Journal of Climate Finance最新文献_第7页

Effect of green investment to reduce carbon emissions in an imperfect production system 不完善生产体系下绿色投资对碳排放的影响

Journal of Climate Finance Pub Date : 2023-03-01 DOI: 10.1016/j.jclimf.2023.100007

S. Priyan

{"title":"Effect of green investment to reduce carbon emissions in an imperfect production system","authors":"S. Priyan","doi":"10.1016/j.jclimf.2023.100007","DOIUrl":"https://doi.org/10.1016/j.jclimf.2023.100007","url":null,"abstract":"<div><p>Climate change is a serious hazard to human life today, and the greenhouse effect is its key cause. The production sector is one of the biggest contributors to climate change as it accounts for a large part of greenhouse gas emissions. Many nations have created ecological policies and regulations to prevent industries from emitting excessive amounts of carbon emissions (CO2) into the ecosystem. Industry experts believe that green technology might aid in making a more sustainable future as a result of the current climate crisis. Investments in green technologies are rising rapidly which may allow more nations to adopt green technology and transition away from non-renewable resources. This research explores the viability of investing in green technology to achieve a sustainable production scheduling considering an uncertain amount of CO2 during the processes of production, transport, and storage, and thus offers managerial insights for policymakers to reduce both CO2 and overall cost. This study assumes an imperfect production process where a fraction of the items is faulty, and the firm employs a rework approach to rectify the faulty items. The firm invests in green technology to significantly cut CO2 and support innovation to address the climate crisis and benefit communities. The main point of the study is to develop a solution procedure of the problem associated with the amount of CO2 where all the CO2 factors might increase or decrease fuzziness. According to the skills gained by the decision maker we fuzzify all CO2 factors as trapezoidal fuzzy numbers, and we use a signed distance method to defuzzify the model. The results imply that the production sector’s outsize ecological CO2 can be reduced without compromising quality by designing optimal production strategies. The findings also confirm that green investment is the greatest economical method for reducing CO2 and overall costs.</p></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"2 ","pages":"Article 100007"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49882878","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: 4

Media coverage of COVID-19 and its relationship with climate change indices: A dynamic connectedness analysis of four pandemic waves COVID-19媒体报道及其与气候变化指数的关系:四次大流行浪潮的动态连通性分析

Journal of Climate Finance Pub Date : 2023-03-01 DOI: 10.1016/j.jclimf.2023.100010

Onur Polat , Rim El Khoury , Muneer M. Alshater , Seong-Min Yoon

{"title":"Media coverage of COVID-19 and its relationship with climate change indices: A dynamic connectedness analysis of four pandemic waves","authors":"Onur Polat , Rim El Khoury , Muneer M. Alshater , Seong-Min Yoon","doi":"10.1016/j.jclimf.2023.100010","DOIUrl":"https://doi.org/10.1016/j.jclimf.2023.100010","url":null,"abstract":"<div><p>This study explores the impact of the COVID-19 media coverage index (MCI) on the return and volatility connectedness of five MSCI Climate Changes Indices (the USA, Emerging Markets (EMU), Japan, Europe, and the Asia Pacific). The sample period was from 11 March 2020–19 January 2022, divided into sub-samples based on four waves of the COVID-19 pandemic. Thus, we use the time-varying parameter vector autoregression (TVP-VAR) model besides the frequency-dependent connectedness network approach. The key findings are as follows. First, the results demonstrate that the MCI is a net receiver of shocks in all waves, and the highest level of connectedness occurs in the first wave. The findings concerning volatility are similar, with the majority of MSCI Climate Change Indices being net transmitters, potentially indicating the severity of the pandemic. Second, estimating the short-, medium-, and long-term return network connectedness indicates the dominance of strong-term connectedness suggesting the spread of shocks within a week. Our results are robust by replacing MCI with Panic Index (PI). These results have implications for investors and policymakers.</p></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"2 ","pages":"Article 100010"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49882880","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: 4

Where is the Carbon Premium? Global Performance of Green and Brown Stocks 碳补贴在哪里?绿色和棕色股票的全球表现

Journal of Climate Finance Pub Date : 2023-01-01 DOI: 10.1016/j.jclimf.2023.100006

M. Bauer, D. Huber, Glenn D. Rudebusch, Ole Wilms

引用次数: 12

Climate finance: What we know and what we should know? 气候融资:我们知道什么，我们应该知道什么?

Journal of Climate Finance Pub Date : 2023-01-01 DOI: 10.1016/j.jclimf.2023.100005

Suwan(Cheng) Long, B. Lucey, Shylendra Kumar, da-Ying Zhang, Zhiwei Zhang

引用次数: 3

Global temperature shocks and real exchange rates 全球气温冲击与实际汇率

Journal of Climate Finance Pub Date : 2022-12-01 DOI: 10.1016/j.jclimf.2022.100004

Sinyoung O. Lee , Nelson C. Mark , Jonas Nauerz , Jonathan Rawls , Zhiyi Wei

引用次数: 9

Using machine learning to predict clean energy stock prices: How important are market volatility and economic policy uncertainty? 使用机器学习预测清洁能源股价：市场波动和经济政策的不确定性有多重要？

Journal of Climate Finance Pub Date : 2022-12-01 DOI: 10.1016/j.jclimf.2022.100002

Perry Sadorsky

{"title":"Using machine learning to predict clean energy stock prices: How important are market volatility and economic policy uncertainty?","authors":"Perry Sadorsky","doi":"10.1016/j.jclimf.2022.100002","DOIUrl":"https://doi.org/10.1016/j.jclimf.2022.100002","url":null,"abstract":"<div><p>The disruptive impacts of climate change have created an urgent need to transition to a low carbon economy and an important part of this transition is an increase in the usage of clean energy. The greater adoption of clean energy is creating new opportunities for clean energy equity investing. The existing literature mostly focuses on the dynamic relationship between clean energy equities, oil prices, technology stock prices, and other important macroeconomic variables like market volatility and economic policy uncertainty. However, there is a shortage of literature on forecasting clean energy stock prices. Forecasting clean energy equity prices is important for making investment decisions. This paper uses machine learning methods to predict the direction of clean energy stock prices. The analysis reveals that random forests, extremely randomized trees, stochastic gradient boosting, and support vector machine have higher prediction accuracy than Lasso or Naïve Bayes. For forecasts in the 10-day to 20-day range, random forests, extremely randomized trees, stochastic gradient boosting, and support vector machine achieve prediction accuracies greater than 85 %. In some cases, prediction accuracy reaches 90%. Lasso prediction accuracy is higher than Naïve Bayes but never greater than 65 %. The MA200, MA50, and WAD technical indicators are, on average, the features most important for predicting clean energy stock price direction. Of the non-technical indicators, VIX and OVX are consistently ranked high in importance. In most cases, EPU is not one of the most important features, Of the forecasting methods considered, extremely randomized trees are very impressive due to high accuracy and short computational time.</p></div>","PeriodicalId":100763,"journal":{"name":"Journal of Climate Finance","volume":"1 ","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949728022000025/pdfft?md5=bb83df58044d9c134277a1b6a3775272&pid=1-s2.0-S2949728022000025-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72292216","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: 8

Climate finance: What we know and what we should know? 气候金融：我们知道什么，我们应该知道什么？

Journal of Climate Finance Pub Date : 2022-12-01 DOI: 10.1016/j.jclimf.2023.100005

Suwan Long Cheng , Brian Lucey , Satish Kumar , Dayong Zhang , Zhiwei Zhang

引用次数: 3

Where is the carbon premium? Global performance of green and brown stocks 碳溢价在哪里？绿色和棕色股票的全球表现

Journal of Climate Finance Pub Date : 2022-12-01 DOI: 10.1016/j.jclimf.2023.100006

Michael D. Bauer , Daniel Huber , Glenn D. Rudebusch , Ole Wilms

引用次数: 13

Using machine learning to predict clean energy stock prices: How important are market volatility and economic policy uncertainty? 使用机器学习预测清洁能源股票价格:市场波动和经济政策不确定性有多重要?

Journal of Climate Finance Pub Date : 2022-12-01 DOI: 10.1016/j.jclimf.2022.100002

Perry Sadorsky

引用次数: 8

Global Temperature Shocks and Real Exchange Rates 全球温度冲击和实际汇率

Journal of Climate Finance Pub Date : 2022-12-01 DOI: 10.1016/j.jclimf.2022.100004

Sinyoung O. Lee, Nelson C. Mark, Jonas Nauerz, Jonathan Rawls, Zhiyi Wei

引用次数: 9