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Do agricultural technical efficiency and technical progress drive agricultural carbon productivity? based on spatial spillovers and threshold effects

Author

Listed:
  • Jindan Liu

    (Chongqing University)

  • Ying Yuan

    (Chongqing University)

  • Chuan Lin

    (Sichuan International Studies University)

  • Litai Chen

    (Chongqing University)

Abstract

Agricultural carbon emissions, as a major carbon emission after industrial carbon emissions, should be taken seriously. Reducing emissions relies heavily on technology. It is possible to explore the main path for reducing agricultural emissions through technology decomposition, which includes technical efficiency and technical progress. The technical efficiency and technical progress components of total factor productivity (broad technology) are first separated using the diethylaminoethyl (DEA)-Malmquist index method. Then, we use spatial panel econometric models and threshold models to analyze the relation between technical efficiency, technical progress, and agricultural carbon productivity. The results indicate that the decline in agricultural carbon emissions is a result of both technical efficiency and technical progress, and the contribution of technical efficiency is greater; this reveals that agricultural technical efficiency is the main technical channel to increase agricultural carbon productivity. Additionally, agricultural technical progress and technical efficiency have significant spatial spillover effects on the promotion of agricultural carbon productivity, which suggests that improvements in agricultural carbon production in nearby provinces are also facilitated by local agricultural technology to some extent. Interestingly, the threshold study further finds significant threshold effects of both technical efficiency and technical progress on agricultural carbon productivity under two threshold variables: farmers' education level and urbanization. These findings can provide effective policy guidance for agricultural emission reduction.

Suggested Citation

  • Jindan Liu & Ying Yuan & Chuan Lin & Litai Chen, 2025. "Do agricultural technical efficiency and technical progress drive agricultural carbon productivity? based on spatial spillovers and threshold effects," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 7701-7725, March.
  • Handle: RePEc:spr:endesu:v:27:y:2025:i:3:d:10.1007_s10668-023-04217-6
    DOI: 10.1007/s10668-023-04217-6
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    References listed on IDEAS

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    1. Haoyue Wu & Hanjiao Huang & Jin Tang & Wenkuan Chen & Yanqiu He, 2019. "Net Greenhouse Gas Emissions from Agriculture in China: Estimation, Spatial Correlation and Convergence," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    2. André Grimaud & Luc Rouge, 2008. "Environment, Directed Technical Change and Economic Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 41(4), pages 439-463, December.
    3. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    4. Ray, Subhash C & Desli, Evangelia, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Comment," American Economic Review, American Economic Association, vol. 87(5), pages 1033-1039, December.
    5. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    6. Xiuquan Huang & Xiaocang Xu & Qingqing Wang & Lu Zhang & Xin Gao & Linhong Chen, 2019. "Assessment of Agricultural Carbon Emissions and Their Spatiotemporal Changes in China, 1997–2016," IJERPH, MDPI, vol. 16(17), pages 1-15, August.
    7. Yihui Chen & Minjie Li & Kai Su & Xiaoyong Li, 2019. "Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China," Energies, MDPI, vol. 12(16), pages 1-23, August.
    8. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    9. Ekins, Paul & Pollitt, Hector & Summerton, Philip & Chewpreecha, Unnada, 2012. "Increasing carbon and material productivity through environmental tax reform," Energy Policy, Elsevier, vol. 42(C), pages 365-376.
    10. Guangyue Xu & Juanjuan Li & Peter M. Schwarz & Hualiu Yang & Huiying Chang, 2022. "Rural financial development and achieving an agricultural carbon emissions peak: an empirical analysis of Henan Province, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12936-12962, November.
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