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Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Author

Listed:
  • Guanghe Han

    (College of Economics and Management, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

  • Jiahui Xu

    (International Education College, Hebei Finance University, Baoding 071051, China)

  • Xin Zhang

    (College of Economics and Management, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

  • Xin Pan

    (College of Economics and Management, Heilongjiang Bayi Agricultural University, Daqing 163319, China)

Abstract

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Suggested Citation

  • Guanghe Han & Jiahui Xu & Xin Zhang & Xin Pan, 2024. "Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms," Agriculture, MDPI, vol. 14(9), pages 1-22, August.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1454-:d:1463872
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    References listed on IDEAS

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    1. Rui Song & Jing Liu & Kunyu Niu & Yiyu Feng, 2023. "Comparative Analysis of Trade’s Impact on Agricultural Carbon Emissions in China and the United States," Agriculture, MDPI, vol. 13(10), pages 1-16, October.
    2. Turconi, Roberto & Tonini, Davide & Nielsen, Christian F.B. & Simonsen, Christian G. & Astrup, Thomas, 2014. "Environmental impacts of future low-carbon electricity systems: Detailed life cycle assessment of a Danish case study," Applied Energy, Elsevier, vol. 132(C), pages 66-73.
    3. Shixiong Song & Siyuan Zhao & Ye Zhang & Yongxi Ma, 2023. "Carbon Emissions from Agricultural Inputs in China over the Past Three Decades," Agriculture, MDPI, vol. 13(5), pages 1-12, April.
    4. Xieqihua Liu & Yongmei Ye & Dongdong Ge & Zhen Wang & Bin Liu, 2022. "Study on the Evolution and Trends of Agricultural Carbon Emission Intensity and Agricultural Economic Development Levels—Evidence from Jiangxi Province," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    5. Chuanhe Xiong & Shuang Chen & Liting Xu, 2020. "Driving factors analysis of agricultural carbon emissions based on extended STIRPAT model of Jiangsu Province, China," Growth and Change, Wiley Blackwell, vol. 51(3), pages 1401-1416, September.
    6. Jianli Sui & Wenqiang Lv, 2021. "Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis," IJERPH, MDPI, vol. 18(15), pages 1-18, August.
    7. Yong Zhu & Congjia Huo, 2022. "The Impact of Agricultural Production Efficiency on Agricultural Carbon Emissions in China," Energies, MDPI, vol. 15(12), pages 1-22, June.
    8. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    9. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    10. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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