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

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.

Research on Green Total Factor Productivity of Yangtze River Economic Belt Based on Environmental Regulation

“…Moreover, foreign direct investment has a significant positive impact on green total factor productivity, while government intervention has a significant negative impact. The regression coefficient of government intervention is negative, meaning that the government intervention is unreasonable and causes the loss of green total factor productivity in the YREB, which is in line with the conclusion of Lu et al [35] The regression coefficient of FDI is positive, which suggests that foreign direct investment can promote green technology spillover and technology innovation in the YREB, thereby helping to improve regional green total factor productivity, which also confirms the study of Ayamba et al [31]. Based on the above conclusions, hypothesis 2 is confirmed.…”

“…Foreign direct investment (FDI): It is generally believed that FDI affects green total factor productivity through capital formation, technology transfer, technology spillover, and environmental spillover, but the results are often regionally heterogeneous and related to the intensity of environmental regulation. This paper adopts the ratio of actual utilized FDI to GDP as an FDI variable [35,36].…”

Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

“…However, human capital and degree of openness show a negative impact [ 28 ]. Some studies stated that there is no significant relationship between environmental regulation and industrial ecological efficiency [ 29 ], but others showed that the interaction between government intervention and environmental regulation has a significant impact on green development [ 30 ]. Although the industrial structure has a negative impact, there is no denying the positive promotion effect of the rationalization of industrial structure and upgrading of industrial structure [ 28 ].…”

How does financial agglomeration affect green development? Evidence from the Yangtze River Delta of China

“…In academia, GD has been studied from both theoretical and practical perspectives, including the conceptualization connotation of GD (Colglazier, 2015; Csete & Horvath, 2012; Grillitsch & Hansen, 2019), its influence factors (Feng et al., 2017; Lu et al., 2020), and evaluations of development level and efficiency (Brito et al., 2019; Wu et al., 2020; Zhang et al., 2020). Scholars have attempted to construct an evaluation index system for GD by studying GD's conceptualization and influencing factors.…”