Sustainable Energy Technologies and Assessments最新文献_第6页

A single − stage AC/DC electric vehicle charging converter with an integrated CLLC resonant circuit 一种集成CLLC谐振电路的单级AC/DC电动汽车充电变换器

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2026.104825

Qing Fu , Yuxi Wang , Benfei Wang , Yiqi Zeng , Xiaoping Zhou , Zhongyu Lu

With the rapid growth of the electric vehicle market, there is an increasing demand for high-power Alternating Current/Direct Current electric vehicles charging converters. The existing charger design requires more switching tubes to meet high-power demands. To address this issue, this paper proposes a high-power single-stage Alternating Current / Direct Current converter that integrates a Capacitor-Inductor-Inductor-Capacitor resonant converter. The proposed converter is configured by integrating a three-phase Alternating Current/Direct Current converter and three Capacitor-Inductor-Inductor-Capacitor resonant circuits, which can effectively reduce the number of electronic components and compress the volume of the converter. The converter is controlled by sine wave pulse width modulation, and power factor correction can be achieved. Additionally, the circuit can achieve electrical isolation through a transformer. Simulation results verify the effectiveness of both the proposed converter and its control method, demonstrating a power factor above 0.99, and the Total Harmonic Distortion of the gird is 0.66%. Additionally, in continuous conduction mode, the converter realizes zero-voltage switching for primary-side switching devices and zero-current switching for secondary-side switching devices. The converter’s highly integrated and efficient design outperforms traditional topologies in material usage and operational energy efficiency, offering a sustainable technical path for greening electric vehicle charging infrastructure.

随着电动汽车市场的快速增长，对大功率交直流电动汽车充电转换器的需求越来越大。现有的充电器设计需要更多的开关管来满足高功率需求。为了解决这个问题，本文提出了一种集成电容-电感-电感-电容谐振变换器的大功率单级交流/直流变换器。该变换器集成了一个三相交流/直流变换器和三个电容-电感-电感-电容谐振电路，有效地减少了电子元件的数量，压缩了变换器的体积。该变换器采用正弦波脉宽调制控制，可实现功率因数校正。此外，该电路可以通过变压器实现电气隔离。仿真结果验证了该变换器及其控制方法的有效性，功率因数在0.99以上，电网总谐波失真为0.66%。此外，在连续导通模式下，变换器实现了一次侧开关器件的零电压开关和二次侧开关器件的零电流开关。转换器的高度集成和高效设计在材料使用和运行能效方面优于传统拓扑结构，为绿色电动汽车充电基础设施提供了可持续的技术途径。

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引用次数: 0

Modeling biohydrogen production from microbial electrolysis cells in the machine learning era: a review 机器学习时代微生物电解细胞生物制氢的建模：综述

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2026.104819

Mohd Nur Ikhmal Salehmin , Tiong Sieh Kiong , Hassan Mohamed , Amir Izzuddin Adnan , Muhammad Fikri Zulkornain , Nur Atiqah Mohamad Aziz , Farah Nurhikmah , Swee Su Lim

Hydrogen production through microbial electrolysis cells (MECs) presents a sustainable solution for renewable energy and wastewater treatment, yet commercialization faces challenges in optimization, scalability, and efficiency. This review explores the evolution of MEC modeling, from traditional mathematical frameworks to advanced hybrid approaches integrating machine learning (ML) and artificial intelligence (AI). ML techniques, including ANNs, ANFIS, and deep learning models (e.g., LSTM, Bi-LSTM), enhance predictive accuracy, parameter optimization, and real-time control, with hybrid models achieving up to 267% higher hydrogen production rates. Despite progress, challenges such as the “black-box” nature of ML models, material costs, and data standardization remain. The review emphasizes the need for technoeconomic and life cycle analyses to assess commercial viability and environmental impacts. Future research should focus on multi-scale modeling, IoT-enabled control systems, and cost-effective catalysts to bridge the gap between lab innovations and industrial deployment, advancing MEC technology toward scalable and sustainable biohydrogen production.

通过微生物电解电池（MECs）制氢是可再生能源和废水处理的可持续解决方案，但商业化面临优化、可扩展性和效率方面的挑战。本文探讨了MEC建模的演变，从传统的数学框架到集成机器学习（ML）和人工智能（AI）的高级混合方法。包括ann、ANFIS和深度学习模型（如LSTM、Bi-LSTM）在内的机器学习技术提高了预测精度、参数优化和实时控制，混合模型的产氢率最高可提高267%。尽管取得了进展，但ML模型的“黑箱”性质、材料成本和数据标准化等挑战仍然存在。该审查强调需要进行技术经济和生命周期分析，以评估商业可行性和环境影响。未来的研究应集中在多尺度建模、物联网控制系统和经济高效的催化剂上，以弥合实验室创新与工业部署之间的差距，推动MEC技术向可扩展和可持续的生物氢生产方向发展。

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引用次数: 0

Advancing sustainable energy systems: integrating safety, efficiency, and circular innovation across emerging technologies 推进可持续能源系统：在新兴技术中整合安全、效率和循环创新

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2026.104816

Bohong Wang , Giovanni Francesco Giuzio , Hrvoje Mikulčić

This paper is the editorial of the virtual special issue of Sustainable Energy Technologies and Assessments dedicated to the 2025 conference series on Sustainable Development of Energy, Water, and Environmental Systems (SDEWES). The thematic coverage of this commentary editorial encapsulates the core findings of 14 articles featured in this special issue, which are synthesised into five key themes. More precise temperature control strategies have been applied to data centre cooling, lithium-ion battery thermal management, and building HVAC (Heating, Ventilation, and Air Conditioning) systems. To enhance the flexibility of transportation systems, approaches such as integrating autonomous driving with public transit and electric vehicle sharing have been explored, alongside discussions of decarbonization strategies for the maritime shipping industry. Emerging technologies in the circular economy have reduced the environmental impacts of waste incineration and recycling processes. The economic viability and operational flexibility of integrating urban renewable energy with regional power and heating systems have become increasingly prominent. However, the urban heat island effect remains a pressing issue requiring mitigation, and the efficiency of ecological innovation still needs improvement. These studies explore multi-dimensional pathways to advance energy transition and sustainable development, highlighting not only the significance of technological innovation but also the pivotal roles of policy guidance, economic incentives, and social participation in achieving energy transition goals.

本文是《可持续能源技术与评估》虚拟特刊的社论，致力于2025年能源、水和环境系统可持续发展系列会议（SDEWES）。这篇评论社论的专题报道概括了本期特刊中14篇文章的核心发现，这些发现被综合成五个关键主题。更精确的温度控制策略已应用于数据中心冷却、锂离子电池热管理和建筑HVAC（采暖、通风和空调）系统。为了提高运输系统的灵活性，人们探索了将自动驾驶与公共交通和电动汽车共享相结合的方法，同时还讨论了海运业的脱碳战略。循环经济中的新兴技术减少了垃圾焚烧和回收过程对环境的影响。将城市可再生能源与区域电力和供热系统相结合的经济可行性和操作灵活性日益突出。然而，城市热岛效应仍是亟待缓解的问题，生态创新的效率仍有待提高。这些研究探索了促进能源转型和可持续发展的多维途径，不仅强调了技术创新的重要性，而且强调了政策引导、经济激励和社会参与在实现能源转型目标中的关键作用。

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引用次数: 0

Synergy mechanism and impacts of the carbon emission trading and tradable green certificate policies: Evidence from Beijing 碳排放权交易与绿色证书交易政策的协同机制与影响：来自北京的证据

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2025.104775

Xiaoqi Sun , Tiantian Feng , Xianyue Shen , Cheng Zhong

To facilitate the realization of dual-carbon goal, China has introduced several key environmental policies, including the carbon emission trading (CET) scheme, Chinese certified emission reduction (CCER) mechanism and tradable green certificate (TGC) mechanism and has also emphasized the importance of fostering synergistic interaction between the carbon market and green certificate market. However, the concrete pathways and effects of such synergy still require in-depth research. This paper first innovatively proposes the green certificate-carbon emission reduction accounting (GC-CERA) mechanism from the perspective of linking CCER and green certificates, providing a specific approach for the synergy of the carbon and green certificate markets. Furthermore, taking Beijing as an example, this paper constructs the logarithmic mean deviation index (LMDI) and the system dynamics (SD) coupled model, and predict the synergy impacts of the CCER and the GC-CERA mechanisms through scenario simulation. The results reveal that:(1) economic and energy intensity factors have the greatest impact on Beijing’s energy consumption;(2) the CCER mechanism and the GC-CERA mechanism will reduce the carbon emission allowance (CEA), have a certain negative impact on GDP, and are conducive to promoting carbon emission reduction and causing energy structure adjustment. The results can provide guidance on carbon − certificate market synergies.

为促进双碳目标的实现，中国推出了碳排放交易（CET）机制、中国认证减排（CCER）机制和可交易绿色证书（TGC）机制等几项关键环境政策，并强调了促进碳市场和绿色证书市场协同互动的重要性。然而，这种协同作用的具体途径和效果还需要深入研究。本文首先从CCER与绿色证书挂钩的角度创新性地提出了绿色证书-碳减排核算（GC-CERA）机制，为碳市场与绿色证书市场的协同提供了具体途径。并以北京市为例，构建对数平均偏差指数（LMDI）和系统动力学（SD）耦合模型，通过情景模拟预测CCER与GC-CERA机制的协同效应。结果表明：(1)经济和能源强度因素对北京市能源消费的影响最大；(2) CCER机制和GC-CERA机制会降低碳排放限额（CEA），对GDP有一定的负面影响，有利于促进碳减排，引起能源结构调整。研究结果可为碳证书市场协同效应提供指导。

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引用次数: 0

Advances in integrated biorefineries for biofuel production: A comprehensive review of energy conversion, techno-economic analysis, and life cycle assessment 用于生物燃料生产的一体化生物精炼厂的进展：能源转换、技术经济分析和生命周期评估的综合综述

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2025.104811

S. Karishma, R. Kamalesh, A. Saravanan, Y.P. Ragini, A.S. Vickram

The rapid growth of the global population and the depletion of non-renewable energy sources have accelerated the pursuit of sustainable energy alternatives. The biorefineries emerge as a promising platform for producing biofuel and value-added products from diverse biomass feedstocks. The integrated biorefinery approach has gained prominence as an advanced framework that combines multiple conversion strategies, thereby enhancing process efficiency, resource utilization, and energy recovery. There is limited understanding of the energy conversion, technoeconomic, and life cycle analysis of an integrated biorefinery system. This study addresses the gap by comprehensively discussing the concept of integrated biorefineries, emphasizing its key components, criteria, and basis for feedstock selection, as well as innovations in pre-treatment strategies. Various conversion strategies, including biochemical, thermochemical, and integrated biorefinery approaches, have been systematically outlined, accompanied by experimental findings. Additionally, the technoeconomic analysis, encompassing cost analysis, economic evaluation, and factors aligned with sustainable indicators, was examined. Furthermore, the assessment of environmental impact caused by the biofuel production from the integrated biorefinery process has been studied through life cycle assessment. This study also examines the key technical challenges, economic barriers, and future prospects towards achieving sustainable, efficient, and economically viable biorefinery systems for biofuel production.

全球人口的快速增长和不可再生能源的枯竭加速了对可持续能源替代品的追求。生物精炼厂是一个有前途的平台，可以从各种生物质原料中生产生物燃料和增值产品。综合生物炼制方法作为一种结合多种转化策略的先进框架而获得突出地位，从而提高了过程效率、资源利用和能源回收。对集成生物炼制系统的能量转换、技术经济和生命周期分析的理解有限。本研究通过全面讨论集成生物精炼厂的概念，强调其关键组件、标准和原料选择的基础，以及预处理策略的创新，解决了这一差距。各种转化策略，包括生物化学、热化学和综合生物炼制方法，已经系统地概述，并伴有实验结果。此外，还审查了技术经济分析，包括成本分析、经济评价和与可持续指标相一致的因素。此外，通过生命周期评价的方法，研究了一体化生物精炼厂生产生物燃料对环境的影响。本研究还探讨了实现生物燃料生产的可持续、高效和经济可行的生物炼制系统的关键技术挑战、经济障碍和未来前景。

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引用次数: 0

Optimizing ulexite-based mineral carbonation for CO2 capture and boron recovery in aqueous systems 优化无水石基矿物碳酸化在水系统中的CO2捕获和硼回收

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2026.104829

Veysel Selimoğlu , Muhammed Bora Akin , Mehmet Muhtar Kocakerim

One of the urgent environmental issues today is the excessive release of CO₂, which accelerates global warming and climate change. Researchers worldwide are developing methods to slow or prevent rise of these emissions. Among the promising approaches, mineral carbonation using calcium- and magnesium-rich minerals has gained attention as an effective way to capture CO₂ from flue gases. Ulexite, a natural boron mineral, shows strong potential for this application. In this study, the CO₂ capture ability of ulexite in aqueous systems was systematically investigated. Experiments were designed and optimized using the Taguchi method, with temperature, particle size, reaction time, and solid-to-liquid ratio selected as key operational parameters. Pure CO₂ was employed under atmospheric conditions. The optimal conditions were identified as 70 °C, particle size below 250 µm, a reaction time of 120 min, and a solid-to-liquid ratio of 125 g ulexite per 500 g water, achieving a theoretical CO₂ retention efficiency of 94.65 ± 8.94%. The results confirm that ulexite can effectively capture CO₂ in aqueous environments, indicating its potential as a cost-effective and environmentally friendly sorbent for CO₂ mitigation technologies. Moreover, the simultaneous formation of sodium pentaborate pentahydrate (NaB₅O₈·5H₂O) provides an economic advantage and enhances industrial applicability of the process.

当今迫在眉睫的环境问题之一是二氧化碳的过度排放，它加速了全球变暖和气候变化。世界各地的研究人员正在研究减缓或防止这些排放增加的方法。在有前途的方法中，利用富含钙和镁的矿物进行矿物碳化作为一种从烟气中捕获二氧化碳的有效方法而受到关注。无脱石是一种天然硼矿物，具有很强的应用潜力。在本研究中，系统地研究了无辉石在水体系中的CO2捕获能力。以温度、粒径、反应时间、料液比为主要操作参数，采用田口法对实验进行设计和优化。在大气条件下使用纯二氧化碳。最佳反应条件为：反应温度70℃，粒径小于250µm，反应时间120 min，料液比为125 g / 500 g水，理论CO2保留率为94.65±8.94%。结果证实，无脱石可以有效地捕获水环境中的二氧化碳，表明其作为一种具有成本效益和环境友好型的二氧化碳减缓技术的吸附剂的潜力。此外，同时生成五水五硼酸钠（NaB5O8·5H2O）具有经济优势，提高了该工艺的工业适用性。

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引用次数: 0

AI-based management and dispatch for a photovoltaic-thermal-electric-hydrogen integrated energy system 基于人工智能的光热电氢一体化能源系统管理与调度

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2025.104814

Xi Lin , Yunjian Song , Zirui Mei , Xinyi Han , Haipeng Yin , Rui Tong , Xiaomin Song , Zengguang Huang

The intermittency of photovoltaic (PV) power generation leads to significant PV curtailment issues, which limits its large-scale application. To overcome the limitations of existing research—such as a narrow focus on single energy forms or reliance on simulations—this study designs and implements a Photovoltaic-Thermal-Electric-Hydrogen Integrated Energy System (IES) that combines multi-energy storage with artificial intelligence (AI) technology. The novel contributions of this work encompass an integrated “prediction–optimization–diagnosis” AI framework deployed on edge hardware (STM32) for real-time control, along with synergistic electric–thermal–hydrogen storage coordinated by AI. Additionally, long short-term memory (LSTM), deep reinforcement learning (DRL), and 1D convolutional neural network (1D-CNN) models are deployed end-to-edge on a low-cost microcontroller. Specifically, the framework employs LSTM for PV prediction (achieving a MAPE of 4.7%), DRL for power dispatch (resulting in 95.8% self-consumption and a 5.2 percentage-point reduction in PV curtailment rate), and a 1D-CNN for fault diagnosis (with 98.8% accuracy). Lightweight deployment on the STM32 platform further enhances operational efficiency. Experimental results demonstrate the superiority of the proposed approach over model predictive control (MPC), improving energy efficiency, economy (with a 13.9% cost reduction), and reliability, while also contributing to sustainability outcomes such as reduced carbon emissions and higher renewable energy penetration.

光伏发电的间歇性导致了严重的弃风问题，限制了其大规模应用。为了克服现有研究的局限性，例如对单一能源形式的狭隘关注或对模拟的依赖，本研究设计并实现了将多种能源存储与人工智能（AI）技术相结合的光伏-热-电-氢集成能源系统（IES）。这项工作的新贡献包括部署在边缘硬件（STM32）上的集成“预测-优化-诊断”AI框架，用于实时控制，以及由AI协调的协同电-热-氢储存。此外，长短期记忆（LSTM）、深度强化学习（DRL）和一维卷积神经网络（1D- cnn）模型部署在低成本微控制器的端对端上。具体而言，该框架采用LSTM进行PV预测（实现4.7%的MAPE）， DRL进行电力调度（导致95.8%的自耗电量和降低5.2个百分点的PV弃电率），1D-CNN进行故障诊断（准确率为98.8%）。在STM32平台上的轻量级部署进一步提高了操作效率。实验结果表明，该方法优于模型预测控制（MPC），提高了能源效率、经济性（成本降低13.9%）和可靠性，同时还有助于减少碳排放和提高可再生能源渗透率等可持续性结果。

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引用次数: 0

Unclean consequences of clean energy? The impact of renewable electricity generation on carbon dioxide emissions 清洁能源带来的不洁净后果？可再生能源发电对二氧化碳排放的影响

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 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.

尽管可再生能源在能源安全和减少排放方面发挥着重要作用，但很少有研究审查不同类型的可再生能源如何影响能源结构和排放结果。这项研究通过分析可再生能源发电如何影响化石燃料需求和由此产生的二氧化碳排放来填补这一空白。采用反映可再生能源引起的燃料间替代的差异燃料分配模型，我们调查了1995年至2023年期间美国电力部门的发电量。我们的研究结果表明，该行业对化石燃料价格变化的反应不太强烈，并表现出煤炭、天然气和石油之间的替代关系。关于可再生能源引起的燃料间替代，水力发电和生物质能发电与减少天然气需求有关，而太阳能发电则减少煤炭需求。相反，生物质发电与煤炭需求上升有关，而太阳能发电则增加了对天然气的依赖。从成本最小化的角度来看，二氧化碳排放的模拟结果表明，太阳能发电有助于进一步减少净排放量，而生物质能的缓解潜力可能会因可再生能源引起的燃料间替代而减弱甚至逆转。

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引用次数: 0

Carbon breakeven analysis of electric versus internal combustion engine vehicles in China: a life cycle assessment 中国电动汽车与内燃机汽车的碳收支平衡分析：一个生命周期评估

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 10.1016/j.seta.2026.104821

Xin Lai , Wentian Zhang , Junjie Chen , Quanwei Chen , Yuejiu Zheng

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.

纯电动汽车（bev）和内燃机汽车（icev）表现出互补的碳排放特征，纯电动汽车的生产阶段排放量较高，但使用阶段排放量较低。本研究采用生命周期评估框架建立综合评估模型，系统量化中国纯电动汽车、纯电动汽车和插电式混合动力汽车（phev）的碳收支平衡点和排放特征。全链模型涵盖了生产、分销和使用阶段，包含了多维变量，如电池类型：镍钴锰氧化物（NCM）和磷酸铁锂（LFP）、区域电网碳强度和插电式混合动力使用模式。主要研究结果显示，尽管纯电动汽车/插电式混合动力汽车的生产排放比纯电动汽车高11% - 132%，但由于运行排放更低，它们实现了碳收支平衡。与基于ncm的电动汽车（70,511-87,231公里）相比，lfp电池汽车在更短的里程内达到收支平衡（例如，PHEV-LFP: 14,509公里；BEV-LFP: 36,751公里）。区域电网强度对盈亏平衡点影响显著，西南电网盈亏平衡点较全国平均水平降低19.7% ~ 38.0%，北部电网盈亏平衡点较全国平均水平提高19.6% ~ 37.8%。在中国2060年碳中和情景下，与2023年相比，纯电动汽车的盈亏平衡里程减少49.1%，生命周期碳足迹减少26% - 48.6%。本研究为制定差别化电动汽车推广策略，支持电网低碳转型提供了科学依据。

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引用次数: 0

How AI shapes the impact of NEV exports on energy transition: Evidence from Chinese provinces 人工智能如何塑造新能源汽车出口对能源转型的影响：来自中国各省的证据

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments

Pub Date : 2026-01-01 DOI: 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.

全球向人工智能（AI）和新能源汽车（nev）的转变为加速能源转型（ET）提供了一个独特的机会。然而，新能源汽车出口和人工智能在影响国内ET方面的相互作用仍未得到探讨。本研究采用双向固定效应面板数据模型来检验新能源汽车出口和工业机器人如何影响可再生电力在总发电量中的份额。利用2017年至2023年中国各省的面板数据，该研究发现，新能源汽车出口可能会通过促进国内制造业从而增加能源需求，以及挤出与ET相关的研发投资来阻碍ET的发展。此外，新能源汽车出口和人工智能之间的相互作用对ET产生了积极影响，因为人工智能通过提高能源效率缓解了出口引起的能源需求增长，从而减轻了新能源汽车出口对ET的抑制作用。研究建议政府，特别是在新能源汽车政策支持力度较大的省份，将新能源汽车出口的扩大与人工智能的部署相结合，以使ET的效益最大化。

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引用次数: 0