extensive energy utilization mode to intensive mode should be accelerated. Furthermore, China’s 30 provincial areas are divided into the eastern region, central region, western region, and northeastern region for observation. It is found from Table 4.5 that the trends of energy resource utilization efficiencies in the eastern, central, western, and northeastern regions are not the same. The efficiencies in the eastern and northeastern regions showed a significant upward trend: the average annual growth rate of energy resource utilization efficiency in the eastern region from 2004 to 2018 was 1.504%, and that in the northeastern region was 1.212%. The northeastern region is an old industrial base in China with years of experience in technology deposition and energy consumption, which can ensure the steady improvement of energy resource utilization efficiency. As a frontier of China’s economic and

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4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.4 Energy resource utilization efficiencies of 30 areas in China City

2004

2005

2006

2007

2008

2009

2010

Beijing

0.6516

0.6645

0.6687

0.6686

0.7203

0.7076

0.6993

Tianjin

0.8579

0.8798

0.8653

0.7989

0.8227

0.7979

0.7451

Hebei

0.3454

0.3513

0.3422

0.3023

0.2948

0.2616

0.2395

Shanxi

0.2766

0.3127

0.3003

0.3152

0.1962

0.1922

0.1783

Inner Mongolia

0.3547

0.3466

0.3426

0.3217

0.3037

0.2918

0.2665

Liaoning

0.4044

0.4016

0.3848

0.3712

0.3716

0.3584

0.3422

Jilin

0.5396

0.5078

0.4633

0.4445

0.4408

0.4375

0.4356

Heilongjiang

0.7267

0.7436

0.7062

0.6168

0.5734

0.5167

0.4585

Shanghai

1.2818

1.2731

1.2697

1.2791

1.2216

1.2548

1.2916

Jiangsu

0.6488

0.6154

0.6166

0.6098

0.6304

0.6571

0.6768

Zhejiang

0.5911

0.6087

0.5969

0.5832

0.6087

0.6001

0.6000

Anhui

0.4911

0.5007

0.4671

0.4257

0.4049

0.3785

0.3733

Fujian

1.0608

1.0534

1.0340

1.0326

1.0324

1.0235

1.0285

Jiangxi

0.5329

0.5326

0.5139

0.5015

0.4836

0.4607

0.4475

Shandong

0.2277

0.1772

0.1602

0.1591

0.1525

0.1519

0.1489

Henan

0.4513

0.4046

0.3796

0.3320

0.3569

0.2877

0.2545

Hubei

0.5613

0.5996

0.5747

0.5220

0.5152

0.4679

0.4347

Hunan

0.5639

0.5185

0.5029

0.4643

0.4525

0.4132

0.3941

Guangdong

0.8445

0.8544

0.8601

1.1371

1.1392

0.8207

0.8122

Guangxi

0.5570

0.5394

0.5149

0.4846

0.4786

0.4461

0.4178

Hainan

1.8830

1.9093

1.9292

1.8978

1.8138

1.7611

1.7313

Chongqing

0.5012

0.4573

0.4432

0.4318

0.4326

0.4243

0.4238

Sichuan

0.4437

0.4668

0.4512

0.4132

0.3912

0.3366

0.3389

Guizhou

0.3070

0.3278

0.3326

0.3324

0.3363

0.3339

0.3326

Yunnan

0.4759

0.4170

0.3972

0.3850

0.3834

0.3654

0.3581

Shaanxi

1.6178

1.6387

1.5616

1.5880

1.7741

1.8939

2.0163

Gansu

0.3055

0.3141

0.3033

0.2929

0.2941

0.2962

0.2941

Qinghai

1.0644

1.0678

1.0744

1.0841

1.0798

1.1078

1.1121

Ningxia

1.0427

1.0538

1.0501

1.0591

1.0909

1.0645

1.0946

Xinjiang

1.1609

1.1448

1.1375

1.1198

1.0823

1.0329

0.7871

City

2011

2012

2013

2014

2015

2016

2017

2018

Beijing

0.7089

0.7089

0.7087

0.9667

0.9838

0.9560

0.9315

0.9563

Tianjin

0.7446

0.7446

0.7475

1.0887

1.1041

1.0915

1.0945

1.1253

Hebei

0.2217

0.2217

0.2115

0.3418

0.3517

0.3120

0.3014

0.3163

Shanxi

0.1483

0.1483

0.1201

0.0544

0.0514

0.0510

0.0502

0.1439

Inner Mongolia

0.1983

0.1983

0.1857

0.1575

0.1568

0.2341

0.2471

0.2460

Liaoning

0.3303

0.3303

0.3163

0.3268

0.3481

0.3900

0.6087

1.0712 (continued)

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

119

Table 4.4 (continued) City

2011

2012

2013

2014

2015

2016

2017

2018

Jilin

0.4484

0.4484

0.4479

0.4839

0.4850

0.5435

0.5554

0.5997

Heilongjiang

0.4459

0.4459

0.4206

0.4119

0.4539

0.5126

0.5198

0.5615

Shanghai

1.3141

1.3141

1.3147

1.3007

1.2634

1.2820

1.2254

1.1800

Jiangsu

0.6796

0.6796

0.6979

0.6809

0.7142

0.7386

0.7494

0.7623

Zhejiang

0.5745

0.5745

0.5619

0.5500

0.5618

0.5599

0.5641

0.5628

Anhui

0.3648

0.3648

0.3438

0.2951

0.2921

0.3136

0.3523

0.3127

Fujian

1.0145

1.0145

1.0128

1.0227

0.5508

1.0055

1.0150

0.5211

Jiangxi

0.4519

0.4519

0.4527

0.4310

0.4189

0.4323

0.4311

0.4136

Shandong

0.1467

0.1467

0.1390

0.1440

0.1400

0.1342

0.1326

0.1299

Henan

0.2331

0.2331

0.2740

0.2594

0.2790

0.2960

0.3066

0.3154

Hubei

0.4014

0.4014

0.3890

0.4196

0.4243

0.4411

0.4440

0.4379

Hunan

0.3641

0.3641

0.3554

0.3559

0.3590

0.3821

0.3879

0.3780

Guangdong

0.7995

0.7995

0.8057

0.7762

0.7697

0.7921

0.8043

0.7971

Guangxi

0.4053

0.4053

0.3992

0.3976

0.3892

0.3998

0.3951

0.3845

Hainan

1.7206

1.7206

1.7036

1.6250

1.5988

1.6235

1.6142

1.3650

Chongqing

0.4294

0.4294

0.4379

0.4804

0.4748

0.5145

0.5219

0.5118

Sichuan

0.3390

0.3390

0.3326

0.3385

0.3334

0.3643

0.3666

0.3627

Guizhou

0.3193

0.3193

0.3097

0.3066

0.3079

0.3089

0.3010

0.3010

Yunnan

0.3593

0.3593

0.3494

0.3552

0.3562

0.3590

0.3512

0.3330

Shaanxi

2.0595

2.0595

1.9881

1.8454

1.7572

1.6373

1.5077

1.2797

Gansu

0.3015

0.3015

0.3073

0.3089

0.3200

0.3302

0.3388

0.3295

Qinghai

1.1295

1.1295

1.1337

1.1370

1.1473

1.1627

1.1613

1.2041

Ningxia

1.0565

1.0565

1.0745

1.0866

1.0994

1.1081

1.1090

1.0044

Xinjiang

0.7454

0.7454

0.6493

0.6020

0.5745

0.5634

0.5542

0.6076

Data source calculated using DEA-Solver 5.0

social development, the eastern region is also a key area for future energy development in China. Due to the reverse distribution trend of energy supply and demand, the eastern region lacks energy resources and has a low energy self-sufficiency rate, which will force the region to develop energy utilization technology to alleviate the developmental problems caused by energy shortage. The utilization efficiencies of energy resources in the central and western regions showed a significant downward trend. The average annual growth rate of energy resource utilization efficiency in the central region was − 6.385%, and that in the western region was − 0.955%. Relying on the advantages of natural energy storage, the central and western regions had made great progress through large-scale energy exploitation. With the gradual depletion of convenient and available energy resources in recent years, there are certain risks in the energy consumption in the future. Therefore, the central and western regions should focus on improving the energy resource utilization technology, appropriately

120

4 Spatial Differences in Water–Energy System Coupling Relationship

control the scale of energy exploitation, and actively promote the improvement of energy resource utilization efficiency. Figure 4.1 shows the histogram of regional energy resource utilization efficiency. It can be seen clearly that the eastern region was a newcomer that took the lead. From 2004 to 2005, the western region was at the highest level. In 2006, the eastern region surpassed the western region and remained at a high level until 2013 when both regions successively reached the highest level of energy resource utilization efficiency. From 2014 to 2018, the utilization efficiency of energy resources in the eastern region was always at the highest level. After 2006, the utilization efficiency of energy resources in the western region gradually decreased, and from 2006 to 2018, the efficiencies in the western region and the central region ranked third and fourth, respectively. According to the classification method of the Development Research Center of the State Council, the 30 areas are classified into eight major economic regions, and the results are shown in Table 4.6. Overall, the energy consumption efficiency index of the eastern coastal comprehensive economic zone is higher than that of other economic zones, belonging to the first echelon; the energy resource utilization efficiencies of the northern coastal comprehensive economic zone, the southern coastal economic zone, and the southwest comprehensive economic zone belong to the second echelon; the northeast comprehensive economic zone, the middle reaches of the Yellow River comprehensive economic zone, the middle reaches of the Yangtze River comprehensive economic zone, and the northwest comprehensive economic zone are in the Table 4.5 Regional energy resource utilization efficiency Year

Eastern region

Central region

Western region

Northeastern region

2004

0.6730

0.4810

0.7011

0.6156

2005

0.6805

0.4958

0.6889

0.6099

2006

0.8343

0.4564

0.5181

0.8021

2007

0.8468

0.4268

0.4775

0.7985

2008

0.8436

0.4016

0.4619

0.8086

2009

0.8037

0.3667

0.4376

0.8110

2010

0.7973

0.3470

0.4121

0.8049

2011

0.7925

0.3273

0.4082

0.8053

2012

0.7925

0.3273

0.4082

0.8053

2013

0.7903

0.3225

0.3950

0.7897

2014

0.8497

0.3026

0.4075

0.7761

2015

0.8038

0.3041

0.4290

0.7634

2016

0.8495

0.3193

0.4820

0.7631

2017

0.8432

0.3287

0.5613

0.7437

2018

0.8294

0.3267

0.6130

0.7287

Data source calculated using DEA-Solver 5.0

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

121

Fig. 4.1 Histogram of regional energy resource utilization efficiency

third echelon, with low energy resource utilization efficiencies. The northern coastal comprehensive economic zone and the southwest coastal comprehensive economic zone had the highest average annual growth rates of energy resource utilization efficiency of 3.99% and 3.99%, respectively, while that of the southern coastal economic zone was -5.12%, with the energy technology development level needing urgent improvement. The utilization efficiency of energy resources in the northeast comprehensive economic zone showed a downward trend first and then an upward trend, and began to rise slowly after reaching 0.4517 in 2013; the efficiency in the northern coastal comprehensive economic zone first showed a steady upward trend, and after 2006, fluctuated around 0.85; the utilization efficiency of energy resources in the eastern coastal comprehensive economic zone began to decline slowly after reaching the highest value of 1.3558 in 2007, and finally fluctuated around 1.15; the utilization efficiency in the southern coastal comprehensive economic zone showed a continuous trend of decline; the efficiency in the middle reaches of the Yellow River comprehensive economic zone fluctuated around 0.39 in 2011, and that of the Yangtze River comprehensive economic zone fluctuated around 0.38 in 2009; the trends of energy resource utilization efficiencies of the southwest region and the northwest region showed a significant difference: the efficiency in the southwest region was first low, and then fluctuated around 0.80 since 2006; while that in the northwest region was first high, and reached the lowest value of 0.395 in 2013. In general, the energy resource utilization efficiencies in coastal economic zones were at high levels, while the security of energy supply in energy-rich areas and border areas are difficult to be guaranteed.

122

4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.6 Energy resource utilization efficiencies of the eight major economic zones Economic zone

2004

Northeast comprehensive economic zone

0.6014 0.6119 0.5091 0.4822 0.4976 0.4798 0.4582

2005

2006

2007

2008

2009

2010

Northern coastal comprehensive economic zone

0.4861 0.4869 0.8278 0.8240 0.8202 0.8374 0.8561

Eastern coastal comprehensive economic zone

0.9555 0.9655 1.2745 1.3558 1.3285 1.2018 1.1907

Southern coastal economic zone

1.0471 1.0507 0.6460 0.6392 0.6577 0.6664 0.6789

Comprehensive economic zone in 0.5395 0.5186 0.5147 0.4784 0.4640 0.4301 0.4124 the middle reaches of the Yellow River Comprehensive economic zone in 0.7524 0.7425 0.4278 0.4094 0.4044 0.3813 0.3743 the middle reaches of the Yangtze River Southwest comprehensive economic zone

0.4573 0.4555 0.8913 0.8890 0.8868 0.8754 0.8220

Northwest comprehensive economic zone

0.7011 0.6889 0.5181 0.4775 0.4619 0.4376 0.4121

Economic zone

2011

2012

2013

2014

2015

2016

2017

2018

Northeast comprehensive economic zone

0.4555

0.4555

0.4517

0.6353

0.6449

0.6234

0.6150

0.6360

Northern coastal comprehensive economic zone

0.8561

0.8561

0.8582

0.8438

0.8465

0.8602

0.8463

0.8407

Eastern coastal comprehensive economic zone

1.1782

1.1782

1.1740

1.1413

0.9731

1.1404

1.1445

1.0761

Southern coastal economic zone

0.6598

0.6598

0.6420

0.5792

0.5611

0.5546

0.5279

0.5018

Comprehensive 0.3956 economic zone in the middle reaches of the Yellow River

0.3956

0.3852

0.3754

0.3736

0.3922

0.4038

0.3935

Comprehensive 0.3704 economic zone in the middle reaches of the Yangtze River

0.3704

0.3657

0.3757

0.3723

0.3893

0.3872

0.3869

Southwest comprehensive economic zone

0.8083

0.8083

0.7912

0.7836

0.7853

0.7911

0.7908

0.7910

Northwest comprehensive economic zone

0.4082

0.4082

0.3950

0.4075

0.4290

0.4820

0.5613

0.6130

Data source calculated using DEA-Solver 5.0

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

123

4.3.2 Measurement Results of Water Resources Utilization Efficiency According to Table 4.7, from 2004 to 2018, the annual average water resources utilization efficiencies of Shanxi, Jiangsu, Liaoning, Beijing, Qinghai, and Inner Mongolia showed a significant upward trend, with an average annual growth rate of 4.22%, 3.47%, 3.32%, 1.73%, 1.31%, and 1.23% respectively, indicating that the above regions paid attention to the high-quality development of industries related to regional water resources consumption when developing the local economy. From 2004 to 2018, Tianjin, Qinghai, and Shanghai ranked the top three in average water resource utilization efficiency. Overall, the eastern coastal areas had the highest level of water resources utilization efficiency, followed by the northeastern region, and finally the western and central regions. Generally, the areas with high water resources utilization efficiency are mainly concentrated in the eastern coastal areas. In 2018, the top three areas with high comprehensive water resources utilization efficiency were Qinghai, Tianjin, and Shaanxi, with efficiency indexes of 1.432, 1.351, and 1.229, respectively; the last three areas were Hunan, Anhui, and Gansu, with efficiency indexes of 0.252, 0.236, and 0.232, respectively. China’s comprehensive water resources consumption index showed a slow downward trend from 0.7179 in 2004 to 0.6184 in 2018 except a few upward fluctuations in certain years. This indicates that China’s overall water resources consumption mode was still at an extensive stage. The 14th Five-Year Plan clearly pointed out that the utilization rate of sewage resources in water-deficient cities at prefecture level and above should exceed 25%, and the water consumption per unit GDP should be reduced by 16% to improve the optimal allocation of water resources and for preventing flood and drought disasters. The primary factor in improving the utilization capacity of water resources is to improve the utilization efficiency of water resources and avoid the massive waste of water resources brought by extensive flood irrigation. Therefore, improving the utilization efficiency of water resources should be emphasized to solve the water-use problem from the source. Table 4.8 shows the calculation results of regional water resources utilization efficiency. The researched 30 areas are classified into the eastern region, central region, western region, and northeastern region for further investigation at the provincial level. It is found that the utilization efficiencies of water resources in the eastern, central, western, and northeastern regions exhibited a significant downward trend as a whole, with an average annual growth rate of − 0.784%, − 1.208%, − 1.570%, and − 0.132%, respectively, from 2004 to 2018. The Research Report on Development Prospect and Strategy of China’s Water Resources Development Industry 2017 to 2022 shows that Ningxia, Hebei, Shandong, Henan, and Shanxi Provinces, as well as Nanjing from Jiangsu Province are areas with extreme water shortage, where water resources are less than 500 m3 . Particularly, the western region of China is an arid zone with water shortage, but its demand for water resources development and utilization is much higher than that of other regions, and the practical problem of water resources utilization efficiency decline should be specially noticed.

124

4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.7 Water-use efficiencies in 30 areas of China City

2004

2005

2006

2007

2008

2009

2010

Beijing

0.7875

0.7944

0.7781

0.7808

1.0049

1.0265

1.0321

Tianjin

1.4508

1.4078

1.4158

1.3648

1.3123

1.2997

1.3246

Hebei

0.5644

0.5438

0.5108

0.4840

0.4664

0.4402

0.4285

Shanxi

0.5046

0.5001

0.4831

0.4920

0.5179

0.5007

0.5003

Inner Mongolia

0.4131

0.3954

0.3994

0.4117

0.4417

0.4511

0.4636

Liaoning

0.6785

0.6106

0.5798

0.5535

0.5615

0.5789

0.5832

Jilin

0.5532

0.4824

0.4391

0.4344

0.4412

0.4544

0.4648

Heilongjiang

0.8137

0.7095

0.6444

0.5773

0.5410

0.4892

0.4415

Shanghai

1.2818

1.2731

1.2697

1.2791

1.2216

1.2548

1.2891

Jiangsu

0.4624

0.4968

0.5152

0.5876

0.6169

0.6583

0.6674

Zhejiang

0.5962

0.6088

0.6395

0.6236

0.6195

0.6278

0.6235

Anhui

0.4133

0.3862

0.3305

0.3031

0.2973

0.2868

0.2756

Fujian

0.8505

0.7210

0.6520

0.5242

0.5315

0.5328

0.5122

Jiangxi

0.3477

0.3375

0.3294

0.3335

0.3298

0.3096

0.2948

Shandong

0.8316

0.8410

0.8334

0.8314

0.8220

0.8162

0.8030

Henan

0.5357

0.4881

0.4306

0.4075

0.3736

0.3498

0.3515

Hubei

0.5131

0.5175

0.4814

0.4417

0.4229

0.3919

0.3712

Hunan

0.4083

0.3912

0.3824

0.3635

0.3541

0.3254

0.3116

Guangdong

0.5699

0.5281

0.5476

0.5146

0.5056

0.4885

0.4784

Guangxi

0.3491

0.3163

0.3017

0.2805

0.2652

0.2642

0.2485

Hainan

1.1970

1.2426

1.2349

1.2240

1.1889

1.1953

1.1890

Chongqing

0.4298

0.4216

0.4120

0.4230

0.4397

0.4554

0.4748

Sichuan

0.4032

0.3924

0.3914

0.3746

0.3613

0.3237

0.3380

Guizhou

0.4383

0.4351

0.4445

0.4516

0.4665

0.4664

0.4434

Yunnan

0.4176

0.3770

0.3691

0.3700

0.3931

0.3865

0.3821

Shaanxi

1.9464

1.9882

1.8929

1.9021

2.1150

2.2718

2.3268

Gansu

0.3116

0.3125

0.2961

0.2882

0.2883

0.2857

0.2869

Qinghai

1.1944

1.1893

1.1982

1.2202

1.1880

1.2544

1.2304

Ningxia

1.0975

1.1016

1.0996

1.0981

1.1341

1.1200

1.1511

Xinjiang

1.1750

1.1547

1.1516

1.1296

1.0941

1.0580

0.6990

City

2011

2012

2013

2014

2015

2016

2017

2018

Beijing

1.0294

1.0158

1.0120

1.0455

1.0616

1.0622

1.0189

1.0009

Tianjin

1.3590

1.3600

1.3678

1.3836

1.3734

1.3725

1.3570

1.3506

Hebei

0.4124

0.3793

0.3924

0.3717

0.3700

0.3815

0.3903

0.3947

Shanxi

0.4855

0.4412

0.4200

0.4213

0.4095

0.4153

0.6449

0.9000

Inner Mongolia

0.4512

0.4498

0.4330

0.4155

0.4763

0.4738

0.4769

0.4904

Liaoning

0.5820

0.5446

0.5837

0.5384

0.5672

0.7937

0.8542

1.0712 (continued)

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

125

Table 4.7 (continued) City

2011

2012

2013

2014

2015

2016

2017

2018

Jilin

0.5074

0.4835

0.5005

0.5018

0.4970

0.5027

0.4768

0.4762

Heilongjiang

0.4218

0.3814

0.3842

0.4330

0.4440

0.4494

0.4534

0.4605

Shanghai

1.3125

1.3057

1.3007

1.2634

1.2820

1.2033

1.1946

1.1332

Jiangsu

0.6606

0.6912

0.7089

0.7262

0.7258

0.7339

0.7384

0.7456

Zhejiang

0.5817

0.5648

0.5753

0.5732

0.5775

0.5853

0.5639

0.5648

Anhui

0.2628

0.2518

0.2529

0.2582

0.2490

0.2573

0.2466

0.2361

Fujian

0.4403

0.4429

0.4467

0.4408

0.4323

0.4507

0.4388

0.4395

Jiangxi

0.2956

0.2927

0.2886

0.2951

0.2938

0.2943

0.2833

0.2618

Shandong

0.8049

0.7979

0.7997

0.7970

0.7815

0.8040

0.8002

0.8096

Henan

0.3511

0.3269

0.3332

0.3461

0.3318

0.3417

0.3286

0.3197

Hubei

0.3443

0.3243

0.3318

0.3301

0.3182

0.3362

0.3332

0.3269

Hunan

0.2905

0.2720

0.2739

0.2723

0.2671

0.2712

0.2635

0.2522

Guangdong

0.4383

0.4217

0.4125

0.3930

0.3929

0.3762

0.3848

0.3523

Guangxi

0.2735

0.2695

0.2766

0.2819

0.2768

0.2824

0.2711

0.2625

Hainan

1.1855

1.1406

1.1740

1.1572

1.1495

1.1219

1.0436

0.9046

Chongqing

0.4826

0.4846

0.4773

0.4836

0.4882

0.4774

0.4392

0.4209

Sichuan

0.3352

0.3164

0.3199

0.3095

0.2916

0.2865

0.2770

0.2793

Guizhou

0.3996

0.3664

0.3797

0.3559

0.3440

0.3367

0.3006

0.2713

Yunnan

0.3700

0.3591

0.3616

0.3596

0.3501

0.3407

0.2951

0.2711

Shaanxi

2.4695

2.2610

2.0976

1.9713

1.8142

1.6529

1.4957

1.2289

Gansu

0.2759

0.2638

0.2583

0.2599

0.2668

0.2622

0.2367

0.2321

Qinghai

1.2468

1.3312

1.3183

1.3948

1.3988

1.3957

1.4345

1.4324

Ningxia

1.1306

1.1449

1.1617

1.1733

1.1780

1.1763

1.1212

1.0687

Xinjiang

0.6974

0.5831

0.5523

0.5584

0.5583

0.5532

0.5912

0.5940

Data source calculated using DEA-Solver 5.0

It can be seen from Fig. 4.2 that the distribution of regional water resources utilization efficiency was relatively stable from 2004 to 2018. The water resources utilization efficiencies in the eastern region were significantly higher than those in other regions, and remained at the highest level consistently. The ranking of efficiency levels from high to low is as follows: the eastern region, northeastern region, western region, and central region. Table 4.9 shows the water resources utilization efficiencies of the eight major economic zone. Overall, the water resources utilization efficiency indexes of the northern coastal comprehensive economic zone, eastern coastal comprehensive economic zone, and northwest comprehensive economic zone—0.8889, 0.8145, and 0.8318, respectively, in 2018—were higher than those of other economic zones, placing these regions in the first echelon. In 2018, the water resources utilization efficiencies of the comprehensive economic zone in the middle reaches of the Yellow

126

4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.8 Regional water resources utilization efficiency Year

Eastern region

Central region

Western region

Northeastern region

2004

0.8592

0.4538

0.6818

0.7433

2005

0.8457

0.4368

0.6008

0.7349

2006

0.8397

0.4062

0.5544

0.7233

2007

0.8214

0.3902

0.5217

0.7227

2008

0.8290

0.3826

0.5146

0.7443

2009

0.8340

0.3607

0.5075

0.7579

2010

0.8348

0.3508

0.4965

0.7313

2011

0.8225

0.3383

0.5037

0.7393

2012

0.8120

0.3181

0.4698

0.7118

2013

0.8190

0.3167

0.4895

0.6942

2014

0.8152

0.3205

0.4911

0.6876

2015

0.8147

0.3116

0.5028

0.6767

2016

0.8091

0.3193

0.5819

0.6580

2017

0.7930

0.3500

0.5948

0.6308

2018

0.7696

0.3828

0.6693

0.5956

Data source calculated using DEA-Solver 5.0

Fig. 4.2 Regional water resources utilization efficiency

River, southern coastal economic zone, and northern comprehensive economic zone were in the second echelon, being 0.7347, 0.5655, and 0.6693, respectively. The average annual growth rate of the eastern coastal economic zone was positive at 0.31%, while that of the southern coastal economic zone and the comprehensive economic zone in the middle reaches of the Yangtze River were − 3.05% and − 3.14%, respectively.

4.3 Measurement and Analysis of Efficiencies of Water and Energy …

127

Table 4.9 Water resources utilization efficiencies of the eight major economic zones Economic zone

2004

Northeast comprehensive economic zone

0.6818 0.6008 0.5544 0.5217 0.5146 0.5075 0.4965

2005

2006

2007

2008

2009

2010

Northern coastal comprehensive economic zone

0.9086 0.8968 0.8845 0.8653 0.9014 0.8957 0.8970

Eastern coastal comprehensive economic zone

0.7801 0.7929 0.8081 0.8301 0.8193 0.8470 0.8600

Southern coastal economic zone

0.8725 0.8306 0.8115 0.7542 0.7420 0.7389 0.7265

Comprehensive economic zone in 0.8499 0.8430 0.8015 0.8033 0.8621 0.8934 0.9105 the middle reaches of the Yellow River Comprehensive economic zone in 0.4206 0.4081 0.3809 0.3605 0.3510 0.3284 0.3133 the middle reaches of the Yangtze River Southwest comprehensive economic zone

0.4076 0.3885 0.3838 0.3799 0.3852 0.3792 0.3774

Northwest comprehensive economic zone

0.9446 0.9395 0.9364 0.9340 0.9261 0.9295 0.8419

Economic zone

2011

2012

2013

2014

2015

2016

2017

2018

Northeast comprehensive economic zone

0.5037

0.4698

0.4895

0.4911

0.5028

0.5819

0.5948

0.6693

Northern coastal comprehensive economic zone

0.9014

0.8882

0.8929

0.8994

0.8966

0.9050

0.8916

0.8889

Eastern coastal comprehensive economic zone

0.8516

0.8539

0.8617

0.8543

0.8618

0.8408

0.8323

0.8145

Southern coastal economic zone

0.6880

0.6684

0.6777

0.6637

0.6582

0.6496

0.6224

0.5655

Comprehensive 0.9393 economic zone in the middle reaches of the Yellow River

0.8697

0.8209

0.7886

0.7580

0.7209

0.7365

0.7347

Comprehensive 0.2983 economic zone in the middle reaches of the Yangtze River

0.2852

0.2868

0.2889

0.2820

0.2898

0.2817

0.2693

Southwest comprehensive economic zone

0.3722

0.3592

0.3630

0.3581

0.3502

0.3448

0.3166

0.3010

Northwest comprehensive economic zone

0.8377

0.8307

0.8227

0.8466

0.8505

0.8468

0.8459

0.8318

Data source calculated using DEA-Solver 5.0

128

4 Spatial Differences in Water–Energy System Coupling Relationship

The utilization efficiencies of water resources in the northeast comprehensive economic zone first decreased and then increased, from being less than 0.5 during 2010 and 2013 to reaching 0.6693 in 2018. The efficiencies in the northern coastal comprehensive economic zone were always at a high level around 0.90. The utilization efficiencies of water resources in the eastern coastal comprehensive economic zone showed a slow upward fluctuation trend to 0.85 before 2015 and decreased slowly and remained at a high level after reaching the peak 0.8618 in 2015. The water resources utilization efficiency of the southern coastal comprehensive economic zone was 0.8725 in 2004, and then decreased gradually, reaching a minimum of 0.5655 in 2018. The efficiency in the comprehensive economic zone in the middle reaches of the Yellow River first rose to 0.9393 in 2011 and then decreased significantly to 0.7347 in 2018, with an average annual growth rate of − 1.17% from 2011 to 2018. The utilization efficiencies of water resources in the middle reaches of the Yangtze River comprehensive economic zone and southwest comprehensive economic zone declined continuously. The efficiencies in the northwest comprehensive economic zone decreased gradually, but remained above 0.83 overall.

4.4 Analysis on Coupling Relationship of Water–Energy Correlation System 4.4.1 Analysis on Coupling Degree of Water–Energy Correlation System Based on the established system coupling coordination model, the coupling degrees of 30 areas in China are calculated first, and the results are shown in Table 4.10. Judging from the coupling results, the water–energy annual average system coupling degrees from 2004 to 2018 were at a high level around 0.95, indicating that the coupling degrees of the water–energy system in the 30 areas of China were at high levels over these years. The degree of coupling reflects the strength of the positive interaction between two systems. Since the coupling relationship mechanism between water resources distribution and energy resource utilization is very complex, and water resources penetrate each stage of energy resources and constrain somewhat the normal utilization of energy resources, a deterioration in the water resources distribution and utilization will negatively affect energy resource utilization quality and efficiency. The water–energy coupling degrees of Beijing in the whole period were all lower than 0.89 except in the year 2005, being lower than those of other areas. Through data analysis we found that the indexes of Beijing were all at medium or high levels except the per capita water resources, and the indexes of energy resources—including total energy production, per capita energy consumption, energy industry investment, energy consumption elasticity, and total power of agricultural machinery—were all at a low level.

4.4 Analysis on Coupling Relationship of Water–Energy Correlation System

129

Table 4.10 Water–energy coupling degrees of 30 areas in China City

2004

2005

2006

2007

2008

2009

2010

Beijing

0.8868

0.9684

0.8099

0.8551

0.8238

0.8850

0.9210

Tianjin

0.8133

0.9186

0.8793

0.9539

0.9568

0.9600

0.9874

Hebei

1.0000

0.9826

0.9583

0.9594

0.9798

0.9624

0.9506

Shanxi

0.9449

0.9533

0.9678

0.9500

0.9808

0.9587

0.9263

Inner Mongolia

0.9911

0.9916

0.9950

0.9978

0.9941

0.9815

0.9685

Liaoning

1.0000

1.0000

0.9994

0.9941

0.9945

0.9941

0.9795

Jilin

0.9906

0.9829

0.9814

0.9989

0.9999

1.0000

0.9970

Heilongjiang

0.9969

0.9906

0.9853

0.9665

0.9607

0.9758

0.9423

Shanghai

0.8190

0.9850

0.9344

0.9869

0.9949

0.9823

0.9990

Jiangsu

0.9771

0.9964

0.9998

0.9916

0.9982

0.9905

0.9828

Zhejiang

0.9517

0.9966

0.9349

0.9734

0.9301

0.9860

1.0000

Anhui

0.9920

0.9948

0.9820

0.9662

0.9737

0.9799

0.9523

Fujian

0.9890

0.9965

0.9878

0.9988

0.9970

0.9968

0.9848

Jiangxi

0.9992

0.9222

0.9955

0.9902

0.9948

0.9985

0.9982

Shandong

0.8266

0.9552

0.9512

0.9721

0.9768

0.9613

0.9207

Henan

0.7912

0.9645

0.8995

0.9180

0.9346

0.9174

0.8724

Hubei

0.9818

0.9965

0.9939

0.9970

0.9746

0.9740

0.9720

Hunan

0.9187

0.9813

0.9817

0.9646

0.9362

0.9221

0.9484

Guangdong

0.8756

0.9941

0.9491

0.9056

0.9361

0.9165

0.8764

Guangxi

0.9210

0.9933

0.9910

0.9563

0.9853

0.9740

0.9294

Hainan

0.9124

0.9851

0.8391

0.9119

0.9249

0.9739

0.9531

Chongqing

0.9990

0.9999

0.9877

0.9808

0.9865

0.9970

0.9995

Sichuan

0.8871

0.9812

0.9783

0.9623

0.9623

0.9494

0.9093

Guizhou

0.9658

0.9829

0.9698

0.9942

0.9934

0.9963

0.9949

Yunnan

0.9793

0.9994

0.9858

0.9933

0.9841

0.9946

0.9999

Shaanxi

0.9393

0.8671

0.9595

0.9925

0.9995

0.9999

0.9958

Gansu

0.9992

0.8787

0.9667

0.9948

0.9966

0.9970

0.9931

Qinghai

0.8435

0.8844

0.8857

0.9149

0.9049

0.8493

0.9257

Ningxia

0.9095

0.9405

0.9122

0.9296

0.9408

0.9574

0.9367

Xinjiang

0.9217

0.9922

0.9279

0.9862

0.9947

0.9997

0.9882

City

2011

2012

2013

2014

2015

2016

2017

2018

Beijing

0.8869

0.8898

0.8987

0.9162

0.8362

0.8548

0.8590

0.8735

Tianjin

0.9498

0.9525

0.9680

0.9687

0.9272

0.9016

0.8890

0.9432

Hebei

0.9518

0.9373

0.9277

0.9123

0.9184

0.9370

0.9390

0.9556

Shanxi

0.9369

0.9234

0.9138

0.9030

0.9036

0.9095

0.9632

0.9941

Inner Mongolia

0.9669

0.9763

0.9809

0.9137

0.9729

0.9512

0.9781

0.9852

Liaoning

0.9936

0.9947

0.9965

0.9901

0.9993

0.9791

0.9933

0.9961 (continued)

130

4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.10 (continued) City

2011

2012

2013

2014

2015

2016

2017

2018

Jilin

0.9984

0.9995

0.9992

0.9977

0.9952

0.9983

0.9989

0.9998

Heilongjiang

0.8871

0.9062

0.9484

0.9254

0.9738

0.9718

0.9572

0.9649

Shanghai

0.9919

0.9925

0.9918

0.9964

0.9628

0.9853

0.9819

0.9915

Jiangsu

0.9879

0.9795

0.9706

0.9602

0.9799

0.9565

0.9562

0.9556

Zhejiang

0.9969

1.0000

0.9994

0.9978

0.9950

0.9999

0.9986

0.9998

Anhui

0.9652

0.9566

0.9396

0.9329

0.9601

0.9254

0.8957

0.9268

Fujian

0.9870

0.9945

0.9888

0.9697

0.9991

0.9987

0.9756

0.9913

Jiangxi

0.9951

0.9953

0.9961

0.9869

0.9982

0.9951

0.9864

0.9803

Shandong

0.9081

0.9082

0.8761

0.8487

0.8863

0.9205

0.9437

0.9581

Henan

0.9123

0.9136

0.8487

0.8453

0.8827

0.8898

0.9002

0.9260

Hubei

0.9703

0.9718

0.9489

0.9504

0.9709

0.9640

0.9329

0.9376

Hunan

0.9461

0.9500

0.9161

0.9208

0.9561

0.9423

0.9091

0.9087

Guangdong

0.9000

0.9024

0.8892

0.8311

0.9148

0.8979

0.8680

0.8579

Guangxi

0.9903

0.9970

0.9951

0.9798

0.9999

0.9904

0.9830

0.9774

Hainan

0.9164

0.9396

0.9062

0.9431

0.8726

0.8918

0.9040

0.9979

Chongqing

0.9965

0.9970

0.9989

1.0000

0.9987

0.9996

0.9951

0.9981

Sichuan

0.9170

0.9227

0.9183

0.8891

0.9354

0.8932

0.9025

0.9218

Guizhou

0.9983

0.9996

0.9997

0.9991

0.9962

0.9986

0.9941

0.9954

Yunnan

0.9996

0.9948

0.9901

0.9855

0.9994

0.9869

0.9870

0.9966

Shaanxi

1.0000

1.0000

0.9957

0.9934

0.9997

0.9947

0.9966

0.9884

Gansu

0.9992

0.9962

0.9870

0.9782

0.9998

0.9949

0.9957

1.0000

Qinghai

0.9072

0.9100

0.9097

0.9181

0.8461

0.8649

0.8550

0.8908

Ningxia

0.9602

0.9403

0.9499

0.9699

0.9463

0.9510

0.9658

0.9651

Xinjiang

0.9952

0.9723

0.9654

0.9245

0.9791

0.9817

0.9901

0.9986

Data source calculated using the coupling coordination model

Figure 4.3 shows the radar map of water–energy coupling degrees in different regions. As shown in the figure, the water–energy coupling degrees in the northeastern region were at the highest level and remained stable, being only slightly lower than those in the eastern region in 2011 and 2012; the water–energy coupling degrees in the eastern region gradually increased since 2004 and remained stable at a high level; there was little difference in the water–energy coupling degree between the northeast region and the western region. Due to the high-level water–energy coupling degrees of all four major regions, conditions of the eight major economic zones will not be analyzed individually.

4.4 Analysis on Coupling Relationship of Water–Energy Correlation System

131

Fig. 4.3 Regional water–energy coupling degree radar map

4.4.2 Analysis on the Coupling Coordination Degree of Water–Energy System Table 4.11 shows the results of the water–energy system coupling coordination degrees in 30 provincial areas of China, the values of which fluctuated between 0.6 and 0.8 from 2004 to 2018 and were in the stage of primary coordination and intermediate coordination. Beijing, Hunan, Guangdong, Sichuan, Henan, Hainan, and Qinghai had been in the primary coupling coordination stage for a long time. Shandong and Tianjin were the first to move beyond the primary coordination but gradually fell back until the system coupling coordination index of Tianjin rebounded to 0.7040 and that of Shandong rebounded to 0.7056 in 2018. The degrees of system coupling coordination in Hebei, Jiangsu, Anhui, and Hubei reverted from the intermediate stage of coupling coordination to the primary stage of coupling coordination. Shanxi and Ningxia fluctuated between the primary coordination stage and the intermediate coordination stage until the system coupling coordination degree of Shanxi was stable at 0.7257 and that of Ningxia was stable at 0.7061 in 2018. After transcending the primary stage of system coupling and coordination, Shaanxi, Guangxi, and Xinjiang were stable in the intermediate coordination stage for a long time. The growth rates of the annual average coupling coordination degrees of Hebei, Heilongjiang, Jiangsu, Anhui, Fujian, Jiangxi, Hubei, Hunan, Guangdong, Sichuan, and Gansu were all negative, respectively, being − 0.298%, − 0.168%, − 0.148%, − 0.160%, − 0.020%, − 0.116%, − 0.238%, − 0.066%, − 0.132%, and − 0.178%. The coordination degree of Gansu decreased slightly, with an average annual growth rate of − 0.007%. ArcGIS is used to construct the spatial distribution map of the water–energy system coupling coordination in 30 provincial areas in China. The years 2004, 2008,

132

4 Spatial Differences in Water–Energy System Coupling Relationship

Table 4.11 Water–energy system coupling coordination degrees of 30 provincial areas in China City

2004

2005

2006

2007

2008

2009

2010

Beijing

0.6632

0.7011

0.6246

0.6519

0.6335

0.6663

0.6867

Tianjin

0.6261

0.6753

0.6644

0.7056

0.7057

0.7075

0.7226

Hebei

0.7303

0.7093

0.7020

0.7031

0.7140

0.7040

0.6961

Shanxi

0.6990

0.6952

0.7111

0.7015

0.7161

0.7059

0.6911

Inner Mongolia

0.7218

0.7152

0.7280

0.7283

0.7273

0.7200

0.7137

Liaoning

0.7202

0.7169

0.7214

0.7192

0.7189

0.7173

0.7118

Jilin

0.7161

0.7095

0.7121

0.7211

0.7223

0.7212

0.7201

Heilongjiang

0.7211

0.7103

0.7165

0.7067

0.7029

0.7100

0.6915

Shanghai

0.6231

0.7102

0.6903

0.7190

0.7221

0.7134

0.7215

Jiangsu

0.7105

0.7162

0.7188

0.7155

0.7194

0.7129

0.7073

Zhejiang

0.7003

0.7159

0.6931

0.7131

0.6897

0.7138

0.7232

Anhui

0.7164

0.7141

0.7129

0.7065

0.7101

0.7110

0.6964

Fujian

0.7166

0.7161

0.7195

0.7251

0.7238

0.7210

0.7164

Jiangxi

0.7189

0.6783

0.7199

0.7164

0.7191

0.7215

0.7219

Shandong

0.6354

0.6979

0.7027

0.7148

0.7158

0.7061

0.6835

Henan

0.6151

0.6995

0.6716

0.6838

0.6934

0.6807

0.6563

Hubei

0.7085

0.7152

0.7158

0.7178

0.7073

0.7056

0.7035

Hunan

0.6760

0.7061

0.7092

0.7001

0.6862

0.6771

0.6912

Guangdong

0.6519

0.7155

0.6927

0.6692

0.6858

0.6735

0.6526

Guangxi

0.6783

0.7145

0.7161

0.6976

0.7143

0.7056

0.6836

Hainan

0.6788

0.7105

0.6395

0.6805

0.6869

0.7134

0.6996

Chongqing

0.7206

0.7168

0.7144

0.7114

0.7148

0.7195

0.7196

Sichuan

0.6623

0.7078

0.7116

0.7032

0.7036

0.6940

0.6735

Guizhou

0.7006

0.7090

0.7055

0.7184

0.7165

0.7179

0.7182

Yunnan

0.7151

0.7160

0.7200

0.7236

0.7196

0.7220

0.7260

Shaanxi

0.6942

0.6488

0.7123

0.7318

0.7346

0.7358

0.7348

Gansu

0.7201

0.6553

0.7054

0.7206

0.7206

0.7191

0.7188

Qinghai

0.6449

0.6534

0.6731

0.6882

0.6825

0.6498

0.6922

Ningxia

0.6736

0.6890

0.6773

0.6863

0.6921

0.6976

0.6868

Xinjiang

0.6904

0.7471

0.6999

0.7273

0.7296

0.7280

0.7243

City

2011

2012

2013

2014

2015

2016

2017

2018

Beijing

0.6665

0.6703

0.6751

0.6863

0.6424

0.6522

0.6574

0.6660

Tianjin

0.7005

0.7023

0.7095

0.7134

0.6907

0.6762

0.6717

0.7040

Hebei

0.6969

0.6890

0.6830

0.6742

0.6782

0.6890

0.6903

0.7006

Shanxi

0.6985

0.6902

0.6842

0.6773

0.6769

0.6775

0.7078

0.7257

Inner Mongolia

0.7130

0.7187

0.7236

0.6864

0.7163

0.7085

0.7193

0.7231

Liaoning

0.7177

0.7182

0.7192

0.7142

0.7186

0.7097

0.7194

0.7248 (continued)

4.4 Analysis on Coupling Relationship of Water–Energy Correlation System

133

Table 4.11 (continued) City

2011

2012

2013

2014

2015

2016

2017

2018

Jilin

0.7208

0.7221

0.7211

0.7196

0.7188

0.7208

0.7213

0.7215

Heilongjiang

0.6593

0.6704

0.6945

0.6804

0.7067

0.7077

0.7003

0.7043

Shanghai

0.7179

0.7189

0.7180

0.7212

0.7029

0.7143

0.7130

0.7188

Jiangsu

0.7115

0.7074

0.7024

0.6972

0.7081

0.6962

0.6960

0.6959

Zhejiang

0.7197

0.7205

0.7202

0.7192

0.7179

0.7205

0.7193

0.7198

Anhui

0.7021

0.6979

0.6880

0.6839

0.6986

0.6816

0.6647

0.6811

Fujian

0.7144

0.7193

0.7165

0.7063

0.7202

0.7222

0.7083

0.7146

Jiangxi

0.7188

0.7188

0.7175

0.7125

0.7188

0.7171

0.7118

0.7074

Shandong

0.6753

0.6756

0.6579

0.6430

0.6633

0.6858

0.6990

0.7056

Henan

0.6764

0.6761

0.6398

0.6375

0.6582

0.6652

0.6721

0.6855

Hubei

0.7028

0.7025

0.6915

0.6914

0.7032

0.7006

0.6840

0.6853

Hunan

0.6908

0.6925

0.6748

0.6761

0.6956

0.6897

0.6716

0.6698

Guangdong

0.6635

0.6650

0.6579

0.6255

0.6710

0.6628

0.6458

0.6400

Guangxi

0.7150

0.7195

0.7188

0.7100

0.7214

0.7162

0.7121

0.7075

Hainan

0.6790

0.6909

0.6744

0.6931

0.6534

0.6653

0.6721

0.7265

Chongqing

0.7186

0.7180

0.7205

0.7200

0.7199

0.7201

0.7178

0.7182

Sichuan

0.6781

0.6812

0.6790

0.6626

0.6871

0.6649

0.6690

0.6789

Guizhou

0.7205

0.7205

0.7209

0.7210

0.7196

0.7197

0.7167

0.7166

Yunnan

0.7249

0.7212

0.7192

0.7156

0.7236

0.7168

0.7157

0.7191

Shaanxi

0.7380

0.7382

0.7384

0.7368

0.7394

0.7368

0.7376

0.7304

Gansu

0.7207

0.7192

0.7145

0.7094

0.7200

0.7170

0.7176

0.7194

Qinghai

0.6821

0.6829

0.6835

0.6893

0.6495

0.6599

0.6571

0.6785

Ningxia

0.7006

0.6906

0.6960

0.7078

0.6953

0.6968

0.7066

0.7061

Xinjiang

0.7259

0.7100

0.7077

0.6848

0.7145

0.7145

0.7186

0.7234

Data source calculated using the coupling coordination model

2013, and 2018 are selected to observe the water–energy temporal and spatial evolution and distribution characteristics. Figures 4.4, 4.5, 4.6 and 4.7 represent the water– energy system coupling coordination distribution in 2004, 2008, 2013, and 2018, respectively. Figure 4.8 is the average system coupling ordination distribution. From Fig. 4.4 we can know that the degrees of water–energy system coupling coordination were mostly high in the northeastern region and central region; among eastern coastal areas, only the degrees of Beijing, Hebei, Jiangsu, and Fujian were at a high level; areas with low coupling coordination degrees were mostly in the western region, such as Qinghai, Henan, and Shandong. In 2008, the areas with high water– energy system coupling coordination degrees were mainly distributed in the northern and western regions of China, including Xinjiang Uygur Autonomous Region, Gansu, Shaanxi, Inner Mongolia, Jilin, and Liaoning. The coupling coordination degrees in the eastern region were relatively high, such as in Jiangsu, Shanghai, and Fujian.

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4 Spatial Differences in Water–Energy System Coupling Relationship

Fig. 4.4 Distribution of system coupling coordination degree 2004

In the central and western regions, only Jiangxi and Yunnan had relatively high coupling coordination degrees. Cities with low coupling coordination degrees were mainly concentrated in inland areas. In 2013, the areas with high water–energy system coupling coordination degrees were mainly concentrated in the northern and southern regions. The former mainly included Inner Mongolia, Gansu, Shaanxi, Liaoning, and Jilin, while the latter included Chongqing, Guizhou, Yunnan, and Guangxi. Only Zhejiang and Fujian in the eastern coastal region had high coupling coordination degrees. Cities with low coupling coordination degrees were mainly distributed in the central region, including Hebei, Shanxi, and Hunan. Among them, Guangdong and Shandong in the eastern region were at a low level of coupling coordination. In 2018, areas with high water–energy system coupling coordination degrees were mainly concentrated in the northern region. In the eastern coastal areas, only Hainan had a high coupling coordination degree. Areas with low coupling coordination degrees were mainly concentrated in the central region, including Anhui, Henan, Hubei, and Hunan. In

4.4 Analysis on Coupling Relationship of Water–Energy Correlation System

135

Fig. 4.5 Distribution of system coupling coordination degree 2008

the eastern region, only Zhejiang and Fujian were at a low level of coupling coordination, and in the central region, only Shaanxi and Jiangxi had high coupling coordination degrees. The coupling coordination degrees of the whole country had generally decreased. Compared with the annual average level, the system coupling coordination levels of Liaoning, Heilongjiang, and Jilin in the northeastern region decreased annually. Heilongjiang had fallen to the third echelon by 2013, and Jilin had fallen below the annual average level in 2018. In the eastern coastal areas, the system coupling coordination of Hebei, Shandong, and Jiangsu also decreased each year. Among them, Shandong showed a fluctuating decline: the coupling coordination level first increased in 2008, and then decreased in 2013, and rebounded slightly in 2018. The coupling coordination situation in Qinghai was gradually optimized, and the coupling coordination degree was above the average level in 2008. On the contrary, the coupling coordination situation in Jiangsu gradually deteriorated, and the degree decreased to below the annual average level in 2013. The coupling coordination levels in the eastern and central regions gradually approached the annual average level of

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4 Spatial Differences in Water–Energy System Coupling Relationship

Fig. 4.6 Distribution of system coupling coordination degree 2013

China. Except Heilongjiang, the coupling coordination degrees in the northeastern region were basically equal to the annual average coupling coordination degree, while the western region showed some fluctuations. Table 4.12 shows that the system coupling coordination degrees were generally high in the western and northeastern regions but low in the eastern and central regions. According to the average system coupling coordination index from 2004 to 2018, the eastern and central regions were at the end of the primary coordination stage and the western and northeastern regions were at the beginning of the intermediate coordination stage. From 2004 to 2018, China’s system coupling coordination degree was generally in a fluctuating upward trend, it fluctuated greatly from 2012 to 2017, and it stabilized to 0.7049 in 2018. Overall, China’s water–energy system coupling coordination degree was stable and in the early stage of intermediate coordination. Figure 4.9 shows the radar chart of regional system coupling coordination degrees. It is obvious that the system coupling coordination degrees of the northeastern region were always at the highest level, except in 2011 and 2012, when it was surpassed by the western region. The northeastern and western regions had values above the

4.5 Conclusions and Policy Recommendations

137

Fig. 4.7 Distribution of system coupling coordination degree 2018

average system coupling coordination degrees for a long time while the eastern and central regions had values that were lower than the national average. The gaps in the system coupling coordination degrees between the eastern region and central region were small except in 2004 and 2006.

4.5 Conclusions and Policy Recommendations 4.5.1 Main Conclusions The super-efficiency SBM model and system coupling coordination model are adopted to measure the water resource utilization efficiency, energy resource utilization efficiency, and water–energy system coupling coordination degree of 30 provincial areas in China from 2004 to 2018. The results show that the energy resource

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4 Spatial Differences in Water–Energy System Coupling Relationship

Fig. 4.8 Distribution of system coupling coordination degree (average)

utilization efficiencies in half of the researched areas showed an upward trend, and were generally the highest in the eastern coastal region, followed by the western region, and finally, the relatively undeveloped northeastern and central regions. In the eight major economic zones, the energy resource utilization efficiencies in the central and eastern coastal comprehensive economic zones were higher than those in other economic zones as a whole, belonging to the first echelon; those in the northern coastal comprehensive economic zone, southern coastal economic zone, and southwest comprehensive economic zone belonged to the second echelon; the efficiencies in the northeast comprehensive economic zone, middle reaches of the Yellow River comprehensive economic zone, middle reaches of the Yangtze River comprehensive economic zone, and northwest comprehensive economic zone were generally undeveloped, falling in the third echelon. As for water resources, only six provinces had increasing utilization efficiencies, and the eastern coastal region generally had the highest efficiencies, followed by the northeastern region and the relatively undeveloped western and central regions.

4.5 Conclusions and Policy Recommendations

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Table 4.12 Regional system coupling coordination degrees Year

Eastern region

Central region

Western region

Northeastern region

Average

2004

0.6736

0.6890

0.6929

0.7191

0.6937

2005

0.7068

0.7014

0.6975

0.7123

0.7045

2006

0.6848

0.7067

0.7058

0.7166

0.7035

2007

0.6998

0.7043

0.7124

0.7157

0.7080

2008

0.6997

0.7054

0.7141

0.7147

0.7085

2009

0.7032

0.7003

0.7099

0.7162

0.7074

2010

0.7010

0.6934

0.7083

0.7078

0.7026

2011

0.6945

0.6982

0.7125

0.6993

0.7011

2012

0.6959

0.6963

0.7109

0.7036

0.7017

2013

0.6915

0.6826

0.7111

0.7116

0.6992

2014

0.6879

0.6798

0.7040

0.7047

0.6941

2015

0.6848

0.6919

0.7097

0.7147

0.7003

2016

0.6884

0.6886

0.7065

0.7127

0.6991

2017

0.6873

0.6853

0.7080

0.7137

0.6986

2018

0.6992

0.6925

0.7110

0.7169

0.7049

Data source calculated using the coupling coordination model Fig. 4.9 Radar map of regional system coupling coordination degrees

The water resources utilization efficiencies in the northern coastal comprehensive economic zone, eastern coastal comprehensive economic zone, and northwest comprehensive economic zone were higher than those in other economic zones, belonging to the first echelon; the utilization efficiencies in the comprehensive economic zone in the middle reaches of the Yellow River, southern coastal economic zone, and northern comprehensive economic zone belonged to the second echelon; those in the middle reaches of the Yangtze River comprehensive economic zone

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4 Spatial Differences in Water–Energy System Coupling Relationship

and southwest comprehensive economic zone were relatively low and in the third echelon. The water–energy coupling degrees of 30 provincial areas in China were all in a high-level coupling stage, and the annual average system coupling degree fluctuated above 0.95 from 2004 to 2018. Overall, the water–energy coupling coordination degrees of 30 areas in China fluctuated between 0.6 and 0.8 from 2004 to 2018, which were in the stages of primary coordination and intermediate coordination. Beijing, Hunan, Guangdong, Sichuan, Henan, Hainan, and Qinghai had long been in the primary stage of water–energy system coupling coordination; areas that crossed the primary coordination stage but gradually returned to it again were Shandong and Tianjin; Hebei, Jiangsu, Anhui, and Hubei moved backwards from the intermediate stage of coupling coordination to the primary stage; Shanxi and Ningxia fluctuated between the primary coordination stage and the intermediate stage; Shanxi, Guangxi, and Xinjiang crossed the primary coupling coordination stage and then remained in the intermediate stage for a long time; other areas had long been stable in the intermediate coordination stage.

4.5.2 Policy Recommendations (1) Improving resource utilization efficiency The super-efficiency SBM measurement results show that the energy resource utilization efficiency indexes of the researched areas were all less than 1 all year round except those of Shaanxi, Hainan, Shanghai, Qinghai, Ningxia, and Fujian. For the western and central areas rich in energy resources, governmental departments should switch from the extensive utilization mode to intensive and economical mode in a timely manner, adhere to the principle of ecological priority, improve the efficiency of green development and energy resources utilization, and adhere to the direction of green circular development. For eastern cities with scarce energy resources, the comparative advantages of each region should be brought into play; material and human capital investments should be increased; and green technology should be introduced to accelerate the implementation of green innovation, deeply promote green ecological protection, and accelerate the development of the circular economy. The water resources utilization efficiency indexes of the researched areas were less than 1 over the years, except those of Shaanxi, Qinghai, Ningxia, Hainan, and Beijing. Due to scarce water resources and low rainfall throughout the year, the western region should advocate water conservation over the years, improve the utilization efficiency of water resources, and prevent the lack of water resources from becoming an important factor restricting high-quality development. Moreover, the western region should strengthen the protection and management of water resources, improve residents’ awareness of water conservation, and enhance the scale of intensive water use. For the eastern coastal areas with relatively rich water resources and the cities around the Yellow River Basin, Yangtze River Basin, and Xin’an River

4.5 Conclusions and Policy Recommendations

141

Basin, they should enhance the protection of water resources; adjust and optimize the use structure of water resources; actively introduce and develop water resources utilization technologies when ensuring the safety of drinking water to improve the water resources utilization efficiencies in industry, agriculture, and life; and improve the overall utilization efficiency of water resources. (2) Improving the coupling coordination level in the eastern coastal areas The water–energy coupling degrees of 30 provincial areas in China were all at high levels from 2004 to 2018, but the system coupling coordination degrees were at the primary and intermediate stages, needing urgent improvement. From the temporal and spatial distribution map, areas with high levels of system coupling coordination in China were mainly concentrated in the northern region, while the levels in the eastern coastal economically developed areas were gradually decreasing. To improve the coupling coordination levels in the eastern coastal areas, institutional mechanisms should be deployed at the national level. Moreover, it is necessary to continuously and deeply promote intensive and economical production and life style, strengthen pollution control and prevention, carry out environmental governance, improve pollution discharge standards of water and energy resources, strengthen the responsibility mechanism of polluters, optimize the resource distribution pattern, promote the comprehensive conservation and recycling of resources, and improve the efficiency of resource utilization to promote the sustainable, steady, and coordinated development of water and energy. The eastern and western regions should continue to further promote the coordinated development of water and energy resources and make a leap to a better system coupling coordination stage. (3) Accelerating the transformation from extensive development to intensive development Taking the Kunlun-Qinling-Dabie Mountain Line as the boundary, water resources and energy resources are inversely distributed in China. The uneven distribution of domestic water and energy resources, and the great pressure of resource allocation have slowed the growth of the regional water–energy coupling coordination degree. In these cases, the traditional extensive resource utilization mode is no longer able to meet the needs of high-quality industrial development, and must be adjusted and transformed urgently. In particular, the eastern and central regions should not simply pursue the economic growth rate, but should focus on improving the development level of the green economy and realize the intensive development of water and energy resources through reasonable planning and technological development to enter the intermediate coupling coordination stage as soon as possible; the northeastern and western regions should continue to pursue intensive and energy-saving resource utilization, and further advance from the intermediate coupling coordination stage to the good coupling coordination stage.

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References 1. Wang, M., Sun, C.Z., Wang, X.L.: Analysis of the water-energy coupling efficiency in China: Based on the three-stage SBM-DEA model with undesirable outputs. Water 11(4), 632 (2019) 2. Dudley, B.: BP Statistical Review of World Energy. British Petroleum, London (2016) 3. Cai, B.M., Liu, B.B., Zhang, B.: Evolution of Chinese urban household’s water footprint. J. Clean. Prod. 208, 1–10 (2019) 4. Liao, X.W., Hall, J.W., Eyre, N.: Water use in China’s thermoelectric power sector. Glob. Environ. Change Human Policy Dimensions 41, 142–152 (2016) 5. Wang, J.X., Rothausen, S.G.S.A., Conway, D., Zhang, L.J., Xiong, W., Holman, I.P., Li, Y.M.: China’s water–energy nexus: greenhouse-gas emissions from groundwater use for agriculture. Environ. Res. Lett. 7(1), 014035 (2012) 6. Li, M., Dai, H., Xie, Y., Tao, Y., Bregnbaek, L., Sandholt, K.: Water conservation from power generation in China: a provincial level scenario towards 2030. Appl. Energy 208, 580–591 (2017) 7. Hong, S.Y., Wang, H.R., Lai, W.L.: Spatial analysis and coordinated development decoupling analysis of energy-consumption water in China. J. Nat. Resour. 32(5), 800–813 (2017) 8. Marsh, D.M.: The Water–Energy Nexus: A Comprehensive Analysis in the Context of New South Wales. Faculty of Engineering and Information Technology University of Technology, Sydney (2008) 9. Gu, A., Teng, F.: Water-saving effect analysis of key industry energy-saving polices during China’s eleventh five-year plan. Resour. Sci. 36(9), 1773–1779 (2014) 10. Guan, W., Zhao, X., Xu, S.: Spatiotemporal feature of the water footprint of energy and its relationship with water resources in China. Resour. Sci. 41(11), 2008–2019 (2019) 11. Hu, J.-L., Wang, S.-C., Yeh, F.-Y.: Total-factor water efficiency of regions in China. Resour. Policy 34(4), 217–230 (2006) 12. Cheng, Y., Shen, M.: Factor endowments, inputs structure and industrial water efficiency: a study based on China’s provincial data during 2002–2011. J. Nat. Resour. 29(12), 2001–2012 (2014) 13. Zhao, L.S., Sun, C.Z., Liu, F.C.: Interprovincial two-stage water resource utilization efficiency under environmental constraint and spatial spillover effects in China. J. Clean. Prod. 164, 715–725 (2017) 14. Chang, Y.J., Zhu, D.M.: Water utilization and treatment efficiency of China’s provinces and decoupling analysis based on policy implementation. Resour. Conserv. Recycl. 168, 105270 (2021) 15. Ding, T., Wu, H., Jia, J., Wei, Y., Liang, L.: Regional assessment of water–energy nexus in China’s industrial sector: an interactive meta-frontier DEA approach. J. Clean. Prod. 244, 118797 (2020) 16. Li, C., Zhang, S.: Chinese provincial water–energy-food coupling coordination degree and influencing factors research. China Popul. Resour. Environ. 30(1), 120–128 (2020) 17. Liu, K.D., Yang, G.L., Yang, D.G.: Investigating industrial water-use efficiency in mainland China: an improved SBM-DEA model. J. Environ. Manage. 270, 110859 (2020) 18. Andersen, P., Petersen, N.C.: A procedure for ranking efficient units in data envelopment analysis. Manage. Sci. 39(10), 1261–1265 (1993) 19. Huang, Y.J., Huang, X., Xie, M., Cheng, W.: A study on the effects of regional differences on agricultural water resource utilization efficiency using super-efficiency SBM model. Sci. Rep. 11(1), 9953 (2021) 20. Ernst, K.M., Preston, B.L.: Adaptation opportunities and constraints in coupled systems: evidence from the US energy-water nexus. Environ. Sci. Policy 70, 38–45 (2017)

Chapter 5

Study on the Economic Effects of Efficient Utilization of Natural Resources

Early studies on natural resource endowment and economic growth mostly considered that rich natural resources were easy to transform into capital, with capital being one of the key elements of economic development. Therefore, natural resource endowment is an important cornerstone for the promotion of economic growth. For example, economist Malthus [1] considered that rich natural resources and fertile land were necessary conditions for economic growth. However, with the deepening of scholarly research on natural resources and economic growth, it has been found that their relationship is complex, and the concept of “resource curse” has been proposed. In summary, the resource curse refers to the phenomenon whereby although an economy has excellent resource endowment, its economic development is stagnant, or even negative. Singer [2] explained this phenomenon from the perspective of international trade, and considered that excessive dependence on primary trade products makes these economies powerless in the face of the deterioration of foreign trade conditions. Hirschman [3] explained the phenomenon from the perspective of industrial correlation, specifically involving the complex relationship among natural resources, capital formation, and economic growth. Thus, there are still many problems worth studying in the relationship between natural resources and economic growth. This chapter explores this relationship from the perspective of natural resources efficiency utilization.

5.1 Research Status of the Relationship Between Natural Resources Efficiency Utilization and Economic Growth The literature related to the relationship between natural resources efficiency utilization and economic growth can be divided into two categories. The first encompasses the research on the natural resources utilization efficiency. Li et al. [4] adopted the three-step logistics analysis (MFA) method to analyze the resource utilization efficiency of Chengde, the second largest vanadium and titanium industrial base in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_5

143

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China, and the results showed that Chengde had low resource utilization efficiency compared with other resource-based cities in China. Miao et al. [5] studied the impact of green technology innovation on natural resources utilization efficiency by using the stochastic frontier analysis method, and analyzed the influencing factors of natural resources utilization efficiency. Their research results indicated that the natural resources utilization efficiency was high under green technology innovation with an increasing trend; the green technology introduction fund and green new product development fund had significantly positive effects on the natural resources utilization efficiency, while the green technology transformation fund and technicians had significantly negative effects. Lu et al. [6] calculated the water footprint of energy–food production in central China from 2001 to 2016 by using a quantitative model, and evaluated the pressure caused by virtual water outflow and the impact of trade on local water resources system. Their research results showed that the water flow of water footprint in energy and food production and that of virtual trade were both on the rise, resulting in a shortage of water resources in local and export areas. There are many such documents, most of which only study the natural resources utilization efficiency from the perspective of one or several resources, while few documents study the issue from the perspective of comprehensive natural resources. The second category of research concerns natural resources and economic growth. Shahbaz et al. [7] explored the relative effects of natural resource richness and natural resource dependence on economic growth in 35 areas with rich natural resources from 1980 to 2015. Wu et al. [8] put forward relevant hypotheses based on the difference between natural resource richness and natural resource-oriented industry dependence, and conducted an empirical test using Chinese provincial panel data. Their research results showed that: rich natural resources are a good foundation for economic growth; natural resource-oriented industry dependence indirectly inhibits economic growth through three transmission mechanisms—inhibiting the crowding out effects of human capital, technological innovation, and foreign investment; the Dutch disease effect hindering the development of the manufacturing industry; and the institutional weakening effect. Sun and Wang [9] verified the impacts of natural resources on environmental pollution and economic growth using the data of 30 areas in China from 2000 to 2019 and the system Generalized Method of Moment (GMM) model. They found that the impact of natural resource development on environmental pollution was significantly negative, and passed the test at the 1% significance level with an elasticity coefficient of − 0.0483; the impact of natural resources on economic growth was significantly negative, with an elasticity coefficient of − 0.0178 at the 10% significance level, confirming the existence of a resource curse in China. In summary, there are relatively few studies on the natural resources utilization efficiency and its economic impacts from the perspective of natural resources overall. This chapter calculates the comprehensive index of natural resources efficiency utilization, and studies the economic effects of the natural resources efficiency utilization level using the entropy method to enrich the research results in this field.

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145

5.2 Calculation of Comprehensive Index of Natural Resources Efficiency Utilization 5.2.1 Introduction of Entropy Method “Entropy” is a physical concept in thermodynamics that was first proposed by German physicist Claude Hughes in 1850; it is used to express the uniformity of energy distribution in space, which is commonly expressed by S. In short, entropy can measure the degree of system chaos. The greater the entropy, the more chaotic the system is. “Entropy increase” is used to describe the increasing degree of chaos in a system. The concept of “information entropy” was developed by Claude Elwood Shannon, the father of information theory, in 1948 to measure the “amount of information.” The function of information is to eliminate uncertainty, and the amount of information is the measurement of information that relates to the probability of random events. The smaller the probability, the greater the amount of information. For example, the probability of “the sun rises from the northeast” is 1, thus, the amount of information contained in this information is very small, which cannot help people eliminate the “uncertainty” of something; however, the information of “the winning number of the lottery is XXXX…” contains a large amount of information as there are many possibilities of winning numbers in the lottery, and this information can help people eliminate uncertainty to a great extent. Shannon defined “information redundancy” according to the occurrence probability or uncertainty of each symbol (number, letter, or word) in the information, called the average amount of information excluding redundancy information entropy, and gavethe mathematical expression for calculating information entropy as: H (x) = − p(xi ) log( p(xi )), in which p(x i ) represents the occurrence probability of a random event X i . Entropy law is an objective weighting method to give weight to indexes according to information entropy. The larger the information entropy of an index, the more information the index contains, and the more important the index is for comprehensive evaluation. Therefore, we can calculate the information entropy of each index, give different weights to the index according to the information entropy, and then comprehensively evaluate different observed objects according to the weights. The entropy method is widely used because of its excellent characteristics. Yang et al. [10] constructed an evaluation index system of green development level including social, economic, and environmental integration using the entropy method, and the results showed that: (1) the green development levels of most mineral resource-based cities in China had improved, with 25% cities lagging behind; (2) there were obvious regional differences in the green development level of mineral resource-based cities, which became increasingly low from east to west; (3) from 2006 to 2020, the green development levels in the western region increased rapidly while those in the eastern region showed a deteriorating trend, and there was a polarization trend in the northeastern region. Zameer et al. [11] explored the coupling degrees among natural resources, financial development, and ecological efficiency in

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eastern, central, and western China, and found that: the financial development levels in the central and western regions were higher than in the eastern region; although lacking natural resources, the resource utilization efficiency in the eastern region was high; the central and western regions were rich in resources, but with low utilization efficiencies. Song et al. [12] calculated the ecological efficiency level and the comprehensive index of environmental regulation based on the super-efficiency SBM model considering undesirable outputs and the entropy method as the global reference. Their research results showed that: environmental regulation had a significant U-shaped relationship with ecological efficiency, and an inverted U-shaped relationship with resource dependence; the relationships between resource dependence and ecological efficiency were different in resource-based and non-resource-based cities. Resource dependence can be used as an intermediary variable of environmental regulation affecting ecological efficiency. Environmental regulation can directly affect the level of ecological efficiency through the mechanisms of “compliance cost” and “costsaving innovation,” and indirectly through the mechanism of resource dependence. Ma et al. [13] applied the entropy TOPSIS method to establish China’s green growth efficiency analysis database, and empirically analyzed the samples of 285 prefecture level cities in China from 2005 to 2016. Their research results showed that there were great spatial differences among Chinese cities in terms of resource investment, socio-economic benefits, and environmental impact indexes of green growth, and the urban resources transformation efficiencies were low. Li and Zeng [14] conducted an empirical study on the operating performance of innovative companies with a dual ownership structure in the Chinese Listed Companies in the United States using the entropy method, and found that: the overall operating performance of such companies had shown a downward trend in the past five years, with low business performance contribution; however, the operating performance of such companies was good under the Ward clustering analysis, and the main reason for the overall decline in operating performance was that such enterprises paid more attention to long-term strategic layout and long-term return. Tian et al. [15] investigated 20 indexes from the six aspects of water resources utilization, water security, water environment protection, water ecological restoration, water culture system, and water management institutions, to comprehensively evaluate the urban water ecological civilization of the three urban agglomerations in the Yangtze River economic belt using the entropy method. The results showed that the level of urban water ecological civilization in the Yangtze River basin gradually increased from west to east with obvious spatial differences. From the above, we know that the entropy method can be widely used in multiindex evaluations. Since the entropy method does not rely on subjective ideas in weight assignment, but follows the amount of information contained in each dimension index itself, it can present satisfying evaluation effects due to high objectivity and relative evaluation of index situations. Therefore, this chapter also uses the entropy method to comprehensively evaluate the natural resources efficiency utilization in 30 areas of China (limited by data availability, Hong Kong, Macao, Taiwan, and Xinjiang are excluded from the research).

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147

5.2.2 Construction of Comprehensive Index System of Natural Resources Efficiency Utilization and Data Source Description Natural resources contain a wide range of contents. Scholars generally agree that natural resources refer to all the tangible and intangible things endowed by nature or left by predecessors that can be directly or indirectly used to meet human needs. Natural resources can be divided into biological resources, agricultural resources, forest resources, land resources, mineral resources, marine resources, climate and meteorology, and water resources according to the resource source. Due to the great difficulty in obtaining the data of all resources and the difficulty in quantitative comparison of some resources, by referring to Zhong et al. [16], the following index system is adopted in this chapter: As shown in Table 5.1, this chapter reflects the natural resources efficiency utilization level from nine indexes in three aspects: water resources, land resources, and energy resources. The indexes in the table are divided into positive and negative ones. Positive indexes mean that when evaluating the comprehensive index of natural resources efficiency utilization in a region, the larger the index value, the better the natural resources efficiency utilization; on the contrary, the smaller the negative index, the better the natural resources efficiency utilization. Limited by the data availability, the period from 2004 to 2017 is taken as the research period, spanning a total of 14 years.

5.2.3 Calculation Steps and Results of Comprehensive Index of Natural Resources Efficiency Utilization To overcome the disadvantage that the existing entropy method can only process cross-sectional data but cannot compare data from different years, this chapter uses the improved panel entropy method of Ma et al. [13] to measure the comprehensive indexes of natural resources efficiency utilization in 30 areas of China from 2004 to 2017. The specific steps are as follows: Step 1: data selection: suppose there are r years, n areas, m indexes, and xi jk refers to the value of the kth index of the jth area in the ith year. Step 2: index standardization. Considering the differences of different index dimensions and units, it is necessary to standardize each index. Standardization xi jk −xmin k ; standardization of negative index: of positive index: xi jk ' = xmax k −x min k xi jk ' =

xmin k −xi jk , xmin k −xmax k

where xmin k and xmax k refer to the minimum and maximum values, respectively, of the kth index of n areas in r years. After index standardization, the value range of xi jk ' is [0, 1], meaning the relative size of index xi jk among n areas in r years.

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Table 5.1 Comprehensive index evaluation system for natural resources efficiency utilization Evaluation system

Secondary index

Tertiary index

Unit

Index type

Comprehensive index of natural resources efficiency utilization

Water resources

Water consumption per unit GDP

m3 /CNY 1

Negative

Per capita water use

10,000 m3 / person

Negative

Industrial wastewater discharge per unit GDP

t/CNY 100 million

Negative

GDP per land unit

CNY 1 trillion/ km2

Positive

Greening coverage rate in built-up area

%

Positive

Average fixed assets investment per land unit

CNY 100 million/km2

Positive

Energy consumption per unit GDP

t of standard coal/ CNY10,000

Negative

Carbon emission per unit energy consumption

t of standard coal

Negative

Per capita energy consumption

10,000 t of standard coal/ person

Positive

Land resources

Energy resources

Note (1) Data source: the original data of the measured indexes come from the China Bureau of Statistics, China Energy Statistical Yearbook, provincial and municipal statistical yearbooks, and the Carbon Emission Account and Datasets; (2) results of the above indexes are calculated by the authors

  Step 3: calculate the weights of indexes. yi jk = xi jk '/ i j xi jk '.   Step 4: calculate the entropy value of the Kth index. Sk = − θ1 i j yi jk ln(yi jk ), where θ > 0 and θ = ln(r n). Step 5: calculate the information utility value of the Kth index. gk = 1 − Sk . Step 6: calculate the weight of the Kth index. wk = gk / k gk . Step 7: Calculate the comprehensive indexes  of natural resources efficiency utilization of all areas every year. h i j = k wk xi jk '. Calculations results are shown in the following Table 5.2. According to Fig. 5.1 and Table 5.2, the comprehensive indexes of the natural resources efficiency utilization of Shanghai, Tianjin, Beijing, Jiangsu, and Zhejiang were relatively high and exhibited a relatively fast growth trend, while those of Guangxi, Heilongjiang, and Xinjiang were relatively low. On the whole, the comprehensive indexes of the natural resources efficiency utilization of all areas were increasing annually.

Fig. 5.1 Comprehensive index of natural resources efficiency utilization of each area

5.2 Calculation of Comprehensive Index of Natural Resources Efficiency … 149

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5 Study on the Economic Effects of Efficient Utilization of Natural Resources

Table 5.2 Comprehensive index of natural resources efficiency utilization of each area Area

2004

2008

2013

2017

Mean value

Anhui

0.1809

0.2064

0.2422

0.2747

0.2258

Beijing

0.2877

0.3410

0.4372

0.5063

0.3924

Fujian

0.1969

0.2214

0.2637

0.3003

0.2433

Gansu

0.1916

0.1908

0.2086

0.2129

0.1988

Guangdong

0.2133

0.2412

0.2747

0.3158

0.2593

Guangxi

0.1547

0.1768

0.2133

0.2346

0.1957

Guizhou

0.1837

0.1965

0.2165

0.2350

0.2062

Hainan

0.1841

0.1937

0.2283

0.2464

0.2111

Hebei

0.2069

0.2344

0.2659

0.2912

0.2503

Henan

0.1999

0.2304

0.2612

0.3005

0.2463

Heilongjiang

0.1765

0.1949

0.2027

0.2025

0.1946

Hubei

0.1868

0.2145

0.2507

0.2769

0.2323

Hunan

0.1803

0.2085

0.2375

0.2636

0.2230

Jilin

0.1873

0.2050

0.2219

0.2244

0.2110

Jiangsu

0.2148

0.2690

0.3531

0.4205

0.3129

Jiangxi

0.1675

0.2032

0.2291

0.2534

0.2146

Liaoning

0.2082

0.2414

0.2826

0.2539

0.2504

Inner Mongolia

0.1869

0.2164

0.2264

0.2407

0.2216

Ningxia

0.1543

0.1917

0.2296

0.2629

0.2073

Qinghai

0.1831

0.2058

0.2346

0.2476

0.2189

Shandong

0.2194

0.2541

0.3070

0.3493

0.2828

Shanxi

0.2047

0.2233

0.2245

0.2174

0.2249

Shaanxi

0.1921

0.2056

0.2278

0.2410

0.2171

Shanghai

0.4309

0.6036

0.7498

0.9419

0.6746

Sichuan

0.1867

0.2095

0.2308

0.2424

0.2186

Tianjin

0.2633

0.3412

0.5113

0.5778

0.4315

Xinjiang

0.1281

0.1552

0.1907

0.2106

0.1712

Yunnan

0.1848

0.2013

0.2186

0.2307

0.2092

Zhejiang

0.2209

0.2493

0.3012

0.3460

0.2774

Chongqing

0.1851

0.2210

0.2645

0.2948

0.2425

Note Limited by article length, this chapter only reports the average data of each area in 2004, 2008, 2013, 2017, and 2004–2017

5.3 Research Design To explore the impact of natural resources efficiency utilization on economic growth, this section sets per capita GDP (pgdp) as the explained variable, the comprehensive index of natural resources efficiency utilization (Guse) of each area from

5.3 Research Design

151

2004 to 2017 calculated by the entropy method as the main explanatory variable, and with reference to the research of Wu [8], the following control variables: (1) fixed capital investment (fix): high-level material capital investment can effectively promote economic growth, and the per capita fixed asset investment is used in this section to reflect material capital investment; (2) human capital level (edu): human capital is an indispensable factor in regional economic development and an important force to promote regional economic growth; the proportion of college students in the population is adopted to reflect the level of human capital; (3) industrial structure (stru); the proportion of the tertiary industry’s output value in GDP is used to reflect the role of industrial structure in economic growth; (4) urbanization rate (urban): the proportion of urban population in the total population is adopted to reflect the role of urbanization level in economic growth; (5) infrastructure level (found): good infrastructure conditions can improve the ability of a region to attract and gather production factors and promote regional economic development; the per capita highway mileage is selected as the proxy variable of infrastructure level to reflect the effect of infrastructure level on economic growth; (6) scientific and technological innovation level (innova): scientific and technological innovation is an important source of economic growth; the number of patent applications authorized per capita is selected as the proxy variable of scientific and technological innovation level to reflect the role of scientific and technological innovation level in economic growth; (7) international trade (trade) is also an important factor driving economic growth, and the effect of international trade on economic growth is reflected through the total import and export per capita. The data of the above variables and the data required to calculate each variable are mainly from statistical websites (the data of the comprehensive index of natural resources efficiency utilization are based on the calculation results in the previous section), and the time dimension is 14 years—from 2004 to 2017. The explanations and descriptive statistics of the variables are shown in Tables 5.3 and 5.4, respectively. Figure 5.2 is the scatter diagram of per capita GDP and the natural resources efficiency utilization index, from which we can know that there is a significant positive linear relationship between per capita GDP and the natural resources efficiency utilization index—that is, with the growth of the natural resources efficiency utilization index, per capita GDP also increases. This phenomenon is in line with the logic and expectations of this chapter, but quantitative and empirical analysis is still needed, which will be discussed later. The benchmark  measurement model set in this section is as follows: pgdpi j = β0 + β1 gusei j + k αk X i jk +u i + λt + εi j , in which pgdpi j is the per capita regional GDP of the ith area in the jth year; gusei j is the comprehensive index of natural resources efficiency utilization of the ith area in the jth year; X i jk is the value of the Kth control variable of the ith area in the jth year; u i is the fixed effect of the ith area; λt is the fixed effect of the tth year; εi j is a random disturbance term; β0 is a constant term; β1 is the estimation coefficient of gusei j that refers to the marginal effect of the comprehensive index of natural resources efficiency utilization; and αk is the estimation coefficient of X i jk .

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Table 5.3 Basic explanations of variables Variable

Symbol Meaning

Calculation method

Explained variable

pgdp

Regional GDP/total population

Per capita GDP (CNY 10,000/person)

Main explanatory variable guse

Comprehensive index of Entropy method natural resources efficiency utilization

Control variable

fix

Per capita fixed capital investment (CNY 10,000)

Total social investment in fixed assets/total population

edu

Human capital level (%)

Number of college students/ total population

stru

Industrial structure (%)

Tertiary industry output value/regional GDP

urban

Urbanization rate (%)

Urban population/total population

found

Infrastructure level (km/ person)

Highway mileage/total population

innova

Scientific and technological Number of patent innovation level (piece/ applications/total 10,000 people) population

trade

Per capita international Total imports and exports/ trade volume (USD/person) total population

Table 5.4 Descriptive statistics of variables Variable

Observed value

Mean value

Standard error

Minimum value

Maximum value

pgdp

420

3.463

2.318

0.422

guse

420

0.256

0.105

0.128

0.942

fix

420

2.465

1.674

0.222

8.181

13.765

edu

420

0.017

0.006

0.005

0.036

stru

420

0.447

0.088

0.298

0.827

urban

420

0.524

0.142

0.254

0.896

found

420

0.0030

0.0020

0.0004

0.0140

innova

420

5.700

8.422

0.130

49.262

trade

420

2386.370

4089.104

37.629

20,283.490

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

153

Fig. 5.2 Scatter diagram of per capita GDP and natural resources efficiency utilization index

5.4 Empirical Analysis of Natural Resources Efficiency Utilization and Economic Growth 5.4.1 Benchmark Regression Results Regression is carried out in this section based on the panel data of 30 areas from 2004 to 2017 by taking the per capita GDP as the explained variable; the comprehensive index of natural resources efficiency utilization as the main explanatory variable; and the per capita fixed capital investment, human capital level, industrial structure, urbanization rate, infrastructure level, the level of scientific and technological innovation, and per capita international trade as control variables. Benchmark regression results are as follows: Column (1) of Table 5.5 shows the results of OLS regression. The coefficient of the main explanatory variable, comprehensive index of natural resources efficiency utilization, is 2.511, which is significant at the level of 1%, indicating that the level of natural resources efficiency utilization can promote the growth of per capita GDP. Moreover, the coefficients of all control variables are positive, in which the coefficients of per capita fixed capital investment, infrastructure level, scientific and technological innovation level, and per capita international trade pass the significance test with expected results, which indicates that their increases can effectively improve the per capita GDP level. Furthermore, the goodness of fit level R2 and the F value are 0.947 and 915.2, respectively, indicating that the interpretation ability and the linearity of the model are both very strong. Column (2) of Table 5.5 shows the results of the panel fixed effect regression, from which we can know that the coefficient of the comprehensive index of natural

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Table 5.5 Benchmark regression results (1)

(2)

(3)

(4)

(5)

pgdp

pgdp

pgdp

pgdp

pgdp

guse

2.511***

7.745***

5.495***

6.518***

5.539***

(4.77)

(9.55)

(7.57)

(9.03)

(8.95)

fix

0.480***

0.193***

0.347***

0.0635**

0.0859***

(16.46)

(5.93)

(12.35)

(2.09)

(2.89)

edu

9.723

28.39**

37.33***

19.24

15.7

(1.29)

(2.00)

(3.20)

(1.35)

(1.33)

stru

0.514

1.773**

1.816***

−

− 2.867***

(1.07)

(2.53)

(2.88)

(− 6.26)

(− 4.26)

urban

0.335

6.716***

1.064

0.0308

1.580**

(0.67)

(5.55)

(1.38)

(0.03)

(2.29)

found

42.10**

66.38**

123.9***

− 35.62

18.67

(2.40)

(2.04)

(5.03)

(− 1.00)

(0.67)

innova

0.0977***

0.0862***

0.0990***

0.0738***

0.0788***

(17.08)

(15.11)

(17.65)

(15.01)

(16.45)

trade

0.000193***

0.000146***

0.000129***

0.000133***

0.000158***

Variable

(8.87)

5.182***

(6.75)

(5.99)

(6.61)

(8.21)

Is the individual fixed No effect controlled?

Yes

Yes

Yes

Yes

Is the time fixed effect No controlled?

No

No

Yes

Yes

− 0.0875

− 4.838***

− 2.073***

1.749***

0.194

(− 0.32)

(− 9.96)

(− 6.01)

(2.69)

(0.54)

420

420

420

420

420

_cons N R2

0.947

0.949

0.944

0.967

0.966

F

915.2

891.4

–

513.7

–

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

resources efficiency utilization is 7.745, and significant at the 1% level, which is higher than and consistent with the results in Column (1), indicating that the natural resources efficiency utilization level can effectively promote the growth of per capita GDP, while the coefficient in OLS regression may underestimate this effect. Moreover, compared with the results of OLS regression, the coefficients of all control variables are positive and pass the significance test. Among them, the coefficients of human capital level, industrial structure, urbanization rate, and infrastructure level are higher than those in the OLS regression; the coefficients of scientific and technological innovation level and per capita international trade are close to the results in the OLS regression, while the coefficient of per capita fixed capital investment is smaller. The results imply that OLS regression may overestimate the impact of per

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

155

capita fixed capital investment on per capita GDP, while it may underestimate the role of other control variables. Moreover, the goodness of fit level R2 and the F value are 0.947 and 915.2, respectively, indicating that the interpretation ability and the linearity of the model are both very strong. Column (3) of Table 5.5 shows the results of the panel random effect regression, which are very similar to those of the panel fixed effect regression. The coefficient of the comprehensive index of natural resources efficiency utilization is 5.495 and significant at the 1% level, and those of other control variables are all positive, except that the coefficient of urbanization rate is insignificant. This shows that even if the random effect model is adopted, the regression results still strongly support the conclusion that the improvement of the natural resources efficiency utilization level can effectively improve the per capita GDP. Column (4) of Table 5.5 shows the results of a two-way fixed effect panel regression that controls both the space fixed effect and time fixed effect. The coefficient of the comprehensive index of natural resources efficiency utilization is 6.518 and significant at the 1% level, which is similar to the result of the panel fixed effect regression, and shows that after further controlling the time fixed effect, the result also supports the conclusion that the improvement of the natural resources efficiency utilization level can effectively improve the per capita GDP. The results also show that the coefficients of most control variables are still positive and close to the results in the panel fixed effect regression, while the regression coefficient of industrial structure becomes negative and significant at the 1% level, which is contrary to the regression results in the previous three columns. This may be because of the multicollinearity problem after controlling for the time fixed effect (note: controlling the time fixed effect may generate a dummy variable of n − 1 years, which is prone to the multicollinearity problem). The regression results excluding this variable will be stated in subsequent regressions in this chapter. Column (5) of Table 5.5 shows the results of the random effect panel regression controlling for both the space fixed effect and time fixed effect. The coefficient of the comprehensive index of natural resources efficiency utilization is 5.539 and significant at the 1% level, which shows that, after further controlling for the time fixed effect, the results support the conclusion that the improvement of the natural resources efficiency utilization level can effectively improve the per capita GDP. In addition, to decide whether to choose the fixed effect model or the random effect model, the Hausman test is conducted on the results of Columns 2 and 3 that only control for the space fixed effect. The results show that the p value of the Hausman test is 0.0000, which strongly rejects the original hypothesis selecting the random effect model. Thus, the fixed effect model should be selected. Meanwhile, the Hausman test is also conducted on the results of Columns 4 and 5 that control for both the space fixed effect and time fixed effect, of which the p value is 0.0001, and the original hypothesis of selecting the random effect model is also strongly rejected. Therefore, in the subsequent analysis, the fixed effect model will be used without reporting the regression results of the random effect model. In conclusion, according to the benchmark regression results in Table 5.5, the improvement of natural resources efficiency utilization can promote the growth of

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per capita GDP; the regression results in Columns (2)–(5) show that the coefficient of the comprehensive index of natural resources efficiency utilization is between 5.495 and 7.745, indicating that for each 1% increase in the comprehensive index of natural resources efficiency utilization, the per capita GDP can increase by about CNY 549.5–774.5; the effects of other control variables on GDP are consistent with expectations, which can all promote the growth of per capita GDP.

5.4.2 Robustness Test Robustness tests are performed in this section to test the stability of the results and to avoid randomness as far as possible. (1) Excluding the influence of monetary inflation To exclude the possible impact of monetary inflation on the results, the consumer price indexes reported by the China Bureau of Statistics from 2004 to 2017 (last year = 100) are adopted to convert the variables measured in currency in previous texts by taking 2004 as the base period. The descriptive statistics of variables before and after the conversion are as follows: In Table 5.6, GDP, pgdp, fix_total, and fix, respectively, refer to the regional GDP (CNY 100 million), per capita GDP (CNY 10,000), total social fixed asset investment (CNY 100 million), and per capita fixed capital investment (CNY 10,000), while gdp(2004), pgdp(2004), fix_total(2004), and fix(2004) are the converted values of the above variables by the consumer price index based on the base period 2004. After variable conversion, the equation is re-estimated, and the regression results are shown in the following Table 5.7. Column (1) of the table shows the results of OLS regression, Column (2) shows the results of panel regression that only controls for the space fixed effect, and Column (3) shows the results of panel regression that controls for both the space fixed effect and time fixed effect simultaneously. Table 5.6 Descriptive statistics of variables converted based on 2004 Variable

Observed value

Mean value

Standard error

Minimum value

Maximum value

GDP

420

15,419.87

14,917.44

443.70

91,648.70

GDP(2004)

420

12,300.90

11,119.08

443.70

65,514.64

pgdp

420

3.46

2.32

0.42

13.76

pgdp(2004)

420

2.77

1.69

0.42

9.84

fix_total

420

10,625.91

10,159.33

289.18

55,202.72

fix_total(2004)

420

8342.48

7406.92

289.18

39,461.40

fix

420

2.47

1.67

0.22

8.18

fix(2004)

420

1.94

1.18

0.22

5.94

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

157

According to the regression results in the table, the values of the comprehensive index of natural resources efficiency utilization are 1.295, 4.574, and 3.543 respectively, and significant at the 1% level, indicating that the impact of natural resources efficiency utilization level on per capita GDP is still significant and positive after excluding the influence of monetary inflation, and the benchmark regression results mentioned above are still stable. Moreover, compared with the previous regression results, the value of the comprehensive index of natural resources efficiency utilization decreases to a certain extent because the marginal effect of the estimated comprehensive index is adjusted accordingly when the variable values are converted as per the price level in 2004. Among the control variables, per capita fixed capital investment, human capital level, scientific and technological innovation level, and per capita international trade Table 5.7 Regression results excluding the influence of monetary inflation Variable

(1) pgdp

pgdp

pgdp

guse

1.295***

4.574***

3.543***

(3.65)

(9.04)

(7.28)

fix

0.373***

0.143***

0.0913***

(13.08)

(4.98)

(3.25)

edu

10.82**

37.49***

25.92***

(2.10)

(4.21)

(2.68)

stru

− 0.0913

− 0.0603

− 3.388***

(− 0.28)

(− 0.14)

(− 6.04)

urban

1.346***

4.738***

1.192

(3.89)

(6.35)

(1.56)

31.65***

49.40**

− 19.55

(2.65)

(2.43)

(− 0.81)

innova

0.0628***

0.0532***

0.0463***

(16.56)

(14.97)

(13.91)

trade

0.000166***

0.0000943***

0.0000854***

(11.33)

(6.96)

(6.21)

Is the individual fixed effect controlled?

No

Yes

Yes

Is the time fixed effect controlled?

No

No

Yes

_cons

0.0163

−

1.051**

(0.09)

(− 8.57)

(2.38)

N

420

420

420

found

(2)

2.450***

(3)

R2

0.954

0.948

0.960

F

1070.2

873.2

424.2

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

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can all promote per capita GDP, and are significant at the 1 and 5% levels; the coefficients of urbanization rate and infrastructure level are positive in Columns (1) and (2) and significant at least at the 5% level, but insignificant in Column 3; similarly, the coefficients of industrial structure in Columns (1) and (2) are positive while insignificant, but the coefficient is negative in the third column and significant at the 1% level, which may be related to the addition of a large number of dummy variables for controlling the time fixed effect. Adding a large number of dummy variables can easily cause multicollinearity problems, which will affect the regression coefficient and significance level of the above control variables. However, according to the regression results in Columns (1), (2), and (3), we still believe that the impacts of these control variables on per capita GDP are positive. Finally, the R2 in Columns (1)–(3) is, respectively, 0.954, 0.948, and 0.960, indicating that the goodness of fit of the model is very good, with high explanatory power, and the F values in all three columns are also very high, indicating that the linearity of the model is significant. (2) Discussions on Endogeneity To prove the robustness of the regression results, the endogeneity problem still needs to be discussed. Although it has been confirmed that an increase in the natural resources efficiency utilization level can significantly promote the growth of per capita GDP, there is still the possibility that the growth of per capita GDP may in turn promote an increase in the natural resources efficiency utilization level. That is, there may be a two-way causal relationship between the comprehensive index of natural resources efficiency utilization and per capita GDP, which may also affect the coefficients and significance estimated before in the article. To confirm the robustness of the regression results, the two-way causal relationship between the comprehensive index of natural resources efficiency utilization and per capita GDP should be controlled, so that it can better reflect the actual impacts of the comprehensive level of natural resources efficiency utilization on per capita GDP. The best way to solve the endogeneity problem is to employ the instrumental variable method. Thus, this section focuses on finding the appropriate instrumental variables. Generally, the selection of instrumental variables should meet two conditions: first, there should be a strong correlation between the chosen instrumental variables and the endogenous variables (the correlation hypothesis); second, the only channel for instrumental variables to affect the explained variables is by excluding other possible influence channels through their related endogenous explanatory variables (the exogeneity hypothesis). Referring to the research of Cai et al. [17], this section selects the forest coverage as the instrumental variable for the panel twostage least squares regression. First, the improvement of the natural resources efficiency utilization level may imply the improvement of relevant technologies and the development of various resources (from destructive development to protective and sustainable development), and the decline of environmental damage. Therefore, the improvement of natural resources efficiency utilization means the improvement of the ecological environment, and forest coverage can be used as a good proxy variable to reflect the conditions of the ecological environment. Taking forest coverage as an

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

159

instrumental variable meets the correlation hypothesis. Second, the impacts of forest coverage on local GDP and per capita GDP are very weak, and the correlation degree is only 0.0258, indicating that there is only a very weak positive correlation between them. Therefore, it is believed that taking forest coverage as an instrumental variable also meets the exogeneity hypothesis. Table 5.8 shows the regression results of the two-stage least squares method with forest coverage as the instrumental variable, in which Columns (1) and (2) show the regression results controlling the space fixed effect, and (3) and (4) show the regression results controlling both space and time fixed effects simultaneously. Columns (1) and (3) are the regression results of the first stage, and Columns (2) and (4) are those of the second stage. According to the regression results in Column (1), the regression coefficient of the instrumental variable (IV) is positive and significant at the 5% level, meaning that there is a strong positive correlation between forest coverage and the comprehensive index of natural resources efficiency utilization. Moreover, the F value in the weak instrumental variable test in the first stage is 45.66, indicating that there is no problem of a weak instrumental variable, and the previous analysis is confirmed. Similar to the results in Column (1), the results in Column (3) also show that there is a strong positive correlation between forest coverage and the comprehensive index of natural resources efficiency utilization, and there is no problem of a weak instrumental variable. Meanwhile, the R2 values of Column (1) and Column (3) are 0.7645 and 0.7986, respectively, indicating that the goodness of fit of the model is adequate. According to the regression results in Column (2), the regression coefficient of the comprehensive index of natural resources efficiency utilization is 33.07 and significant at the 1% level, indicating that after excluding the endogeneity that may be caused by two-way causality, the regression results still support the conclusion that the improvement of the natural resources efficiency utilization level can improve per capita GDP. The regression results in Column (4) are similar to those in Column (2). The regression coefficient of the comprehensive index of natural resources efficiency utilization decreases to 23.17 and is significant at the 5% level, which also supports the previous conclusion. Meanwhile, the R2 values of Column (2) and Column (4) are 0.8192 and 0.9192 respectively, indicating that the goodness of fit of the model is adequate. (3) Placebo test To eliminate random interference, the robustness of the results is further confirmed by a placebo test. The main ideas are as follows: if the previous regression results are robust rather than random, and the original one-to-one correspondence between the explained variable and the explanatory variable is disturbed, the regression results should show that there is no significant positive or negative correlation between the disturbed explained variable and the explanatory variable; that is, the regression coefficient of the disturbed explanatory variable should be very close to 0, and the coefficient obtained by repeating the above steps many times should be a normal distribution curve close to the mean value of 0.

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Table 5.8 Regression results excluding the endogeneity problem Variable

(1)

(2)

(3)

(4)

First stage regression

Second stage regression

First stage regression

Second stage regression

33.07***

guse

23.17**

(2.86)

(2.20)

IV

0.0021309**

0.0018841**

fix

0.0147521*** 7.74

(− 0.97)

(7.29)

(− 1.11)

edu

− 1.158535

53.85*

− 4.993371***

101.3*

(− 1.3)

(1.85)

(− 5.05)

(1.80)

stru

0.2588043***

− 4.952

0.2005656***

− 8.133***

(6.18)

(− 1.49)

(3.36)

(− 3.59)

urban

− 0.3077115***

13.15***

− 0.335844***

5.167

(− 3.97)

(3.55)

(− 4.20)

(1.41)

found

5.167957**

− 59.78

− 6.014742**

73.12

(2.56)

(− 0.71)

(− 2.37)

(0.83)

0.0297

0.0013006***

0.0495***

(2.60)

(2.07) − 0.172

0.0154828***

− 0.175

innova

0.0019738*** (5.60)

(1.07)

(3.69)

(2.90)

trade

0.0000119***

− 0.000156

0.00000674***

0.0000213

(9.79)

(− 1.09)

(4.79)

(0.27)

Is the individual fixed effect controlled?

Yes

Yes

Yes

Yes

Is the time fixed effect controlled?

No

No

Yes

Yes

_cons

0.1580714***

− 9.757***

0.2603306***

− 3.513

(4.95)

(− 4.05)

(5.07)

(− 1.01)

N

420

420

420

420

R2

0.7645

0.8192

0.7986

0.9192

F

45.66

–

49.01

–

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01; (3) the F values in the table are those of the weak instrumental variable test in the first stage

Therefore, the guse variable is disturbed randomly to generate a random_guse variable, and the original one-to-one correspondence between the original guse variable and per capita GDP is also disturbed. Then, the panel regression controlling the space fixed effect and the panel regression controlling both the space and time fixed effects are carried out, and the regression coefficients of Random_guse are recorded

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

161

Fig. 5.3 Kernel density diagrams of coefficients b1 and b2

as b1 and b2, respectively. Repeat the above steps 10,000 times, and then draw the kernel density diagram of coefficients b1 and b2 to observe their distribution images. As shown in Fig. 5.3, the kernel density plots of B1 and B2 are very close to the normal distribution curve with an average value of 0, and the distribution of B2 is more concentrated than that of B1. It indicates that the guse variable with random perturbation is more irrelevant to GDP per capita after considering the time fixed effect. To sum up, the regression results mentioned above are robust.

5.4.3 Heterogeneity Analysis Although the previous empirical analysis has confirmed the basic fact that the improvement of the natural resources efficiency utilization level can improve the per capita GDP, this should not be the only conclusion. We are still curious whether the size of this influence will change with some characteristics of the sample. Thus, the following heterogeneity analysis is carried out to enrich the conclusions of this chapter. (1) Heterogeneity analysis of economic development Is there any difference between economically developed and economically underdeveloped areas in the impact of natural resources efficiency utilization improvement on

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per capita GDP? To answer this question, the 30 areas are divided into economically developed areas and economically underdeveloped areas according to their average per capita GDP from 2004 to 2017. Areas where the average per capita regional GDP is greater than the median per capita GDP of all areas are considered economically developed areas; otherwise, they are considered economically underdeveloped areas. As shown in Table 5.9, the regression samples in Columns (1) and (2) are economically developed areas, with a sample size of 210 (14 years of data from 15 areas), and the regression samples in Columns (3) and (4) are economically underdeveloped areas, also with a sample size of 210 (14 years of data from 15 areas). Meanwhile, the space fixed effect is controlled in Columns (1) and (3), and both space and time fixed effects are controlled in Columns (2) and (4). Table 5.9 Heterogeneity analysis of economic development Variable

guse fix edu stru urban found innova trade

Economically developed areas

Economically underdeveloped areas

(1)

(2)

(3)

(4)

9.093***

7.633***

3.474**

5.399***

(7.25)

(7.50)

(2.02)

(3.53)

0.0332

− 0.0809*

0.285***

0.132***

(0.56)

(− 1.77)

(10.19)

(3.69)

− 39.74

− 21.73

55.76***

46.91***

(− 1.54)

(− 1.05)

(4.96)

(3.99)

1.881

− 8.334***

0.865*

− 1.796***

(1.31)

(− 6.13)

(1.70)

(− 2.93)

16.18***

7.339***

3.512***

− 0.159

(5.95)

(3.17)

(4.23)

(− 0.21)

159.1**

− 29.62

25.72

− 2.891

(2.07)

(− 0.37)

(1.28)

(− 0.14)

0.0772***

0.0592***

0.0677***

0.0402***

(10.13)

(9.88)

(5.03)

(3.36)

0.000105***

0.000115***

0.000475***

0.000318***

(3.72)

(4.45)

(5.46)

(3.81)

Yes

Yes

Yes

Is the individual fixed effect controlled? Yes Is the time fixed effect controlled?

No

Yes

No

Yes

_cons

− 9.663***

− 0.0104

− 2.179***

0.131

(− 8.27)

(− 0.01)

(− 5.63)

(0.30)

N

210

210

210

210

R2

0.951

0.976

0.976

0.986

F

452

340

937.6

595.8

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

163

First, the regression results in Columns (1)–(4) show that the per capita GDP of both economically developed and economically underdeveloped areas increases with the growth of the comprehensive index of natural resources efficiency utilization, and the coefficients of guse except that in Column (3) are all significant at the 1% level (that in Column (3) is significant at the 5% level). Then, by comparing the guse coefficients of both two types of areas, it is found that the guse coefficients of economically developed areas are significantly higher than those of economically underdeveloped areas irrespective of whether the time fixed effect is considered. This indicates that the improvement of the natural resources efficiency utilization level plays a greater role in promoting per capita GDP in economically developed areas. A possible explanation is that economically developed areas are more sensitive to technological updating and progress due to severer market competition and stronger industrial vitality. Thus, once new technologies emerge, the natural resources utilization level can be improved and can affect the whole economy faster and wider. Moreover, both the R2 and F values in Columns (1)–(4) are high, indicating that the model has satisfactory goodness of fit and linearity. (2) Heterogeneity analysis of population size As to whether the impact of natural resources efficiency utilization improvement on per capita GDP varies with the population size, the 30 areas are divided into large population areas and small population areas as per the average year-end resident population of each area from 2004 to 2017. Areas where the regional average yearend resident population is greater than the median of all areas are taken as large population areas, and otherwise as small population areas. As shown in Table 5.10, the 210 regression samples in Columns (1) and (2) are small population areas (14 years of data from 15 areas), and the 210 regression samples in Columns (3) and (4) are large population areas (14 years of data from 15 areas). Meanwhile, the space fixed effect is controlled in Columns (1) and (3), and both space and time fixed effects are controlled in Columns (2) and (4). First, the coefficients of guse in Columns (1)–(4) are positive and significant at the 1% level, indicating that the per capita GDP increases with the growth of the comprehensive index of natural resources efficiency utilization irrespective of whether the concerned area is a large or small population area. It is found by comparing the guse coefficients of both large and small population areas that the guse coefficients of large population areas are greater than those of small population areas, and the gap increases after the time fixed effect is considered in Columns (2) and (4). Therefore, the improvement of natural resources efficiency utilization plays a greater role in promoting per capita GDP in areas with a large population. The possible explanation is that areas with a large population have a larger consumption scale of various natural resources (e.g., energy, water resources, and land resources), making the response of per capita GDP to the improvement of natural resources efficiency utilization more sensitive, and the impact of natural resources efficiency utilization improvement on per capita GDP is also greater. Moreover, both the R2 and F values in Columns (1) to (4) are high, indicating that the model has satisfactory goodness of fit and linearity.

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5 Study on the Economic Effects of Efficient Utilization of Natural Resources

Table 5.10 Heterogeneity analysis of population size Variable guse fix edu stru urban found innova trade

Small population areas

Large population areas

(1)

(2)

(3)

(4)

5.531***

5.175***

17.41***

24.81***

(4.88)

(5.76)

(7.54)

(12.34)

0.171***

− 0.0285

0.116**

− 0.0971**

(3.62)

(− 0.66)

(2.50)

(− 2.30)

83.39***

44.07**

− 30.12

− 53.25***

(3.88)

(2.09)

(− 1.42)

(− 2.65)

0.638

−

3.914***

− 3.124***

(0.57)

(− 6.47)

(5.38)

(− 3.35)

3.915*

− 2.641

6.311***

− 0.395

(1.84)

(− 1.47)

(4.34)

(− 0.30)

78.17*

− 110.9**

165.6***

377.8***

(1.80)

(− 2.22)

(2.84)

(5.02)

0.130***

0.0806***

0.0417***

0.0297***

(12.70)

(8.20)

(5.58)

(4.79)

0.000160***

0.000118***

0.000206***

0.000250***

8.182***

(6.07)

(4.81)

(4.31)

(5.72)

Is the individual fixed effect controlled? controlled?

Yes

Yes

Yes

Yes

Is the time fixed effect controlled?

No

Yes

No

Yes

_cons

− 3.706***

4.737***

− 6.526***

− 2.379***

(− 4.11)

(4.64)

(− 11.63)

(− 3.52)

N

210

210

210

210

R2

0.944

0.969

0.971

0.984

F

393.8

261.8

784.7

507

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

(3) Heterogeneity analysis of geographical differences Two types of geographical differences are studied to explore whether the impacts of natural resources efficiency utilization improvement on per capita GDP will vary with the geographical locations of different regions. First, according to their geographical location, the 30 areas are divided into the eastern region, central region, and western region to investigate the differences in the impacts of natural resources efficiency utilization improvement on per capita GDP. Second, based on whether an area is close to the sea, the 30 areas are divided into coastal areas and non-coastal areas. The results of dividation of 30 areas shows in Table 5.11.

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

165

Table 5.11 Geographic classification Region

Area

Eastern region

Fujian, Guangdong, Beijing, Jiangsu, Liaoning, Hainan, Hebei, Tianjin, Shandong, Shanghai, and Zhejiang

Central region

Guangxi, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangxi, Inner Mongolia, and Shanxi

Western region

Gansu, Guizhou, Ningxia, Qinghai, Shaanxi, Sichuan, Xinjiang, Yunnan, and Chongqing

As shown in Table 5.12, the regression samples in Columns (1) and (2) are areas in the eastern region, with a sample size of 154 (14 years of data from 11 areas), the regression samples in Columns (3) and (4) are areas in the central region, with a sample number of 140 (14 years of data from 10 areas), and the regression samples in Columns (5) and (6) are areas in the west, with a sample number of 126 (14 years of data from 9 areas). Meanwhile, the space fixed effect is controlled in Columns (1), (3), and (5), and both the space and time fixed effects are controlled in Columns (2), (4), and (6). The regression results in Columns (1) and (2) of Table 5.12 show that the regression coefficients of guse are positive and significant at the 1% level, indicating that, in the eastern region, the improvement of natural resources efficiency utilization level has a significant role in promoting the per capita GDP. The regression results in Columns (3) and (4) show that the regression coefficients of guse are positive but insignificant only when the space fixed effect is controlled; after further controlling the time fixed effect, the regression coefficients of guse are all positive and significant at the 1% level. Therefore, it is believed that in the central region, the improvement of the natural resources efficiency utilization level still plays a significant role in promoting the per capita GDP. The regression results in Columns (5) and (6) of Table 5.12 are similar to those in Columns (1) and (2), namely, that the coefficients of guse are positive and significant at the 1% level, indicating that, in the western region, the improvement of the natural resources efficiency utilization level also plays a significant role in promoting the per capita GDP. Under the condition of controlling both space and time fixed effects simultaneously, the comparison of the regression coefficients of guse reveals that the improvement of the natural resources efficiency utilization level has a greater role in promoting per capita GDP in the central region, followed by the eastern region and finally the western region. Moreover, the R2 and F values in Columns (1)–(6) are all high, indicating that the model has satisfactory goodness of fit and linearity. Table 5.13 shows the classification results of the 30 areas according to whether they are adjacent to the sea. As shown in Table 5.14, the regression samples in Columns (1) and (2) are in the coastal region, with a sample size of 154 (14 years of data from 11 areas), and the regression samples in Columns (3) and (4) are in the non-coastal region, with a sample size of 266 (14 years of data from 19 areas). Meanwhile, the space fixed

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5 Study on the Economic Effects of Efficient Utilization of Natural Resources

Table 5.12 Heterogeneity analysis of geographical differences (1) Variable

Eastern region

Central region

Western region

(3)

(4)

(5)

(6)

3.674

10.75***

11.13***

6.887***

(1)

(2)

guse

8.183***

7.398***

(4.86)

(4.95)

(1.25)

(4.41)

(5.66)

(3.19)

fix

0.0226

− 0.0552

0.445***

0.216***

0.205***

0.241***

(0.28)

(− 0.82)

(11.02)

(5.16)

(6.02)

(5.15)

edu

27.73

37.06

− 40.46**

− 40.43*

− 4.12

14.53

(0.96)

(1.37)

(− 2.00)

(− 1.96)

(− 0.22)

(0.74)

stru

4.273**

−

− 0.193

−

−

− 1.803**

(2.05)

(− 3.52)

(− 0.26)

(− 3.11)

(− 2.23)

(− 2.04)

urban

9.349***

2.961

5.850***

1.757*

10.10***

3.509

(2.68)

(0.97)

(4.46)

(1.69)

(6.46)

(1.45)

found

174.8

99.49

221.2***

381.5***

− 48.86**

− 78.29***

(0.92)

(0.53)

(4.53)

(5.77)

(− 2.37)

(− 2.90)

innova

0.0885***

0.0683***

0.0647***

0.0174

0.0659***

0.0963***

(10.15)

(9.01)

(2.97)

(0.98)

(4.77)

(6.59)

trade

0.000122*** 0.000153*** 0.00024 (3.74)

7.528***

2.821***

1.545**

− 0.000143 0.000225*** 0.000174**

(4.43)

(1.35)

(− 0.91)

(3.50)

(2.63)

Is the Yes individual fixed effect controlled?

Yes

Yes

Yes

Yes

Yes

Is the time No fixed effect controlled?

Yes

No

Yes

No

Yes

− 8.337***

0.739

− 2.225*** − 0.901

− 3.975***

− 0.953

(− 5.11)

(0.41)

(− 3.78)

(− 8.22)

(− 0.93)

_cons

(− 1.29)

N

154

154

140

140

126

126

R2

0.945

0.969

0.967

0.985

0.986

0.990

F

288.7

181.1

453.5

334.2

935.5

445.3

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01 Table 5.13 Geographic classification based on whether adjacent to the sea Region

Area

Coastal region

Fujian, Guangdong, Guangxi, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang

Non-coastal region

Anhui, Hunan, Jilin, Jiangxi, Inner Mongolia, Ningxia, Beijing, Gansu, Xinjiang, Yunnan, Chongqing, Guizhou, Henan, Heilongjiang, Hubei, Qinghai, Shanxi, Shaanxi, Sichuan, and Tibet

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

167

effect is controlled in Columns (1) and (3), and both the space and time fixed effects are controlled in Columns (2) and (4). The coefficients of guse in Columns (1)–(4) of Table 5.14 are all positive and significant at the 1% level, indicating that the per capita GDP increases with the growth of the comprehensive index of natural resources efficiency utilization in both coastal and non-coastal areas. Comparing the guse coefficients of coastal areas and non-coastal areas reveals that, when the time fixed effect is not controlled, the guse coefficients of coastal areas are much smaller than those of non-coastal areas, while the guse coefficients in Columns (2) and (4) are 7.805 and 7.982, respectively, after controlling the time fixed effect, and there is no significant difference between them. Therefore, it is believed that there is no significant difference in the impacts of the Table 5.14 Heterogeneity analysis of geographical differences (2) Variable

Coastal area (1)

(2)

(3)

(4)

guse

5.502***

7.805***

10.87***

7.982***

(3.38)

(4.13)

(6.26)

(4.76)

fix

0.144**

0.0584

0.319***

0.242***

(2.06)

(0.88)

(11.06)

(6.53)

48.38

108.4***

18.02

− 7.976

(1.62)

(2.81)

(1.38)

(− 0.61)

stru

6.295***

− 3.238

− 0.117

− 2.866***

(3.61)

(− 1.59)

(− 0.21)

(− 3.75)

urban

6.456**

3.445

4.158***

1.15

(2.36)

(1.26)

(3.99)

(1.15)

43.4

359.4*

11.96

− 67.71**

(0.26)

(1.96)

(0.49)

(− 2.28)

innova

0.0541***

0.0534***

0.112***

0.0984***

(5.77)

(6.37)

(13.94)

(12.20)

trade

0.000263***

0.000322***

0.0000697***

0.0000469***

(5.27)

(6.23)

(3.73)

(2.71)

Is the individual fixed effect controlled?

Yes

Yes

Yes

Yes

Is the time fixed effect controlled?

No

Yes

No

Yes

_cons

− 7.011***

− 3.09

− 3.056***

0.321

(− 5.84)

(− 1.66)

(− 6.84)

(0.55)

154

154

266

266

edu

found

N

Non-coastal area

R2

0.951

0.968

0.971

0.981

F

329

175.8

1017.5

550.8

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

168

5 Study on the Economic Effects of Efficient Utilization of Natural Resources

natural resources efficiency utilization level on per capita GDP between coastal and non-coastal areas. Moreover, the R2 and F values in Columns (1)–(4) are all high, indicating that the model has satisfactory goodness of fit and linearity.

5.4.4 Influence Mechanism Analysis The mechanism through which the comprehensive index of natural resources efficiency utilization affects the per capita GDP is a problem worth exploring. Therefore, several possible channels are proposed and tested through empirical analysis to explore the influence mechanism. (1) Whether the per capita GDP is improved by raising the fixed asset investment level of relevant industries The improvement of the natural resources efficiency utilization level means an improvement in the utilization efficiency, and may also imply an improvement in the utilization technology. Therefore, at the enterprise level, does the improvement of the natural resources efficiency utilization imply an improvement in enterprise efficiency and cost reduction in related industries? In this context, does it translate to a growth in enterprise profits and an improvement of the reinvestment level? To answer these questions, the first possible influence mechanism is proposed: that the improvement of natural resources efficiency utilization stimulates the reproduction investment level of the relevant industries and further promotes the growth of per capita GDP. As the comprehensive index of natural resources efficiency utilization in this chapter mainly focuses on water, land, and energy resources, the fixed asset investment levels of industries related to these three resources are observed with emphasis. Moreover, as the explained variable of this chapter is per capita GDP, for uniformity, the per capita fixed asset investment levels of industries related to these three resources are taken as the research object. The per capita fixed asset investment in the energy industry is adopted to reflect the investment level of the energy industry; the per capita fixed asset investment in the real estate industry is used to reflect people’s investment level in land resources; and the per capita fixed asset investment in the water conservation, environment, and public facilities management industry is used to reflect people’s investment level in water resources. These three variables together are adopted to test the first proposed influence mechanism. As shown in Table 5.15, the explained variable in Columns (1) and (2) is the per capita fixed assets investment of the energy industry, that in Columns (3) and (4) is the per capita fixed assets investment of the real estate industry, and that in Columns (5) and (6) is the per capita fixed assets investment of the water conservation, environment, and public facilities management industry. Meanwhile, the space fixed effect is controlled in Columns (1), (3), and (5), and both the space and time fixed effects are controlled in Columns (2), (4), and (6).

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

169

Table 5.15 Mechanism test (1) Variable

Per capita fixed assets investment in energy industry

(1)

(2)

(3)

(4)

(5)

(6)

guse

0.343*

0.0985

2.282***

2.386***

0.668***

0.797***

(1.71)

(0.47)

(6.22)

(6.48)

(2.81)

(3.56)

edu

− 2.696

− 5.668

27.06***

43.74***

− 8.080*

7.687*

(− 0.72)

(− 1.30)

(3.93)

(5.72)

(− 1.81)

(1.65)

stru

− 0.0466

− 0.315

0.0625

− 0.486

0.462**

− 1.287***

(− 0.25)

(− 1.26)

(0.18)

(− 1.11)

(2.10)

(− 4.82)

urban

0.940***

0.0641

3.342***

1.332**

3.245***

1.915***

(3.50)

(0.19)

(6.80)

(2.28)

(10.20)

(5.39)

found

62.38***

61.90***

− 15

23.22

6.719

21.98*

(7.26)

(5.66)

(− 0.95)

(1.21)

(0.66)

(1.89)

innova

− 0.00248* − 0.00360**

0.0147***

0.0143***

0.00329*

0.00109

(− 1.65)

(5.37)

(5.38)

(1.85)

(0.67)

trade

(− 2.37)

Per capita fixed assets investment in real estate industry

Per capita fixed assets investment in water conservation, environment, and public facilities management industry

− − 0.000000169 0.00000474 − 0.00000246 0.00000401 0.00000846 0.0000275*** (− 0.71)

(− 1.36)

(0.02)

(0.44)

(− 4.13)

(0.37)

Is the Yes individual fixed effect controlled?

Ye

Ye

Ye

Ye

Ye

Is the time No fixed effect controlled?

Ye

No

Ye

No

Ye

0.123

− 2.231***

− 1.277***

− 1.671***

− 0.567***

_cons

− 0.485*** (− 5.70)

(0.62)

(− 14.34)

(− 3.69)

(− 16.60)

(− 2.69)

N

420

420

420

420

420

420

R2

0.463

0.523

0.817

0.851

0.721

0.8

F

47.16

20.31

244.7

105.9

141.7

73.86

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

The coefficient of guse in Column (1) is positive and significant at the 10% level, indicating that the improvement of natural resources efficiency utilization can promote the growth of per capita fixed assets investment in the energy industry. The coefficient of guse in Column (2) is insignificant, but still positive. Therefore, based on the results in Columns (1) and (2), the improvement of the natural resources

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5 Study on the Economic Effects of Efficient Utilization of Natural Resources

efficiency utilization level can promote the growth of per capita fixed assets investment in the energy industry. The coefficients of guse in Columns (3) and (4) are both positive and significant at the 1% level, indicating that the improvement of the natural resources efficiency utilization level can significantly promote the growth of per capita fixed asset investment in the real estate industry. Similarly, the coefficients of guse in Columns (5) and (6) are both positive and significant at the 1% level, indicating that the improvement of the natural resources efficiency utilization level can significantly promote the growth of per capita fixed assets investment in the water conservation, environment, and public facility management industry. It can be found by comparing the coefficients of the three explained variables that the improvement of natural resources efficiency utilization plays the largest role in promoting the per capita fixed assets investment in the real estate industry, followed by the per capita fixed assets investment in the water conservation, environment, and public facilities management industry, and finally the per capita fixed assets investment in the energy industry. Based on the overall regression results in Columns (1)–(6) of Table 5.15, it is believed that the improvement of natural resources efficiency utilization can significantly promote the reproduction investment levels of the relevant industries, so as to promote the growth of per capita GDP. (2) Whether the growth of per capita GDP is improved by promoting the development of tourism One of the important manifestations of natural resources efficiency utilization improvement is that the consumption of a unit resource produces more GDP with less pollution. Hence, can the improvement of natural resources efficiency utilization affect the development of local tourism and per capita GDP by optimizing the local ecological environment? To answer this question, the second possible influence mechanism is proposed: that the improvement of natural resources efficiency utilization stimulates the development of tourism and further promotes the growth of per capita GDP. This section reflects the development of tourism from two aspects: tourism revenue and tourist trips. First, the number of inbound overnight tourists is used to reflect the number of tourists. Second, as the annual data of the tourism income of each area are not available, the international tourism foreign exchange income is adopted to reflect the income level of the tourism industry. Considering that the per capita GDP is taken as the explained variable in the previous text, for uniformity, the per capita levels of the above variables are taken as the explained variables to test the second possible influence mechanism. As shown in Table 5.16, the explained variable in Columns (1) and (2) is the per capita international tourism foreign exchange income and that in Columns (3) and (4) is the per capita number of inbound overnight tourists. Meanwhile, the space fixed effect is controlled in Columns (1) and (3), and both the space and time fixed effects are controlled in Columns (2) and (4). The coefficients of guse in Columns (1) and (2) are positive and significant at the 1% level, indicating that the improvement of the natural resources efficiency

5.4 Empirical Analysis of Natural Resources Efficiency Utilization …

171

Table 5.16 Mechanism test (2) Variable

Per capita international tourist foreign exchange income (1)

(2)

(3)

(4)

guse

1.936***

1.954***

− 0.0041

− 0.056

(6.25)

(5.89)

(− 0.11)

(− 1.57)

fix

0.0745***

0.0939***

− 0.000826

− 0.000225

(5.98)

(6.72)

(− 0.57)

(− 0.15)

edu

38.66***

41.03***

4.976***

4.475***

(7.11)

(6.27)

(7.90)

(6.37)

stru

−

−

−

− 0.216***

(-3.41)

(− 1.97)

(− 3.13)

(− 5.29)

urban

− 2.136***

− 1.897***

0.00507

− 0.116**

(− 4.61)

(− 3.68)

(0.09)

(− 2.10)

found

− 62.80***

− 65.45***

− 3.274**

− 6.823***

(− 5.05)

(− 4.01)

(− 2.27)

(− 3.89)

innova

0.00449**

0.00470**

− 0.000265

− 0.000659***

(2.06)

(2.08)

(− 1.05)

(− 2.71)

trade

0.0000184**

0.0000250***

0.00000602***

0.00000684***

(2.22)

(2.69)

(6.27)

(6.85)

Is the individual fixed effect controlled?

Yes

Yes

Yes

Yes

Is the time fixed effect controlled?

No

Yes

No

Yes

_cons

0.772***

0.547*

0.0196

0.145***

(4.16)

(1.83)

(0.91)

(4.51)

N

420

420

420

420

R2

0.588

0.613

0.407

0.52

F

68.24

27.82

32.72

19.05

0.915***

0.751**

Per capita number of inbound overnight tourists

0.0973***

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

utilization level can significantly increase the per capita international tourism foreign exchange income. However, the coefficients of guse in Columns (3) and (4) are negative but insignificant, indicating that the improvement of the natural resources efficiency utilization level cannot significantly increase the per capita number of inbound overnight tourists. To summarize, the improvement of the natural resources efficiency utilization level mainly improves the per capita GDP by increasing the per capita international tourism foreign exchange income, rather than by increasing the per capita number of inbound overnight tourists.

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5 Study on the Economic Effects of Efficient Utilization of Natural Resources

(3) Whether the growth of per capita GDP is improved by promoting the import and export level Does the improvement of natural resources efficiency utilization mean the improvement of production efficiency and cost reduction at the enterprise level, further improving commodity competitiveness, import and export level, and finally, GDP growth? Thus, the third possible influence mechanism is proposed: the improvement of natural resources efficiency utilization promotes the import and export trade level and further promotes the growth of per capita GDP. The total import and export volume is selected in this section to reflect the import and export trade level of each area. Considering that the per capita GDP is taken as the explained variable, for uniformity, the per capita import and export trade level is taken as the explained variable, and the per capita import and export volume is used to test the third possible influence mechanism. Only the space fixed effect is controlled in Column (1) of Table 5.17, while both space and time fixed effects are controlled in Column (2). First, it is observed that the coefficients of guse in Columns (1) and (2) are both positive and significant at the 1% level, indicating that the improvement of natural resources efficiency utilization can effectively improve the per capita import and export volume. Moreover, it can be found by comparing the coefficients of guse in Columns (1) and (2) that the coefficients of Column (2) are only half of those in the Column (1), indicating that the coefficients of guse in Column (1) may be overestimated without considering the time fixed effect. Overall, the improvement of natural resources efficiency utilization can promote the import and export trade level so as to promote the growth of per capita GDP.

5.5 Conclusions and Policy Recommendations 5.5.1 Main Conclusions In this chapter, the entropy method is used to construct the natural resources efficiency utilization index from the three aspects of water resources, land resources, and energy, with a total of nine sub-indexes, and then the relationship between the natural resources efficiency utilization index and the per capita GDP is investigated. The research results show that the improvement of the natural resources efficiency utilization level can significantly promote the growth of per capita GDP. To confirm the robustness of the regression results, the influence of monetary inflation is excluded, the instrumental variable method is adopted to solve the endogeneity problems that may be caused by the two-way causal relationship between the natural resources efficiency utilization index and per capita GDP, and the placebo test is carried out. In terms of the results of the heterogeneity analysis, the impact of natural resources efficiency utilization improvement on per capita GDP is greater in economically

5.5 Conclusions and Policy Recommendations

173

Table 5.17 Mechanism test (3) Variable guse fix edu stru urban found innova Is the individual fixed effect controlled?

(1)

(2)

trade

trade

16,674.0***

8594.1***

(9.74)

(4.77)

− 304.2***

− 188.1**

(− 4.04)

(− 2.42)

3222.9

− 134,455.7***

(0.10)

(− 3.74)

− 1141.4

2675.4

(− 0.69)

(1.26)

5054.4*

1567.3

(1.78)

(0.54)

− 36,981

− 329,387.9***

(− 0.48)

(− 3.67)

75.27***

56.45***

(5.84)

(4.59)

Yes

Yes

Is the time fixed effect controlled?

No

Yes

_cons

− 3625.1***

− 312.2

(− 3.21)

(− 0.19)

N

420

420

R2

0.55

0.652

F

66.91

34.62

Note (1) Values in parentheses are t values; (2) *p < 0.1, **p < 0.05, and ***p < 0.01

developed areas, areas with a large population, and non-coastal areas, but there is no such significant difference among the eastern, central, and western regions. Regarding mechanism analysis, three possible influence mechanisms are proposed and empirically verified. The results show that natural resources efficiency utilization improvement can stimulate the growth of per capita GDP by increasing the fixed assets investment levels of related industries, promoting the development of tourism, and stimulating the import and export trade.

5.5.2 Policy Recommendations The policy recommendations of this chapter are as follows: the improvement of the natural resources efficiency utilization level can significantly promote the growth of per capita GDP and the improvement of the ecological environment. Therefore, it is

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5 Study on the Economic Effects of Efficient Utilization of Natural Resources

of great significance to increase scientific and technological investment, improve the innovation level, and promote the upgrading of various technologies to promote the improvement of natural resources efficiency utilization. Meanwhile, this chapter also has some limitations and deficiencies. On the one hand, limited by data, the types of natural resources investigated in this chapter are not comprehensive, which might not fully reflect the actual levels of natural resources efficiency utilization in all areas. On the other hand, although the influences of many factors that may affect per capita GDP are controlled, the impacts of possible unconsidered variables cannot be completely ruled out.

References 1. Malthus, T.: Principles of Political Economy (1962) 2. Singer, H.W.: The distribution of trade between investing and borrowing countries. Am. Econ. Rev. 40(2), 56–58 (1950) 3. Hirschman, A.O.: The strategy of economic development. Reg. Stud. 51(2), 348–349 (2017) 4. Li, H., Jia, X., Li, Q., Yu, C.: Improving resource utilization efficiency in China’s mineral resource-based cities: a case study of Chengde, Hebei Province. Resour. Conserv. Recycling 94, 1–10 (2015) 5. Miao, C.L., Fang, D.B., Sun, L.Y., Luo, Q.L.: Natural resources utilization efficiency under the influence of green technological innovation. Resour. Conserv. Recycl. 126, 153–161 (2017) 6. Lu, S., Zhang, X., Peng, H., Skitmore, M., Bai, X., Zheng, Z.: The energy-food-water nexus: water footprint of Henan-Hubei-Hunan in China. Renew. Sustain. Energy Rev. 135, 110417 (2021) 7. Shahbaz, M., Destek, M.A., Okumus, I., Sinha, A.: An empirical note on comparison between resource abundance and resource dependence in resource abundant countries. Resour. Policy 60, 47–55 (2019) 8. Wu, S.M., Li, L., Li, S.T.: Natural resource abundance, natural resource-oriented industry dependence, and economic growth: evidence from the provincial level in China. Resour. Conserv. Recycl. 139, 163–171 (2018) 9. Sun, Z., Wang, Q.: The asymmetric effect of natural resource abundance on economic growth and environmental pollution: evidence from resource-rich economy. Resour. Policy 72, 102085 (2021) 10. Yang, Y., Yang, J., Yuan, L.: Regional analysis of the green development level differences in Chinese mineral resource-based cities. Resour. Policy 61, 261–272 (2019) 11. Zameer, H., Yasmeen, H., Wang, R., Tao, J., Malik, M.N.: An empirical investigation of the coordinated development of natural resources, financial development and ecological efficiency in China. Resour. Policy 65, 101580 (2020) 12. Song, M.L., Zhao, X., Shang, Y., Chen, B.: Realization of green transition based on the antidriving mechanism: an analysis of environmental regulation from the perspective of resource dependence in China. Sci. Total Environ. 698, 134317 (2020) 13. Ma, L., Long, H., Chen, K., Tu, S., Zhang, Y., Liao, L.: Green growth efficiency of Chinese cities and its spatio-temporal pattern. Resour. Conserv. Recycl. 146, 441–451 (2019) 14. Li, L., Zeng, S.: Research on evaluation of operating performance of listed companies with dual ownership structure based on entropy method. Int. J. Soc. Sci. Educ. Res. 4(4), 95–108 (2021) 15. Tian, P., Wu, H., Yang, T., Jiang, F., Zhang, W., Zhu, Z., Yue, Q., Liu, M., Xu, X.: Evaluation of urban water ecological civilization: a case study of three urban agglomerations in the Yangtze River economic belt, China. Ecol. Indicators 123, 107351 (2021)

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16. Zhong, S.J.: Study on Coordinated Development of Regional Resources, Environment and Economy. Jilin University (2013) 17. Cai, X., Lu, Y., Wu, M., Yu, L.: Does environmental regulation drive away inbound foreign direct investment? Evidence from a quasi-natural experiment in China. J. Dev. Econ. 123, 73–85 (2016)

Chapter 6

Research on Environmental Effects of Natural Resources Efficiency Utilization

This chapter first analyzes the current situation of natural resources utilization in China and then uses the entropy method to construct the natural resources efficiency utilization index from the three aspects of water resources, land resources, and energy. Next, based on existing works related to environmental assessment, labor, capital, energy, land resource, and water resource are taken as the input variables; GDP as the desirable output; and industrial wastewater, industrial waste gas, and industrial solid pollutants as undesirable outputs for conducting the SBM-DEA analysis to calculate the environmental efficiency in China. Then, this chapter presents the CCR-DEA analysis, which takes the calculated environmental efficiency as the output and the natural resources efficiency utilization index as the input. Finally, a regression analysis model is established by taking carbon emissions as the main representative index of environmental effects, the comprehensive index of natural resources efficiency utilization as the main explanatory variable, and other environment-related principal components as control variables to draw conclusions and provide suggestions.

6.1 Natural Resources Efficiency Utilization 6.1.1 Analysis of Current Situation of Natural Resources Utilization Before studying the efficient utilization of natural resources, it is necessary to analyze the status quo of natural resources utilization in China. This section will first analyze the overall utilization status of natural resources in China and then analyze the utilization of major natural resources, so as to better understand the current utilization status of natural resources in China and provide a good background for further measuring the natural resources efficiency utilization.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_6

177

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6 Research on Environmental Effects of Natural Resources Efficiency …

At present, China has 9% of the world’s arable land, 6% of water resources, 4% of forests, 1.8% of oil and 0.7% of natural gas, 9% of iron ore, 5% of copper, and less than 2% of bauxite, but it has to feed 22% of the world’s population; the per capita shares of most mineral resources in China are half of the world average; China’s shares of coal, oil, and gas are only 55, 11, and 4% of the world’s per capita share. China’s biggest comparative advantage is its large population, but it also brings disadvantages, that is, a lack of resources. The basic characteristic of natural resources in China is large absolute amounts with small per capita shares and a low utilization rate with serious waste. However, China has continuously innovated and explored ways and methods of natural resources utilization and gradually transformed its natural resources utilization into efficient utilization to prevent and control pollution, and strengthen ecological protection. From the perspective of land resources, the biggest feature in China is that although there is a lot of land, there is not much per capita arable land, high-quality arable land, or exploitable land resources. China has the third largest total area of land resources in the world, yet per capita land resources account for only one-third of the total. Thus, China’s land resource development and utilization efficiency is of vital importance. Moreover, against the backdrop of increasingly scarce land resources, how to use land efficiently has also become a key factor for China and local areas to improve urban competitiveness. All departments should work together to promote the rational flow of land resources, reduce offset construction land, set standards for related industries, and improve the efficient utilization and allocation of land resources. In terms of water resources, China’s freshwater resources account for 6% of the world’s total, ranking the fourth in the world just behind Brazil, Russia, and Canada. However, due to China’s large population, like other resources, the per capita freshwater resources in China are only 2300 m3 , accounting for only a quarter of the world’s per capita freshwater resources. The supply and demand of fresh water in China is in a serious imbalance. As early as 2016, China established the key special project, Efficient Utilization of Water Resources, and some scholars have pointed out that it is necessary to start from all key links to further improve ecological efficiency and truly achieve the efficient utilization of water resources. Energy resources are mainly divided into two categories: renewable energy resources and non-renewable energy resources. Among the non-renewable energy resources, coal accounts for 94% of China’s proven energy reserves, oil 5.4%, and natural gas 0.6%. The basic situation of China’s coal-based energy production will not change for the time being. Generally, China is rich in non-renewable energy resources, but the per capita share is small. Due to the increasing material demand of the people and the needs of economic development, the relationship between the supply and demand of energy resources is tense. The current situation of China’s energy resource utilization is that the proportion of disposable energy is huge and there are few alternative energy sources; at present, 75% of China’s industrial fuel and power and 85% of urban civil fuel are provided by coal. In fact, at present, China still takes output as the main index for the assessment of coal enterprises, which leads to extensive coal mining, and a waste of underground resources to a

6.1 Natural Resources Efficiency Utilization

179

certain extent. Moreover, compared with developed countries, China’s coal storage and mining ratio are lower, with weak deep processing technology and insufficient coal deep processing, resulting in a low degree of coal resources efficiency utilization and further waste of energy resources. In this case, there are also many measures to implement, such as using thin coal seam mining technology to further reduce the waste of underground resources, constantly deepening coal deep processing projects, increasing innovation, breaking through technical barriers, and continuously promoting the overall high-efficiency utilization of the coal industry based on strict industrial energy consumption standards to protect the ecological environment.

6.1.2 Calculation of Comprehensive Index of Natural Resources Efficiency Utilization After studying the current situation of resource utilization in China, this section plans to comprehensively calculate the efficient utilization of natural resources to transform the abstract definition of natural resources efficiency utilization to specific values. With the continuous development of the social economy and modern science, many scholars have also changed their focus from natural resources effective utilization to natural resources efficiency utilization. In-depth study of natural resources efficiency utilization should comprehensively consider both the economic benefits at the micro-level and the social and environmental benefits at the macro-level. This section will further study the environmental effects of natural resources efficiency utilization based on the previous analysis of the economic and social effects of natural resources efficiency utilization in Chap. 5. Natural resources in this section are classified according to the General Rules for Classification and Grading of Natural Resources issued by the Ministry of Natural Resources of China on March 2, 2021. Due to the great difficulty in obtaining the data of all resources and in conducting a quantitative comparison of some resources, by referring to Howells et al. [1], natural resources are classified into water resources, land resources, and energy resources with nine tertiary indexes to reflect the natural resources efficiency utilization level. The indexes in Table 6.1 are divided into positive ones and negative ones. Positive indexes mean that, when evaluating the comprehensive index of natural resources efficiency utilization in an area, the larger the index value, the better the situation. On the contrary, the smaller the negative index, the better the situation. Limited by data availability, the research period of this chapter is from 2004 to 2017, that is, 14 years. The Tibet Autonomous Region is excluded from the research due to too many instances of missing data, and only the comprehensive indexes of natural resources efficiency utilization in the other 30 areas are discussed. After data and index summarization, the index system is further weighted by the entropy method, and finally, the comprehensive index of natural resources efficiency utilization in each area is obtained, as given in Table 6.2.

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6 Research on Environmental Effects of Natural Resources Efficiency …

Table 6.1 Index system of natural resources efficiency utilization Evaluation system

Secondary index

Tertiary index

Unit

Index type

Comprehensive index of natural resources efficiency utilization

Water resources

Water consumption per unit GDP

m3 /CNY 1

Negative

Per capita water consumption

10,000 m3 / person

Negative

Industrial wastewater discharge per unit GDP

t/CNY 100 million

Negative

GDP per land

CNY 100 million/km2

Positive

Greening coverage rate of built-up area

%

Positive

Fixed assets investment per land

CNY 100 million/km2

Positive

Energy consumption per unit GDP

t of standard coal/CNY 10,000

Negative

Carbon emission per unit energy consumption

t of standard coal

Negative

Per capita energy consumption

10,000 t of standard coal/ person

Positive

Land resources

Energy resources

Data source The original data come from the China Bureau of Statistics, China Energy Statistical Yearbook, and provincial statistical yearbooks. Values of the above indexes are calculated by the authors

According to Table 6.2, the average comprehensive indexes of natural resources efficiency utilization in the 30 areas were mainly between 0.1 and 0.3. By taking the difference between the comprehensive indexes in 2004 and 2017 as the trend value in each area for 14 years, it can be seen that the overall situation of natural resources efficiency utilization in China was good. The achievements of natural resources efficiency utilization in Shanghai, Tianjin, Beijing, and Jiangsu were significant as their comprehensive index differences between 2004 and 2017 were large. Areas with small increases are mainly in the northeastern and western regions, where the primary and secondary industries were leading and where the industrial structure was being upgraded and transformed. The results coincide with the setting of an environmental protection tax rate in each area, and the setting of this tax is related to various factors, such as the local environment and people’s awareness of environmental protection. Based on this result, this section infers that the efficient utilization of natural resources may have a certain impact on China’s environment, and the environmental effects of natural resources efficiency utilization must be explored.

6.1 Natural Resources Efficiency Utilization

181

Table 6.2 Results of natural resources efficiency utilization Mean value

Difference

0.275

0.226

0.094

0.506

0.392

0.219

0.264

0.300

0.243

0.103

0.191

0.209

0.213

0.199

0.021

0.241

0.275

0.316

0.259

0.103

0.155

0.177

0.213

0.235

0.196

0.080

Guizhou

0.184

0.197

0.217

0.235

0.206

0.051

Hainan

0.184

0.194

0.228

0.246

0.211

0.062

Hebei

0.207

0.234

0.266

0.291

0.250

0.084

Henan

0.200

0.230

0.261

0.301

0.246

0.101

Heilongjiang

0.177

0.195

0.203

0.203

0.195

0.026

Hubei

0.187

0.215

0.251

0.277

0.232

0.090

Hunan

0.180

0.209

0.238

0.264

0.223

0.083

Jilin

0.187

0.205

0.222

0.224

0.211

0.037

Jiangsu

0.215

0.269

0.353

0.421

0.313

0.206

Jiangxi

0.168

0.203

0.229

0.253

0.215

0.086

Liaoning

0.208

0.241

0.283

0.254

0.250

0.046

Inner Mongolia

0.187

0.216

0.226

0.241

0.222

0.054

Ningxia

0.154

0.192

0.230

0.263

0.207

0.109

Qinghai

0.183

0.206

0.235

0.248

0.219

0.065

Shandong

0.219

0.254

0.307

0.349

0.283

0.130

Shanxi

0.205

0.223

0.225

0.217

0.225

0.013

Shaanxi

0.192

0.206

0.228

0.241

0.217

0.049

Shanghai

0.431

0.604

0.750

0.942

0.675

0.511

Sichuan

0.187

0.210

0.231

0.242

0.219

0.056

Tianjin

0.263

0.341

0.511

0.578

0.432

0.315

Xinjiang

0.128

0.155

0.191

0.211

0.171

0.083

Yunnan

0.185

0.201

0.219

0.231

0.209

0.046

Zhejiang

0.221

0.249

0.301

0.346

0.277

0.125

Chongqing

0.185

0.221

0.265

0.295

0.243

0.110

Area

Year 2004

2008

2013

2017

Anhui

0.181

0.206

0.242

Beijing

0.288

0.341

0.437

Fujian

0.197

0.221

Gansu

0.192

Guangdong

0.213

Guangxi

Note Limited by article length, this paper only reports the average data of these areas in 2004, 2008, 2013, 2017, and the mean and difference for the period between 2004 and 2017

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6 Research on Environmental Effects of Natural Resources Efficiency …

6.2 Environmental Effect Evaluation of Natural Resources Efficiency Utilization With continuous development of the social economy and improvement of per capita GDP, people’s demand for a better life gradually turns to environmental protection and green development. In view of this, many scholars regard environmental efficiency as the main research object and conduct in-depth research based on the measurement of environmental efficiency. In terms of environmental efficiency measurement, Di and Wang [2] selected environmental management, emission standards, and other indexes to analyze the fairness and efficiency of environmental policies in society; Song et al. [3] reviewed relevant literature at home and abroad on the theoretical and practical basis of environmental analysis; Bi et al. [4] explored the relationship between environmental regulation and the energy efficiency of thermal power generation; Wu et al. [5] measured the environmental and energy efficiencies of China’s industrial sectors; Iram et al. [6] explored the role of energy efficiency in CO2 emissions and economic efficiency. Some scholars have also carried out relevant research on environmental efficiency based on spatial measurement. Luo and Wang [7] investigated the impacts of fiscal decentralization and environmental regulations on ecological efficiency by using the spatial Dubin model. Thus, this section intends to measure the environmental efficiency values of areas in China through the SBM-DEA model and conduct the CCR-DEA model analysis by taking the obtained environmental efficiency as output and the comprehensive index of natural resources efficiency utilization as input to obtain the overall impact of natural resources efficiency utilization on China’s environment.

6.2.1 Measurement of Environmental Efficiency in China (1) Basic model of environmental efficiency measurement In this section, the SBM model considering undesirable outputs as proposed by Tone [8] is used for measuring the environmental efficiencies of areas in China, so as to make up for the defects that the traditional DEA model does not consider the impact of input factor slackness and to reduce the deviations of efficiency measurement. The specific SBM model is as follows: X = x1 , x2 , x3 , . . . , xn−1 , xn ∈ R m×n , xi ∈ R m ; q

q

q

q

q

Y q = y1 , y2 , y3 , . . . , yn−1 , ynq ∈ R s1×n , yi ∈ R s1 ; b Y b = y1b , y2b , y3b , . . . , yn−1 , ynb ∈ R s2×n , yib ∈ R s2 ; q

s.t. xi > 0, yi > 0, yib > 0.

6.2 Environmental Effect Evaluation of Natural Resources Efficiency …

183

When the return to scale remains unchanged, P=



  q q xi , yi , yib |xi ≥ X λ, yi ≤ Y q λ, yib ≥ Y b λ, λ ≥ 0 .

(6.1)

Furthermore, ρ =

1+

1 s1 +s2

1 − m1  s1

m

si− i=1 xi0 q

sr r =1 yrq0

+

s2

srb r =1 yrb0



(6.2)

s.t. x0 = X λ + s − q

y0 = Y q λ − s q y0b = Y b λ + s b λ > 0, s − > 0, s q > 0, s b > 0, where X is the input variable; Y q is the desirable output; Y b is the undesirable output; P is the possible output set; λ is the weight; s− is the slack variable of input; and s q and s b are the slack variables of output. When ρ  = 1, it is considered that there is no slack variable of input or output, that is, s− = s b = s q = 0. (2) Data source and index interpretation A total of 31 areas in China are researched for analyzing China’s environmental efficiency. To ensure data consistency and effectiveness, Chongqing and Tibet are excluded from the research scope. Finally, the panel data of 29 provincial areas in China from 2004 to 2017 are selected for further analysis. Considering the selection of China’s environmental efficiency indexes by Song et al. [9], Wang et al. [10], and Chang et al. [11], labor, capital, energy, land resource, and water resource are taken as the input variables; GDP as the desirable output; and industrial wastewater, industrial waste gas, and industrial solid pollutants as the undesirable output. Data processing is as follows: Labor input is reflected in the current labor input of each provincial area in the production process. This paper selects the year-end number of the urban employed population (unit: 10,000 people) in each provincial area as the labor index, and the data come from the statistical yearbooks of each area from 2004 to 2017. Capital input is mainly used to reflect the stock of fixed assets in each area. In this section, the total fixed assets investment (unit: CNY 100 million) is taken as the capital input index of each provincial area. The data come from the statistical yearbooks of these areas from 2004 to 2017. Energy input can reflect to a certain extent the energy demand of an area. This paper selects the total energy consumption (unit: 10,000 t of standard coal) as the energy input index of each provincial area for analysis. The data are from the China Energy Statistical Yearbooks of each area from 2004 to 2017. Land resource input reflects the land utilization rate, such as urban construction and agricultural land. This section selects the proportion of construction land (%) as

184

6 Research on Environmental Effects of Natural Resources Efficiency …

the land resource input index of each area for analysis. The data are from statistical yearbooks of each area from 2004 to 2017. Water resources input can reflect the demand for water resources in an area to a certain extent. In this chapter, the total water consumption (unit: 100 million m3) is selected as the water resources input index of each provincial area for analysis. The data are from statistical yearbooks of each area from 2004 to 2017. GDP is the main indicator of regional economic accounting. This chapter selects the GDP of 28 areas in China from 2004 to 2017 for constant price processing, and the data are from their statistical yearbooks. In this section, industrial wastewater, industrial waste gas, and industrial solid wastes are taken as undesirable outputs and included in the efficiency calculation as air pollution, solid pollutant pollution, and river water resource pollution. The industrial wastewater discharge (unit: 10,000 t), industrial sulfur dioxide discharge (unit: t), and industrial solid waste discharge of each researched area are selected for analysis. The data of each area are from the provincial statistical yearbooks and China Industrial Statistical Yearbooks from 2004 to 2017, as given in Table 6.3. (3) Calculation results of environmental efficiency The aforementioned indexes and data are substituted into MATLAB for calculation to obtain the environmental efficiency values of the 29 areas. Results are given in Table 6.4. This section compares the data of China’s provincial regional environmental efficiency in 2017 with the average value for 2004–2017 and concludes that if the environmental efficiency value in 2017 is lower than the average value for 2004–2017, the provincial regional environmental efficiency shows a downward trend; otherwise, an upward trend. Through the observation of the results of the regional environmental efficiency values at the provincial level in China, it is found that the environmental efficiency values of Beijing, Fujian, Jiangsu, Shanghai, and Zhejiang were all 1 from 2004 to 2017, indicating that these areas performed well in environmental development. Moreover, the economy of these five areas was relatively developed, and each area made large investments in improving environmental efficiency when developing the economy. Certain pollution emissions were reduced at the front end of production due to scientific and technological progress; environmental effect factors in the middle of production were well controlled; and environmental effect factors, such as pollution in the later stage of production, were treated. By comparing the data in 2017 and the average value from 2004 to 2007, it is found that the environmental efficiency values of Anhui, Guizhou, Hainan, Hebei, Hubei, Hunan, Liaoning, Shandong, and Tianjin in 2017 were higher than the overall average value, with an upward trend. Thus, it is inferred that these nine areas had large investments in environmental efficiency, and with the continuous utilization of resources, these provinces’ investment in environment would be also increasing. The environmental efficiency values of the other 14 areas in 2017 were lower than the average value of the overall environmental efficiency. It is considered that these areas showed a downward trend in the development of environmental efficiency

6.2 Environmental Effect Evaluation of Natural Resources Efficiency …

185

Table 6.3 Descriptive statistics of input and output Variable

Variable description

Unit

Minimum

Maximum

Mean value

Deviation

Labor input

Number of the 10,000 employed people population

42.52

1973.28

490.06

334.94

Capital input

Fixed assets investment

CNY 100 289.18 million

55,202.72

10,717.05

10,275.73

Energy input

Total energy consumption

10,000 t of standard coal

742.48

40,138.00

12,734.40

8182.76

Land resource input

Proportion of construction land

km2

105.08

5577.44

1459.34

1026.84

Water resources input

Total water consumption

100 million m3

22.06

591.30

201.58

140.25

GDP

GDP

CNY 100 443.70 million

91,648.70

15,608.41

15,103.30

Industrial waste water

Industrial wastewater discharge

10,000 t

14,287.00

938,261.00

210,416.65

168,993.07

Industrial waste gas

Industrial waste gas emission

t

4680.00

2,002,000.00

681,633.87

449,023.15

Industrial solid wastes

Industrial solid wastes discharge

10,000 t

112.00

45,575.83

8503.51

7868.10

Data source Statistical yearbooks of each area

and lagged behind Beijing, Shanghai, and other frontier areas to a certain extent. Therefore, it is considered that these areas have certain development potentials in energy conservation, emission reduction, and environmental development and should increase investment in pollution control, energy conservation, and emission reduction to further improve environmental efficiency. In summary, it is inferred that economically developed areas had a strong awareness of environmental protection and could support environmental protection with the developed economy. Moreover, as these areas also had more colleges and universities, it was easier for them to attract high-level talent, so as to promote the formation of local environmental protection awareness. The environmental protection efficiency of the western region was low, and reasonable assistance is needed. For example, the implementation of measures, such as a developed area helping an economically undeveloped area, can improve the current situation to a certain extent. Meanwhile, due to China’s vast territory and abundant resources, the local governments in areas

186

6 Research on Environmental Effects of Natural Resources Efficiency …

Table 6.4 Environmental efficiencies of areas in China Area

Year 2004

Mean value 2007

2010

2013

2016

2017

Anhui

0.710

0.625

0.682

1.000

0.753

0.779

0.735

Beijing

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Fujian

1.000

1000

1.000

1.000

1.000

1.000

1.000

Gansu

0.468

0.459

0.466

0.424

0.355

0.371

0.431

Guangdong

1.000

1.000

1.000

1.000

0.794

0.789

0.970

Guangxi

0.611

0.564

0.594

0.575

0.523

0.517

0.567

Guizhou

0.446

0.473

0.527

0.532

0.548

0.538

0.513

Hainan

0.654

0.563

0.633

0.650

0.595

0.621

0.617

Hebei

0.752

0.703

0.746

0.788

0.587

1.000

0.743

Henan

0.797

0.759

0.779

0.675

0.595

0.611

0.722

Heilongjiang

0.517

0.445

0.409

0.416

0.356

0.339

0.425

Hubei

0.592

0.625

0.672

0.793

0.686

0.715

0.680

Hunan

0.661

0.634

0.707

1.000

1.000

1.000

0.787

Jilin

0.502

0.452

0.466

0.457

0.415

0.414

0.457

Jiangsu

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Jiangxi

0.632

0.617

0.681

0.680

0.583

0.571

0.637

Liaoning

0.680

0.589

0.541

0.462

0.566

0.571

0.557

Inner Mongolia

0.506

0.487

0.524

0.582

0.497

0.518

0.522

Ningxia

0.312

0.351

0.412

0.439

0.359

0.368

0.371

Qinghai

0.428

0.450

0.515

0.448

0.440

0.436

0.456

Shandong

0.826

0.804

0.915

1.000

1.000

1.000

0.939

Shanxi

0.703

0.621

0.611

0.522

0.404

0.529

0.583

Shaanxi

0.694

0.682

1.000

1.000

0.625

0.626

0.817

Shanghai

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Sichuan

0.658

0.663

0.707

0.684

0.609

0.621

0.676

Tianjin

0.874

0.788

1.000

1.000

1.000

1.000

0.966

Xinjiang

0.411

0.406

0.436

0.419

0.329

0.331

0.395

Yunnan

0.736

0.577

0.590

0.717

0.586

0.585

0.630

Zhejiang

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Note Limited by article length, this section only reports the data in 2004, 2007, 2010, 2013, 2016, and 2017 and the mean value of 2004–2017

with low environmental efficiencies, such as Gansu and Ningxia, should analyze local environmental conditions and reasonably allocate resources so as to reduce pollution production and further enhance the local environment protection, such as returning farmland to forests. Moreover, there are many areas where the values of

6.2 Environmental Effect Evaluation of Natural Resources Efficiency …

187

environmental efficiency decreased significantly in recent years. The local governments should notice this situation and take corresponding countermeasures to gradually keep up with those frontier areas. Frontier areas should continue to maintain existing scales, constantly improve existing science and technology, and summarize and analyze existing data through technology sharing to make continuous progress and provide good reference for other areas to drive China’s development.

6.2.2 Evaluation of the Effect of Natural Resources Efficiency Utilization on China’s Environment Efficiency After calculating and analyzing the environmental efficiencies in China, this section takes the comprehensive indexes of natural resources efficiency utilization in 29 areas as the input index and their environmental efficiencies as the output index to explore the impacts of natural resources efficiency utilization on China’s environmental efficiency. (1) Basic evaluation model of the effect of natural resources efficiency utilization on China’s environmental efficiency The CCR model is adopted in this section to analyze the impacts of natural resources efficiency utilization on China’s environmental efficiency. The CCR model can study the resource allocation efficiency of the above input and output based on constant returns to scale. The specific model is as below: max h j0 8 vr yr j0 s.t. rm=1 ≤ 1, i=1 vi x i j0

8 vr yr j0 = rm=1 i=1 vi x i j0

(6.3)

j = 1, 2, 3, . . . , n, u ≥ 0, v ≥ 0.

Furthermore, set w = v T1x0 v, μ = v T1x0 u, and the following formula can be obtained through Charnes-Cooper conversion: max h j0 = μT y0 s.t. w T x j − μT y j ≥ 0,

j = 1, 2, 3, . . . , n

w T x0 = 1, w ≥, μ ≥ 0.

(6.4)

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6 Research on Environmental Effects of Natural Resources Efficiency …

Then, introduce the slack variable s+ and the surplus variable s− , Min θ s.t.

n

λ j y j + s + = θ x0

j=1 n

λ j y j − s − = θ y0

j=1

λ j ≥ 0,

j = 1, 2, . . . , n

+

s ≥ 0, s − ≤ 0. (2) Calculation results of the impacts of natural resources efficiency utilization on China’s environmental efficiency DEAP 2.1 is used to calculate the data of natural resources efficiency utilization in the above 29 areas and of China’s environmental efficiency. The results are given in Table 6.5. Overall, the average comprehensive efficiency (meancrste) of the aforementioned 29 areas was 0.635, the average pure technical efficiency (meanvrste) was 0.820, and the average scale efficiency (meanscale) was 0.781. From the perspective of each area, only the CCR-DEA model of Shanghai was effective, with its comprehensive efficiency value (crste) being 1; that (crste) of other areas was lower than the average comprehensive efficiency value (meancrste), with Ningxia scoring the lowest, only 0.132. From the perspective of pure technical efficiency (vrste), only the CCR-DEA model of Shanghai was effective, with pure technical efficiency (vrste) of 1. The pure technical efficiencies (vrste) of Guizhou, Henan, and Shaanxi were higher than the overall average pure technical efficiency (meanvrste), and those (vrste) of other areas were lower than the overall average pure technical efficiency (meanvrste). This indicates that the efficient utilization of natural resources in most areas could not give full play to the maximum economic, social, and environmental benefits. These areas need to further optimize the efficient utilization of natural resources and increase the investment in environment-related indexes, so that the industrial structure can generate the maximum economic, social, and environmental benefits. From the perspective of scale efficiency (scale), when natural resources efficiency utilization was optimized, the environmental effects in many areas fell within a range of 0.4–0.5, with a trend of increasing returns to scale; that is, when natural resources efficiency utilization is further improved, environmental efficiency will rise, showing a positive correlation as a whole. From the preceding analysis, this section infers that there are still many areas in which natural resources efficiency utilization has not reached a good level, impeding environmental efficiency. Further improvement of natural resources efficiency utilization can promote environmental efficiency to a certain extent, leading to the scale optimization of input and output in many areas.

6.2 Environmental Effect Evaluation of Natural Resources Efficiency …

189

Table 6.5 Results of the DEA model Firm

crste

Vrste

Scale

Anhui

0.217

0.518

0.420

irs

Beijing

0.245

0.367

0.668

irs

Fujian

0.168

0.367

0.457

irs

Gansu

0.338

0.760

0.445

irs

Guangdong

0.268

0.542

0.495

irs

Guangxi

0.218

0.608

0.359

irs

Guizhou

0.396

0.929

0.426

irs

Hainan

0.251

0.588

0.427

irs

Hebei

0.224

0.466

0.480

irs

Henan

0.383

0.827

0.464

irs

Heilongjiang

0.220

0.538

0.410

irs

Hubei

0.207

0.477

0.434

irs

Hunan

0.154

0.367

0.418

irs

Jilin

0.214

0.492

0.435

irs

Jiangsu

0.183

0.367

0.498

irs

Jiangxi

0.226

0.582

0.389

irs

Liaoning

0.273

0.564

0.483

irs

Inner Mongolia

0.303

0.699

0.434

irs

Ningxia

0.132

0.367

0.358

irs

Qinghai

0.272

0.639

0.425

irs

Shandong

0.418

0.820

0.509

irs

Shanxi

0.174

0.367

0.475

irs

Shaanxi

0.392

0.880

0.446

irs

Shanghai

1.000

1.000

1.000

–

Sichuan

0.265

0.610

0.433

irs

Tianjin

0.224

0.367

0.611

irs

Xinjiang

0.193

0.649

0.297

irs

Yunnan

0.158

0.367

0.429

irs irs

Zhejiang

0.330

0.643

0.513

Mean value

0.635

0.820

0.781

Note irs means increasing returns to scale, drs means decreasing returns to scale, and – means constant returns to scale

Accordingly, this section believes that the efficient utilization of natural resources can have a certain positive impact on environmental efficiency; many areas currently perform poorly in terms of pollution control and other related environmental effects, and there are great improvement potentials in these areas.

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6 Research on Environmental Effects of Natural Resources Efficiency …

6.3 Impacts of Natural Resources Efficiency Utilization on the Environment The empirical results of the CCR-DEA model indicate that natural resources efficiency utilization has a certain promotion effect on the environment. In this section, the influencing factors will be further analyzed to explore the impacts of natural resources efficiency utilization on the environment by establishing the relevant environmental indexes.

6.3.1 Literature Review and Index Construction To conduct reasonable research and analysis on the factors influencing the environment, this section constructs environment influencing factor indexes and carries out principal component analysis for all indexes after analyzing those used in the existing literature, to obtain representative environment influencing factors and classify them so as to further analyze the environmental effects of natural resources efficiency utilization. Many scholars have conducted in-depth research on environmental effects, but few have selected relevant environmental indexes for natural resources efficiency utilization. This section intends to select the relevant mainstream indexes of environmental effects according to the existing literature. Lv and Lv [12] constructed the main indexes of environmental effects using the carbon emission index to explore the environmental effects of the global value chain; Zhao et al. [13] studied the ecoenvironmental effects of urban land resources from the aspects of resource consumption, pollution emission, and ecological service value; Zhang [14] took the industrial structure, SO2 emission intensity, industrial solid waste, and other factors as the influencing factors of environmental effects and adopted the two-stage least squares method for analysis; Zhang et al. [15] took SO2 , the industrial dust removal rate, and the industrial wastewater treatment rate as the input and output factors to explore the impacts of the local economy on the environment through the DEA method. Sun et al. [16] incorporated factor price distortion, industrial structure upgrading, and industrial environmental efficiency into their analysis framework and adopted the fixed effect model to determine the relationship between factor price distortion and industrial environmental efficiency; Wan et al. [17] pointed out through a literature review that when studying environmental effects, not only the research means of remote sensing and GIS should be utilized, but also the application of other methods should be enhanced to strengthen comprehensive research. Based on GIS and an investment model, Hu et al. [19] adopted remote sensing GIS technology to analyze land transformation and upgrading in central Guizhou and further explored the impacts of land-use transformation on the ecological environment. Based on the literature review, the following indexes are established: total energy consumption (sum-energy), total water consumption (sum-u-water), gross

6.3 Impacts of Natural Resources Efficiency Utilization on the Environment

191

domestic product (GDP), industrial wastewater discharge (industrial-f-water), industrial waste gas emission (industrial-f-air), industrial solid waste discharge (industry waste), environmental consciousness (Environment-cons), industrial structure (industrial-str), innovation consciousness (sen-innovation), environmental investment (Environment-inv), chemical oxygen demand of wastewater (cod), sulfur dioxide emission (SO2 ), per capita electricity consumption (ele-pop), per capita coal consumption (coal-pop), and per capita energy consumption (energy-pop). Furthermore, the data of the selected indexes are collected and sorted, and simple descriptive statistics is carried, as given in Table 6.6. After sorting the 15 indexes of each area, the principal component analysis is carried out to determine the principal components of environment influencing factors. Firstly, the index data are standardized and the sample matrix is constructed. Zi j =

Xi j − X j Sj

(6.5)

X = X i j A × B. Then, the correlation coefficient matrix R is calculated, n eigenvalues λ and the unitary eigenvector are obtained, and the orthogonal matrix A is formed, Table 6.6 Descriptive statistics of indexes N

Minimum Maximum

Mean value

Standard deviation

Coefficient of variation

sum-energy

406 742.48

40,138.00

12,734.40

8182.76

66,957,630.74

sum-u-water

406 22.06

591.30

201.58

140.25

19,670.80

GDP

406 443.70

91,648.70

15,608.41

15,103.30

228,109,630.62

industry-f-water

406 14,287.00 938,261.00

industry-f-air

406 4680.00

2,002,000.00 681,633.87 449,023.15 201,621,786,643.63

industry waste

406 112.00

210,416.65 168,993.07 28,558,658,911.48

45,575.83

8503.51

7868.10

61,906,967.07

Environment-cons 406 0.00

0.04

0.01

0.01

0.00

industrial-str

406 0.30

0.83

0.45

0.09

0.01

sen-innovation

406 70.00

332,652.00

28,041.98

49,915.34

2,491,540,710.69

Environment-inv

406 1921.20

1,416,464.21 201,080.96 189,828.95 36,035,030,207.85

COD

406 3.93

198.25

58.90

41.34

SO2

406 1.23

981,000.00

84,234.57

218,927.33 47,929,176,649.03

ele-pop

406 0.08

2.02

0.39

0.30

0.09

coal-pop

406 0.23

16.20

3.25

2.82

7.96

energy-pop

406 0.86

9.47

3.19

1.56

2.43

Data source Statistical yearbooks of each area

1708.84

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6 Research on Environmental Effects of Natural Resources Efficiency …

⎤ r11 · · · r1n ⎥ ⎢ X  X = ⎣ ... . . . ... ⎦, ⎡

R=

where r I j =

1 m−1



1 m−1

rn1 · · · rnn

X ik X jk . ⎤ a11 · · · a1n ⎥ ⎢ a = ⎣ ... . . . ... ⎦. an1 · · · ann ⎡

By taking the variance contribution rate as the evaluation standard, the contribution rate of the kth principal component λkλi is obtained. Then, as per the characteristics of the contribution rate reaching 85–90%, principal components are chosen. In this section, SPSS software is used for principal component analysis, and the results are given in Table 6.7. According to Table 6.7, it can be inferred that the 15 indexes can be represented by four principal components, and 15 element matrixes can be further obtained, as given in Table 6.8. By analyzing the component coefficients in Table 6.8, it can be observed that the primary components are mainly total energy consumption (sum-energy), gross domestic product (GDP), industrial wastewater discharge (industrial-f-water), and environment consciousness (Environment-cons). They are defined as pollution production. Secondary components mainly include industrial waste gas emission (industrial-f-air) and industrial structure (Industrial-str), which are defined as industrial production. Tertiary components are mainly per capita coal consumption (coalpop) and per capita energy consumption (energy-pop), which are defined as per capita resource consumption. The quaternary component is mainly the per capita electricity consumption (ele-pop). Here, the aforementioned components are further sorted and integrated. Since the meaning of total energy consumption (sum-energy) and per capita energy consumption (energy-pop) are relatively similar, the total energy consumption is excluded. Therefore, the primary components change to gross domestic product (GDP), industrial wastewater discharge (industrial-f-water), and environment consciousness (Environment-cons), which are defined as environmental effects. As the tertiary and the quaternary components are similar with small total variance contribution rates, they are combined, and per capita coal consumption (coal-pop), per capita energy consumption (energy-pop), and per capita electricity consumption (ele-pop) are defined as resource consumption. By taking GDP as the main indicator of regional economic accounting and an environmental effect factor, the GDP of 29 areas in China is analyzed with constant price processing. The data of each area are from their respective statistical yearbooks.

0.114

0.055

0.026

13

14

15

0.173

0.368

0.760

1.328

1.253

0.199

0.188

11

1.994

2.471

12

0.299

10

3.310

0.496

0.371

8

9

5.083

0.763

7

6.557

5.664

0.984

0.850

10.206

5

1.531

4

14.343

11.804

6

2.151

1.771

2

3

100.000

99.827

99.459

98.699

97.446

96.118

94.125

91.654

88.344

83.261

77.597

71.040

60.834

49.030

34.687

Accumulated %

1.531

1.771

2.151

5.203

10.206

11.804

14.343

34.687

Variant %

71.040

60.834

49.030

34.687

Accumulated %

Total

34.687

Variant %

Total

5.203

Select loading sum of squares

Initial eigenvalue

1

Component

Table 6.7 Total variance of 15 influencing factors

1.589

1.897

2.270

4.900

Total

10.595

12.648

15.132

32.666

Variant %

71.040

60.445

47.797

32.666

Accumulated %

Rotate loading sum of squares

6.3 Impacts of Natural Resources Efficiency Utilization on the Environment 193

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6 Research on Environmental Effects of Natural Resources Efficiency …

Table 6.8 Rotating element matrix Element 1

2

3

4 0.188

sum-energy

0.849

0.420

− 0.017

sum-u-water

0.532

0.281

− 0.009

− 0.461

GDP

0.979

− 0.108

− 0.046

0.002

industry-f-water

0.905

0.116

− 0.072

− 0.216

industry-f-air

0.232

0.840

0.000

0.073

industry waste

0.297

0.568

0.017

0.544

Environment-cons

0.869

0.227

− 0.054

− 0.083

industrial-str

0.085

− 0.822

− 0.012

0.229

sen-innovation

0.861

− 0.324

− 0.019

− 0.032

Environment-inv

0.681

0.315

0.045

0.331

COD

− 0.070

0.023

− 0.192

0.545

SO2

0.009

0.069

− 0.379

− 0.034 0.722

ele-pop

− 0.003

− 0.082

0.178

coal-pop

− 0.059

0.066

0.899

− 0.050

energy-pop

− 0.036

0.111

0.929

− 0.051

The discharge of industrial wastewater is used to explain industrial production and is taken as an influencing factor of environmental effects. The data are mainly from the statistical yearbooks of the 29 areas. Environmental consciousness is mainly reflected by the proportion of college graduates in the total population in an area. Gradually increasing environmental consciousness may promote environmental protection while decreasing environmental consciousness may inhibit environmental protection. The data are also mainly from the statistical yearbooks of the 29 areas. Industrial waste gas emission is also used to explain industrial production and is regarded as the main industrial emission that corresponds to the explanatory variable carbon emission in further regression analysis. The data are mainly from the statistical yearbooks of the 29 area. The proportion of industrial output value in GDP is adopted to reflect the proportion of industrial sectors in the overall industry, so as to reasonably define the scale of current industrial sectors. The data are mainly from the statistical yearbooks of the 29 areas. The ratio of the coal consumption of each area to its local population is used to reflect the per capita coal consumption and is taken as an index of resource consumption. The data are mainly from the statistical yearbooks of the 29 areas. The ratio of the energy consumption of each area to its local population is used to reflect the per capita energy consumption and is also taken as an index of resource consumption. The data are mainly from the statistical yearbooks of the 29 areas.

6.3 Impacts of Natural Resources Efficiency Utilization on the Environment

195

Table 6.9 Basic descriptions of variables Variable type

Variable

Meaning

Unit

Explained variable

Cdischarge

Carbon emission

t of standard coal

Main explanatory variable

guse

Comprehensive index of natural resources efficiency utilization

Entropy method

GDP

Gross domestic product

CNY 100 million

industry-f-water

Industrial wastewater discharge

10,000 t

Environment-cons

Environmental consciousness

%

Industrial production

industry-f-air

Industrial waste gas emission

t

industrial-str

Industrial structure

%

Resource consumption

coal-pop

Per capita coal consumption

t/person

energy-pop

Per capita energy consumption

10,000 t of standard coal/ person

ele-pop

Per capita electricity consumption

10,000 kWh/h * person

Control variables

Environmental effects

The ratio of the electricity consumption of each area to its local population is used to reflect the per capita electricity consumption and is also taken as an index of resource consumption. The data are mainly from the statistical yearbooks of the 29 areas. The time dimension of the indexes used in this section is 2004–2017, a total of 14 years. The basic descriptions of variables are given in Table 6.9, and the descriptive statistics of variables are given in Table 6.10.

6.3.2 Benchmark Regression Results The measurement model set in this section is as below: Cdischargei j = β0 + β1 gusei j + ak X i jk + μi + λi + εi j ,

(6.6)

where Cdischargei j is the carbon emission value of the ith area in the jth year; gusei j is the comprehensive index of natural resources efficiency utilization of the ith area in the jth year; X i jk is the value of the Kth control variable of the ith area in the jth year; μi is the fixed effect of the ith area; λi is the fixed effect of the tth year; εi j is

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Table 6.10 Descriptive statistics of variables N

Minimum Maximum value value

Mean value

Standard deviation

Variants

Cdischarge

406 12.80

135.84

47.72

16.22

262.98

guse

406 0.13

0.94

0.26

0.11

0.01

GDP

406 443.70

91,648.70

15,608.41

15,103.30

228,109,630.62

industry-f-water

406 14,287.00 938,261.00

210,416.65 168,993.07 28,558,658,911.48

Environment-cons 406 0.00

0.04

0.01

0.01

0.00

industrial-str

406 0.30

0.83

0.45

0.09

0.01

industry-f-air

406 4680.00

2,002,000.00 681,633.87 449,023.15 201,621,786,643.63

ele-pop

406 0.08

2.02

0.39

0.30

0.09

coal-pop

406 0.23

16.20

3.25

2.82

7.96

energy-pop

406 0.86

9.47

3.19

1.56

2.43

Data source Statistical yearbooks of each area

the random disturbance term; β0 is the constant term; β1 is the estimation coefficient of gusei j that stands for the marginal effect of the comprehensive index of natural resources efficiency utilization; and ak is the estimation coefficient of X i jk . In this section, the panel data of 29 areas from 2004 to 2017 are used for regression, with carbon emission (Cdischarge) as the explained variable, the comprehensive index of natural resources efficiency utilization as the main explanatory variable, and gross domestic product (GDP), industrial wastewater discharge (industrialf-water), industrial waste gas emission (industry-f-air), environmental consciousness (Environment-cons), industrial structure (industrial-str), per capita electricity consumption (ele-pop), per capita coal consumption (coal-pop), and per capita energy consumption (energy-pop) as the control variables. The benchmark regression results are given in Table 6.11. The results of the OLS regression in Column (1) of Table 6.11 show that the coefficient of the comprehensive index of natural resources efficiency utilization is − 0.0137 and significant at the 1% level, indicating that the level of natural resources efficiency utilization can inhibit the growth of carbon emissions. Moreover, the coefficients of GDP, industrial structure (industrial-str), and industrial waste gas emission (industry-f-air) are all negative, while those of industrial waste water discharge (industry-f-water), environmental consciousness (Environment-cons), per capita electricity consumption (ele-pop), and per capita energy consumption (energypop) are all positive. Among them, the coefficients of industrial waste water discharge (industry-f-water) and environmental consciousness (Environment-cons) both pass the significance test with expected results. These results prove that the reduction of industrial waste water discharge (industry-f-water) and the improvement of environmental consciousness (Environment-cons) can both effectively reduce carbon emissions. Moreover, the goodness of fit R2 and the F value are, respectively, 0.746 and 129.2, indicating that our model has strong explanatory power and linearity.

6.3 Impacts of Natural Resources Efficiency Utilization on the Environment

197

Table 6.11 Benchmark regression results (1) (2) (3) Cdischarge Cdischarge Cdischarge

(4) Cdischarge

(5) Cdischarge

X_main

− 0.0137*** (− 32.95)

− 0.0138*** (− 32.4)

− 0.0137*** (− 32.95)

− 0.0138*** (− 32.13)

− 0.0137*** (− 32.51)

GDP

− 1.2E − 05 (− 0.61)

− 2.1E − 05 (− 0.81)

− 0.0000115 (− 0.61)

− 0.00000916 (− 0.34)

− 0.00000581 (− 0.29)

industryfwater

7.38E − 07* (− 0.57)

4.03E − 06* (− 0.97)

0.000000738** 0.00000168* (− 0.57) (− 0.38)

0.000000105* (− 0.07)

Environment-cons 21.0 (− 0.89)

7.566 (− 0.16)

21.01* (− 0.89)

50.48* (− 0.78)

34.89* (− 1.29)

industrial-str

− 0.309 (− 0.24)

− 1.581 (− 0.49)

− 0.309** (− 0.24)

5.147 − 1.17

− 0.101** (− 0.08)

industryfair

− 4.53E − 08 (− 0.17)

− 4E − 07 − 4.53E − 08 (− 0.60) (− 0.17)

− − 0.000000849** 0.000000201 (− 1.03) (− 0.69)

ele-pop

0.357 (− 1.08)

0.537 (− 0.92)

1.357** (− 1.08)

1.645** (− 2.13)

0.584 (− 1.49)

coal-pop

− 0.0161 (− 0.31)

0.00275 − 0.05

− 0.0161 (− 0.31)

0.00582* − 0.11

− 0.015** (− 0.29)

energy-pop

0.00891 (− 0.09)

0.0965 (− 0.93)

0.00891* (− 0.09)

0.113** (− 1.08)

0.00939* (− 0.1)

Is the individual effect is controlled?

No

Yes

Yes

Yes

Yes

Is the time effect is controlled?

No

No

No

Yes

Yes

_cons

4.148***

4.151**

4.148***

1.575

4.203***

− 5.44

− 2.45

− 5.44

− 0.73

− 5.18

N

406

406

406

406

406

R2

0.746

0.749

0.769

0.754

0.769

F

129.2

121.9

–

49.51

–

Note (1) Values in parentheses are robustness standard errors; (2) * p < 0.1, ** p < 0.05, *** p < 0.01

The results of the panel fixed effect regression in Column (2) of Table 6.11 indicate that the coefficient of the comprehensive index of natural resources efficiency utilization is − 0.0138 and significant at the 1% level, which is consistent with the results in Column (1), and the absolute values of the coefficients are greater than those in the first column. This suggests that the level of natural resources efficiency utilization can effectively inhibit the growth of carbon emissions, and the coefficients of OLS regression may underestimate the effect. Moreover, compared with

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the OLS regression results, the coefficients of GDP, industrial structure (industrialstr), and industrial waste gas emission (industry-f-air) are all negative while those of industrial waste water discharge (industry-f-water), environmental consciousness (Environment-cons), per capita electricity consumption (ele-pop), per capita energy consumption (energy-pop), and per capita coal consumption (coal-pop) are all positive. Among them, the absolute values of the coefficients of GDP, industrial structure (industrial-str), per capita electricity consumption (ele-pop), per capita energy consumption (energy-pop), and per capita coal consumption (coal-pop) are all higher than those in the OLS regression, while that of industrial waste water discharge (industry-f-water) is lower. This indicates that OLS regression might overestimate the impacts of industrial-f-water on carbon emission, while underestimating the role of other control variables. Moreover, the goodness of fit R2 and the F value are, respectively, 0.749 and 121.9, indicating that our model has strong explanatory power and linearity. The results of the panel random effect regression in Column (3) of Table 6.11 are very similar to those of the panel fixed effect regression. The coefficient of the comprehensive index of natural resources efficiency utilization is − 0.0137 and significant at the 1% level. The coefficients of gross domestic product (GDP), industrial structure (industrial-str), industrial waste gas emission (industry-f-air), and per capita coal consumption (coal-pop) are all negative, while those of industrial waste water discharge (industry-f-water), environmental consciousness (Environmentcons), per capita electricity consumption (ele-pop), and per capita energy consumption (energy-pop) are all positive. This implies that even if the random effect model is adopted, the regression results still strongly support the conclusion that the improvement of natural resources efficiency utilization level can effectively reduce carbon emissions; that is, the improvement of natural resources efficiency utilization level can effectively promote environmental effects. The results of the two-way fixed effect panel regression that simultaneously controls individual fixed effects and time effects in Column (4) of Table 6.11 shows that the coefficient of natural resources efficiency utilization is − 0.0138 and significant at the 1% level. This is similar to the results of the panel fixed effect regression, which shows that after further controlling the time fixed effect, the results support the conclusion that improving the natural resources efficiency utilization level can effectively reduce carbon emissions. The results also indicate that the coefficients of most control variables are positive; only those of gross domestic product (GDP) and industrial waste gas emission (industry-f-air) are negative and close to the results of the panel fixed effect regression. However, the regression coefficient of per capita coal consumption (coal-pop) becomes positive and significant at the 1% level, which is contrary to the regression results in the previous three columns. This may be because controlling the time effect may lead to multiple collinearity problems (Note: controlling the time effect will generate an (n − 1)-year dummy variable, which is prone to multiple collinearity problems). The results of the random effect panel regression that simultaneously controls individual fixed effects and time effects in Column (5) of Table 6.11 shows that

6.3 Impacts of Natural Resources Efficiency Utilization on the Environment

199

the coefficient of the comprehensive index of natural resources efficiency utilization is − 0.0137 and significant at the 1% level, which is similar to the results of the panel random effect regression. Meanwhile, the coefficients of GDP, industrial waste gas emission (industry-f-air), and per capita coal consumption (coal-pop) are all negative, indicating that after further controlling the time fixed effect, the results support the conclusion that the improvement of the natural resources efficiency utilization level can effectively reduce carbon emissions; that is, the improvement of the natural resources efficiency utilization level can effectively promote the environmental effects. To decide whether to choose the fixed effect model or the random effect model, the Hausman test is carried out on the results of Column 2 and Column 3 of Table 6.11, in which only the individual effect is controlled. The test results indicate that, as the p value is 0.0000, the original hypothesis selecting the random effect model can be strongly rejected, and the fixed effect model should be selected. Similarly, the Hausman test is also carried out on the results of Columns 4 and 5, in which both individual and time effects are controlled. The test results show that the original hypothesis selecting the random effect model can be strongly rejected, and the fixed effect model should be selected, with a p value of 0.0001. To summarize, according to the benchmark regression results in Table 6.11, the improvement of the natural resources efficiency utilization level can effectively reduce carbon emissions, and the regression results in Columns (2)–(5) show that the coefficients of the comprehensive index of natural resources efficiency utilization are between 0.0137 and 0.0138, indicating that for each 1% increase in the comprehensive index, carbon emissions can be reduced by about 137–138 t.

6.3.3 Heterogeneity Analysis The preceding empirical content has confirmed the basic fact that the improvement of the natural resources efficiency utilization level can reduce carbon emissions to a certain extent, but this should not be the whole conclusion. We are still curious whether the size of this influence will show differences with the change of some characteristics of the samples. Thus, the following heterogeneity analysis is carried out to enrich the conclusions of this chapter. China is vast in territory and rich in resources. There are great differences between the southeastern region and the northwestern region. The boundary of the northwestern region starts from the Greater Khingan Range in the north; passes through Hetao Plain in the middle of Ordos Plateau and Yanchi Tongxin District in the west of Ningxia Autonomous Region; then extends to Jingtai, Yongdeng, and Huangshui river valleys; before turning to the southeast edge of Qinghai Tibet Plateau. The southeastern region is the concentrated with cultivated land, forest land, freshwater lakes, and runoff systems. Compared with the results of environmental efficiency in Sect. 6.2 of this chapter, it is found that areas with better environmental development are mainly concentrated in the eastern region. To explore this problem,

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this section will examine the differences in geographical features among different regions. First, according to geographical location, the 29 areas are divided into the eastern region, central region, and western region to investigate the different impacts of natural resources efficiency utilization improvement on carbon emissions. The results suggest that there is no significant correlation between the carbon emission impact of natural resources efficiency utilization and whether an area is in the eastern region, central region, or western region. Then, considering that China is also rich in port site resources, which are characterized by narrow beaches, steep slopes, and deep water, we further classify the 29 areas as coastal areas and non-coastal areas to investigate the different impacts of natural resources efficiency utilization improvement on carbon emissions. Many 5-m to 10-m isobaths in China are close to the coast and can be used as sites for large and medium-sized ports. The muddy coast of China is more than 4000 km long, and the estuary sections of large rivers with relatively stable deep-water channels can be utilized to build large and medium-sized ports. The beaches in China are mainly composed of sands and grit, and there are many types of accumulation landforms, often accompanied by coastal sand bars, tidal channels, and warm lakes. With certain water depth and protection conditions, small and medium-sized ports can be built there. Currently, 164 ports have built berths of an intermediate and above level. Table 6.12 shows the classification results of the 29 areas. As given in Table 6.13, the regression samples in Columns (1) and (2) are in the eastern region, with a sample size of 140 (14 years of data from 10 areas); those in Columns (3) and (4) are in the central region, with a sample size of 140 (14 years of data from 10 areas); and those in Columns (5) and (6) are in the western region, with a sample size of 112 (14 years of data from 8 areas). Meanwhile, the space fixed effect is controlled in Columns (1), (3), and (5), and both space and time fixed effects are controlled in Columns (2), (4), and (6). The samples in Columns (7) and (8) are coastal areas with a sample size of 140 (14 years of data from 10 areas); samples in Columns (9) and (10) are non-coastal areas with a sample size of 252 (14 years of Table 6.12 Classification of areas Region

Area

Eastern

Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang

Central

Guangxi, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangxi, Inner Mongolia, and Shanxi

Western

Gansu, Guizhou, Ningxia, Qinghai, Shaanxi, Sichuan, Xinjiang, Yunnan

Coastal

Fujian, Guangdong, Guangxi, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, Zhejiang

Non-coastal

Anhui, Beijing, Gansu, Guizhou, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangxi, Inner, Ningxia, Qinghai, Shanxi, Shaanxi, Sichuan, Tibet, Xinjiang, and Yunnan

6.4 Conclusions and Policy Recommendations

201

data from 18 areas). Meanwhile, the space fixed effect is controlled in Columns (7), and (9), and both space and time fixed effects are controlled in Columns (8) and (10). According to the regression results in Table 6.13, the impacts of natural resources efficiency utilization on carbon emission are not significant irrespective of the region. Therefore, it is further inferred that the environmental effects of natural resources efficiency utilization have no strong correlation with geographic location. However, Table 6.13 shows that only the relationship between per capita energy consumption (energy-pop) and carbon emissions in the western region is significant with a negative coefficient, while the relationship between per capita energy consumption (energypop) and carbon emissions in the eastern and central regions is not significant. From the regression results (7), (8), (9), and (10) in Table 6.13, it can be inferred that there is no strong significant correlation between the environmental effects of natural resources efficiency utilization and whether the area is coastal.

6.4 Conclusions and Policy Recommendations 6.4.1 Main Conclusions (1) The current situation of natural resources efficiency utilization in developed areas is good Based on the analysis of the economic effect of natural resources efficiency utilization earlier in this chapter, the environmental effect of natural resources efficiency utilization is further analyzed. First, the comprehensive index of natural resources efficiency utilization is obtained using the entropy method. According to the comprehensive index, the natural resources efficiency utilization in China shows a good trend as a whole, especially in Shanghai, Tianjin, Beijing, and Jiangsu Provinces, where the achievements of natural resources efficiency utilization are significant. (2) Great potential in each area for environmental development According to the measurement of the environmental efficiency values of the researched areas with the SBM model considering undesirable outputs, the environmental efficiency values of Beijing, Fujian, Jiangsu, Shanghai, and Zhejiang were all 1 from 2004 to 2017, indicating that these areas performed well in environmental development; those of Anhui, Guizhou, Hainan, Hebei, Hubei, Hunan, Liaoning, Shandong, and Tianjin in 2017 were higher than the overall mean value, indicating that the environmental efficiencies in these areas were in an upward trend; the environmental efficiencies in other areas were relatively low, implying that there are great development potentials in these areas. Furthermore, the CCR model was selected to analyze and study the impacts of natural resources efficiency utilization on China’s environmental efficiency, and the results suggest that the natural resources efficiency utilization can have a certain positive impact on environmental efficiency. Moreover,

0.041 (− 0.21)

− 0.445 (− 1.14)

2.453 (− 0.22)

− 3.00E − 9.54E − 08 − 8.00E − − 2.00E − − 1.00E − − 2.00E − 1.60E − 07 3.30E − 07 − 7.00E − − 2.00E − 07 07 07 06 06 07 06 (− 0.04) (− 0.05) (− 0.13) (− 0.13) (− 0.27) (− 0.05) (− 0.41) (− 0.46) (− 0.46) (− 1.01)

0.758 (− 0.32)

− 44.38 (− 0.22)

7.546 (− 0.96)

− 0.0776 (− 0.26)

− 0.393 (− 0.89)

environmentcons

industrialstr

industryfair

ele-pop

coal-pop

energy-pop

Is the individual Yes effect controlled?

− 9.00E − − 1.20E − − 2.00E − − 1.00E − − 2.00E − − 2.00E − − 1.00E − − 2.00E − − 2.00E − − 2.00E − 07 05 05 05 05 05 05 06 05 05 (− 0.07) (− 0.90) (− 1.13) (− 0.67) (− 0.73) (− 0.55) (− 1.02) (− 0.20) (− 1.34) (− 1.29)

Yes

29.03 (− 1.6)

− 0.802 (− 0.08)

Yes

− 0.43 (− 1.05)

0.0788 (− 0.39)

2.527 (− 0.77)

− 205.7 (− 0.83)

39.91 (− 0.26)

− 8.7513 (− 0.58)

− 8.3763 (− 0.49)

− 4.734 (− 0.85)

− 6.647 (− 0.84)

− 4.908 (− 0.6)

Yes

− 0.228 (− 0.49)

− 0.197 (− 0.60)

14.3 (− 1.55)

33.28* (− 1.83)

137.1 (− 0.51)

Yes

− 0.710** (− 2.15)

0.194 (− 1.22)

0.893 (− 0.51)

11.22 (− 0.66)

202.5 (− 0.8)

Yes

− 0.620* (− 1.71)

0.199 (− 1.17)

3.064 (− 0.96)

8.051 (− 0.4)

261.4 (− 0.77)

Yes

− 0.257 (− 0.59)

− 0.185 (− 0.65)

20.20** (− 2.22)

41.96** (− 2.31)

242.6 (− 0.97)

Yes

− 0.507 (− 1.17)

− 0.0675 (− 0.24)

7.067 (− 0.91)

3.453 (− 0.31)

− 81.48 (− 0.42)

Yes

− 0.569** (− 2.38)

0.131 (− 1.11)

1.086 (− 0.87)

1.283 (− 0.15)

147.1 (− 1.21)

(continued)

Yes

− 0.580** (− 2.37)

0.146 (− 1.2)

1.392 (− 0.74)

7.523 (− 0.67)

66.71 (− 0.42)

− 1.00E − − 9.00E − − 6.00E − − 3.00E − − 1.00E − − 2.00E − 1.70E − 05 05 05 05 05 05 05 (− 0.18) (− 0.17) (− 0.53) (− 0.33) (− 0.39) (− 0.17) (− 0.22)

− 3.983 (− 0.75)

− 4.877 (− 0.97)

industryfwater

0.00026 (− 1.53)

− 4.7553 (− 0.34)

(10)

− 4.00E − 0.000033 05 (− 0.29) (− 0.48)

Non-coastal area (9)

GDP

(8)

− 10.493 (− 0.81)

Coastal area (7)

− 4.3753 (− 0.92)

(6)

guse

Western region (5)

(2)

(4)

Central region (3)

Eastern region

(1)

Table 6.13 Heterogeneity analysis of geographical differences

202 6 Research on Environmental Effects of Natural Resources Efficiency …

140

140

0.0348

N

R2 0.011

0.115

140

− 9.2 (− 0.92)

− 3.732 (− 0.43) 140

Yes

(4)

No

(3)

Central region

0.0823

112

2.731 (− 0.31)

No

(5)

0.052

112

2.597 (− 0.25)

Yes

(6)

Western region

Note (1) Values in parentheses are t values; (2) * p < 0.1, ** p < 0.05, *** p < 0.01

0.0384

5.768 (− 0.92)

4.169 (− 0.63)

_cons

Yes

No

(2)

Is the time effect controlled?

(1)

Eastern region

Table 6.13 (continued)

0.181

140

− 12.29 (− 1.39)

Yes

(7)

Coastal area

0.0425

140

4.235 (− 0.67)

Yes

(8)

0.0489

252

6.473 (− 1.39)

No

(9)

0.0781

252

4.951 (− 0.84)

No

(10)

Non-coastal area

6.4 Conclusions and Policy Recommendations 203

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there is large development room for many areas with poor performance in pollution control and other related environmental issues. (3) Efficient utilization of natural resources can reduce carbon emissions By taking carbon emission as the explained variable, the comprehensive index of natural resources efficiency utilization as the main explanatory variable, and GDP, industrial waste water discharge, industrial waste gas emission, environmental consciousness, industrial structure, per capita power consumption, per capita coal consumption, and per capita energy consumption as control variables for regression analysis, we can know that the coefficients of the comprehensive index of natural resources efficiency utilization are between 0.0137 and 0.0138, indicating that for each 1% increase in the comprehensive index of natural resources efficiency utilization, carbon emissions can be reduced by about 137–138 t. (4) Environmental effect of natural resources efficiency utilization has nothing to do with geographic location The heterogeneity test results of the environmental effect of natural resources efficiency utilization show that there is no significant correlation between natural resources efficiency utilization and carbon emissions; accordingly, it can be inferred that there is no significant difference in the carbon emission impact of natural resources efficiency utilization irrespective of whether an area is in the eastern, central, or western, or coastal/non-coastal region.

6.4.2 Policy Recommendations Through the preceding conclusions, this chapter believes that natural resources efficiency utilization improvement can significantly promote the reduction of carbon emissions; that is, it can improve the environment to a certain extent. Therefore, society, groups, units, and individuals need to work together to promote the efficient utilization of natural resources. From the perspective of environmental protection, it can also be seen from the principal components of environment-related factors that the main influencing factors are environmental consciousness, industrial structure, and industrial discharge. Therefore, people’s environmental awareness should be increased, China’s education level should be improved, and the industrial structure should be optimized. Based on reasonable optimization of the industrial structure, standards for the discharge of industrial waste water, waste gas, and solid wastes should be stipulated. Meanwhile, the industrial chains can be constructed according to the geographical advantages of each area to realize rational allocation of resources and promote the process of environmental protection simultaneously. Indeed, there are still many areas with great development potentials where natural resources efficiency utilization is weak. The high-level efficient utilization of natural resources in economically developed areas may be related to relevant factors such

References

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as scientific research and innovation investment. Therefore, all areas should increase scientific and technological investment, improve their innovation level and promote the upgrading of various technologies. For example, they can establish certain cooperative relations with colleges and universities to obtain certain professional talent to promote the improvement of natural resources efficiency utilization.

References 1. Howells, M., Hermann, S., Welsch, M., Bazilian, M., Segerström, R., Alfstad, T., Gielen, D., Rogner, H., Fischer, H., Van Velthuizen, H., Wiberg, D., Young, C., Roehrl, R.A., Mueller, A., Steduto, P., Ramma, I.: Integrated analysis of climate change, land-use, energy and water strategies. Nat. Clim. Change 3(7), 621–626 (2013) 2. Di, W., Wang, X.: Analysis of fairness and efficiency of environmental policy. China Popul. Resour. Environ. 3 (1997) 3. Song, M.L., An, Q., Zhang, W., Wang, Z., Wu, J.: Environmental efficiency evaluation based on data envelopment analysis: a review. Renew. Sustain. Energy Rev. 16(7), 4465–4469 (2012) 4. Bi, G.B., Song, W., Zhou, P., Liang, L.: Does environmental regulation affect energy efficiency in China’s thermal power generation? Empirical evidence from a slacks-based DEA model. Energy Policy 66, 537–546 (2014) 5. Wu, J., Li, M., Zhu, Q., Zhou, Z., Liang, L.: Energy and environmental efficiency measurement of China’s industrial sectors: a DEA model with non-homogeneous inputs and outputs. Energy Econ. 78, 468–480 (2019) 6. Iram, R., Zhang, J., Erdogan, S., Abbas, Q., Mohsin, M.: Economics of energy and environmental efficiency: evidence from OECD countries. Environ. Sci. Pollut. Res. 27(4), 3858–3870 (2020) 7. Luo, N., Wang, Y.: Fiscal decentralization, environmental regulation and regional ecoefficiency: based on the dynamic spatial Durbin model. China Popul. Resour. Environ. 27(4), 110–118 (2017) 8. Tone, K.: Dealing with undesirable outputs in DEA: a slacks-based measure (SBM) approach. GRIPS Res. Rep. Ser. 5, 44–45 (2003) 9. Song, M.L., Peng, J., Wang, J., Dong, L.: Better resource management: an improved resource and environmental efficiency evaluation approach that considers undesirable outputs. Resour. Conserv. Recycl. 128, 197–205 (2018) 10. Wang, K., Yu, S.W., Zhang, W.: China’s regional energy and environmental efficiency: a DEA window analysis based dynamic evaluation. Math. Comput. Model. 58(5–6), 1117–1127 (2013) 11. Chang, Y.T., Zhang, N., Danao, D., Zhang, N.: Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58, 277–283 (2013) 12. Lv, Y., Lv, Y.: The environmental effect of Chinas participation in global value chain. China Popul. Resour. Environ. 29(7), 91–100 (2019) 13. Zhao, Y., Liu, Y., Long, K.: Ecological and environmental effects of urban land development intensity change. China Popul. Resour. Environ. 7 (2014) 14. Zhang, S.Y.: The environmental effects of international trade in China: measuring the mediating effects of technology spillovers of import trade on industrial air pollution. Sustainability 13(12), 6895 (2021) 15. Zhang, J., Qu, Y., Zhang, Y., Li, X., Miao, X.: Effects of FDI on the efficiency of government expenditure on environmental protection under fiscal decentralization: a spatial econometric analysis for China. Int. J. Environ. Res. Public Health 16(14), 2496 (2019) 16. Sun, X.X., Chen, Z.W., Huang, L.X.: Effects of factor price distortion on industrial environmental efficiency: evidence from environmental pollution in China. Pol. J. Environ. Stud. 29(5), 3803–3812 (2020)

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17. Wei, W., Qian, D., Wei, X., Feng, K.: Progress on the environmental effects of land use and land cover change. J. Fujian Agric. Forestry Univ. 46(4), 361–372 (2017) 18. Yang, Y.Y., Bao, W., Li, Y., Wang, Y., Chen, Z.: Land use transition and its eco-environmental effects in the Beijing-Tianjin-Hebei urban agglomeration: a production-living-ecological perspective. Land 9(9), 285 (2020) 19. Hu, F., An, Y.L., Zhao, H.B.: Study on the eco-environmental effects of “sub Karst” region from the perspective of land use transformation—taking some areas in Central Guizhou as an example. Earth Environ. 44(4) (2016)

Chapter 7

Analysis of Temporal and Spatial Evolution of Natural Resources Utilization

Resources are widely known to be scarce. Scarcity in economics means that the supply of resources is always limited relative to people’s demand in a certain period of time. On the one hand, people strive to have as many resources as possible; on the other hand, the amount of resources is limited. Therefore, there is a contradiction between the limitation of resources and the infinity of people’s desire. It is precisely because of the objective existence of scarcity that economists are forced to study how to use limited resources to produce more products from the perspective of economics, that is, how to make efficient utilization of resources. In the past few decades, to pursue rapid economic development and GDP growth, China has adopted an extensive economic development model. While the country has achieved rapid economic development, this development has come at a steep cost— high consumption of natural resources, rapid reduction of non-renewable resources, weakening trend of renewable resources, great destruction of ecological balance, and frequent occurrence of natural disasters—seriously restricting the development of the social economy and the improvement of people’s living standards. Although China’s economic development mode has improved in recent years, and the quality and effectiveness of economic development are also improving, the utilization efficiency of natural resources in China is still lower than that of developed countries. Moreover, with the deepening of reform and opening up, China’s demand for resources is also increasing. The consumption of main resources increased from 1.34 billion t in the early stage of reform and opening up to 15.12 billion t in 2019. Thus, the constraint of a resource bottleneck on economic development is becoming increasingly prominent. Therefore, based on the premise of ensuring stable economic growth, how to make efficient utilization of resources has become a problem that must be faced in the process of China’s economic development.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_7

207

208

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

7.1 Research Progress on Natural Resources Utilization Efficiency Many scholars have discussed the relationship between natural resources utilization efficiency and economic development, social progress, and the ecological environment. Chen et al. [1] constructed a comprehensive evaluation index system of China’s urbanization level from four aspects—population, economy, society, and land—and undertook a comprehensive evaluation of China’s urbanization process from 1981 to 2006 using the entropy method, pointing out that the continuous improvement of the comprehensive urbanization level in China was characterized by rapid evolution of economic growth and the geographical landscape, followed by population urbanization and social urbanization. The comprehensive evolution of urbanization generated a profound impact on resources, energy, and the environment, putting severe pressure on the security of land resources and energy. Based on the research foundation of sustainable development of resources and the marine economy, Lin [2] constructed the evaluation index system of resources, environment, and the marine economy and concluded that the relationship between the marine economy and ecology had evolved from severe or moderate imbalance to barely balanced. Yu et al. [3], using provincial panel data, verified that there was a significant resource curse in the environmental field, and effective environmental regulations could significantly improve and eliminate this phenomenon that resulted from the resource-driven economic development mode. By taking Wuhan as the research object and establishing the natural resources and socioeconomic index system, Xing et al. [4] analyzed the coupling degree and coupling coordination degree and found that the coordination effect of the economic scenario was the worst while that of the environmental scenario was the best in the short term, and that the resource scenario was quite effective for the long-term coordinated development of the urban regional system. Li et al. [5] evaluated the population–economy–environment coupling coordination of nine central cities in the Yellow River Basin from 2010 to 2017 by establishing a population–economy– environment evaluation system and found that the coupling coordination degree of the Yellow River Basin showed an upward fluctuating trend, and the coupling coordination degrees were obviously different among different areas. By taking data from 2006 to 2016 as samples and Guangdong Province as the research object, Wang et al. [6] established an economy–resources–environment coupling model for analysis and found that the comprehensive development scores of the economy, resources, and the environment system increased with regional differences: most cities in Guangdong were still in the middle or low-level coupling stage, except Guangzhou and Shenzhen. In summary, there is little literature on the coupling of natural resources utilization efficiency with economic utility, social utility, and environmental utility in multiple regions and for an extended period. Thus, this chapter analyzes the coupling degrees among natural resources utilization efficiency, economic utility, social utility, and environmental utility using the entropy method, coupling coordination model, and the data of 30 areas in China from 2008 to 2017.

7.2 Research Method

209

7.2 Research Method 7.2.1 Entropy Method The concept of entropy originates from physics and is called information quantity in information theory, which is used to measure uncertainty. The greater the amount of information contained, the smaller the uncertainty and the smaller the entropy; by contrast, the smaller the amount of information contained, the greater the uncertainty and the greater the entropy. Entropy method is a weighting method that calculates the information entropy of the observed values of each index and then determines the index weight according to the impact of a relative change in each index on the whole system. The basic operation steps are as follows: (1) Dimensionless treatment of indexes There are many dimensionless processing methods for indexes, including the standardization method, extreme value method, linear proportion method, and vector norm method. In this chapter, the extreme value method is used, which can convert all the index values into values in the [0, 1] interval. The maximum value will be 1, and the minimum 0. As the index value may be 0 after dimensionless treatment, to make data processing meaningful, all dimensionless values can be shifted to a minimum unit value. The calculation formula of the extreme value method is as follows: (a) Positive indexes can be calculated according to the following formula: Xi j =

xi j − m j , Mj − m j

where mj is the minimum value of X ij while M ij is the maximum value of X ij . (b) Negative indexes can be calculated according to the following formula: Xi j =

M j − xi j . Mj − m j

It should be noted that the dimensionless values are between [0, 1]. If there are values greater than 1 or lesser than 0, the calculation process should be checked for correctness. i. To calculate the contribution degree or proportion of the ith data under the jth index and record it as Pij xi j Pi j = ∑n i=1

xi j

.

210

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

ii. To calculate the entropy value of the jth index and record it as ej ( ) 1 ∑ pi j ∗ ln pi j . ln(n) i=1 n

ej = −

iii. To calculate the difference coefficient of the jth index and record it as g j gj = 1 − ej. iv. To calculate the weight of the jth index and record it as w j gj w j = ∑m j=1

gj

,

j = 1, 2, . . . , m.

7.2.2 Coupling Coordination Model A coupling coordination model is used to analyze the coordinated development level of phenomena. Coupling degree refers to the interaction between two or more systems, which can reflect the degree of interdependence between the two systems. The calculation method of coupling degree and coupling coordination degree is as follows: Multiply the weight of each index obtained according to the entropy method above with the corresponding index value and then add up the result to obtain the comprehensive evaluation index. The specific calculation formula is as follows: u = pi j ∗

m ∑

wj,

j = · · · . . . m.

j=1

Taking two systems for an example, the calculation formula of the coupling degree is / C =2

u1u2 , (u 1 + u 2 )2

where u 1 and u 2 represent the comprehensive evaluation index of the two different sub systems. The coupling coordination degree is calculated as follows: H= where T = αu 1 + βu 2 , α + β = 1.

√ C ∗ T,

7.3 Current Situation of Natural Resources Utilization Efficiency

211

7.3 Current Situation of Natural Resources Utilization Efficiency 7.3.1 Analysis of the Current Situation of China’s Economic Development (1) Rapid GDP growth Since its reform and opening up, China’s economy has developed rapidly: The GDP of China was only CNY 367.87 billion in 1978, exceeded CNY 10 trillion for the first time in 2000, surpassed that of Japan and became the second largest economy in the world in 2010, exceeded CNY 50 trillion in 2012, and reached CNY 101.6 trillion in 2020. Compared with the CNY 367.87 billion in 1978, China’s GDP has increased 276.2 times as of 2020. (2) Imbalanced economic development In more than 40 years of reform and opening up, China’s economic development has been remarkable, with the focus shifting from the original extensive economic development mode with high resource consumption to high-quality development; however, there are still problems of imbalance and insufficiency and significant regional differences in China’s economic development. The development level of the eastern region is significantly higher than that of the central and western regions. Among the central and western regions, the development level of the former is also significantly higher than that of the latter. The reason is that the eastern region enjoys a good development environment with superior geographical location and convenient transportation, coupled with generous governmental policies, while the central and western regions are restricted by their unfavorable geographical locations and inconvenient traffic conditions. This has led to an imbalance between the development of the eastern coastal areas and that of the central and western regions. Although China has issued relevant policies to support the development of the central and western regions, overall, there are still great regional differences. According to the China Statistical Yearbook 2020, the area with the largest regional GDP in China in 2019 was Guangdong Province, with a GDP (calculated according to the current year price) of CNY 10,767.107 billion, while the area with the smallest GDP was Tibet Province, with only CNY 169.782 billion, followed by Qinghai Province whose GDP was CNY 296.595 billion. The GDP of Tibet in 2019 was only 1.57% that of Guangdong, and that of Qinghai in 2019 was only 2.75% that of Guangdong. From the perspective of GDP ranking, the top ten provincial areas are Guangdong, Jiangsu, Shandong, Zhejiang, Henan, Sichuan, Hubei, Fujian, Hunan, and Shanghai, among which six belong to the eastern region (Guangdong, Jiangsu, Shandong, Zhejiang, Fujian, and Shanghai).

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7.3.2 Current Status of Environment in China (1) China’s ecological environment is facing two major pressures: industry and population From 1978 to 1997, China’s natural population growth rate remained above 10%, with a peak of 16.61% in 1987; only in 1998 did China’s natural population growth rate fall below 10%. Such a high natural growth rate coupled with China’s large population base has led to the rapid growth of China’s population, greatly affecting China’s ecological environment. The large population exceeds the carrying capacity of the ecological environment, and unreasonable human activities, such as reclaiming land from lakes, deforestation, land reclamation, and arbitrary exploitation of mineral resources, have not only worsened the fragile ecosystem, but also destroyed the habitat of many animals, displacing and even endangering them. In the 1860s, Britain took the lead in the first Industrial Revolution, creating an era in which machines replaced manual production. After that, every Industrial Revolution brought rapid improvement of social productivity. However, the improvement of social productivity also strengthened the dependence on and consumption of natural resources. China’s industrialization started late, with a low starting point. Although China took only a few decades to travel the industrialization path that western countries took hundreds of years to reach, the costs of doing so included a low utilization rate of resources, high consumption, and damage to the ecological environment. (2) Environmental status in China China’s ecological environment is grim at present, with increasing ecological deficits, which manifest as soil erosion, land desertification, poor air quality, and garbage pollution, among others. (a) Soil erosion Soil erosion is one significant ecological problem in China. According to statistics, in 2019, China’s soil erosion area totaled 2.7108 million km2 , accounting for 28.3% of China’s land area. Soil erosion causes a large loss of nitrogen, phosphorus, potassium, and other elements in the land, weakening land fertility and reducing crop production. In this case, local residents have to reclaim wasteland or even destroy forests for their survival, which will exacerbate soil and water loss, and further worsen the local ecological environment. (b) Land desertification Land desertification refers to the destruction of the ecosystem by human activities, resulting in the desertification of non-desert areas. According to the observed data of the National Forestry Administration, as of 2014, China’s land desertification area had reached 2,611,600 km2 , mainly distributed across Inner Mongolia, Xinjiang, Tibet, Qinghai, and Gansu.

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Among them, the land desertification in Xinjiang was most serious, with a land area of 1,070,600 km2 . Land desertification causes local houses, buildings, cultivated land, and crops to be eroded by wind and sand; engulfs and inundates nearby roads and railways; reduces visibility; generates poor air quality; produces natural disasters such as sandstorm and haze; seriously affects the daily life of local residents; and causes huge economic losses. (c) Air pollution In recent years, haze weather has been a frequent occurrence in China, and the air quality has become a matter of concern. On the one hand, China’s energy consumption is still dominated by coal, which releases a large amount of dust, sulfur dioxide, carbon monoxide, and other harmful gases in the combustion process; meanwhile, the number of private cars is increasingly year by year with the improvement of people’s living standards, which has also led to a large number of harmful gas emissions. On the other hand, grassland vegetation is being destroyed due to overgrazing, and the forest coverage is being reduced by deforestation. The damaged vegetation can no longer play their function of purifying the air, further aggravating air pollution. (d) Garbage pollution China produces a large amount of industrial and domestic waste every year, and the amount of domestic waste alone reached 242 million t in 2019. Garbage contains a large number of toxic substances and pathogenic microorganisms. The heavy metals and various toxic substances in some landfilled garbage penetrate into underground rivers, resulting in the pollution of water resources. Some refractory substances in the garbage buried in the soil tend to harden the soil and reduce the yield of crops. Moreover, waste incineration produces a lot of toxic gases and dust, pollutes the air, and increases the incidence rate of respiratory diseases. In recent years, China has taken many measures to strengthen the construction of an ecological civilization. For example, in 2012, the Chinese government proposed to attach importance to the construction of an ecological civilization and to integrate it into the general layout of the construction of new China. Moreover, at the 75th United Nations General Assembly in 2020, China proposed to achieve carbon neutrality by 2060. According to the ecological situation bulletin 2019, in recent years, the average annual concentration of PM2.5 in cities at and above the prefecture level in China has shown a downward trend. Since the 13th Five-Year Plan, it has decreased by 21.7%. The proportion of days with good and excellent air reached 82%, and the number of days with heavy pollution decreased from the original average of 10 days to the current average of 6 days. By 2019, China’s ecological environment had significantly improved.

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7.3.3 Analysis of the Current Situation of Chinese Society (1) People’s living standards have improved The changes in the disposable income of Chinese residents from 2000 to 2019 are shown in Fig. 7.1. The per capita disposable income of residents increased rapidly from CNY 3721 in 2000 to CNY 30,733 in 2019. People’s travel became more convenient. By 2019, the number of private cars was 225 million, an increase of about 36 times compared with the 6.253 million in 2000; the highway mileage was 5,012,500 km in 2019, an increase of 2.98 times compared with the 1,679,800 km in 2000. People’s lifestyle has changed dramatically. The emergence of WeChat, Alipay, and online shopping has made people’s daily life more convenient, and people are more inclined to electronic payment rather than the previous cash transactions. (2) People’s livelihood is guaranteed With economic development, China has made great progress in medical, health care, and social services. By 2019, China had 1,007,579 medical and health institutions, 12,928,335 health personnel, and 880,700 medical beds. The number of people guaranteed the minimum standard of living in urban areas decreased from the 22.721 million in 2007 to the 8.609 million in 2019. The urban registered unemployment rate decreased from 4.05% in 2015 to 3.62% in 2019.

Fig. 7.1 Changes in disposable income of Chinese residents from 2000 to 2019

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7.3.4 Resource Utilization Efficiency Analysis (1) Current situation of natural resources in China China has vast and rich natural resources. However, the per capita share of resources in China is far lower than the world average. China has a land area of 9.6 million km2 , subject to various climate types, such as temperate continental climate, temperate monsoon climate, plateau mountain climate, subtropical monsoon climate, and tropical monsoon climate. There are also diversified terrains, such as mountains, hills, plains, plateaus, and basins. The continental coastline is more than 18,400 km long, and the water area of inland and coastal seas is as high as 4.7 million km2 . China’s cultivated land area accounts for 7% of the world, second only to the United States, Russia, and India. However, in recent years, China’s per capita cultivated land area has continued to decrease. Coupled with soil erosion, desertification, and pollution of cultivated land, the average cultivated land area ranks 126th in the world. China is rich in water resources as well. In 2019, China’s total water resources were 2,904.1 billion m3 , but its per capita share ranks only 120th in the world. According to the eighth inventory of China’s forest resources, China has a forest area of 208 million ha and a forest reserve area of 15.137 billion m3 , ranking fifth and sixth in the world, respectively. However, the per capita forest area and per capita forest reserve area are only one-fourth and one-seventh of the world average, respectively. Therefore, from the perspective of sustainable development, improving the utilization efficiency of natural resources has become an urgent need for China. (2) Evaluation system of natural resources utilization efficiency According to the evaluation system of natural resources efficiency utilization established in Chap. 2, this section divides the evaluation system into five subsystems: land resource subsystem, water resource subsystem, energy subsystem, forest resource subsystem, and tourism resource subsystem, comprising 18 indexes in all. The land resource subsystem includes five indexes: cultivated land area, fertilizer application, total agricultural output value, crop sown area, and grain yield per unit area; the water resources subsystem includes four indexes: per capita domestic water consumption, total water resources, COD discharge, and wastewater treatment investment; the energy subsystem includes four indexes: coal consumption, investment in energy industry, total power of agricultural machinery, and total supply of natural gas; the forest resource subsystem includes three indexes: forest coverage rate, area of forest diseases, pests and rodents, and total output value of forestry; and the tourism resource subsystem includes two indexes: the number of inbound tourists and the international tourism income. Details of each index of the natural resources efficiency utilization are presented in Table 7.1. (3) Resource utilization efficiency evaluation According to the aforementioned resource utilization efficiency evaluation system and the data of 30 areas in China from 2008 to 2017, the comprehensive scores of the natural resources utilization of each area are calculated, as given in Table 7.2. The

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Table 7.1 Natural resources utilization efficiency evaluation system Evaluation system

Secondary index

Tertiary index

Unit

Index type

Natural resources

Land resource subsystem

Cultivated land area

1000 ha

Positive

Fertilizer application

10,000 t

Negative

Total agricultural output value

CNY 100 million

Positive

Crop sown area

1000 ha

Positive

Grain yield per unit area

kg/ha

Positive

Per capita daily domestic water consumption

m3 /person

Negative

Wastewater treatment investment

CNY 10,000

Positive

Total water resources

100 million m3

Positive

COD discharge

10,000 t

Negative

Coal consumption

10,000 t

Negative

Energy industry investment

CNY 100 million

Negative

Total power of agricultural machinery

10,000 kW/h

Positive

Natural gas supply

100 million m3

Negative

Forest coverage

%

Positive

Occurrence area of forest diseases, pests, and rodents

10,000 ha

Negative

Total forestry output value

CNY 100 million

Positive

Water resource subsystem

Energy system

Forest resource subsystem

Tourism resource subsystem

Number of inbound tourists received 10,000 people Positive International tourism income

USD 1 million

Positive

table shows that the natural resources utilization efficiencies in the middle reaches of the Yangtze River and southwest China were higher than those in other areas; the utilization efficiency in northwest China was the lowest, resulting from the damaged ecosystem and the serious land drought and desertification there; judging from the changes in the past decade, the resources utilization efficiencies of Beijing, Tianjin, and Shandong were in a downward trend, which must be adjusted in time to avoid a vicious cycle.

7.3 Current Situation of Natural Resources Utilization Efficiency

217

Table 7.2 Comprehensive scores of natural resources utilization efficiency Year

2008

2009

2010

2011

2012

Beijing

0.2488

0.2494

0.2424

0.2478

0.2324

Tianjin

0.1719

0.1812

0.1868

0.1784

0.1705

Hebei

0.3351

0.3246

0.3257

0.3353

0.3396

Shanxi

0.1999

0.2184

0.2232

0.2090

0.2091

Inner Mongolia

0.2686

0.2522

0.2665

0.2538

0.2682

Liaoning

0.2859

0.2804

0.3122

0.3156

0.3180

Jilin

0.2695

0.2754

0.2956

0.2987

0.2982

Heilongjiang

0.3587

0.3743

0.3755

0.3652

0.3773

Shanghai

0.2435

0.2534

0.2548

0.2388

0.2159

Jiangsu

0.3831

0.3923

0.3665

0.3909

0.3594

Zhejiang

0.3746

0.3765

0.4051

0.3827

0.3986

Anhui

0.3164

0.3224

0.3414

0.3469

0.3499

Fujian

0.3461

0.3458

0.3973

0.3669

0.4050

Jiangxi

0.3335

0.3270

0.3791

0.3492

0.3772

Shandong

0.4714

0.4875

0.4703

0.4905

0.4823

Henan

0.4096

0.4131

0.4184

0.4034

0.3994

Hubei

0.2944

0.2986

0.3203

0.3116

0.3138

Hunan

0.3654

0.3750

0.4134

0.3957

0.4101

Guangdong

0.6098

0.5950

0.6787

0.5861

0.5821

Guangxi

0.3566

0.3242

0.3557

0.3631

0.3827

Hainan

0.2085

0.2006

0.2283

0.2395

0.2256

Chongqing

0.2248

0.2356

0.2404

0.2448

0.2387

Sichuan

0.3640

0.3647

0.3636

0.3739

0.3949

Guizhou

0.2438

0.2258

0.2470

0.2311

0.2526

Yunnan

0.3955

0.3609

0.3888

0.3916

0.4194

Shaanxi

0.2374

0.2484

0.2692

0.2961

0.2840

Gansu

0.1889

0.1959

0.2026

0.2029

0.2075

Qinghai

0.1364

0.1433

0.1459

0.1432

0.1458

Ningxia

0.1381

0.1403

0.1464

0.1504

0.1499

Xinjiang

0.2231

0.2364

0.2422

0.2487

0.2443

2013

2014

2015

2016

2017

Mean value

0.2286

0.2241

0.2064

0.1970

0.1850

0.2262

0.1742

0.1800

0.1757

0.1694

0.1541

0.1742

0.3404

0.3291

0.2784

0.2797

0.3110

0.3199

0.2119

0.2005

0.1934

0.1761

0.1856

0.2027

0.2916

0.2574

0.2588

0.2567

0.3206

0.2694

0.2999

0.2751

0.2745

0.2670

0.2625

0.2891 (continued)

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Table 7.2 (continued) 2013

2014

2015

2016

2017

Mean value

0.3051

0.2842

0.2792

0.2756

0.2676

0.2849

0.4172

0.3910

0.3801

0.3775

0.4263

0.3843

0.2256

0.2497

0.2220

0.2569

0.3068

0.2467

0.3281

0.3162

0.3402

0.3754

0.3807

0.3633

0.3988

0.4031

0.4908

0.3586

0.3572

0.3946

0.3463

0.3561

0.3510

0.3877

0.3715

0.3490

0.4231

0.4080

0.4327

0.4296

0.3666

0.3921

0.3501

0.3428

0.3746

0.4501

0.3620

0.3646

0.4150

0.3849

0.3582

0.3960

0.4788

0.4435

0.3971

0.3946

0.3543

0.3464

0.3777

0.3914

0.3188

0.3248

0.3284

0.3498

0.3772

0.3238

0.4069

0.3926

0.3995

0.4051

0.4178

0.3981

0.6158

0.6081

0.6764

0.6187

0.6293

0.6200

0.4060

0.3813

0.3981

0.3708

0.4094

0.3748

0.2210

0.2024

0.1872

0.1905

0.1889

0.2092

0.2352

0.2382

0.2738

0.2245

0.2345

0.2390

0.3813

0.3906

0.3922

0.3752

0.4464

0.3847

0.2538

0.2701

0.2709

0.2632

0.2894

0.2548

0.4029

0.3987

0.4179

0.4036

0.4383

0.4018

0.2596

0.2495

0.2434

0.2334

0.2684

0.2590

0.2101

0.2019

0.1862

0.1985

0.1897

0.1984

0.1490

0.1565

0.1448

0.1327

0.1351

0.1433

0.1660

0.1619

0.1418

0.1443

0.1352

0.1474

0.2539

0.2378

0.2179

0.2231

0.2671

0.2394

7.4 Coupling Analysis of Natural Resources Utilization Efficiency and Economic Utility 7.4.1 Analysis of the Relationship Between Natural Resources Utilization Efficiency and Economic Utility Natural resources utilization efficiency and economic development are closely related. Natural resources development and utilization are the material basis for the existence of human society. Rapid economic development requires the continuous supply of natural resources, and the possession of natural resources also restricts economic development. Sufficient natural resources can help the economy develop rapidly, while the shortage of natural resources will hinder economic development.

7.4 Coupling Analysis of Natural Resources Utilization Efficiency …

219

Generally, economic growth is based on an increase in natural resource consumption. According to the GDP data of all countries in the world, in 2008, the top ten countries were the United States, Japan, China, Germany, the United Kingdom, France, Italy, Brazil, Russia, and Spain. By analyzing the data of energy consumption and mineral resource consumption in these countries, it is found that these countries also ranked high in energy and mineral resources consumption. Thus, it can be concluded that economic development requires a large amount of resources. Having sufficient natural resources is essential for being an economic power. However, the store of natural resources in any country or region is limited, and natural resources cannot be exploited or utilized without restraint. Therefore, one of the problems that must be considered in the process of human economic development is the sustainable and efficient utilization of resources. China is rich in natural resources but has low per capita resources. The distribution of natural resources is uneven, and the quality of natural resources is relatively poor. Moreover, to accelerate economic development, a large number of resources have been exploited and wasted, resulting in the shortage of natural resources, which seriously restricts the development of China’s economy. The energy consumption per CNY 10,000 GDP in China is given in Table 7.3. In Table 7.3, the unit of coal consumption, oil consumption, and crude oil consumption is t/CNY 10,000, while that of power consumption is 10,000 kWh/ CNY 10,000. The GDP from 2007 to 2010 is calculated as per the comparable price of 2005, the GDP from 2011 to 2015 is calculated as per the comparable price of 2011, and the GDP from 2012 to 2018 is calculated as per the comparable price of 2012. Table 7.3 Energy consumption per CNY 10,000 GDP Year

Coal consumption

Oil consumption

Crude oil consumption

Electricity consumption

2005

1.30

0.17

0.16

0.13

2006

1.28

0.17

0.15

0.02

2007

1.20

0.15

0.14

0.14

2008

1.14

0.14

0.13

0.13

2009

1.12

0.13

0.13

0.13

2010

1.09

0.14

0.13

0.13

2011

0.86

0.10

0.10

0.10

2012

0.85

0.10

0.10

0.10

2013

0.81

0.10

0.09

0.10

2014

0.73

0.09

0.09

0.10

2015

0.66

0.09

0.09

0.10

2016

0.53

0.08

0.08

0.08

2017

0.50

0.08

0.08

0.08

2018

0.47

0.07

0.07

0.09

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The level of economic development also determines the utilization efficiency of natural resources. The exploitation and utilization of natural resources need to be supported by considerable capital investments and advanced mining technology. Restricted by limited capital and technology, many natural resources in China are developed under insufficient capital and backward technology. Worse, some small enterprises exploit natural resources without development capacity for their own interests, which not only causes a waste of natural resources, but also greatly increases the accident rate in the mining process, resulting in unnecessary casualties and economic losses. If the government aims to increase the per capita GDP of China to the level of a moderately developed country, it is bound to focus on economic construction, which will inevitably lead to a large consumption of resources. With today’s development mode of high resource consumption, high investment, and high environmental pollution, it is very difficult for China to achieve this goal successfully. Therefore, the economic growth in China must be transformed from the extensive mode to the intensive mode. Natural resources efficiency utilization and economic development are interdependent and interactive. Only the mutual promotion and harmony of resource utilization efficiency and economic development can form a virtuous circle to realize sustainable utilization of natural resources and sustainable economic development.

7.4.2 Economic Utility Evaluation Index System By referring to, modifying, and supplementing the economic development evaluation index system established by Zhang et al. [7] and He et al. [8], an economic utility index system is established, comprising 12 indexes in three dimensions: economic level, economic structure, and economic vitality. The economic level subsystem includes three indexes: regional GDP, per capita regional GDP, and total import and export volume; the economic structure subsystem includes four indexes: the proportion of primary industry in GDP, the proportion of tertiary industry in GDP, the proportion of total industrial output value in GDP, and the urbanization rate; the economic vitality subsystem includes five indexes: science and technology investment, proportion of education investment, social fixed assets investment, unemployment rate, and comprehensive utilization of industrial solid waste. Details of each economic utility index are presented in Table 7.4.

7.4 Coupling Analysis of Natural Resources Utilization Efficiency …

221

Table 7.4 Economic utility evaluation index system Evaluation system

Secondary index

Tertiary index

Unit

Index type

Economic utility

Economic level

Regional GDP

CNY 100 million

Positive

Per capita regional GDP

CNY 1

Positive

Economic structure

Economic vitality

Total import and USD 100 million export volume

Positive

Proportion of % primary industry in GDP

Negative

Proportion of tertiary industry in GDP

%

Positive

Proportion of total industrial output value in GDP

%

Positive

Urbanization rate

%

Positive

Science and technology investment

CNY 100 million

Positive

Proportion of education investment

%

Positive

Social fixed assets investment

CNY 100 million

Positive

Unemployment rate

%

Negative

Comprehensive utilization of industrial solid waste

10,000 t

Positive

7.4.3 Coupling Degree of Natural Resources Utilization Efficiency and Economic Utility (1) Classification of coupling degree and coupling coordination degree Data from 2008 to 2017 (the data are from China’s statistical yearbooks and the statistical yearbooks of each area) are selected to undertake a coupling analysis of the natural resources utilization efficiency and economic utility of 30 areas in China (Tibet is not calculated for data reasons).

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According to Sect. 7.2 of this chapter, the calculation models for the coupling degree and coupling coordination degree are as follows: √ H = C ∗ T;

/ L=2

u1u2 , (u 1 + u 2 )2

where H represents the coupling degree between natural resources utilization efficiency and economic utility; T represents the comprehensive integration index of natural resources utilization efficiency and economic utility; L represents the coupling coordination degree of natural resources utilization efficiency and economic utility; u 1 is the exponent of the natural resources utilization efficiency subsystem; and u 2 represents the exponent of the economic utility subsystem. The larger the L, the better the coordination between natural resources utilization efficiency and economic utility; the greater the H, the better the coupling between the two. Specific classification standards of coupling degree and coupling coordination degree are given in Tables 7.5 and 7.6. (2) Analysis of the coupling degrees of natural resources utilization efficiency and economic utility in different areas According to the coupling coordination model and the data of each area from 2008 to 2017, the coupling degrees between natural resources utilization efficiency and Table 7.5 Division standard of horizontal coupling degree between natural resources utilization efficiency and economic utility level Value of the coupling degree

Coupling level

Coupling characteristics

H =0

Extremely low coupling

The coupling level is very low; the system or internal elements are independent, and the system will develop into imbalance

0.0 < H ≤ 0.3

Low-level coupling

The utilization efficiency of natural resources is low, while the quality of economic development is high

0.3 < H ≤ 0.5

Antagonism

The utilization efficiency of natural resources has entered a period of rapid improvement, and there is a disorderly demand for the quality of economic development

0.5 < H ≤ 0.8

Running-in

A benign system starts to couple; neither of them is optimal

0.8 < H ≤ 1.0

High-level coupling

The utilization efficiency of natural resources and economic development promote each other and develop together

H =1

Superior coupling

The degree of coupling is the highest; the systems achieve benign resonant coupling in harmony; and there tends to be a new orderly structure between natural resources utilization efficiency and economic development

7.4 Coupling Analysis of Natural Resources Utilization Efficiency …

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Table 7.6 Division standard of coupling coordination degree between natural resources utilization efficiency and economic utility level Value of coupling coordination degree

Coordination level

Value of coupling coordination degree

Coordination level

0 < L ≤ 0.1

Severe imbalance

0.5 < L ≤ 0.6

Bare imbalance

0.1 < L ≤ 0.2

Heavy imbalance

0.6 < L ≤ 0.7

Primary coordination

0.2 < L ≤ 0.3

Moderate imbalance

0.7 < L ≤ 0.8

Moderate coordination

0.3 < L ≤ 0.4

Slight imbalance

0.8 < L ≤ 0.9

Good coordination

the economic utility of 30 areas in China are calculated, as given in Table 7.7. The average coupling degrees of 30 areas in China from 2008 to 2017 were between 0.91 and 0.99, exhibiting high-level coupling. Judging from the ten-year change trend, the coupling degrees of Shanghai, Henan, Hubei, Shaanxi, Ningxia, Hainan, and Chongqing were generally on the rise. Shanghai’s coupling degree rose from 0.9271 in 2008 to 0.9738 in 2017, reaching its highest value; that of Henan increased from about 0.98 in 2008 to 0.99 during 2014 and 2017; that of Hubei increased from 0.98 during 2008 and 2012 to 0.99 during 2013 and 2017, reaching a minimum of 0.9716 in 2010 and a maximum of 0.9993 in 2014; that of Shaanxi increased from about 0.98 before 2012 to about 0.99 after 2012, reaching a minimum of 0.9806 in 2010 and a maximum of 0.9997 in 2014; that of Ningxia increased from about 0.98 before 2014 to 0.9938 in 2017, reaching the maximum value. The coupling degree of Hainan gradually increased since 2008 and reached the maximum in nearly a decade later in 2016 after a short decline in 2013 and 2014. The coupling degree of Chongqing was generally on the rise in 2014, and basically remained at about 0.99 after 2014. The coupling degrees of Beijing and Heilongjiang were generally in a downward trend. Beijing’s coupling degree decreased from 0.9459 in 2008 to 0.9050 in 2012, fluctuated around 0.90 from 2013 to 2016, and reached the minimum value of 0.8824 in 2017. The coupling degree of Heilongjiang decreased from 0.9490 in 2008 to 0.8606 in 2017, and that in 2013 decreased by 0.0322 as compared with 2012. The coupling degrees of Tianjin, Hebei, Jilin, Jiangsu, Gansu, and Qinghai were generally in a trend of first decreasing and then increasing. The coupling degree of Tianjin gradually decreased at the beginning, reached the lowest point of 0.9262 in 2014, and increased to 0.9482 after 2014. That of Hebei dropped from 0.9996 to 0.9964, and then began to rise. That of Jilin decreased from the 0.9762 in 2008 to 0.9641 in 2011 and gradually recovered to 0.9816 after 2011. In the next three years, the coupling degree of Jilin was maintained at about 0.96. The coupling degree of Jiangsu was in a downward trend from 2008 to 2014, reached the bottom in 2014, and then rose to 0.9704 in 2017. Gansu was generally in a downward trend before 2011, began to increase gradually after reaching the bottom in 2011, and reached the

224

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.7 Coupling degrees of natural resources utilization efficiency and economic utility in different areas 2008

Year

2009

2010

2011

2012

Beijing

0.9459

0.9345

0.9119

0.9168

0.9050

Tianjin

0.9580

0.9685

0.9645

0.9485

0.9389

Hebei

0.9996

0.9993

0.9992

0.9991

0.9971

Shanxi

0.9954

0.9996

1.0000

0.9966

0.9944

Inner Mongolia

0.9916

0.9958

0.9978

1.0000

0.9978

Liaoning

0.9970

0.9989

0.9998

0.9990

0.9980

Jilin

0.9762

0.9711

0.9666

0.9641

0.9701

Heilongjiang

0.9490

0.9190

0.9270

0.9362

0.9373

Shanghai

0.9271

0.9322

0.9357

0.9187

0.8990

Jiangsu

0.9711

0.9801

0.9645

0.9663

0.9563

Zhejiang

0.9918

0.9964

0.9977

0.9935

0.9964

Anhui

0.9777

0.9704

0.9771

0.9831

0.9829

Fujian

0.9930

0.9922

0.9843

0.9915

0.9884

Jiangxi

0.9348

0.9448

0.9317

0.9672

0.9493

Shandong

0.9994

0.9998

0.9999

0.9996

0.9999

Henan

0.9826

0.9759

0.9749

0.9868

0.9880

Hubei

0.9827

0.9725

0.9716

0.9853

0.9895

Hunan

0.9500

0.9397

0.9260

0.9423

0.9405

Guangdong

0.9933

0.9938

0.9980

0.9932

0.9930

Guangxi

0.9281

0.9396

0.9300

0.9331

0.9277

Hainan

0.9039

0.9323

0.9516

0.9723

0.9710

Chongqing

0.9842

0.9779

0.9800

0.9839

0.9949

Sichuan

0.9368

0.9374

0.9506

0.9536

0.9559

Guizhou

0.9504

0.9562

0.9484

0.9640

0.9579

Yunnan

0.8693

0.8672

0.8637

0.8983

0.8733

Shaanxi

0.9875

0.9828

0.9806

0.9807

0.9888

Gansu

0.9770

0.9629

0.9594

0.9715

0.9751

Qinghai

0.9686

0.9655

0.9587

0.9928

0.9953

Ningxia

0.9866

0.9774

0.9812

0.9864

0.9774

Xinjiang

0.9774

0.9565

0.9694

0.9731

0.9667

2013

2014

2015

2016

2017

Mean value

0.9080

0.9077

0.8996

0.9016

0.8824

0.9113

0.9369

0.9262

0.9444

0.9482

0.9350

0.9469

0.9964

1.0000

1.0000

1.0000

0.9985

0.9989

0.9940

0.9899

0.9926

0.9919

0.9992

0.9954

0.9955

0.9994

0.9973

0.9957

0.9771

0.9948 (continued)

7.4 Coupling Analysis of Natural Resources Utilization Efficiency …

225

Table 7.7 (continued) 2013

2014

2015

2016

2017

Mean value

0.9960

0.9916

0.9997

0.9962

0.9987

0.9975

0.9655

0.9816

0.9600

0.9620

0.9661

0.9683

0.9051

0.9116

0.9065

0.8855

0.8606

0.9138

0.9218

0.9388

0.9270

0.9588

0.9738

0.9333

0.9465

0.9248

0.9578

0.9722

0.9704

0.9610

0.9975

0.9935

0.9990

0.9939

0.9932

0.9953

0.9838

0.9933

0.9827

0.9833

0.9881

0.9822

0.9854

0.9950

0.9770

0.9748

0.9900

0.9871

0.9573

0.9779

0.9409

0.9195

0.9502

0.9473

0.9964

0.9845

0.9908

0.9957

1.0000

0.9966

0.9867

0.9966

0.9920

0.9958

0.9938

0.9873

0.9913

0.9993

0.9945

0.9954

0.9907

0.9873

0.9446

0.9633

0.9452

0.9378

0.9471

0.9437

0.9956

0.9944

0.9995

0.9965

0.9969

0.9954

0.9159

0.9537

0.9110

0.9387

0.9128

0.9291

0.9640

0.9603

0.9835

0.9859

0.9790

0.9604

0.9970

0.9986

0.9894

0.9979

0.9979

0.9902

0.9592

0.9709

0.9521

0.9527

0.9331

0.9502

0.9553

0.9525

0.9458

0.9634

0.9337

0.9528

0.8889

0.8833

0.8643

0.8865

0.8633

0.8758

0.9928

0.9997

0.9952

0.9991

0.9882

0.9895

0.9699

0.9746

0.9855

0.9894

0.9681

0.9733

0.9888

0.9886

0.9919

0.9987

0.9991

0.9848

0.9812

0.9810

0.9906

0.9840

0.9938

0.9840

0.9649

0.9854

0.9800

0.9832

0.9516

0.9708

peak in 2016. The coupling degrees of Qinghai were all above 0.98 except in the year 2009 and the year 2012, generally in a trend of first decreasing and then increasing. The coupling degrees of Shanxi, Inner Mongolia, Liaoning, Zhejiang, and Sichuan generally increased first and then decreased. The coupling degree of Shanxi gradually increased since 2008, reached the peak in 2011, and then began to decline steadily. However, in 2017, the coupling degree of Shanxi suddenly increased to 0.9992. The coupling degree of Inner Mongolia was on the rise before 2011, stabilized at about 0.99 after 2011, and decreased to 0.9771 in 2017. The coupling degree of Liaoning began to decline gradually after reaching its peak in 2011 and reached the minimum value of 0.9916 in 2014. The coupling degree of Zhejiang gradually increased since

226

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

2008 and reached its peak in 2011. After 2011, the coupling degree was in a downward trend on the whole and fell to a level similar to that in 2008. The coupling degree of Sichuan was in the stage of gradual increase before 2014, reaching the maximum value in 2014, and then began to decline steadily, reaching the minimum value in 2017. Anhui, Fujian, Jiangxi, Shandong, Yunnan, Hunan, Guangdong, Guangxi, Guizhou, and Xinjiang did not show an obvious change trend. Among them, the coupling degrees of Shandong and Anhui were stable at about 0.99 and 0.98, respectively. (3) Analysis of coupling degree between natural resources utilization efficiency and economic utility in each region This section further divides the 30 areas into eight economic regions: the northeastern region, the northern coastal region, the eastern coastal region, the southern coastal region, the middle reaches of the Yellow River, the middle reaches of the Yangtze River, the southwestern region, and the northwestern region. The coupling degrees of natural resources utilization efficiency and economic utility of the eight economic regions are calculated according to the established natural resources utilization efficiency index system, economic utility index system, and the index data of each area from 2008 to 2017, as given in Table 7.8. It can be seen from the data changes in the table in the past 10 years that the coupling degrees of the northeastern and the northern coastal regions showed an obvious downward trend while those of the middle reaches of the Yangtze River and the southwestern region showed an obvious upward trend; those of the eastern coastal region, southern coastal region, middle reaches of the Yellow River, and northwestern region fluctuated around their average values, in which the coupling degree of the eastern coastal economic zone showed a wave-like change trend of increasing and decreasing alternately.

7.4.4 Analysis of Coupling Coordination Degree of Natural Resources Utilization Efficiency and Economic Utility (1) Analysis of the coupling coordination degree of natural resources utilization efficiency and economic utility by area Table 7.9 shows the coupling coordination degrees of natural resources utilization efficiency and economic utility from 2008 to 2017, from which it can be known that the coupling coordination degrees of the 30 areas in China were mainly between 0.3 and 0.99. The average coupling coordination degrees of Hainan, Gansu, Qinghai, and Ningxia were between 0.3 and 0.4, in a state of slight imbalance; those of Tianjin, Shanxi, Inner Mongolia, Jilin, Chongqing, Guizhou, Yunnan, Shaanxi, and Xinjiang were between 0.4 and 0.5, on the verge of imbalance; those of Inner Mongolia and Tianjin were more than 0.49, in a transition stage from on the verge of imbalance to bare imbalance; those of Sichuan, Guangxi, Henan, Hunan, Hubei, Anhui, Fujian,

7.4 Coupling Analysis of Natural Resources Utilization Efficiency …

227

Table 7.8 Coupling degree of regional natural resources utilization efficiency and economic utility in each region 2008

Year

2009

2010

2011

2012

Northeastern region

0.9879

0.9659

0.9539

0.9555

0.9521

Northern coastal region

0.9841

0.9834

0.9757

0.9770

0.9746

Eastern coastal region

0.9635

0.9671

0.9680

0.9659

0.9636

Southern coastal region

0.9996

0.9993

0.9991

0.9996

0.9997

Middle reaches of the Yellow River

1.0000

0.9891

0.9930

0.9977

0.9999

Middle reaches of the Yangtze River

0.9177

0.9032

0.8826

0.9273

0.9294

Southwestern region

0.8356

0.8533

0.8618

0.8728

0.8843

Northwestern region

0.9626

0.9018

0.8796

0.9169

0.8813

2013

2014

2015

2016

2017

Mean value

0.9329

0.9424

0.9221

0.8658

0.8909

0.9369

0.9641

0.9630

0.9533

0.9568

0.9542

0.9686

0.9618

0.9665

0.9792

0.9534

0.9556

0.9645

0.9994

0.9993

0.9975

1.0000

0.9991

0.9993

0.9999

0.9992

0.9977

0.9963

0.9999

0.9973

0.9455

0.9678

0.9783

0.9745

0.9910

0.9417

0.8982

0.9019

0.9097

0.9337

0.8998

0.8851

0.8701

0.8883

0.9087

0.9089

0.8950

0.9013

Jiangxi, Shanghai, Heilongjiang, Liaoning, Hebei, and Beijing were between 0.5 and 0.6, in a state of bare coordination; that of Beijing was 0.5903, close to 0.6, just about propelling it from bare imbalance to primary coordination; the average coupling coordination degree of Guangxi was 0.5042, close to 0.5, moving from the verge of imbalance to bare imbalance; those of Jiangsu, Zhejiang, and Shandong were between 0.6 and 0.7, in a state of primary coordination; that of Guangdong was 0.8240 in a state of good coordination, which was the highest value among all the 30 areas in China. Judging from the variation trend of the coupling coordination degree from 2008 to 2017, those of Beijing, Tianjin, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Hainan, Chongqing, Qinghai, and Ningxia showed an inverted U-shaped trend of increasing first and then decreasing, especially in Beijing, Shanxi, and Jilin; that of Beijing was on the rise before 2011, reached its peak value that year, and then gradually decreased to the lowest point in the past 10 years in 2017; the coupling coordination degree of Shanxi increased at first until reaching its peak value in 2013, and then gradually decreased to its original level in 2015, always on the verge of imbalance between 2008 and 2017; the situation in Jilin was similar to that in Shanxi, always in a state of bare imbalance during 2008 and 2017; the coupling coordination degrees of Hebei, Shanghai, Jiangsu, Shandong, Henan, Guangdong, and Shaanxi fluctuated around

228

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.9 Coupling coordination degree of natural resources utilization efficiency and economic utility by area 2008

Year

2009

2010

2011

2012

Beijing

0.5902

0.6016

0.6123

0.6149

0.6050

Tianjin

0.4806

0.4834

0.4948

0.4977

0.4941

Hebei

0.5708

0.5588

0.5596

0.5670

0.5609

Shanxi

0.4692

0.4608

0.4717

0.4765

0.4822

Inner Mongolia

0.4856

0.4796

0.4993

0.5034

0.5009

Liaoning

0.5560

0.5422

0.5642

0.5747

0.5818

Jilin

0.4650

0.4647

0.4769

0.4770

0.4824

Heilongjiang

0.5087

0.4967

0.5031

0.5030

0.5121

Shanghai

0.6008

0.6084

0.6069

0.6021

0.5877

Jiangsu

0.6989

0.6927

0.6930

0.7133

0.6970

Zhejiang

0.6527

0.6402

0.6583

0.6550

0.6588

Anhui

0.5057

0.5020

0.5244

0.5370

0.5389

Fujian

0.5544

0.5524

0.5766

0.5673

0.5894

Jiangxi

0.4796

0.4824

0.5091

0.5189

0.5219

Shandong

0.6984

0.6912

0.6899

0.7103

0.6991

Henan

0.5826

0.5753

0.5776

0.5853

0.5845

Hubei

0.4940

0.4854

0.5017

0.5121

0.5209

Hunan

0.5143

0.5125

0.5272

0.5286

0.5366

Guangdong

0.8277

0.8157

0.8502

0.8115

0.8095

Guangxi

0.4911

0.4764

0.4919

0.4992

0.5085

Hainan

0.3633

0.3706

0.4077

0.4344

0.4205

Chongqing

0.4335

0.4366

0.4433

0.4520

0.4645

Sichuan

0.5026

0.5035

0.5136

0.5235

0.5401

Guizhou

0.4204

0.4087

0.4217

0.4195

0.4336

Yunnan

0.4797

0.4571

0.4725

0.4944

0.4962

Shaanxi

0.4500

0.4539

0.4698

0.4928

0.4943

Gansu

0.3900

0.3854

0.3894

0.3992

0.4070

Qinghai

0.3253

0.3314

0.3300

0.3564

0.3638

Ningxia

0.3422

0.3365

0.3470

0.3569

0.3478

Xinjiang

0.4242

0.4184

0.4342

0.4435

0.4336

2013

2014

2015

2016

2017

Mean value

0.5977

0.5920

0.5740

0.5595

0.5552

0.5903

0.5009

0.5173

0.4972

0.4852

0.4724

0.4924

0.5593

0.5717

0.5258

0.5295

0.5426

0.5546

0.4862

0.4809

0.4674

0.4473

0.4395

0.4682

0.5149

0.5160

0.4903

0.4836

0.5083

0.4982 (continued)

7.4 Coupling Analysis of Natural Resources Utilization Efficiency …

229

Table 7.9 (continued) 2013

2014

2015

2016

2017

Mean value

0.5726

0.5598

0.5303

0.4946

0.4995

0.5476

0.4834

0.4839

0.4576

0.4564

0.4533

0.4701

0.5147

0.5026

0.4923

0.4777

0.4930

0.5004

0.5827

0.5980

0.5739

0.5866

0.6218

0.5969

0.6771

0.6870

0.6762

0.6903

0.6979

0.6923

0.6541

0.6723

0.6854

0.6329

0.6336

0.6543

0.5374

0.5631

0.5394

0.5680

0.5640

0.5380

0.5969

0.6074

0.5902

0.5851

0.5639

0.5784

0.5099

0.5266

0.5131

0.5451

0.5121

0.5119

0.6723

0.6780

0.6406

0.6591

0.6912

0.6830

0.5806

0.6027

0.5586

0.5621

0.5812

0.5790

0.5284

0.5593

0.5437

0.5638

0.5736

0.5283

0.5380

0.5461

0.5336

0.5310

0.5473

0.5315

0.8226

0.8222

0.8354

0.8202

0.8251

0.8240

0.5151

0.5288

0.5068

0.5087

0.5151

0.5042

0.4102

0.3898

0.3948

0.4011

0.3920

0.3984

0.4664

0.4752

0.4864

0.4588

0.4689

0.4586

0.5340

0.5531

0.5348

0.5235

0.5535

0.5282

0.4326

0.4441

0.4398

0.4472

0.4461

0.4314

0.4956

0.4897

0.4903

0.4946

0.5015

0.4872

0.4799

0.4939

0.4698

0.4731

0.4796

0.4757

0.4048

0.4009

0.3961

0.4141

0.3832

0.3970

0.3580

0.3667

0.3570

0.3552

0.3599

0.3504

0.3695

0.3648

0.3515

0.3471

0.3476

0.3511

their average values; and those of Anhui, Hubei, Sichuan, Guizhou, Yunnan, Gansu, and Xinjiang showed an upward trend on the whole. (2) Coupling coordination degree of natural resources utilization efficiency and economic utility by region Table 7.10 shows the coupling coordination degrees of eight economic zones from 2008 to 2017. The coupling coordination degrees of the northern coastal region, eastern coastal region, and southwestern region showed a trend of first increasing and then decreasing, among which the northern coastal region was in a good coordination stage before 2010, decreased from good coordination to moderate coordination in 2011, and stayed there for a long time, until the slight rebound after 2015; the coupling coordination degree in the middle reaches of the Yangtze River showed a change trend of first increasing and then decreasing, rising from bare imbalance to moderate coordination from 2008 to 2016, but returning to primary coordination in 2017; the

230

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.10 Coupling coordination degree of regional natural resources utilization efficiency and economic utility by region 2008

Year

2009

2010

2011

2012

Northeastern region

0.5978

0.5816

0.5967

0.5780

0.5997

Northern coastal region

0.8024

0.8137

0.8058

0.7870

0.7842

Eastern coastal region

0.8161

0.8122

0.8069

0.7972

0.7966

Southern coastal region

0.7100

0.7105

0.7423

0.7033

0.7085

Middle reaches of the Yellow River

0.6301

0.6157

0.6191

0.6235

0.6405

Middle reaches of the Yangtze River

0.5678

0.5655

0.5839

0.5883

0.6161

Southwestern region

0.5347

0.5343

0.5383

0.5486

0.5838

Northwestern region

0.4467

0.4188

0.4065

0.4306

0.4197

2013

2014

2015

2016

2017

Mean value

0.5841

0.5839

0.5680

0.5340

0.5443

0.5768

0.7722

0.7689

0.7520

0.7543

0.7571

0.7798

0.7909

0.7987

0.8304

0.7960

0.8088

0.8054

0.7191

0.7162

0.7377

0.7104

0.7124

0.7170

0.6445

0.6386

0.6272

0.6181

0.6325

0.6290

0.6230

0.6529

0.6684

0.7018

0.6930

0.6261

0.5875

0.5915

0.6119

0.5988

0.6003

0.5730

0.4115

0.4165

0.3987

0.3985

0.4133

0.4161

coupling coordination degree in the northeastern region showed a downward trend and stayed at the stage of bare imbalance for a long time; those in the middle reaches of the Yellow River and the southern coastal regional did not change much and were in primary coordination and moderate coordination, respectively, for a long time; the coupling coordination degree fluctuated around its average value in the northwestern region.

7.5 Coupling Analysis of Natural Resources Utilization Efficiency and Environmental Utility 7.5.1 Analysis of the Relationship Between Natural Resources Utilization Efficiency and Environmental Utility The utilization of natural resources and the protection of the ecological environment are integrated. Unreasonable development of resources will destroy the local ecological environment, and negatively affect local economic development and the living

7.5 Coupling Analysis of Natural Resources Utilization Efficiency …

231

Table 7.11 Main composition of waste gas emissions in China Year

Sulfur dioxide (10,000 t)

Nitrogen hydride (10,000 t)

Smoke dust (10,000 t)

2011

2217.91

2404.27

1278.83

2012

2117.63

2337.76

1235.77

2013

2043.92

2227.36

1278.14

2014

1974.42

2078.00

1740.75

2015

1859.12

1851.02

1538.01

2016

1102.86

1394.31

1010.66

2017

875.40

1258.83

796.26

environment. Excessive demand for natural resources causes excessive pressure on the ecosystem. While demanding natural resources, people discharge dust, harmful substances, wastewater, and waste residue—produced by the processing of natural resources—into the natural environment, resulting in the extreme deterioration of the ecological environment and threats to people’s health. Today, the energy consumption in China is mainly coal based, whether for power generation, heating, or rural household heating. Coal releases a lot of dust and harmful gases during combustion, a primary contributor to haze weather. Table 7.11 shows the main composition of waste gas emissions in China from 2011 to 2017. It can be seen from Table 7.11 that, from 2011 to 2017, the quantity of the main elements in China’s waste gas emissions gradually reduced: The emissions of sulfur dioxide decreased from 22.1791 million t in 2011 to 8.754 million t in 2017; those of nitrogen hydride decreased from 24.0427 million t in 2011 to 12.5883 million t in 2017; and emissions of smoke dust decreased from 12.7883 million t in 2011 to 7.9626 million t in 2017. For a long time, there was serious environmental pollution in rural areas. First, many factories moved from the original urban areas to rural areas, discharging a large amount of wastewater and waste gas. The discharged wastewater directly polluted water sources and indirectly polluted crops when people used the polluted water sources to irrigate farmland. The discharged waste gas formed acid rain, which eroded the land and reduced the land’s fertility and crop harvest. Second, the irrational use of pesticides and fertilizers polluted water sources and the burning of straw polluted the atmosphere. The low utilization of resources continuously deteriorated the rural environment. It was not until recently, when China banned the burning of straw and the abuse of pesticides, that the environmental situation in rural areas improved.

7.5.2 Environmental Utility Evaluation Index System By referring to Zhang et al. [9] and Wang et al. [10], this section establishes an environmental utility index system from three dimensions: resources environment, environmental pollution, and environmental treatment. The resource environment

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7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.12 Environmental utility evaluation index system Evaluation system

Secondary index

Environmental Resources effects environment

Tertiary index

Unit

Index type

Water resources per capita

m3

Positive

Forest area

10,000 ha

Positive

Forestry pest control rate

%

Positive

Wetland area

10,000 ha

Positive

10,000 t

Negative

10,000 t

Negative

Industrial solid waste generation

10,000 t

Negative

Electricity consumption

10,000 million Negative kWh

Environmental Waste water discharge pollution Sulfur dioxide emissions

Environmental Completed investment in industrial CNY 100 treatment pollution control million

Positive

Harmless treatment rate of domestic waste

%

Positive

Afforestation area

Ha

Positive

Green coverage rate of urban green % space and garden built-up areas Daily urban sewage treatment capacity

10,000 m3

Positive Positive

subsystem includes four indexes: per capita water resources, forest area, forestry pest control rate, and wetland area; the environmental pollution subsystem includes four indexes: wastewater discharge, sulfur dioxide emissions, industrial solid waste generation, and electricity consumption; the environmental treatment subsystem includes five indexes: completed investment in industrial pollution control, harmless treatment rate of domestic waste, afforestation area, green coverage rate of urban green space and garden built-up areas, and daily urban sewage treatment capacity. Details of these indexes are presented in Table 7.12.

7.5.3 Analysis of Coupling Degree Between Natural Resources Utilization Efficiency and Environmental Utility (1) Analysis of coupling degree between natural resources utilization efficiency and environmental utility by area Table 7.13 shows the coupling degrees of natural resources utilization efficiency and environmental utility calculated according to the coupling coordination model. It can be seen from the data in the table that the coupling values of environmental

7.5 Coupling Analysis of Natural Resources Utilization Efficiency …

233

utility and the natural resources utilization efficiency of all areas from 2008 to 2017 were between 0.8 and 1.0, exhibiting high-level coupling. Judging from the ten-year average coupling degree, among the 30 areas, Guizhou, Shanghai, and Liaoning ranked the top three while Guangdong, Inner Mongolia, and Qinghai ranked the bottom three. Overall, the coupling degrees of Guizhou were in a trend of first decreasing and then increasing, reaching its lowest point in 2012 and then increasing to the original level; the coupling degrees of Shanghai were always between 0.99 and 1, fluctuating around the average value, except for the year 2011; the coupling degrees of Liaoning were relatively stable with the smallest change amplitude in the 10 years. Judging from the variation trends of coupling degrees from 2008 to 2017, Anhui, Shandong, Guangdong, Qinghai, Ningxia, and Xinjiang showed a U-shaped change that increased first and then decreased; the coupling degrees of Tianjin, Heilongjiang, Zhejiang, Henan, Hunan, Hainan, and Gansu showed an upward trend; Guangxi and Yunnan were generally in a decreasing trend; Guizhou was in an inverted-U trend that decreased first and then increased; Beijing, Hebei, Inner Mongolia, Liaoning, Jilin, Shanghai, Jiangsu, Fujian, Jiangxi, Hubei, Chongqing, Sichuan, and Shaanxi fluctuated around the average coupling degree as a whole with small changes. (2) Coupling degree of natural resources utilization efficiency and environmental utility by region Table 7.14 shows the coupling degrees of resource utilization efficiency and environmental utility in the eight economic regions. The coupling degrees in the northeastern region showed a trend of first rising and then falling and began to decline in a fluctuating manner after reaching its peak in 2011; the southern coastal region showed a wave change of first falling, then rising, and then falling again; the eastern coastal region showed an upward trend, breaking through 0.99 in 2011 and reaching its highest value of 0.9988 in 2017. The coupling degrees of the other five regions had no obvious change trend.

7.5.4 Analysis of Coupling Coordination Degree Between Natural Resources Utilization Efficiency and Environmental Utility (1) Analysis of coupling coordination degree between natural resources utilization efficiency and environmental utility by area Table 7.15 provides the coupling coordination degree of natural resources utilization efficiency and environmental utility, which shows that the average coupling coordination degrees of the 30 areas in China were between 0.4 and 0.7; those in Beijing, Tianjin, Shanxi, Shanghai, Hainan, Chongqing, Gansu, and Ningxia were between 0.4 and 0.5, on the verge of imbalance; Shanghai, Hainan, and Chongqing were on the verge of imbalance in most years; the coupling coordination degrees in Shanxi,

234

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.13 Coupling degree of natural resources utilization efficiency and environmental utility by area 2008

City

2009

2010

2011

2012

Beijing

0.9993

1.0000

1.0000

0.9991

0.9996

Tianjin

0.9925

0.9942

0.9914

0.9929

0.9963

Hebei

0.9962

0.9949

0.9973

0.9960

0.9943

Shanxi

0.9938

0.9966

0.9962

0.9979

0.9966

Inner Mongolia

0.9388

0.9345

0.9402

0.9255

0.9391

Liaoning

0.9993

0.9999

0.9998

0.9999

0.9997

Jilin

0.9992

0.9961

0.9996

0.9978

0.9944

Heilongjiang

0.9954

0.9948

0.9922

0.9934

0.9969

Shanghai

0.9996

0.9996

0.9999

0.9989

0.9999

Jiangsu

0.9997

0.9987

0.9998

0.9990

0.9999

Zhejiang

0.9914

0.9943

0.9914

0.9923

0.9932

Anhui

0.9912

0.9928

0.9915

0.9901

0.9863

Fujian

0.9983

0.9944

0.9952

0.9974

0.9936

Jiangxi

0.9996

0.9997

1.0000

0.9987

0.9984

Shandong

0.9933

0.9868

0.9943

0.9933

0.9944

Henan

0.9799

0.9788

0.9720

0.9777

0.9656

Hubei

0.9998

0.9997

0.9987

0.9990

0.9990

Hunan

0.9971

0.9955

0.9978

0.9987

0.9991

Guangdong

0.9629

0.9555

0.9678

0.9698

0.9758

Guangxi

1.0000

0.9985

0.9997

0.9996

0.9984

Hainan

0.9940

0.9867

0.9946

0.9987

0.9989

Chongqing

0.9991

0.9997

0.9958

0.9991

1.0000

Sichuan

0.9945

0.9994

0.9991

1.0000

0.9975

Guizhou

1.0000

0.9999

0.9999

0.9999

0.9991

Yunnan

0.9975

0.9960

0.9954

0.9995

0.9999

Shaanxi

0.9970

0.9936

0.9894

0.9995

0.9994

Gansu

0.9920

0.9934

0.9889

0.9977

0.9973

Qinghai

0.8243

0.8191

0.8354

0.8175

0.8339

Ningxia

0.9807

0.9928

0.9810

0.9977

0.9985

Xinjiang

0.9788

0.9818

0.9817

0.9872

0.9935

2013

2014

2015

2016

2017

Mean value

0.9991

0.9996

1.0000

0.9988

0.9906

0.9986

0.9950

0.9972

0.9980

1.0000

0.9964

0.9954

0.9966

0.9996

0.9981

0.9993

0.9974

0.9970

0.9968

1.0000

0.9994

0.9973

0.9943

0.9969

0.9396

0.9364

0.9509

0.9343

0.9738

0.9413 (continued)

7.5 Coupling Analysis of Natural Resources Utilization Efficiency …

235

Table 7.13 (continued) 2013

2014

2015

2016

2017

Mean value

0.9993

0.9995

0.9972

0.9998

0.9992

0.9994

0.9967

0.9941

0.9988

0.9992

0.9996

0.9975

0.9975

0.9990

0.9989

0.9982

0.9989

0.9965

0.9998

0.9999

1.0000

1.0000

0.9969

0.9994

0.9971

0.9990

0.9986

0.9998

0.9964

0.9988

0.9982

0.9960

0.9749

1.0000

0.9957

0.9927

0.9990

0.9930

0.9902

0.9895

0.9875

0.9911

0.9972

0.9945

0.9937

0.9972

0.9966

0.9958

0.9995

0.9981

0.9960

0.9900

0.9984

0.9978

0.9978

1.0000

0.9948

0.9993

0.9864

0.9940

0.9809

0.9826

0.9703

0.9948

0.9864

0.9789

1.0000

0.9993

0.9973

0.9999

0.9981

0.9991

0.9976

0.9993

0.9892

0.9994

0.9970

0.9971

0.9670

0.9588

0.9410

0.9818

0.9736

0.9654

0.9961

0.9962

0.9965

0.9988

0.9945

0.9978

0.9960

0.9980

0.9997

0.9957

0.9939

0.9956

0.9995

1.0000

0.9966

1.0000

0.9995

0.9989

0.9986

0.9977

0.9983

0.9984

0.9990

0.9983

0.9991

0.9995

0.9995

0.9994

1.0000

0.9996

0.9999

0.9999

0.9991

0.9999

0.9941

0.9981

0.9947

0.9956

0.9980

0.9976

0.9999

0.9965

0.9979

0.9988

0.9974

0.9975

0.9888

0.9950

0.8402

0.8466

0.8480

0.8072

0.8072

0.8279

0.9969

0.9968

0.9938

0.9968

0.9927

0.9928

Gansu, and Tianjin did not change much, staying on the verge of imbalance for a long time; the coupling coordination degree of Ningxia fluctuated between slight imbalance and verge of imbalance. The average coupling degrees of Hebei, Liaoning, Jilin, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Henan, Hubei, Guangxi, Guizhou, and Shaanxi were between 0.5 and 0.6, in a state of bare imbalance, among which those of Hebei, Liaoning, Jilin, Anhui, Henan, Hubei, and Shaanxi were stable and in a state of bare imbalance for a long time; Jiangsu, Zhejiang, Fujian, and Jiangxi were in a state of bare imbalance in most years, moving from bare imbalance to primary coordination during a few years; Guangxi fluctuated between the verge of imbalance and primary coordination; the coupling coordination degree of Guizhou was generally in a upward trend, from being on the verge of imbalance to bare imbalance. The average coupling coordination degrees of Inner Mongolia, Heilongjiang, Shandong, Hunan, Guangdong, Sichuan, and Yunnan were between 0.6 and 0.7,

236

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.14 Coupling degree of natural resources utilization efficiency and environmental utility by region 2008

Year

2009

2010

2011

2012

Northeastern region

0.9996

0.9997

0.9999

1.0000

1.0000

Northern coastal region

0.9861

0.9856

0.9860

0.9947

0.9887

Eastern coastal region

0.9827

0.9820

0.9805

0.9801

0.9879

Southern coastal region

0.9982

0.9920

0.9917

0.9957

0.9964

Middle reaches of the Yellow River

0.9956

0.9970

0.9967

0.9927

0.9972

Middle reaches of the Yangtze River

0.9937

0.9946

0.9929

0.9896

0.9925

Southwestern region

0.9998

0.9997

0.9994

1.0000

0.9995

Northwestern region

0.9253

0.9428

0.9348

0.9485

0.9431

2013

2014

2015

2016

2017

Mean value

0.9981

0.9954

0.9994

0.9906

0.9943

0.9977

0.9867

0.9979

0.9835

0.9981

0.9988

0.9906

0.9860

0.9740

0.9617

0.9998

0.9876

0.9822

0.9887

0.9689

0.9697

0.9969

0.9928

0.9891

0.9961

0.9969

0.9983

0.9882

0.9959

0.9955

0.9894

0.9748

0.9949

0.9828

0.9891

0.9894

0.9981

0.9951

0.9977

0.9957

0.9934

0.9978

0.9545

0.9521

0.9254

0.9377

0.9406

0.9405

in a state of primary coordination, among which Sichuan, Yunnan, and Heilongjiang were in a state of primary coordination for a long time; the coupling coordination degrees of Inner Mongolia and Shandong were mostly in the state of primary coordination, except the bare imbalance stage in 2015; those of Hunan and Guangdong were in an increasing trend, and Hunan remained at the primary coordination stage from 2010; the coupling coordination degree of Guangdong was mainly in a state of primary coordination before 2016, and then ascended to the moderate coordination stage. (2) Analysis of the coupling coordination degree of natural resources utilization efficiency and environmental utility by region Table 7.16 shows the coupling coordination degrees of the eight economic regions from 2008 to 2017. Judging from the average data of 10 years, the average coupling coordination degrees of all areas were in a range of 0.6–0.7, except that the degree of the southwestern region exceeded 0.7. The northern coastal area and the eastern coastal area showed a trend of first declining and then rising: The northern coastal area fell to the lowest point in 2015, from the original primary coordination stage to the bare imbalance stage, and then gradually increased and returned to the initial primary coordination stage. The coupling coordination degree in the eastern region quickly recovered to the initial level after a short decline and remained in the primary coordination stage. The coupling coordination degree of the northeastern region was

7.5 Coupling Analysis of Natural Resources Utilization Efficiency …

237

Table 7.15 Coupling coordination degree of natural resources utilization efficiency and environmental utility by area 2008

Area

2009

2010

2011

2012

Beijing

0.4897

0.5012

0.4927

0.4875

0.4757

Tianjin

0.4408

0.4493

0.4616

0.4485

0.4310

Hebei

0.5542

0.5416

0.5500

0.5538

0.5523

Shanxi

0.4727

0.4871

0.4934

0.4724

0.4766

Inner Mongolia

0.6202

0.6050

0.6164

0.6149

0.6194

Liaoning

0.5245

0.5250

0.5642

0.5570

0.5565

Jilin

0.5086

0.5020

0.5357

0.5288

0.5180

Heilongjiang

0.6284

0.6439

0.6524

0.6402

0.6387

Shanghai

0.4862

0.4959

0.5018

0.4775

0.4687

Jiangsu

0.6115

0.6109

0.5987

0.6111

0.5964

Zhejiang

0.5730

0.5816

0.5961

0.5813

0.5955

Anhui

0.5263

0.5346

0.5472

0.5489

0.5443

Fujian

0.5714

0.5578

0.6001

0.5845

0.6012

Jiangxi

0.5852

0.5783

0.6177

0.5759

0.5971

Shandong

0.6477

0.6434

0.6501

0.6608

0.6587

Henan

0.5784

0.5793

0.5739

0.5710

0.5532

Hubei

0.5478

0.5533

0.5804

0.5457

0.5477

Hunan

0.5818

0.5841

0.6221

0.6132

0.6270

Guangdong

0.6800

0.6625

0.7244

0.6760

0.6827

Guangxi

0.5979

0.5855

0.6038

0.5945

0.6016

Hainan

0.4823

0.4862

0.5034

0.5019

0.4865

Chongqing

0.4841

0.4909

0.5132

0.5054

0.4886

Sichuan

0.6358

0.6144

0.6158

0.6113

0.6065

Guizhou

0.4938

0.4792

0.5002

0.4848

0.4920

Yunnan

0.6516

0.6281

0.6543

0.6360

0.6424

Shanxi

0.5066

0.5275

0.5583

0.5532

0.5424

Gansu

0.4631

0.4689

0.4851

0.4660

0.4726

Qinghai

0.5090

0.5248

0.5203

0.5255

0.5209

Ningxia

0.4102

0.3977

0.4220

0.4011

0.3978

Xinjiang

0.5240

0.5352

0.5420

0.5405

0.5233

2013

2014

2015

2016

2017

Mean value

0.4883

0.4797

0.4529

0.4551

0.4608

0.4784

0.4388

0.4404

0.4326

0.4127

0.4095

0.4365

0.5598

0.5659

0.5116

0.5388

0.5379

0.5466

0.4791

0.4500

0.4321

0.4355

0.4545

0.4653

0.6455

0.6094

0.5969

0.6105

0.6357

0.6174 (continued)

238

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.15 (continued) 2013

2014

2015

2016

2017

Mean value

0.5578

0.5329

0.5048

0.5119

0.5024

0.5337

0.5304

0.5049

0.5156

0.5144

0.5100

0.5168

0.6691

0.6394

0.6310

0.6333

0.6379

0.6414

0.4801

0.4958

0.4691

0.5061

0.5326

0.4914

0.5951

0.5748

0.5678

0.6195

0.5915

0.5977

0.6128

0.6070

0.6255

0.5967

0.5704

0.5940

0.5757

0.5624

0.5522

0.5790

0.5630

0.5534

0.6265

0.6059

0.6218

0.6313

0.5811

0.5982

0.5826

0.5675

0.5852

0.6250

0.5851

0.5900

0.6232

0.6220

0.5687

0.6177

0.6370

0.6329

0.5711

0.5719

0.5262

0.5592

0.5656

0.5650

0.5627

0.5595

0.5524

0.5872

0.5957

0.5632

0.6163

0.6149

0.6804

0.6256

0.6218

0.6187

0.6889

0.6738

0.6895

0.7144

0.7063

0.6899

0.6095

0.5911

0.6052

0.5944

0.6072

0.5991

0.4916

0.4642

0.4382

0.4571

0.4594

0.4771

0.4925

0.4871

0.5020

0.4733

0.4918

0.4929

0.6013

0.6042

0.6083

0.6301

0.6532

0.6181

0.5145

0.5281

0.5286

0.5220

0.5400

0.5083

0.6382

0.6277

0.6326

0.6319

0.6268

0.6370

0.5365

0.5236

0.5090

0.5002

0.5210

0.5278

0.4734

0.4604

0.4473

0.4616

0.4697

0.4668

0.5230

0.5322

0.5111

0.5113

0.5159

0.5194

0.4238

0.4190

0.3982

0.3954

0.3907

0.4056

first on a rise, broke through from the primary coordination stage to the moderate coordination stage in 2010 and 2012, but fell back from the moderate coordination stage to the primary coordination stage later. The coupling degree of the southwestern region was the highest among those of the eight regions, and remained in the moderate coordination stage except in 2016. The middle reaches of the Yangtze River and the southern coastal areas would soon move from the primary coordination stage to the moderate coordination stage. The northwestern region and the middle reaches of the Yellow River were in the primary coordination stage except in a few years.

7.6 Coupling Analysis of Natural Resources Utilization Efficiency …

239

Table 7.16 Coupling coordination degree of regional natural resources utilization efficiency and environmental utility by region 2008

Year

2009

2010

2011

2012

Northeastern region

0.6559

0.6562

0.7003

0.6729

0.7049

Northern coastal region

0.6747

0.6816

0.6627

0.6706

0.6489

Eastern coastal region

0.6478

0.6482

0.6425

0.6316

0.6424

Southern coastal region

0.6788

0.6546

0.7105

0.6614

0.6873

Middle reaches of the Yellow River

0.6638

0.6892

0.6841

0.6852

0.6699

Middle reaches of the Yangtze River

0.6622

0.6754

0.7101

0.6661

0.7031

Southwestern region

0.7357

0.7054

0.7240

0.7181

0.7403

Northwestern region

0.6266

0.6275

0.6339

0.6267

0.6448

2013

2014

2015

2016

2017

Mean value

0.6837

0.6622

0.6844

0.6562

0.6592

0.6736

0.6209

0.6481

0.5872

0.6298

0.6331

0.6458

0.6318

0.6243

0.6509

0.6878

0.6420

0.6449

0.6780

0.6427

0.6746

0.6849

0.6567

0.6730

0.6794

0.6511

0.6242

0.6395

0.6574

0.6644

0.6857

0.6630

0.7058

0.7164

0.6881

0.6876

0.7209

0.7090

0.7377

0.6895

0.7158

0.7196

0.6286

0.6253

0.6078

0.5965

0.6269

0.6245

7.6 Coupling Analysis of Natural Resources Utilization Efficiency and Social Utility 7.6.1 Analysis of the Relationship Between Natural Resources Utilization Efficiency and Social Utility The development of human society is the process of constantly using natural resources to create wealth. The origin of civilization is an important stage in the development of human society. The four ancient civilizations in the world are Egypt, India, Babylon, and China. There are many similarities between them in terms of geographical environment and location. For example, they were all located between north latitude 30◦ and north latitude 35◦ and originated in river basins, so that are also known as “river civilizations.” Rivers can provide sufficient water for the residents living there to meet the needs of daily life and crop irrigation and deposit a lot of silt that makes the land fertile and suitable for agriculture. Taking ancient Babylon and ancient Egypt as examples, the Babylonian civilization originated from the Euphrates and Tigris rivers, where the climate is arid and the terrain is flat; the Tigris and Euphrates Rivers were often flooded, causing the residents in the two river basins to suffer from flood outbreaks. However, they also provided rich water resources

240

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

for local residents, promoting water conservation and agriculture; the Babylonians created cuneiform characters and formulated the world’s first laws and earliest astronomical calendar. Ancient Egypt was very dry, with the desert occupying 95% of its land area; however, the Nile River brought a lot of water and nutrient-rich silt to ancient Egypt that made the local land extremely fertile and conducive to the growth of crops. Hieroglyphs were created by the ancient Egyptians; Egypt is also well known for its mummies and pyramids. Resources are the foundation of social development and can promote social development if efficiently utilized but can hinder it if not. With an increase in the population, the demand for resources also increases. People exchange resources for speedy economic development, resulting in a serious waste of resources. In the long run, the resources that nature can provide will be insufficient to support people’s increasing resource consumption, which will affect economic development and social progress, and even threaten human survival.

7.6.2 Social Utility Evaluation Index System The evaluation index system of social utility is established from three dimensions: living standard, living security, and infrastructure. Indexes of the living standard subsystem are population mortality, per capita disposable income of urban residents, per capita consumption expenditure, and private car ownership; indexes of the living security subsystem are health technicians per 1000 people, number of medical and health institutions, year-end number of insured people, and basic medical insurance expenditure; indexes of the infrastructure subsystem are domestic waste removal and transportation volume, public vehicles per 10,000 people, per capita park green area, actual year-end road length, and total annual water supply. Details of the indexes of social utility evaluation index system are provided in Table 7.17.

7.6.3 Analysis of Coupling Degree Between Natural Resources Utilization Efficiency and Social Utility (1) Analysis of coupling degree between natural resources utilization efficiency and social utility by area The coupling degrees of natural resources utilization efficiency and social utility of 30 areas in China from 2008 to 2017 are calculated according to the weight of each index of the natural resources utilization efficiency evaluation index system and the social utility evaluation system, as given in Table 7.18. Judging from the ten-year average data, the average coupling degrees of each area were between 0.86 and 1.00, exhibiting high-level coupling.

7.6 Coupling Analysis of Natural Resources Utilization Efficiency …

241

Table 7.17 Social utility evaluation index system Evaluation system

Secondary index

Tertiary index

Unit

Index type

Social utility

Living standard

Population mortality

%

Negative

Per capita disposable income of urban residents

CNY 1

Positive

Per capita consumption expenditure

CNY 1

Positive

Private car ownership 10,000

Positive

Health technicians per 1000 people

Positive

Living security

People

Number of medical and health institutions Year-end number of insured people

Infrastructure

Positive 10,000 people

Positive

Basic medical CNY 100 million insurance expenditure

Positive

Domestic waste removal volume

Positive

10,000 t

Public vehicles per 10,000 people

Positive

Per capita park green area

m2

Positive

Actual year-end road length

km

Positive

Total annual water supply

10,000 m3

Positive

The coupling degrees of Inner Mongolia, Liaoning, Shanghai, Henan, Hunan, Guangdong, Hainan, Chongqing, Sichuan, Guizhou, Shaanxi, and Xinjiang generally showed an upward trend from 2008 to 2017, among which those of Inner Mongolia, Shanghai, Guangdong, Sichuan, Shaanxi, and Xinjiang exhibited a fluctuated rising trend. The coupling degrees of Jilin, Heilongjiang, Jiangsu, Hubei, and Jiangxi mainly showed a downward trend. That of Jilin decreased from the highest value of 0.9911 in 2008 to the lowest value of 0.9324 in 2017, a decrease of 0.0588; that of Heilongjiang decreased greatly from 0.98 in 2008 to 0.9134 in 2017; those of Jiangsu and Hubei decreased slightly, by 0.0182 and 0.0123, respectively. The coupling degrees of Jiangxi showed a downward trend after 2011, from 0.9427 in 2011 to 0.8842 in 2016. From 2008 to 2017, the coupling degrees of Beijing, Tianjin, Zhejiang, and Fujian generally showed a trend of first increasing and then decreasing, while those of Anhui,

242

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.18 Coupling degree of natural resources utilization efficiency and social utility by area Year

2008

2009

2010

2011

2012

Beijing

0.9247

0.9211

0.9281

0.9304

0.9167

Tianjin

0.9795

0.9801

0.9851

0.9838

0.9807

Hebei

0.9958

0.9999

0.9999

0.9996

0.9996

Shanxi

0.9973

0.9967

0.9977

0.9996

1.0000

Inner Mongolia

0.9852

0.9931

0.9895

0.9956

0.9965

Liaoning

0.9926

0.9932

0.9996

0.9997

0.9997

Jilin

0.9911

0.9909

0.9763

0.9714

0.9813

Heilongjiang

0.9800

0.9784

0.9797

0.9693

0.9636

Shanghai

0.9312

0.9472

0.9531

0.9490

0.9421

Jiangsu

0.9879

0.9910

0.9861

0.9923

0.9830

Zhejiang

0.9925

0.9931

0.9959

0.9938

0.9933

Anhui

0.9642

0.9617

0.9606

0.9637

0.9648

Fujian

0.9794

0.9873

0.9813

0.9875

0.9791

Jiangxi

0.9265

0.9437

0.9213

0.9427

0.9341

Shandong

0.9998

0.9998

0.9987

0.9999

0.9995

Henan

0.9600

0.9799

0.9764

0.9798

0.9827

Hubei

0.9984

0.9986

0.9949

0.9964

0.9979

Hunan

0.9694

0.9763

0.9680

0.9664

0.9630

Guangdong

0.9955

0.9947

0.9988

0.9913

0.9888

Guangxi

0.9313

0.9601

0.9431

0.9334

0.9310

Hainan

0.8994

0.9049

0.9047

0.9195

0.9408

Chongqing

0.9828

0.9689

0.9745

0.9835

0.9961

Sichuan

0.9858

0.9946

0.9953

0.9941

0.9933

Guizhou

0.8065

0.8538

0.8497

0.8370

0.8587

Yunnan

0.8970

0.9052

0.8913

0.8890

0.8792

Shanxi

0.9868

0.9912

0.9907

0.9836

0.9797

Gansu

0.9413

0.9160

0.9326

0.9374

0.9376

Qinghai

0.9853

0.9815

0.9882

0.9686

0.9660

Ningxia

0.9972

0.9996

0.9999

0.9994

0.9988

Xinjiang

0.9934

0.9817

0.9808

0.9899

0.9948

2013

2014

2015

2016

2017

Mean value

0.8977

0.9017

0.8908

0.8777

0.8681

0.9057

0.9766

0.9880

0.9854

0.9765

0.9578

0.9793

0.9955

0.9971

0.9996

0.9920

0.9979

0.9977

0.9983

0.9982

0.9990

1.0000

0.9985

0.9985

0.9870

0.9974

0.9975

0.9971

0.9883

0.9927

0.9991

0.9963

0.9982

0.9991

0.9997

0.9977 (continued)

7.6 Coupling Analysis of Natural Resources Utilization Efficiency …

243

Table 7.18 (continued) 2013

2014

2015

2016

2017

Mean value

0.9657

0.9544

0.9653

0.9604

0.9324

0.9689

0.9344

0.9388

0.9319

0.9271

0.9134

0.9517

0.9398

0.9612

0.9465

0.9620

0.9845

0.9517

0.9765

0.9665

0.9726

0.9810

0.9741

0.9811

0.9923

0.9898

0.9988

0.9796

0.9801

0.9909

0.9513

0.9574

0.9572

0.9445

0.9616

0.9587

0.9616

0.9678

0.9614

0.9657

0.9929

0.9764

0.9184

0.9166

0.9070

0.8842

0.9683

0.9263

0.9950

0.9878

0.9681

0.9794

0.9946

0.9923

0.9724

0.9779

0.9875

0.9883

0.9999

0.9805

0.9930

0.9907

0.9954

0.9863

0.9925

0.9944

0.9608

0.9707

0.9756

0.9750

0.9855

0.9711

0.9972

0.9962

0.9991

0.9966

0.9974

0.9956

0.8912

0.9164

0.9068

0.9246

0.9445

0.9282

0.9076

0.9349

0.9491

0.9438

0.9590

0.9264

0.9934

0.9929

0.9850

0.9974

0.9959

0.9870

0.9931

0.9931

0.9932

0.9980

0.9952

0.9936

0.8393

0.8655

0.8876

0.9192

0.9125

0.8630

0.8782

0.8875

0.8716

0.8836

0.9144

0.8897

0.9928

0.9974

0.9968

0.9980

0.9914

0.9908

0.9319

0.9401

0.9393

0.9322

0.9728

0.9381

0.9489

0.9504

0.9452

0.9674

0.9721

0.9674

0.9966

0.9995

0.9999

1.0000

0.9978

0.9989

Shandong, Guangxi, and Qinghai generally showed a trend of first decreasing and then increasing. From 2008 to 2017, the coupling degrees of Hebei, Shaanxi, Yunnan, Gansu, and Ningxia mainly fluctuated around their average values. (3) Analysis of the coupling degrees of natural resources utilization efficiency and social utility by region The researched 30 areas in China are divided into eight economic regions. According to the coupling model, the coupling degrees of natural resources utilization efficiency and social utility are calculated, as given in Table 7.19. The coupling degrees in the northeastern region, northern coastal areas, and middle reaches of the Yangtze River showed a downward trend; those in the eastern coastal region, middle reaches of the Yellow River, southwestern region, and northwestern region also showed a downward trend; the changes of coupling degrees in the southern coastal region were more stable than those in other regions, fluctuating around its average value.

244

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.19 Coupling degree of regional natural resources utilization efficiency and social utility by region 2008

Region

2009

2010

2011

2012

Northeastern region

0.9992

0.9941

0.9840

0.9749

0.9650

Northern coastal region

0.9929

0.9904

0.9846

0.9734

0.9740

Eastern coastal region

0.9881

0.9944

0.9970

0.9972

0.9975

Southern coastal region

1.0000

0.9998

0.9990

0.9941

0.9968

Middle reaches of the Yellow River

0.9890

0.9882

0.9913

0.9968

0.9975

Middle reaches of the Yangtze River

0.9997

0.9997

0.9921

0.9929

0.9866

Southwestern region

0.9651

0.9661

0.9687

0.9828

0.9854

Northwestern region

0.9839

0.9754

0.9954

0.9809

0.9741

2013

2014

2015

2016

2017

Mean value

0.9684

0.9636

0.9619

0.9417

0.9227

0.9675

0.9654

0.9658

0.9461

0.9504

0.9551

0.9698

0.9941

0.9951

0.9993

0.9938

0.9952

0.9952

1.0000

0.9999

1.0000

1.0000

0.9994

0.9989

0.9989

1.0000

0.9985

0.9996

0.9991

0.9959

0.9891

0.9918

0.9897

0.9797

0.9980

0.9919

0.9907

0.9904

0.9896

0.9931

0.9930

0.9825

0.9832

0.9915

0.9973

0.9964

0.9918

0.9870

7.6.4 Analysis of the Coupling Coordination Degree of Natural Resources Utilization Efficiency and Social Utility (1) Analysis of the coupling coordination degree of natural resources utilization efficiency and social utility by area Table 7.20 shows the coupling coordination degrees of natural resources utilization efficiency and social utility of the researched 30 areas in China. The table shows that that the average coupling coordination degrees of the 30 areas were mainly between 0.3 and 0.9. Among them, the average coupling coordination degrees of Hainan, Guizhou, Gansu, Qinghai, and Ningxia were between 0.3 and 0.4, in a state of slight imbalance; those of Tianjin, Shanxi, Inner Mongolia, Jilin, Jiangxi, Chongqing, Yunnan, Shaanxi, and Xinjiang were between 0.4 and 0.5, in a state of bare imbalance; those of Beijing, Hebei, Liaoning, Heilongjiang, Shanghai, Anhui, Fujian, Henan, Hubei, Hunan, Guangxi, and Sichuan were between 0.5 and 0.6, in a state of bare imbalance; the average coupling coordination degrees of Jiangsu, Zhejiang, and Shandong were between 0.6 and 0.7, in a state of primary coordination; and that of Guangdong was 0.8227, in a state of good coordination.

7.6 Coupling Analysis of Natural Resources Utilization Efficiency …

245

Table 7.20 Coupling coordination degree of natural resources utilization efficiency and social utility by area 2008

Year

2009

2010

2011

2012

Beijing

0.6094

0.6133

0.5987

0.6033

0.5957

Tianjin

0.4592

0.4707

0.4714

0.4625

0.4559

Hebei

0.5530

0.5659

0.5738

0.5704

0.5751

Shanxi

0.4308

0.4486

0.4566

0.4510

0.4553

Inner Mongolia

0.4753

0.4735

0.4799

0.4806

0.4967

Liaoning

0.5683

0.5615

0.5664

0.5685

0.5708

Jilin

0.4855

0.4905

0.4871

0.4843

0.4954

Heilongjiang

0.5414

0.5508

0.5535

0.5330

0.5356

Shanghai

0.5973

0.5944

0.5901

0.5752

0.5533

Jiangsu

0.6692

0.6699

0.6583

0.6655

0.6579

Zhejiang

0.6508

0.6508

0.6660

0.6543

0.6692

Anhui

0.4911

0.4933

0.5066

0.5137

0.5170

Fujian

0.5311

0.5428

0.5718

0.5594

0.5740

Jiangxi

0.4738

0.4816

0.5015

0.4969

0.5096

Shandong

0.6933

0.7060

0.7033

0.7060

0.7054

Henan

0.5543

0.5809

0.5796

0.5740

0.5755

Hubei

0.5276

0.5324

0.5380

0.5349

0.5422

Hunan

0.5333

0.5487

0.5656

0.5516

0.5577

Guangdong

0.8191

0.8123

0.8441

0.8179

0.8227

Guangxi

0.4934

0.4932

0.5017

0.4994

0.5109

Hainan

0.3612

0.3568

0.3806

0.3976

0.3981

Chongqing

0.4318

0.4278

0.4374

0.4515

0.4674

Sichuan

0.5542

0.5733

0.5744

0.5791

0.5928

Guizhou

0.3515

0.3561

0.3707

0.3536

0.3787

Yunnan

0.4960

0.4788

0.4883

0.4887

0.4997

Shanxi

0.4490

0.4663

0.4845

0.4968

0.4814

Gansu

0.3646

0.3579

0.3726

0.3756

0.3799

Qinghai

0.3388

0.3437

0.3536

0.3333

0.3346

Ningxia

0.3578

0.3695

0.3800

0.3810

0.3968

Xinjiang

0.4459

0.4415

0.4459

0.4643

0.4697

2013

2014

2015

2016

2017

Mean value

0.6056

0.5966

0.5804

0.5763

0.5647

0.5944

0.4655

0.4587

0.4568

0.4593

0.4551

0.4615

0.5564

0.5523

0.5353

0.5635

0.5763

0.5622

0.4469

0.4347

0.4301

0.4207

0.4430

0.4418

0.4980

0.4895

0.4911

0.4879

0.5243

0.4897 (continued)

246

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.20 (continued) 2013

2014

2015

2016

2017

Mean value

0.5592

0.5474

0.5397

0.5279

0.5188

0.5529

0.4837

0.4570

0.4623

0.4549

0.4281

0.4729

0.5362

0.5225

0.5099

0.5045

0.5261

0.5313

0.5675

0.5757

0.5570

0.5831

0.6052

0.5799

0.6390

0.6411

0.6565

0.6758

0.6922

0.6626

0.6720

0.6821

0.7182

0.6630

0.6609

0.6687

0.5018

0.5144

0.5104

0.5250

0.5294

0.5103

0.5650

0.5616

0.5712

0.5739

0.5703

0.5621

0.4800

0.4738

0.4889

0.5209

0.5296

0.4957

0.6773

0.6710

0.6803

0.6970

0.7288

0.6968

0.5595

0.5650

0.5498

0.5451

0.6111

0.5695

0.5321

0.5321

0.5462

0.5442

0.5775

0.5407

0.5533

0.5543

0.5653

0.5685

0.5933

0.5592

0.8148

0.8146

0.8397

0.8196

0.8222

0.8227

0.4989

0.4995

0.5039

0.4982

0.5395

0.5039

0.3758

0.3737

0.3676

0.3676

0.3757

0.3755

0.4578

0.4597

0.4796

0.4569

0.4628

0.4533

0.5821

0.5893

0.5906

0.5936

0.6360

0.5865

0.3715

0.3946

0.4058

0.4167

0.4329

0.3832

0.4892

0.4922

0.4944

0.4928

0.5341

0.4954

0.4798

0.4817

0.4741

0.4680

0.4851

0.4767

0.3790

0.3762

0.3608

0.3686

0.3871

0.3722

0.3279

0.3368

0.3212

0.3201

0.3261

0.3336

0.3909

0.3960

0.3797

0.3793

0.3800

0.3811

Judging from the changes of the coupling coordination degrees of the 30 areas from 2008 to 2017, the coupling coordination degrees of Anhui, Fujian, Jiangxi, Hunan, Hubei, Guangxi, Chongqing, Sichuan, and Guizhou showed an upward trend, especially those of Anhui, Sichuan, and Guizhou; the coupling coordination degrees of Fujian, Jiangxi, Hunan, Hubei, Guangxi, and Chongqing showed a fluctuated rising trend; and those of Beijing, Tianjin, Liaoning, and Jilin showed a downward trend. The coupling coordination degrees of Shanxi, Inner Mongolia, Zhejiang, Heilongjiang, Hainan, Shaanxi, Gansu, Qinghai, and Ningxia increased first and then decreased; those of Shanghai and Jiangsu showed a trend of first decreasing and then increasing; those of Shanghai were in a decline from 2009 to 2012, and increased gradually after 2013; by 2017, its degree reached 0.6052, evolving from the original bare imbalance state to the primary coordination state; the coupling coordination degree of Jiangsu reached its lowest point of 0.6390 in 2013, and then gradually

7.7 Conclusions and Policy Recommendations

247

increased to 0.6922 in 2017, indicating a move from the primary coordination state to the moderate coordination state. The coupling coordination degrees of Shandong and Hebei showed a trend of first increasing and then decreasing, before increasing again; Shandong jumped from the primary coordination state to the moderate coordination state in 2009 and continued until 2012; in 2013, it fell back to the primary coordination state; however, its coupling coordination degree kept increasing, and finally returned to the state of moderate coordination again in 2017. The changes of the coupling coordination degree in Hebei were similar to those in Shandong, but with a smaller range; Hebei was in a state of bare imbalance from 2008 to 2017; in the same period, the coupling coordination degrees of Guangdong, Yunnan, and Xinjiang fluctuated around their average coupling coordination degrees. (2) Analysis of coupling coordination degree of natural resources utilization efficiency and social utility by region Table 7.21 shows the calculated values of the coupling coordination degrees of eight major economic regions in China, from which it can be concluded that the regions with high coupling coordination degrees were mainly concentrated in coastal areas. Judging from the average coupling coordination degree, those of the northern coastal, eastern coastal, and southern coastal regions were more than 0.7, in a state of moderate coordination. Those of the northeastern region, middle reaches of the Yellow River, middle reaches of the Yangtze River, and southwestern region were between 0.6 and 0.7, in a state of primary coordination; particularly, the coupling coordination degrees of the middle reaches of the Yangtze River and the southwestern region were close to 0.7, and about to enter the state of moderate coordination; the coupling coordination degrees of the northeastern region showed a downward trend, from the original primary coordination state to the bare imbalance state, while those of the middle reaches of the Yellow River were stable in the primary coordination state. The coupling coordination degrees of the northwestern region were the lowest among the eight regions, fluctuating between the verge of imbalance and bare imbalance for a long time.

7.7 Conclusions and Policy Recommendations 7.7.1 Main Conclusions Overall, high coupling coordination degrees are mainly concentrated in coastal areas. By virtue of their favorable geographical locations and the support of Chinese policies, coastal areas have realized rapid economic development, causing technological progress, more effective utilization of natural resources, and further economic development, in a virtuous cycle. Therefore, compared with inland areas, the coupling

248

7 Analysis of Temporal and Spatial Evolution of Natural Resources …

Table 7.21 Coupling coordination degree of natural resources utilization efficiency and social utility by region 2008

Year

2009

2010

2011

2012

Northeastern region

0.6336

0.6289

0.6366

0.6008

0.6141

Northern coastal region

0.7788

0.7959

0.7876

0.7936

0.7853

Eastern coastal region

0.7689

0.7521

0.7378

0.7252

0.7198

Southern coastal region

0.6989

0.6913

0.7410

0.7319

0.7462

Middle reaches of the Yellow River

0.5877

0.6140

0.6146

0.6195

0.6227

Middle reaches of the Yangtze River

0.6918

0.7024

0.7076

0.6748

0.6887

Southwestern region

0.6368

0.6253

0.6272

0.6525

0.6900

Northwestern region

0.4690

0.4717

0.5018

0.4819

0.4836

2013

2014

2015

2016

2017

Mean value

0.6209

0.6058

0.6054

0.5901

0.5674

0.6104

0.7703

0.7647

0.7611

0.7628

0.7560

0.7756

0.7258

0.7363

0.7636

0.7203

0.7297

0.7379

0.7342

0.7338

0.7615

0.7131

0.7092

0.7261

0.6346

0.6262

0.6235

0.6001

0.6418

0.6185

0.6849

0.6964

0.6908

0.7106

0.7178

0.6966

0.6946

0.6951

0.7098

0.6808

0.7148

0.6727

0.4915

0.5001

0.4800

0.4769

0.4926

0.4849

coordination degrees of coastal areas are higher. In terms of the coupling coordination of natural resources utilization efficiency and environmental utility, there is little difference among different regions. The coupling coordination degrees of the southwestern region were slightly higher than those of other regions. That of the northwestern region was lower than those of other regions due to serious damage to ecosystems, such as soil erosion and land desertification. In terms of natural resources utilization efficiency and social utility, the coupling coordination degrees of coastal areas were higher than those of inland areas. Compared with inland areas, the efficient utilization of natural resources has caused the economy of coastal areas and people’s living standards to improve faster. More infrastructure construction, higher investment in scientific research and education, and a better medical security system can directly promote social progress.

7.7.2 Policy Recommendations Through the preceding analysis, it can be seen that the utilization efficiency of natural resources in China is relatively low, and its coupling coordination degrees with

References

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economic utility, environmental utility, and social utility are unbalanced spatially. In view of the above results, this chapter puts forward the following suggestions. (1) Strengthening resources management, reducing environmental pollution, and maintaining ecological balance. The predatory exploitation of natural resources will cause a lot of waste of resources and destroy the local ecological balance. Deforestation and uncontrolled grazing on the grassland destroy the local ecological environment, resulting in land desertification, soil erosion, and other environmental problems. China should strengthen the management of resource development, give full play to the supervisory roles of relevant governmental departments, and reduce unreasonable resources development to improve resources utilization efficiency. (2) Improving the resource utilization efficiency in the central and western regions. China’s overall utilization efficiency of natural resources is relatively low, with uneven spatial distribution. The central and western regions are rich in natural resources, but have low natural resources utilization efficiencies as compared with the eastern coastal areas. With superior geographical locations, the eastern coastal areas have realized rapid economic development and relatively high resource utilization efficiency. Therefore, China should speed up the development of the central and western regions and focus on improving the natural resources utilization efficiency there while ensuring the improvement of natural resources utilization efficiency in the eastern region. (3) Increasing investments in scientific research and improving resources utilization efficiency. Science and technology comprise the primary productive force. The progress of science and technology will improve the resources utilization efficiency and promote economic development. On the one hand, the mining capacity of resources should be enhanced by using advanced mining equipment to reduce the waste of resources in the mining process; on the other hand, the resources processing capacity should be strengthened to reduce the generation of waste and make the best use of resources.

References 1. Chen, M.X., Lu, D.D., Zha, L.S.: The comprehensive evaluation of China’s urbanization and effects on resources and environment. J. Geog. Sci. 20(1), 17–30 (2010) 2. Lin, Y.Q.: Coupling analysis of marine ecology and economy: case study of Shanghai, China. Ocean Coastal Manag. 195, 105278 (2020) 3. Yu, X.Y., Li, Y., Chen, H.Y., Li, C.Y.: Study on the impact of environmental regulation and energy endowment on regional carbon emissions from the perspective of resource curse. China Popul. Resour. Environ. 29(5), 52–60 (2019) 4. Xing, L., Xue, M.G., Hu, M.S.: Dynamic simulation and assessment of the coupling coordination degree of the economy-resource-environment system: case of Wuhan City in China. J. Environ. Manag. 230, 474–487 (2019)

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5. Li, H.M., Jiang, Z., Dong, G., Wang, L., Huang, X., Gu, X., Guo, Y.: Spatiotemporal coupling coordination analysis of social economy and resource environment of central cities in the Yellow River Basin. Discret. Dyn. Nat. Soc. 2021, 6637631 (2021) 6. Wang, J.Y., Wang, S., Li, S., Feng, K.: Coupling analysis of urbanization and energyenvironment efficiency: evidence from Guangdong province. Appl. Energy 254, 113650 (2019) 7. Zhang, X.H., Wang, Y., Qi, Y.-B., Wu, J., Liao, W., Shui, W., Zhang, Y., Deng, S.-H., Peng, H., Yu, X., Qi, H.: Evaluating the trends of China’s ecological civilization construction using a novel indicator system. J. Clean. Prod. 133, 910–923 (2016) 8. He, J.Q., Wang, S., Liu, Y., Ma, H., Liu, Q.: Examining the relationship between urbanization and the eco-environment using a coupling analysis: case study of Shanghai, China. Ecol. Indic. 77, 185–193 (2017) 9. Zhang, J., Fu, X.M., Morris, H.: Construction of indicator system of regional economic system impact factors based on fractional differential equations. Chaos Solitons Fractals 128, 25–33 (2019) 10. Wang, W.J., et al.: Research on a four-dimensional evaluation indicator system for ecocivilization construction: a case study of Guangdong province in China. Chin. J. Urban Environ. Stud. 6(3), 1850017 (2018)

Chapter 8

Price Fluctuation of Natural Resources and Its Impacts on Economic Development

The development of economy and society is inseparable from the utilization and processing of various natural resources. Original natural resources are transformed into products and materials needed for human social life and production through mining, processing, and packaging, among other methods, so as to promote the development of society. However, the impacts of natural resources on social and economic development are not always positive, varying from country to country or region to region. In view of this phenomenon, a large number of scholars have studied the impacts of natural resources utilization on economic development and advanced some explanations and conjectures. Some scholars consider the impacts of natural resources on economic development to be related to the abundance of resources: the more the resources, the slower the economic growth. This view is more about correlation analysis between the abundance of natural resources and economic growth but cannot determine whether the abundance of natural resources is the reason for hindering economic development. Some other scholars deem the reason why natural resources are not conducive to economic development as not concerning the abundance of natural resources, but rather the shocks and risks brought by the price fluctuation of natural resources. The economic benefits gap arising from the adverse impacts of price fluctuation and the utilization of natural resources is the reason for the different impacts of natural resources utilization on economic development in different countries (regions).

8.1 Progress of Research on Price Fluctuation of Natural Resources and Its Impacts on Economic Development With the efficient utilization of natural resources, some natural resources (bulk commodities) have gradually entered the commodity trading market, which increases their proportions in the commodity trading market and their impacts on China’s economic development. However, the phenomenon of resource curse is also widely © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_8

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prevalent in China or regions with relatively abundant resources, hindering local economic development.

8.1.1 Resource Curse Effect The resource curse hypothesis was first put forward by Auty [1] after it was discovered that some countries with rich natural resources had not achieved good economic development. The theory holds that the economic growth in areas rich in natural resources lacks impetus owing to their high dependence on natural resources, where rich natural resources are no longer a “blessing” for local economic development, but a “curse.” Many scholars have conducted in-depth discussions on the resource curse effect, but not reached a consistent conclusion. After studying developing countries, Sachs and Warner [2] found that countries with more natural resources had worse economic growth, affirming the existence of the resource curse effect in developing countries. Through the study of the resource curse effect, Gylfason [3] found that natural resources had a crowding out effect on human capital and could slow down economic growth. Iimi [4] found that the governance of natural resources was the key to determining the effect of natural resources on economic growth; rich natural resources themselves could not ensure economic growth, but insufficient effective governance of natural resources was the reason for the existence of the resource curse. Zhang et al. [5] took China’s interprovincial data as samples and found that the resource curse effect existed in most areas with abundant natural resources in China. By using the data of several coal cities in China, Dong et al. [6] proved that coal mining was conducive to local corruption at all levels, which further demonstrated that corruption was a curse associated with natural resources. Wang et al. [7] analyzed the temporal and spatial differences and driving factors of China’s marine resource curse and found that China’s marine resource curse coefficient showed a downward trend from 1996 to 2014. Yang and Song [8] estimated the resource curse coefficients of 232 prefecture-level cities based on an endogenous growth model and empirically tested the destructive effect of environmental regulation on the resource curse and its internal mechanism. Xie and Zhai [9] empirically proved through path analysis using an SEM model that the resource curse effect was widespread in China. Although the resource curse effect has been proven to exist widely, many scholars still question it. Stijns [10] did not find a significant correlation between resources and economic growth when studying the resource curse effect of oil and mineral resources. Alexeev and Conrad [11] proposed that the resource curse effect did not exist, after adding some important omitted variables. Esfahani et al. [12] found that rich resources had positive effects on economic growth. Hu et al. [13] found that there was no direct resource curse effect based on China’s provincial panel data. Ding and Deng [14] empirically pointed out that the proposition of the resource curse was not obvious in China after controlling for factors such as harbor distance and government intervention.

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In summary, there is no consensus in the research about the resource curse effect, but the phenomenon of resource curse does exist widely, regardless of whether abundant natural resources are negatively correlated with economic growth, especially for developing countries. Countries or regions rich in natural resources should pay attention to the phenomenon of resource curse.

8.1.2 Study on Price Fluctuation of Natural Resources Besides the aforementioned direct research on the relationship between the abundance of natural resources and economic growth to determine the existence of the resource curse effect, other works have paid attention to the price fluctuation of natural resources to clarify why economic growth is slow in areas with abundant natural resources. For example, when studying the price fluctuation of bulk commodities, Combes and Guillaumont [15], Blattman et al. [16], and Cavalcanti et al. [17] found that, regardless of whether the price changes of natural resources were conducive to economic growth, they would eventually be offset by price fluctuation and bring no economic growth. This indicates that the phenomenon of resource curse might not be caused by the abundance of resources, but rather, that the fluctuation in the price of resources on economic growth limited the economic development. Such research findings could explain why the effects of resource abundance on economic growth are inconsistent in different countries or regions; when economic utility is greater than the adverse effect, the utilization of natural resources is conducive to economic growth; otherwise, natural resources utilization limits economic development. Aizenman and Pinto [18] affirmed the role of government regulation ability in coping with commodity price fluctuations: Developed countries or regions were better than developing countries or regions in coping with and preventing the impacts of price fluctuations, which could explain the common phenomenon of the resource curse effect in developing countries. As for commodity price fluctuations, some scholars have analyzed the influencing factors of commodity price fluctuations. For example, Krugman [19], Inamura et al. [20], and Dwyer et al. [21] studied the influencing factors of commodity price fluctuations from the perspective of supply and demand; Gilbert [22] and Basu and Gavin [23] studied the relationship between commodity financialization and commodity price fluctuations; Sanders and Irwin [24] and Stoll and Whaley [25] considered that market investment and speculation exacerbated commodity price fluctuations. Some Chinese scholars have carried out different studies on the price fluctuation of natural resources (bulk commodities). Wang et al. [26] considered resource price fluctuation to be an important factor in the study of resource curse, and that there is a U-shaped relationship between resource dependence and sustainable economic growth; Chen et al. [27] explored the influencing factors of China’s monthly iron ore import price from 2003 to 2013 using the quantile regression and found that under different quantiles, the influence intensity of each factor on the price is different; He et al. [28] studied the pricing relationship between China’s energy prices and

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international energy prices; Lin and Xu [29] studied the main driving forces of China’s commodity price fluctuations using the nonparametric additive model; Chen et al. [27] explored the influencing factors of the international price fluctuations of non-ferrous metals; Luo and Ji [30] took Guangdong as an example to empirically analyze the impacts of commodity price fluctuations on import and export trade; Zhang and Chen [31] analyzed the impacts of the global oil price shock on China’s commodity market and basic industries; and Li et al. [32] discussed the impacts of oil price shock on the stock return of listed companies in China’s oil industry chain based on the SVAR model. In summary, existing literature that studies the price fluctuation of natural resources (bulk commodities) can be divided into the following two categories. One is the study of the impacts of commodity price fluctuations on all aspects of economic development, for example, the impacts of commodity price fluctuations on economic growth, inflation, and trade level. In these studies, the impacts of commodity price fluctuations on economic development in different countries or regions and in different periods are different, resulting from different development levels and abilities to resist and manage market fluctuation risks. The other category of works analyzes the influencing factors of commodity price fluctuations. In this category, some scholars consider that the relationship between supply and demand affects the price of bulk commodities; scholars also consider that the financialization of bulk commodities affects the price fluctuations; some consider that speculation under market conditions exacerbates the fluctuation of bulk commodities; and some have analyzed the impacts of other economic indexes on commodity price fluctuations, such as economic cycle, domestic and foreign market shocks, stock market fluctuations, and futures market fluctuations.

8.1.3 Research on Volatility Modeling Variance or standard deviation refers to the dispersion of a group of data from the mean in a certain period, which is often used as a measure of the fluctuations of data series. Previous studies have shown that the price of an asset tends to fluctuate frequently in the sample range, showing volatility aggregation; that is, the fluctuation range of the return rate changes with time; large fluctuations often appear with large fluctuations, and small fluctuations appear with small fluctuations, which makes it difficult to capture the real fluctuation of asset prices only by sample variance. To understand the volatility characteristics of asset prices within the sample, it is also necessary to model the volatility of the sample to capture the time-varying volatility of the sample data. Engle [33] first proposed the autoregressive conditional heteroskedasticity (ARCH) model for the volatility of asset returns. However, the ARCH model often needs many parameters to describe the process of income fluctuation, and the order of lagging term is relatively large. Bollerslev [34] proposed the new generalized autoregressive conditional heteroskedasticity (GARCH) model based on the ARCH

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model. The GARCH model, which can avoid the problem of too large orders of the lagging term when studying asset return volatility, has been widely used by a large number of scholars at home and abroad to study volatility, such as Luo and Ji [30], Jiang et al. [35], and Lin and Chang [36]. Although the GARCH model has strong practicability in describing volatility aggregation, it also has some defects. First, GARCH assumes that positive and negative “disturbances” have the same impacts on volatility, but in practice, asset prices often show different responses to different disturbances, which the GARCH model cannot explain. Second, assets return often has an excess kurtosis greater than the kurtosis of normal distribution, and it is difficult to describe the characteristics of excess kurtosis, especially when a Gaussian innovation is assumed. Finally, the GARCH model only describes the change of volatility when modeling volatility and cannot explain the causes of income fluctuation. Thus, to overcome the limitations of the GARCH model, scholars at home and abroad have developed different generalizations of the GARCH model. To reflect the asymmetric effect of positive and negative asset returns in the GARCH model, Nelson [37] proposed an exponential GARCH (EGARCH) model considering weighted innovation that describes the asymmetric effect of a disturbance through a weight parameter. Liu et al. [38], Lin [39] and Cui et al. [40] adopted the EGARCH model to carry out empirical research. Different from the EGARCH model, Glosten et al. [41] and Zakoian [42] established a threshold GARCH (TGARCH) model by adding a threshold variable to the volatility model to deal with the leverage effect. With the deepening of research, the GARCH model has been extended to various forms, such as the integrated GARCH model (IGARCH) and the GARCH-M model considering risk premium. Besides research on traditional univariate GARCH models, there are also studies on the multivariate GARCH model and higher-order GARCH model.

8.1.4 Discussions on the Impacts of Natural Resources Price Fluctuation on Economic Development According to Combes and Guillaumont [15] and Blattman et al. [16], the price fluctuation of natural resources will inhibit economic development in areas with resources: When the inhibitory effect of the price fluctuation of natural resources on economic development in an area is greater than that of resources efficiency utilization on economic development, the economic impact of natural resources on the area is negative; when the impact of price fluctuation on economic development is less than that of resources efficiency utilization, the economic impact of natural resources on the area is positive. According to the viewpoint that the price fluctuation of natural resources inhibits economic growth, the impact of natural resources on economic development can be analyzed from the following perspectives. (1) Impact of natural resources price fluctuation on economic level Whether the effect of natural resource price fluctuation on the economic level of an area with resources is positive or negative can be first analyzed from the perspective

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of the overall economic level of the area. In this case, relevant macroeconomic data (e.g., GDP and fiscal revenue) can be analyzed, as these data can directly reflect the overall economic level and the economic changes in a country or region. If the price fluctuation of natural resources can affect the economic development of an area with resources, irrespective of the transmission mechanism and objects, the impact on economic development will ultimately be reflected in the economic level of the area. Studying the impact of natural resource price fluctuation on the economic level is the most intuitive way to study the impact of natural resource price fluctuation on the economic development of an area with resources. Although directly studying the impact of natural resource price fluctuation on the economic level is the most intuitive and convenient way, this method still has some defects. First, there are diversified factors that affect the economic level of a country or region, not just the price fluctuation of natural resources. Directly studying the relationship between the price fluctuation of natural resources and the economic level may lead to conclusions that are contrary to actual situations, as the final performance of the economic level may mostly come from the influences of other factors, and even the impact of natural resource price fluctuation might be offset by the influences of other factors. Second, the price fluctuation of natural resources might not have a large weight among the many influencing factors affecting the economic level, and direct research on the two may lead to insignificant correlation and meaningless conclusions. Thus, the analysis of some other economic indexes (e.g., consumption price level, inflation level, and production price level) that can also measure the economic level and are more closely related to natural resource price fluctuation will overcome some defects in the direct study of the relationship between natural resource price fluctuation and economic level. (2) Impact of natural resource price fluctuation on consumer price level Studying the impact of natural resource price fluctuation on economic development can begin with the perspective of consumer price level. When the price of natural resources rises, it increases the production cost and is directly reflected in the price level of the final commodity. Moreover, the consumer price level reflects the economic level and residents’ living standards in a country or region. The impact of natural resources price fluctuation on consumer price level is one of the transmission mechanisms of the impact of natural resources price fluctuation on economic development. The more violent the price fluctuation of natural resources, the more serious the impact on the consumer price level. (3) Impact of natural resource price fluctuation on inflation The influence of inflation can also be considered when studying the impact of natural resources price fluctuation on the price level. Inflation reflects more the change in price in a certain period than the consumer price level does, reflecting the rise in the price level and the decline of money purchasing power. Moderate inflation can promote economic growth, while excessive inflation is harmful to economic development. The change in inflation can also reflect the level and situation of economic development. As a kind of general commodity, natural resources’ price fluctuation

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should have a significant impact on inflation. Moreover, as a resource commodity, the price fluctuation of natural resources can also affect the price fluctuation of other general commodities. Theoretically, there should be a strong correlation between natural resource price fluctuation and inflation. When the price of natural resources fluctuates more violently, it stimulates greater inflation and limits the development of the economic level. (4) Impact of natural resources price fluctuation on production price level As a resource commodity, the efficient utilization of natural resources is often closely related to the production activities of general commodities. For example, energy resources provide energy supply and mineral resources provide raw materials for various production activities. The production cost rises with the price increase of natural resources. To achieve expected incomes, the price rise of natural resources is eventually reflected in the price of products, and the price change of products is reflected in the change of the production level of the whole society and finally affects economic development. The greater the price fluctuations of natural resources, the greater the restrictions on production activities and economic development. Overall, the impact of natural resource price fluctuation on economic development can be studied from two aspects. The first is to directly study the price fluctuation of natural resources, economic level, and economic growth, an approach with some defects, especially when analyzing big economic entities (e.g., China), that leads to ineffective or wrong conclusions. The second is not to directly study the price fluctuations of natural resources and the economic level, but to study some economic indicators that are closely related to the price fluctuations of natural resources and that reflect the level of economic development to a certain extent. The influence of the price fluctuation of natural resources on economic development is reflected from the relationship between these economic indicators and natural resources price fluctuation.

8.1.5 Research Content and Innovation (1) Research content By reviewing existing literature, this chapter follows the idea that natural resources themselves are beneficial to economic development and considers that the resource curse phenomenon does not arise because of the abundance of natural resources, because the impact of resource price fluctuations on economic development offsets the economic impact of resources in the process of resource utilization. Assume that the economic operation cost of a resource-poor economy is much greater than that of a resource-rich economy. If abundant resources restrict the economic development, it is against the general law of economic operation. Regardless of whether the resources are natural, human, or monetary, they are all economic resources in economics that can be transformed into economic benefits. The purpose of economic operation is to

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reasonably allocate and produce various economic resources and finally maximize economic benefits. Resources will eventually bring benefits; this is the most basic economic law. As for the final result of economic operation, it may fail to meet expectations owing to various uncertain factors in economic operation. However, it cannot be considered that natural resources are harmful to economic development simply because most economies rich in natural resources have slow economic development. The following sections analyze the impact of price fluctuation on economic development from the perspective of natural resource price fluctuation and then analyze the impact of natural resources utilization on economic development. The existing literature on natural resources price fluctuation and its impacts has certain characteristics. First, the research on the price fluctuation of natural resources is based on the price fluctuation of international bulk commodities or some domestic regions, but rarely does it cover the price fluctuation of natural resources in China. Second, when studying resource price fluctuations, most scholars focus on the relationship between natural resource prices and other economic indexes, paying more attention to the direction of price changes, but rarely price volatility. The research on the correlation between price and other economic variables can only explain how the price fluctuates, but cannot derive the size and other characteristics of the fluctuation. The impact of price fluctuations on an economy is often related to the severity of fluctuations. The more stable the price performance, the smaller the impact of price fluctuations. In the research that only considers the correlation between the price change and the change of economic indexes, we can analyze only whether the rise of natural resource price promotes or restricts economic growth, but not whether the impact of price fluctuation on economic development is promoting or restricting; it is also difficult to prove whether the restriction of economic development during the utilization of natural resources is caused by price fluctuations offsetting the impact of natural resources utilization on economic development. Considering some characteristics of previous scholars’ research on natural resource price fluctuation, this chapter conducts volatility modeling research based on China’s natural resource price series; analyzes the magnitude and characteristics of China’s natural resource price fluctuation; studies the relationship between China’s natural resource price fluctuation and relevant variables of economic development based on the volatility modeling; and empirically models and discusses the natural resources price fluctuation and its impacts on economic development in China. (2) Research innovation Compared with previous studies, the research content and perspective of this chapter are innovative from the following aspects. (a) This chapter models the natural resources price volatility and studies the impact of natural resources price volatility on economic development in China. The previous literature on the natural resources price fluctuation is mostly based on the relationship between price change and economic development, but rarely on the size of price fluctuation. The research on the relationship between natural resources price and economic development can only explain the correlation

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between price change and economic change, but cannot explain the reasons for the slow economic development in resource-rich areas. The reason is that the decline in resource prices is only a short-term fluctuation, and prices must rise in the long run; the rise of prices should promote economic development, which contradicts the widespread phenomenon of resource curse. On the contrary, if focusing on price fluctuations rather than price changes—that is, price fluctuations inhibit economic development and price rise promotes economic development—the impact of natural resources on economic development can finally be attributed to the comparison of the impacts of price fluctuations and of price rise on economic development, which can explain the long-term resource curse phenomenon. This chapter innovatively studies the impact of price fluctuation on economic development, which has not been considered in previous studies, and analyzes the impact of natural resources price fluctuation on economic development from the new perspective of volatility. (b) Judging from the perspective of research objects, most scholars prefer to model international commodity prices or natural resource prices in some Chinese regions, but this chapter models and analyzes China’s domestic bulk commodity prices considering China’s reality. Meanwhile, this chapter does not focus on some provinces with special resources, because in terms of provincial economic development, in addition to the advantage of special natural resources, other factors—such as the ability of different provincial governments to control price fluctuations, regional economic pattern, and regional environment, which can affect natural resources utilization and economic development—also differ. Moreover, the administration of a regional government is not as good as that of the national level, which makes it difficult to accurately evaluate the impact of natural resource abundance on economic development. As Aizenman and Pinto [18] state, the role of government in dealing with commodity price fluctuations is definite, and the regulatory capacity of the governments of developed countries is generally stronger than that of developing countries. The regulatory capacity at the national level in China is stronger than that at the provincial level. Thus, this chapter studies the impact of China’s domestic bulk commodity price fluctuations on the country’s economic development.

8.2 Study on Natural Resources Price Fluctuation in China 8.2.1 Model Selection and Discussion To describe the characteristics of China’s natural resource price volatility, this section establishes the GARCH model for China’s natural resource price series and further establishes the EGARCH model for China’s natural resource price volatility series based on the GARCH model to study the asymmetric effect of positive and negative returns. By comprehensively analyzing the modeling effects of the GARCH model

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and EGARCH model, the model to calculate the conditional volatility of China’s natural resource price series is selected. The GARCH model has the following forms: ⎧ ⎨ rt = u t + εt , εt = σt εt∗ m s   2 2 , αi εt−i + β j σt− ⎩ σt2 = α0 + j i=1

(8.1)

j=1

where rt is the series of modeling objects; the GARCH model requires that rt is the time t − 1; εt steady; u t = E(rt |Ft−1 ), and Ft−1 is an information set  until  is a disturbance series; εt∗ is the standardized residual; εt∗ is an independent and identically distributed sequence with a mean value of 0 and variance of 1; the GARCH model usually assumes that εt∗ follows normal distribution (NORM), Student’s tdistribution (STD), and generalized error distribution (GED); σt2 is the conditional variance of rt . To guarantee that the σt2 is constantly positive, it is restricted that αi , β j > 0; to guarantee that the unconditional variance of εt is limited and the max(i, j) conditional variance of εt is time varying, it is required (αi + βi ) < 1 s that i=1 (and when i > m or j > s, αi = 0 or β j = 0). If j=1 β j = 0, the GARCH model degenerates to the ARCH model. The EGARH model has the following forms: ⎧ ⎨ rt = u t + εt , εt = σt εt∗ m s   2 . i εt−i αi |εt−i σ|+γ + β j ln σt− ⎩ ln σt2 = α0 + j t−1 i=1

(8.2)

j=1

Compared with the GARCH model, the EGARCH model has the following advantages: First, the EGARCH model introduces parameter γi that can calculate the contributions of positive and negative disturbances εt to the volatility, reflecting the leverage effect that the GARCH model cannot explain. The contribution of the positive disturbance to the logarithmic volatility is αi (1 + γi )|εt−i |, while that of the negative disturbance is αi (1 − γi )|εt−i |. Whether there is a leverage effect can be judged by the significance of the parameter γi , and the difference of the contributions of positive and negative disturbances to the conditional variance can be compared through the size of γi . Second, the EGARCH model does not directly model the conditional variance σt2 , but uses the logarithmic conditional variance ln σt2 in the model to relax the regularity restriction of the GARCH model on parameters, making parameter estimation freer and easier.

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8.2.2 Data Selection and Description (1) Data source and processing To study the price fluctuation of China’s natural resources, the energy, mineral, and coal price indexes are selected for modeling and analysis, as energy, minerals, and coal are bulk commodities that can be traded in China’s spot market, and for which more accurate and unified price index data can be obtained. Weekly data and monthly data are selected for energy and minerals, respectively. Considering the discontinuity of the weekly coal price index, monthly data are selected for the coal price index. The data cycle runs from June 2006 to March 2021. The length of the weekly data is 764 and that of monthly data is 178. The data type is the base ratio data of the price index with a base period of June 2, 2006, all from the Wind database. Considering that the GARCH model requires a steady series, the logarithmic return rate of the natural resource price index will be used for empirical modeling. The logarithmic return rate of natural resources is obtained from the logarithmic difference of the natural resource price index. The logarithmic difference process is as follows: Logarithmic return rate = Current logarithmic price index − Lagging logarithmic price index = (Current logarithmic price index − Base period logarithmic price index) − (Lagging logarithmic price index − Base period logarithmic price index) = Base ratio of current logarithmic price index − Base ratio of lagging logarithmic price index. (8.3) Through Eq. (8.3), the following five logarithmic return rate series can be obtained: weekly logarithmic return of the energy price index (EW), monthly logarithmic return of the energy price index (EM), weekly logarithmic return of the mineral price index (MW), monthly logarithmic return of the mineral price index (MM), and monthly logarithmic return of coal (CM). (2) Descriptive analysis of samples Figure 8.1 shows the time series of the five types of logarithmic return of the price index, from which we observe that the five types of logarithmic return of the price index fluctuated around zero in an overall stable state. During the sample period, the five types of logarithmic return of the price index all have some abnormal values that significantly deviate from the average level, except CM. The five types all basically show different degrees of non-normality. Table 8.1 shows the statistical characteristics of the sample data and test results. Panel A gives the moment characteristics of the five logarithmic return series, which

0.2

0.0

-0.2

0.2

-0.2 0.0

0.05

2010

2010

2010

Fig. 8.1 Sample sequence diagram

-0.15

CM

EM

2015

2015

2015

EW

2020

2020

2020

0.05 -0.10 0.0 -0.2

2010

2010 MM

2015

2015

MW

2020

2020

262 8 Price Fluctuation of Natural Resources and Its Impacts on Economic …

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263

are the same as the results in the time series diagram, namely that the mean values of the five logarithmic return rates are all almost zero. Compared with the mean value, the standard deviation is larger, indicating that there is an obvious fluctuation of the five types of natural resource price indexes during the sample period. The five logarithmic return rates present the characteristics of non-normality, such as left deviation and excess kurtosis. The results of the Jarque–Bera normality test indicate that the normality assumption is rejected at the significance level of 1% for all five return rates, which is consistent with the non-normality characteristics shown in the time series diagram and Panel A. The stationarity test uses ADF statistics. The test results are all significant at the significance level of 1%, indicating that the stationarity test is passed. From the perspective of autocorrelation, the Ljung–Box test results show that the five logarithmic return series all have significant autocorrelation, and the ARCH effect test results show that the five logarithmic return series all have significant ARCH effects, except MM. The ARCH effect test of MM is significant only at the significance level of 5% when the lagging order is 5, indicating that the MM still has the ARCH effect, although this effect is weak. Table 8.1 Descriptive statistics of sample data EW

MW

EM

MM

CM

Panel A: Sample moment characteristics 0.0003

0.0009

0.0015

0.0041

0.0027

Minimum value

− 0.2268

− 0.1219

− 0.2591

− 0.2453

− 0.1861

Maximum value

0.1919

0.1166

0.2123

0.1635

0.1421

Standard error

0.0301

0.0250

0.0668

0.0572

0.0325

Deviation

− 0.7812

− 0.1928

− 1.0104

− 0.3204

− 0.4066

Kurtosis

14.1049

6.6848

5.9543

4.9893

13.2674

Mean value

Panel B: Sample test 4024.61***

440.54***

97.99***

33.96***

805.50***

− 7.84***

− 12.91***

− 7.40***

− 6.66***

− 5.31***

Q (10)

99.99***

83.57***

47.76***

28.73***

83.38***

Q (20)

107.92***

89.12***

54.57***

51.87***

92.25***

ARCH test (5)

214.76***

42.01***

44.55***

11.58**

65.25***

ARCH test (10)

246.79***

43.16***

44.20***

11.39

67.89***

JB test ADF test

Note JB test means the Jarque–Bera normality test. ADF test is the unit root test to determine the lagging order according to the AIC. Q is the Ljung–Box statistic. The numbers in parentheses in the ARCH test and the Q statistic indicate the lagging order *** and ** indicate significance at the 1% and 5% levels, respectively

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8 Price Fluctuation of Natural Resources and Its Impacts on Economic …

8.2.3 Model Empirical Analysis After analyzing the statistical characteristics of the sample data, this section establishes the GARCH and EGARCH models for all five logarithmic returns and analyzes the model estimation results. The lagging order of the GARCH model and the assumption regarding the distribution of standard residues εt∗ are determined according to the comprehensive performance of parameter significance, likelihood function value, AIC, and BIC in the empirical modeling process. Finally, GARCH (1, 1) and EGARCH (1, 1) are established for each of the five types of return rates. (1) Estimation result analysis Table 8.2 shows the parameter estimation results of the five logarithmic returns in the GARCH (1, 1) and EGARCH (1, 1) models. Panel A is the parameter estimation result of the GARCH (1, 1) model, and Panel B is the parameter estimation result of the EGARCH (1, 1) model. Dist represents the assumed distribution of the standard residue εt∗ during modeling. Log, AIC, BIC, and LR represent the likelihood function value, AIC value, BIC value, and likelihood ratio test statistics, respectively. The standard errors of the estimated parameters are given in parentheses, and the parameter estimation results are above the parentheses. In the GARCH (1, 1) model, the estimation results of parameters α1 and β1 are both significant at a certain significance level. The parameter α1 of the monthly data is larger than that of the weekly data, indicating that monthly logarithmic returns are more sensitive to the market. This may be because the monthly logarithmic return rates have a longer response cycle to market information, and the weekly rates often fail to respond quickly to market information within a shorter cycle. Furthermore, in the GARCH (1, 1) model, the parameter of EM data β1 is smaller than that of EW, and β1 of MM and CM is even more insignificant, indicating that the volatility of the monthly return rates is relatively short and more reflected in the current period, while the weekly rates may lag owing to the short response cycle to market information, and most of the price fluctuations in the current period are reflected until the lagging period. In the EGARCH (1, 1) model, the estimation values of parameter β1 all increase as compared with those in the GARCH (1, 1) model, indicating that the price volatility lasts longer under the EGARCH model. Meanwhile, the estimation values of β1 of EW and MW are all higher than those in the monthly data model, and the gap of β1 between the weekly data and the monthly data decreases significantly as compared with the GARCH (1, 1) model, indicating that in the EGARCH model, the difference between weekly data and monthly data in terms of market sensitivity is not as obvious as in the GARCH model. This can also be seen from the estimation results of parameter α1 . The estimation results of parameter γ1 are significantly positive, indicating that the five volatility series all have a leverage effect in China, and the positive disturbance has a greater impact on price fluctuation than the negative disturbance. This is decided by the supply–demand relationship of natural resource prices. When there is good news in the market, resource consumers often make a large number of transactions to avoid a significant increase in resource utilization costs in the future, resulting in greater fluctuations in market prices. However, when there is bad news in the market,

8.2 Study on Natural Resources Price Fluctuation in China

265

the demand of resource consumers will not change significantly, and there will be no concern about the rising cost of resource utilization in the future. Therefore, the emergence of bad news often will not cause too much fluctuation in the market. As parameter γ1 is introduced, the contribution of parameter α1 conditional variance is partially shared by γ1 , making some estimation results of α1 insignificant. This is also supported by the increase in significance of parameter α1 when the control parameter γ1 becomes 0. In the likelihood ratio test, the results of the five EGARCH models all reject the original hypothesis that γ1 is 0 at the significance level of 1%. (2) Leverage contribution analysis Table 8.3 shows the contribution parameters of the positive and negative lagging disturbances to logarithmic volatility in the EGARCH model. The absolute values of the contribution parameters of the positive lagging disturbance are all greater than those of the negative lagging disturbances. This is because in the five EGARCH models, the estimation results of parameter γ1 are significantly positive, and the volatility series of the five price indexes all have significant positive leverage effects. In the volatility model of the energy price index, the contribution of lagging disturbance to logarithmic volatility is negative; in the volatility model of the mineral price index, the contribution is positive; in the volatility model of the coal price index, the contribution of the positive lagging disturbance to logarithmic volatility is positive, and the contribution of the negative lagging disturbance to logarithmic volatility is negative. Compared with other natural resources, the supply of energy resources has strong monopoly. When the market is good, the energy supplier may give up the current transaction to pursue higher income in the future. When the market is bad, the energy supplier chooses to store more resources, as higher expectations are not met. The impact of the energy market may be very likely to smooth the energy market transactions, making the fluctuation of energy prices moderate. Mineral resources are traded more frequently in the market than energy resources are, and the monopoly of mineral resources is not as strong as that of energy resources. When good news appears, resource demanders choose to increase the proportion of the current transactions to reduce the future resource demand cost, and the resource supplier expands the transmission of information to better realize profits and promote the rapid rise of resource prices. When bad news emerges, users of resources do not increase resource demand, and resource suppliers also control risks by adjusting supply. Thus, the impact of bad news on price fluctuations is often not significant. As a kind of energy, coal is relatively easy to store and has less monopoly than other energy sources. When good news appears, there is more frequent trade of coal in the market, and the market price fluctuation rises significantly. When bad news appears, the coal demanders may reduce the prepurchase demand owing to the lower expected cost in the future rather than increase demand, and the coal suppliers actively respond to the decline in the market price. Thus, it is difficult for bad news to have a positive impact on market volatility.

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Table 8.2 Volatility model estimation results Parameter

EW

MW

EM

MM

CM

Panel A: GARCH model ut

0.0020*** (0.0007)

0.0003** (0.0001)

0.0082** (0.0041)

0.0041 (0.0053)

− 0.0007 (0.0014)

α0

0.0000** (0.0000)

0.0000 (0.0000)

0.0010*** (0.0004)

0.0026*** (0.0006)

0.0001*** (0.0000)

α1

0.2136*** (0.0498)

0.2108** (0.1003)

0.5661*** (0.1615)

0.2551* (0.1496)

0.9990*** (0.1870)

β1

0.7437*** (0.0555)

0.76747*** (0.1256)

0.2586** (0.1297)

Dist

GED

GED

GED

STD

STD

Log

1789.86

1839.95

261.29

265.72

453.11

AIC

− 4.68

− 4.81

− 2.90

− 2.96

− 5.07

BIC

− 4.65

− 4.78

− 2.81

− 2.89

− 5.00

Panel B: EGARCH model ut

0.0018*** (0.0007)

0.0001 (0.0001)

0.0066 (0.0049)

0.0017 (0.0041)

0.0003 (0.0010)

α0

− 0.4979*** (0.1375)

− 0.7484** (0.3068)

− 1.8524*** (0.5980)

− 0.8290 (0.6962)

− 3.4128*** (0.7419)

α1

− 0.1168*** (0.0305)

0.0448* (0.0262)

− 0.1711** (0.0848)

0.0480 (0.0815)

0.2415 (0.1848)

β1

0.9336*** (0.0183)

0.8968*** (0.0412)

0.6810*** (0.1049)

0.8552*** (0.1204)

0.5674*** (0.0917)

γ1

− 0.2928*** (0.0562)

− 0.3435*** (0.0706)

− 0.7182*** (0.1595)

(0.1204) (0.1967)

1.4453*** (0.2324)

Dist

STD

NORM

GED

STD

STD

Log

1801.48

1791.52

262.75

267.56

455.16

AIC

− 4.71

− 4.68

− 2.90

− 2.96

− 5.08

BIC

− 4.67

− 4.65

− 2.79

− 2.85

− 4.97

LR (H0: γ1 = 0)

81.812***

61.908***

23.5794***

8.9414***

57.0678***

Note The standard errors of the parameters are in the parentheses, and the estimated values of the parameters are given above the parentheses ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Dist represents the assumed distribution of the model, Log represents the likelihood function value, AIC and BIC represent the Akaike information criterion value and Bayesian information criterion values, respectively, and LR is the likelihood ratio test statistic Table 8.3 Contributions of positive and negative lagging disturbances to logarithmic volatility EW

MW

EM

MM

CM

α1 (1 + γ1 )

− 0.1510

0.0601

− 0.2940

00.0666

0.5906

α1 (1 − γ1 )

− 0.0826

0.0294

− 0.0482

0.0294

− 0.1075

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Table 8.4 Mean square error ratio of volatility Index

EW

MW

EM

MM

CM

MSER

0.57

0.48

0.52

1.26

2.44

MADR

0.88

0.77

0.85

1.65

0.57

(3) Comparison of volatility fitting effect To compare the volatility fitting effects of the five logarithmic returns under the GARCH model and EGARCH model, the volatility mean square error ratio (MSER) and mean absolute deviation ratio (MADR) of the five logarithmic returns under the EGARCH and GARCH models are calculated. The calculation processes of MSER and MADR are as follows: ⎧ T T

2 

2  ⎪ ⎪ σˆ 0t2 − σ 2 σˆ 1t2 − σ 2 / ⎨ MSER = t=1 t=1 (8.4) T   T   ,  ⎪ 2  2 2 2 ⎪  ⎩ MADR = σˆ 1t − σ / σˆ 0t − σ t=1

t=1

where σˆ 1t2 is the conditional variance under the EGARCH model; σˆ 0t2 is the conditional variance under the GARCH model; σ 2 is the unconditional variance of the sample; and T is the sample size. By comparing the results of MSER and MADR, the gap between conditional variance and unconditional variance under different models can be compared. If the result is less than 1, it indicates that the fitting error of the EGARCH model is smaller than that of the GARCH model; if the result is greater than 1, it indicates that the deviation between conditional variance and unconditional variance under the EGARCH model is greater. Table 8.4 shows the MSER and MADR results of the five volatility rates: The MSER and MADR results of the volatility of the EW, MW, and EM series are all smaller than 1; those of the MM series are all higher than 1; the MSER results of CM are higher than 1, while its MADR results are less than 1. Judging from the MSER results, the volatility fitting effects of EW, MW, and EM under the EGARCH model are better, while MM and CM are more suitable for the GARCH model. Judging from the MADR results, the CM series prefers the EGARCH model.

8.2.4 Analysis of Price Fluctuation of Natural Resources in China Figure 8.2 shows the conditional variance time series of five logarithmic returns under the GARCH and EGARCH models: The four charts on the left show the weekly price index fluctuations, and the six charts on the right show the monthly price index fluctuations. In general, there are about three periods with large price fluctuations in the sample cycle, namely 2008–2009, 2017–2018, and 2020–2021,

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which are basically consistent with the fluctuation trends shown in Fig. 8.1. The fluctuation characteristics of the five logarithmic returns under the GARCH model and EGARCH model are basically the same, but the fluctuation amplitude under the EGARCH model is slightly lower than that under the GARCH model. In addition, except CM, the other four logarithmic returns have higher fluctuation frequencies under the EGARCH model. Jointly considering Fig. 8.1, it is not difficult to find that the EGARCH model can depict more volatility changes when the return series fluctuate more frequently, while the GARCH model can depict more volatility changes when the return series fluctuate relatively gently (e.g., CM). Comparing the volatility rates of EW, MW, and MM, we find that the fluctuation frequencies of weekly return rates are higher with smaller amplitude, while those of monthly return rates are lower with larger fluctuation amplitude. Among the three natural resources, the price index fluctuations of mineral resources are more violent than those of energy resources and coal resources, and the coal price index is most stable. The following conclusions can be drawn from the preceding research on the price fluctuation of natural resources in China. First, there is heteroskedasticity in China’s natural resource price index series, and there are obvious fluctuation aggregation effects in time-varying variance and logarithmic return series and significant non-normality characteristics, consistent with the price fluctuation behaviors of most financial assets. Second, the three types of resources all have a significant positive leverage effect. Natural resources receive greater influences under the positive impact of the market than under the negative impact, and good news is more likely to cause greater volatility changes. This may be because the resource demand does not rise following a decline in prices, while resource suppliers can adjust resource supply and demand in response to an increase in prices to obtain higher benefits, making resource prices more sensitive to good news. Third, the contributions of the lagging disturbance of different types of resources to price volatility are different under the leverage effect. Among them, the contribution of the lagging disturbance of energy resources to price fluctuation is negative, that of mineral resources is negative, and that of coal price fluctuation differs under different market impacts. Fourth, different resources have different performance under price fluctuation, price sensitivity, and market impact. The research on the price fluctuation of natural resources cannot be generalized, and specific resources should be analyzed specifically.

269

Fig. 8.2 Conditional variance time series

8.2 Study on Natural Resources Price Fluctuation in China

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8.3 Impact of Natural Resources Price Fluctuation on Economic Development This section mainly undertakes an empirical analysis of the impact of China’s natural resource price fluctuations on economic development, from several aspects such as sample data selection, sample data testing, model design, and regression analysis.

8.3.1 Data Selection and Processing (1) Data selection and source To study the impact of China’s natural resources price fluctuation on economic development, China’s GDP, CPI, and PPI data are selected as economic samples. GDP can reflect China’s overall economic level. CPI and PPI, as price indicators of consumption and industrial production, can reflect China’s price level. Among them, the data of CPI and PPI are monthly year-on-year data, and those of GDP are quarterly data. To ensure synchronization between the economic samples and the natural resources volatility samples, the quarterly GDP data are transformed into monthly data. The sample period is from June 2006 to March 2021, with a total of 178 samples. To maintain the stability of data, the logarithmic difference series LGDP, LCPI, and LPPI of GDP, CPI, and PPI are used for empirical modeling analysis. LGDP, LCPI, and LPPI no longer measure the size of various indexes, but are the logarithmic growth rates of GDP, CPI, and PPI. The data of GDP, CPI, and PPI all come from the Wind database. Due to the generally significant leverage effects in the modeling process in the second section of this chapter, the volatility data of the monthly price index return rates of natural resources in the second section under the EGARCH model are selected as the volatility sample. The sample period is from July 2006 to March 2021, with a total of 177 samples. The volatility of EM, MM, and CM is VE, VM, and VC, respectively. (2) Descriptive statistics of data Figure 8.3 shows the time series of LGDP, LCPI, and LPPI, from which it is evident that the LGDP series shows strong cyclical characteristics, indicating that China’s GDP has an obvious cyclical effect; LCPI and LPPI series are basically stable and have obvious volatility aggregation. Table 8.5 shows the statistical eigenvalues and some test results of the sample series. Among them, LGDP and LCPI both have a negative deviation, while LPPI has a positive one, and the three series all have excess kurtosis, showing obvious non-normality characteristics. Judging from the Jarque–Bera test results, the three series all reject the normality assumption at the significance level of 1%. Judging from the results of the autocorrelation test, LGDP, LCPI, and LPPI all have significant autocorrelation, which should be fully considered during modeling. The ADF test

8.3 Impact of Natural Resources Price Fluctuation on Economic Development

271

-0.2 0.0

LGDP

2010

2015

2020

2015

2020

2015

2020

-0.02 0

LCPI

2010

-0.04 0.02

LPPI

2010

Fig. 8.3 Sample data time series

results also show that the LGDP series is unstable. Considering the obvious periodicity and instability of the LGDP series and the defects of the research on the impact of natural resources price fluctuation on the economic level as discussed in the first section of this chapter, the research on the relationship between the LGDP series and natural resource price volatility is abandoned, and only the impacts of natural resources price fluctuations on LCPI and LPPI are analyzed. LCPI and LPP series both pass the stationarity test. The ADF test results of three volatility samples show that the three volatility series pass the stationarity test at a certain significance level. Panel E gives the correlation test of the three volatility series. The results show that there is a significant pairwise correlation between the three volatility series, meaning that when establishing the models, a multicollinearity problem arises, especially for VE and VM, as coal itself is an energy resource, and the energy price index itself already contains the information of the coal price index. To avoid multicollinearity in modeling, logarithmic processing of the three volatility series is conducted, and the logarithmic volatility of LVE, LVM, and LVC is used instead of VE, VM, and VC, respectively, for modeling. Logarithmic processing of volatility series can not only eliminate multiple collinearity, but also maintain the correlation between volatility, LCPI, and LPPI.

8.3.2 Granger Causality Test To model and analyze the impacts of volatility on LCPI and LPPI, it is necessary to determine the causal relationship between the three types of volatility and LCPI and LPPI. Only by determining whether there is causality between volatility and LCPI and LPPI, and the cause and result, can we determine the model type, explanatory variables, and explained variables. To study the causal relationship between volatility

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Table 8.5 Descriptive statistics of samples

Panel A: Moment characteristics of economic samples Variable

LGDP

LCPI

LPPI

Minimum value

− 0.2301

− 0.0261

− 0.0441

Maximum value

0.1606

0.0148

0.0393

Mean value

0.0080

− 0.0001

0.0000

Standard error

0.0570

0.0060

0.0097

Deviation

− 1.11

− 0.61

0.028

Kurtosis

5.28

5.08

7.597

Panel B: Economic sample test Variable

LGDP

LCPI

LPPI

JB test

77.45***

44.96***

161.78***

Q (10)

187.53***

36.311***

174.58***

Q (20)

448.95***

111.41***

267.43***

ADF test

− 2.95

− 5.87***

− 3.58**

Panel C: Moment characteristics of volatility samples Variable

VE

VM

VC

Minimum value

0.0012

0.0013

0.0000

Maximum value

0.0356

0.0107

0.0569

Mean value

0.0042

0.0036

0.0012

Standard error

0.005

0.0019

0.0046

Deviation

3.71

1.29

10.37

Kurtosis

19.15

4.64

122.79

Panel D: Volatility sample test Variable

VE

VM

VC

ADF test

− 5.51***

− 3.27*

− 3.84**

Panel E: Volatility sample correlation test Variable

(VE, VM)

(VM, VC)

(VE, VC)

T statistics

2.44**

4.61***

3.57***

Correlation coefficient

0.18

0.33

0.26

Note JB test means the Jarque–Bera normality test. ADF test is the unit root test to determine the lagging order according to AIC. Q is the Ljung–Box statistic. The numbers in parentheses indicate the lagging order. T refers to the T test statistics ***, **, and * indicate being significant at the significance level of 1%, 5%, and 10%, respectively

8.3 Impact of Natural Resources Price Fluctuation on Economic Development

273

and LCPI and LPPI, Granger causality tests are carried out for the three types of volatility and LCPI and the three types of volatility and LPPI. Table 8.6 shows the Granger causality test results of the three types of volatility, LCPI, and LPPI: the left column is to test the original hypothesis, the middle column is F statistics, and the right column presents the conclusion based on the test results. According to the test results, LVE and LCPI are mutually causal, indicating that the price fluctuation of energy can affect the growth of the consumer price index, and in turn, the change in residents’ consumption level affects the price fluctuation of the energy market; the change in residents’ consumption level determines the relative demand of energy and affects the energy market price to a certain extent; LVC can be used as the explanatory variable of LCPI, indicating that coal price fluctuation can affect residents’ consumption level, possibly because coal resources are easy to be used directly by residents, and coal is a part of residents’ consumption in daily life. However, LCPI does not Granger cause LVC, indicating that the change in residents’ consumption level does not change the price fluctuation of coal, which is actually determined by the characteristics of residents’ demand for coal resources; when the coal price rises, the residents’ ability to use other consumer goods begins to decline, while coal demand does not increase due to the improvement of consumption level when the residents’ consumption ability increases, thereby leading to a oneway causal relationship between LVC and LCPI. The Granger causality test results of LVM and LCPI show that there is no significant causality between them. This is because mineral resources do not directly affect residents’ life and do not by themselves meet residents’ daily life needs. The consumption decision of mineral resources in daily life is not related to the price fluctuation of mineral resources. LPPI also has a causal relationship with LVE, but LPPI can only be taken as the explained variable of LVM rather than that of LVC.

8.3.3 Model Design and Regression (1) Model design The results of the Granger causality test show that LVE and LVC Granger cause LCPI, and LVE and LVM Granger cause LPPI; LCPI, LPPI, and LVE are in a mutually causal relationship. According to the causal relationship between the volatility series and LCPI and LPPI series, multiple linear regression models of LCPI and LPPI on volatility are established to analyze the impact of volatility on LCPI and LPPI. Considering the two-way causal relationship between LCPI, LPPI, and LVE, VAR models for LCPI, LPPI, and LVE are established to analyze the interaction among LVE, LCPI, and LPPI. Since the autocorrelation test results of LCPI and LPPI in Table 8.5 all exhibit significant autocorrelation, it is necessary to fully consider the autocorrelation of the explained variables when establishing the multiple linear regression model. Figure 8.4 shows the autocorrelation function diagram of LCPI and LPPI, which

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Table 8.6 Granger causality test Original hypothesis

F statistics

Conclusion

LVE does not Granger cause LCPI

6.9499***

Reject the original hypothesis

LVE does not Granger cause LCPI

6.397**

Reject the original hypothesis

LVM does not Granger cause LCPI

0.0047

Accept the original hypothesis

LCPI does not Granger cause LVM

0.0042

Accept the original hypothesis

LVC does not Granger cause LCPI

4.9323**

Reject the original hypothesis

LCPI does not Granger cause LVC

0.0764

Accept the original hypothesis

LVE does not Granger cause LPPI

4.1386**

Reject the original hypothesis

LPPI does not Granger cause LVE

7.3359***

Reject the original hypothesis

LVM does not Granger cause LPPI

3.6546*

Reject the original hypothesis

LPPI does not Granger cause LVM

0.0666

Accept the original hypothesis

LVC does not Granger cause LPPI

0.2324

Accept the original hypothesis

LPPI does not Granger cause LVC

0.7972

Accept the original hypothesis

Note F statistics means the F test statistics ***, **, and * indicate being significant at the significance level of 1%, 5%, and 10%, respectively

shows that both LCPI and LPPI have a significant 12-order lagging correlation due to the characteristics of the sample data. CPI and PPI are both year-on-year data, that is, the ratio of the current value to the value lagging 12 periods; if the current value is a, the lagging 12-period value is b, and the lagging 24-period value is c, then the current year-on-year data is a/b and the lagging 12-period data is b/c. The current value can be increased either by increasing a or decreasing b; if b is decreased, the year-on-year ratio of 12-period lagging will also decrease; if a is increased, the year-on-year ratio of the future 12-period will decrease. According to the characteristics of year-on-year data, the current values of LCPI and LPPI should be negatively correlated with the value of the 12-period lagging. In addition to the autocorrelation caused by the characteristics of year-on-year data, the LPPI series also has a certain degree of low-order autocorrelation. In the linear regression model, the autocorrelation effect is generally addressed by conducting an auxiliary regression on the residual term and establishing a generalized difference model, which plays a good role in dealing with low-order autocorrelation. However, both LCPI and LPPI have significant 12-order lagging autocorrelation, and the applicability of the generalized difference model is very low. Considering the autocorrelation characteristics of LCPI and LPPI, the method of adding the correlation lagging term to the linear regression model is adopted, and multiple linear regression models with a lagging term are established for LCPI and LPPI. The form of the model is as follows: LCPIt = ω + α1 · LVEt + α2 · LVCt + β1 · LCPIt−12 + εt , LPPIt = ω + α1 · LVEt + α2 · LVMt + β1 · LPPIt−12 + β2 · LPPIt−1 + εt (8.5)

Fig. 8.4 LCPI and LPPI serial autocorrelation diagram

8.3 Impact of Natural Resources Price Fluctuation on Economic Development

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8 Price Fluctuation of Natural Resources and Its Impacts on Economic …

Table 8.7 VAR model sample moment tests and order determination results

(LCPI, LVE)

(LPPI, LVE)

Q (10)

236.0***

424***

Q (20)

343.2***

631***

AIC order

12

2

BIC order

1

2

HQ order

1

2

Note (a, b) refer to the matrix of a and b; Q refers to the multivariate serial correlation test, and the test lagging orders are given in parentheses after Q; AIC order, BIC order, and HQ order are the lagging orders selected according to AIC, BIC, and HQ, respectively *** indicates significance at the 1% significance level

where the subscript t indicates the data period; t − i represents the data i-period lagging compared with period t; parameter α indicates the impact of volatility on LCPI and LPPI; and parameter β indicates the influences of lagging LCPI and LPPI on the current value. According to the year-on-year data characteristics, the value of parameter β1 is estimated to be significantly negative. Table 8.7 shows the correlation tests of the VAR modeling sample matrix and the lagging order determined by relevant information criteria. Judging from the correlation tests, the matrix samples of LCPI and LVE and of LPPI and LVE are significant at a significance level of 1%, indicating that the sample data have serial correlation. The order determination results show that LCPI and LVE select the lagging order 12 under the AIC and lagging order 1 under the BIC and the HQ, while LPPI and LVE select lagging order 2 under all three information criteria. After comprehensively considering the lagging orders under all three information criteria and the empirical modeling effects, a VAR model with a lagging order of 2 is established for LCPI and LVE and for LPPI and LVE, of which the forms are as follows: ⎧

  ⎪ LCPIt ε1t ⎪ ⎪ LCPI + α LCPI + β LVE + β LVE + = ω + α ⎪ 1 t−1 2 t−2 1 t−1 2 t−2 ⎨ LVE ε t

2t  , 

⎪ ε LPPIt ⎪ ⎪ = ω + α1 LPPIt−1 + α2 LPPIt−2 + β1 LVEt−1 + β2 LVEt−2 + 1t ⎪ ⎩ LVE ε t

2t

(8.6) where parameters w, α, and β are all parameter matrixes that each has two rows and one column. (2) Multiple linear regression model analysis Table 8.8 shows the parameter estimation results of the multiple linear regression model, in which the parameter estimation T statistics are in square brackets and the parameter estimation values are in front of the brackets. In terms of significance, parameters α and β both pass the significance test at a certain significance level. In the model of LCPI, the estimation result of parameter α is negative, indicating that there

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277

is a negative correlation between LVE, LVC, and LCPI; that is, when energy and coal prices increase, the consumption level in the current period is reduced; the parameter β1 is significantly negative, indicating that LCPI is negatively correlated with the 12-period lagging value. In the model of LPPI, parameter α1 is also smaller than 0, and parameter α2 is positive, indicating that the price fluctuation of mineral resources generates a higher PPI. Overall, the price fluctuation of energy resources inhibits the rise of CPI and PPI levels and has a certain restrictive effect on economic growth, while the price fluctuation of mineral resources promotes the rise of PPI level, and appropriate price fluctuation is beneficial for economic growth. The estimated value of the parameter β1 is significantly negative, which is consistent with the expectation. The estimated value of the parameter β2 is positive, indicating that the change trend of PPI has a certain sustainability; that is, the growth of PPI in the current period can promote the growth of PPI in the next period. The autocorrelation test results of residual series under multiple linear regression are all insignificant, indicating that the model does not have an autocorrelation effect, which is in line with the original assumption of no correlation between residuals. (3) VAR model analysis Table 8.9 shows the estimation results of the VAR model parameters. Under the VAR model, the synchronous correlation coefficients of residuals are all less than 0, which is consistent with the correlation between LCPI and LVE and between LPPI and LVE in the multiple linear regression model. The estimation result of α in the LCPI model for LVE is positive, indicating that the growth of CPI will continue in the future; the estimation result of parameter β1 is less than 0 while that of β2 is higher than 0, indicating that a change in LVE in previous periods will inhibit the rise of CPI in the current period, but such inhibitory effect will be weakened over time. The estimation result of α in the LVE model for LCPI is negative, indicating that increases in the CPI level in previous periods restrict the energy price fluctuation in the current period; that is, the rise of LVE leads to a decline of CPI in the future, a greater energy price fluctuation, and finally, a long-term negative impact of energy price fluctuation on Table 8.8 Parameter estimation results of multiple linear regression model Parameter

LCPI

LPPI

ω

− 1.5876***

[− 4.364]

0.4732

[0.795]

α1

− 0.1945***

[− 3.778]

− 0.1188*

[− 1.697]

α2

− 0.0563*

[− 1.843]

0.2016**

[2.070]

β1

− 0.5348***

[− 8.383]

− 0.2596***

[− 5.259]

0.6914***

[13.003]

β2 Q (10)

15.873

15.873

Note Values in the square brackets are T statistics, and those before the square brackets are the parameter estimation values ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively Q is the autocorrelation test of the residual term, and the lagging order is in parentheses after Q

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the rise of the CPI level. The estimation result of parameter β1 is positive while that of β2 is insignificant, indicating that the energy price fluctuation also has some sustainability, but not for long. The parameter α1 is significantly positive in the LPPI model for the LVE and is higher than 1, indicating that the current-period PPI change has a strong positive effect on the next-period LPPI; the estimation result of α2 is negative, meaning that the lagging influence of PPI will be weakened with the passage of time, and the positive and negative effects of a short lagging period and long lagging period on the current-period PPI offset each other, but the shorter the interval, the greater the effect intensity of the lagging period on the current period. The significance of parameters β1 and β2 is not strong, indicating that the energy price fluctuation in the lagging period has only weak influence on the current-period PPI level. The estimation result of parameter α1 in the LVE model for the LPPI is negative while that of α2 is positive, and the absolute value of parameters is large, indicating that the lagging PPI growth closer to the current period greatly reduces the current energy price fluctuation, and the further the lagging period, the greater the current-period energy price fluctuation. This is exactly in line with the characteristics of market price fluctuation, namely that the price fluctuates violently in the short term, but becomes relatively stable under the different influences of positive and negative effects. The result of the parameter β1 is significantly positive while that of β2 is insignificant, which is basically the same as the results of the LVE model for the LCPI. This demonstrates that the two VAR models give consistent conclusions on the sustainability of energy price fluctuations. The VAR model estimates the impacts of the lagging period of the two variables on the current period, but does not give the synchronous dependence between the variables. Thus, it is impossible to know the synchronous impact of the two variables on each other from the model results. To obtain the synchronous correlation of the two variables, the VAR model is linearly transformed to a new model including the effect of the current period. The transformation proceeds by conducting a Cholesky decomposition on the model covariance matrix to obtain an upper triangular matrix or lower triangular matrix of which the diagonal elements are all 1. Then, the new model including the effect of the current period can be obtained by left multiplying the inverse matrix of this matrix in the VAR model. Table 8.10 shows the synchronous action parameters of the VAR model variables after linear transformation. According to the parameter results, the correlation between LCPI, LPPI, and LVE is negative, which is consistent with the regression results of the multiple linear model and the synchronous correlation coefficients of the VAR model residuals, and indicates that LCPI and LPPI are synchronously negatively correlated to LVE. The synchronization dependencies of LCPI and LPPI on LVE are − 0.0017 and − 0.0024, respectively, while the synchronization contributions of LCPI and LPPI to LVE are − 12.6062 and − 16.9433, respectively, indicating that the synchronization impacts of LCPI and LPPI on LVE are stronger than their synchronization dependencies on LVE. In other words, although energy price fluctuations can affect the current CPI and PPI levels, they are more vulnerable to the CPI and LPP levels. This may be because when CPI and PPI levels rise, the social demand for energy resources will not increase, and the energy market price changes little;

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Table 8.9 Estimation results of VAR model parameters LCPI

Parameter

LVE

Panel A: (LCPI, LVE) ω

− 0.0043

[− 1.050]

− 2.0363***

[− 5.849]

α1

0.0321

[0.422]

− 16.8659**

[− 2.601]

α2

0.2017***

[2.611]

− 5.9046

[− 0.896]

β1

− 0.0023**

[− 2.577]

0.7673***

[9.968]

β2

0.0016*

[1.823]

− 0.1184

[− 1.589]

Synchronous correlation coefficient

− 0.1478

Q (10)

40.692 [0.996]

− 1.7868***

[− 5.172]

Panel B: (LPPI, LVE) ω

0.0041

α1

1.0898***

[14.247]

− 19.3470***

[− 3.036]

α2

− 0.3322***

[− 4.236]

11.9236*

[1.825]

β1

0.0013

[1.371]

0.7432***

[9.524]

[− 0.637]

− 0.0517

[− 0.668]

β2

− 0.0006

Synchronous correlation coefficient

− 0.2034

Q (10)

20.997

Note Values in the square brackets are T statistics, and those before the square brackets are the parameter estimation values ***, **, and * mean being significant at the significance level of 1%, 5%, and 10%, respectively Q is the autocorrelation test of the residual term, and the lagging order is within the parentheses after Q

when CPI and PPI levels fall, the decline in people’s consumption ability reduces the demand for energy resources. Irrespective of whether the resource supplier wants to increase the price to obtain benefits or reduce the price to increase the trading volume, or whether the energy demander reduces their demand, the energy market price fluctuates greatly. Meanwhile, many other economic factors affect CPI and PPI, resulting in the impacts of energy resources on the CPI and PPI levels being much smaller than those of the CPI and PPI levels on the energy resources price fluctuation. Overall, the fluctuation of the energy market price limits the rise of CPI and PPI, and the change of CPI and PPI causes great fluctuation in the energy market price. Table 8.10 Synchronization parameter Synchronization parameter

LCPI–LVE

LVE–LCPI

LPPI–LVE

LVE–LPPI

− 0.0017

− 12.6062

− 0.0024

− 16.9433

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8.3.4 Result Analysis Through empirical modeling, it can be observed that the fluctuation in the energy market price limits the rise of CPI and PPI in both the multivariate linear model and the VAR model. According to the VAR model, the limiting effect of energy market price fluctuation on the CPI level is mainly reflected in the short term, but when the lagging period is longer, the limiting effect becomes promoting. This means that the fluctuation in the energy price limits the rise of the CPI level in each period, but this restriction does not last constantly. The restrictive effect in the short term and the promotion effect in the longer lagging period offset each other in the long term. Thus, in the long run, price fluctuations in the energy market cannot limit the rise of CPI and PPI levels. The limiting effect of energy market price fluctuation on PPI can be reflected only in the current period, and the lasting effect is not significant. Judging from the multiple linear regression model, the effect of coal price fluctuation on CPI is basically the same as that of energy price fluctuation, but a moderate price fluctuation of mineral resources can promote the rise of the PPI level, which indicates that different resources should be considered differently when analyzing the impact of natural resource price fluctuations on economic development.

8.4 Conclusion and Research Prospects 8.4.1 Main Conclusions Through modeling and analysis of China’s natural resource price volatility, this chapter establishes a model of the impacts of resource price volatility on economic development to study the characteristics of China’s natural resource price volatility and its impacts on economic development. From the model results, the following conclusions can be drawn. (1) Analysis of weekly price index data and monthly price index data of energy, mineral, and coal resources shows that the price volatility of all three kinds of resources has significant volatility aggregation effects and positive leverage effects; monthly price volatility has lower frequency and a greater fluctuation range compared to weekly data, and the characteristics of the price fluctuation of each resource are also different. Thus, each kind of resource should be analyzed specifically when analyzing the price fluctuation of natural resources in China. (2) When analyzing the impact of volatility on economic development, we find that the price fluctuation of energy resources has a strong restrictive effect on economic development, while there is no significant correlation between the price fluctuation of mineral resources and the level of CPI; moderate price fluctuation of mineral resources can promote the rise of the PPI level.

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(3) The model results show that the price fluctuation of natural resources in China does have an impact on economic development, but the effects of energy resources and mineral resources are different. From the perspective of energy resources, resource price fluctuations limit economic development, which supports the hypothesis of resource curse. However, from the perspective of mineral resources, their price fluctuations have very weak impacts on economic development. On the whole, the impacts of price fluctuations of different resources on economic development are different and cannot be explained by just analyzing several types of resources, but rather by comprehensively considering the actual conditions of resource utilization in China and in different regions, to determine whether and how the price fluctuations of natural resources affect economic development, and to clarify whether the phenomenon of resource curse is caused by the price fluctuation of natural resources.

8.4.2 Research Prospects This chapter analyzes the impact of China’s natural resources price fluctuation on economic development from the new perspective of resource price fluctuation, rather than simply considering the correlation between the natural resource price level and economic development. Whether the price fluctuation of natural resources promotes or restricts economic development can be demonstrated from the perspective of volatility rather than by a simple price series correlation study, because, regardless of whether the price is positively or negatively correlated with economic development, it makes economic sense for the price level to rise with the development of the economy and society. However, the research content of this chapter also has certain limitations and can be further expanded. First, due to the instability and strong cyclical effect of the GDP series, research on the relationship between volatility and GDP is not conducted, and other data that can directly measure the economic level (e.g., fiscal revenue) are not considered. Second, this chapter is based only on the analysis of centralized resource prices in bulk commodity trading and does not consider other non-standardized natural resources (e.g., water resources). Moreover, the sample cycle is selected according to the time length of China’s bulk commodity price index. Hence, both sample width and sample length can be further expanded. Finally, this chapter studies only the impact of natural resource price volatility on China’s economic development, without conducting a detailed study of the transmission mechanism and the specific impacts under each mechanism. Moreover, the formation mechanism of resource price volatility is not studied. More detailed research could be carried out from these aspects in the future.

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Chapter 9

Research on Policy Support System and Supervision Mechanism of Natural Resources Efficiency Utilization

Previous chapters focus on the evaluation of natural resources utilization efficiency and analyze its economic, environmental, and temporal and spatial effects. With the deepening of China’s development of natural resources, the negative externalities of natural resources utilization have begun to appear, and the traditional policy system can no longer meet the requirements of the future sustainable utilization of natural resources. Hence, some countries strive to enact efficient and high-quality natural resources utilization policies. This chapter discusses how to realize the policy mechanism and supervision means of natural resources efficiency utilization based on China’s national conditions and specific practice through a comparison of policies and supervision systems of natural resources efficiency utilization in China and abroad.

9.1 Research on Policy, Evaluation, and Supervision System of Natural Resources Efficiency Utilization Abroad 9.1.1 Policy Framework of Foreign Natural Resources Laws and Regulations In foreign countries, natural resources legislation is an important link in natural resources efficiency utilization and is becoming increasingly comprehensive and systematic. Legislation represents the will of the ruling class and reflects various interest relations as a superstructure. Understanding the policy framework of foreign natural resources laws and regulations helps us understand the deep-seated impact of laws on natural resources utilization. In this regard, this section summarizes the development process, status, and trend of foreign natural resources protection laws from a macro-perspective, to provide reference for the legal practice of China’s

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_9

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natural resources protection and utilization based on the current situation of China’s natural resources legislation. (1) Development of international natural resources protection laws International natural resources protection laws were established for protecting the Earth’s natural resources. Before the Industrial Revolution, the impact of human economic activities on the nature was still within the scope of the Earth’s selfpurification, and governments’ awareness of natural resources protection was still in its infancy. After the Industrial Revolution, capitalism developed rapidly. The progress of natural science increased human development of natural resources, and capitalism stepped up its foreign aggression and expansion to compete for raw materials. Therefore, in the early stage of the Industrial Revolution, the international natural resources protection laws mainly protected animals and plants and mostly dealt with local and regional problems, such as the London Convention, Rhine River Fishing Agreement, and World Convention for the Protection of Beneficial Birds. With the accelerated development of the capitalist economy and the rise of economic globalization, the global destruction of natural resources and the pollution of the ecological environment threaten the development of humankind. Environmental problems can no longer be solved by one country alone; these include the increase of global extreme climate caused by the greenhouse effect, the destruction of the soil and water environment by acid rain, the sharp reduction in biological quantity, and the cross-border pollution of hazardous pollutants. As the international natural resources protection laws tend to focus on human interests and aim at protecting biological diversity and sustainable utilization of resources all over the world. The scope of legal protection of natural resources gradually expands, including not only animals and plants, rivers and lakes, and land and minerals, but also meteorological, energy, microorganism, and other resources (Fig. 9.1).

Fig. 9.1 Development of international natural resources protection laws

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(2) Development status of international natural resources protection laws The object of natural resources laws is the various environmental elements on which human beings depend and the behaviors of subjects toward various environmental elements, and the purpose of law is to adjust the various relations generated by subjects in the development, utilization, protection, and improvement of natural resources. The international legislation on natural resources has experienced a transformation from development to sustainable development. With the gradual improvement of public environmental awareness, the natural resources protection laws have entered the stage of environmental transcendence; that is, the development of natural resources and economic growth should not be at the expense of the environment. In the formulation of laws, the central government promulgates principled legal provisions, and local governments formulate specific policies according to the characteristics of regional economic development and resource conditions, taking into account cost–benefit analysis and evaluation of natural resources development. For instance, the natural resources protection laws of the United States have experienced three stages: private free development, adjustment and compatibility, and restriction of development; Congress passes dozens of bills on public resources to determine the overall legal framework, and the state governments formulate laws and regulations independently according to the state conditions. From a global perspective, legislation on natural resources in developed countries is more complete and systematic than that in developing countries. The transfer of heavily polluting industries from some developed countries to developing countries is bound to cause international environmental problems, which cannot be solved within the scope of one country’s sovereignty. Thus, the protection of natural resources should focus on cross-border international cooperation, advocate peaceful consultation, and increase assistance to developing countries. At present, the international protection of natural resources advocates international cooperation among governments and organizations, and many international organizations have been established, such as the WWF, GP, and the IMO. (3) Development trend of international natural resources protection laws Through the comprehensive investigation of international natural resources legislation, several major trends can be summarized. First, the legislation of natural resources pays attention to the role of the market; the management of natural resources requires the participation of not only governments, but also enterprises; therefore, while determining the real right of natural resources, the mode of cooperation between the market and government should be advocated to give play to the role of the price mechanism and integrate the protection of resources into the industrial development of resources. Second, the legislation of natural resources protection moves toward the integration of public and private; it is considered that the disputes over natural resources also fall under the scope of public law, and governments have the right to intervene. Third, the legislation of natural resources protection should reflect the ecological value of resources, so that the investment in the ecosystem should be considered when considering the cost of natural resources development

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to limit the unscientific development of resources. Fourth, the concept of natural resources legislation should be modernized. Natural resources are no longer inexhaustible, and more consideration should be given to the long-term interests of economic development. Steps to be taken include developing resources based on natural laws, making the resource management system more comprehensive, and establishing resource price determination system and resource compensation system.

9.1.2 The Roles of Subjects Abroad in Natural Resources Efficiency Utilization By studying the guiding and normative role of foreign laws in the utilization of natural resources, we can understand that the natural resource protection laws in developed countries have had a development history of nearly 100 years, the scope of legal protection of natural resources has gradually increased, and governments have considered whether natural resources reflect ecological values rather than just centering on economic interests. Laws play a leading role in the overall policy frame, but the implementation of natural resources efficiency utilization policies is also inseparable from the participation of various subjects. Thus, this section summarizes the responsibilities and roles of various subjects abroad in the efficient utilization of resources, clarifies the ways in which they participate in the implementation of policies, and explores the operation mechanism of foreign policies for natural resources efficiency utilization from a new perspective. (1) Government perspective A country is both the owner and manager of its natural resources. Administration is only a part of the management of natural resources. Foreign natural resources management needs the cooperation of governments, professional policies, and professionals to realize the efficient allocation of natural resources, so as to maximize the welfare of the whole society. Foreign natural resource asset management has the following characteristics: (1) Most important natural resources are publicly owned, and the important natural resources here are generally related to the interests of all members of society; (2) provide guidance, advisory services, and supervision on private natural resources when managing public natural resources; (3) incomes are earned by leasing natural resources, mainly transferring the right to use natural resources to obtain financial revenue by handling licenses. In fact, the rental income of natural resources accounts for a considerable part of the national asset income. Moreover, natural resource management fees are collected and turned over to the financial department. Natural resource management is a complex project, which should not only reflect the economic benefits of natural resources, but also realize the protection of resources [11]. Based on summarizing the characteristics of governments implementing natural resource management, this section introduces the policies on natural resources efficiency utilization from five aspects: natural resources registration system, natural

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289

resources public service system, natural resources tax system, natural resources audit system, and natural resources protection association. (a) Natural resources registration system The natural resources registration system came into being to meet the needs of relevant functional departments for the determination of tax standards for natural resources, the formulation of relevant laws and regulations, and the determination of the ownership of natural resources. Since the 2013 Government Work Report pointed out that “it is necessary to improve the property right system and use control system of natural resources,” relevant Chinese departments have established research groups to study the foreign natural resource registration system to abide with relevant policy instructions. From an international perspective, the natural resources registration system is mainly used to record the management status, ownership, natural status, and other relevant information of natural resources, including laws and regulations, planning and judgment information of natural resources, natural resources registration information, archives and records information, administrative examination and approval license generation information, and the information recorded by government departments through open registration. Foreign countries began to implement the natural resources registration system early, and their systems and regulations are relatively complete. Now, information technology is gradually used to integrate the registration information of natural resources. The United States developed the world’s earliest natural resources information system in the middle of the 1960s. Now, its domestic natural resources information system has been gridded and used for practical decision-making. Although the national conditions, political systems, and natural resources reserves of various countries are different, their development paths of natural resources registration system are similar. Thus, countries can learn from each other’s experience of setting up a natural resources registration system. (b) Natural resources public service system The foreign natural resources management system is at a mature stage. The problem of natural resource management is not how to improve the factor market of natural resources, but how to protect and ensure sustainable utilization of natural resources. Governments act more like service providers in the management of natural resources. The foreign natural resources public service system, with a history of over 100 years, has formed a complete system of laws and regulations, not only in agricultural natural resources, but also in other resources. In foreign countries, the public service of natural resources is mainly reflected in the provision of information technology, financial support, and technical guidance [14]. For example, Japan introduced the Agricultural Improvement and Promotion Act to establish an agricultural extension system allowing the central and local governments to coordinate policies, funds, and technologies to help agricultural production activities. Technically, governments allocate special technicians according to the scale of agricultural production and establish special technicians’ offices, agricultural information departments, and scientific research departments to promote the integration of agricultural scientific research

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and administration. Financially, governments have set up two kinds of loans for the promotion of agriculture. One is interest-free loans, including funds for production mode improvement, farm life improvement, business scale expansion, and cultivation of talent in agriculture; and the other is a comprehensive fund with an annual interest rate of 5%. After a series of agricultural extension policies, Japan has developed from a country short of agricultural resources to a rice exporter. (c) Natural resources tax system Foreign countries levy taxes on a wide range of natural resources, most on nonrenewable resources or resources with extremely slow regeneration speed, including mineral resources, water resources, forest resources, and special resources [1]. France collects water intake tax and pollution tax through water resources taxation; the Dutch government collects tap water tax and water resource use tax from enterprises and residents to promote enterprises and residents to save water resources. The taxes required by the natural resources tax system mainly include mining right rent, mining tax, resource rent tax, dividend, depletion subsidy, and royalty [2, 3]. The resource rent tax refers to the tax on profits above the average profit, which generally appears in countries that are rich in oil and gas. Due to Australia’s natural geographical advantages and good industrial policies, the mineral enterprises there have made huge profits due to the sharp rise in ore prices. To balance the national economy, the Australian government put forward this rent tax policy in 2012. Depletion subsidies are usually applicable to countries with private mineral resources, such as the United States, which means that mineral companies need to explore new deposits to replace exhausted deposits. In this case, the depletion subsidies paid to mineral owners can offset taxable income. However, as the mineral resources of most countries are publicly owned, countries that implement depletion subsidies become fewer rather than more plentiful, leading to compensation for the exploration activities of some competitive countries. (d) Natural resources audit system It is generally believed that natural resources audits belong to the field of environmental audits. Similar to other audit activities, the natural resources audit system is an independent economic supervision activity in which national audit institutions, internal audit institutions, and social audit organizations use special methods to supervise, evaluate, and verify the authenticity, legitimacy, and effectiveness of the performance of natural resources responsibilities of auditees (including government organizations and enterprise units) in accordance with national laws, regulations, and policies to urge them to take natural resources protection responsibility and meet the requirements of sustainable development. The main contents of a natural resources audit include financial audit, compliance audit, and performance audit. Financial audit mainly examines the use and management of various special funds in the development and utilization of natural resources and whether the disclosure statements reflect the costs and liabilities of the natural resources environment. A compliance audit mainly evaluates whether the production and operation processes

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of the audited object comply with relevant laws, regulations, standards, and regulations. A performance audit aims to evaluate the economy, efficiency, effectiveness, and sustainability of natural resources development and utilization projects. The research and practice of natural resources audit in developed countries started earlier. For example, in 1956, the US Audit Office audited water conservation projects for the first time under the guidance of the Accounting and Audit Act, focusing on the authenticity of relevant accounting information on the cost income side; to provide sufficient basis for the water resources audit, specific responsibilities and the audit scope of audit institutions in resource audit were stipulated later in written legal form. Since the 1990s, the water resources audit in the United States has gradually improved, and the audit objects have been extended to water pollution prevention and ecological protection, water conservation and water infrastructure management, water resources development, and utilization management, among others. After years of development, the natural resources audit in the United States has shown the characteristics of wide audit theme and coverage, comprehensive audit results and information, diverse audit methods, data emphasis, and so on. Judging from the development experience of the natural resources audit in the United States, the important reason that the United States can take the lead in the world is that it has formulated a set of laws and regulations that can guide actual audit work, so that auditors can carry out audit work under clear standards and basis, and the form of legal provisions also strengthens the supervision and has a deterrent effect on resource users. Moreover, the US Audit Office has many interdisciplinary workers, including audit professionals and technicians, as well as experts in environmental science, engineering, management, computer, and other fields. The diversified disciplinary background makes its natural resources audit more professional, meticulous, and normalized. (e) Natural resources conservation associations There are also many natural resources protection associations in the world, such as the Natural Resources Defense Council (NRDC), TRAFFIC, and the International Water Resources Association. These organizations are usually composed of government officials, enterprise employees, scientists, and members of other non-governmental organizations. For example, the NRDC has 3 million members and supporters at present and is committed to protecting the Earth’s environmental resources and the planet on which all living creatures depend. The NRDC successfully petitioned the US government to formulate the “Clean Power Plan” in 2014—the most stringent set of rules in this regard in history—and also supported Ahmedabad in India in launching the “High Temperature Action Plan” in 2013, as the first city in South Asia to do so. The international wildlife trade monitoring network (TRAFFIC) was established for protecting endangered wild animals and plants and ensuring that trade activities do not endanger the survival of wild animals and plants. The International Water Resources Association (IWRA), founded by several water resources scholars, is a non-governmental academic organization that aims to promote the development of water resources in scientific research, education, planning, development, and management (Fig. 9.2).

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Fig. 9.2 Role of government in the policy system of natural resources efficiency utilization

(2) Market perspective For countries that are rich in natural resources and belong to different market subjects, and whose market economic system has been implemented for many years, leading to a highly developed economy, the role of the market in the allocation of natural resources should be considered when implementing natural resources asset management, and governments should be service providers in the allocation of natural resources rather than just administrative managers. Different from China’s administrative system of natural resource management, departments responsible for natural resource management in foreign capitalist countries where the development degrees of the market economy are higher play a “twoin-one” role whereby governments provide comprehensive technical services and legal administration, which are more standardized. The United States is rich in natural resources and is a federal capitalist country with independent judicial, legislative, and executive power [13]. Since the United States pays more attention to the protection of private property, the property right system is more important in the management of natural resources. In the Coase theorem, resources can be allocated more effectively only when property rights are clear and transaction costs are low. For example, if a federal government wants to use the resources of a state in the United States, it needs to obtain the permission of the state government to purchase the ownership and use right before the resources can be developed. Any entity that wants to exploit and utilize natural resources should obtain the permission of the owner of the natural resources and pay a certain rent and tax before development. For example, marine developers must hold a marine resources license, and mineral resources developers need a mining license. On the premise of

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ensuring the sustainable development of natural resources, governments improve the property right system of natural resources and refine the economic benefit evaluation of natural resources. Therefore, in summary, if the market wants to play a role in the efficient utilization of natural resources, it must be based on the establishment of a perfect natural resources property right system. (3) Enterprise perspective Enterprises play different roles in different periods of natural resources policy development. In the development period of natural resources policies, enterprises play a positive role. To develop the industrial economy of natural resources, some capitalist countries transferred the use right of some unimportant natural resources to enterprises in the early days of founding, which not only reduced the financial pressure of governments, but also promoted the organic combination of macro-control and market economy. For example, Russia has rich natural resources, most of which are concentrated in Siberia and the Far East. The development of the mining industry played a supporting role for Russia during economic difficulties. The Russia Energy Strategy before 2020 stated that Russia would participate in ensuring international energy security as an important energy supplier; consequently, Russia began to encourage joint-stock companies to actively participate in international energy transportation projects. When natural resources policies develop stably, governments will seek a balance between economic benefits and the resources environment, and the operation of enterprises will be strictly controlled by the government. At present, most countries in the world transfer the use right of natural resources to enterprises, and governments manage supervision and service. A series of natural resource protection laws could be promulgated to limit the over-development of natural resources by enterprises. For example, to protect the Danube River, the Joint Declaration on the Establishment of the Lower Danube Green Corridor was adopted in 2000, and the United States promulgated the Surface Mining Control and Reclamation Act, the Mineral Leasing Act, and the General Mining Law to regulate the development of mineral resources. (4) Public perspective Both the government and market play great roles in the traditional natural resources policy system. The government protects natural resources through administrative, economic, and legal means, and the market develops natural resources through the establishment of a property right system and a paid-use system of natural resources. However, these methods have the disadvantages of low efficiency and high cost. Thus, the modern natural resources policy system pays increasing attention to the role of people in natural resources protection and aims to use people’s “self-organization” and “self-management” to effectively protect the ecological environment. Currently, the role of the public in foreign natural resources policy system is mainly reflected in the following aspects.

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(a) Education Developed countries attach great importance to people’s education regarding natural resources, mainly through formal and informal channels. Formal channels refer to promoting knowledge about natural resources through primary and secondary school students’ textbooks to cultivate students’ awareness of environmental protection. For example, the Japanese government integrates environmental protection education into the classroom and requires students to develop basic natural resources knowledge and understand the causes and consequences of natural environment damage in the learning materials for primary, junior, and senior high schools published from 1998 to 2009. Such measures were necessary because Japan is short on natural resources and depends on overseas imports for the production of raw materials, although it is rich in forest and fishery natural resources. Informal channels refer to cultivating residents’ awareness of environmental protection through the media and social practice activities organized in communities. Over the past 15 years, the Ministry of Education, Culture and Sports of Japan has organized various social practice activities, such as the selection of environmental data observation demonstration schools, the comprehensive use and promotion of recycled paper in textbooks, and the construction of environmental protection and ecological schools to enable students to participate in practical actions relating to environmental protection. (b) Rights protection and supervision Due to the negative externality of natural resources, unreasonable development of natural resources has adverse effects on nature and society. For example, improper development of water resources causes pollution of the water environment, and improper development of mineral resources damages surface morphology and causes soil erosion and air pollution, which are harmful to the lives of local community residents. In these cases, residents can use laws and regulations to appeal to courts to safeguard the interests of community groups. In January 2017, more than 1700 residents of Flint in the United States filed a lawsuit in the local federal court, claiming that the federal environmental protection agency had failed to take effective measures to ensure that the state of Michigan and the local government properly solved the water pollution crisis. After a class action lawsuit, the US Environmental Protection Agency announced that it was responsible for the water pollution and would take measures to cut off the polluted water source. Residents can safeguard their common interests, promote natural environment improvement, and supervise the actions of the authorities and governments through joint collective actions. (c) Participation in natural resources legislation The environmental problems caused by the destruction of natural resources first affect the interests at the grass-roots level, while the designation of superstructures, such as laws and regulations, often deviates from the grass-roots groups. Therefore, not only developed countries but also developing countries have become gradually aware of the role of people in the formulation of natural resources laws. The members of the French natural resources management agencies include not only senior government

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officials, but also natural resources experts, scholars, the common people, and natural resource owners. Articles 221 and 222 of the Forest Law stipulate that the formulation of policies must incorporate the opinions of people from all walks of life, encourage the public to participate in the management of natural resources, and protect the rights and interests of all people.

9.1.3 Natural Resources Efficiency Utilization Abroad It is known by studying the role of different subjects in the policy system of natural resources efficiency utilization that governments tend to be a service provider in the management of natural resources and maintain a natural resources policy system centering on the property right system, in which the market determines the price, supply and demand, and competition mechanism to ensure the prices of natural resources better reflect their own values and are allocated efficiently in society. Meanwhile, as the main body of society, members of the public participate in the management of natural resources in the form of education, supervision, and legislation participation. To further understand the development of foreign policies for natural resources efficiency utilization, this section summarizes and analyzes the management contents of various resources in foreign countries. As there are various types of natural resources, by referring to some international classifications of natural resources, this section divides natural resources into five types—biological resources, energy and mineral resources, land resources, agricultural resources, and water resources—to study the natural resources efficiency utilization abroad. (1) Biological resources Biological resources include microbial, animal, and plant resources. Owing to their strong renewability and diversity, they can provide endless products for human society and contribute significantly to the national economy as long as they can be reasonably developed. Countries rich in biological resources are mainly concentrated in tropical and subtropical regions, including Brazil, China, Bangladesh, India, and Mexico. The efficient utilization of biological resources abroad is mainly reflected in the following aspects: paying attention to the preservation and collection of biological resources, giving sufficient financial support to the protection of biological resources, formulating perfect laws and regulations to standardize the development of biological resources, and establishing a perfect biological resource protection system. In the conservation and collection of biological resources, countries all over the world are establishing “seed banks” to prevent the extinction of species, such as the Global Seed Bank and the Millennium Seed Bank in Britain. Countries also establish various wildlife reserves in which hunting is prohibited, to protect the continuity and diversity of animal species, including the well-known Kenya Wildlife Reserve and Masai Mara Wildlife Reserve. Meanwhile, the protection of biological resources needs sufficient financial support. The funds for plant genetic resources in the United States reached USD 29.5 million in 1998, and the total demand funds for

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the United States National Park alone in 2019 were USD 3.2 billion. Detailed laws and regulations on animal and plant resources have also been put forward by most countries, such as the Endangered Species Act of the United States, the Hunting and Hunting Resources Protection Law of Russia, and the Nature Park Act of Japan. (2) Energy and mineral resources In terms of the demand for mineral resources, countries can be divided into three categories: first, countries with rich mineral resources and low demand for mineral resources, such as Australia; second, countries that lack mineral resources but have high demand for mineral resources, such as Japan; and third, countries with rich mineral resources and great demand for mineral resources, such as the United States and China. Therefore, different countries have different policies on mineral resources. Unlike other renewable resources, non-renewability and scarcity are fundamental attributes of energy and mineral resources. From the political and economic point of view, it even affects the national security of a country. In countries except China, there are two ideas around the efficient utilization of mineral resources. One is to develop reserves. A country may generally protect its domestic mineral resources as strategic reserve resources, but import mineral resources from other countries with high resource endowment through political diplomacy, economic cooperation, or controlling other countries’ energy reserves through monopoly capital. For example, after the Cold War, the United States took advantage of its superpower and economic globalization to promote multinational mining companies to invest overseas. In 1997, Exxon, the world’s largest oil company, placed 75% of its mining exploration tasks overseas. The other is to adjust the industrial structure and strengthen the substitution rate of technology for resources. At present, the renewables industry and circular economy in western countries are experiencing vigorous development. Industrial structure directly affects the demand intensity and types of mineral resources. Technological progress is the fundamental way to realize the sustainable development of economy. China is now also adjusting its consumption concept of mineral energy by changing its policy direction and increasing publicity. (3) Land resources Land resources are productive and are the basic materials for human survival [6–8]. Efficient utilization of land resources and scientific land planning cannot only alleviate the contradiction between people and land and promote the sustainable development of agriculture, but also drive the development of the agricultural economy and promote rural urbanization [10]. At present, China’s land use is in a transformation from an extensive mode to an intensive mode. The practice of land resource management abroad has the following characteristics. First, there are complete laws and regulations. For example, the Land Planning Law promulgated by Japan in 1974 stipulated the basic contents and principles of land planning in Japan; Russia promulgated the Land Planning Law and the land code in 2001. Second, there is systematic planning. British land planning is divided into national, regional, county, and district levels. The central government is responsible for the strategic direction—the formulation of draft plans is the responsibility of the

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government, and the plan should reflect the opinions of the public. Third, the protection of ecological environment is emphasized. The reclamation of farmland and the exploration of mineral resources will affect the surface morphology. Therefore, land consolidation planning should consider the protection of the ecological environment simultaneously. Fourth, the ownership of land is important. The ownership of land involves the division of land incomes in the later stage. Therefore, the determination of land property rights in western countries is very strict. Land consolidation projects of the same scale can take a very long time in Germany, where property rights are strictly defined. (4) Agricultural resources Agricultural resources refer to the information, technology, material, and capital that can be used for agricultural production in nature, including both human factors, such as technology and capital, and non-human factors, such as soil and water. Rational use of agricultural resources will not only promote the sustainable development of agriculture, but also protect the ecological environment, promote the increase of farmers’ income, and drive the development of the national economy. Foreign experience in the efficient use of agricultural resources includes first, strengthening the legal construction of agricultural resource protection; second, strengthening the capital investment in agricultural resource protection; third, strengthening the scientific research investment in agricultural development; and fourth, strengthening the organization construction of agricultural resource protection. Regarding the legal construction of agricultural resources, there are sound legal systems in developed countries, and they began to focus on building a sustainable agricultural ecological development system very early. Britain promulgated the first Agricultural Law since World War II and successively promulgated the Rural Protection Law of England and Wales, and the Rural Development Plan to provide guarantee for agricultural development. It is evident that law, as a top-level design, plays a fundamental role in the protection of various resources. In terms of funding for agricultural development, the United States set aside USD 54 billion for agricultural resources and environmental protection in the 2014 Agricultural Act; from 2009 to 2016, it financially supported 1.3894 million rural development projects, with funding of USD 253.434 billion. The scientific research investment and organization construction of agricultural development abroad are similar to those of other natural resources, which will not be repeated here. When solving resource and environmental problems, policies should involve the relevant stakeholders and encourage them to bear the responsibility of reducing management costs. In this regard, the experience of the United States government in the comprehensive management of rural resources and the environment is worth learning from. (5) Water resources Different from other natural resources, water resources play a variety of roles [9]. The efficient utilization of water resources mainly involves extraction, irrigation, breeding, power generation, shipping, and other means to meet people’s needs for

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agricultural irrigation and breeding, industrial production, and ecological protection. The utilization of water resources has both advantages and disadvantages. The development of water resources inevitably affects the ecological environment, and poor utilization causes water pollution, soil erosion, and soil salinization. In foreign countries, the management policies of water resources mainly focus on utilization and protection. In the field of international water resources management, the Southern Water Resources Administration of Florida is a model that has made bold explorations in the fields of water supply policy diversity, river ecological governance, and integrated water resources management, among others. After a series of natural disasters, Florida gradually changed its concept of water management in the application and management of water resources. In terms of water resources protection, the ecological restoration project of Kissimmee Canal restored the ecosystem of the Kissimmee River and floodplain to a self-sustaining level. In 1994, the Florida Parliament enacted a legislation to permanently protect the Everglades, and the Florida government invested significantly in water ecological restoration. Owing to the special form and wide use of water, there are often conflicts in the utilization of international water resources. To efficiently use international water resources, relevant countries have abided by the provisions formulated through consultation and formulated international rules, such as the Convention on the Protection of the Rhine River and the Berlin Rules on Water Resources.

9.2 Research on Policy System of Domestic Natural Resources Efficiency Utilization This section discusses domestic natural resources utilization policies with reference to the discussions on the policy system of natural resources efficiency utilization abroad in the previous section.

9.2.1 Macro-analysis of Domestic Natural Resources Efficiency Utilization Consumption of natural resources increases with continuous economic and social development. At present, China’s natural resources utilization policies are generally behind those of developed countries, retaining the features of the planned economy. Uncontrolled consumption of natural resources will inevitably jeopardize China’s economic development. In this section, the current situation of China’s natural resources utilization policies from the perspectives of the policy development process, development status of natural resources protection laws, and key areas of natural resources policies is analyzed to understand the problems in China’s

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natural resources utilization and provide reference for the optimization of its natural resources policies. (1) Development of natural resources protection policies Compared with western countries, China’s natural resource management started late, with a relatively low utilization mode. The policy development progress is divided into the following stages according to distinct phase characteristics [5]. The first stage was before the implementation of reform and opening up, when China’s natural resources utilization policies were always focused on development under the strong influence of the planned economy. In the second stage, China’s natural resources policies began to emphasize the protection of land and water resources although it was still people centered and prioritized social and economic interests. Although China’s total natural resources are rich, the intensity of resource development for economic development is increasing, which prevents tension in natural resources from being alleviated. Therefore, in the designation of policies, priority should be given to natural resources that contribute greatly to social and economic development. In the third stage, China’s economic development mode advocated transformation from an extensive mode to an intensive mode as well as the mode of resource utilization. At that time, it was proposed to adjust the resource allocation and sort out the price of resource assets through the role of the market. In the fourth stage, the market-oriented reform of natural resources was further promoted and the protection of various natural resources was brought into the government assessment system. The objective of the fifth stage was to fully implement a scientific outlook regarding development during the natural resources policy reform. In this state, governments’ work plans should be geared toward sustainable utilization of natural resources on the premise of guaranteeing natural resources security in China (Fig. 9.3). (2) Development of natural resources audit China’s natural resources audit is mainly led by the National Audit Office, and its development can be divided into the following stages: The first stage started from the official establishment of the National Audit Office of the People’s Republic of China in 1983. In addition to routine audit, relevant pilot

Fig. 9.3 Development of China’s natural resources policies

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audit work was started around various environmental protection funds and projects. In the second stage, the National Audit Office established the Department of Agriculture and Resource Environmental Protection Audit in 1998 to organize and carry out resource environment audits, marking the beginning of China’s resource environment audit as a special audit. Meanwhile, audit institutions at all levels successively established special institutions to engage in natural resources-related audits. In the third stage, the National Audit Office established a coordinating and leading organization for environment audits in 2003 and actively expanded the field of resource environment audit to include, for example, land resources, mineral resources, water resources, and climate resources. Moreover, exploring and summarizing effective ways and methods of natural resource audits that suit China’s national conditions were emphasized. In the fourth stage, the National Audit Office issued the Opinions on Strengthening Resource Environment Audit in 2009 to guide China’s audit institutions at all levels to carry out resource environment audits; the theory and practice of natural resources audit continued to develop, showing the characteristic of diversification. (3) Development status of natural resources protection laws China’s natural resources legal system is based on the Constitution and plays a fundamental role in protecting and developing natural resources. According to different protection domains, China’s natural resources legal system is divided into the resource property right system, natural resources macro-control system, natural resources circulation system, and natural resources administration system. Meanwhile, natural resources legislation should follow the principles of promoting regional economic development, considering economic and ecological benefits, and overall planning, among others, and reflect the direction of resource management. Some guiding ideas are put forward in the opinions of the CPC Central Committee and the State Council on Building a More Perfect Market-Oriented Allocation System issued in 2020. The purpose of the opinions was promoting the construction of a market-oriented allocation mechanism for all factors of natural resources and enriching the value realization forms of natural resources, marking great progress from the previous thinking on natural resources utilization. Nonetheless, there are still great loopholes in China’s current natural resources legislation and management. First, the current natural resources legislation still retains the characteristics of the planned economy in that individual laws cannot be coordinated and communicated, and the demand for natural resources management under the market economy system cannot be met. Second, the legislation of natural resources prioritizes economic interests, and laws that protect natural resources are far from perfect. Some natural resources that do not contribute much or even make no contribution to economic development are not protected by laws. Moreover, the resource values reflected in the current natural resources legislation are too singular and do not reflect the loss of ecological values, resource and environmental restoration compensation, and the resource rights and interests of future generations. Finally, because of the structure of the administrative system, separate natural resources laws and regulations are set by

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relevant departments and approved by the State Council, which may lack legislation efficiency owing to power competition among different departments. (4) Key contents of natural resources policies From the perspective of the types of natural resources, the focus of China’s natural resources protection laws is mostly on resources that make great contributions to social and economic development. Judging from the 13th Five-Year Plan and 14th Five-Year Plan, the Chinese government has invested huge energy in promoting the optimization of land resources, the development of mineral resources, and the protection of water resources. In recent years, China has also actively developed the marine economy and implemented the marine power strategy. From the perspective of policy direction, in recent years, natural resources policies have paid more attention to natural resources protection, ecosystem quality improvement, and inter-regional resource coordination.

9.2.2 Roles of Domestic Subjects in Natural Resources Efficiency Utilization China’s natural resources management policies have made great progress, from focusing on development to reflecting the scientific outlook on development. However, the focus of natural resources policies is still on natural resources that have great economic value to society, and the natural resources protection laws also have some disadvantages, such as retaining the characteristics of the planned economy and ignoring the ecological interests of resources. Similar to the roles played by different subjects in foreign natural resources policies, this section analyzes the roles of three subjects in China’s natural resources efficiency utilization—that is, the government, enterprises, and the public. (1) Government perspective China’s natural resource management is still dominated by administrative management. The government plays a great role in resource management under the market economy, as administrative means are mandatory and can ensure the realization of policy objectives, which is also determined by China’s development situation. The macro-control role of the government in natural resource management is mainly reflected in organization, leadership, and financial support. The central government defines the requirements for the utilization of natural resources, makes plans according to the actual situation of resources, and strengthens the comprehensive utilization of natural resources by issuing instructions or formulating laws and regulations. The regulatory function in natural resource management is usually undertaken by the government [17]. As an important part of regulatory evaluation, the natural resource audit carried out by Chinese audit institutions plays an irreplaceable role in the efficient utilization of natural resources. The main audit work includes auditing

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whether the development of natural resources complies with laws and regulations, whether the distribution of income after the development of natural resources between owners and users is fair and reasonable, and whether the local government performs its work in accordance with policy requirements. The audit objects include natural resource management government organizations, natural resource management units, and each unit subject involved in all links of natural resource development and utilization [19]. In recent years, relevant governments have gradually become service providers and changed their management thinking to allow the market plays a role in the allocation of natural resources. During the 13th Five-Year Plan, China made significant progress in natural resources reform. The regulations the State Council issued during that period, such as the Guiding Opinions on Comprehensively Promoting the Reform of the Property Right System of Natural Resources Assets, the Guiding Opinions on Improving the Secondary Market For the Transfer, Lease and Mortgage of Construction Land-Use Rights, and the Notice on Printing and Distributing the Reform Plan of Mineral Resources Equity Fund System have effectively promoted the development of the natural resources compensation system and natural resources property right system, so that the prices of natural resources are determined by the market and natural resources are used efficiently under the mechanism of competition. (2) Enterprise perspective In the process of natural resource management, Chinese enterprises obey the unified planning and arrangement of relevant governments, engage in business activities according to policy guidance, and are subject to the supervision of the government and citizens. As natural resources are competitive, if there is no government supervision, enterprises may exploit natural resources relentlessly to maximize their profits as business subjects. However, as the micro-subjects of the market economy, enterprises play a positive role in promoting the marketization of natural resource factors. They constantly carry out technological innovation to occupy market share, and technological progress further drives the improvement of natural resources efficiency utilization by reducing the development cost of natural resources and improving the profits of enterprises. For example, the intelligent mining vehicle of China molybdenum can save more than 75% of energy, which greatly reduces the mining costs, brings excess profits, and promotes the improvement of mineral resources utilization efficiency. (3) Public Perspective Chinese people play a small role in the efficient management of natural resources, and their participation in the implementation of policies is also low. In the foreign policy system, the public is involved by being educated, supervising, and participating in legislation, in the implementation of natural resources policies. However, the interaction between Chinese people and the relevant governments is obviously less. Regarding education on natural resources efficiency utilization, although there are relevant contents for protecting natural resources in the curriculum of primary and secondary schools in China, it is only limited to books and lacks offline practice.

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Reasons for the weak sense of public participation in supervision and legislation are that, on the one hand, the people’s awareness of natural resources protection and efficient utilization is weak, which is also caused by the lack of practical education, and on the other, governments do not provide a platform for the public to make suggestions, and there is no administrative system to deal effectively with public opinion.

9.2.3 The Efficient Utilization of Various Natural Resources in China It is evident from the preceding discussion that the government plays a leading role in the policy system of natural resources, enterprises obey the arrangement of policies, and the supervision role of the public is instead played by the government. Next, this section discusses the management of various natural resources in China. The classification of natural resources in the first section is referenced. (1) Biological resources China is rich in biological resources, but due to its large population, China’s utilization of biological resources should aim at resource saving. Like western countries, China also focuses on resource protection in the policy direction of biological resources. China has established an important species resource bank, in which the number of seeds preserved exceeds 520,000, ranking second in the world. The new bank to be launched can preserve 1.5 million seeds, ranking first in the world. China has also established a wild animal cell bank, which makes it possible to undertake future genetic work and a series of scientific research; the establishment of nature reserves is a favorable measure to protect the diversity of biological resources. China’s biodiversity conservation is mainly realized through conservation area management, including nature reserves, scenic spots, and wildlife protection management. The protection and utilization of biological resources also need laws and regulations. Although China’s laws on biological resources have been put into practice, the central government has not yet issued laws on the comprehensive protection of biodiversity. After a long period of development, China’s nature reserves have established a preliminary protection system, including the China National Biodiversity Conservation Action Plan in 1994, China Biological Species Resources Protection and Utilization Plan in 2007, China National Biodiversity Conservation Strategy and Action Plan in 2010, and a series of plans to legally regulate the protection of biological species in China. Moreover, China has adopted advanced and new technologies to promote the sustainable utilization of biological resources. The Strategic Biological Resources Information Center of the Chinese Academy of Sciences is committed to providing an efficient sharing platform for biological resources, transforming biological genetic

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resources into data, and forming an all-round biological resources research and development network. (2) Energy and mineral resources Efficient utilization policies of mineral resources are of great significance to the improvement of economic benefits and environmental protection. According to the summary and sorting of mineral resources policies, the development of China’s mineral resources policies can be divided into the following stages. The first stage is from the founding of the People’s Republic of China to the starting of reform and opening up when the national economy needed to recover urgently. Thus, all of China’s policies gave priority to economic development. China vigorously explored and developed mining resources without considering the ecological environment, leading to the accelerated depletion of mineral resources and the decline of the economic benefits of mineral enterprises. The second stage is from the starting of the reform and opening up to 2005, when governments began to become aware of the comprehensive utilization of mineral resources and gradually established a system of comprehensive utilization of mineral resources. In particular, the promulgation of the Mineral Resources Law comprehensively and clearly put forward the legal responsibilities of each responsible subject for the first time, bringing the development of mineral resources under a legal framework. To arouse the enthusiasm of state-owned enterprises in the comprehensive utilization of mineral resources, the government also issued a series of policy documents, such as the Notice on the Implementation of One-Time Incentives for Comprehensive Utilization Projects of State-Owned Industrial Enterprises to promote the sustainable development of the economy and society. Meanwhile, China started the practice of ecological compensation for mineral resources in this stage. From the initial collection of ecological compensation fees by administrative means to the current collection of comprehensive compensation fees, the ecological compensation system started from scratch and developed from theory to practice. The third stage is from 2005 to the present. The government has established a macro-control system for the comprehensive utilization of resources and implemented the comprehensive utilization of mineral resources in finance, industrial policy, price determination of mineral resources, and other aspects. During the 12th Five-Year Plan period, “resource conservation” was incorporated into the basic national policy. (3) Land resources There are a variety of land development problems, such as small area of cultivated land and small per capita area in China, although the absolute amount of land resources is large. The more complex the social economic structure, the more important the land policy. With the development of China’s economy, the demand for land for the construction of infrastructure is also increasing, making the land resources in China very scarce. Therefore, the Chinese government has established a set of land management systems with the land sustainable utilization as the core, which is a complete scientific system composed of land investigation, planning, use control, supply, arrangement, circulation, and utilization supervision.

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The policies for the efficient utilization of land resources in China were mainly implemented after the reform and opening up. In 1978, the Chinese government pointed out that the household contract responsibility system should be implemented according to the characteristics of agricultural development, and land should be contracted to farmers without privatization. Later, the Land Contract Law in 2002 and the Decision of the CPC Central Committee on Several Major Issues on Promoting Rural Reform and Development in 2008 promoted the transfer of rural land contract management rights. Meanwhile, a paid land-use system that includes collecting landuse fees from Sino–foreign joint ventures, paying for urban operational land transfer, promoting land marketization, and revising the land management law, among other features, was established. In recent years, the land policies have mainly regulated the industrial structure by adjusting the direction of land supply and promoted the intensive use of land resources by improving the land tax system. In the outline of the 13th Five-Year Plan, it is planned to reform the Law of Land Administration and issue a policy documents on establishing a land space optimization system. In terms of protecting cultivated land and saving land, the verification and rectification of information regarding permanent basic farmland and release of land supply should be pushed forward. During the 14th Five-Year Plan, efforts will be made to optimize the spatial layout of land, creating three spatial patterns gradually: urbanized areas, ecological functional areas, and main agricultural product producing areas. (4) Agricultural resources Since the 12th Five-Year Plan, China has made significant progress in the utilization and protection of agricultural resources. Such projects as returning farmland to grassland, establishing China’s pollution prevention and control system, and promoting dry farming and water-saving agriculture have delivered significant achievements. According to the agricultural support policy issued by the Ministry of Rural Agriculture in 2020, China would make efficient utilization of agricultural resources from the aspects of financial subsidies and financial support, advanced technology promotion, and industrial support, among others. In terms of financial subsidies, cultivated land productivity protection subsidies, agricultural machinery purchase subsidies, counties with great grain production, and grassland ecological protection subsidies have been implemented, among which the cultivated land productivity protection subsidies and agricultural machinery purchase subsidies are intended to improve the level of agricultural mechanization and land productivity. Due to the seasonality and instability of agricultural production, individuals are limited in applying for agricultural loans from financial institutions, resulting in a lack of economic support for agricultural development. Agricultural credit guarantee mainly serves farmers’ cooperatives, family farms, agricultural product processing enterprises, and other entities and provides financial assistance for important agricultural products. Technology must be promoted to improve agricultural productivity. The supporting policies related to agricultural technology extension in 2020 include water-saving agricultural technology based on the natural conditions of arid areas,

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information access for households, beneficial agricultural information stations as the first window for convenient services, and promotion of stable and productive high-quality crops. Compared with the early days of the founding of the People’s Republic of China, China’s national economy has made great progress, and agricultural development has been vigorously supported. To effectively coordinate various elements related to agricultural development, China’s macro-policies encourage the construction of advantageous and characteristic industrial clusters, modern agricultural industrial parks, and strong agricultural towns to promote the transformation and upgrading of agriculture and increase farmers’ incomes. (5) Water resources China’s water resources management refers to the unified planning and management of water resources using economic, administrative, legal, and technical means, including the ownership of water rights, the determination of water prices, water allocation, water quality control and protection, and water regime monitoring and prevention. In terms of the establishment of administrative institutions, after the founding of new China, water conservation departments have been established to form a three-level province–city–county management system for local water conservation management, as well as water conservation institutions for the seven major river basins in China. In terms of water resources laws and regulations, the Water Law of the People’s Republic of China, the Law of the People’s Republic of China on Water and Soil Conservation, and the Measures for the Implementation of Water Intake Licensing System were promulgated to clarify the legal efficiency of various policies and the responsibilities of various economic entities in water resources development. The efficient utilization of water resources in China is mainly reflected in the utilization of agricultural water resources, which is very important to China’s agricultural development. In the initial utilization of agricultural water resources, water resources management policies tended to improve the efficiency of water resources, encourage the construction of small-scale agricultural water conservation facilities, improve the irrigation conditions of agricultural water resources through the introduction of advanced irrigation technology, improve the utilization efficiency of water resources, develop the contracting form of water resources, and encourage society to contract idle water surface. In the construction stage of agricultural water resources utilization, ecological benefits were emphasized, and the central and local governments increased the construction of reservoir projects by increasing financial allocation to promote the coordinated distribution of water resources among regions. In the consolidation stage, the government paid attention to the system construction of water resources to prevent water conservation emergencies and strengthened water price reform and the construction of irrigation and water right systems.

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9.3 Problems and Suggestions Regarding the Policy Mechanism of Natural Resources Efficiency Utilization in China 9.3.1 Problems and Suggested Countermeasures of the Policy System (1) The property right system of natural resources is not perfect China’s natural resource property rights are state-owned or owned collectively. Thus, there is no great dispute over the ownership of property rights. However, infringement of collective natural resource ownership by individuals has occurred from time to time. Since the ownership of natural resources is state-owned, China has the right to regulate the distribution and use of natural resources, and the intervention in the transfer of property rights of natural resources is also legal. However, with the development of the economy and society, it has become necessary to establish the property right system of natural resources for the following reasons. First, the property right system of natural resources and the paid system of natural resources are the keys to the rational development and utilization of natural resources. Owing to the imperfect property right system of China’s natural resources, some rents and taxes often appear in the form of administrative fees, which shows the dominant position of the government in the management of natural resources, resulting in the fuzzy pricing of natural resources, and the long-term poor state of China’s paid system of natural resources. Second, when the development of natural resources by economic subjects touches the bottom line of the ecosystem, the government should compensate according to the degree of damage. Therefore, the ecological compensation of natural resources is based on the perfect property right system and tax system. Those two systems can accurately reflect the value of natural resources so that the ecological compensation of natural resources can maintain the balance of the overall number of natural resources. To establish the property right system of natural resources, the following practices are suggested (a) natural resources property rights management organizations should be established in China and entrusted with sorting, clearing, verifying, and counting the quantities and values of natural resources; (b) modern information technology can be utilized to establish a natural resource property right service system, which can be updated regularly to reflect the changes in the natural resource property rights in China; (c) promote the reform of the unified right confirmation system for natural resources and strengthen the legalization of the unified right confirmation system. (2) The degree of marketization of natural resource factors is not high Western developed capitalist countries have long promoted the establishment of a natural resource factor market. By contrast, China has only begun the practice of marketization of natural resource factors in recent years, due its different national

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conditions—namely that China is a socialist country with public economic ownership. For example, although China’s market-oriented system for the mining industry has been preliminarily established, the overall management of mineral resources still has a strong administrative influence. The vague property right system, the unsophisticated tax system, and the lack of laws and regulations have not greatly improved the market management of mining resources. Therefore, the following suggestions are put forward for improving the marketization level of natural resources. (a) Promote the market-oriented management of natural resources, with the government transferring the right to use some non-important natural resources to the market; when the interests of all parties conflict, public interests should take precedence over private interests, while private rights should be protected as well; moreover, the government should pay attention to publicity to mobilize the enthusiasm of all parties to build a natural resources market system. (b) Advocate the construction of a natural resources market support system, including the property right system of natural resources, the paid-use system, the right confirmation hierarchy system, and the natural resources tax system, which complement the natural resources market system. (c) Innovate the value realization forms and market transaction modes of various natural resources, so as to realize equal emphasis on protection and utilization and on cost and income under the action of the price mechanism, competition mechanism, and supply and demand mechanism. (3) The tax system of natural resources needs to be reformed The original intention of the natural resources tax system was to balance the interests of different subjects. In foreign countries, the tax revenues of natural resources are mostly shared by the central government and local governments, and the tax setting is more dynamic and flexible. However, the formulation of natural resource taxes in China is mostly in the mode of quantitative pricing. Although tax reform has been carried out after entering the twenty-first century to give local governments certain authority, the actual bearing capacity of enterprises has still been neglected when deciding taxes. Moreover, the formulation of taxes and fees only reflects the compensation for resource owners, while reflecting no compensation for the ecology and environment. Given the problems in China’s natural resources tax system, we believe that China’s future natural resources tax reform should focus on the following aspects. First, China must expand the scope of tax collection. Most of China’s natural resources taxes are on mineral resources. To adapt the tax system of natural resources to economic and social development, the scope of tax collection should be expanded, such as by gradually bringing grassland, forestry, and water resources into the scope of tax collection. Second, the tax rate should be set scientifically and flexibly. Most of China’s natural resource taxes are static taxes. Thus, the tax system reform should be guided to a comprehensive and dynamic direction by adopting a combination of multiple collection standards to link the tax with the profits of enterprises and prevent enterprises from developing resources without restraint. Third, China should strengthen the legislation of the natural resources tax system at the central level to

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enhance the authority of the tax system. Fourth, while granting certain authority to local governments, responsibilities of governments at all levels should be clarified to mobilize the enthusiasm of local governments for the development and protection of natural resources. (4) The administrative system of natural resources management must be adjusted China’s natural resources administrative system is a hierarchical system with unified planning by the central government and management by local governments and industries. Due to the fuzzy relationship between the central and local governments in the management of natural resources and the fuzzy definition of power and responsibility among various departments, the management of natural resources is difficult to institutionalize and unify. Moreover, some industrial policies and ecological policies need to coordinate with each other in implementation. Otherwise, for individual interests, there will be inconsistency in the implementation of policies among different departments. Although China’s natural resources management system has been established, there are still many defects: (a) little attention has been paid to natural resources with little economic contribution, which makes the natural resource management system lack integrity; (b) natural resources management is mainly based on administrative means and supplemented by legal and economic means; under the influence of the planned economy, many laws and regulations on natural resources rise from administrative provisions, while in most cases, administrative provisions should be guided by laws; and (c) the functions of different departments involved are not clearly defined, creating the phenomenon of “passing the buck” in management. China’s natural resources administration system must be reformed. First, the government should strengthen the comprehensive management level of natural resources and change management ideas under the perspective of sustainable development. It should not only strengthen the management of natural resources with great economic contributions but also pay attention to natural resources that play an important role in the ecological environment and maintain ecological balance to expand the administrative scope of natural resources and make up for the deficiencies in management. Second, a service-oriented government should be built to allow the management and utilization of natural resources be guided by the market. Finally, China should clarify the functions of various departments and implement the natural resource policy responsibility system to promote the coordination of the resource policy, economy, society, and ecosystem. (5) The degree of informatization of natural resource management must be improved Information technology has been introduced into the management of natural resources in foreign countries for a long time. Since 1960, computer technology and remote sensing have been introduced into the management of natural resources to improve the accuracy and efficiency of work. For example, all states in the United States have established natural resources information systems, most of which are based on geographic information systems that can process huge data and solve a wide range of problems. After the establishment of the Ministry of Natural Resources in

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2018, the statistics of natural resources in China have greatly improved, and many natural resources information service systems have been established. However, there are still many problems in the current information service system: (a) each information system is developed independently with different creation standards, and it is difficult to integrate and exchange information; (b) independent systems require independent management, resulting in a waste of human resources; (c) the information system is not fully functional; (d) some natural resource information systems were created early and are not updated in time, making the data processing level of each information system inconsistent. According to the current construction of natural resources information systems in the world, the following suggestions are proposed for the reform of China’s natural resources information system. First, strengthen the top-level design of the information system and reduce the barriers between independent systems so that data and information can be shared among different systems and the wastage of resources can be reduced. Second, the objective behind the construction of an information system is to meet the need to record natural resources statistics; therefore, the reform of the system should be application-oriented and highlight the functions required by the resource in statistics. (6) Public participation must be improved According to the previous analysis of the reasons for the weak sense of public participation in natural resources efficiency utilization, to improve the role of the public in the efficient utilization policy system, the following aspects can be considered. The first is to increase the share of social practice in natural resources education in primary and secondary schools to make students conversant with the situation of China’s natural resources through practical activities and make students realize that economic development cannot rely on the uncontrolled exploitation of natural resources. The second is that the government should actively incorporate public opinions, pay attention to the voice of the people, broaden the collection channels of public opinion in various ways, and ensure the people’s right to know about the government’s policies regarding the efficient utilization of natural resources. Third, legislation governing natural resources should reflect the demands of the majority of people and pay attention to their legitimate rights and interests, especially when the law involves subjects with multiple interests, and should widely reflect the wishes of people from all walks of life.

9.3.2 Analysis of Current Situation of Evaluation and Supervision Mechanism and Improvement Suggestions The efficient utilization of natural resources cannot be completely unified with the economic benefit objectives of enterprises and institutions and may even deviate from

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each other in some cases. In this case, it is necessary to strengthen the independent supervision and evaluation mechanism, ensure natural resources audits to monitor whether enterprises’ decision-making and behavior comply with the efficient utilization of resources, give full play to the positive role of audit institutions in natural resources management, and maintain China’s energy security. Meanwhile, based on the audit results of natural resources, targeted suggestions can be put forward to relevant units to supervise, driving follow-up system construction, rectification, and policy implementation, to prevent the re-occurrence of similar problems and improve the governance mechanism. The following aspects can be further optimized in China’s current natural resources audit practice. (1) The participants of natural resources audit are far from ideal China’s resource audit mainly comprises government audits, with few instances of social or internal audits. However, it must be acknowledged that the government audit plays an irreplaceable role in China’s natural resources audit system. First, based on China’s energy security needs, the vast majority of China’s natural resources are “state-owned” or “collectively owned.” Thus, it is necessary to have government intervention in the definition of property rights, supervision, and governance, and government audit institutions cannot be absent in any natural resources audit. Second, given that the current status of resources and the environment is increasingly serious while the awareness and level of social environmental protection are limited, it is necessary for government audit institutions with obvious external economy and public product attributes to lead and perform audits and supervision of natural resources. Third, at present, China’s natural resources audit is in the early stage of development and construction. The theoretical research on natural resources auditing still lags behind, the audit system and audit standards have not been established, and there is a lack of a mature audit standard model for reference. Therefore, it is necessary for government audit institutions to lead audit practice and exploration for guidance. Fourth, natural resource management is a part of China’s governance. Natural resource audit is one of the means to serve it. One of the internal requirements of China’s governance is to implement natural resource audit by Chinese audit institutions with the relevant authority and audit punishment power. However, relying only on government audit departments to implement external audits cannot form a sound regulatory system. The in-depth development of natural resources audits must be based on the joint participation of various micro-subjects in society [16]. In this regard, it is suggested that relevant departments should strengthen law enforcement and the application of natural resources audit results, promote China’s internal and social audit of natural resources with external strict supervision, and establish a self-conscious and spontaneous evaluation and supervision system for all social subjects. On the one hand, the government should strengthen the implementation of natural resources management, impose strict penalties for violations of natural resources management laws and regulations, and correct illegal acts, to enable enterprises to spontaneously and consciously carry out natural resources internal audits and improve internal management control systems. On the other hand,

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China should emphasize the open application of natural resources audit results and reports, including improving the reporting system of audit results and submitting the audit results to relevant resource management departments as an important basis for evaluating the utilization of natural resources by auditees; improve the post-audit notification system, expose the problems found, establish a model, and publicize the efficient utilization of resources, report the audit treatment and the rectification implementation of the auditees within a certain range, and do a good job in followup audits; and finally, strengthen the audit accountability mechanism, investigate in a timely manner the responsibilities of relevant units, departments, and personnel, give play to the deeper role and effect of the audit results of natural resources, and strengthen the source protection system for the effective utilization of resources. (2) The practice of natural resources performance audit is not sufficient According to the audit announcements issued by the National Audit Office, at present, China’s natural resources audit still focuses on capital audit and compliance audit with little emphasis on performance audit; moreover, the audit methods are mainly traditional, such as internal control evaluation and audit analysis. The audit evaluation of resource utilization effectiveness has not been formed. Comprehensively carrying out performance audits as part of resource audits can provide all-round evaluation information and help arrive at more constructive audit conclusions for natural resource development and utilization projects and improve the quality of the natural resource audit. This requires China to strengthen the research on the systematic theory and technical methods of various natural resources performance audits, speed up the training of natural resources auditors, and jointly strengthen the operability of natural resources performance audits to meet the requirements of resource audits under the new situation. It is suggested that the theoretical research on natural resources should be strengthened, and the applications of empirical analysis, mathematical models, and other methods should be improved to study the theoretical and practical issues related to resource audits from different angles. Meanwhile, theoretical research and practical experience of foreign resource audits that are applicable to China can help compensate for the deficiencies in China’s natural resource audits in system supply research, empirical research, and application theory, among other areas. Moreover, due to the wide scope of performance audits and the high professional and technical requirements, auditors need to have adequate knowledge of laws and regulations, policy documents, and technical standards related to natural resources in addition to the basic audit theory [12]. Audit institutions should establish a scientific training mechanism for natural resources auditors, clarify the requirements of resource audit practice in relation to auditors’ knowledge and skills, build the knowledge and ability framework of China’s natural resources audit, explore the education and qualification certification of corresponding auditors, and solve the bottleneck problems of China’s resources audit.

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(3) The breadth and depth of natural resources audit are insufficient The concept of natural resources audit itself covers a wide range, requiring strong audit professionalism. However, China’s natural resources audit practice fails to match the capital scale and growth rate of the field of resource development and protection, and there is still a big gap in terms of normalization and regularization. The actual supervision needs cannot be satisfied. Moreover, the audit contents in the audit practices are not rich enough; lack specific implementation methods and evaluation standards; and fail to meet the internal requirements of resource audit supervision, evaluation, and authentication. In this regard, the establishment of a basic system of natural resources audit should be expedited as follows. First, China should improve its natural resources audit legal system. The current legal system lacks direct and clear definition and support for a natural resources audit and lacks specific provisions on the content and division of responsibilities for a natural resources audit. Thus, it is suggested to speed up the standardization of natural resources audit legislation, revise the existing audit laws and regulations, and clarify the functions and work contents of audit institutions in the supervision of natural resources protection. Moreover, it is necessary to improve the legal system of China’s natural resources to provide a comprehensive, sufficient, and systematic legal basis for natural resources audit. Second, China should formulate auditing standards and norms for natural resources; establish generally accepted standards for natural resources audit; issue corresponding audit guidelines to refine the audit implementation plan; construct the measurement rules and evaluation scale of natural resources audit; and continuously improve the definition of operable audit techniques and methods to improve the efficiency and quality of social audit practice. All these aspects are of great significance for the standardization and institutionalization of natural resources audit. Third, it is necessary to establish the right and responsibility mechanism of natural resource assets. Clarify the use right, income right, management right, and disposal right of relevant resources and the corresponding responsibilities of relevant managers so that corresponding responsible people can be found for the problems in the process of natural resources audit, and corresponding compensation and recovery responsibilities can be established, to guarantee the effectiveness of natural resources audits [15]. After establishing the basic system, the breadth and depth of natural resources audit should be broadened, such as by expanding the scope of the natural resources audit and paying attention to issues in the field of natural resources, including biodiversity reduction, climate change, over-exploitation of resources, and physical environment degradation [4]. These should include important resource development projects within the scope of natural resource audits to fill the gap in the relevant natural resource audit and gradually move from individual audit cases to routine and institutionalized audits. To deepen the field of natural resources audit, it is necessary to innovate the existing natural resources audit methods, including exploring the physical measurement and monetary measurement system of natural resources, quantifying natural resources, formulating accounting and measurement methods and evaluation index systems for different types of natural resources audits,

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and clarifying the evaluation criteria for the utility of natural resources to build a resource audit technology and method system suited to China’s national conditions and increase the objectivity and operability of audit evaluation. (5) Integration of natural resources audit materials is not enough China’s natural resources audit is still in the primary development stage, and the integration of audit resources needs to be further strengthened. To achieve efficient coordination of resources audit, specific improvement methods include the following aspects. (a) Strengthen the connection between natural resources audit and natural resources accounting; natural resource accounting includes natural asset accounting and enterprise natural resource assets and liability accounting. One of the contents of a natural resource audit is to review the authenticity, rationality, and integrity of the accounting data provided by natural resource accounting, which is the utilization of the natural resource accounting information [18]. Therefore, if auditors can audit whether relevant valuation assumptions and cost accounting methods are reasonable, appropriate, and correct based on accounting records and accounting work; judge and evaluate the fairness, legality, and compliance of accounting materials; and make audit evaluations, then the workload of the audit departments can be reduced to a certain extent, and the improvement of resource utilization efficiency can be promoted. (b) Strengthen the coordination among natural resources audit departments. The wide range of natural resources, the diversity of subjects, and the professionalism of audit requirements all require relevant departments of resource management to strengthen coordination and cooperation between each other in the field of natural resources audit. These departments include China’s environmental protection departments at all levels and local governments at all levels. They should formulate corresponding norms and clarify their respective responsibilities, authorities, and work scopes to improve the efficiency and effect of natural resources audits and strengthen the supervision mechanism of the executive departments of natural resources management. (c) Improve the organization form of natural resources audit trustee. Nowadays, China carries out natural resources audits mainly through relevant training for internal auditors of the audit departments and by hiring relevant external experts to assist the audit when necessary. In this regard, improvement suggestions in two directions are put forward: The first is to set up a special resource audit team comprising experts and scholars in environmental science, engineering management, and other fields required for resource audit projects within the original audit department; the second is to set up special natural resources audit units and institutions to engage in business related to resource audits, which is conducive to developing and strengthening the professional team of natural resources audit and better serving the industry. (d) Strengthen the public sharing of natural resources information and data, monitor the utilization status of natural resources, and ensure the relevant data are linked, updated, and disclosed, to make it easy to obtain such data. Modern audit technologies, such as geographic information systems, cloud computing, and big data, can be used to strengthen the information disclosure and sharing mechanism

References

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of natural resources, improve the information base of natural resources audit, and facilitate the development of audit work.

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Correction to: Efficiency Evaluation of Energy and Resource Utilization at the Regional Level in China

Correction to: Chapter 3 in: M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_3 The original version of the book was inadvertently published with incorrect figures and tables in Chapter 3, which have now been corrected. The book and the chapter have been updated with the changes.

The updated version of this chapter can be found at https://doi.org/10.1007/978-981-99-4981-6_3 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Song et al., Natural Resources Utilization in China, Contributions to Public Administration and Public Policy, https://doi.org/10.1007/978-981-99-4981-6_10

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Correction to: Efficiency Evaluation of Energy and Resource Utilization …

Fig. 3.1 Spatial distribution of economic benefits of energy resources in China 2017 (to ensure the integrity of the map, areas with missing data on economic benefits of energy resources are filled with 0, which is the same in following passage)

Fig. 3.2 Change trends of economic benefits of energy resources in four economic regions

Correction to: Chapter 3 in: M. Song et al., Natural Resources …

Fig. 3.3 Spatial distribution of China’s total factor economic benefits in 2017

Fig. 3.4 Change trends of total factor economic benefits of four major economic regions

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Correction to: Efficiency Evaluation of Energy and Resource Utilization …

Fig. 3.5 Spatial distribution of China’s carbon emission efficiency in 2017

Fig. 3.6 Change trends of carbon emission efficiency in four major economic regions

Correction to: Chapter 3 in: M. Song et al., Natural Resources …

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Fig. 3.7 Spatial distribution of comprehensive utilization efficiency of energy resources in China in 2017

Fig. 3.8 Change trends of comprehensive energy utilization efficiency in four economic regions

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Correction to: Efficiency Evaluation of Energy and Resource Utilization …

Fig. 3.9 Spatial distribution of China’s total factor comprehensive utilization efficiency in 2017

Fig. 3.10 Change trends of total factor comprehensive utilization efficiency in four economic regions

Correction to: Chapter 3 in: M. Song et al., Natural Resources …

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Table 3.1 Information of input and output variables

Input factor

Variable

Unit

Maximum value

Minimum value

Mean value

Capital stock (base period 2000)

CNY 100 million

26,717.13

739.00

26,190.21

Labor input

10,000 people

6,962.71

275.50

2,600.27

Energy resource input

10,000 tons of standard coal

40,138.00

479.95

11,385.69

Desirable output

Regional GDP (base period 2000)

CNY 100 million

59,876.80

263.68

10,128.54

Undesirable output

CO2 emissions

10,000 tons

0.80

270.59

1,553.8

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Table 3.2 Energy resource economic benefits of China, 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.65

0.66

0.68

0.75

0.74

… 0.79

0.80

0.80

0.80

0.83

Beijing

0.58

0.58

0.56

0.57

0.53

… 0.89

0.92

0.97

0.98

1.00

Fujian

0.83

0.95

0.89

0.92

0.77

… 0.80

0.80

0.87

0.90

0.92

Gansu

0.76

0.80

0.76

0.75

0.73

… 0.55

0.54

0.53

0.55

0.54

Guangdong

1.00

0.98

1.00

1.00

1.00

… 0.93

0.92

0.90

0.92

0.94

Guangxi

0.70

0.73

0.66

0.66

0.65

… 0.55

0.55

0.55

0.57

0.59

Guizhou

0.36

0.32

0.37

0.43

0.43

… 0.52

0.51

0.52

0.52

0.51

Hainan

0.56

0.56

1.00

0.56

0.52

… 0.78

0.80

0.83

0.81

0.80

Hebei

0.57

0.61

0.60

0.58

0.51

… 0.50

0.50

0.49

0.49

0.49

Henan

0.74

0.71

0.74

0.74

0.78

… 0.74

0.72

0.78

0.82

0.83

Heilongjiang

0.60

0.63

0.62

0.66

0.59

… 0.60

0.59

0.63

0.66

0.71

Hubei

1.00

0.71

0.62

0.58

0.54

… 0.56

0.57

0.63

0.65

0.68

Hunan

0.78

0.74

0.75

0.76

0.61

… 0.60

0.60

0.67

0.70

0.73

Jilin

1.00

1.00

0.93

0.61

0.72

… 0.66

0.78

0.86

0.68

1.00

Jiangsu

0.86

0.87

0.90

0.90

0.83

… 0.77

0.79

0.83

0.85

0.88

Jiangxi

0.75

0.84

0.75

0.72

0.74

… 0.69

0.70

0.71

0.73

0.76

Liaoning

0.75

0.76

0.64

0.60

0.58

… 0.59

0.62

0.82

0.89

0.94

Inner Mongolia

0.92

0.85

0.96

1.00

0.91

… 0.61

0.59

0.60

0.60

0.62

Ningxia

0.23

0.23

0.24

0.34

0.43

… 0.32

0.29

0.27

0.27

0.31

Qinghai

0.33

0.36

0.36

0.36

0.30

… 0.25

0.24

0.21

0.21

0.21

Shandong

0.62

0.56

0.56

0.55

0.56

… 0.67

0.84

0.85

0.74

0.80

Shanxi

0.72

0.90

0.70

0.71

0.72

… 0.69

0.71

0.70

0.72

0.76

Shaanxi

0.63

0.64

0.62

0.65

0.69

… 0.91

1.00

1.00

0.98

1.00

Shanghai

0.42

0.38

0.68

1.00

0.88

… 1.00

1.00

0.95

0.87

1.00

Sichuan

0.61

0.58

0.61

0.58

0.55

… 0.57

0.61

0.68

0.68

0.69

Tianjin

0.59

0.60

0.63

0.66

0.64

… 0.69

0.73

0.69

0.84

0.91

Xinjiang

0.47

0.47

0.46

0.47

0.45

… 0.36

0.44

0.40

0.33

0.33

Yunnan

0.51

0.54

0.50

0.52

0.45

… 0.54

0.51

0.51

0.49

0.50

Zhejiang

0.71

0.85

0.75

0.72

0.71

… 0.74

0.76

0.78

0.79

0.81

Eastern region

0.70

0.75

0.77

0.73

0.69

… 0.77

0.79

0.81

0.83

0.85

Central region

0.70

0.66

0.68

0.75

0.68

… 0.71

0.71

0.73

0.73

0.78

Western region

0.56

0.56

0.54

0.53

0.53

… 0.50

0.53

0.54

0.50

0.55

Northeastern region 0.80

0.77

0.78

0.78

0.76

… 0.65

0.64

0.74

0.77

0.80

Average

0.67

0.67

0.67

0.64

… 0.65

0.67

0.69

0.69

0.73

0.66

Correction to: Chapter 3 in: M. Song et al., Natural Resources …

C9

Table 3.3 Total factor economic benefits in China, 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.62

0.63

0.63

0.67

0.67

… 0.67

0.67

0.66

0.66

0.67

Beijing

0.68

0.66

0.66

0.67

0.63

… 0.81

0.84

0.90

0.95

1.00

Fujian

0.76

0.80

0.79

0.81

0.77

… 0.71

0.71

0.73

0.74

0.75

Gansu

0.74

0.73

0.70

0.71

0.70

… 0.56

0.55

0.54

0.53

0.53

Guangdong

0.92

0.91

1.00

1.00

1.00

… 0.85

0.81

0.80

0.80

0.80

Guangxi

0.63

0.63

0.60

0.61

0.62

… 0.49

0.49

0.48

0.49

0.51

Guizhou

0.44

0.39

0.39

0.45

0.44

… 0.51

0.47

0.46

0.45

0.43

Hainan

0.53

0.53

1.00

0.56

0.54

… 0.66

0.65

0.66

0.65

0.64

Hebei

0.66

0.66

0.67

0.68

0.67

… 0.57

0.56

0.56

0.55

0.54

Henan

0.77

0.77

0.79

0.81

0.85

… 0.73

0.71

0.73

0.75

0.76

Heilongjiang

0.61

0.62

0.62

0.66

0.64

… 0.55

0.53

0.54

0.55

0.57

Hubei

1.00

0.68

0.67

0.65

0.64

… 0.57

0.57

0.59

0.59

0.60

Hunan

0.67

0.66

0.67

0.68

0.63

… 0.59

0.58

0.61

0.62

0.64

Jilin

1.00

1.00

0.92

0.77

0.87

… 0.79

0.83

0.88

0.77

1.00

Jiangsu

0.75

0.75

0.78

0.79

0.78

… 0.75

0.77

0.79

0.81

0.83

Jiangxi

0.67

0.70

0.66

0.65

0.65

… 0.61

0.62

0.62

0.63

0.64

Liaoning

0.76

0.77

0.73

0.73

0.72

… 0.60

0.60

0.65

0.67

0.70

Inner Mongolia

0.94

0.89

0.97

1.00

0.97

… 0.69

0.68

0.70

0.70

0.72

Ningxia

0.49

0.49

0.49

0.42

0.53

… 0.41

0.38

0.35

0.34

0.40

Qinghai

0.37

0.38

0.38

0.38

0.36

… 0.35

0.33

0.29

0.28

0.28

Shandong

0.56

0.53

0.54

0.54

0.55

… 0.66

0.74

0.74

0.70

0.72

Shanxi

0.69

0.76

0.69

0.72

0.73

… 0.68

0.69

0.69

0.71

0.73

Shaanxi

0.68

0.71

0.72

0.78

0.82

… 0.97

1.00

1.00

0.99

1.00

Shanghai

0.59

0.57

0.82

1.00

0.95

… 1.00

1.00

0.96

0.88

1.00

Sichuan

0.60

0.58

0.59

0.59

0.58

… 0.60

0.62

0.64

0.63

0.64

Tianjin

0.67

0.67

0.71

0.73

0.74

… 0.75

0.75

0.71

0.80

0.83

Xinjiang

0.57

0.57

0.56

0.56

0.57

… 0.50

0.57

0.52

0.44

0.43

Yunnan

0.52

0.54

0.52

0.54

0.50

… 0.48

0.46

0.45

0.45

0.46

Zhejiang

0.71

0.76

0.74

0.72

0.71

… 0.69

0.71

0.71

0.72

0.74

Eastern region

0.70

0.72

0.78

0.75

0.74

… 0.74

0.75

0.76

0.77

0.79

Central region

0.69

0.64

0.68

0.72

0.70

… 0.67

0.66

0.66

0.65

0.69

Western region

0.59

0.58

0.57

0.56

0.57

… 0.54

0.54

0.53

0.51

0.54

Northeastern region 0.82

0.81

0.83

0.85

0.85

… 0.68

0.67

0.69

0.71

0.73

Average

0.67

0.69

0.69

0.68

… 0.65

0.65

0.65

0.65

0.67

0.68

C10

Correction to: Efficiency Evaluation of Energy and Resource Utilization …

Table 3.4 Carbon emission efficiency in China, 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.31

0.32

0.34

0.34

0.34

… 0.22

0.15

0.18

0.22

0.25

Beijing

0.32

0.36

0.36

0.38

0.53

… 0.74

0.75

0.83

0.92

1.00

Fujian

0.75

0.84

0.76

0.69

0.61

… 0.38

0.35

0.34

0.40

0.41

Gansu

0.20

0.21

0.21

0.21

0.21

… 0.29

0.30

0.31

0.33

0.33

Guangdong

0.79

0.84

1.00

1.00

1.00

… 0.69

0.58

0.62

0.63

0.62

Guangxi

0.49

0.52

0.60

0.55

0.44

… 0.25

0.27

0.29

0.28

0.29

Guizhou

0.22

0.27

0.24

0.16

0.16

… 0.17

0.18

0.18

0.18

0.18

Hainan

0.21

0.22

1.00

0.20

0.42

… 0.23

0.25

0.25

0.28

0.21

Hebei

0.26

0.27

0.28

0.29

0.28

… 0.28

0.19

0.21

0.23

0.29

Henan

0.24

0.28

0.31

0.33

0.33

… 0.19

0.21

0.23

0.24

0.26

Heilongjiang

0.37

0.34

0.35

0.29

0.32

… 0.20

0.24

0.22

0.21

0.21

Hubei

1.00

0.34

0.29

0.25

0.22

… 0.33

0.24

0.27

0.30

0.31

Hunan

0.54

0.52

0.47

0.48

0.48

… 0.28

0.34

0.25

0.27

0.28

Jilin

0.18

0.19

0.19

0.24

0.17

… 0.11

0.10

0.10

0.12

0.12

Jiangsu

0.33

0.35

0.38

0.37

0.36

… 0.38

0.34

0.36

0.37

0.39

Jiangxi

0.43

0.43

0.43

0.35

0.26

… 0.26

0.29

0.33

0.36

0.25

Liaoning

0.27

0.28

0.30

0.30

0.32

… 0.24

0.26

0.21

0.21

0.22

Inner Mongolia

0.21

0.25

0.26

0.27

0.27

… 0.23

0.26

0.28

0.27

0.28

Ningxia

0.59

0.57

0.53

0.15

0.10

… 0.06

0.06

0.06

0.06

0.05

Qinghai

0.28

0.25

0.27

0.27

0.28

… 0.16

0.16

0.21

0.21

0.23

Shandong

0.35

0.37

0.34

0.35

0.32

… 0.17

0.13

0.13

0.15

0.14

Shanxi

0.30

0.25

0.24

0.38

0.35

… 0.23

0.25

0.26

0.27

0.29

Shaanxi

0.27

0.29

0.33

0.35

0.41

… 0.77

1.00

1.00

0.97

1.00

Shanghai

0.28

0.29

0.16

0.12

0.13

… 0.05

0.04

0.05

0.05

0.05

Sichuan

0.32

0.32

0.32

0.28

0.25

… 0.33

0.31

0.29

0.35

0.42

Tianjin

0.31

0.34

0.36

0.21

0.23

… 0.29

0.32

0.69

0.56

0.62

Xinjiang

0.20

0.21

0.24

0.23

0.21

… 0.15

0.10

0.10

0.14

0.13

Yunnan

0.20

0.18

0.17

0.12

0.17

… 0.19

0.23

0.28

0.29

0.28

Zhejiang

0.58

0.39

0.39

0.36

0.28

… 0.38

0.29

0.31

0.32

0.33

Eastern region

0.41

0.42

0.51

0.42

0.45

… 0.44

0.43

0.49

0.50

0.52

Central region

0.49

0.37

0.34

0.31

0.29

… 0.22

0.22

0.22

0.23

0.23

Western region

0.30

0.31

0.31

0.26

0.23

… 0.19

0.18

0.20

0.21

0.22

Northeastern region 0.24

0.27

0.29

0.30

0.30

… 0.22

0.24

0.24

0.24

0.25

Average

0.36

0.38

0.33

0.33

… 0.28

0.28

0.30

0.32

0.33

0.37

Correction to: Chapter 3 in: M. Song et al., Natural Resources …

C11

Table 3.5 Comprehensive utilization efficiency of energy resources in China from 2000 to 2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.31

0.32

0.34

0.37

0.35

… 0.33

0.34

0.35

0.37

0.38

Beijing

0.39

0.42

0.44

0.45

0.55

… 0.81

0.83

0.90

0.95

1.00

Fujian

0.82

0.91

0.85

0.81

0.71

… 0.57

0.54

0.57

0.60

0.62

Gansu

0.25

0.25

0.24

0.24

0.23

… 0.22

0.23

0.25

0.28

0.29

Guangdong

0.88

0.91

1.00

1.00

1.00

… 0.80

0.73

0.76

0.78

0.78

Guangxi

0.56

0.60

0.63

0.60

0.52

… 0.32

0.34

0.37

0.38

0.39

Guizhou

0.21

0.24

0.24

0.19

0.20

… 0.16

0.18

0.20

0.21

0.23

Hainan

0.58

0.61

1.00

0.52

0.68

… 0.36

0.36

0.37

0.39

0.40

Hebei

0.24

0.26

0.26

0.26

0.24

… 0.26

0.28

0.23

0.24

0.27

Henan

0.27

0.30

0.34

0.36

0.37

… 0.32

0.32

0.34

0.35

0.37

Heilongjiang

0.44

0.43

0.44

0.40

0.40

… 0.29

0.31

0.35

0.37

0.40

Hubei

1.00

0.43

0.39

0.35

0.32

… 0.36

0.37

0.41

0.44

0.46

Hunan

0.63

0.60

0.57

0.58

0.52

… 0.38

0.40

0.43

0.44

0.46

Jilin

0.26

0.26

0.25

0.23

0.24

… 0.19

0.20

0.21

0.23

0.23

Jiangsu

0.54

0.56

0.60

0.59

0.54

… 0.47

0.51

0.54

0.55

0.58

Jiangxi

0.54

0.59

0.55

0.48

0.43

… 0.40

0.42

0.43

0.44

0.46

Liaoning

0.30

0.31

0.30

0.29

0.30

… 0.30

0.32

0.40

0.44

0.46

Inner Mongolia

0.27

0.30

0.33

0.35

0.32

… 0.27

0.29

0.31

0.30

0.31

Ningxia

0.41

0.41

0.39

0.16

0.13

… 0.10

0.10

0.10

0.10

0.09

Qinghai

0.14

0.14

0.15

0.15

0.15

… 0.17

0.14

0.18

0.19

0.21

Shandong

0.25

0.24

0.23

0.24

0.22

… 0.26

0.25

0.26

0.28

0.29

Shanxi

0.43

0.46

0.38

0.37

0.35

… 0.32

0.33

0.33

0.35

0.37

Shaanxi

0.37

0.39

0.42

0.44

0.49

… 0.81

1.00

1.00

0.98

1.00

Shanghai

0.21

0.25

0.18

0.17

0.18

… 0.14

0.14

0.14

0.15

0.15

Sichuan

0.41

0.41

0.42

0.38

0.36

… 0.42

0.40

0.47

0.52

0.57

Tianjin

0.24

0.26

0.29

0.32

0.33

… 0.42

0.45

0.67

0.66

0.72

Xinjiang

0.15

0.16

0.17

0.17

0.16

… 0.15

0.16

0.16

0.16

0.15

Yunnan

0.31

0.31

0.29

0.26

0.30

… 0.25

0.27

0.30

0.32

0.33

Zhejiang

0.66

0.59

0.55

0.51

0.44

… 0.45

0.48

0.49

0.51

0.52

Eastern region

0.52

0.54

0.58

0.53

0.53

… 0.53

0.55

0.58

0.60

0.63

Central region

0.52

0.44

0.41

0.39

0.37

… 0.32

0.33

0.35

0.37

0.39

Western region

0.30

0.30

0.30

0.26

0.25

… 0.22

0.23

0.25

0.27

0.28

Northeastern region 0.28

0.30

0.32

0.34

0.33

… 0.30

0.31

0.35

0.36

0.38

Average

0.41

0.42

0.39

0.38

… 0.36

0.37

0.40

0.41

0.43

0.42

C12

Correction to: Efficiency Evaluation of Energy and Resource Utilization …

Table 3.6 Total factor comprehensive utilization efficiency values in China 2000–2017 Area

Year 2000 2001 2002 2003 2004 … 2013 2014 2015 2016 2017

Anhui

0.44

0.44

0.45

0.47

0.46

… 0.44

0.45

0.46

0.48

0.49

Beijing

0.53

0.55

0.56

0.57

0.62

… 0.85

0.87

0.92

0.96

1.00

Fujian

0.76

0.81

0.79

0.78

0.73

… 0.63

0.63

0.66

0.69

0.71

Gansu

0.40

0.41

0.41

0.41

0.41

… 0.35

0.35

0.35

0.36

0.36

Guangdong

0.85

0.87

1.00

1.00

1.00

… 0.80

0.74

0.75

0.77

0.78

Guangxi

0.57

0.59

0.60

0.59

0.56

… 0.39

0.40

0.42

0.43

0.44

Guizhou

0.31

0.31

0.29

0.27

0.27

… 0.28

0.28

0.28

0.28

0.29

Hainan

0.57

0.59

1.00

0.54

0.63

… 0.47

0.46

0.46

0.47

0.48

Hebei

0.42

0.42

0.43

0.44

0.43

… 0.38

0.38

0.39

0.40

0.42

Henan

0.45

0.48

0.51

0.54

0.55

… 0.48

0.48

0.50

0.51

0.53

Heilongjiang

0.51

0.51

0.52

0.50

0.51

… 0.39

0.40

0.42

0.43

0.45

Hubei

1.00

0.52

0.51

0.49

0.47

… 0.49

0.50

0.53

0.54

0.56

Hunan

0.61

0.60

0.59

0.60

0.57

… 0.49

0.51

0.54

0.55

0.57

Jilin

0.45

0.46

0.46

0.45

0.41

… 0.35

0.35

0.36

0.38

0.39

Jiangsu

0.59

0.60

0.63

0.64

0.62

… 0.60

0.63

0.66

0.68

0.71

Jiangxi

0.57

0.59

0.57

0.54

0.50

… 0.49

0.51

0.52

0.53

0.55

Liaoning

0.47

0.48

0.47

0.48

0.49

… 0.41

0.43

0.47

0.50

0.52

Inner Mongolia

0.49

0.51

0.55

0.58

0.56

… 0.48

0.48

0.50

0.49

0.51

Ningxia

0.56

0.57

0.57

0.24

0.22

… 0.19

0.18

0.18

0.18

0.17

Qinghai

0.28

0.27

0.27

0.26

0.26

… 0.24

0.23

0.25

0.25

0.26

Shandong

0.37

0.37

0.36

0.36

0.35

… 0.34

0.33

0.34

0.35

0.36

Shanxi

0.53

0.54

0.50

0.50

0.49

… 0.48

0.49

0.49

0.51

0.53

Shaanxi

0.52

0.54

0.56

0.59

0.64

… 0.89

1.00

1.00

0.98

1.00

Shanghai

0.41

0.41

0.39

0.39

0.38

… 0.24

0.24

0.23

0.23

0.24

Sichuan

0.49

0.49

0.50

0.48

0.47

… 0.52

0.51

0.56

0.60

0.64

Tianjin

0.41

0.43

0.46

0.49

0.50

… 0.61

0.64

0.78

0.75

0.77

Xinjiang

0.31

0.31

0.32

0.32

0.32

… 0.29

0.28

0.27

0.28

0.28

Yunnan

0.43

0.42

0.42

0.40

0.42

… 0.36

0.36

0.38

0.38

0.39

Zhejiang

0.67

0.63

0.62

0.59

0.54

… 0.57

0.59

0.62

0.64

0.65

Eastern region

0.58

0.60

0.66

0.61

0.62

… 0.63

0.64

0.67

0.68

0.71

Central region

0.59

0.51

0.50

0.50

0.48

… 0.42

0.43

0.45

0.46

0.48

Western region

0.42

0.42

0.42

0.38

0.37

… 0.33

0.33

0.34

0.35

0.36

Northeastern region 0.47

0.49

0.51

0.53

0.53

… 0.46

0.46

0.49

0.50

0.52

Average

0.51

0.53

0.50

0.50

… 0.47

0.47

0.49

0.50

0.52

0.52