From Finance to Footprints: Environmental Taxes and the Finance–Environment Nexus in Sub-Saharan Africa

Abstract

The finance–environment nexus in Sub-Saharan Africa remains complex, particularly in nations where institutional quality and fiscal policies are in an early stage. To address this, the study evaluates the impact of financial development on environmental sustainability in Sub-Saharan Africa, emphasising the moderating roles of environmental taxes and regulatory quality. Using a balanced panel methodology across 11 SSA nations from 2006 to 2023, the study employs a multi-estimation model (fixed effects (FE), Fully Modified Ordinary Least Squares (FMOLS) and Autoregressive Distributed Lag (ARDL)) to capture both short- and long-run relationships. From the analysis, the FE and FMOLS estimates indicate that financial development significantly increases ecological footprints, while foreign direct investment and government expenditure are associated with lower environmental footprints. However, the ARDL estimates reveal that environmental taxes and regulatory quality significantly reduce the ecological footprint, motivating a policy shift. Most importantly, the moderation estimation reveals that environmental taxes condition the finance–environment nexus in SSA. This depicts that while financial development worsens environmental outcomes, its adverse effects are nullified and reversed under a stronger environmental tax framework. These findings are relevant to the Environmental Kuznets Curve theory and draw insights from the institutional and financial intermediation theory. The study provides evidence that financial development, when integrated with effective environmental taxation and institutional quality, promotes environmental sustainability in SSA. Policymakers are therefore urged to strengthen environmental tax frameworks, integrate green financial intermediation and intensify regulatory institutions to achieve a sustainable finance–environment model and support SDG 13 in SSA.

1. Introduction

Sub-Saharan Africa (SSA) is increasingly faced with a dual challenge of sustaining economic growth while managing rising environmental pressures. Despite contributing a relatively small share of global emissions, the region faces significant environmental risks, including land degradation, deforestation, and increasing carbon intensity linked to rapid urbanisation and industrialisation (Fakhrullah et al., 2025; Prabhakar, 2025; Bennett et al., 2018). However, according to reports, environmental degradation is intensifying due to expanding infrastructure development, population growth, and resource-dependent economic activities. These trends raise concerns about the long-term sustainability of development pathways in SSA, particularly under increasing global climate commitments (Riaz et al., 2025; Aluko & Obalade, 2020).

According to Bashir and Elamin (2026), financial development has improved across many SSA economies, driven by financial sector reforms, technological innovation, and increased access to credit and global capital markets, as well as supporting economic diversification (Taera & Lakner, 2025). In contrast, financial development may also contribute to environmental pressure by promoting foreign investment in carbon-intensive industries, extractive sectors, as well as fossil fuel-based production and consumption activities with high environmental footprints (Shui et al., 2025). This creates a complex policy dilemma, as financial development is essential for economic transformation, but it may simultaneously worsen environmental issues if not aligned with sustainability objectives.

In response to these challenges, the introduction of environmental taxation represents a critical implicit policy instrument in emerging economies. This entails the assigning of economic costs to carbon-intensive activities, imposing environmental taxes to internalise ecological externalities and incentivising green innovation and resource-efficient practices (Masoud, 2024; Ljubičić, 2025). Nonetheless, in many SSA countries, the effectiveness of environmental taxation remains limited by weak institutional capacity, limited enforcement mechanisms, and competing fiscal priorities. As a result, the extent to which environmental taxes can reshape the relationship between financial development and environmental outcomes remains an open empirical question.

Furthermore, the environmental framework across SSA nations varies considerably in scope and implementation. While some countries have formal environmental taxation instruments such as carbon taxes (South Africa), fuel levies, and pollution-related fines, others rely on indirect fiscal measures or have limited implementation capacity (Nigeria, Kenya, and Ethiopia). However, the level of implementation and environmental tax benefits vary across nations, with some countries reporting relatively low levels of environmental fiscal activity, while some are experiencing an environmental system transformation.

Against this backdrop, a growing body of empirical literature (Somoye & Ayobamiji, 2026; Alhassan et al., 2022; Eluyela et al., 2022) have examined the relationship between financial development and environmental outcomes. Nonetheless, their findings remain inconsistent and linked directly to individual nations. Specifically, several studies report that financial deepening can exacerbate environmental degradation by funding carbon-intensive projects and production activities (Bekun et al., 2024; Kayani et al., 2020). In contrast, other studies argue that well-developed financial institutions can transform environmental sustainability by financing cleaner technologies, green investments, and green innovation. Although recent studies have begun to explore the role of environmental fiscal policy in environmental governance, empirical investigations linking financial development, environmental taxes, and environmental outcomes remain limited in the SSA context.

Specifically, studies often examine financial development and environmental degradation independently or focus on carbon emissions as the outcome variable, thereby limiting scope. Furthermore, relatively few studies investigate whether fiscal environmental instruments (such as environmental taxes) can reshape the financial development–environment nexus in SSA. This study addresses these gaps by introducing environmental tax as a moderating mechanism within the finance–environment relationship. The perspective is that while financial development may initially increase ecological footprints, environmental taxes can offset these negative externalities by redirecting finance toward cleaner technologies and sustainable projects.

The study makes four key contributions to the existing literature. First, this study extends the literature by modelling environmental taxation as a conditioning policy mechanism that modifies the effect of financial development on ecological footprint. By incorporating an interaction term within a panel ARDL framework, the study provides new evidence on how the finance–environment relationship evolves over the long run under varying environmental tax regimes. This approach moves beyond traditional EKC and green finance frameworks by embedding fiscal environmental mechanisms into financial development discourse. Second, the study synthesises three complementary theoretical lenses—the Environmental Kuznets Curve (EKC) (conceptually), institutional theory, and financial intermediation theory—to construct a multidimensional analytical framework. Together, these theories offer a comprehensive explanation of how institutional and fiscal structures mediate finance–environment interactions.

Third, the empirical design enhances methodological robustness by combining fixed effect (FE), Fully Modified Ordinary Least Squares (FMOLS), and Autoregressive Distributed Lag (ARDL) estimators. This triangulated approach enables the study to capture both short-run and long-run dynamics, mitigate endogeneity concerns, and validate findings across multiple estimation techniques. Finally, by focusing on Sub-Saharan Africa, the research contributes to an emerging body of evidence from regions with weak institutions and poor environmental regulation contexts often overlooked in the global sustainability finance literature. The SSA perspective is particularly valuable because it reveals how structural weaknesses influence the effectiveness of fiscal and financial instruments in achieving environmental sustainability.

Overall, this study advances the understanding of how financial development interacts with environmental taxation to shape ecological outcomes in emerging economies. It argues that environmental taxes not only correct market failures by pricing environmental externalities but also realign financial incentives toward green investment and innovation. The findings are expected to inform both theory and practice by offering empirical evidence on the transformative role of environmental taxation in aligning financial development with sustainability objectives under Sustainable Development Goal 13 (Climate Action).

The remainder of the paper is structured as follows. Section 2 reviews the relevant literature and develops the theoretical framework underpinning the study. Section 3 outlines the data, variables, and econometric methodology employed. Section 4 presents and discusses the empirical results, including robustness checks. Finally, Section 5 concludes the study and highlights key policy implications and directions for future research.

2. Literature Review

2.1. Financial Development and Environmental Sustainability in Sub-Saharan Africa

Over the years, the complex nexus between environmental outcomes and financial development in Sub-Saharan Africa has received growing scholarly attention (Kelly & Nembot Ndeffo, 2025; Asongu et al., 2024). This complexity cuts across the global sphere as environmental sustainability is an SDG-oriented objective. Specifically, existing literature suggests that financial development may increase environmental pressures by driving higher energy consumption, industrial emissions, and resource extraction (Aluko & Obalade, 2020; Kayani et al., 2020). On the other hand, well-functioning financial systems promote environmental improvements by supporting investments in renewable energy, cleaner technologies, and environmentally sustainable production systems (Prokopenko et al., 2025; Omri & Almoshaigeh, 2025), thereby managing the financing of emissions.

Furthermore, empirical evidence from Sub-Saharan Africa reflects these mixed outcomes. Several studies suggest that financial development contributes to environmental degradation by expanding credit availability for environmentally intensive sectors (Bekun et al., 2024). Conversely, other studies argue that financial deepening may support environmental sustainability through technological innovation and green investment when supported by appropriate regulatory frameworks (Yeboah et al., 2025). These divergent findings suggest that the environmental consequences of financial development depend largely on institutional conditions, policy design, and the structure of economic activity within a country.

2.2. Environmental Taxation and Environmental Quality

Environmental taxation is widely regarded as a market-based instrument for improving environmental quality by internalising negative externalities (Kusumawati et al., 2025). Empirical evidence generally suggests that environmental taxes can reduce emissions and broader environmental pressure by increasing the cost of pollution-intensive activities and promoting cleaner production (Owusu Atuahene et al., 2026; Wolde-Rufael & Mulat-Weldemeskel, 2023). However, empirical results for developing regions remain mixed. While some studies report that environmental taxes lead to lower emissions (Halidu et al., 2025) and improved environmental outcomes, others find weak or insignificant effects (Mehta & Derbeneva, 2024). In SSA, environmental taxes are often implemented primarily for revenue generation rather than for environmental preservation. These findings suggest that the impact of environmental taxation depends not only on its design but also on the broader institutional environment and the extent to which fiscal revenues are channelled toward environmental actions.

2.3. Institutional Quality and Environmental Outcomes

Institutional quality plays a key role in transforming environmental outcomes by shaping policy design, compliance and oversight systems (Gan, 2025). In the presence of strong regulatory institutions, the effectiveness of environmental policies is enhanced. Empirical studies consistently show that higher institutional quality is associated with improved environmental performance, such as a reduced rate of carbon emissions and better resource management (Akpan & Kama, 2024). In contrast, weak governance systems can limit the effectiveness of environmental regulations and fiscal instruments (Ogunkan, 2022). In SSA, where institutional capacity varies significantly across nations, regulatory quality is likely to influence not only environmental outcomes but also the effectiveness of environmental taxation as a policy tool.

2.4. Environmental Taxes as Fiscal Instruments in African Economies

Globally, environmental taxation has emerged as an important policy instrument for addressing environmental externalities and promoting sustainable development (Mpofu, 2022). In SSA, environmental taxation policies have gradually developed in response to growing environmental concerns and international climate commitments, such as the global reporting initiatives and the Paris agreement. Also, several nations have introduced implicit carbon systems targeting carbon emissions, fuel consumption, transport emissions, and natural resource use. For example, in Ghana, implicit environmental taxes have been adopted to address environmental degradation and encourage sustainable development. In South Africa, there is a vast framework for carbon taxes, energy-related levies, and water rates. Furthermore, countries such as Mauritius, Kenya and Ethiopia have introduced various forms of environmental taxes (Abedana et al., 2025; Gamette & Oteng, 2025). This goal is to address environmental degradation and achieve Sustainable Development Goal 13 (SDG 13).

Likewise, existing studies on Sub-Saharan African countries (Omodero, 2025; Gamette & Oteng, 2025; K. I. Okere et al., 2025; Yeboah et al., 2025; Mignamissi, 2026; Ambareen, 2023; Degirmenci & Aydin, 2023) reveal that the effectiveness of environmental taxes in many SSA countries remains constrained by institutional challenges, administrative capacity limitations, and competing fiscal priorities. Additionally, this arises from limited environmental tax objectives, implemented solely for revenue-generation rather than as targeted environmental policy mechanisms. Furthermore, administrative complications, institutional coordination challenges, and political instability are significant limiting factors to the implementation of environmental taxes (Mignamissi, 2026; Ambareen, 2023; Degirmenci & Aydin, 2023).

Furthermore, fuel and transport taxes are perceived to have considerable potential, as they are fairly straightforward to administer and yield significant revenue outcomes (Gamette & Oteng, 2025). Even though this can frequently be overwhelmed by the fuel subsidy provisions. Environmental taxes have been implemented across a range of African countries (Keen, 2024; Cottrell et al., 2023), counting Mauritius (Ambareen, 2023), South Africa (Hlongwane, 2025; Abedana et al., 2025; Nkosi et al., 2021), Nigeria and Ethiopia (Bekele, 2026; Yimam et al., 2025/2026; Fadipe et al., 2025; Hlongwane, 2025), Kenya (Njogu et al., 2025), and Mozambique (Kerckhoven et al., 2015).

Prior studies discovered that there is an avenue to improve the positive impact on the environment; however, most indicators are implemented to boost revenue rather than to mitigate transport-related environmental impacts (Yimam et al., 2025/2026; Fadipe et al., 2025; Hlongwane, 2025). Another tax system that has received attention in the literature is the forestry taxation system (Gamette & Oteng, 2025), as emissions from land use and forestry account for the primary source of greenhouse gas emissions in Sub-Saharan Africa and fiscal policy tools can help curb deforestation (Keen, 2024).

Although existing studies in Sub-Saharan Africa have examined the direct effects of environmental taxation outcomes, relatively few have examined how environmental taxes interact with financial development to shape environmental pressure. In particular, the role of environmental taxation as a conditioning mechanism within the finance–environment nexus remains underexplored, especially in a dynamic long-run context.

2.5. Theoretical Framework

This study employs a multi-theoretical lens that incorporates institutional governance, economic development theory and financial market behaviour frameworks (W. Okere & Ambe, 2026; Kumari & Singh, 2024). To achieve this, the study incorporates three complementary theoretical lenses, the Environmental Kuznets Curve (EKC) theory, institutional theory, and financial intermediation theory, to posit a broad theoretical framework for understanding how environmental taxes mediate the finance–environment nexus.

Firstly, the EKC describes a non-linear dynamic relationship between economic growth and environmental quality (W. Okere & Ambe, 2026; Han & de Vries, 2026; Yeboah et al., 2025; Oladunni et al., 2024; Udeagha & Breitenbach, 2023). The EKC hypothesis postulates that environmental issues initially increase during early stages of economic growth, despite investment in green production and processes (Leal & Marques, 2022). However, as economies develop over the long run, stronger regulatory quality improves environmental outcomes. Nonetheless, despite the EKC’s traditional focus, it extends to financial development (Chaudhry et al., 2022). This is feasible, as financial institutions (with support from strong institutions and regulatory mechanisms) may facilitate the adoption of greener technologies and renewable investments in the long run (Zhang et al., 2023).

For this study, the EKC provides a broad conceptual lens linking development processes to environmental pressures. However, because this study does not explicitly model income dynamics, the EKC is not treated as a testable hypothesis. Instead, the empirical specification is primarily informed by the institutional and financial intermediation theories.

On the other hand, the institutional theory explains the governance and regulatory setting within which fiscal tools operate (Matlala, 2025). Additionally, the institutional theory determines the capacity of governments to design, implement and enforce environmental fiscal policies effectively. Lastly, the financial intermediation theory explains how financial systems distribute finance within an economy with respect to policy signals (Konstantakopoulou, 2023). It highlights the roles of financial institutions (banks, capital markets, and investment institutions) in mobilising savings and directing capital toward productive investments. It explains that the environmental consequences of financial development are not determined by financial expansion but also depend significantly on the policy and institutional environment within which financial systems operate. Therefore, these theories provide a coherent framework of how institutional and fiscal establishments moderate finance–environment relations in Sub-Saharan Africa.

3. Methodology

This study employs a quantitative research design with a comprehensive panel data methodology to capture both cross-sectional and time-series dynamics across countries. To achieve its objectives, the study applies a combination of static and dynamic econometric estimators that capture both short-run associations and long-run equilibrium associations. This ensures a robust and policy-inclusive analysis. Furthermore, the study employs a balanced panel dataset covering 11 SSA countries over the period 2006–2023. The sample period was chosen based on the availability of consistent data for environmental taxation, governance quality indicators, and ecological footprint index across the selected nations.

Importantly, the data were obtained from globally recognised databases. Ecological footprint data were sourced from the Global Footprint Network, the financial development index was obtained from the World Bank Database, and data on foreign direct investment (FDI), government expenditure, and population growth were collected from the World Development Indicators (WDI) database. Additionally, environmental tax revenue data were sourced from the Organisation for Economic Co-operation and Development (OECD) statistical database. Lastly, regulatory quality data were obtained from the World Governance Indicators (WGI) dataset.

Additionally, the financial development index is a composite measure that captures multiple dimensions of financial systems, including financial depth, access, and efficiency across both financial institutions and financial markets. This multidimensional index provides a more comprehensive measure of financial development compared to single indicators such as domestic credit to the private sector.

Furthermore, the ecological footprint index is employed as the environmental indicator because it captures multiple dimensions of environmental resource use (including land use, carbon absorption capacity, and resource consumption) (Khezri et al., 2023). Importantly, compared to employing a single environmental indicator (such as CO₂ emissions), the ecological footprint provides a broader measure of environmental outcomes. This ensures a proper evaluation of environmental sustainability in SSA.

3.1. Model Specification

The baseline model is:

$${ENVSUS}_{\mathrm{it}}={\beta }_0+{\beta }_1{FDEV}_{\mathrm{it}}+{\beta }_2{FDI}_{\mathrm{it}}+{\beta }_3{GOVEXP}_{\mathrm{it}}+{\beta }_4{POPG}_{\mathrm{it}}+{\beta }_5{REGQTY}_{\mathrm{it}}+{\beta }_6{ENVTAX}_{\mathrm{it}}+{\epsilon }_{\mathrm{it}}$$

(1)

To examine whether environmental taxes condition the effect of financial development on environmental outcomes, an interaction model is estimated:

$$\begin{array}{ll}{ENVSUS}_{\mathrm{it}}={\beta }_0& + {\beta }_1{FDEV}_{\mathrm{it}}+{\beta }_2{FDI}_{\mathrm{it}}+{\beta }_3{GOVEXP}_{\mathrm{it}}+{\beta }_4{POPG}_{\mathrm{it}}+{\beta }_5{REGQTY}_{\mathrm{it}}+{\beta }_6{ENVTAX}_{\mathrm{it}}\\ & +{\beta }_7({FDEV}_{\mathrm{it}}\times {ENVTAX}_{\mathrm{it}})+{\epsilon }_{\mathrm{it}}\end{array}$$

(2)

where ENVSUS = environmental sustainability, FDEV = financial development, FDI = foreign direct investment, GOVEXP = government expenditure, POPG = population density, REGQTY = regulatory quality, ENVTAX = environmental tax revenue, and FDEV × ENVTAX = mediation interaction between financial development and environmental tax revenue. Furthermore, β₀ is the intercept term, β₁, β₂, β₃, β₄, β₅, β₆, and β₇ are the coefficients, and ε = error term.

3.2. Measurement of Variables

Table 1. Operationalisation and measurement of variables.

Variable	Definition	Unit	Source
ENVSUS	Ecological footprint index used as a proxy for environmental pressure	Index (0–1)	Global Footprint Network
FDEV	Financial development was measured using the financial development index (depth, access, efficiency)	Index (0–1)	World Bank Global Financial Development Database
FDI	Foreign direct investment inflows (% of GDP)	% of GDP	WDI
GOVEXP	Government expenditure (% of GDP)	% of GDP	WDI
POPG	Population density	Persons per km2	WDI
REGQTY	Regulatory quality index measuring institutional effectiveness	Index (−2.5 to +2.5)	WGI
ENVTAX	Environmental tax revenue	% of GDP	OECD
(FDEVit × ENVTAXit)	This captures the extent to which environmental taxation conditions the effect of financial development on environmental pressure across countries and over time	N/A	Section 3.1

Source: Authors’ computation (2026).

below presents the variables utilised in the study as well as their definitions.

3.3. Estimation Techniques and Diagnostic Strategy

To ensure robust empirical analysis and prevent spurious regressions, the study applies multiple complementary econometric estimators. This includes a fixed effects (FE) panel estimator, which controls for unobserved country-specific heterogeneity that may influence environmental outcomes. The FE model eliminates time-invariant country characteristics, allowing the analysis to focus on within-country variations over time. Furthermore, the analysis employs a two-way fixed effects specification that controls for both country-specific and time-specific effects. Also, a Fully Modified Ordinary Least Squares (FMOLS) is employed to estimate long-run relationships among the variables. The FMOLS model corrects for potential endogeneity and serial correlation that may arise in cointegrated panel data models, thereby producing consistent long-run parameter estimates.

Furthermore, an Autoregressive Distributed Lag (ARDL) estimator (using the Pooled Mean Group (PMG)) is used to capture both short-run dynamics and long-run equilibrium relationships among the variables. The ARDL model is particularly appropriate when variables exhibit mixed orders of integration, such as I(0) and I(1), as confirmed by the unit root tests conducted in this study. While multiple estimators are employed, the ARDL framework is considered the primary estimation framework due to its suitability for mixed-order integration and its ability to capture both short- and long-run relationships.

Moreover, the diagnostic strategy of the analysis includes a correlation analysis to understand the basic relationships among the variables, as well as any indication of multicollinearity. Also, a Variance Inflation Factor (VIF) test was conducted to assess multicollinearity among the independent variables. Additionally, a panel unit root test was employed to determine the stationarity properties of the variables.

4. Results

4.1. Descriptive Statistics

The descriptive analysis in

Table 2. Descriptive test.

	ENVSUS	ENVTAX	FDEV	FDI	GOVEXP	POPG	REGQTY
Mean	0.94	2.70	0.19	4.16	14.65	1.90	−0.37
Median	0.75	1.43	0.12	2.96	13.71	1.94	−0.30
Maximum	2.38	21.92	0.59	30.45	32.53	2.76	0.75
Minimum	0.35	0.00	0.08	−11.19	2.83	0.46	−1.16
Std. Dev.	0.51	3.91	0.13	5.38	5.77	0.53	0.40
Skewness	1.39	3.04	1.72	2.56	0.70	−1.09	0.14
Kurtosis	3.89	12.2	4.98	12.0	3.37	4.22	2.38
Jarque–Bera	70.41	995.8	130.2	886.7	17.1	51.7	3.82
Probability	0.00	0.00	0.00	0.00	0.00	0.00	0.15
Observations	198	198	198	198	198	198	198

Source: Authors’ computation (2026).

below seeks to provide quantitative evidence and insights into the dataset’s variable features. The balanced panel of 11 SSA nations covering the period of 2006–2023 shows significant dispersion in ecological footprint (ENVSUS), with a mean of 0.94 and a standard deviation of 0.51, suggesting significant variation in environmental pressure across SSA. Also, financial development (FDEV) has a mean of 0.19 (standard deviation = 0.13), and is positively skewed (1.72), suggesting diverse and developing financial sectors, institutions and systems in SSA. In contrast, environmental taxes are still low on average (mean = 2.70; std. deviation = 3.91) and significantly skewed (3.04), suggesting inconsistent and weak enforcement across SSA. This outcome shows the relevance of the role of environmental taxation in the models.

Furthermore, the FDI values highlight significant volatility in FDI flows, while government expenditure (GOVEXP) falls around 15% across SSA. Furthermore, population density (POPG) is moderate (mean = 1.90; SD = 0.53) and left-skewed, while regulatory quality (REGQTY) is negative on average, indicating structural, governance and institutional challenges in SSA.

4.2. Diagnostics Tests

4.2.1. Correlation Analysis

The correlation analysis in

Table 3. Correlation matrix.

	ENVSUS	ENVTAX	FDEV	FDI	GOVEXP	POPG	REGQTY
ENVSUS	1.00	−0.06	0.57	0.17	−0.05	−0.58	0.29
ENVTAX		1.00	0.12	−0.15	0.09	0.13	−0.02
FDEV			1.00	−0.09	0.60	0.07	0.53
FDI				1.00	−0.19	−0.39	0.01
GOVEXP					1.00	0.20	0.18
POPG						1.00	0.35
REGQTY							1.00

Source: Authors’ computation (2026).

below was conducted to examine relationships among the variables and provide preliminary insights. Also, the study sought to check for multicollinearity among the independent variables using the generally accepted benchmark of 80%. The analysis shows that financial development shows a moderate positive association with ecological footprint, suggesting that financial expansion may contribute to environmental pressure. Also, population density exhibits a negative correlation with ecological footprint, whereas regulatory quality displays a positive association. Furthermore, it can be seen that there is no issue of multicollinearity, as the highest coefficient in absolute terms is 58% between ecological footprint and population density. This rules out all issues regarding multicollinearity. Nevertheless, the study carried out a Variance Inflation Factor test to confirm the results of the correlation analysis.

4.2.2. Variance Inflation Factor (VIF) Test

The VIF test presented in

Table 4. VIF analysis.

	Coefficient	Uncentered
Variable	Variance	VIF
ENVTAX	3.25 × 10−6	1.226885
FDEV	0.180914	1.639985
FDI	2.80 × 10−6	1.415811
GOVEXP	1.51 × 10−5	1.684340
POPG	0.026929	2.255901
REGQTY	0.001880	1.222345

Source: Authors’ computation (2026).

below was carried out to test for multicollinearity. Based on the generally accepted benchmark of 5, it can be seen that there is no issue of multicollinearity as all VIF values are less than 5.

4.2.3. Stationarity Test

From the unit root test in

Table 5. Unit root test.

Series	Transformation Tested	ADF–Fisher χ2	p-Value	Choi Z-Stat	p-Value	Stationarity Decision	Implied Integration Order
POPG	Level	37.637	0.020	−2.495	0.006	Stationary at level	I(0)
ENVTAX	First difference D(ENVTAX)	66.999	0.000	−5.447	0.000	Stationary after differencing	I(1)
REGQTY	First difference D(REGQTY)	72.668	0.000	−5.582	0.000	Stationary after differencing	I(1)
GOVEXP	First difference D(GOVEXP)	60.040	0.000	−3.991	0.000	Stationary after differencing	I(1)
ENVSUS	First difference D(ENVSUS)	65.828	0.000	−5.062	0.000	Stationary after differencing	I(1)
FDI	First difference D(FDI)	75.022	0.000	−5.656	0.000	Stationary after differencing	I(1)
FDEV	First difference D(FDEV)	69.705	0.000	−5.483	0.000	Stationary after differencing	I(1)

Source: Authors’ computation (2026).

below, it can be seen that the values confirm a mixed stationarity outcome of I(0) and I(1). This result justifies the application of a cointegration test and the use of estimators such as FMOLS and ARDL regressions.

4.2.4. Summary of Key Diagnostics

Table 6. Key diagnostic tests.

Test	Statistic	p-Value	Inferences
Hausman (FE vs. RE)	χ2 = 84.74 (df = 6)	0.00	Fixed effects preferred
VIF range	1.22–2.26	–	No multicollinearity concern
Unit root	ADF-Fisher D(ENVSUS) = 65.83	0.00	Stationary in first difference
	ADF-Fisher POPG = 37.64	0.02	Level-stationary
Kao cointegration	ADF t = 0.195	0.42	Mixed: formal test NS
Durbin–Watson	2.0	-	No autocorrelation
Adj. R-squared	FE-EGLS (0.98); FMOLS (0.98)	-	Strong deterministic model
F-stat/Wald	FE-EGLS (762.95)	0.00	Significant model
Residual ADF (RESID-1)	−0.365	0.00	Supports cointegration
ARDL ECT (COINTEQ)	−0.593	0.00	Long-run equilibrium present

Source: Authors’ computation (2026).

below presents a summary of all key diagnostic tests. The Hausman test supports a fixed effects model, with a significant p-value, implying time-invariant unobservables are correlated with regressors. Also, multicollinearity levels are minimal, with VIFs ranging from 1.22 to 2.26, well below the conventional benchmark. The stationarity test indicates that most series are integrated of order one and become stationary in first differences, while population density is stationary at levels. Furthermore, the Kao cointegration test is mixed: the formal statistic does not reject the null hypothesis; however, the residual-based ADF reveals a negative and significant lag (−0.37, p < 0.00). This supports the ARDL error-correction term and a stable long-run relationship. In addition, the Durbin–Watson statistic is approximately 2, indicating no autocorrelation in the model.

4.3. Inferential Statistics

Table 7. Summary of key regression results.

Variable	FE-EGLS (Levels)	FMOLS (Long-Run)	ARDL (Long-Run)	ARDL Short-Run	ARDL Long-Run (Mod)	ARDL Short-Run (Mod)	GMM (Baseline)	GMM (Mod)
ENVSUS (−1)	—	—	—	—	—	—	0.58 (0.05) *	−0.39 (0.87)
ENVTAX	−0.00 (0.20)	−0.00 (0.88)	−0.01 (0.00) *	−0.04 (0.17)	0.05 (0.00) *	0.32 (0.10)	0.01 (0.90)	0.36 (0.60)
FDEV	0.38 (0.00) *	0.21 (0.62)	−0.25 (0.35)	0.50 (0.10)	0.58 (0.14)	3.41 (0.05) *	−0.03 (0.98)	1.63 (0.45)
FDI	−0.01 (0.00) *	−0.01 (0.00) *	0.00 (0.15)	0.01 (0.12)	−0.00 (0.94)	0.01 (0.30)	0.01 (0.70)	0.04 (0.52)
GOVEXP	−0.01 (0.00) *	−0.02 (0.00) *	−0.00 (0.67)	0.02 (0.21)	−0.00 (0.30)	0.01 (0.40)	0.00 (0.98)	0.05 (0.75)
POPG	0.25 (0.00) *	0.58 (0.00) *	−0.02 (0.92)	−10.7 (0.14)	0.49 (0.01) *	−14.9 (0.23)	0.11 (0.85)	1.79 (0.59)
REGQTY	0.10 (0.00) *	0.21 (0.00) *	−0.13 (0.00) *	0.14 (0.00) *	0.19 (0.00) *	−0.02 (0.80)	−0.32 (0.57)	−1.65 (0.55)
FDEV × ENVTAX	—	—	—	—	−0.24 (0.00) *	−2.03 (0.13)	—	−0.98 (0.68)
C	0.63 (0.00) *	—	—	0.71 (0.00) *	—	0.45 (0.19)	—	—
ECT	—	—	—	−0.59 (0.00) *	—	−0.62 (0.00) *	—	—

Source: Authors’ computation (2026). Note: (*) denotes significance; values in parentheses are p-values; Mod means moderation model.

below summarises all regressions applied in this study, covering the fixed effect (FE) panel OLS, FMOLS, ARDL and Dynamic GMM regression estimators. The FE panel OLS explains changes in environmental sustainability (ecological footprints) with an adjusted R-squared of 99%. Also, in this model, financial development (FDEV) is positively and significantly associated with ENVSUS (0.38, 0.01), consistent with the notion that financial expansion is associated with a higher ecological footprint in SSA. Also, foreign direct investment (FDI) (−0.01, p < 0.00) and government expenditure (GOVEXP) (−0.01, p < 0.00) are significantly negative, indicating that higher foreign direct investments and prudent public expenditure are associated with lower ecological footprints.

Also, there is a positive and significant relationship between regulatory quality and ecological footprint, while environmental taxation had a negative but insignificant impact on ecological footprint in SSA. In addition, population density is positive and significant (0.25; p < 0.00) with ENVSUS in the FE model.

Furthermore, the long-run estimators qualify the outcomes from the FE panel OLS. The FMOLS, which is robust to endogeneity and serial correlation in cointegrated models, confirms that stronger regulatory quality (0.21; p < 0.00) and higher population density (0.58; p < 0.00) are associated with higher ecological footprints. FDI and government expenditure maintain a significantly negative long-run relationship. In contrast, financial development is statistically weak in the long run. Expectedly, the ARDL model provides a clearer result.

In the non-mediated ARDL model, environmental tax is negative and significant (−0.01; p < 0.00), and regulatory quality is also negative and significant (−0.13; p < 0.00), indicating that, when modelled explicitly, environmental tax and regulatory quality reduce ecological footprint in the long run. In the ARDL short-run model, only regulatory quality is positive and significant.

Importantly, the ARDL mediation model (environmental tax x financial development) alters the long-run narrative in a theoretically coherent way. Environmental tax becomes positive and significant, while the interaction variable (FDEV × ENVTAX) is significantly negative (−0.24; p < 0.00). This implies that environmental taxes function significantly as a mediating mechanism, shaping how finance translates into environmental sustainability in SSA. Also, regulatory quality and population density are positive and significant in the long-run mediation model, whereas FDI and GOVEXP remain insignificant in the long-run mediation model. The short-run mediation model is also notable, consistent with the idea that tax-finance mediation is achievable through longer-run investments and innovation channels rather than immediate impact.

Importantly, despite employing a multiple estimation methodology, this study places particular emphasis on the panel ARDL results. This preference is justified based on the unit root indicating mixed order of integration. Therefore, this makes the ARDL model more appropriate than other estimators. Additionally, the ARDL methodology captures both short- and long-run dynamics, which are central to the finance–environment nexus in emerging economies.

5. Discussion

From the analysis, the empirical results yield critical insights into the relationship between financial development, environmental taxation, and environmental outcomes in SSA, employing a multi-econometric estimation strategy. The fixed effects (FE) estimates reveal that financial development is positively associated with ecological footprint, indicating that it may contribute to increased environmental pressure (ENVSUS) in SSA. This finding is consistent with the argument that credit expansion and financial deepening often support industrial growth, infrastructure development, and increased consumption, which, in turn, may intensify environmental degradation in developing economies. Also, the findings are in line with those of Bekun et al. (2024) and Xu et al. (2022), which also highlighted a negative finance–environment nexus.

Furthermore, the long-run results from the ARDL model indicate that environmental taxes and regulatory quality play important roles in reducing ecological footprint in SSA. These results suggest that environmental tax and institutional governance can effectively improve environmental sustainability when consistently and strategically implemented as an implicit pricing system. This supports the finding of Ahmed et al. (2022), which highlight the positive contributions of environmental regulations (e.g., environmental taxes) in reducing the ecological footprint. It also supports the findings of Chen et al. (2022), highlighting the importance of environmental taxation as an intervention mechanism in highly intensive, carbon-intensive economies such as those in SSA.

Additionally, the interaction model (${FDEV}_{\mathrm{it}}\times ENVTAX$) further confirms that environmental taxation significantly moderates the relationship between financial development and environmental pressure in SSA. This indicates a significant intervention, as while financial development may initially increase environmental degradation, effective environmental taxation can reduce this adverse effect by altering economic incentives and encouraging environmentally sustainable investment decisions among stakeholders. This finding is also in line with those of Owusu Atuahene et al. (2026) and Nyantakyi et al. (2023), who postulate that environmental taxation ensures the transition to renewable energy initiatives.

Likewise, foreign direct investment (FDI) and government expenditure indicate a favourable impact on ecological footprint, suggesting that these factors may contribute to environmental improvements in SSA. This is practically achievable as FDI may promote investment in green technologies and green management practices, while government expenditure may support environmental preservation initiatives and sustainable infrastructure development. Overall, these findings underscore the relevance of complementary policy frameworks in shaping the environmental consequences of financial development in an economy. In essence, financial development alone may not significantly promote environmental sustainability. However, when integrated with an implicit environmental taxation system and strong regulatory institutions, financial development can support a transition toward more sustainable economic activities.

Nonetheless, the positive coefficient of environmental tax in the interaction model may appear counterintuitive, as environmental taxation is generally expected to reduce environmental pressure. However, this result can be interpreted within the institutional and policy context of SSA. In many SSA nations, environmental taxes are often implemented primarily as revenue-generating instruments rather than as targeted environmental policy tools. In essence, weak enforcement mechanisms and limited institutional capacity may reduce the effectiveness of environmental taxes in altering production and consumption behaviour. Additionally, environmental tax revenues in SSA nations are not always earmarked for environmental protection or green investments, which may limit their direct impact on reducing ecological footprint.

5.1. Economic Implications

The economic outcome for SSA is that finance and financial development, left unregulated by green actions, could worsen environmental outcomes. However, with the integration of financial deepening and sustainable environmental tax regimes, there can be a shift towards ecological preservation; banks and capital markets internalise environmental costs, adjust risk pricing and adjust their portfolios towards green finance, cleaner production, renewable energy and resource-efficient infrastructure. The long-run reduction in ecological footprints, linked with stronger regulation and tax-finance interaction, is in line with lower transition risk, improved competitiveness in green value chains and reduced environmental costs.

5.2. Theoretical and Practical Implications

The research findings transform the finance–environment theories in emerging nations. They align with a conditional-decoupling assumption, that the financial development environmental effect is contingent on policy instruments that internalise externalities. Practically, these findings affirm the idea of policy complementarity between fiscal instruments (environmental taxes) and financial sector development. For managers and financial intermediaries, the mediation outcomes imply that environmental taxes as a fiscal instrument significantly transform the risk–return landscape, such as credit scoring, collateral policies, and sectoral exposure limits, and should incorporate tax-driven carbon and resource price pathways. Also, risk management committees of organisations should treat environmental tax as a forward-looking incentive to pursue green opportunities and development.

Furthermore, the findings relating to regulatory quality can be further explained through institutional theory. The negative long-run effect of regulatory quality on ecological footprint suggests that stronger institutional frameworks enhance the effectiveness of environmental policies and fiscal instruments. This implies that environmental taxation is more likely to achieve its intended outcomes in contexts where regulatory enforcement, governance quality, and policy credibility are strong.

Additionally, based on the assumptions of financial intermediation theory, the relationship between financial development and environmental taxation reflects how financial institutions in emerging nations respond to policy-induced motivations. Since environmental taxes alter the relative cost structure of economic activities, they influence financing and investing decisions, as well as risk assessment within financial markets. Furthermore, the negative interaction effect indicates that financial intermediaries may, over time, reallocate capital toward less environmentally harmful sectors in response to environmental tax signals and impact.

Nonetheless, the findings are conceptually in tandem with the broader EKC theory, which suggests that environmental outcomes evolve in line with economic and financial development, particularly when supported by appropriate policy interventions. In essence, this study does not directly test the EKC hypothesis. Instead, the results are more interpreted through institutional and financial intermediation lenses.

5.3. Policy Implications

Importantly, the study findings offer key policy pathways for promoting environmentally sustainable development in SSA nations. The empirical findings showcase that financial development, when operating in isolation, worsens environmental pressures by financing environmentally degrading projects and activities. However, the outcomes transform with the introduction of environmental taxation and regulatory governance, which play significant roles in mitigating these adverse environmental effects. Based on the research outcomes, three key policy drivers emerge. First, there is a need to transform environmental tax capacity by broadening its bases (energy, pollution, resources), earmarking a share of environmental tax revenue just for transition investments to ensure environmental sustainability. Therefore, ensuring the transparency and stability of environmental tax models can enhance policy credibility and motivate long-term investment in green technologies.

Second, there is a need to synchronise tax policies with financial regulations through embedding green taxonomies and disclosures (e.g., Global Reporting Initiative (GRI), IFRS S1/S2, Task Force on Climate-Related Financial Disclosures (TCFD)) and supervisory guidance so that financial institutions and investors systematically price environmental risks and opportunities and recognise tax-altered cash flows. Additionally, by increasing the cost of environmentally harmful activities, environmental taxes alter the relative profitability of different sectors and influence capital allocation decisions. Therefore, financial institutions can apply environmental tax signals to risk assessment, lending decisions, and portfolio management strategies.

Thirdly, there is a need to strengthen institutional quality in SSA nations. This can be achieved through contract enforcement, the rule of law, and administrative capacity to improve the long-run effectiveness of both taxes and finance in reducing ecological footprints in nations with limited fiscal space through incremental strategies such as pollution charges and sector-specific levies. Also, concessional tax benefits such as green credit windows can shift companies’ focus toward green activities in SSA. Therefore, the findings suggest that environmental taxation, financial development, and institutional governance should be treated as complementary policy mechanisms rather than independent policy tools.

6. Conclusions

In SSA, the relationship between financial development and environmental sustainability is negative in the short run, but the long-run trajectory is policy-driven for positive outcomes. Environmental taxes and a robust regulatory quality evidently reduce ecological footprints in SSA over time. Also importantly, environmental taxes mediate the finance–environment mechanism. At a higher environmental tax level and revenue, the ecological footprint-raising effect of financial development is nullified and reversed. This champions the relevance of fiscal policy instruments in interpreting the environmental implications of financial development pathways in SSA. Overall, these findings contribute to the growing literature on sustainable finance and environmental governance by revealing that environmental sustainability depends significantly on the fiscal policy and institutional efficiency within which financial systems operate and provide financial services.

Furthermore, environmental taxation operates not only as a direct policy instrument but also as a conditioning mechanism that alters the environmental consequences of financial development over the long run. Therefore, in regions such as SSA with growing financial institutions and emerging environmental policies, policymakers and governments should integrate financial and environmental frameworks to ensure co-development and significant impact. Therefore, SSA governments should seek to stabilise environmental tax reforms and systems, while financial regulators should relate prudential guidelines and disclosures to environmental price signals. Likewise, for corporate organisations, integrating environmental tax implications into corporate capital budgeting and credit policy is now a strategic need for SSA to ensure all stakeholders’ needs are met sustainably.

Limitations and Future Research

Despite the studies’ key contributions to contemporary tax-finance literature, there exist some limitations. First, the time mediation is modest; a longer sample would tighten inferences for the short-run outcomes. Second, environmental sustainability is proxied by the Global Footprint Index (GFI); while comprehensive, it aggregates complex pressures and environmental domains, which may mask environmental-specific responses. Therefore, future studies should test pollutant or sector-specific footprints, explore non-linear environmental tax-finance thresholds and examine heterogeneity across income groups and governance clusters in SSA and scrutinise heterogeneity across income groups and governance clusters in SSA.