Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.107986
Xuerui Wang, Lin Wang, Wuyue An
Carbon allowance price is an important tool to reduce carbon emissions and achieve carbon neutrality. It is necessary to establish a predictive model to provide accurate and reliable information to managers and participants in the carbon trading market. Therefore, a novel probability density prediction model, called TS2Vec-based distribution Transformer (TDT), is proposed. TDT consists of two stages: contrastive unsupervised pre-training and supervised training. In the contrastive unsupervised training stage, time series to vector (TS2Vec) is used to represent the dynamic trends and unique features of the data. Then, these representations are fed into the distribution Transformer (DT) to fit the hypothetical probability distribution. Experimental results show that the prediction results of the proposed TDT are more accurate and reliable than other benchmark models. In addition, our research indicates reliable probability density predictions provide enterprises with opportunities to control carbon emission costs and increase economic returns, thereby improving the competitiveness of enterprises and promoting carbon emission reduction.
{"title":"Probability density prediction for carbon allowance prices based on TS2Vec and distribution Transformer","authors":"Xuerui Wang, Lin Wang, Wuyue An","doi":"10.1016/j.eneco.2024.107986","DOIUrl":"10.1016/j.eneco.2024.107986","url":null,"abstract":"<div><div>Carbon allowance price is an important tool to reduce carbon emissions and achieve carbon neutrality. It is necessary to establish a predictive model to provide accurate and reliable information to managers and participants in the carbon trading market. Therefore, a novel probability density prediction model, called TS2Vec-based distribution Transformer (TDT), is proposed. TDT consists of two stages: contrastive unsupervised pre-training and supervised training. In the contrastive unsupervised training stage, time series to vector (TS2Vec) is used to represent the dynamic trends and unique features of the data. Then, these representations are fed into the distribution Transformer (DT) to fit the hypothetical probability distribution. Experimental results show that the prediction results of the proposed TDT are more accurate and reliable than other benchmark models. In addition, our research indicates reliable probability density predictions provide enterprises with opportunities to control carbon emission costs and increase economic returns, thereby improving the competitiveness of enterprises and promoting carbon emission reduction.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107986"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660762","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108017
Mahdi Ghaemi Asl , Sami Ben Jabeur , Hela Nammouri , Kamel Bel Hadj Miled
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.
{"title":"Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy","authors":"Mahdi Ghaemi Asl , Sami Ben Jabeur , Hela Nammouri , Kamel Bel Hadj Miled","doi":"10.1016/j.eneco.2024.108017","DOIUrl":"10.1016/j.eneco.2024.108017","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108017"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586987","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108005
Wei Shi , Yue-Jun Zhang , Jing-Yue Liu
This paper provides retrospective enterprise-level evidence on the role of the emissions trading system (ETS) in reducing the energy intensity of China's high‑carbon enterprises. The empirical results indicate several key findings: First, in China's ETS pilot regions, the ETS has significantly reduced high‑carbon enterprises' energy intensity by 22.4 % during the sample period, which means ETS has indeed played an anticipated energy-saving effect in China. Second, the ETS has exerted a signal effect on high‑carbon enterprises outside the pilot regions, which suggests that the actual effectiveness of China's ETS may be higher than initially anticipated. Third, the energy-saving effect of China's ETS can be achieved through green technology innovation and digital transformation. Finally, the effect of China's ETS on energy intensity varies significantly by regional development, industry attributes, enterprise characteristics, and carbon market performance.
{"title":"Investigating the role of emissions trading system in reducing enterprise energy intensity: Evidence from China","authors":"Wei Shi , Yue-Jun Zhang , Jing-Yue Liu","doi":"10.1016/j.eneco.2024.108005","DOIUrl":"10.1016/j.eneco.2024.108005","url":null,"abstract":"<div><div>This paper provides retrospective enterprise-level evidence on the role of the emissions trading system (ETS) in reducing the energy intensity of China's high‑carbon enterprises. The empirical results indicate several key findings: First, in China's ETS pilot regions, the ETS has significantly reduced high‑carbon enterprises' energy intensity by 22.4 % during the sample period, which means ETS has indeed played an anticipated energy-saving effect in China. Second, the ETS has exerted a signal effect on high‑carbon enterprises outside the pilot regions, which suggests that the actual effectiveness of China's ETS may be higher than initially anticipated. Third, the energy-saving effect of China's ETS can be achieved through green technology innovation and digital transformation. Finally, the effect of China's ETS on energy intensity varies significantly by regional development, industry attributes, enterprise characteristics, and carbon market performance.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108005"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660772","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108012
Oguzhan Ozcelebi , Rim El Khoury , Seong-Min Yoon
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.
{"title":"Interplay between renewable energy and fossil fuel markets: Fresh evidence from quantile-on-quantile and wavelet quantile approaches","authors":"Oguzhan Ozcelebi , Rim El Khoury , Seong-Min Yoon","doi":"10.1016/j.eneco.2024.108012","DOIUrl":"10.1016/j.eneco.2024.108012","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108012"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579124","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108011
Yujie Huang , Shucheng Liu , Jiawu Gan , Baoliu Liu , Yuxi Wu
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.
{"title":"How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China","authors":"Yujie Huang , Shucheng Liu , Jiawu Gan , Baoliu Liu , Yuxi Wu","doi":"10.1016/j.eneco.2024.108011","DOIUrl":"10.1016/j.eneco.2024.108011","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108011"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594037","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108016
Lingkang Wang , Yiqu Yang , Dongping Yang , Yaying Zhou
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.
{"title":"Role of supply chain disruptions and digitalization on renewable energy innovation: Evidence from G7 nations","authors":"Lingkang Wang , Yiqu Yang , Dongping Yang , Yaying Zhou","doi":"10.1016/j.eneco.2024.108016","DOIUrl":"10.1016/j.eneco.2024.108016","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108016"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586986","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108013
Kaidi Wan , Bing-Yue Liu , Ying Fan , Svetlana A. Ikonnikova
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。
{"title":"Modelling and assessing dynamic energy supply resilience to disruption events: An oil supply disruption case in China","authors":"Kaidi Wan , Bing-Yue Liu , Ying Fan , Svetlana A. Ikonnikova","doi":"10.1016/j.eneco.2024.108013","DOIUrl":"10.1016/j.eneco.2024.108013","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108013"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573142","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}
Pub Date : 2024-10-29DOI: 10.1016/j.eneco.2024.108007
Lucía Barrachina-Fernández , Francisco Sogorb-Mira
This paper investigates the influence of peer financial choices on the capital structure decisions of European and North American listed companies in the oil and gas sector. It also examines how commodity prices, particularly oil and natural gas prices, and their corporate hedging affect capital structure policies. The findings underscore the existence of peer effects in the oil and gas industry, indicating that companies consider their peers' financial decisions when determining their capital structure. Further analysis reveals that there is significant cross-country heterogeneity in capital structure peer effects conditional on financial and institutional development, and disclosure quality. Additionally, the research highlights that oil and natural gas prices, along with the hedging against these prices exposure, impact the capital structure of oil and gas companies, providing invaluable insights for industry practitioners and policymakers.
{"title":"The influence of peer effects, commodity prices and its hedging on corporate capital structure: Evidence from the oil and gas industry","authors":"Lucía Barrachina-Fernández , Francisco Sogorb-Mira","doi":"10.1016/j.eneco.2024.108007","DOIUrl":"10.1016/j.eneco.2024.108007","url":null,"abstract":"<div><div>This paper investigates the influence of peer financial choices on the capital structure decisions of European and North American listed companies in the oil and gas sector. It also examines how commodity prices, particularly oil and natural gas prices, and their corporate hedging affect capital structure policies. The findings underscore the existence of peer effects in the oil and gas industry, indicating that companies consider their peers' financial decisions when determining their capital structure. Further analysis reveals that there is significant cross-country heterogeneity in capital structure peer effects conditional on financial and institutional development, and disclosure quality. Additionally, the research highlights that oil and natural gas prices, along with the hedging against these prices exposure, impact the capital structure of oil and gas companies, providing invaluable insights for industry practitioners and policymakers.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108007"},"PeriodicalIF":13.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660769","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}
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.
{"title":"Evaluating the energy poverty in the EU countries","authors":"Georgia Makridou , Ken’ichi Matsumoto , Michalis Doumpos","doi":"10.1016/j.eneco.2024.108020","DOIUrl":"10.1016/j.eneco.2024.108020","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108020"},"PeriodicalIF":13.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573143","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}
Pub Date : 2024-10-28DOI: 10.1016/j.eneco.2024.108019
Jie Yang , Yun Feng , Hao Yang
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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