AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China
As the largest carbon emitter, China faces an increasingly critical trade-off between the economy and the environment. Despite its recent increasing adoption of renewable energy, China continues to generate excessive emissions, particularly from its dominant thermal power sector. Against this backgr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy Conversion and Management: X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174525000169 |
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| author | Chenhao Huang Zhongyang Lin Jian Wu Penghan Li Chaofeng Zhang Yanzhao Liu Weirong Chen Xin Xu Jinsong Deng |
| author_facet | Chenhao Huang Zhongyang Lin Jian Wu Penghan Li Chaofeng Zhang Yanzhao Liu Weirong Chen Xin Xu Jinsong Deng |
| author_sort | Chenhao Huang |
| collection | DOAJ |
| description | As the largest carbon emitter, China faces an increasingly critical trade-off between the economy and the environment. Despite its recent increasing adoption of renewable energy, China continues to generate excessive emissions, particularly from its dominant thermal power sector. Against this background, this study selected the East China Region, where energy consumption is permanently highest, to implement an AI-based three-step “Indicator Screening − Scenario Prediction − Policy Optimization” framework. Firstly, a highly explanatory system of carbon emission impact indicators in the thermal power industry was established utilizing an Optimal Parameters-based Geographical Detector. Secondly, multi-scenario predictions of carbon emissions from the thermal power industry were conducted based on robust Random Forest models. Lastly, the tailored energy transition strategies were suggested according to the spatial distributions of carbon peak time nodes under each scenario. The results showed that, compared to the baseline, the carbon peak under the Economic Development Scenario will be delayed by three years, with an additional 92.74 Mt CO2; while under the Environmental Protection and Energy Transition Scenarios, the peak will be advanced by five and three years, with 106.48 and 73.86 Mt CO2 reductions, respectively. Leveraging multi-source data-driven AI models, this study efficiently provided reliable quantitative support for measuring policies with various priorities, emphasizing the necessity of implementing balanced energy transition strategies. Furthermore, through intelligent scenario simulation and optimal decision-making, the proposed replicable and scalable methodological framework facilitates achieving relevant Sustainable Development Goals (e.g., SDG 7, 12, and 13) across different industries and regions. |
| format | Article |
| id | doaj-art-137df31acc2247ddb7aa8b2016078532 |
| institution | Kabale University |
| issn | 2590-1745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj-art-137df31acc2247ddb7aa8b20160785322025-01-22T05:43:58ZengElsevierEnergy Conversion and Management: X2590-17452025-01-0125100884AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of ChinaChenhao Huang0Zhongyang Lin1Jian Wu2Penghan Li3Chaofeng Zhang4Yanzhao Liu5Weirong Chen6Xin Xu7Jinsong Deng8College of Environmental and Resource Sciences, Zhejiang University, No.866 Yuhangtang Road, Hangzhou City, Zhejiang Province 310058, PR ChinaZhejiang Institute of Geosciences, No.498 Tiyuchang Road, Hangzhou City, Zhejiang Province 310007, PR ChinaZhejiang Communications Construction Group, Underground Co., Ltd., No. 2031 Jiangling Road, Hangzhou City, Zhejiang Province 310051, PR ChinaCollege of Environmental and Resource Sciences, Zhejiang University, No.866 Yuhangtang Road, Hangzhou City, Zhejiang Province 310058, PR ChinaZhejiang Communications Construction Group, Underground Co., Ltd., No. 2031 Jiangling Road, Hangzhou City, Zhejiang Province 310051, PR ChinaZhejiang Communications Construction Group, Underground Co., Ltd., No. 2031 Jiangling Road, Hangzhou City, Zhejiang Province 310051, PR ChinaCollege of Environmental and Resource Sciences, Zhejiang University, No.866 Yuhangtang Road, Hangzhou City, Zhejiang Province 310058, PR ChinaCollege of Environmental and Resource Sciences, Zhejiang University, No.866 Yuhangtang Road, Hangzhou City, Zhejiang Province 310058, PR China; Zhejiang Ecological Civilization Academy, Two Hills Creator Town, Building 9, Anji Avenue, Changshuo Street, Anji County, Huzhou City 313300, PR ChinaCollege of Environmental and Resource Sciences, Zhejiang University, No.866 Yuhangtang Road, Hangzhou City, Zhejiang Province 310058, PR China; Zhejiang Ecological Civilization Academy, Two Hills Creator Town, Building 9, Anji Avenue, Changshuo Street, Anji County, Huzhou City 313300, PR China; Corresponding author at: Zhejiang University, Zhejiang Ecological Civilization Academy, PR China.As the largest carbon emitter, China faces an increasingly critical trade-off between the economy and the environment. Despite its recent increasing adoption of renewable energy, China continues to generate excessive emissions, particularly from its dominant thermal power sector. Against this background, this study selected the East China Region, where energy consumption is permanently highest, to implement an AI-based three-step “Indicator Screening − Scenario Prediction − Policy Optimization” framework. Firstly, a highly explanatory system of carbon emission impact indicators in the thermal power industry was established utilizing an Optimal Parameters-based Geographical Detector. Secondly, multi-scenario predictions of carbon emissions from the thermal power industry were conducted based on robust Random Forest models. Lastly, the tailored energy transition strategies were suggested according to the spatial distributions of carbon peak time nodes under each scenario. The results showed that, compared to the baseline, the carbon peak under the Economic Development Scenario will be delayed by three years, with an additional 92.74 Mt CO2; while under the Environmental Protection and Energy Transition Scenarios, the peak will be advanced by five and three years, with 106.48 and 73.86 Mt CO2 reductions, respectively. Leveraging multi-source data-driven AI models, this study efficiently provided reliable quantitative support for measuring policies with various priorities, emphasizing the necessity of implementing balanced energy transition strategies. Furthermore, through intelligent scenario simulation and optimal decision-making, the proposed replicable and scalable methodological framework facilitates achieving relevant Sustainable Development Goals (e.g., SDG 7, 12, and 13) across different industries and regions.http://www.sciencedirect.com/science/article/pii/S2590174525000169Thermal power generationEnergy transitionPeak carbon emissionOptimal Parameters-based Geographical DetectorRandom ForestScenario simulation |
| spellingShingle | Chenhao Huang Zhongyang Lin Jian Wu Penghan Li Chaofeng Zhang Yanzhao Liu Weirong Chen Xin Xu Jinsong Deng AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China Energy Conversion and Management: X Thermal power generation Energy transition Peak carbon emission Optimal Parameters-based Geographical Detector Random Forest Scenario simulation |
| title | AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China |
| title_full | AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China |
| title_fullStr | AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China |
| title_full_unstemmed | AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China |
| title_short | AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China |
| title_sort | ai based carbon peak prediction and energy transition optimization for thermal power industry in energy intensive regions of china |
| topic | Thermal power generation Energy transition Peak carbon emission Optimal Parameters-based Geographical Detector Random Forest Scenario simulation |
| url | http://www.sciencedirect.com/science/article/pii/S2590174525000169 |
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