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Has China’s Carbon Emissions Trading Pilot Policy Improved Agricultural Green Total Factor Productivity?

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  • Zhuohui Yu

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, 12 South Avenue, Zhongguancun, Haidian District, Beijing 100081, China
    College of Economics, Northwest Normal University, No. 967 East Road, Anning District, Lanzhou 730071, China)

  • Shiping Mao

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, 12 South Avenue, Zhongguancun, Haidian District, Beijing 100081, China)

  • Qingning Lin

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, 12 South Avenue, Zhongguancun, Haidian District, Beijing 100081, China)

Abstract

The carbon trading system affects all aspects of the economy and society profoundly. Agriculture, as a high-carbon-emitting industry, has been hard-hit. China’s agricultural activities will emit about 820 million tons of carbon dioxide equivalents, accounting for 7% of the country’s total carbon emissions. In order to develop a green and low-carbon economy and control greenhouse gas emissions, China officially launched the pilot carbon emissions trading policy in 2013. The effects and mechanism of this on agricultural carbon emissions are still unclear. Herein, this paper uses China’s provincial panel data from 2000 to 2019 to measure agricultural green total factor productivity regarding the implementation of China’s carbon emissions trading pilot policy in 2013 as a quasi-natural experiment, and uses PSM-DID robustness analysis to evaluate the effect of China’s carbon emission rights trading pilot policy on agricultural green total factor productivity in pilot areas. The propensity score method is a type of statistical method that uses nonexperimental or observational data for intervention-effect analysis, which reduces the effects of bias and allows for more reasonable comparisons between treatment and control groups. “Difference in difference” is an approach to policy-effect evaluation based on a counterfactual framework to assess the change in the observed factors in both cases of policy occurrence and nonoccurrence. PSM-DID is a combination of PSM and DID using the PSM method to match each treatment group sample to a specific control group sample, which can solve the problem of self-selection bias in the DID method and assess the policy implementation effect more accurately. This study found that China’s carbon emissions trading pilot policy has significantly improved China’s agricultural green total factor productivity. Further impact mechanism tests show that China’s carbon emissions trading pilot policy will improve agricultural green total factor productivity through environmental protection policies and technological innovation. Finally, this paper puts forward corresponding countermeasures and suggestions based on the research results.

Suggested Citation

  • Zhuohui Yu & Shiping Mao & Qingning Lin, 2022. "Has China’s Carbon Emissions Trading Pilot Policy Improved Agricultural Green Total Factor Productivity?," Agriculture, MDPI, vol. 12(9), pages 1-21, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1444-:d:912772
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    References listed on IDEAS

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    3. Feng Ye & Lang Wang & Amar Razzaq & Ting Tong & Qing Zhang & Azhar Abbas, 2023. "Policy Impacts of High-Standard Farmland Construction on Agricultural Sustainability: Total Factor Productivity-Based Analysis," Land, MDPI, vol. 12(2), pages 1-13, January.
    4. Feng Ye & Zhongna Yang & Mark Yu & Susan Watson & Ashley Lovell, 2023. "Can Market-Oriented Reform of Agricultural Subsidies Promote the Growth of Agricultural Green Total Factor Productivity? Empirical Evidence from Maize in China," Agriculture, MDPI, vol. 13(2), pages 1-20, January.

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