Energy Economics最新文献

Corrigendum to “New climate policy, resource abundance, and sectoral FDI” “新的气候政策、资源丰富和行业FDI”的勘误表

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-26 DOI: 10.1016/j.eneco.2025.108602

Nadia Doytch , Mohamed Elheddad , Fidel Perez-Sebastian

引用次数: 0

Smart city policies and corporate renewable energy technology innovation: Insights from patent text and machine learning 智慧城市政策和企业可再生能源技术创新：来自专利文本和机器学习的见解

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-22 DOI: 10.1016/j.eneco.2025.108612

Yang Huang , Ni Xiong , Chengkun Liu

引用次数: 0

Energy outages, in-house power generators, and capacity utilization: The case of African manufacturing 能源中断、内部发电机和产能利用：非洲制造业的案例

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-22 DOI: 10.1016/j.eneco.2025.108614

Dengjun Zhang , Nazif Durmaz , John M. Kagochi

引用次数: 0

Is artificial intelligence an impediment or an impetus to renewable energy investment? Evidence from China 人工智能是可再生能源投资的阻碍还是推动力？来自中国的证据

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-21 DOI: 10.1016/j.eneco.2025.108550

Wen Li , Jing-Ping Li , Yun-Feng Wang , Sebastian-Emanuel Stan

{"title":"Is artificial intelligence an impediment or an impetus to renewable energy investment? Evidence from China","authors":"Wen Li , Jing-Ping Li , Yun-Feng Wang , Sebastian-Emanuel Stan","doi":"10.1016/j.eneco.2025.108550","DOIUrl":"10.1016/j.eneco.2025.108550","url":null,"abstract":"<div><div>This study investigates the bidirectional relationship between artificial intelligence (AI) and renewable energy investment, emphasizing their strategic importance in achieving global low-carbon objectives. Using a high-frequency dataset from 2010 to 2024, which includes monthly observations on the artificial intelligence robotics index (AIW) and the renewable energy index (ENI) in China, this research employs a bootstrap subsample rolling window Granger causality test to examine dynamic causal linkages. The findings reveal that AI accelerates renewable energy investment by enhancing energy forecasting, grid optimization, and intelligent energy management. However, its long-term impact is constrained by high capital costs, resource limitations, and regulatory uncertainty. Moreover, renewable energy development reciprocally promotes AI advancements, particularly in energy storage and autonomous energy systems, although this synergy is vulnerable to policy instability and economic downturns. This study makes significant contributions by providing empirical evidence on the evolving role of AI in renewable energy investments and offering practical policy insights. The results inform policy-makers, investors, and energy firms about optimizing AI applications in renewable energy, improving regulatory frameworks, and fostering economic conditions that accelerate the shift towards a sustainable, carbon-neutral economy. These insights have broad implications for countries aiming to leverage AI-driven solutions for sustainable energy innovation.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"147 ","pages":"Article 108550"},"PeriodicalIF":13.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147913","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

Exploring the role of artificial intelligence as a catalyst for energy technology innovation 探索人工智能作为能源技术创新催化剂的作用

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-21 DOI: 10.1016/j.eneco.2025.108578

Mingxing Shao, Lei Wen, Sifei Li, Binyue Huang

{"title":"Exploring the role of artificial intelligence as a catalyst for energy technology innovation","authors":"Mingxing Shao, Lei Wen, Sifei Li, Binyue Huang","doi":"10.1016/j.eneco.2025.108578","DOIUrl":"10.1016/j.eneco.2025.108578","url":null,"abstract":"<div><div>Artificial intelligence (AI) has a strong spillover effect and acts as an essential propellant for advancements in technology and development. Using A-share listed enterprises between 2007 and 2022 as the sample, we assess the influence of AI on energy technology innovation (ETI). Our findings highlight that AI can promote ETI primarily by improving the human capital structure and encouraging enterprises to increase research and development (R&D) expenditure. This effect is more pronounced in enterprises with low-carbon transition strategy, those located in regions with abundant resource endowments, and those situated in clean energy base areas. Moreover, the study reveals that AI can positively affect ETI and contribute to enhanced enterprise environmental performance. The findings provide more thorough understanding of the critical role of AI in energy and innovation, offering practical recommendations for enterprises to leverage AI in boosting energy efficiency and lowering pollutant emissions, thereby aligning with as well as encouraging the attainment of carbon neutrality and peaking targets set by China.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"147 ","pages":"Article 108578"},"PeriodicalIF":13.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130872","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

Impediment ranking and solutions for enhancing electric freight vehicle uptake in urban logistics 城市物流中提高电动货车使用率的障碍排序及解决方案

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-20 DOI: 10.1016/j.eneco.2025.108604

Aalok Kumar , Pooja Goel , Sunil Tiwari

{"title":"Impediment ranking and solutions for enhancing electric freight vehicle uptake in urban logistics","authors":"Aalok Kumar , Pooja Goel , Sunil Tiwari","doi":"10.1016/j.eneco.2025.108604","DOIUrl":"10.1016/j.eneco.2025.108604","url":null,"abstract":"<div><div>The adoption of electric freight vehicles (EFVs) is integral to achieving sustainable urban freight mobility (UFM); however, their large-scale deployment remains constrained by a number of financial, infrastructural, regulatory, policy, and operational barriers. This study employs a robust multi-criteria decision analysis (MCDA) framework, which integrates fuzzy Delphi, decision-making trial and evaluation laboratory (DEMATEL), and interpretive structural modelling (ISM), to systematically identify, rank, and link critical barriers to EFV deployment. Through an initial literature review and expert-driven assessment, we identify 18 barriers, which are subsequently refined to 13 based on their significance and interrelationships. The results highlight six dominant causal barriers, including inadequate government incentives, unstructured urban market layouts, and high operational costs with prolonged payback periods, all of which create systemic resistance to EFV adoption. Additionally, limited charging infrastructure, an immature EFV resale market, and a lack of dedicated repair centres exacerbate the reluctance of logistics service providers to transition to EFVs. The study also categorizes these barriers into four strategic clusters based on their driving and dependence power, offering a novel hierarchical decision roadmap to facilitate effective policy interventions. Advancing existing DEMATEL methodologies, this research contributes to the discourse on sustainable urban logistics by providing an empirically validated, analytically rigorous framework that informs policymakers, logistics service providers, and urban planners in devising effective strategies for EFV integration. The proposed model facilitates strategic decision-making for achieving low-carbon urban freight systems and offers a scalable approach that is applicable to diverse urban contexts.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"147 ","pages":"Article 108604"},"PeriodicalIF":13.6,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147915","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

What REALLY drives clean energy stocks - Fear or Fundamentals? 驱动清洁能源股的真正因素是什么——恐惧还是基本面？

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-20 DOI: 10.1016/j.eneco.2025.108558

Yuqi Zheng , Brian Lucey

引用次数: 0

Corrigendum to “Modelling time-varying volatility spillovers across crises: Evidence from major commodity futures and the US stock market” [Energy Economics Volume 143, March 2025, 108225] “模拟危机期间的时变波动溢出效应：来自主要商品期货和美国股市的证据”的勘误表[能源经济第143卷，2025年3月，108225]

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-19 DOI: 10.1016/j.eneco.2025.108552

Shietal Ramesh , Rand Kwong Yew Low , Robert Faff

引用次数: 0

The economic burden of a carbon tax on Chinese residents: A gender and income perspective 碳税对中国居民的经济负担：性别和收入视角

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-19 DOI: 10.1016/j.eneco.2025.108601

Yan-Yan Yu , Chao-Yun Zhong , Shu-Xin Zhang , Hong-Dian Jiang

{"title":"The economic burden of a carbon tax on Chinese residents: A gender and income perspective","authors":"Yan-Yan Yu , Chao-Yun Zhong , Shu-Xin Zhang , Hong-Dian Jiang","doi":"10.1016/j.eneco.2025.108601","DOIUrl":"10.1016/j.eneco.2025.108601","url":null,"abstract":"<div><div>Consumption-based carbon accounting helps identify key groups that influence emission reduction efforts, but current research primarily focuses on consumption differences among different income groups, neglecting the impact of gender characteristics on emissions and the potential differentiated effects of emission reduction policies on different gender groups. Therefore, on the basis of a detailed description of the consumption structure of residents grouped by income and gender, this study takes China as an example to examine the indirect carbon emissions from residents' product consumption. This study uses an input–output structural decomposition analysis model to investigate the contributions of five influencing factors to changes in consumption-based carbon emissions in China and uses an input–output price model to explore the economic burden of a carbon tax on residents grouped by gender and income. The results show that low-income females have the largest carbon tax burden ratio (i.e., the proportion of the carbon tax cost in the residents' total consumption expenditure), which is 1.12 times and 1.32 times higher than that of low-income residents and the average level, respectively. Specifically, 64.9 % (185.2 yuan) of females' carbon tax cost is related to housing consumption. Targeted implementation of supporting policies can obviously decrease the carbon tax cost of low-income residents and female residents, which can provide support for enhancing the fairness and inclusivity of emission reduction policies.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"147 ","pages":"Article 108601"},"PeriodicalIF":13.6,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147912","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

Persistent energy poverty for renters motivates policy reform 租房者持续的能源贫困促使政策改革

IF 13.6 2区经济学

Energy Economics Pub Date : 2025-05-18 DOI: 10.1016/j.eneco.2025.108577

Rohan Best, Andrea Chareunsy, Fatemeh Nazifi

引用次数: 0