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Unpacking banks’ response to societal expectations: An NLP analysis of European banks’ discussion of climate change

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  • Löfgren, Åsa
  • Elliott, Jasmine
  • Yu, Yinan
  • Scheidegger, Samuel

Abstract

We employ Natural Language Processing (NLP) to analyze how climate change is discussed in the annual sustainability reports of the 35 largest EU banks (2015–2021), assessing alignment with four societal expectations: decarbonizing financial products and services, addressing climate-related risks, reducing operational emissions, and enhancing transparency. These expectations stem from governments, regulators, and civil society. Analyzing over 1.5 million statements, we find that about 7% of content pertains to climate change. Banks increasingly focus on decarbonizing consumer products and their own operations, but devote less attention to financed emissions, transition risks, and concrete commitments. Our study contributes to the application of NLP in climate finance by qualitatively interpreting how banks, in their own words, engage with societal expectations around the climate transition. This complements quantitative studies by contextualizing disclosure patterns and highlighting reporting gaps and under-emphasized but material issues, offering insights that can inform policymakers in designing disclosure requirements.

Suggested Citation

  • Löfgren, Åsa & Elliott, Jasmine & Yu, Yinan & Scheidegger, Samuel, 2026. "Unpacking banks’ response to societal expectations: An NLP analysis of European banks’ discussion of climate change," Research in International Business and Finance, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:riibaf:v:82:y:2026:i:c:s0275531925004635
    DOI: 10.1016/j.ribaf.2025.103207
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    References listed on IDEAS

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    1. Tian, Yingjie & Wen, Haonan & Guo, Kun, 2025. "Machine learning applications in climate finance: An overview," Research in International Business and Finance, Elsevier, vol. 79(C).
    2. Jasmine Elliott & Åsa Löfgren, 2022. "If money talks, what is the banking industry saying about climate change?," Climate Policy, Taylor & Francis Journals, vol. 22(6), pages 743-753, July.
    3. Stefano Battiston & Antoine Mandel & Irene Monasterolo & Franziska Schütze & Gabriele Visentin, 2017. "A climate stress-test of the financial system," Nature Climate Change, Nature, vol. 7(4), pages 283-288, April.
    4. Simone Cenci & Matteo Burato & Marek Rei & Maurizio Zollo, 2023. "The alignment of companies' sustainability behavior and emissions with global climate targets," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Daniel O. Beltran & Pinar Uysal, 2023. "What are Large Global Banks Doing About Climate Change?," International Finance Discussion Papers 1368, Board of Governors of the Federal Reserve System (U.S.).
    6. Bingler, Julia Anna & Kraus, Mathias & Leippold, Markus & Webersinke, Nicolas, 2022. "Cheap talk and cherry-picking: What ClimateBert has to say on corporate climate risk disclosures," Finance Research Letters, Elsevier, vol. 47(PB).
    7. David Friederich & Lynn H. Kaack & Alexandra Luccioni & Bjarne Steffen, 2021. "Automated Identification of Climate Risk Disclosures in Annual Corporate Reports," Papers 2108.01415, arXiv.org.
    8. Moreno, Angel-Ivan & Caminero, Teresa, 2022. "Application of text mining to the analysis of climate-related disclosures," International Review of Financial Analysis, Elsevier, vol. 83(C).
    9. Peter M. Clarkson & Jordan Ponn & Gordon D. Richardson & Frank Rudzicz & Albert Tsang & Jingjing Wang, 2020. "A Textual Analysis of US Corporate Social Responsibility Reports," Abacus, Accounting Foundation, University of Sydney, vol. 56(1), pages 3-34, March.
    10. Porenta, Jan & Rant, Vasja, 2025. "ESG relevance in credit risk of development banks," Research in International Business and Finance, Elsevier, vol. 76(C).
    11. Andreas G. F. Hoepner & Joeri Rogelj, 2021. "Emissions estimations should embed a precautionary principle," Nature Climate Change, Nature, vol. 11(8), pages 638-640, August.
    12. Andres Alonso-Robisco & Javier Bas & Jose Manuel Carbo & Aranzazu de Juan & Jose Manuel Marques, 2025. "Where and how machine learning plays a role in climate finance research," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 15(2), pages 456-497, April.
    13. Bingler, Julia Anna & Kraus, Mathias & Leippold, Markus & Webersinke, Nicolas, 2024. "How cheap talk in climate disclosures relates to climate initiatives, corporate emissions, and reputation risk," Journal of Banking & Finance, Elsevier, vol. 164(C).
    14. Andersson, Malin & Köhler-Ulbrich, Petra & Nerlich, Carolin, 2025. "Green investment needs in the EU and their funding," Economic Bulletin Articles, European Central Bank, vol. 1.
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