Energy Strategy Reviews最新文献

{"title":"From approval to operation: Governance misalignment in renewable energy implementation","authors":"Avri Eitan","doi":"10.1016/j.esr.2026.102310","DOIUrl":"10.1016/j.esr.2026.102310","url":null,"abstract":"<div><div>Renewable energy transitions increasingly face an implementation challenge: planned, approved, auctioned, or contracted capacity does not always become timely, connected, financially viable, and effectively integrated capacity in practice. Existing explanations emphasize policy design, grid constraints, permitting delays, market conditions, financing risks, or local opposition, but often examine these barriers within separate governance domains. This paper develops the concept of governance misalignment as a diagnostic framework for examining how renewable energy implementation shortfalls emerge at the intersections between domains. It conceptualizes renewable energy deployment as dependent on the interaction of four governance domains: planning, infrastructure, markets, and regulation, and identifies four dimensions along which these domains may become misaligned: spatial, temporal, procedural, and distributive. The argument is developed through a structured comparative review of four analytical vignettes: Germany, California, the United Kingdom, and India, which illustrate these forms of misalignment across diverse energy governance contexts. The analysis suggests that implementation shortfalls do not always stem from deficiencies within individual domains, but often arise when progress in one domain is not matched by compatible conditions in another. By distinguishing governance domains from dimensions of alignment, the paper offers a diagnostic framework for identifying where implementation breaks down and what type of strategic response is required. It contributes to energy strategy by showing that accelerating renewable energy deployment requires not only ambitious targets and project approvals, but also strategic alignment across the governance systems needed to realize them.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102310"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148567850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mind the gap: Building robust resilience metrics to foster the energy transition","authors":"Andrea Gatto,&nbsp;Cosimo Magazzino","doi":"10.1016/j.esr.2026.102309","DOIUrl":"10.1016/j.esr.2026.102309","url":null,"abstract":"<div><div>Resilience is increasingly understood as a system's capacity to not only bounce back after shocks, but also to bounce forward through absorption, adaptation, and transformation. As the importance of measuring complex phenomena has increased, resilience metrics have gained relevance. Among other systems, energy is receiving scholarly and policy attention to inform sound strategy design. This issue is particularly important in the context of suitable energy transitions that can support decarbonisation targets and sustainable development. Despite the salience of gauging resilience, existing metrics often fall short in observing energy transition. Because Parag et al. (2026) offer a timely and prominent synthesis of these measurement gaps, it serves as a vital benchmark for the field; therefore, this study critically analyses that paper to highlight its strengths and pitfalls. We learn that a deeper theoretical and methodological understanding of resilience and energy transition scholarship can return more informed deliverables.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102309"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep reinforcement learning for joint short term electricity load and price forecasting in China's smart grids: Evidence from energy economics, market volatility and Financial risk management","authors":"Fang Yu,&nbsp;Nawal Abdalla Adam,&nbsp;Zibeyda Shakaraliyeva,&nbsp;Yegana Hakimova,&nbsp;Alida Huseynova","doi":"10.1016/j.esr.2026.102322","DOIUrl":"10.1016/j.esr.2026.102322","url":null,"abstract":"<div><div>The increasing integration of renewable energy, demand-side flexibility, and market-based electricity trading has intensified the complexity of short-term load and price dynamics in China’s smart grids, where forecasting errors can generate substantial operational inefficiencies, market volatility, and financial risk exposure. This study develops a deep reinforcement learning framework for joint short-term electricity load and price forecasting by integrating grid-side demand behavior, wholesale price fluctuations, renewable generation uncertainty, macro-energy indicators, and financial risk signals into a unified predictive architecture. Unlike conventional forecasting models that treat electricity load and price as independent time-series problems, the proposed framework captures their dynamic interdependence through an adaptive learning mechanism that continuously updates forecasting decisions under changing market and system conditions. The model combines deep neural feature extraction with reinforcement learning-based policy optimization to improve multi-horizon prediction accuracy while accounting for volatility clustering, peak-demand shocks, price spikes, and risk-sensitive decision environments. Using high-frequency electricity market and smart grid data from China, the study evaluates the proposed model against benchmark approaches including ARIMA, support vector regression, random forest, long short-term memory networks, gated recurrent units, and hybrid deep learning models. The empirical results are expected to show that the deep reinforcement learning framework achieves superior forecasting performance in terms of MAE, RMSE, MAPE, and directional accuracy, particularly during periods of high demand variability and electricity price instability. Further analysis links forecasting improvements to energy economics and financial risk management by demonstrating how accurate joint prediction can support optimal dispatch, demand response planning, hedging strategies, grid investment decisions, and electricity market risk mitigation. The study contributes to the literature by providing an integrated forecasting framework that connects smart grid intelligence with energy market volatility and financial risk governance in the context of China’s evolving power sector reform. The findings offer practical implications for grid operators, electricity retailers, energy investors, and policymakers seeking to enhance market efficiency, improve system reliability, and manage financial exposure under the transition toward low-carbon and digitally coordinated energy systems.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102322"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biogenic CO2 as a renewable resource: Drivers, barriers, and pathways for a sustainable circular economy","authors":"Li Bai,&nbsp;Li Na","doi":"10.1016/j.esr.2026.102289","DOIUrl":"10.1016/j.esr.2026.102289","url":null,"abstract":"<div><div>With the increased pace of transition to a low-carbon and circular economy, biogenic CO₂ (bio-CO₂) has become a promising source of renewable carbon produced by biomass-based industrial processes. Through bio-CO₂ use in the production of fuels, chemicals, and materials, bio-CO₂ can be used to help achieve greenhouse gas emission reductions and contribute to the development of circular carbon systems. Nevertheless, the bio-CO₂ utilisation is an immature industry that is fraught with several challenges associated with regulatory systems, technological preparedness, market establishment and investment environment. This paper explores the forces and obstacles that affect the formation of the bio-CO₂ market using a multi-actor approach. Sweden is taken as an example because of its relatively high biogenic sources of CO₂ and active search for strategies of carbon management. Empirical knowledge is based on semi-structured interviews with the stakeholders of the bio-CO₂ value chain, such as the producers, users, supporting actors, industry associations, and government institutions. The results have provided some of the key barriers to the market development that are high capture and processing costs, regulatory uncertainty and lack of infrastructure to transport and use bio-CO₂. The producers stressed the risks in investments and the long payback time, whereas users pointed out the reliability of the supply chain and the quality of the products demanded. Simultaneously, some drivers were distinguished, such as the growing demand for low-carbon products, new opportunities in the production of e-fuel, and the implementation of the bio-CO₂ usage into the current bioenergy systems. The findings emphasise the need to have consistent regulatory frameworks, more transparent carbon accounting regulations, and specific policy tools to minimise investment risks and encourage the development of infrastructure. The synthesis of stakeholder views introduces the study to the insight into the systemic circumstances determining the emergence of bio-CO₂ markets in the early stages and the contribution to the current discourse of the strategy of carbon use to the overall low-carbon transition.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102289"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting EU ETS carbon prices using information on CCU/CDR technologies’ investments","authors":"Maria S. Kasidoni,&nbsp;Nikolaos D. Adamopoulos,&nbsp;Maria D. Loizidou","doi":"10.1016/j.esr.2026.102302","DOIUrl":"10.1016/j.esr.2026.102302","url":null,"abstract":"<div><div>We examine the predictive relevance of an investment-linked proxy capturing market attention toward carbon management technologies, including Carbon Capture Utilization and Carbon Dioxide Removal (CCU/CDR) technologies, for EU ETS carbon prices. Building on evidence that EU ETS prices affect investment, this study examines the reverse relationship by testing whether carbon management investment signals can inform price forecasting. Empirically, we demonstrate that incorporating this proxy into the dataset improves forecast accuracy across statistical, machine learning (ML), and deep learning models. Methodologically, we implement a systematic benchmarking framework and hybrid stacking ensembles, and apply a structured predictor-screening workflow with explicit caveats to ensure transparency regarding measurement uncertainty in the proxy. From a policy perspective, our results provide actionable insights for market participants and regulators seeking to anticipate carbon price dynamics. Future research could explore alternative proxies, higher-frequency data, or causal identification strategies to further enhance forecasting and deepen understanding of carbon market behavior.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102302"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148554984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural lock-in and low-carbon transition in coal-dependent regions: Insights from China's Coal Triangle Region","authors":"Wenhui Chen,&nbsp;Ziwen Ruan,&nbsp;Bofeng Cai,&nbsp;Dan Chen,&nbsp;Yan Li,&nbsp;Li Zhang,&nbsp;Runyu Jiang,&nbsp;Zihua Yin,&nbsp;Zhe Zhang,&nbsp;Jing Guo,&nbsp;Yalin Lei,&nbsp;Jinnan Wang","doi":"10.1016/j.esr.2026.102320","DOIUrl":"10.1016/j.esr.2026.102320","url":null,"abstract":"<div><div>The Coal Triangle Region, comprising Shanxi, Inner Mongolia, Shaanxi, and Ningxia, is the world's largest coal-producing region, accounting for 72% of China's output and 38% of the global total in 2025. As China advances its decarbonization agenda, the region faces a transition problem of reducing coal dependence while maintaining energy security, industrial stability, and employment security. This review synthesizes insights from academic literature, policy reports, and relevant databases to examine the region's structural characteristics, transition pathways, and policy frameworks. It identifies strong structural lock-in, with coal-based industries contributing 20.3% of regional industrial GDP in 2024 and remaining a cornerstone of regional economic development. This dependence constrains both the timing and pace of low-carbon transition. Consequently, the region is expected to peak later than the national average, with a slower post-peak decline in emissions. Deep decarbonization will require a shift from production-control measures to technology-driven mitigation, particularly through renewable power expansion, hydrogen-based industrial pathways, and carbon dioxide removal technologies. The transition also poses major social challenges, as declining coal demand may lead to job losses and skill mismatches among the more than 2 million coal-related workers. Addressing these interconnected challenges requires a comprehensive just transition framework integrating industrial decarbonization, clean-energy substitution, employment resilience, transition finance, and interregional burden sharing. This review provides policy-relevant insights for advancing low-carbon transition in coal-dependent regions in China and beyond.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102320"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of hydrogen transport infrastructure in system operation and flexibility under deep decarbonization","authors":"Philip Guerra,&nbsp;Niklas Hartmann","doi":"10.1016/j.esr.2026.102321","DOIUrl":"10.1016/j.esr.2026.102321","url":null,"abstract":"<div><div>Hydrogen transport infrastructure is widely considered a key enabler of deep decarbonization. In Germany, the planned hydrogen backbone aims to connect industrial demand centers with renewable-rich regions. This study evaluates the system-level implications of a predefined hydrogen backbone using a high-resolution capacity expansion model of Germany, with particular emphasis on the post-gas phase-out period after 2045. Under a modest domestic hydrogen demand scenario without imports, results show that the backbone slightly reduces total system costs. However, its primary impact lies in reshaping the structure of system flexibility by shifting energy balancing toward spatially coordinated hydrogen production, storage, and conversion. This reduces pressure on AC transmission expansion and mitigates transition stress following the fossil phase-out. A targeted demand-side management (DSM) sensitivity further shows that temporal flexibility can reduce overinvestment during critical transition periods, highlighting the complementary roles of hydrogen transport infrastructure and demand-side measures in achieving climate neutrality.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102321"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathway to zero-carbon emission of energy recovery processes from heavy oil reservoirs","authors":"Zeyu Lin,&nbsp;Xinqian Lu,&nbsp;Weifeng Lv,&nbsp;Heyu Liu,&nbsp;Bin Ding,&nbsp;Fanhua Zeng","doi":"10.1016/j.esr.2026.102288","DOIUrl":"10.1016/j.esr.2026.102288","url":null,"abstract":"<div><div>In recent years, declining production of conventional light oil, together with steadily increasing global energy demand, has elevated the strategic importance of heavy oil resources. However, the intrinsically high viscosity of heavy oil severely limits the effectiveness of conventional recovery methods, with typical recovery factors generally below 15%. Thermal recovery technologies enhance oil mobility by injecting heat carriers into the reservoir, thereby substantially improving recovery efficiency. Nevertheless, the generation of thermal agents relies on large-scale fuel combustion and is associated with considerable CO₂ emissions, rendering traditional thermal processes increasingly incompatible with current goals of green and low-carbon oilfield development. Against this background, low-carbon development technologies for heavy oil reservoirs have emerged as major research and application frontiers. Representative approaches include expanding-solvent SAGD (ES-SAGD), non-condensable gas-assisted SAGD (NCG-SAGD), in-situ reflux (ISR), vapor extraction (VAPEX), cyclic solvent injection (CSI) and in-situ hydrogen generation (ISHG). Despite growing research efforts, a comprehensive understanding of their underlying mechanisms, field performance, and carbon emission reduction potential in enhancing oil recovery (EOR) remains limited, which constrains objective assessments of engineering applicability and rational technology selection. To address this gap, this study provides an integrated review of representative mentioned six low-to zero-carbon emission heavy oil EOR technologies, focusing on their fundamental mechanisms, carbon emission reduction potential, technical advantages and limitations, and future development trends. These findings provide scientifically grounded screening criteria and practical references for the low-to zero-carbon development of heavy oil reservoirs, thereby supporting the green and sustainable development of heavy oil reservoirs worldwide.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102288"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience of oil product trade networks along the global oil industry chain: A risk propagation approach","authors":"Sai Chen,&nbsp;Xinran Sun,&nbsp;Yuxi Tian,&nbsp;Tairui Zhang,&nbsp;Guiqin Xue","doi":"10.1016/j.esr.2026.102331","DOIUrl":"10.1016/j.esr.2026.102331","url":null,"abstract":"<div><div>Amidst escalating geopolitical instability and profound shifts in global energy dynamics, the safety of oil trade networks is under mounting pressure. Constructing a resilient oil trading system capable of withstanding risk shocks has thus emerged as a critical strategic imperative for safeguarding global energy security. Accordingly, an improved cascade failure model was applied to simulate the dynamic propagation of supply shortage risks. A three-dimensional resilience assessment framework encompassing structural, functional, and economic dimensions was then developed, based on which the resilience of oil product trade networks along the global oil industry chain (GOIC) was systematically assessed. The findings are fourfold. (1) Trade networks along the oil industry chain exhibit increasing transmission efficiency, tighter regional connectivity, and an increasingly entrenched core-periphery hierarchy from upstream to downstream, with average path length, clustering coefficient, hierarchy coefficient, and assortativity coefficient changing from 5.70, 0.21, 1.14, and −0.09 to 4.19, 0.54, 1.25, and −0.45, respectively. (2) The minimum values of structural, functional, and economic resilience decline to 0.35, 0.60, and 0.60, respectively, under extreme supply shortage scenarios. Upstream network is characterized by functional vulnerability, midstream network by functional or economic vulnerability, and downstream network by structural instability. (3) Saudi Arabia and Russia emerge as the primary risk sources in upstream networks, the United States and Malaysia in midstream networks, and Thailand, Vietnam, and China in downstream networks. Large economies such as the United States and China face higher absolute losses, while smaller economies such as Bhutan are disproportionately exposed to higher relative losses. (4) The average composite resilience of all networks exceeds 0.95, ranking from highest to lowest as upstream, midstream remaining oil product, downstream, and midstream organic chemical networks, indicating relatively high resilience under most risk scenarios but a sharp decline under a few extreme scenarios. Therefore, targeted strategies are needed to address the vulnerable dimensions of each network, prioritizing early-warning and emergency-response capacity for extreme scenarios and diversified supply channels for highly dependent countries, thereby enhancing the resilience of the global oil trade system.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102331"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148540505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated water–carbon–cost tradeoff in microalgae-based biofuel supply chain networks","authors":"Mehraneh Alibeigi,&nbsp;Alireza Goli","doi":"10.1016/j.esr.2026.102330","DOIUrl":"10.1016/j.esr.2026.102330","url":null,"abstract":"<div><div>In this research, a comprehensive multi-objective mathematical optimization framework is developed for the design of a third-generation biofuel supply chain based on microalgae. The proposed model simultaneously addresses the critical trade-offs among total cost, water consumption, and carbon emissions, which are recognized as key challenges in sustainable biofuel systems. To enhance the robustness of the decision-making process, uncertainty in major supply chain parameters, including demand and resource availability, is explicitly incorporated through a robust optimization approach. The supply chain structure is modeled in an integrated manner, encompassing all stages from microalgae cultivation and processing to the production and distribution of biodiesel, glycerin, and biofertilizer. Several exact multi-objective solution methods are implemented and systematically evaluated to generate high-quality Pareto frontiers and to analyze the performance of alternative optimization techniques. Numerical results demonstrate the effectiveness of the proposed framework in capturing the complex interactions among economic, environmental, and social sustainability dimensions, while also highlighting the necessity of resilient design strategies under uncertain operating conditions. The findings provide valuable managerial and policy insights for the development of efficient and sustainable microalgae-based biofuel supply chains.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"66 ","pages":"Article 102330"},"PeriodicalIF":12.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148635041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}