Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR
Abstract
:1. Introduction
1.1. Literature Review
1.1.1. Discussion about Industrial Green Development Efficiency
1.1.2. Influencing Factors of Industrial Green Development Efficiency
1.1.3. Summary
2. Materials and Methods
2.1. Research Methods
2.1.1. Super-Efficiency SBM
2.1.2. Spatial Cold/Hot Spot Analysis
2.1.3. Multiscale Geographically Weighted Regression (MGWR)
2.2. Variable Selection
2.2.1. Calculation of Industrial Green Development Efficiency
2.2.2. Influencing Factors
3. Results
3.1. Spatiotemporal Pattern and Evolution Analysis of Industrial Green Development Efficiency
3.1.1. Analysis of Regions
3.1.2. Analysis of Provinces
3.1.3. Analysis of Cities
3.2. Spatial Exploration of Industrial Green Development Efficiency
3.3. Spatial Pattern Analysis of Driving Factors
3.3.1. Model Comparison
3.3.2. Scale Analysis
3.3.3. Spatial Pattern Analysis of Coefficients
4. Discussion
4.1. Contribution
4.2. Limitations
5. Conclusions
- (1)
- From the regional perspective, IGDE is at a low level in China. All regions showed different degrees of decline except for the northeast region, while the east and the central regions showed the most obvious decline. The ranking of IGDE was as follows: northeast > eastern > western > national > central region. From the perspective of provinces, the IGDE in 2018 increased in some provinces and decreased in others compared with 2008, presenting a “contraction” trend. This was consistent with regional changes. From the perspective of cities, from 2008 to 2018, the number of high-efficiency and low-efficiency cities increased, while that of medium-efficiency cities greatly decreased. There was a serious polarization.
- (2)
- The IGDE presented obvious positive spatial correlation. Compared with OLS and GWR, the MGWR model can better analyze the spatial heterogeneity of the influencing factors. In the MGWR regression results, all variables were significant except for openness. Technological innovation, government regulation, and consumption level belonged to the global scale, with almost no heterogeneity in space. Other influencing factors with spatial heterogeneity were urbanization, industrial structure, economic development, and population density.
- (3)
- The influences of economic development, government regulation, population density, and consumption level on IGDE were positive, while other variables were negative. In terms of the absolute value of the mean coefficient, economic development had the strongest influence, followed by government regulation and consumption level, and openness had the weakest influence. The spatial influence of economic development and technological innovation had a certain circle structure. The influence of population density was mainly concentrated in Gansu, northeast China, the Beijing–Tianjin–Hebei region, the pan-Yangtze River Delta, and the pan-Pearl River Delta. The impact of urbanization level is obvious in most provinces north of the Yangtze River, while the impact of industrial structure is mainly concentrated in most cities south of the YREB. The influence of consumption level was manifested as a distribution trend of decreasing from north to south, and the government regulation had a trend of increasing from west to east and then to northeast.
Policy Suggestions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Target Layer | Rule Layer | Factor Layer | Index Layer |
---|---|---|---|
Input–output index system of IGDE | Input | Labor input | Quantity of employment |
Capital input | Fixed investments | ||
Desirable Output | Industrial output value | The added value of three industries | |
Undesirable Output | Wastewater | The discharge of industrial waste water | |
Waste gas | The discharge of industrial SO2 | ||
Waste residue | The discharge of industrial smoke and dust |
IGDE Values | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|---|---|---|---|---|
National | 0.5346 | 0.4478 | 0.4125 | 0.4728 | 0.4695 | 0.5033 | 0.4335 | 0.3634 | 0.3633 | 0.3682 | 0.3925 |
Eastern | 0.6044 | 0.5369 | 0.4671 | 0.5217 | 0.5127 | 0.5850 | 0.4814 | 0.4032 | 0.3773 | 0.3806 | 0.4083 |
Central | 0.4637 | 0.3767 | 0.3567 | 0.3946 | 0.3973 | 0.4095 | 0.3550 | 0.2666 | 0.2679 | 0.2840 | 0.2995 |
Western | 0.5545 | 0.4602 | 0.4410 | 0.5043 | 0.5109 | 0.5161 | 0.4450 | 0.3856 | 0.3746 | 0.3773 | 0.3959 |
Northeast | 0.4981 | 0.3792 | 0.3554 | 0.4825 | 0.4550 | 0.5129 | 0.4926 | 0.4670 | 0.5546 | 0.5387 | 0.5904 |
Model Indices | MGWR | GWR | OLS |
---|---|---|---|
R2 | 0.617 | 0.579 | 0.426 |
AICc | 641.407 | 647.581 | 680.373 |
Residual Sum of Squares (RSS) | 115.910 | 116.459 | 165.809 |
Number of effective parameters | 36.035 | 44.052 | / |
Variable | The Bandwidth of MGWR | The Bandwidth of GWR |
---|---|---|
Constant term | 118 | 140 |
Economic development. | 92 | 140 |
Consumption level | 287 | 140 |
Technological innovation | 288 | 140 |
Industrial structure | 131 | 140 |
Population density | 82 | 140 |
Urbanization level | 167 | 140 |
Government regulation | 276 | 140 |
Openness | 288 | 140 |
Variable | Min | Median | Max | Mean | Standard Deviation |
---|---|---|---|---|---|
Constant term | −0.286 | −0.036 | 0.399 | −0.021 | 0.158 |
Economic development | 0.253 | 0.600 | 0.855 | 0.549 | 0.162 |
Consumption level | 0.195 | 0.219 | 0.254 | 0.219 | 0.013 |
Technological innovation | −0.150 | −0.135 | −0.120 | −0.135 | 0.004 |
Industrial structure | −0.476 | −0.056 | 0.103 | −0.107 | 0.150 |
Population density | −0.307 | 0.148 | 0.469 | 0.141 | 0.142 |
Urbanization level | −0.243 | −0.131 | 0.028 | −0.117 | 0.072 |
Government regulation | 0.268 | 0.282 | 0.408 | 0.295 | 0.034 |
Openness | −0.061 | −0.053 | −0.048 | −0.053 | 0.003 |
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Liu, K.; Qiao, Y.; Zhou, Q. Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR. Int. J. Environ. Res. Public Health 2021, 18, 3960. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18083960
Liu K, Qiao Y, Zhou Q. Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR. International Journal of Environmental Research and Public Health. 2021; 18(8):3960. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18083960
Chicago/Turabian StyleLiu, Ke, Yurong Qiao, and Qian Zhou. 2021. "Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR" International Journal of Environmental Research and Public Health 18, no. 8: 3960. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18083960