Combining AI and Domain Expertise to Assess Corporate Climate Transition Disclosures

Chiara Colesanti Senni, Tobias Schimanski, Julia Bingler, Jingwei Ni and Markus Leippold
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Chiara Colesanti Senni: University of Zurich - Department of Finance
Tobias Schimanski: University of Zurich
Julia Bingler: University of Oxford
Jingwei Ni: ETH Zurich
Markus Leippold: University of Zurich; Swiss Finance Institute

No 24-92, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Company transition plans toward a low-carbon economy are key for effective capital allocation and risk management. This paper proposes a set of 64 indicators to comprehensively assess transition plans and develops a Large Language Model-based tool to automate the assessment of company disclosures. We evaluate our tool with experts from 26 institutions, including financial regulators, investors, and non-governmental organizations. We apply the tool to the sustainability reports from carbon-intensive Climate Action 100+ companies. Our results show that companies tend to disclose more information related to target setting (talk), but fewer information related to the concrete implementation of strategies (walk). In addition, companies that disclose more information tend to have lower emissions. Our results highlight the need for increased scrutiny of companies' efforts and potential greenwashing risks. The complexity of transition activities presents a major challenge for comprehensive large-scale assessments. As shown in this paper, novel and flexible approaches using Large Language Models can serve as a remedy.

Keywords: Climate disclosure; Large Language Models; RAG system; transition plans; human evaluation; CA100+ (search for similar items in EconPapers)
Pages: 45 pages
Date: 2024-05
New Economics Papers: this item is included in nep-ain, nep-big, nep-ene and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2492

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