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. 2025 Jul 3;20(1):20.
doi: 10.1186/s13021-025-00310-7.

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

Yuxin Zhang  1 , Yao Zhang  2 , Wei Chen  3 , Yongjian Zhang  2 , Jing Quan  2
Affiliations

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

Yuxin Zhang et al. Carbon Balance Manag. .

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.

Keywords: Carbon emission; Carbon emission intensity; LMDI; Scenario prediction.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Scope of the study area
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Fig. 2
Theoretical analysis framework for carbon emissions
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Fig. 3
Research flow chart
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Fig. 4
Change rate of urban population, per capita GDP, energy intensity, carbon intensity of energy consumption in recent 13 years
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Fig. 5
Long time series changes in urban carbon emissions
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Fig. 6
Changes and decline rates of carbon emission intensity in various cities
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Fig. 7
Decomposition results of carbon emission factors in various cities from 2000 to 2020
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Fig. 8
Average contribution rate of driving factors by city
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Prediction trends of carbon emissions in first tier cities under different scenarios
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The proportion of cities in peak years and different time periods under low-carbon scenarios
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The proportion of cities in peak years and different time periods under baseline scenarios
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The proportion of cities in peak years and different time periods under high-carbon scenarios
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Predicting trends in carbon emission intensity under different scenarios
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Sensitivity heat maps of different cities under different scenarios
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Fig. 15
Predict changes in parameters and average sensitivity
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