Regional carbon emission accounting and spatiotemporal characteristics analysis in small and medium scale - A case...

Accurate assessment and monitoring of carbon emissions (CE) are crucial for achieving emission reduction targets and sustainable development. Among the various approaches for CE accounting, the emissions factor method has the widest scope of application. However, the lack of emission factors that can fully reflect regional characteristics limits the accuracy and effectiveness of CE accounting to a certain extent. On the other hand, the existing studies on the structural characteristics and driving factors of the CE spatial correlation network (CESCN) at the district and county scales are not yet sufficient. Therefore, this study innovatively proposes a localized emission factor (LEF) that considers the local energy consumption situation and constructs a monthly grid-scale CE inversion model by combining the accounting results with the night-light image. Taking Jiangsu Province as an example, we obtain the monthly emissions at the provincial, municipal, and county levels from 2013 to 2022, and analyze their spatial and temporal evolution characteristics. Finally, we construct CESCN at the county level based on the modified gravity model and social network analysis (SNA), analyze its structural characteristics, and discuss the core driving factors of county-level CE with the help of the QAP model. The results show that the differences in geographical adjacency, economic development level, and urbanization level positively promote the formation of spatial CESCN.