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Efficiency Measurement of Green Regional Development and Its Influencing Factors: An Improved Data Envelopment Analysis Framework

Yingyu Lu (), Bo Cao (), Yidi Hua () and Lei Ding ()
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Yingyu Lu: Institute of Environmental Economics Research, Ningbo Polytechnic, Ningbo 315800, China
Bo Cao: Department of Public Course Teaching, Ningbo Polytechnic, Ningbo 315800, China
Yidi Hua: Institute of Environmental Economics Research, Ningbo Polytechnic, Ningbo 315800, China
Lei Ding: Institute of Environmental Economics Research, Ningbo Polytechnic, Ningbo 315800, China

Sustainability, 2020, vol. 12, issue 11, 1-1

Abstract: Reasonably assessing the efficiency of green regional development is a key to improving environmental management and implementing sustainable development strategies. From the perspectives of environmental pollutant emissions, energy consumption, and production factor cost, the non-radial data envelopment analysis model based on the Malmquist index was applied to measure the green development efficiency and regional differences of 11 cities in Zhejiang from 2007 to 2016 from both static and dynamic aspects. This paper further analyzes the inherent influencing factors through the panel data model. The result shows: (1) The average static efficiency of green development in Zhejiang Province is 0.844. There is still 15.6% of improvement space from the frontier of production. Pollution emission management has the greatest improvement potential. Pure technical efficiency is the main factor restricting the static efficiency. (2) The dynamic efficiency of Zhejiang’s green development achieves an average annual rate of 0.26%, with a cumulative growth of 2.33%. The improvement of green development efficiency mainly depends on scale efficiency change. (3) The inherent factors affecting the efficiency of green development in the 11 cities mainly include three factors: the industrial structure, environmental regulation, and the urbanization level. The industrial structure has a positive effect, while environmental regulation and the urbanization level have negative effects. (4) The 11 cities are relatively evenly distributed in the four “static–dynamic efficiency” classification quadrants, and there is no "Matthew effect" of high–high, low–low polarization.

Keywords: green development efficiency; data envelopment analysis model; Malmquist index; panel data model; regional difference (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2020
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