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The Impact of Carbon Emissions Trading Pilot Policies on High-Quality Agricultural Development: An Empirical Assessment Using Double Machine Learning

Author

Listed:
  • Shilong Xi

    (School of Economics and Management, Zhejiang Ocean University, No. 1 Lincheng Road, Zhoushan 316022, China)

  • Xiaohui Wang

    (School of Economics and Management, Zhejiang Ocean University, No. 1 Lincheng Road, Zhoushan 316022, China)

  • Kejun Lin

    (School of Economics and Management, Zhejiang Ocean University, No. 1 Lincheng Road, Zhoushan 316022, China)

Abstract

Amid the pressing challenges of global warming, carbon trading policies have gained increasing importance in advancing green development. This study employs Double Machine Learning (DML) to effectively process high-dimensional data and nonlinear relationships, integrating methods such as Difference-in-Differences (DID) to systematically address endogeneity issues. Using an indicator system for High-Quality Agricultural Development (HQAD) covering 30 provinces, municipalities, and autonomous regions, this study aims to evaluate the impact of the pilot carbon emissions trading (CET) policy on HQAD. The findings are as follows: (1) The pilot CET policy significantly enhances HQAD. Though the positive effect has diminished, multi-dimensional robustness checks confirm the results’ credibility and stability. (2) Mechanism analysis shows that the policy promotes HQAD through two key pathways: strengthening environmental regulation (ER) and improving agricultural energy total factor productivity (AETFP). (3) Regional heterogeneity is evident, with the eastern region showing the most substantial policy effects, followed by the western region, while the central region shows minimal impact. Regarding grain functional zones, the policy effect is significant in the main sales and balance areas but weaker in the main producing area. Based on these findings, this study provides three policy recommendations to inform policymaking, facilitate the green transition, and promote High-Quality Agricultural Development.

Suggested Citation

  • Shilong Xi & Xiaohui Wang & Kejun Lin, 2025. "The Impact of Carbon Emissions Trading Pilot Policies on High-Quality Agricultural Development: An Empirical Assessment Using Double Machine Learning," Sustainability, MDPI, vol. 17(5), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1912-:d:1598392
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    References listed on IDEAS

    as
    1. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Jiping Sheng & Xiaoge Gao & Yongqi Sun, 2024. "Sustainability of the Food Industry: Ecological Efficiency and Influencing Mechanism of Carbon Emissions Trading Policy in China," Sustainability, MDPI, vol. 16(5), pages 1-25, March.
    4. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    5. Steven Pressman & Richard P. F. Holt, 2008. "Nicholas Kaldor and Cumulative Causation: Public Policy Implications," Journal of Economic Issues, Taylor & Francis Journals, vol. 42(2), pages 367-373, June.
    6. Philipp Bach & Oliver Schacht & Victor Chernozhukov & Sven Klaassen & Martin Spindler, 2024. "Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study," Papers 2402.04674, arXiv.org.
    7. Lijing Tang & Dongyan Wang, 2018. "Optimization of County-Level Land Resource Allocation through the Improvement of Allocation Efficiency from the Perspective of Sustainable Development," IJERPH, MDPI, vol. 15(12), pages 1-19, November.
    8. Irina Dolgopolova & Bo Hu & Armin Leopold & Stefan Pickl, 2014. "Economic, institutional and technological uncertainties of emissions trading—a system dynamics modeling approach," Climatic Change, Springer, vol. 124(3), pages 663-676, June.
    9. Zhou, Fengxiu & Wang, Xiaoyu, 2022. "The carbon emissions trading scheme and green technology innovation in China: A new structural economics perspective," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 365-381.
    10. Xie, Yan & Guo, Jingjun & Ma, Xiaowen, 2025. "Digital inclusive financial and household fertility:Discoveries based on dual machine learning algorithm," International Review of Economics & Finance, Elsevier, vol. 97(C).
    11. Hongyun Zheng & Wanglin Ma & Quan He, 2024. "Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and greenhouse gas mitigation: a comprehensive review," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 29(4), pages 1-38, April.
    12. Stephen J. Meardon, 2001. "Modeling Agglomeration and Dispersion in City and Country: Gunnar Myrdal, François Perroux, and the New Economic Geography," American Journal of Economics and Sociology, Wiley Blackwell, vol. 60(1), pages 25-57, January.
    13. R. H. Coase, 2013. "The Problem of Social Cost," Journal of Law and Economics, University of Chicago Press, vol. 56(4), pages 837-877.
    14. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    15. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    16. Shuchen Niu & Xiang Luo & Tiantian Yang & Guodong Lin & Chongming Li, 2023. "Does the Low-Carbon City Pilot Policy Improve the Urban Land Green Use Efficiency?—Investigation Based on Multi-Period Difference-in-Differences Model," IJERPH, MDPI, vol. 20(3), pages 1-21, February.
    17. Christina Gravert & Ganga Shreedhar, 2022. "Effective carbon taxes need green nudges," Nature Climate Change, Nature, vol. 12(12), pages 1073-1074, December.
    18. Yidi Wang & Xianzhao Liu & Mengru Song, 2024. "Spatial-Temporal Evolution of Carbon Sequestration and Emission Reduction Capacity of Agricultural Cultivation in Different Functional Grain Areas," Sustainability, MDPI, vol. 16(23), pages 1-23, November.
    19. Heena Panchasara & Nahidul Hoque Samrat & Nahina Islam, 2021. "Greenhouse Gas Emissions Trends and Mitigation Measures in Australian Agriculture Sector—A Review," Agriculture, MDPI, vol. 11(2), pages 1-16, January.
    20. Edward Barbier, 2011. "The policy challenges for green economy and sustainable economic development," Natural Resources Forum, Blackwell Publishing, vol. 35(3), pages 233-245, August.
    21. Steffen Brunner & Christian Flachsland & Robert Marschinski, 2012. "Credible commitment in carbon policy," Climate Policy, Taylor & Francis Journals, vol. 12(2), pages 255-271, March.
    22. Shuai Qin & Zheying Han & Hong Chen & Haokun Wang & Cheng Guo, 2022. "High-Quality Development of Chinese Agriculture under Factor Misallocation," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    23. 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.
    24. Bopp, Carlos & Engler, Alejandra & Jara-Rojas, Roberto & Arriagada, Rodrigo, 2020. "Are forest plantation subsidies affecting land use change and off-farm income? A farm-level analysis of Chilean small forest landowners," Land Use Policy, Elsevier, vol. 91(C).
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