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Does Digital Transformation Promote Agricultural Carbon Productivity in China?

Author

Listed:
  • Ning Xu

    (School of Political Science and Public Administration, Henan Normal University, Xinxiang 453007, China)

  • Desen Zhao

    (School of Business and Tourism Management, Yunnan University, Kunming 650500, China)

  • Wenjie Zhang

    (School of Economics, Yunnan University, Kunming 650500, China)

  • Ming Liu

    (School of Business and Tourism Management, Yunnan University, Kunming 650500, China)

  • He Zhang

    (State Information Center, Beijing 100045, China)

Abstract

Against the background of global climate change and the rapid rise of the digital economy, the digital transformation of agriculture is profoundly changing the agricultural production and operation mode with the help of digital technology, becoming a new driving force for low-carbon and sustainable development of agriculture. However, previous studies rarely examined the impact of agricultural digital transformation on agricultural low-carbon transformation from the perspective of carbon productivity. To fill this gap, this study attempts to build a theoretical analysis framework for the impact of agricultural digital transformation on agricultural carbon productivity (ACP). By using a set of panel data from 30 provinces (cities) in China from 2011 to 2019, this study explores the impact of agricultural digital transformation on ACP, as well as its conduction mechanism and the non-linear mechanism. Empirical results show that the transformation of agricultural digitalization is conducive to the promotion of ACP. A series of robustness analyses support this conclusion. The main transmission mechanisms for digital transformation to affect ACP include agricultural industrial structure upgrading, and the agricultural scale operation. In addition, with the improvement of urbanization level and rural human capital, the impact of agricultural digital transformation on ACP presents a “U” type non-linear feature of inhibition first and promotion later. Furtherly, heterogeneity analysis shows that the impact of digital transformation on ACP will vary greatly depending on the levels of ACP, the geographical location of the studied area and whether it is a main grain-producing area. This study provides a theoretical and empirical basis for the improvement of China’s agricultural carbon productivity from the perspective of the digital economy.

Suggested Citation

  • Ning Xu & Desen Zhao & Wenjie Zhang & Ming Liu & He Zhang, 2022. "Does Digital Transformation Promote Agricultural Carbon Productivity in China?," Land, MDPI, vol. 11(11), pages 1-19, November.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:1966-:d:962058
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    References listed on IDEAS

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    1. Jiang, Song & Zhou, Jie & Qiu, Shuang, 2022. "Digital Agriculture and Urbanization: Mechanism and Empirical Research," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    2. Kiiza, Barnabas & Pederson, Glenn, 2012. "ICT-based market information and adoption of agricultural seed technologies: Insights from Uganda," Telecommunications Policy, Elsevier, vol. 36(4), pages 253-259.
    3. Hoang, Viet-Ngu & Coelli, Tim, 2011. "Measurement of agricultural total factor productivity growth incorporating environmental factors: A nutrients balance approach," Journal of Environmental Economics and Management, Elsevier, vol. 62(3), pages 462-474.
    4. 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.
    5. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    6. Heidi Kaila & Finn Tarp, 2019. "Can the Internet improve agricultural production? Evidence from Viet Nam," Agricultural Economics, International Association of Agricultural Economists, vol. 50(6), pages 675-691, November.
    7. Yu Sheng & Xiaohui Tian & Weiqing Qiao & Chao Peng, 2020. "Measuring agricultural total factor productivity in China: pattern and drivers over the period of 1978‐2016," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(1), pages 82-103, January.
    8. Suresh, Kanesh & Khanal, Uttam & Wilson, Clevo & Managi, Shunsuke & Quayle, Annette & Santhirakumar, Samithamby, 2021. "An economic analysis of agricultural adaptation to climate change impacts in Sri Lanka: An endogenous switching regression analysis," Land Use Policy, Elsevier, vol. 109(C).
    9. Wanglin Ma & R. Quentin Grafton & Alan Renwick, 2020. "Smartphone use and income growth in rural China: empirical results and policy implications," Electronic Commerce Research, Springer, vol. 20(4), pages 713-736, December.
    10. 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.
    11. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    12. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    13. Tang, Le & Sun, Shiyu, 2022. "Fiscal incentives, financial support for agriculture, and urban-rural inequality," International Review of Financial Analysis, Elsevier, vol. 80(C).
    14. Zhang, Wei & Liu, Xuemeng & Wang, Die & Zhou, Jianping, 2022. "Digital economy and carbon emission performance: Evidence at China's city level," Energy Policy, Elsevier, vol. 165(C).
    15. Qi Jiang & Jizhi Li & Hongyun Si & Yangyue Su, 2022. "The Impact of the Digital Economy on Agricultural Green Development: Evidence from China," Agriculture, MDPI, vol. 12(8), pages 1-22, July.
    16. Yi, Ming & Liu, Yafen & Sheng, Mingyue Selena & Wen, Le, 2022. "Effects of digital economy on carbon emission reduction: New evidence from China," Energy Policy, Elsevier, vol. 171(C).
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    Cited by:

    1. Yong Liu & Jixin Yang & Guanghong Zhang & Xufeng Cui, 2024. "Driving factors of green production behaviour among farmers of different scales: Evidence from North China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(10), pages 474-494.
    2. Jian Li & Xiangchen Sheng & Shuhua Zhang & Yixuan Wang, 2024. "Research on the Impact of the Digital Economy and Technological Innovation on Agricultural Carbon Emissions," Land, MDPI, vol. 13(6), pages 1-18, June.
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    4. repec:caa:jnlage:v:preprint:id:188-2024-agricecon is not listed on IDEAS
    5. Yihui Chen & Minjie Li, 2024. "How does the digital transformation of agriculture affect carbon emissions? Evidence from China’s provincial panel data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.

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