Sustainable Macroeconomics, Climate Risks and Energy Transitions: Dynamic Modeling, Empirics, and Policies 3031279816, 9783031279812

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Contributions to Economics

Unurjargal Nyambuu Willi Semmler

Sustainable Macroeconomics, Climate Risks and Energy Transitions Dynamic Modeling, Empirics, and Policies

Contributions to Economics

The series Contributions to Economics provides an outlet for innovative research in all areas of economics. Books published in the series are primarily monographs and multiple author works that present new research results on a clearly defined topic, but contributed volumes and conference proceedings are also considered. All books are published in print and ebook and disseminated and promoted globally. The series and the volumes published in it are indexed by Scopus and ISI (selected volumes).

Unurjargal Nyambuu · Willi Semmler

Sustainable Macroeconomics, Climate Risks and Energy Transitions Dynamic Modeling, Empirics, and Policies

Unurjargal Nyambuu Professor of Economics in the Department of Social Science, New York City College of Technology (NYCCT) The City University of New York (CUNY) Brooklyn, NY, USA

Willi Semmler Henry Arnhold Professor of Economics The New School for Social Research New York, NY, USA

ISSN 1431-1933 ISSN 2197-7178 (electronic) Contributions to Economics ISBN 978-3-031-27981-2 ISBN 978-3-031-27982-9 (eBook) https://doi.org/10.1007/978-3-031-27982-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Dedicate the Book: To Henry Arnhold and Khand Nyambuu

“Act so that the effects of your action are not destructive of the future possibility of life… the new imperative addresses itself to the public policy…Kant’s categorical imperative was addressed to the individual…” “The Imperative of Responsibility” Hans Jonas, former Philosophy Professor of the New School for Social Research.

Preface

This book originates in recent research of the authors and in the many courses and lectures they have delivered at The New School for Social Research, the New York City College of Technology (NYCCT) of the City University of New York (CUNY), New York University’s (NYU) Tandon School of Engineering, and the University of Bielefeld. It includes material that has been presented at a number of conferences. Much of it is based on research and presentations at the German Institute for Economic Research (DIW), Berlin; the French Economic Observatory (OFCE), Paris; the International Institute for Applied Systems Analysis (IIASA), Austria; and at the Economics Department of La Sapienza University, Rome. Valuable assistance and discussions at those research centers are gratefully acknowledged. Further, many of the policy dimensions of the topics discussed here arose during visits to the International Monetary Fund (IMF), the World Bank, the International Labour Organization (ILO), and the European Central Bank (ECB). All of this was of great help in shaping the macro policy orientation of the book. A guiding idea was that of using a macrodynamic framework to study the challenges of climate risks and the green transition in the context of sustainable macroeconomics. Traditionally, in welfare economics, topics such as forces of economic growth, the use of resources (particularly the overuse of fossil fuel resources), and the resultant emissions and climate change risks have been studied from the perspective of growth theory. While this is a useful starting point, the practical challenges of actually addressing and managing those challenges, we thought, need to be studied in the context of a medium-run macroeconomic framework. Thus, the task is to deal with those issues as essential aspects of sustainable macroeconomics. In the latter context, the challenges of climate change, climate risk, and economic, financial, and social disruption are best addressed using the macroeconomic and dynamic tools that have stood the test of time. Climate change, mitigation, and adaptation policies may also create disruptions for short and medium-run macro performance; these are often expressed as shocks in business cycle models. Thus, economically caused climate vulnerabilities and risks, adaptation, and mitigation policies need to become a part of macroeconomic thinking. The specific policies, e.g.,

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Preface

stabilization, fiscal, financial, and monetary, and social policy are often accompanied by public regulatory actions and interventions. Moreover, the transition to renewable energy also has the potential to be disruptive and can entail distributional and labor market challenges. A typical example is the discarding of old energy sources and climate-related infrastructure, in the process of re-buliding something new, often characterized as the Schumpeterian process of “creative destruction.” Thus, for the transition to a low carbon economy, shorter medium-run macro perspectives are needed. That is why we have used Sustainable Macroeconomics instead of Sustainable Growth in the title of our book. We thereby want to underscore the importance of all the tested macro tools, instruments, and policies that have proven their usefulness. We do take the long-run growth perspective into account, but strongly focus on the climate-macro link and climate policies, primarily in a macroeconomic context. For this reason, our book is very suitable as a complementary text for academic courses and for researchers in macroeconomics and growth theory. The book will also be useful for financial practitioners and policymakers who guide macroeconomic and climate policy decisions. We would like to thank Giovanni Di Bartolomeo, Enrico Saltari, Dirk Heine, Alexander Vasa, Ana Paula Gorini, Björn Schlebach, Caroline Horbruegger, Guillermo Studart, Josef Haider, Monica Arevalo, Maria Netto, Marius Cara, Claudia Kemfert, Dorothea Schaefer, Jürgen Zattler, Nicoletta Batini, Prakash Loungani, Tony Bonen, Helmut Maurer, Vladimir Veliov, Gustav Feichtingr, Franz Wirl, Lars Grüne, Ian Parry, Tom Krebs, Giaccomo Corneo, Michael Burda, Andre Semmler, Timo Teraesvirta, Kira McDonald, and colleagues and students for discussions, comments, and important insights. Special thanks for numerous remarks and correspondences go to Jeffrey Sachs, Ottmar Edenhofer, Paul De Grauwe, Marco Gross, Jerome Henry, Christian Proano, Alexander Haider, Stefan Mittnik, Mika Kato, Hans-Helmut Kotz, Nebojsa Nakicenovic, Daniel Samaan, Ekkehard Ernst, Werner Roeger, Sergey Orlov, Elena Rovenskaya, Ibrahim Tahri, Andrea Roventini, Marco and Mauro Gallegati, Domenico Delligatti, Joao Braga, Uwe Koeller, Andreas Lichtenberger, Marieme Toure, and Erin Hayde. We are grateful to the SCEPA staff of the New School for Social Research and its helpful assistance in organizing public lectures and events related to Climate Change each semester, funded by the Thyssen Foundation. Lastly, we owe a particular debt of gratitude to Lucas Bernard for his extensive editing of the manuscript and for his helpful comments and insights. Brooklyn, USA New York, USA

Unurjargal Nyambuu Willi Semmler

Acknowledgment and Statement of Gratitude

We would like to acknowledge a number of colleagues for their outstanding research, papers, books, and ideas which we found crucial for our own work. On occasion, we have adopted some content and/or reprinted important figures/tables in this book. These were all individually cited and referred to within the appropriate sections throughout the book. Note that either both or one of the authors (Unurjargal Nyambuu and Willi Semmler) were co-authors of the publications. Special thanks to the following collaborators: Stefan Mittnik, Anthony Bonen, Prakash Loungani, Sebastian Koch, Alfred Greiner, Helmut Maurer, Lars Grüne, Joao Paulo Braga, Marleen Stieler, Andreas Lichtenberger, Marieme Toure, Erin Hayde, Alexander Haider, Arkady Gevorkyan, Michael Flaherty, Siavash Radpour, Christopher M. Kellett, Steven R. Weller, Timm Faulwasser, Dieter Grass, Giovanni Di Bartolomeo, Behnaz Minooei Fard, Carl Chiarella, Chih-Ying Hsiao, Lebogang Mateane, Pu Chen, and others. Also, we would like to express gratitude to the IMF, the World Bank, Springer Nature, and Elsevier journals including Economic Modelling, Structural Change and Economic Dynamics, Journal of Economic Dynamics and Control, Journal of Economic Behavior & Organization, Applied Mathematics and Computation, Research in International Business and Finance, Annual Reviews in Control, and Econometrics and Statistics.

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Praise for Sustainable Macroeconomics, Climate Risks and Energy Transitions

“The book is a remarkable contribution to macroeconomics in a landscape of scientific literature that has so far kept its focus on written work dealing with the short and long-term sight. A medium-term contribution dealing with the path to greenhouse gas neutrality from a macroeconomic point of view fills an important gap.” —Ottmar Edenhofer, Director of the Potsdam Institute for Climate Impact Research and Professor at the Technical University, Berlin, Germany “Energy security is at the top of the policy agenda. Nyambuu and Semmler present an innovative set of medium-run macro models [and numerical solution techniques] to analyze which policy levers can guarantee lasting energy security by accelerating the adoption of abundant green energy. The models developed here are more suited to understanding the energy transition than either the large-scale macro models or the long-run growth framework embedded in Integrated Assessment Models (IAM). Rich in data description as well as analysis, this book can be used as a complement to traditional macroeconomics textbooks to deepen the discussion of climate change issues.” —Prakash Loungani, Assistant Director, IMF Independent Evaluation Office at International Monetary Fund Director of the MS Econ Program, Johns Hopkins University “Pledges to keep global warming below devastating tipping points, like the Paris Agreement or COP21, are long-term, ranging over decades. They are promises, prone not to be honored. For essentially political-economic reasons, facing up to short-term challenges—‘keeping the lights on’—regularly prevails. Inexorably, this set up increases the social costs of addressing climate change. Short-termism, heavily discounting consequences further down the road, is a major roadblock, preventing our societies to stay within the rapidly exhausting ‘carbon budget’. In their analyses, comprehensive in scope and innovative in tools deployed, Unurjargal Nyambuu and Willi Semmler, two outstanding experts in the field, demonstrate convincingly that it is of the essence to account for the short- and medium-term, that is, the path toward

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Praise for Sustainable Macroeconomics, Climate Risks and Energy …

the long-term goal. Essentially, this bears down to acknowledging the pertinence of macroeconomic background conditions against which policies to address the risks of climate change are implemented. Unurjargal Nyambuu and Willi Semmler’s most instructive and innovative book pushes the economics of climate change significantly beyond the limits of the prevailing growth-theory-oriented approaches. At the same time, and this is crucial, their analyses are practical and applicable—no fearmongering but constructive, solution-oriented proposals.” —Hans-Helmut Kotz, Resident Fellow, Center for European Studies, Harvard University, Former Member of the Executive Board of Deutsche Bundesbank “Recent crises have evidenced how little standard economic thinking can contribute to real World challenges. While the economic theory is already evolving, its application is lacking behind. Institutions such as the World Bank also have some learning to do. This impressive book comes at the right time when shareholders are asking for an overhaul of Multilateral Development Banks’ business model (taking into account global challenges and planetary boundaries).” —Jürgen Zattler, Former Executive Director of the World Bank Group, Director General at the German Ministry for Economic Cooperation and Development–In Charge of Multilateral Institutions, Environment/Climate, and the SDGs “In times in which the introduction of effective climate policies stalls while the climate crisis is becoming ever more pressing, this modern treatment of climate-related macroeconomics is highly welcome as a timely and important contribution. It is ‘modern’ in that it leaves the well-trodden pathways of integrated assessment modeling based on a highly aggregative planner or perfectly competitive economy framework and embraces in a serious analytical way issues such as short-termism; strategic behavior in imperfect resource, energy, and technology markets; financial incentives; negotiations and tipping points; an understanding of all of which is crucial for effective policymaking in a decentralized, imperfect and interest-ridden world.” —Michael Kuhn, Director of Economic Frontiers Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

Contents

1

Introduction and Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 6

2

Sustainable Growth, Welfare, and Short-Termism . . . . . . . . . . . . . . . . 2.1 Sustainability, Natural Wealth, and Welfare . . . . . . . . . . . . . . . . . . 2.2 Intergenerational Welfare Improvements . . . . . . . . . . . . . . . . . . . . . 2.3 Market Dynamics and Short-Termism . . . . . . . . . . . . . . . . . . . . . . . 2.4 Fossil Fuel Resources: Their Use and the Environment . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 11 13 14 16 17

3

Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Resource Extraction and Boom-Bust Cycles . . . . . . . . . . . . . . . . . . 3.2 Foreign Debt Burdens, Borrowing Costs, and Risk Premia . . . . . 3.3 Growth Models with Exhaustible Resources . . . . . . . . . . . . . . . . . . 3.3.1 Basic Growth Model with Resources . . . . . . . . . . . . . . . . . 3.3.2 Modeling Resource Exports and Foreign Debt . . . . . . . . . 3.4 Numerical Solutions—Economic Growth and External Debt Ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

Fossil Fuel Resources, Environment, and Climate Change . . . . . . . . . 4.1 Global Trend in Temperature—The Climate Impact of Fossil Fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Reserves and Production of Fossil Fuels . . . . . . . . . . . . . . . . . . . . . 4.3 Gains and Losses from Changes in Resource Prices . . . . . . . . . . . 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 21 27 32 32 33 36 38 39 40 45 45 48 53 56 56

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Contents

Limits on the Extraction of Fossil Fuels . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Resource Extraction, Its Management, and Prices . . . . . . . . . . . . . 5.2 Modeling Extraction and Price Dynamics of Non-renewable Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Numerical Solutions and Results of Resource Extraction Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fossil Fuel Resource Depletion, Backstop Technology, and Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction to Backstop Technology . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Trends in Energy Prices and Costs . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Modeling Extraction Costs and Backstop Technology . . . . . . . . . 6.4 Numerical Solutions of Growth Models with Backstop Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 62 64 66 67 68 71 71 73 79 80 83 84 84

7

Transition to a Low-Carbon Energy System . . . . . . . . . . . . . . . . . . . . . 87 7.1 Is the Limit of the Carbon Budget Reached? . . . . . . . . . . . . . . . . . . 87 7.2 Emission Rise and the Carbon Budget . . . . . . . . . . . . . . . . . . . . . . . 90 7.3 The Electricity Capacity from Renewable Power Generation . . . . 94 7.4 Environment, Mixed Energy System, and Sustainable Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 7.5 Numerical Solutions–Fossil Fuel Extraction, Emission, and Damages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

8

The Private Sector—Energy Transitions and Financial Market . . . . 8.1 Private Real and Financial Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Some Stylized Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Incumbents and New Entrants in the Energy Sector—Dominant Fossil Fuel-Based Firms . . . . . . . . . . . . . . . . . . 8.4 Renewable Energy-Based Firms as Entrants . . . . . . . . . . . . . . . . . . 8.5 Financing Renewable Energy Firms and Capital Cost . . . . . . . . . . 8.6 Green Assets and Portfolio Performance . . . . . . . . . . . . . . . . . . . . . 8.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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111 111 113 118 120 126 128 131 132

The Public Sector—Energy Transition and Fiscal and Monetary Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 9.1 Public Sector and Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 9.2 Climate-Macro Models with Mitigation and Adaptation . . . . . . . . 137

Contents

9.3 Public Sector, Fiscal Policy, and Climate Change . . . . . . . . . . . . . 9.4 Numerical Solutions on Fiscal Policy Actions . . . . . . . . . . . . . . . . 9.5 Disaster Scenarios and Climate Policies . . . . . . . . . . . . . . . . . . . . . 9.6 Central Banks, Monetary Policy, and Climate Change . . . . . . . . . 9.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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138 140 147 149 154 155

10 Delaying Forces and Climate Negotiation—Games, Lock-ins, Leakages, and Tipping Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Games and Inefficient Outcomes of International Negotiations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 A Dynamic Climate-Macro Model with Leakages . . . . . . . . . . . . . 10.4 Drivers of Nonlinearities and Tipping Points . . . . . . . . . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

161 163 166 168 169

11 Climate Risks, Sustainable Finance, and Climate Policy . . . . . . . . . . . 11.1 Diverse Crises and Delay of Climate Protection Policies . . . . . . . 11.2 Green Innovation and Sustainable Finance . . . . . . . . . . . . . . . . . . . 11.3 Costs of Renewables and Climate Investments . . . . . . . . . . . . . . . . 11.4 Fiscal Policy and Climate Investments . . . . . . . . . . . . . . . . . . . . . . . 11.5 Climate Risk and Monetary Policy . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Global Efforts for Climate Protection . . . . . . . . . . . . . . . . . . . . . . . . 11.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171 171 174 175 181 183 184 186 187

159 159

12 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

Chapter 1

Introduction and Overview

Although many credit John Tyndall with having discovered the greenhouse effect in 1859, it may in fact have first been described by a woman, Eunice Foote, who identified the process in 1856, three years earlier, in a paper in The American Journal of Science and Arts.12 But no matter who discovered it first, since the nineteenthcentury take-off of industrialization, there has been unambiguous evidence that fossil fuels were related to global temperature increases. In recent decades, the planet has experienced a higher frequency and greater severity of climate-related disasters, particularly extreme weather events. The effects not only cause economic damage, but also impact the ecosystem, general welfare, health, agriculture, human migration patterns, and so on. Since the 1980s, the geophysical processes of climate change have been investigated by myriad intellectual communities including climate experts, economists, scientists, data analysts, and sociologists. An early path-breaking study on extreme events was undertaken by Gumbel (1958). Most of the recent research results are well represented in numerous Intergovernmental Panel on Climate Change (IPCC) reports. It is well established that much of the warming we have seen since the beginning of industrialization is most likely due to human activity. More recently, the possibility of various geophysical processes fundamentally changing has become of great concern. These so-called “tipping points” are akin to the nonlinear changes we are familiar with in system theory, economics, and in social and political processes. As an example, the potential shutting down of the Gulf Stream, which would radically change the climate of Europe, is one such possibility. Another tipping point could be the collapse of the Antarctic ice sheet and its subsequent sliding into the ocean. 1

See Foote (1856) at https://archive.org/details/mobot31753002152491/page/382/mode/2up? view=theater. Retrieved 12 November 2022. 2 See Sorenson (2011): “Eunice Foote’s Pioneering Research On CO And Climate Warming” 2 (PDF). Search and Discovery (70092). https://www.searchanddiscovery.com/documents/2011/ 70092sorenson/ndx_sorenson.pdf. Retrieved 25 April 2021. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_1

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1 Introduction and Overview

Leading climate scientists, e.g., Hansen (2015), have written extensively on the topic of tipping points and the associated feedback effects that could be generated by them. These days, extreme weather events and climate disasters affect all countries, see IPCC (2021) and Mittnik (2019), but at particular risk are certain developing and low-income countries, possibly creating poverty traps. For example, in equatorial countries, not only is warming a threat, but there is also the possibility of loss of land due to sea level rise, desert formation, deforestation, and declines in fishery, agricultural productivity, and ecological changes. Although some countries and regions may experience net economic and financial gains from global warming, they may still be impacted by extreme weather events and insidious adverse environmental trends in addition to local disasters. Industrialized and wealthier nations will be able to adapt, i.e., use their wealth and technology to work around the climate-imposed difficulties. However, poorer countries, regions, and population segments and low-income countries will be at a comparative disadvantage socially and economically. With adaptation technology inaccessible to many, the gap between rich and poor will likely widen, allowing the wealthier economies and population segments to not only escape the future costs of climate change, but to exploit the advantage they hold over poorer nations. Thus, the economic consequences of global warming will be quite varied. As we can see, the economics and actual politics do not follow in lockstep with the scientific processes. Rather, the task is a study in complex systems and requires elaborate empirics, sophisticated modeling tools, and considerations of what has been called environmental justice and fair transitions. Concerning policy action for climate protection, historically, the coordination of climate research and mitigation policies began with the IPCC (1990) and the Kyoto Protocol (1997), which took the political stance of insisting on national responsibility with respect to the reduction of greenhouse gas (GHG) emissions. After a large number of international conferences, a watershed agreement was reached at the PCCC (2015). “The Paris Agreement,” as it is called, had the objective of obtaining a global consensus on the goal of limiting temperature increases to below 2 ◦ C above the pre-industrial temperature average. In fact, more ambitiously, the goal was to cap increases at 1.5 ◦ C. Climate scientists have now realized that the climate actions actually specified in the Paris Agreement are too mild and will not even achieve the global 2 ◦ C goal. Also, policy measures to achieve those goals have been left to the numerous countries with little monitoring and weak control mechanisms. Recently, most advanced economies, e.g., the United States and Europe, have targeted reaching zero net carbon emissions in their domestic economies by 2050 and by reducing net emission by fifty percent before 2030. The world consensus seems to be that global warming will be devastating in practical terms, and a large number of national regulatory measures have already been set up. These include limiting emissions from certain sectors of the economy, e.g., heavy industry, automobiles, airlines, and housing. Various approaches, e.g., electronic carbon trading systems and carbon taxes, have been introduced, the latter particularly in Europe. Other approaches, e.g., the issuance of green bonds and investments, may

1 Introduction and Overview

3

also prove transformative to the industries they target, but require new legal frameworks and financial infrastructure to succeed. See Bernard and Semmler (2015) and Semmler (2021) for further elaborations and discussions. The phasing out of fossil fuel energy, the reduction of extreme external energy dependence, and the phasing in of renewable energy, and thus the transformation of the energy system into an environmentally sensitive one are essential. Traditionally, while many of these challenges have indeed been studied within a context of sustainable growth, in this work, we attempt a paradigm shift wherein green policies are actually embedded in the modeling framework of sustainable macroeconomics. Long-run growth prospects need to be translated into medium-run macroeconomic goals, often regime switching models with multiple goals. Available policy tools are pursued with macro policies, such as resource management, fiscal and monetary controls, industrial plans, labor market considerations, and distributional and financial policies. The rich experience researchers have had with these macro models, together with their practicality, their regime switching features, and econometric and policy implications suggest the need for these techniques to be brought to bear on the multifaceted issues of climate change. We also suggest a departure not only from the extreme long run, but also from very short horizon decisions, and short-termism in general, thus aiming macro policy more toward medium-term goals. In the transition to new energy sources, disruptions are likely to occur, and many adjustments will be needed. Macroeconomists have developed a rich set of policy tools to deal with these issues. Sustainable macroeconomics also suggests the inclusion of new welfare measures for the wealth of nations and a moving away from short-termism; the fossil energy basis of economies is folly and a move toward the phasing in of free and unlimited renewable energy thus seems the correct path to follow. Given the variety of economic activities producing and/or using fossil fuel energy and emitting GHGs, and given that the cost and price trends for renewable energy should lead to increasing investments in renewable energy, a regime shift in energy provision seems in order. With this inclusion, we believe that a macro dynamic framework is the appropriate analytical paradigm for assessments. This approach allows us to also incorporate medium-run climate policies, i.e., how a country’s market, sectoral, and macroeconomic strategy can be mobilized for the climate protection agenda.3 It is with this broad perspective of sustainable macroeconomics in mind that our book includes observed trends in carbon-emitting resource use, develops various frameworks of market and macroeconomic dynamic modeling, and suggests strategic policies for moving forward with sustainable macroeconomics and relating it to global climate change challenges. Obviously, the great challenges posed by the energy transformation still lie ahead not only for businesses and households, but particularly for traditional labor markets. It is the ethical/moral component which explicitly underlies the whole effort and 3

See also the extensive work by Castle and Hendry (2020) on the need of multiple goals and instruments for climate protection.

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1 Introduction and Overview

which defines a major change in perspective. These issues have already been explored in a number of studies, e.g., in International Labour Organization (ILO) publications, thus this is an aspect we mainly leave aside in our book. The interested reader can refer to Kato et al. (2015) and Bernard and Semmler (2015). As to the details of our dynamic macro modeling approach, while we recognize that major modeling and calibration achievements are already incorporated into Integrated Assessment Models (IAM), these are mostly in the context of long-run growth theories. Thus, we focus more on the medium-run and macroeconomic approaches and use their dynamic analysis and policy tools as guidelines. Our models stress the role of non-renewable resources, e.g., fossil energy, and incorporate them into the evolution of the economic structure. In this context, we include resource management, fossil fuel energy, and GHG emissions, as well as a gradual transitioning to a green economy, climate-based investments, and mitigation and adaptation policies. In particular, we go beyond the IAM and its standard welfare-theoretic growthoriented framework, e.g., Nordhaus (2000) and Nordhaus (2008). The models presented in our book differ from the growth approach taken by Nordhaus (1994) in his “Dynamic Integrated Climate-Economy” (DICE) model and Tol (2002) in the FUND model. Generally, as physical sciences have found, industrial production impacted by damage functions, our welfare framework has been broadened and captures not only households’ consumption, but also ecological and environmental damages and adaptation efforts in a context where multiple challenges are simultaneously posed for policymakers. Our approach also allows us to refer to work on the Social Cost of Carbon literature. Beginning with the resource extraction modeling initiated by Hotelling (1931) we also consider the progress that has been made since then; see Pindyck (1978). The Hotelling work focused on the optimal policy for the extraction of resources—until they are depleted. As is well known, the use of those non-renewable resources, e.g., fossil fuels, accounts for an extensive share of anthropogenic CO2 emissions; see Greiner (2012). When we work in the framework of such extended dynamic macro models, we assign particular roles to the public sector with regard to fiscal and monetary policies, to climate-related infrastructure, and to mitigation, adaptation, and disaster prevention policies. When we deal with the needed innovative role of the, mostly private, energy sector, we focus on competition, explore entry barriers, and consider how renewable energy can be phased in and fossil fuel energy phased out. There are often both permanent (lock-in) and irreversible effects with respect to market structure; these present special challenges for new firms. We relate this to the concept of sustainable macroeconomics as well. In addition to the above-mentioned issues and modeling strategies, another major contribution is the discussion of numerical analyses as applied to the models. This is done for the more advanced readers of our book. These, together with empirical illustrations and calibrations, are undertaken using Nonlinear Model Predictive Control (NMPC) and Applied Modeling Programming Language (AMPL). These are nonlinear solution methods that take into account the effects of different constraints on sustainable macroeconomic models incorporating both renewable and non-renewable

1 Introduction and Overview

5

resources, environmental and climate effects, as well as country-specific and regime switching features. Many aspects of this research area have been extensively addressed by other disciplines including earth and climate scientists, environmentalists and ecologists, econometricians, and social scientists. We are interested in bringing forward the “Economics of Climate Change” as a specific research and policy area, one that explores the economy-climate interaction in a dynamic macro framework. The topics covered in this book can be outlined as follows. In Chap. 2, we contrast our work with the tradition of growth-oriented welfare economics, its measures of welfare and analysis of what generates the wealth of nations. We stress that it is often the prevailing market short-termism that prevents sustainable and inclusive macroeconomic policies by neglecting natural resources, ecosystems, and the environmental side effects of economic activities. Chapter 3 focuses on non-renewable resources in general. We demonstrate that due to short-termism in economic, financial, and policy decision-making, there are exacerbated resource boom-bust cycles, precarious debt dynamics, excessive and occasionally huge risks, and the creation of high-risk premia and crisis potential; this is often implicit in the short-termism of resources extraction activities. We also model and illustrate those features with respect to obtained numerical solution paths. Chapter 4 deals more specifically with fossil fuel resources, their extraction and use, and, given the often-observed short-termism, the consequent environmental effects. We illustrate what still may lie ahead by analyzing potential reserves, production, and consumption of non-renewable fossil resources and their environmental and climate effects. We also study issues such as price trends and volatility in fossil fuel energy production. In Chap. 5, we elaborate on the limits of the extraction of fossil fuel resources by referring to Hotelling (1931) optimal resource extraction model. We study the issue of what drives the resource price and extraction rates in the longer run, as well as the implications of the Hotelling strategy when resources are optimally depleted— namely moving quickly beyond the earth’s carbon budget. We numerically solve modified dynamic model variants and present the possible evolution of resource prices for a Hotelling-type extraction model. In Chap. 6, we analyze the finiteness of fossil fuel resources and their environmental and ecosystem side effects. Generally, as physical scientists have learned, industrial production moves carbon from the earth into the atmosphere. This generates extensive climate risk and disasters and forces us to envision alternative technologies, i.e., backstop technologies, implementing the transition to a low-carbon economy. The modeling of such a transition is studied and illustrated numerically. For Chap. 7, we demonstrate that the continued use of old fossil energy technology will quickly move us dangerously close to the limit of the carbon budget. As to the control of pollution, we study its dynamics and explore different model variants concerning fossil fuel, renewable energy use, and their solution dynamics with different CO2 emission targets for both advanced and developing economies. In Chap. 8, we focus on the (mostly private) energy market and what market structure and transition paths could support a conversion strategy to the decarboniza-

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tion of the economy. We consider what entry barriers and obstacles do innovative renewable energy firms might face in increasing their market share in the energy sector by competing against large energy oligopolies. Given the decreasing trends in renewable energy costs and prices, and growing investments in low-carbon energy, the competition between renewable and carbon-based energy firms is explored using dynamic models with a game-theoretic setup. In Chap. 9, we study the role of the public sector for the energy transition in the context of sustainable macroeconomics and model implied medium-run policy. We begin with the Nordhaus DICE model and then, by including some further climaterelated macroeconomic features, propose more complex models with extended welfare measures, mitigation and adaptation policy, and regime shifts. This type of sustainable macroeconomics incorporates both the non-renewable and renewable energy resources and allows for fiscal and tax policies, and incentives for energy transition and climate-related infrastructure investments by simultaneously addressing the issue of debt sustainability. We also stress the role of the Central Bank’s decision-making in climate protection and incorporate movements of fundamental macro variables in climate-macro modeling. In Chap. 10, the delaying forces slowing the transition to a low-carbon economy are studied; these typically arise from short-termism, lock-ins, irreversibility, leakages, and certain political strategies. Thus, we explain the snail’s pace evolution of current national and global climate policies. Chapter 11 summarizes the research results in terms of various policies available for sustainable macroeconomics, as guidance for climate protection policies, and as implied in the previous chapter. We focus on a multi-modal approach, i.e., innovation and industrial policy, as well as on macroeconomics and fiscal, monetary, and financial policies. We also discuss possible limitations and constraints concerning new resources, e.g., minerals and metals, needed for the energy transition. Chapter 12 presents some concluding remarks and discusses further research and policy perspectives.

References Bernard L, Semmler W (2015) The Oxford handbook of the macroeconomics of global warming. Oxford University Press Castle JL, Hendry DF (2020) Climate econometrics: an overview. Founda Trends Economet 10(34):145–322. http://dx.doi.org/10.1561/0800000037 Foote E (1856) Circumstances affecting the heat of the sun’s rays. Am J Sci Arts 22(66):382– 383. https://archive.org/details/mobot31753002152491/mode/2up?view=theater. Accessed 7 Nov 2022 Gumbel EJ (1958) Statistics of extremes. Columbia University Press Greiner A, Semmler W, Mette T (2012) An economic model of oil exploration and extraction. Comput Econ 40(4):387–399 Hansen JE (2015) Environment and development challenges: the imperative of a carbon fee and dividend. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, Oxford, pp 639–646

References

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Hotelling H (1931) The economics of exhaustible resources. J Polit Econ 39(2):137–175 IPCC (1990) The first IPCC assessment report: FAR climate change: synthesis. https://www.ipcc. ch/report/ar1/syr/. Accessed 7 Nov 2022 IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press. https://www.ipcc.ch/site/assets/uploads/2018/03/SREX_ Full_Report-1.pdf IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate. Kato, M., Mittnik, S., Samaan D & Semmler W (2015) Employment and output effects of climate policies. In: Semmler W (ed) Bernard L Oxford University Press, The Oxford Handbook of the Macroeconomics of Global Warming, pp 445–476 Kyoto Protocol (1997) UNFCCC. https://unfccc.int/kyoto_protocol Accessed 7 Nov 2022 Mittnik S, Semmler W, Haider A (2019) Climate disaster risks–empirics and a multi-phase dynamic model. International Monetary Fund. Working Paper No 2019/145. Published as: Mittnik S, Semmler W, Haider A (2020) Climate disaster risks—empirics and a multi-phase dynamic model. Econometrics 8(3):1–27 Nordhaus WD (1994) Managing the global commons: the economics of climate change, vol 31. MIT Press, Cambridge, MA Nordhaus WD, Boyer J (2000) Roll the DICE again: the economics of global warming. Yale University Nordhaus WD (2008) A question of balance. Weighing the options of global warming politics. Yale University Press Nyambuu U, Semmler W (2020) Climate change and the transition to a low carbon economy— carbon targets and the carbon budget. Econ Modell 84:367–376. https://doi.org/10.1016/j. econmod.2019.04.026 Paris Conference on Climate Change (2015) UNFCCC. https://unfccc.int/process-and-meetings/ the-paris-agreement/the-paris-agreement. Accessed 7 Nov 2022 Pindyck RS (1978) The optimal exploitation and production of nonrenewable resources. J Polit Econon 86(5):841–861 Semmler W, Braga J, Lichtenberger A, Toure M, Hayde E (2021) Fiscal policy for a low carbon economy, world bank report. https://documents1.worldbank.org/curated/en/998821623308445356/ pdf/Fiscal-Policies-for-a-Low-Carbon-Economy.pdf. Accessed 7 Nov 2022 Sorenson RP (2011) Eunice foote’s pioneering research on CO 2 and climate warming. Sear Dis (70092). https://www.searchanddiscovery.com/documents/2011/70092sorenson/ndx_sorenson. pdf. Accessed 7 Nov 2022 Tol RSJ (2002) Welfare specifications and optimal control of climate change: an application of FUND. Energy Econ 24(4):367–376. https://doi.org/10.1016/S0140-9883(02)00010-5

Chapter 2

Sustainable Growth, Welfare, and Short-Termism

Overview Much theoretically oriented literature has focused on what has been called sustainable and inclusive growth. That literature lays out a framework for evaluating natural resources while considering the environment and addressing distributional issues between generations. In this chapter, we discuss welfare criteria and different approaches that permit us to study social, environmental, as well as longerrun intergenerational equity challenges. This leads to a discussion of alternative measurements of welfare and wealth. We also provide a review of empirical studies and stylized facts on short-termism and its impact on resource extraction and climate change.

2.1 Sustainability, Natural Wealth, and Welfare As the Brundtland report concerning The World Commission on Environment and Development (WCED 1987) points out, for the purposes of sustainability, we need to better conserve and manage natural resources.1 In trying to craft definitions of sustainable development and economic activity, various authors have considered the degradation of resources and the impact of the depletion of resources and environmental problems on long-run economic growth. According to (Pezzey 1989, p. 14), for consumption-oriented measures, while an increase in consumption drives sustainability within economic growth, it is a rise in utility that makes the development sustainable. In contrast, a number of studies, including Repetto (1986) and Pearce 1

The definition of sustainability and the measurement of wealth are discussed in detail in Nyambuu et al. (2014). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_2

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et al. (1990), point to the importance of the stock of natural capital,2 i.e., renewable and non-renewable resources and minerals, over the long term, such that future generations are not affected adversely. The stock of resources available in the future is reduced by the current usage of exhaustible resources as our numerical solutions of theoretical models will demonstrate in Chaps. 3, 5, and 6. According to the WCED (1987), over-extraction of non-renewable resources most probably lead to this problem. To ensure sustainable development, we need to focus on finding more effective ways to manage exhaustible resources—and avoid severe externalities and damages from them, e.g., fossil fuels— and substitute them with alternative sources, e.g., renewable energy. This means that we should be concerned with intergenerational equity, in particular, in cases where severe negative side effects are likely to emerge in the future. Studies, e.g., those by Tietenberg (1984) and Solow (1991), approach this topic by emphasizing a balanced distribution of resources among current and future consumption. The link between nature and society and its relevance for sustainable development is addressed in other papers as well (see Brown 1981; Pearce 1987). In addition, the relationship between climate change and equitable economic development in the long term, along with a discussion of risks, is provided in a number of studies, e.g., IPCC (2014), Halsnaes and Garg (2006), and Halsnaes et al. (2008).3 For example, Halsnaes and Garg (2006) cover different countries and delve further into the connection between energy, sustainable development, and climate change. The measurement of wealth should take all the above-mentioned dimensions of economic activity and their relationship to nature into account.4 Such broader definitions of welfare can be found in numerous recent studies on sustainability. The WorldBank (2011) approach considers shadow values corresponding to reproducible, human, and natural capitals. As an extension to wealth measurement, health and technological progresses were added in Arrow (2012). They used an increase in wealth (per capita) as a proper measure of sustainability there, and natural capital consists of both non-renewable and renewable resources. On the one hand, the wealth of resource exporters and rental values increase; yet on the other hand, due to higher real consumer prices, resources and real wealth decline (see Arrow 2012). Their empirical findings, based on data between 1995 and 2000 for China, Brazil, India, the United States, and Venezuela, emphasized the role of natural capital, technological change, and health capital for sustainable development. Particularly, investments in reproducible capital, as well as in knowledge and human capital have been shown to contribute to wealth significantly. A fall in natural capital

2

For a comprehensive study of natural capital and empirical assessments, see Chami et al. (2020). We discuss climate change and related problems in more detail in Chap. 7. 4 As an alternative, we can consider a production-based model as opposed to one grounded in preferences, i.e., a framework to deal with exhaustible resources and externalities arising in production has been developed in Krause (1981). In the theory of joint production, some of the outputs might exhibit public “bads” that appear to have the features of costless disposal. 3

2.2 Intergenerational Welfare Improvements

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is considered together with the possibly offsetting impacts of higher investment in other types of capital (for details, see Arrow 2012). Regarding modeling sustainability, (Weitzman 1997, p. 1) takes the present value of discounted consumption in the economy as a starting point and describes how the Green Net National Product (GNNP), as a modified national income, can reflect sustainability, thus also relating to technological advancement. The GNNP was adjusted for capital depreciation and non-renewable resource depletion. Thus, the GNNP is assumed to be a function of consumption, price, and capital stock that contains natural resources, yet one needs to account for the depletion of resources as well. Weitzman (1997) argues that no matter how we measure the NNP, this indicator might not be sufficient to fully measure economic sustainability. A further discussion on the modeling of multiple welfare objectives is provided in Chap. 9.

2.2 Intergenerational Welfare Improvements In Nyambuu et al. (2014), the literature on various ways of addressing fairness across different generations is reviewed. In standard measures of intertemporal equity, the discount rate plays the most important role. In that context, intergenerational equity concerns the relationship between present and future generations. In the framework of classical utility theory, for the utility of future generations, a discount rate—large, small, or zero—could be applied. Although this is the standard way to account for future generation’s welfare, it has been challenged from various ethical perspectives. Welfare criteria, such as the Rawlsian criterion, can be used as well (see Ramsey 1928; Von Weizsaecker 1967). For example, “overtaking” criterion (see Von Weizsaecker 1967) compares utility functions based on different consumption paths. Concerning a discount rate such as zero, (Greiner and Semmler 2008, p. 150) argue that there might be some open attainable integral values; overtaking criterion might be of exclusive use for comparing different paths for consumption. According to the Rawlsian criterion (Rawls 1971), decisions made under the “veil of ignorance,” all generations should aim to have the same level of welfare; this is derived from the min-max criterion that society should maximize the worstoff generation first. For such intertemporal decision-making, Solow (1974, p. 41) indicated that there could be two problems with the min-max criterion: a large initial value of the capital stock, and a lack of explanation as to its buildup yields problematic results when technical progress is limitless. The Rawlsian criterion was compared with the discounted utility approach. For a more complete discussion on different criteria, see Greiner and Semmler (2008) and Nyambuu et al. (2014). For preferences to be sustainable, a more advanced approach by Chichilnisky (1996) suggests that axioms should not emphasize the present or the future. It was noted that “the present generation should not dictate the outcome in disregard for the future” and “the welfare criterion should not be dictated by the long-run future,

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and thus requires sensitivity to the present” (Chichilnisky 1996, p. 237). Greiner and Semmler (2008) examined Chichilnisky’s criterion in more detail, where discounted utility is involved. Regarding the limitations of the welfare criterion, (Chichilnisky 1996, p. 235) noted that all the criteria failed to clearly explain the utilities and their discounted sum. As Greiner and Semmler (2008) pointed out, despite its drawback, discounted utility is nevertheless commonly adopted in a large body of research work. Most of the previous research was, however, mainly oriented toward the issue of non-renewable resources as being treated as exhaustible resources. Recent studies focus more on the long-run negative side effects and externalities of the use of non-renewable resources that generate adverse environmental and economic effects.5 In this book, we explore economic and policy instruments that can avoid them and possibly improve welfare. Many studies refer to carbon pricing, the Emissions Trading System (ETS), or a carbon tax; through the use of such instruments, actual welfare can be improved. For the moment, it seems to be more popular in Europe than in the United States. Yet, numerous studies show that it has distributional implications within generations, across households, i.e., some households are burdened more than others by a carbon tax; see Flaherty et al. (2017) and Metcalf and Stock (2020). The actual unevenness of the burden within heterogeneous population groups, in the transition to a low-carbon economy, depends on the type of carbon tax imposed, the availability and elasticity of substitutions with regard to renewable energy, the short- and long-run effects of the tax rate, compensatory measures for households that are affected most by a carbon tax, the recycling of the carbon tax as a citizen dividend, a border adjustment tax, and so on. There are numerous economic and social issues involved; for details, see Semmler (2021) and Chaps. 8 and 9 of this book. Recent work has extensively studied the usage of carbon taxes and other measures, e.g., green bonds,6 for distributing intergenerational costs and benefits of climate policies (see Sachs 2015; Flaherty et al. 2017; Orlov et al. 2018, 2019). The basic problem that might arise with an exclusively carbon tax approach can be illustrated in terms of the Nordhaus DICE model; for details, see Orlov et al. (2018). It can be assumed that abatement costs represent carbon tax due now in order to avoid future environmental damages. In this context, results from the DICE-based model point to the issues that the carbon tax alone would create, i.e., an uneven burden in tackling climate change. Orlov et al. (2018), Orlov et al. (2019) presented optimal solutions of the DICE model with and without welfare mitigation; they demonstrated that the current generation is burdened most. Their results indicate that a carbon tax does not address properly the issue of intergenerational justice. 5

For a production-based approach to evaluating economic externalities using the theory of joint production, see Krause (1981). 6 According to Climate Bonds Initiative, the green bond is defined as “financial debt instrument that is almost entirely linked with green and climate friendly assets or projects” (see https://www. climatebonds.net/certification/glossary).

2.3 Market Dynamics and Short-Termism

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This is the reason why, e.g., in Sachs (2015), Flaherty et al. (2017)) and Orlov et al. (2018), intergenerational borrowing and lending are introduced by way of green bonds. These are long maturity bonds that could be issued to finance green investments. It operates similarly to War bonds or, in current times, like Pandemic bonds that spread the cost of repayment of the urgently needed expenditures over time, and the cost and burden of those measures are shared across generations. As shown in Heine et al. (2019) and Semmler (2021), an important advantage of mixing carbon taxes and green bonds is their respective contributions to greater intergenerational equity and welfare. Those issues are further discussed in Chap. 9, where we also compare them to the DICE model while different welfare objectives will be defined.

2.3 Market Dynamics and Short-Termism Although sustainable economic growth and welfare enhancement seem to be better achieved through the intertemporal lens outlined above, short-term market dynamics (short-termism) has become the dominant perspective. In fact, recent papers in dynamic economics point out that short-termism7 is actually incompatible with sustainable growth. As recently shown in Davies et al. (2014), Semmler et al. (2020), Di Bartolomeo et al. (2019), and Saltari et al. (2021), short-termism seems to have a grip on policymakers, and also drives decision-making for corporations and investors. It neglects the negative externalities of economic growth, e.g., the long-run impact of growth on natural capital, resources, and ecosystems. In particular, it has been shown, using a game-theoretic setup, that short-termism does not properly deal with exhaustible resources and the devastating side effects of competition and market behavior.8 Environmental game models have suggested trends toward the over-extraction of natural resources and the destructive side effects of non-cooperative behavior in markets. Next, we want to discuss the effects of shorttermism on the extraction of resources and the negative side effects associated with such market dynamics, and how those are related to climate change. Let us first refer to the issue of the over-extraction of extractive resources. Given the above-discussed adverse effects of the extraction of resources driven by shorttermism, many policymakers and international investors view the extraction of nonrenewable resources as a major short-run source for generating revenue in certain resource-rich countries. Thus there is often some short-termism dominant, in particular among the international investors who invest in fossil fuel resources with little interest in longer-run development plans. Lower- and middle-income countries seem particularly prone to this in spite of the fact that the usage of such resources is known to have adverse macro effects.

7

Such short-termism is well known in the financial markets where decisions are made within extremely short decision horizons (see Davies et al. 2014). 8 See Di Bartolomeo et al. (2019), Saltari et al. (2021), and Chap. 10.

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In fact, the short-term overuse of exhaustible resources, such as fossil energy, driven by the market dynamics, and the neglect of possible long-run adverse macroeconomic and intergenerational impacts were already addressed in early publications by the Club of Rome entitled “Limits to Growth” (see Meadows er al. 1972). There has been a long-standing debate between economists over how short-term resource extraction, though triggering short-run economic expansions, can have devastating effects on long-run growth, income, and employment. Empirical studies demonstrate that natural resources have also contributed to the volatility of economic growth in certain resource-rich countries, especially those driven by foreign investments.9 These countries export a large amount of their nonrenewable resources, including fossil energy, to other countries. Thus, trade in such resources—and the volatility of prices and volume of such trades—has increased substantially in many developing countries since the 1960s and the 1970s. Based on data for the period of 2006–2010, Berg et al. (2013) and IMF (2012) listed a number of developing countries in terms of share of exports of natural resources in total exports as well as shares of natural resource-driven fiscal revenue in total revenue. High volatility in both volumes and prices characterize non-renewable resources. This also seems to show up in exchange rate volatility and appreciations, hindering better macroeconomic performance, which has been called Dutch disease, and leading to a buildup of debt cycles. Details are discussed in Chap. 3.

2.4 Fossil Fuel Resources: Their Use and the Environment Recently much attention has been given to the side effects of the market dynamics driven by short-termism. Since the industrialization of the nineteenth century, society has largely neglected the negative externalities of economic activity. Yet, the current situation does not come as a complete surprise. In the United States, already aware of the consequences of unrestricted economic growth, between 1901 and 1909, President Theodore Roosevelt increased the number of National Parks. In 1916, President Woodrow Wilson oversaw the creation of the Department of the Interior, which was charged with the protection of 35 national parks, some yet to be created. With little government intervention since those earlier times, environmental side effects, mostly due to the use of fossil fuels, have had a devastating effect on the environment. Climate change has been extensively investigated from many scientific perspectives since 1988; in particular, the International Panel on Climate Change (IPCC)10 has initiated numerous reports on the causes and consequences of the slowly rising global temperatures relative to the period of pre-industrialization. As currently pre9

For example, World Bank paper by Wright and Czelusta (2007) addressed the contribution of natural resources to the economic growth of Chile and Brazil (see Exploration in South America, 2001). 10 IPCC’s Special report on “Global Warming of 1.5 ◦ C” presents a figure on historical change in global temperature compared to pre-industrial levels of 1850–1900 (see Allen et al. (2018) from the link: https://www.ipcc.ch/sr15/chapter/chapter-1/).

2.4 Fossil Fuel Resources: Their Use and the Environment

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dicted, the world can expect a temperature rise of 2–4 ◦ C by the end of the century, an extensive sea level increase due to the probable collapse of the Greenland ice sheets, a likely disappearance of Arctic sea ice within the next 10 to 15 years,11 continuing ocean acidification, and a collapse of ocean circulatory systems, e.g., the Gulf Stream.12 Besides these, weather extremes are likely to occur with higher frequency; also, their severity, i.e., the intensity of storms, hurricanes, typhoons, heavy rainfalls, and heat waves, is likely to increase. Additionally, slow-moving long-term effects are expected; examples include droughts, the decline of agricultural productivity, and human and animal population movements. This could all be magnified due to the existence of “tipping points,” critical boundaries which demarcate major and sudden shifts in climate-related events (see Bernard and Semmler 2015, and Chap. 10). To be more specific, NASA’s data since 2002 show a rapid loss of ice in both Antarctica and Greenland with annual rates of decline of 151 billion and 274 billion metric tons, respectively (see NASA Ice Sheets).13 All the ice melt, together with global warming, is causing ocean expansion and extensive sea level rise sooner than expected: an increase by roughly 10.1 cm in May 2022 relative to 1993 has already occurred (NASAGSFC 2022). Because of this, it is very likely that parts of the earth will see an increase in the number and intensity of weather extremes such as heat waves and desert formation, droughts, and flooding. The low-lying coastal regions may become inundated. Climate change is likely to lead to an increase in the ferocity of tropical storms, hurricanes, and typhoons. Some climate scientists predict additional long-run changes threatening the stability of ecosystems, freshwater supplies, coastal habitation, human health, working conditions, etc. In addition to the above, IPCC (2014, 2018, 2019) addresses the negative impacts of global warming on food production and risks to food security. Quantitative calculations by the (IPCC 2014, p. 497) found that local warming of 1 ◦ C to 2 ◦ C could create tipping points in agriculture and cause a decline in yields of certain crops including maize (corn) and wheat while temperature warming of 3 ◦ C to 5 ◦ C is predicted to lead to a significant drop in yields of rice and other crops. Moreover, rising temperatures will have a more adverse impact on agricultural output, capital accumulation, and the health of low-income developing countries; this was shown in a study conducted by IMF (2017). The most important contributors to the temperature rise and climate change are fossil fuels (see estimations by Friedlingstein et al. (2022)). As we will study in Chap. 4, there are mainly three types of fossil fuel resources—coal, gas, and oil. Those are what has been called the carbon budget of the earth. In Chap. 4, we will further study the enviro-economic dynamics of these resources and how their management is critical to controlling the carbon budget of the earth and climate change.

11

See Rudebusch and Diebold (2021). See Keller and Nicholas (2015). 13 See NASA Ice Sheets from the link: https://climate.nasa.gov/vital-signs/ice-sheets/. Accessed on September 2, 2022. 12

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2.5 Conclusion In this chapter, we presented different approaches and the criteria used to highlight the link between sustainable growth and welfare. First, it is important to define what is included in welfare measures and welfare-enhancing activities; these are natural resources, natural capital, and the ecosystem. Second, an intergenerational perspective is needed, one that incorporates the concept of intergenerational equity and the intertemporal costs/benefits accruing from resource use and climate protection.14 In this context, we also focused on the measurement of wealth, including those aforementioned components, their implications for sustainable economic growth, and the welfare objectives to be considered. We also pointed out how and why more attention should be given to distributional effects within generations and to intergenerational fairness, particularly with respect to the management of climate protection. We defined sustainability and discussed the impact of resources on welfare as well as on sustainable growth. Furthermore, we discussed the literature on empirical findings that view the extraction of resources and the negative side effect of economic growth since the take-off of industrialization. Driven by economic short-termism, it can be modeled with a gametheoretic setup for the dynamics, i.e., a market where non-cooperative behavior and short decision horizons are driving the outcomes. Short-termism prevents countries not only from reasonable sustainable development planning, industrial policy, and finance, but also neglects to consider long-run adverse externalities and limits policy decisions for climate protection (see Chap. 10). The collapse of oil prices, particularly as related to the COVID-19 pandemic, further emphasizes the economic fragility of countries with an economic overdependence on exhaustible resources, particularly fossil fuels. Changes in consumer behavior, lifestyle, remote working conditions, the evolving sentiments of consumers, and institutional and household’s asset holding tendencies, e.g., for more sustainable and “green” finance, may continue to exert downward pressure on fossil fuel resource demand putting a pressure on phasing them out. In the subsequent chapters of our book, we present dynamic models that take into account renewable and non-renewable natural resources, show their evolution over time, and thus highlight their importance for sustainable economic growth and climate protection. The discussion presented in the current chapter will be thus revisited in this Chap. 3–5 where we examine resource booms and busts by addressing the impact of resource price fluctuations on macroeconomic stability, and present modeling of regime changes between high and low prices of non-renewable resources. Excessive external debt, which is often created by short-termism and exacerbated by boom-bust cycles, will also be discussed in more detail in Chap. 3. Chapter 4 will

14

For a social contract approach to deal with externalities, see Brunnermeier (2021). On the difficulties, however, to establish welfare-improving intergenerational social contracts, see Jonas (1978).

References

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more specifically focus on fossil fuel resources and their devastating effects on the environment as drivers of climate change. In Chap. 5, we will present Hotelling’s classical 1931 theory of optimal resource extraction, and its use in fossil fuel extraction.

References Allen MR, Dube OP, Solecki W, Aragón-Durand F, Cramer W, Humphreys S, Kainuma M, Kala J, Mahowald N, Mulugetta Y, Perez R, Wairiu M, Zickfeld K (2018) Framing and context. In: Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, Pirani A, MoufoumaOkia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5 ◦ C. An IPCC special report on the impacts of global warming of 1.5 ◦ C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty 1(5) Arrow KJ, Dasgupta P, Goulder LH, Mumford KJ, Oleson K (2012) Sustainability and the measurement of wealth. Environ Dev Econ 17(03):317–353 Berg A, Portillo R, Yang SCS, Zanna LF (2013) Public investment in resource-abundant developing countries. IMF Econ Rev 61(1):92–129 Bernard L, Semmler W (2015) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, Oxford Brown L (1981) Building a sustainable society. WW Norton & Co Inc, New York, NY, USA Brunnermeier M (2021) The resilient society. Endeavor Literary Press Chami R, Cosimano T, Fullenkamp C, Berzaghi F, Español S, Marcondes M, Palazzo J (2020) On valuing nature-based solutions to climate change: a framework with application to elephants and whales. Available via SSRN. https://dx.doi.org/10.2139/ssrn.3686168 Chichilnisky G (1996) An axiomatic approach to sustainable development. Soc Choice Welfare 13(2):231–257 Climate Bonds Initiative, Glossary. https://www.climatebonds.net/certification/glossary Davies R, Haldane AG, Nielsen M, Pezzini S (2014) Measuring the costs of short-termism. J Financ Stab 12:16–25 Di Bartolomeo G, Saltari E, Semmler W (2019) The effects of political short-termism on transitions induced by pollution regulations, EconStor Preprints 200143, ZBW—Leibniz information centre for economics. (Published as Di Bartolomeo G, Saltari E, Semmler W (2021) The effects of political short-termism on transitions induced by pollution regulations. Dyn Anal Compl Econ Environ 109–122) Flaherty M, Gevorkyan A, Radpour S, Semmler W (2017) Financing climate policies through climate bonds–a three stage model and empirics. Res Int Bus Financ 42:468–479 Friedlingstein, P., Jones, M.W., O’Sullivan, M., Robbie M. Andrew, Dorothee, C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E.M.S Nabel Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter,

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Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido van der Werf, Nicolas Vuichard, Chisato Wada Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng. 2022. Global Carbon Budget 2021, Earth Syst. Sci. Data 14: 1917–2005. https://doi.org/10.5194/essd-14-1917-2022 Greiner A, Semmler W (2008) The global environment, natural resources, and economic growth. Oxford University Press, USA Halsnaes K, Garg A (2006) Sustainable Development as a Framework for Assessing Energy and Climate Change Policies. In: Halsnaes K, Garg A (eds) Sustainable development, energy and climate: exploring synergies and tradeoffs methodological issues and case studies from Brazil, China, India, South Africa. UNEP, Bangladesh and Senegal Halsnaes K, Shukla PR, Garg A (2008) Sustainability development and climate change: lessons from country studies. Clim Policy 8(2):202–219 Heine D, Semmler W, Mazzucato M, Braga JP, Flaherty M, Gevorkyan A, Hayde E, Radpour S (2019) Financing low-carbon transitions through carbon pricing and green bonds. World Bank Policy Research Working Paper 8991. https://openknowledge.worldbank.org/handle/10986/ 32316. Accessed 7 Nov 2022 Hotelling H (1931) The economics of exhaustible resources. J Polit Econ 39(2):137–175 IMF (2012) Macroeconomic policy frameworks for resource-rich developing countries. Int Monet Fund. https://www.imf.org/external/np/pp/eng/2012/082412.pdf. Accessed 7 Nov 2022 IMF (2017) IMF World Economic Outlook, October 2017: Seeking Sustainable Growth: Short-Term Recovery. Long-Term Challenges, International Monetary Fund, Washington DC, USA IPCC (2014) Climate Change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working Group II to the fifth assessment report of the intergovernmental panel on climate change [Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE. https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap7_FINAL.pdf. Accessed 7 Nov 2022 IPCC (2018) Rogelj J, Shindell D, Jiang K, Fifita S, Forster P, Ginzburg V, Handa C, Kheshgi H, Kobayashi S, Kriegler E, Mundaca L, Séférian R, Vilariño MV (2018) Mitigation pathways compatible with 1.5 ◦ C in the context of sustainable development. In: Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5◦ C. An IPCC special report on the impacts of global warming of 1.5 ◦ C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, pp 93–174. https://www.ipcc.ch/site/ assets/uploads/sites/2/2019/05/SR15_Chapter2_High_Res.pdf. Accessed 7 Nov 2022 IPCC, 2019. Mbow, C., Rosenzweig, C., Barioni, L.G., Benton, T.G., Herrero, M., Krishnapillai, M., Liwenga, E., Pradhan, P., Rivera-Ferre, M.G., Sapkota, T., Tubiello, F.N., Xu, Y., 2019. Food Security. In P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D.C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.). Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. 437–550. https://www.ipcc.ch/site/assets/uploads/2019/11/08_Chapter5.pdf. Accessed November 7, 2022 Jonas H (1978) The imperative of responsibility. University of Chicago Press Keller K, Nicholas R (2015) Improving climate projections to better inform climate risk management. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, New York Krause U (1981) A substitution theorem for joint production models with disposal processes. Oper Res Verfahr 41:287–291

References

19

Meadows DH, Meadows DL, Randers J, Behrens WW (1972) The limits to growth: a report for the club of Rome’s project on the predicament of mankind, Earth Island Metcalf GE, Stock JH (2020) The macroeconomic impact of Europe’s carbon taxes. NBER Working paper #27488. Available via NBER. https://www.nber.org/papers/w27488. Accessed 7 Nov 2022 NASA’s Goddard Space Flight Center (2022) Sea Level. https://climate.nasa.gov/vital-signs/sealevel/. Accessed 2 Sept 2022 NASA Ice Sheets. https://climate.nasa.gov/vital-signs/ice-sheets/. Accessed 2 Sept 2022 Nyambuu U, Semmler W, Palokangas T (2014) Sustainable growth: modelling, issues and policies. International Institute for Applied Systems Analysis (IIASA) Interim Report IR-14-019 Orlov S, Rovenskaya E, Puaschunder JM, Semmler W (2018) Green bonds, transition to a lowcarbon economy, and intergenerational fairness: evidence from an extended DICE model. IIASA Working Paper, WP-18-001, IIASA Orlov S, Rovenskaya E, Semmler W (2019) Financing mitigation of climate risks through green bonds—an intergenerational perspective. IIASA Working Paper, IIASA Pearce DW (1987) Foundations of an ecological economics. Ecol Model 38:9–18 Pearce DW, Barbier E, Markandya A (1990) Sustainable development: economics and environment in the third world. Edward Elgar Publishing, Aldershot Pezzey J (1989) Economic analysis of sustainable growth and sustainable development. In: The World Bank Policy Planning and Research Staff, Environment Department Working Paper 15 Ramsey FP (1928) A mathematical theory of saving. Econ J 38:543–559 Rawls J (1971) A theory of justice. Belknap Press Repetto RC (1986) World enough and time: successful strategies for resource management. Yale University Press Rudebusch G, Diebold F (2021) Probability assessments of an Ice-Free arctic: comparing statistical and climate model projections, EGU general assembly 2021, online, 19–30 Apr 2021, EGU2113126. https://doi.org/10.5194/egusphere-egu21-13126. Accessed 7 Nov 2022 Sachs JD (2015) Climate change and intergenerational well-being. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press Saltari E, Semmler W, Di Bartolomeo G (2021) A Nash equilibrium for differential games with moving-horizon strategies. Comput Econ 1–14 Semmler W, Tahri I, Lessmann K (2020) Energy transition, asset price fluctuations, and dynamic portfolio decisions. Working Paper SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_ id=3696295. Accessed 3 Nov 2022 Semmler W, Braga J, Lichtenberger A, Toure M, Hayde E (2021) Fiscal policy for a low carbon economy, World Bank Report, World Bank Group. documents. worldbank.org/curated/en/998821623308445356/Fiscal-Policies-for-a-Low-Carbon-Economy. Accessed 3 Nov 2022 Solow RM (1991) Sustainability: an economist’s perspective. Eighteenth J, Seward Johnson Lecture, Marine Policy Center Solow RM (1974) Intergenerational equity and exhaustible resources. Review of Economic Studies, Symposium on the Economics of Exhaustible Resources, pp 29–45 Tietenberg TH (1984) Environmental and natural resource economics. Scott, Foresman & Company, Illinois Von Weizsaecker CC (1967) Lemmas for a theory of approximate optimal growth. Rev Econ Stud 34(1):143–151 Weitzman ML (1997) Sustainability and technical progress. Scand J Econ 99(1):1–13 World Bank (2011) The changing wealth of nations: measuring sustainable development in the new Millennium. World Bank Publications World Commission on Environment and Development. WCED (1987) Our common future. Oxford University Press, USA Wright G, Czelusta J (2007) Resource-based growth: past and present. In: Lederman D, Maloney WF (eds) Natural resources. World Bank, Stanford University Press, Neither Curse nor Destiny

Chapter 3

Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

Overview In this chapter, we study the perils of non-sustainable growth, which often take the shape of a boom-bust cycle. These frequently emerge from the high volatility seen in resource production, prices, over-leveraging, and from their impact on macroeconomic stability. The evolution of external debt is often related to short-termism in economic, financial, and policy decision-making. In fact, as resource-rich countries become more globalized, the economy becomes more vulnerable through increased commodity exports and fluctuations in their prices. Inevitably, the traditional simple models of a closed economy had to give way to extended versions which would allow for dynamic and open growth, driven by foreign trade, current and capital accounts, and external finance. We focus on countries that borrowed heavily from abroad and thus became vulnerable to external shocks. When resource prices decline sharply, many developing resource-rich countries experience a debt crisis and face a jump in borrowing costs; this, in turn, may trigger internal real and financial crises. We replicate those scenarios by employing numerical solutions of some model variants and trace out the impact of commodity boom-bust cycles, trade balances, and foreign debt. We emphasize those general features of exhaustible resources and their extraction dynamics since they are also inherited, as it were, by the extraction and use of fossil fuels; these will be dealt with in the next chapter.

3.1 Resource Extraction and Boom-Bust Cycles As aforementioned, it is particularly resource-rich developing countries that have tended to rely on mono-production, i.e., an overwhelming share of their exports are natural resources; thus there is natural resource-driven fiscal revenue. Moreover, high volatility in both volumes and prices characterize non-renewable resource extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_3

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This also tends to show up in exchange rate volatility and in the long-run possibility of exchange rate appreciation, thus hindering better macroeconomic performance. These market and macroeconomic dynamics also build up debt cycles. Using data from World Bank’s World Development Indicators, we constructed Fig. 3.1, which illustrates the importance of exports of non-renewable resources, measured as fuel, ores, and metal, and economic growth, the latter expressed as the per-capita-growth-rate of GDP for selected resource-rich countries. We observe that Algeria’s and Nigeria’s merchandise exports consist mostly of fuel. For certain resource dependent countries, e.g., Mongolia, both fuel and ores/metals are exported. In Fig. 3.1, “Resources” indicate export of fuel, ores, and metal measured as a percentage share of merchandise exports, and “GDP” represents the per capita growth of GDP in constant local currency; this is plotted on the right axis. As demonstrated in Fig. 3.2, after oil price reached a peak of over $100 per barrel in 2014, it plunged to around $50. Both demand and supply side forces have contributed to this fall. The major drivers were the U.S. shale oil boom,1 which led to overproduction of oil, and OPEC’s policy on maintaining their market share. Due to oversupply and high inventories (see Fig. 3.2), in early 2016 West Texas Intermediate’s (WTI) monthly average oil price plummeted to $30 per barrel. At the same time, global demand for oil as well as other resources has fallen mainly due to slower economic growth in China. According to the Commodity Markets Outlook from the World Bank (2020), global oil consumption dropped by 16% to 83 million barrels/day between Q2–2019 and Q2–2020. The COVID-19 pandemic had a severe adverse impact on the commodities market, causing a sharp decline in demand.2 During this time, there was also limited storage space available for crude oil. The WTI futures contract, the benchmark for U.S. crude oil prices, traded negatively for the first time, at −$37/barrel, in April 2020, mostly driven by a huge sell-off by WTI futures investors. The WTI monthly average price dropped to around $16/barrel in April 2020. Due to a significant reduction in oil supply by OPEC+,3 oil prices have started rising again significantly with a high of over $115 per barrel in 2022. In addition, coal and natural gas prices started soaring in 2022. Already before the Russian invasion of Ukraine fossil fuel prices went up steeply. See also Fig. 3.2. Also, as shown in Fig. 3.1, resource dependent countries (with a major share of resources in total export), among them fuel, ores, and metal, exhibit highly fluctuating GDP growth rates. Yet, we want to note that historical trends seem to have shown that some other countries have promoted other sectors, e.g., manufacturing and other segments of the economy, to obtain more diversification. Nevertheless, short-termism arising in finance, international investments, and resource extraction seems to have accelerated since the 1970s. 1

U.S. hydraulic fracturing of wells led to a surge in U.S. crude oil and natural gas production: 9.7 million barrels of oil and 92 billion cubic feet per day in April 2015 according to the U.S. Energy Information Administration (EIA) (https://www.eia.gov/petroleum/production/). 2 Following the initial recovery from the pandemic, commodity markets showed signs of improvement. 3 OPEC+ consists of OPEC countries and additional oil exporting countries including Russia, Kazakhstan, and others.

3.1 Resource Extraction and Boom-Bust Cycles

23

Fig. 3.1 Exports of Non-renewable Resources and GDP Growth. Source Constructed using data from the World Bank’s World Development Indicators. In this figure, exports of non-renewable resources are plotted as a percentage of fuel, ores, and metal in merchandise exports—labeled as “Resources”. On the right axis, the per-capita-growth-rate of GDP is labeled as “GDP”. For Algeria, Chile, Indonesia, Malaysia, Mexico, and Nigeria, data between 1964 and 2021 were used. For Kazakhstan and Mongolia, we used available data between 1997 and 2021. As shown in the figure, note that data for certain years are not available, especially for Algeria, Mongolia and Nigeria

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Fig. 3.2 Historical Monthly Prices for Energy (1979–2022). Source Constructed using data from IMF (Primary Commodity Price System). In this figure, monthly energy prices between 1979 (January M1) and 2022 (September M9) are plotted. Crude oil (West Texas Intermediate - WTI) prices are measured in U.S. dollars (US$) per barrel. Coal prices are based on Australian thermal coal and measured in U.S. dollars per hundred metric ton; this is shown on the right axis. On the right axis, natural gas prices from the Henry Hub terminal are plotted; data are measured in U.S. dollars per million metric British Thermal Units (BTU)

This shows up in exchange rates. Changes in exchange rates as well as in terms of trade drive the revenues from exports of those resources. Early studies, e.g., Prebisch (1950) and Singer (1950), note that unlike industrialized countries, developing countries did not perform well. In addition to deteriorating terms of trade, other drivers included specialization in certain resources, i.e., mono-production, and slower technological progress.4 More recent studies by Auty (1990, 1993, 2001), Sachs and Warner (1995, 1999, 2001), and Smith (2004) showed how per capita income was adversely influenced by an extensive share of resources in total exports. Sachs and Warner (2001) argued that higher price levels tend to be present in many resource-rich countries and international investors lacked competition from domestic firms. This was described by Sachs and Warner (2001, p. 837) with the phrase “curse of natural resources.” Those empirical results were subsequently evaluated by econometric methods. On the subject of econometric methods, Manzano and Rigobon (2001)5 noted that there can be an omitted variable problem where some variables might be correlated with exports of resources. They also argue that data used for measuring economic growth, e.g., GDP, contain resource production data and did not take into account its decreasing weight in GDP. They suggested working with panel data and using an adjusted growth rate variable, e.g., GDP excluding resource exports. In addition, they considered debt-related overhang that might be associated with slow economic

4

Maloney (2002, p. 1) also attributed limited technological advancement to slow growth in Latin America. 5 Manzano and Rigobon (2001) found a negative estimated coefficient only for cross-section data.

3.1 Resource Extraction and Boom-Bust Cycles

25

performance. A positive relationship was found in another study by Lederman and Maloney (2003) where panel data between 1975 and 1999 was used in the determination of the causal impact of the Leamer index. Overall, the empirical results were mixed. A wide range of empirical studies have been conducted on the topic of the resource curse and the volatility of resource extraction. The relationship between natural resources and other variables has often been estimated, e.g., Acemoglu et al. (2003) and adopted in Van der (2009, 2017), and others. Based on cross-country analysis, Van der Ploeg and Poelhekke (2009) focus on resource extraction that is driven by volatility associated with unanticipated growth of output, which is not dependent on initial income levels, natural resource dependence, human capital, investment, trade openness, or population. Higher volatility due to resource price fluctuations and restrictions on the current account was found to have a negative impact on a country’s performance. In this context, Van der Ploeg (2011) presents different hypotheses with supporting theories and empirical findings. These include the appreciation of real exchange rates, traded and non-traded sectors’ development, and Dutch disease. The failure of reinvesting resource rents in productive capital in many resource-rich developing countries was discussed. The study concludes that only certain countries experienced a resource blessing because of good quality institutions, greater trade openness, exploration technology, and sound financial system. In more recent study by Van Der Ploeg and Poelhekke (2017), a survey of quantitative findings on the impact of natural resources was provided. In their criticism of traditional cross-country estimations, they stress the importance of the type of data used in the analysis and present approaches for a more uniform econometric identification, e.g., big oil discovery data. When a developing country discovers natural resources and experiences a resource boom, money and credit in circulation may go up, resulting in possible inflation and a real appreciation as described in studies including Edwards (1986). But how does an increase in resource export and its revenue affect the non-resource sectors in the economy? Bruno and Sachs (1982) explain the Dutch disease as a decline in tradable6 goods or the manufacturing sector when a country discovers a new energy source. Three sectors were considered: energy, non-energy tradables, and non-traded goods. Their model of dynamic perfect foresight equilibrium assumes a restricted capital mobility in the short-run and complete capital mobility in the long run. Their simulations on the net effects of the energy sector show that the production of tradable goods decreases in the long run, and final goods’ terms of trade improve. In Nyambuu et al. (2014), a discussion on Dutch disease is provided with a focus on studies by Corden and Neary (1982), Forsyth (1986), and Frankel (2011). As part of growth in the oil and natural gas industries, Corden and Neary (1982) studied a fall 6

Tradables consist of domestically produced goods that can be exported to other countries. Nontradable goods include local services. DeGregorio et al. (1994) stated that tradable sectors should have a share of total export in total production greater than 10% (manufacturing has 45%, mining 31%, agriculture 24%, overall services 4%).

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in the manufacturing sector, in both employment and output, in a small open economy. Their findings demonstrated a worsening trade balance, and real appreciation resulting from a rising relative price of non-tradables to tradables. In addition, they proposed different effects describing movements of resources and spending, namely: “The boom in the energy sector raises the marginal products of the mobile factors employed there and so draws resources out of other sectors, giving rise to various adjustments in the rest of the economy, one mechanism of adjustment being the real exchange rate. This is the resource movement effect. If the energy sector uses relatively few resources that can be drawn from elsewhere in the economy this effect is negligible and the major impact of the boom comes instead through spending effect. The higher real income resulting from the boom leads to extra spending on services which raises their price (i.e. causes a real appreciation) and thus leads to further adjustments” (see Corden and Neary 1982, p. 827). In Forsyth (1986), structural changes, focusing on the impact of Dutch disease on the economies of Britain and Australia, were analyzed. The findings suggested that Britain had significant spending effects and it appeared to reflect Dutch disease. Thanks to the Common Agricultural Policy, primary production in Britain has grown, but the manufacturing sector has fallen significantly. On the other hand, in Australia, a large impact due to resource movements was found. In addition to deindustrialization, Neary and VanWijnbergen (1986) also confirmed that rising relative prices of non-tradables as compared with tradables was driven by the resource boom. Van der Ploeg (2011) presents a Dutch disease model with two sectors, where a household maximizes utility based on the consumption of traded and non-traded goods, when constrained by a budget. In this model, a resource windfall is considered, and resource exports are defined by net imports of traded goods. In other words, the value of the exported resources (defined by price and volume) multiplied by productivity in resource and traded sectors equals the difference between the consumption of traded goods and output of the traded sector (defined by employment in the traded sector and productivity in resource and traded sectors). In this study, the resource boom’s short-term effect is shown to be a higher relative price of non-traded and traded goods, which reflects an appreciation of the real exchange rate,7 and a growth of the non-traded sector, but accompanied by a fall in the traded sector. There are a number of empirical works on the testing of the Dutch Disease Hypothesis. For example, based on a “gravity” model of manufacturing trade and data for the period of 1970–1997, Stijns (2003) estimated that a 1% increase in world energy prices leads to 0.5% reduction of real manufacturing exports in a net energy exporting country. At the same time, it was pointed out that the real manufacturing exports are reduced when net energy exports rise. More empirical evidence is found in Harding and Venables (2016) paper on FX windfalls from natural resource exports and non-resource trade. It covered 7

Empirical tests such as by Chen and Rogoff (2003) showed that higher commodity prices made the real foreign exchange (FX) rate appreciate. Theoretical models and empirical analysis on the real FX rate determination in terms of price levels, tradable and non-tradable goods as well as productivity in these sectors are provided in Nyambuu and Tapiero (2018).

3.2 Foreign Debt Burdens, Borrowing Costs, and Risk Premia

27

41 countries with cross-sectional and panel data analysis of data between 1970 and 2006. Their findings showed that resource revenues reduce non-resource exports by 74 cents per dollar, with a higher impact on the manufacturing sector, predominantly in countries with higher incomes, and increase imports by 23 cents. They emphasized the importance of breaking down the tradable sector into non-resource exports and imports for the analysis of Dutch disease, especially for export-oriented economic growth. Studies of developing economies were reviewed in Mcmahon (1997). It was found that worsening economic growth could be explained by factors other than the Dutch disease. For example, Sala-i-Martin and Subramanian (2003) studied Nigeria and argued that fiscal imperatives and rents played an important role in formulating exchange rate policy. Frankel (2011, p. 12) described some of the negative effects of the commodity boom that corresponds to Dutch disease focusing on how the currency appreciates significantly in real terms. Others effects included a rising relative price of non-traded/traded commodities, changing labor and land, high government spending, and high external debt.

3.2 Foreign Debt Burdens, Borrowing Costs, and Risk Premia As a result of the forces discussed above, many developing countries that discover, exploit, and extract resources have gone through alternating periods of resource booms and busts. Due to globalization, countries have increased access to global markets, thus gaining some opportunities to sell more resources and borrow from abroad. During the phase of the resource boom, driven by high prices and abundant production, export revenues and GDP increased. As shown in the previous section, for certain countries, e.g., Venezuela and Nigeria, fuel, ores, and metals dominate their exports. In addition, these resources make up, in some cases, around 80% of the fiscal revenue. Historically, in the 1970s, when the resource prices were high, these countries borrowed significantly from abroad. The resource price fluctuations that were discussed previously affected export revenues and, in turn, added to the external debt burden of the country. When resource prices suddenly fell in the 1980s, many Latin American countries experienced resource busts with trade and fiscal deficits accompanied by macroeconomic instability. As illustrated in Fig. 3.3, historically external debt stock, as percentage share in GDP, reached very high numbers: 159% in Indonesia, 152% in Argentina, 119% in Kazakhstan, 111% in Nigeria, and 75% in Mexico. They faced challenges repaying the debts, thus defaulted, leading to a debt crisis, e.g., Mexico (1982). According to World Bank data, among developing resource-rich countries, Mongolia’s external debt/GDP ratio reached a record high of almost 250% in 2020. Due to recent resource busts and other domestic and foreign conditions, external debt’s share in GDP for certain other resource dependent countries has shown an increasing trend. We will address those and other related issues next.

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3 Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

Fig. 3.3 External Debt Stock’s Share in GDP (1970–2020). Source Constructed using data from the World Bank’s World Development Indicators. In this figure, external stock as a percentage of GDP between 1970 and 2020 is shown. Data for Argentina and Mongolia are plotted on the right axis

As Frankel (2011) pointed out, oil exporters such as Mexico, in particular, and others, e.g., Russia and Ecuador, could avoid debt payment issues since their obligations reflected the price of oil. As we witnessed in 2014, and the Pandemic meltdown of 2020, prices fluctuate significantly and it would be unrealistic to assume that debt market players are unaware of this. Thus, the indexing of debt to any commodity would likely cause unpredictable changes in the demand and assessment of this same debt. The collapse in oil prices in 2014 was addressed by the IMF (2015), highlighting supply side factors in both their daily and quarterly models. As for the fiscal impact on the oil exporting economy, IMF (2015) estimation indicated the highest loss as 25% of GDP. According to Arezki and Blanchard (2014), although the fall in oil prices could be explained by lower demand, supply side factors, high production quotas, and decisions made by OPEC in particular, had a significant impact. As a result, resource exports were hit hard and caused external debt to surge in several oil exporting developing countries, e.g., Malaysia and South Africa. Many of these countries

3.2 Foreign Debt Burdens, Borrowing Costs, and Risk Premia

29

experienced additional pressure due to FOREX pressure, not least because a substantial part of their debt was denominated in U.S. dollars or other foreign currencies.8 Empirical findings in Nyambuu (2016) highlighted FOREX rate volatility and suggest that many developing resource-rich countries had excess external debt during the 1980s and between 1997 and 1998. Unfavorable external factors led to reversals of capital flow that affected both the public and private sectors.9 While some countries have accumulated long-term foreign debt, others have increasingly taken on large short-term obligations. With trends like those, short-term external borrowing increases liquidity problems and currency risks and makes the country more vulnerable to shocks related to external sources. For example, according to World Bank data, Algeria’s short-term external debt stock accounts for 34% in total external debt stock in 2020; it was only 3% in 2005. This share is calculated for other countries: Venezuela 31%, Argentina 17%, South Africa 16%, Brazil 13%, Mexico 11%, Indonesia 11%, Colombia 9%, Mongolia 9%, and Kazakhstan 6% in 2020. At the same time, we should differentiate between public and private long-term external debt. The data are calculated as an average between certain years and shown in Table 3.1. Private external debt, which has been increasing in many developing countries since the 1990s, declined during the Asian economic crisis of 1997, but it began surging after 2006. In some countries, including Brazil, Kazakhstan, and Mongolia, private external debt exceeded public external debt. For example, Brazil’s average private external long-term debt stock was at $81.6 billion between 2001 and 2005, but it surged to an average of around $300 billion between 2015 and 2020. When resource booms occurred, spending by the government, consumption, and the domestic money supply rapidly increased in many countries. This happened in Mexico in the 1970s because of significant public spending. Such expenditures are typically funded by foreign debt. As a result of insufficient savings, according to World Bank data, Mexico’s total debt rose from $31 billion in 1977 to $86 billion in 1982 (Buffie and Krause 1989). According to Harberger (1985), many developing countries, especially in Latin America, had borrowed abroad mainly to finance their consumption instead of funding productive investments. Studies including Manzano and Rigobon (2001) noted that debt overhang was the main reason why resource-exporting resource-rich countries ran into some growth constraints. An extensive literature review on external debt is provided in Nyambuu et al. (2014). To better understand this problem, many empirical studies were conducted investigating a possible relationship between external debt and economic growth. They demonstrated how the debt to GDP ratio could surpass a certain threshold, where a positive effect was certain to be followed by a negative effect on certain African and

8

Bank for International Settlements provides data on how much of government debt is in foreign currency. 9 Many resource-rich countries found it challenging to repay their high external debt before the COVID-19 hit. We expect the situation to get worse in the coming years.

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3 Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

Table 3.1 Public and private long-term external debt stock (expressed in billion current U.S. dollars) Public 1995–2000 2001–2005 2006–2014 2015–2020 Algeria Argentina Brazil Indonesia Kazakhstan Mexico Mongolia Nigeria South Africa Venezuela, RB Private Algeria Argentina Brazil Indonesia Kazakhstan Mexico Mongolia Nigeria South Africa Venezuela, RB

28.2 73.7 95.4 65.5 2.9 88.9 0.6 25.4 11.4 31.8

20.0 87.2 97.6 73.0 3.1 106.8 1.1 28.3 15.9 33.3

2.6 71.0 105.8 105.6 5.7 160.8 2.4 5.6 38.3 51.5

1.4 114.0 184.5 204.8 22.9 288.4 7.1 20.6 84.1 69.7

– 23.3 81.7 42.9 2.1 34.1 – 0.6 4.3 4.2

0.6 27.7 81.6 34.6 19.6 35.8 – 6.7 10.5 5.2

1.0 37.1 207.4 66.1 103.4 62.2 5.7 12.4 40.0 33.5

0.2 41.0 299.5 111.9 127.7 97.9 18.0 26.3 43.2 57.4

Source Constructed using data from World Bank’s Databank. In this table, we show public as well as private debt for select resource-rich developing countries, expressed in billions of current U.S. dollars. These values are calculated as an average in the time periods: 1995–2000, 2001–2005, 2006–2014, and 2015–2020. Note that data are not available for all the years; this is indicated by “–” in the table

Latin American countries (see Sachs 1989; Cohen 1997; Cordella et al. 2005; Pattillo et al. 2002; Reinhart and Rogoff 2010; Nyambuu and Bernard 2015; Nyambuu 2016; Nyambuu and Semmler 2017). Reinhart and Rogoff (2010) show that when the share of debt in GDP rises to 60%, the growth rate of GDP drops by 2%. Studies such as by Mittnik and Semmler (2014), Semmler and Proano (2015), Nyambuu and Semmler (2017), and Nyambuu and Tapiero (2018) point out that other indicators, e.g., risk premia, borrowing and credit constraints, and financial market stress should also be considered in assessing the debt crisis and fall in GDP. Many early studies had already examined the default risks of debt arising from increased risk premia (see Eaton and Gersovitz 1980, 1981; Obstfeld 1982; Sachs and Cohen 1982; Edwards 1984; Bhandari et al. 1990; Van Der Ploeg 1996). During a resource bust, investment in resource-rich developing countries is usually considered risky. This results in higher risk premia which expands the gap between the

3.2 Foreign Debt Burdens, Borrowing Costs, and Risk Premia

31

Fig. 3.4 Risk premium on lending (1970–2021). Source Constructed using data from The World Bank (World Development Indicators). In this figure, risk premia in percentages are illustrated between 1970 and 2021. Data for Brazil are plotted on the right axis. It is measured as the difference between the lending rate and the T-bill rate. Note that risk premia can be negative

lending rate and the Treasury bill rate (see Fig. 3.4). In general, we would expect the rates to be country specific and affected by other general factors as well. According to World Bank data, some developing countries, e.g., Brazil, had the highest lending rates, with risk premia approaching 60% in the late 1990s and beyond. In contrast, Nigeria’s average risk premium on lending was around 7% between 1991 and 2018, but it reached a peak of 15% in 2009. Nyambuu and Semmler (2017) replicated the empirical observations discussed in this section using a regime-change model. In this chapter, we will present a basic growth model with non-renewable resources–possibly also including fossil fuels–and show its extension to an open economy in a dynamic macro model that incorporates exports, external debt, and borrowing costs. This type of model will numerically be solved using Nonlinear Model Predictive Control (NMPC). We will present the model and the dynamic trajectories of the state variables, focusing particularly on possible nonlinear relationships between the external debt-to-capital ratio, risk premia, and the evolution of capital over time.

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3 Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

3.3 Growth Models with Exhaustible Resources 3.3.1 Basic Growth Model with Resources The modeling of the exhaustible resource’s impact on economic growth was suggested by earlier studies, e.g., Forrester (1971), Meadows et al. (1972), and Dasgupta and Heal (1974). For the production function, exhaustible resource was used as an input in models, e.g., Solow (1973), Dasgupta and Heal (1974), and Stiglitz (1974). Typically, a theoretical model builds-in extractive resources by maximizing a discounted utility from consumption–other welfare enhancing or deteriorating components are not included. Note that no side effects of the use of (fossil fuel) resources are considered subsequently; neither are further welfare function components included.10 Thus, the models from earlier times are simple, but they do frame important issues. Dasgupta and Heal (1974) assume a strictly concave utility function and use consumption, C, and the flow of exhaustible resource, S, as decision variables. As in Dasgupta and Heal (1974), Stiglitz (1974), and in Solow (1973), the production function has two inputs as Y = Q(K , S) with capital, K , and flows of exhaustible resources, S. In the dynamic model, households’ welfare function, U (C), is maximized with two constraints: capital stock, K , and remainder of exhaustible resources, t R, where Rt = R0 − 0 St di. Here, the given initial stock of the initial resource R0 is diminished by S. Therefore, the basic model with constant discount rate, θ, as in Dasgupta and Heal (1974, pp. 8–10) can be outlined as follows11 : ∞ Max

U (C)e−θt dt

(3.1)

0

s.t. K˙ = Y − C R˙ = −S

(3.2) (3.3)

Given our dynamic model shown by Eqs. (3.1)–(3.3), with capital stock dynamics in Eq. (3.2) and the resource dynamics in Eq. (3.3) and decision variables consumption, C, and the extraction rate of the resource, S, we can study the optimal paths for consumption for an economy using capital and resources in production. The dynamic consumption path, depending on the capital stock and resources, is derived in the Appendix of this chapter using the Hamiltonian approach. There, it is also shown that the rate of change in consumption depends on the size of the discount rate in the sense that the growth rate of consumption will decline with the discount

10

For details on both positive and negative effects in the welfare function, see Chap. 9. Extraction costs will be considered in the extended growth model with resource extraction technology in Chap. 6 together with numerical solutions.

11

3.3 Growth Models with Exhaustible Resources

33

rate rising. Moreover, in the Appendix, it is demonstrated that the elasticity of substitution between capital and resource can be different from 1 if a constant elasticity of substitution (CES) production function is used. In this context, if the elasticity of substitution is equal or smaller than 1, the non-renewable resource becomes essential, for further details, see Solow (1973) and Dasgupta and Heal (1974). We leave aside an explicit numerical solution to the above growth model with a non-renewable resource in Eqs. (3.1)–(3.3) but a similar model will be solved numerically in the next section as well as in Chap. 6.

3.3.2 Modeling Resource Exports and Foreign Debt Next, we introduce the domestic economy’s interactions with external markets through foreign trade and foreign borrowing. An extended growth model for a resource-rich country that exchanges commodities with other countries will be presented. We will show how to extend a basic growth model and incorporate the impact of external debt on the economy. Such types of models were proposed by Bardhan (1966) and Bruno (1967). In the study of optimal development and trade, Bruno (1967) addressed the composition of foreign trade, trade activities, and presented an open economy model with constraints on foreign exchange, labor supply, labor skill, and capital. Based on aggregate transformation curves, efficient transformation was analyzed and consumption was maximized subject to four constraints. More recent work can be found in Obstfeld (1980, 1982), Sachs et al. (1981), Sachs (1982), Svensson and Razin (1983), Blanchard (1983), and others. Our proposed model below resembles the work of Blanchard (1983). In Nyambuu et al. (2014)), a detailed discussion of external debt-related models is provided, focusing on studies such as by Sachs (1981, 1982), Eastwood and Venables (1982), Blanchard (1983), Blanchard and Fischer (1989), Mansoorian (1991), and Hamilton (2001). We will describe these earlier prototypical models briefly in this section. For example, Sachs (1982, p. 148) determined the current account as foreign financial claims with a negative value (debtor) or positive value (creditor). In this context, the current account is related to debt payments using real yield and gross domestic product (GDP) that only implicitly contains government expenditure (as fractions of consumption and investments). An intertemporal budget constraint can hold when the discounted surplus of future trade account finances the initial debt, as demonstrated in Sachs (1982, p. 149). In an open economy, a current account deficit can also be caused by both higher consumption and investment. This is discussed in Sachs et al. (1981), where macroeconomic adjustments are also studied. Moreover, they discuss debt problems in less developed countries and describe how increased oil prices may have led to a surge in debt in 1980 due to extensive current account deficits. Presuming an equilibrium in the output market, they derived the current account as the sum of net exports, net factor payments from abroad, and net unilateral transfers.

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3 Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

Blanchard (1983, p. 188), sketches an open economy model for a developing country, using Brazil as an example, with large current account deficits and huge external debt with interest payments on debt. In this model, the world interest rate is taken to be equal to the discount rate, the output function is dependent on capital and fixed labor, and initial values for capital and debt are given. Here, the investment spending includes installation costs for investments, and current account deficits occur when total spending and interest on debt exceed output, as described previously. Next, Blanchard (1983, p. 190), modifies the model by changing the assumptions and introducing time-varying variables, namely Harrod neutral technological progress and a discount rate, which is not equal to the constant world interest rate. In addition, Blanchard (1983, p. 194), introduces a function of disutility of debt in the objective function reflecting external debt’s non monetary cost and variables expressed per efficiency unit. Another model, by Blanchard and Fischer (1989), presented a basic model with dynamic budget constraint and current account deficits, which are financed by external debt; they also included additional terms for installation-related costs of investments, and defined deficit of current account as “excess of absorption over production” (pp. 58–60). Based on national income accounting, they pointed out that when one deducts net exports from interest payments, it results in current account deficits. The above discussed models were adopted in a more recent study such as by Semmler and Sieveking (2000) where a resource extraction was added. Here, welfare depends not only on consumption, but also on resources. The two state variables were resource stock and external debt. They used a welfare function following Beltratti et al. ( 1993, 1994), which is dependent on an exploitable renewable resource, and sketched the dynamics of resources based on a reproduction function, as it is used in fishery and the extraction rate of resources and the accumulated external debt. Semmler and Sieveking (2000, p. 1124), show that dynamics of resource stock depend on the reproduction function and resource extraction rate defining the extraction effort; debt increases due to the interest payment on debt and consumption, and decreases due to the production value that is measured as price times quantity of the goods exported. Next, we consider the Dutch disease12 models where the resource boom’s adverse impact on the domestic manufacturing sector is taken into account (see Nyambuu et al. 2014 for details). In Dutch disease models, with a resource sector as a driver of the economy, Mansoorian (1991, p. 1499), points out that the economy does not have to experience de-industrialization as well as real appreciation. If resource extraction contributes to diversified manufacturing, newly discovered resources may actually promote industrialization in the long run. A model of a country with large external

12

According to Corden (1984), Dutch disease is described as “the adverse effects on Dutch manufacturing of the natural gas discoveries of the nineteen sixties, essentially through the subsequent appreciation of the Dutch real exchange rate” (p. 359).

3.3 Growth Models with Exhaustible Resources

35

debt was presented with a discussion on a possible rapidly worsening position in the net foreign assets.13 Discovery of a resource deposit is also incorporated in dynamic macro models. In a study of oil discovery, Eastwood and Venables (1982, p. 287), built a model prior to oil shock and assumed perfect mobility of capital and “perfect foresight” in the foreign exchange (FX) market. They incorporated the Phillips curve, money market equilibrium, and domestic demand function, which is defined by not only income, domestic price and interest rate, but also by exchange rate. Furthermore, they showed how resource discovery can change demand and oil revenue, which in turn can cause the exchange rate to appreciate.14 The sustainable development of a resource-rich economy was also explored in Hamilton (2001) where output in the production function, Y = Q(K , S), depends on the resource supply in addition to capital. It is assumed that resource exploration cost is determined by resource discovery and accumulated discovery. The investment of resource rents into productive assets contributes to the sustainability of the economy. However, optimal development becomes unsustainable when genuine saving is negative. Genuine saving is positively affected by aggregate output and educational spending, but variables such as consumption, capital depreciation, net stock of pollution, and resource depletion, harvest net of growth can have a negative impact (see Hamilton 2001, p. 38, for detail). In addition to the dynamics of resource discovery, this model also includes the dynamics of depletion and accumulation of resource deposits, and foreign trade with foreign assets, which is reduced by repatriation of assets, but increased by the fixed foreign interest rate paid on debt and international resource price (see Hamilton 2001, pp. 47–48). A number of studies address the rising cost of debt due to a higher risk premium and discuss how it can increase domestic long-run interest rate and lead to a lower capital stock. In Bhandari et al. (1990), the interest rate on external debt depends on world interest rate and a risk premium, which is an increasing function of the debt stock, with a parameter reflecting a shift in country-specific risk. Based on the models proposed in Blanchard (1983) and Blanchard and Fischer (1989), Nyambuu and Semmler (2017, pp. 35–36), presented the following modified dynamic regime-change model for an open economy that includes a risk premium. In particular, low export revenues from low resource prices can lead to current account deficits. Their results demonstrate the impact of different interest rates and risk premia on external debt sustainability. The production function is assumed to be Y = Q(K ) and the welfare function includes a penalty term defined as: −ω((B/K ) − (B/K )∗ )2 , where B/K is debt-tocapital ratio and (B/K )∗ is its steady state. Following Mittnik and Semmler (2014), a state-dependent interest rate that depends on a risk premium is included using an ar ctan function in the dynamics of the external debt. The dynamic model, as in Nyambuu and Semmler (2017), p. 36, is outlined as follows: 13

In Mansoorian (1991, p. 1502), GDP is a function of non-traded goods’ price, labor, capital, and resource extraction. 14 See Eastwood and Venables (1982, pp. 289–290) for more detail.

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3 Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

∞   2  −θt log(cY ) − ω (B/K ) − (B/K )∗ e dt Max c,i

(3.4)

0

s.t. K˙ = i K − δK   B˙ =  (arctan(B/K )) B − Y − cY − i K − η(i K )2

(3.5) (3.6)

where C denotes consumption, I is investment, K is capital that depreciates at a rate of δ, output is represented by Y , external debt is B, and c = C/Y and i = I /K . The latter two are decision variables. To incorporate the extraction of non-renewable resources, the above described model can include a flow of resources in the production function as Y = F(K , S) and one can add an additional constraint of the stock of resources as R˙ = −S that is often used in growth models with resources. As a further extension, Nyambuu (2017) includes human capital, h, in the production function as Y = F(K , S, h), and dynamic of human capital as h˙ = λI − ϑh, where ϑ is human capital’s depreciation and λ represents a contribution of the investment to human capital as discussed in Greiner and Semmler (2008, p. 64). In the next section, we will solve the model defined by the system shown by Eqs. (3.4)–(3.6) and present our numerical solutions corresponding to different scenarios. These reflect a regime switching between resource booms and busts where a nonlinear relationship between the debt-to-capital ratio and risk premia is stylized.

3.4 Numerical Solutions—Economic Growth and External Debt Ratios The dynamic decision models discussed in this book are mostly solved using a modeling device with a finite decision horizon called Nonlinear Model Predictive Control (NMPC). We start with a continuous time model that is discretized and approximates an infinite-horizon control problem with a long time horizon very well.15 This procedure has been adopted in a number of dynamic decision problems in economics (see Grüne et al. 2015; Nyambuu et al. 2014; Nyambuu and Semmler 2017, Nyambuu and Semmler 2020) and for a game theoretical set up of fiscal and monetary policies (see Di Bartolomeo et al. 2019; Saltari et al. 2021). Important empirical facts are replicated by presenting numerical solutions of scenarios for different initial conditions capital, K 0 , and debt, B0 . In this section, we analyze the dynamics of capital and the external debt-to-capital ratio based on the extent of leveraging. As in Nyambuu and Semmler (2017, p. 36), it 15

Grüne and Pannek (2011) and Grüne (2015) show that NMPC can be used with a small number of periods; instead of computing optimal value function for all possible initial states. NMPC only calculates single optimal trajectories. The results are obtained based on a shorter decision horizon with a small number of prediction periods.

3.4 Numerical Solutions—Economic Growth and External Debt Ratios

37

is assumed that the production function depends on capital only: Y = (K )ϕ . We used a steady state value of (B/K )∗ = 0.3, and the following parameter values: ϕ = 0.3 are ω = 0.1, θ = 0.03, δ = 0.05, and η = 0.1. Possible relationships between B/K and K are demonstrated in Figs. 3.5 and 3.6 plotting trajectories for K and B for different interest rates, corresponding to magnifying values representing the risk premium, as well as different initial conditions of the state variables. As described in the modeling section, changes in K in turn drive production. In these figures, the horizontal axis shows K and the vertical axis has B/K . The results presented in Fig. 3.5 show that B/K converges to the steady level of (B/K )∗ = 0.3. Most of the results demonstrate a nonlinear relationship between B/K and K . The dynamic trajectories in Panel (a) show that when initial conditions

Fig. 3.5 Stable dynamics: Nonlinear relationship between debt/capital ratio and capital In these figures, capital, K , is plotted on the horizontal axis and the debt-to-capital ratio, B/K , is plotted on the vertical axis. It is labeled as “stable” dynamics because the B/K ratio stops changing and converges to a steady state

Fig. 3.6 Unstable dynamics: Nonlinear relationship between debt/capital ratio and capital In these figures, capital, K , is plotted on the horizontal axis and the debt-to-capital ratio, B/K , is plotted on the vertical axis. It is labeled as “unstable” dynamics because the B/K ratio continues rising

38

3 Non-sustainable Growth, Resource Extraction, and Boom-Bust Cycles

are very low, both K and B increase over time, but K started rising faster. As a result, B/K , which increased significantly initially, stopped changing as it reached the steady state value. Other possible scenarios and solutions with other variations are presented in Nyambuu and Semmler (2017) where accumulated debt increases first rapidly but starts declining and eventually reaches a low level. Their results show that, with higher initial debt (B0 ), K may start declining rapidly because of high B/K ratio, but when the debt ratio drops and reaches the steady state ((B/K )∗ ), K would start increasing gradually. Next, we consider much higher initial conditions and/or higher amplifiers following Nyambuu and Semmler (2017, p. 37). The results depicted in Fig. 3.6 demonstrate unstable dynamics for the B/K ratio due to the dynamics of continuously rising external debt. The higher the initial debt, B0 , the higher B/K .16 In general, when B/K increases, K rises initially, however, once B/K surpasses a certain threshold, K starts declining. When the magnifying factor is high, e.g.,  = 0.08, as in Panel (b), it generates unstable dynamics as well. In most scenarios presented in this section, there is a nonlinear relationship between B/K and K that corresponds to certain thresholds.

3.5 Conclusion We examined the impact of resource booms and busts driven in tandem with high and low resource prices on the world commodity markets. Resource exporters have become more sensitive to external shocks, e.g., crude oil price fluctuations, economic expansions, and contractions of major trade partners, especially China, as well as debt dynamics and exchange rate fluctuations. As presented in this chapter, a macro model starts with the incorporation of extractive resources by including them in the production function. The prototypical model of earlier times usually represented a dynamic decision model that was constrained by an accumulation of capital stock as well as the extraction of nonrenewable resources.17 Some elements of those models were employed and extended to the open economy macro model we developed, thus resembling Blanchard (1983) approach, which emphasized the relationship between the current account and an economy’s external debt.

16

For the impact of the arctan function on the state-dependent interest rate, see Nyambuu and Semmler (2017). 17 In Chap. 6, backstop technology will be incorporated into the basic growth model together with exhaustible resources, thus highlighting its importance for extraction processes, and demonstrating how it can contribute to the illuminating problem of exhaustible resources. The numerical solutions with different costs will be presented showing dynamic trajectories of the state variables, including capital accumulation and extraction rates for non-renewable resources. There, the impact of these constraints on economic growth is covered in detail.

3.5 Conclusion

39

We focused on a modified dynamic open economy regime-change model that resembles Nyambuu and Semmler (2017). That model also included state-dependent interest rates and risk premia. Similar to the current results, our numerical solutions reflect regime switching between the resource boom and bust and a nonlinear relationship between the debt-to-capital ratio and capital itself. As we demonstrated, low risk premia are associated with sustainable debt, whereas high risk premia are likely to result in surging external debt together with rising risk premia and default probabilities. A number of empirical studies seem to align with this observation. Many of the features above, as observed for non-renewable resources, will be continued to be discussed in the next chapter.

Appendix Following Dasgupta and Heal (1974, p. 10), the current value Hamiltonian with ω and γ as co-state variables of constraints, K and R, respectively, is shown below H = U (C) + ω(Q(K , S) − C) + γ(−S)

(3.7)

The necessary optimality conditions for the optimal control are obtained as 

U (C) = ω ωQS = γ

(3.8) (3.9)

ω˙ = ω(θ − Q K ) γ˙ = θγ with Q S =

∂ Q(K ,S) ∂S

and Q K =

∂ Q(K ,S) . ∂K " ˙

(3.10) (3.11)

˙

C , and ωω˙ = θ − Q K . Based on Rearranging gives us ω˙ = U (C)C or ωω˙ = UU (C)  (C) these equations, Dasgupta and Heal (1974), pp. 10–11, showed the steps on how to " ˙ (C) K −θ derive the consumption: CC = Qε(C) where ε(C) = − CU . U  (C) With x = K /S, (Dasgupta and Heal 1974, p. 11) derived the following elasticity of substitution between K and S that is used in the CES production function "

    q (x) q(x) − xq (x) σ=−  [0, ∞] xq(x)q " (x)

(3.12)

According to Solow (1973), if the share of K exceeds the share of S in CobbDouglas production function, sustained consumption per capita would be a feasible objective. Dasgupta and Heal (1974, pp. 14–19), analyzed the case when S = 0 with different values of σ and showed that only in the cases when σ = 1 and σ < 1, non-renewable resource’s role in production becomes essential.

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References Acemoglu D, Johnson S, Robinson J, Thaicharoen Y (2003) Institutional causes, macroeconomic symptoms: volatility, crises and growth. J Monet Econ 50:49–123 Auty RM (1990) Resource-based industrialization: sowing the oil in eight developing countries. Clarendon Press, Oxford Auty RM (1993) Sustaining development in mineral economies: the resource curse thesis. Oxford University Press, New York Auty RM (2001) Resource abundance and economic development. Oxford University Press Arezki R, Blanchard O (2014) Seven questions about the recent oil price slump. IMFdirect (blog). 22 December 2014. Washington: International Monetary Fund. https://www.imf.org/en/Blogs/ Articles/2014/12/22/seven-questions-about-the-recent-oil-price-slump. Accessed November 3, 2022 Bardhan P (1966) Optimal foreign borrowing. In: Shell K (ed) Essays in the theory of optimal growth. The MIT Press, Cambridge, pp 117–128 Beltratti A, Chichilnisky G, Heal G (1993) Sustainable growth and the green golden rule. NBER Working paper series: 4430. Available via NBER. https://www.nber.org/system/files/working_ papers/w4430/w4430.pdf Beltratti A, Chichilnisky G, Heal G (1994) The environment and the long run: a comparison of different criteria. Ric Econ 48(4):319–340 Bhandari JS, Haque NU, Turnovsky SJ (1990) Growth, external debt, and sovereign risk in a small open economy. IMF Staff Pap 37(2):388–417 Blanchard OJ (1983) Debt and the current account deficit in Brazil. NBER Chapters. National Bureau of Economic Research, Inc. Available via NBER. http://www.nber.org/books/arme83-1. Accessed 3 November 2022 Blanchard OJ, Fischer S (1989) Lectures on macroeconomics. The MIT Press, Cambridge, British Petroleum Bruno M (1967) Optimal patterns of trade and development. Rev Econ Stat 49(4):545–554 Bruno M, Sachs J (1982) Energy and resource allocation: a dynamic model of Dutch disease. Rev Econ Stud 49:845–859 Buffie EF, Krause AS (1989) Mexico 1958–86: From Stabilizing Development to the Debt Crisis. In: Sachs JD (ed) Developing Country Debt and the World Economy. University of Chicago Press, pp 141–168 Chen Y, Rogoff K (2003) Commodity currencies. J Int Econ 60(1):133–160 Cohen D (1997) Growth and external debt: a new perspective on the African and Latin American tragedies. CEPR Discussion Papers 1753. C.E.P.R. Discussion Papers Corden WM (1984) Booming sector and Dutch disease economics: survey and consolidation. Oxf Econ Pap 36(3):359–80 Corden WM, Neary JP (1982) Booming sector and de-industrialisation in a small open economy. Econ J 92(368):825–48 Cordella T, Ricci LA, Ruiz-Arranz M (2005) Debt overhang or debt irrelevance? Revisiting the debt-growth link. IMF Working Paper, 05/223 Dasgupta P (2005) Sustainable economic development in the world of today’s poor. In: Simpson RD, Toman MA, Ayres RU (eds) Scarcity and growth revisited: natural resources and the environment. The New Millennium. Taylor & Francis Dasgupta P, Heal G (1974) The optimal depletion of exhaustible resources. Symposium on the economics of exhaustible resources. Rev Econ Stud 41:3–28 Dasgupta P, Heal G (1979) Economic theory and exhaustible resources. Cambridge University Press De Gregorio J, Giovannini A, Wolf H (1994) International evidence on tradables and nontradables inflation. Eur Econ Rev 38:1225–1244 Di Bartolomeo G, Saltari E, Semmler W (2019) The effects of political short-termism on transitions induced by pollution regulations, EconStor Preprints 200143, ZBW - Leibniz Information Centre for Economics

References

41

Eastwood RK, Venables AJ (1982) The macroeconomic implications of a resource discovery in an open economy. Econ J 92(366):285–99 Eaton J, Gersovitz M (1980) LDC participation in international financial markets: debt and reserves. J Dev Econ 7:3–21. https://doi.org/10.1016/0304-3878(80)90025-5 Eaton J, Gersovitz M (1981) Debt with potential repudiation: theoretical and empirical analysis. Rev Econ Stud 48:289–309. https://doi.org/10.2307/2296886 Edwards S (1984) LDC’s foreign borrowing and default risk: an empirical investigation, 1976–1980. Am Econ Rev 74:726–734 Edwards S (1986) A commodity export boom and the real exchange rate: the money-inflation link. In: Neary JP, Van Wijnbergen S (eds) Natural resources and the macroeconomy, 1st edn. The MIT Press Forrester JW (1971) World dynamics. Wright Allen Press, Cambridge, Mass Forsyth P (1986) Booming sectors and structural change in Australia and Britain: a comparison. In: Neary JP, Van Wijnbergen S (eds), Natural resources and the macroeconomy, 1st edn. The MIT Press Frankel J (2011) Natural resource curse: a survey of the literature. In: For international monetary fund high level seminar, Washington DC Greiner A, Semmler W (2008) The global environment, natural resources, and economic growth. Oxford University Press, USA Greiner A, Semmler W, Mette T (2012) An economic model of oil exploration and extraction. Comput Econ 40(4):387–399 Grüne L, Pannek J (2011) Nonlinear model predictive control theory and algorithms. Springer, Berlin Grüne L, Semmler W, Stieler M (2015) Using nonlinear model predictive control for dynamic decision problems in economics. J Econ Dyn Control 60:112–133 Hamilton K (2001) The sustainability of extractive economies. In: Auty RM (ed) Resource abundance and economic development. Oxford University Press Harberger AC, Smith GW, Cuddington JT (1985) Lessons for debtor-country managers and policymakers. In: International debt and the developing countries, World Bank Harding T, Venables AJ (2016) The implications of natural resource exports for nonresource trade,. IMF Econ Rev 64(2):268–302. Palgrave Macmillan; International Monetary Fund IMF (2022) Primary commodity price system. https://www.imf.org/en/Research/commodityprices. Accessed 12 November 2022 IMF (2015a) 2015 spillover report (Washington). July 23. 2015. http://www.imf.org/external/np/ pp/eng/2015/060815.pdf. Accessed 19 August 2015 IMF (2015b) World economic outlook, Chapter 4: private investment: what’s the holdup?. April (Washington). https://www.imf.org/~/media/Websites/IMF/imported-flagship-issues/external/ pubs/ft/weo/2015/01/pdf/_c2pdf.ashx. Accessed 8 January 2015 Leamer EE (1984) Sources of international comparative advantage: theory and evidence. MIT Press, Cambridge MA Lederman D, Maloney WF (2003) Trade structure and growth. Policy research working paper series 3025. The World Bank Maloney WF (2002) Missed opportunities - Innovation and resource-based growth in Latin America. Policy research working paper series 2935. The World Bank Mansoorian A (1991) Resource discoveries and ‘excessive’ external borrowing. Econ J 101(409):1497–1509 Manzano O, Rigobon R (2001) Resource curse or debt overhang? NBER working paper 8390. National Bureau of Economic Research, Inc Mcmahon G (1997) The natural resource curse: myth or reality? Economic Development Institute, the World Bank, Washington, D.C Meadows DH, Meadows DL, Randers J, Behrens WW (1972). The limits to growth: a report for the club of rome’s project on the predicament of mankind. Earth Island

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Mittnik S, Semmler W (2014) Overleveraging, financial fragility and the banking-macro link: theory and empirical evidence. ZEW Discussion Paper No. 14-110 Neary JP, Van Wijnbergen S (1986) Natural resources and the macroeconomy: a theoretical framework. In: Neary JP, Van Wijnbergen S (eds) Natural resources and the macroeconomy, 1st edn. The MIT Press Nyambuu U (2016) Foreign exchange volatility and its implications for macroeconomic stability: an empirical study of developing economies. In: Bernard L, Nyambuu U (eds) dynamic modeling, empirical macroeconomics, and finance: essays in honor of Willi Semmler. Springer International Publishing, pp 163–182 Nyambuu U (2017) Financing sustainable growth through energy exports and implications for human capital investment. In: Bettina B, Greiner A (eds) Dynamic modeling and econometrics in economics and finance. Springer International Publishing, Inequality and Finance in Aerodynamic, pp 191–220 Nyambuu U, Bernard L (2015) A quantitative approach to assessing sovereign default risk in resource-rich emerging economies. Int J Finan Econ 20(3):220–241. https://doi.org/10.1002/ ijfe.1512 Nyambuu U, Semmler W (2017) Emerging markets’ resource booms and busts, borrowing risk and regime change. Struct Change Econ Dyn 41:29–42. https://doi.org/10.1016/j.strueco.2017. 02.001 Nyambuu U, Semmler W (2020) Climate change and the transition to a low carbon economy-Carbon targets and the carbon budget. Econ Model 84:367–376. https://doi.org/10.1016/j.econmod.2019. 04.026 Nyambuu U, Semmler W, Palokangas T (2014) Sustainable growth: modelling, issues and policies. International Institute for Applied Systems Analysis (IIASA) Interim Report IR-14-019 Nyambuu U, Tapiero CS (2018) Globalization, gating, and risk finance. Wiley Obstfeld M (1980) Intermediate imports, the terms of trade, and the dynamics of the exchange rate and current account. J Int Econ 10(4):461–480 Obstfeld M (1982) Aggregate spending and the terms of trade: is there a Laursen-Metzler effect? Q J Econ 97(2):251–70 Pattillo C, Poirson H, Ricci L (2002) External debt and growth. IMF Working Paper, WP 02/69. https://imf.org/external/pubs/ft/wp/2002/wp0269.pdf Prebisch R (1950) The economic development of Latin America and its principal problems. United Nations Department of Economic Affairs Reinhart MC, Rogoff SK (2010) Growth in a time of debt. Am Econ Rev 100: 573–578. https:// doi.org/10.1257/aer.100.2.573 Sachs JD, Cooper R, Fischer S (1981) The current account and macroeconomic adjustments in the 1970s. Brook Pap Econ Act 1981(1):201–282 Sachs JD (1981) The current account and macroeconomic adjustment in the 1970s. Brook Pap Econ Act 12(1):201–282 Sachs JD (1982) The current account in the macroeconomic adjustment process. Scand J Econ 84(2):147–159 Sachs JD (1989) The debt overhang of developing countries. In: Calvo GA, Findlay R, Kouri P, De Macedo JB (eds) Debt stabilization and development: essays in memory of Carlos Diaz Alejandro. Basil Blackwell, Oxford Sachs JD, Cohen D (1982) LDC Borrowing with default risk. National Bureau of Economic Research Working Paper No. 925. Available via NBER. https://nber.org/system/files/working_ papers/w0925/w0925.pdf Sachs JD, Warner AM (1995) Natural resource abundance and economic growth. NBER Working Paper 5398. National Bureau of Economic Research, Inc.; Sachs JD, Warner AM (1999) The big push, natural resource booms and growth. J Dev Econ 59(1):43–76 Sachs JD, Warner AM (2001) The curse of natural resources. Eur Econ Rev 45:827–838

References

43

Sala-i-Martin X, Subramanian A (2003) Addressing the natural resource curse: an illustration from Nigeria. National Bureau of Economic Research Working Paper 9804. Available via NBER. https://nber.org/system/files/working_papers/w9804/w9804.pdf Saltari E, Semmler W, Di Bartolomeo G (2021) A Nash equilibrium for differential games with moving-horizon strategies. Comput Econ 1–14 Semmler W, Proaño RC (2015) Escape routes from Sovereign default risk in the Euro area, Monetary policy in the context of the financial crisis: new challenges and lessons. In: International symposia in economic theory and econometrics, vol 24. Emerald Group Publishing Limited, Bingley. pp 163–193 Semmler W, Sieveking M (2000) Critical debt and debt dynamics. J Econ Dyn Control 24(5– 7):1121–1144 Singer H (1950) The distribution of gains between investing and borrowing countries. Am Econ Rev 4(2):473–485 Smith B (2004) Oil wealth and regime Survival in the developing world, 1960–1999. Am J Polit Sci 48(2):232–246 Solow RM (1973) Is the end of the world at hand? In: Weintraub A, Schwartz E, Aronson JR (eds) The economic growth controversy. International Arts & Sciences Press, New York Stiglitz J (1974) Growth with exhaustible natural resources: Efficient and optimal growth paths. Symposium on the Economics of Exhaustible Resources. Rev Econ Stud 41:123–137 Stijns J (2003) An empirical test of the Dutch disease hypothesis using a gravity model of trade. International Trade 0305001, EconWPA. http://dx.doi.org/10.2139/ssrn.403041 Stijns Jean-Philippe C (2005) Natural resource abundance and economic growth revisited. Resour Policy 30(2):107–130 Svensson LEO, Razin A (1983) The terms of trade and the current account: the Harberger LaursenMetzler effect. J Polit Econ 91(11):97–125 Van Der Ploeg F (1996) Budgetary policies, foreign indebtedness, the stock market, and economic growth. Oxf Econ Pap 48:382–396 Van der Ploeg F, Poelhekke S (2017) The impact of natural resources: survey of recent quantitative evidence. J Dev Stud 53(2):205–216. http://dx.doi.org/10.1080/00220388.2016.1160069 Van der Ploeg F (2011) Natural resources: curse or blessing. J Econ Lit 49(2):366–420 Van der Ploeg F, Poelhekke S (2009) Volatility and the natural resource curse. Oxf Econ Pap 61(4):727–760

Chapter 4

Fossil Fuel Resources, Environment, and Climate Change

Overview In this chapter, we address the link between CO2 emissions and the extraction and use of fossil fuels. Quite reasonably, many economists presume that CO2 emissions result from economic activity. Thus, it is essential that we study fossil fuel resources, their reserves, their discovery, their extraction, and their consumption. The magnitude and vicissitudes of these processes affect society’s welfare and environment and are the main drivers of climate change. We study carbon-generating fuels, particularly oil, gas, and coal, and discuss the negative externalities thus created. Historical trends in such resource usage and demand are addressed together with resource prices, particularly oil, and their effects on the economy.

4.1 Global Trend in Temperature—The Climate Impact of Fossil Fuels The Earth Policy Institute’s1 compilation using data from NASA shows that average global temperatures have been rising over time: from 13.79 ◦ C in 1880 to 14.68 ◦ C in 2014. The Global Land-Ocean Temperature Index is provided by NASA’s Goddard Institute for Space Studies (GISS)2 ; it describes the change in global temperature compared to a base period, 1951–1980, where an annual anomaly was recorded at 0.85 ◦ C above normal in 2021 (see NASA/GISS 2022, for more detail). As illustrated

1

Earth Policy dataset, http://www.earth-policy.org/data_center/C26. Accessed on April l9, 2021. See NASA/GISS (2022) on Land-Ocean Temperature Index available: https://climate.nasa.gov/ vital-signs/global-temperature/. Accessed on November 9, 2022.

2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_4

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Fig. 4.1 Global Temperature Anomalies. Source Constructed using data from National Oceanic and Atmospheric Administration (NOAA). In this figure, global land and ocean temperature anomalies are illustrated between 1880 and 2021. These are shown in Celsius

in Fig. 4.1, historical data for land as well as ocean temperature anomalies estimated by National Oceanic and Atmospheric Administration (NOAA) also confirm this trend.3 As mentioned previously, the International Panel on Climate Change (IPCC)4 and other researchers have extensively reported the slowly rising global temperatures relative to the period of pre-industrialization. In parallel, we have now also experienced more frequent and more extreme weather events and climate-related disasters; see Mittnik et al. (2019). We have also discussed how there might be tipping points wherein those extreme events may begin to accelerate, e.g., more severe droughts, bringing down agricultural and manufacturing productivity, and an increased incidence of undesirable environmental changes. The IPCC (2014, 2018, 2019) has addressed the negative impacts of global warming on food production and risks to food security. As to the environmental effects, some scientists predict long-run changes threatening the stability of ecosystems, e.g., coastal and ocean habitation, forest reduction, species extinction, and new variants of infectious diseases. This is all supported by quantitative calculations by the IPCC (2014) that found that local warming of 1 ◦ C to 2 ◦ C causes a decline in yields of certain crops including maize (corn) and wheat while temperature warming of 3 ◦ C to 5 ◦ C is predicted to lead to a significant drop in yields of rice and other crops. Rising temperatures will have a more adverse impact on agricultural output, capital accumulation, and the health of low-income developing countries; this was also shown in a quantitative study conducted by the IMF (2017). 3

In Chap. 7, we examine seasonal historical temperature in selected countries (France, Spain, the United Kingdom, and the United States) between 1901 and 2021. 4 IPCC’s Special report on “Global Warming of 1.5 ◦ C” presents a figure on historical change in global temperature compared to pre-industrial levels of 1850–1900 (see Allen et al. (2018) from the link: https://www.ipcc.ch/sr15/chapter/chapter-1/).

4.1 Global Trend in Temperature—The Climate Impact of Fossil Fuels

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The major sources that contribute to the CO2 emission are connected with the use of fossil fuels; these are filling up the earth’s carbon budget. As to the different sources contributing to the carbon budget, electricity and heat generation caused large CO2 emissions and account for around 40% (see International Energy Agency (IEA) 2020a). Friedlingstein et al. (2022) estimate that fossil fuels are overwhelmingly the largest contributors to global carbon emissions, mainly due to the consumption (burning) of solid and liquid fuels. Yet what are the major sources of fossil fuel generating the carbon emission? According to the IPCC Guidelines (2006), in terms of tons of carbon/terajoule coal (tC/TJ), primary coal has the highest carbon intensity with a carbon emission factor of 25.8–29.1 tC/TJ, followed by oil (15.7–26.6 tC/TJ) and gas (15.3 tC/TJ). This is the reason why coal is the greatest contributor to the global emissions from fossil fuel, accounting for around 42%, whereas shares of oil and natural gas were only 36% and 22%, respectively. We also observe that total CO2 emissions from fossil fuels have risen globally, surging since the 1950s. It increased from 1.6 billion tons of carbon in 1950 to 9.4 billion tons of carbon in 2019 (see Panel (a) of Fig. 4.2). The total share of global carbon emissions from coal is still significant and much higher than that for oil or natural gas. CO2 emissions in advanced countries, e.g., the United States, started accumulating much earlier than, as currently defined, developing countries, e.g., India (see Panel (b) of Fig. 4.2). However, due to developing countries’ industrial development since late 1980s, CO2 emissions have risen even more. For example, we observe sharp increases in emissions especially in countries, e.g., China, driven mainly by the consumption of coal. Since the 1990s, this has led to high externalities together with great environmental costs. According to the World Energy Investment report by the IEA (2021a), world investment in coal supplies that increased from $84 billion in 2018 to $99 billion in 2019 started declining in 2020 reaching around $90 billion. While China accounted for almost 70% of this investment, India’s share was around 11%. In Chap. 7, we will analyze emissions for different advanced and developing countries, also accounting for low per capita emissions in China and India due to their large populations. For electricity generation, calculations by Hultman et al. (2011) based on midrange assumptions show that shale gas has less GHG emissions compared to that of conventional gas and coal. The importance of natural gas is also discussed in Kerr (2010), Levi (2013), Gevorkyan and Semmler (2016), and Nyambuu and Semmler (2020). Lowering the emissions from fossil fuels would significantly contribute to the achievement of the temperature limit. Different target levels of emissions in distinct regions should be set to reduce the cumulative CO2 emissions over time (see Nyambuu and Semmler 2020). This issue will be taken up again in Chap. 7.

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Fig. 4.2 Global Carbon Emissions. Source Constructed using data from Boden et al. (2017), Global Carbon Project (2021), Friedlingstein et al. (2022) (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy). In these figures, carbon emissions from the consumption of fossil fuel are shown in billions of metric tons of carbon. While Panel (a) demonstrates global emissions between 1903 and 2020, Panel (b) shows emissions for selected countries: China, India, Japan, and the United States. Note: Values in carbon are multiplied by 3.664 to express them in terms of carbon dioxide (CO2 )

4.2 Reserves and Production of Fossil Fuels Compared to crude oil and natural gas, coal has abundant proven reserves.5 At the end of 2018, the BGR (2020) reports world coal reserves of 1070 Gt with the greatest fraction of reserves in hard coal (almost 70%) and the remainder in lignite; the United States has the largest reserves of hard coal (~220 Gt), followed by China (~133 Gt), India (~101 Gt), and Australia (~73 Gt). BGR data also show that Australia and Asia account for 46% of world hard coal reserves, followed by North America (30%). 5

The BGR (2020) describes proven reserves as “proven volumes of energy resources economically exploitable at today’s prices and using today’s technology” (p. 192).

4.2 Reserves and Production of Fossil Fuels

49

Using the BGR data and 2019 consumption data, the IEA (2020b) calculated that China has coal reserves of almost 40 years and coal resources of almost 1400 years.6 According to the U.S. Energy Information Administration (EIA) (2021), recoverable reserves for the United States totaled 13.2 billion tons, and the Demonstrated Reserve Base (DRB) amounted to 471.8 billion tons of coal with estimated recoverable coal reserves of 251.5 billion tons as of January 1, 2021.7 Historically, high demand for these resources encourages exploration for them and their development; this is illustrated in Fig. 4.3. The share of coal in global fossil energy consumption remains high. The top coal producers include China, the United States, India, and Australia, as well as developing countries, e.g., Indonesia, Russia, and South Africa. Fossil fuel consumption’s share in total energy shows a sharp drop due to the usage of renewable energy. According to the EIA, natural gas plays an important role in electricity generation. Asian countries still consume a large amount of coal; more than half of world’s coal in 2019 was consumed by China (see Fig. 4.4). On the one hand, globally, industrial coal consumption, which had been rising sharply since around 2000, has showed a moderately declining trend in recent years. On the other hand, natural gas has become a popular choice in both industry and residential consumption (see Fig. 4.5). As expected, oil product consumption in transportation has surged over time. In Figs. 4.6 and 4.7, we show proved reserves8 as well as the reserve-to-production (R/P) ratios for different types of fossil fuels including coal and oil. The data are from the British Petroleum (BP) Statistical Review of World Energy (2021). The R/P9 reveals the time to exhaustion in terms of the number of years. Global coal based on data from BP (2021) indicates that it would last 139 years at the current level of production, and its proved reserves amounted to 1.1 trillion tons at the end

6

According to IEA (2020b), resources are defined as “proven energy resources as well as unproven but geologically possible resources that may be exploitable in the future” (p. 18). 7 The EIA (n.d.) explains on their website that the “recoverable reserves at producing mines represent the quantity of coal that can be recovered (mined) from existing coal reserves at active mines” and “demonstrated reserve base is composed of coal resources that have been identified to specified levels of accuracy and that may support economic mining under current technologies”. As for the estimated recoverable reserves, it consists of “coal in the demonstrated reserve base considered recoverable after excluding coal estimated to be unavailable because of land-use restrictions, and after applying assumed mining recovery rates”. (see “How large are U.S. coal reserves?” n.d., para. 1–4, retrieved from https://www.eia.gov/tools/faqs/faq.php?id=70&t=2). 8 Society of Petroleum Engineers (SPE) (2007) defines the proved reserves as “quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions. Reserves must further satisfy four criteria: they must be discovered, recoverable, commercial, and remaining (as of the evaluation date) based on the development project(s) applied” (p. 3). 9 According to the BP (2021), reserves-to-production shows the “length of time that those remaining reserves would last if production were to continue at that rate” (p. 16) (Statistical Review of World Energy: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/ energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf.).

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Fig. 4.3 World Fossil Fuel Production and Final Consumption. Source Constructed using data from International Energy Agency (IEA) (2021b) on World Energy Balances. In Panel (a) of this figure, world production of coal, crude oil, and natural gas is illustrated between 1971 and 2020. Panel (b) shows the final consumption for these fossil fuel sources between 1972 and 2019. Data are measured in millions of tons of oil equivalencies (TOE)

of 2020. We can observe that coal-producing countries have large reserves and very high R/P ratios as shown in Panel (a) of Fig. 4.6. While coal would last 407 years in Russian Federation, 514 years in the United States, and 315 years in Australia, China has only 37 years of coal because of its massive use of coal in industrial production. On the one hand, historical data shown in Panel (b) of Fig. 4.6 illustrate an increasing trend in coal production in China, Australia, Indonesia, Russian Federation, and India. In particular, China’s coal production, which was already on the increase since 1980s, reveals accelerated growth in the 1990s, thus significantly contributing to world production. On the other hand, coal production in the United States started declining during the last ten years. For oil, BP (2021) data show that world oil reserves reached 1.7 trillion barrels at the end of 2020, which has a R/P ratio

4.2 Reserves and Production of Fossil Fuels

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Fig. 4.4 Coal Consumption in Major Coal-Consuming Countries. Source Constructed using data from International Energy Agency (IEA) (2021b) on World Energy Balances. This figure illustrates coal consumption between 1991 and 2019 not only for the world, but also for selected countries: Africa, China, India, Indonesia, Japan, South Africa, and the United States. Note that data for China and world are plotted on the right axis. Data are measured in million tons of oil equivalencies (TOE)

Fig. 4.5 Industry and Residential Final Consumption of Fossil Fuel. Source Constructed using data from International Energy Agency (IEA) (2021b) on World Energy Balances. This figure illustrates the global final consumption of coal, oil products, and natural gas in the industry (labeled as Industry), the residential sector (labeled as Residential), as well as in transportation (labeled as Transportation) between 1971 and 2019. Note that data for oil product consumption in the transportation sector are plotted on the right axis. Data are measured in millions of tons of oil equivalencies (TOE)

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Fig. 4.6 Coal Reserves, Production, and Reserve-to-Production (R/P) Ratios. Source Constructed using data from British Petroleum (BP) Statistical Review of World Energy (2021, 2022). Panel (a) of this figure shows proven coal reserves, measured in billions of tons, as well as reserve/production (R/P) ratios, measured in years, at the end of 2020. In addition to the World, data are depicted for selected countries: Australia, China, India, and the Russian Federation. Panel (b) shows coal production, measured in millions of tons, between 1981 and 2021. Note that World production data are plotted on the right axis

of 53.5 (see Fig. 4.7). It is dominated by major producers, including Venezuela and Saudi Arabia, with excessive oil reserves. In terms of production, the United States has been leading with a significant increase that amounted to 16.6 million barrels per day in 2021 followed by Saudi Arabia and Russian Federation with daily production of around 11 million daily barrels each (see BP, 2022).

4.3 Gains and Losses from Changes in Resource Prices

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Fig. 4.7 Crude Oil Reserves and Reserve-to-Production (R/P) Ratios. Source Constructed using data from British Petroleum (BP) Statistical Review of World Energy (2021). Panel (a) of this figure shows proven oil reserves between 1980 and 2020 for not only the World, but also for selected countries: Canada, Iran, Iraq, Kuwait, the Russian Federation, Saudi Arabia, the United States (USA), the United Arab Emirates (UAE), and Venezuela. Note that data for the World reserves and Venezuela’s R/P ratios are shown on the right axis. These data are measured in billions of barrels. In Panel (b), oil reserve/production (R/P) ratios, measured in years, are depicted between 1992 and 2020

4.3 Gains and Losses from Changes in Resource Prices Prices of non-renewable energy and metals are highly volatile as compared to other commodities, e.g., agricultural goods. In Chap. 3, we illustrated the fluctuations in energy prices, e.g., crude oil, coal, and natural gas, based on monthly data. Historically, as shown in Figs. 3.2 and 4.8, driven by global population growth, rising living standards, and by industrial development, infrastructure, and construction, prices of crude oil, natural gas, coal, copper, iron ore, nickel, gold, and cobalt have increased. Resource booms and busts, and the problems of price fluctuations, external debt, and borrowing costs were addressed in Chap. 3. To demonstrate some of the effects of lower oil prices on both the domestic and the world economy, we constructed Fig. 4.9. Possible gains and losses from low oil prices are described in terms of macroeconomic and microeconomic effects.

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Fig. 4.8 Historical Monthly Prices for Metals (1980–2022). Source Constructed using data from IMF (Primary Commodity Price System). In this figure, monthly metal commodity prices between 1980 (January M1) and 2022 (September M9) are shown. Aluminum (UK ports), cobalt (U.S. spot price), and nickel (European ports) are measured in U.S. dollars (US$) per metric ton, and gold (London Bullion Market Association) is measured in U.S. dollars per troy ounce. Copper spot prices (European ports) and Iron ore, imported to China through the port of Tianjin, are measured in U.S. dollars per metric ton and are shown on the right axis

As shown in Fig. 4.9, oil importers, e.g., Japan, India, and the EU, would benefit from lower costs of imported oil which can, in general, lead to lower inflation rates. For households and firms, they would pay lower prices for gasoline, their demand for automobiles would increase, and production costs would be lower. Low oil prices have a negative impact on oil exporters, as it leads to lower export revenues from the sale of resources. As shown in Chap. 3, because these countries are highly dependent on these revenues, fiscal deficits would arise and economic growth would be negatively affected, and financial crises could emerge in certain cases. Obviously, exchange rates also have an impact on both oil-exporting and importing countries; this can be, arguably, exacerbated as many commodity contracts are typically denominated in U.S. dollar (USD). A strong U.S. dollar coupled with a sharp drop in oil prices has led to the fall in the oil-dependent economies’ currencies, e.g., Nigerian and Russian currencies. Many resource-rich developing countries

4.3 Gains and Losses from Changes in Resource Prices

55

Fig. 4.9 Effects of Low Oil Prices on World Economy and Global Finance. This figure shows economic and financial gains and losses resulting from low oil prices. Both macroeconomic and microeconomic effects are highlighted. For the gains, we showed the possible impact on oil importing countries as well as on oil consumers. For the losses, we listed the possible effects on oilexporting countries as well as on oil-producing firms. Oil importers would benefit from lower costs of imported oil, which, in turn, can help to curb inflation. Due to low oil prices, oil exporters might have lower export revenues, which would cause fiscal deficits especially for the resource-dependent countries

have foreign debt denominated in a foreign currency, thus their liabilities denominated in foreign currency rise and borrowing costs and default risks jump, possibly triggering a currency crisis. After the recent oil price plunge, as in the COVID-19 times in 2020, a number of oil companies experienced default threats due to depressed earnings, lower sales, a fall in share prices, increased debt, and elevated default risk. In addition to the pandemic-related lockdowns, many exploration and production workers in the mining industry were laid off and investment projects were cut or postponed. This supply shock, entailing a reduced capacity to supply fossil fuel resources, gave rise to price run-ups during 2021–2022. Resource-dependent countries—particularly fossil fuel-dependent countries—have become heavily exposed to boom-bust cycles and are likely to experience price peaks, thus impacting inflation rates. A more detailed analysis of long-term trends in real prices of different nonrenewable resources, along with their dynamic paths, and the modeling of those in resource extraction models are provided in Chap. 5.

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4.4 Conclusion The production and consumption of non-renewables have shown a strong upward trend; this was mostly fueled by high demand from both domestic and foreign sources. In global fossil energy consumption, coal still represents a significant share with high levels of production in China and in some developing economies. According to BP data, proven reserves of coal would last over hundred years at current production rates, but highly carbon-intensive coal generates significant negative externalities in the form of CO2 emissions, which affect global temperatures and climate change. We should note that the replacement of carbon-intensive resources with renewable energy is likely to be challenging. For this effort, natural gas has been seen to be a transitional energy source, especially with the development of hydraulic fracturing techniques as discussed in Gevorkyan and Semmler (2016). According to the EIA (2022) estimates, U.S. gas production by 2050 will be dominated by unconventional gas, e.g., shale gas, supplies. To combat climate change and global warming, different strategies and GHG emission targets should be set differently for advanced countries and for developing countries; we will discuss this in Chap. 7. During the pandemic (COVID-19), oil companies were hit hard, demanding subsidies but also laying off workers. Yet, with increasing environmental awareness and the support of large investments in environmentally friendly renewable energy and climate-related infrastructure projects, new replacement jobs can be created for the transformation to a low-carbon economy.10 A recent report by the Global Commission on the Economy and Climate (2018) stresses the benefits from following a “sustainable growth path,” implying avoidance of resource volume and price volatility, and describes many of the economic opportunities, related to climate action, that could generate substantive economic gain. These issues will be explored in the subsequent chapters.

References Allen MR, Dube OP, Solecki W, Aragón-Durand F, Cramer W, Humphreys S, Kainuma M, Kala J, Mahowald N, Mulugetta Y, Perez R, Wairiu M, Zickfeld K (2018) Framing and Context. In: Global Warming of 1.5◦ C. An IPCC Special Report on the impacts of global warming of 1.5 ◦ C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty In Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5 ◦ C. An IPCC special report on the impacts of global warming of 1.5◦ C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty 1(5)

10

See Kato et al. (2015).

References

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BGR (2020) BGR Energy Study 2019 - Data and Developments Concerning German and Global Energy Supplies (23). Hannover. https://www.bgr.bund.de/EN/Themen/Energie/Downloads/ energiestudie_2019_en.pdf. Accessed 3 Nov 2022 Boden TA, Marland G, Andres RJ (2017) Global, regional, and national fossil-fuel CO2 emissions. Carbon Dioxide information analysis center, oak ridge national laboratory, US Department of Energy, Oak Ridge, Tenn, USA. https://doi.org/10.3334/CDIAC/00001_V2017 British Petroleum BP (2021) BP Statistical Review of World Energy 2021. https://www.bp.com/en/ global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed 26 Dec 2021 British Petroleum (BP) (2022) BP Statistical Review of World Energy 2022. https://www.bp. com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed 3 Aug 2022 Friedlingstein P, Jones MW, O’Sullivan M, Robbie MA, Dorothee CEB, Judith H, Corinne LQ, Glen PP, Wouter P, Julia P, Stephen S, Josep GC, Philippe C, Rob BJ, Simone RA, Peter A, Nicholas RB, Meike B, Nicolas B, Laurent B, Thi TTC, Frédéric C, Louise PC, Margot C, Kim IC, Bertrand D, Laique MD, Xinyu D, Wiley E, Richard AF, Liang F, Thomas G, Dennis G, Thanos G, Giacomo G, Luke G, Nicolas G, Özgür G, Ian H, Richard AH, George CH, Yosuke I, Tatiana I, Ingrid TL, Atul J, Steve DJ, Etsushi K, Daniel K, Kees KG, Jürgen K, Jan IK, Arne K, Peter L, Siv KL, Nathalie L, Sebastian L, Junjie L, Gregg M, Patrick CM, Joe RM, David RM, Nabel JEMS, Nakaoka S-I, Niwa Y, Ono T, Pierrot D, Poulter B, Rehder G, Resplandy L, Robertson E, Rödenbeck C, Rosan TM, Schwinger J, Schwingshackl C, Séférian R, Sutton AJ, Sweeney C, Tanhua T, Tans PP, Tian H, Tilbrook B, Tubiello F, van der Werf G, Vuichard N, Wanninkhof CWR, Watson AJ, Willis D, Wiltshire AJ, Yuan W, Yue C, Yue X, Zaehle S, Zeng J (2022) Global Carbon Budget 2021. Earth Syst Sci Data 14:1917–2005. https://doi.org/10.5194/ essd-14-1917-2022. Accessed 3 Aug 2022 Gevorkyan A, Semmler W (2016) Oil price, overleveraging and shakeout in the shale energy sector– Game changers in the oil industry. Econ Model 54:244–259 Global Carbon Project (2021) Supplemental data of Global Carbon Project 2021 (Version 1.0) [Data set]. Global Carbon Project. https://doi.org/10.18160/GCP-2021. Accessed 3 Aug 2022 Global Commission on the Economy and Climate, 2018. The New Climate Economy. https:// newclimateeconomy.report/2018/executive-summary/. Accessed October 20, 2022 Hultman N, Rebois D, Scholten M, Ramig C (2011) The greenhouse impact of unconventional gas for electricity generation. Environ Res Lett 6(4):044008. http://dx.doi.org/10.1088/1748-9326/ 6/4/049504 International Energy Agency (IEA) (2020a) CO2 Emissions from Fuel Combustion. https://www. iea.org/data-and-statistics. Accessed 10 Dec 2020 International Energy Agency (IEA) (2020b) Coal 2020: analysis and forecast to 2025. https://www. iea.org/reports/coal-2020. Accessed 22 Feb 2021 International Energy Agency (IEA) (2021a) World Energy Investment report. https://www.iea.org/ reports/world-energy-investment-2021. Accessed 16 Aug 2021 International Energy Agency (IEA) (2021b) World Energy Balances (database). https://www.iea. org/data-and-statistics/data-product/world-energy-balances-highlights. Accessed 3 Aug 2022 IMF. Primary Commodity Price System. https://www.imf.org/en/Research/commodity-prices. Accessed 12 Nov 2022 IMF (2017) IMF World Economic Outlook, Oct 2017: seeking sustainable growth: short-term recovery, long-term challenges. International Monetary Fund, Washington DC, USA IPCC Guidelines (2006). http://www.ipcc-nggip.iges.or.jp/EFDB/main.php. Accessed 7 Nov 2022 IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the fifth assessment report of the intergovernmental panel on climate change [Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE]. https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap7_FINAL.pdf. Accessed 7 Nov 2022

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IPCC (2018) Rogelj J, Shindell D, Jiang K, Fifita S, Forster P, Ginzburg V, Handa C, Kheshgi H, Kobayashi S, Kriegler E, Mundaca L, Séférian R, Vilariño MV (2018) Mitigation pathways compatible with 1.5◦ C in the context of sustainable development. In: Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5 ◦ C. An IPCC Special Report on the impacts of global warming of 1.5◦ C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, pp 93–174. https://www.ipcc.ch/site/ assets/uploads/sites/2/2019/05/SR15_Chapter2_High_Res.pdf. Accessed 7 Nov 2022 IPCC (2019) Mbow C, Rosenzweig C, Barioni LG, Benton TG, Herrero M, Krishnapillai M, Liwenga E, Pradhan P, Rivera-Ferre MG, Sapkota T, Tubiello FN, Xu Y, (2019) Food security. In: Shukla PR, Skea J, Buendia EC, Masson-Delmotte V, Pörtner H-O, Roberts DC, Zhai P, Slade R, Connors S, van Diemen R, Ferrat M, Haughey E, Luz S, Neogi S, Pathak M, Petzold J, Portugal Pereira J, Vyas P, Huntley E, Kissick K, Belkacemi M, Malley J (eds) Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, pp 437–550. https://www.ipcc.ch/site/assets/uploads/2019/11/08_Chapter-5.pdf. Accessed 7 Nov 2022 Kato M, Mittnik S, Samaan D, Semmler W (2015) Employment and output effects of climate policies. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, pp 445–476 Kerr RA (2010) Natural gas from shale bursts onto the scene. Science 328:1624–1626 Levi M (2013) Climate consequences of natural gas as a bridge fuel. Clim Change 118(3):609–623 Mittnik S, Semmler W, Haider A (2019) Climate disaster risks–empirics and a multi-phase dynamic model. International Monetary Fund. Working Paper No 2019/145. Published as: Mittnik S, Semmler W, Haider A (2020) Climate disaster risks—empirics and a multi-phase dynamic model. Econometrics 8(3):1–27 NASA/GISS. 2022. Land-Ocean Temperature Index. https://climate.nasa.gov/vital-signs/globaltemperature/. Accessed 2 Sep 2022 Nyambuu U, Semmler W (2020) Climate change and the transition to a low carbon economy— Carbon targets and the carbon budget. Econ Model 84:367–376. https://doi.org/10.1016/j. econmod.2019.04.026 Society of Petroleum Engineers (SPE) (2007) Petroleum Resources Management System. https:// www.spe.org/industry/docs/Petroleum-Resources-Management-System-2007.pdf. Accessed 2 Sep 2022 US Energy Information Administration (EIA) (2021). How large are US coal reserves?. https:// www.eia.gov/tools/faqs/faq.php?id=70&t=2. Accessed 2 Sep 2022 US Energy Information Administration (EIA) (2022) Annual Energy Outlook 2022. https://www. eia.gov/outlooks/aeo/index.php. Accessed 1 Sep 2022 World Energy Council (WEC), 2016. World Energy Resources 2016. https://www.worldenergy. org/data/resources/resource/coal/. Accessed 10 Nov 2017

Chapter 5

Limits on the Extraction of Fossil Fuels

Overview In this chapter, we present a study of non-renewable resources and their optimal extraction as it was proposed by Hotelling (1931) together with the predictions of their price movements over time. We contrast Hotelling’s predictions with the actual price movements. While Hotelling mostly assumes competitive markets, we use a monopolistic market structure. Empirical facts are replicated by presenting numerical solutions of the dynamic paths for the extraction of resources, price movements, and their impact on the environment and sustainable growth. Most importantly, we detect U-shaped patterns and monotonically increasing trends in resource prices in later periods; these are associated with a decline in discoveries and availability of reserves.

5.1 Resource Extraction, Its Management, and Prices Reserves of non-renewable resources can only last for a limited time. With rising production and use, the extraction of the stocks of those resources would typically reach a peak and then become depleted. A frequently used measure of the time to exhaustion is the reserves-to-production ratio, which shows a decreasing trend for certain resources, especially in the long run; those ratios for fossil fuels, e.g., oil and natural gas, are much lower than for the coal.1 While worldwide coal has a reserves-to-production ratio of 139 years at the 2020 level of production, crude oil has 53.5 years—based on BP (2021) data. Historical data that were analyzed in Chap. 4 illustrate an increasing trend in coal production 1

Later in Chap. 7, we will focus on damages arising from coal and model their environmental effects in a sustainable growth model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_5

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in many coal-producing countries. In particular, China’s coal production surged in the 1980s from 0.6 billion tons to 4.1 billion tons in 2021 (see BP 2022). World oil reserves reached 1.7 trillion barrels at the end of 2020; this was dominated by major oil producers including Venezuela and Saudi Arabia, with shares of 17.5% and 17.2% in total world reserves, respectively, according to BP (2021) statistics. Other oil producers’ reserve shares are calculated as follows: Canada—9.7%, Iran—9.1%, Iraq—8.4%, Russian Federation—6.2%, Kuwait—5.9%, the UAE— 5.6%, and the United States—4%. Due to innovations in oil production technology,2 the U.S. supply has been rising and reached 16.6 million barrels per day in 2021, followed by Saudi Arabia (11 million) and Russian Federation (10.9 million) (see BP 2022). The extraction of exhaustible resources, and their scarcity, can be related to the extraction costs and price movements in the long run. This has been explored in various studies that suggested an inverse relationship between the resource price and the scarcity level. Early studies, including Hotelling 1931 and Barnett and Morse 1963, are classic papers in this area. To measure the scarcity of resources, economists often use indicators such as resource price, extraction cost, and user cost. While resource prices have been generally rising in the long run, the extraction rates decline as time passes. However, prices of these resources are highly volatile when compared to, for example, manufacturing and even agricultural products. The nominal and real prices for crude oil and U.S. natural gas are illustrated in Fig. 5.1. We note that the inflation-adjusted value of oil prices seems to show a Ushaped pattern. In recent decades, other empirical data suggest an increasing long-run price trend. Additionally, the Annual Energy Outlook of the EIA (2022b) predicts that prices for all types of fossil fuels will continue rising through 2050. In particular, crude oil spot prices are anticipated to increase to around $170/barrel under the “High Oil Price” scenario, or $90 based on “Reference” conditions; however, predictions under the “Low Oil Price” scenario would be around $45.3 There is a vast literature on trends in the long-run prices of extractive resources.4 While some studies suggest a rising price trend, others present a falling trend, or even a U-shaped pattern. The increasing trend in resource prices was discussed in Hotelling (1931), Solow (1974) and others. As expected, due to the exhaustibility of non-renewable resources, prices tend to increase as their scarcity rises. Barnett and Morse’s (1963) theoretical model and empirical findings suggested a decreasing scarcity of mineral resources. Most importantly, resource prices can be affected by a monopolist who has control of a resource supply. For example, in many developing countries, resource companies are owned by the state, and partly by affiliates of foreign companies. According to UNCTAD (2017), state-owned multinational enterprises were concentrated in the natural resource sector and they have been gradually international2

See the U.S. Energy Information Administration’s (EIA 2022a) analysis on “U.S. Oil and Natural Gas Wells by Production Rate” from https://www.eia.gov/petroleum/wells/. 3 See EIA (2022b), Annual Energy Outlook at https://www.eia.gov/outlooks/aeo/. 4 Nyambuu et al. (2014) provide a detailed literature review on trends in resource prices.

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61

Fig. 5.1 Nominal and real prices of oil and natural gas. Source Constructed using data from the U.S. Energy Information Administration (EIA), BP (2022), and U.S. Bureau of Labor Statistics (BLS). In this figure, nominal and real prices for crude oil and U.S. natural gas (residential) are shown. The crude oil prices are measured in U.S. dollars (US$) per barrel and the natural gas prices are measured in U.S. dollars per million cubic feet. Moving averages with a period of 8 for real prices of crude oil as well as natural gas are added. These real prices are calculated using annual U.S. Consumer Price Index (CPI) data from the U.S. BLS

ized through an increase in foreign direct investments. For example, the percentage of foreign assets in total assets in mining, quarrying, and petroleum firms average around 4–6%, which is significantly lower than other industries. In some countries, state-owned mining companies hold large stakes in mining assets, e.g., China’s National Petroleum Corp, Russia’s Gazprom JSC, Petroleo Brasileiro SA, Petróleos de Venezuela SA, Malaysia’s Petronas—Petroliam Nasional Bhd, and Algeria’s Sonatrach. Thus, the transition to a low-carbon economy could hold if appropriate public sector policies are pursued. As to the price trends of resources, certain studies did not find an upward trend of the resource price over time (see Barnett and Morse (1963); and Barnett 1979). Other studies that used more recent data, such as by Sullivan et al. (2000), also claimed a continuous decrease in resource prices. On the other hand, yet studies,

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e.g., Livernois and Uhler 1987, Pindyck (1978), Slade (1982), Liu and Sutinen (1982), Swierzbinski and Mendelsohn (1989) and Nyambuu and Semmler (2014) suggested a nonlinear pattern for the resource price where a decline is followed by an increase. Pindyck (1978) studied how optimally extracted resources are produced and stated that production displays an inverted U-shaped curve because of changes in discovery, reserves, and extraction. According to Liu and Sutinen (1982, pp. 159–160), when the resource exploration expands, its costs may increase, but a rise in a “known resource stock” results in higher net benefits.5 Models by Greiner et al. (2012) and Nyambuu and Semmler (2014) demonstrate that in the case of a small stock of the resource, the price can follow a U-shaped curve. Another empirical study on U-shaped price movements in the long run, conducted by Slade (1982), was based on data for fuels and metals excluding gold between 1870 and 1978. To reflect resource scarcity, a price ratio in the extractive industry as well as in the overall industry was used. Next, we focus on the modeling of extraction and price movements of exhaustible resources by presenting an extension to the Hotelling model as in Greiner et al. (2012) and Nyambuu and Semmler (2014). In these studies, a monopolistic producer is considered and extraction costs and new discoveries are included. With the discovery, the stock of proved reserves will be obviously rising and this seems to be a major driver of the price of the resource, namely going down first and then going up later. We use the numerical method of Nonlinear Model Predictive Control (NMPC)6 to solve the optimal extraction model that allows for new discoveries. In this way, some of the empirical features are replicated and dynamic paths for state variables, including proved reserves and accumulated past extraction, are presented. In particular, we can assess the dynamic paths for extraction rates and prices of resources under different scenarios.

5.2 Modeling Extraction and Price Dynamics of Non-renewable Resources We present theoretical model variants that are grounded in the above considerations. The basic Hotelling (1931) model is based on discounting future profit flows and an interest rate that is correlated to a rise in oil price. This was criticized because of certain assumptions regarding market structure and the model did not consider extraction costs. Livernois (2009) provides a summary of other modifications of the Hotelling rule including issues pertaining to durability, exploration cost, back5

According to Frankel’s (2011) review of various studies, possible price trends might be due to sample data’s ending date: while studies published in the 1970s described an increasing trend, those in the 1980s seem to present a decreasing trend. 6 NMPC method works with a finite horizon decision problem (for detailed Explanation, see Nyambuu and Semmler (2014)). Studies such as by Grüne and Pannek (2011) show that NMPC can be a reliable approximation of a longer horizon decision problem. Greiner et al. (2014), for example, show that the NMPC method provides important information even for a small number of periods.

5.2 Modeling Extraction and Price Dynamics of Non-renewable Resources

63

stop technology, and related risks. Furthermore, Livernois (2009) notes that “if the Hotelling Rule is only one among many supply-side factors that influences price, all kinds of price paths are possible” (p. 37). Pindyck (1978) focuses on the joint dynamics of production and resource exploration and shows that initially falling resource price during the development of reserves would increase at a later time. The pay-off to be maximized is determined as a difference between revenue, i.e., resource price times production rate from the reserve, and costs associated with resource extraction as well as exploration of resources. It was noted that while the resource price is taken as given in the case of competitive producers, but for a monopoly, it is a demand function of the production rate from the resource reserve that drives the price. This model has two state variables that represent the dynamics of cumulative reserves and proved reserves.7 As Pindyck’s (1978) solutions demonstrate, trends in resource price depend on initially available amount of reserves. The price drops from a very high level when initial reserves are small because of exploration and increase in reserves. However, when reserves fall, the resource price starts rising. Thus, this can be shown by a U-shaped curve. In this chapter, we mainly follow Greiner et al. (2012) and Nyambuu and Semmler (2014), where a monopolistic owner of the non-renewable resource is considered. The proved reserves need to be optimally exploited when generating pay-offs. When discounted with some discount rates, the present value of the pay-offs will be maximized. Up to time t, it is assumed that the proved reserves have been discovered. As in Nyambuu and Semmler (2014, p. 274), the model represents a dynamic decision problem with a constant discount rate θ, a control variable, an optimal extraction rate, S, and two state variables—the proved reserves of the resource, R, and the accumulated extraction, m. The model can be presented as follows: T max S

  e−θt P(S, R, m) − C(R 0 − m) Sdt

(5.1)

R˙ = ϕ(R 0 − m − R − R b ) − S m˙ = S

(5.2) (5.3)

0

s.t.

In the pay-off function of the above model, it is assumed that the resource price, P(.), is a function of the amount of the resource extracted, the remaining part of the discovered stock, and the accumulated exploitation. Thus, we presume P(S, R, m). As in Greiner et al. (2012), it is assumed that the exploitation cost is negatively affected by the remaining stock of the resource, denoted as C(R 0 − m). As for the evolution of the proved reserves, it depends on the discovery rate and extraction rate. The volume of the resource that is discovered defines the discovery rate. The stock of proved reserves could still expand despite the extraction rate, S. The discovery is defined as ϕ(R 0 − m − R − R b ) with the initially available total resources, R 0 , which is assumed to be R 0 ≥ m + R, with accumulated extraction in the past (m). The 7

See Pindyck (1978, pp. 843–844) for details on this model and equations that were sketched.

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value of the reserves “below which no new deposits are discovered” (see Nyambuu and Semmler 2014, pp. 273–276) is denoted by R b . A parameter ϕ describes how fast new non-renewable resources can be discovered. The detailed derivation of the solution of this model using the Hamiltonian is relegated to the Appendix of this chapter. As part of the further specification of the model, Greiner et al. (2012) and Nyambuu and Semmler (2014) used a downward sloping demand function of (1/(γ + ιS + κR − m))ν and a cost function of (R 0 − m)−2 with positive parameter values of γ, ι, κ, , ν, and . In the next section, we will numerically solve this dynamic decision model for a finite horizon using the NMPC method and examine the dynamic paths for proved reserves, extraction rate, and the price movement of non-renewable resources.

5.3 Numerical Solutions and Results of Resource Extraction Strategies We use the NMPC method for analyzing a finite time horizon dynamic decision problem and the trajectories of the relevant variables in the model with capital stock and resource extraction.8 Although the model described in the previous section is written for an infinite horizon, a model version with a finite decision horizon serves as an adequate approximation; this is when the time horizon is sufficiently long (see Grüne and Pannek 2011; 2017; Grüne et al. 2015). Dynamic solutions of the variables will be presented for selected initial values of the state variables: R and m. At first, we assume large initial conditions9 of R0 = 2.0 and m 0 = 0.8 as well as R0 = 2.5 and m 0 = 1.2 together with parameter values: θ = 0.03, γ = 0.1,  = 0.04, ν = 2, ι = 3, κ = 0.6, = 4, ϕ = 4, R 0 = 7, and R b = 4. Figure 5.2 shows dynamic paths for R, m, and S, respectively. Corresponding results illustrated in this figure demonstrate that the extraction rate is monotonically falling because of a large initial quantity of proved reserves. When the discovery rates and the proved reserves decline, as predicted by Hotelling (1931) and shown in Nyambuu and Semmler (2014), this scenario would illustrate an increasing trend for the resource price in the long run. Next, small initial conditions of R0 = 0.4 and m 0 = 0.2 as well as R0 = 1.0 and m 0 = 0.8 are considered. The extraction rate demonstrates an inverted U-shaped pattern as shown in Fig. 5.3. As expected, when there is a large amount of undiscovered resource, the extraction rate increases as more resources are discovered; this is 8

As mentioned previously, the solution method of NMPC is a finite time horizon procedure. A number of studies including Grüne et al. (2015), Nyambuu and Semmler (2014, 2017, 2020), and Nyambuu (2017, 2016) demonstrate how the NMPC method can be used in dynamic decision problems specifically in economics. 9 Note that Nyambuu and Semmler (2020, p. 278) used different initial conditions of R = 3.0 and 0 m 0 = 1.0.

5.3 Numerical Solutions and Results of Resource Extraction Strategies

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Fig. 5.2 Numerical solutions displaying dynamic patterns of R, m, and S (large initial conditions). In these figures, S is the extraction rate, R is the proved reserves of the resource, and m is the accumulated extraction. The horizontal axis shows the time, t. Panel (a) demonstrates dynamic paths for R, m, and S when the initial conditions are R0 = 2.0 and m 0 = 0.8. Panel (b) shows the dynamic paths for R, m, and S when the initial conditions are R0 = 2.5 and m 0 = 1.2. Note that S is plotted on the right axis

Fig. 5.3 Numerical solutions displaying dynamic patterns of R, m, and S (small initial conditions). Note S is the extraction rate, R is the proved reserves of the resource, and m is the accumulated extraction. In these figures, S is the extraction rate, R is the proved reserves of the resource, and m is the accumulated extraction. The horizontal axis shows the time, t. Panel (a) demonstrates dynamic paths for R, m, and S when the initial conditions are R0 = 0.4 and m 0 = 0.2. Panel (b) shows dynamic paths for R, m, and S when the initial conditions are R0 = 1.0 and m 0 = 0.8. Note that S is plotted on the right axis

discussed in more detail in Nyambuu and Semmler (2014). Yet, at a later time, since the non-renewable resources are available only in limited quantity, the extraction rate starts falling. The patterns with respect to R and m illustrated in Panel (a) of Fig. 5.4 reflect a nonlinear relationship between the proved reserves, R (with R on the vertical axis), and accumulated resources, m (with m on the horizontal axis): R increases first when m is small, but it starts dropping when m surpasses a certain threshold and then continues to rise. Based on these movements of the extraction, as demonstrated

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Fig. 5.4 Relationships between proved reserves, cumulative extraction, and prices. Panel (a) of this figure shows a nonlinear relationship between proved reserves, R, and cumulative resources, m. While R is plotted on the vertical axis, m is plotted on the horizontal axis. Panel (b) shows a dynamic pattern of the resources prices over time t

in Nyambuu and Semmler (2014), we should expect the price of the non-renewable resource to follow a U-shaped pattern, i.e., declining first and then rising over time (see Panel (b) in Fig. 5.4). Based on the parameter values in the price function, the shape of the price dynamics curve can change as well. This can be shown by varied parameter values: κ and γ. One possibility is that prices drop first and increase later, thus revealing a U shape, but the price growth rates can be different: very slow versus fast. On the other hand, different scenarios can indicate a slow increase in the price followed by a sharp surge, and this might happen mainly due to κ = 0; however, we should note that the timing for the acceleration as well as the price values are different when the value of the parameter γ changes.

5.4 Conclusion As our historical data suggested, the extensive and persistent exploitation of exhaustible resources clearly decreases the length of time until their exhaustion. We reviewed different studies on exhaustible resources and theoretical models of their extraction. A major early contribution and guide to the exploitation of exhaustible resources came from Hotelling (1931), who was also an active conservationist of his time. We have introduced some extensions to Hotelling’s work and modified assumptions that highlight the typical monopolistic market structure found in the resource sector, e.g., Greiner et al. (2012) and Nyambuu and Semmler (2014). Following previous studies on the price dynamics of non-renewable resources, a dynamic decision problem that replicated empirical facts was presented. Our model

5.4 Conclusion

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was numerically solved demonstrating dynamic paths for actual and cumulative extraction, the remaining reserves, the dynamics of discovery, extraction costs, and prices resulting from a downward sloping demand curve. Similar to Nyambuu and Semmler (2014), our numerical solutions also found that the extraction rate can initially increase due to a small initial stock and fast-rising discoveries; but, at a later time, the discovery rates could actually start falling. Thus, the initial quantity of reserves, to some extent, determines the pattern of future resource prices. It can be either U-shaped or monotonically increasing. The first scenario with the falling price at the initial time, and then subsequent increasing price, accompanying a decline in the extraction rate, seems in contrast to what Hotelling had claimed. There are naturally some shortcomings of the resource extraction strategies to be mentioned. First, in terms of intertemporal equity, as discussed in Chap. 2, the current generation should also be concerned about the availability of non-renewable resources for future generations. Second, the negative externalities related to the use of fossil fuels, especially oil and coal, are well known and imply significant CO2 emissions. Thus, these fuels should not be extracted quickly until the depletion, but rather more renewable energy should be phased in earlier so that the excessive CO2 emissions are avoided and the earth’s carbon budget available for the side effects of economic activity is not filled up so quickly. To address these concerns and problems, subsequent chapters provide more comprehensive modeling approaches examining the empirical facts and findings on the overall costs of the use of non-renewables and in particular non-renewable fossil energy resources. In Chap. 6, we will focus on renewable energy sources and take into account those alternative technology (backstop technology). In this context, Chap. 7 introduces a model of renewable energy in addition to fossil fuel-based energy. There, we also study the dynamic evolution of pollution, the limits of the carbon budget, and the risks and damages that may arise from fossil fuels.

Appendix As shown in Nyambuu and Semmler (2014, p. 274), the current-value Hamiltonian H (·), based on an infinite horizon, with ς and ϑ as co-state variables of R and m, respectively, the optimality conditions are presented as follows:     H = P(S, R, m) − C(R 0 − m) S + ς ϕ(R 0 − m − R − R b ) − S + χS (5.4) ∂ H/∂ S = P(·) + S PS (·) + C(·) − ς + χ = 0

(5.5)

ς˙ = r ς − ∂ H/∂ R = r ς − S PR (·) + ςϕ

(5.6)

ϑ˙ = r χ − ∂ H/∂m = r χ − S Pm (·) − SC R 0 −m (·) + ςϕ

(5.7)

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References Barnett HJ (1979) Scarcity and growth revisited. In: Smith VK (ed) Scarcity and growth reconsidered. Johns Hopkins University Press for Resources for the Future Barnett HJ, Morse C (1963) Scarcity and growth: the economics of natural resource availability. Published for Resources for the Future by Johns Hopkins Press, Baltimore British petroleum (BP) (2021) BP statistical review of world energy 2021. https://www.bp.com/en/ global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed 26 Dec 2021 British petroleum (BP) (2022) BP statistical review of world energy 2022. https://www.bp.com/en/ global/corporate/energy-economics/statistical-review-of-world-energy.html. Accessed 2 Sept 2022 Frankel J (2011) Over-optimism in forecasts by official budget agencies and its implications. Oxf Rev Econ Policy 27(4):536–62 Greiner A, Semmler W, Mette T (2012) An economic model of oil exploration and extraction. Comput Econ 40(4):387–399 Greiner A, Grüne L, Semmler W (2014) Economic growth and the transition from non-renewable to renewable energy. Environ Dev Econ 19(4):417–439 Grüne L, Pannek J (2011) Nonlinear model predictive control theory and algorithms, 1st edn. Springer, Berlin Grüne L, Pannek J (2017) Nonlinear model predictive control theory and algorithms, 2nd edn. Springer, Berlin Grüne L, Semmler W, Stieler M (2015) Using nonlinear model predictive control for dynamic decision problems in economics. J Econ Dyn Control 60:112–133 Hotelling H (1931) The economics of exhaustible resources. J Polit Econ 39(2):137–175 Liu PT, Sutinen JG (1982) On the behavior of optimal exploration and extraction rates for nonrenewable resource stocks. Resour Energy 4(2):145–162 Livernois JR (2009) On the empirical significance of the hotelling rule. Rev Environ Econ Policy 3(1):22–41 Livernois JR, Uhler RS (1987) Extraction costs and the economics of nonrenewable resources. J Polit Econ 95(1):195–203 Nyambuu U (2017) Financing sustainable growth through energy exports and implications for human capital investment. In: Bettina B, Greiner A (eds) Dynamic modeling and econometrics in economics and finance. Springer Publishing, Inequality and Finance in Macrodynamics, pp 191–220 Nyambuu U (2016) Foreign exchange volatility and its implications for macroeconomic stability: an empirical study of developing economies. In: Bernard L, Nyambuu U (eds) Dynamic modeling, empirical macroeconomics and finance: essays in honor of willi semmler. Springer International Publishing, pp 163–182 Nyambuu U, Semmler W (2014) Trends in the extraction of non-renewable resources: the case of fossil energy. Econ Model 37(C): 271–279. https://doi.org/10.1016/j.econmod.2013.11.020 Nyambuu U, Semmler W (2017) Emerging markets’ resource booms and busts, borrowing risk and regime change. Struct Chang Econ Dyn 41:29–42. https://doi.org/10.1016/j.strueco.2017.02.001 Nyambuu U, Semmler W (2020) Climate change and the transition to a low carbon economy–carbon targets and the carbon budget. Econ Model 84:367–376. https://doi.org/10.1016/j.econmod.2019. 04.026 Nyambuu U, Semmler W, Palokangas T (2014) Sustainable growth: modelling, issues and policies. International institute for applied systems analysis (IIASA). IIASA interim report. IIASA, Laxenburg, Austria: IR-14-019 Pindyck RS (1978) The optimal exploitation and production of nonrenewable resources. J Polit Econ 86(5):841–861 Slade ME (1982) Trends in natural-resource commodity prices: an analysis of the time domain. J Environ Econ Manag 9(2):122–137

References

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Solow RM (1974) Intergenerational equity and exhaustible resources. Review of Economic Studies, Symposium on the Economics of Exhaustible Resources, pp 29–45 Sullivan DE, Sznopek JL, Wagner LA (2000) 20th century U.S. mineral prices decline in constant dollars. Open-File Report 00-389. U.S. geological survey. http://pubs.usgs.gov/of/2000/of00389/. Accessed 8 Nov 2022 Swierzbinski JE, Mendelsohn R (1989) Exploration and exhaustible resources: the microfoundations of aggregate models. Int Econ Rev 30:175–186 UNCTAD (2017) World investment report 2017. http://unctad.org/en/PublicationsLibrary/ wir2017_en.pdf. Accessed 8 Nov 2022 U.S. energy information administration’s (EIA) (2022a) U.S. oil and natural gas wells by production rate. https://www.eia.gov/petroleum/wells/. Accessed 7 Nov 2022 EIA, US (2022b) Annual energy outlook 2022. Energy information administration. https://www. eia.gov/outlooks/aeo/. Accessed 2 Sept 2022

Chapter 6

Fossil Fuel Resource Depletion, Backstop Technology, and Renewable Energy

Overview In this chapter, we will lay out how economic analysts became aware of the increasing side effects of the use of fossil fuel-based energy and the potential for disasters that could arise therefrom. The issue of what backstop technology will be available and how industrialized economies can move away from fossil fuels has become important. The original motivation for “backstop” Technology was suggested as the basis for resolving the problem of the exhaustibility of existing resources; these days global warming has reinvigorated the importance of the concept. The first basic growth models to include it were developed by Nordhaus (1973) and Heal (1976). These were based on alternative technology and extraction costs, and the availability of new types of resources to be used in production. Using a model with backstop technology, our numerical analysis, which follows this thread, is consistent with other models, and is compatible with empirical data. In addition, our work includes cyclical components and multiple time series. Thus, we are able to study the impact of these resource-based constraints on sustainable economic growth and on the welfare of society.

6.1 Introduction to Backstop Technology Our model is based on some extensions and modifications of basic growth models initially proposed by Forrester (1971), Meadows et al. (1972), Solow (1973), Dasgupta and Heal (1974), and Stiglitz (1974). These early studies explored the role of exhaustible resource constraints on economic growth. Extractive resources are built into growth models that have discounted utility as the sole objective in an intertemporal maximization problem. Then, the objective function is constrained not only by capital stock accumulation, but also by the extraction dynamics of non-renewable resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_6

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Stiglitz (1974) stressed that there are other drivers of growth that could help to overcome the limitations of resources imposed on economic growth. These included technical change, returns-to-scale, and the substitution of production inputs, e.g., capital and non-renewable resources. In this context, we can extend these by introducing a backstop technology or a substitute technology, as, for example, was introduced by Nordhaus (1973) and Heal (1976). In Nordhaus’ (1973) explanations of electricity generation, backstop technology represents a switch from a process that uses a finite resource, e.g., petroleum with no extraction costs, to another process that is based on a resource with infinite availability, e.g., nuclear fuel, as well as capital. The switch was supposed to be driven by the ratio between the supply of petroleum and electricity demand. This model was extended by introducing extraction costs for the technology. Nordhaus (1973) took oil as an example and discussed oil extraction from shale, especially during the transitional period resulting from perceived technological change in the United States. Based on the comparison of different energy sources, including petroleum, shale oil, liquefied coal, gasified coal, strip mined coal, and deep mined coal, it was noted that the estimated costs of certain fuels are cheaper. For example, strip mined coal has a lower estimated cost than liquefied coal, or other types of similar fuels. Dasgupta and Heal (1974) assumed that the average extraction cost function depends on the extraction rate, and the remaining stock as well. Particularly, Heal (1976) highlighted that some resources do not necessarily deplete due to the natural variability of the resources’ grades and costs; e.g., can be obtained from crustal rocks or the sea (p. 371). Dasgupta and Heal (1979) refer to a controlled nuclear fusion or clean breeder technology as contrasted with the availability of shale oil when they describe technology needed for substitution. Of course, this depends on prices. Heal (1976) argued that whether the cost continuously rises depending on the cumulative amount of resources extracted. Initially costs increase, but when the supply surpasses a certain threshold associated with the “backstop,” it stops increasing. Initially, as suggested by Heal (1976), due to low extraction cost, the resource stocks are exhausted with resource pricecost=0. Afterward, the economy follows cost = price. For the optimal path, Heal (1976) states that the type of extraction technology determines how the marginal cost connects to the resource price. Dasgupta and Heal (1979) noted that depending on the resource prices before and after the extraction, “under competitive conditions the backstop technology will be held in reserve until the resource runs out, because so long as stocks are not depleted resource owners will be able to undercut the competitive price of the substitute product. ... The larger is the initial stock... consequently the longer will it be before the backstop technology makes its appearance” (pp. 176–177). They also stress that the resource’s competitive price would be much lower than the production cost of the substitute in the long run under two conditions: (1) resource stock is large, and (2) the difference between the extraction costs of two deposits (same resource but with different quality) are large. More recent studies addressed a transition from exhaustible resources to renewable resources emphasizing backstop (renewable) energy technology. For example,

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Manne and Richels (1992) highlighted the importance of switching to a clean energy source. As part of the extensions of the basic Hotelling model, Krautkraemer (1998) discusses various grades of the deposits of non-renewable resources and suggests a separate user cost variable representing each of these deposits. Furthermore, the focus was directed to a sequence from low to high costs in the case of the extraction cost, and the stock effect and a declining value of the non-renewable resources as in Heal (1976). In Tahvonen and Salo (2001), fossil fuel as well as renewable energy sources are used in production; their respective costs are included in the model. It should be noted that they added technical change into the growth model: technological knowledge not only from learning about extraction, but also related to productive capital. Similarly, studies by Tsur and Zemel (2003) emphasize R&D process and its impact on backstop costs. When we cover a transition to a low-carbon economy and introduce damages arising from fossil fuel in Chap. 7, we will address this issue along with extraction costs in our model with a production function based on two types of resources: fossil fuel and renewable energy. Our research focuses on exhaustible resources used in the production process, the changes in the technology that is used for the extraction of these resources, and the costs of extraction. For this purpose, a dynamic decision model is introduced; it incorporates non-renewable resources and capital as inputs into production. In addition, technology is explicitly considered in order to examine its impact on the extraction of resources, as well as on production in general; this was introduced in early studies by Heal (1976), and others. The model is solved numerically using Nonlinear Model Predictive Control (NMPC). Dynamic trajectories of the state variables are assessed in this chapter. These include capital accumulation and extraction rates for non-renewable resources. Further, the impact of these constraints on economic growth is reviewed. The numerical analysis in this chapter demonstrates changes in the availability of the resources based on different initial conditions of the state variables, as well as the extraction cost functions. We present the incorporation of backstop technology by emphasizing its importance for extraction processes, and how it can contribute to resolving the issues of exhaustible resources and their related externalities.

6.2 Trends in Energy Prices and Costs Energy Production, Prices, and Cyclical Components Low-cost shale gas production based on hydraulic fracturing technology (fracking) has increased since 2005 and has become ubiquitous in the U.S. gas industry. Projections by the U.S. Energy Information Administration (EIA) (2022a) indicate that dry natural gas in the United States is expected to increase from 34 trillion cubic feet in 2021 to 43 trillion cubic feet in 2050 based on “Reference” conditions; it will be

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Fig. 6.1 Natural gas production and prices (1997–2022). Source constructed using monthly data from the EIA (2022b). This figure shows the monthly prices of natural gas between 1997 (January) and 2022 (July): Henry Hub spot prices, measured in U.S. dollars per million British Thermal Units (BTU), and Citygate prices, measured in U.S. dollars per thousands of cubic feet. Natural gas production, measured in billions of cubic feet, is plotted on the right axis

mainly produced from unconventional resources, e.g., shale gas and tight oil. As a result, in general, we observe a significant decline in natural gas prices that is associated with a surge in production (see Fig. 6.1). A relationship between the gas price and its output is determined and explored in studies including Fukui et al. (2017) that predict an estimated future drop in shale gas price. Investments in the shale gas sector have risen substantially (IEA, 2021). For some observers, these trends in supply, investment, costs, and prices indicate that the U.S. shale gas revolution could contribute to a transition to a low-carbon economy; in fact, many experts have treated shale gas as a transition energy. Others view shale gas as a long-run game changer in the energy industry, see Chap. 8. We study the behavior of crude oil prices and oil production by taking the natural log of quarterly data between 1986 quarter 1 and 2019 quarter 1 from the EIA and multiplying by 100. The first difference in the time series is calculated for oil production and oil prices. Historical data in Fig. 6.2 illustrate a monotonic decline in the U.S. oil production until around 2008, but an increasing trend after this period. Historical WTI spot oil prices display more volatility. Next, we apply a Hamilton filter to the oil production and price data instead of the Hodrick-Prescott (HP) filter; this is to avoid problems related to spurious dynamics and other limitations that are explained in Hamilton (2018), De Jong and Sakarya (2016) and Phillips and Jin (2015). Hamilton (2018, pp. 839–840) introduced a method that can “isolate a stationary component from any I(4) series, preserves the underlying dynamic relations, and consistently estimates well-defined population characteristics for a broad class of possible data-generating processes.”1 Based on Hamilton’s (2018) procedure for a variable’s ordinary least squares (OLS) regression 1

Studies such as by Schüler (2019) evaluated Hamilton method’s cycle estimations in contrast to the HP filter by applying them to different time series.

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Fig. 6.2 U.S. crude oil production and oil price growth rate source constructed using data from the EIA (2022b). In this figure, quarterly data between 1986 and 2019 on U.S. crude oil production and prices (nominal, real) are used. Their growth rates are calculated by taking the natural log differences and multiplying by 100. The first difference of the time series is shown

Fig. 6.3 U.S. oil production and prices data with Hamilton filter and regression in this figure, quarterly data between 1986 and 2019 on U.S. crude oil production and real prices are used. Cyclical components of the U.S. oil production and oil prices are shown based on a Hamilton filter and regression results. A random walk through 8 quarters (8Q) change is added for oil production as well as for real oil prices

on its four recent values, we can present cyclical components of the U.S. oil production and oil prices in Fig. 6.3. Hamilton (2018, pp. 838–839) suggested a two-year horizon for examining the business cycle effect, thus in our computation, we use h = 8 for our quarterly data, and compute the residuals of the regression as yt = β0 + β1 yt−8 + β2 yt−9 + β3 yt−10 + β4 yt−11 + υt

(6.1)

and random walk as yt − yt−8 .2 Results indicate that these data are cyclically very volatile. 2

Hamilton (2018) applied these methods to different quarterly time series data and reported standard deviations of their regression residuals as well as a random walk. For example, the calculated

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Fig. 6.4 Prices of solar PV cell, polysilicon, and silicon solar module source constructed using data from Bloomberg terminal In this figure, quarterly prices for Photovoltaic (PV) cell, Polysilicon, and Silicon solar modules are shown. An 8-period (quarters) moving average trend was added to these prices

We also examine solar energy-related price data where quarterly times series are derived from monthly data from Bloomberg. We also added an 8-period moving average trend to each of these variables. The results illustrated in Fig. 6.4 by the blue line clearly show declining trends, shown by the black dashed line, for all the prices over time. We apply the Hamilton approach to other historical quarterly data. These include natural gas price and different prices related to solar energy, e.g., photovoltaic (PV) cell prices, silicon solar module prices, and BNEF survey polysilicon prices. All these data display high cyclical volatility, as shown in Fig. 6.5. Standard deviations presented in Table 6.1 show that solar prices are generally less volatile than oil and gas prices.

Energy Costs On average, historical data on the costs of different fossil fuels for the electric power industry in the United States are shown in Fig. 6.6. In particular, costs for natural gas and distillate fuels have surged since the 1970s, and reached a peak in the 2000s. However, we observe another rise in the costs of some fossil fuels starting in 2016. standard deviations were: around 22 for S&P 500, 13 for investment, 3 for GDP, 3 for consumption, and around 1.5 for 10 year Treasury yield.

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Fig. 6.5 Natural gas, PV cell, silicon solar module and polysilicon price data with Hamilton filter and regression. This figure shows cyclical components of natural gas (a), photovoltaic (PV) cells (b), silicon solar modules (c) and BNEF survey polysilicon (d) prices based on a Hamilton filter and regression results. A random walk through 8 quarters (8Q) change is added for these prices Table 6.1 Standard deviations of the hamilton filter results. This table shows the standard deviations for U.S. oil production and for prices of WTI, natural gas, PV cells, solar, silicon solar modules and BNEF polysilicon based on our own calculations Variable name Regression residual Random variable U.S. oil production U.S. price (WTI) U.S. Real oil price (WTI) Natural gas price PV cell price Silicon solar module price BNEF polysilicon price

10.9 35.5 33.9 44.2 20.9 19.4 9.3

11.7 38.4 37.1 53.8 22.4 23.7 48.7

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Fig. 6.6 Costs of fossil fuels for electricity (1973–2021). Source constructed using data from the EIA (2022c) (Monthly Energy Review). This figure shows the costs of different sources of electricity: coal, natural gas, fossil fuel, and total petroleum between 1973 and 2021. Note that total petroleum data are plotted on the right axis. These data are measured in U.S. dollars per million British Thermal Units (BTU) Table 6.2 Projections of levelized costs of electricity technology entering service in 2027 and 2040 2021 $/MWh 2027 2040 Change Dispatchable Coal (ultra-supercritical) Combined cycle Non-dispatchable Solar Wind (onshore) Wind (offshore)

83

79

–5%

40

44

10%

36 40 137

33 40 98

–8% 0% –28%

Source constructed using data from the EIA (2022d). This table shows projections of two types of Levelized Costs for Electricity (LCOE) technology (dispatchable and non-dispatchable) entering service in 2027 and 2040. The percentage changes are shown for each of the following: coal, combined cycle, solar, and wind (onshore and offshore)

The EIA (2022d) presented estimated Levelized Costs of Electricity (LCOE)3 of newly generated resources starting service in later years up to 2040. The estimated total system LCOEs for different technologies measured by 2021 $/MWh entering service in 2027 and in 2040 are compared in Table 6.2. As expected, while LCOEs for combined cycles are expected to rise on average, LCOEs for wind and solar are anticipated to decline further in 2040. As for the installment, a higher capacity is expected for solar PV. In Chaps. 8 and 11, we will present more data from IRENA (2020, 2022) and LAZARD (2021) and compare renewable sources of electricity generation in detail. 3

The EIA (2022d, p.1) defines LCOE as “estimated revenue required to build and operate a generator over a specified cost recovery period.” LCOE is calculated using “a 30-year cost recovery period, using a nominal after-tax weighted average cost of capital (WACC) of 6.2%” (see EIA, 2022d, p. 6).

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Yet these two different sources indicate that clean energy production costs have declined significantly in the last two decades.

6.3 Modeling Extraction Costs and Backstop Technology In this section, the basic closed economy growth model is extended by introducing a resource extraction technology. It is shown using a switch in the cost of extraction based on the existence of a backstop technology. Following Heal (1976, pp. 373– 377), we presenta model where the resource at time t or the cumulative extraction is t shown by m t = 0 Si di. The resource extraction cost h(m) is assumed to be a convex function defined by two regimes: 

• Low extraction cost: h (m) > 0 for 0 < m < m • High (constant) extraction cost: h(m) = h for m > m where h > 0 A general extraction cost function describing both situations can be denoted by H (m). According to Heal (1976, p. 373), m represents the amount of available resources with low cost that would deplete. When this happens, it was suggested to phase in another supply source, but it will have a high cost that remains unchanged. Based on Dasgupta and Heal (1974) and Heal (1976), we work with a standard growth model with a Cobb-Douglas production function of Y = Q(K , S) = K β S 1−β where capital (K ) and flows of non-renewable resources (S) are used, and households’ welfare function is maximized with two constraints: the capital stock (K ) and the accumulated extraction of the non-renewable resources (m) as follows: T

U (C)e−θt dt

(6.2)

K˙ = Y − C − δK − H (m)S

(6.3)

m˙ = S

(6.4)

max 0

s.t.

Here too, we assume simple preferences, where U (C) is, as usually used, a strictly concave utility function of consumption4 expressed by C, θ > 0 is a discount rate, and δK is the depreciation of capital. A power utility function of U (C) = C 1−η /(1 − η) is employed. To demonstrate the phasing in of the backstop technology and to compare the results, three maximization problems involving a pay-off function—in the above formulation a utility function—will be numerically solved in the next section based on the following specified conditions of the extraction cost: 4

For reasons of transparency, we here refer to preferences over consumption only, but in advanced models, taking into account climate change and adaptation efforts, our objective function has to be considerably redefined—see Chap. 9.

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1. h(m) = μm 2 , μ > 0, for all values of m 2. h(m) = h, for all values of m 3. h(m) = μm 2 if m < m with m > 0 , otherwise h(m) = h if m ≥ m For the details of the analytical solution of the model see the Appendix. The theoretical models sketched in this section will be numerically solved and explained using the NMPC method.

6.4 Numerical Solutions of Growth Models with Backstop Technology Using NMPC, we present numerical solutions of the basic growth model extended with backstop technology. The dynamic paths for the state variables are tracked under the three conditions of the extraction costs that were described in the above section. We first solve the closed economy growth model with backstop technology where the extraction cost depends on the amount of resource that was extracted and accumulated in the past, m, and the function of h that was described in the previous section. The optimal values of the control driving the state variables of the model, corresponding to initial conditions of the state variables of (K 0 , m 0 ), will be identified and assessed. In the next Figs. 6.7–6.9, a blue line shows the dynamic path for K , while a dotted line shows the dynamic evolution of m over time. The parameter values of the maximization problem taken for the NMPC procedure are as follows: θ = 0.03, δ = 0.01, η = 0.5, β = 0.7, and μ = 0.1.

Convex Extraction Cost The dynamic trajectories of capital stock and extracted resources for different initial conditions are illustrated in Fig. 6.7. We observe an increasing pattern or the resources extracted at a higher initial pace, but it starts slowing down because of the limited quantity of available non-renewable resources. When the extraction cost changes proportional to the cumulative extraction, accumulated resources stop rising and reach a value of around 7 or 8 in all cases—see Fig. 6.7. As for the evolution of the capital stock, its pattern corresponds to the changes in resources extracted: the capital stock increases together with an increase in resource extraction, however, it starts declining when the growth rate of resource extraction slows down; eventually, the capital stock reaches a very low value when resources deplete. We also observe that the initial condition of the accumulated resources (m 0 ) affects the growth of the capital stock; the lower the m 0 , the higher the capital accumulation can achieve,—see Panel (a) of Fig. 6.7.

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Fig. 6.7 Dynamic trajectories of capital stock and cumulative extraction with convex extraction cost function. In these figures, K is the capital stock and m is the accumulated extraction. The horizontal axis shows the time, t. Panel (a) demonstrates dynamic paths for K and m when the initial conditions are K 0 = 1.0 and m 0 = 0.1. Panel (b) shows dynamic paths for K and m when the initial conditions are K 0 = 1.0 and m 0 = 3.0. These illustrate results derived from a convex extraction cost function

Given Eq. (6.3) we can observe that with the extraction rate Sa > Sb capital stock will go up faster—see Panel (a) of Fig. 6.7 as contrasted with Panel (b). This is also enforced by the extraction cost shown in Panel (b), at least in the beginning, greater than in Panel (a).

Constant Extraction Cost We solve the model with a constant but high extraction cost and show the results corresponding to different initial values. The same parameter values as before, but different initial conditions of K 0 , and m 0 are taken. The dynamic paths for the state variables illustrated in Fig. 6.8 suggested much higher extracted resources. This implies the possibility of having a lot more used resources depending on the extraction costs. As a result, the evolution of the capital stock depicts an increasing trend when the cost is not so high. However, when the cost is too high, as in Panel (b) of Fig. 6.8, resources are not extracted that much, and the capital stock declines; thus the cost should not be too high. Given Eq. (6.3) we can observe that with lower extraction cost in Panel (a) of Fig. 6.8, h a < h b , the extraction rate is higher: Sa > Sb ; capital stock will go up at the beginning and then stay constant as shown in Panel (a). In Panel (b), with higher extraction level, but lower extraction rate and higher extraction cost than in Panel (a), the capital stock steadily declines.

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Fig. 6.8 Dynamic trajectories of capital stock and cumulative extraction with constant extraction cost. In these figures, K is the capital stock and m is the accumulated extraction. The horizontal axis shows the time, t. Panel (a) demonstrates dynamic paths for K and m when initial conditions are K 0 = 1.0 and m 0 = 3.7 and with a fixed extraction cost of h = 1.4. Panel (b) shows dynamic paths for K and m when the initial conditions are K 0 = 1.0, m 0 = 5.4 and h = 3.0. These illustrate results derived from a constant extraction cost

Switching Extraction Cost Finally, we solve the model with switching costs for the extraction and show the results corresponding to different values of h. The same parameter values and initial conditions of K 0 = 1.0, m 0 = 0.1 are taken for the two numerical analyses (a) and (b). The dynamic trajectories of the state variables are illustrated in Fig. 6.9. We observe that for Panel (a) of Fig. 6.9, as compared to Panel (b), with a lower extraction cost, extractions are steadily rising, and cumulative extraction is increasing. Thus, in the long run, we have Sa > Sb, and output and capital are rising, the latter later becomes constant in Panel (a). Only at the beginning, in Panel (b), is capital stock rising faster due to greater resource extraction and use. Later, it is declining due to high extraction cost and less frequent extraction. Thus, in the case of switching extraction cost, the economy is likely to move to the Backstop Technology instead of employing the usual exhaustible resource. Thus, at least for fossil fuels, the economy is possibly moving to a cheaper renewable energy technology. The Backstop Technology takes effect when the cumulative resource extraction reaches high levels but at the same time, the extraction costs switch as well, and to a much higher level. Compared to previous cases, solutions with the switching cost function are likely to result in a phasing in of a Backstop Technology. This indicates, as Heal (1976) suggested, how a switch to an alternative technology and/or other sources, may offer favorable possibilities and could solve the problem of the exhaustibility of non-renewable resources. We should also point out that the higher the value of the extraction cost, the lower the cumulative use of resources, as shown in Panel (b) of Fig. 6.9; and the stock of capital declines as well.

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Fig. 6.9 Dynamic trajectories of capital stock and cumulative extraction with conditional extraction cost function. In these figures, K is the capital stock and m is the accumulated extraction. The horizontal axis shows the time, t. Panel (a) demonstrates dynamic paths for K and m when the initial conditions are K 0 = 1.0 and m 0 = 0.1 and with an extraction cost of h = 1.4. Panel (b) shows dynamic paths for K and m when the initial conditions are K 0 = 1.0, m 0 = 0.1, and h = 3.0. These illustrate results derived from a conditional extraction cost function

6.5 Conclusion In this chapter, we considered extraction costs and technology as an extension to standard growth models. These growth models have less sustainable and inclusive components, as we are aiming at—and which will be developed later—we could make explicit the idea that non-renewable resources, e.g., ores, metals, or oil can be extracted from a current deposit, which could be cheap, but exhaustible. But these resources might also be extracted from the sea or difficult geological formations, e.g., shale and tar sands, in much larger quantities and at much higher prices (Nordhaus 1974; Heal 1976). Due to this, the problem of the exhaustibility of resources could be resolved by technological change. Our results seem to point to the possibility that the resources need not be extracted to exhaustion any longer, i.e., they do not need to be extracted until depletion. Resources, fossil fuels, in particular, can be left unextracted and capital still can grow. The last case discussed illustrates how moving to a high but constant extraction cost may offer the possibility of a reasonable switch to a new energy technology. As Heal (1976) has suggested, this not only solves the problem of the exhaustibility of non-renewable resources, but also avoids the extreme side effects of the use of fossil energy. In Chap. 7, in addition to exhaustible energy, we introduce renewable energy input into production activities and assume that clean energy is generated using a renewable energy capital stock with significant efficiency. Based on our modified growth model incorporating damages from fossil fuels and a target carbon budget,

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we will examine the effects of these additional components on a model bringing us closer to sustainable growth with less economic, environmental, and eco-system destruction.

Appendix We present the current value Hamiltonian, for the infinite horizon, with ω and γ as costate variables or shadow prices of capital accumulation and accumulated resource constraints, respectively: H = U (C) + ω (Q(K , S) − C − δK − H (m)S) + γ(S)

(6.5)

with Y = Q(K , S) = K β S 1−β . The necessary optimality conditions are obtained as U  (C) = ω ω (Q S − H (m)) = −γ ω˙ = ω(θ − Q K + δ) γ˙ = γθ + ω H  (m)S

(6.6) (6.7) (6.8) (6.9)

with Q S = ∂ Q(K , S)/∂ S and Q K = ∂ Q(K , S)/∂ K . Dasgupta and Heal (1974, pp. 10–11) showed the steps on how to derive the path  " ˙ for the consumption: C/C=(Q K − θ − δ)/(ε(C) where ε(C) = −CU (C)/U (C).

References Dasgupta P, Heal G (1974) The optimal depletion of exhaustible resources. Symposium on the Economics of Exhaustible Resources. Rev Econ Stud 41:3–28 Dasgupta P, Heal G (1979) Economic Theory and Exhaustible Resources. Cambridge University Press De Jong R, Sakarya N (2016) The econometrics of the hodrick-prescott filter. Rev Econ Stat 98(2):310–317 Forrester JW (1971) World Dynamics. Wright Allen Press, Cambridge, Mass Fukui R, Greenfield C, Pogue K, van der Zwaan B (2017) Experience curve for natural gas production by hydraulic fracturing. Energy Policy 105:263–268 Hamilton J (2018) Why you should never use the hodrick-prescott filter. Rev Econ Stat 100(5):831– 843 Phillips PCB, Jin S (2015) Business cycles, trend elimination, and the HP Filter. Cowles Foundation Discussion Paper No. 2005. https://doi.org/10.2139/ssrn.2622477 Heal G (1976) The relationship between price and extraction cost for a resource with a backstop technology. Bell J Econ 7(2):371–378 International Energy Agency (IEA), 2021, World Energy Investment report. https://www.iea.org/ reports/world-energy-investment-2021. Accessed 16 Aug 2021

References

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IRENA (2020) Renewable power generation costs in 2019. International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019 IRENA (2022) Renewable power generation costs in 2021. International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publications/2022/Jul/Renewable-Power-Generation-Costsin-2021 Krautkraemer J (1998) Nonrenewable resource scarcity. J Econ Lit 36(4):2065–2107 LAZARD (2021) Lazard’s levelized cost of energy analysis-version 15.0. https://www.lazard.com/ media/451905/lazards-levelized-cost-of-energy-version-150-vf.pdf. Accessed 22 Aug 2022 Manne A, Richels R (1992) Buying greenhouse insurance: the economic costs of CO2 emission limits. MIT Press, Cambridge, MA Meadows DH, Meadows DL, Randers J, Behrens WW (1972) The limits to growth: a report for the club of rome’s project on the predicament of mankind. Earth Island Nordhaus WD (1973) The allocation of energy resources. Brook Pap Econ Act. The Brookings Institute 3:529–576 Nordhaus WD (1974) Resources as a constraint on growth. Am Econ Rev 64(2):22–26 Schüler SY (2019) On the cyclical properties of hamilton’s regression filter. Deutsche Bundesbank Discussion Paper No 03/2018. https://www.econstor.eu/bitstream/10419/174891/1/1014338883. pdf. Accessed 7 Nov 2022 Solow RM (1973) Is the end of the world at hand? In: Weintraub A, Schwartz E, Aronson JR (eds) The Economic Growth Controversy. Palgrave Macmillan, London, pp 39–61 Stiglitz J (1974) Growth with exhaustible natural resources: efficient and optimal growth paths. Symposium on the Economics of Exhaustible Resources. Rev Econ Stud 41: 123–137 Tahvonen O, Salo S (2001) Economic growth and transitions between renewable and nonrenewable energy resources. European Economic Review 45(8):1379–1398 Tsur Y, Zemel A (2003) Optimal transition to backstop substitutes for nonrenewable resources. Journal of Economic Dynamics and Control 27(4):551–572 U.S. Energy Information Administration (EIA), 2022a. Annual Energy Outlook 2022. https://www. eia.gov/outlooks/aeo/index.php. Accessed September 1, 2022 U.S. Energy Information Administration (EIA), 2022b. Natural Gas Data. https://www.eia.gov/ naturalgas/data.php. Accessed August 8, 2022 U.S. Energy Information Administration (EIA), 2022c. Monthly Energy Review. https://www.eia. gov/totalenergy/data/monthly/index.php. Accessed August 8, 2022 U.S. Energy Information Administration (EIA), 2022d. Levelized Costs of New Generation Resources in the Annual Energy Outlook 2022. https://www.eia.gov/outlooks/aeo/pdf/ electricity_generation.pdf. Accessed September 1, 2022

Chapter 7

Transition to a Low-Carbon Energy System

Overview In this chapter, we present historical trends related to carbon dioxide (CO2 ) and greenhouse gas (GHG) emissions affectings the carbon budget. This documents the fact that the use of fossil fuels, coal in particular, creates a very large negative externality. Mostly through rising temperature, this leads to both extreme climate events and to insidious trends in economic, social, and environmental perils. In modeling, we can include these effects into production, reducing GDP. Another approach is to encode the welfare effects arising from these perils and damages. We adopt a production function that depends on fossil fuel as well as renewable energy. This allows us to highlight new resource discoveries, their extraction and exploitation costs, and their impact on CO2 and GHG emissions. Looking at the dynamics of the carbon budget, our numerical analysis indicates that large fractions of coal reserves should remain untouched, and renewable resources should be phased in faster, thus slowing down CO2 and GHG emissions and fulfilling the requirements of the Paris 2015 agreement. However, much has changed since 2015. The COVID-19 pandemic embodied a serious and unexpected shock to both advanced and developing countries. The sudden shut-down of world economies, shipping, and industrial activity had a corresponding effect on the use of fossil fuels. CO2 emissions declined radically and this should have allowed for a chance to restart the economies on a greener and cleaner foundation and moving to a more sustainable path.

7.1 Is the Limit of the Carbon Budget Reached? There are many studies suggesting that having carbon emission targets would help to cope with climate change and global warming problems. For example, the probabilistic analysis by Meinshausen et al. (2009) both provide emission targets and suggest © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_7

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how they can contribute to the effort of limiting the rise in global temperatures to 2 ◦ C relative to the pre-industrial level, as set by the Paris agreement in 2015. Based on almost 12,000 peer-reviewed scientific studies published between 1991 and 2011, Cook et al. (2013) summarized that 97.1% of the papers discuss how global warming is caused by human activity. O’Neill et al. (2010) studied future carbon emissions based on demographic dynamic changes, such as population, aging, and urbanization, incorporated in a growth model with fossil energy use. In this context, a link between the emissions and temperature is discussed in Rogelj et al. (2011), with emission pathways to achieve the 2 ◦ C temperature limit. Projected emissions using risk-based analysis, e.g., Sheehan et al. (2008), calls for urgent policy responses to climate change; the studies highlight the role of consumption of fossil fuels and related technologies. Central for these types of studies is the carbon budget which defines the permissible carbon concentration in the atmosphere to limit the temperature increase to a particular value. Nature has some self-renewable forces which absorb pollution, but if the creation of pollution is greater than nature’s absorption capacity, the carbon budget will fill up, with a rising concentration of pollution in the atmosphere and temperature rise. Climate scientists have been releasing warnings on the remaining carbon budget and possible tipping points. A series of research papers address the danger of overshooting of the carbon budget and its implication for global warming (Edenhofer et al. 2014; Jakob and Hilaire 2015; Rozenberg et al. 2015; Pfeiffer et al. 2016; Eichner and Pethig 2017; Sartor 2018). Using a techno-economic scenario in line with temperature limits of 2 ◦ C, Sartor (2018) analyzed coal transition pathways for coal-rich countries, including Australia, China, India, Germany, Poland, and South Africa, and concluded that coal can be phased out and alternative energies can be phased in by 2040–2050. It is important to realize that coal reserves are expected to last much longer than other fossil fuels. Coal has the largest fraction of consumption of all fossil fuels and produces the highest emissions. Studies have proposed that society should simply not burn fossil fuel any longer; in other words, maintain legally unburnable reserves. For example, McGlade and Ekins (2015) state that between 2011 and 2050 fossil energy with enormous carbon content exceeds the carbon budget threshold discussed in IPCC (870–1,240 Gt), i.e., the threshold that signifies the borderline separating global warming at less than 2 ◦ C rise, as compared to the pre-industrial level. Based on a model for optimal extraction of resources, Hoel and Kverndokk (1996) suggest reducing the extraction of fossil fuel over a long term. According to World Energy Outlook prepared by the International Energy Agency (IEA, 2020a, 2021a), long-run projections up to 2030 and 2040 and 2050 indicate less demand for coal1 for electricity generation under all scenarios; this is thanks to an expected surge in low-carbon energy and renewables, solar energy in particular, 1

IEA (2020a, p. 19) highlights the “retirement of 275 gigawatts (GW) of coal-fired capacity worldwide by 2025 (13% of the 2019 total), including 100 GW in the United States and 75 GW in the European Union” and coal demand is estimated to drop from the current approximate level of 5500 Mtce to around 1900 Mtce in 2040 under the Sustainable Development

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followed by wind power. A shift to a low-carbon economy is discussed in a number of studies highlighting sustainable infrastructure investment, a reduction in pollution, an increase in efficiency, and other efforts. For example, Stern (2015) treats this shift as a particular growth strategy that involves innovation, co-benefits, energy efficiency, and clean infrastructure in addition to managing climate risk. The United Nations Framework Convention on Climate Change outlined a Summary of the Paris Agreement (n.d.) in 2015 highlighting the mitigation efforts for different countries as “developed countries should continue to take the lead by undertaking absolute economy-wide reduction targets, while developing countries should continue enhancing their mitigation efforts, and are encouraged to move toward economy-wide targets over time in the light of different national circumstances.”2 In this context, the Carbon Pricing Leadership Coalition (CPLC), 2017) presents different efforts and targets for GHG emission for countries based on their individual development category: an advanced country (AC) or a developing country (DC). Using IPCC (2014) data, they noted that the industrialization of advanced countries contributed to GHG emissions significantly especially from the 1970s on. Thus, in an extended growth model by Nyambuu and Semmler (2020), different target carbon budgets for DCs and ACs are specified based on the stage of industrialization and GHGs emissions of these countries. Indeed, concerning the short run, studies suggest a slower reduction of emissions for low-income developing countries that seems appropriate based on these countries’ financial constraints, poverty alleviation purpose, and other reasons (Fleurbaey 2014; Kolstad et al. 2014; Knopf et al. 2012; Stern 2014). For example, based on historic emissions and damage from climate change, Kolstad et al. 2014 address causal and moral responsibility for developed and developing countries, and stress the importance of financial assistance and technology transfers for the mitigation activities in developing countries. The issue concerning the differentiated responsibility and burden sharing is discussed in studies such as by Fleurbaey (2014) highlighting not only the stock of accumulated emissions that contributed to the climate change, but also current emissions that must be constrained. Related to the concept of ‘climate debt’, Pickering and Barry (2012) state the following: “as some countries (which tend to be higher income countries that industrialized earlier) have consumed more than their equal per capita share of the historical global budget, this excess use is offered as an argument for obliging them to provide financial and technological resources to other countries that have used less than their historical share.” (Fleurbaey 2014, p. 320). In the context of the above perspective, Nyambuu and Semmler (2020) present critical dynamic paths for CO2 emissions, caused by fossil fuel stocks, cumulative Scenario (see https://iea.blob.core.windows.net/assets/a72d8abf-de08-4385-8711-b8a062d6124a/ WEO2020.pdf). According to 2050 scenarios by IEA (2021a, p. 58), “coal power plant retirements increase fourfold over the next decade in the net zero pathway” (see https://iea.blob.core.windows. net/assets/4ed140c1-c3f3-4fd9-acae-789a4e14a23c/WorldEnergyOutlook2021.pdf). 2 See UNFCCC website on Summary of the Paris Agreement (n.d.): https://unfccc.int/resource/ bigpicture/#content-the-paris-agreemen.

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extraction, and capital accumulation; they show, looking at carbon budget targets, the possibility of limiting the negative externalities and damages from fossil fuel. This research shows how advanced and developing countries should have different carbon emission targets. These uneven challenges in the transition from fossil fuel to renewable energy are demonstrated in the model with frameworks for two different regions. As expected, fossil resources’ paths would indicate a decreasing pattern, or an increase followed by a fall at the end resulting in one of the two levels: depletes completely3 or reaches a constant level (if renewable resources are phased in). This result is affected by the initially available reserves of non-renewable resources and the amount of the discovered resources. We should note that the extraction rate generally rises with an increase in discoveries. Next, we study extended growth models with production functions which include non-renewable resources as well as renewable energy. For the carbon budget and environmental effects, different variables, e.g., dirty and clean technology, are considered. The numerical solutions suggest that large fractions of fossil fuel reserves should remain underground and renewables should be phased in faster. In this way, with a slower increase in cumulative emissions, we might be able to ensure that the carbon budget is met, thus bringing the world closer to the Paris higher target, i.e., a limit to global temperature rises of 1.5 degree Celsius.

7.2 Emission Rise and the Carbon Budget The development of the carbon budget is crucial. First, historical development and trends in the environmental effects of fossil fuel and alternative clean energy are presented. These include carbon intensity in the atmosphere, CO2 emissions, their relation to the carbon budget, investments in energy, and energy costs. As stated above, historically, CO2 emissions in advanced countries have been much higher compared to developing countries; this is shown in Fig. 7.1. However, in the late 1980s manufacturing accelerated in many developing countries leading to increased CO2 emissions. We show average CO2 emissions and CO2 emissions per capita calculated using data between 1971 and 2019 in Panels (a) and (b) of Fig. 7.2. Based on our data, the United States has the highest CO2 emissions followed by China and Russia. In terms of per capita emissions, the United States, Canada, and Australia are high; as expected, China and India have low average CO2 emissions per capita due to their massive populations. As discussed in Chap. 4, fossil energy in the form of coal contributed greatly to the rapid increase in carbon emissions in China. Other fossil fuel-dependent regions, especially developing and low-income countries, have also had a similar experience. The current share of coal in total global energy demand is still high.4 Based on IEA (2020a) data, the share of different fuels 3

See the results illustrated in Nyambuu and Semmler (2014). See IEA’s (2020) World Energy Outlook at https://www.iea.org/reports/world-energy-outlook2020#.

4

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Fig. 7.1 Historical CO2 emissions for developed and developing countries. Source Constructed using data from IEA (2020b). This figure shows CO2 emissions, measured in millions of tons of CO2 , between 1971 and 2019 for selected countries. Note that data for China and the United States are plotted on the right axis

Fig. 7.2 Average CO2 emissions. Source Constructed using data from IEA (2020b, 2021b). Panel (a) of this figure shows average CO2 emissions from fuel combustion between 1971 and 2019 for selected countries. These data are measured in millions of tons of CO2 . Panel (b) shows per capita CO2 emissions, measured in tons of CO2 , calculated using data between 1971 and 2019

in global energy demand in 2019 is as follows: oil 31%, coal 26%, natural gas 23%, renewables 10%, nuclear 5%, and traditional biomass 4%. As a result, CO2 emissions from the consumption of fossil fuel have been rising at a faster pace, especially since the late 1980s. Next, we present the drivers of the global carbon budget in terms of contributions of CO2 emissions to the atmosphere and the absorption capacity of the CO2 by oceans, forest, and land, thus assessing the net contributions to the trend; see Friedlingstein

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Fig. 7.3 Global carbon budget. Source Constructed using data from earth system science data by Global Carbon Project (2021) and Friedlingstein et al. (2022). Panel (a) of this figure shows fossil emissions (excluding carbonation), atmospheric CO2 growth, and ocean sink between 1951 and 2020. Panel (b) shows emissions from the land-use changes and land sink between 1951 and 2020. Data in this figure are measured in Gigatons of Carbon (GtC) per year

et al. (2022).5 In addition to the CO2 emission from fossil fuels that was discussed earlier, global CO2 emissions include emissions from the usage of land, the resulting changes in land (which also concerns de- and re-forestation), and its absorption capacity6 as shown in Fig. 7.3. The carbon budget, driven by the redistribution of global CO2 emissions in the atmosphere and ocean, is measured in Gigatons of Carbon (GtC) per year (see Panel 5

“The report determines the input of CO2 to the atmosphere by emissions from human activities, balanced by output (storage) in the carbon reservoirs on land or in the ocean.” (See https://www. icos-cp.eu/science-and-impact/global-carbon-budget for details). 6 Emissions from land-use change cover “CO fluxes from deforestation, afforestation, logging, 2 and forest degradation (including harvest activity), shifting cultivation (cycle of cutting forest for agriculture, then abandoning), and regrowth of forests following wood harvest or abandonment of agriculture.” (Friedlingstein et al. 2022, p. 8).

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Fig. 7.4 Seasonal temperature trends. Source Constructed using data from world bank climate change knowledge portal. This figure shows average seasonal temperature in France, Spain, the United Kingdom (UK), and the United States (US) between 1901 and 2021. All data are measured in Celsius. Panel (a) illustrates the U.S. average temperatures in winter (Dec-Feb) and spring (MarMay). Panel (b) shows the U.S. average temperatures in summer (June-Aug) and fall (Sep-Nov). Average temperatures for other countries are shown as follows: Panels (c) and (d)-the United Kingdom, Panels (e) and (f)-France, and Panels (g) and (h)-Spain. A linear trend line is added

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Table 7.1 Summary statistics for the U.S. temperature U.S. Temperature Mar–May Jun–Aug Mean Standard error Standard deviation Sample variance Kurtosis Skewness Range Minimum Maximum

Sep–Nov

Dec–Feb

8.08 0.07 0.72

20.08 0.05 0.54

9.61 0.05 0.60

–2.52 0.09 0.95

0.52 0.12 0.30 3.85 6.37 10.23

0.29 –0.10 0.25 2.39 18.97 21.36

0.36 0.52 0.63 3.22 8.46 11.68

0.90 –0.54 0.12 4.36 –4.71 –0.36

Source Constructed using data from world bank climate change knowledge portal. This table shows summary statistics for March to May (Mar-May), June to August (Jun-Aug), September to November (Sep-Nov), and December to February (Dec-Feb) in the United States. These results are based on data between 1901 and 2021

(a) of Fig. 7.3 covering data between 1951 and 2020). The emissions from the usage of land and land sink are shown in Panel (b) covering data for the same period of time. These are equal to the atmospheric concentration of CO2 (growth rate) reduced by the CO2 sinks, and the absorption by land and oceans. The former component has shown a faster growth rate than the latter two components, thus contributing to filling the carbon budget, especially since the 1960s. Thus, the average global temperature has been rising over time: 13.71 ◦ C between 1880 and 1920, 13.92 ◦ C between 1921 and 1960, 14.16 ◦ C between 1961 and 2000, and 14.59 ◦ C between 2001 and 2014 according to Earth Policy Institute’s compilation of data based on NASA database.7 To examine seasonal changes in temperature, in Fig. 7.4, we demonstrate historical temperature for four seasons in selected countries: France, Spain, the United Kingdom, and the United States. In general, average data display increasing trends in temperature for all four seasons. A summary statistics for the U.S. presented in Table 7.1 indicates that winter season temperature covering December, January, and February has the highest range and standard deviation.

7.3 The Electricity Capacity from Renewable Power Generation In addition to regulatory measures, recent years have witnessed sharply rising investments in renewable energy capacity, especially wind and solar. Due to the continuous decline in the costs of clean energy production, the share of renewable energy 7

See Earth Policy Institute, All Datasets, n.d.

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in global electricity capacity accelerated. There has been an increase for solar from around 1 gigawatt (GW) in 2000 to 855 GW in 2021; wind has also surged from 17 GW to 823 GW (see Fig. 7.5 and IRENA, 2022a, 2022b). While the growth of hydropower capacity has slowed, solar and wind have exhibited fast growth. A more detailed breakdown is shown in Fig. 7.5 for selected countries and technology using IRENA data. IRENA (2020) noted that more than half of the new generation of electricity from renewable sources could provide electricity at a much cheaper price compared to newly established fossil fuel-based alternatives. As we have discussed, the facts and data show that coal has the greatest reserves, with a high reserve-to-production ratio, but the use of it also generates the greatest CO2 emissions, and many developing nations still rely on coal-fired power plants. Next, we will present a growth model focusing mainly on certain features of fossil fuel as well as renewable energy. This model’s numerical solutions together with trajectories of the related variables will be examined in Sect. 7.5.

7.4 Environment, Mixed Energy System, and Sustainable Growth As previously stated, the environmental and economic disasters and damage caused by CO2 and other GHG emissions are generally termed “negative externalities.” Thus, as shown in the earlier chapters, economic growth models have been introduced to accommodate them together with resources consumption and extraction within the same framework. As we discussed in Chap. 6, more recent research has highlighted the limits of fuel use, showing the exhaustion of the carbon budget and pointing to the need of backstop technologies. Our discussion on the perils present in the carbon budget trends has underscored the urgency of the problem. This is also a challenge to build sustainable growth models. Sustainability and climate change were extensively explored by Edenhofer et al. (2014, p. 476), where they stated that “the limiting factor of global energy supply is not the scarcity of fossil fuels, but rather the limited disposal space of the atmosphere implied by climate stabilization targets.” Thus, many of the recent models on sustainable growth work with an extended welfare function. In this context, Jacob et al. (2016) emphasize the importance of pricing carbon emissions in climate change, as well as in sustainable development, by financing investments in infrastructure under various scenarios. In the welfare function, accumulated pollution was introduced by Byrne (1997). Smulders and Gradus (1996) considered net pollution and Greiner (2011), Greiner et al. (2014), and Nyambuu and Semmler (2020) added damage costs stemming from fossil fuel use. Toll (2015, pp. 298–299) estimates climate change effects on welfare using the Social Cost of Carbon (SCC) and damage costs as well. In this study, a summary of different studies was provided predicting the effects of a “doubling of the atmospheric concentration of GHG emissions on the current economy,” implying a large increase

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Fig. 7.5 Electricity capacity (solar, wind, and hydropower). Source Constructed using data from IRENA (2022a, 2022b). This figure shows electricity capacity broken down into hydropower, wind, and solar between 2000 and 2021. Panel (a) shows data for the World; Panel (b)-the United States (USA); Panel (c)-China; Panel (d)-India; Panel (e)-the United Kingdom (UK); Panel (f)-Germany; Panel (g)-Australia; Panel (h)-Japan. These data are measured in gigawatts (GW)

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in the temperature that would lead to disastrous losses, and expose low-income countries in particular. One could argue that the social cost of climate change comes along with large uncertainties and an uneven distributional burden from the effects of climate change. A carbon tax that would promote energy efficiency and transition to a carbonneutral energy is often estimated based on the marginal damage costs of CO2 emissions. However, the SCC, e.g., monetized marginal externality, computations are based on Integrated Assessment Methods (IAMs)8 , but its costs and benefits are not easy to measure. Pindyck (2013) argues that “IAMs are of little or no value for evaluating alternative climate change policies and estimating the SCC... IAM damage functions are completely made up, with no theoretical or empirical foundation” (p. 870). Moreover, the costs of the ecosystem are rarely taken into account.9 The social cost and vulnerability associated with climate change and climate disasters are discussed in Mittnik et al. (2019) assessing the impact of carbon emissions, temperature rise, and climate disasters. Their study demonstrates the benefits of adaptation policy to limit the impact of disasters so that “an increased frequency can be accompanied by lower severity resulting from reduced vulnerability” (p. 35). Another study by Weyant (2017) discusses benefits and costs in IAMs that serve as a basis for calculating the social cost of carbon and addressed some of the challenges faced by the IAMs. The authors believe these models are a good springboard for short-term models, but not adequate for longer-term policy decisions. Metcalf and Stock (2017) examined alternatives to IAM-based SCC methods using certain criteria and suggested improvements for the estimation process of the SCC. They highlighted the measures with uncertainty involved in science on climate change cost.10 In Nyambuu and Semmler (2014), a review of different growth models and their extensions, e.g., Nordhaus, emphasizing environmental effects is provided. Nordhaus (2008) Dynamic Integrated Model of Climate and the Economy (DICE) incorporates CO2 emissions, climate change effects, and damages in production. It also addresses climate change-related mitigation policies that are supposed to reduce the carbon emission through an optimal abatement policy. To examine the effects of climate change and policy, the DICE-2007 model consists of a large number of dynamic and static equations: carbon emission, damage, abatement cost, sum of emissions from land as well as industry, mass of carbon (atmospheric, upper as well as lower ocean), temperature of surface and lower ocean, radiative forcing, and abatement costs with markup and so on. The carbon emission from industries is defined with respect to output and corresponding parameters as well as the damage function.11 We will discuss the DICE model in more detail in Chap. 9 highlighting the missing parts.

8

For the abatement policies, Stern (2007), Nordhaus (2008), and others used the IAM and found different abatement and SCC contingent on the underlying assumptions. 9 See the work by Frances C. Moore and others. 10 For a detailed study on IAM and social costs associated with CO , see Kellett et al. (2019). 2 11 See the model equations in the of Nordhaus (2008, p. 205).

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Other studies including Steckel et al. (2015) presented the factors that contribute to the emissions. These include population, income per capita, energy intensity, and intensity of carbon with further decomposition into different energy sources. Greiner et al. (2014) incorporate changes in GHGs compared to the pre-industrial level and include damages in the welfare function in their model for not only a socially optimal solution, but also for a laissez-faire economy. Their numerical results suggest it is optimal not to extract all the non-renewable resources especially when non-renewable energy production has low efficiency. Bondarev et al. (2013) use variables such as technological efficiency, concentration of GHG, and change in global temperature. In addition, the atmosphere’s recovery, intensity of emissions, abatement rate, and a rise in temperature can be included in these models. According to their findings, endogenous technical change leads to a lower environmental damage associated with less GHGs emissions and a slower increase in temperature. Bondarev et al. (2014) highlight how economic activity increases GHG emissions and how it depends on the rate of abatement, output, emission’s intensity, and “rate of recovery of the atmosphere due to natural absorption” (p. 4). In addition, the temperature was used as one of the constraints where its increase was measured as compared to the pre-industrial levels and emissions. Another constraint represents R&D investments and the progress of technology. Bondarev et al. (2014) emphasized the benefits from (endogenous) technical progress for reducing GHG emissions and avoiding temperature rise. The importance of limiting the consumption of fossil fuel was covered in Bauer et al. (2013) and McCollum et al. (2014). They addressed the importance of constrained fossil fuel consumption to climate stabilization and analyzed long-term energy and emissions scenarios. By studying oil, coal, and gas markets, Bauer et al. (2013) examine the effects of intertemporal scarcity rent, price differentials, and heterogeneity and inertia in their REMIND model. They calculated fossil fuel rent’s net present value until 2100 and indicated that the share of coal would be small. A number of further studies also highlighted the importance of technology for reducing carbon emissions. These include Edenhofer et al. (2006), Van der Ploeg and Withagen (2014), Acemoglu et al. (2012), and Greiner et al. (2014). Various production technologies, dirty, e.g., fossil fuel, and clean, e.g., wind and solar, and further substitution of inputs were considered in Acemoglu et al. (2012) encouraging clean inputs that would contribute to sustainable growth. They proposed innovation and related tax or subsidy schemes for this effort. In their aggregate production function, a positive elasticity of substitution between the two sectors was introduced. As a constraint on the growth model, Acemoglu et al. (2012, p. 137) used a variable representing a quality of environment that depends on rates corresponding to environmental degradation as well as regeneration. Their results stressed the importance of supporting clean technology via R&D and replacing production inputs with clean sources over time. More recently, Acemoglu et al. (2016), in their research paper, covered competition between different technologies (clean and dirty) and used U.S. energy sector data (1576 firms between 1975 and 2004) highlighting the role of an optimal policy

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for this transition. In their microeconomic model, productivity is affected negatively by “atmospheric carbon concentration above the pre-industrial level” (pp. 58–59). Van der Ploeg and Withagen (2014) applied a “Green Ramsey model” and focused on the extraction of non-renewable resources by exploring the optimal usage of fossil fuel and clean energy, and possible transitions. They addressed four regimes that are each dependent on initial stocks of capital and oil. Furthermore, Van der Ploeg and Rezai (2017) demonstrated how to calculate untapped fossil fuel amount based on the extraction cost technology, technical progress in renewable energy, future economic growth, time impatience, and intergenerational inequality aversion. According to their research, “general equilibrium IAM with stock-dependent extraction costs, endogenous energy transitions, and Oxford carbon dynamics shows that with business as usual global warming leads to unacceptable degrees of peak global warming, around 5 ◦ C” (p. 219). Hoel and Kverndokk (1996) presented an optimal depletion and global warming model with negative externalities highlighting the use of a non-renewable resource generating carbon when employed. In the preference function, they included a damage from carbon consumption or negative externality, which is an increasing and convex function of carbon stock compared to its pre-industrial level. In their model, extraction of fossil fuel (and consumption) is expressed in units of carbon and the utility function depends on it. The objective function contains fossil fuel extraction cost, and the dynamics of the CO2 emissions are increased by the extraction of fossil fuel, but decreased by the depreciation rate based on CO2 atmospheric lifetime.12 In a number of studies, e.g., Smulders and Gradus (1996), Byrne (1997), Van der Ploeg and Withagen (2014), and Greiner et al. (2014), a modified growth model that is based not only on fossil fuel (St ), but also on renewable energy created by the use of capital is explored. These models are further developed by Nyambuu and Semmler (2020), including a resource discovery and related exploration cost. In addition, they proposed model variants for advanced countries (ACs) and for developing countries (DCs), and specified corresponding emission targets as an underlying factor for the dynamic pattern of cumulative CO2 emissions. In the model, the production function, Y , depends on efficiency indices for K and S, Z K and Z S , respectively, and is formulated as13 Y = Z (Z K K + Z S S)ω

(7.1)

where 0 < ω ≤ 1, Z > 0 On one hand, fossil fuel energy as input generates output more efficiently if Z S is higher. On the other hand, renewable energy to be used as input is created using a capital stock, K , that produces energy using renewable sources of energy such as wind or solar energy, with associated efficiency. As a result of higher Z K , solar or wind sources are transformed into usable energy with greater efficiency.

12 13

See the model in Hoel and Kverndokk (1996, p. 118) for detail. See Nyambuu and Semmler (Nyambuu and Semmler 2020, p. 370).

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Next, the discovery of non-renewable resources is explained. It can be defined as ϕ(R 0 − m − R − R b ) with total fossil fuel available at the start (R 0 ), stock of the resource R, with cumulative extraction from the past (m), and “value of the fossil fuel reserves below which no new deposits will be discovered” (see Nyambuu and Semmler (2020, p. 370)) that is denoted by R b . A parameter ϕ is used to examine how fast new exhaustible energy can be discovered. It is assumed that these resources’ exploitation costs follow (R 0 − m)−2 as in Greiner et al. (2014). These functions of discovery as well as costs are discussed in Nyambuu and Semmler (2014) and explained in Chap. 5 of this book. The households’ welfare function depends on consumption, C, and CO2 emission’s damages measured by (−τ (E − E ∗ )2 ), as was described in studies such as by Greiner (2011) and Greiner et al. (2014). As shown in Nyambuu and Semmler (Nyambuu and Semmler (2020), p. 371), the dynamic decision problem can be quite complex: With control variables of consumption, C, and fossil fuel extraction rate, S, and state variables including capital stock, K , cumulative fossil fuel extraction, m, fossil fuel stock or proved reserves14 , R, and damages arising from cumulative GHG emissions, E, driven by fossil fuel use, S. The model can be presented as follows: ∞ max C,S

  e−θt ln(C) − τ (E − E ∗ )2 dt

(7.2)

0

s.t.

K˙ = Y − C − δK − (R 0 − m)−2 S R˙ = ϕ(R 0 − m − R − R b ) − S E˙ = ϑS − ς(E − λE ∗ ) m˙ = S

(7.3) (7.4) (7.5) (7.6)

where parameter ϑ denotes the “fraction of GHG not absorbed by the ocean”15 (0 < ϑ < 1), ς reflects atmospheric lifetime of GHGs (0 < ς < 1), and E ∗ stands for the pre-industrial level of GHG emission from fossil fuel. The parameter λ that represents GHG stabilization efforts can be used to specify the target carbon budgets of different countries. As we discussed earlier, DCs’ industrialization has commenced at later times as compared to ACs that have been consuming fossil fuel and generating cumulative GHG emissions for a much longer time. For this reason, Nyambuu and Semmler (2020) explained that DCs should have a higher allowed carbon target level as compared to the pre-industrialization period, thus λE ∗ needs to be higher. The opposite is applied to ACs with lower allowed carbon targets.

14

According to BP, proved reserves are “generally taken to be those quantities that geological and engineering information indicates with reasonable certainty can be recovered in the future from known reservoirs under existing economic and geological conditions.” (Available via https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-worldenergy/using-the-review/methodology.html#accordion_oil-methodology). 15 For details, see Greiner et al. (2014).

7.5 Numerical Solutions–Fossil Fuel Extraction, Emission, and Damages

101

Using again the Nonlinear Model Predictive Control (NMPC) method, the next section focuses on the solutions of the dynamic decision problem as presented in Eqs. (7.2)–(7.6).

7.5 Numerical Solutions–Fossil Fuel Extraction, Emission, and Damages We numerically solve the model described in the previous section, using the NMPC,16 and present results following the dynamic model that was described above. The figures that we provide illustrate dynamic trajectories of the capital (K ), stock of fossil fuel (R), damages resulting from GHG emissions (cumulative) (E), and cumulative extraction (m) assuming different initial levels of state variables and parameter values that correspond to a specific condition and environment. In the solutions, presented in this section, we used the following values of efficiency indices, target carbon budget, and other parameters: θ = 0.03, Z = 1, Z K = 1, Z S = 1, τ = 1, E ∗ = 1, δ = 0.05,  = 4, ϕ = 4, R 0 = 7, R b = 4, ς = 0.08, ω = 0.5, and ϑ = 0.08.17 In addition, different initial conditions of the state variables are selected.18 Following Nyambuu and Semmler (2020), we also note that while a low value of λ reflects the lower GHG emissions target that developed countries need to achieve, a high value of this parameter corresponds to the case for developing countries. That is why the values of λ parameter will be different for the scenarios that we present. First, we focus on low value of λ = 0.3 and analyze the results shown in Fig. 7.6 where the following initial conditions for the state variables are used: K 0 = 0.2, R0 = 0.4, E 0 = 0.3, m 0 = 0.2 in Panel (a) and K 0 = 0.5, R0 = 3.0, E 0 = 1.1, m 0 = 0.5 in Panel (b). In all the cases presented in this section, the general trend for K is very high at the beginning, perhaps driven by the high negative externalities of fossil energy. Presumably, this occurred due to large extraction rates during the initial period. But as time goes by, the fossil fuel stock declines to a very low level and remains unchanged with some stock left in the ground. In most cases, as we explained in Chap. 5, the dynamics of the stock of fossil fuel, R, starts declining quickly because of the high initial condition of R0 . In contrast, numerical solutions depicted in Panel (a) of Fig. 7.6 show an inverted U-shaped pattern for R due to the small initial condition, e.g., R0 = 0.4.

16

See Grüne and Pannek (2011) for the details of this method. The most parameter values are based on Nyambuu and Semmler (2020). 18 We should note that in most of the scenarios in this chapter, we used different initial conditions for the state variables. For example, R0 took values of R0 = 0.4 and R0 = 3.0, compared to Nyambuu and Semmler (2020), where it was assumed to be R0 = 0.5. 17

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Fig. 7.6 Dynamic trajectories of state variables when λ = 0.3. In these figures, K is capital stock, R is the proved reserves of the resource, E is cumulative emissions, and m is the accumulated extraction. The horizontal axis shows the time, t. Dynamic paths for K , R, E, and m are shown when λ = 0.3. As explained in this section, the parameter λ represents GHG stabilization efforts. Panel (a) demonstrates paths for K , R, E, and m when the initial conditions are K 0 = 0.2, R0 = 0.4, E 0 = 0.3, and m 0 = 0.2. Panel (b) demonstrates paths for K , R, E, and m when the initial conditions are K 0 = 0.5, R0 = 3.0, E 0 = 1.1, and m 0 = 0.5

Next, in the same figure, we focus on cumulative emissions, E, and observe declining trends over time in both cases where initial conditions are low and high. This is attributable to a drop in the stock of fossil fuel that helps to reduce cumulative emissions. We should also note that these results seem to be associated with low parameter values for λ. Furthermore, we took different values of λ and compared the dynamic trajectories of cumulative emissions. Optimal dynamic paths for corresponding control variables of consumption, C, and fossil fuel extraction rate, S, are illustrated in Fig. 7.7. Furthermore, we considered high values of λ, e.g., λ = 3.0, reflecting a developing country’s case, and explored its impact on the dynamics of decision and state variables. Other parameter values as well as initial conditions took the same values as in the previous case. From the results shown in Fig. 7.8, we observe that the stock of fossil fuel declined but stops at a certain level leaving some stocks left unextracted in the ground. Initially, when fossil fuel is discovered and extracted at a large volume, the emissions rise rapidly. But when the fossil fuel extraction starts declining, as we expected, the growth rate of emissions slows down significantly as shown by a decreasing slope in the E line (see Panels (a) and (b) in Fig. 7.8). The results also confirm that generally cumulative emissions are higher when E 0 is greater. Optimal dynamic paths for corresponding control variables of consumption, C, and fossil fuel extraction rate, S, are shown in Fig. 7.9. Using numerical analysis of the model, characterizing different countries with various emissions target levels (λ), we illustrate numerical solutions in Fig. 7.10. We present four more cases where λ = 0.05, λ = 0.8, λ = 1.5, and λ = 5.0. For the initial conditions, we assume that K 0 = 0.2, R0 = 0.4, E 0 = 0.3, and m 0 = 0.2

7.5 Numerical Solutions–Fossil Fuel Extraction, Emission, and Damages

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Fig. 7.7 Optimal paths for consumption and fossil fuel extraction rate when λ = 0.3. In these figures, C is consumption, S is resource extraction, K is capital stock, R is the proved reserves of the resource, E is cumulative emissions, and m is the accumulated extraction. The horizontal axis shows the time, t. Dynamic paths for C and S are shown when λ = 0.3. As explained in this section, the parameter λ represents GHG stabilization efforts. Panel (a) demonstrates paths for C and S when the initial conditions are K 0 = 0.2, R0 = 0.4, E 0 = 0.3, and m 0 = 0.2. Panel (b) demonstrates paths for C and S when the initial conditions are K 0 = 0.5, R0 = 3.0, E 0 = 1.1, and m 0 = 0.5

Fig. 7.8 Dynamic trajectories of state variables when λ = 3. In these figures, K is capital stock, R is the proved reserves of the resource, E is cumulative emissions, and m is the accumulated extraction. The horizontal axis shows the time, t. Dynamic paths for K , R, E, and m are shown when λ = 3.0. As explained in this section, the parameter λ represents GHG stabilization efforts. Panel (a) demonstrates paths for K , R, E, and m when the initial conditions are K 0 = 0.2, R0 = 0.4, E 0 = 0.3, and m 0 = 0.2. Panel (b) demonstrates paths for K , R, E, and m when the initial conditions are K 0 = 0.5, R0 = 3.0, E 0 = 1.1, and m 0 = 0.5

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Fig. 7.9 Optimal paths for consumption and fossil fuel extraction rate when λ = 3.0. In these figures, C is consumption, S is resource extraction, K is capital stock, R is the proved reserves of the resource, E is cumulative emissions, and m is the accumulated extraction. The horizontal axis shows the time, t. Dynamic paths for C and S are shown when λ = 3.0. As explained in this section, the parameter λ represents GHG stabilization efforts. Panel (a) demonstrates paths for C and S when the initial conditions are K 0 = 0.2, R0 = 0.4, E 0 = 0.3, and m 0 = 0.2. Panel (b) demonstrates paths for C and S when the initial conditions are K 0 = 0.5, R0 = 3.0, E 0 = 1.1, and m 0 = 0.5

and use the same parameter values that were listed previously. When λ is very high, λ = 5.0, cumulative emissions rise much faster and reach a higher level than other cases (see Panel (a) in Fig. 7.10). As in Nyambuu and Semmler (2020), these results show that initially, due to a large extraction of fossil fuel, there is a sharp rise in emissions, but the growth rate declines over time when λ is high. In contrast, when λ is low, although emissions increase at first, they start falling because of a significant decline in fossil fuel extraction. These results demonstrate that long-run changes in cumulative emissions can be associated with values of λ, thus indicating the importance of the target levels for pollution reduction. Dynamic trajectories of the GHG emissions in all cases are compared and illustrated in Fig. 7.11. Other variables, e.g., efficiency indices and associated parameters, may also have an impact on the trajectories of cumulative emissions. Nyambuu and Semmler (2020) show that when Z S is high, cumulative emissions tend to rise to a higher level than in the case where Z S is low. This can be explained by the cumulative extraction of fossil fuel. In sum, the results from different scenarios show the possibility of reducing the growth rate of CO2 emissions over time.

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105

Fig. 7.10 Dynamic trajectories of state variables for different values of λ. In these figures, K is capital stock, R is the proved reserves of the resource, E is cumulative emissions, and m is the accumulated extraction. The horizontal axis shows the time, t. Dynamic paths for K , R, E, and m are shown when the initial conditions are K 0 = 0.2, R0 = 0.4, E 0 = 0.3, and m 0 = 0.2; results correspond to the following values of λ: 5.0, 1.5, 0.8, and 0.05. The parameter λ represents GHG stabilization efforts

Fig. 7.11 Dynamic trajectories reflecting different target carbon budgets over time. In this figure, dynamic paths for GHG emissions, E, are shown for different levels of the emissions target, shown by the parameter λ. The horizontal axis shows the time, t. These results correspond to the following values of λ: 0.05, 0.3, 0.8, 1.5, 3.0, and 5.0

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7.6 Conclusion In this chapter, we presented trends related to the carbon budget and focused on the importance of transitioning to a low-carbon economy by phasing out fossil fuels, coal in particular, and replacing it with renewable energy. Using an extended growth model, the impact of resource discovery and extraction, available reserves, and the associated costs of fossil fuel use on pollution were examined. In the production function, two types of energy sources, fossil fuel and renewables, were used. The efficiency indices corresponding to each of these production inputs reflect their effectiveness in the transformation of the energy system toward a low-carbon economy. Utilizing the model proposed by Nyambuu and Semmler (2020), the relationship between fossil fuel resources and emissions in various scenarios was studied using NMPC. The results reveal the system sensitivity to energy efficiency parameters, fossil fuel’s discovery and extraction rates, and the impact of carbon emission target levels. We pursued the policy target that the carbon emission allowance should be set at a lower level for advanced countries than for developing countries. The numerical solutions of the model tracking the externality effects of fossil fuel demonstrate an increasing trend in capital stock used for renewable energy. A high discovery rate and extraction rate of fossil fuel cause an initial rise in cumulative CO2 emission. However, a further decline of the stock of fossil fuel, until it reaches a certain level of reserves, coupled with high capital stock, leads to a moderate fall in cumulative emissions. As demonstrated in Nyambuu and Semmler (2020) and above, a low target for carbon emissions, thus a low λ, seems to lead to a monotonic reduction in emissions over time. On the other hand, in developing countries with higher allowable carbon limits, higher λ, emissions continue rising. But note that the developing countries might have a much lower share in total worldwide emissions. So, the emission target for the high-emitting countries matters most. Our results also suggest phasing in renewable energy before fossil fuel is completely depleted, thus leaving some fossil fuel in the ground, is the best path to take. Such a transition has the great benefit of reducing the previously discussed negative externalities, slows the rate of increase in filling the carbon budget, decreases the CO2 concentration in the atmosphere, and diminishes temperature rise. In addition, the relationship between cumulative past fossil fuel consumption and its current stock level can influence decisions pertaining to the amount of resources not to be extracted.

References Acemoglu D, Aghion P, Bursztyn L, Hemous D (2012) The environment and directed technical change. Am Econ Rev 102(1):131–166 Acemoglu D, Akcigit U, Hanley D, Kerr W (2016) Transition to clean technology. J Polit Econ 124(1):52–104

References

107

Bauer N, Mouratiadou I, Luderer G, Baumstark L, Brecha RJ, Edenhofer O, Kriegler E (2013) Global fossil energy markets and climate change mitigation–an analysis with REMIND. Clim Chang :1–14 Bondarev A, Clemens C, Greiner A (2013) Climate change and technical progress: impact of informational constraints. SSRN ID 2207947. Social Science Research Network, Rochester, NY Bondarev A, Clemens C, Greiner A (2014) Climate change and technical progress: impact of informational constraints. In: Moser E, Semmler W, Tragler G, Veliov V (eds) Dynamic optimization in environmental economics, dynamic modeling and econometrics in economics and finance 15. Springer, Berlin, Heidelberg, pp 3–35 British petroleum (BP). https://www.bp.com/en/global/corporate/energy-economics/statisticalreview-of-world-energy/using-the-review/methodology.html#accordion_oil-methodology. Accessed 4 Nov 2022 Byrne MM (1997) Is growth a dirty word? pollution, abatement and endogenous growth. J Dev Econ 54:261–284 Carbon pricing leadership coalition (CPLC) (2017) Report of the high-level commission on carbon prices. https://static1.squarespace.com/static/54ff9c5ce4b0a53decccfb4c/t/ 59244eed17bffc0ac256cf16/1495551740633/CarbonPricing_Final_May29.pdf. Accessed 22 Apr 2019 Cook J, Nuccitelli D, Green SA, Richardson M, Winkler B, Painting R, Way R, Jacobs P, Skuce A (2013) Quantifying the consensus on anthropogenic global warming in the scientific literature. Environ Res Lett 8(2):024024 Earth policy institute. All datasets. http://www.earth-policy.org/data_center/C26. Accessed 4 Nov 2022 Edenhofer O, Kadner S, von Stechow C, Schwerhoff G, Luderer G (2014) Linking climate change mitigation research to sustainable development. In: Atkinson G, Dietz S, Neumayer E, Agarwala M (eds) Handbook of sustainable development, 2nd edn. Edward Elgar Publishing Limited, UK. https://doi.org/10.4337/9781782544708.00044 Edenhofer O, Lessmann K, Kemfert C, Grubb M, Kohler J (2006) Induced technological change: exploring its implications for the economics of atmospheric stabilization: synthesis report from the innovation modeling comparison project. Energy J 27:57–108 Edenhofer O, Steckel JC, Jakob M, Bertram C (2018) Reports on coal’s terminal decline may be exaggerated. Environ Res Lett 13(2):024019 Eichner T, Pethig R (2017) Buy coal and act strategically on the fuel market. Eur Econ Rev 99: 77–92. https://doi.org/10.1016/j.euroecorev.2017.04.001 Fleurbaey M, Kartha S, Bolwig S, Chee YL, Chen Y, Corbera E, Lecocq F (2014) Sustainable development and equity. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA Friedlingstein P, Jones MW, O’Sullivan M, Andrew RM, Bakker DCE, Hauck J, Le Quéré C, Peters GP, Peters W, Pongratz J, Sitch S, Canadell JG, Ciais P, Jackson RB, Alin SR, Anthoni P, Bates NR, Becker M, Bellouin N, Bopp L, Chau TTT, rédéric Chevallier F, Chini LP, Cronin M, Currie KI, Decharme B, Djeutchouang LM, Dou X, Evans W, Feely RA, Feng L, Gasser T, Gilfillan D, Gkritzalis T, Grassi G, Gregor L, Gruber N, Özgür Gürses, Harris I, Houghton RA, Hurtt GC, Iida Y, Ilyina T, Luijkx IT, Jain A, Jones SD, Kato E, Kennedy D,Goldewijk KK , Knauer J, Korsbakken JI, Körtzinger A, Landschützer P, Lauvset SK, Lefévre N, Lienert S, Liu J, Marland G, McGuire PC, Melton JR, Munro DR, Nabel JEMS, Nakaoka SI, Niwa Y, Ono T, Pierrot D, Poulter B, Rehder G, Resplandy L, Robertson E, Rödenbeck C, Rosan TM, Schwinger J, Schwingshackl C, Séférian R, Sutton AJ, Sweeney C, Tanhua T, Tans PP, Tian H, Tilbrook B, Tubiello F, van der Werf G, Vuichard N, Wada C, Wanninkhof R, Watson AJ, Willis D, Wiltshire

108

7 Transition to a Low-Carbon Energy System

AJ, Yuan W, Yue C, Yue X, Zaehle S, Zeng J (2022) Global carbon budget 2021. Earth Syst Sci Data 14: 1917–2005. https://doi.org/10.5194/essd-14-1917-2022 Global carbon project (2021) Supplemental data of global carbon budget 2021 (Version 1.0) [Data set]. Global carbon project. https://doi.org/10.18160/gcp-2021. Accessed 12 Aug 2022 Greiner A (2011) Environmental pollution, the public sector and economic growth: a comparison of different scenarios. Optim Control Appl & Methods 32:527–544 Greiner A, Grüne L, Semmler W (2014) Economic growth and the transition from non-renewable to renewable energy. Environ Dev Econ 19(4):417–439 Grüne L, Pannek J (2011) Nonlinear model predictive control: theory and algorithms. Springer, Berlin Hoel M, Kverndokk S (1996) Depletion of fossil fuels and the impacts of global warming. Resour Energy Econ 18(2):115–136 International energy agency (IEA) (2020a) World energy outlook 2020. https://www.iea.org/reports/ world-energy-outlook-2020#. Accessed 2 Jan 2021 International energy agency (IEA) (2020b) CO2 emissions from fuel combustion: greenhouse gas emissions from energy. https://www.oecd-ilibrary.org/energy/data/iea-co2-emissions-fromfuel-combustion-statistics_co2-data-enAccessed 12 Nov 2022 International energy agency (IEA) (2021a) World energy outlook 2021. https://www.iea.org/reports/ world-energy-outlook-2021. Accessed 1 Sept 2021 International energy agency (IEA) (2021b) Greenhouse gas emissions from energy. https://www. iea.org/data-and-statistics/data-tools/greenhouse-gas-emissions-from-energy-data-explorer. Accessed 12 Aug 2022 IPCC (2014) Mitigation of climate change. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA IRENA (2020) Renewable power generation costs in 2019. International Renewable Energy Agency, Abu Dhabi IRENA (2022) Renewable capacity statistics 2022. International Renewable Energy Agency, Abu Dhabi IRENA (2022) Renewable energy statistics 2022. International Renewable Energy Agency, Abu Dhabi Jacob M, Chen C, Fuss S, Marxen A, Rao DN, Edenhofer O (2016) Carbon pricing revenues could close infrastructure access gaps. World Dev 84:254–265 Jakob M, Hilaire J (2015) Unburnable fossil fuel reserves. Nat 517:150–152 Kellett CM, Weller SR, Faulwasser T, Grüne L, Semmler W (2019) Feedback, dynamics, and optimal control in climate economics. Annu Rev Control 47:7–20 Kolstad C, Urama K, Broome J, Bruvoll A, Cariño Olvera M, Fullerton D, Gollier C (2014) Social, economic and ethical concepts and methods. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds) Climate change 2014: mitigation of climate change, contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA Knopf B, Kowarsch M, Lüken M, Edenhofer O, Luderer G (2012) A global carbon market and the allocation of emission rights. In: Edenhofer O, Wallacher J, Lotze-Campen H, Reder M, Knopf B, Müller J (eds) Climate change. Springer, Justice and Sustainability, pp 269–285 McCollum D, Bauer N, Calvin K, Kitous A, Riahi K (2014) Fossil resource and energy security dynamics in conventional and carbon-constrained worlds. Clim Chang 123(3–4):413–426 McGlade CE, Ekins P (2015) The geographical distribution of fossil fuels unused when limiting global warming to 2◦ C. Nat 517:187–190 Metcalf GE, Stock JH (2017) Integrated assessment models and the social cost of carbon: a review and assessment of U.S. experience. Rev Environ Econ Policy 11(1): 80–99. https://doi.org/10. 1093/reep/rew014

References

109

Meinshausen M, Meinshausen N, Hare W, Raper SCB, Frieler K, Knutti R, Frame DJ, Allen MR (2009) Greenhouse-gas emission targets for limiting global warming to 2◦ C. Nat 458(7242):1158– 1162 Mittnik S, Semmler W, Haider A (2019) Climate disaster risks–empirics and a multi-phase dynamic model. International Monetary Fund. Working Paper No. 2019/145. Published as: Mittnik S, Semmler W, Haider A (2020) Climate disaster risks—empirics and a multi-phase dynamic model. Econ 8(3): 1–27 NASA. National aeronautics and space administration, goddard institute for space studies, global land-ocean temperature index in 0.01 degrees celsius. http://data.giss.nasa.gov/gistemp/. Dataset URL: http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt. Accessed 7 Nov 2022 Nordhaus WD (2008) A question of balance: weighing the options on global warming policies. Yale University Press Nyambuu U, Semmler W (2020) Climate change and the transition to a low carbon economy–Carbon targets and the carbon budget. Econ Model 84: 367–376. https://doi.org/10.1016/j.econmod.2019. 04.026 Nyambuu U, Semmler W (2014) Trends in the extraction of nonrenewable resources: The case of fossil energy. Econ Model 37(C): 271–279. https://doi.org/10.1016/j.econmod.2013.11.020 Nyambuu U, Semmler W, Palokangas T (2014) Sustainable growth: modelling, issues and policies. International Institute for Applied Systems Analysis (IIASA) Interim Report IR-14-019 O’Neill BC, Dalton M, Fuchs R, Jiang L, Pachauri S, Zigova K (2010) Global demographic trends and future carbon emissions. Proc Natl Acad Sci USA 107(41):17521–17523. https://doi.org/10. 1073/pnas.1004581107 Rogelj J, Hare W, Lowe J, van Vuuren DP, Riahi K, Matthews B, Hanaoka T, Jian K, Meinshausen M (2011) Emission pathways consistent with a 2◦ C global temperature limit. Nat Clim Chang 1(8):413–418 Pfeiffer A, Millar R, Hepburn C, Beinhocker E (2016) The ’2◦ C capital stock’ for electricity generation: committed cumulative carbon emissions from the electricity generation sector and the transition to a green economy. Appl Energy 179: 1395–1408. https://doi.org/10.1016/j.apenergy. 2016.02.093 Pickering J, Barry C (2012) On the concept of climate debt: its moral and political value. Crit Rev Int Soc Polit Philos 15: 667–685. https://doi.org/10.1080/13698230.2012.727311 Pindyck RS (2013) Climate change policy: what do the models tell us? J Econ Lit 51(3):860–72 Rozenberg J, Davis SJ, Narloch U, Hallegatte S (2015) Climate constraints on the carbon intensity of economic growth. Environ Res Lett 10(9):095006 Sartor O (2018) Implementing coal transitions: insights from case studies of major coalconsuming economies. IDDRI and Climate Strategies. https://www.iddri.org/sites/default/files/ PDF/Publications/Catalogue Accessed 25 Nov 2022 Sheehan P, Jones RN, Jolley A, Preston BL, Clarke M, Durack PJ, Islam SMN, Whetton PH (2008) Climate change and the new world economy: implications for the nature and timing of policy responses. Glob Environ Chang 18(3):380–396 Smulders S, Gradus R (1996) Pollution abatement and long-term growth. Eur J Polit Econ 12:505– 532 Steckel JC, Edenhofer O, Jakob M (2015) Drivers for the renaissance of coal. Proc Natl Acad Sci USA (PNAS) 112(29): E3775–E3781. https://doi.org/10.1073/pnas.1422722112 Stern N (2007) The Economics of climate change: the stern review. Cambridge University Press, Cambridge and New York. Stern N (2014) Ethics, equity and the economics of climate change paper 1: science and philosophy. Econ Philos 30(3):397–444 Stern N (2015) Economic development, climate and values: making policy. Proc R Soc B: Biol Sci 282(1812). https://doi.org/10.1098/rspb.2015.0820 Toll RSJ (2015) The social cost of carbon. In: Bernard L, Semmler W (eds) The oxford handbook of the macroeconomics of global warming. Oxford University Press, New York United nation climate change (UNFCCC). Summary of the paris agreement. https://unfccc.int/ resource/bigpicture/#content-the-paris-agreemen. Accessed 4 Nov 2022

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Van der Ploeg F, Withagen C (2014) Growth, renewables, and the optimal carbon tax. Int Econ Rev 55(1):283–311 Van der Ploeg F, Rezai A (2017) Cumulative emissions, unburnable fossil fuel, and the optimal carbon tax. Technol Forecast Soc Chang 116:216–222 Weyant J (2017) Some contributions of integrated assessment models of global climate change. Rev Environ Econ Policy 11(1):115–137 World bank. World development indicators. https://data.worldbank.org/data-catalog/worlddevelopment-indicators. Accessed 19 Dec 2020 World bank. Climate change knowledge portal. https://climateknowledgeportal.worldbank.org/. Accessed 12 Aug 2022

Chapter 8

The Private Sector—Energy Transitions and Financial Market

Overview This chapter focuses on the private sector and energy transitions. If renewable energy is the key to a low-carbon economy and is essential to the fulfillment of the Paris agreement, there has to be a large scaling-up of renewable energy supplies. These are mostly produced by the private sector. In this chapter, we study the potential competition between renewable energy and fossil fuel firms and how the phasing in of renewable energy might occur on a large scale. Critical for the success of renewable energy firms are the capital costs and trends in renewable energy production, encouraging new types of investments. In principle, this allows renewable energy firms to enter the market and subsequently increase their market share. We discuss entry barriers for the entrants—the new energy firms—and how they are set-up by the established firms, the incumbents. A related question is if the new firms would have sufficient equity to survive or become overleveraged. A further issue, on the financial market side, is how does asset pricing evolve? Does the stock market valuation of incumbent firms represent “stranded assets” and how would this show up in the balance sheets of the banking system and dynamics of wealth portfolios? Will the holding of fossil fuel assets in a portfolio become too risky?

8.1 Private Real and Financial Sectors Given the 2015 Paris agreement to keep global temperature rises below 2 or even 1.5 ◦ C, there have been a number of policies discussed on the transformation of high-carbon to low-carbon economies. In general, various instruments and regulations, e.g., fuel standards for cars, electric cars, carbon pricing and specific financing instruments are proposed, and their effectiveness for such a transition is assessed. For the financial side of this transition, not only a reduction of fossil fuel subsidies and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_8

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carbon pricing (emissions trading systems, ETS, and carbon tax) are explored, but also the issuance of green bonds1 and other related instruments have been proposed. The first green bond was issued by the World Bank in 2008, and has grown to a total of 200 issue worth around $18 billion as of June 2022.2 The World Bank green bond’s current commitment stands at 72% for mitigation, dominated by renewable energy, energy efficiency, and transportation, and 28% for adaptation purposes (see World Bank 2021). Whereas carbon pricing is relying on a slow substitution process in the production and use of energy, the issuance of green bonds is aiming more at large scale and accelerated climate-related investments in energy production, extensive grids, and transport systems. The investments proposed allow for the provision of infrastructure for a low-carbon economy as well as mitigation and adaptation at an accelerated rate.3 Solely proposing a carbon tax for mitigation policy does not appear sufficient. It is of little help if alternatives to fossil fuels, such as renewable energy sources, are not universally available. Thus, phasing in green energy through the scaling-up of climate-related investments is needed. However, this can be a major challenge for energy transformation policy design. Even if widely adopted, one may need complementary strategies. In particular, one needs to incentivize and support, as the Carbon Pricing Leadership Coalition (CPLC) stresses, some kind of Schumpeterian renewable energy firm as the paradigmatic driver of the transition.4 This changeover needs to scale up not only climate-related investments, but also financial mechanisms specific to those investments. As the existing literature on innovation dynamics shows, this can be expected to display nonlinearities, e.g., thresholds, and complex dynamics. We will also study the major types of entry barriers, i.e., availability of financing sources, e.g., selffinancing, equity finance, and bank loans. The next question is whether there are risks specific to this process, e.g., over-expansion. We will also study the asset price dynamics of entrants, incumbents, and the possible destabilizing effects that might be triggered in the financial sector by carbon based energy. In other words, the following questions arise: How do portfolio holdings with both types of assets perform? Are there “stranded” assets, and how will these affect the banking system and financial markets at large? Yet, these new types of firms, e.g., renewable energy and S&M firms, also face challenges. Recent studies on climate policy modeling have discussed challenges faced by new entrants, particularly those embracing new technologies, in the energy sector. We will elaborate on portfolio holdings and their fragile performance when containing fossil fuel assets. 1

According to Climate Bonds Initiative, a green bond is defined as “financial debt instrument that is almost entirely linked with green and climate friendly assets or projects.” (see https://www. climatebonds.net/certification/glossary). 2 See https://treasury.worldbank.org/en/about/unit/treasury/ibrd/ibrd-green-bonds, Accessed on August 16, 2022. 3 See Orlov et al. (2018) and Maurer et al. (2015). 4 See Braga et al. (2021).

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Looking at the phasing in of renewable energy firms into the energy sectors, major issues include: 1. What are the entry barriers for renewable energy? 2. Are there long-run cost advantages for renewable energy sources, e.g., declining long-run average costs? 3. What are the financing sources available to the new energy firms? 4. What external finance and leveraging are sustainable? 5. What will happen to the asset prices of carbon-based oligopolies and their linked businesses, i.e., will they be “stranded assets” and surpassed by renewable energy businesses, and create some instability in the banking system? 6. Which assets will perform better in long-term portfolios that re-balance assets holding at low frequency? This chapter is structured as follows: after the introduction of related stylized facts, a dynamic model of competition between fossil fuel energy (incumbents) and renewable energy (entrants) firms will be presented.5 We will study the dynamics of market entry for renewable energy firms, and the dynamics of the market shares of the two types of firms in the energy sector. The goal is to define the types of entry barriers established by the incumbents; how they are, or are not, overcome by the new entrants, and if they overcome the entry and competition barriers, will they have sufficient equity to survive? Moreover, we introduce a dynamic portfolio model to compare the financial performance of the two types of firms.

8.2 Some Stylized Facts Some of the stylized facts on trends in prices and costs for fossil fuel (gas, oil, coal) as well as renewable energy, e.g., wind, solar, were presented in Chaps. 6 and 7. The analysis is extended in this chapter by providing further information on the Levelized Costs Of Electricity (LCOE) and the size of climate investments. Clean energy production costs have been declining dramatically, especially since 2000. This, as well as extensive supportive policy measures, have led to higher investment in clean energy. According to the International Renewable Energy Agency (IRENA 2022a), Total Installed Costs associated with different renewable energy investments were significantly reduced in 2021 as compared to 2010: solar photovoltaics (PV) by 82%, and onshore and offshore wind power by 35% and 41% respectively. As shown in Fig. 8.1, in terms of LCOE, expenses associated with renewable power generation continue declining: solar (PV) by 88%, concentrated solar power (CSP) by 68%, onshore wind by 68%, and offshore wind by 60% in 2021 compared to 2010.

5

The model has also some resemblance to Brian Arthur’s work (1989) and Gevorkyan and Semmler (2016).

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Fig. 8.1 Costs of electricity from renewable power generation. Source Constructed using data from IRENA (2022a). This figure shows the costs of electricity from renewable power generation between 2010 and 2021. These data for solar photovoltaics (PV), wind, hydropower, and concentrated solar power (CSP) are measured in 2021 US Dollars per kilowatt-hour (kWh)

“Learning by doing” refers to the increased efficiency gained through experience with technology. A number of studies show that this has happened in both solar PV and wind turbines, contributing to lower costs (Kavlak et al. 2018). While solar PV module real prices declined by around 87% from 2010 to 2021, wind turbine prices fell, on average, around 41% during the same period (see IRENA (2022a)). The rapid decline in costs has driven demand for renewable energy sources and greatly contributed to the investment in the transition to a low-carbon economy. IRENA (2019) compares renewable sources of electricity generation in the Gulf Cooperation Council (GCC) region and presents the following energy costs: The lowest is for solar, costing 2.3 cents/kWh for solar PV and CSP at 7.3 cents/kWh.6 Lazard (2021) analysis of LCOE for energy generation technologies also confirms the declining trends for energy, in particular, solar and wind power. Their costs have become much lower compared to some of the more traditional energy generation technologies such as gas peaker, coal, and combined cycle gas. Major contributors include technological and efficiency and production-related improvements, lower supply-chain and raw material costs, lower capital cost, increased competition, and possible shifts in consumers’ preferences toward more environmentally friendly generated technologies. Due to this declining trend in costs associated with renewable power, we expect more electricity to be generated. In fact, recent IRENA (2022b) data show a significant increase in electricity generation from renewable energy across the world (see Fig. 8.2). Historical data on new global investment in clean energy provided by BloombergNEF (2019) demonstrate increased shares of solar and wind since the late 2000s s and a surge in investments by Asian countries. In recent years, there was a shift to clean energy investments away from Europe and toward Asia, par6

According to IRENA (2019), for fossil fuel, the lowest gas-based generation costs around 3 cents/kWh, LNG 8 cents/kWh, oil at around 10 cents/kWh, and coal 4.5 cents/kWh for the Hassyan Clean Coal Power Plant.

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Fig. 8.2 Electricity generation from renewable energy in different regions. Source Constructed based on data from IRENA (2022b). This figure shows electricity generation from renewable energy for the World, Asia, Europe, North America, and South America between 2000 and 2020. The data are measured in thousand gigawatt-hours (GWh). Note that the data for the World are plotted on the right axis

ticularly China. The International Energy Agency (2021) data between 2015 and 2021 show that on average the renewables in Asia Pacific represented around 50% of world renewables investment, whereas Europe and North America garnered only 20% and 17%, respectively. As illustrated in Fig. 8.3, the majority of investments in power generation was allocated to renewables in different regions. However, the IEA (2020, 2021) still warns that more investments are needed to achieve a sustainable goal. Different methods have been used to finance transitioning to a low-carbon economy. Data from IRENA and CPI (2020) on “Global Landscape of Renewable Energy Finance 2020” show that renewable energy’s financial commitments reached $322 billion in 2018 with almost half of it dedicated to solar energy and almost one-third is allocated to onshore wind energy. According to BloombergNEF (2022), worldwide investments supporting transitioning toward low-carbon energy in 2021 was more than tripled compared to 2010 and reached around $755 billion; the major investors were China ($266 billion), the United States ($114 billion), Germany ($47 billion), United Kingdom ($31 billion), and France ($27 billion). This was dominated by renewable energy, which accounted for almost half of total investments. Other investments include carbon capture and storage (CCS), electrified heat, electrified transport, energy storage, hydrogen production, nuclear power, and sustainable materials. Heine et al. (2019) stress the need for a mix of carbon tax (and some emission trading) and the issuance of climate (green) bonds; these will also scale up green investments. This mix is suggested since, by creating alternative energy sources, green bonds, and investments are essential to allow the substitution away from fossil fuels. It also allows for better sharing of costs and benefits between generations. This scales up green investments. In fact, according to data provided by Climate Bonds

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Fig. 8.3 Investments in power generation in Asia Pacific, Europe, North America, and World. Source Constructed using data from International Energy Agency (2021). This figure shows investments in power generation in Asia Pacific, Europe, North America (N.America), and World in 2015 and 2021. The pie chart demonstrates the breakdown in terms of percentage share in coal, gas and oil, renewables, and nuclear

Initiative,7 cumulative issuance of green bonds reached a record high of almost $1.9 trillion as of the first half of 2022 with around one-third of use-of-proceeds in the energy sector, followed by buildings (27%), and transportation (18%). The number of companies in the clean energy sector has been rising and expanding to more countries including Canada, China, Denmark, Spain, and the United States. According to Investopedia, the biggest renewable energy companies, based on trailing 12-month (TTM) revenues, included Orsted A/S (a Danish company focused on wind and bioenergy solutions), Iberdrola SA (a Spanish electric utility company focused on clean energy), JinkoSolar Holding Co. Ltd. (a Chinese company in the solar power 7

See data from https://www.climatebonds.net/market/data/ (accessed on August 16, 2022).

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Fig. 8.4 Early stage firms’ growth over time. Source Constructed using data from PwC (2021) on “The State of Climate Tech 2021”. In this figure, the number of early stage rounds is shown for early venture capital (VC), seed, and series A rounds that have $1 million or more. Numbers in different years are compared: 2013, 2016, and 2020, corresponding to the first half (H1) and the 2nd half (H2) of the year

area), Vestas Wind Systems A/S (a Danish company), Siemens Gamesa Renewable Energy SA (a Spanish company focused on wind energy), Brookfield Renewable Partners LP (Canadian), First Solar Inc. (a U.S. company), and Canadian Solar Inc. The funding of startups through clean-tech venture capital (VC) has recently been at the center of discussion. A Slaughterhouses (PwC) report on The State of Climate Tech 2021 demonstrates that investments in climate tech8 surged since 2013 constituting cumulative investments of $222 billion at the end of the 1st half of 2021 financing more than 3000 startups. This growth was especially accelerated in 2020 (2nd half) and 2021 (1st half) that had an investment of $87.5 billion mostly attributable to the United States ($57 billion), Europe ($18 billion), and China ($9 billion). In terms of area, almost two-thirds of the capital is allocated to mobility and transportation, dominated by electric vehicles and the micro-mobility sector. Another important area is energy, with almost $31.5 billion of investments between 2013 and 2021 (1st half) and around 2100 deals; however, most of the solar and wind energy projects are funded by project-level debt or equity and by the debt that we discussed earlier (and will be further discussed below). PwC (2021) highlights newly established companies in the market which is confirmed by the observation of a rapid increase in the number of “early stage start-ups” consisting of early VC, seed, and series A rounds that have $1 million or more, from less than 100 in 2013 to several hundred in 2020 (see Fig. 8.4 for a detailed trend analysis).9 8

According to PwC (2020), climate tech covers “technologies that are explicitly focused on reducing GHG emissions or addressing the impacts of global warming, while climate tech startups are companies that are applying those technologies” (p. 12). 9 According to PwC (2020, p. 15), “‘Early VC’ is the default label for rounds of $1-10m with no self-reported label”.

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Although the cost trend for renewable energy production, together with associated prices, is steadily declining, prices are much less volatile than for fossil fuel energy. The investment share of renewable energy is rapidly rising and renewable energy companies, though still having a low market share, are rising in terms of both numbers and size. There are still entry and market barriers slowing their expansion. This is the topic we want to discuss next.

8.3 Incumbents and New Entrants in the Energy Sector—Dominant Fossil Fuel-Based Firms10 Kato and Semmler (2011) and Gevorkyan and Semmler (2016) presented a model that incorporates fossil fuel firms, incumbents, as well as new entrants in the energy market. A general assumption seems to hold that the incumbents are active in maximizing profits over time and setting up entry barriers. The entrants are overwhelmingly reacting to prices set by the incumbents. In addition to these two groups, there might be a third group in between, composed of companies that are either growing or falling back. In the next section, we will then describe the new entrants, which provide renewable energy, as actively attempting to garner increased market share. On the other hand, there are dominant fossil fuel-based firms that have established a dominant market share. They represent big oil and fracking companies shown in Table 8.1.

Table 8.1 Big companies in oil and fracking industry. This table lists some of the big oil and fracking companies Oil companies Fracking companies China Petroleum & Chemical Corp. (SNP) PetroChina Co. Ltd. (PTR) Saudi Arabian Oil Co. (Saudi Aramco) Royal Dutch Shell PLC (RDS. A) BP PLC (BP) Exxon Mobil Corp. (XOM) Total SE (TOT) Chevron Corp. (CVX)

10

Chevron Corp. (CVX) Exxon Mobil Corp. (XOM) ConocoPhillips Co. (COP) Halliburton (HAL)

This section follows Gevorkyan and Semmler (2016) which is based on Kato and Semmler (2011). The type of models introduced there originate in work by Brock (1981), who used such a model when testifying before a congressional committee on the breakup of AT&T.

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Entry Deterrence by Dominant Fossil Fuel-Based Firms Based on Kato and Semmler (2011), Gevorkyan and Semmler (2016) present the following dynamic model where dominant firms, e.g., oligopoly, maximize their profit following an entry deterring strategy. In the context of a multi-period decision strategy, revenue is determined by price, p, and output, q, where p depends on market share, s, and the market demand function is described by q = sd( p). In addition, the production cost is denoted by C(q), and “entry deterring gross investment” by x. There is an adjustment cost ϕ and a deprecation rate, δ E , respectively, see Eq. (8.1). The dynamics of “competition-deterring capital”, E, is shown below in Eq. (8.2). The difference of competitive price, p c , and the monopoly price, p m , drives the monopoly markup when magnified by the market share, see Eq. (8.3).11  T max e−r t [ pq − C(q) − x − ϕ(x)] dt (8.1) x

s.t.

0

E˙ = x − δ E E p(s) = p c + ( p m − p c )s d = b − ap

for 0 ≤ s ≤ 1

(8.2) (8.3) (8.4)

On the one hand, it is presumed that the revenue R(s) is increasing in the market share. On the other hand, the market price, p(s), determines the industry demand, d through Eq. (8.4). The parameters b and a are coefficients. Three scenarios can be studied, characterized by different markups and different attractors and repellors. Gevorkyan and Semmler (2016) show that the higher the markup, the higher the resulting steady state value for the incumbents. The parameter values used in their numerical solutions using nonlinear model predictive control (NMPC)12 are r = .02, δ E = .15, ρ = 5, χ = 10, c = .001, α = .5, p m = 8, 7, 6, p c = 2, b = 10, a = .5 (see Gevorkyan and Semmler 2016, p. 246).

Numerical Solutions and Results Gevorkyan and Semmler (2016) stress the importance of technological advancement as well as support from the government which contributes to lower entry opportunities for new smaller firms. They present numerical solutions showing dynamic paths for the entry barriers deterring capital, E, over time, resulting in multiple equilibria; this is especially prominent in cases where both markup and market shares of dominant 11

The model shown by Eqs. (8.1)–(8.4) was “Reprinted from Economic Modelling, vol. 54, Arkady Gevorkyan and Willi Semmler, Oil price, overleveraging and shakeout in the shale energy sector— Game changers in the oil industry, pp. 244–259, Copyright (2016), with permission from Elsevier.”. 12 See Grüne et al. (2015) for the NMPC algorithm.

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Fig. 8.5 Total loss of dominance, market share shrinking. In this figure, trajectories are shown where the incumbents start with a very low E(0) and a very low market share. It resulted in total loss of dominance, where the market share is shrinking, even with large initial capital and market share. A low markup, p m = 6, is assumed. There are low and high initial conditions: E(0) = 35 and E(0) = 50

firms are high. Because of competition, the market share of the incumbent might be reduced and eventually shrinks to zero. This happens if the markup or the initial entry deterring capital E(0) of the incumbent is too low, for example, below some threshold value E(0). Gevorkyan and Semmler (2016, p. 248) depict the trajectories where the incumbents start with a very low E(0) and a very low market share (see Fig. 8.5). Then, dominance is not feasible.13 We focus on the case with low markup, e.g., p m = 6. In Fig. 8.5, there are two trajectories where initial values are either lower or higher: E(0) = 35 and E(0) = 50. Dynamics of the capital, E, decline over time and reach zero as illustrated by the trajectories.14 We also observe that when E(0) is smaller, it reaches zero much sooner (market share falls) compared to the case when E(0) is higher. Empirically, Gevorkyan and Semmler (2016, p. 247) attributed such a scenario to a rise in market shares of new smaller companies—for example smaller shale companies—in the years 2013–2014.

8.4 Renewable Energy-Based Firms as Entrants Next, we focus on renewable energy-based firms. As discussed above, we will neglect the differences among fossil fuel type firms (providing energy through coal, oil or gas).15 In this section, a dynamic model that focuses on technical change is presented 13

A more complete discussion with other numerical results—where incumbents can maintain a dominant market share—can be found in Gevorkyan and Semmler (2016, pp. 246–247). 14 This Fig. 8.5 was “Reprinted from Economic Modelling, vol. 54, Arkady Gevorkyan and Willi Semmler, Oil price, overleveraging and shakeout in the shale energy sector Game changers in the oil industry, pp. 244–259, Copyright (2016), with permission from Elsevier”. 15 Note that the two types of firms can be mainly viewed as firms providing energy through power generation (electricity) or using such energy in transport, production plants, housing and so on.

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to help understand the emerging competition between the fossil fuel-based incumbents and the renewable energy-based entrants. This model follows the evolutionary approach in economics developed by Arthur (1989). The approach is based on the Schumpeterian dynamics of innovation and diffusion of technology. In addition, we also take into account the features of other competing models. More details of such an evolutionary approach can be found in Braga et al. (2021). In earlier literature, the evolutionary perspective on innovation research has been built on replicator dynamics, but has recently been extended to include richer dynamics by referring to the Lotka-Volterra system (see Pistorius and Utterback 1996 and Utterback et al. 2018). Many aspects of our model can be motivated by those strands of literature. We start from the existence of competitive dynamics between new technology firms implementing renewable energy as innovators and existing carbonintensive energy firms as incumbents.16 However, we do not use a static theory of the firm. We note that, for the most part, a static theory of competition and profit maximizing firms in the energy sector has been considered in the literature. Kotlikoff et al. (2019) present a model of this type for fossil fuel firms as well as for renewable energy firms. However, a strict short-run profit maximization approach ignores induced entry of new firms, currently and in future periods. A dynamic approach allows firms to consider their intertemporal profit flows, balancing current profits and future market shares, taking into account the entry dynamics. In this context, we build up a model for the transformation of the energy system with two types of firms—incumbents and entrants—which drive the substitution of renewable energy for fossil fuel energy. To study this as a dynamic process, we focus on the large traditional fossil fuel oligopolies that are dominant in the energy market, on the one side, and the smaller entrants on the other side. The incumbents, as oligopolies, defend their market share by pursuing entry and competition preventing strategies and investments. Yet, for the low-carbon energy sector, the successful entry of clean energy firms into the energy market plays an important role.

Active Market Entrants and Competition Dynamics Modern evolutionary theory of technical change often borrows from mathematical biology. For the heterogeneous firms presented here, we presume the following three interactions: (a) cooperation, (b) competition, and (c) predator-prey.17 Braga et al. (2021)18 emphasize the interaction between a predator and a prey in the environment 16

For recent contributions, see the innovation model with an entrant and an incumbent proposed by Acemoglu and Cao (2015) and the innovation climate model for clean energy elaborated by Kotlikoff et al. (2019). 17 For further research on this type of modeling of the evolutionary approach to innovation and diffusion of technology, see Pistorius and Utterback (1996), Utterback et al. (2018), and Braga et al. (2021). 18 See also Aftab (2016).

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with expanding innovators. New technology produces more intensive competition. In the study, a demand function captures the effect of the price and markup on profit. The model incorporates the cooperative interaction of firms as well. Yet there are also noncooperative interaction model types studied using a game-theoretic set-up (see Semmler et al. 2022). Semmler (2011) presented a “small scale model” of innovators and incumbents. In the current model, leveraging of the innovative entrants is also introduced. It is assumed that there is an intertemporal payoff function for those innovative entrants that offer new types of energy supply. As a result of new innovations, a reaction is generated from the established oligopolies. Yet, Braga et al. (2021) assume that the incumbents might also go through a learning process and use some of the new technology.19 For simplicity, it is presumed that low-carbon energy firms are the most active firms as they are engaged in innovations with a goal of increasing the number of innovators, y, and the effort represented by w with w ∈ + . As in Semmler (2011) and Braga et al. (2021), these firms’ strategy to maximize their joint payoff is assumed to be f (y, w) = μ(y, w)yw − cw − c0 y . Based on Semmler (2011)20 and Braga et al. (2021)), a dynamic model with the number of incumbents, x, number of innovators,y, and external financing of the renewable energy firms, z, (with a fixed interest rate on debt payments, r ), where entrants have a payoff function for multiple periods, is described as follows:  T e−r t f (y, w)dt (8.5) max w

s.t.

0

x˙ = k − ax y 2 + by − xe/μ y˙ = y(ax y + v f (y, w) − β) z˙ = r z − f (y, w)

(8.6) (8.7) (8.8)

As shown in Braga et al. (2021), the payoff function affects the incumbents through a markup, μ = α/( + yw), where net revenue can be shown as μ(·)yw; in Eq. (8.6). They presume that the new technology creation is represented by w (for example, this could be the number of engineers, research labs, etc.). Semmler (2011) assumes that the “cost per unit of effort,” c, gives rise to cw and there is some proportional cost c0 y. We thus have cw + c0 y for the innovation cost. Furthermore, an increase in positive profit flow denoted by f (y, w) gives rise to new entries v f (·) in Eq. (8.7) with v a coefficient indicating the impact of positive profit flows on the number of entering innovative firms. The entry dynamics are specified in a way that is driven by profit flows; but we should also note that an increase in the number of firms affect the excess profit. The predator-prey relationship is described 19

Innovative firms in the area of renewable energy are allowed to borrow. In the model, we will be focusing more on the expansionary period of the renewable energy firms. 20 Note that Semmler’s (2011) original model had an infinite horizon whereas here we have a finite horizon. It will be solved using NMPC.

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by ax y 2 because the incumbents’ access to new technology increases more, with the new technology produced by firms. In addition, y has an impact on the increase in the diffusion speed. In Eq. (8.6), there is a term by and Semmler (2011) points out that incumbents enhance their productivity with the advent of the new technology. The crowding effect is shown by the term xe/μ in Eq. (8.6), which indicates that the markup will decline with the number of innovating firms, for details, see Braga et al. (2021). We should also note that if f (y, w) = μ(y, w)yw − cw − c0 y > 0 in Eq. (8.8), the innovative firms can build up equity or repay debt. Otherwise, if the term is < 0, liabilities rise leading to an increase of default perils.21

Numerical Solutions and Results In this section, we solve the above model for different variants using NMPC. We study some characteristic cases here. More detailed cases with a further study of the debt dynamics are covered in Braga et al. (2021).

Case 1: High Entry Barriers, Tight Oligopoly, Eventual Shake Out of Entrants Trajectories of the size of incumbents, x, the number of innovators, y, and external debt, z, corresponding to chosen initial values of state variables are illustrated in Fig. 8.6. It demonstrates a possible successful market entry of renewable energy firms into the energy sector where fossil fuel companies are still active and are dominant in the long run. Yet, the new technology entrants can be squeezed out and their market share is reduced at a later period. In other words, a tight oligopoly group introduces barriers to entry and competition.22 However, leapfrogging of the market entrants may exist only for a short time, or it might not take place at all.

Case 2: Leapfrogging of Entrants, with Low Markups and Modest Debt Buildup Figure 8.7 represents the case of the declining incumbents, x, and rising innovators, y, and higher debt burden for the selected initial conditions. The results demonstrate that innovators that have inventive investments successfully enter the energy market, leapfrogging over the old technology. The entrants 21

A more technical study of whether the debt—and under what conditions—is sustainable is studied in Braga et al. (2021). 22 For details, see Kato and Semmler (2010).

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Fig. 8.6 High entry barriers, dominance of incumbents in the long run. This figure demonstrates trajectories of the size of incumbents, x, the number of innovators, y, and external debt, z, corresponding to initial values of state variables: x0 = 1.0, y0 = 1.0, and z 0 = 0.1. High entry barriers resulted in first entry then exit of entrants (shown by red line or y), dominance of incumbents in the long run (shown by green line or x), and debt buildup by entrants (shown by dashed line or z)

Fig. 8.7 Leapfrogging of entrants with low markup Φ = 5. This figure demonstrates trajectories of the size of incumbents, x, the number of innovators, y, and external debt, z, corresponding to initial values of state variables: x0 = 5.0, y0 = 1.0, and z 0 = 0.1. It is assumed that Φ = 5 (weak markup). This scenario presents declining incumbents (shown by green line or x), and rising innovators (shown by red line or y), and higher debt burden (shown by dashed line or z)

succeed and raise their market share. Similar to the previous case, there is first coexistence and then the incumbents decline in the long run. The new technology entrants are doing well but at the cost of piling up of debt. Nevertheless, they may keep a large market share in the long run. The debt dynamics might, however, indicate some vulnerability of the innovating firms.

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Case 3: Leapfrogging of Entrants, with High Markups, and Low Debt Buildup Figure 8.8 illustrates the case of the falling size of incumbents, x, rising innovators, y, and low debt burden for the selected initial conditions. The dynamic trajectories show that innovating firms that invest inventively have entered the energy market and leapfrogging over the old technology. Similar to previous cases, the entrants managed to raise their market share. The number of incumbents in the market has decreased, and their number remains very small in the long run. The entrants with new technology are successful, they have only little debt built up. Their market share is large in the long run.

Case 4: Leapfrogging of Entrants, with High Markups and Subsidies Our final case of subsidies for the new technology is illustrated in Fig. 8.9, which leads to a decline of the number of incumbents, x, and a rise of the number of innovators, y. It was assumed that the entrants are subsidized and operate with a high markup, Φ = 1. An accelerated growth of innovating entrants can also arise from de-risking of investments by the innovators through public low cost credit and credit guarantees, see Braga et al. (2021). The numerical solutions indicate that innovators that commit to inventive investments and are subsidized, are likely to be successful in market entry, allowing them leapfrogging over the old technology. The entrants succeed rapidly and increase their numbers and their market share fast. The incumbents may

Fig. 8.8 Leapfrogging of entrants with high markup Φ = 1. This figure demonstrates trajectories of the size of incumbents, x, the number of innovators, y, and external debt, z, corresponding to initial values of state variables: x0 = 5.0, y0 = 1.0, and z 0 = 0.1. It is assumed that Φ = 1. This scenario presents a declining size of incumbents (shown by green line or x), leapfrogging of entrants, success of entrants (shown by red line or y), and little debt pile up (shown by dashed line or z)

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Fig. 8.9 Leapfrogging of entrants with subsidies and high markup Φ = 1. This figure demonstrates trajectories of the size of incumbents, x, the number of innovators, y, and external debt, z, corresponding to initial values of state variables: x0 = 5.0, y0 = 1.0, and z 0 = 0.1. It is assumed that Φ = 1 (high markup) and subsidies. This scenario presents a declining size of incumbents (shown by green line or x), leapfrogging of entrants (shown by red line or y), and little debt pile up (shown by dashed line or z)

decline and finally dissipate. The new technology entrants are successful and show little debt pile up. Their market share rises fast in the long run. For further analysis, we could also compute the present value of the innovators and incumbents along their respective paths. Semmler (2011) notes that the stock prices for incumbents as well as for innovators can be derived from discounting of the profit flows arising from the model’s solution, respectively, from Eqs. (8.6)–(8.8) and the value function.

8.5 Financing Renewable Energy Firms and Capital Cost Given the modeling method described in the previous section, we want to discuss the issue of capital costs and make some preliminary remarks regarding asset price dynamics. We also explore the possible instabilities that can arise due to what has been called “stranded assets”. These are the assets emerging from fossil fuel firms that are likely to experience substantial depreciation and return losses, creating also perils for banks and the financial sector at large. An important way to support the finance of renewable energy firms is de-risking climate-oriented projects and renewable energy production. Capital cost is essentially impacted by the bonds’ betas. This can be brought down via government guarantees, issuance of green bonds, and/or subsidization of renewable energy—all having the result of de-risking green assets. The empirical effectiveness of bringing down the credit cost for renewable energy firms is evaluated in Braga et al. (2021).

8.5 Financing Renewable Energy Firms and Capital Cost

127

The public sector plays an important role and should act in financial markets to expedite innovative green investments. Stiglitz (1993) highlights the importance of the government when markets are incomplete and projects have social benefits and risks. The Government can lower costs of projects and distribute risk across taxpayers (Arrow and Lind 1970). This is the case for uncertain and green investments (Arrow and Fisher 1974). The Government may internalize these costs, intervening in the financial market to reduce credit constraints and credit costs to expedite the phasing in of renewable energy. Studies covering developing countries, e.g., Sweerts et al. (2019), indicate that renewable technologies often exhibit high fixed costs. Although renewable energy, including sun and wind power, is freely available in unlimited supply, new technologies to exploit those energies have high set-up costs. Yet, we have shown in Sect. 8.2 that renewable energy costs have declined worldwide. It is driven by economies of scale as well as lower credit risk involved in renewable technology projects.23 Credit is the first and foremost main financing source for entrants into the energy market. The interest on credit payments affects capital costs. If credit constraints are relaxed or a de-risking of the project takes place through de-risking of credit flows by the public to the renewable energy sector, this will reduce capital cost for green investments. Although bank credit is an important financing source for entrants into the energy market, the interest payment on capital cost can also be reduced by issuing green bonds as we discussed in Sect. 8.2, exhibiting low yields. This is likely to have an impact on the weighted average cost of capital (WACC) for green investments.24 In general, investors’ preferences can favor green bonds and also impact project capital costs. Larcker and Watts (2019) highlight zero “green premium” meaning that green and non-green bonds cost the same for investors. Others show that green bonds frequently carry a negative premium (see Kapraun and Scheins 2019) which implies a lower capital cost. Using these advantages when the public sector de-risks green bonds can also increase their attractiveness and enhance investor benefits. It can solve existing imbalances between savings and investments. Also, as shown in the literature, green bonds can serve as a good hedge mechanism against riskier assets. Empirical findings in Braga et al. (2021) and Semmler et al. (2021a, 2021b) show that oil price fluctuations have a limited effect on the volatility of returns of green bonds. Besides green bonds, other financing sources with relevant data were discussed in Sect. 8.2. The model in Sect. 8.4 can also allow for a broader set of financing instruments for renewable energy firms. The entrants have, in fact, numerous sources of available financing. They can use self-financing, equity finance, bank loans, bond issuing on the capital markets, venture capital, crowd finance, tax breaks and subsidies, etc. We also might need to note that different sources of finance have become relevant for the different types of energy firms in Europe and the United States. 23

For more details, see Braga et al. (2021). For the role of WACC for renewable energy projects in different countries, for this and the subsequent results, see Braga et al. (2021).

24

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8.6 Green Assets and Portfolio Performance Next, we want to study how fossil fuel and renewable energy-based firms would perform in portfolios composed of a variety of assets. For simplicity, we consider portfolios of one risky and one risk-free asset. Much recent literature has pointed out that large scale oligopolies, such as fossil fuel firms, tend to short-termism as mentioned already in Chap. 2, see also Davies et al. (2014), Haldane (2016). Different criteria have been used in defining short-termism. These include risk aversion, discount rates, and decision horizon. Much of the literature focused on discount rates. Yet Heine et al. (2019) and others demonstrated that there is some effect of the discount rate on portfolio decisions and asset accumulation. Chiarella et al. (2016, Chaps. 4 and 5) cover risk aversion and discount rates in detail regarding an approach to dynamic portfolio decisions. Semmler et al. (2020) work with continuous time, with regard to a finite decision horizon problem of allocation of assets and dynamic saving decisions. Here, for illustration, we introduce a model with one asset and constant returns. Usually, in the literature, this type of problem is formulated as a decision made over consumption and asset allocation. In this context, the objective function is designed to maximize investor’s welfare over consumption. They further analyzed the impact of returns, the discount rate, and variation of risk aversion on the share of consumption in wealth. This approach can be useful in the exploration of changes in wealth over time. The same problem can be addressed in a model that considers two assets, a risky and risk-free asset. The risky asset’s returns may vary with time, see Semmler et al. (2020).25

Dynamic Model of a Portfolio of Assets We continue our study of the firms that produce fossil fuel or renewable energy. Next, it is presumed that investors’ portfolio contains a risky asset in addition to a risk-free asset, and the competition in the energy market is incorporated. The model presented in this section resembles the early model introduced by Merton (1971, 1973) which is also in continuous time and it highlights the asset allocation problem. The lowfrequency movements based on some-wave functions can be applied to the returns of the risky assets that are presented in the model of dynamic portfolio decisions.26 Note that according to Tobin’s theory, two decisions are made in the portfolio theory, namely, asset allocation and saving. Following Chiarella et al. (2016, Chap. 4), the dynamic portfolio model where a fraction of Wealth, W , is spent on innovation efforts, vt , as well as on consumption, ct , with weights (β1 ) and (1 − β1 ), and returns, 25

This section follows Chiarella et al. (2016) and Semmler et al. (2020). For a more detailed study of such an approach, see Semmler et al. (2020) and Chiarella et al. (2016).

26

8.6 Green Assets and Portfolio Performance

129 f

e R, are specified as short-term interest rate, Rt , and equity return, Ri,t , is presented as follows:  T e−θt (β1log(vt Wt ) + (1 − β1 )log(ct Wt ))dt (8.9) max v,c,ξ

s.t.

0 f e Wt + (1 − ξt ) Rt Wt − (vt + ct )Wt W˙ t = ξt Ri,t

x˙t = 1

(8.10) (8.11)

f

In addition to defining Rt , the risk-free interest rate, a constant, a time-varying e , based on Chiarella et al. (2016, Chap. 4) and Semmler et al. (2020), equity return, Ri,t e the return R can be described as below with ξ4 affecting the frequency, and ξ5 as a phase shift: f

Rt = constant

(8.12)

e (xt ) = (ξ2 sin(ξ4 xt ) + ξ5 )(1 ± δ(vt Wt )) Ri,t

(8.13)

It is assumed that returns on the asset Rte Wt can be affected by vt , the share of the wealth that is spent on human capital related to innovation and engineering efforts. The other fraction of wealth, c, will go to households with non-innovating activities. Moreover, while innovation spending, e.g., renewables, has a positive effect on equity return (+ sign), one can also account for a negative externality (− sign), for example, generated by fossil fuel engineers, having a negative impact.

Numerical Solutions We numerically can solve the above dynamic model employing the NMPC method where discount rates could vary. While the iteration period is given, different decision horizons, N , can be used. General results of this type of numerical analysis are reported in Semmler et al. (2020) where it is assumed that ξt ≤ 1.2. In the NMPC procedure used here, one trajectory is computed at a time for the decision variables {c, v, ξ}. In this section, we take N = 6 fixed and 25 time periods (T = 25) and solve the model with an assumption that δ(·) > 0 with + sign in (1 ± δ(vt Wt ), for example for the new innovations brought in by renewable energy firms. Note that there might also be some temporary risk premium harvested by fossil fuel assets so that we have δ(·) > 0. On the other hand, for the fossil fuel asset, due to external costs, there can be a δ(·) < 0, thus with − sign in (1 ± δ(vt Wt )) < 1. The latter effect can be shown by a numerical analysis presented in Fig. 8.10 where the portfolio mostly consists of fossil fuel bonds. This case illustrates the prospect of a carbon tax on stranded assets as well as downgrades via listing of dis-

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Fig. 8.10 Solution path for wealth for different types of externalities for different values of δ(·), and N = 6, T = 25. This figure shows trajectories of wealth for different types of externalities, two upper graphs δ(·) > 0, lower graph with δ(·) < 0. It is assumed that N = 6 and T = 25

closure requirements, associated with C O2 disclosure; this will indicate the negative externalities and higher risk of default. For this case, we can use a computational parameterization of δ(·) = −0.2. The negative externalities are likely to lead to less accumulation of the asset, and in the long run, asset values may dissipate (see the lowest trajectories in Fig. 8.10). The middle graph in Fig. 8.10, also solved with N = 6, is still computed with an additional term (1 + δ(vt Wt )), and δ(vt Wt ) as an effort for building up human capital in fossil fuel industries. So the return on assets may still be higher than the lower graphs, but this can represent some temporary effect where risk premia are captured in returns. The upper graph in Fig. 8.10, obtained with N = 6 and the term (1 + δ(vt Wt )), represents firms with superior asset formation. As indicated in the objective function, when the effort is spent on human capital suitable for renewable energy, a positive externality effect on the returns (or representing the avoidance of future disasters) is shown. We also want to remark that there are short-sighted investors in the financial market, for example, having a decision horizon of N = 2, that are likely to be investors in fossil fuel assets. Based on Davies et al. (2014) who use a short decision horizon for investors who show short-termism in investors’ behavior, Semmler et al. (2020) show that renewable energy assets will not be built up in this case.27 For the extended analysis, other discounting methods as well as liquidity problems can be considered. In addition, a longer decision horizon can be used in the NMPC algorithm and other variations of the model tested. Another approach could be to incorporate the returns’ low-frequency movements as estimated in Chiarella et al. (2016, Chap. 5) and Semmler et al. (2020).

27

In the dynamic portfolio model, assets of renewal technology could be also sold. It is assumed in the model that the share of the asset fluctuates and declines at the end.

8.7 Conclusion

131

8.7 Conclusion In this chapter, we studied the issue of how green energy, based on renewable energy sources, can be phased in to compete with the dominating fossil fuel-based firms. The energy sector and how it is supported by the financial market is essential for the transition to a low-carbon economy. Section 8.2 introduces stylized facts related to this issue including information on types of firms, costs of electricity, green investments, financing methods, and clean-tech venture capital. Section 8.3 showed that entrants with innovating energy technology are not necessarily successful when entry barriers are high and the incumbents defend their market share through entry preventing investment strategies and are heavily subsidized, see IMF working papers for example. Furthermore, as Sect. 8.4 shows, entrants might not be successful if they have low markups, cannot exhaust returns to scale because of low pricing and markup power, and/or have piled up unsustainable debt. Sections 8.3 and 8.4, however, also show that incumbents can be overcome by innovative entrants, particularly if there is some de-risking of their investments by the help of the public sector, reducing capital cost of the entrants. Innovative financing, e.g., crowd funding, may also help to bypass traditional financing sources such as bank credit, bonds and equity. Both the asset prices of incumbents and the innovative entrants are usually driven by financial market valuations, as shown in Sect. 8.5. Here, it is demonstrated that the negative externalities created by the incumbents might lead to a decline (and possibly even sudden collapse) of asset prices; this is as predicted by the “stranded assets” theory and could occur through a confidence drop in conventional fossil fuelbased companies, a sudden stop of subsidies, a C O2 disclosure requirements, and/or a carbon and wealth tax on “dirty”—carbon-intensive assets. On the other hand, the asset price of the innovative entrants can stay high or rise due to the anticipated lower long-run decline in weather extremes and disasters and because positive externality effects can be scaled up by green investments. Usually, both incumbents’ and the entrants’ assets are held in portfolios of individual and institutional investors. Given the possibility of low—but in the long-run rising—returns of the entrants—the Sharpe ratio, which is a measure of the return to risk ratio, may be higher for renewable energy firms since the volatility of returns is usually lower than for fossil fuel-based assets, see Semmler et al. (2021a, b). If there is a low interest rate for some time period, for example, after the Great Recession 2007–2009, this should provide a sufficient incentive for those contemplating a large-scale investment in climate-related infrastructure, energy efficiency, renewable energy-based innovations, transport systems, housing, and other sectors. Moreover, in a recessionary period, a large-scale government spending program should also help to ease the recession through deficit spending oriented toward a green recovery.

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References Acemoglu D, Cao D (2015) Innovation by entrants and incumbents. J Econ Theory 157(2015):255– 294 Aftab I (2016) Modeling firms’ innovation decisions in developing countries, PhD thesis, The New School Arrow KJ, Fisher A (1974) Environmental preservation, uncertainty, and irreversibility. In: Gopalakrishnan C (ed) Classic papers in natural resource economics. Palgrave Macmillan, London, pp 76–84 Arrow KJ, Lind R (1970) Uncertainty and the evaluation of public investment decisions. Am Econ Rev 60(3):364–378 Arthur B (1989) Competing technologies, increasing returns, and lock-in by historical events. Econ J 99:116–131 Bloomberg NEF (2019) Clean energy investment trends, 2019. https://data.bloomberglp.com/ professional/sites/24/BloombergNEF-Clean-Energy-Investment-Trends-2019.pdf. Accessed 20 Jan 2021 Bloomberg NEF (2022) Energy transition investment trends 2022. https://assets.bbhub. io/professional/sites/24/Energy-Transition-Investment-Trends-Exec-Summary-2022.pdf. Accessed 16 Aug 2022 Braga JP, Semmler W, Grass D (2021) De-risking of green investments through a green bond market–empirics and a dynamic model. J Econ Dyn Control 131:104–201. https://doi.org/10. 1016/j.jedc.2021.104201 Brock GW (1981) The telecommunications industry: the dynamics of market structure. Harvard University Press Chiarella C, Semmler W, Hsiao C, Mateane L (2016) Sustainable asset accumulation and dynamic portfolio decisions, vol 18. Springer. https://doi.org/10.1007/978-3-662-49229-1 Climate Bonds Initiative (2019) Green Bonds Market 2019. https://www.climatebonds.net/sites/ all/modules/custom/cbi_market_data/templates/bonds-view/assets/cbi_sotm_2019_vol1_04d. pdf. Accessed 7 Nov 2022 Climate Bonds Initiative (2022) Green Bonds Market 2022. https://www.climatebonds.net/market/ data/. Accessed 7 Nov 2022 Davies R, Haldane AG, Nielsen M, Pezzini S (2014) Measuring the costs of short-termism. J Financ Stab 12:16–25. https://doi.org/10.1016/j.jfs.2013.07.002 Gevorkyan A, Semmler W (2016) Oil price, overleveraging and shakeout in the shale energy sector– game changers in the oil industry. Econ Model 54:244–259 Grüne L, Semmler W, Stieler M (2015) Using nonlinear model predictive control for dynamic decision problems in economics. J Econ Dyn Control 60:112–133 Haldane AG (2016) The cost of short-termism. Wiley, The Political Quarterly, 86:66–76 Heine D, Semmler W, Mazzucato M, Braga JP, Flaherty M, Gevorkyan A, Hayde E, Radpour S (2019) Financing low-carbon transitions through carbon pricing and green bonds. Vierteljahrshefte Zur Wirtschaftsforschung 88(2):29–49 International Energy Agency IEA (2020) World Energy Outlook 2020. https://www.iea.org/reports/ world-energy-outlook-2020#. Accessed 7 Nov 2022 International Energy Agency IEA (2021) World Energy Investment 2021. https://www.iea.org/ reports/world-energy-investment-2021. Accessed 14 Aug 2022 Investopedia (2020) 10 Biggest Oil Companies. https://www.investopedia.com/articles/personalfinance/010715/worlds-top-10-oil-companies.asp. Accessed 7 Nov 2022 IRENA (2019) Renewable energy market analysis: GCC 2019. International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/ Jan/IRENA_Market_Analysis_GCC_2019.pdf. Accessed 27 Apr 2019 IRENA (2022) Renewable power generation costs in 2021. International Renewable Energy Agency, Abu Dhabi

References

133

IRENA (2022) Renewable energy statistics 2022. The International Renewable Energy Agency, Abu Dhabi IRENA and CPI (2020) Global landscape of renewable energy finance, 2020. International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/publications/2020/Nov/GlobalLandscape-of-Renewable-Energy-Finance-2020. Accessed 20 Jan 2021 Kapraun J, Scheins C (2019) (In)-credibly green: which bonds trade at a green bond premium? In: Proceedings of Paris Dec 2019 finance meeting EUROFIDAI—ESSEC. https://papers.ssrn.com/ sol3/papers.cfm?abstract_id=3347337. Accessed 7 Nov 7 2022 Kato M, Semmler W (2011) Dominant firms, competition-deterring investment and antitrust policy. In: Salvadori N, Gehrke C (eds) Keynes, Sraffa and the criticism of neoclassical theory, essays in Honour of Heinz Kurz. Routledge, pp 293–313 Kavlak G, McNerney J, Trancik JE (2018) Evaluating the causes of cost reduction in photovoltaic modules. Energy Policy 123:700–10 Kotlikoff L, Kubler F, Polbin A, Sachs J, Scheidegger S (2019) Making carbon taxation a generational win win, National Bureau of Economic Research. No w25760. Published as Kotlikoff L, Kubler F, Polbin A, Sachs J, Scheidegger S (2021) Making carbon taxation a generational win win. Int Econ Rev 62(1): 3–46 Larcker D, Watts EM (2019) Where’s the Greenium? Working paper, Stanford. Published as Larcker David F, Edward MW (2020) Where’s the greenium? J Account Econ 69.2-3:101312 Lazard (2021) Lazard’s levelized cost of energy analysis—version 15.0. https://www.lazard.com/ media/451905/lazards-levelized-cost-of-energy-version-150-vf.pdf. Accessed 22 Aug 22 2022 Maurer H, Preuß JJ, Semmler W (2015) Policy scenarios in a model of optimal economic growth and climate change. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford Academic, Oxford, UK. https://doi.org/10.1093/oxfordhb/ 9780199856978.013.0005. Accessed 2 Nov 2022 Merton RC (1971) Optimum consumption and portfolio rules in a continuous time model. J Econ Theory 3(4):373–413. https://doi.org/10.1016/0022-0531(71)90038-X Merton RC (1973) An intertemporal capital asset pricing model. Econometrica 41(5):867–887. https://doi.org/10.2307/1913811 Orlov S, Rovenskaya E, Puaschunder J, Semmler W (2018) Green bonds, transition to a low-carbon economy, and intergenerational fairness—evidence from the DICE Model. IIASA Working Paper, Laxenburg, Austria Pistorius CWI, Utterback J (1996) A Lotka-Volterra model for multi-mode technological interaction: modeling competition, symbiosis and predator-prey modes. Sloan WP 155-96. MIT PricewaterhouseCoopers PwC (2020). The state of climate tech 2020. https://www.pwc.com/gx/ en/services/sustainability/assets/pwc-the-state-of-climate-tech-2020.pdf. Accessed 4 Nov 2022 PricewaterhouseCoopers PwC (2021) The state of climate tech 2021. https://www.pwc.com/gx/en/ sustainability/publications/assets/pwc-state-of-climate-tech-report.pdf. Accessed 4 Nov 2022 Semmler W (2011) Asset Prices, Booms and Recessions, Springer Publishing House Semmler W, Tahri I, Lessmann K, Braga J (2020) Energy transition, asset price fluctuations, and dynamic portfolio decisions. https://ssrn.com/abstract=3696295. Forthcoming Annals of Operations Research (2022) Semmler W, Braga J, Lichtenberger A, Toure M, Hayde E (2021a) Fiscal policy for a low carbon economy. World Bank Report. https://documents1.worldbank.org/curated/en/ 998821623308445356/pdf/Fiscal-Policies-for-a-Low-Carbon-Economy.pdf. Accessed 7 Nov 2022 Semmler W, Maurer H, Bonen T (2021b) Financing climate change policies: a multi-phase integrated assessment model for mitigation and adaptation. In: Haunschmied JL, Kovacevic RM, Semmler W, Veliov VM (eds) Dynamic economic problems with regime switches. Springer, pp 137–158 Semmler W, Di Bartolomeo G, Fard B, Braga JP (2022) Limit pricing and entry game of renewable energy firms into the energy sector. J Struct Change Econ Dyn 61:179–190

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8 The Private Sector—Energy Transitions and Financial Market

Stiglitz J (1993) Perspectives on the role of government risk-bearing within the financial sector. In: Sniderman M (ed) Government risk-bearing: proceedings of a conference held at the Federal Reserve Bank of Cleveland. Springer, Dordrecht, pp 109–130 Sweerts B, Dalla Longa F, van der Zwaan B (2019) Financial de-risking to unlock Africa’s renewable energy potential. Renew Sustain Energy Rev 102:75–82 Utterback J, Pistorius C, Yilmaz E (2018) The dynamics of competition and of the diffusion of innovations. MIT Sloan School Working Paper 5519-18. https://dspace.mit.edu/bitstream/ handle/1721.1/117544/Utterback_Dynamics_of_Competition_Revised_2019.pdf. Accessed 4 Nov 2022 World Bank (2019) State and Trends of Carbon Pricing 2019. WorldBank, Washington DC World Bank (2021) The 2021 impact report: IBRD sustainable development bonds and green bonds. https://treasury.worldbank.org/en/about/unit/treasury/impact/impact-report. Accessed 16 Aug 2022 World Bank. Green Bonds. https://treasury.worldbank.org/en/about/unit/treasury/ibrd/ibrd-greenbonds. Accessed 19 Oct 2021

Chapter 9

The Public Sector—Energy Transition and Fiscal and Monetary Policies

Overview After having explored to what extent the private real sector and the financial market can be a roadblock or a bridge to a low-carbon economy, we now move to the public sector and macroeconomic policies. We focus on dynamic macro models as guidance for climate policies that can support mitigation and adaptation efforts concerning climate protection and the role of broader public policies that will promote and incentivize the energy transition. More specifically, we explore the fiscal resources that should be spent on climate-related infrastructure, mitigation, and adaptation efforts. We will also allow for public borrowing, at least as much as it is sustainable. In addition, the effects of damages and disasters are studied in a multi-phase macro model. The important contribution of monetary policy to climate protection is studied in the context of medium-run monetary macro models.

9.1 Public Sector and Policies1 2 After exploring the important roles played by the private real and financial sectors in a transition to a low-carbon economy, we want to move to transition issues incorporating the public sector and public policies. 1

We want to thank Anthony Bonen, Prakash Loungani, Sebastian Koch, Alexander Haider, and Stefan Mittnik for allowing us to use joint work a basis of this chapter. The authors also would like to thank Ian Parry, Manoj Atolia, and Chris Papageorgiou at the IMF for their discussions and suggestions. We are also indebted to Dirk Heine, Stephan Klasen, and Helmut Maurer for their many helpful insights. 2 Research papers that led to this chapter were completed while Willi Semmler was a visiting scholar at the IMF Research Department, where they were produced; see Bonen et al. (2016) and Mittnik et al. (2019). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_9

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The starting point of most of the climate—macro models are the integrated assessment models (IAM), to link climate change and the macroeconomy. A more specific model developed by Nordhaus and his co-authors is economic growth oriented and calibrated; it is called the Dynamic Integrated Climate-Economy (DICE) model; see Nordhaus (2008). We start with this model type, but develop variants allowing us to encompass sustainable and inclusive growth, resource use, and carbon emissions, and explore the role of fiscal and other policies for the transition to a low-carbon economy. We also use new numerical solution methods to solve the model variants. According to Bonen et al. (2016), the DICE-type macro models neglect resources—particularly fossil fuel resources—that are extracted and used for economic growth, as well as the negative external impacts that arise from the use of those resources and that are behind long-run climate changes. It is commonly recognized that the Nordhaus-type DICE models focus on carbon pricing as a fiscal instrument for incentivizing a transition to a low-carbon economy, i.e., a funding source for mitigation policy. Although it explores the effects of disasters due to climate change, it is done only with respect to economic production, as it appears in GDP and growth accounting. Other effects, such as the environmental and ecological impacts are less emphasized. Adaptation efforts to prevent or provide protection against the rising frequency, and possibly severity, of disasters are also less studied in DICE-type models. In Kellett et al. (2019), DICE methodology with an emphasis on the social cost of carbon is extensively discussed. To accomplish this, one needs extended models of sustainable macroeconomics that include resources, the public sector, and fiscal resources. Thus, one also needs to look at mitigation and adaptation policies and how they can be implemented using private or public finance. Besides taxes and subsidies, other sources of finance, e.g., sovereign green bonds (see Chap. 8 for related issues) should also be considered. Following Bonen et al. (2016), this chapter will focus on what share of public resources should be spent for climate-related infrastructure, mitigation, and adaptation. Such an analysis requires us to focus more intensively on the role of the public sector and related policies. Although our proposed model roughly follows overall Nordhaus’ DICE approach, in terms of resources extracted, damages and disaster effects, transition of the energy system, and financing sources, it will be more explicit when mitigation and adaptation policies are considered. This chapter focuses on the effects of policy decisions pertaining to economic and environmental issues—both the causes of the pollution as well as constraining factors. In order to do this, following Fankhauser et al. (2020) and Nordhaus and Boyer (2000), Bonen et al. (2016) and Atolia et al. (2023) extend an integrated assessment model (IAM) with a different welfare framework. In contrast to using carbon intensity as a source of greenhouse gas (GHG) emissions(Nordhaus et al. 2013, and Anthoff and Tol 2013), the Bonen et al. (2016) and Atolia et al. (2023) models work with fossil fuel extraction and usage. Following Mittnik et al. (2019), we also relate that model to adaptation policy, disaster prevention, and recovery policies. This chapter also discusses model variants on detailed fiscal policy that are

9.2 Climate-Macro Models with Mitigation and Adaptation

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connected to sustainable economic growth and development.3 Encouraged by recent papers on monetary policy and climate protection, e.g., Brunnermeier and Landau (2021), we introduce a medium-run monetary macro model, giving us some guidance on monetary policy instruments for climate protection.

9.2 Climate-Macro Models with Mitigation and Adaptation4 Studies in the 1990s and 2000s, e.g., Kaya et al. (1993), Nordhaus and Boyer (2000), Nordhaus (1994, 2008), Tol and Fankhauser (1998), Mendelsohn (2000), and Golosov et al. (2014) were focused in a more generic way on mitigation and less on adaptation policies. In fact, much of the previous work on climate change has concentrated on mitigation policies; the original DICE model was designed for this. As for expected climate disasters and adaptation policies, recent papers, e.g., Tol (2007), address this topic in terms of specifics, e.g., sea level changes, and highlight the topic of adaptation. Bonen et al. (2014) give a general overview of expected future climate-related damages and adaptation policies for different sorts of models. For an empirical study on expected weather extremes and possible adaptation measures, see Mittnik et al. (2019). As to the DICE model, De Bruin et al. (2009) incorporated adaptation into a version of the Integrated Assessment Model (IAM) and concluded that damages related to climate change would be decreased by around one-third thanks to adaptation measures (see De Bruin et al. 2009, p. 79). The IMF working papers by Bonen et al. (2016) and Semmler et al. (2021a) relate to the approach by Bréchet et al. (2013)— those papers study both mitigation and adaptation policies in a model of the climatemacro link. Furthermore, in the former papers, in their modeling of the extraction of resources and carbon emissions, household-related damages were added. In fact, this chapter shifts the emphasis from damages in production to damages for households in general, but also includes adaptation policies, appearing in preferences. Before we discuss Bréchet et al. (2013) in more detail, a theoretical approach by Bosello (2010) should be mentioned; this work emphasizes the importance of not only mitigation, but also adaptation and green investments. In the model presented by Bosello (2010), optimal trajectories of adaptation and abatement, and investments in capital as well as in R&D are studied. As a result of adaptation that is representing a type of long-term investment, abatement rates can be reduced significantly, especially by 2100 (see Bosello 2010, for detail). Based on the Solow-Swan growth model, Bréchet et al. (2013) include physical and adaptation capital and greenhouse gas (GHG) emissions, and show how to measure their harmfulness; these studies suggest the adoption of adaptation policies for 3

See Bose et al. (2007), Semmler et al. (2021a, b), and Bonen et al. (2016). This section is based on an IMF Working Paper by Bonen et al. (2016) where Willi Semmler is one of the co-authors. 4

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certain countries depending on productivity levels. They also demonstrate that the lower the discount rate or the rate of natural decay of pollution, the higher the adaptation benefits.5 In contrast to the DICE or DSGE-based models,6 as in Bonen et al. (2016), we focus on a model with inclusive growth where funds are invested not only in economic growth, but also in adaptation and mitigation efforts in an environment with evolving climate risks. The model also goes beyond a carbon tax as a climate protection policy and introduces bond-financed green investments. Also included are an active public sector and fiscal policy. Thus, we explore the transition efforts to move to a low-carbon economy with renewable energy. Furthermore, we sketch how a macroeconomy can be trapped in a low-growth regime after having experienced large disaster shocks. Since the model presented here is a nonlinear climate-macro model of higher dimensions regarding state and decision variables, even though it resembles Bonen et al. (2016), it is solved with a different methodology than used there. We have five state variables and six decision variables. The solutions and the trajectories are obtained from the Applied Programming Language (AMPL)7 As shown in Semmler et al. (2015, 2021a), AMPL works well with a large-scale system that has a complex objective function, nonlinearities, and many control variables and constraints.

9.3 Public Sector, Fiscal Policy, and Climate Change8 In a previous model developed by Semmler et al. (2011), sustainable economic growth was addressed in terms of government spending on education, infrastructure, and healthcare-related issues. In more recent work, the allocation of funds for alternative environmental purposes, in particular, climate change, is often considered. In that context, in addition to carbon-intensive energy sources, Bonen et al. (2016) incorporated an interaction between economic and environmental forces and climate-related reduced welfare, as well as public capital spending on mitigation and adaptation. Although their model has similarities to Nordhaus and Sztorc (2013), in Bonen et al. (2016) welfare is affected by climate change directly, as will be described in this section. Similar to what we presented in previous chapters, the output production function, Yt , depends on capital stock, kt , and a non-renewable resource extraction rate, u t . It is assumed that inputs for energy production are perfect substitutes. In Bonen et al. (2016), capital is used for the production of green energy, and there is also a public capital, gt , that is built up by public investments. In the following equation, ν1 represents the share of gt spent on production activities; the rest is spent on policies 5

See Bréchet et al. (2013, p. 221). See Golosov et al. (2014). 7 See Fourer et al. (2002)for instructions on how AMPL works. 8 This section is based on an IMF Working Paper by Bonen et al. (2016)) where Willi Semmler is one of the co-authors. 6

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139

related to climate change (for details, see Bonen et al. (2016), p. 11). The efficiencies of the production inputs are indicated by Ak and Au . Yt = A (Ak kt + Au u t )α · (ν1 · gt )β , α + β ≤ 1

(9.1)

In previous chapters, we showed that the state variable for the stock of fossil fuel, Rt , is reduced by a flow of extraction, u t . Bonen et al. (2016) specify the marginal cost, Ct , with C R < 0, that is defined in the following way: C[Rt ] = ψ Rt−τ

(9.2)

For the accumulation of private capital, Bonen et al. (2016) state that it follows a saving formulation similar to Ramsey-Cass-Koopmans. It is represented by the righthand side of Eq. (9.6) below, where etP stands for tax revenue, ct is consumption per capita, and the depreciation rates and population growth rates are denoted by δk and n. The last term represents the cost of extraction of the fossil fuel resource. They also assume that overall government income consists of an optimal tax rate etP and financial support, i F , which could be foreign aid in the case of developing economies, as shown in Eq. (9.3). They noted that i F represents the amount of public funds spent on infrastructure as well as environmental policy (see Bonen et al. (2016), p. 14). Tt = etP + i F

(9.3)

Furthermore, government expenditure is broken down as share of public capital, α1 , social transfers and services, α2 , administrative costs, α3 , and debt service, α4 . Overall, this assumption should hold: α1 + α2 + α3 ≤ 1. Bonen et al. (2016) presented a model with ρ ≡ (ρ¯ − n) where ρ stands for the discount rate and n denotes the growth of the population. As compared to other models of DICE-type, this model has a more elaborate welfare function. Welfare depends on consumption per capita, ct , cost of government operations, α1 etP (solely appearing as cost, see state equations), as well as public goods, α2 etP . Additionally, CO2 emissions as compared to pre-industrial levels are included in the objective function (impacted also by climate damages) together with a government policy for adaptation that is represented by ν2 gt .9  max W =

ct ,u t ,etP

e−ρ·t ·

  1−σ η    − (ν2 gt )ω ct α2 etP Mt − M −1 1−σ

dt

s.t. R˙ t = −u t k˙t = A ( Ak kt + Au u t )α · (ν1 · gt )β − etP − ct − (δk + n)kt 9

(9.4) (9.5)

For including also a substantial part of the ecosystem in a welfare function, see Bernardo et al. (2021).

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9 The Public Sector—Energy Transition …

  − u t · ψ Rt−τ g˙t = + i − (δg + n) · g    − θ(ν3 · gt )φ M˙ t = γu t − μ Mt − κ M b˙t = (¯r − n)bt − (1 − α1 − α2 − α3 ) · etP α1 etP

F

(9.6) (9.7) (9.8) (9.9)

gt , kt , u t ≥ 0, ∀t The state variable of debt, bt , in the above model’s Eq. (9.9), is driven by the primary surplus as well as a fixed interest rate, r¯ , adjusted for population growth, n. Equation (9.7) presents the evolution of the public capital, gt , which depends on depreciation, δg , and depreciation per capita driven by the population growth rate n. Then, the public capital, gt , is invested into climate-related infrastructure, adaptation, and mitigation, the fractions of which are denoted by ν1 , ν2 , and ν3 , respectively.10 Based on Greiner et al. (2014), Eq. (9.8) was added to demonstrate an accumula the absorption of tion of CO2 where the pre-industrial carbon stock is shown by M, the carbon emission by the oceans is denoted by 1 − γ, and κ represents a multiple of the pre-industrial GHG level. Additionally, ν3 gt is added in this equation to incorporate the effect of the mitigation measure. Note that in our modeling procedure we let carbon emission directly relate to the extraction rate of fossil fuel, u t .

9.4 Numerical Solutions on Fiscal Policy Actions Next, we present the numerical results of the dynamic model that was described above by using AMPL. Similar to NMPC, this procedure applied to finite horizon problems represents a good approximation of the solution horizon T → ∞ of a dynamic programming algorithm. Bonen et al. (2016), Atolia et al. (2023), and Semmler et al. (2021a, b) highlight the benefits of using this method in particular for more complex IAMs and nonlinear models. Details of the parameters used in this model are presented11 in the Table 9.1. Numerical solutions with policy scenarios presented in this chapter use the same parameter values as in Bonen et al. (2016, p. 22; see Table 9.1). The initial values of the state variables are assumed to be as follows: k0 = 10, g0 = 0.5, R0 = 2.5, b0 = 0.8, and M0 = 1.8. Next, the endogenous decision variables include consumption, fraction of public capital spent on climate infrastructure, tax rate, and the mitigation and adaptation efforts. Note also that our formulation of the society’s welfare contains

10

See (Bonen et al. 2016, pp. 14–15) for more details. Table 9.1 was “Used with permission of International Monetary Fund, from Investing to Mitigate and Adapt to Climate Change: A Framework Model, Anthony Bonen, Prakash Loungani, Willi Semmler, and Sebastian Koch, working paper WP/16/164, Copyright (2016); permission conveyed through Copyright Clearance Center, Inc.”.

11

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141

Table 9.1 Simulation parameters. This contains a list of parameters with definitions and assumed values that are used to numerically solve the model described in the previous section Parameters Value Definition ρ n η

 σ A Ak Au α β ψ τ δk δg IF α1 α2

0.03 0.015 0.1 1.1 0.05 1.1 1 1 40 0.5 0.5 1 2 0.075 0.05 0.05 0.1 0.7

α3 M˜ γ κ θ

0.1 1 0.9 2 0.01

Pure discount rate Population growth rate Elasticity of transfers and public spending in utility Elasticity of CO2 -eq concentration in (dis-)utility Elasticity of public capital used for adaptation utility Intertemporal elasticity of instantaneous utility Total factor productivity Efficiency index of private capital Efficiency index of the non-renewable resource Output elasticity of privately owned inputs, (Ak k + Au u) Output elasticity of public infrastructure, v1 g Sealing factor in marginal cost of resource extraction Exponential factor in marginal cost of resource extraction Depreciation rate of private capital Depreciation rate of public capital Official development assistance earmarked of public infrastructure Proportion of tax revenue allocated to new capital Proportion of tax revenue allocated to transfers and public consumption Proportion of tax revenue allocated to administrative costs Pre-industrial atmospheric concentration of greenhouse gases Decay rate of greenhouse gases in atmosphere Atmospheric concentration stabilization ratio (relative to M˜ Effectiveness of mitigation measures

many more components than the common DICE model that is solely maximizing a representative consumer’s welfare. The business as usual (BAU) scenario without any climate change policy is studied in Bonen et al. (2016) in detail. This reflects a case without mitigation or adaptation policies. In other words, the public resources, mainly consisting of tax revenue, p α1 et + i F , are allocated to traditional infrastructure only.

State and Decision Variables Numerical solutions solving the proposed large-scale climate-macro model are presented using AMPL. The trajectories of the evolving state variables are discussed

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Fig. 9.1 Trajectory of capital stock over time. In this figure, a dynamic path for private capital stock, k, is demonstrated over time, t. Note that k is shown in per capita terms

Fig. 9.2 Trajectory of consumption over time. In this figure, an optimal path for consumption, c, is demonstrated over time, t. Note that c is shown in per capita terms

first. The variables are shown in per capita terms. First, we observe the changes in the capital stock shown by k illustrated in Fig. 9.1. In the beginning, private capital, k, goes down (partly due to a small u). But at a later time, not only k, but also c increases as well. More fossil fuel is used in production, and the growth rate of output exceeds that of consumption (see Fig. 9.2). Figure 9.3 depicts the case where initial fossil fuel amount is large. As stated in Eq. (9.5), over time fossil fuel usage declines because of increased costs, diminishing MP (marginal product), and the efficiency of extraction of fossil fuel. Other stocks and flows increase, for example, private capital and consumption. Yet, at a much later time, capital stock declines again as shown in Fig. 9.1. This is coming from the fact that there are some terminal constraints set in the AMPL algorithm.

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Fig. 9.3 Trajectory of fossil fuel over time. In this figure, a dynamic path for fossil fuel, R, is demonstrated over time, t

Fig. 9.4 Trajectory of public capital spend for infrastructure, mitigation, and adaptation. In this figure, a dynamic path for public capital, g, is demonstrated over time, t. Note that g is shown in per capita terms

As illustrated in Fig. 9.4, public capital shows mostly a steady increase. Note that the increase of public capital could also be supported by external sources such as development assistance earmarked for public capital. The stock of public debt, b, follows an inverted U shape (see Fig. 9.5). The increasing part can be explained by a very small primary surplus. Similar to what Bonen et al. (2016, p. 25) indicated, higher taxes or less government spending would be needed to avoid a rise in public debt. But note that in our case we already have optimal tax rates and spending and also the public debt increase is counteracted by future tax increases, thus helping to generate a debt decrease: compare Figs. 9.5 and 9.7. Next, Fig. 9.6 shows an evolution of atmospheric GHG concentration that increases first at a fast rate from M0 = 1.8 to nearly M45 = 2.4. However, it starts falling as the extraction of fossil fuel declines and depletes, as shown in Fig. 9.3, and as public

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9 The Public Sector—Energy Transition …

Fig. 9.5 Trajectory of public debt over time. In this figure, a dynamic path for public debt, b, is depicted over time, t. Note that b is shown in per capita terms

Fig. 9.6 Trajectory of atmospheric greenhouse gas concentration. In this figure, a dynamic path for atmospheric greenhouse gas concentration, M, is demonstrated over time, t

capital and mitigation efforts are rising. Moreover, as Fig. 9.8 shows, the extraction of fossil fuel is declining and eventually seizes.

Decision Variables and Welfare In this section, we examine the role of decision variables in more detail. The optimal path for consumption was already illustrated in Fig. 9.2. Other decision variables are taxation, fossil fuel extraction rate, and spending of public funds on climate-related infrastructure, as well as mitigation and adaptation. First, Fig. 9.7 shows the evolution of lump sum tax that rises first, then falls. In general, tax revenue is used to finance

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Fig. 9.7 Trajectory of tax, e.g., lump sum tax. In this figure, an optimal path for lump sum tax is demonstrated over time, t

Fig. 9.8 Trajectory of extraction rate of fossil fuel stock. In this figure, an optimal path for the extraction of fossil fuel stock, u, is demonstrated over time, t

not only public goods, and administrative expenditures, but also public infrastructure that may include climate-related infrastructure. Concerning the extraction rate, it increases first, which would in turn reduce the stock of fossil fuel; but eventually, it will decline to zero, causing the stock of fossil fuel not to change anymore. We should note that at the beginning period, a high stock of fossil fuel and rising extraction rate lead to constant or lower amount of alternative source of energy that is associated with green capital. We also explore another decision variable: mitigation of carbon emissions and adaptation. Consider a case where a little public capital is spent for mitigation efforts; this means ν3 takes a very small value, e.g., ν3 = 0.03. The result is shown in Fig. 9.11. Note that the share of climate-related infrastructure will be very high. For example, it can be set as ν1 = 0.9 so that sufficient funds are allocated to develop renewable energy system and manage climate risks (see Fig. 9.9 for the result). The public

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9 The Public Sector—Energy Transition …

Fig. 9.9 Trajectory of the fraction of public capital spent for (climate-related) infrastructure. This figure depicts an optimal path for fraction of public capital spent for infrastructure, ν1 , over time, t

Fig. 9.10 Trajectory of the fraction of public capital spent for adaptation. This figure depicts an optimal path for the fraction of public capital spent on adaptation, ν2 , over time, t

spending devoted to adaptation can be at around ν2 = 0.1 and lower. Yet, since we have used our optimal solution procedure AMPL, our numerical solutions reflect the optimal paths for allocation decision, ν1 , ν2 , ν3 , that are computed in this chapter (see Figs. 9.9, 9.10, and 9.11). As in Bonen et al. (2016), given our model solutions here, we also would like to encourage increased public investments to support economic growth. This should also include spending on adaptation measures to climate change, with additional measures coming later; see Fig. 9.11. Our numerical results seem to confirm these statements. Since we have also kept track of the debt dynamic made sustainable through appropriate tax rates, see Figs. 9.5 and 9.7, our model also allows for credit-financed productive public expenditure whereby the debt does not become unsustainable. Although, overall, public climate-related taxation does not necessarily coincide

9.5 Disaster Scenarios and Climate Policies

147

Fig. 9.11 Trajectory of the fraction of public capital spent for mitigation. This figure shows an optimal path for the fraction of public capital spent for mitigation, ν3 , over time, t

with the benefits obtained from the avoidance of climate disasters at each period of time, there could still be an intertemporal improvement through tax and creditdriven expenditures and benefits from the mitigation of climate disasters; for details, see Orlov et al. (2018, 2019). Yet, overall, there are still likely to occur phases of the macroeconomy with higher frequency and severity of climate-related disasters whereby the macroeconomy might get trapped.

9.5 Disaster Scenarios and Climate Policies12 Recent research indeed has demonstrated that certain climate-related extreme events have higher chances of turning into unpredictable disasters. Such a higher disaster risk can be incorporated into the model described in Sect. 9.3 by extending it to take into account disaster traps.13 The objective function could be same as before in Eq. (9.4) but the state variables following Mittnik et al. (2019) can be sketched as follows (we omit the time index): K˙ = Y · (ν1 g)β − C − e P − (δ dis K + n)K − u ψ R −ζ , R˙ = −u,  − θ(ν3 · g)φ , M˙ = γ u − μ(M − κ M) b˙ = (rr − n)b − (1 − α1 − α2 − α3 ) · e P + ςk g, 12

(9.10) (9.11) (9.12) (9.13)

Section 9.5 is based on an IMF Working Paper by Mittnik et al. (2019) where Willi Semmler is one of the co-authors. They discussed similarities between financial and climate-related disasters. 13 Note that such disaster phases have also been studied as economic crisis scenario by Barro (2006) and Barro and Ursua (2008). They can be related to poverty traps in growth theory; see Semmler and Ofori (2007) and Kovacevic and Semmler (2020).

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9 The Public Sector—Energy Transition …

g˙ = α1 e P + i F − (δ disg + n)g + ςk g

(9.14)

where it has green private, K , and public, g, capitals, fossil fuel stock, R, debt, b, and CO2 concentration, M, as before. Mittnik et al. (2019) emphasized a tipping point and explained how the next stage reflects losses of capital from a large disaster can be modeled; a strong shock is demonstrated using δ disg = δ dis K where it can surge from 0.1 to 0.17 as well as a risk premium, rr , that increases from rr = 0.04 to rr = 0.12. The model presented above has bond issuance denoted by ςk g, but this is removed in the third stage described below. Tax revenue with tax rate, τ , that is used to repay debt is introduced together with risk premia. As a result, some of the state variables are changed to the following in the third phase while the R˙ and M˙ are unchanged14 : K˙ = Y · (ν1 g)β (1 − τk ) − C − e P − (δ K + n)K − u ψ R −ζ , b˙ = (rrr − n)b − (1 − α1 − α2 − α3 ) · e P − Y · (ν1 g)β τk ,

(9.16)

g˙ = α1 e P + i F − (δg + n)g

(9.17)

(9.15)

We thus characterize shocks with reference to relevant changes in the state equations and with underlying parameters. The detailed results of a multiple phase model are presented in Mittnik et al. (2019) where, for example, while there still might be leveraging, the capital losses will disappear in stage three. This might suggest that in the second and third phases a different interaction of sovereign borrowing and taxation should be adopted. In general, fiscal policy in terms of tax and public spending can contribute to an alleviation of environmental damages that might occur in the future. As Mittnik et al. (2019) stated, a disaster risk affects the welfare and consumption that might lead to poverty traps. In the context of the models presented in this chapter, an intertemporal fiscal policy plays an important role. In addition to adaptation and mitigation of CO2 emissions, one needs to stress the importance of green bonds and lower risk premia for a faster recovery from disasters. The benefits from the issuance of green bonds for both climate mitigation and adaptation policies are extensively discussed in Mittnik et al. (2019). They stressed the reduction of GHG emissions, energy efficiency in different sectors, alternative sources of energy, and the importance of sustainable infrastructure, grids, and transportation. We also discussed green bonds in Chap. 8, where we assumed that those were issued not only by the national government and municipalities, but also by financial corporations and banks, as well as by international organizations such as the World Bank. Semmler et al. (2021b) emphasize the usage of long-term green bonds, and the importance of household’s preferences toward green bonds (see also Chap. 11).

14

See Mittnik et al. (2019) for detail of such a disaster phase, and Semmler et al. (2021a) for a generic setup of a dynamic multi-phase macro model.

9.6 Central Banks, Monetary Policy, and Climate Change

149

9.6 Central Banks, Monetary Policy, and Climate Change Note that the climate-economic models that we described above, as well as DICE-type models, are generally built using growth models. For example, the Nordhaus (2017) model uses economic growth (GDP) driven by an exogenous technical change and an exogenous growth rate of total factor productivity. The steady state is obtained based on a logistic function leading total factor productivity to a stationary value. Since economic activities contribute to carbon emissions over time, carbon emissions can be shown as a time varying fraction of output, thus generating higher temperatures and damages, i.e., the temperature rise affects the level of output negatively through damages; see Nordhaus (2017). In recent years, the relationship of central banks to climate risk has become an important topic, often in the context of short or medium time horizon macro models.15 Monetary policy macro models are, however, usually formulated as stationary models, and for a medium time horizon,16 demonstrating fluctuations over the business cycle. Thus, while the monetary macro models are, in principle, stationary models for a shorter time horizon, they model the dynamics of some macroeconomic gaps. The dynamic macro models for policy purposes are thus oriented more toward business cycles, namely fluctuations of output and inflation rate around some trends. Although the proposal for central banks’ involvement in climate protection is not uncontroversial, we want to sketch a stylized medium-run macro model that may lend support for such an endeavor. We will, however, build in a slowly moving trend, capturing the carbon emission arising from economic growth. Related models include Linear Quadratic (LQ) models such as de Groot et al. (2021) and Nonlinear Quadratic models (NLQ), and taking account of financial market dynamics, as proposed in Faulwasser et al. (2020). We take the latter approach as the basic version of our proposed climate-macro model that incorporates not only carbon emission and climate change, but also movements of relevant macro variables central banks are interested in. We focus on a central bank’s decision problem: setting the interest rate, i, conditional on four constraints. Following Woodford (2018), Faulwasser et al. (2020), Semmler et al. (2022), and Chen et al. (2022), the usual constraints are defined by macro variables such as inflation rate, π, output gap, y, and volume of credit flows, l.

15

π˙ = −α1 π + α2 y,

(9.18)

y˙ = −β1 y − β2 (i + δ(y) − π − r ), l˙ = −γ1 l + γ2 y − γ3 (i + δ(y)) − γ4 π

(9.19) (9.20)

See Brunnermeier and Landau (2021). For an excellent treatment of selective credit flows in a monetary macro model such as presented here, see Roy (2023). 16 For decision-making by the central bank, Brunnermeier and Landau (2021) distinguish three time horizons around 2 years: monetary decisions, business cycle frequency: financial stabilization decisions, and decades: climate policies.

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9 The Public Sector—Energy Transition …

The Phillips curve is denoted by Eq. (9.18). There is an autoregressive process that links the predicted change in the inflation rate to the current inflation rate, and to the output gap, denoted by α2 .17 The IS curve is represented by Eq. (9.19), driven by the real interest rate, and a risk premium, δ(y); the latter driven by the output gap. Equation (9.20) represents credit flow, driven by interest rates and the risk premium, δ(y). Note that all coefficients are supposed to have a positive sign; the exception is γ4 , which is likely to be negative, the impact of the inflation rate on the real value of credit flows. We do not address the issue of the zero lower bound of the interest rate, and neither any Quantitative Easing (QE) policy. Our framework can be altered by introducing the effect of QE, which is practically dampening the risk premia during recessions. We assume a risk premium, δ(y), as proposed by Faulwasser et al. (2020), that exhibits a nonlinear relationship to the output gap, e.g., rising with a negative output gap, which can be affected by unconventional policies, e.g., QE. Parameters for the system (9.18)–(9.20) are also taken from Faulwasser et al. (2020). To account for carbon emissions and CO2 concentration in the atmosphere, we need to extend the above system with one more dynamic equation for the evolution of a newly introduced variable, m, which is the carbon emission dynamics.18 m˙ = −δ1 m + δ2 (y + d(t))

(9.21)

The new state Eq. (9.21) demonstrates the dynamics of carbon emission defined by some decay effects related to the stock of carbon in the atmosphere, δ1 . Various versions of this dynamics equation could be explored, e.g., by allowing δ1 to have a different size or being time dependent. The impact of a change in the output drift can also be modeled by a time and state-dependent adjustment coefficient, δ2 . These parameter values are assumed to be fixed at δ1 = 0.01 and δ 2 = 0.05. Here, however, we want to focus on a specific case: modeling the dynamics of the output gap, y, see below. An additional variable will be a drift term, a time trend d(t), also to be explained below. To be in a position to carry out a variety of monetary policy experiments concerning climate protection by taking seriously the conventional goal, e.g., inflation and output control, we need to complement the monetary policy objective function. For this, in addition to the other objectives of the Central Bank, a control of the stock of carbon in the atmosphere is included. As shown in Semmler et al. (2022) and Chen et al. (2022), an objective function is to be minimized facing constraints that depend on the macro behavior of each variable, as represented by the model’s dynamic state equations:

17 18

See Faulwasser et al. (2020) who introduce a nonlinearity in the Phillips curve. This is based on Semmler et al. (2022).

9.6 Central Banks, Monetary Policy, and Climate Change

T J (π, y, l, i, m) =

151

e−ρ t (wπ (π − πs )2 + w y (y − ys )2

0

+ wl (l − ls )2 + wm (m − m s )2 + wi i 2 ) dt

(9.22)

While Eqs. (9.18)–(9.21) are the constraints of the proposed monetary policy macro model, modifying the formulation of de Groot et al. (2021) and Faulwasser et al. (2020), we hereby propose a dynamic macro policy decision model with Eq. (9.22) as objective function with multiple goals of the Central Bank and state variables in Eqs. (9.18)–(9.21).19 Hereby, a specific weight is assigned to each objective (w j )20 We take the following values as target goals for the macro variables: πs = 0; ys = 0; ls = 0; and m s = 0.1. Usually, output gap, y, is defined as a difference between the actual and potential output for a market economy, but disregarding positive and negative externalities. These effects will be included in our definition of the output gap. In this way, we let potential output be affected, on the one hand, by forces favoring growth and, on the other, some negative externality effects; see Gerlagh et al. (2018).21 We want to consider the positive and negative externalities that do not affect output levels, as in Nordhaus (2017), but rather the growth rate of the potential output. Yet we are still using the output gap, y, in Eqs. (9.18)–(9.20), using the time index (t), to be defined in log form: y(t) = log(Y a (t)) − log(Y p (t))

(9.23)

where y(t) denotes some deviation of log of the actual output, Y a , from log of the potential (trend) output, Y p . Normally, if Y p is constant and normalized to 1, the dynamics of Eq. (9.23) is driven by log(Y a (t)). We introduce a growth of the potential output as well, affected by two forces, a positive externality effect that may drive up, Y p , and a negative externality effect that may drive it down, Y p . The latter is the same force that, in DICE models, makes the net output decrease through carbon emission, creating C O2 concentration in the atmosphere, and producing damages to the level of output through temperature rise; see Nordhaus (2017). In DICE models, an abatement cost is assumed, thus reducing the net output. We presume here that the damages are still greater than the abatement cost, yet output may also rise due to some positive externalities, creating a net slightly positive growth rate.

In the objective function, it is assumed that i s = 0. Brunnermeier and Landau (2021) argue that in case of conflict with the primary Central Bank goal, inflation targeting, for example in the EU, the Central Bank should give priority to the inflation rate. In the context of our model, this could be considered as assigning different weights for the objectives. In the simulations presented in the next section, we have taken equal weights, all equal to 1. 21 For the treatment of those two effects in growth models, see Greiner et al. (2005). 19 20

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9 The Public Sector—Energy Transition …

Thus, the above forces can be assumed to still exert a positive effect on the growth rate of potential output; we could describe potential output as follows: log(Y p (t)) = log(τ ( p ex p(gn t))) = d(t)

(9.24)

In the simulation for d(t), we take τ = 0.05 and the net growth rate of potential output gn = 0.002, and t as the time periods starting with t = 1. Moreover, it is assumed that  p = 20 as initial condition of Y p (0). The initial conditions of Y a and Y p are chosen in a way that log(Y a ) > log(Y p ), the latter growing at a rate gn . Since d(t) in Eq. (9.24) initially can be interpreted as a drift in the output dynamics, we thus can view the dynamics of the macroeconomic state variables, Eqs. (9.18)–(9.21), as detrended or de-drifted, using a filtering procedure to separate actual values of output from trend values, as in Faulwasser et al. (2020). Yet, the potential output shown in Eq. (9.24) is driving carbon emissions in the multiple objective functions of Eq. (9.22). These arise from output growth and cause damaging effects on output, affecting the trend growth rate gn , and affecting monetary policy as well. Our overall assumption is that the macroeconomy will still grow slightly; due to positive growth effects but reduced by damages, whereas the actual damages are slightly reduced through abatement policies. This is what Brunnermeier and Landau (2021) postulate as the monetary challenge facing planners confronted with the growth of the economy; on the one hand an increase of the natural rate, r ∗ , and on the other hand, a decrease of r ∗ caused by climate damage effects. We here assume that the overall effects on r ∗ will be slightly positive. Thus, we simulate the above model by studying the dynamics of the state variable, y, as driven by Eqs. (9.18)–(9.20), the drift term, e.g., the slight trend effect so that the system (9.18)–(9.20) is still defined as a stationary macro system, but including the drift effect defined by Eq. (9.24). In the simulations of the output gap dynamics, y, we will then obtain the deviations from the trend output, e.g., fluctuating around some trend.22 More generically, the above climate-oriented monetary macro policy model may give us some guidance of additional instruments for climate protection, e.g., instruments such as allocating certain weights to different policy objectives, time varying policy interest rate, dampened risk premia through QE (in case climate risk-related financial instability arises), and macro prudential regulations. All those instruments could be used to impact not only macroeconomic variables, e.g., inflation rate, output gap, and credit flows, but also carbon emissions, generating physical climate risks and transition risks, arising from the trend output.23 The model variants presented in this section are again solved by using AMPL, employing some initial conditions of the model (9.18)–(9.24) and time 22

To empirically estimate the output gap, in our case, the y(t) in Eq. (9.23) is an econometric issue, which is addressed in a previous chapter where we have suggested using a Hamilton filter. Other methods are discussed in Faulwasser et al. (2020). 23 See also Brunnermeier and Landau (2021) and their extensive discussion on the Central Bank’s precarious policy decisions when multiple policy objectives for Central Banks exist.

9.6 Central Banks, Monetary Policy, and Climate Change

153

Fig. 9.12 Interest Rate and Output Gap. In this figure, dynamic trajectories of interest rates (Panel a) and output gap (Panel b) are shown over time

discretization.24 For the results, we assume that the macroeconomy is highly out of balance; this is in order to see whether the macro variables can adjust. We explore the effects of cyclical and long-run increases in the carbon emissions. As initial conditions in Fig. 9.12 (Panel b) show, given an initially highly positive output gap, the interest rate moves up significantly; see Fig. 9.12 (Panel a). But given a low inflation rate with an output gap and high interest rate, the output gap falls and becomes negative, although it becomes positive again roughly from period 28 on, and then becoming stationary. While the output gap is high at the beginning, as shown in Fig. 9.13 (Panel b), the inflation rate, Fig. 9.13 (Panel a), and the credit flows have moved up, Fig. 9.13 (Panel b). Yet when the output gap becomes negative, the inflation rate and credit flows drop, and then also become stationary. Overall, when those macro variables move, they are significantly interacting with the risk premia that tend to ramp up for the time period when the output gap is negative. We assume that the rise of the risk premium is constrained and dampened through unconventional monetary policy of the QE-type. Finally, in Fig. 9.14 (Panel a), we observe that carbon emissions have moved cyclically, and the stock of carbon in the atmosphere has significantly increased over the longer run. It resulted from an overall positive trend in economic growth implicit in our Eq. (9.24), affecting the long-run growth of carbon density in the atmosphere. This is a worry that monetary authorities are facing, possibly requiring policy adjustments to help manage the climate challenges. We should also note that sustainability-oriented monetary macro models of medium time horizon, with active central banks, can be suitable to provide guidance for climate protection policies. Those are often used to study shorter-term, but similar models with dynamically linked macroeconomic variables, e.g., interest 24

The solution method and further details of the algorithm, allowing also for regime switching, are reported in Semmler et al. (2021a).

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9 The Public Sector—Energy Transition …

Fig. 9.13 Inflation Rate and Credit Flows. In this figure, dynamic trajectories of the inflation rate (Panel a) and credit flows (Panel b) are shown over time

Fig. 9.14 Carbon concentration and Risk Premium. This figure demonstrates dynamic trajectories of carbon concentration (Panel a) and risk premium (Panel b) over time

rates, inflation rates, output gaps, and credit flows. They are, however, also suitable for the study of monetary policy instruments, e.g., multiple objective targeting, interest rate setting, dampening risk premia through QE, and macro prudential regulations addressing the challenges of climate change. Central banks are likely to impact CO2 emissions only moderately; thus, other policies, as discussed in Chap. 7, 8 and 11, are needed.

9.7 Conclusion As discussed in previous chapters, greenhouse gas emissions have been rising and the severity of climate-related extreme events have become more serious. To combat those climate-related disasters, adaptation policies are needed. Although the private

References

155

sector plays an important role in reducing carbon-based economic activities and energy systems, in this chapter, we focused on models that give us guidance as to how the transition can be enhanced by public sector, fiscal, and monetary policies. We reviewed and discussed a dynamic climate-multi-phase macro model for an improved framework that accounts for detailed mitigation and adaptation policies. GHG emissions and global warming-related disaster risks are addressed as well. We also elaborated on how fiscal and monetary measures might be able to support policies for mitigation, adaptation, and the green transition, and even have a role in the prevention and recovery from disaster risks, thus also affecting long-run trends in the climate as well as the economy. In the long-run macro model (see Sects. 9.3–9.5), instead of relating the impact of global warming and accompanying damages only to production activities, we demonstrated how welfare is affected. The numerical solutions presented in this chapter demonstrate the direct impact of emissions on welfare, capturing both the insufficient abatement, i.e., negative effects due to climate change, and indicating adaptation benefits as well. These numerical solutions also show that public spending on climate-related infrastructure projects would be beneficial in the long run. Public effort and public capital should be devoted to mitigation and adaptation in varying proportions over time. Moreover, similar to Bonen et al. (2016), the results presented in this chapter suggest that certain countries should prioritize climaterelated infrastructure that aids the transition to a low-carbon economy and enhances both economic livelihood and builds resilience to climate change. Most of the macro models are based on some intertemporal optimal solutions. However, doubt has been raised whether there are not numerous decision constraints, irreversibilities, and lock-ins, as well as policy games that slow down actual climate negotiations and policy decisions, even if some optimal and efficient policies are known, designed, and attempted to be implemented. These delaying forces, inefficient policies, and decisions can hinder the speed of effectively reducing carbon emissions leading to an unnecessarily sluggish path to a low-carbon economy. Those issues will be addressed in the next chapter.

References Anthoff D, Tol RJ (2013) The uncertainty about the social cost of carbon: a decomposition analysis using fund. Clim Change 117(3): 515–530. https://doi.org/10.1007/s10584-013-0706-7 Atolia M, Loungani P, Maurer H, Semmler W (2023) Optimal control of a global model of climate change with adaptation and mitigation. Math Control Related Fields 13(2): 583–604. https://doi. org/10.3934/mcrf.2022009 Barro R (2006) Rare disasters and asset markets in the twentieth century. Q J Econ 121:823–66 Barro R, Ursua JF (2008) Macroeconomic crises since 1870. NBER Working Paper 13940. Available via NBER. https://doi.org/10.3386/w13940 Brunnermeier M, Landau LP (2021) Finance, Money, and Climate Change, Manuscript, Princeton University, Published in 74th Economic Policy Panel Meeting (2022) Bonen A, Semmler W, Klasen S (2014). Economic damages from climate change: a review of modeling approaches. Schwartz Center for Economic Policy Analysis, Working Paper 2014-3

156

9 The Public Sector—Energy Transition …

Bonen A, Loungani P, Semmler W, Koch S (2016) Investing to mitigate and adapt to climate change: a framework model. WP/16/164 International Monetary Fund Bose N, Haque EM, Osborn D (2007) Public expenditure and economic growth: a disaggregated analysis for developing countries. The Manch Sch 75(5):533–556 Bosello, F., 2010. Adaptation, Mitigation and ‘ Green’ R&D to combat global climate change. Insights from an empirical integrated assessment exercise. FEEM Working Paper No 22.2010. https://ssrn.com/abstract=1573633. Accessed 7 Nov 2022 Bréchet T, Hritoneko N, Yatsenko Y (2013) Adaptation and mitigation in long-term climate policy. Environ Res Econ 55(2):217–243 Chen P, Semmler W, Maurer H (2022) Delayed monetary policy effects in a multi-regime cointegrated VAR(MRCIVAR). Econometr Stat. https://doi.org/10.1016/j.ecosta.2022s.03.004 De Bruin K, Dellink R, Tol R (2009) AD-DICE: an implementation of adaptation in the DICE model. Clim Change 95:63–81 Published in: Environ Dev Econ, 1–12 Di Bartolomeo G, Fard B, Semmler W (2021) Greenhouse gases mitigation: global externalities and short termism. Working Paper No 196 June 2021. University of Rome La Sapienza, Department of Public Economics De Groot O, Falk M, Motto R, Ristiniemi A (2021) A toolkit for computing Constrained Optimal Policy Projections (COPPs), ECB Working Paper No 2555 Fankhauser S, Tol R, Pearce DW (1997) The aggregation of climate change damages: a welfare theoretic approach. Environ Res Econ 10(3):249–266 Faulwasser T, Gross M, Semmler W, Loungani P (2020) Unconventional monetary policy in a nonlinear quadratic model. Stud Nonlinear Dyn Econometr 24(5):20190099. https://doi.org/10. 1515/snde-2019-0099. Accessed 7 Nov 2022 Fourer R, Gay DM, Kernighan BW (2002). AMPL: a modeling language for mathematical programming, 2nd ed. Cengage Learning. http://ampl.com/resources/the-ampl-book/. Accessed 7 Nov 2022 Gerlagh R, Lupi V, Galeotti M (2018) Family Planning and Climate Change, CESIFO 7421, December Golosov M, Hassler J, Krusell P, Tsyvinski A (2014) Optimal taxes on fossil fuel in general equilibrium. Econometrica 82(1):41–88 Greiner A, Semmler W, Gong G (2005) The forces of economic growth. Princeton University Press, Princeton Greiner A, Grüne L, Semmler W (2014) Economic growth and the transition from non-renewable to renewable energy. Environ Dev Econ 19(4):417–439 Kaya Y, Nakicenovic N, Nordhaus W, Toth FL (1993) Costs, impacts, and benefits of CO2 mitigation, IIASA Collaborative Paper CP-93-002, International Institute for Applied Systems Analysis Kellett CM, Weller SR, Faulwasser T, Grüne L, Semmler W (2019) Feedback, dynamics, and optimal control in climate economics. Ann Rev Control 47:7–20 Kovacevic R, Semmler W (2020) Poverty traps and disaster insurance in a bi-level decision framework. In: Haunschmied J, Kovacevic R, Semmler W, Veliov V (eds) Dynamic economic problems with regime switches. Springer Publishing House Mendelsohn R (2000) Efficient adaptation to climate change. Clim Change 45:5836000 Mittnik S, Semmler W, Haider A (2019) Climate disaster risks–empirics and a multi-phase dynamic model. International Monetary Fund. Working Paper No 2019/145. Published as: Mittnik S, Semmler W, Haider A (2020) Climate disaster risks—empirics and a multi-phase dynamic model. Econometrics 8(3):1–27 Nordhaus WD (1994) Managing the global commons: the economics of climate change, vol 31. MIT Press, Cambridge, MA Nordhaus WD, Boyer J (2000) Roll the DICE again: the economics of global warming. Yale University Nordhaus WD (2008) A question of balance: weighing the options on global warming policies. Yale University Press, New Haven, CT Nordhaus WD (2017) Revisiting the social cost of carbon. Proc Nat Acad Sci 114(7):1518–1523

References

157

Nordhaus WD, Sztorc P (2013) DICE 2013R: introduction and user’s manual, 2nd edn. Yale University and the National Bureau of Economic Research, USA Orlov S, Rovenskaya E, Puaschunder JM, Semmler W (2018) Green bonds, transition to a lowcarbon economy, and intergenerational fairness: evidence from an extended DICE model. IIASA Working Paper, WP-18-001, IIASA Orlov S, Rovenskaya E, Semmler W (2019) Financing mitigation of climate risks through green bonds—an intergenerational perspective. IIASA Working Paper, IIASA Roy A (2023) Three Essays in The Economics of Environment and Climate Change, Dissertation, The New School for Social Research Semmler W, Ofori M (2007) On poverty traps, thresholds and take-offs. Struct Change Econ Dyn 18(1):1–26 Semmler W, Greiner A, Diallo B, Rajaram A, Rezai A (2011) Fiscal policy, public expenditure composition and growth: theory and empirics. Aestimatio the IEB Int J Finance 2:4889 Semmler W, Henry J, Maurer H (2022) Pandemic meltdown and economic recovery—a multiphase dynamic model, empirics, and policy. https://iceanet.org/wp-content/uploads/2022/02/ Henry.pdf. Accessed 7 Nov 2022 published in: Research in Globalization Volume 6, June 2023, 100106 Semmler W, Maurer H (2015) An integrated assessment model for mitigation and adaptation in modeling of climate change, 13th Viennese Workshop on Optimal Control and Dynamic Games Semmler W, Maurer H, Bonen A (2021) Financing climate change policies: A multi-phase integrated assessment model for mitigation and adaptation. In: Haunschmied J, Kovacevic R, Semmler W, Veliov V (eds) Dynamic economic problems with regime switches. Springer Publishing House Semmler W, Braga J, Lichtenberger A, Toure M, Hayde E (2021b) Fiscal policy for a low carbon economy, world bank report. https://documents1.worldbank.org/curated/en/ 998821623308445356/pdf/Fiscal-Policies-for-a-Low-Carbon-Economy.pdf. Accessed 7 Nov 2022 Tol RSJ (2007) The double trade between adaptation and mitigation for sea level rise: an application of FUND. Mitig Adapt Strat Clim Change 12(5):741–753 Tol RSJ, Fankhauser S (1998) On the representation of impact in integrated assessment models of climate change. Environ Model Assess 3:63–74. Woodford M (2018) Monetary policy analysis when planning horizons are finite. NBER Working Papers 24692. Available via NBER. https:// doi.org/10.3386/w24692 Woodford M (2018) Monetary policy analysis when planning horizons are finite. NBER Working Papers 24692. Available via NBER. https://doi.org/10.3386/w24692

Chapter 10

Delaying Forces and Climate Negotiation—Games, Lock-ins, Leakages, and Tipping Points

Overview In practice, not all of the above dynamic decision models yield policies that are as efficient and optimal as might be desired. Thus, the real question is if they are sufficiently accurate to provide useful guidance as to implementable policy. Many of the previously discussed versions of the climate-macro link work with intertemporal optimizing behavior. However, we should acknowledge that there is some legitimate criticism of those same models. For example, behavioral approaches that do not presume optimizing behavior are also widely applied in the field. Endowing decision-making institutions and policymakers with more realistic features will also shed some light on why the environmental and climate policy creation has taken so long, and indeed, may be expected to proceed slowly. We provide three modelbased illustrations exemplifying the proverbial snail’s pace of negotiations and policy creation.

10.1 Literature Review As pointed out in the previous chapters, we acknowledge the limitations of infinite horizon optimization and therefore tend to work with finite horizon decision models. Finite horizon and state constraints, as well as limited information and informationconstrained agents, were previously mostly assumed. Next, we want to take a step further and explicitly introduce decision constraints into our models, particularly those that often arise in the context of environmental and climate games and their application to international negotiations. We first discuss the issue of constraints of international climate negotiations in the context of a game-theoretical setup. We then elaborate, in a simplified dynamic climate-macro model, why well-intended negotiations might be insufficient and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_10

159

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blocked by lock-ins, irreversibility, and leakage which are costly to overcome. This helps us to evaluate the limitations of the intertemporal decision models, often used in this area, and also referred to in previous chapters. By doing so, we will explore behaviorally founded decision-making and policy perspectives, and explain inefficiencies implicit in climate negotiation. The first strand of literature on insufficient mitigation policy was generated by game-theoretic work. These games are designed as cooperative and non-cooperative, whereby the difference between the two perspectives is important for understanding the outcomes of the respective games. Since the Kyoto Protocol, many international negotiations have been guided by various game-theoretical setups; see Chappe (2015), Heal (1994), and Heal and Kunreuther (2011). In Dockner et al. (1996), an excellent overview on resource and environmental games is given, and how they are related in their setup. Both are dealing with externalities and side effects of industrial dynamics; one deals with a resource extraction problem, see Benhabib and Radner (1992), and the other with polluting the environment, see Dockner et al. (1996). A group of researchers at Rome’s La Sapienza University has developed a numerical approach, also based on NMPC, that allows one to study multi-period strategically interacting decision-making for finite horizons, i.e., with shorter decision horizon, whereby also the economic impact of short-termism can be studied; see Di Bartolomeo et al. (2018, 2021), Saltari et al. (2021), and Semmler et al. (2022). Linear-quadratic games in environmental economics are treated in Engwerda (2015). As one can show, a non-cooperative game, by avoiding agreements, can lead to extensive extraction of fossil fuel and increased carbon emissions; these are termed leakage problems; see Reguant (2021). What has also been observed in much empirical literature is that decision-making might be locked-in by prior decisions. Note that not all decisions are easily reversed. A well-known example is if there is a lock-in of previous investment where the capital stock cannot be discarded or quickly replaced. Old capital stock is irreversible: when investment returns turn out to be lower than depreciation, the capital stock is supposed to shrink under the optimal strategy, but if investments are irreversible or very costly, capital can be only slowly reduced (see Bernanke 1979). In general, economic or social lock-ins1 also occur due to past decisions, e.g., lifestyle lock-ins are given by the fossil fuel society as well as by lock-ins of large operating industries and firms defending their interest in fossil fuel energy-based activities. Moreover, there is inertia and resistance to change even if a carbon tax is levied on carbon-intensive products. A larger number of firms, households, and countries still continue operating using fossil fuel energy. This is also a kind of leakage effect where, in spite of new policies or international agreements, fossil fuel is still extracted for energy use.2

1

For the theory of lock-ins and the development and implementation of technology, see Bryan Arthur’s work. 2 Many observers point to the significant use of coal as an energy source in China and other countries in spite of international agreements. If other countries do not participate in existing agreements, the conditions for a cooperative game are not fulfilled.

10.2 Games and Inefficient Outcomes of International Negotiations

161

10.2 Games and Inefficient Outcomes of International Negotiations3 Early literature on insufficient mitigation policy was related to research on environmental games. Di Bartolomeo et al. (2021) highlight emission reduction policies and resulting trade-offs as in Nordhaus (2021a, b) and demonstrate how to compute the effects of international externalities (CO2 concentration) by studying non-cooperative and cooperative games. They also address policies in the short run and focus on damages that could also be significant for delaying effective climate policies. For this purpose, in the context of climate protection policy, we can again utilize Nonlinear Model Predictive Control (NMPC). As shown in Di Bartolomeo et al. (2021), where the numerical solutions involve policy horizons, NMPC can be used to study strategic policymaking. To reduce CO2 emissions and limit temperature rise, it is recognized that global cooperation is needed. However, as Nordhaus (2021b) notes, the problem of free riders should be solved by coordination between players with the consequence that non-cooperative members pay a penalty. In NMPC, the time horizon affects the dynamics, and choosing different decision horizons can be interpreted as changing the degree of rationality; see Di Bartolomeo et al. (2018), Saltari et al. (2021), and Di Bartolomeo et al. (2021). Similar to Di Bartolomeo et al. (2021), for the GHG emission policies, we also incorporate the climate system and relate it to the economy. Di Bartolomeo et al. (2021) study environmental policymakers and compare non-cooperative and cooperative cases. We also can refer to theories that were outlined in Greiner et al. (2005, 2014). The following basic version of a game-theoretical setup of a climate negotiation game is presented in Di Bartolomeo et al. (2021).4 It is assumed that the use of fossil fuel in country i ∈ 1, 2, and xi (t), which has some benefits from using it, contributes to global concentration of carbon, g(t): g(t) ˙ = β(x1 (t) + x2 (t)) − μg(t)

(10.1)

As shown in Di Bartolomeo et al. (2021, p. 5), β denotes the “portion of CO2 that is not absorbed by oceans” and μ, is a parameter reflecting “inverse of the atmospheric lifetime of CO2 ”. Di Bartolomeo et al. (2021) used Eq. (10.2) to show a solution involving NMPC, where t denotes time, j represents opponent’s policy, and i corresponds to a policymaker who faces the following dynamic decision problem in the context of a finite horizon model:

3

This section is based on Di Bartolomeo et al. (2021), where Willi Semmler is one of the co-authors. For further details concerning the weakness of non-cooperative games and international agreements, see Nordhaus (2021a, b).

4

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10 Delaying Forces and Climate Negotiation—Games …

 maxxi =

T

e−ρt Ui (t)dt, i ∈ {1, 2}

(10.2)

t

subject to Eq. (10.1). This defines a non-cooperative game. The decision pay-off function for each player contains the state variable g(t) as well as the player’s decision and the other players’ given decision whereby g (·) is a convex function and ρ ∈ (0, 1) is the discount rate. For the preferences, based on Byrne (1997) and Greiner et al. (2014), Di Bartolomeo et al. (2021) use the following pay-off function for each player:   Ui (t) = xi (t)(1−σ) (g(t) − g)−γ(1−σ) − 1 / (1 − σ) The players’ decision on xi is beneficial for him/her but globally damaging through the state variable g(t), the total carbon emission, with g as a threshold value.5 In this case of a non-cooperative game, no negotiations have been reached and no international agreements or treaties on climate protection exist or are activated. The numerical solution is obtained in an iterative fashion. A finite horizon decision-making process is initiated by one side, taking the decision of the other side as given. This is repeated until there is some convergence. This is done over N steps whereby the first of the N steps are taken as the realization of the game, then moving on to the next iteration with N steps until the time period T is reached.6 The above outcomes of a non-cooperative game can be compared to those resulting from a cooperative game. Di Bartolomeo et al. (2021) incorporate policy horizon and introduce bargaining powers, ω and 1 − ω, as shown in equation (10.3). The preferences under the integral (10.2) are then defined multiplicatively as indicated in equation (10.3): (U1 (t))ω (U2 (t))1−ω

(10.3)

Those two model versions are solved through some calibrations where it assumes equal bargaining power. Next, Di Bartolomeo et al. (2021) present the two cases with a game-theoretic setup. The results show that policymakers in each country are not internalizing global externalities. The second case corresponds to worldwide coordination with credibility that reflects full participation in climate protection policies as suggested by Nordhaus (2008). Technically these two scenarios represent the following features: • In the non-cooperative game, the problem (10.2) is solved assuming the policymakers are individually and separately following each country’s dynamic decision process as the best response strategy to the other countries’ moves. • In the cooperative game, it is assumed that some policymakers jointly maximize the preferences of all countries together with a goal to maximize (10.3). 5 6

See also a very convincing description in Nordhaus (2021a). For details, see Di Bartolomeo et al. (2021).

10.3 A Dynamic Climate-Macro Model with Leakages

163

• The solutions are generated using NMPC as developed in Grüne et al. (2015), but in the case of two players, there are two objective functions and one state variable dynamics; see equation (10.1). Figure 3 in Di Bartolomeo et al. (2021) demonstrates the path of temperature rise, due to carbon concentration, for the two scenarios. It shows a much greater and faster increase for the non-cooperative case. This bad trend is likely to end up with a much higher level increase as compared to the normal level, corresponding to a greater temperature increase, far beyond the pre-industrial level. In contrast, a cooperative solution shows a gradual increase in the stock of carbon and corresponding changes in temperature over time measured against 1959.7 However, this result suggests that even the cooperative game and appropriate international agreements would not achieve the goals of the Paris Agreement. The less positive result, even in the case of a cooperative game, depends of course on the assumptions of the available renewable energy technology and, pessimistically, priors on the parameters β, μ, and the weights in the Nash product, w. Note, however, that the above two scenarios represent two extremes that are modeled in Di Bartolomeo et al. (2021). Actual international agreements, for details see Chappe (2015), have somewhat achieved some results in between these. The international cooperation since the Kyoto Protocol was first targeting carbon emission levels using the pre-industrial level as a benchmark, then later since Copenhagen it was focused on the temperature, first at 2 ◦ C but in the Paris Agreement aiming at 1.5 ◦ C. Given the large lock-ins of countries, the irreversibility of fossil fuel-based capital and energy investments, entry barriers to the energy sector, and large leakages and loopholes that countries take advantage of, some realism for the evaluation of targets is appropriate; one could in fact be motivated to move away from some realization of optimal solutions and should introduce some behavioral forces. There are behavioral economic as well as political forces that are delayed and are likely slowing down “Green New Deal” climate efforts.8

10.3 A Dynamic Climate-Macro Model with Leakages The decision models of the previous chapters, written in continuous time, may mask the problem that a smooth transition through the use of an optimal and efficient decision variable might be too restrictive. In those models, we employed behavioral and state constraints, but now we want to look at a small-scale dynamic decision model,

7

As Di Bartolomeo et al. (2021) show, the above results correspond to the large confidence interval of the IPCC (2014) study. They also note that predictions by IPCC (2014) would mean “an increase in the global mean surface temperature in a range from 2.5 to 7.8 ◦ C.” (see Di Bartolomeo et al., 2021, p. 9). 8 For a more skeptical view, see Nordhaus (2021a, b) and Reguant (2021).

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10 Delaying Forces and Climate Negotiation—Games …

as a shortcut of the model of Sect. 9.3, one that might exhibit control constraints reflecting the slowed forces. As also shown in Reguant (2021), there can be many reasons for such control constraints. They can arise from lock-ins, irreversibility, leakages, and other reasons that will be further discussed below. Being often used as policy constraints, those will give rise to strong inertia, sluggish and costly adjustments, and non-optimal policy decisions with non-smooth transitions—possibly with disruptions arising from tipping points. We want to exemplify more behaviorally oriented non-optimal decision-making in the following two simplified models. These work with three state variables and two decision variables, and we contrast those with a model variant of non-optimal controls. The optimizing model version resembles Barnett et al. (2020) and Reguant (2021) and is a shortened version of the model of Sect. 9.3 that was based on Bonen et al. (2016) and Greiner et al. (2014). We thus show a dynamic model in three state variables of capital stock, k, fossil fuel resources, R, and carbon emissions, M:  max W = ct ,u t

s.t.

T

   dt e−ρ·t · log(ct ) − (Mt − M)

(10.4)

0

k˙t = A (Ak kt + Au u t )α − ct − (δk + n)kt − γu t R˙ t = −u t    M˙ t = γu t − μ Mt − κ M

(10.5) (10.6) (10.7)

ut ≥ 0 As compared to the model of Sect. 9.3, here we leave out the state variables of debt, bt , and the evolution of public capital, gt , invested in climate-related infrastructure, adaptation, and mitigation, denoted there by ν1 , ν2 , and ν3 , respectively. We have also simplified the emission dynamics and preferences, following Greiner et al. (2014), Barnett et al. (2020), and Reguant (2021). As before, we use the following output dynamics, but now with st as a new decision variable that could be chosen optimally or not: Yt = A ( Ak kt + Au st )α As shown in previous chapters, the state variable for the stock of fossil fuel, Rt , is now reduced by a flow of extraction, st . The extraction cost is also simplified and  is again the pre-industrial level of carbon concentration. M For the non-optimizing version, we can formulate a model as follows:

10.3 A Dynamic Climate-Macro Model with Leakages



T

max W = ct ,u t

s.t.

   dt e−ρ·t · log(ct ) − (Mt − M)

165

(10.8)

0

k˙t = A ( Ak kt + Au st )α − ct − (δk + n)kt − γs t R˙ t = −st    M˙ t = γst − μ Mt − κ M st = max(χ, u t )

(10.9) (10.10) (10.11) (10.12)

The last term in Eq. (10.12) means that we also have a decision variable χt that is related to behavioral rules whenever the optimal decision u t moves below a lower threshold. This definition allows us to consider cases of lock-ins in old energy technology, irreversibility, and leakages—a lagging use of old energy technology even if agreements are made. In many countries, service, industrial, and mining activities are historical sources of revenue. Countries are not easily convinced or cooperative with international agreements that encourage rapid transition to other types of economic activity. Thus, those inertial, sticky, and sluggish old energy technologies are likely to remain leaders in the extraction of fossil fuels and in carbon emissions. As discussed in Chap. 2, this might originate with short-termism or from the fact that non-renewable resourceproducing countries cannot or do not want to swiftly switch to other processes due to high costs. Rather, they want to continue earning revenues from extensive fossil fuel resources (see Chaps. 3–5). In this context, constraints have been largely addressed before. In the work of Nordhaus (2008, 2021a, b), the issue of incentives for international cooperative agreements is discussed; it is assumed that the participation rate will not be 100%. So, there will be some leakages—nonparticipating nations still continue extracting, using, and selling fossil energy.9 Figure 10.1 demonstrates the sluggish and delayed decision-making, which may be generated by economic forces or public opinion dynamics. Optimal and behaviorally oriented non-optimal energy policies are in contrast. While the optimal extraction policy is shown by the upper blue line and lower black line, the behaviorally non-optimal policy is demonstrated by the lower blue line and lower black line. The capital stock dynamics shown by the red line is only affected slightly. It should also be noted that the issue of environmental justice and fair transitions is currently being discussed in many countries. A carbon tax and the transition to a low-carbon economy will have distributional impacts through very uneven effects on households and certain population segments; thus, political constituencies are likely to differently evaluate the changes and may tend to bypass optimal solutions. Moreover, in terms of both job destruction and creation through renewable energy, this will be quite costly and uneven, hence resistance is expected. As Wirl and Yegorov (2015) argue, there is also an uncertainty implicit in the process—if subsidies by governments are promised, citizens might not believe that they are permanent, and so households might hesitate to swiftly switch to renewable 9

In order to close such leakages, Nordhaus (2021b) suggests a penalty for non-participation or failure to meet agreed-upon targets.

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Fig. 10.1 Optimal and non-optimal energy policies. In this figure, dynamic paths for three state variables are shown reflecting optimal and non-optimal energy policies. These variables are capital stock, k (red line), fossil fuel resources, R (blue line), and carbon emissions, M (black line). The optimal extraction policy is shown by the upper blue and lower black lines. The non-optimal policy is demonstrated by the lower blue and upper black lines

energy. Some countries might not be able to subsidize the transition in the first place, thus the transition to new energy sources will be sluggish; see Semmler et al. (2022). Financial markets might continue to be dominated by short-termism; see Chap. 2. Overall, non-participation, lock-ins, irreversibilities, and leakages may not adjust as needed to accelerate the decreasing use of fossil energy.

10.4 Drivers of Nonlinearities and Tipping Points In spite of the slow increase of carbon concentration in the atmosphere and gradual rise in temperature, thresholds and tipping points may actually accelerate the frequency and severity of disasters. Intensive work has been undertaken on this issue; see Hansen (2008), Scheffer et al. (2009), Greiner et al. (2010), and Nordhaus et al. (2008). Climate scientists also warn about nonlinearities; see Keller and Nicholas (2015) in the collapse of the ocean circulations. According to Hansen et al. (2008), the earth’s energy balance is shifting, and tipping points have been arising due to slowmoving feedback dynamics. Dietz et al. (2021) explore the tipping point’s relevance for the discounting of future damages. One important physical tipping point is defined by Hansen et al. (2008). It is based on temperature and may shift from low to high, caused by the albedo effect. It is the fraction of incoming energy, α, e.g., sun energy, which is reflected back to space. Because of decreasing α, the fraction absorbed by the earth is rising, (1 − α). The

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Fig. 10.2 Tipping surface in three dimensions. This figure depicts the tipping surface in three dimensions: capital per capita, K , GHG concentration in the atmosphere, M, and temperature, T . There is a stable equilibrium point, red (higher temperature); the other stable equilibrium point is shown in yellow (lower temperature). Note that temperature is measured in degrees Kelvin

latter fraction is likely to go up with the temperature; see Hansen et al. (2008) and Greiner et al. (2010). Using the above shift of energy balance, Greiner et al. (2010) demonstrate that this nonlinearity can lead to a tipping point due to multi-equilibria. Figure 10.2 shows10 that all trajectories above the surface in the M-dimension, which represents the stock of emissions, will move to the high-temperature equilibrium, and below the Msurface, they will go to the low-level temperature. For more explanation of this result, see Greiner et al. (2010). Overall, climate-economic research has found different tipping points that can be related to temperature or to sea level rise. These can be summarized as follows: • Tipping point: Energy and temperature feedbacks as discussed in Hansen et al. (2008); major parts of the world will see long-run climate change sooner. • Tipping point causes: Sudden shifts, due to melting of the Greenland ice shields, Arctic Sea ice likely to disappear, the collapse of ocean currents, e.g., the Gulf Stream, as noted in Keller and Nicholas (2015), release of locked-in CO2 , permafrost leading to a faster release of carbon.

10

This Fig. 10.2 was “Reprinted/adapted with permission from Springer Nature Customer Service Centre GmbH: Springer Nature, Growth and Climate Change: Threshold and Multiple Equilibria by Alfred Greiner, Lars Grüne & Willi Semmler, COPYRIGHT (2010).”)

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• Tipping point effects: Increased weather extremes and intensity of tropical storms, hurricanes, typhoons, and more frequent weather extremes (flooding, extreme cold and hot periods), sea level rise, heat waves (desert formation, droughts). • Tipping point effects: Long-run changes; impact on ecosystem, coastal conditions, health, water supply, decline of productivity in agriculture and food supply, worsening of working conditions of labor. The nonlinearities and tipping points or in the case of Fig. 10.2 the tipping surface11 may allow an increase in the frequency and severity of climate disasters and create medium- and long-run climate change effects.

10.5 Conclusion We summarize the results presented in Chaps. 8, 9 and in this chapter. As loopholes, leakages, and costly reversals become more relevant, this leads to bypassing of the restrictions of the extraction of fossil fuels and a delay of effective climate policies essentially because of the following: 1. Prevention of the entry of renewable energy firms into the energy sector becomes more successful (see Chap. 8), and new needed resources for the transition are limited in supply. 2. Fiscal space12 in public policy shrinks and monetary policy becomes more concerned with other issues, e.g., rising inflation (see Chap. 9). 3. Non-cooperative games in international negotiations, lock-ins, irreversibilities, and leakages substantially continue to hold in this chapter. Also, given the possible tipping points, tipping surfaces, and regime shifts, many climate protection policies may not be very effective in keeping earth below the proposed 2 ◦ C, let alone achieving the 1.5 ◦ C as per the Paris Agreement. This is, in fact, below the target that Hansen et al. (2008) had estimated to be required, namely 1.7 ◦ C as compared to the pre-industrial period—this is seen to be close to a threshold, or surface, triggering irreversible climate processes. Because of the shortcomings discussed above, suggesting a dark perspective on the success of climate protection policies, extensive research and practical policies are needed for the low-carbon transformation, in particular energy and financial policies, which will be discussed next.

11

A possible switch into an irreversible regime is an issue that Golosov et al. (2014) do not consider in their optimal carbon tax solution. 12 Here, it refers to enough space to conduct fiscal policy, without experiencing unsustainable debt.

References

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References Arthur B (1989) Competing technologies, increasing returns, and lock-in by historical events. Econ J 99:116–131 Atolia M, Loungani P, Maurer H, Semmler W (2023) Optimal control of a global model of climate change with adaptation and mitigation. Math Control Relat Fields 13(2):583–604. https://doi. org/10.3934/mcrf.2022009 Barnett M, Brock W, Hansen LO (2020) Pricing uncertainty induced by climate change. Rev Financ Stud 33(3):1024–1066 Benhabib J, Radner R (1992) The joint exploitation of a productive asset: a game-theoretic approach. Econ Theor 2:155–190 Bernanke B (1979) Long-term commitments, dynamic optimization, and the business cycle. PhD dissertation, Massachusetts Institute of Technology. https://dspace.mit.edu/handle/1721.1/ 29839. Accessed 9 Nov 2022 Bonen A, Loungani P, Semmler W, Koch S (2016) Investing to mitigate and adapt to climate change: a framework model. WP/16/164 International Monetary Fund; Byrne MM (1997) Is growth a dirty word? Pollution, abatement and endogenous growth. J Dev Econ 54:261–284 Chappe R (2015) The legal framework of global environment governance on climate change: a critical survey. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, New York Di Bartolomeo G, Di Pietro M, Saltari E, Semmler W (2018) Public debt stabilization: the relevance of policymakers’ time horizons. Public Choice 177:287–299 Di Bartolomeo G, Fard B, Semmler W (2021) Greenhouse gases mitigation: global externalities and short termism. Working paper no. 196 June 2021. Sapienza University of Rome, Published in: Environ Dev Econ, 1–12 Dietz S, Rising J, Stoerk T, Wagner G (2021) Economic impacts of tipping points in the climate system. Proc Natl Acad Sci USA 118(34):e2103081118. https://doi.org/10.1073/pnas.2103081118 Dockner EJ, Van Long N, Sorger G (1996) Analysis of Nash equilibria in a class of capital accumulation games. J Econ Dyn Control 20(6–7):1209–1235 Engwerda J (2015) Prospects of tools from differential games in the study of macroeconomics of climate change. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, New York Golosov M, Hassler J, Krusell P, Tsyvinski A (2014) Optimal taxes on fossil fuel in general equilibrium. Econometrica 82(1):41–88 Greiner A, Semmler W, Gong G (2005) The forces of economic growth. Princeton University Press, Princeton Greiner A, Grüne L, Semmler W (2010) Growth and climate change: threshold and multiple equilibria. In: Crespo Cuaresma J, Palokangas T, Tarasyev A (eds) Dynamic systems, economic growth, and the environment. Dynamic modeling and econometrics in economics and finance, vol 12. Springer, Berlin, Heidelberg, pp 63–77 Greiner A, Grüne L, Semmler W (2014) Economic growth and the transition from non-renewable to renewable energy. Environ Dev Econ 19(4):417–439 Hansen J, Sato M, Kharecha P, Beerling D, Berner R, Masson-Delmotte V, Pagani M, Raymo M, Royer DL, Zachos JC (2008) Target Atmospheric CO2 : where should humanity aim? https:// arxiv.org/ftp/arxiv/papers/0804/0804.1126.pdf. Accessed 7 Nov 2022 Heal G (1994) Formation of international environmental agreement. In: Carraro C (ed) Trade. Springer Publishing House, Innovation, Environment, pp 301–22 Heal G, Kunreuther H (2011) Tipping climate negotiations. NBER working paper 16954. Available via NBER. http://www.nber.org/papers/w16954. Accessed 7 Nov 2022 IPCC (2014) Mitigation of climate change. Contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

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Keller K, Nicholas R (2015) Improving climate projections to better inform climate risk management. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, New York Nordhaus W (2008) A question of balance. Yale University Press Nordhaus W (2021a) Climate solutions—Nobel winner’s evolution from ‘dark realist’ to just plain realist on climate change, the Washington post, 14 June 2021. https://www.washingtonpost.com/ climate-solutions/2021/06/14/qa-william-nordhaus-interview-carbon-pricing. Accessed 7 Nov 2022 Nordhaus W (2021b) Why climate policy has failed and how governments can do better, foreign affairs, 12 Oct 2021. https://www.foreignaffairs.com/articles/world/2021-10-12/why-climatepolicy-has-failed. Accessed 7 Nov 2022 Reguant M (2021) Comment on “Climate change uncertainty spillover in the macroeconomy”. NBER Macroecon Annu 2021 36:321–328. https://www.journals.uchicago.edu/doi/10.1086/ 718669 Saltari E, Semmler W, Di Bartolomeo G (2021) A Nash equilibrium for differential games with moving-horizon strategies. Comput Econ 1–14. https://doi.org/10.1007/s10614-021-10177-8 Scheffer M, Bascompte J, Brock W (2009) Early-warning signals for critical transitions. Nature 461:53–59. https://doi.org/10.1038/nature08227 Semmler W, Braga JP, Lichtenberger A, Toure M, Hayde E (2021) Fiscal policy for a low carbon economy. World bank report. https://documents1.worldbank.org/curated/en/ 998821623308445356/pdf/Fiscal-Policies-for-a-Low-Carbon-Economy.pdf. Accessed 7 Nov 2022 Semmler W, Di Bartolomeo G, Fard B, Braga JP (2022) Limit pricing and entry game of renewable energy firms into the energy sector. J Struct Chang Econ Dyn 61:179–190 Wirl F, Yegorov Y (2015) Renewable energy: models, implications, and prospects. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, New York, pp 349–375

Chapter 11

Climate Risks, Sustainable Finance, and Climate Policy

Overview Given the historically excessive use of fossil fuel energy resources and the perils of moving beyond the earth’s carbon budget, we have explored the challenges in reaching a low carbon economy. In Chap. 7 we started with the dynamic modeling of a mixed energy sector, then in Chap. 8 we considered the market and competition dynamics of how renewable energy can become the dominant energy source while fossil fuel is gradually phased out. In Chap. 9 we elaborated on dynamic macro models and how they can become proper guidance for sustainable macro policies in the transition to better climate protection policy. In Chap. 10 we studied the delayed effects and obstacles to scaling up effective climate strategies. Next, we focus explicitly on a variety of sectoral as well as financial and macro policies that can support the transition. In the last decades there were severe crises and oil market turbulences that impacted climate protection policies. These were financial market meltdown of 2007–2009, COVID-19 pandemic driven recession in 2020–2022, and the invasion of Ukraine by Russia in 2022. All three crises gave rise to contrasting the future benefits and costs of traditional fossil fuel energy compared to renewable energy production. The question of priorities is crucial, i.e., should recovery from economic disasters and meltdowns take precedence over climate protection or the reverse; which should be at the forefront of macro policy decisions?

11.1 Diverse Crises and Delay of Climate Protection Policies First, as has been widely discussed, the financial crisis of 2007–2009 created oil market turbulence that had a great impact on climate protection discourse; these had been on the back burner while climate disasters were being modeled as instances of financial disaster events. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_11

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Second, the COVID-19 (Corona virus) pandemic gave rise to a world-wide recession. Semmler et al. (2023) describe the early waves (of infection) in 2020, associated with the initial conditions of a growing economy, e.g., a boom, which ended up with lower output. Further waves occurred in the years 2021 and 2022. There was also Corona fatigue effecting the public; this further affected the pandemic’s severity and decreased economic activity, resulting in a prolonged and deeper economic meltdown. Eventually, even though new variants of the virus arose, with extensive vaccination efforts, the waves slowed down in most Western countries. When those infection waves caused the output gap to turn negative, carbon emissions fell relative to the trend. Similar to the 2007–2009 financial crisis, unconventional monetary policy in the form of Quantitative Easing was enacted to encourage recovery. Yet, the great challenge of climate change was neglected as the financial recovery was given priority. Concerning more frequently occurring climate disasters, the science was pointing to the urgent need to develop and apply adequate long-run policy measures. Investments for climate protection were then suggested to aid in the exit from the COVID-19 pandemic driven recession. Innovation in the financing of green investments continues to have some success and green securities often outperform carbon-intensive assets in financial markets.1 The third important global event deeply affecting the fossil fuel energy market and climate protection policies started with the Russian invasion of Ukraine in February 2022, highlighting the fossil fuel dependence of many countries, particularly in Europe.2 The extensive sectoral and financial sanctions on Russia impacted the provision of energy to Europe. Because of its dependence on Russian energy supplies, the proposal for a quick boycott did not get through easily—but the Nord Stream 2 gas pipeline was canceled. Russia held at that time roughly 13% of the world crude oil supply and an oil embargo sanction would have meant obtaining oil from other countries, e.g., Iran, Venezuela, Arab countries, and/or Nigeria. Since this would have taken too long and were likely to be insufficient for the existing demand, oil prices were predicted to rise above $120 per barrel, translating into increased inflation, see Hamilton (2022). Since oil is traded on the world market, price controls were not likely to work. What likely constrained the price surge was the consideration of energy conservation and rationing, as had been used during the oil crises of 1973 and 1980. The rationing and reduction of oil demand offered the possibility of keeping oil prices down while providing incentives for quickly moving toward renewable energy, which, in general, could already be implemented at lower cost (see Table 11.1). A tax cut on energy-related products could have reduced the effect of the oil price surge on low income households. Some parts of the massive windfall profits and 1

While the annual performance of the MSCI Global Alternative Energy Index was at 108.5% in 2020 (see https://www.msci.com/documents/10199/40bd4fec-eaf0-4a1b-bfc3-8ed5c154fe3c), the MSCI World Energy Index was at –30.5% (see https://www.msci.com/documents/10199/de6dfd903fcd-42f0-aaf9-4b3565462b5a). See the article by Pastor et al. (2021). 2 For fossil fuel dependence in developing and emerging economies, and for sovereign default risk analysis, see Nyambuu and Bernard (2015).

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Table 11.1 Solar PV module and wind turbine real prices. Source: constructed based on data from IRENA (2022). This table shows annual average real prices for different solar photovoltaic (PV) modules sold in Europe and wind turbines in 2010, 2016, 2017, and 2021. Note that a-Si stands for amorphous silicon. Solar PV module prices are measured in 2021 US dollars per watt ($/W) and wind turbine prices are measured in 2021 US dollars per kilowatt ($/kW). The change is calculated as a percentage growth rate between two years. For example, the percentage change for U.S. wind turbines with higher than 100 megawatts (>100 MW) capacity was calculated using data between 2010 and 2016 Solar PV module prices ($/W) 2010 2017 2021 Change (%) Low-cost Mainstream High-efficiency Thin film a-Si/u-Si Wind turbine prices ($/kW) U.S. (> 100 MW) Chinese Vestas average

0.34 0.51 0.64 2.15 2010 1626 787 1598

2016 973

0.20 0.30 0.40 0.27 2021 425 937

–41 –41 –38 –87 Change (%) –40 –46 –41

a tax increase on the imported oil could have been redirected as subsidies to those households. An additional tax on imported oil would provide further incentives to phase in renewable energy faster, but the outcome for an oil supplier, especially for Russia, in terms of revenue loss would be uncertain. The efforts were made to curtail the Russia’s state revenues through sanctions from the West, particularly blocking of dollar payments in the transactions. The impact of an oil boycott from the United States was estimated to be minor, given the autarky of the fossil fuel energy production in the United States, but embargoes are likely to have a greater impact. Hamilton (2022) estimated a loss of 0.5% of GDP for the United States, but it was expected to be greater for Europe. The downturn in the US did not occur in the US in 2022 and Europe’s growth rate declined only slightly, see Mittnik and Semmler (2022). The latter energy crisis, as well as the other two crises discussed above,3 raised the issue of energy security. As has been realized, securing the energy supply primarily through fossil fuel energy provisions might be disastrous in the long run from both the geopolitical and climate change perspectives.4 Also, temporary measures needed to overcome short-run shortages through pumping more oil, yet producing severe climate damages in the long run, might not be very reasonable. As a short-run policy, energy conservation efforts and rationing schemes would be preferable; as was done in the 1970s. Voices have been raised that global climate mitigation efforts are necessary—even in difficult times—to effectively pro3

For an evaluation of the perils of big economic meltdowns, the COVID-19 crisis, and possible future climate disaster traps, see Brunnermeier (2021) and his discussion on societal resilience and “bouncing back” mechanisms. 4 Nyambuu and Tapiero (2018) highlight the geopolitical risks.

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tect the planet from impacts of greenhouse gas (GHS) emissions in the long run and to combat climate change. Thus, the best policy for energy security seems to be the rapid development of green energy, which is available with infinite supply. We note, however, that short run infrastructure buildup, including the mining of important minerals, e.g., cobalt, lithium, and rare earth metals, will be required and this may slow the transition and add costs. Over the medium run, Bonen et al. (2016) highlighted a trade-off between infrastructure spending for the production and prevention of extreme events, adaptation, and mitigation. Given the long delays in the actual reduction of carbon in the atmosphere, climate-related extreme events could still increase. For the interaction of the market based private and the public sectors, an important mix of policies is needed— innovation policy, financial, fiscal, monetary, sectoral and insurance policies.5

11.2 Green Innovation and Sustainable Finance Indeed, recent research, e.g., Lamperti et al. (2019) and Kemfert et al. (2020), highlight private as well as public innovations in green technology. Stiglitz (1993, p. 110) notes that there can be “an incomplete set of risk markets and imperfect information” and the Government plays an important role in financial risk bearing. In this context, Braga et al. (2021) suggest government de-risking of private sector forays into green finance. This is not unprecedented as similar de-risking of mortgages has been done for more than half a century.6 They also discuss challenges and entry barriers encountered by new renewable energy firms. As to the financing side, in our previous chapters we discussed bank loans, the issuance of green bonds, venture capital, and other methods. Empirical work on green bonds and their importance for climate finance is set out in Lichtenberger et al. (2022). More specifically, the following questions should be asked regarding clean energy innovations: 1. Are there cost advantages in the long run, e.g., declining long-run average costs, as we have explored in Chap. 8? 2. How can the innovation become relevant for firms creating the renewable energy (see Chaps. 4 and 8)? 3. What are the specific financing sources available to the new types of energy firms; what external finance and leverage are sustainable? 4. What will happen to the asset prices of the carbon-based oligopolies and their linked businesses? Should one remove their subsidies and tax carbon wealth assets? See Bastos and Semmler (2022). 5

For a more comprehensive survey of possible climate policy options and their empirical assessment of success, see Castle and Hendry (2021). 6 In the late 1940s, in order to encourage banks to extend home loans (mortgages) to returning servicemen, the US government greatly expanded the Fannie Mae program. By removing default risk and interest rate risk from mortgages, banks were incentivized to originate mortgages.

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5. Which assets will perform better in dynamic portfolios that re-balance asset holdings (green or carbon-intensive assets)? Here, some further remarks are appropriate: renewable energy sources are in principle of unlimited supply, but there are also some operative constraints. New technology innovations—and new resources, are needed to harvest this infinite supply of free energy. As Arthur (1989) argued, there are different paths, with different social and economic spillover effects, used to generate and implement new technologies. There is likely to be history dependence on what will, ultimately, be the final winners and losers. Brian Arthur also pointed out, since there can be lock-ins to wrong, costly, ineffective and harmful technologies, public policy should always leave other options and pathways open.

11.3 Costs of Renewables and Climate Investments As to the actual investment in renewables, it has been rising for some time—especially in low-carbon electricity generations, which has been accompanied by a significant gain in efficiency. If carbon pricing at a sufficiently large scale will effectively accelerate the transition to a low-carbon economy, then parallel investments in related infrastructure is also needed. Users will need to be able to substitute away from fossil fuel. Thus, a broad spectrum of green investments becomes crucial to such programs. As we discussed in Chap. 10, data from the International Energy Agency (IEA) (2018) show there are still lagged forces at play in the transition to renewable energy. On a country level, there are still many, including the United States, Russia, China, and other oil rich nations, which had until recently heavily invested in oil and gas, and some, especially China, who continue to undertake substantial investments in coal mining and related infrastructure. Based on 2021 data, energy investment breakdowns in different regions are shown in Fig. 11.1. Already the IEA’s (2018) Sustainable Development Scenario (SDS), articulating to achieve the targets specified under The Paris Agreement 2015—and later improved by the Glasgow Declaration to reduce coal production—states that the share of fossil fuel in energy supply investment needs to be reduced to 40% by 2030. Triggered by these negotiations, according to the IEA (2017, 2018), on the one hand, global investments in renewables grew faster through the early 2010s and have remained at a high level. On the other hand, investments in fossil fuel supplies, which reached their highest level in 2014, started dropping and its estimated share in total supply investment fell to around 53% in 2021.7 IRENA (2022) data demonstrate that costs for renewables have dropped significantly in the past decade with percentage changes of –88% for Solar Photovoltaic (PV), and onshore and offshore wind by –68 and –60% respectively in 2021 (from 7

In IEA (2021a) data, total supply investments include fossil fuel supply, which consists of fuel supply and power, and renewables, electricity networks, and other supply.

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Fig. 11.1 Energy Investments in 2021. Source Constructed using data from IEA (2021a). In this figure, energy investments in Africa, the Asia-Pacific region, Central and South America, Eurasia, Europe, the Middle East, and North America are plotted using estimated 2021 data. It covers fuel supply and power generation from gas, oil, coal, renewables, electricity networks, and energy efficiency. Investments are measured in billions of 2019 US dollars

Fig. 11.2 Renewable energy investment financing. Source Constructed using data from IRENA and CPI (2020). This figure shows different types of financing of renewable energy investments between 2013 and 2018. These include project-level and balance-sheet financial instruments in terms of debt and equity. Data are measured in billions of US dollars ($)

2010).8 More detailed data on solar PV module real prices show an 87% fall between 2010 and 2021 (for thin film) and around 41% fall between 2017 and 2021 (for mainstream and low-cost types sold in Europe). Similarly, real prices for wind turbines dropped by around 41% in terms of Vestas average selling (see Table 11.1 with some available data for 2021 as well). Concerning renewable energy, when their costs drop, households and firms are widely incentivized to utilize them. Another data source, LAZARD (2021), demonstrates similar downward trends for the Levelized Cost Of Electricity (LCOE) from power generation, e.g., solar 8

According to IRENA (2022), weighted Levelized Cost Of Electricity (LCOE) of solar PV was around $0.05 per kilowatt hour (kWh), onshore wind $0.03 per kWh, and offshore wind $0.07 per kWh in 2021.

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and wind. For example, mean LCOE for solar PV plunged from around $250 per megawatthour (MWh) in 2010 to $36 per MWh in 2021; wind declined by 69% during the same period.9 Their analysis identified lower costs of capital, higher competition, and technological progress as main contributors. This cost trend will contribute to the IEA (2021b) pathways to a “net zero energy system by 2050,” which anticipates a transition to as much clean energy as possible by 2030 together with zero-emission electricity generation. Shifting way from fossil fuel, particularly unabated coal, increasing investments in clean energy, and innovation and technology need further incentives and public support. As we stressed in previous chapters, the IEA (2020) also urges the allocation of more investment in climate related projects. However, they are concerned that total expenditures for this purpose were still lower than required. The latest data from BloombergNEF (BNEF) (2022), show that clean energy investment more than doubled in 2021 ($755 billion) compared to what it was in 2014 ($312 billion), mostly driven by renewable energy (48%) and electric vehicles (36%). The contribution from the Asia-Pacific region accounted for almost half of the 2021 investment. Policy makers and private companies are promoting effective financing methods for projects related to climate change activities. According to the International Renewable Energy Agency (IRENA) and Climate Policy Initiative (CPI) (2020), renewable energy investments in 2017–2018 were funded mainly by project-level financial instruments and balance-sheet financing, e.g., bonds and equity. Trends in these types of financing between 2013 and 2018 are shown in Fig. 11.2. BloombergNEF also provides quarterly historical data on public markets showing the sources from which the new investments in clean energy originate. These include secondary and private investments in public equity (PIPE), initial public offerings (IPO), over-the-counter (OTC) instruments, convertibles, e.g., warrants, and others.10 According to BloombergNEF (2022), corporations raised $165 billion for climate tech in 2021 and it was dominated by the public investors (67%); among the large IPO deals, an electric truck producer Rivian raised $11.9 billion in November, 2021.

9

Conventional energy generation costs declined by only 3% for coal, gas-combined cycle by 27%, and gas peaker by 29% between 2010 and 2021 (see LAZARD 2021). 10 Some of the larger deals in 2019 included: the United Arab Emirates in solar (700MW) and United Kingdom in wind (432MW) in asset finance; the United States and Sweden (smart technology) via venture capital and private equity (VC/PE); Tesla (energy smart technology) and Greencoat UK Wind (wind) in the secondary market, and Xinyi Energy Holdings Ltd (solar) and Sterling & Wilson Solar Ltd (solar) through IPOs (see BloombergNEF 2019, pp. 31–33 for details). For 2020 deals, Netherlands (1540MW) and United Kingdom (1140MW) both in wind via asset finance, the United States, e.g., Altus Power America Inc, using VC/PE, and Jinko Power Technology Co Ltd via IPO (see BloombergNEF 2020). For 2021, China Three Gorges Renewables, Longi Green Energy Technology, and Plug Power (U.S.) raised several billions of dollars via public market; NorthVolt AB (Sweden) and Amp Solar Group Inc (Canada) deals via VC/PE (see BloombergNEF 2021).

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Fig. 11.3 Green bond issuance. Source Constructed using data from Climate Bonds Initiative (2022). This figure shows green bond issuance in 2014 and 2021. The pie chart demonstrates the breakdown in terms of percentage share in Africa, Asia-pacific, Europe, Latin America, North America, and Suprational

Regarding the issuing of green bonds, some illustrations may be appropriate.11 As of the end of the first half of 2022,12 cumulative issuance of green bonds reached a record high of almost $1.9 trillion with the United States at $334 billion, China at $250 billion, Germany at $190 billion, France at $190 billion, Supranational at $144 billion, the Netherlands at $97 billion, Sweden at $66 billion, Spain at $63 billion, the United Kingdom at $53 billion, Canada at $48 billion, and Japan at $45 billion as top countries (see Climate Bonds Initiative 2022). Annual green bond issuance has been increasing across the world from around $37 billion in 2014 to almost $580 billion in 2021, mostly dominated by Europe (see Fig. 11.3). While developed countries issued 74% of total green bonds, emerging economies’ share accounted for 21% in 2021 (share for supranational was 5%). The contribution by emerging markets declined in 2020 from previous year due to COVID-19, but it bounced back in 2021. Among the issuers, the World Bank, the European Union, and Fannie Mae have been at the top of the list; for example, German sovereign green bond issuance was worth $4.1 billion and Deutsche Bank’s deal was for $3.7 billion in the third quarter of 2021 (see Climate Bonds Initiative 2021 for details). According to Climate Bonds Initiative (2022) use of proceeds data, most green bonds are used for energy; this is followed by buildings and then by transportation (see Fig. 11.4). As to the role of the public and private sectors, corporations account for around 52% of 2021 issuance (non-financial and financial almost equal), fol11

For a more technical analysis of the rise and performance of green bonds, see Lichtenberger et al. (2022). 12 For the current and updated cumulative data, check the Climate Bonds Initiative website at https:// www.climatebonds.net/market/data/.

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Fig. 11.4 The Use of Green Bond Proceeds. Source Constructed using data from Climate Bonds Initiative (2022). Based on use of proceeds data, this figure shows usage of green bonds for buildings, energy, land use, transport, waste, and water between 2014 and 2021. These data are measured in billions of US dollars ($)

lowed by sovereigns (15%), government backed entities (15%), development banks (7%), Asset Backed Security (ABS) (5%), local governments (3%), and loans (3%). Another type of green instrument is the environmental loan. According to Bloomberg (2021), green loans in 2019 increased almost 8 times from 2013 but declined to $80 billion in 2020. Thus, we are currently in a transitional period with contradictory policies. There are still slowing forces showing up as heavy investments in fossil fuel energy, in particular coal, but there is simultaneously also a strong rise of investment into renewable energy. Yet, given the rising investment flows into renewable energy, and the use of renewable energy in dominant sectors in advanced countries, e.g., manufacturing, electricity production, automobiles, heating in buildings, and transportation, there will be also a higher demand for some other metals needed for renewable energy production and its use, as well as for semiconductor chips. The IEA (2021c) report focuses on “Minerals in Clean Energy Transitions.” It compares the volume of minerals used for different types of vehicle as well as for energy-related technologies. For example, while an electric car uses 2.4 times more copper than a conventional car, it also requires cobalt, graphite, lithium, manganese, nickel, and rare earths. Certain additional resources are needed for the rise of renewable energy and the more digital economy, in particular cobalt lithium and rare earth. There are likely to be severe resources constraints (including green physical and human capital) that slow down the transition to a low carbon economy. Based on Sustainable Development Scenario (SDS), the IEA (2021c) estimates that demand for these minerals in the energy sector will surge. Specifically, the percentage of lithium will increase from the 2020 amount of 29% to around 90%, cobalt from 15% to around 70%, nickel from 8% to around 60%, and rare earths from 16% to around 40%; all by 2040.13 13

See figure entitled: “Share of clean energy technologies in total demand for selected minerals” on p. 7 of IEA (2021c).

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Fig. 11.5 Metals needed for renewable energy and a digital economy. Source Constructed using data from U.S. Geological Survey (2021). This figure shows reserves and production of lithium, cobalt, and rare earths as of 2020. The pie chart demonstrates the breakdown in terms of percentage share in geographical locations. Note that the lithium production data for the United States are excluded in the U.S. Geological Survey

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The IMF blog by Boer et al. (2021) warns that a future surge in prices for these minerals will peak in only several years.14 These needed resources, their production, discovery, and reserves, will steeply rise in cost—giving rise to new related boom-bust cycles—these are likely to be concentrated in certain geographical regions. China, for example, is the world’s largest producer of rare earth minerals, accounting for almost 60% of annual global production. The current production and reserves as well as geographical locations are depicted in Fig. 11.5. Australia, Chile, and Argentina are in dominant positions regarding production and reserves for lithium. Congo has not only gold and copper, but also cobalt, tin, tantalum and lithium. Besides the economics, Nyambuu and Tapiero (2018) stressed the strategic importance of the rare earth minerals, where a major exporter might use them to advance a political agenda using supply restrictions and price discrimination. Overall, the phasing out of fossil energy to fulfill the 1.5 ◦ C mandate of The Paris Agreement 2015, also kept alive in the Glasgow negotiations 2021,15 will affect certain geographical regions, as Fig. 11.5 shows. Additionally, as demonstrated in Table 11.1, it will make the demand for other metals, geographically concentrated in particular regions, rapidly rise—possibly producing new boom-bust cycles.16

11.4 Fiscal Policy and Climate Investments Ambiguous trends are observable with respect to fiscal policy. If one thinks about climate policies, fiscal strategies also become more relevant. This is not surprising even if government’s limited resources and debt sustainability are always crucial constraints. In fact, in Chap. 9, based on Mittnik et al. (2020) and Bonen et al. (2016), we discussed large-scale model variants related to the integrated assessment model (IAM) but including climate-related fiscal policies; these can be adopted to different countries’ needs. Numerous papers and reports by various agencies provide supporting evidence on the contribution of macroeconomic policies to environmentally sustainable growth. For example, IMF papers, such as those by Krogstrup and Oman (2019) address mitigation of climate change with an extensive literature review that prioritizes fiscal policy. As developing carbon-free technologies may take some time, Mazzucato (2015) discussed the important role of the government in R&D and related incentives; both supply and demand side policies were addressed. 14

In the PIIE working paper by Leruth et al. (2022), a detailed analysis is provided on supply chains of rare earth minerals. 15 Note that the UN Climate Change Conference (COP26) in Glasgow in November 2021 did not achieve binding agreements on phasing out fossil fuels, specifically coal, in the next few years. 16 IMF seminars discuss this topic in detail. For example, Independence Evaluation Office (IEO) Virtual Seminar on “The Market for Rare Metals and Implications for Phasing Out Fossil Fuels: Where Do We Stand?, September 20, 2022: https://ieo.imf.org/en/our-work/Seminars/Past/marketfor-rare-metals..

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As part of fiscal-climate policy, green bonds with longer maturity are highlighted in Orlov et al. (2018) and Braga et al. (2021). In this context, Lichtenberger et al. (2022) noted the importance of bond financing for climate change related policies. In the previous section of this chapter, we provided detailed data and information on green bond issuance. Both current and future generations face costs pertaining to climate policies as these are funded by investors who value long-term environmental benefits (Sachs 2015). Green bonds can also be used as hedge against the volatility of carbonintensive securities (Kapraun and Scheins 2019; Pastor et al. 2021, and Semmler et al. 2021a, b). These green financial instruments can serve as a crucial fiscal tool. Semmler et al. (2021b) showed that green bonds can also perform well within the context of portfolio holdings. They can be held as a successful hedge against heavy dependence on fossil fuel. Therefore, raising funds through green bonds can scale up sustainable infrastructure, support mitigation and adaptation efforts, and reduce portfolio volatility (see Bonen et al. 2016; Mittnik et al. 2020). Mittnik et al. (2020) emphasized fiscal policy allocation among different types of projects and examined its role in mitigation, as well as in spending related to the prevention of extreme events. They explored optimal combinations of policies and dependencies by state, as well as by time; and demonstrated the benefits from not only using bonds, but also grants and taxes. This was covered in the model variants presented in Chap. 9. There are also advantages in mixing green bonds and carbon taxation (see Heine et al. 2019). In a small country study, Catalano et al. (2020) focused on fiscal policy and in particular the role of public debt’s role for climate change and suggested preventive measures as part of the adaptation to better manage the shocks. Their simulation results show the benefits in terms of GDP growth rates. However, bond issuance and other financing methods are not readily available in many low income countries (see UNCTAD 2018). In this context, Banga (2019) highlights institutional support, as well as necessary reductions in the costs of green bonds in developing countries. As Burke et al. (2015) and IMF (2017) stressed, due to greater vulnerabilities, some of these countries encounter greater disaster risk. Several other studies support similar conclusions (see Bevan and Adam 2016; Marto et al. 2018; Kovacevic and Semmler 2020). A related study covering agriculture in less developed countries was provided by Auffhammer and Kahn (2018) addressing challenges faced by farmers including adaptation, capital, and financial resources. There is a widespread concern that many low-income countries might continue relying on coal, especially in the post-COVID world, as it remains one of the cheapest energy sources available and is abundant. It is also the most carbon-intensive fuel and generates the highest negative externality. Numerous empirical studies suggest that the world ought not extract coal anymore and leave it in the ground while phasing in renewable energy.

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The Glasgow COP26 conference in 2021 has specifically put adaptation efforts for the vulnerable countries and their financing at the forefront, with, however, only obtaining vague financial commitments.17

11.5 Climate Risk and Monetary Policy Monetary policy also plays an important role in promoting climate protection. In particular, expansionary monetary policies can provide incentives to undertake large scale investments in climate oriented infrastructure, renewable energy-based innovations, and electricity-driven transportation. For a long time, central banks’ inflation targeting has been considered as an appropriate measure for macro meltdowns associated with climate events.18 Yet, the issues have turned out to be much larger. Brunnermeier and Landau (2021) provide a comprehensive overview and evaluation of the challenges that climate change bring to monetary policy. Some of the more technical modeling issues were covered in Faulwasser et al. (2020) and in our Sect. 9.6. In this chapter, we will briefly address four broader issues that are under discussion. Early climate studies were concerned with inflation and its targeting. Inflationary pressure can be a consequence of climate disasters. This can also lead to higher credit risk, which would require higher premiums to compensate for the risk.19 Semmler and Bernard (2007) provide a selected collection of relevant research papers, discussing important factors involved not only in the measurement of credit risk, but also its modeling and management. The second heavily discussed issue was triggered by public speeches by the Governor of the Bank of England, Mark Carney (2015) and his prediction regarding certain types of wealth, e.g., carbon-intensive wealth, becoming “stranded assets.” As he argued, monetary instruments and bank stress tests can be useful in combating climate risks in particular, as part of broader macro policy actions.20 Central banks have started developing networks and work focused on climate change by relating environmental risks to financial risks. Risks were perceived as physical risk and transition risk, threatening the financial system, and in particular banks’ exposure to non-performing loans, as well as extensive stock market swings. The third issue that has come to the forefront concerns with central banks’ mitigation measures and attempts to prevent climate change through the stimulation of climate finance. It was perceived that monetary instruments can be used to facilitate 17

See Sachs (2021). See Fratzscher et al. (2020), McKibbin et al. (2017), and Monnin (2018). 19 For the borrowing and risk premia discussion, see Nyambuu and Semmler (2017). 20 Hansen (2022, p. 14) stated that “How best to develop and use quantitative research to guide fiscal policy in the face of uncertainty remains a fertile avenue for future research. Monetary policy can support these objectives and promote sound strategies for quantifying longer term impacts of exposure to climate change uncertainty.” 18

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energy transition finance, in particular through equity issuance, support of commercial banks, and bond markets. Recent research points to the issue that central banks should become more focused in their bond purchasing program, focusing on green bonds instead of fossil fuel bonds, see Papoutsi et al. (2021). Lastly, concerning small countries’ policies, others, e.g., Catalano et al. (2020) and Mittnik et al. (2020), address the role of preventive measures. They also encouraged risk-pooling, especially through safety nets and insurance. Campiglio (2016) also underscores the role of policy tools, e.g., reserve ratios that facilitate climate related lending by banks. To improve credit flows, green Quantitative Easing (QE) could be supported by central banks as well (Grauwe 2019). Overall, although Central Banks’ primary target is inflation control, they can still support, conjointly with other public policies, climate protection.

11.6 Global Efforts for Climate Protection As we argued in Chap. 10, climate protection goals, which can be well stylized through some game theoretic set ups, are only slowly being accepted in international climate policy negotiations, mostly because of lag effects. Many multilateral policy-making institutions support a faster transition to a low carbon economy, e.g., the UN, the IMF, and the World Bank. Domestic government policies and enforced targets have played an important role and encouraged emission reduction. These include carbon pricing through cap-and-trade systems, carbon taxes, fuel efficiency standards, and reduced fossil fuel subsidies, sometimes paired with an increase in subsidies for renewables. Sterner and Robinson (2018) present indepth analyses of the advantages and disadvantages of various environmental policies focusing on both price and regulatory type tools as well as on information driven actions. In selecting policies, they considered efficiency, information asymmetries, and distributional effects (political and practical). Already, a joint report by IEA et al. (2011) has underscored commitments of the G20 nations to “rationalize and phase out over the medium term inefficient fossil fuel subsidies that encourage wasteful consumption” (p. 2). In more recent research, Gerasimchuk et al. (2017, p. 42) argue that ending subsidies for production of fossil fuel would significantly reduce CO2 emissions. For climate policy, conventional and prohibitive carbon taxes, and investment taxes are analyzed in Kalkuhl et al. (2019). Edenhofer et al. (2018) address planned coal-fired power plants, and those that under construction, and discuss how they threaten the climate targets. Based on the given climate challenges, the Carbon Pricing Leadership Coalition (CPLC) (2017) proposed a carbon pricing system with estimated levels of carbon price. They discussed country specific climate policy instruments, suggested that revenue recycling (from carbon pricing) be used in advanced countries to support the transition to decarbonization in developing economies, and argued that carbon prices can be lower in low-income countries as compared to high-income countries;

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this was demonstrated in a background paper prepared by the Deep Decarbonization Pathways Project (DDPP) for CPLC (2017).21 In order to combat climate change, the 21st session by United Nations Framework Convention on Climate Change (UNFCCC) parties in Paris (December 2015) agreed to maintain less than 2 ◦ C temperature rise compared to above pre-industrial levels with further efforts to reduce it to 1.5 ◦ C.22 More recently, the COP26 conference in Glasgow has again proposed resolutions for the world community to phase out coal. Note that India and China that did not want to commit to fixed time horizon. In addition, in terms of targets concerning important temperature goals, the UNFCCC has highlighted the following key elements of a successful agreement: • Global peaking of greenhouse gas emissions (GHGs)—developing countries will need more time. • Mitigation—domestic measures are pursued with absolute economy-wide reduction targets, for advanced countries, and longer-term economy-wide targets for developing countries. • Carbon sequestration—sinks and reservoirs of GHGs should be developed. • Voluntary cooperation, i.e., market- and non-market-based approaches— supporting the mitigation of GHG emissions and sustainable development. • Adaptation efforts with stronger resilience, creating less vulnerability to climate change—enhanced support should be directed to developing countries. • Losses and damages—vulnerable countries should be supported with financing. • Finance, technology, and capacity-building support—enhanced support for developing countries. • Climate change education—training, public awareness, public participation, and public access to information is needed. • Transparency—more effective implementation and compliance is needed. • Yearly global assessments—beginning in 2023 an assessment of collective progress should be required. Now more than ever, major countries have committed to climate protection. The “G7 Industrial Decarbonization Agenda” was launched at the recent G7 summit in Carbis Bay (2021), Cornwall. In an effort to phase out production of unabated coal, the G7 countries announced that their member governments will no longer support new unabated coal projects. Some of the developed countries expressed willingness to help developing countries to phase out coal fuel. In the United States, a White House briefing, released June 12, 2021, highlighted the need for faster progress in the reduction of the use of coal energy.23 21

For a further detailed study on the history of international climate negotiations, see Chappe (2015). 22 For details, see https://unfccc.int/resource/bigpicture/#content-the-paris-agreemen. 23 For details, see https://www.whitehouse.gov/briefing-room/statements-releases/2021/06/12/ fact-sheet-g7-to-announce-joint-actions-to-end-public-support-for-overseas-unabated-coalgeneration-by-end-of-2021/.

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Many countries, including the United States, the United Kingdom, European Union (EU), China and India, have encouraged further steps beyond The Paris Agreement to reduce carbon emissions substantially; a number of countries announced their commitment to cut net emissions in half by 2030. Yet at the COP26 conference in Glasgow, only half of that was agreed to. The Paris agreement was still left alive, but a stronger commitment to phase out fossil fuel energy, e.g., gas, oil, and particularly coal, by 2030 was not obtained; this was due to the fact that the commitment of “the willing” could not convince India, China, and other oil-rich countries. Yet, recent developments in 2022, i.e., the issue of whether or not oil and gas should be part of the sanctions against Russia, and energy security have moved to the top of the policy agenda. On the financing side of energy transition and climate protection, $130 billion were promised by the financial market at the COP26 conference,24 which came close to fulfilling the Copenhagen (2009) commitment of raising $100 billion.25 For low-income countries, adaptation efforts were agreed to as a source of development, specifically, the doubling of this amount. Here too, one can expect only a slow pace for the fulfillment of promises. For example, at the COP27 conference in Sharm El Sheik, Egypt, the financial effort was concentrated on “Loss and Damage” for countries of particularly low income which are threatened by potential climate disaster. Yet neither the size of the funds, nor the contributors and recipients were detailed. Moreover, neither was a decision achieved on how to limit global warming to an increase of 1.5 ◦ C, nor was an agreement reached on future limitations in the use of fossil fuels.

11.7 Conclusion Although there were numerous initiatives and tentative agreements announced at the COP26 meeting and thereafter, now there are better tracking, monitoring, and scheduling tools for new meetings. However, it is probably correct to assume that the delaying forces are still strong and predominant in the domestic and international climate agendas. These might nudge countries toward speeding up the pace, in future, possibly resulting in a successful realization of existing agreements and initiatives. Furthermore, as discussed above, national and/or global crises are leading to considerable distraction from the foreseeable climate change and its predicted disasters. The urgency of the more current events and crises management are also likely to lead to the withdrawal of resources for climate protection. In fact, details of current and future financing initiatives, e.g., Sachs (2021), still have yet to be worked out. Still unresolved are the questions of how much funding 24

See the UN news on 13 November, 2021 available at https://news.un.org/en/story/2021/11/ 1105792 (Accessed on November 12, 2022). 25 See detailed information from the UNFCCC link on “Copenhagen Climate Change Conference—December 2009”: https://unfccc.int/process-and-meetings/conferences/pastconferences/copenhagen-climate-change-conference-december-2009/copenhagen-climatechange-conference-december-2009.

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will actually be raised, for example: What share of funds will be grants, versus loans? Can there be de-risking of financing via multilateral institutions? What criteria will be applied for the distribution of funds, and what fraction of these later will be allocated for mitigation or adaption efforts? All of the above-discussed national and global policy measures are important and raise general questions as to where progress and more stringent commitments need to be made.

References Arthur B (1989) Competing technologies, increasing returns, and lock-in by historical events. Econ J 99:116–131 Auffhammer M, Kahn ME (2018) The farmer’s climate change adaptation challenge in least developed countries. In: Dasgupta P, Pattanayak S, Smith K (eds) Handbook of environmental economics, vol 4. Elsevier, pp 193–229 Banga J (2019) The green bond market: a potential source of climate finance for developing countries. J Sustain Financ Invest 9(1):17–32 Bastos Neves JP, Semmler W (2022) A proposal for a carbon wealth tax: modelling, empirics, and policy, SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4114243 Bevan D, Adam A (2016) Financing the reconstruction of public capital after a natural disaster. World bank policy research working paper 7718. SSRN: https://papers.ssrn.com/sol3/Delivery. cfm/7718.pdf. Accessed 8 Nov 2022 BloombergNEF (2019) Clean Energy Investment Trends, 2019. https://data.bloomberglp.com/ professional/sites/24/BloombergNEF-Clean-Energy-Investment-Trends-2019.pdf. Accessed 20 Jan 2021 BloombergNEF (2020). Clean energy investment trends, 1H 2020. https://data.bloomberglp.com/ professional/sites/24/BNEF-Clean-Energy-Investment-Trends-1H-2020.pdf. Accessed 2 April 2022 BloombergNEF (2021) Renewable energy investment tracker, 1H 2021. https://assets.bbhub. io/professional/sites/24/BNEF-Renewable-Energy-Investment-Tracker-1H-2021_FINAL_ abridged.pdf. Accessed 2 April 2022 BloombergNEF (2022) Energy transition investment trends 2022. https://assets.bbhub. io/professional/sites/24/Energy-Transition-Investment-Trends-Exec-Summary-2022.pdf. Accessed 2 April 2022 Bloomberg (2021) The Sustainable debt market is all grown up. https://www.bloomberg.com/news/ articles/2021-01-14/the-sustainable-debt-market-is-all-grown-up?srnd=green. Accessed 20 Jan 2021 Boer L, Pescatori A, Stuermer M, Valckx N (2021) Soaring metal prices may delay energy transition. IMFBlog 10 Nov 2021. Available via International monetary fund. IMF. https://blogs.imf.org/ 2021/11/10/soaring-metal-prices-may-delay-energy-transition/. Accessed 7 Nov 2022 Bonen A, Loungani P, Semmler W, Koch S (2016) Investing to mitigate and adapt to climate change: a framework model. WP/16/164. International monetary fund Braga JP, Semmler W, Grass D (2021) De-risking of green investments through a green bond market: empirics and a dynamic model. J Econ Dyn Control 131:104201. https://doi.org/10.1016/j.jedc. 2021.104201 Brunnermeier M (2021) The resilient society. Endeavor Literary Press Brunnermeier M, Landau LP (2021) Finance, money, and climate change, manuscript, Princeton University, Published in 74th Economic Policy Panel Meeting (2022) Burke M, Hsiang S, Miguel E (2015) Global non-linear effect of temperature on economic production. Nature 527:235–239

188

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Campiglio E (2016) Beyond carbon pricing: the role of banking and monetary policy in financing the transition to a low-carbon economy. Ecol Econ 121:220–230 Carbon Pricing Leadership Coalition (CPLC) (2017) Report of the high-level commission on carbon prices. https://static1.squarespace.com/static/54ff9c5ce4b0a53decccfb4c/ t/59244eed17bffc0ac256cf16/1495551740633/CarbonPricing_Final_May29.pdf. Accessed 22 April 2019 Carney M (2015) Breaking the tragedy of the horizon—climate change and financial stability— speech by Mark Carney. https://www.bankofengland.co.uk/speech/2015/breaking-the-tragedyof-the-horizon-climate-change-and-financial-stability. Accessed 8 Nov 2022 Castle JL, Hendry DF (2021) Can the UK achieve net-zero greenhouse gas emissions by 2050? Economics series working papers 953, Department of Economics, University of Oxford Catalano M, Forni L, Pezzolla E (2020) Climate-change adaptation: the role of fiscal policy. Resour Energy Econ 59:101–111 Chappe R (2015) The legal framework of global environment governance on climate change: a critical survey. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, New York, pp 603–638 Climate Bonds Initiative (2021) Sustainable debt summary Q3 2021. https://www.climatebonds. net/resources/reports/sustainable-debt-summary-q3-2021. Accessed 3 April 2022 Climate Bonds Initiative (2022) Green bonds market. https://www.climatebonds.net/market/data/. Accessed 16 Aug 2022 Edenhofer O, Steckel JC, Jakob M, Bertram C (2018) Reports on coal’s terminal decline may be exaggerated. Environ Res Lett 13(2):024019 Faulwasser T, Gross M, Semmler W, Loungani P (2020) Unconventional monetary policy in a nonlinear quadratic model. Stud Nonlinear Dyn Econ, De Gruyter 24(5):1–19 Fratzscher M, Grosse-Steffen C, Rieth M (2020) Inflation targeting as a shock absorber. J Int Econ 123:103308 Gerasimchuk I, Bassi AM, Ordonez CD, Doukas A, Merrill L, Whitley S (2017) Zombie energy: climate benefits of ending subsidies to fossil fuel production. Working paper February 2017. International institute for sustainable development Grauwe PD (2019) Green money without inflation. Vierteljahrshefte zur Wirtschaftsforschung 88(2):51–54 Hamilton J (2022) Sanctions, energy prices, and the global economy, presentation at a seminar organized by markus Brunnermeier, Princeton University, 17 Mar 2022 Hansen LP (2022) Central banking challenges posed by uncertain climate change and natural disasters. J Monet Econ 125(C):1–15 Heine D, Semmler W, Mazzucato M, Braga JP, Flaherty M, Gevorkyan A, Hayde E, Radpour S (2019) Financing low-carbon transitions through carbon pricing and green bonds. Vierteljahrshefte Zur Wirtschaftsforschung 88(2):29–49 International Energy Agency (IEA), OPEC, OECD and World Bank (2011) Joint report by IEA, OPEC, OECD and world bank on fossil-fuel and other energy subsidies: an update of the G20 Pittsburgh and Toronto commitments. Prepared for the G20 meeting of finance ministers and central bank governors (Paris, 14-15 October 2011) and the G20 summit (Cannes, 3–4 November 2011). International energy agency, Organization for economic cooperation and development, Organization of the petroleum exporting countries, and the World bank International Energy Agency. IEA (2017) World energy investment 2017. https://www.iea.org/ publications/wei2017/. Accessed 5 Jan 2019 International Energy Agency. IEA (2018) World energy investment 2018. https://www.iea.org/ wei2018/. Accessed 5 Jan 2019 International Energy Agency. IEA (2020) World energy outlook 2020. https://www.iea.org/reports/ world-energy-outlook-2020#. Accessed 2 Jan 2021 International Energy Agency. IEA (2021a) World energy investment 2021. https://www.iea.org/ reports/world-energy-investment-2021. Accessed 14 Aug 2022

References

189

International Energy Agency. IEA (2021b) Net Zero by 2050: A Roadmap for the Global Energy Sector. https://www.iea.org/reports/net-zero-by-2050. Accessed 14 Aug 2022 International Energy Agency. IEA (2021c) The role of critical world energy outlook special report minerals in clean energy transitions. https://www.iea.org/reports/the-role-of-critical-mineralsin-clean-energy-transitions. Accessed 14 Aug 2022 International Monetary Fund. IMF (2017) World economic outlook, October 2017: seeking sustainable growth: shortterm recovery, long-term challenges. World economic outlook. International monetary fund. https://doi.org/10.5089/9781484312490.081 International Monetary Fund. IMF (2022) Independence evaluation office (IEO) virtual seminar on the market for rare metals and implications for phasing out fossil fuels: where do we stand?. https://ieo.imf.org/en/our-work/Seminars/Past/market-for-rare-metals. Accessed 14 Nov 2022 IRENA and CPI (2020) Global landscape of renewable energy finance, 2020, international renewable energy agency, Abu Dhabi. https://www.irena.org/publications/2020/Nov/Global-Landscapeof-Renewable-Energy-Finance-2020. Accessed 20 Jan 2021 IRENA (2022) Renewable power generation costs in 2021. International Renewable Energy Agency, Abu Dhabi Kemfert C, Schäfer D, Semmler W (2020) Great green transition and finance. Intereconomics ZBW 55(3):181–186 Kalkuhl M, Steckel JC, Edenhofer O (2019) All or nothing: climate policy when assets can become stranded. J Environ Econ Manag 1–21. https://doi.org/10.1016/j.jeem.2019.01.012 Kapraun J, Scheins C (2019) (In)-credibly green: which bonds trade at a green bond premium?. SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3347337 Kovacevic R, Semmler W (2020) Poverty traps and disaster insurance in a bi-level decision framework. In: Haunschmied J, Kovacevic R, Semmler W, Veliov V (eds) Dynamic economic problems with regime switches. Springer Publishing House, pp 57–83 Krogstrup S, Oman W (2019) Macroeconomic and financial policies for climate change mitigation: a review of the literature. IMF working paper no. 19/185. International Monetary Fund Lamperti F, Mazzucato M, Roventini A, Semieniuk G (2019) The green transition: public policy, finance, and the role of the state, Vierteljahrshefte zur Wirtschaftsforschung, ISSN 1861-1559, Duncker & Humblot, Berlin, 88(2):73–88. https://doi.org/10.3790/vjh.88.2.73 LAZARD (2021) LAZARD’S LEVELIZED COST OF ENERGY ANALYSIS —VERSION 15.0. https://www.lazard.com/media/451905/lazards-levelized-cost-of-energy-version-150-vf. pdf. Accessed 22 Aug 2022 Leruth L, Mazarei A, Régibeau P, Renneboog L (2022) Green energy depends on critical minerals. Who controls the supply chains? PIIE working paper 22–12. Available via Peterson institute for international economics. https://www.piie.com/sites/default/files/documents/wp22-12.pdf. Accessed 8 Nov 2022 Lichtenberger A, Braga JP, Semmler W (2022) Green bonds for the transition to a low-carbon economy. Econometrics 10(1):11. https://doi.org/10.3390/econometrics10010011 Marto R, Papageorgiou C, Klyuev V (2018) Building resilience to natural disasters: an application to small developing states. J Dev Econ 135:574–586 Mazzucato M (2015) The entrepreneurial state: debunking public versus private sector myths, vol 1. Anthem Press McKibbin W, Morris A, Panton A, Wilcoxen P (2017) Climate change and monetary policy: dealing with disruption. CAMA working paper 77/2017, Centre for Applied Macroeconomic Analysis Mittnik S, Semmler W (2022) Die Substitution fossiler Energieträger—die Analyse wirtschaftlicher Kurz-und Langfristwirkungen. Vierteljahrshefte zur Wirtschaftsforschung 91(3):11–44. Accessed 1 Jul 2022 Mittnik S, Semmler W, Haider A (2020) Climate disaster risks-empirics and a multi-phase dynamic model. Econometrics 8(33):1–27 Monnin P (2018) Central banks and the transition to a low-carbon economy. Working paper 2018/1, Council on Economic Policies

190

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Nyambuu U, Bernard L (2015) A quantitative approach to assessing sovereign default risk in resource-rich emerging economies. Int J Financ Econ 20(3):220–241. https://doi.org/10.1002/ ijfe.1512 Nyambuu U, Semmler W (2017) Emerging markets’ resource booms and busts, borrowing risk and regime change. Struct Chang Econ Dyn 41:29–42. https://doi.org/10.1016/j.strueco.2017.02.001 Nyambuu U, Tapiero CS (2018) Globalization, gating, and risk finance. Wiley Orlov S, Rovenskaya E, Puaschunder JM, Semmler W (2018) Green bonds, transition to a lowcarbon economy, and intergenerational fairness: evidence from an extended DICE model. IIASA working paper, WP-18-001, IIASA Papoutsi M, Piazzesi M, Schneider M (2021) How unconventional is green monetary policy? Manuscript, Stanford University. https://web.stanford.edu/~piazzesi/How_unconventional_is_ green_monetary_policy.pdf. Accessed 14 Aug 2022 Pastor L, Stambaugh RF, Taylor L (2021) Dissecting green returns, NBER working paper series. Working paper 28940. https://doi.org/10.3386/w28940. Accessed 14 Nov 2022 Sachs J (2015) Climate change and intergenerational well-being. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, Oxford, pp 248–259 Sachs J (2021) Fixing climate finance. In: social Europe, 17 Nov 2021. https://socialeurope.eu/ fixing-climate-finance. Accessed 3 Nov 2022 Semmler W, Bernard L (2007) (eds), The foundations of credit risk analysis. Edward Elgar publishing Semmler W, Maurer H, Bonen A (2021a) Financing climate change policies: a multi-phase integrated assessment model for mitigation and adaptation. In: Haunschmied J, Kovacevic R, Semmler W, Veliov V (eds), Dynamic economic problems with regime switches, Springer Publishing House, pp 137–158 Semmler W, Braga J, Lichtenberger A, Toure M, Hayde E (2021b) Fiscal policy for a low carbon economy, World bank. https://openknowledge.worldbank.org/handle/10986/35795. Accessed 7 Nov 2022 Semmler W, Henry J, Maurer H (2023) Pandemic meltdown and economic recovery—a multiphase dynamic model, empirics, and policy. Research in Globalization 6. https://doi.org/10.1016/ j.resglo.2022.100106 Sterner T, Robinson EJZ (2018) Selection and design of environmental policy instruments. In: Dasgupta P, Pattanayak S, Smith K (eds), Handbook of environmental economics, vol 4. Elsevier, pp 231–284 Stiglitz J (1993) Perspectives on the role of government risk-bearing within the financial sector. In: Sniderman M (ed), Government risk-bearing: proceedings of a conference held at the federal reserve bank of Cleveland, Dordrecht: Springer, pp 109–130 UNCTAD (2018) The least developed countries report 2017. United nations conference on trade and development United Nations Framework Convention on Climate Change (UNFCCC). Paris agreement. https:// unfccc.int/resource/bigpicture/#content-the-paris-agreemen. Accessed 7 Nov 2022 UNFCCC. Copenhagen Climate Change Conference—December 2009. https://unfccc.int/ process-and-meetings/conferences/past-conferences/copenhagen-climate-change-conferencedecember-2009/copenhagen-climate-change-conference-december-2009. Accessed 7 Nov 2022 United Nations. UN News: Climate and Environment. 13 Nov 2021. https://news.un.org/en/story/ 2021/11/1105792. Accessed 7 Nov 2022 U.S. Geological Survey (2021) Mineral commodity summaries 2021. https://pubs.usgs.gov/ periodicals/mcs2021/mcs2021.pdf. Accessed 8 Jan 2022 U.S. White House. White House briefing on 12 June 2021. https://www.whitehouse.gov/briefingroom/statements-releases/2021/06/12/fact-sheet-g7-to-announce-joint-actions-to-end-publicsupport-for-overseas-unabated-coal-generation-by-end-of-2021/. Accessed 7 Nov 2022

Chapter 12

Concluding Remarks

The need for urgent policy action to protect the earth’s climate, natural wealth, and the welfare of society against climate extreme events is stressed in IPCC (2021, 2022a, b) reports and at climate change summits, e.g., the COP26 Glasgow conference. These reports and meetings have expressed an immediate need for more commitments and climate policy action. Even with a global temperature increase to 1.5 ◦ C, and the accompanying extreme weather events, there are great perils ahead. Yet, if a further rise of temperature up to 2, 3 ◦ C, or even 4 ◦ C, as compared to the pre-industrial era, is likely to occur, then an acceleration of extreme weather events is certain. Dangerous tipping points, e.g., the collapse of ocean circulation, are ahead; additionally, there is the risk of vast ice sheets suddenly moving into the oceans surrounding Antarctica and off Greenland. These events would likely cause extensive sea level rise with devastating impacts on coastal areas and cities. As currently predicted, not only the frequency of extreme weather events will rise, but also their severity—heat waves, storms, wildfires, droughts, and floods may all intensify. As stressed in the IPCC (2021) Working Group I (WGI) report, climate risks have been explored by physical scientists. The report is based on a large amount of new data collected by different scientists and used to create future projections for the world ecosystem.1 The results of various working groups at the COP26 meeting have now yielded better forecasts. Thus, more specific predictions can be made regarding what weather extremes and long-run environmental degradation will occur in certain places. In other words, more accurate projections can now be made for the occurrence of natural disasters and extreme weather events.2

1

The IPCC website (https://www.ipcc.ch/working-group/wg1) describes how the IPCC Working Group I (WGI) focuses on research results on climate system and human activity; climate targets and carbon emissions are also assessed with further analysis of the relationship between climate and land and air quality. 2 How climate disasters and weather extremes are presented in contemporary musical artwork, see Murphy (2023). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 U. Nyambuu and W. Semmler, Sustainable Macroeconomics, Climate Risks and Energy Transitions, Contributions to Economics, https://doi.org/10.1007/978-3-031-27982-9_12

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The above information served as an important foundation for the next report by Working Group II of the IPCC (2022a) that examined the following essential topics: how climate change affects ecosystems, how humans adapt, and how to mitigate climate risks.3 This report is able to spell out specific early warning systems and protection measures including floods, storms, and forest fires management. As previously discussed, an increase in funding for adaptation measures, in particular, for developing economies, has been promised in the Glasgow COP26 conference. However, the specifics of this funding, pertaining to its financing and implementations, are still vague. The IPCC report (2022c) by the Working Group III4 focuses more on mitigation policies, i.e., the major action prioritizes not only a reduction in greenhouse gas emissions (GHGs), but also prevention.5 As this report stresses effective mitigation policies and coordination of actions both nationally and internationally, it would contribute to better management of impacts of climate change and vulnerability. A very focused mitigation policy should be implemented instead of implementing non-cooperative strategies and delaying climate protection measures, as discussed in Chaps. 10 and 11. In particular, the biggest carbon and GHG emitters, e.g., China, the United States, the European Union, India, and Russia, are still far off track to fulfill the Paris Agreement goals,6 and are in urgent need of coordination and the enforcement of international mitigation policies. Driven by domestic and international agendas, and by short-termism in the financial market and in policy circles, too many games are still being played. Distractions rooted in the so-called emergency responses to other crises have resulted in snail’s pace international negotiations and agreement formation, and an overall lack of progress.7 These days, the eminent task is to move away from typical policy short-termism and to pursue joint climate protection and environmental policies and their verification. This can be done in terms of regulations, carbon pricing, green investments, and/or transitioning to renewable energy. As for carbon pricing, cap-and-trade, as well as a carbon tax have been implemented in certain regions to reduce emissions, but on quite a low scale. According to the World Bank (2022) report, although revenues from carbon prices have shown a significant increase, especially in places such as the European Union, California, and New Zealand, direct carbon pricing still covers only a very small percentage of total emissions. Investments in renewable energy and general and climate related infrastructure have been initiated, but not yet sufficiently scaled up. Green investments are urgently needed because the incentives for energy substitution, through carbon pricing, are not operative when the substitutes 3

For more details, see IPCC WGII website: https://www.ipcc.ch/report/ar6/wg2/. For more details, see IPCC WGIII website: https://www.ipcc.ch/working-group/wg3/. 5 For more information, check the following IPCC website: https://www.ipcc.ch/report/sixthassessment-report-working-group-3/. 6 Information on nationally determined contributions (NDCs) by parties to the Paris Agreement are shown on the following website: https://unfccc.int/NDCREG. For example, in the NDC submitted on April 21, 2021, the United States is aiming at 50–52% lower U.S. net GHG emissions in 2030 compared to 2005 levels (see UNFCCC, 2022). 7 At the Paris conference in 2015, a kind of coalition game was suggested. The idea was if one builds a coalition among big nations, the smaller ones will follow. 4

12 Concluding Remarks

193

are not available. Thus, the transition to a low carbon economy may be significantly slowed through consequent and severe bottlenecks. Since the IPCC (2021) report, the COP26 meeting, and the IPCC (2022c) report, climate adaption policies have come to the forefront as a polarizing issue separating advanced economies and low income countries. For the former, extensive industrialization and high per capita income had been achieved; thanks, in large measure, to overuse of natural resources, which has taken a toll on the ecosystem, nature, and the welfare of future generations. Facing extreme climate events, low income countries and future generations are asking advanced economies not only for greater burden sharing of both the mitigation and adaptation costs, but also for the adaptation costs. As discussed in Sachs (2021), finance proposals that combine grants and loan schemes were made to developing countries. This objective is also supported by the European Investment Bank (EIB) with its promise of significant increases in financing for adaptation efforts. As discussed in Chap. 11, we note that the transition to a low carbon economy has become fundamental, but now even more relevant given the geopolitical sanctions against Russia’s revenue sources from oil and gas exports. Thus, national and regionspecific energy security has arrived at the top of the macroeconomic policy agenda of many countries; there are likely to be more actions along those lines. Securing the energy supply predominantly through fossil fuel may prove disastrous from multiple perspectives: availability of non-renewable resources, the geopolitical conflicts, and climate change. In fact, the most sustainable policy for energy security seems to be the accelerated adoption of abundant green energy as its cost is steadily declining. To address the above issues more formally, instead of focusing on long-run growth models, our research is conducted in the context of medium-run macro models and policies in current decision-making. Referring back to the rich macro models and policies developed over decades, long-run growth models have actually provided us with fewer policy tools than those found by simply studying the history of macroeconomics. The older growth models tend to state the neutrality of policy tools, but not the macroeconomic work. In this context, we presented extensive empirical work on how we got here: on the overuse of fossil fuels, unrestricted resource extraction, persistent carbon emissions since the dawn of industrialization, and the passing (soon) of CO2 emissions beyond the earth’s carbon budget, leading to further global warming and climate disasters. We have focused on small scale macro dynamic models to capture the essence of these issues and the effects of policy actions. We undertook such analysis in the context of intertemporal and multi-period models because the future needs to be taken into account. We explored those issues in small or medium scale economic models which were informed by historical macroeconomic modeling. Using numerical solution paths, we can mimic empirical trends and facts. We also can show dynamic paths for certain modeling options and strategies, indicating future pathways, and highlighting new policy instruments and policies for the decarbonization of the economy, energy transition, and climate protection. We have adopted dynamic decision models with finite horizons. This approach allows us to move away from both short-termism and the infinite horizon models that presume model consistent paths, whereby both the objective function and dynamic

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variables and parameters are supposed to hold for an indefinite future. In contrast, our approach is akin to Sim’s theory where agents have information constraints, are boundedly rational, and decision-making is flexibly kept open—being portrayed as a receding horizon that can change when new information and occurrences arise. The procedures developed by Grüne and Pannek (2011) and Grüne et al. (2015) were of great help approximating relevant current and future solution paths. Another model solution method we have used was AMPL, which is, in particular, suitable for studying regime change and models with delay effects. As policy tools, besides the regulations, we recommend a mix of carbon pricing and climate investments for combatting climate change. Although carbon pricing provides incentives to reduce CO2 emissions, studies have revealed the limitations of carbon pricing. This is mostly due to the volatility of cap-and-trade and minimal carbon taxes that are hard to significantly scale up via policy actions. Moreover, as we see in the current discussion on the energy crisis, the question of where should one substitute to when alternatives are not available remains. We should also consider income distribution effects of the carbon taxes. Thus, climate investments for promoting energy transformation, particularly climate related infrastructure, seem to be a needed component of effective climate policy.8 Recent research also shows that scaling up climate investments would lead to more favorable economic outcomes. Furthermore, a climate investment multiplier has become one of the currently studied topics in macroeconomics.9 As we discussed in Chap. 11, other measures involve innovation, fiscal and monetary policies, and financial market instruments. Financial market resources can be mobilized by attracting investors into green assets and holding more green investments in their portfolios. To promote this effort, a tax on carbon-intensive wealth could also be imposed.10 Finally, we want to point out that certain climate-macro policies as well as the transition to a low carbon economy entail significant transition costs. Thus, the fairness of this transition needs to be considered. The expected climate change and disasters have already affected the labor market on a grand scale. On a positive note, some mitigation and adaptation policies might promote better paying jobs.11 Overall, we hope that our reference to the richness of the macro theory, macroeconomic empirics and policy, will help to give some further incentives to the development of what we might want to call sustainable macroeconomics.

8

For a mix of different policies, see details in Castle and Hendry (2021) and Semmler et al. (2021a, b). For climate investments and green bonds, see Braga et al. (2021). 9 For details, see Batini et al. (2022). 10 For details, see Bastos Neves and Semmler (2022). 11 Here a new trend seems to arise namely that the use of industrial policies for a green economy appears to obtain more support now. For details on labor market challenges, see Kato et al. (2015).

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References Bastos Neves JP, Semmler W (2022). A proposal for a carbon wealth tax: modelling, empirics, and policy. Available via SSRN. https://ssrn.com/abstract=4114243. Accessed 14 Nov 2022 Batini N, Di Serio M, Fragetta M, Melina G, Waldron A (2022) Building back better: how big are green spending multipliers?, IMF working paper 2021/087 Braga JP, Semmler W, Grass D (2021) De-risking of green investments through a green bond market—empirics and a dynamic model. J Econ Dyn Control 131:104201. https://doi.org/10. 1016/j.jedc.2021.104201 Castle JL, Hendry DF (2021) Can the UK achieve net-zero greenhouse gas emissions by 2050? Economics series working papers 953, Department of economics, University of Oxford Grüne L, Pannek J (2011) Nonlinear model predictive control theory and algorithms. Springer, Berlin Grüne L, Semmler W, Stieler M (2015) Using nonlinear model predictive control for dynamic decision problems in economics. J Econ Dyn Control 60:112–133 IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate IPCC. WGII (2022a). https://www.ipcc.ch/report/ar6/wg2/. Accessed 8 Nov 2022 IPCC. WGIII (2022b). https://www.ipcc.ch/working-group/wg3/. Accessed 8 Nov 2022 IPCC (2022c) Climate change 2022: mitigation of climate change. https://www.ipcc.ch/report/ sixth-assessment-report-working-group-3/. Accessed 8 Nov 2022 Kato M, Mittnik S, Samaan D, Semmler W (2015) Employment and output effects of climate policies. In: Bernard L, Semmler W (eds) The Oxford handbook of the macroeconomics of global warming. Oxford University Press, pp 445–476 Murphy S. (2023). The Steel Miller’s Daughter- A creation of a climate-conscious reimagination of a Schubert classic, see http://skmmusic.com/the-steel-millers-daughter Sachs J (2021) Fixing climate finance. In: Social Europe. 17 Nov 2021. https://socialeurope.eu/ fixing-climate-finance. Accessed 7 Nov 2022 Semmler W, Maurer H, Bonen A (2021a) Financing climate change policies: a multi-phase integrated assessment model for mitigation and adaptation. In: Haunschmied J, Kovacevic R, Semmler W, Veliov V (eds), Dynamic economic problems with regime switches. Springer Publishing House, pp 137–158 Semmler W, Braga J, Lichtenberger A, Toure M, Hayde E (2021b) Fiscal policy for a low carbon economy, world bank report. https://documents1.worldbank.org/curated/en/ 998821623308445356/pdf/Fiscal-Policies-for-a-Low-Carbon-Economy.pdf UNFCCC (2022). Nationally determined contributions registry. NDC Registry. https://unfccc.int/ NDCREG. Accessed 7 Nov 2022 UNFCCC. The United States of America: nationally determined contribution. https://unfccc.int/ sites/default/files/NDC/2022-06/United Accessed 7 July 2022 World Bank (2022) State and trends of carbon pricing 2022. State and trends of carbon pricing. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/37455. Accessed 7 Nov 2022