基于PSO-GWO的省级能耗强度预测与碳减排潜力估算  

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

作　　者：董福贵[1] 夏美娟 李婉莹 DONG Fugui;XIA Meijuan;LI Wanying(School of Economics and Management,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学经济与管理学院,北京102206

出　　处：《中国电力》2023年第9期226-234,共9页Electric Power

基　　金：国家重点研发计划资助项目(2020YFB1707802);北京市社会科学基金资助项目(21JJB012)。

摘　　要：准确预估省域节能降碳潜力是政策制定与调整的基础。针对目前省级降碳潜力估算方法仍存在难以指导实践的局限性问题,提出一种主客观相结合的新方法,构建包含经济、技术投入、规模效应3个因素的能耗强度学习曲线,并以灰狼算法改进粒子群优化算法优化拟合曲线;充分考虑碳汇技术,构建减排潜力核算框架;以S省为例,设定12种组合情景进行实证研究。结果表明,优化产业结构与调整能源结构是目前降低碳排放量、确保实现“碳达峰”目标的主要手段;零碳、负碳技术现阶段能够为减排做出较少贡献,但可促进碳达峰进程。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.

关 键 词：环境学习曲线 碳减排潜力 粒子群优化算法 灰狼算法 能耗强度 

分 类 号：X321[环境科学与工程—环境工程] TP18[自动化与计算机技术—控制理论与控制工程]