The Impact of Smart City Construction on PM2.5 Concentrations: Empirical Analysis from Chinese Counties
Abstract
:1. Introduction
2. Policy Context and Research Hypotheses
2.1. Policy Context
2.2. Research Hypotheses
2.2.1. Smart Cities Directly Affect PM2.5 Concentrations
2.2.2. Smart Cities Influence PM2.5 Concentrations Through Technological Innovation
2.2.3. Moderating Effect of Economic Agglomeration on the Relationship Between Smart Cities and the Concentration of PM2.5
3. Model Setup and Variable Description
3.1. Modeling
3.1.1. Multi-Temporal Double Difference Models
3.1.2. Mediated Effects Model
3.1.3. Moderating Effect Model
3.2. Variable Description and Source of the Data
3.2.1. Explanatory Variables
3.2.2. Explained Variables
3.2.3. Control Variables
- Economic development level (lnpgdp): quantifying using the logarithmic transformation of per capita gross regional product (GRP), serving as an indicator of regional economic scale;
- Fiscal self-sufficiency (fisdes): assesses the financial autonomy of local governments through the ratio of local general budget revenues to expenditures;
- Industrial structure upgrading (is): the ratio of tertiary to secondary value added and is an indicator of a country’s industrial modernization process;
- Financial sector depth (fd): the year-end loan balance of financial institutions as a percentage of regional GDP is used to reflect the level of regional financial service provision levels;
- Education resource allocation (student): the coverage density of basic education is reflected by the ratio of students enrolled in general secondary schools in the household population;
- Healthcare supply (hos): quantifying the intensity of medical infrastructure investment by the number of hospital beds per 1000 population.
3.3. Sample Selection and Data Sources
4. Empirical Analysis
4.1. Benchmark Regression
4.2. Parallel Trend Test
4.3. Robustness Tests
4.3.1. Replacement of Variables
4.3.2. A Lag Phase
4.3.3. Replacement of Sample Intervals
4.3.4. PSM-DID
4.3.5. Exclusion of Contemporaneous Policies
4.4. Placebo Test
4.5. Mechanism Analysis
4.5.1. Innovation Effects
4.5.2. Moderating Effects of Economic Agglomeration
5. Heterogeneity Analysis
5.1. Digital Infrastructure Heterogeneity
5.2. Regional Heterogeneity
5.3. Heterogeneity of Urban Hierarchies
6. Conclusions and Policy Recommendations
6.1. Conclusions
6.2. Limitation
6.3. Policy Recommendations
- (1)
- Based on the effectiveness of PM2.5 emission reduction in the SC pilot counties, it is recommended to expand the coverage of the policy in a phased manner, prioritizing the focus on regions with serious PM2.5 pollution, high potential for economic agglomeration, and a better level of digital infrastructure.
- (2)
- The mechanism of SC to reduce PM2.5 through technological innovation has been verified, and technological investment needs to be further strengthened to promote the intelligent transformation of highly polluting industries and encourage enterprises to apply cleaner production processes.
- (3)
- In high economic density areas (e.g., central cities), the effect of SC in reducing PM2.5C is more significant, and it is recommended to prioritize the layout of intelligent environmental protection monitoring and sharing of pollution control facilities. In regions with lower economic density, supporting industrial cultivation policies are needed.
- (4)
- Setting up a special fund for “digital infrastructure transfer payments” to subsidize the purchase of smart equipment and provide technical operation and maintenance training to economically backward counties, to break the financial bottleneck of their digital transformation.
- (5)
- In response to digital infrastructure heterogeneity, priority is given to promoting SC in regions with mature digital infrastructure, while strengthening digitalization in low-infrastructure regions to fill in the gaps. Given regional heterogeneity, it is necessary to formulate differentiated promotion strategies. In the western region, relying on the “East Counts, West Counts” project, the layout of the intelligent monitoring network; in the eastern region to build provincial and municipal “air pollution collaborative governance platform”, the integration of the Yangtze River Delta, Beijing Tianjin Hebei region pollution source data, the use of blockchain technology to realize the responsibility for pollution control and ecological compensation mechanism; in the central region to set up heavy pollution control platform, the use of blockchain technology to realize the responsibility for pollution control and ecological compensation mechanism. In the central region, a transition fund for the intelligent transformation of heavy industry has been set up to help build digital infrastructure.
- (6)
- To ensure the long-term effectiveness of smart city policies, there is a need to improve the policy hiring and dynamic adjustment mechanism. In addition, public participation and social cooperation should be strengthened. Referring to the experience of universal action in the “dual-carbon” goal, we should promote the “Citizen Environmental Protection Data Platform”, encourage the public to query the local PM2.5 data in real-time through the government app, and open the channel for reporting pollution sources to form a closed loop of “monitoring-feedback-governance”. Further, we should be carrying out “intelligent pollution control” science popularization actions, popularizing the application scenarios of the Internet of Things and big data in environmental governance in communities and schools, and enhancing the public’s recognition of and participation in smart city policies.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
SC | Smart City |
PM2.5C | PM2.5 concentration |
ISC | Innovative Smarter City |
MDID | Difference-in-differences with multiple time periods |
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Variable | N | Mean | SD | Min | Max |
---|---|---|---|---|---|
PM2.5 | 32546 | 0.419 | 0.199 | 0.010 | 1.393 |
SC | 32546 | 0.178 | 0.382 | 0 | 1 |
lnpgdp | 32546 | 10.060 | 0.836 | 7.394 | 13.430 |
fisdes | 32546 | 0.316 | 0.242 | 0.006 | 9.463 |
is | 32546 | 1.237 | 1.354 | 0.043 | 54.980 |
fd | 32546 | 0.631 | 0.437 | 0 | 7.635 |
student | 32546 | 0.496 | 0.172 | 0.007 | 2.069 |
hos | 32546 | 0.035 | 0.020 | 0 | 0.332 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
PM2.5 | PM2.5 | PM2.5 | PM2.5 | |
SC | −0.015 | −0.015 | −0.015 | −0.015 |
(−12.807) | (−12.802) | (−12.804) | (−13.422) | |
lnpgdp | 0.031 | 0.032 | 0.036 | |
(15.509) | (15.387) | (17.454) | ||
fisdes | −0.041 | −0.041 | −0.040 | |
(−4.978) | (−4.978) | (−5.011) | ||
is | 0.003 | 0.003 | ||
(5.538) | (5.608) | |||
fd | −0.002 | −0.001 | ||
(−1.412) | (−0.422) | |||
student | −0.031 | |||
(−9.246) | ||||
hos | −0.598 | |||
(−15.535) | ||||
_cons | 0.422 | 0.125 | 0.111 | 0.105 |
(1203.184) | (6.674) | (5.482) | (5.277) | |
County FE | √ | √ | √ | √ |
Year FE | √ | √ | √ | √ |
N | 32,341 | 32,341 | 32,341 | 32,341 |
R2 | 0.940 | 0.941 | 0.941 | 0.942 |
(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|
PM1 | PM2 | PM3 | PM2.5 | PM2.5 | PM2.5 | |
SC | −0.013 *** | −0.014 *** | −0.016 *** | −0.009 *** | −0.015 *** | |
(−6.583) | (−13.229) | (−13.089) | (−8.703) | (−13.198) | ||
L.SC | −0.014 *** | |||||
(−10.106) | ||||||
lnpgdp | 0.013 *** | 0.037 *** | 0.039 *** | 0.041 *** | 0.019 *** | 0.036 *** |
(3.888) | (21.140) | (18.648) | (17.438) | (10.071) | (18.827) | |
fisdes | −0.033 *** | −0.060 *** | −0.067 *** | −0.065 *** | −0.031 *** | −0.062 *** |
(−5.230) | (−17.310) | (−15.856) | (−12.199) | (−8.808) | (−16.407) | |
is | 0.003 *** | 0.002 *** | 0.003 *** | 0.002 *** | −0.000 | 0.003 *** |
(3.535) | (5.430) | (5.996) | (4.260) | (−1.178) | (5.210) | |
fd | −0.014*** | −0.000 | −0.001 | −0.005 ** | 0.012 *** | −0.002 |
(−4.722) | (−0.016) | (−0.313) | (−2.206) | (9.159) | (−1.102) | |
student | −0.043 *** | −0.024 *** | −0.036 *** | −0.035 *** | 0.007 * | −0.031 *** |
(−7.379) | (−7.913) | (−9.712) | (−8.527) | (2.428) | (−9.180) | |
hos | −1.275 *** | −0.524 *** | −0.648 *** | −0.668 *** | −0.380 *** | −0.584 *** |
(−16.549) | (−16.011) | (−14.782) | (−12.145) | (−8.901) | (−15.663) | |
_cons | −2.597 *** | 0.029 *** | 0.159 *** | 0.060 ** | 0.271 *** | 0.111 *** |
(−80.674) | (1.672) | (7.642) | (2.685) | (14.857) | (5.809) | |
County FE | √ | √ | √ | √ | √ | √ |
Year FE | √ | √ | √ | √ | √ | √ |
N | 32,341 | 32,341 | 32,341 | 20,138 | 26,325 | 31,324 |
R2 | 0.990 | 0.944 | 0.938 | 0.950 | 0.963 | 0.946 |
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
PM2.5 ipc | PM2.5 nic | PM2.5 lccp | PM2.5 bbc | PM2.5 | |
SC | −0.015 *** | −0.015 *** | −0.015 *** | −0.015 *** | −0.015 *** |
(−13.855) | (−13.926) | (−13.855) | (−13.855) | (−13.926) | |
lnpgdp | 0.036 *** | 0.036 *** | 0.036 *** | 0.036 *** | 0.036 *** |
(20.029) | (20.027) | (20.029) | (20.029) | (20.027) | |
fisdes | −0.061 *** | −0.061 *** | −0.061 *** | −0.061 *** | −0.061 *** |
(−16.626) | (−16.694) | (−16.626) | (−16.626) | (−16.694) | |
is | 0.003 *** | 0.003 *** | 0.003 *** | 0.003 *** | 0.003 *** |
(5.578) | (5.582) | (5.578) | (5.578) | (5.582) | |
fd | −0.000 | −0.000 | −0.001 | −0.000 | −0.000 |
(−0.155) | (−0.141) | (−0.155) | (−0.155) | (−0.141) | |
hos | −0.059 *** | −0.584 *** | −0.590 *** | −0.590 *** | −0.584 *** |
(−15.630) | (−15.516) | (−15.630) | (−15.630) | (−15.516) | |
student | −0.028 *** | −0.029 *** | −0.028 *** | −0.028 *** | −0.029 *** |
(−9.102) | (−9.169) | (−9.102) | (−9.102) | (−9.169) | |
_cons | 0.107 *** | 0.107 *** | 0.107 *** | 0.107 ** | 0.107 *** |
(5.925) | (5.923) | (5.925) | (5.925) | (5.923) | |
County FE | √ | √ | √ | √ | √ |
Year FE | √ | √ | √ | √ | √ |
N | 32,341 | 32,327 | 32,341 | 32,341 | 32,327 |
R2 | 0.947 | 0.947 | 0.947 | 0.947 | 0.947 |
(1) | (2) | (3) | |
---|---|---|---|
patent_grant | patent_density | Economic Aggregation | |
SC_econ | −0.019 *** | ||
(−27.176) | |||
SC | 0.178 *** | 0.185 *** | 0.109 *** |
(0.025) | (0.028) | (25.113) | |
ln_econ_agg | −0.015 *** | ||
(−3.765) | |||
lnpgdp | 0.082 ** | 0.188 *** | 0.050 *** |
(0.032) | (0.039) | (11.43) | |
fisdes | −0.235 *** | −0.318 *** | −0.058 *** |
(0.054) | (0.066) | (−16.180) | |
is | −0.012 | −0.007 | 0.003 *** |
(0.009) | (0.016) | (5.254) | |
fd | 0.066 ** | 0.110 ** | −0.003 * |
(0.033) | (0.055) | (−1.615) | |
hos | 2.929 *** | 3.630 *** | −0.520 *** |
(0.587) | (0.718) | (−14.143) | |
student | 0.139 *** | 0.228 *** | −0.027 *** |
(0.043) | (0.054) | (−8.855) | |
_cons | −0.711 ** | −1.700 *** | 0.055 * |
(−2.153) | (0.419) | (2.296) | |
County FE | √ | √ | √ |
Year FE | √ | √ | √ |
N | 20,291 | 20,291 | 32,116 |
R2 | 0.728 | 0.726 | 0.944 |
(1) | (2) | (3) | |
---|---|---|---|
PM2.5 High dinf | PM2.5 Low dinf | PM2.5 | |
SC | −0.002 | −0.018 *** | −0.010 *** |
(−0.732) | (−14.972) | (−8.363) | |
SC_dinf | −0.029 *** | ||
(−12.551) | |||
lnpgdp | 0.049 *** | 0.024 *** | 0.035 *** |
(11.125) | (12.628) | (19.273) | |
fisdes | −0.073 *** | −0.056 *** | −0.061 *** |
(−10.370) | (−13.740) | (−16.814) | |
is | 0.007 *** | 0.002 *** | 0.003 *** |
(3.487) | (4.757) | (5.533) | |
fd | 0.036 *** | −0.007 *** | −0.001 |
(7.979) | (−3.901) | (−0.456) | |
student | −0.058 *** | −0.026 *** | −0.027 *** |
(−10.507) | (−7.342) | (−8.587) | |
hos | −0.716 *** | −0.571 *** | −0.595 *** |
(−8.836) | (−14.768) | (−15.760) | |
_cons | 0.075 | 0.205 *** | 0.119 *** |
(1.593) | (10.818) | (6.612) | |
County FE | √ | √ | √ |
Year FE | √ | √ | √ |
N | 5984 | 26,356 | 32,340 |
R2 | 0.963 | 0.944 | 0.947 |
(1) | (2) | (3) | |
---|---|---|---|
PM2.5 Landlord | PM2.5 Center | PM2.5 West | |
SC | −0.007 | 0.000 | −0.018 |
(−3.480) | (0.073) | (−10.956) | |
lnpgdp | 0.046 | 0.024 | −0.011 |
(12.090) | (6.674) | (−4.772) | |
fisdes | −0.097 | −0.047 | −0.027 |
(−13.877) | (−7.165) | (−5.171) | |
is | −0.000 | 0.005 | 0.002 |
(−0.246) | (4.033) | (3.257) | |
fd | −0.014 | 0.019 | −0.004 |
(−3.484) | (7.550) | (−2.545) | |
student | −0.101 | −0.014 | 0.006 |
(−15.509) | (−2.639) | (1.478) | |
hos | −0.416 | −0.936 | −0.503 |
(−5.584) | (−11.780) | (−10.570) | |
_cons | 0.140 | 0.264 | 0.491 |
(3.474) | (7.601) | (21.897) | |
County FE | √ | √ | √ |
Year FE | √ | √ | √ |
N | 8215 | 9013 | 11,348 |
R2 | 0.959 | 0.934 | 0.946 |
(1) | (2) | |
---|---|---|
PM2.5 High Administrative Level Cities | PM2.5 Ordinary Cities | |
SC | −0.018 | −0.015 |
(−10.709) | (−13.419) | |
lnpgdp | −0.012 | 0.036 |
(−5.505) | (17.452) | |
fisdes | −0.010 | −0.041 |
(−2.214) | (−5.011) | |
is | 0.002 | 0.003 |
(2.938) | (5.609) | |
fd | −0.004 | −0.001 |
(−2.685) | (−0.422) | |
hos | −0.514 | −0.058 |
(−10.769) | (−15.536) | |
student | 0.006 | −0.031 |
(1.425) | (−9.243) | |
SC_level | ||
_cons | 0.506 | 0.105 |
(22.284) | (5.278) | |
County FE | √ | √ |
Year FE | √ | √ |
N | 11,346 | 32,340 |
R2 | 0.945 | 0.942 |
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Li, C.; Duan, Y.; Zhou, Z.; Zhong, S. The Impact of Smart City Construction on PM2.5 Concentrations: Empirical Analysis from Chinese Counties. Sustainability 2025, 17, 5100. https://doi.org/10.3390/su17115100
Li C, Duan Y, Zhou Z, Zhong S. The Impact of Smart City Construction on PM2.5 Concentrations: Empirical Analysis from Chinese Counties. Sustainability. 2025; 17(11):5100. https://doi.org/10.3390/su17115100
Chicago/Turabian StyleLi, Chenxue, Yuxin Duan, Zhicheng Zhou, and Shen Zhong. 2025. "The Impact of Smart City Construction on PM2.5 Concentrations: Empirical Analysis from Chinese Counties" Sustainability 17, no. 11: 5100. https://doi.org/10.3390/su17115100
APA StyleLi, C., Duan, Y., Zhou, Z., & Zhong, S. (2025). The Impact of Smart City Construction on PM2.5 Concentrations: Empirical Analysis from Chinese Counties. Sustainability, 17(11), 5100. https://doi.org/10.3390/su17115100