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The environmental effect evaluation of the Traffic Power Construction Outline in China based on meteorological normalization and ensemble synthetic control method
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DOI：10.1007/s10668-026-08044-3.png)
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Abstract
En 中文
Mobile source pollution is a major contributor to air pollution, so it is significant to evaluate the environmental effect of transportation policies for facilitating their optimization. The concentration of air pollutants is influenced not only by emissions but also by meteorological factors, so raw air pollution monitoring data cannot be directly used to evaluate the environmental effects of policies. To further explore the true effect of transportation policies on pollutant emission reduction, this study develops a WN-Ensemble-SCM-DID model, which integrates machine learning algorithms and intelligent optimization algorithms with a synthetic control method. This integrated approach is used to analyze the meteorologically normalized pollutant concentration data and evaluate the actual impact of traffic policies on the emission levels in the target city. In this study, the Traffic Power Construction Outline is used as a case to test the feasibility and effectiveness of the proposed model. The results show a significant downward trend in PM2.5 and PM10 levels in both Shenzhen and Chongqing, as well as in NO2 levels in Chongqing. WN-Ensemble-SCM-DID serves as a reliable machine learning-based evaluation tool for assessing the environmental effects of policies. Moreover, the results support the conclusion that the Traffic Power Construction Outline has a positive impact on reducing fine particulate matter emissions within two years of implementation, providing a valuable reference for policy promotion and future related decisions. This study provides a machine learning-based causal model for policy effect evaluation. WN-Ensemble-SCM-DID can be effectively evaluate the environmental effects of transportation policy. ‘Traffic Power Construction Outline’ reduced the emission of PM2.5 and PM10.
Keywords:
Air pollution
Machine learning
Meteorological normalization
Policy evaluation
Synthetic control method
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