As countries pursue economic development andindustrialization, the relationship between economic growth and environmentaldegradation, particularly air pollution, has emerged as a critica
RATIONALE OF THE STUDY
In recent decades, the global decline in environmental quality has raised significant concerns, particularly as nations prioritize economic development and industrialization This has led to increased focus on the connection between economic growth and environmental degradation, especially air pollution A substantial body of research has emerged, primarily investigating the Environmental Kuznets Curve (EKC) hypothesis, which posits an inverted U-shaped relationship between economic development and environmental degradation.
Low and low-middle income countries encounter distinct challenges as they pursue economic growth, which is crucial for alleviating poverty and enhancing living standards However, this growth often leads to significant environmental degradation The tension between advancing the economy and maintaining environmental quality creates intricate policy dilemmas that necessitate thorough empirical research and evidence-based strategies.
Air pollution poses significant risks to public health, ecosystem stability, and climate change, making it a critical environmental issue Long-term exposure to elevated pollution levels leads to respiratory and cardiovascular diseases, as well as decreased life expectancy, particularly impacting vulnerable groups Recognizing the relationship between economic growth and air pollution is essential for creating sustainable policies that harmonize economic development with environmental conservation.
AIMS OF THE STUDY
This research investigates the relationship between economic growth and air pollution in low and low-middle income countries from 2015 to 2020, testing the Environmental Kuznets Curve (EKC) hypothesis It identifies key economic factors that influence air pollution levels at various development stages and examines the sectoral contributions of industrialization, energy consumption, and urbanization Additionally, the study analyzes regional and income-group differences in pollution-growth dynamics Ultimately, the research aims to inform effective environmental policies that promote sustainable economic growth while preserving environmental quality.
OBJECTIVES AND SCOPE OF THE STUDY
Object of the Research
The primary focus of this study is to analyze the relationship between air pollution and economic growth in low and low-middle income countries from 2015 to
2020 The research will examine various economic indicators and their correlation with different air pollutant measures to provide a comprehensive understanding of this relationship.
Specific Objectives
To examine the existence and nature of the Environmental Kuznets Curve in the context of low and low-middle income countries
To identify the key economic factors that influence air pollution levels in these countries
To analyze how different types of economic activities affect various air pollutants
To investigate whether there are significant differences in pollution-growth relationships across different regions or income groups within the sample
To formulate policy recommendations based on empirical findings
Scope of the Research
Temporal Scope: The study covers the period from 2015 to 2020, providing a recent analysis of the relationship between economic growth and air pollution.
Geographical Scope: The research encompasses 76 low and low-middle income countries across different regions of the world.
Methodological Scope: The study employs panel data analysis techniques to examine the relationship between economic indicators and air pollution measures.
This research highlights a significant gap in existing literature by concentrating on low and low-middle income countries, contrasting with the predominant focus on developed nations and emerging economies in prior studies The findings aim to enhance the understanding of sustainable development pathways for nations at earlier stages of economic development.
EMPIRICAL STUDY ON EKC
The Environmental Kuznets Curve (EKC), proposed by Simon Kuznets, suggests an inverted U-shaped relationship between economic development and environmental degradation Initially, as economies industrialize, pollution levels rise However, as income levels increase, accompanied by greater environmental awareness, technological progress, and improved regulations, a decline in pollution levels is observed.
Empirical studies on the Environmental Kuznets Curve (EKC) hypothesis reveal mixed results that vary by country, pollutant, and methodology Notably, Grossman & Krueger (1995) found an inverted U-shaped relationship between economic growth and pollution levels, indicating that while pollution may initially increase, it tends to decrease as economies develop; however, this effect is less pronounced in poorer nations where economic growth does not enhance environmental awareness Stern (2004) highlighted that not all pollutants conform to this pattern, with some showing linear or N-shaped relationships, suggesting that economic growth alone does not ensure environmental improvements This perspective is further supported by Selden & Song (1994), emphasizing that different environmental indicators respond variably based on economic structures and policy measures.
Empirical findings reveal significant variations in environmental issues based on national income levels, regulatory frameworks, and industrial structures Shafik (1994) identified an Environmental Kuznets Curve (EKC) pattern for urban air pollution in certain countries, while noting that deforestation and water pollution did not follow this trend In developing nations like China and India, research by Zhang et al (2017) and Dasgupta et al (2002) indicates that pollution initially increases with economic growth, but environmental improvements are largely contingent upon policy interventions, technological advancements, and a transition to cleaner industries For instance, China's efforts to reduce air pollution in the early 2000s were primarily driven by government regulations and the adoption of clean energy, rather than a natural decline linked to rising income levels.
The Environmental Kuznets Curve (EKC) hypothesis is supported for local pollutants like sulfur dioxide and nitrogen oxides, but shows inconsistency for global pollutants such as carbon dioxide and methane Research by Dinda (2004) indicates that while wealthier nations may reduce visible pollution, they often transfer carbon-intensive production to developing countries through outsourcing and globalization Furthermore, studies by Holtz-Eakin and Selden (1995) reveal that although other pollutants decrease with rising income levels, CO2 emissions continue to rise, particularly in middle- to lower-income nations where economic and population growth is most rapid This highlights the concern that economic growth alone is inadequate for addressing climate change.
Recent studies underscore the influence of economic structure on pollution trends, revealing significant regional disparities in the Environmental Kuznets Curve (EKC) Stern (2017) noted that Latin America and Southeast Asia have seen slower pollution reductions compared to Europe and North America, primarily due to differences in environmental governance and regulatory frameworks Wealthier regions benefit from stricter policies and technological advancements, while many developing countries face high pollution levels due to weaker regulations and enforcement Hossain (2011) highlighted that South Asian economies experience industrial growth without stringent environmental regulations, leading to persistent pollution despite rising income levels This challenges the EKC hypothesis, indicating that economic growth alone does not ensure environmental improvement, as high emissions, urbanization, and weak institutional capacity continue to drive environmental degradation.
A key aspect often neglected in Environmental Kuznets Curve (EKC) research is the impact of environmental policies, technological progress, and institutional quality on pollution trends Research by Panayotou (1993) and Carson (2010) highlights that robust environmental regulations and investments in green technology can accelerate the turning points in the EKC Countries like Japan and Germany exemplify this by successfully reducing air pollution through the adoption of renewable energy, stringent emission regulations, and support for green innovation Japan's proactive environmental strategies since the 1970s and advancements in energy efficiency have significantly diminished industrial pollution, while Germany's Energiewende policy has facilitated a transition to renewable energy and strict emission controls, resulting in better air quality Conversely, nations with lax environmental regulations, especially in developing areas, continue to see rising pollution levels despite economic growth, as industrial development often overshadows sustainability These insights indicate that economic growth does not inherently lead to environmental improvement; rather, strong governance and technological advancements are essential in determining a country's environmental trajectory.
RESEARCH GAP
Educational While numerous studies have explored the EKC hypothesis, several gaps remain in the existing literature
The majority of empirical research has predominantly centered on developed and emerging economies, resulting in a limited focus on low and lower-middle income countries, which are significantly affected by pollution.
- growth dynamic may differ due to weak regulatory frameworks and high dependence on pollution - intensive industries
The lack of consensus on air pollution trends is evident, as studies reveal mixed results regarding various pollutants While some, such as SO2 and NO2, demonstrate Environmental Kuznets Curve (EKC) patterns, others like CO2 and PM2.5 show consistent growth Further research is essential to comprehend the factors influencing these discrepancies, especially in developing economies where industrial growth frequently occurs without regulation.
Understanding regional and sectoral disparities is essential for crafting effective environmental policies, as different industries contribute to pollution in unique ways For instance, the energy sector, which relies heavily on fossil fuels, tends to produce long-term emissions without significant investments in renewable technologies In contrast, industrial manufacturing can see earlier reductions in pollution through stringent regulations and cleaner production methods Additionally, urban areas face immediate pollution challenges due to higher industrialization and transportation emissions, while rural regions experience slower environmental degradation linked to agriculture and resource extraction These differences underscore the importance of detailed analyses that consider specific sectoral and regional contexts, enabling policymakers to create targeted interventions that effectively address pollution across various economic sectors and geographic locations.
Existing research frequently neglects the impact of environmental policies, clean energy transitions, and technological advancements on pollution-growth relationships Exploring the influence of policy interventions on Environmental Kuznets Curve (EKC) dynamics in developing countries is a crucial area for further investigation Additionally, incorporating political economy perspectives can shed light on governmental responses to environmental challenges across various governance structures.
Most studies on the Environmental Kuznets Curve (EKC) emphasize long-term trends in pollution and economic growth, overlooking the significant short-term fluctuations caused by economic crises, industrial changes, or policy shifts These short-term variations are crucial for understanding how pollution levels respond to external shocks and regulatory measures, providing a dynamic view of environmental change For instance, economic recessions typically result in temporary reductions in industrial output and emissions, but pollution levels may rise again as economic activity picks up Additionally, transitions from manufacturing to service-oriented economies can affect pollution trends in complex ways that long-term analyses may not fully capture.
EKC models highlight the significant impact of policy interventions on short-term pollution dynamics Sudden regulatory changes, such as stricter emissions controls and carbon pricing, can result in immediate pollution reductions, but their long-term success relies on effective enforcement and technological adaptation The COVID-19 pandemic exemplifies this, as global lockdowns temporarily decreased air pollution and greenhouse gas emissions However, emissions surged again with the reopening of economies, prompting concerns about the sustainability of these temporary improvements A deeper analysis of these short-term fluctuations is essential for policymakers to develop more effective pollution management strategies that balance immediate responses with long-term environmental objectives.
THEORETICAL FRAMEWORK OF REGRESSION MODEL
Economic growth and environmental sustainability, especially regarding air pollution, are key topics in environmental economics A widely utilized analytical tool for exploring the interplay between these elements is the regression model This model analyzes the effects of economic growth on air pollution levels and the reverse, providing valuable insights into how economic activities affect environmental outcomes.
A regression model is a statistical method used to analyze the relationship between a dependent variable and one or more independent variables Its primary objective is to predict the dependent variable based on the independent variables This article will specifically focus on "Multiple Regression," one of the five key types of regression models.
The Multiple Regression Model can be expressed in the following form:
𝛽𝑖: Coefficients representing the relationship strength
As the multiple regression model gained prominence in analyzing complex relationships, numerous efforts have been made to extend its application in economic analysis, enhancing its precision and adaptability.
THEORETICAL FRAMEWORK OF REGRESSION MODEL
GDP per Capita
GDP per capita serves as a vital economic metric linked to environmental degradation Typically, a higher GDP per capita reflects increased industrial activity, energy consumption, and urbanization, which can result in elevated emissions and air pollution Conversely, affluent countries often possess greater resources to invest in cleaner technologies, which may help mitigate pollution levels.
Research by Grossman and Krueger (1995) indicates an inverted-U relationship between GDP per capita and environmental degradation, implying that as countries achieve higher income levels, they are better equipped to adopt cleaner technologies and enhance air quality.
Net FDI Inflow (Foreign Direct Investment)
Net FDI inflows can significantly impact air pollution by promoting industrial growth, as foreign direct investment typically results in the creation of manufacturing facilities and heightened production activities, leading to increased pollution levels Conversely, FDI has the potential to bring in advanced, cleaner technologies and enhance environmental standards, mitigating some of its negative effects.
Cole (2004) explored the dual role of foreign direct investment (FDI) in relation to pollution, highlighting that its environmental impact is influenced by the nature of the investment and the host country's environmental regulations.
Manufacturing (Value-Added)
The value-added manufacturing sector significantly contributes to air pollution through the emission of pollutants like particulate matter, nitrogen oxides, and sulfur dioxide As manufacturing output increases, so does the potential for environmental harm, highlighting the need for sustainable practices in industrial activities.
Supporting Research: o Shah and Shafik (1992) highlighted the significant relationship between manufacturing output and air pollution, noting that industrial sectors often contribute heavily to environmental degradation through emissions.
Urban Population
Urbanization significantly impacts air pollution, as the rising urban population leads to greater energy consumption, transportation needs, and industrial output, all of which elevate emissions Consequently, cities tend to experience higher pollution levels compared to rural areas, driven by their denser populations and intensified industrial activities.
Research by Zhang and Wen (2011) indicates that urbanization leads to higher energy consumption and an increase in road vehicles, which significantly raises pollution levels They also highlighted that urban areas often lack the necessary infrastructure to effectively address these environmental challenges.
Population Density
Population density is another significant factor influencing air pollution.
Densely populated regions face increased traffic congestion, elevated energy consumption, and greater waste generation, all of which lead to heightened air pollution The environmental strain from industrial activities and urban expansion is intensified by high population density.
Research by Jha et al (2015) indicates that areas with greater population density experience more significant pollution issues, as the increased number of residents results in heightened emissions from transportation, industrial activities, and residential heating.
Total Greenhouse Gas Emission per Capita
Total greenhouse gas (GHG) emissions per capita are a crucial environmental metric that reflects the pollution produced by individuals in a country Elevated per capita emissions often signify a higher dependence on fossil fuels, intensive industrial processes, and inefficient energy consumption, all of which exacerbate air quality issues.
Research by Stern (2006) highlights the significant impact of economic activities and energy consumption patterns on per capita emissions It indicates that developed nations are more likely to reduce emissions by adopting cleaner technologies.
Renewable Energy Consumption
Renewable energy consumption plays a crucial role in mitigating air pollution.
Increasing the share of renewable energy in the overall energy mix significantly lowers pollutant emissions, as wind, solar, and hydro sources generate minimal to no air pollution in contrast to fossil fuels.
Research by Owusu and Asumadu-Sarkodie (2016) indicates a negative correlation between the adoption of renewable energy and air pollution levels, highlighting that investments in clean energy sources lead to improved environmental outcomes.
Population
Population size significantly influences air pollution levels As populations grow, energy demands rise, transportation needs increase, and industrial output escalates, all contributing to the decline in air quality Moreover, urban areas with high population density face intensified emissions from vehicles, industries, and residential energy consumption.
Supporting Research: o Martinez-Zarzoso and Maruotti (2011) observed that population growth significantly influences environmental degradation, as higher populations intensify resource consumption and pollution levels (Martinez-Zarzoso &Maruotti, 2011).
Government Effectiveness Index
Government effectiveness plays a vital role in air pollution control, as strong governance facilitates the implementation of effective environmental policies and stringent emissions regulations Countries with higher government effectiveness scores often achieve better environmental outcomes through proactive pollution management strategies.
Research by Esty and Porter (2005) indicates that countries with superior governance and robust regulatory frameworks tend to have reduced pollution levels This is attributed to the implementation of stricter environmental laws and sustainability initiatives.
Multiple regression models serve as powerful instruments for examining the connections between air pollution and various influencing factors These models enable researchers to evaluate the impact of economic indicators, such as GDP per capita and manufacturing output, alongside demographic elements like urban population and population density, on pollution levels Through multiple regression analysis, the significance of each factor and their interactions are revealed, offering a deeper insight into the underlying causes of air pollution.
Regression models are essential for policymakers and environmental experts to forecast pollution trends and develop strategies to reduce environmental harm By incorporating a variety of predictors, including net FDI inflows and manufacturing value-added, these models recognize the complex relationship between economic growth, industrial activities, and environmental impacts.
METHODOLOGY
Method used to derive the model
Regression analysis is a powerful inference technique that allows us to make conclusions about a population based on a representative sample It estimates population parameters through sample statistics, known as regression coefficients, with the aim of finding optimal estimations for the population model The Ordinary Least Squares (OLS) method is the most common approach for estimating parameters in a linear regression model, as it minimizes all squared residuals When the model adheres to the seven fundamental assumptions of traditional linear regression, OLS estimators are consistent and considered BLUE (Best Linear Unbiased Estimator) according to the Gauss-Markov theorem Therefore, we opted to utilize the Ordinary Least Squares method for our study.
Method Used to Collect and Analyze the Data
This study aims to identify the key factors that influence the relationship between air pollution and economic growth globally from 2015 to 2020, analyzing the effects of nine economic and environmental variables.
Data were sourced from reputable organizations including the World Bank, UNDP, and WHO, as well as national statistical agencies The analysis utilized STATA software and the Fixed Effects Model (FEM) to manage unobserved heterogeneity among countries To enhance the reliability of the results, clustered standard errors were implemented at the country level, addressing potential heteroskedasticity and serial correlation within the panels.
Prior to estimation, the data underwent cleaning and transformation as needed, and were assessed for multicollinearity and essential regression assumptions The results offer valuable insights into the economic-environmental trade-offs in developing economies, forming a basis for policy recommendations.
THEORETICAL MODEL SPECIFICATION
Econometric Model
The Fixed Effects Model (FEM) is a regression technique used in panel data analysis that addresses individual-specific heterogeneity by assigning a unique intercept to each entity, such as a country or firm This approach effectively controls for time-invariant characteristics, thereby reducing potential bias in the estimates.
Our research team established a linear relationship between the dependent variable lnGHG and the independent variables, guided by relevant economic theories and prior studies Consequently, we opted to develop a multiple regression model in a linear format.
The Fixed Effects Model (FEM) is defined by the equation: \[\ln GHG_{it} = \beta_0 + \beta_1 \ln GDP_{it} + \beta_2 \ln FDI_{it} + \beta_3 \ln UP_{it} + \beta_4 \ln REC_{it} + \beta_5 \ln MV_{it} + \beta_6 \ln PD_{it} + \beta_7 \ln P_{it} + \beta_8 \ln GE_{it} + \alpha_i + \epsilon_{it}\]This model incorporates various factors influencing greenhouse gas emissions, including GDP, foreign direct investment, urban population, renewable energy consumption, motor vehicle usage, population density, and energy consumption, while accounting for individual-specific effects and random errors.
i, t represent country and time, respectively
lnGHGp_it: Log of greenhouse gas emissions per capita of country i in year t
lnGDP_it: Log of GDP per capita of country i in year t
lnFDI_it: Log of foreign direct investment inflows of country i in year t
lnUP_it: Log of urban population of country in year i t
lnREC_it: Log of renewable energy consumption of country in year i t
lnRE_it: Log of motor vehicle ownership per capita of country i in year t
lnPD_it: Log of population density of country in year i t
lnP_it: Log of pollution index of country i in year t
lnGE_it: Log of government environmental expenditure of country in year i t
u_it is the error term of country in year i t
Model testing
H0: Model has no omitted variables
H1: Model has omitted variables o The result:
Prob F> = 0.5934 > 0.05 → Accept H0, model has no omitted variables
H0: No individual-specific effects (POLS)
H1: Individual-specific effects exist (FEM) o The result:
Prob > F = 0 < 0.05 → Reject H0, use FEM instead of POLS
H0: differences in coefficients are not systematic (REM)
Ha: differences in coefficients are systematic (FEM) o The result: chi2 (8) = 38.57
Prob > chi2 = 0 < 0.05 → Reject H0, prefer FEM
H0: Homoscedasticity (error variance is constant)
H1: Heteroskedasticity (error variance varies with explanatory variables) o The result: chi2(91) = 115706.27
Prob > chi2 = 0 < 0.05 → Reject H0, heteroskedasticity is present
H1: Presence of first-order autocorrelation o The result:
Prob > F = 0 < 0.05 → Reject H0, indicating autocorrelation is present
Conclusion: Use Fixed Effects Models (FEM) with clustered standard errors for panel data (Correcting autocorrelation and heteroskedasticity)
VARIABLES AND DATA DESCRIPTION
Data source
Variable Name Code Unit Source
Emission per capita GHG Metric tons CO2 per capita World Bank
GDP per capita GDP USD World Bank
Net FDI inflow FDI % of GDP World Bank
(value -added) MV % of GDP World Bank
Urban population UP % of total population World Bank
Population density PD People per km² World Bank
Consumption REC % of total energy World Bank
Effectiveness Index GE World Bank
Data summary
This study analyzed 403 observations from low and low-middle income countries between 2015 and 2020, utilizing nine key variables, which are detailed in the table below.
Table 1: Statistics description of variables Variable Observations Mean Std Dev Min Max lnGHGp 403 1.907473 0.822683 -2.798828 4.318836 lnGDP 403 9.743947 1.020575 6.592386 11.66912 lnFDI 403 1.221184 1.319962 -2.827941 6.114171 lnUP 403 4.166592 0.3898968 2.836502 4.60517 lnREC 403 2.337773 1.399923 -2.302585 4.448516 lnMV 403 2.23479 0.8056166 -1.089513 3.551279 lnPD 403 4.75305 1.60255 1.059149 9.97084 lnP 403 15.50229 2.397168 9.785267 21.06763 lnGE 403 -0.6313152 1.154369 -6.004584 0.8260171
Source: Authors’ calculations in STATA
The mean LnGHG of the researched countries is 1.907473, with the highest at 4.318836, the lowest at -2.798828, and a standard deviation of 0.822683
The mean lnGDP of the researched countries is 9.743947, with the highest at 11.66912, the lowest at 6.592386, and a standard deviation of 1.020575
The mean lnFDI of the researched countries is 1.221184, with the highest at 6.114171, the lowest at -2.827941, and a standard deviation of 1.319962
The mean lnUP of the researched countries is 4.166592, with the highest at 4.60517, the lowest at 2.836502, and a standard deviation of 0.3898968
The mean lnREC of the researched countries is 2.337773, with the highest at 3.551279, the lowest at -2.302585, and a standard deviation of 1.399923
The mean lnMV of the researched countries is 2.23479, with the highest at 3.551279, the lowest at -1.089513, and a standard deviation of 0.8056166
The mean lnPD of the researched countries is 4.75305, with the highest at 9.97084, the lowest at 1.059149, and a standard deviation of 1.60255
The mean lnP of the researched countries is 15.50229, with the highest at 21.06763, the lowest at 9.785267, and a standard deviation of 2.397168
The mean lnGE of the researched countries is -0.6313152, with the highest at0.8260171, the lowest at -6.004584, and a standard deviation of 1.154369
Correlation matrix
lnGHGp lnGDP lnFDI lnUP lnREC lnMV lnPD lnP lnGE lnGHGp 1.00 lnGDP 0.5998 1.00 lnFDI 0.0362 0.1873 1.00 lnUP 0.5324 0.6088 0.1661 1.00 lnREC -0.3387 -0.3074 -0.1526 -0.1708 1.00 lnMV 0.1410 -0.1098 -0.2002 0.0228 0.3691 1.00 lnPD -0.2703 0.1044 0.2145 -0.0673 -0.3831 -0.2157 1.00 lnP 0.1794 -0.0227 -0.2538 0.2385 0.2565 0.6323 -0.0779 1.00 lnGE 0.4166 0.7199 0.1174 0.3752 -0.0896 0.0154 0.1082 0.0007 1.00
Source: Authors’ calculations in STATA
A.Correlation analysis of independent variables and dependent variables:
r(lnGHG, lnGDP) = 0.5998 > 0: Indicates a positive correlation between lnGHG and lnGDP, with a moderate correlation level of 59.98%.
r(lnGHG, lnFDI) = 0.0362 > 0: Indicates a positive correlation between lnGHG and lnFDI, with a very low correlation level of 3.62%.
r(lnGHG, lnUP) = 0.5324 > 0: Indicates a positive correlation between lnGHG and lnUP, with a moderate correlation level of 53.24%.
r(lnGHG, lnREC) = -0.3387 < 0: Indicates a negative correlation between lnGHG and lnREC, with a low correlation level of 33.87%.
r(lnGHG, lnMV) = 0.1410 > 0: Indicates a positive correlation between lnGHG and lnMV, with a very low correlation level of 14.10%.
r(lnGHG, lnPD) = -0.2703 < 0: Indicates a negative correlation between lnGHG and lnPD, with a low correlation level of 27.03%.
r(lnGHG, lnP) = 0.1794 > 0: Indicates a positive correlation between lnGHG and lnP, with a very low correlation level of 17.94%.
r(lnGHG, lnGE) = 0.4166 > 0: Indicates a positive correlation between lnGHG and lnGE, with a low correlation level of 41.66%.
B Correlation analysis among independent variables:
r(lnGDP, lnFDI) = 0.1873 > 0: Indicates a positive correlation between lnGDP and lnFDI, with a very low correlation level of 18.73%.
r(lnGDP, lnUP) = 0.6088 > 0: Indicates a positive correlation between lnGDP and lnUP, with a moderate correlation level of 60.88%.
r(lnGDP, lnREC) = -0.3074 < 0: Indicates a negative correlation between lnGDP and lnREC, with a low correlation level of 30.74%.
r(lnGDP, lnMV) = -0.1098 < 0: Indicates a negative correlation between lnGDP and lnMV, with a very low correlation level of 10.98%.
r(lnGDP, lnPD) = 0.1044 > 0: Indicates a positive correlation between lnGDP and lnPD, with a moderate correlation level of 10.44%.
r(lnGDP, lnP) = -0.0227 < 0: Indicates a negative correlation between lnGDP and lnP, with a very low correlation level of 2.27%.
r(lnGDP, lnGE) = 0.7199 > 0: Indicates a positive correlation between lnGDP and lnGE, with a high correlation level of 71.99%.
r(lnFDI, lnUP) = 0.1661 > 0: Indicates a positive correlation between lnFDI and lnUP, with a very low correlation level of 16.61%.
r(lnFDI, lnREC) = -0.1526 < 0: Indicates a negative correlation between lnFDI and lnREC, with a very low correlation level of 15.26%.
r(lnFDI, lnMV) = -0.2002 < 0: Indicates a negative correlation between lnFDI and lnMV, with a very low correlation level of 20.02%.
r(lnFDI, lnPD) = 0.2145 > 0: Indicates a positive correlation between lnFDI and lnPD, with a very low correlation level of 21.45%.
r(lnFDI, lnP) = -0.2538 < 0: Indicates a negative correlation between lnFDI and lnP, with a low correlation level of 25.38%.
r(lnFDI, lnGE) = 0.1174 > 0: Indicates a positive correlation between lnFDI and lnGE, with a very low correlation level of 11.74%.
r(lnUP, lnREC) = -0.1708 < 0: Indicates a negative correlation between lnUP and lnREC, with a very low correlation level of 17.08%.
r(lnUP, lnMV) = 0.0228 > 0: Indicates a positive correlation between lnUP and lnMV, with a very low correlation level of 2.28%.
r(lnUP, lnPD) = -0.0673 < 0: Indicates a negative correlation between lnUP and lnPD, with a very low correlation level of 6.73%.
r(lnUP, lnP) = 0.2385 > 0: Indicates a positive correlation between lnUP and lnP, with a very low correlation level of 23.85%.
r(lnUP, lnGE) = 0.3752 > 0: Indicates a positive correlation between lnUP and lnGE, with a low correlation level of 37.52%.
r(lnREC, lnMV) = 0.3691 > 0: Indicates a positive correlation between lnREC and lnMV, with a low correlation level of 36.91%.
r(lnREC, lnPD) = -0.3831 < 0: Indicates a negative correlation between lnREC and lnPD, with a low correlation level of 38.31%.
r(lnREC, lnP) = 0.2565 > 0: Indicates a positive correlation between lnREC and lnP, with a low correlation level of 25.65%.
r(lnREC, lnGE) = -0.0896 < 0: Indicates a negative correlation between lnREC and lnGE, with a very low correlation level of 8.96%.
r(lnMV, lnPD) = -0.2157 < 0: Indicates a negative correlation between lnMV and lnPD, with a very low correlation level of 21.57%.
r(lnMV, lnP) = 0.6323 > 0: Indicates a positive correlation between lnMV and lnP, with a moderate correlation level of 63.23%.
r(lnMV, lnGE) = 0.0154 > 0: Indicates a positive correlation between lnMV and lnGE, with a very low correlation level of 1.54%.
r(lnPD, lnP) = -0.0779 < 0: Indicates a negative correlation between lnPD and lnP, with a very low correlation level of 7.79%.
r(lnPD, lnGE) = 0.1082 > 0: Indicates a positive correlation between lnPD and lnGE, with a very low correlation level of 10.82%.
r(lnP, lnGE) = 0.0007 > 0: Indicates a positive correlation between lnP and lnGE, with a very low correlation level of 0.07%.
Conclusion: There is no perfect collinearity among the independent variables.
ESTIMATED RESULTS
The table below presents the results of the fixed-effects model (FEM) with robust standard errors, accounting for heteroskedasticity and autocorrelation:
The regression model is represented as follows: lnGHG = 2.219695 + 0.0483197 lnGDP + 0.0001292 lnFDI + 0.6905985 lnUP -
Regression analysis reveals key factors impacting greenhouse gas (GHG) emissions, with a statistically significant model indicated by an F-statistic of 3.51 and a p-value of 0.0014 This confirms that the chosen variables account for a substantial portion of the variation in emissions However, the within R-squared value of 0.2434 highlights that additional factors influencing emissions are not included in the model, underscoring the necessity for further research.
A significant finding reveals the connection between GDP and GHG emissions, with a coefficient of 0.0483 (p = 0.416) indicating that a 1% increase in GDP correlates with a 0.0483% rise in emissions However, this relationship is statistically insignificant, challenging conventional expectations This may suggest that economic growth in the examined regions does not inherently result in higher emissions, possibly due to advancements in technology or a transition towards cleaner industries.
Foreign direct investment (lnFDI) exhibits a negligible coefficient of 0.0001 and a high p-value of 0.983, indicating its statistical insignificance This implies that FDI does not have a direct effect on emissions The influence of FDI on emissions is likely contingent upon the nature of the industries that receive the investments, specifically whether they are energy-intensive or environmentally sustainable.
Urbanization (lnUP) shows a positive correlation with emissions, indicated by a coefficient of 0.6906 (p = 0.194) This implies that urban growth could lead to higher emissions driven by increased energy consumption, transportation, and industrial activities; however, the findings lack statistical significance Further investigation into urbanization trends and the implementation of policies that encourage sustainable urban development is essential.
Renewable energy consumption (lnREC) significantly reduces emissions, evidenced by a coefficient of -0.1668 (p = 0.022) This result confirms that greater reliance on renewable energy sources effectively lowers emissions To mitigate environmental impacts and support economic growth, policymakers should enhance incentives for adopting renewable energy.
The analysis reveals that manufacturing value-added (lnMV) has a coefficient of 0.0479 (p = 0.616), indicating a weak and statistically insignificant relationship with emissions This suggests that although manufacturing does contribute to emissions, its influence is not predominant in the examined context, possibly due to differences in industry structures or the adoption of cleaner production technologies.
Government expenditure on the environment (lnGE) has a coefficient of
The analysis shows a positive but insignificant relationship between government spending on environmental protection and emissions, with a p-value of 0.413 This suggests that current expenditures may not be sufficient to effectively mitigate emissions growth, or that there may be a delay in the effects of such spending.
The analysis reveals that population density (lnPD) has a negative coefficient of -0.2979 (p = 0.663), indicating a lack of statistical significance in this relationship Although denser areas typically exhibit lower per capita emissions due to more efficient public transportation and energy consumption, the findings imply that additional factors may be affecting emissions in these regions.
The population variable (lnP) exhibits a notable negative coefficient of -1.1263 (p = 0.157), although it lacks statistical significance This surprising negative value may suggest that population growth is linked to enhanced energy efficiency or policy measures that mitigate its environmental effects Additional investigation is required to better understand this relationship.
These findings emphasize the intricate relationship between economic, social, and environmental factors in influencing greenhouse gas (GHG) emissions Notably, renewable energy consumption demonstrates a clear and statistically significant impact, while other variables necessitate additional research to clarify their contributions to emissions reduction.
POLICY IMPLICATIONS
Economic Growth and Industrialization as Drivers of Emissions
Economic growth and industrialization significantly contribute to emissions, as expanding economies result in higher production and energy consumption This trend is particularly pronounced in the manufacturing sector, which is vital for many developing nations Although economic advancement is essential, the heavy dependence on fossil fuel-based industries poses serious concerns regarding long-term sustainability.
Policy Implications: Governments should focus on promoting clean technologies, improving energy efficiency, and implementing stricter environmental regulations to mitigate the negative effects of industrial expansion.
The Role of Urbanization in Emissions Growth
Rapid urbanization significantly contributes to rising emissions as cities grow The expansion of infrastructure, transportation, and residential energy use all lead to increased greenhouse gas emissions Additionally, poorly planned urban development results in traffic congestion, elevated energy consumption, and inefficiencies in waste management.
Policy Recommendations: To address this, policymakers should invest in sustainable urban planning, energy-efficient buildings, and public transportation systems to limit emissions while accommodating urban growth.
Renewable Energy as a Key Mitigating Factor
Renewable energy plays a crucial role in lowering emissions, with countries that focus on clean sources like solar and wind power achieving reduced emissions even amid economic growth Nevertheless, challenges remain, including high initial costs and insufficient infrastructure for the deployment of renewable energy.
Policy Implications: Expanding renewable energy subsidies, modernizing energy grids, and phasing out fossil fuel subsidies can accelerate the transition to cleaner energy sources.
Foreign Direct Investment (FDI) and Government Policies
Foreign investment has a complex impact on emissions, influenced by the specific sectors it supports Some investments lead to increased industrial activity and higher emissions, while others foster the adoption of cleaner technologies Furthermore, effective governance and robust environmental policies are essential in determining emissions trends, as well-regulated economies are better equipped to harmonize development with sustainability.
Policy Takeaways: Attracting "green" FDI and integrating environmental policies into economic
This study explores the factors affecting greenhouse gas (GHG) emissions in low- and middle-income countries (LMICs), highlighting the intricate link between economic development and environmental sustainability Key findings indicate that industrialization, urbanization, and economic growth significantly contribute to emissions In contrast, trade openness and foreign direct investment (FDI) exhibit varying impacts based on each country's environmental policies and industrial framework Furthermore, the adoption of renewable energy and effective environmental governance are essential for reducing emissions, underscoring the critical role of policy interventions in achieving a balance between economic advancement and sustainability objectives.
The findings highlight the necessity for strategic policy measures to balance economic growth in LMICs with environmental protection Governments should focus on green economic policies, including investments in renewable energy, energy efficiency, and stricter environmental regulations Implementing climate-related policies, such as carbon pricing and emission reduction targets, is crucial for minimizing the environmental impact of industrial and urban growth Furthermore, strengthening public-private partnerships will be vital for mobilizing financial resources and fostering technological innovation for sustainable development.
To promote a low-carbon economy, it is essential to prioritize sustainable infrastructure development, including low-carbon transportation, green buildings, and efficient waste management systems Urban planning must incorporate smart city initiatives and green spaces to reduce the environmental effects of rapid urbanization Additionally, countries should participate in global climate agreements and pursue financial and technological assistance from international organizations to expedite their transition to a low-carbon future.
Foreign direct investment (FDI) can serve as a catalyst for environmental sustainability by promoting green investments and attracting multinational corporations that adhere to high environmental standards By enhancing environmental regulations tied to trade and investment, economic activities can be aligned with long-term climate goals Furthermore, fostering regional cooperation enables low- and middle-income countries (LMICs) to exchange knowledge, access clean technologies, and work together on climate adaptation strategies.
In conclusion, LMICs can achieve sustainable development by implementing proactive policies that balance economic growth with environmental protection By focusing on renewable energy, strengthening environmental regulations, and promoting international cooperation, these nations can reduce emissions while expanding their economies The success of this transition to a greener future relies on the collaboration of governments, businesses, and international stakeholders to create a resilient and sustainable global economy.
1 Dinda (2004) Environmental Kuznets Curve Hypothesis: A Survey, https://mimoza.marmara.edu.tr/~mtekce/Dinda_2004.pdf
2 Grossman & Krueger (1995) Economic Growth and the Environment, https://www.nber.org/system/files/working_papers/w4634/w4634.pdf
3 Stern (2004) The Rise and Fall of the Environmental Kuznets Curve , https://www.sciencedirect.com/science/article/pii/S0305750X04000798
4 Selden & Song (1994) Environmental Quality and Development: Is There a
Kuznets Curve for Air Pollution?, https://www.sciencedirect.com/science/article/pii/S009506968471031X
5 Shafik (1994) Economic Development and Environmental Quality: An
Econometric Analysis, https://www.scribd.com/document/81894422/Shafik-N-
1994-Economic-Development-and-Environmental-Quality-An-Eco-No-Metric- Analysis?v=0.201
6 Zhang et al (2017) Does Economic Growth Improve Environmental Quality?
Evidence from China, https://www.sciencedirect.com/science/article/abs/pii/S0140988323003018
7 Dasgupta et al (2002) Confronting the Environmental Kuznets Curve, https://pubs.aeaweb.org/doi/pdfplus/10.1257/0895330027157
8 Holtz-Eakin and Selden (1995) Stoking the Fires? CO Emissions and₂
Economic Growth, https://www.sciencedirect.com/science/article/pii/004727279401449X
9 Stern (2017) The environmental Kuznets curve after 25 years, https://papers.ssrn.com/sol3/papers.cfm?abstract_id'37634
10 Hossain (2011) The Nexus Between Industrialization and Pollution in South
Asia: An EKC Approach, https://www.researchgate.net/publication/369625682_Nexus_Between_Urbaniz ation_Industrialization_Natural_Resources_Rent_and_Anthropogenic_Carbon_ Emissions_in_South_Asia_CS-ARDL_Approach
11 Panayotou (1993) Empirical Tests and Policy Analysis of Environmental
Degradation at Different Stages of Economic Development, https://www.academia.edu/69281307/Empirical_tests_and_policy_analysis_of_ environmental_degradation_at_different_stages_of_economic_development
12 Carson (2010) The Environmental Kuznets Curve: Seeking Empirical
Regularity and Theoretical Structure, https://www.researchgate.net/publication/46511263_The_Environmental_Kuzn ets_Curve_Seeking_Empirical_Regularity_and_Theoretical_Structure
13 Grossman, G M., & Krueger, A B (1995) Economic Growth and the Environment The Quarterly Journal of Economics, 110(2), 353–377.
14 Cole, M A (2004) Trade, the Environment, and the WTO: A Developing Country Perspective Oxford Review of Economic Policy, 20(2), 233-254.
15 Shah, S., & Shafik, M (1992) Environmental pollution and the manufacturing sector: Evidence from developing countries The Journal of Development Economics, 39(2), 333–354.
16 Zhang, J., & Wen, Z (2011) Urbanization and its impact on environmental quality: Evidence from China Environment and Development Economics, 16(6), 663-681.
17 Jha, S., Srivastava, R., & Ghosh, S (2015) The impact of population density on pollution levels in urban regions Environmental Science & Policy, 56, 48-58.
18 Esty, D C., & Porter, M E (2005) National environmental performance: An empirical analysis of policy results and determinants Environment and Development Economics, 10(4), 391-434.
19 Martinez-Zarzoso, I., & Maruotti, A (2011) The impact of urbanization on CO2 emissions: Evidence from developing countries Ecological Economics, 70(7), 1344-1353.
20 Owusu, P A., & Asumadu-Sarkodie, S (2016) A review of renewable energy sources, sustainability issues, and climate change mitigation Cogent
21 Shah, A., & Shafik, N (1992) Economic growth and environmental quality: Time-series and cross-country evidence World Bank Policy Research Working Paper No WPS 904.
22 Stern, D I (2006) Stern Review: The Economics of Climate Change Cambridge University Press.
Data Set: https://docs.google.com/spreadsheets/d/1r0Qby3MdnDCft8Ak_T1x2xqnYST990G3N PpLEdYcrrA/edit?usp=sharing
Ramsey Test for omitted variables:
Kiểm định Wald cho phương sai thay đổi: Có tồn tại phương sai thay đổi
Kiểm định Wooldridge cho tự tương quan: Có tồn tại tự tương quan
Kiểm định Hausman giúp xác định lựa chọn giữa mô hình hiệu ứng cố định (FEM) và mô hình hiệu ứng ngẫu nhiên (REM) phù hợp hơn Mô hình FEM thường được coi là lựa chọn tốt hơn, đặc biệt khi ước lượng FEM (robust) cho dữ liệu bảng có khả năng sửa chữa tự tương quan và phương sai thay đổi.
Log file: encode countryname, gen(ID) xtset ID time gen lnGHG=log( totalgreenhousegasemissionsexclu) gen lnGDP=log( gdppercapitacurrentusnygdppcapcd ) gen lnFDI=log( foreigndirectinvestmentnetinflow ) gen lnUP=log( urbanpopulationoftotalpopulation )