Chongqing Transport Planning and Research Institute, Chongqing 401120, China.
Shanxi Environmental Protection Institute of Transport, Taiyuan 030000, China.
Int J Environ Res Public Health. 2023 Jan 28;20(3):2307. doi: 10.3390/ijerph20032307.
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.
为科学评价交通运输碳排放特征及影响效应，利用 286 个城市面板数据测度交通运输碳排放并分析其空间关联特征，在此基础上，结合已有研究构建交通运输碳排放影响因素指标体系，运用普通最小二乘法、地理加权回归和多尺度地理加权回归模型进行评价分析，选取多尺度地理加权回归模型结果分析交通运输碳排放影响因素的空间异质性，结果表明：（1）中国交通运输碳排放的空间特征表现为东高西低、北高南低，高值区集中于京津冀、长三角、粤港澳和成渝等中心城市，低值区集中于川西、云南、贵州、青海和甘肃等地区。（2）交通运输碳排放的空间异质性在增强，但局部集聚特征明显，呈现出明显的高高集聚，且高-高集聚与低-低集聚空间分布呈正相关，高-高集聚主要集中在东部地区，低-低集聚主要集中在西部地区。（3）人均 GDP、机动车拥有量和道路里程 3 个变量对研究区内交通运输碳排放的影响效应均表现为正向作用，常数项、人口密度和交通运输业从业人数 3 个变量在不同区域具有不同的影响机制，常数项、机动车拥有量和道路里程对交通运输碳排放的影响效应较大。
