Abstract
Following market liberalization, the vehicle population in China has increased dramatically over the past few decades. This paper examines the causal impact of the opening of a heavily used high speed rail line connecting two megacities in China in 2015, Chengdu and Chongqing, on air pollution. We use high-frequency and high spatial resolution data to track pollution along major highways linking the two cities. Our approach involves the use of a novel augmented regression discontinuity in time approach that incorporates machine learning to inform our specification choice in the first stage. Our estimates show that CO is reduced by 6.4% and PM2.5 by 7.1% along the main affected highway. These findings are supported using a difference-in-differences approach.
| Original language | English |
|---|---|
| Article number | 102884 |
| Number of pages | 19 |
| Journal | Journal of Environmental Economics and Management |
| Volume | 122 |
| DOIs | |
| Publication status | Published - Oct 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 11 Sustainable Cities and Communities
Keywords
- Air pollution
- China
- Green infrastructure
- High-speed railway
- Machine learning
- Regression discontinuity
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver