Climate change and climate-related financial disclosures in the banking sector

Risk Governance & Control: Financial Markets & Institutions

article

Dario Aversa¹ 

[1] Department of Social Sciences and Economics, Sapienza University of Rome

关键词: Climate Change;    Disclosure;    Transition Risks;    Physical Risks;    Scenario Analysis;    Text Mining;    Text Analytics;    Unsupervised Learning;    Supervised Learning;   

DOI  :  10.22495/rgcv13i1p6

学科分类：社会科学、人文和艺术（综合）

来源: Virtus Interpress

【 摘 要 】

The aim of the paper is to analyze sustainability report disclosures (Task Force on Climate-related Financial Disclosures [TCFD], 2017a, 2017b, 2019, 2020a, 2020b, 2021, 2022; AlHares & Al-Hares, 2020; Lagasio, 2019; Lucchese, 2020; International Sustainability Standards Board [ISSB], 2022) of the listed banks on FTSE Italia All-Share index of Borsa Italiana through text analytics (Giuliano, 2004). The research questions tend to verify: how and whether physical risk (acute and chronic) is reported; how and whether transition risk (legal, technology, market, and reputational) is reported; how and whether scenario analysis (The Bank of England, 2022; Rogelj et al., 2018) is conducted. Using Iramuteq (www.iramuteq.org) and SAS Viya (www.sas.com), the research combines unsupervised learning (Reinert, 1990) and supervised techniques (SAS, 2019) pointing out the inadequacy, the lack of transparency, and the lack of comparability of the sustainability reports that may increase the potential for uncertainty and financial instability. Disclosing climate information on a mandatory basis allows an increase in the quantity and quality of climate-related reporting, an increase in transparency, and comparability accountability, and provides clearer disclosures to investors and regulators.

【 授权许可】

CC BY-NC