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Technical and environmental efficiency of livestock farms in China: A slacks-based DEA approach

Lena Kuhn, Tomas Baležentis, Lingling Hou and Dan Wang

China Economic Review, 2020, vol. 62, issue C

Abstract: In the wake of economic development and raised living standards, Chinese pork consumption has risen significantly. As a response to the increased demand and active government support, the national hog sector, which was traditionally dominated by small backyard farms, is featuring a quick restructuring towards large-scale breeding enterprises. With the transformation of the hog sector, environmental externalities have become a very serious concern. Foremost non-point source pollution resulting from the leakage of pig waste into surface water and groundwater resources is seen as a serious threat. Via data envelopment analysis (DEA), we approach the question of efficiency gains from both technical and environmental perspective along a 2012 sample of 371 Chinese hog farms. Our results suggest that especially mid-size hog farms in transition face low environmental efficiency and high pollution abatement costs due to limited waste disposal options. Existent policies turn out to be yet hardly effective in decreasing environmental externalities. In some provinces, projects for rural biogas production could help to improve waste management. However, especially small farms require state support towards improving storage and transportations facilities.

Keywords: Technical efficiency; Environmental efficiency; DEA; Slack-based measure; Livestock sector; Hog; China (search for similar items in EconPapers)
JEL-codes: O13 Q15 Q53 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chieco:v:62:y:2020:i:c:s1043951x18301093

DOI: 10.1016/j.chieco.2018.08.009

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China Economic Review is currently edited by B.M. Fleisher, K. X. D. Huang, M.E. Lovely, Y. Wen, X. Zhang and X. Zhu

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