Energy Economics最新文献

Renewable energy production across U.S. states: Convergence or divergence? 美国各州的可再生能源生产：趋同还是分歧？

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-30 DOI: 10.1016/j.eneco.2024.108015

This study explores the degree to which per capita aggregate renewable energy production is converging across U.S. states. Specifically, we examine both relative (club) convergence and weak σ-convergence. The results reject overall convergence in per capita aggregate renewable energy production for the panel of U.S. states, but identifies two convergence clubs. The results also suggest that there is considerable heterogeneity in the number of convergence clubs for the different subcomponents of per capita renewable energy production and consumption (biomass, geothermal, hydropower, solar and wind). We examine the factors which are associated with the emergence of the convergence clubs at the aggregate level. In the case of per capita aggregate renewable energy production, the average marginal effects from the logit analysis indicate that neighboring states with renewable portfolio standards, mandatory green power options, maximum effective retail rate increase, and per capita CO₂ emissions are associated with a higher likelihood of being in the convergence club with higher per capita renewable energy production. However, interconnection standards, having a public benefit fund, renewable energy certificates trading, compliance penalities, and per capita fossil fuel production are correlated with a lower likelihood of being in the convergence club with higher per capita renewable energy production. We also consider the factors correlated with convergence for the subcomponents of per capita renewable energy production and consumption, with the results suggesting considerable heterogeneity of the various factors at the subcomponent level.

本研究探讨了美国各州人均可再生能源总产量的趋同程度。具体而言，我们考察了相对（俱乐部）趋同和弱σ趋同。结果拒绝了美国各州人均可再生能源生产总量的总体趋同，但确定了两个趋同俱乐部。结果还表明，人均可再生能源生产和消费的不同分项（生物质能、地热能、水电、太阳能和风能）的趋同俱乐部数量存在相当大的异质性。我们研究了在总体水平上出现趋同俱乐部的相关因素。就人均可再生能源总产量而言，Logit 分析得出的平均边际效应表明，邻近州的可再生能源组合标准、强制性绿色电力选择、最高有效零售价涨幅和人均二氧化碳排放量都与更高的人均可再生能源产量相关。然而，互联标准、拥有公益基金、可再生能源证书交易、合规处罚和人均化石燃料产量与跻身人均可再生能源产量较高的趋同俱乐部的可能性较低相关。我们还考虑了与人均可再生能源生产量和消费量子项趋同相关的因素，结果表明，在子项层面，各种因素之间存在相当大的异质性。

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引用次数: 0

Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy 量子计算、人工智能和大数据存量对可再生和可持续能源的动态关联性

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-29 DOI: 10.1016/j.eneco.2024.108017

This research aims to evaluate the accuracy of the long-term relationship between renewable and sustainable energy sectors and emerging technologies, including quantum computing, artificial intelligence (AI), and big data. Using a novel methodology that integrates the Time-Varying Parameter Vector Autoregressive (TVP-VAR) frequency connectedness approach with Long Short-Term Memory (LSTM) neural networks, the study examines the long-term interconnectedness, considering the dynamic nature of coefficients and covariance structures. The analysis spans from May 14, 2018, to September 6, 2023. It focuses on six critical clusters within the sustainable and renewable energy sectors: clean energy, green energy, solar energy, the water industry, wind energy, and the low-carbon industry. Additionally, the study explores two contemporary technology domains, AI and big data, alongside quantum computing. The findings reveal that AI and its associated technologies generally exhibit weaker connections to the renewable and sustainable energy sectors. However, specific pairs, such as those involving business intelligence and AI, show notable interconnectedness. Overall, quantum computing entities demonstrate lower levels of connectedness than the AI/significant data sector, with Microsoft standing out for its solid and broad connections to renewable and sustainable industries. Further analysis identifies distinct patterns, with AI and related technologies showing strong long-term memory connections with renewables and green energies. At the same time, platforms centered on business intelligence and AI display comparatively weaker long-term ties. Among the quantum computing companies, IBM and Google have shown superior performance through specific subsectors. Finally, this study offers valuable insights into the evolving dynamics and interconnectedness at the intersection of renewable and sustainable energies, quantum computing, and the AI/big data industries. The findings support strategic decision-making in sustainable energy transitions and underscore the significance of industry-specific factors in shaping long-term collaborations.

本研究旨在评估可再生和可持续能源部门与量子计算、人工智能（AI）和大数据等新兴技术之间长期关系的准确性。该研究采用一种将时变参数矢量自回归（TVP-VAR）频率关联性方法与长短期记忆（LSTM）神经网络相结合的新方法，在考虑系数和协方差结构的动态性质的基础上，对长期相互关联性进行了研究。分析时间跨度为 2018 年 5 月 14 日至 2023 年 9 月 6 日。研究重点关注可持续和可再生能源领域的六个关键集群：清洁能源、绿色能源、太阳能、水行业、风能和低碳行业。此外，研究还探讨了人工智能和大数据以及量子计算这两个当代技术领域。研究结果表明，人工智能及其相关技术与可再生能源和可持续能源行业的联系普遍较弱。不过，一些特定的技术对（如涉及商业智能和人工智能的技术对）显示出明显的相互关联性。总体而言，量子计算实体与人工智能/重要数据部门的关联度较低，而微软则因其与可再生和可持续产业的牢固而广泛的联系而脱颖而出。进一步的分析发现了不同的模式，人工智能和相关技术与可再生能源和绿色能源之间表现出强大的长期记忆联系。与此同时，以商业智能和人工智能为中心的平台则显示出相对较弱的长期联系。在量子计算公司中，IBM 和谷歌通过特定的子行业表现出卓越的性能。最后，本研究对可再生和可持续能源、量子计算以及人工智能/大数据产业交叉点上不断变化的动态和相互联系提供了宝贵的见解。研究结果为可持续能源转型中的战略决策提供了支持，并强调了特定行业因素在塑造长期合作中的重要性。

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引用次数: 0

Interplay between renewable energy and fossil fuel markets: Fresh evidence from quantile-on-quantile and wavelet quantile approaches 可再生能源与化石燃料市场之间的相互作用：量化对量化和小波量化方法的新证据

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-29 DOI: 10.1016/j.eneco.2024.108012

Highlighting the unprecedented rise in CO2 emissions from the global energy sector, the paper discusses the significant shift towards renewable energy, which has reshaped financial markets and investment landscapes. Despite the transition, conventional fossil fuel energy remains pivotal to the global economy, influencing renewable energy markets, especially during financial crises. Using advanced methodologies, quantile-on-quantile regression (QQR) and wavelet quantile regression (WQR), this study investigates the interplay between individual fossil fuel stocks and various renewable energy assets, including exchange-traded funds (ETFs) and yieldcos. The findings reveal substantial interdependencies between these markets, with fossil fuel stocks notably negatively impacting renewable energy assets under extreme market conditions. During turbulent periods, renewable energy assets function as safe havens against the volatility of fossil fuel stocks in the short term. Conversely, under normal market conditions, while renewable energy ETFs and yieldcos can hedge against fossil fuel volatility, they can also serve as diversifiers in the long term. The results underscore the importance of understanding these dynamic interactions to develop effective investment strategies and policies. The study's insights are crucial for investors and policymakers in mitigating investment risks and fostering a resilient transition to sustainable energy systems, emphasizing the need for comprehensive frameworks to manage the interconnectedness between fossil fuel and renewable energy markets.

本文强调了全球能源行业二氧化碳排放量的空前增长，讨论了向可再生能源的重大转变，这种转变重塑了金融市场和投资格局。尽管发生了转变，但传统化石燃料能源仍对全球经济起着举足轻重的作用，影响着可再生能源市场，尤其是在金融危机期间。本研究采用先进的方法，即量化回归（QQR）和小波量化回归（WQR），研究了化石燃料个股与各种可再生能源资产（包括交易所交易基金（ETF）和收益证券）之间的相互作用。研究结果表明，这些市场之间存在着巨大的相互依存关系，在极端市场条件下，化石燃料股票对可再生能源资产产生了显著的负面影响。在动荡时期，可再生能源资产可作为避风港，在短期内抵御化石燃料股票的波动。相反，在正常市场条件下，虽然可再生能源 ETF 和收益证券可以对冲化石燃料的波动，但从长期来看，它们也可以起到分散投资的作用。研究结果强调了了解这些动态互动对制定有效投资战略和政策的重要性。该研究的见解对于投资者和政策制定者降低投资风险、促进向可持续能源系统的弹性过渡至关重要，同时强调需要制定全面的框架来管理化石燃料和可再生能源市场之间的相互联系。

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引用次数: 0

Role of supply chain disruptions and digitalization on renewable energy innovation: Evidence from G7 nations 供应链中断和数字化对可再生能源创新的作用：来自 G7 国家的证据

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-29 DOI: 10.1016/j.eneco.2024.108016

Renewable energy innovations are essential for mitigating greenhouse gas emissions and addressing climate change, guaranteeing a more pristine and healthful environment. Moreover, these advancements stimulate economic expansion by establishing novel sectors and employment prospects while improving energy reliability and ecological viability. For the first time, the current study explores how supply chain disruption and digitalization impact renewable energy innovations. Besides, the study also considered the role of control variables, including human capital, globalization, economic growth, and democracy. The study used moment quantile regression as an estimator focused on the G7 economies, with data from 1990 to 2020. The study findings show supply chain disruption's insignificant and adverse effect on renewable energy innovations. Furthermore, digitalization promotes renewable energy innovations across all quantiles. Besides, this study also found the effectiveness of economic growth in promoting renewable energy innovations across all quantiles. On the contrary, globalization consistently hampers renewable energy innovations across all quantiles, while democracy is seen as an effective tool in increasing renewable energy innovations. The study formulates policies based on these findings.

可再生能源创新对于减少温室气体排放和应对气候变化、确保更纯净和更健康的环境至关重要。此外，这些进步在提高能源可靠性和生态可行性的同时，还通过建立新的行业和就业前景来刺激经济扩张。本研究首次探讨了供应链中断和数字化如何影响可再生能源创新。此外，研究还考虑了控制变量的作用，包括人力资本、全球化、经济增长和民主。研究使用矩量回归作为估计工具，以 G7 经济体为研究对象，数据时间为 1990 年至 2020 年。研究结果表明，供应链中断对可再生能源创新的不利影响并不显著。此外，数字化促进了所有量级的可再生能源创新。此外，本研究还发现，经济增长在所有量级上都能有效促进可再生能源创新。相反，全球化始终阻碍所有量级的可再生能源创新，而民主则被视为增加可再生能源创新的有效工具。研究根据这些发现制定了政策。

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引用次数: 0

How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China 新一代国家人工智能创新发展试验区建设如何推动企业ESG发展？来自中国的经验证据

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-29 DOI: 10.1016/j.eneco.2024.108011

In the context of the rapid development of artificial intelligence (AI) technology and the growing global attention to the ESG performance of enterprises, this study takes the “National New Generation Artificial Intelligence Innovation and Development Pilot Zone” as a quasi-natural experiment. Based on the unbalanced panel data of Chinese Shanghai and Shenzhen listed companies from 2007 to 2022, it uses the multi-period difference-in-differences model (DID) and the propensity score matching-difference-in-differences (PSM-DID) method to explore the impact and mechanism of the AI pilot policy on the ESG performance of enterprises. The empirical results show that this policy significantly improves the ESG performance of enterprises, and the robustness of the conclusion is verified through parallel trend tests, placebo tests, PSM-DID tests, etc. The heterogeneity analysis shows that the policy has different effects in different regions and industries, and the response is more significant in the eastern and central regions, as well as non-state-owned enterprises and heavily polluting industries. The analysis of the impact mechanism confirms the key role of green technology innovation and the level of R&D expenditure. Finally, this paper puts forward policy suggestions such as formulating differentiated policies, building innovation platforms, enhancing R&D investment, and establishing monitoring and evaluation mechanisms to promote the effective implementation of AI technology application by enterprises in ESG performance.

在人工智能（AI）技术飞速发展、企业ESG绩效日益受到全球关注的背景下，本研究以 "国家新一代人工智能创新发展试验区 "为准自然实验。基于2007-2022年中国沪深两市上市公司的非平衡面板数据，采用多期差分模型（DID）和倾向得分匹配-差分模型（PSM-DID）方法，探讨人工智能试点政策对企业ESG绩效的影响和作用机制。实证结果表明，该政策显著改善了企业的ESG绩效，并通过平行趋势检验、安慰剂检验、PSM-DID检验等验证了结论的稳健性。异质性分析表明，该政策在不同地区、不同行业的效果不同，在东部、中部地区以及非国有企业和重污染行业的响应更为明显。对影响机制的分析证实了绿色技术创新和研发支出水平的关键作用。最后，本文提出了制定差异化政策、搭建创新平台、加强研发投入、建立监测评估机制等政策建议，以促进企业在ESG绩效中有效实施人工智能技术应用。

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引用次数: 0

Modelling and assessing dynamic energy supply resilience to disruption events: An oil supply disruption case in China 模拟和评估能源供应对中断事件的动态适应能力：中国石油供应中断案例

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-29 DOI: 10.1016/j.eneco.2024.108013

Energy supply disruptions can have unpredictable and significant economic impacts, making supply resilience a critical concern for policymakers. Assessing and improving supply resilience have become necessary to make energy policies more effective. This study aimed to develop a model for resilience assessment and enhancement. First, we created a Mixed-Supply-side Dynamic Inoperability Input–output Model (M-SDIIM), which could calculate sectors' dynamic inoperability and economic losses under import or production disruptions. Second, a dynamic supply resilience curve was established using M-SDIIM, and the calculating method for robustness and recoverability was used to visualise the resilience characteristics. Finally, given the practical significance of oil security, we incorporated the strategic stock strategy into M-SDIIM to construct a resilience enhancement model. Using the developed model, we conducted a case study of China's oil supply disruption. The results demonstrated that M-SDIIM effectively assessed the energy supply resilience of interdependent infrastructure. In an extremely large oil disruption event, the resilience curves of all sectors in China showed a typical U-shape; however, significant differences were apparent in the robustness and recoverability of the sectors, with six sectors, including Petroleum processing, Transport and Chemical products, among the most vulnerable. Second, the resilience enhancement model enabled a quantitative assessment of strategies, providing a clear improvement target. In China, more than the current stock levels are needed; at least 73-day crude oil imports are required. Thus, we propose targeted policy recommendations to assist countries in formulating energy policies.

能源供应中断会产生不可预测的重大经济影响，因此能源供应的恢复能力成为政策制定者关注的关键问题。为了使能源政策更加有效，有必要评估和提高供应恢复能力。本研究旨在开发一个复原力评估和增强模型。首先，我们创建了一个混合供应方动态不可操作性投入产出模型（M-SDIIM），该模型可计算进口或生产中断情况下各部门的动态不可操作性和经济损失。其次，利用 M-SDIIM 建立了动态供应弹性曲线，并利用稳健性和可恢复性的计算方法将弹性特征可视化。最后，考虑到石油安全的现实意义，我们将战略储备战略纳入 M-SDIIM，构建了弹性增强模型。利用所开发的模型，我们对中国石油供应中断进行了案例研究。结果表明，M-SDIIM 能够有效评估相互依存的基础设施的能源供应弹性。在特大石油供应中断事件中，中国所有部门的恢复力曲线都呈现出典型的 U 型；但各部门的稳健性和可恢复性存在明显差异，其中石油加工、运输和化工产品等六个部门最为脆弱。其次，复原力增强模型能够对战略进行量化评估，提供明确的改进目标。在中国，需要的不仅仅是目前的库存水平；至少需要 73 天的原油进口量。因此，我们提出了有针对性的政策建议，以帮助各国制定能源政策。

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引用次数: 0

Evaluating the energy poverty in the EU countries 评估欧盟国家的能源贫困状况

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-28 DOI: 10.1016/j.eneco.2024.108020

The domain of energy poverty is increasingly recognised as a multifaceted global challenge stemming from limited income, high energy costs, and inefficient housing. The issue affects different social groups and regions unevenly, even within Europe. This paper investigates energy poverty across 32 economies, including EU member states and several non-EU European countries, over the period from 2004 to 2021. By analysing micro-level data from the EU-SILC database and Eurostat, the study identifies that low-income households, smaller households, and those living in overcrowded conditions are particularly vulnerable to energy poverty. Interestingly, the research finds that renewable energy does not contribute to alleviating energy poverty in Europe. Based on these results, the study calls for immediate policy measures to improve housing conditions and lower electricity costs, especially for economically disadvantaged households, to effectively address energy poverty.

人们日益认识到，能源贫困是一个多方面的全球性挑战，它源于有限的收入、高昂的能源成本和低效的住房。这一问题对不同社会群体和地区的影响并不均衡，即使在欧洲内部也是如此。本文研究了 2004 年至 2021 年期间 32 个经济体的能源贫困问题，其中包括欧盟成员国和几个非欧盟的欧洲国家。通过分析欧盟-SILC 数据库和欧盟统计局（Eurostat）的微观数据，研究发现低收入家庭、小家庭和居住条件拥挤的家庭尤其容易陷入能源贫困。有趣的是，研究发现可再生能源无助于缓解欧洲的能源贫困。基于这些结果，研究呼吁立即采取政策措施，改善住房条件，降低电费，尤其是经济困难家庭的电费，以有效解决能源贫困问题。

引用次数: 0

Scrutinizing multi-scale and multi-quantile interactions in commodity markets: A petrochemical industrial chain perspective 审视商品市场中的多尺度和多量纲相互作用：石化产业链视角

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-28 DOI: 10.1016/j.eneco.2024.108019

From the perspective of the petrochemical industrial chain, this paper examines the interactions among five China's petrochemical commodity futures using three innovative methods - wavelet local multiple correlation, frequency connectedness framework, and quantile connectedness framework. The results show China's petrochemical markets exhibit a high degree of market integration at different time scales but decouple from international crude oil markets in the short term. The price dynamics of polypropylene (PP) and linear low-density polyethylene (LL) behave as the dominant factors to impact the price fluctuations of other commodities. The total information spillover level showcases a rapidly decreasing trend with the time scale increasing but a U-shaped curve across various quantiles and reaches the minimum at the 50th percentile. We further identified the net information transmitters and recipients in the industrial chain system and also explored the spillover shocks of two globally traded crude oil benchmarks, i.e., Brent and WTI, at different time scales and under different market conditions. They virtually always serve as net risk transmitters to China's domestic markets, but under extremely bullish market conditions, they are net influenced by the sharply upward trends of China's markets.

本文从石化产业链的角度出发，采用小波局部多重相关性、频率连通性框架和量子连通性框架三种创新方法，研究了中国五种石化商品期货之间的相互作用。结果表明，中国石化市场在不同时间尺度上表现出高度的市场一体化，但在短期内与国际原油市场脱钩。聚丙烯（PP）和线型低密度聚乙烯（LL）的价格动态是影响其他商品价格波动的主导因素。总信息溢出水平随着时间尺度的增加呈快速下降趋势，但在不同数量级之间呈 U 型曲线，并在第 50 个百分位数时达到最小值。我们进一步确定了产业链系统中的净信息传递者和接收者，并探讨了两种全球交易的原油基准（即布伦特原油和 WTI 原油）在不同时间尺度和不同市场条件下的溢出冲击。它们几乎始终是中国国内市场的净风险传递者，但在市场极度看涨的情况下，它们会受到中国市场大幅上涨趋势的净影响。

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引用次数: 0

Discerning the impact of global geopolitical risks on China's energy futures market spillovers: Evidence from higher-order moments 辨析全球地缘政治风险对中国能源期货市场溢出效应的影响：来自高阶矩的证据

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-28 DOI: 10.1016/j.eneco.2024.107981

Global geopolitical risks matter to China's energy futures market. Through a three-stage research framework combining higher-order moment (third- and fourth-order moments), time-frequency spillover model, and the random forest technique, we identify the influence of global energy countries' geopolitical risks on China's energy futures spillovers under different time horizons. Our third-order moment (skewness) spillover results indicate that crude oil and fuel oil futures primarily play the role of net transmitters in the short-term time scale, while coking coal and bitumen futures are mainly net recipients. In the medium- and long- term time scales, however, thermal coal and fuel oil are stable net recipients. For the fourth-order moment (kurtosis) spillovers, the crude oil consistently acts as the transmitters across all time horizons, with a relatively significant net spillover value. Moreover, Russia's geopolitical risk has the most prominent impact on spillovers in China's energy futures market, followed by Saudi Arabia. Additionally, Russia and Saudi Arabia are more likely to impact upside and downside market spillovers, respectively. These results provide some implications for different types of investors and policy makers.

全球地缘政治风险与中国能源期货市场息息相关。通过高阶矩（三阶矩和四阶矩）、时频溢出模型和随机森林技术相结合的三阶段研究框架，我们识别了不同时间跨度下全球能源国家地缘政治风险对中国能源期货溢出的影响。我们的三阶矩（偏度）溢出结果表明，在短期时间尺度上，原油和燃料油期货主要扮演净传递者的角色，而焦煤和沥青期货则主要是净接受者。但在中长期时间尺度上，动力煤和燃料油是稳定的净接受者。在四阶矩（峰度）溢出效应方面，原油在所有时间跨度上都始终是传播者，净溢出值相对较大。此外，俄罗斯地缘政治风险对中国能源期货市场溢出效应的影响最为突出，其次是沙特阿拉伯。此外，俄罗斯和沙特阿拉伯分别更有可能影响市场的上行和下行溢出效应。这些结果为不同类型的投资者和政策制定者提供了一些启示。

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引用次数: 0

Corrigendum to “Incentivizing residential electricity consumers to increase demand during periods of high local solar generation” [Energy Economics, Volume 127, Part A, November 2023, 107028] 对 "激励居民电力消费者在当地太阳能发电量高的时期增加需求 "的更正[《能源经济学》，第 127 卷，A 部分，2023 年 11 月，107028]

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics

Pub Date : 2024-10-28 DOI: 10.1016/j.eneco.2024.107997

引用次数: 0