Energy Economics最新文献

Cold shocks in a warming world: Regional disparities in household climate adaptation and electricity demand 全球变暖中的冷冲击：家庭气候适应和电力需求的区域差异

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109656

Zihan He,Jiayang Shi,Qi Wang,Bing Zhang

引用次数: 0

Electrifying the factory and beyond: The impact of digital proximity on energy transition 电气化工厂及其他：数字化对能源转型的影响

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109651

Yijia Song,Qinyi Huang,Jiaqi Chen,Xitao Li

引用次数: 0

Mind the gap: Emissions adjustment dynamics and firm performance 注意差距：排放调整动态和企业绩效

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109650

Shilpa Garg,Anil Kumar Sharma

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Critical peak pricing or rebate? An impact analysis in a cold climate market 关键峰值定价还是返利？寒冷气候下的市场影响分析

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109644

Debbie J. Dupuis,Pierre-Olivier Pineau,Luca Trapin

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Patent agglomeration and environmental efficiency: effects and mechanisms in Africa 专利集聚与环境效率：非洲的效应与机制

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109647

Rockson Sai,Hongping Yuan,Ebenezer Kwabena Takyi,Hermas Abudu,Stephen Duah Agyeman

引用次数: 0

The role of memory structure in tail risk forecasting for WTI crude oil futures 记忆结构在WTI原油期货尾部风险预测中的作用

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109652

Xinyu Cao,Yan Li,Zongxin Qian

引用次数: 0

Risk premia in the UK electricity market under policy uncertainty and market fragmentation 政策不确定性和市场分散下的英国电力市场风险溢价

IF 12.8 2区经济学

Energy Economics Pub Date : 2026-10-01 DOI: 10.1016/j.eneco.2026.109649

Jalal Siddiki,Prakash Singh

引用次数: 0

Attaining carbon neutrality goals in the energy sector: A MCDM study towards sustainable energy development 实现能源部门的碳中和目标：可持续能源发展的MCDM研究

IF 14.2 2区经济学

Energy Economics Pub Date : 2026-06-01 Epub Date: 2026-05-22 DOI: 10.1016/j.eneco.2026.109393

Anchal Gupta , Ashish Dwivedi , Sachin Kumar Mangla , Harreet Kaur , Manjari Garg , Sarah Ben Amor

{"title":"Attaining carbon neutrality goals in the energy sector: A MCDM study towards sustainable energy development","authors":"Anchal Gupta , Ashish Dwivedi , Sachin Kumar Mangla , Harreet Kaur , Manjari Garg , Sarah Ben Amor","doi":"10.1016/j.eneco.2026.109393","DOIUrl":"10.1016/j.eneco.2026.109393","url":null,"abstract":"<div><div>The energy sector is considered to be one of the major producers of carbon emissions. The carbon emissions represent a significant contributor to global environmental degradation. Thus, there is a need to reduce carbon emissions and promote long-term sustainable development. This study provides insights into strategic policy approaches that target carbon footprint reduction and thereby enhance energy sustainability. This study develops a decision-making framework for assessing carbon emissions and developing carbon reduction programs. Initially, the study addresses significant concerns in the energy sector using the Situation-Actor-Process-Learnings-Actions-Performance (SAP-LAP) approach. After variable identification from SAP-LAP analysis, an Interpretive Ranking Process (IRP) is applied to establish interrelationships between actions and performance measures, and finally, actions are ranked. The results from the study underscore the necessity of proactive efforts such as carbon pricing mechanisms, subsidised and incentive energy utilization plans, and personnel and sustainable infrastructure requirements for enhancing energy resource efficiency to promote sustainability. The results highlight the necessity for strategic efforts and policies to reduce carbon emissions and support the goal of sustainable energy development. The findings from the IRP approach conclude that major initiatives, such as Government approvals, strategic planning, and smart grid integration, are the most effective steps in reaching carbon neutrality. Such findings present actionable recommendations for policymakers and business leaders to help make investment decisions and policy interventions to speed up the shift to clean energy. This study offers a novel contribution by identifying practical policy aligned actions and prioritizing them to strengthen overall performance while reducing emissions in the energy sectors of developing economies.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"159 ","pages":"Article 109393"},"PeriodicalIF":14.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148168579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Environmental dividends of population migration: The collaborative reduction of pollutant and carbon emissions 人口迁移的环境红利：污染物和碳排放的协同减少

IF 14.2 2区经济学

Energy Economics Pub Date : 2026-06-01 Epub Date: 2026-05-20 DOI: 10.1016/j.eneco.2026.109409

Yan Bu, Bole Zhang, Ziteng Wang

{"title":"Environmental dividends of population migration: The collaborative reduction of pollutant and carbon emissions","authors":"Yan Bu, Bole Zhang, Ziteng Wang","doi":"10.1016/j.eneco.2026.109409","DOIUrl":"10.1016/j.eneco.2026.109409","url":null,"abstract":"<div><div>Large-scale interregional population migration (PM) in China exerts a profound impact on the environment, and how PM affects the Collaborative Reduction of Pollutant and Carbon Emissions (CRPC) has become a critical issue in environmental economics. Using panel data from 278 Chinese cities (2006–2021) and quantifying CRPC through a marginal abatement cost approach, this study shows that PM significantly promotes CRPC. The effect is more pronounced in cities governed by local-origin officials, those included in key environmental protection programs, and those with a healthier government-business relationship. From a full production-process perspective, PM fosters CRPC improvement through three interconnected pathways: enhancing green innovation capacity (source prevention), optimizing labor allocation (process optimization), and facilitating industrial agglomeration (end-of-pipe governance). Talent introduction policies further reinforce these mechanisms. Further analysis reveals that relaxing hukou restrictions amplifies the environmental dividend of migration, and that this migration-induced dividend materializes only when local R&D intensity exceeds a threshold of 1.92%.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"159 ","pages":"Article 109409"},"PeriodicalIF":14.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148168576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence-powered hybrid probability density prediction for ultra-short-term offshore wind power: A model integrating TVFEMD, R2CMSE, and MCQRNNG 人工智能驱动的超短期海上风电混合概率密度预测：一个集成TVFEMD、R2CMSE和MCQRNNG的模型

IF 14.2 2区经济学

Energy Economics Pub Date : 2026-06-01 Epub Date: 2026-05-25 DOI: 10.1016/j.eneco.2026.109413

Haiying Liu , Ming Yang

{"title":"Artificial intelligence-powered hybrid probability density prediction for ultra-short-term offshore wind power: A model integrating TVFEMD, R2CMSE, and MCQRNNG","authors":"Haiying Liu , Ming Yang","doi":"10.1016/j.eneco.2026.109413","DOIUrl":"10.1016/j.eneco.2026.109413","url":null,"abstract":"<div><div>The volatility of offshore wind power poses challenges for grid stability, necessitating reliable uncertainty quantification beyond deterministic forecasts. We propose a TRMG model that integrates time-varying filter empirical mode decomposition (TVFEMD), rapid refined composite multi-timescale entropy (R2CMSE), and an AI-based monotonic composite quantile regression neural network (QRNN) with Gaussian kernel estimation (MCQRNNG) for probabilistic prediction. Experiments conducted on TenneT offshore wind data demonstrated the TRMG’s strategic emphasis on uncertainty metrics over point accuracy. Compared to the EMD-based hybrid ERMG, TRMG reduces interval width by 53.846% (prediction interval normalized averaged width (PINAW): 0.036 vs. 0.078) and probabilistic error by 29.062% (pinball loss: 29.745 vs. 41.931 MW). Compared with the sample-entropy-based TSMG model, TRMG achieved superior coverage (prediction interval coverage probability: 0.841 vs. 0.403), 25.000% narrower intervals (PINAW: 0.036 vs. 0.048), and 70.555% lower distributional errors (pinball loss: 29.745 vs. 101.018 MW). Notably, when compared with the unconstrained quantile model TRQG with identical inputs, TRMG delivered 82.439% tighter intervals (PINAW: 0.036 vs. 0.205) through the monotonicity constraints of the MCQRNN, eliminated quantile crossing (0.000 vs. 0.510 MW for the two-layer QRNN), and provided the narrowest prediction bounds in the industry. The TRMG equips grid operators with decision-ready uncertainty estimates, which are essential for renewable integration and energy-security planning.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"159 ","pages":"Article 109413"},"PeriodicalIF":14.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148168577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0