Spatiotemporal Differences and Spatial Spillovers of China’s Green Manufacturing under Environmental Regulation
Abstract
:1. Introduction
2. Theoretical Foundation
3. Methods and Data
3.1. Urban Green Manufacturing Efficiency
3.1.1. Indicator System Construction
3.1.2. Calculation Method
3.2. Kernel Density Estimation
3.3. Hot Spot Analysis
3.4. Space Panel Durbin Model
- (1)
- Geographic distance matrix (). The first law of geography in geography states that spatial influence presents as a characteristic of decay with spatial distance [48], so the inverse of the Euclidean distance between cities within the study sample was used to construct the weight matrix, and the formula is:
- (2)
- Economic distance matrix (). The close economic ties of spatial units with different economic development levels can reflect their actual economic spatial relationships through distance, so the economic distance matrix with the difference in GDP per capita among research units as the measurement index was constructed, and the formula is:
- (3)
- Economic-geographic distance nested matrix (). In fact, the economic development and closeness of linkage of spatial units is the result of the joint action of multiple factors, such as economic, cultural and institutional factors, and measuring the degree of spatial linkage simply by spatial distance or economic distance triggers certain errors. Therefore, the nested matrix of economic-geographical distance was used to measure the difference in regional unit spatial connections, and the formula is:
4. Analysis of the Results
4.1. City Green Manufacturing Efficiency Measurement
4.1.1. Changes in Regional Characteristics
4.1.2. Spatial-Temporal Agglomeration and Differentiation
4.2. Empirical Study on Environmental Regulation Effect
4.2.1. Variable Setting
- (1)
- Explained variable.
- (2)
- Core explanatory variable.
- (3)
- Control variables.
- Industrial Structure (IS). Due to the manufacturing industry belonging to secondary industry, the differentiated green development of industries has heterogeneity in economic output, environmental output and green efficiency [53]. To avoid multicollinearity among influencing factors, the ratio of the value added of secondary and tertiary industries, which reflects industrial upgrading, was applied to characterize the change in industrial structure.
- Economic development level (EDL). Taking the “Environmental Kuznets Curve” as the mainstream hypothesis, this paper expounded the relationship between economic growth and environmental development. So, economic growth was an important indicator that affected urban development, and economic development inevitably affects urban green manufacturing efficiency. Therefore, GDP per person was selected as the quantitative index [54].
- Innovation capability (IC). Digitalization and intelligence are the common trends of current manufacturing development, and innovation drive is an important push to achieve the improvement of manufacturing technology and management technology [34]. To measure the innovation capacity of cities in innovation activities, the proportion of local science and technology expenditure was considered as its proxy.
- Government Intervention Degree (GID). The inhibitory effect of “government failure” on urban green development has been confirmed by scholars, while some scholars believe that appropriate government intervention can effectively promote the urban innovation environment and infrastructure construction to provide basic guarantees for green all-factor improvement [55,56,57]. Fiscal expenditure is an important indicator of the degree of government intervention, so the share of fiscal expenditure was used as a token variable.
- Degree of external openness (DOEO). Foreign direct investment brings the externalities of a “pollution paradise” or “pollution halo”, which leads to a change in the green manufacturing efficiency of the city. Therefore, we chose the proportion of foreign-invested enterprises as the representation.
- (4)
- Variables set pre-test
4.2.2. Model Selection Test and Model Reconstruction
4.2.3. Environmental Regulation Parameter Estimation Analysis
4.3. Theoretical Framework Construction of Manufacturing Green Development from the Perspective of Environmental Regulation
5. Conclusions
5.1. Distribution of Cluster Centers
5.2. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Variable | Obs | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
LnGME | 4544 | −1.4061 | 0.5150 | −3.7213 | 0.2797 |
LnER | 4544 | 0.8346 | 0.6069 | −2.7949 | 4.3102 |
LnIS | 4544 | 10.1645 | 0.8360 | 4.5951 | 13.0557 |
LnEDL | 4544 | 11.8795 | 1.9130 | 1.6094 | 16.9217 |
LnIC | 4544 | −0.2361 | 0.4443 | −2.3591 | 1.4713 |
LnGID | 4544 | 14.0877 | 1.1087 | 10.4058 | 18.2405 |
LnDOEO | 4544 | 9.4171 | 2.2215 | 0.0000 | 14.9413 |
Variable | VIF | 1/VIF |
---|---|---|
LnER | 1.17 | 0.8541 |
LnIS | 1.20 | 0.8305 |
LnEDL | 1.69 | 0.5919 |
LnIC | 1.71 | 0.5851 |
LnGID | 1.19 | 0.8385 |
Variables | ||||||
---|---|---|---|---|---|---|
Coefficient | p | Coefficient | p | Coefficient | p | |
Moran’s I | 41.163 | 0 | 21.885 | 0 | 33.385 | 0 |
LM-error | 992.849 | 0 | 474.483 | 0 | 1021.81 | 0 |
LM-lag | 1552.48 | 0 | 432.727 | 0 | 561.028 | 0 |
LR-lag | 12.44 | 0.0293 | 10.91 | 0.0533 | 19.88 | 0.0013 |
LR-error | 12.5 | 0.0285 | 17.4 | 0.0038 | 20.59 | 0.001 |
Hausman | −135.36 | / | 36.93 | 0 | −114.15 | / |
Variables | |||
---|---|---|---|
LnER | −0.0448 *** | −0.0427 *** | −0.0415 *** |
(−12.46) | (−12.15) | (−11.61) | |
LnIS | 0.345 *** | 0.339 *** | 0.339 *** |
(19.30) | (18.81) | (18.88) | |
LnEDI | −0.0654 *** | −0.0601 *** | −0.0775 *** |
(−4.13) | (−3.81) | (−4.89) | |
LnIC | −0.00144 | −0.0062 | −0.00147 |
(−0.19) | (−0.83) | (−0.20) | |
LnGID | 0.0275 | 0.0574 ** | 0.0211 |
(1.13) | (2.22) | (0.87) | |
Wx * LnER | 0.0877 *** | 0.0078 | 0.0125 |
(2.89) | (1.34) | (0.44) | |
Wx * LnIS | 0.271 | 0.0685 ** | 0.269 * |
(1.60) | (2.31) | (1.93) | |
Wx * LnEDI | −0.00660 | −0.0392 | 0.186 |
(−0.05) | (−1.53) | (1.49) | |
Wx * LnIC | −0.0378 | −0.000917 | −0.0796 |
(−0.89) | (−0.08) | (−1.61) | |
Wx * LnGID | 0.147 | −0.0727 * | 0.129 |
(0.75) | (−1.84) | (0.77) | |
0.318 *** | 0.112 *** | 0.0136 | |
(3.07) | (5.75) | (0.12) | |
R2 | 0.0199 | 0.0326 | 0.106 |
N | 4544 | 4544 | 4544 |
Decomposition Category | Variables | |||
---|---|---|---|---|
Direct effect | LnER | −0.0444 *** | −0.0425 *** | −0.0414 *** |
(−12.07) | (−11.82) | (−11.28) | ||
LnIS | 0.348 *** | 0.343 *** | 0.342 *** | |
(20.38) | (20.02) | (19.80) | ||
LnEDI | −0.0661 *** | −0.0619 *** | −0.0781 *** | |
(−4.35) | (−4.10) | (−5.11) | ||
LnIC | −0.00166 | −0.00636 | −0.00157 | |
(−0.23) | (−0.90) | (−0.22) | ||
LnGID | 0.0280 | 0.0557 ** | 0.0211 | |
(1.17) | (2.21) | (0.88) | ||
Indirect effect | LnER | 0.111 ** | 0.00370 | 0.0130 |
(2.45) | (0.59) | (0.46) | ||
LnIS | 0.556 ** | 0.115 *** | 0.274 ** | |
(2.36) | (4.02) | (2.06) | ||
LnIC | −0.0513 | −0.000606 | −0.0766 | |
(−0.83) | (−0.05) | (−1.54) | ||
LnEDI | −0.0371 | −0.0502 * | 0.193 | |
(−0.18) | (−1.77) | (1.47) | ||
LnGID | 0.243 | −0.0712 * | 0.146 | |
(0.83) | (−1.68) | (0.83) | ||
Total effect | LnER | 0.0664 | −0.0388 *** | −0.0284 |
(1.48) | (−5.56) | (−1.01) | ||
LnIS | 0.903*** | 0.459 *** | 0.615 *** | |
(3.87) | (14.99) | (4.73) | ||
LnEDI | −0.103 | −0.112 *** | 0.115 | |
(−0.51) | (−3.65) | (0.88) | ||
LnIC | −0.0530 | −0.00696 | −0.0782 * | |
(−0.90) | (−0.63) | (−1.65) | ||
LnGID | 0.271 | −0.0154 | 0.167 | |
(0.95) | (−0.39) | (0.99) |
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Tao, J.; Cao, W.; Fang, Y.; Liu, Y.; Wang, X.; Wei, H. Spatiotemporal Differences and Spatial Spillovers of China’s Green Manufacturing under Environmental Regulation. Int. J. Environ. Res. Public Health 2022, 19, 11970. https://doi.org/10.3390/ijerph191911970
Tao J, Cao W, Fang Y, Liu Y, Wang X, Wei H. Spatiotemporal Differences and Spatial Spillovers of China’s Green Manufacturing under Environmental Regulation. International Journal of Environmental Research and Public Health. 2022; 19(19):11970. https://doi.org/10.3390/ijerph191911970
Chicago/Turabian StyleTao, Jie, Weidong Cao, Yebing Fang, Yujie Liu, Xueyan Wang, and Haipeng Wei. 2022. "Spatiotemporal Differences and Spatial Spillovers of China’s Green Manufacturing under Environmental Regulation" International Journal of Environmental Research and Public Health 19, no. 19: 11970. https://doi.org/10.3390/ijerph191911970