Economic, Environmental and Health Consequences of Conservation Capital: A Global Perspective 9819941369, 9789819941360

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Ramesh Chandra Das   Editor

Economic, Environmental and Health Consequences of Conservation Capital A Global Perspective

Economic, Environmental and Health Consequences of Conservation Capital

Ramesh Chandra Das Editor

Economic, Environmental and Health Consequences of Conservation Capital A Global Perspective

Editor Ramesh Chandra Das Department of Economics Vidyasagar University Midnapore, India

ISBN 978-981-99-4136-0 ISBN 978-981-99-4137-7 (eBook) https://doi.org/10.1007/978-981-99-4137-7 © 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

This book is for my Parents, Uncles and Aunts

Foreword

Professor Ramesh Chandra Das of Vidyasagar University should be applauded for putting together an edited book on an extremely important and topical subject. He has managed to persuade scholars from across the globe to write on topics that have far-reaching consequences for the globe. The title of the book Economic, Environmental and Health Consequences of Conservation Capital—A Global Perspective brings out many of the key issues facing the world today in a very concise way. Economists have been very good at focusing on issues facing the world of the day. For example, growth theory was a very busy area when the world economy was growing at an unprecedented rate. Initially, the concerns were the lack of savings, investments in fixed capital, and foreign exchange. Then there was a realization that one needs technological progress in order for labour and physical capital to have the proper impact. With the international mobility of capital, scholars also turned to incorporating the distinction between domestic and foreign capital in growth analysis. Then came the belief that the world also needs the necessary skills in the labour force in order for all the factors of production including new technology to have the necessary impact. This led to the literature on human capital and growth. Concerns for sustainable growth and development came about when activists and scholars realized that much of the growth was destroying the environment, depleting both exhaustible and renewable natural resources, and causing local and global health problems. The negative externalities of growth is harmful to the health of the world population directly, it also has indirect effects by changing climate, causing natural disasters and calamities. The concept of conservation capital to help sustainable development is a very important and new development in the area. There are, of course, sceptics of the concept of sustainable development. They think that any environmental regulation adds to the costs of production and therefore gives the countries enforcing such regulations a disadvantage in the world market. People have been arguing against such view points and we have seen many international treaties on the environment so that there is international coordination in achieving sustainable development. It has also been recognized that green technology itself can bring in more well-paid jobs than the loss of low-paid jobs that vii

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environmental regulations can cause. I remember that in the 1960s and the 1970s, people in India were very nervous about the introduction of computers and protests against “automation” were very common. Now we know that computers in India have created more well-paid jobs than the loss of low-paid jobs that it has caused. There are twenty-two chapters in the volume covering most of the issues that I have raised here, and a lot more. I think this will be a very interesting and important volume for anyone who is interested in sustainable development. I once again commend Professor Ramesh Chandra Das for undertaking this book project. Sajal Lahiri Vandeveer Chair Professor in Economics and Distinguished Scholar Southern Illinois University Carbondale Carbondale, USA

Preface

All economic activities either affect or are affected by natural and environmental resources. Activities such as extraction, processing, manufacture, transport, consumption, and disposal change the stock of natural resources, add stress to the environmental systems, and introduce wastes to environmental media. Moreover, economic activities today affect the stock of natural resources available for the future and have inter- temporal welfare effects. From this perspective, the productivity of an economic system depends in part on the supply and quality of natural and environmental resources. The history of industrial activities, particularly from the industrial revolution in the mid-nineteenth century, brings about the growth of output in most of the economies of Europe and North America. Its trend is still continuing with huge increase in output of the global economy. But there is a serious negative effect, among others, to this output boom which is environmental degradation. Environmental degradation in terms of different types of pollution and extraction of resources affects economic output and welfare since environmental services are considered part and parcel of human civilization. Producers use environmental resources to generate economic output but its degradation in the current period will inevitably affect the future generation in terms of cost of living, loss of aesthetic values of the environment, etc. Hence, the current economic system should account for this cost of degradation in the current period to compute green income or sustainable income. Whenever we think about the sustainability of economic expansion, we need to focus on whether these economic activities are worsening the stock of natural capital or the capacity of the environment. The inputs, labour and physical capital, used in the production pollute the environment by means of direct emissions from the production points in one hand and get incomes and spend which generates further pollution as by-products on the other. As the stock of greenhouse gases (GHGs) are increasing day by day, it is thus evident that the economic activities are not sustainable. The global experience of COVID-19 pandemic is such a remarkable incidence that infected crores of people and claimed lives of lacs of people around the world. It is not just the death toll and number of cases, it has now threatened livelihoods of the people through loss of jobs, trade, productions, and huge health ix

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expenses. Inventions of vaccines for combating Coronavirus may save lives of some people for now or for some more years but rapid climatic changes could force them to mutate frequently and to appear with new arms and ammunitions. Hence, the only solution could hinge around protection of environment which can be done through investment upon conservation capital. Keeping these unsustainability issues in mind the global leaders are now highly emphasizing upon investments on conserving natural capital (which is termed as conservation capital) through technological inventions/innovations towards greener technologies to maintain sustainability or to bringing back the natural capacity intact for all future generations. Expenditures on R&D activities, thus, have been increasing day by day. There have been co-movements of R&D, FDI, investment in conservation capital and income in the major developed and emerging economies. Under this backdrop, the present edited book aims to compile studies on these issues of economic, health, and environmental implications of conservation capital from a wide variety of countries and groups of economies in terms of both the theoretical and empirical exercises. It has a total of twenty-two chapters covering the basic themes of the book. While working on the book project, it should be blameworthy if I miss acknowledging the contributions of different academic persons all around the world. First, I acknowledge the cooperation of the Springer Team, especially Nupoor Singh for her continuous support and guidance in completing the whole project. Second, I am grateful to my research guides Prof. Soumyen Sikdar, formerly of the Indian Institute of Management, Calcutta, India, and former Prof. Sarmila Banerjee of Calcutta University, India, for persistently encouraging me to undertake such types of academic projects. Third, I must express gratitude to Prof. Sajal Lahiri, Vandeveer Chair Professor in Economics and Distinguished Scholar, Southern Illinois University Carbondale, U.S.A., for writing a highly valued Foreword for the book. Fourth, I am highly indebted to all the contributing authors for their valuable chapter contributions and for showing their patience for such a long-haul project. Last but not least, I am indebted to my parents, wife and daughter, brothers, and other members of the family for their unceasing encouragement, support, and sacrifice in carrying out this type of lengthy academic project. Of course, no one other than me, as the editor, discloses to remain entirely responsible for any errors that still stay behind the book. Midnapore, India

Ramesh Chandra Das

Introduction

In traditional economies, incomes were determined mostly by their stocks of labour force and physical capital and the achievements were in the short run. But, with the progress of time, the global economic scenario has dramatically changed and is still changing vastly, and the levels of incomes of the countries are getting dependent upon factors other than the traditional inputs, labour force, and physical capital stock. The new factors become technological advancements through R&D activities, FDI, etc. which makes spill over effects across all the economies and become the crucial determinants of the global output. The channel through which the expansion of output at the global level occurred is termed globalization which broke the barriers across the economic territories of the countries and allowed the free flow of goods and services. There is no doubt that the opening up of the economies through the international flow of goods and services, technology exchange, FDI, etc. has led to betterment from economic fronts. But, the incomes or Gross Domestic Products (GDP) of the countries that were measured by traditional tools did not have the ability to account for all goods, services, assets, etc. in their market transactions. The items which were out of market transactions were taken as nonexisting and there were no externality effects of production, consumption, and above all, the gross national products or gross domestic products. The only erosion from the flow of the income stream was the physical wear and tear of the fixed capital that was termed as ‘depreciation’. The natural sources of capital related to resources and environment work as the externality factors to the accounting of national incomes of countries exclusion of which make the inefficiency of market operations. Exclusions of all these externality factors from the accounting of national income and the effectiveness of the related fiscal and monetary policies upon the income, employment, rate of interest, and general price levels like important macroeconomic determinants disprove the gross national product (GNP) as the measure of welfare (Harris, 2013; Banerjee & Das, 2017). This, in other words, means that the earlier version of GNP was unsustainable. The inclusion of all the natural resource factors into the economic activities gives rise to the concept of green macroeconomics or sustainable macroeconomics. GNP fails to account for environmental degradation and resource depletion. This xi

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issue can be important, especially in developing countries, which depend heavily on natural resources for carrying out their economic activities as well as livelihoods. If a country cuts down its forests, depletes its soil fertility, and pollutes its water supplies, this surely makes the country poorer in some very real sense. But national income accounts merely record the market value of the timber, agricultural produce, and industrial output as positive contributions to GDP. This may lead policy makers to view the country’s development in an unrealistically rosy light—at least until the effects of the environmental damage become apparent, which in some cases may be decades. Since the effects of negative externalities are not incorporated into the determination of market prices of the goods, therefore, it induces the producers or generators of the negative externalities to overproduce than the society or nature has the ability to absorb. The effect of pollution, once created, affects all the persons in the locality or even outside the locality and no one can be getting rid of its adverse effects. This means environmental pollution has the public bad (not goods) character. We thus need to internalize the social cost or dead weight loss of this environmental damage. Some measures to internalize are—Indirect Tax on pollution, Subsidy as an incentive for less pollution, Pollution standards, Marketable Pollution Permits, etc. Internalization of pollution externalities into the economic activities through these controlling measures puts financial burdens on the emitting sources on one hand and indirectly incentivizes them to go for adopting greener technologies in the form of conservation capital. Environmental degradation in terms of different types of pollution and extraction of resources affects economic output and welfare since the environmental services are considered the part and parcel of human civilization. Producers use environmental resources to generate economic output but its degradation in the current period will inevitably affect the future generation in terms of cost of living, loss of aesthetic values of the environment, etc. Hence, the current economic system should account for this cost of degradation in the current period. The statistical division of the United Nations has developed the System of Environmental Economic Accounting (SEEA) to incorporate environmental factors into the System of National Accounts (SNA). A new system of sustainable accounting, known as Green Accounting, has thus emerged. It is a measure of sustainable income level that can be secured without decreasing the stock of natural assets. Whenever we think about the sustainability of economic expansion, we need to focus on whether these economic activities are worsening the stock of natural capital or the capacity of the environment. The inputs, labour and physical capital, used in the production, pollute the environment by means of direct emissions from the production points in one hand and get incomes and spend which generates further pollution as by-products on the other. As the stock of greenhouse gases (GHGs) is increasing day by day, it is thus evident that economic activities are not sustainable. The impact of such unsustainable use of the environment can be divided in a broad sense into economic and natural. On the economic side, it hampers the decisionmaking process of the future generations as they will be incapable to lead their lives in the polluted environment that the present generations are doing. On the other hand,

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the natural impact of such unsustainability may lead to an imbalance in biodiversity whose ultimate impact will be upon the generations of new microorganisms that will invade human civilizations through their nondefendable discharge of toxic materials. The global experience of the COVID-19 pandemic is such a remarkable incidence that infected crores of people and claimed the lives of lacs of people around the world. It is not just the death toll and number of cases, it has now threatened the livelihoods of the people through loss of jobs, trade, production, and huge health expenses. Inventions of vaccines for combating Coronavirus may save the lives of some people for now or for some more years but rapid climatic changes could force them to mutate frequently and to appear with new arms and ammunition. Hence, the only solution that could hinge on the protection of the environment is through investment in conservation capital. Keeping these unsustainability issues in mind, the global leaders are now highly emphasizing investments on conserving natural capital (which is termed as conservation capital) through technological inventions/innovations towards greener technologies to maintain sustainability or bringing back the natural capacity intact for all future generations. Expenditures on R&D activities, thus, have been increasing day by day. There have been co-movements of R&D, FDI, and investment in conservation capital and income in the major developed and emerging economies. With the growing world population as well as with the growing magnitudes of uses of fossil fuels, the ambient environment has now been converted into a chamber of different greenhouse gases within which the contribution of CO2 is the highest. There is a trade-off between the growth of the economy and environmental degradation which calls for explorations into alternative sources of energy as well as regulations for protecting the environment. The alternative sources of energy are targeted for producing conservation capital through proper technological developments to replace the traditional ones since they have the potential of supporting human lives through waste absorption services, securing biodiversity, among others, which further help in achieving further economic growth in a sustainable manner. This is the true stage of growth and development which persists having the long-run phenomenon. According to Pearce et al. (1990), the policy debate on the implications of environmental concerns and regulations for economic growth has made the objective of maintaining the stock of natural capital constant which ensures sustainable growth in terms of inter-generational equity. The generation of conservation capital requires continuous efforts upon technological developments in terms of rising investments in the R&D activities (Romer, 1994). Thus, the linkages of the pair, economic growth, and conservation capital, can be modelled in the endogenous growth theoretic framework. Ramirez et al. (2002) have captured this phenomenon through their model on conservation capital and its relations to sustainable growth. The other studies that had also attempted the similar modelling before Ramirez et al. are Smulders (1994); Porter and Van der Linde (1995), among others. According to these studies, the general inference is that the conservation capital plays a dual role in the production process; it is both resourceconserving and pollution reducing. Differentiated from other capital, the production capital has no role in lowering the pollution intensity of the inputs for production,

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and the conservation capital serves as the key feature for the attainment of both economic and environmental goals. With a variation in contribution, Ramirez et al. (2002) have shown that a market economy can solve the environmental problems if there are strong preferences towards environmental quality coupled with the low rate of interest and low rate of discount of the future; otherwise, there would be the strong interventions of the governments to fight against environmental degradations. Therefore, the role of conservation capital cannot be circumvented for the sake of sustainable growth and development. Under the backdrop, the present edited book has tried to unearth theoretical and empirical analysis on the global economic periphery to examine the roles of conservation capital from economic, health, and environmental perspectives. After a series of scrutiny made by the editorial team of the publisher, Springer, the title which ultimately going to see the day light is decided to be Economic, Environmental and Health Consequences of Conservation Capital—A Global Perspective. There are a total of twenty-two chapter contributions by eminent authors from across the world. The total book content has been divided into two broad sections. Section I deals with the Economic and Environmental Aspects of Conservation Capital with eleven chapters and Section II deals with the Implications of Climate Change and Conservation Capital on Health Issues with eleven chapters. The summaries of all the chapters are given below.

Part I: Economic and Environmental Aspects of Conservation Capital Chapter 1 takes an attempt to capture the impacts of conservation capital inflow on the economy of the rural as well as urban belt through both theoretical and empirical means. Conservation capital may be invested in one or more economic sectors. It is assumed that it flows either in the rural or in the manufacturing sector but consequential effects are seen on the whole economy. It is observed that, in the short run, conservation capital comes as an inflow from outside sources through technology transfer, but in the long run, a nation becomes self-dependent to invest. In the short run, it has an impact on the economy based on Stolper- Samuelson effects and Rybczynski effects, but the long-run equilibrium is stable only if the conservation capital is being invested in the rural sector and not in the urban sector. Chapter 2 aims to examine three objectives related to different aspects of environmental pollution in developing countries. Firstly, types of environmental pollution are discussed. Secondly, some information has been given on policies that can be developed to prevent environmental pollution. Finally, an evaluation has been performed with AHP methodology. It is concluded that environmental pollution is of vital importance, especially for developing countries. Because of this situation, some actions should be taken in time to reduce environmental pollution by these countries.

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The most important cause of environmental pollution in countries is fossil fuels used in energy consumption and it is thus suggested to impose additional taxes upon the companies that pollute the environment as a result of the use of fossil fuels. Additionally, necessary policies should be implemented to increase the use of renewable energy within the country. Chapter 3 explores the high risk associated with the privatization of natural resources in terms of environmental degradation. It develops a general equilibrium framework for an open economy producing two goods using both dirty energy and cleaner energy which are substitutes for each other, along with labour and capital. It shows that government regulations by means of tariffs on dirty input or increased expenditure on clean input are helpful in mitigating pollution. Chapter 4 aims to identify the appropriate Green Corporate Social Responsibility (GCSR) strategies to improve the performance of the companies in selected developing countries using fuzzy AHP approach. The findings demonstrate that using renewable energy resources is the most important GCSR factor for companies to improve their performance. Additionally, it is also defined that ensuring the reuse of raw material waste is another essential issue in this framework. Hence, it is recommended that developing countries should mainly focus on renewable energy usage and government should patronize the initiatives. Chapter 5 aims to develop appropriate policies to reduce the negative environmental factors caused by nuclear energy using the DEMATEL method. It reveals that uranium is the main source of environmental degradation due to energy use. The study thus recommends a relatively less pollutive element thorium for the generation of nuclear energy. Chapter 6 seeks to examine if there is any evidence that sustainable development has impacted the economic growth of Tonga, a Small Island Development State (SIDS). In particular, it investigates the causal relationship between sustainability, economic growth proxied by GDP, agricultural GDP, non-agricultural GDP, overseas development assistance, and oil prices in Tonga for the 1975–2013 period. The bounds F-test for cointegration test yields evidence of a long-run relationship between the above-mentioned variables. Further, it is found that the impact of economic growth on sustainability is negative. That is higher the economic growth, the lower will be the sustainability for the economy of Tonga. Chapter 7 attempts to analyze the current scenario of sustainable and green investing vis-a-vis traditional investing in an emerging economy like India using daily level return based on closing prices data for BSE 100 vis-a-vis BSE 100 ESG and its constituents along with NIFTY 50 vis-a-vis NIFTY 100 ESG considering the standard modern portfolio analysis based on compounded annual growth rate (CAGR) and time-varying risk, namely, ARCH-M framework for the period October 26, 2017, to December 31, 2020, daily level data. The results show that the CAGR for the ESG index is much higher compared to traditional funds for both the normal and turbulent years with low volatilities with sustainable funds. This study thus demonstrates that green investing at the corporate level outperforms conventional indices, and thus advocates for proper policy modelling toward sustainable investment.

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Chapter 8, on the background of the declining forestry contribution to the Nigerian economy, aims to identify which macroeconomic factors could lead to poor forestry contribution to GDP and the factor to improve it. Using the ARDL technique for the data of 1970–2017, a long-run relationship is established between macroeconomic factors and forestry contribution to GDP. Also, the result indicated that unemployment had a negative and significant effect on forestry contribution to GDP, while foreign direct investment had a positive and significant effect on forestry rent as a contribution to GDP. These results lead to suggest the policy makers to prescribe that the activities of unemployed ones can be minimized on forestry through foreign direct investment to enhance future development. Chapter 9 reviews the prospect, opportunities, and challenges towards a low carbon economy in the Indian scenario. As per this review, increasing energy consumption has a positive impact on the economic development of the country. To achieve the goals of a low carbon economy, investment should be promoted in renewable energy sectors and energy-efficient technologies. However, equilibrium should be maintained in the use of renewable energy and fossil fuel to maintain the GDP. Chapter 10 aims to investigate conservation capital investments and policies in the global construction industry by emphasizing the importance of and need for effective conservation capital investments and policies in the construction industry. Furthermore, the effects of the green macro economy and conservation capital on the global construction industry are discussed in the chapter. It recommends that conservation capital investments in the construction industry can be influenced and supported by the relevant effective conservation capital policies. Chapter 11 analyzes the environmental consequences of the use of electric vehicles in leading countries with a special reference to India. The data used on the market share of electrical vehicles at the global level by leading countries and the share of different GHGs for the year 2018. A negative correlation is found between the adoption of electric vehicles (EVs) and greenhouse gas (GHG- CO2 , N2 O, and Methane) across top-20 EV market-sharing countries.

Part II: Implications of Climate Change and Conservation Capital on Health Issues Chapter 12 sheds light on the impacts of the rapid growth of the human population throughout the world upon several environmental problems such as climate change, global warming, loss of biodiversity, etc. leading to alterations in the ecosystem that causes a global crisis in all spheres of life. In addition, human intervention or anthropogenic disturbances aggravate the crisis in the biotic and abiotic surroundings of the environment that are responsible for the origin of many natural hazards including the occurrence of the recent pandemic COVID-19. To prevent the outbreak of the future pandemic like the present SARS-CoV-2 or a more dreadful one, the study

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recommends the conservation of biodiversity and ecosystem and reforestation as an urgent need. Chapter 13 reviews recent findings and developments on the tools and techniques of sustainable green chemistry to usher the world’s people towards a safe environment. Sustainable green chemistry is a process that encourages innovation around all divisions to propose and discover new chemicals, production methods, and product stewardship practices that will offer better performance while meeting the objectives of protecting and enhancing human health and the environment. Therefore, it is highly encouraging to invest in modern green and sustainable chemistry to make our world better for our future generation. Chapter 14 raises concerns about the major challenges of development with reconciling regional development that come up with the mounting evidence of resource depletion and the negative environmental impacts of predominantly urban-based modes of production and consumption. On the backdrop, it aims to analyze the relationship between human development and conservative capital, investigating the linkage existing between the quality of natural capital for the different Italian Regions, as well as the ability of various regional governments to enhance its natural resources, and the state of health of residents expressed as life expectancy using the method of Mazziotta and Pareto Index. The evidence show the existence of a correlation between ECCI and life expectancy at birth and life expectancy in good health. This result shows further that environmental quality can have impacts on residents’ health in a given area. Chapter 15 investigates thoroughly the impact of climate change, pollution, and deforestation on viral pandemic events and host–virus interactions in different countries and regions of the world during 2000–2020. It observes increasing influenza outbreaks and many animal influenza viruses crossing the zoonotic barrier in recent times and becoming fatal for human beings, such as highly pathogenic avian influenza A(H7N9), A(H5N1), and A(H5N6). Increasing deforestation causes more contact of wild animals with humans and may help in disease transmission. The number of viral affected individuals during the study period is positively correlated with global average temperature, nitric oxide emission, and carbon dioxide emission, whereas negatively correlated with the percentage of forest area. Chapter 16 intends to determine whether conservation capital improves healthadjusted life expectancy and ascertain the interactive effect of expenditure on environmental protection and research and development on health-adjusted life expectancy in a panel of 40 countries selected from very high human development, high human development, medium human development, and low human development countries, using data from 2000–2019. Adopting the fixed effects panel regression models with robust standard errors adjusted for clustering of residuals by countries, the finding indicates that conservation capital typically enhances health-adjusted life expectancy. Chapter 17 focuses on the reverse migration in India due to the COVID-19 pandemic, a process of the movements of migrant workers from destination centres to their homeland in an attempt to escape starvation caused by rapid job losses and a lack of efficient social support mechanisms. The study has estimated the probability of occurrence of reverse migration by making use of a probit model based on factors,

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primarily, GDP growth, unemployment, share of urban population, and percentage of COVID-19 affected, for the period of March 2020–August 2020, across states in India. It concludes that the COVID-19 fallout will lead to a heavy loss of biotic resources due to such migration. Chapter 18 examines the response of environmental pollution and climate change to the coronavirus pandemic in 40 SSA countries. It was found that one standard innovation in environmental pollution and climate change produces significant positive effects on the pandemic. The implication of this result is that, with a cumulative continuous rise in pandemic cases by one person, with a continuous observance of lockdown and other protocols, environmental pollution and unfavourable climate change would go down and/or improve by half in the next period. However, the impact of the shock of the pandemic today on future environmental pollution and unfavourable climate change decays or reverts to zero fast. The study, therefore, recommends that people and governments in SSA should strive harder to continue observing pandemic protocols and reduce every activity that breeds environmental pollution. Chapter 19, initiating from a reflection on the importance of rural areas from an economic and environmental point of view, explores the impact of tourism growth due to the current emergency situation in Italian marginal areas. It first looks at the broader economic-political context in terms of national and EU strategies, including the National Strategy of Internal Areas (SNAI), before focusing on the current situation. Specific case studies around Italy have been developed in order to underline territorial strengths and weaknesses. The aim of the work is to identify a series of guidelines and good practices for sustainable tourism in internal areas where natural capital can play a crucial role in local economic growth. Chapter 20 attempts to examine whether vaccination has any sort of linkages with the total number of COVID cases in India and its four states having metro cities for the period February 01, 2021 to May 10, 2021. Using appropriate econometric tools, the study arrives at the conclusion that both the number of vaccination and the total number of cases have long-run relation for India only, with no such results for the states. But the short-run result shows that for India and Delhi, there are bi-directional causal interplays between the two. Only Maharashtra has causal influences from the total number of cases to vaccination, while for Tamil Nadu and West Bengal, there are no such causal interplays. Chapter 21 focused on the effective conservation capital investment and policies on health care policies and expenses as well as on their relationship in a set of countries. Environmental pollution and degradation affect human health adversely. Failure in conservation capital investments can be costly due to the consequences of climate change, environmental pollution, and degradation as well as their domino effects hindering sustainable development and harming human health. Effective conservation capital policies and investments on the conservation capital can support humanity’s well-being and enhance their health. They can contribute to the reduction in the need for health care services and their expenses enabling countries to invest

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more in their development and conservation capital. It recommends designing effective conservation capital investment policies considering their relationship with and their influence on health care policies and sustainable development. The final chapter, that is Chap. 22, examines whether the nexus of environment and quality of life is contingent on the level of economic liberalization in the Sub-Saharan Africa (SSA). The study employs the two-step system GMM estimation technique for the investigation. The study found a complementary interaction between environmental quality (ENVQ) and economic liberalization and concluded that economic liberalization positively moderates the impact of ENVQ on the quality of life in SSA. This study recommended that SSA countries should pursue a guided liberalization policy that would manage the process of industrialization in the region, such that it would mitigate environmental degradation and pollution in the industrial cities of the region. Reviewing the twenty-two chapters included in the book, it is drawn that economic activity has undoubtedly affected the environmental quality whose consequences have fallen upon the health conditions of the people of the global community. All types of countries are with almost the same phenomenon in this regard. Hence, protecting the environmental quality as well as the health quality of the people is highly desirable to proceed for the conservation of nature. Hence, the role of the conservation capital becomes pertinent for discussion. In some studies covered in the book, it has been well justified that investment in conservation capital besides the investment in the traditional physical capital will lead to protection of the nature, its quality will be managed from further degradation whose ultimate effect will fall upon the human health as well as other species’ health in the earth. One of the important sources of making conservation of the environment is to move for reforestation as well as inventing cleaner technologies for industrial and transport activities. The present edited book has tried its best to accommodate studies in the related themes to justify the roles of conservation capital on economic as well as environmental fronts. But it does not mean that everything on the themes has been explored through the book, and also not that nothing remained unexplored. The editor thus invites the academic community to take further initiatives in going through the unexplored regions of the themes that the book has been incapable to accommodate. The book, whichever remains, is assumed to be helpful for the readers and researchers in different fields of pure economics, environmental economics, energy economics, environmental management, development economics, health economics, and policy makers in the related fields. Ramesh Chandra Das

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References Banerjee, D. & Das, R. C. (2017). Macroeconomics: From short run to long run, 1st Edition, Sage India, New Delhi Harris, J. (2013). Environmental and Resource Economics: A Contemporary Approach, 3rd Edition, Chapter: 8, M.E. Sharpe, New York, USA Pearce, D. W., Markandya, A. & Barbier, E. (1990). Sustainable Development: Economy and the Environment in the Third World (London: Earthscan Publications). Porter, M. E. and Van der Linde, C. (1995). “Green and Competitive,” Harvard Business Review, September-October, 120–138. Ramirez, D. T. J., Khanna, M. & Zilberman, D. (2002). Conservation Capital and Sustainable Economic Growth, the paper presented at the AAEA Annual Meeting, Long Beach, California, USA, 26-31 July 2002 Romer, P. (1994). “The Origins of Endogenous Growth,” J of Econ. Perspectives, 8, 3–22. Smulders, S. (1995b). “Environmental Policy and Sustainable Economic Growth: An Endogenous Growth Perspective,” De Economist, 143, 163–195.

Contents

Part I 1

2

3

4

5

6

7

8

Economic and Environmental Aspects of Conservation Capital

The Role of Conservation Capital in Developing Economies: A Static General Equilibrium Analysis with Dynamics . . . . . . . . . . . . Tonmoy Chatterjee and Nilendu Chatterjee

3

Generating Appropriate Policies to Minimize Environmental Pollution in Developing Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hasan Dinçer, Serhat Yüksel, and Ça˘gatay Ça˘glayan

15

Conservation of Resources and the Impact of Privatization: A General Equilibrium Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mainak Bhattacharjee, Dipti Ghosh, and Sanghita Ghosh

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Defining the Most Effective Green Corporate Governance Strategies for Sustainable Performance . . . . . . . . . . . . . . . . . . . . . . . . . . Hasan Dinçer, Hakan Kalkavan, Serhat Yüksel, and Hüsne Karaku¸s

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Evaluating Possible Ways to Decrease Negative Environmental Impact of Nuclear Energy Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serhat Yüksel, Hasan Dinçer, and Gülsüm Sena Uluer

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Does Sustainability Really Lower Economic Growth? In Search of Empirical Evidence from Tonga . . . . . . . . . . . . . . . . . . . . . . . Partha Gangopadhyay, Rina Datt, and Narasingha Das

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Does Green Investing Generate Return Over Conventional Funds? A Comparative Portfolio Analysis with Indian Stock Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debabrata Mukhopadhyay Macroeconomic Determination of Forestry Contribution to the Nigeria Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adenuga Fabian Adekoya

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9

Contents

Impacts of Low Carbon Economy in India: A Review . . . . . . . . . . . . . 111 Tarakeshwar Senapati, Apurba Ratan Ghosh, Krishna Singh, and Palas Samanta

10 Conservation Capital Investments and Policies in the Global Construction Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Begum Sertyesilisik and Egemen Sertyesilisik 11 Environmental Consequences of the Adoption of Electric Vehicle in Leading World Economies with Special Reference to India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Vani Kanojia, Megha Jain, Imran Hussain, and Ramesh Chandra Das Part II

Implications of Climate Change and Conservation Capital on Health Issues

12 Anthropogenic Disturbances on Climate Change, Global Warming, Ecosystem and COVID 19 Pandemic . . . . . . . . . . . . . . . . . . 155 Satyesh Chandra Roy 13 Green and Sustainable Chemistry: A New Blossoming Area to Solve Multiple Global Problems of Environment and Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Sumit Ghosh and Alakananda Hajra 14 Evergreen Conservation Capital Indicators and Life Expectancy in Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Andrea Ciacci, Enrico Ivaldi, and Paolo Parra Saiani 15 Viral Pandemic Caused by Intense Abuse on Environment . . . . . . . . 199 Nandini Ghosh 16 Does Conservation Capital Lead to Improvements in Health-Adjusted Life Expectancy? . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Richardson Kojo Edeme 17 Internal Reverse Migration in India Amid COVID-19 Pandemic: Linking up Human Capital with Natural Capital . . . . . . 229 Sovik Mukherjee 18 COVID-19, Environmental Pollution, and Climate Change Nexus in Sub-Saharan Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Ambrose Nnaemeka Omeje, Augustine Jideofor Mba, Divine N. Obodoechi, Ezebuilo R. Ukwueze, and Chinasa E. Urama 19 Tourism, Environment and Italian Internal Areas at the Time of COVID-19: New Challenges and Opportunities . . . . . . . . . . . . . . . . 259 Stefania Mangano, Pietro Piana, and Mauro Spotorno

Contents

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20 Does Vaccination Influence COVID Cases? An Empirical Investigation for India and Its States . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Imran Hussain and Ramesh Chandra Das 21 Impacts of Effective Conservation Capital Investment and Policies on Healthcare Policies and Expenses . . . . . . . . . . . . . . . . . 289 Egemen Sertyesilisik and Begum Sertyesilisik 22 Environmental Quality and the Quality of Life in Sub-Saharan Africa: Measuring the Role of Economic Liberalization . . . . . . . . . . . 301 Olalekan Charles Okunlola

Editor and Contributors

About the Editor Ramesh Chandra Das is a Professor of Economics at Vidyasagar University, West Bengal, India. He obtained his Ph.D. in Economics from the University of Calcutta. Dr. Das has about twenty-five years of teaching and research experience in different fields of economics consisting of theoretical and applied macroeconomics, financial economics, environmental economics, and political economics. He has contributed more than 100 research papers to journals and books published by leading international publishers and has completed three research projects (MRPs) sponsored by the University Grants Commission, India. Dr. Das has co-authored textbooks on microeconomics and macroeconomics and is Editor-in-Chief in Asian Journal of Research in Business Economics and Management, and Editor of International Journal of Research on Social and Natural Sciences. He has edited a number of volumes on globalization, investment and growth, economic and political convergence, infrastructure, microfinance, military expenditure, terrorism, etc. three of which are by leading scholarly book publishers including Springer. He was also Guest Editor of the IJSEM Special Issue (two parts).

Contributors Adenuga Fabian Adekoya Ph.D., is the Principal Lecturer at the Department of Economics, School of Arts and Social Science, Michael Otedola College of Primary Education, Noforija-Epe, Lagos, Nigeria. His research interests lie in the Development Economics and Environmental Economics. Mainak Bhattacharjee is presently an Assistant Professor in Economics at Loreto College, Kolkata, India and formerly, in The Heritage College, Kolkata, India. He has obtained M.Phil. and M.A. degrees in Economics from the Jadavpur University, Kolkata. He has been working in the areas of Macroeconomics and International xxv

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Editor and Contributors

Trade. He has contributed many research articles in reputed journals and chapters in edited volumes with international publications, along with having a number of undergraduate level text books to his credit. Ça˘gatay Ça˘glayan is currently pursuing the degree in economics and finance with Istanbul Medipol University, where he is also student with the Health Management Department. His research interests include sustainable energy economics, renewable energy, and nuclear energy. He has some articles and international book chapters related to these topics. Satyesh Chandra Roy Ph.D., was Professor and Head, Department of Botany, University of Calcutta, India. Dr. Roy was UGC Emeritus Professor in the University of Calcutta. He had more than 100 publications, published in both International and National Journals. Twelve students got their Ph.D. in various disciplines of Botany. Professor Roy established Protoplast culture Laboratory in Calcutta. He worked on Micropropagation of medicinal plants and the analysis and production of secondary metabolites through somaclonal variation in tissue culture. Tonmoy Chatterjee Ph.D., is an Assistant Professor in Economics at Ananda Chandra College, West Bengal, India. He has also served as faculty member in the Department of Economics of Sidho-Kanho Birsha University, West Bengal. He has worked as Research Assistant in Centre for Studies in Social Sciences, Calcutta, for World Bank project (under CTRPFP). Dr. Chatterjee has obtained Prof. M. J. Manohar Rao Award from the Indian Econometric Society for the best research paper in 2012. He has published a number research articles in several international and national journals of economics. Nilendu Chatterjee Ph.D. is an Assistant Professor in the Department of Economics, Bankim Sardar College, West Bengal, India. He has research interest in Resource economics, General Equilibrium, and Development Economics. He has published a number research articles in several international journals of economics including International Journal of Sustainable Economies Management, Economic Affairs, Foreign Trade Review. Andrea Ciacci is a Ph.D. student in Security, Risk and Vulnerability, curriculum in Management and Security, at the University of Genoa (Italy), Department of Economics and Business Studies. He is Member of Centro de Investigaciones en Econometría (C.I.E., Universitad de Buenos Aires, Argentina), and scientific societies such as Italian Society of Management, Italian Society of Marketing, Academy of Business Administration (AIDEA) and Italian Society of Statistics. Ramesh Chandra Das Ph.D., is currently Associate Professor of Economics at Vidyasagar University, West Bengal, India with more than twenty-two years of teaching and research experience in different fields of the subject. He has obtained Masters, M.Phil. and Ph.D. Degree in Economics from the University of Calcutta. Dr. Das has contributed research papers to reputed national and international journals and written text books on Microeconomics and Macroeconomics and edited books

Editor and Contributors

xxvii

with international publishers such as IGI Global, Emerald, Springer, Routledge and Sage. Narasingha Das Ph.D., obtained his doctoral degree from the Department of Economics and Rural Development, Vidyasagar University, India. Dr. Das does research in Industrial Organization, Environmental Economics and Econometrics. He is also associated with research projects at the Indian Institute of Technology, Kharagpur, India. Rina Datt Ph.D., commenced as a sessional staff member in 2008 and was offered a full time position in August 2015. She completed her Ph.D. in 2016 entitled “Corporate Incentives for External Carbon Emission Assurance: An International Study”. Dr. Datt has industry experience of 25 years, working in private sectors as a Financial Accountant. Her research interests predominantly lie in carbon accounting including carbon disclosure, carbon performance, carbon management and carbon assurance and general sustainability such as waste recycling. Hasan Dinçer Ph.D., is a Professor of Finance with the Faculty of Economics and Administrative Sciences, ˙Istanbul Medipol University, Istanbul-Turkey. He has BAs in Financial Markets and Investment Management from Marmara University. He received his Ph.D. in Finance and Banking by evaluating the new product development process in the banking industry. Partha Gangopadhyay Ph.D., is a Professor of Economics at Western Sydney University, Australia and held chair professorships in Germany and the South Pacific, visiting professorships in the US, Canada, India. He is rated among the top 2% of 50,663 economists of the globe listed by RePEc, a senior editor of several accomplished journals and a book series of Emerald, an executive director of Gandhi Centre in Bangalore, India and the current chair of Economists for Peace and Security—Australia. Dipti Ghosh is currently working as Junior Research Fellow in Economics at Jadavpur University, Kolkata, India. She did her Masters in Economics from Presidency University, Kolkata, India and received M.Phil. degree in Economics from Jadavpur University. Her research interests lie in macroeconomics and Indian economy. She has been associated with Bijoy Krishna Girls’ College, Howrah, India as a College Contractual Temporary Teacher (CCTT) of Economics. Nandini Ghosh Ph.D., is presently Assistant Professor in Microbiology at Vidyasagar University, India. She is working on allergen identification and development of therapeutic strategies. She has expertise in immunology, proteomics, molecular biology and bioinformatics. She has published many papers in internationally reputed journals. She was selected as a Fellow of Indian Aerobiological Society. She has also received P.H. Gregory Young Scientist Award, Abstract Award from European Academy of Allergy and Clinical Immunology. Sanghita Ghosh is presently associated with Jadavpur University, India, as Junior Research Fellow in Economics. She obtained M.Phil. and M.A. degrees in Economics

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Editor and Contributors

from the same institution. Her research interest lies in the area of Development Economics and Econometrics. Previously, she served at IMI, Kolkata in the capacity of Research Associate. She has published articles in reputed journals. Sumit Ghosh completed his B.Sc. in Chemistry from Burdwan University in 2014. He achieved his M.Sc. from IIT Kanpur in 2016. Recently, he is working with Dr. Alakananda Hajra in Visva-Bharati University. Alakananda Hajra Ph.D., is an Associate Professor of Chemistry at Visva-Bharati University. After his Ph.D. in 2002 he joined in SUNY at Albany, USA as a postdoctoral research fellow. He was also a JSPS research fellow in the University of Tokyo. He works on the development of new synthetic methodologies and green synthetic procedures. He has published more than 170 articles with 7250 citations and h-index of 44. Imran Hussain has got his master degree in Economics from Vidyasagar University, India. He is now pursuing his M.Phil. in Economics from the same university. He has research interests on growth economics, environmental economics and development economics. Enrico Ivaldi Research Fellow in social statistic and Ph.D. in Applied Economics and Quantitative Methods at the University of Genoa. He is in the editorial board of Revista de Estudios Andaluces, is a member of the Scientific Committee of the National Nautical Observatory, CIELI—Italian Centre of Excellence in logistics, transport and infrastructure, Centro de Investigaciones en Econometría, University of Buenos Aires and member of the board of Ph.D. in Security, Risk and Vulnerability (University of Genoa). Megha Jain is currently teaching as an Assistant Professor at Daulat Ram College, University of Delhi and is under pursual of her research in Management (Economics) from Faculty of Management Studies (FMS), University of Delhi. She has many national & international publications with papers presented at IIMs, IITs. She is a joint columnist in Financial Express, Firstpost, Pioneer, Hindu Business line etc. Hakan Kalkavan Ph.D., has been working as an Assistant Professor with the Department of Economics and Finance, Istanbul Medipol University since 2015. From 2019 to 2020, he has worked as a TUBITAK Postdoctoral Researcher with Durham University, U.K. His main research interests include political economy, Islamic economics, economic equality, sustainable economy, religion-ethics-economic relations, and business ethics. Vani Kanojia is an aspiring research scholar at the University of Delhi. She has completed post-graduation from Delhi School of Economics in 2015. She has recently published a short opinion article at The Pioneer on Practical implications of Electric Vehicle in India and another on Adopt a Sustainable lifestyle for Earth’s sake which is highly appreciated by the academicians. Hüsne Karaku¸s received the degree from the Banking and Insurance and International Trade and Finance Department, ˙Istanbul Medipol University, Turkey, in 2020.

Editor and Contributors

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Her research interests include energy finance, solar energy, wave energy, and renewable energy projects. She has authored some articles and international book chapters regarding these topics. Richardson Kojo Edeme Ph.D., is a Senior Lecturer in the Department of Economics, University of Nigeria, Nsukka and Research Fellow, Institute of Business Research, University of Economics, Ho Chi Minh City. Stefania Mangano is a researcher of Economic and Political Geography at the Department of Political Sciences of the University of Genoa. Her research interests concern the analysis of tourism in a geographical perspective, with a particular focus on sustainable management and the promotion of cultural heritage for the development of internal areas. Augustine Jideofor Mba is a Ph.D. student in Economics (bias in Public Finance) from University of Nigeria, Nsukka. He is a Lecturer in the Department of Economics of the University. He has published widely in both local and international journals and attended many local and international conferences. Sovik Mukherjee is at present Assistant Professor in Economics, Department of Commerce & Management Studies, St. Xavier’s University, Kolkata, India. His current research interests lie in the areas of applied game theory in industrial organization, economics of criminology and social sustainability and applied growth econometrics. Debabrata Mukhopadhyay Ph.D., is a Professor in the Department of Economics at the West Bengal State University, India. He has published a number of papers in scholarly national and international journals. He has obtained his Ph.D. degree in Quantitative Economics from the Indian Statistical Institute, Kolkata, in 2008. Divine Ndubuisi Obodoechi is a Lecturer in the Department of Economics and a research fellow with Health Policy Research Group, College of Medicine, University of Nigeria. He obtained his M.Sc. degree in Finance and Economics, Manchester Business School, United Kingdom. He is a consultant with Thinkwell global, United States of America. Charles O. Okunlola Ph.D., is currently a research fellow in the Directorate of Defence and Security Studies, Institute for Peace and Conflict Resolution, Abuja, Nigeria. His research areas lie on institutional economics, development economics, international trade and economics, peace and defence economics. He has published a couple of articles in both local and high impact international journals. Ambrose Nnaemeka Omeje is a Lecturer and Doctoral student in the Department of Economics, University of Nigeria, Nsukka. He has published papers in both international and local peer-reviewed journals. He has also researched and consulted for SAVI-DFID, UNICEF and has facilitated research for Central Bank of Nigeria—South-East Entrepreneurship Development Centre (SEEDC) Training on Entrepreneurship and Development, Nigeria.

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Editor and Contributors

Pietro Piana is research assistant in Economic and Political Geography at the Department of Political Sciences of the University of Genoa. His research interests concern the study of landscape change in rural areas and its current social, cultural and environmental implications. Apurba Ratan Ghosh Ph.D., is Professor of Environmental Science, the University of Burdwan, West Bengal. He was Joint-Director of IQAC and Director of UGCAcademic Staff College of the University. He has got authorship of eight books, eight chapters and 107 research papers. Dr. Ghosh has delivered 45 Seminar Lectures, 42 lectures in 17 different HRDC, 76 invited lectures and chaired over 12 National and International Conferences. Paolo Parra Saiani Ph.D. in Sociology at the University of Trieste, is Associate Professor in Sociology at the University of Genoa; he is actually Director of the Curriculum in Political Sciences, Ph.D. in Social Sciences. He is a member of various scientific associations and part of the scientific committee of numerous sociological journals. Palas Samanta Ph.D., is Assistant Professor of Environmental Science at Sukanta Mahavidyalaya, India. He is Ex-DST INSPIRE Fellow, GoI and Ex-BK 21 Plus Fellow, Korea. His research focused on fate and toxicity analysis of environmental contaminants on aquatic organisms, pollution monitoring and risk assessment and environmentally sustainable aquaculture. One book, four book chapters, 56 international articles are in his credit. Gülsüm Sena Uluer is currently pursuing the degree in business administration with Istanbul Medipol University. She is also student of Business Administration Integrated Master with Thesis student in Istanbul Medipol University. Her research interests include sustainable energy economics and project finance, electricity, renewable energy, and nuclear energy. Tarakeshwar Senapati Ph.D., is presently working as Assistant Professor in Environmental Science at Directorate of Distance Education, Vidyasagar University, Midnapore, West Bengal, India. He was former Assistant Professor in Environmental Engineering at Poornima University, Jaipur, Rajasthan India. He has completed his Ph.D. degree in Environmental science from The University of Burdawn in the year 2013. He has published more than thirty research papers in International and national journals of repute. His research area includes ecotoxicology, limnology, and climate change issues. Begum Sertyesilisik Ph.D., teaches at Izmir Democracy University, Turkey. She has been awarded her B.Sc., M.Sc. and MBA from the Istanbul Technical University and her Ph.D. from the Middle East Technical University. She has been specialized in the field of construction project management. Egemen Sertyesilisik Ph.D., is now a freelance consultant at Gozuyilmaz Engineering and Marine Industries Ltd, Turkey. He has been awarded his undergraduate degree from the ˙Ihsan Do˘gramacı Bilkent University, his MA in the field of Politics

Editor and Contributors

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and the Mass Media from the University of Liverpool, his MBA degree from the Yıldız Technical University and his Ph.D. degree from the Marmara University. Krishna Singh Ph.D., is an Assistant Professor at the Department of Economics, University of GourBanga, Malda, West Benga, India. He completed his M.A. in 2008 and Ph.D. in 2015 from the University of Burdwan. He has published more than fifteen papers in international and national journals of repute. His research interests include rural livelihood, Health Economics, Corporate Finance etc. Mauro Spotorno Ph.D., is Professor of Economic and Political Geography at the Department of Political Sciences of the University of Genoa. His research interests concern the analysis of territorial dynamics in internal areas in terms of demographic, economic and social changes, with a particular focus on Mediterranean mountains. Ezebuilo Romanus Ukwueze Ph.D., is a Senior Lecturer at the Department of Economics, University of Nigeria, Nsukka in Enugu State, Nigeria. He has published widely in both local and internationally recognized journals. He teaches and supervises graduate students. Chinasa E. Urama is a Lecturer at the Department of Economics, University of Nigeria. Chinasa does research in Macroeconomics, Health Economics and Development Economics. ˙ Serhat Yüksel Ph.D., is an Associate Professor of Finance with Istanbul Medipol University, Turkey. He has a BS in Business Administration (in English) from Yeditepe University in 2006 with full scholarship; master’s degree in the economics from Bo˘gaziçi University in 2008; and Ph.D. in Banking from Marmara University in 2015.

Abbreviations

Chapter 1 $ ADF CGE CO2 EKC GDP GHG IPS LLC PCFDI PCGDP PP REC

US Dollar Augmented Dicky Fuller Computable General Equilibrium Carbon Dioxide Environmental Kuznets Curve Gross Domestic Product Green House Gases Im-Pearsen-Shin Levin- Lin- Chu Per capita Foreign Direct Investment Per capita Gross Domestic Product Phillips–Perron Renewable Energy Consumption

Chapter 2 AHP R&D

Analytic Hierarchy Process Research and Development

Chapter 3 CPSE

Central Public Sector Enterprises

xxxiii

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Abbreviations

Chapter 4 AHP CSR GCSR

Analytic Hierarchy Process Corporate Social Responsibility Green Corporate Social Responsibility

Chapter 5 CO2 DEMATEL EU GHG NPP RE

Carbon Di Oxide Decision-Making Trial and Evaluation Laboratory European Union Greenhouse gas Nuclear power plant Renewable Energy

Chapter 6 ADF ARDL ECM ECT GDP ODA PICs PP SIDS

Augmented Dickey Fuller Autoregressive distributed lag Error Correction Model Error Correction Term Gross Domestic Product Overseas development assistance Pacific Island Countries Phillips–Perron Small Island Development State

Chapter 7 ARCH-M BSE CAGR ESG GARCH-M GGEI HDFC IMF IPCC

Autoregressive conditional heteroscedasticity in mean Bombay Stock Exchange Compounded annual growth rate Environmental, social and governance Generalized autoregressive conditional heteroscedasticity in mean Global Green Economy Index Housing Development Finance Corporation International Monetary Fund International Panel on Climate Change

Abbreviations

NIFTY OECD SDGs UNCTAD

xxxv

National Stock Exchange Fifty Organization for Economic Co-operation and Development Sustainable Development Goals United Nations Conference on Trade & Development

Chapter 8 ARDL FDI FR GDP

Autoregressive distributed lag Foreign Direct Investment Forestry rents Gross Domestic Product

Chapter 9 CDM CER FSI GDP GHGs ICFRE IIRS IISc INCCA LCD LCE LPG MoEF MoEFCC NAPCC NCDMA NCEF NSC REDD+ SMF UNFCCC WII

Clean Development mechanism Certified Emission Reduction Forest Survey of India Gross Domestic Product Greenhouse gas Indian Council of Forestry Research and Education Indian Institute of Remote Sensing Indian Institute of Science Indian Network of Climate Change Assessment Low carbon development Low carbon economy Liquified Petroleum Gas Ministry of Environment and Forest Ministry of Environment, Forests & Climate Change National Action Plan on Climate Change National Clean Development Mechanism Authority National Clean Energy Fund National Steering Committee Reducing Emissions from Deforestation and Forest Degradation Sustainable Management of Forest United Nations Framework Convention for Climate Change Wildlife Institute of India

xxxvi

Abbreviations

Chapter 10 CI GHG IEA IMF IPCC OECD UN SDGs

Construction Industry Greenhouse Gas International Energy Agency International Moneraty Fund the Intergovernmental Panel on Climate Change Organisation for Economic Co-operation and Development United Nation Sustainable Development Goals

Chapter 11 EVs GHG ICE IEA WHO

Electric vehicles Greenhouse Gas Internal combustion engine International energy agency World Health Organization

Chapter 12 CCN IPCC MEA NWS

Cloud Condensation of Nuclei Intergovernmental Panel on Climate Change Millennium Ecosystem Assessment National Weather Service

Chapter 13 FAO MNRE SECI

Food and Agriculture Organization Ministry of New and Renewable Energy Solar Energy Corporation of India

Chapter 14 BES ECCI GDP

Equitable and Sustainable Well-being Environmental Capital Conservation Index Gross Domestic Product

Abbreviations

MPI OECD WHO

xxxvii

Mazziotta and Pareto Index Organisation for Economic Co-operation and Development World Health Organization

Chapter 15 CO2 HIV Kt MERS N2 O Ppb R R0 SARS CoV

Carbon dioxide Human Immunodeficiency Virus Kiloton Middle East Respiratory Syndrome Nitrous oxide Parts per billion Pearson’s correlation coefficient Reproduction number Severe Acute Respiratory Syndrome Coronavirus

Chapter 16 FEM PRM REM UNDP WHO

Fixed effect model Pooled regression model Random effect model United Nation Development Programme World Health Organization

Chapter 17 CAMPA CARE Ratings CMIE COVID EPF FY GDP ILO INR MGNREGS MSME

Compensatory Afforestation Fund Management and Planning Authority Credit Analysis & Research Ltd. Ratings Centre for Monitoring Indian Economy ‘CO’ stands for corona, ‘VI’ for virus, and ‘D’ for disease Employees’ Provident Fund Financial year Gross Domestic Product International Labor Organization Indian Rupee Mahatma Gandhi National Rural Employment Guarantee scheme MicroSmall and Medium Enterprise

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Abbreviations

NABARD NIT NSDC NSS PDS PMGKP PMGKY PM-KISAN RBI RM SWADES UP VIF

National Bank for Agriculture and Rural Development National Institution for Transforming India National Skill Development Corporation The National Service Scheme Public Distribution System Pradhan Mantri Garib Kalyan Packages Pradhan Mantri Garib Kalyan Yojana Pradhan Mantri Kisan Samman Nidhi Reserve Bank of India Reverse Migration Skilled Workers Arrival Database for Employment Support Uttar Pradesh Variance inflation factor

Chapter 18 ADF FEVD GDP GMM IRF PPE PVAR SSA WHO

Augmented Dickey Fuller Forecast error variance decomposition Gross Domestic Product Generalized method of moments Impulse Response Function Personal Protective Equipment Panel Vector Autoregression Sub-Saharan Africa World Health Organization

Chapter 19 CTAI EU ISTAT NSEA SNAI UNCEM

Comitato Tecnico Aree Interne European Union Italian Institute of Statistics National Strategy of Internal Areas Sviluppo delle Aree Interne Unione nazionale Comuni, Comunità ed Enti Montani

Chapter 20 ADF

Augmented Dickey Fuller

Abbreviations

ARCH GARCH PP RSS TC TD

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Autoregressive conditional heteroscedasticity Generalized autoregressive conditional heteroscedasticity Phillips–Perron Residual Sum Squares Total cases Total Doses

Chapter 21 GHG SLR OECD UNCTAD WHO WMO

Greenhouse Gases Sea level rise Organisation for Economic Co-operation and Development United Nations Conference on Trade and Development World Health Organization World Meteorological Organization

Chapter 22 CO2 EFW ENVQ EQ FAID GDP GMM GS IQ POPG QoL SSA WDI WHO

Carbon Dioxide Economic Freedom Environmental Quality Equation Foreign Aid Gross Domestic Product General Method of Moment Government Expenditure Intelligent Quotient Population Growth Quality of Life Sub-Saharan Africa World Development indicators World Health Organization

List of Figures

Fig. 6.1 Fig. 7.1 Fig. 7.2

Fig. 8.1 Fig. 8.2 Fig. 9.1 Fig. 10.1

Fig. 10.2

Fig. 10.3

Fig. 11.1

Fig. 11.2 Fig. 12.1

CUSUM and CUSUMSQ tests statistics. Source Authors’ estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daily returns of BSE 100 and BSE 100 ESG for the period 27 October, 2017 to December 31, 2020 . . . . . . . . . . . . . . . . . . . . Daily Returns of NIFTY 50 and NIFTY 100 ESG for the period 1 January, 2020 to December 31, 2020. Source Author’s derivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Change in forestry rents (% of GDP). Source Author Computation based on World Development Indicator . . . . . . . . . Stability test. Source Author’s computations . . . . . . . . . . . . . . . . Economics and carbon emission. Source Adapted from Gouldson et al. (2018), an open source . . . . . . . . . . . . . . . . CI related conservation capital investment and policies as an important pillar for green macro economy and sustainable development. Source Authors’ elaborations . . . . Main pillars and facilitators of the conservation capital investments and policies in the CI. Source Authors’ elaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relationship among construction companies’ conservation capital protection performance and their competitiveness. Source Authors’ elaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share of EVs across top 20 countries in 2018. Source Authors’ compilations from the Energy News of the economic times and international energy agency (IEA)-open sources data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow chart depicting determinants of Electric Vehicle Usage. Source Author’s own representation . . . . . . . . . . . . . . . . . Total emissions in 2018. Source Author’s compilation from the data of United States Environmental Protection Agency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

77 90

91 98 108 121

132

133

134

141 148

162

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Fig. 12.2 Fig. 12.3 Fig. 13.1

Fig. 13.2

Fig. 13.3

Fig. 14.1 Fig. 14.2 Fig. 14.3 Fig. 14.4

Fig. 15.1

Fig. 15.2

Fig. 15.3 Fig. 15.4

Fig. 18.1

Fig. 18.2

List of Figures

Fossil fuel CO2 emissions. Source Authors derivations from World Bank Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anthropogenic effects on the environment. Source McMichael (2003)—an open source . . . . . . . . . . . . . . . . . . . . . . . Year-wise solar electricity production in India. Source Mercom India Research-an open source, https://mercom india.com/solar-power-generation-lowest-yoy/ . . . . . . . . . . . . . . . Biofuel production by region. Note Biofuel production is measured in terawatt-hours (TWh) per year, and includes both bioethanol and biodiesel). Source https://ourworldi ndata.org/grapher/biofuels-production-by-region . . . . . . . . . . . . . Global Biodegradable polymer market, 2015–2025 (Kilo Tons) (USD Million). Source Adroit Market Research, 2019-an open source. https://www.adroitmarketresearch. com/industry-reports/biodegradable-polymer-market . . . . . . . . . Map of ECCI results by regions. Source Authors’ presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Map of life expectancy at birth by regions . . . . . . . . . . . . . . . . . . Map of life expectancy in good health at birth by regions . . . . . . Correlation matrix including ECCI, life expectancy, and life expectancy in good health. Source Authors’ calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . World map showing affected countries in swine flu (a) and SARS-CoV-2 (b) pandemic. Source Author’s compilation from https://mapchart.net . . . . . . . . . . . . . . . . . . . . . . World map showing affected countries due to the epidemic of dengue (a), chikungunya (b), Ebola (c) and avian influenza (d). Source Author’s compilation from https:// mapchart.net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Change in global temperature from 2000 to 2020. Source NASA/GISS, Image adopted from https://climate.nasa.gov/ . . . . A comparative account of yearly affected individuals due to viral outbreak with increase in global average surface temperature (a), CO2 and N2 O emissions (b), annual global increase of methane (c) and percentage of forest area (d). Note that CO2 emission was measured in terms of kiloton (kt), N2 O emission was measured in terms of thousand metric tons of carbon dioxide equivalent, methane emission was measured in terms of parts per billion (ppb). Source Author’s computations . . . . . . . . . . . . . The Response of environmental pollution to the COVID-19 pandemic. Source Authors’ Computation from Available Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Response of climate change to COVID-19 pandemic. Source Author’s Computation from Available Data . . . . . . . . . . .

163 166

174

175

176 190 191 191

192

204

205 211

212

250 252

List of Figures

Fig. 18.3 Fig. 19.1

Fig. 20.1

Fig. 20.2 Fig. 21.1 Fig. 21.2 Fig. 21.3

Fig. 22.1 Fig. 22.2

VAR stability test graph. Source Author’s Computation from Available Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The three case studies: Alta Langa, located between Liguria (capital Genoa) and Piedmont (capital Turin), Sassello and Toirano (entirely located in Liguria). Source Elaboration by the authors from ISTAT data . . . . . . . . . . . . . . . . . Trends of Total Cases (TC) for the period of Feb. 1, 2021 to May. 10, 2021 by India and states. Source Authors’ own derivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends of Total Doses (TD) for the period of Feb. 1, 2021 to May. 10, 2021. Source Authors’ own derivations . . . . . . . . . . . Relationship between conservation capital investment and healthcare expenses. Source Authors’ elaborations . . . . . . . . Relationship between conservation capital policies and healthcare policies. Source Authors’ elaborations . . . . . . . . . Conservation capital policies and investments supporting sustainability-enabled well-being. Source Authors’ elaborations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends of different indicators. Source Data sourced from WDI, World Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial Analysis of the Average carbon dioxide emissions (metric tons per capita) in SSA between 1985 and 2017. Source Data sourced from WDI, World Bank. Author’s Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part I

Economic and Environmental Aspects of Conservation Capital

Chapter 1

The Role of Conservation Capital in Developing Economies: A Static General Equilibrium Analysis with Dynamics Tonmoy Chatterjee and Nilendu Chatterjee

1.1 Introduction We know that the whole world is dependent upon natural capital and its stock. Further that, nature provides immensely crucial services along with resistance power to fight out tough conditions and natural disasters. Nature is immensely important for its benefits because bio-diversity, a core component of natural capital provides us wide range of goods starting from foods to medicines and limiting the amount of CO2 and GHG gases. But we hardly quantify these numerous benefits provided by nature and thus do not view or do not find nature as a good investment opportunity. We cannot deny the fact that human activities have been responsible for destroying the benefits generates by natural resources. For example, deforestation is responsible for almost 14% of global carbon emission, volatility in weather change and occurrence of extreme weather events every now and then, according to the report of Global Carbon Budget, 2019. The speed at which mangroves and marine reefs are getting destroyed, the population near coastal belts and its eco system may not survive for long. We have witnessed almost 66% reduction in the wildlife in the last 50 years and we cannot deny that climate change is solely responsible for this worldwide loss in biodiversity (Almond et al., 2020). It’s a fact that the pressure on the remaining wildlife increases the chances of various zoonotic diseases and epidemics or pandemics.

T. Chatterjee (B) Department of Economics, Bhairab Ganguly College, Kolkata, India e-mail: [email protected] N. Chatterjee Departmental of Economics, Bankim Sardar College, Canning, South 24 Parganas, Tangrakhali, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_1

3

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T. Chatterjee and N. Chatterjee

These pressures and the magnitude of these pressures have led the scientists to believe that it is really tough and almost impossible or very low probabilistic to save, preserve and stabilize the remaining natural resources or nature. For reducing the destruction of natural environment, conservationists and environmental planners have asked for permanently conserving at least 30% of surface of nature by 2030, they have asked fore doubling the land conservation and inland water system. Quite naturally, if these are achieved, then it would mean increase in the conservation of international water system which is a good thing. Now, achieving this 30% level is not an easy task. Policy-setters would need continuous cost–benefit analysis with available data and need to design policies. We need such policies and conservation methodologies that would consider the whole scenario of benefits and risks associated with any conservation policy by giving importance to the people related to these policies or to be affected by such policies. Hence, arises the need for conservation capital. Conservation capital or conservation finance being a fairly new concept emphasizes on stock of capital or funds for the sake of preserving or conserving various natural resources, such as, land, water, etc. People who support this cause always believe that there is lack of capital devoted or conserved for this purpose, although governments, corporates, welfare-seekers always contribute a certain amount of money for this purpose. But, it is an approach that has been gaining popularity and being applied in different socio-economic fields, including banking, finance, industries, etc. So, in order to survive, we need to protect the remaining natural resources for which there exists considerable opportunities for business entities to re-design their business strategies that can support this noble cause along with investment from the private bodies, since the investment made by public and philanthropic channels have been falling short of its demand. It was estimated that during 2018, annually we need at least $300–$400 billion for conservation of biodiversity but only $52 billion could be raised. (Huwyler et al., 2015). So, there is a huge shortage of funds. So, conservationists are emphasizing on and looking for different sources for raising this fund, they are even thinking about getting it from capital market (Schuyley, 2005). Few newly suggested nonconventional sources of capital1 are debt-financing, emerging tax-benefits, private equity investments, project financing, etc. These sources are expected to fill up the pool of funds and help in conservation of biodiversity worldwide (Schuyley, 2005). The report on “Valuing Nature Conservation” by McKinsey and Company (2020) suggests that if we double the conservation of nature, it could not only reduce the amount of atmospheric CO2 by 0.9 to 2.6 gigatons but it could also generate 27 to 33 million employment opportunities only in ecotourism and sustainable fishing, which certainly would have a positive, multiple impact on rest of the sectors as well as on GDP. The report also says that doubling natural conservation would reduce the

1

Few popular conventional government sources of capital are Debt-for Nature swaps and help from foreign nations. Few non-government sources are Green Bonds, Payments for Eco system services, etc.

1 The Role of Conservation Capital in Developing Economies: A Static …

5

chances of zoonotic diseases by 10% to 80% and thus would have a positive impact on our health and various socio economic aspects. We must not forget that there are certain facts existing regarding environmental protection and the urge to do it. Firstly, there exists considerable amount of difference in expenditure on research on environment and use of green technologies between developed nations and developing nations. Developing nations are far behind in this regard from all aspects and secondly, whether both types of nations are really willing to forego growth of GDP for the sake of environmental protection, despite their international agreements. But the good thing is that various nations, both developed as well as developing, have began to take initiatives in this regard and started to invest money for the protection of biodiversity. So, conservation capital or investment of capital for conservation of nature is a necessity for all nations, whether it is developed or not. A nation has to frame out its policies properly. It may use the conservation capital in agriculture, it may use it for industrial purpose or simply for the purpose of protection of nature that would help in development of eco-tourism and other aspects. Many nations make a combination of various approaches and decides to invest capital in various economic fields for protecting the environment. In this paper, we shall consider these issues by the help of a general equilibrium framework in Hecksher–Ohlin–Samuelson set up. Rest of the paper is organized as follows. Section 1.2 discusses about the existing literatures in this field. Section 1.3 discusses about the model of our study along with the dynamic analysis in Sect. 1.4. Section 1.5 incorporates the empirical analysis and concluding remarks are made in Sect. 1.6.

1.2 A Brief Review of Existing Literatures Zilberman et al. (2005) have developed an endogenous model to link up pollution with ineffective input-use and it could be reduced by the investment of conservation capital. They have considered both regulated as well as unregulated regimes and have shown how environmental quality can be maintained, depending upon individual choice for environmental protection and private investment for its improvement. They are of the view that in the absence of any regulation, low growth rate contributes to good environmental condition but the longevity of such depends upon choice of the population. Again, in regulated nations, they believe that the rate of interest should be high enough for consumers to sacrifice consumption but low enough for the producers to discourage them from using polluting technologies. They have recommended for a pollution tax on polluting unit but for a subsidy for the investment of conservation capital. Tisdell (1994) has discussed about the benefits and problems of conservation in a globalized market economy. The author is of the view that globalization and its speed have not been able to conserve nature, rather have been responsible for its falling quality. Again, various economic crises combined with few economic policies have further aggravated this situation. In the present world, it is almost impossible

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T. Chatterjee and N. Chatterjee

to forego economic growth and conserve nature, especially the rivalry of politics would not allow that. The author has concluded that politics and lobbying of politics need to play an important role in conservation of nature, investment of capital for this purpose and possible distribution of the benefits of such conservation among different categories of nations in the presence of a globalized market economy. Maler and Munasinghe (1996) have analyzed the effects of macroeconomic policies on environment and suggested remedial measures for the harmful impacts of such policies. They have opined that it is both faulty macroeconomic policies along with institutional failures that lead to cause harm to the environment. They have suggested two alternative policies, first, to change the institutional system and if that is not possible, then they have suggested for a modification of macroeconomic policy as the second option. Case study by Lopez (1993) has focused on Ghana and the author has concluded that if we consider the effect on land-quality, then, if wage reforms and price reforms do not impact the land- management practices, then they have negligible impact on national income. Unemo (1996) have used the Computable General Equilibrium framework to study the environmental impacts of structural adjustment policies in Botswana. Since, livestock is one of the important sectors in Botswana, hence overgrazing is a common phenomenon. In Botswana, several Governmental policies were also taken to encourage this overgrazing. Unemo showed the negative impacts of these macroeconomic policies on environment and suggested remedial measures by analyzing the impacts of five macroeconomic policy changes. Cromwell and Winpenny (1993) have considered a structured model to analyze the environmental impact of liberalization and reforms in Malawi. They have found initial impact of reforms on crop-mix and production intensity has been negative. Mearns (1991) has challenged any one technique to be followed for considering the impact of structural adjustments on environment. He has proposed for following comprehensive analysis along with discussions between the experts and affected people. Several authors have used the Computational General Equilibrium frameworks to analyze the impact of macroeconomic policies on environment. Persson and Munasinghe (1996) have used CGE model by introducing property rights and by modifying the market-functioning. Reed (1996) has done his work on Pakistan. In his CGE model, he has used simulation of tax-policy and dynamic model of long-run growth and showed the impact on EKC. Many authors have individually used various sectors, such as agriculture, forestry, energy, etc. to demonstrate the impact of macroeconomic policies on environmental conservation. Richardson (1996) has shown the effects of structural changes on production of crops and he has found mixed impact of macroeconomic policies on environment due to different technologies of crop-production followed. Mukherjee (1995) has argued that it is not easy to change the land-use practices and techniques in developing economies due to social constraints. Here, farmers and producers do not want to give up the production of profitable crops easily even if it is harmful to the environment.

1 The Role of Conservation Capital in Developing Economies: A Static …

7

Pandey and Wheeler (2001) have used data of 112 developing economies over a period of 38 years to discuss the effects of structural adjustment policies on forestbiodiversity conservation. They have used an econometric model and round wood as a dummy for deforestation. The authors have found that due to structural adjustment policies, income of the people has increased and because of it pressure on forest has decreased but as a bi-product of reform we get population growth and urbanization and these two things have increased the pressure on forests. They have seen that devaluation of currency has a positive impact on the production on forest products, which means it helps in reducing dependency on forestry. Meier et al. (1996) have considered two aspects of association between energy and environmental. They have used the price reform of electricity and the policies to reduce GHG gases in Srilanka. The authors have justified the financial help given to the efforts of both the above mentioned activities on the grounds of reducing environmental damages and achieving economic efficiency. So, various authors have made efforts to consider various aspects of environmental degradation and have asked for the necessity to in conservation of biodiversity, that is, they have actually encouraged investment of conservation capital. But, almost no paper has considered all sectors of an economy in a general equilibrium framework and looked to capture the impact of conservation capital on various sectors of an economy. By this paper we shall look to fill up that research gap and this has been the prime motivation behind this work.

1.3 The Model 1.3.1 Conservation Technology Transfer Function Here we start with the assumption that inflow of conservation capital (KC ) occurs either to domestic rural (Agriculture) or to urban (Manufacturing) sector from the green or conservation sector (C) of the economy. The function through which transfer of KC can be expressed as

/

/

For rural − A R = A R (K C )

(1.1)

For urban − AU = AU (K C )

(1.2)

where, A R > 0 and AU > 0. It implies inflow of conservation capital owing to foreign capital inflow leads to an increase in conservation capital stock of both urban and rural sectors. For sake of simplicity here we assume one of the following cases. / / / / Case 1- A R > 0 and AU = 0, Case 2- AU > 0 and A R = 0.

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T. Chatterjee and N. Chatterjee

1.3.2 Product Market Using specifications (1.1) and (1.2) augmenting with Hicks-neutral kind of technological progress we get the following product market equilibrium conditions A R (K C )PR = E R (W, r )

(1.3)

AU (K C )PU = EU (W, r )

(1.4)

PC = E C (W, rC )

(1.5)

1.3.3 Factor Market Factor market clearing conditions under competitive set up are described as K U (W/rC )L U + K R (W/rC )L R = K

(1.6)

Full employment of labour market L R + LU + LC = L

(1.7)

Demand –supply equality for conservation capital can be expressed as K C (W/rC )L C = K C

(1.8)

1.3.4 National Income National income at domestic factor cost (N D ) is expressed in terms following expression ND = W L + r K

(1.9)

However, national income after foreign repatriation F = N D + tC rC K C

(1.10)

1 The Role of Conservation Capital in Developing Economies: A Static …

9

1.3.5 Comparative Statics Here we want to examine the impact of an increase in K C on the factor returns, N D and also on F. An increase in K C implies transfer of conservation technology with / / / / the assumption that, either AU > 0& A R = 0(case 1) or A R > 0& AU = 0(case 2). In short, at a particular point either sector R or sector U can be benefitted following a rise of stock in conservation capital. Owing to such transfer a technological advancement may be occurred in the subsequent sector (either R or U) and the corresponding sector enjoys an additional Stolper-Samuelson (S–S hereafter) effect apart from usual S–S effect following price system of the general equilibrium (GE hereafter). However, in / / the absence of any transfer of conservation capital, that is, A R = 0& AU = 0, then the accumulation conservation capital stock produces only Rybczynski effect. / / Now, let us start with case 1, i.e., AU > 0& A R = 0. Under such set up an increase in KC shows the same effect what we can get due to an increase in PU . Hence, inflow of KC leads to an increase in r and a fall in W as sector U is more capital intensive (as per the assumption) and therefore claims the usual S–S arguments. Again, a rise in the stock of KC leads to an increase in rC as W declines (from Eq. 1.5). For given PC = 1(as numeriere) and tC , an increase in rC leads to an increase in tC rC KC . Moreover, a rise in KC implies a fall in WL following a reduction in W. Hence, the volume of N D depends on the values of sectoral factor intensities in comparison with / / capital-labour ratio (K/L). Then under the set up AU > 0& A R = 0 one can derive the following comparative static expressions d N D /d K C = (dr/d K C )L[(K /L) − K U ]

(1.11)

Here,d N D /d K C > 0, i.e., N D increases due to a rise in K C , iff K U < (K /L) and (dr/d K C ) > 0. Again, an increase in KC also leads to an increase in tC rC KC and which in turn raises F iff K U < (K /L). Similarly, we can describe case 2, i.e., / / A R > 0& AU = 0 in the following manner. Here, an inflow of KC leads to an increase in W and a fall in R as sector R is more labour intensive (as per the assumption) and therefore claims the usual S–S arguments. Again, a rise in the stock of KC leads to a fall in rC as W increases (from Eq. 1.5). For given PC = 1(as numeriere) and tC , reduction in rC may lead to a decline in the following specification, i.e., tC rC KC . Moreover, a rise in KC implies a rise in WL following an increase in W. Hence, again the volume of N D depends on the values of sectoral factor intensities in comparison / / with capital-labour ratio (K/L). Then under the set up A R > 0& AU = 0 one can derive the following comparative static expressions d N D /d K C = (dr/d K C )L[(K /L) − K R ]

(1.12)

Here, d N D /d K C > 0, i.e., N D increases due to a rise in K C , iff K R > (K /L) as (dr/d K C ) < 0. Again, an increase in KC also leads to an increase in tC rC KC and which in turn raises F iff K R > (K /L).

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1.4 Dynamic Version To set up the dynamic version of the model we assume the following assumptions. First, we assume that a constant fraction s of F is saved and is also invested to augment the domestic capital stock (K). Second, we further consider that a fraction ρ of foreign capital income (rC K C ) is also invested to augment the conservation capital stock (KC ). The rate of depreciation of K and KC are M and MK respectively. Here, both M and MD are constant in nature. Moreover, we assume that the growth rate of labour is zero. Therefore, the equations of motion of K and KC are represented in terms of following specifications respectively, ( K˙ /K ) = (s F/K ) − M

(1.13)

( K˙ C /K C ) = ρrC − MC

(1.14)

and,

Using Eqs. (1.4), (1.10) we rewrite Eqs. (1.13) and (1.14) as K˙ = s[W (K C )L + r (K C )K + tC rC (K C )K C ] − M = Q(K , K C , tC ) K˙ C = ρrC (K C , tC )K C − MC K C = S(K C , tC )

(1.15) (1.16)

/

/

Here, again we consider two different cases, namely, case-1, where, AU > 0& A R = 0 and we also get ∂ W/∂ K C < 0, ∂r/∂ K C > 0&∂rC /∂ K C > 0. Note, case 2, where, / / A R > 0& AU = 0 and we also find ∂ W/∂ K C > 0, ∂r/∂ K C < 0&∂rC /∂ K C < 0.

1.4.1 Long-Run Equilibrium and Stability Long run equilibrium of our system implies that K˙ = K˙ C = 0

(1.17)

Here, the long-run equilibrium values of K and KC are K* and KC * respectively. Here again we shall start with case 1 and later move to case 2 for further discussions. / / Case 1- where AU > 0& A R = 0: To check the stability of dynamic model we shall check both the trace and determinant of the Jacobian (JU ) derived from Eqs. (1.15) and (1.16). From (1.15) and (1.16) we get  JU =

Q1 Q2 S1 S2

 (1.18) (K ∗ ,K C∗ )

1 The Role of Conservation Capital in Developing Economies: A Static …

11

where, Q 1 = (∂ Q/∂ K ) = sr − M < 0 Q 2 = (∂ Q/∂ K C ) = s(∂ N D /∂ K C ) + stC rC (εrC ,K C + 1) > 0

(1.19) (1.20)

  Note, Q 2 > 0 iff, εrC ,K C  ≤ 0 and K R < (K /L). S1 = (∂ S/∂ K ) = 0

(1.21)

S2 = ρ(∂rC /∂ K C )K C∗ > 0

(1.22)

Therefore, the trace of jacobian at equilibrium. JUtr = (Q 1 + S2 )

(1.23)

We can’t derive any conclusive sign of the trace as Q 1 < 0&S2 > 0. Again, the determinant of (1.18) can be derived as |JU | = Q 1 S2 < 0

(1.24)

Equations (1.23) and (1.24) tell us that the long-run equilibrium under case 1 is unstable. Now we have to check case 2. / / Case-2- where A R > 0& AU = 0: To check the stability of dynamic model we shall check both the trace and determinant of the Jacobian (JU ) derived from Eqs. (1.15) and (1.16). From (1.15) and (1.16) we can obtain expression (1.18) once again for further consideration  JR =

Q1 Q2 S1 S2

 (1.25) (K ∗ ,K C∗ )

where, Q 1 = (∂ Q/∂ K ) < 0; Q 2 = (∂ Q/∂ K C ) > 0; S1 = (∂ S/∂ K ) = 0 and S2 = (∂ S/∂ K C ) < 0 as (∂rC /∂ K C ) < 0. Therefore, the trace of jacobian at equilibrium. J Rtr = (Q 1 + S2 ) < 0

(1.26)

Note, here we get negative sign of the trace as Q 1 < 0&S2 < 0. Again, the determinant of (1.25) can be derived as |JU | = Q 1 S2 < 0

(1.27)

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T. Chatterjee and N. Chatterjee

Equations (1.26) and (1.27) tell us that the long-run equilibrium under case 2 is stable. Our theoretical model leads couple of empirically examinable hypothesis. First, whether inflow or transfer of conservation capital affects the destination economy positively? Second, does such inflow or transfer of conservation capital lead to dynamic returns?

1.5 Empirical Analysis In order to perform empirical study on the just-stated theoretical outcome, first we have employed panel unit root test for selected 20 developing nations. These countries are Bangladesh, Brazil, Cameroon, Chile, China, Cuba, Egypt, Ghana, India, Indonesia, Iran, Kenya, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Russian Federation, South Africa and Venezuela. Here, we use the outcome of conservation capital in terms Renewable energy consumption, to measure inflow or transfer of conservation capital we use foreign direct investment inflow and GDP is used to proximate national income. Table 1.1 depicts the results of several panel unit root tests and all the test outcomes suggest that all the three variables, that is, Renewable energy consumption (REC), per capita GDP (PCGDP) and per capita FDI (PCFDI) are integrated at order 1 and hence possibilities of long run associations are emerged. To check the way of causality among the variables we perform vanilla model of Granger causality to the panel. Table 1.2 illustrates the results of granger causality analysis and it suggests that conservation capital inflow or transfer in terms of PCFDI inflow encourages REC, which in turn affects national income of a state in terms of increase in PCGDP. Table 1.1 Panel unit root tests for overall panel At level

At first difference

Variable

LLC test

IPS test

ADF test

PP test

LLC test

IPS test

ADF test

PP test

REC

1.37 (0.75)

2.52 (0.78)

2.01 (0.71)

7.01 (0.65)

−5.01 (0.00)

−6.24 (0.00)

25.03 (0.00)

29.07 (0.00)

PCGDP

0.21 (0.38)

3.35 (0.89)

3.88 (0.88)

3.16 (0.92)

−7.08 (0.00)

−5.98 (0.00)

39.35 (0.00)

43.27 (0.00)

PCFDI

−0.87 (0.32)

−0.86 (0.35)

7.13 (0.37)

7.10 (0.31)

−8.15 (0.00)

−7.97 (0.00)

42.66 (0.00)

45.32 (0.00)

Notes This table reports the test statistic followed by the probability values in parentheses for the four tests performed in ascertaining the stationarity of the variables. Source Authors’ calculations Source Authors’ calculations

1 The Role of Conservation Capital in Developing Economies: A Static …

13

Table 1.2 Panel granger causality tests for overall panel Null hypothesis

Lags

F statistic

Prob. values

REC does not Granger cause PCGDP

4

2.3127

0.0405

PCGDP does not Granger cause REC

4

1.7767

0.1212

PCFDI does not Granger cause REC

2

2.1502

0.0430

REC does not Granger cause PCFDI

2

2.2180

0.0370

Source Authors’ calculations

1.6 Concluding Remarks Conservation Capital is an important concept in modern world. It can be used in any economic sector to protect the environment or biodiversity. The study does both short-run as well as long-run analysis. In the short run, it is considered that it can be invested either in rural or agriculture sector or in urban or manufacturing sector, but not in both the sectors at a time. But, the positive effect of investment of conservation capital may or may not pass on to another sector and consequently have an impact on that sector and rest of the economy based on Stolper–Samuelson Effect or Rybczynski Effect. So, two scenarios have been analysed. Once, when it flows in agro-sector but not in manufacturing sector, and secondly, it flows in manufacturing sector but not in agro-sector. For the long-run two assumptions have been made, firstly, a part of national income post foreign repatriation is saved and used to augment domestic capital and secondly, a fraction of foreign capital income is being used to invest as conservation capital. Under such assumptions, the long-run stability under two conditions which were used in short-run analysis have been checked and it is seen that if conservation capital flows to the rural sector, not to the urban sector, then the long-run equilibrium is a stable one.

References Almond, R. E. A., Grooten, M., & Petersen, M. (2020). WWF living planet report 2020—bending the curve of biodiversity loss. WWF. Claes, J., Conway, M., Hansen, T., Henderson, K., Hopman, D., Katz, J., Magnin-Mallez, C., Pinner, D., Rogers, M., Stevens, A., & Wilson, R. (2020). Valuing nature conservation—a methodology for quantifying the benefits of protecting the planet’s natural capital. Mckinsey & Company. Cromwell, E., & Winpenny, J. (1993). Does economic reform harm the environment? A review of structural adjustment in Malawi. Journal of International Development, 5(6), 623–649. Huwyler, F., Kaeppeli, J., Serafimova, K., Swanson, E., & Tobin, J. (2015). Making conservation finance investable. Stanford Social Innovation Review. Stanford University. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. Jones, R. W. (1965). The structure of simple general equilibrium models. Journal of Political Economy, 73, 557–572.

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Jones, R. W. (1971). A three-factor model in theory, trade and history. In J. Bhagwati, R. W. Jones, R. A. Mundell, & J. Vanek (Eds.), Trade, balance of payments and growth (pp. 3–21). North-Holland. Kao, C., & Chiang, M. (2000). On the estimation and inference of a cointegrated regression in panel data. Advances in Econometrics, 15, 179–222. Levin, A. L., Lin, C.-F., Chu, J. S. J. (2002). Unit root tests in panel data: Asymptotic and finitesample properties. Journal of Econometrics, 108(1), 1–24. Lopez, R. (1993). Economic policies and land management in Ghana. Draft Report, University of Maryland, Washington, D.C. Maddala, G., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics & Statistics, 61(S1), 631–652. Maler, K., & Munasinghe, M. (1996). Macroeconomic policies, second-best theory and the environment. In M. Munasinghe (Ed.), Environmental impacts of macroeconomic and sectoral policies. The International Society for Ecological Economics, World Bank, and UNEP. Mearns, R. (1991). Environmental implications of structural adjustment: Reflections on the scientific method. Discussion Paper 284, Institute of Development Studies, Brighton, England Meier, P., Munasinghe, M., & Siyambalapitiya, T. (1996) Energy sector policy and the environment: A case study of Sri Lanka. In M. Munasinghe (Ed.), Environmental impacts of macroeconomic and sectoral policies. The International Society for Ecological Economics, World Bank, and UNEP. Mukherjee, A. (1995). Structural adjustment program and food security. Munasinghe, M. (1996). Environmental impacts of macroeconomic and sectoral policies. The International Society for Ecological Economics, World Bank, and UNEP. Pandey, K., & Wheeler, D. (2001). Structural adjustment and forest resources: The impact of world bank operations. Policy Research Working Paper 2584. World Bank. Pedroni, P. (1997). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests, with an application to the PPP hypothesis: New results. Working Paper. Indiana University. Persson, A., & Munasinghe, M. (1996). Economywide policies and deforestation: The case of costa rica. In M. Munasinghe (Ed.), Environmental impacts of macroeconomic and sectoral policies. The International Society for Ecological Economics, World Bank, and UNEP. Reed, D. (1996). Environmental impacts of structural adjustment: The social dimension. In M. Munasinghe (Ed.), Environmental impacts of macroeconomic and sectoral policies. The International Society for Ecological Economics, World Bank, and UNEP. Richardson, J. (1996). Structural adjustment and environmental linkages: A case study of Kenya. Overseas Development Institute. Schuyler, K. W. (2005). Expanding the frontiers of conservation finance. In J. N. Levitt (Eds.), From Walden to wall street: Frontiers of conservation finance (p. 110). Tisdell, C. (1994). Conservation, protected areas and the global economic system: How debt, trade, exchange rates, inflation, and macroeconomic policy affect biological diversity. Biodiversity & Conservation, 3, 419–436. Unemo, L. (1996). Environmental impact of governmental policies and external shocks in Botswana: A CGE modeling approach. In M. Munasinghe (Ed.), Environmental impacts of macroeconomic and sectoral policies. International Institute for Ecological Economics, The World Bank, and UNEP. Zilberman, D., Harrington, D. R., & Khanna, M. (2005). Conservation capital and sustainable economic growth. Oxford Economic Papers, 57(2), 336–359.

Chapter 2

Generating Appropriate Policies to Minimize Environmental Pollution in Developing Countries Hasan Dinçer, Serhat Yüksel, and Ça˘gatay Ça˘glayan

2.1 Introduction Perhaps the most development in the history of humanity is experienced today, where globalization and industrialization accelerate. The world population is increasing rapidly and people’s needs are increasing due to the increasing human population. Increasing needs cause the rapid depletion of resources in nature by increasing industrialization more. The basic needs of the increasing human population, such as nutrition and shelter, must be met. Whenever these needs are met, wastes and negatives that may threaten ecology occur. For example, a person who buys a plastic bottle of water every day leaves 7 plastic bottles in a week as waste to the nature. Even though this figure seems low, when the total human population is deducted, millions of plastic bottle wastes are released into the nature every week and pollute the environment. It cannot be said that environmental pollution consists only of plastic left in the environment. Although fossil fuels and nuclear wastes transformed into electricity meet the energy needs of people, they also pollute the environment. When chemicals used in factories are released into the environment, they also pollute the nature. Therefore, while natural resources are being depleted rapidly, disposing of wastes originating from production and consumption to nature without taking precautions provides an environment for the formation of Environmental Pollution. From this perspective, it cannot be inferred that technological development and increase in industrialization is a bad situation. Because it is not the development of H. Dinçer (B) · S. Yüksel · Ç. Ça˘glayan The School of Business, ˙Istanbul Medipol University, ˙Istanbul, Türkiye e-mail: [email protected] S. Yüksel e-mail: [email protected] Ç. Ça˘glayan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_2

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humanity that is harmful to the environment. Unplanned development is harmful to the environment. Unplanned urbanization and industrialization, various nuclear tests, wars, the killing of trees and unconscious chemicals used in agriculture increase environmental pollution. Meeting the raw material needs of humanity is not a bad action, but doing this unplanned and uncontrolled means the waste and destruction of the ecological environment. In summary, environmental pollution is the situation where the natural structure and composition of the environment gradually deteriorates and all living things on Earth are negatively affected. Environmental pollution occurs due to the expansion of need areas, unconscious consumption and production, and lack of supervision. The concept of environmental pollution covers a wide area. Basically, it is said that everything that harms nature causes environmental pollution but there are types of environmental pollution.

2.2 Types of Environmental Pollution 2.2.1 Air Pollution Air pollution means the mixing of chemicals, particles and biological materials in the atmosphere, which may cause some diseases and even deaths in the atmosphere, into the air above normal (Haiyun et al., 2021). Humans, other living organisms, and natural or artificial environment are affected directly or indirectly as a result of the change that occurs with the uncontrolled and above-normal mixing of these substances into the atmosphere. Carbon dioxide, greenhouse gases, are known as major air pollutants (Yüksel et al., 2021). In addition to these, particles, sulfur dioxide, carbon monoxide, and nitrogen oxide gases also cause air pollution. These gases can cause acid rain with reactions in the atmosphere. It is a type of environmental pollution caused by the uncontrolled release of fuels used for heating together with toxic gases from factories, cars, planes, and energy facilities (Dinçer and Yüksel, 2019).

2.2.2 Soil Pollution The deterioration of the physical and chemical properties of the soil with not recycled waste is called soil pollution. Although soil formation takes a long process, it takes a short time to be destroyed by humans. For agriculture, it is essential to have fertile land. From this perspective, soil pollution directly affects people’s health through the nutrients grown in the soil, as well as harming living life. With the increasing need for nutrients, agricultural producers use various fertilizers and pesticides to make the soil more productive. When these are used unconsciously, they make the soil both harmful to human health and inefficient. Garbage thrown into the environment and mixing of industrial wastes into the soil without purifying is also one of the factors

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that pollute the soil (Dinçer et al., 2018). Therefore, the soil, which has the power to strongly affect human life, becomes a great threat to human health when it is polluted.

2.2.3 Water Pollution It can be defined as the change in the natural properties and composition of water in a way that adversely affects the health of living things. This type of pollution can be seen in all areas such as oceans, seas, lakes, rivers, and groundwater. Water pollution occurs when harmful wastewater is discharged into basins without adequate purifying of sewage water (Yüksel et al., 2020). Water pollution is extremely harmful to human health as well as damaging various biological entities. Therefore, this pollution negatively affects not only the creatures living in the seas but the whole natural environment.

2.2.4 Radioactive Pollution This type of pollution occurs when the waste materials spread harmful radiation due to radioactive degradation. Nuclear waste, nuclear power plant explosions, and nuclear weapons cause these contaminations. When it mixes with air, water and soil and comes into contact with living things, it can cause difficult diseases (Yuan et al., 2021).

2.2.5 Noise Pollution Noise pollution is a pollution caused by human, animal, machine, or motor vehicle. Noise pollution, which adversely affects the lives of living things, is generally caused by unplanned urbanization. For example, in a residential area established in an industrial zone, human health is adversely affected due to the noise generated by the industry. Its effects on human health can be seen both physiologically and psychologically. High stress and hearing loss are some of these effects. Although the types of all these pollution types are different, their common point is that they disrupt the ecological environment and harm human health (Apergis, et al., 2020; Xu, et al., 2018). Therefore, environmental pollution has social and economic consequences. People whose quality of life is reduced due to pollution face problems both psychologically and physiologically. They often become ill and cannot do their daily work. Thus, they cannot continue their work. People who cannot continue their jobs cause loss of workforce (Williams, 2002). This situation causes the economies of countries to become fragile. Both the decrease in production and the increase in health costs will cause the budget imbalance and put the economies

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of the country more in a stalemate. The problems caused by environmental pollution will not be limited to this. It will lead to bigger problems in the longer term. Threats such as depletion of water resources, climate change caused by global warming, food and water shortage, energy shortage, loss of agricultural land, decrease in biological diversity, increase in the number of people suffering from hunger, and failure to maintain social peace are problems that can be encountered in the long term. All these problems will deepen the environmental problems even more. Therefore, it is necessary to determine and implement appropriate strategies and policies to reduce environmental pollution.

2.3 Policies to Minimize Environmental Pollution Anything that harms nature causes environmental pollution. The factors that cause pollution are too many. Some of these are uncontrolled migration and urbanization, the widespread use of greenhouse gas-emitting tools, the fact that factories do not limit their waste, the disposal of waste in nature, the non-recycling of domestic and medical wastes, the excessive use of insoluble plastics, the destruction of natural life, and the non-inspection of nuclear power plants. In this context, various policy recommendations can be presented for developing countries.

2.3.1 Environmental Taxes and Charges Multiple steps can be taken to reduce environmentally harmful behavior. Environmental taxes are one of them. Environmental taxes can also be called pollution taxes. The basic principle of this taxation is “the polluter pays” principle (Luppi, et al., 2012). Hence, pollution taxation charges pollutants in proportion to the damage they cause. Environmental taxes increase the cost of environmentally harmful goods, services, or activities, thus directing producers and consumers to activities that are not harmful to the environment. Thus, economic benefits are also produced, as tax revenues increase. Since it forces the pollutants to pollute less, environmental benefit is also produced. Environmental taxes can be summarized as follows; create a price for the damage done to the environment, return the burden caused by the pollution to the polluters, deter from pollution, protect the environment, and gain economic benefit (Krass and Ovchinnikov, 2013; Bosquet, 2000; Parry, 1995; Fredriksson, 1997). There are four types of environmental taxes. Taxes are collected under the heading of Energy, Transportation, Pollution, and Natural Resources. Energy taxes are more often expressed as carbon taxes. Carbon taxes are taxed on carbon emissions. It has been put forward to reduce carbon emissions against global warming (Baranzini, et al., 2000). This type of taxation plays an active role in reducing the effectiveness of environmental damages such as fossil fuels. In this context, the same tax rate is

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not applied to all fossil fuels. Because what matters is how much carbon emission the fuel in question contains. For example, coal causes more carbon emissions than natural gas (Greiner, et al., 2018). Therefore, the taxation to be applied in the use of coal should be more than natural gas. Thus, it can be said that this taxation process should be rational and fair. Otherwise, economic and environmental benefits will not be obtained. Transport taxes require having a motor vehicle that consumes gasoline, diesel, etc. products. The applicable taxes can be collected annually or during trading. It is beneficial in preventing pollution that the most environmentally damaging vehicles are subject to the most taxes and the tax on less polluting vehicles such as electric vehicles is reduced. Apart from these, taxes collected from mines and the land where oil is extracted are called natural resource taxes, and taxes collected from dirty solid and liquid wastes are called pollution taxes (Borck, 2019; Kallbekken and Sælen, 2011). Charges other than taxes may apply. Environmental charges are paid as a result of a provision obtained. Therefore, the financial provisions received from pollutants that cause environmental pollution can be called charges. It is one of the most widely used economic tools within the framework of “the polluter pays principle” together with taxes. It is aimed to change the actions causing pollution with financial pressures of the charges. They can be applied differently, under different names, as emission charges, user charges and product charges. Thus, charges are an important financial tool, along with taxes, at the point of preventing environmental pollution (Bongaerts, 1989).

2.3.2 Incentives and Subsidies Other important financial instruments used with the aim of reducing environmental pollution are incentives and subsidies (Blackman, 2000). Incentives can be used to increase the effectiveness of environmental taxes. Incentives are in place for the use of clean energies and technologies in many developed countries. Incentives can be given in a variety of ways. For example, a tax reduction for electric vehicles is an incentive for electric vehicles to become widespread. Tax incentives can also be applied to green businesses. Incentives can also be applied in the form of cash assistance. For example, cash support for investments in renewable energies can be used in R&D processes and can prevent environmental pollution by increasing the prevalence of renewable energies (Harrington and Morgenstern, 2007; Zheng, et al., 2014; Miliman and Prince, 1989). Incentives and subsidies do not only include cash support. Flexible and lowinterest loans and guarantees can be provided by governments to institutions. Because a business needs new and environmentally friendly technologies in order to have an environmentally friendly structure. Policies that can reduce the financing problems that businesses may experience will enable businesses to take more courage for environmentalism. Incentives and subsidies supported by taxes and charges will

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reduce the environmental pollution risks of developing countries (Elliott and Okubo, 2016; Hosseini and Shahbazi, 2016).

2.3.3 Strict Audit and Administrative Fines In developing countries, environmental procedures should be determined in advance in order to create a more liveable environment and protect the ecosystem. Strict inspections are required to ensure that factories and various businesses comply with the established procedures. Inspections are carried out to reduce the practices of businesses that may harm the environment, minimize the pollution that may occur, improve activities, and impose sanctions on pollutants. Businesses with poor environmental performance during the audit should be subject to administrative fines. Businesses with good environmental performance should be encouraged (Arguedas, 2013; Faure and Heine, 1991; Foley, 2010; Haque, 2017; Malik, 1993).

2.3.4 Environmental Expenditures Environmental expenditures are expenditures made to eliminate and prevent the pollution that occurs during the production and consumption of goods and services. The financing of all activities related to environmental pollution is evaluated within this scope. All expenditures such as waste management and water services constitute environmental expenditures. When environmental expenditures are compared with other expenditure types, it can be seen that the resources allocated to environmental expenditures are generally low. In addition to the low amount of resources, resources can be used environmentally inefficiently. One of the main reasons for the inefficient use of resources is the lack of environmental awareness. In a country that does not have a good environmental management system, managers who do not have environmental awareness cause the effectiveness of environmental expenditures to decrease. Therefore, environmental taxes should be collected systematically and fairly, and these taxes should be spent on the environment again by environmentally conscious managers. The amount of financial resources allocated for environmental expenditures should be increased. Because diseases caused by pollution will cause more financial loss in the long run (Singh, et al., 2016; Baso˘glu and Uzar, 2019; Gholipour and Farzanegan, 2018).

2.3.5 Collaborations Events such as the destruction of forest areas, melting of glaciers, decrease in biodiversity, and acceleration of global warming reveal that environmental pollution is not

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a local problem, but an international problem. Therefore, when it comes to environmental pollution, every nation has responsibility in this problem. In accordance with this responsibility, the issue of environmental pollution should be discussed both nationally and internationally, measures should be taken and binding agreements should be made. Because, considering the large dimensions that the problem has reached today, the solution of this problem will be possible with solution-oriented collaborations. Therefore, what needs to be done is to cooperate closely with national and international organizations and states regarding the environment and development. As a result of this cooperation, an atmosphere of mutual trust should be created, channels for information exchange should be established, rational environmental programs should be prepared, and necessary financial resources for R&D and academic studies should be provided (Hardy and Koontz, 2008; Albino, et al., 2012; Dangelico and Pontrandolfo, 2015; Vitiea and Lim, 2019).

2.3.6 Environmental Literacy Trainings The consequences of environmental pollution are felt more than ever. Therefore, studies on this issue are carried out all over the world, but it cannot be said that environmental awareness has spread to the bottom of the societies. In this context, the concept of environmental literacy is an important concept. Environmental literacy aims to raise awareness about the ecosystem of which individuals are a part. Individuals with an environmental consciousness will avoid as much as possible from the steps that harm the nature in their actions. Accordingly, raising environmental awareness among all members of the society in developing countries will be beneficial in minimizing environmental pollution (Chu, et al., 2017; Negev, et al., 2008; Saribas, et al., 2014; Hsu, 2004).

2.4 An Application with AHP Methodology In this section, it is aimed to find the optimal strategy to minimize environmental pollution for developing countries. In this context, we defined 6 different strategies and the details are given in Table 2.1. 3 different experts made evaluations regarding the significance of these criteria. In this context, 9 different scales are taken into consideration. In the analysis process, AHP methodology is used (Dinçer and Yüksel, 2018, 2019; Dinçer et al., 2017; Wang et al., 2019; Yüksel and Ubay, 2021). Pairwise comparison matrix is demonstrated in Table 2.2. After that, this matrix is normalized, and the weights of the criteria are calculated. The analysis results are shared in Table 2.3. Table 2.3 indicates that environmental taxes and charges (C1) play the most critical role for developing countries to minimize environmental pollution. It is also

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Table 2.1 Environmental pollution reduction strategies Criteria

Supported literature

Environmental Taxes and Charges (C1)

(Krass and Ovchinnikov, 2013; Bosquet, 2000)

Incentives and Subsidies (C2)

Harrington and Morgenstern, 2007; Zheng, et al., 2014)

Strict Audit and Administrative Fines (C3)

(Arguedas, 2013; Haque, 2017)

Environmental Expenditures (C4)

(Singh, et al., 2016; Baso˘glu and Uzar, 2019)

Collaborations (C5)

(Dangelico and Pontrandolfo, 2015; Vitiea and Lim, 2019)

Environmental Literacy Trainings (C6)

(Chu, et al., 2017; Negev, et al., 2008)

Source Authors

Table 2.2 Pairwise comparison matrix Criteria

C1

C2

C3

C4

C5

C6

C1

1.00

3.33

6.67

7.33

9.00

5.00

C2

0.30

1.00

4.67

6.67

7.33

2.67

C3

0.15

0.21

1.00

2.67

5.67

0.31

C4

0.14

0.15

0.38

1.00

2.33

0.18

C5

0.11

0.14

0.18

0.43

1.00

0.14

C6

0.20

0.38

3.27

5.63

7.30

1.00

Source Authors

Table 2.3 Analysis results

Criteria

Weights

C1

0.4505

C2

0.2386

C3

0.0835

C4

0.0428

C5

0.0265

C6

0.1581

Source Authors

understood that incentives and subsidies (C2) and environmental literacy training (C6) are other important items in this framework.

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2.5 Conclusion and Discussion Environmental pollution is one of the most important problems faced by countries today. It is seen that this problem is causing the countries to experience difficulties in different aspects. First, environmental pollution makes countries uninhabitable. For example, in a country where air and environmental pollution is high, the people cannot enjoy social life. This stated situation causes an increase in unhappiness among the people. On the other hand, it is possible to talk about many negative economic consequences of increasing environmental pollution. In this context, the number of sick people is increasing in countries where environmental pollution is increasing. This situation leads to an increase in health expenditures in the country. These increasing expenditures pose a serious risk to the country’s budget. As a result of this situation, the country is likely to experience a budget deficit. This situation makes the country’s economy more fragile. This problem is especially important for developing countries. These countries are not fully developed economically. On the other hand, they take some actions in order to rise to the level of developed countries. In this framework, developing countries aim to increase their investments. In this context, countries may take some risks in order to achieve these goals quickly. For example, they can resort to very fast industrial production in order for their economies to grow rapidly. Meanwhile, there is a risk that some risks may be ignored. In order for the industrial production to increase rapidly, the carbon emission problem caused by the companies may not be given importance. Although this situation creates an effect to increase the industrial production of countries in the short term, environmental pollution will create very serious social and economic problems for these countries in the long term. In this context, firstly, types of environmental pollution are discussed in this section. First of all, air pollution means the mixing of chemicals, particles and biological materials in the atmosphere, which may cause some diseases and even deaths in the atmosphere, into the air above normal. In addition to this issue, soil pollution is another significant type of environmental pollution. The deterioration of the physical and chemical properties of the soil with not recycled waste is named as soil pollution. Moreover, water pollution can be identified as the change in the natural properties and composition of water in a way that adversely affects the health of living things. This type of pollution can be seen in all areas such as oceans, seas, lakes, rivers, and groundwater. Furthermore, radioactive pollution occurs when the waste materials spread harmful radiation due to radioactive degradation. Nuclear waste, nuclear power plant explosions, and nuclear weapons cause these contaminations. Finally, noise pollution is a pollution caused by human, animal, machine, or motor vehicle. Noise pollution, which adversely affects the lives of living things, is generally caused by unplanned urbanization. Following these issues, some information has been given on policies that can be developed to prevent environmental pollution. Environmental taxes are one of the multiple steps that can be taken to reduce environmentally harmful behavior.

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Pollution taxation charges pollutants in proportion to the damage they cause. Environmental taxes increase the cost of environmentally harmful goods, services, or activities, thus directing producers and consumers to activities that are not harmful to the environment. Other important financial instruments used with the aim of reducing environmental pollution are incentives and subsidies. Incentives can be used to increase the effectiveness of environmental taxes. Incentives are in place for the use of clean energies and technologies in many developed countries. Strict inspections are required to ensure that factories and various businesses comply with the established procedures. Inspections are carried out to reduce the practices of businesses that may harm the environment. In addition to them, Environmental expenditures are expenditures made to eliminate and prevent the pollution that occurs during the production and consumption of goods and services. The financing of all activities related to environmental pollution is evaluated within this scope. Additionally, the issue of environmental pollution should be discussed both nationally and internationally, measures should be taken and binding agreements should be made. Furthermore, the concept of environmental literacy is an important concept. Environmental literacy aims to raise awareness about the ecosystem of which individuals are a part. Individuals with an environmental consciousness will avoid as much as possible from the steps that harm the nature in their actions. Finally, an analysis has been performed with AHP methodology. Within this framework, it is aimed to find the optimal strategy to minimize environmental pollution for developing countries. For this purpose, 6 different criteria are defined based on the comprehensive literature review. 3 different experts made evaluations regarding the significance of these criteria. In this context, 9 different scales are taken into consideration. It is identified that environmental taxes and charges (C1) play the most critical role for developing countries to minimize environmental pollution. It is also understood that incentives and subsidies (C2) and environmental literacy training (C6) are other important items in this framework. Based on these points, it is understood that environmental pollution is of vital importance, especially for developing countries. In this context, some actions should be taken in time to reduce environmental pollution by the relevant countries. Otherwise, it is inevitable for these countries to experience both social and economic problems in the long run. In this study, some precautions that developing countries can take to manage the environmental pollution problem more effectively are mentioned. Each of these actions can contribute to the minimization of this important problem. On the other hand, some of these strategy suggestions may be more effective than the countries’ conjuncture. In this context, it would be appropriate to decide on the strategy types on a country basis. The most important cause of environmental pollution of countries is fossil fuels used in energy consumption (Du et al., 2020). The biggest advantage of fossil fuels compared to other energy types is that their costs are very low (Liu et al., 2021; Mikayilov et al., 2020). This is the most important reason why fossil fuels are preferred by companies (Liu et al., 2020). As can be seen, some issues should be taken into consideration in order for companies to choose energy types that do not pollute the environment. First of all, additional taxes must be collected from companies that

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pollute the environment as a result of the use of fossil fuels. This stated situation will eliminate the cost advantage of fossil fuels. This will help companies not to opt for fossil fuels. In addition to this issue, it should be provided to increase the use of renewable energy within the country (Cheng et al., 2020; Li et al., 2021; Qiu et al., 2020). However, the biggest disadvantage of using renewable energy is the very high initial costs (Zhao et al., 2021). Therefore, it is very difficult for investors to prioritize renewable energy projects as such. In this context, it would be appropriate to provide some support from the state to renewable energy projects in order to increase the use of clean energy in the country (Li et al., 2020). Some factors such as low-interest loans and tax deductions will provide a cost advantage to renewable energy projects.

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Chapter 3

Conservation of Resources and the Impact of Privatization: A General Equilibrium Analysis Mainak Bhattacharjee, Dipti Ghosh, and Sanghita Ghosh

3.1 Introduction In recent times, a trend of privatization in almost all the sectors of the economy has become very prominent in the policies of the government. De novo, the union Budget of 2021 has set the target to monetize hundreds of government-owned assets as part of the monetization plan of the central government of India. Along with this, it has also unveiled the strategic disinvestment policy for strategic sectors. According to a report in Financial Express (May 07, 2022), precisely, there are four strategic sectors that are characterized under-national security, critical infrastructure, energy and minerals, and financial services. In a broader sense, it encompasses the sectors viz. atomic energy, space and defence; transport and telecommunications; power, petroleum, coal, and other minerals; and banking, Insurance, and financial services as strategic sectors, while keeping the minimum units of CPSEs (The Central Public Sector Enterprises). Now the flip side of this dispensation implies that in each of the strategic sectors, the rest can be relegated to merger and privatization. Given, it is argued that state-owned enterprises grappling with loss are and a number them are severely cash starved and thereof their monetization will enhance public welfare, the matter doesn’t remain that simple once we focus on the sustainability issues. The consequences of the adaptation of such an extensive privatization policy without any special attention to the sectors with huge strategic importance can prove to be subversive for the development of the nation. At this juncture, the utilization of resources in an optimal M. Bhattacharjee (B) Economics, Loreto College, Kolkata, India e-mail: [email protected] D. Ghosh Economics, Kolaghat Government Polytechnic, Amalhanda, India S. Ghosh Economics, Jadavpur University, Kolkata, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_3

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manner becomes very crucial. It involves minimization of the use of non-renewable resources given the state of technological know-how. There is a high possibility that opening up the sectors that are considerably intensive in natural resources particularly which are non-renewable in nature to the private sector might endanger biodiversity and turn out to be anti-environmental. It is very likely that the profit-seeking private enterprises will keep the environmental concerns at a subordinate place in their plan of action and during the course of their economic activities which will be detrimental to the environment. This necessitates the requirement of strong government intervention either directly by continuously keeping these sectors at the domain of the public sector or indirectly by implementing suitable macro rules to regulate environmental standards and maintain the stock of conservation capital. The primary objective of this chapter is to explore the high risk associated with the privatization of natural resources in terms of environmental degradation. With the help of a suitable theoretical model, it can be exposed exhaustively. Besides this, the model can also be used to find the effect and scope of some specific macro rules in this regard to regulate the utilization of non-renewable natural resources.

3.2 Literature Review Natural resources play an important role in preserving a country’s economic selfdetermination and, in that way, cherish the ideal of economic democracy. There has been a long argument between the resource economists regarding the privatization of natural resources, such as forests, fisheries, and rangelands, promoting economic efficiency (Chueng, 1970; Gordon, 1954; Johnson, 1972). More recently, private rights have been advocated explicitly as a means of improving environmental goals (Gibson et al., 2002, Fujita and Bonzon, 2005, Helson et al., 2010.) Various researchers (Clark, 1973, 1974; Larson & Bromley, 1990; Van Ginkel, 1989) argue that private ownership does not guarantee that the natural resources like forests will be well-managed or conserved. In a study, Acheson (2000) reveals that it has been observed that securing private property right does not ensure conservation since owners may exploit their resources when doing so brings the highest financial benefits in the short term. Natural resources which are subject to property institutions are managed more sustainably and in better condition than those subject to political management or left in open-access commons. But Adler (2019) reports that private property plays an essential role in environmental conservation. There are mounting evidences—from developing countries, formerly socialist economics and developed industrial countries—that the privatization of state-held assets can be carried out in a manner that inordinately benefits certain groups of purchasers and deprives the state (and therefore also the tax-payers) of the rightful value of such asset (Ghosh et al., 1995). According to Bromley (2005), once private rights are implemented, it is very difficult to change. Gilmour et al. (2012) argued that private right can pose a significant challenge before governments in materializing initiatives, for example,

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marine protected areas. It is also maintained that private rights assume lesser importance than are purported to, and even may lower the incentives to sustainability. Thus, various social and environmental limitations, to such rights (Sumaila, 2010), make it imperative that alternative arrangements, concerned with communal (Wingard, 2000) or limited-tenure systems (Bromley, 2005; Costello & Kaffine, 2008), should be considered in greater detail. De Meza and Gould (1987) using an alternative model of resource utilization find that workers may be better off in the privatization regime compared to open access. This finding essentially attributes inefficiency in the open-access regime depletion of resource productivity. Baland and Francois (2005) examine the welfare implications of the privatization of common resources when individuals differ on income possibilities out of resource use and reveal that although privatization could be a worthy solution to resource overuse, common insurance is superior to privatization with insurance in case markets are incomplete. Privatization with an equal allocation of resource use rights is detrimental for the poor in situations where the resource is not very productive, inequality in the private economy is high, and discount rates are high (Okonkwo & Quaas, 2019). As against this backdrop, this chapter develops a general equilibrium framework much in the light of Jones (1965) to investigate in to the issues mentioned in the previous section.

3.3 The Model The basic model develops small open economic structure [i.e., prices of tradable goods are exogenously given as determined in the international market and hence are independent of the domestic demand-supply condition] that consists of two sectors— the X sector which is relatively intensive to renewable energy and the Y sector which is intensive to the use of the non-renewable energy having negative externalities. There is one primary input, labour, denoted by L. Except for this primary input, the production also requires another input, Z (energy). It is assumed that there are two variants of Z, viz. Z1 and Z2 which are substitutes of each other. Z1 is considered as cleaner input whereas Z2 is relatively dirty in nature having by-products like pollution. The cleaner input Z1 is assumed to be comparatively expensive than Z2 , which is a quite plausible assumption, for example, hydropower (cleaner input) is costlier than thermal power (dirty input) in India. The small- and medium-scale sector may be assumed to be more dependent on the dirty energy like thermal power due to economic reasons. Consequently, we can get to see a predominance of the thermal power in the power generating sector in India. At this vein, let us assume that the Y sector principally consists of these small and medium enterprises whereas the large firms belong to X-sector. Z1 and Z2 both inputs are used in the production of X and Y. On the flip side, the large sector may be assumed to be more dependent on clean energy and uses energy-efficient technology unlike the former. This apart from the clean sector is relatively k-intensive than the dirty sector in as much as it uses energy re-cycling technology while the dirty sector is relatively L-intensive. Besides, the

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country is assumed to export the dirty good (X) and import clean good (Y) and the respective prices of the two goods are given exogenously. The production function in each sector is characterized by a fixed input coefficient, denoted by aij [which denotes unit input requirement of ith factor to produce one unit of commodity j] and production is carried out by two primary factor factors, namely capital (K) and labour (L), and energy. The structural equations of the model are as follows: PX = a L X W + a K X r + a1X P1 + a2X P2

(3.1)

PY = a LY W + a K Y r + a1Y P1 + a2Y P2

(3.2)

P1 = (a L1 W + a K 1 r )(1 + m)

(3.3)

P2 = P2 (1 + t)

(3.4)

where W = wage rate, r = rental rate on capital, m = mark-up, and t = tariff imposed by govt. on dirty power used. Z 1 = a1X X + a1Y Y

(3.5)

Z2 = Z2

(3.6)

L = a L1 Z 1 + a L X X + a LY Y

(3.7)

K = a K 1 .Z 1 + a K X X + a K Y Y

(3.8)

Equations (3.1)–(3.4) are the price equations showing the determination of price for the outputs of the two sectors, the cleaner input and the dirty input denoted, respectively, by PX , PY , P1, and P2 . Perfect competition prevails in the market for X and Y. The competitive prices of X and Y are given by Eq. (3.1) and (3.2). The power sector (cleaner input) generated from renewable resources is being organized by a few producers. In this setup, the price determination follows the mark-up rule. Equation (3.3) illustrates this, where m is the fixed mark-up added to the cost of production. The price of dirty input, P2 , in line with the reality, is assumed to be under the regulation of the government, hence treated as exogenous to this model and is denoted by P2 . Moreover, the government regulates the supply of dirty input and that explains (3.6). Equations (3.5)–(3.8) delineated above demonstrate the full employment conditions for the inputs. The extraction of non-renewable resources, Z2, is fixed by the government. The specifications of the model are complete now. There are eight endogenous variables viz. PX , PY , W, r, P1 , Z1 , X, and Y in eight equations which can be solved for the values of the endogenous variables.

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3.3.1 Impact of Tariff on the Use of Dirty Energy as Means of Pollution Curb Herein we shall examine the consequence of the aforementioned move by the government. Let us begin with a logical bent. Following the tariff, the price of dirty energy will shoot (i.e., rise in P1 ). As a result, the cost of production will burgeon in both X and Y, and due to the fixed coefficient type of technology being in use therein, a condition of loss will emerge leading to the exit of some of the firms and the contraction of output. Thus, there will happen a fall in wage rate and interest rate in these sectors. This in turn will pave the way to a reallocation of released labour and capital to Z1 (to keep with the condition of full employment) resulting in the expansion in the production of clean energy subsequently. Thus, we have the following results. 

 θ − θ θ ) + θ θ − θ θ (θ (θ )P 2X K X 2Y K Y K 1 2X 1X 2Y 1Y 2 Wˆ = dt D   (θ2X θ LY − θ2Y θ L X ) + θ L1 (θ2X θ1Y − θ2Y θ1X )P2 rˆ = dt D

(3.9)

(3.10)

where D = (θ L X θ K Y −θ LY θ K X )+θ K 1 (θ L X θ1X −θ LY θ1Y )+θ L1 (θ 1X θ K Y −θ2Y θ K X )+ θ L1 θ K 1 (θ1Y − θ2Y ) < 0. Let us now delve into a plausible explanation of the above results. To start with (3.9), wherein the numerator is positive in sign and the denominator being negative sign. The reason being that the sector X is K-intensive relative to the sector Y to an extent greater than what sector Y is intensive in the use of the dirty energy relative to the sector X. Similarly, the extent to which sector X is intensive to clean input relatively Y is greater than the extent to which sector is intensive to use of dirty energy. Hence, both the bracketed terms in the numerator take positive sign. A similar argument applies to the case of the numerator of (3.10). Let us now reason the sign of the denominator. In all plausibility, it can be argued that sector X is K-intensive relative to sector Y much more than what sector Y is in terms of labour intensity. Hence, the first term in the numerator is negative. Again, for the sign of the second term to be negative, the extent to which X is more intensive in the use of clean energy relative to Y is less than what sector Y is in terms of labour intensity. Similarly, in the case of the third term, the extent to which sector X is relatively intensive to clean energy is greater than that of sector Y being intensive in the use of the dirty energy and the reasoning will suffice to make the last term negative. Thus, for dt > 0, we have,W , r < 0. Hence, we have the following proposition. 



Proposition 3.1 The introduction of tariff on use of dirty energy will lead to the contraction of both clean and dirty for a while, leading to the fall in W and r as an immediate impact. This development would induce the clean energy producing sector to absorb the labour and capital released by X and Y subsequently leading to the increase in the production of clean energy. Now in the long run the expanded

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supply of clean energy can potentially induce X and Y to reduce their dependence on dirty energy enabling the conservation of non-renewable resources and mitigation of pollution of the environment.

3.3.2 Impact of Public Investment in Clean Energy In the next segment, we shall examine the role of public investment in clean energy. This would essentially be the purported entry of government in the production and distribution of clean energy leading to the curtailment of market power by the private sector, i.e., m will fall. Now we shall look into the impact of fall in m as what follows.  (θ2X θ K X − θ1X θ K Y )  P1 (θ L X θ K Y − θ LY θ K X )   (θ1X θ LY − θ2X θ L X )  rˆ = P1 (θ L X θ K Y − θ LY θ K X )    P0 ˆ m  dG, m  < 0 P 1 = (θ L1 W + θ K 1rˆ ) + 1 + m0 Wˆ =



(3.11) (3.12)

(3.13)

Let us now explain the aforementioned results. To begin with (3.13), where the first term signifies the indirect effect of the increase in public investment in clean energy operating factor price changes while the second term indicates the immediate and direct impact. Thus, from the end of the direct impact, P1 < 0, as what is obvious. This fall in P1 would affect W and r in the manner illustrated in (3.14) and (3.12). Now, beginning with (3.11), it is plausible as already indicated in the previous segment that X is relatively intensive in the use of clean energy compared to Y much more than what Y is in dirty energy relative to X. This therefore implies the numerator is positive in sign, given that X is more K-intensive relative to Y and this factor in addition to the case that Y is more relatively labour-intensive compared to X, and makes the denominator positive. Thus implies W , r < 0. Hence, the final effect on the price of clean energy is P1 < 0. Hence, we have the following proposition. 







Proposition 3.2 Increase in public investment leads to the clean energy being cheaper. As a result, there would arise the possibility of cost economy for the other two sectors which they will exploit through modification of production technology towards lowering dependence. However, this would need capital and labour or increase productivity of these factors. In particular increased intensity of capital due to switch clean energy calls for FDI in these two sectors and clean energy sector as well.

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3.3.3 Privatization and Liberalization of Non-Renewable Energy This section is devoted to examining the impact of liberalization of non-renewable energy and its welfare implications. To this end, we make slight modifications to the baseline model. We assume the supply of clean energy being fixed and the government administers its price. The country imports crude oil and exports petroleum production and the industrial setup is perfectly competitive in nature with its price being linked to the international price. Moreover, the specific capital (earning return r2 ) being used in the production of petroleum along with labour and crude oil being (c) imported. Hence, we have the following equations. Moreover, privatization and external liberalization can potentially enable the expansion of the capital stock specific to the production of petroleum and the generation of thermal energy as well. P1 = P1

(3.3)

P2 = a L2 W + a K 2 r2 + ac Pc

(3.4)

Now here we seek to address the impact rise in international crude oil price or the international petroleum price. The results arrived at using comparative statics and ‘hat’ algebra are as follows:  (θ2Y θ K X − θ2X θ K Y )  P2 (θ L X θ K Y − θ LY θ K X )   (θ LY θ2X − θ L X θ2Y )  rˆ = P2 (θ L X θ K Y − θ LY θ K X )

Wˆ =



(3.14) (3.15)



P 2 = θ L2 Wˆ + θ K 2 rˆ2 + θc Pˆc

(3.16)

Following the privatization of petroleum (the primary source of non-renewable energy), the revocation of price control of government takes place with the price of petroleum being in line with the international level. Thus, in the revocation period, the price of non-renewable energy or dirty will shoot, i.e., P2 > 0. This will have a reflection in the cost production of X and Y sector as evident from (3.13) and (3.14), which, respectively, depicts the relation between change in P2 and money wage and interest rate (r). Let us overhaul into these relations. To begin with (3.3), the term in the denominator is negative as already explained. Now the term in the numerator will be positive since sector Y is more intensive to the use of non-renewable energy resource than X is and Y is more L-intensive to labour than X. Thus, we have W < 0 in case P2 > 0. However, the term in the numerator of (3.15) will be positive or negative depending on whether the extent to the sector Y is more intensive to the use of non-renewable energy resource than X is greater or lesser than the extent to 





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which Y is L-intensive to labour relative X. But the factor coefficients, being fixed, disallow firms to switch from costlier dirty energy to relatively cheaper clean energy and thereby will face loss and eventually some of them will leave, this would lead to the contraction of output in sector X and Y and this will result in fall in demand for labour and capital leading to the fall in W and r. Now the released labour and capital will remain unemployed as they can redeploy in sector Z2 for want of K2 . This will result in unemployment and excess capacity in the economy. However, the opposite would be the scenario, if privatization coupled with trade liberalization and deregulation of price cause the price to fall. Similar will be the development if the international price of crude oil gets on the increase, i.e.,Pc > 0. Thus, we have the following proposition. 

Proposition 3.3 Privatization along with liberalization of non-renewable (dirty) energy sector would produce an effect on the economy in terms of contraction of tradable sectors and excess creation and unemployment depending on whether such a move is associated with rise on the price of dirty energy or not. The policy implication is to expand the supply of the clean energy through more investment (public as well as private), enabling the suffering sectors to reduce their dependence on dirty energy. Let us now examine the consequence of such privatization leading to more investment in dirty energy, i.e., k2 > 0. This is understood in terms of the following equations, which are solved for X , Y, and Z 2 . 







0 = β L X Xˆ + β LY Yˆ + β L Z 2 Zˆ 2

(3.17)

kˆ2 = β K Z 2 Zˆ 2

(3.18)

0 = β K X Xˆ + β K Y Yˆ

(3.19)

Therefore, we have the following: For k2 > 0   −β L2 β K 2 ˆ X= kˆ2 < 0, for (β K X β LY − β K Y βl X )    β L2 β K 2 βK X Yˆ = kˆ2 > 0 βK Y (β K X β LY − β K Y βl X ) 

kˆ2 >0 Zˆ 2 = βK 2

(3.20) (3.21)

(3.22)

Proposition 3.4 Expansion of K-stock specific to the production of dirty energy through privatization and liberalization leads to expansion of clean sector (Y) while

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a contraction in the dirty sector (X). However, this goes against the fate of the exportorientated industry and but in favour of import competing industry leaving a slightly ambiguous implication for external sector macroeconomics.

3.3.4 Welfare Implication To this end, we shall adopt the very approach envisaged by Caves and Jones (1985). Thereby we get the change welfare. We begin with the community utility function:



U = U X d , C Y d , Z 2d , P X d , Ux , UC > 0, U P < 0

(3.23)

After total differentiation of (3.20) along slight manipulation, we get



dU = dy = pd X d + dY d + d Z 2d + UC Note : CY = CZ ≈ 1, p =



 UP dP UC

PX PX ≈ , given Pc = P(PY , P2 ), P2 = 1 Pc P2

(3.24) 

Now coming to the external sector balance, we have Mo + Mno = p X + Z 2

(3.25)

After total differentiation of (3.22) assuming Mo (denoting import of crude oil) being fixed, we get dy = pd X d + dY d + d Z 2d   X

< UP ˆ dP 0 = ( pd X + d Z 2 + dY ) − E 0 po P2 + UC >

(3.26)

Proposition 3.5 On welfare this privatization leaves a dicey picture. Nevertheless, the brighter side remains in the reduction of pollution due to the contraction of production in the dirty sector. If privatization leads to lowering energy price and the contraction is outweighed by the degree of the expansion in Y and Z 2 , then the overall welfare will ameliorate.

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3.4 Conclusion This paper highlights the crucial need of government intervention in an otherwise unregulated market economy in order to ensure sustainable development to take place by maintaining a sound environmental standard. This study has essentially shown the impact of tariff on dirty input and increased public investment in clean inputs. It finds that the introduction of tariff on the use of dirty energy will lead to the contraction of both clean and dirty in the short run while opening the possibility of lower dependence on dirty energy sources in the long run enabling the conservation of non-renewable resources and mitigation of pollution of the environment. Another finding of the study shows that increased public investment in cleaner input makes it the cheaper. However, complementary foreign invest could make the result stronger in the case of capital poor developing countries. The government can follow other measures also to curb environmental degradations but the impact would just be reversed and detrimental to the environment if the government follows a privatization policy instead by lowering the intervention.

References Acheson, D. (2000). Health inequalities impact assessment. Bulletin of the World Health Organization: The International Journal of Public Health, 78(1), 75–76. https://apps.who.int/iris/han dle/10665/57048. Adler, J. H. (2019). Introduction: Property in ecology. Natural Resources Journal, 59(1), x–xx. Baland, J. M., & Francois, P. (2005). Commons as insurance and the welfare impact of privatization. Journal of Public Economics, 89, 211–231. Bromley, D. W. (2005). Purging the frontier from our mind: Crafting a new fisheries policy. Reviews in Fish Biology and Fisheries, 15, 217–229. https://doi.org/10.1007/s11160-005-4866-z Caves, R. R., Jones, R. W. (1985). World trade and payments: An introduction, 4 edn. Boston: Little, Brown and Co. Chueng, S. N. S. (1970). The structure of a contract and the theory of a non-exclusive resource. Journal of Law and Economics, 13(1), 49–70. https://doi.org/10.1086/466683 Clark, C. W. (1973). Profit maximization and the extinction of animal species. Journal of Political Economy, 81(4), 950–961. Clark, C. W. (1974). The economics of overexploitation. Science, 181, 630–634. Costello, C. J., & Kaffine, D. (2008). Natural resource use with limited-tenure property rights. Journal of Environmental Economics and Management, 55(1), 20–36. https://doi.org/10.1016/ j.jeem.2007.09.001 de Meza, D., & Gould, J. R. (1987). Free access versus private property in a resource: Income distributions compared. Journal of Political Economy, 95, 1317–1325. Financial Express (2022). Ministerial panel to decide on number of CPSEs to be retained in each strategic sector: DIPAM Secy. https://www.financialexpress.com/economy/ministerial-panelto-decide-on-number-of-cpses-to-be-retained-in-each-strategic-sector-dipam-secy/2187169/ Fujita, R., & Bonzon, K. (2005). Rights-based fisheries management: an environmentalist perspective. Reviews in Fish Biology and Fisheries, 15, 309–312. https://doi.org/10.1007/s11160-0054867-y Ghosh, J., Sen, A., & Chandrasekhar, C. P. (1995). Privatising natural resources. Economic and Political Weekly, 30(38), 2351–2353.

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Gibson, C. C., Lehoucq, F. E., & Williams, J. T. (2002). Does privatization protect natural resources? Property rights and forests in Guatemala. Social Science Quarterly, 83(1), 206–225. https://doi. org/10.1111/1540-6237.00079 Gilmour, P. W., Day, R. W., & Dwyer, P. D. (2012). Using private rights to manage natural resources: Is stewardship linked to ownership? Ecology and Society, 17(3), 1. https://doi.org/10.5751/ES04770-170301 Van Ginkel, R. (1989). Plunders into planters: Zeeland oysterman the marine commons. In J. Borssevain (Ed.) Dilemmas: Anthropologists look at the Netherlands (pp. 89–105). Assen/ Gorcum. Gordon, H. S. (1954). The economic theory of a common property resource: The fishery. Journal of Political Economy, 62(2), 124–142. https://doi.org/10.1086/257497 Helson, J., Leslie, S., Clement, G., Wells, R., & Wood, R. (2010). Private rights, public benefits: Industry-driven seabed protection. Marine Policy, 34(3), 557–566. https://doi.org/10.1016/j.mar pol.2009.11.002 Johnson, O. E. G. (1972). Economic analysis, the legal framework and land tenure systems. Journal of Law and Economics, 15(1), 259–276. https://doi.org/10.1086/466736 Jones, R. W. (1965). The structure of simple general equilibrium models. Journal of Political Economy, 73, 557. Larson, B. A., & Bromley, D. W. (1990). Property rights, externalities, and resource degradation: locating the tragedy. Journal of Development Economics, 33(2), 235–262. Okonkwo, J. U., & Quaas, M. F. (2019). Welfare effects of natural resource privatization: A dynamic analysis. Environmental and Development Economics, 25, 205–225. Sumaila, U. R. (2010). A cautionary note on individual transferable quotas. Ecology and Society, 15(3), 36. http://www.ecologyandsociety.org/vol15/iss3/art36/ Wingard, J. D. (2000). Community transferable quotas: Internalizing externalities and minimizing social impacts of fisheries management. Human Organization, 59(1), 48–57.

Chapter 4

Defining the Most Effective Green Corporate Governance Strategies for Sustainable Performance Hasan Dinçer, Hakan Kalkavan, Serhat Yüksel, and Hüsne Karaku¸s

4.1 Introduction Social responsibility is the behavior of individuals or institutions considering both their own and social benefits (Wang & Sarkis, 2017). Individuals or institutions that attribute importance to social responsibility contribute to social life. The basis of a healthy life is strengthened by social responsibility. However, it increases the sensitivity of individuals or institutions to the environment. In this context, social responsibility becomes important. People or institutions that attach importance to social responsibility focus on many issues. Issues such as health, education, green environmental awareness, gender equality, and protection of social rights are among the important subjects of social responsibility (Liu & Zhang, 2017; Ortas et al., 2015; Tunay & Yüksel, 2017). With the awareness of social responsibility in businesses, the concept of corporate social responsibility (CSR) has emerged. CSR is that businesses operate by considering the expectations of society. Every corporation has certain responsibilities towards the society and its employees (Dinçer et al., 2019a; Li, 2006; Vitell, 2015). Businesses that fulfill these responsibilities strengthen their corporate image (Christmann & Taylor, 2006; Sardinha et al., 2011). In this regard, enterprises should

H. Dinçer · H. Kalkavan · S. Yüksel (B) · H. Karaku¸s The School of Business, ˙Istanbul Medipol University, Istanbul, Türkiye e-mail: [email protected] H. Dinçer e-mail: [email protected] H. Kalkavan e-mail: [email protected] H. Karaku¸s e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_4

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give the necessary value to corporate social responsibility. Corporations have responsibilities towards their employees, consumers, shareholders, the state, and the environment. The aforementioned responsibilities are among the subjects of CSR (Guo et al., 2015; Yüksel & Özsarı, 2017). Businesses that fulfill their responsibilities to their employees go through an efficient production period. Furthermore, the brand awareness of companies which determines CSR strategies in compliance with their consumers increases. This situation ensures that companies’ assets sustain for a long time (Reverte, 2009). The strategies that businesses follow to protect the environment are important. In this regard, the concept of green corporate social responsibility (GCSR) gains importance (Song et al., 2017). GCSR is the development of strategies to minimize the negative effects of all kinds of activities of businesses on the environment (Trumpp et al., 2015). These types of businesses carry out all their activities from the production process to the consumers’ perceptions with an environmentalist approach. Especially in recent times, consumers consider companies engaged in environmental activities as valuable (Barboza, 2018). This situation indicates that businesses should give importance to GCSR strategies (Takala & Pallab, 2000). Businesses that give importance to GCSR develop a number of strategies. These businesses increase their environmental advertisements. In this way, an environmentally friendly company image is created in the eyes of consumers. This situation positively affects the performance of the company (Marquis & Qian, 2014). Furthermore, some businesses adopt green marketing strategies. With green marketing, the production and distribution of environmentally friendly products are carried out in a way that does not harm nature (Widyastuti et al., 2019). Producing in line with all these strategies, companies are working on the use of renewable energy sources. In this way, the damage of energy used to nature is reduced. The utilization of raw materials used in production as waste is also one of the strategies determined in green corporate social responsibility. By this means, businesses perform energy control efficiently. However, companies are shifting their equipment expenditures to this area in order to be environmentalists (Hidayat & Leon, 2020; Laskowska, 2018; Muharam & Tarrazon, 2011). Strategies developed for GCSR affect the performance of companies. Consumers with high environmental awareness prefer companies that attribute importance to GCSR. Therefore, the effect of green ads on consumers is influential (Marquis & Qian, 2014). However, businesses that provide energy in a controlled manner have less effect on environmental pollution. This issue also attracts the attention of consumers and investors (Chuang & Huang, 2018; Wu et al., 2018). GCSR strategies developed both internally and externally affect the performance of companies. These strategies are effective in ensuring companies’ sustainable performance (Popescu & Popescu, 2019). Therefore, the purpose of this study is to define the GCSR strategies that are effective in ensuring companies’ sustainable performance. Developing countries were included in the study. However, 7 considered criteria have been determined as a result of the literature review. After all, the determined criteria have been tested with the fuzzy AHP method.

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This study consists of 5 parts. This section is the introduction, and theoretical information about social responsibility, corporate social responsibility, and green corporate social responsibility is included. In the second part of the study, strategies for GCSR will be determined by making a literature review. In the third part of the study, activities for GCSR will be emphasized. In the fourth part of the study, the determined criteria will be tested through the fuzzy AHP method. Thus, it will be determined which strategies will be effective in achieving the sustainable performance of companies. In the last part of the study, the results obtained will be evaluated.

4.2 Literature Review on Green Corporate Social Responsibility In the literature, the topic of GCSR has been discussed by many researchers. For example; Bhalla (2018) researched the subject of GCSR. In the study, multinational companies in the USA and India were examined. The study has been examined by using the Confirmatory Factor Analysis. As a result, it has been determined that GCSR is substantial for multinational companies. Since GCSR increases the brand image and market value of companies, therefore corporations need to determine strategies for GCSR. There are many studies on this subject in the literature. Wu et al. (2018) examined the relationship between GCSR and innovation. In the study, Chinese companies in the 2006–2015 period were examined by regression analysis. As a result, it was emphasized that there is a relationship between GCSR and innovation. It has been determined that companies should use renewable energy sources to ensure GCSR. Similar to these studies, Muharam and Tarrazon (2011) focused on GCSR. According to their studies, it was stated that companies should reduce their carbon emissions. For this, it was determined that renewable energy sources should be used. One of the important activities to procure GCSR is to ensure the reuse of raw material waste. This topic has attracted the interest of many researchers. In their studies, Rodrigues and Leon (2020) researched the issue of GCSR and innovation. Based on this research, 33 manufacturing companies in Indonesia were examined by regression analysis. As a result, it was emphasized that there is a meaningful relationship between GCSR and innovation. However, it was stated that companies should reduce their energy use in order to achieve GCSR. Energy use should be brought under control with GCSR. For this, the reuse of used raw materials should be ensured. In this way, unnecessary resource usage is prevented (Nguyen & Nguyen, 2018). There are also researches on environmental equipment expenditures in the literature. For example; Wang et al. (2013) focused on the development of green management standards in his study. Restaurants in Taiwan have been examined. The examination was observed by the Delphi method. As a result, it has been identified that the importance of newly established restaurants to spend on green equipment and ensure

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green production is vital for the sustainability of companies. Similar to these studies, Laskowska (2018) examined the effectiveness of green corporate social responsibility in the financial market. The study was supported by a literature review. As a result, it was emphasized that GCSR increases the growth of companies and their effectiveness in the financial market. Therefore, it was stated that companies should give importance to eco-expenditures. Likewise, Guo et al. (2015) evaluated GCSR in terms of industrial law. In the study, manufacturing companies were examined by using the DEMATEL method. As a result, it was emphasized that GCSR is important in terms of ensuring the sustainability of companies. Therefore, it was stated that companies should increase their spending on peripheral equipment. One of the important issues in GCSR is green marketing. There are many studies on this subject in the literature. Companies should give importance to green marketing to ensure their sustainability. Companies develop products that consume less energy with green marketing. Moreover, it creates affordable pricing policies (Lymperopoulos et al., 2012). Similar to these studies, Suki et al. (2016) researched the issue of green marketing awareness in CSR. In the study, the retail sector has been analyzed using the Least Squares method. As a result, it has been stated that green marketing is important in ensuring GCSR for companies. It was emphasized that companies that adopt green marketing increase their customer portfolio. In this way, it has been determined that the performance of the companies has also improved. Additionally, Widyastuti et al. (2019) focused on green marketing. In the study, 225 customers in Unilever were examined by the survey method. It has been determined that companies should give importance to green marketing in order to raise customer awareness. Businesses that attach importance to GCSR should also focus on internal activities. First of all, it is necessary to raise environmental awareness among company employees. Then, company employees are required to provide environmental services to their customers. This issue has been emphasized by many researchers in the literature. E.g., Pop et al. (2011) investigated the issue of promoting environmental activities. The European Union countries were examined in the study. As a result, it was emphasized that necessary training should be given to employees in order to encourage green production within the company. However, Cruz and Pedrozo (2009) evaluated the issue of GCSR strategies in their study. In the study, multinational companies operating in the retail sector were examined by the interview method. As a result, it was stated that companies should develop CSR strategies in order to ensure their sustainable development. One of these strategies is to create consciousness of the green management concept. It was stated that the company’s board of directors and employees should be trained in order to realize this strategy. In parallel with these studies, Banyte et al. (2010) focused on GCSR in their studies. The food sector in Lithuania was included in the scope of the study. It was emphasized that environmentally friendly products are important for customers. However, it has been determined that there are price problems. For this reason, it was stated that the public relations department should be trained and promoted for green marketing to continue.

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It is important to develop environmentally friendly technological systems for businesses that adopt GCSR. These systems do not cause any harm to the environment. Bohas and Poussing (2016) focused on this issue and evaluated green information technologies in terms of CSR. In the study, the survey data of the Luxembourg SocioEconomic Research Institute for the period of 2008–2011 were analyzed using the survey method. As a result, it has been determined that the use of green information technology is important for companies. Therefore, it was stated that the use of clean technologies for companies should be encouraged. Similar to these studies, Abimbola et al. (2010) investigated the corporate image of companies and GCSR. Oil and gas companies in Norway were examined in the study. The study was evaluated by using in-depth interview method. It was emphasized that companies should reflect their customers as a brand that cares about the environment. However, it was stated that environmental technologies should be developed by taking all risks. Chuang and Huang (2018) evaluated the impact of environmental corporate social responsibility on business competition and performance. 358 companies in Taiwan were analyzed in the study. As a result, it has been determined that one of the important issues in environmental corporate social responsibility is information technology. Further, green technologies have been found to increase company performance. In a similar study, Barboza (2018) evaluated consumers’ preferences with GCSR. The literature was searched in the study. It has been determined that companies focus especially on socially responsible consumers in the field of GCSR. It has been determined that green production technologies are used based on the aforementioned consumers. GCSR activities are important in determining the performance of companies. Especially consumers or investors with high environmental awareness affect the performance of the company. Therefore, companies should engage in activities that will create an environmental image. There are many studies on this subject in the literature. For example; Cox (2008) investigated green advertising, which is one of the GCSR activities. British oil companies were examined in the study. As a result, it was stated that green advertising is important in ensuring GCSR. Thus, it is shown that companies attach importance to the environment with green advertising. In addition to these studies, Servaes and Tamayo (2013) examined the effect of GCSR on company value. The study was tested by regression analysis. As a result, it has been determined that GCSR increases firm value. Thus, it was stated that companies should focus on green ads. Customers are one of the factors that affect the financial performance of companies. Companies should pay attention to customer diversity while increasing green financial performance. For this, it is necessary to focus on green advertising (Wang & Qian, 2011). Marquis and Qian (2014) conducted a similar study. The study focused on the reporting of CSR activities. 1600 Chinese companies were evaluated in the 2006–2009 period. As a result, it was emphasized that the reporting of GCSR is important. However, it has been determined that the density of advertisements affects sales and firm value. According to the literature review, many issues are focused on GCSR activities. In general, the studies emphasized that GCSR is important for the sustainability of companies. In addition, it has been observed in the studies that the use of renewable

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energy resources, ensuring the reuse of raw material wastes, increasing the expenditures of environmental equipment, proposing green marketing, providing training for in-house employees, developing environmentally friendly technological systems and promoting green advertising in the GCSR activities of companies. Generally, Chinese companies were examined within the scope of the studies. However, it has been observed that some of the studies dealt with different sectors. In the studies, it is generally stated that GCSR activities are effective in determining company performance. As a result of the literature review, what deficiency in literature is that few studies are explaining the impact of green corporate social responsibility activities on company performance. Therefore, it is thought that this study will contribute to the literature. In this study, it is suggested to evaluate the effect of GCSR activities on company performance. Also, it is aimed to support the study methodologically by using the Fuzzy AHP method. It is thought that this analysis will contribute to the literature.

4.3 Theoretical Background of Green Corporate Governance Activities The concept of corporate social responsibility is first mentioned in the book “Social Responsibilities of the Businessman” written by Howard Bowen in 1953. According to Bowen, CSR is that businesses operate according to the goals and values of the society (Bowen, 2013). However, the definition of CSR in the literature varies. Researchers define the concept of CSR as making and implementing decisions that will reduce social damage (Mohr et al., 2001). However, CSR means that businesses engage in voluntary activities aiming for the future benefits of society and an unpolluted environment (Carroll, 1999). In general, CSR means that businesses voluntarily fulfill their responsibilities towards society and nature. CSR has emerged by being influenced by many factors including political, social, and economic (Post et al., 1996). Corporations are responsible for their employees, consumers, shareholders, environment, nature, society, government, and competitors (Alchian & Demsetz, 1972; Freeman & McVea, 2001). Businesses, which adopt CSR, operate by considering these areas of responsibility. In this way, the brand values of businesses increase. However, the preferences of consumers who attach importance to social responsibility also favor businesses that value CSR (Mohr & Webb, 2005). In this way, businesses gain the trust of the customers and have the opportunity to enter new markets. Therefore, businesses should attach importance to CSR. One of the CSR areas of businesses is the protection of the social and natural environment. However, recently, it is aimed to conduct businesses in all CSR areas in accordance with nature and social environment (Acu & Babos, 2012). At this point, the concept of GCSR has emerged. GCSR is that corporations fulfill their responsibilities towards their employees and society in a way that protects nature and the environment. Current climate changes,

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depletion of natural resources, and increased environmental awareness of consumers push businesses to GCSR (Trumpp et al., 2015). Corporations that attach importance to GCSR gain the trust of consumers, reduce their damage to nature and the environment, and strengthen the future of society. In this respect, the effects of businesses on society are important. The impact of GCSR on businesses is also extremely important. The brand values of businesses that attribute importance to GCSR are increasing (Auger et al., 2003). This situation increases the market values of the enterprises. Recently, corporate investors have focused on social responsibility efforts. These efforts influence the performance of companies (Mohr & Webb, 2005). When all these are considered, businesses should give the necessary value to GCSR. Corporations are engaged in some activities for GCSR. They have been working towards green production in order to minimize the negative impact of the production process on the environment and society. So, they try to turn raw materials used in production into secondary sources (waste materials) (Zhou et al., 2012). In this way, unnecessary use of resources is prevented. Renewable energy sources are energy resources that are constantly found in nature and do not harm the environment (Dinçer et al., 2020). Businesses are working on the use of these resources in the production process. Thus, the damage of the energy used to nature is reduced and continuous production is ensured. However, businesses aim to improve the production process and design environmentally friendly products to ensure green production. Therefore, they increase their spending on peripheral equipment (Johansson & Winroth, 2010). Technological systems used in production make it easier for businesses. Therefore, the existence of technological systems is important for businesses. However, many technological systems used adversely affect the environment. It causes carbon emissions, human and animal health. This situation also threatens the assets of businesses (Zhang et al., 2011). Therefore, one of the important issues in GCSR is the development of environmentally friendly technological systems. Businesses with this precision aim to develop and use technological systems that have positive effects on the environment and society by using as little resources as possible. Besides, studies are carried out to take measures that can reduce the negative effects of existing technological systems on the environment (Du & Li, 2019). E.g., enterprises producing oil and gas in Norway are working on the development of environmentally friendly technological systems. In this way, the harmful effects of technological systems on the environment and society can be reduced (Abimbola et al., 2010). One of the important issues for corporations is the distribution of green products in an environmentally friendly way. Efforts are being made to develop distribution systems that leave no waste to nature and the environment and consume low energy. However, it is aimed not to reflect the costs of environmentally friendly products to product prices -from production to distribution. While businesses focus on these issues, they develop green marketing strategies (Dangelico & Vocalelli, 2017). Thuswise, businesses attract the attention of investors and consumers. Companies focus on advertisements in order to attract the attention of consumers and investors with high environmental awareness and to present an environmentally friendly company image.

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In addition, such companies provide environmental training to their employees, especially the public relations department. Environmental awareness of employees is provided with environmental education (Azzone & Noci, 1998). This environmental awareness is reflected in consumers and investors. For example, businesses operating in the food sector provide environmental training to their employees. In this way, the image of an environmentally friendly company is presented both inside and outside the company (Banyte et al., 2010). Essentially, activities for GCSR ensure that the performance of businesses is sustainable. Accordingly, businesses are valuable in the eyes of consumers and investors, and this situation increases the market and brand value of businesses. Companies with high brand value can enter new markets more easily (Widyastuti et al., 2019). In this context, many businesses that adopt GCSR gain a competitive advantage. Further, it helps the development of countries as it contributes to the environment and the increase of the welfare of society (Kalkavan, 2020; Popescu & Popescu, 2019). When all these are considered, GCSR becomes crucial for society and corporations.

4.4 An Evaluation for GCSR Activities 4.4.1 The List of Indicators In this study, it is aimed to find out which factors are more significant to improve GCSR activities. After making a detailed analysis, 7 different criteria are identified. They are detailed in Table 4.1.

4.4.2 Analysis Results In the second stage of the analysis, it is intended to identify which criteria affect GCSR activities of the companies more. For this purpose, an evaluation has been conducted by using fuzzy AHP methodology. AHP methodology is considered to find the significant weights of different factors (Dinçer & Yüksel, 2018; Dinçer et al., 2017). In this framework, hierarchical relationship between the criteria is taken into consideration (Silahtaro˘glu et al., 2021). With respect to the fuzzy AHP, the calculations are performed by considering triangular fuzzy numbers (Dinçer et al., 2019b). Owing to this situation, it is aimed to handle the uncertainty of decision-making process more effectively (Dinçer et al., 2019c). Within this framework, 3 different experts evaluated these criteria. Table 4.2 gives information about the weights of the criteria. Table 4.2 states that using renewable energy resources (C1) is the most significant factor for the companies to improve their GCSR activities. Moreover, it is also defined that ensuring the reuse of raw material waste (C2) is another essential issue in this

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Table 4.1 List of the criteria Criteria

Explanation

Using renewable energy resources (C1)

Carbon emissions are reduced by using Guo et al. (2015), Wu renewable energy sources. This ensures that the et al. (2018), company’s performance is sustainable Muharam and Tarrazon (2011)

Ensuring the reuse of raw material waste (C2)

Recycling can be achieved by using raw material wastes that will harm the environment in production. In this way, the energy control of the companies is ensured and the damage to the environment is reduced

Increasing environmental equipment expenses (C3)

More expenditures are required to ensure that Guo et al. (2015), the equipment used by companies is more Wang et al. (2013), environmentally friendly. In this way, Laskowska (2018) companies become environmentally friendly and strengthen their brand image. This situation draws the attention of investors

Ensuring green marketing (C4)

Companies ensure their sustainability with green marketing. Green marketing contributes to the development of low energy consuming products, affordable pricing policies and environmentally friendly distribution system for these products. This situation increases the performance of the company

Widyastuti et al. (2019), Suki et al. (2016), Lymperopoulos et al. (2012), Nguyen and Nguyen (2018)

Training of personnel (C5)

The environmental and social responsibility awareness of consumers or investors is increasing day by day. This situation affects companies. For this reason, companies should primarily train their personnel in order to give the image of being a more environmentally friendly company. In this way, the public relations department’s image of an environmentally friendly company to investors or consumers positively affects its performance

Pop et al. (2011), Cruz and Pedrozo (2009), Banyte et al. (2010)

Development of environmental technological systems (C6)

Companies need to develop technological systems that will less harm the environment. In order to reduce the environmental damage of technologies that cause carbon emissions, mechanisms that can provide energy control in the current technological system must be developed

Bohas and Poussing (2016), Abimbola et al. (2010), Chuang and Huang (2018), Wu et al. (2018)

Green advertising (C7) The fact that companies show that they are environmentally friendly with green advertisements attracts the attention of consumers and investors. This situation positively affects the sales performance of companies, especially Source Authors’ compilations

Supported Literature

Guo et al. (2015), Hidayat and Leon (2020), Nguyen and Nguyen (2018)

Cox (2008), Servaes and Tamayo (2013), Wang and Qian (2011), Marquis and Qian (2014)

50 Table 4.2 Weights of the criteria

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Criteria

Weights

Using renewable energy resources (C1)

0.432265

Ensuring the reuse of raw material waste (C2)

0.260171

Increasing environmental equipment expenses (C3)

0.124174

Ensuring green marketing (C4)

0.072182

Training of personnel (C5)

0.054894

Development of environmental technological systems (C6)

0.035222

Green advertising (C7)

0.021091

Source Authors’ calculations

context. On the other hand, development of environmental technological systems (C6) and green advertising (C7) play lower role in comparison with other ones.

4.5 Conclusion Social responsibility is the behavior of individuals or institutions considering both their own and social benefits. Individuals or institutions that attribute importance to social responsibility contribute to social life. With the awareness of social responsibility in businesses, the concept of CSR has emerged. It means that businesses operate by considering the expectations of society. Moreover, GCSR is the development of strategies to minimize the negative effects of all kinds of activities of businesses on the environment. These types of businesses carry out all their activities from the production process to the consumers’ perceptions with an environmentalist approach. In this study, it is aimed to define the GCSR strategies that are effective in ensuring companies’ sustainable performance. Developing countries are included in the scope of the study. Furthermore, 7 considered criteria have been determined as a result of the literature review. After all, the determined criteria have been tested with the fuzzy AHP methodology. The findings indicate that using renewable energy resources is the most significant GCSR factor for the companies to improve their performance. In addition to this issue, it is also identified that ensuring the reuse of raw material waste is another essential issue in this context. Nonetheless, it is also concluded that the development of environmental technological systems and green advertising have lower weights. It is obvious that developing countries should mainly focus on renewable energy usage in order to improve their performance. Within this framework, the governments should give appropriate incentives to encourage companies to make investments in renewable energy. For this purpose, there can be tax reduction which has a direct impact on the cost minimization. Because renewable energy projects have high initial costs, this action can be very helpful to increase these investments. In addition to

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this issue, loans with lower interest rate can be provided to the renewable energy investors. Since there is a need of high amount of financial support for these projects, this situation can contribute to the improvement of renewable energy investments.

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Chapter 5

Evaluating Possible Ways to Decrease Negative Environmental Impact of Nuclear Energy Projects Serhat Yüksel, Hasan Dinçer, and Gülsüm Sena Uluer

5.1 Introduction Rising industrial production since The Industrial Revolution, high and fast international trade volume because of globalization, increment demand and need amount for goods and services with rapid population growth, upgrade competitiveness between sectors and exponential urbanization all around the world have caused an increase in carbon dioxide (CO2 ) emission rate in the atmosphere which triggers inevitably global warming negative effects (Yuan et al., 2021; Zhao et al., 2021). In addition to that, there is superior energy consumption, and it will continue inducing environmental pollution if correct energy resources do not use and allocate consciously. Energy is our life’s main component, and it uses heating, cooling, lighting, cooking, production and services (Zhou et al., 2020). There are two kinds of energy resources which are renewable energy (RE) and non-renewable energy resources. Non-renewable energy resources are fossil fuel-based energy resources such as oil, natural gas and coal. These energy resources are the most commonly used energy resources all around the world and they have high CO2 emission rate. Hence, the most influential reason for environmental pollution is that fossil fuel-based energy resources are used in production, transportation, heating, lighting, cooling and other daily activities. On the other hand, renewable energy resources are wind, solar, water, hydrogen (H), biomass and so on. Besides, these energy resources have low CO2 emission rate. With regard to these, there have been designed many conferences, S. Yüksel (B) · H. Dinçer · G. S. Uluer The School of Business, ˙Istanbul Medipol University, Istanbul, Turkey e-mail: [email protected] H. Dinçer e-mail: [email protected] G. S. Uluer e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_5

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agreement and conversation in order to decrease CO2 emission rates and promoted countries to increase their RE resources’ investments. For example, Kyoto Protocols and Paris Agreements are one of the most known attempts in order to decrease and control CO2 emission rate in the world (Savaresi, 2016). Countries—particularly European Union (EU)—have tried to initiate agreements in order to reduce down CO2 emission rate and mitigate climate change, because the increase in environmental pollution has also a negative impact on human health which leads to decrease in employment and create extra cost for remedying hazards. For example, social factors are the most influential factors in coal energy use for economic development, because environmental pollution, demographic factors and health problems are required to be considerable in coal energy use (Du et al., 2020). Thus, coal energy generation consequences although it is low-cost energy will not be beneficial for economy, because repairing cost of fossil fuel-based energy resources’ losses are higher than obtained revenue thanks to the low-price fossil fuel usage. So, RE and nuclear energy will be provided benefits for economy, environment and society (Haiyun et al., 2021). Technological developments on RE and upgrading RE usage are considered to mitigate climate changes and greenhouse gas (GHG) emission rates, increase energy security which will support energy supply with other resources and contribute to economy, society and environment. Thus, RE investments are so important to create a more sustainable and healthier environment for next generations. However, the problems for RE resources are storage and cost. Also, developing RE technologies provided electricity supply without losing power and RE-generated electricity price becomes cheaper by decreasing RE costs. Moreover, another barrier could be the advanced RE technology export by developed countries are so expensive. In addition to that, geographic, social, governmental and political issues have impact on RE investments (Li et al., 2021; Liu et al., 2021; Xie et al., 2021). As it is mentioned before, energy demand has raised in the world and unconscious energy usage creates hazards, so RE usage should be improved. For example, there are excessive fossil fuel energy and nuclear energy waste in the Middle East. Also, energy crisis and wars have been realized time by time. Therefore, the solution is seen as RE which eliminates problems because solar and wind energy generate electricity supply. For instance, Iran, Turkey, Iraq, Egypt, Yemen and Oman have high wind energy potential (Nematollahi et al., 2016). On the other hand, nuclear energy is one of the lowest CO2 emission rates of energy resources and it carries out huge efficient energy generation potential. Thus, RE and nuclear energy have been promoted instead of fossil fuel-based energy generation. According to researches, nuclear energy decreases GHG emission rates and there is a positive relationship between nuclear energy and economic growth while reducing down CO2 emission rates (Kim, 2020; Noh et al., 2017; Pr˘av˘alie & Bandoc, 2019; Qiu et al., 2020). Also, some researches demonstrated that nuclear energy is more environment-friendly than RE because it has lower CO2 emission rates than RE However, some researches do not agree with this approach strongly (Dong et al., 2018; Li et al., 2020; Qi et al., 2020).

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Nuclear energy reduces foreign energy dependency a lot, because it is one of the most efficient energy resources for satisfying energy demand. For instance, the Turkish government announced that Akkuyu Nuclear Power Plant will be in process by 2023. Turkey is a highly energy-dependent country and this NPP which has been constructed by dealing with Russia will decrease foreign energy dependency— e.g. natural gas import by Russia—of Turkey (Aydın, 2020). Another example is that the Middle East countries support to build NPP in order to get sustainable and efficient energy although they have petroleum (Krane et al., 2016). Besides, nuclear power is a more efficient energy resource than RE and fossil fuel-based energy generation. With regard to increasing clean energy demand, advanced nuclear energy nuclear power plants (NPP) provide a fast and cheaper energy supply (Alekseev et al., 2020). However, there is needed high investment amount for building NPP, and quick revenue response against investment amount could be late if the financing of NPP is not well planned (Dinçer et al., 2020a, 2020b). In addition to that, NPP governance, location choice and construction management are important in order to mitigate risks (Siqueira et al., 2019). However, nuclear energy has also carried out some problems for environment. These are radioactivity, waste nuclear fuel management and long-lived NPP concerns (Kermisch & Taebi, 2017; Liu & Wei, 2019). Therewithal, all these concerns have an impact on nuclear energy public acceptance, and it is negatively triggered after Fukushima and Chernobyl nuclear accidents. In the world, NPP decommissioning trend spread and countries—particularly European countries—have made decision in order to phase out nuclear energy reactors, while some countries were drawing NPP investment plans for future. For example, the German government announced that all nuclear reactors will be phased out in the country by 2022. Otherwise, China, Russia and Canada have planned NPP and studied nuclear waste management (Peng et al., 2019). With advanced nuclear energy technology, waste nuclear fuel recycling has been achieved, and radioactivity danger has become lower (Fernandez et al., 2017). Also, developed nuclear energy technologies have mitigated nuclear accident risk and reduced down cost while they are increasing energy supply security and safety. Moreover, science and communication are so crucial for nuclear energy safety and public support. Environmental concerns have also an impact on nuclear energy acceptance (Wang et al., 2020). On the other hand, nuclear fuel utilization studies have tried to mitigate radiation risk which affects health and environment and to get the maximum productivity. For example, thorium-based nuclear reactors are called green nuclear energy and considered more environment-friendly energy than uranium-based nuclear energy (Demirdogen and Askar, 2017; Humphrey & Khandaker, 2018). Thus, nuclear energy developments provide to improve economic, social and environmental sustainability.

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5.2 Theoretical Background In the world, greenhouse gas emission (GHG) rates are increasing, and countries have tried to find new alternatives for fossil fuels in manufacturing and services in order to reduce down CO2 emission rate, because high emission rates can cause losses in economy, society and environment. Hence, nuclear energy and renewable energy (RE) are alternatives to fossil fuel-based energy resources. Therefore, these energy resources have lower CO2 emission rates than fossil fuel-based energy which could be dangerous for economy, environment and society. For example, Poland CO2 emission rate is so high because of coal-dependent economy in the EU. Thus, they will build nuclear reactor for economic and environmental development. Also, nuclear energy usage reduces down coal and gas consumption because it has become efficient in energy generation and meet populations’ energy demand fast thanks to advanced nuclear technologies (Alekseev et al., 2020) Nuclear energy is important in order to decrease CO2 emission rates, and it has a positive impact in both economy and environment (Baek & Pride, 2014). Also, there is a negative relationship between nuclear energy and CO2 emission rates in short and long runs, but there is a causality and positive relationship between RE and CO2 emission rates and it has a storage problem which can reduce preference for energy generation (Apergis et al., 2010; Menyah & Wolde-Rufael, 2010). However, some researches stated that RE can decrease CO2 rate more than nuclear energy and it is recommended economy and ecology a lot (Dong et al., 2018). Moreover, nuclear energy generation provides efficiency, and it is more beneficial and feasible for environment and economy. In addition to that, Almutairi et al. (2018) constituted two scenarios as business as usual (BAU, existing energy resources usage) and RE, nuclear energy (RNE) scenario. In the RNE scenario, mitigation of climate changes has not appeared by 2030 predictions. However, nuclear energy is risky for health because of radiation rates. So, precautions are important for NPP security (Fiore, 2006). Additionally, Liu and Wei (2019) recommended that public should be informed because low management of radwaste in coastal nuclear reactor power plants will be dangerous for health and environment. Therewithal, Glinskii et al. (2020) have developed radiation level and subsoil status monitoring geo-environmental packet systems for nuclear energy security and environmental protection. On the other hand, nuclear reactors can create hazard for natural balance as different from radioactivity. According to Beheshti (2011)’s research in Iran, nuclear reactors raise temperature of water and it leads to environmental degradation cost. Therefore, this cost ends up arid in Iran and specialists have recommended being careful. However, one of the environmental problems of nuclear energy is waste management, although it is an alternative to fossil fuels for decreasing CO2 emission rate, increasing economic efficiency and sustainability (Noh et al., 2017; Zare, 2016). Besides, nuclear fuel waste management will improve energy generation and productivity (Schlange, 1995). Moreover, the S-CO2 power cycle provides economic efficiency with high technology in solar energy and nuclear energy (Li et al., 2017). Additionally, Bobrowski et al. (2017) remarked that spent nuclear fuel recycling

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is critical for radioactivity and, geography and cladding materials matter. Further, nuclear waste management researches and tests continue internationally in order to develop recycling technology, energy policies and remedy environmental impacts (Lee & Koh, 2002). Besides, moral issues associated with environment, sustainability and radioactivity cannot be ignored in order to provide nuclear reactor safety (Kermisch & Taebi, 2017). Also, weak waste nuclear fuel recycling plan and lack of sustainability development for NPP can create nuclear reactor mistakes and it is measured nuclear energy risks, nuclear energy fuel efficiency and proliferation by considering ADS reactors (Fiori & Zhou, 2016). In other respects, nuclear accidents and atomic bombs damaged environment and human health a lot because of radiation and exposure. Besides, nuclear energy fear and lack of trust in authorities have started with atomic bombs in Japan. Therewithal, radiation level triggers this fear, because foods and lakes expose radiation (Edwards et al., 2019). On the other hand, some countries decided to phase out nuclear reactor power plant (NPP). This decommissioning trend spreads after the Fukishima NPP accident. The reasons are radioactivity, negative environmental impacts because of radioactivity, shortening of the life cycle of nuclear reactors and no impact on the economy. However, researches show that nuclear energy reactor phase-out is so costly and it affects CO2 emission rates. In Belgium, nuclear energy phase-out can cause increasing fossil fuel-based energy generation, high CO2 emission rates and foreign energy dependency, lower energy supply and security. Also, it might not raise RE deployment in the country (Kunsch & Friesewinkel, 2014). In China, CO2 emission rates have declined 43–53% with nuclear energy since 2005 and China has developed and given incentives by government nuclear energy plans against nuclear reactor decommissioning trend in the world (Dong et al., 2018; Peng et al., 2019). Furthermore, Neumann et al. (2020) denoted that low-level democratic countries have high willingness to use, open and support NPP as nuclear warhead countries. Hence, nuclear energy has a negative impact on environmental pollution, and it is low CO2 emission rate energy resources. Also, it influences economy, policies and researches (Mahmood et al., 2020). As it is mentioned before, there is a positive relationship between CO2 emission rate and economic growth. With regard to these, increase in RE and nuclear energy investments will increase environmental protection and economic growth (Chu & Chang, 2012; Lau et al., 2019). Nuclear energy reduces the energy dependency of countries which increases economic growth, because energy dependency raises current account deficits and expenditures. For instance, Turkey is energy-dependent countries and they dealt with Japan and Russia in order to decrease Russia’s gas import dependency, but stakeholder concerns, environmental concerns, trust of government problems and environmental concerns maintain (Aydın, 2020). However, A˘gbulut (2019) stated that economic growth, low electricity price and foreign dependency were achieved with NPP for Turkey. When there is made a comparison between nuclear and non-nuclear energy countries, nuclear energy countries’ energy consumption is higher than other countries and these nuclear countries’ CO2 emission rates are lower. However, there is no evidence of reduction in the usage fossil fuel-based energy and foreign energy independenc (Gralla et al., 2017). On the other hand, nuclear reactor phase-out can reduce economic growth by

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increasing CO2 emission rate. For example, it has damaged economic growth with Italy’s nuclear reactor phase-out decision which cut electricity generation after referendum (Esposto, 2008). As Sarkodie and Adams (2018) noted that some indicators of environmental quality are energy consumption, economic growth and political institutions’ quality. Hence, nuclear energy and RE have been promoted in order to mitigate climate change and reduce down fossil fuel-based countries’ economic vulnerabilities. There is a positive relationship between usage of RE, nuclear energy, fossil fuel-based energy, level of CO2 emission rate and GDP per capita (Ozturk, 2017). However, there could not be a relationship between GDP per capita and nuclear energy in short run. Nuclear energy consumption has an impact on economic growth, income level, labour force, real gross fixed capital and foreign direct investments (FDI) while reducing CO2 rates (Apergis & Payne, 2010; Baek & Pride, 2014; Kim, 2020; Luqman et al., 2019; Pr˘av˘alie & Bandoc, 2019; Wolde-Rufael, 2010; WoldeRufael & Menyah, 2010). Moreover, nuclear energy trade provides a contribution to economy. Russia is one of the nuclear energy exporter countries and they have satisfied energy demand while developing their economy (Alekseev et al., 2020). Nuclear energy power plant has high cost and investment amount for construction, although it has efficient in electricity generation and low CO2 emission rate energy technology. However, NPP investments do not respond quickly as revenue (Gozgor & Demir, 2017). As it is mentioned, nuclear reactor is costly and dangerous, but it generates cheap and secure electricity. Otherwise, the Finland government refused the fifth reactor proposal because of the cost before businesses proposed nuclear energy as green energy (Jensen-Eriksen, 2020). On the other hand, nuclear energy management, governance and policies are important to get benefit and efficiency for future energy generation, reducing cost and security (Galinis & van Leeuwen, 2000; Heffron, 2013). Furthermore, location choice is so significant for NPP project investment, because feasible and convenient locations for NPP can lead to decrease cost and provide economic benefit with environmental protection (Zawali´nska et al., 2020). Moreover, Siqueira et al. (2019) evaluated that the time of NPP construction, energy policies, electricity production management among countries’ various energy resources, public opinion, life cycle, technological developments and cost management of nuclear power reactor are the main issues for achievement in NPP management. So, it is added that strong management system provides electricity generation security. Also, technological improvements are so crucial for nuclear energy supply security, safety and health condition by managing radioactivity and spent nuclear fuels, environmental and economic feasibility. According to Lee et al. (2016) research in South Korea, it was figured out that sodium-cooled fast reactors (Gen IV) are better than pressurized water reactor-based nuclear energy reactors as social, economic and environmental. On the other hand, Hydrogen (H) energy is one of the cleanest energy resources and it contributes to environmental protection in the world. At the same time, H can be obtained from nuclear energy reactors during the process with tritium fuel utilization. Thus, nuclear energy could be considered as clean energy resources (Nowotny et al., 2016). In addition to that, Bildirici (2020) research analysed that

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hydropower failure rates and usage have increased in Japan. Besides, hydropower energy generation increases GDP and decreases environmental pollution. However, there is found that nuclear energy failure rates raises while its life cycle is running out (Pr˘av˘alie & Bandoc, 2018). So, it leads to scale-up environmental pollution and economic growth in any increase nuclear energy usage. Also, Zuba et al. (2020) denoted that the radioisotope of ruthenium Ru-106 uses in nuclear energy generation field and it carries out dangerous as well as other elements which might create huge accidents, although they have benefits for the nuclear field. As technology helps to improve waste nuclear fuel recycling, artificial intelligence (AI) has been used in nuclear reactor technology in order to predict nuclear energy risks and prevent any mistakes and accidents (Fernandez et al., 2017). Therewithal, Bedenko et al. (2019) made a test on a high-temperature gas-cooled nuclear reactor which processes with thorium–plutonium nuclear fuel cycle. Gen IV technologies and development of nuclear field contributes economical condition and cost. Moreover, Yang and Zhan (2017) proposed a ceramic reactor which provides high-level energy generation, energy security and sustainability that is occurred by uranium recycling. With regard to these, Gao et al. (2019) analysed that Gen IV reactor technology is advanced nuclear fuel cycle technology which is also provided uranium utilization with cost-effectiveness and feasibility. Moreover, nuclear energy fuel utilization studies are effective on economic and environmental benefit. For instance, Maiorino et al. (2018) stated that uranium and thorium utilization ((U-Th)O2 ) raises efficiency. On the other hand, there are thorium usage and uranium–thorium utilization in nuclear reactor studies, and it seems more environment-friendly. Humphrey and Khandaker (2018) figured out that thorium is in the ground more than uranium and thorium fuel-based nuclear reactor called green nuclear energy. Also, accelerator-driven system (ADS)-based green nuclear energy reduces GHG emission rates (Hossain et al., 2015). Therewithal, Demirdogen and Askar (2017) remarked that green nuclear energy decreases radioactive waste and nuclear proliferation. In addition to that, CO2 emission rate and information trust affect nuclear energy’s perceived risk and benefit which have impact on willingness to pay for other energy resources (Vainio et al., 2017). Also, Wang et al. (2020) remarked that environmental beliefs influence public acceptance of nuclear energy and nuclear engagement will be weak when place attachment is high. Besides, social perception and developed nuclear energy technology have a role in energy sustainability with willingness to pay (Jun et al., 2010). For example, McKie (2020) studied 100 pound million invested small nuclear energy in the UK with ecological and social consideration. However, there is strong nuclear energy opposer and strong opposers showed high willingness to pay for NPP because of R&D studies (Contu & Mourato, 2020). Therewithal, Hao et al. (2019) emphasized that high safety, perceived benefit, trust and knowledge of nuclear energy and environmental awareness influence positively public acceptance of NPP. On the other hand, economic conditions affect the risk perception of nuclear energy with environmental awareness and safety. For example, public acceptance of nuclear reactors in South Korea was low after the Fukushima accident. However, it has increased with exporting nuclear technology to the UAE (Roh & Kim, 2017).

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5.3 An Examination with DEMATEL In this section, it is intended to define the optimal strategies for the countries to minimize the negative environmental impacts of nuclear energy usage. In this context, four different balance scorecard-based perspectives are taken into account which are finance (C1), customer (C2), technological development (C3) and qualified employees (C4). Hence, it can be possible to consider both financial and nonfinancial issues (Dinçer & Yüksel, 2019; Dinçer et al., 2017, 2019). Three experts evaluated these criteria while considering 5 different scales. DEMATEL approach is taken into consideration to achieve this objective (Dinçer et al., 2020a, 2020b; Yüksel et al., 2017, 2019). This methodology is considered to weigh different items based on their significance (Fang et al., 2021; Meng et al., 2021). For this purpose, expert evaluations or quantitative values can be used (Kalkavan et al., 2021a, 2021b). While considering these values, direct relation matrix is created (Kou et al., 2021). After that, this matrix is normalized (Yang et al., 2021). Next, total relation matrix is generated (Silahtaro˘glu et al., 2021). With the help of this matrix, the weights of different factors can be identified. Firstly, direct relation matrix is created by calculating the average values of the evaluations of the experts. Table 5.1 gives the details of this matrix. After that, this matrix is normalized. Next, mathematical operations are considered, and the weights of the criteria are identified. The analysis results are demonstrated in Table 5.2. Table 5.2 determines that technological development (C3) is the most significant criteriion to minimize negative impacts of the nuclear energy usage. In addition to this issue, customer (C2) is another important factor in this issue. Table 5.1 Direct relation matrix Criteria

C1

C2

C3

C4

C1

0.00

2.67

0.67

1.67

C2

1.67

0.00

1.33

1.33

C3

3.33

4.00

0.00

3.33

C4

1.67

1.00

0.67

0.00

Sources Authors’ calculations

Table 5.2 Analysis results Criteria

D

R

D−R

D+R

Weights

Finance (C1)

0.89

1.18

−0.29

2.08

0.2478

Customer (C2)

0.85

1.33

−0.48

2.18

0.2602

Technological development (C3)

1.80

0.56

1.24

2.36

0.2816

Qualified Employees (C4)

0.64

1.12

−0.48

1.76

0.2105

Sources Authors’ calculations

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5.4 Conclusion Energy is a need that a country has to provide. This is the case for both individuals and companies. Individuals need energy to meet their basic needs such as warming and enlightenment. On the other hand, energy is also one of the most important raw materials in the production process of companies. Since energy is such an important need, countries that do not have their own energy resources have to meet these needs from other countries. This mentioned situation causes many different problems. Payment is made in foreign currency for energy purchased from abroad. When this currency is more valuable, the amount paid for energy increases. On the other hand, this stated situation can also lead to political problems. Due to these problems, countries are looking for ways to produce their own energy. Nuclear energy is also a type of energy obtained as a result of the breakdown of uranium. Thanks to the nuclear power plant established, a country can produce its own energy. This situation also reduces the country’s dependence on energy. On the other hand, it is possible to talk about some negativities of nuclear energy. First of all, nuclear energies are very risky. If the degradation process of the element uranium cannot be effectively controlled, this can lead to a disaster. On the other hand, as a result of obtaining nuclear energy, a significant amount of radioactive material is produced. It is accepted that this situation also threatens the health of people. While considering the information obtained in this study, it has been understood that the most serious problem in nuclear energy use is radioactive waste. It has been seen that the main reason for this is the uranium element used in nuclear reactors. This situation is one of the main reasons for the protests against nuclear power plants. In this context, it is seen that thorium element can also be used in nuclear reactors instead of uranium. In this context, the easiest way to minimize the environmental problems in nuclear energy use is to choose thorium instead of uranium. In this study, an evaluation has been carried out to identify the optimal strategy to reduce the negative environmental impacts of nuclear energy usage. Within this framework, four different criteria are defined by considering balance scorecard-based perspectives which are finance (C1), customer (C2), technological development (C3) and qualified employees (C4). Three experts evaluated these criteria while considering five different scales. DEMATEL approach is taken into consideration to achieve this objective. Firstly, a direct relation matrix is created by calculating the average values of the evaluations of the experts. After that, this matrix is normalized. Next, mathematical operations are considered, and the weights of the criteria are identified. It is concluded that technological development (C3) is the most significant criterion to minimize negative impacts of nuclear energy usage. In addition to this issue, customer (C2) is another important factor in this issue.

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Chapter 6

Does Sustainability Really Lower Economic Growth? In Search of Empirical Evidence from Tonga Partha Gangopadhyay, Rina Datt, and Narasingha Das

6.1 Introduction At country level, sustainability is broadly defined as achieving economic growth without compromising the stock of natural and environmental assets (see Pearce et al., 1990), which has sparked debates on the impacts of sustainability on economic growth. On the one hand, it has been argued that sustainability calls forth environmental regulations, which can—in turn—inhibit economic growth (see Ausubel, 1996; Green & Morton, 2000). On the other hand, an alternative view has gathered momentum that such regulations can trigger technological innovations leading to increased factor productivities and efficiencies and thereby promote economic growth (see Das et al., 2022; Gardiner & Portney, 1994; Porter & Linde, 1995). This book chapter raises a simple question for a Pacific Island nation—specially the Kingdom of Tonga—seriously threatened by the consequences of climate change: does sustainable development inevitably imply lower long-run economic growth? This chapter seeks to assess if there is any evidence that sustainable development has impacted the economic growth of Tonga, which is a Small Island Development State (SIDS). In order to create a coherent framework of analysis, we structure our analysis in two related steps: first, we develop an overall indicator of sustainable development for Tonga. Secondly, we seek to establish—applying the time series analysis—if this sustainability indicator bears a long-term and equilibrium relationship with the economic growth for Tonga. By exploiting a suitable data set, we will

P. Gangopadhyay (B) · R. Datt Western Sydney University, Penrith, NSW, Australia e-mail: [email protected] N. Das Indian Institute of Technology, Kharagpur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_6

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bring two new and important insights to the literature on environmental capital and economic growth for SIDS: . First and foremost, we will apply an Autoregressive Distributed Lagged (ARDL) model to capture the long-term relationship between sustainability and economic growth for Tonga. By doing this, we seek to assess if a statistically significant long-run relationship exists between sustainability and economic growth . Secondly, we will also seek to assess if the economic growth, or lack of it, is predicated upon any other macroeconomic variables of interests for Tonga. This framework, to the best of our understanding, is a new approach to investigate if sustainability shapes economic growth in liaison with other macroeconomic variables relevant to SIDS. If yes, a comprehensive framework will be required to balance the needs of sustainability with economic growth in SIDS. The plan of the chapter is as follows: we provide a brief overview of the existing literature. In Sect. 6.3, we present the data and a detailed description of the methodology. In Sect. 6.4, we discuss the results and highlight the major findings. We conclude in Sect. 6.5.

6.2 Brief Literature Review The Small Island Developing States (SIDS), fairly well known among policy-makers, have started suffering from the initial effects of climate shocks. Especially for the 15 Pacific Island Countries (PICs), the entire Pacific community has been exposed to severe environmental threats. Thus, the consequences of climate shocks led to a public perception in PICs that climate shocks in the Pacific trigger (see Gangopadhyay & Rai, 2020): . Erosion of community coping capacity, . Imminent threat to economic growth and critical infrastructure, . Gradual disappearance of long-term development gains achieved over the long haul, . Worsening of food and water security, and . Unpredictable impacts on human health. Among the PICs, as Gangopadhyay and Rai (2020) argued, Tonga has faced a “unique and existential challenge” from climate change as most of Tongan live and critical infrastructure are located on vulnerable atoll islands. The problem is exacerbated by the concentration of population and economic activities located in the region Tongatapu, which is a low-lying area. Consequently, the country’s people and economic activities are vulnerable to moderate changes in the sea level. The United Nations identified Tonga as one of the most vulnerable members among the PICs to the immediate ravages of climate change, especially from rising sea level. Tonga will have to face these challenges for the foreseeable future. Climate change brings special risks for Tonga and other PICs as ocean water expands causing with

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increasing heat causing the sea level to rise. The melting of glaciers and ice sheets due to a rising global temperature leads to the rising sea level. The doomsday prediction has been stark: most experts believe that most of Tonga will be underwater in less than 50 years. Moreover, Tonga is highly vulnerable to the impacts of climate change due to its geographical location and its socio-economic characteristics. Tonga is susceptible to a wide range of other climate change impacts, including increasingly intense tropical cyclones; extreme rainfall events leading to flooding; coastal erosion; heat waves; drought; ocean acidification; and sea level rise. In order to cope with the above challenges, as Gangopadhyay and Rai (2020) describe, Tonga has enacted several key national climate change policies and strategies and legislation, to create well-concerted and effective measures against climate change catastrophes. As an example, Tonga has pre-committed to the generation of 50% of its energy output from renewable sources by 2020. Moreover, Tonga implemented a series of measures in integrating resilience across all sectors. In this context, external aid has played a critical role in improving climate resilience of this nation. The PICs also benefit from the global humanitarian and disaster risk reduction programs. Many donor countries contribute to the risk reduction program. Australia is a key player in the global climate finance mechanisms, including the Green Climate Fund ($200 million committed by Australia over 4 years from 2015) and the Global Environment Facility ($93 million committed, from 2014–15 to 2017–18) by Australia. These funds support a wide range of resilience building and emission reduction projects in the Pacific region. Australia uses its seat on the Green Climate Fund Board and the Global Environment Facility Council to draft suitable policies and processes, highlight the climate change disasters for the PICs, and the vulnerability of the people in the Pacific for creating Pacific-focused proposals. Australia, under its aid partnership, is a major partner of Tonga and Australia will continue to support the Tongan government and people of Tonga for building climate resilience for creating environmentally sustainable development pathways. As an example, Australia invested an estimated $8.6 million as a climate change support to Tonga from 2015–16 to 2017–18. It has been noted by Gangopadhyay and Rai (2020) that Australia will be a key partner for Tonga’s transition to renewable energy by reducing its reliance on imported fossil fuels by enhancing the energy security of Tonga. Yet, the main challenge for energy security is how to harness reliable, safe, and affordable solar energy in Tonga. By integrating climate risks across the aid program, the donor counties will help increase Tonga’s resilience to the impacts of climate change. As examples: . The current Australia–Tonga Aid Partnership (2016–2019) focuses on “disaster resilience as a cross-cutting issue” and . Active policies are formulated to build resilience and integrate the risks of climate change into all key sectors of the aid program. For the issue of climate resilience for Tonga, this research poses a simple question for this Pacific Island nation—seriously threatened by consequences of climate change: does sustainable development inevitably imply lower long-run economic growth and vice versa? In other words, we seek to assess if there is any evidence

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that sustainable development has impacted the economic growth of Tonga, which is reliant on economic growth to fight poverty and economic deprivation. We will also examine if economic growth impacts on sustainability in Tonga. In order to create a coherent framework of analysis, we structure our analysis in two related steps: first, we develop an overall indicator of sustainable development for Tonga. Secondly, we apply the time series analysis to detect if this sustainability indicator bears a long-term and equilibrium relationship with the economic growth and other variables for Tonga. This will enable us to determine the direction of causality between sustainability and economic indicators for Tonga. By exploiting a suitable data set, we will bring two new and important insights to the literature on environmental capital and economic growth for SIDS as will be explained in Sect. 6.3.

6.3 Methodology By referring to the work of few researchers like Ausubel (1996); Gardiner and Portney (1994); Green and Morton (2000); Pearce et al., (1990); Porter and Linde (1995) who applied the idea and explored the relation between sustainability and economic growth, the standard estimation model can be expressed in its implicit form as follows: LNSUS = f (LNGDP, LNGDPA, LNGDPN, LNOILP, LNODA)

(1a)

where LNGDP represents the natural log of GDP, LNGDPA is the natural log agricultural GDP, LNGDPN is the natural log of non-agricultural GDP, LNOILP implies the natural log of oil price, LNODA is the natural log of overseas development assistance, and LNSUS is the natural log of the Sustainability Index explained in the next sub-section. Now, in order to provide a clear interpretation of the coefficients, the econometric model (explicit form) can be written as LNSUS = α0 + α1 LNGDP + α2 LNGDPA + α3 LNGDPN + α4 LNOILP + α5 LNODA + u t

(1b)

6.3.1 Database and Measurement of Variables All data for Tonga came from the World Bank’s data series (https://data.worldb ank.org/). The sustainability index (LNSUS) is a synthetic variable derived from the principal component analysis of eight development indicators. The variable LNSUS, by construction, is the inverse of the vulnerability index of Tonga, which is explained

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and derived in Gangopadhyay and Rai (2020). In this paper, we utilize the synthetic variable LNSUS along with other economic variables explained after Eq. (1a). In the following sub-section, we explain the methodology to detect the relationship between LNSUS and the chosen economic variables.

6.3.2 Estimation Methodology In this section, we will seek to establish short-run and long-run relationships between the variables through the cointegration analysis via ARDL approach. It is imperative to note that the economic variables—based on time series data—are often found autocorrelated and non-stationary in nature. So, before proceeding to the ARDL analysis, we will need to address the problems of autocorrelation and non-stationarity for making our results reliable. After ensuring the basic properties, we will apply the ARDL bounds testing approach (Pesaran et al., 2001). Apergis and Gangopadhyay (2020), Gangopadhyay and Jain (2019) have postulated that the ARDL method is especially suitable for small sample situations, as the case in the present study, due to the non-availability of reliable long-run data on the selected variables in our study. More importantly, the methodology is capable of handling variables that are stationary and non-stationary (integrated of up to order 1 or even fractionally integrated). Results obtained from this approach are unbiased and efficient since they are robust to problems of autocorrelation. There are two insights involving the ARDL approach. First, as predicted by the theory, testing for the presence of the long-run association between the variables under concern. If it exists, then estimating the short- and long-run parameters of the association (Ozturk & Acaravci, 2013).

6.3.2.1

Stationarity (Unit Root) Tests

The study has undertaken the following tests in order to examine the occurrence of unit roots in the concerned variables (1) the Phillips–Perron (PP) test, (2) the Augmented Dicky–Fuller (ADF) test with optimum lag using the Akaike Information Criterion (AIC) as per Ng and Perron (2001), and (3) test by Clemente et al. (1998) letting for a single structural break (which is known as the additive outlier (Jain & Gangopadhyay, 2020). Table 6.1 presents the unit root results. If the variables are investigated to be stationary either at the level [i.e., I(0)] or at the first difference [i.e., I(1)], then the ARDL bounds approach of cointegration can be safely applied.

6.3.2.2

ARDL Framework for Estimation

Following Pesaran et al. (2001), the error correction representation of the ARDL model is as follows:

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Table 6.1 Results from unit root tests Test procedure Test equation

Variable

I(0) t-value

ADF

Intercept

LNGDP LNGDPA

I(1) Prob

t-value

Prob

−1.052327 0.7243 −5.422219 0.0001 0.125097 0.9636 −5.559121 0.0000

LNGDPN −0.821981 0.8010 −4.123799 0.0027 LNODA

−2.323197 0.1702 −6.995492 0.0000

LNOILP

−0.800232 0.8076 −6.162937 0.0000

LNSUS

−2.197568 0.2104 −6.347902 0.0000

Trend and intercept LNGDP LNGDPA

−1.933579 0.6175 −5.490354 0.0004 −2.818806 0,1999 −5.477430 0.0004

LNGDPN −4.019012 0.0166 −4.067866 0.0148

PP

Intercept

LNODA

−3.130929 0.1139 −6.904076 0.0000

LNOILP

−1.479182 0.8193 −6.125299 0.0001

LNSUS

−2.009626 0.5775 −6.507460 0.0000

LNGDP

−1.052327 0.7243 −5.434229 0.0001

LNGDPA

0.248107 0.9722 −5.856950 0.0000

LNGDPN −0.663299 0.8439 −3.941939 0.0093 LNODA

−2.330361 0.1681 −6.989851 0.0000

LNOILP

−0.799405 0.8079 −6.162937 0,0000

LNSUS

−2.206204 0.2075 −6.383238 0.0000

Trend and intercept LNGDP LNGDPA

−2.045627 0.5584 −5.490354 0.0004 −2.604833 0.2805 −5.741014 0.0002

LNGDPN −2.778716 0.2134 −3.879547 0.0231 LNODA

−3.200889 0.0995 −6.893656 0.0000

LNOILP

−1.489410 0,8158 −6.125582 0.0001

LNSUS

−2.009626 0.5775 −6.789769 0.0000

Source Author’s own estimates

Δyt =α0 +

Σ i=1

+

Σ

pα1i × Δyt−i +

Σ

q 1 α2i × Δx1t−i + · · ·

i=0

q α(k + 1)i × Δxkt−i + β1 yt−1 + β2 x1t−1 + βk+1 xkt−1 + εt k

(1c)

i=0

where parameter β i (for i = 1, 2, …, k + 1) is the corresponding long-run relationship, while parameter α i (for i = 1, 2, …, k + 1) is the short-run dynamic coefficient of the underlying ARDL model. Thus, the ARDL bound test allow to model both I(0) and I(1) variables together. In the bound test, the null hypothesis is formed to test β i since H0 : β 1 = β 2 = … = β k+1 = 0. Thus, the null hypothesis means that there is no cointegration versus the alternative of there is cointegration or H1 : at least, one

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parameter not equal to zero. F-statistics is calculated to compare with Pesaran et al. (2001)’s critical values, knowing that it is derived from the Wald test. If the calculated F-statistics is found below the lower critical values mentioned before, we can’t reject the null hypothesis that there is no relationship between time series. If the calculated F-statistics is between lower and higher bounds of critical values, it is violated to take a certain decision and referred to other cointegration tests. If the calculated Fstatistics is greater than the upper bound of critical values, we can deduce that there is a relationship between time series. In other words, the null hypothesis is rejected. The ARDL model as shown in Eq. (1c) is based on a linear combination of the dependent and independent variables indicating symmetrical adjustments in the long run and the short run of y to any shock the concerned variables of the study. The model used in the study is consistent with Pesaran et al. (2001). In the model of Pesaran et al. (2001), the response of the dependent variable is symmetrical to both the increases and decreases in the magnitudes of the independent variable. The present study relies upon the methodology of ARDL by Pesaran et al. (2001). The ARDL method of cointegration has the super consistency property which defends us not to address the endogeneity problem between the variables (Engle & Granger, 1987). Further, the estimates’ super consistency property will still hold even if the model runs with the omitted variables with the stationary property (Gangopadhyay & Nilekantan, 2018; Hemed et al., 2019; Herzer & Strulik, 2017).

6.4 Results and Discussion The results of ADF with AIC and PP tests are provided in Table 6.1 following the methodologies mentioned above. From the table, we have found that all the variables are stationary after the first difference, i.e., I(1). Thus, we can proceed to the ARDL framework of estimation. With limited observations, this study used the AIC to select an appropriate lag for the ARDL model. Table 6.2 presents the estimated ARDL model that has passed several diagnostic tests that indicate no evidence of serial correlation (Table 6.3), heteroskedasticity (Table 6.4), and non-normality in the error term (Table 6.5) and there is no specification error (Table 6.6). From Table 6.3, we have found that there is no serial correlation at 5% level of significance. Table 6.2 Estimated ARDL models and bounds F-test for cointegration ARDL model LNSUS, LNGDP, LNGDPA, LNGDPN, LNODA, LNOILP Source Authors’ estimates

(4,4,4,4,3,4)

F-statistics

CV 1%

CV 5%

I(0)

I(1)

I(0)

I(1)

12.949625

3.06

4.15

2.39

3.38

76 Table 6.3 Breusch–Godfrey serial correlation LM test

P. Gangopadhyay et al.

F-statistic

0.574122

Prob. F(2,4)

0.6037

Obs*R-squared

7.806259

Prob. Chi-Square(2)

0.0202

Source Authors’ estimates Table 6.4 Heteroskedasticity test: Breusch–Pagan–Godfrey

F-statistic

8.650473

Prob. F(28,6)

0.0062

Obs*R-squared

34.15395

Prob. Chi-Square(28)

0.1959

Scaled explained SS

0.622038

Prob. Chi-Square(28)

1.0000

Source Authors’ estimates Table 6.5 J–B normality test

Source Authors’ estimates Table 6.6 Test for model specification (Ramsey RESET Test)

Value

df

Probability

t-statistic

1.789906

5

0.1335

F-statistic

3.203762

(1, 5)

0.1335

Sum of Sq

df

Mean squares

F-test summary Test SSR

0.076015

1

0.076015

Restricted SSR

0.194648

6

0.032441

Unrestricted SSR

0.118634

5

0.023727

Source Authors’ estimates

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From Table 6.4, we can say that there is no heteroscedasticity in the error term as the null hypothesis of the presence of heteroskedasticity can be rejected from the F-stat. Table 6.5 suggests that there is normal distribution in the error term against the null hypothesis of non-normal distribution of the error term. Table 6.6 represents that the model is correctly specified over the null hypothesis that mis-specification in the model prevails. Now, the bounds F-test for cointegration test yields evidence of a long-run relationship between Sustainability, economic growth proxied by GDP, Agricultural GDP, Non-agricultural GDP, oil prices, and overseas development at 1% significance level in Tonga (see Table 6.2). In addition, due to the structural changes in the economy of Tonga, it is likely that macroeconomic series may be subject to one or multiple structural breaks. For this purpose, the stability of the short-run and long-run coefficients are checked through the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests. Unlike the Chow test, which requires breakpoint(s) to be specified, the CUSUM and CUSUMSQ tests are quite general tests for structural change in that they do not require a prior determination of where the structural break takes place. Figure 6.1 presents the plot of CUSUM and CUSUMSQ tests statistics that fall inside the critical bounds of 5% significance. This implies that the estimated parameters are stable over the periods. Now the short-run ECM model and long-run cointegration model are presented in Tables 6.7 and 6.8, respectively. Now as the ECT theoretically shows how much of the disequilibrium is being corrected; so, from Table 6.7, we can say that approx. 95% of adjustment from short run to long run has taken place each year. In this regard, as the ECT is negative and highly significant, we can also conclude that in the adjustment procedure convergence occurs. For the ECM, we have also performed the Wald test to examine joint significance of the coefficients of this estimated model and we have found that all the variables are jointly significant in short run (see Appendix 1). In long-run analysis, as presented in Table 6.8, we have found all the independent variables are significant at less than 1% except LNGDPN and LNODA (Both

Fig. 6.1 CUSUM and CUSUMSQ tests statistics. Source Authors’ estimates

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P. Gangopadhyay et al.

Table 6.7 Short-run ECM model ECM regression Restricted constant and no trend Variable

Coefficient

Std. error

t-Statistic

Probability

D(LNSUS(−1))

−0.814515

0.095029

−8.571238

0.0001

D(LNSUS(−2))

−1.380502

0.168821

−8.177303

0.0002

D(LNSUS(−3))

−0.954252

0.107089

−8.910822

0.0001

D(LNGDP)

1.331762

0.663262

2.007898

0.0914

D(LNGDP(−1))

7.311413

1.000674

7.306488

0.0003

D(LNGDP(−2))

8.498009

0.817746

D(LNGDP(−3))

3.488851

0.452343

D(LNGDPA)

16.95028

1.592819

10.39200 7.712847 10.64169

0.0000 0.0002 0.0000

D(LNGDPA(−1))

10.50568

2.200720

4.773745

0.0031

D(LNGDPA(−2))

25.24178

3.238199

7.795004

0.0002

D(LNGDPA(−3))

28.33174

2.918343

9.708160

0.0001

D(LNGDPN)

−6.384470

0.593930

D(LNGDPN(−1))

3.702506

0.435152

−10.74954 8.508535

0.0001

D(LNGDPN(−2))

−0.043170

0.417466

−0.103408

0.9210

0.0000

D(LNGDPN(−3))

1.599412

0.499708

3.200691

0.0186

D(LNODA)

−0.340190

0.134208

−2.534796

0.0444

D(LNODA(−1))

−3.509051

0.322223

−10.89012

0.0000

D(LNODA(−2))

−2.118657

0.228809

−9.259500

0.0001

D(LNOILP)

−1.131364

0.128890

−8.777714

0.0001

D(LNOILP(−1))

−1.790892

0.156863

−11.41693

0.0000

D(LNOILP(−2))

−2.276554

0.238532

−9.544018

0.0001

D(LNOILP(−3))

−1.324715

0.162344

−8.159951

0.0002

CointEq(−1)*

−0.952314

0.070727

−13.46457

0.0000

R-squared

0.974741

Mean dependent var

0.002468

Adjusted R-squared

0.928433

S.D. dependent var

0.476078

S.E. of regression

0.127361

Akaike info criterion

−1.039746

Sum squared resid

0.194648

Schwarz criterion

−0.017660

Log likelihood

41.19555

Hannan–Quinn criter

−0.686922

Durbin–Watson stat

2.154096

variables are significant at 5% level of significance). From Table 6.8, we may say that economic growth via GDP inversely affects the sustainability of Tonga. We have also found that the highly significant inverse relationship exists between sustainability and GDP coming from non-agricultural sector (LNGDPN) and sustainability with oil prices (LNOILP). In this regard, Overseas Development Assistance (LNODA) and

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Table 6.8 Long-run cointegration model Levels equation Case 2: restricted constant and no trend Variable

Coefficient

Std. error

t-Statistic

Probability

−4.8553

0.0028

LNGDP

−4.8414

0.99714

LNGDPA

30.9332

6.54992

LNGDPN

−12.493

4.00972

LNODA

4.09181

1.50988

LNOILP

−1.3197

0.29155

−4.5266

0.004

C

−511.29

100.081

−5.1088

0.0022

4.72269 −3.1156 2.71003

0.0032 0.0207 0.0351

EC = LNSUS − (−4.8414*LNGDP + 30.9332*LNGDPA − 12.4926*LNGDPN + 4.0918*LNODA − 1.3197*LNOILP − 511.2932) Source Authors’ estimates

Agricultural GDP (LNGDPA) act as vital and positive ingredients toward sustainability for the economy of Tonga as both the coefficients of the variables are positive and highly significant. From our study, we have also found that there is a unidirectional causality flow from economic growth as proxied by GDP to sustainability for the economy of Tonga (see Appendix 2).

6.5 Concluding Remarks The study investigates the causal relationship between sustainability, economic growth proxied by GDP, agricultural GDP, non-agricultural GDP, overseas development assistance, and oil prices in Tonga for the 1975–2013 period. The bounds F-test for cointegration test yields evidence of a long-run relationship between the above-mentioned variables. From the result, it is found that the impact of economic growth on sustainability is negative. That is higher the economic growth, lower will be the sustainability for the economy of Tonga. In addition, the coefficient of agricultural GDP and overseas development assistance is positive and significant at less than 1% and 5% level of significances which shows that agricultural growth coupled with overseas development assistance will increase the sustainability of the Tonga economy. Finally, GDP from non-agricultural sector and oil prices has s significant and adverse impact on the sustainability of Tonga in the long run. At the 1% level of significance, the predicted ECT coefficient is also negative and significant statistically. Therefore, any deviation from the long-run equilibrium between variables is rectified for each period in order to restore the long-run equilibrium relationship

80

P. Gangopadhyay et al.

between the variables. The Granger causality approach is also used to investigate the causal link among the variables in this research and it is found that a significant and unidirectional causality flows from economic growth to sustainability for the economy of Tonga.

Appendix 1 LNSUS Wald test Equation: untitled Test statistic

Value

df

Probability

F-statistic

7.120135

(4, 6)

0.0183

Chi-square

28.48054

4

0.0000

Value

df

Probability

LNGDP Wald test Equation: untitled Test statistic F-statistic

5.247310

(5, 6)

0.0338

Chi-square

26.23655

5

0.0001

Value

df

Probability

LNGDPA Wald test Equation: untitled Test statistic F-statistic

15.97784

(5, 6)

0.0021

Chi-square

79.88920

5

0.0000

Value

df

Probability

LNGDPN Wald test Equation: untitled Test statistic F-statistic

10.47942

(5, 6)

0.0063

Chi-square

52.39711

5

0.0000

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LNODA Wald test Equation: untitled Test statistic

Value

df

Probability

F-statistic

14.43968

(4, 6)

0.0031

Chi-square

57.75872

4

0.0000

Test statistic

Value

df

Probability

F-statistic

8.039687

(5, 6)

0.0123

Chi-square

40.19844

5

0.0000

LNOILP Wald test Equation: untitled

Appendix 2 Results from Granger Causality

Null hypothesis

Obs

LNGDPA does not Granger Cause LNGDP

37

LNGDP does not Granger Cause LNGDPA LNGDPN does not Granger Cause LNGDP

37

8.E-05

37

0.87517

0.4265

1.01520

0.3737

37

0.01259

0.9875

1.16361

0.3252

37

1.17930

0.3205

2.50041

0.0980

37

0.47361

0.6270

5.85802

0.0068

37

0.15417

0.8578

1.88035

0.1690

1.12225

0.3380

LNGDP does not Granger Cause LNSUS LNGDPN does not Granger Cause LNGDPA LNGDPA does not Granger Cause LNGDPN LNODA does not Granger Cause LNGDPA LNGDPA does not Granger Cause LNODA LNOILP does not Granger Cause LNGDPA

0.1636 0.7285 0.4510

LNGDP does not Granger Cause LNOILP LNSUS does not Granger Cause LNGDP

1.91701 0.31988 12.8917

LNGDP does not Granger Cause LNODA LNOILP does not Granger Cause LNGDP

Probability

0.81636

LNGDP does not Granger Cause LNGDPN LNODA does not Granger Cause LNGDP

F-statistic

37

(continued)

82

P. Gangopadhyay et al.

(continued) Null hypothesis

Obs

LNSUS does not Granger Cause LNGDPA

0.2618

0.03698

0.9637

2.03948

0.1467

37

3.19869

0.0541

1.81895

0.1786

LNGDPN does not Granger Cause LNODA LNOILP does not Granger Cause LNGDPN

37

2.01652

0.1497

1.99118

0.1531

37

0.27226

0.7634

0.64078

0.5335

37

2.43109

0.1040

2.13557

0.1347

LNGDPN does not Granger Cause LNOILP LNSUS does not Granger Cause LNGDPN LNGDPN does not Granger Cause LNSUS LNOILP does not Granger Cause LNODA LNODA does not Granger Cause LNOILP LNSUS does not Granger Cause LNODA

37

LNODA does not Granger Cause LNSUS LNSUS does not Granger Cause LNOILP LNOILP does not Granger Cause LNSUS

Probability

37

LNGDPA does not Granger Cause LNSUS LNODA does not Granger Cause LNGDPN

F-statistic 1.39775

LNGDPA does not Granger Cause LNOILP

37

1.40210

0.2608

0.21577

0.8071

0.01121

0.9889

2.48278

0.0995

References Apergis, N., & Gangopadhyay, P. (2020). The asymmetric relationships between pollution, energy use and oil prices in Vietnam: Some behavioural implications for energy policy-making. Energy Policy, 140(C), 1114–1130. Ausubel, J. H. (1996). Can technology spare the earth? American Scientist, 84, 166–178. Clemente, J., Montañés, A., & Reyes, M. (1998). Testing for a unit root in variables with a double change in the mean. Economics Letters, 59, 175–182. Das, N., Bera, P., & Panda, D. (2022). Can economic development & environmental sustainability promote renewable energy consumption in India? Findings from novel dynamic ARDL simulations approach. Renewable Energy, 189, 221–230. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 55, 251–276. Gangopadhyay, P., & Rai, K. (2020). Overseas development assistance and climate resilience: A case study of Tonga. In: S. Nath, & J. L. Roberts et al. (Eds.), Shaping the future of small islands, roadmap for sustainable development (pp. 283–300). Springer. Gangopadhyay, P., & Jain, S. (2019). Understanding subnational conflicts in Myanmar. Indian Growth and Development Review, 13(2), 339–352. https://doi.org/10.1108/IGDR-08-2019-0084 Gangopadhyay, P., & Nilakantan, R. (2018). Estimating the effects of climate shocks on collective violence: ARDL evidence from India. Journal of Development Studies, 54(3), 441–456. Gardiner, D., & Portney, P. R. (1994). Does environmental policy conflict with economic growth. Resources, Spring, 96, 19–23. Green, K., & Morton, B. (2000). Greening organizations. Organization & Environment, 13(2), 206–225.

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Hemed, I., Faki, S., & Suleiman, S. (2019). Economic growth and environmental pollution in Brunei: ARDL bounds testing approach to cointegration. Asian Journal of Economics, Business and Accounting, 10(4), 1–11. Herzer, D., & Strulik, H. (2017). Religiosity and income: A panel cointegration and causality analysis. Applied Economics, 49, 2922–2938. Jain, S., & Gangopadhyay, P. (2020). Impacts of endogenous sunk-cost investment on the Islamic banking industry: A historical analysis. Journal of Risk and Financial Management, 13(6), 108. Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69, 1519–1554. Ozturk, I., & Acaravci, A. (2013). The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey. Energy Economics, 36, 262–267. Pearce, D. W., Markandya, A., & Barbier, E. (1990). Sustainable development: Economy and the environment in the third world. Earthscan Publications. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326. Porter, M. E., & van der Linde, C. (1995). Green and competitive (pp. 120–138). Harvard Business Review.

Chapter 7

Does Green Investing Generate Return Over Conventional Funds? A Comparative Portfolio Analysis with Indian Stock Market Debabrata Mukhopadhyay

7.1 Introduction The gain in terms of growth in output at the global level due to increasing industrial activities has now come to be halted due to the ill effects of the increasing environmental degradation. To have sustainable growth or the growth in the longer runs there has been the necessity of thinking about investment upon green capital with the relatively low investments upon the dirty physical capital. Rising public treatise over the past couple of decades has led to the beginning of the ‘green economy’ that contemplates environmental, social, and governance (ESG) factors in the selection of the portfolios as well as focusing upon the management of the green firms. ESG stock indices including Green funds are increasingly exhibiting higher returns compared to traditional funds. Recent researches also show that green or sustainable investment is essential for the future economies to cut down the magnitudes of greenhouse gases and other air pollutants, without significantly compromising the level of production and consumption of the non-energy-based goods. India has been emerging as one of the high-level investors in green capital formation. India, according to the Economic Survey 2019–20, has emerged at the global level as the second-largest market for the green bonds with the worth of $10.3 billion transactions in the first half of the year 2019, as the public and private issuers and investors of these bonds have been continuing to implement policies and stratagems linked to the goal of sustainable development, a global mission right in this juncture. This study makes an attempt to understand the state of green investing in India and how does it differ in terms of return and risk with respect to traditional indices.

D. Mukhopadhyay (B) Department of Economics, West Bengal State University, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_7

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The trade-off between economic development and the environment has been well documented in the literature (Trana et al., 2020). To address the issues arising out of climate change, ecological imbalances, and unsustainable development, green investment selection by emerging companies is necessary for their sustainability so that the adverse impact on the economy is arrested and businesses are sustainable. The green investment concept has been increasingly used in the literature where both private and public enterprises should include this in their investment decisions. In the literature (Eyraud et al., 2011; IMF, 2011; Belkaoui, 1976, Bowman & Haire, 1975; Bragdon & Marlin, 1972; Eyraud et al., 2013; Moskowitz, 1972; Tirole, 2001), green investment refers to investment necessary to reduce greenhouse gas and air pollutant emissions, without significantly reducing the production and consumption of non-energy goods. Given such apprehensions on economic sustainability, investors worldwide are showing greater interest in climate change, resource efficiency, and green issues as has been elaborated in an interesting OECD working paper published in 2012. In an influential study, Inderst et al. (2012) demonstrated that the private financing of long-term green growth has been very important in recent times. As a result, sustainable investing has become an attractive strategy for both investors and policymakers (Naffa & Fain, 2020). The green investment or sustainable investment has a long history of about 50 years related to the series of works of Moskowitz, Bragdon, and Marlin, Bowman and Haire, Belkaoui published in the 1970s. Despite heterogeneity in terminology and investment strategies, many important concepts such as responsible investing (RI), sustainable investing (SI), socially responsible investing (SRI), and environmentalsocial-governance (ESG) investing have gained significant attention in the literature. There are two most important basic approaches to sustainable or green investing, namely ESG and SRI. Both the volume and direction of investments are going in favor of green investments in many OECD countries. In this context, ecological and ethical perspectives are very crucial for sustainable investment. Green investments have significant implications toward climate change either in the form of mitigation or adaptation strategies, and the very idea of climate change mainly is drawn from the scientific standpoints. The ESG investment selection criteria have been the most important in the developed as well as emerging countries’ investment strategies. In fact, in mainstream portfolio selection, the ESG factors are attracting increasing fund flows. The ESG framework was first designed in 2005 when Kofi Anna, the former United Nations Secretary-General, met with a group of leading institutional investors at the global level to give a framework to the concept of the Principles for Responsible Investment (PRI) to take care of environmental, social, and governance issues for responsible investing. Although the investing approach based on sustainability is one of the up-to-date ESG strategies, but as per the data of 2012, as low as $70 billion investment took place in ESG-based funds. However, thereafter, this strategy has exhibited marvelous growth potential when total Assets Under Management (AUM) crossed $1,018 million in 2018 and the figure is also consistent with 56.23% CAGR (Morgan &

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Morgan 2019). In a report, UNCTAD, stating to Blackrock, forecasts that the ESG ETF market will outperform $500 billion by the year 2030 (UNCTAD, 2020). There are 521 signatories of PRI with a total investment of US$ 103 trillion in March 2020 following the principles of ESG. The ESG investment assets have also grown at a very high rate of 22% since its inception. The core objective of green investments is to make the world ecologically sustainable through zero carbon emissions. There are reports from Morgan Stanley that sustainable equity-based funds of the US from January 2020 to June 2020 are outperforming the traditional funds such as stocks, exchange-traded funds, and mutual funds by a median of 2.8%. Sustainable funds have become less volatile even in pandemic situations like COVID-19. According to the Global Market Intelligence investigation of 17 exchange-traded and mutual funds by S&P, 14 of these stated greater returns than the S&P 500 in the year 2020. A recent Washington Post article states that investors are eyeing up and down the global supply chains and looking not only for recognized companies but also for the inventive ones at the initial phases of development. The literature concerning sustainable investing or green investing and its social goal has been well documented (see for example, Kölbel et al., 2020). In today’s world, many investors are showing their interest in sustainable capitalizing due to their unselfish motivations (Hartzmark & Sussman, 2017; Riedl & Smeets, 2017). Many international policy-making bodies such as International Panel on Climate Change (IPCC) have considered sustainable investing as a mechanism to mitigate climate change and achievement of sustainable development goals (SDG) (Betti et al., 2018). The origin of green investing goes back to the issue of corporate social responsibility that exemplifies the actions of the group from the environmental, social, and economic standpoints since 1924 (Han et al., 2020). The long-term purpose of green investment is observed to be the achievement of sustainable development (Chitimiea et al., 2021). At the micro level, better environment management with green investment can increase the revenue and profitability of organizations. This is done in a number of ways including the decrease in the quantity of energy use and costs of different materials, the entry to green or decent mutual funds, and the reduction of labor costs (see for instance, Ambec & Lanoie, 2008; Falcone, 2018). Apart from the importance of sustainable investing, the literature has also considered whether sustainable investing outperforms conventional indices. This literature is quite vast and comprehensive (see Managi et al., 2012). There are many studies which make comparisons of the yields of the Standard & Poor’s 500 Stock Index (S&P 500 Index) with the SRI index. The studies characteristically observe that the differences rest on area, year of coverage, and industry (see Sauer, 1997; Statman, 2000; Labatt & White, 2002; Bauer et al., 2005; Renneboog et al., 2008). Katherina (2009) pointed out that the occurrence of socially accountable investing has become more pervasive over the past two decades. But, there are many challenges faced by green investments as the World Bank raised many issues such as instability in the price of carbon, high fossil fuel subsidies, high up-front cost and long payback period, and technological and revenue risks.

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The Global Green Economy Index (GGEI), 2018 demonstrates that the largest economies of the world have made irregular development toward the investments and policies in order to facilitate the transition to a green economy. Out of the five of the largest economies of the world, Germany is the front runner in terms of performing in the overall index (ranked 6th), which is followed by Japan (ranked 19th), China (ranked 28th), the United States (ranked 31st), and India (ranked 36th). In recent years, market participants are investing in companies that take care of environmental, social, and governance factors (ESG) such as the S&P 500 ESG index of the USA and S&P Europe 500 ESG index. It may be pointed out that during the COVID-19 pandemic period although mutual funds and ETFs were losing their assets ESG or sustainable investing remained steady or gained better compared to the conventional funds. Sustainable and ESG investing has also been evolving in India (Sekhar, 2011, SSRN) where market participants are showing interest in ESG factors. India has become the second-largest market globally for green bonds with $10.3 billion worth of transactions in the first half of 2019, as issuers and investors continued to adopt policies and strategies linked to sustainable development goals, according to the Economic Survey 2019–20. There are some reports that the Nifty ESG Index delivered a five-year return of 10.80% CAGR, vis-à-vis 8.99% on Nifty 50 as of October 30, 2020. Many Indian companies like State Bank of India and Axis Asset Management company are either converting or launching ESG funds. Indian companies are now showing their appetite for ESG funds. The study has been organized as follows. Section 7.2 deals with materials and methodology. Section 7.3 deals with results and discussions. The conclusions are made in Sect. 7.4.

7.2 Materials and Methods This study considers the daily level closing prices of the following five indices of the Bombay Stock Exchange (BSE), among which the BSE 100 is the benchmark index and BSE 100 ESG represents the green and social sustainability index to take care of sustainable development goals such as climate change, resource efficiency, and corporate commitment toward society. The indices span for the period from October 26, 2017, to December 2020 considering the availability of the S & P BSE 100 ESG data. We have also considered the three largest performers of the S&P BSE 100 ESG Index which are HDFC Bank limited, Reliance industries limited, and HDFC (Housing Development Finance Corporation). The study also considers Nifty 50 and Nifty 100 ESG for the period January 1, 2020, to 31 December 2020 to compare the returns of these two indices during the turbulent Covid period. The study compares the continuously compounded return and its risk in terms of time-invariant variance along with the benchmark index and decides whether environmentally sustainable indices are outperformed by the traditional benchmark market portfolio. This study

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also compares the time-varying risk measures following generalized autoregressive conditional heteroscedasticity in mean (GARCH-M) framework among these stock indices to understand the risk of sustainable index vis-a-vis BSE 100. We now present a GARCH in mean (GARCH-M) model as suggested by Lilien and Robins (1987) for understanding the fundamental relationship between return and time-variant risk (volatility) in BSE 100, BSE 100 ESG, and its other major constituents by using the following set of equations. rt = β0 + β1rt−1

m Σ

/ ςk rt−k + θ h t + εt

(7.1)

k=1

/ εt = z t h t

(7.2)

2 h t = α0 + +α1 εt−1 + δh t−1

(7.3)

z t ∼ N (0, 1), α0 > 0, α1 ≥ 0,δ ≥ 0, where rt is the continuously compounded return defined as the first logarithmic (natural) difference of the stock index. Equation (7.1) represents the fundamental risk-return relationship in the GARCH-M model. The parameter θ stand for risk premium, the expected sign of which is positive implying given an increase in the time-variant risk leads to an increase in the mean return.

7.3 Results and Discussion This section deals with the empirical results that we obtained by applying the methodology presented in the previous section. As already mentioned, our main purpose of the study is to understand whether a newly developed socially sustainable stock index which accounts for environmental (green), social, and governance factors outperforms conventional stock indices in terms of return and risk both time-variant and time-invariant in the Indian stock market. This study thus uses the two major ESG indices in India, one such index is the BSE 100 ESG index along with its major constituents HDFC, Reliance Industries, and HDFC bank vis-à-vis BSE 100 one of the important representative conventional indices in the country. This study considers daily closing prices for the period October 26, 2017, to December 31, 2020, consisting of 788 observations. This study also considers other two major indices, namely NIFTY 50 and NIFTY 100 ESG daily for the years 2019 and 2020. Now we present the returns (Continuously compounded) of these indices in the following two figures. Figure 7.1 indicates that all the returns move around the constant mean and show variability which is quite dissimilar in different parts of the sample period. The BSE 100 and BSE 100 ESG return series both some long spike during March– April 2020 due to the presence of COVID-19 pandemic affecting the world economy

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including financial markets returns also demonstrate that both the returns fluctuate around zero mean and dissimilar variability around different points of time. ESG returns are less volatile compared to the traditional index NIFTY 50. Both Figs. 7.1 and 7.2 clearly show that during the March–April COVID-19 turbulent period traditional indexes exhibit high volatility. The summary statistics are presented in Table 7.1 for understanding the average return and its variability for BSE 100, BSE 100 ESG, and its major constituents being considered in the study. The results on performance strategies such as return, risk, and return-risk ratios are presented for the series in Table 7.1. We observe from this result on total gross return covering the whole period 26 October 2017 to December 31, 2020, that BSE 100 ESG has a higher return at 38.61% compared to 31.19% of BSE 100, the benchmark broad-based stock index of India. In the same way, CAGR on the BSE 100 ESG portfolio is far more compared to BSE 100. The result shows that for the portfolios BSE 100, BSE 100 ESG, HDFC Bank, Reliance Industries, and HDFC are 8.839, 10.705, −6.790, 26.094, and 13.914%, respectively. Again, among the major three constituents of the sustainability index considering the green investment, the HDFC bank index performs badly providing both gross total and average annual return as negative. However, reliance industries limited company stock one of the major green companies having commitment toward environmental, social, and governance factors performs best in terms of both the returns. The compounded annual growth return of the reliance industries Ltd. and Housing development corporation (HDFC) are 35 and 19%, respectively. However, the volatility risk measure, in terms of standard deviation, differs across these indices. BSE 100 has the lowest volatility at 1.30% followed by BSE 100 ESG at 1.33%. The other indices show high volatility. In the literature, the return-risk ratio has been considered as one of the important deciding factors for investment. 0.1

0.05

27-Oct-20

27-Dec-20

27-Jun-20

27-Aug-20

27-Apr-20

27-Feb-20

27-Oct-19

27-Dec-19

27-Jun-19

27-Aug-19

27-Apr-19

27-Feb-19

27-Oct-18

27-Dec-18

27-Jun-18

27-Aug-18

27-Apr-18

27-Feb-18

27-Oct-17

-0.05

27-Dec-17

0 BSE 100 BSE 100 ESG Close

-0.1

-0.15

Fig. 7.1 Daily returns of BSE 100 and BSE 100 ESG for the period 27 October, 2017 to December 31, 2020

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0.1

0.05

0 Nifty 100 ESG

-0.05

Nifty 50

-0.1

-0.15

-0.2

Fig. 7.2 Daily Returns of NIFTY 50 and NIFTY 100 ESG for the period 1 January, 2020 to December 31, 2020. Source Author’s derivation

Table 7.1 Performance strategies of portfolio selection based on summary statistics BSE 100

BSE 100 ESG

HDFC bank

Reliance industries

HDFC

Gross total return (compounded annual growth return)

31.19% (8.839%)

38.61% (10.705%)

−19.87% (−6.790%)

113.53% (26.094%)

50.68% (13.914%)

Average daily return (continuously compounded)

0.343%

0.412%

−0.285%

0.939%

0.527%

Volatility on daily return

1.30%

1.33%

2.997%

2.141%

2.095%

Return/risk ratio on daily return

0.0264

0.0311

−0.00951

0.0438

0.0252

Skewness

−1.920104

−1.724780

−15.35247

0.015306

−0.587099

Kurtosis

26.99972

24.67423

352.1477

12.43993

10.76345

Source Author’s own computations

In this study, we observe that BSE ESG has a better performance compared to the traditional portfolio BSE 100. The basic performance strategies thus demonstrate that BSE 100 ESG and its major constituents outperform the traditional stock index BSE 100 considering these major representative indices in India. We also calculated compounded annual growth return (CAGR) of two other national-level stock indices, namely Nifty 50 and Nifty 100 ESG for the period

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January 1, 2020, to 31 December 2020 to compare between the returns of these two indices during the turbulent COVID-19 pandemic period. We found that the annual gross returns for NIFTY 50 and NIFTY 100 ESG were 14.77 and 21.45%, respectively. However, the volatilities are lower with the thematic index NIFTY 100 ESG compared to the traditional index NIFTY 50. We observe that during the turbulent period the overall economy thematic index performs better. The compounded annual growth returns (CAGR) on NIFTY 50 and NIFTY 100 ESG were calculated for the period January 1, 2019, to 31 December 2019, which was a normal year. The results show that NIFTY 50 returns are much lower at 14.77% compared to 21.45% during the year indicating higher returns for ESG funds compared to traditional funds. Volatilities for the year 2019 are much lower for NIFTY 100 ESG compared to the benchmark NIFTY 50 index. Now to understand the time-varying risk-return relationship in sustainability indices vis-a-vis traditional indices we apply (G)ARCH-M framework to know the nature of the relationship as well as the magnitude of risk measure. This will allow us to know that in the presence of ARCH, the variance of returns might increase over time, and the agents will ask for greater compensation to hold the asset. A positive l implies that the agent is compensated for any additional risk. But before presenting the GARCH-M framework, we employ unit root test procedures to understand the stationarity status of these returns by applying the Augmented Dickey-Fuller (ADF) test. The results demonstrate that the test ADF test statistic values at the first difference (i.e., in return) for the five series are large enough to reject the null of a unit root in the first difference. Thus, the series is stationary in the first difference. The GARCH-M results are presented in Table 7.2. The results show that all the series demonstrate a significant presence of conditional volatility as GARCH coefficients are significant and follow other regulatory conditions. For instance, in the case of BSE 100 and BSE 100 ESG, both the GARCH coefficients are associated with and are significant at a 1% level. The results demonstrate that the parameter which represents risk premium and the central focus of the model is positive and significant with a p-value of 0.055. However, the risk premium parameter is not significant for BSE 100 ESG and its other constituents. We thus understand that even though the representative investors are asking for compensation for additional risk in the case of traditional funds/indices. This is not true for green investments. This is the most important aspect of this study.

7.4 Conclusions This study has attempted to understand the interest and performance of green and ESG investments in India vis-a-vis traditional stock portfolios as worldwide investors are showing their commitment toward environmental challenges such as climate change in their long-term investment decisions for sustainability. Our analysis considers BSE 100 as the representative traditional portfolio compared to BSE 100 ESG index

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Table 7.2 Estimated GARCH_M Models for return on Five stock Indices Variable/coefficient

BSE100 Coefficient

Standard error

t-statistic

p-value

−0.001538

0.001133

−1.357424

0.1746

rt−1

0.038070

0.042003

0.906352

0.3647

θ

0.254305

0.132546

1.918618

0.0550

Constant

Variance equation Constant

4.56E-06

1.30E-06

3.519233

0.0004

2 εt−1

0.141976

0.019839

7.156437

0.0000

h t−1

0.822742

0.027084

30.37796

0.0000

BSE 100 ESG −0.001139

0.001255

−0.907031

0.3644

rt−1

0.030354

0.042510

0.714038

0.4752

θ

0.211975

0.139900

1.515185

0.1297

Constant

Variance equation Constant

4.21E-06

1.39E-06

3.036953

0.0024

2 εt−1

0.120212

0.016566

7.256722

0.0000

h t−1

0.846849

0.024841

34.09016

0.0000

HDFC bank Constant

rt−3

0.003970

0.000410

9.686245

0.0000

−0.473940

0.047994

−9.875020

0.0000

Variance equation Constant

3.30E-05

6.43E-06

2 εt−1

2.638355

0.228694

h t−1

0.205669

0.022003

9.347232

0.0000

−0.000451

0.002402

−0.187813

0.8510

5.129985 11.53661

0.0000 0.0000

Reliance Industries Ltd. Constant rt−1

0.039762

0.043829

0.907212

0.3643

θ

0.102002

0.140945

0.723698

0.4693

Variance equation Constant

2.45E-05

5.64E-06

4.348688

0.0000

2 εt−1

0.162868

0.022399

7.271344

0.0000

h t−1

0.785528

0.027811

28.24529

0.0000

0.002005

0.153523

0.8780

HDFC Constant

0.000308

rt−1

0.058163

0.038119

1.525840

0.1270

θ

0.032789

0.123958

0.264519

0.7914

Variance equation (continued)

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Table 7.2 (continued) Variable/coefficient

BSE100 Coefficient

Standard error

t-statistic

p-value

Constant

1.31E-05

3.04E-06

4.321816

0.0000

2 εt−1

0.112095

0.018973

5.908063

0.0000

h t−1

0.851985

0.024093

35.36270

0.0000

Source Author’s own computations

which was launched on 26 October 2017, and its other major performers such as HDFC Bank, Reliance Industries, and HDFC. The analysis employs both conventional performance strategies such as return, risk, and return-risk ratio as well as the ARCH-M framework for understanding the time-varying risk-return relationship. Our results show that in terms of both conventional econometric approach and ARCH-M framework ESG/Green indices outperform traditional portfolios perform. The BSE 100 ESG index consists of those securities meeting sustainability criteria. This result would also imply new investment strategies for India. As environmental sustainability is getting increasing attention worldwide, these companies are also taking more energy-efficient cost-saving strategies so that such investments are becoming more sustainable. We also carry out our exercise on comparing CAGR between NIFTY 50 and NIFTY 100 ESG of the national stock exchange (NSE) along with volatility for both the years 2019 and 2020. Our results show that the CAGR for the ESG index is much higher compared to traditional funds for both the normal and turbulent years. Volatilities are also much lower with sustainable funds. Further, the empirical in the context of a leading emerging country like India results also suggest that investors should recognize ESG investing as a superior strategy relative to conventional approaches as they can attain comparable financial performance and still address ESG concerns. Our results are also in line with other similar findings that to address the sustainable goals (SDG) of the United Nations ‘end hunger, achieve food security (SDG2), ‘ensure healthy lives and promote well-being for all at all ages’ (SDG3), and ‘make cities and human settlements inclusive, safe, resilient and sustainable’ (SDG1), such investment strategies can help to achieve.

References Ambec, S., & Lanoie, P. (2008). When and why does it pay to be green? Academy of Management Perspectives, 23, 45–62. Bauer, R., Koedijk, K., & Otten, R. (2005). International evidence on ethical mutual fund performance and investment style. Journal of Banking and Finance, 29, 1751–1767. Belkaoui, A. (1976). The impact of the disclosure of the environmental effects of organizational behavior on the market. Finance Management, 5, 26–31. https://doi.org/10.2307/3665454

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Betti, G., Consolandi, C., & Eccles, R. G. (2018). Supporting sustainable development goals is easier thanyou might think. Sustainability, 10(7), Article 2248. https://doi.org/10.3390/su1007 2248 Bowman, E. H., & Haire, M. A. (1975). Strategic posture toward corporate social responsibility. California Management Review, 18, 49–58. https://doi.org/10.2307/411646389 Bragdon, J. H., & Marlin, J. (1972). Is pollution profitable? Risk Management, 19, 9–18. Chitimiea, A., Minciu, M., Manta, A., Ciocoiu, N. C., & Veith, C. (2021). The drivers of green investment: A bibliometric and systematic review. Sustainability, 13, 3507. https://doi.org/10. 3390/su13063507 Eyraud, L., Clements, B., & Wane, A. (2013). Green investment: trends and determinants. Energy Policy, 60, 852–865. Eyraud L., Wane, A., Zhang, C., & Clements, B. (2011). Who’s going green and why? Trends and determinants of green investment, IMF Working Paper, No. WP/11/296. Falcone, P. M. (2018). Green investment strategies and bank-firm relationship: A firm-level analysis. Economic Bulletin, 38, 2225–2239. Han, S.-R., Li, P., Xiang, J.-J., Luo, X.-H., Chen, C.-Y. (2020). Does the institutional environment influence corporate social responsibility? Consideration of green investment of enterprises— Evidence from China. Environmental Science and Pollution Research, 1–18. Hartzmark, S. M., & Sussman, A. B. (2017). Do investors value sustainability? A natural experiment examiningranking and fund flows. https://www.ssrn.com/abstract=3016092. Inderst, G., Kaminker, C., Stewart, F. (2012). Defining and Measuring Green Investments: Implications for Institutional Investors’ Asset Allocations. OECD Working Papers on Finance, Insurance and Private Pensions, No. 24, OECD Publishing Katherina, G. (2009, April). Understanding socially responsible investing: the effect of decision frames and trade-off options. Journal of Business Ethics, Volume 87(1). Kölbel, J., Heeb, F., Paetzold, F., Bosch, T. (2020). Can sustainable investing save the world? Reviewing the mechanisms of investor impact. Organization and Environment, 33(4), 554–574, https://doi.org/10.1177/1086026620919202 Labatt, S., & White, R. (2002). Environmental Finance A Guide to Environmental Risk Assessment and Financial Products. Wiley Finance Editions. Managi, S., Okimoto, T., & Matsuda, A. (2012). Do socially responsible investment indexes outperform conventional indexes? Applied Financial Economics, 22, 1511–1527. Morgan. J. P. (2019) J.P. Morgan Perspective–ESG Investing 2019: Climate Change Everything. New York, NY. Moskowitz, M. (1972). Choosing socially responsible stocks. Business and Society Review, 1, 71–75. Naffa, H., & Fain, M. (2020). Performance measurement of ESG-themed megatrend investments in global equity markets using pure factor portfolios methodology. PLoS ONE, 15(12), e0244225. https://doi.org/10.1371/journal.pone.0244225 Renneboog, L., ter Horst, J., & Zhang, C. (2008). Socially responsible investments: Institutional aspects, performance, and investor behavior. Journal of Banking and Finance, 32(9), 1723–1742. Riedl, A., & Smeets, P. (2017). Why do investors hold socially responsible mutual funds? Journal of Finance, 72(6), 2505–2550. https://doi.org/10.1111/jofi.12547 Sauer, D. (1997). The impact of social-responsibility screens on investment performance: Evidence from the Domini 400 social index and Domini equity fund. Review of Financial Economics, 6, 23–35. Sekhar, G. V. S. (2011). Green funds & green investing: A new route to Green India. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1872370 Statman, M. (2000). Socially responsible indexes. Journal of Portfolio Management, 32, 100–108. Tirole, J. (2001). Corporate governance. Econometrica, 69, 1–35. Trana, T., Dob, H., Vub, T., & Don, N. (2020). The factors affecting green investment for sustainable development. Decision Science Letter, 9, 365–386.

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UNCTAD. (2020). Leveraging the Potential of ESG ETFs for Sustainable Development. Geneva, Switzerland: UNCTAD; 2020. Report No.: UNCTAD/DIAE/2020/1. Retrieved from https://unc tad.org/en/pages/PublicationWebflyer.aspx?publicationid=2682

Chapter 8

Macroeconomic Determination of Forestry Contribution to the Nigeria Economy Adenuga Fabian Adekoya

8.1 Introduction There is an ongoing debate on deforestation. This debate has been triggered by the continuous decline in the forest area and dwindling rents accrued to it. In the debate there are diverse opinions on what could account for this increase in deforestation. One of the argument centers on foreign direct investment (FDI) that FDI encouraged deforestation especially in developing countries. This is because land areas meant for forest are encroached on due to developmental projects which aids comfort to life. Such development projects include road infrastructures and setting up manufacturing companies. Cole et al. (2006) hypothesized that FDI increases environmental pollution. Though, he contends that this can be controlled if corruption is reduced in the system. Many other researchers had it that FDI must be encouraged in the developing countries due to oor income and capital base. They consented that FDI increases the level of employment generation, lead to high skill labour and technology in low developing countries. Therefore, there is high possibility for FDI to promote forestry economic activities. In Sub-Sahara Africa, Lokonon and Mounirou (2019) found mixed results among SSA countries that FDI be encouraged in some countries while it should be discouraged in others. They showed that FDI increases deforestation in SSA. However, Assa (2017) concluded that FDI did not increase deforestation in most Sub-Sahara African nations. As Moon and Solomon (2018) did not show the statistical relation between FDI and deforestation in Africa but concluded that FDI effect on forestry depends of the rate level of governance. Based on the available literature, the position of FDI on deforestation remain inconclusive especially in Sub-Sahara Africa. A. F. Adekoya (B) Department of Economics Education, Lagos State University of Education, Noforija-Epe Campus, Lagos State, Nigeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_8

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6

0.35

5

0.3 0.25

4

0.2 3 0.15 2

0.1

1

0.05 0

0 1990

1995 2000 World Sub-Saharan Africa

2005

2010

2015

2016 2017 2018 Latin America & Caribbean Nigeria

Fig. 8.1 Change in forestry rents (% of GDP). Source Author Computation based on World Development Indicator

This study extended the debate to Nigeria especially as Lokonon and Mounirou (2019) found zero effect in the case of Nigeria. This further indicated the need for more research work on the debate as regards Nigeria. Especially that forestry rents’ contribution to GDP reduced from 5.25% in 1995 to 2.31% in 2000 and 1.07% and 1.02% in 2017 and 2018 respectively (Fig. 8.1). Also, the link between FDI and forestry rents/deforestation is not yet documented in the country. Thus, would FDI increase forestry rents to indirectly prevent deforestation in Nigeria? Or what is the impact of macroeconomic variables on forestry economic activities in the country? Therefore, the present study becomes necessary for four reasons. One, the role of FDI as a policy instrument is crucial in the tested model because of the double two edge sword it performs. That is FDI reduces the impact of unemployment on forestry rents and further enhanced forest rents in Nigeria. Two, to reduce deforestation and see to means of reducing unemployment in the country. Three, to improve forestry rent contribution to GDP and, lastly, to promote sustainable development in Nigeria. The rest of the chapter is divided into four sections. The sections are literature review (review of empirical evidences); methodology, findings and conclusion.

8.2 Empirical Review of Relevant Literature Cole et al. (2006) considered pollution haven hypothesis in their study where they used panel data of 33 countries for 1982–1992 comprising 13 countries from OECD and 20 from developing countries. Using the IV 2SLS estimation technique, their findings showed that FDI enhances environmental regulation which help to reduce pollution but the interaction of FDI with corruption weakens the environmental regulation thereby increasing the pollution. The existence of corruption in most

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of the developing countries acts against the strength of the environmental regulatory. Supporting this hypothesis Wang et al. (2020) ran the study based on the panel data of 29 provinces in China from 1994 to 2015. The implication of findings in these studies is that control of pollution becomes difficult as pollution tax reduces thereby exposing more population to carbon emission. Therefore, the relevance of these studies is that they provided justification for the model tested in this line, that is, where FDI is well managed it minimizes development issues like unemployment which further improves forestry rent which can mitigate pollution as the rent would be used to enhance forestry business that is capable of reducing pollution in the society. Thus this section is further divided into three parts. The first part reviewed foreign direct investment and forestry; the second focused on unemployment and forestry while the third part paid attention to foreign direct investment and unemployment.

8.2.1 Foreign Direct Investment and Forestry Cole et al. (2006) examined the relationship between the stringency of environmental policies and foreign direct investment (FDI) using panel data from 33 countries. The result reveals that FDI affects environmental policy and the effect is conditional on the local government’s degree of corruptibility. They, however, recommend that there is need for reforms that reduce the level of government corruption in many countries. Moser (2008) studied the factors driving deforestation in Madagascar employing a nation-wide data set for commune-level variables for the year 1990–2000. The result countered the theory that higher prices of crops drive deforestation. In addition, population may be higher where there is higher agricultural potential and it therefore may be the underlying agricultural potential driving forest loss, and not the population numbers per se, He therefore recommended that while promoting rural economic development, improving access to markets must remain the top priority for the government of Madagascar. Boka (2017) examined Foreign Direct Investment’s effect of forest area condition on governance in Sub-Saharan Africa (SSA), using a panel data set of 38 SSA countries for the period of 1996–2011. The result reveals that FDI has a negative and significant effect on the forest area. The findings suggest that in a regime where the rule of law and corruption control are not enforced, FDI leads to more forest degradation. Boris and Ichaou (2019) investigated the relationship between foreign direct investment and deforestation for 35 Sub-Saharan African countries using panel data techniques for the period of 1991–2015. The result indicate the existence of a long-run relationship between deforestation, FDI, economic growth, trade openness and urbanization. The results from the study suggest that SSA countries should continue attracting FDI, while a certain number of them should put more emphasis on controlling deforestation associated with FDI inflows.

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8.2.2 Unemployment and Forestry Iftekhar and Hoque (2005), employed a multistage sampling technique and multiple regression analysis technique to analyse the cause of forest encroachment in Bangladesh from 1966 to 2000. The result shows that population pressure and poverty are the underlying causes of people’s movement into the forests. The authors are of the opinion that judicious measures can maintain degradation at a tolerable level. Nair and Rutt (2009), used world unemployment trends to analyse the major consequences of economic decline on rapid increase in unemployment in developing countries from 2007 to 2009. The empirical evidence reveals that job losses among migrant workers from developing countries, who are particularly vulnerable, lead to reverse migration to their home countries, reduced remittances, loss of livelihood and increasing poverty and food insecurity. They recommend that public investment could create new jobs in afforestation, reforestation, management of natural forests, recreation sites etc. Richard and Michal (2021), demonstrated the causes of Socio-ecological deforestation and forest degradation in Ghana from 1990 to 2020, using the Drivers Pressure-State-Impact-Response (DPSIR) analytical framework. The result shows that Ghana is faced with multi-faceted drivers and pressures leading to massive and ubiquitous deforestation and forest degradation. In addition, the findings indicated the plethora of forest policies, laws, legislations and interventions, implementation and enforcement failures, and lapses due to the absence of appropriate harmonization and coherence forest governance institutions. The authors recommend there is a critical need to approach the issues from the socio-ecological perspectives through the comprehensive analysis of the drivers, pressures, states, impacts and the responses from various sectoral agencies and ministries to address challenges holistically.

8.2.3 Foreign Direct Investment and Unemployment Vasile et al. (2015), employed the econometrical methodology to analyse the shortterm causal relationship between FDI and unemployment for the period of 1991– 2012. The findings of the paper consists of the fact that there is no granger causality relation between foreign direct investment and unemployment. According to Irpan et al. (2016), they investigated the impact of FDI on unemployment in Malaysia from 1980 to 2012 using annual data spanning. The authors found out that FDI and GDP significantly influence the unemployment rate in Malaysia. Therefore, the Government imposed joint venture policy to control FDI on local market by allowing local producers to learn new technological ideas. Widia et al. (2019), examined if foreign direct investment can reduce unemployment in home countries in the ASEAN countries from 1980 to 2017 using Vector Error Correlation Model (VECM). The result explains that in the long run, the effect

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of the unemployment rate on FDI in the ASEAN countries has a negative and significant impact. Therefore, the study recommends that the ASEAN not only encouragess proactive policy to attract FDI but also maintain appropriate environment where there is political stability. Alalawneh and Nessa (2020) employed panel data for the period of 1990–2018 to examine the impact of FDI on unemployment in six countries in MENA Region. The result reveals that there is no causal relationship between FDI and unemployment. In addition, some other hypothesis show that foreign direct investment plays an important role in reducing the problem of unemployment through a direct impact in increasing employment opportunities in industries and government policies to reduce unemployment rate focusing on economic growth whereby it would lead to increase in the employment rate.

8.3 Method 8.3.1 Theoretical Framework Cole et al. (2006) explored the link between foreign direct investment and environmental regulation based on Grossman and Helpman (1996). The model considered two markets involved in environmental pollution generation. These markets are both domestic firms and foreign firms operating within a certain boundary. Four agents operate in these markets, and they are consumers, domestic producers, foreign producers and the government. Further, they considered that the activities of the foreign producers are capable to have positive effect on the economy if all these agents’ activities can be regulated. This would not only improve the economy but it would help in reducing the pollution cause by them. In testing their hypothesis, they came with the model in Eq. 8.1. REGSt = f (FDIt , Z t )

(8.1)

In Eq. 8.1, REGSt is the environmental regulation measured with grams of lead content per gallon of gasoline; FDIt is foreign direct investment, Z t other variables in the model which include regulatory policy like corruption, GDP, manufacturing and urban population. However, Assa (2017) used forestry degradation as an extension to measure environmental regulation. He thus comes up with Eq. 8.2. FAt = f (FDIt , Z t )

(8.2)

In Eq. 8.2, FAt is the forestry degradation that is proxy by the rate of forest area change. Z t remains as other variables like governance, GDP etc., But this study further amended Eq. 8.2 to arrive to Eq. 8.3. Rather using forestry degradation because of limited data, forestry rent is considered as a dependent variable. Also, the other

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variable included now is unemployment as indicated in Eq. 8.3. FRt = f (FDIt , UNEt )

(8.3)

8.3.2 Model Estimated Thus, the tested model presented in Eq. 8.4. This model is also similar to Michinaka and Miyamoto (2013); they examined socioeconomic factors on forestry area in terms of change. In Eq. 8.1, FRt indicates the forestry rent (%), UNEt is the unemployment rate (%) and FDIt is the foreign direct investment (%). Also, β0 isconstant, β1 andβ2 are parameters, μt is the white noise. ln FRt = β0 + β1 UNEt + β2 FDIt + μt

(8.4)

In Eq. 8.1, the a priori expectations of the coefficients are: β1 , < 0: An increase in unemployemnt rate will led to a decrease in forestry rent β2 > 0: An increase in foreign domestic investment will led to an increase in forestry rent

8.3.3 Data In examining the macroeconomic determination of forestry contribution to GDP in Nigeria this study used annual time series from 1970 to 2017. These data are forestry contribution to GDP, unemployment and foreign direct investment. Forestry rents (FR) (% of GDP) and foreign direct investment measured by foreign direct investment, net inflows (FDI) (% of GDP); both are sourced from World Development Indicator (2019). The unemployment rates (UNE) (%) sourced from National Bureau of Statistics of various publications.

8.3.4 Justification of the Variables In line with the objective of this paper which is to determine the impact of macroeconomic variables on forestry contribution to economic activities in the Nigeria, the choice of the variables (FR and FDI) emanated from the theoretical framework in the previous studies (see Cole et al., 2006). Moreover, Forestry rent which is supposed to bring in more revenue to the economy is facing threat mainly caused by the activities

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of those unemployed. Unemployment is high in the country and its inclusion as a variable in the model allows this study to assess the extent of labour policy in Nigeria.

8.3.5 Co-integration Approach To perform the co-integration analysis of any time series, it is utmost to determine the integration order of the variables. That is the number of period which a variable shall be differentiated before becoming stationary. The essence of this is to avoid wrong model been specified and misleading conclusion. Further, it assists to determine the appropriate technique for estimation. For instance, Johansen Juselius’ co-integration approach is best suited for variables with I(1) while the bound test approach of Pesaran et al. (2001) can combine both I(0) and I(1). However, the Philip Perron approach to unit test was adopted and tested. This is to overcome any structural break in any of the variable. Thus, the results as presented in Table 8.3 indicated that variables are integrated in the order of I(0) and I(1). Based on the mix integration of I(0) and I(1), co-integration and estimation of the variables followed proposed the bound test for the long-run relationship proposed by Pesaran et al. (2001). This approach is suited for the model estimated because ARDL generates sufficient lags for variables in the model and its ability to determine residual correlation makes it more superior. Further, it is dynamic as it used one period of lag in determining the optimal lag length. It is good to estimate small sample (Narayan, 2005). In the small sampled tested, the Akaike information criterion (AIC) was used to selected the lagged because it is good for estimation of small sample (Liew, 2004). In model transforming of Eqs. 8.4 to 8.5, the F-statistic in the bound test used to determine the existence of co-integration. Also, the F-statistic tested the joint significance of the coefficients at one period of lag. The null hypothesis of no cointegration shows that H0 : β1 = β2 = β3 = 0 (implies the non-existence of cointegration) and the alternative is H0 : β1 /= β2 /= β3 /= 0 and where at least one of the β1 to β3 /= 0 (implies the existence of cointegration). The short-run dynamics of the ARDL model in Eq. 8.6 and the ECT presented in Eq. 8.7. ΔFRt = β0 + β1 FRt−1 + β2 UNEt−1 + β3 FDIt−1 +

p Σ

γ1 ΔFRt−1

i=1

+

p Σ

γ2 ΔUNEt−1 +

i=0

ΔFRt = β0 A +

p Σ i=1

γ1 ΔFRt−1 +

p Σ

γ3 ΔFDIt−1 + μt

(8.5)

i=0 p Σ i=0

γ2 ΔUNEt−1 +

p Σ

γ3 ΔFDIt−1 + μt

(8.6)

i=0

ectt = FRt − (−β1 UNEt − β2 FDIt + β0 )

(8.7)

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8.4 Findings In putting the data to test for policy consideration, the nature of the data employed is found to be suitable for analysis. This confirms by the result in Tables 8.1, 8.2 and 8.3. In Table 8.1, descriptive statistics showed that mean value of each variable lies within the minimum and maximum value. These variables include FRt , UNEt and FDIt . In Table 8.2, UNEt is significant and negative correlates with FRt but with low value. Besides, the correlation test indicated that there is no correlation problem. Even and if there is, the choice of technique (ARDL) used minimised it (see Pesaran & Shin, 1997). Moreover in Table 8.3, the result of the PP unit root test showed that variables did not suffer from serial correlation as all the variables are found to be integrated of I(0) and I(1). Table 8.1 Descriptive statistics Observations

Mean

Std. Dev

Minimum

FRt

48

2.139271

1.161579

0.695474

UNEt

48

7.481250

4.620521

1.800000

FDIt

48

2.541205

2.147577

Maximum 5.253100 20.40000

−1.15086

10.83256

Source Author’s computations

Table 8.2 Correlation Variables

FRGDP

FRt

1

UNEt

−0.43144

FDIt

UNE

FDI

1

(−3.2436)***

–

0.622699

−0.07793

1

(5.3975)

(−0.5301)

–

Source Author’s computations

Table 8.3 Results of the unit root test Phillip-Perron (PP) Variables

PP Model

Lag length

ADF statistics

Prob. Value

Decision

FRt

Trend and intercept

1

−7.52***

0.000

I(1)

UNEt

Trend and intercept

1

−6.953***

0.000

I(1)

FDIt

Trend and intercept

0

−3.622**

0.038

I(0)

Source Author’s computations Note The figures reported are t-ratio and showed the p-values of MacKinnon (1996) one-sided at various levels of significance. The asterisks (***) are at 1% and (**) at 5%. The lag length 0 means variables are stationary at a level while lag length 1 showed that variables are stationary at the first difference

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Having ensured that data was appropriate for analysis, the result of the ARDL employed is presented in Table 8.4; this includes the bound test, long and short run estimations, and diagnostics tests. Here, Bound Test result of the ARDL specification (1, 1, 1) indicated that F-statistics is above the critical value of 3.87 at the 5% level of significance. This means that there is joint movement of variables in the model. In the long-run estimation, unemployment is significant to determine forestry rent at the 1% level of significance. That is increase in unemployment by 1% reduces forestry rent by 0.15%. This result supports the finding in Kyere-Boateng and Marek (2021). They found that youth unemployment featured in Ghana’s economy and thus, stressed the youths to engage in deforestation illegally. Likewise, Nair and Rutt (2009) affirmed that rising rural unemployment mounts pressure on forests and woodlands which lead to deforestation and degradation. Deininger and Minten (1999) found that poverty increases forestry lost. One way of reducing poverty is to reduce unemployment that could assist in improving rent from forestry to enhance sustainable development. Thus, this a long term approach to reduce deforestation (Miyamoto, 2020). But foreign direct investment is significant and positive to determine forestry rent at 1% level of significance. This means when foreign direct investment increases by 1%, the forestry rent increases by 0.45%. The inclusion of FDI in the model provides the policy to reduce unemployment if forestry rent would increase. Although Lokonon and Mounirou (2019), and Assa (2017) provided that FDI serves as a channel to increase economic activities, it encourages forest resources degradation. Where increase in forestry rent is the target of the government, it is possible to channel more FDI in forestry business. Moreover, the error correction model is −0.4079 and significant at 1% level of significance. It showed that there is an adequate feedback mechanism which could restore the model back to equilibrium. That is the model would restore to equilibrium in a year by 40.79%. Also, as the full equilibrium is 100%, it would take 2.45 years to adjust any deviation in the short-run. This means features of disequilibrium in the model shall take 2.45 years to return to the long-run equilibrium relationship. The diagnostics tests includes normality test (χ N2 ), functionality test (χ F2 ), serial 2 ), heteroscedasticity (χ H2 ) and structural stability test. These postcorrelation (χ SC tests passed at 5% level of significance. Meaning that the series in the study is normally distributed just as Ramsey’s RESET affirms there is no misspecification in the model. Also, present values in the data are not affected by the previous lagged values based Breusch-Godfrey LM test. The residuals of the series have same variance in the model using Breusch-Pagan-Godfrey Heteroscedasticity. Likewise, the structural stability test of CUSUM and CUSUMSQ showed that parameters are stable in the model (Fig. 8.2).

5.00 4.4930 Cointegrated (5%) AIC* = 1.528

Upper

F-stat FR = f (UNE, FDI)

Conclusion (significance level)

Test of ARDL specification significance

0.4523*** 2.0550

FDIt

C

0.0993 0.0207

−0.4079*** −0.0603*** 0.1845***

UNEt−1

FDIt−1 0.0551

Standard error

Coefficients

0.4508

FRt−1

Short-run estimates

0.0437

−0.1478*** 0.0953

Standard error

Coefficients

Variables

SB = 1.764

K

UNEt

Long-run estimates

3.10

4.13

Lower 3.87

5% significance level

1% significance level

Critical bounds (F-test)

Dependent variable: forestry

Table 8.4 Estimates of the forestry rents model using ARDL

3.3465

−2.9083

−4.1043

T-stat

4.5582

4.7438

−3.3821

T-stat

HQ = 1.617

2

3.35

2.63

10% significance level

0.0018

0.0058

0.0002

(continued)

P-value

0.0000

0.0000

0.0016

P-value

Adj-R-square = 0.825

1, 1, 1

ARDL specification

106 A. F. Adekoya

0.0928

−0.4079***

ECM(−1)

Funct i onal t est (χ 2F )

χ 2 = 8.4158 (0.1348)

H et er oskedast i ci t y t est (χ 2H )

F = 0.7565 (0.4537)

J B = 2.1166 (0.3470)

0.0001

0.0083

0.9720

to be rejected at 5%. Variables defined as Forestry Rents is FRt−1 as a dependent variable while Unemployment (UNEt ), Foreign Direct Investment (FDIt ) are independent variables. In the ARDL specification, the F-stat in the bounds test is based on critical upper bounds while relying on AIC

Source Author’s computations 2 , χ 2 are not failed Note The asterisk (*) showed that the estimated coefficients are significant at 1% (***) and 5% (**). Also, the diagnostic tests χ N2 , χ F2 , χ SC H

N or mal i t y t est (χ 2N )

χ 2 = 2.2557 (0.1331)

−4.3917

2.7735

−0.0352

Ser i al C or r el at i on t est (χ 2SC )

Diagnostics tests

0.0421

0.1168***

ΔFDIt

0.0245

−0.0008

ΔUNEt

Dependent variable: forestry

Table 8.4 (continued)

8 Macroeconomic Determination of Forestry Contribution to the Nigeria … 107

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A. F. Adekoya

20

1.4

15

1.2

10

1.0 0.8

5

0.6

0

0.4

-5

0.2

-10

0.0

-15

-0.2

-20

1980 1985 1990 1995 2000 2005 2010 2015 CUSUM

a.

CUSUM Test

-0.4

5% Significance

1980 1985 1990 1995 2000 2005 2010 2015 CUSUM of Squares

b.

5% Significance

CUSUM Sq Test

Fig. 8.2 Stability test. Source Author’s computations

8.5 Conclusion This study examined macroeconomic factors which include unemployment and foreign direct investment to determine forestry rent in Nigeria. The result indicated that unemployment reduces forestry rent. It means activities of unemployed population increases deforestation with no financial improvement to the government purse. In most cases their activities in the forestry are illegal. But foreign direct investment as policy enhances forestry rent as against the opinion that the foreign direct investment leads to forestry degradation in Sub-Saharan Africa countries. The positive link of foreign direct investment to forestry rent shows that forestry investment can be used to reduce the effect of activities of unemployed people on forestry environment/ business. Based on the findings, forestry rent as a source to boost economic development should be strengthened. In doing this, this study proffered the following suggestions. One, activities of the unemployed people in the forestry should be moderated by the government. This moderation is to reduce their non-financial gain to the government. Two, foreign direct investment in forestry business is encouraged for four reasons. The first reason is that foreign direct investment comes with new technology to improve the current state of forestry in the country. Second, it will boost the revenue on forestry thereby improving the GDP. Thirdly, more investment in forestry business leads to more employment in this area thereby reducing the number of unemployed population in the country. Lastly, it preserves the nature and help to enhance the greenhouse.

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References Alalawneh, M., & Nessa, A. (2020). The impact of foreign direct investment on unemployment panel data approach. Emerging Science Journal, 4(4), 228–242. Assa, S. K. (2017). Foreign direct investment, bad governance and forest resources degradation: Evidence in Sub-Saharan Africa. Economics and Politics. https://doi.org/10.1007/s40888-0170086-y Boka, S. (2017). Foreign direct investment, bad governance and forest resources degradation: Evidence in Sub-Saharan Africa. Economic Policy Analysis Unit of CIRES (CAPEC); Cote d’Ivoire, 3–15. Boris, O., & Ichaou, M. (2019). Does foreign direct investment impede forest area in Sub-Saharan Africa? Natural Resources Forum, 43, 230–240. Cole, M., Elliott, R. J. R., & Fredriksson, P. G. (2006). Endogenous pollution havens: Does FDI influence environmental regulations? Scandinavian Journal of Economics, 108(1), 157–178. Deininger, K. W., & Minten, B. (1999). Poverty, policies, and deforestation: the case of Mexico. Economic Development and Cultural Change, 47(2), 313–344. Grossman, G. M., & Helpman, E. (1996). Foreign investment with endogenous protection. In R. C. Feenstra, G. M. Grossman & D. A. Irwin (Eds.), The political economy of trade policy. MIT Press. Iftekhar, M., & Hoque, A. (2005). Causes of forest encroachment: An analysis of Bangladesh. Geo Journal, 62, 97–105. Irpan, H. M., Saad, R. M., Nor, A. S., Noor, A.M., & Ibrahim, N. (2016). Impact of foreign direct investment on the unemployment rate in Malaysia. Journal of Physics: Conference Series, 710, 012028. Kyere-Boateng, R., & Marek, M. V. (2021). Analysis of the social-ecological causes of deforestation and forest degradation in Ghana: Application of the DPSIR framework. Forests, 12, 409. Liew, V.K.-S. (2004). Which lag length selection criteria should we employ? Economics Bulletin, 3(33), 1–9. Lokonon, B. O. K., & Mounirou, I. (2019). Does foreign direct investment impede forest area in Sub-Saharan Africa? Natural Resources Forum, 43, 230–240. Michinaka, T., & Miyamoto, M. (2013). Forests and human development: An analysis of the socioeconomic factors affecting global forest area changes. Journal of Forest Planning, 18, 141–150. Miyamoto, M. (2020). Poverty reduction saves forests sustainably: Lessons for deforestation policies. World Development, 127, 104746. Moon, H., & Solomon, T. (2018). Forest decline in Africa: Trends and impacts of foreign direct investment: A review. International Journal of Current Advanced Research, 7(11-C), 16356– 16361. Moser, C. (2008). An economic analysis of deforestation in Madagascar in the 1990s. Environmental Sciences, 5(2), 91–108. Nair, C. T. S., & Rutt, R. (2009). Creating forestry jobs to boost the economy and build a green future. Unasylva, 233(60), 3–10. Narayan, P. K. (2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37, 1979–1990. Pesaran, M. H., & Shin, Y. (1997). An autoregressive distributed lag modelling approach to cointegration analysis. Revised paper presented at the Symposium at the Centennial of Ragnar Frisch, The Norwegian Academy of Science and Letters, Oslo, March 3–5, 1995. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289–326. Richard, K., & Michal, V. (2021). Analysis of the socio-ecological causes of deforestation and forest degradation in Ghana: Application of the DPSIR Framework, 1–17. Vasile A., Davidescu, A., & Paul, A. M. (2015). FDI and the unemployment causality analysis for the latest EU members. Procedia Economics and Finance, 23, 635–643.

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Wang, S., Wang, H., & Sun, Q. (2020). The impact of foreign direct investment on environmental pollution in China: Corruption matters. International Journal of Environmental Research and Public Health, 17(6477), 1–20. Widia, E., Ridwan, E., & Muharja, F. (2019). Can foreign direct investment reduce unemployment in home countries? Analysis for ASEAN countries. Journal Kajian Ekonomi Islam, 4(2), 143–150.

Chapter 9

Impacts of Low Carbon Economy in India: A Review Tarakeshwar Senapati, Apurba Ratan Ghosh, Krishna Singh, and Palas Samanta

9.1 Introduction India launched the National Action Plan on Climate Change (NAPCC) in the year 2008 that incorporated a multidimensional, long-term and integrated framework to address the climate change as a key development issue. The NAPCC recommended renewable energy, energy efficiency, clean technologies, public transport, resource efficiency, afforestation/reforestation, tax incentives and research, and generation of strategic knowledge as part of its eight mission initiatives (TERI, 2015). The Ministry of Environment, Forests & Climate Change (MoEFCC) managed the implementation of the NAPCC through its various missions which are nodalized by the respective administrative ministries. To achieve the vision of climate justice, the government has to initiate the decentralized bottom-up approach. It has been observed that sub-national institutions have an important and extensive role to play in the transformation process in low-carbon economies. On the basis of a study by UNDP (2010), it was found that to mitigate the greenhouse gas (GHGs) approximately 50 – 80% of the total investment occurred at the sub-national and local levels. Regional and local governments have played an T. Senapati Department of Environmental Science, Sidho Kanho Birsha University, Ranchi Road, Purulia, West Bengal 723104, India A. R. Ghosh Department of Environmental Science, The University of Burdwan, Burdwan, West Bengal 713104, India K. Singh Department of Economics, University of Gour Banga, Malda, West Bengal 732101, India P. Samanta (B) Department of Environmental Science, Sukanta Mahavidyalaya, University of North Bengal, Dhupguri, Jalpaiguri 735210, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_9

111

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important role to execute policies, programmes and fiscal instruments in the areas of generation, supply and distribution of electricity, the regulation of the built environment, waste management, transport and land-use planning. Involving sub-national and local actors in climate action can encourage cross-sector policy interventions and create ‘role models’ that can be replicated/up-shaped domestically and globally. The role of local and sub-national governments as ‘government stakeholders’ in global climate action was recognized in the Cancun Agreement (COP 16) (TERI, 2015). NITI Aayog has recommended that strong States make a strong Nation through the structure of cooperative federalism on the basis of support initiatives and mechanisms with the States on a continuous basis. India is a federal country consisting of 28 states and 7 union territories. In the Seventh Schedule of the Constitution of India, the responsibilities and areas of jurisdiction of the central and the state governments are demarcated through the Union List and the State List. In the context of environment federalism and climate policy segregation of responsibilities played a significant part in the country (Jörgensen, 2011). Out of 97 subjects included in the Union list, trade representation, United Nations organizations, agreements and conventions with foreign countries, atomic power, mineral and oil resources, and control of industries are related to climate change. In addition, the Union Government is responsible for the climate change agreement, given the international context of the problem and the constitutional proficiency (Jörgensen, 2011). In addition, the states are responsible for implementing policies and programmes formulated by the central government. However, due to the degree and urgency of the Climate Change Challenge, state actors need to take an extended role to move from mere ‘executors’ to ‘entrepreneurs’ and ‘innovators’. To transform states into ‘laboratories of invention’ for technological and regulatory innovation, consideration of a bottom-up approach and active participation of states in the process of climate policy-making play an important role (Kashwan, 2007). Moreover, the relevance of states’ active involvement in policy-making increases in the light of the broader socioeconomic and climate-geographical changes across different regions. In addition, states differ in terms of mitigation potential (easy opportunity to reduce/avoid GHGs) and capacity (financial, technical, how-to and awareness). Implementation of a more decentralized, bottom-up climate policy may result in actions customized to local contexts and needs, driving the country’s response to climate change (Kashwan, 2007; Burtraw & Shobe, 2009). Accordingly, International Energy Agency (IEA) highlighted some of the rapid transition measures to mitigate the impacts of climate change. Scientific evidence has also confirmed their importance in an economically feasible way (Sharma & Chaudhry, 2015). More than fifty percent of India’s households don’t have electricity and women spend total of 80 billion hours each year collecting firewood (Sharma & Chaudhry, 2015). Accordingly, to cope with climate change and to promote India’s economic growth and energy security, a low carbon economy should be adopted. Therefore, the aim of this paper is to review the prospect, opportunities and the challenges towards Low carbon economy in Indian scenario.

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9.2 Challenges in Low Carbon Development India contributes near about 5% of global CO2 emissions and it is continuing to increase. During the years 1990–2008, CO2 emissions have been more than doubled (International Energy Agency (IEA) Statistics, 2010). According to Sharma and Chaudhry (2015), CO2 emission in India will be increased up to 2.5 times by the year 2030 from 2008. The CO2 emissions in the country are strongly linked with economic growth, energy supply and generation of thermal power. There were huge growth in Indian GDP (148%) (Economic Survey of India 2005–2006, 2006; Sharma & Chaudhry, 2015), production of thermal power (170%) (Sharma and Chaudhry, 2015), commercial energy supply (100%) during the period 1990–2005. It was also evidenced that CO2 emissions were increased up to 100% in the country during this time. In India, there are some challenging factors in the development of low carbon economy e.g., growing population, dependency on thermal power and poor energy efficiency. At present, India with its 1.3 billion population is the second most populous country in the world (Census of India, GoI, 2011). Indian population contributes to 17.5% global population. With 1.41% growth rate Indian population will be surpassing China in the year 2050 (Sharma and Chaudhry, 2015). Meeting the energy demand will be very challenging for this growing population. Energy consumption in different developmental activities and changing life style will be high. So, India will be facing a major challenge to the preservation of ecological balance and fulfilment of the energy demand of its growing population. Energy sector in India is dominated by coal based thermal power. The CO2 emissions in India were 1229 mt in the year 2005. The amount almost doubled within the years 1990–2005. Energy demands in different industrial sectors like steel, cement, fertilizers, petrochemicals, etc., are primarily fulfilled by coal-based power plants (Chandrasekar & Kandpal, 2007). So, to keep the industrial growth positive and meet their energy demand with the emission reduction will be a huge challenge (Ghosh et al., 2002; Sharma & Chaudhry, 2015). Higher energy efficiency is the key factor for low carbon economic growth. Improving the energy efficiency is vital to achieve better energy security and profitability in Industry and ultimately reducing carbon foot print (Chandrasekar & Kandpal, 2007). India is lacking in energy efficient technology due to higher initial cost, less funding opportunities, government policy, and lack of awareness.

9.3 Metrics for Socio-Economic Considerations and Low Carbon Development The capability at the sub-national level in India was measured by constructing an index that consists of three sub-indices like socio-economic performance, low carbon development performance and carbon footprint (TERI, 2015). Socio-economic

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performance index was constructed by considering the indicators like poverty, literacy, public infrastructure and capacity of local institutions (TERI, 2015). The effectiveness of intervention related to grid and off-grid clean energy and the area of forestation at the state level can be considered as indicators of low carbon development. At the sub-national level to construct the carbon footprint index per capita emissions are not used because low-population states may also be those states that do not do well in terms of socio-economic indicators. Carbon footprint sub-indices were constructed on the basis of the difference between CO2 emissions and storage in Gg. The indicators used for constructing indices are given in Table 9.1. Further computation and standardization of indicator values are done to identify and collect basic data in the range 0–1. The best performer gets value 1, while worst performer gets value 0. Moreover, all values become uni-directional. This standardization is done by using UNDP goal post method. Table 9.2 depicts the socio-economic performance indices, low carbon development (LCD) performance indices and carbon footprint indices for 14 major states in India. States like Kerala, Gujarat, Tamil Nadu, Maharashtra and Karnataka are fairly well in terms of socio-economic indicators. The findings of the study reveals that the performance of a state with respect to LCD was influenced by the policy approaches of the state government as well as existing resource endowments of that state. States like Bihar and Odisha have performed at lower ending terms with respect to socioeconomic performance and low carbon footprints and have achieved a higher rank in LDC by optimizing the path of alternate development. On the other hand, Rajasthan, Punjab, Gujarat, Tamil Nadu and Odisha are fairly well in terms of LCD capacity indicators. Considering the sub-national context of India, LCD strategies need to be considered not only to mitigate carbon emissions but also emphasis should be given on socio-economic capacity and adaptive capacity to promote low carbon development and policy in the equity space. A policy framework would be justified for the low-carbon development strategies inherent in the ‘co-benefit approach’ framework that considers human development and socio-economic viability. Maharashtra, Uttar Table 9.1 Indicators used Index category

Indicators

Socio-economic Performance Index

(i) Average population served per government bed, (ii) Total funds availability and expenditure at Panchayat level for MG-NREGS (iii) Non-BPL Population (iv) Female literacy rates (v) Electrification

Low carbon development (LCD) Performance Index

(i) Grid: Solar RPO performance (ii) Off-grid: Biogas (iii) Forest cover

Carbon footprint index

(i) Difference between CO2 emissions and storage in Gg

Source Authors’ compilations

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Table 9.2 Indices for LCD Performance, Carbon Footprint and Socio-economic Performance for 14 Major States in India (Modified after TERI, 2015) State

LCD capacity index

Carbon footprint index

Adaptive capacity index

Standardized scores

Standardized scores

Standardized scores

Rank

Rank

Rank

Rajasthan

0.8086

1

0.5730

7

0.5690

10

Punjab

0.6791

2

0.4974

9

0.7286

6

Gujarat

0.6523

3

0.8033

3

0.7632

2

Maharashtra

0.6521

4

1.0000

1

0.7375

4

Tamil Nadu

0.5832

5

0.7199

5

0.7470

3 13

Odisha

0.5682

6

0.2962

13

0.3770

West Bengal

0.5578

7

0.7151

6

0.7025

8

Bihar

0.5526

8

14

0.2121

14

02.512

Karnataka

0.5360

9

0.5322

8

0.7334

5

Madhya Pradesh

0.5344

10

0.4365

10

0.5045

12

Kerala

0.5044

11

0.3009

12

0.9244

1

Haryana

0.4333

12

0.4078

11

0.7143

7

Uttar Pradesh

0.3672

13

0.8039

2

0.5068

11

Andhra Pradesh

0.2382

14

0.7951

4

0.6538

9

Source Authors’ compilations

Pradesh, Gujarat, Andhra Pradesh and Tamil Nadu have higher carbon footprints; this is also owing to factors such as higher industrialization and demographics.

9.4 Centre’s Actions on Low Carbon Development in India India is one of the fastest growing economies in the World with 1.3 billion populations. India is now facing the challenge to keep its economic growth positive as well as to reduce GHGs emission as per international agreement to tackle climate change. The Government of India is committed to transform Indian economy into Low carbon economy. In this context, Indian Government has taken some important policies e.g., National Action Plan on Climate Change (NAPCC), Indian Network of Climate Change Assessment (INCCA), Indian Network of Climate Change Assessment (INCCA), Expert Group on a Low Carbon Strategy and Inclusive Growth, Renewable energy related policies and measures, Reducing Emissions from Deforestation and Forest Degradation, India GHG program, Carbon Finance. Details of these policies are described below:

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9.4.1 National Action Plan on Climate Change (NAPCC) To combat the adverse effects of climate change, Indian government is committed to reduce carbon emission and intensify low carbon economy. As per the planning to cut emission, Indian government launched National Action Plan on Climate Change (NAPCC) on 30th June 2008. As per NAPCC, the government has taken eight ‘national missions’ with different objectives under the guidance of different ministries. The core national missions are as follows: i. National Solar Mission: The objective of this mission is to increase the capacity of solar power up to 20,000 megawatt (MW) by the year 2020 under the Ministry of New & Renewable Power. ii. National Mission on Enhanced Energy Efficiency: The objective of this mission is to save 10,000 MW of electricity by the year 2020 under the Ministry of Power. iii. National Mission for Sustainable Habitat: The objective of this mission is to enhance the energy efficiency in residential buildings, commercial buildings, and public transport. Another notable objective of this mission is solid waste management. This mission is supervised by the Ministry of Urban Development. iv. National Water Mission: This mission is running under the guidance of the Ministry of Water Resources with the objective of conservation of river basin and its management. v. National Mission for a Green India: In this mission, MoEF has taken afforestation program to over 6 million hectors (Ha) of degraded forest land under 12th plan. vi. National Mission for Sustaining the Himalayan Ecosystem: Ministry of Science and Technology has taken different methods for conservation and monitoring of glaciers. vii. National Mission for Sustainable Agriculture: Under this mission, the Ministry of Agriculture has taken different measures for drought proofing and risk management in agriculture. Ministry is also promoting agricultural research to fight climate change. viii. National Mission on Strategic Knowledge for Climate Change: The objectives of this mission are vulnerability assessment, research observation, and data management under the supervision of Ministry of Science and Technology.

9.4.2 Indian Network of Climate Change Assessment (INCCA) Ministry of Environment and Forests (MoEF) has launched a research network of 127 research institutions across the country in 2009, as there is a lack of knowledge and

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understanding of the implications of climate change (MoEF, 2010a). The objectives of this program are as follows: i. Assessment of causes and implications through scientific research ii. Preparation of report regarding climate change and its associated vulnerabilities every two years iii. Capacity building towards the management of climate change related risks and opportunities.

9.4.3 Expert Group on a Low Carbon Strategy and Inclusive Growth A concrete strategy and concept are inevitable to achieve the ultimate goal of low carbon growth. In this context to finalize the strategies an expert group was formed by Planning Commission under 12th Five-year Plan of India. The expert Group in its report has detailed the national emissions reduction potential by 2020 for various sectors e.g., power, steel, cement, oil & gas, etc., in the context of annual GDP growth of 8% and 9%, respectively (GIZ, 2014).

9.4.4 Carbon Tax on Coal to Fund Clean Energy Government has a plan to generate National Clean Energy Fund (NCEF) by imposing clean energy tax on domestically produced coal and imported coal. This fund can be used in funding research and innovative projects of clean energy technologies e.g., solar, wind, tidal, geothermal, coal gasification, coal bed methane, shale oil, hydrogen/fuel cells, hybrid vehicles and environmental remedial measures (Carbon Disclosure Project, 2010).

9.4.5 Renewable Energy Related Policies and Measures As per geographic location, the country has huge potential of different renewable resources e.g., solar, wind, tidal, etc. But there is a lack of uniformity in the potential of renewable energy across the country. Government has taken different measures to attract investment in renewable energy sectors and make the project commercially viable (WSP, 2010).

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9.4.6 Reducing Emissions from Deforestation and Forest Degradation (REDD+) Implementation of REDD+ activities are now another intuitive by Govt. of India to climate change issues. The activities under REDD + include setting up of national forest reference emission level or national (sub-national level) forest reference level or implementation of national forest monitoring system. Accordingly, India has set up Green India Mission programme under its National Action Plan on Climate Change (MoEF, 2010b). India has taken the following initiatives under REDD+ policies: i. Submission of “REDD, Sustainable Management of Forest (SMF) and Afforestation and Reforestation (A&R)” report to UNFCCC. ii. Set up of Technical Group to develop SOPs to assess and monitor REDD+ actions. iii. Establishment of National REDD+ Coordinating Agency. iv. Establishment of Different REDD+ cells: Forest Survey of India (FSI), Indian Council of Forestry Research and Education (ICFRE), Indian Institute of Remote Sensing (IIRS), Indian Institute of Science (IISc), Wildlife Institute of India (WII) and the state forest departments. v. Preparation of reference document to explore the mitigation potential of forestry sector.

9.4.7 Carbon Finance Clean Development Mechanism (CDM) is an important tool for cutting carbon emission. As a part of the United Nations Framework Convention for Climate Change (UNFCCC), India can trade Certified Emission Reduction (CER) across the globe (WSP, 2010). India is an attractive market for carbon trading, so, investment in the carbon market as part of CDM can boost Indian economy in near future. As per the rules of Kyoto Protocol, India can trade CER to developed nations in the world which have specific emission reduction target (World Bank, 2008). Till now the registered CDM projects and NCDMA approved projects in India, attract investments of more than 1.6 trillion INR and 5.5 trillion INR, respectively (GIZ, 2014).

9.5 State Action Plan on Low Carbon Development Every state formulated State Action Plan on Climate Change (SAPCC) consistent with NAPCC as per instruction of the Prime Minister of India to deal with climate change issues for developing low carbon economy. State Steering Committee, State Advisory Group and Core Agency are the three pillars for SAPCC development. After SAPCC preparation and subsequent submission, SAPCC undergoes review by Expert

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Table 9.3 Preparation of SAPCC for climate change reduction National Steering Committee on climate change (NSCCC)

NSCCC se up under chairmanship of Secretary, MoEFCC with Secretaries from National Ministries to consider and endorse SAPCC

Expert Committee EC reviews SAPCC draft and provides suggestions to States For improved (EC) final SAPCC reports State Steering Committee (SSC)

SSC set up under chairmanship of Chief Secretary of Stale with representatives from relevant State government departments, academicians and NGOs. SSC provide overall guidance supervision and coordination for SAPCC preparation

State Advisory Croup (SAG) Core Agency

SAG review technical quality, robustness of analysis and feasibility of SAPCC recommendations Core Agency/Agencies of State government prepare final SAPCC

National Steering Final approval Committee (NSC) Source Authors’ compilations from TERI (2015)

Committee (EC), constituted by Central Govt. On receipt of the revised SAPCC incorporating recommendations made by EC, the National Steering Committee (NSC) on Climate Change considers and endorses the SAPCC. The detailed procedure and the role of each compartment are outlined in Table 9.3. Till now, Andaman and Nicobar, Andhra Pradesh, Arunachal Pradesh, Chhattisgarh, Himachal Pradesh, Jammu and Kashmir, Lakshadweep, Madhya Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Odisha, Puducherry, Punjab, Rajasthan, Sikkim, Tripura and West Bengal have submitted the SAPCC and the NSCC at MoEFCC has endorsed. Three SAPCCs (Haryana, Jharkhand and Karnataka) have been considered by EC. The basic idea followed by various states in preparing SAPCCs includes ‘principles of territorial approach to climate change, sub-national planning, building capacities for vulnerability assessment, and identifying investment opportunities based on state priorities’ (TERI, 2015).

9.6 Benefits of LCE LCE is focused globally to tackle the climate change and its consequences. LCE includes climate friendly technologies and policies. In Indian context, it can yield many beneficial effects by improving public health, energy security, conserving the ecosystem and new job opportunities. Public health: A significant portion of Indian population still now is depending on open fires for coking, heating, etc., which are the major cause of indoor air pollution. Indoor air pollution causes different health issues, particularly for women and children. Use of clean energy like solar energy and LPG reduces health risks related

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to indoor air pollution due to air pollution. Use of cleaner fuel and energy efficient technology in transport sector can reduce air pollution in the outdoor environment. Major industries like steel, power, cement, and fertilizer will use cleaner and energy efficient technology when they will be transforming into low carbon development. This will also reduce air pollution. So, air pollution related health risks will be reduced. Quality of public health will be improved. Solid waste management is also a part of low carbon development. Proper solid waste management will significantly improve the quality of environment in urban areas, thus effectively improving the public health of the urban people. Employment generation: Transitioning to low carbon development from traditional development plan will cause a significant change in the job opportunity across different sectors. Volume, types, skill and quality of employment opportunities will be significantly changed (Fig. 9.1). These changes may affect the level and distribution of income. According to research, half of the global workforce in different sectors like agriculture, forestry, power, manufacturing and transport will face major changes (LEDS GP, 2016). So, there will be a significant change in the labour market. Solid waste management as part of low carbon development will attract good investment, thus can have a good potentiality for creation of jobs. As for example, Bangladesh has the potential to create over 200,000 jobs and livelihoods (Gouldson et al., 2018). As per the commitment of Paris Agreement (2015), India will increase renewable energy generation capacity up to 450 gigawatt (GW). So, the investment in renewable energy sector will create lots of job opportunities. Conservation of ecosystem: Different strategies of low carbon development, will be able to combat the adverse effects of climate change. So, these strategies ultimately help to protect biodiversity, forest and aquatic bodies thus conserving the ecosystem. Energy security: Capture of methane gas from waste management plant will significantly reduce the emission of GHGs from nature. This methane can be used as a source of fuel for generation of electricity. Use of modern technology to increase energy efficiency ultimately reduce the wastage of electricity, thus LCE will ultimately improve the energy security across the country.

9.7 Policy Recommendation for Low-Carbon Economy The potential for making rapid cuts in carbon emissions can be achieved if governments improve the following policy areas: Avoid weakening of environmental policies: Although weakening of climate policies helps for better business environment as well as improves benefits, weakening climate policies enhances uncertainty for firms for example electricity production firms, discouraging them from investment and job creation. Therefore, Govt. should avoid weakening of environmental policies. Cut fossil fuel subsidies and strengthen carbon pricing: Govt. should cut fossil fuel subsidies so that people moved to renewable sources. Investment without price signals is not sufficient to achieve low-carbon technologies vis-à-vis low-carbon

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Fig. 9.1 Economics and carbon emission. Source Adapted from Gouldson et al. (2018), an open source

economy, while carbon prices can provide incentives to ailing firms during recovery phase. In addition to this, phasing out tax expenditures and fossil fuel subsidies reduce the pressures on public finances during recovery phase. Therefore, need gradual rising of carbon price to strengthen price signaling both for producers and consumers. Similarly, more actions through regulations and standards need to be taken to complement carbon pricing in driving the transition to introduce and bring out realistic carbon taxes. Further, there need complementary measures to ensure adequate compensatory spending to avoid unfair burden and political acceptability of carbon pricing. Help firms to manage liquidity problems: Direct financial support to ailing companies helps governments to steer investment toward low-carbon production modes and emissions reductions, instead of indirect support like subsidies. Further, efficiency improvement can also help to ensure future viability in a low-carbon world. Apart from this cost-effectiveness of emission-reduction measures of these companies should be given much concern. Direct technological support to firms for environmental improvements: Technological support by the Govt. is very much needed to actively manage and soften the

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transition for the firms which are in recovery phase from fossil fuels to low-carbon technologies. This will help in the recovery phase of that company for setting incentives and adjusting investors’ expectations. Further, it also ensures firm’s long-term viability and competitiveness in a low-carbon economy. Remove barriers to invest in green economy: Generally, fossil fuel underpricing serves as a barrier to investment in energy efficiency and renewable energy sources. Because pollution cost is not accurately priced, as a result, fossil fuel projects appear more competitive than clean infrastructure projects. Apart from these market and regulatory arrangements, unpredictable policy and regulatory environments, high financing costs, and barriers to international trade and investment act as a constraint for low-carbon economy development. These barriers are to be tackled very conveniently on an urgent basis for green investment. Behavioural improvement and support: Behavioural improvement play a vital role for low-carbon economy during transition of a firm changes. This can be achieved through facilitating teleworking, rolling out high-speed broadband, etc. Investing in low-carbon infrastructure: Public investment and infrastructure spending should be based on cost–benefit analysis and cost-effective projects that are financially viable and have a strong public-good component. Public investment and infrastructure spending can help to develop low-carbon economy through investment in different sectors like power system (e.g., smart grids, energy storage, long-distance and cross-border power transmissions), charging stations for electric or hybrid vehicles, public transport infrastructure, energy efficient retrofitting of buildings, carbon capture facilities, and renewable energy deployment. Adoption of advance low-carbon projects for recovery phase: Projects in advance need to be submitted for evaluation for crisis phase based on job gains and emissions intensities. Evaluation of environmental and economic impacts of green policy packages using quantifiable metrics will help to design more effective policies. Green stimulus packages to support during long-term recovery: Green stimulus recovery packages help job creation, reignite growth and resilience during scarce government funds and act as an urgent policy priority for longer-term horizon. It includes the following agendas: . . . . . . .

Investment support with long-term carbon pricing Supporting feed-in tariffs and production tax credits Investment in energy efficient building and retrofitting Providing financing to businesses for developing emerging technologies Policy design as per domestic settings (talents, skills, firms and infrastructure) Impacts of green stimulus policies need to be assessed Governments should evaluate (ex-ante and ex-post) green stimulus packages

Maintaining government support for innovation and start-ups: Development of low-carbon technologies is very much needed to bring us closer to a low-carbon world. This can be achieved through research and development with public sector support. In particular, the public support for private R&D might be in the form of grants, tax credits or innovation prizes along with demand-side policies (OECD, 2011). Apart from public support, government should continuously support research

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and development to develop new low-carbon technologies and provide different start-ups from time to time such as recovery stimulus per se, job- and wage-support policies, etc. In addition to basic research, support for deployment and commercial demonstration is also needed for achieving market scale through risk-sharing between private and public sectors. Align policies across economy and support climate finance: Tackling climate change requires transformative domestic policies based on international trust and co-operation. This can be achieved by leaving behind fossil fuels, but this simultaneously cuts every economical aspect. In addition to this, effective tracking progress is very essential to provide a clear about whether carbon-pricing instruments and other policies are to be implemented or not to address greenhouse gas emissions.

9.8 Conclusions and Future Perspectives Adopting low carbon economy is the only way to deal with conflict between high CO2 emission i.e., climate change and rapid economic growth of India. Apart from these developing low-carbon energy technology, technological innovation, transforming economic growth and social consuming model could play crucial roles. Ghosh et al. (2014) in their study have analyzed the causal relationship between GDP, CO2 emissions and energy consumption of Bangladesh. By the application of time series data during the period 1972–2011, the study revealed that CO2 emissions have a negative and insignificant impact on the economic growth of their country. On the other hand, energy consumption has a positive influence on economic growth. In addition to this, collaboration and stakeholder involvement from different fields including government, academic, industry and civil society is essential to find out the best policies to promote environmental sustainability and to meet poverty reduction of India’s large underserved population. Saidi and Sami (2015) in their study assessed the relationship between economic growth and emissions of 58 countries using the variables like financial development, population, labour and capital, energy consumption, CO2 and GDP. The net outcome derived from the study indicated that financial development has a significant positive impact on the energy consumption of selected countries. The study also revealed that economic growth and CO2 emissions have positively influenced the energy consumption. However, the path to low carbon economy in India is regulated by a set of challenges. Therefore, India should identify the importance of technological innovations to implement low carbon growth-strategies. Policy implications in this regard can go for a long way in enabling Low Carbon Economy. This can be achieved by implementing some stringent measures like GDP should not be compromised by carbon emission, more dependence on renewable energy and less dependence on fossil fuels, maintaining equilibrium between carbon emission and GDP. Misra (2019) has used the Granger causality test to check for the existence of unidirectional and bi-directional causality among the variables. The empirical findings of the Granger causality test revealed that there exists a unidirectional causality running from energy consumption and GDP to CO2 emissions. By using

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auto-regressive distributed lag (ADRL) bound test the study found the existence of a long-run relationship between the variables. Overall, India can substantially increase current GDP rates if access to renewable energy is improved. Acknowledgements Authors would like to thank Dept. of Environmental Science of Sidho Kanho Birsha University, The University of Burdwan, Sukanta Mahavidyalaya, and Department of Economics of University of Gour Banga for allowing working from home during lock-down condition.

References Burtraw, D., & Shobe, B. (2009). State and local climate policy under a national emissions floor. Proceedings of Climate Change Policy: Insights from the U.S. and Europe Paris, held 23-24 March 2009 Carbon Disclosure Project. (2010). Carbon disclosure project 2010: India 200 report. British High Commission, New Delhi Census of India, Government of India, (2011). Chandrasekar, B., & Kandpal, T. C. (2007). An opinion survey-based assessment of renewable energy technology development in India. Renewable and Sustainable Energy Reviews, 11(4), 688–701. Economic survey of India 2005–2006, Ministry of Finance, Government of India, New Delhi (2006). http://www.mycii.org/library/Newarrivals/0602.htm#CII%20PUBLICATIONS Ghosh, C. B, J. K Alam & Md. Osmani (2014). Economic growth, CO2 Emissions and Energy Consumption: The Case of Bangladesh. International Journal of Business and Economic Research, 3(6), pp. 220-27 Ghosh, D., Shukla, P. R., Garg, A., & Ramana, P. V. (2002). Renewable energy technologies for Indian power sector: Mitigation potential and operational strategies. Renewable and Sustainable Energy Reviews, VI(6), 481–512 GIZ. (2014). Carbon market roadmap for India- looking back on cdm and looking ahead. Deutsche Gesellschaftfür Internationale Zusammenarbeit (GIZ), B5/2, Safdarjung Enclave, New Delhi, India. www.giz.de Gouldson, A., Sudmant, A., Khreis, H., & Papargyropoulou E. (2018). The economic and social benefits of low-carbon cities: a systematic review of the evidence. Coalition for Urban Transitions. London and Washington, DC.: http://newclimateeconomy.net/content/cities-workingpapers International Energy Agency (IEA) Statistics. (2010). CO2 emissions from fuel combustion highlights 2010 International energy agency, Paris, France, (2010) Jörgensen, K. (2011). Climate initiatives at the subnational level of the Indian states and their interplay with federal policies. In: Proceedings of 2011 ISA Annual Convention, held 16-19 March 2011, Montreal, Canada Kaswan, A. (2007). The domestic response to global climate change: What role for federal, state, and litigation initiatives? University of San Francisco Law Review, 42(1), 2010–2110. LEDS GP. (2016). Create green jobs to realize the benefits of low emission development. Low Emission Development Strategies Global Partnership (LEDS GP). Retrieved 8 July2016 Misra, K. (2019). The Relationship Between Economic Growth and Carbon Emissions in India, Working Paper Number 447, The Institute for Social and Economic Change, Bangalore

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MoEF. (2010a). Climate change and India: a 4X4 assessment—a sectoral and regional analysis for 2030s. Indian Network for Climate Change Assessment (INCCA), Ministry of Environment and Forests, Government of India MoEF. (2010b). India’s forests and REDD+. Ministry of environment and forests, government of India OECD. (2011). Demand-side innovation policies. OECD Publishing. https://doi.org/10.1787/978 9264098886-en Ramachandra, T. V., & Shwetmala,. (2012). Decentralized carbon footprint analysis for opting climate change mitigation strategies in India. Renewable and Sustainable Energy Reviews, 16(8), 5820–5833. Saidi, K., & Sami, H. (2015). The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Reports, 1, 62–70. Sharma, V., & Chaudhry, S. (2015). India’s developmental strategy under the lowcarbon economy. International Journal of Science and Research (IJSR), 4(4), 911–917. TERI (2015). Sub-national actions on low carbon development in India. Discussion Paper. Supported by shakti sustainable energy foundation. New Delhi: The Energy and Resources Institute UNDP (2010). Down to Earth: A territorial approach to climate change, low carbon and climate resilient strategies at the sub-national level. Retrieved from: http://www.nrg4sd.org/sites/ default/files/default/files/content/public/29climatechange/background/tacc/down_to_earth_d onor_proposalversion_1_mars_2010.pdf World Bank (2008). Scaling up carbon finance in India” background paper on “India: Strategies for Low Carbon Growth”. The World Bank WSP (2010). Resource guide for Indian business: low carbon investment in India. WSP International management consulting Ltd

Chapter 10

Conservation Capital Investments and Policies in the Global Construction Industry Begum Sertyesilisik and Egemen Sertyesilisik

10.1 Introduction Global climate change problem has adversely affected over 500 million people mostly in the third world countries (Zhou et al., 2020). Furthermore, climate change directly or indirectly affects economic activities (Sun et al., 2020). Industrialization and urbanization lead to extreme consumption of mineral resources and accumulation of GHGs (greenhouse gases) resulting in extreme weather conditions globally (IMF, 2017; IPCC, 2014; Sun et al., 2020). Climate change problem has been fostered by the environment footprint of the construction industry (CI). CI causes air and drinking water pollution as well as climate change and landfill wastes (Gocontractor, 2020; Mojumder & Singh, 2021). Cities, as hub of buildings, have significant environmental footprint affecting conservation capital adversely. Zhang et al. (2021)’s research revealed negative correlation of the urbanization ratio with CO2 emissions. The World Bank indicated that cities obtain 72% of their demand for energy from fossil fuels and consume 70% of the renewable energy generated (Li et al., 2021). While 15% of the global population were living in cities in 1900, this percentage increased to 50% in 2008 whereas it is expected to increase to 70% by 2050 (Hurlimann et al., 2018; UNHABITAT, 2009). Effective conservation capital investments and policies in the CI are needed for preserving and regenerating natural and conservation capital in the world despite this intensive urbanization. Even if buildings are vulnerable to climate change, they cause significant amount of GHG emissions further fostering climate change (Hurlimann et al., 2018). Buildings consume approximately 30% of final B. Sertyesilisik (B) Faculty of Architecture, Istanbul University, Istanbul, Turkey e-mail: [email protected] E. Sertyesilisik Gozuyilmaz Engineering and Marine Industries Ltd, Izmir, Turkey e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_10

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energy and majority of electricity (more than 55%) globally (IEA, 2019). Buildings caused 9% of CO2 emissions in G20 countries in 2013 (OECD, 2017). The developments in energy efficiency cannot meet the increase in demand for energy in buildings (IEA, 2016; OECD, 2017). As demand for electricity in buildings increased more than the improvements in the CO2 intensity of power industry since 2000, buildings’ CO2 emissions continue to increase (IEA, 2019). CO2 emissions from buildings have been increased especially due to widespread of air-conditioner and fossil fuel-based assets usage as well as the need for effective policies for energy-efficiency and investments in sustainable buildings (IEA, 2020). World Green Building Council aims to achieve significant amount of CO2 emissions reduction in CI and net-zero emissions buildings by 2050 (Ahmed et al., n.d.; World Green Building Council, 2018). Sustainable and environment-friendly CI, which is respectful to the conservation capital and which supports conservation capital, is vital for the conservation capital protection. For this reason, effective conservation capital investments and policies are vital for reducing CI’s environmental footprint so that global sustainable development can be achieved. For this reason, this chapter aims to investigate conservation capital investments and policies in the global CI.

10.2 Main Drivers for Conservation Capital Investment and Policies in the CI There are many drivers for conservation capital investment and policies in the CI. These drivers can foster establishment and implementation of effective conservation capital investment and policies in the CI. They can act as motivator factors for all CI stakeholders to establish, achieve, and comply with effective conservation capital policies for the CI. Conservation capital investment in the CI in terms of sustainable buildings and energy efficiency investments can support countries’ economies. For example, sustainable buildings have the capacity to enable $1 trillion saving (IEA, 2019). Furthermore, energy efficiency investments can enable economic benefits such as reduced long-term energy costs and increased job opportunities in the CI (Capozza & Samson, 2019). Additionally, Farrell and Beer (2019) emphasised surplus capital opportunity to support ecological economic production. Furthermore, according to recent adaptation need estimations, these investments can provide high benefits (Bhandary et al., 2021; Global Commission on Adaptation, 2019). Similarly, construction companies can get benefit from the sustainability market which can support conservation capital protection. For example, according to Zhang et al. (2016)’s research on housing market in China, people prefer green apartments in case they are accurately informed about apartments’ environmental information/ properties (Zhang et al., 2016, as cited in Gao & Tian, 2020). Effective conservation capital investment and policies in the CI can support protection of water resources as well. Water is an important resource affecting economy and nature. Zhao et al. (2021)’s research revealed important role of water in employment and economy

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indicating main consequences of tolerable water constraint (e.g., economic loss, unemployment). Furthermore, green investments can support financial performance (Chen & Ma, 2021). Effective and successful conservation capital investments and policies in the CI can be considered as important pillars for global peace. As sustainability is the key for global peace (Sertyesilisik, 2018), conservation of natural capital through effective conservation capital investments and policies is a vital enabler and supporter for the global peace. For this reason, effective conservation capital investments and policies in the CI are important not only for the nature but also for the entire humanity. Global peace relies on sustainability (Sertyesilisik, 2018).

10.3 Recommendations for Enhancing Effectiveness of Conservation Capital Investment and Policies in the CI Conservation capital investment and policies in the CI need to focus on energy, water and carbon pillars considering their technical aspects so that they can support enhancement in energy efficiency, reduction in energy demand, wide spread of renewable energy usage, reduction in water/consumed, increase in the water efficiency as well as innovation on reducing energy, water and carbon footprint of the CI. Conservation capital investment and policies in the CI need to support and encourage energy efficiency and relevant investments. Energy efficiency in the CI plays a vital role in mitigating adverse effects of CI to the environment and natural capital. Significant amount of money is spent/allocated for energy related issues in the CI. In 2019, the total global investments of $5.7 trillion in the building sector was allocated to building construction and energy-related fields having approximately 60% and 40% share respectively (IEA, 2020). Furthermore, money needs to be allocated for enhancing building envelopes’ energy efficiency improvements and retrofitting. Building envelopes’ energy efficiency improvements is the largest component with respect to energy related investments in building (IEA, 2020). There is a need for building retrofitting to enhance buildings’ energy efficiency. As 75–90% of buildings in developed countries are estimated to be in service by 2050 and as majority of them do not satisfy the current energy efficiency standards, 30% of these buildings need to be retrofitted by 2030 (IEA, 2017; OECD, 2017). Energy efficiency improving technologies and their innovations can support CI’s contribution to the conservation capital. Technological progress increased energy efficiency about 7.1% annually in the period of 1997–2014 (Zhu et al., 2019). Effective and timely policies and regulations on energy efficiency and effectiveness are vital for conservation capital policies in the CI. Effective energy policies are vital as the actions towards high-performance construction and renovations should not be delayed as any ten-year period delay in actions can be costly and cause emissions and energy loss (IEA, 2019).

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While preparing energy related conservation capital policies for the CI research studies such as Zheng and Lin (2020), Zhang et al. (2020), Ji et al. (2019) and Zhou et al. (2020), Li (2020) and Li and Xu (2020) can be considered as valuable inputs. Energy-saving policies need to be designed and applied considering energy-using entities’ marginal energy-saving costs (Zheng & Lin, 2020). Furthermore, Zhang et al. (2020)’s research on China highlighted significance of electricity conservation strategies throughout the supply chain. Similarly, Ji et al. (2019) emphasised the importance of considering indirect energy consumption related to the CI and designing energy conservation policies for the CI’s upstream sectors (e.g., steel). Additionally, Zhou et al. (2020), Li (2020) and Li and Xu (2020) focused on energy conservation and emission reduction policies. Countries need to give priority/importance to the energy intensity of their economy and of their CI’s energy intensity while preparing their conservation capital policies for the CI. Energy intensity is a target for economic and social development (Chen et al., 2019; Zhu et al., 2019). Technology aspect of energy related conservation capital investments and policies need to be considered for effective conservation capital investments and policies in the CI. CI needs to get benefit from technology to enhance its contribution to the conservation capital. Even if solution to the CI’s inefficiency and low productivity problems can be supported through CI’s adaptation to the digital age, McKinsey Global Institute (2017)’s index points out that CI is among the least digitised sectors in the world (McKinsey Global Institute, 2017, as cited in Pessoa et al., 2021). Furthermore, widespread usage of prefabricated systems is considered as another important factor supporting conservation capital (e.g., Gao & Tian, 2020; Zhu et al., 2019). Gao and Tian (2020)’s research revealed that policies supporting prefabrication can contribute to the labour productivity and reduction materials used. Furthermore, Zhu et al. (2019) recommended governments to “set the minimum prefabrication rate for building construction”. As construction activities contribute to the environment related problems, national development plans and short-run industrial policies of China’s government encourage modern prefabricated construction (Gao & Tian, 2020). Additionally, widespread use of 3D printing technology (Pessoa et al., 2021; Salet & Wolfs, 2016) and electrification of construction vehicles (Zhu et al., 2019) can be considered as other important factors supporting conservation capital. Construction 3D printing provides many benefits as it enables production of complex shapes at lower cost (Pessoa et al., 2021; Salet & Wolfs, 2016). Furthermore, Zhu et al. (2019) recommended usage of electric-powered construction machines to reduce energy consumption and CO2 emissions. For this reason, conservation capital investments in the CI need to include investments in energy efficient machines and equipment at the site level as well. Energy related conservation capital investments and policies in the CI need to cover the innovation aspect as well. CI still depends on the labour force and remains as a low-tech industry due to the lack of/insufficient innovation (Harty, 2008; Pessoa et al., 2021). Innovation in construction material industry is vital for natural capital protection and conservation. Eco-friendly construction materials and their eco-friendly production play an important role for conservation capital.

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For example, Czop and Lazniewska-Piekarczyk (2020) introduced an environmentfriendly method for the cement industry to reduce the CO2 emissions through substitution of 30% of cement by the slag (Czop and Lazniewska-Piekarczyk, 2020, as cited in Al-Hamrani et al., 2021). Conservation capital policies in the CI need to encourage CI to improve its managerial and production processes’ efficiency. Climate change problem can be solved by the reduction in pollution and increase in the efficiency in resource utilization (Saez-Martinez et al., 2016; Zhou et al., 2020). Efficiency and sustainability of the CI should be improved through increased interoperability and productivity to reduce its adverse impact to climate change (Pessoa et al., 2021). Furthermore, McKinsey & Company’s research revealed that majority (60%) of the executives are in the opinion that integration of climate change caused risks into the company’s strategy are needed whereas approximately 50% of respondents expressed that these risks were considered in their investment plans (Gordon, 2008; Sun et al., 2020). Conservation capital investments and policies in the CI need to cover the construction waste management topic. Governments should establish practical solution related with construction waste which needs to be converted into products usable for constructions (Al-Hamrani et al., 2021). Energy related conservation capital investments and policies in the CI need to cover energy generation source’s environment-friendliness. Energy generation resource affects CO2 emission level. For example, substitution of natural gas by biomethane can support decarbonisation of heating and cooling (Herbes et al., 2021). Carbon emission reduction related conservation capital investments and policies in the CI need to cover the technology aspect. Zhang et al. (2021)’s research revealed the relationship between the technology level and CO2 emissions. Furthermore, Qi et al. (2021)’s research revealed low-carbon technological progress’s role in fostering industries’ low-carbon international competitiveness. Carbon related conservation capital investments and policies in the CI can focus on CI’s carbon emission. Li et al. (2021) indicated that their framework for the prediction of peak carbon emission of CI can support policy makers to define an effective low-carbon development roadmap. Conservation capital investments and policies in the CI need to cover water efficiency and management topic. Sustainable water management is a SDG (Zhao et al., 2021). CI can benchmark from other industries to improve its water related conservation capital investments and policies. For example, Saviolidis et al. (2020) emphasised the importance of blue growth and indicated the requirements of the fishing sector. Benchmarking from Saviolidis et al. (2020), CI needs to support water resources and its activities and outputs need to be water efficient. Green finance and green loan strategies need to be considered in the conservation capital investments and policies in the CI. UN Environment (2017) emphasised importance of green financing and indicated that in Hangzhou Summit 2016 seven financial sector options (related with strategic policy; green bond markets; international collaboration; knowledge-sharing, etc.), which can be voluntarily implemented by countries based on their national circumstances, have been determined. Similarly, Bhandary, et al. (2021) emphasised finance’s role in achieving sustainable and climate-resilient economy. Furthermore, Jin et al. (2021) indicated the role of and

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importance of green finance for an energy conservation and environmental protection industry. Similarly, Odeku (2017) emphasised the importance of acceleration in banks’ credits and loans so that green projects can be funded and investments for decarbonisation of the economy and achieving sustainable development.

10.4 Discussion Conservation capital investment and policies in the global CI can act as a leverage and multiplier factor for achieving global sustainability and UN’s SDGs (Fig. 10.1). These conservation capital investment and policies need to be supported by coherent strategies, regulations, standards and targets for environment-friendly built environment and CI. These conservation capital investment and policies in the CI need to be considered as an important pillar for green macro economy globally. They can further affect green micro economy which can be in coherence with the green macro economy. Furthermore, these investments and policies need to be prepared and updated as an integral part of countries’ sustainable development policies and strategic plans. CIs’ performance needs to be assessed and monitored with respect to the achievement level of these conservation capital investments as well as effectiveness and success of these conservation capital policies. Conservation capital investments and policies in the CI need to cover energy, carbon and water pillars as well as their relationship (Fig. 10.2). These investments and policies need to support CI with respect to enhancement in energy efficiency, reduction in energy demand, wide spread of renewable energy usage, reduction in water consumed, increase in the water efficiency as well as innovation on reducing CI’s outputs’ embodied energy, water and carbon and on CI’s environmental footprint. Furthermore, green loan and green finance as well as technical aspects are among the main facilitators for achieving the conservation capital investments and policies in the CI (Fig. 10.2). CI’s and construction companies’ performances with respect to their compliance with the conservation capital policies and their investments in conservation capital can be assessed and certified. This conservation capital protection level certification system can be developed to assess construction companies’ performance on conservation capital protection. Companies in the CI can be required to get a certain level of this certificate, as a compulsory requirement, for applying to the bid in the public

CI related conservation capital investment and policies

Green macro economy at the global level

Sustainable development

Fig. 10.1 CI related conservation capital investment and policies as an important pillar for green macro economy and sustainable development. Source Authors’ elaborations

10 Conservation Capital Investments and Policies in the Global … Fig. 10.2 Main pillars and facilitators of the conservation capital investments and policies in the CI. Source Authors’ elaborations

Main pillars . Energy . Water . Carbon

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Main facilitators . Technology . Green finance and green loan

entities’ tenders. Furthermore, subsidies or certain level of tax exemptions can be enabled to construction companies performing well on conservation capital protection so that they can invest more in their companies’ technologies and infrastructure to support conservation capital protection. Construction companies’ strategic plans and investments as well as green micro economy need to be aligned with the CI’s conservation capital investment and policies. Conservation capital protection can be integrated into the construction companies’ corporate culture as they need to respect and act respectfully to the conservation capital. Construction companies can invest more on conservation capital protection activities, technologies in case they experience that their high performance of conservation capital protection can support their competitiveness. Conservation capital investment in CI can be profitable for all stakeholders in the CI and entire humanity globally. Conservation capital investment of construction companies can support their competitiveness (Fig. 10.3) as they can contribute to their corporate image, brand identity, value creation and resource efficiency. Furthermore, CI companies and professionals can further support conservation capital investments and policies through value creation by design for conservation capital. For example, architects can invent and find new/innovative effective ways for enhancing conservation capital protection and investment (e.g., changing the building designs considering potentials of floating farms, urban farming, vertical gardens, algae bioreactor, bioenergy façade; smart, sustainable and resilient greenhouses, etc.). Construction companies’ compliance with conservation capital policies’ requirements can provide them benefits (e.g., competitiveness, financial benefits, etc.) which can further motivate them to perform conservation capital investments and relevant research and development activities. CI’s conservation capital related innovation investments and policies (e.g., R&D on energy efficiency, renewable energy technologies, sustainable building materials etc.) need to be considered as an important pillar for the conservation capital investments and policies of the CI. Furthermore, CI’s conservation capital investments and policies need to be aligned with UN SDGs. Additionally, green finance and loans can support green infrastructure in the CI needed for conservation capital investment. For this reason, green finance and loans can be considered as an important enabler for achieving conservation capital investment and policies in the CI. Considering the technical aspects of effective conservation capital investments and policies in the CI, these policies can be considered as ‘techno-policies’. Addressing

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Fig. 10.3 Relationship among construction companies’ conservation capital protection performance and their competitiveness. Source Authors’ elaborations

construction companies' image

construction companies' conservation capital protection performance construction companies' resource efficiency

construction companies' brand value

technical characteristics and properties of these policies can support the applicability, accuracy and effectiveness of these policies and relevant investments. For this reason, these ‘techno-policies’ in the CI need to be prepared with the participation and collaboration of policy-makers and CI professionals having technical background, education, experience, and specialisation. Furthermore, other stakeholders’ contribution to these ‘techno-policies’ proposals so that they can share their experience on these proposals can further support effectiveness of these ‘techno-policies’. Effectiveness of conservation capital investments and policies in the CI can be further supported by the financial arrangements (e.g., green finance, green loan). Effective conservation capital investments and policies in the global CI can become strategically more and more important in the near-future as there is only one way forward for the humanity, and that is the way to sustainability so that humanity can sustain itself.

10.5 Conclusion This chapter emphasised the importance of conservation capital investments and policies in the global CI. Effective conservation capital investments in the CI can contribute to conservation capital protection, to reduce its environmental footprint and to minimize/eliminate CI’s harms to the environment. These effective investments in the CI can contribute to the conservation of nature. Conservation capital investments in the CI can be influenced and supported by the relevant effective conservation capital policies.

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Conservation capital investment and policies in the CI need to consider main pillars i.e., energy, carbon, and water pillars due to their effects to the conservation capital. Conservation capital policies covering these pillars can support reduction of the CI’s environmental footprint as well as its outputs’ embodied energy, carbon and water. Technical aspects of these main pillars (i.e., energy, carbon and water) need to be considered in the design of the conservation capital policies in the CI and in investment plans. For this reason, as these conservation capital policies need to be prepared considering their technical aspects, these conservation capital policies can be considered and defined as ‘techno-policies’ which can be prepared more effectively through collaboration of policy-makers with CI professionals enabling other stakeholders’ to share their opinions and experiences on these ‘tech-policies’ proposals. Furthermore, green finance and green loan can act as supporting tools or enablers of conservation capital investments in the CI. For this reason, conservation capital policies in the CI need to be designed considering technical and financial aspects so that their implementation can be enhanced and their applicability, accuracy and effectiveness can be supported. Furthermore, technology, green finance and green loan can act as facilitators for effective conservation capital investments and policies in the CI. Conservation capital investment and policies in the CI can be considered as an important pillar for green micro economy, green macro economy and global sustainable development. Furthermore, they can contribute to the achievement of UN SDGs. CI’s and construction companies’ conservation capital protection performance can contribute to their competitiveness especially through enhanced resource efficiency in their operations, image and brand value. Their performance on conservation capital protection and investments can be tracked and assessed via a certification which can be prepared and proposed as the conservation capital protection level certification system. Construction companies’ willingness to invest in conservation capital and environment-friendly technologies can be increased in case they can observe contribution of conservation capital investments to their competitiveness. Main drivers or motivator factors for conservation capital investments and policies in the CI can encourage all CI stakeholders to establish, achieve, contribute to and comply with effective conservation capital policies for the CI. These drivers can further motivate them to become and act more respectfully to the conservation capital. Effective conservation capital investments and policies in the CI can support and act as a significant tool for supporting global sustainable development. Further research studies are recommended to be carried out for the establishment of the proposed conservation capital protection level certification system at the company and CI levels.

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Chapter 11

Environmental Consequences of the Adoption of Electric Vehicle in Leading World Economies with Special Reference to India Vani Kanojia, Megha Jain, Imran Hussain , and Ramesh Chandra Das

11.1 Introduction Very aptly stated by Wangari Maathai: We have the responsibility to protect the rights of generations, of all species, that cannot speak for themselves today. The global climate change requires that we ask no less of our leaders, or ourselves.

The global community suffers from the pollution generated by the vehicles at their end uses. Thousands tonnes of greenhouse gases are emitted into the open air as a result of these end-use pollutions. The policy-makers at the global level are therefore focusing on shifting the uses of the traditional vehicles (i.e., internal combustion engine (ICE) cars) to electric vehicles in order to eliminate all direct emissions of end uses (VIrTA Blog, 2022). The venture will benefit hugely to large cities with large plying of vehicles in terms of good air quality. World Health Organization (WHO) reports that open-air pollution makes a cause to about 4.2 million premature deaths annually which implies that every 20th fatality is at least partially due to outdoor air pollution. Transport sector is accountable for the large share in air pollution. For example, one fifth of Europe’s greenhouse gases (GHGs) are from the in road transportation sector. Therefore, it is at least deserved from the developed countries to venture for electric mode of vehicles in the transport sector. It is a renewable V. Kanojia University of Delhi, New Delhi, India M. Jain Faculty of Management Studies, Daulat Ram College, University of Delhi, New Delhi, India I. Hussain (B) · R. C. Das Department of Economics, Vidyasagar University, Midnapore 721102, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_11

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energy-supported initiative which will avoid combustion of the fossil fuels which are mainly responsible for air pollution. The manufacturing processes of average electric vehicle (EV) affects about 15% more emissions than the manufacturing of a gasoline car. Though, lifetime discharging emission of EVs are on an average 51 per cent lower than the gasoline cars (World Economic Forum, 2018). Ample evidence is there that confirm the growth of vehicles with the increase in population. World population is expanded to expand from 6 to 10 bn (Chan, 2002). In the next 50 years, vehicles’ size is simultaneously to grow from 700 mn to 2.5 bn. India is ranked third in global greenhouse gas (GHGs) emissions after the USA and China. It is certainly a matter of grave concern as atmospheric pollution level in India has already crossed the dangerous level. In the given framework, the importance and use of renewable energy could be the only inevitable measure to help preventing the harmful historical incidence of global warming. Unfortunately, rising urbanization has provided undue impetus to the demand for vehicles, especially in developing nations like India. This will further intensify the level of local air pollution, carbon emissions, and congestion (Rao et al., 2013; Woodcock et al., 2009). As far as India is concerned, it ranks second in energy-related greenhouse gas (GHG) emissions with sectoral carbon emissions from road transport of a share of 87% approx (Osho et al., 2005). In order to address this problem, the only way is to switch to the electric vehicle mode. In the Gujarat Energy Research & Management Institute (GERMI) White Paper, Bhatt and Magal try to examine the financial viability of an EV in India compared with an Internal Combustion Engine Vehicle (ICEV) from the same segment. From their report, it is obtained that, In India, in January 2013, National Electric Mobility Mission Plan (NEMMP) 2020 was launched by prime minister with an aim to enhance national energy security, mitigate adverse environmental impacts from road transport vehicles, and boost domestic manufacturing capabilities for electric vehicles. Prior to NEMMP’s release, the Ministry of New and Renewable Energy (MNRE) offered an incentive scheme to push sales of EVs in India but the scheme was effective from November 11, 2010, to March 31, 2012 termination of the scheme resulted in the steep downfall of the EV market. After 2 months of its termination, close to 33% of dealers reverted to their earlier business and more than 20% closed their shutters. A similar trend was observed in the sales of India’s only EV car manufacturer— Mahindra Reva; a number of manufactured units fell by 40% after the termination of subsidy. So, there is a need for stable policy and incentives if the government wants to establish a vibrant market of EV in India. Figure 11.1 shows the share of electric vehicle sales for the top 20 countries in 2018. The scenario of the sales and use of electric vehicles is dismal with skewed distribution at the global level. There is a very few countries in the so-called developed zone that have some shares of sale in the EV, but the so-called less developed or developing economies are still away from the concern of not using EV. The figure shows that Norway tops the list in the market for selling EV, according to a report by the World Economic Forum (WEF). The country is with the experience that about half of the sales of all new passenger cars in 2018 were having either electric or hybrid vehicles. The countries to follow Norway are, respectively, Iceland, Sweden,

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0.07% 0.90% 1.57% 1.97% 2.05% 2.09% 2.10% 2.14% 2.22% 2.43% 2.53% 2.54% 3.18% 3.44% 4.44% 4.74% 6.69% 8.01%

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40.00%

50.00%

60.00%

Fig. 11.1 Share of EVs across top 20 countries in 2018. Source Authors’ compilations from the Energy News of the economic times and international energy agency (IEA)-open sources data

the Netherlands, Finland, and China with the corresponding shares of sales are 19, 8, 7, 4.7, and 4.4%. There are huge disparities among the leading countries in the sales of the EV. But if we look at the developing countries like India we see a very gloomy picture in this respect, the EV sales market share is very small with 0.07% in that year. To find the possible impact of this transformation to EV mode of transport on the quality of environment the shares of major pollutants by the above-listed countries may be correlated. There are three major pollutants from the vehicles of traditional mode which are CO2 , N2 O, and methane. Using the data of the World Bank, the shares of CO2 , N2 O, and methane emissions in the year 2018 are calculated and represented in Table 11.1. The remaining study is structured as follows—Section 11.2 discusses the existing review of literature related to the electric vehicles market in India and across. Section 11.3 lays down the key objective of the study. Data sources are mentioned in Sect. 11.4. Section 11.5 entails the research methodology adopted followed by qualitative analysis in Sect. 11.6. The study concludes with key policy remarks in Sect. 11.7.

142 Table 11.1 Share of CO2 , N2 O, and Methane emission of the respective top-20 EV market sharing countries in 2018

V. Kanojia et al.

Countries

Share of CO2 (%)

Share of N2 O (%)

Share of methane (%)

Norway

0.11

0.11

0.06

Iceland

0.01

0.01

0.01

Sweden

0.11

0.16

0.06

Netherlands

0.44

0.26

0.21

Finland

0.13

0.16

0.05

30.30

18.05

15.15

Portugal

0.15

0.10

0.14

Switzerland

0.11

0.07

0.06

Austria

0.19

0.12

0.08

United Kingdom

1.05

0.95

0.63

Belgium

0.27

0.15

0.10

Canada

1.69

1.44

1.15

Denmark

0.10

0.16

0.09

China

France

0.91

1.27

0.71

United States

14.63

8.38

7.62

South Korea

1.85

0.36

0.31

Germany

2.08

1.10

0.65

Ireland

0.11

0.34

0.21

Japan

3.25

0.60

0.26

India

7.15

8.50

8.15

Source Authors’ computations from the World Bank data

11.2 Review of Literature In light of the above perspective, the current study includes eminent literature from Nobel laureates and other renowned theorists who have appended different views on the impacting determinants that could propel the usage the electric vehicles along with key legislative initiatives that could enhance the prospective governance of electric vehicles. Vidhi and Shrivastava (2018) analyzed that pollution emission can be reduced by adopting EVs and only if a high percentage of the electricity mix comes from renewable resources. They recommend to the Indian government to adopt policies that increase the sale of EVs, percentage of renewable energy in the electricity mix, and prevent air pollution caused by battery manufacturing. Guttikunda et al. (2014) present an outline of the emission sources and manage options for better air quality in Indian cities. A particular focus on interventions like urban public transportation facilities and discussed the measures, like public awareness and scientific studies, necessary for building an effective air quality management plan in Indian cities.

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Dhar et al. (2017) introduced model assessment spans of the period 2010–2050 and analyzes future EV demand in India with three scenarios: (i) a “Reference” scenario: continuation of existing EV policies; (ii) “EV policy” scenario: targeted supply-side push policies for EVs, but without the budget constraints; and (iii) “low carbon” scenario: exogenous price for CO2 in line with the global target of 2 ˚C temperature stabilization. In 1834, EV was invented but due to issues in battery and at the same time progression of internal combustion engine vehicles (ICEVs) wiped out an electric vehicle from the scene since 1930 Chan (2002). But now again the world is concerned about the protection of the environment so as an author we need to consider factors which can bring electric vehicles again into limelight for consumer adoption. The study by Jensen et al. (2014) describes that real-life experiences with electric vehicles can impact individual preferences and attitude, they also catered the reasons why customers were not taking electric vehicles as the alternative over the traditional gasoline cars through their market penetration and performance was much higher than vehicles of the early 1990s. They have given their reason through using the methodology consisting of a long panel survey where they interviewed individuals before and after a real-life experience of 3 months of an EV. They finally concluded that preferences can be forecasted and altered by experiences so the skepticism of consumers for electric vehicles like home charging, zero tailpipe emission, limited driving range, etc., can be changed through their first-hand experience and customer wishes to purchase car of their class is important because in stated choice experiment customer who more often preferred small car chosen EV as compared to customers looking for large cars. Other factors are categorized by Li et al. (2017) based on the respective influence on the consumers buying intentions and can be divided into three ways—(i) demographic, (ii) situational, and (iii) psychological. In demographic factors, there are certain further aspects like individual factors including age, gender, education level, occupation, and income. Further Ploz et al. (2014) found that customers engaged in a technical profession are presumably like to adopt BEVs than others. Factor like income also affects consumer preferences. Consumers should have sufficient wealth to purchase BEVs. Secondly, in joined decision making the consumer can be influenced by family members, having more vehicles in consumer’s family he is less likely to purchase BEV because of the limited charging point. In situational category, they included technical features like driving range, refueling time, etc., high purchasing cost of BEVs, and environmental attributes as per Axsen et al. (2012) consumers suspect the BEV’s ability to protect the environment because of the creation of a lot of pollution in the production of BEV’s batteries and electricity. Further government policies and support play a vital role in the adoption of BEVs. Lastly, the psychological factor includes personal experience, emotions, attitudes, societal influence, and symbols. Then there are some factors which can discourage the intention of consumer on this Cherons and Zins (1996) have done exploratory research to assess the relative important factors which are limited range, speed, and a long time for recharge but, according to the author, the biggest fear among customers is power failure owing to a dead battery which compels customers to prefer conventional gasoline car over electric vehicles. Another recent paper by Carley et al. (2013)

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has identified the factors which generally create interest and disinterest among the consumer to purchase EVs using survey method on a group of 2302 drivers in 21 large cities in the U.S. This survey started in 2011 before starting the process of manufacturing and marketing campaigns to know the early impressions and perceptions of consumer intent to purchase electric vehicles. The result of these consumers who have shown interest in PEVs is among those who have highly concerned about the dependence on foreign oil, sensitive towards environment, owners of conventional hybrids, and highly educated. Consumers who intend to purchase PEVs are divided into two categories: early adopters and niche consumers. Early adopters are generally not concerned about saving money or functionality of the car and more concerned about the environment they are among those who already have alternative fuelled vehicles and, on the other hand, niche consumers have very specific needs. Perception of risk which creates disinterest among consumers are safety, space, and esthetics. Environment self-efficacy is one factor which discourages the person which means that one person’s action cannot make any difference Maibach (1993). Some nations chose to use another strategy to make their customers adopt electric vehicles in Norway Mersky et al. (2016) investigated the impact of incentives on per capita EV sales among the municipalities and other Norway regions through collecting the economic data and EV infrastructure data and EV sales data from municipalities and grouped on the basis of vehicle range and its owner. They have used the optimal linear regression for predicting the variables used for sales of per capita EV. Their study has found that the government adopted the method to incentivizing their citizen to adopt the battery EVs like exemption from road tolls, providing charging infrastructure, point of sales incentives, etc., to remove the skepticism for using EVs. There are other strategies also, which have proven to be successful in different countries like the UK, Sweden, France, the US, and Canada, for example, giving tax credits, offering purchase incentives to customers, and rising gasoline prices. In the same direction, Bjerkan et al. (2016) considered the role of incentives which allure customers to adopt BEVs in Norway. Norway has the biggest market share of BEV than any other country because of its incentives for promoting ownership and purchasing of BEVs. They provide financial incentives which bring BEV’s cost at the same level as ICEV like exemption from vehicle registration tax, exemption from VAT (Currently in Norway 25%), BEV’s car owner pays lowest vehicle license fee, exemption from road trolling, free parking on municipal parkings and lastly access to bus lanes. This paper has investigated the role of various incentives in Norway. As per a paper by Seixas et al. (2015), the European Union focuses on two goals that are decreasing greenhouse gas (GHG) emissions and dependency on oil which can be successful adoption of electric vehicles which also requires removing the hindrances like change in energy supply chain, from energy supply to distributed infrastructure and changes in consumer mindset, improvements in grid and battery swapping system feasibility. There are several factors which are contributing to determine the overall competitiveness of electric vehicles like life of batteries and their cost, carbon-neutral biofuel availability, consumer taste and preferences, and improvements in internal combustion engines.

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Marketing becomes an essential tool in today’s world to maintain and create desirable demand for a product so the same can be applied to electric vehicles. Oliver and Rosen (2010) proposed marketing techniques in their paper that can motivate customers and segment people on the basis of their environmental values and environmental self-efficacy. Consumers can be segmented based on their environmental behavior like genuine greens considered environmental activist, not me greens having a strong attitude but no action, go with the flow having the moderate attitude, dream greens with limited green behavior, business first class with less concerned about environmental issues. Another paper by Garling and Thogersen (2001) has focused on how the marketing of electric vehicles can be done. Consumers can be segmented on the basis of their environmental behaviour which is again categorised as genuine greens, not me greens, dream greens etc. The marketing strategy is divided into two phases; firstly, target should be on public organizations, green companies, and multicar households who need a second car and also have technical capabilities. Reason for multicar families is that the disadvantage of EVs can be less considered if they have conventional cars. The second phase deals with single-car households that should not be advertised as the second car by producers but should be advertised on the basis of differential advantages as compared to a conventional car. In a paper by Chan 2002, it depicts the commercialization of all types of EVs. The first issue which arises in promoting EVs is the production of low-cost electric vehicles. A second key issue is the commitment and willingness of producers, industrial partners, public authorities, government, and consumers. The integrations which are successful key for commercialization of EVs are an integration of technical strength, integration of society strength financing interest, policies of government, industry incentives, and academic institutions’ technical support, and apart from this, effective infrastructure and after-sale service are required. Financiers, manufacturers, and consumers cooperate with each other to achieve a win-win situation. The study by Koller et al. (2011) gives insight into how the ecological aspect of usage of electric cars integrates a link between value and loyalty of a customer. According to the authors, the ecological value impacts four value dimensions, functional value, economic value, emotional value, and social value which differently impact loyalty intentions of customers. Another work by Onat et al. (2017) explores the BEV’s suitability in the United States by taking into account factors like potential market share, variations in electric generation profiles by states, social acceptability of technologies, and current government policies. As per reviews, most studies focused on GHG emissions through fossil fuels and nuclear energy but electrification of transports also require a billion gallons of water which in turn can cause a serious problem of water scarcity in states. The authors have chosen three methodological approaches which are Life Cycle Assessment (LCA), Agent-Based Model (ABM), and Data Envelop Analysis (DEA). The study concludes that each state requires to frame its own policies and the federal fund should be allocated to states to strengthen BEV adoption. In the same framework, a study by Axsen et al. (2016) discusses how current pioneers (PEV’s owners) are different from future potential PEV’s buyers. Earlier approaches to PEV’s markets focus on “innovativeness” as a key parameter for

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segmentation which further divides potential buyers into innovators, early adopters, early majority, late majority, and laggards. The authors also divided the population into discrete segments: Pioneers (who currently own PEVs) and Mainstreamers (those who do not own PEVs). They have further divided into potential early mainstream (currently owns conventional petrol car but stated an interest to buy it) and potential late mainstreamers (who have not shown any stated interest in buying PEVs until there will be no changes in policy, cost, cultural norms, and technology). They used the facts and figures from Canadian plug-in electric vehicles study from that, they developed a survey for the population. The conclusion of this study is that pioneers have higher income, education, and middle-aged male. Mainstream respondents have less awareness about PEV’s technology and how these can be refueled and used. This study analyzes consumer’s motivation to encourage them to adopt cleaner environment technologies. The authors divided consumer motivation into two categories, intrinsic motivation and extrinsic motivation, which affect consumer’s behavior (Coad et al. 2009). Intrinsic motivation is an individual act coming from within. So individuals are concerned about the environment out of their behaviour which is guided by “environment morale”, even if there is high cost involved in the process. Extrinsic motivation means behavior described by standard economic theory. In this, individuals base their decision on monetary terms and expected payoffs. Awareness and persuasion of the usefulness of technology and comparing a new technology with former technology so that the relative advantage of new technology will play a substantial role in deciding the adoption of the new product. Central variables which act as guiding factors for consumer’s behavior are status, knowledge, risk preferences, and financial constraints (Roger, 1995). The study by Osho et al. (2005) shows that increasing population increases the demand for crude oil, and the World Energy Outlook states that fossil fuel-based transportation is the second largest source of emission of CO2 in India which is one of the largest automotive markets. Its transportation accounts for one third of the total crude oil consumption and requires cleaner alternative fuel vehicles because it is expected by 2020 that India will import 92% of its fuel which poses a threat to India’s fuel security for future generation use. So, India is required to take crucial measures to decrease the country’s dependence on fossil fuels. Clearly, electric mobility is the alternative solution to this problem.

11.3 Objective The study tries to link the benefits of EVs by reduction of GHG and assessing the status of EVs in different countries and then focusing on the factors which facilitate nations to determine their consumers’ preferences to adopt EVs. Further, it includes its achievements and hindrances that may cause discontent among consumers. The study concludes by identifying the prospective incentives and different policies

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implemented by the governments of different countries to achieve the desired rate of adoption.

11.4 Data Source The present study is based on secondary data. The data relating to the share of EV sales in the global market is collected through Energy News from the economic times and International Energy Agency (IEA) (www.energy.economictimes.indiat imes.com). Considering GHG, the data of carbon dioxide (CO2 —measured in kt), nitrous oxide (N2 O—thousand metric tons of CO2 equivalent), and methane (kt of CO2 equivalent) emissions are collected from the World Bank (www.data.worldb ank.org). Here, CO2 emissions are those stemming from the burning of fossil fuels and manufacturing of cement. These include carbon dioxide produced during the consumption of solid, liquid, and gas fuels and gas flaring.

11.5 Methodology Correlation coefficient is applied to validate the negative relation between the adoption of EVs and GHG. The research design is exploratory in nature. Through synthesizing data from secondary sources, analysis of existing literature on electric vehicle adoption, factors influencing and incentives facilitating the customer to adopt the vehicles has been done. By reviewing the literature, observations are made on the subject matter of electric vehicles from different data sources like JSTOR, Google Scholar, Emerald, Science Direct, and SAGE from 1999 to 2019. Various factors and incentives related to EVs were shown through the analysis of data.

11.6 Findings and Discussion As the finding result shown in Table 11.2, all the pollution emissions (CO2 , N2 O, and methane) are insignificantly and negatively correlated with electric vehicles. The direction of the correlation shows that electrifications in vehicles lead to the reduction in GHG. The present study considered EVs are the substitution tor internal combustion engine (ICE) cars that are responsible for GHG. But the study doesn’t consider the factors which are responsible for GHG, such as manufacturing, agriculture, forestry, etc. This may be the reason behind statistically insignificant result. However, electrification in vehicles is good for the environment; there are some issues that work as a hindrance to the adoption of EVs in India. From the above literature, according to the authors, though India launched the FAME Scheme in 2019

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Table 11.2 Correlation among share of EV, share of CO2 , share of N2 O, and share of Methane emission across top 20 EV market sharing countries in 2018

Correlation

t-statistic

P-value

r 1 = −0.129860906

−0.55566

0.585284

r 2 = −0.144848929

−0.62109

0.542326

r 3 = −0.145112593

−0.62225

0.541580

Note r 1 = correlation between share of EV and share of CO2 emissions, r 2 = correlation between share of EV and share of N2 O, and r 3 = correlation between share of EV and share of methane emissions Source Author’s calculations

to promote sales of EVs, still there are some factors which are creating hindrances in the adoption of electric vehicles in India given below (Fig. 11.2). Lack of infrastructure: According to Goel et al. (2021), India is lacking in providing a charging station for EVs until now. In addition to this infrastructural bottleneck, there are concerns regarding highly expensive battery packs for an EV which works for only few years. These costly batteries are required to be replaced several times in one’s lifetime. Lack of manufacturers: Further, the observation by Goel et al. (2021) shows that the mindset of the manufacturers in India is still away from execution because of the associated risks of selling, raw materials for the batteries of EVs such as lithium,

INFRASTRUCTURE

SUBSIDY SCHEME

FACTORS AFFECTING ADOPTION OF ELECTRIC VEHICLES

MANUFACTURERS

AWARENESS

Fig. 11.2 Flow chart depicting determinants of Electric Vehicle Usage. Source Author’s own representation

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nickel, phosphate and manganese, graphite, and cobalt which are rarely available from the natural sources. China has a huge stock of lithium now which is far away from the capacity of India. Lack of awareness: In India, no one is taking the initiative to make the general public and manufacturers aware of the advantage and usage of EVs. Although there are some financial benefits in the form of subsidies applicable upon undertaking EV projects at the center and state levels, there are no such awareness programs initiated by the authorities. The common people are also reluctant to move to EV mode of transportation (Goel et al. 2021). Lack of subsidy scheme: India has launched FAME-I and FAME-II (Faster Adoption and Manufacturing of (Strong) Hybrid and Electric Vehicles) to provide subsidies to their customers but the government failed to appeal to customers in comparison to the other country’s subsidy schemes. For example, the Government of Norway exempted EVs from VAT.

11.7 Conclusion and Policy Implications The heart of sustainable development lies in the health and well-being of people and our planet. In recent years, air pollution becomes a crucial issue due to the rapid use of fossil fuels. Following the current study, it is concluded that nations desperately need an alternative to the use of conventional vehicles (ICE cars) to curb the increasing pollution and less dependence on fossil fuels. Therefore, the current study explores the relevance of an alternative to conventional vehicle usage, i.e., electric vehicles that is necessary for a sustainable and pollution-free future. Employing the correlation coefficient, the research study found an inverse relationship between uses of electric vehicles and GHG (CO2 , N2 O, and methane) emissions. Though the government of different nations like Norway, Germany, and the USA has made umpteen efforts to make their citizen adopt electric vehicles through consideration of different factors and incentives like providing good charging stations, improvements in grid and battery swapping system, and incentives like point of sales incentives, giving tax credit, rising gasoline prices, etc., which actually have enabled these countries to adopt electric vehicles. Talking about India, it has already touched the deleterious level of pollution, and still, it is never stopping, especially in the case of metropolitan cities. For example, the New Delhi region itself has a population of 22 million which is the highest population density, 12 times denser as compared to New York. With the ever-increasing population of registered personal vehicles, the future of public transport remains in dark (Vidhi & Shrivastava, 2018). The same further suggests that India should not only focus on the stringent policy of planned electric vehicle introduction but the government should also direct efforts to include a greater percentage of people to adopt public transport as a means to travel over owned car efficiently (similar to what is implemented in Japan). Last but not least,

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Success cannot be possible without amalgamated steps of prudent government interventions, good governance of organizations, manufacturers’ support, and end-user self-awareness.

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Osho, G., Nazemzadeh, A., Osagie, J., & Williford, R. (2005). Increased demand for oil in developing countries: Effects on the global oil trade. Southwest Rev. Int. Bus. Res, 15, 221–231. Plotz, P., Schneider, U., Globisch, J., & Dutschke, E. (2014). Who will buy electric vehicles? Identifying early adopters in Germany. Transportation Research Part A: Policy and Practice, 67, 96–109. Rao, Z., Wang, S., Wu, M., Lin, Z., & Li, F. (2013). Experimental investigation on thermal management of electric vehicle battery with heat pipe. Energy Conversion and Management, 65, 92–97. Roger, E. M. (1995). Diffusion of innovations (4th edn). The Free Press. scribd.com/document/ 334334694/Electric-Vehicles-in-India-a-Comprehensi-1 Seixas, J., Simoes, S., Dias, L., Kanudia, A., Fortes, P., & Gargiulo, M. (2015). Assessing the cost-effectiveness of electric vehicles in European countries using integrated modeling. Energy Policy, 80, 165–176. Vidhi, R., & Shrivastava, P. (2018). A Review of electric vehicle lifecycle emissions and policy recommendations to increase EV penetration in India. Energies, 11(3), 483. https://doi.org/10. 3390/en11030483 Woodcock, J., Edwards, P., Tonne, C., Armstrong, B. G., Ashiru, O., Banister, D., & Franco, O. H. (2009). Public health benefits of strategies to reduce greenhouse-gas emissions: Urban land transport. The Lancet, 374(9705), 1930–1943. VIrTA Blog (2022). Electric cars & pollution: Facts and figures. https://www.virta.global/blog/ele ctric-cars-pollution-facts World Economic Forum. (2018). Electric vehicles for smarter cities: The future of energy and mobility. Industry Agenda. https://www3.weforum.org/docs/WEF_2018_%20Electric_ For_Smarter_Cities.pdf

Web Links http://dhi.nic.in/writereaddata/Content/NEMMP2020.pdf http://orbit.dtu.dk/files/104752085/Electric_Vehicle_Scenarios_and_a_Roadmap_for_India_upl oad.pdf http://www.academia.edu/19595787/Electric_Vehicles_in_India_A_Comprehensive_Review https://medium.com/@an223c/trends-challenges-and-future-for-electric-vehicles-in-india-b61 91f4a70b6 https://www.accenture.com/_acnmedia/PDF-37/accenture-electric-vehicle-market-attractiveness. pdf

Part II

Implications of Climate Change and Conservation Capital on Health Issues

Chapter 12

Anthropogenic Disturbances on Climate Change, Global Warming, Ecosystem and COVID 19 Pandemic Satyesh Chandra Roy

12.1 Introduction The normal environment or natural environment of the Earth accomplishes with the interaction of all living organisms, climate, weather and natural resources of the earth. Any disturbances in the environment may affect the survival of microorganisms, plants, animals, human being including the ecosystems of soil, water, rocks and economic activities. With the rapid growth of human population throughout the world many environmental problems such as biophysical environment, climate change, global warming, pollution, loss of biodiversity, alterations in the ecosystem have caused a global crisis in all spheres of life. Biophysical environment implies the biotic and abiotic surrounding of an organism or population that have an influence in their survival, development and evolution. Such radical changes in Earth’s atmosphere are responsible for the origin of many natural hazards including the occurrence of recent pandemic of COVID 19. All these changes are due to the impact of human intervention or anthropogenic activities on the environment. There are other problems also created by humans which is the more and more demand of natural resources such as water, fuel, food, energy and excessive use of land causing disturbances in the biodiversity and ecosystem of the planet. The tremendous growth of human population since the dawn of civilization is the main cause of all these problems leading to the degradation of biodiversity, global warming and conversion of ecosystem to meet their needs. Thus the anthropogenic or human activities cause damage to the environment on a global scale due to overconsumption, overexploitation, pollution, deforestation etc. leading to an existential risk to human race and other living races. The term anthropogenic activity was first S. C. Roy (B) Department of Botany, Centre of Advanced Study for Cell and Chromosome Research, University of Calcutta, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_12

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designate by Russian Geologist Alexey Pavlov and was widely used by the British Ecologist Arthur Tansley. Anthropogenic impact on ecosystems on urban areas brings change in both biotic and abiotic conditions by converting undeveloped land into anthropocentric habitats. The increase in urbanisation in more and more areas through construction of high rise buildings, communication towers, high- ways, fly-overs are detriment to biodiversity and wild life. The physical elements of the ecosystem such as climate, water and soil are mainly modified by human activities leading to the changes in biodiversity and ecosystem of the planet causing major changes in the habitat of wild animals. Humans actually use the natural resources of the earth to a maximum extent to meet their increased demand without thinking of the habitat of other animals throwing them to risk for their survival. It is important to aware that health of all living beings such as human, animal and plants are all deeply connected with environment and natural habitats. These disturbances in the ecosystem through anthropogenic activities may have caused a great chance of causing new dreadful pandemic like that of the recent COVID 19. Anthropogenic disturbance and climate change are the important drivers of terrestrial ecosystem dynamics worldwide (Danneyrolles et al., 2019). There was a rapid change of biodiversity in the last fifty years. The climate change due to human activities (anthropogenic) also leads to ecosystem degradation and loss of biodiversity, There is a strong consensus that human activities are causing climate change. But most of the world’s population are still not aware that human activity is responsible for climate change. The occurrence of recent pandemic COVID 19 gives a reminder that human health is dependent on the health of the planet. Coronaviruses are zoonotic meaning thereby that they are transmitted between animals and people. Thus the outbreak of any future pandemic can be prevented by looking after the restoration of ecosystems and wild life including habitat loss, illegal trade of forest products and wild animals, pollution and climate change.

12.2 Ecological Footprint The term Ecological Footprint was coined by Wackernagel and Rees and further developed by others to assess the societal demands on the regenerative capacity of the biosphere (Chambers et al., 2000; Kites et al., 2009). In 1992 Wackernagel and Rees first termed it as “Appropriated Carrying Capacity” which was further renamed by Rees by the term “Ecological Footprint” in 1996. The global acceptance of this term was done after the publication of the book on “Our Ecological Footprint: Reducing Human Impact on Earth” in 1996. It actually measures the demand on and supply of nature. It is an indicator of natural resource consumption (energy and materials) like that of Economic indicators for measuring Gross Domestic Product or the Real Price Index to indicate Financial Economy. In other words, the ecological footprint is a method to measure how much

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natural resources we have and how much we are using. It can also measure the ecological capacity of the natural resources of the planet. On the demand side, the Ecological footprint includes all the productive areas for which the people compete. The productive surface areas are cropland, grazing land, fishing areas, forest areas, built-up-land for housing and infra-structure etc. There is an another term called Biocapacity which is the productivity of the planet’s ecological assets to regenerate its natural resources and its absorption of other materials such as Carbon dioxide and water from the atmosphere. An increase in human population in the world is gradually decreasing the biocapacity of the earth. This issue can be temporarily resolved through green economy policies and Sustainable Consumption by increasing resource efficiency and promoting sustainable lifestyles. But if the rate of population growth is not controlled then there will be a collapse of the ecosystem resulting in a dangerous environment for humans to survive. Biocapacity is measured in terms of Global hectares (gha) per person showing again that the growth of human population is important. One hectare is equivalent to 2.47 acres. The Global Footprint Network (GFN) calculates the Ecological Footprint of different cities of different countries to produce data showing Ecological Footprint as a metric of sustainability giving per capita global footprint. The Global Footprint Network is taking the data from United Nations and other data from about 200 countries of the world. In 2014 the per capita global footprint was 2.8 gha and the global capacity was 1.7 gha per person showing that the consumption was more than the capacity resulting in the deficit in the biocapacity of the earth. It is also called “Ecological Overshoot”. This overshoot indicates that life-supporting biological resources such as agricultural land, forest resources, fisheries etc. are depleted (Hayden, 2011). If the overshoot goes on for a long time several changes occur in the ecosystems like water shortages, drought, desertification, erosion, reduced productivity of agricultural land, overgrazing, deforestation, rapid extinction of species, global climate change and others. The analysis of the Ecological footprint indicates whether any country or any region is living within the capacity of its territory or is an ecological debtor to the ecological capital of the earth. When the result of Ecological Footprint analysis is compared with measures of human development, it is possible to assess whether the countries are aware on the per capita availability of the biocapacity of that area. If the per capita use of resources is lower than the available per capita capacity then they are on track toward sustainable development. The ecological footprint is measured in relation to population size of the bio-productive area (land and water) of a country with its resource consumption. The bio-productive area includes arable land, forest with forest products and carbon sequestration, pasture, built land and sea space. The measurement of the Ecological Footprint shows that human demand for renewable resources and ecological services increased by nearly 140% (from 7.6 to 18.1 billion global hectares) during the year 1961 to 2010. This result indicates that the planet’s bio-productive area is no longer sufficient to support the human demands (Galli et al., 2015). In addition Carbon emissions are also responsible for giving pressure to ecological assets of the country when the generation of carbon emissions is more than the ecosystems of that country to sequester Carbon. Tracking resource and emissions flows is a key step in addressing pressures on these overburdened ecosystems (Report

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from Global Footprint Network, 2009). Ecological footprint can measure only the renewable resources of the planet that is annually renewed but the measurement of non-renewable sources as well as economic and social aspects are excluded from its measurement.

12.3 Anthropogenic Effects on Biodiversity Biodiversity generally refers to the variety and variability in life of the Earth along with large number of flora and fauna and the environment of the planet. All are interconnected with each other to maintain the well-being of life and planet. Any disturbance in this equilibrium may cause serious environmental threat to civilization. Human activity or anthropogenic effect poses a major threat to the biodiversity. One of the major reason is the population growth rate which grows faster and faster regardless of the population size. Changes in biodiversity due to human activities were more rapid in the past fifty years. The loss of biodiversity is still going on at a rapid pace without any sign of declining. With the decline in Plant biodiversity due to deforestation and land-use, there is a loss of productivity both in flora and fauna leading to a threat of extinction. The Millennium Ecosystem Assessment group has been formed in Washington, USA in 2005 to assess the consequences of ecosystem change and biodiversity loss for human well-being and also to find out the scientific actions needed to enhance the conservation and sustainable use of the ecosystem and biodiversity (Watson et al., 2005). Since 1950 more and more land was converted to agricultural field causing a dramatically changed ecosystem of the Earth by human action mostly in developed and developing countries. The transformation of land to agriculture was very less between 1750 and 1850. The Millennium Ecosystem Assessment (MEA) programme has reported that 7 biomes of the 14 biomes assessed showed 20–50% conversion to human use in temperate and Mediterranean forests (Watson et al., 2005). In the temperate grasslands, the natural habitat of the wild plants have been changed drastically to cultivated lands causing a great change in biodiversity, ecosystem and loss of many rare medicinal plants and wild animals. There is a decline in genetic diversity world-wide particularly among the cultivated species. With the advancement of techniques in Plant Breeding, Animal Breeding and the Impact of globalization, there is a substantial reduction in genetic diversity of cultivated plants and animals in agricultural systems. The gradual decline in genetic diversity has lowered the productivity and adaptability of domesticated species. Thus the Seed Bank and Gene Bank have been developed to conserve the genetic diversity of wild plants for the enhancement of crops and live-stock. Now-a-days an unprecedented deficit or loss in the ecosystem and biodiversity is found all over the world due to human intervention. However, there are some natural ecological disturbances causing Wildfire, Floods and Volcanic eruptions etc. which can change ecosystems drastically by eliminating some species of local populations and transforming biological communities of that

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area. But these natural disturbances are common and temporary. However, the disturbances on biodiversity and ecosystem by human activities are more severe and longlasting. The statement of Wolfgang Sachs rightly pointed out that “The World will no longer be divided by the ideologies of ‘Left’ and ‘Right’ but by those who accept ecological limits and those who don’t” (Goldfinger & Poblete, 2010). Wide diversity of flora and fauna are the important source of food and pharmaceuticals to human. Natural disturbances or perturbations always occur in ocean like typhoon, storm, El-Nino-Southern Oscillation Events etc. resulting in changes in biodiversity which are generally reversible. But changes due to human activities are irreversible.

12.3.1 Population Growth The growth of population is one of the main factors in causing disturbances in biodiversity and ecosystems. The population has been taken as an indirect factor of biodiversity loss as the demand of resources like food, fuel etc. is directly proportional to the growth of the population. The human population has been growing continuously since 1400 and a significant increase started from the last 50–60 years. As of June 18, 2014, the world’s population is estimated to be 7.17 billion by the United States Census Bureau and over 7 billion by the United Nations (Edet et al., 2014). The population is expected to reach between 8 and 10.5 billion between the year 2040 and 2050 (Edet et al., 2014). This huge human population has converted the habitable land of the earth of about 51million square kilometre to agriculture for their food and some fertile lands of the forest area are used for grazing cattle, sheep and other livestock. This conversion of forests, wetlands and other terrestrial ecosystems has caused 60% decline in vertebrates (wild life) worldwide and loss of wild birds (Rafferty, 2021). Thus the primary drivers of biodiversity loss are many such as conversion of natural land to agriculture, deforestation, urbanisation, introduction of invasive species. Thus, overgrowth of human population is a major factor affecting the environment. Thomas Malthus rightly pointed that mankind would outgrow its available resources, since a finite amount of land was incapable of supporting an endlessly increasing population (Malthus, 1998). It has been found that habitat loss is greatest where population density is the highest and regions rich in endemic species have higher than average population densities and population growth rates. This is found in many parts of Asia and Africa where people and threatened species are often concentrated within the same localities (Edet et al., 2014). Habitat loss due to land use is the most important factor for deterioration of the biodiversity and ecosystem. Major habitats including forests, grasslands and coastal zones are mostly affected by human impact as per Millennium Ecosystem Assessment Programme. The habitat loss can also happen due to plantation of any plant in large scale. In order to meet this demand of vegetable oil, plantation of palm oil is increased particularly in Asia (mostly in Indonesia and Malaysia), Latin America and Africa by removing natural forests leading to a habitat loss of large number of species and endangered animals. Again, the use of monoculture of Palm tree will ultimately affect

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the growth of different varieties of flora and fauna leading to a loss of biodiversity and natural habitat of many animals. Whenever there is habitat destruction, pollination will be reduced followed by the yield. If the present trend of growth continues in all spheres such as population, industrialisation, pollution, food production, and use of natural resources, then it will bring a natural disaster leading to eco-catastrophe, famine, war, and dreadful pandemic to reduce the overgrowth of human population by nature. It can be stated that the origin of the recent pandemic COVID-19 may be due to the loss of forest biodiversity and natural habitat of wild animals at an exceptional rate by human pressure of population explosion. In industrial and developed countries, exploitation of the natural resources is increasing their GDP and economic growth despite the loss of capital assets of the planet. The loss of biodiversity and ecosystem affect mostly the poor people as they are directly dependent on biodiversity and ecosystem services.

12.3.2 Climate Change There is now a widely accepted view that the Earth is going warmer every year at an unprecedented rate. The National Weather Service (NWS) is keeping record of the temperature using the Heat Index which depends on both air, temperature and humidity to determine how hot it feels to the human body. The rise of global temperatures is the indicator of climate change. Extreme heat is dangerous for wild life and human being causing change in the atmosphere, biosphere and oceans. Any change or degradation of the biosphere leads to regional or planetary consequences. This gradual warming climate of the Earth will continue unless rise of Carbon emissions is reduced drastically. The Third Assessment Report of the Intergovernmental Panel on Climate change in 2001 stated that “There is new and stronger evidence that most of the warming observed over the last 50 years is likely to be attributable to human activities” due to the release of greenhouse gases from fossil fuels. The alteration in the climate due to human activities is sometimes referred to as Anthropogenic climate change. The main factor responsible for anthropogenic climate change is Global Warming.

12.3.2.1

Global Warming

It was observed as early as 1850 and 1900 (pre-industrial period) that by burning fossil fuel increases heat trapping greenhouse gas level in the atmosphere of the Earth which raises the surface temperature of the earth. Climate change is a long term change in the average weather patterns of local, regional and global climates. Changes in the temperature cause change in the temperature of the earth by altering ocean patterns like El Nino, La Nina and the Pacific Decadal Oscillation, volcanic activity, changes in the output of Solar energy etc. The Intergovernmental Panel on Climate Change (IPCC) showed that the warming of the earth was 0.6 °C between

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1901 and 2000. When it was calculated between 1905 and 2000 the temperature increase was 0.74 °C (Strickland & Grabianowski, 2021). When the day is hot or there is frequent storm then there will be a remark that it is due to global warming. Global warming research by the scientists of IPCC predicted that the average rise of temperature may be between 1.4 and 5.8 °C by 2100. In normal condition the earth absorbs and reflects solar radiation and emits longer wavelength thermal (heat) radiation back into space to balance the incoming solar radiation on earth by outgoing terrestrial thermal radiation from the earth. The energy from this absorbed terrestrial radiation warms the earth’s surface and atmosphere which is known as Natural Greenhouse Effect or Natural Heat Trapping properties (Belic, 2006). In the absence of natural greenhouse effect the temperature of the earth will be about 33 °C lower. With the Second Assessment Report of the Science of climate change, the IPCC concluded that “Human activities are changing the atmospheric concentrations and distribution of greenhouse gases and aerosols. These changes can produce a radiative force by changing either the reflection or absorption of solar radiation, or the emission and absorption of terrestrial radiation” (Belic, 2006). The main cause of Global warming is the use of Thermal power by burning fossil fuel (mainly Coal) which is now replaced by nuclear power or solar power in many countries. The next main source of carbon pollution is in the transportation sector Again global warming initiates evaporation of water from the soil resulting in drought in many places. According to research from NASA, global patterns of drought have been influenced by anthropogenic activities for nearly a century. Another effect of global warming is the rise in sea level. As per IPCC report, sea levels will rise by 7–23 inches by the end of this century due to global warming. This is due to the tremendous increase of Carbon dioxide, water vapour, methane, nitrous oxide and some polluting substances as a result of industrialization, pollution and deforestation and ice-loss from Antarctica. It has been noted that the rise in sea level related with each degree rise of temperature is nearly 2.3 m within the next 2000 years. Melting glaciers, early snow melt and droughts will cause severe water shortages and the risk of storms, cyclones and flash floods (Kumar, 2005).

12.3.2.2

Greenhouse Gases

Greenhouse gases are those that trap heat in the atmosphere. These are: (i) Carbon dioxide—It enters the atmosphere through burning of fossil fuels (Coal, Natural Gas and Oil), trees, waste materials and in case of manufacturing cement and other chemical products. Some Carbon dioxide is removed or sequestered by plants in their Carbon Cycle. (ii) Methane—It is emitted during the production of Coal, Coke from coal mines, natural gas and oil. Methane gas is also produced the decay of organic waste in municipal solid waste and from waste of cattle and others.

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(iii) Nitrous Oxide—It is emitted during agricultural and industrial processing, combustion of fossil fuels, solid waste and during waste water treatment. (iv) Fluorinated gases—Most powerful greenhouse gases that are emitted from industrial processes are Hydrofluorocarbons, perfluorocarbons, Sulphurhexafluoride and nitrogen trifluoride. These gases are man-made and are staying in the atmosphere as powerful greenhouse gases giving a global greenhouse effect up to 23,000 times greater than Carbon dioxide with high emissions. Fluorocarbons and their derivatives are used as refrigerant in commercial refrigeration, industrial refrigeration, air-conditioning systems and others. The total emission of greenhouse gases of 2018 is shown in Fig. 12.1. The atmosphere of the Earth always contains some gases like Oxygen and Nitrogen which have no greenhouse effect as these gases are transparent to terrestrial radiation. The greenhouse effect is due to presence of gases like Carbon dioxide, water vapour and other gases in the atmosphere that absorb the terrestrial radiation coming from the surface of the Earth; Changes in the atmospheric concentrations of the greenhouse gases can alter the balance of energy between the atmosphere, space, land and oceans. The concentration of CO2 has increased from 280 parts per million (ppm) before 1800 to 396 ppm in 2013 due to increased emissions of greenhouse gases. In nature Carbon Cycle remains in balance and is steady. About 25% of Carbon di oxide was absorbed by the ocean making its water acidic and 30% was taken up by land plants and also release CO2 in the atmosphere. The other 45% is also accumulated in the atmosphere leading to an increased level of greenhouse gases as the extra CO2 is unable to be absorbed by land, biospheres and oceans. CO2 emissions from burning fossil fuels Fig. 12.1 Total emissions in 2018. Source Author’s compilation from the data of United States Environmental Protection Agency

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Fig. 12.2 Fossil fuel CO2 emissions. Source Authors derivations from World Bank Data

and other industrial processes have continued to increase steadily in recent years (Fig. 12.2) although it varies from year to year.

12.4 Anthropogenic Activities on Climate Change It has been found that increased pollution from Urban and Industrial areas can increase the concentrations of Cloud Condensation of Nuclei (CCN) which is the important fraction of the atmospheric aerosol influencing the microphysical and radiative properties i.e., CCN activation. Condensation of nuclei provides the nongaseous surface necessary for water vapour to condense into cloud droplets. An increased amount of aerosol can affect the environment by constantly contributing to the formation of ground-level ozone and by scattering and absorbing radiation and by modifying the radiative properties of the cloud and thus altering the precipitation efficiency of the cloud. The emissions from the aircraft give aerosol particles directly into the upper troposphere where dangerous Cirrus clouds are formed. Burning biomass by humans emits large amount of pollutants just like that of burning coal. Burning organic material emits particulate matter (PM), Nitrogen oxides, Carbon monoxide, Sulphur dioxide, Lead, Mercury and other hazardous air pollutants. These pollutants are increasing at a tremendous rate with the development of urbanisation and modern city by human beings. The rate of urbanization and urban population growth are increasing at a faster rate in developing countries than developed countries. This human-induced heat emitted into the atmosphere leads to climate change and global warming. Besides CO2 emissions, other pollutants of particulate matter (PM) which

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may be microscopic solid and liquid suspended in the atmosphere. These particles are very dangerous to human health. Another important effect of global climate change is in the frequency and intensity of tropical cyclones and hurricanes. The anthropogenic addition of greenhouse gases to the atmosphere might lead to an increase in the energy available to tropical cyclones and therefore to an increase in their intensity (Emanuel, 1987). The energy that is required for Cyclones or hurricanes are coming from water and climate change is the cause for increasing the temperature of the sea surface. Increase in precipitation in the atmosphere and higher rates of melting snow and glacier contribute to storm, cyclone and heavy downpour leading to future flood risk. The recent occurrence of flash floods in the Chamoli district of Uttarakhand of India on February 7, 2021 causing many deaths is due to climate change and global warming. According to Kailash Pandey, senior Climatologist, high mountain rocks are gradually fractured heavily due to climate change. These fractures are filled with ice which glues the rock mass. Global warming is causing this ice to melt which is weakening the rock mass leading to collapse of big slope of glaciers. In the last 60 years, Uttarakhand has an increase of temperature of 0.6 °C (Report from The Week, 2021). Different types of changes in the climate like Sweltering heat waves, heavy downpours, hazy skies and polluted water have been noticed in many countries that are responsible for the occurrence of frequent natural disasters and severe alterations in the ecosystem of the Earth throughout the world. The IPCC gives warning that greenhouse gas emissions should be cut by half by 2030 and reach net zero emissions by mid-century in order to avert the most catastrophic consequences of climate change (IPCC, 2018).

12.5 Change in Biodiversity and Rise of Pandemics With the continued natural changes in the environment, biodiversity and ecosystem of the earth along with the human interventions new mutants or variants are produced in zoonotic viruses leading in the emergence of many novel infections such as the recent dreadful SARS-CoV-2 virus causing global pandemic. There is a direct correlation of the emergence of zoonotic diseases from wild life (mammalian) diversity and human population density and anthropogenic environmental and ecosystem changes. It has also been found that biodiversity loss may increase transmission of microbes specially viruses from animals to people in many instances (Report from Convention on Biological Diversity, 2020; Roy, 2020a, 2020b, 2021). Before the onset of the recent pandemic, other dreadful diseases like Ebola, AIDS, SARS, MERS, Avian flu and Swine flu emerged due to overexploitation of nature by human leading to open a new era called ANTHROPOCENE. With the increase of deforestation urbanisation, land-use for meeting their own needs they have brought wild animals and livestock into close contact or very near to the residential area of humans. It has been established that all the dreadful infectious diseases including the recent COVID 19 have originated from the Zoonotic RNA viruses harbouring mainly on wild animals. It is

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also known that Bats are the natural reservoirs of a wide diversity of Coronaviruses including the present SARS-CoV-2 (Forni et al., 2017; Roy, 2020a, 2020b, 2021). But all microorganisms are not causing diseases, most of them co-exist with animals and some are beneficial to animals. Only about 1400 microorganisms are responsible for human infections. Nearly 60% of human infections are from animals and most of them are transmitted from animals to humans and is called Jump Species. These are transmitted generally through passive means through vectors or via food systems. Of the emerging infections, SARS, MERS and SARS-CoV-2 are thought to be originated from wild animals. Bats are the only mammals that can fly and they are more numerous after Rodents (Rat family). Bats are natural reservoirs (hosts) of many microbes. Of the two hundred novel Coronaviruses found in Bats, 61 are potential zoonotic viruses. As Bats are coming nearer to human habitats due to loss of their own place (forest) so viruses are believed to reach humans from their natural bat hosts. As the forests become lesser and lesser bats have to fly longer distances for food and so they are coming to the Animal Farm House which is built now closer to human habitats. When a bat sits on a tree to eats fruits or to animal shed for insects, it leaves traces of its body fluids containing different types of viruses. So Bat is the main culprit for causing pandemic diseases in human. Although Bats are providing us many beneficial services like flower pollination, helping in seed dispersal of many species and in controlling insect populations so we cannot eradicate Bats. As Bats are carrying deadly viruses, people have to take precautions in not coming in contact with such trees, fruits, animals or shed as they become victims of the deadly virus very easily. Thus the chances of pathogens like viruses passing from wild animals to humans have increased to a great extent by the destruction and modification of natural ecosystems and biodiversity through anthropogenic activities.

12.5.1 Changes of Ecosystem, Climate and Emergence of Diseases Ecosystem of our nature has an important role in supporting the living beings of the planet including humans. Any alteration in this natural system can help in emerging and spread of infectious diseases. Human activities are causing drastic changes to the earth in different ways like (i) loss of habitat of wild animals and plants; (ii) modification of natural environments; (iii) decline in biodiversity; (iv) high growth of human population; (v) increase in consumption of food; (vi) changes in land-use, rivers, oceans; (vii) climate system; (viii) pollution and global warming. From the report of Intergovernmental Science-policy Platform on Biodiversity and Ecosystem Services (IPBES), it has been seen that three-quarters of land and two-thirds of the marine environment have been modified by humans causing threat to about 1 million animal and plant species or putting them at the risk of extinction. If these global changes continue in this way without any awareness from the people, there will be an uncertain future for the ecosystem, biosphere and human health.

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The destruction of forests leads to the exposure of pathogenic viruses to human contact from wild species that host them. There are lot of such examples of emerging dreadful infections. For example, Ebola infection was started in Africa following increased incursion into the wild forest which has brought human population to come in close contact with bats that are the reservoir of diverse viruses. In this way, yellow fever disease (transmitted from monkeys through mosquitoes). AIDS from Apes in Central Africa occurred only through close contact with wild life either by entering the forest or by destructing the habitat of wild life. So Biodiversity and human health are closely interlinked. Some of these pathogens grow better in warmer, humid environment and so the warmer climates are disease prone. Thus climate change is an important factor in the emergence of disease. The warm climate and seasonal variations facilitate the geographic distribution and the presence of large number of species like bats, monkeys and rodents (rat family) including their hosts carrying zoonotic pathogens. For this reason, the incidence of Dengue, Chikungunya virus, Flu Virus, Malaria etc. occur in areas with higher degrees of warming and seasonal rainfall. It has been noted in Brazil from extensive literature review that there is a relationship between outbreaks of infectious diseases and (i) cause of extreme climate events like El Nino, La Nina, heatwaves, droughts, floods, heavy rainfall; (ii) environmental changes like deforestation, urbanization, unplanned land-use, loss of habitats of wild animals. This global climate change is found to be an important factor in the spectrum of environmental health hazards to human. The foundation of long term good health in human populations depends on the stability and good functioning of the ecological systems of the earth. The environmental hazard has been shown in Fig. 12.3 by human activities (McMichael, 2003). Vitousek and colleagues stated that “Human alteration of Earth is substantial and growing. Between one-third and one-half of the land surface has been transformed by human action: the carbon dioxide concentration in atmosphere has increased

Fig. 12.3 Anthropogenic effects on the environment. Source McMichael (2003)—an open source

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by nearly 30% since the beginning of the Industrial Revolution: more atmospheric nitrogen is fixed by humanity than by all natural terrestrial sources combined more than half of all accessible surface fresh water is put to use by humanity: and about one quarter of the bird species on Earth have been driven to extinction. By these and other standards, it is clear that we live on a human-dominated planet” (McMichael, 2003). Human activities on land and water can affect ecosystems on climate, habitat loss, permafrost melting, ocean acidification, air pollution and eutrophication. It is the process by which the water body becomes enriched with surplus nutrients leading to overgrowth of one plant (bloom). As for example excessive growth of Algae in water body, known as Algal Bloom, causing deterioration of water and spreading of toxic gases in the atmosphere. The cumulative effects of all the problems have serious impact on ecosystems. In West Uganda it was noted that forest disturbances could affect infection dynamics at a local scale and human encroachment induces cross species infection rates between primates and human. It has also been found that forest disturbances are related with the transmission of infections that is related with the changing patterns of ecosystem and biodiversity. In Australia, anthropogenic land-use changes and forest fragmentation (breaking of large forested areas into smaller pieces of forest separated by roads, agriculture etc.) have tremendous effects on the emergence of zoonotic diseases. In Australia these disturbances lead to many diseases like Lyma disease, rodent-borne haemorrhagic fevers, hanta viruses, arena viruses causing fatal illnesses. Thus vector-borne infections, distribution and increased number of pathogens and hosts are strongly affected by different forms of climate changes. The gradual increase of temperature, deforestation, urbanisation etc. may change the life cycle of vector borne and zoonotic pathogens leading to changes of their hosts and alterations in their transmission pattern causing more spread of infection and pandemic. Experts from different disciplines are required to work together to develop ways to prevent spreading of any future pandemic. One special committee was formed by Dr. Jeanne Fair and others to correctly forecast the outbreak of the pandemic in response to climate change. Similarly another team of Los Alamos National Laboratory having Computer scientists, Disease ecologists, Mathematicians and others also studied to find out the relationship between climate changes and the outbreak of infectious diseases including COVID 19 (Fair, 2019). When the effect of global warming and climate changes occur in regions of high zoonotic potential in the presence of Bats (reservoir of Viruses) and wild animals (hosts) then there is a high chance of spillover to human hosts. Weather conditions can influence virus shedding and spill overs from bats to facilitate pathogenic transmission. Inclement weather associated with scarcity of food can act as stressors to impair the immune system of bats leading to increase in the susceptibility to pathogenic agents. Thus climate changes are the important drivers of viral infections including the effect of extreme weather conditions due to El Nino. The global emergence of the present Coronavirus SARS-CoV-2 in 2019 may be due to the presence of El Nino in the year 2019 (Platto et al., 2021).

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Another driving force for the emergence of the pandemic is the wild life trade which may again increase the human activity in collecting wild animals from the regions of the deep forest leading to new contact among people, animal and novel microbes. In this way the shedding of microbes to human in urban areas and the spread of transmission of viruses from human to human occur enhancing the crossspecies spillover and illness as these animals are kept for longer periods of time in over-crowded places. It has been found that there is a wild market selling meat of wild animals of Civet cats, Rodents, Dog, Pangolins, Fox, Crocodile etc. in the busy city of Wuhan in China. This market is known as Wet Market. There is one view that the present coronavirus causing COVID 19 has been transmitted from this market as the first infection of COVID-19 is found in China. These meats are also trafficked for human consumption to other countries. Another reason for widespread of this type of disease may also be due to the crowding of many animals in a small space of Farmhouse by man for more profit which acts as an amplifier for viral pandemics. The outbreak of Bird Flu in 1997 originated in Chinese Chicken Farms due to crowding of chickens in the farms and also occurred in the United States where Poultry Farmers killed tens of millions of their chickens to prevent the outbreak (Shapiro, 2020). Thus the Bird Flu is giving an alarm to man in making commercial farms of animals. In 2009 again the outbreak of Swine Flu occurred in North Carolina due to the confinement of Pigs in small places. In the editorial of the American Journal of Public Health in 2007 it was mentioned that the mass raising in small spaces and slaughtering of animals for food could be the genesis of the next big global pandemic. Usually the natural immune systems of humans are highly polymorphic and able to adapt to new situations but if the genetic diversity of the affected population is low the invading microbes will suddenly mutate in new situations and create outbreaks of epidemics and pandemic (Maillard & Gonzalez, 2006).

12.6 Concluding Observations In conclusion, it may be stated that to prevent the outbreak of future pandemics like the present SARS-CoV-2 or more dreadful ones the conservation of the biodiversity and ecosystem and reforestation is urgently needed. As the health of all life in the planet is interconnected, people should be aware of that before starting their activities on biodiversity and ecosystem of the planet. The recent COVID 19 has given us a great warning to remind us to take care of the environment of the planet before doing any deforestation, land-use changes and also to rethink for reforestation to re-establish the natural habitat of ethnic people and animals. Acknowledgements The author would like to express his gratitude to his wife Chayna Roy for her constant inspiration in writing the manuscript.

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References Africa Ecological Footprint Report made in collaboration with WWF, African Development Bank and African Development Fund (2012) Belic, D. S. (2006). Global warming and greenhouse gases. Facta Universitatis. Series: Physics, Chemistry and Technology, 4(1), 45–55. Chambers, N., Simmons, C., & Wackernagel, M. (2000). Sharing nature’s interest: Ecological footprints as an indicator of sustainability. Earthscan. Danneyrolles, V., Sebastien, D., Fortin, G., Leroyer, M., et al. (2019). Stronger influence of anthropogenic disturbance than climate change on century-scale compositional changes in northern forests. Nature Communications, 10, 1265. https://doi.org/10.1038/s41467-019-09265-z Ecological Wealth of Nations. (2009). Report from Global Footprint Network, USA (pp. 1–36). Edet, S. I., Samuel, N. E., Etim, A. E., & Etefia, T. E. (2014). Impact of overpopulation on the biological diversity conservation in Boki local government area of cross river state, Nigeria. American Journal of Environmental Engineering, 4(5), 94–98. Emanuel, K. A. (1987). The dependence of hurricane intensity on climate. Nature, 326, 483–485. Fair, J. M. (2019). Climate change is driving the expansion of Zoonotic diseases. www.researcho utreach.org Forni, D., Cagliani, R., Clerici, M., & Cironi, M. (2017). Molecular evolution of coronavirus genomes. Trends in Microbiology, 25(1), 35–48. Galli, A., David, L., Wackernagel, M., Gressot, M., & Winkler, S. (2015). Humanity’s growing ecological footprint: Sustainable development implications. Brief for GSDR 2015. Goldfinger, S., & Poblete, P. (ed.). The ecological wealth of nations: Earth’s biocapacity for as a new framework for International Co-operation (2010). Hayden, A. (2011). Ecological footprint. In Encyclopaedia of consumer culture. SAGE Publications. IPCC. (2018). Global warming of 1.5 °C. https://www.ipcc.ch/sr15/ Kumar, S. (2005). Global warming and its cause and effects: A review. IJRSI, 2(10), 177–179. Kites, J., Moran, D., Galli, A., Wada, Y., & Wackernagel, M. (2009). Interpretation and application of the ecological footprint: A reply to Fiala (2008). Ecological Economics, 68, 929–930. Maillard, J.-C., & Gonzalez, J.-P. (2006). Biodiversity and emerging diseases. Annals of the New York Academy of Sciences, 1081, 1–16. Malthus, T. R. (1998). An essay on the principle of population. McMichael, A. J. (2003). Global climate change and health: An old story writ large. In A. J. McMichael, D. H. Campbell-Lendrum, C. F. Corvalan, K. L. Ebi, A. K. Githeko, J. D. Scheraga, & A. Woodward (Eds.), Climate change and human health. World Health Organization. Platto, S., Zhou, J., Wang, Y., Wang, H., & Carafoli, E. (2021). Biodiversity loss and COVID-19 pandemic: The role of bats in the origin and the spreading of the disease. Biochemical and Biophysical Research Communications, 538, 2–13. Rafferty, J. P. (2021). Biodiversity loss. Encyclopedia Britannica, Inc. Report from Convention on Biological Diversity. (2020). CBD/ SBISTTA-SBI-SS/2/2/INF/1, 10 December 2020. Report from The Week. (2021). Vol. 39, Number 10, March 7. Roy, S. C. (2020a). Corona virus—Origin, replication and remedy for future threat. Science and Culture, 80(5–6), 137–143. Roy, S. C. (2020b). Mystery of corona virus—a review. International Journal of Engineering Applied Sciences and Technology, 5(2), 413–418. Roy, S. C. (2021). Corona virus and pandemic. LAP Lambert Academic Publishing, Member of Omniscriptum Publishing Group. Shapiro, P. (2020). Scientific American, 2(2). Strickland, J., & Grabianowski, E. (2021). How global warming works. HowStuffWorks.com. Retrieved March 28, 2021, from https://science.howstuffworks.com/environmental/green-sci ence/global-warming.htm

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Watson, R. T., Zakri, A. H., & Zedan, A. H. (2005). Ecosystem and human well-being: Biodiversity synthesis. World Resources Institute.

Chapter 13

Green and Sustainable Chemistry: A New Blossoming Area to Solve Multiple Global Problems of Environment and Economy Sumit Ghosh and Alakananda Hajra

13.1 Introduction The modern world in recent days is thriving with incredible economic growth but facing tremendous environmental damage simultaneously (Chakravarty & Mandal, 2020). The environment wherein we survive today is under serious threat from chemicals, and a lack of recognition of their function in our lives (Hoffmann, 1993; Wang et al., 2020). Rachel Carson in 1962 mentions in her book ‘Silent Spring’ that “chemicals are the sinister and little-recognized partners of radiation in changing the very nature of the world––the very nature of life.” Nowadays, with the rapidly increased environmental consciousness, it is very important to develop new ways of preparing the same molecules but with zero waste and zero pollution. The outlook and incentive toward the development of cleaner and sustainable energy resources are growing worldwide due to the rise of fossil fuel price, depletion of petroleum resources, and environmental protection issues. To solve all of these problems, chemists have introduced a new subject, known as Green and Sustainable Chemistry (Anastas & Kirchhoff, 2002; Clark et al., 2014; Li & Anastas, 2012; Saini, 2017). The Green and Sustainable Chemistry aims to abolish or diminish the environmental impact of all influences of chemical endeavour (Höfer & Bigorra, 2007). In the other word, Chemistry in a sustainable direction is one which causes progressively less damage to the living organisms as well as to the environment (Anastas & Eghbali, 2010). The premise of employing green chemistry and green engineering approaches is to contribute to the development of S. Ghosh · A. Hajra (B) Department of Chemistry, Visva-Bharati (A Central University), Santiniketan, India e-mail: [email protected] S. Ghosh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_13

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sustainable manufacturing processes for chemicals and products that simplify the reaction strategy and minimize resource and energy consumption, process time, and environmental impacts throughout the product’s or chemical’s life cycle (Song & Han, 2014). Therefore, it is highly encouraging to lead research and development efforts in the direction of a goal that will generate a powerful tool for cultivating sustainable developments (Chanshetti, 2014). In this chapter, we have discussed some of the developing green and sustainable technologies which are evolved based on twelve principles of green chemistry into practice and which will be indispensable in near future for both the environment and economy.

13.2 Some Important Green and Sustainable Chemical Technologies After two decades of tremendous hard work, several fruitful technologies are given by green chemists to our world, a few of which are discussed here.

13.2.1 Atmospheric CO2 Capturing and Conversion Technology The recent alert on the rapid accumulation of CO2 in the atmosphere creates a very high risk for the ecosystems as well as deep impacts on the environment. Hence, carbon capture, its utilization and storage have achieved significant importance presently. In this context, the chemical community has introduced the integrated carbon capture, utilization and storage (CCUS) technology which is a significant mitigation technology. It includes the CO2 sequestration from fossil fuel combustion or industrial processes, followed by its transportation via pipeline or ship and finally conversion into helpful products or its final storage in the deep underground (Nocito & Dibenedetto, 2020). Most applied approaches for CO2 capture are based on physical or chemical absorption using various substrates, for instance, activated carbon functionalized with amines, alkali metals, metal oxides, ionic liquids etc. (Mukherjee et al., 2019). Moreover, green polymeric membranes and ceramics (Jiang & Ladewig, 2020; Wang et al., 2017) are also broadly used for their operational simplicity and low capital cost. In addition, metal–organic frameworks (MOF), covalent-organic frameworks (COF) technology (Elhenawy et al., 2020), cryogenic chamber technology (Song et al., 2019) and enzyme technology (Bhatia et al., 2019) are also important. These technologies are not only significant for our environment but also indispensable for the economy.

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13.2.2 Water Splitting Technology for the Production of Renewable Hydrogen Fuel With the sharp increase in the global energy crisis, hydrogen (H2 ) is becoming one of the most sustainable, environmental-friendly and clean energies in the twenty-first century for substituting fossil fuel energy which will exhaust soon. Electrochemical water splitting is a sustainable and pollution-free potential approach for the purpose of H2 production and maybe become an effective solution for the energy crisis (Li et al., 2020). Although splitting of water into H2 and O2 is known for a very long time, its maximum efficiency is inhibited by high cost, low stability of electrode and low scale production. Consequently, developing economic and proficient technologies for electrochemical water splitting has been a serious ambition for the chemists around the globe (Rafique et al., 2020). Interestingly, an external power source is necessary to complete electrolytic oxidation and reduction reactions of water. The utilization of green renewal energy sources is very desirable. In this circumstance, by working very hard for decades, chemists have constructed different kinds of green energy systems for H2 production effectively, for example, two-electrode electrolysis of water, water splitting driven by solar cells (Wang et al., 2019), photoelectrode device (Yang et al., 2019), thermoelectric (TE) device (Zhou et al., 2018), triboelectric nanogenerator (TENG) (Wei et al., 2018) and other devices including pyroelectric (Xie et al., 2017) and water–gas shift (WGS) reaction (Ebrahimi et al., 2020). Largescale H2 fuel production by using water splitting driven by green energy systems will be future-crush for both the economy and environment in near future.

13.2.3 Solar Cell Solar energy is a clean and green renewable resource with no emission and has got incredible potential of energy which can be harnessed using a variety of devices. India, being a tropical country receives significant radiation throughout the year and has enormous possibilities to capture solar energy. Moreover, the expert bodies of the Indian government, such as the Ministry of New and Renewable Energy (MNRE) and the Solar Energy Corporation of India (SECI) have played a crucial responsibility in helping India become one of the fastest adopters of solar energy. In addition, the National Solar Mission of MNRE has targeted solar power projects from 20 GW by the year 2020–2021 to 100 GW by the year 2021–2022. Even, government tax rebates can also be granted on the small solar project on houses. Hence it has no doubt that the govt. is paying very much interest on solar energy harvesting to utilize as a sustainable alternative to electricity generated by fossil fuel. Days will not be so far when solar energy harvesting will be considered as the only determining factor for the development of countries around the globe. Figure 13.1 represents year-wise solar electricity production in India from the financial year 2012–13 to year 2019–20.

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Fig. 13.1 Year-wise solar electricity production in India. Source Mercom India Research-an open source, https://mercomindia.com/solar-power-generation-lowest-yoy/

13.2.4 Biofuel Synthetic Technology Biofuels are achieving increasing concern in recent days worldwide as an alternative for petroleum-based transportation fuels to address energy security, energy cost and global warming problems related to liquid fossil fuels (Connor & Atsumi, 2010). The term biofuel is defined as any liquid fuel synthesized from a plant material that can be utilized as a substitute for the petroleum-derived fuel. Synthetic biofuels from agricultural-based origins such as biohydrogen, biogas, biogasoline, biodiesel, and green diesel will emerge as prospective fuels of tomorrow due to their excellent fuel properties and environmental-friendly attributes (Yusup et al., 2019). There are three types of biofuels that are available in the market. The mostly used firstgeneration biofuels contain bioalcohols, including ethanol, propanol and butanol which are made by fermentation of sugar crops, starch crops, oilseed crops, and animal fats. However, second-generation fuels or cellulosic biofuels are generally synthesized from non-edible cellulose biomass, either non-edible residues of food crop production or non-edible whole-plant biomass. Moreover, the third-generation biofuel is defined as the biofuel that would be generated from algal biomass, which has a very distinctive growth yield in comparison to classical lignocellulosic biomass (Lee & Lavoie, 2013). Figure 13.2 denotes year wise biofuel production chart around the globe and it clearly signifies a rapid surge in the production of biofuel in the last two decades which largely encourages economic investment in this area and could act as the saviors of the economy and environment. However, change of land use patterns, air pollution, pressure on water resources and food cost increase may be responsible to inhibit its maximum efficiency (Datta et al., 2019).

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Fig. 13.2 Biofuel production by region. Note Biofuel production is measured in terawatt-hours (TWh) per year, and includes both bioethanol and biodiesel). Source https://ourworldindata.org/gra pher/biofuels-production-by-region

13.2.5 Biobased and Biodegradable Polymers and Plastics Synthetic plastics, such as polystyrene, polyethylene, polypropylene and acrylics are widely used due to their strength, flexibility, and lightness. However, their longevity and stability under soil burial conditions make them non-biodegradable and hence plastic waste creates a serious threat to all kinds of life. To solve this problem, chemists have introduced reusable biodegradable polymers and plastics, such as starch-based plastics, soy-based plastics, bacteria-based plastics, ligninbased plastics, cellulose-based plastics and natural fiber reinforced plastics and have been commercialized in the manufacturing of different kinds of products, for example, garbage bags, poly bags, compost bags, and agricultural mulch films (Faris et al., 2014). Polyhydroxyalkanoates (PHAs) are a renowned class of bacteria-based biodegradable plastics that could gain carbon neutrality and support more sustainability in the industry (Philip et al., 2007). On the other hand, polylactic acid (PLA) is a versatile biodegradable polyester and is synthesized from corn and starch which are 100% renewable and show huge promise in various commodity applications (Drumright et al., 2000). In soy-based plastic, the excessive amount of protein permits the soy to be molded into plastic materials and films which have been largely used in edible food coating, shopping bags and even car components (Swain et al., 2004). Although, lignin-based plastics, cellulose-based plastics and natural fiber reinforced plastics are not very common but slowly accelerating their importance in the present situation in both industries and nature. Figure 13.3 signifies global projected market of biodegradable polymer up to the year 2025.

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Fig. 13.3 Global Biodegradable polymer market, 2015–2025 (Kilo Tons) (USD Million). Source Adroit Market Research, 2019-an open source. https://www.adroitmarketresearch.com/industry-rep orts/biodegradable-polymer-market

13.2.6 Food Coating Technology According to FAO (Food and Agriculture Organization) estimation in ‘The State of Food Security and Nutrition in the World, 2020’ report 14% of the population is undernourished in India and nearly one third of the food produced in the world for human consumption every year gets lost or wasted due to inefficient supply chain management and post-harvest handling (https://www.indiafoodbanking.org/hunger). Hence, the preservation of food is highly desirable. To avoid these problems, chemists invented food coating technology. In the food industry, coating is the addition of a skinny layer of solids or liquids onto a food product for primary packaging. Edible coatings and films assist to conserve the sensory qualities, such as aroma, taste and appearance in different food products, reducing moisture and weight loss, prevent oxidative rancidity in meat and products, delaying ripening in fruits and vegetables, maintain pigments in food products and enlarge shelf life in foods (Ulusoy et al., 2018). Besides, they can be utilized as carriers for oxygen and antimicrobials such as lysozyme, potassium sorbate, nisin, EDTA and provided several advantages against conventional coatings, such as better spreading, diffusivity and solubility (Ramos et al., 2012). This food coating technology is so fruitful that the whole world is amazed by it and soon discovered various methods, such as dipping, spraying, brushing, solvent casting and extrusion methods which are applied based on kinds of food and on the desired effects (Dhanapal et al., 2012). Lastly, with the rapid increase in population, there is a surge in demand of food preservation and therefore food coating could be a game-changer in future.

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13.2.7 Use of Greener Catalysts for Sustainable Synthetic Technology Catalysis performs a key function in the chemical industry and academia since most of the chemical processes required catalysts to speed up reactions, increase selectivity and completed the reactions by using lower energy (Chorkendorff & Niemantsverdriet, 2003). Most of the used catalysts in classical reactions are noble metals which are expensive, toxic and harmful. On the other hand, green catalysts should contain some very important characteristics, such as high selectivity, activity, stability, easy separable and reusable. Moreover, they must be prepared by environmentally benign, easily and cheaply available raw materials, such as organic compounds and even abundant metals (Dubey & Pandey, 2019). In this scenario, enzyme catalysts are getting immense attention (Itoh & Hanefeld, 2017). Visible light photocatalysts are also achieved very much popularity in recent days (Djuriši´c et al., 2020). Magnetsupported catalysts are also showing attractiveness due to their easy separable and reusable nature (Shifrina & Bronstein, 2018). The design and exploration of noble synthetic methods strongly depend on progress in catalysis. The development and utilization of these green catalysts to accomplish the dual goals of economic benefits as well as environmental protection is an imperative task, and is highly indispensable to attain sustainability in the chemical industry (Song & Han, 2014).

13.2.8 Green and Sustainable Solvent The Chemical industries, pharmaceutical industries and research institutes used large amounts of volatile, flammable and toxic organic solvents for multistep chemical processes and purification to synthesize chemicals and materials. Each year ~ 20 million tons of these kinds of solvents are mixed to the atmosphere and creating solvent waste as well as environmental pollution (Jutz et al., 2011). Hence, utilization of greener solvents like water, ionic liquids, task-specific ionic liquids, supercritical fluids, non-toxic liquid polymers etc. in chemical reactions has become one of the main focuses of research among the chemical community. There are certain requirements, such as low toxicity, ease of availability and recycling, and high process efficiency for a solvent to be a green solvent (Clarke et al., 2018). Finally, green solvents may optimize chemical reactions, diminish reaction processing steps, solvent usage and suggest new routes and technologies which could lead to the requirements of sustainability (Song & Han, 2014).

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13.3 Conclusion In summary, in this chapter, we have discussed the developments of modern green sustainable technologies which we hope to be highly demanded in near future. A few success stories should be listed here. For example, photovoltaic cells can be an alternative to fossil fuels and cause a rapid decrease in environmental pollution. Photocatalytic water splitting into hydrogen and oxygen under visible light irradiation has received much attention due to the possibility of generating a clean energy source. Eco flux, a compostable polyester film combines with cassava starch and calcium carbonate to prepare fully biodegradable bags. Synthesis of organic drugs is always in high demand. Therefore, green chemistry, an environmentally benign chemistry has been developed to diminish or demolish the creation of unhealthy or hazardous side-products and to magnify the convenient products in an environmentbenign pathway. Reduce, reuse and recycling are the three main agendas of green chemistry which causes lowering in marine debris as well as pollution of our environment. These are a few solutions for environmental pollution and could be game-changer in economy. However, sustainable development is complex, multidimensional concepts and embracing disciplines and is not generally compared with general subjects, such as chemistry, physics or economics etc. In addition, numerous disadvantages of these green methods and technologies, such as high execution costs, lack of proper information, lack of alternative chemical, raw material inputs and process technology, insecurity about performance impacts, and deficient human resources and skills retard to show its maximum efficiency. Hence, chances and challenges to improve are very high. We hope this chapter will create a center of attention for young scientists of chemistry, environmental science and economics to dig deep inside this topic and remove its barrier.

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Chapter 14

Evergreen Conservation Capital Indicators and Life Expectancy in Italy Andrea Ciacci, Enrico Ivaldi, and Paolo Parra Saiani

14.1 Introduction One of the main problems posed by development has always been how to deal with the depletion of resources and the negative environmental impacts of predominantly urban modes of production and consumption. And yet, there is still talk of ‘externalities’: the achievement of environmental sustainability should not be considered as a cost placed ‘outside’ economic activity, as it is essential to create and maintain a prosperous economy (Raworth, 2017, p. 123). Gross domestic product (GDP) has often been used as an indicator of well-being and health, implicitly accepting the assumption that greater production corresponds to greater well-being, regardless of the usefulness of the production itself: while in the GDP we compute any production as an active item, the losses due to these productions—side effects, or negative externalities, just think of the environmental costs—are not considered with a negative sign. But as John Sterman reminds us, “There are no side effects, only effects”, and all the effects of policies must be considered, not only those considered positive and useful; in their evaluation, it is necessary to broaden the time-scale and consider every useful or harmful aspect (2012, p. 24). In response to these critics, many attempts have been made to include estimates of habitually neglected aspects, such as those relating to the environment, as proposed by Nordhaus and Tobin (1973) and Nordhaus and Kokkelenberg (1999). Thanks to the famous Bruntland Report, the concept of ‘sustainable development’ began to spread rapidly and widely: “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to

A. Ciacci Department of Economics and Business Studies, University of Genoa, Genoa, Italy E. Ivaldi (B) · P. P. Saiani Department of Political and International Science, University of Genoa, Genoa, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_14

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meet their own needs” (World Commission on Environment and Development, 1987, p. 43). In 1990, the United Nations Development Programme (UNDP) began to publish a series of reports on human development, wanting to shift the focus of development policies from national accounts to people. Mahbub ul Haq—one of the initiators of the new UNDP approach—emphasizes that income is only one of the elements that define the quality of human life and that health, education, physical environment, and freedom can be as important as income (1995, p. 23). The growth of GDP, while being a necessary element for human development, is not enough: “income is a means, not an end” (UNDP, 1990, p. 10). The choices of the UNDP are influenced in particular by the ideas of Amartya Sen, who advocates the need to broaden the concept of freedom, including both freedom from and freedom of : “human development is a process of enlarging people’s choice” (UNDP, 1990, p. 10).1 According to Sen (1984, p. 497), the process of economic development “has to be concerned with what people can or cannot do”. It is essential to consider environmental diversities as a source of variation between our real incomes and the advantages—the well-being and freedom—we get out of them (1999, p. 70) and that the presence of infectious diseases or pollution and other environmental handicaps alter the quality of life. Environmental preservation is a fundamental contributor to human capability, a type of ‘public goods’, which people consume together rather than separately (Sen, 1999, p. 128). Even though HDI was born to respond to the criticisms of those who accused the GDP of having neglected non-material needs, HDI does not contemplate the environmental sphere (Hamilton, 1994). Yes, ul Haq said that health, education, physical environment, and freedom were as important as income, but in the calculation of HDI, there is no trace of physical environment or freedom. In September 2009, the Report of the Commission on the Measurement of Economic Performance and Social Progress recommended—among other things— to improve the assessment of health, education, and environmental conditions and to assess the sustainability of well-being. Health can only be protected by promoting healthy behavior, and this will be possible by going beyond individual characteristics and taking into account the context, therefore the physical and social environment (van den Berg et al., 2015, p. 806), adopting a socio-ecological approach. The link between environment and health has not been successful even among the mainstream environmental movement; as Hannigan reminds us, health inequalities linked to environmental conditions have been denounced for many years—for example since the 1960s in the United States—but they have rarely become central to public discourse, except for some ecological political movement (2006, p. 47), as the issues such as the toxicity of lead, pesticide poisoning, and uranium hazards highlighted by Gottlieb (1993).

1

The term capabilities—central to Sen’s approach—has been replaced since the first UNDP report of 1990 by ‘choices’, a more ambiguous and poor term from a theoretical point of view; the reasons are not clear even to Fukuda-Parr, director of the team that has drawn up some reports since 1995 (2003, p. 315).

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Despite the difficulties, the impact of air pollution on health and related mortality and morbidity has been extensively addressed (WHO Regional Office for Europe & OECD, 2015). As can be read in the Lancet Commission Report on Pollution and Health, pollution is the largest environmental cause of disease and premature death in the world, leading to approximately nine million premature deaths in 2015, far outstripping deaths for AIDS, tuberculosis, and malaria put together. In some countries, pollution-related diseases are responsible for more than one in four deaths (Landrigan et al., 2018). A poor environment space is associated with a higher infant mortality and malnutrition, due to a higher incidence of infections and diarrhoea, as well as a greater predisposition to suffer respiratory and dermatological diseases. All this is expressed in multiple weaknesses that hinder the development of capacities in childhood (Clark et al., 2020). Boyce et al. pointed out that environmental stress has a negative influence on public health and that it is (or should be) an issue with pre-eminent political relevance: for some Americans, it is literally a matter of life and death (1999, p. 138). Therborn (2013) reminds us that “Environmental exclusion and marginalization, into barren lands, slums without sanitation, susceptibility to flooding and landslides, pollution” are part of the “Killing Fields of Inequality”, the very rude allegory chosen for the title of his book. Many systematic reviews report results comparing health or well-being in natural and synthetic environments: natural environments may have direct and positive impacts on well-being (Bowler et al., 2010); although Di Nardo et al. (2010) found contradictory and unexpected results, they conclude that urban design and the availability of green spaces are critical elements of individual and collective well-being, influencing both perceived health and ‘objective’ physical conditions; we also know that green space have a beneficial health effect (Lee & Maheswaran, 2011) and that strong positive associations were found between the amount of green space and perceived mental health and all-cause mortality, and a moderate association with perceived overall health (van den Berg et al., 2015). Idrovo (2011) studied the effects of the physical environment on life expectancy at birth for Mexican states, using fifty environmental indicators relating to demography, housing, poverty, water, soil, biodiversity, and forest resources; Four factors were extracted from the exploratory factor analysis: population vulnerability/susceptibility, and biodiversity (FC1), urbanization, industrialization, and environmental sustainability (FC2), ecological resilience (FC3), and free-plague environments (FC4); while FC2, FC3, and FC4 were found to be positively associated with life expectancy at birth, FC1 was negatively associated. Caiazzo et al. (2013) studied air quality to assess the health impacts of major emission sources in the United States: total combustion emissions in the United States account for approximately 200,000 premature deaths annually due to changes in PM2 concentrations, 5 and about 10,000 deaths due to changes in ozone concentrations. The main ‘suspects’ are road transport and energy production (2013, p. 198). According to the 2015 Lancet Commission on Health and Climate Change (Watts et al., 2015), the health impacts of climate change can be direct (e.g. heat waves and extreme weather events such as storms, forest fires, floods, or droughts) or indirect, i.e. mediated by the effects of climate change on ecosystems (e.g. agricultural losses

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and changes in disease patterns), and economies and social structure (e.g. migration and conflicts). In China, it is estimated that particulate matter concentrations have led to a decrease in average life expectancy of about 40 months, a link also found in other contexts (among many, see Turner et al., 2016 and Di et al., 2017 for the United States; European Environment Agency, 2018; Crouse et al., 2015 and Cakmak et al., 2018 for Canada; de Keijzer et al., 2017 for Spain). In an attempt to draw attention to these aspects, the National Institute of Statistics (ISTAT) launched in 2010 the BES project for the study of equitable and sustainable well-being, to evaluate the progress of society not only from an economic point of view, but also from a social and environmental point of view. To this end, traditional economic indicators, including GDP, have been integrated with indicators on the quality of life of people and the environment, starting to publish an annual report. Twelve domains2 and a set of 130 indicators are used to illustrate Italian well-being; among the domains, we find Health (ISTAT, 2020, p. 27), composed by 12 indicators, and Environment (ISTAT, 2020, p. 159), with 18 indicators. Since 2016 some BES indicators have been included among the planning and evaluation tools of the national economic policy and published in a specific annex to the Economic and Financial Document (DEF), helping to define economic policies and focusing attention on what their effects are on some fundamental dimensions of the quality of life. Although it is now common for statistical agencies to compile dashboards of well-being indicators, the decision to insert these dimensions in the DEF makes Italy a pioneer country for designing policies that go beyond GDP (ISTAT, 2020, p. 5).

14.2 Objectives of the Study The objective of the study is to provide a measure of environmental capital conservation for the 20 Italian Regions. To do this, we build an aggregative index (Environmental Capital Conservation Index, ECCI), of a partially non-compensatory nature, known as Mazziotta and Pareto Index (MPI) (Mazziotta & Pareto, 2017). ECCI is structured on the indicators of the environmental dimension proposed by the Equitable and Sustainable Well-being (BES3 ) dashboard (CNEL-ISTAT, 2019). BES indicators are divided into 12 domains. In the definition of national and territorial policies, the BES has an importance that has grown over the years. In these terms, the aims set by the legislator concern the pursuit of well-being in its many dimensions. Moreover, data contained in the BES support documented and transparent government decisions, ex-ante and ex-post evaluation of policies, and their monitoring (CNEL-ISTAT, 2019).

2

Health, Education and training, Work and life balance, Economic well-being, Social relationships, Politics and institutions, Safety, Subjective well-being, Landscape and cultural heritage, Environment, Innovation, Research and Creativity, Quality of services. 3 In Italian BES is the acronym of “Benessere Equo e Sostenibile”.

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Successively, we intend to determine the degree of correlation between ECCI’s results and the life expectancy at a regional level (Alaimo et al., 2021; Bruzzi et al., 2020; Landi et al., 2018). For our purpose, we have decided to decline the life expectancy due to a double way: the life expectancy at birth and the life expectancy in good health at birth. The first is an objective indicator because it identifies the average number of years that a child born in a certain calendar year can expect to live, on the basis of the information taken from the mortality tables of the Italian population. The second can be considered as a subjective indicator, since it describes the life expectancy using the prevalence of individuals who respond positively to the question of perceived health. In other words, we want to determine how the protection of regional environmental heritage can affect life expectancy in different Regions (Ivaldi & Testi, 2011, 2012) and, as a potentially two-way relationship, how high life expectancy stimulates the protection of regional environmental heritage.

14.3 Data The data is taken from the BES, composed of 12 different domains. In order to pursue our aims, the indicators belonging to the domain ‘Environment’ have been selected. The only indicators excluded are the following: internal material consumption (data were provisional), urban air quality (PM10), as the indicator was closely related to the nitrogen dioxide indicator, sea bathing coasts, as not all Regions have coastal areas, the sites contaminated, concern about the loss of biodiversity and the population exposed to the risk of landslides, due to the high number of missing data, the protected areas given the highly indicator number of protected areas present from one Region to another. As far as the test indicators are concerned, we have assumed two indicators, such as life expectancy at birth and life expectancy in good health at birth, both referable to the ‘Health’ domain. All indicators have been oriented according to a positive polarity. Higher values of the ECCI correspond to a better conservation of the environmental heritage (Table 14.1).

14.4 Method ECCI is obtained by applying MPI method. The MPI is a formative composite index to aggregate a set of non-substitutable indicators. Performing an aggregation of non-substitutable indicators implies the researcher must balance all the components (Maggino, 2017; Mazziotta & Pareto, 2018). The balancing consists of a non-linear function introducing a penalty for the statistical units characterized by unbalanced values of the indicators.

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Table 14.1 Indicators’ descriptions and sources

Environnemental Capital Conservation Index (ECCI)

Indicator

Description

Source (year)

Dispersion from municipal water supply

Percentage of total volume of total water losses in municipal drinking water distribution networks (difference between volumes fed into the network and authorized volumes supplied)

Istat (2015)

Landfill of municipal waste

Percentage of municipal waste going to landfill (including municipal waste streams entering and leaving other Regions) in total municipal waste collected

Ispra (2018)

Urban air quality—Nitrogen dioxide

Percentage of units in provincial capital municipalities with valid measurements exceeding the annual limit value for NO2 (40 µg/m3 )

Istat (2018)

Availability of urban greenery

Square meters of urban greenery per inhabitant

Istat (2018)

Satisfaction with the environmental situation

Percentage of people 14 years old and Istat more very or quite satisfied with the environmental situation (air, water, noise) of the area where they live

Population exposed to flood risk

Percentage of the population (2011 Ispra Census) living in areas with average (2017) hydraulic hazard. The indicator calculation is based on the National ISPRA Mosaic of Hydrogeological Planning, with reference to the P2 risk scenario

Wastewater treatment Percentage share of the pollutant loads that flowed into secondary or advanced plants, in equivalent inhabitants, compared to the total urban loads generated Electricity from renewable sources

Istat (2015)

Percentage of electricity consumption Terna covered by renewable sources on total (2018) gross internal consumption

Separate collection of Percentage of separately collected Ispra municipal waste municipal waste out of total municipal (2018) waste collected

Life expectancy

Waterproofing of the soil by artificial covering

Percentage of waterproofed soil in the Ispra total land area (2018)

Life expectancy at birth

Life expectancy expresses the average Istat number of years that a child born in a (2018) certain calendar year can expect to live (continued)

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Table 14.1 (continued) Indicator

Description

Source (year)

Life expectancy in good health at birth

It expresses the average number of years that a child born in a given calendar year can expect to live in good health, using the prevalence of individuals who respond positively (‘well’ or ‘very well’) to the question of perceived health

Istat (2018)

The method for calculating the synthetic index MPI can be summarized as a step-by-step process (De Muro et al., 2011; Mazziotta & Pareto, 2017): 1. Standardization of Indicators Considering the X = {xi j } matrix of n rows (statistical units) and m columns (individual indicators), we indicate with /

Σn Mx j =

i=1 x i j n

Σn i=1

e Sx j =

(

xi j − Mx j n

)2

After having constructed the matrix Z = {z i j }, we can write Z i j = 100 ±

(x i j − Mx j ) 10 Sx j

where X i j is the value of j indicator in i unit and ± is the sign of the relation between j indicator and the phenomenon to be measured (e.g. the environmental capital in the Italian Regions). 2. Calculation of Horizontal Variability It is necessary to calculate the vector of the coefficients of variation C V = {cv i } on the matrix Z = {z i j }, in the following way: CVi =

Szi Mzi

where /

Σm Mzi =

j=1 z i j

n

e Szi =

3. Construction of the Synthetic Index

Σn j=1

(

z i j − Mzi m

)2

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The synthetic index of the i unit M P cvi derives from the following formula: ( ) M P cvi = Mzi 1 − cv i 2 = Mzi − Szi cv i in which the arithmetic mean of the standardized indicators is corrected by subtracting a quantity (the product Szi cv i ) proportional to the average quadratic deviation and a direct function of the coefficient of variation. In this way, units with similar standardized values, i.e. in a similar proportion to the mean vector, are less penalized. In the present case study, all the indicators are oriented with a positive polarity. Therefore, higher values of the ECCI correspond to a better conservation of the environmental heritage.

14.5 Results The analysis of the results will be carried out according to the following scheme: 1. the most relevant results will be highlighted; 2. the correlation between ECCI’s results and life expectancy at birth and life expectancy in good health at birth will be discussed; 3. the difference in years between life expectancy at birth and life expectancy in good health at birth in relation to the level of environmental capital surveyed by ECCI will be discussed. The results of the ECCI show a rather marked gap between the first two classified Regions, where the best conservation of environmental capital is recorded, and the Regions classified in Table 14.2. Trentino Alto Adige and Aosta Valley, placed in the North of Italy, are the best Regions with an ECCI’s coefficient equal to 110.04 and 106.37, respectively. With a detachment of about four points, we find two other Regions of Northern Italy, such as Piedmont (102.26) and Friuli Venezia Giulia (102.02). Umbria (101.91), the island of Sardinia (101.75), Marche (101.64), and Abruzzo (101.17) complete the top eight of the ranking. Observing the first eight positions, we notice the absence of Regions of Southern Italy. The first Region of the South classified is Basilicata, with a score of 100.68. Looking at ECCI’s results, the worst Region is Sicily (90.04), preceded by Campania (92.65), Calabria (94.39), Lazio (95.17), and Molise (95.75), i.e. Regions of Central or Southern Italy. Figure 14.1 gives us a clear territorial breakdown of the results obtained by ECCI. It shows the orientation of the Northern Regions toward the conservation of the environmental heritage, more so than the Central and Southern Regions. On the North side, the Regions adjacent to the Alpine ridge show a marked tendency toward conservation of the environmental heritage (see Trentino Alto Adige, Valle d’Aosta, Piedmont, and Friuli Venezia Giulia among the best four Regions). Liguria is the exception in a picture that tends to be marked by high index values.

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Table 14.2 MPI results and life expectancies for each region Regione

ECCI

Abruzzo

101.17 83

Life expectancy at birth Life expectancy in good health at birth 57.2

Aosta Valley

106.37 81.9

61.1

Basilicata

100.68 82.6

55.9

Calabria

94.39 82.5

52.9

Campania

92.65 81.4

56

Emilia Romagna

97.02 83.5

59.2

Friuli Venezia Giulia 102.02 83.1

60

Lazio

95.17 83

59.3

Liguria

97.54 82.7

58.5

Lombardy

100.16 83.4

58.8

Marche

101.64 83.7

60.1

Molise

95.75 82.7

57.6

102.26 82.6

59.2

Piedmont Puglia Sardinia Sicily Tuscany

96.56 83

57.5

101.75 83.1

57.6

90.04 81.9 97.85 83.6

56 61.7

Trentino Alto Adige

110.04 84

67.7

Umbria

101.91 83.8

58.2

Veneto

98.22 83.6

59

Source Authors’ calculations

In Central Italy, Umbria, Abruzzo, and Marche (on the Adriatic side) show a good environmental capital, better than the Tyrrhenian side, penalized by the low values found in Lazio and Campania. Moving the attention to the Southern Regions, Basilicata appears as the only Region to establish an index result higher than the average distribution (99.16). The picture in the South is not particularly encouraging as far as the protection of the environmental heritage is concerned. The islands of Sardinia and Sicily, on the other hand, appear to be the antipodes. The high conservation of natural assets observed in Sardinia is contrasted with the worst performance of the whole distribution, represented by Sicily. Life expectancy at birth, represented in Fig. 14.2, is higher in northern and central Italy. Graphically, the Regions in the north-west, i.e. Liguria (82.7), Piedmont (82.6), Valle d’Aosta (81.9), Sicily (81.9), and Campania (81.4), where life expectancy is the shortest of all Italian Regions, appear as more disadvantaged on this indicator. Two other territorial macro-clusters can be identified in the North-eastern (Trentino Alto Adige (84), Veneto (83.6), Lombardy (83.4), Friuli Venezia Giulia (83.1)) and Central

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Fig. 14.1 Map of ECCI results by regions. Source Authors’ presentation

Italy (Umbria (83.8), Marche (83.7), Tuscany (83.6), Emilia Romagna (83.5)), where life expectancy is high. For how it concerns the life expectancy in good health (Fig. 14.3), Trentino Alto Adige and Calabria are at the antipodes of the map, as well as in terms of life expectancy in good health, with values equal to 67.7 and 52.9, respectively. Southern Regions tend to show lower life expectancy in good health. The macro-cluster formed by Calabria (52.9), Basilicata (55.9), Campania (56), Siciliy (56), and Puglia (57.5) collects the lowest life expectancies in good health of the entire Peninsula. The correlation analysis (Fig. 14.4) shows the existing intensity of the two-way relationships between the indicators analyzed. It can be noted that between ECCI and life expectancy, the relation is significant, assuming a value of Pearson’s correlation coefficient equal to 0.5. This relationship is reinforced if we consider ECCI and life expectancy in good health (0.7). This implies that the protection of a Region’s environmental heritage affects life expectancy to a certain extent. Since this is a two-way relationship, it is possible to state that higher life expectancy provides more incentives for more investment and environmental protection in that area. In particular, the good level of correlation between ECCI and healthy life expectancy shows that environmental quality can influence the health of residents living in a given area. The data concerning life expectancy in good health follows the ranking of the life expectancy in good health at a level equal to 0.5 of Pearson’s correlation coefficient.

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Fig. 14.2 Map of life expectancy at birth by regions

Fig. 14.3 Map of life expectancy in good health at birth by regions

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Fig. 14.4 Correlation matrix including ECCI, life expectancy, and life expectancy in good health. Source Authors’ calculations

Although the significance of this correlation is high, there is a deviation between the two types of life expectancy in some Regions. Examples can be traced back to Calabria and Sicily, Regions where there is a high differential between the two-life expectancy (29.6 and 25.9) and, at the same time, low index values (Table 14.2). On the contrary, where the difference is smaller (e.g. Trentino Alto Adige (16.3), Aosta Valley (20.8), Friuli Venezia Giulia (23.1), Piedmont (23.4), and Marche (23.6)), higher ECCI values can be found. Analyzing this last data together with ECCI values, it would seem that in the cases where the greatest deviation is found, the protection of the environmental heritage may be a key to increase the years of life lived in good health. Investing in the environment and in the natural resources present in one’s own territory can thus be a policy strategy aimed at pursuing concrete public health objectives (Ivaldi et al., 2018).

14.6 Conclusions Review studies claim that built environment factors such as green space, traffic and air pollution, water treatment, and polluting and non-renewable energy sources affect diverse health outcomes, health behaviors, and risk factors (Lachowycz & Jones,

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2011; Lee & Maheswaran, 2011; Götschi et al., 2008; Papas et al., 2007; WendelVos, 2007). However, existing evidence is assumed to be highly compromised by methodical shortcomings (Schulz et al., 2018; Diez-Roux, 2003). In order to overcome methodological criticalities, we have adopted an aggregative approach based on the use of a partially non-compensatory method. The use of a partially non-compensatory method of aggregation made it possible to limit the balancing effect that would have occurred in the case of uneven values of the indicators analyzed. Given the complexity of the issue, and the need to use multiple indicators in order to provide a precise measure of environmental protection, the use of a partially non-compensatory method would seem to optimize the accuracy of the measurement, limiting the margin of error in a less distortive sense. The evidences that emerge in our study can be connected to the correlation existing between ECCI and life expectancy at birth and life expectancy in good health. This result shows that environmental quality can have impacts on residents’ health in a given area. The protection of the environmental heritage may represent an effective strategy to increase the years of life lived in good health. As a consequence, investments in clear environment and natural resources protection can be considered a policy strategy aimed at pursuing concrete public health objectives. If this does not happen, an environmental trap could occur (Landrigan, 2017). The existence of an environmental trap implies that some Countries (or Regions) may experience both environmental degradation and decay in life expectancy. However, it is important to underline that the environmental trap does not follow automatic sequentially logic. There are other factors that could alter the cause-effect link between environmental degradation and lower life expectancy, first and foremost economic growth. The latter can harm the environment, but also generate additional income to increase (or preserve) longevity (He & Li, 2020; Mariani et al., 2009). Such an approach is of considerable interest, because knowing to what extent different health conditions depend on the protection of the environmental heritage would make it possible to improve resource allocation, investment, and maintenance strategies, activating different social and infrastructural policies that could also be effective in promoting health. In other words, living in a healthy and uncontaminated environment can help to combat some diseases linked to environmental degradation (Deaton, 2013; Ivaldi & Testi, 2012; Barrella & Spandonaro, 2002). A suggestion for future work focused on the analysis of environmental quality and public health concerns the possibility to study the dynamic aspect of indicators (Soliani et al., 2017). In particular, an analysis of the indicators over time seems useful to assess the extent to which situations of worsening environmental conditions, over a medium-long period of time, may be related to a greater risk to health. The analysis based on historical series, with the necessary precautions, can allow to define trends and provide forecasts for future developments. Furthermore, we recognize that the environment and its impacts on health may well be interpreted and experienced differently across social groups. According to Di et al. (2017), studies usually include people with socioeconomic status higher than the national average and who live in urban areas, thus lacking statistical power to estimate

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the health effects in underrepresented groups. Using an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up, they find significant adverse effects on health from exposure to PM2.5 and ozone even at concentrations below current national standards, more pronounced among minorities and people with low income. This finding is in line with Deguen and Zmirou-Navier’s (2010) review of the literature on the mechanisms through which environmental exposure may contribute to social health inequalities in Europe: some studies found that poorer people were more exposed to air pollution, while the opposite was observed in other work. A general pattern, however, is that regardless of exposure, individuals of lower socioeconomic status experience greater health effects from air pollution.

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Chapter 15

Viral Pandemic Caused by Intense Abuse on Environment Nandini Ghosh

15.1 Introduction Emerging viral disease outbreaks are major threats to human life. Every year viral disease outbreaks affect the life of millions of people worldwide. Sometimes, it is restricted within a geographical region affecting a large number of people causing epidemics. Recently, the outbreak of SARS-CoV-2 or Covid-19 spreads worldwide and becomes pandemic. It takes billions of human lives and puts the human race in front of the imminent danger of being swept away. Interestingly, most of the viral diseases attack humans due to species jump from its original host. Although the cause of species jump is debatable, the role of human exploitation of environment causing increasing emergence of viral outbreaks cannot be denied. Spread of virus occurs through skin contacts, droplets, air, water, blood, body fluid, etc., or through vectors like insects, rodents, etc. Climate change affects disease transmission by modulating transmission environment, transmission media, pathogen and the host. The spreading rate of an infection in a population depends on the basic reproduction number denoted as R0 . If R0 > 1, then infection is started spreading within a population, but it does not spread from one infected person to another if R0 < 1 (Obadia et al., 2012). In the case of R0 < 1, disease can only spread from the original source. Studies show that R0 value is greatly altered with climate change; therefore, converting a slow-spreading disease into high spreader. R0 is a temperature-dependent parameter. Global warming increases the R0 of vector-borne diseases. For example, West Nile Virus spreads through mosquito. Birds are the reservoir of this virus. Global warming helps this disease to emerge in newer areas which are at greater risk of outbreak than the place of origin (Kushmaro et al., 2015). Crimean Congo haemorrhagic fever spreads through ticks. It is found that increased temperature favours the rapid growth of ticks thereby accelerating the spread of virus (Wang-Shick, 2017). N. Ghosh (B) Department of Microbiology, Vidyasagar University, Midnapore 721102, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_15

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Many of the viruses infect human due to species jump. Such species jumps sometimes have devastating effects on human host, e.g. the HIV pandemic due to species jump of the virus from primates to human (Woolhouse et al., 2005), SARS and SARS-CoV-2 pandemics are also due to species jump probably from wild animals to human (Dhama et al., 2020). Human intervention in forest area and destruction of habitat for wild animals often cause species jump of virus. It is reported that the Ebola outbreak in 1994 occurs due to spillover of the virus from the forest area due to human interference with wild animals (Alexander et al., 2018). Bats are the reservoir of many viruses. It is thought that the recent pandemic of SARS-CoV-2 might spread from seafood market of Wuhan also due to exploitation of wild animals by human beings (Contini et al., 2020). Increasing exposure of human beings to wild animals is one of the major causes of cross-species virus transmission. Higher adaptability of virus renders them to transmit easily to alternative hosts like human beings. Human-induced changes like deforestation, agriculture expansion, tourism, bush meat hunting, introduction of domestic species, etc., may uphold viral host switching (Domingo et al., 2020). In this study, a systemic approach is taken to find out the effect of deforestation, climate change and pollution levels on viral disease outbreaks during the time period of 2000–2020. During this time, many epidemics took place, and finally, a devastating pandemic occurred all over the globe. The silent role of human being to invite this disaster has been thoroughly investigated in this study.

15.2 Materials and Methods 15.2.1 Overview In this study, the impact of global temperature change, deforestation and human activities on viral disease outbreaks has been assessed. During the period of 2000– 2020, several viral epidemics occurred across the globe, but the SARS-CoV-2 has crossed all of the geographical barriers and becomes a pandemic during 2020. Transmission of virus may occur through direct and indirect modes. Direct transmission occurs through direct contact and droplet spread; modes of indirect transmission are airborne, vehicle-mediated (e.g. water, food, blood, etc.) and vector-mediated (e.g. mosquito, ticks, rodents, etc.). During this time, the global climate also changed which is reflected as a change of temperature. Anthropogenic impacts on environment are another leading cause of climate change. In this study, data are collected from secondary sources and analysed statistically.

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15.2.2 Online Repositories All data are collected from online repositories. Data on viral disease outbreaks at different parts of the globe are collected from the official website of WHO (https:// www.who.int/). Data on global temperature change is collected from the website of NASA (https://climate.nasa.gov/). Data on air pollutants, greenhouse gases and total forest area are collected from World Bank (https://data.worldbank.org/indicator).

15.2.3 Data Collection Data are collected for the period of 2000–2020.

15.2.3.1

Disease Outbreaks

Data on viral disease outbreaks, number of infected and number of deaths across the globe during the period of 2000–2020 are collected. The original host of the virus and new host are investigated to see how many of them crossed zoonotic barriers.

15.2.3.2

Climate Change

From the last decade, global climate has changed due to human activities like industrialization, deforestation, excessive burning of fossil fuels, etc. Global temperature change is an important indicator of climate change. Data on global temperature change is collected to further correlate if it has any impact on viral outbreaks.

15.2.3.3

Data on Air Pollutants and Emission of Greenhouse Gases

Human activities greatly affect the air quality by increasing pollution on the one hand and by increasing greenhouse gases in the atmosphere on the other hand. Data on parameters of air pollution like PM 2.5, and greenhouse gases nitrous oxide (N2 O) emission, carbon dioxide (CO2 ) emission and methane emission are collected to see their effect on viral outbreaks. CO2 emission was measured in terms of kiloton (kt), N2 O emission was measured in terms of thousand metric tons of carbon dioxide equivalent and annual global increase of methane emission was measured in terms of parts per billion (ppb).

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Total Forest Area

The primary host of most of the viral diseases are animals other than human beings; however, human invasion into forest area often helps the virus to cross zoonotic barrier and choose human as their new host. Data on the percentage of land occupied by the forest is collected.

15.2.4 Limitations of Datasets During year-wise data collection, data on some pollution parameters are not found in the database. During the early 2000s, data on suspended particulate matter is found in 5 years interval. To overcome this limitation, linear interpolation has been done to predict the yearly data points. Few data on N2 O and CO2 emissions are not updated after 2018. Linear extrapolation is done for these datasets. Interpolation and extrapolation are performed using the following formula: f (x) = f (x0) + (x − x0) [{ f (x1) − f (x0)}/(x1 − x0)] where x is the required data point, x0 is the initial data point and x1 is the final data point.

15.2.5 Statistical Analysis The number of affected persons due to the viral outbreak is separately correlated with average temperature, forest area, PM 2.5, N2 O, CO2 emission and a global average increase of methane emission. Pearson’s correlation coefficient is calculated at p < 0.05. Lowess smoothening is done before correlating the datasets with smoothening factor of 0.5.

15.3 Result 15.3.1 Epidemics and Pandemics During 2000–2020 After being started from the end of 2019, the entire period of 2020-21 and few months of 2022, the human civilization was under the grip of a tiny virus that is SARS-CoV2 or Covid-19 that has affected the life of billions of people worldwide; however, viral outbreaks are very common and often turn into epidemics and pandemics. Seasonal influenza is the most frequently encountered viral disease by mankind, but

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this seemingly innocent disease takes the life of 290,000–650,000 people each year causing respiratory disease alone (World Health Organization, 2015a). According to WHO (World Health Organization), this estimate is excluding the deaths due to influenza-related cardiovascular disease (Iuliano et al., 2018). Different types of influenza virus caused epidemics and pandemics during 2000–2020. Besides human seasonal influenza, avian influenza and swine flu often cross the animal–human interface and cause disease outbreaks. When a virus is regularly transmitted from animal to human, it is called zoonotic virus and, when the transmission of virus occurs from animal to human for the first time, it is called a spillover event (World Health Organization, 2020). Animal influenza viruses are less commonly transmitted to human; however, they sometimes cross the animal–human interface and infect human. Different variants of avian influenza virus like, H5N1, H5N6, H7N9, etc., caused epidemics during 2000–2020. After crossing the zoonotic barrier, it often undergoes genetic recombination and sometimes becomes more powerful and causes pandemics. The devastating Spanish flu was actually avian influenza that became a pandemic in 1918. Similarly, human is not the host for swine influenza virus, but a close proximity of human to infected pigs may cause viral transmission. Swine flu caused by H1N1 became a pandemic during 2009–2010. It was first reported in the USA and Mexico and then transmitted throughout the world (Fig. 15.1a) (Singer, 2009). More than 25,584,595 people were affected by this flu (Patel et al., 2010) with a death toll of 18,449 as confirmed by WHO. Beside influenza virus, another name of threat is coronavirus. This is a newly emerged highly transmitting severe disease of twenty-first century. At the end of 2002, first case of severe acute respiratory syndrome (SARS) associated coronavirus disease was detected in China and then it became an epidemic in 2003 (Zhong et al., 2003). Over 8000 confirmed cases from more than 25 countries and minimum of 730 deaths are confirmed by WHO. SARS-CoV generally infects civet cats and was transmitted to human in 2002 (World Health Organization, 2020). Another variant of coronavirus is the Middle East respiratory syndrome coronavirus (MERS-CoV) that caused an epidemic in 2012. This virus is transmitted to human from infected dromedary camels. By analysing several viral genomes, it is assumed that this virus may be originated from bat and then changed its host to camel (World Health Organization, 2015b). This disease occurred since 2012 and at least 858 people in 27 countries died as reported by WHO. The most dangerous form of coronavirus arises in 2019 at Wuhan, China which is designated as SARS-CoV-2. Up to the end of 2020, it has affected 79,000,000 people in the world of which 1,700,000 died (Fig. 15.1b). According to the report of WHO, the zoonotic source of this virus is unknown and it is probably originated from wild animals due to the spillover event. The mode of transmission of both influenza virus and coronavirus are dropletmediated. Other than these, measles is another droplet-mediated viral disease that infected 2.9 lakh people in 28 countries during 2000–2020 including 1637 deaths. Besides, the world also faced other viral epidemics and pandemics which are transmitted through vector or through body fluids. The spread of vector-borne diseases depends greatly on facilitating environment for the growth of the vectors. Dengue, chikungunya, Rift Valley fever, yellow fever, West Nile fever, Japanese encephalitis,

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a

b

Fig. 15.1 World map showing affected countries in swine flu (a) and SARS-CoV-2 (b) pandemic. Source Author’s compilation from https://mapchart.net

Zika virus, etc., are the major vector-borne diseases that caused epidemics during 2000–2020. All four panels of Fig. 15.2 represent the affected countries due to some of the viral outbreaks that occurred during 2000–2020. The primary host of this virus are generally birds or non-human primates, but they frequently change their host to human due to several reasons such as less availability of original host, habitat destruction, land use, climate change, human exposure, increasing adaptability, etc. (Semenza & Menne, 2009). High temperature and rainfall facilitate mosquito-borne diseases. Viral diseases that are transmitted through body fluids or direct contact with infected animals or their blood in slaughterhouse increase the disease burden. Among these vehicle-mediated viral diseases, AIDS is the most dangerous. It is creating a global epidemic since 1980 and increase disease burden every year till now. It is believed that, possibly during 1920, it crosses the species barrier from chimpanzee to human (Gao et al., 1999). Ebola virus and Nipah virus are also transmitted through

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a

b

c

d

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Fig. 15.2 World map showing affected countries due to the epidemic of dengue (a), chikungunya (b), Ebola (c) and avian influenza (d). Source Author’s compilation from https://mapchart.net

body fluids or direct contact with infected animals. Their primary host are non-human primates and bats, respectively. It is found that most of the viral diseases change their host from animals or birds to human mostly due to anthropogenic activities and causes epidemics and pandemics. Excessive abusing of wild animals, deforestation, slaughtering of wild animals and climate change are the major factors that facilitate the transmission of virus from their original host to human. Table 15.1 represents the details host shift of the viruses that cause epidemics and pandemics during 2000–2020. These data were collected from the website of WHO representing year-wise disease outbreaks in different parts of the world.

15.3.2 Climate Change During 2000–2020 The global climate is slowly changing over the last decade. Climate represents the average weather of a particular area over a long period of time. According to WMO (World Meteorological Organization) (https://public.wmo.int/en), indicators of climate change are global surface temperature rise, increase in ocean temperature, sea level rise, melting of ice sheets, CO2 emission and ocean acidification. In this paper, global average temperature change from 2000 to 2020 is considered as an indicator of global climate change as surface temperature has a direct impact on spreading of many diseases, especially those which are vector-borne. Temperature change from 2000 to 2020 is represented in Fig. 15.3. Data on year-wise epidemic

Turkey, Mauritania, Pakistan, Kosovo

2006

Crimean Congo haemorrhagic fever Nairovirus

Chikungunya virus

Sudan, Republic of Congo, France, Kenya, Italy, Mombasa, Chad

2006, 2018–20

HIV

Chikungunya

Pakistan

Azerbaijan, Influenza virus Bangladesh, Cambodia, China, Egypt, Lao People’s Democratic Republic, India, Indonesia, Iraq, China, Pakistan, Myanmar, Thailand, Turkey, Vietnam, Netherlands

2019

2007–13, 2016–17

Causal organism

AIDS

Affected area

Year of outbreak

Avian Influenza (H5N1)

Name of the disease

Table 15.1 Epidemics and pandemics during 2000–2020

Cattle

Human/wild primates

Bird

Chimpanzee

Original host/ reservoir host

Human

Human

Human

Human

New host

(continued)

Vector=borne (ticks)/ infected animal blood. Human-to-human transmission occurs through infectious blood or body fluid

Vector borne (mosquitoes—Aedes aegypti and Aedes ablopictus)

Direct contact/ droplet-mediated

Body fluid

Mode of transmission

206 N. Ghosh

Argentine Republic, Panama, USA

Namibia, Sudan, Chad Hepatitis E

2019

2004

Hantavirus pulmonary syndrome

Hepatitis E

Benin, Germany, Liberia, Nigeria, Togo, Sierra Leon, Burkina Faso

2012, 2016–19

2005

Lassa fever

Marburg haemorrhagic fever

Angola, Uganda

India

Japanese Encephalitis 2005

Republic of Congo, Guinea, Liberia, Nigeria, Uganda, Sudan

2014–16, 2018–19

Ebola haemorrhagic fever

Marburg virus

Lassa mammarenavirus

Japanese Encephalitis virus

Orthohantavirus

Ebola virus

Dengue virus

Afghanistan, Brazil, Burkina Faso, EI Salvador, Cote D’Ivoire, Egypt, France, India, Cape Verde, Indonesia, Jamaica, Pakistan, Spain, Srilanka, Sudan, Uruguay, Timor Leste

2004–06, 2008–09, 2017–19

Dengue

Causal organism

Affected area

Year of outbreak

Name of the disease

Table 15.1 (continued)

Non-human primates

Multimammate rat

Bird

Animal (swine)

Rodents

Fruit bat

Non-human primate

Original host/ reservoir host

Human

Human

Human

Human

Human

Human

Human

New host

(continued)

Body fluid/through bat

Contaminated food or household items

Vector-borne (mosquitoes—Culex sp.)

Contaminated water

Body fluids

Blood or body fluid

Vector-borne (mosquitoes—Aedes aegypti)

Mode of transmission

15 Viral Pandemic Caused by Intense Abuse on Environment 207

Malaysia, Singapore, Bangladesh, India

Peru, France

2017, 2020

2018

2016, 2020

2005

Monkeypox

Nipah virus

Oropouche virus

Polio

Indonesia

Congo, Nigeria, Central African Republic

Countries in the Middle East, Africa, South Asia

2012

MERS CoV

Human

Human

New host

Sloth

Bat

Human

Human

Human

Rodents, primates Human

Camels

Cattle

Original host/ reservoir host

Polio virus/enterovirus C Human

Oropouche orthobunyavirus

Nipah virus

Monkeypox

Coronavirus

Many of the European Measles virus countries, Republic of Congo, Korea, Ireland, Chile, China, Colombia, Australia, Brazil, Canada, Ecuador, Ethiopia

2001, 2011, 2015, 2019

Measles

Causal organism

Affected area

Year of outbreak

Name of the disease

Table 15.1 (continued)

Direct contact (continued)

Vector-borne (midge Culicoides paraensis and some mosquito species)

Direct contact with infected animals/body fluids/ consuming food that has been contaminated by body fluids of infected animals

Direct contact/body fluids

Droplet-mediated

Droplet-mediated

Mode of transmission

208 N. Ghosh

2009–10

1999–2010

Swine flu (H1N1)

West Nile fever

Influenza virus

Coronavirus

Coronavirus

Rift Valley fever virus (phlebovirus)

Causal organism

Outbreak occurs in the West Nile virus USA. Other affected countries are Canada, Israel, Rumania, Greece, Albania, Russian Federation

Worldwide

Worldwide

Mostly East Asia

2003

2019–22

SARS

South Africa, Somalia, Niger, Kenya, Madagaskar, Sudan, Yemen, Tanzania, Soudi Arabia, France

Small sporadic outbreak at 2008–09, widespread epidemic in 2010–11

Rift valley fever

SARS-CoV-2

Affected area

Year of outbreak

Name of the disease

Table 15.1 (continued)

Birds

Pig

Unknown (probably bat)

Civet cat

Cattle

Original host/ reservoir host

Human

Human

Human

Human

Human

New host

(continued)

Vector-borne (mosquito—Culex sp.)

Direct contact/ droplet-mediated

Droplet-mediated

Droplet-mediated

Direct contact/body fluids of livestock such as cattle, sheep, goat, etc., or sometimes vector-borne (mosquito of Aedes spp.)

Mode of transmission

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2016

2015–16

Yellow fever

Zika virus

Causal organism

Brazil

Zika virus

Angola, Brazil, China, Yellow fever Bolivarian Republic of virus—Flavivirus Venezuela, Cameroon, Chad, Central African Republic, Cote d’Ivoire, Republic of Congo, French Guinea, Ethiopia, Kenya, Liberia, Nigeria, Mali, Ghana, Netherlands, Paraguay, Peru, Sierra Leon, Sudan, Surinam, Togo, Uganda

Affected area

Source Author’s compilations from the official website of WHO (https://www.who.int/)

Year of outbreak

Name of the disease

Table 15.1 (continued)

Rhesus monkey

Non-human primate

Original host/ reservoir host

Human

Human

New host

Vector-borne (mosquitoes—Aedes sp.)

Vector-borne (mosquitoes—Aedes aegypti)

Mode of transmission

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Fig. 15.3 Change in global temperature from 2000 to 2020. Source NASA/GISS, Image adopted from https://climate.nasa.gov/

events all over the world is retrieved from the website of WHO. The total number of affected persons in yearly epidemic and pandemic events is statistically correlated with global temperature to see the effect of temperature on the events of epidemic. During 2000–2020, mankind encountered two pandemics—swine flu in 2009 and SARS-CoV-2 in 2020. As the number of deaths depends on many factors like efficacy of disease management, healthcare system, availability of medicine, etc., the total number of affected persons is selected as a parameter to represent the severity of epidemic and pandemic. Pearson correlation coefficient is calculated by taking the total number of infected persons in the x-axis and the global yearly average temperature in the y-axis. It shows a positive correlation with R = 0.789 which is significant at p < 0.05 (Fig. 15.4a).

15.3.3 Effect of Pollution on Disease Outbreaks Greenhouse gases and suspended particulate matters are taken as pollution indicators. Carbon dioxide, nitrous oxide, annual rise of methane and PM 2.5 are correlated with the total number of infected persons in yearly epidemic events. Urbanization and industrialization increase greenhouse gases in the air. It increases the global temperature on the one hand and pollutes the environment on the other hand. Nitrous oxide, carbon dioxide emission and annual rise of methane emission are moderately positively correlated with the total number of affected persons with R = 0.578, 0.469 and 0.669, respectively, significant at p < 0.05. No significant correlation was observed with PM 2.5. Figure 15.4b, c represents a comparative account of annual disease burden with annual nitrous oxide, carbon dioxide emission, rise of methane emission and PM 2.5. The result of the correlation is depicted in Table 15.2.

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Fig. 15.4 A comparative account of yearly affected individuals due to viral outbreak with increase in global average surface temperature (a), CO2 and N2 O emissions (b), annual global increase of methane (c) and percentage of forest area (d). Note that CO2 emission was measured in terms of kiloton (kt), N2 O emission was measured in terms of thousand metric tons of carbon dioxide equivalent, methane emission was measured in terms of parts per billion (ppb). Source Author’s computations Table 15.2 Correlation between the total number of viral diseases affected individuals worldwide during 2000–2020 and the global temperature, greenhouse gases, pollutants and forest area Parameters

R-value

Significance

Total number of affected individuals versus increase in global surface temperature

0.789

Significant at p < 0.05

Total number of affected individuals versus nitrous oxide emission

0.578

Significant at p < 0.05

Total number of affected individuals versus carbon dioxide emission

0.469

Significant at p < 0.05

Total number of affected individuals versus annual global rise of methane

0.669

Significant at p < 0.05

Total number of affected individuals versus PM 2.5

−0.395

Not significant

Total number of affected individuals versus forest area (% of land)

−0.588

Significant at p < 0.05

Source Author’s computations

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15.3.4 Effect of Deforestation on Disease Outbreaks Wild animals are the natural reservoirs of many viral diseases. Most of the viruses chose human as their host due to the spillover event or for their zoonotic nature. Loss of habitat of wild animals due to deforestation is a major cause of their death and increasing interaction with human. Here, the effect of deforestation on the total number of yearly affected individuals in viral disease outbreaks is statistically correlated. This is found to be significantly negatively correlated with R = −0.588 at p < 0.05. So, disease burden is inversely proportional to the percentage of forest area (Fig. 15.4d).

15.4 Discussion Coevolution of viruses and animals occurs since the time of origin of life. Animals and birds are the natural hosts of many viruses that sometimes infect human due to crossing the animal-human interface. Human commonly acts as a dead end host for these zoonotic viruses (Hurst & Adcock, 2000). They cannot pass to other humans or go back to their natural host after infecting a human host; however, sometimes, they are mutated and can infect other human beings. At that time, they are very dangerous and can cause epidemics and even pandemics. Antigenic drift and antigenic shift are two major events that turn animal/bird influenza virus into human influenza virus causing seasonal flu and flu pandemic (Wang-Shick, 2017). Antigenic drift is the small mutation that creates a new strain of seasonal influenza virus and antigenic shift is the drastic change causing an exchange of RNA segment producing hybrid virus that cause pandemics (Wang-Shick, 2017). During 2000–2020, the human population faced many epidemics and two pandemics only in 10 years of interval. The severity of some of the viral outbreaks increases in recent times. The Ebola virus was discovered in 1976. The largest outbreak of this virus occurred in 2014– 15 and another large outbreak occurred again in 2018–19, although the fatality rate decreases (Buscema et al., 2020) due to the betterment of treatment strategy. Hantavirus pulmonary syndrome is first reported in 2000, but it comes again in 2012 and then in 2019 (www.who.int/). Until recently, coronavirus is regarded as a relatively harmless virus (Domingo & Rovira, 2020), but SARS in 2003 and MERS in 2012 are large epidemics and a similar type of coronavirus SARS-CoV-2 has shaken the entire human civilization during 2019–22. Measles infect human since long back, but after 2001, large epidemic of measles occurred in 2011 and 2015 (Zipprich et al., 2015) and 2019 (Arendt & Scherr, 2019). In 2020, Mayaro virus disease occurs for the first time in our selected time span (www.who.int/). The underlying cause of this increased viral outbreak is investigated in this study. Climate is a major factor affecting the survival, reproduction and distribution of pathogens, vectors and hosts (Wu et al., 2016). Vector-borne diseases are greatly affected by climatic factors. This paper emphasizes on the role of an average increase

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in global temperature on increasing viral disease outbreaks. In these 20 years, the global average surface temperature increases by 0.88˚C. Beside climate change, role of air pollutants in increasing viral disease outbreaks is also investigated. It is found that pollution level is significantly positively correlated with viral disease outbreaks. This study is in accordance with previous literatures (Domingo & Rovira, 2020). Pollutants continuously damage human respiratory and cardiovascular system (Manisalidis et al., 2020). Studies show that greater exposure to pollutants increases oxidative stress; free radical-mediated damage of the respiratory system may reduce resistance against respiratory viral infections thereby increasing the rate of infection (Ciencewicki & Jaspers, 2007). It is reported that survival, stability and transmission of the influenza A virus are greatly increased by environmental pollutants (Ciencewicki & Jaspers, 2007). Although in this paper no significant correlation of PM 2.5 is found with viral outbreaks, studies suggest reducing exposure of elderly people to PM 2.5 significantly reduces their susceptibility towards respiratory pathogens (Chen et al., 2018). Air pollutants on the other hand increase global temperature which again has a positive role in increasing viral outbreaks. Previously, several regional studies are performed to assess the role of meteorological parameters, pollutants, etc., on disease outbreaks. This is probably the first systemic approach to correlated global climate change, pollution level and deforestation with viral outbreaks. After critical reviewing of WHO data, it is found that some of the epidemics are regional at the beginning, but slowly spreading throughout the world. The recent pandemic of SARS-CoV-2 spreads throughout the world indicating that only the local climate is not responsible for disease outbreaks. So, in this study, several global climatic parameters are taken into consideration keeping in mind the global view of disease spreading. Another most important parameter is deforestation. It increases animal-human interaction and thereby increases the chance of spillover event of virus, e.g. Ebola. Here, global forest area is correlated with disease outbreaks and it gives a significant negative correlation. Increasing medical facilities, the development of vaccines may reduce the fatality rate of viral diseases, but it cannot stop viral outbreaks. The recent pandemic of SARS-CoV-2 is a ready example of that. HIV is controlled in recent times due to increasing awareness, vaccination strategies almost eradicated polio, but still, sometimes, outbreaks of these viruses are encountered. The recent statistics of increasing disease outbreaks are a bit scary. At this point, the foremost necessity is to reduce torture on nature. Climate change is the ultimate result of increasing pollution and deforestation. All these three parameters, i.e. pollution, deforestation and climate change, separately facilitate the survival and spreading of viruses. So, the combined effect of all these parameters may be devastating. This study focused that only 0.88˚C increase in global temperature, 0.57% decrease in total forest area and increase in greenhouse gases (0.005% increase in CO2 , 0.002% increase in N2 O and 38% increase in methane) highly increase global disease burdens. The effect should be manifold if it is viewed at the regional level. So, in addition to inventing new medicines, it is equally important to take measures to keep our planet healthy. That may have some long-term effects on reducing viral disease events.

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Acknowledgements The author is thankful to the Hon’ble Vice-Chancellor of Vidyasagar University to provide infrastructural facility to carry out this research.

References Alexander, K. A., Carlson, C. J., Lewis, B. L., Getz, W. M., Marathe, M. V., Eubank, S. G., & Blackburn, J. K. (2018). The ecology of pathogen spillover and disease emergence at the humanwildlife-environment interface. In The connection between ecology and infectious disease (pp. 267–298). Springer, Cham. Arendt, F., & Scherr, S. (2019). Investigating an issue–attention–action cycle: A case study on the chronology of media attention, public attention, and actual vaccination behavior during the 2019 measles outbreak in Austria. Journal of Health Communication, 24(7–8), 654–662. Buscema, M., Asadi-Zeydabadi, M., Lodwick, W., Nde Nembot, A., Bronstein, A., & Newman, F. (2020). Analysis of the ebola outbreak in 2014 and 2018 in West Africa and congo by using artificial adaptive systems. Applied Artificial Intelligence, 34(8), 597–617. Chen, C. W., Hsieh, Y. H., Su, H. C., & Wu, J. J. (2018). Causality test of ambient fine particles and human influenza in Taiwan: Age group-specific disparity and geographic heterogeneity. Environment International, 111, 354–361. Ciencewicki, J., & Jaspers, I. (2007). Air pollution and respiratory viral infection. Inhalation Toxicology, 19(14), 1135–1146. Contini, C., Di Nuzzo, M., Barp, N., Bonazza, A., De Giorgio, R., Tognon, M., & Rubino, S. (2020). The novel zoonotic COVID-19 pandemic: An expected global health concern. The Journal of Infection in Developing Countries, 14(03), 254–264. Dhama, K., Patel, S. K., Sharun, K., Pathak, M., Tiwari, R., Yatoo, M. I., … Rodriguez-Morales, A. J. (2020). SARS-CoV-2 jumping the species barrier: zoonotic lessons from SARS, MERS and recent advances to combat this pandemic virus. Travel Medicine and Infectious Disease, 101830. Domingo, J. L., & Rovira, J. (2020). Effects of air pollutants on the transmission and severity of respiratory viral infections. Environmental Research, 109650. Gao, F., Bailes, E., Robertson, D. L., Chen, Y., Rodenburg, C. M., Michael, S. F., … Hahn, B. H. (1999). Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes. Nature, 397(6718), 436–441. Hurst, C. J., & Adcock, N. J. (2000). Relationship between humans and their viruses. Viral Ecology, 519. Iuliano, A. D., Roguski, K. M., Chang, H. H., Muscatello, D. J., Palekar, R., Tempia, S., … Mustaquim, D. (2018). Estimates of global seasonal influenza-associated respiratory mortality: A modelling study. The Lancet, 391(10127), 1285–1300. Kushmaro, A., Awerbuch-Friedl, T., & Levins, R. (2015). Temperature effects on the basic reproductive number (R0 ) of west nile virus, based on ecological parameters: endemic vs. new emergence regions. Journal of Tropical Diseases & Public Health, S1–001. Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and health impacts of air pollution: A review. Frontiers in Public Health, 8, 14. Obadia, T., Haneef, R., & Boëlle, P. Y. (2012). The R0 package: A toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Medical Informatics and Decision Making, 12(1), 1–9. Patel, M., Dennis, A., Flutter, C., & Khan, Z. (2010). Pandemic (H1N1) 2009 influenza. British Journal of Anaesthesia, 104(2), 128–142. Semenza, J. C., & Menne, B. (2009). Climate change and infectious diseases in Europe. The Lancet Infectious Diseases, 9(6), 365–375. Singer, M. (2009). Pathogens gone wild? Medical anthropology and the “swine flu” pandemic. Medical Anthropology, 28(3), 199–206.

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Wang-Shick, R. (2017). Chapter 15—Influenza viruses. In Molecular virology of human pathogenic viruses (pp. 195–211). Academic Press, ISBN 9780128008386. Woolhouse, M. E., Haydon, D. T., & Antia, R. (2005). Emerging pathogens: The epidemiology and evolution of species jumps. Trends in Ecology & Evolution, 20(5), 238–244. World Health Organization. (2015a). A manual for estimating disease burden associated with seasonal influenza. World Health Organization. World Health Organization. (2015b). Middle East respiratory syndrome coronavirus (MERS-CoV): Summary of current situation, literature update and risk assessment (No. WHO/MERS/RA/ 15.1). World Health Organization. World Health Organization. (2020). Origin of SARS-CoV-2, 26 March 2020 (No. WHO/2019-nCoV/ FAQ/Virus_origin/2020.1). World Health Organization. Wu, X., Lu, Y., Zhou, S., Chen, L., & Xu, B. (2016). Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environment International, 86, 14–23. Zhong, N. S., Zheng, B. J., Li, Y. M., Poon, L. L. M., Xie, Z. H., Chan, K. H., … & Guan, Y. (2003). Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People’s Republic of China, in February, 2003. The Lancet, 362(9393), 1353–1358. Zipprich, J., Winter, K., Hacker, J., Xia, D., Watt, J., & Harriman, K. (2015). Measles outbreak— California, December 2014–February 2015. MMWR. Morbidity and Mortality Weekly Report, 64(6), 153.

Chapter 16

Does Conservation Capital Lead to Improvements in Health-Adjusted Life Expectancy? Richardson Kojo Edeme

16.1 Introduction Life expectancy is generally accepted as a measure of longevity over the years. However, the desire for paradigm shift for a sustainable human development in relation to sustainable environment has triggered discussions about better measures of healthier life among stakeholders and policy-makers. Even though studies have established a positive correlation between well-being and life expectancy, the concern is whether conservation capital accentuates health-adjusted life expectancy (United Nations Development Programme (UNDP) Human Development Report, 2020). Health-adjusted life expectancy is the number of years an individual is expected to live given the current morbidity and mortality conditions existing in a particular country in relation to available health infrastructure and healthy environment. It is a more comprehensive indicator for measuring the quality of life as it represents the best summary measure for estimating the overall level of health for a population (Labbe, 2010). The World Health Organization (WHO) used health-adjusted life expectancy as an official measurement method to provide information on the average level of health for the population of member states. This is premised on the fact that it represents a wholistic measure that reflects mortality and morbidity. It is also an important measure that could provide information for policy-making decisions (Hyder et al., 2012; Lee et al., 2016). For the past decade, several countries have witnessed improvement in the human development index with sub-Saharan Africa and South Asia exhibiting the greatest progress. The UNDP Human Development Report (2019) highlighted that people R. K. Edeme (B) Department of Economics, Dennis Osadebay University, Anwai, Asaba, Nigeria e-mail: [email protected] Research Fellow, Institute of Business Research, University of Economics, Ho Chi Minh City, Vietnam © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_16

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living in very high-income development countries can expect to live 19 years more than those living in the group of low human development countries. Studies such as Wu (2017), Prady and Sy (2019), Zaninotta and Steptoe (2019) asserted that the improvement in the human development index was driven by changes in health, education, income, and environment. Also, of the 189 countries for which the human development index is calculated, 59 have moved into the very high-income development group, while 38 countries fell in the low human development index group. Ireland had the highest increase in the human development index, moving up 13 places, while Turkey, the Dominican Republic, and Botswana moved up 8 places. Moreso, the Syrian Arab Republic, Libya, and Yemen experienced a decrease in the human development index, falling 27 places, 26 places, and 20 places, respectively. One important channel in promoting environmental sustainability in relation to healthier life is conservation capital. Conservation capital is the application of environmental regulations coupled with technological advancement to protect the environment both for the present and future generations. While environmental regulations are designed to protect the environment, technological advancement determined by market forces protects the environment in the absence of regulations (Adrian & Nadkami, 2001; Green & Morton, 2000). Kinney (2018), Kolasa-Wi˛ecek and Suszanowicz (2019) further argued that as the desire for global natural capital is on the increase, new investment financing arrangement is evolving Apart from private investment, public expenditure is being conducted in order to conserve capital. It has been asserted that, while a reduction in environmental degradation connotes a reduction in input use, it also has the potency to improve the ability of the environment to regenerate and improve its quality which enhances the quality of life (Chen et al., 2013; Dockery & Pope III, 2014). Porter and van der Linde (1995), Jaffer and Palmer (1997), Jouvet et al. (2007) averred that environmental regulations retards human development. At another extreme, Hill et al. (2019) contended that exposure to environmental degradation has the ability of reducing life expectancy. In the last decades, several countries have witnessed environmental degradation which has given rise to global warming. Most worrisome, however, is the attendant consequence on quality of life. Several stakeholders and policy-makers have concentrated on the quality of the environment with the desire to come up with strategies to enhance quality of life through environmental sustainability. Kinney (2018) averred that, in developing countries, it is increasingly becoming difficult to avoid environmental degradation emanating from natural resource depletion. It, therefore, be argued that the quality of life is ultimately affected by the quality of the environment. Upholding this assertion, Muhammad et al. (2015) noted that poor environmental policy coupled with low funding are dangerous to the attainment of healthier life. In recent years, the concern is that this compromises the attainment of sustainable human development, especially in relation to the sustainable development agenda. To understand the full dimensions of the problems, it is necessary to examine whether conservation capital improves health-adjusted life expectancy. Previous studies such as Jouvet et al. (2007) and Nadia (2013) examined the relationship between environmental degradation and life expectancy. Pîrlogea (2012) investigated the influence of energy in the process of human development and found

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that high level of energy is hazardous to human development. Wang et al. (2018) explored the connection between renewable energy consumption, economic growth, and human development in Pakistan. It was reported that renewable energy consumption had a negative effect on human development, while carbon dioxide emission is positively correlated with life expectancy. It was further found that feedback mechanism between environmental factor and human development process in the long run. Both territorial and consumption-based carbon emissions were found to have an effect on life expectancy. However, the interaction effect of carbon emissions and income on life expectancy was stronger than that of carbon emission. The concern is to achieve simultaneous low carbon emissions and high life expectancy; it is only attained with low population growth. While economic and environmental objectives seem to contradict each other, this certainly seems to be the case for countries with high per capita gross domestic product. Studies have demonstrated that a wide range of human development opportunities exist in countries with low level of carbon dioxide emission, this does not necessarily follow a global pattern. Steinberger et al. (2012) found that high life expectancy is associated with low carbon emissions, but high incomes are not. This portends that people living in countries with low environmental sustainability may attain high life expectancy but not necessarily quality health care. The implementation of environmentally sustainable measures increases quality of life. However, the effect is much stronger in middle-income countries than in low- and high-income countries. Sheraj (2017) highlighted that loss of ecosystems supports global atmospheric oxygen and carbon dioxide balance reduces life expectancy. To protect the environment, several ecosystem service protection and human development policies have been introduced. Ideally, linking conservation and human development policies is designed and evaluated by analyzing the impacts of ecosystems on livelihoods and well-being. For the attainment of sustainable human development, there is a need to examine the effect of the environment on quality of life. Garg (2017) opined that improvement of human well-being depends largely on the available indicators which must be enhanced through conservation capital. In the case of Bangladesh, Hossain et al. (2020) explored the impact of environmental degradation on life expectancy and found that environmental degradation has a negative and significant effect on life expectancy in both short run and long run. This is indicative that well implementation of environmental protection laws and regulations is important to enhance life expectancy. In another variant of studies, social policy expenditure has also been found to be associated with life expectancy. Sanya and Yemisi (2017) contended that the greatest shocks to life expectancy are accounted for by government expenditure on health. Sghari and Hammami (2016) contended that health-care expenditure has been rising in many countries, including the Netherlands, it is unclear to what extent increased healthcare spending caused the increase in life expectancy. Abdulganiyu and Tijjani (2021) examined the nature of the relationship between healthcare expenditure and life expectancy in a panel of 45 African countries, disaggregated into different sub-regions, using the fixed effect method. It was found that healthcare spending is an important predictor of life expectancy. Das and Ivaldi (2021) analyzed

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the interlinkages between environmental pollution and health cost in a list of countries and revealed mixed results of their interlinkages. Further, in another study, Das et al. (2021) observed that urbanization and non-agricultural activities affect environmental quality and health quality. In the case of Organisation of Economic Co-operation and Development (OECD) countries, Reynolds and Avendano (2018) concerned in determining whether social spending was positively associated with life expectancy. It was concluded that the size and magnitude of the association play a great role in the positive relationship between healthcare spending and life expectancy. Specifically, it was reported that an increase in health expenditure leads to about a 0.160 increase in life expectancy. Ray and Linden (2020) examined the effects of public and private health expenditures on life expectancy in 195 countries using data from 1995 to 2014. It was found that the effect public health expenditures on life expectancy was greater than that of private expenditure. The growing recognition of sustainable human development manifested in healthier life has provoked research on the role of environmental policy on life expectancy. Empirical research addressing this issue used cross-sectional data to examine how environmental degradation affects life expectancy (Jouvet et al., 2007). This body of research also seeks to determine whether there are processes that are leading to improvement in life expectancy emanating from environmental sustainability. The question still remaining to be answered is: Does conservation capital lead to improvements in health-adjusted life expectancy? The primary aim of this chapter is to determine whether conservation capital improves health-adjusted life expectancy. The finding indicates that conservation capital typically enhances healthadjusted life expectancy. The interactive effect of research and development expenditure and environmental protection expenditure on health-adjusted life expectancy is positive and significant. The result affirms that conservation capital is an imperative enabler of health-adjusted life expectancy with the ripple effect of boosting sustainable human development.

16.2 Materials and Method In this study, a total of 40 countries were selected from very high human development, high human development, medium human development, and low human development. It is a cross-sectional study that used secondary data from 2000 to 2019. Dataset for this study comprises health-adjusted life expectancy and conservation capital. Figures on health-adjusted life expectancy were taken from United Nations Development Programme (UNDP), Human Development Report. Data on environmental protection expenditure, Research and development expenditure, and Gross National Income (GNI) per capita were gotten from World Bank’s World Development Indicator (2020). Environmental protection expenditure and research and development expenditure were measured as a percentage of gross domestic product (GDP), while GNI per capita is measured in millions of US dollars. All variables are in their natural logarithm for consistency of analysis. Based on the environmental degradation and

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human development framework, the analysis is premised that environmental degradation influences health-adjusted life expectancy. Conservation capital is considered to be rival and excludable in its productive use and can be enhanced through public investment as well as socio-economic sustainability and technological advancement (Prady & Sy, 2019). The primary aim of this chapter is to determine whether conservation capital improves health-adjusted life expectancy and examine the interactive effect of expenditure on environmental protection and research and development on health-adjusted life expectancy. Following Jouvet, Pestieau and Ponthiere (2007) and Sheraj, (2017), the model for empirical estimation is specified as HLEPit = β1 EVPGDPit + β2 RDEGDPit + β3 HTEX GDPit + β4 GNIPit + εit (16.1) Similarly, the model for the interactive effect is stated as HLEPit = ϕ1 EVPGDPit + ϕ2 RDEGDP ∗ EVPEGDPit + ϕ3 HTEXGDPit + ϕ4 GNIPit + εit

(16.2)

where HLEP is health-adjusted life expectancy; EVPEGDP = environmental protection expenditure as percentage of GDP; RDEGDP = research and development expenditure as percentage of GDP; HTEXGDP = expenditure on health as percentage of GDP; GNIP = gross national income per capita; E = Disturbance error term; I = cross-sectional unit, 1…0.40; t = time period, 2000–2019. In Eqs. (16.1) and (16.2), the error term is stated independently. In order to capture the characteristics of each country which are assumed to be fixed on time, the individual effects are incorporated into the general model. In line with this, Eqs. (16.1) and (16.2) are respecified as HLEPit = αi + β1 EVPGDPit + β2 RDEGDPit + β3 HTEXGDPit + β4 GNIPit + εit (16.3) HLEPit = αi + ϕ1 EVPGDPit + ϕ2 RDEGDP ∗ EVPEGDPit + ϕ3 HTEXGDPit + ϕ4 GNIPit + εit

(16.4)

where αi denotes individual-specific effects. Another way of extending the model is to allow the intercept may vary across individual state and time. The essence of this is to incorporate possible events, such as economic shocks, that may affect the set of countries, which yields in HLEPit = αi + ζ1 + β1 EVPGDPit + β2 RDEGDPit + β3 HTEXGDPit + β4 GNIPit + εit (16.5)

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HLEPit = αi + ζ1 + ϕ1 EVPGDPit + ϕ2 RDEGDP ∗ EVPEGDPit + ϕ3 HTEXGDPit + ϕ4 GNIPit + εit

(16.6)

In this study panel data is used which covered 40 countries and a 20-year period, 2000–2019. As such pane data give more accurate information, given the variability of the data. Ordinarily, in analyzing panel data, three methods can be employed: pooled regression model, fixed effect, and random effect. In the pooled regression model, the concern is in both the intercepts and slopes. In estimating the model, therefore, even though there are differences in the constant terms and the intercept terms in the regression model, it is instructive to combine all the data. In the case of the fixed effect model, the differences across cross-sectional units are captured. On the part of the random effect model, the individual effects are randomly distributed across all the cross-sectional units covered by the study. To capture the specific effect, the regression model is specified with an intercept which denotes the overall constant term (Seddighi & Lawler, 2000). For consistency of analysis and robustness of result, both the fixed effect model and random effect model are employed in this present sent. For the suitability of either the fixed effect model or the random effect model, the Hausman test was conducted. For the null hypothesis, if the individual effects are random, the estimators yield similar result. In the alternative hypothesis, the estimators are inconsistent and therefore differs (Wooldridge, 2003).

16.3 Results and Discussion The descriptive summary statistics presented in Table 16.1 shows that the average health-adjusted life expectancy 0.6821 was public health expenditure stood at 3.10 as the percentage of GDP Mean expenditure on research and development, 0.56 average gross national income per capita, 18,070 and environmental protection expenditure stood $0.233. The Jarque–Bera statistic for health-adjusted life expectancy is 2.3990 with a p-value of 0.242, gross national income per capita is 4.2974 with a p-value of 0.117 and environmental protection expenditure is 0.4200 with a p-value of 0.1809 and for research and development expenditure is 2.3480 with a p-value of 0.3722. All the respective p-values are greater than 0.05, an indication of the acceptance of null hypothesis that the respective residuals of the variables are normally distributed. The results of our analyses are presented in Table 16.2. In model 1, the result depicts that gross national income per capita has a positive effect on health-adjusted life expectancy, where health-adjusted life expectancy improves approximately by 0.45% for each 1% growth in gross national income per capita. A similar result was reported for model 2, although the effect of gross national income per capita is not significant. Health expenditure has a positive effect on health-adjusted life expectancy. As indicated, 1% increase in health expenditure corresponds with 0.35%

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Table 16.1 Descriptive summary statistics of the variables for empirical estimation Variable

Mean

Jarque–Bera

Probability

Health-adjusted life expectancy

0.6821

2.399

0.242

Gross Nation Income per capita

18,070

4.297

0.117

Public health expenditure

3.10

1.098

1.091

Research and development expenditure

0.56

2.348

2.348

Environmental protection expenditure

0.232

0.420

0.181

Source Author’s computation

increase in health-adjusted life expectancy. Research and development and environmental protection expenditure have a substantial positive effect on health-adjusted life expectancy. This is in tandem with the finding by Reynolds and Avendano (2018), Abdulganiyu and Tijjani (2021) that public health expenditure is positively associated with life expectancy. In Model 2, the interaction between research and development expenditure and environmental protection expenditure is positive and significant. This indicates that the effects of these two factors are intertwined, requiring a subtle interpretation since the effect of gross national income per capita on health-adjusted life expectancy varies based on the level of conservation capital and the effect of conservation capital varies based on GDP per capita. The finding is suggestive that in high-income countries, expenditure on environmental protection improves healthadjusted life expectancy than it does in poorer nations. It also indicates that countries with low levels of research and development expenditure and gross national income per capita experience high levels of health-adjusted life expectancy more than countries with low levels of conservation capital. Intuitively, conservation capital coupled with high level of gross national income per capita tends to accentuate healthadjusted life expectancy. This collaborates with the view of Garg, (2017). Although the results may appear paradoxical, the findings have identified potential channels through which conservation capital coupled with gross national income per capita may improve health-adjusted life expectancy. In essence, environmental degradation and low per capita income reduces health-adjusted life expectancy (Hill et al., 2019; Steinberger et al., 2012).

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Table 16.2 Fixed effects panel regression model of the influence of conservation capital on healthadjusted life expectancy Model 1

Model 2

Gross National Income per capita

0.450*** (0.134)

1.762 (0.209)

Health expenditure

0.350*** (0.134)

0.231** (0.543)

Research and development expenditure

0.564 (0.621)

0.230* (0.091)

Environmental protection expenditure

0.123 (0.35)

0;092 (0,127)

Interaction: research and development exp*environmental protection expenditure

0.122* (0.087)

R2 within

0.557

0.504

R2 overall

0.609

0.691

Note Standard errors are in parenthesis Note *p < 0.05, **p < 0.01 ***p < 0.001 Source Author’s computation

16.4 Conclusion The desire for paradigm shift for a sustainable human development in relation to sustainable environment has triggered discussions on the influence of the ecosystem on well-being. Even though studies have found a positive relationship between a county’s well-being and life expectancy after controlling for poverty and education, the concern is whether conservation capital accentuates health-adjusted life expectancy. The primary aim of this chapter was to determine whether conservation capital improves health-adjusted life expectancy and examine the interactive effect of expenditure on environmental protection and research and development on healthadjusted life expectancy. The study covers 40 countries selected from very high human development, high human development, medium human development, and low human development, using data from 2000 to 2019. It was found that conservation capital typically enhances health-adjusted life expectancy. The interactive effect of research and development expenditure and environmental protection expenditure on health-adjusted life expectancy is positive and significant. The finding is suggestive that countries with more expenditure on research and development and environmental protection coupled with high gross national income per capita have the tendency of experiencing higher growth in health-adjusted life expectancy. The result affirms that conservation capital is an imperative enabler of health-adjusted life expectancy with the ripple effect of boosting sustainable human development. The policy implication is that efforts to improve capital conversion in developing nations may be an important part of mitigating environmental degradation while trying to reduce the overall effect of climate change.

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Chapter 17

Internal Reverse Migration in India Amid COVID-19 Pandemic: Linking up Human Capital with Natural Capital Sovik Mukherjee

17.1 Background Internal migration indicates the process when people migrate within the country, but it can be across states (inter-state) or within the state itself (intra-state) for the purpose of education, employment or any other economic reasons or correlated requirements. Conferring to the 2011 Census data, India had 450 million internal migrants when there were no restrictions on internal mobility, and their population represents only 4% of the population in India. There was a whopping increase of around 45% over the 309 million recorded as per the 2001 Census. The nature of movements, as per the 2011 Census data, is in bulk within the same district in a state with 62%; 26% is between districts within the same state and only 12% of the movement belongs to the inter-state category (Kone et al., 2018). Obviously, these numbers have gone up over a period of almost 10 years as the next census is due. Network and community links are often held as central factors in migration theory. With the unprecedented disruption in the global economy came to the forefront an issue which was not considered to be of concern in the Indian subcontinent—reverse migration or return migration and not migration per se. We discuss in the paper this phenomenon in the times of a pandemic like COVID-19. The conditions, restrictions and the economic downturn were completely unprecedented for and the conclusions are as blurred as hypothesized. Nevertheless, the fact that the metro cities are totally dependent on migrant labor became evident when the reverse migration forces hit the workforces and the businesses adversely to the extent of complete lockdown (Kumar & Choudhury, 2018). In some states, migrant laborers back home are finding it difficult to feed themselves to two square meals a day despite putting in hard work due to less options and limited resources. The sustainability challenges crop up. Agents are ex post different S. Mukherjee (B) Faculty of Commerce and Management, St. Xavier’s University, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_17

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in terms of their intention to stay considering the impacts of a shock like COVID-19 to their preferences. Those having a taste for city life, staying longer in the city is the general convention but with shocks the result becomes ambiguous and migrants having very strong preferences and capability to afford will continue to stay in the city; rest will reverse migrate (Mukhra et al., 2020). The data for testing the empirical model has been compiled from newspaper reports, railway lists, press releases, census database, NITI Aayog database and other government documents released amid the pandemic between the March–August 2020 under consideration with apt interpolation-extrapolation techniques to fill in the missing data points. We have estimated the probability of occurrence of reverse migration making use of a probit model based on factors, primarily, GDP growth rates, unemployment figures (%), share of urban population (%) and percentage of COVID-19 affected which have triggered such a form of reverse migration movements in the 6 months period (March–August 2020) across states in India. Moreover, having lost the jobs and the money it brought home, pandemic exposed the comfort of being home and safety thereto. The alternatives were few and limited, but survival was a key concern. The problem lies herein. The question is—is there enough natural capital (read resources) in the villages to support the influx of these reverse migrants? The paper puts some general comments on the Neo-Malthusian aspect of this reverse migration process and the sustainability issues thereof in view of the pressure on the rural ecosystem in terms of ecosystem’s potential capacity of resource supply to support human consumption. In case the ecological footprint exceeds bio-capacity, which is expected to widen after such reverse internal migration movements, this COVID-19 fallout will lead to a heavy loss of biotic resources and might result in a lop-sided impact on the rural economy. The rest of the chapter is as follows. The effect of the COVID-19-induced lockdown on the reverse migration has been discussed in Sect. 17.2 followed by the methodology in Sect. 17.3 and estimation of the probability of occurrence of reverse migration conditioned on certain macroeconomic factors in Sect. 17.4. The discussion on the Neo-Malthusian aspect of the reverse migration process and the sustainability issues thereof where such human capital flows might create pressure on the limits of bio-capacity of the natural capital follows after this. Finally, the chapter ends with a conclusion.

17.2 The Beginning of the Lockdown The nationwide lockdown as a strategy to slow the spread of the COVID-19 pandemic has disproportionately impacted unskilled and semi-skilled migrant laborers. The distressing images of the migration of these “marginal” and “invisible” drivers of the informal economy (Dandekar & Ghai, 2020) of urban India between March and June 2020 continue to haunt us. Many of these poor pedestrians were seen traveling back to their hometowns carrying their sophisticated things in packages on the top of their heads. On top of that, migrants who had a relatively shorter distance to cover made

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attempts to reach their endpoint from medium-sized towns and cities. A scenario of “crowding back to villages”1 that constitute the dried up “source” cropped up. Field reports suggested that the “destination points” didn’t have enough food and water for the migrants who were reverse migrating and coming back home. The rabi crop harvest and kharif sowing did provide some relief but cannot be relied upon to bear the additional burden of the reverse migrants. At that moment, more than 120 to 140 million people were either walking back or stranded in various camps, according to approximate estimates.2 The great majority of slums that characterize our cities and shelter migrants are not included in this figure. The number of those who wished to return home at that point would be rather big. According to ILO estimates, the 400 million people employed in the informal economy are at a risk of falling into deeper poverty during the crisis.3 As Mukherjee (2020) points out, “For the 467 million belonging to the selfemployed and the non-salaried class (including both contractual and non-contractual and especially, migrant laborers), the prolonged lockdown became far more life threatening than the danger of being affected with COVID-19 and many might have died from hunger, fatigue, suicides etc.—a question of life vs. livelihood. The worst hit was the MSME sector.” In its April 2020 research report, Barclays estimates that the lockdown was costing the Indian economy INR 35,000 crore per day. Using NSS data, Saini and Khatri (2020) found that about 20 crore households grouped in the fourth to tenth income deciles lost between INR 65,333 crore and INR 1,30,000 crore every month due to lockdown, with nearly INR 18 lakh crore being the overall loss for the economy. It’s no surprise, then, that the Home Ministry chose to allow migrants to return home because, in the lack of work, their living expenditures will drop significantly, and they may be able to find some modest agricultural labor in the village to support themselves, but nothing was certain.

17.3 Methodology A response variable, Y (reverse migration, RM has taken place or not in a particular month), is binary, 1 or 0 being two possible outcomes. X, being a vector of regressors, is supposed to affect Y (which is also the outcome). The model is structured in the following form: Pr(R M ∗ = 1/ X ) = Φ(X T β)

1

https://blogs.lse.ac.uk/socialpolicy/2020/06/18/migrant-workers-in-india-the-pandemic-pre ssure/.last accessed on 24.05.2021. 2 https://www.bbc.com/news/world-asia-india-52360757. last accessed on 24.05.2021.

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Table 17.1 VIF results

Variable

VIF

GDP_gr

2.79

Unemp

1.59

urb_pop

4.52

COV_aff

3.01

Mean VIF

2.55

Source Author’s own estimates using STATA 12

This can be modified as { } 1 RM = i f R M ∗ > 0; 0 other wise 0 Without any loss of generality, I will make use of a latent variable model of the form: R M = X T β + ε where ε is assumed to follow a standard normal distribution and X is as usual the vector of regressors in line with the theoretical model and it consists of • • • •

GDP growth, GDP_gr; unemployment, Unemp; share of urban population, urb_pop; percentage of COVID affected, COV_aff .

Before the estimation of the model begins, there is one more methodological challenge that must be addressed, i.e.—the multicollinearity among the variables chosen in this regard is investigated3 (see Table 17.1). The data for testing the empirical model has been compiled from newspaper reports, railway lists, press releases, census database, NITI Aayog database and other government documents released amid the pandemic during March–August 2020 under consideration with apt interpolation-extrapolation techniques to fill in the missing data points.

17.4 Results and Discussions Thereof To understand the probit model, one needs to understand the concept of P-seudo R square given in the model. STATA reports the one proposed by McFadden (McFadden, 1974) as

3

Given the Indian context, the author characterizes pseudo exogenous variables as those variables which are to a certain degree correlated with the other regressors, but multicollinearity here does not cause a problem.

17 Internal Reverse Migration in India Amid COVID-19 Pandemic … Table 17.2 Probit regression results

Variables

March–August 2020

233

Contributor to reverse migration

GDP_gr

0.03*

Likely

Unemp

0.44*

Likely

urb_pop

0.09*

Likely

COV_aff

0.31*

Likely

Note * indicates significance at a 5% level Source Author’s own estimates using STATA 12

R2 = 1 −

ln L(M f ull ) ln L(Minter cept )

In the words of Veall and Zimmermann (1992), “If an ‘only intercept’ model has a very low likelihood, then the log of the likelihood will have a larger magnitude than the log of a more likely model. In that case, a small ratio of log likelihoods indicates that the full model is a far better fit than the intercept model considered. Consequently, the P-seudo R squared value will be very high”. Thus, the interpretation of the coefficients has to be done very carefully. When the coefficient is positive, it signifies that as the predictor value rises, the predicted probability rises as well. A negative coefficient, on the other hand, indicates that as the predictor increases, the projected likelihood decreases. Furthermore, a unit rise in the value of a predictor powers the probability value dependent on the initial value of the predictor from which it is varying, conditioned upon the values of the other predictors. Even if the magnitude of change is the same, a change from 1 to 2 may not be the same as a change from 3 to 4. The results have been reported in Table 17.2. The probit model’s results clearly indicate that unemployment and the percentage of COVID-19 affected have been influential for such a form of reverse migration. A one unit rise in the number getting affected from COVID-19 increases the probability of reverse migration by 0.31. Similarly, a one-unit rise in the number of unemployed raises the probability of reverse migration by 0.44 consistent with the fact that the major cause of reverse migration is COVID-19-induced unemployment as per the model. Similar results can be found for GDP growth and urban population in influencing the probability of occurrence of reverse migration, but the magnitude of influence is comparatively less. Following this, the paper discusses the policy initiatives taken by the government to address the unemployment issue.

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17.5 Policy Initiatives for Migrant Workers During Pandemic Some key points have developed in response to the pronouncements made by the Union and State governments. The Prime Minister initially declared PMGKP valued at INR 1.7 crore on March 26, 2022. Following this, some notable programs to directly target the impoverished were launched under the PMGKP. These included a cash transfer program for women under the PM Jan Dhan Yojana, free food for the migrant workers with an allocation of INR 3500 crore; INR 3000 crore allocations for cash transfers to the “senior citizens, widows, and physically handicapped”; INR 17,500 crore packages for PM-KISAN as a “frontloading expenditure” (though the entire allocation in budget 2020–21 was INR 75,000 crores); INR 13,000 crores for the Ujjawala Scheme, coupled with an increase of INR 40,000 crores for MGNREGS; INR 6000 crores for employment of tribals/adivasis (CAMPA); and INR 2500 crores for EPF. Besides, the Union Government announced free food distribution for three months, from April 2020 onwards, under the Public Distribution System, as well as health insurance for the related professionals. Additionally, funds for the construction employees (about INR 31,000 crores) and the District Mineral Fund (INR 35,925 crores) were provided. On May 12, 2020, 48 days after the nationwide lockdown was announced, Hon’ble Prime Minister announced a package worth INR 20 lakh crores, with direct transfers amounting to roughly 3.5 lakh crores. Some analyses, such as Das’s (2020), indicate that a direct transfer package of such a huge amount was needed to restart the economy. The package’s specifics were revealed later. As Mukherjee (2020) puts it, “Only by combining the packages with RBI stimulus it sums to the announced stimulus of Rs. 20,97,053 crore under the Atma Nirbhar Bharat Abhiyan. It is important to highlight that only Rs. 1.70 lakh crore were announced from the Union Budget share. The remaining came from collateral-free loans for MSMEs to the tune of Rs. 3 lakh crores, and through Kisan Credit Card around Rs. 2 lakh crores as ‘concessional credit’, RBI liquidity infusion for around Rs. 8 lakh crore, Infrastructure fund of Rs. 1 lakh crore from NABARD, and around Rs. 1.9 lakh crore from other liquidity measures.” However, many unresolved questions remained in the stimulus’ fine print. The fiscal and monetary policy got intertwined. This had drawn criticism from eminent economists like Prof. Arvind Panagariya, and Barclays and CARE ratings pointed out that the package cost the government around 1.1–1.5% of GDP (out of its treasury), with the rest coming from RBI infusions, government guarantees and other non-fiscal (or monetary) measures. As a result, there is simply no clarity on the magnitude of the fiscal deficit being expanded in India because of this stimulus package. The Center in view of the emergency-like situation announced a number of universal plans for the disadvantaged population groups. The PMGKY is the most well-known of these. As previously stated, an emergency relief package of INR 1.7 lakh crores was announced. Despite the importance of these direct cash transfers, there are no Union-level initiatives aimed at migrant workers (Sikdar & Mishra,

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2020). While it is difficult to identify migrant laborers, the administrations of various panchayats and other local bodies were requested to collect data on them shortly after the lockdown was enforced; nonetheless, the database is still not complete at this time (Hembram & Garai, 2020). Back then, several states in India have announced their own one-time income supports to the migrant population in their own states as well as their migrant workers beached in the other parts of the country. This is very much consistent with the results of the model (refer to Table 17.2) where policies were taken up by the respective state governments across states in India, primarily, to meet the needs of the unemployed migrant workers who reverse migrated. Andhra Pradesh and Uttar Pradesh each granted a one-time financial assistance of INR 1,000. Bihar and Haryana declared that registered migrant workers will receive INR 1,000, while the Government of Tamil Nadu decided that all migrant workers in the state will receive INR 500.6 “Sneher Paras” was set up in West Bengal to provide an “ex gratia”4 financial aid of INR 1,0006 to the migrant workers from the state who were trapped in other states through the DBT scheme. Jharkhand adopted a similar approach. The state government of Odisha declared income assistance of INR 20006 per migrant worker subject to the fulfillment of the quarantine period, while the Punjab Government offered INR 3000.6 In May 2020, the center declared that all migrant workers who do not have a central or state PDS card would receive 5 kg grain and 1 kg chana per household each month from June to July 2020. Besides, Telangana gave all rice cardholders Rs. 1,500 to spend on necessities like groceries and vegetables. Following schemes like Bihar’s Jeevikasamuh, West Bengal’s Prachesta and other cash distribution programs under the National Food Security Act, Madhya Pradesh, Chhattisgarh and other states came up with similar schemes. In addition, Uttar Pradesh announced the availability of ration cards for all migrant laborers. The most difficult challenge of the lockdowninduced crisis has been, and continues to be, the urgent necessity for the Union and State governments to generate employment for the individuals who have lost their jobs. Though no precise appraisals of the number of jobless workers have been obtained, CMIE’s approximate estimations for April 2020 put the figure at around 122 million. According to CMIE unemployment figures, the national unemployment rate reached 9.1% in the week ending August 16, 2020, which is higher than the rate of 8.67% in the week ending August 9, 2020.5 The Center enhanced the budget for MNREGS by INR 40,000 over and above the INR 61,500 crore budget allotment for 2020–21 as a precautionary measure. While this rise is good, there are a few factors to consider. To begin with, INR 61,500 crore budgeted for the 2020–21 fiscal was obviously an underestimation. The actual expenditure on the rural work program in FY20 was INR 71,000 crores. Second, this larger allocation of INR 1,01,500 crores must clear lingering liabilities of INR 11,000 crores from the 2019–20 fiscal. According to the CMIE website, the NSDC’s SWADES database documented 4

An ex-gratia payment is not legally necessary, but is made to show good intentions. https://cmie.com/kommon/bin/sr.php?kall=warticle&dt=2020-06-15%2012:26:25&msec=493. last accessed on 24.05.2021.

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roughly 15,634 returnee migrant workers (reverse migrants) and professionals, with 59% of them losing their jobs during March–May 2020.6 However, explicit policy announcements targeted at incentivizing migrant workers to stay in destination centers now, or in the future, reducing the scope of willful reverse migration are yet to be made.

17.6 The Neo-Malthusian Aspect: Human Capital Puts Pressure on Natural Capital This brings us to the key highlight of the study where we discuss how human capital (read reverse migrants) creates pressure on the natural capital and the Neo-Malthusian aspect of the story. The current form of the COVID-19 pandemic is not a Malthusian event (Sengupta, 2020). Although the pressure of population is not a significant explanatory factor for the occurrence of COVID-19 or its scale, the event still carries a Neo-Malthusian message (Dasgupta, 2020). Malthus’ major concern had been that the population size would be exceeding the carrying capacity of the ecosystem where the latter is defined in terms of the capacity of the land to produce food, i.e., the maximum size of the population that can be fed with a subsistence food basket. The fixity of land and Ricardian law of diminishing returns can explain the population size, without preventive checks to overshoot the carrying capacity. Neo Malthusians do not define the carrying capacity of the ecosystem in terms of capacity of food production, but in terms ecosystem’s potential capacity of resource supply to support human consumption of various kinds of goods and services and waste absorption including change of land use for building up fixed infrastructure or changing product mix of the economy as per the changing preference structure of the people with growth of income and change in technology. This should also include the requirement of land use change for carbon sequestration of the carbon dioxide unabsorbed by the ocean or existing photosynthetic green cover. The concept of an ecological footprint as defined and estimated globally and country-wise by the Global Footprint Network captures this appropriation of all such demands for resources and eco-services expressed in units of land use of various types as normalized aggregate in terms of per capita cropland of average photosynthetic productivity. Given the availability of land for various types of uses, the regenerative capacity or bio-capacity of a country or the earth can be taken to be the aggregate of availabilities of crop land, pasture, forest, water area (both inland and coastal) of fisheries, etc. in a similar normalized unit for meeting the need of resource supply for the humans. In case the ecological footprint exceeds bio-capacity, the measure of excess can be called the bio-deficit of our ecosystem. With the COVID-19-induced lockdown, deprivation of livelihood and basic needs for the poor and marginalized section of the population, particularly, the migrant workers, it became a matter of the Darwinian survival of the fittest. They were fighting over the limited finite natural resources they had for their survival. Dasgupta

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(2020) has addressed this important issue in his provisional report on the Economics of biodiversity for the Government of UK. In his report, he takes, in fact, the measure of ecological footprint to represent the impact of humanity on the biosphere per unit of time. He represents essentially the same measure as defined by the global ecological footprint network alternatively as the ratio of GDP or Y to the rate of conversion of resources of biosphere for supplying inputs to production and absorbing wastes, into GDP denoted by, say, α. However, GDP undervalues the value of the contribution of resources of the biosphere as there are nonmarket resources and eco-services which provide human life support and support to economic activities. In any case, the paper considers the ecological footprint to be Y/α = y. N/α where N is the size of the population of the economy and y per capita GDP or income per unit of time, following the report. On the supply side, eco-services are determined by the stock of ecological resources of the biosphere which grows over time as these resources are regenerative, the size of growth per unit of time depending on the size of the stock S and can be represented by G(S), G being the rate of regeneration. The excess of ecological footprint over G(S) is the bio-deficit yielded by the impact inequality—N.y/α > G(S) (as the report calls it). So far as the impact of the 2020 lockdown in India was concerned, it is important to mention the impact on labor, particularly the migrant laborers who have been the worst sufferer. In India, there existed an inter-state migrant population of 5.47 crores as per the 2011 census. Of this migrant population, migrant laborers (including petty self-employed ones) are engaged in the unorganized sector with subsistence income and no social security. They live mostly in slums or slum-like living conditions, with entire families mostly living in one-room accommodation with poor water supply and unhygienic sanitary conditions. With the announcement of the lockdown, these laborers lost almost instantaneously their jobs, income and their rented accommodation as they were evicted by their landlords immediately.6 These laborers with their family members were forced to return to their homes in different states and the reverse migration flow started, while the government had no planning and preparedness to facilitate the return of these people. The return of migrants had put stress on flimsy rural health systems, where there were inadequate health infrastructure, human resources and trained personnel, coupled with low testing capacity during the period March–June 2020.7 The author points out that mass reverse migration has shown that structural reforms and robust inter-state migration policies should be in place in the areas of rural development to support urban livelihood guarantees, and inter-state portability of government subsidies and entitlements; otherwise, the pressure on natural capital would be huge and overharvesting of resources of the biosphere exceeding the bio-capacity could trigger severe loss of biotic stock to support such quantum of bio-deficit eventually resulting in extreme events such as cyclones, floods and sea-level rise; submergence coastal areas among others would 6

https://www.hindustantimes.com/gurugram/migrants-students-distressed-anxious-as-landlordspush-for-rent/story-VqxqxlwtaceoLb0asU7aeJ.html. last accessed on 24.05.2021. 7 https://www.financialexpress.com/opinion/pandemic-impact-necessary-to-stop-reverse-migrat ion/2241322/. last accessed on 24.05.2021.

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lead to the destruction of habitats of wildlife, plants and animal species and their eventual extinction (Sengupta, 2020).

17.7 Concluding Remarks The ecological balance between human appropriation of biosphere resources and the bio-capacity of our ecosystem—a relationship between human and natural capital— was highlighted in the preceding sections of this chapter. These occurrences provide us with a difficult choice today between life and livelihood, and between economic growth and halting the eroding biosphere’s environmental capital stock. Since the beginning of the settled societies at the dawn of agrarian civilization, man often tended to exceed the carrying capacity of the ecosystem and faced problems of scarcity, poverty, war, famine, disease and devastation from time to time. In times of environmental crises, all religions and faiths around the world have been very vocal about the virtues of sharing, charity, compassion and empathy so that society does not break down due to disorder and humanity can survive. The pandemic is still raging in India and many other nations. To meet the challenge in the face of today’s existential crisis, we must resolve conflicts between growth and environmental preservation, as well as between life and livelihood. In line with the 2020 reverse migration scenario discussed in this chapter, the question is whether the “destination points” to which reverse migration flow goes to have enough resources to sustain the flow. At least to stop reverse migration, employment provisioning schemes by the government for the urban and semi-urban areas are a must so that their jobs are at least secure. Greenfield infrastructure projects could be one possible source of such employment. The infrastructure development projects under the Central Sector Schemes, like road works, metro projects and construction works for the army, have already been allocated by the Union Government in the current budget. This will fulfill the simultaneous goals of increasing productivity and giving low-skilled workers the required purchasing power that have been affected by the lockdown. It is especially essential to resurrect general demand in the economy and provide economic activity a much-needed Keynesian boost.

References Dandekar, A., & Ghai, R. (2020). Migration and reverse migration in the age of COVID-19. Economic and Political Weekly, 55(19), 28–31. Das, R. C. (2020, December). COVID-19 Pandemic: Trends, possible causes, impacts and remedies, with special reference to India. Vidyasagar University Journal of Economics, 24, 112–129. Dasgupta, P. (2020). The Dasgupta Review–Independent Review on the Economics of Biodiversity Interim Report. Retrieved 24 May, 2021, from http://bibliotecadigital.ciren.cl/handle/123456 789/32687.

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Hembram, R., & Garai, U. (2020). Inter-state Labor migration in India: The normal and reverse phase. COVID-19 Pandemic Trajectory in the Developing World, pp. 257–274. doi: https:// doi.org/10.1007/978-981-33-6440-0_11. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/art icles/PMC7981502/. Kone, Z. L., Liu, M. Y., Mattoo, A., Ozden, C., & Sharma, S. (2018). Internal borders and migration in India. Journal of Economic Geography, 18(4), 729–759. Kumar, S., & Choudhury, S. (2021). Migrant workers and human rights: A critical study on India’s covid-19 lockdown policy. Social Sciences & Humanities Open, 3(1), 100130. McFadden, D. (1974). Conditional logit analysis of qualitative choice behaviour. In: P. Zarembka (Ed.), Frontiers in Econometrics. New York: Academic Press. Mukherjee, S. (2020). India’s exit strategy: Trade-off between life vs. livelihood. Business Economics, June 1–15, 11–12. Mukhra, R., Krishan, K., & Kanchan, T. (2020). COVID-19 sets off mass migration in India. Archives of Medical Research, 51(7), 736–738. Sengupta, R. (2020). Extreme events in nature: An ecological history of the present. The India Forum, November Issue, 1–11. Sikdar, S., & Mishra, P. (2020). Reverse Migration during Lockdown: A Snapshot of Public Policies. Retrieved from https://nipfp.org.in/media/medialibrary/2020/09/WP_318_2020.pdf. Veall, M. R., & Zimmermann, K. F. (1992). Pseudo-R 2’s in the ordinal probit model. Journal of Mathematical Sociology, 16(4), 333–342.

Chapter 18

COVID-19, Environmental Pollution, and Climate Change Nexus in Sub-Saharan Africa Ambrose Nnaemeka Omeje, Augustine Jideofor Mba, Divine N. Obodoechi, Ezebuilo R. Ukwueze, and Chinasa E. Urama

18.1 Introduction With the declaration of coronavirus infectious disease (COVID-19) as a global pandemic (WHO, 2020a), it became recognized rapidly among the countries of the world. The world emergency committee stated that early discovery, quarantine, and treatment are all essential (Sohrabi et al., 2020). Many countries began to announce the statistics of the casualties, which became shocking and alarming (MacLean et al., 2020; WHO, 2020). Within a short time, the impact was spontaneous as it impacted on the economy, health, society, education, tourism, and even the environment in many countries (Wuyts et al., 2020; Fine et al., 2020). When the Coronavirus epidemic expanded throughout Sub-Saharan Africa (SSA), the region’s political leaders were confronted with an almost unthinkable policy challenge (Amewu et al., 2020). The absence of vaccine and treatment methods created a precarious concern about the health systems and was made worse by severe shortages of intensive care beds and ventilators, and the population’s underlying poor health (Bishop, 2020); slowing viral spread became critical for countries to manage amidst rising patient numbers and keeping mortality rates as low as possible. Faced with all of these problems, the only way to prevent the virus’s growth is to implement policies that restrict people’s physical interaction and movement, or, in extreme cases, force key businesses to shut down. In the region where the majority of the population is poor, a sudden economic shock can have catastrophic consequences for people’s food security and health. This has numerous environmental ramifications. According to Wang et al. (2020), the coronavirus outbreak has created serious health risks worldwide. National lockdowns are viewed as undesirable by most governments and agencies, particularly in Africa (Bhorat et al., 2020). According to A. N. Omeje · A. J. Mba · D. N. Obodoechi · E. R. Ukwueze (B) · C. E. Urama Department of Economics, University of Nigeria, Nsukka, Enugu State, Nigeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_18

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Bai et al. (2020) and Lai et al. (2020), the pandemic has not only created a significant threat to public health, but has also hampered global economic progress. People were warned and asked to use personal protective equipment (PPE), such as face shields and face masks, after the COVID-19 outbreak (Chintalapudi et al., 2020; WilderSmith & Freedman, 2020). Almost every country, as well as the WHO, has called for strategies to halt COVID-19 from spreading. Regrettably, the issue of how to appropriately handle PPE waste has received little attention in public debate (Arimiyaw et al., 2021). This demonstrates the growth of PPE as an environmental problem as a result of improper disposal both on land and in water, posing environmental risks. Additional PPE would exacerbate the existing environmental problems (waste management) as the epidemic develops and surgical masks are worn, especially if suitable waste management is not in place. The severity of the problem is highlighted by low-slung assortment rates, insufficient and poorly integrated management systems, and casualness in SSA countries (Arimiyaw et al., 2021; Gugssa, 2012; Jambeck et al., 2015; Quartey et al., 2015). Linkages between climate change and environmental degradation arise out of the fact that changes in climate produce some impacts on the environment. So, the environmental effects of climate change could be enormous; for example, the environmental effect of flood cannot be overestimated. The pollution which could be produced by changes in weather or climate and the activities related to treatment and management of the spread of coronavirus in various countries are also of serious concern. Environmental dangers arising from the health sector due to the efforts to contain the horrible impacts of the COVID-19 pandemic, particularly medical waste (such as face shields, face masks, hand gloves, surgical masks, aprons, protective medical suits, gowns, and so on) (Oruonye & Ahmed, 2020). All these tools and equipment are disposed of after use and with restriction of movement, “stay at home” policy (due to lockdowns), the waste collectors do not go to work; the wastes, which are not properly disposed of, litter on highways, enter drainages, block passages of water and pollute the environment and because they contain some dangerous compounds chemicals pose health threats to citizens and also add to emissions. The bulk of PPEs is constructed of polymers such as polypropylene (PP), polyurethane (PU), polycarbonate (PC), low-density polyethylene (LDPE), and polyvinyl chloride (PVC), whereas the plastics used in packaging materials are mostly recycled (Kleme et al., 2020). Plastic waste management has also become a serious worry as a result of declining gasoline and petroleum prices and decreased transportation activity during pandemic-induced lockdowns (BIR, 2020; Eco-Business, 2020; Kaufman & Chasan, 2020). The quality of air is very important when discussing environmental quality. Climate change produces low air quality. In two large Chinese cities, Wuhan and Xiao, it was found a strong association between air quality index and COVID-19 (Lin et al., 2020). In China, a similar study found a link between air pollution and verified COVID-19 cases (Li et al., 2020; Zhang et al., 2020). Furthermore, Ali and Islam (2020) claim that air pollution and COVID-19 infections have a substantial relationship. Air pollution, according to their findings, increased COVID-19 patients’ vulnerability and impaired their prognosis. However, there is disagreement because

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Muhammad et al. (2020) and He et al. (2020) claim that the COVID-19 pandemic lowered pollution significantly, and so concluded that pollution is decreasing as a result of COVID-19. As can be seen from the above, earlier investigations on the linkage between climate change, pollution, and the COVID-19 epidemic have generated a lot of debate. Following the controversies of previous studies, this study tried to investigate the response of environmental pollution and climate change to the COVID-19 pandemic in Sub-Sahara Africa. This would also contribute to our understanding of the links between climate change’s environmental effects and the COVID-19 epidemic. The next section is a review of relevant literature; the third section shows the methodology used in the study. The fourth and fifth sections are the presentation and discussion of results, and conclusion, respectively.

18.2 Literature Review There is a plethora of literature on environmental issues, how men have interacted with the environment, and how their lives have been affected by changes in the environment. Environmental degradation and climate change have influenced man, his activities, and his living conditions. The pollution theory hypothesis was first proposed by Copeland and Taylor (2004) and it hypothesizes that strict environmental regulation in a country forces companies to migrate to countries where their environmental laws are not strict. As a result of the transfer of these enterprises from industrialized economies with rigorous environmental restrictions, less developed countries may have a comparative advantage in the manufacturing of pollutionintensive commodities. The impact of trade on the environment is fully examined by composition, scale, and technology effects (Farhani et al., 2014). The scale effect reveals the negative environmental consequences of economic growth due to international trade as a result of the increase in the demand for energy. The composition effect, on the other hand, is defined by Acharyya (2009) as a change in the share of unclean items in GDP as a result of a price change that favors their production. The Environmental Kuznets Curve is a popular way to explain the linkage between economic growth and environmental quality. It suggests that economic output per capita and several environmental quality indices have an inverted U-shaped connection. It goes on to claim that as per capita GDP rises, so does environmental degradation. On the other hand, an increase in GDP per capita would eventually lead to a reduction in environmental effects. Economists have researched so much on the linkage between pollution and climate changes and their impact on economies, especially with the arrival of COVID-19 pandemic. The coronavirus disease took the world by surprise and it was first found in the city of Wuhan, China, in 2019 and was later named COVID-19 pandemic as a result of its devastating effect on world economies (Chen et al., 2020). It spreads mostly by respiratory droplets and contact with an infected person (WHO, 2020). Many countries throughout the world went into lockdown to try to stop the virus

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from spreading (Tosepu et al., 2020). Most industrial activities were shut down internationally as countries went into lockdown, drastically reducing pollution levels due to firms’ limited industrial activities, therefore limiting social and economic activities (Dutheil et al., 2020). The shutdown lowered emissions in most countries in Sub-Saharan Africa, which were previously high due to fossil fuel combustion and traffic congestion. Nitrogen oxide is a highly reactive pollutant, and transportation pollution is a major source of nitrogen emissions in Sub-Saharan African cities (He et al., 2020). Despite its catastrophic effects, COVID-19 can be viewed as a “gift in disguise” for many areas throughout the world, as pollution levels have decreased significantly. The COVID19 epidemic substantially reduced environmental degradation, and as a result, nature is reclaiming itself (Muhammad et al., 2020). In contrast to the findings above, Ali and Islam (2020) claim that COVID-19 infections and air pollution are linked. Air pollution made patients more susceptible to COVID-19 infection and impaired their prognosis, according to their findings. Furthermore, Pansini and Fornnacca (2020) investigated eight countries in relation to COVID-19 and air pollution, and their findings suggest that nations with high PM2.5 and nitrogen NO2 have higher illnesses, implying that air pollution is substantially and positively associated with COVID-19 disease. In six nations, with the exception of Spain and Germany, the researchers discovered a substantial linkage between air pollution levels and COVID-19 infections and mortality. Even though their pollution magnitude and density were not the highest, Italy was struck the hardest by the virus. Lin et al. (2020) found a strong association between air quality index and COVID19 in two large Chinese cities, Wuhan and Xiao, in a retrospective study. PM2.5 and Nitrogen, among four primary air pollutants, were substantially linked to COVID-19 infection, according to the scientists. A similar study in China discovered a relationship between confirmed COVID-19 cases and air pollution (Li et al., 2020; Zhang et al., 2020). After accounting for temperature, SO2, relative humidity, CO, O3 , and NO2 , the case fatality rate (CFR) in China showed a positive correlation with PM2.5 and PM10 (Yao et al., 2020). In Wuhan, China, a strong positive connection (p < 0.01) was discovered between air quality indicator (AQI), particularly for PM2.5 , and daily COVID-19 deaths (Jiang & Xu, 2020). A positive link was discovered between air pollution (PM10 and PM2.5 ) and COVID-19 infected infections in three French cities (Magazzino et al., 2020). 110 Italian cities were studied by Setti et al. (2020) and they established a link between the early development of COVID-19 and the topographical distribution of daily PM10 . The study projected that PM10 acts as a transporter for droplet nuclei, allowing COVID-19 to spread more quickly in Italian cities during the pandemic. Another study of similar nature discovered a relationship between COVID-19 infections and air pollution, implying that the virus spreads swiftly and broadly (Coccia, 2020).

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18.3 Methodology and Data To investigate the reaction of environmental pollution and climate change to the COVID-19 pandemic in Sub-Sahara Africa, researchers used the panel IRF (Impulse Response Function) of the PVAR (panel vector autoregressive) model. The panel vector autoregressive model is estimated by fitting a multivariate panel regression on the lags of the dependent variable itself, the lags of additional dependent variables, and any other exogenous factors. With the generalized method of moments (GMM), the PVAR (Panel Vector Autoregression) model is calculated. It is worthy of note here that panel VAR models look like VAR models. This is because they have similar structure and all variables of the model are assumed to be interdependent and, as such, are endogenous. However, in the panel VAR model, cross-sectional or cross-country dimension is added in the model (Canova & Ciccarelli, 2013). In this regard, this study specifies the PVAR model as given below: yit = P0i (t) + Pi (λ)Yt−1 + μit

i = 1 . . . N; t = 1 . . . T

(18.1)

where Yt is the stacked version of yit = (y1t ,, y2t ,, . . . , y N t ,) and represents a vector of endogenous variables (COVID-19, environmental pollution (evpoltn), and climate change (climatechg)). In this study, i is generic and indicates countries in the SSA. μit is a vector of innovations or random disturbances that are correlated across countries, i, and are also independently and identically distributed (i.i.d.) with zero mean and constant variance; P0i (t) and Pi are vectors of constant terms which may depend on the units of the innovations. λ represents the matrices of coefficients to be estimated. Equation (18.1) suggests that a PVAR has the assumption of dynamic interdependence (which implies that lags of all endogenous variables of all units enter the model for unit i), the assumption of static interdependence (implying that μit are correlated across countries, i), and the assumption of cross-sectional heterogeneity (which implies that the intercept, the slope, and the variance of the shocks, μit , may be unit specific. The PVAR, on the other hand, must pass the stability test if all moduli of the companion matrix A are strictly less than one. This can be specified as given below: ⎡ ⎢ ⎢ ⎢ A=⎢ ⎢ ⎢ ⎣

A1 A2 A z A z−1 Ik 0k · · · 0k 0k 0k Ik 0k 0k .. .. .. . . . 0k 0k · · · Ik 0k

⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(18.2)

With a representation of an infinite order vector moving average (VMA), the panel VAR is invertible, according to Eq. (18.2). This clarifies the significance of the estimated impulse response function (IRF) and forecast error variance decomposition.

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Therefore, the objective of the study (the response of environmental pollution and climate change to COVID-19 pandemic in Sub-Sahara Africa) is achieved by applying the PVAR’s impulse response function (IRF). The study specifies the panel IRF as given below: This makes the calculated IRF and forecast error variance decomposition more meaningful. θi =

i Σ

θt− j A j

Ik , i = 0, j = 1, 2, . . .

(18.3)

j=1

The implication here is that there is no causal interpretation to the normal IRF but innovations, μit , may be contemporaneously correlated and, as such, implies that a shock on one variable may probably be accompanied by shocks in other variables of the model. Therefore, with the matrix given by Z, such that Z ,Z = Σ, Z may be applied to orthogonalize the innovations given as μit Z −1 and to transform the parameters of the vector moving average into the orthogonalized impulse responses given as Z θi . Further, apart from the PVAR-IRF, the forecast variance error decomposition mechanisms were also examined. It demonstrates how much uncertainty in the time series for the future is attributable to forthcoming shockwaves in the system’s other time series (Schenck, 2016). Because this changes over time, shocks to time series, Y, may be insignificant in the short run but critical in the long run. Therefore, the h-step ahead variance decomposition can be specified below as follows: Yit+h − E[Yit+h ] =

h−1 Σ

ei (t + h − i)ϕi

(18.4)

i=0

where Yit+h is the observed vector at time t + h and E[Yit+h ] is the h-step ahead predicted vector at time t. The orthogonalized shocks, like impulse response functions, use the matrix Z to isolate each variable’s contribution to the variance of the forecast error. The covariance matrix Ik of the orthogonalized shocks ei Z−1 provides for a simple breakdown of the forecast error variance. More precisely, the role of a variable m to the h-step forward forecast error variance of the variable “n” may be computed as follows: h−1 Σ i=0

θ2mn =

h−1 ( )2 Σ , in Zϕ i im

(18.5)

i=0

where is is the sth column of Ik . In application, the contributions are often normalized relative to the h-step ahead forecast error variance of variable n as can be seen below:

18 COVID-19, Environmental Pollution, and Climate Change Nexus … h−1 Σ

θ2mn =

i=0

h−1 ( ) Σ Σ , , in ϕi ϕi in

247

(18.6)

i=0

Data and Sources The study utilized secondary high-frequency (quarterly) panel data generated from 40 SSA countries for the year 2020. The cumulative number of coronavirus cases was used as a proxy for COVID-19 pandemic. CO2 emission was used to proxy climate change while environmental pollution emissions were used to capture environmental pollution. The COVID-19 pandemic data for all the 40 SSA countries were sourced from Worldometers (2021) data set while the environmental performance data set was used to generate the climate change and environmental pollution data. The 40 SSA countries where the data were generated and applied for the study include Angola, Botswana, Burkina Faso, Burundi, Cape Verde, Central African Republic, Chad, Congo Democratic Republic, Côte d’Ivoire, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

18.4 Results and Discussions 18.4.1 Descriptive Statistics In a bid to examine the response of environmental pollution and climate change to the COVID-19 pandemic in sub-Sahara Africa, the study first looked at the descriptive statistics in order to inspect the characteristics and nature of the variables of the model. In line with this, the study presents the results of the descriptive statistics as given in Table 18.1. Table 18.1 shows that all of the model’s variables have enough variety, with various means, standard deviations, and minimum and maximum values. It further revealed that the overall panel observations (N) (that is, the total number of quarterly observations in the study) is 160 for all the variables, the between panel observations (n), which corresponds to the quarterly observation, is 4, while the within panel observations, which corresponds to the number of countries used in the study, is 40. In another vein, the study also looked at the unit root in order to examine whether the panel series contain unit root, using panel data unit-root tests. As a result of this, the Fisher ADF panel unit-root test was adopted for the study. The Fisher ADF panel unit-root test’s null hypothesis is that all panels have unit root. Under the alternative hypothesis, as N approaches infinity, the number of panels without unit root should expand at the same pace as N. The Fisher ADF panel unit-root test can be seen summarily in Table 18.2.

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Table 18.1 Summary results of the descriptive statistics of the variables of the model Variable Covid_19 overall

Evpoltn

Climat ~ g

Mean

Std. Dev.

Min

Max

Observations

73.80625

43.24168

1

147

N = 160

Between

4.89606

70.425

80.925

n=4

Within

43.03198

−6.11875

147.3813

T = 40

Overall

22.17787

0

90.2

N = 160

Between

0

44.005

44.005

n=4

Within

22.17787

0

90.2

T = 40

Overall

44.005

241.0678

0.0043491

1016

N = 160

Between

0

77.96808

77.96808

n=4

Within

241.0678

0.0043491

1016

T = 40

Overall

77.96808

Source Authors’ Computation from available data

Table 18.2 Summary results of the panel unit-root test of the variables Variable

Inverse chi Square P

Inverse normal Z

Inverse logit L*

Modified inv. Chi Squared Pm

P-values

Order of integration

COVID-19

128.9253

−10.3506

−18.1694

30.2313

0.0000

I(0)

Evpoltn

167.7543

−12.0753

−23.6416

39.9386

0.0000

I(0)

Climatechg

128.3388

−10.3705

−18.0868

30.0847

0.0000

I(0)

Source Authors’ Computation from available data

Table 18.2 shows the Fisher ADF panel unit-root test, which integrates the p-values from the four specific panel unit-root tests which Choi (2001) proposed. Three of the four methods are different in the way they utilize the inverse χ2 , inverse normal, or inverse logit transformation of p-values. However, the fourth is a variant of the inverse two transformation that works well when N approaches infinity. The inverse normal and inverse logit transformations can be used regardless of whether N is finite or infinite. As shown in Table 18.2, all four Fisher ADF panel unit-root tests significantly reject the null hypothesis that all of the panels have unit roots. Essentially, Table 21.2 indicates that the inverse logit L* test typically agrees with the inverse normal Z test. In the same way, the inverse χ2 P test also agrees with the modified inverse χ2 Pm test. The p-values of all the variables were shown to be statistically significant at levels and, as such, are integrated of order zero (that is, I (0) process).

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18.4.2 Presentation of the Panel VAR Model Results The panel VAR model, its related impulse response function (IRF), and forecast error variance decomposition were used to investigate the reaction of environmental pollution and climatic change to the COVID-19 pandemic in Sub-Saharan Africa. In view of this, the summary results of the panel VAR model are presented in Table 18.3. The panel vector autoregressive (PVAR) model, just like all VAR models, cannot be easily interpreted as its output results are very cumbersome and difficult to comprehend and/or to interpret. Therefore, its interpretation would make the study very clumsy and uninteresting. The study therefore resorts to the impulse response function (IRF) generated from the panel VAR model for its interpretation in a bid to find the response of environmental pollution and climate change to the COVID-19 pandemic in sub-Saharan Africa. Table 18.3 Summary results of the panel VAR model Variable

Coef

Std. Err

z

P > |z|

−0.0578549

0.1422002

−0.41

0.684

0.0252377

0.2805607

0.09

0.928

0.0109192

0.0173956

0.63

0.530

0.1553216

0.0632914

2.45

0.014

0.109665

0.1351243

0.81

0.417

−0.0243841

0.0075365

−3.24

0.001

1.292345

0.7965335

1.62

0.105

5.092171

2.007011

2.54

0.011

−0.085344

0.0284669

−3.00

0.003

Covid-19 Covid-19 L1 Evpoltn L1 Climatechg L1 Evpoltn Covid_19 L1 Evpoltn L1 Climatechg L1 Climatechg Covid_19 L1 Evpoltn L1 Climatechg L1

Source Authors’ Computation from available data

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18.4.3 Results of the Impulse Response Function (IRF) In order to investigate the study’s goal, the impulse response function (IRF) from the panel VAR model was used. The findings on the response of environmental pollution to the COVID-19 epidemic in Sub-Saharan Africa can be found here:

18.4.3.1

The Panel VAR Impulse Response Function (IRF) Results Capturing the Response of Environmental Pollution to COVID-19 Pandemic in Sub-Saharan Africa

Because the panel VAR model is difficult to interpret, the orthogonalized impulse response function (oirf) of the panel VAR model was used to investigate if environmental contamination responds significantly to the COVID-19 pandemic in SSA. Consequently, Fig. 18.1 shows the response of environmental pollution to the COVID-19 pandemic in sub-Sahara Africa. Figure 18.1 indicates that when COVID-19 produces impulse, environmental pollution (evpoltn) responds by emitting significant positive effects at the initial period up to period two where the effect emitted by environmental pollution on

Fig. 18.1 The Response of environmental pollution to the COVID-19 pandemic. Source Authors’ Computation from Available Data

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COVID-19 turned out to be negative. After this period, the effects emitted by environmental pollution on COVID-19 increased to point zero (0) at the third period but started dying gradually till the tenth period where it finally dies off. In other words, a one standard innovation in environmental pollution produces significant positive effects on COVID-19 pandemic as indicated by the results. These effects are within the 95% confidence interval. The implication here is that with a cumulative continuous rise in COVID-19 cases by one person, even with a continuous observance of lockdown and other COVID-19 protocols, environmental pollution (evpoltn) would go down by half (1/2) in the next period. However, the impact of a shock on COVID19 today on future environmental pollution (evpoltn) decays to zero (0) fast. This implies that, as time passes, the effects of a shock on environmental pollution today decay to zero (0).

18.4.3.2

The Panel VAR Impulse Response Function (IRF) Results Capturing the Response of Climate Change to COVID-19 Pandemic in Sub-Sahara Africa

The orthogonalized impulse response function (oirf) of the panel VAR model was also employed to examine the response of climate change to COVID-19 pandemic in sub-Saharan Africa. This is shown in Fig. 21.2. Figure 18.2 shows that when COVID-19 generates impulse, climate change (climatechg) responds by emitting large positive impacts for the first two periods, until the influence emitted by climate change on COVID-19 becomes negative in period two. After this period, the effects emitted by climate change on COVID-19 increased around point zero (0) at the third period. However, it started dying gradually till the tenth period where it finally dies off. In other words, one standard innovation in climate change produces significant positive effects on COVID-19 pandemic as indicated by the results. These effects are also within the 95% confidence interval as shown by the results. The implication of this result is that with a cumulative continuous rise in COVID-19 cases by one person, even with a continuous observance of lockdown and other COVID-19 protocols, unfavorable climate change (climatechg) would go down or improve by half (1/2) in the next period. However, the impact of a shock on COVID-19 today on future unfavorable climate change (climatechg) decays or reverts to zero (0) fast. This implies that, as time passes, the effects of a shock on climate change (climatechg) today also decay to 0.

18.4.4 Stability Test This study employed the Eigenvalue stability condition test on the PVAR model so as to carry out the stability test. Therefore, the PVAR stability test table and graph were used in the study. This can be seen from Table 18.4.

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Fig. 18.2 The Response of climate change to COVID-19 pandemic. Source Author’s Computation from Available Data

Table 18.4 Panel VAR stability test table

Eigenvalue Real

Imaginary

Modulus

−0.0166921

0.3083615

0.3088129

−0.0166921

-0.3083615

0.3088129

−0.0001496

0

0.0001496

Source Authors’ Computation from Available Data

The PVAR stability test table results seen in Table 18.4 shows that all the eigenvalues are less than one and, as such, lie inside the unit circle. Therefore, the PVAR satisfies the stability condition. This condition can also be seen clearly in the graph shown in Fig. 18.3. Figure 18.3 above presents where all the roots of the Eigenvalues lie inside the unit circle. This implies that the panel VAR process is stationary. Therefore, the results confirm that the PVAR model estimated passed the Eigenvalue stability test; hence, the PVAR satisfies the stability condition.

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Fig. 18.3 VAR stability test graph. Source Author’s Computation from Available Data

18.4.5 Forecast Error Variance Decomposition (FEVD) The forecast error variance decomposition (FEVD) was also utilized to help interpret the panel vector autoregressive (PVAR) model. However, unlike the PVAR-IRF, the FEVD reveals the amount of contributive effect each variable has on the other variables in the autoregressive model. Exogenous shocks to the other variables in the model influence how much of the forecast error variation of each variable may be determined by FEVD. Hence, the summary results of the forecast error variance decomposition (FEVD) can be seen in Table 18.5: The forecast error variance decomposition (FEVD) table result shows how much shock and/or effect to one variable, say, environmental pollution (evpoltn) and climate change (climatechg) impacts COVID-19 in SSA. In the study’s case, 0.11431, 5.61883, and 5.62388% of the variance in the forecast error of COVID-19 seems to be explained by a unit orthogonal shock in environmental pollution (evpoltn) in the first, second, and third horizons, respectively. However, from the 4th up to the 10th horizon, 5.62442% of the variance in the forecast error of COVID-19 seems to be explained by a unit orthogonal shock in environmental pollution (evpoltn). In another vein, 0.03475, 1.00456, and 1.00522% of the variance in the forecast error of COVID-19 seems to be explained by a unit orthogonal shock in climate change (climatechg) in the first, second, and third horizon,s respectively. However, from the 4th up to the 10th horizon, 1.00523% of the variance in the forecast error

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Table 18.5 Summary results of the forecast error variance decomposition (FEVD) Response variable and Forecast horizon

Time variable

Response variable and Forecast horizon

0

0

0

0

1

0.0011431

1

0.0003475

Evpoltn

Impulse variable

climatechg

2

0.0561883

2

0.0100456

3

0.0562388

3

0.0100522

4

0.0562442

4

0.0100523

5

0.0562442

5

0.0100523

6

0.0562442

6

0.0100523

7

0.0562442

7

0.0100523

8

0.0562442

8

0.0100523

9

0.0562442

9

0.0100523

10

0.0562442

10

0.0100523

Source Authors’ Computation from Available Data

of COVID-19 seems to be explained by a unit orthogonal shock in climate change (climatechg). Therefore, the variance in the forecast error of all the variables in the model is completely explained by each of the variables alone (i.e. environmental pollution (evpoltn) and climate change (climatechg). This means that orthogonal shocks to other system variables have no effect on the model’s forecast error variance.

18.5 Conclusion and Policy Recommendations This study empirically examined COVID-19, environmental pollution, and climate change nexus in sub-Saharan Africa. The study utilized secondary high-frequency panel data that was generated among 40 SSA countries and applied the impulse response function (IRF) of the panel vector autoregressive (PVAR) model to examine the response of environmental pollution and climate change to COVID-19 pandemic. The forecast error variance decomposition (FEVD) was also applied in the study to further buttress the results of the impulse response function. It was revealed by the study’s finding that when COVID-19 produces impulse, environmental pollution (evpoltn) responds by emitting significant positive effects at the initial period up to period two where the effect emitted by environmental pollution on COVID-19 turned out to be negative. After this period, the effects emitted by environmental pollution on COVID-19 increased to point zero (0) at the third period but started dying gradually till the tenth period where it finally dies off. In other words, a one standard innovation in environmental pollution produces significant positive effects on COVID-19 pandemic as indicated by the results. These

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effects are within the 95% confidence interval; hence, the implication is that with a cumulative continuous rise in COVID-19 cases by one person, with a continuous observance of lockdown and other COVID-19 protocols, environmental pollution (evpoltn) would go down by half (1/2) in the next period. However, the impact of a shock on COVID-19 today on future environmental pollution (evpoltn) decays to zero (0) fast and thus shows that as time passes, the effects of a shock on environmental pollution today go back to zero (0). Therefore, people and governments in SSA should strive harder to continue observing COVID-19 protocols and reduce every activity that breeds environmental pollution. Further, SSA countries should try to explore other country-specific fundamental innovations with respect to COVID-19 containment instead of locking down their economies, given the level of poverty in sub-Sahara Africa. People should as well try to reduce waste dumps which makes the environment unclean and/or contaminate the environment thereby exposing people more to other life-threatening infections/diseases that could trigger adverse health challenge in the face of COVID-19. The study also found that when COVID-19 produces impulse, climate change (climatechg) responds by emitting significant positive effects at the initial period up to period two, where the effect emitted by climate change on COVID-19 turned out to be negative. After this period, the effects emitted by climate change on COVID-19 increased around point zero (0) at the third period. However, it started dying gradually till the tenth period where it finally dies off. In other words, a one standard innovation in climate change produces significant positive effects on COVID-19 pandemic as indicated by the results. These effects are also within the 95% confidence interval, hence, implying that with a cumulative continuous rise in COVID-19 cases by one person, with a continuous observance of lockdown and other COVID-19 protocols, unfavorable climate change (climatechg) would go down or improve by half (1/2) in the next period. However, the impact of a shock on COVID-19 today on future unfavorable climate change (climatechg) decays or reverts to zero (0) fast. This shows that as time passes, the effects of a shock on climate change (climatechg) today also decay to zero (0). Therefore, governments in SSA and the people at large should encourage a cleaner environment by reducing the level of CO2 emissions in the environment. SSA governments should try to formulate environmental and climate change policies that would discourage more bush burning, increased use of firewood for cooking among its people, increased deforestation, use of crude production processes that increase CO2 emission, and increased utilization of engines (cars, motorcycles, generators, among others) that contribute heavily to greenhouse gas concentration.

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Chapter 19

Tourism, Environment and Italian Internal Areas at the Time of COVID-19: New Challenges and Opportunities Stefania Mangano, Pietro Piana, and Mauro Spotorno

19.1 Introduction Following the emergency of the first Covid-19 infections and the rapid spread of the epidemic in some Northern regions in February 2020, Italy became the first European country to impose a national lockdown, on 10 March 2020. Between March and early May 2020, some 60 million people experienced dramatic changes in their habits and everyday life, with profound impacts on the perception and use of space due to strict movement limitations, particularly in urban contexts. On 21 April 2020, an interview to one of Italy’s most famous architects, Stefano Boeri, stimulated a debate on a renewed centrality of Italian internal areas and the so called “Borghi” as potential alternatives to polluted and overcrowded cities in the time of the pandemic and beyond. Boeri reflected on the importance of green areas in and outside cities not only in the context of the Covid-19 lockdown, but within a larger process of “retreat from the urban”. He argued that the Covid-19 emergency offers a new stimulus to re-populate marginal and abandoned areas, a process which is facilitated by the possibility of working from home, thanks to increasingly faster and widespread Internet connection facilities. This suggestion was positively received by Marco Bussone, president of UNCEM (Unione nazionale Comuni, Comunità ed Enti Montani), who, in a public letter addressed to Boeri, emphasized the various recent national initiatives aimed at reviving the social and economic structure of Italian internal areas, characterized by chronic underdevelopment, depopulation and spontaneous re-naturalization. According to Bussone, however, many problems remain, mostly concerning lack of services, digital divide unequal taxation and lack of specific tourism policies. S. Mangano · P. Piana (B) · M. Spotorno Dipartimento Di Scienze Politiche e Internazionali-DISPI, Università Di Genova, Piazzale Brignole, 3, 16124 Genova, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_19

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Tourism in particular is seen as a very important mean of sustainable development, but its impact on the environment and society of marginal areas, as well as the relation with natural capital conservation are controversial. Jones and Wynn (2018) argue that academic research on the relation between natural capital and tourism is diverse and generally fragmented. In analyzing this complex nexus, many authors emphasize the predominance of economic interests over conservation and the instrumental use of terms such as “ecotourism”, “alternative tourism” and “sustainable tourism” related to activities not supported by adequate conservation planning policies (Collins, 1999; Dell’Agnese, 2018; Fletcher, 2018). Others acknowledge the increasing importance of bottom-up tourism and landscape policies planning in environmentally and culturally sensitive areas through community involvement (Visentin & Vallerani, 2018; Zhang et al., 2020). As shown below, new and emerging nature-based tourisms offer development opportunities for marginal rural areas characterized by re-naturalization processes and high natural capital, as is the case of Italian internal areas (Comitato per il Capitale Naturale, 2021). Due to the Covid-19 pandemic and the significant reduction of international travel, these areas saw a remarkable growth of the so called “proximity tourism” (Mangano, 2020), with potential economic benefits but also consequences in terms of social and environmental pressure. This chapter aims at assessing the perception of proximity tourism growth, its impact and management amongst local users in Italian internal areas. It first analyses the situation on a national scale, focusing on issues of depopulation, natural capital and recent national policies. It then examines three case studies between Liguria and Piedmont (NW Italy) where three surveys amongst local users were carried out in the summer of 2020.

19.2 Materials and Methods Data collection and analysis on the socio-economic situation of Italian internal areas and the effects of Covid-19 on tourism entailed both primary and secondary sources and focussed on the summer of 2020. General information on Italian internal areas draws upon a well-established and up to date literature on the theme amongst scholars. This includes geographical analysis of the impact of depopulation and recent territorial planning policies put in place to revive the economy of these areas, including tourism. Data on tourism, related to 2019, are part of the annual report by the Italian Institute of Statistics (ISTAT). They concern tourist movement generated by arrivals and overnights (national and international) for hotels and other accommodation facilities, disaggregated by single municipality. Data for 2020 are still provisional and concern the overall tourism movement for the three quarters of the year. Although they cannot be compared with those of the previous year, that provide information for each Italian municipality, data for 2020 allowed a preliminary assessment of tourism movement variations in the first pandemic summer compared to 2019 for the various destinations categories identified by ISTAT.

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Primary data relate to three surveys we carried out in the summer of 2020 within a broader research project on internal areas of Liguria and Piedmont, North West Italy (Fig. 19.1), where some questions were devoted to the impact of the Covid-19 Pandemic. The first survey, administered face to face 180 people, was aimed at investigating local users’ perception of the area of 15 municipalities of the Alta Langa area, between the Province of Savona (Liguria) and that of Cuneo. Local users are people who reside, work or spend a significant amount of time in the area (for example second homes owners). The second case study is Sassello, a municipality of the inland area of Savona Province, in the Ligurian Apennines, where a survey on the perception of tourism by local users was administered via social media to 180 people. A third survey was carried out amongst tourists (90 in total and interviewed face to face) in Toirano, an inland village located near the coast in the Western Riviera of Liguria.

Fig. 19.1 The three case studies: Alta Langa, located between Liguria (capital Genoa) and Piedmont (capital Turin), Sassello and Toirano (entirely located in Liguria). Source Elaboration by the authors from ISTAT data

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19.3 Results and Discussion 19.3.1 Between Marginalization and Socio-Economic Revival, Italian Internal Areas The situation of Italian internal areas today reflects a long process of urbanisation and industrial development that has characterised coasts, floodplains and valley floors, leading to progressive and seemingly irreversible marginalisation and depopulation of the Alps and Apennines. In 2018, Italy was Europe’s fifth-largest country by urban population, 49 million people out of a total of 61 million (81.1%) with an urbanisation rate of 33.8% (Basile & Cavallo, 2020). Depending on local situations and dynamics, the beginning of this long-lasting process of rural abandonment dates back to the late nineteenth century, although it is in the post-war period, as the country was going through an incredible economic development (the so called “economic miracle”, Nardozzi, 2003), that this phenomenon, commonly referred to as “rural exodus” (Macchi, 2019), became particularly significant and somehow dramatic. This is particularly true of internal areas of Liguria and Piedmont (NW Italy); in Liguria particularly, the depopulation of peripheral internal areas was − 32.90% for the period 1951–71, −36.01% for 1971–2001, −28.92% for 2001–2011 and −3.77% for 2011–2017 (Marchioro, 2018). The consequences of this process are well visible in both the socio-economic and physical landscape of these areas. In terms of demographic structure, rural areas are characterised by rapid ageing processes. Ageing rates in these territories are higher than the average of Liguria and Piedmont, both being characterised by population ageing at the regional level (Mangano et al., 2020a, 2020b). In terms of physical changes, rural depopulation meant progressive loss of traditional agro-sylvo-pastoral practices that for centuries have characterised landscape management in Apennines (Cevasco, 2007). The most evident effect of the abandonment of the countryside is spontaneous woodland growth, as trees are replacing formerly cultivated and managed areas. According to ISPRA (2018), in Liguria, trees cover 80.8% of the region, a value which is well above national average (39.3%). In the mountains, a rich heritage of local practices and uses, traditional activities and individual landscapes is rapidly disappearing and a process of rewilding is taking place, with the return of big mammals like the wolf and the wild boar (Hearn et al., 2014; Piana et al., 2018). In addition, many villages are today completely abandoned and several buildings have collapsed and have been erased by invasive vegetation. Lack of landscape management, particularly in the upper mountainsides, has had negative consequences on hydrogeological instability and the number of floods and landslides has increased (Piana et al., 2019). As well as being beneficial in terms or biodiversity and carbon sequestration (Comitato per il Capitale Naturale, 2021), this process stimulates the development of new nature-based economies in areas with high potential conservation capital where traditional agrarian practices have significantly decreased. The conservation and enhancement of natural capital through the management and control of this

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spontaneous re-naturalization process and habitat restoration can offer development opportunities in terms of ecosystem services improvement and tourism. Nature-based economies, particularly ecotourism and wildlife observation, have been developed in the central Italy by Rewilding Apennines and associated partners as part of a wider effort towards rewilding at the European scale (https://rewilding-apennines. com/). Local stakeholders and population always need to be involved (Hall, 2019), and this is particularly true of Italian internal areas, subject to demographic decrease and rewilding but still characterised by significant population size and rich cultural heritage. In this sense, surveys and interviews such as those discussed in this paper can offer insights into sustainable tourism management policies. Due to their socio/economic as well as physical marginality, inner and internal areas are seen as crucial sectors where to promote specific policies to increase cohesion at the European and national level (Scanu et al., 2019). In 2012, the Minister of Cohesion Fabrizio Barca promoted the Strategia Nazionale per lo Sviluppo delle Aree Interne (SNAI) supported by European as well as national funding and managed by the Comitato Tecnico Aree Interne (CTAI), coordinated by the Department of Cohesion Policies of the Presidency of the Council of Ministers. The SNAI is aimed at encouraging sustainable territorial competitiveness and at contrasting depopulation, at the same time increasing occupation, improving essential services (local public transport, education, social-health care services), landscape maintenance and natural capital. The SNAI has been funded with 90 million euros in 2014, further 90 million euros for 2015–2017 and 10 million euros for 2016–2018. With the 2018 budget law, the project has been financed further 91,2 million euros, of which 30 million for 2019 and 2020, and 31,18 million for 2021 (Strategia Nazionale per le Aree Interne). Further to the SNAI, other initiatives have been promoted to implement cohesion in Italian marginal areas, including the “Benessere Italia” control room of the Presidency of the Council of Ministers, which provides technical-scientific support in well-being policies and citizens’ life quality assessment. One of the purposes of the control room is the development and coordination of specific projects between different territories that show geographical continuity but are not necessarily included within the same administrative unit (region or metropolitan area). Recent initiatives aimed at reviving the economy of internal areas are quality certifications that characterise the so-called “Borghi”, traditional Italian villages with artistic, historical, cultural and aesthetic values, most of them located in mountain contexts. Amongst the main labels are the “Borghi più Belli d’Italia”, “Bandiere Arancioni” and “Borghi Autentici” certifications’, that in 2020 gathered respectively 293, 247 and 229 places, with 88 villages being included in at least two lists, and one in three. As part of this national effort to revive these areas, on 17 July 2020 (Law number 77, article 182) a new classification of Italian tourist municipalities was established. This classifies municipalities both according to their potential tourist vocation based on anthropic geographical elements (elevation, proximity to the sea, etc.), and on “tourist density”. The latter is defined by a set of statistical indicators defined to measure the presence of infrastructures and tourist fluxes, and to assess economic return in terms of productivity and employment of tourism-based activities. The six following categories, whose characteristics are summarised in Table

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Table 19.1 Italian municipalities by tourist category, number, inhabitants and presences (2019) Categories

N. municipalities

N. inhabitants 2019

Large cities

12

9,231,543

15.3

19.7%

Cultural, historical, artistic and landscape vocation

431

7,371,534

12.2

7.8%

Maritime vocation

414

4,504,784

7.5

19.6%

Mountain vocation

497

739,981

1.2

4.0%

Lake vocation

168

734,960

1.2

4.0%

Thermal vocation

51

438,413

0.7

1.2%

Maritime + cultural, historical, artistic and landscape vocation

240

5,831,480

9.7

20.0%

Mountain + cultural, historical, artistic and landscape vocation

244

760,276

1.3

8.8%

Other municipalities with two or more vocations

151

1,685,266

2.8

6.9%

Not belonging to a specific category

4,014

25,296,927

41.9

8.0%

Non-touristic

1,704

3,764,382

6.2

Total

7,926

60,359,546

100.00

% inhabitants (%)

% overnights on tot. 2019 (436,7 million)

// 100.00%

Source Elaboration by the authors from ISTAT (2020a, 2020b)

19.1, are identified as: large cities; municipalities with cultural, historical, artistic and landscape vocation; municipalities with maritime vocation; municipalities with mountain vocation; lake tourism municipalities; thermal tourism municipalities. In addition, municipalities with two or more tourist vocations are found, for example, those with maritime and cultural, historical, artistic and landscape vocation, and those with mountain and cultural, historical, artistic and landscape vocation. This classification is established by the contextual presence of one of the above geographical characters and a significant degree of tourist density (ISTAT, 2020a, 2020b).

19.3.2 The Impact of Proximity Tourism in the Summer of Covid-19: From Emergency to Opportunity? The concept of proximity tourism is not recent, but it is increasingly associated with tourism activities linked to the Covid-19 emergency. Its definition is complex, as the term “proximity” might relate to an idea of physical, as well as figurative

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closeness (Mangano, 2020). Literature review on proximity tourism is quite limited (Diaz-Soria & Llurdés Coit, 2013), but various examples of approaches on the theme, mainly connected with reflections on tourism sustainability, involved French, Spanish and Northern European scholars from the 1980s (Mangano, 2020). By considering the idea proximity only in terms of physical distance, proximity tourism can be associated to domestic tourism. This type of tourism involves people who visit areas located within, or immediately around their area of residence. In Italy, proximity tourism necessarily includes internal areas, characterised by rich cultural heritage offer and natural protected areas that are suitable to various types of sustainable and experiential tourism. Tourism in Italian internal areas can be divided into two macro groups: natural tourism and cultural tourism. Nature tourism considers naturebased activities that range from the mere contemplation of the natural environment to outdoor. It includes rural tourism, outdoor tourism, active tourism, adventure tourism and ecotourism. Rural tourism in Italy is linked with agritourism, an original form of tourism typical of rural areas, where tourists can enjoy various services and activities connected to the rural world. In Italy, agritourism accommodation has grown by +32.1% between 2002 and 2019. These structures are mainly concentrated in Centre and North East Italy, but North West Italy is the macro area that saw the highest growth in recent years (Mangano, 2020). Outdoor tourism can be associated to particular sports (Rivera Mateos, 2018) or simply to recreational activities in rural or green areas in urban contexts, such as public parks. Active tourism includes a series of recreational and adventure activities that take place in rural areas including cycling tourism, a relatively recent type of practice that in Italy has considerably grown in last years, also thanks to the development of cycling infrastructures (Inart-Legambiente, 2020). Cultural tourism in internal areas relates to the “borghi” and to natural protected areas characterised by the presence of natural and cultural landscapes (Mangano, 2018). The “borghi” conservation and tourist enhancement is one of the targets of the Italian Strategic Plan for Tourism 2017/22, as these destinations provide an opportunity to diversify and broaden the national tourism offer and reduce over-tourism from classic destinations (Mangano & Ugolini, 2020). In internal areas, nature and cultural tourism share some subcategories that include Smart, Food and Wine, Proximity and Slow Tourism. The above-described activities can be also be practiced by excursionists: in this case, the only difference is that excursionists do not stay overnight in the area where they practice natural and cultural tourist activities. Like tourists, excursionists practice activities outside their usual environment, as also established by the definition of tourism by the UNWTO: «Tourism comprises the activities of persons travelling to and staying in places outside their usual environment for not more than one consecutive year for leisure, business and other purposes» (UNWTO, 1994). The definition of UNWTO shows some ambiguities (Díaz-Soria, 2017; Mangano, 2020; Vacher, 2014); after the first pandemic summer (2020), and in view of the following summer (2021), still conditioned by Covid-19, it is necessary to reflect on how appropriate is to exclude excursionist activities from touristic ones. These considerations also offer

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some reflections on tourist categories of ISTAT based on law n. 77/2020 that does not consider movements generated by excursionists who do not stay overnight. With this classification, movements to minor centres with lack of accommodation facilities are under assessed, as is the case of some Alta Langa municipalities discussed in the next section of this work.

19.3.3 Surveys Results The first case study involves 15 municipalities of the Alta Langa area, between the Province of Savona (Liguria) and that of Cuneo (Fig. 19.1), where the total population amounts to 16,074 (ISTAT, 2021). The area went through massive depopulation, averagely by −27.0% between 1951 and 2017 and with a peak of—81.0% in the municipality of Torresina (Spotorno, 2019; Piana & Spotorno, 2020). Although the territory is administratively divided between two regions, it shows similar geographical features, typical of Italian mountain areas. The majority of the Alta Langa territory (65%) lies between 600 and 700 m asl, while only 3.0% is located below 300 m asl. It is an area which is easily reached from various parts of Liguria and Piedmont, just like Sassello and Toirano, where visitors can practice typical activities of proximity tourism in the day. According to Law 77, 12 July 2020, four out of 15 municipalities (Castelnuovo di Ceva, Montezemolo, Sale delle Langhe and Torresina) fall under the “non touristic municipalities” category, as either they have no accommodation infrastructures or they had no tourist movement in 2019. Despite this, these areas have a rich cultural, historical, artistic and landscape heritage which attracts daily excursionists from both large centres and the area around. Similarly, there are seven places (Camerana, Cengio, Dogliani, Millesimo, Priero, Roccavignale, Sale San Giovanni) that are classified as “Tourist centres not belonging to any specific category” as they have very low tourism movement. The case of Millesimo is particularly significant, as the village is in the list of the “Borghi più belli” label, but it does not fall under any specific category. The only municipalities with a recognised tourist characterisation are Belvedere Langhe, Mombarcaro, Murazzano and Paroldo that have a mountain vocation due to local tourism characters and the fact that they are located at more than 600 m asl. In 2019 overnights in the municipalities of Alta Langa overall were 21.663 (0.005% of the total national) while the accommodation capacity (number of beds) was 857 (0.02%). Sassello (1,742 inhabitants), in the inland area between Genoa and Savona and Toirano (2,655 inhabitants) internal municipality but located within less than 10 km from the coastline are recognised as tourist municipalities with cultural, historical, artistic and landscape vocation. Due to its characteristic and well-kept historical centre, Sassello was awarded the “Bandiera Arancione” label in 1998, first Italian municipality to acquire this certification. In addition, part of its territory lies within Beigua Natural Regional Park, one of Liguria’s protected areas, which played a crucial role in its tourism development. Despite sharing with the Alta Langa area issues of marginalisation and depopulation, in recent times Sassello

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diversified its offer which today includes trekking/outdoor activities and food and wine tourism. In 2019 Sassello had 15,502 overnights and the total number of beds was 399. Toirano has also been awarded the “Bandiera Arancione” due to the rich artistic and architectural heritage of its well-kept historical centre which includes a sixteenth-century bridge. In 2019 it had 4,290 overnights, while the total amount of beds in the municipality was 105. Local users and tourists involved in the surveys emphasised the important role of internal areas in the 2020 pandemic summer (Table 19.2). In the Alta Langa, almost 65% local users declared to agree or strongly agree with a sentence stating that tourists in rural, internal areas increased because of the Covid-19 emergency situation. In the case of Sassello, just less than 65% of the interviewees declared that, following the pandemic emergency, tourists chose to visit this village as part of a renewed interest on proximity tourism in internal areas, while according to almost 50% there has been a higher number of tourists in the summer of 2020. This has occurred despite users of Sassello felt that Liguria regional authority and other local institutions did very little or nothing to enhance internal areas through the promotion of local craftsmanship (66.7%), the use of social media and websites (64.4%) and cultural or folklore activities. The promotion of agri-food products has been the activity that enhanced the most the tourist offers of municipalities of internal areas. A large majority of interviewed tourists in Toirano argued that they would have visited the place regardless the pandemic, although almost 47.0% of interviewees declared to have visited inland destinations as social distancing is facilitated (40.5%) and they are little frequented (33.3%). Such behaviours are also confirmed by a survey carried out by ISTAT (2020a) for Italian tourism in the first three quarters of 2020. In this time range tourism in general decreased by −68.6% and foreign tourists by −68.6%, with large cities being particularly affected (−73.2%). The situation is very different for what concerns domestic tourism in little municipalities of both internal areas and the coast in the summer quarter, suggesting a renewed interest for Italian villages. Between July and September Italian mountain destinations show the same number of tourists as 2019 (−0.4% of overnights), while municipalities with cultural, historical, artistic and landscape vocation register a growth of +6.5% compared to 2019.

19.4 Conclusions Tourism and excursionism in Italian internal areas represent a real chance of socioeconomic development that can contribute to reverse depopulation, offering new opportunities and perspectives particularly to young generations. Proximity tourisms in particular will be crucial in tourism revival in Italy, although foreign tourism, accounting for almost 50% of arrivals and overnights should not be neglected. Alongside domestic tourists who can re-discover small villages and rural landscapes, a promotion of such less-known destinations amongst foreign tourists can contribute to diversify the offer and relieve pressure on consolidated and often overcrowded

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historical-artistic cities. The Covid-19 pandemic has constituted a boost to a tourist phenomenon that in recent years had already shown some positive, although still very preliminary, development trends. The “borghi” and natural resources around them were one of the priorities not only of the SNAI, but also of the Italian Strategic Plan for Tourism 2017/22. By underlying the importance of proximity tourism during the Table 19.2 Prevalent tourist opinions of local users in the surveys of Alta Langa and Sassello and of tourists in Toirano Survey

Target

Questions

Prevalent opinions

Alta Langa

Local user

Following the Covid-19 emergency, there has been a growth of tourists in rural areas

✓ 64.4% of interviewees agreed or strongly agreed

Sassello

Local user

In the pandemic summer 2020

✓ for 63.9% of interviewees Sassello was part of a renewed interest on holidaying in inland areas ✓ for 48.9% of interviewees Sassello had a higher number of tourists and excursionists ✓ for 24.4% of interviewees Sassello had a lower number of foreign tourists ✓ for 24.4% of interviewees tourists stayed in Sassello for a longer time

In the pandemic summer 2020, Liguria regional authority and other local institutions promoted Ligurian inland areas like Sassello very little of nothing

✓ for 66.7% through local craftsmanship (e.g. local markets) ✓ for 64.4% through social media and websites (e.g. Museo Perrando) ✓ for 61.1% through folklore and cultural initiatives (e.g. Aperilibro) ✓ for 54.4% through tourist activities where distancing is facilitated (e.g. open air cinema) ✓ for 48.3% through the promotion of agri-food products (e.g. excursions and visits to farms, typical food tastings)

Toirano

Tourist

You would have visited Toirano anyway, ✓ Yes for 91.1% of regardless the on-going emergency? interviewees Following the Covid-19 emergency, are you visiting the inland area more than before?

✓ Yes for 46.7% of interviewees (continued)

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Table 19.2 (continued) Survey

Target

Questions

Prevalent opinions

People frequent inland areas destinations ✓ for 40.5% of cases because more than before because these are safer destinations due to easier social distancing ✓ for 33.3% of cases because they are less frequented destinations ✓ for 16.7% of cases for no specific reason Source Elaboration by the authors from ISTAT (2020a, 2020b)

Covid-19 emergency, it is important to emphasize how each type of tourism needs to be sustainable and respectful of the environment. This has to be true for classic historical cultural tourism that made Italy one of the most popular tourist destinations worldwide, the fifth overall in 2019 in terms of international tourist arrivals, according to UNWTO. At the same time, proximity tourism in areas subject to renaturalization such as internal Italy needs to be carefully planned and managed in order to reduce potential negative impacts on these fragile natural environments. Here the increase of natural capital can offer innovative economic opportunities, including nature-based tourism. However, the sustainability of such development policies strictly depends on the direct and active involvement of local stakeholders through focus groups, interviews and surveys.

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Chapter 20

Does Vaccination Influence COVID Cases? An Empirical Investigation for India and Its States Imran Hussain and Ramesh Chandra Das

20.1 Introduction The distressing effects of the COVID-SARS 2, simply the COVID-19 virus, are a well-known fact to the global community today. Ending its first wave in most of the countries of the world in 2020, it has started its second-round evasion in many of the countries of the West and then to the countries of the East. Lacs of people have already died and crores of people have been affected till date. India is not an exception to its second-round aggression. A list of its provinces, mainly those with metro cities have been affected largely. A common recommendation by the scientific community is toward the proper vaccination policy to be taken by all the countries at a time to keep the citizens away from its common as well as mutational effects. It has been observed in the second round of the attack that the aged persons over the age of sixty are now less impacted by the virus due to the fact that a major part of them have already been vaccinated. Further, those with a vaccination age of over 45 years are now observed to be less affected as because a part of this age group population has got at least the first dose of the vaccines. The third wave is thus expected to attack the young population group (below 44 years of age) as they are mostly unvaccinated. Hence, there is an association between the number of COVID incidences and vaccination across the countries. A list of studies had attempted earlier to predict the spread of the virus across different countries in the world most of which turned up with the near actual figures (Bayyurt & Bayyurt, 2020; Das, 2020; Hernandez-Matamoros et al., 2020, among others). Since the last couple of months, India has been the worst-hit country in I. Hussain · R. C. Das Department of Economics, Vidyasagar University, West Bengal, Midnapore, India R. C. Das (B) Economics, Vidyasagar University, West Bengal, Midnapore, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_20

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the world. According to Rao (2020, September), the National Herald has mentioned that India has been the new epicenter of the global pandemic. The effects of Britain strain and a deadly variant, Indian strain, have been identified as the central cause of the new spread in the sub-continent. The Lancet (2021, May) very recently opined in its editorial that there would be about one million deaths by August 1, 2021 for India. Several causes have been identified by the report for this massive death rates, out of which policy failure on the part of the government regarding vaccination and capacity buildings have been in the priority list. Keeping in mind the positive associations between the COVID incidences and vaccination, the present study aims to examine whether vaccination in India and its metro states has long run and short run linkages with the number of cases due to COVID.

20.2 Literature Review Although the subject matter is new in the research field, a list of available studies are there in the literature to justify the basis of a new study. The present study accounts for some of them to proceed for the execution of the research objectives. The study of Miller et al. (2020) compares BCG vaccination policies for a large number of countries under morbidity and mortality circumstances for COVID-19 and it finds that BCG immunization appears to reduce mortality rates related with COVID-19 in a significant manner. In 183 most affected countries Hussein et al. (2020) find an association between the fatality rates of COVID-19 and the proportion of coverage under BCG vaccination. In a detailed study Banik et al. (2020) observe that the factors like population age structure, public health system, poverty level, and BCG vaccination are influential causative factors in shaping the COVID19 fatality rates. Kanupriya (2020) has attempted to trace the pandemic in order to view the challenges and to provide possible remedies from the perspectives of social, economic, political, and medical sciences. Her study contends fragmentary method to sort out the crisis through an all-inclusive raft comprising all the noted areas (New York Times, 2020). In order to find the impact of COVID-19 on personality levels and emotional levels, the study of Sahni et al. (2021) reveals the following results-extraversion, conscientiousness, and neuroticism are directly associated with emotional bounciness for working adults; agreeableness is directly associated with emotional resilience for the homemakers; and assiduousness and frankness to experience are directly associated with emotive flexibility for the students. The findings of the study are at par with that of Oshio et al. (2018). There are some studies also in this line which are of Ong et al. (2006); Oshio et al., (2018), among others. In an attempt of examining the association between BCG vaccination and the sternness of Covid-19 Dolgikh (2020) establishes that the vaccine may also improve the immune system to fight against any virus over the lifetime. Solis-Soto and Nicoli (2020) show that in Indian children the poor nutritional status is closely related to incomplete immunization which is also reported before in the work of Anekwe and Kumar (2012). There are other study reports on the impacts of immunization

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275

such as those of Kristensen, Aaby, and Jensen 2000; McGovern and Canning 2015 who reported on the reduced mortality rates; the results of decreased morbidity are observed by Otto et al. 2000; and antibiotic use is by Wilby and Werry, 2012. In the study related to the business field, Kaur and Kaur (2020) point out that COVID-19 has taught a lesson to the business houses to make preparations to defend against any such natural and economic crises which may appear at any time without alerting. With respect to the response against any banking business volatility, Bhatia and Gupta (2020) have shown that unstable behavior has been adequately robust to continue in the market with the leverage effect which was prevailing during the US sub-prime crisis. But that effect vanished for Nifty Bank Indices and Private Sector Bank Indices in comparison to Public Sector Bank Indices during the COVID-19 period.

20.3 Research Gap and Rationale of the Study The extract of the literature review in the related fields shows that a few studies have been done by the correlation analysis on Covid-19 cases and the vaccination. But the correlation analysis doesn’t provide an adequate explanation of cause-effect relationship. It shows only the degree of association between the variables. Thus, the present study aims to investigate the specific short run and long run causal relationship between COVID-19 cases and vaccination in India as well as four major states having metro cities, namely, Maharashtra, Tamil Nadu, West Bengal, and Delhi.

20.4 Variables and Data Sources The present study has incorporated the effectiveness of vaccination in India as well as four major states (Maharashtra, Tamil Nadu, West Bengal, and Delhi) to the total number of COVID-19 cases. Vaccination is proxied by Total Doses (TD) and total number of Covid-19 cases is proxied by Total Confirmed Cases (TC). Considering these two variables, we have collected daily basis data on Total Cases (TC) from ‘PRS LEGISLATIVE RESEARCH’ (www.prsindia.org) and Total Doses (TD) from ‘Ministry of Health and Family Welfare’ (www.cowin.gov.in). The period of study is February 01, 2021 to May 10, 2021 which covers a major part of the second wave of the spread of the virus.

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20.5 Hypotheses of the Study The present study is based on the following hypotheses for testing the cointegration and causality between TC and TD. 1. Does vaccination (as proxied by TD) have a significant long run impact on COVID-19 cases (proxied by TC) for the time period February 01, 2021 to May 10, 2021? 2. Is there a causal relationship between TC and TD for India as well as these four major states?

20.6 Methodological Ladders Firstly, a descriptive analysis has been carried out to understand data characteristics for these two variables with mean and standard deviation and then Karl Pearson’s correlation coefficient is applied to have a quick view on the degree of associations between the two. Then standard time series exercise is done to investigate the long run and short run associations between the two. First, the stationarity test has been done in line with the augmented Dickey and Fuller (1979) and Phillips and Perron (1988) tests. Second, Engel and Granger (1987) cointegration test is carried out for the long run associations, and third, Granger (1969) causality test is done to see the short run interplays between the two.

20.7 Augmented Dickey-Fuller (ADF) Test The Augmented Dickey-Fuller (ADF) test is a common technique for testing the unit root problem (or the non-stationary problem) of a time series data which is free from the problem of autocorrelation. The ADF test includes different (say, m) lags of the dependent variable (ΔYt ) to spot-on any serial correlation in the random disturbance term. The null hypothesis of H0 : δ = 0 for no unit root problem is tested by using a τ-statistic in three following situations (or models): ΔYt = δYt−1 +

m Σ

γi ΔYt−i + u t

(20.1)

i=1

ΔYt = α + δYt−1 +

m Σ

γi ΔYt−i + u t

(20.2)

i=1

ΔYt = α + βt + δYt−1 +

m Σ i=1

γi ΔYt−i + u t

(20.3)

20 Does Vaccination Influence COVID Cases? An Empirical Investigation …

277

where ut is a white noise error term. These equations incorporate different terms ΔYt−1 = (Yt−1 − Yt−2 ), ΔYt−2 = (Yt−2 − Yt−3 ), etc. The decision rules under the ADF test are the same as the DF test. The null hypothesis, that is, H0 : δ = 0 will be rejected (or, when the time series will be stationary) when the computed absolute value of the tau statistics (τ) surpasses the Mackinnon critical values. In the reverse situation, there will be non-stationary issues with the series. Further, the ADF test has the restrictive condition that the errors are serially independent and with a constant variance. Phillips-Perron test is another technique that relaxes these restrictive assumptions. The study also goes with the Phillips-Perron test as an alternative test.

20.8 Phillips-Perron (PP) Test Under the Phillips and Perron (1988) test procedure the test regression is also the AR(1) process with the expression ΔYt = δYt−1 + u t

(20.4)

Unlike the ADF test, the PP test makes a modification to the computed τ-statistic of the estimated regression coefficient of δ to take into account the problem of serial correlation in u t . So, the PP τ-statistic is just a reform of the ADF τ-statistic. Additionally, the asymptotic distribution of the τ-statistic of ADF and PP are identical making the same critical values of the two tests.

20.9 Engle-Granger (EG) Cointegration Test After the confirmation of the series as I(1) the subsequent step is to inspect whether the series are cointegrated. If so, then it is said that a long run or equilibrium relationship between the variables exists throughout the period of the study. To test for cointegration the study uses the well-known Engle and Granger (1987) test. It is a simple test of cointegration based on residual estimation. The method runs through the following steps: Step I: If it is found that both the series of the variables are I(1), OLS method is applied to estimate the equation [Yt = α + bX t + εt ] and then derive the series of ˆ t . The series for the estimated residual has time estimated residuals εˆ t = Yt − aˆ − bX series property again. It is required to exercise the time series results from the series. Step II: To check the stationarity of the series of εˆ t to arrive at the results of whether the variables are really cointegrated, or having an equilibrium relation. For the said purpose, the ADF test is applied in the same way as that of the two series of variables. The structure of the ADF test equation here is as follows:

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I. Hussain and R. C. Das /\

/\

Δεt = δε t−1 +

m Σ

/\

αi Δε t−1 + vt

(20.5)

i=1

Here the null hypothesis is H0 : δ = 0, and the alternative hypothesis is H1 : δ # 0. It is now required to see whether εt is I(0) (i.e., stationary) or not. If ε t , the estimated residual is found to be stationary at levels, that is εˆ t ∼ I (0) then it is concluded that the variables are cointegrated. /\

/\

20.10 Error Correction Mechanism (ECM) After the confirmation of the presence of cointegration between the variables, the next step is to investigate the short run dynamics in the long run relation which requires the construction of the error correction mechanism. The persistence of the ECM is to indicate the speed of adjustment from the state of the short run equilibrium to that of the long run equilibrium. To present the model of ECM, let us consider two series Yt and Xt which are I(1) so that their linear combination ε t = Yt − a − b X t is I(0). Then the equilibrium relation between the two variables is represented by Y t = a + b X t . Corresponding to this equilibrium relation, the ECM can be written as /\

/\

/\

/\

/\

/\

ΔYt = φ + γ ΔXt + λˆεt−1 + wt

(20.6)

where γ = Short-run coefficient which captures the immediate impact of a change in Xt will have on the changes in Yt . It is sometimes called short run propensity of Y with respect to X. ‘λ’ = Coefficient of the estimated lagged residual which stands for the feedback effect, or the adjustment effect, or error correction coefficient and shows how much of the disequilibrium is being corrected. εt−1 = error correction term. Here εt−1 = (Yt−1 − Y t−1 ) = (Yt−1 − a − b X t−1 ) is one period lagged value of the error from the cointegrating regression. Finally, wt = white noise error term in the ECM. When εt−1 is non-zero (positive or negative), there is disequilibrium in the short run. However, equilibrium will be restored (or converged) in the long run if and only if λ < 0 and statistically significant, otherwise the deviation will be diverging. /\

/\

/\

/\

/\

/\

20.11 Granger Causality Test Granger (1969) was the first econometrician to offer a formal test of the direction of causality between the variables. It is basically a statistical test. To understand the Granger causality test, suppose the following set of questions are attempted to answer is it that Y causes X (written as Y → X) or X causes Y (X → Y)? To answer these questions, the Granger test may be applied which involves estimating the following pairs of equations.

20 Does Vaccination Influence COVID Cases? An Empirical Investigation …

Yt = a1 + X t = a2 +

Σn i=1

Σn i=1

αi X t−i + γi X t−i +

Σm j=1

Σm j=1

279

β j Yt− j + ε1t

(20.7)

δ j Yt− j + ε2t

(20.8)

where ε1t and ε2t are uncorrelated white noise error terms. Here both the variables are taken in their level forms. However, if they are I(1) then the first or second differenced forms of the two variables are to be taken and inserted in the two equations. In terms of the above model, four different cases can be visualized to justify directions of causality between the variables. They are as follows /\

1. If all the α i s in Eq. 20.8 are significantly different from zero in statistical terms and all the δ j s in Eq. 20.9 are not significantly away from zero, it is then the situation of having unidirectional or one-way causal influence from X to Y. 2. If all the α i s in Eq. 20.8 are not significantly different from zero in statistical terms and all the δ j s in Eq. 20.9 are significantly away from zero, then it is the situation of having unidirectional or one-way causal influence from Y to X. 3. If all the α i s, β j s, γ i s and δ j s are significantly different from zero statistically, then it is the situation of two-way, bilateral, or feedback causal influence among the variables. 4. If all the α i s, β j s, γ i s and δ j s are not significantly different from zero statistically then it is the situation of unrelatedness or independence among the variables where neither of the variable causes the other one and vice-versa. /\

/\

/\

/\

/\

/\

/\

/\

/\

/\

/\

To know which of the above cases holds, Granger’s test is applied. Here the null hypotheses are H0 : αi = 0 (i = 1, 2, . . . , n) [For Eq. 20.8]. H0 : δ j = 0 (i = 1, 2, . . . , m) [For Eq. 20.9]. For this, the F-statistics is calculated as F∗ =

(RSS R − RSS U R )/m RSS U R /(n − k)

(20.9)

where RSS R = Restricted residual sum square (Regressed Yt on Yt-j but do not include Xt-i or regressed X t on X t−i but do not include Yt− j ). RSS U R = Unrestricted residual sum square (Regressed Yt on Yt-j and Xt-i or regressed X t on X t−i and Yt− j ). m = number of lagged X terms (for Eq. 20.1) or number of lagged Y terms (for Eq. 20.2). If the computed-F exceeds the critical-F value [i.e., F ∗ > Fλ (m, n-k)] then reject the H0 and conclude that ‘X Granger causes Y’ (for Eq. 20.1) or ‘Y Granger causes X’ (for Eq. 20.2).

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Total Number of Cases

25000000

20000000

India Maharashtra Tamil Nadu West Bengal Delhi

15000000

10000000

5000000

0

Fig. 20.1 Trends of Total Cases (TC) for the period of Feb. 1, 2021 to May. 10, 2021 by India and states. Source Authors’ own derivations

20.12 Empirical Results and Discussion 20.12.1 Graphical Presentation of the Data Before attempting the econometric exercises, the graphical presentation is done to have the idea on the movements of the series for the period of the study. In Figs. 20.1 and 20.2 the trends of number of Covid-19 cases (TC) and vaccination, i.e., number of doses (TD) are shown for India as well as its four states. By observing both the figures, it is seen that both the variables increase over time. The trend is similar (approximately coincides) for Tamil Nadu, West Bengal, and Delhi. Maharashtra is the outlier state in the number of TC. In case of vaccination (Fig. 20.2), fluctuations for India as well as its selected states are observed. It is to be noticed that Maharashtra is in the first position for both TC and TD among the state level.

20.12.2 Descriptive Statistical Analysis of the Data In the descriptive statistical analysis, three major measures are considered, the mean, variance, and correlation between the variables across three equal phases (i.e., 33 time points each) of the 99 data points. Table 20.1 presents the value of mean and standard deviation of the variable Total Cases (TC) for the period of Feb. 01, 2021 to Mar. 05, 2021 (33 days), Mar. 06, 2021 to Apr. 07, 2021 (33 days), and Apr. 08, 2021 to May. 10, 2021 (33 days), respectively. It is observed that the mean values of TC have significantly increased over time but the variances don’t (i.e., equal variance). Considering the states, mean value

20 Does Vaccination Influence COVID Cases? An Empirical Investigation … 4000000

Total number of Doses

3500000 3000000

281 India Maharashtra Tamil Nadu West Bengal Delhi

2500000 2000000 1500000 1000000 500000 0

Fig. 20.2 Trends of Total Doses (TD) for the period of Feb. 1, 2021 to May. 10, 2021. Source Authors’ own derivations

is highest for Maharashtra in all the three sub-periods and that is lowest for West Bengal. The mean values of all the states have increased significantly in TC over all the three phases. In case of TD (refer to the lower panel of the table), as noticed similar, mean has significantly increased over different phases for Maharashtra, West Bengal, and Delhi but for India and Tamil Nadu, there do not have significant increase in the TD. The case for West Bengal is further different in the sense that its mean TD has decreased in the third phase leading to a positive t value. But the figures for the variance in TD have not changed for all the selected units. In case of Tamil Nadu (t23 = −1.18924), mean has not significantly increased during the sub-period from (Mar. 06, 2021 to Apr. 07, 2021) to (Apr. 08, 2021 to May 10, 2021) and the variance is statistically equal for all the period. Considering Maharashtra and Delhi, both mean and variance statistically changed over time. It indicates of non-stationary time series by weak sense. Table 20.2 shows the values of the correlation coefficient between the variables, TC and TD, for the period of Feb. 1, 2021 to May 10, 2021. For India, Maharashtra, Tamil Nadu, and Delhi, variables are significantly positively correlated. Similar findings were given by Miller et al. (2020) and Hussein et al. (2020). In case of West Bengal, TC and TD positively but insignificantly correlated (i.e., acceptance of null hypothesis of no correlation). Behind the reason is that vaccination provided by the Government of West Bengal in a very limited extents, assembly election campaigns were there for eight phases, and at the same time Covid-19 cases don’t hold constant, however, Covid-19 cases increase significantly during the last subperiod as compared to previous two sub-periods. This may explain the absence of a correlation between TC and TD for the state. A similar result was shown by Li and Zhao (2020) in the countries with BCG and without BCG. The correlation coefficient is the highest and significant for Maharashtra (r = 0.6228).

19,988

37,376

12,652

West Bengal

Delhi 38,223

170,045

82,300

215,878

2,079,109

Mean2

652,464

582,464

871,706

2,527,929

11,813,964

S.E.2

16,182

49,410

12,729

70,818

706,253

S.E.2

12,539

5849

15,609

281,214

506,173

66,842

143,645

89,353

276,014

2,364,301

Mean3

984,827

747,074

1,084,464

4,149,868

17,335,653

Mean3

27,849

55,235

31,603

90,275

651,341

S.E.3

211,005

124,729

141,360

596,632

3,145,255

S.E.3

−3.01 −1.19 2.05 −5.10

−14.5 −25.8 −14.3 −8.87

−1.70

−13.3

−9.03

−6.87

t23

−7.57

−8.99 t12

−14.1 −8.59

−8.90 −9.10

t23 −9.95

t12 −9.37

Apr. 08,2021 to May. 10,2021 t-statistic

0.05

0.16

0.45

0.01

0.02

F12

0.01

0.09

0.08

0.03

0.06

F12

F-statistic F23

0.34

0.80

0.16

0.61

1.18

F23

0.01

0.01

0.01

0.22

0.03

Note Test is conducted at 5% level of significance. * t12 : t-Statistic for Mean1 minus Mean2 * t23 : t-Statistic for Mean2 minus Mean3 * F12 : F-Statistic for S.E.1 and S.E.2 and F23 : F-Statistic for S.E.2 and S.E.3 Source Authors’ calculations

3531

8057

8527

35,450

13,340

109,685

S.E.1

1488

1743

Maharashtra

420,151

India

48,099

4514

Tamil Nadu

637,344

Mean1

Delhi

572,911

West Bengal

TD

2,085,524

845,964

Maharashtra

Tamil Nadu

125,878

S.E.1

Mean2

Mean1

10,962,631

TC

India

Mar. 06,2021 to Apr. 07,2021

Feb. 01,2021 to Mar. 05,2021

Table 20.1 Descriptive statistics for total cases (TC) and total doses (TD) in India and states

282 I. Hussain and R. C. Das

20 Does Vaccination Influence COVID Cases? An Empirical Investigation … Table 20.2 Correlation coefficient between TC and TD by India and states

283

r

t-statistic

Probability

India

0.3920

4.196629

0.0001

Maharashtra

0.6228

7.840001

0.0000

Tamil Nadu

0.2965

3.057682

0.0029

West Bengal

0.1201

1.191472

0.2364*

Delhi

0.5940

7.272189

0.0000

Notes r = Karl Pearson’s correlation coefficient between TC and TD. * P-value is greater than 0.05 Source Author’s own calculations

20.12.3 Unit Root Test Results Following different models of stationarity test (Eqs. 20.1, 20.2 and 20.3), the unit root tests are carried out and the results of the ADF Test and PP Test with first/second difference of series for India, Maharashtra, Tamil Nadu, West Bengal, and Delhi are shown in Table 20.3. From the above ADF test results, it is observed that the series belonging to TC and TD are not stationary in level values for all the states and India but they become stationary only when first difference or second differences are taken. It is found that the hypotheses of unit root for the series are rejected under PP Test also. Thus, the PP Test reinforces our conclusion that the series are in first difference/second difference form and don’t have a unit root and have a deterministic trend. For India, there is no unit root in the first difference, i.e., the series are I(1). Thus, the exercise for the cointegration test is done for India only to confirm long run relationship between TC and TD. But all the states’ series are second differenced stationary that forbids us to test for cointegration for them.

20.12.4 Cointegration Test Results As mentioned in the methodology, the study uses the Engle and Granger (1987) test technique to examine the presence of a cointegrating relationship (i.e., long run relationship) between the variables. According to the methodology, if the variables are I(1) and the estimated residuals are stationary in level, i.e., I(0) then the variables are said to be cointegrated. The ADF test is conducted to test whether the estimated residual is stationary in level or not for India. The result is given in Table 20.4. The findings show that the estimated residual is not stationary at level, i.e., it is not I(0). Thus, the results conclude that there is no long run relationship between COVID-19 cases and number of vaccinations in case of India. The non-cointegrating result can be supported by the survey results by MIT in collaboration with Johns Hopkins University and Facebook across 68 countries in the world including India. The news published by ‘TOI plus’ on May 23, 2021 on the survey reports shows that

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Table 20.3 ADF and PP test results for the period of Feb. 1, 2021 to May 10, 2021 India

Variables

Intercept and trend

Conclusion

ADF

Δ TC

−4.269485 (0.0054)

Stationary at first difference

Δ TD

−10.85731 (0.0000)

Δ TC

−1.667712 (0.0581)

Δ TD

−11.09132 (0.0000)

ΔΔTC

−17.63919 (0.0000)

ΔΔTD

−10.12381 (0.0000)

ΔΔTC

−22.35758 (0.0000)

ΔΔTD

−18.92804 (0.0000)

ΔΔTC

−12.99348 (0.0000)

ΔΔTD

−10.41582 (0.0000)

ΔΔTC

−12.91924 (0.0000)

ΔΔTD

−34.15730 (0.0001)

ΔΔTC

−6.555098 (0.0000)

ΔΔTD

−14.23155 (0.0000)

ΔΔTC

−7.022634 (0.0000)

ΔΔTD

−37.96681 (0.0001)

ΔΔTC

−9.484982 (0.0000)

ΔΔTD

−9.767824 (0.0000)

ΔΔTC

−9.661717 (0.0000)

ΔΔTD

−210.7161 (0.0001)

PP Maharashtra ADF PP

Stationary at second difference

Tamil Nadu ADF PP

Stationary at second difference

West Bengal ADF PP

Stationary at second difference

Delhi ADF PP

Stationary at second difference

Notes tau statistics, τ-value is denoted by underlined value and P-value is in first bracket, Δ-First difference, ΔΔ-Second difference Source Authors own calculations Table 20.4 ADF test for estimated residual in level for India

Variable

Intercept

Intercept and trend

Conclusion

Residual

2.47 (0.12)

−2.38 (0.3838)

Not stationary at level

Notes tau statistics, τ- value is denoted by underlined value and P-value is in first bracket Source Authors own calculations

20 Does Vaccination Influence COVID Cases? An Empirical Investigation …

285

25% Indians are either unsure or will not participate in the vaccination drive by the government. As having no cointegration result the study does not attempt for error correction mechanism for short run dynamic analysis for India.

20.12.5 Granger Causality Test Results Now it is the time to proceed for the final stage of investigation of the study and the short run causal interplays between TC and TD in line with Granger (1969). The results are presented in Table 20.5. It is obtained that India and Delhi have bilateral causal relationships between TC and TD. This implies that Covid-19 cases depend on vaccination and at the same time, vaccination depends on Covid-19 cases. It may be that low vaccination rate influences high number of cases and at the same time, high vaccination rate influences the number of cases to decline. For Maharashtra, the result shows a unidirectional relationship between TD and TC and is running from TC to TD but not the reverse. It means vaccination depends on Covid-19 cases but Covid-19 case doesn’t depend on vaccination. It further means that the pressure of highly increasing number of cases forces the Government of Maharashtra to undergo vaccination drives. But there is no causal relationship between TC and TD for Tamil Nadu and West Bengal, i.e., the variables are independent of each other. While total cases increase over time, vaccination didn’t increase. It may be that other factors except TC and TD have worked for these states to produce no causal results. These two states had faced their assembly election in the phase of the study period which could have changed the causal interplays between TC and TD. Further, the shortage of vaccines is another Table 20.5 Granger causality test for the period of Feb. 1, 2021 to May 10, 2021 India Maharashtra Tamil Nadu West Bengal Delhi

Null hypotheses

Lag

F- stat

Prob

Conclusion

ΔTC ⇸ ΔTD

8

3.83140

0.0008

ΔTC ↔ ΔTD

ΔTD ⇸ ΔTC

8

3.61654

0.0014

ΔΔTC ⇸ ΔΔTD

6

3.32286

0.0058

ΔΔTD ⇸ ΔΔTC

6

0.18777

0.9794

ΔΔTC → ΔΔTD but ΔΔTD ⇸ ΔΔTC

ΔΔTC ⇸ ΔΔTD

9

0.82619

0.5943

No causality

ΔΔTD ⇸ ΔΔTC

9

1.30775

0.2490

ΔΔTC ⇸ ΔΔTD

3

0.03134

0.9925

ΔΔTD ⇸ ΔΔTC

3

0.20538

0.8924

ΔΔTC ⇸ ΔΔTD

6

3.77669

0.0024

ΔΔTD ⇸ ΔΔTC

6

2.29082

0.0434

No causality ΔΔTC ↔ ΔΔTD

‘⇸’ implies ‘does not Granger cause’, ‘ → ’ denotes the direction of causality, and ‘ ↔ ’ implies two--way causality, *Lag order selected from majority decision by AIC, SIC, and HQ Source Author’s own calculations

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problem during this period for all the states and all India levels (THE HINDU on June 1, 2021 and TOI on May 31, 2021). A similar result is shown by Li et al. (2020) by employing correlation analysis.

20.13 Conclusion Raising an important question on whether vaccination has any sort of linkages with the number of COVID cases in the short as well as long runs for the states of India and India as a whole the present study applied the relevant time series econometric exercises and arrived at the conclusion that both the indicators have long run or equilibrium relations in case of India as a whole but no such relations are observed for the four selected states. The series for the states are integrated of order two being the crucial cause for the no cointegration relation between the two in the states. But the results of the short run analysis show that India and Delhi have bi-directional causal interplays between the two indicators and West Bengal and Tamil Nadu are the extreme states where there are no such causal interplays. Only Maharashtra has the unilateral causal relation from a total number of COVID cases to the number of vaccinations which means that the pressure of a highly increasing number of cases forces the Government of Maharashtra to undergo vaccination drives. Hence, in an overall sense, it cannot be ensured that vaccination is the only way to combat the severity of the COVID-19 virus. There might have other causes like that of awareness, physical distancing, uses of masks and sanitizers, etc. which could defend against the virus.

References Anekwe, T. D., & Kumar, S. (2012). The effect of a vaccination program on child anthropometry: evidence from India’s Universal Immunization Program. Journal of Public Health, 34(4), 489– 497. Banik, A., Nag, T., Chowdhury, R., & Chatterjee, R. (2020). Why do COVID-19 fatality rates differ across countries? An explorative cross-country study based on select indicators. Global Business Review. https://doi.org/10.1177/0972150920929897 Bayyurt, L., & Bayyurt, B. (2020). Forecasting of COVID-19 cases and deaths using ARIMA models. medRxiv. https://doi.org/10.1101/2020.04.17.20069237 Bhatia, P., & Gupta, P. (2020). Sub-prime crisis or COVID-19: A comparative analysis of volatility in indian banking sectoral indices. FIIB Business Review, 9(4), 286–299. Das, R. C. (2020). Forecasting incidences of COVID-19 using box-jenkins method for the period July 12–Septembert 11, 2020: A study on highly affected countries. Chaos, Solitons, and Fractals, 140, 110248. https://doi.org/10.1016/j.chaos.2020.110248 Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive timeseries with a unit root. Journal of the American Statistical Association, 74 Dolgikh, S. (2020). Further evidence of a possible correlation between the severity of Covid-19 and BCG immunization. Journal of Infectious Diseases and Epidemiology. https://doi.org/10. 23937/2474-3658/1510120

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Engle, R. F., & C. W. Granger. (1987). Co-integration and error correction: Representation, estimation and testing. Econometrica, 55 Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross spectral methods. Econometrica, 37(3) Hernandez-Matamoros, A., Fujita, H., Hayashi, T., & Perez-Meana, H. (2020). Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Applied Soft Computing, 96. https://doi.org/10.1016/j.asoc.2020.106610 Hussein, M., et al. (2020). Correlation between COVID-19 case fatality rate and percentage of BCG vaccination: Is it true the vaccine is protective? The Egyptian Journal of Bronchology. https:// doi.org/10.1186/s43168-020-00022-1 Kanupriya. (2020). COVID-19: A socio-economic perspective. FIIB Business Review, 9(3), 161– 166. https://doi.org/10.1177/2319714520923 Kaur, G., & Kaur, C. (2020). COVID-19 and the rise of the new experience economy. FIIB Business Review, 9(4), 239–248. Kristensen, I., Aaby, P., & Jensen, H. (2000). Routine vaccinations and child survival: follow up study in Guinea- Bissau, West Africa. BMJ (Clinical Research Ed.), 321(7274), 1435–1438. Ong, A. D., Bergerman, C. S., Bisconti, T. L., Wallace, K. A. (2006). Psychological resilience, positive emotions and successful adaptation in later life. Personality and Individual Differences, 127, 730–749. Oshio, A., Taku, K., Hirano, M., Saeed, G. (2018). Resilience and big five personality traits: A meta-analysis. Journal of Personality and Social Psychology, 127, 54–60. Otto, R. K., Edens, J. F., & Barcus, E. H. (2000). The use of psychological testing in child custody evaluations. Family Court Review, 38, 312–340. https://doi.org/10.1111/j.174-1617.2000.tb0 0578.x The Lancet (2021). Editorial-India’s COVID-19 emergency. 397(10286), P1683 Li, Y., Zhao, S., Zhuang, Z., Cao, P., Yang, L., & He, D. (2020). The correlation between BCG immunization coverage and the severity of COVID-19. https://doi.org/10.2139/ssrn.3568954 McGovern, M. E., & Canning, D. (2015). Vaccination and All-Cause Child Mortality From 1985 to 2011: Global Evidence From the Demographic and Health Surveys. American Journal of Epidemiology, 182(9), 791–798. Miller, A., Reandelar, M. J., Fasciglione, K., Roumenova, V., Li, Y., & Otazu, G. H. (2020). Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19: An epidemiological study. https://doi.org/10.1101/2020.03.24.20042937 Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. Rao, V. V. (2020). India: The new global epicentre for COVID-19. National Herald. https://www. nationalheraldindia.com/ Sahni, S., Kumari, S., & Pachaury, P. (2021). Building emotional resilience with big five personality model against COVID-19 pandemic. FIIB Business Review, 10(1), 39–51. Solis-Soto, P., & Nicoli, D. (2020). Relationship between vaccination and nutritional status in children analysis of recent demographic and health surveys. Demographic Research, 42(1), 1–14. https://doi.org/10.4054/DemRes.2020.42.1 Wilby, K. J., & Werry, D. (2012). A review of the effect of immunization programs on antimicrobial utilization. Vaccine, 30(46), 6509–6514.

Chapter 21

Impacts of Effective Conservation Capital Investment and Policies on Healthcare Policies and Expenses Egemen Sertyesilisik and Begum Sertyesilisik

21.1 Introduction Fast-growing economy-enabled increase in the GDPs has acted as a driver for the increase in production and fast industrialisation despite their adverse environmental impacts and environmental degradations’ costs and consequences endangering economy, people’s welfare and well-being at the global level. GDP per capita in the USA has increased 28 times between 1776–2016 (Roser, n.d.). Hence, 2-weeks output per person today is more than the annual output per person in the past (Roser, n.d.). While the economic growth increases the living standards in the short run, it can jeopardise the nature and environment essential for achieving sustainable welfare in the long term (ESA, 2013). Environmental pollution and degradation have been accelerated. Environmental quality deterioration and fast economic development go hand in hand (Liu et al., 2021; Mohmmed et al., 2019). Economic growth in the world continues at an increasing rate. Although this situation can result in economic improvement and economic development, in case this, development is not environmentally sensitive and in case environmentally sensitive growth is not achieved, the world can continue to become more and more environmentally polluted. Nature and conservation capital need to be protected while achieving economic growth as adverse consequences of harmed and deteriorated nature have started to be observed and experienced. Demaria (2018) emphasises economy’s incompatibility with the environmental sustainability as, at the global economy level, approximately 10% of materials are recycled and 50% of processed materials are not recycled as E. Sertyesilisik (B) Gozuyilmaz Engineering and Marine Industries Ltd, Izmir, Turkey e-mail: [email protected] B. Sertyesilisik Faculty of Architecture, Istanbul University, Istanbul, Turkey © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_21

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they are used for energy supply. Even if industries might differ from each other with respect to the size of their environmental footprint, all industries contribute to the environmental footprint, all industries contribute to the environmental degradation. For example, textile industry consumes a high amount of water and puts pressure on water resources globally (Hussain & Wahab, 2018; Saxena et al., 2017). The textile industry’s chemical-intensive processes can be harmful to human life and environment (Hussain & Wahab, 2018). The textile industry also needs a high volume of water in production (Hussain & Wahab, 2018; Kant, 2012). Many researches emphasise that environment quality degrades due to the fast-growing economy which consumes non-renewable resources and which causes GHG emissions (Klaassen & Opschoor, 1991; Liu et al., 2021). Climate change threatens economy as well as people’s welfare and well-being globally. World real GDP per capita is estimated to be reduced by about 7.22% by 2100 due to a steady increase in average global temperature (Franck, 2019). The population living in extreme poverty is estimated to increase to 16 million in 2030 due to climate change, whereas, according to a more pessimistic scenario, this population can reach 35–122 million (Hallegatte et al., 2016; OECD, 2017). Intergovernmental Panel on Climate Change expects that, by 2100, the crop yields in various African countries to fall by 90% due to climate change (OECD, 2013). Furthermore, reduced amount of crop yields can affect people’s well-being adversely as well. Another example of climate change’s impacts is the sea level rise (SLR) endangering people’s well-being and welfare as well as requiring investment to enhance humanity’s resilience to deal with disasters’ risks and consequences. Ng and Mendelsohn (2005) emphasise the cost of protecting Singapore’s coasts against SLR (Ng and Mendelsohn 2005, as cited in Asuncion & Lee, 2017). Furthermore, flooding caused annual losses in coastal cities are expected to reach USD 1 trillion by 2050 unless flood defences are improved/necessary precautions are taken (Hallegatte et al., 2013; OECD, 2017). There is an opportunity cost of not investing in conservation capital. For this reason, this chapter aims to investigate the relationship between conservation capital investment and healthcare expenses as well as between conservation capital policies and healthcare policies.

21.2 Relationship Among Climate Change, Conservation Capital and Health Care Climate change threatens humanity’s well-being and health. Climate change has impacts on health (e.g. mortality, morbidity) and on occupational health (WHO and WMO, 2012; WHO, 2014; OECD, 2015). These impacts’ economic costs are difficult to estimate as they need to cover market costs (e.g. morbidity’s impact on productivity) and non-market costs (e.g. pain and suffering) (OECD, 2015). Climate change and conservation capital affect health care and humanity’s health.

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As climate change has a significant impact on health determinants (e.g. drinking water and food), climate change-related health problems (e.g. malnutrition, malaria, diarrhoea and heat stress) are estimated to cause about 250,000 deaths annually in the 2030–2050 period (WHO, 2018). If governments fail to launch effective conservation capital policies and investments to solve the climate change’s health impact problems, pressure to healthcare services and healthcare expenditures can increase in the long run. Climate change negatively affects water resources. Safe water shortages together with hygiene issues can increase the diarrhoeal disease risk which causes death of more than 500,000 children under 5 years annually (WHO, 2018). Many people lose their lives in our world due to the polluted and/or lack of water resources which can cause a lack of hygiene leading to many diseases. This situation eventually raises the healthcare expenditures of the countries. Governments need to find solutions to solve water scarcity problems and provide adequate water resources to their society as well as establish relevant water infrastructure in their countries so that they can save people, especially children, dying from diseases. These policies can further help to decrease their healthcare expenditures. For this reason, these policies can act as proactive healthcare policies as well. Climate change affects food production adversely leading to less production of food resulting in malnutrition and diseases. Malnutrition can affect people’s immune system causing them to be vulnerable to diseases. In other words, people, who do not get enough food, can lose their health and lives. Governments’ effective agriculture policies and investments to reduce food waste and enhance climate resilience of agriculture can contribute to the solution of malnutrition. These policies and investments can further support people’s health and well-being reducing their demand for healthcare services which can result in reduced healthcare expenditures. For this reason, effective conservation capital investments and policies as well as healthcare investments and policies need to cover agriculture and food production topics. Loss in natural capital can endanger humanity’s well-being. For example, loss in biodiversity including endemic plants can harm the balance among the livelihoods as well as the food chain vital for humanity’s well-being. Another example can be provided focusing on the impacts of deforestation. In Panama, deforestation caused mosquito populations’ displacement in the canopy resulting in the increased Yellow Fever cases which is a zoonotic disease (ESA, 2013). Even if zoonotic diseases outbreak is not easy to foresee, they can escalate quickly resulting in loss of human lives and billions of dollars (ESA, 2013). Even if economic growth caused resource depletion and biodiversity loss can harm ‘carrying capacity of ecological systems’, the cost of this harm is uncertain due to the unknown benefit of the loss of genetic maps (Pettinger, 2019). The amount of money spent on the healthcare expenditures is influenced by climate change and damaged natural capital as well as their adverse consequences. Climate change-related healthcare expenditures were investigated by Bosello et al. (2012), Chima et al. (2003), OECD (2015). Healthcare cost projections to 2060 reveal increased demand for health services, mainly/especially in Asia, Brazil, the Middle East and North Africa, whereas demand for health services is expected to

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decrease in countries such as Canada, Germany and France (OECD, 2015). Reduced well-being of people can increase the need for increase in the healthcare expenses and in medicines. Increased demand for medicines can further result in governments to increase the budget allocated to medicines. Governments need to consider the return-on-investment from medical interventions as they need to use public funds and resources cost-effectively (Brunetti et al., 2013; Cook et al., 2017). Ineffective healthcare policies and expenses can increase the need for increasing the budget allocated to healthcare expenses due to the increased need for healthcare services and medicines. This requirement can further result in the reduction in the amount of budget allocated to the conservation capital investments due to the budget constraints and investment priorities. This reveals that ineffective healthcare policies and investments have adverse impacts on environmental sustainability. Furthermore, as ineffective healthcare policies can fail to protect human health in a proactive way, they can have adverse impacts on social sustainability as well. Healthcare expenses can have impacts on the sustainable development of the countries affecting their investment priority. For example, countries’ increased healthcare expenses can obstacle and challenge sustainable development investments (e.g. investments in sustainable infrastructure, renewable energy) due to budget limitations. These obstacles and challenges can further reduce conservation capital investments. Failure in allocating budget to the conservation capital investments can further cause increase in healthcare expenses due to the harmed nature and climate change’s adverse consequences on people’s health and well-being. An increase in effective conservation capital investment can contribute to the reduction in healthcare expenses (Fig. 21.1). Conservation capital investment policies and healthcare policies affect each other and complement each other (Fig. 21.2). Effective healthcare policies can support budget allocation for conservation capital investment which can further support humanity’s well-being. Effective conservation capital policies can influence the effectiveness of healthcare policies. Successful conservation capital investment policies can support the well-being of people through enhanced and protected nature. Enhanced well-being of people can reduce their need and demand for healthcare services and medicines. Effective conservation capital investment

Health care expenses

Fig. 21.1 Relationship between conservation capital investment and healthcare expenses. Source Authors’ elaborations

Conservation capital investment policies

Health care policies

Fig. 21.2 Relationship between conservation capital policies and healthcare policies. Source Authors’ elaborations

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The policy-making process of healthcare sector has become complicated as healthcare professionals, politicians and planners need to deal with diversity, complexity and change while considering factors affecting health conditions (González-Block et al., 2009; Topçu et al., 2020). Healthcare policies are multidimensional and they need to be prepared and updated in an interdisciplinary approach (Topçu et al., 2020). Topçu et al. (2020) have emphasised political economy of the occupational health and safety in the construction industry. The health targets of the sustainable development goals can be achieved through suitable factors of political economy which affects the reform process and which covers the interaction of politics and economics as well as their consequences as a result of specific outcomes (Poole, 2011; Reich, 2019). As although health policy-makers know the importance of political economy analysis, they do not have enough ability to make and apply it, the WHO can help and support the health policy-makers by providing them with evidence-based political economy analysis so that they can manage the change more effectively (Reich, 2019). There is a need for effective conservation capital investment and policies as well as healthcare policies and expenses so that sustainable development of the countries can be supported. As conservation capital investments and policies can influence healthcare expenses, conservation capital investment policies need to be integrated to the healthcare policies.

21.3 Recommendations for Enhancing Effectiveness of Conservation Capital Policies in Reducing Healthcare Expenses of Countries Effectiveness of conservation capital policies in reducing healthcare expenses of countries and in enhancing people’s well-being can be supported by maintained natural resources. Natural capital assets need to be maintained with the help of changes in technology and behaviour in favour of improving human well-being as well as reducing waste and demand for material so that sustainable development can be achieved (ESA, 2013). Countries’ economic growth is influenced mainly by factors related with natural resources, human capital, financial development, industry value added and innovation (Rahim et al., 2021). Enhanced sustainability can contribute to people’s well-being and result in reduced healthcare expenses (Fig. 21.3). Conservation capital policies need to encourage investment in renewable energy and energy efficiency so that natural capital’s protection can be supported. The International Renewable Energy Agency indicates the vitality of transition to renewable energy so that decarbonisation can be supported (Khan et al., 2021). Based on the REN21, Global Status Report (2018), Lui et al. (2021) highlighted countries’ attention to develop and invest in renewable energy. Investment in the Research and Development activities has become key to support productivity of energy and usage

294 Fig. 21.3 Conservation capital policies and investments supporting sustainability-enabled well-being. Source Authors’ elaborations

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Achieving sustainable development

Increased investments for achieving sustainable development

Reduced health care expenses

Achieving environmental sustainability

Enhanced wellbeing

of renewable energy sources (Khajehpour et al., 2020; Sim, 2018). Green innovations and their political economy can contribute to the sustainability performance of the industries (Sertyesilisik, 2019). Energy prices can affect energy efficiency-related investments and widespread of renewable energy usage. Following the increase in energy prices in the USA in the 1970s, energy efficiency in manufacturing has increased (Schipper & Meyers, 1992; Davidson, 2000). Reduction in solar and wind power costs accelerated widespread of renewable energy usage (IEA, 2017; Mohsin et al., 2021). For this reason, research and development and energy pricing in favour of renewable energy usage and energy efficiency can be used as policy tools for supporting natural capital protection. Investing in research and development can support efficiency in production which can further support return of this investment, conservation capital as well as human health. For example, investment in R&D specifically made in the field of energy can support sustainable and clean energy production which can support conservation capital through more environment-friendly production which can support human health and well-being. More environment-friendly production can contribute to the efforts in conservation capital protection and in preventing the pollution of water resources and air. This situation is important for the protection of human health. Protection of human health can provide people high quality of life and reduced death risks. Furthermore, these improved health conditions can help to reduce governments’ health expenditures and enable governments to invest in other needs which can enable people to get better education, transport, infrastructure, etc. Similarly, Topçu et al. (2020) have emphasised the adverse impacts of high rates of occupational accidents and occupational diseases on health expenditures, countries’ sustainable development and budgets due to their opportunity cost. Conservation capital policies need to encourage carbon reduction targeting policies to support natural capital protection vital for humanity’s health and well-being.

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For example, Chinese president Xi Jinping announced that, by 2060, they intend to achieve net zero emissions, whereas their carbon usage can be expected to reach peak by 2030 (McGrath, 2021). Furthermore, trade policies can be a tool for natural capital protection as well. Removing trade barriers for green goods and services can support the green growth transition of developing countries (OECD, 2013). Natural capital protection necessitates actions to be taken for ensuring sustainability and resilience of the ecosystem. Even if it is important that ecologically sustainable development protects ecosystem resilience, many ecosystems management strategies fail to be sustainable reducing biodiversity and ecosystem as they concentrate only on a single service and neglect others (ESA, 2013). Sertyesilisik (2018) has emphasised the importance of global sustainability leadership and the need for global sustainability renaissance. Conservation capital investment policies need to cover effective budget allocation and investment to achieve natural capital protection. Achievement of the Sustainable Development Goals necessitates approximately $4.5 trillion worth of investments annually (UNCTAD, 2014; Gnych et al., 2020). Decarbonisation of the economy requires investment in innovation activities to achieve changes in technologies to produce and consume energy (Dechezleprêtre, et al., 2016). Investment in technology improvement and its research and development can foster environment-friendly economic growth and conservation capital investments. Improved technologyenabled economic growth can achieve a higher amount of output with less pollution (Pettinger, 2019). Technological developments enable efficient and environmentfriendly solutions, increase in efficiency, reduction in cost and adverse impacts on the environment (e.g. substitution of fossil fuel-run cars by renewable energy-based cars) (Pettinger, 2019). Green/environment-friendly economy, growth and policies are vital for conservation capital investments and policies. Green/environment-friendly economy and growth need to be based on international collaboration among developing and developed countries (Sertyesilisik & Sertyesilisik, 2016). Furthermore, for fostering green/ environment-friendly economy and growth, interrelation among different aspects (i.e. political, social and economic) need to be considered (Sertyesilisik & Sertyesilisik, 2016). Human resources need to be considered as important enablers for natural capital protection vital for their health. They need to consider themselves as responsible for conservation capital protection. Increased awareness of renewable energy can foster transition to the renewable energy usage. As developing countries encounter the dilemma regarding how to meet their energy demand while preventing environmental degradation which endangers sustainable development, efforts are being spent to increase awareness of renewable energy’s environmental compatibility (Mahjabeen et al., 2020). Furthermore, education should be provided at the individual and corporate levels on topics such as recycling, usage of energy and responsible citizenship (Zafar et al., 2019). Conservation capital policies and investments can support healthcare policies and investments through the synergy to be created by the sustainability enhanced and enabled well-being (Fig. 21.3). Achieving sustainable development enables

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achieving environmental sustainability which can contribute to the people’s wellbeing reducing the need for healthcare expenses and increasing investments for achieving sustainable development and natural capital protection (Fig. 21.3). Effective conservation capital policies and investments necessitates an assessment of their political economy aspects as well as their relation with the healthcare policies, investments and their political economy aspects. Conservation capital can act as a multiplier factor for humanity’s health. Conservation capital policies and investments can be considered as proactive healthcare policies as they can focus on and support protecting human health. For this reason, effective conservation capital policies and healthcare policies complement each other. Protection of humanity’s health can reduce healthcare expenditures enabling governments to invest more in conservation capital and their people’s needs. Conservation capital investments and policies’ impacts on humanity’s health emphasise their role in sustainability as well. Conservation capital investments and policies as well as healthcare investments and policies have impacts on sustainable development and on all pillars of sustainability.

21.4 Conclusion This chapter focused on the effective conservation capital investment and policies on healthcare policies and expenses as well as on their relationship. Environmental pollution and degradation affect human health adversely. Failure in conservation capital investments can be costly due to the consequences of climate change, environmental pollution and degradation as well as their domino effects hindering sustainable development and harming human health. Effective conservation capital policies and investments in the conservation capital can support humanity’s well-being and enhance their health. They can contribute to the reduction in the need for healthcare services and their expenses enabling countries to invest more in their development and conservation capital. In other words, effective conservation capital policies and investments can support sustainable development and sustainability enabled wellbeing. Furthermore, healthcare policies and conservation capital investment policies influence and affect each other. This chapter emphasises the importance of and the need for effective conservation capital investment and policies to reduce healthcare expenses and support global sustainable development. Effective conservation capital investment policies need to be designed considering their relationship with and their influence on healthcare policies and sustainable development. As the world’s GHG emission level has started to increase mainly due to economic and industrial growth, global warming could not have been prevented due to the insufficient precautions taken by previous and current generations. For this reason, global warming can become even more noticeable and detrimental in the future affecting humanity’s health as long as we do not pay required attention to sustainable economic growth and conservation capital. This situation can harm our conservation capital including air and result in pollution affecting the present and next generations of humanity’s health. A Native Americans’ proverb says “We do not inherit the Earth

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from our ancestors, we borrow it from our children”. As a Native Americans’ proverb reveals, this world does not just belong to our generation as we borrowed it from our children. If required importance is given to conservation capital investments and policies today, a healthy environment can be inherited by the next generations. Maintained natural resources and natural capital is the key for enhancing the effectiveness of conservation capital policies in reducing healthcare expenses of countries and in enhancing people’s well-being. Conservation capital policies need to encourage investment in energy efficiency and renewable energy as well as carbon reduction targeting policies so that natural capital’s protection can be supported. Governments need to launch effective conservation capital policies and investments so that climate change’s health impact problems can be solved. These effective conservation capital policies and investments can further support their healthcare policies and investments as they can help reduce the potential pressure to healthcare services and healthcare expenditures with the help of protected and enhanced health of their people. Effective conservation capital policies and healthcare policies complement each other as conservation capital policies can be considered as proactive healthcare policies. Effective conservation capital policies and investments can achieve creation of synergy through sustainability-enhanced well-being. Furthermore, conservation capital policies need to cover human resource aspects and ways for increasing their awareness of the importance of natural capital as human resources are the main enablers for natural capital protection. It is further important that conservation capital investment policies support effective budget allocation and investment to achieve natural capital protection vital for human health. As conservation capital investments and policies as well as healthcare investments and policies support, affect and complement each other, they need to be considered in an integrated way. All these investments and policies are vital to accomplish sustainable development as they can affect all pillars of sustainability. This chapter can be beneficial to academics and policy-makers as well as all stakeholders.

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Chapter 22

Environmental Quality and the Quality of Life in Sub-Saharan Africa: Measuring the Role of Economic Liberalization Olalekan Charles Okunlola

22.1 Introduction Quality of life (henceforth QoL) is a multidimensional concept with no welldeveloped theoretical background (Okunlola & Akinlo, 2021). The QoL has been viewed from objective and subjective dimensions (Felce & Perry, 1995; Rokicka, 2014; Terhune, 1973; WHO Group, 1995). While Terhume (1973) viewed the QoL subjectively as personal satisfaction or a prerequisite for happiness, Rokicka (2014) defined the QoL to mean a good life in terms of consumption, depending on the possession of particular material goods. Many studies have investigated the factors influencing the QoL (Martinez-Martin et al., 2012; Okunlola & Akinlo, 2021; Nikolaev, 2014; Graafland, 2020; Joshua, 2017; Scully, 2001; Easterlin & Angelescu, 2007). For example, Martinez-Martin et al. (2012) found health, family, and finances significant factors influencing QoL. Similarly, economic freedom, economic growth, and government consumption expenditure have been established to affect the QoL (Okunlola & Akinlo, 2021; Easterlin & Angelescu, 2007; Scully, 2001). In addition to all these factors that have been demonstrated to influence the QoL, ENVQ has also been linked to the QoL (Streimikiene, 2015; Keles, 2011; Amuka et al., 2018; Borhan et al., 2018; Milner et al., 2020; Nkalu & Edeme, 2019; Azam et al., 2015; Deng et al., 2020; Diener & Suh, 1997; UNECE, 2009). It is a known fact that human life is greatly affected by the health of the physical environment. Pollutants and hazardous substances have enormous side effects on human health (Streimikiene, 2015). According to Brajša-Žganec et al. (2011), the ENVQ matters O. C. Okunlola (B) Directorate of Defence and Security Studies, Institute for Peace and Conflict Resolution, Abuja. PMB 349, Garki, Nigeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. C. Das (ed.), Economic, Environmental and Health Consequences of Conservation Capital, https://doi.org/10.1007/978-981-99-4137-7_22

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intrinsically because human beings see very important the beauty and health of the place where they live and care about the depletion of its natural resources. Furthermore, the ENVQ is a fundamental factor in well-being because the physical environment strongly affects the QoL (Holman & Coan, 2008; Kahn, 2002; Van Liere & Dunlap, 1980; Reto & Garcia-Vega, 2012). For instance, environmental events such as natural disasters and epidemics may lead to death, injury, and disease. Similarly, Ahmad and Yamano (2011) claimed that severe environmental changes might damage human health through climate change. Also, the United Nations (2002) outlined the major health problems associated with environmental pollution as reduced IQ, anemia, neurological damage, physical growth impairments, nerve disorders, pain and aching in muscles and bones, memory loss, kidney disorders, retardation, tiredness and headaches, lead colic, seizures, delirium, coma and, in some cases, death. Furthermore, the emissions of lead, mercury, chromium, and carbon dioxide (CO2 ) have a dangerous impact by poisoning infants, pregnant women, and children between 5 and 14 (Blacksmith Institute, 2011). Aside from affecting human health, ENVQ is also a key factor influencing QoL through household consumption (Boyd & Uri, 1991; EEA, 2012; Narayan & Narayan, 2008; Odusanya et al., 2014; Boachie et al., 2014; Yahaya et al., 2016; Yazdi & Khanalizadeh, 2017; Mujtaba & Shahzad, 2021; UNEP/MAP-Plan Bleu, 2009; Alimi et al., 2020). In their work, Boyd and Uri (1991) demonstrated that regardless of the strategy employed to improve ENVQ, both output and consumption decline, as does household utility. Furthermore, they noted that the aggregate loss in production and economic welfare (measured by consumption expenditures and utility) is less under a policy that stresses reliance on alternative fuels (brought about by taxation) than through one that requires the installation of pollution abatement devices (that is, regulation). Similarly, UNEP/MAP-Plan Bleu (2009) claimed that climate change causes the following: reduced yields in agriculture and fishing, reduced attractiveness of tourism (heatwaves, rarefied water resources), coastal zones and infrastructures (high exposure to waves, coastal storms, and other extreme climatic events, higher salination, depletion of underground freshwater resources, seawater penetration in aquifers), and negative impact on public health (heat waves). Also, Alimi et al. (2020) demonstrated that carbon emission exerts a positive and statistically significant impact on public and national healthcare expenditure, while no relationship exists between environmental pollution and private healthcare expenditure. Also, Narayan and Narayan (2008), Yazdi and Khanalizadeh (2017), Mujtaba and Shahzad (2021), Alimi et al. (2020), and Yahaya et al. (2016) demonstrated a positive impact of ENVQ on consumption expenditure in the health sector. However, Boachie et al. (2014) found no statistically significant effects of ENVQ on consumption expenditure. Furthermore, another aspect of QoL that has been linked with ENVQ is economic well-being measured by per capita income or growth. Some viewed the link between economic well-being to ENVQ (Kasman & Duman, 2015; Uddin et al., 2017). For instance, the environmental Kuznets curve views the ENVQ-economic well-being nexus as one running from economic growth to ENVQ. The Environmental Kuznets

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curve argues that as the economy develops, it has more impact on the environment (Jeffords & Thompson, 2019). Many country-specific studies have demonstrated an inverted U-shaped curve in the ENVQ and per capita income relationship (Hamit-Haggar, 2012; Hooper et al., 2012; Yavuz, 2014; Ratanavaraha & Jomnonkwao, 2015; Onafowora & Owoye, 2014; Ahmed & Long, 2012). Other results from the cross-national and panel studies are mixed and are not conclusive (see Shafik & Bandyopadhyay, 1992; Panayotou, 1993; Grossman & Krueger, 1995; Unruh & Moomaw, 1998; Unruh & Moomaw, 1998; List & Gallet, 1999; Stern & Common, 2001; Apergis & Payne, 2009; Cole & Neumayer, 2004; Chen, 2009; Lean & Smyth, 2010). While many of these studies supported inverted U-shaped relationships or the environmental Kuznets curve hypothesis, others found an N-shaped (Friedl & Getzner, 2003) and monotonically increasing (Akbostanci et al., 2009) relationship between income and CO2 emission. However, in looking at the links between ENVQ and the QoL, many studies did not consider the role of economic liberalization, which can stimulate industrial growth. For example, growing effluent pollution associated with industrialization may reduce dissolved oxygen in higher-income countries (Shafik., 1994). Also, Halicioglu (2009), Machado (2000), Ang (2009), and Jalil and Mahmud (2009) have demonstrated the existence of a positive relationship between liberalization and carbon dioxide emissions. Given these established linkages, it will be instructive to show the role economic liberalization plays in the ENVQ-QoL relationship. Sub-Saharan Africa (henceforth SSA) suffers from serious environmental problems, including deforestation, soil erosion, desertification, wetland degradation, and insect infestation (Mabogunje, 1995). Despite the low QoL in the region, SSA has enormous natural resources in its rural areas, including forests and grasslands, wetlands, cultivable soils, and other biological resources (World Bank, 1989). These natural resources extracted contributed to environmental degradation in the region. For instance, in the ranking of global greenhouse emissions, only three countries in the SSA region (i.e., South Africa, Zaire, and Nigeria) ranked among the top 50 countries in terms of their 1991 contributions to global greenhouse emissions (World Resources Institute, 1994). In 2018 however, no African country was among the first 10 on this list, but the Democratic Republic of Congo, South Africa, and Nigeria have moved to the 12th, 16th, and 26th position, respectively (Climate Watch, 2020). This study will depart from other empirical studies to investigate economic liberalization’s role in the relationship between the ENVQ and QoL.

22.2 Materials and Methods This study adopts the panel data analysis to examine economic liberalization’s role in the relationship between ENVQ and QoL, as Oseni (2016) has used. This chapter uses the dynamic generalized method of moment (GMM) to estimate the phenomena under consideration. To achieve this, a framework dynamic panel regression model

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to represent the role of economic liberalization in the relationship between ENVQ and QoL in SSA is specified as follows: Qol it = a + γ QoL it−1 + β E N V Q it +

k Δ

δ j X jit + εit ;

j=1

j = 1, . . . , k; i = 1, . . . , n; t = 1, . . . , T

(22.1)

In Eq. (22.1), Qol it represents the Quality of life for country i over period t; QoL it−1 entails the lagged value of the Quality of life for a nation’s i over time t; E N V Q it stands for environmental Quality (ENVQ) for a nation’s i over time t; X jit represents the remaining regressors in the model, including the moderating variable (economic liberalization measured by economic freedom) and control variables for country i over time t, and j is the number of control and moderating variables included. A country-specific fixed effect is assumed for the disturbance term as follows: εit = ei + u it

(22.2)

where εit represents error term; which entails ei , the country-specific fixed effects that are time-invariant, while u it is assumed to be independent and normally distributed and has zero (0) mean and constant variance σu2 overtime and across countries, that is, u it ≈ n(0, u it ). To empirically analyze economic liberalization’s role in the relationship between Quality of life and ENVQ, this paper uses a dynamic panel approach with the system GMM estimator. In a dynamic panel, including a lagged dependent variable as an independent variable violates the orthogonality assumption. This is because the lagged value of the dependent variable (QoL it−1 ) depends on εit−1 , which is a function of εit . Because εit = ei + u it , absolutely the expected value of the lagged dependent variable and error term E(QoL it−1 εit ) /= 0. From this correlation, dynamic panel data estimation suffers from bias which vanishes as t tends to infinity. To get rid of this country-specific effect, we differenced Eq. (22.1) as follows: Δln Qol it = βΔE N V Q it a + γ Δln QoL it−1 +

k Δ

δ j Δln X jit + Δu it

(22.3)

j=1

But, the converted error term Δu it is correlated with Δln QoL it−1 as they both contain Δu it−1 . Compared to the astatic model, the ordinary least square on the first differenced data in a dynamic model generates inconsistent parameter esti( ) mates since E(QoL it−1 εit ) /= 0. We must take note that E QoL it−g u it = 0 where g ≥ 2, t = 3, . . . T . Then, the chances of using instrumental variable IV estimations, using the lagged variables as instruments. Going by this, Anderson and Hsiao proposed )IV estimation using ln QoL it−2 as instrument for Δln QoL it−1 since (1982) ( E QoL it−2 Δu it = 0.

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Citing from Oseni (2016), Blundell and Bond (1998) argued that if the explained variable is close to the random walk, this will perform the difference GMM poorly because the previous levels convey little information about future changes. In this case, untransformed lags are weak instruments for transformed variables. Therefore, to increase the efficiency, we assume that orthogonality moment condition E(ϕit εit ) = 0 for all i and t. Arellano and Bover (1995) first employed this method to make the instruments exogenous to the fixed effects, and they transformed the difference. Therefore, this assumption is only valid if the variations in instrumental variables are linked to the fixed effect. Going by this assumption, Δϕ it is valid for all variables in levels since E(ϕit−1 εit ) = 0. If N > T, the GMM estimators are suitable. The bias in the GMM model will vanish in the large T panel. This shows that the changes to the economy’s fixed effect demonstrated by the error term reduce with time, and the link between the lagged explained variable and the error term would not be significant (Judson & Owen, 1997; Roodman, 2009). For the dynamic GMM, the problem of endogeneity is resolved when we use static and OLS models, which excludes internally generated instruments. Similarly, according to Arellano (2003), Han et al. (2013), and Horváth, Hušková, Rice, and Wang (2015), the variables from the regression model are not associated with the error factor and are valid as instruments. It allows a condition where N > T helps manage dynamic panel bias (Baum et al., 2007). The study employs a dynamic panel model adopting the system GMM in this study because it has an advantage over difference GMM in a variable that is ‘ random walk’ or close to being a random walk variable (Arellano, 2003; Baltagi, 2008; Baum et al., 2007; Han et al., 2013). Since this study’s model includes primarily macroeconomic variables characterized by random o difference- GMM by improving precision and reducing the finite sample bias (Baltagi, 2008). In conducting the tests of overidentifying restrictions, whether the instruments, as a group, are exogenous, either Sargan or Hansen J statistics or both are used. Sargan statistic is reported for a onestep non-robust estimation which minimized the value of the one-step GMM criterion function. Further, Sargan is reported for all two-step estimations and reduces the value of the two-step GMM criterion function, which is robust. This study uses the Sargan test to account for the over-identifying restrictions based on this criterion. As mentioned, this study’s primary goal is to determine if the level of economic liberalization positively/negatively moderates the impact of ENVQ on the QoL. Then, an interactive term and other variables are incorporated in Eq. 22.1. Equation 22.1 then becomes QoL i,t =β1 QoL i,t−1 + α1 E N V Q i,t + α2 E F W i,t + α3 (E N V Q ∗ E F W i,t ) + α4 P O P G i,t + α5 F AI D i,t + α6 G S i,t + μi + εi,t

(22.2)

where E N V Q ∗ E F W i,t is the interactive term of ENVQ and economic liberalization, P O P G i,t represents population growth rate, F AI D i,t measures the foreign aid inflow into country i at the period t, and G S i,t indicates government size measured by government size as a percentage of GDP. The definition of other variables can be

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seen in Eq. 22.1; ENVQ is measured by CO2 emissions (metric tons per capita). The carbon dioxide emissions stem from burning fossil fuels and cement manufacturing. They include carbon dioxide from solid, liquid, and gas fuels and gas flaring. Also, for validation purposes, fossil fuel energy consumption (% of total) was used to measure ENVQ. Fossil fuel comprises coal, oil, petroleum, and natural gas products. CO2 emissions and fossil fuel energy consumption are the commonly used indicators in the literature. The study used three indicators of the QoL. Per capita income was used to measure economic well-being, consumption per capita was used to capture the standard of living, while life expectancy measures the health component of the QoL (Okunlola & Akinlo, 2021; Okunlola & Ayetigbo, 2021; Akinlo & Okunlola, 2022). This study hypothesized that the coefficient of the interactive term α3 may have a negative value but is not significant. Economic liberalization improves industrialization, which worsens ENVQ. Also, economic liberalization improves the QoL; economic liberalization has a positive effect on QoL but a negative effect on ENVQ. The impact of the interactive term depends on where the impact of economic liberalization is higher on the environment or QoL. Data of per capita GDP, consumption per capita, life expectancy, CO2 emissions (metric tons per capita), fossil fuel energy consumption (% of total), foreign aid, population growth rate, and government expenditure (%GDP) were sourced from World Development Indicators of the World Bank. Economic freedom data was sourced from the Economic Freedom of the World (EFW) report published by the Fraser Institute.

22.3 Results and Discussion Although there are claims that SSA countries are not meticulous with their environmental issues (Mabojuje, 1995), the region still has a meager contribution to global greenhouse emissions (Climate Watch, 2020). For instance, Panel A of Fig. 22.1 shows the trend of CO2 emissions and per capita income in SSA between 1985 and 2017. Chart B in Fig. 22.1 compares the trend of CO2 in SSA and other regions. Chart B shows that despite SSA’s claim to have a nonchalant attitude toward her environment, the region still has low average CO2 emissions compared with other regions. For instance, aside from the European Union with a lower average CO2 emission, the United States and China have average CO2 emissions higher than SSA (see chart B in Fig. 22.1). Chart A in Fig. 22.1 shows the trend of average CO2 emissions and GDP per capita in SSA within the study period. Chart A shows that average CO2 emissions and GDP per capita exhibit a similar trend. Also, spatial mapping of the average CO2 emissions in SSA shows that most countries with a higher value are in the southern part of Africa (Fig. 22.2). It is not a coincidence that those with high average CO2 emissions are among the region’s countries with the highest economic freedom level. This may validate the relationship that has been hypothesized between economic liberalization and CO2 emissions. The countries with the highest average CO2 emissions are represented in deeper green

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A: Trend of CO2 Emissions and Per Capita GDP in SSA

2000

2

(1985-2017)

1500

1.5

1000

1

500

0.5

0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

0

GDPPC

CO2

B:Trend of CO2 Emissions in some Regions in SSA (19852017)

21 16 11 6 1

2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985

-4

SSA

China

Middle East and North Africa

World

European Union

United States

Fig. 22.1 Trends of different indicators. Source Data sourced from WDI, World Bank

color, while the countries represented with lighter green color have lower average CO2 emissions. And white represents an absence of data. This study carried out all the pre-estimation tests, such as the stationarity and cointegration tests. In addition, it examined the characteristics of the data used. However, due to the limited number of tables/figures/graphs allowable, I will present the results of the tests by request. Then, this study moves to the primary analysis of this work. Table 22.1 presents the analysis results of the impact of economic liberalization on the nexus between ENVQ and the QoL in SSA. The two-step system GMM results are shown in Table 22.1. First, the study used final household consumption, per capita GDP, and life expectancy as proxies for QoL. Also, this study used CO2 emission per capita and fossil fuel energy consumption (% of total) as the proxies for ENVQ.

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Fig. 22.2 Spatial Analysis of the Average carbon dioxide emissions (metric tons per capita) in SSA between 1985 and 2017. Source Data sourced from WDI, World Bank. Author’s Computation

In Fig. 22.1, CO2 emission per capita coefficients have a negative and significant sign for all the models. This result suggests that CO2 emission harms economic wellbeing (measured by GDP per capita), the standard of living (measured by consumption per capita), and health (measured by life expectancy), which are all proxies used for QoL. These findings are consistent with a priori expectations and results in the literature. For example, Ahmad and Yamano (2011), Reto and Garcia-Vega (2012), United Nations (2002), and Blacksmith Institute (2011) all demonstrated how ENVQ negatively impacts human health. Furthermore, environmental degradation negatively impacts consumption and per capita income (Boyd & Uri, 1991; UNEP/ MAP-Plan Bleu, 2009; Alimi et al., 2020; Hamit-Haggar, 2012; Hooper et al., 2012; Grossman & Krueger, 1995; Moomaw & Unruh, 1997). Similarly, in the second estimation, where fossil fuel consumption is the indicator for QoL, the result shows that fossil fuel consumption has a negative and significant impact on economic well-being and living standards only (Table 22.1). As found in Okunlola and Akinlo (2021), Akinlo and Okunlola (2022), Nikolaev (2014), and Graafland (2020), this study also found a positive effect of economic freedom on QoL. Here, an increase in economic freedom improves the QoL of

0.006*

(0.000)

0.026*

EFW*CO2 0.004*

Hausman test

AR(2)

AR(1)

C

GEGDP

LNFAID

POPG

0.046*

2.56

(0.010)

1.83

(0.167)

(0.012)

−0.97

(0.334)

9.57

0.051*

(0.000)

−2.51

5.383*

(0.000)

0.058*

(0.000)

(0.000)

−0.005* -0.003

−0.001

(0.763)

0.001*

(0.000)

14.83

(0.000)

4.765*

(0.159)

(0.000)

(0.000)

(0.000)

(0.000)

(0.030)

(0.000)

−0.007* 0.060*

(0.233)

(0.207)

(0.709)

(0.000)

0.056*

(0.000)

0.098*

(0.000)

−0.001** 0.047*

0.014*

(0.007)

−0.014

(0.000)

−0.003

(0.000)

0.089*

(0.000)

EFW

−0.007*

(0.000)

(0.005)

−0.015

(0.790)

−0.030*

(0.000)

(0.060)

−9.561**

(−0.210)

−1.25

(0.095)

−1.67

55.05

(0.022)

−6.703**

0.086***

(0.000)

2.099*

(0.000)

1.748*

Hausman test

AR(2)

AR(1)

C

GEGDP

LNFAID

POPG

(0.000)

−0.009

(0.000) (0.594)

0.012

(0.713)

0.37

(0.009)

−2.61

(0.000)

0.732*

(0.268)

−0.001

(0.000)

16.31

(0.000)

6.089*

(0.000)

0.011*

(0.000)

−0.035* 0.036*

(0.000)

0.101*

SGMM (0.000)

0.987*

(0.173)

1.36

(0.091)

−1.69

(0.064)

SGMM

(0.808)

−0.003

(0.848)

0.127

(0.802)

0.036

(0.000)

0.971*

(0.078)

0.006***

(0.000)

0.066*

(0.057)

68.07

(0.000)

(0.806)

0.016

(0.000)

1.623*

(0.001)

1.777*

(0.030)

−0.024**

(0.001)

1.428*

(0.000)

0.303*

F-effect

(0.857)

−0.18

(0.812)

−0.24

(0.020)

(continued)

23.64

(0.110)

−8.336** 6.197

(0.714)

0.014

(0.004)

0.410*

(0.753)

−0.059*** 0.371

(0.000)

0.004*

(0.928)

−0.002

(0.000)

-0.016*

F-effect

0.308*** 5.387*

0.226)

-0.002

(0.004)

0.010*

(0.001)

0.034*

(0.001)

0.001*

(0.626)

−0.011

(0.002)

−0.013* −0.009* (0.001)

0.004*

−0.026

(0.001)

−0.008

(0.000)

F-effect

−0.370*** EFW*Fossil 0.002*

(0.083)

(0.032)

SGMM 0.974*

(0.647)

EFW

FOSSIL

Lag (DEP)

(0.324)

3.082 (0.000)

(0.408)

−0.025

(0.000)

0.634*

(0.937)

−0.032

(0.043)

0.527**

(0.235)

−0.505

(0.076)

(0.000) (0.048)

−0.034* −0.210* −3.253*** 2.412**

CO2

(0.000)

F-effect

(0.000)

SGMM 0.962*

0.981*

F-effect

R-effect SGMM

SGMM

Lag (DEP) 1.0026*

Table 22.1 Empirical dynamics of system GMM dynamic panel (two-step estimate)

22 Environmental Quality and the Quality of Life in Sub-Saharan Africa … 309

535

(0.787)

558

(0.000)

(0.000)

582

239.44

273.23

535

(0.999)

8.32

F-effect

(0.022)

R-effect SGMM

(0.144)

612

(0.922)

3.83

SGMM

F-effect

558

(0.000)

33.64

(0.000)

F-test

Sargan test

286

(0.659)

8.6

SGMM

F-effect

307

(0.000)

51.31

(0.012)

288

(1.000)

1.31

SGMM

F-effect

301

(0.000)

163.08

(0.000)

313

(0.946)

2.22

SGMM

F-effect

325

(0.000)

46.6

(0.001)

Note The probability values for the fixed effects and system GMM estimates are in parenthesis. *, ** and *** denote the significance of the individual coefficients at 1%, 5%, and 10% levels, respectively. The Sargan test is for over-identifying restrictions. AR(1) and AR(2) represent the Arellano–Bond test of first-order and second-order autocorrelation, respectively. The F-test examines if the panel has an individual-specific effect. Hausman’s test determines if the difference in coefficient is systematic. Dependent variable: CO2 and Fossil emissions. Source Authors’ Computation

OBS

F-test

Sargan test 13.76

SGMM

Table 22.1 (continued)

310 O. C. Okunlola

22 Environmental Quality and the Quality of Life in Sub-Saharan Africa …

311

people. But in the second estimation in the table, there is no significant effect of economic freedom on QoL. The table shows that the interactive term of economic liberalization and ENVQ has a positive sign for all the models using the system GMM. For instance, the interaction of the economic freedom variable and CO2 emission positively impacts GDP per capita, consumption per capita, and life expectancy. While in the second estimation, the result shows a positive and significant impact on GDP per capita and consumption per capita only. This result suggests a complementary interaction between economic freedom and QoL. By complementary interaction, an increase in the value of the moderator (in this case, economic freedom) will increase the impact of the explanatory variable on the explained variable (Cartwright et al., 2018). This implies that a unit increase in economic freedom will increase the effect of CO2 on QoL indicators and vice versa. Given the harmful impact CO2 has on QoL, an increase in economic freedom should reinforce this negative impact on QoL. However, this is not the case, as the interactive terms positively impact QoL. Earlier in this paper, we hypothesized that economic freedom also stimulates QoL (established by the result of this study). The impact of the interactive term depends on which variable—ENVQ or QoL, economic freedom has more impact. From this result, however, it is evident that economic freedom’s effect on QoL outweighs its effects on ENVQ. Therefore, this explains why the interactive terms of economic freedom and ENVQ have positive signs. Next, the study looks at the economic interpretation of the marginal effect of the interactive terms. For instance, in case 1: the conditional marginal impact of CO2 D P PCit = on GDP per capita when there is economic freedom is presented as dΔG dC O2it ] [ −0.030 0.004 − ∗ E F Wit . Thus, the study calculates the statistical significance (0.000) (0.000) of this impact for a realistic value of economic freedom. This implies that when the economic freedom index is at its mean value (such that the economic freedom index = 0), the marginal effect of CO2 emission is −0.030. Given the complementary interaction term, a rise in the value of the economic freedom index will reduce the impact of CO2 on GDP per capita and vice versa. In other words, improvement in the level of economic freedom in SSA increases the effects of CO2 on GDP per capita. This also applies to consumption per capita and life expectancy models. For case 2: the conditional marginal effect of economic freedom on the GDP D P PCit = per capita when there is CO2 emission in Table 22.1 is given by dΔG d E F Wit ] [ −0.007 0.004 − ∗ C O2it . This implies that when the mean value of CO2 emis(0.000) (0.000) sion is reached (i.e., CO2 emission = 0), the marginal effect of economic freedom is 0.007. In this case, with a complimentary interactive term, an increase in CO2 emissions will reduce economic freedom’s impact on human development and vice versa. This also applies to the consumption per capita and life expectancy models. From these findings, we can conclude that the interaction of ENVQ and economic liberalization is favorable for improving QoL in SSA.

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22.4 Conclusion This study examined whether the ENVQ-QoL nexus is contingent on SSA’s economic liberalization level. The study employs the two-step system GMM estimation technique to investigate the role of economic liberalization in the ENVQ-QoL relationship. The study concludes that economic liberalization positively moderates the impact of ENVQ on QoL in SSA. It also found that the direct effects of economic freedom on the QoL offset its indirect effect on QoL through ENVQ. Therefore, this study will recommend that SSA countries pursue a guided liberalization policy that would manage the process of industrialization in SSA to mitigate environmental degradation and pollution in the industrial cities of the region. Given a complementary interaction between ENVQ and economic liberalization in the region, a guided liberalization policy that will take environmental preservation as its hallmark will improve the people’s QoL.

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