Fintech and Sustainability: How Financial Technologies Can Help Address Today’s Environmental and Societal Challenges 303140646X, 9783031406461

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Edited by Thomas Walker · Harry J. Turtle · Maher Kooli · Elaheh Nikbakht

Fintech and Sustainability How Financial Technologies Can Help Address Today’s Environmental and Societal Challenges

Fintech and Sustainability

Thomas Walker · Harry J. Turtle · Maher Kooli · Elaheh Nikbakht Editors

Fintech and Sustainability How Financial Technologies Can Help Address Today’s Environmental and Societal Challenges

Editors Thomas Walker John Molson School of Business Concordia University Montreal, QC, Canada

Harry J. Turtle Finance & Real Estate Colorado State University Fort Collins, CO, USA

Maher Kooli School of Management (ESG) Université du Québec À Montréal Montreal, QC, Canada

Elaheh Nikbakht John Molson School of Business Concordia University Montreal, QC, Canada

ISBN 978-3-031-40646-1 ISBN 978-3-031-40647-8 (eBook) https://doi.org/10.1007/978-3-031-40647-8 © 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 Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

In the ever-evolving landscape of financial technology (fintech), transformative advancements are reshaping every aspect of our lives. As scientists and practitioners delve into the various possibilities to utilize these innovations, new horizons emerge where fintech intersects with sustainability. Fintech innovations have the potential to enhance accessibility, transparency, and accountability, and unlock new opportunities for sustainable finance. With the power of data analytics, artificial intelligence, and machine learning, financial institutions can assess environmental risks, identify sustainable investment opportunities, and allocate resources more efficiently. Moreover, fintech solutions enable the integration of environmental, social, and governance (ESG) factors into investment decisions, fostering responsible investing practices and encouraging businesses to adopt sustainable practices. This book investigates the effect and application of fintech on different aspects of sustainability in various industries ranging from the maritime sector to banking. Contributors to this collective comprise distinguished scholars from the international community and experienced practitioners who have extensively explored the intersection of fintech and sustainability. The book examines how fintech can improve sustainability in three different dimensions. First, the book explores how fintech can improve sustainability in an environmental context covering topics such as climate change and CO2 emissions. The book continues by exploring whether fintech can enhance the financial inclusion of marginalized groups. The last chapter of the v

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PREFACE

book is dedicated to exploring fintech at the governance level and shows how financial technologies can support sustainable business models and pave the way for sustainable financing and green investment. Montreal, Canada Fort Collins, USA Montreal, Canada Montreal, Canada

Thomas Walker Harry J. Turtle Maher Kooli Elaheh Nikbakht

Acknowledgments

We acknowledge the financial support provided through the Jacques Ménard—BMO Centre for Capital Markets at Concordia University. In addition, we appreciate the excellent copy-editing and editorial assistance we received from Eimear Rosato, Gabrielle Machnik-Kekesi, Maya Michaeli, Meaghan Landrigan-Buttle, Mauran Pavan, Miles Murphy, and Victoria Kelly.

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Contents

Part I Introduction 1

Fintech and Sustainability: An Overview Thomas Walker, Harry J. Turtle, Maher Kooli, and Elaheh Nikbakht

3

Part II Fintech and Environmental Sustainability 2

Scaling up Climate Finance Through Blockchain-Based Digital Green Bonds Yushi Chen

3

Green Energy, Emissions, and Blockchain Technology Tony Erwin and Baozhong Yang

4

The Role of Green Finance in Supporting Maritime Sustainable Development Massimo Arnone and Tiziana Crovella

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Part III Fintech and Social Sustainability 5

Does Fintech Contribute to Fair and Equitable Outcomes? Lakshmi Shankar Ramachandran

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CONTENTS

Fintech, Financial Inclusion, and Social Challenges: The Role of Financial Technology in Social Inequality Simona Cosma and Giuseppe Rimo The Metaverse’s Inspiration for Sustainable Business: Restructuring Economic Logic, Capital, Assets, Organization, and Industry Yushi Chen

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Part IV Fintech and Governance Sustainability 8

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Circular Economy: A Fintech Driven Solution for Sustainable Practices Vincent Grégoire and Kevin Guay The Role of Fintech in the Field of Sustainability and Financing Niccole Jordan, Patrick Röthlisberger, Julia Meyer, and Beat Affolter

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The Mediating Role of Fintech on ESG and Bank Performance Nur Badriyah Mokhtar and Ashraful Alam

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Integrating AI to Increase the Effectiveness of ESG Projects Sean Stein Smith

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Index

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Notes on Contributors

Beat Affolter is Professor of Financial Management at the ZHAW School of Management and Law and heads the Center for Corporate Performance and Sustainable Financing. His research focuses on corporate finance and sustainability with a particular focus on financial innovation. Beat Affolter holds a Ph.D. in Banking and Finance from the University of Zurich and has worked in the financial consulting industry. Ashraful Alam is a lecturer in the Accounting, Finance, & Economics (AF&E) Department at the University of Salford. He is also working as a program director for the M.Sc. in International Corporate Finance. Before joining Salford, he taught at the University of York and Leeds Beckett University. Moreover, he previously held an assistant professor position at the University of Dhaka, Bangladesh. He obtained his Ph.D. from the University of York. Massimo Arnone is currently a fixed-term researcher in Political Economy at the Department of Economics and Business of the University of Catania in the context of a research project on the topic of FINTECH and Social Impact Finance. He has a Ph.D. in Economic Analysis, Technological Innovation, and Management of Territorial Development Policies and was Research Fellow at the ISSIRFA Institute of the CNR and also at the Department of Political Sciences of the University of Bari. Over the years, he has had various collaborations with national and international research centers (CASMEF-LUISS Guido Carli, EURICSE,

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SRM-Studies and Research for Southern Italy, OBI-Osservatorio Banche e Imprese). He is the author of numerous publications investigating the relationship between finance and economic growth and new planning models of local development. Yushi Chen is a doctoral researcher at the Science Policy Research Unit of the University of Sussex, UK (ranked third in the world among science and technology think tanks, and first in the UK). He received his M.Sc. in Climate Change, Management, and Finance from Imperial College London and a Bachelor of Science in Business Administration (BSBA) in Energy Management (STEM) and Finance from the University of Tulsa. He serves as Chief Researcher at the Digital Alliance Institute for Digital Finance Research (DAIDFR), Financial Advisory Fellow at 2060 Advisory, Climate FinTech Advisor at FinTech4Good, Member of the Financial Technology Committee at Guangzhou Digital Finance Association, Blockchain Expert at Shenzhen-Hong Kong-Macao Fintech Program, Member of ISO/TC322 TAG01 Sustainable Fintech, and as Visiting Scholar at the China Institute for Science and Technology Policy at Tsinghua University (CISTP). Simona Cosma is Associate Professor in Financial Markets and Institutions at the University of Bologna, Italy. From 2011 to November 2022, she was Associate Professor in Financial Markets and Institutions at the University of Salento, Italy, where she taught Risk Management in Banks and the Economics of Financial Intermediaries. She previously worked as Affiliated Professor at the SDA Bocconi School of Management (Banking and Insurance Department), Milan, Italy. Her research mainly focuses on risk management, corporate governance, and sustainability. She is an author and co-author of many books and articles in international scholarly journals such as Business Strategy and the Environment, Corporate Social Responsibility and Environmental Management, and the Journal of Management & Governance. She is a board member of a financial company (Banca Popolare Pugliese) and a listed non-financial company (Monrif). Tiziana Crovella is Assistant Professor in Commodities Sciences at the University of Bari, Department of Economics, Management and Business Law (Italy). She holds a Ph.D. in Technology and Management from the Department of Economics, Management and Business Law, University of Bari Aldo Moro, Italy, and a Master’s Degree in Economics

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and Management of Tourism. During a research fellowship, she studied the circular economy in agriculture. She is also a scientific tutor in a II Level of Master Port City School in collaboration with the University of Venice. Her scientific activity deals with these items: maritime sector and tourism, material flow analysis (MFA), life cycle assessment (LCA), big data, sustainability indicators, and the circular economy. She is the author of numerous publications investigating the relationship between the maritime sector and sustainability. Tony Erwin is Senior Principal Systems Engineer at Extreme Networks. Tony has more than 20 years of high-tech sales and business development experience performing consulting and high-tech sales for cloud, IoT, and 5 G-related solutions. He has designed and developed automation software for large mobile operators and performed consultative selling to Verizon, Ericsson, AT&T & T-Mobile. Tony has been part of various green energy projects in renewable energy as an active investor and executive including executing off-take agreements with British Petroleum (BP). He has a Master’s in Finance (MSF) with a Fintech concentration from Georgia State University, J. Mack Robinson College of Business. Tony is also actively involved with machine learning, crypto, blockchain, and AI and does consultative work with Fintech companies in Atlanta and serves on the Technology Association of Georgia Fintech Society steering committee. Charlotte Esme Frank completed her bachelor’s degree in the Humanities at Carleton University, Ottawa. She holds an M.A. in English Literature and Creative Writing from Concordia University, Montreal, where she is a research associate at the John Molson School of Business. Charlotte is currently completing a Ph.D. with a focus on the poet Elizabeth Bishop at McGill University. Vincent Grégoire is tenured Associate Professor of Finance at HEC Montr´eal, where he holds Research Professorship in Financial Big Data Analytics. He holds a Ph.D. in Finance from the University of British Columbia and degrees in Computer Engineering and Financial Engineering from Universit´e Laval in Quebec. His academic research interests are in information economics, market microstructure, and big data and machine learning applications in finance. His research has been published in leading academic journals such as the Journal of Financial Economics,

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the Journal of Accounting Research, and the Journal of Financial and Quantitative Analysis. Kevin Guay holds a B.B.A. in Finance from Bishop’s University in Sherbrooke, Canada. He is pursuing an M.Sc. in Finance at HEC Montréal, where he was a teaching assistant for the graduate-level course Empirical Finance. Kevin currently works at Caisse de dépôt et placements du Québec (CDPQ), a leading global asset manager, as an analyst under the Private Debt Team. Kevin’s research interests are focused on sustainable finance and impact investing. Niccole Jordan is a senior lecturer at the Center of Competence of Corporate Performance & Sustainable Finance at the ZHAW School of Management and Law. She holds a master’s degree in finance from the University of Zurich and has gained a wealth of practical experience within the financial sector. Her research interests include linking sustainability and digitalization within the fields of finance and accounting. Victoria Kelly recently graduated from Concordia University (Montreal) with a B.Sc. in Biology with an additional major in Irish Studies. She plans to pursue her studies with an independent master’s degree examining the 1832 cholera epidemic and its management on a social, urban, economic, and medical level, drawing parallels with the recent COVID-19 pandemic. Maher Kooli is Professor of Finance in the Department of Finance of the School of Management (ESG), Université du Québec à Montréal (UQAM), and head of the Finance Department. He is also Caisse de Depot et Placement de Québec (CDPQ) research chair-holder in portfolio management, founder of the trading room at ESG UQAM, and Autorité des marchés financiers (AMF) and Finance Montreal research co-chair-holder in Fintech at ESG UQAM. Previously, Professor Kooli worked as a senior research advisor for la Caisse de dépôt et placement du Québec. His research interests include initial public offerings, mergers and acquisitions, venture capital, hedge funds, Fintech, portfolio management, and corporate finance. He has published in many prestigious academic journals and has several books on financial management, venture capital, and hedge funds. He is also a member of the Editorial Board of the Journal of Asset Management, the Journal of Wealth Management, and Risk Management.

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Meaghan Landrigan-Buttle holds a Master’s degree in History from Concordia University (Montreal), with a focus on Irish Studies. Meaghan has experience in project management, conference planning, and tutoring, and holds a professional development certificate in Professional Editing from the University of Waterloo. She has worked as a teaching assistant in the History Department and at the School of Irish Studies at Concordia. Her research interests include the First World War, the uses and misuses of history, the consumption of history via popular culture and commemoration, memory studies, and genealogy. Gabrielle Kathleen Machnik-Kekesi is Ph.D. Candidate and Hardiman Research Scholar at the Centre for Irish Studies at the National University of Ireland, Galway. She holds an Individualized Program master’s degree from Concordia University, which was funded by both the Social Sciences and Humanities Research Council (SSHRC) and the Fonds de Recherche du Québec en Société et Culture (FRQSC) and a Master’s in Information Studies from McGill University. Gabrielle’s research interests include modern Irish history, food, domestic space, and cultural heritage. Julia Meyer works as a senior lecturer and researcher at the ZHAW School of Management and Law. After her studies in economics, she worked in financial consulting with a focus on compensation, valuebased management, and valuation. In her current research, she studies the measurement of sustainability and impact and the related capital market effects (e.g., performance and information asymmetry). Julia holds a Ph.D. in Finance from the University of Zurich. Nur Badriyah Mokhtar is a Ph.D. student at the University of Salford. She formerly worked as a bank executive for 8 years at Kuwait Finance House, a Middle Eastern bank, in Malaysia. In 2018, she graduated with a Master’s in Banking from the University of Utara Malaysia. Miles Murphy is a graduate of the School of Irish Studies at Concordia University. He has been a researcher, writer, and editor on a variety of projects and publications. He is former Professional Test Developer and Standards Manager and was Director of Exam Design and Development with Moody’s Analytics. He has experience in the areas of adult education, finance, the built environment, mining, energy, health, and public safety.

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Elaheh Nikbakht holds an M.Sc. degree in Finance from the John Molson School of Business, Concordia University. She currently serves as Research Associate in the Emerging Risks Information Center (ERIC) and the Jacques Ménard/BMO Center for Capital Markets at Concordia University. In addition, she works as Senior Data and Reporting Analyst in Global Entity Services at Maples Group. Elaheh completed her undergraduate degree and M.B.A. in Iran. She has been awarded several scholarships and awards for her academic performance, including the Arbour Foundation Scholarship and the Bourse D’études D’excellence du Centre Desjardins D’innovation en Financement D’entreprises. Mauran Pavan is an undergraduate student in Computer Science-Web Services and Applications at Concordia University. He has previous experience working at a start-up in the entertainment and tourism sector. His interests lie in FinTech, sustainability, and emerging new technologies. Lakshmi Shankar Ramachandran is Associate Professor in the Practice of Finance at the Goizueta Business School, Emory University. Previously, he served as Assistant Professor of Banking and Finance at the Weatherhead School of Management, Case Western Reserve University. He earned his Ph.D. in Finance from EdHEC, M.S. in Computational Finance from Carnegie Mellon University, and B.S. in Technology (B.Tech) from IIT (Indian Institute of Technology), Madras. Shankar’s primary research interest lies in the area of Fintech and empirical asset pricing. His papers have appeared, among other journals, in the Journal of Financial Economics (JFE). He has received several prestigious research grants, including the inaugural grant for capital markets instituted by New York University and the National Stock Exchange of India. Shankar has more than fifteen years of experience teaching graduate-level courses on Artificial Intelligence, Blockchain Technology, Fintech, Quantitative Risk Modeling, and Corporate Risk Management. Giuseppe Rimo is Ph.D. Candidate in Digital Transformation and Sustainability at the University of Salento, Lecce, Italy. He has a master’s degree in economics, finance, and insurance and a bachelor’s degree in economics and finance. His research interests broadly concern banking, digital transformation, and sustainability in financial intermediation.

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Eimear Rosato is Ph.D. Candidate in the Department of History and School of Irish Studies at Concordia University, Montreal, Tiohtià:ke. Her research takes an oral history and memory studies approach to examining the intergenerational memory of the Troubles in Northern Ireland. She is the 2022 inaugural winner of the John and Pat Hume Foundation Award in association with the American Conference of Irish Studies. Rosato is also a copyeditor with the Emerging Risks Information Center at Concordia University. Patrick Röthlisberger is a researcher at the Center for Corporate Performance and Sustainable Financing at the ZHAW School of Management and Law. He gained additional professional experience in the banking sector. He holds a Master’s degree in Business Administration from the University of Zurich and is working on his Ph.D. in Sustainable Financing. Sean Stein Smith is an assistant professor at the City University of New York—Lehman College. He serves on the Advisory Board of the Wall Street Blockchain Alliance, where he chairs the Accounting Working Group. Sean sits on the Advisory Board of Gilded and is a strategic advisor to the Central Bank Digital Currency Think Tank. Sean also serves as an Advisor to Crescent City Capital, a crypto asset investment fund. He is the immediate past chairperson of the NJCPA’s Emerging Technologies Interest Group, is the president of the NYSSCPA Manhattan-Bronx Chapter, and is a member of the NJCPA Board of Trustees. Sean also serves on the Board of Governors of Fairleigh Dickinson University. He has served as Visiting Research Fellow at the American Institute for Economic Research (AIER). Sean has developed and presented courses on Artificial Intelligence, Cryptoassets, and Blockchain for both the AICPA and IMA. Sean has been named one of the Top 100 Most Influential People in Accounting and has been named a past winner of the NJCPA Ovation Award for Innovation. He has also been named top 40 under 40 in the accounting profession from 2017 to 2021 by CPA Practice Advisor and the NYSSCPA in 2021. Sean was named on the NJBIZ Power 50 in Accounting in 2021 & 2022. He was also awarded the Outstanding Young CPA of the Year Award by the AICPA in 2022. Harry J. Turtle is Professor of Finance, and Tinberg Business for a Better World University Professor in the Department of Finance & Real Estate at Colorado State University. Previously, Harry held the positions

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of Chair at the Department of Finance, Insurance, and Real Estate, Washington State University; Omer L. Carey Chair in Finance at Washington State University; Fred T. Tattersall Distinguished Chair of Finance at West Virginia University; and Chair, Department of Finance, and Real Estate, at Colorado State University (CSU). Harry enjoys teaching investment theory, portfolio management, international finance, and capital markets at the undergraduate and graduate levels. Harry’s research is published in outlets including the Journal of Business and Economic Statistics, the Journal of Financial Economics, the Journal of Financial and Quantitative Analysis, and Management Science. He examines a variety of topics in the areas of household finance, investments, portfolio management, and international finance. Harry served as Associate Editor for the Journal of Financial Research and for the Finance Division of the Canadian Journal of Administrative Sciences. He is a member of the Editorial Advisory Review Board for the American Journal of Business, and the Topic Board for the Journal of Risk and Financial Management and served as a founding member of the CSU Other Post-Employment Benefits (OPEB) Investment Committee. Thomas Walker is Professor of Finance, Director and Academic Lead of the Emerging Risks Information Center (ERIC), inaugural Director for the Jacques Ménard/BMO Center for Capital Markets, and Concordia University Research Chair in Emerging Risk Management (Tier 1) at Concordia University in Montreal, Canada. He previously served as Associate Dean, Department Chair, and Director of Concordia’s David O’Brien Centre for Sustainable Enterprise. Prior to his academic career, he worked for firms such as Mercedes Benz, KPMG, and Utility Consultants International. He has published over 70 journal articles and books. Baozhong Yang is the H. Talmage Dobbs Jr Chair in Finance and Associate Professor of Finance at the J. Mack Robinson College of Business at Georgia State University. He is also the director of the Fintech Lab at Robinson College, one of the first such labs associated with a business school in the nation. He has founded and organized the GSU-RFS Fintech Conference, a leading annual Fintech conference that offers dual submission to the premier journal Review of Financial Studies. Professor Yang’s research interests span theoretical and empirical studies in Fintech, Investments, and Corporate Finance. Professor Yang’s research has been published in leading academic journals, including

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the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. Professor Yang’s research has been also well cited and widely covered by the media, including the NBER Digest, Bloomberg, Wall Street Journal, Financial Times, and Forbes. Professor Yang received his Ph.D. in Finance from Stanford University and his Ph.D. in Mathematics from the Massachusetts Institute of Technology.

List of Figures

Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig. Fig.

2.1 3.1 3.2 3.3 3.4 3.5 3.6 3.7 4.1 4.2 6.1 7.1 9.1

Fig. 10.1 Fig. 10.2

Implication of digital finance for sustainable development Evolution of global carbon pricing revenues over time The global volume of carbon credit/offset issuances Low carbon fuel standard credits and deficits Pathways from feedstock to renewable energy Example lifecycle of a renewable identification number The pipeline from landfill to renewable natural gas Proposed blockchain platform for the RNG-RIN market European roadmap on sustainability Topics in green finance Main barriers to financial inclusion in 2021 Atomistic-mechanistic view of knowledge and development Sustainability-related dual materiality and the focus areas of sustainability in financing Theoretical framework Robustness test

19 30 31 38 40 41 42 45 54 57 112 133 171 200 210

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List of Tables

Table 4.1 Table 4.2 Table Table Table Table Table

4.3 4.4 6.1 6.2 6.3

Table 9.1 Table Table Table Table Table Table

10.1 10.2 10.3 10.4 10.5 10.6

Definitions of green bonds Benefits and threats of green bond issues for issuers and investors Cold ironing diffusion Barriers and limitations to energy efficiency investments Indicators of financial inclusion in 2021 Key roles of different actors Some of the 50 inclusive startups selected by the InclusiveFintech50 program Summary of technological approaches and possible applications to sustainable financing Definition of variables Distribution of sample by country Sample descriptive statistics Correlations of ESG indexes PLS-SEM results Sobel test

68 69 71 73 111 114 120 174 203 204 204 205 208 211

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PART I

Introduction

CHAPTER 1

Fintech and Sustainability: An Overview Thomas Walker, Harry J. Turtle, Maher Kooli, and Elaheh Nikbakht

1.1

Introduction

The majority of the current economic models were created in the resource abundance era. As a result, most of these models do not incorporate the cost of using natural resources or potentially destructive environmental

T. Walker · E. Nikbakht (B) John Molson School of Bussiness, Concordia University, Montréal, QC, Canada e-mail: [email protected] T. Walker e-mail: [email protected] H. J. Turtle Finance & Real Estate, Colorado State University, Fort Collins, CO, USA e-mail: [email protected] M. Kooli School of Management (ESG), Université du Québec à Montréal, Montréal, QC, Canada e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_1

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outcomes. Mass production and economies of scale have led to the overconsumption of natural resources, long working hours, and pollution (Schoenmaker & Schramade, 2018). Global warming and climate change, child labor, ozone depletion, ocean acidification, poverty and hunger, and gender discrimination are only a few examples of the environmental and social concerns that will continue to have a detrimental effect if they are not resolved immediately (Walker et al., 2019). Despite the existing environmental and social concerns and challenges, outdated models and behaviors have remained popular. Making a smooth transition to a sustainable economy is the only solution to managing these challenges (Schoenmaker & Schramade, 2018). Sustainable development is defined as an organizing principle leading humans toward achieving their goals while maintaining the ecosystem’s ability to continue providing natural resources. In recent years, politicians and governments have established various public policies, such as the 2030 Agenda and Sustainable Development Goals (Environmental, Social, and Corporate governmental goals or ESGs) to guide this transition (Gutterman, 2021). One of the areas targeted by these new sustainable development policies is the financial system. As the main distributor of financial resources in an economy, the financial system can ensure that financial resources are devoted to sustainable corporations. Any type of financial support, including investing or lending, aimed toward sustainable goals can radically increase the number of sustainable projects and accelerate the transition to a sustainable society (Kaur & Kautish, 2022). Sustainable finance covers various types of concepts including environmental finance, social responsibility, ethical finance, and green finance. Sustainable finance differs from traditional finance in several respects, including the primacy of risk and profit, economic value, awareness, and public responsibility (Ziolo & Sergi, 2019). On the other hand, financial systems themselves have been revolutionized by new technologies and innovations. Emerging financial technologies (fintech) have dramatically changed the way financial processes through which banks and financial institutions provide services to their clients (Singh & Singh, 2019). Fintech technologies like artificial intelligence (AI), blockchain, cryptocurrencies, and machine learning (ML) enable financial institutions to (1) provide their traditional services faster and make services more widely available to customers, (2) perform complex data analysis to produce optimal decisions, (3) save staff time and increase the efficiency of financial procedures through the use of

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robots and computers, and (4) offer new services and investment tools that were impossible to provide before the advent of these technologies (Cocco et al., 2017; Rath et al., 2021; Thompson, 2021). Fintech paves the way for financial institutions to follow a sustainable pathway by reducing negative environmental outcomes, as well as by offering up sustainable assets. In particular, “green fintech” refers to the programs, start-ups, or businesses that follow ESG standards (Kaur & Kautish, 2022). Fintech provides powerful tools for financial institutions to improve data integrity regarding green processes while reducing costs and improving efficiency with respect to ESG standards. Also, concepts like “green bonds” and “green crowdfunding” describe other ways in which fintech achieves sustainable goals by allocating financial resources to sustainable projects (Abdelli et al., 2021). The application of fintech to achieving sustainability goals is not limited to the finance sector and financial institutions. Fintech innovations like AI or ML can be used directly to create systems to minimize water waste or to control CO2 emissions. Additionally, fintech can facilitate the development of policies, standards, and regulations to meet ESG goals and provide effective platforms to monitor and oversee regulatory practices (Taghizadeh-Hesary & Hyun, 2022). Despite the obvious advantages offered by fintech, there is debate among environmentalists about the negative consequences of the technologies. Opponents warn that the negative consequences of using fintech innovations might surpass their benefits. For example, cryptocurrency miners’ overconsumption of electricity demonstrates these technologies’ negative impacts on sustainable development (Akkucuk, 2021). Thus, fintech and its application in sustainability is a double-edged sword that calls for both careful investigation and considerable caution. This book explores how financial technologies can enhance the sustainability of investment and corporate decisions and thereby contribute to the fulfillment of the Sustainable Development Goals (SDGs). In particular, it examines (1) whether and how fintech can be used to improve sustainable practices, (2) the potential threats that fintech applications may pose and possible solutions, and (3) policies and regulations designed to maximize the benefits of fintech in sustainability. This topic is relatively new, and the literature remains scarce. Previous publications focus on specific aspects of fintech related to sustainability or explore specific policies in this area. Thus, the literature suffers from a lack

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of comprehensive studies that examine the interplay of fintech and sustainability. This collection addresses this gap by including a comprehensive review of different fintech applications in meeting ESG standards and exploring sustainable finance’s future. This book utilizes both academic insights and practitioner-oriented approaches. It also reviews the potential negative impacts of fintech and the challenges and risks fintech places on policymakers and practitioners. The goal of the book is to encourage the reader to think about how fintech can strike a balance between the costs and benefits of sustainability and potential challenges with respect to regulation, monitoring, supervision, and management.

1.2

Overview of Content

The book begins with an introduction to fintech and sustainability, including some definitions, as well as a discussion of its importance in finance today. This is followed by three main sections that review the application of fintech in sustainability at the environmental, social, and corporate governance levels. 1.2.1

Part I: Introduction

Chapter 1 summarizes the background and importance of fintech innovations and explores the opportunities and challenges concerning environmental and social sustainability goals. This chapter also provides an overview of the following ten chapters. 1.2.2

Part II: Fintech and Environmental Sustainability

Chapter 2, Scaling up climate finance through blockchain-based digital green bond issuance provides an overview of the climate challenges and how climate fintech can be used for climate financing considering the current advancements in digital technology. Chen reviews the traditional models that are merely based on the stakeholders’ interests. He then uses green bonds as an example to discuss how these tools can create a balance between the stakeholders’ interests and environmental goals. In Chapter 3, Green energy, emissions, and blockchain technology, Erwin and Yang study blockchain technology as a tool to reduce greenhouse gas emissions. After providing an overview of blockchain technology, they explore its applications in cases like secure green energy trading,

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green energy, efficient tracking of CO2, and air pollution reduction. Finally, they review the potential challenges of blockchain-based solutions and how the technology might be used to support the Environmental Protection Agency (EPA) programs. Chapter 4, The role of green finance in supporting maritime sustainable development, shifts the attention to the maritime transport industry and the sustainability goals in this sector. Arnone and Crovella review the importance of this sector in international trading and its negative impacts on the environment. Next, they investigate how fintech tools like green bonds can pave the way for reaching sustainability in this sector. They review different articles and compare fintech tools with other sustainable strategies. 1.2.3

Part III: Fintech and Social Sustainability

This section, Chapter 5, Does fintech lead to fair and equitable outcomes?, begins by examining the equitability and fairness of fintech in financial settings. Shankar reviews evidence from both media and academia to address the question of whether or not machine learning algorithms used by financial institutions yield ethical outcomes. Chapter 6, Fintech, financial inclusion, and social challenges: The role of financial technology in social inequality, looks at fintech in terms of inclusion. Specifically, Cosma and Rimo explore the literature on social inequality and financial inclusion and investigate how fintech and fintech companies can facilitate the implementation of the United Nations’ 2030 Agenda and achieve the Sustainable Development Goals. Chapter 7, The Metaverse’s inspiration for sustainable business, provides an overview of the metaverse and how the idea can be used to rethink the current economic and financial worldview. Chen investigates the history and evolution of the metaverse concept in Asia and its interface with financial and social assets. He also looks at new types of organizations created with the help of this technology and how their culture and social system differ from traditional businesses. 1.2.4

Part IV: Fintech and Governance Sustainability

Chapter 8, Circular economy: A fintech-driven solution for sustainable practices, looks at circular business models in different industries to discover the most successful examples. Grégoire and Guay examine

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how fintech companies can improve efficiency through case studies of businesses using fintech solutions and the effects of these on society. Chapter 9, The role of fintech in the field of sustainability and financing, changes the focus to the corporate level of sustainability. Meyer, Rothlisberger, Affolter, and Jordan investigate small and medium-sized enterprises that may not directly have access to the capital market. By exploring real-life case studies, they discuss how fintech can facilitate sustainable finance and minimize the drawbacks of classic bank financing. Chapter 10, The mediating role of fintech on ESG and bank performance investigates if fintech can improve commercial banks’ performance by supporting ESG-oriented practices. Mokhtar and Alam employ PLSSEM, a quantitative research method, to examine European Union (EU) commercial banks. They also provide some lessons learned for the banks and customers. The last chapter of this section, Chapter 11, Green assets & sustainable capital markets: AI and green investment, reviews different types of green investments and how artificial intelligence can facilitate these investments in different ways. In this chapter, Smith explores green assets including green bonds and green asset-backed securities and discusses how AI is revolutionizing their development, selection, and funding. He also includes an illustration of the application of fintech in assessing and optimizing green financial projects.

References Abdelli, M. E. A., Youssef, W. A. B., Özgöker, U., & Slimene, I. B. (Eds.). (2021). Big data for entrepreneurship and sustainable development. CRC Press. https://doi.org/10.1201/9781003090045 Akkucuk, U. (2021). Disruptive technologies and eco-innovation for sustainable development. IGI Global. https://doi.org/10.4018/978-1-7998-8900-7 Cocco, L., Pinna, A., & Marchesi, M. (2017). Banking on blockchain: Costs savings thanks to the blockchain technology. Future Internet, 9(3), 25, 1–20. https://doi.org/10.3390/fi9030025. Gutterman, A. S. (2021). Sustainable finance and impact investing. Business Expert Press. https://www.oreilly.com/library/view/sustainable-fin ance-and/9781637420034/ Kaur, G., & Kautish, S. (2022). Assessment of service quality of payment wallet services in India using the Servqual model. In: AI-enabled agile Internet of Things for sustainable fintech ecosystems. IGI Global. https://doi.org/10. 4018/978-1-6684-4176-3

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Rath, G. B., Das, D., & Acharya, B. (2021). Modern approach for loan sanctioning in banks using machine learning. In: Advances in machine learning and computational intelligence. Springer, Singapore. https://www.springerp rofessional.de/en/modern-approach-for-loan-sanctioning-in-banks-using-mac hine-lear/18215650 Schoenmaker, D., & Schramade, W. (2018). Principles of sustainable finance. Oxford University Press. https://global.oup.com/academic/product/princi ples-of-sustainable-finance-9780198826606?cc=ca%26lang=en%26 Singh, N. P., & Singh, D. (2019). Chatbots and virtual assistant in Indian banks. Industrija, 47 (4), 75–101. https://doi.org/10.5937/industrija47-24578 Taghizadeh-Hesary, F., & Hyun, S. (2022). Green digital finance and the sustainable development goals. Springer Singapore. https://doi.org/10.1007/978981-19-2662-4 Thompson, S. (2021). Green and sustainable finance: Principles and ractice (Vol. 6). Kogan Page Publishers. https://www.koganpage.com/product/pri nciples-and-practice-of-green-finance-9781789664546 Walker, J., Pekmezovic, A., & Walker, G. (2019). Sustainable development goals: Harnessing business to achieve the SDGs through finance, technology and law reform. John Wiley & Sons. https://www.wiley.com/en-us/Sustainable+Dev elopment+Goals:+Harnessing+Business+to+Achieve+the+SDGs+through+Fin ance,+Technology+and+Law+Reform-p-9781119541813 Ziolo, M., & Sergi, B. S. (2019). Financing sustainable development: Key challenges and prospects. Palgrave Macmillan. https://www.amazon.ca/FinancingSustainable-Development-Challenges-Prospects/dp/3030165248

PART II

Fintech and Environmental Sustainability

CHAPTER 2

Scaling up Climate Finance Through Blockchain-Based Digital Green Bonds Yushi Chen

2.1

The Climate Reality We Face

The Paris Agreement, which was launched in 2015, created a shared global “hard target” for countries. Strengthen the global response to the threat of climate change by keeping global average temperature rise well below 2 °C over pre-industrial levels, with the goal of limiting warming to 1.5 °C (UNFCCC, 2015). Most countries worldwide are participating in this effort to combat global climate change, with 194 countries alongside the European Union signing the Paris Agreement and 147 signatories ratifying it. The Paris Agreement includes promises from all countries to decrease emissions and collaborate on climate change adaptation, as well as a call for countries to increase their commitments. The agreement establishes a framework for transparent monitoring and reporting on national climate targets, as well as mechanisms for developed countries to assist developing countries in mitigating and adapting to climate change. However, there is a significant financing shortfall in developing

Y. Chen (B) Science Policy Research Unit, University of Sussex, Brighton, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_2

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nations to address climate change, whereas wealthy countries’ commitment to low-income countries’ yearly climate financial support of US$100 billion has not been fulfilled (Timperley, 2021). Regardless, current national responses to climate change are insufficient. According to NASA statistics, global greenhouse gas emissions fell by an unprecedented 5.4% following the outbreak of the Covid19 epidemic in 2020, only to return to pre-epidemic levels in 2021 (NASA, 2021). According to the World Meteorological Organization’s (WMO) State of the Global Climate 2021 report (WMO, 2022), atmospheric greenhouse gas concentrations continue to rise, and the last seven years were the warmest on record, with 2021 being “only” one of those seven warmest years. Four key climate change indicators—greenhouse gas concentrations, sea level rise, ocean heat, and ocean acidification—all set new records in 2021, indicating that human activities are causing changes to land, oceans, and the atmosphere on a global scale. Furthermore, in Emissions Gap Report 2022, the United Nations Environment Programme (UNEP) indicates that, despite the establishment or updating of national autonomous contribution targets announced by many countries at COP27, there is a significant risk that global temperatures will rise by 2.6 °C by the end of this century, far exceeding the Paris Agreement target and leading to catastrophic changes in the planet’s climate (UNEP, 2022). To limit global warming to 1.5 °C this century, the world must cut annual greenhouse gas emissions in half over the next eight years.

2.2

Key Challenges of Climate Finance

Countries must direct more finance to address climate change to meet their nationally owned contribution targets and low-carbon development goals. However, current climate finance-flows fall far short of estimated needs, and in order to achieve the transition to a sustainable, net-zero emission, within this decade, climate investment must increase significantly, and climate finance commitments must be translated into action. Climate finance is a visible solution as well as a moral imperative. According to the United Nations Framework Convention on Climate Change (UNFCCC), climate finance is a type of financing from public, private, or other sources that assists local, national, and transnational regions that are severely affected by/or sensitive and vulnerable to climate change. It assists them in mitigating climate change and improving their

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capacity to adapt to it (UNFCCC, 2022b). In developing nations, the World Bank and other development finance institutions have increasingly employed Results-Based Climate Finance (RBCF) to promote climate action and assist governments in meeting their Nationally Determined Contributions (NDCs) to the Paris Agreement. The World Bank has identified three areas that are especially suited to RBCF financing: (1) Agriculture, forestry, land use, marine, and other sectors that sustain important services and natural capital assets are the focus of Natural Climate Solutions. Outcome-based climate payments, for example, might aid mangrove restoration by earning emission reduction credits and attracting further private investment from carbon markets; (2) Infrastructure for energy, water, transportation, cities, and other areas that is sustainable. For example, outcome-based climate payments might hasten the decommissioning of coal-fired power facilities by selling emission reduction credits from the coal transition in carbon markets. This monetisation will aid in the attraction of private finance, the expansion of clean energy capacity, and the access of local workers and affected communities to new economic prospects as they migrate to clean energy; (3) Fiscal and financial solutions that supply or mobilise money for climate change, whether directly or indirectly. Carbon taxes, the elimination of damaging subsidies (such as those for fossil fuels), green public financial institutions, and sustainability-related loans are a few examples (World Bank, 2022). However, four major issues arise when questioning how to measure and report on climate finance activities; (1) How can we accurately determine how much money is spent on climate change mitigation, adaptation, and resilience initiatives? (2) How can we measure the relative effectiveness of different decarbonisation interventions? (3) How can we assess where the most urgent need for investment is? (4) How can we get enough and high-quality data? The lack of agreement on the four challenges listed above clouds action on climate finance. Inconsistencies in statistical methods and criteria have resulted in some capital flows not being classified as climate finance activities, even when they provide positive climate change mitigation or adaptation benefits to improve climate resilience. These phenomena compound the scientific ambiguity of assessment and measurement efforts. Some issues emerge. For example, should transition funding operations be included in the scope of climate finance? Whether some financial flows that are incompatible with climate finance objectives, such as financial acts that have a direct

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influence on rising greenhouse gas (GHG) emissions, should also be regulated. This calls into question various interpretations of the definition of climate finance. Everyone seems to understand that the response to climate change must be transnational, but the data flow can easily “hit a wall” at the micro level, even within a country. The agriculture, forestry, other land use, and fisheries (AFOLU) sector, for example, accounts for nearly 20% of global emissions but receives relatively little climate finance because these key sectors lack data on private-sector climate investment, making it difficult to track progress towards climate targets (CPI, 2021; Henderson et al., 2021; Lamb et al., 2021).

2.3

Which is the Priority of Climate Finance

In recent years, funding for climate change mitigation has consistently lagged far behind funding for climate change adaptation. According to data from the Committee on Climate Policy Initiative (CPI) 2021, climate change mitigation accounts for more than 90% of total climate finance. Renewable energy dominates climate change mitigation finance, accounting for nearly 70% of total funding (CPI, 2021). The renewable energy sector is maturing and competitive, with a return on investment up to seven times that of fossil fuels (AbdulRafiu et al., 2022). On the other hand, low-carbon transportation has been the most rapidly growing climate change mitigation option in the last five years, attracting funding from various sources due to the scale of investment and commercial viability. In 2021, electric vehicle sales set a new peak. China has the most EV sales, with 3.3 million (tripling 2020 sales), followed by Europe, with 2.3 million (up from 1.4 million in 2020). In the United States, EV sales will more than double to 4.5% in 2021, with 630,000 units sold (IEA, 2022). The disparity in growth between climate mitigation and adaptation financing over the last two years suggests that adaptation finance will most likely be the “tortoise” in the tortoise and the hare. According to the data, funding for climate change adaptation increased by nearly 53% in 2021, with an average annual growth rate of 16.7%. The average annual growth rate for climate change mitigation finance, on the other hand, is only 6% (CPI, 2021). Adaptation finance encompasses funds provided by developed countries to developing countries as well as funds invested by governments (both developing and developed) to cover climate change

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adaptation activities within their own borders. The report of the Intergovernmental Panel on Climate Change (IPCC) Working Group II has emphasised the importance of increasing long-term climate adaptation efforts, and increasing the share of adaptation finance has become a priority in the fight against climate change (IPCC, 2022). We must recognise that the higher priority given to adaptation funding does not imply that humanity has abandoned the pace of climate change mitigation but rather that it has only previously invested in mitigation projects. We must acknowledge that our efforts to mitigate climate change remain woefully inadequate and that no matter how determined or how hard we try, the rate of global warming cannot be reversed overnight. We have entered the era of the climate crisis. There is now a recognition that both must go hand in hand, and that projects that increase climate resilience should be given even more priority. New pledges totalling more than US$230 million to the Climate Resilience Enhancement Fund were also made at COP27 (UNFCCC, 2022a). Through concrete adaptation programmes, these pledges will assist more climate-vulnerable communities in becoming more climateresilient. The new “Loss and Damage” fund for climate-vulnerable countries is also intended to aid in climate change adaptation. The Sharm El Sheikh Implementation Plan emphasises that the global transition to a low-carbon economy will require at least US$4–6 trillion in annual investment and that providing such finance will necessitate a rapid and comprehensive transformation of the financial system and its structures and processes, involving governments, central banks, commercial banks, institutional investors, and other financial actors (UNFCCC, 2022c).

2.4

Huge Climate Financing Gap

According to the CPI report, the private sector has contributed roughly half of the incremental climate finance over the last decade, but at a compound annual growth rate of 4.3%. In comparison, the public sector grew at an average annual rate of 9.1% from 2011 to 2020 (CPI, 2021). Private climate finance is far from reaching the scale and speed required for a low-carbon transition and must be rapidly scaled up. According to Nick Robins, a climate finance expert at the London School of Economics and Political Science, emerging markets and developing countries, excluding China, will need to spend approximately US$1 trillion per year by 2025 (4.1% of GDP, up from 2.2% in 2019) and

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approximately US$2.4 trillion per year by 2030 (6.5% of GDP) to implement climate action. Up to US$1 trillion of the funds required by these countries by 2030 will have to come from outside sources, including aid from investors, developed countries, and multilateral institutions (Robins, 2022). Developed countries “pledged to mobilise US$100 billion per year” to meet the needs of developing countries at COP15 in Copenhagen in 2009, but this pledge from rich countries has been long overdue (OECD, 2021). The “$100 billion” figure was based on a technical report issued by the UNFCCC secretariat in 2007, which estimated developing countries’ mitigation needs at $92–$96 billion and adaptation needs at $28–$67 billion in 2030, far lower than current estimates (UNFCCC, 2008). Public climate financing alone is still insufficient. Even if rich countries keep their obligations, they will barely cover one-tenth of the climate finance gap. To close the gap, more private finance must be mobilised in addition to public finance commitments to climate finance. For example, by strengthening the carbon market’s market-based nature, developing more blended finance projects, and encouraging the development of impact investments in the primary market. People are looking at different dimensions of solutions in the face of the huge financial gap, and perhaps the climate issue is also waiting for a qualitative leap in technology.

2.5

Climate Fintech---Solutions in a New Dimension

Climate fintech, according to New Energy Nexus, is the intersection of climate change, finance, and digital technology. These digital innovations, applications, and platforms have the potential to serve as a critical financial intermediary and a bridge between all stakeholders seeking to decarbonise ( New Energy Nexus, 2020). When delving into the definition of climate fintech, one should first recognise that the shift in production factors is driving climate fintech development. Climate fintech should be a financial mechanism driven by data as a factor of production to address climate change. Because data as a factor of production is shareable, replicable, and almost infinitely available, it can break the constraints of the limited supply of traditional factors of production, such as land and capital, to drive economic growth, and the data factor has a greater multiplier effect than traditional factors of production in driving economic growth. The

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value orientation of the financial sector can be repositioned and the potential of digital technology unlocked through the twin transitions brought about by climate fintech—digital transformation and sustainability transition—realising the pursuit of multiple values rather than just financial indicators (Blüm, 2022). According to the Green Digital Finance Alliance report (Sustainable Digital Finance Alliance, 2018), digital finance promotes inclusion and innovation in the real economy by improving the potential of information and efficiency in the financial sector, as well as by expanding sustainability options and providing new sources of finance. By making better use of sustainability-related data for financial decision-making and supporting new business models through more diverse financing options, digital finance can unlock the full potential of sustainable finance (Fig. 2.1). In terms of climate fintech, digital green bonds could be one approach to increase both public and private involvement in climate finance. I will discuss how blockchain-based green bond solutions can facilitate domestic investment flows and support climate change investments, while improving the delivery of projects throughout their lifecycle by optimising processes and increasing transparency. Current blockchain-based green bonds can be divided into three phases. Stage 1: Blockchain-based green bond certification; Stage 2: Blockchain-based clearing and settlement system; Stage 3: Issuance of digital green bonds through tokenisation.

Fig. 2.1 Implication of digital finance for sustainable development (Source Sustainable Digital Finance Alliance, 2018)

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2.6

The Potential Role of Digital Green Bonds

Considering here green bond certification, global case studies can provide worthwhile examples to build from. The traditional green bond certification process requires the third-party verifier of a green bond to collect information from all stakeholders’ step by step to determine the bond’s green attributes. On the other hand, the blockchain-based approach to green bond certification changes the way information flows: stakeholders only need to upload the information needed to certify their green bonds, and all stakeholders must agree on the authenticity and greenness of their information in order to complete the certification. For example, the International Capital Markets Association (ICMA) Green Bond Principles require four areas of use of proceeds, including the process for project evaluation and selection, management of proceeds, and reporting (ICMA, 2021). When all stakeholders agree that the information required to certify the four aspects of the green bond is correct, the certification process will be completed automatically via a smart contract. This is a blockchain-based hybrid of a green bond system (industry system) and a smart contract system (digital system). The blockchain-based approach to green bond certification has the potential to significantly improve certification efficiency and thus replace the traditional third-party verification process. The mBridge project is an example of a blockchain-based clearing and settlement system (BIS, 2022b). The mBridge project is one of the initiatives under the HKMA’s FinTech 2025 strategy, which includes strengthening research on Central Bank Digital Currency (CBDC) to make Hong Kong a CBDC-ready financial centre. The HKMA has expanded the Cross-Border Corridor Network initiative through the mBridge project to support the interfacing of more international currencies with new or traditional payment systems. Due to a lack of process automation and lengthy upstream processes, the traditional primary bond issuance and settlement process has a typical settlement cycle of up to T + 4 days for a single bond issue. Clearing and settlement through digital assets is a necessary step in the development of digital green bonds. The clearing and settlement of digital assets, on the other hand, includes interaction with and substitution of traditional clearing and settlement services. This may create new technological and institutional challenges, such as the mutual trust of digital infrastructure across countries.

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Goldman Sachs is developing a system for clearing bonds using digital assets (Sachs, 2021). Goldman Sachs’ blockchain-based digital asset platform can handle the issuance, transaction, and transfer of ownership of securities, as well as cash clearing and settlement via mBridge, by performing an atomic swap exchange. When investors are informed about their bond allocation, a ticket is booked, and transaction confirmation is sent. A new bond is created at the Central Securities Depository (CSD) after receiving instructions from the issuer that there is a commitment to pay. The blockchain system records a series of events, and the transfer of funds and securities occurs on the settlement date. The transaction is considered settled once the CSD has credited the purchased securities to the buyer’s account (with the corresponding cash amount) and credited the securities to the seller’s account (with the corresponding cash amount). This is the entire process of Delivery and Payment (DvP). Between trade execution and settlement—the final layer in which the CSD is involved when ownership of the securities is transferred and recorded—the clearing system effectively manages counterparty credit risk. In general, blockchain eliminates some intermediaries and functions from the entire process. The case for the issuance of digital green bonds via tokenisation stems from the Genesis project, an experiment conducted by the BIS Innovation Hub and launched in 2021 in collaboration with the Hong Kong Monetary Authority (HKMA) (BIS, 2021). The project aims to track the returns of green bonds using blockchain technology, providing credible data to demonstrate that green bond financing has the same impact in the real sector as promised by the issuers, while also increasing the financial liquidity of the bonds in the market and increasing the transparency and traceability of information in the secondary market to retail investors. Six institutions are involved in the Genesis project (BIS, 2021, 2022a): Digital Asset (Switzerland), in collaboration with GFT Technologies (Hong Kong), has deployed a prototype Daml, a multi-party permissioned blockchain application for green bond issuance and trading on the federated chains Hyperledger Fabric and Hyperledger Besu; secondly, the Liberty Consortium, formed by SC Ventures, Standard Chartered Bank and Shareable Asset, who are developing the Liberty prototype of permissionless blockchain infrastructure for green bond issuance and trading on the public chain Stellar Network; and finally, Hong Kong startup Allinfra, a green data provider that connects climate-related data with infrastructure, finance, and other solutions.

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The Genesis project has been conducted to address the following five difficulties in the issuance and trading of green bonds (BIS, 2021, 2022a). First, Data silos exist between placement agencies and information sharing is inefficient. Data is re-entered and workflows are constantly repeated during the preparation phase of a green bond issue, from combing through the list of investment terms and conditions to creating the order book for investor subscriptions to registering the bonds in the central depository. These highly manual paper-based processes frequently necessitate multiple reviews and constant amendment coordination among participants, and the entire issuance phase can take one to two months to complete. The lack of standards in these manual processes raises bond issuance costs. The placement agent must consolidate, review, and count data from retail investors during the subscription phase of a green bond issue and pass it to the issuer in a consistent format. The issuer reconciles the data to avoid duplicate subscriptions by retail bond investors across multiple placement agencies. Again, manual processing is heavily used throughout the data consolidation, processing, and statistics process. Second, freezing the subscription deposit leads to the problem of capital tied up in cash. As traditional bonds require a certain amount of deposit before subscription, this leads to cash-tied-up problems, which significantly increases the cost of capital for small and medium-sized investors. Third, retail investors are vulnerable to intermediary risk. Retail investors obtain retail bonds indirectly by placing institutions, and issuers’ accounts with CMU, the central clearing and settlement system for debt instruments, differ. Bond placing banks can open accounts directly with CMU, whereas stockbrokers rely on accounts with Hong Kong Securities Clearing Company Limited HKSCC, which is part of CMU, for centrally registered transactions. This means that the income rights of the Green Retail Bonds are held by the bond placement agent on behalf of the investors and then allocated to various investors via the bond placement agent. Furthermore, there is a lack of transparency in the processing of relevant information in this process, including late information sharing and ambiguous methods. Fourth, issuers must calculate accurate and fair interest on green bonds. The issuer will account for the bond’s market value in terms of the green bond investment at the issuance stage, which determines the calculation of interest on the bond. However, the current process of issuing all green

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bonds suffers from a lack of transparency in information on interest calculations. Issuers frequently face the inability to calculate bond interest in a truly fair manner. Fifth, there is the issue of green bond secondary market trading. Due to data synchronisation issues between the Hong Kong Monetary Authority and the Hong Kong Exchange, it typically takes T + 2 to complete the trading process of green bonds in the secondary market, which is not conducive to developing the green bond market. Genesis enables retail bond investors to easily and efficiently participate in the secondary market and learn about the specific operation of each green bond project. In response to the five issues listed above, the Genesis project demonstrates the five features listed below (BIS, 2021, 2022a). First, retail investors have direct access to transactions. Through a dedicated retail investor mobile app, retail investors can invest as little as HK$100. The blockchain network simplifies the process and lowers the cost of issuing and trading green bonds, increasing retail investor participation in green project financing. Growing market demand for demonstrable green investment products requires cost reduction. Second, both traditional and on-book stablecoin payments are supported by the system. Genesis accepts traditional payment methods as well as stablecoin payments. The prototype demonstrates the value of building a smart contract layer on top of existing infrastructure for financial institution applications using the traditional fiat currency-based approach. The current infrastructure deployment aims to enable the rapid adoption of green bond applications and encapsulate settlement options via a CBDC-scalable stablecoin. Third, tokenisation is permitted for the completion of secondary market transactions. To assist stakeholders in realising end-to-end benefits, the prototype models the entire lifecycle of a tokenised retail government green bond. Standardised elements from Digital Asset’s open-source financial library, such as the standardised secondary trading market workflow, are used in the project. Fourth, go digital to simplify issuer workflows. Some previous blockchain cases that attempted to tokenize bonds typically ignored default risk. The Genesis system would require the issuer to allocate the funds to a payment account and approve the payment rather than executing the transaction entirely on its own. This is a very practical way to eliminate the risk of pass-through bond default. Fifth, Genesis can track capital market products’ real-time/estimated green. Companies will face increased demand for transparency and auditability

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as demonstrable green investments grow in the emerging investment space. There are numerous opportunities for industry players to participate in the global transformation of addressing climate change due to the inherent benefits of blockchain and following the example set by BIS through the Genesis project. It should be noted that the government side of the Genesis design is fully cooperating. The HKMA is in charge of regulating the rules for bond issuance and depository, whereas the HKEx is in charge of setting secondary trading rules and providing market support for transactions on the exchange. The HKMA and HKEx both act as blockchain operators, with each hosting multiple participant nodes. These nodes ensure physical data segregation between participants while also enforcing the visibility of need-to-know data. This design improves market participants’ application because each market participant can maintain its own logical and physical data segregation. Market participants can also benefit from shared, automated workflows and data flows at the infrastructure level. Ulrich Volz, Director of the Centre for Sustainable Finance at the School of Oriental and African Studies, University of London, and I collaborated on the article Scaling Up Sustainable Investment Through Blockchain-Based Project Bonds, which highlights how well digital green bonds can complement traditional bond market functions (Chen & Volz, 2021). In this article, we propose blockchain-based bonds for long-term investment. This is evident in five areas. For starters, in terms of public policy, digital green bonds can be an effective hedge against foreign currency risk. Digital green bonds, on the one hand, can stimulate the development of local small and medium-sized infrastructure. On the other hand, they can strengthen the development of local currency bond markets. This is also a way to investigate CBDC’s internationalisation. From a regulatory standpoint, blockchain provides a transparent platform for mitigating the risks of digital green bonds. Regulators can contribute to the innovation process by acting as regulatory nodes, proactively regulating while maintaining regulatory vigilance. This is a regtech innovation, similar to the approach taken by the UK Conduct Authority FCA in much of its sandpit regulation. Digital green bond also enables digital governance by monitoring and controlling data flows while avoiding the risk of funds being misappropriated. Through product thinking, investors can participate in low-default risk assets and encourage small and medium-sized investors to invest in

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small and medium-sized climate change projects in villages and towns. Simultaneously, digital green bond aggregates several small and mediumsized climate-friendly projects to attract institutional investors while also leveraging the power of logos to attract impact investors. The digital green bond supports local environmental improvements and climate resilience for the general public. This is beneficial to local jobs and livelihoods. From the perspective of a development agency, the digital green bond helps the host country combat climate change while also contributing to digital governance, anti-corruption efforts, and the development of local financial markets. Our proposal aims to address key issues in three key phases of the green bond project lifecycle (Chen & Volz, 2021): setup and fundraising, project construction, and project operation. Using timestamps, public and private key mechanisms, and smart contract technology, blockchainbased project bond issuance can enable transparent recording and proof of project proceeds, quantification of climate change mitigation and adaptation impacts, and statistical revenue streams. Digital green bonds enable investors of all sizes to purchase assets in local currency and issuers such as municipalities to raise funds for long-term infrastructure investments. Project management can be facilitated once a project is operational (for example, through metering and billing). Full transparency is established throughout the investment’s life cycle, reducing the problem of fund misappropriation. In summary, blockchain’s disruptive nature reduces information asymmetry at the system level, allowing business processes to be reconfigured based on new information access, and achieves business process disintermediation by building decentralisation of business systems while decentralising information systems. Finally, overall efficiency is increased, and benefits are redistributed. This will be accomplished through more inclusive participation of multiple stakeholders, including transparency and quantifying environmental benefits throughout the project cycle. Green bonds based on blockchain technology can thus supplement traditional capital markets and help raise additional funds for climate finance.

References AbdulRafiu, A., Sovacool, B. K., & Daniels, C. (2022). The dynamics of global public research funding on climate change, energy, transport, and industrial decarbonisation. Renewable and Sustainable Energy Reviews, 162, 112420.

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BIS. (2021). A prototype for green bond tokenisation by Digital Asset and GFT. https://www.bis.org/publ/othp43_report3.pdf BIS. (2022a). Genesis 2.0: Smart contract-based carbon credits attached to green bonds. https://www.bis.org/publ/othp58.htm BIS. (2022b). Project mBridge: Connecting economies through CBDC. https://www.bis.org/about/bisih/topics/cbdc/mcbdc_bridge.htm Blüm, S. (2022). What is the ‘twin transition’—and why is it key to sustainable growth? https://www.weforum.org/agenda/2022/10/twin-transitionplaybook-3-phases-to-accelerate-sustainable-digitization/ Chen, Y., & Volz, U. (2021). Scaling up sustainable investment through blockchain-based project bonds. ADBI Working Paper Series 1247 . https:// www.adb.org/publications/scaling-sustainable-investment-blockchain-basedproject-bonds CPI. (2021). Global landscape of climate finance 2021. https://www.climatepo licyinitiative.org/wp-content/uploads/2021/10/Full-report-Global-Landsc ape-of-Climate-Finance-2021.pdf Henderson, B., Frank, S., Havlik, P., & Valin, H. (2021). Policy strategies and challenges for climate change mitigation in the Agriculture, Forestry and Other Land Use (AFOLU) sector. ICMA. (2021). Green bond principles: Voluntary process guidelines for issuing green bonds. https://www.icmagroup.org/assets/documents/Sustainable-fin ance/2022-updates/Green-Bond-Principles_June-2022-280622.pdf IEA. (2022). Electric vehicles—Technology deep dive. https://www.iea.org/rep orts/electric-vehicles IPCC. (2022). Climate change 2022: Impacts, adaptation and vulnerability. https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_ WGII_SummaryForPolicymakers.pdf Lamb, W. F., Wiedmann, T., Pongratz, J., Andrew, R., Crippa, M., Olivier, J. G., Wiedenhofer, T., Mattioli, G., Khourdajie, A. A., House, J., Pachauri, S., Figueroa, M., Saheb, Y., Slade, R., & de la Rue du Can, S., Chapungu, L., … Minx, J. (2021). A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environmental Research Letters, 16(7), 073005. NASA. (2021). Emission reductions from pandemic had unexpected effects on atmosphere. https://www.jpl.nasa.gov/news/emission-reductions-frompandemic-had-unexpected-effects-on-atmosphere New Energy Nexus. (2020). Climate fintech: Mapping an emerging echosystem of climate capital catalysts. https://www.newenergynexus.com/climate-fin tech-report/ OECD. (2021). Climate Finance and the USD 100 Billion Goal. https://www. oecd.org/climate-change/finance-usd-100-billion-goal/

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Robins, N. (2022). The $468 trillion climate question: how the financial system is starting to shift. LSE Business Review. https://www.lse.ac.uk/granthaminst itute/news/the-468-trillion-climate-question-how-the-financial-system-is-sta rting-to-shift/ Sachs, G. (2021). Project mBridge. https://www.goldmansachs.com/worldw ide/greater-china/materials/goldman-sachs-research-paper-case-study.pdf Sustainable Digital Finance Alliance. (2018). Digital technologies for mobilizing sustainable finance: Applications of digital technologies to sustainable finance. Timperley, J. (2021). The broken $100-billion promise of climate finance— and how to fix it. Nature. https://www.nature.com/articles/d41586-02102846-3 UNEP. (2022). Emissions Gap Report 2022: The closing window: Climate crisis calls for rapid transformation of societies. https://www.unep.org/resources/ emissions-gap-report-2022 UNFCCC. (2008). Investment and financial flows to address climate change: An update. https://unfccc.int/sites/default/files/resource/docs/2008/tp/ 07.pdf UNFCCC. (2015). Adoption of the Paris agreement. I: Proposal by the President (Draft Decision), United Nations Office, Geneva (s 32). UNFCCC. (2022a). COP27 Reaches breakthrough agreement on new “Loss and Damage” Fund for vulnerable countries. https://unfccc.int/news/ cop27-reaches-breakthrough-agreement-on-new-loss-and-damage-fund-forvulnerable-countries UNFCCC. (2022b). Introduction to climate finance. https://unfccc.int/topics/ introduction-to-climate-finance UNFCCC. (2022c). Sharm el-Sheikh Implementation Plan. https://unfccc.int/ documents/624441 WMO. (2022). State of the global climate 2021. https://library.wmo.int/doc_ num.php?explnum_id=11178 World Bank. (2022). What you need to know about results-based climate finance. https://www.worldbank.org/en/news/feature/2022/08/17/what-youneed-to-know-about-results-based-climate-finance#:~:text=Examples%20i nclude%20carbon%20taxes%2C%20the,for%20electricity%20and%20natural% 20gas.

CHAPTER 3

Green Energy, Emissions, and Blockchain Technology Tony Erwin and Baozhong Yang

3.1

Introduction

The costs of climate change, if uncontrolled, could be catastrophic for humankind. Therefore, there is a global effort and coordination to contain the emissions of carbon dioxide and other greenhouse gases, and to promote the use of green, renewable energy, as demonstrated by the collective goals enshrined by the Kyoto Protocol in 1997 and the Paris Accord in 2015. To the extent that companies are profit-driven, financial instruments are among the most powerful mechanisms to achieve the goals of carbon reduction and green energy utilization. There are several categories of carbon pricing instruments: carbon taxes/subsidies, carbon emissions trading systems (ETS), carbon credit/offset, and green energy markets. Carbon taxes are taxes that are directly levied on carbon emissions activities as an incentive to encourage carbon reduction by productive companies. Governments can also provide tax subsidies to

T. Erwin · B. Yang (B) J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_3

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encourage the use of clean technology and reduction of emissions. For example, the Inflation Reduction Act of the United States provides $369 billion in incentives and funding for clean energy, providing tax credits for green energy and carbon reduction projects (Dvorak et al., 2023). ETS markets are cap-and-trade programs where regulatory agencies set emission caps and issue credits. Companies can either consume such credits for their own emissions or trade them to other companies that need more credits to emit. In the carbon offset markets, companies or organizations actively engage in activities that remove carbon from the atmosphere or reduce carbon emissions to generate carbon offset credits, which can then be traded in certain regulatory markets or voluntary markets. The Kyoto Protocol and the Paris Accord consider carbon pricing mechanisms as the main instruments for achieving net-zero emissions and limiting temperature rise to within 1.5 degrees Celsius. While carbon taxes can be helpful for reducing emissions, ETS markets are a more powerful mechanism since governments can set implementable goals (caps) which can help achieve specific emissions targets, e.g., netzero emissions within a certain time frame. Figure 3.1 depicts the growth of the carbon tax and ETS markets in recent years. ETS markets have grown substantially, reaching 54 billion USD and exceeding the size of carbon taxes for the first time in 2021.

Fig. 3.1 Evolution of global carbon pricing revenues over time (Source World Bank [2022])

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The carbon credit/offset markets, where voluntary organizations and companies often operate, aim to reduce or remove carbon from the atmosphere. In such markets, companies create credits or offsets through projects on renewable energy production and utilization, forest and wetland conservation/restoration, transportation efficiency, and others. Figure 3.2 depicts the global volume of carbon credits and offsets through various mechanisms. Note that while the international Clean Development Program (CDM) set up by the Kyoto Protocol contributed significantly to carbon reduction, the program expired in 2012. In recent years, the voluntary or independent carbon markets have seen considerable growth, driven by well-recognized programs such as Verified Carbon Standard (Verra), Gold Standard, and American Carbon Registry. A survey conducted by the International Emissions Trading Association (IETA) found that market participants expect both regulated and voluntary carbon markets to grow rapidly to meet global demand, as countries and companies double down on climate goals (IETA & PWC, 2022). Furthermore, several government-initiated programs, such as the Low Carbon Fuel Standard program in California and the Renewable Fuel Standard program that the EPA introduced, are specifically designed to track and promote the production and use of green energy. We discuss these programs and the blockchain solutions related to these in more detail later in this chapter.

Fig. 3.2 The global volume of carbon credit/offset issuances (Source World Bank [2022])

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While carbon and green energy markets have seen rapid growth as a result of government and corporate commitments to reach net-zero carbon emissions, there are several factors that limit the real-world impact of these efforts; examples of these factors include double counting of carbon offsets, the quality of the projects, issues with transparency and permanence of records, and the lack of a unified platform for data and trades (Niiler, 2022). Blockchain technology provides an effective mechanism that can resolve many of these issues and lead to greater efficiencies in green energy and emissions markets. Blockchain is a distributed ledger technology that allows a decentralized system to store and share data and conduct transactions. The main characteristics of a blockchain include transparency, immutability, security, and resilience, which make it a popular choice for business applications. Indeed, since its inception in 2009, various industries have utilized blockchain technologies in a wide range of applications, including financial services (such as trading, payments, and financing), accounting, supply chain management, healthcare data management, and many others. Carbon and energy markets have seen adoptions of blockchain and digital asset technologies (Balch, 2022) and the U.S. government has called for an examination of the potential uses of blockchain for climate and energy policies. For example, the Biden administration directs various government agencies to investigate (Executive Order 14,067, March 9, 2022, United States): (a) potential uses of blockchain that could support monitoring or mitigating technologies to climate impacts, such as exchanging of liabilities for greenhouse gas emissions, water, and other natural or environmental assets; and (b) implications for energy policy, including as it relates to grid management and reliability, energy efficiency incentives and standards, and sources of energy supply.

The remainder of the paper is structured as follows. In Sect. 3.2, we review some fundamentals of blockchain technology as well as its unique features and potential applications. In Sect. 3.3, we review the literature on potential applications and discuss example use cases of blockchain solutions in the environmental ecosystem. In Sect. 3.4, we propose and analyze a new blockchain use case for the Renewable Fuels Standard (RFS) program in detail. Throughout our discussion, we consider the

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challenges and issues with the existing system, study the benefits and characteristics of blockchain-based solutions, and also delineate the potential challenges and opportunities for blockchain adoption. Section 3.5 offers concluding remarks.

3.2

Blockchains and Business Applications

Blockchains are a type of distributed ledger technology that allows decentralized parties to perform and record transactions without the need for a trusted intermediary. Blockchains rely on advances in computing technologies such as hashing, digital signatures, distributed systems, and consensus mechanisms, to ensure reliability and security. Blockchain technology was first expressed through Bitcoin, a peer-to-peer distributed transaction and ledger system proposed by Nakamoto (2008), and the first decentralized currency that solves the double-spending problem. Due to its decentralized nature and various encryption and security features, a blockchain system offers transparency, immutability, and resilience, which allows its application in a large number of business and financial problems. In the past six years, there have been a large number of business and economic studies related to blockchain technology, we provide a brief survey of this literature below. For additional information and surveys on blockchain technology, we refer the reader to Narayanan et al. (2017), Yermack (2017), and John et al. (2021). Ethereum provides a general-purpose blockchain platform on which “smart contracts,” or programs on the blockchain that process data and automate business logic, can be written. Smart contracts have enabled a wide range of applications, especially applications in decentralized financing (DeFi). For example, decentralized exchanges, such as Uniswap, 0x, and Augur, employ smart contracts to enable the exchange of digital assets and the implementation of complicated securities such as derivatives in a trustless, decentralized setting. Smart contracts that allow the issuance and trading of non-fungible tokens (NFT) are another example of DeFi applications. Harvey et al. (2021) and John et al., (2022a, 2022b) survey the recent developments in DeFi. Antonopoulos and Wood (2018) give an in-depth introduction to Ethereum and smart contract design. Unlike public blockchains, such as Bitcoin or Ethereum, which allow any node to join the network, a permissioned blockchain only includes identified and trusted nodes. One advantage of permissioned blockchains is that they can utilize more efficient consensus algorithms, such as

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majority voting, which can prevent the energy waste caused by mining and proof-of-work, as seen in Bitcoin and other cryptocurrencies. Permissioned blockchains also offer higher security and can handle higher transaction throughput. Some open-source software for permissioned blockchains includes Corda (by R3), Hyperledger Fabric (by IBM), and Quorum (by J.P. Morgan). In addition, several companies have developed proprietary permissioned blockchain systems, such as Digital Asset Holdings, which helped the Australian Stock Exchange transition to a new system based on permissioned blockchains for trading and settlements. There is an increasingly large body of literature on blockchains in finance and economics. One strand of literature mainly focuses on the mechanisms and economics of public blockchains, cryptocurrencies, and tokens (e.g., Halaburda & Sarvary, 2016; Biais et al., 2019; Easley et al., 2019; Li & Mann, 2020; Tsoukalas & Falk, 2020; Lehar & Parlour, 2020; Chod & Lyandres, 2021; Cong et al., 2021a, 2021b; Saleh, 2021). There has also been increasing attention on permissioned blockchains. Cong and He (2019) study the use of permissioned blockchains in information distribution and decentralized consensus. Cao et al. (2019) explore the application of permissioned blockchains in financial reporting to maintain privacy and facilitate the automatic reconciliation of transaction accounts, and the economic implications of blockchain adoption. John et al. (2021) examine the potential of permissioned blockchains in overcoming scalability limitations. Chod et al. (2020) demonstrate the benefits of using blockchain technology for enhancing transparency and financing operations efficiently through the verifiability of transactions. Another related body of research is on the use of blockchain technology in supply chains and operations management. A number of studies explore how the strengths of blockchains, including transparency, immutability, and traceability, can help to improve supply chain operations, and related economic issues (e.g., Babich & Hilary, 2020; Cui et al., 2020; Hastig & Sodhi, 2020; Iyengar et al., 2022; Ma et al., 2022). Most of these studies also focus on the use of permissioned blockchains, which is more suitable for many enterprise applications. In this study, we propose a new application of permissioned blockchains in the renewable energy market.

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3.3 Blockchain Applications to Emissions and Green Energy Markets 3.3.1

Literature Review on Blockchains in Emissions and Energy Markets

In this section, we provide a brief review of the emerging literature of proposals and studies for blockchain solutions for the carbon and energy markets. Some studies examine how the adoption of blockchain technology can improve the carbon markets in general. For example, Khaqqi et al. (2018) explore the role of a reputation system of buyers and sellers in a blockchain-based emissions trading system. Hartmann and Thomas (2020) apply a well-established design process to develop a blockchain design for the Australian Carbon Market and consider the various choices in the blockchain design for practical implementation of the system to improve transparency, efficiency, and equity of the carbon market. Al Sadawi et al. (2021) propose a comprehensive framework for carbon emission trading with a multi-level infrastructure that consists of three levels: an upper level of public blockchains that allow trading between buyers and sellers, a lower level of permissioned blockchains that are connected to sensors and record measurement of emissions, and a cross-transfer level that facilitates the transfer of data via inter ledger protocols. A few studies focus on reducing emissions in specific industries or markets and study how blockchain design can be adapted to the particular nature of the industries/markets. For example, Fu et al. (2018) consider an environmentally sustainable solution for the fashion apparel manufacturing industry based on blockchain. The system connects manufacturers and consumers to track and reduce emissions in different steps of clothing making and promote the reusing and recycling of used clothes. Li et al. (2021) propose a new blockchain-based framework of an ETS for road transport, in which the government sets a cap on emissions and allocates initial permits, with fuel producers, vehicle manufacturers, and vehicle users as regulated entities. The coordination of the regulated entities can help to increase vehicle fuel economy and reduce miles traveled and emissions. A smart contract on a decentralized blockchain can reduce administrative costs, improve transparency and traceability, and eliminate fraud. Zhao et al. (2022) propose a blockchain solution to the blue carbon system (sea-based carbon reduction). They design a peer-to-peer

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system that considers participating parties’ roles in blue carbon production, circulation, and trading processes, with the potential to promote the under-recognized blue carbon sink projects, utilize marine resources, and contribute to the goal of “emission reduction without reduction in production” (Zhao et al., 2022, p. 1). Another stream of research considers how blockchains can be used to improve the efficiency of energy markets and encourage the use of green energy. Ashley and Johnson (2018) discuss the potential benefits of blockchain solutions to the energy market such as the Low Carbon Fuel Standard (LCFS) program, including security, transparency, and traceability. Hua et al. (2020) propose a blockchain-based peer-to-peer trading framework that connects the energy and emissions market and allows the trading of trading energy and carbon allowance, aimed at addressing the challenges faced in tracing carbon emissions and formulating pricing schemes for prosumers (individuals who both produce and consume energy). The framework incentivizes presumption behaviors to achieve energy balance and reduce carbon emissions through the use of decentralized low-carbon incentives. They also show in a case study that the proposed framework can improve energy generation and reduce carbon emissions. In contrast to the above studies, which are mostly proposals of blockchain systems, Sipthorpe et al. (2022) analyze the existing blockchain ecosystem, including blockchain-powered carbon market projects from 39 organizations. The study shows that some existing projects, such as the Toucan Protocol and Energy Web Origin, are in advanced stages and technologically ready to improve carbon tracking and trading. The study lists scalability and skills shortage, system integration, and regulatory uncertainty as examples of the most important barriers to the greater adoption of blockchain technology in carbon markets. 3.3.2

Examples of Applications in the Energy Markets

There are several benefits to the applications of blockchain to green energy and emissions solutions. First, blockchain can help improve market efficiency. Second, blockchain can help to mitigate and reduce the harmful generation of GHG and other byproducts of human activity. Third, blockchain usage can enhance the use of environmentally friendly energy or green energy production. To illustrate these ideas, we discuss some concrete examples below.

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We first consider an example of a blockchain use case in the energy commodities trading market. In 2018, a group of major oil companies, including Shell, Chevron, and British Petroleum, oil traders, and oil finance providers, formed the blockchain consortium VAKT. These companies took a manual and error-prone process, revolutionized it on a blockchain platform, and created digital records of their transactions. VAKT created a single digital record where different partners can record, review, and agree on the details and stages of trade, providing opportunities to improve trades and reduce potential risks in real time. This example highlights that even multinational corporations can reap great benefits and efficiencies through cooperation in energy markets. Although this is not necessarily an environment-benefiting platform in and of itself, it certainly directly impacts many of the world’s largest sources of fossil fuels. The idea of a single source of truth in commodity transactions can also find applications in many other energy markets, including solar, wind, natural gas, or geothermal power since electricity from any source is still electricity and can be tracked and traded. Next, we examine the application of blockchains to the Low Carbon Fuel Standard (LCFS) mandated in California. This program aims to reduce California’s greenhouse gas (GHG) emissions by decreasing the use of fossil fuels while increasing that of renewable alternatives. As with other regulatory programs, the LCFS mandates monitoring, tracking, reporting, and verification of renewable energy use. Compliance with these regulations can be very labor-intensive, as it involves gathering and reporting data and fulfilling regulatory requirements. To help simplify some of the regulatory burdens, the Clean Energy Blockchain Network (CEBN), in partnership with Silicon Valley Power, applies blockchain technology to simplify their participation in the LCFS program. As with any data monitoring, reporting, or tracking system, efficiency can be gained when automation systems or intelligent metering systems replace human activity. This program relies heavily on credit accounting and management systems to track when regulated entities have either a credit or a deficit in compliance with the program. As seen in Fig. 3.3, (Randolph, 2023) the cumulative bank of LCFS credits has been quickly increasing. The rapid credit growth means that compliance with the program can benefit from replacing humanintensive processes with automation. With the CEBN solution, data from intelligent metering systems is fed into a cloud database to generate

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the credits, which then become tokenized on a blockchain, providing tracking, auditing, and redemption capabilities. This example illustrates three essential elements of successfully implementing blockchain with regulatory standards and contributing to the goals of increasing the use of green energy and reducing GHG emissions. First, a crucial aspect of success is having an audit system for pre-qualifying and verifying assets as part of the clean energy program. This initial audit and verification, which may initially be human-intensive, can help ensure the program’s integrity. The second vital element is the implementation of “smart metering,” also known as intelligent metering or Internet of Things (IoT), i.e., systems that feed data into a blockchain. The third critical component is the blockchain network. The blockchain brings all of the benefits described earlier in this chapter, such as tracking, auditing, and trading capabilities via tokens. We revisit these three elements in our use case for the renewable fuel standard in the next section.

Fig. 3.3 Low carbon fuel standard credits and deficits (Source Randolph [2023])

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3.4 Increasing Green Energy production---A Blockchain Use Case In this section, we propose and discuss a blockchain-based solution to the renewable energy ecosystem. 3.4.1

The Renewable Fuels Standard Program

First, we provide background information on the renewable energy credit market in the United States. The Environmental Protection Agency (EPA) implemented the Renewable Fuels Standard (RFS) program as part of the Energy Independence and Security Act of 2007 (EISA). The EPA explains that “Congress adopted the RFS program to reduce the nation’s dependence on foreign oil, help grow the nation’s renewable energy industry and achieve significant greenhouse gas emissions reductions” (EPA, 2023a). In this program, the EPS aims to increase the use of renewable fuel for transportation in the United States. In recent years, EPA further developed the RFS program in collaboration with renewable fuel producers and refiners. The RFS program provides a mandatory market for obligated parties, which include major petroleum-based fuel producers, refiners, importers, and exporters. The obligated parties, such as Chevron and Exxon, are required to produce a given volume of renewable fuel in their annual transportation fuel production. The obligated volume for a company is called “Renewable Volume Obligation” or “RVO.” The obligated parties have the flexibility within the program to purchase renewable credits instead of meeting their obligations for renewable fuel volumes. They can also sell those credits if they have excess credits above their obligation. We discuss these renewable credits in more detail later. The RFS program not only increases green energy production but also reduces GHG emissions by encouraging and incentivizing the conversion of methane (CH4 ), a hydrocarbon that is a primary component of natural gas, into transportation fuels. Methane is an important environmental factor because it is the second most abundant GHG after carbon dioxide (CO2 ) and accounts for about one-fifth of global emissions; furthermore, methane is more than 25 times as potent as carbon dioxide at trapping heat in the atmosphere (EPA, 2023b).

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3.4.2

Renewable Identification Numbers

The Renewable Identification Number (RIN) may be likened to the store of value and method of accounting for the RFS program. For the RFS program to function, the EPA must track the production and use of renewable fuel that qualifies for the program. The RIN tracks the amount of renewable fuel produced, sold, or consumed. The fuel must meet certain “pathway” criteria to qualify as a renewable fuel. A fuel pathway consists of three components: the feedstock, the production process, and the resulting fuel type. (EPA, 2023b) Feedstock is the raw organic input material used to produce renewable fuel. Just as crude oil is the feedstock for gasoline and corn starch is the feedstock for ethanol, there are many types of feedstocks for renewable fuel, including waste (from landfills or farms) and crops. Figure 3.4 (Tanigawa, 2017) shows several potential fuel pathways from various feedstocks to renewable energy. The EPA determines whether a fuel pathway qualifies for the RFS program through an assigned “D-code.” This code “identifies the renewable fuel type – based on the feedstock used, fuel type produced, energy inputs and GHG reduction thresholds, among other requirements” and provides important information about the production process (EPA, 2023c). For example, a D3 type is assigned to biogas-based fuel and D6

Fig. 3.4 Pathways from feedstock to renewable energy (Source Tanigawa [2017])

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Fig. 3.5 Example lifecycle of a renewable identification number (Source EPA [2023d])

is assigned to corn-based ethanol. A RIN is a 38-character number associated with a physical gallon of renewable fuel produced or imported. RINs are generated by renewable fuel producers or importers as a renewable fuel is created and is attached to transportation fuel. When the fuel is blended or distributed, the RINs become separated and can then be either retired to fulfill obligations or sold to third parties. Figure 3.5 (EPA, 2023d) shows the lifecycle of a RIN as a renewable fuel is produced and blended. 3.4.3

Renewable Natural Gas and the RIN Market

Although our proposal generally applies across the broad spectrum of the RFS program, we consider Renewable Natural Gas (RNG) in what follows to provide a more concrete context. We examine the RNG industry as part of the RFS program and identify a potential solution of applying blockchain technology to the relevant ecosystem, with potential impacts on various aspects of our society, including electricity production and transportation. We choose to focus on the RNG sector due to its tremendous potential. The United States currently has 2,300 operating biogas systems in 50 states, but there is potential to build more than 15,000 new systems. This represents an unrealized potential of a seven-fold increase over the current state. If fully built to capacity, the U.S. biogas infrastructure could produce 100 trillion kilowatt-hours of electricity per year that could power 9.3 million homes, or transportation fuel equivalent to 15.4 billion

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gallons per year that can supply 32 million vehicles (American Biogas Council, 2022). Biogas systems also create substantial additional environmental benefits as they protect air, water, and soil by recycling organic waste into renewable energy and soil products. In fact, biogas-based renewable fuel is typically assigned by the EPA as a D3-type biofuel, which meets the requirements of 60% lifecycle GHG reduction (EPA, 2023a). Therefore, upgrading the biogas infrastructure would create significant economic, environmental, and energy benefits. Biogas can be transformed into renewable transportation fuel in the form of compressed natural gas (CNG) or liquefied natural gas (LNG) through a number of steps, as Fig. 3.6 (Coffelt, 2015) illustrates. First, the naturally occurring process of organic material decomposition within the landfill creates biogas, which contains methane. Processing plants then process the methane out of the landfill biogas and “upgrade” it to pipeline quality. The “upgrade” of methane is the removal of biogas impurities, making it pipeline-quality natural gas. Transfer stations, known as “skids,” measure the injected volume of upgraded gas from the processing plant, and also typically compress that gas to prepare it to be transferred into a pipeline. From the transfer station, it is injected into the pipeline. The pipeline then transports the gas to an endpoint and the gas gets converted via chemical processes to create transportation fuel such as CNG or LNG. Currently, the EPA Moderated Transaction System (EMTS) handles RIN registration and management. When a producer generates a batch of renewable fuel, it needs to register with the EPA through the EMTS and generate the RINs. Further RIN transactions, including buys, sells, separations, and retirements, also need to be reported to the EMTS by

Fig. 3.6 The pipeline from landfill to renewable natural gas (Source Coffelt [2015])

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the relevant parties (Poloncarz et al., 2019). Transactions involving RINs occur through private contracts, either between buyers and sellers or between biofuel providers and downstream processors and distributors. The individual parties report the details of the transactions to the EMTS but do not disclose them to the public. 3.4.4

Problems with the Current System

Despite the significant potential benefits of increasing biogas production and integrating it with the RFS market, there are several issues and inefficiencies in the market that hinder its development. First, biogas systems require a significant amount of capital investment to create. For example, from the author’s personal experience in projects of this type, as of the beginning of 2023, a medium-sized landfill gas upgrading project to create renewable natural gas could require a capital investment of $11–15 million USD. A stable and efficient market for biofuel and its associated RINs is therefore important to encourage investment and entry into this market. Second, biogas transportation and distribution also involve substantial costs. Biogas must reach a pipeline or be carried over a “virtual pipeline” via tanker trucks to a pipeline connection point. Producers need to negotiate offtake agreements to sell the gas and RINs to buyers or “off-takers.” There are substantial contracting and search costs involved with signing such offtake agreements. Further, buyers and sellers of RINs also have to search for counterparties, sign private contracts, and manually report transaction information to the EMTS. The current system depends on private bi-party contracts, which can be labor- and time-intensive, error-prone, and inefficient. Third, prices in the RFS market can be unpredictable and unstable due to the private nature of transactions. Indeed, prices have been driven high by manipulation such as stockpiling of RIN credits and are volatile thanks to the lack of transparency in the market. For example, compliance with the Renewable Fuel Standard by the refining industry was more expensive in 2021 than at any other point in the program’s 15-year history due to surging RIN prices from RIN shortages (AFPM, 2021), Such price hikes can generate substantial costs for obligated parties in the RFS market. Furthermore, the over-the-counter nature of RIN trading also implies inefficiency and information asymmetry in the market. A more

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transparent and stable market would reduce the uncertainty for market participants and encourage more production of biofuel. 3.4.5

Proposal: A Blockchain Solution for the Biogas and RNG Market

In this section, we propose a new blockchain-based solution to the RNG market that can potentially resolve the above issues. The proposed system is a permissioned blockchain platform, operated by authorized parties in the market and the government. The permission nature of the blockchain will prevent malicious parties from sabotaging the system. Producers, processors, distributors, buyers, and sellers will all need to have accounts on the blockchain with verified identities to prevent fraud. Each party will provide digital signatures to verify their transactions and activities. We describe below the different parties’ roles and responsibilities. First, renewable fuel producers generate gas and RINs. They would create tokens in smart contracts on the blockchain that correspond to RINs. Together with the tokens, the producers provide data about the volume and specifications of the RNG. The volume and specifications can be automatically provided via smart metering and control systems at the delivery point of the gas to the pipeline. The meters would be pre-verified as coming from a validated source and feed time-stamped data into the blockchain system. Although this method of utilizing smart metering is not required for the system to work, it is a much more efficient manner to provide the data. An alternative is for the producer to provide data to the blockchain via some software, which is then validated by a third-party auditor as seen in Fig. 3.7. Second, third-party auditors (Quality Assurance Program providers) can provide validation and audit information to the smart contract governing a particular RIN. For example, a certified engineer representing the auditor can examine the company, their facilities, their production process, and the end product. Following the inspection, the auditor needs to upload verification information on the blockchain with their signature. Such validation will then become visible to all parties on the blockchain, including potential buyers and the regulator, and provide assurance of the quality and provenance of the renewable gas. Third, the blockchain serves as a decentralized exchange of RINs associated with renewable fuel, in a similar manner to the decentralized exchanges of digital assets such as

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Fig. 3.7 Proposed blockchain platform for the RNG-RIN market

Uniswap and OpenSea. Potential sellers and buyers of RINs can initiate bids and trades on the blockchain. Transaction information, including volume, pricing, and counterparties, would be automatically recorded on the blockchain and verified by signatures from both transaction parties. This can facilitate the matching between buyers and sellers and eliminate errors from manual data entering. Fourth, an oracle service could provide market-based pricing information of natural gas, e.g., the Henry Hub Inside FERC Monthly Index, on the blockchain to enable transacting parties to base their pricing bids on active physical market prices. This would also enable different parties to contract on these prices and potentially write new contracts, including derivatives such as futures and options. Finally, the regulator (the EPA) has a special account with the blockchain that allows them to directly export the relevant data to the EMTS database. This will automate and unify the data entry process, reducing paperwork imposed on buyers and sellers in the current system. Furthermore, the blockchain can provide real-time updates about transactions and prices, which is more efficient than the existing system.

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3.4.6

Benefits of the Proposal

We delineate the potential benefits of the proposed system in this subsection. First, the primary benefit of the blockchain system is to facilitate and encourage the entrance of small companies into biogas investment and production. As discussed earlier, small biogas providers must work to obtain a contract from a major oil and gas producer as obligated parties, to act as buyers for their gas and RINs. Obtaining such contracts can be difficult, expensive, and time-consuming for smaller companies without established relationships with buyers. With the proposed blockchain ecosystem in place, smaller companies will find it much easier to enter the market. Second, automatic data entry and verification with signatures by transacting parties can ensure data correctness at the time of transactions and reduce paperwork. It would significantly reduce the time invested manually to report, track, buy, and sell, making the RFS program more efficient for all involved. Third, the inclusion of smart metering systems and the connection of qualified auditors through a common blockchain would streamline and speed up the monitoring and auditing activities, resulting in pre-qualified and verified clean energy. The immutability of blockchain records can also reduce the potential manipulation of records. Fourth, there are multiple benefits to the smart contract acting as an exchange between multiple buyers and sellers. This would make the prices and transactions much more transparent and remove some of the uncertainty and price volatility that many major market participants experience today. Tokenizing renewable energy credits, including RINs and the associated renewable fuel, in a secure digital form via a blockchain can dramatically reduce the costs and barriers to entry, and result in more widespread adoption and implementation of the RFS program. Finally, renewable natural gas (RNG) can have other uses than transportation fuels. Such production is not tracked today because it does not qualify for RFS credits. For example, if a major oil refiner sells RNG to a buyer that wants a smaller carbon footprint, the current practice is to send the buyer an attestation report certifying the purchase. Being able to track these transactions via blockchain for ESG reporting purposes is appealing to such producers, as it could provide additional incentives for renewable energy production.

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Blockchain technology in the energy market generated about $220 million in 2018 and is predicted to grow at more than 50% annually until 2025 (Global Market Insights, 2019). We note that some of the functions in our proposed blockchain can be realized through the Solana-based Powerledger TraceX blockchain platform. TraceX is a digital blockchain-based marketplace that potentially enables the trading of Energy Attribute Certificates, including Renewable Energy Certificates, Guarantees of Origin, and carbon credits.1 Powerledger also works in partnership with M-RETS, an environmental nonprofit organization that owns and manages an environmental attribute tracking system to track renewable electricity certificates and renewable thermal certificates. However, Powerledger does not serve the full spectrum of the RFS market (in particular the RNG market), nor does it provide all the functions we proposed. It is our hope that our proposed solution can be implemented through these and other blockchain efforts to help the RFS program reach its full potential. 3.4.7

Challenges to the Proposal

As a new and disruptive technology, blockchain solutions will necessarily face a number of challenges in adoption. Below, we discuss several potential challenges to our proposal.2 First, one major challenge is that our solution disrupts the status quo. Implementing the solution would require cooperation from multiple parties, especially the EPA. The EPA would have to transform its data exchange and the EMTS system into a blockchain-friendly system and allow connections between its system and the blockchain. An additional challenge is to engage the major oil companies, who are not currently pushing for an RFS blockchain. Therefore, in order for this proposal to be adopted and successful, the EPA must be the main driver for blockchain adoption within the RFS program. Another challenge in implementing smart metering and blockchains is that many sites are remote and have limited or no internet access. Data glitches from bad or slow connections can also be a hurdle. This challenge can be partially mitigated by the use of satellite and wireless

1 For more information, see https://www.powerledger.io/platform-features/tracex. 2 We benefited from discussions with some leading industry experts, who wished to

remain anonymous.

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internet technologies, such as 5G. Given that most of the globe can be reached via either satellite communications, cellular or Wi-Fi, this should be achievable. Finally, the adoption of blockchains requires the development and installation of the new system, as well as the associated training and education program. However, such fixed costs can be recuperated from the benefits of running the blockchain system in the long term.

3.5

Conclusion

In this chapter, we delve into the current state and literature surrounding the use of blockchain technology in the renewable energy and emission reduction sectors. Blockchain technology has already found applications in various industries, particularly finance and supply chain management, and holds promise for disrupting and optimizing the energy industry. Our review of the literature and proposed use case in the renewable energy market suggests that blockchain can boost compliance with government programs, lower transaction costs and errors, streamline trading, increase transparency, and create a unified, verifiable database of records. Despite these benefits, the adoption of blockchain in the energy sector faces challenges such as the need for regulatory support, industry collaboration, and technological advancement. Our proposal provides an example of how an impactful solution can be actualized by the government and leading companies actively joining forces to develop blockchain technology and harness its power to promote the expansion of green energy and tackle climate change.

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American Biogas Council. (2022). Benefits of biogas. Championing the biogas industry, advancing a biogas future. https://americanbiogascouncil.org/res ources/why-biogas/ Antonopoulos, A. M., & Wood, G. (2018). Mastering ethereum: Building smart contracts and DApps. O’Reilly Media. Ashley, M. J., & Johnson, M. S. (2018). Establishing a secure, transparent, and autonomous blockchain of custody for renewable energy credits and carbon credits. IEEE Engineering Management Review, 46(4), 100–102. Babich, V., & Hilary, G. (2020). Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22, 223–240. Balch, O. (2022, September 26). Brand Watch: Can blockchain help carbon markets turn the tide on deforestation, Reuters. Biais, B., Bisiere, C., Bouvard, M., & Casamatta, C. (2019). The blockchain folk theorem. Review of Financial Studies, 32, 1662–1715. Cao, S., Cong, L. W., & Yang, B. (2019). Financial reporting and blockchains: Audit pricing, misstatements, and regulation. Working paper. Chod, J., & Lyandres, E. (2021). A theory of ICOs: Diversification, agency, and information asymmetry. Management Science, 67 (10), 5969–5989. Chod, J., Trichakis, N., Tsoukalas, G., Aspegren, H., & Weber, M. (2020). On the financing benefits of supply chain transparency and blockchain adoption. Management Science, 66(10), 4378–4396. Coffelt, L. (2015). An introduction to the renewable fuel standard & The RIN credit program. The Renewable Fuel Standard (RFS2)—How The Biogas Pathway Works. https://cleancities.energy.gov/files/u/news_events/ document/document_url/84/2_-Session_0_-_RIN_101_-_FINAL.pdf Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. Review of Financial Studies, 32, 3412–3460. Cong, L. W., He, Z., & Li, J. (2021a). Decentralized mining in centralized pools. Review of Financial Studies, 34(3), 1191–1235. Cong, L. W., Li, Y., & Wang, N. (2021b). Tokenomics: Dynamic adoption and valuation. Review of Financial Studies, 34(3), 1105–1155. Cui, Y., Hu, M., & Liu, J. (2020). Values of traceability in supply chains. Working Paper. Dvorak, P., Strasburg, J., & Mackrael, K. (2023, January 25). U.S., Europe Tussle over frenzy of clean-energy subsidies. The Wall Street Journal. https://www.wsj.com/articles/u-s-energy-subsidies-kick-off-globalbattle-for-jobs-billions-in-investment-11674645814?mod=article_inline Easley, D., O’Hara, M., & Basu, S. (2019). From mining to markets: The evolution of bitcoin transaction fees. Journal of Financial Economics, 134, 91–109.

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CHAPTER 4

The Role of Green Finance in Supporting Maritime Sustainable Development Massimo Arnone and Tiziana Crovella

4.1

Introduction

Following the global crisis of 2008 and the recent pandemic, the application of sustainability in finance has become increasingly relevant, not only academically but also practically. In this regard, the European Commission, with the 2018 Action Plan on Sustainable Finance codified guidelines to guide public and private finance towards a single objective,

Arnone, M: elaboration parts on sustainable finance and green bond, Crovella, T: elaboration part on maritime sector and cold ironing. Introduction, Methodology and conclusion are elaborated in equal parts. M. Arnone Department of Economics and Business, University of Catania, Catania, Italy T. Crovella (B) Department of Economics, Management and Business Law, University of Bari Aldo Moro, Bari, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_4

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that is, the transition towards an eco-compatible model (Fig. 4.1). This attention to the implementation of sustainable finance was also reaffirmed in the EU Next Generation program, which established that at least 30% of financial resources be allocated to public and private initiatives aimed at countering climate change as a long-term goal for the 2021–2027 period. This program is tasked with identifying investments that are consistent with the European Union’s 2019 “Green Deal” strategy and related to the environment, the digitalization, and the resilience of economic and social systems. This strategy’s long-term goal is to make the European economy increasingly competitive and, above all, capable of reducing the emissions of greenhouse gases by 2050. This chapter introduces an observation of the achievement of a green economy that requires a stable and growing financial system. In this regard, the announcement of the European Green Deal, at the end of 2019, testified to the commitment to strengthening one of the crucial factors for achieving an increasingly sustainable economy, i.e., the financial instrument represented by Green Bonds. For this reason, this study

Transforming the EU’s economy for a sustainable future

Financing transition

A zero pollution ambition for a toxic-free environment

Preserving and restoring ecosystems and biodiversity

From "Farm to Fork": a fair, healthy and environmentally friendly food system

Mobilising industry for a clean and circular economy The Europenan Green Deal

Accelerating the shift to sustainable and smart mobility

Supplying clean, affordable and secure enerrgy

Increasing the EU's climate ambition for 2030 and 2050

Financing the transition

Leave no one behind (Just Transition)

Fig. 4.1 European roadmap on sustainability (Source Authors elaboration on European Commission [2019])

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focuses on Green Bonds which, within the European Union, from the very early stages of drafting the post-Covid-19 Recovery Plan, have been attributed a crucial role in achieving the objectives of the ecological transition. To strengthen the green bond market, it is necessary to mobilize the resources of investors on environmental issues. The underlying logic that contributed to the birth of these new financial instruments, and differentiates them from traditional bonds, is the need to finance investment projects capable of minimizing the negative effects produced by business operations. Green finances are primarily applied for carbon neutrality, in the fight against pollution and climate change, in renewable energy, and in energy efficiency (Flaherty et al., 2017; Fragiacomo & Genovese, 2020). As such, Green Bonds have known no setbacks during these recessions. Cotugno et al. (2022) highlight that Green Bonds can be considered a subset of Environmental, Social, and Governance (ESG) risks. The outbreak of the pandemic reinvigorated attention to the issue of environmental sustainability and led investors to make more responsible choices aimed at obtaining maximum returns while also reducing ESG risks (Semieniuk et al., 2021, European Bank Federation, 2017). The inclusion of these risk factors along with financial risks allows investors to broaden the parameters used to assess their performance and no longer limit themselves only to the financial dimension (Van Duuren et al., 2016). Both companies and individual investors share the need to “green” their portfolios. Observing recent market trends, territorial differences have consolidated with the United States as the country with the largest issue of Green Bonds (51.1 billion dollars), followed by Germany (40.2 billion dollars), France ($32.1 billion), China, and the Netherlands ($17.2 and $17 billion respectively). In terms of market share, Green Bonds currently account for 50% of the total sustainable bond market and 5% of the global bond market (Frisma Prisandy & Widyaningrum, 2022). Amongst Asian countries, particularly China has been the protagonist of growth that has known no setbacks since 2015 (Waddams Price et al., 2012). In 2016, 77% of green bond issues were carried out by the Chinese banking system (with 62% and 68% in the following years). This growth was mainly caused by a significant domestic demand for green investments that could benefit from adequate financial support exclusively through Green Bonds. Another factor that may explain China’s position in the green bond market is fewer regulatory constraints across Green Bonds

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facing commercial banks with weak ratings and a lack of liquid assets. The creation of a shadow banking system has been favored above all since 2018, with the new regulations issued to the China Banking Regulatory Commission (CBRC4) for stricter supervision against off-balance sheet financial transactions that can fuel systemic banking risks. In this context, the use of Green Bonds represents a form of alternative finance solution for obtaining the liquidity necessary to finance one’s assets. About 80% of the entire green bond market in 2020 was composed of Asia and Europe (Rizzello, 2022). Data from the Sustainable Finance Forum (2020), highlights that Italy, the focus of this paper, holds a pre-eminent position in Europe; in 2019, eleven Green Bonds were issued in favor of three categories of objectives: increase in production from renewable energy, reduction of CO2 emissions from the electricity system, and reduction of land use and impact on terrestrial biodiversity. With the reflections and analyses proposed in this study, we intend to highlight the importance of green finance, and in particular the product of Green Bonds, in achieving a more environmentally friendly economy (Fig. 4.2), considering their roles in the fintech revolution. Globally, sustainable investments amount to 35.3 trillion dollars, of which more than 80% are in the United States and Europe (Global Sustainable Investment Alliance—GSIA, 2020), highlighting the centrality of the topic investigated in this paper. Particularly, the sustainable Italian financial portfolio amounted to 151.1 billion dollars (Banca d’Italia, 2022). The sector of application of green finance in this study is maritime transport. This is because, as Fig. 4.2 shows, the transport sector can help green finance to meet the objectives related to each of the main natural elements. In particular, maritime transport accounts for over 80% of the volume of international trade. This percentage is even higher in developing countries (UNCTAD, 2021). The research objectives aim to: 1. Provide an analysis of the literature on the main characteristics of Green Bonds to highlight the differences between these and conventional bonds, as well as the effects of this new approach to finance on the decision-making and strategic process of individual investors and the business models of bank intermediaries;

Fig. 4.2 Topics in green finance (Source Authors’ elaboration on UNEP [2016])

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2. Propose an empirical reflection on the application of green finance to Cold Ironing technology1 in the maritime transport sector as fintech instruments. This confirms the important role that Green Bonds can play in the transition toward an efficient low-carbon economy (Banga, 2019). The results presented in this chapter can start a new reflection among local actors about the nature of Green Bonds as a tool for completing the financial market in the Fintech field. Many studies adopting a microeconomic perspective show that the issuance of Green Bonds leads to a change in practitioner and investor behaviors (Flammer, 2021; Löffler et al., 2021; Tang & Zhang, 2020). According to Baker et al. (2018), investors are willing to sacrifice a portion of their financial return by preferring Green Bonds to conventional ones to obtain more profitable results in terms of the financial sustainability of environmental investments. According to Löffler et al. (2021) what differentiates Green Bonds from conventional bonds is a higher premium (“greenium”) which makes this source of funding less expensive for the issuer. Amongst the social determinants of greenium in the literature, the greater preference of investors towards socially responsible investments, ESG criteria, environmental protection of nature, human rights and diversity, the governance structure, and labor relations with employees have been identified (Endelman, 2018; Maltais & Nykvist, 2020). Bollen (2007) argues that green bond investors may have a multi-attribute utility function in terms of standard investment, risk minimization, and maximization of a set of personal and social values. Cotugno et al. (2022) show that in periods of high economic uncertainty when companies face liquidity crises and insolvency risk, Green Bonds have a dynamic high Beta, (i.e., offering higher risk premiums). According to the latter authors, it is possible to identify at least four stages of the dynamics of credit spreads of green and conventional bonds during Covid-19. When the virus led to the first public health crisis in China, corporate credit spreads remained stable. Only after 24 February 2020, when 11 municipalities in Northern Italy went 1 Cold Ironing technology is a supply system for ships through connections to the local electricity grid via cable in order to avoid the use of fossil fuels and greenhouse gas emissions (Abu Bakar et al., 2023).

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into lockdown, did green bond credit spreads start to rise, outperforming conventional bonds and peaking in mid-March 2020. Green bond credit spreads fell to below conventional ones in October and retreated significantly after 9 November, when US-based Pfizer and Germany’s BioNTech revealed positive trial results of their Covid-19 vaccine. Green bond credit spreads narrowed further in the following month through to 31 December 2020. However, other studies, which adopt a logic of market aggregation (macroeconomic perspective), conclude that the enrichment of funding instruments in the financial market through Green Bonds could allow investors to transfer risks from one market to another by exploiting the similarities between the different types of bonds. Furthermore, they could more easily achieve significant environmental results than in traditional financial markets (Arif et al., 2021; Flammer, 2021; Reboredo, 2018). For example, using a Vector Autoregressive (VAR) structural model, Reboredo (2018) proves the transmission of financial shocks across green and financial bond markets, including bond, currency, equity, energy, and high-yield corporate bond markets. This interdependence is even more valuable after systemic crisis periods (the global financial crisis of 2008, and the most recent Covid-19 crisis) when the focus of the companies is to increase their liquidity, and many of them are interested in making investments in sectors with strong impact on environmental and social sustainability (energy, waste, climate). The pandemic has increased the already existing economic inequalities in the territories, particularly in the PEPFAR countries,2 and has brought out new forms of poverty. Therefore, the results of this study are increasingly topical as they reflect the ways to achieve sustainability in the financial sector. It is necessary to look at this goal from a broader perspective in the processes of development and economic growth. This broader perspective is that of the ecological transition (OECD, 2021) aimed at placing economies within agreements, businesses and structures with zero carbon emissions.

2 PEPFAR countries: Angola, Botswana, Burundi, Cameroon, Côte d’Ivoire, Dominican Republic, Democratic Republic of Congo, Eswatini, Ethiopia, Haiti, Kenya, Lesotho, Malawi, Mozambique, Namibia, Nigeria, Rwanda, South Africa, South Sudan, Tanzania, Uganda, Ukraine, Vietnam, Zambia Zimbabwe.

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This chapter is structured as follows: the second section following the introduction, highlights the peculiarities of the maritime transport industry. The third section provides the theoretical framework of the contribution, through an analysis of the literature on the topic of sustainable finance, with a focus on application to the maritime transport sector through the prism model. The objective of this section is to provide a critical analysis of this new approach to finance, capturing the main differences concerning traditional finance. A part of this section is dedicated to an analysis of Green Bonds’ features. The content of this section is of a descriptive nature and can be divided into three parts: (1) analysis of the state of the art of the application of sustainable finance measures; (2) effects of sustainable finance measures; and (3) estimate of the reduction of impacts with the application of cold ironing.

4.2

The Maritime Sector

The maritime sector has a considerable impact on both the economy and the environment as it accounts for over 80% of the volume of international trade and this percentage is even higher for most developing countries (UNCTAD, 2021). In fact, maritime transport represents one of the most polluting sectors, with greenhouse gas emissions of over one billion tons of CO2 , equal to about 3% of global emissions. Furthermore, with the growth of ship traffic and the absence of rapid measures to mitigate emissions, this figure is set to increase to over 15% by 2050. Emissions from shipping are an important source of air pollution, especially sulfur oxides (SOx), nitrogen oxides (NOx), and particulate matter, which impacts not only human health but also marine fishery resources and the marine ecological environment (Du et al., 2019). In the context of this analysis, EU maritime transport represents up to 4% of the territory’s total CO2 emissions (European Commission, 2022). Particularly, among the ten most polluting ports, Rotterdam in the Netherlands emits 13.7 million tons (Mt) of CO2 , Antwerp in Belgium emits 7.4 Mt, and Hamburg and Bremerhaven in Germany emit respectively 4.7 and 2.3 Mt of CO2 (Transport & Environment, 2018). These emission values showcase the urgent need to reduce supply chain emissions related to European ports. Shipping is thus considered a major contributor to global CO2 emissions. This pollution is mainly due to the ships docked in the port having their propulsion engines turned off during the stop on the quay, to ensure

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the provision of services on board they use auxiliary diesel engines. These engines involve high fuel consumption and exhaust gas emissions. A cruise ship parked for 10 hours generates CO2 emissions equal to those generated by 25 cars in a year. In order to reduce this impact, the use of electric power as an alternative fuel source is an important way to decarbonize the shipping industry. Electrifying maritime transport to achieve a “cleaner ocean” and decarbonization attracts considerable research attention (Horvath et al., 2018). Driven by the need to decarbonize the energy sector, renewable capacity has reached a total of 161 GW in 2016 (REN21, 2017). The emergent “green port” strategy recognizes that seaports must minimize emissions from all existing and future port activities and not just those related to the logistics area. However, ports must not only focus on reducing emissions and adopting environmental practices but also maintain a focus on port growth (Fahimnia et al., 2015). Thus, a green port strategy should meet both economic and environmental objectives, leading to sustainable development on an economic, social, and environmental level. Considering that 90% of European ports are located in urban areas and that pollutants can extend hundreds of kilometers from the coast, their impact trickle to the hinterland, inconveniencing citizens, mainly due to noise, air pollution, and traffic which stem from port-related operations. Communities are therefore reticent to accept the establishment of ports nearby. The sustainable development of ports must therefore also take into consideration the sustainability of port environments. This becomes a primary concern for port authorities as well as local administrations. In 2020, the Covid-19 pandemic disrupted shipping, causing maritime trade to contract by 3.8% in 2020, recording a nascent, albeit asymmetrical, recovery in the second half of the year (UNCTAD, 2021). In particular, the lockdowns imposed by various countries, travel restrictions, and production cuts reduced the demand for fuel. Consequently, transportation and trade in crude oil, refined oil, derivatives, and gas decreased by 7.7% (UNCTAD, 2021). Nevertheless, as the industry recovers from these restrictions and growth is on the horizon, Cold ironing (Zis, 2019) represents a possible solution for the reduction of environmental pollution in port areas. This practice consists of an onshore power supply from the electrical grid or other means to satisfy the energy demand of ships at berth. Ships can actually produce energy through onboard diesel generators, and this has a significant positive impact on the environment (Colarossi & Principi, 2020).

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4.3

Theoretical Framework and Literature Review 4.3.1

Sustanaible Finance

Applying the concept of sustainability to finance is difficult as it requires companies to change their perspective. Companies must evaluate development processes with regard to both their organizational boundaries and the mindset of adopting tactical and strategic approaches that involve all internal and external stakeholders. Potential investments must be selected by considering both financial returns, as well as the operational environment of the enterprise. Gallucci (2021) highlights that the modus operandi of companies must go in the direction that promotes the integration between corporate profitability and the interests of communities and the environment. In this way, financial choices would no longer have to follow parallel paths concerning the application of ESG principles. Phenomena such as climate change, energy efficiency, environmental degradation, the enhancement of biodiversity, the circular economy, waste management, and other social issues represent challenges that investors will no longer be able to overlook if they want to maintain a certain degree of competitiveness on the market. The topic of sustainable finance is quite recent in the scientific debate (Rizzi et al., 2018). Sustainable finance is one of the perspectives for analyzing the multidisciplinary concept of sustainability applied to business; it is a key condition to successfully apply sustainable entrepreneurship where decisions do not have the sole objective of achieving economic and financial returns on investments, but also of producing a social impact. In particular, sustainable entrepreneurship, which still lacks a formal definition, makes it possible to seize all those opportunities representative of market failures in the field of sustainability (Hoogendoorn et al., 2019). Some authors who have coined the expression “ecoentrepreneurship” have investigated the business world exclusively by looking at its relationship with the environment (Isaak, 2002; Shrivastava, 1995). For these authors, the competitive capacity of a company is based on the pursuit of environmental objectives. Another author who has dealt with “social entrepreneurship” focuses on social issues and obtaining adequate financial support for them (Bull, 2008).

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This nascent interest in sustainable finance, particularly green finance, is justified by the lack of financial resources to be allocated to the 17 Sustainable Development Goals (SGD) of the United Nations Agenda 2030. It follows the assumed importance of climate change in the field of action of the public and private sectors. Therefore, it appears increasingly necessary to build public–private partnerships in support of an economy increasingly attentive to environmental problems. Utting (2015) provides the following definition of green finance: “an economy that focuses economic development on the action of business organizations in which people play a crucial role. This economy bases its multidisciplinary nature on the integration of economic approaches, typical of the traditional economy, and the social, environmental, political, and holistic ones, typical of solidarity economies”. The centrality of personal initiative and entrepreneurial skills are key factors in the realization of successful forms of innovation while simultaneously following the logic of the market and environmental sustainability (Schaltegger & Wagner, 2011). The term “creative capitalism” has also been used in literature to indicate this hybrid form of enterprise (Taylor, 2010). However, since no formal definition of the term exists, sustainable finance has become an umbrella term for several proliferations of the concept, which lack categorization. Some examples of this are ethical finance, sustainable and responsible investment, microfinance, social impact investment, crowdfunding, and green finance (Chiappini, 2017; Warner, 2013; Weber & Remer, 2011). To develop a categorization of sustainable finance, Grandin and Saidane (2011) identify four main characteristics of this new approach to corporate finance by: 1. innovative approaches and new individual behaviors adopted by financial intermediaries; 2. sustainable growth; 3. proximity to people; 4. inclusive logic. Ryszawska (2016) defines sustainable finance as finance concerning development under three dimensions (economic, environmental, and social). Some studies have investigated the investors’ methods for selecting sustainable finance projects. For example, the Global Sustainable Investment Alliance-GSIA (2019) has estimated negative screening, and the

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companies and shareholder involvements in Europe as the primary selection criteria in Japan, as well as the integration of ESG factors (Environmental, Social, Governance factors) in decisions in the USA, Canada, and Australia. The presence of these characteristics, according to Schoenmaker (2017), captures another important difference between traditional finance and sustainable finance: the adoption of a long-term time horizon. According to the author: “sustainable finance is a tool to promote sustainable development, for example by financing healthcare, green buildings, and wind farms. The starting point is a positive selection of investment projects based on their potential to generate positive social and environmental impacts. In this way, the financial system serves the medium-long term sustainable development agenda”. More recently, Schoenmaker (2018) has developed the concept of “Sustainable Finance 3.0”, i.e., finance that intends to maximize economic returns not only for shareholders but for all stakeholders. Regarding social impact investments, the Global Impact Investing Network—GIIN (2019) specifies the peculiarities of the intervention methods: (1) definition of a social and financial goal; (2) setting up qualitative and quantitative measures of the impacts identification of potential risks associated with the target objectives and implementation of risk mitigation techniques. 4.3.2

The Application and Categorization of Sustainable Finance

To date, there is no agreement on the models for measuring social and environmental impact. This complicates the parameters of the sustainable finance definition. A first attempt to place sustainable finance within a regulatory framework comes from the European Union in 2016 which set up a Task Force on Sustainable Finance to adopt a homogeneous treatment between investments with climate objectives and investments in the environmental sector. In March 2018, this Committee of Experts published ten guidelines (“The Action Plan”) on sustainable finance. Among the main challenges were the introduction of a taxonomy of ecosustainable activities, greater transparency of information on sustainable investments and environmental risks, and the adoption of targets taken as a reference to evaluate the achievement of climate objectives. Applying the concept of sustainability to the banking business requires a transformation of traditional operating models (Carè, 2018; La Torre et al., 2019) and the adoption of new financing instruments, and social

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impact bonds based on a participatory logic. These bonds also influence the production of social and environmental impact. Sustainable Banks, defined by Jeucken and Bouma (2001) as a category of intermediaries who link their performance, not to the objective of achieving the highest financial return rate, but rather to the highest sustainable rate of return. This type of bank offers financial products and services to customers without neglecting environmental protection (Yip & Bocken, 2018), the role of banks in achieving the SDGs has been highlighted by Lindenberg and Volz (2016) through both direct impacts on the environment related to banking operations and indirect products from customers. Literature has established the following characteristics for green banks: digital transparency, customer inclusiveness in designing and offering financial products and services, supporting sustainable initiatives (e.g., better waste management), and creating green products (Ahuja, 2015; Bose et al., 2017; Schub, 2015). The creation of a green bank can involve different types of stakeholders: employees, customers, banking operations, and strategy. In addition to green banks and sustainable banks (Fuchs et al., 2021), other financial operators that can intervene in the green finance market are “alternative banks” (Weber & Remer, 2011), i.e., ethical banks and social banks. The first market initiative in support of green finance was the United Nations Environment Program Finance (UNEP-FI) signed in 1991 and replaced the following year by the UNEP Declaration of the Financial Institutions on the Environment and Sustainable Development. This initiative involves more than 200 financial institutions belonging to the banking, insurance, and investment sectors. In Italy, initiatives in support of green finance are quite recent. For this reason, the CONSOB Regulation implementing EU Directive 95/2014, Law 232/2016. 4.3.3

Green Bonds as a Tool for Sustainable Finance

This chapter focuses on Green Bonds, which were at the center of the guidelines codified in 2018 by the International Capital Market Association (ICMA) aimed at ensuring their greater diffusion in compliance with the logic of reporting transparency. Particularly, ICMA has since promoted the required credentials for external reporting agents. Green bonds can greatly help issuers in achieving sustainability goals and offer higher returns in both economic and environmental terms. The green bond market still represents less than 1% of total bonds issued worldwide

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(G20 Green Finance Study Group, 2016). In recent years, the European Union has seen its competitive position in the market grow, reaching a share of 50.09% in 2018 (against 33.4% in the previous year). Private entities entered the green bond market before public actors (in 2013 and 2016 respectively) and Poland was the country where the first issuance of sovereign Green Bonds took place. In accordance with the green bond standards (GBS) established by the EU in March 2019, in order to be defined as such, each green bond must satisfy four characteristics: green project, green bond framework, reporting, and verification. A project can be defined as green if the capital raised through issuing bonds is used to finance or refinance at least one of the following objectives: climate change mitigation, adaptation to climate change, sustainable use and protection of water and marine resources, the transition towards circular economy models, waste recycling and waste prevention, and pollution control and prevention. The first Green Bond issue dates back to 2007 in Europe by the European Investment Bank “Climate Awareness Bond”. Thanks to this issue, 8:24 billion euros were raised to be used to finance projects relating to renewable energy, and energy efficiency. The first attempt to formulate a definition of green finance comes from Lindenberg and Volz (2016) who claim that thanks to its tools, green finance can guarantee financial support for green investments in both the public and private actors. It can incite policies that promote projects for environmental sustainability and ensure greater diversification of the banking business. The OECD (2017) defined green finance as a strategy for economic growth without neglecting environmental protection. Heinkel et al. (2001) are the first authors who analyzed the adoption of exclusionary ethical investing strategies and their impact on the polluting companies’ cost of capital, and this was the beginning to broach the topic of green finances. Gallucci (2021) conducted a bibliometric analysis of the green finance literature, based on a sample of 165 scientific articles published between 2001 and 2020, using the Web of Science as a database. Among his conclusions, it emerges that the main theme when it comes to green finance is sustainable finance. It represents the macrotheme that encompasses other basic themes of green investments, climate finance, and sustainable development (Schoenmaker & Schramade, 2018; Steckel et al., 2017). The basic themes of climate finance and Green

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Bonds present a marked overlap with that of sustainable finance, highlighting a common trend in the use of these new financial approaches to counter the problem of climate change (Table 4.1). The main difference between Green Bonds and traditional bonds is the intended use: in the first case, it must favor green investments. Green Bonds are equated to climate risk mitigation tools. The achievement of the objective of environmental sustainability improves the reputation of the issuing company as perceived by investors and in a cascade generates an improvement in the economic/financial performance (higher sales, higher profits, lower production costs linked to anti-pollution initiatives, etc.). Investors who subscribe to Green Bonds are more interested in environmental and social returns than in economic/financial ones. In the long term, sustainable finance becomes more effective if it manages to guarantee an integration between financial objectives and green-type objectives. Therefore, we are witnessing the consolidation of a bond of interdependence between Green Bonds and conventional bonds and financial instruments. Among the factors driving the development of this market, attention to the climate of investors is the commitment of policymakers towards the problem of climate change. The literature has highlighted both the advantages and the critical issues associated with the issuance of Green Bonds for both investors and issuers (Table 4.2).

4.4

Final Remarks

Environmental damage in the maritime sector is addressed by the International Convention for the Prevention of Pollution from Ships (MARPOL) developed by the International Maritime Organization (IMO). These conventions, updated over time, cover various issues of maritime pollution such as emissions, oil spills, and ballast water (Lister et al., 2015). The IMO has defined Emission Control Areas (ECAs) with limits of 0.1% sulfur and a global sulfur limit of 0.5% effective from 2020, reduced since then by the current limit of 3.5%. However, there is still no global CO2 limit imposed by the IMO, therefore this fundamental mention is missing in the most important international legislation. Some port authorities are starting to provide incentives for the implementation of sustainable measures in ports: some large ports in the United States are taking independent actions, for example, the port of Long Beach has launched a voluntary program, called the “Green flag incentive”, which provides discounts for port dues on arriving ships reducing

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Table 4.1 Definitions of green bonds Reference

Definition

Revelli and Paranque (2017) Sean and Padraig (2014)

Green Bonds as instruments of social impact finance Differentiate Green Bonds from traditional bonds, not in terms of the issuer but the objectives for which they are issued, i.e., environmental protection, use of renewable energy, and combating climate change Use of Green Bonds is linked to the fight against climate problems Green Bond acts as a signal of information transparency in the relationship between the issuing company and its investors regarding its commitment to environmental protection Missions of Green Bonds denote the company’s commitment to financing projects that protect the environment and that benefit society rather than selecting projects exclusively using the criterion of economic performance Green Bonds are characterized by lower returns and risks than traditional bonds Risk-return relationship of Green Bonds is due to the still limited diffusion of this financial instrument in Europe and the limited investor base Did not find significant differences between the yields of Green Bonds and traditional ones Yields on Green Bonds are very volatile because they are influenced by the occurrence of unforeseen events such as political actions in response to climate change Other factors affecting the pricing of Green Bonds include the rating of the issuing company (expression of insolvency risk), the size of the issue, and the issue period Other aspects must be considered in Green Bonds issue: environmental, social, governance, and the macroeconomic situation of the country in which the issuing company is located and the types of monetary policy adopted Path of Green Bonds benefits from positive externalities from other financial markets Green Bonds produce positive impacts on the performance of the company both from a financial and an environmental point of view Determinants of the Green Bond market are the same as those of traditional bonds

Climate Bond Initiative (2022) Kaminker and Stewart (2012) Lyon and Maxwell (2010)

Flammer (2013) Mocanu et al. (2021)

Löffler et al. (2021) Ehlers and Packer (2022)

Kapraun et al. (2021) Karpf and Mandel (2017) Antoniuk and Leirvik (2021)

Wang et al. (2013)

Anh Tu et al. (2020) Dan and Tiron-Tudor (2021)

Elsayed et al. (2022) Zhou and Cui (2019)

Banga (2019) Source Authors’ elaboration

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Table 4.2 Benefits and threats of green bond issues for issuers and investors

Issuers

Investitors

Advantages

Disadvantages

• Presentation/implementation of the issuer’s approach to the issue of ESG bonds are factors related to environmental protection, society, and corporate governance • Making investment decisions • Improving the diversification of the investor base of Green Bonds • Buy-and-hold green bond investors may contribute to reducing volatility in the secondary market • Significant investor demand for bonds can lead to oversubscription and an increase in issuance • Reputation improvement Increased credibility of the sustainable development strategy • Investors can offset risk-adjusted financial returns with environmental benefits • Meets ESG requirements • Better risk assessment in an otherwise opaque fixed-income market • Recognized by the UN Framework Convention on Climate Change as a non-state actor taking “climate action” • Engagement and private dialogue with issuers on ESG • Traceability of issue proceeds and reporting leads to improved internal governance structures

• Reputational risk, should the green credentials of the bonds be questioned • Transaction costs associated with administration, certification, reporting verification, and monitoring • Investors can claim penalties for a so-called “green default”

• Small and emerging markets, small size of bond issues • Lack of harmonized standards can cause confusion and increase reputational risk • Limited possibilities of legal enforcement of ecological integrity • Lack of standardization can lead to complications in the research • The need for additional due diligence may not always be met

Source Authors’ elaboration on Dyduch et al. (2022)

their speed in the port area (Ahl et al., 2017), one of the main strategies available to ports to reduce emissions from ships. In Asian ports, Roh et al. (2016) found little attention paid towards reducing impacts by providing incentives to shipping companies to use clean, low-sulfur fuels or reducing shipping speed while close to the port. However, some studies have explored the potential of green port policies in Asian ports

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such as slow steaming (Chang & Wang, 2012), but little research has been done on current policy enforcement. It is not true that all ports in developed countries are applying sustainable strategies as indeed many ports remain reluctant to increase costs for themselves or their shipping line customers. Instead, ports in developed countries, which also prefer to act more on intermodal connections, but are less motivated to take actions that can increase costs for carriers, are showing some interest in green strategies to encourage fewer polluting ships in ports. At the European level, the port policy promotes the allocation of environmental costs by seaports. For example, the port of Rotterdam is one of the 30 major European ports that already applies green fees to use their infrastructures. It has developed Green Award rebates for ships based on their environmental performance to incentivize ships to reduce their polluting emissions. In Italy, on the other hand, these types of systems have already been studied in Civitavecchia (Naso et al., 2006), Venice, Livorno (PRPL, 2014), and Genoa (Sciutto & Pinceti, 2019), the latter being the only port where the system became operational. These projects are all connected to a single network, although in Italy the use of the network is associated with high electricity costs. For this reason, many studies have shown negative results, due to a lack of economic feasibility (Colarossi & Principi, 2020). In relation to the implementation of cold ironing, it is also recognized (Zis et al., 2014) that the type of vessel plays an important role as this can be a determinant of the time spent at berth which in turn can be a decisive factor for whether to use cold ironing or not. Innes and Monios, in 2018, quantified 28 wears with cold ironing, and Zis (2019) counted 43 with both cold ironing already installed or planned. Another relevant factor taken up by Zis (2019) is the economic feasibility of cold drawing for ship operators. This question is heavily intertwined with the volatility of energy prices and the question of when a given vessel refuels as bunker prices tend to differ sub-essentially between different places. Despite the challenges mentioned above for the implementation of cold ironing, there are good reasons to assume that the penetration of this technology will be observable in the coming years. One reason is that ports may consider using mandatory cold ironing. The latter must be understood against the context that some argue that “the main barrier to further implementation of the solutions [cold ironing] […] is the associated high installation cost ” (Zis, 2019).

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Utilities and Limitations to the Application of Cold Ironing

Analyzing the articles selected through our systematic literature review, a new common thread emerges: authors highlight utilities and limitations in the application of the Cold Ironing system in the maritime sector, an approach for reducing the environmental impact associated with ships that fall under the tools of Fintech when supported by green finance. Currently, in terms of diffusion (Table 4.3) cold ironing installations in Europe are mainly collected in the northern part (e.g., Finland, Norway, and Sweden) and followed by the USA with seven plants (Piccoli et al., 2021). Table 4.3 Cold ironing diffusion Year

Country

Port

Ship type

Capacity

Frequency

Voltage

2000

Sweden

Gothenburg

1.25–2.5

50&60

6.6&11

2000 2001 2004

Belgium USA USA

Zeebrugge Juneau Los Angeles

1.25 7–9 7.5–60

50 60 60

6.6 6.6&11 6.6

2005 2006 2006 2006 2008 2008 2009 2010 2010

USA Finland Finland Finland Belgium Germany Canada USA USA

12.8 n.d. n.d. n.d. 0.8 2.2 16 16 16

60 50 50 50 50&60 50 60 60 60

6.6&11 6.6 6.6 6.6 6.6 6.6 6.6&11 6.6&11 6.6&11

2010 2011 2011 2011 2011

Sweden USA USA Norway Canada

Cruise Cruise Container Cruise Container

2.5 16 7.5 4.5 7.5

50 60 60 50 60

11 6.6&11 6.6&11 11 6.6

2012 2012 2015 2017

Netherlands Sweden Norway France

Seattle KEMI Kotkal Oulu Antwerp Lübeck Vancouver San Diego San Francisco Karlskrona Long Beach Oakland Oslo Prince Rupert Rotterdam Ystad Bergen Marseille

RoRo, RoPax RoRo Cruise Container, Cruise Cruise RoPax RoPax RoPax Container RoPax Cruise Cruise Cruise

RoPax Cruise Nd Ferry

2.8 6.25 1 4

60 50&60 50&60 60

11 11 0.440/0.690 11

Note Ship type RoRo (i.e., Roll-on/Roll-off) and RoPax (Roll-on/roll-off Passengers) Source Authors’ elaboration on Piccoli et al. (2021)

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Observing Table 4.2, we highlight that the first cold ironing plant was built in Sweden in 2000 for RoRo and RoPax ships, subsequently, the United States implemented three plants, and in the following years, Northern Europe continued to spread cold ironing. We must wait until 2017 to see the first Mediterranean port of Marseille build the docks for cold ironing available for ferry ships. Particularly, Italian ports are now presenting various studies for implementation. Cold ironing systems have been sponsored in several regulations. They were first considered by international IMO regulations, with the goal of promoting the respect of keeping sulfur limits in fuel to 0.5% (Sun et al., 2022). Shipowners must therefore make careful investments that allow them to equip their ships with less harmful systems while considering the current uncertainty of long-term fuel prices. Despite this basic legislation, there are several factors that influence shipowners’ investment decisions towards more sustainable approaches. Analyzing a sample of shipowners, it emerges that the main problems are associated with the distribution of incentives and the lack of information that renders companies reluctant to invest (Longarela-Ares et al., 2020). Furthermore, energy efficiency measures are unlikely to be implemented in older ships, possibly due to the difficulty associated with recovering the investment. Shipowners are more likely to invest in efficiency improvements in larger and newer ships and regulation encourages their adoption (Longarela-Ares et al., 2020). For this reason and based on this evidence, investing in the efficiency of the docks themselves through measures such as cold ironing, which would benefit all vessel types, represents a sustainable and worthy investment solution. Longarela-Ares et al. (2020) presented an interesting analysis of the barriers and limitations that prevent energy efficiency investments in the maritime sector (Table 4.4). Since cold ironing depends on docking time, an accurate estimate of the mooring duration is required to help the port operators optimally manage the mooring assignments and energy planning. Ports must therefore know the energy consumption and departure time of the ship to use the energy management system (EMS) and the problem of berth allocation (BAP) (Bakar et al., 2022). Moreover, technology analyses provided in scientific literature provide authorities and policymakers in the sector, highlighting the added value of selected and inexpensive actions for energy efficiency and hybrid mobility.

Subtypes

Examples

X X

X

Hobson et al. (2007)

X

X

Kollamthodi et al. (2013)

Barriers and limitations to energy efficiency investments

Behavioral barriers Organizational barriers Technical barriers Social barriers Legislative barriers Institutional barriers

Types

Table 4.4

X

X

Maddox Consulting (2013)

X

Jafarzadeh and Utne (2014)

X

X

(continued)

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Market barriers

Economic barriers

Capital constraints Heterogeneity Hidden costs Risk and uncertainty Regulation and other Asymmetric information Split incentives Adverse selection Moral hazard

Examples

X

Hobson et al. (2007)

Source Authors elaboration on Longarela-Ares et al. (2020)

Nonmarket failures Market failures

Subtypes

(continued)

Types

Table 4.4

X

X

Kollamthodi et al. (2013) X

Maddox Consulting (2013) X

Jafarzadeh and Utne (2014) X

Rehmatulla and Smith (2015a, 2015b)

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Konstantinos et al. (2022) believe that expensive and seemingly mandatory actions under current European legislation, such as cold ironing and LNG, are robust and necessary. However, the technology of cold ironing (or shore-to-ship power) which can significantly reduce greenhouse gases and air pollutant emissions from ships at berth collides with economic, legal, and environmental factors which still makes this technology less attractive in southern Europe (Piccoli et al., 2021). These authors further analyzed the main regulatory bottlenecks occurring in different European jurisdictions on the development of cold ironing, while evaluating the legal and economic consequences of implementing cold ironing considering the future inclusion of the maritime sector in the EU emissions trading. We also found that Hobson et al. (2007) were the first to identify the technical, economic, social, and legislative barriers that limit the adoption of low-carbon technologies in shipping. Kollamthodi et al. (2013) analyzed risks, hidden costs, information problems, technical and operational measures, and the principal-agent problem, defining the barriers as technological, institutional, and financial. Instead, Maddox Consulting (2013) distinguishes between technological, operational, and physical barriers, regulatory, economic, market failures, and administrative barriers. Jafarzadeh and Utne (2014) identified information, economic, inter-organizational, technological, political, geographical, and intra-organizational barriers, while Rehmatulla and Smith (2015a, 2015b) considered behavioral, organizational, and economic barriers (market barriers and market failures). Other scholars examined 22 potential pathways, including conventional marine Heavy Fuel Oil (HFO) as a reference case, alternative “blue” fuel produced from natural gas, and “green” fuels produced from biomass and solar energy. From a methodological point of view, the paths are compared in terms of quantifiable parameters: fuel mass, fuel volume, life cycle energy intensity, cost, greenhouse gas emissions (GHG), and non-GHG emissions, estimated from literature and various modeling (Law et al., 2021). The results showed that from an energy point of view, renewable electricity with battery technology is the most efficient, albeit still impractical route for long-distance shipping due to the low energy density of today’s batteries (Law et al., 2021). However, as evidenced by Yigit et al. (2016) the use of shore-side electricity to serve ships in ports has increasingly been considered a measure to improve their energy efficiency and environmental performance.

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4.4.2

Pros and Cons of Cold Ironing

Ballini and Bozzo (2015) propose the following advantages of Cold Ironing: 1. Shore power supply can effectively reduce the hazardous emissions (e.g., SOx, NOx, VOC, PM, CO, N2 O, CH4 ) in the local environment significantly, 2. Since the energy supplied from the national grid (i.e., from power plants) is subject to stricter emission control 16 (including CO2 ) than the energy supplied by PE, the overall level of flue gas emissions of ships using shore power is significantly reduced, 3. Although ships will always need AE for power at sea, operational operating costs and capital investment for these engines will be lower if AE use is limited, 4. Cruise ship labor costs related to generating electricity using PE while at berth will be reduced. The main benefit of cold drawing technology, however, lies in the reduction of the local, port of call, global emissions, and the reduction of noise from running engines. Depending on the region, the effects naturally look different (Spengler & Tovar, 2021). The disadvantages or limitations consist of: 1. AE-powered electricity is generally cheaper than shore-based electricity supply, 2. Electricity powered by AE is exempt 17 from national energy and electricity tax within the EU, 3. Considerable capital investment must be made in shore power services, 4. Shore power may only be supplied while vessels are at berth and not while maneuvering or underway. Port environments would therefore still be subject to a certain level of emissions.

4.4.3

Other Sustainable Strategies

Throughout the last few decades, different approaches to avoiding or mitigating emissions and the associated external costs that are caused by

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shipping have been promoted. In particular, in ports that are in the direct vicinity of densely populated areas. One of the approaches to reduce said emissions from vessels in ports, cold ironing, is often perceived (Pettit et al., 2018) as one way to a cleaner and more environmentally friendly sea transport. However, with the aim to reduce emissions while in port and at berth, other short-term options are being considered: the first option is the use of alternative energy sources such as liquefied natural gas (LNG) instead of current fuels. The second option is the vessel speed reduction strategy is effective in reducing fuel consumption and costs, as well as emissions. The weakness of this strategy requires both port authorities and shipping companies to invest in related power transmission equipment, which increases expenses. Indeed, the strategy of implementing emission control areas to regulate ships and switch to the use of low-sulfur fuel is difficult to achieve in the short term because the implementation requires an agreement with the IMO which will increase the fuel costs for shipowners by 37.2% (Chang & Wang, 2012).

4.5 Conclusion, Limitation, and Future Implications Cold ironing is a port-based emission reduction technology that reduces emissions from berth shipping, especially from a ship’s auxiliary engines using shore-based electrical power. Currently, the traditional cold ironing application considers the shore-to-ship link to be one-way by allowing the ship to turn off its auxiliary engines. For this reason, better knowledge in the field of sustainable finance for the maritime sector could help businesses and governments act more sustainably. Therefore, investing in sustainable finance is a real challenge for all the main local players (banks, financial intermediaries, issuing companies, and public and private bodies). Despite the important purposes of green finance, the lack of a univocal definition of sustainable finance and green finance has certainly contributed to fueling the risk of greenwashing, i.e., the risk of making an activity appear eco-sustainable when it is not. The generating cause of this problem is the failure of communication between the investor in green finance projects and the stakeholders. This problem generates a misalignment or “decoupling” between communication and the actions

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of organizations and individuals. There is also talk of “attention deflection” because a communication, falsely green, diverts the attention of stakeholders from unethical issues and actions (Gatti et al., 2019). To avoid the incorrect use of sustainable finance instruments with the consequence of creating greenwashing, it is necessary to assist investors when they have to choose between alternative investment projects and to be able to select the most performing one, especially in environmental terms. Furthermore, investors and stakeholders must be trained and informed about the nature of investments by taking advantage of the greater availability of information assets. Moreover, as highlighted by the previous literature review, these tools may also have critical issues. First, the existence of numerous definitions of green investments can make it increasingly difficult to narrow down their nature and scope. Therefore, a first challenge that involves both academics and practitioners is to reduce the plurality of these definitions to be able to assess the intensity of the link more easily between the company’s performance and the environment in which it is located. Furthermore, it appears increasingly necessary to improve the transparency of rating methods on environmental and sustainability objectives, perhaps by systematically using all the data available for reporting (financial and otherwise). Another challenge is the promotion of greater involvement of subjects external to the company in conducting the disclosure of its green performance. Analyzing a model based on cruise ships in a European port, this study shows the total external potential benefit in terms of costs incurred by cruise ships using cold ironing compared to diesel fueling. The capital cost of the cold ironing infrastructure to be implemented in new cruise ship docks covers almost 100% energy demand of two hotel cruise moored vessels (22 MWh), with a cost of $110,000 amortized over 30 years of investment. Moreover, it has been calculated that cold ironing reduces total shipment-related greenhouse gases by less than 0.5%; although of greater importance are the benefits related to the reduction of SOx, NOx, and PM and the improvement of local air quality. Thus, not only the environment but also the local population living near the port could gain a calculable benefit as an equivalent of the avoided damage from the effects of the ship’s emissions. Therefore, after this examination, it emerges that the role of the port is to merge corporate social responsibility, port strategy, and the need to implement national and local environmental regulations. And already

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some of the main European and American hub ports have adopted their own green port strategies for full sustainable development. However, due to variable pollutant targets, different operating conditions, terminal types, and hub configurations, there is no single approach that is the same for all ports and each adoptable measure must be analyzed on a case-by-case basis (Du et al., 2019). Future extensions of the study are both theoretical and empirical, expanding the literature on the impacts of sustainable finance on the business models of banks and comparing green banks and traditional banks. Moreover, we intend to propose a brief analysis of the trend of the Green Bond market in Italy compared to the rest of Europe. Credit Author Statement Arnone, M.: introduction, methodology, elaboration parts on sustainable finance and green bond, conclusion. Crovella, T.: introduction, methodology, elaboration part on maritime sector and cold ironing, final remarks, conclusion, revision, and formatting.

References Abu Bakar, N. N., Bazmohammadi, N., Vasquez, J. C., & Guerrero, J. M. (2023). Electrification of onshore power systems in maritime transportation towards decarbonization of ports: A review of the cold ironing technology. Renewable and Sustainable Energy Reviews, 178, 113243. https://doi.org/ 10.1016/j.rser.2023.113243 Ahl, C., Frey, E., & Steimetz, S. (2017). The effects of financial incentives on vessel speed reduction: Evidence from the Port of Long Beach Green Flag Incentive Program. Maritime Economics & Logistics, 19, 601–618. Ahuja, N. (2015). Green banking in India: A review of literature. International Journal for Research in Management and Pharmacy, 4(1), 11–16. Anh Tu, C., Rasoulinezhad, E., & Sarker, T. (2020). Factors Influencing the Green Bond Market Expansion: Evidence from a Multidimensional Analysis. Journal of Risk and Financial Management, 13, 126. Antoniuk, Y., & Leirvik, T. (2021). Climate transition risk and the impact on green bonds. Journal of Risk and Financial Management, 14, 597. Arif, M., Hasan, M., Alawi, S. M., & Naeem, M. A. (2021). Covid-19 and timefrequency connectedness between green and conventional financial markets. Global Finance Journal, 49, 100650. Bakar, N. N. A., Bazmohammadi, N., Çimen, H., Uyanik, T., Vasquez, J. C., & Guerrero, J. M. (2022). Data-driven ship berthing forecasting for cold ironing in maritime transportation. Applied Energy, 326, 119947. https://doi.org/ 10.1016/j.apenergy.2022.119947

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Baker, M. P., Bergstresser, D. B., Serafeim, G., & Wurgler, J. A. (2018). Financing the response to climate change: The pricing and ownership of U.S. GreenBonds (Working Paper). Ballini, F., & Bozzo, R. (2015). Air pollution from ships in ports: The socioeconomic benefit of cold- ironing technology. Research in Transportation Business & Management, 17 , 92–98. https://doi.org/10.1016/j.rtbm.2015. 10.007 Banca d’Italia. (2022). Report on sustainable investments and climate-related risks, p. 42. https://www.bancaditalia.it/pubblicazioni/rapporto-investimenti-sos tenibili/2022/en-RISC-2022.pdf?language_id=1 Banga, J. (2019). The green bond market: A potential source of climate finance for developing countries. Journal of Sustainable Finance & Investment, 9, 17–32. Bollen, N. P. (2007). Mutual fund attributes and investor behavior. Journal of Financial and Quantitative Analysis, 42(3), 683–708. Bose, S., Khan, H. Z., Rashid, A., & Islam, S. (2017). What drives green banking disclosure? An institutional and corporate governance perspective. Asia Pacific Journal of Management, 35, 501. https://doi.org/10.1007/s10 490-017-9528-x Bull, M. (2008). Challenging tensions: Critical, theoretical and empirical perspectives on social enterprise. International Journal of Entrepreneurial Behavior & Research, 14(5), 268–275. Carè, R. (2018). Sustainable banking: Issues and challenges. Palgrave Macmillan. Chang, C. C., & Wang, C. M. (2012). Evaluating the effects of green port policy: Case study of Kaohsiung harbor in Taiwan. Transportation Research Part D: Transport and Environment, 17 (3), 185–189. https://doi.org/10. 1016/j.trd.2011.11.006 Chiappini, H. (2017). Social impact funds: Definition, assessment and performance. Springer. Climate Bond Initiative. (2022). https://www.climatebonds.net/ Colarossi, D., & Principi, P. (2020). Technical analysis and economic evaluation of a complex shore-to-ship power supply system. Applied Thermal Engineering, 181, 115998. https://doi.org/10.1016/j.applthermaleng.2020. 115988 Cotugno, M., Cicchiello, A. F., Monferrà, S., & Perdichizzi, S. (2022). Credit spreads in the European green bond market: A daily analysis of the Covid-19 pandemic impact. Journal of International Financial Management Account, 33, 383–411. https://doi.org/10.1111/jifm.12150 Dan, A., & Tiron-Tudor, A. (2021). The determinants of green bond issuance in the European Union. Journal of Risk and Financial Management, 14, 446.

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ICMA. (2018). Summary of green—social—sustainable bonds database providers. https://www.icmagroup.org/assets/documents/Regulatory/Green-Bonds/ 2018-Sept_Green-and-Social-Bond-Databases-140918.pdf Innes, A., & Monios, J. (2018). Identifying the unique challenges of installing cold ironing at small and medium ports–The case of Aberdeen. Transportation Research Part D: Transport and Environment, 62, 298–313. https://doi.org/ 10.1016/j.trd.2018.02.004 International Maritime Organization (IMO), Smith, T. W. P., Jalkanen, J. P., Anderson, B. A., Corbett, J. J., Faber, J., Hanayama, S., O’Keeffe, E., Parker, S., Johansson, L., et al. (2014). Third IMO GHG study 2014. https://research.manchester.ac.uk/en/publications/third-imo-greenh ouse-gas-study-2014 Isaak, R. (2002). The making of the ecopreneur. Greener Management International: The Journal of Corporate Environmental Strategy and Practice, 38, 81–92. Jafarzadeh, S., & Utne, I. B. (2014). A framework to bridge the energy efficiency gap in shipping. Energy, 69, 603–612. Jeucken, M., & Bouma, J. J. (2001). Sustainable banking: The greening of finance. Greenleaf Publishing. Kaminker, C., & Stewart, F. (2012). The role of institutional investors in financing clean energy (OECD Working Papers on Finance, Insurance and Private Pensions. OECD: No. 23). Kapraun, J., Latino, C., Scheins, C., & Schlag, C. (2021). Which bonds trade at a green bond premium? https://papers.ssrn.com/sol3/papers.cfm?abstract_id= 3347337 Karpf, A., & Mandel, A. (2017). Does it pay to be green? (Working paper). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2923484 Kollamthodi, S., Pueyo, A., Gibson, G., Narkeviciute, N., Hawkes, A., Cesbron, S., Milnes, R., Harries, J., Zamparutti, T., Hernandez, G., Kaltsouni, S., Vancauwenbergh, S., Goldenman, G., Pålsson, C., Bengtsson, N., Rydbergh, T., Nilsson, L., Krantz, A., Weber, K., … Lindstad, H. (2013). Support for the impact assessment of a proposal to address maritime transport greenhouse gas emissions. European Commission—DG Climate Action. Konstantinos, K., Nikas, A., Daniil, V., Kanellou, E., & Doukas, H. (2022). A multi-criteria decision support framework for assessing seaport sustainability planning: The case of Piraeus, A multi-criteria decision support framework for assessing seaport sustainability planning: The case of Piraeus. Maritime Policy and Management. https://doi.org/10.1080/03088839.2022.2047815 La Torre, M., Trotta, A., Chiappini, H., & Rizzello, A. (2019). Business models for sustainable finance: The case study of social impact, bonds. Sustainability, 11, 1887. https://doi.org/10.3390/su11071887

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PART III

Fintech and Social Sustainability

CHAPTER 5

Does Fintech Contribute to Fair and Equitable Outcomes? Lakshmi Shankar Ramachandran

5.1

Introduction

In an insightful report titled “Data Point: Credit Invisibles” published in 2015, the CFPB (Consumer Financial Protection Bureau) found that about 11% of the adult population in the United States were credit invisible.1 In other words, more than 25 million consumers did not have any credit history with any of the three nationwide credit reporting companies. The authors also noted that a further 8.3% of the adult population had credit records that were classified as “unscorable” by traditional credit scoring models. These unscorable customers were more likely to be Black, Hispanic, or those living in low-income neighborhoods. The economic and societal implications of these findings are staggering: about one in five adults in the United States is caught in a vicious debt cycle. For example, these consumers will face significant challenges in 1 The report can be downloaded from the following link: https://files.consumerfina nce.gov/f/201505cfpbdata-point-credit-invisibles.pdf.

L. S. Ramachandran (B) Goizueta Business School, Emory University, Atlanta, GA, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_5

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accessing credit, and even if they are successful in getting credit, they will have to pay higher interest rates compared to other consumers. Finally, if they are unable to pay back these debts on time, their credit history will suffer. The situation is further exacerbated by the fact that consumers who are disadvantaged are not randomly distributed across the population; rather, there is significant racial and economic disparity. To address this disparity and to make credit available for a larger section of the population at fair terms, a cohort of entities—both within established financial institutions and independent start-ups, collectively referred to as Fintech firms—are attempting to get better insights by employing Machine Learning algorithms (ML) on alternate data sets. The attraction of these datasets stems from the fact that while some of them directly relate to repayment capacity (history of rent payments or payment of mobile bills), a large number of the variables do not (education level, work history, social network information, internet activity, devices) (Hiller, 2020). While these Fintech initiatives have a noble promise, the question remains: have they lived up to their potential? Anecdotal evidence, particularly those instances that capture the attention of the media and popular culture, paint a conflicting picture. For example, in August 2019, Apple launched Apple Card with much fanfare, but, within months, the product was plagued with controversies.2 Importantly, users noted that women were offered smaller lines of credit than men. In one specific case, a popular tech entrepreneur tweeted that his wife was issued one-twentieth of the credit limit that he received.3 Apple co-founder Steve Wozniak also expressed similar frustration. He tweeted that he and his wife had joint bank accounts and similar credit limits.4 Apple Card, he said, was the only exception: the credit limit offered him was ten times that extended to his wife. The popular magazine Wired picked up this story and reported, “The response from Apple just added confusion and suspicion. No one from

2 For more details, refer to the article titled “Viral Tweet About Apple Card Leads to Goldman Sachs Probe” published by Bloomberg and available at https://www.bloomberg.com/news/articles/2019-11-09/viral-tweet-about-applecard-leads-to-probe-into-goldman-sachs. 3 The tweet cited can be accessed from https://twitter.com/dhh/status/119254090 0393705474. 4 The tweet cited can be accessed from https://twitter.com/stevewoz/status/119342 2616016519168.

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the company seemed able to describe how the algorithm even worked, let alone justify its output.”5 When confronted, Goldman Sachs, the bank that issued the card, vehemently defended its algorithm saying that it did not use gender as an explanatory variable. It further asserted that its algorithm had been independently evaluated for potential biases. Responding to the outrage, the New York State Department of Financial Services launched a legal investigation into whether the Goldman Sachs Apple Card violated fair practices.6 The controversy surrounding Apple Card is not an isolated incident. Upstart Network is a consumer lending platform that uses sophisticated machine learning models and alternate datasets to auto-mate lending decisions. Besides traditional credit scores, it uses several alternate nonfinancial data such as educational information.7 In 2017, it sought a no-action letter8 from the Consumer Financial Protection Bureau (CFPB). In its request, Upstart stated that “Upstart requests a NoAction Letter regarding regulatory uncertainty that hinders development and expansion of loan products offered through Upstart. Specifically, Upstart is requesting a No-Action Letter to address regulatory uncertainty surrounding the sufficiency of its efforts to ensure compliance with ECOA and Regulation B, with respect to a model for applicants for unsecured non-revolving credit who would otherwise not receive such credit on as favorable terms”.9 In essence, as a young start-up firm using proprietary models and alternate datasets, Upstart sought immunity from 5 For more details, refer to the article titled “The Apple Card Didn’t ‘See’ Gender - and That’s the Problem” that is available at https://www.wired.com/story/the-applecard-didnt-see-genderand-thats-the-problem/. 6 For more details, refer to the article titled “Apple’s ‘sexist’ credit card investigated by US regulator” that is available at https://www.bbc.com/news/business-50365609. 7 Hiller (2020) provides an excellent review of the Upstart case study. 8 As clarified by US Securities and Exchange Commission (SEC), “An individual or

entity who is not certain whether a particular product, service, or action would constitute a violation of the federal securities law may request a “no-action” letter from the SEC staff. Most no-action letters describe the request, analyze the particular facts and circumstances involved, discuss applicable laws and rules, and, if the staff grants the request for no action, concludes that the SEC staff would not recommend that the Commission take enforcement action against the requester based on the facts and representations described in the individual’s or entity’s request.” For more inputs, please visit SEC’s website: https:// www.investor.gov/introduction-investing/investing-basics/glossary/no-action-letters. 9 Upstart’s letter can be accessed from https://files.consumerfinance.gov/f/docume nts/201709_cfpb_upstart-no-action-letter-request.pdf.

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being charged with fair lending law violations with respect to its underwriting algorithms. In September of 2017, as requested, CFPB provided Upstart with a no-action letter. This response represented a significant turning point in the Fintech industry as it demonstrated that the regulator was willing to accept lenders using sophisticated ML models and alternate datasets. In 2020, Upstart received another no-action letter from CFPB; however, in July, the Student Borrower Protection Center (SBPC) published a scathing analysis of Upstart’s lending decisions.10 In particular, it noted that Upstart was charging higher interest rates for graduates from historically Black colleges. It is interesting to juxtapose this anecdotal evidence with some recent results from academic literature. For example, Bartlett et al. (2022) document that African American and Latino borrowers were charged between 3.6 and 7.9 basis points more for original and refinance mortgages. Importantly, the authors find that Fintech lender rate disparities are similar to—and occasionally less than—that of traditional lenders. In particular, they find that “Fintech lenders’ rate disparities were similar to those of non-Fintech lenders for GSE mortgages, but lower for FHA mortgages issued in 2009–2015 and for FHA refi mortgages issued in 2018–2019.” These studies reinforce the need for us to think more deeply about whether ML models and alternate datasets have had material success in mitigating racial and economic disparities in consumer finance. In this chapter, we draw upon the evidence, both from academia and industry, to provide an unbiased response to this question. In the next section, we briefly review Machine Learning (ML) models, their relevance for consumer lending, and the course of proxy variables. In the third section, we delve further into evaluating fairness. Specifically, we review the regulatory guidance and define the relevant fairness measures. In the final section, we discuss the path forward.

10 The article titled “Fintech Lenders’ Responses to Senate Probe Heighten Fears of Educational Redlining” can be accessed from here: https://protectborrowers.org/fintechlenders-response-to-senate-probe-heightens-fears-of-educational-redlining.

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5.2 Machine Learning, Alternate Datasets, and Consumer Lending 5.2.1

Machine Learning in Finance

Over the past few decades, significant strides have been made in algorithms that are successful in finding patterns in high-dimensional datasets. These techniques, broadly referred to as Machine Learning (ML), help researchers and businesses make better predictions. This edge in prediction accuracy can be attributed to several factors, including (but not limited to) the ability of these flexible models to uncover non-linear relationships in a high-dimensional space (Gu et al., 2020; Kleinberg, Lakkaraju, et al., 2018). Indeed, Kleinberg, Lakkaraju et al. (2018) note that “machine learning represents a pragmatic breakthrough in making predictions, by finding complex structures and patterns in data.” Over the years, predictions have not only become better but also cheaper and faster (Agrawal et al., 2018). This fundamental shift in the economics of predictions helps us better appreciate the widespread adoption of Machine Learning techniques in various industries. As might be expected, ML techniques have been widely embraced by the banking and finance industry. Focusing on consumer lending, the two problems of interest are: (a) should credit be extended to a particular consumer? (b) if yes, what is the appropriate rate to charge the consumer? In answering these questions, Fintech firms have leveraged ML models because such models can not only find unexplored patterns among variables that have been traditionally used in such applications, but they can also find patterns while using a multitude of new variables, the high dimensionality of the resultant problem notwithstanding. For example, Berg et al. (2020) show that digital footprints left by users while interacting with a website can be used to build effective credit scoring models. The credit scores thus obtained can supplement traditional credit scores. This, in turn, affects access to credit and reduces delinquency rates. Fuster et al. (2019) show that Fintech lenders who use end-to-end online platforms, centralized mortgage underwriting, and processes automated by algorithms, process applications faster. More importantly, they show that this efficiency does not come at the expense of higher defaults.

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5.2.2

The Curse of Proxies

As adoption of advanced ML models and alternate datasets becomes more widespread, and stakeholders and regulators are increasingly interested in: are these models mitigating or exacerbating extant disparities in lending decisions? Before we answer the above question, it might be beneficial to briefly review the two-step procedure typically employed for making predictions using ML techniques.11 ,12 In the first step, called the training stage, the model is fed historical data containing explanatory variables (referred to as features) and the outcome variable. The model then infers the relation between the outcome variable and the explanatory variable. In the second step, this inferred relation is applied to new data where a prediction is sought. In the context of lending decisions, the model would be fed a multitude of inputs about the consumer (i.e., features) and the final outcome (either a continuous variable such as the interest rate for the loan or a binary variable representing future default). The model then tries to understand the relationship between consumer features and the final lending decision. A basic understanding of how ML models work helps us to immediately understand one of its severe limitations: because ML models find patterns in data that they are trained on, it merely codifies any discrimination or disparity in existing data. Left unchecked, these models can perpetuate and accelerate bias. Indeed, Weber et al. (2020) assert that “machine learning can become a vehicle for perpetuating and even accelerating prejudice in a reinforcing loop of bias training data.” We can illustrate this crucial point with the Goldman Sachs Apple Card example. The issuer bank, here Goldman Sachs, presumably trained its model on a historical lending dataset. We can assume that the lending decisions—here, credit limits to be extended to the new consumer—in the dataset were made by human loan officers. Now, if these loan officers had discriminated against consumers on the basis of gender (or its proxy) in the past, it would be reflected in the dataset. More importantly, since ML

11 For a standard textbook treatment on Machine Learning, please refer to the second chapter of the excellent book by James et al. (2013). 12 Given the prediction problem under consideration, we focus only on supervised learning techniques.

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models find patterns in existing data, the predictions made by the trained ML model would be similarly discriminatory in nature. It is important to appreciate that the defense of the issuer bank that its algorithms were not discriminatory on the grounds that it did not use gender as an explanatory variable is potentially misleading. Although gender may not have been included as a feature, it is plausible that variables that are highly correlated to gender—say, the shopping history of the consumer—were fed as inputs to the model during the training stage. Because ML models excel in finding patterns in high-dimensional datasets, it is quite likely that the model picked up the discriminatory relation by zooming in on one such correlated variable. These variables are commonly referred to as proxy variables and their use has been hotly contested. Not surprisingly, proxy variables have been at the center of intense regulatory debate, even during the pre-ML era. The curse of the proxy variable is also evident from a deeper analysis of the second case study that we considered: Upstart Network. In detailed responses provided to a Senate subcommittee, Upstart claimed that its algorithm used over 1500 variables.13 Further, it asserted that no single variable, or even a small subset of variables, dominated the final prediction. However, it qualified this by saying that “the education variables are one subset of variables present in our model that help us to identify which consumers are creditworthy, and in combination with the others significantly increase the accuracy and predictive value of our model.” However, there is a wide body of academic literature that has repeatedly affirmed the high correlation between race and educational factors.14 Hence, as with Apple Card, while Upstart might not have used race as an input variable in its model, employing educational factors that act as a proxy for race could potentially lead to discriminatory decisions. This conclusion receives further support from the SBPC report, which was based on the responses that Upstart provided to the regulators.15 In particular, they note that Upstart groups schools through the use of average incoming standardized test scores. Based on further inputs available from the Department of Education College Scoreboard, they note 13 Upstart’s responses to the Senate subcommittee can be accessed from https://www. banking.senate.gov/imo/media/doc/Educational%20data%20-%20appendix.pdf. 14 See Hiller (2020) and the references therein. 15 SBPC’s comments can be accessed from https://protectborrowers.org/Fintech-len

ders-response-to-senate-probe-heightens-fears-of-educational-redlining/.

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that only two Historically Black Colleges or Universities (HBCU) would fall within the top half of Upstart’s educational groups. Furthermore, 96 percent of HBCUs would fall in the bottom half of Upstart’s educational groups. To the extent that educational details play a key role in Upstart’s lending decisions, it is difficult to argue that the firm was fully compliant with fair lending practices. Not surprisingly, the Senate Committee called on CFPB to closely scrutinize lenders’ usage of educational data as they may violate fair lending laws. Can modelers successfully win the battle against proxies? In a recent article, Fuster et al. (2022) argue that that battle might be a difficult one to win. They show that Black and Hispanic borrowers are less likely to gain from the introduction of machine learning to the credit disbursal process. They build an equilibrium model that predicts that machine learning increases disparity based on race. More importantly, they attribute this disparity to the flexibility of these models, a key factor that enables these models to make better predictions. To quote the authors (pp. 6–7). Default outcomes can generically depend on both “permissible” observable variables such as income or credit scores, and “restricted” variables such as race or gender. We focus on the case in which lenders are prohibited from using the latter set of variables to predict default but can freely apply their available technology to the permissible variables. In this setting, the first flexibility mechanism that we consider is that the additional flexibility available to the more sophisticated technology allows it to more easily recover the structural mapping between permissible variables and default outcomes. Another possible mechanism, triangulation, is that the structural relationship between permissible variables and default is perfectly estimated by primitive technology but the more sophisticated technology can triangulate the effect of the unobserved restricted variables on the outcome by more effectively and accurately combining the observed permissible variables. In the latter case, some groups may be penalized or rewarded based on realizations of the permissible variables, as the more sophisticated technology “de-anonymizes” group identities in the data using the permissible variables.

They validate their intuition through the use of rigorous empirical analysis. They start with a large administrative dataset of about ten million mortgages originating in the United States between 2009 and

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2013. From this dataset, they observe several borrower characteristics (such as race, gender, and ethnicity), mortgage characteristics, and default outcomes. They then develop several baseline and advanced ML models for predicting borrower default. Based on their analysis, they conclude: We confirm that the machine learning technology delivers statistically significantly higher out-of-sample predictive accuracy for default than the simpler logistic models. We also find that predicted default propensities across race and ethnic groups look very different under the more sophisticated technology than under the simple technology. In particular, while a large fraction of borrowers who belong to the majority group (e.g., White non-Hispanic) “win,” that is, experience lower estimated default propensities under the machine learning technology than the less sophisticated Logit technology, these benefits do not accrue to some minority race and ethnic groups (e.g., Black and Hispanic borrowers) to the same degree. We show that these inferences are robust to numerous changes to the set of covariates, the sample used for estimation, and the estimation approach.

The effect of proxy variables on algorithmic discrimination is wellstudied and documented. Given the significance of consumer lending to the economy, it is reasonable to wonder what the regulator’s stance on evaluating fairness in algorithmic lending is. Indeed, this discussion raises two important questions: (i) How do regulators measure and evaluate fairness? and (ii) How do financial institutions test their models for fairness?

5.3 5.3.1

Evaluating Fairness Regulatory Guidelines

Consumers in the United States are protected against discrimination by two laws: the Fair Credit Reporting Act (FCRA) and the Equal Credit Opportunity Act (ECOA). FCRA requires credit bureaus to ensure that the information in consumer credit reports are accurate, fair, and private. ECOA, on the other hand, prohibits lenders from discriminating against consumers based on attributes such as gender, race, religion, age, and marital status. Such attributes are collectively referred to as protected attributes. (In this chapter, we focus on the ECOA guidelines.) It is worth noting that these acts were in response to documented cases of discrimination based on attributes such as race and gender. For example,

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the eminent sociologist Joe McKnight introduced the term “redlining” to describe the practice of discriminating against Black neighborhoods; consumers in such neighborhoods were deemed to pose higher mortgage default risks. An associated form of discrimination, referred to as “reverse redlining,” has also been widely documented. Under this practice, predatory lenders extend credit to targeted minorities at usurious terms that they cannot afford. ECOA clearly spells out two forms of discrimination: disparate treatment and disparate impact . Disparate treatment is the act of intentionally treating a consumer differently based on protected characteristics such as gender or race. While creating alternate datasets, it is important to be aware that such treatment could occur either in the input stage (when a protected attributed is used as a feature) or in the design stage (when a close proxy of a protected attribute is used as a feature). Disparate impact, on the other hand, does not require an explicit intent. Rather, the policy of a lender is said to have disparate impact if it has a disproportionately adverse effect on a protected group. While there is a rich literature that reflects on the differences between these two forms of discrimination, for the sake of simplicity, we will not distinguish between these two forms. Given the clear articulation of what constitutes discrimination for a lending policy, it is indeed surprising that an episode of discrimination as blatant as that of the Goldman Sachs Apple Card was allowed to occur at all. Clearly, we are missing a piece of the puzzle; it seems inconceivable that a sophisticated lender such as Goldman Sachs would be negligent about basic compliance requirements; and while we might never know what transpired behind the scenes, Weber et al. (2020) present an excellent analysis based on various reports that were available at that time. In this section, we draw heavily from their insights. Before we delve further into the situation, it will be useful to review certain regulatory guidelines. While building sophisticated ML models that make accurate predictions is important for a financial institution, it is equally important for the institution to monitor the risk attached to such models. In handling such model risk, a financial institution must adhere to two regulatory operational guidelines. First, it needs to ensure that model builders are not privy to the processes and measures that model validators use to verify the fairness of models. This guideline mitigates the chances of model builders gaming the system. Second, neither model builders nor model validators are permitted to access protected features such as gender and race. In essence, all stakeholders are expected to be blind to

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such protected data. As Weber et al. (2020) emphasize, this policy guideline poses an operational challenge: if validators are blind to protected features, how can they ensure that the model is fair on these grounds? To address this challenge, CFPB provides guidance on how to leverage publicly available data to construct proxies for protected features such as race and ethnicity.16 These guidelines constitute the bedrock of model validation as it applies to validating models for fairness. While these operational guidelines ensure a fair process, the underlying question remains: how do financial institutions measure fairness? 5.3.2

Fairness Measures

5.3.2.1 Group Fairness Mathematical measures of fairness come in two flavors: group fairness and individual fairness. Let us begin with the more commonly used measure in consumer lending: group fairness (also referred to as statistical parity). To measure fairness, the financial institution first partitions its users based on a certain protected attribute (or more appropriately, its proxy). Next, for each partition, it computes a certain group-level metric. If this grouplevel metric is nearly equal across all partitions, then the model is said to be fair. In practice, banks employ a range of metrics, each signaling a different dimension of fairness. For example, a bank could partition users on a proxy for race and compare the percentage of loans approved for each partition. If equal, it can be concluded that there is no racial disparity in loan approvals. Alternatively, institutions could use conditional metrics such as false positive or true negative rates to ensure that the conditional odds are not dramatically disparate across different partitions. It is worth emphasizing that different metrics could potentially lead to different conclusions.17 Are group-level fairness measures adequate defenses? Weber et al. (2020) argue that they are not. Referring to the Goldman Sachs Apple Card fiasco, they note that it is unreasonable to assume that the issuing bank would not have tested its model for group-level fairness based on gender. To explain the disparity, the researchers introduce the notion of 16 CFPB’s Office of Research creates a proxy for race and ethnicity from census data on geography and surname using a methodology called Bayesian Improved Surname Geocoding. 17 For an interesting case study on this subject, refer to Humerick (2019).

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subgroup discrimination. They use a hypothetical example to show how such a disparity might have come about in the Apple Card example. Suppose that the model used by the issuer bank was constrained to ensure group fairness using gender as a constraint, i.e., it ensures that the predicted outcome (here, the credit limit) is not disparate across different genders. Further, suppose that while trying to maximize accuracy within this constrained optimization problem, the model focuses on personal income as the primary explanatory variable. The predictions of this model are easy to visualize. Assume there are three levels of personal income: high, medium, and low. For each level, the approved credit limit would be similar across men and women. Now, let us consider an applicant who is female and a homemaker. Since she falls within the lowest income category, her approved credit limit would be low, but not lower than a male with no or low income. This, in turn, leads to a disturbing possibility: women homemakers, who share assets with their spouses and file joint taxes, will be approved at a significantly lower credit limit than their spouses. Weber et al. (2020) note that many of the complaints against Apple Cards stemmed from married homemakers with shared assets. The example illustrates why a model that is seemingly non-discriminatory on a protected attribute under group fairness could still discriminate against users of a random subgroup. How might financial institutions address this problem? It is tempting to argue that the issuer could have tested relevant subgroups at the intersection of gender and occupation for subgrouplevel parity. Unfortunately, this solution misses the big picture. As the number of features used by an ML model increases, the number of potential subgroups that it must consider increases exponentially. Evaluating fairness for this bewildering array of subgroups might not be feasible, or even desirable. What then is the way forward? 5.3.2.2 Individual Fairness Our discussion provides a natural segue to the second kind of fairness measure commonly examined in literature: individual fairness. While the goal of group fairness is to ensure that the average member of a group is not discriminated against, individual fairness strives to ensure that there is no disparity at the individual level. Proposing individual fairness as the more appropriate measure for fairness (as against group fairness, or statistical parity), Dwork et al. (2012) write in their highly impactful article titled “Fairness Through Awareness” (p. 215):

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Statistical parity is the property that the demographics of those receiving positive (or negative) classifications are identical to the demographics of the population as a whole. Statistical parity speaks to group fairness rather than individual fairness, and appears desirable, as it equalizes outcomes across protected and non-protected groups. However, we demonstrate its inadequacy as a notion of fairness through several examples in which statistical parity is maintained, but from the point of view of an individual, the outcome is blatantly unfair.

While seemingly more appropriate, a reasonable implementation of individual fairness poses several hurdles. First, the objective that similar individuals should receive similar treatment assumes that there is an unambiguous way of quantifying similarity across individuals. Second, individual fairness may not necessarily imply group fairness, the principal factor underpinning current regulations. Finally, it is not apparent whether improving individual fairness leads to an improvement in prediction accuracy. Recent developments in research on fairness have helped us to address these concerns. For example, building on the work of Dwork et al. (2012), Yurochkin et al. (2019) introduce a measure called Distributionally Robust Fairness (DRF) that might be viewed as the first practical approach to achieving individual fairness. Weber et al. (2020) propose an algorithm called SenSR to enforce DRF. To better understand the impact of their approach, the authors employ their algorithm in two different experiments initially discussed in Yurochkin et al. (2019). In the first experiment, Weber et al. (2020) posit that in a world devoid of racism, common African-American names should elicit the same sentiment as other names. However, they note that when traditional Natural Language Processing (NLP) models are trained using existing data to predict a sentiment for names, the resultant model has a sharp racial bias. Weber et al. (2020) find that their approach effectively removes this bias. More importantly, this improvement does not come at the cost of less accuracy. In the second experiment, which is more relevant to the Goldman Sachs Apple Card case study, Weber et al. (2020) attempt to predict individual’s income based on the census data. Baseline models tend to overlook the fact that one of the key variables—relationship status—is closely correlated to gender and could inadvertently result in subgroup discrimination. The authors find that their approach produces a

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fairer result. Interestingly, they note that using group fairness yields results that are inferior even to some baseline models.

5.4

The Path Forward

Four dominant themes emerge from our discussion: 1. While Machine Learning models can achieve high levels of prediction accuracy, left unchecked, they are likely to perpetuate and accelerate any bias in the underlying training data. 2. The gravity of such bias creep is particularly significant in consumer lending, where consumers of certain races, ethnicity, and gender have faced historical discrimination. 3. While existing regulations have well-intentioned checks and balances to mitigate the curse of proxy variables, Machine Learning models can inadvertently propagate newer forms of discrimination, such as subgroup discrimination. 4. Measuring fairness is not trivial. Regulators have traditionally relied on group fairness. However, given the bewildering array of subgroups that are possible, it might be better to constrain models on the basis of individual fairness. (The literature in this area is still at a preliminary stage.) What is the path forward for algorithmic decision-making in consumer finance? We believe that answers to this question fall into two camps. The first camp, which we dub the cynical camp, believes that fairness in Fintech is an eternally uphill battle. This camp holds the view that the challenges of biases in historical data are insurmountable. Further, despite research to the contrary, this group also believes that increased fairness and reduced accuracy are not compatible objectives. The second camp, which we refer to as the optimistic camp, subscribes to the notion that fairness in Fintech is a work in progress. This camp understands the limitations of the current models but is hopeful that future research will be effective in addressing model limitations. We would like to conclude this chapter by presenting another interesting side of the debate on algorithmic fairness that we hope would shed a different light on the issue. In a popular New York Times article provocatively titled “Biased Algorithms Are Easier to Fix than Biased People,” Sendhil Mullainathan, one

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of the co-authors of an influential paper by Kleinberg, Ludwig and et al. (2018) and Obermeyer et al. (2019), summarized several interesting findings from these two studies.18 First, while algorithms are prone to propagate bias in the training data, it is easier to uncover algorithmic biases than human biases. Second, once a bias is discovered, it is easier to fix in algorithms than it is in humans. Mullainathan drives this point home by painting a rather amusing picture: “Changing algorithms is easier than changing people: software on computers can be updated; the ‘wetware’ in our brains has so far proven much less pliable.” We believe that therein lies the promise of resolving a principal problem of Machine Learning for Fintech: while decades of discrimination based on gender, race, and ethnicity almost guarantees that discrimination in algorithms is inevitable, it is easier to discover and remedy biases in algorithms than it is in humans.

References Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Press. Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the Fintech era. Journal of Financial Economics, 143(1), 30–56. https://doi.org/10.1016/j.jfineco.2021.05.047 Berg, T., Burg, V., Gombovi´c, A., & Puri, M. (2020). On the rise of Fintechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897. https://doi.org/10.1093/rfs/hhz099 Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pp. 214–226. https://doi.org/10.48550/arXiv. 1104.3913 Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? the effects of machine learning on credit markets. The Journal of Finance, 77 (1), 5–47. https://doi.org/10.1111/jofi.13090 Fuster, A., Plosser, M., Schnabl, P., & Vickery, J. (2019). The role of technology in mortgage lending. The Review of Financial Studies, 32(5), 1854–1899. https://doi.org/10.1093/rfs/hhz018

18 The article can be accessed https://www.nytimes.com/2019/12/06/business/alg orithm-bias-fix.html.

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Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10. 1093/rfs/hhaa009 Hiller, J. S. (2020). Fairness in the eyes of the beholder: Ai; fairness; and alternative credit scoring. West Virginia Law Review, 123, 907. Humerick, J. D. (2019). Reprogramming fairness: Affirmative action in algorithmic criminal sentencing. HRLR Online, 4, 213. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer. Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human decisions and machine predictions. The Quarterly Journal of Economics, 133(1), 237–293. https://doi.org/10.1093/qje/qjx032 Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein, C. R. (2018). Discrimination in the age of algorithms. Journal of Legal Analysis, 10, 113–174. https://doi.org/10.1093/jla/laz001 Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342 Weber, M., Yurochkin, M., Botros, S., & Markov, V. (2020). Black loans matter: Distributionally robust fairness for fighting subgroup discrimination. https:// doi.org/10.48550/arXiv.2012.01193 Yurochkin, M., Bower, A., & Sun, Y. (2019). Training individually fair ml models with sensitive subspace robustness. https://doi.org/10.48550/arXiv. 1907.00020

CHAPTER 6

Fintech, Financial Inclusion, and Social Challenges: The Role of Financial Technology in Social Inequality Simona Cosma and Giuseppe Rimo

6.1

Financial Inclusion and the 2030 Agenda

In September 2015, the United Nations General Assembly adopted the 2030 Agenda for Sustainable Development. This agenda, born out of an awareness of the unsustainability of the current development model, aims to achieve 17 Sustainable Development Goals (SDGs) and 169 related targets. The 17 goals address a range of key development issues that balance the three dimensions of sustainable development: economic, social, and environmental (Agency for Territorial Cohesion, 2020). The United Nations SDGs cover many aspects of human life. They are linked to two fundamental principles: no country or individual should be left

S. Cosma (B) University of Bologna, Bologna, Italy e-mail: [email protected] G. Rimo University of Salento, Lecce, Italy e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_6

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behind, and no SDG should be pursued to the detriment of others (Ferrata, 2019). In the sustainable development agenda, equitable and inclusive growth takes a leading role. It is, therefore, essential to link macroeconomic growth with a social growth process (Mok, 2000). Socio-economic development can therefore favor the achievement of new development goals and can contribute to the reduction of poverty, especially in emerging countries (Mazumder & Lu, 2015; Montgomery & Weiss, 2011). Presently, poverty is one of the biggest issues that affect society. The lack of economic resources and the inability of the poor to obtain financial resources make them even more vulnerable to cyclical poverty (Samat et al., 2018). External financing is indeed one of the factors that can lift these people out of this precarious situation (Noreen et al., 2011). In their study, Galor and Zeira (1993) demonstrate that in the presence of imperfect credit markets characterized by information asymmetries, poor families cannot invest in their education. Similarly, Banerjee and Newman (1993) show that credit market imperfections hamper entrepreneurial initiatives by low-income households. Such evidence in the literature suggests that wider access to finance can aim to reduce poverty and inequality (Demir et al., 2022). To this end, financial inclusion is a key issue whose treatment is critical to achieving socioeconomic development at the individual level (Bruton et al., 2015), to improve individuals’ livelihoods and facilitate their access to the formal financial system (Montgomery & Weiss, 2011; Solesbury, 2003). Extending access to financial services to people with difficulty was the subject of discussion during the consultations that led to the publication of the 2030 Agenda. However, the final document takes an unambitious approach to this issue (Queralt et al., 2017). Indeed, financial inclusion is not represented among the 17 SDGs; instead, its importance is diluted through reference to the 169 associated goals. Greater access to financial services such as loans, deposits, and payment systems can be a factor that facilitates the implementation of the Sustainable Development Agenda (Ferrata, 2019). According to Ferrata (2019), finance is heavily involved in at least nine of the 17 SDGs, directly impacting issues related to living conditions and economic development. Achieving the 17 SDGs would therefore be difficult without including the banking system (Worldbank.org, 2016). According to a report by the McKinsey Global Institute, the spread of digital finance could add $3.7 trillion to the GDP of emerging economies within ten years (McKinsey

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Global Institute, 2016). The results of a study on the long-term impact of a mobile money service in Kenya show that mobile money, or the provision of electronic money on cell phones, has lifted up to 194,000 households, or 2% of Kenya’s population, out of poverty (Suri & Jack, 2016). There is also growing evidence of financial inclusion as a factor that can add stability to the financial system (UNCFD, 2020). According to Queralt et al. (2017), including financial inclusion in the SDGs would have raised global awareness of the urgent financial needs of the poor. Therefore, the downgrade to a mere target represents a missed opportunity to strengthen global efforts to build more inclusive financial systems.

6.2

Financial Inclusion: Data and Evidence

Financial inclusion (FI) refers to a process that facilitates access to formal financial services for all members of an economy (Sarma & Pais, 2011). Financial inclusion has become the new buzzword in sustainable development practices. Its popularity is likely a result of the awareness that universal access to financial services can play a key role in alleviating poverty, reducing inequality, and achieving sustainable economic development (Beck et al., 2007; Demirgüç-Kunt & Levine, 2009; DemirgüçKunt et al., 2015). This awareness has led to the creation of the Global Partnerships for Financial Inclusion by G20 leaders (GPFI, 2017). According to Sarma and Pais (2011), an inclusive financial system can facilitate the efficient allocation of productive resources, improve day-today finance management, and counter the emergence of informal money lending businesses. Thus, a comprehensive financial system can improve efficiency and well-being by providing secure savings mechanisms and a range of efficient financial services (Sarma & Pais, 2011). The phenomenon of financial inclusion arises from the speculative phenomenon of financial exclusion, a current problem that has increasingly attracted the attention of institutions and regulators. According to a European Commission report, financial exclusion refers to a process in which “people experience difficulties in accessing and using financial services and products on the regular market that meet their needs and enable them to lead a normal social life in the society to which they belong” (European Commission, 2008). Unemployment, low income levels, and excessive transaction costs are among the main factors of

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financial exclusion. Excluding factors also include poor housing conditions, high distance from bank branches, loss of trust in the services provided, insecurity in the community resulting from cybercrimes, and cultural factors (Anyanwu & Anyanwu, 2017). This means that barriers to financial inclusion can be related to supply and, therefore the cost of products, organization, level of development, and the nature of the financial market in a given region, but they can also be related to demand and therefore caused by the user’s financial incapacity or cultural factors (European Commission, 2008). In 2014, approximately 2 billion adults were excluded from the financial system (Demirgüç-Kunt et al., 2018). To date, an estimated 31% of the world’s population is “unbanked”, and an even higher percentage does not have access to secure forms of credit or savings (Demirgüç-Kunt et al., 2018). The Global Financial Inclusion Database provided by the World Bank, with over 800 indicators of financial inclusion in more than 150 economies, provides a quick overview of financial exclusion figures and the reasons that reinforce this phenomenon. Analyzing the data in Table 6.1, grouped based on the per capita income of the various economies, it is clear how the percentage of accounts held by banks or other financial institutions drops dramatically as one moves towards low-income countries. While in high-income countries, the percentage of respondents who report having a bank account is 96.35%, in low-income countries this percentage drops to 23.88%. The same is true when the analyzed variable concerns the percentage of people with internet access, which is much lower in low-income countries, or when the percentage of credit card holders is analyzed. Among the reasons why people are unbanked, that is, that they do not have an account at a bank or financial institution, are organizational barriers, such as being too far away from bank branches and, in general, from financial institutions, but also socio-cultural and income-related factors. Figure 6.1 shows the main reasons why individuals are unbanked. The most important reason is income-related: a high percentage of respondents say they do not have an account because they do not have enough money to use an account; another major barrier is the perceived high cost of financial services. In addition, Table 6.1 shows that in low-income countries, a high percentage of respondents report not having saved or set aside money for any specific purpose (43.96% in low-income countries). However, the number of respondents who deposited their savings in a bank is much

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Table 6.1 Indicators of financial inclusion in 2021 Indicators (%)

Low income

Banked and unbanked Financial institution accounts Inactive account Made digital payment Own a credit card Saved any money Saved at a financial institution Have access to the internet The Why of the excluded Financial institutions are too far away Financial institutions are too expensive Lack of trust in financial institutions Religious reasons

Lower middle income

Upper middle income

High income

23.88

58.5

83.75

96.35

2.76 32.71

15.12 30.4

2.38 76.42

0.6 92.42

3.29

4.08

32.73

57.42

43.96 8.49

32.55 12.67

54.02 35.86

76.14 57.94

24.85

38.18

81.35

90.62

36.46

32.95

25.27

NA

32.76

37.05

35.68

NA

27.64

21.35

24.66

NA

11.18

10.01

8.9

NA

Notation High income: per capita income greater than $12,696. Upper middle income: per capita income between $4,096 and $12,695. Middle income: per capita income between $1,046 and $12,695. Lower middle income: per capita income between $1,046 and $4,095. Low income: per capita income less than $1,045 Source Authors’ processing of The World Bank data retrieved from the database Global Financial Inclusion https://databank.worldbank.org/source/global-financial-inclusion

lower (8.49% in low-income countries). This shows that there is a large percentage of savers who prefer or tend to use non-traditional savings instruments. This may be due to the reasons mentioned above as well as to a lack of trust in financial institutions or to the excessive bureaucracy required by banks to open a checking account (see Fig. 6.1). Such barriers exacerbate financial exclusion even in high-income or better-off countries, especially in crisis periods. The 2007 global financial crisis and

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100% No account because of religious reasons (% without an account, age 15+)

90% 80%

No account because of insufficient funds (% without an account, age 15+)

70%

No account because of a lack of trust in financial institutions (% without an account, age 15+)

60% 50%

No account because of a lack of necessary documentation (% without an account, age 15+)

40% 30%

No account because financial services are too expensive (% without an account, age 15+)

20%

No account because financial institutions are too far away (% without an account, age 15+)

10% 0% Low Income

Lower middle income Upper middle income

Fig. 6.1 Main barriers to financial inclusion in 2021 (Source Authors’ processing of The World Bank data retrieved from the database Global Financial Inclusion https://databank.worldbank.org/source/global-financial-inclusion)

the Eurozone debt crisis have had various consequences, including a sharp credit crunch and an increase in unemployment, which inevitably have repercussions on social and financial inclusion (Gómez Urquijo, 2015). According to the European Consumers’ Union, the financial crisis exacerbated difficulties in accessing financial services and has raised new concerns and doubts among consumers about the future of their deposits, loans, and personal savings. These problems clearly affect the most vulnerable groups, such as the unemployed, immigrants, people with low incomes, and/or people with low financial literacy (Sarma & Pais, 2011).

6.3 The Role of Fintech in the Pursuit of Financial Inclusion Where information asymmetries, market segmentation, and excessive transaction costs do not allow the most vulnerable individuals to access the financial markets and nor aspire to change their status, new technologies can play the role of financial inclusion accelerator (Demir et al.,

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2022). Fintech, understood as the application of technology in the provision of financial services, foresees new financing models emerging outside the traditional financial system. These are often based on online platforms or websites that connect fundraisers directly with funders and investors. Such models include peer-to-peer lending tools, digital payment systems, or crowdfunding platforms. Alternative finance is rapidly changing the landscape of financial inclusion, proving to be able to overcome all the limitations that characterize the traditional financial system (Makina, 2019). 6.3.1

Call to Action for Financial Inclusion

To date, academics, practitioners, and regulators recognize the role of fintech as a catalyst for financial inclusion. The Global Partnership for Financial Inclusion (GPFI) think tank of G20 leaders has officially recognized digital solutions as key to advancing financial inclusion (GPFI, 2016). In 2017, the G20 Financial Inclusion Action Plan (FIAP) underwent revision to emphasize the role digital can play in promoting financial inclusion that benefits all countries and all people, particularly less affluent and vulnerable groups (GPFI, 2017). In November 2018, the United Nations established a special task force to recommend strategies for harnessing the potential of fintech to advance the goals of the 2030 Agenda (UN, 2018). The creation of the task force stems from findings related to the growth in the number of people with financial or mobile money accounts in developing countries between 2010 and 2017. Indeed, it is estimated that in just six years, 1.2 billion people gained access to financial services thanks to the spread of technology (Arner et al., 2020). The Task Force’s latest report illustrates how digitization has the potential to provide large amounts of funding to achieve the SDGs through more data and innovative business models. This report includes a call to action with three recommendations for using digitization in creating a citizen-centric financial system in line with the SDGs (UN, 2020). The recommendations include: • Promoting financing that catalyzes improvements in SME finance and encourages consumer spending that is consistent with the Sustainable Development Goals. • Establishing a foundation for sustainable digital infrastructure to drive the development of digital finance.

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Table 6.2 Key roles of different actors Actors

Key roles

Policymakers

Create standards to foster cooperation with innovators, drive market development in support of sustainable development priorities, and empower citizens about digital finance risks Cooperate to build common infrastructure, regulations, and approaches Innovate products and services to meet consumer demand while channeling finance towards sustainable development goals Identify the growth margins of its products and systems and collaborate in the definition of international standards Providing technical assistance and support for the development of inclusive infrastructures at the service of citizens Sharing its know-how to help governments formulate projects aligned with the Sustainable Development Goals Document problems of various interest groups and propose possible solutions

Member states Fintech companies and platforms

Financial institutions

International development community

Development finance institutions

Civil society organizations

Source https://www.un.org/en/digital-financing-taskforce

• Developing regulations and standards for digital finance. The Call-to-Action invites public and private actors to take action in seven different categories, each of which is assigned a key role (Table 6.2). The objectives set by the Task Force are ambitious and their realization depends on collaboration between all the subjects called to act. Fintech companies, financial institutions, and the collaboration between them will play pivotal roles in promoting inclusive digitization in the service of sustainable development. The United Nations Task Force strongly supports the view that fintech is the most important accelerator for achieving the SDGs (Arner et al., 2020). 6.3.2

Fintech and Financial Inclusion: Evidence in the Literature

According to a World Bank report (2016), more households own a mobile phone than those with access to electricity or clean water.

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Increasing mobile penetration can play an important role in promoting financial inclusion and reducing income inequality, especially in developing countries (Demir et al., 2022). According to Alexander et al. (2017), the Fintech sector attracted $19 billion from investors in 2016, up from $12.2 billion in 2014. The report highlights the three main channels through which Fintech companies are influencing the financial sector: • Promoting more efficient financial services. Indeed, more and more banks are turning to fintech innovations to improve the delivery of digital services, especially in emerging markets. • Greater flexibility in providing affordable and accessible products and services. In fact, unlike traditional banks, fintech companies are particularly quick to adapt their service offerings based on consumer behavioral data. Their ability to innovate also allows them to rapidly expand their customer base. • Offering non-formal banking services. Fintech offers similar services to traditional banks but are not subject to the same regulation as banks. PricewaterhouseCoopers’ 2017 Global Fintech Report highlights two parallel phenomena, the rise of consumers who plan to increase their use of non-traditional financial service providers and, at the same time, the rise of traditional financial institutions embracing the fintech phenomenon and seeing it as an opportunity to expand their customer base by capitalizing on the community of unbanked customers and those who are reluctant to rely on traditional financial solutions. Various studies suggest that mobile technology can extend access to the formal banking system in countries where most of the population is unbanked but has a mobile phone. Much of the scholarship concludes that information and communication technologies (ICTs) and Fintech can promote financial inclusion, reduce the cost of services, and improve the speed of money transfers even from remote areas (Ghosh, 2016; Gosavi, 2018; Jack & Suri, 2011). There is evidence of a strong relationship between mobile penetration and financial inclusion within and across countries. Indeed, mobile financial services can reduce transaction costs and administrative costs for physical bank branches; they can also improve

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data collection on depositors allowing for more efficient analysis of creditworthiness and more effective credit monitoring (Andrianaivo & Kpodar, 2012). Finally, there is evidence of a positive relationship between the use of mobile money and the financial inclusion of households and businesses, which offer mobile financial services tools that can adapt to their needs and solve problems associated with distance to bank branches and high transaction costs for formal financial services (Morawczynski, 2009; Ouma et al., 2017). Furthermore, according to neoclassical growth models, the technological factor can represent the engine of economic and human development in poor countries (Abramovitz, 1986; Bernard & Jones, 1996). ICTs, in fact, improve access to information, a factor considered crucial for development activities, as it improves users’ access to development inputs, expands their capabilities, and limits existing barriers (Smith et al., 2011). Based on this theory, several studies have examined the role that fintech can play in promoting social inclusion. Asongu and Le Roux (2017) show a positive impact of ICTs on inclusive growth in 49 sub-Saharan countries from 2000–2012. Another study by Asongu and Odhiambo (2019) highlights how the spread of mobile telephony and the growth of Internet subscriptions can reduce inequalities and thus promote equality and social inclusion. 6.3.3

How Can Fintech Promote Financial Inclusion?

The proliferation of fintech and digital financial instruments in emerging markets, characterized by a higher presence of vulnerable groups, has been made possible by ten years of investments that have highlighted the role of fintech as a catalyst for financial inclusion, especially in African countries (Bollou, 2006). Since 1995, several African countries have begun to invest in ICT infrastructure and establish high-tech parks to attract and promote entrepreneurial initiatives in the sector. The proliferation of infrastructure in less developed countries has led to the adoption of various technologyenabled services by end users. Among these, a fundamental role is played by mobile money, a fintech innovation that allows financial transactions to be carried out simply using a mobile phone (Donovan, 2012), in an ecosystem made up of users, service providers, merchants, agents, banks, and regulators (Donovan, 2012; Senyo et al., 2019). These financial services include financial transactions such as paying bills, depositing savings, transferring funds, borrowing money, and purchasing products

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and services. There are several competitive advantages that allow these alternative financial instruments to gain a foothold even in low-income countries: • Easier access than traditional bank accounts. Often, only a cell phone number and an ID card are needed to access a mobile money account (Mugambi et al., 2014). • Increased ability to access from remote areas that are generally not served by physical branches of traditional banks (Demirgüç-Kunt et al., 2018). • Lower transaction costs (Maurer, 2012). Banna et al. (2022) describe the case of Wizzit, a provider of microfinance services that has partnered with the World Bank Group to establish itself in sub-Saharan Africa with its smartphone-accessible microloan offering targeted at unbanked or low-income individuals who lack access to traditional financial services. Since 2004, Wizzit has offered South Africans a low-cost bank account that they can access to make and receive payments via any cell phone or phone network. More than 400,000 people in the country have adopted this solution and opened accounts with the mobile banking company. As reported by the World Bank Group, by setting up such services and interacting with the platform’s officials, people can better manage their finances and control the future of their businesses. In several countries, the proliferation of electronic payments and digital money has enabled faster and broader access to government assistance. Indeed, digital payments enable governments to provide subsidies through low-cost cash transfers rather than through water and food deliveries. Such government-to-person (G2P) payment systems have been widely used in Brazil, Colombia, and Pakistan (Arner et al., 2020). Telecommunications companies have mainly driven fintech developments in Africa. Vodacom and Safaricom, two private mobile phone companies, launched the most successful project, M-Pesa (mobile money in Swahili), in East Africa. This project is based on providing payment services with electronic money registered on a cell phone. They launched the project in Kenya in 2007. In 2016, there were more than 100 million mobile money users in sub-Saharan Africa. Between 2006 (the year before M-Pesa was launched) and 2019, the percentage of the population with bank accounts in Kenya more than tripled from 26.7% to

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82.9% (Central Bank of Kenya, 2019). The financial inclusion recorded in these geographic areas was also characterized by high gender inclusion. Compared to other emerging regions, the gender gap in mobile money use is 19.5%, about half the average for all low- and middle-income countries (Makina, 2019). This process of financial inclusion subsequently saw the introduction of other mobile money services such as loans, savings, and insurance, for example, through the M-Shwari service, a lending service introduced by the Commercial Bank of Africa and linked to MPesa, which allows you to save and borrow money via smartphone without having to visit a bank or fill out forms (Bharadwaj & Suri, 2020). This service stipulates that amounts must be repaid within 30 days and credit approval involves a rating based on data about the user’s spending by phone. In addition to mobile-based services, there has been a rapid growth of fintech in Africa related to bringing innovative financial products to market. In Kenya, Tala was the first to launch its mobile app to offer credit and unsecured loans to consumers and has disbursed more than $2.7 billion in loans to date. FairMoney, launched in Nigeria in 2017, has followed a similar path. Starting as an online lender that offered instant loans and bill payments, it now also provides a bank account with free money transfers and a debit card and holds a microfinance license from the Central Bank of Nigeria. In 2020, FairMoney extended $93 million worth of loans to more than 1.3 million users who submitted more than 6.5 million loan applications (McKinsey & Company, 2022). Further lessons can be drawn from the IndiaStack fintech strategy implemented in India. The government, businesses, and other entities utilize IndiaStack, which comprises a set of application programming interfaces (APIs), as a digital infrastructure to deliver paperless and cashless services (Arner et al., 2020). IndiaStack represents a complete digital identity, payment, and data management system. Its rapid adoption by billions of individuals and businesses has helped promote financial and social inclusion, position the country in the Internet age, and expand access to financial services in a heavily cash-based economy (IMF, 2019). The explosion of technology in emerging markets has led to the emergence of many fintech startups, which appear to be driven by the logic of profit and offer themselves as catalysts for financial inclusion. In 2020, the InclusiveFintech50 program, founded by Visa and the MetLife Foundation and co-funded by Blackrock, identified 50 high-potential startups

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according to the criteria of inclusiveness, innovation, the potential to scale, and traction (Table 6.3). The highlighted solutions can potentially improve the accessibility, convenience, and reliability of financial services in advanced and emerging markets. In the literature, however, there are also currents of thought attributing a potential negative effect on the financial inclusion process to fintech, as the following section reveals. 6.3.4

The Other Side of Fintech

Despite the prevalent notion in the literature that financial technology can contribute to inclusive growth and financial inclusion, multiple authors emphasize potential adverse outcomes. According to Ozili (2018), digital finance can have a negative impact on financial inclusion. In particular, the author highlights the potential geographic biases associated with providing fintech services. Indeed, service providers may autonomously decide, based on their own internal assessments, to withdraw or interrupt the provision of services in areas considered particularly risky or in communities that do not have adequate infrastructure. Moreover, the companies providing such services seek to maximize their own profitability or that of their affiliated financial institutions. This means that companies may decide to limit the provision of services based on their profitability assessments, which harms poor and uneducated communities (Ozili, 2018). Further considerations in this sense are raised by Yue et al. (2022). According to the authors, digital finance amplifies household participation in the credit market. This tends to stimulate the marginal propensity to consume but increases the risk that they will face financial problems. Facilitating access to often complex financial services can pose high risks for populations with low financial literacy. According to Panos and Wilson (2020, p. 297), “fintech developments may also damage financial well-being by triggering impulsive consumer behavior when interacting with financial technologies and platforms”. Mobile apps, for example, may target individuals who are impulsive and lack the ability to anticipate and plan for future needs. Hundtofte and Gladstone (2017) believe that mobile credit products are often too easily accessible and therefore enable actions based on fleeting needs. These considerations take on added importance, given recent attention from policymakers and researchers to a sharp increase in household debt

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Table 6.3 Some of the 50 inclusive startups selected by the InclusiveFintech50 program Startups

Category

Operating countries

Description

Aflore

Credit

Colombia

Akiba

Savings and personal financial management

Mexico, Peru

Alfi

Savings and personal financial management

Chile, Mexico, Peru

Asaak

Credit

Uganda

Bamba

Insurance

Mexico

Bankly

Savings and personal financial management

Nigeria

Climb Credit

Payments and remittances

United States

Aflore combines tech and data with existing personal relationships to expand financial services to the middle class in Latin America Akiba aims to improve the financial well-being of low-income employees by enabling savings and providing access to emergency lines of microcredit Alfi is a platform and app that connects users to a marketplace of financial products while improving their financial management skills Asaak is an asset financing company that provides credit by lending motorcycles, fuel, and smartphones to drivers to improve the lives of informal workers in Uganda Bamba is a platform that brings low-income domestic workers into the formal financial system Bankly helps its users digitize and grow their cash in a safe, simple way manner attraverso technology Climb Credit provides accessible and affordable payment options for partner schools that focus on jobs with strong earning potential in today’s economy

(continued)

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Table 6.3 (continued) Startups

Category

Operating countries

Description

Coink

Savings and personal financial management Credit

Colombia

DreamStart Labs

Savings and personal financial management

Dvara SmartGold

Savings and personal financial management

Bangladesh, Benin, Ethiopia, Philippines, Rwanda, Sri Lanka, Tanzania, Uganda, Vietnam, Zambia India

Coink is an app that brings the traditional experience of the piggy bank into the digital age Davinta is an AI- based digital platform that offers credit and other financial products to unbanked and underbanked customers in India DreamStart Labs offers digital banking solutions for informal community savings groups

Esusu

Credit

United States

EthicHub

Credit

Mexico, Spain

Eversend

Payments and remittances

France, Ghana, Kenya, Uganda

Extramile Africa

Savings and personal financial management

Kenya, Nigeria, United States

Davinta

India

Source https://www.inclusivefintech50.com/2020-cohort

Dvara SmartGold creates financial security for lower- and middle-income households by encouraging micro-savings Esusu uses rental payment data to help underserved populations build their credit histories and unlock new opportunities EthicHub connects smallholder farmers with lenders and direct buyers from around the world Eversend is a mobile banking solution that provides essential financial services attraverso smartphones and basic mobile phones Extramile Africa helps transform savers into investors while enabling MSMEs and smallholder farmers to access capital to grow their businesses

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levels (Feng et al., 2019). Excessive household debt may pose a threat to the global economy, and this threat may be exacerbated by digital finance (Chen et al., 2020).

6.4

The Mitigating Role of Financial Literacy

The G20 and the World Bank are among the key promoters of the inclusive growth process that can facilitate the achievement of many of the goals of the 2030 Agenda. In this context, regulation has an essential role, as does the financial education, the single factor that can reduce the risk of families getting into financial trouble (Gabor & Brooks, 2017). Financial literacy reflects how well a person understands various financial concepts and should be assessed using several questions (Feng et al., 2019). Making effective decisions relies heavily on individuals’ ability to make informed financial decisions (Lusardi & Mitchell, 2014). At the same time, financially ill-informed individuals are particularly vulnerable because they are often unaware of the consequences of their debt decisions. A study by Feng et al. (2019) highlights the importance of financial literacy, particularly for developing countries. In China, most households have limited financial knowledge about basic concepts such as the composition of interest rates, inflation, and risk diversification. This systematically affects households’ financial position and leads to a surge in debt. Fintech may reduce supply-side barriers to financial services, such as excessive distance from bank branches, but demand-side barriers must also be considered to achieve inclusive financial development. Wellfunctioning financial markets need not only good infrastructure but also informed customers who are able to make better financial decisions for themselves and others (Grohmann et al., 2018). Fintech tools and digital money can facilitate access to credit and payment instruments, but less financially literate consumers will continue to misjudge the risks they face. Financial services based on mobile and particularly accessible fintech platforms can lead financially illiterate users to make irrational purchases, apply for short-term loans (“payday loans”), and find themselves in vicious circles that can determine the state of financial difficulty (Panos & Wilson, 2020). To overcome this problem, mobile apps and fintech platforms could integrate an educational component in order to explain the basic concepts of financial services to the end user. Better financial literacy can strongly impact financial inclusion by removing the barriers preventing people from participating in traditional financial

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services (Hasan et al., 2021). The combined effect of financial education and financial inclusion leads to financial standing, a concept that combines the ability to trade and the ability to trade (Sherraden, 2013). This represents people’s ability to “understand, assess, and act in their best financial interests” (Johnson & Sherraden, 2007, p. 123). The findings and theoretical constructs presented offer insights for policymakers, as well as providers of alternative financial services. Financial education can be an important tool for financial development, in addition to conventional infrastructure development policies. Indeed, the proliferation of digital money, digital finance, and fintech platforms cannot happen without comprehensive and long-term education programs capable of training marginalized populations. This is the only way to achieve inclusive growth.

6.5

Concluding Remarks

The study explores the relationship between fintech and financial inclusion to understand if and how technologies serving finance can be considered a tool to promote sustainable and inclusive growth. The literature review shows that technological advances, mobile money systems, and fintech companies can be considered important accelerators of financial inclusion, as they facilitate access to financial services and reduce costs. On the other hand, it also shows that financial markets are often characterized by barriers to access or use, both on the demand and supply side of financial services. Technology in the service of finance currently makes it possible to overcome mainly supply-side barriers, for example, by enabling end consumers to use banking services without having to visit a branch, which is often non-existent or scarce in emerging markets. However, there are still barriers related to demand, such as the lack of sufficient financial literacy, which hinders rational and informed use of the services offered by innovative solutions. Otherwise, the ease of use of fintech platforms may even worsen the financial situation of the excluded, as it encourages impulsive and irrational behavior. To this end, it is essential to increase knowledge about financial services, the logic, and the mechanisms underlying them. To this end, it is essential to deepen the knowledge of financial services, and the logic and mechanisms that underlie them through training and awareness programs, financial literacy campaigns, partnerships between educational institutions, government agencies, and financial institutions. The development

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of mobile training applications, online courses, and interactive videos can help people learn more about financial services. It is also essential to involve local communities by organizing seminars and financial literacy events. Measures to expand ICT infrastructure can only be an instrument that promotes inclusive growth and contributes to the goals of the 2030 Agenda, whose implementation inevitably depends on financial inclusion if they are accompanied by financial literacy measures.

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McKinsey & Company. (2022). Fintech in Africa: The end of the beginning. https://www.mckinsey.com/~/media/mckinsey/industries/financial%20s ervices/our%20insights/fintech%20in%20africa%20the%20end%20of%20the% 20beginning/fintech-in-africa-the-end-of-the-beginning.pdf. Accessed 10 December 2022. McKinsey Global Institute. (2016). How digital finance could boost growth in emerging economies. https://www.mckinsey.com/featured-insights/employ ment-and-growth/how-digital-finance-could-boost-growth-in-emerging-eco nomies. Accessed 11 December 2022. Mok, K. (2000). Economic growth and people’s livelihood. Social and political development in post-reform China (pp. 19–38). Palgrave Macmillan. Montgomery, H., & Weiss, J. (2011). Can commercially-oriented microfinance help meet the millennium development goals? Evidence from Pakistan. World Development, 39(1), 87–109. Morawczynski, O. (2009). Exploring the usage and impact of “transformational” mobile financial services: The case of M-PESA in Kenya. Journal of Eastern African Studies, 3(3), 509–525. Mugambi, A., Njunge, C., & Yang, S. C. (2014). Mobile—Money benefits and usage: The case of M-PESA. IT Professionals, 16(3), 16–21. Noreen, U., Imran, R., Zaheer, A., & Saif, M. I. (2011). Impact of microfinance on poverty: A case of Pakistan. World Applied Sciences Journal, 12(6), 877– 883. Ouma, S. A., Odongo, T. M., & Were, M. (2017). Mobile financial services and finance inclusion: Is it a boon for savings mobilization? Review of Development Finance, 7 (1), 29–35. Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Istanbul Stock Exchange Review, 18(4), 329–340. Panos, G. A., & Wilson, J. O. (2020). Financial literacy and accountability finance in the FinTech era: Capabilities and challenges. The European Journal of Finance, 26(4–5), 297–301. PwC. (2017). Redrawing the lines: FinTech’s growing influence on financial services (PwC Global FinTech Report 2017). https://www.pwc.com/gx/ en/industries/financial-services/assets/pwc-global-fintech-report-2017.pdf. Accessed 12 November 2022. Queralt, J., Fu, J., & Romano, M. (2017). Financial inclusion and the 2030 agenda for sustainable development: A missed opportunity. Enterprise Development & Microfinance, 28(3), 200–211. Samat, N., Rashid, S. M. R., & Elhadary, Y. A. (2018). Analyzing spatial distribution of poverty incidence in northern region of Peninsular Malaysia. Asian Social Sciences, 14(12), 86–96. Sarma, M., & Pais, J. (2011). Financial inclusion and development. Journal of International Development, 23(5), 613–628.

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Senyo, P. K., Liu, K., & Effah, J. (2019). Digital business ecosystem: Literature review and a framework for future research. International Journal of Information Management, 47 , 52–64. Sherraden, M. S. (2013). Building blocks of financial capability. Financial education and capability: Research, education, policy, and practice, 3–43. Smith, M. L., Spence, R., & Rashid, A. T. (2011). Mobile phones and expanding human capabilities. Information Technologies & International Development, 7 (3), 77–88. Solesbury, W. (2003). Sustainable livelihoods: A case study of the evolution of DFID policy (Vol. 217). Overseas Development Institute. Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288–1292. United Nations. (2018). Task force on digital financing of sustainable development goals. https://www.un.org/sg/en/content/sg/personnel-appoin tments/2018-11-29/task-force-digital-financing-sustainable-development. Accessed 15 November 2022. United Nations. (2020). The task force on digital financing of the sustainable development goals. https://www.un.org/en/digital-financing-taskforce. Accessed 12 November 2022. UN Capital Development Fund. (2020). Financial inclusion and the SDGs. https://www.uncdf.org/financial-inclusion-and-the-sdgs. Accessed 10 November 2022. World Bank Group. (2016). World development report 2016: Digital dividends. World Bank Publications. Worldbank.org. (2016). Financial inclusion has a big role to play in reaching the SDGs. https://blogs.worldbank.org/developmenttalk/financial-inclusionhas-big-role-play-reaching-sdgs. Accessed 19 November 2022. Yue, P., Korkmaz, A. G., Yin, Z., & Zhou, H. (2022). The rise of digital finance: Financial inclusion or debt trap? Finance Research Letters, 47 , 102604.

CHAPTER 7

The Metaverse’s Inspiration for Sustainable Business: Restructuring Economic Logic, Capital, Assets, Organization, and Industry Yushi Chen

7.1

Genesis: Sustainable Business and Metaverses

The Earth was created about 4.5 billion years ago, and modern humans have been around for about 315,000 years. However, humanity and the way we feed, fuel, and finance our communities and economy are threatening nature and the services that power and sustain us. Humans have destroyed about a third of the planet’s natural resources in just three decades, and we continue to consume the world’s resources at an alarming rate. When human demands on nature exceed what ecosystems can give, this is referred to as an ecological overshoot. Today, approximately 1.8 planets are required to provide the resources we consume and absorb our waste. By 2030, we will require two planets. In fact, we only have one (The World Counts, 2023).

Y. Chen (B) Science Policy Research Unit, University of Sussex, Brighton and Hove, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_7

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To solve the problems of a sustainable future, a paradigm shift and holistic approach are required: sustainable development necessitates concurrently tackling environmental, economic, and social transformation. The following are some characteristics of a path to a sustainable economy, as outlined by (Jackson, 2009): • A system aimed to maximize social and environmental advantages over just pursuing economic growth; • A system built on collaboration and sharing, not fierce rivalry, to improve human creativity and talents by offering a meaningful, gratifying work experience for all; • A closed loop system, also known as a circular economy model, in which nothing is wasted or abandoned into the environment, and where rethink, refuse, reduce, reuse, and recycle. These types of advancements necessitate major changes in the goal of company as well as nearly every aspect of how it is carried out, resulting in what is known as sustainable business (Bocken et al., 2014). Metaverses could be venues where these value propositions can be realized. Matthew Ball, a venture capitalist, envisions the metaverse as persistent, synchronous, and live, with no limit on concurrent users, a fully functioning economy, an expansive experience, unprecedented interoperability, and a diverse range of contributors who create content and experiences (Ball, 2020). Since Facebook changed its name to Metaverse, the word metaverse is gaining popularity globally. In China, the metaverse was mentioned for the first time in the “14th Five-Year Plan” for the development of Shanghai’s electronic information industry. The local government emphasizes forward-looking research and development on the metaverse’s underlying core technologies and basic capabilities. Shanghai government agency also aims to promote the development of new terminals and systematic virtual content that deepen perception and interaction (Cheng, 2021). In the United States, tech giants are actively negotiating with policymakers, academics, and other groups, hoping to collaborate with all parties to create standards and protocols for the metaverse. In South Korea, the Seoul government issued a statement claiming to invest about 3.9 billion won in five years to establish a city-level metaverse platform exclusive to Seoul. At the same time, South Korea has also

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established the “Metaverse Alliance” led by the government and includes more than 200 companies and institutions (Keane, 2022). However, the metaverse concept is still in the experimental stage and early cognition phase. As there are different opinions on the interpretation of the metaverse, people have mixed feelings about the future development direction of the metaverse. Metaverse has not formed a stable business model yet. At present, there are mainly three kinds of people’s cognitive interpretations of the metaverse: The first view is the online virtual space advocated by the internet giants. This is a way to create an immersive experience provided by augmented reality (AR) and virtual reality (VR) as the main technical basis explained from the existing business model, which is similar to the ultra-immersive virtual reality depicted in the movie of “Ready Player One”. The second view is the pursuit of digital sharing status represented by the blockchain, which reflects the vision of the internet in the future and dissatisfaction with the existing closed and monopolistic state of Web 2.0, the so-called Web 3.0. The blockchain community believes that the front-end experience is only the surface layer of the metaverse. More importantly, the aim is to create a highly interoperable, decentralized open virtual world through the blockchain, decentralized organization (DAO), non-fungible token (NFT), and other technical methods. The third view is to emphasize the concept of time in the metaverse rather than the concept of space. Just as the proposed moment of singularity, the metaverse is not a virtual place but a specific point in time. Similar to the moment when artificial intelligence leaves human intelligence far behind singularity, it marks the moment when people’s digital life is more valuable than physical life. The above three explanations have their starting points, but technology is only a tool. Discussing the extent to which the metaverse will influence the future economic system and company development patterns necessitates a thorough examination of the metaverse’s various forms. Because metaverse may meet the value propositions of sustainable business, such as collecting and establishing multiple value systems, forming new capital, assets, organizational and industrial forms, and finally, changing the future business direction and developing more sustainable business models.

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7.2

Rethinking Economic Logics

The term “metaverse” was originated in 1992 by Neal Stephenson’s science fiction novel Snow Crash (Stephenson, 2003). When it came to life with the film Ready Player One (Spielberg et al., 2018), the image of metaverse is exhibited in front of us. These types of science fiction and movies act as dystopian warnings to our current unsustainable economic logics. When our technological level gets closer to the scenes depicted in these science fiction literatures and movies, we should be warned that if our current unsustainable economic model is maintained, we will lose our environment and be forced to live in a virtual metaverse. With the increasing frequency of global epidemics, floods, and wildfires, as well as debates on climate change, biodiversity, and the right to development at every COP summit, there is some evidence that the current model of economic and social development is unsustainable and that we urgently need to reverse this economic system and business model that has been in place since the industrial revolution. The traditional economic model is based on incomplete scientific principles from a cognitive standpoint. In the early seventeenth century, Isaac Newton’s Principia Mathematica summarized and formalized classical, atomic-scale physics. People’s understanding of the world at the time was mostly based on Newton’s three laws of motion. Newton likened the universe to a functioning properly “machine”. Newton’s three laws depicted a universe that was basic, regular, predictable, and controllable. This concept has enormous power. Newton’s three laws are claimed to have given birth to modern thinking and had a major impact, particularly on the business level, as Adam Smith in economics and Frederick Taylor in management science. Thus, the socioeconomic-technological paradigm is influenced by the trinity of contemporary science, advanced technology, and capitalism, and it is responsible for the growth of the economy at the moment. Norgaard (1988) argues that Western development has traditionally followed a linear development model based on an atomic view (shown in Fig. 7.1)—a body of knowledge established through observation and practice; science dedicated to the development of new technologies and social organizations; and technology that facilitates material production, leading to further exploitation and consumption of natural resources. The manner in which economic progress affects societal values and lifestyles, which in turn affects our organizational forms and technology choices. For example, technologies are being introduced to

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Fig. 7.1 Atomistic-mechanistic (Norgaard, 1988)

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define new production models, ranging from steam engines to massproducing factories, as well as to develop new modes of cooperation and collaboration, such as the structure of the company. Neoclassical economics based on static and atomic theories became the mainstream in twentieth century (Colander, 2000; Hall et al., 2001). The rational expectation hypothesis and equilibrium models of neoclassical economics have traditionally followed a pattern of formulating assumptions, developing logical reasoning, and then quantitatively testing them. However, neoclassical economics excludes the influence of dynamic and “irrational” real-world characteristics such as organizational culture, institutional model, entrepreneurial spirit, and so on in their representation of reality (Nelson & Winter, 1982; Schumpeter, 2013). Figure 7.1 demonstrates the expanding gap between mainstream Western science and economics’ view of economic progress and how the systems actually work. The unsustainable development patterns were caused by a lack of awareness to recognize the gap. The feedback loop in the past is not linear. It is becoming increasingly difficult to predict the strengths and weaknesses of development under the current model, as well as potential disasters (Hulme, 2005). Thus, in order to find a new sustainable growth pathway, metaverses need to fundamentally establish a cognitive system including quantum science and evolutionary economics to supplement the existing neoclassical physics and economics based on static and atomic theory. The

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concepts of relativity and quantum physics are radically altering the classical mechanics-based worldview. It describes how quantum physics explains the concept of objective information based on matter, energy, and force (Von Bertalanffy, 1973). In 1865, German physicist Rudolf Clausius coined the term “entropy” to describe the degree of disorder in a system (Cropper, 1986). The rule of entropy rise well reflects thermodynamics’ discovery of the world, that is, in an isolated system, things always spontaneously and irreversibly tend toward increasing entropy and chaos, from “ordered” to “disordered”. Because all of our present economic models and social institutions are based on the intense pursuit of efficiency and productivity, concerns such as climate change and biodiversity loss will arise. Such spillover effects, also known as “externalities”, were not recognized until the 1920s. An externality is an unrecognized impact of a market transaction that results in additional profit or expense elsewhere or at a different time and is not accounted for in a legitimate cost-benefit analysis. Even so, we’ve been looking at externalities as a side note to the market exchange system up until now. Evolutionary economics is based on Darwinism and treats the economic system from a complex scientific point of view. Compared with the static equilibrium analysis of neoclassical economics, evolutionary economics focuses on “change”, emphasizing the relationship of coevolution relationship between systems and the role of institutional changes. The guiding ideology of evolutionary economics is of great significance to the interpretation of the path of the metaverse, including the interpretation of the changes in the environment to individual behavior, the interaction between different actors, the variation of economic organization models, and the process in which innovation occurs as “creative destruction” (Garud et al., 2010; Nelson & Winter, 1982; Schumpeter, 2013).

7.3

Reorganizing Three Capitals

Metaverses will reconstruct the interaction between natural capital, financial capital, and technological capital. As a traditional production factor, capital is accumulated labor in the form of money and commodities (Marx, 2020). As the techno-economic paradigm shifts toward clean technology and the digital economy, capital’s monetization process and value realization as a commodity have changed dramatically. In summary, the logic of which fields we invest in and in what form to obtain a return on

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investment is changing. Changes in the interaction of natural, financial, and technological capital will change the current pattern of carbon lockin, in which the economy is locked in a carbon-based technology system, resulting in unsustainable development. Natural capital usually refers to the foundation of natural resources, such as land, air, water, and biodiversity ecosystems. The current energy system mainly relies on a fossil fuel-based model. The Paris Agreement is a long-term goal adopted by 196 nations to maintain global temperature rise below 2 °C and attempt to restrict global temperature rise to 1.5 °C. To achieve the goal of the Paris Agreement, international and national policies encourage decarbonization through low-carbon energy production while supporting the use of renewable energy and improving the efficiency of power generation, transportation, and green lifestyle (Di Silvestre et al., 2018). As carbon neutrality and net-zero emissions have become an international consensus, the energy system is undergoing a process of creative destruction by renewable energy, which is also a transition of a techno-economic paradigm shift to clean technologies (Cherp et al., 2018; Mathews, 2013). This also marks the transition of the economic model from an extensive resources-driven economic development model to a technology-driven economic development model. The digital economy represented by the metaverse needs to follow the logic of carbon neutrality to avoid the massive consumption of natural capital. At the same time, realizing carbon neutrality in the future is inseparable from the support of digital tools. We need to utilize metaverse to more accurately measure the carbon emissions of projects, enterprises, and industries, and then implement more precise emission reduction strategies. For example, Nvidia intends to construct the world’s most powerful artificial intelligence supercomputer dedicated to climate change prediction. Earth-2 is the name of the system, which will produce a digital twin of Earth. As part of the European Commission’s Green Deal and Digital Strategy, Destination Earth aims to develop a highly accurate digital model of the Earth on a global scale to monitor and predict the interaction between natural phenomena and human activities. Wind farms’ maintenance needs can be remotely monitored utilizing industrial metaverse, and operational efficiency can be raised by at least 20%. The enlightenment of the metaverse for sustainable development lies in establishing a healthy interaction between the physical and digital worlds. Recognizing natural capitalization is also a way to rethink the relationship between humans and nature. The realization of the value of an

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ecological product is a good example. In the future, natural capital may circulate and develop in the form of digital assets. Financial capital refers to the transformation of mainstream financial standards and paradigms toward decarbonization and digitalization, and the specific manifestations are green finance and financial technology. The flow of capital should not only pursue efficiency but also aims to capture more universal social and environmental values, such as integrating with environmental, social, and governance (ESG) concepts. In the current financial system, the three statements (balance sheet, income statement, and cash flow statement) are represented as major performance indicators to evaluate one company. From monetary measurement to formulating international accounting standards, these indicators reflect on the capital delivery process by directly related parties. However, this logic of maximizing shareholders’ value has brought economic prosperity in the industrial age, but it is difficult to reflect multiple values for sustainable business. In the digital age, digital ledgers must include relationships and the value of assets of related parties. The financial sector faces a wider range of stakeholders to judge the flow of capital. When the perspective changes over time, the credit boundaries and the credit relationships are reshaped. For example, due to the impact of climate change, more investors are asking their asset managers to actively reduce the carbon emissions of their investment portfolios and include the quantities of emission reduction as one of the performance indicators in the scope of assessment. With the support of digital technology, the ways of financial capital flow are more diversified. Proposals from green finance, and financial technology to stakeholder capitalism are calling for changes in the structure of financial capital. The prosperity of the metaverse requires the realization of financial inclusion. Financial inclusion means everyone has financial instruments that can not only meet their daily needs but also create wealth for themselves, their families, and communities. In turn, financial inclusion can support a more resilient and robust economy. According to the Global Findex database (Demirguc-Kunt et al., 2018), approximately 1.7 billion people in the world have no access to bank accounts. Women, povertystricken communities, and young people are disproportionately excluded from the financial system. From community currency, algorithmic stable currency to decentralized finance, the format of currency has undergone tremendous changes and carries richer content. At present, the amount of total value locked in decentralized finance is around 110 billion US

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dollars (Pulse, 2021). Decentralized finance may become one of the paths to achieving financial inclusion. From the perspective of technology capital, we are living in a period of rapid development of the digital economy. Metaverse possesses strong technological capacities, including artificial intelligence, digital twins, blockchain, cloud computing, mixed reality, robotics, brain-computer interfaces, 5G, etc. At the same time, the evolution of technology capital is not only reflected in the fact that digital technologies are reconstructing the physical world into the digital world, but also in heights that metaverse represents the in-depth interaction, mutual learning, and integration between the digital world and the real world. The prosperity of the digital world is inseparable from the support of energy and material systems in the real world. With the flow and evolution of technological capital, Metaverse will better meet the diversified needs across different populations. The driving force for the future development of the economy is mainly reflected in the coevolution process between clean technologies and information technologies. Especially in the energy system transition, renewable energy such as solar and wind will become the primary drivers in supporting the metaverse’s vast technical system. The development of the metaverse will be reflected in the reconstruction of the relationship between natural capital, financial capital, and technological capital, which is also a process of coexistence of virtuality and reality.

7.4

Reconstructing Social Assets

Digital assets have become important social assets, constituting important social wealth for enterprises and individuals. The volume of digital assets is constantly expanding. With the development of the metaverse, data will be the main production factor in the future, and digital assets will also play a more important role in the value system. The digital economy has four major trends: digital industrialization, industrial digitization, data governance, and maximizing data value. As the coevolution between data-driven assets, emerging technologies, finance, law, and industrial movement, digital assets have emerged. Digital assets based on digital natives and digital twins have increased significantly. For example, the carbon asset represents the carbon emission rights that need to be converted into carbon dioxide equivalent values. This is the basic unit for measuring the greenhouse impact and environmental performance.

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In terms of manifestation, carbon is a digital asset in the metaverse. Under the framework of Article 6.4 of the Paris Agreement, Metaverse can create a new marketplace for the global carbon market, and improve the liquidity, transparency, and credibility of the international carbon trading market by introducing technical mechanisms such as smart contracts, blockchain, and digital twins. People can automatically create digital carbon trading certificates through metaverse to avoid excessive or insufficient certificates and help stabilize market prices. Governments can thus establish a more effective carbon market and implement more accurate green financial policies, thereby providing additional capital flows for green projects and industrial transformation. In the metaverse, the flow of social assets will change. The future ledger system and accounting system will be displayed in a digital ledger and represented as a digital asset. For example, the new monitoring, reporting, and verification method (MRV) can record more corporate reporting data, so that stakeholders have a clearer understanding of the company’s performance. This is helpful for all stakeholders to understand and distinguish the activities advocated by participating organizations. The popular NFTs represent a typical way for the Metaverse to change the flow of assets. People are more familiar with the application of NFT in the field of digital art trading. Currently, NBA Top Shot, the highestgrossing NFT project, has generated $780 million in sales and attracted more than 1.1 million registered users. Although many NFT products are still in the speculative market, there is a rapid decline. However, this digital carrier has laid a technical foundation for both digital identity and green digital financial products. The application scope of NFT transactions is actually more extensive. For example, financial products can also be conducted through NFTs. Each NFT is an investment portfolio. Returning to the topic of carbon emissions, people can also create carbon trading NFTs in the metaverse to improve the liquidity, transparency, auditability, and credibility of the international carbon trading market, and help stabilize market prices. At present, the BIS Innovation Hub is cooperating with Hong Kong Monetary Authority and exchanges to carry out the practice of issuing digital green bonds through tokenization (BIS, 2021). The Green Digital Finance Alliance and HSBC have also proposed the advantages of digital green bonds (SDFA, 2019). In the future, green bond products can be presented in the form of NFTs. Each NFT is a green investment portfolio, which may include equity

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and debt parts, and these rights and interests are reflected and circulated through digital ledgers (Chen & Volz, 2021). Under the technology and transaction framework of the Metaverse, the pace of green finance is accelerating.

7.5

Creating New Type of Organizations

In 2020, the first International Conference on Algorithms, Computation and Artificial Intelligence, ACAI 2020, was held in the well-known game “Animal Crossing”, with the aim of creating a meaningful interactive space for AI researchers. In 2021, the South Korean company Com2Us released a product video of working in the Metaverse, where employees can clock in to work, receive work emails, hold meetings with colleagues, and even “get lazy”. Com2Us says their goal is to migrate social, cultural, economic, and other real-world systems into the digital world, making it a space where everyday life takes place. At the same time, it was also announced that in 2022, the company’s employees will gradually enter this office metaverse. This indicates that the emergence of the metaverse is changing the traditional organizational operation mode. Although the above cases are still at the experience level, deeper changes have already occurred. Distributed autonomous organization (DAO) and distributed autonomous corporation (DAC) are entering people’s field of vision. Hailed as the cornerstones of the metaverse organization, DAO and DAC refer to virtual companies or organizations based on digital rules constructed on open and transparent procedural codes. These digital rules can empower organizations to operate autonomously, individuals can choose to join and withdraw at any time, and they can become participants of the company or organization to get rewarded by contributing to their expertise or strength. Rocas-Royo (2019) believes that DAO can be an effective model for realizing the healthy development of the gig economy. For example, individuals can implement software outsourcing, participate in charitable activities, and even participate in venture capital in DAO. The automated protocol and codified token system can democratize corporate governance, support online voting and decision making, and limit to the minimum of human intervention, based on the peer-to-peer transaction networks (Kraus et al., 2019). It is also expected to become the fourth form of organization outside of the country, market, and

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company to maximize the effectiveness and value of the organization (See et al., 2022). DAO could form a new business transformation. The organizational form of DAO usually has four characteristics: • Disintermediation: By reducing the complexity of the decisionmaking process, DAO aims to shift the management level from a top-down multiple-level approach to a flat structure. To create a flat enabling organization. Flat management is carried out from both inside and outside the organization, making the organization itself an enabler. • De-boundary: DAO aims to break the barriers of knowledge flow between traditional enterprises, build an ecosystem from the perspective of open innovation, and enable organizations to realize knowledge interaction. • De-administration: DAO aims to break the inherent administrative, bureaucratic order and rules of the industry and switch the model to consensus-based digital rules governed by the participant, which fully respect people’s independent innovation spirit, and stimulate individuals’ willingness to realize themselves. • Decentralization: DAO aims to break the command system dominated by the company’s will and establish a multi-center parallel operation mechanism with the needs of the participant and personal will as the core. The formation of an organization stems from the dynamic allocation of resources. Each person may play a different role in different projects. Different people form an organization for the same interest or purpose. DAOs allow different people to form an organization only for the same goal or purpose. Individuals can get revenue and incentives by participating in intriguing initiatives produced by various organizations through DAOs. These attempts provide useful references for dealing with problems such as excessive centralization and organizational failure in traditional organizations. In contrast, the typical internet-based freelancing platform may still have the problem of having a platform fee that is excessive and has no legal consequence. In March 2021, the state of Wyoming became the first in the US to approve and legally recognize a

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DAO. This means the DAO has been recognized by the local government and has become another form of economic organization other than a limited liability company (Crank, 2021). Although there are already many DAO projects in the metaverse, DAO still has many shortcomings from the perspective of technical support and institutional environment, such as security issues, legal issues, technical limitations, and so on. For example, the GDPR environment may impede DAO development, necessitating more technical system changes to allow the development of this organizational form. DAO is currently unable to support large-scale applications. Still, with further in-depth theoretical research and the advancement of various technical tools, these problems will be gradually solved, and DAO has the potential to achieve more sustainable business models. It can be said that the future DAO and DAC are cooperative organizations and social enterprises in the metaverse. People can form organizations through consensus and make their contributions to emerging topics.

7.6

Embracing the Quaternary Sector of the Economy

In the past, we used to divide the industry into three categories; the primary industry (agriculture), which meets the most basic survival needs; the secondary industry (industry and construction), which meets the basic material needs of humankind, such as the production of vehicles, trains, and housings; and finally, the tertiary industry which is to personalize products directly to individuals as a service. The concept of the fourth industry is taking shape, which is an industry that solves people’s demand for information, including information processing and intelligence enhancement. The quaternary sector of the economy is based on economic activities related to intellectual or knowledge services and data as the main production factor (Franck, 2019). Metaverse is one of the representative carriers in the quaternary sector of the economy. The significance of the fourth industry is not only to incubate more digitally native business models but also to transform and upgrade traditional industries. Agriculture is the art and science of tilling the soil, growing crops, and rearing livestock at the appropriate time and place. Any innovation in the field of agriculture requires more than one year of verification and adjustment on the land. Metaverse could be a way to achieve a smart

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agriculture system by deploying technologies such as digital twins and virtual reality. In October 2021, Alibaba established an XR laboratory at its Dharma Institute to explore the application of in agriculture metaverse. Currently, they have launched an agricultural picking robot that is connected to the metaverse. By modeling the orchard environment, the robot can perform motion planning in virtual space to guide realworld picking operations. At the same time, XR Lab has also built a set of agricultural service management platforms to collect relevant data and perform calculations in the virtual world to understand the real world better. This set of tools and products has been put into trial operation at the Shaanxi Apple Base. By strengthening the digital twin, the research and development process can be completed in the metaverse, and the simulation of the growth process of plants in a virtual environment can also help shorten the innovation cycle and solve a major pain point in agricultural development. Fundamentally, the metaverse aims to change the characteristics of traditional agricultural improvements that are difficult to quantify. A similar situation occurs in the industrial field. At present, the core of Industry 4.0 that many countries are actively promoting lies in the Cyber-Physical System. It can be said that the industrial metaverse is the path and method of upgrading the existing industrial system. At the end of 2020, Nvidia launched a collaboration platform called Omniverse, known as the Metaverse of Engineers. It has attracted more than 17,000 customers in just one year, including large companies such as BMW, Ericsson, and Adobe. In Omniverse, engineers can simulate various things and share and collaborate in a virtual space. For example, BMW has simulated a complete factory model in Omniverse, including employees, robots, buildings, assembly parts, etc., allowing global product engineers to collaborate in a virtual environment to complete a series of complex processes such as design, simulation, and optimization. It is said to improve efficiency by 30%. As digital models can reflect more physical influencing conditions, more complete and broader virtual objects and spaces will be constructed in the metaverse. Relying on more powerful communication environments, processing platforms, and sensing devices such as 5G and edge computing, the industrial metaverse can achieve better risk prediction, capacity management, and consumption of renewable energy. Furthermore, the metaverse can achieve fully refined industrial production, which is an unavoidable aspect of achieving a circular

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economy. Technical system support is required throughout the design, processing, application, and recycling processes. In addition, the education field is an important chapter for the value of the metaverse. Human beings have entered the historical stage of “life is learning, learning is life”, and learning has become a lifelong mission when we have solved survival issues. Traditional educational infrastructure and institutions face the problem of shortage and imbalance of educational resources. Metaverse provides the largest space and the technical foundation for education. For example, open universities are suitable to be conducted in the metaverse so that everyone is no longer bound to the campus (Duan et al., 2021). Metaverses represent current digitization and digital transformation tendencies, and sustainable business is the path we will take in our future lives and development. As time passed, metaverse should not follow the current economic development model, or just become the next economic growth point under the current model. Still, it should develop a new pathway for sustainable business growth. The values of metaverse for sustainable business are reflected in the rethinking of economic logic as a dynamic view; reconstruction of the relationship between nature, technological and financial capitals; guiding social assets to pay attention to low-carbon transition; through the formation of distributed autonomous organizations to achieve open innovation ecosystem; while transforming the existing agriculture, industry, and service industries, we shall also promote the landing of emerging industries sustainably. Conflicts and dependencies emerge between the metaverse’s progress and the physical restrictions of sustainability. A metaverse created to accommodate mankind may not have a uniform operating or governing system, and it may even be divided by national borders, but it must have a long-term direction. We cannot disregard the metaverse’s high-carbon side. We are trapped in a deadly vortex of disintegrating matter. We must ensure that the metaverse points to our era’s carbon-neutral development.

References Ball, M. (2020). The Metaverse: What it is, where to find it, and who will build it. MatthewBall.Vc (blog). https://www.matthewball.vc/all/themetaverse BIS. (2021). A prototype for green bond tokenisation by digital asset and GFT . https://www.bis.org/publ/othp43_report3.pdf

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Bocken, N. M., Short, S. W., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production, 65, 42–56. Chen, Y., & Volz, U. (2021). Scaling up sustainable investment through blockchain-based project bonds (ADBI Working Paper Series 1247). https:// www.adb.org/publications/scaling-sustainable-investment-blockchain-basedproject-bonds Cheng, E. (2021). Shanghai doubles down on the metaverse by including it in a development plan. CNBC. https://www.cnbc.com/2021/12/31/shanghaireleases-five-year-plans-for-metaverse-development.html Cherp, A., Vinichenko, V., Jewell, J., Brutschin, E., & Sovacool, B. (2018). Integrating techno-economic, socio-technical and political perspectives on national energy transitions: A meta-theoretical framework. Energy Research & Social Science, 37 , 175–190. Colander, D. (2000). The death of neoclassical economics. Journal of the History of Economic Thought, 22(2), 127–143. Crank, J. (2021). Wyoming DAO LLCs: Potential pitfalls for the novel entity. Available at SSRN 3950916. https://papers.ssrn.com/sol3/papers.cfm?abs tract_id=3950916 Cropper, W. H. (1986). Rudolf Clausius and the road to entropy. American Journal of Physics, 54(12), 1068–1074. Demirguc-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2018). The Global Findex Database 2017: Measuring financial inclusion and the fintech revolution. World Bank Publications. Di Silvestre, M. L., Favuzza, S., Sanseverino, E. R., & Zizzo, G. (2018). How decarbonization, digitalization and decentralization are changing key power infrastructures. Renewable and Sustainable Energy Reviews, 93, 483–498. Duan, H., Li, J., Fan, S., Lin, Z., Wu, X., & Cai, W. (2021). Metaverse for social good: A university campus prototype. Paper presented at the Proceedings of the 29th ACM International Conference on Multimedia. Franck, G. (2019). The economy of attention. Journal of Sociology, 55(1), 8–19. Garud, R., Kumaraswamy, A., & Karnøe, P. (2010). Path dependence or path creation? Journal of Management Studies, 47 (4), 760–774. Hall, C., Lindenberger, D., Kümmel, R., Kroeger, T., & Eichhorn, W. (2001). The need to reintegrate the natural sciences with economics: Neoclassical economics, the dominant form of economics today, has at least three fundamental flaws from the perspective of the natural sciences, but it is possible to develop a different, biophysical basis for economics that can serve as a supplement to, or a replacement for, neoclassical economics. BioScience, 51(8), 663–673.

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Hulme, P. E. (2005). Adapting to climate change: Is there scope for ecological management in the face of a global threat? Journal of Applied Ecology, 42(5), 784–794. Jackson, T. (2009). Prosperity with growth: Economics for a finite planet. London Earthscan Publication. Keane, J. (2022). South Korea is betting on the metaverse—And it could provide a blueprint for others. CNBC. https://www.cnbc.com/2022/05/30/southkoreas-investment-in-the-metaverse-could-provide-a-blueprint.html Kraus, D., Obrist, T., & Hari, O. (2019). Blockchains, smart contracts, decentralised autonomous organisations and the law. Edward Elgar Publishing. Marx, K. (2020). Theories of surplus value: Volume 1 (Vol. 20). Pattern Books. Mathews, J. A. (2013). The renewable energies technology surge: A new technoeconomic paradigm in the making? Futures, 46, 10–22. Nelson, R. R., & Winter, S. G. (1982). The Schumpeterian tradeoff revisited. The American Economic Review, 72(1), 114–132. Norgaard, R. B. (1988). Sustainable development: A co-evolutionary view. Futures, 20(6), 606–620. Pulse, D. (2021). Total value locked in DeFi. https://defipulse.com/ Rocas-Royo, M. (2019). Decentralization as a new framework for the sharing economy. In Handbook of the sharing economy (pp. 218–228). Edward Elgar Publishing. Schumpeter, J. A. (2013). Capitalism, socialism and democracy. Routledge. See, G., Perumall, A., & Zhanassova, A. (2022). Are ‘decentralized autonomous organizations’ the business structures of the future? World Economic Forum. https://www.weforum.org/agenda/2022/06/are-dao-the-businessstructures-of-the-future/ Spielberg, S., Silvestri, A., Penn, Z., Cline, E., & De Line, D. (2018). Ready player one. Warner Bros USA. Stephenson, N. (2003). Snow Crash: A Novel. Spectra. (Original work published 1992). Sustainable Digital Finance Alliance & HSBC. (2019). Blockchain: Gateway for sustainability linked bonds—widening access to finance block by block. Sustainable Digital Finance Alliance and HSBC Centre of Sustainable Finance. https://www.sustainablefinance.hsbc.com/mobilising-finance/ blockchain-gateway-for-sustainability-linked-bonds The World Counts. (2023). State of the planet. https://www.theworldcounts. com/challenges/planet-earth/state-of-the-planet/overuse-of-resources-onearth Von Bertalanffy, L. (1973). The meaning of general system theory. General system theory: Foundations, development, applications (pp. 30–53). Penguin Books.

PART IV

Fintech and Governance Sustainability

CHAPTER 8

Circular Economy: A Fintech Driven Solution for Sustainable Practices Vincent Grégoire and Kevin Guay

8.1

Introduction

The growing climate change issues have been discussed for quite some time. More precisely, global warming and the depletion of the ozone layer became more prominent in public and political debates across nations during the 1980s (United Nations, 2007). The current linear model of consumption, where goods are purchased and then thrown into the trash once used or defective, contributes to these challenges. Sadly, one famous example is the Great Pacific Garbage Patch, a massive floating trash island in the Pacific Ocean made up mostly of plastic (National Geographic, n.d.). Global recycling infrastructures and their low efficiency may be the cause (OECD, 2022). However, product design is also a root cause, where manufacturers do not use biodegradable materials. As part of the

V. Grégoire (B) · K. Guay HEC Montréal, Montreal, QC, Canada e-mail: [email protected] K. Guay e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_8

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problem comes from the beginning of the product lifecycle, and another stems from the product’s end of life, this inspired a more sustainable model of consumption: the circular economy. Disruptive financial technologies enable the potential of circularity as they reduce the barriers to adopting this sustainable concept. In Sect. 8.2, we first examine the rise of fintech which dates back to the nineteenth century. In 2014, the industry gained significant traction and is now viewed as the union of financial services and information technology. Today’s era of Fintech 4.0 is marked by disruptions to traditional financial services. Big data and machine learning are becoming more important, not only in the day-to-day business decision-making process but also in the product or service development process. Next, in Sect. 8.3, we describe the concept of circular economy in more detail. With growing waste management challenges and countries directing more resources to develop sustainable practices, researchers have devised many definitions around the 3Rs: reduce, reuse, and recycle. However simple the concept seems, there are considerable barriers to adopting circular business models. Nonetheless, some industries offer actionable solutions in the short term, which are discussed in Sect. 8.4. Plastics and packaged goods, fashions and textiles, and food and agriculture are the three main sectors that can significantly reduce waste, costs, and greenhouse gas emissions by implementing the concept of circularity in their operations. Section 8.5 explains a critical component of the successful implementation of circularity: information traceability and transparency. Material-flow data allows us to identify structural waste in operations and guide effective decision-making. Sharing information among stakeholders is a way of taking responsibility for the environmental impact of business operations and allows participants to understand their exposure to various risks. Finally, we discuss the areas where fintech helps enable circular business models in Sect. 8.6 before offering concluding remarks in Sect. 8.7. Alternative financing solutions, such as crowdfunding, bridge the gap between traditional financing and projects that cannot otherwise receive capital from banks or venture capitalists. Information tracking solutions leverage financial technologies to provide insights for environmentally cautious consumers and businesses, allowing for more informed decision-making. Moreover, the rapid growth in industries centered around digital resale platforms can be attributed in part to several key factors. These include

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the buying preferences of younger generations, their cost-effective nature, and the representation of sustainability that they embody.

8.2

The Rise of Financial Technology

At its core, fintech refers to the use of modern technology to improve the delivery of financial solutions. The relationship between finance and technology has a long history and can be traced back to the nineteenth century, according to Arner et al. (2015). The industry gained significant traction in 2014 and is often viewed as an especially recent union of financial services and information technology. Attracting over 220 billion U.S. dollars of investment worldwide in 2021 (Statista, 2022a), the industry is still growing rapidly. The Americas is the region with the most fintech startups, counting 10,755 entities in November of 2021 (Statista, 2022b). 8.2.1

Fintech 1.0 (1866–1967)

The first phase of financial globalization, which lasted until the beginning of World War I, started in the late nineteenth century due to the convergence of finance and technology. Technology from this era, including the telegraph, railways, and canals, supported cross-border financial links, enabling quick transmission of financial data, transactions, and payments. The post-war period that followed saw several technological changes, especially in communications and information technology. In other words, this first stage was about laying the foundation of the infrastructure that would support globalized financial services. 8.2.2

Fintech 2.0 (1967–2008)

The beginning of this phase is characterized by the installation of the world’s first ATM in 1967 (Reuters, 2017). It marked the start of the modern period for financial services, which saw a switch from analog to digital. The first digital stock market in the world, NASDAQ, was founded in the 1970s, along with the Society for Worldwide Interbank Financial Telecommunications (SWIFT), a protocol for financial institutions to communicate with one another that facilitated the high number of international transfers. Growth continued into the 1980s with the rise of bank mainframe computers and the introduction of platform solutions

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such as Bloomberg. The following decade brought the advent of online banking as the internet boomed, alongside a plethora of e-commerce business models. Innovative payment solutions like PayPal were born and offered to both businesses and citizens to facilitate online transactions. Banks’ internal operations, communications with outside parties, and interactions with retail customers had all been fully digitized by the beginning of the twenty-first century, the key characteristic of this phase (Zigurat Global Institute of Technology, 2022). 8.2.3

Fintech 3.0 (2008–2014)

The Great Financial Crisis of 2008 caused a shift in individuals’ perspective of traditional banking institutions as they lost confidence in their ability to provide financial services. Combined with widespread digitization movements, this mindset shift paved the way to what we recognize as the fintech industry today. A combination of changes in regulations and policies and economic recovery led to an increase in the number of startups in the industry. For example, the 2012 Jump Start Our Business Startups (JOBS) Act in the United States was instrumental in providing funding for entrepreneurs. For startups in general, the fintech industry was able to benefit from such a policy. Crowdfunding, cryptocurrencies such as Bitcoin, and digital wallets are examples of technologies or services that were created in this era that are still commonly used today. 8.2.4

Fintech 3.5 (2014–2017)

This period marks a shift away from the financial industry that is dominated by the West and considers the global spread of digital banking. Specifically, the focus is put on consumer behavior and how people in developing countries use the internet. For instance, markets in China and India, which had lagging physical banking infrastructure compared to Western countries, were more receptive to innovative ideas. The quick adoption rate of these technologies is partly explained by the population’s demographic characteristics as well as inefficiencies in the local financial markets. This opened opportunities for Eastern tech giants like Alibaba to introduce technology not yet available elsewhere, namely facial recognition payment systems (Smith, 2015).

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Fintech 4.0 (2017–Today)

In our current fintech climate, it is difficult to predict how long this phase will last and what new technologies it will bring. What we can predict, however, is that this era will change the current way we do business. Artificial Intelligence is already a game changer in many industries with various applications. Combined with Big Data, Machine Learning is proving to be insightful and is used in predicting consumer behavior, among other things. For instance, an application of such technology is used to combat credit card fraud and money laundering by detecting odd behavior without human intervention. On the side of financial services, blockchain technologies and open banking are also driving innovation. The rise of neo-banks challenges traditional banks by offering digital-only experiences with low-to-no fees (MaltaToday, 2018). In addition to disrupting current processes, some fintechs leverage technology and information to create new products and services that enhance sustainable practices. For example, digital technologies such as artificial intelligence and blockchain technology are used to improve information transparency and traceability during a product’s life (Fogarassy & Finger, 2020). Such real-time insights improve remanufacturing and recycling opportunities, a critical component of circularity.

8.3

Circular Economy for a Sustainable Future

Across the world, we are encountering considerable environmental challenges. Given the increasing rate of animal extinction and the growing unpredictability of weather patterns, adopting sustainable practices has become crucial to economic progress. Resource efficiency has been identified by the European Union (E.U.) as one of the pillars of its Europe 2020 strategy as it acknowledges the importance of the transition toward a regenerative circular economy (Michelini et al., 2017). Waste management is an ever-increasing challenge as the population grows. There is no doubt that the current linear socioeconomic system— that is, discarding a product at the end of its life—is fueling this challenge. Not only is this model contributing to pollution due to waste, but it is also not sustainable in the long term (Korhonen et al., 2018). There are growing concerns about resource depletion worldwide, given their finite amounts. For instance, Australia’s ore grades have been declining, requiring more complex processing and inputs such as energy and water

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(Prior et al., 2012). Both waste and resource exhaustion are two elements, among others, that have sparked discussions around the need to improve the current system to a more sustainable paradigm. 8.3.1

The Concept of Circularity

Now popular among several national governments and businesses worldwide, the concept of circular economy emerged a few decades ago. In the 1970s, when waste generation became an issue, the first iteration of the concept was brought to light with the idea of the 3Rs: reduce, reuse, recycle (Schröder et al., 2020). Since then, the concept has gained interest from scholars and practitioners, and many definitions of the circular economy have been used. Kirchherr et al. (2017) analyzed 114 different definitions of the concept and found that the most frequently used definition revolves around the 3Rs, without considering the systemic shift required for the circular economy. With the aim of sustainable development, we should take a more holistic approach to the concept and view it as as Kirchherr et al. (2017) describe: an economic system that replaces the ‘end-of-life’ concept with reducing, alternatively reusing, recycling and recovering materials in production/ distribution and consumption processes. It operates at the micro level (products, companies, consumers), meso level (eco-industrial parks), and macro level (city, region, nation, and beyond), with the aim to accomplish sustainable development, thus simultaneously creating environmental quality, economic prosperity, and social equity, to the benefit of current and future generations. (p. 229)

This definition illustrates the need for collaboration between the various stakeholders—such as businesses, policymakers, and researchers—to coordinate efforts in implementing the concept. 8.3.2

Barriers to Circular Economy Business Models

The idealized concept of circular economy has immense potential benefits for societies worldwide. However, there are quite a few roadblocks preventing or slowing the adoption of the concept in current business models. Rizos et al. (2016) identified seven categories of barriers that

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small and medium-sized enterprises (SMEs) may face when trying to implement a circular business model. We explain these categories below. 1. Financial barrier. The need for more capital resources is widely recognized as a major obstacle for SMEs in adopting circular economy practices. Transitioning from a linear to a circular production or business model requires significant investments in areas such as distribution planning, inventory management, production planning, and the management of a reverse logistics network. These changes can entail substantial upfront and indirect costs, such as the investment of time and human resources. Additionally, the time required to recoup these investments is particularly important for SMEs, as they tend to be more sensitive to additional costs associated with implementing environmentally friendly business practices than larger enterprises. Adopting a circular economy business model also requires ongoing monitoring and improvement of the product’s lifecycle, which can be resource-intensive for companies. 2. Regulatory barrier. One of the biggest challenges to SMEs adopting environmental initiatives is the need for adequate government backing and appropriate regulations. This can be caused by, among other things, a need for more funding opportunities, education, an efficient tax system, and laws and regulations. With a clear-cut, welldefined legal framework, SMEs might be more inclined to consider incorporating sustainable practices into their daily operations. This is made worse by weak market signals, such as low raw material costs, discouraging resource efficiency, or shifting to a circular economy. Furthermore, resource taxes are frequently low and environmental costs, or externalities, are not taken into account when determining product prices, which can deter businesses from using recycled materials because they may require additional processing. 3. Technological barrier. Transforming a linear business model into a circular form can be challenging. It requires the integration of new sustainable production and consumption technologies such as eco-design, clean production, and life cycle assessment into existing linear businesses. Historically, these technologies generated little demand until recent interest in the concept. As such, the lag in developing new technologies is creating a bottleneck for firms, which may have to use a business model they are familiar with instead of an innovative concept. In terms of infrastructure, the

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increasing complexity of the material content of new products puts pressure on the current recycling infrastructure. There is a need to leverage new technologies for current recycling systems to improve their capacity and efficiency. 4. Information barrier. The effective shift to a circular economy can only be accomplished through collaboration, necessitating information sharing, and innovation among various value chain parties. However, businesses frequently hold information in strict confidence, and people frequently find it challenging to share their knowledge, which hinders the development of circular economy business models and their widespread adoption. There must be a method for exchanging information to implement circular business models effectively. Co-production, innovation, and efficient product end-of-life management are all hampered by the inability of corporations to share expertise and information about their products due to concerns about confidentiality, lack of trust, and competition. 5. Supply and demand barrier. Suppliers and customers need to engage in the adoption of sustainable practices. Collaboration among all supply chain parties is critical to successfully implementing the circular economy. Adopting such a model will likely cause added complexity in the supply chain, affecting the logistics, financial, and legal aspects, thus impacting the value chain of products, processes, or services. This can be a dealbreaker for suppliers, who may decide to no longer provide for the business. Additionally, a lack of customer knowledge about the advantages of environmentally friendly products can prevent a shift in purchasing habits. Thus, businesses may face minimal consumer demand to implement sustainable practices or adopt circular economy business models— ultimately slowing down the adoption process for a sustainable future. 6. Company culture barrier. The philosophy, habits, and attitudes of a company’s management and employees toward implementing circular economy business practices can be a barrier to adopting circular business models. Managers with strong risk aversion could be reluctant to implement such a business model due to the systemic shift that would be required. The actions and reactions of workers also fall under the same umbrella. While some employees may be motivated by working for a company that prioritizes the environment, others may be hesitant or unaware of how to alter their usual

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work processes to align with the company’s environmental goals. Additionally, managers who focus on immediate benefits rather than long-term sustainability can also impede the implementation of sustainable practices. This highlights the importance of providing employees with the necessary training, resources, and support to help them understand and adopt circular economy practices. These require, however, some additional costs to be borne by the company. 7. Administrative barrier. Due to the costs and resources required, monitoring and reporting environmental performance data may be complex for SMEs. For instance, SMEs frequently need to submit the same data in different formats and to different authorities, but the necessary expertise is frequently needed from outside experts. Additionally, implementing a circular company model may require more active, experienced management, and planning procedures.

8.4

Sectors with High Short-term Circular Economy Potential

While it would be optimal for all sectors to implement the concept of circularity in their business models, this is not realistic today. Some technological or demand-based barriers may be too persistent, leading to a slower short-term potential. In contrast, some sectors have actionable opportunities, given the current state of business, which can lead to lower pollution levels and economic benefits in the near term. The Ellen Macarthur Foundation, a global thought leader on the subject, has identified three distinct sectors with high short-term potential in their 2020 report “Financing the Circular Economy: Capturing the Opportunity.” We explore these sectors below. 8.4.1

Plastics and Packaged Goods Sector

Transitioning to a circular economy for the plastics and packaged goods sector is a high-potential short-term solution to the problem of rising demand for these materials. Combined with the high volume of such materials in use and the waste stream, it creates a significant opportunity for circular solutions. Currently, most plastics and packaged goods are not designed for circularity, meaning there is significant potential for design, production, and consumption improvements. This transition can provide

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significant economic opportunities, such as cost savings from reduced raw material and energy costs and new revenue streams from the sale of recycled materials in circular business models. Additionally, there is increasing pressure from consumers, governments, and investors to move away from single-use plastics and packaging, as well as increasing awareness of the environmental impacts of these materials. Advancements in technology and innovation, such as new materials, new recycling technologies, and digital platforms, are making it increasingly possible to create circular solutions. Incorporating circularity in plastics and packaged goods can have immediate and tangible benefits, such as reducing pollution and greenhouse gas emissions. Chemical recycling is an example of a solution developed for this sector via innovation. The process converts plastics back to the quality level of virgin resin, providing an alternative to landfilling and incineration of plastic waste. This is an actionable opportunity in the short term to reduce the dependence on virgin material and help close the loop of plastic waste. 8.4.2

Fashion and Textiles Sector

As one of the largest industries globally, the fashion and textiles sector has a high potential for circularity. The high demand for new clothing and textiles creates an opportunity for designers to innovate their designs and production processes to meet the growing consumer demand for sustainable practices. Advancements in technology and government policies are driving the shift toward circular economy strategies such as rental, resale, and circular design. These strategies could lead to a decrease in the production of new clothes and textiles and an increase in the use of existing ones. The successful implementation of a circular business model in the fashion industry is Rent the Runway. Offering rental subscriptions allows customers to rent clothing instead of purchasing them, creating a new business model for the industry, and reducing the environmental impact of fast fashion. 8.4.3

Food and Agriculture Sector

Similar to the fashion and textiles sector, the food and agriculture business model is not currently designed for circularity. There is considerable waste in these industries, leaving room for significant improvements. In

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addition, some agricultural practices still used today are not environmentally friendly. Changing policies and environmental activist groups are forcing the industry toward sustainable practices. In recent years, regenerative agriculture, precision agriculture, and agroforestry have been implemented in regions to meet the changing dynamics. These can lead to sustainable food production, reduced environmental impact, and improved soil health, thus alleviating concerns about environmental degradation. Moreover, immediate action in this industry can have tangible benefits, such as reducing greenhouse gas emissions and chemical products overuse. For instance, precision agriculture is a concept by which farmers optimize crop production using information technology. By analyzing data collected from various sources, farmers can then make informed decisions on operations to be more efficient, minimizing the environmental impact of farming. 8.4.4

Other Sectors with Increasing Potential

The report of the Foundation identifies a few additional sectors with increasing potential for growth in the near future. The engineering and construction sector can benefit from circular economy strategies such as building retrofits, modular and prefabricated construction, and integrating recycled and bio-based materials. Given the large volume of materials and energy used in the sector and the long lifespan of buildings, this creates many opportunities to implement circularity in the industry. Electronics is also another sector that has potential, given the rapid pace of technological change and the high volume of materials used. Strategies such as product-as-a-service models, where companies lease rather than purchase products, can help to create a more circular system. Also, the industrial manufacturing sector can, by designing waste out of production processes as well as implementing de-manufacturing in their operations for example, substantially close the loops of material flows in their operations. This is only a partial list of sectors. There are opportunities to implement circular concepts in many other sectors that are not mentioned above as technology progresses.

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Information: Critical Component for Success

Understanding the history of fintech enables us to identify a critical factor that has significantly impacted the advancement of circular business models: Information technology. From the Fintech 3.0 era onward, there have been major advancements in information technology and digitization. These technological advancements significantly improved our ability to collect, store, and analyze information to improve business operations (Fawcett et al., 2007). For circular business models specifically, the ability to track and share information among stakeholders has been critical in the concept’s success. Traceability and transparency, which we discuss below, are two characteristics of information that are required for the circular economy. 8.5.1

Traceability

Complexity reductions and uncertainty in manufacturing systems have historically been linked to the capacity to localize components, materials, and products across the supply chain using precise information (Cheng & Simmons, 1994). Today, digital technology can identify the issues with material flows, highlight the primary sources of structural waste, guide more effective decision-making on how to solve these issues, and offer systemic solutions (Ellen Macarthur Foundation, 2017). Specifically, Pagoropoulos et al. (2017) concluded that three main architectural layers significantly influence the circular economy: data collection, analysis, and integration. Technologies such as radio frequency identification (RFID) and the internet of things (IoT) impacted the first architectural layer, data collection. Artificial Intelligence and Big data analytics come in handy for the data analysis stage. They also identified Product Lifecycle Management (PLM) and Relational Database Management Systems (RDBMS) as technologies that play a crucial role in data integration. Notably, Jabbour et al. (2019) indicate that the circular economy idea might not be effective without sufficient big data assistance. Implementing such technologies is challenging. For instance, IoT features have a lot of drawbacks, including decentralization, weak interoperability, privacy issues, and security flaws. Some of these can be addressed by blockchain technology (Kouhizadeh et al., 2019). This emerging technology has a high potential for traceability of information due to its features, such as immutable data, consistent operations, and record-keeping, that produce a reliable

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business ecosystem. Additionally, implementing this growing technology in business models promotes information transparency throughout the supply chain (Sahoo et al., 2022). 8.5.2

Transparency

The necessity for actors involved in every stage of global supply chains, not only producers and consumers, to share responsibility for putting production systems on a more sustainable footing is becoming increasingly evident. These include traders, processors, retailers, and investors that make up global supply chains, without forgetting the key role that states and regulators play in the process (Gardner et al., 2019). However, this requires information to be accessible and available to the stakeholders involved in the supply chain. Access to such critical information allows participants to assess and control their exposure to various risks by understanding increasingly complex supply chains (Laurell, 2014). Furthermore, this sharing of information allows stakeholders within the supply chain to better understand their environmental footprint and develop strategies to meet their sustainability goals (Kashmanian, 2017). There are, however, some challenges in making information accessible and readily available. One is the necessity to simplify and standardize the data, a process that can be resource-intensive and result in some characteristics being more visible than others (Gardner et al., 2019). Another considerable challenge is the competitive advantage, for example, keeping information about suppliers and sub-suppliers private can benefit a company. These hurdles can lead to corporations remaining cautious about information dissemination (Egels-Zandén et al., 2015). Transparency in the supply chain thus remains voluntary (Ebinger & Omondi, 2020). Similar to traceability, emerging technologies, such as blockchain, improve information sharing due to enhanced privacy of information, audit capabilities to ensure information accuracy, and process efficiency improvements (Zelbst et al., 2019).

8.6 Fintech Solutions: Enablers of Circular Economy Studies have shown that insufficient use of industry 4.0 technologies may lead to inefficient circular business models. Fortunately, growing numbers of fintechs leverage emerging technologies to provide solutions to some

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of the roadblocks to adopting a circular economy. Specifically, innovative solutions related to financing, information tracking, and digital platforms are enabling circular business models. We discuss these enablers below. 8.6.1

Financing Solutions

One significant barrier to adopting circular business models is the lack of capital. The costs associated with transitioning from linear to circular systems can be significant. This can be a considerable hurdle for SMEs, where their innovative business models may not be appropriate for traditional financing solutions from banks due to the greater risks involved (Mach et al., 2014). Alternative funding sources such as crowdfunding and so-called marketplace (peer-to-peer) lending, which first started during the Fintech 3.0 era, grew to become important facilitators of the circular economy and social investing. These platforms decentralize the risks by dispersing them among their users, creating an advantage compared to traditional financial institutions that collect credit and liquidity risks on their balance sheets when offering loans. Additionally, these platforms tend to have a simple application process requiring fewer documents than banks, making them an attractive choice for entrepreneurs (Lenz, 2016). The impact of these solutions is considerable for small and medium businesses. For instance, the crowdfunding platform Indiegogo has raised over 1 billion U.S. dollars for over 650k projects globally (Indiegogo, n.d.). Peer-to-peer lending platform LendingClub is also a major source of capital, with over 75 billion U.S. dollars in loans since its inception in 2007 (LendingClub, n.d.). While these may not be specifically focused on sustainable projects, they are two examples of fintech that disrupted traditional financing and capital solutions. The German crowdfunding platform Ecoligo is an example of a startup that leverages crowdfunding to provide services in the energy transition of emerging markets (Ecoligo, n.d.). An estimated 1 million tons of carbon emissions will be saved from the projects that are about to be completed (Allen, 2022). Abundance Investment, a European firm, has succeeded in deploying a peer-to-peer lending platform. Investing in renewable energy, circular economy, and affordable housing projects, the firm has lent over 100 million Euros for 40 different projects since it was founded (Kobayashi-Solomon, 2020). While there is no guarantee that sustainable projects will be successful and deliver attractive investor returns, it offers investors concerned with

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preserving the environment a channel for early investment in innovative concepts (Lam & Law, 2016). While remaining a relatively minor source of funding compared to, for example, Blackrock’s circular economy fund nearing 1.8 billion U.S. dollars of assets under management (Blackrock, n.d.), these financing solutions bridge a gap for early seed funding that is otherwise even more scarce. 8.6.2

Information Tracking

Lack of information is a substantial hurdle to circular economy business models. Without traceability and transparency in the supply chain, it is difficult to determine the origin and journey of products. This makes it hard for environmentally cautious consumers to make informed choices about the products they buy and for companies to manage risks and take action to achieve their sustainability targets. Supply chain management platforms focused on connecting stakeholders to improve information tracking using financial technologies are examples of solutions reducing the information and administrative barriers. Provenance, a technological firm that helps with sustainability transparency, uses blockchain and artificial intelligence to track products in global networks (Provenance, n.d.). Leveraging financial technologies allows the company to offer the end customer assurance about facts. The end customer knows the origin of the materials and all the transformative steps involved in making the product. This allows customers to make better-informed decisions when purchasing products knowing the origins and carbon emissions released in the process. Inherently, this can also reduce the incidents of greenwashing that can have a negative impact on consumers’ confidence for sustainable products (Delmas & Burbano, 2011). ChainPoint offers similar solutions using blockchain and A.I. but is more focused on helping businesses gain insights into their supply chain to identify inefficiencies that lead to waste (ChainPoint, n.d.). In addition, it offers audit, certifications, and regulatory requirements compliance solutions to meet external regulations. Their solution also helps companies with sustainability reporting by transforming raw and complex data into readily available reports. These platforms are examples of financial technologies used to make information traceable, transparent, secure, and available at all times, which are critical components of circular economy and sustainable practices. This enables users—end consumers or companies—to make educated decisions to meet their own sustainable objectives.

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8.6.3

Digital Platforms

The secondhand and resale industry is increasingly popular around the globe. The world’s largest consumer market, the United States, had an estimated secondhand market size of 96 billion U.S. dollars in 2021, and it is growing fast. Younger generations, competitive pricing, and sustainability’s core attributes can partly explain this trend (Wertz, 2022). Resale and rental platforms such as Poshmark and BikeClub are examples of the definition of circular economy, where they contribute to the extension of the lifecycle of products which limits the need for consumers to purchase new unsustainably sourced products. They also have the potential to reduce the amount of donated clothing that ultimately ends up in the landfills of poorer countries. A fintech company, Twig, is pushing the concept of resale to a new level. Using sophisticated valuation tools and the secondary market value of over 100 million products, Twig allows its users to contribute to the circular economy by valuing their lifestyle assets and instantly converting them into cash if they desire to part ways. The U.K.-based company then takes charge of the resale of the items. Users receive funds on Twig Visa debit cards, which allow them to complete domestic and international money transfers. This innovative business model has caught the attention of many large corporations in both the fashion and banking sectors. For instance, LVMH, L’Oréal, Barclays, and Goldman Sachs are some investors who invested part of the 35 million U.S. dollars in their Series A financing round (Lomas, 2022). This digital platform is an instance where financial technology meets circular economy at its core and incentivizes the adoption of sustainable resale practices via the friction-free process and rapid conversion of items to money.

8.7

Conclusion

This chapter presents an overview of the circular economy and its potential, fueled by fintechs’ innovative solutions. We are still early in the adoption of circular business models. As such, it is hard to assess whether the concept will remain as is or change as technology evolves. Nonetheless, it is undeniable that the benefits of such a concept are essential for a sustainable future. Whether by changing their processes or leveraging financial technologies, businesses need to rethink their current operations from start to finish to incorporate circularity. More importantly, all

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stakeholders must take responsibility for their environmental impact. As cooperation and accountability rise, we are likely to see innovative solutions alleviate sustainability concerns. Although the circular economy has gained significant attention in recent years, it is still a relatively novel area of research. Conducting further studies, especially those utilizing quantitative methods to assess the advantages and disadvantages of circular business models, could greatly enhance our understanding of the full potential of this concept.

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Prepared for United Nations Environment Programme. http://www.scpcle aringhouse.org/sites/default/files/sustainability_of_supply_chains_and_sustai nable_public_procurement.pdf LendingClub. (n.d.). Corporate profile | LendingClub corporation. Retrieved 18 January 2023, from https://ir.lendingclub.com/home/default.aspx Lenz, R. (2016). Peer-to-peer lending: Opportunities and risks. European Journal of Risk Regulation: EJRR, 7 (4), 688–700. Lomas, N. (2022, January 11). Twig takes $35M to turn stuff you own into a way to pay. TechCrunch. https://techcrunch.com/2022/01/11/twig-series-a/ Mach, T. L., Carter, C. M., & Slattery, C. R. (2014). Peer-to-peer lending to small businesses (FEDS Working Paper No. 2014-10). https://doi.org/10. 2139/ssrn.2390886 MaltaToday. (2018, November 12). Revolut deploys AI to combat card fraud and money laundering. Retrieved 14 May 2023, from http://www.maltat oday.com.mt/sponsored/sponsored/90854/revolut_deploys_ai_to_combat_ card_fraud_and_money_laundering Michelini, G., Moraes, R. N., Cunha, R. N., Costa, J. M. H., & Ometto, A. R. (2017). From linear to circular economy: PSS conducting the transition. Procedia CIRP, 64, 2–6. https://doi.org/10.1016/j.procir.2017.03.012 National Geographic. (n.d.). Great Pacific Garbage Patch | National Geographic Society. Retrieved 23 January 2023, from https://education.nationalgeograp hic.org/resource/great-pacific-garbage-patch OECD. (2022, February 22). Plastic pollution is growing relentlessly as waste management and recycling fall short, says OECD. Retrieved 15 May 2023, from https://www.oecd.org/newsroom/plastic-pollution-is-growingrelentlessly-as-waste-management-and-recycling-fall-short.htm Pagoropoulos, A., Pigosso, D. C. A., & McAloone, T. C. (2017). The emergent role of digital technologies in the circular economy: A review. Procedia CIRP, 64, 19–24. https://doi.org/10.1016/j.procir.2017.02.047 Prior, T., Giurco, D., Mudd, G., Mason, L., & Behrisch, J. (2012). Resource depletion, peak minerals and the implications for sustainable resource management. Global Environmental Change, 22(3), 577–587. https://doi.org/10. 1016/j.gloenvcha.2011.08.009 Provenance. (n.d.). Sustainability communications solutions | provenance. Retrieved 19 January 2023, from https://www.provenance.org/solutions Reuters. (2017, June 27). World’s first ATM machine turns to gold on 50th birthday. Retrieved 10 January 2023, from https://www.reuters.com/article/ us-atm-anniversary-idUSKBN19I166 Rizos, V., Behrens, A., Van der Gaast, W., Hofman, E., Ioannou, A., Kafyeke, T., Flamos, A., Rinaldi, R., Papadelis, S., Hirschnitz-Garbers, M., & Topi, C. (2016). Implementation of circular economy business models by small and

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CHAPTER 9

The Role of Fintech in the Field of Sustainability and Financing Niccole Jordan, Patrick Röthlisberger, Julia Meyer, and Beat Affolter

9.1

Introduction

The transition toward a sustainable economy is currently framed by 17 sustainable development goals (SDGs) set forth by the United Nations (UN) in 2015.1 The transition, though, began years before these goals were created and is driven by various international and regional regulatory initiatives with different goals and objectives. Many of the initiatives are based on the Paris Agreement of 2015 which strives to limit global warming to 1.5 degrees Celsius, lower greenhouse gas emissions, and

1 The UN SDG goals can be found here: https://sdgs.un.org/goals

N. Jordan · P. Röthlisberger (B) · J. Meyer · B. Affolter ZHAW School of Management and Law, Center for Corporate Performance and Sustainable Financing, Winterthur, Switzerland e-mail: [email protected] N. Jordan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_9

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foster climate-resilient development.2 Other initiatives focus on biodiversity loss and the social aspects of sustainability. The financial sector is expected to play an important role in the transition to a sustainable economy. To date, assets under management that include sustainability amount to roughly one-third of all assets managed on a global scale (Global Sustainable Investment Alliance, 2020). Investment strategies apply different approaches to sustainability, such as the exclusion of certain non-sustainable companies or industries and the integration of environmental, social, and governance (ESG) ratings. This is also relevant in the context of financing decisions. For example, there is evidence that ESG risk factors are relevant for pricing credit instruments. While these strategies are powerful in relation to their sheer size and they certainly create awareness, the real-world impact is not so straightforward (Kölbel et al., 2020). By only applying strategies of exclusion and ESG integration to financial decisions, it is very likely that the economy will fall short of achieving the UN SDGs. New forms of (sustainable) financing are needed, to enable all companies (not only those with access to the capital markets) to contribute to a sustainable future. The question of how to finance a more sustainable economy is intimately related to the societal push to create sustainable business models with which to finance the transition. The Organization for Economic Co-operation and Development (OECD, 2020) estimates that the amount of global investment needed to finance the reduction of CO2 alone is between USD 2.5 and 4.2 trillion annually. These numbers show the vast dimensions of the transition to sustainability. Classic bank financing will, in all likelihood, reach its limits and not be able to close this large gap (Affolter et al., 2022). Fintech raises the hope that it can make a significant contribution toward achieving these goals. The aim of this chapter is to identify and discuss the contribution of fintech to sustainable financing.

2 The Paris Agreement can be found under: https://www.un.org/en/climatechange/ paris-agreement.

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9.2 Sustainability in Financing and Financing of Sustainability One must differentiate two distinct approaches to sustainable financing: sustainability in financing (e.g., consideration of ESG risk factors), and the financing of sustainability (e.g., fostering the development of new products to bridge the financing gap) (see Fig. 9.1). This distinction closely follows the contours of the widespread concept of double materiality, namely the distinction between financial materiality, and environmental and social materiality (European Commission, 2019). Sustainability in financing centers on the concept of financial materiality—an assessment of the financial risks stemming from sustainability (e.g., pricing of financial and sustainability risks). Financial materiality motivates companies to avoid stranded assets. Financial materiality also refers to opportunities for growth in the form of new markets that emerge from arising threats, such as global warming, e.g., the solar industry. The Task Force on Climate-Related Financial Disclosures (TCFD) focuses on the climate-related aspects of financial materiality (acute risks, or eventdriven, and chronic risks, due to longer-term impacts of climate change), by introducing a framework of disclosure standards.3 The framework aims to provide information for both the company, as well as the financier and thus enables better-informed financing decisions and risk-adjusted financing terms.

Fig. 9.1 Sustainability-related dual materiality and the focus areas of sustainability in financing (adapted from Affolter et al. [2022])

3 See https://www.fsb-tcfd.org for further information.

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In financial markets, the consideration of sustainability in the financing may be pursued through two basic approaches: (1) excluding nonsustainable companies or only choosing the “greenest” company in a non-sustainable industry (exclusion) and (2) considering sustainability opportunities and risks in financing decisions, often by taking ESG ratings into account (ESG integration). Financing sustainability centers on the concept of environmental and social materiality with a focus on sustainable opportunities. It involves the financing of promising approaches to achieve specific sustainability goals within the framework of the UN SDGs. While many companies have already begun implementing strategies within the province of financial materiality, environmental and social materiality has proven to be more challenging. This is mainly because environmental and social materiality involves the long-term transition of entire business models to impact a sustainability objective. From a financing perspective, this encompasses specifically designed financial instruments (e.g., green bonds/loans, sustainability-linked bonds/loans), which are only accessible through capital markets or on the large-scale bank market. Small and mediumsized companies often do not have access to these markets and traditional bank loans may be unattainable to them, resulting in an inability to fully cover their sustainable financing needs. As highlighted earlier, relying simply on traditional bank lending will very likely fail to fully cover the enormous financing needs to meet global sustainability goals. Companies and projects that contribute to environmental and social materiality may be exposed to higher levels of risk due to longer maturities, lack of collateral and secondary markets, as well as too uncertain profits. There are also challenges within financial materiality, where the problem is less about an uncertain outcome, as in obtaining and measuring the right data and then converting the data into usable information (Berg et al., 2022). These challenges require new solutions, such as specialized (impact-oriented) intermediaries, public–private partnerships, and non-traditional financing structures, as well as technological solutions (i.e., fintech). In summary, financing of sustainability consists of two areas: (1) financing the transformation of existing businesses and (2) financing new sustainability solutions that have a positive social or environmental impact. Figure 9.1 summarizes the different dimensions of sustainable financing, considering double materiality and other approaches. It should

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be kept in mind, however, that distinctions between the different approaches are not clear cut and there may be a great deal of overlap.

9.3

The Role of Fintech in Sustainable Financing

The term fintech may have different definitions, depending on who is doing the defining (Allen et al., 2020). In this chapter, we define fintech as the use of technology to support financing. This meets the requirements of the definition given by Berg et al. (2021). Fintech makes financial services more accessible, efficient, and affordable, while also being an important driver for inclusion and sustainable development (Vergara & Agudo, 2021). Between the years 2010 and 2019, an estimated $165.5 billion was invested in fintech companies (Imerman & Fabozzi, 2020), which clearly demonstrates investor interest in the area. These large investments initiate a trend of technology companies moving into the financial services sector. Based on the taxonomy proposed by Imerman and Fabozzi (2020), fintech may be categorized along the lines of emerging technological innovation for financial services: automation/robotics, big data analytics, artificial intelligence, distributed ledger technology/block chain and quantum computing. Table 9.1 summarizes these technologies and offers a general description of the technology, its impact, and possible applications within sustainable finance, as well as how it differentiates the two areas of sustainability in financing and financing of sustainability.

9.4

Automation/Robotics

Automation/robotics is the use of technology to automate business processes and the flow of data either partially or fully between business systems or platforms. It is a process-driven technological innovation, as opposed to being data-driven. This chapter prioritizes financial business processes, but essentially all value-added processes of a company are within the scope of automation. Automation involves the use of digital tools to automate repetitive tasks, reduce errors, and improve efficiency, thereby making processes less repetitive and leaving room for more analysis. Automation is an incremental innovation, meaning that although the underlying process may remain the same, it is now more efficient. From an economic point of view, this leads to lower transaction costs and may enable new market-built solutions or new intermediaries.

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Table 9.1 Summary of technological approaches and possible applications to sustainable financing

Emerging Technologies

Automation/ Robotics

Big Data Analytics

Description of the Technology The use of technology to mimic human actions by automating tasks and processes, as well as integrating different systems and platforms using minimal or no human interaction. Impact: Increased efficiency and reduction of transaction costs.

Possible Applications in Sustainable Financing (Examples) Sustainability in Fi- Financing of Susnancing tainability Sustainability ratings may be automated and added to the credit process, resulting in a better evaluation of the borrower and fairer credit conditions.

New market solutions become feasible, e.g., crowdlending for new sustainability solutions. The transformation to a more sustainable business model may also be supported by automated reporting of sustainability metrics. Inclusion of new borrowers who had not previously had access to credit may also be reached. The use of large data sets to Inclusion of ESG data The inclusion of more reveal patterns and trends and using rating agencies, as encompassing sustainato improve decision-making. well as alternative data bility data sources may Data sources are varied and sources, may be incorpo- be incorporated into a may include alternative rated in real time, allow- company’s internal and sources, e.g., social media, ing for more accurate external reporting needs satellite imagery, online credit pricing and inand financing decisions. transactions, and sensory sights that may result in data. better decision-making. Impact: Better decisions based on better data.

(continued)

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Table 9.1 (continued)

Artificial Intelligence

Distributed Ledger Technology/ Blockchain

Quantum Computing

The simulation of human intelligence using machines to perform tasks that previously were reserved for humans while, at the same time, using the data produced to improve their own programs and processes. Impact: More effective and faster decisions. The use of electronic databases to be shared across several systems and locations while ensuring security and verification. Impact: Decentralization and broader access to financial markets. The use of technology to solve problems simultaneously (not sequentially) at significantly higher speeds. Impact: Revolutionize all technologies.

Different forms of data (including alternative data) are converted into information (i.e., ESG ratings or impact on SDG goals) in real time and may subsequently lead to the detection of greenwashing.

Intelligent decisionmaking process, with better information quality helping to make better decisions while tracking more relevant information (such as the overall societal or ecological impact of a company). Rewarding tokens for Inclusion of new market low emissions is already participants as interacvery common. tions are based Additionally, data on on peer-to-peer relationsustainability perforships without mance can be stored and intermediaries. This shared transparently means financial transacwith little or no transac- tions are no longer lotion costs. cally (regionally) bound. When applied to day-to-day processes, the technology will be disrupting and improve all use cases mentioned in this table. Currently, the technology is not widely used and is still in its infancy.

A common adaptation of this technology is robotic process automation (RPA). RPA can be used to automate a wide range of tasks in finance, including data entry, data extraction, and documentation. RPA software often mimics the actions of a human user and can perform tasks with greater efficiency. Overall RPA can also help to reduce costs and improve compliance. The transition toward digital process automation may result in better analysis and insights into the data in the form of reports, dashboards, and performance indicators. This allows, for example, for the inclusion of sustainability metrics in financing decisions. 9.4.1

Sustainability in Financing

One example of process automation is the automated collection of sustainability data and its integration into the credit process via ratings (e.g., ESG scores). Data collection and rating generation itself would theoretically also be possible without automation, but only automation

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makes it efficient and enables scaling. The addition of sustainability ratings not only enables the financier to better assess the current and future financial condition of the company, but also provides a greater resiliency of the company in the longer term. The extraction and uploading of ESG scores and geographical information may be accomplished through RPA. Example: A recent study based on residential properties considered the risk of rising sea levels and concluded that the inclusion of climate risk models within the credit pricing process, had led to a sea level risk premium of 7.5 basis points on properties that are exposed to sea level rise as opposed to those that are not (Nguyen et al., 2022). Banks are also incorporating climate change risk criteria into their lending policies.

Automation allows comparison among various standards and regulations (i.e., Global Reporting Initiative (GRI) Standards, Task Force on Climate-related Financial Disclosures (TCFD), SASB Standards, EU Taxonomy) and among rating agencies (i.e., MSCI, ISS ESG, Sustainalytics, Refinitiv). Industry scores may also be easily integrated into the analysis. One of the challenges in these comparisons is the variability in ESG scores and methodologies (Berg et al., 2022). Automation, therefore, does not necessarily lead to better decisions (effectiveness). In addition, ESG scores are only currently available for publicly traded companies, although a number of private companies are also becoming increasingly interested in ESG scores. The lack of data for certain ESG statistics presents a great challenge (or potential for further automation). Factoring in ESG scores with more complete data could mean a decrease in credit costs for more sustainable borrowers but would also entail an increase for others (e.g., see the example of rising sea levels above) when sustainability data is included in credit ratings. This can lead to automatic exclusions and rankings of companies and industries in the financing process. 9.4.2

Financing of Sustainability

The transformation to a more sustainable business model may also be supported through new market solutions that take advantage of automation and robotics. Automation means gains in efficiency, thereby lowering

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transaction costs. Through this reduction of transaction costs new alternative intermediaries may arise, e.g., crowdlending or operating in a new market space. Lending money is not a new idea; however, novel solutions or specific new marketplaces allow investors to contribute to small to medium-sized businesses. Investors choose the type of projects/ companies they would like to invest in and are increasingly doing so with a focus on sustainability. This opens up new potential in the financing of new sustainability solutions or business transformations. Several crowdfunding platforms have dedicated themselves to sustainable projects/ companies (Maehle et al., 2020). Example: bettervest, an online German crowdfunding platform, was launched in 2012 with the goal of financing energy efficiency projects run by individuals. The projects are chosen based on their impact, technical feasibility, economic viability, and risk mitigation measures. This process also includes sustainability metrics of the various projects. The projects range in size, location, and estimated returns. One project includes a lending goal of EUR 2.2 million in the form of a subordinated loan to finance the expansion of portable cooking stoves in Africa over a 5-year period and offers a 6.0% return. Another project includes a funding goal of EUR 100,000 in the form of participation rights in various impact projects involving CO2-saving technologies that aim to save 900,000 tons of CO2.4

This example shows that new forms of financial intermediaries are shaping the financing of sustainability and can step in to fill the gap to help finance small to medium businesses when classic bank financing is not available. Due to bettervest’s automated investment process, transaction costs are reduced, and efficiency is improved.5 The use of digitalization to support the sustainability of financing may also be observed in the automated reporting of sustainable metrics and the incorporation of this data into internal reporting and controlling processes. This may also include more efficient digital processes within the business itself. These efficiency gains matter not only for larger companies but also for the financing of small-scale projects, for example through loans provided by microfinance institutions. Automation provides enormous benefits to the microfinance industry as transaction costs are usually a large hindrance in the growth of microfinance services. Using process 4 See https://www.bettervest.com for further information. 5 See https://www.microfund.org.jo for further information.

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automation and robotics compensates for this disadvantage. Not only does it allow for cost and efficiency savings within the microfinance institute itself (the International Finance Corporation, a large global development institution, estimates the annual cost to serve a customer is reduced by 80% and the cost-income ratio reduced by 18% through the implementation of digital process automation), but also promotes financial inclusion for all members of society (Flaming & Jenik, 2021). Example: Microfund for Women (MFW) was launched in Jordan in 1996. Jordan’s first and largest non-profit organization is dedicated to helping women entrepreneurs who lack access to traditional financing through various financial and non-financial services. In 2017, the organization began to digitalize its loan application process, beginning with tablet-based onboarding and streamlining the digital process flows of approval and disbursement. By doing so, the organization was able to reduce the average loan processing time by over 60%. In addition, the organization has integrated with the national e-money and payments infrastructure, thereby allowing 36% of its applicants to receive loan disbursements without a physical visit (Flaming & Jenik, 2021).6

9.5

Big Data Analytics

As data usage becomes ubiquitous, companies are increasingly interested in using data to promote operational efficiency and positively impact their profits. Advancements in computing allow for the processing and analyzing of large data sets (or big data) in real time. Data itself refers to unorganized facts, whereas the term information refers to structured, processed, or organized data. It is important to understand the role that the quality of data plays in its later stages as information (Batini & Scannapieco, 2016). Big data analytics is all about generating information from large sets of data that can be used to identify underlying patterns, trends, and correlations. This can be applied to large amounts of sustainability data, despite the fact that the data may be incomplete and inconsistent.

6 See https://www.microfund.org.jo for further information.

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Sustainability in Financing

The need for transparent and high-quality data is essential in assessing and meeting sustainability goals. This presents a challenge, however, as currently there is an overall lack of data. Potential sources of ESG data include disclosed public, quasi-public, and private data. Publicly disclosed data includes company financial and sustainability reports, press releases, and media reports from listed companies. Quasi-public data includes data in government, regulatory, and NGO databases. Private data might include data provided by the company itself in response to specific questionnaires. Despite the seemingly endless wealth of data sources, the unavailability of data is truly remarkable. The UN’s latest report on measuring sustainability in 2021 indicated that there is not enough data to assess progress on 58% of all environmental-related SDGs. Many of these indicators correspond to issues that have not received as much attention as others, such as CO2 levels. These difficult to measure indicators include the assessment of biodiversity, ecosystem health, and the concentration of pollution and waste in the environment. However, not only are there gaps in the data, but there is also a need for tools and analytical methodologies for interpreting the data and its interactions. Sustainability data is not yet a requirement in standardized corporate (external) reporting (although there are currently advanced initiatives from the EU and the IASB that concern the disclosure of climate data, based on the recommendations set forth by the TCFD). Many companies now consider incorporating ESG and SDG data into their internal reports, thereby integrating sustainability into their company controlling processes. Working with current types of disclosed data sets presents challenges: incompleteness of data, lack of standardization, bias, and inconsistency. A common taxonomy of ESG metrics and methodology would certainly ease the problems of standardization and consistency; however, data integrity remains an issue. Alternative data sources can help bridge this data gap in terms of providing more complete information and reducing bias. Alternative data sources must be identified to address this issue and fill the data gap. Sources of alternative data include transactional data, email records, geospatial, satellite and weather data, remote sensing data, internet data, and social media feed data. Using these alternative data sources can enable more efficient and evidence-based decision-making. Big data and new technology can be used to help achieve SDGs. For

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example, spending patterns on mobile phone services can provide an indication of income levels, or sensors connected to water pumps can track access to clean water. Example: The Canadian-based company, Orenda, is an alternative data vendor. The alternative data sources stem from public social media data (primarily from, X, formerly known as Twitter). Each unstructured data point is individually analyzed to determine its impact on the various ESG elements, which is then incorporated into the real-time ESG rating of the company. The real-time ESG data also has predictive capabilities and can identify how corporate events impact the ESG rating. For instance, in May 2020 when the international mining company, Rio Tinto, mined a sacred Aboriginal site in Australia, the public was outraged, and many people took to social media to express their opinions. The public backlash eventually led to the CEO’s resignation in September 2020. Orenda’s alternative ESG data correlates with the stock price and can connect the market impact and ESG-focused events, while also providing predictive information.7

This example shows that alternative sources of ESG data offer new insights for companies and investors alike. 9.5.2

Financing of Sustainability

Big Data not only allows for a better sustainability risk assessment in financing but also allows for a more impact-oriented allocation of capital. As customers and employees become more conscious of sustainability, global value chains are also reshaping to meet the new demands. Sustainability is the catalyst for the transformation of business models, which touches all aspects of the company. The transformation toward sustainability is most successfully achieved when companies use the advantages of digitalization to achieve their desired ESG outcomes. This enables companies to do more with the data that they are collecting. For example, accounting for greenhouse gas emissions represents an important element within all sustainability frameworks. Greenhouse gas emissions are classified according to the Greenhouse Gas Protocol (GHG Protocol), which was established in 2001, as Scope 1 (direct emissions), Scope 2 (indirect 7 See https://www.orendasolutions.com for further information.

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emissions from the generation of purchased energy), and Scope 3 (all other indirect emissions, also referred to as carbon emissions).8 All three emission types are included in the product life cycle and are important to help companies to analyze their value chain carbon footprint. Most large companies account for their Scope 1 and 2 emissions. Scope 3 emissions, however, are difficult to quantify (despite being the largest source of emissions and, therefore, offering the most significant impact following a reduction) and entail emissions both upstream and downstream in the value chain. There are many advantages for companies to account for Scope 3 emissions to identify risks in their supply chain and positively improve their energy balance. This makes supply chains a conduit for a change in Scope 3 emissions. The GHG Protocol Value Chain (Scope 3) Accounting and Reporting Standard gives guidance to companies to understand their full value chain emissions in a cost-effective manner. Example: Scandens, a Swiss company, prioritizes decarbonization in the building sector. They offer software that can assess the climate impact and climate risks of buildings in order to work towards achieving the 1.5 degrees Celsius climate target. The software that the company has developed may be incorporated into the calculation of Scope 3 emissions.9 This helps banks and other financing intermediaries to include CO2 reductions in their financing decisions (as a target or as a decision criteria).

9.6

Artificial Intelligence

As mentioned previously, data is a key component in moving sustainable finance forward. The next level of technological innovation is artificial intelligence (AI) which is the ability of machines or computers to perform tasks that previously were reserved only for humans. These processes revolve around intelligent decision-making and continuous improvement. Machine learning (ML) is a subset of artificial intelligence and refers to the ability to collect new information and identify patterns without human intervention, such as external supervision from managers, engineers, or developers (Milana & Ashta, 2021). A big part of fintech solutions rely either on AI or ML in some form, and it is expected that the technology will continue to gain importance 8 See https://www.ghgprotocol.org for further information. 9 See https://www.scandens.ch/ for further information.

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in the financial sector (Ryll et al., 2020). AI and ML have several implications for fintech. This section focuses on those which help to reduce shortcomings in sustainable finance. 9.6.1

Sustainability in Financing

Collecting, analyzing, and interpreting non-financial and ESG data is important to understand how the environment influences companies. Here, AI and ML come into play, as they can process data at a rate at which humans are unable to match. ESG ratings differ from rating agency to rating agency, especially as the data underpinning their ratings differs (Berg et al., 2022). AI and ML powered solutions can search and analyze the different reporting styles and criteria (which can vary greatly by region) in real time and by using additional alternative data can provide new perspectives for investors analyzing the ESG performance of a company. Alternative data consists of data that is not usually used for investment decision-making, like digital sensor or satellite data, and does not appear in financial reports (In et al., 2019). ML can process, incorporate, and synthesize unstructured data, such as images, which is normally not in numerical format. ESG rating agencies are already adapting this new technology. Example: RepRisk is a company from Switzerland which specializes in measuring ESG risks using artificial intelligence (AI) and machine learning (ML). By doing so, they can process data from 23 languages, creating a daily risk score for each company analyzed, not only in the industrial world but also in emerging markets. This use of alternative data and high-speed processing allows for much deeper insight and analysis. According to RepRisk, they analyze over 500,000 risk incidents from more than 100,000 sources.10

As the ability to analyze data grows with AI and ML, so does the ability to detect greenwashing. Greenwashing is the practice of misleading communications about the environmental performance of a company (Lyon & Montgomery, 2015). As can be seen with ESG and alternative data, detecting greenwashing is difficult due to the different communication channels used to present sustainable performance. AI helps to 10 See https://www.reprisk.com/ for more information.

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compare claims data with actual performance (Cojoianu et al., 2020). Using sophisticated tools for language and data analysis, it is possible to compare specific commitments of a company with their actual climate initiatives (Bingler et al., 2022). 9.6.2

Financing of Sustainability

When using AI or ML for extensive data analysis and greenwashing detection, it is possible to predict future risks for a company. Banks can use this information to make intelligent credit decisions and assess the ability of a company to repay their obligations. Overall, it facilitates investment decisions in sustainable projects which normally would have been assessed as too risky. With AI it is possible to track after-investment performance, screening documents and maintaining data permanently and accessibly, and responding to the need for transparency. Checking performance after investment (e.g., Scope 1–3 data) is essential. AI offers this service and supports transparency. In its best form, AI can predict the impact of an investment. Alongside the advantages of AI, there are also challenges which must be addressed. For one, AI is only as good as the data provided. It can process data quickly that cannot be readily processed by humans, but still, quality matters. In addition, AI is vulnerable to a number of biases. This is because it learns from historical data which might be inaccurate, not applicable to future events, and may be mismatched or manipulated (Roselli et al., 2019). Because the algorithms represent a black box, it is very difficult to determine the reasons when an AI tool offers a result that is wrong. This may also result in regulatory scrutiny. One possible solution to the problem might be “explainable AI (XAI)”, where the decisionmaking process is explainable, however, “explain” may be difficult to define (Burgt, 2020). Example: Clarity AI11 is using AI in the form of Natural Language Processing and Machine Learning to measure the impact of investments for wealth managers or banks. This approach combines AI with alternative (big) data. It helps to evaluate new data that could not be analyzed by human 11 See https://clarity.ai/ for more information.

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resources because of the quantity of data and the large investment of time required. Their focus also includes the UN SDGs by combining theoretical and practical approaches to impact measurement.

9.7

Distributed Ledger Technology/Blockchain

Quatrosi (2022) summarized why distributed ledger technology (DLT) is a key component for overcoming the limitations of traditional finance and making it a highly promising tool for climate-related use cases. DLT provides security, transparency, decentralization, lower transaction costs, and higher speed for financial operations. In one of its forms, DLT results in redundancy of banking infrastructure, as it allows electronic databases to be shared across several systems and locations. At its core, DLT works where there is no need or use for a central power structure, as one of its main goals is decentralization. As previously mentioned, DLT spreads financial services over multiple participants and potentially eliminates the bank as a market maker. Where this is not needed, the easier (organizational and technological) path is that of a central database or entity. Most of the implementation cases today build on tokenization, but still use a central party, making the technology redundant. Blockchain, as a subset of the DLT technology stores information on chains or blocks of data. These blocks are then validated by all participants (Proof of Work or Proof of Stake) by linking the information to the previous block. In the beginning, blockchain technology was used to create virtual currencies and assets but is now reaching the ability to tokenize real-world assets (Macchiavello & Siri, 2022). 9.7.1

Sustainability in Financing

One of the first applications of blockchain technology has been the use of tokens to reward energy producers who demonstrate low emissions. These tokens can be traded or bought and sold by consumers or other producers. This is intended as an incentive for “green” production. It is also possible to link tokens with financial benefits or governance rights. Example: Green Asset Wallet (GAW) is a fintech company from Sweden, that evolved from a project by Stockholm Green Digital Finance with the goal to meet

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the demands of fast-growing investments in green and sustainable (-linked) bonds. Gathering data and turning the data into information requires a lot of effort by the investors if they want to track the sustainable performance of their investments. With the use of blockchain technology, GAW provides a secure way for green bond issuers in emerging markets to verify data on their green bond performance. This reduces the cost of validation (depending on the location, this can be a very difficult task, if the local financial industry lacks the respective know how) and significantly speeds up the process.12

As previously described, collecting transparent ESG data is a large challenge in the current environment of different ratings. A DLT-based system could help collect, securely store, and evaluate data in real time across ratings organizations. The main benefit would include the reduction of coordination efforts. Of course, there are still limitations as organizational complexity (within companies, across investors, and due to legislation, for example) is high and there must be a uniform understanding of the whole process, which does not, at present, exist (Cerchiaro et al., 2021). Overall, the main benefit of DLT in sustainability in financing is the added transparency and verification built into the technology while also providing real-time information to all participants. 9.7.2

Financing of Sustainability

Emerging markets have a large potential for sustainable development, but there are not enough banks available to fund the opportunities. This restricts companies from accessing financing opportunities. DLT is one potential solution to this problem as the technology is not limited by borders or distance and could potentially bring investors and companies from distant parts of the globe together, thus overcoming financial exclusion. The characteristics of DLT can improve the trust of investors and the traceability of the project. Local monetary and financial authorities can play an important role in facilitating DLT (e.g., providing the required supervision) (Dikau et al., 2022). In addition, tokenization can lower the minimum amount of money needed to invest in green assets. This would further reduce financial exclusion for both the investor and the company. For example, fair trade producers in South America could 12 See https://greenassetswallet.io/ for more information.

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receive financing from investors in Europe. The investor receives a token for their investment and can either keep it, sell it on the secondary market, or use it to purchase the product in a local supermarket. The issuance of green or sustainability-linked bonds is more expensive than the issuance of a standard bond. Furthermore, extensive reporting and disclosure is required. By issuing such bonds with the help of DLT, a study from the HSBC Bank (2019) finds cost to be up to ten times lower. Possible applications of DLT are in structuring, distribution, transfer of ownership, and reporting. This could also help with the implementing new financial frameworks without having to worry about higher disclosure requirements. Example: Security token offerings (STOs), as implied by the name, are tokenized issuance of securities. These STOs represent part of a security, debt, or equity. An STO can improve the supply (lower issuance amount, allowing for more green projects to be financed) and demand (lower investment hurdle, contributing to financial inclusion) of green investments (Schletz et al., 2020).

9.8

Quantum Computing

Up until this point, all the technologies we have presented in this chapter can be implemented on a standard computer. These computers work with binary logic (“0” and “1”) and can process inputs that are either numerical, alphabetical, video, or audio. In contrast, quantum computers are designed with the use of quantum principles at atomic and subatomic levels. Currently, quantum computing is only possible at a very small scale and in laboratory environments. When they finally become applicable on a larger scale, quantum computers will be able to solve problems simultaneously rather than sequentially, and at significantly higher speeds (Casati, 2020). Applying this innovative technology to finance, the main benefit is a significant improvement in technological speed and depth of analysis on a much more granular level (Orús et al., 2019). AI or ML could perform at higher speeds with lower energy usage. Even more (alternative) data would be able to be processed the quantum computer processes large data sets and translates them into combinatoric challenges and predictions.

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This would potentially reduce ESG confusion and provide a measure of sustainable risk and performance for every market participant. Investors would, therefore, be able to allocate their resources in the most efficient way possible. This technology is still a long way from being applied in an actual sustainable finance context, but it has the potential to disrupt and enhance all focus areas of sustainable finance.

9.9

Conclusion

While sustainability is becoming increasingly relevant in financial markets, the prevailing financing gap calls for new and more innovative solutions. One of the main drivers supporting this development is fintech. In fact, fintech has the potential to make the overall business of finance more resilient and sustainable (Vergara & Agudo, 2021). This chapter reflects on current fintech technologies and discusses how they affect sustainable financing, thereby focusing on both aspects of the double materiality, namely sustainability in financing and the financing of sustainability. This overview attempts to outline the current state of each technology, illustrates its potential to contribute to current challenges in sustainable financing, and offers a rich variety of use cases. It is clear that technologies diverge and differ in both implementation and future potential but there are also convergences and overlaps. Many real-world use cases combine technologies and build upon each other in order to be successful, while each of them has their own primary impact: – “Automation / Robotics” leads to greater efficiency. – “Big Data Analytics” and “Artificial Intelligence” contribute to more effective results. – “Decentralized Ledger Technology / Blockchain” enables decentralization and facilitates access to financial markets. – “Quantum Computing” will improve the performance of all the technologies outlined above. The myriad ways in which fintech can add value and facilitate sustainable financing have only just begun.

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Flaming, M., & Jenik, I. (2021). Digitization in Microfinance: Case Studies of Pathways to Success. CGAP and World Bank. https://www.cgap.org/res earch/publication/digitization-in-microfinance-case-studies-of-pathways-tosuccess Global Sustainable Investment Alliance. (2020). Global Sustainable Investment Review 2020. http://www.gsi-alliance.org/wp-content/uploads/2021/08/ GSIR-20201.pdf HSBC & Sustainable Digital Finance Alliance. (2019). Blockchain. Gateway for sustainability linked bonds. https://www.sustainablefinance.hsbc.com/-/ media/gbm/reports/sustainable-financing/blockchain-gatewayfor-sustainab ility-linked-bonds.pdf Imerman, M. B., & Fabozzi, F. J. (2020). Cashing in on innovation: A taxonomy of FinTech. Journal of Asset Management, 21(3), 167–177. https://doi.org/ 10.1057/s41260-020-00163-4 In, S. Y., Rook, D., & Monk, A. (2019). Integrating alternative data (also known as ESG data) in investment decision making. Global Economic Review, 48(3), 237–260. https://doi.org/10.1080/1226508X.2019.1643059 Kölbel, J. F., Heeb, F., Paetzold, F., & Busch, T. (2020). Can sustainable investing save the world? Reviewing the mechanisms of investor impact. Organization & Environment, 33(4), 554–574. https://doi.org/10.1177/108 6026620919202 Lyon, T. P., & Montgomery, A. W. (2015). The means and end of greenwash. Organization & Environment, 28(2), 223–249. https://doi.org/10.1177/ 1086026615575332 Macchiavello, E., & Siri, M. (2022). Sustainable finance and fintech: Can technology contribute to achieving environmental goals? A preliminary assessment of ‘green fintech’ and ‘sustainable digital finance.’ European Company and Financial Law Review, 19(1), 128–174. https://doi.org/10.1515/ecfr2022-0005 Maehle, N., Piroschka Ott, P., & Drozdova, N. (2020). Crowdfunding sustainability. In R. Schneor, L. Zhao, B. T. Flåten (eds.), Advances in crowdfunding (pp. 393–422). Palgrave Macmillan. https://doi.org/10.1007/978-3-03046309-0_17 Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189– 209. https://doi.org/10.1002/jsc.2403 Nguyen, D. D., Ongena, S., Qi, S., & Sila, V. (2022). Climate change risk and the cost of mortgage credit. Review of Finance, 26(6), 1509–1549. https:// doi.org/10.1093/rof/rfac013 OECD. (2020). Global outlook on financing for sustainable development 2021: A new way to invest for people and planet. OECD Publishing. https://doi.org/ 10.1787/e3c30a9a-en

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CHAPTER 10

The Mediating Role of Fintech on ESG and Bank Performance Nur Badriyah Mokhtar and Ashraful Alam

10.1

Introduction

Environmental, social, and governance (ESG) concerns have recently risen to the top of the business agenda. ESG practices have become a priority for major players in the industry due to climate change and other environmental influences. Even the financial services industry has been affected. A survey in 2013 from United Global Compact found that nearly 93% of CEOs viewed ESG policies as crucial to success of their companies (Khan, 2022). The European Union, or EU, started to make ESG reporting requirements more stringent starting in 2021 with a “nonfinancial reporting requirement.” It demonstrates that the EU has begun to take ESG very seriously.

A. Alam BPP Business School, BPP University, Manchester, United Kingdom e-mail: [email protected] N. B. Mokhtar (B) Salford Business School, University of Salford, Salford, United Kingdom e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_10

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Research demonstrates that the involvement of banks practitioner in the environmental, social, and governance marketplace has provided stakeholders with a new perspective. It has aided numerous corporations to enhance their public image while, at the same time, continuing to successfully operate their businesses. Tesla and Amazon have demonstrated how their ESG ratings help them to keep up with the competition and win over the hearts of their customers, employees, and investors. Share prices provide evidence that these companies were able to rise to the top and to grow their business, while at the same time, maintaining their ESG commitments as a key component of their operations. There remain, however, many divergent opinions and arguments over how ESG affects performance in the commercial banking industry. Numerous global studies have examined the Environmental, Social, and Governance (ESG) perspective. Al Hawaj and Buallay (2022), Aouadi and Marsat (2018), Minutolo et al. (2019), and Xie et al. (2019) have conducted such studies. Country-specific research includes Ruan and Liu’s (2021) study in China, Ting et al. (2019) and Garcia et al. (2017) in developed and emerging markets, and Fahad and Busru’s (2021) and Buallay et al.’s (2020) work on OIC Members Islamic banks. In the United States, studies have been conducted by Alareeni and Hamdan (2020), Consolandi et al. (2020), Brogi and Lagasio (2019), and Fatemi et al. (2018). Garcia et al. (2017) have focused on the BRICS nations (Brazil, Russia, India, China, and South Africa). ESG studies conducted in other countries include Chen and Yang’s (2020) study in Taiwan, Yoon et al.’s (2018) in the Korean market, Dalal and Thaker’s (2019) in India, Atan et al.’s (2018) and Atan et al.’s (2016) in Malaysia, and Lokuwaduge and Heenetigala’s (2017) in Australia. Examples from the European market include Landi and Sciarelli’s (2019) study in Italy, Velte’s (2017) in Germany, as well as Buallay’s (2019) and Chiaramonte et al.’s (2021) research. Studies on the banking industry include Miralles-Quirós et al. (2019), Birindelli et al. (2018), and Buallay (2019). Fintech’s role in transforming the banking sector has sparked debates across industries. With the rise of new technologies, fintech has become increasingly significant, driving transformative efforts in businesses, including banks. While studies have evaluated various sectors within the financial industry, such as start-ups, venture capital, and cryptocurrencies, there is a need for further exploration to deepen our understanding and gain comprehensive insights. Existing studies have shed light on the

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impacts and dynamics of fintech, but there is still much to discover to enhance our understanding. Researchers and experts are actively engaged in ongoing research and analysis to uncover additional insights into fintech’s performance, challenges, and opportunities. Fintech, as an alternative to traditional financial instruments, has emerged in the context of the digital revolution and sustainable financing. The Financial Stability Board (2017) defines “fintech” as encompassing a wide range of financial technology applications, operational procedures, and innovative products at the forefront of financial innovation. According to Gomber et al. (2018), digital technologies including the Internet, mobile computing, and data analytics, are referred to as fintech when they facilitate, innovate, or disrupt financial services. Fintech, in general, refers to technological advancements in a variety of retail financial services. Examples include online banking and mobile payments. Fintech is able to improve the overall effectiveness of financial services by extending the boundaries of traditional finance and altering consumer spending habits (Demertzis et al., 2018; Lee et al., 2021). The business economy is impacted by fintech innovation, including revenue, costs, and profit margins (Schueffel, 2016). A review of the existing fintech literature (Bashayreh & Wadi, 2021; Chang et al., 2021; Cheng & Qu, 2020; Cho & Chen, 2021; Choubey and Sharma, 2021; Chueca Vergara & Ferruz Agudo, 2021; Haddad & Hornuf, 2018; Lee et al., 2021; Phan et al., 2020; Wang et al., 2021; Zhao et al., 2022) concludes that fintech has a positive and significant impact on organizational performance. However, there is a lack of relevant studies exploring how fintech influences global sustainable development. Fintech has the potential to address environmental issues associated with paper waste in traditional banking operations. For example, online loan applications reduce paper consumption. The use of big data and artificial intelligence ensures secure digital data storage, while blockchain technology enables efficient electronic record management. Additionally, fintech improves the social aspects of banking by simplifying tasks, reducing human error, and enhancing employee well-being. It also streamlines routine processes, resulting in a more satisfied workforce. Although fintech reduces the need for certain banking tasks that rely on human capital, it also creates new job opportunities as the demand for fintech specialists increases. There is growing optimism that technological advancements will benefit banks worldwide (Cho & Chen, 2021).

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Blockchain integration in fintech has proven effective in enhancing the “know your customer” (KYC) process for credit applications, minimizing fraud, and ensuring better governance. Its widespread usage has reduced the risk of human error, lowered operational risk, and improved data storage and security (Ji & Tia, 2022; Sinha & Bathla, 2019). The transparency and accountability provided by blockchain technology enhance business intelligence and overall operational efficiency in banks. Investing in fintech enables banks to become more competitive and promotes sustainability practices, such as ESG, leading to improved bank performance. Policymakers should focus on enhancing operational performance, offering diverse financial services at lower costs, and fostering sector competitiveness to adapt to the expanding influence of financial innovation in the economy. These goals have broad economic and policy implications, necessitating further research into the impact of new technologies on banks. Prior studies on ESG and its impact on bank performance as well as research on fintech in the banking sector result in the following conclusions: first, there is a dearth of research on ESG and its effects on bank performance in the commercial banking industry. Prior research, such as Buallay (2019), which used a banking sample from countries in Europe specifically, produced conflicting results. Additionally, none of the current research used a sizable sample or studied specific commercial banking characteristics. All the research, therefore, falls short of understanding the connection between ESG and bank performance in other regions. Second, based on our research, the majority of earlier studies merely served to broaden the body of knowledge about ESG factors and bank performance. None of them included fintech in their studies. Our research is intended to address these gaps in the literature. We do so by investigating the effects of ESG in the banking sector using commercial banks from the EU. Additionally our inclusion of fintech and its impacts on ESG and bank performance makes this research unique. Our study contributes to the literature in a number of ways. To begin with, the study includes a diverse sampling of regions. Using the most recent EU sample, we are able to see new results in the relationship between ESG and bank performance. Second, this research adds to the growing body of knowledge about fintech as a mediator in the relationship between ESG and bank performance. Finally, this study contributes to methodology by employing a method from the second generation of data analysis, namely partial least square structural equation modelling, or

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PLS-SEM. To our knowledge, previous work has focused on only firstgeneration methods like GMM and OLS regression (Aslam & Haron, 2021; Boachie, 2023). The remainder of the paper is structured as follows: Sect. 10.2 explores the theory behind our research, reviewing prior research on the relationship between ESG and bank performance as well as previous research on fintech in order to develop our hypotheses. Section 10.3 looks at methodology, covering sample selection, data sources, variable explanation, and model specification. Section 10.4 considers the empirical data on the relationship between ESG and bank performance and the role of fintech mediation. Section 10.5 sets out our tests for robustness. Section 10.6, the final section of the paper summarizes our findings and explores their ramifications for the banking industry.

10.2

Theory and Hypotheses

Stakeholder theory proposes that business managers have responsibilities to certain groups of stakeholders (Freeman, 2015). According to this theory, the objective of an organization is to create value for its stakeholders. Stakeholders may be defined as involved parties who have the potential to directly or indirectly impact the company (Freeman, 2015). Internal and external stakeholders make up the two categories of stakeholders. Company management, employees, and investors are examples of internal stakeholders. External stakeholders are those who are not part of the business, such as the neighborhood in which the business is located, its clients and suppliers, government and non-governmental organizations, as well as investors and creditors. Stakeholders play a crucial role in ensuring the viability and performance of the company (Freeman et al., 2010), and as a result, their impact on business operations is significant. As a result, stakeholders serve as the foundation for fintech investment and ESG practices. 10.2.1

ESG and Bank Performance

ESG investment is already substantial and continues to increase (Khan, 2022). There is an extensive body of literature on the connection between ESG activities and corporate performance with mixed results. Buallay et al. (2021) examine 882 banks from developed and developing countries covering an eleven-year period after the 2008 financial crisis. Using

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pooling regression and instrumental variable GMM, the study finds that ESG weakens banks’ performance in both developed and developing countries. Ruan and Liu (2021) analyze samples of China’s Shanghai and Shenzhen A-share listed companies using OLS regression and ESG rating data between 2015 and 2019. They find that corporate ESG initiatives have a significantly negative effect on firm performance. Fahad and Busru (2021) look at the impact of CSR disclosure using panel regressions for a final sample of 386 Indian companies listed on the BSE 500 index, representing all of the major players in the capital market over a ten-year period from 2007 to 2016. The research reveals a pattern of negative impacts of CSR disclosure, as reflected by ESG, on Indian company profitability and firm value. In their study on listed Italian companies utilizing Panel data analysis, Landi and Sciarelli (2019) discover a negative and statistically significant impact of ESG on market premiums, while for firms engaging in socially responsible investing (SRI). In their comparative study of Malaysia and Denmark, Atan et al. (2016) found no correlation between ESG disclosure level and firm financial success for the Top 100 largest companies listed in Bursa Malaysia and Nasdaq OMX Copenhagen financial markets. The bulk of the research, however, shows that ESG information disclosure, ESG ratings, and other ESG activities have a favorable impact on business performance. In their study employing a sample of European banks operating in 21 countries between 2005 and 2017, Chiaramonte et al. (2021) found that the total ESG score, as well as its sub-pillars, reduces bank fragility during times of financial difficulty. The impact of ESG performance on the economic success of the Standard & Poor’s 500 (S&P 500) Index was assessed by Cek and Eyupoglu (2020). Using longitudinal data covering the years 2010 to 2015, structural equation modeling, and linear regression to assess the overall and individual influence of environmental, social, and governance (ESG) performance on economic performance, ESG approaches, and economic success were significantly correlated. They discover a significant correlation between economic performance and the entire ESG strategy. Alareeni and Hamdan (2020) examine if there are associations between corporate disclosure of (ESG) and firm operational (ROA), financial (ROE), and market performance (Tobin’s Q), and whether these associations are favorable, unfavorable, or neutral. U.S. S&P 500 listed businesses from 2009 to 2018 are included in the study sample. ESG disclosure is shown to have

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a favorable impact on firm performance metrics using panel regression analysis. The importance of ESG materiality in determining stock return was examined by Consolandi et al. in 2020. Using the Sustainability Accounting Standards Board (SASB) classifications of materiality and data from a sizable sample of U.S. companies represented in the Russell 3000 Index from January 2008 to July 2019, they discovered that not only do ESG rating changes (ESG momentum) have a consistent impact on equity performance, but also that the market appears to favor companies that operate in sectors with a high level of concentration of ESG materiality. According to Chen and Yang (2020), financial markets have ESG momentum as a result of investors routinely exaggerating corporate ESG information. Investors react positively to positive news about companies with higher ESG scores but negatively to negative news about companies with lower ESG ratings. According to these findings, an ESG momentum strategy can produce significant short-term gains and long-term losses, supporting the overreaction theory. Through several measures of ROA and Tobin’s Q ratio, Dalal and Thaker (2019) investigate the impact of ESG issues on the profitability and firm value of Indian public limited enterprises. The NSE 100 ESG Index database contains annual ESG data for 65 Indian companies listed for the years 2015 to 2017. Their research found that high business ESG performance improves financial performance as measured by accounting and market-based indicators using random effect panel data regression analysis. In the context of emerging markets, Shakil et al. (2019) investigate the influence of bank ESG performance on financial performance. They use ESG performance data for 93 emerging market banks from 2015 to 2018 and the generalized method of moments (GMM) technique for estimation purposes due to the dynamic nature of the data and to correct for endogeneity. They discover a positive correlation between the environmental and social performance of emerging market banks and their financial performance. In their analysis of 467 S&P 500 companies from 2009 to 2015, Minutolo et al. (2019) show that ESG scores have a positive impact on business performance as assessed by Tobin’s Q and ROA. ESG and corporate market value are studied by Aouadi and Marsat (2018). Surprisingly, their principal finding demonstrates that ESG is connected with higher business value using a unique dataset of more than 4000 firms from 58 countries between 2002 and 2011. Using return on assets as a metric of profitability, Brogi and Lagasio (2019) looked into

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the relationship between (ESG) disclosure and business success (ROA). The statistical model analyzes the association of ROA to the three main dimensions of ESG score using the ESG scores of a large sample of 17,358 observations from U.S. listed businesses based on MSCI ESG KLD STATS data from 2000 to 2016. They discover a strong and positive link between ESG and profitability. In her study for the European banking sector, Buallay (2019) analyzes 235 institutions over a ten-year period (2007–2016), yielding 2,350 observations. ESG disclosure serves as the independent variable, while the performance indicators (return on assets (ROA), return on equity (ROE), and Tobin’s Q serve as the dependent variables. The author finds that ESG has a positive impact on performance. To determine whether businesses concerned with ESG can still be successful and efficient, Xie et al. (2019) consider the relationship between corporate sustainability and efficiency. They discover that ESG disclosure has a positive association with corporate efficiency at the moderate disclosure level (as opposed to the high or low disclosure level), by estimating corporate efficiency using data envelopment analysis (DEA) and the nonlinear relationship between efficiency and ESG disclosure. Following governance information disclosure are social and environmental information disclosure, which have the next strongest positive relationships with corporate effectiveness. Ting et al. (2019) look at how ESG activities within businesses affect their financial performance. Additionally, they contrast how corporate social performance initiatives affect valuation in both developed and emerging market enterprises. This study finds that ESG activities have a significant beneficial impact on firm performance using ESG ranking scores from the Thomson Reuters database and a sample of 1,317 emerging market firms and 3,569 developed market firms. Fatemi et al. (2018) look into how ESG actions and their transparency affect firm value. They use data on ESG strengths and ESG concerns as compiled and reported by KLD Research and Analytics as proxies for firm ESG performance and use Bloomberg’s ESG disclosure score (DISC) as an indicator of the extent of the ESG disclosure of a company using empirical analysis based on data for 1,640 firm-year observations for publicly traded U.S. firms for the years 2006 to 2011. The research finds that ESG firm value increases with identified strengths and decreases with shortcomings. Yoon et al. (2018) investigate whether company corporate social responsibility (CSR) has a substantial impact on enhancing its market

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value in Korea, a rising market. To assess CSR performance and see how they affect firm valuation, the study uses (ESG) scores. The study finds that CSR policies have a favorable and considerable impact on a company. Aouadi and Marsat (2018) explore the association between (ESG) and corporate market value. Using a unique dataset of over 4,000 enterprises from 58 countries between 2002 and 2011, their analysis reveals that ESG is connected with higher firm value. Velte (2017) focuses on environmental, social, and governance performance (ESGP) as a whole and divides it different components, evaluating their impact on financial performance (FINP). The study covers a sample of firms listed on the German Prime Standard (DAX30, TecDAX, MDAX) from 2010 to 2014, a total of 412 firm-year observations. A correlation and regression analysis is performed to assess potential relationships between ESGP as determined by the Thomson Reuters Asset4 database and accounting and market-based FINP measures (Return on Assets (ROA) and Tobin’s Q). The study finds that ESGP has a positive effect on ROA but has no effect on Tobin’s Q. Although the outcomes on ESG toward firm performance vary, most studies find positive financial associations. As a result, we propose: Hypothesis 1: ESG has a positive impact on bank performance.

10.2.2

Fintech and Bank Performance

Zhao et al. (2022) use patent data and a fintech development index to assess the gauge of the influence of financial technology innovation on Chinese bank performance. They conclude that fintech innovation reduces bank profitability and asset quality. In their study of fintech in Chinese banking using the system-GMM model, Cho and Chen (2021) find that the greater the proportion of mobile device transactions and the volume of third-party payment transactions, the higher the cost productivity growth rate. It shows that fintech has an effect on productivity.

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10.2.3

Fintech and ESG

Chueca Vergara and Ferruz Agudo (2021) conduct a study using a literature review and case study approach to examine the relations between fintech, sustainable finance, and general sustainability, in both theory and practice. Their findings suggest that sustainable finance and fintech have many commonalities and that fintech can make financial organizations more sustainable overall by supporting green financing. Chang et al. (2021) use data envelopment analysis (DEA) and panel data analysis to investigate the interactions between digital finance and ESG performance on corporate financing efficiency. The findings show that higher ESG performance and digital finance improve corporate financing efficiency at the 1% significance level and that digital finance mitigates the positive marginal effect of ESG performance on corporate financing efficiency (Fig. 10.1).

.. .

Fig. 10.1 Theoretical framework

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In conclusion, the literature presents mixed results regarding how fintech affects ESG and bank performance. Our hypothesis on the mediating role of fintech on ESG and bank performance is as follows: Hypothesis 2: Fintech mediates the relationship between ESG and bank performance.

10.3

Methodology 10.3.1

Data

Secondary data is collected from financial and annual reports published by commercial banks in 28 EU nations during a ten-year period between 2010 and 2019. European countries take the lead when it comes to advocating sustainable development (Buallay, 2019). We select the listed commercial banks of the EU countries as our research object because they are the financial enterprises supporting social and economic development and which have the same asset base for comparison. A total of 138 commercial banks operating in the European Union (EU) were included in this study, utilizing the Orbis bank database. However, due to limitations in obtaining ESG scores from the Refinitiv platform, a few banks had to be excluded from the analysis. Currently, the banking sector plays an important role for the development and growth of the European economy by facilitating the financial transactions. Furthermore, our data show that commercial banks in the European region focus more on fintech innovations and digital transformation, including electronic payments, online and mobile services (EBF, 2020) compared to other types of banking such as investment banking and corporate banking. As a result, we focus on commercial banking rather than other types of banking. We incorporate a number of variables in our study in order to analyze the factors that influence commercial bank performance. Three of these are the ESG score ratings, which we use to construct the major independent variable on assessing ESG: Environmental score, Social score, and Governance score. The results come from Refinitiv’s database (previously known as Thomson Reuters data). Our study aims to shed light on the intervention of fintech in the relationship between ESG and bank performance. While there have been recent discussions regarding potential biases in Refinitiv data such as from Berg et al. (2021), we have chosen to utilize it due to its numerous advantages. Refinitiv data offers robust

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and comprehensive coverage of ESG metrics, providing a reliable foundation for our analysis. By leveraging this data, we can effectively measure the ESG performance of banks and gain valuable insights into the impact of fintech interventions. At the same time, we use profitability, namely Return on Assets (ROA), to assess the success of the bank (Brogi & Lagasio, 2019). We choose return on assets (ROA) as our benchmark because this statistic shows how profitable a business is in comparison to its total assets. Management, analysts, and investors assess ROA to see if company resources are being used profitably. At the bank level, we also include two control factors: bank size and bank growth. Bank size can have implications for performance as larger banks may have different operational structures, market presence, and economies of scale compared to smaller banks. By including bank size as a control variable, the researchers can assess whether the observed relationships between the independent variables (ESG scores and ROA) and bank performance are influenced by bank size. Bank growth is another control factor that captures the rate at which a bank is expanding its operations. The growth of a bank can impact its performance, profitability, and risk profile. Including bank growth as a control variable allows the researchers to examine whether the effects of ESG scores and ROA on bank performance are influenced by the growth trajectory of the bank. Additionally, we include two macroeconomic level controls: GDP growth and inflation, for each country. Macroeconomic factors play a significant role in shaping the overall business environment and can affect the performance of commercial banks. GDP growth is a commonly used indicator of economic activity and is included as a control variable to account for the influence of the broader economic conditions on bank performance. Inflation is another macroeconomic variable that can impact the financial performance of banks. Higher inflation rates can erode the purchasing power of money and have implications for interest rates, loan quality, and overall profitability. Including inflation as a control variable helps to capture the potential effects of inflation on bank performance. The impact of other variables on bank performance can be better understood once we know these metrics. Fintech measurement is now being studied through a variety of approaches by academics. Some of the difficulties that scholars have encountered in measuring fintech are the lack of readily available data and clearly defined measures. Fintech is a broad term that may include every bank innovation, service enhancement, or product creation. In this

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study, we use commercial bank expenses on fintech investment, digital transformation (including digital banking innovation), and investment in third-party fintech firms (outsourcing) to measure fintech. In summary, the annual report of each of the banks is checked to ensure that bank subsidiaries are not also included as separate entities in our final data set to reduce the impact of double counting (Table 10.1). The descriptive data presented in Table 10.2 illustrates the percentage of participation from each country included in our study. With the European Union comprising 28 countries as of 2019, it is important to note that Cyprus and Slovakia lack consistent ESG and fintech data. Consequently, we have excluded these two countries from our analysis. Based on the available data, Austria and Italy have the highest number of banks with published ESG scores in Refinitiv, representing 8.7% each. The United Kingdom closely follows with a representation of 8.0%. Table 10.1 Definition of variables Variables

Definition

Type of data

Bk performance Profitability ESG Index (ESGIndex)

Fintech (Fin)

Bank Size (BkSz) Bank Growth (BkGro) Growth Domestic Product (GDP) Inflation (Inf)

Sources

Return on Asset (ROA) • Environmental Score (Env_score) Scores/Rating for each of • Governance Score (Env_Score) banks that • Social Score (Soc_Score) published in the database The expenses in fintech investment, Annual digital transformation, including digital Financial banking innovation, and investment to Report collaboration with third-party fintech firms Natural Log of Total Asset for Bank Total Asset (Log in Million USD) Growth in Total Assets Percentage growth in total assets for bank Percentage of annual GDP growth GDP growth % (annual)

Orbis Bank Focus Refinitiv

Percentage of annual inflation

World Bank Indicator

Inflation % (consumer price index)

Orbis Bank Focus

Orbis Bank Focus Orbis Bank Focus World Bank Indicator

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Table 10.2 Distribution of sample by country Country

Frequency

Percent

Country

Frequency

Percent

Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Total Banks

12 5 6 2 0 1 10 1 2 9 10 4 4 4

8.7 3.6 4.3 1.4 0.0 0.7 7.2 0.7 1.4 6.5 7.2 2.9 2.9 2.9

Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

12 1 1 8 1 5 3 7 4 0 1 10 4 11 138

8.7 0.7 0.7 5.8 0.7 3.6 2.2 5.1 2.9 0.0 0.7 7.2 2.9 8.0 100

Table 10.3 provides descriptive results. Profitability has a minimum of -0.0952 and maximum of 5.779. It is normal for certain banks to have a negative ROA as they could be the process of earning profits after a period of instability. Meanwhile, the ESG index shows a minimum of 28.60 and a maximum of 83.61. Table 10.3 Sample descriptive statistics

BkPerf ESGIndex Env_score Gov_score Soc_score Fin BkSz BkGro GDP Inf Valid N (listwise)

Minimum

Maximum

Mean

Std. Deviation

−0.952 28.640 23.140 25.660 18.550 1.432 1014.274 −7.075 0.267 0.551 1380

5.779 83.160 94.990 82.370 96.110 9326.179 1,644,059.490 35.332 6.286 2.792

0.684 68.468 70.774 59.720 74.909 632.406 262,888.191 4.635 1.914 1.489

0.738 10.439 15.785 13.058 14.445 1440.624 408,460.835 6.080 1.135 0.466

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Table 10.4 Correlations of ESG indexes ESGIndex ESGIndex Pearson Correlation

Env_score

1

Sig. (1-tailed) Env_score Pearson Correlation Sig. (1-tailed) Soc_score

Pearson Correlation Sig. (1-tailed)

.742

**

Soc_score .767

Gov_score

**

.653**

.000

.000

.000

1

.349**

.184*

.000

.015

1

.311** .000

Gov_score Pearson Correlation

1

Sig. (1-tailed)

Table 10.4 denotes the results of the correlation among ESG indexes whereby all three pillars of ESG—Environmental, Social, and Governance—have positive and significant correlations with the ESG index. 10.3.2

Model

The following model was developed to investigate the impact of ESG on bank performance and the mediating role of Fintech. 10.3.2.1 Econometric Model Controlling the effect of time or year is indeed an important consideration when analyzing data that spans multiple years, such as a 10-year dataset. It helps address potential confounding factors and ensures that any observed relationships are not solely attributed to the passage of time. In order to account for the potential influence of time or year in our analysis, we implemented a control for the year effect. This was accomplished by including a categorical variable for each year of the 10-year dataset in our regression model. By introducing these year-specific variables, we aimed to capture and control for any temporal variations or trends that could potentially impact both ESG metrics and bank performance. The inclusion of year as a control variable allows us to isolate and examine the independent effects of the variables of interest, namely ESG metrics and fintech interventions. By holding constant the year-specific factors, we can better ascertain the relationship between these variables

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without the potential bias introduced by changes occurring over time. Additionally, controlling for the year effect enhances the internal validity of our study. It helps us distinguish between the effects that can be attributed to our variables of interest and those influenced by external factors associated with specific years. This approach strengthens the reliability and robustness of our findings, allowing for a more accurate assessment of the impact of fintech interventions on the relationship between ESG and bank performance. To address the possibility of reverse causality in our study, we have implemented two key actions. Firstly, we conduct robustness checks and employ alternative model specifications to validate our findings. By analyzing the relationship between ESG metrics and bank performance using different time periods or alternative statistical models, we strengthen the argument against reverse causality. If the relationship consistently holds across various time periods or models, it provides stronger evidence that reverse causality is less likely to be present. Secondly, we include additional control variables in our analysis. These variables capture relevant factors that may influence both ESG metrics and bank performance. By incorporating variables such as bank size, bank growth, GDP, and inflation, we account for potential confounding factors that could lead to spurious relationships or reverse causality. By implementing these actions, we enhance the robustness of our study and minimize the potential impact of reverse causality, thereby providing more reliable insights into the relationship between ESG metrics, fintech interventions, and bank performance. Bank Per f or mance(Bk Per f ) = β0 + β1 (E SG I ndex) + β2 (Fin) + β4 (BkG D P) + β6 (I n f ) + e

10.3.3

Method

We run our data through SmartPLS 3.0. This study utilizes the SEM partial least squares (PLS) method along with a multilevel analysis function. SEM based on covariance can be replaced with this second generation PLS-SEM technique (Wold, 1985; Chin et al., 2010; Hair et al., 2019). This is especially beneficial when data is not dispersed evenly (Hair et al., 2014; Monecke & Leisch, 2012). PLS-SEM is to be used to accurately measure the mediating role of fintech. In its most basic form,

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mediation describes how or how a dependent variable (Y) is impacted by an independent variable (X) through an intermediate variable, called a mediator (M) (Baron & Kenny, 1986). The most extensively used method for testing mediation is regression analysis (MacKinnon et al., 2002; Wood et al., 2008). We find PLS-SEM to be superior to the regression technique for determining mediator value (Chin, 2009; Preacher & Hayes, 2004). To ensure the reliability and robustness of our model, we conducted additional tests and subjected it to OLS regression analysis. This approach allowed us to evaluate if the results and findings obtained from our model hold up consistently across different methodologies. By employing OLS regression, we can assess the stability and consistency of our model’s performance and validate its effectiveness in capturing the underlying relationships and patterns in the data.

10.4

Result and Discussion

The results of the Partial Least Squares Structural Equation Modelling (PLS-SEM) analysis are presented in Table 10.5. Model A demonstrates that the Environmental, Social, and Governance (ESG) index has a positive influence on bank profitability. The coefficient for the ESG index is 0.211, indicating that a 10% increase in ESG score leads to a 21.1% increase in bank profitability. This finding supports previous research conducted by Chiaramonte et al. (2021), Alareeni and Hamdan (2020), Dalal and Thaker (2019), Shakil et al. (2019), Minutolo et al. (2019), Brogi and Lagasio (2019), Buallay (2019), Ting et al. (2019), and Velte (2017). Thus, our hypothesis suggesting a significant positive impact of ESG on bank performance is supported. Examining the control variables, we find that bank size negatively affects bank profitability. The path coefficient for bank size is -0.245, indicating that a 10% increase in bank size leads to a 24.5% decrease in bank profitability. Additionally, the path coefficient for bank growth is -0.058, but the corresponding p-value is not statistically significant, suggesting that bank growth does not have a significant impact on profitability. On the other hand, GDP has a positive impact on bank profitability, with a coefficient of 0.040 and a p-value of 0.038. This implies that a 10% increase in GDP results in a 4.0% rise in bank performance. Furthermore, inflation has a detrimental effect on performance, with a 10% increase in inflation leading to a 3.9% decrease in profitability. Interestingly, the inclusion of the fintech variable in Model A indicates a positive effect of 3.5%

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Table 10.5 PLS-SEM results Variables

BkPerformance Model A

Model B

ESGIndex t−1 Env_score Gov_score Soc_score BkSz

0.211*** (0.000) 0.751*** (0.000) 0.414*** (0.000) 0.214*** (0.000) −0.245*** (0.000)

0.246*** (0.000) 0.768*** (0.000) 0.416*** (0.000) 0.205** (0.008) − 0.244*** (0.001)

BkGro GDP Inf Fin Fin > ESGIndex Fin > Env_score Fin > Gov_score Fin > Soc_score

− 0.058 (0.182) 0.040 (0.038) − 0.039 (0.120) 0.035 (0.040)

− 0.053 (0.188) 0.054 (0.039) − 0.042 (0.118) 0.185*** (0.042) 0.213*** (0.049) 0.235*** (0.047) 0.254*** (0.056) 0.053* (0.061)

R2 R2 adj Q2 Total Observations

0.590 0.577 0.539 1380

0.585 0.574 0.536 1380

on bank performance, but this effect is not statistically significant. Therefore, it can be concluded that the performance of banks is not directly influenced by fintech. In Model B, we explore the mediating impact to investigate the direct and indirect effects of fintech on both ESG and bank performance. While fintech does not directly affect bank performance, we observe a significant indirect effect on improving ESG performance, which subsequently enhances bank performance. The mediating effect is supported by a significant p-value of less than 0.001 and a positive path coefficient of 0.213. This suggests that the indirect boost in bank performance due to the mediating influence of fintech amounts to 21.3%. Hence, our second hypothesis proposing that fintech mediates the relationship between ESG and bank performance is confirmed. Furthermore, we conduct additional analyses to determine the impact of individual score indices on ESG and the favorable impact of each score when fintech mediates the relationship between ESG and bank performance. In Model A, the environmental score demonstrates a substantial

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positive influence on ESG, with a coefficient of 0.751. This implies that a 10% increase in the environmental score leads to a 75.1% improvement in bank performance, and vice versa. In Model B, the mediating effect of fintech further enhances the impact of the environmental score on bank performance by 23.5%. Similarly, the governance score exhibits a significant beneficial influence. In Model A, the coefficient for the governance score is 0.414, indicating that a 10% improvement in governance score results in a 41.4% increase in bank performance. Moreover, the mediating role of fintech enhances the impact of the governance score on bank performance by 25.4%. Regarding the social score, a positive and significant path coefficient of 0.214 is observed in Model A, suggesting that a 10% growth in the social score leads to a 21.4% increase in bank performance. In Model B, the mediating effect of fintech yields a positive impact of 5.3% on bank performance related to the social score. In summary, the analysis reveals that the governance score has the most significant impact on bank performance, followed by the environmental and social scores. The mediating role of fintech enhances the relationship between ESG and bank performance, indirectly improving bank performance. These findings provide a comprehensive understanding of the relationships among ESG, fintech, and bank performance.

10.5

Robustness Test

To ensure the reliability of our model, we conducted a robustness analysis in SPSS using an alternative method called Ordinary Least Squares (OLS) regression. We followed standard procedures to ensure the accuracy of our findings. In the analysis, we first examined the overall effect between variable X and variable Y through bivariate regression. This allowed us to understand the direct relationship between the two variables. Next, we assessed the direct effect between variable X and the mediator variable M using bivariate regression. This step helped us understand the specific impact of X on M. Subsequently, we examined the direct effect between the mediator variable M and the outcome variable Y using multiple regression. In this analysis, we included both X and M as predictors and Y as the dependent variable. This allowed us to determine the direct influence of M on Y, while accounting for the impact of X. Finally, we estimated and tested the indirect effect, focusing on its statistical significance. To measure the indirect effect, we employed the Sobel Test and evaluated the standard error (SE) and beta coefficient

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results. By following these rigorous procedures and utilizing the OLS regression method, we ensured the reliability and validity of our findings. These steps allowed us to thoroughly investigate the relationships between X, M, and Y, and provide robust evidence for our conclusions (Fig. 10.2). In this robustness analysis Table 10.6, we examined the implications of various measures to assess the strength and reliability of the reported effects. The findings provide valuable insights into the role of Fintech in enhancing the impact of bank performance on ESG performance. Our analysis uncovered significant implications regarding the magnitude of the reported effects. Firstly, we observed full mediation, indicating that the relationship between bank performance and ESG performance is not direct, but rather mediated through Fintech. This implies that the effect of bank performance on ESG performance operates through the intermediary mechanism of Fintech. This finding suggests that without the involvement of Fintech, the impact of bank performance on ESG performance may not be as

Fin a 0.005** (0.002)

b 0.369*** (0.019)

ESG

BkPer c 0.002** (0.001)

Fig. 10.2 Robustness test

c’ 0.000 (0.000)

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Table 10.6 Sobel test Sobel Test (z- score)

T/Statistic

Standard Error (SE)

pValue

2.480

0.001

0.013

pronounced. Furthermore, we found a positive route coefficient, indicating that Fintech strengthens the relationship between bank performance and ESG performance. This suggests that the influence of Fintech amplifies the positive impact of bank performance on ESG performance. The presence of a positive route coefficient suggests that the adoption and utilization of Fintech tools and technologies can lead to improvements in bank performance, which, in turn, positively affects ESG performance. Our analysis also demonstrated the significance of the path coefficients a, b, and c, indicating the statistical relevance of the relationships between bank performance, Fintech, and ESG performance. This further supports the notion that Fintech plays a crucial role in mediating the relationship between bank performance and ESG performance, contributing to their observed positive association. In addition, we examined the significance of the p-value for c' , which measures the direct correlation between Fintech and ESG performance when controlling for bank performance. Interestingly, we found an insignificant p-value for c' , suggesting that there is no direct association between Fintech and ESG performance when considering the influence of bank performance. However, it is important to note that despite the lack of a direct correlation, Fintech still exerts an indirect influence on ESG performance through its mediation of the relationship with bank performance. To further validate our findings, we conducted the Sobel test, which assesses the significance of the mediating effect. The criterion of -/+1.96 was met, with a specific value of 2.475, indicating a substantial magnitude of the mediating effect. This outcome strengthens our key findings derived from the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach and provides additional evidence for the robustness of our model. In conclusion, our robustness analysis revealed important insights into the magnitude of the reported effects. The findings highlight the role of Fintech as a mediator, strengthening the relationship between bank performance and ESG performance. While there may not be a direct correlation between Fintech and ESG performance when controlling for bank performance, Fintech’s influence is significant and operates indirectly. These findings contribute to a better understanding of the factors

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driving the relationship between bank performance, Fintech, and ESG performance, and underscore the importance of integrating technological innovations in the pursuit of sustainable and socially responsible banking practices.

10.6

Conclusion and Implications

The study has important implications for various stakeholders, including consumers, investors, managers, and policymakers. Firstly, consumers should be more aware of the ESG initiatives undertaken by banks and actively support those banks that engage in sustainability practices. This support can encourage banks to continue their efforts in promoting ESG values and ultimately benefit society as a whole. Investors can utilize ESG ratings as a reliable indicator of the volume of corporate social responsibility (CSR) activities undertaken by banks. Understanding the negative impact of CSR activities on corporate performance can help investors make informed decisions regarding their investment portfolios. Managers within the banking sector need to recognize the significance of ESG and fintech, allocating their resources wisely and conducting thorough studies before implementing new initiatives. It is essential for them to communicate their ESG practices and how they leverage fintech solutions to their advantage, fostering transparency and trust among stakeholders. Policymakers play a crucial role in promoting ESG practices, particularly in reaching consumers residing in rural areas who may have limited access to sustainable financial services. Increasing their knowledge of ESG practices and exploring how fintech solutions can facilitate implementation will help in creating an inclusive and sustainable banking ecosystem. From a theoretical standpoint, our study contributes to the existing literature by exploring the relationship between ESG and bank performance within the context of EU commercial banks. Prior research has predominantly focused on either bank performance or the association between fintech and performance, with little attention given to investigating the indirect impact of these variables. By addressing these gaps, our research provides valuable insights into the positive and significant impact of ESG on bank performance, aligning with earlier studies and expanding the understanding of this relationship. Overall, our study highlights the importance of ESG practices in the banking sector and emphasizes the potential benefits of integrating fintech solutions to enhance performance.

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The lessons learned from this research can guide consumers, investors, managers, and policymakers in their decision-making processes, ultimately leading to a more sustainable and responsible banking industry. ESG practices improve bank performance, reinforcing Friedman’s Theory (Friedman & Miles, 2002) claim that a company’s duty is to make a profit. The role of fintech provides the most interesting findings. Our model provides convincing evidence for how fintech mediates the relationship between ESG and bank performance. Our findings show that fintech has a negative or no influence on performance, but there is a positive association between fintech and ESG, and by improving ESG performance, bank performance is improved. This indicates that fintech indirectly contributes to enhancing bank performance. These findings help to advance research on fintech, banking, and ESG. This study has a few limitations. First, the study focuses primarily on commercial banks in EU countries while ignoring the other regions of the globe. Using diverse samples from other regions to run the model could serve to confirm the results of our research. Second, Refinitiv’s competitive policy prevents them from disclosing the specific formula used to determine the ESG score. In addition, there are no established methods for determining sustainability scores. This could provide a starting point for future research to determine whether the association between ESG and business performance is biased in favor of the choice of the rating agency. Additionally, the impact of each of the ESG pillars on other indicators like leverage and efficiency might be explored in future fintech research.

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CHAPTER 11

Integrating AI to Increase the Effectiveness of ESG Projects Sean Stein Smith

11.1

Introduction

Two forces continue to dominate headlines, investment decisions, and public attention in the world of finance that seemingly represent disparate fields of knowledge: artificial intelligence and sustainable investing goals. Artificial intelligence, long heralded (and feared by many) in the realm of philosophy, speculative fiction, and information technology, continues to make inroads in business. One of the early adopters of AI has been the financial services sector, which includes a wide array of actors including accounting, financial reporting, wealth management, and tax planning services. Automation, and analytics layered on top of these automated processes, are key tools that promise rapid economic growth, but the reality of the situation is far from that currently being advertised.

S. S. Smith (B) City University of New York—Lehman College, Bronx, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8_11

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Sustainable investing and asset allocation strategies also occupy an ever-larger share of public discourse, business decision-making, and institutional strategy. Several goals need to be achieved to develop a selfsustaining cycle of investment and reinvestment; several goals need to be achieved. First, the support and expansion of projects that either have a minimal environmental impact or actively reverse detrimental effects of past projects should be differentiated from other, sustainability-adjacent, projects. Second, these sustainable investment projects need to deliver returns that are comparable to other projects to attract capital (both financial and intellectual) on a recurring basis. Finally, and building on the second point, sustainable projects need to be able to support themselves without reliance on government support. No single tool, even those supported by the most powerful AI engines, can guarantee such lofty goals. By building more transparency into these projects, investors, creditors, and policymakers will have the data necessary to accurately assess and evaluate sustainable projects accurately.

11.2

A Word About FTX

The cryptocurrency exchange FTX, once lauded as the most responsible and well-managed firm in the crypto sector, collapsed spectacularly in a matter of days in November 2022. In addition to financial accounting issues, corporate governance failures, and allegations of illegal activity, the collapse also highlights the criticisms being leveled at ESG and other sustainability projects. On top of the rise of anti-ESG products such as exchange-traded funds, the fall from grace of both FTX and its founder, Samuel Bankman-Fried, has tarnished many of the sustainability values the firm espoused. This was made worse by increasing evidence that green donations and contributions might have been made with misallocated customer funds. Given this and the growing criticisms of ESG and sustainability projects at large, the need for more transparency, better analytics, and real-time feedback will only continue to increase going forward. 11.2.1

Issues With Sustainable Investing

A number of problems arise with regard to sustainable investing, but one that immediately comes to mind is the concept of “greenwashing”. As defined by Anne Smith (2022), greenwashing is when a project, series

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of projects, or even an organization purports to represent green, environmentally friendly, or sustainable values, whereas, in reality, this is not the case. Greenwashing, in addition to misrepresenting the facts of a specific project, can also tarnish the reputation of all sustainabilityoriented projects. Projects, companies, and entire industries have suffered reputational and financial penalties for insufficient disclosure, inaccurate reporting, and other data irregularities in the marketplace. Reputational damage, once incurred, can forestall the investment or allocation of assets to genuine and worthy projects in space. Environmental, social, and governance (ESG) issues have recently moved to the forefront of the investment landscape. Large institutional funds have aggressively allocated funds to ESG-related products, projects, and firms are aggressively investing in these ideas and initiatives (Stanton, 2022). Forecasts predict that over 20% of total asset management will be allocated to ESG projects by 2026. Despite the recent economic downturn and the COVID-19 Pandemic, the enthusiasm and interest for ESG investing, and sustainability-oriented projects, continues to grow; however, this enthusiasm has also begun to be questioned by some market actors. There is a definite need for increased due diligence around these projects (Ross Sorkin et al., 2022). Whether it takes the form of organizations voting down ESG ballot initiatives, or that of states in the U.S. choosing to end business relationships with certain investment funds based on association with green or sustainable projects, the impact is the same: there is a definitive shift and pushback occurring against ESG, green, or sustainability projects (Bhagat, 2022). The November 2022 initiative announced by the European Union to examine the issues around greenwashing is another example of how the pressure around quantifying results is increasing, alongside the interest.

11.3

ESG Positives and Negatives

The ESG trend that has dominated the asset management class and asset allocation process has also led to a renewed interest in the factors that underpin ESG, as well as the projects that ESG supports. Renewable energy, zero or reduced carbon projects, and integrating sustainable energy projects into the mainstream are attracting investment and the focus of policymakers. Before discussing the implication of ESG policies some definitions might be helpful:

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1. Environmental: Environmental concepts and ideas have a long history, with roots in the first conversations around stakeholder capitalism and the impact of negative externalities on the wider community (Peterdy, 2023). 2. Social: The impact of wider societal forces and change has been a part of the business decision-making process ever since the advent of business in the marketplace (Horton, 2022). Whether it relates to socio-economic, socio-political, or other industry-specific issues, the impact of social trends and changes on business is unavoidable. 3. Governance. The topic of governance is a unique aspect of the ESG landscape, as it applies to the broader topic of how a company is managed but is also unique to the individual firm in question (S&P Global, 2020a). Reflecting the reality of how each firm is managed differently, every firm must customize its governance approach to reflect the specifics of market segments, customers, and how the firm interacts with its environment Clark et al. (2022). One of the most cited problems when it comes to developing better or more consistent ESG standards is the lack of established evaluative criteria used to rank, judge, and categorize these projects. Given that some disreputable firms are included within an ESG category or investing portfolio (such as the recently bankrupted FTX)—complete with accusations of fraud—a tool that would assist investors, creditors, and policymakers in identifying these projects would be useful. A lack of comparable standards, however, is just one of several fundamental issues that continue to generate frustration in the ESG investment landscape.

11.4

Issues With ESG Investing

As with any new technology or way of doing business, there are several issues that have arisen because of the rush toward ESG investment. These are issues that could be addressed with better analytics and access to more real-time, transparent, and comparable data: 1. Incomplete or inaccurate data. This is a problem that applies to any asset class, but for ESG investors and users it is even more pronounced due to both the novelty of the sector and the lack of consistent accounting and reporting standards around these sources

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of information. Data is the lifeblood of any organization, and not having access to consistent and comparable information makes proper analysis difficult. 2. Misrepresenting an ESG. There have been numerous instances (Engler et al., 2022) where an ESG, green, or sustainably oriented fund has been found to include multiple firms that are neutral to these initiatives or even actively anti-ESG both in terms of its operations and how it deals with investors. ESG funds and sustainability funds will not achieve promised results from either a financial or environmental perspective without common and comparable definitions of what ESG entails. 3. Regulatory inconsistency. Regulation is desperately needed in the sustainability and ESG space. Since regulatory standards for what is considered green or sustainable do not yet exist, the marketplace has been left to rely on a patchwork of policies and regulations. Both accurate representation and regulation are essential for a wellfunctioning marketplace, and this includes ESG initiatives and projects.

11.5

ESG Category Samples

Taking a higher-level view, the larger types of projects and activities around ESG and sustainable investing would include the following: Electric and electrification projects . Electric vehicles and the pivot away from fossil-fuel power have led to a deluge of funding and projects that have sought to capitalize on this interest. A significant amount of investment will be needed to electrify the grid to the extent necessary to support the forecasted numbers of electric vehicles that are projected to account for over 60% of all vehicles sold globally by 2030 (IEA, 2022). The serious discussion has also begun to extend to other forms of transportation such as locomotives, aircraft, and ships. (DOE, 2022). One aspect of this discussion that continues to complicate the analysis and opens the door for greater integration of real-time analytics is the question of how the electricity to power these vehicles is to be generated. Water and hydroelectric power. Water and hydroelectric power have featured prominently for the last hundred years as an almost ideal type of sustainable, green power supply, and a perfect investment project or asset class. Hydroelectric dams and other technologies used to harness the 70% of the globe that consists of water have served as examples of how

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sustainable projects can support other environmentally sensitive efforts (such as electrification of the power grid more generally) and do so in a fiscally responsible manner. While there have been calls to make better use of the abundant water resources around the world and while there are obvious electrical and power generation benefits achievable through hydropower, there are also other environmental implications that need to be assessed on a real-time basis. Sustainable and environmentally friendly real estate. Numerous studies point to the reality that real estate, both commercial and residential, leads real estate to a large percentage of carbon output and general pollution (Carlin, 2022). An obvious, but simplistic, solution, is to incorporate more green space into urban and other real estate planning, but the economic costs of doing so are not insubstantial; by lowering the density of housing and real estate the costs of achieving these goals continue to increase as well (Boland, et al., 2022). While there is general agreement on the development of healthier buildings and communities, the costs of doing so—and societal pushback against changing the nature of certain neighborhoods—have complicated the development and implementation of sustainable communities more complicated. A common thread through the discussion is that these projects tend to focus on reducing the carbon footprint in larger, urban settings. Given the continued shift of population from more rural to urban centers, such projects represent the best return-on-investment (ROI) opportunities.

11.6

Role of AI In Sustainable Investing

For the purposes of this text, AI can be defined as follows: AI is a tool or a suite of tools that can augment, automate, or otherwise streamline underlying business processes, transactions, or job tasks.

The specific implementations of AI can, and will, vary from firm to firm, and this can sometimes cause some confusion when AI is being discussed (IBM, 2020). AI does not offer a magic wand for solving underlying business problems, but the ability to automate and streamline business processes can deliver significant benefits to the ESG assessment process. It is good to keep in mind, however, that a core attribute and use case for AI is that these tools are financially self-sustaining.

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How can artificial intelligence be used to better assess, rank, and assist with investing in sustainable projects? AI is not going to solve all of the underlying problems associated with sustainable investing: greenwashing, capital misallocation, and meddling from policymakers will continue to exist. That said, AI has an important role to play in (1) providing more consistent and comparable data, (2) evaluating the components of proposed sustainable projects, and (3) helping decision-makers assemble and publish better assessment tools to rank these ideas (S&P Global, 2020b). The following are some of the specific ways in which AI can assist decision-makers (Colson, 2019): 1. Quantifying non-standard data. One of the biggest issues is a large amount of non-standardized, non-quantitative, and confusing data, which is not amenable to analysis through traditional tools. Since most of the data is used to help evaluate and rank sustainability and ESG projects there is an opportunity for users and investors to make effective use of AI technology and analytics. 2. Real-time feedback. One of the issues around analyzing financial projects is the data lag that almost always exists; AI can assist with obtaining better and more real-time/on-demand information, which is essential for developing better, more sustainable, and more financially viable projects. 3. Ability to redirect resources and personnel. Projects that are marginal in terms of viability, financial success, or sustainability of these efforts will need to be able to quickly extract and monitor information and redirect resources, personnel, and information on an on-demand basis. AI can help to identify and redirect resources. Additionally, sustainability projects are the focus of attention from politicians, regulators, and policymakers. These stakeholders expect and demand real-time updates on projects and the ability to quickly address any issues that come up during the assessment process.

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11.7 Assessing Sustainable Investing Projects with AI Financial assessment and ranking of sustainability projects involve multiple stakeholders, numerous metrics, and a wide range of information. These are not factors unique to ESG, green investing, or the sustainability sector, but what is unique to this space, however, is the multiplicity of factors that make up the assessment, i.e., the success or failure of any project is going to be determined by a range of factors, versus a single financial metric or tool. Some of the most important factors are listed as follows: 1. Is the project able to be scaled up, scaled down, or otherwise modified on the fly in response to market changes? 2. Does the organization in question have access to the necessary information to assess how changing macro-economic, and geo-political factors might influence and affect project’s affect the viability of the project? 3. Have processes been established within the organization and with external stakeholders regarding how the data collected from AI will be leveraged? 4. Do the parties that are invested in and involved in this project have the capabilities to effectively implement the AI tools effectively? 5. Have workflows and internal controls been updated appropriately to deal with the increase in automation and analytics that AI integration will bring? No matter what specific tools or assessment processes are integrated within the company to evaluate project success there are a number of core components that need to be a part of this process. Clarity, consistency, and comparability across both different projects and different firms are essential to achieving a standardized framework for evaluating ESG and sustainability initiatives.

11.8

Green Bonds and AI

One of the most common products associated with green investing has been the continued development of green bonds. Given the billions of dollars that have been allocated to these bond issuances throughout the last decade (The World Bank, 2021), the level of scrutiny that should

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be brought to bear on these projects should match the levels that are conducted on traditional bond offerings. For the purposes of this discussion, green bonds can be defined as the following: Green bonds are traditional bonds that have been issued with the explicit intent to support and finance green, sustainable, or otherwise oriented projects.

Green bonds are a fast-growing sector of the debt marketplace, tend to offer appealing interest rates to investors, and provide a readily available way for market actors to advertise green credentials (Segal et al., 2022). That said, especially given the dual forces of increasing interest rates and higher inflation, bond issues and debt financing are experiencing increased scrutiny and pressure to operate as advertised. There are several specific ways in which AI can assist with both the issuance of green bonds and the connection of green bonds to their associated projects. First, AI can help monitor, trigger, or restrict payouts, interest rate changes, or other changes to bond financials connected to the outcomes of the projects themselves. For example, if a certain metric or benchmark is achieved on a project that is financed by a green bond and there needs to be some sort of verification process, AI can help with that. AI can measure and monitor benchmarks and other metrics that play a key role in determining how the finances of the green bond evolve and impact both the issuer and the recipients of these projects. Second, AI can enable market regulators and other external users to appropriately assess the viability and manner in which these bonds can be traded and exchanged. Bonds of all kinds are widely traded, with trading volumes and amounts for fixed-income instruments far exceeding those of equity securities. That said, the lack of transparency and liquidity in the green bond market has made these instruments much less liquid and tradeable when compared to other fixed-income instruments. As the demand for green financing continues to grow, the necessity for policymakers and investors to understand the mechanisms of green finance also grows apace. Third, better integration of information between project outputs and finance projects will help to create a more sustainable and transparent framework for assessing the viability of certain ESG projects. Especially given the aftermath of the collapse of FTX, and with the ripple effects

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echoing through the crypto marketplace, more information and transparency around just where promised funding is coming from will be in demand. Specifically, the connection between FTX and various environmental projects has cast into doubt the viability of sustainability projects and invited criticism.

11.9

Action Steps for AI Implementation

Several facts should now be apparent following a review of the global financial landscape. The first is that automation, analytics, and the interest of firms seeking to leverage tools related to these areas is on the rise. Second, sustainability represents a fundamental shift in how assets are going to be allocated and managed moving forward. Despite a short-term pushback, allocating capital to projects that both generate a reasonable rate of return and are sustainable for the environment makes good business sense. As a result, it also makes sense that managers and other market actors will continue to seek out recommendations to assist them with the implementation of AI to help with the evaluation of ESG projects. Some questions follow: 1. Which type of project will be assessed using AI? As outlined throughout this chapter, sustainable and ESG investing can encompass a broad array of projects, investments, and asset decisions. The first decisions, therefore, that need to be made are (a) what type of sustainability projects will the firm seek to invest in? and (b) how can existing AI tools and frameworks be leveraged to assist with the evaluation of projects? 2. What is the goal of the AI implementation? AI implementations are neither quick nor easy, and this fact needs to be incorporated into the budgeting and planning process. Some projects might be more focused on harnessing the predictive analytic power of AI implementation, whereas others might be used to construct forecasts as to how certain initiatives might have to change in response to market forces. What will be the feedback loop for AI implementation? Simply collecting data is not enough, and the corrective steps taken in response to the collection of data is where the strength of AI can be realized. As

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with any other technology, the information to be processed and analyzed is more important than the tool itself.

11.10 Predictive Analytics and Modeling with AI for Green Projects Environmental conditions, like many other market forces, can be notoriously difficult to forecast or predict accurately, but this is an area in which AI can be of some assistance. Adoption and implementation of AI have to do almost exclusively with the ability of various AI tools to accurately model, forecast, and predict the outcomes of certain situations and to do so with a higher level of accuracy than their human counterparts. Some examples of where AI predictive abilities can be used to great effect include the following: 1. Gauging public policy interest in certain projects, especially those that might have a higher-than-normal risk profile. Especially, given the combination of higher inflation rates and higher interest rates, the capital costs for projects—even those with a lower risk profile— have doubled or even tripled since 2021. 2. Analyzing how other trends, such as technological advancement and the development of more cost-effective options for energy generation, storage, and distribution might change the cost-benefits for certain sustainable projects. Predicting and forecasting the viability of projects is always a difficult task, but it is also an area in which AI excels. 3. Assessing the probability of default or other financial stressors being brought to bear on organizations seeking to actively invest in, develop, and expand sustainability projects, especially considering that several of the leading firms in the sustainability sector are startups or relatively new to the space. Examples of firms that have struggled to develop a sustainable financial strategy include new entries such as Rivian, as well as more established market actors such as Tesla. Even more, telling are the financial pressures brought more generally on institutions such as power producers and distributors who simultaneously seek to develop more sustainable projects, products, and services.

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No matter how predictive analytics are leveraged with regard to ESG and sustainability projects, the end goal should be the same: allowing managers to make more effective business and capital allocation decisions.

11.11

The Green Transition

Although there have been multiple efforts and initiatives to jump-start sustainability, especially in Western Europe and the Americas, the reality is that this will be a process versus a one-time effort. To develop a self-sustaining cycle of investment, project management, and project completion, the reality is that the ESG sector needs better, more transparent, and real-time information. Many green or sustainability-related projects have been hamstrung by incomplete, inaccurate, or out-of-date information. While AI is not a guarantor of better results tools and suites of tools that make more data available on a continuous basis will help to create clarity and transparency in this sector.

11.12

Future Directions

Given the rapid rise of sustainability and the widespread questioning around ESG, AI can contribute to a meaningful discussion in a number of ways. First, the more transparency and analytic capability that is brought to bear around sustainability and ESG projects the better the end results will be for investors, creditors, and policymakers alike. For investors, gaining better insight into how these types of projects are developed, assessed, and judged will help with more accurate pricing. Second, creditors will also gain better transparency and access to the finances of the projects, which in a business environment dealing with both rising interest rates as well as higher-than-previous inflation figures, is essential. Risk assessment and accurate pricing of risks is an important role that, without better and more real-time data, can become distorted and lead to the misallocation of capital, both financial and intellectual. Perhaps the greatest beneficiaries of better AI integration within ESG and sustainability projects, are the policymakers and business leaders that are tasked with charting the path forward. Decisions have serious ramifications from a financial perspective but also have larger political and social consequences. ESG and sustainability projects and initiatives have the support of individuals, institutions, and policymakers alike. What AI can do is add much-needed transparency, real-time data monitoring, and

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the ability to course correct in real-time. Both AI and ESG projects are here to stay, and both will benefit from further integration.

References Anne Smith, K. (2022). Greenwashing and ESG: What you need to know. Forbes. August. Available at https://www.forbes.com/advisor/investing/greenwash ing-esg/ Bhagat, S. (2022). An inconvenient truth about ESG investing. Harvard Business Review. March. Available at https://hbr.org/2022/03/an-inconvenienttruth-about-esg-investing Boland, B., Levy, C., Palter, R., & Stephens, D. (2022). Climate risk and the opportunity for real estate. McKinsey. February. Available at https://www.mckinsey.com/industries/real-estate/our-insights/cli mate-risk-and-the-opportunity-for-real-estate Carlin, D. (2022). 40% of emissions come from real estate; Here’s how the sector can decarbonize. Forbes. April. Available at https://www.forbes. com/sites/davidcarlin/2022/04/05/40-of-emissions-come-from-real-estateheres-how-the-sector-can-decarbonize/?sh=48c7364663b7 Clark, H., Cain, K., Lipton, M., Rosenblum, S., & Lipton, W. (2022). Thoughts for boards: Key issues in corporate governance for 2023. Harvard Law School Forum on Corporate Governance. December. Available at https://corpgov.law.harvard.edu/2022/12/01/thoughts-for-boardskey-issues-in-corporate-governance-for-2023/ Colson, E. (2019). What AI-driven decision making looks like. Harvard Business Review. July. Available at https://hbr.org/2019/07/what-ai-driven-decisionmaking-looks-like DOE. (2022). SunShot 2030. Solar Energy Technologies Office. Available at https://www.energy.gov/eere/solar/sunshot-2030 Engler, H., Robson, A., & Mikkelsen, R. (2022). Special report: ESG under strain. Thomson Reuters Institute. Available at https://www.thomsonreuters. com/en/reports/esg-under-strain.html Horton, C. (2022). Explainer: What is the ‘S’ in ESG investing? Reuters. July. Available at https://www.reuters.com/business/sustainable-business/ what-is-s-esg-investing-2022-07-19/ IBM. (2020). Artificial Intelligence (AI). IBM Cloud Education. June. Available at https://www.ibm.com/cloud/learn/what-is-artificial-intelligence IEA. (2022). By 2030 EVs represent more than 60% of vehicles sold globally, and require and adequate surge in chargers installed in buildings. International Energy Administration—Technology Report. September. Available at

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https://www.iea.org/reports/by-2030-evs-represent-more-than-60-of-veh icles-sold-globally-and-require-an-adequate-surge-in-chargers-installed-in-bui ldings Peterdy, K. (2023). ESG (Environment, Social and Governance). Corporate Finance Institute. May. Available at https://corporatefinanceinstitute.com/ resources/esg/esg-environmental-social-governance/ Ross Sorkin, A., Giang, V., Gandel, S., Hirsch, L., Livni, E., Gross, J., Gallagher, D., & Schaverien, A. (2022). The pushback on E.S.G. investing. New York Times. May. Available at https://www.nytimes.com/2022/05/11/business/ dealbook/esg-investing-pushback.html S&P Global. (2020a). What is the “G” in ESG? S&P global. February. Available at https://www.spglobal.com/en/research-insights/articles/what-is-theg-in-esg S&P Global. (2020b). How can AI help ESG investing? S&P. February. Available at https://www.spglobal.com/en/research-insights/articles/how-can-aihelp-esg-investing Segal, T., Brock, T., & Munichiello, K. (2022). Green bond: Types, how to buy, and FAQS. Investopedia. September. Available at https://www.investopedia. com/terms/g/green-bond.asp Stanton, R. (2022). ESG-focused institutional investment seen soaring 84% to Us $33.9 trillion in 2026, making up 21.5% of assets under management. PwC. October. Available at https://www.pwc.com/gx/en/news-room/press-rel eases/2022/awm-revolution-2022-report.html The World Bank. (2021). World Bank sustainable development bonds & Green bonds impact report 2021. The World Bank—IRBD Funding Program. Available at https://treasury.worldbank.org/en/about/unit/treasury/ibrd/ibrdgreen-bonds

Index

A Application programming interface (API), 118 Artificial intelligence (AI), 4, 5, 8, 121, 131, 135, 137, 139, 153, 160, 163, 173, 181–183, 186, 187, 193, 219, 220, 224–231 Atomic swap exchange, 21 Augmented reality (AR), 131 B Bank financing, 8, 170, 177 Bank fragility, 196 Big data analytics, 160, 173, 178, 187 Biodiversity ecosystem, 135 Biogas-based fuel, 40 Blockchain, 4, 6, 7, 20, 21, 23–25, 31–39, 41, 44–48, 131, 137, 138, 153, 160, 161, 163, 184, 185, 193, 194 Blockchain-based green bond certification, 19 Blue carbon system, 35 Brain-computer interface, 137

Bureaucracy, 111

C Capital misallocation, 225 Carbon-based technology system, 135 Carbon emissions, 29, 30, 32, 35, 36, 59, 135–138, 162, 163, 181 Carbon emissions trading system (ETS), 29 Carbon footprint, 46, 181, 224 Carbon tax, 15, 29, 30 Cash clearing, 21 Central bank digital currency (CBDC), 20, 23, 24 Centralized mortgage underwriting, 95 Central Securities Depository (CSD), 21 Chemical recycling, 158 Circular economy model, 66, 130 Citizen-centric financial system, 113 Clean Development Program (CDM), 31

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 T. Walker et al. (eds.), Fintech and Sustainability, https://doi.org/10.1007/978-3-031-40647-8

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INDEX

Clean Energy Blockchain Network (CEBN), 37 Climate financing, 6, 17, 18 Climate fintech, 6, 18, 19 Climate-resilient development, 170 Cloud computing, 137 Cold Ironing technology, 58 Compressed natural gas (CNG), 42 Consumer Financial Protection Bureau (CFPB), 91, 93, 94, 98, 101 Consumer lending, 93–95, 99, 101, 104 Corporate social responsibility (CSR), 78, 196, 198, 199, 212 Covid-19, 14, 55, 58, 59, 61, 221 Creative destruction, 134, 135 Crowdfunding, 5, 63, 113, 150, 152, 162, 177 Cryptocurrency exchange, 220 Cybercrime, 110 Cyber-Physical System, 142 Cyclical poverty, 108

D Data envelopment analysis (DEA), 198, 200 Data extraction, 175 Decarbonization, 61, 135, 136, 181 Decentralized financing (DeFi), 33 Digital asset, 20, 21, 32, 33, 44, 136–138 Digital finance, 19, 108, 113, 114, 119, 122, 123, 184, 200 Digital money, 117, 122, 123 Digital signature, 33, 44 Digital transparency, 65 Digital twins, 135, 137, 138, 142 Digitization, 54, 113, 114, 137, 143, 152, 160 Disparate impact, 100

Disparate treatment, 100 Distributed autonomous corporation (DAC), 139, 141 Distributed autonomous organization (DAO), 131, 139–141, 143 Distributed ledger, 32, 33, 173, 184 Distributionally Robust Fairness (DRF), 103 E Eco-entrepreneurship, 62 Electrification project, 223 E-money, 178 End-to-end online platform, 95 Energy balance, 36, 181 Energy consumption, 72 Entropy, 134 Environmental asset, 32 Environmental degradation, 62, 159 EPA Moderated Transaction System (EMTS), 42, 43, 45, 47 Equal Credit Opportunity Act (ECOA), 93, 99, 100 ESG disclosure score (DISC), 198 Ethereum, 33 European Green Deal, 54 F Financial globalization, 151 Financial Inclusion Action Plan (FIAP), 113 Financial inclusion (FI), v, 7, 107–116, 118, 119, 122–124, 136, 137, 178, 186 Fixed-income instrument, 227 Fossil fuel-based model, 135 Freelancing platform, 140 G Generalized method of moments (GMM), 195–197, 199

INDEX

Global Impact Investing Network (GIIN), 64 Global Reporting Initiative (GRI), 176 Global warming, 4, 14, 17, 149, 169, 171 Government-to-person (G2P) payment system, 117 Green Asset Wallet (GAW), 184, 185 Green bank, 65, 79 Green bond, 5–8, 19–25, 54–56, 58–60, 65–69, 79, 138, 172, 185, 226, 227 Green energy trading, 6 Greenhouse gas (GHG), 6, 14, 16, 29, 32, 37, 39, 54, 58, 60, 75, 78, 150, 158, 159, 169, 180 Greenhouse Gas Protocol, 180 Greenium, 58 Green port, 61, 69, 79 Greenwashing, 77, 78, 163, 182, 183, 220, 221 Group fairness, 101–104

H Heavy fuel oil (HFO), 75 Hydroelectric power, 223

I Individual fairness, 101–104 Industrial transformation, 138 Information asymmetry, 25, 43 Information transparency, 68, 153, 161 Intergovernmental Panel on Climate Change (IPCC), 17 Internet of Things (IoT), 38, 160

K Know your customer (KYC), 194

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Kyoto Protocol, 29–31 L Liquefied natural gas (LNG), 42, 75, 77 Liquidity risk, 162 Low-carbon energy production, 135 M Market segmentation, 112 mBridge project, 20 Metaverse, 7, 130–139, 141–143 Microfinance institution (MFI), 177 Microfund for Women (MFW), 178 Mixed reality, 137 Mobile credit product, 119 Momentum strategy, 197 N Nationally determined contribution (NDC), 15 National Plan for Recovery and Resilience (PNRR), 53 Natural language processing (NLP), 103, 183 Neo-bank, 153 Neoclassical economics, 133, 134 Non-fungible token (NFT), 33, 131, 138 O Ocean acidification, 4, 14 Operational risk, 194 Outcome-based climate payment, 15 P Panel data analysis, 196, 200 Paris Agreement, 13–15, 135, 138, 169, 170

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INDEX

Partial least squares (PLS), 206 Payday loan, 122 Peer-to-peer lending tool, 113 Precision agriculture, 159 Product lifecycle management (PLM), 160 Q Quantum science, 133 R Radio frequency identification (RFID), 160 Relational Database Management Systems (RDBMS), 160 Renewable energy, 16, 29, 31, 34, 37, 39, 40, 42, 46, 48, 55, 56, 66, 68, 135, 137, 142, 162, 221 Renewable identification number (RIN), 40–44 Results-based climate finance (RBCF), 15 Reverse redlining, 100 Robotic process automation (RPA), 175, 176 Robotics, 137, 173, 176, 178, 187 S Sea level rise, 14, 176 Security token offering (STOs), 186 SenSR, 103 Small and medium-sized enterprise (SME), 8, 113, 155 Smart contract, 20, 23, 25, 33, 35, 44, 46, 138 Social asset, 7, 137, 138, 143 Social inequality, 7 Socially responsible investing (SRI), 196

Society for Worldwide Interbank Financial Telecommunications (SWIFT), 151 Statistical parity, 101–103 Student Borrower Protection Center (SBPC), 94, 97 Sustainability Accounting Standards Board (SASB), 197 Sustainability-linked bond, 172, 186 Sustainable Development Goals (SDGs), 4, 5, 7, 63, 65, 107–109, 113, 114, 169, 170, 179 Sustainable model of consumption, 150

T Task Force on Climate-Related Financial Disclosures (TCFD), 171, 176, 179 Techno-economic paradigm shift, 134, 135 Tobin’s Q, 196–199 Tokenization, 138, 184, 185 TraceX, 47

U United Nations Environment Programme (UNEP), 14, 57, 65

V Vector autoregressive (VAR), 59 Verified carbon standard (Verra), 31 Virtual reality (VR), 131, 142

W Waste management, 62, 65, 150, 153