Despite the growing integration of artificial intelligence (AI) into business models, studies of its impact on corporate climate change exposure remain scarce. Through an examination of AI-related innovations among US-listed firms from 2001 to 2019, we present compelling evidence that AI innovation effectively mitigates firms’ climate change exposure. In particular, it reduces firms’ exposure to regulatory and physical risks related to climate change through improved carbon management efficiency, with computer vision and control and planning being the most effective types in this context. Our findings are particularly pronounced for mature firms and those facing greater regulatory intervention. The results withstand rigorous tests that address endogeneity concerns. Our study provides strong support for firms to adopt AI innovations to achieve carbon neutrality, contributing to the ongoing discourse regarding AI trade-offs. Our findings also offer valuable insights into the development of climate risk mitigation strategies.

Few studies have examined the reputational damage of environmental and social (E&S) scandals ex-post. Using an international sample of mergers and acquisitions (M&A) across 18 countries, we examine whether heightened E&S risks arising from acquirers’ E&S incidents affect the cost of acquisition. We find that the severity of the incident increases acquisition premium and this effect is explained by incident acquirers compensating target shareholders for heightened reputation risks, rather than seeking to repair their reputation through the acquisition. At the country level, incident acquirers in countries with stronger E&S standards and policies also experience higher premium. Further analysis shows that E&S incidents reduce shareholder value upon the announcement of an M&A and increase the time taken for M&A completion. Firms are less likely to make acquisitions after experiencing an E&S incident, but this decision depends on the cost of delay. Finally, we show that the adverse impact on acquisitions dissipates after six months.

We investigate the price reaction of listed companies in response to blockchain-related announcements. The average abnormal return based on a global sample of 713 firm announcements is approximately 5% on the announcement day, with significantly higher returns for U.S. firms, smaller firms and announcements in late 2017 and early 2018. We show that abnormal returns are linked to the performance of bitcoin. Additionally, speculative announcements exhibit higher returns than non-speculative announcements, and blockchain-related Form 8-K disclosures have negligible difference in performance compared to their U.S. peers. Whilst we acknowledge the possibility of a latent variable that affects both the abnormal returns and the performance of bitcoin, we hypothesise that investors have confused bitcoin and blockchain, and used the performance of bitcoin as an indicator of the expected success of the blockchain technology.