The first Global Climate Strike on March 15, 2019, represented a historical turning point in climate activism. We investigate the cross-section of stock price reactions to this event for a large sample of European firms. The strike's unanticipated success caused a decrease in the stock prices of carbon-intensive firms. The effect appears to be driven by the increased public attention to climate activism. Furthermore, after the first Global Climate Strike financial analysts downgraded their longer-term earnings forecasts on carbon-intensive firms.

We compare prices of financial derivatives whose payouts are based on future weather outcomes to CMIP5 climate model predictions as well as observed weather station data across eight cities in the US from 2001 through 2020. Derivative prices respond both to short-term weather forecasts for the next two weeks and longer-term warming trends. We show that the long-term trends in derivative prices are comparable to station-level data and climate model output. The one exception is February in the northeastern US, where financial markets price in a polar vortex-induced cooling effect, a recent scientific finding that was not present in the older CMIP5 climate output. When looking at the spatial and temporal heterogeneity in trends, futures prices are more aligned with climate model output than observed weather station trends, suggesting that market participants closely align their expectations with scientific projections rather than recent observations.

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.

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.

This paper investigates the nexus between climate-related variables, commodity price co-movements and financial stability. First, we project the commodity price time series onto a multilayer network. Centrality measures computed on the network are used to detect the existence of common trends between the series and to characterize the role of different nodes during phases of market downturns and upturns, unveiling the onset of financial instability. Then, an econometric analysis is introduced to show how climate-related variables affect financial stability by influencing co-movements of commodity prices. Overall, the paper reveals how synthetic indicators of commodity price co-movements generate valuable signals to study the nexus between climate-related conditions and the dynamics of financial systems.

We find robust empirical evidence that firms in locations with higher exposure to climate change pay significantly higher spreads on their bank loans. To alleviate the concerns related to using firms' headquarters in determining climate risk exposure, we exploit the economic link between a firm and its customers and find that the exposure of a firm's customers to climate risk also adversely affects that firm's cost of borrowing. In the cross-section, we find that the long-term loans of poorly rated firms drive the effect. Overall, our evidence suggests that lenders increasingly view climate change as a relevant risk factor.

We study the Markov perfect equilibrium in a dynamic game where agents have non-constant time preference, decentralized households determine aggregate savings, and a planner chooses climate policy. The article is the first to solve this problem with general discounting and general functional forms. With time-inconsistent preferences, a commitment device that allows a planner to choose climate policy for multiple periods is potentially very valuable. Nevertheless, our quantitative results show that while a permanent commitment device would be very valuable, the ability to commit policy for “only” 100 years adds less than 2% to the value of climate policy without commitment. We solve a log-linear version of the model analytically, generating a formula for the optimal carbon tax that includes the formula in Golosov et al. (2014, Econometrica, 82, 41–88) as a special case. More importantly, we develop new algorithms to solve the general game numerically. Convex damages lead to strategic interactions across generations of planners that lower the optimal carbon tax by 45% relative to the scenario without strategic interactions.

We show that housing markets provide information about the appropriate discount rates for valuing investments in climate change abatement. Real estate is exposed to both consumption and climate risk and its term structure of discount rates is downward sloping, reaching 2.6% for payoffs beyond 100 years. We use a tractable asset pricing model that incorporates features of climate change to show that the term structure of discount rates for climate-hedging investments is thus upward sloping but bounded above by the risk-free rate. At horizons at which risk-free rates are unavailable, the estimated housing discount rates provide an upper bound.

We estimate how the mortality effects of temperature vary across U.S. climate regions to assess local and national damages from projected climate change. Using 22 years of Medicare data, we find that both cold and hot days increase mortality. However, hot days are less deadly in warm places while cold days are less deadly in cool places. Incorporating this heterogeneity into end-of-century climate change assessments reverses the conventional wisdom on climate damage incidence: cold places bear more, not less, of the mortality burden. Allowing places to adapt to their future climate substantially reduces the estimated mortality effects of climate change.