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Temperature effects on rural household outmigration: Evidence from China

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Abstract

Changes in temperature extremes under climate change are expected to affect outmigration in rural areas through an agricultural mechanism. This study examines the effect of rising temperatures on rural outmigration in Jiangxi Province, China, a cold region where agricultural livelihoods predominate. The study contributes novel results by using large-scale household smart meter data to identify rural household outmigration. These data are combined with temperature data from meteorological stations to reveal a nonlinear effect of temperature on rural outmigration through an agricultural livelihood mechanism. The study projects the influence of rising temperatures on rural outmigration based on two representative concentration pathways (RCPs): RCP4.5 and RCP8.5. The results of the study show that extremely low temperatures significantly increase rural outmigration in Jiangxi Province, China, a rice-growing region. Moreover, projections show that warmer temperatures will improve rice yields and diminish outmigration. According to the medium-term (2041–2060) and long-term (2061–2080) prediction results, rural household outmigration will decrease by 0.55–1.40% and 1.23–2.96%, respectively. These findings contribute to research showing that rising global temperatures affect rural areas in cold regions by improving crop yields and diminishing outmigration.

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Data availability

Weather data is publicly available at http://data.cma.cn. Economic data is publicly available at http://www.epsnet.com.cn. Projected data on temperature rise is publicly available at https://dataserver.nccs.nasa.gov.

Notes

  1. One US dollar is approximately equivalent to 7 yuan.

  2. These 21 temperature prediction models were developed by research institutes in different countries around the world. The 21 temperature models are as follows: the ACCESS1-0 model, bcc-csm1-1 model, BNU-ESM model, CanESM2 model, CCSM4 model, CESM1-BGC model, CNRM-CM5 model, CSIRO-Mk3-6–0 model, GFDL-CM3 model, GFDL-ESM2G model, GFDL-ESM2M model, inmcm4 model, IPSL-CM5A-LR model, IPSL-CM5A-MR model, MIROC5 model, MIROC-ESM model, MIROC-ESM-CHEM model, MPI-ESM-LR model, MPI-ESM-MR model, MRI-CGCM3 model, and NorESM1-M model.

  3. Since the number of days per month is between 28 and 31 days, we normalize the temperature bins to ensure that the sum of the temperature bins equals 30. These results are reported in the “Robustness check” section.

  4. According to the seemingly unrelated regression, the p value is 0.0020; thus, the group regression results are comparable.

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Funding

This study is supported by the National Natural Science Foundation of China (Grant No. 72304140), the Philosophy and Social Sciences Foundation in Universities of Jiangsu Province (Grant No. 2022SJYB0016), and the Fundamental Research Funds for the Central Universities (Grant No. 30922011206), the Natural Science Foundation of Jiangsu Province (Grant No. BK20220971). The National Natural Science Foundation of China, 72304140, Yefei Sun, The Philosophy and Social Sciences Foundation in Universities of Jiangsu Province, 2022SJYB0016, Yefei Sun, The Fundamental Research Funds for the Central Universities, 30922011206,Yefei Sun, The Natural Science Foundation of Jiangsu Province, BK20220971, Yefei Sun.

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  1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yefei Sun

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Yefei Sun has contributed in all roles as this is a single-authored paper.

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Sun, Y. Temperature effects on rural household outmigration: Evidence from China. Popul Environ 45, 25 (2023). https://doi.org/10.1007/s11111-023-00441-4

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