Applied Energy最新文献

{"title":"Data-driven multi-stage distributionally robust scheduling for coupled electricity-hydrogen-refinery systems","authors":"Chao Ning ,&nbsp;Aokai Ma ,&nbsp;Zhaoyang Dong","doi":"10.1016/j.apenergy.2025.126620","DOIUrl":"10.1016/j.apenergy.2025.126620","url":null,"abstract":"<div><div>Green hydrogen production via electrolysis has fueled decarbonization for refineries, revealing a promising roadmap towards an electricity-hydrogen-refinery integration. This paper proposes a novel multi-stage distributionally robust scheduling framework for a coupled electricity-hydrogen-refinery system under renewable energy uncertainty. Power systems, hydrogen devices, and process units are seamlessly integrated to facilitate the comprehensive optimization of the refinery, achieving both economic and sustainable benefits. To effectively hedge against the high-dimensional uncertainty arising from scheduling-stage proliferation and uncertainty-type multiplicity, we develop an innovative structured moment-Wasserstein-based ambiguity set, along with theoretical set-inclusion relationships and probabilistic guarantees. Based on this ambiguity set, the refinery scheduling problem is then formulated to achieve robust optimal dispatch strategies under a given production target. In this formulation, the variables for refinery processes are considered as here-and-now decisions due to inflexibility and production requirements, while electricity and hydrogen dispatches are treated as multi-stage recourse decisions for flexible regulation. To efficiently solve the resulting scheduling problem, we exploit a lifted decision rule, based on which an equivalent mixed-integer linear programming reformulation is derived. Case studies demonstrate the effectiveness and superiority of the proposed approach compared with state-of-the-art decision-making methods. The electricity-hydrogen-refinery coupled scheduling scheme exhibits significant advantages over the conventional scheduling scheme without distributed hydrogen generation, achieving a 3.77 % reduction in scheduling costs and a 20.59 % decrease in carbon emissions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126620"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing electric bus charging station locations: An integrated land-use and transportation approach","authors":"Shaopeng Zhong ,&nbsp;Ao Liu ,&nbsp;Meihan Fan ,&nbsp;Yan Song ,&nbsp;Yu Jiang","doi":"10.1016/j.apenergy.2025.126649","DOIUrl":"10.1016/j.apenergy.2025.126649","url":null,"abstract":"<div><div>Existing research on optimizing electric bus charging station locations often assumes an exogenous demand, overlooking the feedback effects of station locations on demand. Moreover, the long-term implications of location strategies are deeply influenced by the complex interactions between land-use and transportation systems. To address these two challenges simultaneously, this study develops a bi-level programming model—a hierarchical decision-making framework involving two interconnected problems. Specifically, the upper-level problem is formulated as a mixed integer nonlinear programming model that minimizes the electric bus system's investment, operation, and passenger waiting time costs by optimizing the fleet size of electric buses, the corresponding frequency setting, and the location and capacity of charging stations. The lower-level model is an integrated land-use and transportation model that captures the long-term impacts of upper-level location decisions on transportation and land-use systems. To solve the proposed model, an iterative solution method is devised, which employs Gurobi to generate upper-level decisions via solving a linearized upper-level model and subsequently evaluates the decisions via TRNUS, which is an integrated land-use and transportation model, in the lower-level. Case studies are carried out using real data from Jiangyin City, China. The results demonstrate that the optimal design considering the interaction between land use and transportation attracts a higher number of bus users across various routes and increases the share of passenger kilometers traveled by bus from 19.9 % to 20.5 %. Meanwhile, it contributes to alleviating traffic congestion by 2.7 %, improving regional accessibility by 0.4 %, and reducing vehicle carbon emissions by 1.1 %, promoting urban sustainability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126649"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI explained data-driven framework for electricity theft detection with optimized and active machine learning","authors":"Nadeem Javaid,&nbsp;Muhammad Hasnain,&nbsp;Muhammad Ammar","doi":"10.1016/j.apenergy.2025.126632","DOIUrl":"10.1016/j.apenergy.2025.126632","url":null,"abstract":"<div><div>Electricity theft is a major problem that causes significant financial losses and inefficient power distribution. Effective theft detection systems play a critical role in detecting fraudulent consumption patterns. However, the performance and generalization of traditional theft detection systems are hindered by issues such as class imbalance, lack of labeled data, suboptimal hyperparameter tuning, and limited model interpretability. To overcome these issues, we propose a novel framework that combines active learning and metaheuristic optimization to enhance theft detection performance. Initially, the proposed framework addresses the data imbalance in the State Grid Corporation of China dataset by employing localized randomized affine shadow sampling. Next, two models are proposed to increase classification accuracy: Active Stochastic Gradient Descent (ASGD) and Cuckoo Stochastic Gradient Descent (CSGD). The ASGD uses entropy-based active learning to prioritize informative samples, whereas CSGD incorporates cuckoo search optimization to improve parameter tuning. The proposed ASGD and CSGD models show significant improvements of 36.67 % and 35 %, respectively, over the baseline SGD in accuracy, demonstrating enhanced performance in electricity theft detection. The experimental results demonstrate that ASGD and CSGD outperform state-of-the-art models with an improvement score of 6.57 % and 7.89 % in accuracy, 7.89 % and 9.21 % in F1-score, and 7.14 % and 8.33 % in the precision-recall area under the curve. Furthermore, the results of the proposed models are validated using a 10-fold cross-validation technique to ensure their reliability. Additionally, the statistical significance of ASGD and CSGD is confirmed using a <em>t</em>-test. Finally, two explainable artificial intelligence methods: local interpretable model-agnostic explanations and Shapley additive explanations, are employed to uncover the interpretability and explainability of the proposed models’ predictions. The proposed framework is useful for detecting electricity consumption anomalies as it enhances both classification performance and model interpretability, ensuring more reliable predictions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126632"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinated operation of multi-energy microgrids considering green hydrogen and congestion management via a safe policy learning approach","authors":"Xueyong Jia ,&nbsp;Yang Xia ,&nbsp;Ziming Yan ,&nbsp;Hongjun Gao ,&nbsp;Dawei Qiu ,&nbsp;Josep M. Guerrero ,&nbsp;Zhengmao Li","doi":"10.1016/j.apenergy.2025.126611","DOIUrl":"10.1016/j.apenergy.2025.126611","url":null,"abstract":"<div><div>Multi-energy microgrids (MEMGs) with green hydrogen have attracted significant research attention for their benefits, such as energy efficiency improvement, carbon emission reduction, as well as line congestion alleviation. However, the complexities of multi-energy networks coupled with diverse uncertainties may threaten MEMG's operation. In this paper, a data-driven methodology is proposed to achieve effective MEMG operation, considering the green hydrogen technique and congestion management. First, a detailed MEMG modelling approach is developed, coupling with electricity, green hydrogen, natural gas, and thermal flows. Different from conventional MEMG models, hydrogen-enriched compressed natural gas (HCNG) models, and weather-dependent power flow are thoroughly considered in the modelling. Meanwhile, the power flow congestion problem is also formulated in the MEMG operation, which could be mitigated through HCNG integration. Based on the proposed MEMG model, a reinforcement learning-based method is designed to obtain the optimal solution of MEMG operation. To ensure the solution's safety, a soft actor-critic (SAC) algorithm is applied and modified by leveraging the Lagrangian relaxation and safety layer scheme. In the end, case studies are conducted and presented to validate the effectiveness of the proposed method.</div><div>© 2017 The Authors. Published by Elsevier Ltd.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126611"},"PeriodicalIF":11.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early cross-sector decarbonisation for Europe’s hard-to-abate sectors: Insights from Denmark’s 2030 target","authors":"Frederik Fristed ,&nbsp;Simon Tønning ,&nbsp;Zhiyuan Xie ,&nbsp;Lissy Langer ,&nbsp;Gorm Bruun Andresen","doi":"10.1016/j.apenergy.2025.126568","DOIUrl":"10.1016/j.apenergy.2025.126568","url":null,"abstract":"<div><div>With a 70 % reduction target by 2030, Denmark is among the first countries to require deep decarbonisation in hard-to-abate sectors, such as transport and agriculture, after more accessible options are exhausted. Collectively with other ambitious countries, this might set a precedent for Europe. Within a full European energy system, this study explores early decarbonisation pathways in the hard-to-abate sectors, evaluating outcomes for energy equity, supply security, and sustainability in Denmark under enacted policies. We model 33 European countries using PyPSA-Eur and impose cross-sector carbon budgets from national 2030 commitments to optimise capacity expansion and dispatch of electricity, heating, transport, hydrogen and biomass, including CCS. For Denmark, we apply 9-node spatial resolution and extend the carbon budget setting to include sectoral decarbonisation trajectories, including agriculture. Results show that carbon abatement costs can double if agriculture fails, heating electrification delays, or biomass is diverted from dual use for CCS. System reliability during critical periods relies on thermal storage, strategic biomass use, and flexible electrolysis. Furthermore, early renewable hydrogen adoption may yield export advantages to less decarbonised grids. We recommend: (1) building infrastructure that utilises sector coupling (electrified district heating, CCS, flexible electrolysis), (2) coordinating heat pumps and central heating with CCS to use biomass strategically, and (3) applying comparable carbon pricing across agriculture, heating, and industry, with follow-up actions if voluntary measures fail. While the share of hard-to-abate sectors varies by country, the framework is applicable to other European states with ambitious near-term targets, and the cross-sector dynamics are relevant across national contexts.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126568"},"PeriodicalIF":11.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven sustainable energy saving: Pathways for enhancing energy efficiency in Chinese listed firms","authors":"Chuntao Wu ,&nbsp;Haoran Li ,&nbsp;Bingbing Yuan","doi":"10.1016/j.apenergy.2025.126607","DOIUrl":"10.1016/j.apenergy.2025.126607","url":null,"abstract":"<div><div>Artificial intelligence (AI) offers unprecedented opportunities for energy management and optimization through data-driven, precision decision-making. This paper investigates how AI development influences energy efficiency (EE) by analyzing an unbalanced panel dataset of 54,657 observations from 4453 listed firms in China over the period 2007 to 2023. Studies have found that the development and diffusion of AI can significantly enhance EE at the firm level, while also inducing short-term energy rebound effects. Mechanistic analysis suggests that AI enhances EE mainly through innovative inputs and green innovation outputs. However, the relationship between AI and labor—where AI serves as a substitute for human labor—limits the role of human capital investment in fostering innovation. Further, heterogeneity analysis reveals that firms in non-high-tech, labor-intensive, and privately owned sectors, as well as those located in the Midwest or with established market positions, are particularly likely to benefit from AI-driven EE improvements. This study not only extends the application of technology diffusion theory to the domain of AI, but also draws comparative insights from other BRICS nations, aiming to offer actionable guidance for China's transition toward intelligent manufacturing and a low-carbon economy.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126607"},"PeriodicalIF":11.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wind-solar complementarity in the Northwest Pacific: Implications for renewable energy planning and policy guidance","authors":"Xingzhi Yuan ,&nbsp;Yanji Wei ,&nbsp;Hongxing Yang","doi":"10.1016/j.apenergy.2025.126600","DOIUrl":"10.1016/j.apenergy.2025.126600","url":null,"abstract":"<div><div>This work investigates the wind-solar complementarity characteristics over large-scale marine regions, with the aim of offering potential planning and policy insights for the integrated development of marine energy. The study first examines the optimal installed capacity proportion of wind and solar energy in various regions based on the coefficient of variation to minimize power output fluctuations. Building upon this premise, the output fluctuation characteristics of wind-solar hybrid systems are quantitatively analyzed through metrics including power change rate, the frequency of extreme events, and the proportion of generated power. A multidimensional comparative analysis highlights the advantages of wind-solar complementarity utilization while also underscoring the need for adequate storage and flexible generation capacity–an insight crucial for decision-makers planning large-scale deployments. Furthermore, the study examines the limitations of wind-solar complementarity by focusing on the frequency and varying durations of zero-output events, which are caused by meteorological conditions with neither wind nor sunlight, thereby uncovering the spatiotemporal distribution patterns of these constraints across broad geographical scales. Research conducted in the Northwest Pacific region demonstrates that wind-solar complementary utilization can effectively reduce power output fluctuations, bringing the frequency of extreme events below 10 % in most areas. However, in equatorial regions where solar resources dominate, zero-output events lasting 13 consecutive hours can exceed 30 %, highlighting the necessity of robust energy storage or cross-regional power exchange. These findings are expected to contribute preliminary insights for decision-makers in formulating policies that support strategic storage deployment and enhanced system interconnectivity, with the goal of promoting both economic viability and reliable operation of large-scale wind-solar projects.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126600"},"PeriodicalIF":11.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanism insights and system-level operation analysis of cathode recirculation for durability enhancement in automotive PEMFC","authors":"Ze Liu ,&nbsp;Mingyang Yang ,&nbsp;Xingwang Tang ,&nbsp;Lei Shi ,&nbsp;Sichuan Xu ,&nbsp;Quan Zhou","doi":"10.1016/j.apenergy.2025.126647","DOIUrl":"10.1016/j.apenergy.2025.126647","url":null,"abstract":"<div><div>Cathode recirculation (CR) has emerged as a promising strategy to mitigate accelerated degradation in proton exchange membrane fuel cells (PEMFCs) under low-load conditions. While previous studies have primarily focused on CR's external performance impacts, the fundamental mechanisms underlying its durability enhancement and operational characteristics in high-power self-humidifying systems remain insufficiently understood. This study firstly systematically investigates CR-enhanced durability mechanisms through rigorously controlled single-cell tests. Macroscopic analyses demonstrate that CR significantly mitigates polarization curve degradation, with maximum attenuation reduction reaching 58.4 % at 0.3 A/cm². Microscopic characterization reveals CR primarily alleviates the increases in both charge transfer and diffusion resistance, slowing electrochemical surface area (ECSA) loss by 62.7 % compared to non-recirculation (NCR) mode and reducing cathode catalyst layer (CCL) crack propagation rate by 2.4 percentage points. Following the mechanistic insights obtained at the single-cell level, system-level validation is conducted in a high-power automotive self-humidifying fuel cell system. The results show that optimal CR operation requires progressively higher pump speeds with increasing current density to maintain equivalent voltage reduction, governed by competing oxygen dilution and humidification effects. Additionally, CR demonstrated the capability to reduce idle power output while maintaining protective voltage levels, resulting in reduced energy management pressure. Overall, the presented framework bridges single-cell mechanistic understanding with system-level optimization strategies, advancing durable automotive fuel cell development.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126647"},"PeriodicalIF":11.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards physics-guided graph neural network for hydrogen gas explosion simulation at urban scale","authors":"Jihao Shi ,&nbsp;Junjie Li ,&nbsp;Haoran Zhang ,&nbsp;Jinyue Yan","doi":"10.1016/j.apenergy.2025.126592","DOIUrl":"10.1016/j.apenergy.2025.126592","url":null,"abstract":"<div><div>As hydrogen and fuel cell technologies become integral to the urban energy transition, their widespread adoption in densely populated areas necessitates robust safety measures. The rapid upscaling of hydrogen energy applications introduces risks associated with accidental hydrogen gas explosion, resulting in substantial blast loads and posing catastrophic threats to both structures and people. Machine learnings have been employed to efficiently evaluate the consequence of obstructed gas explosion, which however exhibits poor accuracy in blast load dynamics prediction due to the lack of considering the interactions between congestion, flame propagation, and blast wave dynamics. This paper aims to develop a physics-guided graph neural network approach, termed Physics_GNN, to simulate the dynamics of hydrogen vapor cloud explosion at the urban scale. The underlying physics of the interactions between congestion, flame propagation, and blast wave dynamics are integrated to enhance prediction accuracy regarding overpressure peaks and their arrival times. The OpenFOAM solvers, validated using public experimental data, are utilized to construct a benchmark dataset. Sensitivity analysis of the empirical coefficient affecting physical interaction is conducted. Results demonstrate Physics_GNN approach achieves a higher prediction accuracy with an <em>R</em>^<em>2</em> of 0.97 compared to the state-of-the-art deep learning models. Additionally, it offers a 1000-fold computational speed-up compared to CFD model for simulating hydrogen gas explosions at urban scale. Physics_GNN approach has the potential to efficiently and accurately analyze the destructive effects of hydrogen gas explosions at urban scale, supporting decision-making to improve urban resilience in the context of the energy transition.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126592"},"PeriodicalIF":11.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Route-based time-dependent life cycle greenhouse gas and NOₓ emissions analysis of heavy-duty trucks","authors":"Arnav Sinha ,&nbsp;Yen Cheng Wang ,&nbsp;Harsh Sapra ,&nbsp;Saurabh Gupta ,&nbsp;Sage Kokjohn ,&nbsp;Constandinos Mitsingas ,&nbsp;Chol-Bum M. Kweon","doi":"10.1016/j.apenergy.2025.126616","DOIUrl":"10.1016/j.apenergy.2025.126616","url":null,"abstract":"<div><div>The electrification of Class-8 heavy-duty (HD) trucks is gaining traction due to the potential for greenhouse gas (GHG) emissions reductions to combat climate change. Hence, a life-cycle analysis (LCA) must be used to conduct a comprehensive emissions analysis of the vehicle's lifecycle. The current literature lacks studies conducted on Class-8 trucks, time-of-day impact on GHG emissions from electricity generation, and nitrogen oxide (NO_x) emissions. Therefore, the goal of this paper is to investigate the potential GHG and NO_x emissions reductions achieved with battery electric (BE) and series-hybrid (SHE) trucks when compared to a conventional diesel internal combustion engine (ICE) Class-8 truck using a regional and time-of-day dependent electricity grid. A vehicle simulation tool was used to connect modular powertrain components to simulate the real-time performance, energy consumption, and emissions of the three powertrains with dynamic drive cycle data as input. Additionally, the LCA accounted for emissions uncertainties from manufacturing, maintenance, fuel, and electricity production using a Monte Carlo simulation. The truck's emissions were evaluated over a cross-country highway “long-haul” route and two urban “drayage” routes for last-mile delivery. A future grid assumption was applied to the time-dependent grid to take the increase in the use of renewables over ten years into account. The LCA results show that for drayage routes in regions with a large share of renewable sources of electricity, BE trucks are best suited for operation, emitting 22 % less GHG and comparable NOₓ to the SHE truck. On the other hand, along the long-haul and the drayage route in regions with significant use of non-renewable sources of energy, the SHE truck had the lowest emissions overall with 40 % and 48 % less GHG and NOₓ respectively than the BE truck. After considering future grid emissions reduction from 2023 to 2032, the BE truck's emissions were reduced by 10–13 % compared to the ICE truck. Despite this reduction, the BE truck had greater GHG and NOₓ emissions during long-haul and drayage operation in regions with emissions intensive grids than the SHE truck.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126616"},"PeriodicalIF":11.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}