Spatial distribution and driving factors of carbon emission in a furnace city using Luojia1–01 nighttime data and optimal parameters-based geodetector

Rapid urbanization has increased carbon dioxide (CO 2 ) emissions, exacerbating ecological issues and prompting global shift towards low-carbon development. However, current studies at the county-level face challenges such as incomplete monitoring systems and insufficient statistical granularity, which restrict the detailed analysis of carbon emission spatial distribution and driving mechanisms. To address this, the study utilized high-resolution Luojia1–01 nighttime light (NTL) data combined with the optimal parameters-based geographical detector (OPGD) model, taking Fuzhou, a typical “furnace city” as a case study to reveal the spatial differentiation characteristics and driving mechanisms of carbon emissions at the county-level. The results indicate that carbon emissions in Fuzhou exhibit a “core-edge” spatial differentiation pattern, with the central urban areas having higher emissions than the surrounding counties, and a positive spatial correlation was observed; the proportion of the tertiary production (PTP), the proportion of the primary production (PTP), the urbanization rate (UR), and the level of social capital (SC) are core driving factors of carbon emissions, with dual-factor interactions exhibiting significant bilinear enhancement effects. Based on the carbon emission differentiation characteristics, the study proposes a “five-zone differentiated” governance strategy, which includes low-carbon transformation of the service industry in the core urban areas, green industrial upgrading in high-emission zones, and strengthening the carbon sink function in ecological protection areas. This study provides methodological support and decision-making guidance for refined carbon emission management and low-carbon development planning at the county-level. • Luojia1–01 NTL data were used to estimate carbon emissions at county scale. • High-high and low-low zones of carbon emissions were revealed by spatial clustering. • OPGD was employed to explore the driving factors of carbon emission. • PTP, PFP, UR and SC were driving factors of carbon emissions. • Policy recommendations for low-carbon development and urban sustainability were proposed.