气候变化领域集成服务门户(CCPortal): Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method

CCPortal

DOI	10.1016/j.jenvman.2024.120922
	Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method
	Ling, Shuai; Jin, Shurui; Wang, Haijie; Zhang, Zhenhua; Feng, Yanchao
发表日期	2024
ISSN	0301-4797
EISSN	1095-8630
起始页码	358
卷号	358
英文摘要	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.
英文关键词	Transportation infrastructure upgrading; Double machine learning; Green development; Synthetic difference in difference; Regional effect
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001233720600001
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300983
作者单位	Tianjin University; Zhengzhou University; Lanzhou University; Lanzhou University
推荐引用方式 GB/T 7714	Ling, Shuai,Jin, Shurui,Wang, Haijie,et al. Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method[J],2024,358.
APA	Ling, Shuai,Jin, Shurui,Wang, Haijie,Zhang, Zhenhua,&Feng, Yanchao.(2024).Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method.JOURNAL OF ENVIRONMENTAL MANAGEMENT,358.
MLA	Ling, Shuai,et al."Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method".JOURNAL OF ENVIRONMENTAL MANAGEMENT 358(2024).