Wang Yiping, Wu Qunqi, Song Jingni
College of Transportation Engineering, Chang'an University, Middle-Section of Nan'er Huan Road, Xi'an, 710064, China.
School of Economics and Management, Chang'an University, Middle-Section of Nan'er Huan Road, Xi'an, 710064, China.
Environ Sci Pollut Res Int. 2023 Apr;30(18):52266-52287. doi: 10.1007/s11356-023-25963-0. Epub 2023 Feb 24.
This study explores the spatial and temporal evolution characteristics of transportation carbon emissions from multiple scales. Based on the integrated DMSP/OLS-NPP/VIIRS nighttime light data, a transportation carbon emission estimation model was constructed, and the spatial and temporal evolution characteristics of transportation carbon emissions in 30 provinces and some counties in China from 2000 to 2019 were analyzed. The main findings are as follows: (1) The goodness-of-fit of the estimation model improved from 51.2 to 87.15% by introducing the GDP variables. (2) At the provincial scale, the provinces with high carbon emissions from transportation were mainly distributed in the eastern region, with the highest value increasing from 19,171.6 million tons in 2000 to 71,545.98 million tons in 2019. The spatial distribution has a significant and positive spatial spillover effect, and the H-H aggregation was mainly distributed in the east-central region, showing a trend of expansion from the coast to the inland. Trend analysis showed that Shandong, Guangdong, Shanghai, and Jiangsu were areas with a rapid growth of high carbon emissions. (3) The county scale displayed a northeast-southwest evolutionary pattern, with the center of gravity in Henan. The spatial distribution showed a significant spatial agglomeration phenomenon. Trend analysis indicated that the transportation carbon emissions in 184 counties need to be controlled urgently, which was the focus of carbon emission reduction. This paper theoretically enriches the measurement method of transportation carbon emissions and overcomes the problem of insufficient spatial information of statistical data. In practice, it provides a scientific basis for accurate emission reduction and low-carbon development of transportation.
本研究从多尺度探究交通碳排放的时空演变特征。基于整合后的DMSP/OLS-NPP/VIIRS夜间灯光数据，构建了交通碳排放估算模型，并分析了2000年至2019年中国30个省份及部分县域交通碳排放的时空演变特征。主要研究结果如下：（1）通过引入GDP变量，估算模型的拟合优度从51.2%提高到了87.15%。（2）在省级尺度上，交通碳排放高值省份主要分布在东部地区，最高值从2000年的19171.6万吨增长到2019年的71545.98万吨。空间分布具有显著的正向空间溢出效应，H-H集聚主要分布在中东部地区，呈现出从沿海向内陆扩展的趋势。趋势分析表明，山东、广东、上海和江苏是高碳排放快速增长的地区。（3）县域尺度呈现出东北-西南演变格局，重心在河南。空间分布呈现出显著的空间集聚现象。趋势分析表明，184个县域的交通碳排放急需控制，这是碳排放减排的重点。本文在理论上丰富了交通碳排放的测算方法，克服了统计数据空间信息不足的问题。在实践中，为交通精准减排和低碳发展提供了科学依据。
