Journal of Climate Finance最新文献_第3页

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 : 2025-06-01 Epub 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 : 2025-06-01 Epub 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":"2025-06-01","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

Portfolio selection from risk transfer mechanisms in a time of crisis for renewable energy markets 可再生能源市场危机时期风险转移机制下的投资组合选择

Journal of Climate Finance Pub Date : 2025-03-01 Epub Date: 2024-12-28 DOI: 10.1016/j.jclimf.2024.100059

Yu-Ann Wang , Chia-Lin Chang

{"title":"Portfolio selection from risk transfer mechanisms in a time of crisis for renewable energy markets","authors":"Yu-Ann Wang , Chia-Lin Chang","doi":"10.1016/j.jclimf.2024.100059","DOIUrl":"10.1016/j.jclimf.2024.100059","url":null,"abstract":"<div><div>This study investigates risk transmission in financial markets, focusing on investor’s hedging decisions and how risk moves between renewable and fossil fuel energy assets within energy ETFs during the Global Financial Crisis (GFC) and the COVID-19 pandemic. A novel approach is introduced to assess how the volatility of a single energy asset affects the risk of an entire energy portfolio, providing valuable insights for policymakers, investors, and energy producers in managing financial risk. The analysis focuses on three major renewable energy ETFs (solar, wind, and hydro) and three major fossil fuel energy ETFs (oil, coal, and natural gas). During the COVID-19 crisis, asset combinations like (solar, coal), and (wind, coal), were found to effectively minimize losses. Although not ideal for solar-related risks, the (solar, oil) combination proved advantageous, particularly oil-related shocks. The study also finds that combining solar with oil and wind with oil was effective in mitigating losses during the GFC and before the COVID-19 pandemic. In non-crisis periods, asset combinations such as (solar, oil) or (solar, coal) offer robust risk management strategies. This research highlights the interconnectedness of energy assets and the importance of using crisis-specific forecasting models, which significantly improve forecasting accuracy. Further research could explore similar impacts from events like the Russia-Ukraine war, which could affect energy markets.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100059"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135553","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

Corporate carbon emission effectiveness and corporate green transformation: Based on a quasi-natural experiment from China 企业碳排放有效性与企业绿色转型——基于中国的准自然实验

Journal of Climate Finance Pub Date : 2025-03-01 Epub Date: 2024-12-27 DOI: 10.1016/j.jclimf.2024.100056

Xin Yun, Yang Hu

{"title":"Corporate carbon emission effectiveness and corporate green transformation: Based on a quasi-natural experiment from China","authors":"Xin Yun, Yang Hu","doi":"10.1016/j.jclimf.2024.100056","DOIUrl":"10.1016/j.jclimf.2024.100056","url":null,"abstract":"<div><div>Green finance policies serve as a critical driver for the green transformation of enterprises, significantly influencing the environmental decision-making of corporate leaders. Publicly listed companies disclose carbon reduction data through various channels, including corporate social responsibility reports, sustainability reports, and corporate bylaws. This aggregation of information enhances market transparency and strengthens the interconnectedness between listed companies and the market, thereby enriching the external information environment for these firms. Such data dissemination facilitates the implementation of incentive-compatible market governance mechanisms within companies, thereby promoting their green transformation efforts. This study employs the Carbon Emission Disclosure (Cid) and a Difference-in-Differences (DID) model to examine the impact of carbon reduction measures on the green transformation of listed companies before and after the establishment of green finance pilot zones, utilizing data from 2008 to 2021. We analyze how these enhance corporate governance, increase media attention, stimulate government research and development investments, and ultimately influence the green innovation and overall productivity of these enterprises. Our findings indicate that the effects of these processes depend on factors such as the degree of marketization within the industry, company size, and geographic location. Empirical results provide robust support for the implementation of green finance pilot zone policies in China and offer policy recommendations for publicly listed companies to enhance energy conservation, emission reduction, and green development.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135554","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

Nonlinear interaction of climate risk and stock market 气候风险与股票市场的非线性相互作用

Journal of Climate Finance Pub Date : 2025-03-01 Epub Date: 2024-12-22 DOI: 10.1016/j.jclimf.2024.100055

Xin Xu , Haizhong An , Brian M. Lucey , Shupei Huang

{"title":"Nonlinear interaction of climate risk and stock market","authors":"Xin Xu , Haizhong An , Brian M. Lucey , Shupei Huang","doi":"10.1016/j.jclimf.2024.100055","DOIUrl":"10.1016/j.jclimf.2024.100055","url":null,"abstract":"<div><div>This paper investigates both the direct and indirect pathways through which climate change risks influence the fossil energy stock market in China, focusing on the mediating effect of investor attention and the moderation effect of the crude oil market. Utilizing China’s daily climate risk data and energy stock return data from September 4, 2017, to June 30, 2022, we employ the partial linear function coefficient model, zero-inflated negative binomial regression, and the Bootstrap technique to unravel these complex relationships. Our analysis reveals pivotal findings: (1) Climate transition risk has a U-shaped nonlinear direct effect on fossil energy stock returns, with a critical inflection point identified at 0.3364. This risk also positively mediates the return rate by shaping investor attention. Notably, exceeding a risk threshold of 0.3 intensifies the adverse impact of oil price volatility on returns. (2) Climate physical risk does not exert a discernible direct effect or mediation on the return rate of fossil energy stocks. These insights offer a comprehensive perspective on the complex interplay between climate change risk and China’s fossil energy stock market, shedding light on the nonlinear dynamics that govern these relationships. From a practical perspective, these findings underscore the need for policymakers to design risk mitigation strategies tailored to transition risks, while investors should remain vigilant to oil price volatility when climate risk exceeds critical thresholds.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100055"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135556","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

Generating shareholder value through the announcement of sustainability-linked bond issuance 通过宣布发行与可持续发展挂钩的债券，为股东创造价值

Journal of Climate Finance Pub Date : 2025-03-01 Epub Date: 2024-12-18 DOI: 10.1016/j.jclimf.2024.100057

Jannis Poggensee

引用次数: 0

Dynamic connectedness and portfolio strategies: Insights from fintech, robotics, renewable energy, and green bonds in China 动态连接和投资组合策略：来自中国金融科技、机器人、可再生能源和绿色债券的见解

Journal of Climate Finance Pub Date : 2025-03-01 Epub Date: 2025-01-04 DOI: 10.1016/j.jclimf.2025.100060

Nader Naifar

{"title":"Dynamic connectedness and portfolio strategies: Insights from fintech, robotics, renewable energy, and green bonds in China","authors":"Nader Naifar","doi":"10.1016/j.jclimf.2025.100060","DOIUrl":"10.1016/j.jclimf.2025.100060","url":null,"abstract":"<div><div>This study investigates the dynamic interactions between fintech, robotics, renewable energy, and green bonds in China's market, focusing on sectoral spillovers, their determinants, and optimal portfolio strategies. While prior research has often examined these sectors in isolation, their interconnected dynamics within China's green finance framework remain underexplored. Employing an extended joint connectedness approach and quantile regression techniques, this study analyzes the interdependencies among these sectors and evaluates the influence of macroeconomic factors, including Chinese sovereign credit risk, the Yuan-Dollar exchange rate, and global volatility indices. The findings highlight fintech's fundamental role in return transmission and the robotics sector's pronounced sensitivity to external shocks. Moreover, the study demonstrates the feasibility of a minimum connectedness portfolio strategy, which optimizes investment outcomes by leveraging reduced intersectoral correlations during environmental policy shifts. These insights offer valuable guidance for investors, policymakers, and financial advisors, emphasizing the strategic importance of fostering cross-sectoral collaboration to drive technological advancement, job creation, and environmental sustainability in China's market.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135552","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

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 Epub Date: 2025-02-22 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

Rural credit market imperfection in financing climate change adaptation: Evidence from Pakistan 农村信贷市场在适应气候变化融资中的不完善：来自巴基斯坦的证据

Journal of Climate Finance Pub Date : 2025-03-01 DOI: 10.1016/j.jclimf.2025.100062

Muhammad Nawaz , Misak Avetisyan

{"title":"Rural credit market imperfection in financing climate change adaptation: Evidence from Pakistan","authors":"Muhammad Nawaz , Misak Avetisyan","doi":"10.1016/j.jclimf.2025.100062","DOIUrl":"10.1016/j.jclimf.2025.100062","url":null,"abstract":"<div><div>With the increasing negative consequences of climate change on agriculture, adaptation has emerged as a viable alternative to mitigation. Adaptation strategies for farmers heavily dependent on availability of credit financing from formal, semi-formal and informal lenders. However, there has been limited access to the credit for adaptation because of inefficiency and inequity in credit markets, caused mostly by the variations in farmer’s socio-economic status. Therefore, in this study we analyze the inefficiency and inequity measures of credit market imperfection by utilizing field data of 400 wheat growing farmers from various agro-ecological zones in Pakistan. In this study we find presence of imperfection in credit markets for financing climate change adaptation strategies. Only 36 % and 37 % of farmers have access to loans from formal (banks) and semi-formal lenders (MFIs), respectively, while 95 % of farmers have access to credit from informal lenders contributing to inequity issues in credit markets. The findings further suggest that 75 % of farmers use ‘personal security’ for loans and travel from far-off areas to get credit which may strengthen the imperfection of credit markets in allocating and financing loans for climate vulnerable farmers. Climate vulnerable farmers mostly use the climate and non-climate adaptation strategies that include ‘better and expensive seed’ (57 %) and the ‘use of tractor’ (83 %). The results of our regression analysis suggest that marginal and small farmers have limited access to credit from all types of lenders, which negatively affects their ability to invest in climate change adaptation strategies. Application cost (registration and trip to lenders) reduces the access to credit and ability to invest in climate adaptations. Finally, credit market imperfection can be minimized for climate vulnerable farmers through direct provision of agricultural inputs at local markets.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548875","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

How important are climate change risks for predicting clean energy stock prices? Evidence from machine learning predictive modeling and interpretation 气候变化风险对预测清洁能源股票价格有多重要？来自机器学习预测建模和解释的证据

Journal of Climate Finance Pub Date : 2025-03-01 Epub Date: 2024-12-27 DOI: 10.1016/j.jclimf.2024.100058

Syed Abul Basher , Perry Sadorsky

{"title":"How important are climate change risks for predicting clean energy stock prices? Evidence from machine learning predictive modeling and interpretation","authors":"Syed Abul Basher , Perry Sadorsky","doi":"10.1016/j.jclimf.2024.100058","DOIUrl":"10.1016/j.jclimf.2024.100058","url":null,"abstract":"<div><div>The clean energy equity sector plays an important role in the transition to a low-carbon economy. This paper explores the role of climate change risks in predicting the direction of clean energy stock prices (solar, wind, nuclear). We employ machine learning models, including random forests, boosting, extremely randomized trees, and support vector machines, to make our predictions. Variable importance is determined using Shapley/SHAP values. Notably, tree-based ensemble and boosting models show an accuracy exceeding 85 % for the 10 day to 20 day forecast period. For the stock prices of solar, wind, and nuclear energy, inflation expectations and technical indicators (which account for behavioral factors) such as on-balance volume and Williams’ accumulation/distribution are important features within this forecast range. For wind and solar energy stocks moving averages are also important additional features while for nuclear energy stocks economic policy uncertainty and stock market volatility are additional important features. In the five day to twenty day forecast horizon, climate change risks are not important features. These results align with a body of literature that raises concerns about equity prices not fully reflecting climate change risks. An equally weighted portfolio of wind, solar, and nuclear energy stock prices that used trading signals from an Extra Trees prediction model outperformed a buy and hold portfolio in terms of risk adjusted returns. These results are robust to trading costs and weekly or monthly portfolio rebalancing.</div></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"10 ","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135555","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