ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Economics and Management
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1603809
Key Factors Influencing CO₂ Emissions in the Southern Power Grid of China
Provisionally accepted
- Southern Power Grid Artificial Intelligence Technology Co., LTD, Guangdong, China
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The power sector is the largest contributor to global carbon emissions, particularly in China, where its decarbonization is critical for achieving a high-efficiency transition towards carbon neutrality. Understanding the driving factors behind carbon emissions in this sector is essential for designing and implementing effective policies. This study focuses on the Southern Power Grid of China, employing decomposition techniques to identify and analyze the driving factors of carbon emissions from 2013 to 2021. The findings reveal that GDP per capita is a significant driver of electricity-related carbon emissions across provinces. Guangdong, Guangxi, and Hainan have made notable progress in reducing per capita emissions in electricity supply, while Guizhou and Yunnan have achieved reductions through distinct drivers. Categorization based on electricity generation highlights substantial provincial disparities, which are closely linked to economic output. Furthermore, analysis of contribution rates underscores variations in progress toward carbon peak targets and differences in electricity supply methods among provinces. This study provides critical insights into the driving factors of carbon emissions in the power sector, particularly within the Southern Power Grid. It emphasizes the need for tailored policies to address provincial disparities and accelerate the transition to carbon neutrality.
Keywords: carbon emissions, Power sector, Decomposition analysis, Driving factors, carbon neutrality
Received: 01 Apr 2025; Accepted: 25 Jul 2025.
Copyright: © 2025 Huang, Wang, Zhang, Tan and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yanlu Huang, Southern Power Grid Artificial Intelligence Technology Co., LTD, Guangdong, China
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