The Spatial Interaction Effect of Environmental Regulation on Urban Innovation Capacity: Empirical Evidence from China
Abstract
:1. Introduction
2. Research Hypotheses
3. Materials and Methods
3.1. Study Area
3.2. Variable Selection
3.2.1. Explained Variable
3.2.2. Explanatory Variable
- 1.
- Normalize the positive indicators, the normalized indicator is:
- 2.
- Calculate the proportion of indicator j from region i in year t:
- 3.
- Calculate the information entropy of variable j:
- 4.
- Calculate the j variable weight :
- 5.
- Calculate the environmental regulation score of city i in year t:
3.2.3. Control Variables
- The level of urban informatization (). The informatization level of a city can reflect the ability to exchange and share information of a city. Furthermore, the construction of information exchange platforms and the efficiency of information exchange can ensure that a city introduces innovative resources for re-innovation. This paper used the number of Internet broadband access users to express the informatization level of a city [39].
- The level of urban science and technology investment (). Investment in science and technology can provide financial support for urban innovation activities and reduce the risk of innovation activities due to a long R&D cycle and uncertain R&D results. This paper used the expenditure for science and technology within the general public budget expenditure to express the level of urban science and technology investment [40].
- City size (). The size of a city will affect the employment choice of innovative personnel, the location of innovative enterprises and the interaction among innovation entities [23]. We use the urban registered population to represent the city size.
- The level of urban economic development (). The economic level of a city reflects the economic growth rate and growth potential of a city. The economic scale will affect the aggregation of innovative talents and enterprises [41]. This paper used GDP to measure the urban economic development level.
- The level of urban industrial development (). Industrial enterprises constantly carry out technological innovation and invention in the process of daily production and upgrading [41]. In 2015, after the “Industry 4.0” strategic cooperation between China and Germany, to build intelligent industry, industrial companies further became one of the main innovation bodies of cities. Therefore, the number of units of industrial enterprises above a designated size was chosen to represent the level of industrial development.
- The education level of the urban population (). As the incubators of innovation, colleges and universities provide a favorable environment for urban innovation and cultivate potential innovative talents [38]. Therefore, this paper selected the number of students enrolled in regular institutions of higher education to represent the education level of the urban population.
3.3. Spatial Econometric Model
3.3.1. Spatial Autocorrelation Test
3.3.2. Model Selection and Design
3.4. Data Sources
4. Results and Discussion
4.1. Descriptive Statistical Analysis of Variables
4.2. Spatial Characteristics of the Main Variables
4.3. Discussion of SAR Regression Results
4.4. Discussion of Spatial Effect of SAR
4.5. Robustness Test
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year | ||
---|---|---|
2009 | 0.5292 *** (4.6427) | 0.0116 (0.1128) |
2010 | 0.5371 ***(4.7076) | 0.0191 (0.3712) |
2011 | 0.4592 *** (4.0607) | 0.0942 (1.0009) |
2012 | 0.4447 *** (3.9482) | 0.2351 ** (2.1874) |
2013 | 0.4568 *** (4.0492) | 0.3344 *** (3.0060) |
2014 | 0.4844 *** (4.2695) | 0.1688 * (1.6500) |
2015 | 0.5175 *** (4.5433) | 0.4527 *** (4.0232) |
2016 | 0.4729 *** (4.1721) | 0.5594 *** (4.9449) |
2017 | 0.4653 *** (4.1096) | 0.6150 *** (5.4108) |
2018 | 0.4731 *** (4.1748) | 0.6143 *** (5.3794) |
Null Hypothesis | Statistic | p-Value |
---|---|---|
LM test no spatial | 148.6278 | 0.0000 |
robust LM test no spatial lag | 28.3518 | 0.0000 |
LM test no spatial error | 122.0444 | 0.0000 |
robust LM test no spatial error | 1.7684 | 0.1840 |
Statistic | Degrees of Freedom | p-Value |
---|---|---|
29.9527 | 8 | 0.0002 |
Test Statistic | Statistic | Freedom | p-Value |
---|---|---|---|
LR-test joint significance spatial fixed effects | 389.0234 | 41 | 0.0000 |
LR-test joint significance time-period fixed effects | 153.3537 | 10 | 0.0000 |
Variables | Mean | Std. Dev. | Min | Max | Obs | Unit |
---|---|---|---|---|---|---|
inno | 11,922.66 | 15,762.83 | 43.00 | 92,055.00 | 410 | piece |
regu | 75.99 | 9.33 | 53.96 | 98.06 | 410 | % |
info | 119.25 | 123.98 | 7.00 | 773.00 | 410 | 104 households |
exp | 163,820.93 | 463,029.80 | 1100.00 | 4,263,655.00 | 410 | 104 yuan |
size | 202.81 | 222.06 | 29.00 | 1462.00 | 410 | 104 persons |
ec | 2214.84 | 4040.90 | 112.18 | 32,679.87 | 410 | 108 yuan |
ent | 1385.75 | 1992.07 | 119.00 | 17,611.00 | 410 | unit |
edu | 105,628.02 | 158,315.00 | 4100.00 | 859,555.00 | 410 | person |
Variables | Estimated Value | z-Value | p-Value |
---|---|---|---|
Wlninno | 0.3850 *** | 7.4212 | 0.0000 |
regu | 1.0319 *** | 5.2115 | 0.0000 |
info | 0.3954 *** | 3.7453 | 0.0002 |
exp | 0.0910 ** | 2.0923 | 0.0364 |
size | −0.9753 *** | −5.3036 | 0.0000 |
ec | 0.6094 *** | 3.4905 | 0.0005 |
ent | 0.3683 *** | 3.6455 | 0.0003 |
edu | −0.0680 | −0.5840 | 0.5592 |
Variables | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|
regu | 1.0677 *** (5.2420) | 0.6076 *** (3.5788) | 1.6753 *** (4.8624) |
info | 0.4119 *** (3.7483) | 0.2330 *** (3.0888) | 0.6450 *** (3.6774) |
exp | 0.0952 ** (2.1072) | 0.0541 * (1.8799) | 0.1494 ** (2.0631) |
size | −1.0113 *** (−5.4148) | −0.5762 *** (−3.5606) | −1.5875 *** (−4.9377) |
ec | 0.6310 *** (3.4537) | 0.3609 *** (2.6873) | 0.9919 *** (3.2661) |
ent | 0.3845 *** (3.8341) | 0.2182 *** (3.0646) | 0.6027 *** (3.7173) |
edu | −0.0743 (−0.6055) | −0.0428 (−0.5979) | −0.1171 (−0.6066) |
Variables | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|
regu | 0.0176 (0.7855) | 0.0102 (0.7566) | 0.0278 (0.7803) |
info | 0.4585 *** (4.2142) | 0.2664 *** (3.4425) | 0.7249 *** (4.1840) |
exp | 0.0963 ** (2.1606) | 0.0563 * (1.9169) | 0.1526 ** (2.1065) |
size | −0.7789 *** (−4.1425) | −0.4575 *** (−3.0444) | −1.2364 *** (−3.8730) |
ec | 0.6863 *** (3.6500) | 0.4039 *** (2.7754) | 1.0901 *** (3.4291) |
ent | 0.1341 (1.6016) | 0.0783 (1.5064) | 0.2124 (1.5894) |
edu | −0.1030 (−0.8003) | −0.0606 (−0.7818) | −0.1636 (−0.7996) |
Year | 2009–2013 | 2014–2018 | ||||
---|---|---|---|---|---|---|
Variable | Direct Effect | Indirect Effect | Total Effect | Direct Effect | Indirect Effect | Total Effect |
regu | 1.6247 *** (4.4674) | 0.6445 ** (2.2812) | 2.2691 *** (3.9570) | 0.5120 ** (2.5980) | 0.3839 ** (2.1011) | 0.8958 ** (2.4881) |
info | 0.7440 *** (4.2270) | 0.2930 ** (2.3064) | 1.0370 *** (3.8732) | 0.0772 (0.8417) | 0.0569 (0.7932) | 0.1340 (0.8321) |
exp | 0.2511 ** (2.5467) | 0.0986 * (1.8396) | 0.3497 ** (2.4616) | 0.1094 ** (2.6649) | 0.0830 ** (2.0734) | 0.1924 ** (2.4993) |
size | −0.7785 ** (−2.4660) | −0.3033 * (−1.8887) | −1.0818 ** (−2.4274) | −0.6285 ** (−2.3180) | −0.4730 ** (−1.9162) | −1.1015 ** (−2.2240) |
ec | 0.5842 ** (2.1505) | 0.2318 (1.6153) | 0.8160 ** (2.0750) | 0.3492 (1.5684) | 0.2536 (1.4947) | 0.6028 (1.5789) |
ent | 0.1436 (0.8869) | 0.0538 (0.7840) | 0.1975 (0.8722) | 0.2707 * (1.8502) | 0.2062 (1.5608) | 0.4769 * (1.7660) |
edu | 0.0672 (0.2908) | 0.0241 (0.2442) | 0.0912 (0.2801) | 0.0395 (0.3343) | 0.0316 (0.3408) | 0.0711 (0.3403) |
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Zhou, R.; Zhang, Y.; Gao, X. The Spatial Interaction Effect of Environmental Regulation on Urban Innovation Capacity: Empirical Evidence from China. Int. J. Environ. Res. Public Health 2021, 18, 4470. https://doi.org/10.3390/ijerph18094470
Zhou R, Zhang Y, Gao X. The Spatial Interaction Effect of Environmental Regulation on Urban Innovation Capacity: Empirical Evidence from China. International Journal of Environmental Research and Public Health. 2021; 18(9):4470. https://doi.org/10.3390/ijerph18094470
Chicago/Turabian StyleZhou, Ruomeng, Yunsheng Zhang, and Xincai Gao. 2021. "The Spatial Interaction Effect of Environmental Regulation on Urban Innovation Capacity: Empirical Evidence from China" International Journal of Environmental Research and Public Health 18, no. 9: 4470. https://doi.org/10.3390/ijerph18094470