Energy Economics最新文献

{"title":"A climate stress testing exercise on loans to European small and medium enterprises","authors":"Yujia Chen ,&nbsp;Zhenghong Ding ,&nbsp;Luca Barbaglia ,&nbsp;Raffaella Calabrese ,&nbsp;Serena Fatica","doi":"10.1016/j.eneco.2026.109177","DOIUrl":"10.1016/j.eneco.2026.109177","url":null,"abstract":"<div><div>We develop a micro-level climate stress testing framework to evaluate the financial performance of small business loans under diverse climate scenarios. Focusing on European small and medium-sized enterprises (SMEs), we estimate the impact of coastal, flash, and river floods on loan default risk using a discrete-time survival model. Our analysis reveals that flood events significantly increase SME loan default probabilities in countries such as Spain and France. However, this effect is notably reduced in regions with strong infrastructure or effective support mechanisms. To complement the empirical findings, we conduct a forward-looking stress testing exercise that projects default probability trajectories under varying flood severity scenarios. This approach enables financial institutions and regulators to quantify the loan-level credit risk associated with climate-related flooding, offering valuable insights for risk management and policy design.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109177"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135459","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":"Special issue of energy economics: Sustainable governance and energy transition","authors":"Renatas Kizys ,&nbsp;Ramzi Benkraiem ,&nbsp;Panagiotis Tzouvanas","doi":"10.1016/j.eneco.2026.109204","DOIUrl":"10.1016/j.eneco.2026.109204","url":null,"abstract":"<div><div>This Special Issue on <em>Sustainable Governance and the Energy Transition</em> brings together 14 papers that examine the ways in which corporate governance, financial markets, public policy, and social factors shape the energy transition. Across a diverse set of contexts, the papers examine the impact of sustainable finance, corporate decision-making, and policy interventions on the low-carbon energy transition. Several papers show that climate transition risks are increasingly reflected in credit and equity markets, although the extent and mechanisms of this incorporation vary across countries and economic regimes. Strong governance emerges as an important condition for translating sustainable finance initiatives and divestment strategies into meaningful changes in investment behaviour. Other papers demonstrate that market-based climate policies, incentives for electric vehicles, and financial innovations driven by digital technologies can accelerate shifts in energy production and consumption, particularly when supported by credible and stable institutions. At the household and community level, the evidence points to the importance of perceptions of affordability, fairness, and institutional trust in shaping public acceptance and behavioural responses to the energy transition. These findings emphasise that governance acts as a critical enabling framework for the energy transition, influencing how technologies are adopted, how risks and costs are distributed, and how benefits are shared. Strengthening sustainable governance across financial markets, firms, public institutions, and local communities is therefore fundamental to achieving a low-carbon energy transition that is effective, resilient, and socially inclusive.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109204"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208844","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":"The role of socially responsible firms and government subsidies in the sustainability transition: A multi-objective game","authors":"Lijue Lu,&nbsp;Fouad Ben Abdelaziz","doi":"10.1016/j.eneco.2026.109198","DOIUrl":"10.1016/j.eneco.2026.109198","url":null,"abstract":"<div><div>This paper develops a multi-stage game-theoretic model to examine how different institutional mechanisms influence firms’ incentives to invest in sustainability. In the base model, two competing firms decide first whether to join a sustainability initiative. Then, participating firms choose both their production quantities and levels of sustainability effort. Sustainability investments generate a green premium, reflecting consumers’ willingness to pay for environmentally responsible products, but also produce spillover effects that benefit competitors. We further analyse two contrasting scenarios. In Scenario <span><math><mi>G</mi></math></span>, a government offers subsidies to incentivize participation and align private actions with social goals. In Scenario <span><math><mi>R</mi></math></span>, firms are intrinsically motivated by corporate responsibility and pursue both profit and environmental performance. Using the <span><math><mi>ϵ</mi></math></span>-constraint method, we characterize the Pareto frontier for each scenario compare the results. Our findings reveal that well-calibrated subsidies can overcome free-rider problems and coordinate firms toward higher joint performance, while voluntary participation alone may lead to inefficient outcomes. However, at high levels of environmental ambition, both mechanisms converge in effectiveness. The results offer policy and managerial insights on how to design incentive systems that support industry-wide sustainability transitions.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109198"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208847","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":"Will hydrogen prolong the price coupling between gas and electricity?","authors":"Martin Densing ,&nbsp;Yi Wan ,&nbsp;Evangelos Panos ,&nbsp;Tom Kober ,&nbsp;Thomas J. Schmidt ,&nbsp;Russell McKenna","doi":"10.1016/j.eneco.2026.109221","DOIUrl":"10.1016/j.eneco.2026.109221","url":null,"abstract":"<div><div>Currently, market prices of methane gas influence prices of electricity. In future energy markets, the influence may be bidirectional via the energy carrier hydrogen, which can be produced both from electricity and methane. To investigate how future hydrogen deployment may affect market prices, we develop a numerical model of coupled spot markets of electricity, methane gas, and hydrogen, where wholesale hydrogen is assumed to be traded on an hourly market with elastic demand in an equilibrium model with conjectural variations. While our numerical study is stylized, input data reflects the Central Western Europe region, and we consider an exogenous energy-systems’ capacity expansion scenario in years 2040 and 2050 under a stringent climate policy. We consider price changes induced by switching off electrolyzers and hydrogen storage, and by increasing import gas prices. While electrolyzers lower hydrogen prices and increase electricity prices, they also increase electricity price volatility in our model because peak-demand periods of electricity and hydrogen may intersect. Storage of hydrogen barely reduces the short-term volatility of electricity prices. By allowing hydrogen production other than through electrolyzers, price influences become complex: for example, hydrogen storage favors mostly steam-methane reforming, and electrolyzers amplify electricity price hikes under gas import price hikes. Hence, future market designs for promoting renewables may consider guidelines on the usage of hydrogen storage and may closely monitor the marginal technologies that determine the prices on the hydrogen and electricity markets.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109221"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778889","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":"The role of artificial intelligence in global green energy products trade pattern evolution: Based on the international trade network perspective","authors":"Gang Wu ,&nbsp;Xiaomin Liu ,&nbsp;Yu Zhao","doi":"10.1016/j.eneco.2026.109210","DOIUrl":"10.1016/j.eneco.2026.109210","url":null,"abstract":"<div><div>Artificial intelligence (AI) technology has been extensively applied in green technological innovation and green energy system, effectively facilitating production and consumption of green energy. However, the role of AI in shaping the green energy products trade pattern remains understudied. From the perspective of the international trade networks, this study constructs the global green energy trade networks (GGETNs) and global industrial robot trade networks (GIRTNs) representing AI interaction among economies from 2010 to 2022, analyzing their evolution of network structure and node importance. Using the temporal exponential random graph model (TERGM), this study explores the influencing mechanism of AI on the evolution of the GGETNs. The research findings suggest that (1) Although most structural indicators of GGETNs exceed those of GIRTNs, their evolution trends and growth directions demonstrate remarkable similarities. Trade reciprocity and trade clique among economies play important roles in their evolution. Both GGETNs and GIRTNs gradually exhibit “China-the United States-Germany” trade pattern, emerging markets and developing economies play pivotal bridging roles in GGETNs, with Ireland and South Africa as key connectors in GIRTNs. China, Japan, and the United States wield more significant trade influence on both networks. (2) AI trade interaction among economies significantly promotes network evolution, reciprocal trade and trade clique of GGETNs. Economies with higher trade strength and stronger central influence for industrial robot products are more likely to develop green energy products trade, and economies with higher trade strength and stronger central influence are more likely to engage in reciprocal trade and trade clique. The GGETNs exhibit strong evolutionary stability when embedded in the GIRTNs. (3) Heterogeneity tests demonstrate that the promoting effect of AI on GGETNs' evolution is more pronounced in emerging markets and developing economies. These findings carry significant implications for harnessing AI to accelerate the global green energy transition.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109210"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778888","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 to sustainably spur R&D of energy storage? Policy options and combinations","authors":"Haowei Jin ,&nbsp;Ju-e Guo ,&nbsp;Yanzhao Li ,&nbsp;Shouyang Wang","doi":"10.1016/j.eneco.2026.109226","DOIUrl":"10.1016/j.eneco.2026.109226","url":null,"abstract":"<div><div>Governmental incentives (e.g. production subsidy, R&amp;D subsidy, and tax benefit) could spur the research and development (R&amp;D) of energy storage enterprises (ESEs), thereby reducing the cost of energy storage. However, long-term high-intensity incentives may increase fiscal burden and disrupt the sustainability of incentive policies. We develop a compound real option model to identify and compare the influence mechanisms and consequences of individual or combined incentives on ESEs’ R&amp;D decisions, and further explore the effects of different incentives on fiscal burden. The main conclusions are as follows. (1) Production subsidy and R&amp;D subsidy can promote early start of R&amp;D, while tax benefit does the opposite. R&amp;D subsidy leads to longer duration of R&amp;D and faster cost reduction than other incentives. (2) With increasing incentive intensity, R&amp;D subsidy enables faster cost reduction and more investment revenue than production subsidy and tax benefit, thereby increasing ESE’s total taxes and reducing governmental fiscal burden. (3) Incentive combinations bring longer duration of R&amp;D and more social welfare, but increase fiscal burden and fail to spur early start of R&amp;D. There are also differences in the effects of these incentive combinations. (4) Simulations of dynamic changes in incentives for China and the United States reveal that China’s approach yields longer duration of R&amp;D and faster cost reduction, but with bigger fiscal burden. We finally discuss how governments select reasonable incentive policies and combinations according to regional endowments.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"156 ","pages":"Article 109226"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147412545","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":"The impact of solar panel installation on electricity consumption and production: A firm’s perspective","authors":"Natalia D’Agosti ,&nbsp;Facundo Danza","doi":"10.1016/j.eneco.2026.109202","DOIUrl":"10.1016/j.eneco.2026.109202","url":null,"abstract":"<div><div>Since 2010, the Uruguayan government has fostered the installation of solar panels among firms to promote the production of small-scale renewable electricity. Under this policy, firms that have installed solar panels are allowed to feed any surplus electricity into the grid. Using electricity consumption and grid injection data from every firm that installed a microgenerator between 2011 and 2022, we study the economic and environmental consequences of this policy. First, we find that installing a solar panel reduces the amount of electricity extracted from the grid. Second, we find that it increases the electricity injected into the grid. Third, we find that it reduces CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions only marginally. Fourth, we provide evidence of a rebound effect, which ranges from 18% to 20%. Lastly, we propose an alternative policy that allows firms to store their excess electricity in batteries rather than immediately injecting it into the grid. This policy would further reduce CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions by 2.7%, incentivizing the injection of electricity at night, when fossil-fuel-based facilities meet the demand at the margin. Our results highlight the importance of integrating storage solutions into renewable energy policy design.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"156 ","pages":"Article 109202"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778366","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":"Unraveling environmental threads: Bitcoin prices, energy consumption, and crypto market volatility","authors":"Nishant Sapra ,&nbsp;Imlak Shaikh ,&nbsp;David Roubaud","doi":"10.1016/j.eneco.2026.109216","DOIUrl":"10.1016/j.eneco.2026.109216","url":null,"abstract":"<div><div>Bitcoin has evolved into a major decentralized asset, yet its escalating electricity consumption has raised concerns. Because mining is profit-driven, higher prices intensify computational activity and energy demand, directly linking market dynamics to electricity use. This study empirically investigates the dynamic relationships among Bitcoin electricity consumption, Bitcoin prices, and cryptocurrency market volatility across time and frequency domains, complemented by nonlinear causal assessment. The study applies Biwavelet Coherence, Partial-Wavelet Transform Coherence, and Diks &amp; Panchenko techniques to 1761 daily observations from March 2020 to January 2025. The frequency-domain analysis indicates that electricity consumption leads Bitcoin prices in the long run, whereas the nonlinear causality results reveal bidirectional feedback between the two variables. Collectively, these findings suggest that Bitcoin's energy use and its price influence each other over time, forming a feedback loop rather than operating in isolation. Further, Bitcoin's electricity consumption has bidirectional nonlinear causality with cryptocurrency volatility. The findings suggest that Bitcoin's electricity consumption is more sensitive to cryptocurrency market sentiment and geopolitical risk than to traditional economic and trade policy uncertainties, implying that behavioural and geopolitical indicators may offer more informative signals for monitoring fluctuations in energy consumed within the Bitcoin network. Given the linkage between Bitcoin prices and electricity consumption, policymakers may consider upper and lower trading circuits on cryptocurrency exchanges to moderate price-driven surges in mining-related energy demand. Policymakers may also consider cryptocurrency market volatility, investor sentiment, and geopolitical risk indicators for monitoring fluctuations in Bitcoin's electricity consumption, given its observed sensitivity to these factors.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"156 ","pages":"Article 109216"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147278909","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":"Corrigendum to ‘Comparison between inclusive finance and green finance in alleviating energy poverty and the mediating role of energy structure’ [Energy Economics Volume 147 June 2025 108597]","authors":"Meirui Zhong ,&nbsp;Ti Zhou ,&nbsp;Qingtian Wu","doi":"10.1016/j.eneco.2026.109188","DOIUrl":"10.1016/j.eneco.2026.109188","url":null,"abstract":"","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109188"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146253","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":"Artificial intelligence and green development: Evidence from China on efficiency and equity","authors":"Tielong Wu","doi":"10.1016/j.eneco.2026.109201","DOIUrl":"10.1016/j.eneco.2026.109201","url":null,"abstract":"<div><div>This study investigates the impact of Artificial Intelligence (AI) policy on urban green development, focusing on both efficiency and equity perspectives. We first apply a green growth model to analyze the green development. Then, using panel data from 286 cities in China between 2000 and 2023, we assess the Green Development Efficiency (GDE) of cities through Data Envelopment Analysis and analyze the impact of AI policy on efficiency with a Difference-in-Differences model. Finally, we do convergence analysis to explore the equity of green development across cities. The results show that AI policies lead to a “dividend” in the form of enhanced overall GDE, driven by improvements in technical and scale efficiency, increased patent outputs, and urban agglomeration. However, the study also reveals a “divide”, as the benefits are not equally distributed across cities. Convergence analysis reveals no global convergence across cities but identifies six convergent clubs and one divergent club. Ordered probit analysis shows that AI policies do not significantly affect a city's likelihood of transitioning between clubs. In the meantime, regional heterogeneity is observed, with the positive impact of AI policy being more pronounced in eastern regions, while smaller improvements are observed in central and western regions. Negative spillover effects are also found in cities within a 50 km radius of AI policy, where GDE decreases due to resource competition. While AI policies improve efficiency, they do not promote equitable green development. Future AI policies should focus on a more equitable green transition.</div></div>","PeriodicalId":11665,"journal":{"name":"Energy Economics","volume":"155 ","pages":"Article 109201"},"PeriodicalIF":14.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385405","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}