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Smart city article details
| Title | The Impacts Of Smart City Construction On Carbon Total Factor Productivity: Empirical Evidence From China |
|---|---|
| ID_Doc | 55799 |
| Authors | Wu Z.; Wang X. |
| Year | 2025 |
| Published | Clean Technologies and Environmental Policy, 27, 1 |
| DOI | http://dx.doi.org/10.1007/s10098-024-02865-4 |
| Abstract | Today, low-carbon development and smart city pilots are prevalent. Against this backdrop, an urgent need exists to clarify the impact of smart city construction on low-carbon development. However, studies on the low-carbon development effects of smart city construction remain scarce. Therefore, this in-depth study focuses on China, the world's largest developing country, to examine the role of smart city construction in promoting low-carbon development. First, we calculate the carbon total factor productivity of 182 prefecture-level cities in China using the slacks-based global Malmquist-Luenberger index. Second, to empirically examine the impact of smart city construction on carbon total factor productivity, we employ a multi-period difference-in-difference (DID) model and a machine learning-based propensity score matching DID (PSM-DID) model. The results reveal that smart city construction significantly enhances carbon total factor productivity and low-carbon technological efficiency, while its impact on low-carbon technological progress is nonsignificant. Mechanism tests indicate that smart city construction can improve carbon total factor productivity through the following three channels: green technological innovation, industrial structure upgrading, and resource allocation. Heterogeneity tests indicate that all three batches of smart city construction improve carbon total factor productivity, and that the positive effect of the third batch is greater than that of the first and second batches. Furthermore, the carbon total factor productivity promotion effect of smart city construction is stronger in megacities and cities in the central region. Finally, we propose relevant policy implications. |
| Author Keywords | Carbon total factor productivity (CTFP); Difference-in-difference (DID); Heterogeneity; Machine learning PSM-DID; Smart city construction (SCC) |
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