Ab initio calculation of transition properties and infrared absorption spectrum of the NaS molecule.

Breaking the resource curse: The impact of green finance on the energy transition of resource-based cities

Narrating the energy transition: A critical geopolitical analysis of Czech media discourse

Energy transitions increasingly depend on collective decision-making (CDM) by households, firms, and civic organizations that co-invest, co-operate, and co-govern local energy resources. Yet mainstream energy models still treat behavior as that of a single, perfectly rational actor, under-representing the social dynamics of CDM. This paper presents a decade-scale (2015-2025) systematic review of CDM modeling at community scale. Using a broad search string pairing synonyms of “collective decision” with “energy” across leading databases, hundreds of records were screened and 84 peer-reviewed studies retained that simulate interactions among multiple autonomous actors. A supplementary search-robustness check indicates that broader retrieval adds more game-theoretic and governance-oriented studies, but does not materially alter the main patterns. Methodologically, agent-based simulation is a major strand, alongside game-theoretic formulations, hybrid optimization-agent approaches, and an emerging line of multi-agent reinforcement learning. Most studies focus on electricity communities, especially solar PV, battery storage, and peer-to-peer trading, while heat, mobility, and multi-energy integration remain limited. Peer influence and heterogeneity are often represented, whereas governance processes and diverse actor roles are still rarely modeled. Empirical calibration and validation are improving but remain uneven, limiting policy credibility in practice. The review identifies three field-defining gaps: (i) limited behavioral realism and weak validation; (ii) thin representation of governance and institutional change; and (iii) weak multi-scale socio-technical integration across sectors and levels. In response, the Multi-Scale Integrated Collective-Energy Decision (MICED) framework is proposed as a modular way forward for linking heterogeneous actors, group-decision processes, and socio-technical system dynamics. Together, the review and framework outline an agenda for more credible, equity-aware, and policy-relevant analysis of community-scale energy transitions. • Decade-scale systematic review of energy-related collective decision-making models. • Key gaps: weak behavioral realism, governance, and multi-scale coupling. • Propose framework MICED: multi-scale, learning agents, and explicit governance.

The decarbonization of fossil fuel is crucial for balancing energy security and carbon management. Underground coal gasification (UCG) is a promising process because of its potential for mitigating ecological impacts, but its conventional autothermal approach with oxygen injection still generates substantial CO 2 . By utilizing thermal plasma to simultaneously provide heat and highly reactive species, can enable rapid coal seam heating and drive directed carbon-reduction pathways without carbon combustion, thereby effectively mitigating CO 2 generation. Experimental results demonstrate that, plasma-driven underground coal gasification (PUCG) initiates within 1 min, producing >94% effective gas and reducing CO 2 concentration by over 86%. Under plasma treatment, aromatic clusters in coal undergo cleavage and recombination into longer, more planar polycyclic assemblies, then carbon defect sites develop on these aromatic clusters, driving the carbon structure toward a more reactive state. The carbon defects serve as attack sites for reactive species, promoting the formation of oxygen-containing functional group (C O ). The activation energy for the reaction between a coal model compound and OH· species to form syngas is only 27.65 kJ·mol −1 . Compared with coal gasification by ground-state water, the activation energy is reduced by more than 80%. Therefore, PUCG offers a promising novel pathway for low-carbon transition of coal-based energy. • A novel in-situ enhanced coal seam gasification system via plasma for H 2 production • Plasma-driven gasification achieves >94% effective gas with >86% CO 2 reduction • Active carbon sites are continuously generated under conversion • Reactive oxygen-containing functional group (C O ) formed under plasma conditions • Activation energy for gasification is reduced by over 80%

Practical guidance about tradeoff choices between accuracy, efficiency and deployment ability in deep convolutional neural network architectures for land use and land cover classification has been largely unavailable because each study evaluates architectures differently. This paper provides a controlled comparative assessment of four popular convolutional neural network architectures for land use and land cover classification visual geometry group19, ResNet50, Inception_Version3 and MobileNet_V2 using the Euro_SAT benchmark which includes 27,000 Sentinel2 red green blue images that have been cut into 10 land uses classes. The convolutional neural network architectures were all trained and evaluated through the same preprocessing, augmentation, data splitting, training procedure and metric as follows: Overall accuracy/F1 macro averaging class by class confusion matrix convergence dynamics efficiency metrics (number of parameters and inference-oriented considerations). The results indicate that modern architectures provide significantly better than older sequential baselines: ResNet50 provided the highest total accuracy (>97%), along with consistent convergence behavior; InceptionV3 improved discrimination for classes with both ambiguous visual appearances and linear structures (river, highway); MobileNetV2 was able to achieve high accuracy (>94%), but had an order of magnitude less number of parameters than the other architectures and is well suited to lower source or real time application scenarios. Finally, this paper maps convolutional neural network outputs into an energy transition decision workflow (Renewable Siting → Corridor Constraints → Monitoring), and demonstrates how land use and land cover layers derived from convolutional neural networks can support net zero resource planning. • Benchmarks 4 CNNs on EuroSAT using identical training and evaluation setup. • ResNet50 achieved highest accuracy (>97%) for LULC classification. • MobileNetV2 reduced parameters by ∼6× while maintaining >94% accuracy. • InceptionV3 improved corridor feature detection (roads, rivers). • Enables decision-grade LULC for CCUS, geothermal, and net-zero planning.

Taiwan’s nuclear energy debate has intensified amid rising electricity demand, recurring blackout incidents, and growing decarbonization pressures, yet public acceptance remains polarized following the 2021 referendum rejecting nuclear plant reactivation. This study examined the determinants of public acceptance using a hybrid multi-criteria decision-making framework integrating Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). Thirty Taiwanese citizens completed a DEMATEL-based pairwise comparison questionnaire covering four dimensions and sixteen criteria related to perceived risk, perceived benefit, trust and governance, and knowledge and communication. DEMATEL results showed that perceived benefit and perceived risk were the main causal drivers, while trust and governance and knowledge and communication were dependent dimensions. Key causal criteria included environmental contamination, low-carbon electricity, and complementarity with renewable energy. ANP weighting analysis indicated that trust and governance was the most influential dimension, with safety oversight, environmental contamination, public education, emergency preparedness, and regulatory competence ranked as the top criteria. The findings reveal a governance paradox: although benefit framing may increase openness to reactivation, public acceptance ultimately depends on credible regulatory competence, transparent safety assurance, and participatory communication. Finally, the DANP framework offers a robust, generalizable, and decision-oriented tool for systematically evaluating nuclear energy acceptance within complex and contested energy transition landscapes. • A DEMATEL–ANP framework was applied to evaluate nuclear reactivation acceptance. • Perceived risks and benefits were identified as primary causal drivers of acceptance. • Trust and governance were determined as the most influential evaluative dimension. • Safety oversight and environmental contamination were ranked as top-priority criteria. • Climate-benefit framing was shown effective only when credible regulation was ensured.

Unintended delays: Green technology, finance, and energy transition

The International Maritime Organization has outlined a strategy for the shipping sector to reach net-zero greenhouse gas emissions by 2050. As ongoing research continues to evaluate new technologies and energy sources, their potential, effectiveness, costs, and challenges evolve year by year. Conducting a comprehensive review of existing research and past efforts can provide a solid basis for upcoming research for the maritime sector. This review updates the current state of research on maritime defossilisation, focusing on promising alternative fuels, propulsion systems, their associated challenges and cost ranges, as well as regulatory frameworks, and energy efficiency and emission reduction measures. It also identifies gaps in the existing techno-economic literature, particularly regarding cost elements that remain insufficiently addressed. The maritime sector's energy transition is analysed from a systemic perspective, and potential solutions to overcome bottlenecks are assessed. The findings reveal that, in the long term, e-ammonia is commonly viewed as the most promising alternative fuel for achieving net-zero targets, while liquefied natural gas may take a transitional role, and biofuels may play a more limited role due to their limited supply potential. During the past decade, research interest in marine fuels has shifted from fossil fuels to alternative fuels such as ammonia, hydrogen, and methanol. These fuels together comprised approximately 40% of all fuel-focused studies in 2020-2024. The review highlights that scaling up new energy sources and technologies remains a major bottleneck in the maritime energy transition, and that carbon pricing and taxes can facilitate a shift towards a defossilised shipping sector. • Research shows a clear shift from fossil fuels to e-fuels and biofuels. • e-Ammonia is the most promising among alternative fuels for net-zero shipping. • Dual-fuel and hybrid systems facilitate the transition to net-zero shipping. • Techno-economic studies vary widely and often omit externality costs. • Carbon pricing, emission trading, and policy support are key to scaling up green fuels.

Alternative community participation models for a just energy transition in Ireland: A comparative analysis of community benefit funds and share schemes under ORESS1

Exploring impact of AI-robotics, energy transition, energy utilization, and economic growth on environmental degradation: Evidence from leading Asian countries by KRLS model

Systematic review of justice-based transitions in climate, renewable energy, and food security from 2009 to 2025

Energy transition for sustainable development in Saudi Arabia: Renewable energy growth and emission reduction

A comprehensive review and framework on the applications of digital twins for energy transition at the district level

Hydrogen-argon power cycle for next-generation zero-emission energy transition

This paper offers a comprehensive optimisation tool for the design and assessment of hybrid maritime power systems that combine internal combustion engines, fuel cells, and battery energy storage systems. Using a surrogate-assisted NSGA-II algorithm, the framework concurrently reduces operational expenditure, CO₂ emissions, and life cycle cost assessment. Under constant technical criteria, including system weight and volume, with and without waste heat recovery, four fuel pathways—diesel, LNG, methanol, and ammonia—are evaluated. The results reveal considerable economic and environmental differences compared to the diesel baseline: LNG increases LCCA by 0.5 % (€1.2M) and global warming potential (GWP) by 2 % (1752 kg), while acidification potential (AP) and aerosol formation potential (AFP) decrease by 91 % (914 kg and 1118 kg, respectively). Methanol reduces LCCA by 14.3 % (€35.3M), GWP by 36 % (35,540 kg), and AP/AFP by 81 %, offering a cost-effective and environmentally balanced solution. Ammonia eliminates GWP, AP, and AFP, though with a 10.7 % (€60M) increase in LCCA, demonstrating its potential for long-term decarbonisation. The findings show clear Pareto fronts for every fuel, suggesting that the possible design area is significantly influenced by fuel type. The framework offers practical guidance for designing energy-efficient, low-emission vessels, aiding in sustainable marine energy transitions.

Prospects for solar photovoltaics in highly renewable energy transition scenarios towards a dominant future energy source

How low-carbon power policies affect the green hydrogen industry? A case of China

The effect of new energy demonstration city policy on energy consumption transition: Evidence from China.

In the last ten years, the North African Countries (NAC) have reached remarkable rates of the development of renewable energy in last ten years, with an overall growth of the EU’s renewable generation up to 40%. This can be attributed to 4.5 GW of new onshore and offshore wind, PV and solar thermal capacity that has been added to the system. If we take hydropower out of the mix, capacity from renewable sources is up by nearly 560% (with the overall total growing just 80%). This has happened, incredibly enough, at a time of seismic activity from socio-political earthquakes in the area. The purpose of this article is to provide full overview of renewable energy in NAC, assessing its resource potential, deployment, investment, and policies. By assessing key drivers, challenges, and opportunities, the article will provide strategic suggestions and policy recommendations that could help to accelerate renewable energy growth and long-term sustainability in the region. With respect to data collection, this study primarily investigates the adoption trajectory and current status of renewable energy deployment across the NAC, drawing on evidence synthesized from IRENA, the IEA, and the World Bank. In addition, the article incorporates an assessment of the region’s prospective capacity for renewable energy installations, contextualizing deployment patterns against the underlying resource potential and enabling conditions. The region, statistically, has considerable renewable energy potential, with estimates of annual generation of 147,729, 181,718, and 168,205 TWh for CSP, PV, and wind, respectively. By 2040, Algeria's solar PV could achieve a levelized cost of energy (LCOE) of only €0.019/kWh and wind energy in Morocco could come in at a range of €0.077 - €0.111/kWh. Energy efficiency measures could also save a total of 35 TWh of electricity consumption per year by 2040, amounting to an 8.6%, or 35 TWh less than the current demand. The total consumption savings would be a total of 437 TWh and savings of $39.7 billion in electricity bills. In addition, these savings will amount to emissions • Mapping the technical potential of solar, wind, green hydrogen, and storage technologies across North Africa. • Assessing investment requirements and financial feasibility for large-scale deployment. • Presenting transition scenarios from 2025–2040 based on realistic energy demand, CO 2 reduction pathways, and grid integration constraints. • Identifying key opportunities and challenges for accelerating the clean energy transition. • Providing strategic policy recommendations for regional decision-makers and international partners.