Virtual power plants (VPPs) aggregate distributed energy resources into system-facing flexibility portfolios, yet deployment remains uneven across jurisdictions. This study examines institutional conditions associated with durable VPP deployment through an exploratory comparative review of 26 cases across six continents. The VPP Institutional Transition Framework (VITF) evaluates five dimensions—aggregator recognition, market or procurement access, remuneration design, governance coherence, and pilot-to-durable transition design—and separates four institutional stages from three strategic pathways: Project/Sandbox-to-Market, Programmatic-to-Market, and Durable Procurement or Administrative Embedding. We assessed cases through standardized evidence extraction, independent LLM-assisted preliminary coding, author adjudication, and sensitivity analysis. Twelve cases are Stage 1, ten Stage 2, and four Stage 3; none meet the Stage 0 definition. Two high-scoring cases remain Stage 2 because Stage 3 requires formal recognition and repeatable access. Five cases follow Pathway A, three Pathway B, and four Pathway C, while 14 remain pathway indeterminate; four of five Pathway A routes are retrospective. Remuneration design has the lowest mean (1.27), reflecting the absence of service-linked payment in most Stage 1 cases. Comparable PV–battery configurations occur across all occupied stages, while more durable cases are characterized by recognized authority, recurring access, service-linked remuneration, coherent governance, and either a documented transition mechanism or durable endpoint. Durable institutionalization can therefore occur without open-market integration. The VITF provides a reproducible institutional diagnostic and transition framework for identifying first-order weaknesses, distinguishing current institutional position from routes to durable endpoints, and assessing the institutional basis for VPP contributions to flexibility, resilience, and energy security.
Limiting energy-system emissions increases the strategic value of reliable renewable sources. Geothermal energy stands out for its high capacity factor and low emissions, yet its historical deployment is often described through visually defined periods rather than statistically dated regimes. This study develops a data-driven framework to statistically validate geothermal development stages and benchmark major markets against a reference pathway. Using annual cumulative installed capacity for leading countries (1945–2024), we estimate endogenous break dates with the Bai–Perron multiple structural change methodology and summarize global diffusion via a sample-aggregate series. An ACC–ACA visualization highlights long-run growth and volatility. Country trajectories are benchmarked to the U.S. reference market through correlation-based time-shift alignment and stage-by-stage similarity scores; Dynamic Time Warping distances serve as a robustness diagnostic. The U.S. series shows five statistically identified breaks over 1960–2024, including a post-2015 regime of moderated growth. The sample-aggregate series displays three breaks (1977, 1989, 2012) with accelerated expansion after 2012. Benchmarking reveals heterogeneous pathways, ranging from strong multi-stage similarity (Italy, Japan) through partial alignment with weaker late-stage recovery (Mexico, Philippines) to limited stage completion (Iceland, Türkiye). The statistically dated staging and alignment framework provides a replicable basis for interpreting geothermal deployment pathways, informing national energy strategies, and supporting policy design for accelerated clean-energy transitions under SDG 7.
Population ageing is increasingly reshaping the consumption structure of urban energy technologies, yet its household-level implications remain poorly understood. Using micro-level survey and interview data from 2050 households in Beijing, this study examines how the age of the household head shapes per capita household energy consumption, and how these effects are conditioned by intra-urban spatial structure and China's hukou system. We find pronounced intergenerational differences in household energy consumption and associated socio-technical impact outcomes. Electricity and natural gas dominate the energy mix, with electricity consumption rising systematically with age, reflecting more home-centred daily routines, longer television-viewing time, and differences in appliance conditions. Older household heads are also more likely to be retired or not working, to occupy larger floor area on a per capita basis, and to live in dwellings with weaker thermal conditions. Age-related effects are most pronounced in the core area, where older residents are more concentrated and older housing stock with weaker dwelling conditions is more prevalent. Institutional status further amplifies these patterns: local hukou significantly strengthens the association between age and household energy consumption. Together, the findings show that the energy implications of population ageing emerge from the interaction of demographic change, spatial context and institutional stratification, underscoring the need for differentiated urban energy strategies that support affordable and clean energy consumption in ageing cities.
Energy justice is central to achieving a fair and inclusive energy transition. Using balanced panel data for 282 prefecture-level cities in China from 2010 to 2022, this study employs a spatial difference-in-differences approach to examine the relationship between New Energy Demonstration City pilot designation and urban energy justice while accounting for inter-city spatial spillovers. The analysis yields three main findings. First, the SDID estimates suggest that NEDC pilot designation is associated with higher local energy justice, while the spatial estimates indicate positive cumulative spillovers across spatially connected cities. Second, industrial structure upgrading and regional development inequality moderate the estimated policy relationships in different ways, with industrial structure upgrading strengthening both the local relationship and spatial spillovers, whereas regional development inequality weakens the local relationship but has no significant moderating effect on spatial spillovers. Third, the local estimates are relatively larger in eastern and central regions and in large cities, while the estimated spatial spillovers are stronger in resource-based cities and cities with higher renewable energy endowments. These findings highlight the importance of incorporating spatial linkages and local development conditions into the design and evaluation of new energy policies aimed at advancing energy justice.
Digitalisation is transforming modern energy systems into complex socio-technical ecosystems where interconnected devices, user practices and governance structures must be considered together. To analyse this landscape and address relevant research questions concerning controllable household appliances, a multilevel conceptual framework has been developed by organising the investigation into five interconnected thematic domains: digital control of the appliances, flexibility analysis, user engagement, management by aggregators, and relations with energy communities. Through discussions of selected scientific articles and real-world projects, this article reviews the categorisation of household appliances, methods for controlling them, and indicators to assess load-shifting capabilities and flexibility. It then addresses user engagement based on users’ behaviour, active role, and comfort. Operational strategies for aggregators are identified to manage controllable appliances, develop suitable business models, and participate in flexibility markets. Finally, the deployment of smart household appliances within energy communities is discussed as a further opportunity to involve more users and share their available resources. The synthesis distinguishes comparatively established approaches from promising but still weakly validated combinations of appliance types, control strategies, engagement mechanisms, and aggregator models.
The growing pressure for a green transition has coincided with heightened economic policy uncertainty (EPU), creating a dilemma for firms seeking to signal sustainability commitments while coping with uncertain external conditions. This study examines the impact of firm-perceived economic policy uncertainty (FEPU) on corporate greenwashing (GW) behavior. The results indicate that higher in FEPU is associated with a greater degree of GW. Mechanism analysis shows that this effect operates through two channels: tighter financing constraints and greater managerial myopia. Further heterogeneity tests reveal that the FEPU-GW effect is stronger among firms facing greater regulatory distance, weaker environmental regulation, lower female executive representation, and more intense industry competition. In contrast, interlocking director networks do not transmit FEPU-induced GW behavior across firms. These findings underscore the need to strengthen external regulatory oversight, improve managerial awareness, and reduce external pressure to mitigate GW. They also enrich understanding of how FEPU distorts corporate sustainability strategies.
Understanding how humanizing energy reconciles the synergies and trade-offs among the three dimensions of the energy trilemma—energy equity, energy security, and environmental sustainability—is crucial for advancing affordable and clean energy. However, evidence regarding its role remains limited. This study addresses this gap by examining the direct and indirect effects of country-specific contextual factors—macroeconomic stability, government effectiveness, and innovation capability—on the energy trilemma across the world's top nine energy trilemma performers over the period 2000-2023. Regarding the direct effects, the Markov regime-switching panel VAR model identifies two distinct regimes and reveals downward, upward, and upward states for energy equity, energy security, and environmental sustainability, respectively. Furthermore, the impulse-response analysis yields three key findings. First, the adverse effects of unfavorable macroeconomic conditions on the dimensions of the energy trilemma are mitigated under both regimes. Second, government effectiveness enables countries to achieve a more balanced energy trilemma, particularly under regime 1. Third, innovation capability does not necessarily improve energy system performance under either regime. Building on these findings, the mediation analysis reveals four principal mediation mechanisms. First, macroeconomic stability establishes a threefold mediating relationship among the dimensions of the energy trilemma, thereby facilitating a more balanced management of the complex interactions among its three dimensions. Second, energy security acts as a mediating mechanism through which government effectiveness influences environmental sustainability. Third, environmental sustainability mediates the relationship between government effectiveness and energy security. Fourth, environmental sustainability also serves as a mediating mechanism linking innovation capability to energy security. Collectively, these findings underscore that sustaining leadership in the energy transition requires adaptive, integrated, context-sensitive, and human-centered policy frameworks that reflect each country's institutional capacity, economic structure, technological capability, and energy system characteristics.
This study assesses how material constraints influence the performance and resilience of renewable energy systems during low-carbon transitions. A bottom-up energy system model is used to simulate the evolution of the energy system including photovoltaic, wind, solar thermal, battery storage, and hydrogen technologies along with fossil-fuel based technologies under alternative climate policy pathways from 2026 to 2050. Material requirements are explicitly linked to technology deployment, allowing consistent evaluation of resource extraction, imports, and system-level impacts. Energy security is assessed using a set of indicators covering technology performance, material availability, economic conditions, primary energy scarcity, and environmental outcomes, that are aggregated into a composite index.Results show that carbon pricing policies accelerate renewable and storage deployment but significantly increase material demand and supply-chain exposure. In contrast, emission constraint policies moderate storage expansion and material use, reducing import dependence and improving overall energy security despite lower renewable penetration. The findings highlight the importance of material-aware planning for renewable energy systems and provide practical insights for designing sustainable and secure energy transition pathways.
The rapid expansion of ultra-low-energy buildings in China has increased interest in power recuperation systems that recover otherwise wasted electrical and mechanical energy from elevators, ventilation equipment, heat pumps, and other building services. However, evidence concerning the economic feasibility and energy-cost benefits of these systems remains limited. This study evaluates the effects of power recuperation technologies on building energy consumption, operating expenditure, and investment returns in China. A panel dataset covering ultra-low-energy residential and commercial buildings across 30 Chinese provincial-level regions from 2015 to 2025 is constructed using building energy-monitoring records, electricity tariffs, climatic conditions, building characteristics, and regional economic indicators. A propensity-score-matched difference-in-differences model is employed to estimate the causal effect of power recuperation system adoption, while building and year fixed effects are used to control for unobserved heterogeneity and common temporal shocks. The analysis is supplemented by system generalized method of moment's estimation to address potential endogeneity between technology adoption and building energy performance. Economic feasibility is assessed through annual energy-cost savings, discounted payback period, net present value, internal rate of return, and sensitivity to electricity prices, capital costs, climatic zones, and building-use intensity. The econometric results indicate that power recuperation systems significantly reduce electricity consumption and annual operating costs, with stronger effects in high-rise commercial buildings, buildings located in regions with higher electricity tariffs, and facilities characterized by intensive elevator and ventilation use. The estimated economic returns are sensitive to initial investment costs and system utilization rates, although most installations remain financially viable under medium- and long-term operating scenarios. Regional heterogeneity analysis further shows that eastern and northern Chinese cities achieve comparatively greater financial benefits because of higher energy prices, denser building utilization, and longer equipment operating periods. The study concludes that power recuperation systems can improve both the energy efficiency and lifecycle economic performance of ultra-low-energy buildings in China. Targeted subsidies, performance-based incentives, standardized energy monitoring, and region-specific technology deployment strategies could accelerate their adoption in China's low-carbon building sector.
Model-based scenario analysis is often used to evaluate the impact of national climate policies on global greenhouse gas (GHG) emissions. ‘Current Policies’ scenarios are thus normative scenarios that attempt a realistic representation of projected emissions that include policies adopted and implemented by national governments. In this work we present the Climate Policy Modelling Protocol (CPMP), a comprehensive protocol to translate climate policies on a national level into inputs to integrated assessment models (IAMs). Furthermore, we apply the protocol to develop the ‘Current Policies’ scenario using the IMAGE model, and present relevant results. The protocol can be used to guide the implementation of climate policies in national and global models, highlighting the importance of policy selection and quantification in IAMs, and the development of climate policy scenarios that add to the literature on baseline scenarios used by e.g. the IPCC assessment reports and the UNEP Emissions Gap report.
The Sustainable Development Goals (SDGs) provide a global framework for addressing interconnected challenges in energy access, environmental sustainability, and social equity. Energy policy is central to this agenda because it shapes access, affordability, decarbonization, and the broader social and economic conditions required for sustainable development. This paper presents a structured narrative and policy-oriented review of the alignment between energy policies and the SDGs. The particular emphasis of this study is on SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) while also examining links with equity, economic growth, governance, and sustainable consumption. Drawing on selected peer-reviewed studies and policy-relevant literature published from 1992 through early 2026, the review identifies recurring policy gaps, cross-regional differences, and major trade-offs in policy design and implementation. The paper moves beyond descriptive cataloguing by 1) comparing contradictory findings, 2) tracing the evolution of energy–SDG policy thinking, and 3) outlining future pathways for more coherent and inclusive governance. This review highlights the need for integrated policy frameworks, stronger institutional coordination, and context-sensitive strategies that can better align national energy planning with broader sustainability objectives.
Fusion is a promising sustainable energy source that can potentially contribute to achieving the most ambitious climate goals with firm carbon-free baseload electricity. Due to recent advancements, fusion is transitioning from a purely research stage to a pre-commercialisation phase. However, many aspects related to its commercialisation are uncertain. Energy scenarios within the EUROfusion TIMES model (ETM) have been used to explore the conditions that could enable fusion to play a significant role in a future decarbonised global energy system, assuming moderate socio-economic development. The results indicate that a maximum share of 21% in a ∼140 PWh global electricity generation is achievable by 2100 under the following concurrent conditions: fusion is commercialised by 2040; the capacity doubling time is as large as that experienced by fission in the ‘70s and a stable tritium supply chain is established; the average overnight cost of the first fleet of fusion plants does not exceed 7200 USD/kW with a learning rate of 10%, and solar and wind generation is limited to 75% in each model timeslice. In contrast, the share of fusion under the most conservative assumptions reaches only a few percent, despite corresponding to an operating fleet comparable in size to the current fission fleet. The study also proves two insights: firstly, that early availability alone cannot guarantee high fusion shares in power markets. Secondly, deployment speed along with restriction to variable renewable energy contribution are dominant structural factors.
This paper introduces PHOS-UK, an open-source graphical user interface (GUI) and optimisation-based decision-support tool for optimal planning of integrated energy infrastructure in the United Kingdom (UK), including electricity, hydrogen, heating, and CO2 infrastructures. PHOS-UK is structured around two main architectures: a hydrogen optimisation framework and a hybrid power–hydrogen optimisation framework. The platform provides a flexible environment that eliminates the need for coding or direct interaction with complex optimisation models. Users can execute models using a wide range of commercial and open-source solvers and explore results through an interactive energy dashboard with diverse spatial and temporal resolutions. A distinctive feature of PHOS-UK is its capability to support decision-making under uncertainty in the hydrogen optimisation framework, including variability in renewable resources, energy demand, technology efficiency, and cost projections. For this architecture, the platform implements two-stage stochastic programming with scenario generation and reduction, alongside deterministic and robust (adaptive and static) optimisation methods, thereby allowing users to configure, reduce, and execute custom scenarios. In contrast, the hybrid electricity–hydrogen module is currently implemented under deterministic optimisation. A demonstration case study illustrates that PHOS-UK can handle large-scale optimisation problems and communicate results effectively through effective visualisation. This platform represents an important step towards open, transparent, and accessible decision-support tools, enabling stakeholders to evaluate electrification and hydrogen pathways for the UK’s net-zero transition.
The accelerating adoption of rooftop photovoltaic (PV) systems and residential electric vehicles (EVs) is reshaping households into active energy prosumers, intensifying the need for intelligent and sustainable Smart Home Energy Management Systems (SHEMS). Despite extensive research on optimization- and machine learning–based control, existing studies remain fragmented, often focusing on algorithmic performance while overlooking cyber–physical integration, scalability, and system-level sustainability. This paper presents a critical review of PV–EV integrated SHEMS from a control-oriented and sustainability-driven perspective. The literature is systematically analyzed across architectural paradigms, uncertainty modeling, energy storage coordination, EV charging strategies, and reinforcement learning (RL) applications. The review identifies a persistent gap between advanced learning algorithms and deployable, grid-interactive residential implementations aligned with decarbonization objectives. To bridge this gap, a reinforcement learning–enabled cyber–physical framework is proposed that integrates data-driven forecasting, structured state–action modeling, degradation-aware reward design, and hierarchical coordination of PV, ESS, and EV resources. The proposed framework emphasizes transparency, computational feasibility, and compatibility with real-time operation. By linking algorithmic intelligence to renewable integration, peak demand mitigation, and emission-reduction goals, this work outlines a scalable pathway toward resilient, sustainability-oriented PV–EV-integrated smart homes.
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.
The present study inspects the link between combustible renewable and renewable waste (CRW), clean energy poverty, energy justice, and carbon discharges across twenty-five developing nations from 1980 to 2017. Thus, the primary objective is to determine long and short-run elasticities among energy and environmental pollution factors. For empirical investigation CS-ARDL method is used and outcomes are revealing a negative relationship between carbon emissions and CRW. In other words, the results endorsed that the integration of CRW into manufacturing and electricity generation could support the economies evolution toward low-carbon development trajectories. These outcomes are crucial for the developing nations where industrial growth and energy demand both are growing swiftly. Thus technological evolution for efficient use of CRW can reduce the carbon intensity of GDP, and may support climate mitigation goals. Moreover, renewable energy poverty is recognized as a contributing factor to amplified carbon emissions, while enhancements in energy justice determine a mitigating effect on emissions. Furthermore, the study highlights that policymakers, governments, and stakeholders should prioritize strategies to assimilate CRW into manufacture sectors, yielding numerous benefits such as cost-effective production, reduced emissions, and advancement toward SDG-11. The study proposed that alleviating energy poverty and cultivating energy security through renewable clean energy channels could be an operative strategy for carbon mitigation, whereas pursuing the same objective through carbon-intensive sources such as fossil fuels likely to exacerbate the environmental challenges. Finally, the study emphasizes that progressing energy justice safeguards the endowment of clean and affordable energy, which is a long-term resolution for reducing carbon emissions and accomplishing SDG-7 and SDG-13 targets.
This study investigates the economic performance and financial risk dynamics associated with energy-related economic activities in China over the period 2005–2023, with a primary focus on macro-financial efficiency rather than technical system design. The analysis is grounded in an energy–economics interface, where energy consumption, pricing structures, and transition policies are treated as key economic drivers influencing profitability, capital allocation, and risk exposure across regions. A balanced provincial panel dataset is constructed to capture variations in economic performance indicators alongside financial risk measures under evolving policy and market conditions. To empirically examine these relationships, the study employs a dynamic panel econometric framework based on the two-step system Generalized Method of Moments (System GMM), which effectively addresses endogeneity, unobserved heterogeneity, and persistence in economic performance. Economic performance is proxied by indicators such as return on investment, value-added growth, and cost efficiency, while financial risk is quantified using volatility measures, leverage sensitivity, and downside risk proxies. The baseline specification incorporates lagged dependent variables and key explanatory factors including energy price fluctuations, energy intensity, capital structure, and policy uncertainty indices, allowing for a comprehensive assessment of both short-run adjustments and long-run equilibrium effects. The empirical findings reveal that energy-related economic activities exert a statistically significant and nonlinear influence on economic performance, with moderate levels of energy cost exposure enhancing efficiency, while excessive volatility in energy prices amplifies financial risk and erodes profitability. The results further indicate that financial risk is highly sensitive to policy uncertainty and carbon-related regulatory shifts, particularly in provinces undergoing rapid structural transformation. Regions with diversified economic structures and stronger financial development exhibit greater resilience, demonstrating lower risk transmission from energy price shocks to economic outcomes. Robustness checks using alternative estimators, including fixed-effects models with Driscoll–Kraay standard errors and quantile regression techniques, confirm the stability of the results across different distributional conditions. The study also identifies threshold effects, suggesting that beyond certain levels of energy price volatility, the marginal impact on financial risk increases disproportionately. These findings underscore the importance of stable policy frameworks and financial risk management strategies in sustaining economic performance. Overall, the study contributes to the energy economics and financial economics literature by providing an integrated empirical assessment of how energy-related factors shape economic efficiency and financial risk under uncertainty in China. The policy implications highlight the need for coordinated economic and financial reforms, improved risk hedging mechanisms, and more predictable regulatory environments to support and resilient economic growth.
