In recent decades, the scientific literature gauging the macroeconomic cost of climate change has treated it primarily as a public good problem. A stable climate is non-excludable and largely non-rival. While greenhouse gas emissions generate global externalities, the exposure to (and damages from) climate-driven shifts in average temperature vary substantially across space. This leaves no productive units and geographic locations untreated per se. As a result, statisticians have typically relied on the local variability of historical climate fields over time to econometrically predict country- or region-level GDP deviations (Burke et al., 2015; Kalkuhl and Wenz, 2020; Linsenmeier, 2023; Berg et al., 2024; Kotz et al., 2024, among others). This approach, backed by a long line of increasingly granular studies, has structured the applied physical-risk modules used by institutions, central banks, scenario providers, financial-risk and stress-testing frameworks (i.e. Oxford Economics (2024) uses Burke et al. (2015); the NGFS has consumed Kalkuhl and Wenz (2020) in the earlier vintage; and Kotz et al., 2024 in the latest Phase-V1) consumed by the industry at large. As shown later, this methodological approach has also been the natural anchor point of EDHEC Climate Institute (ECI)’s applied scientific developments.
Adrien Bilal, Assistant Professor at Stanford University, received the 2026 Prix du Meilleur Jeune Économiste on 30 March 2026, awarded by Le Monde and Le Cercle des Économistes. The award drew renewed attention to his work with Diego Känzig, “The Macroeconomic Impact of Climate Change: Global Versus Local Temperature”, published in the Quarterly Journal of Economics (QJE) in 2026. The differentiating point of Bilal and Känzig (2026; Bilal–Känzig hereafter) is that they depart from this standard approach. They instead model country- and global-level GDP changes as functions of global temperature variability. While this change in scale is methodologically consequential (because it modifies the nature of the climate signal used for causal identification in econometrics), it also points to materially larger aggregate damage estimates and naturally reopens the central question of how macroeconomic climate damages should be modelled in general.
This raises several questions. What have we learned about this topic over the past two decades? What are the comparative strengths and limitations of the global versus local approaches? And what do these perspectives suggest for ongoing and future research? This position paper aims to give substance to these questions and clarify the stakes of the debate.
