Energy Strategy Reviews最新文献_第9页

Power Islands. The geography of electricity diffusion in Indonesia, 1975–2018 权力的岛屿。1975-2018年印度尼西亚电力扩散的地理特征

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.esr.2026.102077

Isfandiarni S. Rosidin , Henri L.F. de Groot , Peter Mulder

Using the case of Indonesia, we provide novel evidence that the spatial diffusion of electricity technology in the global South is a lengthy and non-linear process that is influenced by local geographic, economic and demographic conditions. We do so on the basis of newly developed consistent time series data on installed electricity generation capacity and household electrification ratios from previously unpublished energy statistics at the (sub-)regional level in Indonesia, for the period 1975 to 2018. We identify spatial interactions in historical electricity adoption rates that are mediated by geographical distance, exploiting the spatial variation stemming from the country's size and archipelagic nature. We find that regions located farther from Jakarta experienced delayed electrification, with geographic remoteness and fragmented infrastructure emerging as key constraints. Differences in power supply development, population density, per capita GDP, and landscape complexity further explain variation in electrification speed. Using a simulation exercise, we show that some eastern provinces could have reached a 50 % electrification rate up to 26 years earlier if they had shared the structural characteristics of Jakarta. Our findings underscore the importance of within-country spatial heterogeneity in understanding electricity diffusion and highlight the policy relevance of targeting local barriers to accelerate access. The study contributes to global electrification research by documenting diffusion patterns at subnational scale in a lower-middle-income country and offers a replicable framework for assessing spatial inequality in electricity access.

以印度尼西亚为例，我们提供了新的证据，证明电力技术在全球南方的空间扩散是一个漫长的非线性过程，受当地地理、经济和人口条件的影响。我们基于1975年至2018年期间印度尼西亚（分）区域一级未公布的能源统计数据中新开发的关于装机容量和家庭电气化比率的一致时间序列数据。我们确定了由地理距离介导的历史电力采用率的空间相互作用，利用了源于国家大小和群岛性质的空间变化。我们发现，距离雅加达较远的地区电气化延迟，地理位置偏远和基础设施分散成为主要制约因素。电力供应发展、人口密度、人均GDP和景观复杂性的差异进一步解释了电气化速度的差异。通过模拟演习，我们发现，如果东部一些省份具有雅加达的结构特征，它们可以在26年前达到50% %的电气化率。我们的研究结果强调了国家内部空间异质性对理解电力扩散的重要性，并强调了针对当地障碍加速获取的政策相关性。该研究通过记录中低收入国家次国家层面的扩散模式，为全球电气化研究做出了贡献，并为评估电力获取的空间不平等提供了一个可复制的框架。

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引用次数: 0

Historical baselines and empirical constraints on global and sectoral energy intensity pathways 全球和行业能源强度路径的历史基线和经验约束

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-03-11 DOI: 10.1016/j.esr.2026.102197

Jorge Manuel Barrios-Sánchez , Ignacio de Blas , Tommaso Brazzini , Luis Javier Miguel-González

Energy intensity is a key variable in energy-demand analysis, long-term scenario building, and strategic energy planning. Although global energy intensity has declined over recent decades, substantial heterogeneity persists across countries and economic sectors, complicating the interpretation of future pathways. Moreover, many long-term energy and climate scenarios rely on assumptions about sustained improvements in energy intensity that are only weakly justified by historical evidence. This study develops empirically grounded reference pathways for global and sectoral energy intensity based on historical data for more than 110 countries over the period 1990–2021. Countries are grouped using a data-driven clustering approach that captures similarities in the temporal evolution of energy-intensity trajectories across four major sectors: agriculture, services, industry, and manufacturing. For each cluster, historical trends are summarised using transparent exponential fits to derive conditional baseline pathways extended to mid-century. To account for historically observed variability, empirical uncertainty envelopes are constructed from the dispersion of country-level trajectories within each cluster. These envelopes define plausible ranges of energy-intensity evolution conditional on past dynamics and provide empirical constraints on long-term projections. The resulting baselines reveal a systematic deceleration in energy-intensity improvements and only partial convergence across countries and sectors, with several sectors exhibiting early stabilisation. Comparisons with widely used global and sectoral scenarios indicate that many climate-aligned pathways imply rates of energy-intensity decline that lie outside historically observed empirical ranges, even under optimistic assumptions. Rather than offering forecasts or normative prescriptions, this analysis provides transparent empirical benchmarks to support scenario interpretation, strategic planning, and cross-sectoral benchmarking.

能源强度是能源需求分析、长期情景构建和战略性能源规划中的一个关键变量。尽管近几十年来全球能源强度有所下降，但各国和经济部门之间的巨大异质性仍然存在，这使得对未来路径的解释变得复杂。此外，许多长期的能源和气候情景依赖于关于能源强度持续改善的假设，而这些假设只有很少的历史证据可以证明。本研究基于1990年至2021年期间110多个国家的历史数据，开发了基于经验的全球和行业能源强度参考路径。采用数据驱动的聚类方法对各国进行分组，该方法捕捉了农业、服务业、工业和制造业四个主要部门能源强度轨迹在时间演变方面的相似性。对于每个集群，使用透明的指数拟合来总结历史趋势，以得出延伸到本世纪中叶的条件基线路径。为了解释历史上观察到的变异性，经验不确定性包络是根据每个集群内国家一级轨迹的分散度构建的。这些包络定义了以过去动态为条件的能源强度演化的合理范围，并为长期预测提供了经验约束。由此得出的基线显示，能源强度改善出现系统性减速，各国和行业之间只有部分趋同，一些行业表现出早期企稳。与广泛使用的全球和部门情景的比较表明，许多与气候相关的路径意味着，即使在乐观的假设下，能源强度下降的速度也超出了历史上观察到的经验范围。该分析不是提供预测或规范的处方，而是提供透明的经验基准，以支持情景解释、战略规划和跨部门基准。

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引用次数: 0

Dynamic evolution and frontiers of energy decentralization research: A bibliometric-based review 能源分散研究的动态演变与前沿：基于文献计量学的综述

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.esr.2026.102097

Fengfu Mao , Jing Lin , Yuqiao Hou

Energy decentralization signifies a fundamental shift in energy production and governance from conventional, centralized systems towards distributed, localized, and end-user-centric paradigms. This transition is of paramount importance for advancing energy efficiency and bolstering energy security. Employing bibliometric analysis and literature review methodologies, we utilize CiteSpace to delineate the intellectual landscape, research hotspots, and evolutionary trends within the energy decentralization literature from 2008 to 2024, based on the Web of Science (WoS) core collection. Our analysis reveals that: ①Robust collaborative networks exist at the international and institutional levels, while scholars tend to form distinct collaborative clusters. China, USA, and Denmark emerge as prominent contributors to the field. ②The research landscape is thematically diverse, with focal points on emerging technology applications, energy management and optimization, and energy governance and institutional arrangements. ③Prospectively, future research is poised to concentrate on the coordination mechanisms between technology diffusion and institutional alignment, the trade-off between community energy expansion and distributive justice, and the interplay between energy system resilience and governance constraints. The dynamic knowledge map constructed herein provides a scientific foundation for understanding the academic trajectory of the energy decentralization field and for identifying promising avenues for future research.

能源去中心化标志着能源生产和治理从传统的集中式系统向分布式、本地化和以最终用户为中心的范式的根本转变。这一转变对于提高能源效率和加强能源安全至关重要。基于Web of Science （WoS）核心文献，采用文献计量学分析和文献综述的方法，利用CiteSpace对2008 - 2024年能源去中心化文献的知识格局、研究热点和演化趋势进行了梳理。研究结果表明：①国际和机构层面存在稳健的合作网络，学者倾向于形成不同的合作集群；中国、美国和丹麦成为该领域的主要贡献者。②研究领域主题多样，重点关注新兴技术应用、能源管理与优化、能源治理与制度安排。③展望未来，技术扩散与制度结盟之间的协调机制、社区能源扩张与分配正义之间的权衡、能源系统弹性与治理约束之间的相互作用将成为未来研究的重点。本文构建的动态知识图谱为理解能源去中心化领域的学术轨迹和确定未来的研究方向提供了科学基础。

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引用次数: 0

Evaluating the embodied carbon emissions of power infrastructure construction towards carbon neutrality: A study of Fujian province, China 面向碳中和的电力基础设施建设隐含碳排放评估——以福建省为例

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.esr.2026.102096

Zewen Ge , Chenjun Yan , Junyi Guo , Chenyi Zhang

The transformation of the power sector toward renewable energy is crucial for achieving China's carbon neutrality goals and mitigating global climate change. However, the construction phase of power infrastructure generates substantial embodied carbon emissions. This study focuses on Fujian Province, a strategic hub for clean energy development along China's southeastern coast, to quantify and project these emissions. Using an environmentally extended input–output model, we quantify cumulative embodied carbon emissions from all economic activities associated with power infrastructure construction in Fujian over 2011–2022. A Random Forest machine learning model is then used to forecast future embodied carbon emissions under three climate change scenarios for 2023–2060. Results show that cumulative embodied carbon emissions from power infrastructure construction increased from 8.63 million tons in 2011 to 337 million tons over 2011–2022. Among generation technologies, nuclear power exhibited the highest embodied carbon intensity per unit of installed capacity after 2016, while solar photovoltaics consistently maintained the lowest values. Over 2023–2060, cumulative embodied carbon emissions are projected to reach 6.79 billion tons under the carbon neutrality scenario. Based on these results, policy recommendations are proposed from technical, economic and regulatory perspectives to mitigate construction-phase embodied emissions. These findings highlight the importance of detailed construction-phase embodied carbon assessment and provide a scalable analytical framework for forecasting and mitigating emissions in power infrastructure transitions.

电力行业向可再生能源的转型对于实现中国的碳中和目标和减缓全球气候变化至关重要。然而，电力基础设施建设阶段产生了大量隐含碳排放。本研究的重点是福建省，中国东南沿海清洁能源发展的战略中心，量化和预测这些排放。采用环境扩展投入产出模型，量化了2011-2022年福建省电力基础设施建设相关经济活动的累积隐含碳排放量。然后使用随机森林机器学习模型来预测2023-2060年三种气候变化情景下的未来隐含碳排放量。结果表明，2011 - 2022年，电力基础设施建设累计隐含碳排放量从2011年的863万吨增加到3.37亿吨。在发电技术中，核电在2016年之后表现出最高的单位装机碳强度，而太阳能光伏一直保持最低的碳强度。在碳中和情景下，2023-2060年累计隐含碳排放量预计将达到67.9亿吨。基于这些结果，从技术、经济和监管的角度提出了政策建议，以减少施工阶段的隐含排放。这些发现强调了详细的建设阶段隐含碳评估的重要性，并为电力基础设施转型中的预测和减少排放提供了可扩展的分析框架。

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引用次数: 0

Artificial Intelligence–driven cyber–physical energy resilience framework for secure and sustainable smart distribution networks 安全可持续智能配电网的人工智能驱动的网络-物理能量弹性框架

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-03-03 DOI: 10.1016/j.esr.2026.102168

T. Yuvaraj , D. Buvana , M. Thirumalai , Savitha Venkatesan , Mohit Bajaj , Vojtech Blazek , Lukas Prokop

This paper presents an Artificial Intelligence-driven Cyber-Physical Energy Resilience Framework for next-generation smart distribution networks, responding to the critical demand for secure, adaptive, and sustainable grid operation amid complex cyber-physical disruptions. The framework combines cybersecurity-aware control with renewable energy sources to make it possible for systems to heal themselves, stay stable, and use energy in a way that is good for the environment in changing and unpredictable situations. It is tested on modified IEEE 33-bus and 118-bus test networks that include distributed energy resources (DERs) like photovoltaic systems, wind turbines, battery energy storage systems, and battery electric vehicles. The proposed model's main purpose is to allow secure inter-microgrid coordination (SIMC), adaptive network reconfiguration, and dynamic microgrid formation. This will make sure that critical loads are restored and faults are quickly isolated during disruptive events. A multi-objective optimization function (MOF) is created to maximize resilience, cybersecurity strength, and trading revenue at the same time, while minimizing energy not delivered (END), operational cost, and energy losses. Adaptive weighting coefficients dynamically prioritize objectives across three operational scenarios: (A) renewable uncertainty and economic operation; (B) fault recovery and cybersecurity restoration; and (C) multi-threat coordination with balanced objectives. To solve the MOF quickly, an AI-enhanced metaheuristic optimization mechanism is created by combining the Artificial Gorilla Troops Optimization (AGTO) algorithm with the Grey Wolf Optimizer (GWO) in the SIMC layer. The AGTO is nearly 30% faster than the baseline Hunter–Prey Optimization Algorithm (HPOA) when it comes to adaptive convergence. It also improves MOF by more than 20%, END by 85%, and critical load restoration by 95% in all cases. Comparative results show that the suggested AGTO–SIMC framework gives cyber-secure, resilient, and sustainable energy management a scalable, smart, and self-healing operational base. This supports the strategic growth of AI-enabled cyber–physical resilience in smart energy distribution systems.

本文提出了用于下一代智能配电网络的人工智能驱动的网络物理能量弹性框架，以响应在复杂的网络物理中断中对安全、自适应和可持续电网运行的关键需求。该框架将网络安全感知控制与可再生能源相结合，使系统能够自我修复，保持稳定，并在变化和不可预测的情况下以有利于环境的方式使用能源。它在改进的IEEE 33总线和118总线测试网络上进行了测试，包括分布式能源（DERs），如光伏系统、风力涡轮机、电池储能系统和电池电动汽车。该模型的主要目的是实现安全的微网间协调（SIMC）、自适应网络重构和动态微网形成。这将确保在破坏性事件期间恢复关键负载并快速隔离故障。创建了多目标优化函数（MOF），以最大限度地提高弹性、网络安全强度和交易收入，同时最大限度地减少未交付能源（END）、运营成本和能源损失。自适应加权系数在三种运行情景中动态地对目标进行优先排序：(A)可再生不确定性和经济运行；(B)故障恢复和网络安全恢复；(C)平衡目标的多威胁协调。为了快速解决MOF问题，将人工大猩猩部队优化算法（AGTO）与SIMC层的灰狼优化器（GWO）相结合，建立了一种人工智能增强的元启发式优化机制。当涉及到自适应收敛时，AGTO比基准猎人-猎物优化算法（HPOA）快近30%。在所有情况下，MOF提高了20%以上，END提高了85%，临界负载恢复提高了95%。对比结果表明，建议的AGTO-SIMC框架为网络安全、弹性和可持续能源管理提供了可扩展、智能和自修复的操作基础。这支持了智能能源分配系统中人工智能网络物理弹性的战略增长。

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引用次数: 0

A novel fractal fuzzy decision-making model for ESG-based prioritization of energy poverty alleviation strategies 基于esg的能源扶贫战略优先排序分形模糊决策模型

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-01-23 DOI: 10.1016/j.esr.2026.102059

Serhat Yüksel , Serkan Eti , Hasan Dinçer , Orhan Özaydın , Hakan Yıldız

Energy poverty has emerged as a multidimensional challenge encompassing social, economic, and environmental dimensions, requiring decision frameworks capable of handling complexity, uncertainty, and heterogeneous stakeholder judgments. Although the literature offers numerous policy instruments to address energy poverty, there is limited methodological consensus on how such strategies should be systematically prioritized under uncertainty. This study addresses this gap by proposing a novel fractal fuzzy multi-criteria decision-making framework grounded in environmental, social, and governance dimensions. The primary contribution of the study lies in the development and integration of fractal fuzzy sets with an expert weighting system, entropy-based criterion weighting, and MARCOS-based strategy ranking, complemented by ARAS-based robustness analysis. An illustrative case application based on a limited expert panel is employed as a proof-of-concept to demonstrate how the proposed framework operates and translates expert judgments into structured prioritization outcomes. The numerical results are presented to showcase the internal consistency, stability, and interpretability of the method rather than to provide context-independent policy prescriptions. Overall, the proposed framework offers a flexible and transparent methodological tool that can be adapted to different geographical, institutional, and policy contexts for evaluating energy poverty alleviation strategies under uncertainty.

能源贫困已经成为一个包含社会、经济和环境维度的多维挑战，需要能够处理复杂性、不确定性和不同利益相关者判断的决策框架。尽管文献提供了许多解决能源贫困的政策工具，但在不确定性下如何系统地确定这些战略的优先顺序方面，方法论上的共识有限。本研究通过提出一种基于环境、社会和治理维度的新型分形模糊多准则决策框架来解决这一差距。本研究的主要贡献在于将分形模糊集与专家加权系统、基于熵的准则加权和基于marcos的策略排序相结合，并辅以基于aras的鲁棒性分析。一个基于有限专家小组的说明性案例应用程序被用作概念验证，以演示所提议的框架如何运作并将专家判断转化为结构化的优先级结果。给出数值结果是为了展示该方法的内部一致性、稳定性和可解释性，而不是提供与上下文无关的政策处方。总的来说，拟议的框架提供了一种灵活和透明的方法工具，可以适应不同的地理、体制和政策背景，以评估不确定性下的能源扶贫战略。

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引用次数: 0

Energy price crisis and crash early warning system 能源价格危机与崩盘预警系统

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.esr.2026.102069

Turgut Yokuş, Ahmet Ay, Nihal Yokuş

Over the past fifty years, numerous energy price crises and energy price crashes have occurred worldwide, defined as cases exceeding two standard deviations (large increases or decreases) from the mean of the Energy Market Pressure Index, which is constructed from energy prices and U.S. inflation. These crises and crashes have caused numerous economic, political, military, social, and environmental issues in countries, depending on their energy import-export structures. The objective of this study is to develop an Early Warning System model that provides sufficient time for preventive measures before energy crises and crashes occur. The 14-variable model, created using Multinomial Logistic Regression analysis and based on monthly data from January 1973 to December 2023, provides the ability to predict the probabilistic changes of crisis or crash cases on a monthly basis from month 1 to month 6, using lagged variable values, and thus enables forecasting of potential crises or crashes in the upcoming 6th month. The empirical results of the model show that these cases are determined by indicators related to energy supply-demand imbalances, economic and financial disruptions, energy investments (drilling activities), and geopolitical risks and expectations. Furthermore, the model is able to predict energy trends (non-crisis/crash months) with 99.5 % accuracy, crises with 75 %, crashes with 60 %, and all cases overall with 98.3 % accuracy. In conclusion, this model, which can anticipate energy crises and crashes in advance, offers a practical and effective tool for governments, energy market actors, and analysts to use in policy development, investment planning, and risk management.

在过去的50年里，世界范围内发生了许多能源价格危机和能源价格崩溃，其定义为能源市场压力指数（由能源价格和美国通货膨胀构成）的平均值超过两个标准差（大幅上涨或大幅下跌）的情况。这些危机和崩溃在各国造成了许多经济、政治、军事、社会和环境问题，这取决于它们的能源进出口结构。本研究的目的是建立一个早期预警系统模型，为能源危机和崩溃发生前的预防措施提供足够的时间。使用多项逻辑回归分析创建的14变量模型基于1973年1月至2023年12月的月度数据，提供了从第1个月到第6个月每月预测危机或崩溃案例概率变化的能力，使用滞后变量值，从而能够预测即将到来的第六个月的潜在危机或崩溃。该模型的实证结果表明，这些案例是由与能源供需失衡、经济和金融中断、能源投资（钻井活动）以及地缘政治风险和预期相关的指标决定的。此外，该模型能够预测能源趋势（非危机/崩溃月份），准确率为99.5% %，危机为75% %，崩溃为60% %，所有情况的总体准确率为98.3% %。总而言之，该模型可以提前预测能源危机和崩溃，为政府、能源市场参与者和分析师在政策制定、投资规划和风险管理中提供了一个实用而有效的工具。

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引用次数: 0

Breaking the pollution cycle: Green energy transition, financial innovation, and climate resilience in Türkiye 打破污染循环：绿色能源转型、金融创新和气候适应能力

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.esr.2026.102056

Mohammed Musah , Isaac Adjei Mensah , Thomas Appiah , Kwadwo Boateng Prempeh , Gertrude Amoakohene

Türkiye's rapid economic development has heightened environmental degradation, underscoring the need for a green energy transition supported by sustainable financial innovation. This study investigates how financial innovation moderates the relationship between green energy and environmental quality in Türkiye from 1996 to 2021. A multidimensional environmental pressure index, capturing CO₂ damage, energy depletion, forest depletion, mineral depletion, and particulate emission damage, is developed to measure environmental degradation. Using the Kernel Regularized Least Squares (KRLS) machine learning approach, the results show that green energy significantly reduces environmental pressure, advancing Sustainable Development Goals 7 and 13. In contrast, financial innovation intensifies environmental pressure, while its interaction with green energy weakens the latter's environmental benefits. Foreign direct investment has no significant effect. These findings highlight the dual role of financial innovation as both an enabler and a constraint on environmental sustainability. Policymakers should therefore embed environmental safeguards within financial innovation frameworks, expand green finance instruments, and align financial sector development with Türkiye's low-carbon transition goals. The study contributes to ecological modernization theory and the finance–environment nexus by offering novel evidence from an emerging economy.

日本经济的快速发展加剧了环境的恶化，强调了在可持续金融创新的支持下实现绿色能源转型的必要性。本文研究了1996 - 2021年金融创新如何调节我国绿色能源与环境质量之间的关系。建立了一个多维环境压力指数，包括二氧化碳损害、能源枯竭、森林枯竭、矿物枯竭和颗粒排放损害，以衡量环境退化。使用核正则化最小二乘（KRLS）机器学习方法，结果表明绿色能源显着降低了环境压力，推进了可持续发展目标7和13。相反，金融创新加剧了环境压力，与绿色能源的相互作用削弱了后者的环境效益。外商直接投资没有显著影响。这些发现突出了金融创新在环境可持续性方面的双重作用：既是推动者，也是制约因素。因此，政策制定者应将环境保护措施纳入金融创新框架，扩大绿色金融工具，并使金融部门的发展与世行的低碳转型目标保持一致。该研究为生态现代化理论和金融-环境关系提供了来自新兴经济体的新证据。

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引用次数: 0

Green ammonia in the global research spotlight: Bibliometric insights and a review of renewable pathways and applications 全球研究焦点中的绿色氨：文献计量学见解和可再生途径和应用综述

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.esr.2026.102070

Hanane Ait Lahoussine Ouali, Otman Abida, Nisar Ali, Mohamed Essalhi

Ammonia plays a pivotal role in global agriculture as a key precursor to nitrogen-based fertilizers and is increasingly recognized as a promising carbon-free energy carrier. However, the conventional Haber–Bosch synthesis process is energy-intensive, relying heavily on fossil fuels. To address this, the shift towards green ammonia production driven by renewable hydrogen and clean technologies is an increasingly important decarbonization strategy. This study offers a comprehensive bibliometric analysis using Scopus database on green ammonia spanning from 2006 to 2024, mapping the research trends, top-ranked countries, institutions, authors, and journals. In addition to the bibliometric analysis, the paper includes an integration of a technical overview of different renewable energy sources used for green ammonia, life cycle assessment (LCA), as well as emerging studies dealing with its applications in fertilizers, shipping fuels, and energy storage. The findings reveal accelerated growth in research output, dominated by collaborations in Europe, Asia, and North America, with a strong focus on electrolyzer development, ammonia cracking, and integration with solar and wind power. The combined bibliometric–technical approach highlights critical knowledge gaps, such as techno-economic feasibility, large-scale storage infrastructure, and safety protocols for ammonia transport. This dual analysis not only captures the advancement of scientific understanding but also provides practical guidance for policymakers, business leaders, and researchers. The results can inform technology roadmaps, foster international collaboration, and guide strategic investments in scaling up green ammonia as a cornerstone of the hydrogen economy and global net-zero transition.

作为氮基肥料的关键前体，氨在全球农业中发挥着关键作用，并日益被认为是一种有前途的无碳能源载体。然而，传统的Haber-Bosch合成过程是能源密集型的，严重依赖化石燃料。为了解决这个问题，由可再生氢和清洁技术驱动的绿色氨生产的转变是一项日益重要的脱碳战略。本研究利用Scopus数据库对2006年至2024年的绿色氨进行了全面的文献计量分析，绘制了研究趋势、排名国家、机构、作者和期刊。除了文献计量学分析之外，本文还整合了用于绿色氨的不同可再生能源的技术概述，生命周期评估（LCA），以及处理其在肥料，船舶燃料和能源存储中的应用的新兴研究。研究结果显示，研究产出加速增长，主要是欧洲、亚洲和北美的合作，重点是电解槽开发、氨裂解以及与太阳能和风能的整合。文献计量学和技术相结合的方法强调了关键的知识差距，如技术经济可行性、大规模储存基础设施和氨运输的安全协议。这种双重分析不仅抓住了科学理解的进步，而且为政策制定者、商业领袖和研究人员提供了实践指导。研究结果可以为技术路线图提供信息，促进国际合作，并指导扩大绿色氨作为氢经济和全球净零转型基石的战略投资。

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引用次数: 0

A time-varying analysis of the responsible AI and energy crisis link: Fresh findings from TVP-VAR-SV 负责任的人工智能与能源危机联系的时变分析：tpv -var - sv的新发现

IF 7.9 2区工程技术 Q1 ENERGY & FUELS

Energy Strategy Reviews

Pub Date : 2026-03-01 Epub Date: 2026-01-24 DOI: 10.1016/j.esr.2026.102080

Le Thanh Ha

The development of responsible AI shows how technological progress interacts with evolving energy governance frameworks. Our research employs time-varying parameter structural vector autoregression (TVP-VAR) with a stochastic volatility model to analyze the correlation between responsible AI and energy uncertainty spanning from November 2018 to November 2023. Our findings show that the stochastic volatility of responsible AI, S&P 500, and oil volatility were positive and stable in the whole period. The result of energy uncertainty is much larger than the other, indicating that it is highly sensitive to external shocks and must be adapted flexibly according to the evolving context. In the 4-period ahead, impulse responses of responsible AI to energy uncertainty and oil volatility were opposite in the 1-period ahead. Impulse responses of responsible AI to energy uncertainty declined sharply into negative territory in early 2020 and late 2022, reaching their lowest point near early 2023. With responsible AI shocks, impulse responses of energy uncertainty peaked sharply around early 2021 and again near mid-2022 before dropping steeply to its lowest point in early 2023. In the short term, Responsible AI has limited influence on reducing energy uncertainty, but in the long run, it strengthens system resilience and sustainability through improved data governance, adaptive learning, and ethical integration.

负责任的人工智能的发展显示了技术进步如何与不断发展的能源治理框架相互作用。我们的研究采用时变参数结构向量自回归（TVP-VAR）和随机波动模型，分析了2018年11月至2023年11月期间负责任的人工智能与能源不确定性之间的相关性。我们的研究结果表明，在整个时期，负责任的人工智能、标准普尔500指数和石油波动率的随机波动率都是正的、稳定的。能源不确定性的影响比其他影响大得多，这表明能源不确定性对外部冲击高度敏感，必须根据不断变化的环境灵活调整。在未来4个周期内，负责任的人工智能对能源不确定性和石油波动的脉冲响应在未来1个周期内相反。负责任的人工智能对能源不确定性的脉冲响应在2020年初和2022年底急剧下降至负值，在2023年初附近达到最低点。在负责任的人工智能冲击下，能源不确定性的脉冲响应在2021年初左右达到峰值，在2022年年中再次达到峰值，然后在2023年初急剧下降至最低点。在短期内，负责任的人工智能对减少能源不确定性的影响有限，但从长远来看，它通过改进数据治理、自适应学习和道德整合，增强了系统的弹性和可持续性。

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引用次数: 0