Decomposition of driving factors and peak prediction of carbon emissions in key cities in China

Abstract Urban areas are pivotal contributors to carbon emissions, and achieving carbon peaking at the urban level is crucial for meeting national carbon reduction targets. This study estimates the carbon emissions and intensity changes of 19 cities from 2000 to 2023 using urban statistical data. By...

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Bibliographic Details
Main Authors: Yuxin Zhang, Yao Zhang, Wei Chen, Yongjian Zhang, Jing Quan
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Carbon Balance and Management
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Online Access:https://doi.org/10.1186/s13021-025-00310-7
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Summary:Abstract Urban areas are pivotal contributors to carbon emissions, and achieving carbon peaking at the urban level is crucial for meeting national carbon reduction targets. This study estimates the carbon emissions and intensity changes of 19 cities from 2000 to 2023 using urban statistical data. By employing the logarithmic mean Divisia index (LMDI) method, the driving factors of carbon emissions across these cities are analyzed. Additionally, a multi-scenario prediction approach is utilized to forecast the timing of carbon peaking and trends in carbon emission intensity under various scenarios. The findings reveal that, during the study period, carbon emissions exhibited an overall upward trend, while carbon emission intensity demonstrated a year-by-year decline. The population effect and per capita GDP effect were identified as significant drivers of urban carbon emissions during urban development. Conversely, reducing energy intensity and the carbon intensity of energy consumption can effectively curb the growth of carbon emissions. Under the low-carbon scenario, all cities are projected to achieve carbon peaking before 2030. In the baseline scenario, the vast majority of cities (89.47%) are expected to reach carbon peaking before 2030. However, under the high-carbon scenario, only 63.16% of cities are anticipated to achieve carbon peaking by the same deadline.
ISSN:1750-0680