LEADER 10656nam 2200541 450 001 9910677700103321 005 20230415154628.0 010 $a1-394-19237-1 010 $a1-394-19235-5 024 7 $a10.1002/9781394192373 035 $a(MiAaPQ)EBC7155929 035 $a(Au-PeEL)EBL7155929 035 $a(CKB)25657500300041 035 $a(OCoLC)1356742291 035 $a(OCoLC-P)1356742291 035 $a(CaSebORM)9781786308061 035 $a(EXLCZ)9925657500300041 100 $a20230415d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aClimate investing $enew strategies and implementation challenges /$fEmmanuel Jurczenko, editor 210 1$aHoboken, NJ :$cJohn Wiley & Sons, Inc.,$d[2023] 210 4$dİ2023 215 $a1 online resource (396 pages) 225 0 $aInnovation, entrepreneurship and management series 311 08$aPrint version: Jurczenko, Emmanuel Climate Investing Newark : John Wiley & Sons, Incorporated,c2023 9781786308061 327 $aCover -- Title Page -- Copyright Page -- Contents -- Foreword -- Chapter 1. The Financial Materiality of Climate Change: Evidence from a Global Survey -- 1.1. Introduction -- 1.2. Survey design and demographic data -- 1.2.1. Survey design -- 1.2.2. Demographic data -- 1.3. Survey results -- 1.3.1. Importance of climate change for investment decisions -- 1.3.2. Financial materiality of climate risk -- 1.3.3. Challenges for the disclosure and use of climate change information -- 1.4. Summary and conclusion -- 1.5. References -- Chapter 2. Looking Forward with Historical Carbon Data -- 2.1. Introduction -- 2.2. Data -- 2.3. How stale is historical carbon data? -- 2.4. Are historically brown firms getting greener? Might green firms become browner? -- 2.5. Nowcasting financed emissions using historical data -- 2.6. Conclusion -- 2.7. Appendix -- 2.7.1. Measures of portfolio greenhouse gas emissions -- 2.8. References -- Chapter 3. Portfolio Construction with Climate Risk Measures -- 3.1. Introduction -- 3.2. Climate risk measures -- 3.2.1. Carbon footprint -- 3.2.2. Carbon transition pathway -- 3.2.3. Other metrics -- 3.3. Portfolio optimization -- 3.3.1. General framework -- 3.3.2. Portfolio decarbonization -- 3.3.3. Portfolio alignment -- 3.4. Conclusion -- 3.5. Appendices -- 3.5.1. Appendix 1: Scope 3 emissions -- 3.5.2. Appendix 2: Data -- 3.6. References -- Chapter 4. Hedging Climate Risks: A Cross-asset Approach -- 4.1. Introduction -- 4.2. Factor-mimicking portfolios methodology -- 4.2.1. General FMP approach -- 4.2.2. Errors-in-variable estimates -- 4.3. Hedging climate risk factors -- 4.3.1. Setup -- 4.3.2. Climate textual risk factors data -- 4.3.3. Base assets data -- 4.3.4. In-sample hedging results -- 4.3.5. Out-of-sample hedging results -- 4.4. Conclusion -- 4.5. Appendices -- 4.5.1. Appendix 1: General FMP portfolio optimization program. 327 $a4.5.2. Appendix 2: Principal components instrumental variables FMP estimator -- 4.6. References -- Chapter 5. A Framework for Achieving Net-Zero-Carbon Alpha Portfolios -- 5.1. Introduction -- 5.2. Carbon emission in the capital market -- 5.3. Passive approach to zero-carbon portfolios -- 5.4. Active approach to zero-carbon portfolios -- 5.4.1. Backward-looking data: carbon efficiency -- 5.4.2. Present-time data: "nowcasting" of environmental news -- 5.4.3. Forward-looking data: corporate climate alignment and adaptation plans -- 5.4.4. Case study: sustainable global equity strategy from PanAgora Asset Management -- 5.5. Carbon offsets -- 5.6. Conclusion -- 5.7. Appendix -- 5.8. References -- Chapter 6. Active Paris-aligned Equity Investing -- 6.1. Introduction -- 6.2. Standards of Paris-aligned benchmarks -- 6.3. Climate-aware alpha drivers -- 6.3.1. Carbon resource efficiency -- 6.3.2. Green patents -- 6.3.3. Corporate target setting -- 6.4. Empirical results -- 6.4.1. Decarbonization pathway -- 6.4.2. Climate-aware alpha -- 6.4.3. Incorporating climate-aware alphas and decarbonization -- 6.4.4. Systematic active Paris-aligned strategies -- 6.5. Conclusion -- 6.6. Appendix: Paris-aligned equity strategy screens -- 6.7. References -- Chapter 7. Green Alpha -- 7.1. Introduction -- 7.2. Research methodology -- 7.2.1. Region classification -- 7.2.2. ESG-specific industry classification -- 7.2.3. Common style factors -- 7.2.4. Backtesting methodology -- 7.3. MSCI ESG rating -- 7.3.1. MSCI ESG data -- 7.3.2. Data coverage and average rating -- 7.3.3. An overview of MSCI ESG rating methodology -- 7.3.4. ESG pillars, themes and key issues -- 7.4. Characteristics of ESG - a factor perspective -- 7.4.1. The basics -- 7.4.2. Difference across sectors -- 7.4.3. Factor exposure -- 7.5. ESG as stock-selection factors. 327 $a7.5.1. Aggregated ESG rating and the three pillars -- 7.5.2. Revenue, country and industry adjustment -- 7.5.3. Other adjustment -- 7.5.4. ESG momentum -- 7.5.5. Performance of aggregate ESG and three pillar scores -- 7.6. Environmental factors -- 7.6.1. Zooming into clean technology -- 7.6.2. Carbon emissions along the supply chain -- 7.7. ESG signals are additive to traditional stock-selection factors -- 7.7.1. Performance comparison with traditional stock-selection factors -- 7.7.2. Correlation with traditional factors -- 7.7.3. The diversification benefit offered by ESG factors -- 7.8. Conclusion -- 7.9. References -- Chapter 8. Enhancing Environment-driven Portfolios with Traditional Factors -- 8.1. Introduction -- 8.2. Framework -- 8.2.1. ESG overlays: the classic overlay -- 8.2.2. The factor embedding - the factor overlay -- 8.3. Empirical tests -- 8.3.1. Data and protocol -- 8.3.2. Baseline results -- 8.3.3. Statistical significance -- 8.3.4. Sector exposure -- 8.3.5. Transfer coefficients -- 8.4. Robustness checks -- 8.4.1. The sample size -- 8.4.2. A more passive benchmark -- 8.5. Conclusion -- 8.6. Appendix: Distribution of variables -- 8.7. References -- Chapter 9. Enhancing the Accuracy of Firm Valuation with Multiples Using Carbon Emissions -- 9.1. Data -- 9.1.1. Carbon data -- 9.1.2. Financial data -- 9.2. Multiple construction methodology -- 9.2.1. Identifying and composing suitable peer group -- 9.2.2. Constructing and aggregating multiples -- 9.2.3. Determining firm valuation errors -- 9.3. Constructing new multiples using carbon data -- 9.4. Constructing peer groups using carbon data -- 9.5. Combining carbon emission multiples and carbon emission enhanced peer groups -- 9.6. Robustness -- 9.7. Recommendation for using carbon emissions for multiples and further research -- 9.8. References. 327 $aChapter 10. Risk Management Challenges in Sustainability Themed Portfolios: An Application to GHG-constrained Portfolios -- 10.1. Introduction -- 10.2. Methodology -- 10.3. Data description -- 10.4. Results -- 10.5. Conclusion and implications -- 10.6. References -- Chapter 11. Absolutely Sustainable Investing Across Asset Classes with Paris-aligned Benchmarks: An Application to AP2 -- 11.1. Introduction -- 11.2. The climate benchmarks -- 11.2.1. Minimum benchmark requirements -- 11.2.2. Benchmark decarbonization and inflation adjustment -- 11.3. Absolutely sustainable investing -- 11.4. Case study: implementation of PAB at Andra AP-fonden -- 11.4.1. The Swedish pension system and the AP-funds -- 11.4.2. Development of sustainability integration and benchmarks at AP2 -- 11.4.3. Implementing the EU Paris-aligned Benchmark at AP2 -- 11.4.4. Specific aspects -- 11.4.5. Discussion -- 11.5. Conclusion -- 11.6. References -- Chapter 12. Delegated Philanthropy in Mutual Fund Votes on Climate Change Externalities -- 12.1. Introduction -- 12.2. Sample, data sources, variables and descriptive statistics -- 12.2.1. Mutual fund votes -- 12.2.2. Mutual fund characteristics -- 12.2.3. Mutual fund holdings -- 12.2.4. Descriptive statistics -- 12.3. Empirical analysis -- 12.3.1. Impact of the percentage of SRI on the support for climate resolutions -- 12.3.2. Resolutions on other corporate externalities -- 12.3.3. Drivers of support for climate change resolutions -- 12.3.4. Robustness -- 12.4. Conclusion -- 12.5. Appendix: Classification of shareholder resolutions -- 12.6. References -- Chapter 13. Creditworthiness and Buildings' Energy Efficiency in the Mortgage Market -- 13.1. Introduction -- 13.2. Portfolio analysis -- 13.2.1. Energy efficiency -- 13.2.2. Descriptive statistics -- 13.3. Methodology -- 13.3.1. Logit regression. 327 $a13.3.2. Cox proportional hazards model -- 13.4. Results -- 13.4.1. Estimates from the logit regression -- 13.4.2. Estimates from the Cox regression -- 13.4.3. Additional findings -- 13.5. Conclusion -- 13.6. Appendix -- 13.7. References -- Chapter 14. The Thesis for Green Investing and Other ESG through the Looking Glass of China and the US -- 14.1. Introduction -- 14.2. Who and what does Green investing impact? -- 14.3. Who should set the Green investing agenda? -- 14.3.1. Should Green Initiatives be determined by elected civil servants or by rating services, investment funds and corporate CEOs? -- 14.3.2. The Milton Friedman take on who should drive ESG -- 14.3.3. American ESG in conflict with American democracy? -- 14.3.4. Who drives environmental protection policy and other ESG issues in China? -- 14.3.5. Good intentions but bad skills? -- 14.4. Earning a Green alpha?! -- 14.5. Market efficiency and ESG -- 14.6. Conclusion -- 14.7. References -- List of Authors -- Index -- EULA. 330 $aThis edited book consists of a collection of original articles written by leading industry and academic experts in the area of climate investing. The chapters introduce the reader to some of the latest research developments in the area of low-carbon investing and climate change solutions. Each chapter deals with new methods for estimating portfolio carbon footprints, constructing Paris-aligned equity and multi-asset portfolios and hedging climate risks. This title will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge and understanding of climate investing. 606 $aInvestments$xEnvironmental aspects 606 $aEnvironmental economics 615 0$aInvestments$xEnvironmental aspects. 615 0$aEnvironmental economics. 676 $a332.6 702 $aJurczenko$b Emmanuel 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910677700103321 996 $aClimate investing$93089290 997 $aUNINA