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Microstructure & physicochemical properties dataset of NaCl-based salt mixtures for concentrating solar power | 10.1038/s41597-025-06437-z | https://doi.org/10.1038/s41597-025-06437-z | Scientific Data | 2,026 | Feng, Y.; Wu, Y.; Wang, W. | Abstract
Concentrating solar power is a pivotal technology in global transition toward renewable energy, providing a viable pathway for dispatchable and base-load electricity generation. An important component of the concentrating solar power system is molten salts, particularly NaCl-based mixtures, which serve as both efficient heat transfer fluids and high-capacity thermal energy storage media. The influence mechanisms of micro-ionic interactions and microstructure on physico | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Policy & Social Factors | |
An electronic product carbon footprint dataset for question answering | 10.1038/s41597-026-06544-5 | https://doi.org/10.1038/s41597-026-06544-5 | Scientific Data | 2,026 | Zhao, K.; Koyatan Chathoth, A.; Balaji, B.; Lee, S. | Abstract
The embodied carbon of computing systems constitutes a significant portion of their greenhouse gas (GHG) emissions. To support environmental initiatives and meet evolving standards, many companies now disclose product carbon footprints in sustainability reports, often with detailed breakdowns. Yet these reports appear in diverse and unstructured formats—text, tables, and graphs embedded in PDFs—creating major challenges for extracting and analyzing component-specific e | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | LCA & Sustainability | |
Carbon footprint dataset of concrete based on field surveys at commercial mixing plants in Shandong, China | 10.1038/s41597-026-06789-0 | https://doi.org/10.1038/s41597-026-06789-0 | Scientific Data | 2,026 | Niu, D.; Zhou, J.; Guo, B. | Abstract
Carbon dioxide (CO
2
) emissions from concrete have grown rapidly, ranking second after the power sector. Current emission factors often overlook regional heterogeneity. To bridge this knowledge gap, this study takes Shandong Province, a typical region in China, as a case study. Considering the difference in geography, history, culture, and economic development, Shandong is divided into five subregions: Easte | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Policy & Social Factors | |
Heterogeneity in public attitudes and preferences for the deployment of aquifer thermal energy storage | 10.1038/s41560-026-01977-z | https://doi.org/10.1038/s41560-026-01977-z | Nature Energy | 2,026 | Liu, T.; Hanna, R.; Kountouris, Y. | Abstract
Aquifer thermal energy storage (ATES) can contribute to heating and cooling decarbonization by utilizing the thermal capacity of natural aquifers. Securing acceptance and support for deploying ATES at scale requires acknowledging public perceptions and designing systems compatible with public preferences. Here we characterize attitudinal stances and preferences for the deployment of ATES in public buildings in the UK. Using data from a social surve | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Collaboration can secure carbon capture’s future | 10.1038/s41560-025-01916-4 | https://doi.org/10.1038/s41560-025-01916-4 | Nature Energy | 2,026 | Wilcox, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | ||
Global gridded dataset of heating and cooling degree days under climate change scenarios | 10.1038/s41893-025-01754-y | https://doi.org/10.1038/s41893-025-01754-y | Nature Sustainability | 2,026 | Lizana, J.; Miranda, N.; Sparrow, S.; Wallom, D.; Khosla, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
Carbon sequestration for geological negative emissions of the shale gas value chain in China | 10.1038/s41467-026-68829-y | https://doi.org/10.1038/s41467-026-68829-y | Nature Communications | 2,026 | Hong, P.; Guo, M.; Liang, S.; Shi, W.; Li, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | ||
Self-driven recycling of spent Li-ion battery materials with electricity generation | 10.1038/s41467-026-69868-1 | https://doi.org/10.1038/s41467-026-69868-1 | Nature Communications | 2,026 | Huang, S.; Huang, S.; Li, M.; Zhang, H.; Wang, X. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | ||
Aligning EU energy security and climate mitigation through targeted transition strategies | 10.1038/s41467-025-67595-7 | https://doi.org/10.1038/s41467-025-67595-7 | Nature Communications | 2,026 | Lal, A.; Tavoni, M.; Preuss, N.; You, F. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
A multi strategy optimization framework using AI digital twins for smart grid carbon emission reduction | 10.1038/s41598-026-38720-3 | https://doi.org/10.1038/s41598-026-38720-3 | Scientific Reports | 2,026 | Sakthivel, S.; Arivukarasi, M.; Charulatha, G.; Nithisha, J.; Abirami, B. | Abstract
This research presents an AI-enabled digital twin framework to achieve carbon neutrality in smart grids through optimal management of heterogeneous energy storage systems. The proposed structure integrates battery, thermal, and hydrogen storage technologies with AI-driven forecasting models to address the challenge of renewable integration, while maintaining grid stability and economic viability. This paper presents a comparative analysis of three distinct optimization | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Optimized scheduling of integrated energy systems considering waste-to-power plants and advanced adiabatic air compression energy storage machines | 10.1038/s41598-026-37485-z | https://doi.org/10.1038/s41598-026-37485-z | Scientific Reports | 2,026 | Wang, W.; Liu, M.; Zhao, H.; Wu, Y.; Tian, Y. | Abstract
To achieve carbon peaking and carbon neutrality goals, improve energy utilization efficiency, and accelerate the decarbonization of energy structure, this paper proposes a model that integrates Waste Incineration Power Plant (WIP) and Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) to reduce carbon emissions and enhance system economics. First, based on the coupled WIP and Power-to-Gas (P2G) model, a waste heat recovery unit is introduced to recover exhaust | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Optimized economic scheduling of demand response in integrated energy systems considering dynamic energy efficiency and dynamic carbon trading | 10.1038/s41598-025-33497-3 | https://doi.org/10.1038/s41598-025-33497-3 | Scientific Reports | 2,026 | Mao, H.; Deng, Q.; Zhang, Z.; Yang, X. | CrossRef | EnergiTrade | Energy & Carbon Trading | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | ||
Climate change will increase high-temperature risks, degradation, and costs of rooftop photovoltaics globally | 10.1016/j.joule.2025.102218 | https://doi.org/10.1016/j.joule.2025.102218 | Joule | 2,026 | Wu, H.; Kong, Q.; Huber, M.; Sun, M.; Craig, M. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Climate Mitigation | ||
Democratizing life cycle assessment by developing a streamlined model of greenhouse gas emissions from US natural gas supply chains | 10.1016/j.crsus.2025.100554 | https://doi.org/10.1016/j.crsus.2025.100554 | Cell Reports Sustainability | 2,026 | Srikanth, A.; Ramesh, S.; Heath, G.; Jordaan, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | ||
Aligning circular economy and low-carbon economy for a sustainable built environment | 10.1016/j.crsus.2025.100609 | https://doi.org/10.1016/j.crsus.2025.100609 | Cell Reports Sustainability | 2,026 | Zhang, C.; Behrens, P.; Myers, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | ||
Heterogeneous impacts of fear and policy on building energy use during COVID-19 in South Korea | 10.1016/j.isci.2025.114479 | https://doi.org/10.1016/j.isci.2025.114479 | iScience | 2,026 | Yoo, J.; Kim, D.; Kim, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Unpacking the growth of global agricultural greenhouse gas emissions | 10.1126/sciadv.aeb8653 | https://doi.org/10.1126/sciadv.aeb8653 | Science Advances | 2,026 | Ortiz-Bobea, A.; Pieralli, S. |
Agriculture, forestry, and other land use contribute about a fifth of total anthropogenic greenhouse gas (GHG) emissions. Mitigation efforts have emphasized “decoupling” that sustains production while lowering emissions per unit of output. However, the underlying decoupling mechanisms have not been fully characterized. We rely on a mathematical identity to decompose agricultural GHG emission growth (
| CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Reassessing boreal wildfire drivers enables high-resolution mapping of emissions for climate adaptation | 10.1126/sciadv.adw5226 | https://doi.org/10.1126/sciadv.adw5226 | Science Advances | 2,026 | Eckdahl, J.; Nieradzik, L.; Rütting, L. | The expansive carbon reservoirs of the boreal region are becoming some of the most rapidly growing sources of greenhouse gasses under a positive feedback between intensifying fire activity and climate change. However, current regional-scale methods lack the spatial precision needed to improve understanding of the drivers of these fluxes to inform strategies aimed at maximizing landscape carbon storage. Here, we develop an alternative and highly constrained procedure for estimating wildfire emiss | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Global dataset combining open-source hydropower plant and reservoir data | 10.1038/s41597-025-04975-0 | https://doi.org/10.1038/s41597-025-04975-0 | Scientific Data | 2,025 | Shah, J.; Hu, J.; Edelenbosch, O.; van Vliet, M. | Abstract
Hydropower is a crucial renewable source that depends heavily on water availability. Analyzing drought and climate change impacts on hydropower potential requires detailed data on both hydropower plant attributes (e.g. plant type and head) and reservoir characteristics (e.g. area, depth and volume). However, existing open-source datasets are poorly integrated: hydropower plant datasets often lack reservoir information, while reservoir datasets commonly miss hydropower plant in | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Climate Mitigation | |
Global photovoltaic solar panel dataset from 2019 to 2022 | 10.1038/s41597-025-04985-y | https://doi.org/10.1038/s41597-025-04985-y | Scientific Data | 2,025 | Li, A.; Liu, L.; Li, S.; Cui, X.; Chen, X. | Abstract
Solar photovoltaic (PV) power generation, known for its affordability and environmental benefits, is a key component of the global energy supply. However, the lack of comprehensive, timely, and precise global PV datasets has limited spatial analysis of PV potential. We developed a new method to identify PV panels globally, producing an annual 20-meter resolution dataset for 2019–2022. This dataset offers unprecedented detail and accuracy for future research and policy-making. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Policy & Social Factors | |
A dataset of structural breaks in greenhouse gas emissions for climate policy evaluation | 10.1038/s41597-024-04321-w | https://doi.org/10.1038/s41597-024-04321-w | Scientific Data | 2,025 | Tebecis, T.; Crespo Cuaresma, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Vulcan version 4.0 high-resolution annual carbon dioxide emissions in the U.S. for the 2010–2022 time period | 10.1038/s41597-025-06391-w | https://doi.org/10.1038/s41597-025-06391-w | Scientific Data | 2,025 | Gurney, K.; Dass, P.; Kato, A.; Gawuc, L.; Aslam, B. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | ||
Disaggregated Municipal Energy Consumption and Emissions in End-use Sectors in Germany and Spain for 2022 | 10.1038/s41597-025-05938-1 | https://doi.org/10.1038/s41597-025-05938-1 | Scientific Data | 2,025 | Patil, S.; Pflugradt, N.; Weinand, J.; Kropp, J.; Stolten, D. | Abstract
Sectorally-detailed municipal energy consumption and emissions datasets are crucial for localized policy-making, resource allocation, and climate action planning. While some large municipalities develop bottom-up inventories, smaller ones often lack the capacity to do so. Existing studies have spatially disaggregated national totals, yet no dataset to date provides both energy consumption and emissions data across multiple sectors at the municipal level. This study addresses t | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Policy & Social Factors | |
Global greenhouse gas emissions mitigation potential of existing and planned hydrogen projects | 10.1038/s41560-025-01892-9 | https://doi.org/10.1038/s41560-025-01892-9 | Nature Energy | 2,025 | Terlouw, T.; Moretti, C.; Harpprecht, C.; Sacchi, R.; McKenna, R. | Abstract
Hydrogen will play a critical role in decarbonizing diverse economic sectors. However, given limited sustainable resources and the energy-intensive nature of its production, prioritizing its applications will be essential. Here, we analyse approximately 2,000 (low-carbon) hydrogen projects worldwide, encompassing operational and planned initiatives until 2043, quantifying their greenhouse gas (GHG) emissions and mitigation potential from a life cyc | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Implications of policy-driven transmission expansion for costs, emissions and reliability in the USA | 10.1038/s41560-025-01921-7 | https://doi.org/10.1038/s41560-025-01921-7 | Nature Energy | 2,025 | Senga, J.; Botterud, A.; Parsons, J.; Story, S.; Knittel, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Author Correction: US industrial policy may reduce electric vehicle battery supply chain vulnerabilities and influence technology choice | 10.1038/s41560-025-01799-5 | https://doi.org/10.1038/s41560-025-01799-5 | Nature Energy | 2,025 | Cheng, A.; Fuchs, E.; Michalek, J. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | ||
Policymakers and academics envision energy demand reductions beyond typical policies in the United Kingdom | 10.1038/s41560-025-01897-4 | https://doi.org/10.1038/s41560-025-01897-4 | Nature Energy | 2,025 | Sharmina, M.; Broad, O.; Barrett, J.; Brand, C.; Garvey, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Policymaker-led scenarios and public dialogue facilitate energy demand analysis for net-zero futures | 10.1038/s41560-025-01898-3 | https://doi.org/10.1038/s41560-025-01898-3 | Nature Energy | 2,025 | Sharmina, M.; Broad, O.; Barrett, J.; Brand, C.; Garvey, A. | Abstract
Demand-side energy reductions have so far received less policy support than supply-side net-zero technologies. Here we undertake a demand-focused process for energy scenario analysis, led by policymakers and evaluated through public dialogue. We codesign, describe and model four societal futures that aim to achieve the UK’s 2050 net-zero target. The uniquely close involvement of policymakers leading the project generates markedly different narratives that reflect polic | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Demand-side strategies enable rapid and deep cuts in buildings and transport emissions to 2050 | 10.1038/s41560-025-01703-1 | https://doi.org/10.1038/s41560-025-01703-1 | Nature Energy | 2,025 | van Heerden, R.; Edelenbosch, O.; Daioglou, V.; Le Gallic, T.; Baptista, L. | Abstract
Decarbonization of energy-using sectors is essential for tackling climate change. We use an ensemble of global integrated assessment models to assess CO2 emissions reduction potentials in buildings and transport, accounting for system interactions. We focus on three intervention strategies with distinct emphases: reducing or changing activity, improving technological efficiency and electrifying energy end use. We find that these strategies can reduce emissions by 51–85% in bui | CrossRef | DigiEnergy | Renewable Energy Simulation Tools | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Strategizing renewable energy transitions to preserve sediment transport integrity | 10.1038/s41893-025-01626-5 | https://doi.org/10.1038/s41893-025-01626-5 | Nature Sustainability | 2,025 | Xu, B.; Liu, Z.; Yan, S.; Schmitt, R.; He, X. | Abstract
Hydropower is vital for climate mitigation by enabling low-carbon energy systems, but hydropower dams also trap sediment, a crucial resource for ecosystems and climate adaptation along downstream coastlines. Here we present a multisectoral integrated water–sediment–energy planning framework that fully internalizes the impacts of hydropower expansion, both on energy system costs and on foregone ecosystem services from reduced sediment supply for the Mekong River Basin. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Energy- and cost-efficient CO2 capture from dilute emissions by pyridinic-graphene membranes | 10.1038/s41893-025-01696-5 | https://doi.org/10.1038/s41893-025-01696-5 | Nature Sustainability | 2,025 | Micari, M.; Hsu, K.; Bempeli, S.; Agrawal, K. | Abstract
Membrane-based carbon capture offers an energy-efficient and environmentally friendly alternative to conventional absorption-based processes, yet adoption remains limited by its performance with dilute CO
2
sources such as natural gas power plants. Here we present a techno-economic assessment of pyridinic-graphene membranes—porous graphene membranes hosting pyridinic nitrogen—that yield increasingly high CO
| CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | |
Embodied emissions of chemicals within the EU Carbon Border Adjustment Mechanism | 10.1038/s41893-025-01618-5 | https://doi.org/10.1038/s41893-025-01618-5 | Nature Sustainability | 2,025 | Minten, H.; Hausweiler, J.; Probst, B.; Reinert, C.; Meys, R. | Abstract
The European Union’s Carbon Border Adjustment Mechanism (CBAM) aims to avoid carbon leakage by pricing the production emissions of imported goods. Currently, the CBAM applies to iron and steel, cement, aluminium, fertilizers, electricity and hydrogen. As the European Union considers extending the CBAM to chemicals by 2030, its effectiveness in this complex industry remains uncertain. Here we assess how well the CBAM would capture emissions in the chemical industry by u | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Effects of demand and recycling on the when and where of lithium extraction | 10.1038/s41893-025-01561-5 | https://doi.org/10.1038/s41893-025-01561-5 | Nature Sustainability | 2,025 | Busch, P.; Chen, Y.; Ogbonna, P.; Kendall, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | ||
Electrochemical lithium recycling from spent batteries with electricity generation | 10.1038/s41893-024-01505-5 | https://doi.org/10.1038/s41893-024-01505-5 | Nature Sustainability | 2,025 | Wang, W.; Liu, Z.; Zhu, Z.; Ma, Y.; Zhang, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | ||
Economic costs of wind erosion in the United States | 10.1038/s41893-024-01506-4 | https://doi.org/10.1038/s41893-024-01506-4 | Nature Sustainability | 2,025 | Feng, I.; Gill, T.; Van Pelt, R.; Webb, N.; Tong, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Urban and non-urban contributions to the social cost of carbon | 10.1038/s41467-025-59466-y | https://doi.org/10.1038/s41467-025-59466-y | Nature Communications | 2,025 | Estrada, F.; Lupi, V.; Botzen, W.; Tol, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Carbon trade biases and the emerging mesoscale structure of the European Emissions Trading System network | 10.1038/s41467-025-59913-w | https://doi.org/10.1038/s41467-025-59913-w | Nature Communications | 2,025 | Flori, A.; Spelta, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Emerging green steel markets surrounding the EU emissions trading system and carbon border adjustment mechanism | 10.1038/s41467-025-64440-9 | https://doi.org/10.1038/s41467-025-64440-9 | Nature Communications | 2,025 | Johnson, C.; Åhman, M.; Nilsson, L.; Li, Z. | Abstract
The global steel industry accounts for 8–10 % of global CO2 emissions and requires deep decarbonisation for achieving the targets set in the Paris Agreement. However, no low-emission primary steel production technology has yet been commercially feasible or deployed. Through analysing revisions and additions of European Union climate policy, we show that green hydrogen-based steelmaking in competitive locations achieves cost-competitiveness on the European market starting 2026. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Policy & Social Factors | |
Quantifying the trade-offs between renewable energy visibility and system costs | 10.1038/s41467-025-59029-1 | https://doi.org/10.1038/s41467-025-59029-1 | Nature Communications | 2,025 | Tsani, T.; Pelser, T.; Ioannidis, R.; Maier, R.; Chen, R. | Abstract
Visual landscape impacts on scenic and populated places are among significant factors affecting local acceptance of large-scale renewable energy projects. Through the combination of large-scale reverse viewshed and techno-economic energy system analyses, we assess their potential impacts for nationwide energy systems. In our case study of Germany, moderate consideration of visual impact by placing renewables out of sight of the most scenic and densely populated areas d | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Gram-scale selective telomerization of isoprene and CO2 toward 100% renewable materials | 10.1038/s41467-025-62409-2 | https://doi.org/10.1038/s41467-025-62409-2 | Nature Communications | 2,025 | Lutz, M.; Kracht, F.; Marumoto, K.; Nozaki, K. | Abstract
Carbon dioxide (CO2) is an ideal chemical feedstock due to its abundance, low cost, low toxicity and its role as a greenhouse gas. Telomerization with butadiene give rise to functional small molecules and polymers with significant CO2 content, but the fossil origin of the olefin offsets sustainability benefits. Here, we present a palladium-catalyzed telomerization of CO2 with isoprene, two of the most prevalent organic compounds in the atmosphere, yielding “COOIL”, an ideally | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | LCA & Sustainability | |
Quantifying the global climate feedback from energy-based adaptation | 10.1038/s41467-025-59201-7 | https://doi.org/10.1038/s41467-025-59201-7 | Nature Communications | 2,025 | Abajian, A.; Carleton, T.; Meng, K.; Deschênes, O. | Abstract
Many behavioral responses to climate change are carbon-intensive, raising concerns that adaptation may cause additional warming. The sign and magnitude of this feedback depend on how increased emissions from cooling balance against reduced emissions from heating across space and time. We present an empirical approach that forecasts the effect of future adaptive energy use on global average temperature over the 21st century. We estimate that energy-based adaptation will lower g | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Energy and climate policy implications on the deployment of low-carbon ammonia technologies | 10.1038/s41467-025-56006-6 | https://doi.org/10.1038/s41467-025-56006-6 | Nature Communications | 2,025 | Chyong, C.; Italiani, E.; Kazantzis, N. | Abstract
The economic feasibility of low-carbon ammonia production pathways, such as steam methane reforming with carbon capture and storage, biomass gasification, and electrolysis, is assessed under various policy frameworks, including subsidies, carbon pricing, and renewable hydrogen regulations. Here, we show that employing a stochastic techno-economic analysis at the plant level and a net present value approach under the US Inflation Reduction Act revea | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | |
Planning the electric vehicle transition by integrating spatial information and social networks | 10.1038/s41467-025-66072-5 | https://doi.org/10.1038/s41467-025-66072-5 | Nature Communications | 2,025 | Wu, J.; Salgado, A.; González, M. | Abstract
The transition from gasoline-powered vehicles to plug-in electric vehicles (PEVs) offers a promising pathway for reducing greenhouse gas emissions. Spatial forecasts of PEV adoption are essential to support power grid adaptation, yet forecasting is hindered by limited data at this early stage of adoption. While different model calibrations can replicate current trends, they often yield divergent forecasts. Using empirical data from states with the highest levels of ado | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | |
The urgent electrolyte sustainability challenges for electric vehicle batteries | 10.1038/s41467-025-60711-7 | https://doi.org/10.1038/s41467-025-60711-7 | Nature Communications | 2,025 | Burton, T.; Gómez Urbano, J.; Zhu, Y.; Balducci, A.; Fontaine, O. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | LCA & Sustainability | ||
Lithium-ion battery recycling relieves the threat to material scarcity amid China’s electric vehicle ambitions | 10.1038/s41467-025-61481-y | https://doi.org/10.1038/s41467-025-61481-y | Nature Communications | 2,025 | Zhang, B.; Xin, Q.; Chen, S.; Wang, B.; Li, H. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | LCA & Sustainability | ||
Temperate forests can deliver future wood demand and climate-change mitigation dependent on afforestation and circularity | 10.1038/s41467-025-58463-5 | https://doi.org/10.1038/s41467-025-58463-5 | Nature Communications | 2,025 | Forster, E.; Styles, D.; Healey, J. | Abstract
Global wood demand is expected to rise but supply capacity is questioned due to limited forest resources. Additionally, the global warming potential (GWP) impact of increased wood supply and use is not well understood. We propose a framework combining forest carbon modelling and dynamic consequential life-cycle assessment to evaluate this impact. Applying it to generic temperate forest, we show that afforestation to double productive forest area co | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Avoiding misuses of energy-economic modelling in climate policymaking | 10.1038/s41558-025-02280-7 | https://doi.org/10.1038/s41558-025-02280-7 | Nature Climate Change | 2,025 | Kaufman, N.; Bataille, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Modelling the impacts of policy sequencing on energy decarbonization | 10.1038/s41558-025-02497-6 | https://doi.org/10.1038/s41558-025-02497-6 | Nature Climate Change | 2,025 | Luo, H.; Peng, W.; Fawcett, A.; Green, J.; Iyer, G. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Individualized cost–benefit analysis does not fit for demand-side mitigation | 10.1038/s41558-025-02330-0 | https://doi.org/10.1038/s41558-025-02330-0 | Nature Climate Change | 2,025 | Berger, S.; Creutzig, F. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
Reply to: Individualized cost–benefit analysis does not fit for demand-side mitigation | 10.1038/s41558-025-02331-z | https://doi.org/10.1038/s41558-025-02331-z | Nature Climate Change | 2,025 | Tan-Soo, J.; Qin, P.; Quan, Y.; Li, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
Governance challenges for domestic cross-border carbon capture and storage | 10.1038/s41558-025-02250-z | https://doi.org/10.1038/s41558-025-02250-z | Nature Climate Change | 2,025 | Zhang, X.; Li, F.; Gu, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | ||
Influence of climate change and accidents on perception differs among energy technologies | 10.1093/pnasnexus/pgaf079 | https://doi.org/10.1093/pnasnexus/pgaf079 | npj Clean Energy | 2,025 | L’Her, G.; Duncan, N.; Jenkins-Smith, H.; Deinert, M. | Abstract
Risk perceptions of energy systems, and their evolution under climate change and after accidents, affect public acceptance of generation technologies. Despite this, little is understood about how such factors impact public perception at different timescales and the drivers for perception. We use state-of-the-art natural language processing to measure temporal changes in sentiment toward energy technologies using the full Twitter archive for 2009–2022. We find that percept | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Single-fibril Förster resonance energy transfer imaging and deep learning reveal concentration dependence of amyloid β 42 aggregation pathways | 10.1093/pnasnexus/pgaf342 | https://doi.org/10.1093/pnasnexus/pgaf342 | npj Clean Energy | 2,025 | Sohail, S.; Yoo, J.; Chung, H. | Abstract
Amyloid fibril formation is a highly heterogeneous process as evidenced by polymorphism in fibril structure. It has been suggested that different polymorphs are associated with different diseases or disease subtypes. Detailed characterization of this heterogeneity is a key to understanding the aggregation mechanism and, possibly, the disease mechanism. In this work, we develop Förster resonance energy transfer (FRET) imaging of amyloid fibril formation in real time and | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Global consistency of urban scaling evidenced by remote sensing | 10.1093/pnasnexus/pgaf037 | https://doi.org/10.1093/pnasnexus/pgaf037 | npj Clean Energy | 2,025 | Xu, Z.; Xu, G.; Lan, T.; Li, X.; Chen, Z. | Abstract
The urban scaling theory (UST) strives for a universal taxonomy that depicts relationships among urban indicators (e.g. energy consumption, economic output) with city size. However, the lack of international agreement on city definitions and statistics complicates cross-country comparisons of urban scaling performance. Remote sensing provides a uniform standard for measuring cities around the world. To scrutinize the consistency of UST, we quantified changes in remotely s | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Climate action literacy interventions increase commitments to more effective mitigation behaviors | 10.1093/pnasnexus/pgaf191 | https://doi.org/10.1093/pnasnexus/pgaf191 | npj Clean Energy | 2,025 | Goldwert, D.; Patel, Y.; Nielsen, K.; Goldberg, M.; Vlasceanu, M. | Abstract
Reducing lifestyle carbon emissions is a critical component of decarbonizing society. However, people hold substantial misperceptions about the relative efficacy of different behavioral changes, such as comprehensively recycling or avoiding long flights, and these misperceptions may lead to the suboptimal allocation of resources. In a preregistered experiment in the United States, we tested the effects of two literacy interventions on correcting misperceptions and increas | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | |
The equity implications of pecuniary externalities on an electric grid | 10.1093/pnasnexus/pgaf356 | https://doi.org/10.1093/pnasnexus/pgaf356 | npj Clean Energy | 2,025 | Sims, C.; Ali, G.; Holladay, J.; Roberson, T.; Chen, C. | Abstract
The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of ro | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Policy & Social Factors | |
Satellite analysis of methane emissions connects war and urban sustainability | 10.1038/s44284-025-00312-z | https://doi.org/10.1038/s44284-025-00312-z | Nature Cities | 2,025 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | |||
Synergistic action on mitigation and adaptation pilot policies to enhance low-carbon resilience of Chinese cities | 10.1038/s44284-025-00303-0 | https://doi.org/10.1038/s44284-025-00303-0 | Nature Cities | 2,025 | Wang, D.; Chen, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
Low-carbon solutions for water infiltration in urban buildings under climate change | 10.1038/s44284-025-00259-1 | https://doi.org/10.1038/s44284-025-00259-1 | Nature Cities | 2,025 | Xiao, J.; Yu, C.; Xia, B.; Xiao, X.; Wang, F. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
Integrating energy justice and economic realities through insights on energy expenditures, inequality, and renewable energy attitudes | 10.1038/s41598-025-12410-y | https://doi.org/10.1038/s41598-025-12410-y | Scientific Reports | 2,025 | Volodzkiene, L.; Streimikiene, D. | Abstract
Energy justice is a cornerstone of the European Union’s pursuit of climate neutrality by 2050, addressing both environmental and societal challenges. This research employs a representative survey to analyze household expenditures on electricity, natural gas, and heating, evaluating the extent of energy inequality and its implications for affordability and access across income groups. The study also explores public attitudes toward renewable energy, focusing on perceive | CrossRef | FLEXERGY | Socioeconomic & Energy Consumption | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Solar photovoltaic feed-in tariffs: viability analysis and policy recommendations | 10.1038/s41598-025-32105-8 | https://doi.org/10.1038/s41598-025-32105-8 | Scientific Reports | 2,025 | Mekonnen, T.; Tsegaye, S.; Belete, B.; Selvaraj, J.; Negewo, A. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Policy & Social Factors | ||
An examination of the decoupling effect and influential mechanisms of energy consumption and economic growth in Chinese urban areas | 10.1038/s41598-025-16262-4 | https://doi.org/10.1038/s41598-025-16262-4 | Scientific Reports | 2,025 | Cheng, H.; Li, C.; Huangmei, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
The impact of China pilot carbon market policy on electricity carbon emissions | 10.1038/s41598-025-00975-7 | https://doi.org/10.1038/s41598-025-00975-7 | Scientific Reports | 2,025 | Zhang, Z.; Xiao, Y.; Zhang, K.; Tang, M.; Ma, T. | Abstract
The electric power industry is the pillar of the national economy but also the largest carbon emission sector in China, facing great pressure to reduce emissions. Existing research often lacks the analysis of the carbon market on electricity carbon emission reduction. Based on the panel data of 30 provinces (cities) in China from 2003 to 2020, we combine the multi-period difference-in-differences model with the spatial Durbin model to explore the effectiveness of the pilot car | CrossRef | EnergiTrade | Energy & Carbon Trading | Carbon Trading & New Business Models | Policy & Social Factors | |
The impact of carbon emissions trading on innovation bubbles in manufacturing enterprises | 10.1038/s41598-025-99814-y | https://doi.org/10.1038/s41598-025-99814-y | Scientific Reports | 2,025 | Lyu, Z.; Li, G. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Research on the impact of the digital economy on carbon emissions based on the dual perspectives of carbon emission reduction and carbon efficiency | 10.1038/s41598-025-87098-1 | https://doi.org/10.1038/s41598-025-87098-1 | Scientific Reports | 2,025 | Liu, X.; Chen, L.; Lu, Y.; Chang, M.; Xiao, Y. | Abstract
China’s digital economy is currently thriving, with the “dual carbon” targets representing a significant pursuit of economic development. The role of the digital economy in achieving these targets warrants detailed discussion. Using urban panel data from China spanning 2011 to 2021, this paper empirically examines the impact of the digital economy on urban carbon emissions. The findings reveal several key points: Firstly, the digital economy significantly reduces urban carbon | CrossRef | EnergiTrade | Urban Carbon Footprint | Carbon Trading & New Business Models | Policy & Social Factors | |
Individual perceptions of renewable energy investment in Somali firms | 10.1038/s41598-025-11581-y | https://doi.org/10.1038/s41598-025-11581-y | Scientific Reports | 2,025 | Nor, B. | Abstract
Somalia’s energy sector is seen as potential for development and investment. financing this sector is crucial for development and economic growth. Small and medium-sized private-sector enterprises are the primary electricity generators and distributors, operating diesel-powered systems via off-grid networks This study investigates the factors influencing investment intentions in renewable energy in Somalia. This study utilized a quantitative research approach employing a descr | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Optimal energy management of multi-carrier energy system considering uncertainty in renewable generation | 10.1038/s41598-025-10404-4 | https://doi.org/10.1038/s41598-025-10404-4 | Scientific Reports | 2,025 | Garg, A.; Niazi, K.; Tiwari, S.; Sharma, S.; Rawat, T. | Abstract
This paper presents a structured approach for the efficient operation of multi-carrier energy systems under the uncertainty of renewable energy sources. As the penetration of wind and solar energy increases, managing the resulting variability becomes critical to maintaining both economic efficiency and operational flexibility. To address this, a two-stage multi objective optimization framework is proposed. In the first stage, the objective is to minimize daily operational cost | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Policy & Social Factors | |
Agricultural carbon footprints, renewable energy and sustainable development in Asia | 10.1038/s41598-025-17491-3 | https://doi.org/10.1038/s41598-025-17491-3 | Scientific Reports | 2,025 | Liu, H.; Liu, Y. | Abstract
There is a growing concern over environmental degradation and climate change in rapidly developing Asian nations. However, little research has been conducted on the impact of agricultural carbon emissions and renewable energy use on sustainable development outcomes in Asia. This research looks at the relationship between agricultural carbon footprints (ACF), renewable energy consumption (RE), and sustainable development (SD) in nine Asian nations from 2000 to 2022. The study e | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Climate Mitigation | |
Capabilities of battery and compressed air storage in the economic energy scheduling and flexibility regulation of multi-microgrids including non-renewable/renewable units | 10.1038/s41598-025-06768-2 | https://doi.org/10.1038/s41598-025-06768-2 | Scientific Reports | 2,025 | Naghibi, A.; Akbari, E.; Veisi, M.; Shahmoradi, S.; Pirouzi, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Policy & Social Factors | ||
Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties | 10.1038/s41598-025-87689-y | https://doi.org/10.1038/s41598-025-87689-y | Scientific Reports | 2,025 | Zhang, H.; Xi, Q.; Chen, L.; Min, Y.; Fan, X. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | ||
Mitigating anthropogenic climate change with aqueous green energy | 10.1038/s41598-025-86042-7 | https://doi.org/10.1038/s41598-025-86042-7 | Scientific Reports | 2,025 | Olim, S.; Nickoloff, A.; Moffat, L.; Weaver, A.; Eby, M. | AbstractReaching net zero emissions and limiting global warming to 2 °C requires the widespread introduction of technology-based solutions to draw down existing atmospheric levels and future emissions of CO2. One such approach is direct air CO2 capture and storage (DACCS), a readily available, yet energy-intensive process. The combination of DACCS and ocean thermal energy conversion (OTEC) allows for independently powered carbon capture plants to inject concentrated carbon into deep marine sedim | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | |
Solar potential assessment using machine learning and climate change projections for long-term energy planning | 10.1038/s41598-025-23661-0 | https://doi.org/10.1038/s41598-025-23661-0 | Scientific Reports | 2,025 | Reddy, B.; Gautam, K.; Pachauri, N. | Abstract
This work proposes a novel method for evaluating solar potential, essential for the development, installation, and operation of solar power systems. The approach forecasts solar energy potential for specific sites by utilizing integrated geospatial, meteorological, and infrastructural multidimensional data. A new application has been released to assess the solar capacity globally. The study evaluated various machine learning methods, ultimately selecting an XGBoost mod | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Climate Mitigation | |
A novel approach to wind energy modeling in the context of climate change at Zaafrana region in Egypt | 10.1038/s41598-025-90583-2 | https://doi.org/10.1038/s41598-025-90583-2 | Scientific Reports | 2,025 | Kamel, B.; Abdelaziz, A.; Attia, M.; Khamees, A. | Abstract
Global warming, driven by the excessive emission of greenhouse gases from the combustion of fossil fuels, has emerged as a critical environmental challenge which is considered as a motivation for this research. Where, the switch to sustainable energy sources is crucial because of the pressing need to slow down climate change and lower carbon footprints. Of all the renewable energy sources, wind energy is particularly important as a means of reducing carbon emissions from the g | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Climate Mitigation | |
The role of environmental awareness, renewable energy, and green innovation in shaping climate change perceptions | 10.1038/s41598-025-24815-w | https://doi.org/10.1038/s41598-025-24815-w | Scientific Reports | 2,025 | Hussain, A.; kanwel, S.; Erum, N.; Pasha, U.; Asad, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
A smart grid data sharing scheme supporting policy update and traceability | 10.1038/s41598-025-10704-9 | https://doi.org/10.1038/s41598-025-10704-9 | Scientific Reports | 2,025 | Yang, X.; Yao, K.; Li, S.; Du, X.; Wang, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure | 10.1038/s41598-025-04984-4 | https://doi.org/10.1038/s41598-025-04984-4 | Scientific Reports | 2,025 | Sharma, A.; Rani, S.; Shabaz, M. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Climate Mitigation | ||
Identification and suppression of low-frequency oscillations using PMU measurements based power system model in smart grid | 10.1038/s41598-025-88389-3 | https://doi.org/10.1038/s41598-025-88389-3 | Scientific Reports | 2,025 | Zuhaib, M.; Rihan, M.; Gupta, S.; Sufyan, M. | Abstract
Low-frequency oscillations (LFO) are inherent to large interconnected power systems. Timely detection and mitigation of these oscillations is essential to maintain reliable power system operation. This paper presents a methodology to identify and mitigate low-frequency oscillations ( forced and inter-area) using a wide area monitoring system (WAMS) based power system model utilizing phasor measurement units (PMUs). These models accurately identify the behavior and location of | CrossRef | DigiEnergy | Renewable Energy Simulation Tools | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Flexible renewable integrated energy system capabilities to improve voltage stability with power quality and economic environmental operation of smart grid | 10.1038/s41598-025-29052-9 | https://doi.org/10.1038/s41598-025-29052-9 | Scientific Reports | 2,025 | Hassankashi, A.; Dini, A.; Pirouzi, S.; Veisi, M.; Bahreini, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Comprehensive performance analysis of an electric vehicle using multi-mode Indian drive cycles | 10.1038/s41598-025-02238-x | https://doi.org/10.1038/s41598-025-02238-x | Scientific Reports | 2,025 | Kondru, J.; Obulesu, Y. | Abstract
The constant advancements in the research and development society of vehicle manufacturing made the customer’s attention towards EV ownership due to the better economic profile in the view of maintenance and operations. Even though the vehicles have better features the performance of the EV can be estimated with the consideration of the designed drive cycle for the region. According to that the procurement of EVs should be in the approved range of drive cycles with the concern | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | |
Strategic design of wind energy and battery storage for efficient and sustainable energy systems | 10.1038/s41598-025-18863-5 | https://doi.org/10.1038/s41598-025-18863-5 | Scientific Reports | 2,025 | Eroğlu, H.; Kurtuluş, O. | Abstract
The intermittent nature of renewable energy sources, particularly wind power, necessitates advanced energy management and storage strategies to ensure grid stability and economic viability. This study investigates the techno economic benefits of integrating Battery Energy Storage Systems (BESS) into wind power plants by developing and evaluating optimized hybrid operation strategies. Using real world Data from a 70 MW wind farm, ten distinct operational strategies were simulat | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
A spatial decision making framework using neutrosophic VIKOR for wind energy investment in Turkey | 10.1038/s41598-025-18799-w | https://doi.org/10.1038/s41598-025-18799-w | Scientific Reports | 2,025 | Eroğlu, H. | Abstract
The growing demand for clean energy and the urgency of reducing carbon emissions have made wind power a key element of Turkey’s renewable energy strategy. However, identifying optimal regions for wind energy investment remains a complex task due to the interplay of technical, spatial, and economic factors, all of which are characterized by varying degrees of uncertainty. Although GIS-based site selection and multi-criteria decision-making (MCDM) methods are widely used, few ap | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | |
Economic and environmental assessment of different energy storage methods for hybrid energy systems | 10.1038/s41598-025-09732-2 | https://doi.org/10.1038/s41598-025-09732-2 | Scientific Reports | 2,025 | Liu, Y.; Zhang, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Carbon footprint analysis and carbon neutrality potential of desalination by reverse osmosis for different applications basd on life cycle assessment method | 10.1038/s41598-025-24518-2 | https://doi.org/10.1038/s41598-025-24518-2 | Scientific Reports | 2,025 | Zhang, M.; Yu, S.; Shi, C.; Wang, H.; Chang, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | LCA & Sustainability | ||
Uncovering the carbon footprint of minimally invasive axillary osmidrosis surgery in China through life cycle assessment | 10.1038/s41598-025-09293-4 | https://doi.org/10.1038/s41598-025-09293-4 | Scientific Reports | 2,025 | Tan, K.; Zhang, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | LCA & Sustainability | ||
Carbon footprint analysis and emission reduction pathways of Bogie frame manufacturing process in Urban Rail Transportation | 10.1038/s41598-024-83407-2 | https://doi.org/10.1038/s41598-024-83407-2 | Scientific Reports | 2,025 | Zhou, J.; Wang, R.; Liu, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Climate Mitigation | ||
Temporal-spatial evolution and formation mechanism of energy consumption carbon footprint at county scale in the Yellow River Basin | 10.1038/s41598-025-86383-3 | https://doi.org/10.1038/s41598-025-86383-3 | Scientific Reports | 2,025 | Zhang, L.; Song, M.; Gao, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Policy & Social Factors | ||
Carbon footprint assessment and reconstruction redesign of recycled discarded military training uniforms | 10.1038/s41598-025-87733-x | https://doi.org/10.1038/s41598-025-87733-x | Scientific Reports | 2,025 | Huang, G.; Shi, S.; Wang, Q.; Li, F.; Li, X. | Abstract
In response to the problem of resource waste and environmental pollution caused by the large amount of waste textiles in China, taking waste military training uniforms as an example, a thorough investigation was conducted to draw a life cycle diagram of military training uniforms, establish a recycling system for waste military training uniforms, and use Taiyuan University of Technology as a pilot for recycling. The carbon footprint of different recycling methods was c | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | LCA & Sustainability | |
Quantifying the energy and emissions implications of consumption redistribution in the UK through sustainable consumption corridors | 10.1038/s41598-025-01495-0 | https://doi.org/10.1038/s41598-025-01495-0 | Scientific Reports | 2,025 | Betts-Davies, S.; Owen, A.; Barrett, J.; Brockway, P.; Norman, J. | Abstract
Reducing inequality to ensure decent living standards alongside climate mitigation, are frequently posited as dual goals of a just transition. Energy sufficiency has received attention as a solution to these crises, but there has been limited exploration of the impact sufficiency principles could have on energy and GHG emissions. Addressing this gap, we utilise a consumption-corridor approach to develop three redistributive scenarios of final consumption for the UK. Each scena | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | |
Assessment of electrode materials in EDM of SS316L: energy consumption, electrode wear, dielectric consumption, GHG emissions, and economic viability for sustainable development | 10.1038/s41598-025-24430-9 | https://doi.org/10.1038/s41598-025-24430-9 | Scientific Reports | 2,025 | Ali, M.; Raza, M.; Ehsan, S.; Sana, M.; Farooq, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system | 10.1038/s41598-025-85175-z | https://doi.org/10.1038/s41598-025-85175-z | Scientific Reports | 2,025 | Dey, B.; Sharma, G.; Bokoro, P.; Dutta, S. | AbstractThe cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal. An incentive-based demand response (IBDR) is introd | CrossRef | FLEXERGY | Demand Response | Carbon Trading & New Business Models | Policy & Social Factors | |
Day-ahead economic dispatch of wind-integrated microgrids using coordinated energy storage and hybrid demand response strategies | 10.1038/s41598-025-11561-2 | https://doi.org/10.1038/s41598-025-11561-2 | Scientific Reports | 2,025 | Meng, Q.; He, Y.; Hussain, S.; Lu, J.; Guerrero, J. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | ||
Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency | 10.1038/s41598-025-86232-3 | https://doi.org/10.1038/s41598-025-86232-3 | Scientific Reports | 2,025 | Singh, A.; Dey, B.; Bajaj, M.; Kadiwala, S.; Kumar, R. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Policy & Social Factors | ||
Climate change, biodiversity, and the energy transition: The potential role of the UN’s declaration on peasants’ rights | 10.1016/j.oneear.2024.11.013 | https://doi.org/10.1016/j.oneear.2024.11.013 | One Earth | 2,025 | Kemp, D.; Owen, J.; Schuele, W.; Loginova, J.; Ern Ang, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Climate Mitigation | ||
Co-deploying biochar and bioenergy with carbon capture and storage improves cost-effectiveness and sustainability of China’s carbon neutrality | 10.1016/j.oneear.2024.12.008 | https://doi.org/10.1016/j.oneear.2024.12.008 | One Earth | 2,025 | Deng, X.; Teng, F.; Zhang, X.; Fan, J.; Forsell, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage | ||
Technoeconomic analysis of distributed energy resources for rapid deployment of the US national charging network | 10.1016/j.crsus.2024.100303 | https://doi.org/10.1016/j.crsus.2024.100303 | Cell Reports Sustainability | 2,025 | Poudel, S.; Wang, J.; Zhou, Y.; Reddi, K.; Elgowainy, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Policy & Social Factors | ||
Sustainability trade-offs across modeled floating solar waterscapes of the Northeastern United States | 10.1016/j.crsus.2025.100423 | https://doi.org/10.1016/j.crsus.2025.100423 | Cell Reports Sustainability | 2,025 | Gallaher, A.; Kalies, E.; Grodsky, S. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | LCA & Sustainability | ||
Unit-level monitoring data reveal the effectiveness of China’s national emissions trading scheme | 10.1016/j.crsus.2025.100339 | https://doi.org/10.1016/j.crsus.2025.100339 | Cell Reports Sustainability | 2,025 | Yan, G.; Ruan, J.; Qin, Z.; Lyu, C.; Qian, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Comparative techno-economic analysis of synthetic renewable natural gas production via reactive CO2 capture and conversion | 10.1016/j.crsus.2025.100408 | https://doi.org/10.1016/j.crsus.2025.100408 | Cell Reports Sustainability | 2,025 | Aui, A.; Goldstein, H.; Ellebracht, N.; Li, W.; Pang, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Policy & Social Factors | ||
Sustainability challenges demand social science insights | 10.1016/j.crsus.2025.100556 | https://doi.org/10.1016/j.crsus.2025.100556 | Cell Reports Sustainability | 2,025 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | LCA & Sustainability | |||
Rising sea level reduces carbon sequestration and CO2 and N2O fluxes while promoting CH4 flux from mangroves | 10.1016/j.crsus.2025.100520 | https://doi.org/10.1016/j.crsus.2025.100520 | Cell Reports Sustainability | 2,025 | Qiao, P.; Chen, L.; Krauss, K.; Guo, X.; Xu, L. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Carbon Capture & Storage |
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