The low-carbon transformation of the Yangtze River Delta urban agglomeration plays an important leading role in promoting high-quality development in other regions.This paper built a CO2 emission inversion model based on nighttime lighting data,and used trend analysis,spatial autocorrelation analysis,and geographic weighted regression models to explore the temporal and spatial changes and the influencing factors of CO2 emissions in counties of the Yangtze River Delta urban agglomeration from 2000 to 2019.The results showed that the counties with high CO2 emissions were located in the central regions of major cities such as Shanghai and Suzhou,while the counties with low CO2 emissions were mainly located in the southwest region.However,these counties showed a rapid upward trend since 2011.The spatial distribution of CO2 emissions at the county level was dominated by high-high concentration and low-low concentration,with low-low concentration areas were in a trend of a continuous decreasing.The impacts of total population,per capita GDP,and total foreign capital use on CO2 emissions was positive.The total population had the greatest impact on CO2 emissions.The results of this paper provided scientific support for the precise implementation of emission reduction measures and higher quality integrated development of the Yangtze River Delta urban agglomeration.
关键词
夜间灯光/CO2排放/空间自相关/地理加权回归
Key words
night light/CO2 emissions/spatial autocorrelation/geographically weighted regression
万方数据