Multi-source data assessment and multi-factor analysis of urban carbon emissions: A case study of the Pearl River Basin, China

• Published in: Urban climate 2023-09, Vol.51, p.101653, Article 101653
• Main Authors: Zhang, Bin, Yin, Jian, Jiang, Hongtao, Chen, Shihui, Ding, Yi, Xia, Ruici, Wei, Danqi, Luo, Xinyuan
• Format: Article
• Language: English
• Subjects: Carbon dioxide emissions; Geographical detector; Influencing factor; LMDI decomposition approach; Multisource data
• ISSN: 2212-0955; 2212-0955
• DOI: 10.1016/j.uclim.2023.101653

The Pearl River Basin (PRB) is of strategic importance to China's economic development and ecological sustainability, while carbon emissions (CES) are currently exerting immense pressure on the region. This study attempts to study the spatial-temporal evolution and driving factors of CES to achieve regional joint emission reduction. Through the integration of multi-source data, this study has developed a coupled model for calculating CES from both carbon sources and sinks. The space-time patterns and clustering characteristics of CES in the PRB were analyzed at multiple scales. And the main driving factors of CES were revealed from two scales of urban and basin, respectively. The results show that CES exhibited an increasing trend, with cities with high CES mainly located in the downstream of the PRB. The CES between neighboring cities exhibited strong spatial dynamics and dependence, resulting in a collaborative development trend of CES with adjacent cities. The social consumption level, land use degree, and fiscal expenditure were significant drivers of CES. Additionally, the interaction impact between influencing factors on CES exerted more significantly than any single factor. This study improved the methods of calculating CES with multi-source data and provided support for different-type cities to formulate targeted CES reduction policies. [Display omitted] •A coupled model for calculating carbon emissions was developed based on multisource data.•The static and dynamic characteristics of carbon emissions were analyzed at multiple scales.•The drivers of carbon emissions were explored by integrating LMDI decomposition and geographical detector.•A strong positive synergistic evolution of urban carbon emissions was observed.•The interaction between influencing factors had a more significant influence on carbon emissions.