Study on the Impact of Industrial Agglomeration on Ecological Sustainable Development in Southwest China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Source of Data
2.2. Method for Measuring the Degree of Industrial Agglomeration
2.2.1. Measuring Methods for Specialization
2.2.2. Measuring Methods for Related and Unrelated Diversification
2.3. Measuring Methods for Industrial Eco-Efficiency
2.3.1. Global SBM-DDF Model
2.3.2. Selection of Indicators
2.4. Panel Tobit Regression Model
2.4.1. Model Determination
2.4.2. Selection of Control Variables
3. Results and Discussion
3.1. Industrial Agglomeration in Southwest China in 2006–2018
3.2. Level of Industrial Eco-Efficiency in Southwest China in 2006–2018
3.3. Influence of Industrial Agglomeration on Eco-Efficiency in Southwest China
4. Conclusions
4.1. Effects of Different Types of Industrial Agglomeration on Ecological Efficiency in Four Southwest Chinese Provinces, Besides Tibet
4.2. Effects of Different Types of Industrial Agglomeration on Ecological Efficiency in Tibet
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Variable Type | Variable Name | Variable Explanation | |
---|---|---|---|
Input indicator | Natural resource input | Water consumption | Total industrial water |
Land consumption | Area of industrial construction land | ||
Energy consumption | Total industrial energy consumption | ||
Social and economic factor input | Labor input | Number of industrial employees | |
Capital input | Industrial fixed assets | ||
Output indicator | Desirable output | Economic value creation | Industrial value added |
Undesirable output | Wastewater discharge | COD emissions industrial wastewater | |
Ammonium nitrogen emissions from industrial wastewater | |||
Waste gas discharge | SO2 emissions from industrial emissions | ||
Smoke (dust) emissions from industrial waste gas | |||
Solid waste discharge | Emissions from industrial solid waste |
Type | Region | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Specialization | Chongqing | 0.83 | 0.82 | 0.82 | 0.81 | 0.80 | 0.82 | 0.81 | 0.77 | 0.79 | 0.80 | 0.80 | 0.81 | 0.81 |
Sichuan | 0.82 | 0.83 | 0.83 | 0.85 | 0.86 | 0.82 | 0.80 | 0.79 | 0.78 | 0.78 | 0.79 | 0.79 | 0.78 | |
Guizhou | 0.89 | 0.89 | 0.86 | 0.87 | 0.88 | 0.90 | 0.89 | 0.86 | 0.83 | 0.72 | 0.75 | 0.77 | 0.80 | |
Yunnan | 0.97 | 0.98 | 0.92 | 0.91 | 0.89 | 0.88 | 0.86 | 0.85 | 0.84 | 0.84 | 0.85 | 0.86 | 0.86 | |
Tibet | 0.04 | 0.03 | 0.03 | 0.03 | 0.03 | 0.04 | 0.03 | 0.03 | 0.03 | 0.03 | 0.03 | 0.03 | 0.03 | |
Mean | 0.71 | 0.71 | 0.69 | 0.69 | 0.69 | 0.69 | 0.68 | 0.66 | 0.65 | 0.63 | 0.64 | 0.65 | 0.66 | |
Related diversification | Chongqing | 1.58 | 1.58 | 1.60 | 1.61 | 1.61 | 1.58 | 1.58 | 1.57 | 1.59 | 161 | 1.63 | 1.63 | 1.64 |
Sichuan | 1.55 | 1.56 | 1.59 | 1.61 | 1.62 | 1.62 | 1.62 | 1.63 | 1.63 | 1.64 | 1.64 | 1.65 | 1.66 | |
Guizhou | 1.55 | 1.46 | 1.40 | 1.37 | 1.31 | 1.34 | 1.34 | 1.36 | 1.41 | 1.55 | 1.54 | 1.55 | 1.57 | |
Yunnan | 1.63 | 1.63 | 1.61 | 1.61 | 1.60 | 1.61 | 1.61 | 1.65 | 1.69 | 1.71 | 1.70 | 1.71 | 1.71 | |
Tibet | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Mean | 1.26 | 1.25 | 1.24 | 1.24 | 1.23 | 1.23 | 1.23 | 1.24 | 1.26 | 1.30 | 1.30 | 1.31 | 1.32 | |
Unrelated diversification | Chongqing | 1.25 | 1.26 | 1.25 | 1.25 | 1.25 | 1.24 | 1.24 | 1.25 | 1.26 | 1.26 | 1.25 | 1.24 | 1.21 |
Sichuan | 1.29 | 1.27 | 1.24 | 1.22 | 1.20 | 1.20 | 1.19 | 1.17 | 1.18 | 1.20 | 1.21 | 1.21 | 1.22 | |
Guizhou | 1.21 | 1.23 | 1.24 | 1.24 | 1.25 | 1.25 | 1.26 | 1.26 | 1.27 | 1.28 | 1.28 | 1.29 | 1.31 | |
Yunnan | 1.32 | 1.29 | 1.30 | 1.30 | 1.29 | 1.30 | 1.30 | 1.30 | 1.31 | 1.30 | 1.29 | 1.29 | 1.28 | |
Tibet | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
Mean | 1.01 | 1.01 | 1.01 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.01 | 1.01 | 1.01 | 1.00 |
Region | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Chongqing | 1.09 | 1.11 | 1.11 | 1.13 | 1.14 | 1.19 | 1.21 | 1.21 | 1.22 | 1.24 | 1.24 | 1.26 | 1.27 |
Sichuan | 1.01 | 1.03 | 1.03 | 1.04 | 1.05 | 1.05 | 1.05 | 1.07 | 1.08 | 1.12 | 1.11 | 1.12 | 1.13 |
Guizhou | 0.97 | 1.01 | 0.96 | 0.89 | 0.83 | 0.75 | 0.73 | 0.71 | 0.66 | 0.72 | 0.72 | 0.75 | 0.75 |
Yunnan | 1.04 | 1.11 | 1.03 | 1.17 | 1.06 | 1.18 | 1.19 | 1.16 | 1.20 | 1.19 | 1.18 | 1.20 | 1.21 |
Tibet | 0.32 | 0.35 | 0.35 | 0.36 | 0.37 | 0.37 | 0.37 | 0.38 | 0.39 | 0.40 | 0.41 | 0.41 | 0.40 |
Mean value | 0.89 | 0.92 | 0.90 | 0.92 | 0.89 | 0.91 | 0.91 | 0.91 | 0.91 | 0.93 | 0.93 | 0.95 | 0.95 |
Explanatory Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 |
---|---|---|---|---|---|---|
spe | −0.433 *** | −1.862 ** | ||||
(−2.85) | (−2.25) | |||||
spe2 | 0.813 | |||||
(1.56) | ||||||
rd | −0.147 | −4.376 *** | ||||
(−1.13) | (−2.97) | |||||
rd2 | 1.451 *** | |||||
(2.65) | ||||||
urd | −0.436 * | −0.287 | ||||
(−1.95) | (−0.07) | |||||
urd2 | −0.061 | |||||
(−0.06) | ||||||
pgdp | −0.174 *** | −0.162 *** | −0.117 *** | −0.146 *** | −0.088 * | −0.087 * |
(−3.75) | (−3.49) | (−2.74) | (−3.45) | (−1.98) | (−1.79) | |
pgdp2 | 0.036 *** | 0.034 *** | 0.029 *** | 0.033 *** | 0.025 *** | 0.025 *** |
(4.65) | (4.39) | (3.84) | (4.47) | (3.27) | (3.21) | |
estur | −0.944 *** | −0.999 *** | −0.963 *** | −0.936 *** | −0.934 *** | −0.935 *** |
(−5.51) | (−5.76) | (−5.57) | (−5.89) | (−5.65) | (−5.51) | |
open | −0.162 | −0.196 | 0.047 | 0.026 | 0.279 | 0.279 |
(−0.22) | (−0.25) | (0.06) | (0.04) | (0.38) | (0.38) | |
tech | 6.78 ** | 7.609 ** | 6.975 ** | 5.073 * | 6.781 ** | 6.804 ** |
(2.17) | (2.44) | (2.14) | (1.75) | (2.10) | (2.05) | |
envir | 2.825 | 3.25 | 4.180 | 2.662 | 6.967 ** | 6.954 ** |
(0.89) | (1.07) | (1.27) | (0.82) | (1.98) | (1.96) | |
mark | 0.019 * | 0.008 | 0.022 * | 0.018 * | 0.014 | 0.014 |
(1.68) | (0.58) | (1.85) | (1.67) | (1.01) | (1.01) | |
Constant | 1.762 *** | 2.413 *** | 1.454 *** | 4.546 *** | 1.776 *** | 1.677 |
(8.28) | (5.39) | (6.27) | (4.01) | (5.98) | (0.53) | |
Observations | 50 | 50 | 50 | 50 | 50 | 50 |
Number of ID | 5 | 5 | 5 | 5 | 5 | 5 |
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Gao, L.; Li, F.; Zhang, J.; Wang, X.; Hao, Y.; Li, C.; Tian, Y.; Yang, C.; Song, W.; Wang, T. Study on the Impact of Industrial Agglomeration on Ecological Sustainable Development in Southwest China. Sustainability 2021, 13, 1301. https://doi.org/10.3390/su13031301
Gao L, Li F, Zhang J, Wang X, Hao Y, Li C, Tian Y, Yang C, Song W, Wang T. Study on the Impact of Industrial Agglomeration on Ecological Sustainable Development in Southwest China. Sustainability. 2021; 13(3):1301. https://doi.org/10.3390/su13031301
Chicago/Turabian StyleGao, Lei, Fang Li, Jingran Zhang, Xu Wang, Yue Hao, Chao Li, Yu Tian, Chao Yang, Weiming Song, and Tielong Wang. 2021. "Study on the Impact of Industrial Agglomeration on Ecological Sustainable Development in Southwest China" Sustainability 13, no. 3: 1301. https://doi.org/10.3390/su13031301