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This study explores the driving factors of carbon emissions in Wuhan, China, and forecasts future emission trends using the STIRPAT model. Analyzed data from 2007 to 2022 includes population, GDP per capita, energy intensity, electricity consumption, urbanization rate, and electrical machinery industry investment. PCA and regression identify key drivers and simulate future emissions. PCA extracts two components explaining 94.69 and 4.75% of variance. PC1 is primarily influenced by investment and technology, while PC2 reflects a balance between affluence and technological progress. The regression analysis based on PCA showed that PC1 has a negative coefficient (− 0.139), indicating that increased investment lowers PC1 values, while technological advancement raises PC1 values, leading to lower carbon emissions. PC2 has a positive coefficient (0.221), suggesting that higher affluence increases PC2 values, thereby raising carbon emissions. The model exhibits strong explanatory power (R2 = 0.927). Scenario analysis projects future emissions under 32 development pathways for 2030, 2040, and 2050. Low population and GDP growth with high energy intensity reduction and electrical investment best curb emissions. This underscores the importance of energy efficiency and innovation for low-carbon development. Targeted investments and sustainable policies are crucial for carbon reduction.

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Driving Factors and Scenario Analysis of Carbon Emissions in Wuhan City: A STIRPAT Model Approach for Low-Carbon Development

  • 会议论文

  • 发表日期：

    2026年

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  • Chenxi Hu

摘要

This study explores the driving factors of carbon emissions in Wuhan, China, and forecasts future emission trends using the STIRPAT model. Analyzed data from 2007 to 2022 includes population, GDP per capita, energy intensity, electricity consumption, urbanization rate, and electrical machinery industry investment. PCA and regression identify key drivers and simulate future emissions. PCA extracts two components explaining 94.69 and 4.75% of variance. PC1 is primarily influenced by investment and technology, while PC2 reflects a balance between affluence and technological progress. The regression analysis based on PCA showed that PC1 has a negative coefficient (− 0.139), indicating that increased investment lowers PC1 values, while technological advancement raises PC1 values, leading to lower carbon emissions. PC2 has a positive coefficient (0.221), suggesting that higher affluence increases PC2 values, thereby raising carbon emissions. The model exhibits strong explanatory power (R2 = 0.927). Scenario analysis projects future emissions under 32 development pathways for 2030, 2040, and 2050. Low population and GDP growth with high energy intensity reduction and electrical investment best curb emissions. This underscores the importance of energy efficiency and innovation for low-carbon development. Targeted investments and sustainable policies are crucial for carbon reduction.