Bridging the Climate Finance Gap: Behavioral and Market Barriers to Efficient Climate Risk Pricing in Emerging Economies

ABSTRACT: Climate risk represents an increasingly vital issue to countries, companies, and institutional investors, making it a reality but not a distant threat to humanity. Considering the effects of climate risks on firms' financial indices and financing options, the study investigates whether climate risk is priced by the capital markets of South Africa. The study used reported carbon emissions as a measure of climate risk of 81 listed companies in the Johannesburg Stock Exchange from 2011 to 2020 to examine whether climate risk is considered and priced by the South Africa capital market. Data was sourced from DataStream database- a global financial and macroeconomic time-series database providing data on equities, stock market indices, currencies, company fundamentals, fixed income securities, and key economic indicators. We used the two-step system Generalized Method of Moments estimation technique that copes with endogeneity concerns to corroborate the effects of climate risk on cost of capital and capital structure. We find that climate risk is priced in both cost of debt capital and cost of equity capital. Specifically, we find that an increase in a firm's exposure to climate risk increases the cost associated with issuing debt and equity capital. We also find that climate risk exposure decreases the debt-equity ratio. Additionally, the study showed that firm size, leverage ratio, capitalization, profitability, and turnover affect both cost of capital and capital structure of listed firms. The study concludes that climate risk is priced in cost of financing in the capital market of South Africa. The study recommends that firms should invest in installing eco-friendly machinery that aligns with changing market expectations in order to reduce their carbon emissions. The study therefore highlights the need for companies to proactively assess and manage climate risks, incorporate climate considerations into their strategic decision-making, and enhance their resilience to climate-related challenges.

Climate change research on transportation systems: Climate risks, adaptation and planning

Climate Change Risk Indicators (CCRI) for seaports in the United Kingdom

<p>Climate risk, and related impacts, are determined by a variety of natural, climatological and socio-economic factors. In its fifth Assessment Report, the Intergovernmental Panel on Climate Change has adapted the concept and terminology in this respect. The challenge is: How can relevant influencing factors be identified and integrated? And, how can these factors be represented spatially and integratively in order to provide decision makers with a sound basis for adaptation measures? The central starting question is: Where do I do what (and when)? Within the Austrian ACRP project 'RESPECT', a novel climate change risk analysis for the natural hazard 'flooding' was developed. Special attention is paid to the modelling of socio-economic and physical vulnerability and its integration into a spatially explicit climate risk analysis. As a result, spatial and thematic hotspots of social and physical vulnerability and climate risk for Austria are identified, which serve as a basis for the identification of adaptation measures.</p><p>As a result, climate risk maps are available for Austria, which show risk and vulnerability hotspots as homogeneous spatial regions, independent from administrative boundaries and traditional raster-based approaches. These hotspots are quantitatively evaluated by an index value as a measure of climate risk. In addition to the purely quantitative evaluation, it is also possible to characterise and present the spatial units qualitatively, in terms of 'problem areas' and contributing factors. This is a significant development compared to 'traditional' spatial units (grid cell based; based on administrative units). Thus the question mentioned at the beginning can be answered - where are which intervention measures necessary. The results are available for socio-economic and physical climate risk, which are flanked by corresponding hazard and vulnerability maps. Results for the present and the future have been produced using proxy indicators from the high-resolution Austrian climate change scenario data (ÖKS15). This makes it possible to identify future hot spots under the assumption of different climate scenarios. The presentations presents the adapted risk concept and methodological approach, respectively, and reflects critically on the opportunities and challenges of climate risk analysis in Austria and in general for the planning of climate change adaptation measures.  </p>

PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan’s (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West’s model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

Climate change is leading to changing patterns of precipitation and increasingly extreme global weather. There is an urgent need to synthesize our current knowledge on climate risks to water security, which in turn is fundamental for achieving sustainable water management. Climate Risk and Sustainable Water Management discusses hydrological extremes, climate variability, climate impact assessment, risk analysis, and hydrological modelling. It provides a comprehensive interdisciplinary exploration of climate risks to water security, helping to guide sustainable water management in a changing and uncertain future. The relevant theory is accessibly explained using examples throughout, helping readers to apply the knowledge learned to their own situations and challenges. This textbook is especially valuable to students of hydrology, resource management, climate change, and geography, as well as a reference textbook for researchers, civil and environmental engineers, and water management professionals concerned with water-related hazards, water cycles, and climate change.

Taming the Green Swan: a criteria-based analysis to improve the understanding of climate-related financial risk assessment tools

<p>Climate risk analysis and assessment studies are typically conducted relying on historical data. These data, however, represent just one single realization of the past, which could have unfolded differently. As an example, Hurricane Irma might had struck South Florida at Category 4 and, had it done so, damages could have been as high as 150 billion, about three times higher than damage estimated from the actual event. To explore the impacts of these potentially catastrophic near-misses, downward counter-factual risk analysis (Woo, Maynard and Seria, 2017) complements standard risk analysis by exploring alternative, plausible realization of past climatic events. As downward counter-factual risk analysis frames risk in an event-oriented manner, corresponding more closely to how people perceive risk, it is expected to increase climate risk awareness among people and policy makers (Shepherd et al., 2018).</p><p>We present a counter-factual risk analysis study of climate risk from tropical cyclones on the Caribbean islands. The analysis is conducted using the natcat impact model CLIMADA (Aznar-Siguan and Bresch, 2019). Impact is estimated based on forecasts of past tropical cyclones tracks from the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset, as they all represent plausible alternative realizations of past tropical cyclones. The goal is to study whether, and to what extent, the estimated impacts from forecasts provide new insights than those provided by historical records in terms of e.g. cumulated annual damages, maximum annual damages and, in so doing, perform a worst-case analysis study to support climate risk management planning.</p><p><br>Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085-3097, doi.org/10.5194/gmd-12-3085-2019, 2019.</p><p>Woo, G., Maynard, T., and Seria, J. Reimagining history. Counterfactual risk analysis. Retrieved from: https://www.lloyds.com/~/media/files/news-and-insight/risk-insight/2017/reimagining-history.pdf, 2017.</p><p>Shepherd, T.G., Boyd, E., Calel, R.A. et al.: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change 151, 555–571, doi.org/10.1007/s10584-018-2317-9 , 2018.</p>

We investigate how the application of advanced predictive models could help investors to assess and manage climate risk in their portfolios, contributing to the development of more sustainable and resilient investment practices. We highlight the possible applications of predictive analytics as a key tool in climate finance. It emerges how emerging technologies (blockchain and Artificial Intelligence) can improve transparency, efficiency, and climate risk analysis in sustainable investments. Further lines of research are highlighted, focusing on how investors and portfolio managers can develop strategies to manage the risks associated with climate events and the integration of climate risks into the management of Supply Chain Finance to ensure greater resilience and sustainability. Some generalized models are analyzed focusing the most important aspects and features by which modeling Climate risks and related issues in financial frameworks.

The Asia-Pacific region is extremely vulnerable to climate variability and change. This reflects high exposure to hydroclimatic hazards such as tropical cyclones, floods, droughts, and heatwaves. Rapidly growing cities and low-lying coastal zones/estuaries also face threats from sea level rise and storm surges. However, climate model projections remain very uncertain about most of these risks, so water infrastructure and operations need to consider a range of plausible futures. Against this background, the Asian Development Bank (ADB) has been developing frameworks, tools, and capacities in climate risk and adaptation assessment and management. Project teams are often operating in data-scarce situations and under significant time constraints, so the emphasis has been on creating pragmatic guidance and training resources. This paper charts the transition of climate risk management (CRM) within the ADB from a predominantly scenario-led to decision-led approach to adaptation. Examples are given of light-touch procedures for screening climate risks, strengthening the transparency and rigour of scenario analysis, raising awareness of a broad range of adaptation options, streamlining identification of CRM options, and embedding allowances for climate change in detailed engineering designs. Such practical innovations would benefit communities of practice beyond the Asia-Pacific region.

Earth's forests harbor extensive biodiversity and are currently a major carbon sink. Forest conservation and restoration can help mitigate climate change; however, climate change could fundamentally imperil forests in many regions and undermine their ability to provide such mitigation. The extent of climate risks facing forests has not been synthesized globally nor have different approaches to quantifying forest climate risks been systematically compared. We combine outputs from multiple mechanistic and empirical approaches to modeling carbon, biodiversity, and disturbance risks to conduct a synthetic climate risk analysis for Earth's forests in the 21st century. Despite large uncertainty in most regions we find that some forests are consistently at higher risk, including southern boreal forests and those in western North America and parts of the Amazon.

The challenges associated with climate change are increasing, so there is an urgent need for modern tools to effectively assess, predict and minimise climate risks. This research paper presents the results of the development of the innovative Community Climate Change Impacts Service (C3IS) system, which represents a paradigm shift in climate risk analysis. C3IS is a module that includes a pioneering set of tools with an interactive application programming interface (API) fully integrated with the Google Earth Engine (GEE) platform. The C3IS module enables lightning-fast collection and visualisation (in real time) of critical climate risk data through flexible integration with GEE. The advantages of this integration are the ability to use the GEE platform to access an extensive petabyte-scale catalogue of geospatial data and an ever-expanding database of satellite imagery. The defining feature of the developed module is accessibility and usability due to the exclusion of operations such as the time-consuming preliminary processing of “big data”; complex modelling; and large-scale data storage. The study shows the promising application of the C3IS module for the operational decision making and development of sound strategies for effective climate change mitigation.

Fruticulture constitutes an important sector of the Brazilian agricultural industry. Despite technological and scientific advances, climate is still the most important variable defining crop productivity. Because of this, agroclimatic zoning should be one of the first factors to consider when starting to plant a particular crop. The objective of this work was to conduct climate risk zoning for guava (Psidium guajava L.) in Paraná river basin 3, Paraná, Brazil, using meteorological data from 43 stations collected between 1976 and 2018. The climate risk analysis was based on the climatic factors that impact the species, such as rainfall, annual water deficit, average annual temperature, coldest month temperature, and risk of frost. The findings of this study suggest that the basin has areas with a low climate risk for guava cultivation. Precipitation and water balance were sufficient under all tested scenarios. The most limiting factor for production was frost, but with risk only present during the first years of cultivation. Despite this, planting restrictions were only predicted to occur in the far west portion of the basin. Agricultural techniques that reduce the risk of frost and avoiding areas with greater frost incidences are the two most important aspects to consider to ensure greater success for guava in the region.

Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

<p>There has been rapid progress in the development of capabilities to analyse infrastructure networks on very large scales, up to global scales. This is enabled by the growing availability of geospatial data products with global coverage and computational capabilities, which enable processing of these datasets and analytics on large-scale. Global analyses of the risks from climatic hazards to infrastructure networks serve several important purposes:</p><ul><li>Quantified risk estimates in future climate scenarios contribute to the overall picture of the scale of climate risks worldwide, which helps to motivate climate mitigation and adaptation.</li> <li>Geospatial analysis of hotspots of infrastructure vulnerability helps to target adaptation actions.</li> <li>Cost-benefit analysis of adaptation enables the prioritization of scarce adaptation resources.</li> <li>Quantified climate risk analysis is increasingly required for financial disclosure of physical climate risks by infrastructure investors.</li> </ul><p>There are inevitable limitations to global-scale analyses, but they enable cross-country comparisons, and the monitoring of changing risks and national infrastructure resilience. Global analyses also provide a convenient starting point for national analyses and a motivation to collect better data to inform national-scale decisions.</p><p> </p><p>Here we present recent developments in capability for global-scale climate risk analysis to infrastructure networks. The analysis combines (i) global-scale probabilistic hazard layers (including floods, hurricanes and coastal storm surges); (ii) infrastructure asset and network exposure, for energy, transport and telecommunications networks (iii) analysis of the people and economic activities that are dependent upon these networks. This quantified risk analysis framework has been efficiently implemented for global-scale computations, yielding new results on the scale of climate-related risks. Analysis of resource flows on networks and their connection to infrastructure users is enabling calculation of the numbers of people and economic activity that may be disrupted in catastrophic events. A recent development has been in the introduction of probabilistic event sets for hurricanes and flooding, which enables accurate estimation of the impacts from spatially extensive extreme events. The research is being made available as part of the Global Resilience Index Initiative https://www.cgfi.ac.uk/global-resilience-index-initiative/ and as an open source toolset and interface for geospatial visualisation.</p><p> </p><p> </p><p> </p><p> </p>