Spatio-temporal pattern evolution of carbon emissions at the city-county-town scale in Fujian Province based on DMSP/OLS and NPP/VIIRS nighttime light data

Timely and accurate spatio-temporal carbon emission evolutions at different scales is essential to formulate strategies to reduce region-specific carbon emissions. However, current research on carbon emissions predominantly focuses on national and provincial levels, with few investigations at the city, county, and town levels. This study addresses this gap by examining the Fujian Province as a case study. This study combined DMSP/OLS and NPP/VIIRS nighttime light data to generate a long-term dataset. Based on this extended nighttime light data time series and statistical energy carbon emissions, we constructed a carbon emission estimation model. Carbon emissions were estimated at the city, county, and town scales in Fujian Province between 2000 and 2020. Presenting the research findings below: (i) The optimal R2 for the fusion of the two nighttime light datasets was 0.8878, and the carbon emission estimation model achieved an R2 of 0.6925. (ii) Fujian Province carbon emissions increased from 47.67 million tons in 2000 to 69.15 million tons in 2020. (iii) Fuzhou and seven coastal counties experienced rapid carbon emission increases, with an additional 13, 33, and 32 counties exhibiting fast, moderate, and slow growth, respectively. (iv) County-town scale carbon emissions exhibited spatial clustering; however, the local correlation decreased at the county level. (v) High-carbon regions were concentrated in coastal areas and large cities, with the city size demonstrating a nonlinear impact on carbon emissions. Our findings reveal the spatio-temporal patterns and regional heterogeneity of carbon emissions in the Fujian Province, offering valuable data to formulate region-specific carbon reduction policies and promote low-carbon economies.