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The Impact of Global Warming on U.S. Agriculture: An Econometric Analysis of Optimal Growing Conditions

Open access
Schlenker, Wolfram, Hanemann, W. Michael, Fisher, Anthony C.·2006·Review of Economics and Statistics

Abstract

We link farmland values to climatic, soil, and socioeconomic variables for U.S. counties east of the 100th meridian, the historical boundary of agriculture not primarily dependent on irrigation. Degree days, a nonlinear transformation of the climatic variables suggested by agronomic experiments as more relevant to crop yield, gives an improved fit and increased robustness. Estimated coefficients are consistent with the experimental results. The model is employed to estimate the potential impacts on farmland values for a range of recent warming scenarios. The predictions are very robust, and more than 75% of the counties in our sample show a statistically significant effect, ranging from moderate gains to large losses, with losses in the aggregate that can become quite large under scenarios involving sustained heavy use of fossil fuels.

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Metadata

Authors
Schlenker, W., Hanemann, W. M., & Fisher, A. C.
Venue
Review of Economics and Statistics
Published
2006-02
Volume
88
Issue
1
Pages
113-125
ISSN
  • 0034-6535 (ISSN-L)
  • 1530-9142
OpenAlex
W2147703849
Cited by
510
References
0
FWCI
17.24
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: The Impact of Global Warming on U.S. Agriculture: An Econometric Analysis of Optimal Growing Conditions. https://app.cochat.ai/papers/10.1162/rest.2006.88.1.113
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Schlenker, W., Hanemann, W. M., & Fisher, A. C. (2006). The Impact of Global Warming on U.S. Agriculture: An Econometric Analysis of Optimal Growing Conditions. Review of Economics and Statistics. https://doi.org/10.1162/rest.2006.88.1.113

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.