TX_1~AT/TX_2~AT International Journal of Economics and Financial Issues ISSN: 2146-4138 available at http: www.econjournals.com International Journal of Economics and Financial Issues, 2021, 11(4), 97-104. International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 2021 97 Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study Sujan Chandra Paul1*, Md. Harun Rosid1, Md. Jamil Sharif2, Anjuman Ara Rajonee3 1Department of Accounting and Information Systems, University of Barishal, Bangladesh, 2Department of Accounting and Information Systems, University of Dhaka, Bangladesh, 3Department of Soil and Environmental Sciences, University of Barishal, Bangladesh. *Email: sujan9099@gmail.com Received: 04 May 2021 Accepted: 09 July 2021 DOI: https://doi.org/10.32479/ijefi.11535 ABSTRACT To investigate the effects of foreign direct investment on CO2, CH4, N2O, and other greenhouse gas emission the study was conducted. The panel data from 200 countries were collected for the period of 1990 to 2018. Ordinary least square (OLS), pooled ordinary least square (POLS), Driscoll-Kraay (DK), Second stage least square (2SLS), generalized methods of moments (GMM) model has been performed. The findings showed that foreign direct investment has positive impact on CO2 in all the models. The study also showed that FDI had negative impact on CH4 emission and positive impact on N2O emissions in all models except GMM model. Finally, FDI had mixed impact on greenhouse gas emission but the results were statistically insignificant except OLS model. Keywords: CO2, CH4, N2O, Greenhouse Gas emission, FDI JEL Classifications: F21, O44, Q5 1. INTRODUCTION The environmental impacts of FDI (foreign direct investment); sustainability of FDI and its effect on the environment; and cross-border environmental implications are the areas of debate in which the FDI-Environment Relationship considers its status for study. The literature has progressed to the point that no clear or conclusive consensus on the meaning has been reached (Cole et al., 2017; Pazienza, 2014), which is particularly true in the first vein of the sentence, for which it is commonly argued that further research is required (Shao, 2018; Zheng and Sheng, 2017; Seker et al., 2015; McAusland, 2010; OECD, 2002a). It has been noted that there has been a greater focus on the relationship between FDI and the atmosphere in this specific thematic area. The majority of the work has been completed, and continues to be done, using various aggregated FDI statistics (e.g. Bakhsh et al., 2017; Shahbaz et al., 2015, 2011; Liang, 2006). According to Marques and Caetano (2020), the supply of goods and services would begin to rise as a result of globalization, which would inevitably encourage a nation’s economic growth. However, one aspect of globalization that has piqued economists’ interest is the flow of polluting industries between countries. This issue may be caused by inconsistencies in environmental regulations and the failure of instruments to control pollution. The Panel on Autoregressive Distributed Lag was used to calculate the impact of FDI on carbon dioxide and other significant greenhouse gas emissions with this viewpoint in mind. Their data were collected from 21 countries with different income levels for a period of 2001 to 2017. This approach permitted the study of the resulting emission dynamics in the short and long term. A deeper understanding of the consequences of FDI flows requires the qualities of efficiency, imagination, and power. Control continues to increase pollution in high-income countries, so it’s This Journal is licensed under a Creative Commons Attribution 4.0 International License Paul, et al.: Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 202198 worth debating more. The Pollution Haven Theory states that FDI reduces emissions in high-income countries while increasing emissions in middle-income countries. However, middle-income countries’ willingness to absorb technology will become crucial in the long run. Environmental policy has a major impact on trade in middle-income countries. Our mission is to comprehend the transfer of emissions from polluting industries, which is why we conducted a thorough examination of the industrial sector’s total green house gas pollution. It has also been discovered that policymakers do not pay enough attention to how innovation contributes to environmental degradation. This paper has five sections. Section two discusses the review of the literature. Section three is the methods. Section four is about the findings and discussion and finally section five of this paper give some recommendations and conclusion. 2. LITERATURE REVIEW While panel data analyses using aggregated data, such as those conducted by Hoffmann et al. (2005), Sadorsky (2010), Pao and Tsai (2011), and Kim and Adilov (2012), have been unable to confirm a near relationship between energy intensity and FDI or emission and FDI, firm level analyses conducted by Blackman and Wu (1999) and Fisher-Vanden et al. (2004) have shown that FDI has a reduced impact on energy intensity. Furthermore, Hoffmann et al. (2005) discover that the causal relationship transfers to country groups that were identified by the Granger Causality Approach screen as having high per capita income. In addition, Eskeland and Harrison (2003), Merican et al. (2007), Lee (2009), Tang (2009), and Chang (2012) have identified a transformation in bilateral relations using time series analyses. Panel data analyses produce more reliable and statistically powerful results than cross-section and time series analyses since the sample size is larger. There may be some variation in the estimated parameters for each particular panel, however (country). Furthermore, the topic of heterogeneity will influence bias estimation. Furthermore, cross-sectional dependence may lead to erroneous conclusions. The chosen panel data approach should then take into account variability and cross-sectional dependency concerns. Adams (2009) revealed that FDI had an initial negative influence on DI and subsequent positive effect in later periods for the panel of countries investigated. The sign and size of the present and delayed FDI coefficients imply a net crowding out impact. The study's findings and analysis of the literature show that the continent need a tailored strategy to FDI, increased absorption capacity of local companies, and government-MNE cooperation to achieve mutual benefit. Azomahou et al. (2005) used a panel of 100 nations to look at the empirical relationship between CO2 emissions per capita and GDP per capita from 1960 to 1996. They discovered evidence of the relationship's structural integrity. They then design a country- specific nonparametric panel data model. The findings of the estimation reveal that the connection is upward sloping. Another concern in the literature is the conflicting findings on the relationship of FDI-energy power and FDI-pollution. For example, Eskeland and Harrison (2003) found that FDI helps Mexico save electricity. Cole and Elliott’s (2005) findings supported the carbon haven hypothesis for the aforementioned countries. Several studies, including Blackman and Wu (1999), Hübler and Keller (2010), Sadorsky (2010), and Herrerias et al. (2013), have assumed that if FDI had contributed to energy production, per capita emissions would have decreased. Variations in processes, time intervals, or factors may have caused conflicting results in various experiments. As a consequence, the two lines of literature should be reviewed together in order to achieve reliable data. If there are contrary results, reducing emissions by energy savings enhanced by inward FDI cannot be obvious. Muhammad and Khan (2019) contributed to factors that help Asian countries grow economically, with an emphasis on often-forgotten bilateral FDI, electricity use, CO2 emissions, and a central position in the economy. In their study, they used the Generalized Approach of Moments (GMM), OLS regression, Fixed Effect and Random Effect Estimates. Between 2001 and 2012, data was gathered from 34 Asian host countries and 115 source countries. The study found that oil use, FDI inflows and outflows, CO2 emissions, and other services all play a significant role in Asia’s economic growth. The current study shows that improved energy use strategies, such as the use of appropriate and innovative energy technologies, as well as attracting international investors both in and out of the countries, are being implemented in Asian countries, resulting in increased economic growth as the global economy grows due to both inflows and outflows of FDI, oil use, and CO2 emissions. Fauzel (2017) looked at the long- and short-term effects of FDI on CO2 emissions in Mauritius (disaggregated into manufacturing and non-manufacturing sectors). In this study, the bounds checking approach to co-integration is used. For time series data from 1980 to 2012, the autoregressive distributed lag (ARDL) model is used. The study’s main findings show that foreign investment in the manufacturing industry is adverse to the environment, while FDI in non-manufacturing sectors is not. Furthermore, an increase in demand is thought to result in an increase in CO2 emissions. Energy consumption in the world has already been found to result in an increase in CO2 emissions. The results also affirm the stability of the model for the small island economy in Mauritius. Saini and Sighania (2019) focused on long-term growth and carbon emissions, as well as their effect on the environment. They tried to gather all available information on the topic and discovered that, in the present scenario, the problem is gaining high priority due to the growing pace of development in developing countries. Many of the study supported Kuznets’ environmental curve theory, and they discovered a wide body of literature advocating for cleaner FDI as a way to reduce the negative environmental effects of economic growth. Carbon pollution and foreign direct investment have a negative relationship, according to Yüksel et al. (2020). As a result, a comparison analysis is conducted for all E7 and G7 countries. The analysis framework incorporates Pedroni panel co-integration (PPC), Kao panel co-integration (KPC), and Dumitrescu Hurlin panel causality (DHPC) analyses. Gas emissions have a detrimental impact on foreign direct investment for all countries, according to the findings. This bond, on the other hand, is stronger Paul, et al.: Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 2021 99 with the G7 economies. There is also no evidence of a causal relationship between these factors. Countries should follow ambitious policies to reduce carbon emissions, according to the experts. In this way, a new tax might be imposed on businesses that emit a lot of pollution. Policymakers, on the other hand, may be willing to support policies that aim to reduce carbon emissions. In this scenario, lowering the tax rate and increasing the supply of technical assistance are examples. Li and Liu (2011) used absolute and comparative metrics representing the volume of CO2 released from 30 Chinese provinces from 2000 to 2008 to divide the entire county into eastern and western regions based on economic and geographical factors. The thesis investigates the effect of foreign direct investment on CO2 emissions across a technical channel. According to the findings, FDI’s effect on CO2 emissions in China is erratic. FDI in the east has a significant positive impact on local CO2 emissions; the role of FDI in the central region is unclear; and FDI in the west of the country had a negative impact on CO2 emissions. The effect of international trade and foreign direct investment (FDI) on CO2 emissions in Turkey was investigated by Haug and Ucal (2019). They looked at both linear and non-linear ARDL models and discovered that exports, imports, and FDI have a significant asymmetrical effect on per capita CO2 emissions. FDI, on the other hand, has no statistically significant long-term effects. The reduction in exports reduces per capita CO2 emissions in the long run, but the increase in exports has no statistically meaningful effect. Imports increase CO2 emissions per capita, while reductions in imports have no long-term effects. Exports and imports, on the other hand, have little effect on CO2 power, which measures CO2 emissions per unit of oil. Instead, financial development and urbanization are aided. They also discovered that the Kuznets environmental curve is current for both CO2 indices, implying that increases in actual per capita GDP have led to lower CO2 emissions for at least the last decade, after accounting for other competing causes. Furthermore, in two of the four markets, the sectoral share of CO2 emissions in total CO2 emissions asymmetrically changes with foreign trade, with export growth leading to a lower share of CO2 and imports having the opposite impact. Fereidouni (2013) indicated that actual FDI states do not add to emissions of CO2. Consumption of energy, urbanization and economic growth has also been described as significant determinants of CO2 emissions. Mugableh (2013) and Borhan et al. (2012) studied the association between CO2 emissions and economic growth in Malaysia in separate ways, but the results were similar: an increase in the economy causes CO2 emissions. To re-analyse CO2 pollution, Mugableh (2013) used a self-regressive lag strategy. From 1971 to 2012, data was collected. The results show that economic development is dependent on energy demand, but that this can be harmful to the environment because it can result in CO2 emissions. Borhan et al. (2012) used FDI to conduct their research. From 1965 to 2010, they used a larger number of comments in the study. Revenue, FDI, population, exports and imports were included as parts of their CO2 feature. The non-linear model has been used and the findings suggest that a rise in FDI implies a rise of CO2 in the atmosphere. For 15 years, Maddison and Rehdanz (2008) looked at the relationship between GDP and carbon emissions in 134 countries (1990 to 2005). When variability is ignored, CO2 emissions in North America, Asia, and Oceania are not compared to GDP. Han and Lee (2013) used a hierarchical panel data model to study the directional relationship between pollution and economic growth in OECD countries from 1981 to 2009. The connection between economic growth and pollution implies the need for technological advancement in order to achieve economic growth with minimal pollution, which supports Kuznets’ environmental curve hypothesis. 3. METHODS A analysis using a composite model was carried out. Using STATA 15, describe the relationship between FDI and emission-related variables. The OLS (ordinary least squares) model was used. STATA 15 was used to describe the relationship between FDI and emission variables using the Pooled Ordinary Least Squares (POLS model). Using STATA 15, the Drisc/Kraay (DK) model was used to determine the relationship between FDI and emission variables. The two stage least square model (2SLS) was used to describe the relationship between FDI and variables related to emissions using STATA 15. Finally, using STATA 15, a Generalized Method of Moments (GMM) model was used to define important explanatory variables that can describe the reasons for the interaction between FDI and emission variables. Variables and Description Sl. No. Variable Description Unit 1 lnFDI Log normal of Foreign direct investment, net inflows (BoP, current) USD 2 LnCO2EKT Log normal of CO2 emissions (kt) 3 LnCO2EMTPC Log normal of CO2 emissions (metric tons per capita) 4 LnCH4E Log normal of CH4 emissions (kt of CO2 equivalent) 5 LnN2OE Log normal of N2O emissions (thousand metric tons of CO2 equivalent) 6 LnTGHGE Log normal of Total greenhouse gas emissions (kt of CO2 equivalent) Hypotheses No. Hypotheses H1 A significant positive relationship between FDI and CO2 emissions (kt) of a country H2 A significant positive relationship between FDI and CO2 emissions (metric ton per capita) of a country H3 A significant positive relationship between FDI and CH4 emissions of a country H4 A significant positive relationship between FDI and N2O emissions H5 A significant negative relationship between FDI and total greenhouse gas emissions Paul, et al.: Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 2021100 4. RESULTS AND DISCUSSION 4.1. Descriptive Statistics The following table summarizes the informative data for all of the variables considered in this study’s models. For each element, the table shows the number of observations, mean value, standard deviations, minimum and maximum score. Table 1 summarizes the data gathered over a 29-year period for 200 countries on six variables (Appendix 1). The major dependent variable, FDI, shows an average of 17.276 billion dollars for the countries surveyed, with a very high standard deviation of 7.291 billion dollars, indicating that there is a significant difference in FDI among the world’s countries. The average LnCO2EKT is 7.598, while the average LnCO2EMTPC is 0.554, according to the table. LnCO2EKT and LnCO2EMTPC have standard deviations of 4.163 and 1.557, respectively. The average LnCH4E, on the other hand, is 6.57, the average LnN2OE is 5.845, and the average LnTGHGE is 7.458. LnCH4E, LnN2OE, and LnTGHGE have standard deviations of 4.212, 4.007, and 5.122, respectively. 4.2. Pair Wise Correlation Matrix First, we’ll look at the associations among the variables we found in the literature and see whether there’s a connection between FDI and different types of emissions. The variables are reported in a combined correlation matrix shown in Table 2. Table 2 indicates that the factors have no correlation, suggesting that endogeneity is unlikely. Only the correlation coefficient matrices and collinearity test results are given due to the layout constraints. The findings, on the other hand, pass the correlation coefficient and VIFs tests. Furthermore, both of the variables display importance at the 0.10 mark. There is no correlation between any of the variables at the 0.90 mark. 4.3. Econometric Models Multiple regression models have been run with the dependent (LnFDI) and independent variables (LnCO2EKT, LnCO2EMTPC, LnCH4E, LnN2OE and LnTGHGE). In the following section the results of those models are presented and interpreted below. CO2 emissions (both kt and metric ton per capita) have a strong positive association with FDI, as seen in Table 3. The higher a country’s foreign direct investment, the higher its CO2 emissions. On the contrary CH4 emissions has significant negative relationship with the FDI which indicates that a country having high more FDI does not significantly affect the CH4 emission of a country. N2O emissions and total greenhouse gas emissions have a substantial positive relationship with FDI, indicating that more FDI produces more N2O and total greenhouse gas emissions in a region. CO2 emissions (both kt and metric ton per capita) and nitrous oxide emissions (both kt and metric ton per capita) have a strong positive relationship with FDI, as seen in Table 4. The higher a country’s foreign direct investment, the higher its CO2 and N2O emissions. On the contrary methane emissions has significant negative relationship with the FDI which indicates that a country having high more FDI does not significantly affect the CH4 emission of a country. Total greenhouse gas emissions have a negative relationship with FDI, but the relationship is insignificant, even though the overall model is significant at the 10% stage. CO2 emissions (kt) and nitrous oxide emissions (kt) have a significant beneficial association with FDI, as seen in Table 5. The higher a country’s foreign direct investment, the higher its CO2 and nitrous oxide emissions. Methane emissions, on the other hand, have a substantial negative association with FDI, indicating that a nation with a high level of FDI has no impact on its CH4 emissions. CO2 emissions (metric ton per capita and gross greenhouse gas emissions) have a favorable relationship with FDI, but the relationship is negligible, despite the overall model Table 3: Ordinary least squares model LnFDI Coef. St. Err. t-value P-value [95% Conf Interval] Sig LnCO2EKT 0.595 0.037 16.02 0 0.522 0.668 *** LnCO2EMTPC 0.282 0.067 4.21 0 0.151 0.413 *** LnCH4E –1.715 0.123 –13.95 0 –1.956 –1.474 *** LnN2OE 1.592 0.12 13.25 0 1.357 1.828 *** LnTGHGE 0.098 0.055 1.78 0.075 –0.01 0.206 * Constant 13.825 0.192 72.07 0 13.449 14.201 *** Mean dependent var 17.276 SD dependent var 7.291 R-squared 0.127 Number of obs 5800.000 F-test 168.958 Prob>F 0.000 Akaike crit. (AIC) 38726.869 Bayesian crit. (BIC) 38766.862 ***P<0.01, **P<0.05, *P<0.1 Table 2: Pairwise correlations matrix Variables (1) (2) (3) (4) (5) (6) (1) LnFDI 1.000 (2) LnCO2EKT 0.306 1.000 (3) LnCO2EMTPC 0.150 0.344 1.000 (4) LnCH4E 0.188 0.745 0.018 1.000 (5) LnN2OE 0.214 0.727 –0.012 0.982 1.000 (6) LnTGHGE 0.193 0.702 0.009 0.947 0.940 1.000 Table 1: Descriptive statistics Variable Obs Mean Std. Dev. Min Max LnFDI 5800 17.276 7.291 0 27.879 LnCO2EKT 5800 7.598 4.163 0 16.147 LnCO2EMTPC 5800 0.554 1.557 –4.773 4.249 LnCH4E 5800 6.57 4.212 0 14.376 LnN2OE 5800 5.845 4.007 -4.155 13.283 LnTGHGE 5800 7.458 5.122 0 16.338 Paul, et al.: Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 2021 101 being important at the 10% stage. The next model is presented to improve the findings’ robustness. CO2 emissions (both kt and metric ton per capita) have a strong positive association with FDI, as seen in Table 6. The higher a Table 6: Two stage least square model Instrumental variables (2SLS) regression LnFDI Coef. St. Err. t-value P-value 95% Conf Interval Sig LnCO2EKT 0.595 0.037 16.02 0 0.522 0.668 *** LnCO2EMTPC 0.282 0.067 4.21 0 0.151 0.413 *** LnCH4E –1.715 0.123 –13.95 0 –1.956 –1.474 *** LnN2OE 1.592 0.12 13.25 0 1.357 1.828 *** LnTGHGE 0.098 0.055 1.78 0.075 –0.01 0.206 * Constant 13.825 0.192 72.07 0 13.449 14.201 *** Mean dependent var 17.276 SD dependent var 7.291 R-squared 0.127 Number of obs 5800.000 F-test 168.958 Prob>F 0.000 ***P<0.01, **P<0.05, *P<0.1 Table 4: Pooled ordinary least squares model Regression results LnFDI Coef. St. Err. t-value P-value 95% Conf Interval Sig LnCO2EKT 0.331 0.031 10.64 0 0.27 0.392 *** LnCO2EMTPC 0.817 0.112 7.29 0 0.598 1.037 *** LnCH4E –0.853 0.155 –5.49 0 –1.157 –0.548 *** LnN2OE 0.622 0.16 3.89 0 0.308 0.936 *** LnTGHGE –0.028 0.052 –0.53 0.595 –0.129 0.074 Constant 16.48 0.305 54.06 0 15.883 17.078 *** Mean dependent var 17.276 SD dependent var 7.291 Overall r-squared 0.053 Number of obs 5800.000 Chi-square 282.463 Prob>chi2 0.000 R-squared within 0.046 R-squared between 0.063 ***P<0.01, **P<0.05, *P<0.1 Table 7: Generalized method of moments model Regression results of system GMM model LnFDI Coef. St.Err. t-value P-value 95% Conf Interval Sig L.LnFDI 0.2 0.019 10.50 0 0.163 0.237 *** LnCO2EKT 0.064 0.028 2.30 0.022 0.009 0.118 ** LnCO2EMTPC 0.969 0.182 5.34 0 0.613 1.326 *** LnCH4E 0.83 0.27 3.07 0.002 0.299 1.36 *** LnN2OE –1.1 0.3 –3.67 0 –1.689 –0.512 *** LnTGHGE 0.062 0.071 0.88 0.38 –0.077 0.201 Constant 13.47 0.366 36.85 0 12.753 14.186 *** Mean dependent var 17.438 SD dependent var 7.187 Number of obs 5400.000 Chi-square 184.165 ***P<0.01, **P<0.05, *P<0.1 Table 5: Driscoll-Kraay pooled OLS model Regression with Driscoll-Kraay standard errors Number of obs=5800 Method: Pooled OLS Number of groups=200 Group variable (i): ID F (5, 28)=53.59 Maximum lag: 3 Prob>F = 0.0000 R-squared=0.1273 Root MSE=6.8146 Drisc/Kraay LnFDI Coef. Std. Err. T P>t 95% Conf. Interval LnCO2EKT 0.595 0.290 2.050 0.050 0.001 1.190 LnCO2EMTPC 0.282 0.214 1.320 0.199 –0.157 0.720 LnCH4E –1.715 0.448 –3.830 0.001 –2.632 –0.797 LnN2OE 1.592 0.450 3.540 0.001 0.670 2.514 LnTGHGE 0.098 0.082 1.200 0.240 –0.069 0.265 _cons 13.825 2.583 5.350 0.000 8.534 19.117 Paul, et al.: Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 2021102 country’s foreign direct investment, the higher its CO2 emissions. On the other hand, CH4 emissions have a major negative association with FDI, indicating that a nation with a high level of FDI has no impact on its CH4 emissions. N2O emissions and total greenhouse gas emissions have a significant beneficial association with FDI, implying that more FDI causes more N2O emissions and total greenhouse gas emissions. The next model is run to ensure that the findings are more reliable. Table 7 reveals a significant positive association between CO2 emissions (kt), CO2 emissions (metric ton per capita), and CH4 emissions and FDI. The higher a country’s foreign direct investment, the higher its CO2 and methane emissions. In the other hand, N2O emissions have a major negative association with FDI, indicating that a nation with a high level of FDI has no impact on its N2O emissions. Total greenhouse gas emissions have a favorable relationship with FDI, but the relationship is negligible, despite the overall model being meaningful at the 10% stage. 5. CONCLUSION To investigate the effects of foreign direct investment on CO2, CH4, N2O and total greenhouse gas emission this study is conducted. Panel data for 200 countries over a period of 29 years (1990- 2018) has been used as the sources of information. Ordinary Least Square (OLS), Pooled Ordinary Least Square (POLS), Driscoll- Kraay (DK), Second Stage Least square (2SLS), Generalized Methods of Moments (GMM) models have been performed and the result shows that there is a positive relationship between FDI and different types of green house gas emission. With economical advancement the emission green house gases (CO2, CH4, N2O and others) increase simultaneously. The findings are very important in case of formulating environmental policies. Therefore, the developing country should find alternative sources of energy to ensure that there is no harmful effect on environment as there is an increase rate of energy consumption with economic growth. The use of natural gas, biomass, green technology etc. may be some important way to reduce CO2 emission. Data were collected only from 200 countries because there is a lack of data availability from remaining countries of the world. 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Rep. 81 Hong Kong SAR, China 2 Albania 42 Congo, Rep. 82 Hungary 3 Algeria 43 Costa Rica 83 Iceland 4 American Samoa 44 Cote d’Ivoire 84 India 5 Andorra 45 Croatia 85 Indonesia 6 Angola 46 Cuba 86 Iran, Islamic Rep. 7 Antigua and Barbuda 47 Cyprus 87 Iraq 8 Argentina 48 Czech Republic 88 Ireland 9 Armenia 49 Denmark 89 Isle of Man 10 Aruba 50 Djibouti 90 Israel 11 Australia 51 Dominica 91 Italy 12 Austria 52 Dominican Republic 92 Jamaica 13 Azerbaijan 53 Ecuador 93 Japan 14 Bahamas, The 54 Egypt, Arab Rep. 94 Jordan 15 Bahrain 55 El Salvador 95 Kazakhstan 16 Bangladesh 56 Equatorial Guinea 96 Kenya 17 Barbados 57 Eritrea 97 Kiribati 18 Belarus 58 Estonia 98 Korea, Dem. People’s Rep. 19 Belgium 59 Eswatini 99 Korea, Rep. 20 Belize 60 Ethiopia 100 Kosovo 21 Benin 61 Euro area 101 Kuwait 22 Bermuda 62 Fiji 102 Kyrgyz Republic 23 Bhutan 63 Finland 103 Lao PDR 24 Bolivia 64 France 104 Latvia 25 Bosnia and Herzegovina 65 French Polynesia 105 Lebanon 26 Botswana 66 Gabon 106 Lesotho 27 Brazil 67 Gambia, The 107 Liberia 28 Brunei Darussalam 68 Georgia 108 Libya Paul, et al.: Foreign Direct Investment and CO2, CH4, N2O, Greenhouse Gas Emissions: A Cross Country Study International Journal of Economics and Financial Issues | Vol 11 • Issue 4 • 2021104 29 Bulgaria 69 Germany 109 Liechtenstein 30 Burkina Faso 70 Ghana 110 Lithuania 31 Burundi 71 Gibraltar 111 Luxembourg 32 Cabo Verde 72 Greece 112 Macao SAR, China 33 Cambodia 73 Greenland 113 Madagascar 34 Cameroon 74 Grenada 114 Malawi 35 Canada 75 Guatemala 115 Malaysia 36 Chad 76 Guinea 116 Maldives 37 Chile 77 Guinea-Bissau 117 Mali 38 China 78 Guyana 118 Malta 39 Colombia 79 Haiti 119 Marshall Islands 40 Comoros 80 Honduras 120 Mauritania 121 Mauritius 161 Singapore 122 Mexico 162 Slovak Republic 123 Micronesia, Fed. Sts. 163 Slovenia 124 Moldova 164 Solomon Islands 125 Mongolia 165 Somalia 126 Morocco 166 South Africa 127 Mozambique 167 South Asia 128 Myanmar 168 Spain 129 Namibia 169 Sri Lanka 130 Nauru 170 St. Kitts and Nevis 131 Nepal 171 St. Lucia 132 Netherlands 172 St. Vincent and the Grenadines 133 New Caledonia 173 Sudan 134 New Zealand 174 Suriname 135 Nicaragua 175 Sweden 136 Niger 176 Switzerland 137 Nigeria 177 Syrian Arab Republic 138 North America 178 Tajikistan 139 North Macedonia 179 Tanzania 140 Norway 180 Thailand 141 Oman 181 Timor-Leste 142 Pakistan 182 Togo 143 Palau 183 Tonga 144 Panama 184 Trinidad and Tobago 145 Papua New Guinea 185 Tunisia 146 Paraguay 186 Turkey 147 Peru 187 Turkmenistan 148 Philippines 188 Uganda 149 Poland 189 Ukraine 150 Portugal 190 United Arab Emirates 151 Qatar 191 United Kingdom 152 Romania 192 United States 153 Russian Federation 193 Uruguay 154 Rwanda 194 Uzbekistan 155 Samoa 195 Vanuatu 156 Sao Tome and Principe 196 Venezuela, RB 157 Saudi Arabia 197 Vietnam 158 Senegal 198 Yemen, Rep. 159 Seychelles 199 Zambia 160 Sierra Leone 200 Zimbabwe Appendix 1: (Continued)