Spatio temporal variation of urban multi-scale carbon emissions and its influencing factors based on night light data:-A case study of Pearl River Delta urban agglomeration

Abstract

Abstract Background: It is of great significance to formulate differentiated carbon emission reduction policies to clarify spatio-temporal characteristics and driving factors of carbon emissions in different cities and cities at different scales. By fitting nighttime light data of long time series from 2000 to 2020, a carbon emission estimation model of Pearl River Delta urban agglomeration, at city, county, and grid unit levels was built to quickly and accurately estimate carbon emission in the Delta cities above county level. Combining spatial statistics, spatial autocorrelation, emerging spatio-temporal hotspot analysis, and Theil index, this study explored the spatio-temporal differentiation of urban carbon emissions in the Delta , and used a geographical detector, to dig influencing factors of the differentiation. Results: The results of the study showed that night light data could replace a statistical yearbook in calculating carbon emissions of cities at or above county level. The calculation error was less than 11% in Pearl River Delta urban agglomeration. The three levels of carbon emissions in the Delta increased in a fluctuating manner, and the spatial distribution difference of carbon emissions at municipal and county levels was small. Therefore, a combination of municipal and county scales can be implemented to achieve precise emission reduction at both macro and micro levels. The central and eastern parts of the agglomeration, including Guangzhou, Shenzhen, Zhongshan, and Huizhou, were a high-value clustering and spatio-temporal hot spots of carbon emissions. Zhaoqing City in the northwestern part of the agglomeration had always been a low-value clustering and spatio-temporal cold spot, because of its population, economy, and geographical location . The carbon emission differences of the Delta cities were mainly caused by carbon emission differences within the cities at municipal level, and the cities faced the challenge of regional differences in the reduction of per capita carbon emissions. As the most influential single factor, spatial interaction between economic development and various factors was the main driving force for the growth of carbon emissions. Conclusions: Our study provide scientific theory and information support for carbon emission estimation and prediction, differentiated emission reduction measures, and carbon neutrality of cities in Pearl River Delta.