Energy Economics最新文献

{"title":"Renewable energy production across U.S. states: Convergence or divergence?","authors":"James E. Payne ,&nbsp;James W. Saunoris ,&nbsp;Saban Nazlioglu ,&nbsp;Russell Smyth","doi":"10.1016/j.eneco.2024.108015","DOIUrl":"10.1016/j.eneco.2024.108015","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 108015"},"PeriodicalIF":13.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive capacity to climate change: Does energy aid matter?","authors":"Rabindra Nepal ,&nbsp;Yang Liu ,&nbsp;Kangyin Dong","doi":"10.1016/j.eneco.2024.108018","DOIUrl":"10.1016/j.eneco.2024.108018","url":null,"abstract":"<div><div>The global call for augmented financial support to satisfy the growing requirements for adaptation funding, essential for enabling vulnerable groups to withstand the ramifications of climate change, has been resonant. Channeling energy assistance to developing countries may serve to ameliorate this shortfall. Through an empirical analysis using a balanced panel dataset comprising 64 countries from 2002 to 2020, the research investigates the role of energy assistance in enhancing the adaptive capacities of developing nations. Our analysis identifies critical areas with pronounced deficiencies in adaptive capacity, primarily situated in sub-Saharan Africa, South Asia, Central Asia, and western South America. The study reveals that energy aid, notably in the forms of non-renewable, policy-related, and distribution assistance, significantly elevates adaptive capabilities. Although this assistance yields significant benefits for countries across various income levels, the impact is relatively greater for higher-income nations. Additionally, energy aid indirectly bolsters adaptive capacity by stimulating innovation, and an improvement in the quality of governance aids in enhancing the effectiveness of energy assistance implementation, especially for lower-income countries. The study concludes by offering nuanced policy insights aimed at donors and recipients alike, with the goal of augmenting the efficacy of aid to improve climate change adaptation.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"141 ","pages":"Article 108018"},"PeriodicalIF":13.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electricity storage or transmission? Comparing social welfare between electricity arbitrages","authors":"Chihiro Yagi ,&nbsp;Kenji Takeuchi","doi":"10.1016/j.eneco.2024.107969","DOIUrl":"10.1016/j.eneco.2024.107969","url":null,"abstract":"<div><div>Electricity storage and inter-regional transmission are expected to play a greater role in mitigating the power surplus caused by the large-scale introduction of solar power generation. In this study, we evaluate the impacts of these two power arbitrages and provide their welfare implications. We develop a simple analytical framework based on the demand and supply in the power market, and apply the framework to Kyushu area in Japan to quantify the social benefits of current storage and transmission practice. We estimate electricity demand curves and define supply curves from the data to describe the social impacts of the two arbitrages. Our main findings can be summarized as follows. First, the estimation results indicate that the price elasticity of electricity demand is <span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>228</mn></mrow></math></span> and <span><math><mrow><mo>−</mo><mn>0</mn><mo>.</mo><mn>252</mn></mrow></math></span> in high and low solar hours, respectively. Second, the results show that the current storage and transmission operations provide social benefits of 73,000 and 59,000 USD per day, respectively. Third, both arbitrages lead to external benefits by reducing CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions from thermal power generation. These results suggest that the current storage and transmission operations provide positive social benefits without detrimental effects on consumer, producer or environment. Therefore, both storage and transmission are preferable measures for mitigating the impact of variable solar power generation on society and the environment.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"140 ","pages":"Article 107969"},"PeriodicalIF":13.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probability density prediction for carbon allowance prices based on TS2Vec and distribution Transformer","authors":"Xuerui Wang,&nbsp;Lin Wang,&nbsp;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}

{"title":"Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy","authors":"Mahdi Ghaemi Asl ,&nbsp;Sami Ben Jabeur ,&nbsp;Hela Nammouri ,&nbsp;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}

{"title":"Investigating the role of emissions trading system in reducing enterprise energy intensity: Evidence from China","authors":"Wei Shi ,&nbsp;Yue-Jun Zhang ,&nbsp;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}

{"title":"Interplay between renewable energy and fossil fuel markets: Fresh evidence from quantile-on-quantile and wavelet quantile approaches","authors":"Oguzhan Ozcelebi ,&nbsp;Rim El Khoury ,&nbsp;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}

{"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 ,&nbsp;Shucheng Liu ,&nbsp;Jiawu Gan ,&nbsp;Baoliu Liu ,&nbsp;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&amp;D expenditure. Finally, this paper puts forward policy suggestions such as formulating differentiated policies, building innovation platforms, enhancing R&amp;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}

{"title":"Role of supply chain disruptions and digitalization on renewable energy innovation: Evidence from G7 nations","authors":"Lingkang Wang ,&nbsp;Yiqu Yang ,&nbsp;Dongping Yang ,&nbsp;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}

{"title":"Modelling and assessing dynamic energy supply resilience to disruption events: An oil supply disruption case in China","authors":"Kaidi Wan ,&nbsp;Bing-Yue Liu ,&nbsp;Ying Fan ,&nbsp;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}