Abstract
This study assesses the carbon disclosure practices of financial institutions and insurance companies listed in the MSCI World based on a mixed-method content and panel regression analysis. Previous empirical investigations could not relate performance of financial institutions and insurance companies with risk or impacts to society. The guiding research question for this work is how and to what extent are financial institutions and insurance companies disclosing carbon-related information. The results show that similar to previous studies, the performances of the financial sector cannot be assessed with respect to climate change and they cannot be related to market valuation either. However some insurers have suffered insurance cases from climate adaptation and hazards leading them to stop insuring carbon-loaded assets alongside with divesting from some carbon-intensive assets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
MSCI climate value at Risk: Powering better investment decisions for a better world available at https://www.msci.com/documents/1296102/16985724/MSCI-ClimateVaR-Introduction-Feb2020.pdf/f0ff1d77-3278-e409-7a2a-bf1da9d53f30?t=1580472788213
- 4.
Schroders available at https://www.schroders.com/en/ch/asset-management/themes/climate-change-dashboard/carbon-var/, Ecofact available at https://www.ecofact.com/?gclid=Cj0KCQjw0rr4BRCtARIsAB0_48PNG8eEpFB7g5Je-hMmhPTtcnOxPnV2ieoR-NKv17JOeDo33YKnf6AaAhBsEALw_wcB, MSCI Carbon Delta available at https://www.carbon-delta.com/climate-value-at-risk-zur-bewertung-von-unternehmen/
- 5.
In reviewing more than 1600 corporate adaptation strategies, we find significant blind spots in companies’ assessments of climate change impacts and in their development of strategies for managing them (Goldstein et al. 2019).
- 6.
See Unfriend Coal, https://unfriendcoal.com
- 7.
Major Public Companies Describe Climate-Related Risks and Costs: A Review of Findings from CDP 2011–2013 Disclosures (CDP 2014). Available at https://www.ourenergypolicy.org/wp-content/uploads/2014/05/CDP.pdf
- 8.
See Unfriend Coal
- 9.
- 10.
- 11.
The definition of carbon footprinting and the distinction between Scope 1, 2, and 3 emissions have been developed by the Greenhouse Gas Protocol under leadership of the World Resources Institute.
- 12.
- 13.
Is a multi-stakeholder partnership of businesses, non-governmental organizations (NGOs), governments, and others convened by the World Resources Institute (WRI), a US-based environmental NGO
- 14.
Further partners are NGOs, and the World Business Council for Sustainable Development (WBCSD), a Geneva-based coalition of 170 international companies.
- 15.
Distinction: GHG Protocol Corporate Accounting and Reporting Standard – a step-bystep guide for quantifying and reporting their GHG emissions. GHG Protocol Project Quantification Standard (for quantifying reductions from GHG mitigation projects).
- 16.
Please refer to the overview of voluntary initiatives presented in Annex 1.
- 17.
Bloomberg is extracting the data from company reports; due to incompleteness Bloomberg is only using Scope 1 and Scope 2 data.
- 18.
Global Industry Classification Standard (GICS); see https://www.msci.com/gics
- 19.
The Carbon Disclosure Project is an NGO consisting of industry members having the ambition to establish transparency and reporting on carbon emissions in business running a global disclosure system for investors, companies, cities, states, and regions to manage their environmental impacts, available at https://www.cdp.net/en.
- 20.
- 21.
Weblyzard Technology GmbH Vienna, https://www.weblyzard.com
- 22.
- 23.
Sentiment analysis with https://sentione.com/pro#/dashboards/show/156370
- 24.
IRIS Data Intelligence Tool, http://iris.lmsal.com/itn26/iris_level2.html
- 25.
Big data analytics encompasses a range of techniques that can be used to uncover hidden patterns, discover unknown correlations, highlight market trends, and reveal customer insight from the data. The results can lead to more effective marketing, boost in revenue, improved customer service, increased operational efficiency, and a competitive edge over rival companies. The primary focus of big data analytics is to provide companies and organizations with the necessary information to make more knowledgeable decisions and find hidden and undisclosed patterns, for example, in Web server logs, social media content, text from customer emails, survey responses, mobile phone call detail records, and many more. This research, with the aid of knowledge extraction from social media and paid services will explore the capabilities of big data analytics when applied to carbon strategy insurance scenario.
- 26.
- 27.
- 28.
Using PitchBook trial access
- 29.
The Guardian 2018
- 30.
The letter was signed by 350.org; Avaaz; Divest Invest Individual; Friends of the Earth – France; Greenpeace, Switzerland; Market Forces; Re:Common; ShareAction; the Sierra Club; The Sunrise Project; Rainforest Action Network; Urgewald; and the Waterkeeper Alliance
- 31.
- 32.
Unfriend Coal: https://unfriendcoal.com/scorecard/
References
BankTrack.org. (2018). A climate strategy for banks. https://www.f.org/download/a_climate_strategy_for_banks_know_your_financed_emissions_1/1165opm_factsheet_banken_deflow.pdf
BankTrack.org. (2021). Banking on thin ice. https://www.banktrack.org/download/banking_on_thin_ice/210202_banking_on_thin_ice.pdf
Botta, Jochen et al.2012: Carbon accounting und controlling – Grundlagen und Praxisbeispiel Deutsche Post DHL (J. Weber, Hrsg.) (Advanced controlling, Band 83, S. 15–24). Wiley. ISBN 978-3-527-50697-2, 3 Relevanz von Treibhausgasemissionen.
CDP. (2000). The Carbon Disclosure Project. https://www.cdp.net/en
CDP. (2014). https://www.cdp.net/en/reports/archive?page=3&per_page=20&sort_by=last_post_revision_published_at&sort_dir=asc. Accessed Nov. 10, 2020.
CDSB 2007. https://www.cdsb.net/what-we-do/reporting-frameworks
Challenge, C. (2000). The insurance company case. Amsterdam: Sentient Machine Research. Also a Leiden Institute of Advanced Computer Science.
Darbyshire, J. H. (2016). The PRICING and TRADING of interest rate derivatives.
Dietrich, D. (Ed.). (2015). Data science and big data analytics: Discovering, analyzing, visualizing and presenting data. Indianapolis: Wiley.
Economist. (2016, March 12). Technology quarterly. Retrieved from After Moore’s law. http://www.economist.com/technology-quarterly/2016-03-12/after-moores-law
Eitelwein, O., & Goretzki, L. (2010). Carbon Controlling und Accounting erfolgreich implementieren – Status Quo und Ausblick. In: ZfCM Controlling & Management. Band 50, Nr. 1, February 2010, https://doi.org/10.1007/s12176-010-0010-6. EMC Education Services.
Elkan, C. (2001). Magical thinking in data mining: Lessons from CoIL challenge 2000. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, pp. 426–431.
EMC Education Services.
ERM. (2018). https://www.unglobalcompact.org/participation/report/cop/create-and-submit/advanced/421921. Accessed Nov. 10, 2020.
Fodor, I. K. (2002). A survey of dimension reduction techniques (Vol. 9, pp. 1–18). Livermore: Center for Applied Scientific Computing, Lawrence Livermore National Laboratory.
Fortune 500 Index. http://fortune.com/fortune500/
Gartner, I. (2018). IT glossary: Big data. Retrieved from http://www.gartner.com/it-glossary/big-data/17STATUS_HOME_ENVIRONMENT_FIRE_PRIVATE_POLICIES_FAMILYs_ACCIDENT_POLICES_CUSTOMER_TYPE_PROPERTY_COVERHOUSE_OWNER_RENTED_and_ECONOMICALL_DEPRIVED
Goldstein, A., Turner, W. R., Gladstone, J., & Hole, D. G. (2019). The private sector’s climate change risk and adaptation blind spots. Nature Climate Change, 9(1), 18–25. https://www.nature.com/articles/s41558-018-0340-5?proof=trueMay#Bib1.
Google. (2012). Internet live stats. Retrieved from Google Search Statistics: http://www.internetlivestats.com/google-search-statistics/
Guyon, I. A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.
IBM. (2013). Bringing big data to the enterprise. Retrieved from What is big data? https://www-01.ibm.com/software/data/bigdata/what-is-big-data.html
IPPC 2018 : https://www.ipcc.ch MSCI climate value at Risk: Powering better investment decisions for a better world available at https://www.msci.com/documents/1296102/16985724/MSCI-ClimateVaR-Introduction-Feb2020.pdf/f0ff1d77-3278-e409-7a2a-bf1da9d53f30?t=1580472788213
IPPC Report. (2018). https://www.ipcc.ch/2018. Accessed 10 Nov, 2020.
Jolliffe, I. (2002). Principle component analysis. Wiley. https://royalsocietypublishing.org/doi/full/10.1098/rsta.2015.0202
King, J. E. (2008). Binary logistic regression. In Best practices in quantitative methods (pp. 358–384). Thousand Oaks: SAGE.
Kirsten, S., & Edeltraud, G. (2012). Carbon accounting: A systematic literature review. Journal of Cleaner Production, 36. https://doi.org/10.1016/j.jclepro.2012.02.021.
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety (META Group research note, 6, p. 70). http://www.sciepub.com/reference/190800
Mac Nally, R. (2000). Regression and model-building in conservation biology, biogeography and ecology: The distinction between–and reconciliation of ‘predictive’ and ‘explanatory’ models. Biodiversity and Conservation, 9(5), 655–671.
Marr, B. (2015, December 15). How big data is changing insurance forever. Retrieved from Forbes: http://www.forbes.com/sites/bernardmarr/2015/12/16/how-big-data-is-changing-the-insurance-industry-forever/#9e78bce435e8
Mills. (2018). https://acomstaff.acom.ucar.edu/mmills/. Accessed Nov. 10, 2020.
Pitchbook. (2018) Data platform. https://pitchbook.com/products
Putter, P. V. (2000). Insurance company benchmark (COIL, 2000) data set. Retrieved from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Insurance+Company+Benchmark+%28COIL+2000%29
Ramavajjala, V. (2012). Policy iteration based on a learned transition model. Joint European conference on machine learning and knowledge discovery in databases, pp. 211–226.
Rouse, M. (2014). Big data. Big data and cloud business intelligence, tech target, 30. Retrieved from Big Data and Cloud Business Intelligence.
Schücking. (2015). In K. Wendt (Ed.), Responsible Investment Banking 2015. https://springerlink.bibliotecabuap.elogim.com/chapter/10.1007/978-3-319-10311-2_28
Science Based Targets Initiative. https://sciencebasedtargets.org
Sentione Data Platform. https://sentione.com/pro#/dashboards/show/156370
Smith, H. (2012, March 23). Big data FAQs. Retrieved from ARC Community, arcplan, Inc: https://community.arcplan.com/blogs/communityannouncement/big-data-faqs
Stefan, S., & Csutora, M. (2012). Carbon accounting for sustainability and management. Status quo and challenges. Journal of Cleaner Production, 36. https://doi.org/10.1016/j.jclepro.2012.06.024.
TechAmerica. (2012). Demystifying big data: A practical guide to transforming the business of Government. Retrieved from http://www.techamerica.org/Docs/fileManager.cfm?f=techamerica-bigdatareport-final.pdf
UNEP EcoResearchNEP. https://unep.ecoresearch.net/weblyzard/en/
UNEP Live Webintelligence. http://www.uneplive.org/webintelligence
WRI 2011: World Resources Institute, World Business Council on Sustainable Development (Hrsg.). (2011). Corporate value chain (scope 3) accounting and reporting standard: Supplement to the GHG Protocol Corporate Accounting and Reporting Standard (ghgprotocol.org [PDF]).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive licence to Springer Nature Switzerland AG
About this entry
Cite this entry
Wendt, K. (2021). The Impact of Carbon Disclosure on the Market Value of Financial Industry Companies: A Review of the Current Status. In: The Palgrave Handbook of Climate Resilient Societies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-32811-5_122-2
Download citation
DOI: https://doi.org/10.1007/978-3-030-32811-5_122-2
Received:
Accepted:
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-32811-5
Online ISBN: 978-3-030-32811-5
eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences
Publish with us
Chapter history
-
Latest
The Impact of Carbon Disclosure on the Market Value of Financial Industry Companies: A Review of the Current Status- Published:
- 24 October 2021
DOI: https://doi.org/10.1007/978-3-030-32811-5_122-2
-
Original
The Impact of Carbon Disclosure on the Market Value of Financial Industry Companies: A Review of the Current Status- Published:
- 07 August 2021
DOI: https://doi.org/10.1007/978-3-030-32811-5_122-1