Water Risk Modeling: Developing Risk-Return Management Techniques in Finance and Beyond 3031238109, 9783031238109

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Edited by Dieter Gramlich · Thomas Walker · Maya Michaeli · Charlotte Esme Frank

Water Risk Modeling Developing Risk-Return Management Techniques in Finance and Beyond

Water Risk Modeling

Dieter Gramlich · Thomas Walker · Maya Michaeli · Charlotte Esme Frank Editors

Water Risk Modeling Developing Risk-Return Management Techniques in Finance and Beyond

Editors Dieter Gramlich Department of Banking & Finance DHBW-Baden-Württemberg Cooperative State University Heidenheim an der Brenz Baden-Württemberg, Germany Maya Michaeli Department of Finance Concordia University Montreal, QC, Canada

Thomas Walker Department of Finance Concordia University Montreal, QC, Canada Charlotte Esme Frank Department of Finance Concordia University Montreal, QC, Canada

ISBN 978-3-031-23810-9 ISBN 978-3-031-23811-6 (eBook) https://doi.org/10.1007/978-3-031-23811-6 © 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

Acknowledgments

We acknowledge the financial support provided through the Global Risk Institute and the Jacques Ménard—BMO Centre for Capital Markets. We appreciate the excellent copy-editing and editorial assistance we received from Victoria Kelly, Meaghan Landrigan-Buttle, Miles Murphy, Mauran Pauvan, and Eimear Rosato.

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Contents

Introduction Water Risk-Return Modeling and Management: Threats and Opportunities Dieter Gramlich, Thomas Walker, Charlotte Esme Frank, and Maya Michaeli

3

Frameworks for Water Risk-Return Modeling and Management Water Cycle Changes in a Warming World: The Scientific Background Karsten Haustein and Quintin Rayer

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Water: Textile’s Danger—Financial Risks, State of Practice and Moving Forward Navya Bhayana and Laureline Josset

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Financial Risks Due to Residential Flooding: Incorporating Household Perceptions to Better Understand Behaviors James I. Price and Diane P. Dupont

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Water Stewardship—Bridging the Knowledge and the Financial Gaps Pratibha Singh, Nidhi Nagabhatla, and Karin Kreutzer

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CONTENTS

A Framework for Global Warming Induced Extreme Weather and Water Investment Risks Quintin Rayer, Karsten Haustein, and Pete Walton

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(Investment) Strategies for Water Risk-Return Modeling and Management Measuring Water Risk: The Challenges for Passive Index Investment Markus Barth Using the CWR APACCT 20 Index to Re-Calibrate Chronic Tail Risks and Re-Assess Long-Term Capital Allocation Decisions Given Rising Locked-In Coastal Threats Dharisha Mirando, Debra Tan, and Chien Tat Low Water Neutrality in Investment Portfolios Nadja Franssen

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217 247

Making Water Count—Integrated Risk-Return and Knowledge-Based Models for Water Investments Josien Sluijs, Blanca Méndez, and Dieter Gramlich

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Water Risk in Real Estate: An Introduction to the Climanomics Platform Isabelle Jolin and Maya Michaeli

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The Water Credit Risk Tool and Corporate Sensitivity to the Shadow Price of Water Dieter Gramlich and Henrik Ohlsen

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Index

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Notes on Contributors

Markus Barth CFA, is a seasoned investment professional and an index pioneer. He has designed, developed, and launched hundreds of highly successful proprietary indices throughout his 30+ year career in the financial services industry. In June 2019, he founded Anatase Ltd. as an independent consulting firm in order to provide his extensive expertise in index development, structured products, and investment solutions globally. Prior to founding Anatase, he was Global Head of Systematic Indices at Deutsche Bank and DWS from 2002 to 2019 and Global Head of International Quantitative Research at Merrill Lynch. Navya Bhayana is a penultimate year student at the National University of Juridical Sciences, Kolkata, India pursuing law studies on the Aditya Birla Scholarship. She is working as a Graduate Student Researcher at the Columbia Center on Sustainable Investment, in close association with the Columbia Water Center, wherein she analyzes issues related to water quality reporting and standards in different industrial sectors. She is intrigued by the measures undertaken by businesses to align their strategies with the SDGs and works toward examining the adequacy of the reporting frameworks adopted by businesses to measure, monitor, and disclose their ESG performance. Diane P. Dupont is a Professor in the Economics Department at Brock University where she held the Chancellor’s Chair for Research Excellence from 2006 to 2009. The Canadian Water Network, SSHRC, CIHR,

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Health Canada, Environment Canada, and the Donner Foundation have funded her work. She works with researchers in other fields, as well as researchers across Canada, in the United States, England, and Australia. Her current research program concentrates upon examining ways to encourage more efficient and sustainable use of water resources both on the supply and demand side. On the supply side, she is looking at factors that help to identify which water utilities operate most efficiently and sustainably. On the demand side, she has undertaken a number of nonmarket valuation studies to determine the value of good quality water as it relates to individuals’ perceptions of the health effects of tap water. Professor Dupont was the elected Researcher representative to the Canadian Water Network Board of Directors (2010-2012). She is also a Member of the Board of Directors for the North American Association of Fisheries Economists and served as a Member (and Chair for one year) of the Scientific Advisory Committee for WorldFish Centre, Penang, Malaysia. Prior to that, she served as a Member of the Executive Council of the Canadian Economics Association and as a Member of the Social Sciences and Humanities Adjudication Committee (Economics). She has served as an Associate Editor for the Australian Journal of Agricultural and Resource Economics and Society and Natural Resources and is currently an Associate Editor for the Canadian Water Resources Journal, Water Resources Research, and Water Resources & Economics. 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. She is currently completing a Ph.D. in English literature at McGill University. Nadja Franssen has been working as a Responsible Investment Officer in the Sustainability and Strategy team at Cardano-ACTIAM since 2020. Her focus is on the screening of companies and the development of policies. The emphasis of her work is on water-related risks and opportunities. She recently worked on the update of ACTIAM’s Water Policy and contributed to the publication of thought leadership articles and reports on the topic. Prior to joining Cardano-ACTIAM, she worked as a Portfoliomanager Socially Responsible Investing at SPF Beheer where she was responsible for the development and implementation of the socially responsible

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investment strategy, drafted financial analyses and contributed to the selection and monitoring of external managers in the field of impact investing. Nadja holds an M.B.A. in Finance and Responsible Investment from the Sustainability Management School in Gland and an M.Sc. degree in Public Policy and Human Development at the United Nations University—MERIT in Maastricht M.Sc. Public Policy and Human Development, Maastricht Graduate School of Governance in cooperation with United Nations University. Dieter Gramlich is a Professor of Banking and Finance at DHBW— Baden-Württemberg Cooperative State University in Heidenheim, Germany, where he serves as the Head of the Banking Department. He previously studied at the University of Mannheim and was an interim professor and Chair of Banking and Finance at the University of Halle. His research focuses on sustainable finance. Karsten Haustein is a Research Associate at the Meteorological Institute of the University of Leipzig, Germany. He is linking extreme weather events and the loss of biodiversity with human-induced climate change. One key question he is trying to answer is to what extent observed changes can be attributed to anthropogenic causes. Previously, he has worked as a postdoctoral researcher at GERICS (German Climate Service Center Hamburg) as well as at the Environmental Change Institute (ECI) of the University of Oxford. At ECI Oxford, he helped in the development of the rapid event attribution framework, now widely known as World Weather Attribution. Isabelle Jolin (B.Comm. 2018, M.Sc. 2021), a two-time John Molson School of Business alumna, specializes in residential investments and asset management at Ivanhoé Cambridge. After completing her undergraduate degree, she began working at Ivanhoé Cambridge while pursuing a master’s degree in Finance. She now manages a variety of residential products through portfolios across the United States, notably in New York City and the Sunbelt. She is very passionate about sustainability, a central concept in her investment and asset management approach. Laureline Josset Associate Research Scientist at the Columbia Water Center, specializes in the quantification of risks due to uncertain climate and data to inform decisions. Collaborating with actors from governmental agencies, NGOs and advocacy groups, the financial industry, she

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assesses water data needs, availability, gaps, and biases, and examines the associated regulatory, infrastructural, and financial consequences. Her work has led her to consider the educational needs for better water data practices, for high-schoolers, practitioners, policy-makers, and academics. Before joining Columbia, she obtained a bachelor’s and master’s degrees in Physics at the EPFL (CH) and a Ph.D. in Earth Sciences at the University of Lausanne (CH). She teaches for the Sustainable Management program classes on water system analysis and groundwater management with a particular emphasis on conceptual modeling and system thinking. Karin Kreutzer holds the Chair of Social Business at EBS University. In addition, Professor Kreutzer is the head of the EBS Impact Institute and Vice Dean of Research. Her research focuses on social business models, strategic partnerships between companies and NGOs, and questions of strategic management and innovation in non-profit organizations. She received her doctorate from the University of St. Gallen and studied International Business at the Universities of Passau and Parma. At Bocconi University in Milan, she completed a master’s degree in the Management of Non-Profit organizations. She has worked for international NGOs in Germany and Italy, and serves on the supervisory board of various large social enterprises in Germany. She teaches courses at the undergraduate, graduate, and doctoral levels in programs in business administration, corporate social responsibility, social entrepreneurship, and qualitative research methods. Chien Tat Low heads CWR’s geospatial analysis work, which is being used to identify risk hotspots to plan for better resilience. His 3D flood maps have triggered many corporates and banks to start assessing their coastal threats and his models have been included in the annual reports of a number of publicly listed firms. His work has also been cited by mainstream media outlets, such as Bloomberg TV and SCMP. Low’s geospatial models were a key input for the groundbreaking CWR APACCT 20 Index that benchmarks coastal threats across 20 cities in APAC. Currently, he is leading CWR’s Re-IMAGINE HK initiative and sits on HKUST’s Shaping a Sustainable Northern Metropolis steering group to drive “low-regret adaptation” in HK against coastal threats. Low believes that we need to think creatively to raise awareness of climate change and has thus played a major role at CWR integrating visual arts and science in various collaborative projects with the likes of CUHK’s Museum of Climate Change, M+ Museum and HK Ballet.

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He is also an advocate for “education unusual” and has designed waternomic coursework for a M.Sc. in Investment Management program and a climate hackathon workshop for HKUST. As a certified GIS Professional, he has also taught GIS modules at universities. Low has a Ph.D. and prior to joining CWR completed a postdoctoral fellowship at HKU. His geospatial research is published in multiple prominent international peer-reviewed journals and book chapters. Blanca Méndez is a Communications Manager at Aqua for All. For more than 25 years, she has managed group communications of international companies, primarily in the private sector. In the last 15 years, she has worked in impact investing and venture philanthropy financing financial institutions, sustainable agriculture, fair trade, water and sanitation, and renewable energy worldwide. Blanca followed her Bachelor and Master in Communication studies in Peru. She holds a M.Sc. in Corporate Communications from the Rotterdam School of Management, Erasmus University. Furthermore, she graduated in Gender Studies and Digital Marketing. She is also certified in Change Management and as a Lean Six Sigma Green Belt. She also has extensive experience advocating for human and women’s rights and environmental issues. She is a passionate advocate for gender equality, diversity, and inclusion. Besides working for Aqua for All, she is currently a member of the Supervisory Board Wo=Men, Dutch Gender Platform. Maya Michaeli works as a Research Associate in the Department of Finance, at Concordia University (John Molson School of Business), Montreal. She has a natural passion for financial markets and the future of sustainable investments and has participated in numerous research projects in those areas. Dharisha Mirando hails from the finance industry and leads CWR’s engagements related to Water Risk Valuation. She joined the team as she believes that climate and water factors are downplayed by the financial sector. Since joining, she has published reports with Manulife Asset Management, the Asia Investor Group on Climate change, and CLSA on how water and climate risks could impact investment portfolios and how to take action to tackle these before it’s too late. She has also spoken at multiple finance conferences and conducted meetings with investors on water and climate risks in Asia. She hopes to help build consensus on

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how to value water risks, bridge the gap between finance and science, and engage with investors to incorporate these risks. This could also lead innovative Green Finance instruments to become more prevalent. Prior to joining CWR, she worked in the investment team of a longonly public equities fund. She has also worked in the impact investment space in London and Singapore, where she provided technical assistance to social enterprises, helped them raise equity investments, and managed a debt portfolio. Nidhi Nagabhatla is a Senior Fellow and Cluster Coordinator for Nature, Climate & Health at the United Nations University (CRIS) Belgium. A sustainability science specialist and a systems analyst with more than 20 years of work experience, she has led, coordinated, and implemented transdisciplinary projects in various geographical regions such as Asia, Africa, Europe, and the Americas, working with international organizations and leading research and capacity development initiatives. She is also affiliated with Oxford University (UK) and Leibniz University (Germany). She serves as Adj/Associate Professor at the School of Earth, Environment & Society at McMaster University, Canada, as Guest Professor at Universidad Mayor de San Andrés, Bolivia, and as visiting Professor at Imo State University, Nigeria. She has published more than 200 papers (journals, chapters, technical reports). Henrik Ohlsen has been a member of the management board of VfU e.V. since 2012. There, he coordinates the association network of the 50+ financial institutions in various functions. He supervises and moderates various projects and working groups or forums on questions of integrating sustainability criteria into financial decision-making processes. Recently (2017–2019), he was also leading sub-project of the joint project CARIMA “Carbon Risk Management and Financed Emissions Quantification, Management and Reporting of Carbon Risks.” Previously, he, together with colleagues from UNEP FI and GIZ, co-led a project consortium to develop an approach for analyzing and assessing water risks in the credit sector, with which water risks can be made assessable and calculable using the “Total Economic Value’s (TEV)” tool. Before joining VfU, he worked in the field of environmental management at the City of Munich and at the consulting firm imu Augsburg GmbH. He holds a university degree (M.A.) in political science and international law, specializing in governance research.

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James I. Price is an Assistant Professor and Environmental Economist at the School of Freshwater Sciences, University of Wisconsin—Milwaukee. He was formerly a postdoctoral research fellow at the U.S. Environmental Protection Agency, Brock University, and the University of British Columbia—Okanagan, and holds master’s and doctoral degrees in economics from the University of New Mexico, with concentrations in economic theory, environmental and natural resource economics, and econometrics. His research focuses on non-market valuation and municipal water modeling, including issues related to source water protection, in situ water quality, drinking water quality, small-scale irrigation, residential water demand, and urban flooding. He has extensive experience working in interdisciplinary settings and with partners from local government agencies and research institutes. Quintin Rayer has worked for actuarial and investment consultancy firms as well as a multi-national European bank for nearly ten years. Projects have included substantial and innovative development of quantitative fund selection and analysis techniques, risk-monitoring, and portfolio optimization, including in-house training for analysts and relationship managers. In addition, he is a Chartered Fellow of the Chartered Institute for Securities and Investments, a Chartered Wealth Manager and holds a Physics degree from Imperial College London and a Physics doctorate in atmospheric physics from Oxford University and is a Fellow of the Institute of Physics. He has applied his knowledge and experience in computational and analytical analysis from nuclear and aerospace engineering to areas in finance. He has also completed the Sustainable Investment Professional Certification (SIPC) with the John Molson Business School, becoming this program’s first graduate in the Channel Islands and the second in the UK. In January 2017, he joined P1 Investment Management Ltd, founding their ethical and sustainable investing proposition. Pratibha Singh has dedicated close to a decade of her career working with NGOs, think tanks, and associations in India, Thailand, and Germany on issues such as gender equality, climate change, migration, conflict management, and sustainable development. Adept in content creation & storytelling, she has published around 40 articles, book chapters, issue briefs, and papers. She holds two master’s degrees in Gender & Development Studies & Public Policy. Currently, she aims to maximize positive impact of financial institutions in consonance with Agenda 2030

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at Agents for Impact where she is working as an SDG Rating Analyst. Simultaneously, she is pursuing her Ph.D. from EBS University of Business and Law on sustainability transitions in small- and medium-sized cities of the Global South. Josien Sluijs has been the Managing Director at Aqua for All since 2019. Josien has been active in the field of inclusive finance for over 19 years. In her former position as Director of NpM, Platform for Inclusive Finance, she was responsible for the representation of the Dutch Inclusive Finance sector and facilitated cooperation, knowledge development, and sharing among the members of NpM. Before NpM, she held a senior position at Rabo Development. She was responsible for restructuring Rural Banks in the Middle East and Asia. She was also responsible for different advisory trajectories for international organizations, such as the International Finance Corporation (IFC) and research projects with regard to Value Chain Finance. Other functions she held at Rabobank International were Project Manager Securitization, and Credit Structurer Food and Agribusiness. For the World Food Programme (WFP) in India, she carried out an evaluation and advised the WFP on the Mother and Child program. Debra Tan heads the CWR team and has steered the CWR brand from idea to a leader in the water risk space globally. Reports she has written for and with financial institutions analyzing the impact of water risks on the Power, Mining, Agricultural, and Textiles industries have been considered groundbreaking and instrumental in understanding not just China’s but future global water challenges. One of these led the fashion industry to nominate CWR as a finalist for the Global Leadership Awards in Sustainable Apparel; another is helping to build consensus toward water risk valuation. She is a prolific speaker on water risk, delivering keynotes, participating in panel discussions at water prize seminars, numerous investor & industry conferences as well as G2G and academic forums. Before venturing into “water,” she worked in finance, spending over a decade as a chartered accountant and investment banker specializing in M&A and strategic advisory. She left banking to pursue her interest in photography and also ran and organized philanthropic and luxury holidays for a small but global private members travel network. She has lived and worked in Beijing, HK, KL, London, New York, and Singapore and spends her spare time exploring glaciers in Asia.

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Thomas Walker is Professor of Finance and Concordia University Research Chair in Emerging Risk Management at Concordia University, Montreal, Canada. Prior to academia, he worked in the German consulting and industrial sector at Mercedes Benz, Utility Consultants International, Lahmeyer International, Telenet, and KPMG Peat Marwick. Pete Walton is the Impact Translation Fellow at the UK Climate Resilience Programme, University of Leeds developing ways that the outputs and learning from the program can be implemented into policy and decision-making, especially at the national level. Previously having worked at the UK Climate Impacts Programme, University of Oxford, he brings 14 years’ experience of working in the adaptation world, researching and supporting stakeholder engagement with climate change adaptation science. Initially, this was using the UK Climate Projections and more recently in how to use the science of extreme weather event attribution. This follows the completion of his doctoral thesis that examined how climate science can be more successfully communicated to the non-academics using online technologies. He is a qualified teacher with over twenty years’ experience teaching at school and university levels. His research has provided him with an opportunity to link his background as an environmental geographer with his interest in the role of technologies as tools for engagement.

List of Figures

Water Risk-Return Modeling and Management: Threats and Opportunities Fig. 1

The complex nature of water risk: Interactions between ecology, economy, society, and finance

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Water Cycle Changes in a Warming World: The Scientific Background Fig. 1

Fig. 2 Fig. 3

Depiction of the present-day water cycle based on the IPCC assessments (Abbott et al., 2019; Rodell et al., 2015; Trenberth et al., 2011) with adjustments for groundwater flows (Luijendijk et al., 2020; Zhou et al., 2019) seasonal snow (Pulliainen et al., 2020) and ocean precipitation and evaporation (Allan et al., 2020; Gutenstein et al., 2021; Stephens et al., 2012) Climatic drivers of drought, effects on water availability, and impacts Synthesis of assessment of observed change in heavy precipitation and confidence in human contribution to the observed changes in the world’s regions

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LIST OF FIGURES

Water: Textile’s Danger—Financial Risks, State of Practice and Moving Forward Fig. 1

Water risks associated with the stages of the life cycle of textile

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Water Stewardship—Bridging the Knowledge and the Financial Gaps Fig. 1

Mapping actors in the water stewardship program

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A Framework for Global Warming Induced Extreme Weather and Water Investment Risks Fig. 1

Hypothetical climate liability framework

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Using the CWR APACCT 20 Index to Re-Calibrate Chronic Tail Risks and Re-Assess Long-Term Capital Allocation Decisions Given Rising Locked-In Coastal Threats Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11

Wide-ranging input to the CWR APACCT 20 Index from 100+ finance professionals Estimated likelihood of global warming scenarios—Responses from 100+ finance professionals Inclusion of sea level rise in financial valuation—Responses from 100+ finance professionals Concerns about the impact from coastal threats—Responses from 100+ finance professionals Concerns about the effects from SLR at the start and end of the survey—Responses from 100+ finance professionals Land affected from SLR at different temperature scenarios in the case of Bangkok and Tianjin Agreement about government actions addressing coastal threats—Responses from 100+ finance professionals Types of government action to include in a coastal threat index—Responses from 100+ finance professionals Cities included in the CWR APACCT 20 Index People and GDP at risk from SLR in the 20 cities in the CWR APACCT 20 Index City rankings in the CWR APACCT 20 Index with and without government actions

221 222 223 224 226 227 228 229 231 232 234

LIST OF FIGURES

Fig. 12 Fig. 13

Fig. 14 Fig. 15

Locked-in SLR levels for the modeling of flooding and preferences—Responses from 100+ finance professionals Flooding from locked-in sea level rise in Hong Kong—NASA SRTM-30 m (left) versus Hong Kong DTM (right) model Government adaptation categories and indicators in the CWR APACCT 20 Index Weightings of government adaptation categories in the CWR APACCT 20 Index

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238 240 241

Water Neutrality in Investment Portfolios Fig. Fig. Fig. Fig. Fig. Fig. Fig.

1 2 3 4 5 6 7

Safe and just zone for humanity in the doughnut economy Planetary boundaries Two strands of water neutrality Portfolio water footprint Portfolio water quality footprint Mitigation hierarchy of water-related risks and impacts Water footprint including positive company impacts

248 250 255 258 259 262 268

Making Water Count—Integrated Risk-Return and Knowledge-Based Models for Water Investments Fig. 1 Fig. 2 Fig. 3

WASH framework of Aqua for All WASH Loan Facility as an integrative model—cooperation of Aqua for All and Sidian Bank The Naivasha Ushirika Water Project (NUW Project)—partnership between Family Bank and Aqua for All

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Water Risk in Real Estate: An Introduction to the Climanomics Platform Fig. 1 Fig. 2 Fig. 3 Fig. 4

Evolution 2020–2100 of relative water risk by asset type (% of Asset value) Evolution 2020–2100 of relative water risk by REIT (% of Asset value) Distribution of boston properties’ MAAL over time by property Distribution of Hudson Boulevard 3 + MAAL by Water Hazard

324 325 325 326

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LIST OF FIGURES

The Water Credit Risk Tool and Corporate Sensitivity to the Shadow Price of Water Fig. 1 Fig. 2

Fig. 3 Fig. 4

Fig. 5 Fig. 6 Fig. 7

Potential exposure from water as the difference between shadow price and market price Shadow price of water in the total economic value concept as the combination of economic, ecological, and societal value Structure of the GIZ/NCD/VfU Water Credit Risk (WCR) tool Companies ordered based on the relation of EBITDA/Revenue ratios before and after including a shadow price of water (SPW) Analyzing water risk sensitivity of companies—Sensitivity to changes in operations Composition of Eskom’s power generation portfolio 2013 and 2020 Operating countries of Femsa, 2013 and 2020—Baseline water stress projections within a range from 0 to 5 from WRI Aqueduct

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336 339

340 343 347

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List of Tables

Water Risk-Return Modeling and Management: Threats and Opportunities Table 1

Opportunities and threats from water challenges (financial perspective)

7

Water: Textile’s Danger—Financial Risks, State of Practice and Moving Forward Table 1

Financial risks and their risk profile dimensions as a function of water risks

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Financial Risks Due to Residential Flooding: Incorporating Household Perceptions to Better Understand Behaviors Table 1 Table 2 Table 3 Table 4 Table 5

Characteristics of Canadian population and survey respondents Descriptive statistics Results for hurdle model of perceived flood risk Results for non-insurance flood risk mitigation models by respondent with zero and non-zero perceived flood risk Results for insurance models by respondent with zero and non-zero perceived flood risk

99 105 107 109 112

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LIST OF TABLES

Water Stewardship—Bridging the Knowledge and the Financial Gaps Table 1 Table 2

Types of financing mechanisms Water Stewardship initiatives, labels, and certification

125 141

Measuring Water Risk: The Challenges for Passive Index Investment Table 1 Table 2 Table Table Table Table Table

3 4 5 6 7

Maximum and minimum reported raw water metric data (December 31, 2021) Maximum and minimum reported water utilization intensity (December 31, 2021) TSC Water Security Index selection pool criteria TSC Water Security Index industry activity exclusion list TSC Water Security Index performance characteristics Water Footprint and carbon footprint percent reductions TSC Water Security Index historical sector exposures

200 200 205 205 209 210 211

Making Water Count—Integrated Risk-Return and Knowledge-Based Models for Water Investments Table 1 Table 2

Instruments used in the innovative approach of Aqua for All SIINC metrics—Application to KWSH

286 293

Water Risk in Real Estate: An Introduction to the Climanomics Platform Table 1 Table 2

Summary of water-related physical hazards in climanomics Descriptive statistics

318 323

The Water Credit Risk Tool and Corporate Sensitivity to the Shadow Price of Water Table 1

Table 2

Operational sensitivity of EBITDA/Revenue (E/R) and net debt/EBITDA (N/E) ratios to not including (n) and including (w) SPW 2013 for the financial years (FY) 2013 and 2020 Price sensitivity of EBITDA/Revenue (E/R) and net debt/EBITDA (N/E) ratios to the SPW 2013 and SPW 2020

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LIST OF TABLES

Table 3

Sensitivity of EBITDA/Revenue (E/R) and net debt/EBITDA (N/E) ratios to low (LO) and high (HI) projections of the SPW 2040

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Introduction

Water Risk-Return Modeling and Management: Threats and Opportunities Dieter Gramlich, Thomas Walker, Charlotte Esme Frank, and Maya Michaeli

This edited book “Water Risk Modeling: Developing Risk-Return Management Techniques in Finance and Beyond” addresses the challenges posed to financial modeling by a rapidly changing water environment. Modeling challenges arise both from the translation of the multifold physical and transitional effects of water risk into a monetary framework, and from the evaluation of water risk for financial assets and

D. Gramlich DHBW – Baden-Württemberg Cooperative State University, Heidenheim, Germany e-mail: [email protected] T. Walker · M. Michaeli Concordia University, Montreal, Canada e-mail: [email protected] M. Michaeli e-mail: [email protected] C. Esme Frank (B) McGill University, Montreal, Canada e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_1

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D. GRAMLICH ET AL.

institutions. This book provides a series of frameworks aimed at guiding the assessment of water-related opportunities and threats and offers a variety of strategies for coping with these challenges. The underlying assumptions are, first, that water-related risks exist in many dimensions and will further increase in the mid and long term; second, that changes in the hydrological and meteorological systems interact with multiple related ecological, economic, and social systems creating feedbacks and feedforwards with the financial system; and third, that changes in the water environment should not only be considered as negative or dangerous; rather, they also provide opportunities to prepare for situations which could pose potential risks. The term water challenges is thus a synonym for water-related opportunities and threats.

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Water-Related Challenges

Water-related challenges have recently emerged as a major risk (WEF, 2021). The decreasing availability and quality of water as well as an increase in water-related extreme events (droughts, floods) represent significant threats to societies, the environment, and the economy. Changes in hydrological and meteorological conditions, with subsequent effects on the natural environment as a whole, affect humans’ nutrition and health, and impact production chains and our way of living (Howard & Livermore, 2021). Water risks also influence the financial value of impacted assets. In 2022, the Carbon Disclosure Project (CDP) reported a potential water-related value at risk of about US$ 225 billion for 499 top public-listed companies reporting through the CDP (CDP and Planet Tracker, 2022, p. 4). In the age of climate change, the economic and social consequences of water risk and their interaction with finance can only increase. The lack of access to freshwater for industrial and personal use, as well as the effects of droughts, floods, and water contamination, are not new problems, yet they are amplifying in the face of climate change, population growth, and rapid economic development (Veldkamp et al., 2015). The industries most exposed to water scarcity are agriculture, energy, mining, and manufacturing (Ceres & GIWS, 2022). A recent report from China Water Risk (CWR) indicates that the sea-level rise will seriously hit

WATER RISK-RETURN MODELING AND MANAGEMENT: …

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the largest twenty cities in Asia (CWR, 2020).1 Moreover, because of deteriorating economic and social conditions, the United Nations (UN) expect that water scarcity could displace 700 million people by 2030 (UN, 2020).

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Interactions

The basic interactions between water and the economy have been well documented (Russ, 2020; Zaveri et al., 2021). The economic impact of changes in water quantity and quality is particularly high when water represents the product itself or is a major ingredient of products (e.g., beverages), or when it is important in the production/cooling process of a product (e.g., mining, chemicals, etc.). Many companies use groundwater and rainwater within their own production processes (e.g., irrigation in agriculture) or depend on it via the production chain of their suppliers (cooling power plants for energy). Water also serves as a means of transportation, therefore low water levels in rivers and channels can restrict shipping. Finally, water risks may have simultaneous adverse impacts, such as when insufficient water quantity restricts the extracting of metal ore, and the deficiency cannot be corrected through higher water pressure, because the supply of electricity is also constrained due to the limited availability of cooling water. Generally, water is considered a “connector” (Rudebeck & Breslin, 2021, p. 8): water interacts with multiple ecological and social environments, and in that way impacts the economy. In a study conducted by the World Bank, the authors relate rainfall variability to migration rates (Zaveri et al., 2021). They use global data based on a gridded distribution of population and data on local rainfall deviation from the long-term mean. The authors find that (repeated) periods of low rainfall have an impact on the number of migrants originating from a region. The migrants base their decision on a comparison of income possibilities between industries and regions and the related patterns of rainfall. Particularly, workers from the rural sector are sensitive to income conditions, which vary depending on the intensity of rainfall. Water resources distributed across regions and countries typically involve cooperation to jointly use these resources. However, water is

1 See also the contribution of CWR in Chapter 8 of this book.

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also a driver of conflicts. Borgomeo et al. (2021, pp. 24–25) provide an overview of water-induced wars, particularly in the Middle East and North Africa. They see shared water resources as a matter of rivalry and as a driver of tensions between communities. The decreasing availability of water, mainly due to climate change, along with increasing populations and military power will further intensify existing tensions (Sustainable Fitch, 2022). In addition, wars that originate from water and other conflicts impact water resources, depriving people of access to sufficient and clean water (Sowers & Weinthal, 2021). This book focuses on the interactions between water and the financial system. Financial institutions may be directly affected by disruptions in the water environment if, e.g., floods or droughts impact the operations of local branches. More often, however, water risks affect financial assets and institutions indirectly by posing challenges to the local ecology, economy, and society (Dumont-Bergeron & Gramlich, 2021). Financial assets constitute claims on the value of companies, private households, and public entities, and may be jeopardized by water risk. Where water risk does not materialize directly as a physical risk for financial institutions, the physical risk for customers may cause a decline in the value of financial assets and higher probabilities of default. Given the indirect transmission of water risk across various entities, the assessment and modeling of financial water risk are highly context-sensitive and demanding (see Fig. 1).

Fig. 1 The complex nature of water risk: Interactions between ecology, economy, society, and finance

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7

Opportunities

In the face of threats, opportunities emerge. As outlined in Table 1, these include investments in water infrastructure to ensure a more efficient use of water, technologies to purify and recycle water, hydropower as a clean source of energy, as well as the development of instruments to protect against water-related exposures (Heiberg & Truffer, 2022; TNFD, 2022). As financial investors develop an interest in assessing both the vulnerability and resilience of companies and increasingly assimilate environmental risks and social responsibilities into their investment decisions, financial markets also become more sensitive to the sustainability of their investments. In fact, many activist investors argue that the current financial structure should evolve into a larger socio-ecological system where finance, social well-being, and planetary health are interlinked. Table 1 Opportunities and threats from water challenges (financial perspective) (own representation based on Dumont-Bergeron & Gramlich [2021]; OECD [2022]) Water challenges

Threats

Opportunities

Physical

Scarcity (local, regional); contamination; water-related extreme weather events (droughts, floods, blizzards); sea-level rise Restricting access to water; implementation of water markets

Investments into desalination and purification systems; investments into wastewater recycling plants; rainwater and stormwater harvesting Preparation for expected future regulation (e.g., water trading systems) Financial water derivatives; financial instruments to hedge against water risks; improvement of credit rating systems (incorporation of water risks); development of water stress tests Funding opportunities related to water affordability

Regulatory

Economic

Societal

Technical

Disruption of economic processes due to water scarcity; increase of water prices with effects on the value and default risk of companies and households Migration inside and between countries; political conflicts; water wars Outdated water pipeline systems; water leakages

Improvement of the pipeline system; digitalization to improve the efficiency of water usage and to reduce leakages; continued development of hydropower and fuel cell technology

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It is essential for multiple stakeholders to properly measure and manage water-related opportunities and threats. Water risk affects companies in the real economy as well as private households and governments. Given the many interactions of these stakeholders with the financial markets, financial investors must assess the risks related to their stakes in companies as well as their affectedness as financiers of private households and public authorities. In addition to material damages from the devaluation of loans, bonds, equities, derivatives, and insurance claims, financial firms must consider regulatory and reputational risks as these can affect costs and brand reputation, among other things. A major hindrance to holistically measuring and managing water risk is the slow development of appropriate frameworks. This edited book aims to shed light on the topic of water risk by examining the measurement and management challenges associated with financial water risk while considering the interaction between water risk, the real economy, society, and finance. It explores various approaches to operationalizing water risk from the perspective of financial investors and managers. Specifically, it (1) provides a collection of frameworks to assess the dimensions of water risk. These frameworks provide (2) the basis for the development of appropriate tools to develop quantitative water risk-return management techniques in finance and beyond. Approaches include both actions to protect against the threats of water risk, and measures to benefit from opportunities in the field. The book will also address this topic from a risk and return perspective of corporate financial managers. The contributions take into account recent developments in finance and apply these considerations to the assessment and management of water risk. Water risk creates unique challenges for individuals and institutions as well as for our society, which traditional risk management approaches are typically inadequate in addressing. Academics, policymakers, and practitioners are currently developing new tools and methods to include ecological, socio-political, and economic factors in the way they evaluate and make financial decisions. However, these concepts are still in their infancy, and many of the proposed ideas and approaches have been highly controversial. Our book fills a gap in the literature by providing new insights regarding financial water threats and their related opportunities. It follows a previous book from the editors on water risk and its impact on finance and society (Walker et al., 2021).

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9

Chapter Overview

The first part of this book presents a series of frameworks for water riskreturn modeling and management. In Chapter 2, Karsten Haustein and Quintin Rayer review human responsibility for the unprecedented, and soon to be potentially irreversible, global warming since the industrial revolution. Further, they examine the effects of this warming on the global water cycle. This chapter provides the geophysical and meteorological context for the subsequent contributions on the financial dimensions of water challenges. Navya Bhayana and Laureline Josset point to the risks posed to the global water supply by the textile industry in Chapter 3. Arguing that the textile industry affects the global water supply on both a global and local scale, the authors introduce a conceptual framework to understand the correlation between water and financial risks. They also discuss two survey instruments which they use to review the existing reporting frameworks, uncovering the frameworks’ inadequacies, and offering recommendations for their improvement. In Chapter 4, James Price and Diane Dupont seek to understand Canadian homeowners’ perceptions of flood risk, and their efforts to mitigate possible flood-related losses. They argue that not just in Canada but around the world, homeowners are increasingly vulnerable to their homes being flooded, and that this flood risk exposes the broader financial system to considerable risk. Particularly, they investigate the factors explaining the households’ risk behavior in the context of flood risks. Pratibha Singh, Nidhi Nagabhatla, and Karin Kreutzer posit water stewardship as a viable approach to ensuring that communities will still be allowed just and equitable access to water as water supplies dwindle. In Chapter 5, they argue that water stewardship incentivizes members of the private sector to manage water responsibly and allows them to mitigate water risks. In the final chapter of this first section, Chapter 6, Karsten Haustein, Quintin Rayer, and Pete Walton develop a framework for global warminginduced extreme weather and water investment risks. Such a framework, they explain, may be helpful for estimating investment losses based on firms’ historical carbon emissions, and furthermore allows for the sensitivity of losses to uncertain inputs to be explored for climate risk stress-testing.

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Part Two of this book pertains to strategies, mainly investment strategies, for water risk-return modeling and management. Markus Barth, in Chapter 7, describes the importance of standardizing the reporting of water data, such that companies and portfolio managers can assess and control their exposure to water risk with a tilt toward water security and good water stewardship. Statistical techniques are required to address this issue, and the presented approach creates a proper distribution from best to worst water risk at the company level. The indexes presented in the study have been used in practice and have demonstrated superior returns while generating a lower water and carbon footprint. In Chapter 8, Dharisha Mirando, Debra Tan, and Chien Tat Low explore the creation of an index which reflects sea-level rise (SLR) risks for four climate scenarios across key indicators (population, land area, and key infrastructure assets) for a number of major cities in Asia–Pacific; and also analyzes storm surge threats and subsidence. This index from China Water Risk also considers government adaptation actions to reduce physical risks, and the authors evaluate it through the support of over 100 finance professionals. Chapter 9, written by Nadja Franssen, points to the potential impacts of water risks on and opportunities for investment portfolios, and outlines the asset manager ACTIAM’s management of water risks. The strategy of water-neutral investment portfolios offers guidance to investors seeking to improve their understanding and management of portfolio water risk. The study further discusses water footprinting as a tool for measuring a firm’s progress toward water neutrality, with both water quantity and water quality being considered as factors. Josien Sluijs, Blanca Mendez, and Dieter Gramlich show how to bridge the service and financial gaps in the water and sanitation economies in Chapter 10. They present the strategy and models employed by the Dutch foundation Aqua for All which implements comprehensive approaches in order to enhance water access by supporting innovations and enabling small and medium enterprises (SMEs) to scale. Aqua for All, the authors explain, provides instruments to bridge the funding gap in investments for water, sanitation, and hygiene and at the same time offers technical assistance (capacity building) and de-risking to institutions. Furthermore, their integrative modeling enables financial return and social impact. In Chapter 11, Maya Michaeli and Isabelle Jolin argue that there is a growing demand for a better understanding of the implications of climaterelated risks, more specifically water risks, for the real estate market. They

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identify the necessity of relevant instruments to assist stakeholders in identifying and quantifying water risks as there is an inevitable increase in the occurrences and severity of events such as hurricanes, severe storms, and droughts, all of which are related to the increase in extreme climate volatility. Following that, the Climanomics platform as an approach to quantify climate-related (water) risk is introduced in detail. In conclusion, a case study is presented to demonstrate the advantages of considering climate risks in real estate investments, and how Climanomics can help to facilitate this analysis. Finally, Dieter Gramlich and Henrik Ohlsen, in Chapter 12, address the effects of the shadow price of water (SPW) as the total economic value of water. The authors present the Water Credit Risk tool developed from GIZ, NCD, and VfU and apply it to companies from three different industries. They analyze the sensitivity of these companies to alternative SPWs, thereby considering changes in the companies’ operational structure across time, changes of the SPW between two different points in time, and projections of the SPW into the future.

References Borgomeo, E., Jägerskog, A., Zaveri, E., Russ, J., Khan, A., & Damania, R. (2021). Ebb and flow. Volume 2: Water in the shadow of conflict in the Middle East and North Africa. World Bank. https://doi.org/10.1596/978-1-46481746-5 CDP – Carbon Risk Disclosure Project & Planet Tracker. (2022, May). High and dry: How climate issues are stranding assets. Ceres & GIWS – Global Institute for Water Security. (2022, April). Global assessment of private sector impacts on water. CWR – China Water Risk. (2020). CWR APACCT 20 index city factsheets. https://www.chinawaterrisk.org/notices/cwr-apacct-20-index-city-fac tsheets/ Dumont-Bergeron, A., & Gramlich, D. (2021). Introducing water risk: A framework for (integrated) water risk assessment and management. In T. Walker, D. Gramlich, K. Vico, & A. Dumont-Bergeron (Eds.), Water risk and its impact on the financial markets and society (pp. 1–19). Springer International Publishing. https://doi.org/10.1007/978-3-030-77650-3_1 Heiberg, J., & Truffer, B. (2022). The emergence of a global innovation system—A case study from the urban water sector. Environmental Innovation and Societal Transitions, 43, 270–288. https://doi.org/10.1016/j.eist. 2022.04.007

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Howard, P. H., & Livermore, M. A. (2021). Climate–society feedback effects: Be wary of unidentified connections. International Review of Environmental and Resource Economics, 15(1–2), 33–93. https://doi.org/10.1561/101.000 00129 OECD – Organization for Economic Co-operation and Development. (2022). Financing a water secure future. OECD Studies on Water, OECD Publishing. https://doi.org/10.1787/a2ecb261-en Rudebeck, T., & Breslin, S. (2021). Accounting for water in active ownership. Integrating assessments of water risks and impacts into equity portfolio management. Stockholm International Water Institute and Sweden’s Sustainable Investment Forum. https://siwi.org/publications/accounting-for-waterin-active-ownership/ Russ, J. (2020). Water runoff and economic activity: The impact of water supply shocks on growth. Journal of Environmental Economics and Management, 101, 102322. https://doi.org/10.1016/j.jeem.2020.102322 Sowers, J., & Weinthal, E. (2021). Health and environmental tolls of protracted conflicts in the Middle East and North Africa. Current History, 120(830), 339–345. https://doi.org/10.1525/curh.2021.120.830.339 Sustainable Fitch. (2022, July 15). Political risks and climate change: Where are the flashpoints? Sustainable Insight. TNFD – Task Force on Nature-Related Financial Disclosures. (2022, June). The TNFD nature-related risk and opportunity management and disclosure framework: Beta v0.2. https://framework.tnfd.global/wp-content/uploads/2022/ 06/TNFD-Framework-Document-Beta-v0-2.pdf UN – United Nations. (2020). Water and climate change: The United Nations world water development report 2020. https://en.unesco.org/themes/watersecurity/wwap/wwdr/2020 Veldkamp, T. I., Wada, Y., de Moel, H., Kummu, M., Eisner, S., Aerts, J. C., & Ward, P. J. (2015). Changing mechanism of global water scarcity events: Impacts of socioeconomic changes and inter-annual hydro-climatic variability. Global Environmental Change, 32, 18–29. https://doi.org/10.1016/J.GLO ENVCHA.2015.02.011 Walker, T., Gramlich, D., Vico, K., & Dumont-Bergeron, A. (Eds.). (2021). Water risk and its impact on the financial markets and society: New developments in risk assessment and management. Springer International Publishing. WEF – World Economic Forum. (2021). The global risks report 2021 (16th ed.). Davos. Zaveri, E., Russ, J., Khan, A., Damania, R., Borgomeo, E., & Jägerskog, A. (2021). Ebb and flow. Volume 1: Water, migration, and development. World Bank. https://doi.org/10.1596/978-1-4648-1745-8

Frameworks for Water Risk-Return Modeling and Management

Water Cycle Changes in a Warming World: The Scientific Background Karsten Haustein and Quintin Rayer

1

Introduction

Currently, climate change has raised average global temperatures by around 1.25 °C above pre-industrial levels. This is relative to targets set out in the Paris Agreement, with the more ambitious 1.5 °C target being considered the ‘safe’ limit, while 2 °C is the upper threshold which should not be crossed if we want to avoid the initiation of irreversible climate tipping points (UNFCCC, 2015). Relative to human inputs, the natural component of the temperature rise is very small. Essentially all of the 1.25 °C average global temperature rise is attributable to anthropogenic factors (Haustein et al., 2017). This fact should be a source of major

K. Haustein (B) Institute for Meteorology, Leipzig University, Leipzig, Germany e-mail: [email protected] Q. Rayer P1 Investment Management Ltd, Exeter, UK e-mail: [email protected] UK Centre for Greening Finance and Investment, Oxford, UK

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_2

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concern, as it means we only have 0.25 °C left of the margin recommended by the Intergovernmental Panel on Climate Change (IPCC) to avoid the worst excesses of global warming. Recent years have seen an accumulation of extreme weather and hydrological events relating to excessive heat, prolonged drought or intense rainfall and flooding (Emanuel, 2017; Kreienkamp et al., 2021; Otto et al., 2018; Promchote et al., 2016). One might wonder whether these events have been caused by climate change. The public, decision-makers or media commentators, who are concerned by these events, would like a straightforward ‘yes’ or ‘no’ answer. Is an individual extreme weather event with its concomitant damages and loss of life due to anthropogenic climate change or not? However, the question has to be more nuanced, as the answer is not simply ‘yes’ or ‘no’. Rather, it is a probabilistic statement about the altered risks of an event happening. So the question needs to be: ‘has climate change made a certain extreme event more or less likely?’ This is exactly what the relatively young field of extreme weather event attribution is trying to quantify. Already, considerable progress has been made to link human factors to meteorological disasters during the last decade or so (Eyring et al., 2021). To better appreciate the linkage, we refer the reader to Rayer et al. (2020), Rayer et al. (2021) and Chapter 6 in this volume. For those seeking a short answer, extreme weather and hydrological events are often (although not always) linked to global warming. While the details can be complex, for financial market practitioners familiar with theories for pricing options and derivatives, the process is not entirely dissimilar. That human activities have led to global warming (hence anthropogenic global warming), is based on well-established science with a long pedigree of over 200 years. As many readers are likely to be unaware of this long history of climate science, we review work commencing with Fourier from around 1807 in Sect. 2 below. The historical context provides a backdrop for our review of the current state of the climate sciences in general, and water and hydrological risks in particular. This section is accompanied by a discussion of the robustness of the understanding of the links between human activities and the climate. We commence in Sect. 3, by looking at water in ecosystems, the global water cycle and the fundamental thermodynamics that constrain it. This discussion leads to the consideration of changes in rainfall and intensity, and of the role of human activity in driving climate changes. Regional shifts in precipitation and the water balance and seasonal changes are

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also discussed. We round off Sect. 3 by looking at drought and groundwater levels. Having introduced the fundamentals of the global water cycle in a warming climate, we provide an overview of particular extreme hydrological events and associated water risks in Sect. 4. We discuss rainfall extremes, tropical cyclones and the attribution of such events to climate change. We complete Sect. 4 with a brief review of some specific events relating to drought, hurricane precipitation and flooding; including the European floods during the summer of 2021. In the final section (Sect. 5), we identify remaining gaps and challenges to the current understanding of extreme hydrological events.

2

Historical Background to the Climate Science

It may seem that human-induced warming of the planet is a recent phenomenon, but that is actually not the case. Climate science has developed over more than two centuries, with key results identified earlier than most may suspect (Rayer, 2022). So how long have scientists known about global warming, and when did their discovery first became evident? 2.1

Two Centuries of Climate Research

He may not have been the first, but in 1824, the French mathematician Joseph Fourier reported his investigations into the Earth’s temperature that started as early as 1807 (Hawkins & Jones, 2013). Pierrehumbert et al. (2011) felt that Fourier’s contribution was profound, highlighting that he introduced the problem of planetary temperature, thereby establishing a largely correct physical framework for global warming. Building on work from as early as 1807 (Fleming, 1999), Fourier effectively stated that the Earth’s temperature is increased by the atmosphere because it tends to trap the heat generated by visible light (Fleming, 1999): “…the temperature is augmented by the interposition of the atmosphere, because the heat has less trouble penetrating the air when it is in the form of light, than it has exiting back through the air after it has been converted to dark heat” (Fourier, 1824, 1827). In modern parlance, energy in the form of visible light passes through the atmosphere more easily than after it has been converted to radiant (‘dark’) heat. As a result, the atmosphere keeps the Earth warmer than it would have been otherwise (Pierrehumbert et al., 2011). By 1836, Claude Pouillet had concluded “the atmospheric stratum … exercises a greater absorption upon the terrestrial than on

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the solar rays” (Fleming, 1999; Pouillet, 1837). In modern terms, we would call the ‘terrestrial rays’ infrared or longwave radiation and ‘solar rays’ visible light or shortwave radiation. The inevitable conclusion of this imbalance is that the atmosphere traps heat in what is also known as the Greenhouse effect. An often-overlooked contribution was made in 1857 by Eunice Foote (Foote, 1857; Jackson, 2020). She sealed air in one glass cylinder and carbon dioxide (CO2 ) in another. When exposed to sunlight, the CO2 cylinder heated much more than the other. Foote commented that a CO2 atmosphere would give the Earth a high temperature (Foote, 1857). She had effectively demonstrated that CO2 was a greenhouse gas. Unaware of Foote’s work (Jackson, 2020), in 1859, John Tyndall worked on heat radiation through different gases (Tyndall, 1859). By 1861, Tyndall had identified that both water vapour and CO2 , as well as other ‘hydrocarbon vapours’, were transparent to light but trapped heat (Fleming, 1999; Tyndall, 1861). Tyndall’s experiments were much more precise than Foote’s. He discovered that CO2 was less transparent to infrared radiation than visible light. Tyndall had isolated the effect of infrared radiation on CO2 , whereas Foote used the full spectrum of sunlight. Between them, Tyndall and Foote identified many of the characteristics that would fit the modern definition of CO2 as a greenhouse gas. Tyndall commented that small changes in CO2 concentrations would produce great effects on terrestrial heating and corresponding changes of climate and the “mutations of climate which … geologists reveal” (Tyndall, 1861), although his main emphasis was on atmospheric humidity. However, we know today that CO2 is the principal control knob when it comes to global warming, with water vapour levels being the slave of CO2 -induced warming (Lacis et al., 2010). In essence, the average atmospheric concentration of water vapour is governed by thermodynamics in general and the hydrological cycle in particular (see details in Sect. 3). 2.2

The First Comprehensive Theory of Climate Change

In 1896 Svante Arrhenius directly explored how surface temperature is increased by rising concentrations of atmospheric CO2 (Arrhenius, 1896; Fleming, 1999). He quantified the contribution that CO2 concentrations could make to global temperatures and went on to question whether variations of atmospheric CO2 would be sufficient to account for longterm variations in climate. His discussion extended to putting atmospheric

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CO2 concentrations against the backdrop of the (then) global production of coal, which he described as “the industrial development of our time, which must be conceived of being of a temporary nature” (Arrhenius, 1896). Although his words may appear portentous to a modern-day reader, his context suggests that it was not how he intended them to sound. Arrhenius (1896) also lists the mechanisms by which CO2 might be expected to enter or be removed from the atmosphere. In short, Arrhenius provided the first comprehensive theory of climate change, effectively inventing climate modelling (Pierrehumbert et al., 2011). 2.3

The Earth’s Warming Is Due to CO2 from Fossil Fuels

By the 1930s, scientists had noted the rise in temperatures at the poles and the beginning of the retreat of Arctic Sea ice (Hilton, 2008). Building on Arrhenius’ work, in 1938, Guy Stewart Callendar (Callendar, 1938) concluded, “By fuel combustion man has added about 150,000 million tons of CO2 to the air during the past half century. The author estimates from the best available data that approximately three quarters of this has remained in the atmosphere”. Current estimates are that around half of the emitted CO2 remains in the atmosphere, with the other half being absorbed by the biosphere and the ocean (Pierrehumbert et al., 2011). Callendar estimated that the then ‘artificial production’ of CO2 was warming the global climate by 0.3 °C per decade (estimates of contemporary temperature increases are averaging 0.25 °C per decade). However, Callendar’s findings were largely dismissed by the scientific community until further work had been completed in 1941. While not immediately accepted at the time, Callendar’s temperature measurements are in remarkable agreement with modern analyses (Hawkins & Jones, 2013). One question which did remain open for longer was how quickly CO2 levels would rise given the increased emissions of greenhouse gases (GHGs), not just from burning fossil fuels directly, but forest clearance or general loss of biodiversity which otherwise create natural sinks for CO2 . Starting in 1957 Charles Keeling commenced collecting the data he published in 1960 which shows that the atmospheric concentration of CO2 was rising at a rate close to that expected from the combustion of fossil fuel (Keeling, 1960). Keeling later wrote “If the human race survives into the twenty-first century… the people living then… may also face the threat of climatic change brought about by an uncontrolled release in atmospheric CO2 from fossil fuels” (Keeling, 1970).

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This statement, in turn, started to raise the question of what would happen if atmospheric CO2 concentrations were to double from preindustrial levels of 280 ppm to 560 ppm. This metric, now known as Climate Sensitivity, can be deduced from variations seen during geological history. Early estimates of around 4 °C per CO2 doubling are remarkably close to the latest range provided by the 6th IPCC assessment report. Their best estimate is 3 °C with a likely range of 2.5 °C to 4 °C (IPCC, 2021). Additionally, we can estimate how much of the observed warming up until now is due to human interference with the climate system. Based on continuous temperature observations around the globe, we know that each of the last four decades has been warmer than any that preceded it since 1850. During 2011–2020, mean global surface air temperature (GSAT) was approximately 1.1 °C higher than during the 1850–1900 period, which is as close to pre-industrial conditions as possible. Land areas have warmed by 1.6 °C and oceans by 0.9 °C (IPCC, 2021). The attributable fraction of that warming is virtually 100%, i.e., all of the observed temperature increase since 1850 is caused by human burning of fossil fuels, which has released vast amounts of extra CO2 into the atmosphere. Therefore, the best estimate of human-induced warming during the 2011–2020 period is around 1.1 °C as well (IPCC, 2021). It is likely that well-mixed GHGs contributed a warming of 1.0 °C to 2.0 °C, whereas other human drivers (most importantly anthropogenic aerosols) contributed a cooling of 0 °C to 0.8 °C (equivalent to negative centigrade). Natural drivers have not contributed to warming over the past 170 years or so. Using the method introduced in Haustein et al. (2017), attributable warming levels in 2022 have already reached 1.25 °C compared to pre-industrial conditions. With the current rate of warming assessed to be around 0.25 °C per decade, the 1.5 °C warming target as highlighted in the Paris Agreement will likely be reached between 2030 and 2035. 2.4

The Final Pieces

To complete a quantitative estimate of the effect of CO2 on atmospheric temperature, three additional elements were required, which were provided by Manabe and Wetherald (1967). These were the radiative properties of water vapour, the effects of atmospheric convection on the atmosphere, and the top-of-the-atmosphere energy balance. Their groundbreaking work is described by Pierrehumbert et al. (2011) as

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the first fully sound estimate of the warming that would arise from a doubling of CO2 . Later developments by Manabe and Wetherald (1975) demonstrated that global mean precipitation increases as the world warms, pointing towards the maturing discipline of climate changeinduced extreme weather event attribution seen today. Ironically, their predictions have already turned into observable evidence of changes in the water cycle and associated rainfall extremes. Globally averaged precipitation over land has increased since 1950, with a faster rate of increase since the 1980s. There is also a growing body of evidence that human influence has contributed to the pattern of observed precipitation changes since the mid-twentieth century (IPCC, 2021). These changes are accompanied by changes in ocean salinity, deep ocean warming, sea level rise, changes in atmospheric circulation pattern, the retreat of glaciers as well as the melt of the Arctic ice sheet and the loss of substantial ice mass from Greenland and Antarctica (IPCC, 2021). Thus, we can conclude that the scientific community has been making significant inroads into comprehending climate change since 1807 (Fleming, 1999), with the first comprehensive theory in 1896 (Arrhenius, 1896). By 1938, fossil fuel emissions had been linked to global warming (Callendar, 1938), although it was not until 1941–1960 that this theory became more widely accepted (Keeling, 1960; Plass, 1956), and quantified (Manabe & Wetherald, 1967). Humanity has known about global warming since the 1960s, if not earlier. Today, human influence has already warmed the climate at a rate that is unprecedented in at least the last 2000 years. One can only regret that timely warnings from scientists were not acted upon before the global climate was verging on a crisis. Yet it is not too late to avoid most critical global and regional climate tipping points (McKay et al., 2022).

3

The State of the Science

The coupled atmosphere–ocean system can be considered a global-scale heat engine which moves heat from the equator to the poles. The enhanced GSAT arising from global warming means that the ‘global heat engine’ is likely to run with greater intensity, with potential for stronger storms (Laliberté et al., 2015; Peixoto & Oort, 1992; Zielinski, 2015). This analogy extends to the global water cycle, as increased evaporation leads to enhanced latent heat release into the atmosphere, and is associated with more water vapour in the atmosphere.

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3.1

Water in the Ecosystem

Water is vital to all life on Earth. Approximately 70% of the planet is covered by water, with the bulk accounted for by the oceans (97%). Non-saline terrestrial freshwater from lakes and glaciers represents less than 2% of all water on Earth, with the remainder ( 5% per year from now on until net carbon-zero conditions are achieved (which would take ~ 45 years) to reach the 1.5 °C temperature target, it is extremely unlikely that this goal can be met without the implementation of major carbon removal mechanisms (Leach et al., 2018). The UN Framework Convention on Climate Change’s (UNFCCC) Paris Agreement acknowledges that even in the case of stabilizing global temperatures below the target of 1.5 °C and well below 2.0 °C above preindustrial levels, the impacts of global warming will continue to be felt. Such impacts will include consequences such as more frequent extreme weather events (UN FCCC, 2015). On a planetary scale, this is unsurprising. If the atmosphere–ocean system is considered a global-scale heat engine, the greater temperatures which arise from global warming will lead to the “global heat engine” running “faster” or with greater intensity, and with subsequent potential for stronger storms (Laliberté et al., 2015; Peixoto & Oort, 1992; Zielinski, 2015). This analogy is also applicable from a global water cycle perspective, as global warming leads to more energy available to be transported via latent heat in the atmosphere. Accordingly, hydrological risks are considerably higher than they used to be, mainly because of the increased water holding capacity of warmer air. Not only does that mean that there are more dry days, but also that there

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is an expected increase in extremely wet days (Vogel et al., 2020). There is observational evidence that extreme precipitation has already intensified in Europe (Zeder & Fischer, 2020). By the same token, tropical cyclones can become more intense as evident in an increase in Atlantic tropical cyclone season activity over the last 40 years or so (Pfleiderer et al., 2022). Yet, dynamical aspects often lead to counterintuitive and regionally heterogeneous results. For example, concurrent dry and wet spells are one possible manifestation of AGW (De Luca et al., 2020). The cooccurrence of extended dry spells and heavy precipitation events during the warm season—all while mean precipitation is seemingly unchanged— is another form of a climate change related increase in risk across many densely populated areas in the Northern hemisphere (Hoffmann et al., 2021). Agricultural droughts and hydrological disasters such as flash floods or river flooding are essentially two sides of the same coin, virtually emerging at the same place and time. Another example of climate changerelated risk is the reduced latitudinal temperature gradients due to Arctic (temperature) amplification, which have the potential to slow the jet stream down and cause more severe winter weather in the mid-latitudes (Cohen et al., 2018). While not all types of severe weather are expected to change (e.g., tornadoes or large hail events), the trend toward more EWH events due to global warming is indisputable. Such events include the number of the most intense tropical cyclones. Most of these events are straightforwardly attributable to well-understood thermodynamic changes in the atmosphere. As alluded to above, possible changes in atmospheric circulation patterns can cause additional harm, which is particularly relevant when one takes into consideration the large uncertainty associated with changing dynamics, and how these hamper a reliable risk assessment. 2.2

Corporate Emissions and Responsibilities

The “corporate activities” branch of Fig. 1 commences with the selection of an organization (or class of organizations) and their associated historical accumulated GHG emissions. These cumulative emissions are considered against total global human historical emissions to estimate their contribution to the whole. GHG emissions cause harmful changes to the Earth’s delicate climate balance. When evaluating the proportion of climate damages attributable to an organization, it is also necessary to consider how aware that organization was of the harm they were causing,

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and what steps the organization has taken to reduce or prevent past or future potential harm. Regrettably, once the harms caused by GHG emissions became known, some organizations sought to evade responsibility, rather than reduce or prevent future and existing harms (Oreskes & Conway, 2010). For example, in the 1950s the American Petroleum Institute (API) was supporting research on the concentration of atmospheric CO2 originating from fossil fuel combustion, with industry leaders being warned of the dangers of CO2 accumulation in 1959 (Franta, 2018). There is also evidence that the API was publicly downplaying the threat of climate change in 1980 (Franta, 2021). We also consider the producer versus user share of responsibility (scopes of emissions1 ) for products that generate climate-damaging emissions. Such aspects can be informed by the comparison of industrial to non-industrial emissions, and furthermore also point to the need for a societal judgment, which can often be informed by moral considerations. 2.3

Hypothetical Climate Liability

Bringing together the EWH event damages attributable to AGW, with a company’s share in responsibility for emissions, results in the organization’s hypothetical climate liability (HCL) for the event, or class of events, in question. The HCL is usually stated as a monetary value (typically in billions of US dollars) in inflation-adjusted terms to a specific date. To increase salience for investors and other parties, it can also be expressed as a fraction (or percentage) of the market capitalization or enterprise value of the emitting firm. While a firm’s market capitalization is based on its listed stock market equity and may be useful for shareholders, the enterprise value of a firm also includes its borrowing, and so may be of interest to investors in the corporate bonds of the firm, or to a company’s lenders. Monetary HCLs in US dollars may be helpful for those considering reparations, policy developments, or litigation for damages. HCLs 1 Scope 1 emissions, or direct emissions, originate from sources that are owned and controlled by a company, including, for example, fuel used by company vehicles. Indirect emissions are covered by scopes 2 and 3; scope 2 emissions result from energy used by a company, including electricity, steam, heating, and cooling, while scope 3 emissions cover all other indirect emissions arising due to company activities. Scope 3 emissions also include upstream and downstream value chain emissions, including those of suppliers and customers using their products (Greenhouse Gas Protocol, 2022).

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expressed as a percentage of the emitter’s value (whether market capitalization or enterprise value) may be useful for financial analysts assessing climate investment risks, and investors including institutional investors and pension funds.

3 Attributing Damages from Extreme Climate Events This section details the steps involved in the calculation of the “climate” branch of Fig. 1. The sub-sections below deal with climate change attribution, financial damage estimation, inflation adjustment, increased frequency, or intensity of events due to AGW, proportion of damages attributable to AGW, and for the event in question, the damages attributable to AGW. 3.1

Climate Change Attribution

The first step is to identify which EWH event (or class of events) is to be considered. In Fig. 1, this event is referred to as “extreme weather event [3.1]” (referring to this section). As noted by Otto (2017), it is the impacts of an event that often determine its definition as an extreme event. Aside from the specific examples for extreme events provided above, there are other examples of recent extremes that have led to major economic impacts: the 2010 Western Russian heatwave (the warmest July since at least 1880) (Dole et al., 2011; Otto et al., 2012; Rahmstorf & Coumou, 2011; Schiermeier, 2018); the 2020 Siberian heatwave (with a record-breaking temperature of 38 °C within the Arctic Circle) (Ciavarella et al., 2020). In terms of flooding, events include Hurricane Harvey (van Oldenborgh et al., 2017), and the 2011 Thailand flooding (Gale & Saunders, 2013; Promchote et al., 2016; van Oldenborgh et al., 2012). As an example of drought, Rayer et al. (2021a) mention the 2015 to 2017 Western Cape province of South Africa drought and possible “day zero” in Cape Town (Otto et al., 2018b). More generally, the changing intensity, frequency, and duration of regional heatwaves from a global perspective have been discussed by Perkins-Kirkpatrick and Lewis (2020). They found that heatwave frequency demonstrates the most rapid and significant change in almost all regions.

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In these examples, events have been identified in which the role of AGW is suspected. Detailed analyses are then required (such as those above) to estimate whether climate change did play a role, and how weather or water cycles were affected and to what degree. However, such events may have occurred before the onset of global warming. AGW’s role may have been to alter the probability of an event of a given magnitude, or to change the intensity of an event relative to what would have occurred without AGW. Such aspects are explored in later sections. For this step, it is sufficient to clearly identify the EWH event or class of events under consideration. In Fig. 1, we illustrate the process based on Rayer et al. (2021a)’s analysis of floods and droughts over the 2012–2016 period. 3.2

Financial Damage Estimation

Having identified an event (or class of events) in Fig. 1, the financial damages must be determined. Typically, these damages may arise from insurance company claims, or from payments made to address the consequences of the event, by bodies such as local and national authorities. These damages will be in contemporary monetary values at the period of assessment or disbursement, typically valued at millions or billions of US dollars. Rayer et al. (2020) take damages estimates from National Hurricane Centre (2018), and Blake and Gibney (2011), while Rayer et al. (2021a) cite EM-DAT (2020). One problem with damage estimates is that it takes time for them to be assessed. For example, in preliminary work, Rayer and Millar (2018a, 2018b) used the initial damage estimates of $200 billion reported in TIME magazine (Johnson, 2017), which were later revised to $265 billion (Rayer et al., 2020). In some cases, true damages may never be known (loss of development potential). In many cases, monetary damages represent an inadequate measure of the harms caused, which may also include, for example, lives lost, and fragile ecosystems destroyed. In Fig. 1, for 2012–2016 global floods and droughts Rayer et al. (2021a) cited damages from EM-DAT (2020) of $256 billion.

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Inflation Adjustment

The reported damage estimates will typically be in monetary values at the time of assessment or disbursement. For later analyses, it may be necessary to allow for any change in purchasing power in the interim. Alternatively, it may be useful for comparison for monetary value to be presented at a common date. For classes of events that have taken place over an extended period, it may be necessary to allow for inflation during that period to be able to present a single monetary estimate of damages incurred. Rayer et al. (2020) present damages for a single year in 2017 inflationadjusted US dollars. For global flood and drought damages over the fiveyear period 2012–2016, Rayer et al. (2021a) note that the total damages each year, as accrued, were $256 billion. To allow for inflation over the five-year period, they used World Bank global inflation rates (The World Bank, 2020) to adjust this figure to $265 billion in 2016 US dollars, as shown in Fig. 1. Global inflation figures may not accurately reflect inflationary conditions in the localities where the EWH events have occurred. Differences between global and local inflation could also exacerbate recovery from extreme events. Inflation figures are also averages across baskets of goods. As different products’ prices will experience different price changes, the inflation experienced by goods that differ from those used to calculate reported inflation figures will differ from such figures. Over short periods of time, the resulting inaccuracies may be relatively small, but over longer periods, additional or specific inflation data may be required relating to the specific products and localities affected. This may mean that rather than relying on broad inflation estimates across the global economy, it would be necessary to research appropriate inflation adjustments in detail. 3.4

Quantification of the Climate Change Fingerprint

For inflation adjusted EWH event damages, the next question to ask is to what extent event damages can be attributed to AGW. As observed by Allen (2003), the saying “climate is what you expect, weather is what you get”2 is incorrect when it comes to the attribution of EWH events. “Climate” means “possible weather” in a statistical sense (Allen, 2003). 2 This quote and close variants on it are variously attributed to Mark Twain (1887, “Climate lasts all the time and weather only a few days”), Andrew John Herbertson

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The climate determines a range of possible weather outcomes, envisaged as a statistical distribution, which includes the statistically expected weather and its variability for the time of year. The magnitude of a weather event outcome (e.g., rainfall) can be mapped as a statistical distribution which indicates the probability of levels of (say) rainfall from low to high. Typically, such distributions are (very approximately) bell-shaped curves, with the central outcomes occurring much more frequently than extreme low or high outcomes (extreme rainfall, for example, follows a Gumbel distribution). Considering rainfall, an extreme rainfall event would be when rainfall is above a certain threshold, a level high enough to overwhelm infrastructure, cause flooding, structural damage, or loss of life. In terms of the rainfall probability distribution, the likelihood of an extreme rainfall event is given by the area under the probability distribution that is above a critical level. The rainfall distribution can be seen as a manifestation of climate. An effect of AGW on climate is that increasing global temperatures may shift the entire rainfall distribution to higher rainfall levels or broaden it to include higher rainfall levels. Harrington and Otto (2018) illustrate this idea in the context of temperatures. Thus, if rainfall is above some critical level, it would be considered “extreme.” As AGW shifts the rainfall distribution, the probability of rainfall above the critical “extreme” level may be increased. Alternatively, a manifestation of a dry extreme event (rainfall below some critical level) could result in a hydrological drought where upper-level soil moisture can essentially become completely depleted, pushing it below the critical level for river flow, farming, and vegetation in general. The changes in probabilities of such EWH events can be rigorously quantified by established attribution techniques (Otto et al., 2018a; Philip et al., 2020) which allow for formal probabilistic attribution statements (Allen, 2003; Stott et al., 2004). Following Allen (2003), if AGW has trebled the risk of some event X compared with the state of the preindustrial climate, as the risk has increased from 1 to 3, in a probabilistic sense, AGW is “responsible” for two-thirds of the risk of X occurring. This is not to say that X “was caused” by AGW, but that AGW made it more likely.

(1901, “Climate is what on an average we may expect, weather is what we actually get”), and Robert Heinlein (1973, “Climate is what we expect, weather is what we get”).

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As in Rayer et al. (2020) and Rayer et al. (2021a), we refer to a comparison with the state of the climate during pre-industrial times, commonly the 1850–1900 period. Because of AGW, EWH events do often occur with altered frequency in a statistical sense, alongside associated changes in losses and damages. This need to allow for the pre-industrial baseline is often expressed as a factual versus counter-factual Earth in climate science literature (Otto, 2017; Yiou et al., 2017). The “factual” Earth has the climate we experience, while the “counter-factual” Earth experiences climate as if AGW had not occurred. Event attribution studies require the study of extreme weather and climate-related events, as they would have been in a world without anthropogenic influence (Otto, 2017; Otto et al., 2018a; Philip et al., 2020). As such direct observations are not possible, studies depend on physical and statistical climate modeling and are conditional on the assumption that the model reliably simulates the EWH event in question. Otto (2017) gives a summary of different methods of EWH event attribution. Approaches include analysis of the increase in intensity of a type of event, or the frequency of occurrence for an event of a given magnitude. We focus on risk-based approaches, which consider the frequency of occurrence of an event of a given magnitude. Each EWH event is unique, as it results from a unique combination of natural and human-induced drivers, climate variability, and noise. Due to these facts, no one event can be solely attributed to climate change alone, and each must be assessed in a statistical way to determine whether the chances of an event occurring have changed. The risk-based attribution approach simulates possible weather under current climate conditions to identify the likelihood of occurrence of an event in question in today’s climate and compares it to the likelihood of occurrence of the same event in a counterfactual climate with AGW “removed.” The counterfactual climate may be generated from computer simulations, or else a historical period may be used to estimate possible weather in a world where the anthropogenic influence is thought to be minimal. To assess changes in risk associated with climate forcing by AGW, Otto (2017) suggests focusing on the return time of the event in question. Following Allen (2003), if an event had a 100-year average return period, but now occurs every 33 1/3 years, its frequency has increased threefold. For this step, the analysis is dependent on research carried out and published in the climate attribution literature (e.g., Philip et al., 2020). For hurricane Harvey, Rayer et al. (2020) cited Emanuel (2017)’s study

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which concludes that the frequency of Harvey-like precipitation intensities had increased from 1-in-100-year events since the late twentieth century to around 1-in-16-year events in 2017, a sixfold increase in frequency. Considering global floods and droughts, Rayer et al. (2021a) included sources such as Ciavarella et al. (2020) which state that the 2020 Siberian heatwave had become at least 600 times more likely because of AGW; and Otto et al. (2018b) who conclude that the prolonged lack of rain causing an acute water shortage in Cape Town leading to the possibility of “day zero” in 2018 (when the city’s water supply would run dry), had its likelihood increased by a factor of 3.3 due to AGW. 3.5

Proportion of Damages Attributable to AGW

The previous section has explored how AGW has resulted in the increased frequency or intensity of EWH events. Some elements of the costs associated with such events are due to AGW, but to what extent? Rayer et al. (2021a) define FAGW , the fraction of the impact of EWH events that can be attributed to AGW. Rayer et al. (2020) effectively use FAGW , without the terminology. Rayer et al. (2021a) define FAGW in terms of either frequency or intensity. As an increase in frequency, if some damaging EWH events were to occur six times as often because of global warming, then there would be an increase in frequency from one to six per unit time, resulting in FAGW = (6 − 1)/6 = 0.83. In this form, FAGW is equivalent to Allen (2003)’s fraction of attributable risk (FAR) (Otto, 2017; Yiou et al., 2017). Following Stott et al. (2004) and Yiou et al. (2017), we define FAGW = F A R =

p1 − p0 p1

The yearly probability of the EWH event occurring in the factual climate, as impacted by AGW, is p1 . The yearly probability of the event occurring in the counterfactual climate is p0 (no AGW). If an event occurs six times more frequently because of AGW, then p1 = 6 p0 , and FAGW =

6−1 6 p0 − p0 = 0.83 = 6 p0 6

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We could also take an earlier period as the counterfactual climate when AGW is small. Suppose an event used to occur (on average) with a 1-in100-year return period during the pre-industrial era but is now assessed to have a return period of 1-in-16 years, then (on average) p0 = 1/100 per year, and p1 = 1/16 per year. Thus, FAGW =

1 16

− 1 16

1 100

=

16 1 − 100 84 = = 0.84 1 100

Rayer et al. (2021a) consider FAGW not only in terms of altered frequency, but also in terms of changed intensity of an event due to AGW. On further reflection, we now consider an approach based on changed intensity not only to be problematical for the estimation of the proportion of damages attributable to AGW, but also to complicate matters, seeing as both frequency and intensity changes are closely intertwined. EWH event damages depend not only on the event intensity, but also on the robustness of the affected infrastructure, including its exposure, sensitivity, and vulnerability to the event in question. AGW may significantly increase the intensity of one type of event, however if existing infrastructure can adequately resist the event, no damage may be caused. In another instance, AGW might only modestly increase the intensity of an event, but if that modest increase is sufficient to overwhelm (say) flood defenses, the outcome could be catastrophic. In one locality, river levels resulting from a doubling of rainfall intensity might be contained by flood defenses, in another locality, a 10% increase in rainfall might overtop defenses and lead to significant flooding. Thus, the damages caused by an increased event intensity could be highly nonlinear. As a result, for future applications of our framework, we recommend that FAGW be interpreted based on an increase in frequency or probability (or equivalently a reduction in return period) to make it equivalent to FAR. The strength of the probabilistic approach is that the question asked is of the form “for this event, as it actually occurred, and which caused these damages, how much more likely was it to occur because of AGW?” By phrasing the question in this way, questions as to the state and robustness of current infrastructure and the damages caused are automatically addressed, without having to resort to estimates linking increased intensities to increased damages, or whether infrastructure would have been overwhelmed or not. We note that some authors, such as Grinsted et al. (2019) address climate liabilities in terms of “normalized damages.” These approaches

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may have merit for other applications, but we feel they would not be appropriate within our HCL framework. For climate change attribution, we focus firmly on physical changes in water cycles and weather caused by AGW. Once physical climate changes are valued, the resulting monetary sums, when considered over the extended periods relevant to AGW, become subject to considerations such as inflationary revaluations (in monetary terms), the value of investments in infrastructure over the period, the fragility or increased robustness of such infrastructure, and so on. As an incomplete analogy, when comparing “equivalent” storms between (say) 1900 versus 2020, we feel it more appropriate to compare the number of “roofs blown off” from “historically equivalent” houses, between 1900 and 2020, rather than to take the cost of roof repairs (in USD say) in 1900 and compare it with the USD cost of roof repairs in 2020. Effectively, we feel that the number of “historically equivalent” roofs is a better measure of changes in physical weather activity (and therefore of physical climate) than changes in the resulting repair costs which are subject to many human, economic, and societal inputs unrelated to climate. Figure 1 uses the value of FAGW = 0.79 from Rayer et al. (2021a). 3.6

Event-Specific Damages

Our conclusions from Sect. 3.3 point to the inflation-adjusted event damages, to give a monetary value at a specific date. From Sect. 3.5, we have the proportion of the event damages attributable to AGW. The next step is to bring these together and estimate the damage estimate caused by AGW for a specific event (or class of events). Following the approach used in Rayer et al. (2020) and Rayer et al. (2021a), we use FAGW directly as the proportion of EWH event damages that would be attributed to AGW. Thus, FAGW = 0.83 implies that 83% of recent occurrences of the event in question are due to AGW, so 83% of damages are attributable to human emissions. This probabilistic evaluation of damages is akin to that used in other areas of finance, for example, in the valuation of financial options. Suppose an option is to be valued by a simple one-step binomial model. Let us suppose outcome A has a probability of 1/6 and a payout of nothing, while outcome B has a probability of 5/6, with a payout of $100. The option value would be based on an expected value of 16 × $0 + 56 × $100 = $83.33. Many options have both payouts and liabilities that are valued

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based on statistical probabilities, for example see Hull (1999) or Natenberg (2014) and others. Thus, it does not seem inappropriate to value climate damages in a similar manner, which approaches based on FAGW (interpreted as FAR) allow us to do. For an EWH event, we are interested in damages above those that would have been caused on a probabilistic basis in the absence of AGW. We consider annual probabilities of the event occurring without AGW ( p0 ) and with AGW ( p1 ). Consider some event causing damages D. In the counterfactual world, each year there is probability p0 of damage D, and probability (1 − p0 ) of no damage. The annual expected damages without AGW are d0 = p0 D + (1 − p0 ) × 0 = p0 D. In the factual world (with AGW), the annual probability of damage D is p1 , with probability (1 − p1 ) of no damage. The annual expected damages with AGW are d1 = p1 D + (1 − p1 ) × 0 = p1 D. When an EWH event occurs in the factual world (with AGW), the additional expected damages due to AGW (d1 − d0 ), as a fraction of the expected damages in the factual world (d1 ), are ( p1 − p0 ) (d1 − d0 ) ( p1 D − p0 D) = = = FAGW d1 p1 D p1 If none of the expected damages for the EWH event were due to AGW (fraction 1 − FAGW ), the additional damages due to AGW are zero. Thus, for some event, the expected liability is (1 − FAGW ) × 0 + D FAGW = D FAGW Considering Fig. 1, if the EWH event has inflation-adjusted damages of $265 billion, and FAGW = 0.79, then the damages attributable to AGW are D FAGW = $265bn × 0.79 = $209.35bn. As AGW is caused by total global cumulative anthropogenic GHG emissions, this figure reflects the damages relating to total global cumulative anthropogenic GHG emissions for the EWH event in question. We consider what share of responsibility should be assigned to an individual emitter (or collective emitters) in the “corporate activities” leg of Fig. 1.

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4 Assigning Corporate Responsibilities for Emissions Having determined the event damages attributable to AGW in Sect. 3 (“Climate” in Fig. 1), we consider the role of an organization’s emissions (typically a company, hence “corporate” emissions, but strictly any organization or group of organizations). In the “corporate activities” leg of Fig. 1, we consider how responsibility could be assigned for such emissions. The sections below cover the identity of the organization(s) and their cumulative emissions, the proportion of global anthropogenic emissions these represent, and the potential share of climate damages these might represent. Having established the damaging effect of humans’ cumulative carbon emissions on the Earth’s delicate climate balance over time, there is a moral dimension to these seemingly neutral emission numbers. Factors such as knowledge of the climate damage caused, steps taken to reduce or prevent harm, or, alternatively, to evade responsibility, become important. So too may be the extent of the period of time companies knowingly ignored the problem. Additionally, as emissions are generated both directly by an organization itself, as well as through its clients’ use of its products, the division of responsibility with clients, and potentially more widely within society, must also be taken into consideration. For fossil fuel extraction companies, the question of producer versus user apportionment of responsibility is particularly important. Combined with the moral aspect above, estimation of an organization’s share of emissions responsibility must incorporate a wider societal judgment, which will often be informed by the moral considerations to which we have just alluded. 4.1

Who Are the Emitters?

All anthropogenic GHG emissions contribute to AGW. However, analyses are likely to focus on specific (typically high emitting) organizations. One challenge to such analyses is the availability of historical as well as current emissions data. As an increased focus on achieving net-zero emissions develops, it is likely that such data will increasingly become available. Much focus has (rightly) been on firms involved in high emission activities

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such as those involved in fossil fuel production. The historical accumulated emissions of these firms are well-documented, for example see Griffin (2017) or Heede (2019). While in previous analyses Rayer et al. (2020) and Rayer et al. (2021a) have focused on groups of companies, the approach presented here can readily be applied to individual organizations, providing the necessary data is available. Following Rayer et al. (2021a), Fig. 1 focuses on the accumulated historical emissions of nine fossil fuel extraction companies over the period from 1751 to 2017. 4.2

Organizations’ Proportion of Total Emissions

Once an organization (or group of organizations) has been identified, together with the historical accumulated emissions, the next step is to place these emissions in the context of total anthropogenic emissions. Some cumulative emissions studies, such as Griffin (2017) and Heede (2019), include the proportion of total anthropogenic emissions represented. In other cases, it may be necessary to obtain cumulative emissions for the organization in question and compare these emissions with the cumulative total of anthropogenic emissions. As a tautological statement, the totality of current AGW must be generated by the totality of historical anthropogenic emissions to date. While some GHG emissions “decay” to become less harmful to the climate, AGW is caused by those that do not. Furthermore, before they decayed, short-lived climate pollutants contributed to the state of the current climate. Several crucial GHGs are also extremely long-lived, particularly CO2 , and play a critical role in AGW. We conclude that the proportion of total cumulative anthropogenic emissions that an organization’s historical cumulative emissions represent, is a key measure of that organization’s contribution to AGW. Following Rayer et al. (2021a), we accept that estimating global warming from a range of different greenhouse gases, including CO2 , CH4 , and N2 O, is complex; it is not straightforward to determine the effects of various greenhouse gases, with different atmospheric lifespans, upon the climate (Skeie et al., 2017). Consequently, we focus, to be pragmatic, primarily on cumulative CO2 emissions because these are the primary cause of global climate system changes (Allen, 2016). This

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approach allows for a relatively straightforward assignment of historical responsibility. In Fig. 1, based on Heede (2019), Rayer et al. (2021a) noted that between 1751 and 2017, the nine firms of interest collectively accounted for 14.5% of accumulated Scope 1 and 3 emissions. Ideally, their analysis should have included Scope 2 emissions as well, however considering the nature of the fossil fuel companies’ activities, this omission may be considered to be relatively minor. Heede (2019) justifies omitting Scope 2 emissions based on tracking the lithospheric carbon extracted by these firms and ultimately released to the atmosphere, as these companies’ primary focus of activity. 4.3

Share of Climate Damages

We identified the proportion of total cumulative anthropogenic emissions that an organization’s historical cumulative emissions represent as a key measure of that organization’s contribution to AGW. Following Allen (2016), we focus primarily on cumulative CO2 emissions, as these are the primary cause of global climate system changes. On this basis, we assign historical responsibility. We pro-rate the share of historical responsibility, based on the proportion of cumulative emissions above. Following Fig. 1, Rayer et al. (2021a) noted a 14.5% proportion of cumulative emissions for the nine firms they considered. Using this to pro-rate the share of historical responsibility, the potential share of responsibility for climate damages is 14.5%. 4.4

Knowledge of Harms Caused

Companies carrying out activities that result in GHG emissions are therefore causing harm. This introduces a moral aspect into the discussion. Following Shue (2017), Rayer et al. (2021a) explore the question of moral responsibility. Shue (2017) notes that society classifies responsibilities into positive and negative, general, and special, and backward-looking and forward-looking. By the 1960s, once it became clear that continuing CO2 emissions would progressively endanger the climate, major carbon producers could see that they were marketing harmful products. Shue argues that the negative responsibility to “do no harm” required major carbon producers to rapidly reduce the harm they were causing, by either modifying the product to capture its dangerous emissions, or

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by developing safe substitutes, such as carbon-free energy. Harm reduction also includes ending the search for additional fossil fuels. Shue sees the half century of corporate carbon producers’ failure to reduce the harm caused by their products as grounds for assigning them additional responsibility, including asking them to correct the damages they caused through decades of neglecting the negative responsibility. If major carbon producers wished to make more than a minimal positive contribution (i.e., do more than only reduce the harm caused by their products), their distinctive political power, wealth, and expertise qualify them to lead the transition to an energy regime upon which future generations could safely rely (Shue, 2017). A key aspect of this moral argument is whether or not emitters were aware of the harms caused by their activities, since one cannot take steps to address harms of which one is unaware. Society tends to be more forgiving of harms caused unknowingly. In terms of the assignment of responsibility for emissions, one defense, although it is perhaps an incomplete one, might be that one was ignorant that the activities in question caused harm. However, in 1965, the US President, Lyndon B. Johnson, said in a message to Congress: “this generation has altered the composition of the atmosphere on a global scale through radioactive materials and a steady increase in carbon dioxide from the burning of fossil fuels” (Jamieson, 2014). Later that year, the President’s Science Advisory Committee issued a report treating CO2 as a pollutant, with an appendix on “Atmospheric Carbon Dioxide” (United States White House, 1965). Furthermore, knowledge of the role of CO2 emissions in altering climate had been emerging in the scientific community from a much earlier date, in work by scientists such as Fourier (1824), Foote (1856), Tyndall (1861), Arrhenius (1896), Callendar (1938), Plass (1956) and Keeling (1960), and others. We cite these authors to illustrate the period over which awareness had been developing. Like Shue (2017), we feel it reasonable that a defense based on ignorance of the harms caused ceases to be plausible from around the 1960s. We might suppose a date at which such climate ignorance was dispelled to be circa 1965, although it may have been much earlier. As Shue (2017) states, once it became clear that CO2 emissions were damaging to the climate, the negative responsibility to “do no harm” required CO2 emitters to act to reduce that harm rapidly. Decisive action

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at that point by an emitting organization might not render the organization blameless, but it would certainly make them less blameworthy for historical emissions. It would also have reduced (or perhaps ultimately eliminated) ongoing climate harm, as emissions would have been reduced. Figure 1 identifies the question, did emitters take steps to reduce harmful emissions? Once aware of the harms caused by emissions, rather than reduce emissions, some organizations instead took actions to evade responsibility, particularly in the fossil fuel industry (Oreskes & Conway, 2010). Attempts to obfuscate the science, or place undue emphasis on uncertainties, appear to have formed a part of climate denial activities, some of which continue to the present day (Oreskes & Conway, 2010). It is morally highly questionable that the response to the awareness of causing harm, was not only by not acting to reduce harm, but actively to seek to defend an entitlement to cause harm. To explore how moral responsibility for harm caused can be assigned, we posit two firms with identical historical cumulative emissions prior to the date when climate ignorance was dispelled (we suppose 1965 as determined above). To illustrate these moral arguments, let us designate these firms as “A” and “B.” Once aware of their responsibility for harmful emissions, these firms took different courses of action. “A” accepted responsibility for its historical emissions and chose to significantly reduce future emissions, while “B” chose to increase emissions while taking steps to evade responsibility, perhaps through “climate denial” or other activities. In terms of emissions post-1965, A’s emissions would thus be reduced, unlike B’s. What about responsibility for pre-1965 emissions? In both cases, ignorance could be considered at least a partial defense against responsibility. However, following the moral arguments, we suspect that society (and potentially ultimately legal practice) would be far more forgiving of A than of B. For post-1965 emissions, while A’s emissions would be reduced, we suspect that it is also quite possible that society would be far more forgiving of harms caused by A (per unit of A’s emissions) than of harms caused by B (per unit of B’s emissions). We thus propose that for two organizations with identical emissions, it would be entirely reasonable that the proportion of responsibility that would be assigned to each unit of their emissions could be different, depending on their responses to the harms caused. The proportion of

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responsibility thus assigned would be a societal judgment, which we suggest would be likely to be strongly informed by moral arguments. In summary, we suppose that a key input to decisions on emissions responsibility would be the morality of the response taken to the climate damages caused by emissions. An at least partial defense for responsibility for the harms caused would be ignorance. Once ignorance was dispelled, we might conclude that meaningful steps to reduce harms would reduce the degree of responsibility for the emissions (as well as reducing future emissions). However, following awareness of the harms caused, the alternative response to attempt to deny or evade responsibility should be seen as augmenting the responsibility for harms caused by emissions. This logic is illustrated in Fig. 1. 4.5

Producer Versus User Responsibility

One standard society could adopt for the apportionment of responsibility between producers and users of high emitting products, would be alignment with social norms, such as historical example. Rayer et al. (2020) consider the split between industrial and non-industrial emissions, noting that 77% of all anthropogenic emissions in 2015 were industrial, leaving 23% emissions non-industrial. On the basis that fossil fuel extraction firms are industrial, they therefore suggest a 77% producer share of responsibility and a 23% user share. They support assigning the majority share to producers on the basis that users, such as individuals, often have more constrained choices and less resources when making decisions. Rayer et al. (2021a) amplify the argument relative to users, accepting the 77% producer share. They then use Sect. 4.4’s moral argument that the producer share of responsibility for climate damages should be augmented due to active efforts to evade responsibility, to propose a division of 88.5% to producers. This figure is obtained by splitting the difference between 77 and 100%. This augmentation of the producer share of responsibility mimics a societal judgment based on a combination of historical industrial versus non-industrial emissions, and a moral judgment reflecting the emitting firms’ response to knowledge of the climate harms their activities cause. These figures are shown in Fig. 1, as “0.77 industrial” emissions, and producer versus user share “responsibility 0.885.”

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4.6

Company Emissions Responsibility

Having determined the potential share of climate damages attributable to an organization (or group of organizations) based on their historical emissions in Sect. 4.3, we can now adjust this value to reflect the appropriate division between the producers and users of the emitting product. The resulting company (producer) emissions responsibility is the product of their share of historical global emissions, and the producer share of responsibility (including a societal judgment) from Sect. 4.5. For the example given in Rayer et al (2021a) and illustrated in Fig. 1, the potential share of damages based on proportion of accumulated global anthropogenic emissions was 14.5%. Following Sect. 4.5, the producer share of responsibility, including a moral societal judgment was 0.885. Thus, 14.5% × 0.885 = 12.83% represents the organizations’ share of the cumulative global anthropogenic emissions that should be considered as causing the damages arrived at in Sect. 3.6. In other words, it represents the share of the damages relating to total global cumulative anthropogenic GHG emissions for the EWH event in question.

5

Determining Hypothetical Climate Liability

We next determine the hypothetical climate liability (HCL) of the organizations considered for the EWH event in question. The HCL is usually stated as a monetary value (typically in billions of US dollars) in inflation-adjusted terms to a specific date. The HCL depends on the damages caused by the EWH event considered, the role that AGW has played in the occurrence of the event, the cumulative emissions of the firms being assessed, and their producer versus user share of responsibility, including societal judgment. As well as being stated in monetary terms, it can also be useful to state the HCL as a fraction of an emitting company’s market value, as this may increase salience for investors and financial analysts. 5.1

Definition and Calculation of HCL

We now consider how the results derived above can be used to estimate an event-specific hypothetical climate liability, in relation to the EWH event(s) identified in Sect. 3.1, due to the organizational emissions discussed in Sect. 4.1.

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In Sect. 3.6, we estimated the inflation-adjusted damages for an EWH event attributable to AGW. For the example in Fig. 1, these were estimated as$265bn × 0.79 = $209.35bn. In Sect. 4.6, we estimated the share of the damages relating to total global cumulative anthropogenic GHG emissions for the EWH event in question. For the example in Fig. 1,14.5% × 0.885 = 12.83%. We now bring these together as a single figure representing the “hypothetical climate liability” (HCL) for the organization in question, as a share of the EWH event damages attributable to AGW. The HCL is simply the fractional share of emission responsibility which can be attributed to an organization for event damages due to AGW. Thus, in Fig. 1, the product of the inflationadjusted damages due to AGW ($209.35bn) and the company emissions responsibility share of$209.35bn × 0.1283 = $26.9bn. The HCL is a monetary value (typically $bn) in inflation-adjusted terms to a specific date. 5.1.1 Definition of HCL Hypothetical Climate Liability (HCL) is first mentioned by Rayer and Millar (2018a, 2018b) as “if, hypothetically, [seven high-emitting companies] contributed 9.5% of the hurricane damage from 2017” with the 9.5% based on a share of their cumulative emissions (Rayer & Millar, 2018a, p. 36). Rayer et al. (2020) also mention “hypothetical damage contributions.” Rayer et al. (2021a) discuss an HCL regime, where the hypothetical damages caused by high-emitting companies may refer to insurance payouts, successful legal cases, carbon taxation, or payment following societal pressures leading to voluntary contributions. We note it is not the harms caused due to emissions that are hypothetical, as much evidence links emissions via AGW to EWH and other climate responses that have inflicted deaths and damage on individuals, companies, and wider society (EM-DAT, 2020; National Academies of Sciences, Engineering, and Medicine, 2016; Stern, 2006). Rather, the word “hypothetical” refers to the potential for legally enforceable “damages” in the sense that a harmed party would be able to seek monetary compensation for hurts arising from the action of another party. The legal aspect of this issue is developing rapidly but will not be further explored within the scope of this chapter. Currently, in many cases, financial recompense for harms caused will likely be borne by insurers and governments. Finally, Rayer

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et al. (2021a) note that liabilities may be paid on a voluntary basis, because of wider social pressures to address harm caused. Rayer et al. (2021a) also define hypothetical climate liabilities as “the percentage of market capitalization for the firms in question” (p. 170). However, while liabilities can be defined as either dollar amounts or as fractional value of another asset (such as market capitalization), for clarity, we find it more useful to focus on HCL as defined in terms of monetary value, typically in US dollars, unless otherwise stated. 5.2

Presentation of HCL

As noted above, usage of the term HCL has not always been entirely consistent. For example, Rayer et al. (2021a) defined HCL in terms of an emitting firm’s market capitalization. This inconsistency raises difficulties, as a firm’s market capitalization may fluctuate daily as financial market share prices move. It also neglects the possibility that a more appropriate measure of an emitting firm’s value might be its enterprise value, which includes the value of the firm’s debt capital in addition to shareholder equity. We therefore recommend that an organization’s HCL be presented as a monetary value at a specific date. For example, the HCL presented in Fig. 1 is $26.9bn expressed in 2016 US dollar terms (Rayer et al., 2021a). 5.3

Uses of HCL and Salience for Stakeholders

To increase the salience of HCL estimates for investors and other parties, it can be useful for HCL estimates to be expressed as a fraction (or percentage) of the market capitalization or enterprise value of the emitting firm. Thus, the HCL for the firm(s) in question can be divided by a suitable measure of the total value of the emitting organizations. Figure 1 shows that as the HCL of $26.9bn was attributable to the emissions of firms with a market capitalization of $1,358bn, the HCL amounted to 2% of those companies’ total values. Expressing the HCL in this way is advantageous to investors or other interested parties, as it allows them to immediately see the scale of damages caused by the historical emissions for the event in question relative to a measure of the firm’s worth. If the financial market’s measure of the firm’s worth is deemed to be a measure of its value to society, this measure can ultimately be seen as an indication of whether the firm’s emissions are more damaging to society than

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its activities are beneficial (in simple monetary terms). Thus, the HCL figures estimated become immediately salient. Firms’ market capitalizations are based on their number of issued shares, and those shares’ prices. If the number of a firm’s shares remains unchanged, then a percentage change to market capitalization corresponds to a change in a firm’s share price. Financial markets often assess investment risks in terms of share price losses, using metrics such as Value-at-Risk (typically defined as the maximum loss which can occur at some defined confidence level over a given holding period) (Choudhry & Wong, 2013). Thus, presenting HCL in terms of market capitalization provides a link to a potential fall in share price due to climate risk. Expressing climate-related EWH event damages due to corporate emissions in terms of potential share price falls, provides a means of linking emissions to more conventional financial risk measures such as Value-at-Risk. Despite this convenience, as a measure, expressing HCL relative to market capitalizations has challenges. Firms’ market capitalizations are directly linked to their stock market share prices, which means they can fluctuate significantly over short periods of time. Long-term investors may doubt that market capitalizations truly reflect the underlying value of a firm. Indeed, much active management is based on the premise that stock market valuations deviate from a firm’s “true value.” Rayer et al. (2021a) draw attention to this point, by noting that during the COVID pandemic in 2020, the market capitalizations of the firms they considered declined significantly to $538bn. Expressed as a percentage, their HCL of $26.9bn rose from 2.0% of market capitalizations to 5.0%. The use of market capitalization as a measure of a firm’s value can also face other challenges. Companies raise capital for their activities by means other than equity. They may also issue bonds or take loans from other sources such as banks. Enterprise value adds short- and long-term debt to a firm’s market capitalization, as well as any cash on its balance sheet. As such, enterprise value may be a more appropriate measure of the capital used to resource a firm, although it ultimately does not address the huge variation of value that can be seen within market capitalization. A further critique of Rayer et al. (2021a) relates to the timing of liabilities when calculating HCL and the firm’s values (whether calculated by market capitalization or enterprise value). The HCL was estimated in 2016 US dollars, while the market capitalizations used related to 2018 and 2020. Given this period was one of relatively low inflation and market

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capitalizations fluctuated much more widely, this discrepancy would not be expected to be significant. The objective of expressing the HCL in terms of market capitalizations was to increase its salience, rather than to provide an exact calculation. However, best practice would require for inflation-adjustments of HCLs, and that estimates of emitter organization value should be as close to the same date as reasonably possible. Notice that the above concerns only relate to expressions of the HCL relative to emitting firm value, which demonstrate the importance of defining HCL as a monetary value relative to a specific date, in the case of Fig. 1, $26.9bn in 2016. We feel that monetary HCL figures (in, say, US dollar terms) may be helpful for those considering reparations, policy developments, or litigation for damages. HCLs expressed as a percentage of the emitter’s value (whether market capitalization or enterprise value), meanwhile, may be useful for financial analysts assessing climate investment risks, and investors including institutional investors and pension funds, even though such values are prone to significant fluctuation.

6

Conclusion

Financiers and investors need tools to assess the climate risks of their portfolio holdings. High emissions from carbon-intensive sectors have been connected with global warming induced extreme weather and hydrological (EWH) events by the evolving field of extreme weather event attribution. Thus, potential liabilities for EWH event damages due to emissions, present risks that may not be reflected in current equity or bond prices. As EWH event attribution could devalue holdings of equities, bonds, and loans, financial supervisors and regulators need to be able to identify downside risks. We propose a framework to estimate investment losses based on firms’ historical emissions. This framework would also allow for the exploration of the sensitivity of losses to uncertain inputs, for climate risk stress-testing. The framework considers financial damages arising from an EWH event. Developments in extreme event analysis now permits probabilistic attribution, so that the fraction of the damages that are attributed to AGW can be estimated. This can be for a discrete event, or for a class of events over a period of years. The approach used is not dissimilar to methods adopted in areas of finance for the valuation of options. How

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the extreme event damages relate to an individual organization (or group of organizations) is based on their historical emissions. While this should encompass all scopes of emissions under the Greenhouse Gas Protocol, consideration also needs to be given to the share of responsibility for emissions resulting from the use of a product between the producing firm and its clients. A further aspect relates to the harms caused by emissions. Once an organization became aware that its activities were damaging the climate, its response, such as steps taken to reduce or prevent harms, or alternatively to evade responsibility, raises important moral questions. Our discussion also explores how “hypothetical climate liabilities” (HCLs) should be presented, to increase salience for those using them, such as those considering reparations or litigation for damages, and policymakers, as well as for financial analysts, investors (particularly institutional investors) and pensions funds. Our framework enables financial market practitioners and policymakers to estimate, for individual listed companies, potential equity or bond price falls arising from a company’s historical emissions following climate change induced extreme weather or water events.

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Rayer, Q., Pfleiderer, P., & Haustein, K. (2020). Global warming and extreme weather investment risks. In T. Walker, D. Gramlich, M. Bitar, & P. Fardnia (Eds.), Ecological, societal, and technological risks and the financial sector (Vol. Palgrave Studies in Sustainable Business In Association with Future Earth, pp. 39–68). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-388 58-4_3. Schiermeier, Q. (2018, August 2). Climate as culprit. Nature, pp. 20–22. Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., Zhou, B. (2021). Weather and Climate Extreme Events in a Changing Climate. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., B. Zhou, Climate Change 2021: The physical science basis. Contribution of working group I to the Sixth assessment report of the intergovernmental panel on climate change (pp. 1513– 1766). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. https://doi.org/10.1017/9781009157896.013 Shue, H. (2017). Responsible for what? Carbon producer CO2 contributions and the energy transition. Climatic Change, 144, 591–596. https://doi.org/ 10.1007/s10584-017-2042-9 Skeie, R. B., Fuglestvedt, J., Berntsen, T., Peters, G. P., Andrew, R., Allen, M., & Kallbekken, S. (2017). Perspective has a strong effect on the calculation of historical contributions to global warming. IOP, Environmental Research Letters, 12(2). Stern, N. (2006). Stern Review executive summary. New Economics Foundation. Stott, P. A., Stone, D. A., & Allen, M. R. (2004). Human contribution to the European heatwave of 2003. Nature, 432, 610–614. https://doi.org/ 10.1038/nature03089 The World Bank (2020). Inflation, consumer prices (annual %). Retrieved June 11, 2020, from https://data.worldbank.org/indicator/FP.CPI.TOTL. ZG?end=2019&start=1960&view=chart. Tyndall, J. (1861). The Bakerian Lecture: On the absorption and radiation of heat by gases and vapours, and on the physical connexion of radiation, absorption, and conduction. Philosophical Transactions, 151, 1–36, The Royal Society of London. http://www.jstor.org/stable/108724 UN FCCC (2015). Adoption of the Paris agreement. United Nations framework convention on climate change. United States White House (1965). Restoring the quality of our environment, Report of the Environmental Pollution Panel, President’s Science Advisory Committee Washington. US Government Printing Office.

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van Oldenborgh, G. J., van der Wiel, K., Sebastian, A., Singh, R., Arrighi, J., Otto, F., Haustein, K., Li, S., Vecchi, G., Cullen, H. (2017). Attribution of extreme rainfall from Hurricane Harvey, August 2017 . IOP Environmental Research Letters, 12, 124009. http://iopscience.iop.org/article/https://doi. org/10.1088/1748-9326/aa9ef2. van Oldenborgh, G. J., van Urk, A., & Allen, M. (2012). The absence of a role of climate change in the 2011 Thailand floods. Bulletin of the American Meteorological Society, 93, 1047–1049. https://doi.org/10.1175/BAMS-D12-00021.1 Vogel, M. M., Hauser, M., & Seneviratne, S. I. (2020). Projected changes in hot, dry and wet extreme events’ clusters in CMIP6 multi-model ensemble. Environmental Research Letters, 15, 094021. https://iopscience.iop.org/article/10. 1088/1748-9326/ab90a7/pdf. Yiou, P., Jézéquel, A., Naveau, P., Otto, F., Vautard, R., & Vrac, M. (2017). A statistical framework for conditional extreme event attribution. Advances in Statistical Climatology, Meteorology and Oceanography, 3, 17–31. https://doi. org/10.5194/ascmo-3-17-2017 Zeder, J., & Fischer, E. M. (2020). Observed extreme precipitation trends and scaling in Central Europe. Weather and Climate Extremes, 29, 100266. https://reader.elsevier.com/reader/sd/pii/S2212094719301720?token=F38 55CDB21BFC8A2B041AB2AE7CFF6FD79BD4B7F51BDD1801E10A8 0DF069BB90E54B0FE58D8D24FA594CE689EFA20388&originRegion= eu-west-1&originCreation=20220606132726. Zielinski, S. (2015, January 29). Climate change is altering the global heat engine. https://www.smithsonianmag.com/science-nature/climate-cha nge-altering-global-heat-engine-180954079/. Summary for Policymakers. In V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., B. Zhou, & V. P. Masson-Delmotte (Ed.), Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 3-32). Cambridge, UK and New York, USA: Cambridge University Press. doi:10.1017/9781009157896.001

(Investment) Strategies for Water Risk-Return Modeling and Management

Measuring Water Risk: The Challenges for Passive Index Investment Markus Barth

1 Why Is Water so Important to Investment Decision-Making? 1.1

Water Informs Climate

Water is essential to life, but is not typically a commodity that is considered by investors when selecting and weighting companies in their portfolios. According to the World Economic Forum (WEF), nine out of the ten worst global risks are linked to water (Berggren, 2019; WEF, 2019) and as a result, investors increasingly use water risk as a proxy for climate risk. Water stewardship will directly impact future corporate earnings and risks (AWS, 2019), and consequently, stock prices and investment portfolio performance.

M. Barth (B) CFA, Billericay, Essex, UK e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_7

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Water Impacts Company Earnings Much More Than Carbon Emissions

There is no doubt that CO2 emissions have been the primary input to climate investment strategies since the first climate indices were launched in the early 2000s. It appears that every climate investment product appears to be entirely focused on CO2 emission levels as the basis for stock selection and constituent weighting. This is counterintuitive because companies can continue to spew carbon into the atmosphere while not impacting their ability to manufacture or process products. So, while we completely agree that carbon emissions are highly detrimental to the environment, they have little impact on corporate earnings and therefore shouldn’t be considered a source of return to investors. A low carbon index should not be expected to outperform the market merely because it has a lesser carbon footprint. And yet the authors of various low carbon indices claim that this factor generates market outperformance when it may be due to sector exposures relative to the benchmark or single stock selection (Kolostyak, 2021). Consider a low carbon investment strategy that has large underweights in the big carbon emitting Energy sector, which was offset by much higher exposure to the low carbon emitting Technology sector. Between 2016 and 2021, the MSCI World Information Technology Sector Index outperformed the MSCI World Energy Index by 39.36% on an annualized basis (MSCI, 2022), and there is no doubt that this outperformance was not due to low carbon emissions. It was a huge sector bet that paid off while energy prices were flatlining, and Technology stocks soared. However, during the first half of 2022, this pattern changed dramatically, where a spike in energy prices boosted the sector at the same time as the Technology sector corrected. During the first half of 2022 through June 30, the MSCI World Energy Sector Index outperformed the MSCI World Information Technology Sector Index by 54.36%! During this period, some low carbon indices underperformed the market. So much for low CO2 emissions being alpha-generators. Furthermore, it has recently come to light that many of the so-called “Climate” investment products include companies from highly carbonintensive industries such as Oil & Gas and Utilities. This is most likely a result of the Climate Index providers’ desire to reduce the amount of sector exposure risk in their indices from excluding entire high carbon emissions sectors. For obvious reasons, including Energy companies in a

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Low Carbon Index is counterintuitive. In 2022, financial regulators in the United States and Europe began to issue warnings to asset managers who advertised low carbon investment strategies that, upon closer inspection, are not. Water is the other side of the climate coin. Global water scarcity is well-documented, and investors who fail to account for water risk in their portfolios face significant financial risk (CDP, 2020). Water risk has a meaningful and direct impact on future corporate earnings, and investors who fail to account for water risk in their portfolios may experience significant market underperformance in the future. However, it is not widely recognized that nearly all companies rely on water to varying degrees and therefore possess some portion of water risk. In addition to the obvious agricultural requirements, industries such as beverages, industrials, textiles, mining, utilities, and semiconductors (to name a few) all require vast amounts of water as a critical input to their production and operating processes. For example, most fashionconscious investors may not realize that it takes over 7,000 liters of water to manufacture a single pair of denim jeans (Mukherji, 2020). Consider the stark contrast between carbon and water usage today. A company that emits carbon into the atmosphere can still manufacture products, produce revenues, and grow earnings—which support our premise that high carbon emissions do not directly or materially impact corporate profitability. While planned European Union (EU) Emissions Trading Scheme (ETS) reforms may have an impact on costs incurred by companies in the future, their materiality is unknown at this point and there is still the United States (US) and the rest of the world to contend with. If a manufacturing facility can’t obtain one economic source of power, there are alternatives, whereas if a Coca-Cola plant cannot obtain water, the firm must close the plant or at the very least, pay a much higher price to transport the water to the plant from another location. Either way, Coke’s costs would increase, and their earnings could be negatively impacted. Why aren’t investor portfolios focused more on water risk when considering climate investments? Two of the seventeen Sustainable Development Goals (SDGs) are directly related to water, with water linked to many of the other SDGs. A. Poberezhna, Founder of ClearHub/Smart4tech states that the “total cost of water is US$1.9

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trillion per year, when including the full economic, social, and environmental costs of water pollution, flooding, and drought. With an estimated US$670 billion of required annual spending by 2030 to meet the Sustainable Goals related to water, it is unlikely that those targets will be met” (Poberezhna, 2021). According to a recent study by the Carbon Disclosure Project (CDP) Global Water Forum, “the financial penalty for failing to mitigate water risk is over five times larger than the mitigation costs” (Lamb, 2021; see also CDP, 2020). Access to water is therefore a concern not only for humanity that needs clean, potable water to sustain life, but also to businesses for whom it is the lifeblood of their operations. Managing water in a sustainable manner is good for the environment, but also good for investors too as water risk is poised to impact the performance and ratings of companies that rely on water to produce goods and run their operations.

2

Water Scarcity Portends Water Risk 2.1

What Is Water Risk?

The term water risk denotes all uncertainties and challenges relating to water availability (Dumont-Bergeron & Gramlich, 2021). Water risk is closely connected to climate. Changing climate, which is substantially manifested through water scarcity, portends unprecedented disruption in supply chains, which pose threats to production and distribution channels. Essentially, water informs climate. Water risk is not only environmental, but also ubiquitous across all sectors; it impacts future earnings, AND it is wholly unaccounted for in market benchmarks. The drivers of water risk include climate change/climatic events, failing infrastructure, pollution, weak regulations, and poor company water stewardship. These risk drivers result in operational, reputational, and regulatory financial risk effects, which can lead to earning shortfalls, litigation, and penalties. The scarcity of water will directly impact the earnings of companies which will translate into lower share prices for those companies that fail to manage water properly.

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How Is Water Risk Measured?

There are many complexities with water data. Primarily, there are no accounting standards for reporting of water data (CDP, 2020), therefore, quantitative techniques must be applied to ensure comparability across companies and geographies. The greatest challenge in sourcing raw water data is the wide disparity in the availability and reported levels of water usage, disposal, and recycling. Some companies report tens of billions of cubic meters, while others barely report thousands. A mathematical approach helps to design a workable distribution from lowest to highest water risk at the company level. This enables index construction in the aggregate to lead to a lower water risk exposure. There is minimal incentive for companies to report and disclose water data because there are no regulatory restrictions. Over the past few years, we have observed a marked increase in the number of companies who are reporting water utilization and water stewardship metrics (CDP, 2020). However, we are still a long way from a world where water data is as consistently reported as traditional financial statement data. The change needs to come from within the company managements and for this to occur, regulators and the company management boards must apply pressure to modify the mandate of a CEO from “maximizing shareholder value” to “maximizing shareholder value while minimizing environmental impact.” When this becomes the new CEO mantra, disclosure will improve, and investors will be able to better understand and assess water risk and water security. It is also not surprising that company managements do not wish to report water data that could highlight risk or negative environmental impact as this could lead to lower stock prices as the market prices this risk into the stock’s valuation. Considerable research and analysis are required to develop a means to systematically stratify water risk at the company level. After several years’ analysis, we have developed an approach that applies certain statistical techniques to enable water risk to be quantified, which results in a company-level ranking system across the broader capital markets. 2.3

What Are the Main Determinants of Water Risk?

There are two aspects of water risk: Water Utilization—How well has a company used water? These metrics can be measured by total water withdrawal, freshwater withdrawal,

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water discharged, water pollution, and water recycled as reported by each company as part of their annual report. These also assess a company’s Water Footprint and reflect where they stand in terms of water usage. For example, a beverage company requires water as a key input to production, and they source water from either utilities or from the ground or by purchasing it—which is their freshwater utilization. Companies that utilize water in their production processes have to expel that water in some way (draining into a sewer or trucking it to another disposal area) which represents their water discharged. Water Stewardship—Is the company doing anything to mitigate future water risk? Stewardship is a more forward-looking measure of water risk, and it focuses on the existence (or lack) of corporate water procedures such as (AWS, 2019): i. Is there a water policy? ii. Does the company target water conservation? iii. Does the company use technology to mitigate water risk? 2.4

How Does Water Risk Differ from Water-Themed Investments?

There is a big difference between Water Indices and Water Security. The market is flooded with so-called “water indices” with assets exceeding $30 billion (Citywire, 2022), but their approach completely ignores water risk and water security. They are nothing more than highly concentrated (30–40 stocks) portfolios of companies in the water purification/water recycling equipment manufacturing industry as well as some water utilities. These indices are purely speculative, based on the assumption that as water becomes scarcer, these companies will benefit from the increased demand for their products. While this is a viable thematic, none of these water investment strategies incorporate water risk or water security. “Water security is the reliable availability of an acceptable quantity and quality of water for health, livelihoods and production, coupled with an acceptable level of water-related risks” (Grey & Sadoff, 2007). Water Security (Taka et al., 2021) relates to all companies and not just companies from a few industries. Every company has a measure of water risk and water stewardship which permits them to be analyzed at the portfolio construction level to assess and regulate portfolio exposure to water risk with a bias toward water security and

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good water stewardship. In addition, a typical water-themed strategy has a substantially higher level of market risk compared to benchmarks because of the high concentration and exclusion of most sectors. 2.5

Introducing the Water Footprint

Most climate investors are familiar with the concept of a carbon footprint (Harkiolakis, 2013). It is essentially a measure of the amount of a company’s carbon emissions that are reported each year. There are different levels of CO2 emissions that inform the carbon footprint including direct emissions—which are the amount of CO2 emissions from the company’s operations; indirect emissions—which measure emissions from the supply chain to the company as well as the impact from utilities that provide energy to the company and its suppliers. It should be simple to apply a similar methodology to calculate a water footprint (Hogeboom, 2020). Instead of measuring carbon emissions, a water footprint can be measured by the water utilization metrics described in Sect. 2.3. This footprint can be calculated for each company that reports at least one of the water utilization metrics. The water footprint of a portfolio or an index of companies can also have a combined water footprint by simply aggregating each constituent’s water footprint and then weighting that footprint by the percentage of each company’s weight in the portfolio or index. The water footprint is an accurate measure of a portfolio’s environmental impact from water risk, and it enables investors to compare the level of water risk across different investment alternatives, portfolios, and indices.

3 Mitigating Water Risk in a Passive Investment Strategy Developing a methodology for pricing water risk into securities has proven to be quite a challenge. All the major index providers have tried with no success. The issues with data, quantifying water risk and how to construct an index all present significant hurdles. However, a collaboration between Thomas Schumann Capital (TSC) and Anatase Ltd Consulting, has cracked the code. A systematic methodology for measuring and quantifying water risk at the company level was developed and applied to the construction of a suite of Water Security Indices which

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were finalized in November 2020 and launched in January 2021 on the Moorgate Benchmark Index platform (Moorgate Benchmarks, 2022). Stratifying water risk results in a ranking system that enables portfolio constituent weights to be adjusted to reflect the degree of water risk. This effectively reduces portfolio water risk as companies with low water risk are overweighted while companies with higher water risk are underweighted. By including many large capitalization companies from all sectors, the risk exposure to market benchmarks has been minimized. This has facilitated the creation of an index that allows investors to mitigate water risk while not having to deviate from country, regional, and sectoral weights in the benchmarks. Performance of the indices regularly outperformed comparable market benchmarks with similar risk levels, and the indices have shown a much lower water footprint and carbon footprint than their corresponding equity market benchmarks. Similar to how CO2 emissions can be used to determine a company’s carbon footprint and, consequently, the weighted carbon footprint of a portfolio, a portfolio’s water footprint can also be calculated using a similar approach. Water utilization informs the water footprint which enables such a footprint to be calculated at the corporate and portfolio level. By using the water risk metrics described in Sect. 2.3, it is relatively simple and transparent to reweight a broad universe of companies across all industries, resulting in an average 53% lower water footprint than traditional market benchmarks such as the S&P 500, MSCI World, and the EuroSTOXX 50. This approach can significantly mitigate water risk without sacrificing diversification across sectors, countries, and regions. Investors may hedge potential future negative earnings impact from high water risk without making large bets and accepting unintended and undesirable risks. 3.1

Calculating the First Component of Water Risk—Water Utilization

As mentioned earlier in Sect. 2.3, there are two aspects of water risk—the first one is water utilization, which is a measure of a company’s water footprint. The inputs to calculating water utilization are defined by Refinitiv (our source for the analysis) and are:

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Total Water Withdrawal: The total volume of water (in cubic meters) from any water source that was either withdrawn directly by the reporting organization or through intermediaries such as water utilities—different sources of water like well, town/utility/municipal water, river water, surface water, etc. are considered. Freshwater Withdrawal: Total freshwater withdrawal in cubic meters—freshwater refers to water with low salt content. Sources of freshwater include surface, underground, well, boreholes, rain, and distributed/purchased water—municipal water, industrial water, and tap/drinking water. Saline, gray, and brackish water are not considered. Total Water Discharged: The total volume of water discharged in cubic meters. This includes water discharged for which there is no further use by the company which is considered wastewater—treated wastewater and discharged information are also in scope. Total Water Recycled: Amount of water recycled or reused in cubic meters. Recycled or reused water refers to water being sourced internally by recycling or reusing water in place of additional withdrawals. Treated water does not qualify. Most water data is reported by companies in their annual reports, but some of it is also available via government and municipal water suppliers. Refinitiv is one of the few collectors of this type of data which is why we have chosen them after considerable comparison across the various ESG data vendors. As previously stated, converting raw water data into a measure of water risk requires some statistical techniques. The fundamental issue with water data relates to the scale of water metrics reported by companies. As the table below highlights, the range of values reported by companies can be extreme, which makes creating a viable ranking system difficult at best. Due to the wide variance in how companies use water, depending on the industry, these measures can be either very, very large or very small (some even zero). The table indicates the maximum and minimum reported water utilization measures values of 1,500 of the largest by market capitalization global market public companies as of December 31, 2021 (Table 1). To facilitate a more productive distribution of water metrics, we proportionately scale each company’s water data by dividing the water metric by the company’s annual revenues in millions of US dollars.

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Table 1 Maximum and minimum reported raw water metric data (December 31, 2021)

Water utilization metric

Maximum

Minimum

Total water withdrawal (cubic meters) Freshwater withdrawal (cubic meters) Total water discharged (cubic meters) Total water recycled (cubic meters)

94,394,100,000

2,689

9,337,000,000

1,016

12,159,000,000

1,358

5,800,000,000

0

Source Anatase Ltd, Refinitiv, used by permission from paid subscription for non-commercial use

Refinitiv does this for part of their water data but not for all, therefore we must make the required adjustments as needed. In environmental reporting, it is a common practice to divide by revenues; the resulting measure is often referred to as “intensity.” For example, Total Water Withdrawal is converted into Total Water Withdrawal Intensity and so on. This helps to adjust the magnitude of water utilization to the scale of the company’s revenues. Smaller companies should have smaller water utilization than larger ones. However, when extracting water data from company annual reports or using other sources, it is crucial to note whether they are reporting these measures in cubic meters or metric tons or cubic meters per a million US dollars of revenue. Once we have converted all of the required water data, the maximum and minimum water metric intensities reflect what is shown in Table 2. Clearly, the revenue adjustment has narrowed the wide range of reported values, however, we aren’t quite ready to move to the derivation Table 2 Maximum and (December 31, 2021)

minimum

reported

water

utilization

intensity

Water utilization intensity metric

Maximum

Minimum

Total water withdrawal (cubic meters/m$) Freshwater withdrawal (cubic meters/m$) Total water discharged (cubic meters/m$) Total water recycled (cubic meters/m$)

14,948,308 740,572 511,290 445,785

2,689 1,016 1,358 0

Source Anatase Ltd, Refinitiv, used by permission from paid subscription for non-commercial use

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of water risk just yet. While the conversion into intensity has reduced the magnitude of the water risk metrics, there are still individual data observations for individual companies that are extremely high compared to most of the other companies. A process called “winsorization” is applied to render the data set more manageable (Wilcox, 2005). Winsorizing mitigates the effects of outliers by replacing them with less extreme values, thereby curbing in the extreme levels and reducing their impact on the overall data set. Without going into too much detail, the process of winsorization simply reduces the extreme levels to a level that is statistically very high but not as high as the raw data. For example, let’s assume we have 1,000 different data points for the Total Water Withdrawal intensity. As we see from Table 2, the maximum is 14,948,308 which is considerably larger than the minimum of 2,689. While it is critical to realize that the very high levels are still of the highest risk, the specific numerical value of that risk is not important as we are only looking to rank the universe of companies and assign them into quartiles—the top quartile (top 25% of companies) by utilization would have the lowest level of risk, while the next quartile (next 25%) would have the next lowest level of risk, and so forth. It should be clear that, as long as these extreme companies are in the proper quartile, it is not important that their water intensity is ten times larger than those in the 2nd quartile…only that they are in the highest water risk category. By applying winsorization to each of the company water metrics, we can reduce the extremes to a more manageable level which still results in their being in the exact same quartile. We are performing this to prepare for the following statistical adjustment, which is much more critical to the measurement of water risk and will determine how each company is weighted in a Water Security Index. To keep this chapter on point and avoid lengthy quantitative formulas, we will not go into detail about the next statistical adjustment as there are plenty of statistics textbooks that handle that quite well (e.g., Forbes et al., 2011; Krishnamoorthy, 2016). For our discussion, we will simply describe what happens rather than the complex mathematical process that gets us there. The subsequent adjustment to the water data is called “applying a gamma distribution.” In simple terms, what the gamma distribution accomplishes is to spread the distribution of each of the water metrics across a scale of 1 to 100, so that we eliminate the “clusters” at either

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end and smooth the data. This is required because even after winsorization, we are still left with what is called a barbell distribution. As the name suggests, the number and magnitude of the data points are heavily clustered at each end of the distribution (therefore looking a lot like a barbell). We need to be able to separate all the companies into four quartiles based on their water risk and utilization, therefore, the data distribution is “spread-out” so that the individual quartiles are accurately reflecting the degree of water risk for each company. Once each company is assigned to a water risk quartile, we can use this information to determine the water risk adjustment to their calculated weighting in the index. 3.2

Creating the Second Component of Water Risk—Water Stewardship

The second aspect to consider when calculating water risk is the presence of water policies as reported by the company in their annual report. These results will also contribute to the adjustment of each constituent in the index. While water utilization is a numerical measurement of a company’s inputs to their water footprint, water stewardship indicates whether a company is aware of its water risk, has a water policy in place, targets water conservation, and/or uses technology to mitigate water risk. These inputs are digital and not numerical—either the company does or doesn’t have any of the above water stewardship policies. The three key policies as defined by Refinitiv are: Water Efficiency Policy: Does the company have a policy to improve its water efficiency? Is there a system or set of formal documented processes for efficient use of water and driving continuous improvement? In scope are the various forms of processes/mechanisms/procedures to improve water use in operations efficiently. Targets Water Efficiency: Has the company set targets or objectives to be achieved on water efficiency? In scope are the short-term or long-term reduction targets to be achieved on efficiently using the water at business operations. Water Technologies: Does the company develop products or technologies that are used for their own water treatment, purification or that improve water use efficiency? In scope are the products or

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services addressing water purification or greater water conservation or efficiency as well as those using technology and/or software to detect water leaks. Given that these three data points are either yes or no, there is no need for any statistical techniques such as those used in the water utilization components, to measure water risk. However, they are equally important to ascertaining water risk because they point to whether companies are incorporating water risk in their day-to-day operations (i.e., the future). This forward-looking measure of water risk management is, in our view, sometimes more important than where the company has been (its water footprint). It also suggests that companies with superior water stewardship policies will be more likely to minimize their water footprint going forward—thereby highlighting lower water risk. 3.3

One Last Sanity Check—Environmental Controversies

When designing a water security investment strategy, it made sense to reduce the index weighting of companies which have had an environmental controversy over the past twelve months (i.e., is the company under a public spotlight because of an environmental accident?). Focusing on the past 12 months makes more sense than a prolonged historical period because a longer period might penalize a company for an accident that occurred in the distance past and therefore already long since remediated. It might even suggest that companies which have already experienced environmental controversies in the further past are less likely to endure them again in the future by having learned how to better prevent them from occurring in the first place. However, we haven’t taken this concept so far as to further increase index weighting based on environmental mishaps that occurred in the more distant past. This last perspective in determining the index weighting is more about avoiding overweighing companies that may have low water risk but still are well-known to have had recent environmental accidents.

4

Constructing a Water Security Index

Establishing a methodology for pricing water risk into securities has proved to be quite a challenge. The major index providers have attempted but with no success. The issues with data, how to quantify water risk

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and how to construct an index all present significant hurdles. However, a collaboration between TSC and Anatase resulted in an innovative and first of its kind investment solution that generates an average 53% reduction in water footprint and an average 34% reduction in carbon footprint compared to traditional equity benchmark indices. In addition to the more environmentally friendly climate profile, the Water Security Indices are over 99% correlated to traditional benchmarks with essentially the same market risk (volatility). This new and innovative approach to mitigating climate change by investing in good water stewardship enables investors to hedge their water risk exposure without taking unnecessary and unintended bets on sectors, countries, and regions. The process of identifying a selection universe of companies, recalculating their water risk, determining their new index weights, and reconstituting the positions (called “index rebalancing”) is performed four times per year to ensure that the data is as up-to-date as possible. 4.1

Determining the Selection Universe of Companies for Each Index

The methodology for the three permutations of the TSC Water Security Indices (USA, Eurozone, and Global) is all rebalanced in the same manner with only the number of constituents and geographic allocation differing between them. For illustrative purposes, we will refer to a single index—the TSC Water Security Index. Each selection pool (the universe of companies from which an index is constructed) consists of a fixed number of the largest stocks in a geographical region according to free-float market capitalization (the number of shares available to the public for trading in the secondary market). Table 3 highlights for each Water Security Index, what those selection pool parameters are. In addition to selecting the number of companies eligible for each index, there are also exclusions based on certain ESG and Business Lines. These exclusions represent common types of companies that are frequently regarded as undesirable in certain investment communities and countries. They are excluded to ensure the resulting indices are more palatable to a broader geographical and cultural set of investors. The aggregate number of companies excluded from the Global selection universe (out of the 1,500) is relatively insignificant. Table 4 indicates

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Table 3

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TSC Water Security Index selection pool criteria

Region

Number of companies

Countries included

USA Eurozone

600 250

Global

1,500

The United States Austria, Belgium, Finland, France, Germany, Ireland, Italy, Netherlands, Portugal, Spain Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Norway, Singapore, Spain, Sweden, Switzerland, the UK, the United States

Table 4 TSC Water Security Index industry activity exclusion list

Aerospace and defense Aerospace and defense electronics Arms and ammunitions Manufacturing Casinos and gaming industry Coal industry

Drone manufacturing Internet gaming Military aircraft manufacturing Military clothing and accessories Military vehicles manufacturing Tobacco industry

which companies are excluded when their activities are classified in any of the listed categories. 4.2

Measuring Water Risk and Determining Weighting Adjustments

Each of the remaining companies is eligible to be in the TSC Water Security Index and the water utilization, stewardship, and number of recent environmental controversies are collected for each company, if available. When only a limited amount of data is available, we use what we can obtain to measure the water risk. As more companies are reporting and increasing amounts of water data become available, we are confident that the quality and quantity of water data reporting will continue to improve over time. The process of determining an index constituent’s weight is relatively straightforward. The simplicity is based on the theory that companies with

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lower water risk will be future beneficiaries in terms of earnings’ growth and stock price appreciation, whereas the companies with higher water risk will suffer the opposite. Therefore, to “tilt” the portfolio toward lower water risk and a consequently lower water footprint, we merely adjust each constituent weight in the index either up or down (bonus or penalty) based upon their water risk ranking. Companies that do not report any water data whatsoever are left unadjusted. The underlying principle is that the company’s free float market capitalization is how most traditional indices are weighted. The larger the company, the larger the weighting and vice versa. By adjusting the market capitalization up or down, we effectively end up with a portfolio that is overweight low water risk and underweight high water risk. This approach is also sometimes referred to as “fundamental weighting,” “alternative weighting,” or “smart beta.” Each of these terms basically means the same thing—an index weighted with some adjustment to skew the index toward a factor other than market capitalization. Without delving into too much mathematical detail, there are a series of weight adjustments that get us from raw data to final index constituent weights: i. Water Utilization Quintile Adjustment—All companies are ranked by water utilization and then assigned to one of four quartiles with the first quartile having the companies with the lowest water utilization and the second containing the next highest water usage, and so forth. Companies in the first quartile get the largest increase in their weight followed by the next largest increase for the second quartile. The third and fourth quartile companies have their weight decreased by the negative of the first and second quartile adjustment percentage amounts with the fourth quartile having the largest decrease applied. ii. Water Stewardship Adjustment—Each of the three water stewardship policy adjustments is based on whether the answer to each is “yes” or “no.” Companies who answer a stewardship policy question “yes” have their weight increased while those who answer “no” have their weight decreased. The three stewardship adjustments are applied cumulatively to each company. iii. Environmental Controversies Adjustment—The number of reported environmental controversies occurring in the latest fiscal year is multiplied by a fixed reduction percentage to a maximum

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of four occurrences. It is not specifically a water risk measure and is only present to avoid potentially overweighting a company that has been in the news for polluting the environment. While there is also no way to separate water-related environmental controversies from other types, typically, when the environment is damaged, water supplies are negatively impacted. iv. Determining the Aggregate Weight Adjustment—Once the above three adjustments are calculated, they are summed to form the Aggregate Weight Adjustment. This number is multiplied by the company’s free float market capitalization as of the rebalancing date to generate a water risk-adjusted free float. That water risk-adjusted free float is how the index constituents are weighted. 4.3

Minor Modifications to the Weighting Scheme

The original TSC Water Security Indices were designed as market water risk benchmarks and therefore as unrestricted as possible. The number of constituents in the resulting indices may be too large for some investors to manage, especially in smaller notional amounts. Fortunately, there are several simple solutions to significantly reduce the number of final constituents without impacting the performance of the index or the financial characteristics, water and carbon footprint. i. Remove smallest weighted constituents—For example, if we remove all constituents that have below 0.01% index weight and then simply reallocate that weight proportionally to the remaining constituents, the number of index constituents reduces by as much as half with basically only 10–25% of the index actually changing and ostensibly no impact to the index characteristics. If fewer constituents are desired, the minimum weight cutoff could be increased to 0.02%, or 0.05% all with very little impact to the index characteristics. ii. Optimization—A very common portfolio management technique that uses complex quantitative modeling to create a portfolio or index of significantly fewer constituents but with nearly the exact same “factor” exposure, which basically means that a mathematical set of formulas is able to replicate the “look and behavior” of an index with a fraction of the number of constituents. This is often applied to Exchange Traded Funds (ETFs) that track indices with

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unwieldy numbers of constituents. A more detailed discussion of these techniques is outside the scope of this chapter and there is plentiful information available in the literature for those interested.

5 Measuring the Performance of the Water Security Indices To analyze the performance of our water risk investment methodology, we performed a backtest, which is common in the industry and merely reflects what would have happened had we started performing this process beginning in October of 2015. We have the benefit of a robust historical database containing as reported water data, free float market capitalization, and the presence of all historically listed companies. It is simple to build the backtest. We perform the rebalancing process at each quarterly period and identify the selection universe, measure the water metrics, and determine the constituents and weights. This is repeated until our live launch date which was on January 4, 2021. From that point forward, the index calculation and administration were taken over by a Benchmark Administrator—Moorgate Benchmarks Ltd in London, England at the time of launch. All the historical backtests were vetted by Moorgate and are part of the published index history. 5.1

TSC Water Security Indices Financial Characteristics Through December 31, 2021

Table 5 indicates the performance and risk characteristics of the three regional water security indices compared to the traditional equity benchmarks. While there is a lot of information in the above tables, the key takeaways are: i. All the indices outperformed their traditional benchmarks over the analysis period by between 1 and 2% per year on a compound annual growth rate (CAGR). ii. On an individual annual basis, each of the indices demonstrated the best outperformance over the past few years which may be indicating that water risk is beginning to attract more investor focus.

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TSC Water Security Index performance characteristics

Oct. 30 2015 Dec 31 2021

TSC US Water S&P 500 Security NR Index NR Index 175.5% 149.0% CAGR 17.8% 15.9% 18.8% 18.2% 13.7% 13.0% Dividend Yield 1.19% 1.26% 99.7% Tracking Error (Ann) 1.62% Oct. 30 2015 Dec 31 2021

TSC Euro Water S&P Euro 75 Security NR Index NR Index 57.0% 42.8% CAGR 7.6% 5.9% l 18.3% 19.3% M 14.0% 14.1% Dividend Yield 2.24% 2.28% n 99.1% Tracking Error (Ann) 2.71% Oct. 30 2015 Dec 31 2021

TSC Global Water MSCI World Security NR Index $ NR Index 124.0% 112.7% CAGR 14.2% 13.2% l 16.0% 15.2% M 11.8% 11.1% Dividend Yield 1.57% 1.66% n 99.7% Tracking Error (Ann) 1.47% The TSC US and Euro Water Security Indices went live on January 4, 2021 and the TSC Global Water Security Index has not yet been launched and was in development at the time of this publication. All performance data prior to the live date was retrospectively calculated by Moorgate Benchmarks using the Index Methodology and by Anatase for the Global Index. For further information, please see Moorgate Benchmarks (2022). Past performance whether live or simulated is not indicative of future results. Returns include reinvestment of dividends net of local taxes and do not include product fees or transaction costs. Source Anatase Ltd, Refinitiv, MSCI, Standard & Poor’s

iii. All indices have correlations greater than 99% and relatively low tracking error indicating that risk exposures to the benchmarks were minimized. iv. The overall risk (volatility) of each index is within tolerance of the benchmarks on both the past eighteen months as well as the entire

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period. This means that investors can meaningfully reduce water and carbon risks without taking additional unintended risk. v. Each index offered a similar dividend yield to its benchmark suggesting that investors do not have to forego income to have lower water risk. vi. Investment in water security doesn’t need to come at a detriment to performance. 5.2

TSC Water Security Indices Water and Carbon Footprints on December 31, 2021

By examining the Table 6, each of the TSC Water Security Indices was able to generate significantly lower water and carbon footprints, despite taking very little benchmark risk. The Global Index has the greatest overall reduction in water footprint (63%) as well as a carbon footprint reduction of 38%. The footprint reduction numbers have been relatively stable over the last few years, so this is not a recent phenomenon. Logically speaking, reducing the weighting of constituents with the highest water footprints should result in a basket of stocks that has a lower aggregate water footprint. Table 6

Water Footprint and carbon footprint percent reductions

Source Anatase Ltd, Refinitiv as of December 31, 2021 based on the current constituents and weights of the TSC Indices and Market Benchmarks

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TSC Water Security Indices Sector Exposure on December 31, 2021

The charts in Table 7 illustrate the sector and country and regional exposures of each TSC Water Security Index back through 2015. Table 7

TSC Water Security Index historical sector exposures

TSC US Water Security Index

(continued)

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Table 7

(continued)

TSC Euro Water Security Index

(continued)

Clearly the indices have been broadly diversified across all sectors and countries through time and as the tables showing the exposures on December 31, 2021, vs. the benchmarks suggest, there is minimal deviation in exposures compared to the benchmarks. This explains why the correlations are so high and the tracking errors are so low. This

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(continued)

TSC Global Water Security Index

Source Anatase Ltd, Refinitiv as of December 31, 2021 based on the current constituents and weights of the TSC Indices and Market Benchmarks. The TSC US and Euro Water Security Indices went live on January 4, 2021 and the TSC Global Water Security Index has not yet been launched and was in development at the time of this publication. All performance data prior to the live date was retrospectively calculated by Moorgate Benchmarks using the Index Methodology and by Anatase for the Global Index.

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also provides further historical evidence that investment in a water security index constructed across a broad universe of stocks across markets and economic sectors should have a considerably lower risk exposure for investors than a concentrated basket of stocks that are solely in the water purification industry.

6

Conclusion and Next Steps 6.1

Conclusion

The purpose of this report is to provide insight on the importance of water risk in investment space—a focus that has been sorely lacking in light of the overwhelming focus on CO2 emissions as the relevant source of climate risk. While it is clear that CO2 emissions are part of the climate issue, the financial services industry has largely ignored water risk in its reporting on climate and provision of climate-based investment products. It is compelling that over the past few years, more and more climate experts have written about water scarcity and its impact on the environment, and yet there is a dearth of commentary about the impact of water scarcity on corporate earnings and future security prices. The work that has been done in quantifying water risk into an investment strategy as explained in this chapter is groundbreaking and can enable investors to reduce their exposure to water risk in their portfolios, however, it is only a first step. The lack of water metric reporting standards and regulatory requirements for all public companies to report their water metrics hinders investors from truly understanding the inherent water risk when investing in equities. 6.2

Next Steps

The author states that the most important way forward is to develop a framework for consistently and accurately measuring water risk. This requires regulated standards for all listed companies in the same way that there are financial reporting standards for publishing income statements, balance sheets, and other financial statement content in annual reports. The problem with this approach is that there are numerous ESG data providers who claim to have the “best” data available when in fact all of these providers source their data from company annual reports. So, they all source the same data but each processes it in their own “special” way

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to create a unique water measurement. The question that comes to mind is “do investors really need so many different water risk metrics?” When investing in bonds, people often look to a bond rating for a measure of credit risk. There are really only two bond rating agencies, do we really need more? The author would propose the same argument holds true for water risk ratings and hope that at some point in the future, companies are required to follow regulatory standards when reporting their water metrics and ESG data, and ratings providers are reduced in number. While these next steps may take years to appear, what can investors do in the meantime? The TSC Water Security Indices are available for investment, and while they are a first generation of water security investment, they are currently the only means (short of individual company analysis by investors) to hedge portfolios against water risk. Overall, the relevance of water challenges in investment portfolios will further increase, and information about water risk and water scarcity will become more prominent in theory and practice (see for example Foster [2022] as a recent cover story in the Barron’s magazine).

References AWS—Alliance for Water Stewardship. (2019, March 22). International water stewardship standard, Version 2.0, North Berwick. Berggren, J. (2019, January 24). 9 of the 10 worst global risks are linked to water, The Stockholm International Water Institute. https://siwi.org/latest/9-ofthe-10-worst-global-risks-are-linked-to-water/ Citywire. (2022). Citywire selector. Equity—Water. https://citywireselector. com/sector/water-funds/i3741/?periodMonths=3&page=1&sortBy=maxdra wdown&expandedList=true CDP—Carbon Disclosure Project. (2020). Global water report 2020. A wave of change. The role of companies in building a water-secure world. Dumont-Bergeron, A., & Gramlich, D. (2021). Introducing water risk: A framework for (integrated) water risk assessment and management. In T. Walker, D. Gramlich, K. Vico, & A. Dumont-Bergeron (Eds.), Water risk and its impact on the financial markets and society: New developments in risk assessment and management (pp. 1–19). Springer International Publishing. https://doi.org/ 10.1007/978-3-030-77650-3 Forbes, C., Evans, M., Hastings, N., & Peacock, B. (2011). Statistical distributions (4th ed.). Wiley. https://ebookcentral.proquest.com/lib/kxp/detail.act ion?docID=706567

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Grey, D., & Sadoff, C. W. (2007). Sink or Swim? Water security for growth and development. Water Policy, 9(6), 545–571. https://doi.org/10.2166/ wp.2007.021 Harkiolakis, N. (2013). Carbon footprint. In S. O. Idowu & N. Capaldi (Eds.), Springer reference. Encyclopedia of corporate social responsibility (pp. 309–313). Springer. https://doi.org/10.1007/978-3-642-28036-8_38 Hogeboom, R. J. (2020). The water footprint concept and water’s grand environmental challenges. One Earth, 2(3), 218–222. https://doi.org/10.1016/ j.oneear.2020.02.010 Kolostyak, S. (2021, August 11). Do ESG stocks outperform? Report. https:// www.morningstar.co.uk/uk/news/214249/do-esg-stocks-outperform.aspx Krishnamoorthy, K. (2016). Handbook of statistical distributions with applications (2nd ed.). CRC Press. Lamb, C. (2021, March 19). Cost of water risks to business five times higher than cost of taking action, CDP. https://www.cdp.net/en/articles/media/cost-ofwater-risks-to-business-five-times-higher-than-cost-of-taking-action Moorgate Benchmarks. (2022). Reaching new heights for indices. https://moo rgatebenchmarks.com/clients/thomasschumanncapital/ MSCI World. (2022). Sector indexes. https://www.msci.com/our-solutions/ind exes/market-cap-indexes/sector-indexes Mukherji, R. (2020, February 21). It takes 7,000 liters of water to make your jeans. The Hindu Business Line. https://www.thehindubusinessline.com/ blink/know/it-takes-7600-litres-of-water-to-make-your-jeans/article30871 977.ece Poberezhna, A. (2021, February 10). Demystifying water; redesigning systems, Aquatech Trade. https://www.aquatechtrade.com/news/urban-water/annapoberezhna-on-demystifying-water/# Taka, M., Ahopelto, L., Fallon, A., Heino, M., Kallio, M., Kinnunen, P., Niva, V., & Varis, O. (2021). The potential of water security in leveraging Agenda 2030. One Earth, 4(2), 258–268. https://doi.org/10.1016/j.oneear.2021. 01.007 WEF—World Economic Forum. (2019). The global risks report. Wilcox, R. (2005). Trimming and Winsorization. In P. Armitage & T. Colton (Eds.), Encyclopedia of biostatistics (2nd ed.). Wiley. https://doi.org/10. 1002/0470011815.b2a15165

Using the CWR APACCT 20 Index to Re-Calibrate Chronic Tail Risks and Re-Assess Long-Term Capital Allocation Decisions Given Rising Locked-In Coastal Threats Dharisha Mirando, Debra Tan, and Chien Tat Low

1 Introduction: The Current Policy Path Is Accelerating Global Warming and Sea Level Rise The goal of the 2015 Paris Agreement is to ensure the planet warms less than 2 °C, and preferably less than 1.5 °C by 2100 compared to pre-industrial levels (UNFCCC, n.d.). However, the World Meteorological Organization’s (WMO) latest estimate (2022) is that there is a 50% chance that already by 2026 the world will warm by 1.5 °C compared to

D. Mirando (B) · D. Tan · C. T. Low China Water Risk, Hong Kong, China e-mail: [email protected] D. Tan e-mail: [email protected] C. T. Low e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_8

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the pre-industrial period (WMO, 2022). The speed of climate change is thus rapidly accelerating. Just one year ago, the Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report from Working Group I (IPCC AR6 WGI) estimated that there is more than a 50% chance that by 2040 global temperatures will warm by 1.5 °C (IPCC, 2021). Sadly, while pledges and targets by governments to reduce carbon emissions will get the world to 2.1 °C (range: 1.7–2.6 °C) by 2100, actual current policies and actions put the world on track to warm by 2.7 °C (range: 2.0–3.6 °C) (Climate Action Tracker, 2021). To keep global warming to below 1.5 °C requires significant action— annual GHG emissions must be no more than 31GtCO2 by 2030 and 9GtCO2 by 2050 (IPCC, 2022b). Current annual GHG emissions, however, are of about 55GtCO2 (2019 levels) (IPCC, 2022b). A cut of 24GtCO2 or a 43% reduction by 2030 is therefore necessary. For these results to be achieved, we would have to see a 3GtCO2 reduction every year for the next 8 years, as compared to the 2.3GtCO2 of emissions reduced in 2020 globally due to COVID-19 (Tollesfson, 2021). But given the significant economic impact of COVID-19, governments are highly unlikely to want to continue with 8 consecutive years of COVID-19-like living conditions. Due to this fact, there is significant hope for the success of carbon capture technology, especially for the oil sector where it has been used the longest. Yet, it is hard to rely on carbon capture technology because for a technology that has existed for over 30 years in the oil sector, it captures very little carbon. For example, in Canada, despite at least US$5.8bn invested in subsidies for carbon capture, utilisation and storage (CCUS), the technology has only captured 0.05% of Canada’s emissions (Environmental Defence, 2022). Given the current emissions policy path and accelerated warming, the reality is that SLR is likely accelerating as well. The IPCC now warns that 2 m of SLR by 2100 and 5 m by 2150 “cannot be ruled out” as possible potential scenarios, “due to deep uncertainty in ice sheet processes” (IPCC, 2021). There are multiple reasons for this potential future; besides glacial melt, warming oceans cause thermal expansion which accounts for about 30–50% of SLR (IPCC, 2013). In the last 25 years (1987–2019), our oceans have warmed at an unprecedented rate of 450% greater than the period before (1955–1986) (CNN, 2020). Scientists warn that if the world continues to warm at its current rate to about 3 °C by 2100, an abrupt jump in the loss of the Antarctic ice sheet

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could happen after around 2060 making SLR of more than 1 m by 2100 possible (Deconto et al., 2021; International Cryosphere Climate Initiative, 2021; IPCC, 2021). This discovery has led the IPCC to warn that multi-metre SLR by 2100 also “cannot be ruled out” (IPCC, 2021). The latest observations are more worrying: an ice shelf holding the Thwaites Glacier, a critical glacier in Antarctica that is the size of Florida, could break apart within the next 5 years and cause 0.65 m of SLR within 80 years (Pettit et al., 2021). Such a collapse could accelerate the melt of the whole West Antarctica Ice Sheet, which holds an equivalent of 3–4 m of SLR (Pettit et al., 2021). With multi-meter SLR, coastlines around the world, which house millions of people and billions of dollars of assets, will be permanently submerged. Such events are not hundreds of years away—the China Water Risk (CWR) APACCT 20 Index’s 1.5 °C scenario assumes 2.9 m of locked-in SLR would be felt far in the future, but new observations and accelerated warming could accelerate this timeline (CWR, 2020a; Deconto et al., 2021; International Cryosphere Climate Initiative, 2021). This risk is significant and must be factored into valuations because a permanent loss of land will shorten the lifespan of all assets located there. Their valuation will be affected the way leasehold property is valued lower than freehold property (Mirando & Tan, 2021). Also, properties/assets may lose their insurability status leading to mortgage and/or loan defaults. Naturally, central banks have started to worry about how these risks will affect the financial system and have begun executing large scale financial stress tests. These tests have highlighted the materiality of these risks: for example, the Hong Kong Monetary Authority’s (HKMA) pilot stress test of its banking sector involving 27 banks revealed that almost HK$1 trillion worth or 32% of participating banks’ mortgage and property lending are vulnerable to climate impacts mainly from flooding and typhoons (HKMA, 2021). The results are not, however, as sizeable as they could be, given accelerating SLR, as warned by the recent IPCC AR6 WGI. Given the materiality of coastal threats due to climate change, it is important for the financial sector to “see” the consequences of its actions—many key policymakers, business leaders, bankers and investors are unaware of the severity of the impact of their decisions and of rising emissions. The China Water Risk APAC Coastal Threat Index for 20 APAC capitals and economic hubs (CWR APACCT 20 Index) was built

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to help gauge risks across regions and close knowledge gaps regarding coastal threats. The index assesses the absolute and relative coastal threats facing 20 APAC cities and economic hubs. Here, it is important to note that the index includes adaptation action/inaction which can reduce/increase the city’s exposure to coastal threats. For example, both New York and Singapore are working off “noregret” levels—New York is planning to build resilience to 2 m of SLR by 2100 (CWR, 2022), while Singapore has indicated that it is building resilience to 3 m (CWR, 2020b). Yet, Hong Kong, despite having significant exposure as indicated by the HKMA pilot study (HKMA, 2021), is only building resilience to a medium emissions scenario by 2050 (Civil Engineering & Development Department Civil Engineering Office of HKSAR, 2022). While the study does not provide an adaptation level, the Hong Kong Observatory’s projections under this scenario is SLR of 0.13–0.28 m by 2050 or 0.37–0.82 m by 2100 (Hong Kong Observatory, n.d.). Such actions are not comforting, as the imminent collapse of the Thwaites glacier is likely to bring forward 0.65 m of SLR (Pettit et al., 2021). The CWR APACCT 20 Index was recently created to assess the coastal threats facing 20 APAC capitals. This chapter explores the creation of the index with the support of over 100 finance professionals. The chapter and the charts have been summarized, with the permission of CWR, from its five-report series “CWR Coastal Capital Threat Series” (2020), which benchmarks risks for 20 coastal capitals and economic hubs in APAC from Tokyo to Sydney. The five reports in the series include an overview of the latest science on rising seas; a ground-breaking CWR APACCT 20 Index to benchmark risks; physical threats levels of 20 APAC cities; what governments are doing or not doing to protect from existential threats; analyses of sovereign and clustered financial risks from GDP and trade to bank loans; as well as next steps and to-do lists for multiple stakeholders to waterproof APAC.

2

The CWR APACCT 20 Index

To create a practical index that was useful to the financial sector, 100+ finance professionals inputted in to CWR’s index development. The majority of these professionals provided feedback through a survey, while a core group helped CWR frame the work, select cities and/or design the survey.

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As Fig. 1 shows, the 100 finance professionals ranged from chairs or directors of bank boards to research analysts as well as financial industry associations, asset owners and financial regulators. In addition, only 34% of those surveyed classified themselves as “sustainable finance/ESG” indicating the increasing urgency by “mainstream finance” to understand and benchmark tail risks from long-term coastal threats.

Fig. 1 Wide-ranging input to the CWR APACCT 20 Index from 100+ finance professionals (Source These charts are extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

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2.1

Finance: Tail Risks Should Be Factored into Valuations

There is growing consensus and resignation in the financial industry to the fact that a warming of 3–4 °C should be used as the base case to assess climate impacts given the stasis around global decarbonisation efforts and the Paris Agreement. The 100 + finance experts that inputted into the index development confirmed this fact, as Fig. 2 shows. There was also agreement that this development would lead to greater physical risks, including coastal threats, which would affect valuations. As Fig. 3 shows, the majority of those surveyed “Strongly Agreed” and “Agreed” that tail risks from coastal threats will impact project valuations, sovereign credit ratings, corporate credit ratings and equity valuations. It is also clear that views shifted towards “Strongly Agree” after the survey across the four types of valuations. Note that zero per cent “Strongly Disagree” with the inclusion of such risks in valuations; project valuations and sovereign credit ratings carried the least “Disagree” votes for inclusion at 1% and 3%, respectively.

Fig. 2 Estimated likelihood of global warming scenarios—Responses from 100+ finance professionals (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

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Fig. 3 Inclusion of sea level rise in financial valuation—Responses from 100+ finance professionals (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

2.2

Building the Index

Though indices and tools to evaluate coastal threats exist, they are not being used. When receiving feedback from finance experts, it was clear that most had either “never heard of” existing tools, or had heard of one tool which only 4% of respondents “use regularly”. Thus, CWR sought feedback from the financial sector to see if a consensus could be reached as to the type of coastal threat index to build in terms of: (1) physical threat factors and proxy indictors; (2) impacts across various scenarios; and (3) government action. The answers received helped CWR to finalise the risks, indicators and weightings used in the index. These are discussed in greater detail below. 2.2.1 Physical Threat Factors and Proxy Indictors When asked to rank their preference on the types of physical risks to assess, results revealed that everyone was concerned with SLR as can be seen in Fig. 4. However, the magnitude and frequency of storm surge/tide garnered more votes than SLR in the “very concerned” and “extremely concerned” categories, reflecting their shorter time frame impacts as compared to SLR. Feedback also clearly shows that those surveyed were additionally worried about interlinked SLR and storm surge risks. Given that 94% expressed varying levels of concern over subsidence exacerbating SLR and storm surge, subsidence was factored in by scoring

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Fig. 4 Concerns about the impact from coastal threats—Responses from 100+ finance professionals (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

cities that on average are sinking by 1 cm or more per year. Therefore, the CWR APACCT 20 Index assesses the impact of SLR on proxy indicators (as explained below), storm surge as well as subsidence. To gauge the absolute level of SLR risk, a set of proxy indicators were used to assess the impact on Gross Domestic Product (GDP) due to trade and economic activity disruptions. The following were chosen: • Percentage of people affected: flooding could lead to mass migration of populations away from coastal cities, disrupt social stability and cause labour shortages; • Ports and airports: the flooding of key logistics infrastructure would affect food and energy security and the economy as trade plays a significant role in APAC economies; • Percentage of land affected: key property will be exposed and could damage economic activity. As highlighted in the CWR, Manulife Asset Management and AIGCC report “Are Asian Pension Funds Ready for Climate Change?” (CWR, Manulife Asset Management & Asia Investor Group on Climate Change, 2019), the percentage of land affected will also affect the financial sector, as mortgage and corporate loan books could be left exposed especially as more expensive real estate is typically found by the coast. Sea wall data is not

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included even though they can reduce flood risks as it is currently not possible to find all sea wall data for the 20 cities; • Central Business District (CBD): as the economic and financial centre of a city, its flooding could lead to large disruptions; and • Stock exchange: not all cities have a stock exchange as they are not all financial hubs, but if a city is home to a stock exchange, its flooding could force trading to a halt and pose a significant risk to the financial system as a large percentage of financial institutions are typically located by the exchange. When asked the same question at the start and end of the survey, 95– 98% of respondents said they were concerned about impacts across almost all these indicators. The only indicator to which respondents responded differently at the end of the survey was that of the stock exchange, as can be seen in Fig. 5. Although there was only slight movement in the total level of concern over each indicator before and after the survey, views clearly shifted towards higher levels of concern after the survey. At the end of the survey, when asked to rank their concern over what was impacted, residents of the cities were most concerned with 70% saying they were very and extremely concerned compared to 58% before the survey. This category was followed by that of ports and airports, and then city land area, and finally CBDs. Those surveyed were least concerned over the stock exchange, so a lower weighting towards it was applied, but it is important to note that key data centres and power generation assets that supply electricity to the cities and thus the stock exchange should be assessed in the future studies. 2.2.2 Impacts Across Various Scenarios Both the Network for Greening the Financial System (NGFS) and the Task Force on Climate-Related Financial Disclosures (TCFD) recommend running scenarios (Network for Greening the Financial System, n.d.; Task Force on Climate-Related Financial Disclosures, 2017). So far, these scenarios have focused on carbon transition risks and acute (eventdriven) physical climate risks. They are starting to shift, however, towards assessing all physical impacts including underlying environmental risks and long-term chronic risks. And yet, running climate scenarios is expensive, and it is therefore important to choose scenarios wisely. Climate models which predict the frequency and magnitude of storm surges can be expensive to run, and

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Fig. 5 Concerns about the effects from SLR at the start and end of the survey—Responses from 100+ finance professionals (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

their accuracy is debatable as they rely on historical data. Also, it is important to preserve consistency to ensure that the results are comparable across cities. Yet, it transpires that typhoons/hurricanes/tropical cyclones are not measured on the same scale in APAC. Rather, each county/territory/region has its own scale and measurements. Therefore, it was decided to map and reflect stacked risks for locked-in SLR but not for storm surges. Cities are impacted differently by SLR at each degree of warming due to their unique geographical locations and elevation characteristics. Furthermore, the location of key infrastructure differs from city to city. Impacts for each degree of warming, therefore, differ significantly. For example, Fig. 6 shows the land area affected by locked-in SLR in Bangkok (LHS) and Tianjin (RHS). At 4 °C, they have a similar percentage of land at risk (83–84%), but at 1.5 °C Tianjin has 35% of its land at risk whereas Bangkok has only 9%. It is clear from the charts that each city faces different impacts at each temperature scenario (1.5 °C, 2 °C, 3 °C, 4 °C), which means the exposure profile differs between the two cities, and the risks “stack-up” differently once all climate scenarios are considered. Taking into account such stacked risks, the CWR APACCT 20 Index scoring reflects that Tianjin is more at risk to SLR than Bangkok.

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Fig. 6 Land affected from SLR at different temperature scenarios in the case of Bangkok and Tianjin (Source These charts are extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

Since finance feedback overwhelmingly agreed that stacked risk exposure provided a better idea of exposure of SLR threats (see Fig. 6), the index includes SLR impacts for all four climate scenarios on all key indicators for each city. 2.2.3 Government Action Government action can significantly reduce the risks faced by a city, as it can alleviate the threats of storm surge and SLR. For example, while the Verisk Maplecroft Sea Level Rise Index highlights that of the 500 cities evaluated, Guangzhou is one of the most vulnerable cities to SLR (Verisk Maplecroft, 2020), government action including the building of 600 km of sea walls to protect the region, and turning Guangzhou into a “sponge city” to reduce flooding risks (Department of Water Resources of Guangdong Province, 2017), has been significant. As can be seen from Fig. 7, an overwhelming 90% agree, and furthermore 50% “strongly agree” that government action should be included in a city’s coastal threat index. When it comes to tackling climate change, governments can (1) push for a reduction in carbon emissions so that the worst risks are avoided (mitigation) and (2) invest in protecting people and assets from the expected physical threats due to a warming planet (adaptation). The CDP

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Fig. 7 Agreement about government actions addressing coastal threats— Responses from 100+ finance professionals (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

Cities A List is a combined/blended index which ranks cities based on both their adaptation and mitigation actions (CDP, 2020). This list is a good first step, as cities should be rewarded for decarbonizing, but this blended approach could conceal that cities could still be at serious risk due to a lack of local efforts to adapt. In fact, in order for mitigation to be effective, it must be carried out globally, while in order for adaptation to be effective it needs to be carried out locally. Indices should thus

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reflect results separately. As Fig. 8 shows, half of those surveyed wanted adaptation and mitigation separated and a further 11% wanted an adaptation only index compared to 1% who wanted a mitigation only index. Meanwhile, 38% said a blended index was still useful. As the CWR APACCT 20 Index was built to reflect actual physical threats ahead, it only factors in government adaptation actions, not mitigation. However, benchmarking government adaptation action is difficult

Fig. 8 Types of government action to include in a coastal threat index— Responses from 100+ finance professionals (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

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as impacts and thus actions differ for each city. To reduce subjectivity, proxy indicators were identified to score adaptation action. For more detail, please see 4.5. 2.2.4 Cities Included 20 coastal cities from 14 countries/territories in the Asia Pacific region (APAC) were included in this first version of the CWR APACCT 20 Index (see Fig. 9). They were selected as they are coastal capitals or key cities that are heavily populated and contribute significantly to their country/territory’s GDP. While only 20 APAC cities have been included in this inaugural benchmark index, there are many more coastal cities as well as manufacturing/trade hubs in the APAC region, and the index can be expanded to cover more cities in the future to help gauge sovereign risk. The 20 cities are home to 207 million people and account for US$5.7trillion GDP or over a fifth of the GDP of the countries/territories (see Fig. 10) (CWR, 2020a). The ports and airports that serve these 20 cities ship around a quarter of global sea and air cargo volumes (CWR, 2020a). All these are at risk as they are exposed to increasing coastal threats of SLR as well as storm surges for those in the path of typhoons/tropical cyclones/hurricanes.

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CWR APACCT20 Index---Results in Brief 3.1

Key Considerations

Below are the key considerations in this index: • 20 APAC cities were chosen. These cities are the capitals and economic hubs of the leading countries in the APAC and are also located in coastal regions. • Population growth was not factored in, rather we decided to use current population data, because we are dealing with long timelines and because the assessment of SLR impacts requires knowledge of the geo-spatial spread of the population per city. • Impacts from 1.5 °C to 4 °C were evaluated. • Locked-in SLR rather than SLR at 2100 was used to gauge chronic locked-in tail risks. Sea levels will not stop rising in 2100, yet many financial scenarios have only accounted for SLR risks until then.

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Fig. 9 Cities included in the CWR APACCT 20 Index (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

The CWR APACCT 20 Index thus uses locked-in SLR to ensure that such guaranteed tail risks are factored in. However, though the 1.5 °C scenario assumes 2.9 m of locked-in SLR that would be felt far in the future, new observations and accelerated warming mean that the same level of flooding could be felt by 2100 (CWR, 2020a; Deconto et al., 2021; IPCC, 2021; International Cryosphere Climate Initiative, 2021). • Locked-in SLR impacts on key indicators are analysed and factored in. Scoring depends on the degree of exposure of each key indicator to locked-in SLR at every temperature warming scenario (1.5 °C, 2 °C, 3 °C and 4 °C). These key indicators include land area

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Fig. 10 People and GDP at risk from SLR in the 20 cities in the CWR APACCT 20 Index (Note The impacts on population are based on estimates for 2020 and do not include any further population growth and increases/ decreases in urbanization in the future. For more details, please see CWR [2020a]. Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

and population affected as well as key infrastructure such as ports, airports, the stock exchange and the CBD as they act as proxies for the impact on GDP due to trade and economic activity disruptions. • The 2 °C, 3 °C and 4 °C CWR APACCT 20 Indices accounts for stacked SLR risks, as cities are impacted differently at each degree of warming due to their unique geographical location and elevation characteristics. • The median level of locked-in SLR was used for each climate scenario as it received the highest votes from the finance survey.1

1 See Sect. 4. Challenges in creating the CWR APACCT 20 Index for more.

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• The 30 m-grid NASA SRTM (SRTM-30 m) elevation data was used to map SLR risks for regional consistency in modelling SLR flooding. • Physical risks from storm surges were also assessed.1 • Subsidence of land naturally occurs, but some cities are sinking due to the over-extraction of ground water and the weight of buildings. The index takes into account cities that on average are sinking by 1 cm or more per year as the sinking brings forward coastal threats. For example, 1 m of SLR will be felt in cities facing subsidence sooner than others due to the sinking of the city. • Government adaptation action to reduce coastal threats was also included.1 • Index scoring—a lower score reflects lower risks. Cities that are more vulnerable to coastal threats will score higher on physical risks than those which are less vulnerable. Because government adaptation action can reduce the physical risks faced, cities’ scores are reduced depending on the amount of action they take. Therefore, index scores go up with SLR, subsidence and storm surge whereas government action reduces scores. Hence, a city that is most vulnerable to threats after taking into account government adaptation action will rank at the bottom of the CWR APACCT 20 Index at #20 with the highest score. On the other hand, a city that is least vulnerable after taking into account government adaptation action will rank at the top of the index at #1 with the lowest score. 3.2

1.5 °C CWR APACCT 20 Index

At 1.5 °C, 2.9 m of SLR is locked in. At this level, 28 million people from just 20 capitals and cities in APAC would lose their homes, and urban real estate equivalent to 22 Singapores, and 20 ports and 12 airports would be permanently submerged (CWR, 2020a). However, given accelerating risks, this could be felt by 2100 once tides are taken into consideration. Yet, each city will be affected differently. So, knowing which cities are more/less exposed to coastal threats can help drive capital allocation away from more vulnerable areas, and it may also affect sovereign credit ratings. These indices can also be used to gage/spread investment risks across APAC. Figure 11 shows the full 1.5 °C CWR APACCT 20 Index with government adaptation action on the left and without government adaptation on the right. Even though this index was built to reflect locked-in

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coastal threats of 2.9 m at 1.5 °C, the acceleration of risks due to current policies and action means this could be felt earlier than by 2100. Some key points to note: • Cities vulnerable to typhoons/tropical cyclones face more threats and have higher risk scores. 13 of the 20 cities in the index are in the path of typhoons/tropical cyclones and have recently experienced the strongest level of typhoon. They are Hong Kong, Macao, Taipei, all five cities in mainland China, all three cities in Japan, Manila and Seoul. Generally, these cities will have a higher physical score to reflect storm surge risk, but government action could lower scores. • Financial hubs Hong Kong and Singapore are at either end of the spectrum: Singapore is not subject to typhoons and its government is taking serious action to adapt to climate change, scoring top marks in adaptation action. Meanwhile, though Hong Kong’s government is taking some action, not much is being done vis-à-vis the rest of the cities in the index.

Fig. 11 City rankings in the CWR APACCT 20 Index with and without government actions (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

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• Accounting for physical risks alone, Manila is most at risk at #20 as it faces material subsidence as well as storm surges. However, since its government is taking action to reduce risks, it moves up six places from #20 to #14. • Bangkok and Jakarta have a similar physical risk profile but move in opposite directions—Bangkok slips down in ranking from #4 to #7 after adjusting for government action while Jakarta moves up from #5 to #3 with an ambitious plan to build a giant sea wall and move the administrative capital. But just because it says it is doing something doesn’t mean it actually is—this index does not score government adaptation action for effectiveness. 3.3

4 °C CWR APACCT 20 Index

As the majority of the finance sector believes that in the base case the world will warm by 3–4 °C by 2100, a 4 °C index was built. At 8.9 m of SLR, 102 million people from the 20 APAC cities would be displaced, urban real estate equivalent to 59 Singapores would be underwater including 11 CBDs, all 23 ports would be permanently submerged, and only 2 out of the 25 airports serving the 20 cities would still be operational. These potential consequences demonstrate a real need for transformative adaptation and resilience. For more detailed descriptions and ranking of the 1.5 °C and 4 °C indices please see (CWR, 2020a).

4 Challenges in Creating the CWR APACCT 20 Index Many challenges were faced when building this index even after input was received from the finance sector, because of research data gaps as well as timing and budget constraints primarily. In order to ensure that it is useful, practical and available, various components of the index were simplified. Set out below are the five key challenges faced in the making of this index, and the ways in which they were overcome.

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Determining SLR Exposure—Which Level of Locked-In SLR Should Be Used?

Due to global warming, sea levels will continue to rise beyond 2100, and each temperature scenario (1.5 °C, 2 °C, 3 °C and 4 °C) will “lockin” a certain amount of irreversible SLR. Therefore, although they are in the distant future, negative outcomes are guaranteed, and we should thus recalibrate terminal values for certain chronic tail risks accordingly. As the CWR APACCT 20 Index was created to assess long-term tail risks, SLR levels that will be locked-in at each temperature have been used to highlight long-term tail risks if we are to reach these temperatures. The locked-in SLR data we used in the CWR APACCT 20 Index is based on peer reviewed scientific research developed by Benjamin Strauss and Scott Kulp of Climate Central, in collaboration with Anders Levermann of the Potsdam Institute of Climate Impact Research (Strauss, Kulp, & Levermann, 2015). Please see CWR (2020a) for why this set of SLR was used. However, for each temperature scenario, there is a range of potential SLR—for example, at 4 °C the range is 6.9 m-10.8 m (66% confidence) as Fig. 12 shows. As the finance sector feedback showed a clear preference (69%) towards using the median, this was used in the index for each temperature scenario. For more information on how to build base & worst-case SLR projections, please see CWR (2020b).

Fig. 12 Locked-in SLR levels for the modeling of flooding and preferences— Responses from 100+ finance professionals (Source These charts are extracted from CWR (2020a) for the use in this chapter with permission from China Water Risk)

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Flooding Methodology—Bathtub or Hydrological?

Ideally, the method used to map SLR would only flood areas connected to the sea or river, that is, areas that are hydrologically connected. However, hydrodynamic modelling is expensive, hard to implement at a large scale, and the data required to implement hydrodynamic modelling may not be available publicly for most cities. The second option is to use the bathtub approach (CWR, 2020a) to identify which areas are lower than the SLR projection that would be flooded. The main idea behind the bathtub method is that an area which lies under a certain height gets flooded, in the same way water overflows from a bathtub. This method does not consider hydrological connectivity criteria and dynamics of water motion. As 76% of those surveyed favoured the bathtub approach, it was used for the index. However, while the bathtub method used in this report is an efficient screening tool, a hydrological model may be worth commissioning, to identify which highly impacted areas might warrant further investigation. It is also important to note here that current sea walls which could add a degree of protection were not considered, as it is currently not possible to ascertain the details of all sea walls for the 20 cities. However, in the CWR APACCT 20 Index, government adaptation scores were adjusted for cities that have built sea walls and other nature-based solutions as these would reduce threats from SLR. 4.3

Mapping Granularities—Trade-Off Granularity for Consistency?

Some cities have developed their own elevation data that is more granular and detailed than the global elevation data sets with grids of 5 m instead of 30 m. The difference in granularity provides a very different picture as can be seen from Fig. 13. Flooding from locked-in SLR in Hong Kong at 4 °C uses the global elevation map—NASA’s SRTM-30 m is shown on the map on the left while results with Hong Kong’s own Digital Terrain Model (DTM) with a 5 m grid is shown on the right. For more details on impact differences with greater granularity, please see (CWR, 2020a). Clearly, higher granularity provides a more accurate picture of flooding. However, such granular terrain data is not available for all cities, and some cities have not made the required data public. Furthermore, each city uses its own methodology to assess terrain, yielding different granularities. These data gaps and differences in methodologies make it difficult to

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Fig. 13 Flooding from locked-in sea level rise in Hong Kong—NASA SRTM30 m (left) versus Hong Kong DTM (right) model (Source These charts are extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

benchmark cities. As 85% of survey respondents voted to use the global elevation dataset (see Fig. 13), that was what was used for the index. However, as the granularity of elevation data makes a marked difference to the severity of flooding impacts, it is recommended to use more granular maps, where available, for identified hotspots. 4.4

Storm Surge Projections—Simple Metrics vs. Expensive Modelling

Feedback from the finance sector revealed an overwhelming concern for the magnitude and frequency of storms, as well as the interlinkages of these phenomena with SLR as shown in Fig. 4 (see Sect. 2.2.1). However, the risks from storms are not measured on the same scale between countries, and running storm models is costly across four scenarios. Worse still, they may not be useful as storm forecasts are modelled on historical storms which no longer point to the future. As warned by BlackRock, “New climate patterns mean long-dated historical data are a poor guide to the future. Investors using models overly reliant on the past are missing the big picture” (BlackRock, 2019). These facts relate merely to the storm, meanwhile, and not the storm surge which is dependent on the storm’s angle. Additionally, there is the time of arrival of the storm—at high tide or low tide. If a storm arrives at high tide, it will bring higher storm tides than if it arrives at low tide. The storm tide must thus be estimated when projecting the impacts of a storm.

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Given that modelling the magnitude and frequency of storms is expensive and may not be accurate, simple metrics consisting of a set of proxy indicators for storm surge were used in the index: • The city is in the path of typhoons/hurricanes/tropical cyclones as this automatically increases all risks; • Increasing intensity of typhoons/hurricanes/tropical cyclones affecting the city as these will become more damaging; • Increasing frequency of typhoons/hurricanes/tropical cyclones affecting the city as there will be more periods of interrupted economic activity and damage; • The city has experienced one T10 in the past 5 years (or equivalent—hurricane force wind is blowing or expected to blow with sustained speed reaching 118 km/h or above and gusts that may exceed 220 km/h) as these typhoons are the most damaging and show how vulnerable a city is; and • The city has experienced more than one T10 or equivalent in past 5 years—as the city will be even more at risk. For more information on how to build base and worst-case extreme storm tides, please see (CWR, 2020b). 4.5

Government Actions Are Inconsistent and Unique—What Proxy Indicators to Use?

Government adaptation actions can reduce the impacts of physical risks, and as a result have been included in the CWR APACCT 20 Index. Indeed, 90% of those surveyed either “agreed” or “strongly agreed” with this inclusion. As this index was created to gauge chronic tail risks facing cities, only adaptation measures were considered as they can directly reduce the risks locally. However, because each city faces its own unique climate threats, government adaptation actions are neither consistent nor standardised across the 20 APAC cities. Government action can range from disaster management policies all the way to implementing soft and hard infrastructure to protect vulnerable areas. Unfortunately, most governments do not disclose all actions being taken to reduce impacts of physical risks, let alone the budget being set aside for it. Also, the exact impacts of

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Category

Indicator

Acknowledgement and buy-in

• Government publicly admits country/city is vulnerable to risks from climate change • Senior official e.g. President/PM/Mayor acknowledges vulnerability • Appointment of a climate change commissioner • Set up dedicated climate change dept / working group nationally / in city

Research and planning

• • • • •

Commissioned study to understand the impact Study is publicly available Resilience plan / adaptation roadmap exists Ports/airport mentioned + plan to protect if exposed Costed parts of resilience plan / adaptation roadmap

Adaptation investments

• • • •

Started to invest in adaptation but no detail disclosed Disclosed for: Sea walls and/or elevation of coastal areas Disclosed for: Nature Based Solutions e.g. mangroves Disclosed for: Storm / flood drainage

Commitments

• Joined the Global Resilient Cities Network (GRCN)

Fig. 14 Government adaptation categories and indicators in the CWR APACCT 20 Index (Source This table is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

government actions to adapt for coastal threats are difficult to quantify as they vary across cities. Thus, several indicators as proxies were used within the four categories as the table in Fig. 14 shows. As Fig. 15 shows, scoring is not equally weighted across the four different categories. The weightings for the four categories and indicators within them were influenced by the responses from the financial sector. The rational and finance sector preferences for proxy indicators are discussed in more detail in CWR (2020a).

5

Conclusion

The CWR APACCT 20 Indices reveal the potential for a bleak future at 1.5 °C, but one which would be dire at 4 °C. Though they are far from perfect, the indices nonetheless predict a dire future for APAC capitals and cities if we are not more aggressive at closing the emissions gap to steer the world away from 3–4 °C, and to reach 1.5 °C by 2100 instead of 2030. At 4 °C no city is safe, but even a warming of 1.5 °C will trigger systemic shocks that put APAC banks/finance at risk of collapse. The

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Fig. 15 Weightings of government adaptation categories in the CWR APACCT 20 Index (Source This chart is extracted from CWR [2020a] for the use in this chapter with permission from China Water Risk)

NGFS recognised in 2019 that “climate change is one of many sources of structural change affecting the financial system” (Network for Greening the Financial System, 2019). There is no doubt that there are tail risks from coastal threats. The unpreparedness of governments, banks and other institutions for potential negative outcomes will trigger systemic shocks to the financial system. While acute risks are better accounted for, significant tail risks from chronic risks have so far been ignored. Valuing these tail risks does not require a structural shift in the finance system as the current way of valuing takes into account the long term through terminal values. Yet, when the terminal value is being calculated, chronic climate risks are currently not being factored in. Until such tail risks are readjusted, financial valuations will negatively affect the new risk landscape, which will result in widening valuation gaps as climate risks accelerate. The financial sector thus has an important role to play in reducing these long-term chronic tail risks, and the CWR APACCT 20 Indices can be used to prepare for systemic shocks ahead: • Central banks/banks must check clustered risk spread across the 20 cities to ensure capital adequacy: Banks must be able to survive coastal threats at 1.5 °C through 4 °C as APAC savings are at stake. The clustered nature of coastal threats means that tail risk impacts across the region must be well understood to ensure proper allocation of capital and alleviate the threat of risk. Sectoral and company analyses are no longer the only concerns when allocating capital:

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locational impact analysis will need to be performed due to clustered assets facing coastal threats. Impacts on assets such as ports and airports will also have knock-on impacts across multiple sectors. Worse still, there will likely be loss of insurance coverage placing all coastal threat risks squarely on banks; as the HKMA stress test has shown, risks are very material (HKMA, 2021). In addition to using the index, banks should carry out appropriate stress tests to ensure the risks are being seen. Recalibrate for tail risks to ensure financial resilience: sovereigns, credit and equity valuations: Financial mispricing of fundamentals has not allowed climate strategies to pervade across governments and all sectors—these must be adjusted. More specifically, the CWR indices can be used to inform credit rating agencies on their re-rating of sovereign and corporate risks as governments and companies are exposed to these 20 cities (CWR, 2020c). Drive APAC governments and corporates to step up proper adaptation for 1.5 °C: The 1.5 °C CWR APACCT 20 Index shows that risks are still material at this level of warming. Almost all the 20 cities’ ports and over half the airports are impacted, the financial sector should ensure trade resilience by raising rates/withholding capital from these ports and airports unless they adapt to survive coastal threats ahead. These risks are inevitable. However, as the IPCC warns of multi-meter SLR by 2100, it is best that APAC governments use the 1.5 °C CWR APACCT 20 Index as the “low-regret” benchmark, and, further, use it to catalyse adequate transformative resilience and avoid maladaptation. Push APAC governments and corporates to decarbonise to avoid 4 °C: As all cities are significantly impacted at 4 °C, APAC must act to avoid this temperature/amount of warming at all costs. Cities that face more threats must do much more to fast-track decarbonisation. They should not only be mitigating their own impact risks by cutting carbon emission in their jurisdictions, but also by coordinating/leading efforts across the region and beyond. Penalise governments for mitigation strategies that head to 4 °C but adapting for 1.5 °C: Currently, many cities as well as corporations have climate strategies (mitigation and adaptation) that do not make sense. As many have net-zero/carbon neutrality policies aiming for 1.5 °C, they are also adapting for 1.5 °C. However,

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they should be adapting for 3–4 °C, as that is where we are actually heading given our current policies and actions (Climate Action Tracker, 2021). Such mismatched mitigation and adaptation strategies should be re-aligned, and finance should penalise those for not taking action. For case studies on no-sense climate strategies by governments and corporates are discussed in CWR (2020c). • Work to resolve nitty gritty issues regarding data gaps through collaboration: Although finance sector feedback signals consensus, the ways in which coastal threats and government adaptation actions are measured can be improved. Here, science, academia, finance, corporates, investors and governments all have a role to play and must collaborate. • Follow the science, which continues to evolve: In 2021, the IPCC warned that 2 m of SLR by 2100 and 5 m by 2150 “cannot be ruled out”. This fact was considered important enough to be included in the Summary for Policy Makers for the first time in 2021 (IPCC, 2021). With new findings and observations, this too will continue to be updated. Therefore, finance must keep up to date with fast evolving climate science. While the CWR indices are helpful in gauging risks, only by clearly assessing future physical impacts through proper stress tests will finance be able to truly value what is at risk. Such assessment will allow the finance sector to move away from the current negative feedback loop which sees capital continuing to flow to carbon intensive sectors and vulnerable locations (Mirando & Tan, 2021). Stress testing with “low-regret” scenarios that reflect current policies and actions will ensure finance avoids funding projects that could lead to maladaptation, which according to the IPCC “refers to actions that may lead to increased risk of adverse climate-related outcomes, including via increased greenhouse gas emissions, increased or shifted vulnerability to climate change, more inequitable outcomes, or diminished welfare, now or in the future. Most often, maladaptation is an unintended consequence” (IPCC, 2022a, p. 9). Unfortunately, maladaptation is already happening due to limited adaptation financing, which constrains adaptation plans. Inadequate stress tests (using wrong levels and timelines) exacerbate maladaptation, as members of the financial and corporate sectors are unable to “see” the real impacts.

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The short-term bias of the financial sector impacts our financial and business systems. The IPCC also warns that adaptation decision-making is “driven by short-term thinking or vested interests, funding limitations, and inadequate financial policies and insurance” (IPCC, 2022a). All the above block the path to effective adaptation and increases physical risks. Once the financial sector as well as governments and corporations start to use the right stress tests, such tests will provide stakeholders with real impetus to change capital allocation decisions away from carbon intensive sectors and towards adaptation to ensure a more resilient future. Therefore, valuations can be re-calibrated for chronic tail risks, paving a path towards transformative adaptation as well as accelerated decarbonisation.

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Environmental Defence. (2022). Buyer beware: Fossil fuels subsidies and carbon capture fairy tales in Canada. HKMA. (2021). Pilot banking sector climate risk stress test. Hong Kong Observatory. (n.d.). Mean sea level projection data for Hong Kong. Retrieved from Hong Kong Observatory. https://www.hko.gov.hk/en/cli mate_change/proj_hk_msl_med_conf_info.htm International Cryosphere Climate Initiative. (2021). State of the cryosphere 2021— A needed decade of urgent action. IPCC. (2013). Climate change 2013: The physical science basis. Contribution of working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC. (2021). Climate change 2021: The physical science basis. Contribution of working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC. (2022a). Climate change 2022: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC. (2022b). Climate change 2022: Mitigation of climate change. Working Group III Contribution to the IPCC Sixth Assessment Report. Mirando, D., & Tan, D. (2021). Chronic coastal water threats warrant a valuation re-think. In T. Walker, D. Gramlich, K. Vico, & A. DumontBergeron, Water risk and its impact on the financial markets and society: New developments in risk assessment and management (pp. 189–216). Springer International Publishing. https://doi.org/10.1007/978-3-030-77650-3_7 Network for Greening the Financial System. (2019). A call for action: Climate change as a source of financial risk. Network for Greening the Financial System. (n.d.). NGFS scenario portal. https://www.ngfs.net/ngfs-scenarios-portal/ Pettit, E. C., Wild, C., Alley, K., Muto, A., Truffer, M., Bevan, S. L., … Benn, D. (2021). C34A-07—Collapse of Thwaites eastern ice shelf by intersecting fractures. AGU Fall Meeting. Strauss, B., Kulp, S., & Levermann, A. (2015). Carbon choices determine US cities committed to futures below sea level. Proceedings of the National Academy of Sciences, 112(44), 13508–13513. https://doi.org/10.1073/pnas. 1511186112 Task Force on Climate-Related Financial Disclosures. (2017). Recommendations of the task force on climate-related financial disclosures. Tollesfson, J. (2021, January 15). COVID curbed carbon emissions in 2020— but not by much. Retrieved from Nature, 589(343). https://www.nature. com/articles/d41586-021-00090-3

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UNFCCC. (n.d.). The Paris agreement. Retrieved from United Nations Climate Change. https://unfccc.int/process-and-meetings/the-paris-agreement/theparis-agreement Verisk Maplecroft. (2020, February 27). China’s manufacturing heartland most at risk from rising seas: Environmental Risk Outlook 2020. By Will Nichols and Rory Clisby. https://www.maplecroft.com/insights/analysis/chinas-man ufacturing-heartland-mostat-risk-fr WMO. (2022, May 9). Press release: WMO update: 50:50 chance of global temperature temporarily reaching 1.5°C threshold in next five years. https:// public.wmo.int/en/media/press-release/wmo-update-5050-chance-of-glo bal-temperature-temporarily-reaching-15%C2%B0c-threshold

Water Neutrality in Investment Portfolios Nadja Franssen

1

Relevance of Water Risks for Investors

Water is a resource under threat. Although ostensibly a renewable resource, in many parts of the world, the current rate of replenishment lags well behind the rate of depletion. Yet agriculture, industry, and the economy and society at large are all immensely reliant on water as a resource. Due to its scarcity and dependency, water poses a major risk to investment portfolios. As such, it deserves to receive more attention from investors than it currently does. ACTIAM is a globally operating asset manager that offers sustainable investment strategies and solutions to insurance companies, pension funds, banks, and distribution partners, as well as private investors. ACTIAM has approximately EUR 20 billion in assets under management (as of December 2021). ACTIAM is part of Cardano Group. ACTIAM’s sustainable investment philosophy (ACTIAM, 2021a) is based on a combination of the planetary boundaries (Rockström et al., 2009) and the doughnut economy (Raworth, 2018). This philosophy contends first

N. Franssen (B) Cardano-ACTIAM, Rotterdam, The Netherlands e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_9

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that, in a sustainable society, environmental pressures do not overshoot planetary boundaries, and second that wellbeing does not fall short of the minimum universal social and governance norms affecting people’s health and wealth, for current and future generations, as well as for nearby and distant regions. Companies that do not violate planetary boundaries and do not fall short on the social foundations, which operate in the ‘safe and just space for humanity,’ are said to operate sustainably. This philosophy is depicted in Fig. 1. Water integrates within this philosophy at many points. The most direct impact is through the planetary boundary for freshwater withdrawals. Freshwater withdrawals are defined as “the volume of freshwater abstraction from surface or groundwater. Part of the freshwater withdrawal will evaporate, another part will return to the catchment where

Fig. 1 Safe and just zone for humanity in the doughnut economy (Source ACTIAM [2021a])

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it was withdrawn and yet another part may return to another catchment or the sea” (Hoekstra et al., 2011, p. 197). This boundary is divided into two separate but interlinked components: green and blue water (see Fig. 2).1 When the green-water boundary was introduced early in 2022, it immediately became apparent that it already exceeded safety limits. Researchers at the Stockholm Resilience Centre discovered widespread changes in soil moisture relative to pre-industrial conditions that have the effect of destabilizing ecological, atmospheric, and biogeochemical processes (Wang-Erlandsson et al., 2022). All throughout the world, forests are being pushed closer to a tipping point where they might turn into savannah-like regions due to changes in soil moisture brought on by climate change and deforestation. Current levels of human blue-water consumption, on the other hand, remain within allowable limits and do not (yet) exceed global water availability. However, here too, global figures mask severe water stress at regional and local levels. Analysis by NASA (2015) concluded that 13 of the world’s 37 largest aquifers are being depleted while receiving little to no recharge. Also, a study by De Graaf et al. (2019) found unsustainable forms of groundwater pumping in 15% to 21% of watersheds worldwide. In these regions, levels of groundwater pumping exceed recharge from precipitation and rivers, causing a decline, stop, or reversal of streamflows from groundwater systems to surface water streams. This is an imbalance that severely compromises the health of the surrounding ecosystems. The same study estimated that, without better water management, environmental flow limits will be reached by 2050 in approximately 42 to 79% of watersheds in which there is groundwater pumping. It is not only groundwater stocks that are under threat. In 2018, Northern Africa, Central Asia, and Southern Asia reported levels of freshwater stress of above 70%, while Western Asia and Eastern Asia experienced stress levels of 60% and 45%, respectively (UN DESA, 2021). Water is also directly linked with many of the other planetary boundaries, such as:

1 The planetary boundary on freshwater withdrawals is divided into two components.

Blue water refers to “fresh, surface and groundwater, in other words, the water in freshwater lakes, rivers and aquifers.” Green water is the “precipitation on land that does not run off or recharge the groundwater but is stored in the soil or temporarily stays on top of the soil or vegetation” (Hoekstra et al., 2011, p. 189). See also Wang-Erlandsson et al. (2022).

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Fig. 2 Planetary boundaries (Source Stockholm Resilience Centre [2022])

– biosphere integrity (a lack of water and degrading water quality lead directly to changes in ecosystems and could be detrimental to biodiversity); – climate change (wetlands and big lakes act as carbon sinks); – ocean acidification (water quality degraded by untreated wastewater or runoff could have irreversible effects on the ocean environment and living organisms). Moreover, a lack of sufficient clean water has a direct impact on the shortfall of social norms of the doughnut economy. Clean drinking water and sanitation are a basic human need, where a lack of this basic necessity can have a detrimental impact on human health and food security. The world’s most stressed basin, the Arabian Aquifer System, is an important water source for more than 60 million people (NASA, 2015).

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Additionally, water scarcity hampers equality, decent work and economic development and is an important driver of conflict. Some 34% of global deaths caused by extreme weather events between 1970 and 2019 were the result of droughts, even though these represented only 6% of reported disasters (WMO, 2021). Droughts caused USD 252 million worth of economic damage in the same period. The figures are increased/worsened if floods and storms are also included. The IPCC—Intergovernmental Panel on Climate Change predicts that 700 million people are at risk of being displaced by 2030 as a result of droughts (IPCC, 2022). In other words, water is a growing risk that investors must be aware of. Despite a clear need, research from the CDP—Carbon Disclosure Project found that over one-third of listed financial institutions did not assess their investment portfolio’s exposure to water risks (CDP, 2020a). The overarching objective of this chapter is to provide guidance to financial institutions in analyzing water risks and impacts in relation to both portfolios and individual companies. It suggests a possible method for identifying and prioritizing water risks. The outcome of this exercise can be used both as input for portfolio construction as well as guidance on where to focus engagement.

2

Water Neutrality

The concept of water neutrality was coined for the first time at the World Summit for Sustainable Development in 2002, as a potentially efficient means of combating water scarcity. Since then, many commentators have elaborated on the concept, with Hoekstra (2008) being one of its main promotors. Hoekstra formulated two requirements that entities must meet in order to become water neutral. First, water consumption and pollution must be reduced as far as reasonably possible. Second, appropriate investments must be made to offset the negative impacts of the remaining water consumption and pollution. The concept of water neutrality can also be applied to an investment portfolio. In 2017, as one of the first asset managers to do so, ACTIAM set itself the target of achieving a water neutral investment portfolio by 2030 (ACTIAM, 2021b). The stocks included in a water neutral investment portfolio consume no more water than nature can replenish and emit no more pollution than is acceptable for the health of humans and natural ecosystems. For ACTIAM, this means encouraging its portfolio companies to pursue strong, regionally appropriate

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water management strategies, and guaranteeing transparent reporting of relevant water metrics. Water neutrality is influenced by changes in water quantity and water quality. The preservation of natural ecosystems is another critical factor in reaching water neutrality. 2.1

Water Quantity

Water quantity refers to the volume of water used. By 2030, the global gap between the supply and demand of water is projected to reach 40%, assuming there are no further efficiency gains (The 2030 WRG, 2021). Population growth and economic development increase the amount of water required for irrigation, industry, and domestic purposes, while climate change and deforestation put a growing strain on the available water resources. There are two different ways of defining water use: i. Water withdrawal refers to the total amount of water extracted from its source. Water withdrawal is a good proxy of company risk as it reflects the amount of water a company requires to sustain its operations. ii. Water consumption, on the other hand, refers to the amount of water withdrawn from its source without being returned. This portion of the water is incorporated into products or plants or lost during the production process due to leakages or evaporation, for example. The concept of water consumption is more closely aligned with a company’s (adverse) impact on its environment—either the natural environment or other stakeholders. An impact may turn into a risk, for example, as a result of stakeholder opposition, threats to a company’s license to operate, or reputational damage. Investors can benefit from measuring and monitoring both a company’s water withdrawal and its water consumption, allowing them to obtain the most comprehensive proxy of company risks and associated impacts. 2.2

Water Quality

The second threat to water neutrality is the degrading quality of the available freshwater resources (UNESCO & UN Water, 2020). The link

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between water quality and water quantity is often overlooked; however, deteriorating water quality is a major contributor to global water stress because it reduces the quantity of water available for use without incurring significant treatment costs. Water pollution occurs when harmful substances—such as chemicals or pesticides—contaminate a water body, thereby rendering it unsafe for humans and its environment. Nutrients, chemicals, and metals are among the substances that are often seen as threats to water bodies. Simultaneously, new contaminants such as pharmaceuticals, synthetic fibers, and microplastics continue to emerge, posing an increasing threat to the health of water bodies. As the complexity of waste flows grows, maintaining the quality of water supplies requires an ever-increasing effort. Companies can influence the quality of water in two ways: 1. Point-source pollution: pollution originating from a single, identifiable source. Industry and power plants are major sources of point-source pollution: discharged wastewater may contain harmful substances such as heavy metals, solvents, or toxic chemicals. The problem is exacerbated by leaches, leaks, and spills. The amount of point-source pollution could be significantly reduced by increasingly effective wastewater treatment. Unfortunately, 80% of all wastewater remains either untreated or is not adequately treated before being discharged into waterways (UNESCO, 2017). 2. Non-point-source (diffuse) pollution: pollution that is not attributable to a single source, but instead is caused by runoff moving across the land and picking up pollutants along the way. Nutrient pollution from agricultural runoff is a main contributor to non-point-source pollution. The presence in water of excess nutrients such as nitrogen or phosphorus stimulates algal growth and is the primary cause of the eutrophication2 of surface waters, thereby threatening the survival of both freshwater and saltwater species, and limiting the capacity of the world’s lakes and oceans to provide

2 Eutrophication is defined by the Oxford Dictionary (2022) as “excessive richness of

nutrients in a lake or other body of water, frequently due to run-off from the land, which causes a dense growth of plant life.” Excess nutrients often cause an increase in algae and aquatic plant life thereby depleting the level of oxygen in the water. The result is a decrease in the diversity of species (biodiversity) or, in extreme cases, a complete loss of life in an area, also referred to as dead zones.

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sustainable food sources and act as carbon sinks. Equally, runoff from cities, industry, mines, and oil fields can also be major sources of non-point-source pollution. Year by year, the findings published by CDP show that corporate efforts to address the water quality problem tend to lag behind their efforts in relation to water quantity problems (CDP, 2019; CDP, 2020b). The most common way for investors to identify a company’s risks and impacts, in relation to water quality, is through effluent discharges. However, these metrics do not provide a full picture, as they do not elaborate on the density and solubility of different harmful substances in the effluent, the carrying capacity of the receiving body and the vulnerability of the ecosystem. Measuring the quantity of hazardous emissions in discharge water would yield more accurate information—information that is not widely available yet.

3

Measuring Risk and Impact

This chapter introduces the concept of water footprinting as a tool for indicating portfolio risk and measuring progress toward the goal of water neutrality. The second section of the chapter explains how a water footprint can help identify the companies that ought to be prioritized for further qualitative analysis and offers suggestions for what such analysis should focus on. Having identified the main water risks embedded in the investment portfolio, the third part of the chapter explains how active ownership can be used to ensure that companies adequately manage such risks. Lastly, attention is being paid to the opportunities companies may have in transitioning to a water neutral society. 3.1

Measuring Progress: Water Risk Assessment

3.1.1 Water Footprinting Methodological Aspects Transitioning to a water neutral portfolio requires investors to have information on the main water-related risks and effects in their investment portfolio, and on how such risks and impacts can be mitigated. Water neutrality is not about using no water at all; rather, it focuses on water use that is sustainable in the long term. In other words, the amount of

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Fig. 3 Two strands of water neutrality (Source Own representation)

water used must remain within a region’s planetary boundaries or must be appropriately compensated. A complicating factor in measuring the water risks of an investment portfolio is that such risks are very different in nature from the water risks of a single (non-financial) company. What is relevant to investors is the consolidated risks and impacts of their portfolio. Contrary to climate change, which impacts the global economy, water risks are less likely to affect the entire investment portfolio simultaneously and can therefore be partially diversified away. In order to best measure water-related risks and impacts, investors must obtain a picture of their aggregated portfolio exposure to high-risk industries and water-scarce regions. Singling out the most highly exposed companies from this aggregate provides investors with an idea of how they can prioritize their actions. As is apparent from Hoekstra’s theory (Hoekstra, 2008), there are two principal ways in which companies in an investment portfolio can interfere with water neutrality (see Fig. 3). i. Negative impacts: negative impacts occur when the extraction of freshwater from its source is greater than the natural rate of replenishment, or when insufficiently treated wastewater degrades the quality of freshwater stocks and renders them unavailable for future use. In order to prevent negative impacts, water withdrawal, consumption, and pollution must be reduced as far as reasonably

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possible. As not all water use and discharge can be avoided, reasonable investments must be made in offsetting the negative impacts of the remaining water consumption and pollution. ii. Positive impacts: companies could increase their beneficial impact on watershed health by producing or designing products, services, and technologies that help resolve water quantity and/or water quality issues. Wastewater treatment, the provision of alternative water (desalination plants or rainwater harvests), water recycling, and smart metering technologies are some examples. The restoration, rehabilitation, and enhancement of degraded catchments3 could also enhance the global stock of clean freshwater. Overlapping such negative and positive impacts with the local hydrological context is critical to accurately measuring portfolio impacts on the goal of water neutrality. Company withdrawals and discharges must be mapped with local circumstances of scarcity, vulnerability, and biodiversity levels to best estimate the real-life impacts of such actions. A wide variety of sectors use—and abuse—water resources, either the industries themselves or within areas of the value chain. The Consumer Staples sector, including food, beverage, and livestock production, is the sector with the biggest impacts (Ceres & GIWS, 2022). Agricultural production accounts for 70% of global water withdrawals. Additionally, fertilizer use and wastewater discharges from slaughterhouse processing contribute significantly to nutrient pollution. However, also many other industries contribute to water scarcity and pollution. The first step in assessing portfolio water risks and impacts involves identifying high-risk sectors. High-risk sectors are those with high levels of water usage and carry out their operations in water-scarce or biodiversity-sensitive areas/regions. As far as water quality is concerned, sectors with large, polluting water discharges need to be prioritized. Sources of input for this type of mapping exercise are SASB—Sustainability Accounting Standards Board, Ceres, CDP, and data-providers such as Sustainalytics and MSCI. 3 A catchment is defined by the Cambridge Dictionary (2022) as “the area of land from which water flows into a river, lake, or reservoir.” Landscape and forest restoration can improve the hydrological cycle in a catchment, for example, by improving water retention and infiltration of the water into the ground. This positively impacts water supply and quality.

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Once the high-risk sectors have been identified, the second step in an investor water risk assessment, and a good way of measuring progress toward water neutrality goals, is footprinting of those sectors. Hoekstra (2008, p. 11) defines a product’s water footprint as “the total volume of freshwater used to produce the product, summed over the various steps of the production chain.” The water footprint of a product is equivalent to its ‘virtual water content,’ but this metric is more than a number alone. The water footprint of a product shows not only the total volume of water consumed (the virtual water content), but also where and when the water is used. The water footprint of an entire business is a sum of these individual water footprints and reflects “the total volume of freshwater that is used directly and indirectly to run and support a business” (Hoekstra, 2008, p. 194). The overall company’s water footprint thus consists of two components: it includes the direct water use by the producer for manufacturing and supporting activities, and the indirect water use, i.e., the water use in the producer’s supply chain. Water Quantity Footprinting ACTIAM has used this definition to design its proprietary model for measuring the water footprint of its investment portfolio (ACTIAM, 2017). The portfolio water footprint reflects the absolute water consumption by all portfolio companies from high-risk sectors. Figure 4 illustrates the aggregation of single investments’ footprints to the overall portfolio level. The percentage of company value owned by ACTIAM is used to calculate the share of water consumption for which ACTIAM is responsible. For non-reporting companies, the missing data is estimated by using the sector consumption per unit of production as a proxy for the water consumption of a company operating in a high-risk sector. Depending on the objective, i.e., measuring risks or impacts, investors could opt to replace water consumption by water withdrawal in their footprint calculations, or could decide to calculate both a consumption footprint and a withdrawal footprint. Due to the localized nature of water-related problems, the quantity of water consumed alone does not say much. The impact of water consumption on a catchment varies across regions and over time, as it depends on many variables, including water scarcity levels, the speed of aquifer recharge, the vulnerability of the ecosystem, the carrying capacity of a water body, other claims on water rights, and the time of year. For

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Fig. 4 Portfolio water footprint (Source ACTIAM [2017])

this reason, an account needs to be taken of the regional and temporal characteristics of a company’s operations. In calculating the water footprint of its investment portfolio, ACTIAM only uses the water consumption of companies operating in highrisk areas. The riskiness of an area is currently based on water stress levels obtained from the WRI—World Resources Institute tool Aqueduct (Hofste et al., 2019), but could potentially also incorporate additional factors affecting ecosystem vulnerability, such as the degree of water pollution in a region or whether the region is a hotspot for biodiversity. Water consumption per region or production facility is not yet widely reported by companies. ACTIAM, therefore, works with a proxy of geographic spread of revenue (i.e., gross income), on the assumption that the level of water consumption per region correlates directly with the amount of revenue earned. In other words, if 50% of a company’s revenue comes from region A, the model allocates 50% of water consumption to that region. In reality, this assumption will not hold, especially if there are differences in water use efficiency across production locations or if different facilities produce different products. However, until companies report more precise data, this remains the best estimate.

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Fig. 5 Portfolio water quality footprint (Source Own representation)

Water Quality Footprinting A similar model could be used to provide further information on water quality risks, mainly those risks resulting from point-source pollution. Instead of water consumption figures, a company’s wastewater discharges should then be used to calculate its water quality footprint. The model would be as follows (Fig. 5): There are a number of additional complexities that should be taken into account when interpreting a water quality footprint. Firstly, many different substances can be found in wastewater discharges (see, e.g., Akpor et al., 2014). The use of hazardous and/or polluting substances varies widely from one industry to another, and not all of them are equally well-known or adequately reported on. Knowledge of the toxicity of such substances sometimes also evolves over time. PFAS (perand polyfluoroalkyl substances) used to be regarded as having all sorts of useful characteristics and were used in numerous applications. However, PFAS are water-soluble and do not easily break down in the environment, hence the name ‘forever chemicals’ (EPA, 2022). They are now commonly found in water bodies near industrial facilities. Research has demonstrated the existence of highly negative impacts caused by certain PFAS families on both environmental and human health (EPA, 2022).

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Secondly, polluting potential varies from one hazardous substance to another, depending on its toxicity, but also on its degradability and solubility in water (see, e.g., Shiu et al., 1990, and Yaws et al., 2010. For a list of priority substances, see EPA, 2021; EU, 2013). The potential impact on waterways is also influenced by the density of the substance in wastewater discharges, the degree of treatment of the discharge, and the carrying capacity of the catchment into which substances are discharged. While replacing absolute water consumption figures with absolute wastewater discharge figures in the above calculation provides a good initial estimate of a firm’s water quality risks, it does not address all the complexities. It would be better to take the volume and polluting potential of hazardous emissions in wastewater discharges as the sole basis of footprint calculations. Unfortunately, this information is not widely available yet and there are no common standards for translating substances with different polluting potentials into a single metric. The EU’s Sustainable Finance Disclosure Regulation (SFDR), which requires investors to report on emissions to water as an adverse impact indicator, could potentially encourage better reporting in the future. Until then, a more qualitative in-depth assessment will undoubtedly be needed to interpret water quality footprinting results. 3.1.2 Extended Qualitative Assessment While water footprints provide very valuable information, they do not convey the full picture. First, a water footprint reflects the situation at a specific point in time, or at best—if conducted as part of an annual assessment—provides information on the annual changes in water use. It does not, however, give an investor a clear picture of a company’s water management and how it plans to reduce water-related risks and impacts in the future. A company with a high volume of water use could have an extensive program and time-bound targets for reducing its environmental footprint, whereas a company with a more moderate level of water usage might be less inclined to implement efforts to reduce its footprint. In addition, the footprint model shown above does not reveal much about the type of water used. This is essential as a company’s true risk and impact depends on the amount of water being extracted from the natural environment and on the likelihood of this water running out. Companies that recycle water or use alternative sources of water, for example, by means of water desalination or rainwater harvesting, tend to be less prone to water shortages, but could be susceptible to other

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risks. An ideal means of adjusting figures for this caveat would be by using only freshwater consumption in water footprint calculations. The problem is that companies often only report on their total water consumption and do not always distinguish between different sources of water used. Therefore, relying solely on freshwater consumption data reported by companies could therefore further compromise/taint the dataset available for footprinting purposes. Due to such caveats, ACTIAM believes that although a company’s water footprint can act as a red flag in analyzing investment portfolios, a thorough understanding of a company’s water management necessitates further research. A water footprint can therefore be most efficiently used to draft a short list of high-priority companies for further qualitative analysis. Mitigation Hierarchy Companies in ACTIAM’s investment portfolio are expected to transition toward a more sustainable water management strategy (ACTIAM, 2021b). By screening its investment universe, ACTIAM determines which companies will be able to transition toward a water neutral society in time. Water strategies will differ from one sector to another, and sometimes even from one company or operating location to another, depending on what is feasible in terms of water reduction, water recycling or the replacement of freshwater use, for example. Over time, the emergence of new technologies may create new opportunities. Based on the work of Hoekstra et al. (2011) and following the guidelines of the mitigation hierarchy introduced by the CSBI—Cross Sector Biodiversity Initiative (2015), ACTIAM has defined four key levers for change that can help achieve water neutrality. The levers are used by ACTIAM to help direct engagement processes and conduct a qualitative analysis of a company’s water management. Ideally, organizations would follow the mitigation hierarchy in order to decide which actions to prioritize in their water management. The levers are outlined in Fig. 6. The First Lever: Avoidance One of the most important strategies for attaining water neutrality is avoiding water consumption and pollution as much as reasonably possible, especially in vulnerable, high-risk and/or water-stressed geographies. Among the issues involved in this first lever are the extent to which companies account for water availability in their (re)location decisions,

Fig. 6 Mitigation hierarchy of water-related risks and impacts (Source Own representation, based on the work of Hoekstra et al. [2011] and CSBI [2015])

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thereby avoiding water-scarce or vulnerable regions as much as possible; and the extent to which companies move toward more circular business models to avoid the extraction of water and/or replace hazardous chemicals with less harmful alternatives. It is worth bearing in mind that companies cannot always achieve market breakthroughs on their own. Cooperation among companies, across sectors, within supply chains, and even within governments and consumers is required for more systemic shifts, such as the transition from an animal-based to a more plant-based food system. For such transitions, ACTIAM assesses whether companies play an active role in the development and promotion of new products and technologies, participate in sector initiatives, and seek to achieve the necessary forms of cooperation. The lobbying activities and behaviors of a company may also provide valuable information. The Second Lever: Minimization As water use could be greatly reduced but never fully eliminated, a second important strand in a company’s water management is minimizing unavoidable water use, such as making operational improvements, adopting technologies that enhance water use efficiency, fixing leaks and spills, and reducing the use of (hazardous) chemicals. The Third Lever: Water Creation Also on the supply side, companies can take action to reduce their dependency on freshwater. The most straightforward way is by means of wastewater treatment to enable water to be reused. This could be either within the same company or across a number of neighboring or interconnected companies. An interesting example is the use of hot water released in electricity generation to heat greenhouses. Alternatively, ‘new’ supplies of freshwater could be generated by capturing and storing rainwater or—more artificially—by means of desalination. The Fourth Lever: Compensation Finally, companies can compensate their water-related impacts, for instance, by funding initiatives to improve catchment management or catchment restoration programs. A company could even become waterpositive by enhancing a catchment beyond its original state.

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3.2

Fostering Progress: Engagement

Furthermore, having identified the main water risks embedded in the investment portfolio, investors have a responsibility to ensure that companies adequately manage such risks. Different tools are available for investors that can be used to understand and exert influence on the management of water risks by individual securities. One of the important tools is active ownership. Many of the largest investors already have an active engagement strategy, aimed at gathering information on and changing corporate management of ESG-related risks. By rendering water an integral part of this strategy, investors can make portfolio companies aware of water-related risks, encourage them to take further action, and speed up the pace of transition. Different organizations have issued engagement guides for investors that are specifically focused on water risks, including a set of potential questions to be used; for example, Ceres (n.d.), the VBDO—Vereniging voor Beleggers Duurzame Ontwikkeling (VBDO, 2021), South Pole (2020), and the UN PRI—United Nations Principles for Responsible Investment (UN PRI, 2018). Additionally, the topic should be embedded in investors’ proxy voting guidelines. Initially, awareness raising should be the primary focus of active ownership strategies. Research by ENGIE Impact (2020) has shown that companies that have identified their main sustainability risks and impacts tend to react faster and be more successful in managing these risks than their counterparts. Companies should be encouraged to conduct a risk assessment that identifies their most material water-related risks. Both operational risks that arise directly from operations and risks that arise from the supply chain are relevant. To monitor progress, investors should advocate for increased transparency and improved data. If not doing so already, companies should measure, monitor, and publicly disclose their water withdrawal, consumption, and discharge. Becoming a signatory of organizations dedicated to corporate reporting on water can help increase the pressure on companies to report. CDP is a well-known example of such organization. Also, further enactment of disclosure regulation, such as the Sustainable Finance Disclosure Regulation in the EU, can stimulate increased transparency (EU, 2019). As soon as firms have developed a profound understanding of their main water-related risks, investors can use their engagement efforts to

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gain insight in a company’s management of such risks. A good management strategy is supported by ambitious and science-based targets that align with the local context. A guidance on how to draft science-based targets for nature is currently being developed by the SBTN—Science Based Targets Network (SBTN, 2020). Both targets and performance evaluations ought to be made public. Additionally, a successful water management strategy considers not only the company’s own water use, but all competing demands for water such that the total water use fits within the replenishable reserves of the catchment. As companies rarely are the sole consumers of water in a catchment, they should develop a strategy to consult with other stakeholders active in a catchment area. Stakeholders can include other companies, the local population, farmers and landowners, local (water) authorities, and NGOs, for example. Therein, it is also necessary to take into account the geographically adjacent landscapes, seascapes, and catchments. Lastly, companies must be motivated to participate in catchment restoration and enhancement. These are activities that return the ecosystem as close as possible, or even beyond, its state prior to a company’s activities in the region. Numerous initiatives exist that commit companies to improving their water management and that support them in their journey. Through engagement, investors can encourage companies to commit to such initiatives, hold those involved accountable, and transform these initiatives into standard metrics. Some (non-exhaustive) examples of initiatives are: . The CEO Water Mandate, initiated by the UN Global Compact. The initiative helps companies to advance their water stewardship, by identifying critical water risks, seizing water-related opportunities, and contributing to water security (CEO Water Mandate, 2022). The initiative focusses on own operations, the supply chain, collective action and interaction with other stakeholders. . The Alliance for Water Stewardship, which sets a global standard for water stewardship and certifies member companies against this standard (AWS, 2022). . The Glasgow Declaration on Fair Water Footprints, by Water Witness and CDP. An initiative that calls on businesses, governments, civil society, and external support groups in a collaborative effort to make water footprints more sustainable (Water Witness, 2021). Topics addressed are sustainable withdrawals, eliminating

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water pollution, access to sanitation, and the protection of nature and planning for droughts and floods. . The Wastewater Zero Commitment from the WBCSD—World Business Council for Sustainable Development is focused specifically on water pollution. Companies are committed to eliminating industrial wastewater pollution by 2030 (WBCSD, 2021). Active ownership strategies can be implemented both independently and in collaboration with other investors. Collaboration between investors on engagement initiatives can strengthen the credibility of engagement initiatives and enhance the investors’ leverage over a company. The localized nature of water-related problems makes this the ideal topic to collaborate with. Finding a sustainable solution for water-related problems in a specific basin requires cooperation between stakeholders as well as local knowledge about the region. Investors are in the ideal position to unite the different companies active in a basin and to motivate them to make a collective effort, addressing their common interests/benefits. NGOs and local authorities can be involved to share their knowledge on which solutions best fit the local circumstances. This approach is relatively resource-intensive, and collaborating with other investors makes it easier to scale up and expand to different regions and/or sectors. 3.3

Increasing Positive Impacts

In the transition to a water neutral society, investors can encourage companies to improve their water efficiency, reuse, and recycling, or reduce hazardous discharges—in other words, to reduce their negative impacts. Alternatively, investors can actively look for companies that support the transition through their products, services, and activities. According to the WEF—World Economic Forum (WEF, 2020), an estimated USD $670 billion in annual spending is needed until 2030 to address the challenge of water security. This is a great opportunity for investors to help speed up the transition to a water-neutral society. Portfolio companies can create positive water-related impacts in different ways, by reverting to either tech-based or natural solutions. First, companies can provide products, services, and technologies that reduce global reliance on freshwater resources. Research by the 2030 Water Resources Group shows that technological advances play a key role in the transition to a water-neutral society (The 2030 Water Resources

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Group, 2009). Less than half of the projected gap between water demand and supply by 2030 is expected to be closed by further improvements in productivity and increased water capture. The bulk of it, however, needs ground-breaking technologies or other innovative solutions in the field of water management. Alternatively, companies could mitigate negative risks and impacts by engaging in catchment restoration and rehabilitation efforts. There is a general consensus that not all water use can be avoided, so compensation must be found for part of the remaining usage. Different metrics can be used to measure positive efforts toward greater water neutrality. Which is the most suitable depends on the activity at stake: . For activities targeting water efficiency, the amount of water usage avoided would be a suitable metric. Avoided water use is calculated by taking the average amount of water saved by clients using the new technology or product compared to a baseline situation in which no water-efficient technologies or products were used. . For firms involved in water treatment, the volume of wastewater treated over a predefined period would form a better measure. The water treatment level and end-use of the water treated should also be taken into account. . For companies involved in the production of alternative water, such as rainwater harvesting or desalination, the amount of (potable) water produced would be a relevant impact metric. . For companies involved in restoration efforts, information on the size and type of the area restored would be relevant. Even more than is the case with negative water impacts, positive impact reporting by companies has not been standardized to any great extent. Guidance is provided by the IRIS + metrics created by the GIIN— Global Impact Investing Network. In most cases, however, companies develop their own proprietary methodologies for reporting beneficial water impacts. Including positive impacts as a compensatory factor in the water footprint model would require a change in the model as shown in Fig. 7. For the water quality footprint, absolute water avoided or restored would have to be replaced by the amount of water treated.

Fig. 7 Water footprint including positive company impacts (Source Own representation)

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This model is only a generalized representation of how both positive and negative impacts could be combined in a single footprint. If positive impacts are included as a compensatory factor, both positive and negative impacts must be accurately mapped, both geographically and chronologically. Negatives in geography A cannot simply be outweighed by positives in geography B, just as water consumption during the dry season cannot be offset by discharges during the wet season. Also, in order to fully offset the negative impacts, it is vital to align the size of an investment with the vulnerability of the region where the negative impacts occur. Impacts in water-stressed regions or periods require a larger offset effort than in water-abundant areas or periods. If a number of stakeholders collaborate on a rehabilitation project, double counting must be avoided and only each party’s fair share of the project can be included in water neutrality calculations. The offsetting impact must be adjusted for the aggregate water consumption of all participants combined and not merely for one company’s share of it. Lastly, ACTIAM believes that every portfolio company’s water management should at least adhere to a minimum standard. The compensation of negative impacts could be a solution in situations in which water use cannot be further reduced, but it should not serve as a free pass for continuously overextracting and polluting water bodies. It is due to these complexities, combined with a lack of quantitative company reporting on positive impacts, that ACTIAM does not currently use offsets in its water footprint calculations.

4

Main Challenges and Steps Ahead

Poor data disclosure by companies about their water use poses a distinct challenge for financial institutions in adequately comprehending water risks. In order to help investors on their path toward water neutral investment portfolios, the level and quality of reporting by companies must be improved. Additionally, more granular information is needed to improve the informative value of a water footprint and boost the accuracy of portfolio risks and impact assessments.

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4.1

Data Gaps and Inconsistencies

Footprints are still affected by large data gaps, eroding their reliability. Sustainalytics (2021) reports that only 7% of companies in their respective industry disclosed water withdrawal data in 2019. The percentage was even lower for water consumption, with only 3% of companies disclosing. Investors, therefore, need to step up the pressure on companies to enhance disclosure. Sector-wide initiatives such as the SASB (2022), the GRI (2021), the CDP (2022) and the TNFD—Taskforce on Naturerelated Financial Disclosures (2022) have an important role to play in standardizing such disclosures. 4.2

Contextual Nature

Unlike climate change, water is a problem with very local or regional implications. Moreover, geographical risk depends on much more than just water scarcity levels. A region’s vulnerability and biodiversity play a role, as do the competing demands for water, upstream and downstream impacts, and dependencies. For this reason, a profound understanding of the contextual nature of a company’s operations and their impact on specific geographies is key in understanding risks and impacts. Site-specific data is still difficult to obtain, however. More work is required to improve site-specific transparency. 4.3

Relative versus Absolute Values

Water intensity is a useful starting point for measuring company risk. Water intensity is the number of cubic meters of freshwater a company needs in order to generate a dollar of revenue. The greater a company’s water intensity, the more reliant its business model is on water; thus the greater the potential effects if the water supply is interrupted. However, information on water intensity must be combined with more absolute data, as risks and effects are also affected by the absolute levels of available water. The absolute water use of a company must be consistent with the overall level of demand and supply in a specific region. A company may use very low levels of water either due to the nature of its operations or because it uses water much more efficiently than its competitors—but it may still encounter buffers if the water supply runs out.

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Many companies monitor, report, and set targets for water intensity. Such figures tend to conceal a portion of the risk. Even if a company improves its water intensity, if its business grows, this could still adversely affect its absolute water consumption. ACTIAM deliberately employs absolute figures rather than relative intensity. If relative low water use were to be taken as an indicator, this would say something about the company’s efficiency. However, a low figure does not necessarily imply that the company’s risks and environmental impacts are equally low.

5

Conclusion

Water is a distinct challenge that touches on nearly every aspect of the economy. Both water risks and opportunities have an impact on investors, which is likely to intensify in the future. Despite the fact that Sandra Postel, the founding director of the Global Water Policy project and the 2021 Laureate of the Stockholm Water Prize, stated during the award ceremony that “we just don’t have [the] time to solve the water, climate, and biodiversity crisis in a piecemeal fashion,” this appears to be what is occurring (Postel, 2021, min. 35:20). The majority of mainstream investors tend to focus much of their sustainability efforts on climate change, with biodiversity now also emerging as a topical issue. Water, however, is underrepresented in investors’ environmental policies. Advancing investors’ understanding and managing of water risks is therefore essential. This chapter discusses ACTIAM’s management of water risks (ACTIAM, 2021b). In 2017, ACTIAM set itself the goal of attaining a water neutral investment portfolio by 2030. This means that the volume of water consumed by companies in the portfolio should not be greater than the volume that nature can replenish. To achieve this goal, both water quantity and water quality must be considered. This chapter discusses how investors can use water footprinting to measure progress toward water neutrality goals. A water footprint reflects the aggregate water consumption that portfolio companies in high-risk sectors retrieve from high-risk areas. The results of a water footprinting exercise can be used to select those parts of a portfolio in which urgent action is needed. Companies with high water footprints ought to be subjected to a more qualitative assessment that reflects on their water management. Depending on the results of the qualitative assessment, decisions can be taken on portfolio composition and engagement.

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What is essential in this respect is the action that a company takes to avoid, minimize, and compensate its water usage, or to find alternative sources of water. Companies with the most advanced water management have developed site-specific action plans with targets that cater for the needs of the specific catchments in which they are active. Companies collaborate with other water users in a local watershed to ensure that their combined water use and pollution remain within the region’s natural limits, while leaving sufficient water for the local population and the conservation of local biodiversity. Where needed, companies engage in catchment restoration and enhancement efforts. Investors can help speed up the transition by consciously selecting for their investment portfolios those companies whose products, services, and activities support the transition toward a water-neutral society, and by engaging with those companies that do not (yet).

References ACTIAM. (2017). Case study: aiming for portfolio-level water neutrality. https://www.actiam.com/4addb1/siteassets/4_verantwoord/documenten/ en/ri_case_study_water.pdf ACTIAM. (2021a). ACTIAM sustainable investment policy. A: Sustainable investment framework. https://www.actiam.com/491aac/siteassets/4_v erantwoord/documenten/en/a-actiam-sustainable-investment-policy.pdf ACTIAM. (2021b). The freshwater crisis. ACTIAM’s investor perspective on how to address water risks and opportunities. https://www.actiam.com/4a833d/ siteassets/4_verantwoord/documenten/en/actiam-water-policy.pdf Akpor, O., Otohinoyi, D. A., Olaolu, T. D., & Aderiye J. B. (2014). Pollutants in wastewater effluents: Impacts and remediation processes. International Journal of Environmental Research and Earth Science, 3(3), 50–59. https:// www.researchgate.net/publication/261834688_POLLUTANTS_IN_WAST EWATER_EFFLUENTS_IMPACTS_AND_REMEDIATION_PROCESSES AWS – Alliance for Water Stewardship. (2022). The AWS international water stewardship standard. https://a4ws.org/the-aws-standard-2-0/ Cambridge Dictionary. (2022). English dictionary. https://dictionary.cambridge. org/de/ CDP – Carbon Disclosure Project. (2019). Cleaning up their act. Are companies responding to the risks and opportunities posed by water pollution? https://cdn. cdp.net/cdp-production/cms/reports/documents/000/005/165/original/ CDP_Global_Water_Report_2019.pdf?1591106445

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CDP – Carbon Disclosure Project. (2020a). The time to green finance. CDP financial services disclosure report 2020a. https://cdn.cdp.net/cdp-produc tion/cms/reports/documents/000/005/741/original/CDP-Financial-Ser vices-Disclosure-Report-2020.pdf?1619537981 CDP – Carbon Disclosure Project. (2020b). A wave of change. The role of companies in building a water-secure world. https://cdn.cdp.net/cdp-pro duction/cms/reports/documents/000/005/577/original/CDP_Water_ana lysis_report_2020b.pdf?1614687090 CDP – Carbon Disclosure Project. (2022). Water. https://www.cdp.net/en/ water CEO Water Mandate. (2022). Endorse the mandate. https://ceowatermandate. org/endorse/ Ceres. (n.d.). Investor water toolkit. https://www.ceres.org/resources/toolkits/ investor-water-toolkit Ceres & GIWS. (2022). Global assessment of private sector impacts on water. https://www.ceres.org/resources/reports/global-assessment-privatesector-impacts-water CSBI – Cross Sector Biodiversity Initiative. (2015). A cross-sector guide for implementing the mitigation hierarchy. http://www.csbi.org.uk/our-work/mitiga tion-hierarchy-guide/ De Graaf, I. E. M., Gleeson, T., Beek, P. H. van, Sutanudjaja, E. H., & Bierkens, M. F. P. (2019, October). Environmental flow limits to global groundwater pumping. Nature, 574, 90–94. https://www.nature.com/articles/s41 586-019-1594-4 ENGIE Impact. (2020). Global executive survey: Known gaps and blind spots in corporate sustainability. https://www.engieimpact.com/insights/globalexecutive-survey-preview EPA – Environmental Protection Agency. (2021). Toxic and priority pollutants under the Clean Water Act. https://www.epa.gov/eg/toxic-and-priority-pol lutants-under-clean-water-act EPA – Environmental Protection Agency. (2022). PFAS explained. https://www. epa.gov/pfas/pfas-explained EU – European Union. (2013). Directive 2013/39/EU of the European Parliament and of the Council of 12 August 2013 amending Directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policy. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:320 13L0039 EU – European Union. (2019). Regulation (EU) 2019/2088 of the European Parliament and of the Council of 27 November 2019 on sustainability-related disclosures in the financial services sector. https://eur-lex.europa.eu/legal-con tent/EN/TXT/?uri=CELEX:32019R2088

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GRI – Global Reporting Initiative. (2021). The GRI Standards update 2021. https://www.globalreporting.org/standards/download-the-standards/ Hoekstra, A. Y. (2008). Water neutral: Reducing and offsetting the impacts of water footprints. https://www.waterfootprint.org/media/downloads/Rep ort28-WaterNeutral.pdf Hoekstra, A. Y., Chapagain, A. K., Aldaya, M. M., & Mekonnen, M. M. (2011). The water footprint assessment manual: Setting the global standard. https://waterfootprint.org/media/downloads/TheWaterFootprintAsse ssmentManual_2.pdf Hofste, R. W., Kuzma, S., Walker, S., Sutanudjaja, E. H., Bierkens, M. F. P., Kuijper, M. J. M., Faneca Sanchez, M., Van Beek, R., Wada, Y., Galvis Rodriguez, S., & Reig, P. (2019). Aqueduct 3.0: Updated decision-relevant global water risk indicators. Technical note, World Resources Institute, Washington, DC. https://www.wri.org/research/aqueduct-30-updated-decisionrelevant-global-water-risk-indicators IPCC – Intergovernmental Panel on Climate Change. (2022). IPCC Sixth Assessment Report. Impacts, adaptation and vulnerability. https://www.ipcc. ch/report/ar6/wg2/ NASA. (2015). Study: A third of big groundwater basins in distress. https:// climate.nasa.gov/news/2297/study-a-third-of-big-groundwater-basins-in-dis tress/ Postel, S. (2021). Stockholm water prize award ceremony and meet the laureates. https://www.youtube.com/watch?v=5Zl7jeJPR94 Raworth, K. (2018). Doughnut economics: Seven ways to think like a 21st-century economist. Random House Business Books. Rockström, J., Steffen, W., Noone, K., Persson, A., Chapin, F. S., Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., & Nykvist, B. (2009, September). A safe operating space for humanity. Nature, 461, 472– 475. https://www.nature.com/articles/461472a SASB – Sustainability Accounting Standards Board. (2022). The materiality map. https://www.sasb.org/standards/materiality-map/ SBTN – Science Based Targets Network. (2020). Science-based targets for nature: Initial guidance for business. https://sciencebasedtargetsnetwork. org/wp-content/uploads/2020/11/Science-Based-Targets-for-Nature-Ini tial-Guidance-for-Business.pdf Shiu, W. Y., Ma, K. C., Mackay, D., Seiber, J. N., & Wauchope, R. D. (1990). Solubilities of pesticide chemicals in water. Part I: Environmental physical chemistry. https://link.springer.com/chapter/10.1007/978-1-46123434-0_1 South Pole. (2020). An investor guide on basin water security engagement: Aligning with SDG 6. https://www.southpole.com/uploads/media/an-inv estor-guide-basin-water-security-engagement.pdf

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Making Water Count—Integrated Risk-Return and Knowledge-Based Models for Water Investments (The model of Aqua for All)

Josien Sluijs, Blanca Méndez, and Dieter Gramlich

1

Challenges of Sustainable Water Investments

The Sustainable Development Goal 6 (SDG 6) addresses the availability and conservation of water and sanitation for all (UN, 2015). It is one of the most important objectives to achieve from the international community through the year 2030. However, the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) estimate

J. Sluijs · B. Méndez (B) Aqua for All, The Hague, The Netherlands e-mail: [email protected] J. Sluijs e-mail: [email protected] D. Gramlich DHBW – Baden-Württemberg Cooperative State University, Heidenheim, Germany e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_10

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that, at the current rate of progress, only 81% of the world´s population will be equipped with safely managed drinking water by 2030. This statistic leaves about 1.6 billion people uncovered (WHO & UNICEF, 2021, p. 7). Regarding basic handwashing facilities, the coverage ratio is 78%, where 1.9 billion of people worldwide remain restricted as to basic human needs. The coverage ratio for safely managed sanitation facilities is even lower at 67%, with about 2.8 billion people remaining unserved (UN, 2022a, p. 38). WASH is the acronym for “water, sanitation, and hygiene.” It is an umbrella term for initiatives to achieve the SDG 6 (UNICEF, 2016). WASH comprises multiple solutions and enterprises to improve access to water and sanitation services such as, for example, water resources management, water supply services, irrigation systems, and wastewater management (OECD, 2022). Following the SDG declaration in 2015, UN member states have drawn plans and committed budgets. Water and sanitation have been perceived as part of the public sector’s domain and have been traditionally financed by grants and public funding (Afonso et al., 2022). SDG 6 investments therefore continued to being undertaken primarily from governments and public organizations such as UNICEF or the WHO as well as from donors and non-governmental organizations (NGOs). The world is not on track to achieve SDG 6 by 2030 because of the huge service and financial gap in the water and sanitation economy (UNWater, 2021). Progress toward achieving the goal is at risk of stagnation or even regression if action is not taken. To close this gap and achieve SDG 6 through 2030, large investments in water-related infrastructure and services are necessary. The OECD (2022) estimates that achieving access to water and sanitation for all through 2030 requires an amount of US$ 1,000 billion per year.1 However, in 2020 private sources only contributed US$ 17 billion to water-related investments (OECD, 2022, p. 12). Private investments—including impact investments, i.e., initiatives that strive to create both economic and social value (BCG et al., 2019)—are by far not enough, and there is evidence that the COVID19 pandemic has further decreased expenditure in SDG 6 projects (UN 1 In their Financing for Sustainable Development Report, the UN & Inter-agency Task Force (2022, p. 14) estimate that overall investments in water and sanitation, electricity, and roads in least developed and other low-income countries will amount to 9.8% of the countries´ gross domestic product.

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& Inter-agency Task Force, 2022). Despite the opportunities to achieve both financial and social return (UN, 2022b), several factors restrict water-related investments (see also Lardoux de Pazzis & Muret, 2021; OECD, 2022): – Investments in water and sanitation are perceived as not yet commercially promising; particularly, there is a lack of clearly defined revenue streams. – In many cases, WASH enterprises do not have sufficient collateral. Therefore, enterprises that want to engage in the water and sanitation sector struggle to get credit for their projects. – Public and donor funds that represent the majority in providing money alone are not sufficient to bridge the gap. – Existing funding opportunities mostly focus on piloting projects rather than targeting the projects to become investment ready and viable in the long run. The water and sanitation sector has been traditionally financed by public funds (OECD, 2019). However, these public and semi-public utilities cannot service the entirety of the population. Many nonprofit organizations have tried to solve this issue through development projects thereby providing methods of water collecting, such as rainwater harvesting and recycled wastewater methods that have been around for decades. The implemented methods were successful from an operational point of view (Wiegant et al., 2018). However, in the long term, the concepts have not been sustainable as they have depended on time-limited grants which cause a high financial burden for the partners involved. While there are many grants for piloting solutions, there are less funds available to support enterprises to scale and become investment ready. Particularly, small and medium enterprises (SMEs) established in water and sanitation struggle to get access to credit from public and private financial institutions (Lardoux de Pazzis & Muret, 2021). Mobilizing more private funds requires raising understanding of the sector and its potential to achieve social and financial performance (Afonso et al., 2022). This fact will support more water and sanitation enterprises to scale up their service delivery, reach more people, and sustain quality services. Impact investors and local financial institutions are willing to fund water and sanitation SMEs, but they do not have enough experience with the

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sector and therefore do not understand the risk-return profiles. These constraints restrict initiatives in the WASH sector that otherwise provide an economically and socially promising potential (UNESCO, 2019). To address this challenge, new approaches are necessary that make projects affordable and manageable for all participants. There is a need of innovative models to provide these SMEs with the tools to efficiently organize their WASH businesses, establish relationships with investors and financiers as well as to continue these relationships—not only bridging the initial funding gap but allowing the continuation of projects even post the establishment. In this chapter, we present Aqua for All’s objectives and strategies. Aqua for All is an international foundation based at The Hague, the Netherlands, which develops comprehensive approaches reducing the service gap by supporting innovations and enabling SMEs to scale. The foundation develops innovative finance instruments to bridge the finance gap in partnership with impact investors, global funds, and local financial institutions. By offering de-risking capital and technical assistance (capacity building), Aqua for All is able to have a leverage of 1:10 when mobilizing investments in water and sanitation. In the following sections, we introduce Aqua for All as an international foundation with the mission and expertise to fostering innovation, sustainability, and impact to transform the water and sanitation sector (Sect. 2). Aqua for All enables water-related investments by developing a unique set of blended finance solutions to fund WASH SMEs and to optimally coordinate the involved financing parties. In this chapter, we present these elements and their underlying strategy (Sect. 3). Furthermore, we elaborate on two innovative finance models developed by Aqua for All to serve as case studies to provide further evidence of the foundation’s groundbreaking and transformative approach to address water and sanitation challenges (Sect. 4). The chapter ends with concluding remarks pointing to potential avenues ahead (Sect. 5).

2 Catalyzing Water Investments---The Innovative Approach of Aqua for All Aqua for All is an international foundation working toward the achievement of SDG 6, primarily in Africa and Asia. The foundation develops and encourages market-based initiatives that facilitate access to safe and affordable drinking water and adequate and equitable sanitation for all (Aqua

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for All, 2022a; Sluijs, 2020). Aqua for All strives to create an innovative, sustainable, and inclusive water economy worldwide. As such, Aqua for All supports WASH enterprises and solutions that offer affordable, good quality, and reliable services for low-income families and communities. The foundation strives to economically empower people by building capacity and stimulating job creation. It also promotes climate change mitigation and adaptation to ensure WASH resilience in the communities served. Activities center around three thematic areas—drinking water, sanitation, and water resources management. The foundation was established in the Netherlands in 2002, and the Netherlands Ministry of Foreign Affairs is the main funder of Aqua for All providing further support and collaboration. Aqua for All works with different stakeholders toward transforming the water and sanitation sector. The foundation has a long-term approach to make the sector independent of subsidies and grants. Furthermore, Aqua for All involves regular market parties—for example, financiers offering commercial de-risking or providing liquidity—in its work at an early stage, so the exit is guaranteed. Aqua for All addresses the current service and finance gaps in the water and sanitation sector (Jones-Russell, 2021) and wants to bridge the discrepancy between water-related business solutions on the one hand and available financial resources on the other. Particularly, the foundation contributes to market development by supporting and financing innovative and market-based viable solutions and enterprises that allow a business model approach for WASH projects. Aqua for All also finances technical assistance and business acceleration trajectories. To increase access to finance and catalyze capital for the sector, Aqua for All provides de-risking for water-related investments, shares its knowledge on WASH with investors, and facilitates the integration of different stakeholders in the activities. While bringing together innovation, sustainability, and finance, Aqua for All acts as a thought leader aiming to transform the water sector by supporting the economic viability of WASH enterprises and catalyzing impactful investments in water. Furthermore, the foundation wants to motivate others to either work with them or use their methods. Figure 1 provides an overview of how Aqua for All integrates with stakeholders in the context of WASH enterprises and the incentives provided to them. Main partners in the initiatives are the local companies

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(SMEs) providing affordable fee-based services for households, institutions, and communities. Direct financiers, such as local banks and microfinance institutions, mainly provide loans. Additional investment partners include guarantee providers, multilateral agencies, global funds, impact investors, companies, and technical assistance experts. These partners provide either liquidity, guarantees, or expertise to facilitate the development of loan facilities locally. Aqua for All plays a critical role by connecting to all participants in the initiative, as the foundation is involved in the initiative’s early design and the search for investors and partners. Moreover, Aqua for All takes an active role in the organization of the overall initiative and provides funding and de-risking both for the enterprise itself (SMEs) and for the financial institutions (FIs) involved. It also contributes by providing technical assistance to improve sector understanding and by contributing to embedding WASH impact management in the initiative. For over two decades, Aqua for All has supported water and sanitation service providers worldwide. Since 2002, the foundation has contracted around e 200 million in funds to support over 300 organizations in 65 countries worldwide, mainly in Africa and Asia. Due to its activities, private service providers were able to complement public efforts by giving

Fig. 1 WASH framework of Aqua for All

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access to water and sanitation services to a vast majority of the population. This is especially the case for developing countries, where people have low income and live in rural areas, and where the social and healthrelated challenges from insufficient water and sanitation are high (Danert et al., 2020). In these countries, innovative and scalable solutions as well as capital are instrumental in order to bridge the existing service delivery and financial gaps in water and sanitation.2 Aqua for All fills these gaps by supporting market-based innovations and enabling business solutions to scale and to become viable and investment ready. While pursuing the overarching objective to support impactful and economically viable companies, Aqua for All uses multiple instruments and strategies (see Sect. 3) and combines them as it is suitable in the relevant context (integrative approach). The choice of using a single instrument and or combining various is flexible and tailor-made to the reality of the country. For this, Aqua for All follows basic principles (Sluijs, 2020) that may also be considered as the success factors of their engagement. These principles include: – Social and climate impact The overall mandate of Aqua for All and its specific criterion when selecting suitable companies is achieving sustainability and impact in the access of people to water and sanitation services. The SMEs must clearly address SDG 6 as the overarching target and service the communities. To achieve this end, Aqua for All has developed an impact management system with metrics that assess the social and climate risks and benefits of WASH enterprises. The system includes mechanisms to monitor the progress of the activities on an ongoing basis. The foundation uses this information to improve its operations and impact and reports this progress as well to its principal funder, the Netherlands Ministry of Foreign Affairs. – Long-term orientation Aqua for All aims at achieving progress in WASH enterprises and sustaining it in the long run. Rather than merely launching projects, Aqua for All ensures that the invested companies are successful on a continued timeline (Sluijs, 2020). This is also due to the finding

2 Toan and Angh (2022, p. 7) present a list of relevant factors to attract private capital supporting rural water supply.

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that many SDG investments “have positive financial returns, but long gestation periods” (UN & Inter-agency Task Force, 2022, p. 15). Considering a long-term perspective in the sense of strategic planning will also help to better assess potential factors affecting SDG 6 investments in the future as, for example, effects from climate change (OECD, 2022, p. 13). Considering the lifecycle process in total is also a consequence of less encouraging experiences with other models which mainly focused on piloting or replicating projects but did not sufficiently prepare them for the future by, for example, promoting their self-sustainability. In line with this fact, Aqua for All also strives to keep continued and even lifelong cooperation with the engaged financiers and to make use of this relationship in multiple initiatives. This will contribute to scalability and effectiveness, which in turn, safeguard access to WASH beyond the support or investment period in the SME. – Economic viability This principle expresses the need to enable service providers to make their ideas profitable. Within the scope of Aqua for All, all eligible enterprises must have plans to overcome their barriers to scale and thus become investment ready. Next to having clear longterm objectives, they need to have a forward-looking business plan to ensure profitability. Economic viability is a prerequisite to reduce dependency from grant funding. If the WASH SMEs involved are able to demonstrate their business case, they can also become eligible for commercial funding. – Cooperation Aqua for All brings together key public and private stakeholders to create an enabling environment for scaling up innovations and enterprises and attracting private capital. Particularly, the foundation connects with strategic partners in finance such as microfinance institutions, commercial banks, and impact investors to develop innovative and blended finance solutions. Aqua for All also brings together relevant institutions such as businesses, governments, knowledge institutes, and civil society organizations that can contribute to transforming the water and sanitation sector. These stakeholders do not regularly work together but have a common goal, namely, to contribute to achieving water and sanitation for all. These partnerships work on several levels, but all of them are important to build the international water and sanitation economy. They share the common goal that every person on the planet will benefit from affordable access to clean water and decent sanitation.

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3 Aqua for All’s Innovative Approach---Catalyzing Instruments 3.1

Overview

The innovative approach of Aqua for All comprises the use of different types of instruments to support WASH enterprises directly as well as engaging in partnerships with investors, corporates, and institutions for catalyzing and promoting investments in water and sanitation. Instruments and partners are selected depending on the specific context of the enterprises and initiatives (Sluijs, 2020). Aqua for All develops tailored solutions combining several instruments to achieve optimal results regarding the enterprises’ social impact and its business case. Particularly, the combination of instruments and institutions is customized to match the potential lack of knowledge in WASH projects—both from the side of interested investors and financiers—and to address gaps in the enterprises’ funds and collateral. Table 1 provides an overview of potential actions to be used from Aqua for All while enabling private investments in water and sanitation (see also the characterization of different institutions from Lardoux de Pazzis & Muret, 2021, p. 18). It also shows how the implementation of the different instruments may be split between Aqua for All and its partners. Although there are multiple instruments to choose from and various ways to combine them, the actions are guided by three main directions: – Fostering innovation and scalability as well as providing knowledge for business solutions and SMEs. The main objective is to make SMEs commercially viable and eligible for funding. – Using grants to catalyze public and private funds. Together with impact investors and funders, Aqua for All develops innovative and blended finance models to fund viable innovative solutions and SMEs. – De-risking investments in water and sanitation. Aqua for All aims at enabling viable water and sanitation service providers to overcome their barriers to scale and become investment ready. To foster innovation and scalability, Aqua for All shares expertise in the field of SDG 6 investments and provides technical assistance. The foundation implements business accelerators in almost all countries

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Table 1

Instruments used in the innovative approach of Aqua for All

Instrument

Participation of Aqua for All

Participation of partners (WASH SMEs, investors, WASH donors, others)

1. Grant funding for innovation and scale

Grants for scaling water and sanitation businesses: Identification of (a) market-based solutions and (b) commercially viable enterprises. Search for co-financiers, service providers partners, liaison with (local) authorities, etc. Review of applications, assessment of business plans and risks, advice, and support Design and funding of tailor-made acceleration programs that match the needs of the local water and sanitation sector. Selection of acceleration and technical assistance service providers. Connecting selected enterprises with potential financiers Consulting/coaching SMEs. Transfer of water-related and financial knowhow, providing expert support to WASH SMEs. Coordination and co-creation with technical assistance providers Developing and participating in initiatives to increase sector understanding among private financiers

WASH SMEs send applications and progress reports, develop business plans, participate in acceleration programs WASH donors co-fund the SMEs and contribute with technical assistance and network

2. Business acceleration

3. Technical assistance

WASH SMEs participate in the business acceleration trajectory. Responsibility for continued operations. Buy-in of senior management Acceleration or technical assistance providers co-design and implement the programs WASH SMEs promote training of their staff and management Staff of FIs and impact investors receive training on WASH financing Impact investing networks support Aqua for All to promote and create greater understanding of the sector

(continued)

where it operates. In Burkina Faso and Ethiopia, for example, Aqua for All launched business acceleration programs to support local WASH enterprises (Aqua for All, 2021a, 2021b). The programs included training, coaching, and technical assistance to improve the enterprises’ internal

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(continued)

Instrument

Participation of Aqua for All

Participation of partners (WASH SMEs, investors, WASH donors, others)

4. Risk-taking (de-risking)

Grants for first/second-loss capital, guarantees. Investigation of potential risk takers. Substitutions where collateral is unavailable Assistance to local financial institutions for building capacity and/or market studies, development of blended and innovative finance solutions for the sector, addressing donors, connecting with private and public investors and funders, facilitating/supporting search for guarantors. Providing Impact-Linked Finance and performance-based incentives Development of WASH indicators and tools to measure social, climate, and financial risks and return Support to embed WASH IMM in WASH SMEs and financiers’ systems

Public or private financiers provide guarantees or additional second- and third-loss capital

5. Catalyzing investments/ external funding/ blended finance

6. Impact measurement and management (IMM)

Private financiers provide liquidity for financial institutions to establish WASH SME loan portfolios Local banks and FIs provide liquidity to develop their WASH portfolios Identification of pipeline of potential investees

WASH SMEs and financiers integrate WASH IMM in their systems Financiers report back on the loan purpose and the financial and social (and environmental) impacts of the loans

capacity and business plans. Participating enterprises that complete the acceleration program can become eligible to receive Aqua for All funding. A major strategy of the foundation is engaging SMEs to scale and investing in digitalization, renewable energy, or the modernizing of operations to ensure affordable and reliable WASH services. The reduction of the production cost of water and sanitation services reduces the breakeven point of profitability and increases the demand for these services with a further potential for economies of scale. As WASH SMEs become economically promising, they also become eligible to receive private finance and be more sustainable in the long run.

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The access to private capital reduces or even ends their dependency on public or philanthropic grants. Particularly, Aqua for All develops innovative finance facilities and programs to attract private and institutional funds. This contribution ensures access to finance for investment-ready WASH SMEs. Where in the past the public sector and donors have financed most water and sanitation projects, additional capital from private financiers is needed to involve and stimulate activities in the future (Afonso et al., 2022). New sources of finance have emerged, but these new sources and WASH enterprises need to be brought together (Roelofs, 2022). Blended finance (OECD, 2019) involves the combination of “private sector investment for social purpose […] with public or philanthropic funds” (Buckland et al., 2020, p. 15). Aqua for All develops innovative, blended finance models in partnership in local financial institutions and impact investors to develop loan facilities for WASH SMEs. Besides, Aqua for All has developed an Impact-Linked Finance program for WASH enterprises aiming to scale while creating additional impact.3 Besides providing funding and technical assistance to make WASH SMEs more efficient and commercially viable, Aqua for All facilitates investments in the water sector by providing de-risking and technical assistance to financial institutions and (impact) investors. In the last years, private financiers—in particular, impact investors—have become more interested in investing in water and sanitation, but due to the little knowledge of the sector among investors, its risk is also not understood. Aqua for All develops benchmark indicators to improve risk-return analysis and provide a more transparent calculation basis. This can encourage impact investors to step in. The foundation also participates in initiatives that help impact investors and local financial institutions understand and embrace the water and sanitation market. These instruments contribute to the strong leverage of Aqua for All’s grants. In 2021, Aqua for All’s total contracted portfolio had a leverage of 1:4.2 (Aqua for All, 2022a, p. 5).4 In the case of the innovative finance facilities, Aqua for All grants can have a targeted leverage up to 1:15. Aqua 3 See Sect. 3.2 below. Impact-Linked Finance is a finance concept, which links “financial incentives to social purpose organisations to the achievement of social performance targets” (Buckland et al., 2020, p. 6). 4 In 2021, the innovative finance portfolio itself had a leverage ratio of 1:8.8 (Aqua for All, 2022a, p. 15).

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for All focuses on catalyzing private capital because of the huge funding gap to create a sustainable and resilient water and sanitation economy. However, it believes that local, private parties are best situated to enter a dialogue with their governments in this transformation process and to shape the enabling environment. Examples of this phenomenon include networks built by local banks with (semi) government agencies. In this way, compliance with the law and regulation is also observed. After all, their local investments are at risk. 3.2

Selected Catalyzing Instruments

Aqua for All uses the different innovative finance instruments within its portfolio in combination, whereby single instruments are designed according to the WASH SMEs’ characteristics and their interaction with other instruments. While the impact of a single instrument should be measured within the overall approach, we present the following selected instruments on their own for a better understanding. 3.2.1 Business Acceleration and Technical Assistance Aqua for All has funded and developed several business acceleration programs and technical assistance trajectories to build pipelines and strengthen WASH SMEs in selected countries. Business acceleration programs aim to support innovators in developing solid and fundable (business) plans. Aqua for All’s accelerator programs support entrepreneurs in validating their business models and prepare them for attracting funding. The programs work at the individual level as they support peer-to-peer learning and align with partners to foster development and growth. Aqua for All is involved in the design of these trajectories but does not implement them. Rather, local providers implement business acceleration and technical assistance as they are better acquainted with the country’s realities and needs. Aqua for All provides technical assistance to WASH SMEs and utilities to build up or grow their water and sanitation activities. For enterprises that are not yet fully eligible for investing, Aqua for All provides technical assistance support to capacitate the enterprise to become investment ready. This technical support can be provided in different ways, as, for instance, in the case of the Impact-Linked Fund for WASH. Technical assistance is also a key instrument for catalyzing investments in water and sanitation. Aqua for All funds technical assistance support for

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impact investors and local financial institutions to build internal capacity and reduce their knowledge gaps on WASH. Technical assistance can also be provided for market exploration and preparation before the local financial institution embarks in developing a loan facility for WASH SMEs. 3.2.2 De-Risking In many low-income countries, private service providers provide access to water and sanitation to the majority of the population. Like SMEs, in other areas contributing to the SDGs, these enterprises face a relatively high risk and show unfavorable risk-return ratios what makes them unattractive for traditional financial investors (Naeve, 2022). Therefore, a major requirement to make water and sanitation SMEs economically sound and to attract financiers is assuring that their business models are solid, risks are manageable, and risk-return ratios improve (Toan & Angh, 2022). Aqua for All’s risk management approach comprises different elements providing benefits for the enterprise itself and its stakeholders. As previously established, WASH SMEs continue to be uncommon, and often operate in fragile environments (Toan & Angh, 2022). It is therefore first important to assess and evaluate the potential risk factors of the businesses. Aqua for All is already involved in the conceptual phase of WASH investment initiatives and co-develops their design and strategy. This early involvement and ongoing technical assistance keeps risks low and operations transparent. In addition, Aqua for All initiated the development of benchmarks and indicators to increase the risk-return perception and to measure the financial impact of water and sanitation investments. In partnership with the European Microfinance Platform (e-MFP), Aqua for All has set up, and currently leads, the WASH Action Group (Afonso et al., 2022). The WASH Action Group of impact investors was created to fill in information and knowledge gaps among impact investors and to define WASH indicators that can be used for making investment decisions. To catalyze investments and to facilitate the involvement of private financiers, Aqua for All can provide different de-risking options. In some cases, the foundation assumes the first (or second) loss from the investment itself. As Aqua for All is one of the facilitators, it has extended expertise funding WASH SMEs and is in contact with all stakeholders. Thus, it can make a reliable risk analysis, which is a prerequisite for taking risks of its own. In other cases, Aqua for All cooperates with institutions

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providing de-risking (guarantees). Namely, international development or multilateral organizations have both a public and economic interest in fostering SDG initiatives while taking over risks. Due to the reputation of Aqua for All, its experience in the sector, its in-house knowledge, and repeated cooperation with guarantee providers, the involvement of institutions for joint risk-taking is facilitated. 3.2.3 Impact-Linked Finance (Impact-Linked Fund for WASH) Under the special program Impact-Linked Fund for Water, Sanitation and Hygiene (ILF for WASH), Aqua for All supports WASH SMEs that are commercially viable, offer affordable services, and contribute to SDG 6. As such, they can become attractive for impact investors who want to sustain their invested capital (financial performance) and at the same time support sustainability goals and create impact. Through innovative finance models, commercial and impact objectives can be integrated into the design of the return profile from impact investments in WASH SMEs. It also allows the foundation to address the “missing middle,” i.e., those SMEs that are sustainable but cannot qualify for microfinance and are perceived as too risky and small for commercial lending (Naeve, 2022). In this context, Impact-Linked Finance is the practice of linking financial rewards for commercial enterprises to the “achievement of positive social outcomes, thereby improving the risk-return profile” (BCG et al., 2019, p. 4). The creation of social benefits is specifically rewarded, whereby the financial return can take the form of additional payments, reduced interest costs, or other types of compensation. Integrating economic performance and positive impact combines elements of impact investing, results-based finance, and blended finance (Buckland et al., 2020). Aqua for All has co-developed ILF for WASH. This program enables water and sanitation enterprises in Africa, Asia, and the Middle East to access finance under “better terms for better impact” (Aqua for All, 2022b). ILF for WASH is a partnership between Aqua for All and Roots of Impact, a manager of catalytic capital based in Germany (Roots of Impact, 2020). ILF for WASH targets established, impact-driven water, and sanitation enterprises with a clear business model and a strong revenue model. To be considered in the program, these enterprises must have reached—or be close to reaching—financial sustainability and must be raising investments of a minimum of e 500,000.

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ILF for WASH uses several Impact-Linked Finance instruments as well as technical assistance to tackle challenges related to impact measurement and management. The program’s main funding instrument is the Social Impact Incentives (SIINC). SIINC is part of the suite of Impact-Linked Finance instruments and provides payments for the impact achieved (Abbas et al., 2022). This additional revenue stream enables enterprises to improve their profitability, attract investments, and accelerate scaling. In the long run, these actions leverage enterprises’ potential to reach self-sustainability and profitability. The program implementation includes different phases, namely deal sourcing, diligence, deal structuring, outcome contract, and impact verification and management. Deal structuring is the core of the SIINC process. The crucial point of this phase is to identify first where frictions lie between the business model and the social impact, and second how SIINC can help the company deepen the impact. Of particular importance is the assessment of the baseline impact generated to date by the impact enterprise and the potential for an incremental increase of impact by scaling the operations. The baseline assessment is crucial for defining a viable benchmark for incentivizing additional impact via outcome-based payments. Khmer Water Supply Holding (KWSH) provides access to safe and reliable on-premises piped water in (semi) rural areas in Cambodia. KWSH was selected as a pilot transaction on SIINC, which is one of the instruments under ILF for WASH (KWSH et al., 2021). Due to the high cost of investment, low-income households living in areas harder to reach and sparsely populated are often left unserved. The two SIINC metrics were co-defined in agreement with KWSH. They include growing the number of households connected in Puok and Chhlong districts, and the additionality5 level of new license areas. Table 2 presents the criteria and metrics upon which the assessment of additionality for KWSH is based. The lack of impact baseline data (UN-Water, 2021) is a major barrier for impact enterprises to receive Impact-Linked Finance. So too is, more importantly, the lack of a system that allows sustainable practice and

5 Roots of Impact defines additionality as a “measure of whether the financial investment

would otherwise have been made. At Root Capital we consider a loan high additionality if no other lender (social or commercial) would make it on the same terms; medium additionality if only another social or public lender would make it on the same terms; and low additionality if any other lender (social or commercial) would make it on the same terms” (Naeve, 2022, p. 3).

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SIINC metrics—Application to KWSH

Table a: Metrics

Metric 1

Incentive

Increase the percentage of Increase outreach to more households connected in Puok and impactful license areas Chhlong 60% (average) See Table b) 72% (average) See Table b) e 350,000 3 years Every 12 months

Baseline SIINC target Payments (up to) Transaction period Impact verification Table b: Criteria for Metric 2* IDPoor** households living in the area Pipeline coverage rate upon purchase Households with access to clean water

Metric 2

Low additionality (%)

Medium additionality (%)

High additionality (%)

< 16

16–17

> 17

> 37

26–37

< 26

> 43

30–43

< 30

*SIINC for WASH will only reward medium and high additionality areas. **IDPoor: Identification of Poor Households (DIPHC, 2022). Source Own representation based on KWSH et al. (2021, p. 6)

implementation of impact measurement and management (Lardoux de Pazzis & Muret, 2021). These barriers are particularly important to overcome for investors seeking to achieve social benefit from their participation in WASH projects and build on reliable measures of both financial and social returns. Roots of Impact is responsible for modeling impact, while the impact enterprise will be responsible for the financial modeling. This stage is also often the one in which the quality and maturity of impact data, or impact gaps will be revealed. The ILF-readiness Bootcamp is therefore incorporated to address such gaps and improve the efficiency of the program. Through the ILF-Readiness Bootcamp, the facilitators work directly with enterprises and get them up to speed in a shorter timeframe. To further strengthen the capacity of wider eco-system players, i.e., the service providers, ILF for WASH includes Train-the-Trainer modules. Through the Foundational Support, enterprises get technical assistance which allows them to receive Impact-Linked Finance within one to two

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years. All these elements also aim to facilitate the long-term development of a pipeline of ILF-ready impact enterprises. Aqua for All will be closely involved throughout the structuring of the SIINCs to make sure that the incentives reflect its impact objectives and to provide its sectoral expertise on the scope of the intervention. The amount of SIINC payments in the payment phase will be directly linked to the social contribution of the enterprise and depend on the impact model (Abbas et al., 2022). The modeling will be undertaken in close cooperation with the enterprise to ensure that both parties agree as to the payment triggers and the conditions surrounding the disbursements. Aqua for All will provide non-repayable funds to the impact enterprises engaged with the program. Aqua for All strives to establish long-term and system-changing solutions for social problems. In line with this goal, the issue of the “exit strategy” is addressed explicitly in the selection criteria for relevant social enterprises, and a concrete plan should already be in place from the side of the enterprise. This means that every supported impact enterprise needs to have a feasible plan in place about how to achieve financial sustainability without dependency on grants or donations after the intervention period. 3.2.4 Impact Measurement and Management Managing, measuring, and monitoring social and environmental development impact and results are core to Aqua for All’s work. The foundation realizes that all of its interventions have consequences—not just for individual stakeholders, but also for whole communities and for the market at large. The interventions can expand the provision of water and services and create jobs. They may also have positive and negative effects on the wider society and the environment. When Aqua for All works on innovative finance transactions, the foundation wants to make sure that the grant funding is both mobilizing the much-needed capital to bridge the SDG 6 financing gap and contributes to impact areas targeted from Aqua for All: – – – – –

Increased access to safe drinking water, Increased access to safe sanitation facilities, Inclusive and pro-poor WASH portfolios, Improved business performance, Low carbon and climate resilient WASH infrastructure,

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– Women’s empowerment. Aqua for All has developed an integrated impact measurement and management (IMM) system. Aqua for All’s IMM system is inspired by the Impact Management Project. Managing the impact of an investment, or portfolio of investments, means taking into account the positive and negative impacts of the underlying enterprises/assets, as well as the investor’s own contribution. Aqua for All’s approach considers IMM as a process which is integrated in all stages of any initiative: – Origination: Familiarization between Aqua for All and the financial institution (FI). – Due diligence and concept development: Information sharing, screening, and co-creation. – Full proposal approval and partnership agreement: Ensuring that the initiative with the FI is aligned with Aqua for All’s mandate and expectations as well as securing and formalizing commitments. – Execution, monitoring, and reporting: Appropriate oversight and alignment of objectives working toward creating impact. Responsive management of results. When working on innovative finance transactions, Aqua for All aims to track the impact of the partner organization’s (e.g., a bank) interventions in the water and sanitation sector as well as to track progress made on the side of the investees/borrowers. Having access to timely and verified data helps both Aqua for All as well as the partner to make better-informed decisions about how to manage and monitor the efforts in the water and sanitation sector. When working with an FI, the FI will strive toward developing a WASH portfolio that is inclusive and climate resilient. The ultimate development results that Aqua for All achieves are generated indirectly, through the FI loans, systems, and processes. Aqua for All has developed an IMM system that includes social and climate considerations into the approach when working with FIs. The foundation developed a “FI Toolkit” in 2022. The FI Toolkit is a bundle of “living documents” which are intended to guide the Aqua for All team as well as the FI throughout the partnership process, to ensure that the partnership and operational

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implementation is aligned with the development and climate mandate and impact management system of Aqua for All. 3.2.5 Mobilizing the Impact Investing Sector for WASH According to the Global Impact Investing Network “2022: Sizing the Impact Investing Market” report, 3,349 organizations are currently managing US$ 1,164 trillion in impact investing (Hand et al., 2022, p. iv). However, investments by private and impact investors only represent a very small part of the total water-related investments: US$ 17 billion as of 2020 (OECD, 2022, p. 12). Aqua for All shares its work and expertise in global industry platforms aiming at showcasing the business case for water investments. Aqua for All also participates in campaigns and conferences and organizes events to promote impact investments in water and sanitation and to raise awareness on the need to put water as a priority for business and economic decisions.

4 4.1

Aqua for All Case Studies Kenya Partnership with Sidian Bank Ltd in Kenya

4.1.1 Situation in Kenya In 2020, the population of Kenya was 53.7 million where 38.7 million people (72%) lived in rural areas (World Bank, 2022). The country’s GDP amounted to US$ 100.7 billion with a GDP per capita of US$ 1,872. As per the UN-Water SDG 6 data portal (UN-Water, 2022), in 2020, 71% of the population had access to a safely managed drinking water facility (rural areas: 52%), 19% used surface water (rural areas: 24%), and 10% of the population did not have regular access to safe water (rural areas: 13%). 58% of the population could use safely managed basic sanitation services (48% in rural areas), 33% had access to unimproved sanitation services (rural areas 41%), and 9% of the population (rural areas: 11%) remained without safely managed sanitation systems (UN-Water, 2022).6 By 2020, 32.7% of the Kenyan population (17.6 million people) had access to drinking water through a piped improved facility. 38.5% has access to drinking water through a non-piped improved facility. In its

6 In comparison, the ratios for Africa in 2020 are: 39% of the population used safely managed drinking water, 27% used safely managed sanitation, and 37% used basic hygiene (UNICEF & WHO, 2022, p. 1).

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Impact Report 2022, the Water Services Regulatory Board (WASREB) of Kenya reported that water coverage in regulated areas improved from 57 to 60% between the years 2020 and 2021 (WASREB, 2022, p. ii), growing only 1% from the coverage levels in 2019 (WASREB, 2021, p. 12). There is a need to improve the access to water and sanitation for the underserved population, and the Kenyan government has established several programs to support the achievement of SDG 6 by 2030. Achieving universal access to WASH by 2030 in Kenya requires US$ 12.9 billion in WASH investments to expand and improve WASH services. However, the Government of Kenya only has US$ 5.6 billion available, which means that the current government budget for water and sanitation is leaving a US$ 7 billion gap (USAID, 2022). In the Kenyan WASH sector, mostly micro, small, and medium-sized enterprises (MSMEs) provide services. Public water utilities have the capacity to serve about 23% of the 53,7 million population, enterprises provide water services to almost 70% of the population (Afonso et al., 2022, p. 10). For example, about 28.4 million people (53.6%) of the population rely on small-scale service providers to access water services in peri urban and rural neighborhoods. It has been estimated that there are between 15,000 and 20,000 small-scale water operators. One-third of them (around 6,900) provide piped water supplies to low-income households via household connections. These enterprises need access to finance to maintain and, preferably, scale their business to reach the approximately 3.5 million Kenyans (7%) who do not have regular access to safe water. These companies have limited financial resources, a comparatively low creditworthiness, and unfavorable cost conditions because of their small size. As in many countries, Kenyan water and sanitation service providers struggle to access commercial finance. In turn, catalyzing private investments in water is a challenging task. Local commercial banks are willing to step in, but they are not fully acquainted with the dynamic risk-return model and potential impact of investing in water. 4.1.2 Engagement of Aqua for All in Kenya Among the various countries in Africa and Asia in which Aqua for All is operating, Kenya plays a major role. About 46% of Aqua for All’s overall portfolio is invested in East Africa with Kenya as the top single country. Within the total portfolio of e 52 million funds contracted in 2021, Aqua

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for All has allocated e 22 million for projects in Kenya (Aqua for All, 2022a, p. 6). The foundation cooperates with various financial institutions and service providers in Kenya as well as with international donors to advance the availability of water and sanitation for households and communities. Aqua for All implements its dual approach in Kenya to develop the market and increase access to finance. To promote market development, the foundation funds WASH SMEs directly. In 2021, it launched the WA-KE UP Business Acceleration Program in partnership with cewas and Opero Services (Aqua for All, 2021c). The program supported 11 WASH SMEs to develop tailored acceleration strategies to scale up their service coverage and attract private investments. Further to the need to develop the WASH sector in general, the COVID-19 pandemic in 2020 and 2021 created additional frictions in available services and WASH investments in Kenya.7 Local service providers have been struggling because of the pandemic and needed support to maintain their current operations and regain their previous creditworthiness status (USAID, 2022). In April 2020, Aqua for All’s survey of a group of Kenyan water and sanitation SMEs has shown that these enterprises needed capital urgently for the expansion, repair, and maintenance of infrastructure or to transition to renewable energy sources as fuel prices rose. In addition to needing to further develop their capacities and strengthen their operational knowhow, there was a need for immediate assistance to secure their existence. These events led to the establishment of the WASH Loan Facility in partnership with a local financial institution, Sidian Bank, for the first time ever. Based on this experience, Aqua for All partnered with two other Kenyan banks to develop similar WASH loan portfolios. In total, the three Kenyan banks together initially committed to build a portfolio of e 15 million and overtime to a portfolio of e 52 million. Aqua for All allocated e 1.19 million for de-risking and technical assistance. For these partnerships, the targeted leverage of Aqua for All grants ranges from 1:8 up to 1:15. It is important to emphasize that these blended finance facilities were developed and implemented in collaboration with the local 7 In 2020, the Kenyan government had proclaimed free water for handwashing after the first COVID-19 case in Kenya. However, this led to a huge increase in the demand for water service. This increase caused rising operating costs for enterprises in the WASH sector and exceeded their capacities.

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actors and took into account local needs and challenges. To date, these initiatives have proved effective to secure sustainable access to water to low-income households and communities in Kenya. The blended finance approach can be scaled and replicated with other financial institutions in the country and globally. 4.1.3 Innovative WASH Loan Facility with Sidian Bank Ltd. With the multiple objectives to further advance investments in water and sanitation, to assist institutions and the government to overcome the challenges from the pandemic, and to support the marketability of the projects, Aqua for All has partnered with Sidian Bank. Together, the two institutions have established the COVID-19 WASH Loan Facility in 2020. The focus of the program is to provide sufficient capital to MSMEs to have an immediate effect on WASH projects. In light of the many restrictions the COVID-19 pandemic had imposed, the facility further had to respond to the higher risk in the MSMEs’ projects and specifically provide de-risking services. Sidian Bank was founded in 1984 with the aim of providing grants and technical assistance to NGOs who in turn offered loans to microenterprises (Sidian Bank, 2022). Since then, the bank has further developed its mission to now act as a partner realizing the entrepreneurial potential in the MSME sector. To achieve this goal, the bank offers an array of financial services designed to transform the Kenyan society in general. The WASH Loan Facility developed by the cooperation of Sidian Bank and Aqua for All involves a combination of short-term funding, de-risking services, and technical assistance (Aqua for All, 2022a). While the facility is technically managed by Sidian Bank, Aqua for All provides knowledge to Sidian Bank and own funds to cover part of the risk inherent in the WASH projects. An important feature of this partnership was to include Davis and Shirtliff, an equipment supplier that offered borrowers a 30% discount and free installation on water equipment, as well as manufacturers of small water storage tanks. Particularly, the modeling of the WASH Loan Facility includes the elements described below. – De-risking elements: Aqua for All’s financing includes part of the de-risking component as it provides a second loss on their WASH portfolio. Further to the second-loss piece from Aqua for All, the

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Fig. 2 WASH Loan Facility as an integrative model—cooperation of Aqua for All and Sidian Bank

funds granted include, e.g., a reduction in the processing fees to make them more affordable to the service providers. The facility involved the participation of an institution that secured the loans and carried senior risk. Particularly, the US International Development Finance Corporation provided the first loss guarantee. – Technical assistance: The facility involves the transfer of knowledge to Sidian Bank, training for loan officers and business teams, and market activation and promotion. The shared expertise involves elements to both monitor adequately the applicants for funds as well as to measure appropriately the impact from the supported projects. Figure 2 provides an overview of the modeling components of the WASH Loan Facility and their connectivity. The integrative approach of the funding and risk management model includes Aqua for All and Sidian Bank as the originators, the MSMEs as the WASH service providers, the Development Finance Corporation as insurer of default risk as well as further stakeholders. Based on the collaboration, the facility surpassed the e 4 million target initially set by August 2022, raising US$ 10 million in liquidity, having supported over 610 MSMEs, which then served about 1.9 million people.8

8 Sidian Bank plans a larger portfolio for the next phase of e 15 million.

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The partnership between Aqua for All and Sidian Bank was the first innovative finance facility of its kind to mobilize commercial funds for WASH MSMEs in Kenya and elsewhere. This successful initiative received public recognition and raised interest of other banks in the country and abroad. 4.2 From a Guarantee to Naivasha Ushirika Water Project to Developing a WASH SME Loan Portfolio with Family Bank 4.2.1 The Naivasha Ushirika Water Project In addition to commercial enterprises and public organizations, community-based water providers (CWPs) offer water services in Kenya. They are self-organized institutions with the objective to assume own responsibility for the supply of water and advance the social impact of water for their members (Daluwate et al., 2020; Dupuits & Bernal, 2015). CWPs are an integral part of the society and important in the development of their community as well in taking individual responsibility to achieve the SDG 6. However, as they constitute a private social initiative and are governed by representatives of the participating group, they are also considered to be economically less efficient and profitable in their operations. This perception and their lack of collateral further restrict their access to funding opportunities. During the COVID-19 pandemic, the CWPs in Kenya faced similar challenges in continuing their operations as their commercial enterprise counterparts. Particularly, they have been challenged by customers who were not able to pay their water bills because of the pandemic. The Naivasha Ushirika Community Water Project (NUW Project) is a community water service provider operating in the outskirts of Naivasha town in Nakuru Country since 2006. It provides water to 20,000 people in the community through a network of house connections and water kiosks. Due to the COVID-19 crisis, this community provider experienced a surge in demand for water, both for drinking and domestic use, including hand washing at household and marketplaces. Water pumping costs—electricity from grid connection—more than doubled, consuming about one-half of the revenues on a monthly base. Besides, many customers had difficulties paying water bills and, as a result, the NUW Project ran into negative cash flow. Looking at its operational expenses, NUW Project decided to invest in replacing the current grid energy supply with solar energy, which will significantly reduce costs

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in the long run. Experiencing payment problems from their customers and a decrease of their liquidity, they approached the commercial banks. However, given their perceived low creditworthiness, the COVID-19 related cash flow problems, and the lack of collateral, the approached banks denied their application for loans. 4.2.2 Funding the Hybrid Solar Water Pumping System To increase the efficiency of their operations and to look at their operational expenses, the NUW Project managers decided to invest in the replacing of the current grid energy supply with solar energy, which would significantly reduce costs in the long run. Before, Kenya Power provided power, yet at a rather expensive price. However, even the upgrade of their energy supply projecting a higher economic efficiency and performance was not sufficient to become considered eligible from commercial banks for the needed loan. As regards Aqua for All, the foundation considered the cost-cutting strategy of the NUW project as economically promising and also as contributing to an environmentally friendly energy production and having a favorable social impact. Therefore, Aqua for All decided to cooperate with Family Bank in facilitating the loan for this CWP. Aqua for All provided a first loss guarantee on the loan given to the NUW Project by Family Bank. By providing de-risking, a bank can estimate the actual risk and determine whether it finds the water and sanitation interesting to include it in its portfolio in the future. Besides, the CWP can access capital to meet its investment needs and build a track record for future funding. It fills the missing piece to build the NUW Project´s financial reputation and thus providing the needed funds. It may potentially materialize into a longer-lasting relationship between the CWP and the bank. Figure 3 shows the overall approach developed from Aqua for All, Family Bank, and the NUW Project. Because of the de-risking facility provided by Aqua for All to Family Bank to lend to CWPs, the NUW Project received financing to invest into solar energy, which enabled the NUW Project to continue its operations at much lower expenses and to provide WASH services to the community. In addition, the modeling of the shared funding comprises these success factors: – The CWP has got the means to sustain its operations. CWPs also find it hard to prioritize their investment needs and often need support on the business side of their organization. Through this process, they

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Fig. 3 The Naivasha Ushirika Water Project (NUW Project)—partnership between Family Bank and Aqua for All

indeed get acquainted to how to apply for funding from a bank as well as what is needed in this process. – The growth potential of the NUW Project is sustained, which will help establish its presence within the community to ensure access to drinking water. – Aqua for All has provided the NUW Project with the personalized tools that were previously absent in its organizational structure. The foundation has thus provided the organization with the base to flourish and succeed in a notably economic environment. – The project is further eligible for loans and is more attractive for investors. The guarantee to the NUW Project can be seen as a pilot for the partnership between Aqua for All and Family Bank to launch a larger initiative to finance CWPs without collateral. NUW Project is an example of the CWPs that would be supported under the initiative. 4.2.3 Results from the Partnership Family Bank—Aqua for All As a further result from the NUW Project, Aqua for All and Family Bank engaged in an ongoing cooperation to support similar demands from community-based organizations. In July 2022, Aqua for All and Family Bank launched a first-of-its-kind un-secured loan facility to facilitate lending to community-based water service providers (CWPs). Cooperatives, societies, trusts, sole proprietors, associations, Self-Help Groups, and NGO social projects are considered as CWPs. The partnership will

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contribute to increasing Family Bank’s capacity to finance investments which will result in better access to clean water and sanitation services in unserved or underserved areas. This lending program will specifically target community-based organizations by design and take into account the challenge of inadequate access to safe drinking water. Family Bank’s extended branch network will be instrumental to create more impact on eligible customers in a wider and farther scope. Under this partnership, Aqua for All will provide Family Bank with risk-sharing instruments (first loss) and technical assistance for loans to community-based water projects. This will allow Family Bank to expand its internal capacity to assess risk and increase the quality of the portfolio of targeted infrastructure projects. Thanks to the blended finance facility and technical assistance, the community-based initiatives can significantly improve their operations and expand their services to households that would otherwise remain unserved.9

5

Conclusions

The experiences in Kenya have set the foundations for future similar collaborations in the country and abroad. At the same time, Aqua for All continues to develop new blended finance structures to address the barriers which block water enterprises from accessing commercial finance. 5.1

Further Directions

– Partnership with Oikocredit to scale up the development of WASH loan facilities by local financial institutions In early June 2022, Aqua for All and Oikocredit International launched a two-year partnership to increase access to finance for water and sanitation enterprises and households in Africa and Asia (Oikocredit, 2022). Never before have an international foundation and an impact investor set up a facility at this scale to promote financial inclusion in water and sanitation. The blended finance facility for local MFI/FIs includes de-risking 9 It is expected that 140 community-based projects will be supported in the future and 280,000 people will gain improved access to clean safe drinking water and improved sanitation. The program is the first of its kind to provide unsecured loan facility to water service providers.

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capital, technical assistance, and access to liquidity to support local financial institutions enter the water and sanitation market. The partnership will benefit from Oikocredit’s large portfolio of financial institutions in combination with Aqua for All’s extensive expertise funding WASH SMEs and facilitating local financial institutions to build WASH loan portfolios. Aqua for All’s funds committed to this partnership will be leveraged by 1:10. Aqua for All will allocate e 1.5 million for technical assistance, de-risking and/or performance-based incentives. In turn, Oikocredit International will invest up to e 15 million to finance the development of the water, sanitation, and hygiene portfolios of its current and new financial inclusion partners (Oikocredit, 2022). Thanks to this partnership, local financial institutions’ capacity in WASH lending will be improved. Local WASH SMEs and low-income households will get increased access to finance, and WASH SMEs could expand and offer affordable water services to more people. This will result in healthier communities, more resilient water infrastructures, job creation, and reduction of the burden on women and girls as they would not have to take the long journey to fetch water far from their communities. The partnership kicked off in Senegal in September 2022. Currently, two Cambodian FIs are being supported with technical assistance. – Water Access Acceleration Fund (W2AF): Blended finance equity fund In 2023, the Water Access Acceleration Fund (W2AF) is about to be launched. This blended finance equity fund, with Danone as anchor investor, will be managed by impact investor Incofin Investment Management. Its committed capital comes from several public, private, and institutional investors, among them Aqua for All. The fund aims to accelerate access to safe drinking water globally by investing directly in water enterprises. Examples of eligible water enterprises include safe water enterprises, water technology companies, and enterprises providing water through decentralized piping infrastructure. Besides financing, W2AF would also offer pre and post investment technical assistance to the water enterprises to improve their operations and environmental and social impacts. Aqua for All is a strategic partner and investor in the fund and had been involved since the early stages of development of the fund and will provide de-risking capital (catalytic first

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loss) and funds for technical assistance. Further, Aqua for All provides technical expertise on water as well as market intelligence on the frontier markets where the fund will make investments. – The Challenge Fund: revenue-based loan financing In 2021, Aqua for All and Kenya Markets Trust (now Gatsby Africa) commissioned a study to assess the investment opportunity and barriers to small-scale water service providers in Kenya. The results of the study showed that the market of small-scale water service providers offers a viable investment opportunity for domestic lenders to contribute toward increasing access to water.10 However, these small enterprises have limited ability to access to finance because of inadequate licensing framework, their lack of bank-worthy collateral due to their informal nature. In partnership with local commercial banks, water service regulatory agencies, and private sector service providers, Aqua for All is in the process of setting up a Challenge Fund. This financing facility aims to enhance creditworthiness of small-scale water service providers to attract investment finance for micro and small water infrastructure projects.11 Combining technical assistance, targeted capex grant incentives and technology, the Challenge Fund will support a first-of-its-kind “revenue-based loan financing” program for water providers in Sub-Saharan Africa. Through the program, two local commercial banks will increase their capacity to rollout cashflow financing products for water infrastructure projects in Kenya. The Challenge Fund comprises multiple innovative ingredients with the potential to transform the sector, including creating revenue-based lending processes, establishing an investment platform (as a social enterprise) and developing a scalable approach to provide technical assistance in a cost-effective way for small-scale water service providers. The program builds on Aqua for All’s previous and current work in the water and 10 A survey of 368 small-scale water service providers located in Nairobi City Metropolis showed that 52.6% of them needed capital to expand their coverage, among others. 11 It is expected that the Challenge Fund will assist 150 small-scale water service providers to raise commercial finance up to e 2.7 million for micro and small water infrastructure investment projects. This could provide new or improved access for up to 200,000 people across the country and designed to contribute to climate resilience and adaptation.

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impact finance sectors and leverages on its current innovative finance partnerships in Kenya, including the facilities with Sidian Bank and Family Bank. 5.2

Accelerating Sector Transformation Toward Sustainability and Inclusion

Aqua for All has pioneered the development of innovative finance structures aimed to mobilize public and private investments in water and sanitation MSMEs and utilities that contribute to achieving SDG 6. The foundation has been the first one to use blended finance aimed to partner and support financial institutions to build substantial WASH SME loan portfolios. Aqua for All’s approach is transformative and drives the WASH sector toward becoming more inclusive and sustainable. By combining the smart use of grants for market development as well as for de-risking and technical assistance to catalyze investments, Aqua for All aims to accelerate the transformation of the WASH sector faster in an attempt to reach the targets of SDG 6. The initial accelerated transformation and positive results achieved show the effectiveness of using integrated risk-return and knowledgebased models for catalyzing the much-needed private capital. Without involving the private sector—as financiers or service providers—and without addressing both the service and financial gaps that hinder progress to creating a sustainable economy, there awaits a challenging—if not almost impossible—road ahead toward achieving the SDGs by 2030. Acknowledgements The authors want to thank Aarno Keijzer, Ramah Rugut, and Shabana Abbas from Aqua for All for their valuable comments to specific sections of this manuscript.

References Abbas, S., Struewer, B., & Baffioni, P. (2022). Pushing the water boundaries: How social impact incentives can make WASH enterprises more innovative, impactful and catalytic. NextBillion guest article, 20 June 2022. https://nextbillion.net/water-social-impact-incentives-wash-ent erprises-innovative-impactful-catalytic/

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Afonso, J., Kumar, S., & Ma, A. (2022). A brief Introduction to WASH for Impact Investors. European Microfinance Publication, Luxemburg. https:// www.e-mfp.eu/sites/default/files/resources/2022/09/A-brief-introductionto-WASH-%20for-impact-investors.pdf Aqua for All. (2021a). The PME performantes WASH programme. Aqua for All news, published 16 June 2021a. https://aquaforall.org/news/the-pme-perfor mantes-wash-programme/ Aqua for All. (2021b). Call for applications: Water and Sanitation Incubation Programme in Ethiopia. Aqua for All news, published 17 May 2021b. https://aquaforall.org/news/call-for-applications-water-and-san itation-incubation-programme-in-ethiopia/ Aqua for All. (2021c). Ready to WA-KE UP: Kick-off business accelerator programme in Kenya. Aqua for All news, published 4 October 2021c. https://aquaforall.org/news/ready-to-wa-ke-up-kick-off-business-acc elerator-programme-in-kenya/ Aqua for All. (2022a). Annual Report 2021, The Hague. Aqua for All. (2022b). Impact-linked fund for water, sanitation and hygiene (ILF for WASH ). Aqua for All news. https://aquaforall.org/ilf-for-wash/ BCG – Boston Consulting Group, Roots of Impact, & BCG Henderson Institute. (2019, January). Accelerating impact-linked finance. Buckland, L., Burnand, G., & Ho, M.-L. (2020). A review of impact-linked finance: Does incentivizing impact work? Report by Investing for Good for the Esmée Fairbairn Foundation. https://www.roots-of-impact.org/impactlinked-finance/ Daluwatte, D., Sivakumar, S., & Mutua, F. (2020, October). Community empowerment with community based water societies in rural areas driving them for community development equipping for development and policy dialogue. Global Scientific Journal, 8(10), 518–525. Danert, K., Adekile, D., & Canuto, J. G. (2020). Striving for borehole drilling professionalism in Africa: A review of a 16-year initiative through the rural water supply network from 2004 to 2020. Water, 12(12), 3305. https://doi. org/10.3390/w12123305 DIPHC – Department of Identification of Poor Households of Cambodia. (2022). About IDPoor. https://idpoor.gov.kh/en/about/ Dupuits, É., & Bernal, A. (2015). Scaling-up water community organizations: The role of inter-communities networks in multi-level water governance. Flux, 99, 19–31. https://www.cairn-int.info/journal--2015-1-page-19.htm Family Bank Ltd. (2021). Integrated Report & Financial Statements 2021. Hand, D., Ringel, B., & Danel, A. (2022). Sizing the impact investing market: 2022. The Global Impact Investing Network (GIIN).

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Water Risk in Real Estate: An Introduction to the Climanomics Platform Isabelle Jolin and Maya Michaeli

1

Introduction

Given that climate change creates weather patterns that are increasingly volatile and unpredictable, there is an increasing demand from the commercial real estate industry for tools and methods to protect their assets against potential catastrophic weather events. Indeed, the growing impact of climate-related risks has compelled various sectors to strive for and discover new practices to avoid disastrous financial consequences (European Central Bank, 2020). Warming oceans are expected to have a greater impact on coastal environments, while inland properties devoid of water will face more fire and soil-subsidence issues based on the occurrence of the catastrophic weather events. In other words, climate change will inevitably result in unprecedented changes to our land and its development as well as significant disruptions to our way of life.

I. Jolin Ivanhoé Cambridge, Concordia University, Montreal, QC, Canada e-mail: [email protected] M. Michaeli (B) Department of Finance, Concordia University, Montreal, QC, Canada e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_11

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The real estate industry has pushed for the development of new tools and technologies to measure and report all kinds of environmental disasters, from forest fires to floods and tornadoes. For instance, since hurricane season in the Unites States lasts through late November, current regulations on new building developments enforce protection from the strength of some common pattern storms. Architects have also been designing modern structures intended to increase resilience to severe weather conditions. However, a large portion of the United States’ current real estate stock existed before their local policymakers required designs to be more grounded (Smith, 2022) to withstand these environmental disasters, which points to a large number of vulnerable structures. Along with wildfires and other natural catastrophes, hurricanes, floods, and severe storms have continued to wreak havoc on all local communities, posing a significant financial burden for asset owners and financial institutions. Not only is the financial burden to homeowners devastating and life changing, but we must also not forget the magnitude of their emotional loss. They have built homes filled with memories, some for years and others for decades, and all these meaningful memories and emotional is destroyed. Land experts warn that climate change will act as a motivator for owners who are unprepared for the challenges posed by upcoming and changing weather patterns. Despite large corporations pouring many resources to assess the threat of climate change to properties, even the most refined data cannot keep up with the constant changes in climate and its associated impacts. According to the Consumer News and Business Channel (CNBC), in 2017, floods, hurricanes, and other natural disasters amounted to more than US$306 billion in damages to the US real estate market (Mooney, 2018), including the impact of hurricane Harvey that caused extreme flooding in Houston which totaled to US$125 billion in damages. All real estate stakeholders are affected by these environmental disasters, including corporations that sponsor, fund, and invest in Real Estate Investment Trusts (REITs) and other types of real estate investments. As these water-related risks are bound to continue to emerge with increasing consequences to our environment, companies must find the tools to understand these risks and further mitigate their water risk exposure. One example is the Climanomics platform, developed by The Climate Service team at S&P Global. As this chapter shows, Climanomics specifically addresses challenges in quantifying climate-related

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risks, including a variety of water risks, and allows users to make informed decisions to mitigate these risks and protect asset values. In the following, we present the platform and apply it to assess the water risk of a portfolio of real estate assets. As our analysis shows, the effects of water risk for the projected period 2020–2100 affect all types of the included real estate assets but are highest for investments in offices.

2 2.1

Water Risk in Real Estate Increasing Water Cost of Real Estate

In spite of some voices who deny anthropogenic climate change, there are undoubtedly significant changes happening in the climate around the globe. These climate-related consequences are also impacting the real estate market at every stage of the business, from higher operating expenses to the decline in market attractiveness at specific locations. Lately, the recurrence of storms and unexpected variations in weather has driven debates regarding future consequences of such detrimental events. Furthermore, a rise in the recurrence of hurricanes will surely increase risks of property damage (and/or destruction) caused by the whirlwinds. Waterfront communities are generally at the highest risk and are therefore the most vulnerable to property value depletion, as flooding adversely influences the structural integrity and overall value of the property. In addition, tracking the occurrence rate of and variations in these natural catastrophes by specific location has also been challenging. As the challenges continue to grow, many players are attempting to develop tools to help find the patterns of the expected natural disasters in order to provide an accurate asset valuation. With sea levels rising, there is a pressured incentive to avoid investing in high-risk areas. According to a study conducted by the Union of Concerned Scientists (UCS), over 300,000 dwellings and commercial buildings in the United States are in danger of severe and disruptive flooding over the next 30 years as a result of rising sea levels, which is predominantly caused by climate change and aggravated by major rainfall and storms (UCSUSA, 2018). Inherently, these risks create unanticipated expenses for the whole real estate market as the total value of these properties at risk combines to approximately US$136 billion (USCUSA, 2018). Although current property values do reflect these risks, communities, investors, and all other parties will eventually face these unaccounted financial losses.

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Steps can be taken to alleviate these burdens and risks by implementing ecological mindfulness in the real estate industry. To build homes that can withstand the challenges of our current climate circumstances, we should make more efficient use of resources and information, while simultaneously reducing our carbon footprint by choosing quality materials and creating land efficiencies, which is also part of the 2030 Agenda for Sustainable Development, more specifically the United Nations Sustainable Development Goal 11 (SDG-11) for sustainable cities and communities (Marquard et al., 2020). Two fundamental ways that we can implement the improvements required to support our current situation are through energy use and collection. A crucial first step is the development of designs that are more energy-efficient, i.e., the development of properties designed to withstand current conditions through changes in building materials or foundation structure. These improvements will eventually increase the overall value of these properties. Collecting data, assessing information, and growing knowledge will bring us to cooperate and make improvements to mitigate climate change risks. Otherwise, “we will end up underwater physically and financially” (Lewis, 2019). The particular climatic conditions that have become the new norm in today’s reality put pressure on the need for change in policies and regulations. “Floods are the most common natural disaster type worldwide” (Hennighausen & Suter, 2020, p. 366). For instance, recent research has revealed that Canada regularly experiences extreme weather events like flooding, heat waves, forest fires, and others. The yearly cost of the Federal Disaster Financial Assistance Arrangements (DFAA) program, which provides fiscal help to provinces and territories in the event of catastrophic natural disasters, will be nearly one billion dollars. Flood events are responsible for 75% of this cost. In the past decade, flooding has emerged as the country’s most pressing and impactful climate-related extreme weather event, causing financial and emotional stress to those impacted (Moudrak & Feltmate, 2019). These statistics are taken into consideration as Canada has similar climate and asset types to those of the United States, which can be used as relevant comparables. 2.2

The Implications of Water Risk for Various Stakeholders

The majority of partners have a few formal or informal strategies for coping with water scarcity, remembering their long-term reliance on

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communities and optional forms of agriculture. Although water shortages lower overall productivity for those who can least afford it, although in some models, it can spur productive change toward a more sustainable state by learning to recycle water previously used in the manufacturing stage for example. Learning to manage water use may diminish risk as some become more adaptable to using less water and perhaps even develop water-saving technologies. Nonetheless, it is important to note that there are limits as these hazards could overwhelm social structures or natural frameworks. Water stress and its environmental implications have undoubtedly many financial consequences that are not unique to the real estate market but also to agriculture, the food and beverage industries, and the energy sector. Many complications are caused by merely the location of the property or land. According to research conducted by BlackRock, findings show that the entire US Southwest (i.e., California, Arizona, New Mexico, and Utah) will suffer extreme water stress challenges by 2030. “Buildings in locations with severe water stress may face more stringent regulations on water efficiency in the future. Severe water stress can reduce the attractiveness of a location […] This, in turn could affect migration trends and local demand for property” (Bertolotti & Suo, 2020, p. 9). Communities in these areas will likely endure many physical and financial damage, which could be detrimental to overall local markets and their economic environments. Additionally, financial institutions hold a huge stake in the real estate market and thus have high exposure to property and climate related risks. As a result, financial institutions must consider water stress implications of investments related to real estate— notably investments in Real Estate Investment Trusts (REITs)—such as water management, regulations, and operational efficiencies. All these water risks have led to stakeholders requiring a better understanding and a stronger ability to evaluate the environments surrounding their investments, as they “expect regulators to increasingly demand more efficient use of water across industries” which will inevitably require additional expenditures for data collection and analysis, new technologies, and any damages caused by water stress disasters (Bertolotti & Suo, 2020). As escalating costs from natural catastrophes affect tenants, landowners, property owners, investors, insurers, mortgage holders, and financial institutions, there is a need to adapt to and prepare for these disasters in order to mitigate associated risks. Land benefactors and financial corporations are beginning to consider climate risk as a

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major factor in their decision-making. As water risks vary from floods to water shortages, the amount of land affected is on the rise. The Center for Research on the Epidemiology of Disasters (CRED) report on natural disasters in 2021 indicates the majority of the US$252 billion in global economic damage is composed of water related catastrophic events. Mainly, floods and storms dominated other disasters, as these were the most frequently recorded events in 2021 with 223 and 121 occurrences, respectively, out of the 432 catastrophic events recorded that year (CRED, 2022). The CRED database, for instance, “provides objective and evidence-based information to assess communities’ vulnerability to disasters, assisting policymakers to set priorities” (CRED, 2022, p. 8). Many other organizations and companies are developing new tools, technologies, databases, and methodologies that will benefit the world as a whole.

3

The Climanomics Platform 3.1

Introduction to Climanomics

The Climate Service, now a part of S&P Global, has developed the Climanomics platform based on a proprietary methodology that incorporates insights relating to decision-making while simultaneously assessing vulnerabilities and associated financial losses. Embracing its commitment to a sustainable future, S&P Global provides its clients with a software that helps model and assess their real estate portfolios’ climate-related risk exposure. More specifically, the Climanomics platform “quantifies climate risks in financial terms” by combining climate and socioeconomic data on climate-related risks, integrating hazard inputs and business data into econometric models, and converting the resulting risk exposure into financial terms (The Climate Service, 2021). Through this methodology, The Climate Service team at S&P Global provides relevant insights that enable users to perform an effective climate risk analysis using easily accessible real estate information, such as market value and geographical location. Moreover, Climanomics’ methodology is aligned with the well-known framework developed by the Task Force on Climate-Related Financial Disclosures (TCFD) . Climate-related risks addressed by the platform are classified in the same manner, which allows users to access

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Climanomics outputs to support their Environmental, Social and Governance (ESG) reporting in accordance with the TCFD framework. Climanomics’ methodology responds to three core issues: hazard, vulnerability, and risk. Hazards are defined as “changes in environmental or economic conditions associated with climate change,” whereas vulnerabilities refer to the responses of a specific asset to changes in these hazards (The Climate Service, 2021). Finally, these two concepts are tied together to measure risk as the financial impact resulting from the asset’s vulnerability at a given hazard level. These topics are covered in greater depth in the following section. 3.2

Climanomics Methodology and Modeling

3.2.1 Hazard-Change Modeling As mentioned above, “the climate-related change in the level of hazard exposure of an asset over time, relative to a historical baseline” is referred to as hazard modeling (The Climate Service, 2021). Each hazard is assigned to a specific metric based on data stemming from various climate models and other sources of data. The Climanomics platform addresses both physical and transition hazards since it is in line with the TCFD framework. Given our emphasis on water risks in particular, some of the physical risks are outside the scope of this study and will not be addressed in this chapter (i.e., extreme temperature, wildfire, and tropical cyclone). Additionally, we will disregard transition risks as they relate to the “transition to a lower-carbon economy” (TCFD, 2017), which is unlinked to water risks. Table 1 below summarizes the relevant physical hazards: The drought hazard metric is defined as the “annual probability of drought conditions above the historical 90th percentile, as compared to the baseline period (1980–2000) at the asset’s location” (The Climate Service, 2021). Using localized climate-model data for temperature and precipitation, Climanomics calculates a drought severity index. Both temperature and precipitation data come from the Coupled Model Intercomparison Project (CMIP). The CMIP5 represent the fifth phase of CMIP, developed by the World Climate Research Programme (WCRP) in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2014). Additionally, the variance in the values of projected climate variables at each location is estimated using NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP).

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Table 1

Summary of water-related physical hazards in climanomics

Physical hazard

Hazard type

Underlying variables

Data source

Drought

Chronic

CMIP5; NEX-GDDP

Water Stress

Chronic

Coastal Flooding Fluvial Basin Flooding

Acute

Temperature, precipitation Withdrawal, available renewable water Sea level rise, storm surge

Acute

Annual frost days, consecutive dry days, five-day precipitation, basin area, slope, impervious surface area, lake-storage area in each basin

World Resources Institute (WRI) Kopp et al., 2014; Muis et al., 2016 CMIP5; NEX-GDDP; World Wildlife Fund; HydroBASIN

As for water stress, Climanomics relies on Aqueduct 3.0, a well-known tool developed by the World Resources Institute (WRI). WRI defines Baseline Water Stress as the level of competition for available water and the degree to which freshwater availability remains an issue by measuring “the annual water withdrawals divided by the mean of available blue water” (Reig et al., 2013, p. 5). The platform incorporates WRI’s current Baseline Water Stress metric combined to projections from the 2020s to 2040s by multiplying the Baseline value by the change in WRI projections (The Climate Service, 2021). Climanomics models coastal flooding using data from two primary studies: data gathered by Kopp et al. (2014) indicates sea-level rise and storm surge, while Muis et al. (2016) provide flood-level return periods for thousands of coastal segments worldwide. Combining this data, the metric for coastal flooding calculates the localized annual probability of a 100-year flood event caused specifically by sea-level rise (The Climate Service, 2021). It is important to note that only assets within 10 km from the coast and above a certain elevation threshold relative to mean sea level are considered by Climanomics as being exposed to coastal flooding risk. In addition to elevation and distance from the coast, the speed at which coastal flooding is projected to increase depends on other location-based factors, such as changes in ocean dynamics, heat content, and salinity (Kopp et al., 2014).

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Finally, to calculate fluvial basin flooding, Climanomics employs a “statistical model of fluvial-basin flood volumes and depth that estimates changes in return period for the historical 100-year flood in catchment basins using three climate variables and four topographical variables” (The Climate Service, 2021). The climate variables include annual frost days, consecutive dry days, and maximum five-day precipitation, and are computed using the same model as for droughts (i.e., CIMP5 and NEX-GDDP). The topographic variables are extracted from the WWF HydroBASINS data and include basin area, slope, impervious surface area, as well as lake-storage area for each basin. Unlike coastal flooding, an asset does not need to be located nearby to a body of water to be exposed to climate-related changes in fluvial basin flooding given that the model includes flooding from all water networks within the basin in which the asset is located. 3.2.2 Vulnerability Modeling Climanomics’ methodology for vulnerability modeling estimates the direct financial impacts of each hazard is expected to have on a given asset type. The vulnerability of each asset type is characterized using impact pathways, which are defined as the specific ways “in which a particular asset type is impacted by a given climate hazard” (The Climate Service, 2021). These impact pathways are then incorporated into impact functions, which are used to model the risk based on both hazard and vulnerability. Impact functions can be either general or specific to an asset type and are based on an extensive body of peer-reviewed literature as well as government and industry sources. Impact functions estimate the financial losses—such as revenue, operating expenses, and capital expenditures—incurred by a specific asset class due to a hazard of varying intensity (The Climate Service, 2021). Importantly, a single hazard may harm an asset via diverse impacts, in which case multiple impact pathways would be required to characterize the impact function. For instance, a flood at a manufacturing facility could drive up repair and maintenance costs, degrade the equipment, and reduce productivity due to downtime. Additionally, impact functions are modeled according to three financial materiality perspectives based on asset ownership and investment structure: investor-owner, owner-occupier, and tenant (The Climate Service, 2021). Investor-owner impact functions refer to a situation in which an investment manager owns the asset and leases it out to others. While

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the manager is financially impacted by direct damage to the building, rental income, and operating expenses, they are not liable for the damage caused to tenants’ equipment, inventory, and revenue losses. The owneroccupier perspective reflects the impact functions of a company that both owns and uses the building. In this case, owner-occupiers are financially impacted through revenues, operating expenses, and capital expenditures related to employee productivity, utilities, and direct building damages. Finally, the tenant perspective reflects the situation of building tenants who lease either the entire building or a portion from investor-owners. Here, tenants are responsible for impacts linked to revenue and operating expenses, but not for direct building damage, which would impact the investor-owners instead. Some impact functions are also specific to locational and geographic considerations, meaning they vary depending on “whether the asset is located in a rural or urban area, and whether it is located in a tropical or temperate region” (The Climate Service, 2021, p. 17). Given that these factors do not impact on an asset’s vulnerability to the water risks addressed above, they are not directly relevant to this study. However, it is worth nothing that rural assets are more vulnerable to wildfires as opposed to urban assets due to their proximity to vegetation. Moreover, for some agricultural assets, “impact functions have been developed that distinguish between the physiological response of crops in tropical versus temperate climates” (The Climate Service, 2021, p. 17). 3.2.3

Business Data, Risk Modeling and Climate-Change Scenarios Business data is gathered from users to inform the vulnerability modeling as well as the risk calculation portions of the climate risk analysis. Depending on the type of user, Climanomics incorporates either business data regarding operations (i.e., corporate customers) or holdings (i.e., investment managers) based on the following five characteristics: asset type, asset ownership, asset location, asset value, and greenhouse gas (GHG) emissions data (The Climate Service, 2021). The last characteristic will not be addressed in this chapter since it is unrelated to water risks. As mentioned in the previous section, the asset type determines which impact function should be selected to model vulnerability. Similarly, data on asset ownership is required to select the appropriate financial materiality perspective, which will also determine the impact function.

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Information on percentage ownership may also be useful depending on the type of analysis. The asset’s location is also critical, not only to ensure climate data is processed for the correct location but also to allow the Climanomics software to add elevation data for each location as part of the data augmentation process. Finally, asset value is required to quantify the financial impact. More often than not, “corporate customers use replacement value or total insured value while real-estate investors use market value” (The Climate Service,2021, p. 24). The Climanomics platform employs the Modeled Average Annual Loss (MAAL) metric to measure the direct financial impacts caused by climate change. The MAAL is defined as “the sum of climate-related expenses, decreased revenue, and/or business interruption” and is reported as an annual value for each ten-year period (The Climate Service, 2021). One of the most notable features of the Climanomics output is its ability to present results in absolute and relative terms. Absolute risk, reported in millions of dollars, is a function of hazard, vulnerability, and asset value. In other words, this measure indicates the projected financial impacts in dollar terms. In this case, an asset with a high value but low hazard exposure and vulnerability may still pose a significant risk due to the asset’s considerable value. Alternatively, relative risk represents only a function of hazard and vulnerability, and it is expressed as a percentage of asset value (i.e., the MAAL divided by asset value). The benefit of this measure is that “it provides a perspective on exposure and vulnerability across assets, independent of their value” (The Climate Service, 2021). For instance, a low-value asset could hold relatively more risk compared to more valuable assets, despite its smaller MAAL. It is also important to note that the MAAL is expressed relative to a baseline period. The Climanomics output should be interpreted as “the expected delta in financial risk due to climate change” (The Climate Service, 2021). Historical baselines are also specific to each hazard. Similarly, MAAL outputs should be viewed as the annual average loss in any given year over a decadal period. To compute the risk for an entire decade, the MAAL for that particular decade should be multiplied by ten. It is also specified that the Climanomics software only provides a raw output of results without applying any discount rate to their projections, which is up to the user in subsequent analyses. Finally, while they do not apply to our study of water risks, the platform provides the financial impacts of climate-related opportunities. Expressed as Modeled Average Annual

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Gain (MAAG), they reflect the sum of climate-related financial benefits (The Climate Service, 2021). Finally, the Climanomics platform offers users with a selection of climate-change scenarios based on the Representative Concentration Pathways (RCPs) from the IPCC. The four RCPs included in the IPCC AR5 are available on the platform: RCP8.5, RCP6, RCP4.5, and RCP2.6. These scenarios vary based on human emissions of GHGs and the resulting global temperature increase by 2100, representing a broad range of possible climate outcomes. “The synthesis of climate variables for each emissions/RCP pathway forms the basis of Climanomics hazard metrics” (The Climate Service, 2021). In the “High Emissions” scenario, or RCP8.5, it is assumed that no major global effort will be undertaken to limit GHG emissions. Here, GHG emissions keep increasing over time and the global mean surface temperature will rise by 4.2 to 5.4°C by 2100. On the other hand, the “Low Emissions” scenario, or RCP4.5, assumes coordinated action to limit the global temperature increase around 2°C by limiting GHG emissions (The Climate Service, 2021).

4 Case Study: Mapping the Water Risk Exposure of REITs In this case study, we take on the role of an investor holding a diversified portfolio of US REITs. The objective is to map the portfolio’s exposure to water risks and identify the main source of risk that should be mitigated. We begin by gathering a sample of eight major US REITs, two for each of the four main asset classes: residential, retail, industrial, and office. The REITs were also selected to be geographically diverse in order to cover various regions across the United States. This geo-sectoral diversification should allow for a variety of water risks and levels of exposure. For each of those eight REITs, we extract property data using the S&P Global SNL Real Estate Property dataset for the following variables: Property Name, Property Type, Property size (sqft), Street Address, Latitude, Longitude, and Property Value. We then select the top-ten properties for each REIT based on Property size for which data is available as of June 30, 2022. For simplicity, we assume that all properties are held at 100% by the corresponding REIT. Results would likely differ if we used actual

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Table 2

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Descriptive statistics

Sector

REIT

Properties

Value (US$)

Industrial

EastGroup properties Rexford industrial Boston properties Cousins properties AvalonBay Mid-America apartments Simon property group The macerich company

10 10 10 10 10 10 10 10 80

US$1,329.8 M US$ 2,296.4 M US$11,231.4 M US$3,332.8 M US$2,585.0 M US$1,803.0 M US$5,528.6 M US$4,290.0 M US$32,396.9 M

Office Residential Retail Total

ownership percentages, as it would change the absolute and relative exposure of the REIT to each asset. Table 2 summarizes the resulting sample of 80 properties: We then uploaded the business data to the Climanomics platform. For each property, the software requires the following inputs: Property Name, Property Type, Latitude, Longitude, and Property Value. The RCP8.5 scenario is then selected to adopt a conservative approach. After exporting the Climanomics outputs, we clean the data to include only the relevant water risks, namely: Drought, Water Stress, Coastal Flooding, and Fluvial Basin Flooding. We also retain results over the entire 2020–2100 period to observe variations over time. Due to the complexity of estimating discount rates over such a long period, the raw values remain undiscounted for our analysis. We begin our analysis by examining the relative water risk exposure by asset type. For this portion of our analysis, relative risk is more relevant as it is not skewed by the asset value, which varies greatly between sectors and REITs. As shown in Fig. 1 below, the projected MAAL increases over time for all asset classes. As climate change worsens due to rising global temperatures, so do the financial impacts of water risks on real estate assets regardless of the sector. However, it is clear that the office sector REITs within our sample will be the most affected. By the 2090s, average annual losses are expected to range close to 6% of total asset value for the two office REITs, representing a significant write-off. This could be due to the underlying assets’ location, or the asset type itself being more vulnerable to physical water risks.

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Fig. 1 Evolution 2020–2100 of relative water risk by asset type (% of Asset value)

To provide another perspective, we break down the relative water risk exposure by individual REITs. Figure 2 shows these results below. In line with our previous findings, an office REIT stands out as a clear outlier. Indeed, Boston Properties shows significantly higher relative water risk compared to all the other REITs in our sample. Given that Cousins Properties (i.e., the other office REIT) holds among the lowest relative risk over the period, we can therefore conclude that the high office risk exposure in Fig. 1 is mostly due to individual characteristics of the properties included in Boston Properties rather than factors specific to the asset type. Based on these findings, we explore the composition of Boston Properties in greater detail to understand the main source of this water risk exposure. As a first step, we identify which assets are the top contributors by observing the distribution of the REIT’s MAAL over time. Per Fig. 3, Hudson Boulevard 3 + is the property that will be mainly responsible for the REIT’s projected financial losses stemming from water risks. Our results support the hypothesis that asset location is a critical factor for determining the magnitude of water risk exposure in real estate because this asset is located in the heart of New York City, a city well-known for its exposure to a variety of water risks. The final step to our analysis is to specifically determine which water risk, or hazard, is expected to have the greatest financial impact on

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Fig. 2 Evolution 2020–2100 of relative water risk by REIT (% of Asset value)

Fig. 3 Distribution of boston properties’ MAAL over time by property

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Hudson Boulevard 3 + during the sample period. As Fig. 4 demonstrates, drought and water stress do not pose any risk to the property. Instead, coastal flooding is revealed to be the primary water hazard for Hudson Boulevard 3 + . The MAAL imputed to coastal flooding increases exponentially, representing as much as US$126 M per year toward the end of the century. As an investor-owner, this is a very useful indicator for decisionmaking. First, we mapped our portfolio’s water risk exposure and found that, not only it would grow exponentially over the century, but office assets are the most vulnerable even today. Upon further analysis, to identify both the asset and the specific water risk that are likely to be responsible for the greatest financial losses within our portfolio. This aspect is crucial for real estate investors, who are increasingly under pressure to better understand, measure and report ESG risks and to seize the related opportunities. Conducting a water risk analysis through Climanomics also helps to better target capital investments in risk mitigation efforts to maximize their impact on portfolio value. In this specific example, addressing the risk of coastal flooding at Hudson Boulevard 3 + through innovative

Fig.4 Distribution of Hudson Boulevard 3 + MAAL by Water Hazard

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solutions would be the most efficient way to contribute to long-term resilience, thereby protecting the REIT’s market value as the asset will become more attractive compared to similar office properties in the same area. This case study is only one example of the uses and benefits of the Climanomics platform and could be developed in greater depth. For example, an investor would most likely adjust asset values according to their ownership share, and they would attempt to discount the MAAL using an appropriate discount rate based on their long-term view of the market. They would also analyze their entire portfolio, whereas our case study only looks at the top-ten assets in terms of property size. Furthermore, some limitations of the Climanomics platform could also be addressed through further analysis. The methodology only takes into account the asset’s type and geographic location, disregarding the property’s vintage as well as mitigation measures already in place or other water-efficiency initiatives, such as low-flow toilets or water pumps in the basement. Because of this simplification inherent to Climanomics’ methodology, it is possible that an asset’s MAAL could be over- or underestimated. Therefore, in-place water risk mitigation measures should be identified and taken into account by the users when analyzing the platform’s output.

5

Conclusion

As climate risk awareness continuously becomes a more pressing topic and is integrated in various industries, we must acknowledge its importance not only for our environment, but for our economy as well. According to scientific studies by the United States Environmental Protection Agency (EPA), human-induced climate change is predicted to increase the frequency or intensity of extreme weather events including heat waves and major storms (EPA, 2022). Awareness of these water risks in relation to the real estate market is key to minimizing potential financial losses to all parties involved. Fortunately, a variety useful tools are emerging, such as the Climanomics platform developed by The Climate Service team at S&P Global. As this case study demonstrates, the software’s holistic methodology based on an extensive body of literature from a variety of academic, scientific, and business fields allows all types of users to make informed decisions that are beneficial for both our planet and our wallet. This

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methodology enables investor-owners to mitigate unprecedented water risks which, although difficult to predict, could be accounted for with the right tools. The industry must have a better understanding of the effects of physical climate hazards on pricing and as well as the greater effects of these risks on future valuations. The availability of tools like Climanomics that facilitate this transition will only inspire more market participants to follow this market trend, as we must push toward a sustainable society and more resilient global economy.

References Bertolotti, A., & Suo, Y. (2020). Troubled waters: Water stress risks to portfolios. BlackRock Investment Institute. https://www.blackrock.com/us/individual/ literature/whitepaper/bii-water-risks-july-2020.pdf Centre for Research on the Epidemiology of Disasters (CRED) (2022). Disasters in numbers 2021 - EMDAT report. https://cred.be/sites/default/files/ 2021_EMDAT_report.pdf EPA – United States Environmental Protection Agency (2022). Climate change indicators: Weather and climate. Retrieved on October 25, 2022, from https://www.epa.gov/climate-indicators/weather-climate European Central Bank (2020). Guide on climate-related and environmental risks: Supervisory expectations relating to risk management and disclosure. https://www.bankingsupervision.europa.eu/legalframework/publiccons/ pdf/climate-related_risks/ssm.202005_draft_guide_on_climate-related_and_ environmental_risks.en.pdf Hennighausen, H., & Suter, J. F. (2020). Flood risk perception in the housing market and the impact of a major flood event. Land economics 96(3), 366– 383. https://www.muse.jhu.edu/article/758939. IPCC – Intergovernmental Panel on Climate Change (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland. Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X., Oppenheimer, M., Rasmussen, D. J., Strauss, B. H., & Tebaldi, C. (2014). Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future, 2(8), 383–406. Lewis, R. K. (2019, March 8). Factoring the effects of climate change into Real Estate Investments. The Washington Post. Retrieved October 22, 2022,

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from https://www.washingtonpost.com/realestate/factoring-the-effects-ofclimate-change-into-real-estate-investments/2019/03/07/aa60f186-3f7f11e9-a0d3-1210e58a94cf_story.html Marquard, E., Bartke, S., Gifreu i Font, J., Humer, A., Jonkman, A., Jürgenson, E., Marot, N., et al. (2020). Land consumption and land take: Enhancing conceptual clarity for evaluating spatial governance in the EU context. Sustainability, 12(19), 8269. MDPI AG. Retrieved from https://doi.org/ 10.3390/su12198269 Mooney, C. (2018, January 8). Hurricane Harvey was year’s costliest U.S. disaster at $125 billion in damages. The Texas tribune. Retrieved October 22, 2022, from https://www.texastribune.org/2018/01/08/hurricane-har vey-was-years-costliest-us-disaster-125-billion-damages/ Moudrak, N., & Feltmate, B. (2019). Ahead of the storm: Developing floodresilience guidance for canada’s commercial real estate. Intact Centre on Climate Adaptation. Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H., & Ward, P. J. (2016). A global reanalysis of storm surges and extreme sea levels. Nature Communications, 7 , 11969. Reig, P., Shiao, T., & Gassert, F. (2013, January). Aqueduct water risk framework. (World Resources Institute Working Paper). https://www.wri.org/res earch/aqueduct-water-risk-framework Smith, M. B. (2022). How is climate change affecting real estate? CoreLogic® . Retrieved October 22, 2022, from https://www.corelogic.com/intelligence/ how-is-climate-change-affecting-real-estate/https://www.corelogic.com/int elligence/how-is-climate-change-affecting-real-estate/ S&P Global (2022). S&P global climanomics. https://www.spglobal.com/esg/ solutions/the-climate-service The Climate Service (2021). Introduction to Climanomics modeling methodology. TCFD – Task Force on Climate-related Financial Disclosures (2017). Recommendations of the task force on climate-related financial disclosures. UCSUSA – Union of Concerned Scientists (2018). Underwater: Rising seas, chronic floods, and the implications for US Coastal Real Estate report. https:// www.ucsusa.org/sites/default/files/attach/2018/06/underwater-analysisfull-report.pdf

The Water Credit Risk Tool and Corporate Sensitivity to the Shadow Price of Water Dieter Gramlich and Henrik Ohlsen

1

Introduction

Forward-looking management is a key to preserve water as an essential and limited natural resource. The current use of water must consider the quantity and quality that will be available over the long run, both within the planet’s boundaries and capacities to regenerate water. Future population, economic expansion, and the effects of climate change will increase the demand for clean water, which will exacerbate the existing restrictions and disruptions in the hydrological system (Liu et al., 2017; WWF, 2020) and increase the competition for water among different stakeholders. These issues will, particularly, affect companies that are relying on water as a product, as a vital element of the production process, or as

D. Gramlich (B) DHBW—Baden-Württemberg Cooperative State University, Heidenheim, Germany e-mail: [email protected] H. Ohlsen VfU—German Association for Environmental Management and Sustainability in Financial Institutions, Frankfurt, Germany e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6_12

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a key component within their supply chain. Depending on their perceptions and reactions, water challenges may jeopardize the companies or even provide new opportunities. Water prices allocate water to different users and uses (Zetland, 2021). They are primarily influenced by economic factors such as the cost of production and transportation of water. Via water regulations, policymakers can also intervene on markets and directly or indirectly influence the price of water. Economic considerations reflect the current cost of water and adjust to extant relationships between available and required quantities. Changes in the water relevant context are typically priced when they occur but prices do not sufficiently anticipate these effects. From a business standpoint, this indicates that decisions based on current prices do not adequately account for the future. Consequently, the companies do not well prepare for the mid- and long-term water restrictions, and their ability to develop mitigation strategies is limited. Potential risks and lost opportunities apply similarly for all stakeholders who have shares in water-sensitive companies, namely financial investors (RMS et al., 2017). In addition to focusing on the value of water based solely on current needs, thus contributing to the “tragedy of the horizon” (Carney, 2015, p. 2; see also Hardin, 1968), current concepts of water pricing undervalue water because they center on economic aspects, while failing to sufficiently consider the value of water for further stakeholders. Conflicting interests arise where industrial water use reduces its availability for the general population or has an impact on the hydrological system as well as on biodiversity and nature as a whole (OECD, 2017). However, the expected restrictions in the availability of water and the increase of water regulations will imply a more serious trade-off between alternative uses of water within the economic, societal, and ecological system. This will also rebalance the weights of alternative stakeholders in their claim for water. The limited consideration of the value of water both from an intertemporal and inter-stakeholder perspective raises serious doubts about the usefulness of current market prices for water to adequately inform market participants and efficiently allocate water resources. Basing decisions on the present cost of water and an economic perspective only contradicts comprehensive and forward-looking (i.e., sustainable) strategies, notably those of companies and financial investors, reduces their ability to prepare for the future, and further decreases the effectiveness of water allocation in general (NCFA & PwC, 2018). Instead, a more comprehensive valuation

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framework and future water stress scenarios are demanded (Delevingne et al., 2020). Thus, company valuation concepts, including credit risk analysis, must consider emerging water challenges. There is an urgent need to analyze companies’ reliance/dependency on water and the risk-return effects from reduced quantity and quality of water as well as from increased water regulations. The majority of these effects will result in higher costs for water. While accounting for future scenarios, a company’s valuation must specifically include representative water prices, in addition to assessing the general sensitivity of companies to changing water scenarios and their ability to adjust to these changes. Tools to analyze the profitability and solvency of companies must be developed with respect to water related challenges. Cost effects as a major factor in the water-related company valuation can be captured by considering alternative and forward-looking prices for water, also known as the shadow prices for water (Bierkens et al., 2019). The shadow price of water (SPW) represents potential upper bounds for the future water costs (Ohlsen & Ridley, 2020), and it is calculated based on assumptions on upcoming water challenges and the integrative use of water. Financial analysis tools can incorporate shadow prices for water to assess their impact on the performance of the firm, estimate companies’ sensitivity to these shadow prices as well as their ability to adjust to them, and make comparisons among companies and industries. Aside from being useful for company management, water-adjusted valuation tools support financial investors and further stakeholders in their risk-return analysis as well as their engagement toward a better water management of companies (TNFD, 2022). The Water Credit Risk (WCR) tool, developed by German Society for International Cooperation (GIZ), Natural Capital Declaration (NCD), and German Association for Environmental Management and Sustainability in Financial Institutions (VfU), uses a shadow price concept for water to assess the impact of higher water costs on the profitability and solvency of companies (GIZ et al., 2015). In the following, we present the tool and the inherent shadow price concept as an approach to assess the price effects from water scarcity for corporate financial analysis. Section 2 demonstrates how the SPW, the core element of the tool and input for further financial analysis, is derived and calculated. We present the overall approach in Sect. 3 and apply it to selected companies. The tool is used in Sect. 4 to further conduct sensitivity analyses of companies, whereby the

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vulnerability of companies is obtained from a SPW that is different from current market prices and a comparison across industries and time.

2 Valuation of Natural Capital---Total Economic Value and the Shadow Price of Water 2.1

The Rationale for the Shadow Price Approach

There is a large consensus that global water problems can be attributed to the difference between the price and value of water (United Nations, 2021). Whereas water prices only reflect the current conditions of trading water on economic markets, the value of water is a more comprehensive concept. It incorporates the benefits of water for different economic and non-economic stakeholders, from alternative direct and indirect uses, from current consumption and future availability, as well as from economic and environmental externalities caused from the use of water (Bierkens et al., 2019). At present, market prices for water are considered to be much lower than the true value of water. However, as water demand and extraction rates for private use will likely continue to rise and increase the scarcity of water,1 the private costs will increase as well and more closely reflect the true costs to society. Furthermore, water governance is highly politicized and affects water pricing, particularly at the local and regional levels. As a result, current water tariffs are thus not a reliable indicator for local and global supply–demand dynamics, and they do not represent the comprehensive worth of water. To cope with this “false information,” the concept of social value has been developed—the total economic value of water to society (OECD, 2017). Particularly, this concept provides a basis for analyzing corporate financial management, evaluating the creditworthiness of companies, and assessing the default risk of corporate loans and bonds. The social value of water is considered as a “shadow price” for water—a theoretical price that reflects the total value of water to society as a whole. In the event of continued pricing that does not reflect social values, water is not allocated efficiently among the stakeholders, and pressure on the supply of water is likely to grow. This may result in restrictions 1 The 2030 Water Resources Group has calculated that global water demand by 2030 could be 40% higher than current available water supplies (WRG, 2021).

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Fig. 1 Potential exposure from water as the difference between shadow price and market price (Source: Own representation based on Ridley and Boland [2015b, p. 10])

on access to water for competing water users, including business and industry. The market price (effective price) of water will continue to rise as capital and operating expenditures required to source, treat, and transport water resources to users increase. Companies in sectors such as food and beverage, mining, and utilities, in particular, are considered to be financially impacted due to their general exposure to nature (TNFD, 2022), and increasingly incurring direct costs for infrastructure to source and manage water used in production, processing, and cooling. Figure 1 shows how the market and production driven cost of water (market or effective price) will increase over time toward the comprehensive and multidimensional social value (shadow price) of water. The further the current market price of water is distinct from its shadow price, the higher is the exposure of an entity and/or a region to water. 2.2

Using the Total Economic Value Formula to Calculate the Shadow Price

Although it is impossible to precisely predict how quickly or how much prices or effective costs might rise, it is possible to quantify the potential rise—the magnitude of exposure—by measuring the gap between shadow price (social value) and market price (current cost) of water. To quantify the magnitude of exposure, we must first calculate the social value of water at the location of interest. According to general evidence and the wide array of metrics for the valuation of natural capital (TNFD,

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2022), several approaches have been suggested to assess the value of water (Bierkens et al., 2021). Here, we will use the concept of Total Economic Value (TEV) to identify the benefits of water to society (Pascual et al., 2012). The TEV framework has evolved to capture environmental economic values that were previously largely ignored by markets or neglected in neoclassical economics (NCFA & PwC, 2018). The TEV refers to the sum of all the values that are received from nature, including market traded and non-market perceived values, as well as values from the use (consumption) and non-use (altruism, existence value) of nature (Pascual et al., 2012). Here, the TEV framework is applied to assess the value of water, i.e., the value that water provides to society (OECD, 2017). Figure 2 refers to the characteristics of the TEV of water (shadow price) and contrasts it to the economic cost of water (market price). The WCR tool applies the TEV concept to calculate the shadow price of water at a specific location. It tests the sensitivity of corporate credit ratings to water risk by using the shadow price of water (alternatively the difference between the shadow price and the market price of water) as an indicator of potential financial risk. The dependent variables in the equation are alternative uses of water at a specific location: These include agricultural values, domestic supply values, human health impacts, and environmental impacts. Water stress and population are the independent variables that determine the four different values (Ridley & Boland, 2015b). The quantification of the shadow price requires assumptions such as the magnitude of potential increases of the price of water and the future demand for water in various locations. The model developers based their

Fig. 2 Shadow price of water in the total economic value concept as the combination of economic, ecological, and societal value

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assumptions on evidence from the literature and empirical findings for the price of water worldwide. Indicators of water stress and stress predictions are obtained from the World Resources Institute (WRI) Aqueduct database. The data includes the Baseline water stress raw data and Baseline water stress score (10 km by 10 km area). Information about population density in a region as a driver of the demand for water is provided by the Socioeconomic Data and Applications Centre (SEDAC) at Columbia University, in New York (population within 50 kilometers). The SPW is derived based on the following equation (Ohlsen & Ridley, 2020, p. 148; see also Ridley & Boland, 2015b): Shadow Price of Water = (2W/5) + P * (4/5(W + 1)) + P * D * (2 *10−8 *W2 +10−8 * W + 10−7 ) ( ) + P * (W/10) * 0.031W2 +0.015W where D = DALY = disability-adjusted life year (lost year of healthy life) P = population weight W = baseline water stress score To assess the company-specific shadow price, the SPW for the different locations of the company is calculated and weighted based on the water use per location or assets per location. Following that, we apply the SPW concept within the WCR tool and apply it to selected companies.

3

The GIZ/NCD/VfU Water Credit Risk (WCR) Tool

Several approaches to assess the water-related opportunities and threats of companies have been developed recently (Gilsbach et al. (2019), and Rudebeck & Breslin (2021) provide overviews). A first category models the general impact of changing hydrological conditions on the firms’ production and supply and assigns scores of high or low vulnerability. These indicators of relative vulnerability to water help prioritize actions in risk management and allow for comparisons between companies. A second category attempts to quantify the monetary effects of changing water conditions on the performance and value of firms. These models generate measures of profitability and liquidity with further consequences for financial risk analysis and investment management.

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The WCR tool developed from GIZ, NCA, and VfU assesses the effects from adverse conditions of water scarcity on the credit risk of companies (GIZ et al., 2015; Ohlsen & Ridley, 2020; Ridley & Boland, 2015a; Ridley & Boland, 2015b). The tool maps the impact of changing water availability and water prices on the firms’ income, cash flow, and associated financial ratios. Based on the SPW, it accounts for locationspecific hydrological conditions as well as for the concurrent demand and supply of water at the companies’ sites. The model expresses the volume and price effects from water challenges as additional operating expenses (opex) that affect the profitability of companies. In addition, the WCR tool allows companies to include their investments in water-related infrastructure and technology (capex). These investments result from actions/initiatives to improve the water efficiency or relate to requirements from regulators, which impact the firms’ cash flow and capital structure. Figure 3 shows how the tool is structured. In a top-down approach related to the income statement, it first considers additional operating expenses for water that result from a higher consumption and a higher price of water (Ohlsen & Ridley, 2020). This has further effects on measures of profitability such as EBIT and net income as well as on the liquidity of companies. In a subsequent step, expenses related to new water-related infrastructure have an additional impact on cash flow ratios such as the Free Cash Flow and Cash Flow to Equity. The WCR tool addresses the need to assess the location-specific value of water (Delevingne et al., 2020), thereby considering its alternative use by concurrent stakeholders at a specific site and balancing today’s benefits against future water needs. The model thus recognizes that “additional local modeling would provide better information on whether or not water scarcity is of significant concern” (Morgan & Dobson, 2020, p. 21). It derives the SPW as a quantitative measure of direct water affectedness of the company itself; however, does not yet include water issues in the company’s supply chain or the company’s principal access to water (Ohlsen & Ridley, 2020). Though aspects of water quality (Xabadia et al., 2021) and regulatory issues are not explicitly included in the computation of the SPW at this stage, the equation can be expanded to accommodate further dimensions of the water challenge. It can also be argued that additional constraints from water quality and regulation finally materialize in water price. Therefore, the current SPW framework is already suitable to incorporate these dimensions.

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Fig. 3 Structure of the GIZ/NCD/VfU Water Credit Risk (WCR) tool

As previously mentioned, the model uses information from WRI Aqueduct about water stress at different company locations. When computing the SPW, the model principally aggregates water conditions per site and recognizes the importance of the location by weighting local SPWs with the production per site. In the beverage industry, where there are numerous local production units and a higher flexibility to relocate within a region, the SPW is calculated based on country characteristics instead of local or regional properties (Ridley & Boland, 2015b). The tool further allows to include future conditions of water availability in the analysis of companies and their effect on production and performance. The model user can apply assumptions about the growth of companies and the related consumption of water. Forward-looking SPWs are also obtained based on the WRI Aqueduct’s projections on future water stress in the regions. Figure 4 shows the results from the tool’s first application when it was developed. At this time, the model developers have used financial data from 2013 to demonstrate the effects from higher SPWs. The figure compares the effects of water cost on the EBITDA (scaled by the revenue of the company) of 16 companies from three different industries, beverages, mines, and power generation. The EBITDA without considering a SPW (EBITDA/Revenue (no SPW)) is divided by the EBITDA/Revenue (with SPW) representing the EBITDA with the inclusion of a shadow

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Fig. 4 Companies ordered based on the relation of EBITDA/Revenue ratios before and after including a shadow price of water (SPW). Higher ratios mean higher risk (Source: Data from 2013. Representation is based on data from GIZ et al. [2015])

price. The higher the ratio, the more exposed to changing water prices is the company, and the more it is at risk.

4 Sensitivity Analysis of Companies to the Shadow Price of Water (SPW) 4.1

Overview

The degradation of the environment increasingly affects companies. As climate change and climate-related extreme weather events are expected to worsen, as are the challenges posed by depleting/expiring natural resources, a forward-looking perspective on the effects of this transition is needed. Scenarios, as perceptions of the future combine multiple components to model a potential context. They are a major element within simulation and sensitivity analyses that attempt to capture the effects from changing environments on affected entities (Kumar & Gupta, 2022; O’Neill et al., 2020). Further to modeling the potential outcome from a

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business as usual, these analyses allow to assess the effects from different possible reactions and thus provide opportunities to prepare mitigation strategies (WWF, 2020). Simulation and sensitivity analyses are technical approaches that use functional relationships to assess the consequences from a changing context (scenario) on affected parties (Pianosi et al., 2015; Savolainen et al., 2022). The terms are frequently used interchangeably, where sometimes sensitivity analysis refers to a procedure addressing specifically the vulnerability that results from the variation of a single input factor and is thus used as a pre-step for simulation as a larger set of iterations (Ugwuegbu, 2013). To assess non-linear connectivity, simulation as the repetition of alternative cause-effect relationships and providing a range of outputs is also considered. Scenarios used in simulations and sensitivity analyses must be both innovative and prospective, while also pragmatic and reliable. They often build on extrapolations from the past and adjust these for potential future disruptions. For example, Savolainen et al. (2022) model future prices based on the outcome of mining operations using information about drift and volatility from previous price movements. In the context of waterrelated scenarios, Delevingne et al. (2020) use WRI Aqueduct projections to construct water stress scenarios for mines. Morgan and Dobson (2020) apply projections from the WWF water filter to assess the water risk of mining companies that are identified based on sites data from Standard and Poor’s. The outcome from this simulation is the sensitivity of mining companies to water-related physical, regulatory, and reputational risk. Similarly, the WWF (2020) Water Risk Filter Scenarios, which ground on climate change scenarios, incorporate these three dimensions of water risk to derive resilience strategies. The WCR tool developed by GIZ et al. (2015) employs income statements and cash flow statements as the functional framework to analyze the impact of water risk on the profitability and liquidity of firms. The tool derives the sensitivity of firms to water particularly by extrapolating the quantity of water needed in the future and by introducing the SPW as the expected potential cost of water (Ohlsen & Ridley, 2020). The two parameters have a significant impact on a company’s opex and capex. The inclusion of additional expenses related to expected changes in the consumption and the water price affects companies’ profitability and liquidity, as well as their credit ratios.

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In the initial application of the WCR approach, Ridley and Boland (2015b) estimate shadow prices for the years 2010 and 2040 and apply them to the companies’ statements based on the year 2013. The authors map the effects from these price scenarios on the firms’ financial ratios, showing that both EBITDA and debt ratios can significantly deteriorate following an increase in the SPW. To specifically extract the sensitivity to changing water prices, they use the same numbers for the operations (production cost) and sales (revenues) of companies in the different scenarios. The WCR tool also allows for alternative growth ratios of production as well as for alternative consumption ratios of water to assess additional water-related effects. Ridley and Boland (2015b) make multiple suggestions for expanding the model’s application. The user may calculate the SPW differently, i.e., use production data for mines instead of reserves, use alternative percentages for water consumption, apply the WCR tool to further economic sectors, and relate it more comprehensively to credit analysis and portfolio management. In addition to their recommendations, new evidence for water-related challenges can be obtained by including further types of water risk, such as water pollution, and considering additional indicators of physical water risk on top of baseline water stress. In the following, we extend their analysis in two ways. First, we refer to more recent company data, here for the year 2020, and compare it with the year 2013. In this step, the SPW is kept constant when comparing the companies’ financial ratios for the two years. This allows to determine the water-related effects from changes in the companies’ operational and product structures across the two points in time (water sensitivity from operations). Here, we are particularly interested in understanding how changes in the companies’ locations, infrastructure, and output between 2013 and 2020 have altogether affected their water sensitivity. Additionally, the results may provide evidence about the companies’ ability and willingness to adjust to water-related challenges (see Sect. 4.2). Figure 5 represents the structure of the comparison across time. Second, we analyze how sensitive companies are to changes in water prices (sensitivity to changes of the SPW). This will be accomplished in two steps. In a first step, we update the water stress assessments from WRI Aqueduct using the new Aqueduct framework 3.0 (Hofste et al., 2019). Although the modified methodology from Aqueduct produces similar results for the overall sample of companies included in the WRI assessment, we test for the effects on selected individual companies where

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Fig. 5 Analyzing water risk sensitivity of companies—Sensitivity to changes in operations

the SPW is further affected by changes in the companies’ consumption of water and locations (see Sect. 4.3). In a further step, we use the WRI Aqueduct’s new predictions for countries’ water stress as well as the projected stress scenarios for the year 2040. It is shown, how much the performance of companies changes if minimal and maximal values from the Aqueduct forecasts are applied (see Sect. 4.4). Considering the results from the initial application of the VfU tool (Ridley & Boland, 2015b), we choose the three companies that have been most affected by the SPW application in 2013. Within the three considered industries, these companies are Femsa (beverages), Glencore (mines), and Eskom (power generation) as also previously illustrated in Fig. 4. 4.2

Operational Sensitivity—Comparison Across Time Based on the SPW 2013

To assess the overall effect from changes in infrastructure and production on companies’ water-related sensitivity, we refer to the two financial years 2013 and 2020. For both years and for each company, we compare the financial results before including (no SPW, n) and with including the SPW (with SPW, w). At this stage, the SPW used in the initial application of the tool for the year 2013 (referred to as SPW 2013) is also applied to the year 2020, which allows to keep the effects of changes in the SPW

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out.2 The additional cost of water for the two years is obtained considering the specific water consumption per year (accessed via the companies’ integrated or sustainability reports) and the SPW 2013. The inclusion of shadow costs for water changes the financial performance with subsequent effects on financial ratios. The two ratios considered are the EBITDA/Revenue (E/R) ratio as a measure of profitability3 and the Net Debt/EBITDA (N/E) ratio to express the companies’ liquidity profile. The EBITDA focuses on operational revenues and expenses and is calculated from the income statement. The Net Debt is obtained from the balance sheet as the result from Gross Debt (total debt securities and borrowings plus subordinated loans from shareholders) minus cash and marketable securities. The N/E ratio is also affected from changes in the capital structure between 2013 and 2020. First, we calculate the relation of a company’s result including the SPW (w) and the results without including the SPW (n). The results of this step are shown in Table 1 for the three companies and the years 2013 and 2020. For example, the E/R ratio 2013 for Femsa is 15.42% (n) and 10.14% (w). Adjusting for the effective value of water as expressed in the SPW would reduce the profitability of Femsa by approximately one-third. The case becomes even more compelling in 2020, when the value drops from 14.46% to 6.65%. Then, we compare the N/E sensitivities between 2013 and 2020 (ratios (w/n)). A lower ratio 2020 means that the vulnerability of the company has increased, as seen with Femsa and Glencore. The figures for Eskom show an increase, indicating that the company’s operational setting and capital structure has become more resilient to the cost of water. The results allow for a preliminary estimate of companies’ sensitivity to the SPW due to their operational setting. They may also indicate how efficient changes in the companies’ infrastructure and production (between 2013 and 2020) have been with respect to their water-related resilience as well as the companies’ flexibility in addressing water challenges. However, a closer inspection is required. The year 2020 is insofar special as effects from the COVID-19 crisis may have had a notable impact. Also, evidence from the general operational setting of companies does not necessarily 2 The SPW used in the initial calculation for the year 2013 is derived from the WRI ‘s estimations of water stress for the year 2010. As we focus on the year 2013 as a baseline, we refer to the SPW similarly as SPW 2013. 3 EBITDA as the earnings before interest, taxes, depreciation, and amortization.

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Table 1 Operational sensitivity of EBITDA/Revenue (E/R) and net debt/EBITDA (N/E) ratios to not including (n) and including (w) SPW 2013 for the financial years (FY) 2013 and 2020 Beverages Femsa Shadow Price of Water (SPW) SPW 2013 EBITDA/Revenue (E/R) FY 2013(n) FY 2013(w) FY 2020(n) FY 2020(w) Net Debt/EBITDA (N/E) FY 2013(n) FY 2013(w) FY 2020(n) FY 2020(w)

Mines (%)

4.67

Power Plants

Glencore (%)

4.33

Eskom (%)

4.61

15.42% 10.14% 14.46% 6.65%

ratio(w/n) 65.74 ratio(w/n) 46.01

4.50% 2.69% 6.44% 3.31%

ratio(w/n) 59.88 ratio(w/n) 51.44

16.73% 4.04% 15.85% 8.52%

ratio(w/n) 24.15 ratio(w/n) 53.75

1.24 1.89 1.14 2.47

ratio(w/n) 152.12 ratio(w/n) 217.36

3.43 5.73 3.90 7.59

ratio(w/n) 167.00 ratio(w/n) 194.42

9.80 40.59 12.11 22.53

ratio(w/n) 414.08 ratio(w/n) 186.03

reveal the specific impact on the efficiency companies use water. Additional measures must include links between water metrics and financial figures, such as the volume of water used per unit of production or unit of revenue. At first glance, the results for Eskom appear to be very promising. Between 2013 and 2020, both the E/R(w) and the N/E(w) ratios have improved, and Eskom’s operational vulnerability from including an SPW decreased. This is obtained from the higher (w/n) profitability ratio of 53.75% in 2020 compared to the (w/n) ratio of 24.15% in 2013. Similarly, the negative effect on liquidity of including an SPW has diminished from 414.08% in 2013 to 186.03% in 2020. However, the lower vulnerability to the SPW 2013 is also supposed to be an effect of the specific year 2020. Mainly related to the COVID-19 crisis, the sales of electricity have decreased from 217,903 GWh in 2013 (Eskom, 2014, p. 31) to 191,852 in 2020 (Eskom, 2021, p. 66). A lower demand for water is associated with the 12% less volume, which may mitigate the effect of including the SPW.

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A closer examination of water-related metrics reveals that the Eskom’s water-use efficiency has further deteriorated. According to the company, the volume of water needed to send out one kilowatt hour (kwh) in 2013 was 1.35 liter (Eskom, 2014, p. 128), while the number increased to 1.42 l/kwh in 2020 (Eskom, 2021, p. 99). The company mentions aging infrastructure and states that “water performance remains very disappointing” (Eskom, 2021, p. 102). Moreover, Delevingne et al. (2020) outline general measures that businesses can take to improve their water efficiency. Sparks et al. (2014) explain dry-cooling versus wet-cooling systems as a special alternative for power generation firms. Lower vulnerability to water can also be achieved by changing the technology in generating power, mainly to solar and wind driven systems. However, as Fig. 6 shows, the composition of power generating entities in the case of Eskom has barely changed between 2013 and 2020. 4.3

Price Sensitivity—Comparison of the Effects from SPW 2013 and SPW 2020

The focus now is on determining how changes in the SPW affect the companies’ financial performance (price sensitivity to the SPW). Keeping the operational setting and the capital structure constant, we determine the effects from a variation in the SPW. The reference for the setting of the three companies is the year 2020. Additional operational expenses for water result from the inclusion of the SPW 2013 and the SPW 2020. The analysis begins with the data pulled from 2020 financial statements, which are based on the specific production and sales activities of the year. We further calculate the SPW 2020 using the most recent water assessments from WRI Aqueduct, the updated water consumption of the companies, and accounting for the companies’ locations in 2020.4 The companies’ integrated records provide information on the volume of water consumed in 2020. The data provided is the total number for the company. We assign it to the different locations based on the production volume of the site or, as in the case of Femsa being part of the beverage industry, based on the activity per country. Table 2 depicts the results when including additional operational expenses for water obtained as the water consumption 2020 multiplied 4 Following GIZ et al. (2015), we refer to the 20 largest mining sites in the case of Glencore.

Fig. 6 Composition of Eskom’s power generation portfolio 2013 and 2020 (Source:Data from Eskom [2014], Eskom [2021])

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by the SPW 2013 and SPW 2020, respectively. Since the SPW for the three companies has not changed in an identical way, due to each companies’ specific water consumption quantities and structure of locations, the results vary among the three firms. We scale the absolute opex value to the companies’ costs before water opex, and we also calculate the EBITDA to revenue (E/R) and net debt to EBITDA (N/E) ratios. The results in Table 2 show that the SPW rises for all companies between 2013 and 2020. The magnitude is greater for Femsa (12.63%) and Glencore (14.09%), whereas the solely South Africa based Eskom experiences a lower increase of 5.86%. The increase in the SPW is mainly driven from the most recent assessment of water risk from WRI Aqueduct. Higher scores for physical water risk negatively impacts almost all locations. As regards Femsa, Fig. 7 illustrates the country structure of the beverage company 2013 and 2020 as well as the associated baseline water stress for the two years. The financial ratios of the companies become less favorable as a result of the higher SPW 2020 in comparison to the SPW 2013. The relative importance of water opex in terms of overall production costs is highest for Femsa. As a beverage company, Femsa is fundamentally dependent on water as its primary production component. Also, while the company may Table 2 Price sensitivity of EBITDA/Revenue (E/R) and net debt/EBITDA (N/E) ratios to the SPW 2013 and SPW 2020 Beverages

Shadow Price of Water (SPW) SPW 2013 SPW 2020 Income Statement Cost 2020 Δ Water opex SPW 2013 Δ Water opex SPW 2020 EBITDA/Revenue (E/R) E/R 2020 SPW 2013 E/R 2020 SPW 2020 Net Debt/EBITDA (N/E) N/E 2020 SPW 2013 N/E 2020 SPW 2020

Mines

Power Plants

Femsa

(%)

Glencore

(%)

Eskom

(%)

4.67 5.26

delta 12.63

4.33 4.94

delta 14.09

4.61 4.88

delta 5.86

22,608 1,935 2,181

ratio 8.56 9.65

140,321 4,450 5,069

Ratio 3.17 3.61

201,191 15,175 16,071

ratio 7.54 7.99

6.65% 5.66%

3.31% 2.88%

8.52% 8.09%

2.47 2.90

7.59 8.73

22.53 23.74

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Fig. 7 Operating countries of Femsa, 2013 and 2020—Baseline water stress projections within a range from 0 to 5 from WRI Aqueduct (in 2013, Femsa did not yet operate in Chile, Ecuador, the United States, and Uruguay)

potentially select other locations to extract water and adjust its logistics, it cannot principally substitute water. This is different for Eskom who can switch to less or no water consuming power generation technologies. Similarly, Glencore has several options for mitigating water challenges and improving the water efficiency of mining processes by applying desalination technology for the use of seawater and by increasing the recycling of wastewater (Glencore, 2020b). Consequently, Femsa focuses on reducing the water consumption that is related to the production process of beverages. The company reports major efficiency improvements, where in 2013 Femsa needed 1.75 liter of water to produce 1 liter beverage, while in 2020 the ratio decreased to 1.49 liter of water per 1 liter beverage (Femsa, 2020a, p. 31). Figure 7 shows how the different countries in which Femsa operates contribute to the company’s overall water stress. The figure shows the projections of baseline water stress (BWS) as a dimension of physical water risk and as provided from WRI Aqueduct. The BWS is a major component in the calculation of the SPW and weighted by the company’s production per country. The new projections from Aqueduct (Hofste, 2019) do not always exacerbate countries’ water risk. Notably, the most recent projections show decreases in the BWS score for Argentina and Brazil, among

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others. Depending on their geographical proximity, Femsa may be able to compensate for water scarcity challenges in one country with more favorable conditions in another. 4.4

Sensitivity to Low and High Projections of the SPW 2040

The WRI Aqueduct tool provides estimations of future water stress. Aqueduct distinguishes between three possible scenarios in the future (Luck et al., 2015): The optimistic scenario (OP) represents a stable economic development. Carbon emissions peak and decline by 2040 (stabilize at 650 ppm CO2 ). Temperatures rise by 1.1–2.6 °C by 2100 (relative to 1986-2005 levels). The business-as-usual scenario (BU) also represents a stable economic development. Global carbon emissions rise steadily (CO2 concentration reaches 1,370 ppm by 2100). Temperatures rise by 2.6–4.8 °C by 2100 (relative to 1986–2005 levels). The pessimistic scenario (PE) represents an uneven economic development and higher population growth. Global carbon emissions rise steadily (CO2 concentration reaches 1,370 ppm by 2100). Global temperatures rise by 2.6–4.8 °C by 2100 (relative to 1986–2005 levels). Due to the construction of the scenarios and the different factors affecting the forecast of water conditions for sites worldwide, the scenarios provide varying results. Particularly, the pessimistic scenario does not always result in higher water stress than the business-as-usual scenario. Rather than comparing optimistic and pessimistic scenarios, we consider the lowest (LO) and highest (HI) value for water from the three scenarios for each company. This yields two projections for every company for the year 2040, which are a SPW 2040(LO) and SPW 2040(HI). Table 3 captures the differences in the companies’ sensitivity as represented by the effects of the two SPW projections on E/R and N/E. It can first be observed that the varying SPW 2040 projections per company are very similar, which then will also result in similar consequences for the companies’ performance ratios. Compared to the SPW 2020, the SPW

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2040 for Femsa and Glencore is clearly distinct, whereas Eskom’s projections do not vary significantly from the SPW 2020. The differences in the E/R and N/E ratios as a consequence from the low and high projections for the SPW 2040 are small and do not sufficiently differentiate the companies. However, the SPW projections for 2040 are only one factor in the assessment of water-related challenges for the companies. While the beverage industry and power generation will be affected by the availability of water, their business model based on the production of beverages and Table 3 Sensitivity of EBITDA/Revenue (E/R) and net debt/EBITDA (N/E) ratios to low (LO) and high (HI) projections of the SPW 2040 Beverages Femsa SPW (USD/m3 ) SPW 2040(OP) SPW 2040(PE) SPW 2040(BU) FY 2020 (E/R) no SPW with SPW 2040(OP) with SPW 2040(PE) with SPW 2040(BU) delta LO/HI (2040) FY 2020 (N/E) no SPW with SPW 2040(OP) with SPW 2040(PE) with SPW 2040(BU)

Mines (%)

Glencore

Power Plants (%)

Eskom

(%)

5.48

5.33

4.74

5.57

5.32

4.85

5.51

5.41

4.74

15.85% 8.75%

Δ LO/HI -2.00

14.46% 5.30%

Δ LO/HI

6.44% 2.59%

5.15%

-2.98

2.60%

Δ LO/HI

8.58%

2.53%

-2.75

8.75%

5.25%

1.14 3.10

Δ HI/LO

3.90 9.70

3.19

2.89

9.66 9.92

3.13

12.11 21.94

Δ HI/LO

Δ HI/LO

22.38

1.96

2.68

21.96

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electricity will persist due to the continuous demand for these products. In the mining industry, it must be considered that with increased exploitation of mines it becomes more difficult to extract metals from the ores and more water is required (Bloomberg & NCFA, 2015). Finally, the mines will be fully depleted and the assessment of credit risk for mining companies must consider this as well. The WCR tool enables higher capex to be incorporated into models of potential investments in technology for higher water efficiency and/or recycling techniques (Ridley & Boland, 2015a). It also allows to vary the growth rates of the companies’ activities as well as associated revenue and cost numbers. In the case of mines, however, this would need a specific assessment of the geophysical properties of the sites. Though the WCR tool can incorporate data for business variations across time, the proper inclusion of mining output and the lifespan of mines would require additional technical analysis.

5

Conclusion

This study analyzes the WCR tool developed from GIZ, NCD, and VfU and its potential for assessing the sensitivity of companies to alternative water prices. The WCR tool represents a financial statement-based methodology for assessing the effects from location- and company-specific shadow water prices on different ratios of return and risk. The SPW is in the center of the model and calculated from a total economic value approach, thereby representing alternative and conflicting uses of water in ecology, economy, and society. The shadow price is derived from assumptions about the impact of location-specific water stress and population dependent factors as well as the companies’ activities related to specific geographic areas, rather than merely modeling simple variations of the current water price. Particularly, the WCR tool is suitable to assess the sensitivity of companies to alternative water-related scenarios. It reflects the vulnerability caused by changing water prices and technology, thereby allowing comparisons across companies and industries as well as across time. The results from the sensitivity analysis are incorporated to the company’s financial analysis. They may benefit both the company itself in developing more water efficient production structures and water risk mitigation policies, as well as financial investors to assess the risk in their assets and to reach out to affected companies for the development of appropriate

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strategies. As a result, the WCR model serves as a tool for redirecting financial flows into more sustainable investments, thereby supporting a green (blue) economy. In its current version, the model considers price variations from the availability of water for companies. This could potentially be extended to include additional cost effects from a changing quality of water as well as indirect effects resulting from the supply chain (Ohlsen & Ridley, 2020). However, including quality aspects will also require modeling the complex relationships between factors that may potentially contaminate water, including functions to assess the intensity and dynamic of this contamination as well as the ability of the water system to renature or to be regenerated technically (Xabadia et al., 2021). Though the model provides a notable approach for the water-related valuation of companies, it focuses on profitability based on water price. Further dimensions of water risk for companies may include reputational concerns and transition risks as the regulatory framework for the use of water evolves. Water-related conflicts or the effects from political conflicts on water may provide additional challenges. Finally, scenarios must be considered in which the reduced quantity and quality of water does not only have price effects but may include restrictions on water access and available volume. Application problems mainly refer to the availability of water-related data for water areas and the locations of companies in these areas. This also includes the reliability of extrapolations of water stress. In the future, new location-specific water data from sustainability reporting frameworks will fill the data gap. As an improvement, company-specific water data can be used to replace the weightings in the calculation of the SPW that are currently based on assets or reserves per location, providing more realistic information. Based on the model, investors will be able to estimate the affectedness of companies both from water challenges and opportunities, with further implications for risk premiums, the extent of financial investments into companies, the diversification of financial portfolios, and the stability of the financial market itself. Even though the assessment of these effects is still in its early stages, the shadow water price concept and the water credit risk tool provide the foundation for future steps and initiatives.

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Index

A Acceleration, 234, 286, 287, 289, 298 Acidification, 250 Adaptation, 10, 85, 220, 227–229, 233, 234, 237, 239, 242–244, 281 Agenda 2030, 122 Alliance for Water Stewardship (AWS), 140, 265 Anthropogenic global warming (AGW), 156, 158–172, 176, 177, 180 APAC capitals, 220 APAC Coastal Threat Index, 219 APACCT 20 Index, 219, 220, 224, 226, 229–231, 233, 236, 237, 239, 242 Aqueduct, 133, 258, 337, 339, 341, 342, 346, 348–350 Aquifer, 31, 56, 58, 249, 257 Asia pacific region (APAC), 219, 220, 226, 230, 233, 235, 240

Attribution, 16, 17, 21, 35, 36, 38, 154, 158, 161, 163–165, 168, 180 Awareness, 52, 86, 115, 173–175, 264, 296

B Base case, 222, 235 Baseline water stress (BWS), 318, 337, 342, 348, 349 Biological oxygen demand (BOD), 59, 60 Blended finance, 127, 128, 144, 284, 285, 288, 291, 298, 299, 304, 305, 307 Blue bond(s), 128, 129 Bootcamp, 293 Business acceleration, 281, 286, 289, 298

C Capacity building, 10, 135, 280 Capex, 57, 306, 338, 341, 352

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Gramlich et al. (eds.), Water Risk Modeling, https://doi.org/10.1007/978-3-031-23811-6

359

360

INDEX

Capital adequacy, 241 Carbon disclosure project (CDP), 4, 52, 55, 62, 63, 67, 69, 70, 72, 74–76, 84, 140, 194, 227, 251, 254, 256, 264, 265, 270 Carbon footprint, 10, 192, 197, 198, 204, 207, 210, 314 Catchment, 38, 122, 135, 139, 140, 143, 256, 257, 260, 263, 265, 267, 272 Central business district (CBD), 225, 232, 235 CEO Water Mandate, 124, 140, 265 Challenge fund, 306 Chemical oxygen demand (COD), 59 China water risk (CWR), 4, 219, 220, 223, 224, 233, 236, 237, 239–243 Climanomics, 11, 316–319, 321–323, 326–328 Climate, 4, 6, 9–11, 15–22, 25–27, 29–40, 56, 61, 62, 66, 85, 86, 91, 92, 114, 121, 122, 128, 153–156, 159–177, 179–181, 191–194, 197, 204, 214, 218, 219, 222, 225–227, 232, 234, 238, 239, 241–243, 249, 250, 252, 255, 270, 271, 281, 283, 284, 287, 294–296, 311–317, 319–323, 327, 328, 331, 340, 341 Climate attribution, 35, 36, 165, 168 Climate change, 4, 6, 15–17, 19, 21, 30, 31, 33, 35–40, 56, 67, 91, 92, 121, 122, 128, 155, 159–162, 165, 168, 181, 194, 204, 218, 219, 227, 234, 241, 243, 249, 250, 252, 255, 270, 271, 281, 284, 311–314, 317, 321, 323, 327, 331, 340, 341 Climate-change scenario, 322

Climate disclosure standards board (CDSB), 62, 63, 67–72 Climate impact, 219, 222, 236 Climate liability, 156, 176 Climate risk, 9, 11, 62, 153, 154, 179, 180, 191, 214, 225, 241, 283, 315, 316, 320, 327 Climate science, 16, 17, 40, 165, 243 Climate Sensitivity, 20 Climate service, 312, 316–322, 327 Coastal flooding, 318, 319, 323, 326 Coastal threat, 219–223, 227, 230, 233, 234, 240–243 Collateral, 126, 279, 285, 301–303, 306 Community-based water providers (CWPs), 301–303 Consumer News and Business Channel (CNBC), 312 Coupled Model Intercomparison Project (CMIP), 317 Credit risk, 215, 333, 338, 352 Credit risk tool, 353 Cross-sectional, 116 Cross Sector Biodiversity Initiative (CSBI), 261 Cyclone, 17, 34, 35, 37, 157, 159, 226, 234, 239 D De-risking, 10, 280–282, 285, 288, 290, 291, 298, 299, 302, 304, 305, 307 Development Bank of Seychelles (DBS), 128 Disaster Financial Assistance Arrangements (DFAA), 314 Discharge, 37, 51, 59, 66–71, 75–77, 254, 256, 260, 264 Disclosure, 52, 55, 56, 62, 63, 66–76, 84–86, 134, 140, 143, 195, 264, 269, 270

INDEX

Discount rate, 321, 323, 327 Distribution, 5, 10, 27, 29, 58, 67, 73, 99, 101, 103, 104, 164, 194, 195, 199, 201, 202, 247, 324 Doughnut economy, 247, 248, 250 Drinking water, 22, 69, 76, 199, 250, 278, 280, 281, 294, 296, 303–305 Drought, 4, 6, 11, 16, 17, 31, 36, 37, 39, 40, 57, 60, 63, 66, 67, 121, 142, 154, 156, 157, 159, 161, 163, 164, 194, 317, 326 E EBITDA, 339, 340, 342, 344, 345, 348, 351 El Niño Southern Oscillation (ENSO), 26 Environmental Protection Agency (EPA), 260, 327 Environmental, Social and corporate governance (ESG), 53–55, 85, 199, 204, 214, 215, 317, 326 European Union (EU), 193, 260, 264 Eutrophication, 253 Evaporation, 21–23, 25, 26, 28, 29, 154, 155, 252 Extreme event, 4, 16, 35, 158, 161, 163, 164, 180 Extreme weather, 9, 16, 21, 35, 40, 67, 73, 91, 153–158, 165, 180, 181, 251, 314, 327, 340 Extreme weather and hydrological (EWH), 153–156, 158–167, 169, 176, 177, 179, 180 F Facility, 193, 258, 290, 296, 298–304, 306, 319 Federal Emergency Management Agency (FEMA), 92, 96

361

Feedback, 4, 26, 28, 29, 56, 220, 223, 227, 236, 243 Feedforward, 4 Finance gap, 280, 281 First loss, 300, 304, 306 Flood experience, 97, 98, 100, 101, 103, 107, 110, 113, 114 Flooding, 16, 17, 31, 37, 38, 40, 62, 67, 91–96, 98, 100, 104, 105, 113, 115, 116, 154, 159, 161, 164, 167, 194, 219, 224, 225, 227, 231, 233, 237, 238, 312–314, 318, 319, 326 Flood insurance, 93–97, 102, 104, 111, 113–116 Flood risk perceptions, 97, 116 Flood(s), 4, 6, 9, 17, 38, 56, 57, 61, 63, 66, 78, 91–111, 113–116, 121, 128, 136, 141, 154–156, 159, 162, 163, 166, 167, 225, 237, 251, 266, 312, 316, 318, 319 Fluvial Basin Flooding, 318, 319, 323 Forward-looking, 172, 196, 203, 284, 331–333, 339, 340 Foundation, 10, 56, 92, 136, 137, 248, 280–285, 287, 288, 290, 291, 294, 295, 298, 302–304, 307, 314, 353 Freshwater, 4, 22, 23, 51, 58, 59, 121, 122, 132, 195, 196, 199, 248, 249, 252, 253, 255–257, 261, 263, 266, 270, 318

G German Society for International Cooperation (GIZ), 11, 139, 333, 338, 341, 352 Global Climate Change, 15 Global reporting initiative (GRI), 62, 67, 68, 70, 72, 76, 270

362

INDEX

Global warming, 9, 16–18, 21, 23, 25, 33, 35, 37, 40, 153, 154, 156, 158, 159, 162, 166, 171, 180, 218, 236 Governance, 61, 62, 70, 85, 123, 129, 135, 136, 141–143, 248, 334 Granularity, 237, 238 Greenhouse gases (GHGs), 19, 20, 23, 27, 28, 35–37, 156, 159, 160, 169, 171, 172, 176, 177, 218, 320, 322 Gross Domestic Product (GDP), 92, 220, 224, 230, 232, 296 Groundwater, 5, 17, 22, 23, 31–33, 39, 56, 60, 61, 131, 248, 249 Guarantee, 125, 126, 129, 231, 236, 282, 291, 300, 302, 303 H H&M, 54, 73, 134, 135 Hazard, 95, 96, 315–317, 319, 321, 324, 326, 328 Heat extreme, 16 Higg Index, 54, 134 Hong Kong Monetary Authority’s (HKMA), 219, 220, 242 Household survey, 93, 98 Hurdle model, 99, 101, 106–108 Hydrological cycle, 18 Hydrological event, 16, 17, 38 Hydrological sensitivity, 25–28, 33 Hygiene, 10, 123, 143, 305 Hypothetical climate liability (HCL), 154–156, 158, 160, 168, 176–181 I Impact, 4–6, 8, 52, 54, 55, 60, 61, 67–71, 73, 77, 83–85, 95, 96, 122, 123, 134, 166, 191–195,

197, 198, 201, 207, 214, 218, 219, 222, 224, 232, 237, 242, 248, 250, 252, 256, 257, 260, 267, 269–271, 278, 280, 282–286, 288–297, 300, 304, 305, 307, 311, 312, 317, 319–321, 324, 326, 332, 333, 337, 338, 341, 344, 345, 352 Impact investing, 55, 85, 291, 296 Impact-Linked Finance (ILF), 287, 288, 291–293 Impact measurement, 292, 293 Index, 10, 54, 192, 197, 202–210, 220, 222, 223, 227–230, 233–239, 242, 317 Index constituent, 205–207 Indicator, 10, 73, 100, 101, 134, 140, 223–225, 227, 230, 231, 239, 240, 260, 271, 288, 290, 326, 334, 336, 337, 342 Industrial, 4, 9, 15, 20, 40, 51, 52, 56, 59, 122, 132, 133, 135, 160, 175, 199, 249, 259, 266, 322, 332 Instrumental variable, 101–103 Insurance, 8, 93–97, 102, 104, 111, 113–116, 129, 162, 177, 242, 247 Insured value, 321 Integrated Water Resources Management (IWRM), 137, 138 Integrative approach, 283, 300 Intergovernmental Panel on Climate Change (IPCC), 16, 20, 21, 24, 32, 35, 36, 39, 40, 158, 218, 219, 242–244, 251, 317, 322 Investment, 7, 9, 10, 55, 61, 65, 72, 82, 96, 123, 127, 129, 130, 140, 141, 154, 161, 179, 180, 191–193, 197, 203, 204, 208, 214, 215, 233, 247, 251, 254, 255, 257, 258, 261, 264, 269,

INDEX

271, 272, 279, 282–285, 288–290, 292, 295, 302, 305, 306, 319, 320, 337 Investment strategy, 192, 203, 214

K Kenya, 129, 137, 296–299, 301, 302, 304, 306, 307 Kenya Innovative Finance Facility for Water (KIFFW), 129 Knowledge, 31, 36, 38, 39, 124, 155, 170, 173, 175, 220, 230, 266, 281, 284, 285, 288, 290, 291, 299, 300, 314

M Market value, 176, 316, 327 Methodology, 197, 203, 204, 208, 209, 213, 237, 316, 317, 319, 327, 342, 352 Metrics, 55, 61, 154, 179, 195, 197–199, 201, 208, 214, 215, 239, 252, 254, 265, 267, 283, 292, 293, 322, 335, 345, 346 Microfinance, 129 Microfinance institution’s (MFI’s), 130 Micro, small, and medium sized enterprises (MSMEs), 297, 299–301, 307 Mitigation, 40, 57, 62, 67, 68, 70–72, 76, 85, 93, 95, 97, 98, 102, 103, 109, 111, 113–116, 126, 227–229, 242, 243, 261, 281, 326, 327, 332, 341, 352 Modeled Average Annual Loss (MAAL), 321, 323, 324, 326, 327

363

N National Association of Mutual Insurance Companies (NAMIC), 92 Nature Conservancy, 124, 137, 138 Network for Greening the Financial System (NGFS), 225, 241 Neutrality, 242, 252 Non-governmental organizations (NGOs), 54, 85, 136, 140, 265, 266, 278, 299, 303

O Office, 322–324, 327 Office of Parliamentary Budget Officer (OPBO), 94 Operating expenses (opex), 338, 341, 348 Organization for Economic Co-operation and Development (OECD), 7, 52, 91, 93, 124, 125, 127, 128, 134, 137, 141, 157, 278, 279, 284, 288, 296, 332, 334, 336

P Paris Agreement, 15, 20, 158, 217, 222 P-E pattern, 29 Perceived risk, 97–104, 106–108, 110, 114, 115 Perception, 93, 103, 114, 116, 290, 301, 332, 340 Physical risk, 6, 10, 61, 66–68, 222, 223, 233, 235, 239, 244, 317 Planetary boundaries, 247–249, 255 Pollution, 29, 40, 51, 52, 56–67, 69, 70, 72–74, 77–79, 82, 85, 134, 136, 194, 196, 251, 253–256, 258, 259, 261, 266, 272, 342

364

INDEX

Polyfluoroalkyl substances (PFAS), 259 PR, 142, 144 Precipitation, 16, 17, 21, 23, 25–31, 33, 35–37, 39, 56, 91, 154, 157, 159, 166, 249, 317–319 Principles of Responsible Investments (PRI), 141 Probit, 100, 101, 103 PRODES, 131 Producer responsibility, 154, 160, 170, 172, 175, 176 Projection(s), 11, 31, 33, 37, 39, 40, 92, 220, 236, 318, 321, 339, 341, 349–351 Protection actions, 115 Protective actions, 104, 105

R Rainfall, 5, 16, 17, 21–23, 25–29, 31, 33, 34, 36–38, 154, 155, 164, 167, 313 Real estate, 10, 11, 224, 233, 235, 311–316, 323, 324, 326, 327 Real Estate Investment Trusts (REITs), 312, 315, 322–324, 327 Regulatory risk, 68, 69 Replacement value, 321 Replenishment, 247, 255 Reporting framework, 9, 55, 56, 61, 63, 353 Representative Concentration Pathways (RCPs), 322 Reputational risk, 8, 55, 56, 61, 64, 68–70, 341 Residential, 59, 92, 96, 322 Residential flooding, 93, 94 Resilience, 7, 128, 220, 235, 242, 281, 312, 327, 341, 344

Responsibility, 9, 94, 124, 130, 139, 141, 143, 154–156, 160, 169, 170, 172–177, 181, 264, 301 Restricted substance lists (RSL), 66 Retail, 53, 54, 62, 72, 322 Risk, 4, 6–9, 11, 31, 34, 35, 37, 39, 40, 52, 55, 56, 68, 71, 72, 85, 92, 93, 95–111, 113, 114, 116, 123, 126, 127, 129, 133, 139, 142, 154, 158, 159, 164–166, 179, 180, 192–195, 197, 198, 201, 204, 208–210, 214, 219, 224–228, 230, 234, 235, 239–241, 243, 247, 251, 252, 254–258, 260, 261, 264, 270, 271, 278, 280, 287–291, 297, 299, 300, 302, 304, 307, 313, 315–324, 326, 333, 334, 336, 337, 340, 352, 353 Risk-return, 8–10, 280, 288, 290, 297, 307, 333 S Sanitation, 10, 22, 55, 69, 74–76, 123, 125, 127, 128, 130, 131, 141, 143, 250, 266, 277–291, 294–299, 302, 304, 305, 307 Scalability, 284, 285 Scale, 9, 10, 21, 22, 25, 30, 39, 56, 61, 125, 137, 139, 141, 144, 158, 173, 178, 199–201, 219, 226, 237, 238, 266, 279, 280, 283–288, 297, 298, 304, 306, 348 Scarcity, 4, 5, 7, 22, 56, 58, 61–63, 66, 67, 73, 78, 85, 193, 194, 214, 215, 247, 251, 256, 257, 270, 314, 333, 334, 338, 350 Scenario, 37, 219, 220, 226, 231, 232, 236, 322, 323, 341, 350 Science Based Targets Network (SBTN), 265

INDEX

Scope, 115, 130, 131, 144, 177, 199, 202, 208, 284, 294, 304, 317 Sea-level rise (SLR), 10, 218–220, 223, 224, 226, 227, 230–233, 235–238, 242, 243 Sensitivity, 9, 11, 28, 154, 167, 180, 333, 336, 340–344, 346, 348, 350, 352 Service gap, 280 Seychelles Conservation and Climate Adaptation Trust (SeyCCAT), 128 Shadow price of water (SPW), 11, 333, 334, 337–346, 348–353 Simulation, 30, 34, 37, 39, 40, 165, 340, 341 Small and medium enterprises (SMEs), 10, 284, 298, 307 Small Island Developing States (SIDS), 128 SME lending, 291 Social impact, 10, 285, 292, 301, 302, 305 Stewardship, 71, 74, 84, 122–125, 132, 134, 135, 137–140, 142–144, 194–197, 202–206, 265 Stress testing, 243 Survey, 9, 93, 97, 98, 114, 220, 222, 225, 232, 238, 298 Sustainability, 7, 52–55, 61, 72–76, 78, 84, 86, 124–126, 134, 136, 139, 264, 271, 280, 281, 283, 284, 291, 292, 294, 307, 344, 353 Sustainability Accounting Standards Board (SASB), 62, 66, 68–72, 76, 256, 270 Sustainable, 6, 54, 122, 134, 193, 251, 260, 264, 266, 277, 314 Sustainable Development Goal 6 (SDG 6), 277

365

Sustainable Development Goals (SDGs), 86, 122, 130, 141, 193, 290, 307 T Task Force on Climate-Related Financial Disclosures (TCFD), 62, 225, 316, 317 Technical assistance, 10, 280–282, 285, 286, 288–290, 292, 293, 298, 299, 304–307 Terminal value, 236, 241 Textile industry, 51, 52, 54, 55, 58–61, 67–69, 71 Textile life cycle, 58 Textile risk, 55 The Center for Research on the Epidemiology of Disasters (CRED), 316 Thermodynamic(s), 16, 18, 23, 25, 29, 33, 34, 39, 159 Tipping point, 15, 21, 38, 40, 249 Topographic, 319 Total economic value (TEV), 11, 334, 336, 352 Total suspended solids (TSS), 59 Transformative adaptation, 235, 244 Transition, 34, 39, 173, 225, 261, 263, 264, 266, 298, 317, 328 Transition risk, 225, 317, 353 Typhoon, 234 U UNEP, 52, 121, 122 UN Framework Convention on Climate Change (UNFCCC), 15, 158 Union of Concerned Scientists (UCS), 313 United Nations (UN), 5, 124, 265, 277, 278

366

INDEX

United States Agency for International Development (USAID), 74, 129, 298 United States (US), 37, 54, 73, 160, 162, 163, 176, 178, 179, 193, 199, 300, 312–314, 322, 327, 349 User responsibility, 175

V Value-at-Risk, 154, 179 Viability, 130, 281, 284 Virtual water, 133, 257 Vulnerability, 7, 67, 92, 93, 101, 114, 115, 128, 167, 243, 254, 256–258, 269, 270, 316, 317, 319–321, 334, 337, 341, 344–346, 352

W WA-KE UP, 298 Wastewater, 56, 60, 61, 70, 72, 77, 131, 134, 141, 199, 250, 255, 256, 259, 260, 263, 267, 279, 349 Water, 3–11, 16–18, 20–23, 25–28, 30–34, 37–40, 51–76, 78–82, 84, 85, 92, 94, 98, 101, 103, 121–144, 154, 155, 158, 162, 166, 168, 181, 191, 193–208, 210, 214, 215, 233, 237, 247, 249–267, 269–272, 277–299, 301–307, 311–324, 326–328, 331–346, 348–353 Water challenge, 4, 7, 9, 135, 136, 139, 142, 143, 215, 332, 333, 338, 344, 349, 353 Water consumption, 51, 52, 54, 63, 67, 68, 70, 74, 75, 84, 133, 139, 249, 251, 252, 256–261,

269–271, 342, 344, 346, 348, 349 Water Credit Risk tool, 11, 353 Water cycle, 9, 16, 17, 21–24, 26, 27, 30, 31, 38–40, 133, 154, 158 Water data, 10, 195, 199–201, 205, 206, 208, 353 Water discharged, 196, 199 Water efficiency, 76, 132, 134, 135, 202, 266, 267, 315, 338, 346, 349, 352 Water footprint, 132, 133, 140, 143, 196–198, 202–204, 206, 210, 254, 257, 258, 260, 261, 265, 267, 269, 271 Waterfront, 313 Water investment, 9, 141, 196, 296, 307 Water neutrality, 10, 251, 252, 254–257, 261, 267, 269, 271 Water quality, 10, 61, 67, 69, 70, 76, 250, 252–254, 256, 259, 260, 267, 271, 338 Water recycled, 196, 199 Water risk, 3, 4, 6, 8, 10, 40, 58, 67, 191, 193–199, 201–208, 210, 214, 215, 257, 312, 313, 323, 324, 326, 327, 336, 341–343, 348, 349, 352, 353 Water risk index, 10, 195, 197, 198, 203, 204, 206 Water risk quantification, 68, 336 Water, Sanitation and Hygiene (WASH), 69, 123, 130, 131, 278–295, 297–302, 305, 307 Water security, 10, 22, 122, 124, 139–144, 195, 196, 203, 208, 210, 214, 215, 265, 266 Water security index, 201, 204, 205, 209, 211, 213, 214 Water sensitivity, 342

INDEX

Water stewardship, 9, 10, 65, 71, 76, 122–124, 139, 191 Water stress, 7, 66, 68, 133, 138, 249, 253, 258, 315, 318, 326, 333, 336, 337, 339, 341–343, 349, 350, 352, 353 Water-themed indices, 196 Water utilization, 195, 197–200, 202, 203, 205, 206 Water withdrawal, 67, 69, 76, 195, 201, 252, 255–257, 264, 270 Winsorization, 201, 202

367

Withdrawal, 51, 57, 68, 199, 248, 256, 265 World Business Council for Sustainable Development (WBCSD), 266 World Climate Research Programme (WCRP), 317 World Resources Institute (WRI), 133, 258, 318, 339, 341, 342, 346, 348–350 Z Zero flood risk, 108, 110, 111, 113