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Does Digital Technology Application Promote Carbon Emission Efficiency in Dairy Farms? Evidence from China

Author

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  • Chenyang Liu

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China)

  • Xinyao Wang

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China)

  • Ziming Bai

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China)

  • Hongye Wang

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China)

  • Cuixia Li

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China
    College of Economics and Management, Suihua University, Suihua 152001, China)

Abstract

The implementation of digital technology has become paramount to facilitating green and low-carbon development in dairy farms amidst the advent of digital agriculture and low-carbon agriculture. This study examined the impact of digital technology implementation on the carbon emission efficiency of Chinese dairy farms via an assessment of micro-survey data, incorporating an Undesirable Outputs-SBM model, a Tobit model, the propensity score matching technique, a quantile regression model, and an instrumental variable approach. This study examined the potential moderating influence of environmental regulations on digital technology applications and the carbon emission efficiency of dairy farms. The findings of the research indicate that the implementation of digital technology had a considerable beneficial consequence on the carbon emission proficiency of dairy farms. The statistical significance level of the mean treatment effect was 0.1161, with the most profound influence of precision feeding digital technology on the carbon emission efficiency in dairy farms. The application of digital technology has a more pronounced effect on dairy farms with lower levels of carbon emission efficiency compared to those with medium and high levels of carbon emission efficiency. The application of digital technology toward the carbon emission efficiency of dairy farms is positively moderated by environmental regulations. Finally, this paper puts forward some specific policy recommendations to achieve the strategic goal of low carbon and efficient development in dairy farms through the application of digital technology, which enriches the existing research on carbon emission reduction in dairy farms from theoretical and practical aspects.

Suggested Citation

  • Chenyang Liu & Xinyao Wang & Ziming Bai & Hongye Wang & Cuixia Li, 2023. "Does Digital Technology Application Promote Carbon Emission Efficiency in Dairy Farms? Evidence from China," Agriculture, MDPI, vol. 13(4), pages 1-23, April.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:904-:d:1128544
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    References listed on IDEAS

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    1. Xiaoqin Chen & Shenya Mao & Siqi Lv & Zhong Fang, 2022. "A Study on the Non-Linear Impact of Digital Technology Innovation on Carbon Emissions in the Transportation Industry," IJERPH, MDPI, vol. 19(19), pages 1-18, September.
    2. Wanglin Ma & Kathryn Bicknell & Alan Renwick, 2019. "Feed use intensification and technical efficiency of dairy farms in New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(1), pages 20-38, January.
    3. Yuhang Bai & Kuixing Han & Lichun Xiong & Yifei Li & Rundong Liao & Fengting Wang, 2022. "Differences and Factors of Raw Milk Productivity between China and the United States," Agriculture, MDPI, vol. 12(11), pages 1-13, November.
    4. Chenyang Liu & Lihang Cui & Cuixia Li, 2022. "Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    5. Silvia Rolandi & Gianluca Brunori & Manlio Bacco & Ivano Scotti, 2021. "The Digitalization of Agriculture and Rural Areas: Towards a Taxonomy of the Impacts," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
    6. Bin Fan & Mingyang Li, 2022. "The Effect of Heterogeneous Environmental Regulations on Carbon Emission Efficiency of the Grain Production Industry: Evidence from China’s Inter-Provincial Panel Data," Sustainability, MDPI, vol. 14(21), pages 1-27, November.
    7. Liang Liu & Yuhan Zhang & Xiujuan Gong & Mengyue Li & Xue Li & Donglin Ren & Pan Jiang, 2022. "Impact of Digital Economy Development on Carbon Emission Efficiency: A Spatial Econometric Analysis Based on Chinese Provinces and Cities," IJERPH, MDPI, vol. 19(22), pages 1-21, November.
    8. Hafiz Muhammad Abrar Ilyas & Majeed Safa & Alison Bailey & Sara Rauf & Marvin Pangborn, 2019. "The Carbon Footprint of Energy Consumption in Pastoral and Barn Dairy Farming Systems: A Case Study from Canterbury, New Zealand," Sustainability, MDPI, vol. 11(17), pages 1-15, September.
    9. Yang, Lisha & Li, Zhi, 2017. "Technology advance and the carbon dioxide emission in China – Empirical research based on the rebound effect," Energy Policy, Elsevier, vol. 101(C), pages 150-161.
    10. Yun Qing & Bingjian Zhao & Chuanhao Wen, 2023. "The Coupling and Coordination of Agricultural Carbon Emissions Efficiency and Economic Growth in the Yellow River Basin, China," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    11. Gilbert E. Mushi & Giovanna Di Marzo Serugendo & Pierre-Yves Burgi, 2022. "Digital Technology and Services for Sustainable Agriculture in Tanzania: A Literature Review," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    12. Eastwood, C.R. & Chapman, D.F. & Paine, M.S., 2012. "Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia," Agricultural Systems, Elsevier, vol. 108(C), pages 10-18.
    13. Kaustia, Markku & Rantala, Ville, 2015. "Social learning and corporate peer effects," Journal of Financial Economics, Elsevier, vol. 117(3), pages 653-669.
    14. 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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    Cited by:

    1. Chenyang Liu & Xiuyi Shi & Cuixia Li, 2023. "Digital Technology, Factor Allocation and Environmental Efficiency of Dairy Farms in China: Based on Carbon Emission Constraint Perspective," Sustainability, MDPI, vol. 15(21), pages 1-22, October.
    2. Fagang Hu & Hongjun Liu & Yuxia Guo & Heping Ding & Kun Wang, 2024. "Coupling and Coordinated Development of Carbon Emission Efficiency in Industrial Enterprises and the Digital Economy: Empirical Evidence from Anhui, China," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    3. Suhan Zhang & Xue Li & Zhen Nie & Yan Wang & Danni Li & Xingpeng Chen & Yiping Liu & Jiaxing Pang, 2024. "The Significance of Agricultural Modernization Development for Agricultural Carbon Emission Efficiency in China," Agriculture, MDPI, vol. 14(6), pages 1-27, June.
    4. Guoqing Zhao & Chenhui Ye & Denis Dennehy & Shaofeng Liu & Antoine Harfouche & Femi Olan, 2024. "Analysis of barriers to adopting Industry 4.0 to achieve agri‐food supply chain sustainability: A group‐based fuzzy analytic hierarchy process," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 8559-8586, December.
    5. Tiantian Su & Cuixia Li, 2024. "Has the Digital Economy Boosted Carbon Reduction in Livestock Farming in China?," Agriculture, MDPI, vol. 14(9), pages 1-23, September.

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