A multi-source data-driven innovative evaluation approach for assessing low-carbon city performance

• A Multi-Source Data-Driven (MSDD) LCC evaluation model was developed. • A case study of 38 districts during 2012 to 2022 in Chongqing were conducted. • The proposed model can dynamically and accurately assess LCC performance. • LCC performance in the 38 districts of Chongqing has gradually increased. • The spatial association network in Chongqing increased apparent. Practicing Low-carbon city (LCC) is an important step for achieving urban sustainable development goal, and a thorough and accurate evaluation of LCC performance can provide reference for improvement. As big data technology developed, multi-source data (such as POI and remote sensing data) became widely used in evaluation studies. However, few studies have used multi-data sources to evaluate LCC performance from a holistic perspective. This study innovatively proposed a Multi-Source Data-Driven (MSDD) model for evaluating LCC performance, using data from 38 districts in Chongqing during 2012 to 2022 as a case study . The results show that: 1) The MSDD evaluation model can effectively integrate multi-source data to evaluate LCC performance from county level. 2) LCC performance in the 38 districts of Chongqing has gradually increased, presenting a significant spatial difference. 3) The spatial association network in Chongqing increased apparent, and exchanges in different regions became more frequent. Theoretically, this study broadens the application of multi-source data to the area of LCC, which can draw a holistic picture of LCC performance from the county level. Practically, the findings can provide city managers with the tools to identify LCC issues and develop effective strategies for carbon reduction.