Energy nexus最新文献

{"title":"Reducing carbon footprint and acquiring carbon credits in intensively cultivated rice wheat system by tillage and residue management","authors":"Dipak Kumar Gupta ,&nbsp;Arti Bhatia ,&nbsp;Amit Kumar ,&nbsp;Tapas Kumar Das ,&nbsp;Bidisha Chakrabarti ,&nbsp;Ranjan Bhattacharyya","doi":"10.1016/j.nexus.2026.100633","DOIUrl":"10.1016/j.nexus.2026.100633","url":null,"abstract":"<div><div>The conventional rice-wheat cropping system of the northern-Indo-Gangetic Plains is tillage, energy, and water-intensive leading to greenhouse gases emission (GHG) and soil-carbon loss. Practices such as direct seeding, zero-tillage, and residue-retention are promoted for sustainable-farming and environmental-benefit, however the impact on the net-carbon footprint (NCF) has been less evaluated. Measurement of GHG emission and soil organic carbon was carried out for three consecutive years in a long-term experiment in rice–wheat system. Six treatment combination of zero-till direct-seeded rice (ZTDSR), transplanted-puddled rice, zero-tilled wheat (ZTW), conventionally-tilled wheat (CTW), summer-fallow, zero-tilled green-gram (ZTGG), rice-residue retention (RS), rice residue burning (RRB), and green-gram residue (GGR) were field experimented. The zero tillage (ZT) and crop residue retention significantly reduced energy input and net carbon footprint (NCF) of rice wheat system through energy saving, water saving and net positive ecosystem carbon balance (NECB). The CH₄, N₂O and CO₂ emissions from the field burning of rice residue were 2.56±0.52 g kg⁻¹, 0.3 ± 0.05 g kg⁻¹ and 1035±0.82 g kg⁻¹, respectively. The NCF differed significantly (<em>p</em> &lt; 0.001) among the treatments, and ZT-systems with and without residue retention, had 64–78% lower NCF than conventional-system due to lower methane emission with significantly higher net ecosystem carbon balance (NECB). Residue retention in ZT-treatments reduced NCF by 7–39% varying with type of residue and frequency of application. The triple ZT treatment of ZTDSR+GGR–ZTW+RS–ZTGG significantly reduced GHG emissions (on-farm+off-farm) by 52.6%, energy input by 23.5%, carbon intensity by 64.1%, and NECB by 105% compared to conventional rice-wheat-fallow system. Higher Dehydrogenase activity in the triple ZT-treatments indicated better microbial activity and soil health. Additionally, this approach of triple ZT based rice-wheat-green gram system can yield up to 11 carbon credits per hectare annually, providing additional income for smallholder farmers of the region. However, policymakers need to invest in strengthening agricultural extension services to provide farmers with the necessary knowledge, training, and technical support to adopt these practices and participate in carbon markets.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"21 ","pages":"Article 100633"},"PeriodicalIF":9.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-economic assessment of solar photovoltaic systems in Appalachian Virginia: Geospatial multivariate approach","authors":"Mary Asare-Addo ,&nbsp;Will W. Couch ,&nbsp;Galen Maclaurin ,&nbsp;Bevin Etienne ,&nbsp;Christine Mahoney","doi":"10.1016/j.nexus.2026.100629","DOIUrl":"10.1016/j.nexus.2026.100629","url":null,"abstract":"<div><div>The SDG7 does not only serve as a framework for achieving sustainable and affordable energy for all but also serves as an economic growth model. This research explores the potential of solar energy that could be leveraged to improve the socioeconomic resilience in Appalachian Virginia by proposing two photovoltaic systems as well as revenue share. A utility-scale 20<strong>+</strong> MW ground mounted, and large-scale 25<strong>+</strong> kW solar rooftop PV systems were modeled using monocrystalline PV module. Methods used included the Boolean and AHP multivariate methods combined with geospatial techniques to evaluate the geographical potential to select suitable locations to maximize generation and minimize community oppositions, and the technical potential to quantify generation and installed capacities by the two photovoltaic systems. The revenue model was used to estimate and understand the share of revenue that could be accrued from the two systems. For geographical potential, ∼ 53 % of the total land is suitable for the utility-scale PV system and about 45.4 % for the large-scale rooftop systems. The study estimated a total of ∼ 1.62 TWp of installed capacity by the utility-scale system and about 10.08 GWp for the large-scale rooftop system. About $42.64 billion could be accrued from the utility-scale system and about $265.89 million from the large-scale rooftop system over the 25-year system lifetime. In summary, these PV systems can either be developed together, or as separate systems to provide economic benefits to residents in the region especially the economically distressed and at-risk regions.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"21 ","pages":"Article 100629"},"PeriodicalIF":9.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid deep learning models for water demand forecasting in greenhouses: Exploring the energy Nexus in Urban agriculture","authors":"Arash Moradzadeh ,&nbsp;Lazhar Ben-Brahim ,&nbsp;Ali Arefi ,&nbsp;Arman Oshnoei ,&nbsp;S.M. Muyeen","doi":"10.1016/j.nexus.2025.100546","DOIUrl":"10.1016/j.nexus.2025.100546","url":null,"abstract":"<div><div>Precise water demand forecasting (WDF) is crucial for sustainable irrigation and resource efficiency in urban greenhouse systems. This study introduces a cutting-edge hybrid deep learning approach designed for short-term WDF, while also considering the energy nexus between water, energy, and environmental factors. The model integrates the least squares generative adversarial network (LSGAN) for data pre-processing and noise reduction, convolutional neural networks (CNN) for feature selection, and bidirectional long short-term memory (BiLSTM) for time-series state modeling, and named as LSGAN<img>CBiLSTM. Using real-world data from the Wageningen Research Centre in Bleiswijk, Netherlands, the model significantly outperformed benchmark approaches, achieving an R-value of 99.57 % with minimal forecasting errors. The model demonstrated exceptional stability, minimal bias, and strong handling of environmental variability, improving short-term WDF accuracy, optimizing water management in urban agriculture, enhancing sustainable irrigation, and addressing the energy nexus for efficient resource use.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100546"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain based on proof-of-work consensus algorithm: Evolution and future potential","authors":"Yehia Ibrahim Alzoubi ,&nbsp;Alok Mishra ,&nbsp;Ali Aljaafreh","doi":"10.1016/j.nexus.2025.100577","DOIUrl":"10.1016/j.nexus.2025.100577","url":null,"abstract":"<div><div>The Proof-of-Work (PoW) consensus algorithm has been a foundational element of blockchain technology, designed to establish decentralized security, enable trustless transactions, and ensure tamper-proof record-keeping. Despite the emergence of various other consensus algorithms, PoW remains widely adopted, though it has been criticized for its high resource consumption and inefficiency. Over the past five years, PoW has remained the focus of numerous studies, underscoring its ongoing relevance. Given its sustained use, it is important to examine both the current state and prospects of PoW. This study conducts a thorough literature review of publications indexed in Scopus up to July 2025, tracing the evolution of PoW and its potential trajectory. The analysis indicates that PoW is likely to continue its prominence, particularly in public blockchain applications, in the foreseeable future. However, it's important to note that this study is limited by the availability of research in commonly accessed databases at the time of writing. Future investigations could expand the scope by incorporating additional databases and leveraging the latest developments in this rapidly evolving field.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100577"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovation ecosystem in agriculture economy","authors":"Hari Kumar suberi","doi":"10.1016/j.nexus.2025.100603","DOIUrl":"10.1016/j.nexus.2025.100603","url":null,"abstract":"<div><div>Innovation in the Agri-economy face challenges due to insufficient policy instruments that support closed-loop energy and material metabolism for rural development. Implementing the Research-through-Design (RtD) approach within the Analysis–Projection–Synthesis framework, and supported by the Vester Sensitivity software tool, the innovative Agri-economy is analysed in detail by calculating non-linear normative values based on eighteen identified global variables: Energy sources, Agriculture farming, Transport Service, Technology Innovation, Local Climate, Incubation Centre, Recycling Service, Market Growth, Financial Services, Built Environment, Political Influence, Community Participation, Environment Protection, Forest Management, Infrastructure Availability, Enabling Conditions, Soil Quality, and Water Availability.</div><div>The normative assessment and scenario projection indicate that the integration of three key technologies—pyrolysis of dry biomass, anaerobic digestion of wet biomass, and Agri-PV systems—can enable closed-loop material and energy flow management, with the potential to add value to the Agri-economy. The observation of the system’s self-regulating behaviour, achieved by modifying the negative feedback cycle using ‘Technology Innovation’ as the starting variable, shows that all system variables reach saturation after 32 simulation rounds. Assuming each simulation round represents one year, this indicates that the impact of the intervention becomes fully evident only after 32 years. However, the study presents multiple alternative interventions corresponding to different policy choices, which may serve as additional research directions in the future</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100603"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements and integration strategies of solar water heaters in buildings: A comprehensive review","authors":"Walid Zaafouri ,&nbsp;Walid Ben Amara ,&nbsp;Abdallah Bouabidi ,&nbsp;Saif Ali Kadhim ,&nbsp;Ali Habeeb Askar","doi":"10.1016/j.nexus.2025.100535","DOIUrl":"10.1016/j.nexus.2025.100535","url":null,"abstract":"<div><div>The integration of renewable energy into buildings is a key strategy to reduce energy consumption and heating costs. This review comprehensively examines solar water heater (SWH) systems, focusing on their major components such as storage tanks, solar collectors (flat plate, evacuated tube, parabolic trough, and others), and heat transfer fluids. Two integration strategies are discussed: building-integrated systems (roof and façade applications) and standalone thermosiphon systems. Recent advancements in SWHs include the use of selective coatings, nanofluids, and phase change materials (PCMs), which significantly enhance thermal efficiency. Key factors influencing performance such as collector orientation, tilt angle, and thermal losses are analyzed. The review concludes with design optimization recommendations and integration strategies that support higher system efficiency and broader adoption in both residential and commercial applications.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100535"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermochemical and elemental characterization of aromatic seed residues for solid biofuel applications in a circular economy context","authors":"Pablo Roig-Madrid ,&nbsp;Miguel Carmona-Cabello ,&nbsp;Alberto-Jesus Perea-Moreno ,&nbsp;M.P. Dorado ,&nbsp;David Muñoz-Rodriguez","doi":"10.1016/j.nexus.2025.100594","DOIUrl":"10.1016/j.nexus.2025.100594","url":null,"abstract":"<div><div>Aromatic seed waste (ASW) has high valorisation potential due to their rich composition. Biorefineries can optimize ASW by extracting valuable aromatic compounds through pyrolysis and gasification, reducing waste and promoting sustainability. This study explores the thermal valorisation of 16 different aromatic seed residues, assessing their potential as a solid fuel and in pyrolysis. Characterization following ISO 18,122 standards showed a moisture content below 10 % (w/w), while ash content varied between 3.13 % and 18.80 % (w/w), exceeding normative limits in some cases. The higher heating value (HHV) ranged from 13.55 to 20.31 MJ/kg, similar to woody and other herbaceous biomass, with higher-density benefits for handling and storage. Thermogravimetric analysis (TGA) identified two main degradation stages: 30–250 °C, linked to simple carbohydrate decomposition, and 250–500 °C, associated with lignocellulose breakdown. These variations impact combustion and pyrolysis performance. Elemental analysis revealed an H/C ratio between 1.58 and 1.90, when this is greater than 1.7, it indicates better heating value in combustion processes and better bio-oil quality in pyrolysis processes. Volatile content of about 70 % (w/w), these results show a sufficiently high volatile matter content, similar to that of other biomasses, which would improve ignition at low temperatures, making combustion more efficient. This property favours bio-oil production, yielding a product rich in aromatics. The results highlight ASW’s potential as a renewable energy source and its suitability for thermal conversion processes.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100594"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water-energy nexus and GHG emissions of cropping systems under varying field management practices in arid India","authors":"Vijay Singh Rathore ,&nbsp;Narayan Singh Nathawat ,&nbsp;Seema Bhardwaj ,&nbsp;Bhagirath Mal Yadav ,&nbsp;Mahesh Kumar ,&nbsp;Ravindra Singh Shekhawat ,&nbsp;Dinesh Kumar ,&nbsp;Banwari Lal ,&nbsp;Priyanka Gautam","doi":"10.1016/j.nexus.2025.100565","DOIUrl":"10.1016/j.nexus.2025.100565","url":null,"abstract":"<div><div>Agricultural systems involve interdependencies among water, energy, food, and the environment. Understanding these linkages is crucial for developing resource-efficient systems and reducing greenhouse gas (GHG) emissions to achieve sustainability. This study evaluated energy input-output analysis and GHG emissions, and assessed the interconnections among water, energy, and GHG emissions using a nexus approach of three cropping systems [cluster bean–wheat (CB-W), cluster bean–Indian mustard (CB-IM), cluster bean–isabgol (CB-IG)]under different field management practices in arid region of India. Field management practices included two tillage types [conventional (CT), deep tillage (DT)] and three farmyard manure (FYM) application rates (0, 5, 10 Mg ha⁻¹). A cradle-to-farm gate life cycle assessment was used to estimate energy use and GHG emissions. Non-renewable inputs such as electricity, fertilizers, and diesel accounted for over 80% of total energy use, with nitrogen fertilizer, diesel, and electricity being the main sources of GHG emissions. GHG emissions were highly correlated with energy use (r² = 0.93). Diversified systems (CB-IM, CB-IG) reduced groundwater irrigation, energy use, and GHG emissions by 27.9–37.2%, 22.9–37.4%, and 20.0–46.0%, respectively, compared to the traditional CB-W system. Field management practices influenced agro-economic performance, energy efficiency, and GHG emissions. DT and FYM improved water and energy productivities. The water-energy-food-GHG nexus scores were higher for CB-IG (0.773) and CB-IM (0.507) than for CB –W (0.182), with CB-IG under DT and FYM (10 Mg ha⁻¹) achieved the highest score (0.854). These results suggest that integrating suitable field management practices (e.g. deep tillage and FYM application) with diversified cropping system can enhance sustainability by reducing water and energy use and lowering environmental impacts. This approach reduces water and energy consumption while aligning with long-term environmental and economic goals, offering valuable insights for improving agricultural management in other resource-limited regions.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100565"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A synergistic framework integrating statistical design and machine learning for enhancing biogas yield","authors":"Yuxuan Li ,&nbsp;Mahuizi Lu ,&nbsp;Luiza C. Campos ,&nbsp;Shikun Cheng ,&nbsp;Zifu Li ,&nbsp;Yukun Hu","doi":"10.1016/j.nexus.2025.100583","DOIUrl":"10.1016/j.nexus.2025.100583","url":null,"abstract":"<div><div>Anaerobic digestion (AD) of waste-activated sludge is hindered by slow hydrolysis and complex sludge structure, which limit biogas recovery and threaten process stability. Although pretreatment methods such as microwave pretreatment (MP) have shown promise, their energy efficiency and underlying nonthermal effects remain poorly understood. In parallel, existing predictive models often neglect pretreatment conditions, restricting their applicability for process optimization. To bridge these gaps, this study proposes a synergistic framework that integrates statistical optimization with advanced machine learning (ML) to enhance biogas production from MP-assisted AD. Response Surface Methodology (RSM) identified optimal pretreatment conditions—holding time, pH, total solids (TS%), and microwave power—predicting a cumulative biogas yield (CBY) of 1020.5 mL/gVS, closely validated by experiments (1013.8 mL/gVS). These optimized datasets were further used to train ensemble ML models (AdaBoost, LightGBM, XGBoost), achieving high predictive accuracy (XGBoost R² = 0.992) with strong external validation. SHapley Additive exPlanations (SHAP) revealed TS% as the dominant factor, contrasting with RSM’s linear emphasis on microwave power, thereby highlighting nonlinear interactions and hidden effects. Complementary fluorescence microscopy and ImageJ analysis confirmed microwave-induced structural disruption of extracellular polymeric substances (EPS) and microbial accessibility, particularly at moderate power (600 W), evidencing distinct nonthermal contributions. By combining RSM’s statistical optimization with ML’s predictive and interpretive capacity, this framework not only refines operational conditions for maximum yield but also elucidates the mechanisms of MP, balancing energy input and performance. These insights support the design of more energy-efficient, scalable, and sustainable AD systems.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100583"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the sustainability of the Water-Food-Energy Nexus within an optimization framework: A case study of Ningxia, Northwest China","authors":"Jingyu Lyu ,&nbsp;Shuhong Mo ,&nbsp;Won-Ho Nam ,&nbsp;Lan Zhang ,&nbsp;Hui Sun ,&nbsp;Siyu Yan","doi":"10.1016/j.nexus.2025.100591","DOIUrl":"10.1016/j.nexus.2025.100591","url":null,"abstract":"<div><div>Water, food, and energy are essential resources for human survival and socio-economic development. The complex interconnections have positioned the Water-Food-Energy Nexus (WFEN) as a critical framework for achieving integrated and effective resource allocation, while maintaining the stable operation of coupled systems. This study, for the first time, proposes a novel WFEN-based multi-objective optimization framework that integrating system dynamics (SD) modeling, the non-dominated sorting genetic algorithm III (NSGA-III), and the weighted technique for order preference by similarity to ideal solution (TOPSIS) approach. This integrated framework enables dynamic, year-by-year optimization and comprehensive performance assessment across multiple scenarios, thereby facilitating rational resource allocation and enhancing overall system sustainability. The framework quantifies the impacts of key decision variables such as crop planting areas, irrigation quotas, and energy quotas on the system mechanisms, thereby revealing the complex coupling between feedback loops and policy regulation. A regional case study in Ningxia, northwest China, was conducted to implement and validate the proposed method. The SD model demonstrated high reliability, with simulation relative errors consistently below 10 % during the study period, indicating its suitability for representing the real-world conditions in the study area. The solution sets obtained from the multi-objective optimization exhibited substantial diversity and convergence, underscoring the effectiveness of the proposed integrated method. Under the top1 ranked solution selected by the weighted TOPSIS method, from 2011 to 2022, regional security indicators improved significantly: the Water Security Indicator (WSI) increased from 1.31 to 1.76, the Food Security Indicator (FSI) increased from 0.31 to 0.80, the Energy Security Indicator (ESI) increased from 0.51 to 1.05, and the Water-Food-Energy Security Indicator (WFESI) increased from 0.59 to 1.14. Incorporating integrated management principles into policymaking enables this study to break away from conventional single-sector policy frameworks. Moreover, by adjusting key variables that link across sectors, the proposed framework offers a promising pathway toward win-win synergies and sustainable prosperity in the regional water, agriculture, and energy systems.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"20 ","pages":"Article 100591"},"PeriodicalIF":9.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}