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Corrigendum to “Porter in China: A quasi-experimental view of market-based environmental regulation effects on firm performance” [Energy Economics Volume 126, October 2023, 106966]
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-18 DOI: 10.1016/j.eneco.2025.108405
Abd Alwahed Dagestani , Yuping Shang , Nicolas Schneider , Javier Cifuentes-Faura , Xin Zhao
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引用次数: 0
Geographical Bayesian second stage analysis for operating efficiency of Brazilian electricity distribution system operators
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-17 DOI: 10.1016/j.eneco.2025.108371
Marcelo Azevedo Costa , Aline Veronese da Silva , Leandro Brioschi Mineti
Since 2011, the Brazilian electricity regulator has applied data envelopment analysis to estimate regulatory operating costs for distribution and transmission companies. Despite the availability of environmental or contextual variables, second-stage analysis has been avoided, primarily due to inconsistent statistical results, including estimated coefficients contrary to technical evidence and significant changes in operating efficiencies for selected companies. Previous studies have shown that environmental adjustments are critical for companies’ revenues operating in harsh environments in Brazil. Additionally, climate changes are affecting expenses with varying effects nationwide. To tackle this challenge, a second-stage model in which changes in efficiencies are also affected by geographical location of companies is proposed. Coefficient constraints and multiple environmental variables are applied to estimate regulatory efficiencies of Brazilian Distributor System Operators for upcoming years. Results indicate maximum efficiency changes of +5.35% and an increase of 1.1% in the total regulatory OPEX if the proposed second stage is applied.
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引用次数: 0
Corrigendum to “The oil price-inflation nexus: The exchange rate pass- through effect” [Energy Economics Volume 125, September 2023, 106828].
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-15 DOI: 10.1016/j.eneco.2025.108355
Shusheng Ding , Dandan Zheng , Tianxiang Cui , Min Du
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引用次数: 0
Does artificial intelligence promote green technology innovation in the energy industry?
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-15 DOI: 10.1016/j.eneco.2025.108402
Cong Li , Yue Zhang , Xihua Liu , Jiawen Sun
The lack of incentives for energy corporations to engage in green technology innovation (GTI) is a problem which has long plagued economic growth and sustainable development. The widespread integration of artificial intelligence (AI) innovation in the domain of environmental protection has given new impetus to GTI, sparking interest into its role in green transformation. This study investigates the impact of AI on GTI in Chinese energy corporations to explore whether it is diverting resources from GTI, or overcoming the lack of GTI incentives. The results indicate that AI indeed contributes to GTI, and that it does so by enhancing human capital and alleviating financial pressure. Additionally, this effect is more pronounced in the central and eastern regions, areas with stricter environmental regulations, and the midstream and downstream of the energy industry. These findings offer specific insights to simulate GTI, helping balance economic growth with sustainable development.
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引用次数: 0
Global reach, local impact: How China's outward foreign direct investment shapes corporate carbon risk management?
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-13 DOI: 10.1016/j.eneco.2025.108391
Miaomiao Tao , Jianda Wang , David Roubaud , Lingli Qi
Outward Foreign Direct Investment (OFDI) has emerged as a pivotal driver of globalization, enabling firms to broaden their reach across borders and tap into diverse resources. We present evidence supporting the role of OFDI in mitigating corporate carbon risk. However, the extent of the underlying mitigation effect varies depending on factors such as corporate nature, ESG ratings, and financial constraints. We also identify several intermediaries through which OFDI exerts its mitigating influence, such as corporate reputation, downside risk exposure, debt financing costs, and the firm's ability to innovate in green technologies. Further exploration substantiates the synergistic effects between OFDI and the emissions trading scheme, which together help further reduce corporate carbon risk. These findings offer valuable insights into how enhancing OFDI can mitigate carbon risk, thus supporting China's transition toward a low-carbon economy.
对外直接投资（OFDI）已成为全球化的重要推动力，它使企业能够扩大其跨国业务范围并利用各种资源。我们提出的证据支持对外直接投资在降低企业碳风险方面的作用。然而，基本缓解效应的程度因企业性质、环境、社会和治理评级以及财务限制等因素而异。我们还发现了对外直接投资发挥缓解作用的几个中介，如企业声誉、下行风险敞口、债务融资成本以及企业在绿色技术方面的创新能力。进一步的探索证实了对外直接投资与排放交易计划之间的协同效应，它们共同有助于进一步降低企业的碳风险。这些研究结果为加强对外直接投资如何降低碳风险，从而支持中国向低碳经济转型提供了宝贵的见解。
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引用次数: 0
Retirement decision and household’s gasoline consumption: Evidence from a Regression Discontinuity Design
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-11 DOI: 10.1016/j.eneco.2025.108374
Nicola Francescutto
I employ household-level data over 2006–2017 to quantify the impact of retirement on gasoline consumption. Based on a fuzzy regression discontinuity design, I show that gasoline consumption declines by 32–36 percent on average over my different specifications. The reduction reaches 59–66 percent when I restrict the sample to single-person households. I further find that the probability to use any gasoline decreases by 5–6 percent at retirement (13–16 percent for single-person households). These findings suggest that demographic trends represent an important driver of CO2 emissions associated with private mobility in developed countries.
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引用次数: 0
Optimizing green subsidy policies for decarbonization in Southeast Asia's real estate sector
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-10 DOI: 10.1016/j.eneco.2025.108372
Huake Liu , He Nie , Di Sang , Yu Wang , Xueren Zhang
The real estate sector is a significant contributor to carbon emissions, especially in lesser-developed countries in Southeast Asia, where the adoption of energy-efficient technologies remains limited. This paper evaluates the effectiveness of heterogeneous green subsidy policies aimed at reducing carbon emissions in the real estate industry. Using a dynamic stochastic general equilibrium (DSGE) model, we assess the impact of these policies on building materials manufacturers, real estate enterprises, and regulatory bodies. Our findings indicate that targeted subsidies for low-carbon production factors significantly reduce carbon emissions and support sustainable economic growth in the real estate sector. The results offer crucial insights into formulating green subsidy policies that align with decarbonization goals while fostering sustainable development in Southeast Asia's emerging economies. This research provides practical recommendations for policymakers to optimize green subsidies and promote a transition to a low-carbon future.
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引用次数: 0
Corrigendum to “The asymmetric impact of input prices, the Russia-Ukraine war and domestic policy changes on wholesale electricity prices in India: A quantile autoregressive distributed lag analysis” [Energy EconomicsVolume 132, April 2024, 107428]
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-09 DOI: 10.1016/j.eneco.2025.108357
Charanjit Kaur , Jalal Siddiki , Prakash Singh
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引用次数: 0
Financial risk management innovation in energy market: Evidence from a machine learning hybrid model
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-08 DOI: 10.1016/j.eneco.2025.108360
Zepei Li , Feng Ma , Xinjie Lu
This study employs a novel hybrid machine learning model that combines principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) methods. It explored the relationships between 19 kinds of commodities and 14 international stock market indices, taking the volatility of international stock market indices as a predictive factor. We discover that the LASSO-PCA model has significant predictive power for the energy market. Furthermore, through the analysis of different special periods (such as periods of high and low volatility, the COVID-19 pandemic, and the Russia-Ukraine conflict), it is verified that the model can still stably predict the energy market in various market environments. This research result showcases the application value of machine learning methods in analyzing the energy market, which is of great significance for financial risk management innovation and investor decision-making in the energy market.
{"title":"Financial risk management innovation in energy market: Evidence from a machine learning hybrid model","authors":"Zepei Li ,&nbsp;Feng Ma ,&nbsp;Xinjie Lu","doi":"10.1016/j.eneco.2025.108360","DOIUrl":"10.1016/j.eneco.2025.108360","url":null,"abstract":"<div><div>This study employs a novel hybrid machine learning model that combines principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) methods. It explored the relationships between 19 kinds of commodities and 14 international stock market indices, taking the volatility of international stock market indices as a predictive factor. We discover that the LASSO-PCA model has significant predictive power for the energy market. Furthermore, through the analysis of different special periods (such as periods of high and low volatility, the COVID-19 pandemic, and the Russia-Ukraine conflict), it is verified that the model can still stably predict the energy market in various market environments. This research result showcases the application value of machine learning methods in analyzing the energy market, which is of great significance for financial risk management innovation and investor decision-making in the energy market.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"144 ","pages":"Article 108360"},"PeriodicalIF":13.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Supply chain challenges and energy insecurity: The role of AI in facilitating renewable energy transition
IF 13.6 2区 经济学 Q1 ECONOMICS
Energy Economics
Pub Date : 2025-03-07 DOI: 10.1016/j.eneco.2025.108378
Lingxiao Li , Jun Wen , Yan Li , Zi Mu
The global energy industry has been undergoing a transition toward renewable energy due to high energy insecurity, disruption in the global supply chain, and industry 4.0 technologies. Given this, it is imperative to identify the factors influencing renewable energy transition by examining the impact of artificial intelligence, supply chain pressure, and energy insecurity in emerging countries. This study employs the method of moments quantile regression on monthly data of selected countries from 2010 to 2022. The findings show that supply chain pressure significantly reduces renewable energy transition, with the negative effects being most prominent at lower quantiles. However, artificial intelligence and energy insecurity stimulate renewable energy transition, with profound impacts observed at lower quantiles. The interaction term of supply chain pressure and artificial intelligence indicates that when nations integrate supply chains with artificial intelligence, it significantly promotes renewable energy transition by addressing supply chain disruptions, with positive effects being pronounced at lower quantiles. These regression parameters are validated using alternative estimators and offer valuable policy suggestions.
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