Energy nexus最新文献

{"title":"Collaborative swarm robotics for sustainable environment monitoring and exploration: Emerging trends and research progress","authors":"Belkacem Khaldi ,&nbsp;Fouzi Harrou ,&nbsp;Ying Sun","doi":"10.1016/j.nexus.2025.100365","DOIUrl":"10.1016/j.nexus.2025.100365","url":null,"abstract":"<div><div>This study explores the application of swarm robotics and swarm and evolutionary computing techniques in environmental management and sustainability, a highly specific and increasingly demanding niche research area. Through a bibliometric analysis of two collections of peer-reviewed papers, key trends and emerging research areas are identified. The first collection, comprising approximately 450 papers, focuses on specific applications of swarm robotics systems in environmental use cases, including swarms of UAVs, AUVs, and USVs, particularly in tasks such as ecological monitoring, agricultural management, and disaster response. This analysis highlights essential keyword clusters, with ``ecological restoration'' emerging as a significant topic, and ``agricultural robots'' and ``remote sensing'' as active frontiers. Building on this analysis, eight directions are proposed to address environmental challenges across five categories. The second collection, consisting of around 198 papers, examines the different swarm and evolutionary computing algorithms employed in this niche area, identifying ten significant research clusters. Notably, the ``secure incentive mechanism'' is a trending area, emphasizing the development of reliable and secure cooperative multi-robot systems. Recent methods in this cluster utilize deep reinforcement learning and heuristic algorithms to enhance cooperation efficiency. Five potential directions categorized into two main groups are explored to address security and reliability challenges within swarm robot systems in environmental tasks. The findings underscore the critical role of swarm robotics in environment-focused tasks such as ecosystem recovery and the importance of secure cooperation mechanisms, paving the way for advancements in agriculture, resource management, intelligent infrastructure, and urban systems.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100365"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring interlinkages in land, energy, and water in cooking and agriculture sectors: A case study in Kenya","authors":"Roberto Heredia-Fonseca,&nbsp;Francesco Gardumi,&nbsp;Will Usher","doi":"10.1016/j.nexus.2025.100366","DOIUrl":"10.1016/j.nexus.2025.100366","url":null,"abstract":"<div><div>This study contributes to the Climate, Land, Energy, and Water system (CLEWs) framework by developing an integrated model for Kenya capturing the interdependencies between climate, land, energy, and water systems. Focusing on cooking and crop production, it examines their contributions to land use changes, mainly deforestation, and emissions. We evaluate three scenarios—BAU, SC1, and SC2- that target clean cooking transitions and reduced crop imports, covering seven crops representing 72 % of Kenya's cultivated area. We detail the challenges of gathering data to populate such a model through document examination and literature review, and we identified uncertain input parameters. Results show that forest loss from cooking varies with the fraction of non-renewable biomass (fNRB). Under BAU, forest cover loss could range from 300 km² at an fNRB of 0.3 to 900 km² at 0.9. Scenarios SC1 and SC2 mitigate these impacts through cleaner cooking solutions. By 2050, under the clean cooking scenario (SC2), LPG stoves could achieve up to 96 % penetration, reducing CO₂ emissions to 8.3 MTon and PM_2.5 to 0.8 kTon, compared to high emissions in the BAU scenario dominated by wood and charcoal stoves. In agriculture, land use expands by 56 %, 69 %, and 33 % across the scenarios, while fossil fuel use rises from 2.46 PJ to 5.9 PJ by 2050, increasing CO₂ emissions, from 183 kTon to 436 kTon. The findings highlight the need for integrated policies promoting clean cooking, sustainable agriculture, and deforestation mitigation. This integrated CLEWs approach provides actional insights for reducing deforestation and emissions in energy and agriculture sectors.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100366"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of food wastes from the hotel industry as a potential feedstock for energy production","authors":"Emily Machuma Muchele ,&nbsp;Booker Osodo ,&nbsp;Isaiah Omosa ,&nbsp;Emmanuel Yeri Kombe","doi":"10.1016/j.nexus.2025.100364","DOIUrl":"10.1016/j.nexus.2025.100364","url":null,"abstract":"<div><div>Food waste contribute to 38% of total Municipal Solid Wastes (MSW) in Kenya and end up in landfills. Due to high competition in the available space, most cities, including Nairobi, do not have enough space for landfills. Therefore, there is a need for efficient ways to manage the generated waste. Developed countries have embraced Waste-to-Energy technologies, minimizing waste generation and converting generated waste into energy and other resources. Waste characterization is a key element in the energy generation process not only to identify important parameters but also to guide biomass source segmentation. In this study, food wastes were collected from 21 hotels within Nairobi City County, in different mixed ratios and subdivided into five samples for investigation and analysis. The average feedstock characteristics were observed to be moisture content (6.0%, <em>p</em> &lt; .001, <em>R²</em> = 90.70 %), total solid (93.7%, <em>p</em> &lt; .001, <em>R²</em> = 99.97 %), volatile solid (84.3%, <em>p</em> &lt; .001, <em>R²</em> = 99.80 %), ash content (4.2%, <em>p</em> = .005, <em>R²</em>= 48.54 %), fixed carbon (5.4%, <em>p</em> &lt; .001, <em>R²</em> = 88.61%), nitrogen (3.6%, <em>p</em> = .04, <em>R²</em> = 36.81 %), carbon to nitrogen ratio C/N (4.0), crude protein (22.4%, <em>p</em> = .004, <em>R²</em> = 49.36 % ), crude lipids (12.1%, <em>p</em> &lt; .001, <em>R²</em> = 89.06 %), total organic carbon (44%, <em>p</em> &lt; . 001, <em>R²</em> = 94.70%), potassium (0.6%), sodium (1.2%), calcium (0.2%), and phosphorus (0.4%). The potassium, sodium, calcium and phosphorus <em>p</em> and <em>R²</em> values all calculated together were p &lt; .001 and <em>R²</em>= 72.35%. The results showed a significant difference in the means of the samples with the majority of the parameters registering a strong positive correlation of above 50%. The analysis revealed that the feedstock under investigation contained well-balanced parameters for briquette, biogas, syngas and biochar production. Therefore, the findings of this research provide vital knowledge in integrating energy production from food wastes thereby improving the efficiency of food waste utilization.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100364"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of 5G network in revolutionizing agriculture for sustainable development: A comprehensive review","authors":"Wasif ur Rehman ,&nbsp;Mohsin Ali Koondhar ,&nbsp;Samandar Khan Afridi ,&nbsp;Lutfi Albasha ,&nbsp;Idris H. Smaili ,&nbsp;Ezzeddine Touti ,&nbsp;Mouloud Aoudia ,&nbsp;Wassim Zahrouni ,&nbsp;Ibrahim Mahariq ,&nbsp;M.M.R. Ahmed","doi":"10.1016/j.nexus.2025.100368","DOIUrl":"10.1016/j.nexus.2025.100368","url":null,"abstract":"<div><div>The deployment of 5G technologies in the agricultural sector promises to revolutionize smart farming practices by enabling unprecedented levels of connectivity, data exchange, and real-time monitoring. This paper presents a comprehensive review of the challenges, considerations, and future directions of integrating 5G technologies into smart agriculture, aligning with Sustainable Development Goals (SDGs) such as SDG 2 (Zero Hunger) and SDG 9 (Industry, Innovation, and Infrastructure).Key topics discussed include the necessity of dense network infrastructure, optimization strategies for cross-deployment of 5G and sensing networks, and the role of edge computing in 5G-enabled farming production. Additionally, the paper explores development optimization of various nodes, fault detection, self-healing mechanisms, AI application optimization, and security issues specific to 5G-enabled smart agriculture. Furthermore, the paper examines the potential impact of 5G technology on crucial agricultural tasks such as real-time monitoring, UAV operations, augmented reality (AR), virtual reality (VR) applications, virtual consultation, predictive maintenance, AI-driven robotics, and data analytics. Through a thorough analysis of these topics, the paper underscores the potential of 5G technology in enhancing productivity, reducing environmental impact, and advancing sustainable agricultural practices. The paper identifies critical areas for further research and emphasizes the importance of collaborative efforts among stakeholders to maximize the benefits of 5G-enabled smart farming, thereby contributing to global efforts to achieve SDGs related to food security, innovation in technology, and sustainable infrastructure.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100368"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interval-valued type 2 intuitionistic fuzzy theory-based approach to assess the biofuel production and adoption drivers in emerging economies: Implications for sustainability","authors":"Shah Murtoza Morshed ,&nbsp;Md Shihab Shakur ,&nbsp;Rafat Rahman ,&nbsp;Mohammad Mynul Islam Mahin ,&nbsp;Binoy Debnath ,&nbsp;Arman Hossain Apu ,&nbsp;Fairuz Al Nafiz ,&nbsp;A.B.M. Mainul Bari","doi":"10.1016/j.nexus.2025.100369","DOIUrl":"10.1016/j.nexus.2025.100369","url":null,"abstract":"<div><div>Biofuels, obtained from locally developed biomass, provide a sustainable energy alternative to reduce reserve depletion, environmental pollution, and rising energy demand in emerging economies like Bangladesh. These fuels can deal with the concerns about energy security through the diversification of the energy mix and mitigation of dependence on expensive imported fossil fuels. Given the ongoing energy shortages, inadequate policy frameworks, and escalating energy demands prompted by population growth and industrial expansion, biofuels have emerged as a sustainable solution. Therefore, this study tries to investigate the biofuel production and adoption drivers employing an integrated multi-criteria decision-making (MCDM) approach. Specifically, it combines the interval-valued type 2 intuitionistic fuzzy (IVT2IF) theory and the decision-making trial and evaluation laboratory (DEMATEL) method to determine, rank, and assess the correlation among the drivers that affect the sustainable production and adoption of biofuel in emerging economies like Bangladesh. The drivers were initially extracted through a systematic literature review of the existing literature. Followed by expert validation, 17 drivers were chosen for analysis utilizing the IVT2IF-DEMATEL technique. The findings suggest that \"facilitating advanced R&amp;D and efficient training regimen\", \"promoting technological advancements\", \"enhanced energy security and resilience,\" and \"development of the diversified renewable energy mix\" are the most significant drivers, with prominence values 15.616, 15.467, 15.164, and 15.067, respectively. Furthermore, \"streamlining bio-waste management processes\" holds the highest significance as a causal driver (with a causal weight of 1.290), which is trailed by \"commercialization of biofuel retrofits\" and \"efficient agricultural resource management\" (which have causal weights of 0.696 and 0.505, respectively). The study's actionable insights can potentially aid policymakers and decision-makers in formulating investment policies and long-term strategic planning focusing on areas including R&amp;D, infrastructure development, technology, waste management, and renewable energy to achieve energy security, sustainability, and carbon neutrality in Bangladesh.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100369"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and analysis of development risks under uncertainty: The impact of disruptive technologies on renewable energy development","authors":"Ibrahim Alrashdi ,&nbsp;Ahmed M. Ali ,&nbsp;Karam M. Sallam ,&nbsp;Mohamed Abdel-Basset","doi":"10.1016/j.nexus.2025.100371","DOIUrl":"10.1016/j.nexus.2025.100371","url":null,"abstract":"<div><div>Renewable energy (RE) is gaining great attention nowadays, as opposed to traditional energy, such as fossil fuels, which have various negative impacts and issues. RE faces multiple risks and challenges, so these risks need to be evaluated and mitigated such as technical risks, environmental risks, security risks, policy risks, and technological risks. This study proposed a multi-criteria decision-making (MCDM) method for assessing RE risks. The MCDM method is integrated with neutrosophic sets (NSs) to deal with inconsistent data in the evaluation process. Two MCDM methods are used in this study: Entropy and Ranking of Alternatives with Weights of Criterion (RAWEC). The neutrosophic entropy is used to compute the criteria weight, and the neutrosophic RAWEC method ranks the alternatives. This study applied the proposed method to two case studies. In the first case study, the RE risks are ranked. In the second case study, various strategies are proposed by blockchain, artificial intelligence (AI), the Internet of Things (IoT), big data, and zero-trust to reduce RE risks. There are six main factors; 31 sub-factors and 19 risks are used in the first case study, and 19 factors and 20 strategies are used in the second case study. The sensitivity analysis was conducted to show the stability of the rank. The proposed methodology was compared with MCDM methods such as neutrosophic TOPSIS, neutrosophic VIKOR, and fuzzy CoCoSo. The results show various proposed strategies can reduce RE risks.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100371"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diverse pathways to decarbonization: cluster-specific impacts of energy sources on CO2 emissions in the European Union","authors":"Jana Chovancová ,&nbsp;Igor Petruška ,&nbsp;Ugur Korkut Pata ,&nbsp;Peter Adamišin","doi":"10.1016/j.nexus.2025.100367","DOIUrl":"10.1016/j.nexus.2025.100367","url":null,"abstract":"<div><div>This study examines the relationship between different energy sources and CO₂ emissions across European Union (EU) countries through a regression clustering panel data analysis. Using a dataset from 2011 to 2022, we identify distinct country clusters and analyse the impact of energy sources, including gas, coal, oil, wind, biofuels, solar, hydro and nuclear, on CO₂ emissions within these clusters. The regression cluster analysis reveals significant differences in the impact of these energy sources on emissions across the EU. In particular, gas, coal and oil all have positive and significant coefficients, with coal having the largest impact on CO₂ emissions across all clusters. Conversely, biofuel shows a consistently negative and significant effect, indicating its potential to reduce CO₂ emissions. Wind shows mixed behaviour, with both positive and negative significance in certain clusters, highlighting the complexity of integrating wind energy into existing infrastructures. Coefficients of determination R² for individual clusters ranges from 0.9818 to 0.9940, indicating the high reliability of the models. The variables Solar, Hydro and Nuclear show the least significant coefficients. These findings underscore the need for tailored energy policies that consider the specific conditions of each country cluster in order to achieve an effective transition away from fossil fuels and maximise the benefits of renewable energy sources. This study provides critical insights for policymakers aiming to meet climate change targets and underlines the urgent need for strategic energy and climate change policies aligned with the unique characteristics of each EU country cluster to facilitate a successful energy transition.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100367"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Modeling of soil salinity integrating remote sensing and soil variables: An ensembled deep learning approach","authors":"Sana Arshad ,&nbsp;Jamil Hasan Kazmi ,&nbsp;Endre Harsányi ,&nbsp;Farheen Nazli ,&nbsp;Waseem Hassan ,&nbsp;Saima Shaikh ,&nbsp;Main Al-Dalahmeh ,&nbsp;Safwan Mohammed","doi":"10.1016/j.nexus.2025.100374","DOIUrl":"10.1016/j.nexus.2025.100374","url":null,"abstract":"<div><div>Accurate predictions of soil salinity can significantly contribute to achieving the UN- Sustainable Development Goal (SDG-2) of ensuring ‘zero hunger.’ From this perspective, the current research aimed to predict soil electrical conductivity (EC) from remote sensing and soil data using advanced deep learning (DL) architectures. A total of 109 soil samples were analyzed for agricultural land use in the Middle Indus Basin of Pakistan. Seven salinity indices (SI-1 to SI-7) were derived from the 10m to 20m wavelength bands of Sentinel-2, along with vegetation and topographic covariates. Initially, Recursive Feature Elimination was implemented as a feature-selection method to select the most effective predictors. Subsequently, deep learning architectures, including a Feedforward Neural Network (FFNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), were employed to predict soil salinity. Research findings showed that EC ranged between 0.57dS/m to 11.5 dS/m in the study area. The evaluation metrics of the DL models revealed that a simple FFNN with three fully connected dense layers achieved the highest R² = 0.88 for model training. However, the ensemble of improved FFNN and LSTM outperformed with the highest R² and NSE = 0.84, and the lowest RMSE and MAE = 1.38 and 1.01, respectively, on the testing dataset. Optimized deep learning architectures with adjustments to the learning rate, dropout rate, and activation functions achieved the highest prediction accuracy with the lowest validation loss. Finally, SHapely Additive exPlanations (SHAP) revealed that elevation, pH, NDVI, SI-1, and SI-7 had highly significant impacts on EC predictions. This research provides insight into implementing advanced and interpretable DL architectures, supporting informed decision-making by agricultural stakeholders.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100374"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of mineral nutrition and biopriming on crop performance, energetics, and the carbon footprint in rainfed castor bean (Ricinus communis L.)","authors":"Revappa Mohan Kumar ,&nbsp;Yamanura Madival ,&nbsp;Mahantesh Basangouda Nagangoudar ,&nbsp;Nagesha Narayanappa ,&nbsp;Gopalaswamy Ranganath ,&nbsp;Sugganahalli Channappa Ranganatha ,&nbsp;Venkatesh Paramesh ,&nbsp;Dinesh Jinger","doi":"10.1016/j.nexus.2025.100370","DOIUrl":"10.1016/j.nexus.2025.100370","url":null,"abstract":"<div><div>Suboptimal nutrition augmented by soil moisture stress adversely impacts crop performance in rainfed agriculture. At the same situation, conjunctive use of bioinoculants with mineral nutrients alleviates moisture stress and amplifies nutrient availability that improves the crop performance. With this background, a four-year fixed plot field experiment was conducted to determine the impact of mineral nutrition and biopriming on the productivity, economics, energetics, and carbon (C) footprint in rainfed castor. Three fertilizer rates and five bioinoculants were evaluated in a split-plot design with three replications. The result revealed that, application of recommended rate of fertilizer (RRF) recorded ∼22.13 % and 82.49 % higher yields than 75 % and 50 % RRF, respectively. The highest gross returns (₹ 80,412 ha^-1), net returns (₹ 50,511 ha^-1) and benefit-cost ratios (2.68) were also found higher with RRF. Likewise, plots applied with RRF even increased the seed oil content (50.9 %), oil productivity (722 kg ha^-1), and rain-water use efficiency (1.60 kg ha^-1 mm^-1). The plots treated with RRF required in 8.12–18.64 % higher energy input (12,275 MJ ha^-1) over 75 % and 50 % RRF, respectively. Furthermore, RRF appeared to be convincing by recording significantly highest C-outputs (1,582 kg CE ha⁻¹) and also other C quotients over its counterparts. Among the binoculants, seed priming with <em>Bacillus megaterium</em> and <em>Pseudomonas fluorescens</em> surpassed the productivity potential of other bioinoculants. Bioinoculants being energy diffident inputs they did not showed significant variation in energy input, while they showed significant variation in energy output. The energy output of castor bioprimed with <em>B. megaterium</em> (47,011 MJ ha^-1) surpassed the remaining bioinoculants. Again, <em>B. megaterium</em> recorded significantly lower C-footprint (0.22 kg CE kg castor seed yield⁻¹) while it displayed highest C-outputs (1,556 kg CE ha⁻¹), C-efficiencies (4.53 kg kg⁻¹ CE), net C-gains (1,278 kg CE ha⁻¹), and C-sustainability index (4.60). Consequently, the application of RRF, i.e.<em>,</em> 40–40–20 kg N-P₂O₅-K₂O ha^-1, along with the biopriming of <em>B.megaterium</em> appeared to be promising in enhancing productivity, economic returns, and resource use efficiency besides optimizing energy flow and C footprint in rainfed castor bean cultivation.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100370"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging technologies in water desalination: A review and future outlook","authors":"Anwur Alenezi ,&nbsp;Yousef Alabaiadly","doi":"10.1016/j.nexus.2025.100373","DOIUrl":"10.1016/j.nexus.2025.100373","url":null,"abstract":"<div><div>This review analyses emerging desalination technologies that offer sustainable solutions to global water scarcity and address unresolved issues. This study examines solar electrochemical distillation (SED), integrated solar capacitive deionisation (SCDI) and capacitive deionisation hybrid systems (CDI-HS), solar-powered passive desalination (SPPD), membrane distillation (MD), and forward osmosis (FO). A comprehensive literature review evaluates recent research and advancements in each technology. From each study, the authors extracted key characteristics, including the year of publication, research methods, data collection techniques, and the direction or strength of the research outcomes. Each study in this review serves as a unit of analysis, with the literature forming a database to interpret trends and draw conclusions about emerging desalination technologies. Key challenges were identified, and recommendations for future studies proposed, based on existing data and experimental findings. The review's findings underscore the need to address unresolved issues in desalination technologies to enhance their efficiency, scalability, and sustainability. Implementing the recommended research strategies could optimise these technologies, ensuring a reliable and sustainable supply of fresh water. Continued innovation, supported by targeted research and robust policy frameworks, is essential to mitigate global water scarcity and ensure water security for future generations.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100373"},"PeriodicalIF":8.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}