Research on the Effect of Manufacturing Agglomeration on Green Use Efficiency of Industrial Land
Abstract
:1. Introduction
2. Materials and Methods
2.1. Empirical Methodology
2.2. Variable Setting
- (1)
- Explained variable: green use efficiency of industrial land
- (2)
- Core explanatory variable: manufacturing agglomeration
- (3)
- Control variables
2.3. Data Sources
3. Results
3.1. Spatial and Temporal Variation Characteristics of MA and GUEIL
3.1.1. Measurement Results and Spatio-Temporal Analysis of MA
3.1.2. Measurement Results and Spatio-Temporal Analysis of GUEIL
3.1.3. Comprehensive Analysis of MA and GUEIL
3.2. The Effect of MA on GUEIL
3.2.1. Model Testing and Identification
3.2.2. Analysis of National-Scale Regression Results
3.2.3. Regional Heterogeneity Analysis
3.2.4. Robustness Tests
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Indicator Type | Element Name | Indicator Name |
---|---|---|
Input Indicators | Capital Investment | Total assets of industrial enterprises above average per land(Million Yuan/km2) |
Labor input | Number of manufacturing workers per land (million people/km2) | |
Land input | Industrial land area (km2) | |
Output Indicators | Expected output | industrial value added per land (billion yuan/km2) |
Non-desired outputs | industrial sulfur dioxide emissions per land (tons/km2) | |
industrial wastewater discharge per land (million tons/km2) | ||
industrial smoke (dust) emissions per land (tons/km2) |
Variables | Variable Name | Measurements | Mean | Min | Max | Sample Number |
---|---|---|---|---|---|---|
lneil | green use efficiency of industrial land | Super-SBM model | −1.252 | −3.135 | 0.286 | 4464 |
agg | manufacturing agglomeration | location entropy | 0.889 | 0.022 | 3.189 | 4464 |
lnep | environmental regulation | index of “industrial waste” | −3.062 | −14.526 | 3.558 | 4464 |
lnpgdp | economic development level | GDP per capita | 10.076 | 7.603 | 13.245 | 4464 |
is | Industrialization degree | share of secondary industry in GDP | 0.473 | 0.107 | 0.910 | 4464 |
od | opening degree | Total import and export trade/ regional GDP | 0.204 | 0.000 | 8.134 | 4464 |
si | technology input level | science and technology expenditure/public finance expenditure | 0.013 | 0.000 | 0.131 | 4464 |
gil | government intervention level | public finance expenditure/GDP | 0.171 | 0.040 | 1.027 | 4464 |
ifi | Informationalized level | number of international Internet users/year-end population | 0.160 | 0.001 | 1.987 | 4464 |
gc | eco-environmental endowment | greening coverage of built-up areas | 0.376 | 0.004 | 0.696 | 4464 |
ri | regional Innovation Level | number of invention patents granted/total population at the end of the year | 0.914 | 0.000 | 48.132 | 4464 |
Year | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |
Moran’s I | 0.078 *** | 0.078 *** | 0.062 *** | 0.044 ** | 0.051 *** | 0.043 ** | 0.040 ** | 0.023 |
sd statistics | 4.403 | 4.367 | 3.509 | 2.542 | 2.930 | 2.502 | 2.353 | 1.448 |
Year | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
Moran’s I | 0.033 * | 0.028 * | 0.067 *** | 0.060 *** | 0.057 *** | 0.066 *** | 0.092 *** | 0.107 *** |
sd statistics | 1.953 | 1.725 | 3.774 | 3.429 | 3.296 | 3.766 | 5.160 | 5.921 |
Test Method | Test Statistic Results | p Value | Test Conclusion |
---|---|---|---|
LM-Lag | 81.106 | 0.0000 | Can choose SEM model |
Robust LM-Lag | 3.858 | 0.0490 | |
LM-Error | 1047.219 | 0.0000 | Can choose SAR model |
Robust LM-Erro | 969.971 | 0.0000 | |
LR-SDM-SEM | 244.98 | 0.0000 | Compared with SEM and SAR models, choosing SDM models is better |
LR-SDM-SAR | 201.23 | 0.0000 | |
Wald-SAR | 73.14 | 0.0000 | |
Wald-SEM | 70.98 | 0.0000 | |
Hausman | −208.80 | - | Choosing the SDM model, the fixed effect model is better |
Time Effect | 4392.19 | 0.0000 | Choosing the SDM model, the dual fixed model of time and space is better |
Spatial effects | 160.87 | 0.0000 |
Variables | eil | ||||
---|---|---|---|---|---|
Countrywide | Eastern | Central | Western | Northeast | |
(1) | (2) | (3) | (4) | (5) | |
agg | −0.4963 *** | −0.2461 ** | −0.4176 *** | −0.9803 *** | −0.7956 *** |
(0.0384) | (0.0765) | (0.0830) | (0.0781) | (0.1411) | |
agg2 | 0.1046 *** | 0.0113 | 0.0756 * | 0.4039 *** | 0.3242 *** |
(0.0145) | (0.0239) | (0.0393) | (0.0388) | (0.0792) | |
lnep | −0.0418 *** | −0.0640 *** | −0.0398 *** | −0.0267 *** | −0.0525 *** |
(0.0030) | (0.0061) | (0.0054) | (0.0055) | (0.0080) | |
lnpgdp | 0.1693 *** | −0.1566 | 0.0714 | 0.3369 *** | 0.3869 ** |
(0.0405) | (0.1099) | (0.0751) | (0.0630) | (0.1192) | |
is | 1.1312 *** | 0.8055 *** | 0.8245 *** | 1.3369 *** | 0.6410 *** |
(0.0678) | (0.1868) | (0.1283) | (0.1058) | (0.1836) | |
od | 0.0534 *** | 0.0678 *** | −0.1644 * | −0.0062 | 0.0893 * |
(0.0142) | (0.0153) | (0.0920) | (0.0532) | (0.0529) | |
si | 1.9923 *** | 2.2173 ** | −1.5584 ** | 3.5111 ** | 2.5782 |
(0.4505) | (0.7086) | (0.7104) | (1.5000) | (1.7332) | |
gil | −0.6275 *** | −0.9827 *** | −1.0132 *** | −0.4445 *** | −1.7832 *** |
(0.0816) | (0.2709) | (0.2431) | (0.1111) | (0.1904) | |
ifi | 0.0687 ** | 0.0205 | 0.6316 *** | 0.0590 | 0.2553 ** |
(0.0335) | (0.0427) | (0.1249) | (0.0713) | (0.1211) | |
gc | −0.0014 | −0.1583 | −0.1901 ** | 0.0003 | 0.3994 ** |
(0.0495) | (0.1009) | (0.0829) | (0.0861) | (0.1499) | |
ri | 0.0184 *** | 0.0164 *** | 0.0071 | 0.0633 *** | −0.0303 * |
(0.0019) | (0.0022) | (0.0081) | (0.0119) | (0.0180) | |
City Fixed | YES | YES | YES | YES | YES |
Time fixed | YES | YES | YES | YES | YES |
N | 4464 | 1360 | 1264 | 1312 | 528 |
Variables | 2004–2009 | 2010–2014 | 2015–2019 |
---|---|---|---|
(6) | (7) | (8) | |
lneil | lneil | lneil | |
agg | −0.6371 *** | −0.5239 *** | −0.9552 *** |
(0.0900) | (0.0570) | (0.0883) | |
agg2 | 0.1482 *** | 0.1078 *** | 0.2451 *** |
(0.0400) | (0.0212) | (0.0348) | |
Control variables | YES | YES | YES |
City Fixed | YES | YES | YES |
Time fixed | YES | YES | YES |
N | 1674 | 1395 | 1395 |
Variables | lneil | |
---|---|---|
(9) | (10) | |
FE | SYS-GMM | |
L.lneil | - | 0.6397 *** |
- | (0.0426) | |
L.lnec | - | - |
- | - | |
L.lntc | - | - |
- | - | |
agg | −0.5394 *** | −0.3300 *** |
(0.1223) | (0.0877) | |
agg2 | 0.1110 * | 0.0723 ** |
(0.0604) | (0.0331) | |
_cons | −3.1822 *** | 0.2163 |
(0.7464) | (0.75) | |
Control variables | YES | YES |
City Fixed | YES | - |
Time fixed | YES | - |
R2 | 0.515 | - |
N | 4464 | 4185 |
AR(1) | - | 0.000 |
AR(2) | - | 0.527 |
Hansen | - | 0.191 |
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Wang, Y.; Zhang, A.; Min, M.; Zhao, K.; Hu, W.; Qin, F. Research on the Effect of Manufacturing Agglomeration on Green Use Efficiency of Industrial Land. Int. J. Environ. Res. Public Health 2023, 20, 1575. https://doi.org/10.3390/ijerph20021575
Wang Y, Zhang A, Min M, Zhao K, Hu W, Qin F. Research on the Effect of Manufacturing Agglomeration on Green Use Efficiency of Industrial Land. International Journal of Environmental Research and Public Health. 2023; 20(2):1575. https://doi.org/10.3390/ijerph20021575
Chicago/Turabian StyleWang, Yuan, Anlu Zhang, Min Min, Ke Zhao, Weiyan Hu, and Fude Qin. 2023. "Research on the Effect of Manufacturing Agglomeration on Green Use Efficiency of Industrial Land" International Journal of Environmental Research and Public Health 20, no. 2: 1575. https://doi.org/10.3390/ijerph20021575
APA StyleWang, Y., Zhang, A., Min, M., Zhao, K., Hu, W., & Qin, F. (2023). Research on the Effect of Manufacturing Agglomeration on Green Use Efficiency of Industrial Land. International Journal of Environmental Research and Public Health, 20(2), 1575. https://doi.org/10.3390/ijerph20021575