Climate finance is the study of local and global financing of public and private investment that seeks to support mitigation of and adaptation to climate change. In 2017, the Review of Financial Studies launched a competition among scholars to develop research proposals on the topic with the goal of publishing this special volume. We describe the competition, how the nine projects featured in this volume came to be published, and frame their findings within what we view as a broader climate finance research program.

We propose and implement a procedure to dynamically hedge climate change risk. We extract innovations from climate news series that we construct through textual analysis of newspapers. We then use a mimicking portfolio approach to build climate change hedge portfolios. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. We discuss multiple directions for future research on financial approaches to managing climate risk.

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.

Counties more likely to be affected by climate change pay more in underwriting fees and initial yields to issue long-term municipal bonds compared to counties unlikely to be affected by climate change. This difference disappears when comparing short-term municipal bonds, implying the market prices climate change risks for long-term securities only. Higher issuance costs for climate risk counties are driven by bonds with lower credit ratings. Investor attention is a driving factor, as the difference in issuance costs on bonds issued by climate and nonclimate affected counties increases after the release of the 2006 Stern Review on climate change.

This paper studies whether house prices reflect belief differences about climate change. We show that in an equilibrium model of housing choice in which agents derive utility from ownership in a neighborhood of similar agents, prices exhibit different elasticities to climate risk. We use comprehensive transaction data to relate prices to inundation projections of individual homes and measures of beliefs about climate change. We find that houses projected to be underwater in believer neighborhoods sell at a discount compared to houses in denier neighborhoods. Our results suggest that house prices reflect heterogeneity in beliefs about long-run climate change risks.

Lemoine and Rudik (2017) argues that it is efficient to delay reducing carbon emissions, due to supposed inertia in the climate system’s response to emissions. This conclusion rests upon misunderstanding the relevant earth system modeling: there is no substantial lag between CO 2 emissions and warming. Applying a representation of the earth system that captures the range of responses seen in complex earth system models invalidates the original article’s implications for climate policy. The least-cost policy path that limits warming to 2°C implies that the carbon price starts high and increases at the interest rate. It cannot rely on climate inertia to delay reducing and allow greater cumulative emissions.

Mattauch et al. (2020) claims that the quantitative conclusions in Lemoine and Rudik (2017)—henceforth, LR17—are not robust to using a climate model consistent with recent scientific results. We observe that LR17 in fact analyzes an extension to a more realistic carbon model that generates an efficient emission tax trajectory very similar to that in Mattauch et al. (2020), and we here show that simplifications in the temperature model of LR17 do not qualitatively affect their policy conclusions. Accounting for inertia reduces the initial emission tax by 42 percent and reduces the present value of abatement cost by 39 percent.