Pub Date : 2026-01-01DOI: 10.1016/j.esr.2026.102040
Huan Huu Nguyen, Duc Huu Nguyen, Vu Minh Ngo
This study examines the economic and environmental impacts of fossil fuel subsidies in the European Union (EU) during the energy crisis triggered by the 2022 Ukraine war, focusing on their implications for corporate performance and sustainability. Using a panel dataset of 100 publicly listed energy firms across the EU from 2019 to 2023, including 44 fossil fuel companies and 56 renewable energy companies, and employing a Difference-in-Differences (DiD) methodology, we assess the causal effects of increased fossil fuel subsidies on financial performance and Environmental, Social, and Governance (ESG) outcomes. Our findings reveal that these subsidies did not significantly increase fossil fuel consumption or reduce price elasticity of demand, challenging their conventional justification as tools to stimulate energy use or stabilize market sensitivity. However, the surge in subsidies during 2022 significantly improved the financial performance of fossil fuel firms, as reflected by higher Return on Assets (ROA) and Return on Equity (ROE). This financial gain, however, came at the expense of ESG performance, with firms reallocating resources from sustainability initiatives to enhance short-term profitability. These results underscore a critical interaction between immediate financial stabilization and long-term environmental commitments.
{"title":"Fossil-fuel subsidies and energy-firm outcomes in the European Union: Economic and environmental performance","authors":"Huan Huu Nguyen, Duc Huu Nguyen, Vu Minh Ngo","doi":"10.1016/j.esr.2026.102040","DOIUrl":"10.1016/j.esr.2026.102040","url":null,"abstract":"<div><div>This study examines the economic and environmental impacts of fossil fuel subsidies in the European Union (EU) during the energy crisis triggered by the 2022 Ukraine war, focusing on their implications for corporate performance and sustainability. Using a panel dataset of 100 publicly listed energy firms across the EU from 2019 to 2023, including 44 fossil fuel companies and 56 renewable energy companies, and employing a Difference-in-Differences (DiD) methodology, we assess the causal effects of increased fossil fuel subsidies on financial performance and Environmental, Social, and Governance (ESG) outcomes. Our findings reveal that these subsidies did not significantly increase fossil fuel consumption or reduce price elasticity of demand, challenging their conventional justification as tools to stimulate energy use or stabilize market sensitivity. However, the surge in subsidies during 2022 significantly improved the financial performance of fossil fuel firms, as reflected by higher Return on Assets (ROA) and Return on Equity (ROE). This financial gain, however, came at the expense of ESG performance, with firms reallocating resources from sustainability initiatives to enhance short-term profitability. These results underscore a critical interaction between immediate financial stabilization and long-term environmental commitments.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102040"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2025.102004
Abbas Khan , Muhammad Yar Khan , Abdulrahman Alomair , Abdulaziz S. Al Naim
This study examines carbon taxation's impact on UK households' energy choices, focusing on socioeconomic factors and fuel substitution using household microdata in a Multinomial Logit (MNL) framework. Rural households, reliant on costly, carbon-intensive fuels like heating oil and LPG, are significantly affected by carbon price changes, while urban households have cheaper alternatives. Findings show that rising carbon tax increases carbon-intensive fuel costs, prompting urban households to boost combined electricity and mains gas use from 32.8 % to 50.6 %, while reducing LPG and electricity use from 12.3 % to 6.6 %, and heating oil and electricity from 9.7 % to 6.1 %. Conversely, rural households, facing fewer choices, turn to more expensive fuels; combined electricity and LPG use rises from 14.1 % to 39.3 %, and electricity and heating oil use from 9.1 % to 36.1 %. This study provides novel UK-specific insights into regional and socio-economic disparities in energy transitions under carbon taxation. It highlights the disproportionate burden on low-income and rural households, emphasizing the need for targeted revenue recycling, regional support, and measures to combat energy poverty. The findings offer practical guidance for policymakers to design equitable carbon tax policies and ensure a just transition to a low-carbon economy.
{"title":"Carbon tax and household energy choices: A regional study of the UK","authors":"Abbas Khan , Muhammad Yar Khan , Abdulrahman Alomair , Abdulaziz S. Al Naim","doi":"10.1016/j.esr.2025.102004","DOIUrl":"10.1016/j.esr.2025.102004","url":null,"abstract":"<div><div>This study examines carbon taxation's impact on UK households' energy choices, focusing on socioeconomic factors and fuel substitution using household microdata in a Multinomial Logit (MNL) framework. Rural households, reliant on costly, carbon-intensive fuels like heating oil and LPG, are significantly affected by carbon price changes, while urban households have cheaper alternatives. Findings show that rising carbon tax increases carbon-intensive fuel costs, prompting urban households to boost combined electricity and mains gas use from 32.8 % to 50.6 %, while reducing LPG and electricity use from 12.3 % to 6.6 %, and heating oil and electricity from 9.7 % to 6.1 %. Conversely, rural households, facing fewer choices, turn to more expensive fuels; combined electricity and LPG use rises from 14.1 % to 39.3 %, and electricity and heating oil use from 9.1 % to 36.1 %. This study provides novel UK-specific insights into regional and socio-economic disparities in energy transitions under carbon taxation. It highlights the disproportionate burden on low-income and rural households, emphasizing the need for targeted revenue recycling, regional support, and measures to combat energy poverty. The findings offer practical guidance for policymakers to design equitable carbon tax policies and ensure a just transition to a low-carbon economy.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102004"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2026.102035
Paola Arrigoni, Claudia Mariotti, Luca Germano, Renata Lizzi, Laura Mastroianni, Andrea Prontera
Nuclear energy is a technology that has long been subject to controversy, shaped by major global events, shifting public opinion and volatile policy agendas. Among industrialised democracies, Italy is a particularly distinctive case. Following a pioneering role in the 1950s and an ambitious, albeit partially implemented, nuclear programme in the 1970s, the Chernobyl disaster and the 1987 referendum resulted in the complete phase-out of nuclear power. A second referendum in 2011, following the Fukushima disaster, reaffirmed public opposition to any attempt to revive nuclear power. Despite these precedents, nuclear energy has recently returned to the political agenda. This initiative is embedded in broader European debates on the potential of nuclear power in decarbonization and energy security in the context of the crisis triggered by Russia's invasion of Ukraine. This article uses discourse network analysis of media data to investigate the re-emerging debate on nuclear policy in Italy. Specifically, it examines the structure of the debate, the types of actors involved, their framing strategies, how discourse has evolved over time and how pro- and anti-nuclear coalitions have formed. The findings reveal that the Italian nuclear policy debate exhibits a hub-and-spoke structure, with influence concentrated among a small number of dominant actors. Over time, the debate has evolved from initial structuration to consolidation and pluralization in terms of actors, coalitions, and concepts. Nevertheless, despite some differences, pro-nuclear actors and coalitions retain significant visibility and influence.
{"title":"Back to the past? Discourse coalitions in Italy's emerging nuclear policy debate","authors":"Paola Arrigoni, Claudia Mariotti, Luca Germano, Renata Lizzi, Laura Mastroianni, Andrea Prontera","doi":"10.1016/j.esr.2026.102035","DOIUrl":"10.1016/j.esr.2026.102035","url":null,"abstract":"<div><div>Nuclear energy is a technology that has long been subject to controversy, shaped by major global events, shifting public opinion and volatile policy agendas. Among industrialised democracies, Italy is a particularly distinctive case. Following a pioneering role in the 1950s and an ambitious, albeit partially implemented, nuclear programme in the 1970s, the Chernobyl disaster and the 1987 referendum resulted in the complete phase-out of nuclear power. A second referendum in 2011, following the Fukushima disaster, reaffirmed public opposition to any attempt to revive nuclear power. Despite these precedents, nuclear energy has recently returned to the political agenda. This initiative is embedded in broader European debates on the potential of nuclear power in decarbonization and energy security in the context of the crisis triggered by Russia's invasion of Ukraine. This article uses discourse network analysis of media data to investigate the re-emerging debate on nuclear policy in Italy. Specifically, it examines the structure of the debate, the types of actors involved, their framing strategies, how discourse has evolved over time and how pro- and anti-nuclear coalitions have formed. The findings reveal that the Italian nuclear policy debate exhibits a hub-and-spoke structure, with influence concentrated among a small number of dominant actors. Over time, the debate has evolved from initial structuration to consolidation and pluralization in terms of actors, coalitions, and concepts. Nevertheless, despite some differences, pro-nuclear actors and coalitions retain significant visibility and influence.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102035"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2026.102042
Shunshun Yu , Yuxin Zhang , Lu Jin , Jianzhong Xiao , Jiachao Peng
Global warming and energy security challenges have intensified globally, compelling nations to pursue sustainable energy transition pathways. Artificial intelligence (AI) plays a pivotal role in addressing complex systemic challenges and enhancing industrial efficiency, and is widely recognized as a critical catalyst for efficiency transformation and economic advancement. This study employs panel data from 282 prefecture-level cities in China, spanning the period 2007–2021, to empirically examine the relationship between AI and energy transition. The key findings are that: AI significantly promotes energy transition development, and this effect is mediated through its role in driving digital upgrades and green transformation of industrial chains. The association between AI and energy transition exhibits nonlinearity: at low levels of AI development, AI exerts the strongest driving effect on the Energy Transition Index; at moderate AI levels, this driving effect weakens; and at high AI levels, the driving effect strengthens again, presenting a distinct U-shaped pattern. AI exerts a significant positive spatial spillover effect on energy transition, indicating that technological diffusion generates favorable impacts on neighboring regions. Regional heterogeneity analysis reveals marked disparities in AI's influence on energy transition across China, exhibiting a gradient effect characterized by “strengthening in eastern regions, stabilization in central territories, and differentiation in western regions”. This research provides valuable guidance for policymakers by highlighting AI's pivotal role in advancing energy transformation.
{"title":"Stoking the flames of change: AI-driven energy transition in urban China through nonlinear dynamics and spatial spillover effect","authors":"Shunshun Yu , Yuxin Zhang , Lu Jin , Jianzhong Xiao , Jiachao Peng","doi":"10.1016/j.esr.2026.102042","DOIUrl":"10.1016/j.esr.2026.102042","url":null,"abstract":"<div><div>Global warming and energy security challenges have intensified globally, compelling nations to pursue sustainable energy transition pathways. Artificial intelligence (AI) plays a pivotal role in addressing complex systemic challenges and enhancing industrial efficiency, and is widely recognized as a critical catalyst for efficiency transformation and economic advancement. This study employs panel data from 282 prefecture-level cities in China, spanning the period 2007–2021, to empirically examine the relationship between AI and energy transition. The key findings are that: AI significantly promotes energy transition development, and this effect is mediated through its role in driving digital upgrades and green transformation of industrial chains. The association between AI and energy transition exhibits nonlinearity: at low levels of AI development, AI exerts the strongest driving effect on the Energy Transition Index; at moderate AI levels, this driving effect weakens; and at high AI levels, the driving effect strengthens again, presenting a distinct U-shaped pattern. AI exerts a significant positive spatial spillover effect on energy transition, indicating that technological diffusion generates favorable impacts on neighboring regions. Regional heterogeneity analysis reveals marked disparities in AI's influence on energy transition across China, exhibiting a gradient effect characterized by “strengthening in eastern regions, stabilization in central territories, and differentiation in western regions”. This research provides valuable guidance for policymakers by highlighting AI's pivotal role in advancing energy transformation.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102042"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2025.102030
Muhammad Khalid Anser , Dua Hassan , Saira Tufail , Shahzad Alvi , Mehboob Ul Hassan
The global transition toward renewable energy is vital for achieving climate goals and strengthening energy security. The primary objective of the study is to examine the impact of different stances of monetary policy, namely price-based, quantity-based, and outcome-based monetary policy, on renewable energy dynamics in G10 economies, contributing to the evolving discourse on green central banking and the integration of low-carbon transition objectives within modern monetary policy. The empirical evidence of the study is based on the Fully Modified Ordinary Least Squares approach and Vector Error Correction Mechanism for both short and long run dynamics, a dynamic common correlated estimator to capture slope heterogeneity, quantile regression for asymmetric relationship, and panel structural VAR for shock propagation and variance contribution. The empirical findings reveal that monetary policy exerts heterogeneous yet significant effects on renewable energy and energy transition across G10 economies. Price-based policy exhibits a partially green effect, restricting renewable investment under high interest rates but supporting long-term transition via structural efficiency gains. Quantity-based policy shows consistently positive impacts by easing liquidity constraints and stimulating renewable deployment. Outcome-based policy, proxied by financial development, yields limited or even adverse effects due to structural lock-ins and path dependence in carbon-intensive finance. The results also confirm asymmetric and dynamic transmission, with liquidity shocks generating the strongest and most persistent responses. This study has critical policy implications regarding the integration of renewable energy development into the monetary policy framework by leveraging quantity-based tools and by addressing the carbon lock-in and path dependencies associated with financial development through accelerating divestment from fossil fuels.
{"title":"Renewable energy sector and energy transition in G-10 countries: Role of price, quantity and outcome based monetary policy","authors":"Muhammad Khalid Anser , Dua Hassan , Saira Tufail , Shahzad Alvi , Mehboob Ul Hassan","doi":"10.1016/j.esr.2025.102030","DOIUrl":"10.1016/j.esr.2025.102030","url":null,"abstract":"<div><div>The global transition toward renewable energy is vital for achieving climate goals and strengthening energy security. The primary objective of the study is to examine the impact of different stances of monetary policy, namely price-based, quantity-based, and outcome-based monetary policy, on renewable energy dynamics in G10 economies, contributing to the evolving discourse on green central banking and the integration of low-carbon transition objectives within modern monetary policy. The empirical evidence of the study is based on the Fully Modified Ordinary Least Squares approach and Vector Error Correction Mechanism for both short and long run dynamics, a dynamic common correlated estimator to capture slope heterogeneity, quantile regression for asymmetric relationship, and panel structural VAR for shock propagation and variance contribution. The empirical findings reveal that monetary policy exerts heterogeneous yet significant effects on renewable energy and energy transition across G10 economies. Price-based policy exhibits a partially green effect, restricting renewable investment under high interest rates but supporting long-term transition via structural efficiency gains. Quantity-based policy shows consistently positive impacts by easing liquidity constraints and stimulating renewable deployment. Outcome-based policy, proxied by financial development, yields limited or even adverse effects due to structural lock-ins and path dependence in carbon-intensive finance. The results also confirm asymmetric and dynamic transmission, with liquidity shocks generating the strongest and most persistent responses. This study has critical policy implications regarding the integration of renewable energy development into the monetary policy framework by leveraging quantity-based tools and by addressing the carbon lock-in and path dependencies associated with financial development through accelerating divestment from fossil fuels.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102030"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2025.102028
Hui Jun Wang , Wei Ying Chong , Bing Nan Wang
The digital transformation of the energy sector is vital to achieving carbon neutrality and sustainable development goals. In China, significant disparities in the adoption of these technologies persist across regions and enterprise types, yet the underlying institutional and organizational mechanisms remain underexplored. This study addresses this gap by conducting a comparative case study of Ningxia's solar energy sector, grounded in an integrated Technology-Organization-Environment (TOE) and Socio-Technical Systems (STS) theoretical framework. Based on semi-structured interviews, field observations, and policy analysis, the findings reveal divergent digital trajectories for large enterprises (LEs) and small and medium-sized enterprises (SMEs). LEs benefit from advanced capabilities, strong policy support, and organizational readiness, creating a virtuous cycle of adoption. In contrast, SMEs face a self-reinforcing socio-technical trap where high costs, limited skills, and unequal policy access create interconnected barriers. The study theorizes these findings into testable propositions and concludes that without differentiated policy instruments and targeted capacity-building, broad digitalization policies may unintentionally widen the existing digital divide. The findings contribute a nuanced, mechanism-based explanation of uneven informatization with implications for energy governance in developing and transition economies.
{"title":"Institutional barriers to digital transformation in China’s renewable energy: Evidence from Ningxia","authors":"Hui Jun Wang , Wei Ying Chong , Bing Nan Wang","doi":"10.1016/j.esr.2025.102028","DOIUrl":"10.1016/j.esr.2025.102028","url":null,"abstract":"<div><div>The digital transformation of the energy sector is vital to achieving carbon neutrality and sustainable development goals. In China, significant disparities in the adoption of these technologies persist across regions and enterprise types, yet the underlying institutional and organizational mechanisms remain underexplored. This study addresses this gap by conducting a comparative case study of Ningxia's solar energy sector, grounded in an integrated Technology-Organization-Environment (TOE) and Socio-Technical Systems (STS) theoretical framework. Based on semi-structured interviews, field observations, and policy analysis, the findings reveal divergent digital trajectories for large enterprises (LEs) and small and medium-sized enterprises (SMEs). LEs benefit from advanced capabilities, strong policy support, and organizational readiness, creating a virtuous cycle of adoption. In contrast, SMEs face a self-reinforcing socio-technical trap where high costs, limited skills, and unequal policy access create interconnected barriers. The study theorizes these findings into testable propositions and concludes that without differentiated policy instruments and targeted capacity-building, broad digitalization policies may unintentionally widen the existing digital divide. The findings contribute a nuanced, mechanism-based explanation of uneven informatization with implications for energy governance in developing and transition economies.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102028"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2026.102051
T. Yuvaraj , R. Krishnamoorthy , S. Arun , Mohit Bajaj , Vojtech Blazek , Lukas Prokop
The accelerating penetration of electric vehicles (EVs) is reshaping modern distribution networks by introducing substantial, time-varying charging demand and new operational uncertainties. At the same time, Vehicle-to-Grid (V2G) capable EVs offer unprecedented flexibility to support grid stability when coordinated effectively. However, utilities still face major challenges in jointly managing large-scale EV charging, intermittent renewable generation, and feeder-level operational constraints. To address these gaps, this study develops an integrated scheduling framework that simultaneously optimizes solar PV, wind-based renewable distributed generators (RDGs), and V2G-enabled EV charging stations within the IEEE 85-bus radial distribution system. A user-priority-based charging strategy is formulated to accommodate diverse mobility behaviors, including stochastic arrival/departure patterns, state-of-charge variability, and urgency-based service requirements. A multi-objective optimization model is constructed to minimize charging costs, peak demand, charging duration, active power losses, and voltage deviations, while maximizing net grid-trading revenue over a 24-h horizon. The problem is solved using the Cheetah Optimization Algorithm (COA) and benchmarked against Particle Swarm Optimization (PSO), Honey Badger Optimization Algorithm (HPOA), and the Grey Wolf Optimizer (GWO). System uncertainties are rigorously examined through sensitivity tests, Monte Carlo–based error-band analysis, and stability assessments. The coordinated EV–RDG framework yields substantial technical and economic improvements: a peak load reduction of nearly 50 %, voltage deviation reduction exceeding 25 %, active power loss reduction of 20–30 %, and a total operational cost reduction of above 40 % compared to the uncoordinated baseline. COA demonstrates rapid and stable convergence, delivering consistently high-quality solutions across scenarios. Overall, the findings highlight the value of priority-aware EV integration and advanced optimization in developing scalable, cost-effective, and renewable-supportive charging infrastructures for future smart distribution networks.
{"title":"Smart energy strategies for dynamic EV charging and renewable integration in distribution systems: A multi-objective optimization framework","authors":"T. Yuvaraj , R. Krishnamoorthy , S. Arun , Mohit Bajaj , Vojtech Blazek , Lukas Prokop","doi":"10.1016/j.esr.2026.102051","DOIUrl":"10.1016/j.esr.2026.102051","url":null,"abstract":"<div><div>The accelerating penetration of electric vehicles (EVs) is reshaping modern distribution networks by introducing substantial, time-varying charging demand and new operational uncertainties. At the same time, Vehicle-to-Grid (V2G) capable EVs offer unprecedented flexibility to support grid stability when coordinated effectively. However, utilities still face major challenges in jointly managing large-scale EV charging, intermittent renewable generation, and feeder-level operational constraints. To address these gaps, this study develops an integrated scheduling framework that simultaneously optimizes solar PV, wind-based renewable distributed generators (RDGs), and V2G-enabled EV charging stations within the IEEE 85-bus radial distribution system. A user-priority-based charging strategy is formulated to accommodate diverse mobility behaviors, including stochastic arrival/departure patterns, state-of-charge variability, and urgency-based service requirements. A multi-objective optimization model is constructed to minimize charging costs, peak demand, charging duration, active power losses, and voltage deviations, while maximizing net grid-trading revenue over a 24-h horizon. The problem is solved using the Cheetah Optimization Algorithm (COA) and benchmarked against Particle Swarm Optimization (PSO), Honey Badger Optimization Algorithm (HPOA), and the Grey Wolf Optimizer (GWO). System uncertainties are rigorously examined through sensitivity tests, Monte Carlo–based error-band analysis, and stability assessments. The coordinated EV–RDG framework yields substantial technical and economic improvements: a peak load reduction of nearly 50 %, voltage deviation reduction exceeding 25 %, active power loss reduction of 20–30 %, and a total operational cost reduction of above 40 % compared to the uncoordinated baseline. COA demonstrates rapid and stable convergence, delivering consistently high-quality solutions across scenarios. Overall, the findings highlight the value of priority-aware EV integration and advanced optimization in developing scalable, cost-effective, and renewable-supportive charging infrastructures for future smart distribution networks.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102051"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2026.102041
Ijaz Ahmed , Muhammad Rehan , Mohammed Alqahtani , Muhammad Khalid
The magnitude and scope of the application of artificial intelligence (AI) and information-based computing methods to green and sustainable power generation systems have been significantly expanded to include research, initial development, implementation, and deployment. Over the past five years, this study has investigated various prominent AI modeling and optimization strategies for sustainable power systems. The following methodologies are included: cognitive neural network (NN) approaches, adaptive NNs, recurrent NNs with long-term dependencies, statistical approaches, sequential data processing networks, feature extraction networks, meta-heuristic approaches, evolutionary swarm algorithms, and hybrid optimization methodologies. The current study conducted a trend analysis of AI frameworks, with an emphasis on the performance, flexibility, and efficacy of some of the most frequently employed computational techniques for the development of sustainable power sources. Furthermore, this investigation examined the utilization of novel evaluation criteria, including computational complexity, accuracy, and ROC curve performance, in the context of power generation systems. The findings section is a systematic compilation and categorization of numerous distinct studies that were conducted over the past five years, with a focus on the methodology used and the application area. We have concluded by providing a concise summary of the results and outlining potential future research directions.
{"title":"A systematic review on AI-driven computational strategies for sustainable power systems","authors":"Ijaz Ahmed , Muhammad Rehan , Mohammed Alqahtani , Muhammad Khalid","doi":"10.1016/j.esr.2026.102041","DOIUrl":"10.1016/j.esr.2026.102041","url":null,"abstract":"<div><div>The magnitude and scope of the application of artificial intelligence (AI) and information-based computing methods to green and sustainable power generation systems have been significantly expanded to include research, initial development, implementation, and deployment. Over the past five years, this study has investigated various prominent AI modeling and optimization strategies for sustainable power systems. The following methodologies are included: cognitive neural network (NN) approaches, adaptive NNs, recurrent NNs with long-term dependencies, statistical approaches, sequential data processing networks, feature extraction networks, meta-heuristic approaches, evolutionary swarm algorithms, and hybrid optimization methodologies. The current study conducted a trend analysis of AI frameworks, with an emphasis on the performance, flexibility, and efficacy of some of the most frequently employed computational techniques for the development of sustainable power sources. Furthermore, this investigation examined the utilization of novel evaluation criteria, including computational complexity, accuracy, and ROC curve performance, in the context of power generation systems. The findings section is a systematic compilation and categorization of numerous distinct studies that were conducted over the past five years, with a focus on the methodology used and the application area. We have concluded by providing a concise summary of the results and outlining potential future research directions.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102041"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2025.102018
Stefania Benetti , Daniel Delatin Rodrigues , Marco Grasso
The diffusion of solar energy initiatives is shaped by a dynamic interplay of actions supporting and opposing them, influenced by governance structures, socio-economic conditions, environmental concerns, and public perception. This systematic review synthesizes findings from 150 studies published since 2015, identifying twenty key factors that determine the support or opposition to solar energy initiatives. While regulatory frameworks, financial incentives, and community participation by and large enhance support, challenges such as land-use conflicts, economic inequities, and fossil fuel incumbency contribute to opposition. Our review highlights the necessity of integrated policy approaches, participatory governance, and equitable economic models to mitigate opposition and ensure inclusive approaches to solar energy. By providing a comprehensive analysis of the mechanisms behind support and opposition, this systematic review informs policymakers, industry stakeholders, and researchers on strategies to facilitate a sustainable and socially responsible expansion of solar energy.
{"title":"Here comes the sun…? A systematic literature review of factors supporting or opposing solar energy initiatives","authors":"Stefania Benetti , Daniel Delatin Rodrigues , Marco Grasso","doi":"10.1016/j.esr.2025.102018","DOIUrl":"10.1016/j.esr.2025.102018","url":null,"abstract":"<div><div>The diffusion of solar energy initiatives is shaped by a dynamic interplay of actions supporting and opposing them, influenced by governance structures, socio-economic conditions, environmental concerns, and public perception. This systematic review synthesizes findings from 150 studies published since 2015, identifying twenty key factors that determine the support or opposition to solar energy initiatives. While regulatory frameworks, financial incentives, and community participation by and large enhance support, challenges such as land-use conflicts, economic inequities, and fossil fuel incumbency contribute to opposition. Our review highlights the necessity of integrated policy approaches, participatory governance, and equitable economic models to mitigate opposition and ensure inclusive approaches to solar energy. By providing a comprehensive analysis of the mechanisms behind support and opposition, this systematic review informs policymakers, industry stakeholders, and researchers on strategies to facilitate a sustainable and socially responsible expansion of solar energy.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"63 ","pages":"Article 102018"},"PeriodicalIF":7.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.esr.2025.102015
Cynthia Omondi , Francis Njoka , Francesco Tonini , Edo Abraham
Kenya has one of the fastest electrification rates in Sub-Saharan Africa. Despite the increase in electrification rates, rural and underserved regions remain a critical challenge requiring a cost-effective strategy that maximises the use of stand-alone and off-grid solutions. This paper uses the Open-Source Spatial Electrification Tool coupled with a binomial logistic regression model of urbanisation to explore least-cost electrification scenarios for universal access in Kenya. The premise is that as more areas are electrified and the population increases, more regions will likely become urban, leading to changes in their electricity demand. The regression model reveals at least four regions where new urban settlements will likely be concentrated: central Kenya, the coastline, and the border regions to the west and north of Kenya. Electrification scenarios prioritising off-grid ($5.2 billion) and stand-alone solutions ($1.8 billion) significantly reduce the required investment compared to scenarios prioritising grid extension ($8.1 billion). Given the crucial role of stand-alone solutions in minimising costs associated with electricity access, this paper suggests a shift in policy to promote the uptake of stand-alone systems over the previous focus on grid extension and large-scale projects that have dominated Kenya's energy policy landscape.
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