Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO
Accurate estimation of provincial energy saving and carbon reduction potential is the basis for policy formulation and adjustment, but the current methods for estimating provincial carbon reduction potential still has limitations, which make it difficult to guide practice. Therefore, a new method wa...
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| Format: | Article |
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State Grid Energy Research Institute
2023-09-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302055 |
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| author | Fugui DONG Meijuan XIA Wanying LI |
| author_facet | Fugui DONG Meijuan XIA Wanying LI |
| author_sort | Fugui DONG |
| collection | DOAJ |
| description | Accurate estimation of provincial energy saving and carbon reduction potential is the basis for policy formulation and adjustment, but the current methods for estimating provincial carbon reduction potential still has limitations, which make it difficult to guide practice. Therefore, a new method was proposed by combining subjective and objective approaches. An energy intensity learning curve was constructed, which contains such three factors as economy, technology input and scale effect, and the grey wolf algorithm was used to improve the particle swarm optimization algorithm to optimize the fitting curves. An accounting framework for emission reduction potential was constructed with full consideration of carbon sink technologies. Taking the Province S as an example, 12 combination scenarios were set for the empirical study. The results show that optimizing the industrial structure and adjusting the energy mix are the main means for reducing carbon emissions and ensuring the realization of the ‘peak carbon’ target; zero-carbon and carbon-negative technologies can make a relatively small contribution to emissions reduction at this stage, but can facilitate the process of reaching the peak carbon target. |
| format | Article |
| id | doaj-art-71a90a42523943fca03ad027f5520b6e |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-71a90a42523943fca03ad027f5520b6e2025-08-20T02:56:52ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-09-0156922623410.11930/j.issn.1004-9649.202302055zgdl-56-09-dongfuguiPrediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWOFugui DONG0Meijuan XIA1Wanying LI2School of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaSchool of Economics and Management, North China Electric Power University, Beijing 102206, ChinaAccurate estimation of provincial energy saving and carbon reduction potential is the basis for policy formulation and adjustment, but the current methods for estimating provincial carbon reduction potential still has limitations, which make it difficult to guide practice. Therefore, a new method was proposed by combining subjective and objective approaches. An energy intensity learning curve was constructed, which contains such three factors as economy, technology input and scale effect, and the grey wolf algorithm was used to improve the particle swarm optimization algorithm to optimize the fitting curves. An accounting framework for emission reduction potential was constructed with full consideration of carbon sink technologies. Taking the Province S as an example, 12 combination scenarios were set for the empirical study. The results show that optimizing the industrial structure and adjusting the energy mix are the main means for reducing carbon emissions and ensuring the realization of the ‘peak carbon’ target; zero-carbon and carbon-negative technologies can make a relatively small contribution to emissions reduction at this stage, but can facilitate the process of reaching the peak carbon target.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302055environmental learning curvecarbon emission reduction potentialparticle swarm optimization algorithmgray wolf algorithmenergy consumption intensity |
| spellingShingle | Fugui DONG Meijuan XIA Wanying LI Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO Zhongguo dianli environmental learning curve carbon emission reduction potential particle swarm optimization algorithm gray wolf algorithm energy consumption intensity |
| title | Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO |
| title_full | Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO |
| title_fullStr | Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO |
| title_full_unstemmed | Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO |
| title_short | Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO |
| title_sort | prediction of provincial energy consumption intensity and estimation of carbon emission reduction potential based on pso gwo |
| topic | environmental learning curve carbon emission reduction potential particle swarm optimization algorithm gray wolf algorithm energy consumption intensity |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302055 |
| work_keys_str_mv | AT fuguidong predictionofprovincialenergyconsumptionintensityandestimationofcarbonemissionreductionpotentialbasedonpsogwo AT meijuanxia predictionofprovincialenergyconsumptionintensityandestimationofcarbonemissionreductionpotentialbasedonpsogwo AT wanyingli predictionofprovincialenergyconsumptionintensityandestimationofcarbonemissionreductionpotentialbasedonpsogwo |