Pub Date : 2025-11-01DOI: 10.1016/j.esr.2025.101881
Sabah Mariyam , Mohammad Alherbawi , Gordon McKay , Tareq Al-Ansari
This study presents the first comprehensive assessment of Sustainable Aviation Fuel (SAF) readiness in the Gulf Cooperation Council (GCC) region, examining the interplay between regional feedstock potential, technology pathways, and policy frameworks. Utilizing a hybrid narrative-quantitative approach, we identify municipal solid waste (MSW), halophytes, and algae as the most viable feedstocks, based on availability, conversion potential, and regional adaptability. We analyze ASTM-approved SAF pathways and provide techno-economic data, with Gasification Fischer–Tropsch (FT) and Hydroprocessed Esters and Fatty Acids (HEFA) emerging as the most promising for GCC conditions. A phased strategy is proposed: short-term (pilot projects), medium-term (infrastructure scaling and SAF blending targets), and long-term (regional hubs and export potential). A policy scorecard offers actionable guidance for GCC nations, underlining the importance of blending mandates, tax incentives, and international certification. Despite uncertainties around costs and technology scalability, targeted investments and collaboration can support the GCC in establishing a competitive SAF industry. The study provides practical policy pathways that align SAF with broader energy transition and decarbonization goals in the region.
{"title":"Sustainable aviation fuel as a catalyst for decarbonizing Gulf Aviation: Technology and policy insights based on biomass feedstocks","authors":"Sabah Mariyam , Mohammad Alherbawi , Gordon McKay , Tareq Al-Ansari","doi":"10.1016/j.esr.2025.101881","DOIUrl":"10.1016/j.esr.2025.101881","url":null,"abstract":"<div><div>This study presents the first comprehensive assessment of Sustainable Aviation Fuel (SAF) readiness in the Gulf Cooperation Council (GCC) region, examining the interplay between regional feedstock potential, technology pathways, and policy frameworks. Utilizing a hybrid narrative-quantitative approach, we identify municipal solid waste (MSW), halophytes, and algae as the most viable feedstocks, based on availability, conversion potential, and regional adaptability. We analyze ASTM-approved SAF pathways and provide techno-economic data, with Gasification Fischer–Tropsch (FT) and Hydroprocessed Esters and Fatty Acids (HEFA) emerging as the most promising for GCC conditions. A phased strategy is proposed: short-term (pilot projects), medium-term (infrastructure scaling and SAF blending targets), and long-term (regional hubs and export potential). A policy scorecard offers actionable guidance for GCC nations, underlining the importance of blending mandates, tax incentives, and international certification. Despite uncertainties around costs and technology scalability, targeted investments and collaboration can support the GCC in establishing a competitive SAF industry. The study provides practical policy pathways that align SAF with broader energy transition and decarbonization goals in the region.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101881"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462943","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101995
Amir Imeri , Gloria Claudio-Quiroga , Luis A. Gil-Alana
This study explores the characteristics of price persistence and greenhouse gas emissions in the EU27. We use fractional integration, which is a more general approach than the classical methods based on stationary/unit roots tests since fractional degrees of differentiation are permitted. For gas emissions, the findings indicate that mean reversion occurs in the majority of the series. For prices, mean reversion take place in the cases of Italy, Spain, Portugal, and Greece. For the rest of the countries, the differencing parameter is found to be 1 or significantly above 1, thus rejecting mean reversion. These findings are not only statistically significant but also highly policy-relevant: identifying whether shocks are temporary or permanent directly informs the design of appropriate responses. Temporary shocks may be addressed through short-term, cyclical interventions, whereas permanent shocks call for structural and long-term policy measures.
{"title":"The impact of price persistence on greenhouse gas emissions: A fractional integration approach","authors":"Amir Imeri , Gloria Claudio-Quiroga , Luis A. Gil-Alana","doi":"10.1016/j.esr.2025.101995","DOIUrl":"10.1016/j.esr.2025.101995","url":null,"abstract":"<div><div>This study explores the characteristics of price persistence and greenhouse gas emissions in the EU27. We use fractional integration, which is a more general approach than the classical methods based on stationary/unit roots tests since fractional degrees of differentiation are permitted. For gas emissions, the findings indicate that mean reversion occurs in the majority of the series. For prices, mean reversion take place in the cases of Italy, Spain, Portugal, and Greece. For the rest of the countries, the differencing parameter is found to be 1 or significantly above 1, thus rejecting mean reversion. These findings are not only statistically significant but also highly policy-relevant: identifying whether shocks are temporary or permanent directly informs the design of appropriate responses. Temporary shocks may be addressed through short-term, cyclical interventions, whereas permanent shocks call for structural and long-term policy measures.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101995"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145620579","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101981
Naomi Tan , Ioannis Vrochidis , Hannah Luscombe , Emma Richardson , Fernando Plazas-Niño , Kane Alexander , Leigh Martindale , Neve Fields , Mark Howells , Vivien Foster , John Harrison
As global environmental challenges increase, the need for integrated energy modelling to facilitate data-driven decision-making in energy policy and finance is critical. However, most existing integrated frameworks are limited in their applicability, granularity, and accessibility, risking the exclusion of developing countries from the global energy transition. Social dimensions are also often insufficiently addressed, and financial planning is not integrated, leaving gaps between technical analysis, social considerations, and actionable investment pathways. To address this, the article presents the Integrated Model for Policy, Actions and Collaborative Climate Transitions (IMPACCT), a new comprehensive framework that soft-links seven significant open-source tools—MAED; OnSSET; OSeMOSYS, including CLEWs and SIBs; FlexTool; PathCalc; MINFin; and FINPLAN—for the first time. IMPACCT estimates energy demand from electrified and unelectrified populations and calibrates the least-cost capacity mix to meet demand while accounting for land availability, water use, carbon emissions, and social factors. The capacity mix is further refined to ensure power system flexibility, and the technical outputs are visualised in an engaging interface. Financial strategies at national and utility levels complete the framework, supporting the practical realisation of the technical plans. By outlining a new process with open-source, user-friendly interfaces, this paper increases accessibility and ease of use, supports capacity building in developing countries, and facilitates collaboration across institutions and disciplines. It delivers a significant leap in energy modelling, social inclusion, and financial planning, advancing a more integrated approach to sustainable development. Overall, IMPACCT enables more transparent and collaborative decision-making, accelerating financial mobilisation for a just energy transition.
{"title":"IMPACCT: An integrated assessment model for policy and financial decision-making in energy planning","authors":"Naomi Tan , Ioannis Vrochidis , Hannah Luscombe , Emma Richardson , Fernando Plazas-Niño , Kane Alexander , Leigh Martindale , Neve Fields , Mark Howells , Vivien Foster , John Harrison","doi":"10.1016/j.esr.2025.101981","DOIUrl":"10.1016/j.esr.2025.101981","url":null,"abstract":"<div><div>As global environmental challenges increase, the need for integrated energy modelling to facilitate data-driven decision-making in energy policy and finance is critical. However, most existing integrated frameworks are limited in their applicability, granularity, and accessibility, risking the exclusion of developing countries from the global energy transition. Social dimensions are also often insufficiently addressed, and financial planning is not integrated, leaving gaps between technical analysis, social considerations, and actionable investment pathways. To address this, the article presents the Integrated Model for Policy, Actions and Collaborative Climate Transitions (IMPACCT), a new comprehensive framework that soft-links seven significant open-source tools—MAED; OnSSET; OSeMOSYS, including CLEWs and SIBs; FlexTool; PathCalc; MINFin; and FINPLAN—for the first time. IMPACCT estimates energy demand from electrified and unelectrified populations and calibrates the least-cost capacity mix to meet demand while accounting for land availability, water use, carbon emissions, and social factors. The capacity mix is further refined to ensure power system flexibility, and the technical outputs are visualised in an engaging interface. Financial strategies at national and utility levels complete the framework, supporting the practical realisation of the technical plans. By outlining a new process with open-source, user-friendly interfaces, this paper increases accessibility and ease of use, supports capacity building in developing countries, and facilitates collaboration across institutions and disciplines. It delivers a significant leap in energy modelling, social inclusion, and financial planning, advancing a more integrated approach to sustainable development. Overall, IMPACCT enables more transparent and collaborative decision-making, accelerating financial mobilisation for a just energy transition.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101981"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690263","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}
This study investigates the impact of energy efficiency (EE) and renewable energy (RE) on carbon dioxide (CO2) emissions across 25 Asian countries from 1992 to 2019, accounting for the region's diverse economic development stages and energy profiles. Using total factor energy efficiency (TFEE) and panel quantile regression, we explore the heterogeneous effects of EE and RE on CO2 emissions across our sample countries. Our findings reveal that EE improvements and RE expansion reduce CO2, though their influence varies across emission quantiles. EE exerts greater mitigating effects at lower quantiles, whereas RE is more influential at higher quantiles. Overall, EE improvements yield consistently larger emission reduction effects than RE expansion across all quantiles. We find no evidence of synergistic effects between EE and RE or energy rebound effects from improved EE. Our analysis confirms the environmental Kuznets curve hypothesis, although the implied turning point is unrealistically high. Furthermore, analyses utilizing traditional energy intensity or productivity measures may overstate EE's CO2 reduction effects. Our results highlight the importance of tailoring policies for decarbonization in Asian countries, suggesting that while EE and RE are crucial, their relative effectiveness varies based on the country's emission levels.
{"title":"How do energy efficiency and renewable energy impact carbon emissions in Asian economies?","authors":"Satoshi Honma , Yoshiaki Ushifusa , Farhad Taghizadeh-Hesary , Lilu Vandercamme","doi":"10.1016/j.esr.2025.101993","DOIUrl":"10.1016/j.esr.2025.101993","url":null,"abstract":"<div><div>This study investigates the impact of energy efficiency (EE) and renewable energy (RE) on carbon dioxide (CO<sub>2</sub>) emissions across 25 Asian countries from 1992 to 2019, accounting for the region's diverse economic development stages and energy profiles. Using total factor energy efficiency (TFEE) and panel quantile regression, we explore the heterogeneous effects of EE and RE on CO<sub>2</sub> emissions across our sample countries. Our findings reveal that EE improvements and RE expansion reduce CO<sub>2</sub>, though their influence varies across emission quantiles. EE exerts greater mitigating effects at lower quantiles, whereas RE is more influential at higher quantiles. Overall, EE improvements yield consistently larger emission reduction effects than RE expansion across all quantiles. We find no evidence of synergistic effects between EE and RE or energy rebound effects from improved EE. Our analysis confirms the environmental Kuznets curve hypothesis, although the implied turning point is unrealistically high. Furthermore, analyses utilizing traditional energy intensity or productivity measures may overstate EE's CO<sub>2</sub> reduction effects. Our results highlight the importance of tailoring policies for decarbonization in Asian countries, suggesting that while EE and RE are crucial, their relative effectiveness varies based on the country's emission levels.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101993"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690296","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101991
Ehtisham Asghar , Ibrahim Sengor , Martin Hill , Conor Lynch
Energy communities are expected to play an essential role in the low-carbon transition, yet their performance is often constrained by volatile weather conditions, limited historical data, and a lack of deployable Energy Management Systems (EMSs) suited to emerging settings. This study develops a data-efficient EMS that combines eXtreme Gradient Boosting for day-ahead forecasting with a model-free Deep Q-Network (DQN) scheduling optimiser, designed to reduce operational costs and CO2 emissions while operating reliably with only 6–12 months of data. The approach is evaluated using real community data from Ireland and Vietnam, representing contrasting climatic conditions, to assess transferability and robustness. Results show that the forecasting models provide accurate predictions of demand and solar generation, while the DQN-based scheduler achieves operational costs within 17.96% and 13.14% of a perfect-information baseline and maintains stable performance across seasons. The EMS demonstrates scalability as community size increases and is deployed in real time using an API-based architecture with an average response time of 0.46 s. By enabling reliable operation under uncertainty with minimal data, the proposed EMS offers a practical and adaptable tool for strengthening the economic and environmental performance of emerging energy communities and supports future policy and investment decisions in decentralised energy systems.
{"title":"Deep Q-Network for intelligent energy management system in an energy community","authors":"Ehtisham Asghar , Ibrahim Sengor , Martin Hill , Conor Lynch","doi":"10.1016/j.esr.2025.101991","DOIUrl":"10.1016/j.esr.2025.101991","url":null,"abstract":"<div><div>Energy communities are expected to play an essential role in the low-carbon transition, yet their performance is often constrained by volatile weather conditions, limited historical data, and a lack of deployable Energy Management Systems (EMSs) suited to emerging settings. This study develops a data-efficient EMS that combines eXtreme Gradient Boosting for day-ahead forecasting with a model-free Deep Q-Network (DQN) scheduling optimiser, designed to reduce operational costs and CO<sub>2</sub> emissions while operating reliably with only 6–12 months of data. The approach is evaluated using real community data from Ireland and Vietnam, representing contrasting climatic conditions, to assess transferability and robustness. Results show that the forecasting models provide accurate predictions of demand and solar generation, while the DQN-based scheduler achieves operational costs within 17.96% and 13.14% of a perfect-information baseline and maintains stable performance across seasons. The EMS demonstrates scalability as community size increases and is deployed in real time using an API-based architecture with an average response time of 0.46 s. By enabling reliable operation under uncertainty with minimal data, the proposed EMS offers a practical and adaptable tool for strengthening the economic and environmental performance of emerging energy communities and supports future policy and investment decisions in decentralised energy systems.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101991"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690262","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101954
So-Bin Cho , Junyung Kim , Václav Novotný , Rami M. Saeed , Todd R. Allen , Xiaodong Sun
Meeting the need for large-scale geographically concentrated industrial heat demand poses a unique challenge. Unlike the power sector, which can transmit electricity over long distances via established grids, the heat distribution faces significant infrastructure costs and heat losses at high temperatures. Enhancing energy security in adjacent industrial parks provides a dual benefit: reduced exposure to volatile fossil fuel prices and improved economic viability, largely driven by economies of scale in energy supply and distribution. This study presents a comprehensive decision maker-ready framework for high-temperature gas-cooled reactors (HTGR) coupled with thermal energy storage (TES) to supply combined heat and power. Synthetic load profiles for chemical, refinery, and steel plants are generated with autoregressive moving-average models, and a multi-objective optimization traces Pareto-optimal trade-offs between electricity sales and on-site self-sufficiency. The findings establish actionable sizing rules that bridge engineering modeling and practical investment decisions: Pareto-efficient reactor capacity remains below 60 % of peak site demand in balancing net profits and energy self-sufficiency, and TES sized at 1–2.5× the reactor capacity effectively reduces natural gas consumption. The analysis highlights that the entire heat demand with nuclear energy alone is costly, particularly for industries with seasonally fluctuating heat demand. Achieving 99 % self-sufficiency in electricity supply is feasible, representing virtually no grid imports during modeling. However, this increases reliance on natural gas backup boilers. This study informs the early-stage planning of nuclear-integrated energy hubs for industrial sites and the findings remain applicable to industries beyond those examined in this study, as long as these idiosyncratic differences are considered.
{"title":"Multi-objective decision-making for nuclear-integrated energy hub planning and operation for industrial heat and power supply","authors":"So-Bin Cho , Junyung Kim , Václav Novotný , Rami M. Saeed , Todd R. Allen , Xiaodong Sun","doi":"10.1016/j.esr.2025.101954","DOIUrl":"10.1016/j.esr.2025.101954","url":null,"abstract":"<div><div>Meeting the need for large-scale geographically concentrated industrial heat demand poses a unique challenge. Unlike the power sector, which can transmit electricity over long distances via established grids, the heat distribution faces significant infrastructure costs and heat losses at high temperatures. Enhancing energy security in adjacent industrial parks provides a dual benefit: reduced exposure to volatile fossil fuel prices and improved economic viability, largely driven by economies of scale in energy supply and distribution. This study presents a comprehensive decision maker-ready framework for high-temperature gas-cooled reactors (HTGR) coupled with thermal energy storage (TES) to supply combined heat and power. Synthetic load profiles for chemical, refinery, and steel plants are generated with autoregressive moving-average models, and a multi-objective optimization traces Pareto-optimal trade-offs between electricity sales and on-site self-sufficiency. The findings establish actionable sizing rules that bridge engineering modeling and practical investment decisions: Pareto-efficient reactor capacity remains below 60 % of peak site demand in balancing net profits and energy self-sufficiency, and TES sized at 1–2.5× the reactor capacity effectively reduces natural gas consumption. The analysis highlights that the entire heat demand with nuclear energy alone is costly, particularly for industries with seasonally fluctuating heat demand. Achieving 99 % self-sufficiency in electricity supply is feasible, representing virtually no grid imports during modeling. However, this increases reliance on natural gas backup boilers. This study informs the early-stage planning of nuclear-integrated energy hubs for industrial sites and the findings remain applicable to industries beyond those examined in this study, as long as these idiosyncratic differences are considered.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101954"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690976","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101959
Khalil Gholami , Mohammad Taufiqul Arif , Md Enamul Haque , Ali Arefi , S.M. Muyeen
Although the integration of renewable energy sources (RES), battery storage systems, vehicle-to-grid (V2G) technologies, and power-to-gas (P2G) have been introduced for expediting the net-zero target achievements, however they introduce significant challenges related to resource management, operational coordination, system stability, and so on. Virtual power plants (VPPs) have emerged as effective aggregators of the aforementioned distributed resources to facilitate coordinated operations within evolving power systems. However, understanding the cutting-edge advancements in VPP development requires a comprehensive review and criticalcomparison of existing research in this field. This article therefore presents a comprehensive review associated with the recent technologies that enable VPPs to strategically participate in future electricity markets. This study particularly examines key topics such as demand response programming, uncertainty management, multi-level VPP interactions across the power system, V2G integration, P2G applications, ancillary services, and multi-energy market participation. The existing methodologies, benefits, limitations, and applications are thus analyzed across different market contexts, offering a thorough comparative assessment. This utter review not only presents valuable insights for researchers and industry stakeholders to grasp the role of VPPs and their transformative impacts on modern energy markets but also enhances their strategic integration into future power systems.
{"title":"Comprehensive review of cutting-edge virtual power plant advancements for flexibility enhancement in future power grids","authors":"Khalil Gholami , Mohammad Taufiqul Arif , Md Enamul Haque , Ali Arefi , S.M. Muyeen","doi":"10.1016/j.esr.2025.101959","DOIUrl":"10.1016/j.esr.2025.101959","url":null,"abstract":"<div><div>Although the integration of renewable energy sources (RES), battery storage systems, vehicle-to-grid (V2G) technologies, and power-to-gas (P2G) have been introduced for expediting the net-zero target achievements, however they introduce significant challenges related to resource management, operational coordination, system stability, and so on. Virtual power plants (VPPs) have emerged as effective aggregators of the aforementioned distributed resources to facilitate coordinated operations within evolving power systems. However, understanding the cutting-edge advancements in VPP development requires a comprehensive review and criticalcomparison of existing research in this field. This article therefore presents a comprehensive review associated with the recent technologies that enable VPPs to strategically participate in future electricity markets. This study particularly examines key topics such as demand response programming, uncertainty management, multi-level VPP interactions across the power system, V2G integration, P2G applications, ancillary services, and multi-energy market participation. The existing methodologies, benefits, limitations, and applications are thus analyzed across different market contexts, offering a thorough comparative assessment. This utter review not only presents valuable insights for researchers and industry stakeholders to grasp the role of VPPs and their transformative impacts on modern energy markets but also enhances their strategic integration into future power systems.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101959"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462945","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101966
Xiaoqing Wang , Fengzi Lu , Adnan Safi , Xin Li
Determining the influence of climate policy uncertainty (CPU), fossil fuel dynamics, and renewable energy (REE) adoption on carbon market volatility (CTM) is essential for ensuring its stability and sustainable development. Therefore, this study captures the dynamic relationships among CTM, CPU, crude oil (COP), coal (COA) and REE across different time horizons utilizing a Time-Varying Parameter Structural Vector Autoregression with Stochastic Volatility (TVP-SVAR-SV) model. Results reveal that in the short term, shocks from CPU, COP, COA, and REE all significantly intensify carbon price fluctuations. In the medium term, the carbon market exhibits heightened sensitivity particularly to CPU and COA shocks, while the effects of all factors diminish over the long term. Furthermore, the analysis confirms pronounced time-varying characteristics, with the influence of oil prices on carbon price volatility notably strengthening over time. By comparing influence degree, CPU and COP emerge as the more influential and volatile drivers, whereas the impact of COA remains more stable. Finally, all shocks are significantly amplified during periods of major external disruption, especially during the Russia-Ukraine conflict. These findings highlight the importance of maintaining clear climate policy signals and stabilizing energy market dynamics to enhance the resilience and efficiency of carbon markets.
{"title":"Unraveling the dynamics of carbon price volatility: A comprehensive analysis of impacts from climate policy, fossil fuel and renewable energy shocks","authors":"Xiaoqing Wang , Fengzi Lu , Adnan Safi , Xin Li","doi":"10.1016/j.esr.2025.101966","DOIUrl":"10.1016/j.esr.2025.101966","url":null,"abstract":"<div><div>Determining the influence of climate policy uncertainty (CPU), fossil fuel dynamics, and renewable energy (REE) adoption on carbon market volatility (CTM) is essential for ensuring its stability and sustainable development. Therefore, this study captures the dynamic relationships among CTM, CPU, crude oil (COP), coal (COA) and REE across different time horizons utilizing a Time-Varying Parameter Structural Vector Autoregression with Stochastic Volatility (TVP-SVAR-SV) model. Results reveal that in the short term, shocks from CPU, COP, COA, and REE all significantly intensify carbon price fluctuations. In the medium term, the carbon market exhibits heightened sensitivity particularly to CPU and COA shocks, while the effects of all factors diminish over the long term. Furthermore, the analysis confirms pronounced time-varying characteristics, with the influence of oil prices on carbon price volatility notably strengthening over time. By comparing influence degree, CPU and COP emerge as the more influential and volatile drivers, whereas the impact of COA remains more stable. Finally, all shocks are significantly amplified during periods of major external disruption, especially during the Russia-Ukraine conflict. These findings highlight the importance of maintaining clear climate policy signals and stabilizing energy market dynamics to enhance the resilience and efficiency of carbon markets.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101966"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462946","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 : 2025-11-01DOI: 10.1016/j.esr.2025.101975
Taeyoung Jin
This study presents a meta-analysis of the Value of Lost Load (VoLL) to support countries seeking to implement value-based reliability planning in electricity markets. VoLL serves as a key indicator in addressing the missing money problem and promoting efficient investment in generation and transmission infrastructure. However, its estimation is often costly, resource-intensive, and inaccessible to many countries with limited data availability. To overcome these barriers, we construct a comprehensive database of outage cost estimates drawn from 56 studies covering 47 countries. The database incorporates various estimation methods and contextual variables, forming the basis for a meta-regression model. This framework enables benefit transfer of VoLL values by identifying how outage costs vary systematically with macroeconomic conditions, estimation methods, sectoral differences, and outage characteristics. Using this approach, we estimate context-specific VoLL values for South Korea as a case study, illustrating how the model can inform policy in environments where direct empirical estimation is not feasible. Our findings highlight that VoLL estimates are strongly associated with income level, electricity prices, and estimation methodology, while commonly used grid reliability metrics such as SAIDI and SAIFI are statistically insignificant. By offering a replicable analytical framework and an openly accessible database, this study provides practical tools for policymakers and system planners. These results contribute to advancing more economically efficient and reliability-oriented electricity systems in both advanced and developing markets.
{"title":"A meta-analysis of outage cost for value-based reliability planning","authors":"Taeyoung Jin","doi":"10.1016/j.esr.2025.101975","DOIUrl":"10.1016/j.esr.2025.101975","url":null,"abstract":"<div><div>This study presents a meta-analysis of the Value of Lost Load (VoLL) to support countries seeking to implement value-based reliability planning in electricity markets. VoLL serves as a key indicator in addressing the missing money problem and promoting efficient investment in generation and transmission infrastructure. However, its estimation is often costly, resource-intensive, and inaccessible to many countries with limited data availability. To overcome these barriers, we construct a comprehensive database of outage cost estimates drawn from 56 studies covering 47 countries. The database incorporates various estimation methods and contextual variables, forming the basis for a meta-regression model. This framework enables benefit transfer of VoLL values by identifying how outage costs vary systematically with macroeconomic conditions, estimation methods, sectoral differences, and outage characteristics. Using this approach, we estimate context-specific VoLL values for South Korea as a case study, illustrating how the model can inform policy in environments where direct empirical estimation is not feasible. Our findings highlight that VoLL estimates are strongly associated with income level, electricity prices, and estimation methodology, while commonly used grid reliability metrics such as SAIDI and SAIFI are statistically insignificant. By offering a replicable analytical framework and an openly accessible database, this study provides practical tools for policymakers and system planners. These results contribute to advancing more economically efficient and reliability-oriented electricity systems in both advanced and developing markets.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101975"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462954","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}
In response to climate change, China has implemented a carbon trading market to reduce greenhouse gas emissions from thermal power generation. However, the resulting carbon costs are reshaping the behavior of market participants. This study aims to investigate how key players in the thermal power market, i.e., thermal power companies, power selling companies, and energy consumers—adjust their strategies under the influence of carbon trading policies. By integrating evolutionary game theory and system dynamics modeling, we construct a three-agent evolutionary game to analyze strategy stability and key influencing factors. The model is then translated into a system dynamics framework for scenario simulation and sensitivity analysis. Results indicate that under most carbon market conditions, thermal power companies tend to adopt emission reduction strategies, while power selling companies and energy consumers are more inclined toward green power options. This study contributes a novel approach by combining game-theoretic analysis with dynamic simulation, providing strategic insights for optimizing participant behavior and improving the effectiveness of carbon trading mechanisms.
{"title":"Exploring the evolution of thermal power market participants' strategies under carbon trading policy: An evolutionary game and system dynamics approach","authors":"Tian-tian Feng , Hui-min Zhang , Jia-dong Xuan, Cheng Zhong","doi":"10.1016/j.esr.2025.101973","DOIUrl":"10.1016/j.esr.2025.101973","url":null,"abstract":"<div><div>In response to climate change, China has implemented a carbon trading market to reduce greenhouse gas emissions from thermal power generation. However, the resulting carbon costs are reshaping the behavior of market participants. This study aims to investigate how key players in the thermal power market, i.e., thermal power companies, power selling companies, and energy consumers—adjust their strategies under the influence of carbon trading policies. By integrating evolutionary game theory and system dynamics modeling, we construct a three-agent evolutionary game to analyze strategy stability and key influencing factors. The model is then translated into a system dynamics framework for scenario simulation and sensitivity analysis. Results indicate that under most carbon market conditions, thermal power companies tend to adopt emission reduction strategies, while power selling companies and energy consumers are more inclined toward green power options. This study contributes a novel approach by combining game-theoretic analysis with dynamic simulation, providing strategic insights for optimizing participant behavior and improving the effectiveness of carbon trading mechanisms.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"62 ","pages":"Article 101973"},"PeriodicalIF":7.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145462950","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}
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