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

Abstract

With increasingly severe constraints on resources and the environment, it is the mainstream trend of economic development to reduce industrial pollution emissions and promote green industrial development. In this paper, a super-efficiency slacks-based measure (SBM) model is adopted to measure the industrial green development efficiency (IGDE) of 289 cities in China from 2008 to 2018. Moreover, we analyze their spatiotemporal differentiation pattern. On this basis, the multiscale geographical weighted regression (MGWR) model is used to analyze the scale differences and spatial differences of the driving factors. The results show that the IGDE is still at a low level in China. From 2008 to 2018, the overall polarization of IGDE was relatively serious. The number of high- and low-efficiency cities increased, while that of medium-efficiency cities greatly decreased. Secondly, the IGDE presented an obvious spatial positive correlation. MGWR regression results show that the technological innovation, government regulation, and consumption level belonged to the global scale, and there was almost no spatial heterogeneity. Other driving factors were urbanization, industrial structure, economic development, and population density according to their spatial scale. Lastly, the influence of economic development and technological innovation had a certain circular structure in space; the influence of population size mainly occurred in the cities of the southeast coast and northeast provinces; the influence of urbanization was more obvious in the most northern provinces of the Yangtze River, while that of industrial structure was mainly concentrated in the most southern cities of the Yangtze River Economic Belt (YREB). Spatially, the influence of consumption was manifested as a distribution trend of decreasing from north to south, and the government regulation was manifested as increasing from west to east and then to northeast.

Exploring the green development path of the Yangtze River Economic Belt using the entropy weight method and fuzzy-set qualitative comparative analysis

“…The higher the GDE, the higher the GDL. Methods for evaluating GDE have included data envelopment analysis (DEA) [ 14 ], stochastic frontier analysis [ 15 ], super-efficiency DEA [ 16 , 17 ], and super-efficiency SBM [ 18 ]. For example, Feng et al estimated the global patterns of green development performance and its influencing factors using a green development performance index based on DEA and the panel data of 165 countries [ 14 ].…”

“…For example, Feng et al estimated the global patterns of green development performance and its influencing factors using a green development performance index based on DEA and the panel data of 165 countries [ 14 ]. Meanwhile, Liu et al measured the industrial green development efficiency of 289 cities in China based on a super-efficiency slacks-based measure (SBM) model [ 17 ]. In addition, Wu et al used the super-efficiency SBM-undesirable model to calculate the green total factor productivity of 30 provinces in China [ 18 ].…”

“…GDL is affected by many factors. Studies have investigated these factors in terms of industrial structure (IS) [ 24 ], science and technology innovation [ 25 ], environmental regulation (ER) [ 26 ], government investment [ 27 ], technological progress [ 24 ], urbanization [ 27 ], economic development level (EDL) [ 14 ], energy structure [ 14 ], foreign direct investment [ 24 ], openness degree [ 27 ], consumption level [ 17 ], population density and size [ 17 ], environmental tax [ 28 ], energy conservation [ 29 ], emission reduction policy [ 29 ], human capital [ 29 ], industrial agglomeration [ 30 ], and resource endowment [ 24 ]. It is generally believed that areas with a high EDL have a high GDL.…”

“…Guo et al demonstrated that ER showed a significantly negative effect on regional green growth performance and technological innovation had a positive impact on regional green growth performance [ 25 ]. Overall, the majority of studies employed regression analysis method, such as spatial autoregressive models [ 24 ], panel vector autoregressive models [ 18 ], multiscale geographical weighted regression [ 17 ], spatial regression [ 21 ], spatial Durbin models [ 34 ] and random-effect panel Tobit models [ 35 ].…”

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

“…With increasingly severe resource and environmental constraints, the major concern of economic development has become the reduction in industrial pollutant emissions and the promotion of green industrial development. The fundamental change of China's industrial development mode is to comprehensively improve the green total factor productivity of industrial development [4]. Green total factor productivity is an indicator to measure the quality of economic growth.…”