Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO

doi:10.11930/j.issn.1004-9649.202302055

Abstract

Abstract: 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.

Key words: environmental learning curve, carbon emission reduction potential, particle swarm optimization algorithm, gray wolf algorithm, energy consumption intensity

DONG Fugui, XIA Meijuan, LI Wanying. Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO[J]. Electric Power, 2023, 56(9): 226-234.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202302055

https://www.electricpower.com.cn/EN/Y2023/V56/I9/226

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