Energy Economics最新文献

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Nowcasting Italian industrial production: The predictive role of lubricant oils 临近铸造意大利工业生产：润滑油的预测作用

IF 12.8 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-20 DOI: 10.1016/j.eneco.2025.109108

Marco Fruzzetti, Tiziano Ropele

引用次数: 0

Complexity analysis of automotive supply chains considering online reputation under dual policies 双重政策下考虑网络声誉的汽车供应链复杂性分析

IF 12.8 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-20 DOI: 10.1016/j.eneco.2025.109105

Xiaobin Wang, Abudureheman Kadeer, Huanying He

引用次数: 0

Distributional impacts of heterogenous carbon prices in the EU 欧盟异质性碳价格的分布影响

IF 12.8 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-18 DOI: 10.1016/j.eneco.2025.109102

Magnus Merkle, Geoffroy Dolphin

引用次数: 0

Price vs policy: The impact of cost uncertainty on decarbonization pathways 价格与政策：成本不确定性对脱碳途径的影响

IF 12.8 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-18 DOI: 10.1016/j.eneco.2025.109088

Nathan de Matos, Madeleine McPherson

引用次数: 0

The economics of negative price phenomenon in renewable-integrated electricity markets 可再生整合电力市场负价格现象的经济学

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109086

Nima Rafizadeh

Understanding how renewable energy integration affects electricity market efficiency and price formation is an important challenge in energy economics and environmental policy. Negative electricity prices, where generators pay to produce power, now occur with increasing frequency across wholesale markets, yet their economic drivers require better understanding. This paper addresses this gap by developing a theoretical framework linking generator behavior to market outcomes, then testing it empirically using over 14 million observations from New York’s wholesale markets (2010–2022). The theoretical analysis demonstrates that negative prices can achieve welfare-maximizing allocations under operational constraints and production subsidies. The empirical analysis, using binary response and count data models with high-frequency data, identifies a clear hierarchy of drivers: renewable energy integration emerges as primary, with solar energy reducing negative price occurrences while wind energy increases them. Weather conditions rank second in importance, while grid constraints show limited influence, contrary to policy focus on transmission expansion. These findings can inform policy discussions by suggesting that rather than suppressing negative prices through regulatory constraints, policymakers should preserve these efficient price signals while prioritizing technology-specific renewable policies and weather-responsive mechanisms over transmission expansion to enhance investment signals and market stability.

了解可再生能源整合如何影响电力市场效率和价格形成是能源经济学和环境政策的一个重要挑战。负电价（发电商为发电付费）现在在批发市场越来越频繁地出现，但其经济驱动因素需要更好地理解。本文通过建立一个将发电机行为与市场结果联系起来的理论框架来解决这一差距，然后使用来自纽约批发市场（2010-2022）的1400多万次观察结果对其进行实证检验。理论分析表明，在经营约束和生产补贴条件下，负价格可以实现福利最大化的分配。实证分析使用二元响应和高频数据计数数据模型，确定了驱动因素的明确层次：可再生能源整合成为主要因素，太阳能减少了负价格的发生，而风能增加了负价格的发生。天气条件的重要性排在第二位，而电网约束的影响有限，这与政策关注的输电扩张相反。这些发现可以为政策讨论提供信息，建议政策制定者不应通过监管约束来抑制负价格，而应保留这些有效的价格信号，同时优先考虑针对技术的可再生能源政策和天气响应机制，而不是输电扩张，以增强投资信号和市场稳定。

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引用次数: 0

Sentiments and risks: A spillover tale under climate policy uncertainty 情绪与风险：气候政策不确定性下的溢出效应

IF 12.8 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-17 DOI: 10.1016/j.eneco.2025.109101

Yingying Huang, Weizhong Liang, Kun Duan, Mamata Parhi, Tapas Mishra

引用次数: 0

Green transformation in the era of intelligence: How artificial intelligence affects disruptive green innovation in firms 智能时代的绿色转型：人工智能如何影响企业的破坏性绿色创新

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-14 DOI: 10.1016/j.eneco.2025.109093

Jie Chen , Lisha Yang , Huahuang Su

Artificial intelligence (AI) drives disruptive technological breakthroughs and fosters paradigm shifts in firms' green innovation (GI). This study constructs a Green Citation Disruption Index based on green patent citation networks to measure disruptive green innovation (DGI) in Chinese listed manufacturing firms. The results show that about 12.43 % of firms have achieved DGI. In our empirical analysis, we decompose firms' DGI into two dimensions: the probability of achieving DGI and the innovation capability of firms that have already achieved it. The results show that a one-unit increase in AI level raises the probability of a firm achieving DGI by about 4.33 percentage points and enhances its DGI capability by about 9.71 percentage points, after controlling for firm and year fixed effects as well as other covariates. The mechanism analysis reveals that AI facilitates disruptive breakthroughs in green technologies through capability enhancement and capability compensation. The capability enhancement effect operates as AI attracts patient capital and overcomes organizational inertia, thereby strengthening firms' intrinsic ability to undertake DGI. The capability compensation effect arises as AI reinforces inter-firm collaboration, enabling firms to draw on external capabilities and resources. Moreover, we find evidence of a gradual deepening pattern in AI-enabled collaboration: AI first strengthens relationship-based collaboration formed through managerial interlocks, and subsequently enhances capital-based collaboration via shared ownership ties over a longer horizon, providing increasingly substantive external support for DGI. Heterogeneity analyses suggest that AI more strongly enhances the DGI capability of firms located in regions with environmental tribunals. The effect is also more pronounced for high-pollution firms. Moreover, state-owned firms exhibit both a higher probability of achieving DGI and stronger improvements in their innovation capability through AI. This study extends the measurement framework for DGI, clarifies the mechanisms through which AI enables such innovation, and provides new empirical evidence on how AI drives firms' engagement in it.

人工智能（AI）推动了颠覆性的技术突破，促进了企业绿色创新（GI）的范式转变。本文构建了基于绿色专利引文网络的绿色引文中断指数来衡量中国制造业上市公司的颠覆性绿色创新。结果表明，12.43%的企业达到了DGI。在实证分析中，我们将企业DGI分解为实现DGI的概率和已经实现DGI的企业创新能力两个维度。结果表明，在控制了企业和年份固定效应以及其他协变量之后，人工智能水平每提高一个单位，企业实现DGI的概率就会提高约4.33个百分点，其DGI能力提高约9.71个百分点。机制分析表明，人工智能通过能力增强和能力补偿促进了绿色技术的颠覆性突破。能力增强效应是通过人工智能吸引耐心资本，克服组织惯性，从而增强企业承担DGI的内在能力。当人工智能加强了企业间的合作，使企业能够利用外部的能力和资源时，能力补偿效应就会出现。此外，我们发现了人工智能支持的协作模式逐渐深化的证据：人工智能首先加强了通过管理联锁形成的基于关系的协作，随后通过更长期的共享所有权关系增强了基于资本的协作，为DGI提供了越来越实质性的外部支持。异质性分析表明，人工智能更强地增强了环境法庭所在地区企业的DGI能力。对于高污染企业，这种影响也更为明显。此外，国有企业通过人工智能实现DGI的可能性更高，其创新能力也有更强的提高。本研究扩展了DGI的测量框架，阐明了人工智能实现这种创新的机制，并为人工智能如何推动企业参与创新提供了新的经验证据。

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引用次数: 0

The effect of external assurance on corporate carbon disclosures: Empirical evidence from Europe 外部保证对企业碳披露的影响：来自欧洲的经验证据

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-13 DOI: 10.1016/j.eneco.2025.109084

Georgios Papadopoulos

Company carbon disclosures are a fundamental piece of information for assessing firms’ environmental impact, and many policy actions are associated with them. Responding to a rising demand for transparency and regulatory requirements, firms disclose information on their greenhouse-gas (GHG) emissions and voluntarily engage with external assurance of the reported data. However, the possible existence of systematic differences in reported emissions with respect to their assurance status is under-explored. This study investigates the causal effect of external assurance on carbon disclosures in a sample of European companies. Findings suggest that, on average, non-assuring firms under-report their direct GHG emissions by a share similar to the largest annual emissions reduction in the EU, compared to the assured reports of their peers. Instead, assurance’s effect is much weaker in indirect emissions. These results demonstrate that third-party assurance could provide more prudent estimates of corporate GHG emissions which are important for all stakeholders, including companies, investors and policymakers, to mitigate climate change.

公司碳信息披露是评估公司环境影响的基本信息，许多政策行动都与之相关。为响应日益增长的透明度需求和监管要求，企业披露其温室气体（GHG）排放信息，并自愿参与报告数据的外部保证。但是，报告的排放量与它们的保证状况之间是否可能存在系统性差异，尚未得到充分探讨。本研究以欧洲公司为样本，探讨外部保证对碳披露的因果效应。研究结果表明，平均而言，与同行的有保证的报告相比，无保证的公司少报其直接温室气体排放量的比例与欧盟最大的年度减排量相似。相反，保险对间接排放的影响要弱得多。这些结果表明，第三方担保可以提供更谨慎的企业温室气体排放估计，这对包括公司、投资者和政策制定者在内的所有利益相关者都很重要，有助于减缓气候变化。

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引用次数: 0

The predictive effects of Fintech-ESG dynamic interdependence: A global perspective on Cleantech energy transition risk 金融科技与esg动态相互依赖的预测效应：清洁技术能源转型风险的全球视角

IF 14.2 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-13 DOI: 10.1016/j.eneco.2025.109090

Martin Enilov , Edna Delantar , Mamata Parhi

Fintech plays an instrumental role in advancing global ESG objectives, leveraging a more inclusive, transparent, and accountable financial system. Our paper explores the occurrence of dynamic linkages between Fintech and ESG across various dimensions, examining how the strength of their interconnectedness drives the energy transition towards clean technology. Using daily data from 31st May 2018 to 1st August 2024, we apply a time-varying parameter robust Granger causality method coupled with quantile technique to provide the first attempt in the literature on the dynamic causal patterns between the strength of Fintech-ESG connection and Cleantech energy transition risk (CETR). We find asymmetry in the connectedness across different quantiles, with Fintech sectors acting primarily as shock transmitters, while most ESG indexes are receivers. The 2022 Russia-Ukraine conflict reduces the connectedness between Fintech and ESG, with minimal effects on spillover direction. Our results show a heterogeneous response to shocks in developed markets, while developing ones tend to react more homogeneously. Additionally, we find strong evidence of a time-varying causal relationship between Fintech-ESG connectedness and CETR, with the conflict exacerbating asymmetry, especially at the lower quantile. Recent trends suggest a modest resurgence in this connection, signalling a re-emergence of the Fintech-ESG connection influence on CETR. The impact of extreme events tends to taper-off over time, suggesting that the prolonged conflict-driven market environment may have stabilized sufficiently to restore Fintech's role in promoting ESG initiatives, thereby supporting the ongoing transition to clean technology.

金融科技在推进全球ESG目标、推动建立更加包容、透明和负责任的金融体系方面发挥着重要作用。我们的论文探讨了金融科技和ESG之间在各个维度上的动态联系，研究了它们相互联系的强度如何推动能源向清洁技术的过渡。利用2018年5月31日至2024年8月1日的日常数据，我们采用时变参数鲁棒格兰杰因果关系方法结合分位数技术，首次在文献中尝试了金融科技- esg连接强度与清洁技术能源转型风险（CETR）之间的动态因果关系模式。我们发现不同分位数之间的连通性不对称，金融科技行业主要充当冲击发射器，而大多数ESG指数都是接收器。2022年俄罗斯-乌克兰冲突降低了金融科技与ESG之间的联系，对溢出方向的影响微乎其微。我们的研究结果显示，发达市场对冲击的反应各不相同，而发展中市场的反应往往更为均匀。此外，我们发现强有力的证据表明，金融科技- esg连通性与ccr之间存在时变因果关系，这种冲突加剧了不对称性，尤其是在较低的分位数。最近的趋势表明，这一联系略有复苏，标志着金融科技- esg联系对ctr的影响再次出现。随着时间的推移，极端事件的影响往往会逐渐减弱，这表明长期冲突驱动的市场环境可能已经足够稳定，可以恢复金融科技在促进ESG倡议方面的作用，从而支持正在进行的向清洁技术的过渡。

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引用次数: 0

Navigating green innovation: The role of carbon regulation and green finance in corporate strategy 引导绿色创新：碳监管和绿色金融在企业战略中的作用

IF 12.8 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2025-12-11 DOI: 10.1016/j.eneco.2025.109089

Qingying Zheng, Handan Li, Guochao Bu, Hongxun Liu

引用次数: 0

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