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Publication Type
Collection of articles
Editor
Supervisor
Stouthuysen, Kristof
Verdonck, Tim
Verdonck, Tim
Publication Year
2025-05-15
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Book
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Publication Issue
Publication Begin page
1
Publication End page
231
Publication Number of pages
231
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Abstract
Climate change significantly affects the way firms do business. Firms must not only manage and mitigate the financial impacts of climate-related risks—such as transition risks from policy uncertainty or physical risks from exposure to extreme weather—but also take responsibility for the externalities they impose on society, including biodiversity loss, greenhouse gas emissions, and deforestation. With societal awarenss of climate change being at an all-time high, this underscores the importance for firms to be transparent about their exposure to climate change, not only to inform decision-making in capital markets, but also to establish and maintain legitimacy toward its stakeholders, ultimately increasing accountability. However, while we have witnessed an increase in the disclosure of climate-related information (Lin et al., 2024; Müller et al., 2024), these disclosures are not without criticism. Investors have repeatedly voiced concerns about the value-relevance of climate risk disclosure (Ilhan et al., 2023), and empirical evidence documents substantial use of boilerplate language (Bingler et al., 2022, 2024; Lin et al., 2024). Meanwhile, popular concern about greenwashing is soaring (Montgomery et al., 2024). In response to these concerns, policymakers globally are increasingly considering mandating the disclosure of nonfinancial information, including climate-related information, to enhance firms' transparency and, ultimately, accountability. Yet, recent regulatory developments have illustrated the challenges that persist in designing and implementing effective nonfinancial disclosure standards and regulations. Motivated by the relevance of climate disclosure and the challenges firms and regulators face in designing optimal disclosure practices, this dissertation aims to provide fundamental insights into how firms report climate-related information in their annual reports, a key source of both financial and nonfinancial information for investors and other stakeholders (Lin et al., 2024). Moreover, it explores factors influencing firms' reporting choices and the relevance of climate disclosure to external stakeholders. To uncover these "dynamics" of climate disclosure, this dissertation draws on innovations in machine learning, enabling the large-scale analysis of several dimensions of firms' textual climate disclosures. The first chapter examines how the disclosure narrative in firms' annual reports has developed over the last decade. To model this narrative, the chapter introduces a machine learning approach that combines ClimateBERT (Webersinke et al., 2022) with a structural topic model (STM) (Roberts et al., 2016), enabling the discovery of a latent set of disclosure themes in firms' annual reports. The findings indicate that, over the period 2010-2022, the disclosure narrative has significantly shifted toward topics that are expected to be primarily of interest to investors. Additional analyses reveal that these disclosure changes are likely driven by the introduction of the Non-Financial Reporting Directive, which presented an EU-wide shift from voluntary toward mandatory nonfinancial disclosure. Overall, this study contributes to developing a better understanding of the pathways to the current state of climate disclosure and contributes to the debate on the effects of nonfinancial disclosure regulation. The second chapter zooms in and explores whether and when climate risk disclosure influences capital market participants' decision-making, focusing on financial analysts. The findings reveal no general relationship between climate risk disclosure and analysts' earnings forecasts. Nevertheless, results of additional tests show that when firms face negative news exposure, analysts are more likely to downgrade their earnings forecasts, with analysts' revision depending on climate risk disclosure and the materiality of climate risk. These findings illustrate the complex role of climate risk disclosure in capital markets, thereby contributing to the debate on the value-relevance of these disclosures. Another key finding of this chapter is that the perceived credibility of disclosure also influences analysts' judgements of a firm's disclosures, which might lead them to overract to these disclosures. The third chapter, in turn, explores the dynamics of climate disclosure in buyer-supplier relationships. The findings illustrate that there is significant alignment in the way buyers and suppliers report climate-related information. Interestingly, this is driven by the suppliers' imitation of their buyers' disclosure practices, as well as by assortative matching, where buyers and suppliers having similar disclosure practices match in the contracting process. These findings illustrate the importance of moving beyond the focal firm if we are to fully understand firms' climate disclosure choices. Moreover, they should be of interest to policymakers globally, as they illustrate how disclosure practices might spill over from regulated toward unregulated firms due to supply chain dynamics.
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Keywords
Machine Learning