Geophysicists examine and document the repercussions for the earth’s climate induced by alternative emission scenarios and model specifications. Using simplified approximations, they produce tractable characterizations of the associated uncertainty. Meanwhile, economists write highly stylized damage functions to speculate about how climate change alters macroeconomic and growth opportunities. How can we assess both climate and emissions impacts, as well as uncertainty in the broadest sense, in social decision-making? We provide a framework for answering this question by embracing recent decision theory and tools from asset pricing, and we apply this structure with its interacting components to a revealing quantitative illustration.

Addressing climate change requires individual behavior change and voter support for pro-climate policies, yet surprisingly little is known about how to achieve these outcomes. In this paper, we estimate causal effects of additional education on pro-climate outcomes using new compulsory schooling law data across 20 European countries. We analyze effects on pro-climate beliefs and behaviors, as well as novel data on policy preferences and voting for green parties. Results show that a year of education substantially increases pro-climate beliefs, behaviors, and policy preferences.

We examine firms' trade‐offs in their voluntary disclosure decisions following negative media coverage of climate change incidents. By combining a keyword discovery algorithm and a fine‐tuned BERT model, we identify “hard” and “soft” climate disclosures on Twitter. Our findings indicate that firms tend to issue climate tweets as a rapid response to negative climate incidents. Additionally, firms with a history of hard climate change disclosure, as measured by ESG reports, are more likely to issue climate‐related responses than firms without such a history. Furthermore, we show that prior hard disclosure is associated with hard responses when the incident receives moderate media attention, but with soft responses when the incident receives low media attention. Our findings provide empirical insights for dynamic disclosure theory by illustrating how prior disclosure shapes firms' response strategies to negative media coverage.

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Donald Trump’s 2016 election and his nomination of climate skeptic Scott Pruitt to head the Environmental Protection Agency drastically downshifted expectations about U.S. policy toward climate change. Joseph Biden’s 2020 election shifted them dramatically upward. We study firms’ stock-price movements in reaction to these changes. As expected, the 2016 election boosted carbon-intensive firms. Surprisingly, firms with climate-responsible strategies also gained, especially those firms held by long-run investors. Such investors appear to have bet on a “boomerang” in climate policy. Harbingers of a boomerang appeared during Trump’s term. The 2020 election marked its arrival.

This paper uses international trade data to examine the effects of climate shocks on economic activity. At the aggregate level, Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken (2008) (hereafter, DJO) have demonstrated that higher temperatures in a given year reduce the growth rate of GDP per capita, but only in poor countries. The analysis of trade data in this paper builds on that finding, with three principal motivations. First, international trade links the fortunes of countries, providing potentially important conduits for geographically limited climatic impacts to have global economic effects. Second, international trade data is the best available source for identifying economic activity worldwide separately by narrowly defined sectors. Examining international trade data, one can thus say more precisely what sectors are affected by climatic changes. Finally, the trade data, collected by the importing country, provides a check on the potentially low-quality national accounts data provided by the home country.

We provide novel evidence on the role of firms’ banking relationships with government banks (GOBs) during a climate-related shock when relief funds are unavailable. Using variation in the locations of rainfall shocks and firms’ banking relationships, we find that firms maintaining exclusive banking relationships with GOBs (GOB firms) secure more debt relative to other firms during rainfall shocks. We do not find such effects for firms that maintain exclusive relationships with private banks, foreign banks, or maintain multiple banking relationships. We also find that GOB relationships are particularly beneficial for firms that are more vulnerable to rainfall shocks, have long-term relationships with GOBs, and are, at the same time, healthier compared to other firms. With regard to real effects, GOB firms invest more and remain profitable than other firms during rainfall shocks. Overall, our results highlight the benefits of GOB relationships for firms during climate shocks.

We estimate the impacts of temperature on time allocation by exploiting plausibly exogenous variation in temperature over time within counties. Temperature increases at the higher end of the distribution reduce hours worked in industries with high exposure to climate and reduce time allocated to outdoor leisure for the nonemployed, with this time reallocated to indoor leisure. At the lower end of the distribution, time allocated to labor is nonresponsive to temperature increases, but outdoor leisure increases while indoor leisure decreases as temperature warms. We also find suggestive evidence of short-run adaptation to higher temperatures through temporal substitutions and acclimatization.

We survey 861 finance academics, professionals, and public sector regulators and policy economists about climate finance topics. They identify regulatory risk as the top climate risk to businesses and investors over the next five years, but they view physical risk as the top risk over the next 30 years. By an overwhelming margin, respondents believe that asset prices underestimate climate risks rather than overestimate them. We also tabulate opinions about the expected correlation between growth and climate change, social discount rates appropriate for projects that mitigate the effects of climate change, most influential forces for reducing climate risks, and most important research topics.

Very little. A plethora of integrated assessment models (IAMs) have been constructed and used to estimate the social cost of carbon (SCC) and evaluate alternative abatement policies. These models have crucial flaws that make them close to useless as tools for policy analysis: certain inputs (e.g., the discount rate) are arbitrary, but have huge effects on the SCC estimates the models produce; the models' descriptions of the impact of climate change are completely ad hoc, with no theoretical or empirical foundation; and the models can tell us nothing about the most important driver of the SCC, the possibility of a catastrophic climate outcome. IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.