The Impact of Urban E-Commerce Transformation on Carbon Emissions in Chinese Cities: An Empirical Analysis Based on the PSM-DID Method
Abstract
:1. Introduction
2. Theoretical Background
3. Policy Background and Research Hypotheses
3.1. Policy Background of the National E-Commerce Demonstration City
3.2. Theoretical Analysis and Research Hypotheses
4. Data and Methodology
4.1. Data Sources
4.2. Research Methodology
4.2.1. PSM
4.2.2. PSM-DID
4.2.3. Mechanism of Mediation
4.3. Selection of Variables and Descriptive Statistics
5. Results
5.1. PSM Results
5.2. Results of PSM-DID Analysis
5.3. Parallel Trend Test and Dynamic Effects Analysis
5.4. Robustness Tests
5.5. Mechanism Analysis
5.6. Heterogeneity Test
6. Discussion and Implications
6.1. Discussion
6.2. Implications
7. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Observations | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
2862 | 8.0751 | 1.1096 | 4.6535 | 12.7001 | |
2862 | 5.9722 | 1.1550 | 2.1168 | 9.5078 | |
2862 | 0.0999 | 0.2999 | 0.0000 | 1.0000 | |
2862 | 7.4907 | 1.6099 | 1.7918 | 11.5801 | |
2862 | 0.4871 | 0.1044 | 0.1490 | 0.851 | |
2862 | 10.4550 | 0.6554 | 4.5951 | 13.0557 | |
2862 | 0.0369 | 0.0372 | 0.0003 | 0.5000 | |
2862 | 15.0854 | 0.6866 | 12.1335 | 17.3395 | |
2862 | 0.5337 | 0.1506 | 0.0010 | 0.9557 | |
2862 | 0.0828 | 0.1152 | 0.0015 | 0.9637 | |
2862 | 0.3238 | 0.2059 | 0.0105 | 2.4436 |
Variables | Sample | Mean | Standard Bias (%) | Standard Bias Reduction (%) | t-Statistic | p > t | |
---|---|---|---|---|---|---|---|
Treatment Group | Control Group | ||||||
Unmatched | 10.8550 | 10.3330 | 83.9 | 92.0 | 19.16 | 0.000 | |
Matched | 10.8140 | 10.8560 | −6.7 | −1.25 | 0.211 | ||
Unmatched | 0.0207 | 0.0418 | −70.1 | 94.6 | −13.27 | 0.000 | |
Matched | 0.0214 | 0.0226 | −3.8 | −1.52 | 0.128 | ||
Unmatched | 15.4720 | 14.9670 | 79.4 | 95.5 | 17.50 | 0.000 | |
Matched | 15.4410 | 15.4180 | 3.6 | 0.75 | 0.454 | ||
Unmatched | 0.5539 | 0.5275 | 18.6 | 62.4 | 3.99 | 0.000 | |
Matched | 0.5529 | 0.5429 | 7.0 | 1.20 | 0.230 | ||
Unmatched | 0.1389 | 0.0656 | 58.1 | 80.3 | 14.99 | 0.000 | |
Matched | 0.1286 | 0.1430 | −11.4 | −1.86 | 0.063 | ||
Unmatched | 0.4334 | 0.2903 | 64.8 | 93.4 | 16.47 | 0.000 | |
Matched | 0.3990 | 0.4086 | −4.3 | −0.93 | 0.354 |
Variables | |||||||
---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
−0.0958 *** (0.0195) | −0.0969 *** (0.0191) | −0.0954 *** (0.0191) | −0.0957 *** (0.0194) | −0.0958 *** (0.0194) | −0.0942 *** (0.0197) | −0.0945 *** (0.0198) | |
−0.0585 *** (0.0213) | −0.0733 *** (0.0248) | −0.0742 *** (0.0260) | −0.0743 *** (0.0259) | −0.0759 *** (0.0257) | −0.0766 *** (0.0257) | ||
−0.1160 * (0.1104) | −0.130 * (0.1152) | −0.1292 * (0.1141) | −0.1261 * (0.1151) | −0.112 * (0.1126) | |||
−0.0148 * (0.0728) | −0.0146 * (0.0730) | −0.0225 * (0.0694) | −0.0123 * (0.0667) | ||||
−0.0146 ** (0.0291) | −0.0291 ** (0.1410) | −0.0144 ** (0.0290) | |||||
−0.0743 * (0.0976) | −0.0758 * (0.0963) | ||||||
−0.0496 ** (0.0620) | |||||||
YFE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
CFE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Constant | 0.9693 *** (0.0080) | 1.5670 *** (0.2178) | 1.5057 *** (0.2063) | 1.2836 (1.1291) | 1.2902 * (1.1346) | 1.1689 * (1.0828) | 1.3535 * (1.0431) |
Observations | 2690 | 2690 | 2690 | 2690 | 2690 | 2690 | 2690 |
R2 | 0.6406 | 0.6443 | 0.6453 | 0.6453 | 0.6454 | 0.6457 | 0.6459 |
Variables | Replaced Explained Variable | Replaced PSM Method | ||
---|---|---|---|---|
(1) Kernel Matching | (2) Local Linear Regression Matching | (3) Radius Matching | (4) Mahalanobis Matching | |
−0.0505 *** (0.0146) | −0.084 *** (0.016) | −0.059 *** (0.024) | −0.095 *** (0.016) | |
Controls | Yes | Yes | Yes | Yes |
YFE | Yes | Yes | Yes | Yes |
CFE | Yes | Yes | Yes | Yes |
Constant | −1.0872 * (0.8532) | −0.9165 ** (0.870) | −1.0861 ** (0.953) | −1.1652 * (0.9376) |
Observations | 2690 | 2705 | 2643 | 2620 |
R2 | 0.7225 | 0.8654 | 0.8623 | 0.8702 |
−0.0945 *** (0.0198) | 0.1351 *** (0.0208) | −0.0682 *** (0.0102) | 0.1079 ** (0.0143) | −0.0755 *** (0.0162) | |
−0.1953 *** (0.2169) | |||||
−0.1758 ** (0.016) | |||||
Controls | Yes | Yes | Yes | Yes | Yes |
YFE | Yes | Yes | Yes | Yes | Yes |
CFE | Yes | Yes | Yes | Yes | Yes |
Constant | 1.3535 * (1.0431) | 0.8537 * (0.9087) | 1.0638 * (1.0435) | 0.7546 * (0.5463) | 1.0925 * (1.3042) |
Observations | 2690 | 2690 | 2690 | 2690 | 2690 |
R2 | 0.6459 | 0.7275 | 0.8109 | 0.9375 | 0.9278 |
Urban Regions | Urban Types | ||||
---|---|---|---|---|---|
(1) Eastern Region | (2) Central Region | (3) Western Region | (4) Non-Resource-Based | (5) Resource-Based | |
−0.0847 (0.1763) | −0.124 ** (0.8754) | −0.0735 *** (0.0865) | −0.1093 (0.0865) | −0.039 *** (0.0323) | |
Controls | Yes | Yes | Yes | Yes | Yes |
YFE | Yes | Yes | Yes | Yes | Yes |
CFE | Yes | Yes | Yes | Yes | Yes |
Constant | 1.0236 (0.1364) | 1.0376 * (0.1073) | 0.9865 (0.1420) | 1.3470 ** (0.1171) | 1.2982 * (0.1271) |
Observations | 886 | 1072 | 732 | 1005 | 1685 |
R2 | 0.7052 | 0.8161 | 0.6936 | 0.6743 | 0.7105 |
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Wen, L.; Sun, S. The Impact of Urban E-Commerce Transformation on Carbon Emissions in Chinese Cities: An Empirical Analysis Based on the PSM-DID Method. Sustainability 2023, 15, 5659. https://doi.org/10.3390/su15075659
Wen L, Sun S. The Impact of Urban E-Commerce Transformation on Carbon Emissions in Chinese Cities: An Empirical Analysis Based on the PSM-DID Method. Sustainability. 2023; 15(7):5659. https://doi.org/10.3390/su15075659
Chicago/Turabian StyleWen, Limin, and Shufang Sun. 2023. "The Impact of Urban E-Commerce Transformation on Carbon Emissions in Chinese Cities: An Empirical Analysis Based on the PSM-DID Method" Sustainability 15, no. 7: 5659. https://doi.org/10.3390/su15075659