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Digital Technology, Factor Allocation and Environmental Efficiency of Dairy Farms in China: Based on Carbon Emission Constraint Perspective

Author

Listed:
  • Chenyang Liu

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

  • Xiuyi Shi

    (School of Economics and Management, Southeast University, Nanjing 211189, China)

  • Cuixia Li

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

Abstract

The emission of carbon pollutants stemming from dairy farms has emerged as a significant obstacle in mitigating the effects of global warming. China, being a prominent nation in the field of dairy farming, encounters significant challenges related to excessive component input and elevated environmental pollution. Digital technology presents an opportunity to enhance the factor allocation of dairy farms and thus increase their environmental efficiency. This study utilizes survey data from 278 dairy farms in China to examine the effect of digital technology on the allocation of land, labor, and capital variables in dairy farms. The IV-Probit model, IV-Tobit model, treatment effect model, and two-stage least square technique are employed to empirically analyze these impacts. Simultaneously, the intermediate effect model was employed to examine the mediating function of factor allocation in the effect of digital technology on environmental efficiency. The findings indicate that digital technology has the potential to greatly enhance land transfer and land utilization rates in dairy farms. Additionally, it has been observed that digital technology may lead to a decrease in both the proportion and time of labor input. Furthermore, digital technology has the potential to decrease short-term productive input while simultaneously enhancing long-term productive input within dairy farming operations. Digital technology has been found to have an indirect yet beneficial influence on environmental efficiency. This is mostly achieved through the facilitation of resource allocation, specifically in terms of land, labor, and capital aspects. The article presents a set of policy recommendations, including the promotion of extensive integration of digital technology within dairy farms, the facilitation of optimal allocation of production factors in dairy farms, and the implementation of specialized training programs focused on digital technology.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15455-:d:1270907
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    References listed on IDEAS

    as
    1. Chong Lu, 2020. "Does household laborer migration promote farmland abandonment in China?," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1804-1836, December.
    2. Jonathan Walsh & Robert Parsons & Qingbin Wang & David Conner, 2020. "What Makes an Organic Dairy Farm Profitable in the United States? Evidence from 10 Years of Farm Level Data in Vermont," Agriculture, MDPI, vol. 10(1), pages 1-13, January.
    3. Georgina Milne & Andrew William Byrne & Emma Campbell & Jordon Graham & John McGrath & Raymond Kirke & Wilma McMaster & Jesko Zimmermann & Adewale Henry Adenuga, 2022. "Quantifying Land Fragmentation in Northern Irish Cattle Enterprises," Land, MDPI, vol. 11(3), pages 1-16, March.
    4. Zhao, Shikuan & Cao, Yuequn & Hunjra, Ahmed Imran & Tan, Yan, 2023. "How does environmentally induced R&D affect carbon productivity? A government support perspective," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 942-961.
    5. Wanglin Ma & Alan Renwick & Kathryn Bicknell, 2018. "Higher Intensity, Higher Profit? Empirical Evidence from Dairy Farming in New Zealand," Journal of Agricultural Economics, Wiley Blackwell, vol. 69(3), pages 739-755, September.
    6. Xinyi Du & Qi Wang & Yingying Zheng & Jinming Gui & Songhuai Du & Zhengxiang Shi, 2023. "Sustainable Planning Strategy of Dairy Farming in China Based on Carbon Emission from Direct Energy Consumption," Agriculture, MDPI, vol. 13(5), pages 1-15, April.
    7. 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.
    8. Minglu Wang & Bruce A. McCarl, 2021. "Impacts of Climate Change on Livestock Location in the US: A Statistical Analysis," Land, MDPI, vol. 10(11), pages 1-20, November.
    9. Gao, Yang & Niu, Ziheng & Yang, Haoran & Yu, Lili, 2019. "Impact of green control techniques on family farms' welfare," Ecological Economics, Elsevier, vol. 161(C), pages 91-99.
    10. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    11. Ledgard, Stewart F. & Wei, Sha & Wang, Xiaoqin & Falconer, Shelley & Zhang, Nannan & Zhang, Xiying & Ma, Lin, 2019. "Nitrogen and carbon footprints of dairy farm systems in China and New Zealand, as influenced by productivity, feed sources and mitigations," Agricultural Water Management, Elsevier, vol. 213(C), pages 155-163.
    12. Gilbert Cette & Sandra Nevoux & Loriane Py, 2022. "The impact of ICTs and digitalization on productivity and labor share: evidence from French firms," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 31(8), pages 669-692, November.
    13. Galloway, Craig & Conradie, Beatrice & Prozesky, Heidi & Esler, Karen, 2018. "Opportunities to improve sustainability on commercial pasture-based dairy farms by assessing environmental impact," Agricultural Systems, Elsevier, vol. 166(C), pages 1-9.
    14. Zetian Yu & Hao Liu & Hua Peng & Qiantong Xia & Xiaoxia Dong, 2023. "Production Efficiency of Raw Milk and Its Determinants: Application of Combining Data Envelopment Analysis and Stochastic Frontier Analysis," Agriculture, MDPI, vol. 13(2), pages 1-25, February.
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