Green total factor productivity of dairy cow in China: Key facts from scale and regional sector

Shen Zhong, Junwei Li and Yi Qu

Technological Forecasting and Social Change, 2022, vol. 183, issue C

Abstract: In China, people have a great demand for milk in their daily life. However, the process of dairy farming could produce greenhouse gases, resulting in global warming. Therefore, this paper analyzes the green total factor productivity of dairy cow (GDC) of China's 27 provinces in different scales and regions from 2006 to 2018, in order to improve the production efficiency of dairy cows and reduce pollution emissions. Based on Slack Based Measure (SBM) model and three-level Meta-frontier Malmquist Luenberger (MML) index, this paper analyzes the GDC from aspect of scale structure efficiency, technical efficiency and management efficiency. The following conclusions are drawn: (1) From 2006 to 2018, China's GDC has been in a state of fluctuation, which showing an upward trend on the whole, with an average annual growth rate of 2.45 %. In particular，TE increased by 0.40 %, ME increased by 2.48 %, and SE decreased by 0.44 %. (2) In terms of scale, the GDC of middle-scale is the highest, followed by large-scale, and then the GDC of small-scale is the lowest. It is obvious that the SE of small-scale and middle-scale is low, and the TE of large-scale is low. (3) In terms of regional distribution, the GDC in the western region is the highest, followed by eastern region, and then the central region. It should note that SE is low in the eastern and central regions, and TE is low in the western region. This paper provides theoretical guidance for the development of low-carbon dairy farming in China.

Keywords: Dairy cow; Green total factor productivity; Three-level meta-frontier model; Pollution control; Unexpected output (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:183:y:2022:i:c:s004016252200470x

DOI: 10.1016/j.techfore.2022.121949

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