The impact of green finance, gdp growth, and natural resource rent on carbon emissions in asian countries, with policy implications for vietnam

HÒ CHÍ MINHBAO CAO TONG KET ĐỀ TÀI NGHIÊN cứu KHOA HỌC THAM GIA XÉT GIẢI THƯỞNG ‘’ NHÀ NGHIÊN CỨU TRẺ UEH ” NĂM 2024 THE IMPACT OF GREEN FINANCE, GDP GROWTH, NATURAL RESOURCES RENTS ON C

OVERVIEW

Research background and motivation

Climate change poses a significant challenge for humanity in the 21st century, jeopardizing ecosystems and human life, and directly impacting sustainable development (Murshed et al., 2021) This situation necessitates the exploration of sustainable economic development methods, requiring collaboration among nations and the international community Modern economies are increasingly recognizing the importance of fostering green growth and promoting harmonious coexistence between humans and nature, both essential for survival As a result, society is actively shifting towards green development (Kaftan et al., 2023).

Carbon dioxide (CO2) emissions play a significant role in environmental degradation, with the UK's Met Office predicting that atmospheric CO2 levels will surpass critical thresholds this year To combat the pollution resulting from these emissions, researchers are exploring potential policies and methods to effectively address the issue.

Green finance, GDP growth, income from natural resource exploitation, and carbon dioxide (CO2) emissions are intricately linked, as economic growth often leads to increased CO2 emissions and heightened pollution levels Consequently, efforts to reduce emissions may inadvertently hinder economic growth (Mardani et al., 2019) In Asia, which accounts for around 60% of global CO2 emissions (Net Zero Economy Index 2023, PwC), it is essential to prioritize CO2 emission reductions to mitigate the impacts of climate change Accurate data on this relationship is vital for leaders and policymakers, facilitating targeted recommendations for governments and businesses to minimize environmental harm This approach not only fosters sustainable development but also establishes a strong foundation for effective policy-making.

The research team has selected the topic "The Impact of Green Finance, GDP Growth, and Natural Resource Rent on Carbon Emissions in Asian Countries, with Policy Implications for Vietnam" to forecast and tackle future challenges This study aims to promote green financial initiatives that can effectively reduce adverse environmental effects.

Research objectives and questions

This research aims to explore the relationship between green finance, economic growth, and natural resource rent in relation to CO2 emissions The study is designed to meet specific objectives that will elucidate how these factors influence environmental outcomes.

Does GDP affect CO2 emissions?

Does Square of Per Capita GDP have an impact on CƠ2 emissions?

Does Natural Resources Rents affect CO2 emissions?

Does Green Finance have an impact on CO2 emissions?

Research methods

This study was conducted through two steps: preliminary research using qualitative methods and official research using quantitative methods.

Qualitative method: collect information, compare, analyze, and synthesize data on CO2 emissions information, GDP growth, natural resources rents and green finance of 31 Asian countries.

Quantitative method: the research team used the Autoregressive Distributed Lag (ARDL) model (Pesaran & Shin, 1998; Pesaran et al., 200/; Pesaran &

Pesaran, 2009) to analyze the impact of green finance, GDP growth and natural resource rent on CO2 emissions in the period 2000 - 2021 in 31 countries in Asia by StataMP 17.

Research contribution

The research paper explores the relationship between green finance, economic growth, natural resource rent, and CO2 emissions, offering valuable theoretical and practical insights It clarifies the interactions among these factors and enhances the applicability of the ARDL model in relevant studies By employing this model, the study provides a thorough analysis that contributes to environmental and development policy literature, specifically highlighting methods to reduce carbon emissions in select Asian countries through green finance Additionally, it offers policymakers critical insights into the effectiveness of green resources in mitigating CO2 emissions, aiding in the formulation of targeted environmental policies However, the authors note limitations in previous research, particularly regarding the narrow focus on small samples like China when assessing the impact of economic policy instability.

In the 2023 study by Zhong Yu, the influence of green finance, GDP growth, and natural resource rent on CO2 emissions is analyzed across a diverse sample of countries, categorized by their economic characteristics This research offers significant theoretical insights and practical implications, enhancing the understanding of how these factors interact with CO2 emissions.

The structure of the study is divided into 5 chapters Which includes:

Chapter 1 Overview This chapter introduces the research topic, research objective, methodology, and the contribution of the topic.

Chapter 2 Literature review and hypothesis development In this chapter, we briefly and critically review the theoretical basis and the related studies on the subject.

Chapter 3 Research methodology This chapter discusses in detail the research model, data, and econometric approaches.

Chapter 4 Research results Empirical results and discussions will be illustrated in this chapter.

Chapter 5 Conclusion The last chapter will conclude the paper with research implications followed by some limitations and provide recommendations and suggestions to policymakers.

Summary of Chapter 1

This chapter highlights the importance of examining the effects of green finance, GDP growth, and natural resource rent consumption on CO2 emissions Utilizing data from 31 Asian countries between 2000 and 2021, the research employs the ARDL estimator to ensure precise findings The research team aims to deliver accurate results that will serve as a valuable resource for developing more focused and effective environmental policies in both theoretical and practical contexts.

LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT 5 2.1 Theoretical background

EKC curve

The Environmental Kuznets Curve (EKC) is a theoretical model that depicts the correlation between pollution levels and a nation's economic development, measured by per capita income Initially, as a country industrializes and develops economically, pollution levels rise until they reach a certain threshold After this turning point, pollution begins to decline as the economy achieves a higher level of wealth, allowing the nation to invest in improving environmental quality and living conditions.

Due to increasing environmental pollution, there is growing interest in examining the connection between societal living standards and environmental degradation The Environmental Kuznets Curve (EKC) hypothesis, introduced in the early '90s, posits a bell-shaped relationship between economic growth and indicators of environmental degradation.

The relationship between economic growth and pollution is characterized by an initial increase in pollution due to scale effects, followed by a potential decline as technological advancements and shifts in production structures occur For example, moving from heavy industry to a service-oriented economy can significantly reduce pollution levels, illustrating the composition effect in action.

As GDP per capita rises, the demand for a cleaner environment intensifies, leading individuals to prioritize environmental quality after fulfilling basic needs This shift encourages stronger regulations and more conscientious consumer choices However, weak regulations in developing countries often attract polluting industries, driven by globalization and trade, a phenomenon known as the pollution haven hypothesis.

Developed countries, despite having cleaner environments, can inadvertently contribute to environmental degradation in developing nations by transferring polluting activities through trade and investment, which alters global pollution patterns Nonetheless, the Environmental Kuznets Curve (EKC) has its limitations and does not fully address local pollution issues.

Developed countries, with stable production structures, lend to prioritize environmental protection more than developing ones As economies develop, the costs of pollution control rise, leading to decreased pollution levels.

Theoretical Linkage of Natural Resource Rent and Carbon Emissions

Natural resource rents (NRR) encompass the revenues generated from oil, natural gas, coal (both hard and soft), minerals, and forestry These estimates are derived from the methodologies outlined in the World Bank's 2011 report, "The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium."

Current research on the impact of natural resource exploitation on CO2 emissions is limited Typically, the advantages of resource extraction are accompanied by environmental drawbacks The extraction process is energy-intensive, resulting in high energy consumption and the irresponsible release of chemical waste into ecosystems In many developing nations, illegal logging and overfishing are rising, often employing outdated methods that harm the environment The tension between resource utilization and environmental issues fuels differing viewpoints on this matter.

Moreover, revenues from natural resource extraction can stimulate economic growth and industrialization, leading to increased energy demand and consumption

As global energy demand increases, reliance on fossil fuels escalates, resulting in heightened carbon emissions Countries that depend heavily on non-renewable resources (NRR), like Nigeria, often prioritize resource extraction and exports, neglecting vital environmental and sustainable development concerns This is evident in Nigeria's oil industry, which significantly contributes to the nation's GDP and budget but has also caused severe environmental degradation, including oil pollution, biodiversity loss, and health issues for local communities, as reported by the United Nations Environment Programme in 2011.

Figure 2.1: Theoretical linkage of Natural Resource Rent and Carbon Emissions

Over Reliance on Extractive Industries

Theoretical Linkage of Green Finance and Carbon Emissions

This study explores the environmental impact of green finance on CO2 emissions Green finance involves the use of financial tools and investments to promote sustainable initiatives, specifically targeting the reduction of CO2 emissions and the mitigation of climate change.

Green finance plays a crucial role in promoting renewable energy sources, which is vital for achieving environmental sustainability According to Soha Khan and colleagues (2022), enhancing green finance is necessary to meet global targets for reducing CO2 emissions By investing in renewable energy, green finance can significantly contribute to lowering CO2 emissions through various mechanisms The theoretical link between green finance and CO2 reduction underscores its importance in addressing climate change challenges.

Figure 2.2: Theoretical Linkage of Green Finance and Carbon Emissions

Green finance plays a crucial role in advancing renewable energy technologies by offering financial support and resources By investing in clean energy sources such as wind, hydroelectric, and solar power, it reduces reliance on fossil fuels and fosters a diverse energy mix This shift not only promotes sustainability but also significantly lowers carbon emissions linked to energy production (Anwar et al., 2022; Abbasi et al., 2022).

Nathaniel et al., 2021; Habiba et al., 2022; Huang et al., 2020).

Secondly, green finance supports projects and initiatives for energy efficiency

Funding efficient energy consumption models in industries, transportation, and construction; it contributes to reducing energy use and associated CƠ2 emissions

(Huang et al., 2023a,b,c; De Rosa et al., 2023; Anwar et al., 2021b; Cai et al., 2022)

This may involve sponsoring energy-saving devices, improvement projects, and sustainable transportation systems.

Green finance plays a crucial role in fostering sustainable infrastructure development By offering financial backing for projects like public transportation, waste management, and renewable energy systems, it supports the transition towards reduced emissions and the establishment of climate-resilient infrastructure (MacArthur et al., 2020).

Empirical evidence and hypothesis development

2.2.1 The effect of GDP on Carbon emissions

Gross Domestic Product (GDP) plays a pivotal role in understanding environmental emissions, as an increase in GDP typically results in heightened consumption and production, leading to elevated carbon dioxide (CO2) emissions Researchers have explored the Environmental Kuznets Curve (EKC) theory to illustrate the intricate relationship between GDP and CO2 emissions, as highlighted in recent studies (Wang et al., 2022; Frodyma et al., 2022; Fakher et al., 2023; Huang et al., 2023a, b, c).

The Environmental Kuznets Curve (EKC) posits that as a country's per capita GDP rises, environmental degradation initially increases before eventually declining, forming an inverted U-shape In the early phases of economic growth, industries tend to focus on production and profits, which leads to heightened pollution and resource depletion However, once a country surpasses a certain income level, factors such as technological advancements and improved financial efficiency foster the adoption of cleaner technologies and better resource management Additionally, as public awareness and environmental education grow, the demand for sustainable practices also escalates (Wan et al., 2017; Sun et al., 2022; X Li et al., 2022; Chin et al., 2021a, b; Wang et al., 2022).

Governments are implementing stricter environmental regulations to encourage industries to reduce their ecological impact This shift is accompanied by an economic transition from polluting sectors to cleaner industries, such as services and knowledge-based fields, which further enhances environmental quality While the Environmental Kuznets Curve (EKC) offers important insights into the relationship between GDP and environmental degradation, it is crucial to recognize that the curve's shape and income thresholds can differ among various economies.

Hypothesis Hl: GDP Per Capita has a positive effect on Carbon emission.

Hypothesis H2: Square of GDP Per Capita has a negative effect on Carbon emission.

2.2.2 The Effect of Natural Resource Rent on Carbon Emissions

Natural resources are vital to the global economy, but their exploitation significantly affects the environment A pressing concern is the rise in CO2 emissions, which have reached a record high of 40.9 billion tons in 2023, as reported by the Global Carbon Budget Report This alarming increase has prompted researchers to investigate the connection between natural resource rent and CO2 emissions.

Huang et al (2021) examine the effects of natural resource rents, financial development, and urbanization on carbon emissions in the United States from 1990 to 2015 Their findings indicate that increased natural resource rents can help mitigate long-term environmental degradation However, they also highlight that higher levels of financial development, urbanization, and resource utilization exert significant pressure on the environment The study calls for urgent action to address the negative impacts of excessive financial growth, urban expansion, and resource exploitation, advocating for the adoption of green strategies to foster positive environmental results.

Kwakwa et al (2020) explored the relationship between Natural Resource

A study examining the relationship between rent (NRR) and CO2 emissions in Ghana from 1971 to 2013 highlights the negative environmental impacts of mining and oil/gas exploration activities.

Jahanger ei al (2022a,b) examined the relationship between resource rent and

CO2 emissions in 73 emerging economies Their results show a negative association between resource rent and carbon emissions.

The intricate relationship between natural resource rent and CO2 emissions is highlighted by varying research findings; some studies indicate a positive correlation, while others suggest a negative one This dynamic interplay is influenced by several factors, including the type of natural resources, the stage of economic development, and the effectiveness of resource management policies.

Hypothesis H3: Natural Resource Rent has a negative effect on Carbon emission.

2.2.3 The effect of Green Finance on Carbon Emissions

Xu and Li (2020) highlight that the anticipated reduction in financial obligations through green credit can significantly benefit eco-friendly organizations, thereby promoting the growth of sustainable enterprises Similarly, Priyan (2023) points out that investing in green technology is the most cost-effective approach to minimize CO2 emissions and overall expenses, even when accounting for the uncertain CO2 levels associated with production, transportation, and storage processes.

Research on the ecological impact of green finance remains limited, primarily focusing on specific financial instruments such as green loans and green bonds (Benlemlih et al., 2022; Meo and Abd).

Karim 2022) For example, (Zhang, Hong, et al., 2021) find that green credit can fundamentally lessen fossil fuel byproducts by building edge models (Liu et al.,

Research shows that green credit arrangements enhance environmental efficiency by limiting investments in high-emission industries and offering low-interest loans to green enterprises Additionally, responsible green bonds significantly improve the environmental performance of organizations and their ability to achieve green innovations.

In their 2022 study, Muhammad Saeed Meo and Mohd Zaini Abd Karim employed the ỌỌR two-stage least squares method to analyze the connection between green finance and CO2 emissions in prominent economies, including the USA, Sweden, UK, Hong Kong, and Denmark The research highlights green bonds as a key indicator of green finance The overall results reveal a significant inverse relationship, suggesting that increased green finance correlates with reduced CO2 emissions.

In summary, green finance is essential for lowering carbon emissions as it supports the financial requirements of sustainable initiatives, encourages clean energy development, reduces pollution, and limits high-pollution practices.

High-energy-consuming businesses are increasingly required to enhance their production technology and adopt energy-efficient methods due to limited capital availability In contrast, companies focused on low-carbon and environmentally friendly practices are receiving greater financial support.

Hypothesis H4: Green Finance has a positive effect on Carbon emission.

This study explores the connections between GDP, GDP squared, Natural Resource Rents, and Green Finance in relation to carbon emissions across selected Asian countries Building on established theories and prior research regarding the Environmental Kuznets Curve (EKC), the authors propose hypotheses to investigate how these economic factors influence carbon emissions, addressing gaps identified in existing literature.

Data and sample selection

The research sample includes 31 countries in the Asian region, compiled from

2000 to 2021 Information has been sourced from the World Bank Database, including indices related to Carbon Emissions, GDP, and Natural Resource Rent

Additionally, the authorial team collected Green Finance data from the International Renewable Energy Agency (IRENA) report.

Table 3.1: Summary of research variables

Symbol Variables Measurement Source Time Span

CEM Carbon Emissions CƠ2 emissions (Kt) WDI 2000-2021 GDP Per Capita GDP Per Capita GDP (current us$) WDI 2000 - 2021 GDPSQ Square of Per Capita GDP Square of Per Capita GDP WD1 2000 - 2021

NRR Natural Resource Rent Total (% of GDP) WD1 2000-2021

GRF Green Finance Total (2020 USD million) IRENA 2000 - 2021

Model specific

3.2.1 Optimal Lag Selection for ARDL Model

The Panel Autoregressive Distributed Lag (ARDL) model is superior to traditional regression methods as it delivers unbiased estimates even when variables exhibit different orders of integration, such as 1(0) or 1(1), and is effective with small panel data Its flexibility allows for varying lags among variables based on data characteristics, making it more advantageous than typical Vector Error Correction Model (VECM) estimates To determine the optimal lag structure for the panel ARDL model, we utilize the method by Kripfganz et al (2018), running the ARDL model for each country to identify the best lag configuration The most frequently occurring lag count from individual country results is then used as the lag structure for the overall model.

According to prior research, the estimation of the ARDL model can be achieved through three techniques: Mean Group (MG), Pooled Two-Way Group (PMG), and Dynamic Two-Way Fixed Effect (DFE), as outlined by Pesaran et al (1999) and Pesaran & Smith (1995).

(1999) employed the ARDL model (p, q, q, , q) To assess the degree of environmental decline, the study utilizes the subsequent functional formulations depicted in equation-1:

CEM = f(GDP,GDPSQ,NRR,NRR * GRF) (Ỉ )

The econometric representation of both models is provided in equation-2 as follows:

CEMt = a + ^GDPt + p2GDPSQt + ^NRRt + faNRR * GRFt + £t (2)

In the model's equation-2, the constant term is represented by 'a', while the elasticities of the explanatory factors are indicated by p1, p2, p3, and p4 The variables are transformed into logarithmic form, and the error term is denoted as Et, with 't' representing the time period from 2000 to 2021 Descriptive statistics for the variables utilized in this research are provided after their conversion into logarithmic form on a quarterly basis.

This study utilizes the Autoregressive Distributed Lag (ARDL) method, as introduced by Pesaran and Pesaran in 1997, to analyze both short-term and long-term relationships among the variables To confirm the presence of cointegration within the model, the research employs the error correction version of the ARDL technique developed by Pesaran et al.

2001) is utilized Equation-3 of the error correction version is provided below:

ACEMt = a0 + y r bịACEMî y s CịAGDP^ + y u dịAGDPSQî + ^ e^NRRî V i=l i = l i=l

+ ^ftANRR * GRFt-i + 8ỵCEMt_ỵ + 82GDPt_1 + S^DPSQt^ i=l

Equation 3 uses the operator A to express the first difference, which includes the short-run dynamics represented by the coefficients bi, ci, di, ei, and fl The long- run elasticities 81, 82, 83, 84, and 85 represent long-term relationships In addition, equation-4 is used within the ARDL framework to calculate the model’s long-term coefficients: r

To better understand the characteristics of the three different estimation tools mentioned above, the following assumptions are related to each estimation.

• Mean Group Regression - Mean Group (MG)

The Mean Group (MG) technique, introduced by Pesaran & Smith in 1995, estimates separate regressions for each country and calculates unweighted coefficients for these estimates This method allows for varying and heterogeneous coefficients in both the short and long run, without imposing any restrictions However, for the MG approach to be consistent and valid, it is essential to have a sufficiently large time series dataset, along with a sample size of approximately 20 to 30 countries.

Additionally, for small N, the mean (MG) estimators in this approach arc quite sensitive to outliers and small model permutations.

• Dynamic Fixed Effects (DFE) regression

The dynamic fixed effects (DFE) technique imposes a constraint that the slope coefficient and error variance are uniform across countries in the long run, while allowing for country-specific intercepts This model equalizes the adjustment coefficients and short-run coefficients among the sampled countries, and includes a clustering option to estimate within-group correlation, as noted by Blackburne & Frank.

In their 2000 study, Baltagi et al highlight that the model proposed in 2007 is affected by simultaneous equation bias, particularly in small sample sizes, due to the correlation between the error term and the lagged dependent variable.

• Pooled Mean Group (PMG) regression

The pooled mean group (PMG) regression technique combines the features of mean group (MG) and dynamic fixed effects (DFE) methods, allowing for short-term coefficients, including intercepts, to adjust based on long-run equilibrium values and varying heteroskedastic error variances across countries This approach is particularly beneficial when the long-run equilibrium relationships among variables are expected to be similar across countries or specific subsets Additionally, PMG accommodates country-specific short-term adjustments, reflecting the unique impacts of financial vulnerability, external shocks, and distinct stabilization and monetary policies in each nation.

The basic assumptions of the PMG technique are as follows:

First, the errors are uncorrelated with each other and uncorrelated with the independent variables in the regression model, that is, the independent variables must all be extraneous variables, born;

The analysis reveals a significant long-term relationship between the dependent variable and the independent variables, indicating that the estimated coefficient of the error correction term should be negative and statistically significant.

Third, the long-run regression coefficients are similar across countries Such conditions can be met by introducing ARDL lags (p, q) for the dependent (p) and independent (q) variables in error-corrected form;

The relative sizes of T and N are essential for employing dynamic panel techniques, which help reduce biases in average estimation tools and tackle heterogeneity issues This approach enables a more reliable examination of the effects of economic growth and environmental pollution variables across multiple countries, yielding more trustworthy results than traditional methods.

3.2.3 Data analysis and research process

The research will be performed using regression testing using Stata 17.0, and is performed in turn according to the following steps:

Firstly, the authors perform descriptive statistics of the studied variables

Thereby providing an overview of the characteristics of the research variables, namely through mean, standard deviation, minimum and maximum values.

The Pesaran cross-sectional dependence test (2004) is essential for identifying interdependencies among entities, aiding in model specification, and enhancing estimation accuracy By utilizing this test, researchers can significantly improve the reliability and credibility of empirical analyses in panel data studies.

Thirdly, use CIPS test (Pesaran, 2007) to verify whether the variables under consideration are non-stationary or possess unit roots.

Fourthly, use Cointegration Test of Wester Iund (2007) to examine whether there exists a shared long-term relationship among variables across different entities in the panel.

Fifthly, perform the regression according to the PMG, MG, and DFE methods to choose the most suitable model by Hausman Test (Pesaran, Shin and Smith 2001).

Variable measurement

Carbon dioxide (CO2) emissions, measured in kilotons (Kt), serve as a vital index for evaluating environmental quality and impact This metric is widely utilized to assess the effects of human activities on the environment, with data obtained from the World Development Indicators.

This report encompasses fossil CO2 emissions derived from various sources, including the combustion and flaring of fossil fuels, industrial processes such as cement, steel, chemicals, and urea production, as well as product usage However, it excludes emissions from short-cycle carbon CO2.

Gross Domestic Product (GDP) represents the monetary value of all finished goods and services produced within a country during a specific period, serving as a key indicator of economic growth It is utilized to assess the overall economic performance of a nation or region, while also allowing for the evaluation of individual industry sectors' contributions GDP encompasses all forms of private and public consumption, government expenditures, and investments, and is typically expressed in current monetary terms.

US dollars, and the data is sourced from World Development Indicators.

To test hypothesis Hl, when the GDP Per Capita (GDP) increases, it will reduce the CO2 emission, from which the coefficient p 1 is expected to have a positive impact.

To test hypothesis H2, when the GDP Per Capita (GDPSQ) increases, it will increase the CO2 emission, from which the coefficient p2 is expected to have a negative impact.

The World Bank (WDI) calculates natural resource rent by determining the difference between a commodity's market price and its average production cost This involves estimating the unit price of specific goods and subtracting the average unit cost of extraction or harvesting The resulting rent units are multiplied by the quantity of resources extracted or harvested, contributing to the overall Gross Domestic Product (GDP) of each country.

In sustainable development analysis, assessing the economic contribution of natural resources is crucial, as they often represent a significant share of GDP in many countries, particularly through income from fossil fuels and minerals These revenues typically manifest as economic rents, which are earnings that surpass the costs associated with resource extraction Natural resources yield substantial profits due to their inherent availability, unlike manufactured goods and services that face competition and consequently experience reduced profitability.

To test hypothesis H3, an increase in Natural Resources Rents (NRR) is expected to reduce CƠ2 emissions, hence, the coefficient P3 is expected to have a positive impact.

Green Finance (GRF) is a crucial indicator for measuring the extent of financial investments allocated to environmentally friendly initiatives and sustainable development The total amount of Green Finance is quantified in millions, based on 2020 figures.

US dollars (USD million), as sourced from the International Renewable Energy

Agency (IRENA) report, encapsulates the comprehensive value of financial endeavors with positive environmental impacts.

Green Finance refers to investments aimed at reducing environmental harm and promoting sustainable development, covering areas such as renewable energy, energy efficiency, greenhouse gas reduction, and conservation efforts The "Green Finance Total" data provides valuable insights into the scale of eco-friendly financial initiatives and acts as a crucial tool for evaluating progress and commitments towards activities that enhance environmental health, ultimately aiding the transition to a sustainable economy.

To test hypothesis H4, when the Green Finance (GRF) increases, it will reduce the CO2 emissions, from which the coefficient P4 is expected to have a positive impact.

This study uses data from 31 Asian countries from 2000 to 2021 The data is taken from many reputable sources, such as the International Renewable Energy

The research conducted by ỈRENA, in conjunction with the World Development Indicators, employs a model grounded in high-ranking scientific literature In this study, CO2 emissions serve as the dependent variable, while GDP, GDP squared, Natural Resource Rent, and Green Finance are identified as independent variables Various relevant tests and models were applied to derive the regression results.

RESEARCH RESULTS

Descriptive statistics

Table 4.1: Descriptive statistics of variables

The table provides descriptive statistics for the study's data sample, including the mean, standard deviation, maximum, and minimum values for each variable analyzed.

Variable Obs Mean Std dev Min Max

Between 2000 and 2021, the average GDP and GDPSQ values for certain Asian countries were 3.312864 and 11.2051, respectively, indicating the monetary value of final goods and services purchased by end users The GDP ranged from a minimum of 2.551179 to a maximum of 4.54583, while the GDPSQ fluctuated between 6.508514 and 20.66457, highlighting significant volatility in the monetary value of these goods and services during this period, which aligns with the economic conditions in Asia.

The Net Resource Rent (NRR) averages 0.18452, with fluctuations ranging from -2.971629 to 1.89999, while the Gross Resource Flow (GRF) has a mean of 0.7120101, varying between -3.464929 and 3.529796 This data highlights the uneven attention to environmental sustainability issues in various Asian countries from 2000 to 2021.

Test for Cross-Sectional Dependence

Table 4.2: Cross-section dependence of variables

Variable CD-test p-value corr abs(corr)

In the first step, the cross-sectional dependence (CD) tests developed by Pesaran

In 2004, an analysis was conducted to test for cross-sectional dependence among a panel of countries, as detailed in Table 4.2, which displays the results for specific variables and average correlation coefficients The statistical evaluation indicates a clear rejection of the hypothesis of cross-section independence, with corresponding p-values supporting this conclusion The findings strongly demonstrate significant cross-correlations, highlighting the substantial interdependence among the countries in the sample.

Unit root analysis

The study employed the second-generation Pesaran's (2007) C1PS unit root test to examine variable stationarity, effectively accounting for cross-sectional dependence among the series This approach yields more accurate results than traditional first-generation unit root tests.

The test results indicate that NRR_GRF is stationary at the level, while GDP, GDPSQ, and Natural Resources Rents are stationary at the first difference However, none of the variables show stationarity at the second difference based on both first and second-generation unit root tests Consequently, we utilized the ARDL method to assess the long-run relationship among the variables under consideration.

Table 4.4: Symmetric panel cointegration test of Westerlund

The Table 4.4 shows that with a p-valuc of 0,023, suggests cointegration across the entire panel dataset at the 5% significance level.

The Westerlund test results indicate a significant cointegration relationship among carbon emissions, GDP, GDP squared, natural resource income, and its growth rate over the long term This suggests a stable connection between economic development, natural resource use, and environmental impact, paving the way for further research into their interactions over time.

After analyzing the optimal lag structure for 31 countries in our dataset, we identified the most frequently occurring lag count among these countries to establish a unified lag structure for the global model As a result, the optimal lag structure for the global ARDL model is determined to be ARDL(1,2,2,0,0).

CEM GDP GDPQS NRR NRR_GRF idl 10 0 0 0 id2 12 2 0 2 id3 10 0 0 0 id4 10 0 2 0 ids 2 0 12 2 id6 2 112 2 id7 110 2 2 id8 112 0 0 id9 2 2 2 12 d10 dll dl2 dl3 dl4 dl5 dl6 dl7 dl8 dl9 d20 d21 d22 d23 d24 d25 d26 d27 d28 d29 d30 d31

4.6 Regression result of model (PMG - ARDL approach)

4.6.1 ARDL Panel Data Estimation Results in Long Term

Table 4.6: ARDL long run analysis of all variablesCoefficient Std Error t-Statistic Prob.Variable

Long-term estimations using carbon emissions as the dependent variable reveal crucial insights, analyzed through the Autoregressive Distributed Lag (ARDL) method After conducting the Hausman test, the Pooled Mean Group (PMG) method is identified as the most appropriate approach for this analysis.

At a 5% significance level, GDP demonstrates a positive correlation with carbon emissions, suggesting that economic growth contributes to increased emissions Conversely, the Environmental Kuznets Curve (EKC) theory is validated by the negative coefficient of the GDP squared term, indicating that while carbon emissions rise with GDP initially, the rate of increase slows down over time This suggests the potential existence of a tipping point where further economic growth leads to reduced carbon emissions, aligning with findings from previous studies such as Farooq et al (2021) and Anwar et al (2021a).

2023b), and Esmaeili et al (2023) are in line with this result.

Research indicates a significant positive correlation between Natural Resource Revenue (NRR) and carbon emissions, with a 1% significance level suggesting that nations with greater natural resource income tend to produce higher carbon emissions This trend may be attributed to the environmental impacts associated with resource extraction and utilization, as highlighted by studies from Jahanger et al (2023a, b), Dallas et al (2021), and Dou et al (2023).

Recent findings indicate that the interplay between Natural Resource Revenue (NRR) and Green Finance significantly reduces CO2 emissions at a 1% significance level This underscores the potential of integrating effective green finance strategies with the management of natural resource revenues to mitigate carbon emissions It implies that governments adopting sustainable investment practices and channeling resource income towards eco-friendly initiatives can lessen the environmental consequences of resource exploitation (Mngumi et al., 2022; Liang et al., 2023).

4.6.2 ARDL Panel Data Estimation Results in Short Term

Table 4.7: ARDL short run analysis of all variables

Variable Coefficient Sid Error t-Statistic Prob.

Table 4.7 reveals a significant negative correlation in error correction at the 1% level, highlighting a strong long-term relationship between the model's variables and the dependent variable Conversely, in the short term, all variables exhibit statistical insignificance.

Investing in energy-intensive industries that rely on fossil fuels can significantly raise CO2 emissions in the short term According to the Intergovernmental Panel on Climate Change (IPCC), approximately half of global energy consumption stems from burning fossil fuels across various sectors, including energy production, industry, transportation, and construction.

Global warming is responsible for 46% of climate change, and addressing this issue requires long-term strategies Encouraging sustainable development, investing in renewable energy, and reducing reliance on non-renewable sources are crucial steps However, businesses often prioritize profits, which can delay the realization of environmental benefits and the significant reduction of CO2 emissions.

4.6.3 PMG regression results for individual countries

Variable Long term Short term

Armenia Afghanistan Bangladesh Bhutan Cambodia China Georgia Jordan

India Indonesia Kazakhstan Korea Kyrgyzstan Lao PDR Malaysia Maldives

Myanmar Nepal Pakistan Philippines Sri Lanka Tajikistan Thailand Timor Leste

Turkmenistan Uzbekistan Viet Nam Yemen Lebanon Mongolia Turkiye

Note: * **, *** significant at 10%, 5% and 1 % /eve/ respectively

Table 4.8 illustrates the impact of economic variables on CƠ2 emissions for each country in the research sample.

Vietnam's GDP shows a significant positive correlation with carbon emissions at the 5% significance level, suggesting that economic growth is a key driver of rising carbon emissions Notably, Vietnam's carbon emissions exceed the average levels observed in other low to middle-income countries across Southeast Asia.

In Vietnam, GDP per capita and population significantly influence carbon emissions, yet these can be alleviated through reductions in energy and carbon intensity Failure to implement effective adaptation and mitigation strategies could lead to an estimated annual GDP loss of 12% to 14.5% by 2050 due to climate change, according to the World Bank's Development Report for Vietnam (2022).

The Environmental Kuznets Curve (EKC) theory suggests that there is a turning point in GDP where economic growth can lead to a decrease in carbon emissions, as indicated by the negative coefficient on the GDP square This concept is supported by research findings from various Asian countries and aligns with earlier studies, including those conducted by Esmaeili et al.

The relationship between GDP and CO2 emissions is not static; it fluctuates based on factors like economic structure, technology, and policies For Vietnam, key strategies to reduce emissions while increasing GDP include transitioning to clean energy sources, enhancing energy efficiency, and fostering innovation and improvement.

Optimal Lag Selection

After analyzing the optimal lag structure for each of the 31 countries in our dataset, we identified the most frequently occurring lag count to establish the overall model's lag structure As a result, the optimal lag structure for the global ARDL model is determined to be ARDL(1,2,2,0,0).

CEM GDP GDPQS NRR NRR_GRF idl 10 0 0 0 id2 12 2 0 2 id3 10 0 0 0 id4 10 0 2 0 ids 2 0 12 2 id6 2 112 2 id7 110 2 2 id8 112 0 0 id9 2 2 2 12 d10 dll dl2 dl3 dl4 dl5 dl6 dl7 dl8 dl9 d20 d21 d22 d23 d24 d25 d26 d27 d28 d29 d30 d31

Regression result of model (PMG - ARDL approach)

4.6.1 ARDL Panel Data Estimation Results in Long Term

Table 4.6: ARDL long run analysis of all variablesCoefficient Std Error t-Statistic Prob.Variable

Long-term estimates of carbon emissions, analyzed using the Autoregressive Distributed Lag (ARDL) method, reveal several key insights After conducting the Hausman test, we identify the Pooled Mean Group (PMG) method as the most appropriate technique for this analysis.

At the 5% significance level, GDP demonstrates a positive correlation with carbon emissions, suggesting that economic growth contributes to increased emissions However, the negative coefficient on the GDP square supports the Environmental Kuznets Curve (EKC) theory, indicating that while carbon emissions initially rise with GDP, the rate of increase slows down over time This suggests the existence of a potential tipping point where further economic growth could lead to reduced emissions, aligning with findings from previous studies, including those by Farooq et al (2021) and Anwar et al (2021a).

2023b), and Esmaeili et al (2023) are in line with this result.

Research indicates that Natural Resource Revenue (NRR) significantly contributes to increased carbon emissions, with a 1% significance level highlighting that nations with substantial natural resource incomes tend to exhibit higher carbon outputs This relationship may stem from the environmental impacts associated with the extraction and utilization of these resources, as evidenced by studies from Jahanger et al (2023a, b), Dallas et al (2021), and Dou et al (2023).

Recent research highlights a significant finding: the interplay between Natural Resource Revenue (NRR) and Green Finance negatively influences CO2 emissions at a 1% significance level This indicates that integrating effective green finance strategies with the management of natural resource revenues can lead to a reduction in carbon emissions Therefore, governments that adopt sustainable investment practices and channel resource income towards eco-friendly initiatives can mitigate the environmental consequences of resource exploitation (Mngumi et al., 2022; Liang et al., 2023).

4.6.2 ARDL Panel Data Estimation Results in Short Term

Table 4.7: ARDL short run analysis of all variables

Variable Coefficient Sid Error t-Statistic Prob.

Table 4.7 reveals a significant negative correlation in error correction at the 1% level, suggesting a strong long-term relationship between the model's variables and the dependent variable Conversely, in the short term, all variables appear to be statistically insignificant.

Investing in energy-intensive industries that rely on unclean energy sources can lead to a significant rise in CO2 emissions in the short term According to the Intergovernmental Panel on Climate Change (IPCC), the combustion of fossil fuels for energy production across various sectors, including industry, transportation, and construction, accounts for approximately half of global energy consumption.

Global warming is significantly influenced by human activities, accounting for 46% of the issue To combat this, it is essential to promote sustainable development projects, invest in renewable energy, and decrease reliance on non-renewable resources However, these measures may require considerable time to yield noticeable reductions in CO2 emissions, as businesses often prioritize profits over environmental benefits, which can delay the realization of positive impacts on the environment.

4.6.3 PMG regression results for individual countries

Variable Long term Short term

Armenia Afghanistan Bangladesh Bhutan Cambodia China Georgia Jordan

India Indonesia Kazakhstan Korea Kyrgyzstan Lao PDR Malaysia Maldives

Myanmar Nepal Pakistan Philippines Sri Lanka Tajikistan Thailand Timor Leste

Turkmenistan Uzbekistan Viet Nam Yemen Lebanon Mongolia Turkiye

Note: * **, *** significant at 10%, 5% and 1 % /eve/ respectively

Table 4.8 illustrates the impact of economic variables on CƠ2 emissions for each country in the research sample.

Vietnam's GDP shows a significant positive correlation with carbon emissions at the 5% significance level, suggesting that economic growth is a key driver of increased carbon emissions Notably, Vietnam's carbon emissions exceed the average levels found in low to middle-income countries across Southeast Asia.

In Vietnam, GDP per capita and population significantly impact carbon emissions, but these effects can be lessened through reductions in energy and carbon intensity Without effective adaptation and mitigation strategies, projections indicate that Vietnam could face annual GDP losses of approximately 12% to 14.5% by 2050 due to climate change, as highlighted in the World Bank's 2022 Development Report for Vietnam.

The Environmental Kuznets Curve (EKC) theory suggests that economic growth may eventually lead to a decrease in carbon emissions, as evidenced by the negative coefficient on GDP squared This finding is consistent with research conducted in Asian countries and aligns with earlier studies, including those by Esmaeili et al.

The relationship between GDP and CO2 emissions is dynamic and influenced by factors such as economic structure, technology, and policies For Vietnam, there are positive opportunities to reduce emissions while increasing GDP through the transition to clean energy sources, enhancing energy efficiency, and fostering innovation and improvements.

In Chapter 4, our analysis revealed significant fluctuations in key variables across several Asian countries from 2000 to 2021 Utilizing the ARDL estimator, we found a positive correlation between GDP and Natural Resources Rents (NRR) with carbon emissions Conversely, the square of GDP and the interaction between NRR and green finance demonstrated an inverse relationship with emissions Consequently, it is essential for Asian nations to enhance green finance initiatives to mitigate the adverse effects of CO2 emissions.

CONCLUSION AND RECOMMENDATION

Summary of research results

This study explores the relationship between factors influencing CO2 emissions in Asian countries, including Vietnam, focusing on GDP, natural resource rent, and green finance Utilizing ARDL analysis, the research confirms the Environmental Kuznets Curve (EKC) hypothesis, revealing that while GDP positively correlates with carbon emissions initially, further growth leads to a reduction in emissions Additionally, it highlights that Natural Resource Rent (NRR) contributes to increased carbon emissions, indicating the environmental costs associated with resource dependence Importantly, the interaction between NRR and green finance is shown to negatively impact CO2 emissions, underscoring the potential of green finance to alleviate the adverse environmental effects of natural resource extraction.

Limitations of research

Despite the study's efforts to highlight the significance of green finance, several limitations persist that future research must address A key challenge is the lack of standardized metrics for Green Finance (GRF), which creates ambiguity and hinders accurate evaluation of green financial initiatives' impacts This absence of reliable measures complicates researchers' ability to quantify the influence of green financing on environmental sustainability, underscoring the necessity for robust, universally accepted assessment tools for GRF.

The study is limited to 31 Asian countries due to data constraints, excluding key developed nations like Japan and Singapore, which may impact the representativeness of the findings This highlights the urgent need for enhanced data collection across all Asian countries, particularly in developing nations poised to leverage Green Finance for sustainable development By broadening data collection efforts, future research can yield more comprehensive insights into the connection between Green Finance and environmental outcomes throughout the diverse Asian region.

Policy Implications for Vietnam

The findings from the study examining the relationship between green finance, GDP growth, and natural resources on carbon emissions in various Asian countries can serve as a foundation for proposing sustainable economic development solutions in Vietnam, with a strong emphasis on environmental considerations.

Firstly, policymakers should focus on implementing measures to decouple economic growth from carbon emissions, especially in the early stages of GDP growth.

Enhancing energy-saving technologies and promoting renewable energy sources are essential strategies for reducing carbon intensity in economic activities This involves investing in research and development to improve energy efficiency through high-efficiency equipment, streamlined energy management systems, and optimized production processes Additionally, encouraging the adoption of renewable energy sources like solar, wind, hydro, and thermal power in both production and consumption can significantly contribute to sustainability efforts.

Secondly, efforts should he made to reduce countries' dependence on natural resources by diversifying the economy and promoting sustainable activities.

Overreliance on natural resources can cause environmental degradation and unsustainability, making economic diversification crucial By promoting the development of various industries, such as the service sector, information technology, and tourism, economies can reduce dependence on a single resource Furthermore, encouraging sustainable activities like investing in organic food production, green technology, and effective natural resource management can create new business opportunities while alleviating pressure on the environment.

Thirdly, promoting green financial activities is also an important measure to minimize the negative impact on the environment from the consumption of natural resources.

The interplay between Natural Resources Rents (NRR) and green finance is essential for sustainable development By promoting policies that foster investment in renewable energy, effective resource management, and innovative green technologies, we can reduce the adverse effects of resource consumption Encouraging the creation of green financial instruments, such as green bonds and investment funds, will further advance Green Finance initiatives Additionally, implementing incentives for sustainable renewable energy investments is crucial for minimizing environmental harm and promoting a greener future.

Fourthly, there should he a focus on improving the management processes of government agencies through enhanced regulations and detailed enforcement of rules.

The government must enhance support for research and development (R&D) and education to ensure adequate resources for establishing a robust legal framework for emission reduction and environmental pollution prevention, emphasizing strict compliance with environmental regulations Additionally, effective development and implementation of regulations to control industrial activities are essential By leveraging media and community education programs, the government can disseminate information on green growth and sustainable development, promoting green lifestyles and low-carbon consumption habits among all population segments.

Fifthly, encourage robust scientific research and technological advancements in energy, waste treatment, and recycling sectors, alongside rhe increased utilization of modern technologies in climate forecasting.

This can be achieved by facilitating conditions for research organizations, providing financial and human resources for research and development projects, and encouraging collaboration between research organizations and businesses.

Enhancing effective cooperation mechanisms is essential for achieving a balanced distribution of benefits among all parties engaged in bilateral and multilateral collaborations This is particularly important in the context of sharing natural resources and fulfilling responsibilities related to environmental protection.

To enhance environmental protection in Vietnam, it is essential to establish clear cooperation agreements, implement effective monitoring and evaluation mechanisms, and involve all stakeholders in decision-making processes Vietnam has previously taken significant steps and must continue these efforts Notably, the collaboration between the US Environmental Protection Agency (EPA) and Vietnam aims to strengthen environmental laws, improve air quality, and reduce pollution from harmful toxins like mercury and dioxin Additionally, a cooperation agreement between the United States Agency for International Development (USAID) and the Korea International Cooperation Agency (KOICA) focuses on reducing pollution and addressing climate change in the Mekong Delta, while promoting a transition to clean energy in Vietnam.

Vietnam can enhance its journey toward a sustainable and resilient economy by utilizing effective strategies and building on the achievements of carbon taxation, ensuring a harmonious balance between environmental protection and economic growth.

Bekun, F V., Alola, A A., & Sarkodie, s A (2019) Toward a sustainable environment:

Nexus between CO2 emissions, resource rent, renewable and nonrenewable energy in 16-EU countries Science of the Total Environment, 65 7, 1023-1029 https://doi.org/10.1016/j scitotenv.2018.12.104

Cai, X., Li, K., Wang, w., Lu, Y., & Wang, R (2023) The role of resource rent in shaping CO2 emissions in BRICS countries: A panel data approach Resources

Policy, 85, 103857-103857 https://doi.Org/10.1016/j.resourpol.2023.103857

Canh, N p., Schinckus, c., & Thanh, s D (2020) The natural resources rents: Is economic complexity a solution for resource curse? Resources Policy, 69,

Fernando, J (2023, November 30) Gross domestic product (GDP): Formula and how to use it Investopedia https://www.investopedia.eom/terms/g/gdp.asp

Huang, S.-Z., Sadiq, M., & Chien, F (2021) The impact of natural resource rent, financial development, and urbanization on carbon emission Environmental

Science and Pollution Research https://doi.Org/l 0.1007/s 11356-021-16818-7

JV (2023, December 4) Fossil CO2 Emissions at Record High in 2023 Global Carbon

Budget, https://globalcarbonbudget.org/fossil-co2-emissions-at-record-high-in- 2023/

Kaftan, V., Kandalov, w., Molodtsov, I., Sherstobitova, A., & Strielkowski, w (2023)

Socio-Economic Stability and Sustainable Development in the Post-COVID Era: Lessons for the Business and Economic Leaders Sustainability, 15(4), 2876 https://doi.org/10.3390/sul5042876

Khan, s., Akbar, A., Nasim, L, Hedvicáková, M., & Bashir, F (2022) Green finance development and environmental sustainability: A panel data analysis Frontiers in Environmental Science, 10 https://doi.org/10.3389/fenvs.2022.1039705

Kwakwa, p., Adjei, A., Alhassan, H., & Adu, G (2018) Munich Personal RePEc

The extraction of natural resources in Ghana significantly influences energy consumption and carbon dioxide emissions This relationship highlights the environmental impact of resource extraction activities, which often lead to increased energy demands and higher greenhouse gas emissions Understanding this dynamic is crucial for developing sustainable practices and policies aimed at mitigating climate change while balancing economic growth Addressing the challenges posed by resource extraction can help Ghana transition towards a more sustainable energy future.

Lô Thông, T., & Võ Thị Thúy, K (2020) Tác động của FDI len môi trường trong điều kiện tôn tại Đường cong Môi trường Kuznets (EKC) Tạp Chí Nghiên Cứu Kinh

Tẻ và Kinh Doanh Cháu A, 30(8), 26-44. http://jabes.ueh.edu.vn/Home/SearchArticle?article_Idb6blc-bfb6-4567- bb7a-a61adcadf81b

In their 2023 study, Maamar Sebri, Ousseini Issoufou Ahmed, and Hajer Dachraoui explore the relationship between public spending and the resource curse in West African Economic and Monetary Union (WAEMU) countries Utilizing a hidden cointegration and non-linear panel ARDL framework, the authors conduct an asymmetrical analysis to uncover the dynamics of public expenditure in the context of resource wealth Their findings contribute valuable insights into how resource dependency impacts fiscal policies and economic stability in the region.

Militar, u., Granada Colombia, N., Lora, T., Carlos, J., Carrillo Bermudez, & Vizcaíno

(2013) The environmental Kuznets curve (EKC): An analysis of the landfilled solid waste in Colombia, https://www.redalyc.org/pdf/909/90930501002.pdf

Nawaz, M A., Seshadri, u., Kumar, p., Aqdas, R., Patwary, A K., & Riaz, M (2020)

Nexus between green finance and climate change mitigation in N-l 1 and BRICS countries: empirical estimation through difference in differences (DID) approach

Environmental Science and Pollution Research, 28(6), 6504-6519 https://doi.org/' 10.1007/s 11356-020-10920-y

Nghiên cứu của Ngô Thái và cộng sự (2023) tập trung vào mối liên hệ giữa đầu tư xanh, phát triển tài chính và tăng trưởng kinh tế tại Việt Nam, đồng thời phân tích ảnh hưởng của các yếu tố này đến lượng khí thải CO2 Kết quả cho thấy đầu tư xanh không chỉ thúc đẩy phát triển tài chính mà còn góp phần giảm thiểu ô nhiễm môi trường, từ đó hỗ trợ tăng trưởng kinh tế bền vững Nghiên cứu này cung cấp cái nhìn sâu sắc về tầm quan trọng của chiến lược đầu tư bền vững trong bối cảnh biến đổi khí hậu hiện nay.

Đầu tư xanh là một yếu tố quan trọng trong phát triển tài chính và tăng trưởng kinh tế, đặc biệt trong bối cảnh biến đổi khí hậu và lượng khí CO2 gia tăng Nghiên cứu tại Việt Nam chỉ ra rằng việc áp dụng các giải pháp đầu tư bền vững không chỉ giúp giảm thiểu tác động tiêu cực đến môi trường mà còn thúc đẩy sự phát triển kinh tế bền vững Các chính sách khuyến khích đầu tư xanh cần được triển khai mạnh mẽ để đảm bảo sự phát triển hài hòa giữa kinh tế và môi trường.

Ritchie, H., & Roscr, M (2020) CƠ2 and Greenhouse Gas Emissions Our World in

Data, https://ourworldindata.org/co2/country/vietnam

Saeed Meo, M., & Karim, M z A (2021) The role of green finance in reducing CO2 emissions: An empirical analysis Borsa Istanbul Review, 22(1) https://doi.org/10.1016/j bir.2021.03.002

Samargandi, N., Fidrmuc, J., & Ghosh, s (2015) Is the Relationship Between Financial

Development and Economic Growth Monotonic? Evidence from a Sample of Middle-Income Countries World Development, 68, 66-81.https://doi.org/10.1016/j worlddev.2014.11.010

The World Bank (2022, July 1) Key Highlights: Country Climate and Development

Report for Vietnam World Bank. https://www.worldbank.org/en/country/vietnam/brief/key-highlights-country- climate-and-development-report-for-Vietnam

Thi Lam Ho, Bao-Chau Xuan Nguyen, & Thu Hoai Ho (2024) Is There a Trade-off

Between Economic Growth, Foreign Direct Investment, International Trade and Environmental Degradation? A Comparison Between Asian Developed and Developing Countries Foreign Trade Review. https://doi.org/10.1177/00157325231214319

US EPA, o (2014, April 7) EPA Collaboration with Vietnam US EPA https://www.epa gov/international-cooperation/epa-collaboration-vietnam

USAID and KOICA Sign First MOU in Vietnam on Climate Change and Environmental

Protection I Vietnam I Press Release (2022, December 9) U.S Agency for

International Development, https://www.usaid.gov/vietnam/press-releases/jun- 08-2022-usaid-and-koica-sign-first-mou-vietnam-climate-change-and- environmental-protection

Võ Hồng, Đ., Nguyền Công, T., & Võ Thể, A (2019) Tạp chí Nghiên cứu Kinh té và

Kinh doanh Châu A Mã phán loại JEL: 012; 015; 047 https://jabes.ueh.edu.vn/Content/ArticleFiles/5c0a4a02-be8f-4db8-bbe4-

Wang, J., Tian, J., Kang, Y., & Guo, K (2023) Can green finance development abate carbon emissions: Evidence from China International Review of Economics &

Finance https://doi.Org/10.1016/j.iref.2023.06.011

Wu, G., Xu, L., & Cai, Y (2023) The impact of green finance on carbon emission efficiency Heliyon, c23 803-c23 803. https://doi.Org/10.1016/j.hcliyon.2023.c23 803

Yu, z (2024) Do natural resources promote carbon neutrality: The role of green finance Resources Policy, 88, 104424-104424. https://doi.Org/10.1016/j.resourpol.2023.104424

summarize CEM GDP GDPSQ NRR GRF NRR_GRF

Variable Obs Mean std dev Min Max

CEM 682 4.453036 9874415 2.224274 7.070641 GDP 682 3.312864 4799666 2.09691 4.54583 GDPSQ 682 11.2051 3.213136 4.397032 20.66457 NRR 682 18452 1.015153 -2.971629 1.89999 GRF 682 7120101 1.38068 -3.464929 3.529796 NRR GRF 682 225115 1.207939 -4.639746 4.776326

xtcd CEM GDP GDPSQ NRR GRF NRR_GRF

Average correlation coefficients & Pesaran (2004) CD test

Variables series tested: CEM GDP GDPSQ NRR GRF NRR_GRF

Group variable: id Number of groups: 31 Average # of observations: 22.73

Variable CD-test p-value corr abs(corr)

Notes: Under the null hypothesis of cross-section independence CD ~ N(0,1)

Pesaran's test of cross sectional independence = 20.959, Pr = 0.0000

3 Unit root test (CIPS) xtcỉps CEM, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for CEM Deterministics chosen: no constant nor trend

Dynamics: lags criterion decision Portmanteau (Q) test for white noise

H0 (homogeneous non-stationary): bi = 0 for all i

Critical values at -1.46 -1.55 -1.69 xtcips d.CEM, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for D.CEM Deterministics chosen: no constant nor trend

Individual ti were truncated during the aggregation process

Critical values at -1.46 -1.55 -1.69 xtcips GDP, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for GDP Deterministics chosen: no constant nor trend

xtcips d.GDP, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for D.GDP Deterministics chosen: no constant nor trend

xtcips GDPSQ, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for GDPSQ Deterministics chosen: no constant nor trend

xtcips d.GDPSQ, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for D.GDPSQ Deterministics chosen: no constant nor trend

Dynamics: lags criterion decision Portmanteau (0) test for white noise

xtcips NRR, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for NRR Deterministics chosen: no constant nor trend

xtcips d.NRR, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for D.NRR Deterministics chosen: no constant nor trend

xtcips NRR_GRF, maxlag(l) bglags(l) q noc

Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for NRR_GRF Deterministics chosen: no constant nor trend

xtcointtest westerlund CEM GDP GDPSQ NRR NRR_GRF , allpanels

H0: No cointegration Number of panels = 31

Ha: All panels are cointegrated Number of periods = 22

2 ardl CEM GDP GDPSQ NRR NRR_GRF if (id == 'i'), maxlag(2 2222)

Prob > F R-squared Adj R-squared Root MSE

CEM Coef std Err t p>ltl [95% Conf Interval]

CEM Coef std Err t p>|t| [95% Conf Interval]

CEM GDP GDPSQ NRR NRR_GRF rl 1 2 2 0 2

CEM Coef std Err t p>1t1 [95% Conf Interval]

GDP GDPSQ NRR NRR_GRF

F( 7, Prob > F R-squa red Adj R-squ Root MSE obs =

CEM Coef Std Err t p>|t| [95% Conf Interval]

CEM Coef std Err t p>|t| (95% Conf Interval]

Adj R-squared • 0.9836 Log likelihood ■ 68.675894 Root MSE s 0.0116

CEM Coef Std Err t p>|t| [95% Conf Interval]

CEM GDP GDPSO NRR NRR_GRF rl 1 1 0 2 2

R-squared Adj R-squared Root MSE

NRR -.0987371 1068207 -0.92 0.375 -.3338479 1363736 NRR_GRF 035068 0303907 1.15 0.273 -.0318215 1019574 _cons 3.126219 1.837551 1.70 0.117 -.9182026 7.170641 e(lags)[1,5]

CEM Coef std Err t p>ltl 195% Conf Interval]

CEM Coef std Err p>|t| [95% Conf Interval]

Number of obs = 20 F( 11, 8) = 118.02 Prob > F = 0.0000 R-squared = 0.9939 Adj R-squared = 0.9855 Root MSE = 0.0213

CEM Coef std Err t p>|t| [95% Conf Interval)

GDP -.5729487 5.409365 -0.11 0.917 -12.17488 11.02899 GDPSQ 1745411 1.049287 0.17 0.870 -2.075956 2.425038 NRR -.3204379 2924902 -1.10 0.292 -.9477669 3068912 NRR_GRF 2228217 1462349 1.52 0.150 -.0908208 5364643 _cons 1.248119 8.121764 0.15 0.880 -16.17133 18.66757 e(lags)[1,5]

Coef std Err t p>ltl 195% Conf Interval]

CEM Coef std Err t p>l 11 [95% Conf Interval]

GDP 1.055564 1.054247 1.00 0.335 -1.221999 3.333127 GDPSQ -.1010061 1426838 -0.71 0.492 -.4092557 2072435 NRR -.0181463 0618866 -0.29 0.774 -.1518442 1155517 NRR_GRF 0023197 0114687 0.20 0.843 -.022457 0270964 _cons 5.358279 2.063507 2.60 0.022 9003422 9.816216 e(lags) [1,5]

GDP 3136282 1.685334 0.19 0.855 -3.301053 3.928309 GDPSQ -.0344563 2194834 -0.16 0.877 -.5052013 4362887 NRR -.0307432 0387611 -0.79 0.441 -.1138776 0523911 NRR_GRF 0159985 0119015 1.34 0.200 -.0095276 0415246 _cons -.6893568 3.197549 -0.22 0.832 -7.54741 6.168709 e(lags)[1,5]

F( 13, Prob > F R-squa red Adj R-squ Root MSE obs =

CEM Coef std Err t P>|t| [95% Conf Interval]

When comparing and selecting between MG/DFE and PMG, we accept H0 and opt for PMG The variables considered include xtpmg, d.CEM, d.GDP, d.GDPSQ, d.NRR, and d.NRR_GRF The analysis indicates that lr(l.CEM, GDP, GDPSQ, NRR, NRR_GRF) effectively replaces MG in this context.

Mean Group Estimation: Error Correction Form

(Estimate results saved as mg)

D.CEM Coef std Err z p>|z| [95% Conf Interval]

GDP -5.559993 4.360888 -1.27 0.202 -14.10718 2.98719 GDPSQ 7249969 5481736 1.32 0.186 -.3494036 1.799397 NRR 0561986 1134986 0.50 0.620 -.1662546 2786517 NRR_GRF 0331769 06201 0.54 0.593 -.0883605 1547143

xtpmg d.CEM d.GDP d.GDPSQ d.NRR d.NRR_GRF, lr(L.CEM GDP GDPSQ NRR NRR_GRF) replace pmg

Iteration 0: log likelihood = 1327.4818 (not concave)

(Estimate results saved as pmg)

Panel Variable (i): id Number of obs = 651

Time Variable (t): YEAR Number of groups = 31

Obs per group: min = 21 avg = 21.0 max = 21

D.CEM Coef std Err z p>l*l (95% Conf Interval]

NRR 0561986 0931548 -.0369563 1968763 NRR_GRF 0331769 -.0116397 0448166 0881068 b = consistent under Ho and Ha; obtained from xtpmg

B = inconsistent under Ha, efficient under Ho; obtained from xtpmg Test: Ho: difference in coefficients not systematic

xtpmg d.CEM d.GDP d.GDPSQ d.NRR d.NRR_GRF, lr(l.CEM GDP GDPSQ NRR NRR_GRF) replace dfe

Dynamic Fixed Effects Regression: Estimated Error Correction Form

(Estimate results saved as DFE)

Coef std Err z p>|z| [95% Conf Interval]

Tiêu đề	The impact of green finance, gdp growth, natural resource rent on carbon emissions in asian countries, with policy implications for vietnam
Trường học	Trường Đại Học Kinh Tế TP. Hồ Chí Minh
Chuyên ngành	Kinh tế
Thể loại	Báo cáo
Năm xuất bản	2024
Thành phố	TP. Hồ Chí Minh

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Số trang	88
Dung lượng	1,87 MB