Search Results (161)

The circular economy offers effective strategies to support the transition from fossil fuels to renewable energy. However, research at the nexus of the circular economy and energy transition remains fragmented across disciplines and lacks a systematic and integrative overview of its intellectual structure and thematic evolution. To address this gap, this study conducts a large-scale bibliometric analysis of 2977 journal articles published between 2008 and 2025 to examine the development, knowledge structure, and global distribution of this field. Performance analysis and scientific mapping are employed to evaluate research output, subject areas, thematic structures, intellectual foundations, journal dissemination, and international collaborations. The results indicate that the circular economy–energy transition nexus is a rapidly growing and multidisciplinary field. It is anchored by conceptual and policy-oriented works and complemented by applied studies on waste management, bioenergy, and decarbonization technologies that directly relate to energy production, conversion, and system efficiency. The geographical distribution shows a multi-pillar but uneven research landscape, with Europe and China emerging as leading contributors, while other regions remain comparatively underrepresented, shaped by regional priorities and collaborative networks. The study highlights emerging research gaps and future directions, offering insights into how circular economy strategies such as resource circularity and waste-to-energy applications can contribute to sustainable and equitable energy transitions and inform future energy-focused research agendas in the context of low-carbon transformation. Full article

An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES is often overlooked, which can limit the IES’s effective market participation and stability. To address this issue, this study introduces a two-layer collaborative model to coordinate VPP scheduling for multiple IES units, aiming to improve collaboration efficiency. The upper level involves the VPP setting electricity prices based on load conditions, guiding IES units to adjust their market strategies. At the lower level, the model encourages integration and optimization of different energy types within the IES through enhanced energy interactions. Additionally, the application of the Shapley value method ensures fair benefit distribution among all IES members. This approach supports equitable economic outcomes for all participants in the energy market. The model employs a multi-strategy improved Dung Beetle Optimizer (FSGDBO) combined with commercial solver techniques for efficient problem-solving. Experimental results demonstrate that the model significantly enhances the VPP’s peak-shaving and valley-filling capabilities while preserving the economic interests of the IES alliances, thereby boosting overall energy management effectiveness. Full article

Urban crises rarely occur in isolation but emerge as interconnected disruptions across space, time, and institutions. The COVID-19 pandemic intensified existing vulnerabilities and intersected with other crises, producing cascading effects. This paper asks how cascading effects analysis can be used as a planning-oriented method to map and govern compound urban crises, drawing on case studies from Cape Town, Dortmund, and São Paulo. In Cape Town, South Africa, the pandemic intersected with high HIV and tuberculosis rates and load shedding, straining health and social services. In Dortmund, Germany, COVID-19’s economic disruptions overlapped with an energy price crisis, while in São Paulo, Brazil, lockdowns coincided with increased gender-based violence and constrained access to support services. Together, these cases show how pre-existing socio-political and economic conditions shape the impacts of crises, exacerbating marginalization and deepening systemic inequalities. Cascading effects analysis is used to visualize and address interdependencies in compound crises, helping planners move beyond sectoral silos, identify key intervention points for crisis management, and support more resilient and equitable urban planning. The paper calls for a methodological shift in urban crisis research toward tools that better communicate systemic risk and bridge risk assessment, social vulnerability, and planning practice. Full article

Semantic Communication (SC), driven by a deep learning (DL)-based “understand-before-transmit” paradigm, transmits lightweight semantic information (SI) instead of raw data. This approach significantly reduces data volume and communication overhead while maintaining performance, making it particularly suitable for UAV communications where the platform is constrained by size, weight, and power (SWAP) limitations. To alleviate the computational burden of semantic extraction (SE) on the UAV, this paper introduces federated learning (FL) as a distributed training framework. By establishing a collaborative architecture with edge users, computationally intensive tasks are offloaded to the edge devices, while the UAV serves as a central coordinator. We first demonstrate the feasibility of integrating FL into SC systems and then propose a novel solution based on Proximal Policy Optimization (PPO) to address the critical challenge of ensuring service fairness in UAV-assisted semantic communications. Specifically, we formulate a joint optimization problem that simultaneously designs the UAV’s flight trajectory and bandwidth allocation strategy. Experimental results validate that our FL-based training framework significantly reduces computational resource consumption, while the PPO-based algorithm approach effectively minimizes both energy consumption and task completion time while ensuring equitable quality-of-service (QoS) across all edge users. Full article

The global energy transition (ET) is widely portrayed as a technological shift toward low-carbon systems; however, it also entails profound geopolitical and socio-environmental transformations. While energy justice (EJ) has become a key framework for assessing fairness in energy systems, it seldom incorporates the geopolitical restructuring of material, energy, and economic flows that underpin contemporary transitions. This article develops a geopolitically informed approach to EJ, trying to capture how the new flows of energy, matter, and power shape—and are shaped by—enduring centre–periphery inequalities. Using a guided literature synthesis that combines EJ, political ecology, decolonial critiques, and green extractivism, the study enhances classical EJ tenets by incorporating transboundary flows, ecological unequal exchange, ontological plurality, and local self-determination. An illustrative application to copper extraction in Chile and Peru demonstrates how critical-mineral supply chains reproduce new sacrifice zones within emerging geopolitical configurations. By connecting local socio-environmental conflicts to global energy dynamics, the framework advances a more comprehensive, multidimensional approach to justice in the ET. The findings offer conceptual and practical insights for designing more equitable and geopolitically aware sustainability policies. Full article

Electric mobility is increasingly recognised as a sustainable transportation solution worldwide. While the economic and environmental aspects of e-mobility have been explored extensively, social dimensions such as equity, accessibility, and inclusiveness remain underexplored. Existing literature on these social aspects is fragmented across disciplines, shaped by varying regional contexts, which complicates efforts to form a coherent understanding of the field. To address this gap, a bibliometric analysis was conducted using the R-studio software via the Biblioshiny app. Version 4.3.0. This analysis systematically maps the intellectual landscape, identifies dominant themes, and highlights critical research gaps at the intersection of e-mobility and social sustainability. A total of 490 publications were extracted from the Scopus database as of 23 March 2025. The findings reveal a sharp increase in scholarly attention since 2018, peaking at 110 publications in 2024. The top-ranked country is China, which has 130 publications. In addition, the research has clustered around four thematic areas: energy and charging infrastructure, social and economic impacts, public policy and regulations, and technological innovations. Despite this growth, persistent gaps remain, particularly concerning social equity, inclusive policy design, socio-economic disparities, and the real-world effects of emerging technologies on vulnerable populations. Future research should specifically explore how e-mobility initiatives can reduce regional access inequalities, generate quality green employment, and ensure that technologies such as vehicle-to-grid systems are equitably deployed to benefit low-income and marginalised populations. Full article

The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with Türkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems. Full article

This paper addresses the highly important and timely issue of the energy transition, a topic of particular relevance within the European Union (EU), which has long been a global leader in pursuing climate neutrality. The article proposes a novel framework for monitoring energy transition progress and its temporal dynamics across the EU countries, adopting a decade-long analytical horizon. The research employs the Dynamic Energy Transition Assessment (DETA) method, which is structured around five key pillars of the energy transition: (1) decarbonization and the shift toward clean energy; (2) energy security and system resilience; (3) energy justice, health impacts, and affordability; (4) energy efficiency and energy management; (5) development, innovation, and modernization of energy infrastructure. Applying this method enabled the study to meet its central objective: evaluating the level of development of these pillars, analyzing the balance among them, and examining both the direction and speed of changes over time. This dynamic approach integrates three core components of transformation processes, state, quality (coherence), and pace of change, offering an innovative combination of structural and temporal perspectives. The originality of this framework lies in its ability to capture the multidimensional and evolving nature of the energy transition. The study is based on 19 indicators, with indicator weights determined through Entropy and Criteria Importance Through Intercriteria Correlation (CRITIC) analytical methods, while pillar weights were assigned using the AHP method in alignment with EU strategic priorities. The findings reveal substantial variation and dynamism in the implementation of energy transition processes across the EU countries. Denmark, Sweden, Germany, France, Portugal, and Spain demonstrate the highest performance in terms of both quality and dynamism, whereas Malta, Cyprus, and Luxembourg perform the weakest. The proposed methodology and the resulting assessment of the level, quality, and dynamics of transformation processes offer broad practical applications. In particular, they can support the monitoring of progress toward EU climate and energy policy goals and inform management and decision-making aimed at achieving a resilient, sustainable, and equitable energy transition. Full article

With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial for achieving economical and low-carbon operation of IES. To this end, this paper unveils a comprehensive modeling and optimization framework: Firstly, a Spatio-Temporal Diffusion Model (STDM) is proposed, which generates high-quality wind power forecasting data by accurately capturing its spatio-temporal correlations, thereby providing reliable input for IES dispatch. Subsequently, a stochastic optimal scheduling model for electricity–heat–carbon coupled IES is established, comprehensively considering carbon capture equipment and a carbon quota mechanism. Finally, a multi-IES Nash bargaining cooperative game model is developed, encompassing bilateral energy trading and bilateral carbon trading, to equitably distribute cooperative benefits. Simulation results demonstrate that the STDM model significantly outperforms baseline models in both forecasting accuracy and scenario quality, while the designed bilateral market mechanism enhances system economics by reducing the total operating cost by 19.63% and lowering the total carbon emissions by 4.09%. Full article

Electric vehicles (EVs) are widely promoted as a key strategy for reducing carbon dioxide (CO₂) emissions and advancing sustainable development. However, the real-world benefits of EV adoption may vary across countries with different income levels and energy systems. This study investigates the relationship between EV adoption and CO₂ emissions per capita, as well as overall sustainable development performance (SDG Index), across 50 countries from 2010 to 2023. Using panel quantile regression, we find that EV adoption is significantly associated with reduced CO₂ emissions particularly in the high-emitting countries in high-income countries (interaction coefficient at the 90th quantile = −0.24, p < 0.05) but positively associated with emissions in lower- and middle-income countries at lower quantiles of the emissions distribution. Similarly, while EV adoption correlates positively with the SDG Index in high-income countries, it shows negative effects at the median and several quantiles. These findings challenge the “zero-emission” assumption and demonstrate that the climate and development benefits of EV diffusion are context-dependent and unevenly distributed, highlighting the need for policies that link electrification to renewable energy deployment, infrastructure development, and equitable access. Full article

Urban Green Spaces (UGS) are integral components of the built environment, significantly contributing to its ecological, social, and performance dimensions, including microclimate regulation, occupant well-being, and energy efficiency. This decadal review (2015–2025) systematically analyzes 70 high-impact studies to propose a “Symbiotic Intelligence” framework. This framework integrates Generative AI, ethical algorithms, and innovations from the Global South to revolutionize the planning, design, and management of UGS within building landscapes and urban fabrics. Our analysis reveals that Generative AI can optimize participatory design processes and generate efficient planning schemes, increasing public satisfaction by 41% and achieving fivefold efficiency gains. Metaverse digital twins enable high-fidelity simulation of UGS performance with a mere 3.2% error rate, providing robust tools for building environment analysis. Ethical algorithms, employing fairness metrics and SHAP values, are pivotal for equitable resource distribution, having been shown to reduce UGS allocation disparities in low-income communities by 67%. Meanwhile, innovations from the Global South, such as lightweight federated learning and low-cost sensors, offer scalable solutions for building-environment monitoring under resource constraints, reducing model generalization error by 18% and decreasing data acquisition costs by 90%. However, persistent challenges-including data heterogeneity, algorithmic opacity (with only 23% of studies adopting interpretability tools), and significant data gaps in the Global South (coverage < 15%)-hinder equitable progress. Future research should prioritize developing UGS-climate-building coupling models, decentralized federated frameworks for building management systems, and blockchain-based participatory planning to establish a more robust foundation for sustainable built environments. This study provides an interdisciplinary roadmap for integrating intelligent UGS into building practices, contributing to the advancement of green buildings, occupant-centric design, and the overall sustainability and resilience of our built environment. Full article

Long-duration energy storage (LDES) can deliver system-wide flexibility and decarbonization benefits, yet investment is often hindered because these benefits are diffuse and not fully monetized under conventional market structures. A public-asset-oriented valuation and cost-allocation framework is proposed for LDES. First, LDES externality benefits are quantified through a system-level optimization-based simulation on a stylized aggregated regional network, with key indicators including thermal generation cost, carbon penalty, renewable curtailment cost, involuntary load shedding, and end-user electricity expenditures. Second, LDES investment costs are allocated among thermal generators, renewable operators, grid entities, and end users via a benefit-based Nash bargaining mechanism. In the case study, introducing LDES reduces thermal generation cost by 3.92%, carbon penalties by 5.59%, and renewable curtailment expenditures by 7.07%, while eliminating load shedding. The resulting cost shares are 46.9% (renewables), 28.7% (end users), 22.4% (thermal generation), and 0.5% (grid entity), consistent with stakeholder-specific benefit distributions. Sensitivity analyses across storage capacity and placement further show diminishing marginal returns beyond near-optimal sizing and systematic shifts in cost responsibility as benefit patterns change. Overall, this framework offers a scalable, economically efficient, and equitable strategy for cost redistribution, supporting accelerated LDES adoption in future low-carbon power systems. Full article

Urban heat islands (UHIs) have intensified in rapidly urbanizing regions like New York, exacerbating thermal discomfort, public health risks, and energy consumption. While previous research has highlighted various environmental and socioeconomic contributors, most existing studies lack interpretable, fine-scale models capable of quantifying the effects of specific drivers—limiting their utility for targeted planning. To address this challenge, we develop an interpretable machine learning framework using Random Forest and XGBOOST to predict land surface temperature across 1800+ census tracts in the New York metropolitan area, incorporating vegetation indices, water proximity, urban morphology, and socioeconomic factors. Both models performed strongly (mean R² ≈ 0.90), with vegetation coverage and water proximity emerging as the most influential cooling factors, while built form features played supporting roles. Socioeconomic vulnerability indicators showed weak correlations with temperature, suggesting a relatively equitable thermal landscape. Optimization simulations further revealed that increasing vegetation to a threshold level could lower average surface temperatures by up to 6.38 °C, with additional but smaller gains achievable through adjustments to water access and urban form. These findings provide evidence-based guidance for climate-adaptive urban design and green infrastructure planning. More broadly, the study illustrates the potential of explainable machine learning to support data-driven environmental interventions in complex urban systems. Full article

Urban lighting is evolving from a basic technical infrastructure to a strategic tool for sustainable regeneration, energy efficiency, and public space reactivation. This paper explores the potential of smart and adaptive lighting systems as enablers of 24 h services, equitable access, and environmental resilience. By integrating lighting strategies with urban planning instruments (PRIC, PEC, PMU), cities can reduce energy consumption, limit light pollution, and foster new urban centralities. The study outlines regulatory gaps, technical solutions, and cultural shifts needed to transform lighting into a key asset for livable, inclusive, and digitally enabled urban futures. Full article

The transition to a low-carbon economy is profoundly reshaping European labour markets, creating both opportunities for sustainable employment and challenges for regions reliant on carbon-intensive sectors. Assessing how prepared EU Member States are for this shift remains difficult due to the lack of unified evaluation tools. This study introduces the Green Labour Market Readiness Index (GLMRI)—a composite measure assessing the adaptability of national labour markets to the energy-driven green transformation in nine EU countries: Germany, France, Sweden, Spain, Italy, Greece, Poland, Romania, and the Czech Republic. The index integrates five dimensions—education and skills, investment and infrastructure, policy and institutional quality, labour market structure, and innovation—based on harmonized data from 2010 to 2024. Panel econometric models (Fixed and Random Effects), combined with Hausman tests, are used to examine how structurally independent external energy-system characteristics, institutional capacity, and macro-structural labour-market conditions are associated with observed variation in labour-market readiness, as captured by the GLMRI composite outcome. Machine learning algorithms (Random Forest, XGBoost, LSTM) are employed to forecast readiness trajectories until 2040 under alternative policy scenarios. Results reveal persistent asymmetries between Northwestern and Southeastern Europe, showing that successful energy transition is closely associated not only with investment and innovation but also with human capital and governance quality. These associations are interpreted as diagnostic rather than causal, highlighting how external structural conditions shape the translation of energy-transition pressures into differentiated labour-market outcomes. The GLMRI provides a methodological and policy-relevant framework, helping decision-makers prioritize resources and design measures that make Europe’s energy transition sustainable, inclusive, and equitable. Full article