Abstract In 2017, the European Union Emissions Trading System underwent a policy intervention that resulted in a surge in carbon prices. Using this setting as a quasi-natural experiment, we focus on employment, productivity, and emission outcomes among covered enterprises. Results show that emission-intensive private firms, particularly those with financial constraints, are more likely to downsize by divesting production assets, reducing both workforce and emissions. Smaller, cash-strapped listed firms are also prone to downsize by decreasing their operating leverage while maintaining emission output and asset levels. Positive productivity outcomes indicate that both private and listed firms become leaner postintervention.

This paper examines the extent to which polluting firms covered by the world's largest multinational emission trading scheme – the European Union Emission Trading Scheme (EU ETS) – engage in corporate tax avoidance. We exploit the sudden and significant increase in carbon prices after decisions made by the EU Council on February 28th 2017, and test whether firms' tax avoidance behavior subsequently changes. We find that pollution intensive firms engage in more corporate tax avoidance after this sudden price shock. This tax avoidance response is economically sizable as the difference between the effective tax rates between the least and most polluting firms is 5.13 percentage points. Supplemental tests indicate that the tax avoidance response of the more pollution intensive firms is dependent on their operating cost structure, while we find no significant difference in the corporate tax avoidance response depending on their financing needs. Moreover, we find some evidence of reputational concerns moderating this relationship. Additional analyses also demonstrate that increased internal and external monitoring, as reflected by higher regulatory quality and board independence, mitigates the corporate tax avoidance response. Overall, our findings are consistent with the notion that firms transfer carbon costs on to governments under the form of lower societal contributions whereas more internal and external monitoring plays a vital role in preventing this transference.

We apply a novel topic modeling method to map Initial Coin Offerings’ (ICOs’) white paper thematic content to analyze its information value to investors. Using a sentence-based topic modeling algorithm, we determine and empirically quantify 30 topics in an extensive collection of 5,210 ICO white papers between 2015 and 2021. We find that the algorithm produces a semantically meaningful set of topics, which significantly improves the model performance in identifying successful projects. The most value-relevant topics concern the technical features of the ICO. However, we find that white paper's informativeness substantially diminishes after the token is listed. Moreover, we show that credibility-enhancing mechanisms (i.e., regulations and ICO analysts) reinforce the information value of ICO white papers. Overall, our results suggest that the topics discussed in white papers and the attention devoted to each topic are useful ICO performance indicators.