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Modeling and Forecasting of Climate Risks
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Modeling and Forecasting of Climate Risks

A special issue of Climate (ISSN 2225-1154). This special issue belongs to the section "Climate and Economics".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 10605

Special Issue Editors

Prof. Dr. Afees Adebare Salisu

E-Mail Website
Guest Editor
Centre for Econometrics and Applied Research (CEAR), Ibadan, NG, Nigeria
Interests: applied econometrics; econometric analysis; econometric modeling; economic modeling; economic forecasting; time series forecasting
Dr. Raymond Swaray

E-Mail Website
Guest Editor
Hull University Business School, University of Hull, Hull HU6 7RX, UK
Interests: commodity markets; energy economics; agricultural economics and applied econometrics

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue [SI] titled “Modelling and Forecasting of Climate Risks”.

The impact of extreme weather events, which are intensified by climate change, is already causing widespread devastation across different countries and industries. From prolonged droughts in sub-Saharan Africa to tropical storms in Southeast Asia, the Caribbean, and the Pacific, the consequences of climate change are evident. Europe has also experienced fatal heat waves, while wildfires have been reported in several countries, such as South Korea, Algeria, and Croatia. Pakistan has been hit by severe flooding, and Madagascar is facing a severe drought that has left millions of people without access to food. As businesses strive to limit greenhouse gas emissions, various climate change models have impacted their profits. On the other hand, governments are committed to achieving net-zero emissions to combat the adverse effects of climate change. However, the issue of climate change remains contentious, with developing countries arguing that their contribution to the problem is minimal compared to their developed counterparts. Despite ongoing research on climate risks, there are still gaps in our understanding of the issue. This article explores the modelling and forecasting of climate risks, offering policymakers alternative predictive models to make more precise predictions about the potential macroeconomic consequences of climate change. As this is an emerging area of study, discussions around climate change forecasts will undoubtedly continue to be a crucial part of climate change mitigation and adaptation efforts.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Climate risk predictability;
  • Climate risks and economic conditions;
  • Climate risks and business sustainability;
  • Climate risks and financial markets;
  • Climate change and commodity markets;
  • Climate risks and fiscal sustainability;
  • Climate risks and monetary policy;
  • Climate risks and trade.

We look forward to receiving your contributions.

Prof. Dr. Afees Adebare Salisu
Dr. Raymond Swaray
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate risk measures
  • climate risk predictability
  • sustainability
  • climate adaptation
  • green finance
  • green economy
  • global warming
  • climate change economics
  • climate change modeling
  • climate econometrics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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22 pages, 4009 KiB  
Article
Fossil Fuel CO2 Emissions and Economic Growth in the Visegrád Region: A Study Based on the Environmental Kuznets Curve Hypothesis
by Mohammad Fazle Rabbi and Masuk Abdullah
Climate 2024, 12(8), 115; https://doi.org/10.3390/cli12080115 - 7 Aug 2024
Cited by 5 | Viewed by 2193
Abstract
The relationship between fossil fuel CO2 emissions and economic growth in the Visegrád (V4) countries (Czechia, Hungary, Poland, and Slovakia) is examined through the lens of the environmental Kuznets curve (EKC) hypothesis. Employing the modified environmental Kuznets curve (MEKC) hypothesis, time-series data [...] Read more.
The relationship between fossil fuel CO2 emissions and economic growth in the Visegrád (V4) countries (Czechia, Hungary, Poland, and Slovakia) is examined through the lens of the environmental Kuznets curve (EKC) hypothesis. Employing the modified environmental Kuznets curve (MEKC) hypothesis, time-series data from 2010 to 2022 were analyzed. The methodology encompasses a range of econometric techniques, including temporal, comparative, correlational, and regression analyses, to unravel the intricate relationship between economic development (measured by GDP per capita) and environmental pollution (CO2 emissions). Results reveal a complex nonlinear correlation between GDP per capita and CO2 emissions in the V4 countries, following an inverted U-shaped pattern. Specifically, Czechia and Hungary exhibited peak emissions at approximately USD 5000 and USD 4500 GDP per capita, respectively, with corresponding emission levels of 1.15 and 0.64 metric tons. In contrast, Slovakia’s emissions decreased after its GDP per capita exceeded USD 5000 and carbon dioxide emissions reached 0.15 metric tons. However, Poland’s data deviate from the MEKC pattern, exhibiting a consistent rise in CO2 emissions across all levels of GDP per capita. The study highlights that the power industry is the largest source of CO2 emissions in all four countries, contributing 88.09% of total emissions. The transportation and industrial combustion sectors account for about 2.12% and 1.28% of annual emissions, respectively. GDP–CO2 emission correlations vary across the V4 countries. While Czechia exhibits a positive correlation of 0.35, Hungary (−0.37), Poland (−0.21), and Slovakia (−0.11) display negative relationships. Notably, Poland experiences the most significant increase in CO2 emissions from both road transport and air traffic. The conclusions drawn from this study provide a robust foundation for developing tailored environmental policies that support sustainable growth in the Visegrád region and other transitioning economies. Full article
(This article belongs to the Special Issue Modeling and Forecasting of Climate Risks)
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19 pages, 2881 KiB  
Article
Combining E-Scores with Scenario Analysis to Evaluate the Impact of Transition Risk on Corporate Client Performance
by Rudolf van der Walt, Gary van Vuuren, Janette Larney, Tanja Verster and Helgard Raubenheimer
Climate 2024, 12(7), 107; https://doi.org/10.3390/cli12070107 - 19 Jul 2024
Viewed by 1901
Abstract
Scenario analysis is a comprehensive approach to assess the impact of climate-related transition risk on businesses. Environmental, social, and governance (ESG) scores are popular tools with financial institutions (FI’s) for green-scoring practices and since they characterise a company’s performance from an ESG perspective, [...] Read more.
Scenario analysis is a comprehensive approach to assess the impact of climate-related transition risk on businesses. Environmental, social, and governance (ESG) scores are popular tools with financial institutions (FI’s) for green-scoring practices and since they characterise a company’s performance from an ESG perspective, they have been criticised for enabling “greenwashing” when used within the context of climate risk. Commercially available ESG scores are also available for listed entities, while FI counterparties are often unlisted. This study develops a methodology for creating in-house environmental scores (E-scores), which are then used to effectively choose appropriate transition pathways to be used in company-specific forward-looking scenario analysis. Such scenario analysis can be used to forecast the company’s financial position, including the cost of its greenhouse gas (GHG) emissions, and quantify the impact of transition climate risk on specified metrics. The choice of metrics depends on what the results of the analysis are used for. Two metrics are identified for being useful for risk management and credit decisions: future profitability and weighted average carbon intensity. Finally, the study demonstrates how this process can be implemented with a practical worked example, using only publicly available data. Full article
(This article belongs to the Special Issue Modeling and Forecasting of Climate Risks)
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14 pages, 327 KiB  
Article
Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa
by Kejin Wu, Sayar Karmakar, Rangan Gupta and Christian Pierdzioch
Climate 2024, 12(5), 68; https://doi.org/10.3390/cli12050068 - 8 May 2024
Cited by 3 | Viewed by 2570
Abstract
Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only [...] Read more.
Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers. Full article
(This article belongs to the Special Issue Modeling and Forecasting of Climate Risks)
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14 pages, 4966 KiB  
Perspective
The Umlindi Newsletter: Disseminating Climate-Related Information on the Management of Natural Disaster and Agricultural Production in South Africa
by Reneilwe Maake, Johan Malherbe, Teboho Masupha, George Chirima, Philip Beukes, Sarah Roffe, Mark Thompson and Mokhele Moeletsi
Climate 2023, 11(12), 239; https://doi.org/10.3390/cli11120239 - 5 Dec 2023
Viewed by 2881
Abstract
The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from [...] Read more.
The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from climate-related monitoring products obtained from an integration of remote sensing and in situ data from weather stations. It contains useful indicators, such as rainfall, vegetation, and fire conditions, that provide an overview of conditions across the country. The present study demonstrates how these natural resource indices are integrated and consolidated for utilization by farmers, policy-makers, private organizations, and the general public to make day-to-day decisions on the management and mitigation of natural disasters. However, there is a need to expand these baseline observation initiatives, including the following: (1) forecasting future conditions to strengthen coping mechanisms of government, farmers, and communities at large; and (2) incorporating information on other natural disasters such as floods and extreme heat. In the context of South Africa, this information is important to improve disaster preparedness and management for agricultural productivity. In a global context, the Umlindi newsletter can be insightful for developing and disseminating natural resources information on adaptation to and mitigation of climate change and variability impacts to other regions facing similar risks. Furthermore, while international organizations also provide natural resource information, the Umlindi newsletter may be distinguished by its regional focus and linkages to individual communities. It bridges the gap between global environmental data and local decision-making by illustrating how global scientific knowledge may be applied locally. Full article
(This article belongs to the Special Issue Modeling and Forecasting of Climate Risks)
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