Pub Date : 2026-01-01DOI: 10.1016/j.seta.2026.104823
Dong Hee Suh , Sung-Kwan Joo
Despite the important role of renewable energy for energy security and emission mitigation, few studies have examined how different types of renewable energy sources affect energy mix and emission outcomes. This study fills this gap by analyzing how renewable electricity generation influences fossil fuel demand and resulting CO2 emissions. Employing a differential fuel allocation model that reflects renewables-induced interfuel substitution, we investigate electricity generation in the U.S. electric power sector over the period between 1995 and 2023. Our results show that this sector is not very responsive to fossil fuel price changes and demonstrates substitutable relationships among coal, natural gas, and petroleum. With respect to renewables-induced interfuel substitution, hydroelectric and biomass generation are associated with a reduction in natural gas demand, while solar generation reduces coal demand. Conversely, biomass generation is linked to a rise in coal demand, while solar generation increases reliance on natural gas. From a cost-minimization perspective, the simulation results for CO2 emissions show that solar generation contributes to further reductions in net emissions, whereas the mitigation potential of biomass energy may be weakened or even reversed due to renewables-induced interfuel substitution.
{"title":"Unclean consequences of clean energy? The impact of renewable electricity generation on carbon dioxide emissions","authors":"Dong Hee Suh , Sung-Kwan Joo","doi":"10.1016/j.seta.2026.104823","DOIUrl":"10.1016/j.seta.2026.104823","url":null,"abstract":"<div><div>Despite the important role of renewable energy for energy security and emission mitigation, few studies have examined how different types of renewable energy sources affect energy mix and emission outcomes. This study fills this gap by analyzing how renewable electricity generation influences fossil fuel demand and resulting CO2 emissions. Employing a differential fuel allocation model that reflects renewables-induced interfuel substitution, we investigate electricity generation in the U.S. electric power sector over the period between 1995 and 2023. Our results show that this sector is not very responsive to fossil fuel price changes and demonstrates substitutable relationships among coal, natural gas, and petroleum. With respect to renewables-induced interfuel substitution, hydroelectric and biomass generation are associated with a reduction in natural gas demand, while solar generation reduces coal demand. Conversely, biomass generation is linked to a rise in coal demand, while solar generation increases reliance on natural gas. From a cost-minimization perspective, the simulation results for CO2 emissions show that solar generation contributes to further reductions in net emissions, whereas the mitigation potential of biomass energy may be weakened or even reversed due to renewables-induced interfuel substitution.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104823"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) exhibit complementary carbon emission profiles, with BEVs having higher production phase but lower use phase emissions. This study employs a life cycle assessment framework to develop a comprehensive evaluation model, systematically quantifying carbon breakeven points and emission characteristics for ICEVs, BEVs, and plug-in hybrid electric vehicles (PHEVs) in China. The full-chain model, covering production, distribution, and use phases, incorporates multidimensional variables such as battery type: nickel cobalt manganese oxide (NCM) and lithium iron phosphate (LFP), regional grid carbon intensity, and PHEV usage patterns. Key findings reveal that while BEVs/PHEVs have 11 %–132 % higher production emissions than ICEVs, they achieve carbon breakeven due to lower operational emissions. LFP-battery vehicles reach breakeven at significantly shorter mileages (e.g., PHEV-LFP: 14,509 km; BEV-LFP: 36,751 km) compared to NCM-based BEVs (70,511–87,231 km). Regional grid intensity markedly influences breakeven points, with the Southwest grid reducing them by 19.7 %–38.0 % and the North grid increasing them by 19.6 %–37.8 % relative to the national average. Under China’s 2060 carbon neutrality scenario, BEV breakeven mileage decreases by 49.1 % and life cycle carbon footprint by 26 %–48.6 % compared to 2023 levels. This study provides a scientific basis for formulating differentiated EV promotion strategies and supporting power grid low-carbon transitions.
{"title":"Carbon breakeven analysis of electric versus internal combustion engine vehicles in China: a life cycle assessment","authors":"Xin Lai , Wentian Zhang , Junjie Chen , Quanwei Chen , Yuejiu Zheng","doi":"10.1016/j.seta.2026.104821","DOIUrl":"10.1016/j.seta.2026.104821","url":null,"abstract":"<div><div>Battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) exhibit complementary carbon emission profiles, with BEVs having higher production phase but lower use phase emissions. This study employs a life cycle assessment framework to develop a comprehensive evaluation model, systematically quantifying carbon breakeven points and emission characteristics for ICEVs, BEVs, and plug-in hybrid electric vehicles (PHEVs) in China. The full-chain model, covering production, distribution, and use phases, incorporates multidimensional variables such as battery type: nickel cobalt manganese oxide (NCM) and lithium iron phosphate (LFP), regional grid carbon intensity, and PHEV usage patterns. Key findings reveal that while BEVs/PHEVs have 11 %–132 % higher production emissions than ICEVs, they achieve carbon breakeven due to lower operational emissions. LFP-battery vehicles reach breakeven at significantly shorter mileages (e.g., PHEV-LFP: 14,509 km; BEV-LFP: 36,751 km) compared to NCM-based BEVs (70,511–87,231 km). Regional grid intensity markedly influences breakeven points, with the Southwest grid reducing them by 19.7 %–38.0 % and the North grid increasing them by 19.6 %–37.8 % relative to the national average. Under China’s 2060 carbon neutrality scenario, BEV breakeven mileage decreases by 49.1 % and life cycle carbon footprint by 26 %–48.6 % compared to 2023 levels. This study provides a scientific basis for formulating differentiated EV promotion strategies and supporting power grid low-carbon transitions.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104821"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2026.104818
Junjie Zhang , Shiwei Yu , Wenqing Zhang , Xing Hu , Haimei Liu
The global shift toward artificial intelligence (AI) and new energy vehicles (NEVs) offers a unique opportunity to accelerate the energy transition (ET). However, the interaction between NEV exports and AI in influencing the domestic ET remains unexplored. This study employs a two-way fixed-effects panel data model to examine how NEV exports and industrial robotics affect the share of renewable electricity in total electricity generation. Using panel data from Chinese provinces from 2017 to 2023, the study finds that NEV exports can hinder the ET by boosting domestic manufacturing and thus increasing energy demand, as well as by crowding out investments in ET-related research and development. Furthermore, the interaction between NEV exports and AI positively influences the ET, as the AI mitigates the export-induced growth in energy demand through enhanced energy efficiency, thereby alleviating the inhibitory effect of NEV exports on the ET. The study recommends that governments, especially in provinces with robust NEV policy support, align the expansion of NEV exports with AI deployment to maximize benefits for the ET.
{"title":"How AI shapes the impact of NEV exports on energy transition: Evidence from Chinese provinces","authors":"Junjie Zhang , Shiwei Yu , Wenqing Zhang , Xing Hu , Haimei Liu","doi":"10.1016/j.seta.2026.104818","DOIUrl":"10.1016/j.seta.2026.104818","url":null,"abstract":"<div><div>The global shift toward artificial intelligence (AI) and new energy vehicles (NEVs) offers a unique opportunity to accelerate the energy transition (ET). However, the interaction between NEV exports and AI in influencing the domestic ET remains unexplored. This study employs a two-way fixed-effects panel data model to examine how NEV exports and industrial robotics affect the share of renewable electricity in total electricity generation. Using panel data from Chinese provinces from 2017 to 2023, the study finds that NEV exports can hinder the ET by boosting domestic manufacturing and thus increasing energy demand, as well as by crowding out investments in ET-related research and development. Furthermore, the interaction between NEV exports and AI positively influences the ET, as the AI mitigates the export-induced growth in energy demand through enhanced energy efficiency, thereby alleviating the inhibitory effect of NEV exports on the ET. The study recommends that governments, especially in provinces with robust NEV policy support, align the expansion of NEV exports with AI deployment to maximize benefits for the ET.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104818"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2025.104813
Wojciech Bącalski , Krzysztof Abucewicz , Jan Wajs , Joanna Krakowiak
This article presents a novel, 3D-printed lab-scale redox flow battery (RFB) cell featuring a “lid and jar” assembly method instead of the conventional sandwich-like structure connected by bolted joints. The cell is sealed by screwing a threaded ring onto the complementarily threaded casing of the reactor module instead of multi-bolt assembly requiring specific tools, assembly methods and experience, resulting in a simpler and faster assembly process without the need for additional sealing. This approach is particularly beneficial for research applications requiring frequent assembly and disassembly of the cell. Additionally, utilization of 3D printing allows for comparatively easy manufacturing and personalized modifications of the device, resulting in less material loss compared to conventional production methods.
The prototype, manufactured using stereolithography (SLA) technology, was subjected to electrochemical testing, including charge and discharge cycling as well as electrochemical impedance spectroscopy (EIS). The obtained results were compared with those from a commercially available lab-scale RFB cell by Pinflow energy storage s.r.o. Electrochemical measurements were performed to verify the basic functionality of the proposed design and as a proof of concept. Detailed analysis of electrochemical behavior is beyond the scope of this study, as the aim is to improve the assembly method rather than the overall battery efficiency.
{"title":"Simple twist of fate – novel boltless construction for a lab-scale redox flow battery cell","authors":"Wojciech Bącalski , Krzysztof Abucewicz , Jan Wajs , Joanna Krakowiak","doi":"10.1016/j.seta.2025.104813","DOIUrl":"10.1016/j.seta.2025.104813","url":null,"abstract":"<div><div>This article presents a novel, 3D-printed lab-scale redox flow battery (RFB) cell featuring a “lid and jar” assembly method instead of the conventional sandwich-like structure connected by bolted joints. The cell is sealed by screwing a threaded ring onto the complementarily threaded casing of the reactor module instead of multi-bolt assembly requiring specific tools, assembly methods and experience, resulting in a simpler and faster assembly process without the need for additional sealing. This approach is particularly beneficial for research applications requiring frequent assembly and disassembly of the cell. Additionally, utilization of 3D printing allows for comparatively easy manufacturing and personalized modifications of the device, resulting in less material loss compared to conventional production methods.</div><div>The prototype, manufactured using stereolithography (SLA) technology, was subjected to electrochemical testing, including charge and discharge cycling as well as electrochemical impedance spectroscopy (EIS). The obtained results were compared with those from a commercially available lab-scale RFB cell by Pinflow energy storage s.r.o. Electrochemical measurements were performed to verify the basic functionality of the proposed design and as a proof of concept. Detailed analysis of electrochemical behavior is beyond the scope of this study, as the aim is to improve the assembly method rather than the overall battery efficiency.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104813"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2025.104785
Farzana , Fahd A. Nasr , Mushtaq Ahmad , Mohammed Al-zharani , You-Cai Xiong , Lina M. Alneghery , Hong-Yan Tao , Najeeb Ullah , Ahmad Mustafa , Shazia Sultana
The global shift towards renewable energy has intensified the need for sustainable technologies. The present study investigates the invasive weed Verbesina encelioides (Cav.) Benth. & Hook. Ex A. Gray as a potential feedstock for biodiesel production, while managing ecological issues. The seeds contain large amounts of oil (33 wt%) and a very low level of free fatty acids (0.16 wt%), which allows one-step transesterification using a synthesized cobalt oxide (Co3O4) nanocatalyst prepared from seed husk (a waste product) as a precursor. The nanocatalyst was characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), dynamic light scattering (DLS), and X-ray diffraction (XRD). These techniques verified the porous morphology, cobalt-oxygen-rich composition, and highly crystalline structure, thereby confirming the presence of active catalytic sites. Response Surface Methodology (RSM) with a Box-Behnken Design (BBD) was used to optimize the reaction parameters. Such as oil methanol molar ratio of 1:3, catalyst loading of 0.4 % wt., reaction temperature of 60 °C, and reaction time of 120 min, confirming the conversion of triglycerides into fatty acid methyl esters (FAMEs) and yielding a maximum biodiesel production of 97 %. The resulting biodiesel was characterized by FTIR, 1H and 13C nuclear magnetic resonance (NMR), and gas chromatography-mass spectrometry (GC–MS), indicating complete transesterification, with oleic acid methyl ester as the dominant FAME. Compared to conventional diesel, the synthesized biodiesel exhibits high oxidative stability and standard combustion properties; the nanocatalyst also maintains recyclability and catalytic activity across multiple catalytic cycles. These results highlight the dual advantages of controlling an invasive species and generating high-quality biodiesel, which aids in designing bio-powered energy systems, efficient application technologies, and a circular bioeconomy.
{"title":"Ecotechnological Valorization of Verbesina encelioides: A dual strategy for sustainable biodiesel production and invasive weed mitigation","authors":"Farzana , Fahd A. Nasr , Mushtaq Ahmad , Mohammed Al-zharani , You-Cai Xiong , Lina M. Alneghery , Hong-Yan Tao , Najeeb Ullah , Ahmad Mustafa , Shazia Sultana","doi":"10.1016/j.seta.2025.104785","DOIUrl":"10.1016/j.seta.2025.104785","url":null,"abstract":"<div><div>The global shift towards renewable energy has intensified the need for sustainable technologies. The present study investigates the invasive weed <em>Verbesina encelioides</em> (Cav.) Benth. & Hook. Ex A. Gray as a potential feedstock for biodiesel production, while managing ecological issues. The seeds contain large amounts of oil (33 wt%) and a very low level of free fatty acids (0.16 wt%), which allows one-step transesterification using a synthesized cobalt oxide (Co3O4) nanocatalyst prepared from seed husk (a waste product) as a precursor. The nanocatalyst was characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), dynamic light scattering (DLS), and X-ray diffraction (XRD). These techniques verified the porous morphology, cobalt-oxygen-rich composition, and highly crystalline structure, thereby confirming the presence of active catalytic sites. Response Surface Methodology (RSM) with a Box-Behnken Design (BBD) was used to optimize the reaction parameters. Such as oil methanol molar ratio of 1:3, catalyst loading of 0.4 % wt., reaction temperature of 60 °C, and reaction time of 120 min, confirming the conversion of triglycerides into fatty acid methyl esters (FAMEs) and yielding a maximum biodiesel production of 97 %. The resulting biodiesel was characterized by FTIR, 1H and 13C nuclear magnetic resonance (NMR), and gas chromatography-mass spectrometry (GC–MS), indicating complete transesterification, with oleic acid methyl ester as the dominant FAME. Compared to conventional diesel, the synthesized biodiesel exhibits high oxidative stability and standard combustion properties; the nanocatalyst also maintains recyclability and catalytic activity across multiple catalytic cycles. These results highlight the dual advantages of controlling an invasive species and generating high-quality biodiesel, which aids in designing bio-powered energy systems, efficient application technologies, and a circular bioeconomy.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104785"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2025.104802
Mustafa İnci , Ömer Berber , Mehmet Büyük , Necdet Sinan Özbek
This study presents an eco-friendly charging solution with an improved charging methodology for a high-efficiency off-board Vehicle-to-Vehicle (V2V) charging interface supported by photovoltaic (PV) power to facilitate energy exchange between light electric vehicles (EVs). The proposed method uses dynamic current control to adapt charging current in real time according to solar irradiance and the state of charge (SoC) of the vehicles. This approach improves energy transfer compared to conventional constant current (CC) and multi-stage CC methods, which use fixed or stepwise charging profiles and cannot fully utilize variable PV power. A high-gain quadratic buck-boost (QBB) converter is employed to enable both step-up and step-down operation, making the system suitable for vehicles with different voltage levels. The control strategy combines dynamic current control with an enhanced incremental conductance (InC) maximum power point tracking (MPPT) algorithm to maximize solar energy use. Performance results from processor-in-the-loop simulations show that the proposed system achieves more stable voltage regulation, better SoC improvement (+0.056 %), and higher charging efficiency than conventional CC and multi-CC methods under varying conditions. The performance findings show that the proposed V2V–PV interface provides a robust and efficient charging approach, supporting sustainable and grid-independent electric mobility.
{"title":"Dynamic current control of High-Gain Buck-Boost power transfer for electric Vehicle-to-Vehicle (V2V) charging with PV integration","authors":"Mustafa İnci , Ömer Berber , Mehmet Büyük , Necdet Sinan Özbek","doi":"10.1016/j.seta.2025.104802","DOIUrl":"10.1016/j.seta.2025.104802","url":null,"abstract":"<div><div>This study presents an eco-friendly charging solution with an improved charging methodology for a high-efficiency off-board Vehicle-to-Vehicle (V2V) charging interface supported by photovoltaic (PV) power to facilitate energy exchange between light electric vehicles (EVs). The proposed method uses dynamic current control to adapt charging current in real time according to solar irradiance and the state of charge (SoC) of the vehicles. This approach improves energy transfer compared to conventional constant current (CC) and multi-stage CC methods, which use fixed or stepwise charging profiles and cannot fully utilize variable PV power. A high-gain quadratic buck-boost (QBB) converter is employed to enable both step-up and step-down operation, making the system suitable for vehicles with different voltage levels. The control strategy combines dynamic current control with an enhanced incremental conductance (InC) maximum power point tracking (MPPT) algorithm to maximize solar energy use. Performance results from processor-in-the-loop simulations show that the proposed system achieves more stable voltage regulation, better SoC improvement (+0.056 %), and higher charging efficiency than conventional CC and multi-CC methods under varying conditions. The performance findings show that the proposed V2V–PV interface provides a robust and efficient charging approach, supporting sustainable and grid-independent electric mobility.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104802"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2026.104820
Lucas M. Machin Ferrero , Agustín Almaraz , Fernando D. Mele
This prospective study evaluates the environmental performance of manure-derived biomethane for use in passenger cars in Argentina. It compares the performance of fossil natural gas with that of carbon capture and storage (CCS) pathways under current conditions and three 2050 Integrated Assessment Model (IMAGE, REMIND, and TIAM-UCL) scenarios. Life cycle inventories were modeled using Brightway2/premise and the ecoinvent v3.10 database. Under current conditions, the global warming potential of fossil natural gas is 0.207 ± 0.034 kg CO2-eq/km. Biomethane with CCS significantly outperforms this baseline: specifically, when accounting for synthetic fertilizer displacement via digestate, the system achieves a net negative carbon balance of −0.248 ± 0.037 kg CO2-eq/km. However, multi-criteria analysis reveals burden shifting, with acidification and toxicity indicators increasing due to digestate logistics and CCS energy penalties. Long-term projections indicate that decarbonizing the Argentine electricity grid is decisive for sustainability, with the TIAM-UCL scenario enabling GHG reductions of ∼ 80% relative to the 2050 fossil baseline. To maximize mitigation potential while minimizing local trade-offs, the findings recommend prioritizing decentralized production clusters, powering CCS units with renewable energy, and enforcing rigorous methane leak control throughout the supply chain.
这项前瞻性研究评估了阿根廷乘用车中使用的粪便衍生生物甲烷的环境性能。它比较了化石天然气在当前条件下和三种2050综合评估模型（IMAGE， REMIND和TIAM-UCL）情景下与碳捕集与封存（CCS）途径的性能。生命周期清单是使用Brightway2/premise和ecoinvent v3.10数据库建模的。在当前条件下，化石天然气的全球变暖潜势为0.207±0.034 kg CO2-eq/km。具有CCS的生物甲烷显著优于这一基准：具体而言，当考虑到通过消化的合成肥料排气量时，该系统的净负碳平衡为- 0.248±0.037 kg CO2-eq/km。然而，多标准分析揭示了负担的转移，酸化和毒性指标由于消化物流和CCS能源惩罚而增加。长期预测表明，阿根廷电网的脱碳对可持续性具有决定性作用，TIAM-UCL情景使温室气体排放量相对于2050年化石基准减少约80%。为了最大限度地发挥缓解潜力，同时最大限度地减少当地的权衡，研究结果建议优先考虑分散的生产集群，用可再生能源为CCS装置供电，并在整个供应链中实施严格的甲烷泄漏控制。
{"title":"Life cycle Assessment of Manure-Based biomethane with carbon capture and storage for Argentina’s transport sector","authors":"Lucas M. Machin Ferrero , Agustín Almaraz , Fernando D. Mele","doi":"10.1016/j.seta.2026.104820","DOIUrl":"10.1016/j.seta.2026.104820","url":null,"abstract":"<div><div>This prospective study evaluates the environmental performance of manure-derived biomethane for use in passenger cars in <em>Argentina</em>. It compares the performance of fossil natural gas with that of carbon capture and storage (CCS) pathways under current conditions and three 2050 Integrated Assessment Model (IMAGE, REMIND, and TIAM-UCL) scenarios. Life cycle inventories were modeled using Brightway2/premise and the ecoinvent v3.10 database. Under current conditions, the global warming potential of fossil natural gas is 0.207 ± 0.034 kg CO<sub>2</sub>-eq/km. Biomethane with CCS significantly outperforms this baseline: specifically, when accounting for synthetic fertilizer displacement via digestate, the system achieves a net negative carbon balance of −0.248 ± 0.037 kg CO<sub>2</sub>-eq/km. However, multi-criteria analysis reveals burden shifting, with acidification and toxicity indicators increasing due to digestate logistics and CCS energy penalties. Long-term projections indicate that decarbonizing the Argentine electricity grid is decisive for sustainability, with the TIAM-UCL scenario enabling GHG reductions of ∼ 80% relative to the 2050 fossil baseline. To maximize mitigation potential while minimizing local trade-offs, the findings recommend prioritizing decentralized production clusters, powering CCS units with renewable energy, and enforcing rigorous methane leak control throughout the supply chain.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104820"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2025.104810
Thomas Arya , Ingela Tietze , V.K. Srineash , Manasa Ranjan Behera
In recent years, interest has grown in floating offshore wind systems, though current developments are largely limited to prototypes with limited economic insight. This study evaluates the Levelized Cost of Energy (LCOE) for a floating offshore wind farm off the Gujarat coast using the latest data and parametric cost models. Wind speed data from ERA5 and bathymetry from the GEBCO dataset support the site assessment. A 5MW wind turbine mounted on a SPAR platform is analysed under coupled wind–wave conditions using OpenFAST. Based on this generated power under the influence of wind–wave–floater dynamics, the energy output from the farms having aligned and staggered layouts with 5MW wind turbines is estimated and LCOE is calculated using three different cost models, resulting in a range of 110–120 €/MWh cost per unit energy for 16.5GWh per turbine of output energy. The sensitivity analysis reveals that CAPEX and project lifetime is critical for LCOE calculations in the Indian context, particularly given the limited number of studies on FOWTs. The findings suggest that floating offshore wind is economically viable in the Indian context and the adopted methodology can inform future planning and investment decisions as the sector evolves.
{"title":"Coupled wind–wave simulation and LCOE analysis of a SPAR floating offshore wind farm off Gujarat, India","authors":"Thomas Arya , Ingela Tietze , V.K. Srineash , Manasa Ranjan Behera","doi":"10.1016/j.seta.2025.104810","DOIUrl":"10.1016/j.seta.2025.104810","url":null,"abstract":"<div><div>In recent years, interest has grown in floating offshore wind systems, though current developments are largely limited to prototypes with limited economic insight. This study evaluates the Levelized Cost of Energy (LCOE) for a floating offshore wind farm off the Gujarat coast using the latest data and parametric cost models. Wind speed data from ERA5 and bathymetry from the GEBCO dataset support the site assessment. A 5MW wind turbine mounted on a SPAR platform is analysed under coupled wind–wave conditions using OpenFAST. Based on this generated power under the influence of wind–wave–floater dynamics, the energy output from the farms having aligned and staggered layouts with 5MW wind turbines is estimated and LCOE is calculated using three different cost models, resulting in a range of 110–120 €/MWh cost per unit energy for 16.5GWh per turbine of output energy. The sensitivity analysis reveals that CAPEX and project lifetime is critical for LCOE calculations in the Indian context, particularly given the limited number of studies on FOWTs. The findings suggest that floating offshore wind is economically viable in the Indian context and the adopted methodology can inform future planning and investment decisions as the sector evolves.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104810"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2026.104817
Wei Liu, Kaiyuan Hu, Zhijin Lyu, Lu Yan, Fan Hua
The long-term development stages division of new power system balancing is crucial preliminary research that guides the development of the system. The development of system balancing is influenced by various factors, including technology, policy, and economy. However, existing research tends to evaluate these factors separately. Hence, this study proposes a development stage division method for new power system balancing that comprehensively considers multiple factors to assess the level of development and predict the inflection points of the development stages. First, the method analyses the factors from various dimensions, including technology, policy, economy and balance and establishes an evaluation index system. Then, an entropy Technique for Order Preference by Similarity to Ideal Solution-logistic(E-TOPSIS-L) development stage division model is constructed, comprehensively considering multiple factors to predict future inflection points and avoiding the drawbacks of the traditional logistic model that can only fit a single variable. The logistic model fitting results show a root mean squared error of 0.0397 and standard error of 0.0086, indicating a good fitting result. Finally, the feasibility and superiority of the proposed method are validated, with the system balancing maturity forecasted to occur by 2025 and saturation by 2060.
{"title":"Data-driven development stage division method of new power system balancing based on E-TOPSIS-L model","authors":"Wei Liu, Kaiyuan Hu, Zhijin Lyu, Lu Yan, Fan Hua","doi":"10.1016/j.seta.2026.104817","DOIUrl":"10.1016/j.seta.2026.104817","url":null,"abstract":"<div><div>The long-term development stages division of new power system balancing is crucial preliminary research that guides the development of the system. The development of system balancing is influenced by various factors, including technology, policy, and economy. However, existing research tends to evaluate these factors separately. Hence, this study proposes a development stage division method for new power system balancing that comprehensively considers multiple factors to assess the level of development and predict the inflection points of the development stages. First, the method analyses the factors from various dimensions, including technology, policy, economy and balance and establishes an evaluation index system. Then, an entropy Technique for Order Preference by Similarity to Ideal Solution-logistic(E-TOPSIS-L) development stage division model is constructed, comprehensively considering multiple factors to predict future inflection points and avoiding the drawbacks of the traditional logistic model that can only fit a single variable. The logistic model fitting results show a root mean squared error of 0.0397 and standard error of 0.0086, indicating a good fitting result. Finally, the feasibility and superiority of the proposed method are validated, with the system balancing maturity forecasted to occur by 2025 and saturation by 2060.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104817"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.seta.2025.104812
Jiaqi Liu , Yuwei Liu , Qiang Shen , Keyan Li , Yinuo Chen
This study proposes a novel hybrid forecasting architecture that synergistically integrates artificial intelligence and deep learning techniques. This approach effectively overcomes the limitations of conventional single models in characterizing complex weather-power coupling relationships. First, we propose an enhanced honey badger algorithm based on self-learning factors, which incorporates three key innovations: (1) the good point set principle for population initialization, (2) an adaptive dynamic learning mechanism, and (3) a Gaussian mutation strategy. Comprehensive case studies demonstrate that the proposed algorithm achieves superior performance in iterative optimization compared to existing methods. Second, we develop a short-term forecasting model for wind and photovoltaic power generation, explicitly designed to balance model effectiveness and generalizability. The self-learning honey badger algorithm is employed to optimize model parameters, enabling robust feature extraction from renewable energy output sequences. Finally, extensive validation and analysis are conducted using real-world hybrid power generation datasets combining wind and solar energy. The results show that the proposed model achieves the lowest root mean square error values and consistently exceeds 99% in R2 across diverse weather conditions. These findings provide a scientific foundation for enhancing the economic operation and dispatch of renewable energy generation systems.
{"title":"A hybrid artificial intelligence and deep learning architecture for accurate renewable energy forecasting: comprehensive case studies on wind and PV power","authors":"Jiaqi Liu , Yuwei Liu , Qiang Shen , Keyan Li , Yinuo Chen","doi":"10.1016/j.seta.2025.104812","DOIUrl":"10.1016/j.seta.2025.104812","url":null,"abstract":"<div><div>This study proposes a novel hybrid forecasting architecture that synergistically integrates artificial intelligence and deep learning techniques. This approach effectively overcomes the limitations of conventional single models in characterizing complex weather-power coupling relationships. First, we propose an enhanced honey badger algorithm based on self-learning factors, which incorporates three key innovations: (1) the good point set principle for population initialization, (2) an adaptive dynamic learning mechanism, and (3) a Gaussian mutation strategy. Comprehensive case studies demonstrate that the proposed algorithm achieves superior performance in iterative optimization compared to existing methods. Second, we develop a short-term forecasting model for wind and photovoltaic power generation, explicitly designed to balance model effectiveness and generalizability. The self-learning honey badger algorithm is employed to optimize model parameters, enabling robust feature extraction from renewable energy output sequences. Finally, extensive validation and analysis are conducted using real-world hybrid power generation datasets combining wind and solar energy. The results show that the proposed model achieves the lowest root mean square error values and consistently exceeds 99% in R2 across diverse weather conditions. These findings provide a scientific foundation for enhancing the economic operation and dispatch of renewable energy generation systems.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"85 ","pages":"Article 104812"},"PeriodicalIF":7.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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