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The distributional effect of climate change on agriculture: Evidence from a Ricardian quantile analysis of Brazilian census data

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  • DePaula, Guilherme

Abstract

The economic impact of global warming likely varies across farms because of differences in climate, technology, and adaptive capacity. Therefore, aggregate estimates of the average effect of warming may be insufficient to model climate change vulnerability. In this study, I propose a quantile model for the distributional effect of climate change. I estimate interquantile regressions of land value on climate using agricultural census data for 464,277 commercial farms in Brazil. I find that the effects of climate change in Brazilian agriculture vary significantly by climate, land quality, and irrigation choice. A 1 °C of warming is more detrimental to farms in warm climates, those with high-quality land, and those using irrigation. A 100-mm decrease in annual precipitation is more damaging to farms in dry climates, those with low-quality land, and those using irrigation. The heterogeneity in climate change effects is particularly large within the subset of farms in the warmest or the driest climates, as the most vulnerable farms appear to be those that have reached their limits for climate adaptation.

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  • DePaula, Guilherme, 2020. "The distributional effect of climate change on agriculture: Evidence from a Ricardian quantile analysis of Brazilian census data," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    • Handle: RePEc:eee:jeeman:v:104:y:2020:i:c:s0095069620301017
    DOI: 10.1016/j.jeem.2020.102378
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    Cited by:

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    2. Xun Su & Minpeng Chen, 2022. "Econometric Approaches That Consider Farmers’ Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
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    4. Katarzyna Kocur-Bera & Anna Lyjak, 2021. "Analysis of Changes in Agricultural Use of Land After Poland’s Accession to the EU," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 517-533.
    5. Litao Feng & Wei Liu & Zhihui Zhao & Yining Wang, 2023. "Rainfall fluctuations and rural poverty: Evidence from Chinese county‐level data," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(3), pages 633-656, July.
    6. Anny Mulyani & Budi Mulyanto & Baba Barus & Dyah Retno Panuju & Husnain, 2022. "Geospatial Analysis of Abandoned Lands Based on Agroecosystems: The Distribution and Land Suitability for Agricultural Land Development in Indonesia," Land, MDPI, vol. 11(11), pages 1-19, November.
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    8. Xu, Haitao & Pan, Xiongfeng & Guo, Shucen & Lu, Yuduo, 2021. "Forecasting Chinese CO2 emission using a non-linear multi-agent intertemporal optimization model and scenario analysis," Energy, Elsevier, vol. 228(C).
    9. Matsuki, Takashi & Pan, Lei, 2021. "Per capita carbon emissions convergence in developing Asia: A century of evidence from covariate unit root test with endogenous structural breaks," Energy Economics, Elsevier, vol. 99(C).

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    More about this item

    Keywords

    Climate change; Distributional effects; Agriculture; Quantile regression; Census data; Brazil;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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