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A Non-Linear Exploration of the Digital Economy’s Impact on Agricultural Carbon Emission Efficiency in China

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  • Shiying Zhu

    (School of Economics, Shandong University of Finance and Economics, Jinan 250014, China)

  • Jiawen Huang

    (Business School, University of International of Business and Economics, Beijing 100029, China)

  • Yansong Li

    (School of Economics, Shandong University of Finance and Economics, Jinan 250014, China)

  • Paravee Maneejuk

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Jianxu Liu

    (School of Economics, Shandong University of Finance and Economics, Jinan 250014, China)

Abstract

As the global climate crisis intensifies, improving agricultural carbon emission efficiency has become crucial for achieving the sustainable development goals (SDGs). This study investigates the complex, non-linear relationship between China’s digital economy and agricultural carbon emission efficiency, utilizing panel data from Chinese provinces spanning 2012–2022. We employ a multi-method approach, including the Super-SBM model for efficiency measurement, two-way fixed effects models, quantile regression, and Generalized Additive Models (GAMs) for empirical analysis. Our findings reveal: (1) The digital economy significantly enhances agricultural carbon emission efficiency, but with distinct non-linear characteristics across different dimensions. (2) The impact varies among digital economy aspects: the digital economy foundation shows the most substantial influence, followed by the rural digital industry level, while rural digital infrastructure has a relatively minor effect. (3) A threshold effect is observed, with the digital economy’s impact more pronounced in regions with higher agricultural carbon emission efficiency. (4) GAM analysis unveils complex non-linear patterns: the rural digital industry’s impact initially decreases before increasing, the digital economy foundation shows an overall increasing trend with plateaus, and rural digital infrastructure exhibits a near-linear relationship. (5) Sensitivity analysis indicates that agricultural carbon emission efficiency is most responsive to changes in the digital economy foundation, followed by the rural digital industry level. These findings provide nuanced insights into the digital economy’s role in enhancing agricultural sustainability. We propose targeted policy recommendations, including accelerating rural digital infrastructure development, optimizing the rural digital industry structure, and implementing context-specific digital facility construction. These strategies aim to fully leverage the digital economy’s potential in improving agricultural carbon emission efficiency, contributing to China’s “dual carbon” goals and sustainable agricultural development.

Suggested Citation

  • Shiying Zhu & Jiawen Huang & Yansong Li & Paravee Maneejuk & Jianxu Liu, 2024. "A Non-Linear Exploration of the Digital Economy’s Impact on Agricultural Carbon Emission Efficiency in China," Agriculture, MDPI, vol. 14(12), pages 1-22, December.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2245-:d:1538997
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    References listed on IDEAS

    as
    1. Joseph S Shapiro, 2021. "The Environmental Bias of Trade Policy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(2), pages 831-886.
    2. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    3. Daria Taglioni & Deborah Winkler, 2016. "Making Global Value Chains Work for Development," World Bank Publications - Books, The World Bank Group, number 24426.
    4. Asongu, Simplice A. & Le Roux, Sara & Biekpe, Nicholas, 2018. "Enhancing ICT for environmental sustainability in sub-Saharan Africa," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 209-216.
    5. Wellington, Michael & Kuhnert, Petra & Lawes, Roger & Renzullo, Luigi & Pittock, Jamie & Ramshaw, Peter & Moyo, Martin & Kimaro, Emmanuel & Tafula, Miguel & van Rooyen, Andre, 2023. "Decoupling crop production from water consumption at some irrigation schemes in southern Africa," Agricultural Water Management, Elsevier, vol. 284(C).
    6. Eric Williams, 2011. "Environmental effects of information and communications technologies," Nature, Nature, vol. 479(7373), pages 354-358, November.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. 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.
    9. Zhou, P. & Ang, B.W. & Han, J.Y., 2010. "Total factor carbon emission performance: A Malmquist index analysis," Energy Economics, Elsevier, vol. 32(1), pages 194-201, January.
    10. Changming Cheng & Jieqiong Li & Yuqing Qiu & Chunfeng Gao & Qiang Gao, 2022. "Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China’s Carbon Neutrality Targets," IJERPH, MDPI, vol. 19(23), pages 1-18, November.
    11. 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.
    12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    13. Stephen Casler & Adam Rose, 1998. "Carbon Dioxide Emissions in the U.S. Economy: A Structural Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 11(3), pages 349-363, April.
    14. Bai, Ling & Guo, Tianran & Xu, Wei & Liu, Yaobin & Kuang, Ming & Jiang, Lei, 2023. "Effects of digital economy on carbon emission intensity in Chinese cities: A life-cycle theory and the application of non-linear spatial panel smooth transition threshold model," Energy Policy, Elsevier, vol. 183(C).
    15. Cezary A. Kwiatkowski & Małgorzata Pawłowska & Elżbieta Harasim & Lucjan Pawłowski, 2023. "Strategies of Climate Change Mitigation in Agriculture Plant Production—A Critical Review," Energies, MDPI, vol. 16(10), pages 1-27, May.
    16. Wu, Haitao & Xue, Yan & Hao, Yu & Ren, Siyu, 2021. "How does internet development affect energy-saving and emission reduction? Evidence from China," Energy Economics, Elsevier, vol. 103(C).
    17. 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.
    18. 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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