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Ricardian analyses of farmland values have become a cornerstone of the literature valuing the impacts of climate change on agriculture. However, concerns about the lack of a formal econometric strategy to deal with omitted farmland characteristics have raised doubts about the identification of such impacts. This paper proposes an original method for estimating Ricardian models with plot fixed effects to control for confounding omitted variables. Specifically, we use plot-level repeat-sales French data from 1996 to 2019 to investigate how differences in farmland prices between two sale dates are explained by differences in climate evolutions. We show that our repeat-Ricardian estimates suggest greater benefits of climate change than those found with standard Ricardian analyses. In particular, our repeat-Ricardian estimates indicate that warmer summers benefit French agriculture, in complete opposition to our pooled Ricardian estimates or to the remainder of the literature. Our repeat-Ricardian results are robust to several specifications, climate length-definitions and sub-samples. We provide elements suggesting that the repeat-Ricardian analysis is better able to capture crop-switching towards high-value crops requiring particular soil conditions (e.g. vineyards). Our repeat-Ricardian analysis also indicates greater benefits of climate change compared to those estimated with short-term weather-based approaches, shedding new lights on previous inconsistent findings from the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a simple two-step estimation method (C limate A daptive R esponse E stimation - CARE) to estimate sectoral climate damage functions, which account for long-run adaptation. The paper applies this method in the context of residential electricity and natural gas demand for the world's fifth largest economy — California. The advantage of the proposed method is that it only requires detailed information on intensive margin behavior, yet does not require explicit knowledge of the extensive margin response (e.g., technology adoption). Using almost two billion energy bills, we estimate spatially highly disaggregated intensive margin temperature response functions using daily variation in weather. In a second step, we explain variation in the slopes of the dose response functions across space as a function of summer climate. Using 18 climate models, we simulate future demand by letting households vary consumption along the intensive and extensive margins. We show that failing to account for extensive margin adjustment in electricity demand leads to a significant underestimate of the future impacts on electricity consumption. We further show that reductions in natural gas demand more than offset any climate-driven increases in electricity consumption in this context. [ABSTRACT FROM AUTHOR]