Pa ge 1 Pa ge 13 0 American Journal of Environmental Economics (AJEE) Foreign Direct Investment, Institutional Quality, and Environmental Pollution in Selected African Countries Kehinde John Akomolafe1* Volume 3 Issue 1, Year 2024 ISSN: 2833-7905 (Online) DOI: https://doi.org/10.54536/ajee.v3i1.3939 https://journals.e-palli.com/home/index.php/ajee Article Information ABSTRACT Received: October 20, 2024 Accepted: November 27, 2024 Published: December 07, 2024 African countries increasingly attract foreign direct investment as they strive for economic growth and development. However, there have been concerns over the rising effects of FDI on pollution. These concerns are rooted in the potential for industrial activities associated with FDI to have adverse environmental impacts. Hence, the needs to tackle the problem of pollution cannot be overemphasized. One way to do this is by strengthening the quality of African institutions. This study was conducted to examine the effects of institutional quality and FDI on environmental pollution in selected African countries. Environmental pollution was measured using CO2 emission.The result of the PCSE estimator showed that institutional quality had negative effects on environmental pollution, but the effect of FDI was positive and insignificant. Also, the effect of moderating effect showed that institutional quality has the potential to reduce the positive effect of FDI on environmental pollution. It was recommended that African governments should strengthen the quality of their institutions by strengthening environmental governance frameworks. Keywords Africa, Environment, FDI, PCSE, PHH, Pollution 1 Department of Economics, Afe Babaola University, Ado Ekiti, Nigeria * Corresponding author’s e-mail: akjohn@abuad.edu.ng INTRODUCTION Foreign Direct Investment (FDI) is vital to a country’s economic growth. This is especially true in less developed countries. It is an important tool for employment creation, poverty reduction and economic growth (Crescenzi et al., 2022). However, while FDI can provide significant benefits to an economy, it is not without possible drawbacks. One of these is environmental pollution. FDI can sometimes result in environmental harm, especially if the host country’s environmental rules are weak.This may allow multinational corporations (MNCs) to engage in environmentally detrimental practices than they might have done in countries with stronger environmental laws (Chirilus & Costea, 2023). This is referred to as the pollution haven hypothesis (Raihan, 2023). It implies that multinational corporations, particularly those in pollution-intensive industries, may invest in or relocate production to nations with less stringent environmental regulations to avoid the higher costs of compliance in their home countries. This frequently results in developing countries with weak environmental enforcement attracting global corporations eager to cut regulatory costs, potentially leading to increasing pollution and environmental deterioration in such areas (Bashir, 2022; Raihan, 2023). In recent times, Foreign direct investment is becoming more and more popular in African nations as they work to build their economies (Adegboye & Okorie, 2023). From $2,845 million in 1990, FDI inflows to Africa increased to $82,196 million in 2021 (United Nations Conference on Trade and Development (UNCTAD), 2023). However, the growing impact of foreign direct investment on pollution has raised worries. These worries stem from the possibility that industrial operations linked to foreign direct investment could negatively affect the environment. Many African countries have less stringent or poorly enforced environmental regulations (Baajike et al., 2022). This creates a situation where foreign investors are not held to high environmental standards, leading to pollution concerns. Also, there are concerns that FDI are attracted into resource extraction industries, such as mining and oil exploration, and these pose environmental challenges (Kimiagari et al., 2023). According to Adegboye and Okorie (2023), the African region had the highest return on investment, at almost 11%, compared to Asia’s 9.1%, Latin America and the Caribbean’s 8.9%, and the global average of 7.1%. One of the reasons for this is because most of the FDI flows to Africa are attracted to the resource sector with the intention of exploiting Africa’s resources to their advantage (Geda & Yimer, 2023). This is more so because of the weak regulation and compliance in African countries. Ironically, as FDI inflows in Africa continue to rise, CO2 emissions are also on the increase. For instance, Carbon dioxide (CO2) emissions from industrial processes in Sub- Saharan Africa increased from 23.6232 metric tons (MT) in 71.6857 metric tons (MT) in 2022. Similarly, Carbon dioxide (CO2) emissions from Industrial combustion in Sub-Saharan Africa increased from 58.9712 metric tons (MT) in 91.7708 metric tons (MT) in 2022 (World Bank, 2023). Air pollution is one of today’s most serious environmental issues, posing a risk to human health. Every year, air pollution claims the lives of approximately 1.1 million people throughout Africa (Capitanio et al., 2024). Similarly, many African countries have annual mean pollution concentrations exceeding the World Health Organization guideline (Fisher et al., 2021). Fisher et al. (2021) also argued that in 2019, air pollution- related illness and mortality cost Ethiopia $3.02 billion, Pa ge 13 1 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 130-136, 2024 On the other hands, there have been several studies that found a negative effect of FDI on environmental pollution. Studies such as (Balsalobre-Lorente et al., 2019; Demena & Afesorgbor, 2020; Hao et al., 2020; Jiang et al., 2018; Kisswani & Zaitouni, 2023; Mert & Caglar, 2020; Nejati & Taleghani, 2022; Nguyen-Thanh et al., 2022; Tayyar, 2022) found evidence that FDI reduces pollution. The studies discovered that foreign direct investment can help to reduce pollution by encouraging the use of cleaner technologies and sustainable practices. They argue that FDI encourages host countries to raise their standards in response to pressures from multinational corporations, which frequently bring advanced, environmentally friendly technologies. They conclude that FDI reduces pollution through technology diffusion,especially in high- regulation economies where environmental norms are actively enforced. Tayyar (2022), for instance, found that foreign direct investment (FDI) reduces pollution by promoting technology transfer and raising environmental awareness in host nations, and that this effect is stronger in countries with strict environmental rules. Nguyen-Thanh et al. (2022) concluded that FDI helped to reduce pollution by boosting industrial modernization and bringing better environmental standards and cleaner manufacturing processes. According to Nejati and Taleghani (2022), FDI is related with a decrease in air pollutants in developing nations due to the adoption of cleaner technology and practices, which is especially beneficial in industries with historically high emissions. While numerous studies imply that FDI alone leads to increased pollution, the interplay of FDI and environmental control can mitigate this effect. For instance, Fahad et al. (2022) investigated the conditional impacts of FDI on environmental quality and found that stricter environmental regulations in host nations increase FDI’s positive influence on pollution reduction. It suggested that when laws are strong, FDI firms are more inclined to use cleaner technology to meet local norms. According to Fu et al. (2024), the environmental impact of FDI varies with regulatory strictness. FDI in countries with strong environmental policies leads to fewer emissions and improved sustainability practices. In contrast, in less-regulated countries, FDI may contribute to higher levels of pollution, implying that regulatory quality is an important driver. Qian-qian et al. (2019) concluded that FDI firms alter their practices in response to the host country’s regulatory framework. The study argued that in areas with strict environmental legislation, FDI promotes eco-friendly behaviors, whereas in less-regulated environments, investment can occasionally result in “pollution havens.” Also, Xie and Zhang (2024) revealed that the environmental impact of FDI depends on regulatory enforcement. In countries with strict environmental legislation, FDI brings in eco- friendly technology and practices, but in countries with laxer restrictions, it may contribute to increased pollution due to inadequate control. According to Yang et al. (2021), Ghana $1.63 billion,and Rwanda $349 million. Hence, the needs to tackle the problem of pollution cannot be overemphasized. One way to do this is by strengthening the quality of African institutions. Stronger institutions may create, implement, and enforce more stringent environmental legislation to reduce emissions and pollutants. They can move the focus from pollution- intensive sectors to those that support long-term growth by actively pushing FDI in renewable energy and environmentally friendly industries. This study therefore examines the moderating roles of institutional quality in the relationship between FDI and environmental pollution in selected African countries. LITERATURE REVIEW Pollution Haven Hypothesis and Pollution Halo Hypothesis Pollution Haven Hypothesis and Pollution Halo Hypothesis are two important theories that have been used to explain FDI-Pollution relationships in the literature. The Pollution Haven Hypothesis argues that foreign direct investment may increase pollution in the host nations, particularly in countries with inadequate environmental standards (Akbulut & Yereli, 2023). This implies that multinational corporations from nations with stronger environmental rules transfer their polluting activities to countries with looser regulations, resulting in “pollution havens” (Uche et al., 2024). This move enables major companies to reduce their manufacturing costs while increasing pollution levels in the host countries. The Pollution Halo Hypothesis, on the other hand, contends that FDI can improve environmental quality in host countries.According to Xu et al., (2021), multinational firms bring cleaner technology, sophisticated managerial methods, and higher environmental standards from their home countries to the host country. This transfer can have a “halo” effect, in which local businesses and industries adopt more sustainable practices, boosting overall environmental outcomes (Duan & Jiang, 2021; Padhan & Bhat, 2024). Review of Past Studies Over the years, the relationship between FDI and pollution has been widely examined. For instance, studies such as (Abbas et al., 2023; Abdo et al., 2020; Adeel- Farooq et al., 2021; Achuo & Ojong, 2024; Assamoi et al., 2020; Boamah et al., 2023; Gharnit et al., 2019; Khan & Ozturk, 2020; Nadeem et al., 2020; Ren et al., 2014; Shao et al., 2019; Quang, 2023; Wang et al., 2024; Zheng et al., 2024 ) found evidence that FDI increases environmental pollution. They noted that greater FDI inflow is frequently associated with higher levels of industrial emissions and air pollution. They ascribe this to weaker environmental rules in these countries, which make them appealing destinations for corporations looking for cheaper compliance costs. They confirmed that lax environmental regulations allow for polluting industry expansion via foreign investment. Pa ge 13 2 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 130-136, 2024 the impact of foreign direct investment on environmental results is mediated by local regulatory norms. The study highlighted that in countries with strict environmental regulations, FDI has a positive impact on environmental quality, whereas in less-regulated environment, FDI may exploit low standards, causing environmental harm. This study contributes to the debate by building on these findings, notably by investigating the impact of institutional quality in the FDI-pollution link. By adding institutional elements, the study gives a more in-depth knowledge of how governance systems, regulatory efficacy, and institutional strength in host nations may influence FDI environmental consequences. MATERIALS AND METHODS Two models were used for the analysis. The first was used to examine the effects of FDI and institutional quality on environmental pollution, while the second model was used to investigate how institutional quality moderate the effect of FDI on environmental pollution in the selected African countries. Model One LOG_CO2it= δ0+δ1 LOGFDIit+ δ2 IQSTQUALit+ δ3 LOG_CREDITit+ δ4LOG_CPIit+ δ_5 LOGEXCHAit+ ϵit ………..(3.1) Model Two LOG_CO2it= δ0 + δ1 LOGFDIit+ δ2 IQSTQUALit+ δ3 INSTFDIit + δ4LOG_CREDITit + δ5LOG_CPIit + δ6 LOGEXCHA_it + ϵit ………..(3.2) Where LOG_CO2 is the log of CO2 emission, LOGFDI is the log of FDI inflows, IQSTQUAL is institutional quality index, LOG_CREDIT is the log of credit to the private sector which was used as the proxy for financial development, LOG_CPI is the log of consumer price index, and LOGEXCHA is the log of exchange rate, and INSTFDI is moderating variable. INSTFDI = LOGFDI * IQSTQUAL Institutional quality was measured as the composite index from six governance indicators which are voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, and rule of law. Environmental Pollution was measured as CO2 emissions (metric tons per capita), FDI Inflows:was measured using FDI inflows as percentage of GDP, Domestic Credit was measured as the domestic credit to the private sector, Consumer Price Index: was used to measure inflation, Exchange Rate was measured as the Official exchange rate (LCU per US$, period average). This study was based on panel data consisting of time series data from 2002 and 2022. For the cross-sectional data, three countries were chosen from each region in Africa. Hence, the countries that were considered are Tunisia, Egypt, and Morocco, from North Africa, Benin, Nigeria, and Senegal from Western Africa, Cameroon, Chad, and Gabon, from Central Africa, Kenya, Rwanda and Mauritania from Eastern Africa, and Botswana, Namibia, Namibia, and South Africa. Using the Variance Inflation Factor, the study begins by checking for multicollinearity. The test for heteroskdesticity was followed using modified wald test The Pesaran Cross-Sectional Dependence test was used to test for cross-sectional dependence. The panel unit root tested using Cross-Sectional Augmented ADF (CADF) test. The Panel co-integration test was done using the Pedroni co-integration test, while Panels Corrected Standard Errors was used as the primary estimator. RESULTS AND DISCUSSION Testing for the Multicollinearity The variance inflation factor (VIF) and tolerance factor (TF) are measures used to assess multicollinearity among the predictor variables in models. Table 1 shows that the variance inflation factor is low with the highest value of 2.21 while the tolerance factor has the lowest value of 0.45 in the first model. The second model has the highest value of 2.29 and lowest tolerance factor value of 0.43. Given that the VIF values are below 5, there is no significant multicollinearity issue among the predictor variables in the two models (Shrestha, 2020). Table 1: Results of the VIF Test of Multicollinearity Model One Model Two Variable VIF TF VIF TF LOG_CREDIT 2.21 0.451662 2.29 0.437020 LO_GEXCHA 1.83 0.545362 1.84 0.542995 IQSTQUAL 1.76 0.569608 2.56 0.391313 LOG_CPI 1.18 0.844904 1.18 0.844742 LOG_FDI 1.03 0.974278 1.12 0.890316 INSTFDI 2.16 0.462577 Source: Computed by the Author Testing For Serial Correlation in the Models Table 2 shows that the Woodrige Test of Serial Correlation has probability values that is less than 5% in both models, implying that there is evidence of serial correlation in the residuals. Because the probability value is less than 5%, the null hypothesis of no serial correlation is rejected in the two models. Pa ge 13 3 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 130-136, 2024 Table 2 : Woodrige Test of Serial Correlation Model One Model Two F(,20) 346.156 355.181 Prob > F 0.0000 0.0000 Source: Computed by the Author Table 3: Result of the Modified Wald Test for Groupwise Heteroskedasticity Model One Model Two F(,20) 346.156 355.181 Prob > F 0.0000 0.0000 Source: Computed by the Author Table 4: Result of the Pesaran and Yamagata.Test Model One Model Two Delta 13.983 16.934 12.376 15.514 Prob Value 0.000 0.000 0.000 0.000 Source: Computed by the Author Table 5: Results of the Cross-Sectional Dependence Model One Model Two Variable CD-test p-value CD-test p-value LOG_CO2 + 47.372 0.000 47.372 0.000 IQSTQUAL + 3.096 0.002 3.096 0.002 LOGFDI + 4.014 0.000 4.014 0.000 Table 6: The Result of the CADF Unit Root Without Difference With Difference Variable t-bar P-value t-bar P-value LOG_CO2 -2.595 0.080 -2.735 0.018 IQSTQUAL -2.510 0.162 -3.422 0.000 LOGFDI -2.334 0.453 -3.588 0.000 L O G _ CREDIT -2.585 0.088 -3.780 0.000 LOG_CPI -2.070 0.067 -2.780 0.000 LOGEXCHA -2.489 0.189 -2.764 0.013 INSTFDI -2.618 0.064 -3.508 0.000 Source: Computed by the Author Table 7: The Result of Pedroni Co-integration Test Model One Model Two Statistics Statistic P-value Statistic P-value Modified Phillips -Perr -on 4.9890 0.0000 6.0836 0.0000 Phillips- Perron -3.0077 0.0013 -2.0945 0.0181 Augmented Dickey-Fuller -2.4268 0.0076 -1.2297 0.1094 Source: Computed by the Author Testing For Heteroskedasticity in the Models Table 3 reveals that the Modified Wald test for groupwise heteroskedasticity for the two models yields probability value of less than 1%. It provides significant evidence for rejecting the null hypothesis of no groupwise heteroskedasticity. It implies that the error variances in regression models are not uniform across groups or clusters. This breach of the homoscedasticity assumption may result in biased coefficient estimations and wasteful standard errors. As a result, our work solved this issue by determining the optimal estimator that is robust to heteroskedasticity. Slope Homogeneity Test Table 4 shows that the probability value for this test is less than 1% for the two models. It suggests strong evidence against the null hypothesis of slope homogeneity, indicating that the coefficients vary significantly across different units. Testing for Cross-Sectional Dependence The Pesaran cross-sectional dependence test findings demonstrate that most of the variables in the two models have probability values less than 1%, indicating strong evidence against the null hypothesis of no cross-sectional dependence. Cross-sectional dependence can cause coefficient estimates to be biased and standard errors to be erroneous in panel data models. This study addressed this challenge by considering the estimator that is robust to cross-sectional dependence. INSTFDI -.175 0.861 L O G _ CREDIT + 35.397 0.000 35.397 0.000 LOG_CPI + 65.949 0.000 65.949 0.000 LOGEXCHA + 31.22 0.000 31.22 0.000 Source: Computed by the Author Panel Unit Root Test The CADF test results provide insight into the stationarity of the variables in the panel data model. The results demonstrate that the CADF probability values were greater than 5% when the variables were not diferrenced. When they were differenced ones, the probabilities were less than 5%. This means that when the variables were not diferrenced, they were non-stationary, i.e. they showed trends or had unit roots. After differencing, the variables became stationary, which means they no longer showed trends or unit roots. Testing for the Co-integration in the Models Table 7 displays the Pedroni Co-integration test results for the models. The test provides three statistics: modified Phillips-Perron, Phillips-Perron, and augmented Dickey- Fuller. All the three statistics have probability values that are less than 1%. This provides extremely strong evidence against the null hypothesis of no co-integration in the two models. This implies a long-run relationship between the variables in the model. Pa ge 13 4 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 130-136, 2024 Results of The Panels Corrected Standard Errors Table 8 shows that in model one, there is a negative relationship between institutional quality and CO2 emissions. This shows that higher institutional quality leads to lower CO2 emissions. This implies that improving institutional quality, such as upgrading governance structures, strengthening regulatory frameworks, and fostering openness and accountability, may contribute to minimizing environmental deterioration. Similarly, there was a positive relationship between FDI and CO2 emissions, but it is not statistically significant at the 5% level. This shows that an increase in foreign direct investment is connected with higher levels of CO2 emissions. The lack of significance suggests that although FDI has the potential to increase pollution in Africa, the effect is relatively weak. This may imply that FDI has not grown enough to significantly contributes to pollution in Africa. In model two, there is also a negative relationship between institutional quality and CO2 emission. This suggests Table 8: Results Of The Panels Corrected Standard Errors Coef. P>z. Coef. P>z. IQSTQUAL -.0897178 0.000 -.1774559 0.000 LOGFDI .0024163 0.655 .0021982 0.816 INSTFDI -.0012278 0.755 LOG_CREDIT .181255 0.001 .2327117 0.004 LOG_CPI .3883593 0.000 .50382 0.000 LOGEXCHA -.4233512 0.000 -.5277795 0.000 _cons 8.53751 0.000 220.69 0.0000 Wald chi2(5) 128.64 220.69 Prob > chi2 0.0000 0.0000 Source: Computed by the Author that higher institutional quality is associated with lower levels of CO2 emissions. Similarly, a positive relationship between Foreign Direct Investment (FDI) and CO2 emissions was found, but the effect was not statistically significant at the 5% level. The moderating effect of institutional quality with FDI on CO2 is negative, but it is not statistically significant. The negative coefficient suggests that improved institutional quality may dampen the effect of FDI on CO2 emissions.Stronger institutional quality, as evidenced by good governance and regulatory frameworks, may reduce the environmental effect of FDI by encouraging cleaner technologies, sustainable behaviours, and compliance with environmental standards. However, the lack of significance in the moderating effect of FDI on the relationship between FDI and CO2 emissions may indicate that FDI levels are too low to have a noticeable effect on CO2 emissions.It may also imply that institutional quality must improve to a certain level for it to be able to have a significant effect on the way FDI affects pollution in Africa. Summary and Conclusion This study was conducted to examine the effects of FDI and institutional quality on environmental pollution in selected African countries. The findings showed that institutional quality had negative effects on environmental pollution, while the effect of FDI was positive but insignificant. Also, the effect of moderating effect showed that moderating FDI with institutional quality reduced pollution, but the effect was insignificant. The study concludes that FDI has the potential to increase pollution in Africa. Also, institutional quality has the potential to mitigate the potential effects of FDI on environmental pollution in Africa. This implies that higher levels of institutional quality may mitigate the CO2 emissions associated with FDI activities through stricter environmental regulations. It is therefore recommended that African governments should strengthen institutional quality by strengthening environmental governance frameworks, regulatory institutions, and enforcement mechanisms. REFERENCE Abbas, S. J., Iqbal, A., Hussain, M. M., & Anwar, A. (2023). The environmental cost of FDI and spatial implications of CO2 emissions in Sub-Saharan Africa. Environmental Science and Pollution Research, 30(29), 74441-74451. Abdo, A. B., Li, B., Zhang, X., Lu, J., & Rasheed, A. (2020). Influence of FDI on environmental pollution in selected Arab countries: a spatial econometric analysis perspective. Environmental Science and Pollution Research, 27, 28222-28246. Achuo, E., & Ojong, N. (2024). Foreign direct investment, economic growth and environmental quality in Africa: revisiting the pollution haven and environmental Kuznets curve hypotheses. Journal of Economic Studies. https://doi.org/10.1108/JES-02-2024-0065 Adeel-Farooq, R. M., Riaz, M. F., & Ali, T. (2021). Improving the environment begins at home: Revisiting the links between FDI and environment. Energy, 215, 119150. Adegboye, F. B., & Okorie, U. E. (2023). Fragility of FDI Pa ge 13 5 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 130-136, 2024 flows in sub-Saharan Africa region: does the paradox persist?. Future Business Journal, 9(1), 8. Akbulut, H., & Yereli, A. B. (2023). A new look at the pollution halo hypothesis: The role of environmental policy stringency. Estudios de economía, 50(1), 31-54. Assamoi, G. R., Wang, S., Liu, Y., & Gnangoin, Y. T. B. (2020). Investigating the pollution haven hypothesis in Cote d’Ivoire: evidence from autoregressive distributed lag (ARDL) approach with structural breaks. Environmental Science and Pollution Research, 27, 16886-16899. Baajike, F. B., Ntsiful, E., Afriyie, A. B., & Oteng-Abayie, E. F. (2022). The effects of economic growth, trade liberalization, and financial development on environmental sustainability in West Africa. The role of institutions. Research in Globalization, 5, 100104. Balsalobre-Lorente, D., Gokmenoglu, K. K., Taspinar, N., & Cantos-Cantos, J. M. (2019). An approach to the pollution haven and pollution halo hypotheses in MINT countries. Environmental Science and Pollution Research, 26, 23010-23026. Bashir, M. F. (2022). Discovering the evolution of Pollution Haven Hypothesis: A literature review and future research agenda. Environmental Science and Pollution Research, 29(32), 48210-48232. Boamah, V., Tang, D., Zhang, Q., & Zhang, J. (2023). Do FDI Inflows into African Countries Impact Their CO2 Emission Levels?. Sustainability, 15(4), 3131. Capitanio, L., Ratte, S., Gautier, S., & Josseran, L. (2024). Impact of air pollution on mortality: Geo- epidemiological study in French-speaking Africa. Heliyon, Volume 10, Issue 20, 30 October 2024, e39473 Chirilus, A., & Costea, A. (2023). The effect of FDI on environmental degradation in Romania: Testing the pollution Haven hypothesis. Sustainability, 15(13), 10733. Crescenzi, R., Ganau, R., & Storper, M. (2022). Does foreign investment hurt job creation at home? The geography of outward FDI and employment in the USA. Journal of economic geography, 22(1), 53-79. Demena, B. A., & Afesorgbor, S. K. (2020). The effect of FDI on environmental emissions: Evidence from a meta-analysis. Energy policy, 138, 111192. Duan, Y., & Jiang, X. (2021). Pollution haven or pollution halo? A Re-evaluation on the role of multinational enterprises in global CO2 emissions. Energy Economics, 97, 105181. Fu, L., Long, R., Sun, X., & Wang, Y. (2024). Foreign direct investment and pollution emissions: a perspective from heterogeneous environmental regulation. Management of Environmental Quality: An International Journal, 35(2), 378-401. Gharnit, S., Bouzahzah, M., & Soussane, J. A. (2019). Foreign direct investment and pollution havens: evidence from African countries. Archives of Business Research, 7(12), 244-252. Hao, Y., Wu, Y., Wu, H., & Ren, S. (2020). How do FDI and technical innovation affect environmental quality? Evidence from China. Environmental Science and Pollution Research, 27, 7835-7850. Fahad, S., Bai, D., Liu, L., & Baloch, Z. A. (2022). Heterogeneous impacts of environmental regulation on foreign direct investment: do environmental regulation affect FDI decisions?. Environmental Science and Pollution Research, 29(4), 5092-5104. Fisher, S., Bellinger, D. C., Cropper, M. L., Kumar, P., Binagwaho, A., Koudenoukpo, J. B., ... & Landrigan, P. J. (2021). Air pollution and development in Africa: impacts on health, the economy, and human capital. The Lancet Planetary Health, 5(10), e681-e688. Jiang, L., Zhou, H. F., Bai, L., & Zhou, P. (2018). Does foreign direct investment drive environmental degradation in China? An empirical study based on air quality index from a spatial perspective. Journal of cleaner production, 176, 864-872. Khan, M. A., & Ozturk, I. (2020). Examining foreign direct investment and environmental pollution linkage in Asia. Environmental Science and Pollution Research, 27(7), 7244-7255. Kimiagari, S., Mahbobi, M., & Toolsee, T. (2023). Attracting and retaining FDI: Africa gas and oil sector. Resources Policy, 80, 103219. Kisswani, K. M., & Zaitouni, M. (2023). Does FDI affect environmental degradation? Examining pollution haven and pollution halo hypotheses using ARDL modelling. Journal of the Asia Pacific Economy, 28(4), 1406-1432. Geda, A., & Yimer, A. (2023). What Drives Foreign Direct Investment into Africa? Insights from a New Analytical Classification of Countries as Fragile, Factor-Driven, or Investment-Driven. Journal of the Knowledge Economy, 1-36. Mert, M., & Caglar, A. E. (2020). Testing pollution haven and pollution halo hypotheses for Turkey: a new perspective. Environmental Science and Pollution Research, 27, 32933-32943. Nadeem, A. M., Ali, T., Khan, M. T., & Guo, Z. (2020). Relationship between inward FDI and environmental degradation for Pakistan: an exploration of pollution haven hypothesis through ARDL approach. Environmental Science and Pollution Research, 27, 15407- 15425. Nguyen-Thanh, N., Chin, K. H., & Nguyen, V. (2022). Does the pollution halo hypothesis exist in this “better” world? The evidence from STIRPAT model. Environmental Science and Pollution Research, 29(58), 87082-87096. Padhan, L., & Bhat, S. (2024). Pollution haven or pollution halo in the context of emerging economies: a two- step system GMM approach. Environment, Development and Sustainability, 1-21. Qian-qian, Z. H. A. N. G., Rui, Z. H. A. N. G., & Yi-bing, Z. H. A. N. G. (2019). The influence mechanism of foreign direct investment on environmental quality under environmental regulation: a comparative study based on different industry groups. Commercial Pa ge 13 6 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 130-136, 2024 Research, 61(5), 61. Quang, P. T. (2023). Assessment of impacts of inward and outward FDIs on environmental protection in Vietnam. Journal of Environmental Assessment Policy and Management, 25(02), 2350009. Raihan, A. (2023). Exploring environmental Kuznets curve and pollution haven hypothesis in Bangladesh: the impact of foreign direct investment. Journal of Environmental Science and Economics, 2(1), 25-36. Ren, X., An, Y., He, F., & Goodell, J. W. (2024). Do FDI inflows bring both capital and CO2 emissions? Evidence from non-parametric modelling for the G7 countries. International Review of Economics & Finance, 95, 103420. Shao, Q., Wang, X., Zhou, Q., & Balogh, L. (2019). Pollution haven hypothesis revisited: a comparison of the BRICS and MINT countries based on VECM approach. Journal of Cleaner Production, 227, 724-738. Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39-42. Sun, H., Liu, Z., & Chen, Y. (2020). Foreign direct investment and manufacturing pollution emissions: A perspective from heterogeneous environmental regulation. Sustainable Development, 28(5), 1376-1387. Tayyar, A. E. (2022). Testing pollution haven and pollution halo hypotheses for the energy sector: evidence from Turkey. Business and Economics Research Journal, 13(3), 367-383. Uche, E., Omoke, P. C., Silva-Opuala, C., & Al-Faryan, M. A. S. (2024). Re-estimating the pollution haven– halo hypotheses for Brazil via a machine learning procedure. Journal of International Development, 36(2), 1274-1292. UNCTAD(2023). World Investment Report 2023— INVESTING IN SUSTAINABLE ENERGY FOR ALL. United Nations Conference on Trade and Development, New York and Geneva, 2023. Available at https://unctad.org/system/files/official-document/ wir2023_en.pdf Wang, H., & Liu, H. (2019). Foreign direct investment, environmental regulation, and environmental pollution: an empirical study based on threshold effects for different Chinese regions. Environmental Science and Pollution Research, 26, 5394-5409. Wang, F., Ye, L., Zeng, X., & Zhang, W. (2024). The impact of FDI on energy conservation and emission reduction performance: A FDI quality perspective. Heliyon, 10(4). Xie, R., & Zhang, S. (2024). Re-examining the impact of global foreign direct investment (FDI) inflows on haze pollution—considering the moderating mechanism of environmental regulation. Energy & Environment, 35(6), 3186-3209. Xu, C., Zhao, W., Zhang, M., & Cheng, B. (2021). Pollution haven or halo? The role of the energy transition in the impact of FDI on SO2 emissions. Science of the Total Environment, 763, 143002. Yang, T., Dong, Q., Du, Q., Du, M., Dong, R., & Chen, M. (2021). Carbon dioxide emissions and Chinese OFDI: From the perspective of carbon neutrality targets and environmental management of home country. Journal of Environmental Management, 295, 113120. Zheng, J., Assad, U., Kamal, M. A., & Wang, H. (2024). Foreign direct investment and carbon emissions in China:“Pollution Haven” or “Pollution Halo”? Evidence from the NARDL model. Journal of Environmental Planning and Management, 67(3), 662-687.