Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China

Tao Wang, Kai Zhang, Keliang Liu, Keke Ding and Wenwen Qin ()
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Tao Wang: Chongqing Transport Planning and Research Institute, Chongqing 401120, China
Kai Zhang: Shanxi Environmental Protection Institute of Transport, Taiyuan 030000, China
Keliang Liu: School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400000, China
Keke Ding: School of Economics and Business Administration, Chongqing University of Education, Chongqing 400000, China
Wenwen Qin: Faculty of Traffic Engineering, Kunming University of Science and Technology, Kunming 650504, China

IJERPH, 2023, vol. 20, issue 3, 1-17

Abstract: In order to scientifically evaluate the characteristics and impact outcomes of transportation carbon emissions, this paper uses the panel statistics of 286 cities to measure transportation carbon emissions and analyze their spatial correlation characteristics. Afterwards, primarily based on the current research, a system of indicators for the impact factors of transportation carbon emissions was established. After that, ordinary least squares regression, geographically weighted regression, and multiscale geographically weighted regression models were used to evaluate and analyze the data, and the outcomes of the multiscale geographically weighted regression model were selected to analyze the spatial heterogeneity of the elements influencing transportation carbon emissions. The effects exhibit that: (1) The spatial characteristics of China’s transportation carbon emissions demonstrate that emissions are high in the east, low in the west, high in the north, and low in the south, with high-value areas concentrated in the central cities of Beijing-Tianjin-Hebei, the Yangtze River Delta, the Guangdong-Hong Kong-Macao region, and the Chengdu-Chongqing regions, and the low values concentrated in the Western Sichuan region, Yunnan, Guizhou, Qinghai, and Gansu. (2) The spatial heterogeneity of transportation carbon emissions is on the rise, but the patten of local agglomeration is obvious, showing a clear high-high clustering, and the spatial distribution of high-high agglomeration and low-low agglomeration is positively correlated, with high-high agglomeration concentrated in the eastern region and low-low agglomeration concentrated in the western region. (3) The effects of three variables—namely, GDP per capita, vehicle ownership, and road mileage—have a predominantly positive effect on transportation carbon emissions within the study area, while another three variables—namely, constant term, population density, and number of people employed in transportation industry—have different mechanisms of influence in different regions. Constant term, vehicle ownership, and road mileage have greater impacts on transportation carbon emissions.

Keywords: transportation carbon emission; spatial autocorrelation; spatial heterogeneity; multiscale geographically weighted regression; China (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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