Demographic and Investment Drivers of CO2 Emissions in West Africa: Testing the Pollution-Haven Hypothesis

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This study examines the demographic and investment drivers of CO2 emissions in 14 ECOWAS countries over the period 1996 to 2020, with a focus on testing the pollution-haven hypothesis. Using panel data estimations Fixed Effects with Driscoll-Kraay standard errors to address heteroskedasticity, serial correlation, and cross-sectional dependence the study investigates the effects of population growth, deforestation, and foreign direct investment (FDI) on carbon emissions. The findings indicate that population growth significantly increases CO2 emissions, confirming that demographic pressures are a key contributor to environmental degradation in the region. FDI also exhibits a weak but statistically significant positive effect, suggesting a pollution-haven effect in West Africa, particularly in least developed member states where industrial infrastructure and access to electricity are limited. Deforestation was not statistically significant, reflecting heterogeneous land-use patterns and the complex role of forest management in CO2 dynamics. The study highlights that economic growth and foreign investment in West Africa can exacerbate environmental pressures unless mitigated through strategic policy interventions. Climate diplomacy, clean technology promotion, and regional climate governance emerge as critical tools to balance development objectives with environmental sustainability. These results provide empirical guidance for ECOWAS policymakers and international stakeholders seeking to design targeted interventions that harmonize economic growth, foreign investment, and environmental stewardship. Environmental Policy CO2 Emissions Population Growth Foreign Direct Investment Pollution-Haven Hypothesis ECOWAS West Africa Figures Figure 1 Figure 2 1.0 Introduction Africa’s economic integration and development have accelerated over the past decades, yet the environmental consequences of these processes remain understudied. In particular, the Economic Community of West African States (ECOWAS) faces a dual challenge. These are, harnessing economic growth and foreign investment while mitigating the environmental costs of rising CO2 emissions. Despite regional efforts to promote industrialization, trade, and foreign direct investment (FDI), environmental degradation especially increased carbon emissions pose a serious threat to long-term sustainable development and human well-being in the region. International relations and development studies suggest that foreign policy, regional cooperation, and economic integration can influence environmental outcomes, particularly in developing regions. Recent developments, such as the inclusion of African Union (AU) member states in the G-20 framework, underscore the growing global influence of African nations, highlighting the importance of integrating environmental considerations into economic and diplomatic strategies. At the same time, population growth, urbanization, and industrial expansion compound environmental pressures, making it imperative to identify the socio-economic drivers of carbon emissions in West Africa. CO2 emissions are a principal contributor to climate change, with global levels rising from 22.75 billion metric tonnes to 37.15 billion metric tonnes over the last three decades, a 63.2% increase (Statista, 2025 ). Sub-Saharan Africa, including ECOWAS countries, has experienced a significant rise in per-capita emissions, averaging an annual increase of 2.5% over the last decade (World Bank, 2025 ). While climate agreements such as the Paris Accord seek to limit global warming, regional implementation remains uneven, particularly in Least Developed Countries (LDCs) where electricity access is low and industrial activity is energy-constrained (UNCTAD, 2025 ). The literature indicates that FDI can either exacerbate environmental degradation (pollution-haven effect) or promote cleaner technologies and reduced emissions (pollution-halo effect) (Cole & Fredriksson, 2009 ; Zafar et al., 2019 ). Similarly, demographic pressures, especially population growth, can increase energy consumption and CO2 emissions, complicating the region’s sustainability agenda. Despite these insights, empirical evidence on the interaction between population, FDI, and environmental quality in West Africa remains limited. This study addresses this gap by examining the effects of population growth, foreign direct investment, and deforestation on CO2 emissions in 14 ECOWAS countries over the period 1996–2020. By testing the pollution-haven and pollution-halo hypotheses in a West African context, this research provides actionable insights for policymakers seeking to balance economic development, investment inflows, and environmental sustainability. Ultimately, the study contributes to understanding how demographic and investment drivers shape environmental outcomes, offering guidance for regional climate governance and sustainable development strategies. 2.0 Literature Review The CO2 Situation in the ECOWAS Region The Economic Community of West African States (ECOWAS) comprises 15 countries with a combined population of 446 million and an average population density of 73 persons per km² (Worldometer, 2025). The region’s combined GDP stands at US $ 734.8 billion, with a 2023 growth rate of 3.9% (IMF, 2025). Over the past three decades, CO2 emissions have steadily increased, rising from approximately 130,000 kilotons (kt) in 2010 to 190,000 kt in 2020 (Fig. 1 ). Similarly, per capita emissions rose from 4.53 metric tons (mt) in 2010 to 5.87 mt by 2020 (Fig. 2 ). This upward trajectory reflects the combined effects of population growth, industrialization, and deeper integration into the global economy, often accompanied by increased foreign investment. Understanding these dynamics is critical for designing policies that balance economic development with environmental sustainability. Despite this importance, research explicitly examining CO2 trends and their drivers in ECOWAS remains limited, underscoring the need for the present study. Source: Authors’ design based on data from WDI (2025). Source: Authors’ design based on data from WDI (2025). Drivers of CO2 Emissions: Evidence from Africa and Beyond A growing body of literature highlights the multifaceted determinants of CO2 emissions. Globally, fossil fuel consumption remains the principal driver of rising CO2 levels (Dong et al., 2018; Jardón, Kuik, & Tol, 2017 ). Empirical evidence from Europe indicates that energy consumption is positively correlated with CO2 emissions, whereas deployment of renewable energy reduces emissions (Acaravci & Ozturk, 2010 ; Cherni & Essaber Jouini, 2017 ; Jebli, Youssef, & Ozturk, 2016; Shafiei & Salim, 2014). In sub-Saharan Africa, Acheampong, Dzatora, and Savage ( 2021 ) documented a direct causal link between economic growth and CO2 emissions, echoing earlier studies that caution that environmental mitigation measures can inadvertently slow economic growth in carbon-intensive sectors such as manufacturing (Acheampong, 2018 ; Fan, Zhang, & Zhu, 2010; Hsu & Chou, 2000). Foreign direct investment (FDI) presents a complex dynamic. In the EU, environmental regulations combined with innovative FDI have mitigated CO2 emissions in both the short and long run (Neves, Marques, & Patrício, 2020). Conversely, in developing regions, the transfer of pollution-intensive technologies can exacerbate emissions, giving rise to the pollution-haven hypothesis (PHH), while clean technology transfers support the pollution-halo hypothesis (PH) (Albulescu, 2019; Bakhsh et al., 2017 ; Cole & Fredriksson, 2009 ). Within sub-Saharan Africa, mixed evidence emerges. Adams, Adom, and Klobodu ( 2016 ) found that trade openness and GDP are inversely related to environmental degradation in Ghana. Acheampong, Adams, and Boateng ( 2019 ) report that while FDI can reduce emissions, trade openness and population growth tend to increase them. Studies across 28 sub-Saharan African countries indicate that GDP growth raises CO2 emissions, though urbanization may decrease emissions depending on estimation techniques and datasets used (Adams & Nsiah, 2019 ; Adams & Klobodu, 2017 ; Adams, Boateng, & Acheampong, 2020 ; Appiah, Li, & Korankye, 2020). This empirical ambiguity regarding FDI, population growth, and urbanization highlights the importance of region-specific analysis. The current study addresses this gap by examining how demographic growth, deforestation, and FDI shape CO2 emissions in the ECOWAS region. It provides insight into the applicability of the pollution-haven and pollution-halo hypotheses in a West African context. 3.0 Theoretical Considerations Environmental Kuznets curve, Pollution Halo and Pollution-Haven Hypotheses West African countries, particularly those in the ECOWAS region, are pursuing accelerated economic development and deeper integration into the global economy. As population and economic activities expand, the environmental consequences on air, forests, and water become increasingly salient, particularly amid rising global temperatures and sea levels. The Intergovernmental Panel on Climate Change (IPCC, 2023 ) attributes the majority of recent global warming to human activities, notably CO2 emissions. Empirical studies underscore the significant impact of economic activities on environmental degradation, prompting growing attention in policy and diplomatic circles (Mardani et al., 2019 ). The Environmental Kuznets Curve (EKC) hypothesis provides a theoretical lens for understanding this relationship (Dinda, 2004 ; Liu et al., 2019 ). The EKC posits that environmental degradation initially increases with economic development but eventually declines after a threshold level of income is reached, producing a characteristic inverted-U relationship when environmental quality is plotted against per capita income. Empirical applications of the EKC yield heterogeneous results, with variations across countries, pollutants, and time periods (Cansino, Román-Collado, & Molina, 2019 ; Hanif et al., 2019 ). In the context of international economic integration, foreign direct investment (FDI) introduces additional dynamics captured by the pollution-haven and pollution-halo hypotheses. The pollution-haven hypothesis suggests that firms from developed countries may relocate pollution-intensive operations to developing economies with weaker environmental regulations, thereby exacerbating environmental degradation in host countries (Bakhsh et al., 2017 ; Cole & Fredriksson, 2009 ; Kastratović, 2019 ). Some host countries actively leverage lax environmental standards as a strategy to attract FDI (Pao & Tsai, 2011 ; Zafar et al., 2019 ). Conversely, the pollution-halo hypothesis posits that FDI can improve environmental quality in host countries through the transfer of advanced technologies and cleaner management practices (Bakhsh et al., 2017 ; Zafar et al., 2019 ). This effect is more pronounced where domestic firms rely on older, pollution-intensive production methods, making foreign investment a conduit for environmental improvement (Bakhsh et al., 2017 ). Building on these theoretical frameworks, this study examines how three key drivers of economic activity population growth, deforestation, and FDI affect environmental quality in the ECOWAS region. By doing so, it contributes to the ongoing empirical debate on whether FDI acts as a pollution haven or a pollution halo in the context of West Africa’s developing economies. 4.0 Methodology Data Description, Variables and Data Sources This study covers the span of the ECOWAS region. A panel data on 14 ECOWAS countries from 1996–2020 was used in this study. Liberia was exempted from the study due to too much missing data. Panel data sets have become comparatively more widely used in econometric studies, relative to cross-sectional data. This is due to the volume of information which can be contained in a panel data format. Having more information is profitable for more precise estimation of parameters (Hoechle, 2007 ). Table 1 below contains the sources and descriptions of the variables that were used in this study. Table 1 Description of variables and Data Sources Description Variable Data source Carbon dioxide emissions (in kiloton) lnCO2_emkt World Development Indicators Citizen Population lnPop World Development Indicators Area of land covered in forest lnForest World Development Indicators Foreign Direct Investment lnFDIin World Development Indicators Manufacturing Value Addition lnManVA World Development Indicators Agricultural Value Addition lnAgVA World Development Indicators Telephone Subscription lnTel World Development Indicators Disturbance terms ε Source: Author’s own design (2025) Model Specification and Research Hypothesis The empirical model is specified in Eq. 1 below: lnCO2_emkt = ρ + ζ + δlnPop + ղlnForest + αlnFDIin + βlnManVA + γlnAgVA + φlnTel + ε……………………………………………………………………………. (1) Where the number of observations: n = N × T (number of groups × temporal observations) ∀I ∈ [1, N] and ∀ t ∈ [1, T]. ρ capture the unobserved country-specific effects, while ζ capture the unobserved time-specific effects, and εi,t is the error term which is assumed to be i.i.d. with mean and variance equal to \(\:{\sigma\:}_{\epsilon\:}^{2}\) . The key variables of this study are δlnPop, ղlnForest and αlnFDIin. In order to enhance the validity of the study and limit the influence of extraneous and confounding variables, three control variables; Manufacturing and Agriculture value addition (i.e. lnManVA and lnAgVA) and Telephone subscription rate (lnTel) were included in the model. Study Hypotheses In light of the theory, the following hypotheses are formulated; Hypothesis 1 Population growth (lnPop) has a significant positive effect on CO2 emission (pollution-haven behaviour). Therefore δ > 0 . Hypothesis 2 Deforestation (lnForest) has a significant positive effect on CO2 emission (pollution-haven behaviour). Therefore ղ >0 . Hypothesis 3 Foreign Direct Investment (lnFDIin) positively and significantly affects CO2 emission (pollution-haven behaviour). Therefore α > 0. Estimation Strategy The methodology addresses key econometric challenges inherent in panel data: Test for The Multicollinearity Variance inflation factors (VIFs) were computed, with all values below 2 (mean VIF = 1.77), indicating low multicollinearity among regressors. The results are reported in Table 2 below Table 2 Test for The Multicollinearity Variable VIF 1/VIF FDIin 2.010 0.498 Pop 1.920 0.521 ManVA 1.770 0.566 AgVA 1.740 0.573 Tel 1.590 0.630 Forest 1.570 0.637 Mean VIF 1.770 Source: Authors’ design (2025). Model selection Breusch-Pagan Lagrange Multiplier test was applied to detect panel effects, confirming significant country-level heterogeneity ((p < 0.001)). Hausman test guided the choice between fixed and random effects, rejecting the null ((p < 0.001)) and indicating fixed effects are preferred. Serial correlation and heteroskedasticity : Wooldridge test confirmed first-order serial correlation in the residuals ((p < 0.001)). Modified Wald test for groupwise heteroskedasticity also indicated heteroskedastic errors ((p < 0.001)). Results from the Wooldridge Test for Serial Correlation and modified Wald test for groupwise heteroskedasticity are summarized in Table 3 below: Table 3 Tests for Serial Correlation and Heteroskedasticity in Fixed Effects Regression Wooldridge Test for Serial Correlation Modified Wald Test for Group Wise Heteroskedasticity H o : No First Order Serial Correlation H o : = 2 for all i F(1, 13) 2800.041 χ2 (14) 21959.00 p-val > F 0.000 p-val > χ2 0.000 Source: Authors’ design (2025). To address these issues simultaneously, Driscoll-Kraay standard errors were applied, ensuring robust inference despite cross-sectional dependence, serial correlation, and heteroskedasticity. Robustness checks The model was estimated using OLS, Generalized Least Squares (GLS) random effects, and fixed effects with Driscoll-Kraay errors to verify consistency and stability of results. The results are presented in Table 4 below. Table 4 Empirical Results Ordinary Least Square Estimation Generalized Least Squares Random Effects Estimation Fixed Effects Estimation Fixed Effects with Driscoll-Kraay standard errors lnFDIin 0.179*** (0.0238) 0.120*** (0.0229) 0.0292 (0.0261) 0.0292* (0.0166) lnPop 0.775*** (0.0326) 1.170*** (0.0959) 2.249*** (0.235) 2.249*** (0.311) lnManVA 0.248*** (0.0878) -0.0161 (0.112) 0.00436 (0.113) 0.00436 (0.0399) lnAgVA -0.665*** (0.103) -0.198 (0.156) 0.221 (0.179) 0.221 (0.181) lnTel 1.11e-06*** (2.16e-07) 2.33e-07 (1.98e-07) 2.81e-07 (1.92e-07) 2.81e-07 (2.16e-07) lnForest 0.0291 (0.0314) -0.0900 (0.0862) 0.0380 (0.380) 0.0380 (0.352) Constant -6.887*** (0.734) -11.70*** (1.696) -29.92*** (6.964) -29.92*** (8.866) Observations 349 349 349 349 R-squared 0.848 0.497 Number of ID 14 14 14 14 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Source: Authors’ design (2025). 5.0 Results and Discussions The empirical results reveal distinct relationships between population growth, foreign direct investment (FDI), and environmental quality in the ECOWAS region. Consistent with the pollution-haven hypothesis, population growth exerts a strong and statistically significant positive effect on CO2 emissions (2.249% increase per 1% population growth at 99% confidence). This underscores that demographic pressures in urbanizing and industrializing contexts remain a critical driver of environmental degradation. The result aligns with Acheampong et al. ( 2019 ) and reinforces the importance of integrating population management and environmental policy into regional development planning. FDI inflows also exhibit a positive but weaker effect on CO2 emissions (0.0292% per 1% increase), suggesting that foreign investment, in the absence of stringent environmental regulations, can exacerbate ecological pressures. This result supports the notion that least developed ECOWAS countries, which lack advanced energy infrastructure and robust regulatory frameworks, may attract environmentally polluting investments, confirming the pollution-haven effect in this regional context (Cole & Fredriksson, 2009 ; Kastratović, 2019 ). By contrast, studies in advanced economies, such as the EU, demonstrate a pollution-mitigating effect of FDI, highlighting the context-specific nature of environmental spillovers. Interestingly, deforestation (lnForest) and sectoral contributions (lnManVA, lnAgVA) were largely insignificant in the adjusted fixed-effects models, implying that land-use change and sectoral value addition alone may not drive CO2 emissions in ECOWAS. This suggests that emissions are more sensitive to demographic and investment patterns than sector-specific production, emphasizing the importance of macro-level regulatory interventions rather than focusing solely on industrial sectors. Finally, the robustness checks using Driscoll-Kraay standard errors indicate that the fixed-effects estimates are reliable, accounting for heteroskedasticity, serial correlation, and cross-sectional dependence. This strengthens confidence in the validity of the findings and their applicability for policy formulation. For policy, the study proposes the following; As rapid population growth significantly impacts environmental quality. Policies promoting education, family planning, and urban infrastructure are essential to mitigate emissions. Targeted interventions in health, family planning, and urban development can reduce CO2 emissions without constraining economic expansion. Regional guidelines and environmental standards should be reinforced to prevent ECOWAS countries from becoming pollution havens. Clean technology incentives and conditional FDI could redirect investment toward sustainable pathways. Given the transboundary nature of environmental effects, ECOWAS-level climate diplomacy and collective policy action are crucial. Harmonized emissions standards and carbon monitoring frameworks can curb the environmental impact of economic integration. Thus, a structured regional climate diplomacy mechanism is needed to translate pledges into concrete actions, including carbon monitoring, policy implementation toolkits, and cross-country accountability frameworks. 6.0 Conclusions This study highlights that population growth and foreign direct investment are key drivers of CO2 emissions in the ECOWAS region, confirming the pollution-haven hypothesis in the sub-Saharan African context. The empirical evidence suggests that demographic pressures and investment patterns outweigh sectoral or deforestation effects in determining regional environmental quality. Overall, this study emphasizes that sustainable economic development in ECOWAS requires a coordinated regional approach. An approach which integrates population management, environmentally conscious FDI, and robust climate diplomacy. Only through such collective action can the region reconcile economic growth ambitions with environmental sustainability. Theoretical Implication : By situating environmental degradation within the interplay of international relations, foreign investment, and demographic dynamics, the study extends the application of the Environmental Kuznets Curve, pollution-haven, and pollution-halo hypotheses to a sub-Saharan African context. It demonstrates that global frameworks must be adapted to account for regional socio-economic and infrastructural constraints. Limitations and Future Research : While the study provides robust evidence, it is limited in a number of ways. Its focus on 14 ECOWAS countries due to data availability, which may limit generalizability to other African regions. Secondly, the analysis spans 1996–2020. Thus, post-2020 trends, particularly under AfCFTA implementation, remain unexplored. Lastly, the model only focuses on population, FDI, and deforestation. Future research should incorporate energy mix, technological adoption, and policy stringency indicators to provide a more comprehensive understanding. Future studies should also adopt dynamic panel estimators and cross-region comparisons to validate these findings and explore policy interventions in broader African contexts. Declarations Data Availability Statement : The data that support the findings of this study are available from the corresponding author upon reasonable request. Authors Contributions: All works pertaining to the study conception, design, material preparation, data collection and analysis, as well as the drafting of the manuscript were performed by the author. Funding: The author declares that no funds, grants, or other support were received during the preparation of this manuscript. 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06:04:21","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78342,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8542204/v1/f0ebc0a32f9a45b804f53d2a.html"},{"id":99757333,"identity":"340e5fb5-fb7b-40b9-8a44-34974a894fb3","added_by":"auto","created_at":"2026-01-08 06:04:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99705,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual CO2 Emissions (kt) in the ECOWAS Region\u003c/p\u003e\n\u003cp\u003eSource: Authors’ design based on data from WDI (2025).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8542204/v1/94e25b12c81e98adf6d469e4.png"},{"id":99798553,"identity":"d378854f-995f-4ace-b269-d09855d6a62a","added_by":"auto","created_at":"2026-01-08 13:48:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89012,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual CO2 emissions (metric tons per capita) in the ECOWAS Region\u003c/p\u003e\n\u003cp\u003eSource: Authors’ design based on data from WDI (2025).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8542204/v1/b9729e72edf8368046547cf3.png"},{"id":99805606,"identity":"5f018238-bd96-4059-9204-164f4a5ca71d","added_by":"auto","created_at":"2026-01-08 14:16:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":770267,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8542204/v1/9bae6f8f-6703-4567-9d79-ff33e2d9e69f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDemographic and Investment Drivers of CO2 Emissions in West Africa: Testing the Pollution-Haven Hypothesis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eAfrica\u0026rsquo;s economic integration and development have accelerated over the past decades, yet the environmental consequences of these processes remain understudied. In particular, the Economic Community of West African States (ECOWAS) faces a dual challenge. These are, harnessing economic growth and foreign investment while mitigating the environmental costs of rising CO2 emissions. Despite regional efforts to promote industrialization, trade, and foreign direct investment (FDI), environmental degradation especially increased carbon emissions pose a serious threat to long-term sustainable development and human well-being in the region.\u003c/p\u003e \u003cp\u003eInternational relations and development studies suggest that foreign policy, regional cooperation, and economic integration can influence environmental outcomes, particularly in developing regions. Recent developments, such as the inclusion of African Union (AU) member states in the G-20 framework, underscore the growing global influence of African nations, highlighting the importance of integrating environmental considerations into economic and diplomatic strategies. At the same time, population growth, urbanization, and industrial expansion compound environmental pressures, making it imperative to identify the socio-economic drivers of carbon emissions in West Africa.\u003c/p\u003e \u003cp\u003eCO2 emissions are a principal contributor to climate change, with global levels rising from 22.75\u0026nbsp;billion metric tonnes to 37.15\u0026nbsp;billion metric tonnes over the last three decades, a 63.2% increase (Statista, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Sub-Saharan Africa, including ECOWAS countries, has experienced a significant rise in per-capita emissions, averaging an annual increase of 2.5% over the last decade (World Bank, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While climate agreements such as the Paris Accord seek to limit global warming, regional implementation remains uneven, particularly in Least Developed Countries (LDCs) where electricity access is low and industrial activity is energy-constrained (UNCTAD, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe literature indicates that FDI can either exacerbate environmental degradation (pollution-haven effect) or promote cleaner technologies and reduced emissions (pollution-halo effect) (Cole \u0026amp; Fredriksson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zafar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, demographic pressures, especially population growth, can increase energy consumption and CO2 emissions, complicating the region\u0026rsquo;s sustainability agenda. Despite these insights, empirical evidence on the interaction between population, FDI, and environmental quality in West Africa remains limited.\u003c/p\u003e \u003cp\u003eThis study addresses this gap by examining the effects of population growth, foreign direct investment, and deforestation on CO2 emissions in 14 ECOWAS countries over the period 1996\u0026ndash;2020. By testing the pollution-haven and pollution-halo hypotheses in a West African context, this research provides actionable insights for policymakers seeking to balance economic development, investment inflows, and environmental sustainability. Ultimately, the study contributes to understanding how demographic and investment drivers shape environmental outcomes, offering guidance for regional climate governance and sustainable development strategies.\u003c/p\u003e"},{"header":"2.0 Literature Review","content":"\u003cp\u003e \u003cem\u003eThe CO2 Situation in the ECOWAS Region\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Economic Community of West African States (ECOWAS) comprises 15 countries with a combined population of 446\u0026nbsp;million and an average population density of 73 persons per km\u0026sup2; (Worldometer, 2025). The region\u0026rsquo;s combined GDP stands at US\u003cspan\u003e$\u003c/span\u003e734.8\u0026nbsp;billion, with a 2023 growth rate of 3.9% (IMF, 2025). Over the past three decades, CO2 emissions have steadily increased, rising from approximately 130,000 kilotons (kt) in 2010 to 190,000 kt in 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, per capita emissions rose from 4.53 metric tons (mt) in 2010 to 5.87 mt by 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This upward trajectory reflects the combined effects of population growth, industrialization, and deeper integration into the global economy, often accompanied by increased foreign investment. Understanding these dynamics is critical for designing policies that balance economic development with environmental sustainability. Despite this importance, research explicitly examining CO2 trends and their drivers in ECOWAS remains limited, underscoring the need for the present study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Authors\u0026rsquo; design based on data from WDI (2025).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Authors\u0026rsquo; design based on data from WDI (2025).\u003c/p\u003e \u003cp\u003e \u003cem\u003eDrivers of CO2 Emissions: Evidence from Africa and Beyond\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA growing body of literature highlights the multifaceted determinants of CO2 emissions. Globally, fossil fuel consumption remains the principal driver of rising CO2 levels (Dong et al., 2018; Jard\u0026oacute;n, Kuik, \u0026amp; Tol, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Empirical evidence from Europe indicates that energy consumption is positively correlated with CO2 emissions, whereas deployment of renewable energy reduces emissions (Acaravci \u0026amp; Ozturk, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cherni \u0026amp; Essaber Jouini, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jebli, Youssef, \u0026amp; Ozturk, 2016; Shafiei \u0026amp; Salim, 2014).\u003c/p\u003e \u003cp\u003eIn sub-Saharan Africa, Acheampong, Dzatora, and Savage (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) documented a direct causal link between economic growth and CO2 emissions, echoing earlier studies that caution that environmental mitigation measures can inadvertently slow economic growth in carbon-intensive sectors such as manufacturing (Acheampong, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fan, Zhang, \u0026amp; Zhu, 2010; Hsu \u0026amp; Chou, 2000).\u003c/p\u003e \u003cp\u003eForeign direct investment (FDI) presents a complex dynamic. In the EU, environmental regulations combined with innovative FDI have mitigated CO2 emissions in both the short and long run (Neves, Marques, \u0026amp; Patr\u0026iacute;cio, 2020). Conversely, in developing regions, the transfer of pollution-intensive technologies can exacerbate emissions, giving rise to the pollution-haven hypothesis (PHH), while clean technology transfers support the pollution-halo hypothesis (PH) (Albulescu, 2019; Bakhsh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cole \u0026amp; Fredriksson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin sub-Saharan Africa, mixed evidence emerges. Adams, Adom, and Klobodu (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that trade openness and GDP are inversely related to environmental degradation in Ghana. Acheampong, Adams, and Boateng (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) report that while FDI can reduce emissions, trade openness and population growth tend to increase them. Studies across 28 sub-Saharan African countries indicate that GDP growth raises CO2 emissions, though urbanization may decrease emissions depending on estimation techniques and datasets used (Adams \u0026amp; Nsiah, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Adams \u0026amp; Klobodu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Adams, Boateng, \u0026amp; Acheampong, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Appiah, Li, \u0026amp; Korankye, 2020).\u003c/p\u003e \u003cp\u003eThis empirical ambiguity regarding FDI, population growth, and urbanization highlights the importance of region-specific analysis. The current study addresses this gap by examining how demographic growth, deforestation, and FDI shape CO2 emissions in the ECOWAS region. It provides insight into the applicability of the pollution-haven and pollution-halo hypotheses in a West African context.\u003c/p\u003e"},{"header":"3.0 Theoretical Considerations","content":"\u003cp\u003e \u003cem\u003eEnvironmental Kuznets curve, Pollution Halo and Pollution-Haven Hypotheses\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWest African countries, particularly those in the ECOWAS region, are pursuing accelerated economic development and deeper integration into the global economy. As population and economic activities expand, the environmental consequences on air, forests, and water become increasingly salient, particularly amid rising global temperatures and sea levels. The Intergovernmental Panel on Climate Change (IPCC, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) attributes the majority of recent global warming to human activities, notably CO2 emissions. Empirical studies underscore the significant impact of economic activities on environmental degradation, prompting growing attention in policy and diplomatic circles (Mardani et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Environmental Kuznets Curve (EKC) hypothesis provides a theoretical lens for understanding this relationship (Dinda, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The EKC posits that environmental degradation initially increases with economic development but eventually declines after a threshold level of income is reached, producing a characteristic inverted-U relationship when environmental quality is plotted against per capita income. Empirical applications of the EKC yield heterogeneous results, with variations across countries, pollutants, and time periods (Cansino, Rom\u0026aacute;n-Collado, \u0026amp; Molina, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hanif et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of international economic integration, foreign direct investment (FDI) introduces additional dynamics captured by the pollution-haven and pollution-halo hypotheses. The pollution-haven hypothesis suggests that firms from developed countries may relocate pollution-intensive operations to developing economies with weaker environmental regulations, thereby exacerbating environmental degradation in host countries (Bakhsh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cole \u0026amp; Fredriksson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kastratović, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Some host countries actively leverage lax environmental standards as a strategy to attract FDI (Pao \u0026amp; Tsai, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zafar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, the pollution-halo hypothesis posits that FDI can improve environmental quality in host countries through the transfer of advanced technologies and cleaner management practices (Bakhsh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zafar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This effect is more pronounced where domestic firms rely on older, pollution-intensive production methods, making foreign investment a conduit for environmental improvement (Bakhsh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Building on these theoretical frameworks, this study examines how three key drivers of economic activity population growth, deforestation, and FDI affect environmental quality in the ECOWAS region. By doing so, it contributes to the ongoing empirical debate on whether FDI acts as a pollution haven or a pollution halo in the context of West Africa\u0026rsquo;s developing economies.\u003c/p\u003e"},{"header":"4.0 Methodology","content":"\u003cp\u003e \u003cem\u003eData Description, Variables and Data Sources\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study covers the span of the ECOWAS region. A panel data on 14 ECOWAS countries from 1996\u0026ndash;2020 was used in this study. Liberia was exempted from the study due to too much missing data. Panel data sets have become comparatively more widely used in econometric studies, relative to cross-sectional data. This is due to the volume of information which can be contained in a panel data format. Having more information is profitable for more precise estimation of parameters (Hoechle, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e ). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below contains the sources and descriptions of the variables that were used in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of variables and Data Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon dioxide emissions (in kiloton)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnCO2_emkt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizen Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnPop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of land covered in forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign Direct Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnFDIin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing Value Addition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnManVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Value Addition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnAgVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelephone Subscription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnTel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Development Indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisturbance terms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eε\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Author\u0026rsquo;s own design (2025)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cem\u003eModel Specification and Research Hypothesis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe empirical model is specified in Eq.\u0026nbsp;1 below:\u003c/p\u003e \u003cp\u003elnCO2_emkt\u0026thinsp;=\u0026thinsp;ρ\u0026thinsp;+\u0026thinsp;ζ\u0026thinsp;+\u0026thinsp;δlnPop + ղlnForest\u0026thinsp;+\u0026thinsp;αlnFDIin\u0026thinsp;+\u0026thinsp;βlnManVA\u0026thinsp;+\u0026thinsp;γlnAgVA\u0026thinsp;+\u0026thinsp;φlnTel\u0026thinsp;+\u0026thinsp;ε\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;. (1)\u003c/p\u003e \u003cp\u003eWhere the number of observations: n\u0026thinsp;=\u0026thinsp;N \u0026times; T (number of groups \u0026times; temporal observations) \u0026forall;I \u0026isin; [1, N] and \u0026forall; t \u0026isin; [1, T]. ρ capture the unobserved country-specific effects, while ζ capture the unobserved time-specific effects, and εi,t is the error term which is assumed to be i.i.d. with mean and variance equal to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{\\epsilon\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e. The key variables of this study are δlnPop, ղlnForest and αlnFDIin. In order to enhance the validity of the study and limit the influence of extraneous and confounding variables, three control variables; Manufacturing and Agriculture value addition (i.e. lnManVA and lnAgVA) and Telephone subscription rate (lnTel) were included in the model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Hypotheses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn light of the theory, the following hypotheses are formulated;\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003ePopulation growth (lnPop) has a significant positive effect on CO2 emission (pollution-haven behaviour). Therefore \u003cem\u003eδ\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/em\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eDeforestation (lnForest) has a significant positive effect on CO2 emission (pollution-haven behaviour). Therefore \u003cem\u003eղ \u0026gt;0\u003c/em\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eForeign Direct Investment (lnFDIin) positively and significantly affects CO2 emission (pollution-haven behaviour). Therefore \u003cem\u003eα\u0026thinsp;\u0026gt;\u0026thinsp;0.\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEstimation Strategy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe methodology addresses key econometric challenges inherent in panel data:\u003c/p\u003e \u003cp\u003e \u003cem\u003eTest for The Multicollinearity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eVariance inflation factors (VIFs) were computed, with all values below 2 (mean VIF\u0026thinsp;=\u0026thinsp;1.77), indicating low multicollinearity among regressors. The results are reported in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest for The Multicollinearity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/VIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDIin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean VIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors\u0026rsquo; design (2025).\u003c/p\u003e \u003cp\u003e \u003cem\u003eModel selection\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBreusch-Pagan Lagrange Multiplier test was applied to detect panel effects, confirming significant country-level heterogeneity ((p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHausman test guided the choice between fixed and random effects, rejecting the null ((p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)) and indicating fixed effects are preferred.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSerial correlation and heteroskedasticity\u003c/em\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWooldridge test confirmed first-order serial correlation in the residuals ((p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModified Wald test for groupwise heteroskedasticity also indicated heteroskedastic errors ((p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eResults from the Wooldridge Test for Serial Correlation and modified Wald test for groupwise heteroskedasticity are summarized in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTests for Serial Correlation and Heteroskedasticity in Fixed Effects Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWooldridge Test for Serial Correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModified Wald Test for Group Wise\u003c/p\u003e \u003cp\u003eHeteroskedasticity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eH\u003cb\u003eo\u003c/b\u003e: No First Order Serial Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eH\u003cb\u003eo\u003c/b\u003e: = 2 for all \u003cem\u003ei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF(1, 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2800.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ2 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21959.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-val\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-val\u0026thinsp;\u0026gt;\u0026thinsp;χ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSource: Authors\u0026rsquo; design (2025).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo address these issues simultaneously, Driscoll-Kraay standard errors were applied, ensuring robust inference despite cross-sectional dependence, serial correlation, and heteroskedasticity.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRobustness checks\u003c/strong\u003e \u003cp\u003eThe model was estimated using OLS, Generalized Least Squares (GLS) random effects, and fixed effects with Driscoll-Kraay errors to verify consistency and stability of results. The results are presented in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEmpirical Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrdinary Least Square Estimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneralized Least Squares Random Effects Estimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFixed Effects Estimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed Effects with\u003c/p\u003e \u003cp\u003eDriscoll-Kraay standard errors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnFDIin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003cp\u003e(0.0238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120***\u003c/p\u003e \u003cp\u003e(0.0229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0292\u003c/p\u003e \u003cp\u003e(0.0261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0292*\u003c/p\u003e \u003cp\u003e(0.0166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnPop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.775***\u003c/p\u003e \u003cp\u003e(0.0326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.170***\u003c/p\u003e \u003cp\u003e(0.0959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.249***\u003c/p\u003e \u003cp\u003e(0.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.249***\u003c/p\u003e \u003cp\u003e(0.311)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnManVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.248***\u003c/p\u003e \u003cp\u003e(0.0878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0161\u003c/p\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00436\u003c/p\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00436\u003c/p\u003e \u003cp\u003e(0.0399)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnAgVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.665***\u003c/p\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003cp\u003e(0.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003cp\u003e(0.181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnTel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11e-06***\u003c/p\u003e \u003cp\u003e(2.16e-07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33e-07\u003c/p\u003e \u003cp\u003e(1.98e-07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.81e-07\u003c/p\u003e \u003cp\u003e(1.92e-07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81e-07\u003c/p\u003e \u003cp\u003e(2.16e-07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0291\u003c/p\u003e \u003cp\u003e(0.0314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0900\u003c/p\u003e \u003cp\u003e(0.0862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0380\u003c/p\u003e \u003cp\u003e(0.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0380\u003c/p\u003e \u003cp\u003e(0.352)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.887***\u003c/p\u003e \u003cp\u003e(0.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.70***\u003c/p\u003e \u003cp\u003e(1.696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-29.92***\u003c/p\u003e \u003cp\u003e(6.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-29.92***\u003c/p\u003e \u003cp\u003e(8.866)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eStandard errors in parentheses\u003c/p\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors\u0026rsquo; design (2025).\u003c/p\u003e"},{"header":"5.0 Results and Discussions","content":"\u003cp\u003eThe empirical results reveal distinct relationships between population growth, foreign direct investment (FDI), and environmental quality in the ECOWAS region. Consistent with the pollution-haven hypothesis, population growth exerts a strong and statistically significant positive effect on CO2 emissions (2.249% increase per 1% population growth at 99% confidence). This underscores that demographic pressures in urbanizing and industrializing contexts remain a critical driver of environmental degradation. The result aligns with Acheampong et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and reinforces the importance of integrating population management and environmental policy into regional development planning.\u003c/p\u003e \u003cp\u003eFDI inflows also exhibit a positive but weaker effect on CO2 emissions (0.0292% per 1% increase), suggesting that foreign investment, in the absence of stringent environmental regulations, can exacerbate ecological pressures. This result supports the notion that least developed ECOWAS countries, which lack advanced energy infrastructure and robust regulatory frameworks, may attract environmentally polluting investments, confirming the pollution-haven effect in this regional context (Cole \u0026amp; Fredriksson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kastratović, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By contrast, studies in advanced economies, such as the EU, demonstrate a pollution-mitigating effect of FDI, highlighting the context-specific nature of environmental spillovers.\u003c/p\u003e \u003cp\u003eInterestingly, deforestation (lnForest) and sectoral contributions (lnManVA, lnAgVA) were largely insignificant in the adjusted fixed-effects models, implying that land-use change and sectoral value addition alone may not drive CO2 emissions in ECOWAS. This suggests that emissions are more sensitive to demographic and investment patterns than sector-specific production, emphasizing the importance of macro-level regulatory interventions rather than focusing solely on industrial sectors.\u003c/p\u003e \u003cp\u003eFinally, the robustness checks using Driscoll-Kraay standard errors indicate that the fixed-effects estimates are reliable, accounting for heteroskedasticity, serial correlation, and cross-sectional dependence. This strengthens confidence in the validity of the findings and their applicability for policy formulation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFor policy, the study proposes the following;\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAs rapid population growth significantly impacts environmental quality. Policies promoting education, family planning, and urban infrastructure are essential to mitigate emissions. Targeted interventions in health, family planning, and urban development can reduce CO2 emissions without constraining economic expansion.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRegional guidelines and environmental standards should be reinforced to prevent ECOWAS countries from becoming pollution havens. Clean technology incentives and conditional FDI could redirect investment toward sustainable pathways.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGiven the transboundary nature of environmental effects, ECOWAS-level climate diplomacy and collective policy action are crucial. Harmonized emissions standards and carbon monitoring frameworks can curb the environmental impact of economic integration. Thus, a structured regional climate diplomacy mechanism is needed to translate pledges into concrete actions, including carbon monitoring, policy implementation toolkits, and cross-country accountability frameworks.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"6.0 Conclusions","content":"\u003cp\u003eThis study highlights that population growth and foreign direct investment are key drivers of CO2 emissions in the ECOWAS region, confirming the pollution-haven hypothesis in the sub-Saharan African context. The empirical evidence suggests that demographic pressures and investment patterns outweigh sectoral or deforestation effects in determining regional environmental quality. Overall, this study emphasizes that sustainable economic development in ECOWAS requires a coordinated regional approach. An approach which integrates population management, environmentally conscious FDI, and robust climate diplomacy. Only through such collective action can the region reconcile economic growth ambitions with environmental sustainability.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTheoretical Implication\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eBy situating environmental degradation within the interplay of international relations, foreign investment, and demographic dynamics, the study extends the application of the Environmental Kuznets Curve, pollution-haven, and pollution-halo hypotheses to a sub-Saharan African context. It demonstrates that global frameworks must be adapted to account for regional socio-economic and infrastructural constraints.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLimitations and Future Research\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eWhile the study provides robust evidence, it is limited in a number of ways. Its focus on 14 ECOWAS countries due to data availability, which may limit generalizability to other African regions. Secondly, the analysis spans 1996\u0026ndash;2020. Thus, post-2020 trends, particularly under AfCFTA implementation, remain unexplored. Lastly, the model only focuses on population, FDI, and deforestation.\u003c/p\u003e \u003cp\u003eFuture research should incorporate energy mix, technological adoption, and policy stringency indicators to provide a more comprehensive understanding. Future studies should also adopt dynamic panel estimators and cross-region comparisons to validate these findings and explore policy interventions in broader African contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003c/strong\u003e \u003cem\u003eAll works pertaining to the study conception, design, material preparation, data collection and analysis, as well as the drafting of the manuscript were performed by the author.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e \u003cem\u003eThe author declares that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: \u003cem\u003eThe author has no known financial or non-financial conflicts of interests.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent to Publish\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcaravci A, Ozturk I (2010) On the relationship between energy consumption, CO2 emissions and economic growth in Europe. 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Evidence from Pakistan. Environ Sci Pollut Res 26:29172\u0026ndash;29184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-019-06318-8\u003c/span\u003e\u003cspan address=\"10.1007/s11356-019-06318-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CO2 Emissions, Population Growth, Foreign Direct Investment, Pollution-Haven Hypothesis, ECOWAS, West Africa","lastPublishedDoi":"10.21203/rs.3.rs-8542204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8542204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAfrica’s rapid economic integration and rising foreign investment bring both opportunities and environmental risks. This study examines the demographic and investment drivers of CO2 emissions in 14 ECOWAS countries over the period 1996 to 2020, with a focus on testing the pollution-haven hypothesis. Using panel data estimations Fixed Effects with Driscoll-Kraay standard errors to address heteroskedasticity, serial correlation, and cross-sectional dependence the study investigates the effects of population growth, deforestation, and foreign direct investment (FDI) on carbon emissions. The findings indicate that population growth significantly increases CO2 emissions, confirming that demographic pressures are a key contributor to environmental degradation in the region. FDI also exhibits a weak but statistically significant positive effect, suggesting a pollution-haven effect in West Africa, particularly in least developed member states where industrial infrastructure and access to electricity are limited. Deforestation was not statistically significant, reflecting heterogeneous land-use patterns and the complex role of forest management in CO2 dynamics. The study highlights that economic growth and foreign investment in West Africa can exacerbate environmental pressures unless mitigated through strategic policy interventions. Climate diplomacy, clean technology promotion, and regional climate governance emerge as critical tools to balance development objectives with environmental sustainability. These results provide empirical guidance for ECOWAS policymakers and international stakeholders seeking to design targeted interventions that harmonize economic growth, foreign investment, and environmental stewardship.\u003c/p\u003e","manuscriptTitle":"Demographic and Investment Drivers of CO2 Emissions in West Africa: Testing the Pollution-Haven Hypothesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 06:04:16","doi":"10.21203/rs.3.rs-8542204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82925a94-ad87-4820-9dc7-ecfb2b82d7b5","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60750680,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2026-01-08T06:04:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 06:04:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8542204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8542204","identity":"rs-8542204","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}