Abstract
This chapter examines environmental aspects of ESG and risks and opportunities for using big data (BD) and artificial intelligence (AI) to capture these in ESG ratings. It starts by outlining the difference between relative and absolute sustainability and what this means for delivering on globally agreed upon targets, such as the Sustainable Development Goals. We then look at what the state-of-the-art climate and Earth System science has to offer investors interested in absolute environmental sustainability. Next, we discuss the risks associated with a blurring of concepts relating to sustainability and materiality, and examine and contrast conventional ESG rating procedures with new approaches informed by BD and AI to understand what this new generation of tools can offer investors interested in sustainability. We note a current misalignment between stated ambitions of investors, and the ability to deliver on stated goals through the use of current ESG metrics and ratings. We therefore finish with suggestions for how to better align these and how those interested in ESG can become more ‘sustainability savvy’ consumers of such ratings.
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Notes
- 1.
- 2.
See for example p. 8, Fig. 2, in Statement of Intent to Work Together Towards Comprehensive Corporate Reporting” by SASB, GRI, CDP, IIRC and CDSB: https://29kjwb3armds2g3gi4lq2sx1-wpengine.netdna-ssl.com/wp-content/uploads/Statement-of-Intent-to-Work-Together-Towards-Comprehensive-Corporate-Reporting.pdf.
- 3.
- 4.
See e.g. https://www.esg-integrate.com/sustainability-integrated-reporting, but many examples exist.
- 5.
Refinitiv Eikon ESG data for the period 2014–2019 was extracted in May 2020 and included all 7252 publically listed companies with an ‘environmental pillar’ score in 2019.
- 6.
Morningstar changed its methodology in 2019, as Sustainalytics changed from their previous ESG ratings to ESG Risk Ratings measuring “the degree to which a company’s economic value is at risk driven by ESG factors.” https://www.sustainalytics.com/esg-risk/.
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- 10.
Example of companies include Arabesque, Sensefolio, or TruvalueLabs.
- 11.
“It´s not about what companies say, it´s what they do” (https://truvaluelabs.com/trends/esg-integration).
- 12.
- 13.
Eikon download details: Equity(active,public,primary); TR.EnvironmentPillarScore(Period = FY0) > = 0;
Fields used: TR.ControvEnv & TR.RecentControvEnv; StartDate’: ‘2015–01-01’; EndDate’: ‘2020–12-31’.
- 14.
p45 in Sensefolio’s technical paper: https://sensefolio.com/Sensefolio_TechnicalPaper.pdf.
- 15.
Sensefolio scores use three primary sources of information, analyzed via NLP, to assess companies’ degree of involvement in ESG topics: Financial News, Sustainability and ESG Reports & Earnings Calls, and Social Media Posts & Company Reviews (all accounting for 1/3 respectively). The source is: https://medium.com/@sensefolio/presentation-of-sensefolio-55699bd144a5, where Sensefolio in a series of articles explains the methodology behind their scores. That the medium account @sensefolio indeed belongs to sensefolio, is stated on the last page of the sensefolio technical paper reference above.
- 16.
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Crona, B., Sundström, E. (2023). Sweet Spots or Dark Corners? An Environmental Sustainability View of Big Data and Artificial Intelligence in ESG. In: Rana, T., Svanberg, J., Öhman, P., Lowe, A. (eds) Handbook of Big Data and Analytics in Accounting and Auditing. Springer, Singapore. https://doi.org/10.1007/978-981-19-4460-4_6
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