Achieving net-zero emissions requires rapid deployment of renewable and low-carbon energy technologies. Although these systems emit fewer greenhouse gases than fossil fuels, their broader environmental impacts are less well understood. Life cycle assessment (LCA) is applied to evaluate multiple environmental impact categories and increasingly supports sustainability assessment of energy production. This review examines LCA studies (2015–2024) on wind, solar, geothermal, hydropower, e-fuels, and nuclear energy in Europe, following the PRISMA 2020 methodology. Studies reporting quantitative results per kWh were included. Nearly 90% (n = 94) of studies assessed climate impacts, while acidification (56%), resource use (minerals and metals, 51%), and freshwater eutrophication (43%) were analysed less consistently. Climate findings aligned with previous literature, but other impacts showed substantial variability due to methodological differences and data gaps. Key gaps persist in toxicity, biodiversity, and local environmental assessments. More standardised, comprehensive LCA approaches are needed to capture these effects and guide sustainable energy transitions.
Artificial intelligence (AI) is positioned as a strategic enabler of low-carbon and renewable energy systems, yet the knowledge base at this intersection has expanded more rapidly than it has consolidated. This study examines the evolving landscape through a Scopus-based bibliometric analysis combined with a SWOT appraisal to clarify how AI is contributing to low-carbon and renewable energy production for sustainable development. The review integrates descriptive performance analysis and science mapping, including bibliographic coupling, citation, co-authorship, co-citation, and keyword co-occurrence. Available evidence were extended through a coded core sample used for strategic interpretation. The findings show that the field has moved from an emerging niche into a rapidly expanding research domain, with annual scientific output rising from 239 documents in 2021 to 2,366 in 2025. Its technical core is strongly concentrated around machine learning and deep learning, particularly in forecasting/prediction and optimization. The corpus contains 5,192 documents linked to sustainable development.
This paper presents a comprehensive study of improving cobalt recovery from spent lithium-ion batteries via electrowinning. A constant-current power supply with closed-loop control (CCPS) is developed and applied as the power source for electrowinning. The optimal operating conditions are then determined using the synthetic electrolyte and subsequently applied to a real leachate obtained from spent Li-NMC batteries. The CCPS can operate effectively in constant-current mode from 50 to 200 A/m2 with over 98% accuracy. The optimal conditions are an initial cobalt concentration of 5,000 ppm, a current density of 110 A/m2, a temperature of 45 °C, an electrowinning time of 100 min, and a pH of 4. For the synthetic electrolyte, the maximum current efficiency and cobalt recovery exceed 90% and 96%, respectively. In the real leachate application, the maximum current efficiency remains high at 99.21% and the cobalt recovery is at 60% due to co-deposition with other metals.
This work presents the design, modelling, and experimental investigation of a hybrid vibration energy harvester (HVEH) using double orthogonal spiral cantilever, integrating piezoelectric and electromagnetic transduction mechanisms. A centrally mounted magnetic proof mass oscillates between two stationary coils, while piezoelectric strips bonded at high-strain regions convert structural deformation into electrical energy. The harvester geometry and transduction mechanisms were optimized through numerical analysis and experimentally characterized under harmonic excitation. The performance of the hybrid system is compared with standalone piezoelectric and electromagnetic configurations to evaluate the benefits of combined energy conversion. Under hybrid operation at an excitation amplitude of 0.1 mm and resonant frequency of 18.9 Hz, the piezoelectric and electromagnetic subsystems generated maximum powers of 962 & micro;W and 2624 & micro;W, respectively, resulting in a combined output power of 3.58 mW. The proposed double orthogonal spiral geometry enables efficient low-frequency hybrid energy harvesting and offers significant potential for self-powered sensing applications.
The intention-behavior gap in sustainable consumption is especially visible in the electronics sector, where consumers frequently express strong environmental values that do not translate into purchasing decisions. This study examined that gap by combining survey-based perceptions (n = 64) with large-scale analysis of 138,488 Amazon reviews containing sustainability related content. Survey participants prioritized convenient return options, recyclability, and the use of sustainable materials, and reported a willingness to adopt eco-friendly practices when these options were easy to access. By contrast, online reviews focused primarily on affordability, convenience, and product functionality; sustainability was mentioned only when it affected quality or usability. Welch’s t-test and Mann-Whitney U test confirmed significant differences between self-reported survey responses and consumer generated reviews, and topic modeling showed that trust and transparency concerns were salient in surveys but largely absent from reviews. These findings indicated that physical, informational, and psychological frictions prevent sustainable intentions from becoming actionable.
Seaports require a large amount of energy for their daily operations. Nuclear-renewable hybrid energy systems (Nu-R HES) offer a promising solution to address the energy needs of seaports and facilitate their transition away from fossil fuels. This paper evaluates the feasibility of implementing a Nu-R HES consisting of a small modular reactor (SMR), a solar photovoltaic (PV) power plant, and a hydroelectric power plant (HPP), for the seaport region of Durr & euml;s, Albania. The study evaluates technological performance, estimating energy consumption, Nu-R HES, Solar PV, and HPP output. Moreover, the research provides insight into potential costs and estimates the cost of energy (COE). Monte Carlo simulations are employed to model the uncertainties for the whole operational life of the Nu-R HES. The findings confirm Nu-R HES as an affordable solution, delivering technical performance and a competitive COE with the current household and industrial electricity tariffs in Albania.
Decarbonising deep-sea shipping is difficult because vessels operate over long distances with high energy demand and limited refuelling opportunities. Most transition scenarios emphasise alternative fuels and efficiency measures, while nuclear propulsion offers a structurally different pathway by shifting propulsion energy from voyage-dependent expenditure to capital-dominated expenditure. This study examines operational implications of this shift using 2023 IMO Data Collection System dataset, covering 17,420 vessels, or about 30% of the global fleet. We compare observed operational speeds with design speeds and model a counterfactual nuclear propulsion scenario in which economic incentive for slow steaming disappears. Results show that the average observed speed is 11.49 knots, far below the design speed of 17.35 knots. Holding transport demand constant, a return to design speed would reduce required fleet capacity by approximately 31%. These findings suggest that nuclear propulsion could enable both operational CO₂ elimination and major productivity gains, representing a regime shift in fleet deployment.
Resistance to renewable energy projects among residents is increasingly linked to concerns about distributive justice. This study examines how perceptions of fairness in the distribution of benefits and burdens shape residents' preferences regarding hypothetical onshore wind power projects in Japan. Using a hypothetical choice experiment based on an online survey of 2,016 residents across 14 prefectures in wind-suitable regions of Japan, we varied four attributes: (i) project proponent (non-local company, local government, local stakeholder), (ii) community contribution activities, (iii) scale of CO2 emissions reduction, and (iv) household decarbonization levy. Mixed logit estimation revealed stronger preferences for projects led by local governments or local stakeholders, and for projects implementing community contribution activities, compared to projects led by external companies. Latent class analysis revealed heterogeneity: the positive and moderately negative groups valued local governance and benefit distribution mechanisms, while the strongly negative segment derived minimal utility from these attributes.
The increasing deployment of inverter based renewable energy sources in the Sultanate of Oman’s power system is reducing overall system inertia and leading to higher rate of change of frequency (ROCOF). To support higher renewable penetration in line with national energy targets, effective virtual inertia solutions are required. This study evaluates the impact of increased share of renewable energy on the actual inertia of the Oman Main Interconnected System (MIS) and proposes a novel interval Type-2 Fuzzy-PID-based virtual inertia control strategy. Unlike conventional approaches, the proposed method is assessed under multiple operating scenarios, including high renewable penetration, uncertainty, and severe disturbances. The system inertia is evaluated using DIgSILENT Power Factory, while the controller is designed and validated in MATLAB/Simulink. The proposed controller is successfully tested under renewable energy variability and uncertainty conditions at a penetration level of 20% of total system demand. Simulation results show a significant improvement in frequency stability, including reduced ROCOF, enhanced damping, and improved frequency response compared to conventional methods. These findings demonstrate the effectiveness and practical applicability of the proposed controller for real-world low-inertia power systems.
The rapid growth of electric vehicles (EVs) challenges urban energy systems, particularly in cities with limited historical charging data. Accurate forecasting of EV charging demand is essential for sustainable energy planning, grid stability, and infrastructure design. This study proposes a simulation-based forecasting approach that integrates urban traffic modeling with data-driven demand prediction to estimate charging loads under varying fleet sizes and charging strategies. Using Ljubljana as a case study, synthetic traffic and charging datasets are generated through MATSim simulations to represent diverse operational conditions. A feedforward neural network combined with least-squares approximation is applied to predict daily charging profiles. The framework demonstrates stable predictive performance across scenarios and provides insights into peak formation and load distribution dynamics. The approach offers a practical tool for planning EV integration in data-scarce urban environments.
ShinyEnet is an open-source software tool for modelling waste-to-energy processes, including gasification and pyrolysis. Developed at IT4Innovations, the National Supercomputing Centre of the Czech Republic at VSB - Technical University of Ostrava, it utilizes operational data from experimental facility at the Centre for Energy and Environmental Technologies—Explorer (CEETe), also part of the same university. The software models a modular, mobile, and scalable system that converts waste into gaseous or liquid fuels. ShinyEnet supports dynamic simulation, including component-failure cases, optimization, and scenario analysis. The platform thus facilitates development and assessment of compact and mobile waste-to-energy units and provides tools for addressing municipal waste-management challenges. ShinyEnet is based on real operational datasets, with continuously updated data currently available for the pyrolysis process via real-time monitoring system. The interactive web application is implemented using open-source Python libraries and employs validated historical data and machine-learning models to simulate and optimize system performance.
This study presents an integrated framework for assessing the interdependent impacts of urban mobility, land use, walkability, and air pollution on environmental outcomes in New York City. It offers a holistic approach to understanding how urban form and transportation choices shape air quality patterns. We evaluated optimized routes across five scenarios (Car, Electric Car, Transit + Active, Multimodal Baseline, and Multimodal Complete Streets) under alternative time-emissions weightings. The analysis demonstrates that multimodal transport scenarios consistently outperform car-dominated alternatives: while the introduction of electric vehicles and congestion-driven speed improvements contributes to lower emissions (-47.5%), more integrated modes of transportation provide greater benefits in combined optimizations for time and emissions (up to -91.3%)-reduction by an idealized policy horizon under a complete streets framework. The study demonstrates the value of evaluating these dimensions jointly rather than in isolation and offers a replicable framework for planners and policymakers to support targeted interventions.
With increasing global attention on sustainable energy solutions, hydrogen Fuel Cells (FCs) have gained prominence as a viable technology for addressing environmental concerns. Despite increasing research, a systematic bibliometric analysis focusing on the safety, reliability, and performance of these methods remains limited. This gap restricts the ability of regulators and risk–assessment communities to identify critical risks and research priorities. This study presents a comprehensive bibliometric analysis to map the global research landscape on hydrogen FC technologies from 2020 to 2024. Scopus-indexed data is used to get citation metrics and VOSviewer-based network visualizations. A detailed assessment of publication trends, author collaborations, and thematic developments is carried out. Results indicate a significant increase in research activity, with 18,722 publications generating 380,174 citations. China, India, and the United States lead in publication volume, while Switzerland and Sweden show the highest citation impact per paper. The Dominant research themes include FC performance, hydrogen production, and emerging areas, such as microbial FCs and hydrogen storage. The study provides valuable insights for researchers, institutions, and policymakers, supporting informed decisions in journal selection, funding allocation, and strategic research planning. The need for interdisciplinary collaboration and real-world data integration is emphasised with recommendations on future research focused on commercialization, policy alignment, and expanding application domains.
This study advances the concept of waste-to-hydrogen (WtH₂) hubs as integrated industrial ecosystems that jointly address waste valorization and low-carbon hydrogen supply for hard-to-abate sectors. Conversion pathways—thermochemical, biochemical, hydrothermal, and electro/photo-assisted—are critically compared using harmonized techno-economic and life-cycle metrics to enhance cross-study consistency. Thermochemical routes show the highest readiness, achieving 40–80 g·kg−1 feedstock and 3–6 USD·kg−1 H₂, while biochemical and electro-assisted pathways enable superior handling of wet wastes and renewable coupling. Results reveal that feedstock logistics, purification efficiency, electricity carbon intensity, and industrial co-location outweigh pathway selection in determining viability, with life-cycle outcomes highly sensitive to grid conditions. Digital twins, model predictive control, and federated analytics emerge as pivotal for ensuring purity, flexibility, and resilience. A TRL-informed roadmap identifies thermochemical and hybrid hubs, co-located with demand and supported by robust MRV frameworks, as the most bankable near-term deployment pathway.
The global shift toward energy sustainability has intensified the need for alternative energy sources to mitigate the environmental impact of fossil fuels, which currently highly contribute to CO2 emissions. Solar energy presents a viable solution, particularly in alignment with the energy strategy, which aims for 50% clean energy adoption. However, despite their potential, solar power systems face challenges such as low conversion efficiency, security vulnerabilities in remote locations, and a lack of autonomous coordination. This study presents a hybrid machine learning (ML) framework that combines K-means clustering with long short-term memory (LSTM) networks. The proposed framework enhances feature extraction by incorporating short-term and long-term consumption trends to improve forecasting accuracy, external influences such as time-of-day and seasonal variations to capture contextual factors affecting energy usage, and anomaly detection to identify abnormal energy consumption patterns. The proposed hybrid K-means-LSTM model demonstrates superior prediction accuracy and effective identification of unusual consumption patterns.
This study investigates the integration of climate-related courses with a focus on energy planning in the curricula of Planning Accreditation Board-accredited urban and regional planning programs in North America. It addresses two main research questions: the current state of energy-related course offerings and the obstacles and opportunities these programs face in preparing future planners to respond to climate and energy challenges. Findings reveal a significant emphasis on sustainability and climate, yet a notable lack of energy-focused courses. More specifically, the analysis shows that while sustainability, environmental planning, and climate change are reflected in program missions and curricula, energy-related topics are far less visible in both strategic documents and course offerings. In many cases, energy appears only as a subset of broader environmental or sustainability themes, but not a priority. Recommendations include strengthening curricula through visible energy-related classes and creating interdisciplinary and industry connections, along with stronger connections to professional practice.
Wind energy is a low-carbon source that has experienced significant technological advancements in recent decades. The random and unpredictable nature of wind poses distinct challenges for accurate wind power prediction. Accurate forecasts of wind power generation are crucial for the effective management of energy grids. This study introduces a novel, transformer-based, dynamic context-aware power forecasting gated recurrent unit (GRU) hybrid model that adapts the transformer architecture, incorporating innovative modifications to address the complications of wind power forecasting. It improves the extraction of contextual features while simplifying the model's structure. The results for the Jhimpir dataset demonstrate normalised mean absolute error (NMAE) values of 0.636, 1.747, 1.581, and 1.640 for the hybrid dynamic context-aware GRU model, transformer, long short-term memory (LSTM), and single GRU model, respectively. The proposed model outperforms other models, achieving the lowest prediction error, and provides comprehensive knowledge to decision-makers interested in wind turbines and energy optimisation.
This study applies threshold and wavelet quantile regression to investigate the relationship between nuclear energy and carbon emissions in South Africa. The findings reveal a U-shaped relationship between the two variables. The paper used lag GDP as the threshold variable, and the threshold value is 28.983. This threshold value determines the lower and upper regimes. The findings indicate that below the threshold, nuclear energy has a negative effect on carbon emissions, whereas above the threshold, there is a positive effect. With the application of WQR, the results reveal that in the short term, there is a negative effect of nuclear energy on carbon emissions, but the effect is weak. However, in the medium to long term, there is a strong negative effect. The findings may suggest that policymakers are encouraged to promote nuclear energy development cautiously, ensuring that it remains below the threshold level to prevent adverse environmental impacts.
This study presents a performance evaluation of a conventional steam power plant integrated with a chemical energy storage system based on the reversible methane steam reforming. By enabling the plant to run in two modes, charging and discharging, operational flexibility is attained. In cases of low grid demand, a portion of the boiler steam output is redirected to an endothermic reformer, where additional thermal energy is preserved as synthesis gas. The stored energy is released through the exothermic methanation process and used to power a secondary cycle in higher demand. At design condition, the system achieves a round-trip thermal efficiency of 83%. In addition, the operation points of the primary turbine, increased the efficiency from 33.6% to 41%, resulting in a total plant efficiency of 31.43%, Parametric evaluation suggests that steam diversion and machine scale enhance overall performance. Overall, the integrated system provides an efficient solution to energy time shifting.
SOFC-powered marine DC microgrids offer a promising path for maritime decarbonization but face large-signal instability risks due to low system inertia and slow fuel cell dynamics during abrupt load changes. Traditional small-signal methods fail to predict stability under such large disturbances. This paper overcomes this limitation by introducing a mixed potential theory-based stability analysis framework, tailored for SOFC-battery hybrid shipboard systems. We derive explicit analytical criteria that directly link controller parameters to large-signal stability boundaries, enabling prior robustness assessment without local linearization. Simulations and hardware-in-the-loop experiments confirm the criterion’s accuracy in predicting instability thresholds under severe transients and provide actionable insights for co-designing SOFC operating limits, storage sizing, and controller gains. This work delivers both a novel theoretical tool and practical guidance for stable, high-performance operation of next-generation SOFC-based marine power systems.
