Emerging Green Theories to Achieve Sustainable Development Goals (Industrial Ecology) 9819963834, 9789819963836

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Industrial Ecology

Syed Abdul Rehman Khan Muhammad Jawad Sajid Yu Zhang

Emerging Green Theories to Achieve Sustainable Development Goals

Industrial Ecology Series Editor Syed Abdul Rehman Khan, Tsinghua University, Beijing, Beijing, China

Industrial ecology and circular economy is a peer-reviewed book series that focuses on different disciplinary approaches to waste management, sustainable practices & strategies on different scientific, societal, pyschological, technological, economic, governance, and cultural and political aspects of the ongoing and emerging debate. This primary goal of this series is to offer scientists from different school of thoughts and institutions a platform for scientific analysis and debate. Undeniably, Industrial ecology is a rapidly growing field that systematically examines local, regional and global materials and energy uses and flows in products, processes, industrial sectors and economies. It focuses on the potential role of industry in reducing environmental burdens throughout the product life cycle from the extraction of raw materials, to the production of goods, to the use of those goods and to the management of the resulting wastes. Industrial ecology is ecological in that it (1) places human activity—industry in the very broadest sense—in the larger context of the biophysical environment from which we obtain resources and into which we place our wastes, and (2) looks to the natural world for models of highly efficient use of resources, energy and byproducts. By selectively applying these models, the environmental performance of industry can be improved. Industrial ecology sees corporate entities as key players in the protection of the environment, particularly where technological innovation is an avenue for environmental improvement. As repositories of technological expertise in our society, corporations provide crucial leverage in attacking environmental problems through product and process design.

Syed Abdul Rehman Khan · Muhammad Jawad Sajid · Yu Zhang

Emerging Green Theories to Achieve Sustainable Development Goals

Syed Abdul Rehman Khan School of Management and Engineering Xuzhou University of Technology Xuzhou, China

Muhammad Jawad Sajid School of Management and Engineering Xuzhou University of Technology Xuzhou, China

Yu Zhang School of Economics and Management Chang’an University Xi’an, China

ISSN 2730-5775 ISSN 2730-5783 (electronic) Industrial Ecology ISBN 978-981-99-6383-6 ISBN 978-981-99-6384-3 (eBook) https://doi.org/10.1007/978-981-99-6384-3 This work was partially supported by the National Natural Science Foundation of China (72250410375). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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.

Contents

1

2

Consumers’ Willingness to Participate in Internet Trading of Waste Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Consumers’ Cognition of Internet Transaction of Waste Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 The Social Benefits of Internet Transaction of Waste Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 The Economic Benefits of Internet Transaction of Waste Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Consumers’ Evaluation of Internet Recyclers . . . . . . . . . . . . . . . . . 1.2.1 Internet Trading Ability of Internet Recyclers . . . . . . . . . 1.2.2 Consumers Evaluation of Internet Recyclers’ Transaction Management . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 The Evaluation of the Internet Recycler’s Capability of Creating Customer Experience . . . . . . . . . . 1.3 Value of Waste Products Held by Consumers . . . . . . . . . . . . . . . . . 1.3.1 Measurement of the Value of Waste Products Held by Consumers in Internet Transactions . . . . . . . . . . . . . . . 1.3.2 The Impact of Consumers’ Value Judgment of Waste Products on Consumers’ Transaction Intention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 The Impact of Internet Recyclers’ Judgment on the Value of Waste Products on Consumers’ Transaction Intention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 The Impact of Consistent Value Judgment of Both Parties on Consumers’ Transaction Intention . . . . . . . . . . 1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 11 11

Cooperation Mode for Refurbished and Remanufactured Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Online Recycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15

1 1 1 2 5 5 6 7 8 8

8

9

v

vi

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2.2 2.3

3

4

5

TPR (Third-Party Recycler) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Remanufacturing and Refurbishing of Used Products . . . . . . . . . . 2.3.1 CLSC (Closed-Loop Supply Chain) . . . . . . . . . . . . . . . . . 2.3.2 Reverse Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 The Cooperation Between Internet Recycler and OEM . . . . . . . . . 2.5 The Cooperation Between an OEM and a Retailer Under Internet Recycling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 The Retailer Does Internet Recycling Under Distribution Sales Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 The Retailer Does Internet Recycling Under Consignment Sale Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 16 17 17 17

Technological Innovations in Reverse Supply Chain . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 The Role of Internet in Taking Back Used Products from Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 The Role of Internet in Reprocessing of Used Products . . . . . . . . 3.4 The Role of Internet in Retailing of Recovered Products . . . . . . . 3.5 The Role of Internet in Reverse Logistics . . . . . . . . . . . . . . . . . . . . 3.6 The Internet Can Speed the Capital Flow of a Reverse Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 31

Introduction and Problem Analysis of Resource Recycling Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction of the Value of Resource Recycling Industry . . . . . . 4.2 Introduction of the Chain of Resource Recycling Industry . . . . . . 4.3 Technical Requirements and Business Models of Resource Recycling Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Suggestions for the Development of Resource Recycling Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technological Innovation in Business Operations for Sustainability: Current Practices and Future Trends . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Intellectual Growth and Its Impact by Years . . . . . . . . . . 5.3.2 Top-20 Most Productive Countries . . . . . . . . . . . . . . . . . . 5.3.3 Top-20 Most Influential Institutions . . . . . . . . . . . . . . . . . 5.3.4 Top-20 Most Prolific Researchers . . . . . . . . . . . . . . . . . . .

22 24 25 29 29

32 34 36 40 41 42 43 45 45 49 51 52 53 57 57 59 60 60 61 63 65

Contents

vii

5.3.5

6

7

8

Top-20 Most Frequently Used Sources of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.6 Top-20 Most Influential Articles . . . . . . . . . . . . . . . . . . . . 5.3.7 Top-20 Author’s Used Keywords . . . . . . . . . . . . . . . . . . . . 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Limitations and Future Direction . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65 67 69 70 73 73 74

Environmental Policies and Decarbonization: Leading Towards Green Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Previous Empirical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

77 77 78 79 80 81 83 84

Nexuses Between Technological Innovations, Macro-environmental and Economic Factors . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Research Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Variables and Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Empirical Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Estimated Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 87 89 90 90 91 91 92 92 93 95 96

Introduction to the Theory of Fear Industries and Its Implications for United Nations SDGs 1, 2 and 16 . . . . . . . . . . . . . . . . 8.1 Background to Some Main Economic Theories . . . . . . . . . . . . . . . 8.2 Recent Evidence for the Presence of Short- and Long-Term Fear Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Short-Term Fear Industries . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Long-Term Fear Industries . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 The Theory of the Fear Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 The Linkage Between United Nations SDGs 1, 2, and 16 and the Fear Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Data Sources and Methodology . . . . . . . . . . . . . . . . . . . . . 8.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

99 99 101 101 103 104 106 106 109

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Contents

8.5.1

The Proof of the Presence of the Long-Term Fear Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 The Proof of the Presence of the Short-Term Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.1 Prolonged Short-Term Fear Event . . . . . . . . . . . . . . . . . . . 8.6.2 De-evolution of Public . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.3 Non-economic (Non-financial) Incentives for Crime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.4 Emergency Supplies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

An Application of the Long-Term Fear Industry Theory to Environmental Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 A Simple Explanation of Net Demand and Supply-Side Environmental Linkages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Methods and Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Total CO2 Emissions and Direct Intensity . . . . . . . . . . . . 9.4.2 Contribution of the Defense Sector to Final Demand Embedded Emissions . . . . . . . . . . . . . . . . . . . . . . 9.4.3 Contribution of the Defense Sector to Industrial Supply Factors Embedded Emissions . . . . . . . . . . . . . . . . 9.4.4 Defense Sector’s Effect on Pushing and Pulling Emissions of Other Sectors . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.5 Sector-Wise Decomposition of the Defense Sector’s Demand and Supply-Pushed Emissions . . . . . . . 9.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5.1 Limitations and Future Research . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Short-Term Fear industry’s Environmental Consequences and Its Implications for SDGs 1, 2, 3, and 16 . . . . . . . . . . . . . . . . . . . . . 10.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Pertinent Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.1 CO2 Hazard of the Selected Short-Term Fear Industry of Air Carriage . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.2 CO2 Benefit of the Selected Short-Term Fear Industry of Air Carriage . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.3 Net CO2 Hazard/Benefit of the Selected Short-Term Fear Industry of Air Carriage . . . . . . . . . . . .

109 109 111 113 113 113 113 114 117 117 120 122 122 124 125 125 126 127 127 128 132 136 137 141 141 143 145 146 146 147 148

Contents

10.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5.1 Robustness Analysis of the Methodology . . . . . . . . . . . . . 10.5.2 CO2 Emissions from the Air Carriage of the COVID-19 Inoculation . . . . . . . . . . . . . . . . . . . . . . . 10.6 CO2 Reduction in Global Aviation Emissions During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.1 Net CO2 Hazard/Benefit of the Short-Term Fear Industry During the CD-19 . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 A Study of the Diverse Socioeconomic and Environmental Risks of the Long- and Short-Term Fear Industries . . . . . . . . . . . . . . . 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Different Socio-environmental Hazards of the Long-Term Fear Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Land Use by the Long-Term Fear Industry . . . . . . . . . . . . 11.2.2 Biodiversity Loss from the War Machine . . . . . . . . . . . . . 11.2.3 Direct and Indirect CO2 Emissions from the War Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.4 Disruptions to Technological Progress and Investments in Green Technologies . . . . . . . . . . . . . . 11.2.5 Illegal Mining and Logging . . . . . . . . . . . . . . . . . . . . . . . . 11.2.6 Additional Carbon Releases from Recovery and Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.7 Loosening of Environmental Monitoring and Related Laws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.8 Environmental Consequences of Refugee Crises . . . . . . . 11.2.9 Different Socio-environmental Hazards of the Short-Term Fear Industry . . . . . . . . . . . . . . . . . . . . . 11.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 The Path from Economic to Environmental Shortand Long-Term Fear Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 The Path from Economic to Environmental Short-Term Fear Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Definition of the Short-Term Fear Industry . . . . . . . . . . . 12.2.2 Summarized Literature Review and Research Gaps . . . . 12.2.3 Major Research Gaps Fulfilled by Our Work on Short-Term Fear Theory . . . . . . . . . . . . . . . . . . . . . . . .

ix

148 148 149 152 156 156 159 159 163 163 164 164 164 168 169 170 171 171 172 172 173 173 174 177 177 178 178 178 180

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12.2.4 Brief Overview of the Economic and Environmental Implications of Short-Term Fear Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 The Road from Economic to Environmental Long-Term Fear Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.1 Definition of the Long-Term Fear Industry . . . . . . . . . . . 12.3.2 Summarized Literature Review and Research Gaps . . . . 12.3.3 A Brief Overview of the Economic and Environmental Implications of Long-Term Fear Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

180 181 181 182

183 183

Chapter 1

Consumers’ Willingness to Participate in Internet Trading of Waste Products

1.1 Consumers’ Cognition of Internet Transaction of Waste Products 1.1.1 The Social Benefits of Internet Transaction of Waste Products Facing serious environmental problem caused by waste products, all groups in the society should be responsible for the waste management. Waste products acquisition from consumers, waste products processing, and selling the transformed waste products, all the activities can be driven by economic benefits. So, the environmental responsibility and social responsibility during the waste products recycling are difficult to guarantee. When the cost of the waste products is a profitable way for the consumers who have the waste products for sale. The transforming of the waste products can bring the downstream manufacturer economic profit. When environmental cost for the processing waste products is cut, the recycler can benefit more economically. Thus, they have the intention to cut the environmental cost. If the transformed waste products are sold in irregular market with irregular price, the recycler can benefit more. The recycler also can be driven to do irregular sale. But the society will undertake risk of irresponsible economic behaviors. In terms of social benefits, Internet transactions of waste products ensure the regular flow of waste products to formal channels (Mashhadi et al. 2016; Jin et al. 2021; Wang et al. 2021). With an increasing number of different recyclers participating in waste products recycling, it is difficult for consumers to distinguish between regular recycler and irregular recycler. Driven more by economic benefits, irregular recyclers make waste products flow into irregular channels, which is difficult to ensure the environmental protection of processing the waste products and transaction compliance of transformed waste products. Due to the unacceptance of government authorities, the irregular recyclers keep their businesses from the public. For example, the irregular recycler acquires the End-of-Life vehicles recycling of ELVs cannot © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_1

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ensure the environment protection degree of vehicle processing, and also brings risk to public safety. In an open and complex network environment, the Internet recyclers cannot easily avoid public eye easily. The transparency of the transaction process makes the behavior of Internet recyclers subject to the supervision of all sectors. At the same time, the application of information technology ensures a more regular and transparent flow of waste products, so that waste products can be processed formally and sold in regular market (Li et al. 2019).

1.1.2 The Economic Benefits of Internet Transaction of Waste Products In fact, Internet recyclers with modern ideas are more professional in the waste products transaction than traditional recycling enterprises in terms of protecting consumers’ privacy and handling disputes. For example, when the internet recyclers, Internet recycling enterprises of aihuishou and huishoubao acquire used mobile phones and other privacy related electronic products, they can correctly deal with consumers’ privacy in waste products through cleaning consumers’ data of the used products face to face. The leakage or stealing of confidential data or information of consumers during the transaction could cause a great cost to the consumers, and also can make the Internet recycler in legal disputes. Therefore, unprofessional recyclers might pollute the trading environment, which bring fear into the consumers who have the waste products related to their privacy. Consumers’ participation in the Internet trading of waste products is the basis for ensuring the operation of a good social environment and purifying the trading environment of waste products. The stronger sense of social responsibility consumers have, the easier it is for the consumers to understand the social value of waste product trading through internet, and the stronger willingness to trade with internet recyclers the consumers would have (Wang and Herrando 2019; Zhang et al. 2014; Khan et al. 2023a; Bai et al. 2021). As a fundamental part of reverse supply chain, waste product trading itself provides the raw materials for Value Creation Activities with waste products (Kang et al. 2020; Miao et al. 2020; Wang et al. 2021). After acquiring the waste products from consumers, the manufacturer can process the waste products into the remanufactured products that can be used in production activities and peoples’ daily life, and then the sales activities of remanufactured products can be conducted. The Internet transaction with consumers for waste products can also lay the foundation for the renewable resources industry, such as, secondary copper, secondary lead and zinc, and secondary precious metals can be obtained through processing waste phones and waste household Electric Appliances (Wu et al. 2020). Therefore, the recycler would like to pay for the waste products to the consumers according to the value the waste products can create. In the traditional recycling mode, the recyclers need to driving their vehicles from door to door for acquiring the waste products, which requires high logistics costs. Therefore,

1.1 Consumers’ Cognition of Internet Transaction of Waste Products

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recyclers have to squeeze corporate profit margins and provide consumers with a lower recycling transaction price (Li et al. 2019; Khan et al. 2023b). Through online communication with the consumers, Internet recyclers get the information of waste products and organize efficient logistics activities for waste products. Then, the logistics cost for waste products can be reduced than traditional recycling mode. Thus, a higher transaction price for waste products can be provided to consumers. In order to reduce the logistics cost of waste products, Internet recycling enterprises, such as aihuishou, huidhoubso and Lehuishou build the partnership with logistics suppliers, such Zhongtong Express, Yunda Express, etc. (Zuo et al. 2019). Due to economies of scale, logistics service providers can provide low-cost logistics services of waste products for Internet recyclers. With the application of information technology, the new trading model for waste products between recycler and consumers would enhance consumers’ cognition of value creation of their waste products. The higher transaction price provided will attract more consumers to do transactions with Internet recycler. With the continuous popularity of innovative ideas, there are more and more new products in our daily life, it takes some time for consumers to recognize the value of new products after scrapping (Wu et al. 2019). Our private information are easily to be recorded by electronic products, the cameras, computers and phones. Though these products are more valuable than other waste daily products, the immature recycling industry and people’s awareness of recycling the new developed waste products makes insufficient recycling knowledge among the consumers. A survey for the treatment of waste mobile phones was conducted among a group of consumers, and it is found that more than 40% of the group had never participated in the trading of waste mobile phones. Nearly 50% consumers do not think that the benefits they can obtain by trading waste mobile phones with Internet recycler will be greater than the efforts they need invest in the trading process (Wang et al. 2021). As most people work longer hours and take fewer holidays, the value of their leisure time naturally increases, and the waste products recycling need the recycler to sacrifice their leisure time (Li et al. 2019; Khan et al. 2023c). Consumers are more inclined to choose waste products transactions that require less time and effort and more convenience. The Internet recycling creates trading conditions for both parties of transactions that span time and space, increasing the flexibility in transaction time and place, providing more convenience to the busy consumers in the process of waste products transactions. With the application information technology, the internet trading of waste products between consumers and Internet recycler can be well managed. The Internet recyclers can get the information about the waste product they will acquire, and then, they organize the recycling activities and decide the cost need to invest in the transaction. The consumers can choose the time to start the transaction of the waste products with Internet recyclers, and select the service provided by Internet recyclers according to their conditions. Also, the flow of waste products can be ensured due to the transparency of Internet transaction. In order to enhance consumers’ awareness of the value creation of Internet trading of waste products they hold, Internet recyclers can consider publicizing the advantages of Internet trading of waste products through

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social media advertising, so that the consumers could understand the economic value the waste products can bring to them and the social value they can create due to their participation in Internet trading of waste products, so that the willingness of consumers to trade the waste products with Internet consumers could be encouraged. In addition, social organization and governmental sectors can also participate in the advertising campaigns for more participation in the modern industrialization of recycling system (Esenduran et al. 2017) (Fig. 1.1; Table 1.1). Only when consumers recognize the social and economic value of waste product trading and dispel consumers’ concerns about possible losses in trading activities can they have or even enhance their willingness to trade waste products with internet recyclers.

way 10 way 9 way 8 way 7 way 6 way 5 way 4 way 3 way 2 way 1 0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

Fig. 1.1 The proportion of the people who handling their used phone in different ways

60.00%

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Table 1.1 The description of how the people who participated in the survey handle their used phones Ways in Fig. 1.1

Description

Way 1

Leave the used phone at home without handling it

Way 2

Send the used phone to relatives or friends

Way 3

Sell the used phone to Internet recycling company (aihuishou, huishoubao etc.)

Way 4

Sell the used phone to individuals

Way 5

Sell the used phone to manufacturer

Way 6

Put the used phone to waste collection points that the government set

Way 7

Trade in the used phone for new

Way 8

Donate the used phone to public welfare organizations

Way 9

Sell the used phone to pedlar

Way 10

Throw the used phone away along with domestic garbage

1.2 Consumers’ Evaluation of Internet Recyclers 1.2.1 Internet Trading Ability of Internet Recyclers Similarly, the consumers would decide to trade the waste products with Internet recycler according to their’ evaluation of the Internet recycler’s Internet trading ability of waste products, which is an influential factor for the transaction cost that the consumers need to invest and consumer experience during the transaction. From the perspective of consumers, the Internet trading ability of Internet recyclers is reflected in the transaction management and the consumer experience they can provide. Due to the distance between Internet recycler and the consumers, the Internet transaction of waste products between consumers and Internet recycler will take time. Before the consumers get the payment of their waste products, the waste products need to be inspected by the Internet recycler. The Internet recycler and consumers need to reach an agreement on the quality level of the waste products. Then, how to organize the activities during the transaction is associated with the transaction cost that both sides need to invest. How to communicate with the consumers and facilitate the consumers to accomplish the transaction would create consumer experience. Before deciding whether to trade with internet recyclers, consumers will probably predict their possible benefits form the Internet transaction of waste products, and the transaction cost is part of it. From submitting an order online to receiving the waste price payment from the Internet recycler, consumers need to pay the transaction cost and effort for accomplishing the waste transaction. The Internet recycler can arrange the inspection activities closing to the consumers’ location, or making videoconferencing for the consumers and inspectors. So, the early ownership transfer of the

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waste products can be achieved. Then, the consumer has no need to take the risk of transaction failure and may reduce the transaction cost of the consumers. The Internet recycler’s management ability for waste products transaction affects the transaction cost of both parties. The offline network for the waste products transaction and the logistics service for the waste products are associated with both sides’ transaction cost for the waste products. The consumer experience during Internet transaction of waste products is the most direct evaluation basis for consumers, such as whether consumers feel the respect from the recyclers and honesty of the recycler. With the support of information technology, consumer experience has been transformed into Internet word-of-mouth and widely spread, which has become the evaluation basis for potential consumers to evaluate the trading ability of Internet recyclers (Cheung and Thadani 2012).

1.2.2 Consumers Evaluation of Internet Recyclers’ Transaction Management Internet transaction denotes that there are time and space distance between the transaction parties. Different from the traditional waste product transaction, the completion of the transaction needs the coordination of online transaction and offline logistics activities (Ramzan et al. 2020). Different from the Internet transaction of new products (Fu et al. 2019), the agreement on the transaction price of Internet waste products requires both parties to unify the test results. In the traditional trading process of waste products, recyclers and consumers conduct quality inspection of waste products face to face and complete the ownership conversion of waste products. Then recyclers are responsible for the transfer of waste products. Consumers only need to negotiate face to face with recyclers for the recycling price in the whole trading process, and before the ownership transfer of waste products is completed, waste products are transparent and visible to consumers. In the traditional trading mode of waste products, consumers’ transaction cost investment is low, and the probability of transaction disputes is also low (Huang and Wang 2017). In the Internet waste product trading, consumers submit orders online, and offline logistics will transport the waste products to the recycler for inspection, and the recycler will determine the transaction price. In the transaction of waste products, if face-to-face quality inspection cannot be realized, consumers need to invest in logistics activities for transporting the waste products to the Internet recycler without capital income. Thus, consumers need to passively bear the transaction risk of failure transaction. After receiving the waste products, the Internet recycler will conduct quality inspection, determine the consumer’s payment, and then the transaction can be accomplished. Internet transactions are difficult to ensure consumers’ visibility of waste products before completing the transfer of ownership of waste products, that is, the logistics activities of waste products are difficult to be visible to consumers. If the Internet recycler’s inspection result is far too different from the consumer’s inspection

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result in the online order, the two parties also need to invest in communication cost to achieve consistency of the waste product’ quality inspection. Internet transaction of waste product may not be attractive to traditional and risk averse consumers (Wu et al. 2019). Therefore, when dealing with internet recyclers that can realize faceto-face detection and complete settlement, consumers need to pay lower transaction costs. Such Internet recyclers are also more able to attract consumers to trade waste products with them. At present, AI recycling and other Internet recycling enterprises have set up many waste product trading places offline to realize face-to-face detection and settlement. When recyclers cannot realize face-to-face detection and settlement, consumers need to pay the transaction cost—mail cost in advance under the condition of uncertain income. If the cost is too high, it will inevitably reduce the willingness of consumers to trade waste products with internet recyclers (Liu 2014). Therefore, Internet recyclers can also provide consumers with low-cost logistics services through the management of recycling logistics, so as to enhance consumers’ willingness to participate in waste product trading (Wan et al. 2021). In addition, the density of offline service outlets of Internet recyclers is also an aspect of the transaction cost management ability of Internet recyclers.

1.2.3 The Evaluation of the Internet Recycler’s Capability of Creating Customer Experience The consumer privacy protection ability, transaction settlement efficiency and dispute handling ability of Internet recyclers are associated with customer experience during the transaction (Feng et al. 2017). Completing an Internet transaction take a long time, the consumers’ privacy sometimes needs to be exposed to the Internet recycler, which makes the consumers feel unsafe. So, Internet recycler’s ability to protect Internet privacy is the key to win consumer trust, especially during the transaction of the waste products related to consumers’ privacy, such as computer, and cellphone. The higher the settlement efficiency, the higher satisfaction and trust the consumer will get. Due to the longer transaction time and non face to face transaction, the internet recycler and the consumers are easier to be involved in transaction disputes. If the Internet recycler can reasonably and properly handling the psychological demands of consumers, consumers need to invest a lower communication cost in the process of dispute, and get the best customer experience at the same time. Then, there will be more consumers would like to take transaction with the Internet recycler. These evaluation contents reflect the trading ability of waste products of Internet recyclers, which is closely related to the time consumption, effort investment and the risk that the consumer need to take in the Internet transaction of waste products. Therefore, when consumers have low evaluation on the trading ability of Internet recyclers, consumers probably believe that more costs need to be invested in the

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trading process of waste products, but the final profit cannot be increased, which will inevitably reduce the willingness of consumers to trade waste products with internet recyclers (Chen and Gao 2021). In order to improve consumers’ evaluation of the trading ability of Internet recyclers, Internet recyclers not only need to improve their transaction management level, deeply understanding the behavior psychology of consumers who hole waste products and improve their communication ability with consumers, but also need to learn how to manage and guide Internet word-of-mouth (Racherla and Friske 2012).

1.3 Value of Waste Products Held by Consumers 1.3.1 Measurement of the Value of Waste Products Held by Consumers in Internet Transactions The value of waste products is the basis for consumers to decide how much they should invest in the transaction of waste products with Internet recycler, and it is also the basis for the reverse supply chain to create economic profit (Wang et al. 2021). In the non face-to-face Internet transaction of waste product, consumers will judge the value of the waste products they hold in advance based on their understanding of the quality level of their waste products before submitting orders online. Internet recyclers have professional knowledge and equipment of waste products quality inspection. When receiving waste products from consumers, they determine the value of waste products according to professional quality inspection of waste products. The real quality of waste products determines the profit margin of the products processed and transformed by manufacturer. The cost of remanufacturing the waste product depends on its degree of damage, which also affect the renewable materials that are extracted out from the waste product. Therefore, the Internet recycler will determine the payment for the waste products to the consumers based on its own professional quality inspection results.

1.3.2 The Impact of Consumers’ Value Judgment of Waste Products on Consumers’ Transaction Intention The quality of waste products kept in consumers’ mind before the order submission in the Internet transaction is the basis for consumers to decide whether to start the transaction (Wang et al. 2021). Also, the Internet recycler does not want to invest more transaction cost for the waste products with lower quality. The value cognition of consumers to their waste products is the basis for prediction of the profit they

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can get from the Internet recycler. So, the higher value cognition the consumers have, they will be more willing to do transaction with Internet recycler, and the higher transaction costs will be more easily accepted by the consumers (Pangburn and Stavrulaki 2014; Sarigollu et al. 2020). The Internet recycler will have more transaction objects: consumers with high value cognition of their waste products are more willing to participate in the Internet transaction of waste products (Fortuna and Diyamandoglu 2017). This is why we can always see the advertisements that show that the consumers who do transaction of products recycled with Internet recyclers will get high payment, so that the potential consumers can be convinced that they will at higher recycling prices on the Internet transaction of waste products (http://sho uji.jd.com), while few consumers actually can get high transaction price like that in the Internet transaction of waste products. Consumers’ understanding of their waste products’ value is not always accurate, because they have insufficient product quality knowledge and professional inspection equipment. The quality level judgment made with limited knowledge and inspection equipment is prone to subjective error (Zuo et al. 2019). This error means that consumers need to bear the risk that the transaction cost does not match their economic profit for the waste products transaction. Therefore, the consumers’ confidence for their inspection results and risk preference will affect consumers’ willingness to start Internet transactions of their waste products.

1.3.3 The Impact of Internet Recyclers’ Judgment on the Value of Waste Products on Consumers’ Transaction Intention The limited quality detection ability of consumers means that there is likely to be a difference between the quality level of waste products in submitted orders and the quality level of waste products identified after Internet recycler’s quality inspection. In order to correct the consumers’ quality cognition of their waste products, Internet recyclers need to pay communication costs. The process of correction is also increasing consumers’ transaction costs. Even if the consumers accept the inspection results of Internet recycler, the total profit of consumers from waste product transactions will be lower than expected. However, when consumers’ valuation of their waste products in the submitted orders is lower than the inspection result given by the Internet recycler, and the Internet recycler is willing to correct the orders submitted by consumers. Thus, the consumers can obtain higher profit than expected. After that, consumers’ willingness to trade with internet recyclers is likely to increase (Wang et al. 2021). But, the correction for the Internet recycler is to kill the chance for taking low cost on recycling the waste products. On the contrary, if consumers’ valuation of the waste products they hold in their submitted orders higher than the recycling price given by the Internet recycler according to their own quality test results, and the Internet recycler chooses to corrects this. If consumers accept the recycling price proposed by Internet recyclers, the economic profit obtained by consumers from the

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transaction of waste products will be greatly different from the expected due to the communication cost and their limited inspection ability. The psychological discrepancy in the Internet transaction is likely to reduce consumers’ willingness to trade with the internet recyclers. If the consumers refuse to accept the correction of recyclers, the transaction costs previously invested by consumers cannot be compensated. Similarly, it is difficult for consumers to have the willingness to conduct the next waste product transaction with the Internet recycler.

1.3.4 The Impact of Consistent Value Judgment of Both Parties on Consumers’ Transaction Intention As in the traditional transaction mode of waste products, the consistency between Internet recyclers and consumers on the quality level of waste products does not only reflect transaction fairness between Internet recycler and consumers, but also is the premise of completing the ownership conversion of waste products. The Internet environment realizes the transaction of waste merchants cross region and time, but it also creates some difficulties for both parties to reach an agreement on the value of waste products (Xiao et al. 2019). For the waste products holders—consumers, the visibility of waste products can ensure that the value of their waste products will not be eroded and the economic profit will not be affected by the exogenous environment before that the transaction settlement and the ownership transfer of waste products are completed (Peng and Su 2014). Therefore, in order to ensure and enhance the willingness of consumers to trade waste products with Internet recyclers, Internet recyclers need to avoid any activities during the transaction that can affect the quality of waste products before completing the ownership transaction, so as to avoid transaction disputes and the waste products transaction profit reduction. aihuishou, huishoubao and other large Internet recyclers have established multiple offline trading networks around urban and rural areas. Consumers can carry their waste products to offline shops to accomplish the quality inspection of their waste products with professional recyclers face to face. Then, the two sides could easily reach an agreement on the quality level of waste products. After the transaction price is determined and the consumers get the payment, consumers will transfer waste products to the recycler in the shops. Thus, the consumers’ visibility of waste products before the ownership of waste products is transferred, which ensures to a certain extent that consumers’ willingness to trade with Internet recyclers will not be reduced due to the opaque information in the trading process.

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1.4 Conclusion When the concept of Internet recycling has not been popularized among consumers, the Internet recyclers, as the main body of Internet recycling, need to continuously improve consumers’ willingness to participate in waste products Internet transactions. Moreover, facing serious resource shortage and environmental pollution caused by waste products generation, the government and environmental protection organizations should also participate in the campaign of enhancing consumers’ willingness to trade the waste products with Internet recycler and make this efficient resource recycling activity deeply rooted in the hearts of the consumers as soon as possible. Consumers focus on the economic benefits they can obtain from the waste products transaction, the risks they need to bear, their own customer experience and also their responsibility of environmental protection. However, it is difficult for consumers and Internet recyclers to reach a face-to-face agreement on the value of waste products in the Internet transaction across regions and time. The Internet transaction time is significantly longer than the traditional waste products recycling mode, and the consumers may be unclear about the economic benefits they can obtain from the Internet transaction for a long time, It is also difficult for recyclers to improve the customer experience of consumers in this recycling model in the long-time transaction with unclear transaction profit. So, the willingness of consumers to trade with internet recyclers is significantly affected. Therefore, under the new recycling mode, recyclers need to make full use of network and information resources, improve the transaction management ability from the perspective of improving consumers’ willingness, and help consumers better understand the Internet transaction of waste products.

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Huang YT, Wang ZJ (2017) Dual-recycling channel decision in a closed-loop supply chain with cost disruptions. 9(11). https://doi.org/10.3390/su9112004 Jin L, Zheng BR, Huang SJ (2021) Pricing and coordination in a reverse supply chain with online and offline recycling channels: a power perspective. J Clean Prod 298. https://doi.org/10.1016/ j.jclepro.2021.126786 Kang Y, Chen JH, Wu D (2020) Research on pricing and service level strategies of dual channel reverse supply chain considering consumer preference in multi-regional situations. Int J Environ Res Public Health 17(23). https://doi.org/10.3390/ijerph17239143 Khan SAR, Tabish M, Yu Z (2023a) Mapping and visualizing of research output on waste management and green technology: a bibliometric review of literature. Waste Manage Res 41(7):1203–1218. https://doi.org/10.1177/0734242X221149329 Khan SAR, Zia-Ul-Haq HM, Ponce P, Janjua L (2023b) Re-investigating the impact of nonrenewable and renewable energy on environmental quality: a roadmap towards sustainable development. Resour Policy 81:103411. https://doi.org/10.1016/j.resourpol.2023.103411 Khan SAR, Yu Z, Ridwan IL, Irshad AUR, Ponce P, Tanveer M (2023c) Energy efficiency, carbon neutrality and technological innovation: a strategic move towards green economy. Econ Res Ekonomska Istraživanja 36(2). https://doi.org/10.1080/1331677X.2022.2140306 Li Z, Guo Q, Nie J (2019) Research on recycling and resale strategies of the recycler: online recycling VS traditional recycling. Indus Eng Manage 24(6):164–172 Liu DW (2014) Network site optimization of reverse logistics for E-commerce based on genetic algorithm. Neural Comput Appl 25(1):67–71 Mashhadi AR, Esmaeilian B, Behdad S (2016) Simulation modeling of consumers’ participation in product take-back systems. J Mech Des 138(5). https://doi.org/10.1115/1.4032773 Miao SD, Liu D, Ma JF, Tian F (2020) System dynamics modelling of mixed recycling mode based on contract: a case study of online and offline recycling of E-waste in China. Mathe Comp Model Dyn Syst 26(3):234–252 Pangburn MS, Stavrulaki E (2014) Take back costs and product durability. Eur J Oper Res 238(1):175–184 Peng WJ, Su D (2014) Development of an online system for recycling consumer electronic products using the internet, NFC and RFID technologies. Key Eng Mater 572:90–99 Racherla P, Friske W (2012) Perceived ‘usefulness’ of online consumer reviews: an exploratory investigation across three services categories. Electron Commer Res Appl 11(6):548–559 Ramzan S, Liu CG, Xu Y, Munir H, Gupta B (2020) The adoption of online e-waste collection platform to improve environmental sustainability: an empirical study of Chinese millennials. Manag Environ Qual 32(2):193–209 Sarigollu E, Hou CX, Ertz M (2020) Sustainable product disposal: consumer redistributing behaviors versus hoarding and throwing away. Bus Strategy Environ 30(1):340–356 Wan C, Shen GQ, Choi S (2021) The place-based approach to recycling intention: integrating place attachment into the extended theory of planned behavior. Resour Conserv Recycl 169. https:// doi.org/10.1016/j.resconrec.2021.105549 Wang YC, Herrando C (2019) Does privacy assurance on social commerce sites matter to millennials? Int J Inf Manage 44:64–177 Wang C, Zhang XY, Sun Q (2021) The influence of economic incentives on residents’ intention to participate in online recycling: an experimental study from China. Resour Conserv Recycl 169. https://doi.org/10.1016/j.resconrec.2021.105497 Wu D, Chen JH, Yan RY, Zhang RJ (2019) Pricing strategies in dual-channel reverse supply chains considering fairness concern. Int J Environ Res Public Health 16(9). https://doi.org/10.3390/ije rph16091657 Wu D, Chen JH, Li P, Zhang RJ (2020) Contract coordination of dual channel reverse supply chain considering service level. J Clean Prod 260. https://doi.org/10.1016/j.jclepro.2020.121071

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Chapter 2

Cooperation Mode for Refurbished and Remanufactured Products

2.1 Online Recycling A recycler builds its recycling e-platform to attract consumers who hold used products, and the consumers who hold used products submit orders for their used products on the internet recycler’s e-platform will do the online transaction with the internet recycler (Wang et al. 2022; Li et al. 2019). After the online transaction, the used products will be transferred from the consumer to the internet recycler through offline service. With the integration of the internet, recycling cost is reduced, recycling efficiency is enhanced, and the consumers who hold used products are gathered in an e-platform. In online recycling, consumers are gathered on recycling e-platform because of used product transactions, which is a great opportunity for product sales. Therefore, most online recyclers usually have their e-platform for sale and recycling, such as aihuishou and huishoubao in China (Huang and Liang 2022). The online transaction reduces the recycler’s searching cost, and the online platform helps her create a customer base, which is a great advantage for the Internet recycler for other businesses. Not all Internet recycler has enough support for offline service system. Therefore, the internet recycler, aihuishou, cooperates with Shunfeng, a logistics service company, on offline recycling services (Chen and Gao 2021; Xi et al. 2021).

2.2 TPR (Third-Party Recycler) The business body that takes back the used products from consumers as a third party and resells them to the corporate who want to reuse them. The TPR in this chapter is referred to as Internet recyclers when they cooperate with downstream enterprises to reuse used products. In the past, recyclers needed to search for used products by traveling around different societies, and they had no efficient sales platform, such as an e-platform, to resell the used products. Therefore, internet recyclers primarily © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_2

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choose to cooperate with a powerful downstream company to purchase the used products they acquired. So, in the past, the recycler mainly acts as a TPR in the reusing industry. After integrating with the internet and building their e-platforms, the recyclers will not just act as a TPR in the reusing industry. OEM (original equipment manufacturer): an OEM who manufactures new products similar to the used products discussed in this chapter. The manufacturer also acts as a downstream company for reusing used products through remanufacturing and/or using the used products as raw materials. Due to manufacturing new products, the OEM is the starting point of CLSC. Meanwhile, the manufacturer is the one that has the authority to well reuse their used products, such as remanufacturing. Furthermore, they may have the authority to restrict the reusing of their products, so the invasion of other manufacturers who want to produce and remanufacture their products would be difficult or impossible. Alternatively, the OEM’s permission to produce the same products of different versions is necessary for other manufacturers. So, the reusing of used products can be restricted by the OEM. Retailer: the business body that sells new products to consumers. With the integration of the internet, the retailer starts his business on the online platform–web sales, which is different from the past: store sales, or the retailer has two channels for sales business, such as JingDong in China for selling electronics and Amazon in America for selling books. The retailer has an offline service system for Web sales, such as Amazon in America, Taobao, and JingDong in China. These retailers have a good customer base, so it will be easier for them to develop their online recycling business when they have e-platforms, such as JingDong, an online retailer, has developed his online recycling business since 2019.

2.3 Remanufacturing and Refurbishing of Used Products In this chapter, remanufacturing is to respire the used products relatively comprehensively. Due to remanufacturing processes, the quality of an RM product will be close to a new product. Compared to remanufacturing, refurbishing is to repair the surface damage of a used product to make it look good and easier to accept by consumers. Therefore, internal quality cannot be ensured for the refurbished products. In reality, the quality of refurbished products is mostly lower than RM products, which is why the manufacturer usually does not choose to refurbish the used products so that his reputation can be protected. No matter what kind of processing ways will be used on the used products, resource-saving can be achieved, and the carbon emissions during the production process may also be reduced as compared to producing a new product (Liu et al. 2017; Xiang and Xu 2019; Liu et al. 2020; Khan et al. 2023). Though the two products are processed or repaired by the formal corporate, the consumers still feel risky when they make purchasing decisions, which is reflected in consumers’ willingness to pay.

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2.3.1 CLSC (Closed-Loop Supply Chain) CLSCs couple the conventional forward supply chain processes with reverse logistics processes, ranging from producing new products, used product recovery, refurbishing, remanufacturing, disassembly, and part reusing. The final aim is to capture the values of products being consumed and used by customers with the possibility of reducing the environmental impact of the whole CLSC.

2.3.2 Reverse Supply Chain A reverse supply chain is to add new value to a used product so that the used product can be sold in the market again (Sarada and Sangeetha 2021; Khan et al. 2022a, b). So, in a reverse supply chain, the used products should be taken back from consumers. The used products should be processed according to the customer’s requirements, which can be refurbishment, remanufacturing, or others. Finally, the processed, used products would be sold to consumers again. Therefore, in a reverse supply chain, three functions are necessary. When the source of new product production and sale is considered in the reverse supply chain, the reverse supply chain would be called the closed-loop supply chain.

2.4 The Cooperation Between Internet Recycler and OEM The TPR, with an e-platform called internet recycler, will not just be a third-party recycler but also a seller of RF products. Under the cooperation between internet recyclers and OEM, internet recyclers take back used products from consumers and select high-quality used products for resale after refurbishing. Then, the OEM will purchase the remaining used products from the internet recycler. The remaining products can be reused as raw materials for producing new products by the OEM or remanufactured by the OEM. Figure 2.1 shows the cooperation mode when the OEM uses the used products from internet recyclers as raw materials for new products. Figure 2.2 shows the cooperation mode between OEM and IR when the OEM uses the used products for remanufacturing. The cooperation between OEM and internet recyclers for high-technological products usually depends on OEM’s restrictions on their products. The resale of secondhand products represents part of the quality of the new, and reprocessing may let the new and high technologies flow to others when the reprocessor is not the OEM, threatening the market position of OEM. Because the OEMs want to protect their monopoly position and their reputation, restrictions for recycling usually happen. Thus, how the cooperation for recycling the used products can be achieved is mainly associated with OEM’s restriction policies. What kind of policy the OEMs would

18 Fig. 2.1 The cooperation mode between IR and OEM when OEM produce new products with used products

2 Cooperation Mode for Refurbished and Remanufactured Products

Raw materials

Recycled materials processing

OEM

IR

Used products

RF products

New products

Consumer

Fig. 2.2 The cooperation mode between IR and OEM when OEM produce does remanufacturing with the used products

Raw materials

OEM

New products

IR

RM products

Used products

RF products

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make depends on the environment they are involved in and the profit of recycling their used products (Yu et al. 2019). Internet recyclers profit by reselling RF products and providing recycling services for downstream companies, and the resale of RF products is OEM’s primary income source. So, when the internet recycler cannot get the authority to recycle OEM’s products, cooperation between OEM and the internet recycler cannot happen. Therefore, we will discuss the cooperation without OEM’s restrictions. The OEM produces new products, and when the life cycle of the products ends, the internet recycler takes back the used products from consumers. The high-quality ones will be selected and

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refurbished by an internet recycler, and the remaining ones will be sent to the OEM for further processing. When the internet recycler supplies the manufacturer with used products, cooperation between them happens. So, the key to differentiating the cooperation mode is how the OEM reuses the used products. As is shown in Fig. 2.1, the OEM uses recycled materials from the used products to produce new products. While Fig. 2.2, the OEM uses the used products to produce remanufactured products, and the outcomes of the OEM will be new and remanufactured products. If the manufacturer chooses to use used products to produce the new, there will just be one outcome for the OEM, new products (Li et al. 2013). Thus, when the internet recycler starts to sell RF products, the two main parties in the CLSC will supply two kinds of products with the same function: the internet recycler supplies the RF products, and the manufactured supplies the new products. While the manufacturer chooses to produce remanufactured products with the used products, the two main parties in the CLSC will supply three products with the same function. The manufacturer will supply the two (RM products and new products), and the internet recycler will supply the RF products. It might be challenging to tolerate the competitor decreasing his/her market share. The upstream internet recycler might take actions to control the manufacturer’s inflow of used products by adjusting her recycling quantity so that the product outflow of OEM can be influenced. When the application of used products in new products production gives a significant cost advantage for OEM, the OEM will purchase less used products from internet recyclers. As a supplier for OEM, the internet recycler has to reduce the OEM’s inflow of used products so that his/her market position can be ensured or enhance the reputation of RF products to be more competitive. The downstream OEM may also take actions to influence the quantity of internet recycler’s RF products. When consumers prefer RF products, the OEM may reduce the used products inflow of internet recyclers with technological protection, which makes refurbishing used products more difficult. Both parties’ behaviors can be affected by consumers’ preferences changing. The consumer’s value evaluation of one product is the base of its market share (Tansel 2020). Here, one thing needs to be noticed: to some extent, products’ quality can be reflected by their selling price, and products’ quality also affects the value creation of recycling used products (Liu et al. 2016; Khan et al. 2022c). Products with high quality are usually sold at a high price. The used products with high quality usually make more profit than the used products with low quality when they are recycled. When an internet recycler selects high-quality products, the manufacturer has to choose low-quality ones if he is the cooperator of the OEM. The low-quality ones usually cannot make the value as much as the high-quality used products for the OEM. While the low-quality used products cannot be refurbished and sold by the internet recycler. Therefore, the OEM will put pressure on the price of low-quality products if they do not have a firm agreement. If the manufacturer does not provide a price that can cover the internet recycler’s cost, the internet recycler may not take back the low-quality used products. Then, the cooperation will not happen. So, it is better to assume they have an agreement on the price of low-quality used products when discussing their cooperation. In reality, low-quality used products will be sold

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through an auction with many buyers (aihuishou.com), so the price can be promoted as much as possible. Besides, the substitution among RM, RF, and new products should also be considered when discussing the cooperation between internet recyclers and OEMs. The different value evaluation for products with the same functions is beneficial for implementing a discriminatory pricing strategy. The product with higher consumer value evaluation at a higher price according to the market environment can be made when all the products with different consumer preferences are sold simultaneously. However, the profit of selling the product with higher consumer preference will not increase continuously by its price increase. The market share and its price would affect the seller’s profit. When the manufacturer chooses to remanufacture the used products, his new products’ market position will be shaken because of his choice. While, whatever his choice is, the RF products from his cooperator will shake his market position. Then, before he makes a choice, he needs to think about the results in advance according to his market position. Further, an interesting cooperation mode may happen between the two parties due to the power of the internet. The internet recycler has an e-platform for collecting used products and selling RF products. The customer base makes internet recycler a good retailer of manufacturer’s products, which has the same function as the RF products. In 2020, the electronic product manufacturer HUAWEI started cooperating with aihuishou and huishoubao for his new products sold through a trade-in project. The consumers who sell their new product to the internet recycler will get some proof of enjoying the discount when buying the new product, which makes the OEM share the customer base from the internet recycler. In these circumstances, the consumers who want a discount for buying new products and have used products will do used product transactions with the TPR, and the TPR will get more used products (Zhao et al. 2021). Meanwhile, the consumer group who gets the discount proof will probably buy the new products from the manufacturer, and then the manufacturer’s new product sales will increase. Both parties, for more profit, have the same intention to depress the procurement price of used products, so cooperation is easy to be achieved for them. Figure 2.3 shows the cooperation mode between internet recycler and OEM. When the internet recycler works as a supplier of used products and seller of manufacturer’s products in CLSC, he sells his RF product. The OEM pays the commission to the internet recycler for selling her products. The internet recycler’s role for the CLSC will be more complex, acting as an agent retailer, supplier for the OEM, and independent seller of refurbished products. JingDong APP is just the internet recycler mentioned above. Though the customer base of the internet recycler might be a cooperation base for the OEM and internet recycler, this cooperation mode will be more complex for both of them. It will be challenging for the internet recycler to decide how much effort to sell his products and OEM’s products. So, there should be contracts between the internet recycler and OEM for how much investment is in promoting the sales of both parties’ products so that the OEM can accept this cooperation mode. The quality and quantity of the internet recycler’s customer base, consumer’s value evaluation difference for those products, and the commission that the OEM needs to pay could be significant factors to be considered in this cooperation

2.4 The Cooperation Between Internet Recycler and OEM

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Contract

OEM

IR

Used products

Discount proof

Discount proof

New product with discount

Consumers

Fig. 2.3 Cooperation mode of internet recycler and OEM for customer base sharing

mode. Figure 2.4 shows the cooperation mode between internet recycler and OEM, and the OEM sell his products on internet recycler’s e-platform. This cooperation is still not widely implemented between the two parties. It might be because not many internet recyclers have a solid customer base and sufficient infrastructure, or the law environment is not strong enough for this cooperation mode. In fact, for both parties, both parties can build their reverse supply chain for reusing used products, which means cooperation may not happen between them, and the competition for taking back the used products and selling the recovered products. Thus, competition between different e-platforms will happen. High-quality used products are usually associated with low remanufacturing costs for the OEM, a low cost of refurbishing for internet recycling, and more customer preference for recovered products. Facing internet recycler shaking market position of new products, it does not seem very easy for the OEM to start remanufacturing their used products, especially when the RM products are less value creative than the new ones. The RM products would share the market of new products so that the profit of OEM may be reduced. Therefore, in the cooperation between internet recycler and OEM, remanufacturing may not happen when RM products are not competitive. However, with the increasing environmental deterioration and policy change, the profit margin difference between RM products and new products would be changing. With the internet application, the recycler becomes a potential and practical cooperator for the manufacturer to build the CLSC. While with the continued worsening of the environment, how to cooperate well with the upgraded recycler is a practical

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2 Cooperation Mode for Refurbished and Remanufactured Products Contract Used products

OEM

IR New product /RM products Used products

New product / &RM products +RF products

Consumers

Fig. 2.4 The cooperation between IR and OEM when IR is also a sales platform for the OEM

problem for OEMs. The internet recycler may be a recycler for the OEM, retailer, and competitor.

2.5 The Cooperation Between an OEM and a Retailer Under Internet Recycling Implementation of integrating a retailer and internet is earlier than the idea of integrating the recycler and internet, such as Amazon and Taobao. The retailers sell their new products on their e-shops, which also gather many customers. So, retailers with e-platforms also have the advantage of collecting their used products. Suning, JingDong, and other retailers have opened their channels for collecting used products. The original responsibility of the retailer is to sell the products from OEM. While due to the first connection with consumers, the e-retailer has the advantage of taking back the used products (Xiang and Xu 2020). So the CLSC, composed of an eretailer and OEM, also can be found in the market. The e-retailer takes back used products through their e-platform, which is also an e-shop for new products. When the e-retailer takes back the used products, and the OEM buys the used products from the e-retailer, the cooperation between the e-retailer and OEM happens. This cooperation mode may happen easily because the e-retailer has a stronger cooperation base with OEM than the internet recycler. They may reach an agreement on profit sharing policy easily. Because an e-retailer may not be a professional in processing the used products as an internet recycler, such as refurbishing used products, they are

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mostly just a recycling service provider for the OEM, as shown in Fig. 2.5. However, when the e-retailer recovers used products, they will be an official shop for selling recovered products, shown in Fig. 2.6. Refurbished products from e-retailer usually have more customer preference than internet recyclers. Therefore, when the retailer takes back the used products and is just a “transfer station” for used products, the OEM is the main body of the reverse supply chain. The main body–OEM, needs to decide the commission he needs to pay to the e-retailer so that this recycling system can be well operated. How to exploit the “transfer station” to decrease the recycling cost is a prominent subject for this cooperation mode. Taking back the used products requires the retailer to pay the consumers and provide more warehouses for the used products, which will be a considerable cost Fig. 2.5 The cooperation between OEM and e-retailer when e-retailer just do online recycling and sale

Raw materials

New products/RM products

OEM

e-Retailer Used products Used products

New products/RM products

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Fig. 2.6 The cooperation between OEM and e-retailer when the e-retailer does online recycling and refurbishing

Raw materials

New products

OEM

e-Retailer

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New products+RF products

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for the retailer. If the recycling business cannot make enough profit for the retailer, it will be hard for him to involve in unprofessional activity. When the e-retailer makes the recovery of used products, he will be the main body of the reverse supply chain, and his business will impact the OEM’s new products business. Therefore, the OEM may restrict the e-retailer’s sales of recovered products so that they can get some economic compensation from the e-retailer and decrease reputational damage during the sales of recovered products.

2.5.1 The Retailer Does Internet Recycling Under Distribution Sales Mode Under distribution sales mode, the e-retailer is relatively independent when taking back and refurbishing the used products. The e-retailer’s wholesale price for the new products may be connected with profit sharing between the two parties in the reverse supply chain. When the retailer is a wholesaler, he will wholesale products from the manufacturer and then sell them out, which requires the retailer to invest more and undertake more responsibility than being a mediator (agent). Thus, the financial capacity of the retailer will be an influential factor when the retailer decides to recycle. Thus, reducing the financial burden is necessary if the retailer takes back the used products and reduces the risk when selling the products from the OEM. To make the retailer a partner of CLSC, some incentives, like cost-sharing or revenue sharing, etc., may be needed so that the retailer would like to make more efforts in CLSC activities (Jian et al. 2019; Khan et al. 2022d). Quantity and purchasing price of the new products are critical parameters for the OEM to decide the retailer’s wholesale price of used products. When the OEM wants to show more social responsibility in reusing his products, he needs to increase the purchasing price of his used products. Moreover, increasing the profit margin of his new products made from recycled materials is also necessary. Therefore, more demand will be created. So the technologies of recycling the used products and marketing tools are essential for the responsible manufacturer because the retailer will choose products with high-profit margins for wholesale. KFC, the fast food manufacturer, has been involved in designing and choosing an environmental-friendly package for its food, such as thermal insulation paper boxes and bags. Meanwhile, multi modes of propaganda are conducted. Besides, because Huawei, the electronic manufacturer, is upgrading its product design, aihuishou’s (seller of second-hand products) sales of Huawei’s RF products have kept increasing these years. Further, the responsible manufacturer should also make the retailer take back more used products. While the most effective and efficient way to take back more used products may be to pay more to the consumers or create more convenience for the consumers when doing the recycling transaction, which both need investment (Chi et al. 2014). The

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manufacturer may connect the wholesale price of new products and the wholesale price of used products if he is the leader of CLSC.

2.5.2 The Retailer Does Internet Recycling Under Consignment Sale Mode Under consignment sale mode, the e-retailer is dependent on returning the used products and refurbishing used products. The commission OEM needs to pay to the e-retailer may be connected with the contract of profit sharing between the two parties in the reverse supply chain. When the e-retailer is a commission merchant or an agent seller, the OEM needs to pay the commission to the retailer for the retailing service. The retailer does not need to pay the manufacturer for the new products and RM products but takes commissions from the manufacturer, such as Taobao, Suning in China, and Amazon in America. This sale form might be more accessible for the retailer to take back the used products. Still, the premise is that the retailer has sufficient financial support on his own or from the manufacturer if he independently takes the business of used product acquisition. Taobao and Suning have built their channel attached called “xianyu” and “suninghuishou” on their e-platform to back the used products (https://baike.baidu.com/item/%E9%97%B2%E9%B1%BC/17335724, https:// www.163.com/news/article/FU4DPG1O00019OH3.html). The retailer returns the used product and sells it all to the manufacturer. The manufacturer still has two choices for reusing the used products: using the used products to produce new products and remanufacturing the used products. In these circumstances, the manufacturer will take longer-time responsibility and more risk for the products he produces than when the retailer is a wholesaler. So, he needs to be more sensitive to consumer value evaluation of his products before making production decisions. When the retailer chooses to collect commissions from the manufacturer, i.e., the retailer just provides the service for the manufacturer, such as JD in China. The manufacturers need to promise to pay some commissions to JD in advance, a significant internet retailing platform, and JD will provide an online recycling service. Moreover, the manufacturers will decide the acquisition price, and the manufacturer will not just pay the commission to the retailer but to the consumers. Therefore, in the CLSC composed of the retailer and manufacturer, when the manufacturer makes decisions related to used products recovery, not only the recycling cost does he need to know, but he also needs to think of the downstream sales cost of his products. It can be understood that the retailer’s commission impacts the manufacturer’s production decision. The advantage of this cooperation mode is that the manufacturer will get the consumption information directly, such as the quantity and category of product consumption, just as the manufacturer rents a place for the retailer to sell his products. The commission can be connected with the sales profit so that both parties would like to make efforts for more sales.

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Which sale form is better or more acceptable for both parties and profitable for the CLSC operation? How to intervene and design suitable contracts for them in a different situation? For the retailer, what should be considered while making a pricing decision to take back used products, and how much commission must be asked from the OEM? For the manufacturer, how to decide his purchasing price of used products and what should be considered when he decides the price of new products so that the retailer will be willing to involve in the recycling of CLSC and the manufacturer’s profit can be ensured. The win–win strategy will be made accordingly if the problems above are properly considered. Fortunately, internet retailers usually have their distribution system, which can be used in offline taking-back services if the distribution system can be well managed. Taobao has built reverse logistics based on its forward logistics system, and so has Suning’s internet retailer. The cooperation will be interesting when the retailer starts refurbishing or remanufacturing the used products and selling them, just like a TPR that sells the RF products independently. The manufacturer may not need to consider the recovery of used products and social responsibility. The situation will be more interesting when the retailer starts to refurbish or remanufacture the used products. The retailer will sell his products and the products from the OEM, and his choice might impact the manufacturer’s production and profit. The manufacturer would take some actions on the retailer’s refurbishing and remanufacturing behavior through technical protection or involvement in the retailer’s recovery behavior of used products. The retailer may not remanufacture the manufacturer’s products if the manufacturer applies for technical protection from the government. When the retailer intends to be involved in the recovery of used products, he must agree with the OEM. Then, the manufacturer can get part of the profit from the retailer’s recovery activity. Even the manufacturer may invest in the retailer’s recovery activity and then get profit from the retailer’s recovery activity. If the physical distance between consumers and manufacturers is much longer than between consumers and retailers, and/or the used products are challenging to transfer, the retailer involved in refurbishing and remanufacturing used products will be a better choice. More and more manufacturers are implementing the trade-in project. Trade-in is a marketing strategy in which the consumers can sell their used products to the seller but do not get the cash directly in this transaction but get a certain discount when they buy the new products that the seller recommends. Thus, the seller will get more loyalty from the consumers, and the consumer can lower their consumption costs. Also, the used products can be recycled formally. When the retailer participates in the trade-in project that the manufacturer launched, more and more retailers are involved in refurbishing the used products, such as electric vehicle retailers of Yadea, the first listed enterprise of electric two-wheeled vehicles in China. For the Yadea company, primarily, the retailer is not just selling the electric vehicle but providing the consumer aftersale service, which means the retailers have specific repair skills. Thus, for social responsibility and cost saving (Yan et al. 2021). Yadea’s retailers have

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become the best choice for recycling used products. Meanwhile, the manufacturer Yadea gets more consumer loyalty for new products. When the manufacturer uses the used products from the retailer to produce new products, the retailer may sell new products as before if the consumer has the same value evaluation for the new products made from raw and recycled materials. When the manufacturer uses the used products to produce new products, because the new products will probably not be underestimated, the reservation value of the used product will be the crucial factor for the cooperation between retailer and manufacturer. Therefore, the manufacturer would like the high-quality used products to be taken back by the retailer. The profit from using the used products for producing new products is mainly reflected by the decreasing cost of producing new products, which enables the manufacturer to decrease the new product wholesale price, and the retailer will buy more from the manufacturer. Furthermore, if the retail price does not change too much, the retailer will profit more from the sales of new products. When the manufacturer chooses to produce RM products with the used products from the retailer, the CLSC can be sustained if the retailer wholesaler and retails both new and RM products at an acceptable price and quantity. The manufacturer can profit from his production activity and the retailer’s business activities. Except for retailing, the activities for used products are all the cost for the whole system. The trades between the retailer and OEM have relieved the two parties’ burden while waiting for the final profit. Before the products are sold to the consumer, trade activities can be considered risk-transferring. When the manufacturer buys the used products from the retailer, the retailer will receive financial compensation for taking back the used products. In contrast, the OEM will only get financial compensation for purchasing the used products once the RM products are bought from the retailer. If the retailer finds that the consumers take a low-value evaluation for the used products, he will not wholesale the used products, making the CLSC unable to start. Therefore, before the products flow into one of them, the consumer’s value evaluation of RM products is the key for them to be considered, which is the basic insurance of profit form involving CLSC. The consumer’s value evaluation for one product must be higher than its cost, and the difference is called its profit margin. Value evaluation is the base of a product’s profit margin, which is the key to the product’s supply chain well operation. With higher consumer value evaluation, the retail price can be set higher, and its supply chain system would profit more if the cost is relatively low. For the manufacturer, the more profit margin the product has, the more will be wholesaled by the wholesaler, bringing him more profit. The profit margin is the basis for the retailer to decide the quantity to wholesale. So, the OEM decides on remanufacturing based on the customer preference if his market information is consistent with the wholesaler (retailer). To increase the profit margin, they can both enhance the consumer’s value evaluation of RM products through marketing and technological ways. Huawei and JD jointly developed the advertisement plan for sales of Huawei electronic products so that the consumers could understand the electronic products’ performance as much as possible. Because the retailer understands more about consumers and the manufacturer has more power in product designing, they should cooperate to promote consumer value evaluation. Also, the manufacturer

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should adjust the wholesale price of used products so that the inflow of used products can be controlled accordingly. The retailer serves as a recycler of used products and a retailer of manufacturer’s products in the CLSC; the manufacturer is just the processor of used products. However, the manufacturer is the leader of CLSC, especially when the retailer needs more financial support to take back the used products. Suppose the manufacturer wants to exploit the advantage of the retailer in taking back the used products. In that case, he might provide some financial support to the retailer, such as paying a commission and decreasing the wholesale price of new and remanufactured products. It may not be an OEM, as a downstream enterprise for the internet recyclers and e-retailers in a reverse supply chain. An independent remanufacturer also can be a cooperator of them. Profit drives different enterprises to be involved in the recycling industry. Good management of the reverse supply chain can promote the value creation of the whole chain. The profit from recycling the used products is mainly from the sales price of recovered products, such as RF products, RM products, and new products made from recycled materials. The consumers’ willingness to pay for these products is the main factor of these products’ profit margin, which drives the operators of the reverse supply chain to involve (Shen et al. 2021). Due to less environmental impact than the new products, consumers’ environmental awareness would positively impact these products’ promotion. At the same time, the quality of these products is an essential factor in the consumers’ willingness to pay (Feng et al. 2017). For the consumers, quality sometimes cannot be judged by products’ appearance, so the sellers and producers with higher reputations usually positively impact consumers’ willingness to pay. So when they face recovered products, the producer and seller usually are the base for their willingness to pay. Besides, Quality Certification from some qualified third parties also becomes a tool for the seller of recovered products to promote consumers’ willingness to pay. To develop the reusing industry well, the recycling industry’s organization is important, and it is also essential for the operators to improve consumers’ valuation of recovered products. The new products already have a mature system to ensure their quality, while there is still no mature quality insurance system for the recovered products. On the one hand, the recovered products’ value has yet to be widely recognized. On the other hand, the quality of recovered products remains uneven, which creates trouble for the authorities in providing proper official quality insurance. Therefore, reverse supply chain operators need to provide innovative quality insurance, such as comprehensive after-service, to reduce the consumers’ purchasing risk (Ren et al. 2020).

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2.6 Conclusion As the starting point of CLSC, OEM produces the new products, and the reverse supply chain recycles the used products. How the OEM design and produce their products undoubtedly will affect the reverse supply chain, such as the cost of recycling, the quality of recovered products, and the sales of recovered products. The cooperation between the OEM and the operators of the reverse supply chain would enhance the value creation and environmental performance of the reverse supply chain. With the integration of the internet and a reverse supply chain, the cost of the reverse supply chain has been reduced, and the reverse supply chain’s efficiency has also been promoted. Therefore, it is an excellent opportunity for the operators to implement a reverse supply chain to improve resource efficiency and environmental quality. The internet application changed the roles of recyclers and retailers in the reverse supply chain, which should be considered in coordinating different parties in the reverse supply chain. When the internet recycler/e-retailer becomes an independent reverse supply chain operator, refurbishing and selling recovered products, the competition and cooperation between OEM and internet recycler/e-retailer may coexist. To avoid poor efficiency caused by the competition between the two parties, some powerful outsiders intervene, such as the government authorities, and contracts are also needed to restrict the two parties’ business behaviors.

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Khan SAR, Tabish M, Yu Z (2023) Mapping and visualizing of research output on waste management and green technology: a bibliometric review of literature. Waste Manage Res 41(7):1203–1218. https://doi.org/10.1177/0734242X221149329 Li CF, Feng LP, Luo SY (2019) Strategic introduction of an online recycling channel in the reverse supply chain with a random demand. J Clean Prod 236. https://doi.org/10.1016/j.jclepro.2019. 117683 Liu H, Lei M, Deng H et al (2016) A dual channel, quality-based price competition model for the WEEE recycling market with government subsidy. Omega 59(3):290–302 Liu Z, Tang J, Li BY, Wang Z (2017) Trade-off between remanufacturing and recycling of WEEE and the environmental implication under the Chinese Fund Policy. J Clean Prod 167:97–109 Ren X, Michael H, Zhao L (2020) Optimal price and service decisions for sharing platform and coordination between manufacturer and platform with recycling. Comp Indus Eng 147. https:// doi.org/10.1016/j.cie.2020.106586 Sarada Y, Sangeetha S (2021) Coordinating a reverse supply chain with price and warranty dependent random demand under collection uncertainties. In: Operational research, pp 1–40 Shen L, Fan RJ, Yu ZQ, Wang YY (2021) The service strategy and influencing factors of online recycling of used mobile phones. Mathematics 9(21). https://doi.org/10.3390/math92 12690 Tansel B (2020) Increasing gaps between materials demand and materials recycling rates: a historical perspective for evolution of consumer products and waste quantities. J Environ Manage 276:111196. https://doi.org/10.1016/j.jenvman.2020.111196 Wang JJ, Xu ML, Zou LF (2022) Pricing decisions of the Internet plus + recycling platform considering consumer behaviour. Comp Indus Eng 174https://doi.org/10.1016/j.cie.2022. 108831 Xi T, He X, Liu Y, Ding W (2021) Design and simulation of a secondary resource recycling system: a case study of lead-acid batteries. Waste Manage 126:78–88 Xiang Z, Xu M (2019) Dynamic cooperation strategies of the closed-loop supply chain involving the internet service platform. J Clean Prod 220(5):1180–1193 Yan YL, Yao FM, Sun JY (2021) Manufacturer’s cooperation strategy of closed-loop supply chain considering corporate social responsibility. Rairo-Oper Res 55(6):3639–3659 Yu L, Chen M, Yang B (2019) Recycling policy and statistical model of end-of-life vehicles in China. Waste Manage Res 37(4):347–356 Zhao SL, You ZZ, Zhu QH (2021) Quality choice for product recovery considering a trade-in program and third-party remanufacturing competition. Int J Prod Econ 240:108239

Chapter 3

Technological Innovations in Reverse Supply Chain

3.1 Introduction For recycling one used product, three main functions of a reverse supply chain should be ensured: taking back the used products from consumers, reprocessing the used products and sales of the recovered products. A reverse supply chain this chapter would discuss is shown in Fig. 3.1. When the operators and customers in a reverse supply chain have physical distance, the logistics in the reverse supply chain should also be considered in a reverse supply chain. The reverse logistics is to move the materials from one site to another, so that the functions in different site can be well completed. Moreover, economic profit is the basic motivation of every party in a reverse supply chain, and information is the decision support of them. So, capital flow and information flow are the key to achieve well operation of the three functions. To further understand the association between a reverse supply chain and internet, this chapter will analyze the problems that every function of a reverse supply chain are facing and the role of internet in the operation of the functions. Internet is a kind of information technology. The participants who are involved in internet would receive the information from each other with a high speed (Chen et al. 2014). The internet technology enhances the speed and volume of information transferring and overcomes the physical distances among different participants. Therefore, people from different places can communicate on the internet platforms and receive all kinds information from many different places. The internet has improved the development of society, economics, and culture through enhancing information transferring. To provide better decision support for every participants of a reverses supply chain and strengthen the cooperation of them, it is necessary for the operators to enhance the information transferring of the reverse supply chain. So, the application of internet in a reverse supply chain would be a great opportunity to get better performance. Every party of a reverse supply chain need related information for their decision making. The recyclers need to know the downstream customers’ demand for

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_3

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3 Technological Innovations in Reverse Supply Chain The consumers who need recovered materials

Consumer A Used Consumer B products Consumer C Consumer D ...

Recyclers

Used products

Reprocessors

Recovered products

Retailer

The consumers who need RF materials

The consumers who need RM products

Fig. 3.1 Reverse supply chain

used products and decide the quantity and quality of used products; the reprocessors (manufacturers) need to know the quality and quantity of used products that they would receive and make a processing plan; the retailer need to understand the demands before they make an order of recovered products. The capital is the basic motivation of every participant in a reverse supply chain. They need to pay the cost for operating the functions of reverse supply chain so that the value regeneration of a used products can be achieved. Because the internet can enhance the information transferring, it can be an effective tool for reverse supply chain operation. This chapter will discuss the problems of every function in a reverse supply chain and analyze how the internet can be applied in a reverse supply chain to enhance the operations and coordination of a reverse supply chain.

3.2 The Role of Internet in Taking Back Used Products from Consumers For the consumers who hold used goods, the recyclers are very few, and their locations are fixed and single. While, the number of consumers who hold used products is huge, and their locations are not certain, which is spreading at different places, and the quality level of the used goods they hold is also uncertain. When the consumers who hold used products want to sell their used products, they want to know the economic benefits and the degree of convenience they will experience in used product transaction. In the past, the recyclers would look for the consumers who hold used products through driving through streets, but the results are uncertain. The quality and quantity of used products every day they can get are uncertain but the cost they need to invest in recycling every day is fixed and high (Sozoniuk et al. 2022; Khan et al. 2021; Huang et al. 2022). For recyclers, it is necessary to obtain the information of the consumers who hold used products that the recyclers need before the recyclers start taking-back activity, such as the quality information of used products, consumers’ expected recovery price and their locations. After obtaining these information, the recyclers can take corresponding measures for taking back used products, such as selecting proper

3.2 The Role of Internet in Taking Back Used Products from Consumers Fig. 3.2 One recycler takes back used products from different consumers

A

B A

D

Recycler

C

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B

C C D

A-D is the quality level of used products.

staff and vehicles for taking used products from consumers, and determining an acceptable price for the consumers, etc. So, the cost of recycling would be controlled. Therefore the transaction with consumers for used products can be facilitated with a high probability. For the consumers, it is necessary to know the information of the recyclers’ acquisition price for their used products and recyclers’ recycling services as the basis for judging whether it is necessary to participate in the recycling activity (Matsui 2022). So, during the recycling process between recyclers and consumers, both parties need to communicate to obtain the information they need before the transaction, and they can make a better decision for the transaction (Fig. 3.2). The traditional recycling transaction is that itinerant hawkers for recycling go through the streets to take back the waste products by taking a chance. The quantity and quality of used products they can get are highly uncertain, resulting in the mismatch between the cost of recycling logistics and the benefit of recycled used products. Therefore, the economic value creation ability of recyclers has always been at a low level in society. At that time, the information transmission capacity was limited, so the physical distance between recycler and consumer who hold used products make the two parties spend a lot of costs to obtain necessary and useful information from each other. The recyclers have to pay for searching correct consumers, and when the consumers cannot sell their used products as soon as possible, they need to pay the depreciation cost of their used goods. For the connection between recyclers and the consumers who hold used products, an internet platform is established. The consumers can communicate with recyclers and they can transmit the information from each other on the internet platform no matter where they are (Dhaigude and Mohan 2023). Consumers provide the recyclers with the quality information of their used products, their addresses, and their expected acquisition price in an online order on the internet platform. After receiving the information from the online order, the recycler arranges appropriate staff to take back the used products and conducts quality inspection on the used products. With the inspection results, the recycler determines the acquisition price and informs the consumers with a certain time durance. With the support of the Internet, recyclers no longer need to pay the search cost, and they can well organize recycling activities according to the quality information of used products and the personal information of

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the consumers. Similarly, consumers would not need to wait to find suitable professional buyers, and they can find a proper buyer from the Internet platform in time to avoid losses caused by product depreciation or the transaction with non-professional buyers. By building an effective bridge between recyclers and consumers for information transferring, the internet has greatly improved recycling efficiency and reduced the recycling cost. So the internet can play a significant role in taking back used products for the recyclers. The internet also creates more convenience for the consumers to involve in recycling.

3.3 The Role of Internet in Reprocessing of Used Products The manufacturer (reprocessor) purchase the used products from a recycler, and reprocess the used products into different products for different customers, which is shown in Fig. 3.3. Generally, the used products can be reprocessed into three products: RF products, RM products, and raw materials, which depends on the quality of used products and the feasible process technologies. The quality of used products does not only depend on the quality when it was new, but also depend on their using frequency, using performance, using accuracy, maintenance and so on. Therefore, the quality of recycled used products is highly uncertain. While the process technology on used products is still limited. Not every used product can be processed as their customer wants. The manufacturer (reprocessor) needs to classify the used products they received according to the damaged degree and damaged sites of the used product, and then selects the appropriate processing technology for the used products (Khan et al. 2022a; Yang et al. 2022). The selection of reprocessing plan for used products and corresponding facilities and equipment depends on the requirements of downstream customers, and the quality of used products.

Recycler

The consumers who need RF products

Reprocessor

The consumers who need recovered materials

The consumers who need RM products

Fig. 3.3 The function of reprocessing the used products in reverse supply chain

3.3 The Role of Internet in Reprocessing of Used Products

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The upgrading and improvement of process technology has increased the possibility of repairing used products. However, when facing used products with different quality levels, manufacturers (reprocessor) still needs to consider the cost of reprocessing and chooses the appropriate processing plan for used products on the basis of reprocessing cost. To ensure more social value can be created from reprocessing the used products, the customers’ requirement for the processed products should be the base for the selecting a proper processing plan and processing quantity for the used products. Although the products with different quality levels can meet the customer’s requirements after processing, the costs required for reprocessing the used products with different quality levels into products of same quality are different. So, before reprocessing the used products, the manufacturer needs to classify the used products according to the quality level and processing cost of the used products, so as to reduce the reprocessing cost of the used products but not influence the outcome. Therefore, timely and accurate access to quality information of used products to be processed is an very essential part of reprocessing cost control. The demand information from downstream customers is important for manufacturer’s decision making on processing plan. First of all, the manufacturer needs to timely receive the customer demand information, such as the requirements information for product quality and quantity. And the manufacturer also need to understand the quality information of the recycled used products they would receive, so as to understand the processing cost and output quantity and quality in advance. When downstream customers need low-cost recycled raw materials, manufacturers need to disassemble, separate and further process the purchased used products to produce raw materials required by downstream customers. Recycled metal has been one of the indispensable sources in the world’s industrial production process. When downstream consumers need RM products, manufacturers need to deeply repair the purchased used products to make the used products quality level close to the new ones. When consumers pay more attention to the price of recycled products and need RF products, manufacturers may only need to repair the appearance of used products. Whether it is the production of new products or the reprocessing of used products, the processing plan selection needs to be completed on the premise of understanding customers’ demand information, so as to avoid the loss due to the failure to meet customers’ demand in the later stage. The decision of processing facilities, equipment and production process is significantly affected by the quality of used products and the requirement of customers, otherwise the later adjustment is likely to cause a huge cost for the manufacturer (Pourmehdi et al. 2022; Khan et al. 2022b). When using raw materials to produce new automobiles, the quality level of raw materials is uniform, the quality information of raw materials is fixed and can be accessed quickly. Therefore, new automobile manufacturers do not need to adjust the processing and production facilities because the supply of raw material. The adjustment of the production process and related facilities for producing new automobiles with raw materials is mainly due to the upgrading of production technology. And the aim of adjustment is to improve the production quality and efficiency. Due to quality uncertainty of the used products, there are many processing plans and production facilities and equipment. For

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example, the screen of the recycled old mobile phone A is damaged, while the battery of mobile phone B is damaged, so there will be two differences processing channels for repairing mobile phone A and mobile phone B. It is very useful and valuable for manufacturers to get quality information of the used products as early as possible, so that an processing arrangements can be made in advance, and the production adjustment costs caused by the untimely acquisition of quality information can be reduced (Turner et al. 2022). Useful information for the manufacturers (reprocessors) to reprocess used products can be obtained from upstream recycler and downstream retailers. So the information transferring among the three parties is significant for the reprocessing. The link between the Internet and manufacturers’ suppliers and retailer can better achieve the efficient and high-quality information transmission. The EPR platform based on the Internet is to connect suppliers, manufacturers and sellers to achieve this purpose, so as to well connect the different parties in a supply chain, helping manufacturers adjust production processing plans and production facilities and equipment in time.

3.4 The Role of Internet in Retailing of Recovered Products Retailers sell recovered products to downstream consumers, such as RF products, RM and new products made from recycled materials. Retailing of recovered products is the key to re-create the social value of used products. The key to the success of this function of a reverse supply chain relies on retailers’ capability of influencing consumers’ purchase decisions. The supply of production materials and processing process of recovered products makes it difficult for the recovered products to obtain the same consumer preference as new products made from raw materials. In addition, the quality information of recovered products that consumers can obtain is limited, and the recognized quality evaluation system of recovered products has not yet formed, resulting in consumers’ value evaluation of recovered products always at a low level (Zhang et al. 2022a, b). Therefore, it is necessary for retailers to establish efficient and effective pre-sale and after-sale services for the sales of recovered products so that consumers would promote their willingness to pay for the recovered products, reducing the impact of imperfect quality evaluation system of recovered products. Retailers’ pre-sale service and after-sales service for the sales of recovered products are included in retailers’ sales activity. Effective sales activity should be a positive response to consumer psychology and consumer behavior. Retailers’ sales activity should cater to consumers’ psychology and improve consumers’ value evaluation of the products that are sold by the retailers (Wang et al. 2022a), enhancing retailers’ brand quality. Therefore, the retailers who sell recovered products need to understand consumer psychology and consumer behavior before making an effective sales strategy. The sales models that sellers take can be divided into online sales mode

3.4 The Role of Internet in Retailing of Recovered Products

37

and offline sales mode. The offline sales mode has been implemented since commercial activities emerged, which is a traditional sales mode. Consumers can do transaction face to face with sellers under offline sales mode. So, in this mode, consumers can have a more comprehensive and intuitive feeling for the products they are buying. Consumers’ value evaluation for the products they are purchasing based on their own intuitive feelings and external advertising information from the retailers. Online sales mode, also known as internet sales mode. Because the sellers and consumers communicate online and the consumers would get the information of the products from online platform in this sales mode, so they cannot have intuitive feelings for the products. The consumers can only use the advertising information of the product to make their value evaluation for the products they are buying. Therefore, the sales behavior strategies of offline sales model and online sales model should be different. When offline sellers cannot clearly convey product advantages to consumers before the consumers make purchasing decisions, or cannot well resolve consumers’ doubts about their products, the consumers’ risk perception will increase, and consumers’ willingness to buy will decrease. On the other hand, when after-sales service cannot be guaranteed, the sellers’ brand images will be influenced. Online sales mode has been widely accepted by the public because of its convenience. Because the consumer’s purchase decision is not based on intuitive feeling for the products they are buying, and consumer can only passively accept the product information provided by the seller, this kind of non-face-to-face transaction mode is more likely to cause transaction disputes between the two parties. In this sales mode, the sellers need to invest more on advertisement and after-sale service for the products so that the transaction disputes can be reduced (). When the consumer’s understanding of the quality in advertisement and real quality of the products is inconsistent, or when the solid product which is sold out is inconsistent with the quality information that is provided in the advertisement, the product quality disputes would happen. Because consumers can’t actively obtain products information through their own senses like offline purchase, consumers’ risk perception in an online transaction process is always higher than that in an offline transaction process, that is, consumers will realize that the products they are purchasing in online sales mode may be more likely to be inconsistent with what they want, such as the product quality is not up to standard, the product quantity is not the one they booked, and so on. Whether the recovered products are sold in traditional offline sales mode or online sales modes, sellers need to make their sales strategies based on consumers’ psychology when they are purchasing recovered products. It is necessary to reduce consumers’ risk perception, so as to improve consumer willingness to pay and further improve the probability of transaction success. The Internet sales platform established on the combination of Internet and sales enables consumers to do transactions with sellers online. This sales mode can not only effectively reduce consumers’ transaction time, but also help consumers achieve cross-area purchase and select what they want from a bigger market, thus improving consumers’ purchasing experience. However, the information consumers can get from the internet mostly is screened by the seller. So, the establishment of real-time communication channels for sellers and consumers during online transaction would provide the sellers an opportunity to send

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the information the consumers they want to know. When the consumers want to know whether the coat sold on taobao is suitable for them, they would send the questions to the professional online sellers through the online communication channel. If the answer can be fast obtained, the purchasing decision would be made in a short time. The Internet communication channels not only enable consumers to communicate with sellers in real time when they are making purchasing decision, but also provide a good opportunity for the sellers to influence consumers’ purchasing decisions online. Both Amazon, Taobao and JD’s Internet sales platforms have established pre-sales real-time communication channels for sellers and consumers, and this communication system has done great help in increasing successful trading volume for online sellers (Shen et al. 2021). Real-time communication between trading parties is very vital for online sellers to understand consumers’ demands and reduce consumers’ risk perception. Realtime and efficient online pre-sale communication can precisely provide the information that consumers need so that they can be attracted. So, the application of realtime online channels in online sales of recovered products cannot be ignored. Due to the imperfect quality certification system of recycled products, consumers have more demands for the quality information of recovered products, such as manufacturer information, processing history and production materials (used products) of recovered products. Similarly, the after-sales service system for recycled products is also the key to affect consumers’ purchase decisions for the recovered products. In the absence of a complete quality evaluation system, after-sales service is an important means to solve consumers’ concerns about the quality of recovered products. Lenovo, Dell and other enterprises have built after-sales service platforms based on the Internet, so that their consumers can quickly contact manufacturers or sellers, so that manufacturers or sellers can establish maintenance solutions based on consumer problems, and help consumers accurately and quickly solve product quality problems as soon as possible. A speedy and effective after-sales service system can reduce consumers’ risk perception of recovered products, which is also an effective approach to build sellers’ brand attractiveness and enhance consumer trust (Gurnani et al. 2022; Khan et al. 2023). The Internet has established a fast, effective and low-cost communication platform for sellers and consumers, and also helped sellers establish an efficient after-sales service system. So, the online after-sale platform plays a significant supporting role in improving consumers’ value evaluation of recovered products by strengthening the communication with their consumers after sale. The Internet can not only help sellers expand their market, but also help sellers improve their sales strategies, further promoting consumers’ willingness to pay for their recovered products, and improving the environment for the development of reverse supply chain. Advertising is one of the important approaches for sellers to gain market share. Advertising is not only one of the important ways for sellers to provide consumers with product information, but also one of the important ways for sellers to enhance consumers’ willingness to pay. The embodiment of advertising effect requires not only advertising itself, such as design, but also the efficiency of advertising spreading

3.4 The Role of Internet in Retailing of Recovered Products

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channels. The Internet technology has greatly improved the speed of information dissemination and further expanded the channels of information dissemination. The acquisition of advertising effect requires the joint action of information dissemination speed of the advertising platform and the audience width of the information dissemination channels. The information transit speed and the audience width of internet can effectively help sellers to achieve more potential consumers and improve the image of their products. With the improvement of internet technology and the increase of the number of the internet audience, in recent years, the amount of advertising for online channel is significantly more than that of other channels (Wang et al. 2022a). In June 2021, China Internet Network Information Center (CNNIC) released the 48th Statistical Report on the Development of Internet in China, which showed that the number of Internet users in China has reached 1011 million, an increase of 21.75 million over December 2020, and the Internet penetration rate reached 71.6%. China has formed a digital society with the largest scale and the strongest application penetration in the world. The extensive penetration of Internet applications and services has built a good environment for online advertising (https://new.qq.com/rain/a/202 20113A04I5F00). The reverse supply chain is a demand-pulled supply chain, so it has to focus on consumers’ demand of recovered products, such as RM products, and RF products. Demand data is the basis of reverse supply chain’s well operation. Base on what kind of recovered products consumers need and how many they need, the reverse supply chain can be established and well operate. Therefore, investigating consumers’ demand is very primary for a reverse supply chain. The arrangement of all procurement activities in a reverse supply chain should based on consumer demand for recovered products. The more accurate the demand data is, the more scientific the arrangement of production activities will be. Thus, the reverse supply chain is able to maximize its own economic benefits through reducing unnecessary cost. Therefore, the acquisition of demand data is very important for every parties in a reverse supply chain. Big data technology is the application of high-end computing technology (cloud computing, etc.) to analyze and process the collected massive data, making accurate prediction of the development trend of things based on the analysis results of massive data collected (Attar-Khorasani and Chalmeta 2022). Without the support of the Internet, it is almost impossible to collect massive data. Taobao can accurately predict the number of consumers’ consumption in the next quarter. The massive historical data of consumers comes from its own Internet platform. Facebook can predict the massive data of the President of the United States and the historical users’ behavior data on social media platform. The ability of the Internet platforms to record data is the basis for the development of big data technology, which lays a powerful foundation for the stable development of reverse supply chains. Usually, recovered products are at a lower price than the new. Though most enterprises claims that the quality of their RM products is the same as the new, most consumers still doubt on that. On the one hand, the RF products and RM products’ quality assurance is a problem for a reverse supply chain. On the other hand, the profit margin of a new product is usually bigger than the recovered products so that the investment on recovered products may not be sufficient to ensure the real quality

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of used products (Zhang et al. 2022a, b; Zou et al. 2022). So, when the consumers make purchasing decisions, they have more concerns on the quality of the recovered products than the quality of the new (Zhou et al. 2022). The concerns significantly decrease consumers’ demand for recovered products. To solve the impact of consumers’ concerns about the recovered products on the sales of recovered products, it will be very necessary for the reverse supply chain operators to implement real-time communication for providing consumers with the information they want and make a precise prediction of consumers’ demand. For now, because the price difference between recovered products with new products, the demand for the recovered products in developing and underdeveloped countries is more than developed countries. The refurbished cars and smartphones from Japan are mostly sent to Pakistan and some other developing and underdeveloped countries for sales. But, when a better world comes, where the recovered products can be consumed? The consumers with more environmental awareness would prefer to purchase recovered products, but how to promote the sales of recovered products in a society with lower environmental protection awareness. Internet would be a solution for that.

3.5 The Role of Internet in Reverse Logistics The logistics activities of a reverse supply chain is to make sure the materials can be processed in the right place at the right time, supporting every functions of a reverse supply chain can well operate (Rajput and Singh 2021). The information flow of a reverse supply chain can help a reverse supply chain operators arrange their production and business activities, and can help operators take timely response measures to emergency situations. The information of “things” in during reverse logistics is essential to the participants in a reverse supply chain. When taking back used products, recyclers need to classify used products by collecting the quality information of used products to be better prepared for the transactions with downstream buyers. During reprocessing, the manufacturer needs to design the processing plan and arrange the facilities and equipment on the premise of understanding the quality information of the used products (damage degree and damage location, etc.) they will receive, which requires the logistics operator to be able to better package the information of the used products transported to do the matching work of Information and physical products (Pourmehdi et al. 2022). The combination of Internet technology and logistics fulfills the requirement that downstream company requires. The Internet of Things technology refers to the connection of any object with the network through information sensing equipment according to the agreed agreement, and the information exchange and communication of the object through the information transmission media to achieve intelligent identification, positioning, tracking, supervision and other functions. The application of the Internet of Things technology in a reverse supply chain helps the operators of a reverse supply chain to better obtain the quality information of used products and better arrange production on this basis

3.6 The Internet Can Speed the Capital Flow of a Reverse Supply Chain

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of supply information. The Internet of Things technology can not only transmit the static information of “things”, but also transmit and record the dynamic information of “things”, so the quality fluctuation information of used products in the upstream of the reverse supply chain can also be transmitted to the downstream (Gil et al. 2016). It provides consumers an good opportunity to fully understand the quality information of recovered products they are purchasing, helps consumers make objective and comprehensive value evaluation of recovered products. So, internet technology indirectly provides solutions for the construction of quality evaluation system of recovered products.

3.6 The Internet Can Speed the Capital Flow of a Reverse Supply Chain Capital flow is a significant motive of reverse supply chain operation. The payment happens among the operators in a reverse supply chain so that they can pay the bills for reverse supply chain activities and make economic benefits. How much the operators can get and how fast they can get would influence their willingness to participate in a reverse supply chain. The capital from downstream participants would flow to the upstream participants, and the source of capital is the consumers’ payment for the recovered products they purchased. In taking-back transaction of used products, the transaction payment of recyclers can affect the transaction experience of consumers (Pretner et al. 2021). When participating in the recycling transaction, consumers have to get the payment of their used products from the recycler after the recycler make a professional evaluation of their used products, and both parties have reached an agreement on the evaluation results. In online transaction of used products, if consumers need to mail the used products to the detection location of the recycling dealer, the waiting time from the beginning of the transaction to the receipt of the recycling payment will be longer. In an offline transaction of used products, after both parties reach an agreement on the valuation, consumers would get the payment of their used products at once. The recycler would easily get consumer trust in offline transactions, and the recycler can also achieve better public praise. In an online transaction of used products, the longer the time that consumers need to wait for the payment of their used products since mailing out their used products, the worse the transaction experience of consumers will be (Dhaigude et al. 2023). Shortening the waiting time for payment of consumers in the process of recycling transactions can effectively improve the transaction experience of consumers and enhance their willingness to participate in recycling. In addition, the capital flow is also one of the important driving forces for batch trade of used products between recyclers and manufacturers. The transaction payment of manufacturers is an important basis for recyclers to implement their recycling activities. The recycler needs to pay the upstream consumers to get used products, and also needs to pay the cost for the classification and refurbishment of used products.

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The faster the capital inflows for the recyclers is and the more the inflow can guarantee the quality and efficiency of their activities. Therefore, the manufacturer’s payment for purchasing used products from recyclers also needs to be fast and secured. If the financial support from downstream manufacturer from the transaction of used product is insufficient or the capital inflow is slow, the recycler may not participate in a reverse supply chain. The efficiency and quality of manufacturer’s processing work for used products will be difficult to be guaranteed if the capital inflow from the downstream retailer is slow and not sufficient, which would affect the value creation ability of entire reverse supply chain. Therefore, the reprocessing work of the manufacturer needs the financial support from downstream distributors. Because the parties of a reverse supply chain are connected, the financial support from the downstream to the upstream can promote the work efficiency and value creation ability of entire reverse supply chain. The payment from the consumers who buy recovered products from sellers is the reflection of value creation ability of a reverse supply chain. Therefore, the payment efficiency of each party can should be promoted and secured. The willingness to pay and the speed of payment for recovered products from consumers would be the key to well operation of the reverse supply chain. The combination of the Internet and transaction payment has accelerated the speed of capital flow through connecting the payment subjects and banks, which not only effectively promotes the efficient operation of the reverse supply chain, but also lays the foundation for the promotion of the willingness of cooperation among the participants in a reverse supply chain.

3.7 Conclusion The deterioration of the environment and the shortage of resources promote the development of reverse supply chains that is to regenerate the value of used products. Internet of things helps dynamic transmission of used product quality information and enhance the match work of quality information and the used products with uncertain quality, which solves the problem of uncompleted quality evaluation system of recovered products, and also provides the operators in a reverse supply chain with high-quality decision-making support. The Internet-based big data technology provides a technical tool for precise demand forecast of recovered products. The Internet payment technology accelerates the capital flow of the reverse supply chain, and promotes the cooperative relationship among the parties in a reverse supply chain. Different parties in the supply chain have different needs for information. As an information transmission tool, the Internet can be reasonably developed to solve information transmission problems of each subjects in a reverse supply chain. Internet technology is a practical and valuable support for the development of reverse supply chains, further optimizing the overall environmental performance and economic benefits of reverse supply chains.

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References Attar-Khorasani S, Chalmeta R (2022) Internet of Things data visualization for business intelligence. Big Data. https://doi.org/10.1089/big.2021.0200 Chen M, Mao SW, Liu YH (2014) Big Data: a survey. Mobile Netw Appl 19(2):171–209 Dhaigude SA, Mohan BC (2023) Logistics service quality in online shopping: a bibliometric analysis. J Internet Comm 22(1):157–188 Gil D, Ferrandez A, Mora-Mora H, Peral J (2016) Internet of Things: a review of surveys based on context aware intelligent services. Sensors 16(7). https://doi.org/10.3390/s16071069 Gurnani H, Singh S, Tang SM, Wang HQ (2022) Service provision in distribution channels. J Mark Res 59(5):926–940 Huang YT, Liu SJ, Liang YQ (2022) Government policies for e-commerce supply chain with fairness concerns towards sustainable remanufacturing. Kybernetes. https://doi.org/10.1108/K08-2021-0755 Khan SAR, Yu Z, Sarwat S, Godil DI, Amin S, Shujaat S (2021) The role of block chain technology in circular economy practices to improve organisational performance. Int J Log Res Appl 25(4– 5):605–622. https://doi.org/10.1080/13675567.2021.1872512 Khan S, Piprani AZ, Yu Z (2022a) Digital technology and circular economy practices: future of supply chains. Oper Manag Res 15:676–688. https://doi.org/10.1007/s12063-021-00247-3 Khan SAR, Waqas M, Honggang X, Ahmad N, Yu Z (2022b) Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic. Oper Manag Res 15:1115–1133. https://doi.org/10.1007/s12063-021-00222-y Khan SAR, Tabish M, Zhang Y (2023) Embracement of industry 4.0 and sustainable supply chain practices under the shadow of practice-based view theory: ensuring environmental sustainability in corporate sector. J Clean Prod 398:136609. https://doi.org/10.1016/j.jclepro.2023.136609 Matsui K (2022) Optimal timing of acquisition price announcement for used products in a dualrecycling channel reverse supply chain. Eur J Oper Res 300(2):615–632 Pourmehdi M, Paydar MM, Ghadimi P, Azadnia AH (2022) Analysis and evaluation of challenges in the integration of Industry 4.0 and sustainable steel reverse logistics network. Comp Indus Eng 163. https://doi.org/10.1016/j.cie.2021.107808 Pretner G, Darnall N, Testa F, Iraldo F (2021) Are consumers willing to pay for circular products? The role of recycled and second-hand attributes, messaging, and third-party certification. Resour Conserv Recycl 175. https://doi.org/10.1016/j.resconrec.2021.105888 Rajput S, Singh SP (2021) Industry 4.0 model for integrated circular economy-reverse logistics network. Int J Logistics-Res Appl 25(4–5):837–877 Shen L, Fan RJ, Yu ZQ, Wang YY (2021) The service strategy and influencing factors of online recycling of used mobile phones. Mathematics 9(21) Sozoniuk M, Park J, Lumby N (2022) Investigating residents’ acceptance of mobile apps for household recycling: a case study of New Jersey. Sustainability 14(17). https://doi.org/10.3390/su1 41710874 Turner C, Okorie O, Emmanouilidis C, Oyekan J (2022) Circular production and maintenance of automotive parts: an Internet of Things (IoT) data framework and practice review. Comp Industry 136. https://doi.org/10.1016/j.compind.2021.103593 Wang JJ, Xu ML, Zou LF (2022a) Pricing decisions of the Internet plus + recycling platform considering consumer behaviour. Comp Indus Eng 174. https://doi.org/10.1016/j.cie.2022. 108831 Yang L, Zheng C, Ji JN (2022) Disruption management for a remanufacturer with dual collection channels. Int J Logistics-Res Appl. https://doi.org/10.1080/13675567.2022.2091120 Zhang HL, Nie JJ, Cleverdon F, Xiao HS (2022a) Competitive reselling channel choices of recyclers with online retailer encroachment. Int J Logistics-Res Appl. https://doi.org/10.1080/13675567. 2022.2081673

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Zhang Y, Khan AR, Sharif A (2022b) Game analysis on the internet plus closed-loop supply chain considering the manufacturer’s impact on promotional effect. Oper Manage Res. https://doi.org/ 10.1007/s12063-022-00311-6 Zhou Q, Yuen KF, Meng C, Sheu JB (2022) Impact of intercompetitor licensing on remanufacturing market competition and cooperation. IEEE Trans Eng Manage. https://doi.org/10.1109/TEM. 2022.3158398 Zou ZB, Wang C, Zhong QJ (2022) How does retailer-oriented remanufacturing affect the OEM’s quality choice? Sustainability. 14(13). https://doi.org/10.3390/su14138028

Chapter 4

Introduction and Problem Analysis of Resource Recycling Industry

4.1 Introduction of the Value of Resource Recycling Industry Resource recycling refers to the process of recycling and reprocessing waste products, transforming them into reusable resources and entering a new round of value appreciation (Rene et al. 2021). It is a kind of refined treatment for the waste products, such as recycling waste cars and waste electronic products. The resource recycling industry can not only produce alternative renewable resources, but also avoid the pollution effects of waste products (Jiang et al. 2021; Khan et al. 2023a). Therefore, the resource recycling industry has both economic value and environmental public welfare value. Economic value 1. The shortage of new resources has led to higher development costs For industrial production, there are many natural resources in shortage, such as mineral resources, petroleum resources, timber resources, water resources, etc. Due to the previous extensive operations with low efficiency but high accumulation and investment of production, all kinds of natural resources have been overexploited, leading to global resource tension (Luo et al. 2022; Khan et al. 2023b). Further, the environmental problems caused by economic development have gradually become prominent. Therefore, the development cost of new resources has therefore increased has become one of the significant reasons for slow economic growth. The consumption of electronic products has been increasing year by year, becoming one of the daily consumption products for consumers. However, the production of these products requires a large amount of rare metals and other resources. With the increasing consumption of metal resources year by year, the reserves of metal mineral resources supplied to the manufacturers continue to decrease, and the environmental problems caused by rare metal mining have also caused troubles to local areas (Wojnowska-Baryla et al. 2020; Khan et al. 2023c). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_4

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Fig. 4.1 The environmental disruption caused by resource exploitation

Then, tightening supply and raising prices have become an inevitable choice, which makes it more necessary to seek alternative resources (Fig. 4.1). 2. Renewable resources are partial substitution for the new resources The development of resource recycling industry can significantly reduce waste generation and environmental problems caused by waste products. On the other hand, it improve resource utilization, which helps achieve resource conservation through reusing the resources inside of the waste products. Waste products often need to be disposed of through incineration or landfill so that their occupation of natural space and their threat to the human living environment can be avoided. Waste products were previously considered as the products that have no economic value but have physical volume, weight and harmful chemical substances. So, they do not have compatibility with the natural environment, and improper disposal can affect human normal survival (Tiwari et al. 2023). Therefore, incineration and burial were the initial environmentally friendly treatment methods with lower costs. However, because the scarcity of land resources and the pollution of groundwater resources by burying waste products have been increasingly serious, and the air pollution caused by the incineration process has been more and more prominent, the social cost caused by these extensive treatments method is gradually increasing. In fact, it is not waste items themselves that do not have economic value. Although they are at the end of their lifecycle, they still store various resources that can be reused, such as waste mobile phones shown in Fig. 4.2. Although their lifespan has ended, the amount of precious metals used in their manufacturing process has not reduced significantly. The precious metals extracted through disassembly can still

4.1 Introduction of the Value of Resource Recycling Industry

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Rare-earth element Indium tin oxide (ITO)

Copper Nickel Circuit Silicon

Screen

Tin and lead

Aluminum silicate

Bromine

Aluminum Battery Graphite Manganese and cobalt

Phone Case

Magnesium alloy Plastic Nickel

Fig. 4.2 The recyclable materials of a smartphone

create value in the next round of production. Before 2015, the gold in non smartphones was 60–100 times that of ore of the same amount. About 400 g of gold, 2300 g of silver, and 172 g of copper can be extracted from 1 ton of waste mobile phones; Compared to mining 1 ton of gold sand, only about 5 g of gold can be extracted. The renewable material content in waste cars has become an indispensable support for the circular economy. Public welfare value Due to value decline of waste products, people always choose to dispose of them in various ways. Abandonment is one of the main methods. To avoid the impact of indiscriminate disposal on the human living environment, the government would like choose to collect waste products abandoned by consumers and conduct centralized incineration or burial. This is the traditional way of disposing of waste products (Cai et al. 2023). Currently, waste items that cannot be recycled are still processed in these ways. After burying waste products, if the components in them that are difficult to be degraded will pollute the soil and groundwater nearby, and even destroy the balance of nature in the soil (Lange 2021). Due to the fact that some harmful substances in waste products are not eliminated by high temperatures, harmful gases would generated during incineration process, such as dioxins, which poses a threat to surrounding creatures. Moreover, the increase of the number of waste products for landfilling and incineration will always exacerbate environmental degradation. Therefore, reducing landfill and incineration of waste products, and reasonably reusing waste products would create environmental public welfare value. The resource recycling of waste products is to regain the value of waste products. This process can select and extract renewable materials from waste products, making them usable for another production as a substitution of raw materials, thereby reducing environmental pollution and economic costs caused by the development

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of new materials (Beydoun and Klankermayer 2020). At the same time, after the extraction of renewable materials, the number of waste products that need to be incinerated and buried decreases accordingly. After extracting renewable materials (such as precious metals or components) from waste electronic products, the weight to be reduced of a complete mobile phone can be a big amount (Liang et al. 2023), and the resource recycling rate of a waste car may reach 80%. Due to the presence of harmful components such as heavy metals in electronic products, such as waste oil in car cylinders, refrigeration gases in air conditioners, refrigerants in refrigerators, the waste products need to be properly handled (Wojnowska-Baryla et al. 2020). These harmful substances can leak out during disassembly and processing, posing a threat to the environment. The environmental public welfare value of resource recycling will be affected by the process of resource recycling. Therefore, Environmental friendliness is also one of the factors that need to be considered in the design of waste disposal processes. Resource recycling not only reduces the amount of waste incineration and landfill, and reduces environmental pressure, but also is a necessary way to solve the shortage of natural resources for social production (Huysveld et al. 2022). Therefore, the resource recycling industry has both economic value and environmental public welfare value. However, there is a possibility of secondary pollution in the process of resource recycling of waste products, leading to uncertainty of value creation of the resource recycling industry (Kusenberg et al. 2022). The value of resource recycling industry not only requires the practical results, but also depends on the environmental friendliness of the entire process of resource recycling, which ensures its healthy development and sustainability (Mao et al. 2020a). Carbon reduction policies have become one of the effective means for countries around the world to improve environmental quality. China has proposed the “dual carbon” goal, which aims to achieve carbon peak by 2030 and carbon neutrality by 2060 (Geng 2021). The early carbon tax systems of Nordic countries such as Finland and the Climate Change Levy (CCL) in the UK are all aimed at accelerating the formation of low-carbon industrial chains. It is important to objectively and comprehensively evaluate the carbon reduction benefits of the resource recycling industry, which can quickly achieve green transformation of the industry based on the idea of environmental protection, hitching a ride of the fast train of carbon reduction policies. On the one hand, it can reflect the carbon emission reduction performance of the substitution capacity of raw resources in production, reducing the carbon emission of developing new resources. On the other hand, it makes carbon emission reduction of landfill and incineration of waste products through decreasing the amount. Of course, the carbon emission reduction performance of the resource recycling process will affect the overall evaluation of resource recycling industry.

4.2 Introduction of the Chain of Resource Recycling Industry

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4.2 Introduction of the Chain of Resource Recycling Industry As shown in Fig. 4.3, the waste products of resource recycling industry consists various sources, such as individuals, manufacturers, and municipal department. The role played by collectors is to gather scattered waste products and classify them according to the requirements of downstream processors. Therefore, the collectors need to engage in transactions with consumers and producers to obtain ownership of waste items. At the same time, they also need to reasonably arrange reasonable logistics processes of waste items from the source places to the sorting point to ensure cost control. The selection of sorting methods and sorting points for waste items is also one of the decisions of recycling companies. The waste products contains various products, such as electronic products, automobiles, product packaging, etc. So, the economic value and environmental public welfare value vary. Although the take-back price of waste products is low, they are scattered and require a lot of manpower to collect and classify them accordingly. Moreover, the waste materials generated during production processes can also become reusable resources through resource recycling. In addition, municipal waste can also provide waste products for resource recycling industry. In order to ensure environmental performance of waste products, the municipal department has been improving the efficiency of sorting waste products, separating renewable waste from non renewable waste from starting point, which also creates advantages for the integration of municipal recycling network and resource recycling network, making municipal recycling one of the supply sources of resource recycling industry. Due to the close correlation between the distribution information of waste items and the subsequent transportation routes, network distribution, and selection of classification methods, in order to quickly obtain the distribution information of waste

Individuals

Manufacturers

Municipal department

Collector

Processors

Waste to resource Fig. 4.3 The chain of resource recycling industry

Manufcturer

Consumers

New production with renewable resources

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products, internet recycling platforms have become one of the important tools for collection (Wang et al. 2022). The Internet recycling platform establishes information communication channels for collectors and consumers, as well as other waste products suppliers. It provides information support for collectors’ decisions (collection price and route planning) and reduces collection costs. After obtaining the classified waste items from the upstream collector, the processor transform the waste products into renewable resources, through disassembling the waste products, and abstracting useful material. For example, during processing waste cars, the car parts are first disassembled. Then, physical or chemical methods are used to separate the metal from other materials, ultimately achieving the extraction of various metals. Finally, various renewable resources are sold to downstream manufacturer. The parts that cannot be reused will be landfilled or incinerated. The resource recycling rate during the regeneration processes is the basic guarantee for the sustainability of resource recycling industry chain, and the environmental performance of processing waste products would influence public welfare value of resource recycling industry chain, such as controlling the generation of secondary pollution. With the increasing labor cost, technologies have become the key to promoting resource recycling and decreasing the risk of second pollution. Therefore, the mastery of resource recycling technologies, facilities and equipment, as well as the professional ability of staff will all affect the sustainability and value creation of the entire chain of resource recycling. Manufacturers purchase renewable resources from waste processing companies and use them as production materials, achieving the ultimate value of resource recycling industry. Therefore, although the resource recycling industry has both economic value and environmental public welfare value, whether manufacturers will choose renewable resources as their production materials and the transaction price of renewable resources are the fundamental driving force of the resource recycling industry. The resource recycling industry should continuously enhance its market competitiveness, such as cost advantages and quality advantages. From waste to new products, waste products require multiple stages to achieve their economic value, and the long chain of resource recycling industry means that investors cannot obtain actual economic returns for a long time. Therefore, how to enhance the confidence of investors in participating in the resource recycling industry, or provide direct or indirect financial support for the resource recycling industry, is also one of the main issues that the government and entrepreneurs needs to think.

4.3 Technical Requirements and Business Models …

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4.3 Technical Requirements and Business Models of Resource Recycling Industry Before, both labor costs and technological were at a low level, and the industry threshold was low. The business model of the resource recycling industry was mostly a family small workshop business model, where collection of waste products, resource recycling processing, sales, and other links were mostly completed by small unit households. The processes in this business model was simple and extensive. Although the risk of secondary pollution was high, it had cost advantages. However, although the increase in environmental awareness has led to a more comprehensive understanding of the resource recycling industry from all sectors of society, and continuous improvements have been made in both environmental and production performance (Quan et al. 2010). Due to the immature technologies of resource extraction and environmental protection, the quantity and quality of resource recycling from waste products are limited (Ding et al. 2022a, b). Moreover, in order to avoid second pollution during processing waste products, the enterprises in resource recycling industry require a large amount of costs and advanced technologies (Mao et al. 2020a, b). Therefore, with the increase in labor costs and the increase in environmental regulations, the family workshop production and operation model still has considerable market competitiveness. In order to obtain economic benefits and meet government environmental regulatory standards, formal enterprises in resource recycling industry need technical support to achieve a higher level of resource recycling rate and achieve low cost for controlling secondary pollution risk, thereby enhancing the market competitiveness of renewable resources (Rashid et al. 2023). In order to reduce environmental protection costs and enhance the resource recycling performance, the business model of hi-tech industrial agglomeration in one park has attracted the attention of resource recycling entrepreneurs and government. In this business model, resource recycling enterprises for different types of waste products can gather for processing waste products, sharing environmental protection facilities and equipment, and even directly supply renewable resources to production enterprises. At the same time, it can shorten the distance between suppliers and customers, reduce logistics costs, strengthen the trust between merchants, and effectively decrease the investment return period of the resource recycling industry. One renewable resources industry in Dingzhou, Hebei Province has undergone 30 years of development. With the guidance and support of the Chinese government, it is currently in a critical period of transformation from a family workshop business model to a park aggregation business model. Now, the resource recycling industry is gradually moving towards standardization, and the market competitiveness of renewable resources is steadily increasing, laying the foundation for the sustainable development of the local resource recycling industry. Resource recycling industry is a crucial part of circular economy and a new growth point of current economy under environmental constraints. However, the replacement of renewable resources for new resources is still a relatively long process. The market cannot evaluate the environmental cost of new resources, nor can it

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evaluate the environmental values of renewable resources. Under the background of environmental constraints, the two resources still cannot achieve reasonable market competition (Yu et al. 2021). Therefore, the government needs to play a coordinating role in this process and establish the market position of renewable resources before the environment deteriorates (Arain et al. 2020).

4.4 Suggestions for the Development of Resource Recycling Industry It is difficult for environmental public welfare value to be reflected together with economic value in the transaction process. The limited economic value creation leads to insufficient incentive value for economic entities to participate in resource recycling industry, and the market competitiveness of renewable resources is still insufficient (Lu et al. 2023). Therefore, as the main driving entity of public welfare production activities, the government have to enhance the value of resource recycling industries through fiscal policies, such as tax means. Resource recycling entities can obtain tax exemptions for engaging in economic activities related to resource recycling, thereby enhancing the overall value of the resource recycling industry from the perspective of economic cost control. The carbon tax policy is currently one of the tax collection policies that is highly related to the development of the resource recycling industry. As resource recycling can effectively reduce the exploitation of new resources and the environmental problems caused by waste products, it can reduce carbon emissions from two dimensions, thereby improving the level of environmental quality. Therefore, the carbon tax policy can better transform the environmental public welfare value of the resource recycling industry into economic value, making it reflected in the transaction processes of the resource recycling industry, which enhances the incentive for economic entities to participate in resource recycling production activities. In addition, in order to better achieve the public environmental value of renewable resource and ensure fair competition in the renewable resource market, the government also needs to impose environmental regulations on resource recycling enterprises. Environmental regulations can not only control secondary pollution caused by the resource recycling process of waste products, ensure industry standardization, but also prevent the entry of non compliant resource recycling enterprises, ensuring fair competition and healthy development of the renewable resource market. The difference between the economic value of resource recycling of high-value waste products and low-value waste products makes the collection quantity difference between high-value waste products and low-value waste products (Tsai 2021). Some low-value waste products with high public environmental values at a low level of collection, such as plastic products and glass products. The shortage of resources is constantly highlighted, and the severity of environmental pollution caused by production is constantly increasing. The conversion of waste products into resources

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has become the key to the green transformation of production, but their economic value is still the main driving force for resource recycling. So, the participation of nonprofit organizations may enable the regeneration of low value waste resources, such as environmental welfare organizations that can provide free or low-cost collection and classification services to reduce resource recycling costs. In addition, enhancing citizens’ awareness of environmental protection and advocating for their participation in classified recycling activities can also reduce the cost of resource recycling for low-value waste products. The long chain of resource recycling industry leads to a long investment payback period and a lack of market investment confidence. Firstly, the government needs to design appropriate policy solutions to enhance the market position of renewable resources while reducing market risks. In addition, banks need to develop financial products that are suitable for the characteristics of the resource recycling industry, providing high-quality convenience for the capital flow of investors and the participants of the resource recycling industry. The value of resource recycling requires the quantity and quality of output (Mao et al. 2020a; b), as well as the environmental friendliness of the production process, which is also the foundation of the value creation of the resource recycling industry. Technology is the key to improving the quantity and quality of output, and environmental friendliness of the production process. Due to the high cost of environmental protection technology, non compliant resource recycling enterprises have become the powerful competitor for the formal resource recycling enterprises, making it difficult for formal enterprises to survive. Therefore, in order to ensure the sustainable development of the resource recycling industry, the most effective way should be to encourage and support the economic entities of resource recycling to engage in technological development while carrying out production activities, so that the market competitiveness of renewable resources and the sustainability of resource recycling industry can be ensured. The economic and environmental performance of the resource recycling industry is the foundation for maintaining its operation. Therefore, establishing scientifically and reasonably evaluation system for the economic and environmental performance of the resource recycling industry is crucial for its long-term development.

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Ding ZK, Nie WQ, Wu HY (2022a) Investigating the connection between stakeholders’ purchase intention and perceived value of construction and demolition waste recycled products. Environ Dev Sustain 24(7):9285–9303 Ding ZK, Zhang ZY, Chen WL (2022b) The influence of media in purchasing decisions for recycled construction and demolition waste products: an functional near infrared spectroscopy study. Front Neurosci 16. https://doi.org/10.3389/fnins.2022.881537 Geng Z (2021) Under the goal of “double carbon”, China’s renewable resources industry has ushered in new development opportunities. Resour Recycl 228(7):16–17 Huysveld S, Ragaert K, Demets R, Nhu TT, Civancik-Uslu D, Dewulf J (2022) Technical and market substitutability of recycled materials: calculating the environmental benefits of mechanical and chemical recycling of plastic packaging waste. Waste Manage 152:69–79 Jiang J, Shi K, Zhang XN, Yu K, Zhang H, Liu JL (2021) From plastic waste to wealth using chemical recycling: a review. J Environ Chem Eng 10(1). https://doi.org/10.1016/j.jece.2021. 106867 Khan SAR, Tabish M, Yu Z (2023a) Investigating recycling decisions of internet recyclers: a step towards zero waste economy. J Environ Manage 340:117968. https://doi.org/10.1016/j.jenvman. 2023.117968 Khan SAR, Piprani AZ, Yu Z (2023b) The decision-making of internet recycler considering Internetof-Things application. Int J Retail Distrib Manage ahead-of-print(ahead-of-print). https://doi. org/10.1108/IJRDM-03-2023-0177 Khan SAR, Tabish M, Yu Z (2023c) Mapping and visualizing of research output on waste management and green technology: a bibliometric review of literature. Waste Manage Res 41(7):1203–1218. https://doi.org/10.1177/0734242X221149329 Kusenberg M, Roosen M, Zayoud A, Djokic MR, Thi HD, Van Geem KM (2022) Assessing the feasibility of chemical recycling via steam cracking of untreated plastic waste pyrolysis oils: feedstock impurities, product yields and coke formation. Waste Manage 141:104–114 Lange JP (2021) Managing plastic waste-sorting, recycling, disposal, and product redesign. ACS Sustain Chem Eng 9(47):15722–15738 Liang Q, Wang JQ, Chen SQ, Hu L, Qin JC, Han YH, Shu JC (2023) Electrolyte circulation: Metal recovery from waste printed circuit boards of mobile phones by alkaline slurry electrolysis. J Clean Prod 409. https://doi.org/10.1016/j.jclepro.2023.137223 Lu LL, Fan W, Meng X, Xue LL, Ge SB, Lam SS (2023) Current recycling strategies and highvalue utilization of waste cotton. Sci Tot Environ 856(1). https://doi.org/10.1016/j.scitotenv. 2022.158798 Luo JC, Deng Y, Yan JW, Xu ZY, Han SS, Chen SC (2022). Research on high-quality development path of China’s resource recycling industry under the goals of emission peak and carbon neutrality. World Environ 194(1):28–31 Mao SH, Gu WH, Bai JF, Dong B, Huang Q, Zhao J, Zhuang XN, Zhang CL, Wang JW (2020a) Migration of heavy metal in electronic waste plastics during simulated recycling on a laboratory scale. Chemosphere 245. https://doi.org/10.1016/j.chemosphere.2019.125645 Mao SH, Gu WH, Bai JF, Dong B, Huang Q, Zhao J, Zhuang XN, Wang JW (2020b) Migration characteristics of heavy metals during simulated use of secondary products made from recycled e-waste plastic. J Environ Manage 266 Quan C, Li AM, Gao N, Dan Z (2010) Characterization of products recycling from PCB waste pyrolysis. J Anal Appl Pyrol 89(1):102–106 Rashid ME, Khan MR, Khan M, Ul Haque R, Hasanuzzaman M (2023) Challenges of textile waste composite products and its prospects of recycling. J Mater Cycles Waste Manage. https://doi. org/10.1007/s10163-023-01614-x Rene ER, Sethurajan M, Ponnusamy VK, Kumar G, Dung TNB, Brindhadevi K, Pugazhendhi A (2021) Electronic waste generation, recycling and resource recovery: technological perspectives and trends. J Hazardous Mater 416. https://doi.org/10.1016/j.jhazmat.2021.125664 Tiwari R, Azad N, Dutta D, Yadav BR, Kumar S (2023) A critical review and future perspective of plastic waste recycling. Sci Tot Environ 881.https://doi.org/10.1016/j.scitotenv.2023.163433

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Chapter 5

Technological Innovation in Business Operations for Sustainability: Current Practices and Future Trends

5.1 Introduction The present study aimed to analyze the scholarly literature on business management, technology and sustainability (BMTS) indexed in Scopus database. The sustainable growth of business management is entirely dependent on the best adaptation of emerging technologies and continuous innovative practices (Hargadon 1998; Khan et al. 2022a). Trumpp et al. (2015) stated the word “business” that deals with all the commercial and economic actions performed in the shape of either goods or services to get monetary benefits. Further, the business activities such as production, distribution and consumer satisfaction are meant to improve the living standard as well as the quality of life. The modern competitive business environment is determined by the requirement for ongoing novelty in the productive structures, allowing their performance to be enhanced and their revenue to be escalated continually (Newman et al. 2020; Lekic and Rajakovic–Mijailovic 2018; Khan et al. 2022b; Reichheld 1993). Due to the significance of information technology as a necessary input for business activity, it became a value-producing indicator in an innovative practice (Doms et al. 1995). Information technology has become a vital tool for the appropriate growth of business and putting a huge impact on different productive structures as well as leading to automate of their fundamental tasks (Doherty and King 1998; Khan et al. 2023a). Advancement in technology such as Big Data, Blockchain Technology and Artificial intelligence is the most significant modern technology that altering the business as evidenced by Amazon, Google, Uber, Alibaba along with various other companies have transformed their business models and enhanced their annual revenue (Lee et al. 2019). Sustainability is a critical source of competitive recompenses for creating and surviving values in the circular business (Ferasso et al. 2020). Certainly, one of the most challenging issues that humanity has ever encountered in business management is sustainability or sustainable development. Achieving sustainability involves tackling various primary challenges at different levels from local to regional and from regional to global. Every business organization needs to be addressed these issues to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_5

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accomplish its objectives in befitting manner (Khan et al. 2023b; Gomes et al. 2015). Damke et al. (2021) opined that for the long-term sustainable development of the business, management needs a successful business model that evaluates the impact and consequence of its policies, and analyzes the ecological and community characteristics of its revenue (Damke et al. 2021). Technology and sustainability are two fundamental ingredients of an effective business model, which can lead enterprises to higher profitability and improved their reputation in the market. It has been a pervasive fact that business and entrepreneurship are the driving forces that move forward the economy of the country (Dauletova and Al-Busaidi 2022; Gorman et al. 1997). This recognition has led to a growing attentiveness to establishing business schools and developing educational programs to motivate and boost business. Furthermore, researchers and professional business managers started to document the best practices and successful case studies in form of research (Gorman et al. 1997; Ben Amara and Chen 2021). Human performance can be gauged in higher education institutions and business schools on the basis of their research productivity. Quality research is critical for developing a knowledge-based society (Flagg et al. 2011). White et al. (2012) stated that the area of research growth in business had been under-explored. The study explored the factors of low and high research productivity among the 236 faculty members of business schools. The study shed light that the prolific authors enjoyed higher academic status, kept optimum time management ability, gave high priority to research, relish the institutional sustenance and had fewer teaching assignments. The research in the areas of business and management extensively flourished during the late 90s and now it is evolving from an ambitious faculty to a developed academic arena (Hambrick and Chen 2008; Khan et al. 2023c; Bernardi and Zamojcin 2013). Hassan et al. (2014) review the publication growth on sustainable development published from 2001 to 2010. The maximum research was produced by the US, followed by China. This study also explored the research performance in the sub-areas of sustainability by countries. Pan et al. (2022) studied the Web of Science indexed literature on sustainable business model innovation (SBMI) published from 1990 to 2020. A total of 4,509 documents were selected, and a remarkable growing trend was observed after 2008 because the theories of SBMI started to develop in 2009 onward. The highest number of documents (43.66%) were published on “business, management and accounting” followed by “environmental sciences”. Among the preferred sources of publications, Sustainability and Journal of Cleaner Production were on the top with 274 and 267 documents, respectively but Harvard Business Review got the highest citation impact (184 cites/doc). China contributed the maximum number of documents (n = 681), followed by the US (n = 622) England (n = 581), Italy (n = 448) and Germany (n = 384). The thematic distribution of documents showed that innovation, sustainability, business models, and management were the focused areas of research. The current study reviews the existing literature on this subject by using the quantitative research method of bibliometric. Also, bibliometric is a significant instrument for evaluating scholarly publications and their various characteristics. The valuable

5.2 Research Methodology

59

information has been obtained by applying statistical, mathematical, and methodological applications to the retrieved dataset (Allen et al. 2009). The objective, transparent and systemic assessment extensively enhanced the quality of the literature. It also identifies the research trends, studies gaps, and extracts directions for policymakers to revisit their research policies and strategies (Pizzi et al. 2020; Khan et al. 2023d; Lowry et al. 2013). Yu et al. (2022) and Khan et al. (2023e) measured the researches on sustainable supply chain management and green technologies published from 2001 to 2021. Their results show that the involvement of technological innovation is increasing in sustainable supply chain operations, which has a significant role to improve the sustainability and green practices in business operations. Sahoo (2021) analyzed 89 articles on big data analytics in the manufacturing sector in business management, the authors from US, China and UK contributed the great number of researches. Ferreira et al. (2021) scrutinized the 161 documents on sustainability in family businesses published from 2003 to 2019. Progressive growth in publications was seen after 2013 and 80% of the literature was published in the last five years (2015–2019) of the study. The highest number of literature was published in the journal Sustainability-Switzerland. Ejsmont et al. (2020) reviewed 162 papers on sustainability about industry 4.0. These papers were published from 2010 to 2020 and 90% of papers were published during 2018 to 2020. Twenty-nine papers were published in Sustainability-Switzerland, followed by Journal of Cleaner Production (n = 9). India emerged as the most productive country, followed by the US, and Spain. Bhatt et al. (2020) evaluated the literature on sustainable manufacturing published during the last 25 years and 19% of the papers were published in Journal of Cleaner Production. Another bibliometric study focused on sustainability and collaborative economy analyzed 729 papers published from 2010 to 2017 and highest number of papers contributed by North America (29%) followed by the US (25%) and Canada (3%). The authors affiliated with University of California Berkeley, US contributed the maximum number of papers (Ertz and Leblanc-Proulx 2018). The key objective of the current research is to evaluate the previously published articles on Technological innovation, Business Management and Sustainability during 1990 to 2022. Therefore, this study will evaluate and list-down the most influential institutions, top researchers in the field, top international journals, list of most productive countries, and top author’s used keywords in the field of business management, innovation and sustainability.

5.2 Research Methodology The current study aims to evaluate the publication growth on “Business Management, Technology and Sustainability”. The meta-data was retrieved from the Scopus database on December 18th, 2022. The reason for choosing the Scopus database because it provides comprehensive coverage of management sciences compared to WOS database (Falagas et al. 2008). The following research query has been applied to extract the required data.

60

5 Technological Innovation in Business Operations for Sustainability …

(TITLE-ABS-KEY (business AND management) AND TITLE-ABS-KEY (technology) AND TITLE-ABS-KEY (sustainability)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “re”)) AND (EXCLUDE (PUBYEAR, 2023)). Two filters have been used, firstly, articles and reviews were selected and all other types of documents were excluded, secondly, six documents published in 2023 have also been excluded. Search query yielded 1336 papers, published from 1991 to the date of data retrieval. Suriyankietkaew and Petison (2019) carried out a bibliometric analysis of Strategic Management for Sustainability and that study also found the first recorded document published in 1991. To verify the accuracy of the data, the search query was repeated. The data-set was checked to avoid duplicate records, but no duplicate record was found. The retrieved data was evaluated by using Microsoft Excel (v.16) and VOSviewer software (Van Eck and Waltman 2013). The retrieved dataset is limited to the Scopus database only under the pre-defined keywords. The data was obtained on December 18th, 2022, so the data for the year 2022 has been limited to that date.

5.3 Results 5.3.1 Intellectual Growth and Its Impact by Years A total of 1336 papers (articles and review articles) have been published on “Business on Management, Technology and Sustainability” during the period of 33 years, from 1990 to 2022. To assess intellectual growth by periodic, the meta-data has been divided into three equal intervals consisting of eleven years each. Only 13 papers were published during 1990 to 2000. A gradual improvement was observed in the next interval from 2001 to 2011 and 17.58% (n = 235) of the papers were published. A remarkable intellectual growth was detected during the last interval (2012–2022) and more the eighty percent (n = 1088; 81.43%) of the researches were published during this period. During 2019 to 2022, a drastic growth with slightly more than half of the literature (n = 693; 52%) was depicted. Figure 5.1 also highlights that a fluctuation has been seen in intellectual growth from 2008 to 2016 but a growing tendency was explicated from 2017 onward. All the selected papers gained 32,118 citations with an average of 24.04 citations per paper. The instability in the ratio of citations has also been detected by year. In Fig. 5.1, the first little curve was found against the year 2000, five papers were published this year and gained 236 citations with an average of 47.20 citations per paper. The second citation curve against the nine papers published in 2003 and these papers were cited 955 times with an average of 106.11 citations per paper. It was recorded as the highest citation impact by year. In the following years (2004–2005), although 28 papers were published but the citation impact has been decreased (9.58 cites/paper). Another citation curve was seen against the articles in 2011 and 2012.

5.3 Results

61 250

6000 5000

Citations

150

3000 100

2000

50

1990

1996

1995

1997

2000

1999

2001

2002

2003

2004

2005

2007

Articles

2006

2008

2009

2010

2011

2012

2014

2013

2015

2017

2016

2019

2018

2020

2022

2021

1000 0

Articles

200

4000

0

Citations

Fig. 5.1 Distribution of articles and citations by year

Then a decline in the ratio of citations was depicted in the next two years. A growing trend of citations was found after 2014 and the last peak represented the 115 papers published in 2019, and these papers have cited an average of 42.37 cites/paper. Usually, the recent papers gained less number of citations as evident in the last three years of study.

5.3.2 Top-20 Most Productive Countries The researchers belonging to 124 countries contributed to the intellectual productivity of the targeted theme. The list of top-20 most productive countries in terms of a number of papers have been shown in Fig. 5.2. More than one-fourth of the total papers (n = 360; 26.94%) were contributed by two countries the US (USA) (n = 204; 15.26%) and UK (UK) (n = 156; 11.67%). The top four countries (USA, UK, India and China) contributed more than 100 papers each, while the seven countries (Spain, Italy, Malaysia, Australia, Germany, Netherlands and Brazil) contributed a range from 50 to 100 papers each. Table 5.1, exposed that USA was not only found most productive in terms of papers but also most impressive in citation impact. Its intellectual productivity gained 41.48 cites/paper, followed by UK, Netherlands and Denmark with 38.89, 37.35, and 37.03 cites/papers, respectively. The lowest citation impact was found against South Africa and Indonesia with 7.36, and 8.46 cites/papers, respectively.

5.3.2.1

Co-authorship Occurrence Network of Top-20 Countries

The network of co-authorship occurrence among the top-20 countries was generated by VOSviewer’s software shown in Fig. 5.3. The co-authorship network has been distributed in four clusters. The green balls represented the co-authorship among

62

5 Technological Innovation in Business Operations for Sustainability … 204 156 117

104 79

77

73

71

68

52

51

47

44

37

36

33

32

31

27

Fig. 5.2 Top-20 most productive countries with number of articles Table 5.1 Top-15 most productive countries with articles, citations and citation impact Serial No.

Country’s name

Total articles

Total citation

Citation impact

1

US

204

8461

41.48

2

UK

156

6067

38.89

3

India

117

2660

22.74

4

China

104

3454

33.21

5

Spain

79

2270

28.73

6

Italy

77

2290

29.74

7

Malaysia

73

1579

21.63

8

Australia

71

2554

35.97

9

Germany

68

2451

36.04

10

Netherlands

52

1942

37.35

11

Brazil

51

1163

22.80

12

Canada

47

1615

34.36

13

France

44

1227

27.89

14

Indonesia

37

313

8.46

15

South Korea

36

563

15.64

16

South Africa

33

243

7.36

17

Taiwan

32

1012

31.62

18

Sweden

31

735

23.70

19

Denmark

27

1000

37.03

20

Russian Federation

27

279

10.33

27

5.3 Results

63

Fig. 5.3 Network of co-authorship occurrence in top-20 countries

the seven countries (US, Canada, UK, Germany, Sweden, Netherlands, Denmark) in the first cluster. Most of the authors who belonged to these countries collaborated with each-others. The red balls signified the second cluster consisted of eight countries (South Korea, China, India, Australia, Malaysia, Taiwan, South Africa, and Indonesia), while the third cluster of blue colored balls showed the co-authorship among Italy, Spain and Russian Federation. The co-author occurrence between Brazil and France showed in yellow balls of the fourth cluster.

5.3.3 Top-20 Most Influential Institutions A total of 4091 authors affiliated with 3083 organizations/institutions belonging to 124 countries contributed to the 1336 papers on “Business on Management, Technology and Sustainability”. Table 5.2 elaborated on the details of top-20 most influential institutions, their active research productivity period, the total number of papers, citations and citation impact. The authors affiliated with the Universiti Teknologi MARA contributed 16 papers from 2015 to 2022, with an average of 2 papers per year, followed by Wageningen University and Research with 12 papers but these papers were contributed in 15 years (2008 to 2022). Albeit, Universiti Teknologi MARA produced the highest number of papers but its research gained the lowest citation impact (5.13 cites/paper). The authors of Worcester Polytechnic Institute contributed seven papers but gained the highest citation impact, with an average of 205.57

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5 Technological Innovation in Business Operations for Sustainability …

Table 5.2 Top-20 most influential institutions Serial No. Name of institutions

Active phase Total articles Total citations Citation impact

1

Universiti Teknologi MARA

2015–2022

16

82

5.13

2

Wageningen University and Research

2008–2022

12

721

60.08

3

Parthenope University of Naples

2017–2022

11

567

51.55

4

Universidade de São Paulo

2010–2022

11

111

10.09

5

The University of Manchester

2012–2022

9

168

18.67

6

Universiti Kebangsaan Malaysia

2015–2022

9

105

11.67

7

Universiti Sains Malaysia

2011–2022

8

588

73.50

8

Delft University of 2006–2022 Technology

8

300

37.50

9

Universidad Rey Juan Carlos

2019–2022

8

163

20.38

10

Curtin University

2007–2022

8

155

19.38

11

Indian Institute of Technology Kharagpur

2011–2022

8

117

14.63

12

Worcester Polytechnic Institute

2012–2022

7

1439

205.57

13

Universidad de Granada

2012–2022

7

414

59.14

14

Chinese Academy of Sciences

2005–2022

7

406

58.00

15

Vilniaus Gedimino 2018–2022 Technikos Universitetas

7

354

50.57

16

University of Sussex

2015–2022

7

336

48.00

17

Universiti Malaya

2014–2022

7

223

31.86

18

University of Zagreb

2017–2022

7

198

28.29

19

Cardiff University

2005–2022

7

192

27.43

20

Universiti Malaysia Pahang

2016–2022

7

176

25.14

5.3 Results

65

cites/paper, followed by Universiti Sains Malaysia and Wageningen University and Research with 73.50 and 60.08 cites/papers.

5.3.4 Top-20 Most Prolific Researchers The particulars of the top-20 most prolific researchers, who contributed papers ranging from three to eight have been shown in Table 5.3. Mangla, S.K. of the O.P. Jindal Global University, India was found to be the most prolific author with eight papers, followed by Sarkis, J. of Worcester Polytechnic Institute, US with seven papers. Mangla, S.K. contributed papers from 2018 to 2022, while Sarkis, J. produced papers from 2006 to 2022, so the time coverage of Sarkis, J. was longer than Mangla, S.K., that’s why, Sarkis attained a higher number of citations, even the highest among the top-20 authors. Ramayah, T. of the Universiti Sains Malaysia, Minden, Malaysia was found to be the second most influential author in terms of citation impact. Chatterjee, S. and Chaudhuri, R. contributed four papers and all of their research was published during the one calendar year, 2022 and their papers gained the lowest citation impact. The top-20 authors belonged to 14 countries, the maximum, of five authors affiliated from India followed by two from the US, Italy, and Malaysia.

5.3.5 Top-20 Most Frequently Used Sources of Publications About one-third of the total papers (n = 411; 30.76%) have been published in top-20 most frequently used sources of publications and these papers gained 40.20% (n = 12,912) of the citations. The details of top-20 sources/journals have been given in Table 5.4. The maximum amount of papers (n = 182; 13.62%) was published in Sustainability Switzerland, followed by Journal of Cleaner Production (n = 48), Business Strategy and the Environment (n = 20), and Resources Conservation and Recycling (n = 18). Albeit, International Journal of Production Research found in the last of the top-20 journal but the five papers published in this journal gained the highest citation impact with an average of 260.6 citations per paper, followed by Resources Conservation and Recycling and Journal of Cleaner Production with 69.11 and the 63.25 citations per paper, respectively. Out of 20 journals, except one journal, all other journals have CiteScore ranging from 0.2 minimum to 28.5 maximum. The highest number of journals (n = 17) having a scale of CiteScore is more than 4. There are two journals with less than 4 CiteScore, that have the lowest citation impact.

66

5 Technological Innovation in Business Operations for Sustainability …

Table 5.3 Top-20 Most prolific researchers Serial No.

Name of researchers

Affiliation of researchers

Active phase

Total articles

Total citations

Citation impact

1

Mangla, S.K

O.P. Jindal Global University, Sonipat, India

2018–2022

8

416

52.00

2

Sarkis, J

Worcester 2006–2022 Polytechnic Institute, Worcester, US

7

1292

184.57

3

Luthra, S

All India Council for 2018–2022 Technical Education, New Delhi, India

6

273

45.50

4

Tseng, M.L

Asia University, Taichung, Taiwan

2013–2022

6

265

44.17

5

Wells, P

Cardiff Business School, Cardiff, UK

2005–2020

5

139

27.80

6

Kazancoglu, Y

Ya¸sar Universitesi, Department of International Logistics Management, Izmir, Turkey

2021–2022

5

33

6.60

7

Chatterjee, S

Indian Institute of Technology Kharagpur, Kharagpur, India

2022

4

14

3.50

8

Chaudhuri, R

National Institute of Industrial Engineering, Mumbai, India

2022

4

14

3.50

9

Ramayah, T

Universiti Sains Malaysia, Minden, Malaysia

2011–2020

3

524

174.67

10

Zailani, S

Universiti Malaya, Kuala Lumpur, Malaysia

2011–2021

3

499

166.33

11

Benitez-Amado, J

Universidad de Granada, Department of Management, Granada, Spain

2012–2015

3

341

113.67

12

Zavadskas, E.K

Vilniaus Gedimino Technikos Universitetas, Vilnius

2019–2021

3

333

111.00

13

Baumgartner, R.J Universitat Graz, Graz, Austria

2014–2022

3

320

106.67 (continued)

5.3 Results

67

Table 5.3 (continued) Serial No.

Name of researchers

Affiliation of researchers

Active phase

Total articles

Total citations

Citation impact

14

Di Vaio, A

Parthenope 2020–2021 University of Naples, Department of Law, Naples, Italy

3

178

59.33

15

Sueyoshi, T

Tokyo Institute of Technology, School of Environment and Society, Tokyo, Japan; New Mexico Institute of Mining and Technology, US

2014–2017

3

178

59.33

16

Perboli, G

Politecnico di Torino, Turin, Italy

2019–2020

3

128

42.67

17

Teuteberg, F

Osnabrück 2009–2020 University, Department of Accounting and Information Systems, Osnabruck, Germany

3

116

38.67

18

Donnelly, K

Nokia Corporation, Espoo, Finland

2004–2006

3

114

38.00

19

Bai, C

University of Electronic Science and Technology of China

2015–2022

3

107

35.67

20

Narkhede, B.E

National Institute of Industrial Engineering, Department of Operations and Supply Chain Management, Mumbai, India

2019–2022

3

78

26.00

5.3.6 Top-20 Most Influential Articles The top-20 most cited articles have been cited 8,091 times with an average of 404.55 cites/article and these articles were published from 2003 to 2020. These articles were published in 17 different journals and the highest number of articles (n = 4) were published in Journal of Cleaner Production, while the other 16 articles were published in 16 different journals. The researchers belonged to US contributed seven articles, followed by the authors from UK contributed four, while the authors from China, Oman, Malaysia and Spain contributed two papers each, respectively. In

68

5 Technological Innovation in Business Operations for Sustainability …

Table 5.4 Top-20 Most frequently used sources of publications Serial No. Journal’s name

CiteScore Total articles Total citations Citation impact

1

Sustainability Switzerland

5.0

182

3395

18.65

2

Journal of Cleaner Production

15.8

48

3055

63.65

3

Business Strategy 11.9 and the Environment

20

605

30.25

4

Resources Conservation and Recycling

17.9

18

1244

69.11

5

Emerald Emerging Markets Case Studies

0.2

15

5

0.33

6

Technological Forecasting and Social Change

13.7

14

650

46.43

7

Science of the Total Environment

14.1

12

473

39.42

8

Environmental Monitoring and Assessment

4.5

12

223

18.58

9

Environment Development and Sustainability

4.4

11

280

25.45

10

Energies

5.0

11

161

14.64

11

Wit Transactions in Ecology and the Environment

0.8

9

13

1.44

12

IEEE Transactions on Engineering Management

6.2

8

67

8.38

13

Renewable and Sustainable Energy Reviews

28.5

7

268

38.29

14

Management Decision

7.9

7

175

25.00

15

Energy Policy

12.4

7

158

22.57

16

Journal of Environmental Management

11.4

7

152

21.71

17

Espacios

N/A

6

35

5.83

18

Management of 6.7 Environmental Quality an International Journal

6

113

18.83

(continued)

5.3 Results

69

Table 5.4 (continued) Serial No. Journal’s name

CiteScore Total articles Total citations Citation impact

19

Water Resources Management

6.6

20

International Journal 14.6 of Production Research

6

537

89.5

5

1303

260.6

three articles, authors from three different countries collaborated with each other’s, (1: Canada, Denmark and Germany, 2: Taiwan, China and Philippines, 3: Brazil, UK and Mexico). In five articles, authors from two different countries collaborated, (1: US and Australia, 2: China and US, 3: Ireland and UK, 4: Oman and Malaysia in two papers), while the authors belonged to India, Austria and Hungary also contributed in one article each.

5.3.7 Top-20 Author’s Used Keywords A total of 4,088 keywords were used by the authors in 1,336 papers with an average of 3 keywords per paper. The list of top-20 keywords was generated by the VOSviewers software with occurrence scale and total link strength has been shown in Table 5.5. The keyword, “sustainability” has been used 366 times, followed by “Sustainable development” (n = 70), Circular economy (n = 67), Innovation (n = 64) and Industry 4.0 (n = 52). The highest total link strength has gone to the keyword “sustainability” followed by “innovation”. Figure 5.4 describes the occurrence network of top-20 keywords and these keywords have been divided into five clusters. Cluster 1 represented in red balls consisted of 7 items/keywords (sustainability, entrepreneurship, innovation, knowledge management, management, strategy, and technology). The six items/keywords in green-colored balls comprised of cluster 2 (corporate social responsibility, corporate sustainability, digital transformation, digitization, industry 4.0, sustainable development). The three blue balls signified cluster 3 (blockchain, supply chain, and supply chain management), followed by two yellow balls in cluster 4 (business model, circular economy) and two indigo balls in cluster 5 (business models, climate change). The author’s used keywords help to understand the subject dispersion of the targeted theme.

70

5 Technological Innovation in Business Operations for Sustainability …

Table 5.5 Top-20 Author’s used keywords Serial No.

Keyword

Occurrences

Total link strength

1

Sustainability

366

239

2

Sustainable development

70

54

3

Circular economy

67

70

4

Innovation

64

82

5

Industry 4.0

52

66

6

Blockchain

35

33

7

Supply chain management

33

29

8

Business model

31

27

9

Supply chain

28

40

10

Technology

28

37

11

Digital transformation

22

24

12

Knowledge management

22

18

13

Entrepreneurship

21

32

14

Corporate sustainability

19

13

15

Management

19

26

16

Business models

18

15

17

Corporate social responsibility

18

21

18

Strategy

18

31

19

Climate change

16

14

20

Digitalization

15

21

5.4 Discussion The adapting and evaluating the existing technologies are indispensable for the sustainability of a business (Bhandari et al. 2019). The process of business management has concerned the internal and external environmental factors that comprised from native to the international context. For a successful business model, higher education in business management and the ability to conduct operational research to tackle challenges are mandatory in the contemporary competitive environment (Gunasekaran and Ngai 2012; Prasad and Babbar, 2000; Roth et al. 1997). The share of scholarly research in business management had a very small proportion before the 1980s and developing innovative business theories gained momentum in research during the 1990s (Donthu et al. 2020). The authors from the developed world and talent-rich countries contributed promising research and these intellectual efforts put a positive impact on the quality of human lives. It is pertinent to examine the research growth in a particular area of knowledge to assess the status of a concerned profession (Naseer and Mahmood 2009). For this purpose, the quantitative method of bibliometric has frequently been used (Garfield 1955). Bibliometric methods consisted of various indicators that support understanding the collaborative

5.4 Discussion

71

Fig. 5.4 Co-occurrence networks of top-20 authors’ used keywords

patterns, subject dispersion, periodic growth and its impact (Tanveer et al. 2020; Alfadley et al. 2022; Yu et al. 2022). The current study scrutinized 1,336 articles on BMTS published from 1990 to 2022. Very slow growth was observed during the first eleven years (1990–2000) and medium growth was seen in the next phase (2001–2011) but extraordinary growth in research was found in the last period (2012–2022). The possible reasons for this growth are, establishing new business schools, flourishing the research culture, emergence of new sources of publications, patronizing the research by governments and universities, and advancement of information communication technologies (Yu et al. 2022; Pan et al. 2022). The amount of research in every field of knowledge increased remarkably in the twenty-first century, even the valuable share contributed by the authors of developing and third-world countries (Shalaev et al. 2019; Sotudeh 2012). Our study reported that 1336 selected researches were contributed by the authors from 124 countries but more than one-fourth of the research was produced by the US and UK and their research have been more impact than other research contributed by other countries. India and China, the two most populated countries of the world, also produced an appreciated portion of the research. There is no doubt that funding bodies and ministry of education are providing research platform to the Chinese and Indian researchers, which encouraging researchers to conduct more valuable studies

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in their domains. The supremacy of the US in research productivity has been reported by various studies. Pan et al. (2022) reviewed the research growth on sustainable business model innovation published from 1994 to 2020. Bulk of literature was published in the last five years and the highest number of literature was contributed by the China, US and UK but the citation impact of Chinese research had less than the US and UK. Donthu et al. (2020) examined the 5344 papers published in Journal of Business Research from 1973 to 2017. About one-fourth (24.15%) of the papers were published in the first 28 years (1973–2000) and 75.85% of the papers were published from 2001 to 2017. More than half of the papers (n = 2775; 52%) were contributed by the US. There had been a huge gap between the US and other countries. The second most productive country was UK (9.84%), followed by Australia (7.48%), Canada (6.86%) and Spain (5.91%). The analysis of top-20 most productive institutions showed that all these institutions started research in the twenty-first century. The most productive institution, Universiti Teknologi MARA produced their research between 2015 and 2022. The earliest articles from the top-20 band showed that authors belonging to Chinese Academy of Sciences and Cardiff University published their work in 2005. These institutions that started research earlier, gained a higher ratio of citations because the older papers gained more citations as compared to recently published papers. The articles contributed by the Worcester Polytechnic Institute have a maximum compound impact, similarly, the author, Sarkis J. belonged to this institute had the highest citation impact. Another fact revealed that Sarkis was among the list of authors, who produced the most-cited article, “Blockchain technology and its relationships to sustainable supply chain management”. This article was published in 2019 and gained 1,017 citations, with an average of 254.25 citations per year, due to this most cited article, which the author himself and his institution found most impressive in compound impact. Journals are considered a vital source for sharing the findings of scholarly communication (Warriach and Ahmad, 2011; Tanveer et al. 2020). Regarding the preferred sources of publications, the findings of our study reported that most of the articles have been published in Sustainability followed by Journal of Cleaner Production. In line with these findings, the other studies also endorsed the same findings (Pan et al. 2022; Ferreira et al. 2021) stated that most of literature on “sustainable business model innovation” and “sustainability in family business” were published in Sustainability and Journal of Cleaner Production. In fact, Sustainability is an important open accessed journal in the area of environmental, economic and social sustainability of human beings, started its publication in 2009. A bibliometric study on this journal denoted that a total of 6459 papers were published until 2018. China contributed the highest number of papers (29.30%), followed by the US (15%) but citation impact of the papers contributed by the US has been almost double than China. “Sustainability” and “Management” were the top-two most used keywords (Tang et al. 2018). The topological distribution of articles based on authors’ used keywords disclosed that sustainability, sustainable development and circular economy were the most used theme in BMTS, while more research is required on climate change and digitalization.

5.5 Conclusion

73

5.5 Conclusion A total of 1,336 articles on the topic of “Business Management, Technology and Sustainability” have been published globally. An obvious growth of research was observed from 1 article in 1990 to 229 articles in 2022. More than one-fourth (26.94%) of the research was produced by the authors from the US and UK and their research had a higher citation impact as compared to the rest of the world. The most prolific authors were Mangla, S. K. of O.P. Jindal Global University India and Sarkis, J. of Worcester Polytechnic Institute US, while the highest research producing institutions were Universiti Teknologi MARA Malaysia and Wageningen University and Research Netherland. A top-preferred journal for BMTS researchers was Sustainability, a MPDI journal, publishing from Switzerland, followed by Journal of Cleaner Production. From the perspective of the expanding business schools and growing number of dynamic business managers, more research studies are required the policy guidelines that will improve the global business outlook.

5.5.1 Limitations and Future Direction The study evaluated the meta-data that was indexed in Scopus database until the date of data extraction. This study offered a quantitative bibliometric analysis of selected indicators; however, a systematic review on BMTS could shed further information on this subject. The intellectual growth on BMTS presented in the current study offers valuable insights to researchers and practitioners for better understanding the influence of BMTS in corporate environment, its findings also support to comprehend the importance of BMTS for achieving competitive economic benefits. Policymakers may acquire some ideas from the findings of present study to formulate or revisit their strategic policies in accordance with decision-making, infrastructure advancement and technological development. A study comparing the growth on BMTS on any particular region or explicit period would be a useful addition to the current body of knowledge. Since the Scopus database was only used for this study, bibliometric studies employing other citation and abstract databases, including Web of Sciences and Google Scholar, may be beneficial.

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Chapter 6

Environmental Policies and Decarbonization: Leading Towards Green Economy

6.1 Introduction Since the last couple of decades, due to massive industrialization, climate change has been a recent challenge for the governmental bodies and corporate sector (Khan and Qianli 2017), which is not only destroying the environmental performance but also creating alarming situations for human lives in terms of different diseases such as ischaemic heart disease, stroke, lung cancer, and chronic obstructive pulmonary disease (WHO 2022). Undoubtedly, economic operations, including production and transportation, are mainly burning fossil fuels that improve the country’s financial performance (Fig. 6.1). On the other hand, nobody can close their eyes to the harmful impact of burning fossil fuels, which severely affects the world. Several studies have been conducted in different dimensions to highlight the recent challenges of climate change and global warming. Li et al. (2023) conducted a study to examine the impact of urbanization on agricultural carbon emissions. They used the panel data of 30 Chinese provinces from 2005 to 2019. Their findings show that urbanization has a positive effect on agricultural carbon emissions. Zhengxia et al. (2023) highlighted that developing countries are mainly facing the problem of climate change and global warming. Khan et al. (2023a) emphasized recycling and green practices to combat air pollution and waste management challenges. Amin et al. (2023) conducted research on waste management and waste-to-energy in Asian countries, particularly Pakistan, India, and Bangladesh. Their findings show some positive developments have been made in some big cities. However, they also highlighted the need for stakeholder coordination, which slows progress and creates hurdles. Emerging and developed countries primarily deal with poor solid waste management systems, negatively impacting the environment and society (Cremation et al. 2018; Malav et al. 2020). On the other hand, developed countries have wellestablished mechanisms for solid waste management, and they strictly follow environmental policies, which ultimately mitigate the harmful effects on the environment and society. Also, several European developed nations aggressively adopt sustainable © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_6

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Fig. 6.1 Fossil fuel consumption in production

practices and a circular economy approach in their business operations. Khan et al. (2022) highlighted the importance of circular economy practices in business operations. Their findings show that technological advancement with circular economy practices can further foster the effectiveness of gaining sustainability ideas in the global supply chain. Tanveer et al. (2022) conducted a systematic literature review on circular economy and technological innovation. They reviewed a total of 1118 articles from 2000 to 2021. They found that the circular economy and technological advancement have a significant impact on waste management, further promoting recycling and remanufacturing opportunities.

6.1.1 Research Objective This study finds the impact of GDP per capita, trade openness, foreign direct investment, carbon emission, renewable energy, fossil fuel consumption, agriculture valueadded, and industry value-added on policy for environmental sustainability in East African countries (Burundi, Democratic Republic of the Congo, Kenya, Rwanda, Tanzania, and Uganda) from 2006 to 2020. This chapter organizes as follows: The first section discusses the background of the research problem and the motivation of this study with the research objective. Section 6.2 covers the recently published literature on environmental policies, fossil fuel consumption, renewable energy, carbon emission, and economic and environmental indicators. Section 6.3 discussed the research approach and statistical tools adopted in this study. Section 6.4 presents the results and discusses the key findings with the support of previously published literature. Section 6.5 provides the concluding remarks and future avenues.

6.2 Previous Empirical Studies

79

6.2 Previous Empirical Studies Due to urbanization and the lack of environmental awareness in public, environmental challenges are increasing for the governmental bodies in emerging and leastdeveloped nations (Rousseau and Deschacht 2020; Nocera et al., 2018). In scientific literature, several researchers highlighted different factors which contribute to poor environmental sustainability, such as lack of environmental awareness (Khan et al., 2019), traditional and/or polluted practices in business operations, burning of fossil fuel, lack of technology and lack of technical expertise to dispose or recycle the waste (Khan et al. 2023a; Sajid et al. 2022). Previous studies showed that economic activities, including logistical operations and international trade, heavily consume fossil fuel, creating environmental and societal risks (Sarrazin et al. 2016; Sajid and Gonzalez 2021; Khan et al. 2023b). Jianguo and Solangi (2023) highlighted that poor practices in manufacturing are the primary cause of environmental degradation and create adverse effects on natural beauty. Environmental challenges are putting pressure on the regulatory authorities and corporate sector. Therefore, firms are switching their processes towards circular economy practices and adopting green practices in their business operations (Luthra et al. 2016; Sinaga et al. 2019). Alay et al. (2016) researched the textile sector and argued that this industry has a complex process and pollutes environmental sustainability in different processing stages. The textile industry was also criticized by other researchers and highlighted that this sector heavily uses toxic chemicals and non-renewable sources fueling global warming and water pollution (Beton et al. 2014). The technological role in business operations to enhance financial and environmental performances is not avoidable for practitioners, researchers, and corporate sectors. AL-Khatib (2023) collected the data from manufacturing enterprises and employed the SEM technique. The results revealed that Big Data Analytics is positively and significantly correlated with green supply chain practices, which showed that environmental practices could be much more effective by adopting advanced technology in business operations. According to Xie et al. (2022), Green supply chain practices play a vital role in mitigating the polluted practices in manufacturing and logistical operations of firms. Similarly, Al-Khatib and Shuhaiber (2022) emphasized adopting green practices in firms’ processes, and they argued that green supply chain practices reduce social and environmental risk. Globally, circular economy practices are widely adopted in corporate sectors to combat climate change and global warming (Geissdoerfer et al., 2018). A circular economy is gaining attention in scientific literature, and legislative bodies are more focused on integrating technological innovation with circular economy practices, which increases the effectiveness of greening business operations (Dev et al. 2020; Khan et al. 2023c). The mainly circular economy is based on the recovery, reuse, and recycling of products, which reduces waste and enhances the firms’ performance in all aspects. Ethirajan et al. (2021), due to the adoption of a circular economy in supply chain operations, firms are shifting their traditional processes into eco-friendly

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practices that eliminate and/or reduce waste. Khan et al. (2023d) conducted a study on recycling decisions and revealed that environmental issues and challenges could be significantly mitigated by implementing recycling practices in the corporate sector. Besides the circular economy practices, digital technology can be utilized to green business operations and reduce error margins (Khan et al. 2022).

6.3 Methodology This chapter discussed and examined the relationship between GDP, environmental policy, renewable energy, trade, carbon emission, fossil fuel, industry, and agricultural value added in a panel of East African countries, including Burundi, Kenya, Uganda, Tanzania, Rwanda, and Congo. We have used the data from 2006 to 2020 to examine the association. According to previous studies, environmental policies are mainly influenced due to consumption of fossil fuel, manufacturing, and logistical activities, which contribute to carbon emissions. E Mit = α0 + β1 Economicit + β2 Envir onmentit

(6.1)

where EM denotes policy for environmental sustainability, and Economic represents the economic factors, including FDI, Industry and agricultural value added, GDP, and international trade. Similarly, the environment shows the carbon emission, fossil fuel, and renewable energy, while t and I represent the period and number of East African countries, respectively. The data downloaded from the WDI website and the description of the variables are included in Table 6.1. EMit = α0 + β1 GDPit + β2 TOit + β3 FDIit + β4 COit + β5 REit + β6 FFit + β7 AGRit + β8 INDit + Vt + εit (6.2) Table 6.1 List of variables Variable

Explanation of variables

EM

Policy and institutions for environment sustainable rating (1 low, 6 max)

GDP

GDP per Capita (constant 2018)

TO

Trade openness

FDI

Foreign direct investment

CO

Carbon emission

RE

Renewable energy

FF

Fossil fuel consumption

AGR

Agriculture value added

IND

Industry value added

6.4 Results and Discussion

81

Environmental requirements

Economic activities

Policies for environmental sustainability

Fig. 6.2 Theoretical model

In this chapter, we have executed fixed and random effects to examine the impacts of environmental and economic factors on policy for environmental sustainability in East African countries. A theoretical model is presented in Fig. 6.2.

6.4 Results and Discussion After testing the hypotheses, we have presented the results in this section. Table 6.2 shows the mean, Standard deviation, minimum and maximum values of the environmental policy, renewable energy, trade, GDP, carbon emission, fossil fuel, industry and agricultural value added. The correlational matrix is presented in Table 6.3. The independent variables, such as international trade and industry value added, negatively affect environmental sustainability. However, the results of FE and RE effects models will display an accurate picture. Table 6.2 Descriptive statistics Variable

Mean

Std. Dec

Min

Max

EM

1.311818

0.161603

0.916291

1.704748

GDP

7.411471

0.558627

6.567179

8.434074

TO

3.793635

0.297251

3.101892

4.508109

FDI

0.43125

1.486646

− 4.60517

CO

− 2.36842

0.825052

− 3.91202

1.673851

− 3.21888

2.347558

RE

0.344712

1.334447

− 4.60517

0.00995

AGR

1.447785

1.05383

− 0.75502

3.125444

IND

3.080447

0.346039

FF

− 2.07224

2.543175 − 0.82098

2.373975

3.767228

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6 Environmental Policies and Decarbonization: Leading Towards Green …

Table 6.3 Correlational matrix Variable EM

GDP

TO

FDI

CO

RE

FF

AGR

EM

1

GDP

0.5179

1

TO

− 0.302

− 0.0873 1

FDI

0.1819

0.1245

0.4839

CO

0.4058

0.9382

− 0.1639 0.0286 1

RE

0.3169

0.3118

0.5504

0.3527 0.3244

1

FF

0.2826

0.8206

0.1122

0.2496 0.8530

0.6354 1

AGR

0.0368

0.6210

0.2938

0.4227 0.6277

0.8358 0.8844 1

IND

− 0.3298 − 0.0683 0.659

IND

1

0.6002 − 0.1126 0.7704 0.3389 0.6497 1

It is difficult to find a single cause of environmental degradation as several contributing factors include manufacturing, logistical and transportation operations, lack of consumer awareness, increase in plastic and electronic waste, and so on. However, in developing and underdeveloped countries, the primary cause of environmental degradation is the need for more expertise and technology to green the manufacturing and logistical operations. Tables 6.4 and 6.5 present the RE and FE model results, respectively. Also, we have checked the Hasumen test = 15.2 (0.000) for the suitability of the FE and RE models. The results show that economic growth is creating heavy pressure on environmental sustainability and is one of the causes of pollution. Due to the consumption of fossil fuels in manufacturing and logistical operations, carbon emissions are increasing, which translates into global warming and climate change. Similarly, Geissdoerfer et al. (2018) emphasized adopting sustainability in business models and provided their benefits. Ethirajan et al. (2021) analyzed the risk of adopting circular economy modeling in manufacturing firms. The findings of Khan and Qianli (2017) revealed that green supply chain practices significantly improve environmental and financial performance. Khan et al. (2022) studied circular economy applications in supply chain operations. They revealed that firms could execute different circular economy practices such as the ecological design of products, circular manufacturing, and promoting recycling and refurbishment, which will improve the firm’s reputation and mitigate the harmful effects on environmental sustainability. The findings also revealed that industry value added is negatively associated with environmental policies, which reflect that the industries are using non-ecofriendly practices such as non-renewable energy and burning fossil fuel to manufacture the products. Similarly, Jianguo and Solangi (2023) emphasized green practices in supply chain and business operations to mitigate polluted practices and improve environmental sustainability. Sajid et al. (2022) revealed that governmental bodies could only achieve sustainability by injecting circular economy and decarbonization practices into their environmental policies and restricting firms from implementing sustainable practices. Khan et al. (2023b) conducted a study on recycling and IOT

6.5 Conclusion

83

Table 6.4 Random effect (RE) Random effect Variable GDP

Coef.

Std. Err.

Probability

95% Conf.

0.28571

0.065223

0.000

TO

− 0.07251

0.059791

0.225

0.157876

FDI

0.04222

0.011154

0.000

CO

− 0.22709

0.066457

0.001

− 0.35734

RE

0.04834

0.017552

0.006

0.08274

FF

0.113852

0.031359

0.000

0.05239

− 0.1897 0.020358

AGR

− 0.04151

0.05247

0.429

− 0.14435

IND

− 0.13882

0.114795

0.227

− 0.36381

Constant

− 0.32938

0.633297

0.603

− 1.57062

Table 6.5 Fixed effect (FE) Fixed effect model (EM) Variable

Coef.

Std. Err.

Probability

95% Conf.

GDP

− 0.13262

0.249541

0.597

− 0.63178

TO

− 0.02428

0.055501

0.663

− 0.1353

0.009275

0.028

0.0024

0.054023

0.112

− 0.19519

FDI CO

0.020952 − 0.08713

RE

0.099566

0.038446

0.012

FF

0.091843

0.029378

0.723

AGR

0.029496

0.048217

0.543

− 0.06695

0.15848

0.620

− 0.39608

1.57066

0.111

− 0.60277

IND Constant

− 0.07907 2.539019

0.022663 0.033078

Hasumen test = 15.2 (0.000)

(Internet-of-Things). They indicated that technological innovation in recycling and green practices is effective and reduces the entire cost of the business with improved organizational performance (Khan et al. 2023c).

6.5 Conclusion This chapter examines the association between policy for environmental sustainability, economic factors (GDP, industry value-added, and Agricultural value added), and environmental factors (carbon emission, renewable energy consumption, and fossil fuel consumption). The results revealed that GDP, industry value added, and trade activities are negatively associated with environmental policies, which

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6 Environmental Policies and Decarbonization: Leading Towards Green …

confirmed that environmental sustainability is reducing due to greater economic activities. The findings of this study provide a fundamental direction for the policymakers and legislative bodies to promote green and circular economy practices in manufacturing and economic activities to improve environmental sustainability without creating any disturbance in economic indicators. This study used data from East African countries (Burundi, Congo, Kenya, Rwanda, Tanzania, and Uganda) from 2006 to 2020. It employed a random and fixed effects model to test the association between endogenous and exogenous variables. However, future researchers may expand the panel of countries and include other African countries. Also, this study mainly focused on identifying the relationship between variables. However, future researchers can broaden the scope of the study. They may include other objectives such as a comparison of the two-panel countries, expansion of the research framework, testing the cause and effects of the results, and employing the predictive modeling approach.

References Alay E, Duran K, Korlu A (2016) A sample work on green manufacturing in the textile industry. Sustain Chem Pharm 3:39–46. https://doi.org/10.1016/j.scp.2016.03.001 AL-Khatib AW (2023) The impact of big data analytics capabilities on green supply chain performance: is green supply chain innovation the missing link? Bus Process Manage J 29(1):22-42. https://doi.org/10.1108/BPMJ-08-2022-0416 Al-Khatib AW, Shuhaiber A (2022) Green intellectual capital and green supply chain performance: does big data analytics capabilities matter? Sustainability 14(16):10054. https://doi.org/10.3390/ su141610054 Amin S, Khandaker MK, Jannat J, Khan F, Rahman SZ (2023) Cooperative environmental governance in urban South Asia: implications for municipal waste management and waste-to-energy. Environ Sci Pollut Res 30:69550–69563. https://doi.org/10.1007/s11356-023-27152-5 Beton A, Dias D, Farrant L et al (2014) Environmental improvement potential of textiles (IMPRO Textiles). Rep EUR 26316 EN. https://doi.org/10.2791/52624 Cremation R, Mastellone ML, Tagliaferri C, Zaccariello L, Lettieri P (2018) Environmental impact of municipal solid waste management using life cycle assessment: the effect of anaerobic digestion, materials recovery, and secondary fuels production. Renew Energy 124:180–188 Dev NK, Shankar R, Qaiser FH (2020) Industry 4.0 and circular economy: operational excellence for sustainable reverse supply chain performance. Resour Conserv Recycl 153(2020):104583. https://doi.org/10.1016/j.resconrec.2019.104583 Ethirajan M, Arasu MT, Kandasamy J, Kek V, Nadeem SP, Kumar A (2021) Analyzing the risks of adopting circular economy initiatives in manufacturing supply chains. Bus Strateg Environ 30(1):204–236. https://doi.org/10.1002/bse.2617 Geissdoerfer M, Vladimirova D, Evans S (2018) Sustainable business model innovation: a review. J Clean Prod 198(2018):401–416. https://doi.org/10.1016/j.jclepro.2018.06.240 Jianguo D, Solangi YA (2023) (2023) Sustainability in Pakistan’s textile industry: analyzing barriers and strategies for green supply chain management implementation. Environ Sci Pollut Res 30:58109–58127. https://doi.org/10.1007/s11356-023-26687-x Khan SAR, Qianli D (2017) Impact of green supply chain management practices on firms’ performance: an empirical study from the perspective of Pakistan. Environ Sci Pollut Res 24:16829–16844. https://doi.org/10.1007/s11356-017-9172-5

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Khan S, Piprani AZ, Yu Z (2022) Digital technology and circular economy practices: future of supply chains. Oper Manag Res 15:676–688. https://doi.org/10.1007/s12063-021-00247-3 Khan SAR, Sharif A, Golpira H, Kumar A (2019) A green ideology in Asian emerging economies: from environmental policy and sustainable development. Sustain Dev 27(6):1063–1075. https:// doi.org/10.1002/sd.1958 Khan SAR, Yu Z, Ridwan IL, Irshad AUR, Ponce P, Tanveer M (2023a) Energy efficiency, carbon neutrality and technological innovation: a strategic move towards green economy. Econ Res Ekonomska Istraživanja 36(2). https://doi.org/10.1080/1331677X.2022.2140306 Khan SAR, Piprani AZ, Yu Z (2023b) The decision-making of internet recycler considering Internetof-Things application. Int J Retail Distrib Manage ahead-of-print(ahead-of-print). https://doi. org/10.1108/IJRDM-03-2023-0177 Khan SAR, Tabish M, Zhang Y (2023c) Embracement of industry 4.0 and sustainable supply chain practices under the shadow of practice-based view theory: ensuring environmental sustainability in corporate sector. J Clean Prod 398:136609. https://doi.org/10.1016/j.jclepro.2023.136609 Khan SARK, Tabish M, Yu Z (2023d) Investigating recycling decisions of internet recyclers: a step towards zero waste economy. J Environ Manage 340:117968. https://doi.org/10.1016/j.jen vman.2023.117968 Li J, Huang X, Yang T, Su M, Guo L (2023) Reducing the carbon emission from agricultural production in China: do land transfer and urbanization matter? Environ Sci Pollut Res 30:68339– 68355. https://doi.org/10.1007/s11356-023-27262-0 Luthra S, Mangla SK, Xu L, Diabat A (2016) Using AHP to evaluate barriers in adopting sustainable consumption and production initiatives in a supply chain. Int J Prod Econ 181:342–349. https:// doi.org/10.1016/j.ijpe.2016.04.001 Malav LC, Yadav KK, Gupta N, Kumar S, Sharma GK, Krishnan S, Bach QV (2020) A review on municipal solid waste as a renewable source for waste-to-energy project in India: current practices, challenges, and future opportunities. J Clean Prod 277:123227 Nocera S, Galati OI, Cavallaro F (2018) On the uncertainty in the economic valuation of carbon emissions from transport. J Transport Econ Pol 52(1):68–94 Rousseau S, Deschacht N (2020) Public awareness of nature and the environment during the COVID19 crisis. Environ Resour Econ 76:1149–1159 Sajid MJ, Gonzalez EDRS (2021) The impact of direct and indirect COVID-19 related demand shocks on sectoral CO2 emissions: evidence from major Asia Pacific countries. Sustainability 13:9312 Sajid MJ, Ali G, Gonzalez EDRS (2022) Estimating CO2 emissions from emergency-supply transport: the case of COVID-19 vaccine global air transport. J Clean Prod 340:130716. https://doi. org/10.1016/j.jclepro.2022.130716 Sarrazin F, Pianosi F, Wagener T (2016) Global sensitivity analysis of environmental models: convergence and validation. Environ Model Software 79:135–152 Sinaga O, Mulyati Y, Darrini A et al (2019) Green supply chain management organizational performance. Int J Supply Chain Manag 8:76–85 Tanveer M, Khan SAR, Umar M, Yu Z, Sajid MJ, Haq IU (2022) Waste management and green technology: future trends in circular economy leading towards environmental sustainability. Environ Sci Pollut Res 29:80161–80178. https://doi.org/10.1007/s11356-022-23238-8 WHO (2022) Household air pollution/ World Health Organization, https://www.who.int/newsroom/fact-sheets/detail/household-air-pollution-and-health#:~:text=The%20combined%20effe cts%20of%20ambient,(COPD)%20and%20lung%20cancer. Accessed on May 2023 Xie X, Hoang TT, Zhu Q (2022) Green process innovation and financial performance: the role of green social capital and customers’ tacit green needs. J Innov Knowl 7(1):100165 Zhengxia T, Batool Z, Ali S, Haseeb M, Jain V, Raza SMF, Chakrabarti P (2023) (2023) Impact of technology on the relation between disaggregated energy consumption and CO2 emission in populous countries of Asia. Environ Sci Pollut Res 30(68327–68338):68327–68338. https:// doi.org/10.1007/s11356-023-26980-9

Chapter 7

Nexuses Between Technological Innovations, Macro-environmental and Economic Factors

7.1 Introduction In the last few years, policymakers and researchers have been increasingly interested in the variables determining the economic growth of any economy (Al Mamun et al. 2014; Khan et al. 2019). The growth experience of various world economies indicates that globalization-based economic integration is vital for achieving higher economic growth. For instance, globalization facilitates the transfer of resources that could play an essential role in achieving financial objectives (Shahbaz et al. 2016). Therefore, trade as a necessary tool of economic integration has become a crucial factor in enhancing economic growth and development (Nepal 2020). The economic growth cycle is merely related to energy production and consumption; however, energy productions via non-renewable energy methods are a greater threat to the environment and cause climate change (Dobrowolski and Drozdowski 2022; Khan et al. 2022a; Nguyen 2020). Researchers are continuously looking to identify significant macroeconomic factors to help countries promote trade and achieve higher economic growth. Literature indicates various essential factors that might significantly influence a country’s trade. For instance, energy consumption is one factor with strategic importance in both environment and economics. Mainly, in economics, energy products as a crucial input in the production system determine cost factors in any country, thus, indicating a strong association between energy use and economic prosperity (Al Mamun et al. 2014). Additionally, energy demand is continuously increasing because of its strong association with technological innovations and rising living standards (Razali et al. 2015). Therefore, the trade of energy resources has become a critical agenda in global politics. However, traditional energy resources are limited and significantly cause environmental degradation (Kongbuamai et al. 2020; Khan et al. 2022b). Thereby, renewable energy (RE) resources have emerged as a powerful solution to environmental adversities and economic problems (Khan et al. 2020a, b). The RE appears to be the world economy’s most active transformer. New financing structures, decline in cost, and technological advancements have made the RE sector one of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_7

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the significant factors of economic growth (Liu and Song 2020; Charaia et al. 2020). As far as empirical literature is concerned, existing studies have reported inconclusive evidence regarding the impact of RE consumption on economic growth (Chen et al. 2021a, b). Likewise, very few studies (Khan et al. 2020a, b) have analyzed the impact of RE usage on trade. These studies have also reported inconclusive results. Moreover, the literature also indicates that foreign direct investments (FDI) are another significant macroeconomic factor determining a country’s economic growth. As FDI inflow creates jobs and cause technology transfer (Modarress et al. 2014). Limited public resources are generally prioritized toward national needs, such as paying salaries and covering the budget; therefore, FDIs and private investments are essential to boost productivity and economic advancement (Ciobanu et al. 2020). Likewise, military expenditures (ME) are also considered critical for economic growth (Ajmair et al. 2018). For instance, Sozen and Tufaner (2020) indicate a two-way association between ME, tax burden, and financial freedom. Meanwhile, their study also reports a unidirectional effect of ME on the trade freedom of an economy. Moreover, macroeconomic factors such as industrial value-added and technological innovations could also play an essential role in determining a country’s trade performance (Abbasi et al. 2021). Improvements in industrial development cause a significant increase in productivity and resource efficiency, thus, leading to improved sustainability outcomes. At the same time, the technological innovations represented by the number of patent applications (PA) in the country play a vital role in facilitating economic and business activities (Fang et al. 2022). Most of the previous literature has mainly focused on investigating the association of a country’s trade openness with economic growth (Tahir et al. 2018). Several studies have examined the determinants of either environmental or traditional indicators of economic growth (Usman et al. 2021a, b). Likewise, few studies have explored the nexus between various macroeconomic characteristics and crude oil imports (Yu et al. 2022a). However, determining a country’s trade volume has remained an unanswered issue in the existing literature. This highlights the need to conduct a detailed investigation of macroeconomic determinants of trade and compare different groups of countries. The recent decline in trade volumes and economic growth across the globe has raised concerns regarding factors and strategies that could help economies recover from adverse situations. Hence, it has become crucial to investigate various factors and explore strategies that could help countries enhance trade performance and economic growth. Hence, this research intends to explore crucial macroeconomic factors that determine the trade performance of China. Specifically, we analyze the association of technological innovations, RE consumption, FDI, CO emissions, ME, and industrial value-added with the trade volume. The ARDL test has been used to test relationships among various macroeconomic factors. The results significantly contribute to the existing knowledge by providing empirical evidence on the macroeconomic factors that play a critical role in enhancing or reducing international trade activities.

7.2 Literature Review

89

Based on the motivation mentioned above, this research significantly contributes to the literature in macroeconomics. This study mainly underpins the role of various factors relating to technological innovations, industrial development, and globalization in the recent economic downfall. Several previous studies have investigated the association of various macroeconomic factors with a country’s economic growth and environmental performance. Etokakpan et al. (2021) and Charaia et al. (2020) have analyzed the association between globalization and electricity consumption factors on pollutant emissions in China. Etokakpan et al. (2021) investigated natural gas consumption’s role in achieving China’s environmental sustainability. In addition, various other studies have also explored the roles of different macroeconomic factors, such as RE consumption, FDI, and technological advancement, in achieving sustainability and carbon-free development (Khan et al. 2021a). However, there is a lack of existing literature investigating the role of critical macroeconomic factors, particularly technological innovations, in determining trade performance as an indicator of economic growth. Thus, leaving a gap regarding the critical factors that could play a vital role in enhancing a country’s trade volume. Hence, the novelty of this study lies in investigating the nexus between technological innovations, RE consumption, FDI, ME, industrial value-added, and trade performance.

7.2 Literature Review The economic competitiveness of any country is associated with its growth in productivity, employment, and welfare of its citizens. Therefore, authorities aim to stimulate growth by raising and expanding their exports, which is an essential signal for economic advancement. Continuous growth in social, environmental, and economic performances is crucial for sustainable economic development (Armeanu et al. 2018; Khan et al. 2023a). Likewise, other researchers have found a positive impact of FDI on China’s economic prosperity through increasing productivity and encouraging exports. Recent literature has also provided positive association of FDI with economic prosperity (Ciobanu et al. 2020). It is argued that FDI help a country improve in several ways, such as developing human capital, adopting modern technologies, developing banking activities, and improving international trade. Various existing studies have also proved the positive role of RE consumption in determining economic growth (Da Silva et al. 2018). Recently, the impact of RE consumption on international trade has acquired great attention from researchers. For instance, Bilan et al. (2019) indicated that using RE helps promote the international trade of a country. Khan et al. (2020a, b) have analyzed the influence of RE consumption in achieving sustainable goals. Their findings proved that RE consumption plays a positive role in boosting international trade and improving environmental quality. Moreover, with rapid industrial development, the researchers have given significant attention to the association between CO emissions and economic growth. Several studies have also reported a significant association of CO emissions with several economic indicators such as trade, financial development, population, and economic

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prosperity (Shahbaz et al. 2017). Further, Khan et al. (2020a, b) reported a negative and positive relationship between exports, imports, and carbon dioxide emission. In European and developed nations, the consumption of fossil fuel is mitigating (Khan et al. 2023b), but their governmental bodies and policymakers are actively promoting to the green practices and technology-enabled circular economy models, which ultimately improving to the environmental quality without creating pressure on economic prosperity (Guo et al. 2022; Balsalobre-Lorente et al. 2021). Since last few years, the countries in European Union have several eco-friendly policies in different sectors to discourage polluted practices such as electricity vehicles with zero pollution by 2035. Also, these countries have embossed strict laws on maritime transport to mitigate the water pollution and increase natural beauty of coastal areas (Reuters 2022). These are few of many steps, which have taken by developed nations to protect their environmental sustainability. It is argued that ME significantly influence trade freedom, which implies that a country with more ME has a strong bilateral trade standing. Employing timescale regression analysis, Khalid and Habimana (2021) have reported a negative impact of ME on economic prosperity in the long run. These results contradict the neoclassical view that ME may enhance economic growth. Further, some studies have shown an insignificant association of ME with economic growth (Maher and Zhao 2021). Abbasi et al. (2021) showed a positive and significant relationship of industrialization with economic prosperity. Recently, few researchers also argued that industrial development brings significant improvements in resource efficiency and economic productivity, which ultimately help a country to achieve sustainable outcomes. The literature argues that modern technologies significantly help countries develop efficient production systems, ultimately boosting economic growth (Chen et al. 2021a, b; Zafar et al. 2021). Khan et al. (2021b) argued that modern technologies contribute to economic development by facilitating circular economies and enhancing organizational performance. Similarly, implementing advanced technologies promotes green practices, improving social, environmental, and economic performance. Some studies have also shown a significant association between technological advancement, CO emissions, and economic prosperity (Ali et al. 2021).

7.3 Research Method 7.3.1 Variables and Data Sources This research incorporated annual time series data of the China covering the period from 1975 to 2020. The used variables are mentioned in Table 7.1; with used abbreviations and source of data.

7.3 Research Method

91

Table 7.1 Variable description and data sources Abbreviation

Variable

Source

Patent applications (PA)

Total number of patent applications

WDI

CO (CO emissions)

CO emissions (metric tons per capita)

WDI

Foreign direct investments (FDI)

FDI, net inflows (% of GDP)

IMF

Renewable energy consumption (RE)

RE (% of total energy consumption)

IEA

Trade

Trade (% of GDP)

WDI

Military expenditures (ME)

ME (% of GDP)

WDI

Industry value added (IND)

IND (% of GDP)

WDI

7.3.2 Empirical Design Based on the major objective of exploring macroeconomic determinants of trade, this study devises the following Eq. 7.1 to regress trade on RE consumption, FDI, CO emissions, ME, PA, and IND. L N Tt = α + β1 L N R E t + β2 L N F D It + β3 L N C Ot + β4 L N M E t + β5 L N P At + β6 L N I N Dt + εt

(7.1)

LN represents the natural logarithm and similarly, α, β, and ε denotes the model’s intercept, coefficient, and error term. In last, “t” denotes the time.

7.3.3 Data Analysis 7.3.3.1

Unit Root Testing

In the first step, this research conducts unit root testing to evaluate the stationarity of the data. This is crucial in time series analysis, where non-stationary data can lead to complications and inaccuracy (see Table 7.2).

7.3.3.2

ARDL Co-integration Technique

After unit root testing, this research employed the ARDL technique to analyze the co-integration relationship among different variables. ARDL is selected due to its specific advantages over other techniques. For instance, ARDL is considered suitable for handling small sample sizes. Likewise, it is also appropriate for both short and long run analyses. The following Eq. 7.2 under the ARDL technique to examine short and long run associations among different variables of the model:

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Table 7.2 Unit root test results Variables

Augmented Dicky-Fuller

Phillips-Peron

Level

First difference

Order

Level

First difference

Order

lnCO

− 0.155 (0.959)

− 3.475 (0.013)*

First

0.2017 (0.969)

− 3.402 (0.016)*

First

lnFDI

− 2.061 (0.201)

− 4.496 (0.000)**

First

− 1.820 (0.365)

− 4.446 (0.001)**

First

LnIND

− 1.117 (0.699)

− 4.289 (0.001)**

First

− 1.049 (0.726)

− 4.312 (0.001)**

First

LnME

− 3.249 (0.024)*

− 4.109 (0.002)**

Level

− 3.111 (0.033)*

− 4.388 (0.001)**

Level

LnPA

0.947 (0.995)

− 5.572 (0.000)**

First

0.946 (0.995)

− 5.567 (0.000)**

First

LnRE

0.125 (0.963)

− 4.206 (0.002)**

First

− 0.435 (0.893)

− 4.414 (0.001)**

First

lnTRADE

− 3.152 (0.030)*

− 3.257 (0.000)**

Level

− 3.05 (0.038)*

− 4.946 (0.000)**

Level

*, ** shows the significance on 10%, 5% respectively

ΔL N Tt = β0 + ∑β1 j ΔL N R E t− j + ∑β2i ΔL N F D It− j + ∑β3 j ΔL N C Ot− j + ∑β4 ΔL N M E t− j + ∑β5 j ΔL N P At− j + ∑β6 j ΔL N I N Dt− j + γ1 L N R E t−1 + γ2 L N F D It−1 + γ3 L N C Ot−1 + γ4 L N M E t−1 + γ5 L N P At−1 + γ6 L N I N Dt−1 + εt

(7.2)

In ARDL Eq. 7.2 of this study, “β1 ” to “β6 ” denotes the short-run parameters while “γ1 ” to “γ6 ” represents the long-run parameters of the model. F-test is generally applied to examine the hypothesized relationship of no–co-integration. we also compare F-statistics value with the lower and upper bound critical value at 1%, 2.5%, 5%, and 10% by Pesaran et al. (2001), if F statistics value is greater than the upper bound, we will reject Ho of no cointegration relations and vice versa.

7.4 Results and Discussion 7.4.1 Estimated Results First and foremost, this research offers descriptive data analysis findings. Table 7.3 presents descriptive statistics for variables. The next step was to perform a co-integration analysis to assess the co-integration relationship between the factors. The test’s null hypothesis is “no co-integration,” whereas the alternative hypothesis is “the existence of co-integration.” The F-statistic values for the China (7.257) are greater than their respective upper bound values of

7.4 Results and Discussion

93

Table 7.3 Descriptive statistics Variable

lnCO

lnFDI

lnIND

lnME

lnPA

lnRE

lnTrade

Mean

1.458

1.106

3.817

0.9394

10.969

3.150

3.511

Median

1.275

1.278

3.837

1.0620

10.793

3.322

3.605

Maximum

2.147

1.972

3.892

1.2508

14.248

3.705

4.181

Minimum

0.900

04.49E−0.5

3.678

0.1110

8.439

2.541

2.365

Std. dev

0.442

0.594

0.056

0.3061

1.983

0.370

0.454

Observations

42

42

42

42

42

42

42

Table 7.4 Co-integration results F statistic 7.257, K = 6 (lag: 4, 3, 0, 3, 3, 2, 0)

Significance (%)

Critical values Lower bound

Upper bound

10

1.99

2.94

5

2.27

3.28

2.5

2.55

3.61

1

2.88

3.99

3.99 at the 1% level of significance, as shown in Table 7.4. Hence, the test’s alternative hypothesis is accepted, showing that all of the model’s variables are highly associated in the long term. In the case of China, results reported in Table 7.5 specify that the positive coefficient of FDI (0.390) is found significant at 10% in the long run. Thus, implying that FDI play a critical role in positively determining the trade volume of China in the long run. Whereas results also suggest that FDI do not have a significant role in the short term. The findings also show that the estimated coefficient of CO (− 1.031) is significant at 10% in the longer period, whereas insignificant in the short term. Further, it is also found that coefficients of PA in both the long term (0.174) and short term (1.0256) are significant at 5% and 10%, respectively. Next, the positive coefficient (2.5033) of IND is discovered to be highly significant at 1% in the short run. Additionally, RE consumption and ME have insignificant influences on China’s trade in both the short and long term. In the last, Table 7.6 indicate the post estimation diagnostic test. Furthermore, the CUSUM (cumulative sum) and CUSUMSQ (cumulative sum of squares) also indicate stability of model (see Fig. 7.1).

7.4.2 Discussion While investigating the determinants of trade performance of China, this study shows that FDI has a significant influence on trade performance of China only in the long

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Table 7.5 Estimated results for China Dependent variable (trade) (lag: 1, 3, 1, 3, 2, 0, 2) Long run

Short run

Variable

Coefficient

T-ratio

P-value

Variable

Coefficient

lnCO

− 1.031

− 1.688

0.098*

ΔlnCO

0.186

0.747

0.390

lnFDI

0.390

1.921

0.073*

ΔlnFDI

0.072

1.117

0.280

lnIND

− 0.900

− 0.614

0.547

ΔlnIND

2.503

4.361

0.000***

lnME

0.253

0.180

0.180

ΔlnME

0.457

2.673

0.416

lnPA

0.174

2.392

0.029**

ΔlnPA

1.025

3.365

0.089*

lnRE

− 0.895

− 0.751

0.199

ΔlnRE

− 1.068

0.145

0.258

8.676

1.170

0.259

ECM(-1)

− 1.041

− 9.135

0.000

Constant

T-ratio

P-value

*, **, and *** shows the significance on 10%, 5% and 1% respectively

Table 7.6 Diagnostics tests Diagnostics tests (A) Serial correlation (Breusch-Godfrey) F statistic = 2.182, probability value = 0.366 (B) Normality test

Jarque–Bera = 1.63824, probability value = 0.5266

(C) Heteroscedasticity

F statistic = 1.9757, probability value = 0.85613

Fig. 7.1 CUSUM plots for China

run. This could implicate that with fewer FDI, China would face substantial economic difficulties in the long run. Besides, this finding is also consistent with the previous literature, which explains the direct influence of FDI on economic growth in the long-term. For instance, Azarhoushang et al. (2021) argued that FDI inflows significantly contribute to industrial production, which helps economies to achieve higher economic growth in the long-term. In the case of China, industrial value-added is found significantly influencing trade in the short term only. This implies that value addition in terms of industrial production could help China enhance its exports in the short run, thus, ultimately promoting trade. In line with this finding, existing literature also indicates industrial value-added as a crucial factor in enhancing a country’s

7.5 Conclusion

95

trade performance. Recently, Yang and Khan (2022) stated that industrial development significantly improves resource efficiency and economic productivity, which ultimately results in achieving sustainable economic growth. Likewise, technological innovations significantly influence China’s trade performance in both short and long runs. The positive role of the number of PA explains the significance of technological innovations in contributing towards improved economic growth. Existing literature states that technological advancements bring efficiency to the production system, which significantly triggers economic growth (Zafar et al. 2021; Chen et al. 2021a, b). Specifically, in the case of China, it is implied that technological innovation is a vital factor influencing China’s trade in both the short and long run. Therefore, Chinese authorities are implicated in encouraging PA to recover from their recent economic decline. Previous literature also supports these findings by indicating technological innovations as an effective solution to the problem of slow economic growth. Some existing studies have recently proved a significant and positive relationship between technological advancement and economic growth (Ali et al. 2021). In support to our results, existing research has also demonstrated the beneficial role of modern technologies in achieving operational sustainability (Khan et al. 2023c). Additionally, Khan et al. (2021b) showed that technology adoption significantly improves organizational performance; hence, increasing a country’s economic productivity. ME are found insignificant in the case of China, which is also consistent with the previous study stating insignificant association of between ME and economic growth (Maher and Zhao 2021). In addition, macroeconomic variables such as RE and CO emissions are found to have relatively insignificant impacts on the trade performance. Specifically, RE is insignificantly related with trade performance for China. Previous studies such as Yildirim et al. (2012) also reported insignificant association of RE consumption with economic performance. These studies support neutrality hypothesis stating that RE has minor role in determining the economic growth, thus, policies of energy conservation would not affect real economic performance (Chen et al. 2021a, b). On the other hand, carbon emissions significantly and negatively influence China’s trade in the long-term only. It indicates that CO emissions cause country risk to increase, which inversely influences trade performance. This finding is supported by the existing studies, which reported a significant and inverse association between CO emissions and trade (Khan et al. 2020a, b).

7.5 Conclusion The current research identifies macroeconomic determinants of trade in the China. Based on the ARDL it is found that the number of PA is revealed to be a significant determining factor of China’s trade in both short and long run. Meanwhile, FDI and CO emissions are significantly linked with China’s trade in the long run. Further, IND determines the trade volume of China only in the short term. Based on the findings, it is implied that technological innovations, FDI, and industrial value addition are the vital factors influencing trade performance in the China. Mainly, technological innovations

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play crucial role in facilitating economic operations and ensuring efficient production system. Likewise, FDI positively influence a country’s productivity and economic output. Whereas industrial value addition help in achieving competitive advantage over competitors to enhance exports. Moreover, it is also argued that environmental quality is a crucial factor for the developing economies, therefore, emerging countries needs to devise policies to control carbon emissions. There are some limitations of this study, which are the future avenue for the researchers. First, this study used FDI, CO emissions, IND, trade openness and RE consumption variables to examine the nexuses in the context of China. However, the future researchers may used the similar indicators data of other country to examine the relationship. Also, the future researchers can conduct a comparative analysis between two different countries. Second, this study employed ARDL approach due to small datasets, but future researchers may adopt more advanced econometric tools to test their hypotheses. Third, the future researchers can extend the model and include some new economic and environmental explanatory variables.

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Chapter 8

Introduction to the Theory of Fear Industries and Its Implications for United Nations SDGs 1, 2 and 16

8.1 Background to Some Main Economic Theories Economics deals with the problem of the utilization of scarce resources (Steven and David 2017). Since we have scarce resources, their efficient use in the long and short run is essential. Since Smith (1776), different schools of economists have discussed the long-term and short-term use of resources by economies. Short-term economics is mostly presented in the context of business cycles and the Leontief empirical input–output model and its various extensions (such as the Ghosh (1964) supply model). In contrast, long-term economics is mostly based on classical and neo-classical economics concepts. The economy strives to utilize its scarce resources in the short or long run. Due to the scarcity of resources, there is always a trade-off between the allocation of resources between different industries, which is usually referred to as the opportunity cost or resource constraint typically presented through the production possibilities curve (frontier) (Steven and David 2017). The well-known short-term economic cycle consists of recession, growth, peak, and short-term decline. There are several explanations for the triggering of business cycles. Some claim that inventions and innovations cause business cycles (Schumpeter 1939). Others, such as the monetarist and Austrian business cycle (ABCT), focus on government monetary policy. The Austrian business cycle (ABCT) blames the government’s monetary intervention (mainly through central banks) for triggering the business cycle; the ABCT states that short-term economies are entering a boom period due to excess money supply by central banks and that after realizing their inefficient misinvestment, economies go into recession or depression (Rothbard 2000, 2019). In comparison, the monetarist (Nobel laureate Milton Friedman, etc.) advocates a constant not very high and not a very low supply of money by exercising more control over the central banks, where the money supply rate is sufficient to bring the nominal interest rate to zero (Woodford 1990). In contrast, Keynesian economics encourages government intervention through monetary and fiscal policy tools to stimulate demand and move the short-term economy out of depression (Keynes 1923). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_8

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Leontief (1936, 1941, 1949) is the creator of the empirical estimate of the (shortterm) general equilibrium (Akhabbar and Lallement 2010; Jorgenson 1998). The main objective of the Leontief input–output model is to present the interdependence between the different industries of the economy (Miller and Blair 2009). Longterm economics is mostly based on classical and neo-classical economics. Smith (1776) and Ricardo (1817) are among the pioneers of classical economics; classical economics mainly focuses on self-interest and the free market, leading to the common good. In comparison, neo-classical economics is grounded in the demand– supply price equilibrium, potential GDP, and flexible prices in the long term (in contrast to the Keynesian assumption of sticky prices in the short run) (Steven and David 2017). Marshall (1890, 1920) explained most of the famous concepts of neoclassical economics, such as demand–supply price equilibrium and marginal utility. Walras (1954a, b, c) contributed to the neo-classical school by presenting both the partial equilibrium between demand and supply-prices for individual products or markets and the aggregate equilibrium between all the products or markets. Before Walras, the neo-classical economists only showed the partial equilibrium for the individual markets. Post-Walras modern general equilibrium theories are based on “unrealistic assumptions” and thus have lost the “goal of economics” (Davar 2015). There are also other theories such as: • Game theory (focusing on behavioral economics), • Marxism and socialism (presented by the German philosophers Karl Marks and Friedrich Engels in 1848 advocating a classless system without private ownership), • social Darwinism (obsolete today, supported the application of Darwinian principles of natural selection and the survival of the fittest to the economies and societies), • Endogenous growth theory (proponents of this theory, such as Nobel Prize winner Paul Romer, etc., argued that human capital development leads to technological progress and improved productivity, leading to economic development). • A more recent one known as ‘Ergodicity Economics’(Peters 2011, 2019; Peters and Gell-Mann 2016; Peters and Klein 2013) has also received a fair deal of criticism (Toda 2023). Nevertheless, both short- and long-term economics mostly ignore the formation and demise of long- and short-term fear industries. This chapter is novel in the following aspects. First, the chapter presents the conceptual framework for the lifecycle of both the long- and short-term fear industries. Second, this chapter offers theoretical evidence for the presence of both long- and short-term fear industries. Third, in the chapter, theoretical evidence is backed by empirical evidence, i.e., empirical calculations. Fourth, this chapter related the theory of fear industry with the United Nations’ key Sustainable development goals for 2030, including SDG 1 (poverty elimination), SDG 2 (zero hunger), SDG 3 (climate action), and SDG 16 (peace and justice). Finally, the chapter discusses the limitations of the proposed theory of the fear industry.

8.2 Recent Evidence for the Presence of Short- and Long-Term Fear Industries

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The rest of the chapter is organized in the following manner. Section 8.2 demonstrates some recent evidence for the presence of short- and long-term fear industries. Section 8.3 introduces the proposed theory of the fear industry. Section 8.4 links United Nations SDGs 1, 2, and 16 with the theory of the fear industry. Section 8.5 discusses the data sources and methodology for the empirical estimations. Section 8.6 shows the results of our empirical estimates and tries to empirically demonstrate the proof for the presence of both the long- and short-term fear industries. Finally, Chap. 7 concludes the chapter and discusses some limitations of our proposed theory of the fear industry.

8.2 Recent Evidence for the Presence of Shortand Long-Term Fear Industries 8.2.1 Short-Term Fear Industries As evident from the preceding discussion on some mainstream economic theories, the theory of fear industries is not available in the economic literature. However, in previous works, the concepts of creating short-term industries in case of disasters such as the current COVID-19 and the extra profits have been explained in detail (Sajid 2021b). As per Sajid (2021b), “disaster blessed industries” (from here onwards, short-term fear industry) are already active in the economy, producing goods and services at a set price and quantity. Real or perceived fears lead their prices and volumes to increase above typical levels; they become short-term fear industries. The lifecycle of short-term fear industries includes the following stages. Figure 8.1 depicts the supply model for the short-term fear industry. The three significant steps in creating a short-term fear industry are as follows. In Fig. 8.1, a point labeled N depicts the average price and quantity of a specific item that people frequently desire. In the short-term market, commodity prices remain constant. A “sticky market” exists where manufacturers are prepared to sell additional product units at the same price. When the fear event occurs, people will desire more at higher prices. Phase 1 occurs when the prices and quantities of a particular industry exceed their normal levels. Because of a rare event such as the COVID-19 pandemic, consumers will be willing to purchase more even if the price rises. Similarly, producers will be delighted to give more and raise prices. People who manufacture things will utilize more short-term resources, such as greater labor hours, shifts, short-term income, or labor. However, the industry will reach its maximum capacity once a certain production quantity has been reached. Even if demand prices rise, the industry will not produce anymore. MSO is an acronym that stands for maximum short-term output with the current amount of fixed capital (assets). That is the second phase of the short-term fear business, and MSO designates it. Because the product is in high demand, the corporation will begin to raise its prices at this time. However, even if the consumer has a great need at a certain price increase, they will

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Fig. 8.1 The supply curve for the short-term fear industry. The Y-axis presents the supply prices, and the X-axis presents the supply quantity (output). O represents the point of origin where both the price and quantity are equal to zero, Q0 represents the normal supply quantity, P0 represents the normal price, Q1 and Q2 represent the changes in prices and quantities above the normal levels, and Pr and Qr represent the randomly selected price and quantity on the line below the normal. N (normal), MSO (point of maximum short-term output), MPD (maximum price paid by demand), and POR (point of return) represent the point of transition (change) from one phase to another. In contrast, BN (below normal) presents a randomly selected point on the line presenting the prices and quantities below the normal. The figure is retrieved from Sajid (2021b)

be unwilling to buy. Figure 8.1 depicts the MPD (maximum price paid by demand). This point signals the beginning of phase 3, which is the third phase, in which the corporation must spend more money on capital to meet demand or create more money. That will be an extremely unusual instance in which an unexpected disaster takes a long time to finish because there was insufficient planning or resources to deal with it or specific inventions or innovations weren’t discovered for years. Alternatively, one disaster may be followed by another similar disaster. In Fig. 8.1, this moment is labeled POR (point of return). Another hypothesis is that people purchased and stockpiled more of a specific commodity than they required during the short-term fear industry. When the fear subsides, demand may not recover to pre-fear levels. Because there is insufficient demand, providers will be forced to reduce supply and prices, representing any position below the typical demand at the red dotted line, such as point BN (below normal) in Fig. 8.1. For a period, there may not be any demand for the products of a particular short-term fear industry.

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8.2.2 Long-Term Fear Industries Military and police industries are defined as long-term fear industries. These industries are created because of society’s everlasting fears of crime and wars. Sajid (2021a) described these industries as a “preputial industry of fear” and discussed the theory behind the life cycle of these long-term fear industries. The following stages are elaborated upon behind the creation and eventual demise of the long-term fear industries. Figure 8.2 depicts the economy’s initial long-term supply curve as a straight line. In the long run, rational society will recognize that while defense spending contributes to GDP, it is not a proper investment that allows them to spend more money on nondefense items or invest in their businesses. Instead, it is an unnecessary expense that must be eliminated. People who work in the “fear business” produce and sell goods and services that contribute to the GDP (including military and local law enforcement agencies). Sajid (2021a) coined the terms social GDP (SGDP) and free GDP (FGDP) to represent how defense spending impacts the overall amount of money available for citizens to spend. This means that the SGDP is defined as GDP minus military expenditure. GDP, fewer government costs, equals FGDP. Tax incentives, deficit financing, and a desire to spend more on nondefense and private investment will eventually cause the long-term fear industry to cut back on expenditure, shifting the original aggregate supply curve Q0 to the right. Finally, the elimination and reallocation of surplus resources (earned by eliminating the longterm fear business) will push the Q0 curve to Q1 . This graph depicts the SGDP or FGDP of an economy that lacks a long-term fear industry. If defense spending

Fig. 8.2 The long-term fear industry paradigm (retrieved from Sajid 2021a). The Y-axis represents the price, whereas the X-axis represents the real SGDP or real FGDP. Q0 depicts the original supply curve, Q1 depicts the supply curve if the long-term fear industry is eliminated, and Q2 illustrates any arbitrary increase in the amount of fear industry (military and police) spending. The arrowheads indicate the direction of the supply quantity shifts (curves)

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rises, the supply curve will shift to the left, as it did in Q2 . Subsequently, factors such as unsustainable deficit financing, a tax breakpoint, and the planned growth in nondefense expenditure and private investment will cause rational economies to reduce their long-term fear spending, bringing the curve back to Q0 and then to Q1 . In the long term, the money spent on the military and police to keep people fearful all the time will be significantly reduced at first. It will be abolished entirely in the industrialized world in the best-case scenario. Following this, other developing and impoverished countries will reach the same conclusion, leading to the end of the fear industry in the long or very long term. In the long run, intelligent societies will understand that this preparatory fear business is a waste of money and will refuse to pay more for it to exist. Taxes, deficit financing (which generates inflation), foreign loans, and other similar costs are frequently incurred. And in the form of resources that could have been spent to help the long-term fear industry. This, in turn, limits the number of resources and money available for nondefense and private investment.

8.3 The Theory of the Fear Industry Figure 8.3 graphically explains the creation of the short- and long-term fear industries. According to a review of major economic theories and recent evidence, an economy contains two distinct types of fear industries: short-term and long-term fear industries. The title implies that the short-term fear industry results from short-term fearful events. However, we do not mean that this creation results in the formation of an entirely new industry. However, by establishing a short-term sector, we mean that the industry experiences abnormal demand in response to fear events (such as the current COVID-19 pandemic, flood, or earthquake) at a time when the majority of other industries are experiencing significant declines in demand (the so-called “disaster blessed industry”). For example, China’s largest mask factory in Shanqi has a total workforce of 800 people. Eighty percent of these workers are hired only to fulfill the demand surge in the wake of the recent CD-19 pandemic (Khan et al. 2022a, 2023a; b; Goldthread 2020). Similarly, China built two hospitals specifically to house CD19-affected patients (BBC News 2020; Talmazan and Associated Press 2020). Both of these provide concrete examples of our short-term fear industry theory in action. Neither the mask factory nor the hospitals would have been built in the absence of the CD-19 pandemic. Simultaneously, other sectors of China’s economy and demand were contracting rapidly (UNDP 2020). As a result of circumstantial evidence and recent literature, we can conclude that the theory underlying the development and eventual demise of the short-term industry has a strong logical foundation. On the other hand, Sajid (2021b) initially defined the long-term fear industries (military and police). As we all know, the military industry is required to wage wars and defend us against wars and terrorist attacks. The police department is tasked with defending us against criminals. As a result, it is clear that both of these industries were born out of our fears. These industries’ eventual demise or extinction is contingent upon society’s rationality. That is, rational societies will eventually

8.3 The Theory of the Fear Industry

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Fig. 8.3 The graphical presentation of the theory of the fear industry. Here, the short-term fear event (such as COVID-19) leads to the creation of the short-term fear industry. In contrast, the long-term fear event (such as the ever-present fear of war) creates the long-term fear industry

refrain from making irrational investments in things such as war machines and even policing. There is no consensus on the effects of military spending on the economy; some studies suggest positive (Ando 2009) effects, while others suggest neutral (Heo 2015; Robert and Alexander 1990) or negative (Brasoveanu 2010; Desli and Gkoulgkoutsika 2020; Zhao et al. 2015) effects. However, whether positive, negative, or neutral, the fact remains that because every economy has limited (scarce) resources, defense (military) spending entails a massive opportunity cost (trade-off). Governments are also increasing their reliance on deficit financing to fund expenditures such as defense. Additionally, it obstructs the long-term realization of free-market economies. Finally, it is less productive than other sectors (Robert and Alexander 1990). Thus, rational societies will seek alternative peaceful, less resource-intensive, and less expensive (less wasteful) means of resolving their international and domestic conflicts peacefully rather than investing in fear industries. These options include, but are not limited to, resolving border disputes, establishing more effective international

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dispute resolution tribunals, conducting community surveillance, and reinvesting funds saved by reducing policing in criminal rehabilitation.

8.4 The Linkage Between United Nations SDGs 1, 2, and 16 and the Fear Industry The United Nations SDG 1 is related to “no poverty” globally. The United Nations, “Department of Economic and Social Affairs,” has explicitly linked CD-19 with increased poverty worldwide (United Nations 2021). As per the department, 119 to 124 million people were pushed into poverty due to the economic effects of COVID19 in 2020 (United Nations 2021). It is inevitable that the 2030 target of no poverty will not be achieved, while poverty of approximately 7% is expected by 2030 (United Nations 2021). Similarly, the COVID-19 pandemic has enforced hunger on 70 to 160 million people (United Nations 2021). Compared to 160 million in 2014, the number of undernourished people increased from 720 to 811 million in 2020 (United Nations 2021). COVID-19’s role in increasing worldwide hunger is evident from the above facts provided by the United Nations. Similar to COVID-19, other short-term fear events, such as floods, earthquakes, tsunamis, and draughts, also push the population towards poverty and hunger. Furthermore, the long-term fear industry, i.e., the defense industry, is related to increased poverty. In particular, several studies have noted that increased defense and military spending are related to increased poverty (Henderson 1998; Khan et al. 2022b; Wittner 2021). During wars and conflicts, short-term hunger increases, and destruction enforces long-term hunger (DeRose et al. 1998). Moreover, SDG 16, especially SDG 16.1, focuses on reducing all forms of violence plus related death rates, and SDG 16.4 emphasizes reducing illicit arms flows (United Nations 2021). To some extent, the SDG’s first two goals are also dependent on SDG 16 because worldwide peace can also help reduce poverty and hunger. However, the role of short- and long-term industries in sustainable economic progress has received little attention. The theory developed in this chapter can help the potential achievement of SDGs 1, 2, and 16. Or it can at least help us in getting closer to these sustainable development goals.

8.4.1 Data Sources and Methodology 8.4.1.1

Data Sources

The individual year 2009 version 2011 national input–output tables from the World input–output database (WIOD) are used to estimate social (SDGP) and free GDP (FDGP) (World Input–Output Database 2020). Sector-specific final demand (GDP)

8.4 The Linkage Between United Nations SDGs 1, 2, and 16 and the Fear …

107

estimates based on input–output tables make it simple to quantify SGDP. In other words, the input–output data can be used to quantify the effect of resources allocated to the long-term fear industry on national GDP. Although Sajid (2021b) proposed the concept of FGDP, it is more closely related to the free market economy than to the long-term fear industry. Researchers rely heavily on the WIOD database as a reliable source of national (Sajid et al. 2019b, 2020) and international data (Sajid et al., 2019a, c; Wang et al., 2017). Additionally, the presence of short-term fear industries is demonstrated using “hospital-bed” use data (i.e., medical services demand) from Yin et al. (2021) for various regions of China during the normal period just before and after COVID-19. Finally, the short-term fear industries’ presence in an economy is further established using data on hospital beds in Wuhan city from Zhuang et al. (2021) for February 2020.

8.4.1.2

Methods

Sajid (2021a; b) provides the basic procedure for the estimation of both the shortand long-term fear industries. However, here, we have modified the approaches for the estimations of both types of fear industries.

Long-Term Fear Industry A long-term fear industry exists when a country’s GDP includes the defense industry. Therefore, the subtraction of the defense industry’s GDP share can provide us with the presence of a long-term fear industry and the resources allocated that would have otherwise been used for nondefense expenditures, i.e., SGDP. GDP = FD = H + C + G + N E =

n  i=1

+

n  i=1



Ei − 

n  i=1

Hi +

n  i=1

Ci +

n 

Gi

i=1

Mi ,

(8.1)



NE

where G D P presents the gross domestic product of a target nation. F D shows that the final demand is used to estimate the GDP. The elements H, C, G, and NE (E – M) present the final demand categories of household purchases, capital formation, government expenditure, and net exports (final exports minus final imports), respectively. By removing the defense expenditure, we can estimate the SGDP of a given economy. SG D P = F D = (H − d) + (C − d) + (G − d) + (N E − d)

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=

n−d 

Hi +

i=1

n−d  i=1

Ci +

n−d 

Gi +

i=1

n−d  i=1



Ei − 

n−d  i=1

Mi ,

(8.2)



NE

where SG D P is the social gross domestic product of a given economy. d presents the final demand of the defense industry. Therefore, the term n − d signifies the removal of the defense and security sector’s final demand from a given economy. Although the chief demand of the defense and security industry usually comes from government expenditure, a minor portion also comes from other categories of final demand. The percentage effect of this removal or the share of resources allocated can be estimated using the following equation.  LFR = 1 −

G D P GDP



 × 100 = 1 −

SG D P−G D P GDP



× 100,

(8.3)

where LFR presents the percentage effect of resource allocation to the long-term fear industry.

Short-Term Fear Industry The estimation of the short-term fear industry is quite simple. It is estimated by comparing the demand (or profits depending upon data availability) before and after the short-term fear event. During this short-term fear period such as COVID-19, the overall economy (GDP) suffers from disruptions in supply chains, production operations, demand reductions (due to the job market downfall), etc. Therefore, the positive effects are almost minimal. Based on these facts, it is not very important to estimate the relatively minor positive effects of the short-term fear industry on the GDP of a particular nation. However, the numerical estimation of the presence of the fear industry can prove that it is necessary for the survival of the so-called short-term disaster-blessed industries after the extra demand and profits diminish with the end of a particularly disastrous event. The difference between the regular and demand under a particular disaster such as the current COVID-19 can be presented with the following equation. S D = S F D − R D,

(8.4)

where S D depicts the difference between the demand (S F D) under a short-term fear event and the regular (or average) demand of a particular industry. The presence or nonpresence of a short-term fear industry can be ensured with the help of the following rules (necessary conditions). E S F = ∃∀S D > 0 → E S F,

(8.5)

8.5 Results

109

N S F = ∃∀S D ≤ 0 → N S F,

(8.6)

where E S F presents the presence of a short-term fear industry, and N S F represents the nonpresence of fear industries. The condition or rule equation number 5 demonstrates that for all (∃), there exists (∀) if S D greater than (>) zero than ESF. Equation number 6 states that for all, there exists if S D is less than or equal to (≤) zero than NSF.

8.5 Results 8.5.1 The Proof of the Presence of the Long-Term Fear Industry The GDP, defense expenditure, SDGP, and percentage (proportion) of resources (GDP) allocated to defense expenditure for the world’s top six defense budget nations are presented in Table 8.1. As shown in Table 8.1, the United States (USA) invested the highest percentage of its GDP (19.53%) in the security and defense industry in 2009, totaling 11,595,579 million US dollars (USD). Meanwhile, it had the highest GDP and SDGP in 2009, totaling 14,409,432 and 11,595,579 million USD, respectively. Following the United States, the Russian Federation allocated nearly 92,722 million USD to defense spending, representing 8.71% of GDP. However, it spent the least on the defense of any of the remaining top five spending nations in absolute terms. Russia’s GDP (1,064,234 million USD) and SDGP (971,513 million USD) were the smallest of the top six nations. France closely followed the Russian Federation, with the defense and security sector accounting for nearly 8.70% of GDP. It had the fourth largest GDP (2,639,257 million dollars), SDGP (2,409,600 million dollars), and defense spending (2,409,600 million dollars) (229,658 million USD). The United Kingdom and Germany spent approximately 8.30% and 7.44% of their GDP on defense. China, which had the second-highest GDP (5,660,380 million USD), SGDP (5,324,026 million USD), and defense expenditure (336,354 million USD), had the lowest defense expenditure volume at 5.94% of GDP.

8.5.2 The Proof of the Presence of the Short-Term Industry The presence of a short-term fear industry is demonstrated in Fig. 8.4. This figure depicts the average hospital bed utilization rate between 2010 and 2018 and the bed utilization rate in Wuhan city (which reported the first CD-19 cases and was one of the most affected cities) during CD-19. As illustrated in Fig. 8.4, China’s hospital bed utilization rate was 87% under normal conditions. However, during CD-19, Wuhan

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8 Introduction to the Theory of Fear Industries and Its Implications …

Table 8.1 The situation of long-term fear industry’s presence in the world’s top-six defense budget countries Country

Original GDP (Million USD)

Defense expenditure* (Million USD)

SGDP (Million USD)

Long-term fear industry’s effect on GDP (LFR%)

United States of America (USA)

14,409,432

2,813,853

11,595,579

19.53

China

5,660,380

336,354

5,324,026

5.94

France

2,639,257

229,658

2,409,600

8.70

United Kingdom (UK)

2,193,628

181,970

2,011,659

8.30

Russian federation

1,064,234

92,722

971,513

8.71

Germany

3,338,800

248,417

3,090,383

7.44

* Defense

industry here represents the “Public Admin and Defense; Compulsory Social Security.”

hospital bed utilization was 95%, a difference of 8% between the national average and Wuhan hospital bed utilization during CD-19. Another approach toward further establishing a short-term fear industry during the current CD-19 pandemic is to utilize the supply-side approach. That is, hospital bed supply shortages fulfilled the increased demand during CD-19 in Wuhan city. Figure 8.5 presents the details of the hospital bed shortages during February 2020 for CD-19 patients with “mild, severe, and critical” conditions in Wuhan. It is evident from Fig. 8.5 that during this period, almost 44 thousand less than the required Fig. 8.4 The case for the presence of a short-term fear industry based on hospital bed utilization in China’s Wuhan city

8.6 Conclusion

111

Fig. 8.5 Hospital bed shortages for different CD-19 patients in Wuhan during February 2020. Here, a, b, and c show bed shortages for mild, severe, and critical CD-19 patients, respectively. The authors’ construct is based on Zhuang et al. (2021)

number of beds were available for patients with mild CD-19. Approximately three thousand beds were in shortage for severe CD-19 patients. Meanwhile, almost 200 beds were not available for critical patients. All these figures show an extraordinary increase in hospital services demand during the short-term fear period of CD-19 and thus can also serve as proof of the presence of the short-term fear industry.

8.6 Conclusion This chapter discussed the fundamental concepts underlying the establishment and eventual demise of the long-term and short-term fear industries. We began by discussing some of the major economic theories and demonstrating that the concept of “fear industries” is completely absent from them. Following that, some recent evidence and the short- and long-term fear industries’ lifecycle processes were discussed. Sajid’s previous papers (2021a, b) discussed the life cycles of both of these types of industries. However, neither of these pieces combines these two concepts

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into a single theory. We connect these two seemingly disparate works through the lens of short- and long-term fear industry theory. Our work also elaborates on fear industry theory and argues for its validity through practical and logical justifications. The results showed that during 2009, across the top six defense budget nations, the USA had the largest proportion of 19.53% of its GDP to the defense industry. In other words, a significant portion of the USA’s production capacity was allocated to defense and security products and services. The USA was followed by the Russian Federation (8.71%), France (8.70%), the UK (8.30%), Germany (7.44%), and China (5.44%). On the other hand, the presence of the short-term industry was shown by presenting an 8% increase in Wuhan’s hospital bed utilization rate compared to the national average. The presence was also shown via the significant hospital bed demand for “mild, severe, and critical” COVID-19 patients during February 2020 in Wuhan city. Understanding the theory of fear industries can aid government policymakers, industrial managers, and economists in understanding the fear-based industries’ life cycle. The short-term fear industries, in particular, will experience increased demand for a shorter duration, similar to COVID-19. After the short-term fear period passes, a significant portion of the additional resources used by industries, such as labor and fixed capital investments, may become idle. Thus, understanding the theory of short-term fear industries can assist managers and policymakers in preparing in advance for the end of a short-term fear period (e.g., the COVID-19 pandemic) for better resource reallocation to mitigate the ramifications of the demise of short-term fear industries. For instance, labor departments in various countries can reallocate the additional labor force to the medical equipment industry (such as masks and ventilators). The preparations before and during the short-term disaster (such as the COVID-19 pandemic) can help with the postpandemic adverse effects of economic losses and even the closure of so-called disaster-blessed industries. That, in turn, can further impact the recently recovered economies by job losses, GDP growth rate decrease, and non-availability of vital supplies. It can increase poverty and hunger, thus hindering the achievement of UN SDGs 1 and 2. Additionally, long-term fear industry theory can persuade government policymakers to cut defense spending to avoid public confrontations and eventually move toward free markets. Understanding the adverse effects of long-term fear industries can promote defense cuts among major defense expenditure economies. The basic economic principle of “resource trade-offs” will provide more funds for nondefense expenditures and clears the road for much more free-market economies. This can help with the achievement of SDGs 1 and 2, but reducing the chances of both internal and external conflicts can also directly promote SDG 16. Along with rational people, sunk costs, terrorism, and defense exports, Sajid’s (2021b) theory for the long-term fear industry is constrained by the following limitations. Furthermore, the chapter only focused on the economic implications and thus did not discuss the relationship between SDG 3 plus others and fear theory. This relationship is discussed in Chap. 2 on the environmental implications of the feat industry theory.

8.6 Conclusion

113

8.6.1 Prolonged Short-Term Fear Event In the event that a short-term fear event such as the current COVID-19 is prolonged for a longer period, perhaps several decades and beyond (a highly unlikely event), additional resources such as labor and fixed capital assets will be utilized for a much longer period. In that case, these resources may not be idle or reallocated in the short term, as predicted by the short-term fear industry theory.

8.6.2 De-evolution of Public The theory of the long-term fear industry is based on the assumption that people will evolve to be more rational with time. However, this may not be the case; contrarily, some unseen event can lead people to de-evolve instead of becoming more rational.

8.6.3 Non-economic (Non-financial) Incentives for Crime Elimination of domestic preputial fear industries (local law enforcement agencies) is based on eliminating crime in rational economies over the long run due to the economic benefits received and the opportunity cost savings (by reducing or eliminating expenditure on local law enforcement agencies). However, some crimes are motivated by noneconomic incentives such as hatred, jealousy, pride, etc. However, again, rational economies (people) are supposed to be competitive rather than jealous or hate each other. Alternatives such as community and voluntary policing can also be rational to keep these non-economics-stimulated crimes under control at a minimum cost.

8.6.4 Emergency Supplies The transport sector may witness an increase in demand for emergency supplies during the short-term fear event. However, this increased demand will not be sufficient to offset the losses from the disruptions of transport activities. For example, during the current COVID-19 period, to supply one shot per capita of the CD-19 inoculation internationally, at least 8000 “Boeing 747” airlifts and to provide the required number of shots, almost 14,912 flights will be required (Sajid et al. 2022). However, it is well known that due to COVID-19-related lockdowns and other restrictions, the airline industry has witnessed net losses. Another example is that the destruction of infrastructure such as roads, bridges, and others may halt general-purpose transport. The increasing demand to provide emergency supplies during disasters such

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as earthquakes, floods, and landslides will not generate extra demand and additional labor force as expected for other disaster-blessed industries.

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Peters O (2019) The ergodicity problem in economics. Nat Phys 15(12):1216–1221. https://doi.org/ 10.1038/s41567-019-0732-0 Peters O, Gell-Mann M (2016) Evaluating gambles using dynamics. Chaos: Interdiscip J Nonlinear Sci 26(2):023103. https://doi.org/10.1063/1.4940236 Peters O, Klein W (2013) Ergodicity breaking in geometric Brownian motion. Phys Rev Lett 110(10):100603. https://doi.org/10.1103/PhysRevLett.110.100603 Ricardo D (1817) On the principles of political economy and taxation. John Murray Robert W, Alexander J (1990) The impact of defence spending on economic growth. Def Econ 2(1):39–55. https://doi.org/10.1080/10430719008404677 Rothbard MN (2000) Americas greatest depressions (Fifth). Ludwig Von Mises Institute Rothbard MN (2019) Austrian business cycle theory, explained. Mises Wire, Mises Institute. https:// mises.org/wire/austrian-business-cycle-theory-explained Sajid MJ (2021a) Continued increases in military and police spending can lead to economic collapse. 03036:1–6. https://doi.org/10.1051/e3sconf/202125303036 Sajid MJ (2021b) Economic consequences of resource trade-offs for special disaster-blessed industries: the case of COVID-19. In: 2020 International conference on new energy technology and industrial development (NETID 2020), vol 235, 02007. https://doi.org/10.1051/e3sconf/202123 502007 Sajid MJ, Ali G, Santibanez EDR (2022) Estimating CO2 emissions from emergency-supply transport: the case of COVID-19 vaccine global air transport. J Clean Prod 340(February):130716. https://doi.org/10.1016/j.jclepro.2022.130716 Sajid MJ, Cao Q, Kang W (2019a) Transport sector carbon linkages of EU’s top seven emitters. Transp Policy 80:24–38. https://doi.org/10.1016/j.tranpol.2019.05.002 Sajid MJ, Li X, Cao Q (2019b) Demand and supply-side carbon linkages of Turkish economy using hypothetical extraction method. J Clean Prod 228:264–275. https://doi.org/10.1016/j.jclepro. 2019.04.234 Sajid MJ, Shahni N, Ali M (2019c) Calculating inter-sectoral carbon flows of a mining sector via hypothetical extraction method. J Min Environ 10(4):853–867. https://doi.org/10.22044/jme. 2019.8263.1701 Sajid MJ, Qingren C, Ming C, Shuang L (2020) Sectoral carbon linkages of Indian economy based on hypothetical extraction model. Int J Clim Change Strateg Manage:1756–8692. https://doi. org/10.1108/IJCCSM-11-2018-0075 Schumpeter J (1939) Business cycles. In: Fels R (ed) A Theoretical, historical and statistical analysis of the capitalist process. McGraw-Hill Book Company. https://doi.org/10.4324/978131527054 8-20 Smith A (1776) In: Strahan W, Cadell T (eds) An inquiry into the nature and causes of the wealth of nations, 1st ed Steven AG, David S (2017) Principles of macroeconomics, 2nd ed. OpenStax Rice University Talmazan Y, Associated Press (2020) China’s coronavirus hospital built in 10 days welcomes patients. https://www.euronews.com/2020/02/03/china-s-coronavirus-hospital-built10-days-opens-its-doors-n1128531 Toda AA (2023) “Ergodicity economics” is pseudoscience. ArXiv, Preprint. https://doi.org/10. 48550/arXiv.2306.03275 UNDP (2020) Assessment report on impact of COVID-19 pandemic on Chinese enterprises United Nations (2021) The sustainable development goals report. In: Jensen L (ed) United Nations publication issued by the Department of Economic and Social Affairs Walras L (1954a) Elements of pure economics (Routledge). Routledge Walras L (1954b) In: Walker DA, Van Daal J (eds) Elements of theoretical economics: or, the theory of social wealth Walras L (1954c) Theory of pure economics (trans: Jaffe W, Allen and Unwin) Wang H, Ang BW, Su B (2017) A multi-region structural decomposition analysis of global CO2 emission intensity. Ecol Econ 142:163–176. https://doi.org/10.1016/j.ecolecon.2017.06.023

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Chapter 9

An Application of the Long-Term Fear Industry Theory to Environmental Impacts

9.1 Background The term “monopsonic” or “oligopsonic market” is frequently utilized when referring to the defense industry.1 ,2 The majority of the time, there is either a single primary buyer (the government) or a group of a few large buyers competing against a large number of sellers. At the international level, there are a small number of large exporting nations and a large number of buying nations that are dispersed all over the globe (CAAT 2011), with the majority of transfers taking place at the governmental level3 (Kytömäki 2014). The sector of the economy known as the “defense industry” is consistently ranked among the most contentious in the world. Spending on defense creates jobs directly, and it also has the potential to improve economic output indirectly through the transfer of knowledge, skills, and other resources from the military sector to the civilian economy (Bryan et al. 2021). However, there is a cost associated with missed opportunities in regard to defense spending. This is because the money that is spent on defense is diverted from other government programs that could be more effective at fostering economic expansion (Bryan et al. 2021). According to SIPRI (2010), the total amount spent on defense all over the world in 2009 was 1531 billion USD. The United States of America came in first place, contributing 43% of the worldwide total. China, France, the United Kingdom, and Russia ranked second, third, fourth, and fifth, respectively, accounting for 6.6%, 4.2%, 3.8%, and 3.5% of the world’s total expenditure. The United States of America was the leading exporter of conventional arms, accounting for thirty percent of the worldwide market share, followed by Russia (thirty-three percent), Germany (eleven 1

Monopsony refers to a market in which there is only one buyer. Oligopsony is the term given to a market in which there are only a few very large buyers (OECD 2002). 2 In this study “Public Admin and Defence; Compulsory Social Security” sector is used as a proxy for the log-run fear industry’s environmental effects. For ease of presentation the “Public Admin and Defence; Compulsory Social Security” is referred to as defense sector throughout this chapter. 3 The majority of governments support global trade in arms in order to achieve what are called “scale economies” (Price Water House Coopers 2005). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_9

117

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percent), France (eight percent), and the United Kingdom (four percent). Between 2005 and 2009, the total amount of arms exported from these five nations accounted for 76% of the total amount exported from all countries around the world (SIPRI 2010). In general, most of the focus has been on direct industrial emissions, while compound inter-industrial relations have mostly been ignored (Wang et al. 2013). Industrial linkages are defined by an industry’s relation with rest through direct and indirect intermediate inputs and outputs (Miller and Lahr 2001). There are three methods: the classical multiplier approach (Chenery and Watanabe 1958; Chen et al. 2017); hypothetical extraction (Clements 1990), and the modified hypothetical extraction technique (Duarte et al. 2002) (Liao et al. 2017). Both the classical multiplier and HEM methods have been used to describe industrial linkages and environmental problems in different setups, including water usage (Duarte et al. 2002); carbon emissions in the country (Zhao et al. 2015; Khan et al. 2022a; Zhang et al. 2017; Perobelli et al. 2015; Wang et al. 2013; Ali 2015; Sajid et al. 2021, 2022) and at the city level (Liao et al. 2017; Tian et al. 2017); energy consumption (Guerra and Sancho 2010; Khan et al. 2022b); buildings (Song et al. 2006) and food production (Cai and Leung 2004). In many countries, defense forces’ environmental management commitment is an increasing reality (Ramos and Melo 2005; Khan and Qianli 2017). Sectors such as ‘Energy, Tourism, Transport, Agriculture, etc.,’ have mostly been focused on, resulting in a small amount of work conducted in this field (Ramos et al. 2007). Little literature can be found on defense industry-specific carbon emissions and interindustrial linkages. Table 9.1 contains the list of environment-related literature in the context of defense or military industry. Few studies have attempted to estimate the demand- and supply-driven carbon linkages in the defense industry, despite the numerous works that have been published on the subject of carbon emissions in the defense industry. Second, there is a paucity of research on the conventional direct carbon emissions and intensities that are produced by the defense industry. This is especially true for the countries that are the largest spenders in the defense industry, such as the United States of America, Russia, Germany, France, the United Kingdom, and China. Third, the final demand and industrial supply embedded carbon emissions estimates for the defense sector, particularly for key nations that spend much on defense, are not currently available. The following are some of the ways in which this study stands out from others. First, the research estimates the demand- and supply-driven various types of linkages between the major defense spenders of the United States of America, Russia, Germany, France, and the United Kingdom, as well as China. In particular, the research makes use of a relatively new and straightforward method for estimating demand and supply-driven linkages. This method, which is known as “estimation via diagonalization of final demand/primary supply factors and environmental satellite accounts,” was first presented by the Sajid et al. (2022). It should be noted that it has been shown in the research that the well-known Leontief demand model is only acceptable for the estimation of backward industrial linkages. This is something that should be kept in mind (Sajid et al. 2019b, 2021, 2022). The Ghosh supply model is

9.1 Background

119

Table 9.1 List of environment-related literature in the context of defense or military industry Reference

Region

Method

Main focus

Bildirici (2017)

USA (1984–2015)

ARDL; FMOLS; DOLS; CCR

Tested Carbon emissions, biofuel consumption, militarization, and economic progress’ casual and cointegration relation

Fiott (2014)

European Union

Conceptual model

Analyzed the ‘greening behavior’ of EU countries’ defense sector by virtue of categorization

Ramos et al. (2007) Portugal

SEPI

Established ‘environmental performance policy indicators’ explicitly for the defense sector

Ramos and Melo (2005)

Portugal

Survey

Studied Portuguese military environmental commitments

Closson (2013)

USA

Analysis

Analyzed the motivation and challenges of reduction in oil consumption for the department of defense

Strakos et al. (2016)

USA

Review and analysis

Studied energy strategy, research, and regulation related to the department of defense

(Isard 1990)

World

LINK

Reported advances in global arms control and environmental management

appropriate for the calculation of forward environmental linkages (Sajid et al. 2019b, 2021, 2022). Here, we take into account this issue as well, and we have utilized the Leontief demand for the estimation of the upstream or backward sectoral carbon linkages, while we have utilized the Ghosh supply for the calculation of the forward or downstream sectoral carbon linkages. Also quantified in our research are factors that are rarely estimated, such as carbon emissions and the intensity of the defense sector for major defense-spending nations. In addition, the study also investigates the understudied final demand as well as the primary sectoral factors that supply embedded carbon linkages of the defense sector. This presentation of the direct and indirect effects that sectoral demand and supply have had on carbon emissions from major economies can help in mitigating the climate effects that the long-term fear industry has caused. This, in turn, can help the United Nations work toward achieving Sustainable Development Goal 1 (the eradication of poverty), Sustainable Development Goal 2 (the elimination of hunger), Sustainable Development Goal 3 (the combating of climate change), and Sustainable Development Goal 16 (ensuring that no one goes hungry) (peace and justice). The remaining portions of the chapter are laid out in the following structure. In Sect. 9.2, the idea of demand- and supply-driven ecological linkages is broken down into its most fundamental components. In Sect. 9.3, the methodology and the sources of the data are presented. The findings of our investigation are discussed in Sect. 9.4.

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The work is brought to a close in Sect. 9.5, which also contains a discussion of the constraints.

9.2 A Simple Explanation of Net Demand and Supply-Side Environmental Linkages We attempted to explain the concepts of net demand and supply-side linkages before moving on to the numerical estimates in this section. Before moving on to the numerical estimates presented in this section, we made an effort to explain the concepts of net demand and supply-side linkages (without considering technical aspects such as the Leontief and Ghosh inverse matrices explained in the methods section). A straightforward explanation of demand pulled backward linkage estimation is depicted in Fig. 9.1. In this instance, our intended market is the purchasing sector, which is one of our target sectors (s). The final demand in the target sector drives the purchases of intermediate goods and services from upstream sectors by the target sector. The demand of the target sector, in addition to the upstream sectors’ ecological intensity (s), is what determines the number of emissions produced by those sectors (Ω s). Figure 9.2 depicts the process that is used to estimate the net supply-pushed carbon linkages between sectors of the economy. The availability of primary resources, such as labor and fixed capital, enables the target sector of a system to push environmental impacts onto other sectors of the system further downstream (region, country, or international). Therefore, the supply-driven ecological impacts are determined not

Fig. 9.1 Graphical explanation of net demand-pulled intersectoral ecological linkages

9.2 A Simple Explanation of Net Demand and Supply-Side Environmental …

121

only by the availability of resources in the target sector (s) but also by the ecological intensities (sigmas) of the sectors that import the resources (s). The differences between internally generated supplies and demand-driven ecological linkages are broken down graphically in Fig. 9.3. In other words, the availability of resources to the target sector is what drives the ecological linkages of the internal supply, whereas the final demand of the target sector is what drives the purchases made by the target sector’s internal organizations. In the meantime, the target sector’s own ecological intensity (s) can have an effect on the values of both the internal demand and supply linkages (Ω s).

Fig. 9.2 Graphical explanation of net supply-pushed intersectoral ecological linkages

Fig. 9.3 Graphical explanation of net demand and supply-pushed intrasectoral ecological linkages

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9.3 Methods and Data Sources 9.3.1 Methodology 9.3.1.1

Leontief Demand Model

Leontief (1936) first introduced the basic input–output model as follows: x = Ax + y.

(9.1)

x = (I − A)−1 y = L(f + e),

(9.2)

After isolation of X we have:

where the total yield vector of a given economy is presented by x, I represents a n × n dimensioned identity matrix, A is the technology matrix4 whose element amn = xxmnn represents the amount of total output required from sector m to produce one unit at sector n, (I − A)−1 represents the Leontief inverse matrix denoted as L. f is a vector of final domestic demand, and e is a vector of external demand for domestic goods (exports). Here, A we mean intermediate matrix excluding intermediate imports.5 The environmentally extended Leontief demand model for the estimation of final demand-embedded emissions and intensities can be provided in the following manner: c = τ(I − A)−1 y = τL(f + e),

(9.3)

where c represents the total demand embedded emissions or, in other words, the direct emissions of a country. τ = xc depicts the direct sectoral carbon emissions intensity of a given nation. By diagonalizing the vectors of carbon emissions intensity and of final demand, we can obtain a matrix of demand-embedded intermediate inter- and intrasectoral carbon linkages for a country as (Sajid et al. 2022):   C = τˆ (I − A)−1 yˆ = τˆ L ˆf + eˆ ,

(9.4)

where C represents a matrix of intermediate industrial carbon linkages. τˆ and yˆ represent the diagonalized vectors of sectoral carbon emission intensity and of final demand. According to recent evidence, Leontief’s demand model is only appropriate for estimating a sector’s backward or, in other words, intermediate industrial 4

A Matrix is also referred as inter-industry, intermediate demand, technology matrix and direct requirement matrix in related literature. 5 WIOD provides separate intermediate matrixes for domestic and imported goods. So, no further treatment of A matrix is required to separate domestic from intermediate imported demand for calculation of country’s total economic output.

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consumption linkages (Sajid et al. 2019b, 2021, 2022). On the other hand, the Ghosh supply model (Ghosh 1964) is more appropriate for estimating an industry’s forward or, in other words, supply-pushed downstream sectoral carbon linkages (Sajid et al. 2019a, b, c, 2021, 2022). Therefore, based on the above evidence, we can only derive demand-driven backward carbon linkage for a target sector in the following manner: Backward carbon linkage: dc = dϑ,ϑ +

n−ϑ ∑

d∅,ϑ ,

(9.5)

∅=1

where dc represents a scalar value for the demand-pulled backward carbon linkage of the target sector ϑ. dϑ,ϑ presents the value of the target sector’s internal demandpulled carbon linkages. d∅,ϑ represents the target sectors’ virtual carbon imports from other sectors ∅ of an economy.

9.3.1.2

Ghosh Supply Model

The Ambica Ghosh model has been extensively used to estimate the supply-pushed carbon linkages for a target sector (Sajid et al. 2019b, 2021, 2022). Conventionally, the model only considers the labor and capital-embedded industrial carbon linkages for an economy (Sajid et al. 2021). However, here, in addition to labor remuneration and fixed capital investments, we will modify the model to also include the role of net taxes (taxes minus subsidies), that is, government policy (or, in other words, government income), in pushing the carbon emissions of other downstream sectors. The Ghosh model, also referred to as the Ghosh price (Dietzenbacher 1997) or supply (Miller and Lahr 2001) model, was first presented by Gosh (1964) as follows: x∗ = (I − B)−1 p = G(v + i + t),

(9.6)

where B = x−1 A, represents the direct output coefficient matrix, whose element bi j represents the fraction of sector i output used at sector j (Miller and Lahr 2001), and G = (I − B)−1 is the matrix of ‘output inverse’ (Miller and Blair 2009). And p represents a vector of the sector-wise value of supply factors. where v, i, and t represent the vectors of sectoral value added, international trade margin, and net taxes, respectively. The Ghosh supply model can be extended to the environmentally extended supply model by introducing the vector of sectoral carbon emissions intensity as follows: c∗ = τ(I − B)−1 p = τG(v + i + t).

(9.7)

Again, by introducing the diagonalized vector of sectoral carbon emission intensity and the vector of supply factors, we can obtain the matrix of supply-pushed intermediate sectoral linkages as follows:

124

9 An Application of the Long-Term Fear Industry Theory …

C∗ = τˆ (I − B)−1 p = τˆ G(v + i + t).

(9.8)

where C∗ depicts the matrix of supply-pushed intermediate inter- and intrasectoral linkages. As mentioned above, the Ghosh supply model is appropriate for estimating a sector’s supply-pushed downstream carbon linkages. Therefore, the following equation can be used to extract the supply-push effect of the target sector on the downstream sectors’ carbon linkages: sc = sϑ,ϑ +

n−ϑ ∑

sϑ,∅ ,

(9.9)

∅=1

where sc represents a scalar value for the supply-pushed forward (downstream) carbon linkage of the target sector ϑ. sϑ,ϑ presents the value of the target sector’s intrasectoral supply-driven carbon linkages. sϑ,∅ represents the target sectors’ supplies, i.e., intersectoral export impact on pushing the other sectors ∅ of an economy.

9.3.2 Data Sources The national input–output table (IOT) data were obtained from the open-access “World Input–Output” database (WIOD) (Timmer 2015). The WIOD uses the International Standard Industrial Classification version 4 (ISIC Rev. 4) to classify data for 35 industries (The World Input–Output Database 2022). The WOID IOTs adhere to the 2008 System of National Accounts (SNA) (The World Input–Output Database 2022). WIOD IOTs have been widely applied in economic (Hübler and Pothen 2017; Timmer et al. 2014; Fajgelbaum and Khandelwal 2016) and environmental research (Rocchi et al. 2018; Sajid et al. 2019a, c; Khan et al. 2022c, d). Other databases, such as the EORA MRIO (free for academic use), provide the relatively new year 2016 national IOTs for the relevant countries. However, the national IOTs under the EORA MRIO do not use consistent sectoral classifications across nations. As a result, for the national IOT data in our study, we used the well-known open-access WIOD. Furthermore, the 2013 version of the WIOD was preferred over the 2016 version. This is mainly because the environmental satellite accounts provided under the 2016 version are not available directly from the WIOD.

9.4 Results

125

9.4 Results 9.4.1 Total CO2 Emissions and Direct Intensity Table 9.2 provides a comparison of the direct carbon emissions and intensity levels of the defense sectors of a number of different countries. The United States of America was the country that contributed the most to the total carbon emissions from the defense sector in both 2005 and 2009, with a total of 624.54 million tons and 254.43 million tons, respectively. Russia came in at number two with 69.38 million tons of carbon emissions and 72.03 million tons, respectively. Germany, the United Kingdom, France, and China had totals of 18.33 million tons, 17.64 million tons, 10.55 million tons, and 1.48 and 2.60 million tons of carbon emissions in 2005 and 2009, respectively. China and Russia’s defense sectors have actually seen an increase in emissions over time, in contrast to the trend seen in most other countries’ military industries, which have seen a decrease. Even though the emission intensity of Russia’s defense sector decreased to 5.72 t/ $104 in 2009, Russia still topped the list in 2009 as the country with the highest carbon-intensive sector of defense. This is because Russia has the highest direct emission intensity. In 2005, it had an intensity of 9.87 t/$104 . In 2005 and 2009, the United States of America had the second most carbon-intensive sector of defense, with 2.60 t/$104 and 0.86 t/$104 , respectively. In 2005, the defense industry in the United Kingdom was the third most carbon-intensive in the world, with 0.87 t/$104 . At 0.36 t/$104 in 2009, the United Kingdom’s defense sector emission intensity was almost cut in half, but the United Kingdom still had the third most intensive sector of defense. Germany, France, and China ranked second, third, and fourth, respectively, with 0.82 t/$104 and 0.21 t/$104 , 0.51 t/$104 and 0.22 t/$104 , and 0.09 t/$104 and 0.08 t/$104 for 2005 and 2009, respectively. The levels of CO2 emissions produced by every country have decreased over time. Table 9.2 A comparison of direct carbon emissions and intensities of the defense sector from selected countries Direct emission (Mt)

Emission intensity (t/$104 )

2005

2009

2005

2009

624.54

254.43

2.60

0.86

China

1.48

2.60

0.09

0.08

France

10.55

5.22

0.51

0.22

UK

17.64

7.16

0.87

0.36

Russia

69.38

72.03

9.87

5.72

Germany

18.33

5.67

0.82

0.21

Item USA

126

9 An Application of the Long-Term Fear Industry Theory …

9.4.2 Contribution of the Defense Sector to Final Demand Embedded Emissions In 2005, the defense sector was responsible for 15.35% of all sectoral emissions in the USA, and in 2009, it was responsible for 15.93% of those emissions. It was responsible for a staggering 98.80% and 98.57% of all governmental emissions in the United States in 2005 and 2009, respectively. It was responsible for 5.19 and 5.91% of China’s total emissions in 2005 and 2009, respectively. However, they account for 46.40 and 42.25% of the total emissions from the government, respectively. The contribution of the defense industry to the total emissions of France, the United Kingdom, Russia, and Germany in 2005 was 4.12%, 4.05%, 5.33%, and 2.98%, respectively. In 2009, those figures dropped to 3.71%, 3.91%, 5.99%, and 3.07%. It was responsible for 32.16%, 38.19%, 36.67%, and 36.65% of all governmental emissions in these countries in 2005, and it was responsible for 29.63%, 38.18%, 34.31%, and 37.21% of all governmental emissions in these countries in 2009. It should come as no surprise that the defense sector accounts for a significant portion of governmental emissions in each of these countries. The contribution of the defense industry to almost all other categories of final demand is almost nominal. Figure 9.4 provides specific information regarding the defense industry’s contribution to a variety of different types of emissions. In 2005, government consumption was responsible for 86.24%, 99.71%, 95.98%, 93.80%, and 79.79% of all defense emissions in the United States of America, China, France, the United Kingdom, Russia, and Germany, respectively. For 2009, those percentages were 85.64%, 99.83%, 96.83%, 94.84%, and 95.82%, respectively. According to the data presented above, there is only one major player (buyer) in the market for goods and services related to defense, and that player is the government. That player, from the point of view of final consumption, is almost entirely responsible for the emissions caused by the defense industry. The details of the final demand contribution to the defense sector’s emissions are displayed in Fig. 9.5.

Fig. 9.4 The defense industry’s contribution to a variety of different types of emissions

9.4 Results

127

Fig. 9.5 Final demand contribution to the defense sector’s carbon emissions

9.4.3 Contribution of the Defense Sector to Industrial Supply Factors Embedded Emissions A comparison of the effects of the defense industries’ supplies (both internal and intersectoral) on the rate at which their respective economies are pushing their carbon emissions is presented in Table 9.3. It is clear from Table 9.3 that the United States military produces the highest number of emissions out of the group of nations that were chosen. This sector is responsible for 401.54 Mt and 172.28 Mt, respectively. The supply-push impact of the United States military sector is equivalent to between nearly 10% and 5% of total supply-side emissions from the United States. The Russian defense industry had the second highest supply-push impact in terms of both the total and percentage contributions. This was because they produced the most weapons in the world. In 2005 and 2009, respectively, the Russian defense industry was responsible for approximately 4.1% and 4.7% of Russia’s total supplypushed carbon emissions. In 2005, this number was 4.1%. The defense industries of Germany, France, the United Kingdom, and China ranked second, third, and fourth, respectively, in terms of absolute and percentage contributions to the total carbon emissions pushed by supply.

9.4.4 Defense Sector’s Effect on Pushing and Pulling Emissions of Other Sectors The impact of the defense industry on the pulling (backward linkages) and pushing (forward linkages) of other industries out of the target country is outlined in Table 9.4. The United States Department of Defense had the largest backward carbon linkage in 2005 and 2009, with values of 261.42 Mt and 351.10 Mt, respectively. This indicates that purchases made by the United States military in the defense sector have a significant impact on pulling emissions from other industries. At the same time, the defense sector in the United States was responsible for the greatest volume of supply-push impact on the emissions produced by other sectors, with 18.13 Mt and 16.72 Mt,

128

9 An Application of the Long-Term Fear Industry Theory …

Table 9.3 A comparison of defense sectors’ supply impact on pushing the carbon emissions of their respective economies Item

USA China France

Defense sector supply-side emissions (Mt)

Total supply-side emissions (Mt)

Contribution %

2005

2009

2005

2009

2005

2009

401.54

172.28

4118.94

3736.50

9.75

4.61

0.88

1.72

385.86

526.06

0.23

0.33

8.67

5.04

220.64

194.01

3.93

2.60

UK

10.11

4.62

368.27

320.30

2.75

1.44

Russia

51.73

58.62

1259.23

1248.47

4.11

4.70

Germany

21.08

14.21

523.06

488.09

4.03

2.91

Table 9.4 A comparison of the defense sector’s industrial carbon linkages across selected nations Items

2005

2009

Backward linkage (Mt)

Forward linkage (Mt)

Backward linkage (Mt)

Forward linkage (Mt)

USA

261.42

18.13

351.10

16.72

China

13.58

0.09

17.41

0.29

France

4.63

1.07

3.46

0.94

UK

9.17

1.25

8.88

1.02

37.52

16.46

96.25

58.62

9.13

8.33

12.21

10.36

Russia Germany

respectively. However, the impact of the United States’ supply push had much less of a downstream effect than the linkage that occurred in the opposite direction. In addition to the United States of America, a significant value of backward carbon linkage was contributed by the defense sectors of Russia (37.52 Mt and 96.25 Mt), China (13.58 Mt and 17.41 Mt), and Germany (9.13 Mt and 12.21 Mt) when they made purchases from upstream sectors. In terms of the supply push effect that the defense sector has on the emissions of other sectors, Germany (8.33 Mt and 10.36 Mt) and Russia (16.46 Mt and 58.62 Mt) had a significantly larger forward carbon linkage.

9.4.5 Sector-Wise Decomposition of the Defense Sector’s Demand and Supply-Pushed Emissions Further dissection of the forward carbon linkage of selected countries’ defense sectors is presented in Table 9.5. As shown in Table 9.5, the primary source of the overall supply-pushed impact was the defense sector’s intrasectoral supplies in all of the

0.28

1.65

0.15

0.01

0.09

0.49

0.27

1.13

0.10

0.28

0.50

0.28

Mining and quarrying

Food, beverages and tobacco

Textiles and textile products

Leather, leather and footwear

Wood and products of wood and cork

Pulp, paper, printing and publishing

Coke, refined petroleum and nuclear fuel

Chemicals and chemical products

Rubber and plastics

Other non-metallic mineral

Basic metals and fabricated metal

Machinery, Nec

0.08

1.17

1.27

0.04

1.02

0.46

0.52

0.21

0.00

0.06

0.56

0.35

0.47

0.00

0.01

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

2005

0.36

China

2005

2009

USA

Agriculture, hunting, forestry and fishing

Sectors

0.00

0.03

0.04

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.01

2009

0.01

0.05

0.04

0.01

0.10

0.02

0.02

0.00

0.00

0.01

0.09

0.00

0.02

2005

France

0.00

0.06

0.08

0.00

0.05

0.05

0.02

0.00

0.00

0.00

0.05

0.01

0.03

2009

0.02

0.06

0.02

0.01

0.05

0.03

0.01

0.00

0.00

0.01

0.03

0.02

0.01

2005

UK

0.00

0.03

0.02

0.01

0.03

0.01

0.01

0.00

0.00

0.00

0.01

0.02

0.01

2009

Table 9.5 A further decomposition of the defense sector’s supply-side carbon linkage of selected countries

0.47

1.51

0.17

0.04

0.57

0.58

0.09

0.03

0.02

0.09

0.62

3.08

0.36

2005

Russia

0.40

1.38

0.15

0.06

0.57

0.64

0.08

0.03

0.01

0.03

0.88

2.59

0.44

2009

0.06

0.22

0.15

0.03

0.24

0.04

0.06

0.01

0.00

0.02

0.16

0.07

0.03

2005

0.01

0.17

0.24

0.01

0.16

0.07

0.03

0.00

0.00

0.00

0.05

0.05

0.03

2009

(continued)

Germany

9.4 Results 129

0.53

0.15

2.87

1.08

0.10

0.21

0.65

1.06

0.54

Transport equipment

Manufacturing, Nec; recycling

Electricity, gas and water supply

Construction

Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of fuel

Wholesale trade and commission trade, except of motor vehicles and motorcycles

Retail trade, except of motor vehicles and motorcycles; repair of household goods

Hotels and restaurants

Inland transport

1.12

0.45

0.35

0.07

0.03

0.16

4.80

0.02

0.10

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.05

0.00

0.00

0.00

2005

0.17

China

2005

2009

USA

Electrical and optical equipment

Sectors

Table 9.5 (continued)

0.01

0.00

0.00

0.00

0.00

0.00

0.16

0.00

0.00

0.00

2009

France

0.04

0.02

0.01

0.03

0.01

0.09

0.13

0.02

0.04

0.02

2005

0.05

0.01

0.01

0.02

0.01

0.02

0.19

0.02

0.01

0.00

2009

UK

0.21

0.03

0.04

0.03

0.01

0.08

0.14

0.02

0.03

0.01

2005

0.40

0.00

0.01

0.01

0.00

0.02

0.13

0.01

0.00

0.00

2009

Russia

1.46

0.16

0.28

1.15

0.11

1.24

1.82

0.10

0.27

0.18

2005

1.71

0.25

0.38

1.37

0.10

2.21

2.20

0.10

0.39

0.21

2009

0.03

0.05

0.05

0.03

0.01

0.18

6.32

0.03

0.14

0.07

0.04

0.01

0.03

0.01

0.00

0.04

9.12

0.00

0.01

0.01

2009

(continued)

Germany 2005

130 9 An Application of the Long-Term Fear Industry Theory …

0.78

0.00

Private households with employed persons

Public admin and defence; 383.40 compulsory social security

Other community, social and personal services

0.23

Renting of M&Eq and other business activities

1.30

0.84

Real estate activities

0.55

0.17

Financial intermediation

Health and social work

155.56

0.25

Post and telecommunications

Education

0.33

0.05

Other supporting and auxiliary transport activities; activities of travel agencies

0.00

0.49

0.48

0.15

0.03

0.08

0.20

0.30

0.88

0.59

0.43

0.43

0.00

0.00

0.00

0.00

0.79

0.00

0.00

0.00

0.00

0.00

0.00

0.00

2005

Air transport

China

2005

2009

USA

Water transport

Sectors

Table 9.5 (continued)

0.00

0.00

0.00

0.00

1.43

0.00

0.00

0.00

0.00

0.00

0.00

0.01

2009

France

0.00

0.18

0.01

0.01

7.60

0.02

0.01

0.01

0.00

0.00

0.04

0.02

2005

0.00

0.15

0.00

0.00

4.11

0.02

0.00

0.00

0.00

0.00

0.05

0.01

2009

UK

0.00

0.02

0.05

0.01

8.86

0.03

0.10

0.02

0.01

0.00

0.12

0.05

2005

0.00

0.01

0.01

0.00

3.59

0.02

0.01

0.00

0.00

0.00

0.19

0.03

2009

Russia

0.00

0.46

0.27

0.15

35.27

0.16

0.22

0.23

0.05

0.19

0.28

0.06

2005

0.00

0.58

0.33

0.20

39.36

0.19

0.44

0.33

0.08

0.35

0.50

0.08

2009

Germany

0.00

0.06

0.06

0.02

12.75

0.02

0.04

0.01

0.02

0.02

0.05

0.01

2005

0.00

0.03

0.02

0.01

3.85

0.03

0.02

0.00

0.02

0.04

0.07

0.01

2009

9.4 Results 131

132

9 An Application of the Long-Term Fear Industry Theory …

countries. This was the case for all defense sectors. Carbon linkages were primarily pushed by the “Electricity, Gas, and Water Supply” industry, which was the second largest source of supplies for the defense industry. Table 9.6 presents the upstream carbon linkage supply chains of the selected countries organized according to sector. Again, the largest single source of this demand-pulled upstream carbon impact was the intrasectoral demand from the defense sector. In general, the demand for “Electricity, Gas, and Water Supply” from the defense sector was the second largest source of its demand-pulled upstream impact.

9.5 Conclusions The amount spent on defense has a significant influence on the programs that provide public welfare (Sajid 2021). In this chapter, we show that the defense industry has both direct and indirect effects on the environment in terms of carbon emissions. These effects can be traced back to the use of fossil fuels. In particular, by taking the United States of America, China, France, the United Kingdom, and Russia as examples, we analyzed the demand- and supply-side effects of their respective defense expenditures on the carbon emissions of their respective economies for the years 2005 and 2009. This was done for the period of time spanning from 2005 to 2009. According to the findings of our research, the United States of America, Russia, and Germany had the highest levels of direct emissions from the defense industry. In terms of the intensity of carbon emissions, Russia had the highest value in 2005 (9.87 t/$104 ) and the lowest value in 2009 (5.72 t/$104 ), making it the leader among all of the countries that were taken into consideration for this study. Nevertheless, between 2005 and 2009, all of the countries displayed a healthy trend of reduction in the carbon intensity of the defense sector, which also translated into decreased emissions for the majority of the countries. This was the case for all of the countries. The majority of the defense sector’s emissions were found to be incorporated into the government’s final demand, which our findings demonstrated once again reaffirms the monopsonic or oligopsonic nature of the military industry. In addition to the traditional estimations of the demand side, our research on the supply side revealed that the defense industries of the United States (9.75%), Russia (4.11%), and Germany (4.03%) were the primary contributors to the overall supply-pushed emissions in 2005 and 2009, respectively. After further decomposing the demand-pulled and supply-pushed carbon linkages of the defense sector, it was discovered that the largest source of demand-driven downstream and supply-pushed upstream carbon linkages for all of the countries was the defense sector’s intrasectoral purchases and sales. In the meantime, “Electricity, Gas, and Water Supply” was the second largest source of carbon linkages in both the upstream and downstream directions. In addition to presenting the socioeconomic impact of the long-term fear industry, which is referred to as the defense sector in this study, presenting the carbon impact of the defense sector can further guide both public policy and the United Nations’

5.78

10.97

1.45

0.08

0.54

9.74

15.58

13.50

1.39

1.91

3.37

1.80

2.97

6.61

1.28

92.03

13.48

Mining and quarrying

Food, beverages and tobacco

Textiles and textile products

Leather, leather and footwear

Wood and products of wood and cork

Pulp, paper, printing and publishing

Coke, refined petroleum and nuclear fuel

Chemicals and chemical products

Rubber and plastics

Other non-metallic mineral

Basic metals and fabricated metal

Machinery, Nec

Electrical and optical equipment

Transport equipment

Manufacturing, Nec; recycling

Electricity, gas and water supply

Construction

2.74

183.76

0.19

1.80

0.74

0.57

8.97

10.56

0.47

8.90

30.25

8.92

1.57

0.03

0.83

2.82

13.17

1.80

0.03

8.99

0.00

0.05

0.01

0.02

0.54

0.66

0.03

0.46

0.32

0.40

0.04

0.01

0.11

0.12

0.35

0.23

2005

2.07

China

2005

2009

USA

Agriculture, hunting, forestry and fishing

Sectors

0.03

11.15

0.01

0.06

0.02

0.04

0.70

0.75

0.04

0.65

0.39

0.50

0.06

0.01

0.13

0.16

0.48

0.23

2009

0.51

0.88

0.05

0.25

0.03

0.12

0.15

0.11

0.02

0.05

0.24

0.17

0.01

0.00

0.02

0.09

0.01

0.08

2005

France

0.11

1.00

0.04

0.04

0.01

0.03

0.15

0.22

0.01

0.02

0.25

0.12

0.01

0.00

0.01

0.04

0.02

0.09

2009

0.71

3.14

0.08

0.11

0.06

0.25

0.23

0.20

0.07

0.15

0.38

0.28

0.02

0.00

0.01

0.09

0.20

0.03

2005

UK

Table 9.6 A further decomposition of defense sector’s demand-side carbon linkage of selected countries

0.29

4.33

0.03

0.02

0.01

0.04

0.22

0.34

0.05

0.04

0.17

0.21

0.02

0.00

0.01

0.05

0.26

0.03

2009

1.27

7.80

0.55

0.42

0.34

1.59

1.83

0.21

0.08

1.19

2.49

0.54

0.06

0.02

0.11

0.79

2.78

0.58

2005

Russia

1.35

7.88

0.55

0.46

0.36

1.79

1.88

0.15

0.10

1.29

2.33

0.47

0.05

0.04

0.17

0.93

2.83

0.62

2009

0.45

3.68

0.01

0.08

0.06

0.18

0.32

0.23

0.05

0.12

0.29

0.25

0.03

0.00

0.00

0.19

0.10

0.10

2005

0.15

7.07

0.00

0.01

0.01

0.03

0.39

0.39

0.01

0.07

0.43

0.13

0.01

0.00

0.00

0.06

0.10

0.14

2009

(continued)

Germany

9.5 Conclusions 133

7.76

10.16

2.16

7.97

1.33

4.49

3.34

6.72

11.67

Inland Transport

Water transport

Air transport

Other supporting and auxiliary transport activities; activities of travel agencies

Post and telecommunications

Financial intermediation

Real estate activities

Renting of M&Eq and other business activities

3.80

Retail trade, except of motor vehicles and motorcycles; repair of household goods

Hotels and restaurants

4.87

Wholesale trade and commission trade, except of motor vehicles and motorcycles

17.55

0.36

1.64

3.54

5.81

7.92

2.74

20.85

2.81

1.81

2.21

0.20

0.04

0.02

0.01

0.03

0.07

0.24

0.14

0.32

0.11

0.02

0.01

0.00

2005

0.78

China

2005

2009

USA

Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of fuel

Sectors

Table 9.6 (continued)

0.07

0.01

0.01

0.04

0.07

0.67

0.17

0.31

0.21

0.02

0.02

0.00

2009

France

0.23

0.04

0.06

0.06

0.01

0.09

0.01

0.58

0.08

0.05

0.09

0.06

2005

0.21

0.01

0.03

0.02

0.00

0.06

0.01

0.54

0.02

0.02

0.05

0.04

2009

UK

0.29

0.12

0.26

0.20

0.02

0.41

0.01

0.44

0.06

0.24

0.22

0.11

2005

0.25

0.02

0.03

0.13

0.03

0.72

0.01

0.97

0.01

0.11

0.13

0.05

2009

Russia

1.44

1.76

0.65

0.73

0.58

0.65

0.16

3.37

0.79

0.68

1.68

0.22

2005

1.58

2.46

0.82

0.88

0.98

0.91

0.14

3.54

0.91

0.84

1.94

0.21

2009

0.25

0.17

0.26

0.28

0.07

0.88

0.01

0.10

0.07

0.15

0.13

0.07

0.45

0.12

0.06

0.26

0.15

1.45

0.01

0.18

0.02

0.11

0.08

0.03

2009

(continued)

Germany 2005

134 9 An Application of the Long-Term Fear Industry Theory …

3.86

0.82

7.14

0.00

Health and social work

Other community, social and personal services

Private households with employed persons

0.00

4.13

0.61

0.81

245.79

0.00

0.14

0.01

0.04

1.47

2005

602.33

China

2005

2009

USA

Education

Public admin and defence; compulsory social security

Sectors

Table 9.6 (continued)

0.00

0.27

0.03

0.10

2.56

2009

France

0.00

0.40

0.01

0.05

10.03

2005

0.00

0.29

0.00

0.01

5.01

2009

UK

0.00

0.37

0.05

0.36

16.08

2005

0.00

0.15

0.01

0.13

6.65

2009

Russia

0.00

2.13

0.01

0.05

53.23

2005

0.00

2.04

0.01

0.08

55.66

2009

Germany

0.00

0.36

0.02

0.14

16.77

2005

0.00

0.26

0.01

0.05

5.23

2009

9.5 Conclusions 135

136

9 An Application of the Long-Term Fear Industry Theory …

2030 Sustainable Development Goals (SDGs). Particularly, Sustainable Development Goal 1 (elimination of poverty), Sustainable Development Goal 2 (zero hunger), Sustainable Development Goal 3 (climate action), and Sustainable Development Goal 16 (peace and justice) can all be supported by doing the following: (1) Cutting spending on the defense sector and reallocating resources to climate protection and public welfare (i.e., poverty elimination) programs. Specifically, in the context of this chapter, the government, which is the primary purchaser or user of the products and services provided by the defense sector, has the ability to influence not only these emissions through the implementation of policy initiatives but also through the reduction of its final demand. In addition, there is room for improvement in the carbon intensity of the defense sector by encouraging cleaner production and consumption within this sector. Additionally, intrasectoral improvements in the defense industry’s power consumption, such as its “Electricity, Gas and Water Supply” providers, can be influenced toward the production of renewable energy to further reduce the effects of the defense industry’s demand as well as its supply-pushed effects.

9.5.1 Limitations and Future Research The long-term fear industry, especially the “Public Admin and Defence; Compulsory Social Security” depicted in this study as the defense industry, has a wide-ranging environmental effect. In this chapter, we only discussed the long-term fear industry’s demand and supply-driven direct and indirect carbon emissions effects. In particular, wars that are directly or indirectly influenced by defense spending can have a massive impact on socioeconomic well-being. War rips apart neighborhoods and families, and it frequently disrupts the process of building a nation’s social and economic fabric as well (Srinivasa and Rashmi 2006). In addition to causing long-term damage to children and adults’ physical and mental health, the effects of war also include the depletion of both material and human capital (Srinivasa and Rashmi 2006). Furthermore, wars can affect the environment in different ways. For example, according to Global Citizen (Joe 2022), wars have the following environmental consequences: (a) The military is responsible for a significant portion of the world’s greenhouse gas emissions because of the enormous amounts of fossil fuels it uses. If the United States military were a country, for instance, it would rank 47th in the world in terms of the total amount of emissions it produces. (b) Attacks with bombs and other forms of modern warfare have a direct and negative impact on wildlife and biodiversity. The unintended consequences of armed conflict can result in the deaths of as many as 90% of the large animals living in an area. (c) The pollution caused by the war contaminates the bodies of water, soil, and air, making the areas unfit for human habitation. In conclusion, a particular industry can have a wide variety of direct and indirect environmental impacts (on land and water, for example, as well as emissions of

References

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greenhouse gases and other pollutants) and, consequently, environmental linkages (Sajid and Rahman 2021). In this particular study, we focused solely on analyzing the demand- and supply-side direct and indirect carbon effects associated with the defense industry. As a result, to gain a deeper comprehension of the environmental impact that the long-term fear industry has on the world as a whole, future works can estimate the environmental burdens that are associated with war. In addition, quantification of the environmental linkages between the demand and supply sides of the defense industry from other sources is another option for gaining a deeper comprehension of the environmental impacts of the long-term fear industry.

References Ali Y (2015) Measuring CO2 emission linkages with the hypothetical extraction method (HEM). Ecol Ind 54:171–183 Bildirici M (2017) The effects of militarization on biofuel consumption and CO2 emission. J Clean Prod 152:420–428 Bryan R, Grant J, Miranda P (2021) How does defense spending affect economic growth? RAND Corporation, Santa Monica. Retrieved from https://www.rand.org/content/dam/rand/pubs/res earch_reports/RRA700/RRA739-2/RAND_RRA739-2.pdf CAAT (2011) An introduction to arms trade. Campaign Against Arms Trade, London Cai J, Leung P (2004) Linkage measures: a revisit and a suggested alternative. Econ Syst Res 16:65–85 Chen G, Hadjikakou M, Wiedmann T (2017) Urban carbon transformations: unravelling spatial and inter-sectoral linkages for key city industries based on multi-region input–output analysis. J Clean Prod 163:224–240 Chenery H, Watanabe T (1958) International comparisons of the structure of production. Econometrica 4:487–521 Clements B (1990) On the decomposition and normalization of interindustry linkages. Econ Lett 33:337–340 Closson S (2013) The military and energy: moving the United States beyond oil. Energy Policy 61:306–316 Dietzenbacher E (1997) In vindication of the Ghosh model: reinterpretation of a price model. J Reg Sci 37:629–651 Duarte R, Sánchez-Chóliz J, Bielsa J (2002) Water use in the Spanish economy: an input_output approach. Ecol Econ 43:71–85 Fajgelbaum PD, Khandelwal AK (2016) Measuring the unequal gains from trade. Q J Econ 131:1113–1180. https://doi.org/10.1093/qje/qjw013 Fiott D (2014) Reducing the environmental bootprint? Competition and regulation in the greening of Europe’s defense sector. Organ Environ 27:263–278 Ghosh A (1964) Experiments with input-output models. Cambridge University Press, London Guerra A, Sancho F (2010) Measuring energy linkages with the hypothetical extraction method: an application to Spain. Energy Economics 32:831–837 Hübler M, Pothen F (2017) Trade-induced productivity gains reduce incentives to impose strategic tariffs. Econ Model 61:420–431. https://doi.org/10.1016/j.econmod.2016.11.006 Isard W (1990) Progress in global modeling for world policy on arms control and environmental management. Conflict Manag Peace Sci 11:57–94 Joe M (2022) How war impacts climate change and the environment: few things fuel the climate crisis quite like war. Retrieved 3 Sept 2022, from Global Citizen. https://www.globalcitizen.org/ en/content/how-war-impacts-the-environment-and-climate-change/

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Chapter 10

Short-Term Fear industry’s Environmental Consequences and Its Implications for SDGs 1, 2, 3, and 16

10.1 Background The short-term fear industry, which emerges in response to disasters or short-term fear events, is a topic discussed in the introduction of Chap. 1. It is reasonable to assume that the environmental effects of these industries, both positive and negative, will be temporary. For example, natural disasters like floods, earthquakes, and landslides have momentary impacts on the environment. To further understand the potential environmental benefits and risks associated with short-term fear events, it is necessary to categorize them accordingly. In Chap. 2, we focus on carbon emissions and how the disruption of economic activities, such as industrial production and commuting, can reduce daily, monthly, or yearly carbon emissions. However, activities like rescue efforts, emergency supply distribution, and infrastructure rebuilding can lead to additional pollutant emissions. In this section, we expand on the empirical evidence of the short-term fear theory, using the example of the COVID-19 pandemic, to illustrate the environmental risks posed by the short-term fear industry. Specifically, we examine carbon emissions from the worldwide air transport of the COVID-19 inoculation as an environmental risk, as well as the overall decrease in carbon emissions resulting from restrictions on international air travel during the peak of the pandemic. We compare the benefits of carbon reduction against the risks posed by the short-term fear industry, particularly in terms of international transport, to determine the net impact of the COVID-19-related industry. By presenting the environmental hazards and benefits of the short-term industry within the context of ongoing macro disasters like COVID-19, policymakers can develop effective policy instruments to harness the short-term environmental benefits. This is particularly valuable during international disasters like COVID-19, allowing decision-makers to prepare for the adverse environmental effects of shortterm fear events. Moreover, this research enables academia and students to extend the understanding of carbon benefits and hazards from COVID-19 to other types of natural disasters and environmental consequences, such as water and air pollutants, guided by our methodology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_10

141

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10 Short-Term Fear industry’s Environmental Consequences and Its …

Considering the ongoing dynamics of CO2 emissions during the COVID-19 pandemic and other global disruptions, the available data are currently insufficient (Liu et al. 2020b). There is an urgent need for more research, investigation, data collection, and methodological improvements (Liu et al. 2020b). Sajid et al. (2022) developed a straightforward method based on the total number of required airlifts for worldwide air transport of the COVID-19 inoculation, the population of inoculation-receiving countries, and the average distance between countries. Their method accounts for data scarcity associated with natural disasters, specifically the current COVID-19 pandemic. They believe that their approach provides a reliable estimate of the COVID-19 carbon emissions related to air transport. The authors also validated their methodology by comparing it with other distance datasets, such as the worldwide distance between capitals and key cities/agglomerations from the “Centre d’Etudes Prospectives et d’Informations Internationales (CEPII)” (Mayer and Zignago 2011). In our study, we adopt the methodology described by Sajid et al. (2022) to estimate the carbon emissions caused by the worldwide air transport of COVID-19 inoculation. Additionally, instead of comparing various datasets, we verify the reliability of their direct distance dataset between a random center location/country (e.g., the United States of America) and 243 other countries by simulating the dataset to a hundred thousand destination data points using the Monte Carlo simulation technique. This verification aims to confirm the accuracy of their direct distance dataset. Furthermore, data on the reduction of CO2 emissions from air transport related to COVID-19 were obtained from relevant literature. Understanding the short-term fear industry, including the worldwide CO2 emissions of the COVID19-affected air aviation sector, can guide post-COVID-19 technological and policy tools. Controlling global warming can help mitigate the adverse effects of climate change, particularly in developing countries with high climate risk (Sajid 2020). This can contribute to achieving the United Nations Sustainable Development Goals (SDGs), including SDG 1 (poverty eradication), SDG 2 (zero hunger), SDG 3 (climate action), and SDG 16 (peace and justice). The remaining sections of this chapter are structured as follows: Sect. 10.2 presents the most recent research on COVID-19 and explains the research gap that our study aims to address. Section 10.3 describes the sources used to compile the data for this chapter. Section 10.4 outlines the procedures followed to estimate various factors, including the carbon hazards and benefits of the selected short-term fear industry of air transport. Section 10.5 presents and discusses the results of our numerical estimations. Section 10.6 concludes the work by providing recommendations for public policy and discussing the research constraints. Figure 10.1 provides a clear categorization of the environmental (carbon) repercussions of the short-term fear industry.

10.2 Pertinent Literature Review

143

Environmental benefit (EB)

Short-run fear industry's environmental impact

Environmental hazard (EH)

Net environmental benefit/hazard= EB minus EH

Fig. 10.1 A simple classification of the environmental (carbon) consequences of the short-term fear industry

10.2 Pertinent Literature Review With the highly anticipated release of COVID-19 inoculation, the opportunity arises to protect more than 7.8 billion people worldwide from the health risks and fatalities posed by the virus. However, the distribution of the COVID-19 vaccine to such a vast population necessitates extensive organizational and logistical efforts (Berkley 2020; Khan et al. 2018). Notably, air carriage emerges as the primary method for timely international distribution (International Air Transport Association [IATA] 2020), yet it heavily relies on fossil fuels, making the air distribution of COVID-19 inoculation a climate hazard. Furthermore, while each individual typically requires only one shot of the vaccine, many countries have stocked up on multiple doses per person (Mullard 2020). This presents additional social (Callaway 2020) and environmental risks, as less-developed countries may face limited access to the COVID-19 vaccine (Callaway 2020). Additionally, the delivery of extra vaccine doses by air is likely to lead to a significant increase in carbon dioxide emissions. Complicating matters further, the emergence of new mutant variants of the virus may necessitate the development and distribution of entirely new vaccines (Liu et al. 2021; Nature News 2021), yet there is a general lack of reference frameworks for estimating carbon emissions related to COVID-19 vaccine distribution, particularly from essential air carriage. Therefore, it is crucial to estimate these emissions and develop mitigation methods to address the carbon footprint associated with COVID-19 vaccine distribution via air carriage. In general, various aspects of the impact of COVID-19 on the environment and carbon emissions have been extensively studied. The environmental impact of the pandemic has been investigated in relation to air contamination (Acharya et al. 2021; Dumka et al. 2020; Ranjan et al. 2020; Wang et al. 2020), waste management (Sharma

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10 Short-Term Fear industry’s Environmental Consequences and Its …

et al. 2020; Khan et al. 2019, 2020; Vanapalli et al. 2021), circular economy (IbnMohammed et al. 2021), water usage (Kalbusch et al. 2020), sustainable development (Barbier and Burgess 2020; Khan et al. 2021; Rume and Didar-UlIslam 2020), climate (Forster et al. 2020), and public impact (Rousseau and Deschacht 2020). Regarding CO2 emissions, studies have examined the influence of COVID-19 policies on emissions (Gerlagh et al. 2020; Shan et al. 2020; Wang and Wang 2020), daily CO2 emissions patterns (Liu et al. 2020a; Khan et al. 2023; Quéré et al. 2020; Turner et al. 2020; Zheng et al. 2020), power usage (Aktar et al. 2021; Cui et al. 2020; Han et al. 2021; Nižetic 2020; Sajid et al. 2022; Sajid and Gonzalez 2021), and the role of renewable energy (Naderipour et al. 2020). Specifically related to COVID-19 transportation, several studies have examined the adverse effects of the pandemic on transportation activities. Mitr˛ega and Choi (2021) investigated how small and medium-sized transportation companies managed asymmetric client relationships during the COVID-19 epidemic. Amankwah-Amoah (2020) studied the response of the international airline industry to the environmental shocks caused by the pandemic. Cui et al. (2021) explored the impact of COVID-19 shocks on China’s transportation sector. Hensher et al. (2021) conducted research on the influence of remote work on strategic modeling of transportation systems. Kim and Kwan (2021) conducted a longitudinal study on how the epidemic affected people’s movement. Zhang et al. (2021) analyzed the effects of COVID-19 transportation policy measures in six developed countries. Warnock-Smith et al. (2021) investigated the consequences of the pandemic on the Chinese passenger air carriage sector. Hasselwander et al. (2021) explored the implications of COVID-19 for transportation policy in Manila, a megacity. Tiikkaja and Viri (2021) studied the impact of the pandemic on public transportation usage in the Tampere region of Finland. Zhu et al. (2021) examined the influence of COVID-19 recovery preparedness on the international aviation sector. Gössling et al. (2021) conducted an analysis to determine whether the pandemic provides an opportunity to put the sector on a reliable low-carbon trajectory. Their approach considered the observed decrease in air travel demand, proposing a recovery plan with a carbon price to account for externalities and incentivize fuel efficiency, as well as a feed-in quota for nonbiogenic synthetic fuels to decarbonize fuels by 2050. However, none of the aforementioned works, except for Sajid et al. (2022), have taken into account the carbon emissions produced by emergency supplies during a period characterized by a short-term fear industry, such as the ongoing COVID19 pandemic. Additionally, Sajid et al. (2022) did not address their research in the contexts of the short-term fear industry, nor did they differentiate between the environmental hazards and benefits associated with it. Furthermore, the concept of net environmental benefit/hazard is absent from their work. Our research addresses these limitations by comparing the environmental hazards and benefits of the short-term fear industry in terms of the impact on carbon emissions from air carriage during the COVID-19 pandemic. Moreover, unlike Sajid et al., we establish internal consistency in our model by conducting a Monte Carlo simulation of the distance dataset, overcoming the challenge they faced when using Student’s t-test to compare original results with simulated dataset results.

10.3 Data Sources

145

10.3 Data Sources We utilized data on “CO2 emissions from global air dispatch of the COVID-19 inoculation” published by Sajid et al. (2022) for this study. The following method was employed to analyze the data due to the general lack of information on COVID-19 inoculation dispatch operations. Online distance calculators from DistanceFromTo (2021) and MapCrow (2021) were used to develop comprehensive distance data from the United States to 243 other countries and major autonomous regions. Using this data, an estimate of the total number of flying hours required for a “Boeing 747” to travel from the United States to each of the 243 destinations was derived. The average (cruise) speed of a “Boeing 747” in miles per hour was obtained from various sources, including Baciu (2010) and Hopkins (2020). The number of flight hours required to travel between countries was estimated based on this information. Finally, the total expected emissions from the worldwide air distribution of the COVID-19 inoculation were calculated by considering mean flying hours, the total number of “Boeing 747” airlifts required to deliver at least one shot of the COVID-19 inoculation to the global population (Guoxian 2020; Lei 2020; Wenqian 2020), and the per-hour carbon emissions released by a “Boeing 747” during cruise speed (Carbon Independent 2019). Furthermore, data on country-specific worldwide population statistics from the UN’s “World Population Prospects 2019” (United Nations 2019) and COVID19 inoculations secured per capita from Nature’s news article by Mullard (2020) were used to estimate countrywide consumption-based carbon emissions resulting from the shipment of one shot per capita of the COVID-19 inoculation, as well as the accumulated and nation-specific CO2 emissions from the total COVID-19 inoculation shots (one shot per capita plus additional secured dosages). Additionally, data for the year 2020 regarding the percentage reduction in aviation CO2 emissions due to the COVID-19-related demand were obtained from Quéré et al. (2020). These data were utilized to estimate the impact of COVID-19 on reducing CO2 emissions from worldwide commercial aviation, with the year 2019 serving as the base year to estimate day-to-day CO2 reductions during 2020 (Quéré et al. 2020). However, the aforementioned source does not provide information on the total CO2 emissions caused by commercial aviation during the base year. Therefore, we estimated the annual CO2 reductions related to COVID-19 by using data on worldwide aviation CO2 emissions provided by Statista on a yearly basis (Erick Burgueño Salas 2022).

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10.4 Methodology 10.4.1 CO2 Hazard of the Selected Short-Term Fear Industry of Air Carriage The total CO2 emissions can be calculated by simply multiplying the amount of CO2 that is emitted during one hour of flight aboard a “Boeing 747” by the number of total flight hours that are necessary (TH) to deliver the necessary one shot of COVID-19 inoculation to all of the remining nations (Sajid et al. 2022). C =c×TH

(10.1)

where C is the total amount of CO2 emissions produced by a single flight made by a “Boeing 747” from a particular destination to all other countries. The term c denotes the amount of CO2 emissions that a “Boeing 747” flying at maximum speed produces each hour. This encompasses all 243 locations, which equates to a total of 243 airlifts aboard a “Boeing 747”. On the other hand, we are aware that to deliver at least one shot of the COVID-19 inoculation to each and every person on the planet, at least eight thousand “Boeing 747” airlifts will be required (Guoxian 2020; Lei 2020; Sajid et al. 2022; Wenqian 2020).  TC = c ×

TH Tr

 ×TL

(10.2)

where TC is the total amount of CO2 emissions that would need to be released to deliver at least one COVID-19 shot to every  person on the planet. The value that is the number of flight hours that are is represented by the equation AH = TH Tr required on average for a “Boeing 747” to travel from one country r to another country or autonomous/semi-autonomous region. T r stands for the total number of locations that can be visited. TL is the total number of airlifts that would need to be completed by a “Boeing 747” cargo aircraft to transport at least one shot of the COVID-19 inoculation to each and every person on the planet. The following equation can be used to estimate the worldwide shipment of the COVID-19 inoculation’s contribution to total emissions, which is based on the consumption (acquisition) of at least one shot per capita by various nations: T Cl = T C × Pr

(10.3)

where TC l denotes the country’s proportion of the total CO2 emissions that are generated by the air carriage of the COVID-19 inoculation, and TC represents the total CO2 releases. P l (l = 1, 2, 3, . . . , n) represents the percentage contribution of each country to the overall population of the world. However, certain countries have already placed orders for a certain quantity of COVID-19 inoculations, which is sufficient to vaccinate their entire population multiple times over the course of their

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147

lifetime. The following equations can be used to estimate the total amount of CO2 emissions that are anticipated to result from the air carriage of the necessary single shot of the COVID-19 inoculation in addition to the additional shots that have been requested. RT C = T C × R P

(10.4)

RT C l = RT C × R P l

(10.5)

where RTC stands for the total amount of CO2 emissions that have been revised after accounting for the additional inoculations (i.e., more than one per capita distribution of inoculation shots to certain nations). RT C l represents the newly revised contribution by country l to the anticipated CO2 emissions from the worldwide shipment of the COVID-19 inoculation. This was done after adjusting the number (quantity) of preorders received by each country. TC is the amount of CO2 emissions that would be produced if at least one shot of the COVID-19 inoculation were shipped out to each individual in the world’s population. The revised world population is represented by RP. After the adjustment of their preordered quantities of COVID-19 inoculation, each country’s contribution to the world population as a percentage has been recalculated, and this is represented by the variable RPl . The following equation can be used to estimate the increased number of “Boeing 747” cargo aircraft required to cover this hypothetically increased world population based on the per capita delivery of COVID-19 inoculation shots. RT F =

RT C (c × AH )

where RTF represents the revised number “Boeing 747” cargo airlifts that are required to deliver shots of the COVID-19 inoculation to the revised total population of the world (RP).

10.4.2 CO2 Benefit of the Selected Short-Term Fear Industry of Air Carriage Under the new COVID-19 regulations, there has been a significant decrease in demand for aviation in the air. This has led to sizeable reductions in the total quantity of annual CO2 releases in the first year of the spread of CD-19, which is 2020 (Gössling et al. 2021). It is possible for us to make a rough estimate of this phenomenon by using 2019 as the base year for the analysis of the decreases in CO2 emissions that occurred during the first year of COVID-19 as follows: G AC = BC × %DC

(10.6)

148

10 Short-Term Fear industry’s Environmental Consequences and Its …

where GAC stands for the worldwide air aviation emissions during the first year of the COVID-19 model. In the meantime, BC depicts the worldwide aviation emissions in the base year, and %DC represents the percentage decrease in CO2 emissions as a result of CD-19-related economic activity restrictions. Both of these figures can be found in the table.

10.4.3 Net CO2 Hazard/Benefit of the Selected Short-Term Fear Industry of Air Carriage If we make the hypothetical assumption that worldwide air carriage of COVID-19 will be carried out in 2020, then the CO2 emissions from air carriage of the required shots of the COVID-19 inoculation should be added to the after COVID-19 CO2 emissions of worldwide air aviation after COVID-19 CO2 releases to represent the actual emissions. This is a hypothetical scenario. AC F = G AC + RT C

(10.7)

where ACF represents the total CO2 emissions after COVID-19 (i.e., the CO2 emissions for 2020) after including the emissions from the worldwide air carriage of the required shots of COVID-19 inoculation. It is possible to estimate the short-term fear industry’s annual net CO2 impact on worldwide air aviation’s CO2 release by first finding the total emissions during COVID-19 and then subtracting those from the emissions that occurred either immediately before or just after CD-19. In contrast, a positive balance will indicate a net environmental (CO2 ) hazard of the selected short-term fear industry, whereas a negative balance will indicate an overall environmental benefit in terms of CO2 emission reductions from the selected short-term fear industry. N et impact = BC − AC F

(10.8)

10.5 Results 10.5.1 Robustness Analysis of the Methodology The primary methodology used to estimate CO2 emissions from the required one shot, as well as any additional shots, is based on the distances calculated between a target country (the United States) and 243 major destinations worldwide. This estimation serves as the basis for determining total flight hours and the average flight hours required for a “Boeing 747” to travel between countries. However, there is a

10.5 Results Table 10.1 Results of the one-sample t-test analysis

149

Statistic

Value

Mean

6209.4

Standard deviation

2247.1

Standard error mean (SE)

144.2

t-value

1.896

Degrees of freedom (df)

242

P-valueb

0.06

95% CI lower boundc

− 10.58

95% CI upper bound

557.32

= 243, test value (population mean) = 5936 significance c CI = Confidence interval aN

b 2-tailed

possibility of sample bias arising from the choice of the target country or destinations. Calculating the distance between all countries or major destinations is challenging and time-consuming. To address this, a hundred thousand destination data points were randomly generated using the “Monte Carlo simulation method” in the “NIST Uncertainty Machine” to test for sample bias and ensure the robustness of the methodology and results. The one-sample t-test analysis in SPSS was conducted using the randomly generated data. With a significance level of P = 0.06 and a confidence interval ranging from − 10.58 to 557.32, the null hypothesis was not rejected, indicating no statistically significant difference between the randomly generated data and the sample of 243 destinations. Therefore, the findings based on the 243 destinations can be considered reliable even when estimating distances using other nations. (See Table 10.1 for the results of the one-sample t-test analysis.)

10.5.2 CO2 Emissions from the Air Carriage of the COVID-19 Inoculation It was estimated that the global air carriage of COVID-19 inoculation would result in approximately 8.1 metric kilotons of CO2 emissions. This estimation takes into account approximately 8000 airlifts of “Boeing 747” cargo aircraft to deliver the inoculation to 7.8 billion people. The average flight time for a “Boeing 747” cargo plane trip between nations was estimated to be ten hours. Considering these factors, countries with larger populations contribute more to the total CO2 emissions. For example, China, India, the European Union, the United States, and Indonesia are responsible for approximately 1,494, 1,435, 462, 343, and 284 metric tons (t) of emissions, respectively, accounting for nearly 50% of the total emissions from all 206 countries. (See Table 10.2 for the CO2 emissions caused by the shipment of at least one shot of the COVID-19 inoculation.)

150

10 Short-Term Fear industry’s Environmental Consequences and Its …

Table 10.2 Country-level CO2 emissions resulting from the air carriage of at least one shot of the COVID-19 inoculation Country Afghanistan

Our study 40.4

Country

Our study

Country

Our study

Country

Our study

Equatorial Guinea

1.46

Maldives

0.56

Sao Tome and Principe

0.23

Albania

2.99 Eritrea

3.68

Mali

21.01

Saudi Arabia

36.13

Algeria

45.51 Ethiopia

119.3

Marshall Islands

0.06

Senegal

17.38

American Samoa

0.06 European Union

462.05

Martinique

0.39

Serbia

9.07

Andorra

0.08 Falkland Islands

0

Mauritania

4.83

Seychelles

0.1

0.05

Mauritius

1.32

Sierra Leone

8.28

0.93

Mayotte

0.28

Singapore

6.07

French Guiana 0.31

Mexico

133.8

Sint Maarten

0.04

French Polynesia

0.29

Micronesia

0.12

Solomon Islands

0.71

Angola

34.11 Faroe Islands

Anguilla

0.02 Fiji

Antigua and Barbuda

0.1

Argentina

46.9

Armenia

3.08 Gabon

2.31

Moldova

4.19

Somalia

16.49

Aruba

0.11 Gambia

2.51

Monaco

0.04

South Africa

61.55

Australia

26.46 Georgia

4.14

Mongolia

3.4

South Korea

53.2

Azerbaijan

10.52 Ghana

32.25

Montenegro

0.65

South Sudan

11.62

Bahamas

0.41 Gibraltar

0.03

Montserrat

0.01

Sri Lanka

22.22

Bahrain

1.77 Greenland

0.06

Morocco

38.3

Sudan

45.5

Grenada

0.12

Mozambique 32.43

Suriname

0.61

Guadeloupe

0.42

Myanmar

56.46

Swaziland

1.2

Bangladesh

170.9

Barbados

0.3

Belarus

9.81 Guam

0.18

Namibia

2.64

Switzerland

8.98

Belize

0.41 Guatemala

18.59

Nauru

0.01

Syria

18.16

13.63

Nepal

30.24

Taiwan

24.72

0.06 Guinea-Bissau 2.04

New Caledonia

0.3

Tajikistan

9.9

0.8

0.82

New Zealand 5

Tanzania

61.99

11.83

Nicaragua

6.87

Thailand

72.43

10.28

Niger

25.12

Timor-Leste

1.37

7.78

Nigeria

213.92 Togo

8.59

0.35

Niue

0

Tokelau

0

26.75

Tonga

0.11

Benin Bermuda Bhutan Bolivia

12.58 Guinea

Guyana

12.11 Haiti

Bosnia and Herzegovina

3.4

Honduras

Botswana

2.44 Hong Kong

Brazil

220.58 Iceland

British Virgin Islands

0.03 India

1432.07 North Korea

(continued)

10.5 Results

151

Table 10.2 (continued) Country Brunei

Our study

Country

0.45 Indonesia

Our study

Country

Our study

Country

Our study

283.84

Northern Mariana Islands

0.06

Trinidad and Tobago

1.45

Burkina Faso

21.69 Iran

87.16

Norway

5.63

Tunisia

12.26

Burundi

12.34 Iraq

41.74

Oman

5.3

Turkey

87.52

Cambodia

17.35 Isle of Man

0.09

Pakistan

229.23 Turkmenistan 6.26

Cameroon

27.55 Israel

8.98

Palau

0.02

Turks and Caicos Islands

0.04

Canada

39.17 Ivory Coast

27.37

Palestine

5.29

Tuvalu

0.01

Cape Verde

0.58 Jamaica

3.07

Panama

4.48

Uganda

47.47

Cayman Islands

0.07 Japan

131.25

Papua New Guinea

9.28

Ukraine

45.38

Central African Republic

5.01 Jordan

10.59

Paraguay

7.4

United Arab Emirates

10.26

17.05 Kazakhstan

19.49

Peru

34.22

United Kingdom

70.45

Chile

19.84 Kenya

55.8

Philippines

113.72 United States 343.49

China

1493.63 Kiribati

0.12

Puerto Rico

2.97

United States 0.11 Virgin Islands

Kuwait

4.43

Qatar

2.99

Uruguay

3.6

Kyrgyzstan

6.77

Republic of the Congo

5.73

Uzbekistan

34.73

Vanuatu

0.32

Chad

Colombia

52.8

Comoros

0.9

Cook Islands

0.02 Laos

7.55

Reunion

0.93

Costa Rica

5.29 Lebanon

7.08

Russia

151.44 Vatican City

0

11.75 Lesotho

2.22

Rwanda

13.44

Venezuela

29.51

Curacao

0.17 Liberia

5.25

Saint Barthelemy

0.01

Vietnam

101.01

Djibouti

1.03 Libya

7.13

Saint Kitts and Nevis

0.06

Wallis and Futuna

0.01

Dominica

0.07 Liechtenstein

0.04

Saint Lucia

0.19

Western Sahara

0.62

Cuba

Dominican Republic

11.26 Macau

0.67

Saint Martin

0.04

Yemen

30.95

DR Congo

92.94 Macedonia

2.16

Saint Pierre and Miquelon

0.01

Zambia

19.08

(continued)

152

10 Short-Term Fear industry’s Environmental Consequences and Its …

Table 10.2 (continued) Country

Our study

Ecuador

18.31 Madagascar

Egypt

106.2

El Salvador * Country

Country

Malawi

6.73 Malaysia

Our study

Country

Our study

Country

Our study

28.74

Saint Vincent and the Grenadines

0.12

Zimbabwe

15.42

19.85

Samoa

0.21

Total (t)

8088.67

33.59

San Marino

0.04

codes are assigned in accordance with ISO3 standards

After accounting for additional shots per capita secured by certain countries, the total number of required shots increases from 7.8 billion to 14.3 billion, resulting in approximately 1.8 shots per capita of the world’s current population. This adjustment leads to an increase in total CO2 emissions from 8.1 to 15 metric kilotons (kt), representing an 85% increase compared to the emissions from one shot per capita. Countries like Canada, the United States, the United Kingdom, Australia, and the European Union show significant increases in consumption-based CO2 emissions, ranging from approximately 400–800%. The adjustment also reshapes the leaderboard of CO2 emissions, with the United States surpassing China as the largest producer. India, the United States, and the European Union are each responsible for approximately 19%, 16%, and 15% of the total emissions from the air carriage of globally required COVID-19 inoculations, respectively. China drops to fourth place after adjusting for secured shots per capita. Indonesia and Brazil become the fifth and sixth largest contributors. (See Table 10.3 for country-specific CO2 emissions after adjusting for additional COVID-19 inoculation shots.)

10.6 CO2 Reduction in Global Aviation Emissions During the COVID-19 Pandemic A comparison of the original CO2 emissions from global air carriage with those affected by COVID-19 is presented in Fig. 10.2. In 2019, which was the year before the COVID-19 conference, global CO2 emissions were approximately 905 million metric tons (Mt). According to published research, 75% of these emissions were due to a decrease in the daily average during 2020. Therefore, we are able to assume that the global emissions from commercial air travel have decreased to a level of merely 226.25 Mt. Based on these figures, it can be deduced that the short-term fear event of COVID-19 contributed significantly to the protection of the environment in terms of the reduction in emissions from global air carriage.

0.3

9.81

0.41

Belarus

Belize

170.9

Barbados

Bangladesh

1.77

Bahrain

Aruba

0.41

0.11

Armenia

10.52

3.08

Argentina

Bahamas

46.9

Antigua and Barbuda

Azerbaijan

0.1

Anguilla

158.77

0.02

Angola

Australia

0.08

34.11

Andorra

0.06

American Samoa

2.99

45.51

Algeria

40.4

CO2 emission (Metric tons (t))

Albania

Afghanistan

Country

119.3

3.68

1.46

CO2 emission (Metric tons (t))

Guatemala

Guam

Guadeloupe

Grenada

Greenland

Gibraltar

Ghana

Georgia

Gambia

Gabon

French Polynesia

French Guiana

Fiji

Faroe Islands

18.59

0.18

0.42

0.12

0.06

0.03

32.25

4.14

2.51

2.31

0.29

0.31

0.93

0.05

Falkland Islands 0

European Union 2310.24

Ethiopia

Eritrea

Equatorial Guinea

Country

Nauru

Namibia

Myanmar

Mozambique

Morocco

Montserrat

Montenegro

Mongolia

Monaco

Moldova

Micronesia

Mexico

Mayotte

Mauritius

Mauritania

Martinique

Marshall Islands

Mali

Maldives

Country

0.01

2.64

56.46

32.43

38.3

0.01

0.65

3.4

0.04

4.19

0.12

133.8

0.28

1.32

4.83

0.39

0.06

21.01

0.56

CO2 emission (Metric tons (t))

Syria

Switzerland

Swaziland

Suriname

Sudan

Sri Lanka

South Sudan

South Korea

South Africa

Somalia

Solomon Islands

Sint Maarten

Singapore

Sierra Leone

Seychelles

Serbia

Senegal

Saudi Arabia

Sao Tome and Principe

Country

Table 10.3 Country-level CO2 emissions from air carriage after adjusting for the additional COVID-19 inoculation shots secured

18.16

8.98

1.2

0.61

45.5

22.22

11.62

53.2

61.55

16.49

0.71

0.04

6.07

8.28

0.1

9.07

17.38

36.13

0.23

(continued)

CO2 emission (Metric tons (t))

10.6 CO2 Reduction in Global Aviation Emissions During the COVID-19 … 153

5.01

Central African Republic

352.49

Canada

0.07

27.55

Cameroon

0.58

17.35

Cambodia

Cayman Islands

12.34

Cape Verde

21.69

Burundi

0.45

Brunei

Burkina Faso

0.03

441.16

British Virgin Islands

Brazil

2.44

Botswana

Bolivia

3.4

0.8

12.11

Bhutan

Bosnia and Herzegovina

0.06

12.58

CO2 emission (Metric tons (t))

Bermuda

Benin

Country

Table 10.3 (continued)

Jordan

Japan

Jamaica

Ivory Coast

Israel

Isle of Man

Iraq

Iran

Indonesia

India

Iceland

Hong Kong

Honduras

Haiti

Guyana

Guinea-Bissau

Guinea

Country

10.59

262.5

3.07

27.37

17.96

0.09

41.74

87.16

567.69

2864.14

0.35

7.78

10.28

11.83

0.82

2.04

13.63

CO2 emission (Metric tons (t))

Paraguay

Papua New Guinea

Panama

Palestine

Palau

Pakistan

Oman

Norway

Northern Mariana Islands

North Korea

Niue

Nigeria

Niger

Nicaragua

New Zealand

New Caledonia

Nepal

Country

7.4

9.28

4.48

5.29

0.02

229.23

5.3

5.63

0.06

26.75

0

213.92

25.12

6.87

5

0.3

30.24

CO2 emission (Metric tons (t))

United Arab Emirates

Ukraine

Uganda

Tuvalu

Turks and Caicos Islands

Turkmenistan

Turkey

Tunisia

Trinidad and Tobago

Tonga

Tokelau

Togo

Timor-Leste

Thailand

Tanzania

Tajikistan

Taiwan

Country

10.26

45.38

47.47

0.01

0.04

6.26

87.52

12.26

1.45

0.11

0

8.59

1.37

72.43

61.99

9.9

24.72

(continued)

CO2 emission (Metric tons (t))

154 10 Short-Term Fear industry’s Environmental Consequences and Its …

0.02

5.29

Cook Islands

Costa Rica

0.07

11.26

92.94

18.31

106.2

Dominica

Dominican Republic

DR Congo

Ecuador

Egypt

6.73

1.03

Djibouti

El Salvador

0.17

Curacao

11.75

0.9

Cuba

52.8

China

Comoros

19.84

1493.63

Chile

Colombia

17.05

CO2 emission (Metric tons (t))

Chad

Country

Table 10.3 (continued)

Malaysia

Malawi

Madagascar

Macedonia

Macau

Liechtenstein

Libya

Liberia

Lesotho

Lebanon

Laos

Kyrgyzstan

Kuwait

Kiribati

Kenya

Kazakhstan

Country

33.59

19.85

28.74

2.16

0.67

0.04

7.13

5.25

2.22

7.08

7.55

6.77

4.43

0.12

55.8

19.49

CO2 emission (Metric tons (t))

San Marino

Samoa

Saint Vincent and the Grenadines

Saint Pierre and Miquelon

Saint Martin

Saint Lucia

Saint Kitts and Nevis

Saint Barthelemy

Rwanda

Russia

Reunion

Republic of the Congo

Qatar

Puerto Rico

Philippines

Peru

Country

0.04

0.21

0.12

0.01

0.04

0.19

0.06

0.01

13.44

151.44

0.93

5.73

2.99

2.97

113.72

34.22

CO2 emission (Metric tons (t))

0.01

202.02

29.51

0

0.32

34.73

3.6

0.11

2404.43

422.68

CO2 emission (Metric tons (t))

Total (t)

Zimbabwe

Zambia

Yemen

14,973.41

15.42

19.08

30.95

Western Sahara 0.62

Wallis and Futuna

Vietnam

Venezuela

Vatican City

Vanuatu

Uzbekistan

Uruguay

United States Virgin Islands

United States

United Kingdom

Country

10.6 CO2 Reduction in Global Aviation Emissions During the COVID-19 … 155

156

10 Short-Term Fear industry’s Environmental Consequences and Its …

CO2 emission (Mt)

905

226.25

Original emisisons

After COVID-19

Fig. 10.2 A comparison of original and COVID-19 affected global air carriage CO2 emissions

10.6.1 Net CO2 Hazard/Benefit of the Short-Term Fear Industry During the CD-19 The reductions in global air emissions are shown in Fig. 10.2; however, these numbers do not take into account the expected increases in emissions that will result from the worldwide distribution of COVID-19 inoculation via air travel. After factoring in both the actual emissions and the expected emissions from the global air carriage of the necessary shots of the COVID-19 inoculation, Fig. 10.3 illustrates the net reduction in CO2 emissions that would result from the creation of a particular shortterm fear industry (air aviation), which would be beneficial. It is anticipated that the establishment of the COVID-19-related short-term fear industry will result in a net reduction of CO2 emissions of a staggering 678,749 Mt.

10.7 Conclusions The devastation caused by an outbreak of an epidemic is distinct from that caused by other kinds of natural disasters (Govindan et al. 2020; Ivanov 2020). The short-term effects of the fear industry, such as increased or decreased demand brought on by fear-inducing events (disasters) such as floods, earthquakes, landslides, hurricanes, tsunamis, and pandemics, can have opposite effects on the environment. On the other hand, there is no such work that analyzes and contrasts the positive and negative effects that the development of the short-term fear industry has had on the surrounding

10.7 Conclusions

157

Fig. 10.3 Net emissions reduction from the creation of selected (air aviation) short-term fear industry

environment. We have demonstrated both the environmental dangers and the benefits of the short-term fear industry here by using the example of CO2 emissions from global air aviation during the time of CD-19. In addition, both the net benefits and losses associated with CO2 emissions caused by the short-term fear industry were discussed in this chapter. Not only this but also our work reasserted the robustness of the provirus literature methodology (Sajid et al. 2022) by using a one-sample t-test between the 243 destination sample distance data and the hypothesized population mean of 100,000 destination distance data generated using “Monte Carlo simulation.” The null hypothesis was accepted at P = 0.06 (95% CI = − 10.58, 557.32), and the results showed that the provirus literature methodology is accurate. Because of the ongoing COVID-19 pandemic, governments have been forced to place restrictions not only on people’s ability to travel but also on their ability to engage in commercial activity (Amankwah-Amoah 2020). With this in mind, the majority of related studies have concentrated on the reduction of environmental pressures (such as CO2 emissions and pollution) and economic activities as a result of the CD-19-mandated reduction in economic and non-economic activities. This is because the reduction in economic and non-economic activities is expected to have the greatest impact on reducing environmental pressures. However, very few studies have considered the CO2 emissions that are associated with the worldwide distribution of COVID-19 inoculation (Sajid et al. 2022). According to the findings, almost 8,000 airlifts using a “Boeing 747” to deliver at least one shot of COVID19 inoculation to 7.8 billion people will produce approximately 8.1 kilotons (kt) of CO2 emissions. After accounting for the additional shots that were secured by some countries, this number will increase to 12,772 airlifts, and there will be approximately

158

10 Short-Term Fear industry’s Environmental Consequences and Its …

13 kt of CO2 emissions. It is possible that China is not the country that contributes the most to climate change. Countries with smaller populations, such as India, the United States, and the European Union, might have higher consumption-based emissions. In addition, the global air aviation emissions that were saved during the first year of COVID-19 were approximately 75% of 2019s level and decreased from 905 Mt to merely 226.15 Mt. If we assume that each of the 12,772 airlifts required to deliver the required shots is a one-time operation that takes place within one year, then the volume of net emissions that would be saved would be approximately 678.749 metric tons. Policymakers have the ability to ensure that emergency CO2 emissions are kept to a minimum both after COVID-19 and during the transport of COVID-19 by following the steps outlined in this article. Because of the potential for additional COVID19 inoculation shots to have negative effects on both people and the environment, governments across the globe ought to reevaluate their total number of stockpiled shots and bring it down to an acceptable level. In addition, it is absolutely necessary that the anticipated rise in emissions after COVID-19 be mitigated through the implementation of specific policy actions (Sajid and Gonzalez 2021). IATA’s net-zero CO2 emissions by 2050 target was proposed at the organization’s 77th Annual General Meeting (IATA’s Pressroom 2021). The United Nations, local governments, and industrial managers should pay particular attention to this goal. In particular, special attention should be paid to this goal by the International Air Transport Association (IATA). This commitment will also align with the goal of the Paris Agreement for global warming to not exceed 1.5 °C (IATA’s Pressroom 2021) and the United Nations Sustainable Development Goals (UN SDGs). This could be accomplished through the provision of additional subsidies for carbon–neutral synthetic fuels, environmentally friendly operations, and other such initiatives. In the long run, by providing support for environmentally friendly technological innovations as well as environmentally friendly infrastructure. To avoid post-COVID-19 increases in the amount of CO2 emissions produced by the transportation sector, other studies have also proposed the decarbonization of the transportation industry as a whole (Turner et al. 2020). When planning their economic responses to CD-19, which is likely to influence the pathway of CO2 emissions for decades to come, world leaders should take into consideration the net-zero emissions targets and the imperatives of climate change, according to other studies (Quéré et al. 2020). In the meantime, other studies have suggested that world leaders should consider net-zero emissions targets. In conclusion, the task of maintaining CD-19-related decreases in global emissions on the order of billion tonnes of CO2 per year while simultaneously supporting economic recovery and human development, as well as improvements in health, equity, and well-being, lies in both current and future actions (Shan et al. 2020). The urgent nature of the situation is constantly brought into focus by the rapid progression of severe climate impacts (Shan et al. 2020). Therefore, in conclusion, the respite from CO2 emissions that the short-term fear industry, such as air aviation, provided during the COVID-19 pandemic needs to be capitalized on by post-COVID-19 technological and other policy tools. Controlling global warming can help mitigate the adverse effects of climate change, especially in high-climate-risk developing countries (Sajid

References

159

2020). This will assist with the elimination of poverty and hunger (SDGs 1 and 2), which in turn can assist with the United Nations Sustainable Development Goals (SDGs) 1 (poverty elimination), SDG 2 (Zero hunger), and SDG 16 (Climate action) (peace and justice).

10.7.1 Limitations The following are some of the limitations and uncertainty associated with the study: This study takes into account all of the data that are currently available, calculates the average emissions caused by the air carriage of COVID-19 inoculations around the world, and then allocates those emissions to different countries according to the total population of those countries as well as the total number of COVID-19 inoculation shots that are required (or secured). However, as more data become available, more specific CO2 emissions from the air shipment of COVID-19 inoculations across and within nations can be calculated. This will be possible as the amount of available data increases.

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Chapter 11

A Study of the Diverse Socioeconomic and Environmental Risks of the Longand Short-Term Fear Industries

11.1 Background In Chaps. 2 and 3, we discussed the CO2 emissions that result from the creation of short-term and long-term fear industries, as well as the implications these emissions have for Sustainable Development Goals 1 (eradicating poverty), 2 (achieving zero hunger), 3 (taking action on climate change), and 16 of the United Nations (peace and justice). We focused our attention, in particular, on COVID-19 and the defense industries as case studies for the long- and short-term fear industries. In the following, we will investigate some of the threats to the climate and the environment that are posed by the long-term and short-term fear industries. In this section, we concentrate on the risks that the long-term fear industry poses to the environment. In particular, expanding further on the discussion that was presented in chapter three, which primarily concentrated on the direct and indirect industrial CO2 emissions as well as the linkages of the long-term fear industry. Within the scope of this study, a summary of some of the additional environmental dangers posed by the defense industry will be presented. Additionally, this section will extend the discussion to the most environmentally and socioeconomically hazardous aspect of the utilization of the byproducts of the defense industry, namely, war and the militarization of society. Remember that in the first chapter’s explanation of the theory of the long-term fear industry and Sajid’s (2021a) work on military spending, it was argued that the fear of war, conflicts, or crime leads to the creation of the long-term fear industry. This finding should be kept in mind. Analyzing the socioenvironmental role of wars and militarization as an extension of the environmental effects of the long-term fear industry is, therefore, logical in the context of our study. This can be done as an extension of the environmental effects of the long-term fear industry. In the final part of this chapter, we will examine the most recent evidence regarding the effects that the short-term fear industry of COVID-19 has had on the environment. The work that is presented in this chapter can supplement the discussion that took place in earlier chapters and can further guide progress toward Sustainable Development

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_11

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Goals 1 (eradicating poverty), 2 (achieving zero hunger), 3 (taking action on climate change), and 16 (peace and justice). The remaining portions of the chapter are laid out in the following structure. In the second part of this article, we will discuss the various socio-environmental dangers posed by the long-term fear industry. In the third section, we will discuss the various socio-environmental dangers posed by the short-term fear industry. This chapter comes to a close with Sect. 11.4, which addresses some of the restrictions imposed by our methodology.

11.2 Different Socio-environmental Hazards of the Long-Term Fear Industry 11.2.1 Land Use by the Long-Term Fear Industry The military-industrial complex often leaves a sizeable CO2 footprint on the land it uses. The footprint can be roughly cut into three distinct sections in its entirety. The land that is directly utilized in the installation of production facilities comes in at number one (manufacturing sites). The second type of land is that which is utilized by the military for the construction of barracks and garrisons. The third is the land that is used for dumping or storing defense equipment that is either obsolete, destroyed, or in reserve. A straightforward categorization of these three distinct types of land that are utilized by the defense and military sector is shown in figure one. Specifically, the Department of Defense of the United States of America owns almost 26 million acres of land (Koop 2022). According to the author’s estimation, based on data from (Koop 2022) and (USDA 2012), this amounts to approximately 1.13% of the total land area in the United States. The following table provides information regarding the percentage of each state’s total land area that is occupied by the United States military (Fig. 11.1 and Table 11.1). • This does not include the massive foreign land footprint of the USA military. • The data are based on (Koop 2022).

11.2.2 Biodiversity Loss from the War Machine In addition, the defense industry’s use of land over the long term has an impact on the environment, particularly on biodiversity. Wars have a significant impact not only on human lives but also on the loss of biodiversity. The destruction of biological diversity as a result of the conflict is portrayed in Fig. 11.2. For instance, previous research carried out on African protected areas between 1946 and 2010 (Daskin and Pringle 2018) found that the frequency of conflicts predicted the prevalence and severity of population reductions among large wild herbivores. During this time

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Fig. 11.1 A simple classification of three main types of land used by the defense and military sector

Lnad use

Production facilities

Military garrisons and barracks

Dumping or storing

period, seventy-one percent of protected areas were involved in a conflict, and the frequency of these conflicts was the factor that proved to be the most significant predictor of changes in animal population trends among those that we investigated. Both the complete dataset spanning 65 years and a more recent-only analysis showed that population trajectories were stable during times of peace, fell significantly below replacement with only slight increases in conflict frequency (one conflict year every two to five decades) and were almost always negative in high-conflict areas. This was demonstrated by the fact that there was only one conflict year every two to five decades (1989–2010). Gorongosa National Park in Mozambique has suffered another devastating loss of its animal population. As a direct consequence of the civil war in the country, the park was left in ruins and was eventually closed down in 1983. By the time the war was over, more than ninety percent of the park’s animals had been either eliminated or taken captive (DW 2017). Other conflicts around the world have had a negative impact on local biodiversity in areas where these conflicts have taken place (UNEP 2016). The following are some examples from around the world that illustrate the loss of biodiversity as a result of war. • During the Vietnam War, the United States used a chemical that became known as the “Orange agent” to systematically destroy forests in an effort to deny Viet Cong guerillas the cover they required to launch attacks on American forces. This was done in an effort to reduce the number of casualties suffered by the United States.

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Table 11.1 Percentage of land occupied in each state by the USA’s military State

Percent of State land occupied by US military (%)

Percent of State land occupied by US military (%)

Percent of land occupied by US military (%)

Hawaii

0.056

Colorado

0.70

Pennsylvania

0.20

Nevada

0.05

Kentucky

0.70

Missouri

0.20

New Mexico

0.05

Indiana

0.70

Vermont

0.20

Arizona

0.042

Mississippi

0.60

Ohio

0.10

District of Columbia

0.039

South Carolina

0.60

Maine

0.10

California

0.037

Alaska

0.60

Illinois

0.10

Utah

0.036

Tennessee

0.60

North Dakota 0.10

Washington

0.022

Alabama

0.50

Iowa

0.10

Florida

0.02

Oklahoma

0.50

Montana

0.10

Maryland

0.019

New York

0.50

Connecticut

0.10

Georgia

1.60

Wisconsin

0.50

New Hampshire

0.10

New Jersey

1.50

Rhode Island

0.30

Wyoming

0.10

North Carolina 1.30

Delaware

0.30

Nebraska

0.04

Virginia

1.20

Kansas

0.30

Michigan

0.04

Texas

1.00

Arkansas

0.30

West Virginia 0.02

Louisiana

1.00

Idaho

0.30

South Dakota 0.02

Massachusetts

0.80

Oregon

0.20

Minnesota

0.01

• The marshes in Iraq were reduced to less than ten percent of their original extent by a series of dikes and channels, which also transformed the landscape into a desert with salt crusts. In more recent years, extremists affiliated with the Islamic State have set fire on oil wells in the southern city of Mosul, releasing a hazardous mixture of chemicals into the environment. Democratic Republic of the Congo (Civil war has had a devastating effect on wildlife populations that have served as a source of bushmeat for combatants, civilians struggling for survival, and commercial traders). • Afghanistan’s forests (more than half of the country’s forests have been destroyed as a result of decades of violence in the nation). • Nepalese ecosystems (during the armed conflict that occurred between 1996 and 2006, insurgents and civilians alike in places such as Khaptad National Park in the Makalu Barun Conservation area recklessly exploited wildlife and plant resources such as medicinal herbs such as Yarsagumba (Cordyceps sinensis) and Chiraito (Swertia Chiraita), among others.)

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Fig. 11.2 A biodiversity loss scene as a result of the war machine

The United Nations Environment Programme (UNEP) acknowledged the alarming decline in biodiversity that can be attributed to war (UNEP 2016). In particular, the United Nations General Assembly acknowledged in 2001 that the environment is frequently the unsung victim of conflict by designating November 6 as the International Day for Preventing the Exploitation of the Environment in War and Armed Conflict. This was done in an effort to draw attention to the fact that the environment is frequently the victim that is not given credit for its suffering. On May 27, 2016, the United Nations Environment Assembly voted to approve a resolution reaffirming its firm commitment to the full implementation of the Sustainable Development Goals and recognizing the significance of healthy ecosystems and sustainably managed resources in reducing the risk of armed conflict. The resolution also acknowledged the importance of recognizing the importance of Sustainable Development Goals.

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11.2.3 Direct and Indirect CO2 Emissions from the War Machine Wars have the potential to have both direct and indirect effects on the environment, in addition to the direct and indirect emissions that are produced by the manufacturing activities of the long-term fear industry. The use of attack and defense vehicles (tanks, fighter jets, ships, etc.), cooking and heating (burning wood and coal, etc.), and other activities can have a direct impact. These activities can take place on land, in water, or in the air. However, the indirect effects can take on a variety of forms, such as upstream emissions from activities such as the consumption of food, the washing of clothing, and the purchase of fuel. For instance, Colombia, Libya, Syria, and Iraq are just some examples of countries whose oil industries have been directly impacted as a direct result of recent wars and conflicts (Conflict and Environment Observatory 2014). Fires and spills are the most common causes of emissions, but sometimes oil infrastructure is used as a weapon, which also contributes to emissions. It is believed that the oil fires that broke out during the Gulf War in 1991 were responsible for more than 2% of the world’s fossil fuel CO2 emissions in that year (Conflict and Environment Observatory 2014). These emissions have resulted in both far-reaching and long-lasting effects. The soot from the fires, which has contributed to the rapid melting of Tibetan glaciers, is included in this (Conflict and Environment Observatory 2014). The following may describe some of the indirect CO2 emissions caused by the wars, according to the Conflict and Environment Observatory (2014). • War emissions are difficult to evaluate because they have an effect on many businesses and will continue to do so in the future. Early sources of emissions will include things such as damaged infrastructure, the loss of vegetation, and the transportation of humanitarian aid. Understanding the relationship between social change and the environment is necessary for the effective monitoring of indirect emissions. Many societal developments imply changes in people’s environmental practices, so it is imperative that this relationship be comprehended. • In the event that the demand for fuel continues despite disruptions in the traditional energy industry and the markets that serve it, people may look to sources that are less reliable and pose a higher risk. The artisanal refining of oil in Syria has led to a catastrophe, but it is unclear how much contribution this activity has made to emissions. There is evidence that people in the Democratic Republic of the Congo, Yemen, South Sudan, and Syria are cutting down trees to make charcoal and firewood. Because of the influx of Syrian refugees, cross-border human displacement may result in an increase in annual emissions in the countries that border Syria. • A significant CO2 footprint can be attributed to the humanitarian industry because of the resources that are required to provide victims of violence with food, drink, and shelter. In 2017, the cost of fuel consumption was $1.2 billion, which represented 5% of total aid spending. The majority of these costs went toward logistics and powering generators. Donors and organizations have turned to nature-based

11.2 Different Socio-environmental Hazards of the Long-Term Fear Industry

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solutions as a means of mitigating CO2 release following changes in the landscape, such as the deforestation that has occurred close to camps for Rohingya refugees in Bangladesh.

11.2.4 Disruptions to Technological Progress and Investments in Green Technologies It is possible for wars to have a significant impact on the technological progress of a country that is currently under attack. Due to underdevelopment, a lack of outside investment, and poor governance, obsolete and polluting technologies may continue to be used in places where they would otherwise have been replaced (Conflict and Environment Observatory 2014). This phenomenon is referred to as an increase in flaring intensity. Research has shown that the intensity of flaring has significantly increased over the course of the crises unfolding in Libya, Syria, and Yemen. This is despite the fact that there has been a general decrease in total output as a result of the cessation of oil production. The same pattern was noticeable throughout the Iraq War, and more importantly, it has continued to be a problem ever since. It is common for countries that are experiencing unrest or war to follow this pattern (Conflict and Environment Observatory 2014). Not only can the advancement of hard technology be significantly slowed down, but the development of soft technology can also be significantly slowed down in regions that are affected by war and conflict. “Soft technology” can refer to a variety of different things, including technological know-how, intangible resources, and organizational applications. The body of information, or the body of knowledge, methods, disciplines, and/or skills that underpin the creation of goods and services, is referred to as “know-how,” and the term is used to describe this body of information. The term “technology” refers to the scientifically organized body of knowledge that is used to create items or services that assist humans in successfully adapting to the media that surrounds them. Technology can be either “soft” or “hard.” There are many distinct types of technologies, and these categories are based on the processes of their respective development as well as the research methodologies that are used to analyze these technologies (lifepersona 2022). A general description of the various types of technology that may be impacted is provided in Fig. 11.3, which can be found in an area that has been affected by war. It is difficult to imagine investments in conventional green technologies (such as solar, wind, and hydro energy generation technologies) as well as innovative green technologies (such as carbon capture and storage, direct air capture, biofuel, synthetic fuels, clean hydrogen fuels, etc.) in war-torn regions. This is in addition to the more general physical and soft-technology impacts. In addition, the upkeep and repair of existing environmentally friendly technologies will be quite difficult in areas that are currently experiencing conflict. Finally, wars and conflicts can also cause significant damage to the infrastructure that is already available for the generation of green energy. As a result, wars and other forms of

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Fig. 11.3 A basic classification for industrial technology

conflict can have a significant impact on technological advancement, which in turn can have an impact on CO2 emissions and related efforts to mitigate their effects.

11.2.5 Illegal Mining and Logging In times of conflict, a lack of authority to maintain law and order can lead to the illegal mining and housing of civilians, both of which can have far-reaching consequences. Illegal mining has the potential to exhaust natural resources, as does the provision of illegal lodging. According to UNEP (UNEP 2016), the Revolutionary Armed Forces of Colombia (FARC) are responsible for wreaking havoc on the environment in areas of the country where gold was mined for decades without the appropriate permits or oversight. Mining, along with other illegal methods of extracting natural resources such as logging, was a significant source of revenue for insurgent groups. In particular, it resulted in the contamination of the land as well as the waterways in the basin of the Quito River by mercury. Even though war and armed conflict pose significant dangers to the environment and natural resources and may either cause

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violent conflicts or make them worse, there are still promising avenues for cooperation between environmental protection and the building of peace. Together with the UN Environment, the Environmental Law Institute, the Earth Institute at Columbia University, Duke University, and the University of California, Irvine, have created a ground-breaking open online course on Environmental Security and Sustaining Peace.

11.2.6 Additional Carbon Releases from Recovery and Reconstruction The reconstruction and construction that occur in the aftermath of war and conflict can also pose significant risks to the environment. The construction industry consumes enormous quantities of raw materials, which results in the production of enormous quantities of emissions (3560 Mt CO2 e across the globe in 2019). The production of cement is a very carbon-intensive industry because it is responsible for 8% of the world’s total greenhouse gas emissions. Even less productive can be older plants that are situated in precarious environments. In the areas of Syria that have been impacted by the conflict, nearly 10% of the available housing stock has been completely destroyed, and nearly 25% of it has suffered partial damage. It is anticipated that the reconstruction process will generate approximately 22 metric tons of carbon dioxide (Conflict and Environment Observatory 2014).

11.2.7 Loosening of Environmental Monitoring and Related Laws The ability of different governmental and nongovernmental organizations to monitor environmental hazards can be severely hindered by war and other armed conflicts. This can have a massively negative impact. In addition, it may also have an impact on academic institutions and researchers who are monitoring environmental hazards such as carbon emissions. Carbon taxes, carbon markets, and the taxation of carbon at the final consumer level are some of the legal mechanisms that are currently being utilized or suggested for the purpose of controlling carbon emissions (Sajid et al. 2021; Khan and Qianli 2017; Khan et al. 2018). As a result of conflicts and wars, the implementation of laws and regulations designed to control carbon emissions can be weakened or derailed entirely. Therefore, in addition to the problems that were discussed earlier, that can further add to the disastrous effects that the war machine has on the environment.

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11.2.8 Environmental Consequences of Refugee Crises Wars and other types of conflicts have a long history of causing massive numbers of people to be forced to flee their homes, both internally and externally. Camps for displaced people and refugees can have a significant impact on the surrounding environment, particularly if they are not well planned or equipped to deal with waste, water, and sanitation issues (Conflict and Environment Observatory 2020). Campers might have to rely on nearby supplies such as firewood, which could lead to a decrease in the availability of that resource. Migration from rural areas to urban centers as a result of internal conflict can contribute to even faster growth of local populations and put a strain on municipal infrastructure and environmental services. Management of waste is an essential requirement in refugee camps as well as in cities that are currently experiencing war. It is common for there to be an increase in the rates of trash burning, dumping, and poor management when there is conflict. During times of conflict, certain aspects of environmental governance, such as the systems that manage waste, may become dysfunctional. If local environmental rules and regulations are disregarded, it may compromise the ability of both local and national governments to identify, evaluate, and respond to environmental issues (Conflict and Environment Observatory 2020).

11.2.9 Different Socio-environmental Hazards of the Short-Term Fear Industry COVID-19’s negative effects include, among others, a rise in medical waste; the haphazard use and disposal of disinfectants, masks, and gloves; and the burden of untreated wastes that pose a constant threat to the environment, although GHG emissions are reduced as a result of lockdowns and economic activity slowdowns (Rume and Islam 2020; Khan et al. 2022a). Extensive works have been conducted on the positive effects of COVID-19 in positively addressing environmental issues such as carbon emissions (Turner et al. 2020; Zheng et al. 2020; Han et al. 2021; Liu et al. 2020; Quéré et al. 2020; Shan et al. 2020), climate change (Forster et al. 2020; Khan et al. 2022b), the circular economy (Ibn-Mohammed et al. 2021; Khan et al. 2023a), air pollution (Acharya et al. 2021; Dumka et al. 2020; Ranjan et al. 2020; Wang et al. 2020), sustainability (Barbier and Burgess 2020; Khan et al. 2023b; Rume and Didar-UlIslam 2020), waste management (Sharma et al. 2020; Vanapalli et al. 2021), public responsiveness (Rousseau and Deschacht 2020), and water usage (Kalbusch et al. 2020). According to research on a holistic level, COVID-19 has a more negative than positive impact on the environment. In particular, massive quantities of masks, plastic items (personal protective equipment (PPE) kits, face shields, etc.), and chemicals (chloroxylenol, chlorine, H2 O2 , etc.) will be produced as trash as a result of current and future efforts against coronavirus (Ankit et al. 2021). Additionally, the average amount of water used per person will rise. These results

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may have unintended consequences for the natural world. Other ecological effects on human-animal relationships are also apparent, such as the potential for people to avoid keeping pets in the future out of concern for their health (Ankit et al. 2021).

11.3 Conclusions In this chapter, we will discuss the various risks to society and the environment that are posed by long-term and short-term fear industries. In addition to the direct and indirect carbon releases from the long-term defense sector (see Chap. 2), our research and analysis show that spending on defense can have far-reaching effects on the surrounding environment. In addition, our investigation showed that contrary to the common belief that the short-term fear industry of COVID-19 is playing a positive role in the environment (see Chap. 3 for CD-19’s effects on carbon emissions), our chapter reveals that the overall negative environmental impacts of COVID-19 could be greater than any positive environmental gains. This is because our chapter shows that COVID-19’s carbon emissions affect the environment. The discussion of the short-term and long-term environmental consequences of the fear industry that was presented in earlier chapters is further enhanced by this chapter. Keeping in mind the massive environmental effects that the long-term fear industry has, it is our hope that the work we do will be able to encourage a reduction in defense spending, which will help with the United Nations Sustainable Development Goals (SDGs), in particular with the accomplishment of goals 1 (eradicating poverty), 2 (achieving zero hunger), 3 (taking action on climate change), and 16 (peace and justice). In addition, it has been demonstrated in the past that climate change has a significant direct or indirect impact on disasters such as COVID-19 that lead to the establishment of short-term fear industries. These disasters are a direct result of climate change. Carbon emissions are one of the primary causes of climate change and, by extension, global warming. Therefore, policymakers can help reduce the risks of the creation of short-term fear industries by mitigating carbon emissions, and they can also help avoid the related socioeconomic and environmental effects of the creation of short-term fear industries by avoiding the related socioeconomic and environmental effects of the creation of short-term fear industries.

11.4 Limitations This chapter was limited to analyzing and discussing the various socioeconomic effects of the creation of the short-term and long-term fear industries that were not elaborated upon in the chapters that came before it. These effects included both short-term and long-term effects. In future research, it will be possible to quantify the effects of various socioeconomic factors on the environmental consequences of long-term and short-term fear industries by using readily available effective tools

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such as structural decomposition analysis (Cao et al. 2019a, b; Sajid 2020, 2021b; Sajid et al. 2022), index decomposition analysis (Cao et al. 2019a, b), and sensitivity analyses [global and local] (Sajid et al. 2020; Sajid 2021b; Sajid and Gonzalez 2021).

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Chapter 12

The Path from Economic to Environmental Short- and Long-Term Fear Theory

12.1 Background Despite the distinct evidence for the presence of the long-term and short-term fear industries, no economic theories that explain the creation of the fear industry are available. Indeed, well-known economic schools of thought and theories such as Economic cycles (Schumpeter 1939; Rothbard 2000, 2019; Woodford 1990), Keynesian economics (Keynes 1923), Computable general equilibrium modeling (Akhabbar and Lallement 2010; Jorgenson 1998; Ghosh 1958, 1964; Leontief 1941, 1944, 1946a, b, 1949, 1951; Miller and Blair 2009), Classical economics (Smith 1776; Ricardo 1817), Neoclassical economics (Steven and David 2017; Marshall 1890, 1920; Walras 1954a, b, c), and other modern general equilibrium theories (see Davar (2015) on the drawbacks of some modern theories) ignore the theory underlying the creation and eventual demise of the short- and long-term fear industries. Not only is the basic economic theory unavailable, but so are any environmental extensions. As a result, any economic and environmental remedies and preparations in the event of the establishment of long-term and short-term fear industries cannot be directly guided by available economic theories and/or their environmental extensions. As a result, the authors felt it was critical to explain both the economics and related environmental effects of the creation of long-term and short-term fear industries. We have briefly explained and summarized the path from economic to environmental short- and long-term fear theory below.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. A. R. Khan et al., Emerging Green Theories to Achieve Sustainable Development Goals, Industrial Ecology, https://doi.org/10.1007/978-981-99-6384-3_12

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Short-run fear industry

Economic effect

Decrease in overall economic activity

Increase in economic activity of the shortrun fear industry

Environmental effect

Decrease in overall environmental impact

Increassed environmental impact from the short-run fear industry

Fig. 12.1 A summary of the road forms the economic to environmental effects of the short-term fear industry

12.2 The Path from Economic to Environmental Short-Term Fear Theory 12.2.1 Definition of the Short-Term Fear Industry Fear theory argues that in modern economies, there are vital short- and long-term industries that came to naissance because of the fears of societies. The fears can be wide raging. In particular, the short-term fear industry relates to the industries that come into presence out of the fear (risk and damage) of disasters such as floods, earthquakes, pandemics, tsunamis, cyclones, etc. Its particularity lies in the fact that during the era of the short-term fear industries, although other activities are halted, the activities of short-term fear industries are expected to increase. A prime example is the ongoing COVID-19 (“COVID-19”) pandemic. During the early stages of the COVID-19 pandemic, preventive measures resulted in an overall decrease in the demand for products and services. However, at the same time, the demand for masks (surgical, N95, and KN95 masks), hospital beds, ventilators, emergency staff, etc., increased drastically. Figure 12.1 depicts a summary of the road forms and the economic to environmental effects of the short-term fear industry.

12.2.2 Summarized Literature Review and Research Gaps Several studies have been conducted on the commercial impact of the COVID-19 pandemic. For example, short-term costs on worldwide macroeconomic aftermaths

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and monetary markets were analyzed by McKibbin and Fernando (2020) in seven different setups based on the possible spread of CD-19. Decreased workforce supply, business closures, and exoduses are all examples of “Keynesian supply shocks” that Guerrieri et al. (2020) argue can cause even larger shifts in aggregate demand than supply shocks alone. Under a variety of circumstances, Fernandes (2020) analyzed the impact of COVID-19 on GDP in 30 nations. The impact of COVID-19 on the liquid assets of listed businesses in twenty-six countries was presented by Vito and Gómez (2020). The effects of the COVID-19 pandemic on international monetary markets were estimated by Zhang et al. (2020). The financial and economic impact of COVID-19 in the region has also been the subject of some research. Possible American monetary and economic responses to COVID-19 were outlined by Hafiz et al. (2020). The latent lockdown of Tokyo due to COVID-19 was analyzed by Inoue and Todo (2020), who calculated the economic impact on other economies. An analysis of CD-19’s financial effects on Africa was presented by Ataguba (2020). The expenses and repayments of social distance were weighed by Thunström et al. (2020). Meanwhile, Sajid (2021b) conducted research on so-called disaster blesses industries, pointing out that despite the halting of production, transportation, and other economic activities, there are some industries whose demand increases in the event of a mega-disaster. However, his work ended with this particular argument. They did not demonstrate the concept of the short-term fear industry or the environmental consequences of its establishment. On the other hand, most research on short-term fear occasions (disasters), such as CD-19, has focused merely on environmental impact reductions without considering any increase in environmental effects (e.g., COVID-19 inoculation transportation) or net environmental benefits/losses. For example, many studies have been conducted on reducing the environmental impact of aerosols and air pollution (Acharya et al. 2021; Shen et al. 2021; Wang et al. 2020), the circular economy (Ibn-Mohammed et al. 2021; Khan et al. 2023; Khan et al. 2021), climate (Forster et al. 2020), community consciousness (Rousseau and Deschacht 2020), waste management (Vanapalli et al. 2021), sustainable development (Barbier and Burgess 2020; Khan et al. 2022a; Rume and Didar-UlIslam 2020), and water usage (Kalbusch et al. 2020). Specifically, for CO2 emissions, the impact of CD-19’s related policy on CO2 emissions (Gerlagh et al. 2020; Sajid 2022a, b; Khan et al. 2022a, b, c; Shan et al. 2020), industrial CO2 linkages (Sajid and Gonzalez 2021), daily CO2 emissions (Liu et al. 2020; Quéré et al. 2020; Zheng et al. 2020), energy and CO2 impact (Aktar et al. 2021; Han et al. 2021), and renewable power (Naderipour et al. 2020) has been studied in detail. Later, Sajid et al. (2022a, b) demonstrated that the air carriage of COVID-19 inoculations could also result in significant carbon releases. However, their work only presented the emissions estimates. Their research did not explain these emissions within the context of the short-term fear industry. They did not demonstrate any net benefits or losses that could be expected from the creation of short-term fear industries.

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12.2.3 Major Research Gaps Fulfilled by Our Work on Short-Term Fear Theory Based on the above literature review, the following issues regarding the economic and environmental effects of the creation of short-term fear industries remain unresolved. 1. Sajid’s (2021b) work did not explicitly introduce the term “short-term fear industry.” 2. His work did not numerically prove (showed) the presence of the “short-term fear industry”. That is, the presence of the so-called “disaster-blessed industry” was completely missing from their work. 3. Sajid’s (2021b) and Sajid et al. (2022a, b) works did not show the net environmental benefits or losses as a result of the formation of the short-term fear industry. The following novelties were added to the existing literature as a result of our research on short-term fear industries. The terminology of the short-term fear industry was introduced. The presence of the short-term fear industry was demonstrated numerically. The economic theory of the short-term fear industry was extended to the theory of the environmental short-term fear industry. The following sections summarize the economic and environmental implications of our proposed short-term fear theory.

12.2.4 Brief Overview of the Economic and Environmental Implications of Short-Term Fear Theory According to our findings, short-term fear industries may see increased demand during a short-term fear event. Industries may idle labor and fixed capital investments following the short-term fear period. Understanding the theory of short-term fear industries can thus assist managers and policymakers in better preparing for the end of a short-term fear period (e.g., the COVID-19 pandemic) by reallocating resources. For example, labor departments in various countries can redirect extra labor to the medical equipment industry (such as masks and ventilators). Preparing for and responding to a short-term disaster, such as the COVID-19 pandemic, can reduce economic losses and the closure of disaster-affected industries. This can harm recently recovered economies by slowing GDP growth, causing job losses, and causing supply shortages. Increases poverty and hunger, putting UN SDGs 1 and 2 at risk. Our findings can assist policymakers in reducing emergency carbon emissions during and after short-term fear events such as the ongoing CD-19. Because more COVID-19 inoculation shots could harm people and the environment, governments around the world should reduce their stockpiles. Furthermore, specific policy actions must be implemented to reduce CD-19-related emissions increases. The International

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Air Transport Association’s 77th Annual General Meeting proposed achieving netzero carbon emissions by 2050. This goal should be prioritized by the UN, local, and industrial leaders. This goal should be prioritized by the International Air Transport Association. This pledge will also support the Paris Agreement’s 1.5 °C global warming limit and the United Nations Sustainable Development Goals. Subsidies for carbon–neutral synthetic fuels, environmentally friendly operations, and other initiatives may be able to accomplish this. In the long run, green technology and infrastructure should be promoted. Other studies have suggested that the transportation industry be decarbonized to avoid post-COVID-19 increases in carbon emissions. Other studies suggest that when planning their economic responses to COVID19, world leaders should take into account net-zero emission targets and climate change. Other research suggests that world leaders consider setting net-zero emission targets. To summarize, maintaining COVID-19-related global emissions reductions of billions of tons of CO2 per year while promoting economic recovery, human development, health, equity, and well-being necessitates both current and future actions. Rapid climate impacts highlight the urgency of the situation. As a result, post-COVID-19 technological and policy tools should capitalize on CO2 emission reductions provided by short-term fear industries such as air travel. Controlling global warming has the potential to mitigate climate change’s negative consequences, particularly for high-risk developing countries. This will contribute to the achievement of SDGs 1 (poverty eradication), 2 (zero hunger), and 16 (climate action).

12.3 The Road from Economic to Environmental Long-Term Fear Theory 12.3.1 Definition of the Long-Term Fear Industry The long-term fear theory describes the economic effects of industries that arose as a result of society’s persistent fears. It primarily consists of defense and law enforcement agencies. However, we concentrated on the defense industry in this work. We demonstrated that, as a result of the economics of the production possibilities frontier curve, an economy and the global economy as a whole must sacrifice funding and other finite resources to the defense industry. This has an impact on overall economic well-being and has been criticized (i.e., fewer resources available for public welfare projects and private businesses). That, according to logical economic agents, can lead to civil unrest (for example, anti-war protests and protests against police brutality, to name a few). The long-term fear industry can have both direct and indirect environmental effects. Upstream purchases, production activities, waste, wars, military installments, and military waste, for example, all have significant environmental impacts. Figure 12.2 depicts a graphical presentation of the economic and socioenvironmental effects of the long-term fear industry.

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Long-term fear industry

Defense sepnding

Economic effects

Production Possibility Frontier (PPF)

Production activity

Social and environmental effects

Less resources for welfare and other projects

Carbon emission/ environmental impact increase

Fig. 12.2 A graph of the economic and socioenvironmental effects of the long-term fear industry

12.3.2 Summarized Literature Review and Research Gaps Despite the distinct evidence for the presence of long-term fear industries, few works have taken into account the social and/or economic reasons for the creation and eventual demise of long-term fear industries. The majority of the related economic literature is typically focused on the economic sacrifices of the defense and related industries [for example, see Garfinkel (1990), Ando (2009), Heo (2015), Robert and Alexander (1990), Brasoveanu (2010), Desli and Gkoulgkoutsika (2020), Zhao et al. (2015), Gascón and Foglesong (2010)]. However, few studies have been conducted to date on the concept of long-term fear industries. More recently, Sajid (2021a) argued that resource allocation to the defense sector due to scarcity of resources can lead to civil unrest and possible civil wars, requiring governments to take preventative measures now (by cutting defense spending and, in a hypothetical ideal scenario, zero defense spending) to avoid future disaster.1 This work, however, also failed to explicitly introduce the concept of the long-term fear industry and its potential environmental extensions. We concentrated on the direct and indirect carbon linkages of the defense sector (a type of long-term fear industry) from major defense-spending nations in this work. Previously, there was little research on the direct and indirect effects. There has previously been little research on the topic of cross-national and 1

Some research finds a positive effect of military spending on the economy (Ando 2009), some find no effect (Heo 2015; Robert and Alexander 1990), and some find negative effects (Brasoveanu 2010; Desli and Gkoulgkoutsika 2020; Zhao et al. 2015). Whether defense spending is seen as a positive, negative, or neutral contributor to the economy, the fact remains that every economy has limited (scarce) resources, and as a result, there is a massive opportunity cost (tradeoff) associated with it. To cover costs like defense spending, governments are increasingly turning to deficit financing.

References

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even national direct and indirect carbon linkages in the defense sector [see, for example, Bildirici (2017), Closson (2013), Fiott (2014), Ramos et al. (2007), Strakos et al. (2016)].

12.3.3 A Brief Overview of the Economic and Environmental Implications of Long-Term Fear Theory We have summarized some of the most important economic and environmental implications of our work below. Long-term fear industry theory can persuade government policymakers to reduce defense spending to avoid public clashes and move toward free markets. Understanding the long-term effects of fear in the industry can encourage defense cuts in major defense expenditure economies. “Resource trade-offs” will boost nondefense spending while allowing for more free-market economies. While this can contribute to SDGs 1 and 2, reducing internal and external conflicts directly promotes SDG 16. The carbon footprint of defense can influence public policy and the UN’s 2030 Sustainable Development Goals (SDGs). The following contribute to SDGs 1 (elimination of poverty), 2 (zero hunger), 3 (climate action), and 16 (peace and justice): (1) Redirecting defense funds to programs for climate protection and poverty alleviation. In this chapter, the government, the primary customer of the defense sector, can reduce its final demand and implement policy initiatives to reduce emissions. To reduce the carbon intensity of the defense sector, cleaner production and consumption should be encouraged. Intra-sectoral improvements in power consumption in the defense industry, such as its “Electricity, Gas, and Water Supply” providers, can be influenced to produce renewable energy to further reduce the effects of demand and supply.

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