Can digital trade promote the low-carbon development of China's transport sector

被引:6
作者
Wen, Huwei [1 ]
Zhu, Keying [1 ]
机构
[1] Nanchang Univ, Sch Econ & Management, Nanchang 330031, Jiangxi, Peoples R China
关键词
Carbon emissions; Transport sector; Digital trade; STIRPAT model; Mediating effect model; Threshold effect model; ECONOMIC-GROWTH; CO2; EMISSIONS; POLLUTION; URBANIZATION; POLICY;
D O I
10.1007/s10098-024-02781-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The transport sector is a key contributor to the growth of carbon emissions in developing countries, and digital trade could potentially improve efficiency in the transport sector. In this study, the entropy weight TOPSIS model is used to calculate the digital trade index. Based on China's provincial panel data from 2010 to 2021, this study constructs the extended STIRPAT model, a mediating effect model, and panel threshold model to investigate the mechanism and influence of the digital trade on carbon emission reduction of the transportation sector. Empirical results indicate that digital trade significantly reduces carbon emissions in the transport sector. The mediating effect of industrial scale significantly promotes carbon emissions, while the mediating effect of technological innovation and industrial structure upgrading significantly suppresses carbon emissions. Compared to the central and low-income regions, the emission reduction effect of digital trade is greater in the eastern and high-income regions. Digital trade has a threshold effect on carbon emissions in the transportation sector. Specifically, when the clean energy structure and transportation structure exceed a certain level, the carbon reduction effect of digital trade in the transportation industry is greater. Our findings could help shape policies to leverage the role of digital trade in the pursuit of carbon neutrality in the transport sector.
引用
收藏
页码:3089 / 3105
页数:17
相关论文
共 57 条
[1]   Linking Information Communication Technology, trade globalization index, and CO2emissions: evidence from advanced panel techniques [J].
Ahmed, Zahoor ;
Le, Hoang Phong .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (07) :8770-8781
[2]   THE MODERATOR MEDIATOR VARIABLE DISTINCTION IN SOCIAL PSYCHOLOGICAL-RESEARCH - CONCEPTUAL, STRATEGIC, AND STATISTICAL CONSIDERATIONS [J].
BARON, RM ;
KENNY, DA .
JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY, 1986, 51 (06) :1173-1182
[3]   Dynamic and Static Analysis of Carbon Emission Efficiency in China's Transportation Sector [J].
Chen, Benchang ;
Ji, Xiangfeng ;
Ji, Xiangyan .
SUSTAINABILITY, 2023, 15 (02)
[4]   ECONOMIC GROWTH, AIR POLLUTION, AND GOVERNMENT ENVIRONMENTAL REGULATION: EVIDENCE FROM 287 PREFECTURE-LEVEL CITIES IN CHINA [J].
Chen, Jiandong ;
Qi, Jie ;
Ga, Ming ;
Li, Yijing ;
Song, Malin .
TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY, 2021, 27 (05) :1119-1141
[5]   The nonlinear effect of land freight structure on carbon emission intensity: new evidence from road and rail freight in China [J].
Chen, Rujia ;
Wang, Xiaoning ;
Zhang, Yaping ;
Luo, Qian .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (52) :78666-78682
[6]   Development and Influencing Factors of International Trade in Digitally Deliverable Services [J].
Di, Yuna ;
Zhi, Ruixin ;
Song, Huaixi ;
Zhang, Lu .
FRONTIERS IN PSYCHOLOGY, 2022, 13
[7]   ECONOMIC-GROWTH AND THE ENVIRONMENT [J].
GROSSMAN, GM ;
KRUEGER, AB .
QUARTERLY JOURNAL OF ECONOMICS, 1995, 110 (02) :353-377
[8]   Assessment to China's Recent Emission Pattern Shifts [J].
Guan, Yuru ;
Shan, Yuli ;
Huang, Qi ;
Chen, Huilin ;
Wang, Dan ;
Hubacek, Klaus .
EARTHS FUTURE, 2021, 9 (11)
[9]   Threshold effects in non-dynamic panels: Estimation, testing, and inference [J].
Hansen, BE .
JOURNAL OF ECONOMETRICS, 1999, 93 (02) :345-368
[10]   Improving the consumer welfare of rural residents through public support policies: A study on old revolutionary areas in China [J].
He, Congxian ;
Zhou, Can ;
Wen, Huwei .
SOCIO-ECONOMIC PLANNING SCIENCES, 2024, 91