Journal of Climate Finance最新文献

Enhancing sustainable development in West Africa despite uncertainty: The transformative potential of green financing 在不确定的情况下促进西非的可持续发展：绿色融资的变革潜力

Journal of Climate Finance Pub Date : 2025-09-11 DOI: 10.1016/j.jclimf.2025.100074

Joshua Adeyemi Afolabi

引用次数: 0

Lending over the technology lifecycle: Strategies for information search by banks in renewable energy project finance 技术生命周期内的贷款：可再生能源项目融资中银行信息搜索策略

Journal of Climate Finance Pub Date : 2025-09-05 DOI: 10.1016/j.jclimf.2025.100073

Anurag Gumber , Bjarne Steffen

引用次数: 0

Structural changes and volatility forecasting in China’s carbon markets: A hybrid approach integrating GARCH-SVR with LASSO-weighted windows method 中国碳市场结构变化与波动性预测：GARCH-SVR与lasso加权窗口法的混合方法

Journal of Climate Finance Pub Date : 2025-08-15 DOI: 10.1016/j.jclimf.2025.100072

Gaoxiu Qiao , Jinghui Wang , Feipeng Zhang , Yijun Pan

{"title":"Structural changes and volatility forecasting in China’s carbon markets: A hybrid approach integrating GARCH-SVR with LASSO-weighted windows method","authors":"Gaoxiu Qiao , Jinghui Wang , Feipeng Zhang , Yijun Pan","doi":"10.1016/j.jclimf.2025.100072","DOIUrl":"10.1016/j.jclimf.2025.100072","url":null,"abstract":"<div><div>As a newly established and rapidly developing financial market, China’s carbon markets are highly susceptible to structural changes driven by policy adjustments, external shocks, and immature trading mechanisms, which pose critical challenges for volatility forecasting. This study focuses on volatility forecasting of China’s carbon markets, proposing a novel hybrid forecasting approach to address these challenges. We first apply the hybrid GARCH-SVR method that combines the strengths of GARCH models in capturing volatility clustering with SVR’s ability to model nonlinear dynamics. To address model uncertainty arising from structural changes, we further develop a LASSO-weighted window method, where weights of GARCH-SVR forecasts across different windows are determined via LASSO regression. Empirical results show that LASSO-weighted window approach outperforms standalone GARCH models and GARCH-SVR in forecasting accuracy. Robustness tests with alternative windows and volatility proxies confirm this superiority. Importantly, economic value evaluation based on portfolio performance demonstrates that our method enhances asset allocation efficiency, providing practical guidance for carbon asset management.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"12 ","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Public climate concern and policy shifts: Understanding the joint impact on carbon prices in China 公众气候关注与政策转变：了解对中国碳价的共同影响

Journal of Climate Finance Pub Date : 2025-08-08 DOI: 10.1016/j.jclimf.2025.100071

Haoxi Chen

引用次数: 0

What determines the price of carbon? New evidence from phase III and IV of the EU ETS 是什么决定了碳的价格？来自EU ETS第三和第四阶段的新证据

Journal of Climate Finance Pub Date : 2025-07-21 DOI: 10.1016/j.jclimf.2025.100070

Bente Dittmann , Tobias Lauter , Marcel Prokopczuk , Philipp Sibbertsen

引用次数: 0

Extreme temperatures and the profitability of large European firms 极端气温和欧洲大公司的盈利能力

Journal of Climate Finance Pub Date : 2025-05-08 DOI: 10.1016/j.jclimf.2025.100068

Gian Pietro Bellocca , Pilar Poncela , Esther Ruiz

引用次数: 0

Did we open the flood gates? climate risk and infrastructure loans probability of default 我们打开闸门了吗？气候风险与基础设施贷款违约概率

Journal of Climate Finance Pub Date : 2025-04-30 DOI: 10.1016/j.jclimf.2025.100066

Abderrahim Assab

{"title":"Did we open the flood gates? climate risk and infrastructure loans probability of default","authors":"Abderrahim Assab","doi":"10.1016/j.jclimf.2025.100066","DOIUrl":"10.1016/j.jclimf.2025.100066","url":null,"abstract":"<div><div>I provide a novel approach to estimating asset-level expected damage from flooding for 952 airports, ports, and power plants globally. This study contributes to the understanding of climate risks in infrastructure finance by focusing on the impact of flood damage on loan default probabilities—a critical aspect for investors and policymakers managing climate adaptation in high-risk areas. Using multivariate regression models with sectoral and geographic controls, I find that the expected damage from flood increases the probability of default on infrastructure project finance loans and that the presence of stringent flood adaptation standards decreases it. A standard deviation increase in the expected damage from flood increases the probability of default by one percent, while the presence of enforced flood adaptation standards leads to a 4 percent decrease in the probability of default. I find that the effect of expected damage from floods is higher for long-maturity loans, as well as projects including financial risk mitigation mechanisms such as Power Purchase Agreements. I also find that flood adaptation standards decrease probability of default only when these are enforced. The presence of non-enforced flood management policies leads to an increase in probability of default. These findings have important implications for project finance as an instrument to finance infrastructure and infrastructure as a distinct financial asset class.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"11 ","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Greenhouse gas emissions and bank lending in Japan 日本的温室气体排放和银行贷款

Journal of Climate Finance Pub Date : 2025-04-17 DOI: 10.1016/j.jclimf.2025.100067

Koji Takahashi , Junnosuke Shino

引用次数: 0

Spillovers between hydrogen, nuclear, and AI sectors: The impact of climate policy uncertainty and geopolitical risks 氢能、核能和人工智能部门之间的溢出效应：气候政策不确定性和地缘政治风险的影响

Journal of Climate Finance Pub Date : 2025-03-26 DOI: 10.1016/j.jclimf.2025.100065

Adnan Aslam

{"title":"Spillovers between hydrogen, nuclear, and AI sectors: The impact of climate policy uncertainty and geopolitical risks","authors":"Adnan Aslam","doi":"10.1016/j.jclimf.2025.100065","DOIUrl":"10.1016/j.jclimf.2025.100065","url":null,"abstract":"<div><div>This study investigates the spillover effects between hydrogen energy, nuclear energy, and artificial intelligence (AI) sectors in the context of the global clean energy transition, with a particular focus on the impact of climate policy uncertainty (CPU) and geopolitical risks (GPR). Employing the TVP-VAR extended joint connectedness approach, the findings show a high connectedness that indicates significant spillovers among these sectors. Hydrogen energy emerges as a dominant transmitter of shocks, reflecting its sensitivity to regulatory changes and fluctuating demand. However, nuclear energy acts as a stabilising force that offers hedging opportunities and resilience against market turbulence. The AI sector exhibits strong connectedness, primarily as a net receiver of shocks, driven by its dependency on clean energy sources and vulnerability to energy market volatility. Using the GARCH-MIDAS framework, the study identifies a temporal asymmetry in market responses to CPU and GPR. CPU triggers immediate but short-lived disruptions, while GPR induces delayed yet persistent effects that intensify cross-sector spillovers over time. These results underline the vulnerabilities of sectors reliant on regulatory clarity and geopolitical stability. This study provides practical insights for investors, policymakers, technology, and energy companies to better manage systemic risks at the crossroads of clean energy, technological innovation, and uncertainty.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"11 ","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic forecasting of transition towards sustainable energy production 向可持续能源生产过渡的概率预测

Journal of Climate Finance Pub Date : 2025-03-01 DOI: 10.1016/j.jclimf.2025.100063

Victor E. Gluzberg , Yuri A. Katz

{"title":"Probabilistic forecasting of transition towards sustainable energy production","authors":"Victor E. Gluzberg , Yuri A. Katz","doi":"10.1016/j.jclimf.2025.100063","DOIUrl":"10.1016/j.jclimf.2025.100063","url":null,"abstract":"<div><div>It is observed that cumulative production of electricity by renewable technologies increases exponentially with time, while the unit cost exponentially decreases. However, the exponential growth of <em>annual</em> production even by a sustainable technology will eventually flatten due to saturation of the global market. This should result in a linear growth of <em>cumulative</em> production with a passage of time. To capture this qualitative picture, we introduce the novel model of cumulative production by a sustainable technology with negative feedback in the different form than in the traditional logistic model. To account for irregularity of technological progress, we utilize the method of cumulant expansion that allows to make probabilistic forecasts for the unit cost, which do not depend on the specific model of underlying stochastic processes and a scenario of the future production growth. The derived results demonstrate that during the period of initial exponential growth of production, the effective rate of decrease of the unit cost is slightly lower than its average value. The respective confidence interval is growing linearly with the square root of the forecasting horizon. In the long run, the model of sustainable production forecasts the power law decline of the expected unit cost with time. In this regime, the forecasting error of the logarithm of the unit cost is slowly growing as the square root of the logarithm of the forecasting horizon. To illustrate the method, we make probabilistic forecasts for prices of solar photovoltaic modules up to 2060.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100063"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0