Climate Investing

Emmanuel JURCZENKO is Director of Graduate Studies and Professor of Finance at Glion Institute of Higher Education, Switzerland. Prior to this, he spent 13 years as Associate Professor of Finance at ESCP-Europe and worked for ABN-AMRO as Head of Quantitative Analysts where he was in charge of quantitative fund selection. His research focuses on portfolio construction in particular on risk budgeting, factor investing and machine learning estimation techniques.

• Foreword xiiiFiona FRICKChapter 1 The Financial Materiality of Climate Change: Evidence from a Global Survey 1Amir AMEL-ZADEH1.1 Introduction 11.2 Survey design and demographic data 41.2.1 Survey design 41.2.2 Demographic data 51.3 Survey results 81.3.1 Importance of climate change for investment decisions 91.3.2 Financial materiality of climate risk 121.3.3 Challenges for the disclosure and use of climate change information 191.4 Summary and conclusion 251.5 References 26Chapter 2 Looking Forward with Historical Carbon Data 29Steffen BIXBY, Alfie BRIXTON and Lukasz POMORSKI2.1 Introduction 292.2 Data 322.3 How stale is historical carbon data? 332.4 Are historically brown firms getting greener? Might green firms become browner? 352.5 Nowcasting financed emissions using historical data 382.6 Conclusion 432.7 Appendix 442.7.1 Measures of portfolio greenhouse gas emissions 462.8 References 46Chapter 3 Portfolio Construction with Climate Risk Measures 49Théo LE GUENEDAL and Thierry RONCALLI3.1 Introduction 493.2 Climate risk measures 513.2.1 Carbon footprint 513.2.2 Carbon transition pathway 583.2.3 Other metrics 623.3 Portfolio optimization 633.3.1 General framework 633.3.2 Portfolio decarbonization 643.3.3 Portfolio alignment 723.4 Conclusion 833.5 Appendices 843.5.1 Appendix 1: Scope 3 emissions 843.5.2 Appendix 2: Data 843.6 References 85Chapter 4 Hedging Climate Risks: A Cross-asset Approach 87Emmanuel JURCZENKO and Jérôme TeilETCHE4.1 Introduction 874.2 Factor-mimicking portfolios methodology 894.2.1 General FMP approach 894.2.2 Errors-in-variable estimates 914.3 Hedging climate risk factors 944.3.1 Setup 944.3.2 Climate textual risk factors data 944.3.3 Base assets data 974.3.4 In-sample hedging results 994.3.5 Out-of-sample hedging results 1024.4 Conclusion 1044.5 Appendices 1044.5.1 Appendix 1: General FMP portfolio optimization program 1044.5.2 Appendix 2: Principal components instrumental variables FMP estimator 1054.6 References 107Chapter 5 A Framework for Achieving Net-Zero-Carbon Alpha Portfolios 109Sebastian LANCETTI5.1 Introduction 1095.2 Carbon emission in the capital market 1115.3 Passive approach to zero-carbon portfolios 1115.4 Active approach to zero-carbon portfolios 1145.4.1 Backward-looking data: carbon efficiency 1155.4.2 Present-time data: “nowcasting” of environmental news 1165.4.3 Forward-looking data: corporate climate alignment and adaptation plans 1165.4.4 Case study: sustainable global equity strategy from PanAgora Asset Management 1175.5 Carbon offsets 1185.6 Conclusion 1205.7 Appendix 1215.8 References 121Chapter 6 Active Paris-aligned Equity Investing 123Katharina SCHWAIGER, Jim SNOW, Viktoria-Sophie WENDT and Andrew ANG6.1 Introduction 1236.2 Standards of Paris-aligned benchmarks 1246.3 Climate-aware alpha drivers 1266.3.1 Carbon resource efficiency 1266.3.2 Green patents 1276.3.3 Corporate target setting 1276.4 Empirical results 1286.4.1 Decarbonization pathway 1296.4.2 Climate-aware alpha 1296.4.3 Incorporating climate-aware alphas and decarbonization 1316.4.4 Systematic active Paris-aligned strategies 1336.5 Conclusion 1376.6 Appendix: Paris-aligned equity strategy screens 1376.7 References 139Chapter 7 Green Alpha 141Yin LUO7.1 Introduction 1417.2 Research methodology 1417.2.1 Region classification 1427.2.2 ESG-specific industry classification 1437.2.3 Common style factors 1447.2.4 Backtesting methodology 1457.3 MSCI ESG rating 1467.3.1 MSCI ESG data 1467.3.2 Data coverage and average rating 1477.3.3 An overview of MSCI ESG rating methodology 1487.3.4 ESG pillars, themes and key issues 1497.4 Characteristics of ESG – a factor perspective 1517.4.1 The basics 1517.4.2 Difference across sectors 1537.4.3 Factor exposure 1577.5 ESG as stock-selection factors 1617.5.1 Aggregated ESG rating and the three pillars 1617.5.2 Revenue, country and industry adjustment 1627.5.3 Other adjustment 1657.5.4 ESG momentum 1657.5.5 Performance of aggregate ESG and three pillar scores 1657.6 Environmental factors 1707.6.1 Zooming into clean technology 1707.6.2 Carbon emissions along the supply chain 1747.7 ESG signals are additive to traditional stock-selection factors 1797.7.1 Performance comparison with traditional stock-selection factors 1807.7.2 Correlation with traditional factors 1827.7.3 The diversification benefit offered by ESG factors 1837.8 Conclusion 1887.9 References 188Chapter 8 Enhancing Environment-driven Portfolios with Traditional Factors 191Guillaume COQUERET, Christian MORGENSTERN, James KELLY, Sascha STIERNEGRIP, Johannes FREY-SKÖTT and Björn ÖSTERBERG8.1 Introduction 1918.2 Framework 1938.2.1 ESG overlays: the classic overlay 1938.2.2 The factor embedding – the factor overlay 1958.3 Empirical tests 1978.3.1 Data and protocol 1978.3.2 Baseline results 1998.3.3 Statistical significance 2028.3.4 Sector exposure 2048.3.5 Transfer coefficients 2058.4 Robustness checks 2068.4.1 The sample size 2068.4.2 A more passive benchmark 2078.5 Conclusion 2088.6 Appendix: Distribution of variables 2098.7 References 210Chapter 9 Enhancing the Accuracy of Firm Valuation with Multiples Using Carbon Emissions 213Martin NERLINGER9.1 Data 2189.1.1 Carbon data 2189.1.2 Financial data 2199.2 Multiple construction methodology 2199.2.1 Identifying and composing suitable peer group 2209.2.2 Constructing and aggregating multiples 2209.2.3 Determining firm valuation errors 2229.3 Constructing new multiples using carbon data 2229.4 Constructing peer groups using carbon data 2289.5 Combining carbon emission multiples and carbon emission enhanced peer groups 2339.6 Robustness 2369.7 Recommendation for using carbon emissions for multiples and further research 2399.8 References 240Chapter 10 Risk Management Challenges in Sustainability Themed Portfolios: An Application to GHG-constrained Portfolios 245Ryan M. BROWN, Harindra DE SILVA and David W. KRIDER10.1 Introduction 24510.2 Methodology 24810.3 Data description 25310.4 Results 25810.5 Conclusion and implications 26410.6 References 265Chapter 11 Absolutely Sustainable Investing Across Asset Classes with Paris-aligned Benchmarks: An Application to AP2 267Claes EKMAN, Andreas G.F. HOEPNER, Peter MANNERBJÖRK, Tomas MORSING and Gabija ZDANCEVICIUTE11.1 Introduction 26711.2 The climate benchmarks 26911.2.1 Minimum benchmark requirements 27011.2.2 Benchmark decarbonization and inflation adjustment 27211.3 Absolutely sustainable investing 27311.4 Case study: implementation of PAB at Andra AP-fonden 27411.4.1 The Swedish pension system and the AP-funds 27411.4.2 Development of sustainability integration and benchmarks at AP2 27511.4.3 Implementing the EU Paris-aligned Benchmark at AP2 27811.4.4 Specific aspects 28511.4.5 Discussion 28811.5 Conclusion 29111.6 References 292Chapter 12 Delegated Philanthropy in Mutual Fund Votes on Climate Change Externalities 295Marie BRIÈRE, Sébastien POUGET, Martin SCHMALZ and Loredana URECHE-RANGAU12.1 Introduction 29512.2 Sample, data sources, variables and descriptive statistics 29812.2.1 Mutual fund votes 29812.2.2 Mutual fund characteristics 29912.2.3 Mutual fund holdings 30012.2.4 Descriptive statistics 30012.3 Empirical analysis 30212.3.1 Impact of the percentage of SRI on the support for climate resolutions 30212.3.2 Resolutions on other corporate externalities 30412.3.3 Drivers of support for climate change resolutions 30712.3.4 Robustness 31112.4 Conclusion 31812.5 Appendix: Classification of shareholder resolutions 31912.6 References 321Chapter 13 Creditworthiness and Buildings’ Energy Efficiency in the Mortgage Market 325Monica BILLIO, Michele COSTOLA, Loriana PELIZZON, Francesco PORTIOLI, Max RIEDEL and Daniele VERGARI13.1 Introduction 32513.2 Portfolio analysis 32713.2.1 Energy efficiency 32713.2.2 Descriptive statistics 33013.3 Methodology 33313.3.1 Logit regression 33413.3.2 Cox proportional hazards model 33513.4 Results 33713.4.1 Estimates from the logit regression 33713.4.2 Estimates from the Cox regression 33913.4.3 Additional findings 34313.5 Conclusion 34313.6 Appendix 34613.7 References 346Chapter 14 The Thesis for Green Investing and Other ESG through the Looking Glass of China and the US 349Brad CORNELL and Jason C. HSU14.1 Introduction 34914.2 Who and what does Green investing impact? 35014.3 Who should set the Green investing agenda? 35114.3.1 Should Green Initiatives be determined by elected civil servants or by rating services, investment funds and corporate CEOs? 35114.3.2 The Milton Friedman take on who should drive ESG 35114.3.3 American ESG in conflict with American democracy? 35414.3.4 Who drives environmental protection policy and other ESG issues in China? 35414.3.5 Good intentions but bad skills? 35614.4 Earning a Green alpha?! 35714.5 Market efficiency and ESG 36114.6 Conclusion 36214.7 References 363List of Authors 365Index 369