31 4.3.1 The impact of climate change on economic growth of high-income countries.. 40 4.3.4 The impacts of climate change on economic growth of high-income countries .... Gross Domestic
INTRODUCTION
Introduction
According to the United Nations, since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil, and gas In recent decades, there have been numerous reports from the UNFCCC and global organizations, as well as research on the impact of climate change on society and the economy Although there are differences in specific data, the majority of research finds that human activities are the primary cause of climate change, and this phenomenon has a negative impact on social and economic growth The effects include slowed economic growth, overexploitation of natural resources, competition for resources, environmental pollution, and climate change itself While climate change may have some positive effects on certain communities, regions, and industries, overall, the negative impacts outweigh the positive ones
The economic losses from climate change, combined with the costs of addressing these losses, are slowing the growth pace of many nations According to the AR6 report by the IPCC, “Under high warming (>4°C) and limited adaptation, the magnitude of decline in annual global GDP in 2100 relative to a non-global warming scenario could exceed the economic losses experienced during the Great Recession of 2008-2009 and the COVID-
19 pandemic of 2020.” The Swiss Re Institute warns that “The largest impact of climate change could be a reduction of up to 18% of global GDP by 2050 if global temperatures rise by 3.2°C.” According to the Climate Vulnerability Monitor: A Guide to the Cold Calculus of a Hot Planet by the Climate Vulnerable Forum, “The United States, China, and India are particularly expected to incur enormous losses By 2030, the economic costs for these three countries alone could total $2.5 trillion, with over 3 million deaths per year, or half of all mortality, the majority in India and China.”
It has become a societal norm that climate change can have significant economic consequences Growing concern over these negative impacts has heightened fears and pressures on millions of people Increased awareness of the issue has created a growing demand for solutions that benefit future generations It is crucial to recognize and address the current situation to develop recommendations for adapting to this phenomenon at both national and international levels.
Objective of the thesis
This study aims to determine the impact of climate change on the economic growth performance of countries worldwide Additionally, the research seeks to propose effective adaptation measures to address these challenges
Evaluate and determine the relationship between climate change and Economic Growth and determine which factors affected mostly
Identify the impacts of climate change on the economic growth of high-income countries, middle- and low-income countries
Propose measures to respond to climate change (impact mitigation and adaptation measures) appropriate to the reality of the research area.
Research questions
Economic analysis of climate change is an umbrella term for a range of investigations into the economic costs around the effects of climate change, and for preventing or softening those effects These investigations can serve any of the following purposes: i What is the relationship between climate change and economic growth? ii The degree of impact to high income countries, middle- and low-income countries? iii What solution should be taken in order to decrease the impact of climate change?
Subject and scope of the thesis
The subject of this thesis is gross domestic product growth and determinants of climate change include temperature, precipitation, carbon emission, forest depletion
Space: This thesis proceeds by pooling 121 countries around the world
Time: The study period spans from 2000 to 2022 The year 2000 was selected as the starting point to analyze economic growth within the 21st century The data collection ends in 2022 as climate data is available as a time series up to that year.
Research methodology
Data collection method: This study mainly collect data from World Bank website and desk study method is used in General research to: (1) Mention the definitions related to climate change and economic growth (2) Reality of climate change and economy in the period of 2000 and 2022 (3) Which factors contribute to the CC and impact the economy
Employing Stata 15, this study investigates the impact of climate change on economic growth through a comparative analysis of pooled OLS, fixed effects, and random effects regression models Model selection will be based on F-tests and the Hausman test To address potential autocorrelation and heteroscedasticity, the chosen model will undergo diagnostic tests and subsequently corrected using Feasible Generalized Least Squares (FGLS).
Research structure
This chapter provides an overview of the research, including the research rationale, questions, objectives, gap, methodology, scope, and structure
Chapter 2 Theoretical Background and Literature Review
This chapter presents the theoretical framework underpinning the factors influencing economic growth and identifies research gaps in the existing literature
Based on the theoretical ground and literature review discussed in the previous chapter, this chapter presents the details of data collection and research models that are used to examine the impacts of climate change on economic growth This chapter also introduces regression methodology, which includes FEM, REM and FGLS
Chapter 4 Research result and discussion
Chapter Four presents the empirical results derived from applying Pooled OLS, REM, and FEM to the data To enhance model robustness, the study employs FGLS to address potential model shortcomings The robustness of the findings is further assessed through Independent Sample T-tests The chapter concludes with a comprehensive discussion of the empirical outcomes
The concluding chapter offers a comprehensive summary of the research findings, highlighting key results and their implications Moreover, the chapter provides actionable recommendations to mitigate the adverse effects of climate change on economic growth
Chapter 1 provides a foundational overview of the research, outlining the rationale for the study and formulating the research questions within a defined scope Additionally, the chapter introduces the econometric methodologies to be employed, including Pooled Ordinary Least Squares, Fixed Effects Model, Random Effects Model, and Feasible Generalized Least Squares, which will serve as the empirical analysis in subsequent chapters.
THEORETICAL BACKGROUND AND LITERATURE REVIEW
Theoretical of climate change
In the late 19 th century, scientists first argued that greenhouse gas emissions could change Earth’s energy balance and climate Since then, CC has aroused the world’s attention and there are plenty of definitions of climate change introduced by scientists and international organizations According to the United Nations, climate change refers to long-term shifts in temperature and weather patterns Same definition is mentioned by Nasa Science, World Meteorological Organization, IPCC…
The causes of climate change stem from both natural processes and human activities Natural factors include volcanic eruptions, which release significant amounts of ash and gases, and periodic fluctuations in solar radiation that can lead to warming or cooling trends over time Additionally, shifts in oceanic circulation patterns, such as the El Niủo- Southern Oscillation, can cause significant short-term climate variations by altering heat distribution across the globe However, these natural factors play only a minor role compared to human activities
Numerous studies provide strong evidence that human activities are the primary driver of climate change (Mifont et al., 2017; Trenberth, 2018; Lynas and Perry, 2021) In its
2007 report, the IPCC stated that climate change was very likely caused by human activities By 2014, the IPCC confirmed that it is extremely likely that human activities were responsible for more than half of the observed increase in global average surface temperatures since 1951 According to the report published on February 28, 2022, human-induced climate change is causing widespread and dangerous disruptions in nature and affecting the lives of billions of people around the world, despite ongoing efforts to mitigate the risks
Climate change impacts all aspects of life in numerous and interconnected ways Rising temperatures increase the incidence of heat-related illnesses and deaths, particularly among vulnerable populations such as the elderly and those with preexisting health conditions Changes in weather patterns and higher temperatures also exacerbate air pollution, leading to respiratory problems like asthma and chronic obstructive pulmonary disease Additionally, warmer climates and expanded habitats for mosquitoes and other vectors facilitate the spread of vector-borne diseases such as malaria and dengue fever (Smoyer, 1993; Bezirtzoglou et al., 2011) Hong et al (2019) use a combination of climate, air quality, and epidemiological models to assess future air pollution deaths in China, estimating that 95% of the population will be affected annually, with increased vulnerability among China’s aging population by 2050
Climate change also negatively impacts agriculture and food security by altering growing conditions, reducing crop yields, and increasing the prevalence of pests and diseases These changes threaten food availability and stability, especially in vulnerable regions, exacerbating hunger and malnutrition (Wheeler & Braun, 2013; Gregory, Ingram, & Brklacich, 2005; Devereux & Edwards, 2004) Ringler and Wang (2010), using sample data collected from 31 provinces and cities in China spanning from 1984 to 2020, examine the mechanisms through which climate change has affected China over four decades and explore the moderating influence of agriculture.
Theoretical of economic growth
Economic growth refers to the sustained increase in an economy's capacity to produce goods and services over time Typically measured by the rise in real Gross Domestic Product (GDP) or Gross National Product (GNP), it reflects advancements in productivity, technology, capital accumulation, and human capital development
According to the Cambridge Dictionary, economic growth is defined as "an increase in the economy of a country or an area, especially of the value of goods and services the country or area produces." Solow emphasized the role of physical capital investment, while Aghiton and Howitt highlighted the increase in real output Various metrics, including GNP, GNP per capita, welfare, and social indicators (Jhingan, 2011; Todaro and Smith, 2020), can be used to assess economic development
Economic growth is a primary goal for many governments due to its correlation with higher living standards, increased employment opportunities, and improved overall societal well-being
2.2.2 Climate factors affecting economic growth
Temperature, a measure of heat or cold, significantly impacts economic outcomes While a physical property representing the average kinetic energy of particles, temperature directly influences economic growth, affecting gross regional product (Kalkuhl & Wenz,
2018), export growth (Jones & Olken, 2010), and overall economic prosperity (Abidoye
& Odusola, 2015; Alagidede, Adu, & Frimpong, 2016; Samuel Fankhauser & Tol,
2005) Dell, Jones, & Olken (2012) found that higher temperatures negatively affect economic growth predominantly in poorer countries Temperature influences both the growth rate and the overall level of economic output Additionally, extreme temperatures can contribute to political instability and broader economic impacts beyond agriculture Newell and E Sexton (2021) analyzed 800 models and found a wide range of potential GDP impacts due to temperature changes, from significant losses to substantial gains However, models focusing on GDP levels suggest more modest, typically negative impacts of 1-3% The study also highlights the importance of lagged temperature effects on GDP levels rather than growth rates While temperature impacts agricultural production in poorer countries, its influence on non-agricultural sectors and GDP growth in wealthier nations is less pronounced
Rainfall, a crucial component of the water cycle, is the precipitation of condensed water vapor from the atmosphere Its volume and variability significantly impact economic growth Research indicates a complex relationship between rainfall and economic outcomes Ali (2012) found a negative correlation between rainfall changes and economic growth in Ethiopia Sangkhaphan and Shu (2020) observed a similar negative impact of rainfall on gross provincial product (GPP) Moreover, studies consistently demonstrate that rainfall deficits, particularly in developing countries, hinder long-term economic growth (Berlemann and Wenzel, 2015; Dell, Jones, and Olken, 2008; Cabral, 2014; Gilmont, et al., 2018)
Deforestation, the purposeful reduction of forest areas, is driven by land-use changes for agriculture, grazing, and industrial development The relationship between economic growth and forest cover is complex.Cuaresma (2017) suggests that the impact of per capita income on forest cover is most pronounced during early stages of economic development, diminishing in more advanced economies Asıcı (2013), using panel fixed effects instrumental variable methodology on 213 countries from 1970 to 2008, confirms this pattern, finding a stronger correlation between income and environmental pressure in developing nations Similar findings are reported by Walker (1993) and Siregar, Sentosa, & Satrianto (2024)
Carbon dioxide emissions, primarily generated from fossil fuel combustion and cement production, have been a focal point of environmental concern Several studies have explored the complex relationship between these emissions and economic growth Narayan, Saboori, and Soleymani (2016) found a negative correlation between CO2 emissions and income growth using a cross-correlation analysis of 181 countries While Chen and Huang (2013) identified a long-term relationship between CO2 emissions and GDP growth A growing body of research suggests a decoupling of carbon emissions from economic growth in various regions: globally (Chen, Chen, Hsu, & Chen, 2016), China (Zhang & Da, 2015), Europe (Acaravci & Ozturk, 2010), OECD countries (Mercan & Karakaya, 2015), and West Africa (Muftau, Iyoboyi, & Ademola, 2014) Mardani et al (2019) conducted a comprehensive review of 175 studies, concluding that a bidirectional causality exists between economic growth and CO2
Hypothesis All the climate change factors have negative relationship with economic growth
2.2.3 Economic factors affecting economic growth
Government expenditures refer to public spending on goods and services, including healthcare, education, and social programs like pensions and unemployment insurance (OECD) The relationship between government spending and economic growth has been a subject of extensive scholarly debate While some studies have indicated a positive correlation between government expenditures and economic growth (Ansari, Gordon, &
Akuamoah, 1997; Al-Faris, 2002; Devarajan et al., 1993), the prevailing consensus suggests that government spending can stimulate economic growth, irrespective of government size or growth measurement (Wu, Jenn-Hong, & Lin, 2010; Ahuja & Pandit, 2020; Barro, 1990; Ram, 1986; Grossman, 1988) Wu, Jenn-Hong, & Lin (2010) employed a panel Granger causality test on 182 countries from 1950 to 2004 to strongly support this hypothesis However, contrary to the general view, Sinha (1998) found evidence of a long-term relationship between government spending and economic growth, despite supporting the theory of a negative correlation
A robust body of research affirms a positive correlation between employed workforce and economic growth Employment growth is a cornerstone of economic expansion, contributing to increase per capita income and mitigated inflationary pressures Consequently, it is a pivotal determinant of long-term economic prosperity (Kim, Loayza, & Balcazar, 2019; Englander & Mittelestadt, 1988; Baier, Jr., & Tamura, 2007; Limam & M.Miller, 2004) The global economy, particularly in regions like East Asia and India, has benefited significantly from expanding workforces (Chen E K., 2002; Arvind, 2004; Beugelsdijk, Klasing, & Milionis, 2018)
Extensive empirical research has examined the relationship between exchange rates and economic growth While findings are varied, a significant body of literature suggests a positive correlation between real exchange rates and economic growth, particularly in developing countries Rodrik (2008) provides evidence supporting this claim, using various measures of real exchange rates and estimation techniques Rapetti, Skott, & Razmi (2012), Habib, Mileva, & Stracca (2017), and Razmi, Rapetti, & Skott (2012) corroborate these findings Korkmaz (2013) analyzes a broad sample of developing economies and confirms the impact of exchange rate fluctuations on economic variables
Missio, Jr., Britto, & Oreiro (2015) further contributes to the literature by identifying a non-linear relationship between real exchange rates and growth
Previous studies have consistently demonstrated a negative relationship between inflation and economic growth Fischer (1993) identified key channels through which inflation hampers long-term growth, aligning his findings with the new growth theory (Barro, 1995) While the immediate impact of inflation on growth might appear modest, its long-term consequences are substantial Barro (1996) further reinforces this negative correlation, emphasizing the importance of factors like education, fertility, and low inflation for economic growth Ghosh and Phillips (1998) provide additional empirical evidence supporting a statistically significant negative relationship between inflation and growth using a large dataset
2.2.4 Theory background about economic growth and climate change
Ecological economics theory explores the intricate relationship between economic systems and the natural environment, positing that economic growth must be harmonized with ecological sustainability This theory challenges the traditional economic model, which often prioritizes short-term financial gains over long-term environmental health
It emphasizes the finite nature of natural resources and the need to account for environmental costs in economic decision-making By integrating ecological principles into economic analysis, ecological economics theory advocates for a shift towards sustainable practices that minimize environmental degradation, such as reducing pollution and conserving resources It aims to create an economy that not only supports human well-being but also preserves the ecological balance necessary for future generations The ecological economics paradigm provides a refreshing and appropriate viewpoint on the interaction between economy and ecology for progressives seeking long-term alternatives to existing patterns of economic expansion and environmental deterioration (Sheeran, 2006) According to Costanza (2010), ecological economics is a cross-disciplinary topic that includes not just ecology and economics, but also psychology, anthropology, archaeology, and history
Being introduced the Risk, Uncertainty, and Profit, published in 1921 by Frank H Knight, risk and uncertainty theory examines how uncertainties and risks related to climate change impact economic decisions and growth This theory emphasizes that climate change introduces significant unpredictability into the economic system, affecting everything from investment decisions to long-term planning Extreme weather events, shifting climate patterns, and other environmental uncertainties can disrupt economic stability and productivity Businesses and policymakers may face increased risks, leading to reduced investment and slower economic growth The theory suggests that addressing these risks requires better forecasting, adaptive strategies, and robust risk management practices By understanding and mitigating the potential economic impacts of climate-related uncertainties, economies can better navigate the challenges posed by a changing climate
Overview of related studies
The effect of climate change on sectors such as agriculture, animal husbandry, and tourism, which have share in economic growth, have been the subject of research in many studies (Dellink, Lanzi, & Chateau, 2019; Tol, 2015; Eboli, Parrado, & Roson, 2009; Bosello, Eboli, & Pierfederici, 2012; Samuel Fankhauser & Tol, 2005) Matthew, et al (2019), have studied the long-term impact of climate change on economic activity across 174 countries in the period of 1960 to 2014 Using a novel econometric strategy, the result of the research shows that persistent changes in climate have long-term negative impacts on economic growth And these effects are universal, which means all countries, developed, developing, underdeveloped are affected Furthermore, they suggest that by 2100 a persistent increase in average global temperature by 0.04C per year will reduce world real GDP per capita by 7.22% Akram (2012) has analysed the impact of climate change on economic growth for selected Asian countries during the period 1972-2009 By using fixed effect model and seemingly unrelated regression to estimate the model, the results indicated that economic growth is negatively affected by changes in temperature, precipitation, and population growth while urbanization and human development increase the economic growth
The degree to which countries are affected by climate change varies depending on the level of development Regarding developed countries, with ability to bear the cost of reducing effect of climate change, these nations tend to be affected in short-term as much research have point out (Ding Du, 2017; Colacito, Hoffmann, & Phan, 2019) Kadanali
& Yalcinkaya (2020), using linear and nonlinear procedures within the scope of new generation pel data analysis to examine the symmetric and asymmetric effect of climate change on the economic growth in the top 20 biggest GDP in the world, the study found that CC has negative and statistically significant effects on economic growth There was an upward trend for the impact to appear from 1990s and the trend happened in long- term, the result in short-term might be different Along with U.S economy is China, Yuan, Yang, Wei, & Wang (2020), using historical weather data during 1970-2015 and annual economic data during 200-2015 for 258 cities, covering 30 provinces found that a 1 temperature rise is associated with a reduction in economic growth of about 0.07 in warm season, and an increasement in economic growth about 0.36% in cold season The negative effect of warm seasonal temperature is larger than that estimated for the US Regarding poor countries, they are the damaged largest and the most vulnerable of climate fluctuation Dell, Jones, & Olken (2008) construct temperature and precipitation data from 1950 to 2003 and the main result show large and negative effects of higher temperature on growth but only in poor countries In poorer countries, they estimate that 1C rise in temperature each year reduced economic growth in that year by about 1.1 percentage points Moreover, they also found that the output climate shift to poor countries is reduction in agricultural productivity, industrial output, aggregate investment, scientific research, and political stability.
Research gap
Based on previous studies, we have identified several research gaps on a global scale Most studies are based on a fixed research region or a specific country, such as developed or developing countries As a result, there are relatively few studies conducted on a global scale Furthermore, research often focuses on analyzing two factors related to climate change: temperature and precipitation Therefore, there is a need for studies that also analyze other climate change factors such as forest cover and carbon emissions The current literature often treats economic sectors separately An approach that combines climate science, economics, and social sciences could provide a more complete understanding of the complex interactions between climate change and economic growth Finally, there is the issue of currency; most previous studies stopped at the period before the Covid-19 pandemic or focused on future predictions, so there are very few studies on the post-Covid-19 period For these reasons, the author is conducting this study to provide an updated perspective and contribute to filling the gaps in previous research
Chapter 2 has introduced the theoretical background on climate change and economic growth It also reviews previous studies on the relationship between factors and economic growth, and the impact of climate change on economic growth in developed and developing countries Chapter 2 lays the theoretical foundation for the study.
RESEARCH METHODOLOGY
Estimation procedure
There are six stages that this thesis uses to address the research question
The author will conduct a review of the background theory related At the same time, the author explores empirical studies in the world factors affecting economic growth The serve the research, the author uses the method of collecting secondary data by taking the published data from The World Bank from 2000 to 2022
This thesis then conducts the methodological review to find out the determinants applicable to the dataset Next, by using Stata 14 statistical software, a summary of description of the data characteristics of the dependent variable and the independent variables such as mean, maximum, minimum and standard deviation is performed
Step 3: Analyze the correlation matrix
The correlation matrix is done to analyze and check the correlation between the research variables Subsequently, the article implements the Variance Inflation Factor (VIF) to detect multicollinearity
Step 4: Test the model by Pool OLS, FEM and REM
The regression methods, including the FEM and REM, are used to generate appropriate results based on time series and cross-sectional data The author then conducts several tests including the F-test and Hausman test to discover the best-fitted regression model for this study
Step 5: Check the model defects
To ensure the most appropriate results, the author uses the System GMM estimator to handle the endogeneity problem Finally, an Independent Sample T-Test would be used to check the robustness of the models
Step 6: Conclusion and policy implications
Based on the results of the regressions, the author draws conclusions, and makes recommendations to improve economic growth.
Data collection
The data used in the research were collected from The World bank in the period of 2000 to 2022 Because of limited research time and incomplete data sources, the researcher selected countries which have full information of climate factors and economic factors The data was then imported into an Excel file to be edited and encoded The following stage was to conduct data cleaning to detect errors including bank cells with lack or incorrect information and complete the data matrix Finally, using Stata 15 software, the data was processed and calculated.
Model specification
In the line with prior studies, specifically studies of Esra and Omer (2020) and Jonathan and Emmanuel (2017) , this research employs regression with dependent variables is GDP growth and independent variables are temperature, rainfall, carbon emissions, forest depletion, government expenditure, inflation, government expenditure, employed workforce, exchange rate Therefore, this thesis examines the relationship between climate change and economic growth by estimating the following model
In which i is the number of countries in the time (t= 2000-2022), 𝛽 0 is Intercept, 𝜀 is the random error Measuring economic growth by GDPG, Gross domestic product growth Climate change variables include (TEM) changes of annual temperature (ARF) changes of annual rainfall, (FDL) changes of forest, (CEM) Carbon emission, (GEXP) government expenditure, (ER) exchange rate, (EL) employment workforce, and (INFLA) Inflation
Table 3-1 Identification of the Variables Used in the Model
Definitions of Variables Data Sources of Variables
GDPG Gross Domestic product growth rate
TEM Average of annual temperature
The World Bank Group Climate Change Knowledge Portal-CCKP ARF Total annual rainfall The World Bank Group Climate
FDL Forest depletion WB – The World Bank
CEM Carbon emission WB- The World Bank
GEXP Government expenditure WB- The World Bank
ER Exchange rate WB- The World Bank
EL Employed Workforce WB- The World Bank
INFLA Inflation WB- The World Bank
Variable description
The concept of gross domestic product was developed by economist Simon Kuznets GDP growth refers to the rate at which a country's Gross Domestic Product (GDP) increases over a specified period, typically measured quarterly or annually It quantifies the overall economic performance by indicating how much more value an economy produces compared to a previous period Positive GDP growth suggests economic expansion, while negative growth indicates contraction
The independent variables are synthesized and interpreted as in the following table, including explanations, units of measurement, expected signs, and sources of the variables
GDPG Gross Domestic product growth rate
ARF Average total annual rainfall mm - (Ali, 2012)
2020) (Berlemann & Wenzel, 2015) (Dell, Jones, & Olken, 2008)
FDL Forest depletion % of land area - (Cuaresma, et al.,
2013) (Chen, Chen, Hsu, & Chen, 2016) (Zhang & Da, 2015)
TEM Average annual temperature °C - (Jones & A.Olken,
2010) (Abidoye & Odusola, 2015) (Alagidede, Adu, & Frimpong, 2016) (Dell, Jones, & Olken,
(Devarajan, Vinaya, & Zou, 1993) (Wu, Jenn- Hong, & Lin, 2010) (Ahuja & Pandit,
Government Spending in a Simple Model of Endogeneous Growth,
ER Exchange rate LCU per $ + (Rodrik, 2008)
(Rapetti, Skott, & Razmi, 2012) (Habib, Mileva, & Stracca,
2013) (Chen, Chen, Hsu, & Chen, 2016) (Zhang & Da, 2015)
Data analysis methods
Paul Alliosn describes a class of regression model which can be possible to control for variables that have not or cannot be measured In specific A fixed effects regression is an estimating approach used in a panel data set that allows for the management of time- invariant unobserved individual attributes that can be associated with the observed independent variables It being confirm that panel data is the key ingredient for fixed effect regression in statistic research (Wolf & Best, 2013) (Bai, 2009) This regression assumption that those time-invariant characteristics are unique and should not be correlated with another individual characteristic
However, due to the assumption of the models, there is limitation when error terms are correlated there are up to 12 limitations including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions has been pointed out (Hill, Davis, French, & Roos, 2019) Especial, when assigning weights to the different studies, we can largely ignore the information in the smaller studies (Borenstein, Hedges, Higgins, & Rothstein,
2009) and we need to consider the random-effects model
This is a type of regression model for panel data -Random Effect Model (REM) is a statistical method that has been widely used across different areas such as Econometrics and Biostatistics for the analysis of data possibly with some inherent variability among different groups or clusters Unlike the fixed effects model, which assumes that individual-specific effects are constant for all individuals, random-effects models allow these parameters to be independently normally distributed across the population (Raudenbush and Bryk 2002) This is especially useful when one has hierarchical or panel data, as observations may be nested within larger units which are countries in this thesis By adding random variability, a REM can allow researchers to draw inferences more generalizable and robust for the underlying population capturing both within-group variance as well as between-group variance
3.5.3 FGLS - Feasible Generalized Least Squares
Feasible Generalized Least Squares (FGLS) identifies that it’s a statistical technique used frequently in econometrics to resolve the issue of heteroscedasticity, serial correlation while performing regression analysis It generalizes the Generalized Least Squares framework by adding estimators to accommodate inefficient estimation of heteroscedasticity and dependent errors The efficiency and consistency improvements come from iterative computation of FGLS estimators with initial estimates of the variance-covariance matrix The motivation is that the OLS estimators may no longer have desirable properties when econometrician deals with an issue in which error variances do non-constant and correlated errors across observations especially for complex economic relationships FGLS improves on this by adjusting for these reasons
Chapter 3 introduces the factors affecting economic growth, the data sources, and the use of data to derive test results The author also introduces the regression that will be used, including Pool OLS (Ordinary Least Squares), FEM (Fixed Effects Model), REM (Random Effects Model), and FGLS (Feasible Generalized Least Squares).
RESEARCH RESULTS
Descriptive statistics
Through collecting data from 121 countries spanning from 2000 to 2022, the results of the descriptive statistical analysis are displayed in table 4-1 The descriptive statistics encompass the following criteria: the number of observations, mean, standard deviation, minimum value, and maximum value
Table 4-1: Descriptive statistics of the world economic and climate variables
Variable Obs Mean Std.dev Min Max
Economic growth indicator (GDPG): Based on the table above, we can see that the average economic growth rate of the world from 2000 to 2022 is 3.4% The highest GDP growth rate reached 87%, and the lowest GDP growth rate reached -5%
Average annual temperature (TEM): The average temperature was 20.159 degrees, with the highest increase being 29.78 degrees and the lowest decrease being -4.9 degrees
Average annual rainfall (ARF): The average rainfall in the world was 1206.74mm The highest increase in rainfall reached 4897.4mm, while the lowest rainfall was only 6.11 mm
Average annual forestland (FDL): On average, there was a 31.1% increase in forest cover Specifically, the highest forest cover reached 92%, while the lowest increase in forest cover was also notable at 0%
Carbon emissions (CEM): On average, there was a 1.17% detrimental effect on economic growth due to carbon emissions Specifically, the highest increase in carbon emissions reached 27%, while the lowest increase was observed at 0%
Through collecting data from 19 countries spanning from 2000 to 2022, the results of the descriptive statistical analysis are displayed in table 4-2
Table 4-2: Descriptive statistics of high-income countries economic and climate variables
Variable Obs Mean Std.dev Min Max
Economic growth indicator (GDPG): Based on the table above, we can see that the average economic growth rate of high-income countries from 2000 to 2022 is 2.9% Among them, the highest GDP growth rate reached only 65%, and the lowest GDP growth rate reached -36.7%
Average annual temperature (TEM): Over the 22-year period, the average temperature change was 11.46 degrees, with the highest being 28.19 degrees and the lowest decrease being -4.9 degrees
Average annual rainfall (ARF): The high-income countries have an average of
1069.747mm annual rainfall The highest increase in rainfall reached 2011.22mm, while the lowest rainfall was 125.67mm
Average annual forestland (FDL): On average, there is 31% in forest cover
Specifically, the highest forest cover reached 68.5% which is lower than the world, and the lowest percentage of forest cover was 3%
Carbon emissions (CEM): It's noteworthy that carbon emissions in high-income countries are much lower compared to the world On average, there was 0.8% carbon emission damage Specifically, the highest carbon emissions reached 3.8%, while the lowest increase was observed at 2%
4.1.3 Middle- and low-income countries
Through collecting data from 109 countries spanning from 2000 to 2022, the results of the descriptive statistical analysis are displayed in table 4-3 The descriptive statistics encompass the following criteria: the number of observations, mean, standard deviation, minimum value, and maximum value
Table 4-3: Descriptive statistics of middle- and low-income countries economic and climate variables
Variable Obs Mean Std.dev Min Max
Economic growth indicator (GDPG): Based on the table above, the average economic growth of middle- and low-income countries from 2000 to 2022 is 3% Among these, the lowest GDP growth value was -36.4%, and the highest GDP growth was 65%
Average annual temperature (TEM): Over the 22-year period, the average annual temperature was 21.84 degrees, with the highest increase being 29.78 degrees and the lowest decrease being -4.56 degrees
Average annual rainfall (ARF): The rainfall in middle- and low-income countries saw a maximum of 4897.4mm, and minimum was 6.11mm
Average annual forestland (FDL): The forest area did not change significantly overall However, on average, the forest area of these countries increased by 31%, with the highest being 92% and the lowest increase being 0.1%
Carbon emissions (CEM): Despite the significant increase in forest cover compared to high-income countries, middle- and low-income countries face challenges in managing carbon emission, middle- and low-income countries witness an average increase of 2% The highest carbon emission was 27% and the lowest was 2%
Correlation
Correlation analysis is utilized to measure the relationship between variables in the research model The statistical outcomes, illustrating the matrix of correlation coefficients between pairs of independent variables, are displayed in the table below
GDPG TEM ARF FDL CEM GEXP ER EL INFLA
Source: Analysis from Stata 15.0 Overall, the correlations are generally weak In table 4-4, the correlation coefficients for all other pairs of variables are less than 0.8 This indicates that there is no multicollinearity when the variables are included in the model The multicollinearity can also be seen through the Variance inflation factor
TEM ARF FDL CEM GEXP ER EL INFLA Mean VIF 2.03 2.02 1.51 1.45 1.10 1.06 1.05 1.00 1.40
The result of VIF test shows that all coefficients are less than 10 and are down to satisfactory value which is less than 2, this indicates that that the multicollinearity does not seriously interfere with the estimated models.
Regression analysis
4.3.1 The impact of climate change on economic growth of the world
Table 4-6: Regression analysis table for variables of the world
Note ***, **, * denote significant level at 1%, 5% and 10% respectively
The results of the Pooled OLS regression analysis indicate that among the independent variables included in the model, annual rainfall, deforestation, carbon emissions, and government expenditure are statistically significant at the 5% and 1% significance levels This implies that changes in these variables have a statistically significant impact on the dependent variable Notably, FDI emerges as the most influential factor among the significant variables, suggesting that it has the strongest impact on the dependent variable in this model
When applying the Fixed Effects Model, the analysis reveals that the independent variables TEM, ARF, GEXP and INFLA are statistically significant in explaining the variations in GDP growth at the 1%, 5%, and 10% significance levels This indicates a robust relationship between these variables and GDP growth within the entities over time However, the variables EL and ER do not show statistical significance in the model In contrast, other economic variables such as GEXP and INFLA are found to be statistically significant at the 5% and 10% levels, underscoring their importance in influencing GDP growth within the fixed effects framework
The Random Effects Model regression results demonstrate that only the independent variables GEXP, FDI, and INFLA are statistically significant in explaining the variations in GDP growth This finding suggests that, when accounting for both within-entity and between-entity variations, government expenditure remains a crucial determinant of GDP growth The other independent variables included in the model do not exhibit statistical significance, indicating that their impact on GDP growth is not robust when considering random effects
Table 4-7: Model selection test (the world)
Hausman test Chi2 (7) = 43.07 Prob > chi = 0.0000
To select the best model, the author performs model comparison tests using the F-test and the Hausman test The F-test results indicate that the p-value is less than 5%, leading to the rejection of the null hypothesis (H0) and the acceptance of the alternative hypothesis (H1), suggesting that the FEM (Fixed Effects Model) is more suitable than the Pooled OLS model Subsequently, the Hausman test is conducted to distinguish between the FEM and the REM (Random Effects Model) The results show that the p- value (Prob > chi) is less than 5%, so we reject the null hypothesis (H0) and accept the alternative hypothesis (H1), indicating that the FEM is more suitable than the REM As a result, the FEM is determined to be the most suitable model
Table 4-8: Model deficiencies test (the world)
Test Hypothesis Statistic Value P-value
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
H0: sigma(i)^2 sigma^2 for all i chi2(107) 22301.63
Prob >F 0.6952 Source: Analysis from Stata 15.0
Heteroskedasticity testing is used to determine whether the errors in a regression model have homoscedasticity or heteroskedasticity When heteroskedasticity is present, it can make the estimates in the regression model inefficient and unreliable In this research, the author uses the Modified Wald test, which shows a p-value (Prob) of less than 0.05 Therefore, we reject the null hypothesis (H0), indicating that the model exhibits heteroskedasticity at the 5% significance level Testing for autocorrelation is a method to determine whether the residuals in a regression model are correlated with each other
If the residuals at different observations are correlated, autocorrelation exists, which can make the model's estimates inaccurate and inefficient In this research, the author uses the Wooldridge test, which yields a result of Prob = 0.7874 Since the p-value is greater than 0.05, we accept the null hypothesis (H0), concluding that the model does not exhibit autocorrelation
To address heteroskedasticity, the author employs FGLS, a method developed by economist and statistician Arnold Zellner in 1962 When applying FGLS, the analysis reveals that the independent variables TEM, ARF, FDL, and GEXP are statistically significant in explaining the variations in GDP growth at the 1%, 5%, and 10% significance levels This indicates a robust relationship between these independent variables and GDP growth over time However, the variables EL (Employment Level),
ER (Exchange Rate), and INFLA (Inflation) do not show statistical significance in the model In contrast, GEXP remains statistically significant at the 5% and 10% levels, underscoring its importance in influencing GDP growth within the fixed effects framework
Moreover, the empirical results indicate a positive relationship between GDP growth and the variable ARF, while TEM, FDL, and GEXP exhibit a negative association with GDP growth
4.3.1 The impact of climate change on economic growth of high-income countries
Table 4-9: Regression analysis table for variables of high-income countries
VARIABLE OLS FEM REM FGLS
Note ***, **, * denote significant level at 1%, 5% and 10% respectively
The author conducted model estimation for 19 high-income countries The OLS results indicate that only the independent variable ER is statistically significant at the 1% level within the model All other variables are not statistically significant Similar findings were observed for both the FEM and the REM
Table 4-10: Model selection test (high-income countries)
Hausman test Chi2 (7) = 20.06 Prob > chi = 0.0054
The author also conducted various tests to select the best model The results of the F- test, with a p-value of 0.000, indicate that the FEM is superior to the OLS model Additionally, the Hausman test results, with a p-value of 0.000, suggest that the FEM is the most appropriate model for high-income countries
Table 4-11: Model deficiencies test (high-income countries)
Test Hypothesis Statistic Value P-value
Modified Wald test for groupwise heteroskedasticity
In fixed effect regression model
H0: sigma(i)^2 sigma^2 for all i chi2(119)= 25381.50 Prob>chi20.0000
Wooldridge test for autocorrelation in panel data
In this research, the author use the Wooldridge test to test autocorrelation and the result shows that Prob = 0.1650 Therefore, with Prob > 0.05, we accept the null hypothesis H0 Thus, we conclude that the model does not exhibit autocorrelation Furthermore, the model exhibits heteroskedasticity at the 5% significance level as Prob = 0.0000
To address heteroskedasticity, the author also use FGLS and the empirical results indicate a positive relationship between GDP growth and the variables ARF and INFLA All of these variables are found to be statistically significant
4.3.2 The impact of climate change on economic growth of middle and low- income countries
Table 4-12: Regression analysis table for variables of middle- and low-income countries
VARIABE OLS FEM REM FGLS
Note ***, **, * denote significant level at 1%, 5% and 10% respectively
The author conducts model estimation for high-income countries The OLS results indicate that only the independent variables TEM, FDL, and GEXP are statistically significant at the 5% level within the model, with the independent climate variables TEMP and FDL showing a negative relationship with GDP growth All other variables are not statistically significant The FEM results reveal that the variables statistically significant at the 5% level include TEM, FDL, CEM, and INFLA, with GEXP being statistically significant at the 10% level All these variables exhibit a negative relationship with GDP growth Among the climate variables, CEM has the strongest impact on GDP growth
The Random Effects Model (REM) results show that only three economic variables are statistically significant, and none of the climate variables have an impact on GDP growth
Table 4-13: Model selection test (middle- and low-income countries)
Hausman test Chi2 (7) = 13.80 Prob > chi = 0.0005
The author performs various tests, as shown in the above table, indicating that the FEM is the most suitable model for assessing the impact of climate change on economic growth in middle- and low-income countries However, the FEM exhibits heteroskedasticity and autocorrelation issues, as evidenced by the test results presented in the table
Table 4-14: Model deficiencies test (middle- and low-income countries)
Test Hypothesis Statistic Value P-value
Modified Wald test for groupwise heteroskedasticity
In fixed effect regression model
H0: sigma(i)^2 sigma^2 for all i chi2(80)07.30 Prob>chi2=0.0000
Wooldridge test for autocorrelation in panel data
F(1,93) = 0.043 Prob > F0.8354 Source: Analysis from Stata 15.0
To address heteroskedasticity, the author employs FGLS, a method developed by economist and statistician Arnold Zellner in 1962 When applying FGLS, the analysis reveals that the independent variables considered are statistically significant in explaining the variations of the dependent variable, GDP growth, are TEM, ARF,FDL, GEXP at the 1%, 5%, and 10% significance levels This indicates a robust relationship between the independent variables and GDP growth within the entities over time However, the variable EL, ER and INFLA does not show statistical significance in the model In contrast, other economic variables such as the GEXP, are found to be statistically significant at the 5% and 10% levels This underscores the importance of these economic factors in influencing GDP growth within the fixed effects framework Moreover, the empirical results indicate a positive relationship between GDP growth and the variables ARN, whereas TEM, FDL, GEXP exhibit a negative association with GDP growth
4.3.3 The impacts of climate change on economic growth of the world
The author's research findings reveal that all independent variables impact global economic growth The specific effects of these variables are as follows:
CONCLUSIONS AND IMPLICATIONS
Conclusion
Climate change, characterized by global warming and rising sea levels, poses one of the most pressing challenges of the 21st century Its profound impacts on ecosystems, environmental resources, and human livelihoods necessitate urgent global action To comprehensively understand the relationship between climate change and economic growth, this study examines the effects of environmental factors such as temperature, precipitation, deforestation, and carbon emissions, alongside other economic variables, on economic growth Employing Stata 15 software, the research analyzes unbalanced panel data from 121 countries spanning from 2000 to 2022
Environmental variables significantly impact global economic growth Our findings indicate negative relationships between economic growth and temperature, precipitation, deforestation, and carbon emissions Deforestation emerged as the most detrimental factor, followed by carbon emissions, temperature, and precipitation High-income countries also experienced these negative impacts, albeit to a lesser extent compared to developing and poorer nations Surprisingly, our analysis revealed a positive relationship between temperature changes and economic development in these latter contexts.
Recommendations
In the research findings, forest depletion and carbon emissions emerge as prominent factors significantly impacting economic growth Hence, afforestation and reforestation represent vital solutions Trees effectively sequester CO2 from the atmosphere, facilitating ecosystem restoration and addressing environmental concerns such as soil protection, water regulation, and erosion control Successful implementation involves identifying appropriate afforestation sites based on ecological, social, and economic criteria, selecting tree species suited to local climate conditions and biodiversity needs, and ensuring diligent monitoring and management to foster establishment and growth These efforts align with global climate objectives articulated in agreements like the Paris agreement, underscoring the imperative of bolstering carbon sinks and preserving natural habitats According to author research data, three countries with lowest forest land cover would be Qatar, Netherland and Malta who should be aware and apply these mitigation measures the most
Carbon emissions play a significant role as the second strongest influencing factor that requires reduction and elimination One of the essential measures to achieve this goal is carbon pricing Carbon pricing brings several benefits, including encouraging emission reductions where costs are lowest, fostering innovation in low-carbon technologies, and contributing to global efforts to combat climate change and achieve emissions reduction targets Carbon pricing has been implemented in various countries and regions For example, carbon taxes have been effective since 1991 in Sweden, 1990 in Finland, and
1991 in Norway Major cap-and-trade systems include the European Union Emission Trading System (EU ETS), the California Cap-and-Trade Program in the USA, and the South Korea ETS However, the impact of carbon emissions remains substantial, indicating that carbon pricing measures have not fully addressed this influencing factor Furthermore, it is notable that most cap-and-trade systems are prevalent in developed countries Therefore, middle- and low-income countries and high-income countries need to exert efforts to develop and implement measures related to carbon emissions
Regarding temperature factors, although the overall impact of temperature does not significantly affect economic development, research results indicate that all countries worldwide, including both developed and developing nations, are influenced by temperature changes Therefore, the solutions proposed by the author include transitioning to renewable energy and enhancing energy efficiency With advancing technology, countries need to focus more on utilizing renewable energy sources such as solar, wind, and hydropower Additionally, there is a need to explore and adopt more clean energy sources Enhancing energy efficiency is also crucial This can be achieved by setting and enforcing standards for buildings, appliances, and industrial processes, as well as encouraging businesses and households to adopt energy-efficient practices Transitioning to renewable energy not only reduces reliance on fossil fuels but also mitigates the adverse effects of temperature changes on the economy By investing in solar, wind, and hydropower, countries can significantly lower greenhouse gas emissions, contributing to global efforts to combat climate change Furthermore, ongoing research and development of new clean energy sources should be prioritized to ensure a sustainable energy supply Enhancing energy efficiency plays a vital role in mitigating the impact of temperature on economic growth Governments should establish stringent standards for energy consumption in buildings, appliances, and industrial processes to ensure minimal energy waste Promoting energy-efficient practices among businesses and households can lead to substantial energy savings and reduced emissions Education and incentives can drive the adoption of energy-efficient technologies, further contributing to economic resilience against temperature fluctuations
Regarding the factor of annual average rainfall, while the research results indicate no significant impact on countries worldwide, mitigating its effects is crucial, especially for nations with economies primarily reliant on agriculture, such as Vietnam One necessary action is to establish and implement policy and institutional measures It is essential to have water rights and regulations to ensure the fair and sustainable distribution of water resources Policies related to water management that can be adopted include Riparian Rights, Prior Appropriation, and the Public Trust Doctrine from the USA; Water Entitlements and Allocations and the Murray-Darling Basin from Australia; and Water
Law and Irrigation and Agriculture from China Additionally, countries heavily affected by rainfall variability should diversify their economic activities This can be achieved by encouraging the cultivation of a variety of crops, including drought-resistant and water- efficient species, to reduce dependence on a single type of crop that might be vulnerable to rainfall variability Furthermore, developing non-agricultural sectors such as manufacturing, services, and tourism can reduce the overall vulnerability of the economy to changes in rainfall patterns
The study, which examined data from 318 countries worldwide between 2000 and 2022, successfully identified the impacts of climate change on economic growth and addressed the research questions posed However, several limitations remain, which future research can address and build upon
Firstly, there is a deficiency in data availability Economic data such as government expenditure, inflation, exchange rates, and employment workforce statistics are comprehensively collected by the World Bank over the years, providing a substantial dataset of over 121 countries for analysis However, many countries lack information on average temperature, average rainfall, and deforestation, which the author could not obtain Consequently, the study has not truly encompassed all countries worldwide Secondly, the author employed four models, including OLS, FEM, REM, and FLGS However, these models are not entirely optimal, as endogenous variables may still exist within the models More advanced models such as GMM, D-GMM, or system GMM are needed to test and provide the most accurate results Thirdly, the author's contributions regarding solutions are not sufficiently specific and practical for developed, developing, and impoverished countries The proposed solutions remain general and need to be more concrete and applicable to the specific contexts of these nations
Future research can address several key limitations identified in this study to advance the understanding of climate change’s impacts on economic growth more comprehensively Firstly, expanding the quantity and coverage of data collection efforts across all countries, particularly in areas such as climate variables economic indicators will enhance the scope and accuracy of analyses Secondly, extending the study period to include both short-term and long-term impacts of climate variability on economic outcomes will provide a more nuanced assessment Thirdly, incorporating a broader range of variables, spanning climate-related factors and economic metrics, will deepen the understanding of the complex relationships involved Moreover, employing advanced econometric techniques like GMM, D-GMM, or system GMM can help address model limitations and improve result reliability by mitigating endogeneity issues Lastly, future studies should aim to develop specific, practical solutions tailored to the diverse needs of developed, developing, and less high-income countries, ensuring relevance and effectiveness in addressing climate challenges at a global scale By focusing on these areas, future research can contribute significantly to refining policies and strategies aimed at mitigating climate change’s impacts on global economies
Chapter 5 proposed solutions based on the research results to mitigate the impact of each climate factor on the economy It also highlighted the limitations of the study and provided directions for future research
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