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9 September 2019 Study on the Measurement and Influencing Factors of Fishery Economic Efficiency in China—Environmental Perspective
Lingyan Zheng, Haohan Wang, Fan Lv
Author Affiliations +
Abstract

Zheng, L.-Y., Wang, H.-H., and Lv, F., 2019. Study on the measurement and influencing factors of fishery economic efficiency in China-environmental perspective. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 701–706. Coconut Creek (Florida), ISSN 0749-0208.

Under the new normal of China's economy, China's fishery economic efficiency should take environmental constraints into account and improve the efficiency of China's fisheries economy under environmental constraints. Firstly, based on the inter-provincial panel data from 2007 to 2016, this paper uses the factor analysis method, DEA, Malmquist index and non-expected output to measure the economic efficiency of China's fisheries under environmental constraints. Secondly, it explores environmental constraints from a linear perspective through panel regression. Under the influence factors of China's fishery economic efficiency, we use the quantile regression to study the economic efficiency of China's fisheries under different environmental constraints at different levels, and the impact of various factors on the economic efficiency of China's fisheries under environmental constraints. Finally, relevant policy recommendations are made based on empirical conclusions.

©Coastal Education and Research Foundation, Inc. 2019
Lingyan Zheng, Haohan Wang, and Fan Lv "Study on the Measurement and Influencing Factors of Fishery Economic Efficiency in China—Environmental Perspective," Journal of Coastal Research 94(sp1), 701-706, (9 September 2019). https://doi.org/10.2112/SI94-139.1
Received: 7 February 2019; Accepted: 8 March 2019; Published: 9 September 2019
KEYWORDS
China's fishery economic efficiency
environmental constraints
influence factors
non-expected output
panel regression
quantile regression
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