Does the opening of high-speed rails improve urban carbon efficiency? Evidence from a spatial difference-in-difference method

Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as 'dimensional curse' and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading.

The impact of smart city construction (SCC) on pollution emissions (PE): evidence from China

Based on panel data from 210 prefecture-level cities in China from 2003 to 2021, this study employs the Time-Varying Differences-in-Differences (Time-Varying DID) approach to systematically examine the impact of smart city construction on pollution emissions and its underlying mechanisms. Additionally, the Propensity Score Matching-Differences-in-Differences method is employed for further validation. The research findings indicate that Smart City Construction (SCC) significantly reduces urban Volume of Sewage Discharge (VSD), sulfur dioxide emissions (SO2), and Emissions of Fumes and Dust (EFD), thereby mitigating pollution emissions (PE) and enhancing environmental quality. Mechanism analysis reveals that SCC achieves these effects through scale effects, structural effects, and technological effects. City heterogeneity analysis shows that provincial capital cities exhibit a stronger suppression effect on pollution emissions compared to non-provincial capital cities. Moreover, cities with lower levels of education attainment demonstrate a stronger ability to curb pollution emissions, while larger cities exhibit a more pronounced impact on mitigating pollution emissions. The marginal contributions of this study mainly consist of three aspects: Firstly, it enriches the literature on environmental impact factors by assessing, for the first time, the influence of SCC on PE. Secondly, a comprehensive approach is employed, integrating VSD, EFD, SO2 data, and economic and pollution data at the city level. Time-Varying DID is used to evaluate the policy effects of SCC. Finally, the study analyzes the impact mechanisms of SCC policy on environmental emissions from various perspectives.

Carbon neutrality endeavors: the impact of carbon emission trading policies on total factor carbon emission efficiency

Deepening the carbon market allocation reform is a strong support for carbon peaking and carbon neutrality. China has been piloting carbon trading in batches since 2013. However, there is little literature on whether and how carbon emission trading (CET) improves total factor carbon emission efficiency (TFCEE). To fill this gap, this study first calculates urban TFCEE using an extended Epsilon-based model and then designs a quasi-natural experiment based on causal inference. The staggered DID model is adopted to investigate the impact of CET pilot policies on urban TFCEE and its impact mechanism. Our results show that (1) the CET policy has a significant promoting effect on TFCEE in pilot cities, and this conclusion is reconfirmed by adopting robustness tests. (2) The mediating effect models are employed to confirm the mediating role of green technology innovation, industrial structure upgrading, and resource allocation efficiency in CET policy promoting regional TFCEE. (3) The heterogeneity test of CET policy depicts a considerable positive impact on TFCEE in cities with abundant resources. Eastern and developed cities are more likely to experience better carbon efficiency promotion through this policy than other cities. This study provides evidence for the important role of CET in promoting the development of a low-carbon economy and carbon neutrality.