This study exploration delves into the crucial question of whether adopting strategies that ensure vulnerability and readiness at a specific level to climate change can effectively influ
INTRODUCTION
Background
In recent years, heightened awareness of climate change and its potential effects on global economies has underscored the need to understand its complex impacts A significant concern is the relationship between climate change, sovereign risk, and credit ratings, as governments globally grapple with the challenge of addressing climate-related risks while ensuring financial stability and maintaining their creditworthiness.
This study investigates the impact of adopting climate change strategies on sovereign risk and credit ratings It highlights that countries with greater exposure to climate vulnerability tend to experience increased sovereign risk By implementing effective vulnerability and readiness strategies, nations may enhance their credit ratings and mitigate associated risks (Beirner et al., 2021).
The risk of economic activity disruption associated with natural disasters and extreme weather is one of the macroeconomic risks associated with natural disasters (Schuler Ct al.,
Climate change-induced events, including extreme weather, rising sea levels, and agricultural disruptions, can lead to significant consequences such as increased infrastructure damage, strained public finances, and worsening social inequalities These impacts threaten a nation's fiscal health and creditworthiness in international financial markets Consequently, tackling the complexities of climate change has evolved from being solely an environmental issue to a crucial component of effective economic policy and responsible governance.
The findings of this study will offer critical insights for policymakers, financial analysts, and researchers, enabling them to make informed decisions that align climate action with fiscal stability and improved creditworthiness in a changing world Climate change adaptation directly impacts public budgets, particularly on the expenditure side, while mitigation investments, like clean energy, can also strain public finances Additionally, climate mitigation policies, such as carbon taxes, influence revenue streams According to the United Nations Conference on Trade and Development (2019), 31 developing nations will need to significantly increase their public debt-to-GDP ratios from 47% to 185% to fund essential investments aimed at achieving Sustainable Development Goals in poverty alleviation, nutrition, healthcare, and education, primarily through borrowing.
A 2019 study by Battiston and Monastcrolo found that countries heavily reliant on carbon-intensive industries tend to see higher yields on government bonds Additionally, research by Klusak and colleagues in 2021 indicates that a country's susceptibility to climate change impacts can negatively influence its sovereign credit ratings.
Investing in sustainable infrastructure and transitioning to low-carbon economies can protect nations from the adverse effects of climate change Furthermore, international credit rating agencies now recognize climate-conscious policies, which adds a significant dimension to this discussion Countries that commit to reducing their carbon footprint and enhancing climate readiness are more likely to achieve favorable credit ratings These ratings impact borrowing costs and reflect a government's credibility in managing risks, thereby attracting foreign investments and promoting economic growth.
This study aims to examine case studies and empirical data from 42 economies from 2004
In 2022, the connection between vulnerability, readiness, climate change, sovereign risk, and credit ratings was established, underscoring the significant impact of proactive climate policies These policies have the potential to transform the global financial landscape, urging governments to implement sustainable practices that benefit both their nations and the planet.
Research Objectives
This research explores the connection between vulnerability and readiness in the context of climate change, focusing on their effects on sovereign risk and national credit ratings By examining empirical data, case studies, and relevant literature, the study seeks to offer valuable insights for policymakers, investors, and financial institutions.
1 Does vulnerability and readiness in climate change affect sovereign risk? And are there any differences between regions?
2 Does climate change vulnerability and readiness have an impact on credit ratings?
Scope of the Study
The primary objective of this research is to comprehensively assess the worldwide implications of vulnerability and readiness within the context of global financial markets.
This study critically relies on accessible historical data of 10-year sovereign bonds from 2004 to 2022, sourced from Investing.com The countries analyzed are categorized into three distinct groups: Advanced economies, Emerging economies, and members of the Association of Southeast Asian Nations (ASEAN) This classification facilitates a nuanced examination of the unique vulnerabilities and readiness responses within each specific economic context.
Data Description
The research data selection process is thorough, prioritizing reliability and relevance The author specifically targets countries with complete 10-year sovereign bond yield data available on investing.com from 2004 to 2022, ensuring a robust dataset for analysis.
42 countries were chosen as the basis for conducting the research.
The author analyzes the effects of climate change on 42 countries by categorizing them into three distinct groups, allowing for a more nuanced understanding of the issue The research is further enhanced by incorporating supplementary data from reputable sources like The World Bank and the IMF, which includes credit ratings and key macroeconomic indicators such as current account/GDP, GDP per capita, real GDP growth, public debt/GDP, fiscal balance/GDP, and VIX This comprehensive approach enriches the analysis and offers a broader perspective on the economic and financial factors influencing the findings.
Research Methodology and Model
This research utilizes panel data from 42 countries to explore the complex relationship between climate change and macroeconomic variables in the context of sovereign credit risk assessment It employs two methodologies: Fixed Effects Models (FEM) to analyze the connections between climate change and various macroeconomic factors influencing sovereign credit rankings, and Structural Vector Autoregression (SVAR) to identify interdependencies and impacts among these variables The SVAR analysis, guided by the Akaike Information Criterion (AIC), suggests a maximum lag of 2, with the response order of variables determined through Cholesky decomposition This comprehensive methodology allows for an in-depth examination of the nuanced interactions between climate change and sovereign credit risk across a diverse range of countries.
Findings and Contributions
Our research indicates a significant positive correlation between climate change vulnerability and the countries studied, particularly highlighting the ASEAN region Additionally, in line with earlier studies, we found a notable negative relationship between climate change readiness and 10-year sovereign bond yields, suggesting that climate change preparedness exerts a more substantial influence on Emerging Market Economies (EMEs) and ASEAN nations compared to advanced countries.
This study reveals a lack of correlation between macroeconomic fundamentals and sovereign risk across different countries, attributed to varying economic development policies Additionally, our research indicates that factors such as climate vulnerability and readiness significantly influence Moody's country ratings; specifically, higher climate change vulnerability is associated with lower ratings from Moody's.
This study emphasizes the transformative potential of proactive climate policies, which can significantly reshape global economic landscapes By adopting these policies, nations can effectively tackle the challenges of climate change while promoting economic readiness and sustainability.
Structure of the thesis
Based on the criteria of a standard research article, this study is also divided into 5 chapters as follows:
Chapter I: Introduction: a brief introductory giving overview information of this research.
Chapter 2: Literature Review: a more detailed analysis of climate change on other economic factors, presents more specific aims or hypotheses for this study.
Chapter 3: Data and Methodology: a detailed description of the dataset and introduction that provides a justification and explanation of the methodological approach the author chose
Chapter 4: Results: describe and present the outcome of the analysis
Chapter 5: Conclusions: summarize the main findings of the research The author comments on the results and identifies any limitations of this study.
LITERATURE REVIEW
Theoretical Literature
The understanding of climate change dates back to the 19th century when John Tyndall discovered the heat-trapping capabilities of greenhouse gases in 1859 Nobel laureate Svante Arrhenius further advanced this knowledge in 1896 by linking fossil fuel combustion to rising global temperatures In 1957, Roger Revelle and Charles Keeling conducted groundbreaking measurements of carbon dioxide levels at Mauna Loa, leading to the creation of the Keeling Curve That same year, Humble Oil, which later became Exxon, published a report detailing the significant carbon dioxide emissions produced by fossil fuels.
By 1968, the Stanford Research Institute highlighted the serious impacts of fossil fuels on global warming, such as ice cap melting and rising sea levels Despite this, scientists noted policymakers' focus on isolated events rather than overarching warming trends In 2022, Hurricane Ian caused severe damage in Southwest Florida, prompting a meeting between Florida Governor Ron DeSantis and President Joe Biden, where Biden claimed the hurricane settled the debate on climate change and the necessity for human action Following the 2008 global financial crisis, investors began to recognize climate change and global warming as critical hidden risks that had become increasingly evident.
Table 1.1: The Climate Risk Index (CRI) for the Extended Duration: The top 10 nations experiencing the greatest impact from 1999 to 2018 (averaged annually).
Total losses in million us$ PPP
Losses per unit GDP in %
Source: Global Climate Risk Index 2020
Southeast Asia is highly susceptible to climate change, facing severe challenges such as droughts, floods, typhoons, rising sea levels, and heat waves The 2020 Global Climate Risk Index highlights that Myanmar, the Philippines, Vietnam, and Thailand are among the most affected nations from 1999 to 2018, with Myanmar ranked first with a CRI of 10.3, followed by the Philippines at 17.67, Vietnam at 29.83, and Thailand at 31.0, as reported by Eckstein, Kunzel, Schafer, and Winges in 2019.
Figure 2.1: Historical occurrences of extreme weather events in ASEAN
Over the last two decades, carbon dioxide (CO2) emissions from energy use in the ASEAN region have steadily increased, largely due to growth in the electricity and heat generation, manufacturing, and transportation sectors.
In the ASEAN region, the power sector is the largest contributor to direct CO2 emissions, with the exception of Cambodia, where transportation leads in emissions Overall, sectoral CO2 emissions in ASEAN align with global averages, highlighting the collective goal of decarbonizing the power, industry, and transportation sectors to combat climate change Rising energy-related CO2 and GHG emissions from land-use change and forestry have further increased emissions in the region While a few ASEAN nations have managed to decouple GDP growth from energy-related CO2 emissions, it remains crucial to reduce energy intensity through decreased consumption and improved energy conservation Additionally, there is an urgent need to lower emission intensity by promoting the development of renewable energy sources.
The ASEAN region relies heavily on agriculture and natural resources, with some countries seeing these sectors contribute over 20% to their national GDP Although economies are diversifying to lessen this dependence, rural communities that depend on agriculture remain particularly vulnerable to the effects of climate change.
ASEAN countries face numerous natural disasters annually, leading to substantial economic and human losses Factors such as climate change are intensifying the frequency and severity of events like droughts, floods, and typhoons, while increasing migration to vulnerable areas exacerbates the situation As the 'rice basket of the world,' ASEAN produces nearly 28% of global rice and 31% of Asia's rice supply, making it crucial for food security Consequently, shifts in rainfall patterns could have profound effects on food availability both within the region and internationally.
Figure 2.2: Average annual agricultural loss due to climate change as a percentage of GDP
The growing trend of migration within the region is straining densely populated areas, leading to resource depletion, increased competition, and congestion Many migrants are likely to settle in regions at high risk for natural disasters and rising sea levels, which exacerbates vulnerability Therefore, it is crucial to address the challenges of migration and mitigate potential vulnerabilities.
ASEAN Member States are proactively strengthening their readiness for a more rigorous transparency framework while sharing insights and learning from one another's experiences A key focus of this effort is improving their capacity to forecast future emissions and reductions, which is vital for linking transparency capacity building with the formulation of medium- to long-term emission reduction strategies This foundational step is essential for driving transformative changes across multiple sectors and enhancing their commitment to achieving net-zero emissions.
2.1.2 Sovereign risk and Climate change
Climate change and sovereign risk are interconnected challenges that threaten the economic and political stability of nations worldwide Driven by human activities like greenhouse gas emissions and deforestation, climate change leads to more frequent and severe extreme weather events, disruptions in agricultural productivity, and rising sea levels These phenomena jeopardize the infrastructure and economic foundations of countries, resulting in significant financial repercussions Natural disasters and extreme weather can disrupt economic activities, negatively affecting tax revenues and public income while increasing the demand for social welfare payments Additionally, the physical and transitional impacts of climate change can create shocks to both aggregate supply and demand, further exacerbating economic instability.
Extreme weather events often lead to short-term shocks that can significantly impact economic growth and public financial stability Research by Acevedo (2014) and Klomp & Valckx (2014) highlights the lasting effects these events can have, underscoring the importance of addressing their implications for long-term economic resilience.
Climate change introduces transition risks that affect economies through changes in policies, regulations, market demands, and technology adoption Countries dependent on fossil fuel exports face the risk of stranded assets and fiscal stress For example, fluctuations in commodity prices can reduce government revenue or increase expenditures on subsidies for fossil fuels and food, while supply and demand shocks may impact inflation and interest rates Additionally, these financial challenges can lead to debt-related issues, undermining a nation's capacity to meet its financial obligations.
Climate change significantly threatens political stability, as highlighted by Clark (1997), who notes that political events can influence the likelihood of sovereign default Cuadra and Sapriza (2008) further argue that politically unstable and polarized nations experience higher default rates, leading to increased default risk premiums in international credit markets As environmental degradation leads to resource scarcity, competition for vital resources like water and arable land may incite conflicts and unrest Additionally, climate-induced migration, driven by droughts and rising sea levels, can overwhelm host countries' resources, potentially destabilizing regions and escalating geopolitical tensions.
Climate change significantly impacts sovereign risk by altering international trade and capital flows, as highlighted by research from Dellink et al (2017), UNCTAD (2019), Wilbanks et al (2007), and the WTO These changes can lead to substantial effects on a nation's balance of payments, which are vital for assessing sovereign bond yields, as examined by Beirne & Fratzscher.
In 2013, climate-related extreme events and disasters significantly disrupted trade, leading to increased sovereign risk The long-term effects of global warming further threaten resources and production, while transitional impacts on international trade also emerge, highlighting the multifaceted challenges posed by climate change.
Empirical Studies
This study provides a thorough review of research on the factors affecting sovereign risk pricing Edwards (1984) highlighted the critical role of domestic macroeconomic elements, such as public debt, foreign reserves, current account balance, and inflation, in influencing government bond spread fluctuations Furthermore, Beirne and Fratzscher (2013) analyzed 31 advanced and emerging economies, finding that the decline in economic fundamentals is the main driver of changes in sovereign risk pricing.
Research by Favero and Missale (2012) highlights that during times of increased global risk aversion, fiscal fundamentals significantly influence sovereign risk pricing Additionally, climate-related natural disasters can elevate sovereign risk due to their fiscal impacts Macroeconomic risks from such disasters and extreme weather events may disrupt economic activity, leading to decreased public revenues and increased social transfer payments (Schuler et al., 2019).
Climate change significantly affects a country's sovereign borrowing costs due to its broad macroeconomic implications The gradual increase in global temperatures and the shift towards sustainable practices can result in fundamental structural changes in the economy, potentially hindering long-term growth, as noted by Burke et al (2015) Furthermore, climate change is expected to impact the long-term productive capacity and potential output of numerous nations The extent of these effects on financial stability can also influence sovereign borrowing costs and the assessment of sovereign risk.
Extreme weather conditions can increase credit risk for banks by damaging the operational assets and production capacity of borrowers, leading to a higher incidence of non-performing loans This situation negatively impacts the sovereign risk profile and can adversely affect public debt ratios, as shown in Acharya et al (2014) Additionally, climate-related factors may induce political instability, further raising the likelihood of sovereign defaults, a connection that has been extensively studied in research by Clark (1997) and Cuadra & Sapriza (2008).
A series of studies highlight the significant relationship between climate vulnerability and sovereign debt costs Kling et al (2018) analyzed annual data from 46 countries between 1996 and 2016, revealing that nations with greater climate vulnerability, as indicated by ND-GAIN indices, face higher debt costs Similarly, Beirner et al (2021) examined Southeast Asia using monthly data from 2002 to 2018, concluding that elevated debt costs hinder essential investments in public infrastructure and climate adaptation, exacerbating debt sustainability issues and limiting development prospects Their research, which also covered 40 countries from 2002 to 2018, utilized a structural panel VAR approach to demonstrate that both climate change vulnerability and readiness significantly affect sovereign bond yields Furthermore, Cevik and Jalles (2022) confirmed that countries more resilient to climate change benefit from lower bond yields and spreads compared to those facing greater climate-related risks, emphasizing the critical impact of climate factors on government borrowing costs.
Table 2.1: Previous studies about the effects of climate change vulnerability on sovereign risk
DATA AND METHODOLOGY
Data Description
The author employs a fixed effects panel model utilizing yearly data from 2004 to 2022, analyzing 42 countries, including advanced economies, emerging markets, and ASEAN nations The study first investigates the determinants of sovereign bond yields, highlighting the influence of sovereign ratings alongside various macroeconomic indicators and two climate-related factors: climate risk vulnerability and readiness Key macroeconomic variables examined include the current account-to-GDP ratio, GDP per capita, real GDP growth rate, public debt-to-GDP ratio, fiscal balance-to-GDP ratio, and the VIX, which reflects global risk aversion Data for these variables was meticulously sourced to support the analysis.
"Investing.com" These variables were obtained from the International Monetary Fund's International Financial Statistics, the OECD, and The World Bank.
The climate vulnerability and readiness indicator utilizes an enhanced version of the ND-GAIN framework to evaluate susceptibility and preparedness for climate risks To mitigate endogeneity concerns, the refined vulnerability measure incorporates components from the ND-GAIN vulnerability index that exhibit minimal correlation with economic variables This comprehensive analysis is based on data collected from various sources spanning from 2004 to 2022, providing yearly insights into climate-related vulnerabilities and readiness.
Investing 10-ycar government bond yield
Sovereign Rating Moody's A measurement of a government's ability to repay its debts
Vulnerability ND-GAIN 2021 Measures a country's exposure, sensitivity, and capacity to adapt to the negative effects of climate change.
Readiness ND-GA1N 2021 Measures a country’s ability to leverage investments and convert them into adaptation actions
Cuncnl account/GDP The World Bank The current account balance to GDP ratio
GDP per capita The World Bank Real GDP per capita at constant 2010 USS.
Real GDP growth IMF International
The real GDP growth rate.
Public debt/GDP IMF International
The public debt as a share of GDP, defined as general government gross debt-to-GDP ratio
Fiscal balance/GDP IMF International
The fiscal balance, a share of GDP, is a cyclically adjusted primary balance-to-GDP ratio.
VIX Investing.com VỈX stands for the Chicago Board Options
Exchange (CBOE) Volatility Index, a measure ofglobal risk aversion.
Model Methodology
This research is conducted by using this equation: yij= Pxij-1 + yZi.t-1 + xVIXt-1 + Si + Ci,.; i = 1, , N, t = 1, , T where:
Yi.t represents the government bond yield and sovereign rating;
Xi.t represents a set of domestic macroeconomic fundamentals;
Zj denotes our climate vulnerability and readiness indicators;
VIX stands for the Chicago Board Options Exchange (CBOE) Volatility Index, a measure ofglobal risk aversion;
Si is country fixed effects; ehI is the error term.
These variables are lagged by one period to mitigate against endogeneity concerns.
A structural panel VAR analysis investigates how sovereign bond yields react to climate vulnerability and readiness shocks This panel SVAR is applied to 42 countries from 2002 to 2022, utilizing a general configuration defined by structural shocks identified through a recursive constraint.
Our identification strategy is based on a block recursive restriction (Christiano Ct al., 1999), which results in the following matrix A to fit ajust-identified model: ô1.1 0 0
In a recursive variable ordering, top variables, such as climate vulnerability, remain unaffected by immediate shocks to lower variables, like readiness for climate change Conversely, lower variables are influenced by shocks from upper variables Thus, the authors place climate vulnerability at the top, indicating that the readiness for climate change is only impacted by simultaneous shocks.
The author identifies sovereign yields as the final variable in the analysis, considering both the influence of climate risk on bond yields and initial empirical findings related to macroeconomic factors Positioned centrally in the ordering are the selected macroeconomic fundamentals For the selection of lags in the SVAR model, the author utilized the AIC criterion, recommending the inclusion of two lags for optimal model performance.
Research Hypothesis
This research investigates the relationship between low vulnerability and high readiness for climate change and their effects on sovereign risk and credit ratings The authors aim to determine if such an association exists and to identify the factors influencing a country's risk profile The hypothesis outlined in the development section serves as a foundation for the analysis conducted in this study.
Climate change vulnerability is raising sovereign bond yields across ASEAN, emerging, and advanced countries due to heightened risks from climate-related events Investors demand higher returns to offset the uncertainties and potential financial repercussions of climate change on national economies and government finances The effect on bond yields, however, is influenced by the severity of climate impacts, the effectiveness of government responses, and prevailing global market conditions.
Hypothesis I: The vulnerability of climate change has a positive and higher impact on ASEAN, Emerging countries, and Advanced countries 's ỈO-year sovereign bond yield respectively.
In ASEAN nations, the level of readiness significantly influences bond yields, with higher readiness resulting in lower yields due to enhanced investor confidence This trend is also evident in emerging economies, where increased readiness leads to lower bond yields as investors prioritize safety amid fragile economic conditions Even in advanced economies, readiness plays a crucial role, negatively impacting bond yields by demonstrating a proactive stance on risk mitigation Therefore, readiness is a key factor affecting sovereign bond yields across diverse economic landscapes.
Hypothesis 2: Readinessfor climate change has a negative and higher impact on ASEAN, Emergingcountries, andAdvanced countries's J 0-y ear sovereign bond yield respectively.
Domestic macroeconomic fundamentals significantly affect the 10-year sovereign bond yields across various economies, particularly in the ASEAN region, where factors like inflation rates, fiscal policies, and trade dynamics vary widely Emerging economies, marked by rapid growth and deeper global integration, are especially sensitive to these domestic fundamentals, as they can greatly influence investor sentiment and risk perception, thereby impacting bond yields Advanced economies, despite their established financial markets and stable conditions, are also shaped by domestic macroeconomic factors, which are critical determinants of their sovereign bond yields For both emerging and advanced economies, effectively understanding and managing these fundamentals is essential for governments and policymakers aiming to navigate the complexities of the global bond market.
Hypothesis 3: Domestic macroeconomic fundamentals have a higher impact on ASEAN, Emerging countries, and Advanced countries's ỈO-year sovereign bond yield respectively.
Domestic macroeconomic fundamentals play a pivotal role in influencing the 10-year sovereign bond yields of ASEAN nations, emerging economies, and advanced countries
In ASEAN countries, varying levels of fiscal discipline lead to dynamic financial markets, where investors vigilantly assess policies to evaluate investment risks, as even minor macroeconomic shifts can influence their sentiments Emerging economies, characterized by higher inflation and currency fluctuations, exhibit stronger responses in their bond markets to central bank actions Conversely, in advanced economies, deviations from anticipated growth trends or alterations in global trade dynamics can result in adjustments to bond yields.
Hypothesis 4: VIX has a positive and higher impact on ASEAN, Emerging countries, and Advancedcountries 's 10-year sovereign bond yield respectively.
The VIX, known as the "fear gauge," significantly influences the 10-year sovereign bond yields of ASEAN nations and emerging economies due to their heightened economic volatility When the VIX rises, investors tend to seek safer assets, leading to increased demand for sovereign bonds and consequently driving up their yields.
The VIX has a diminished effect on bond yields in advanced countries, as these bonds are considered safe-haven assets During periods of increased market uncertainty indicated by a rising VIX, investors tend to seek refuge in these bonds, leading to higher prices and lower yields This behavior helps to lessen the impact on advanced countries' bond yields compared to those in emerging and ASEAN nations.
Hypothesis 5: The vulnerability of climate change has a negative impact on Moody's numeric rating.
Climate change vulnerabilities significantly undermine Moody's numeric rating system by introducing complex risks that impact the creditworthiness and financial stability of countries and entities.
The rising frequency and intensity of extreme weather events, such as hurricanes, floods, and wildfires, lead to considerable economic losses and disruptions in key industries These disasters place a strain on government resources, drive up insurance costs, and damage infrastructure, negatively impacting a nation's fiscal health and its capacity to meet debt obligations, ultimately resulting in lower credit ratings Additionally, the long-term effects of climate change on economic growth, agricultural productivity, and public health can weaken a nation's economic fundamentals, increasing vulnerability to fiscal imbalances Consequently, this situation influences Moody's evaluation of a country's creditworthiness.
Hypothesis 6: Readiness of climate change has a positive impact on Moody '.S ’ numeric rating.
Countries that demonstrate readiness for climate change adaptation and mitigation can positively influence their Moody's credit ratings A proactive stance in tackling climate-related risks, through investments in resilient infrastructure and effective climate policies, enhances economic stability By promoting sustainable practices, these nations are better positioned to manage climate disruptions, ultimately reducing the risk of default on debt obligations and leading to improved credit ratings.
Effective climate change readiness is linked to robust governance and regulatory frameworks Governments that emphasize climate action demonstrate enhanced risk management and transparency, qualities that credit rating agencies such as Moody's highly regard This improved governance fosters stable economic conditions, ultimately leading to elevated credit ratings.
RESULTS
Data Statistic
Variable Obs Mean Std Dev Min Max
Source: Author’s estimation using Stata Ỉ6
Table 4.1 presents detailed descriptive statistics, including the number of observations, mean values, standard deviations, minimum and maximum values for all variables related to the regression analysis This analysis examines the influence of climate change vulnerability and readiness on sovereign risk levels across 42 countries from 2004 to 2022, utilizing a total sample size of 798 observations for each variable However, two variables, specifically the 10-year bond and the Fiscal balance/GDP, have fewer than 798 observations due to data limitations in certain countries.
The analysis of the two dependent variables, the 10-year bond yield and sovereign rating, reveals the credit risk levels of various countries, with average values of 4.22 and 12.49, respectively The 10-year government bond yields exhibit significant fluctuations, ranging from a low of 0.5267 to a high of 23.6125, indicating uneven credit risk across countries Meanwhile, the average sovereign rating of 12 aligns with an A2 rating, suggesting a relatively safe credit risk level, although the stability of this assessment remains uncertain.
The variables of Vulnerability and Readiness reflect each country's climate vulnerability and adaptation capabilities to climate change While the average vulnerability score hovers around 24 for all countries, there is a notable disparity in the adaptation scores, with developed nations exhibiting significantly higher readiness levels than their developing counterparts This highlights a clear gap in climate adaptation readiness, emphasizing the need for targeted efforts to enhance resilience in vulnerable nations.
The macroeconomic factors of each country, including Current account/GDP, GDP per capita, Real GDP growth, Public debt/GDP, and Fiscal balance/GDP, reflect their economic growth rates and development policies through debt ratios and liquidity measures An analysis of 798 variables across 42 countries from 2004 to 2022 reveals significant disparities in economic development, with considerable variations in average, maximum, and minimum values indicative of each nation's unique development strategy.
Correlation
Source: Author's estimation using St at a /6
The correlation coefficients among independent variables are all below 0.8, indicating the absence of multicollinearity among the explanatory variables However, since the correlation coefficient matrix alone does not comprehensively assess the impact of independent variables on one another, it is essential to conduct a variance inflation factor (VIF) test for a more thorough evaluation.
Regression Results
Multicollinearity occurs when independent variables in a regression model are correlated, leading to inaccurate estimates of regression coefficients This correlation increases the standard errors of the variables, resulting in larger confidence intervals and unreliable t-statistics To assess the impact of multicollinearity on regression coefficients, the variance inflation factor (VIF) test is utilized, which evaluates the sensitivity of these estimates to collinearity.
This one will be used to determine whether our model meets this assumption Multicollinearity exists when the VIF value exceeds 5 (Ringle, Wende, and Becker,
2015) As the results from Table 2, our sample is free from multicollinearity
Table 4.3: Multicollinearity test results using VIF
Source: Author's estimation using Stata 16 Cross-section dependence test
The author employs the Pasaran CD test to examine the hypothesis of dependence among cross-sectional observations in panel data The findings derived from the analysis conducted using Stata software reveal significant results.
Table 4.4: Cross-section dependence test
Variable CD-test p-value average joint T mean p mean abs(p) yearbond 74.127 0.000 18.86 0.58 0.64
Notes: Under the null hypothesis ofcross-section independence, CD - N(0,l)
P-values close to zero indicate data are correlated across panel groups.
Source: Author's estimation using St ata J 6
The p-value of 0.0000 is less than 5%, leading the author to reject the null hypothesis (H0) of no cross-sectional dependence, thereby concluding that the panel data exhibits cross-sectional dependence among observations Consequently, the authors will employ the CIPS test of stationarity (Pesaran, 2007) for each variable to analyze this dependence.
Source: Author's estimation using Stata 16
The analysis reveals that the absolute value of the test statistic coefficients for most variables exceeds the absolute value of the critical values at significance levels of 10%, 5%, and 1%, or at the first difference The only exception is the Sovereign Rating variable, which is stationary at the second difference Consequently, the authors conclude that all variables in the research model are stationary Moving forward, the author will employ the Fixed Effects Model (FEM) to estimate the model.
Table 4.6: Westerlund test for cointegration
Source: Author's estimation using Stata 16
The Westerlund test, which operates under the null hypothesis of no cointegration, indicates that the series included in the model do not exhibit cointegration This finding enhances the reliability of the estimated values derived from the model.
Test: Ho: difference in coefficients not systematic
Source: Author's estimation using Stata 16
To determine the appropriate model for this research, a Hausman test was conducted to compare the random effects and fixed effects models The results indicated a prob>chi2 value of 0.0000, which is less than the 5% significance level This clearly demonstrates that the Random Effects Model is the suitable choice for this analysis.
Using the FEM in Stata 16 to run the model, the results of this research for each country category are as followed:
Table 4.8: The determinants of sovereign bond yields
Climate risk vulnerability and readiness
Countries fixed Yes Yes Yes Yes effects
Time effects No No No No
Note: Standard errors in parentheses; ***p < 0.01, **p < 0.05, *p |t| [95% Conf :[ntervalj
Source: Author's estimation using Stata 16
The analysis reveals a negative correlation between sovereign ratings and climate change vulnerability, indicated by a regression coefficient of -0.1941 This suggests that increased vulnerability to climate change corresponds with lower Moody's numeric ratings Additionally, the findings align with the performance of 10-year government bonds, while also indicating that factors such as Current Account, Public Debt, and Fiscal Balance similarly impact Sovereign Ratings in relation to climate change vulnerability.
The coefficient of 029 indicates a direct correlation between climate change readiness and Moody's credit rating, suggesting that enhanced preparedness for climate change can positively influence credit score bands.
Table 4.12: Robustness: The determinants of sovereign bond rating
Robust Std Err. t p>|t| [95% Conf Interval]
Source: Author's estimation using Stata Ỉ6
A summary of findings compared to the hypotheses
The findings of this study
Readiness has a negative and higher impact on Emerging countries, ASEAN, and Advanced countries respectively.
The impact ofeach variable has significantly changed through each group
VỈX has a positive and higher impact on Advanced, Emerging countries, and ASEAN respectively.
Source: Author's estimation using Stata Ỉ6
The author employed a structural panel VAR methodology to analyze the interplay between sovereign bond yields and climate risk exposure and preparedness The Cholesky ordering was established with vulnerability first, followed by readiness, macroeconomic fundamentals, and concluding with the 10-year sovereign bond price.
Figure 4.1: Panel SVAR according to Cholesky Order
Response to Cholesky One S.D (d.f adjusted) Innovations + 2 S.E.
Response of 10-YEAR-BOND to VULNERALITILY Response of 10-YEAR-BOND to READINESS
Response to Cholesky One S.D (d.f adjusted) Innovations + 2 S.E.
Response of 10-YEAR-BOND to VULNERALITILY Response of 10-YEAR-BOND to READINESS
Response of 10-YEAR-BOND to VULNERALITILY Response of 10-YEAR-BOND to READINESS
Response to Cholesky One S.D (d.f adjusted) Innovations ± 2 S.E.
Response of 10-YEAR-BOND to VULNERALITILY Response of 10-YEAR-BOND to READINESS
Response to Cholesky One S.D (d.f adjusted) Innovations Ỉ 2 S.E.
Source: Author's estimation using Eviews 12
Research indicates that a positive shock to climate risk vulnerability results in increased sovereign bond yields, while a positive shock to readiness causes a decrease in these yields This trend is consistent across a dataset of 40 countries, with ASEAN economies showing a particularly pronounced impact The statistical significance of these findings is established for ASEAN nations over a 19-year period, maintaining consistent significance in subsequent analyses.
Bond yields react significantly and persistently to climate risk readiness across all countries, with effects lasting for about 19 years This long-term influence highlights the urgent need for policymakers to enhance efforts in mitigating the impacts of physical climate risks, particularly in economies vulnerable to climate change Neglecting these measures could lead to serious repercussions for fiscal stability and overall economic growth.
CONCLUSIONS
Conclusion
This research investigates the influence of climate vulnerability and readiness on sovereign risk within the Vietnamese market from 2004 to 2022 The findings reveal a statistically significant positive correlation between climate change vulnerability and sovereign risk, affecting all groups of countries analyzed, with the most pronounced impact observed in ASEAN nations.
The author found a significant negative correlation between climate change readiness and 10-year sovereign bond yields, indicating that this readiness has a more pronounced impact on Emerging Market Economies (EMEs) and ASEAN countries compared to Advanced economies.
This study reveals an absence of a clear correlation between macroeconomic fundamentals and sovereign risk, even when analyzing different groups of countries This lack of correlation can be attributed to the varying economic policies implemented by developing nations.
Our research indicates a correlation between climate vulnerability and Moody’s country ratings, revealing that increased climate vulnerability leads to lower ratings and vice versa The acknowledgment of climate-conscious policies by international credit rating agencies introduces a significant aspect to this dialogue These ratings impact borrowing costs and reflect a government's credibility in managing risks, which in turn attracts foreign investment and supports economic growth.
Vulnerability, while initially challenging, can drive significant positive change across ASEAN, emerging, and advanced nations For advanced countries, it acts as a catalyst for investing in preparedness and sustainability, enhancing long-term stability In emerging nations, vulnerability encourages innovation and adaptability, paving the way for accelerated economic growth and development In the context of ASEAN, it promotes regional cooperation among member states, fostering solidarity and creating a more integrated community to tackle common threats.
In summary, climate change and sovereign risk are interconnected issues that carry significant consequences It is crucial to grasp the intricate relationships among environmental, economic, and political elements to develop effective risk mitigation strategies Coordinated efforts by governments and international organizations are necessary to enhance preparedness, diversify economies, and support sustainable practices Additionally, financial institutions and investors should incorporate climate risks into their decision-making and promote responsible corporate conduct The future stability and prosperity of nations hinge on our ability to address these challenges during this period of environmental change.
Limitations and Future Research
This study investigates the relationship between empirical evidence of climate change and sovereign risk, focusing on 42 countries out of 195 globally However, the limited sample may hinder a comprehensive analysis, as data is lacking for several nations severely affected by climate change.
3 over 10 countries most affected by climate from 1999 to 2018 got examined.
Robust regression is commonly used in financial mathematics and econometrics, but its effectiveness is limited due to the author's lack of understanding regarding the presence of accurate outliers and the correct values of relative potencies Moreover, robust procedures may give the false impression of having no implementation conditions, leading researchers to apply them inappropriately While these methods relax the assumptions of normality and outlier occurrence, other conditions still apply In cases where the classic method's conditions are fully met, robust approaches can be less powerful than traditional methods (Courvoisier & Renaud, 2010) Despite efforts to control confounding errors and minimize bias, potential contradictions may still exist in the data.
Future research should explore additional variables related to climate change, focusing on the interplay between environmental policy and climate impacts, as well as the associated risks The significance of this topic is heightened by the rapid advancement of modern technology Therefore, upcoming studies must expand their scope to include the effects of technological innovations and convergence, which are essential for effective environmental protection.
Acevedo, s (2014) Debt, growth, and natural disasters: A Caribbean trilogy Washington, DC: International Monetary Fund IMF Working Paper No 14/125.
Acharya, V V., Drechsler, I., & Schnabl, p (2014) A pyrrhic victory? Bank bailouts and sovereign credit risk The Journal of Finance, 69(6), 2689-2739.
Bachner, G., Bednar-Friedl, B., & Knittel, N (2019) How does climate change adaptation affect public budgets? Development of an assessment framework and a demonstration for Austria Mitigation and Adaptation Strategies for Global Change, 24, 1325-1341.
Batten, s., Sowerbutts, R., & Tanaka, M (2016) Let's talk about the weather: the impact of climate change on central banks Social Science Research Network.
Battiston, s., & Monasterolo, I (2019) A climate risk assessment of sovereign bonds. Zurich: University of Zurich Mimeo.
Beirner, J., & Fratzscher, M (2013) The pricing of sovereign risk and contagion during the European sovereign debt crisis Journal of International Money and Finance, 34, 60-82.
Beirner, J., Renzhi, N., & Volz, u (2021) Feeling the heat: Climate risks and the cost of sovereign borrowing International Review of Economics & Finance, Volume 76, 2021, 920-936.
Beirner, J., Renzhi, N., & Volz, u (2021) Bracing for the Typhoon: Climate change and sovereign risk in Southeast Asia Sustainable Development 3, 537-551.
Brannon, H R., Daughtry, A c., Perry, D G., Whitaker, w w., & Williams, M (1957). Radiocarbon evidence on the dilution of atmospheric and oceanic carbon by carbon from fossil fuels Transactions, 38(5), 643.
Climate change significantly impacts the cost of capital in developing countries, as highlighted by Buhr et al (2018) Additionally, Burke, Hsiang, and Miguel (2015) reveal that rising temperatures have a non-linear effect on global economic production, emphasizing the urgent need to address climate-related challenges to ensure sustainable economic growth.
Burke, M., Hsiang, s M., & Miguel, E (2015b) Climate and conflict Annual Review of Economics, 7, 577-617.
Cai, p., Gan, ọ., & Kim, s (2018) Do sovereign credit ratings matter for foreign direct investments? Journal of International Financial Markets, Institutions and Money, 55, 50 64.
Christiano, L J., Eichenbaum, M., & Evans, c L (1999) Monetary policy shocks: What have we learned and to what end? In J B Taylor, & M Woodford (Eds.), 1 Handbook of macroeconomics (pp 65-148) (Amsterdam: Elsevier).
Clark, E (1997) Valuing political risk Journal of International Money and Finance, 16, 477-490.
Cuadra, G., & Sapriza, H (2008) Sovereign default, interest rates, and political uncertainty in emerging markets Journal of International Economics, 76(1), 78-88.
Ccvik, s., & Jallcs, J T (2022) This changes everything: Climate shocks and sovereign bonds* Energy Economics, 107, 105856.
Dcllink, R., Hwang, H., Lanzi, E., & Chateau, J (2017) International trade consequences of climate change OECD Trade and Environment Working Papers.
Edwards, s (1984) “LDC foreign borrowing and default risk: An empirical investigation, 1976-80 The American Economic Review, 74(4), 726-734.
Favero, c., & Missale, A (2012) Sovereign spreads in the eurozone Which prospects for a Eurobond? Economic Policy, 27, 231-273.
Farhi, E., & Gabaix, X (2016) Rare disasters and exchange rates * Quarterly Journal of Economics, 131(1), 1-52.
Kling, G., Lo, Y c., Murinde, V., & Volz, u (2018) “Climate vulnerability and the cost of debt.’* center for global finance working paper No 12/2018 London: SOAS University of London.
Klomp, J., & Valckx, K (2014) Natural disasters and economic growth: A meta-analysis Global Environmental Change, 26, 183-195.
Klusak, p., Agarwala, M., Burke, M., Kraemer, M., & Mohaddes, K (2021) Rising temperatures, falling ratings: The impact of climate change on sovereign credit ratings.
Revelle, R., & SueB, H E (1957) Carbon Dioxide Exchange Between Atmosphere and Ocean and the Question ofan Increase of Atmospheric CO2during the Past Decades Tellus B: Chemical and Physical Meteorology, 9(1), 18-27.
Schuler, p., Oliveira, L E., Mele, G., & Antonio, M (2019) Managing the fiscal risks associated with natural disasters In M A Pigato (Ed.), In fiscal Policies for Development and climate action (pp 133-153) Washington, DC: World Bank.
United Nations Conference on Trade and Development (2019) Trade and development report 2019: Financing a global green new deal Geneva, Switzerland: United Nations Conference on Trade and Development.
Volz, u (2021) The IMF and the macro-criticality of climate change In K p Gallagher,
& H Gao (Eds.), 108-118Building back a better global financial safety net.
Boston, MA: Global Development Policy Center, Boston University.
Volz and Ahmed (2020) discuss the macro-financial risks faced by climate-vulnerable developing countries and emphasize the crucial role of the IMF in addressing these challenges They propose a collaborative action agenda between the V20 group and the IMF to enhance resilience against climate-related financial risks This initiative aims to support sustainable finance and adaptation strategies, highlighting the need for effective policy responses in the face of increasing climate uncertainties.
Volz, u., J Beirne, N Ambrosio Preudhomme, A Fenton, E Mazzacurati, N Renzhi and
J Stampe 2020 Climate Change and Sovereign Risk London, Tokyo, Singapore, and Berkeley, CA: SOAS University of London, Asian Development Bank Institute, World Wide Fund for Nature Singapore, and Four Twenty-Seven.
Wilbanks, T J., & Sathaye, J (2007) Integrating mitigation and adaptation as responses to climate change: a synthesis Mitigation and Adaptation Strategies for Global Change, 12(5), 957-962.
Table AI: List of Countries
Table A2: Measures for Climate Vulnerability
Projected change in cereal yields Projected population change
Projected change of annual runoff Projected change of annual groundwater recharge Freshwater withdrawal rate
Projected change in deaths from climate-induced diseases Projected change in vector-borne diseases
Projected change in biome distribution Projected change in marine biodiversity Ecological footprint
Protected biome Engagement in international environmental conventions Habitat
Projected change ofwarm periods Projected change in flood hazard
Projected change in sea level rise impacts Dependency on imported energy
Population living less than 5m above sea level
Table A3: Measures for Climate Readiness
Political Stability and Non-violence Control of Corruption
Rule of Law Regulatory Quality
Social Inequality ICT Infrastructure Education
Table A4: Conversion of Moody's ratings into numeric ratings