Energy nexus最新文献

{"title":"Optimizing energy use efficiency and environmental performance in cotton and canola production using the Imperialist Competitive Algorithm","authors":"Mousa Mirmoradi ,&nbsp;Mohammad Gholami Parashkoohi ,&nbsp;Hamed Afshari ,&nbsp;Ahmad Mohammadi","doi":"10.1016/j.nexus.2025.100392","DOIUrl":"10.1016/j.nexus.2025.100392","url":null,"abstract":"<div><div>This study focuses on optimizing energy efficiency and environmental performance in the production of cotton and canola through the application of the Imperialist Competitive Algorithm (ICA). Conducted in the Dasht-e Gorgan region of Iran, the research provides a comprehensive analysis of energy inputs and outputs for both crops. The findings reveal distinct differences in energy utilization, with cotton requiring significantly more labor (120 h) and machine energy (6,270 MJ) compared to canola, which utilizes less labor (79 h) and machine energy (2,821.5 MJ). However, canola's dependency on diesel fuel is higher, consuming 6,757.21 MJ against cotton's 5,631 MJ. While cotton demonstrates greater nitrogen energy utilization at 7,810 MJ, canola's nitrogen consumption by volume is 10,153 MJ. Furthermore, cotton production incurs higher biocide energy inputs (1,750 MJ) due to pest management challenges. Total energy consumption per hectare is slightly higher for cotton (26,083.80 MJ) relative to canola (25,747.04 MJ), yet cotton yields greater output (2,900 kg vs. 2,300 kg), indicating superior yield efficiency. Energy use efficiency favors canola with a conversion rate of 2.23 compared to cotton's 1.31, as well as a significantly higher net energy gain (31,752.96 MJ ha^–1 for canola versus 8,136.20 MJ ha^–1 for cotton). Environmental impacts also differ; canola's fertilizer use contributes more nitrogen oxides and ammonia, potentially affecting water quality, while cotton's labor-intensive methods lead to increased emissions of heavy metals and CO₂. In terms of human health impacts, cotton shows a lower Disability-Adjusted Life Years (0.064 DALY) compared to canola (0.089 DALY). Financially, cotton demonstrates lower resource intensity (115.36 USD2013) than canola (187.56 USD2013). To mitigate the environmental effects associated with both crops, this study recommends strategies such as precision agriculture, the integration of renewable energy, and enhancements in soil health, all aimed at improving overall sustainability in cotton and canola production.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100392"},"PeriodicalIF":8.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420715","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":"Recrystallization of tri-sodium phosphate from Thai monazite concentrate decomposition as solid catalyst for biodiesel production","authors":"Dussadee Rattanaphra ,&nbsp;Wilasinee Kingkam ,&nbsp;Sasikarn Nuchdang ,&nbsp;Chantaraporn Phalakornkule ,&nbsp;Unchalee Suwanmanee","doi":"10.1016/j.nexus.2025.100385","DOIUrl":"10.1016/j.nexus.2025.100385","url":null,"abstract":"<div><div>Tri-sodium phosphate (TSP) obtained from alkaline baking process of Thai monazite concentrate was used as raw material to synthesize the solid catalyst for biodiesel production. The TSP catalysts were prepared via recrystallization method with the ratio of Na₃PO₄·12H₂O: H₂O of 1:15 by lower temperature from 80 to 30 °C using stirring rate of 400 rpm and calcined at 300–700 °C. The catalytic performances were evaluated in the transesterification of palm oil with methanol. According to the results, the radioactive material (uranium) of &lt; 10 mg kg^-1 was detected after recrystallization, which was considered safe to use as catalyst. The TSP calcined at 600 °C showed active pure tetragonal phase with high basic sites strength and basicity, and can produce the highest fatty acid methyl ester (FAME) content of 91 % under the reaction conditions: the molar ratio of oil to methanol of 1:9, the catalyst loading of 5 wt %, the reaction temperature of 80 °C and the reaction time of 5 h. There was a significant leaching of active Na⁺ during the reaction. The improvement of stability and reusability of the catalyst and economic analysis will be further investigated for its utilization in the large-scale biodiesel production. This development can enhance the economic value of TSP as by product obtained from mineral monazite processing and also provides an idea for designing the economic viability of rare earth production.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"17 ","pages":"Article 100385"},"PeriodicalIF":8.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394906","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":"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":"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":"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}