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Short-term impact of climate change on labor market in China: Quantitative spatial analysis based on an oligopsony model

Hui Xu and Huai Deng

China Economic Review, 2024, vol. 84, issue C

Abstract: Assessing the impact of climate change on the economy is an important issue for designing climate policy. By constructing an oligopsony model to depict the incompleteness of the labor market and using the quantitative spatial model analysis method, we quantitatively estimated the short-term impact of climate change on the labor market in China. Then, to control climate change, the optimal climate change governance intensities (CCGI) of multi-governments satisfying CD preferences and Stackelberg game are designed and solved. The results show that climate change leads to a decrease in wages and labor supply in labor market equilibrium and a loss of market aggregate income and labor scale. Climate change has led to a shift in labor from the agricultural and dirty sectors to the clean sector. The regional labor distribution is also being “disrupted” due to climate change. Moreover, declining migration costs and increased subsidies help to reduce the negative impacts of climate change. The optimal CCGI of the central government is higher than that of the provincial government. The optimal CCGI is higher in the eastern provinces than in the central and western regions. Implementing CCGI would mitigate the negative climate change shocks to the labor market in China and increase welfare. The conclusions of this paper provide strong support for preventing climate change risks and actively participating in global climate change governance.

Keywords: Climate change; Labor market; China; Oligopsony model (search for similar items in EconPapers)
JEL-codes: Q51 Q54 R23 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chieco:v:84:y:2024:i:c:s1043951x24000312

DOI: 10.1016/j.chieco.2024.102142

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China Economic Review is currently edited by B.M. Fleisher, K. X. D. Huang, M.E. Lovely, Y. Wen, X. Zhang and X. Zhu

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