In a nationwide field experiment involving all Chinese listed companies, we created demand for ESG actions by randomly conveying ESG rating concerns to company management teams via public online platforms. We find that many companies actively addressed these concerns by supplying detailed ESG strategies and actions. High-productivity and low-transparency companies were more likely to respond to such demand for actions. Moreover, companies that received ESG concerns improved their ESG performances over time and published more ESG reports after the experiment. In the long run, stock price responded positively to E and S inquiries while negatively to G inquiries. This divergence can be attributed to investors interpreting E and S inquiries as positive signals and G inquiries as negative signals, demonstrated through their platform interactions. Overall, the results show that companies’ ESG actions are mainly value-driven, rather than values-driven. Corporate ESG actions can be rationalized by a simple signaling model, where companies utilize costly ESG actions (similar to advertisements) to signal their quality under information asymmetry.

This paper investigates how firms adjust their environmental disclosure strategies in response to environmental sanctions. Using natural language processing (NLP) techniques, we analyze the environmental content of earnings conference calls held by U.S.-listed firms from 2012 to 2022, focusing on changes around the announcement of environmental violations.

Our findings document a “green evasion effect”, where CEOs talk less about environmental topics in calls immediately preceding the announcement of environmental penalties. This effect is stronger for firms that are expected to uphold high environmental standards. Conversely, analysts increase their focus on environmental topics after the announcement of an environmental penalty, particularly for firms with higher environmental reputations. We further document that investors respond negatively to environmental sanctions, with more pronounced market reactions to penalties issued to firms perceived as environmentally responsible.

How do ESG labels, that are assigned to firms by ESG rating agencies, affect their ownership by ESG-focused institutional investors and their perceived cost of capital? I identify this effect using the unique institutional setting that MSCI ESG rating methodology provides. I employ a regression discontinuity design around the two cutoffs where the MSCI continuous ESG scores are converted into three distinct ESG labels. Everything else being equal, high-ESG firms with the best ESG labels have higher ownership by ESG institutional investors and lower perceived cost of capital compared to similar firms with average ESG labels. Surprisingly, low-ESG firms with the worst ESG labels also have higher ownership by ESG institutional investors and lower perceived cost of capital compared to similar firms with average ESG labels. I provide evidence that these opposite effects are driven by different investment strategies that institutional investors pursue based on the firms' MSCI ESG labels.

This study contributes to two streams of academic discussion, namely, ESG performance evaluation based on disclosures and the link between banks’ financial performance and ESG performance. Our analysis covers 95 European banks during the 2006–2022 period. We claim that using text mining may result in objective, comprehensive and replicable ESG disclosure scores for banks. Therefore, we employ advanced text-mining techniques to evaluate banks’ ESG performance from a disclosure perspective. The results of the exploration of the link between both types of performance are ambiguous. We find that high profitability reduces banks’ ESG disclosure scores, whereas the ESG variables lack statistical significance with respect to financial performance. Before 2018, some banks had outstanding ESG disclosure scores, and after the implementation of the NFRD, the banks were on the same track.