Environmental degradation as a result of climate change is at the forefront of policy discussions at the academic, scientific, and policy levels ( Sakariyahu et al., 2024). The impact of economic activities, especially land use and transportation, on climate change has reached unprecedented levels ( IPCC, 2019). As such, the quality of the environment reflects the extent to which increased economic activities, especially dependence on natural resources and unsustainable energy sources, inflict danger to the environment ( Amer et al., 2024). While Ngarava (2021) argued that increased economic activity is a threat to environmental sustainability, the theoretical view for this relationship is contested (see Zhang et al., 2024; Burgess and Barbier, 2025). The Environmental Kuznets Curve (EKC) suggests that institutional framework and economic advancement eventually lead to environmental recovery ( Stern, 2004; Zuo et al., 2022). Additionally, Treadmill of production theory argues that accumulation of capital leads to rent seeking and prioritisation of profit leads to continued industrial expansion to maintain economic growth at the expense of the environment ( Curran, 2017; York and Foster, 2005). For low-income countries, characterized by high levels of poverty, inequality, and unemployment, there is pressure for economic development and prioritisation of growth which leads to increased pollution due to limited clean technology adoption for production, sustainable storage, transportation, and waste disposal ( Hunjra et al., 2024).

Environmental degradation is varied across jurisdictions, with greatest divide being between global north versus global south ( Lauer, Llases, and López-Muñoz, 2025; Li, Wei, Chen et al., 2025). On the other hand, even among peer economies, differences of managing environment, especially ensuring sustainable production, depends on level of financial development and law quality and enforcement (part of institutional quality) ( Khan, Weili, & Khan, 2022b; Hussein, Warsame, and Abdi, 2025; Naseer, Hunjra, Palma, and Bagh, 2025). Understanding of this development in the global South context is critical, especially Africa ( OSS, 2022; Sakariyahu et al., 2024), and this study advances inquiry in the case of Namibia.

Namibia, like many developing countries, has the least carbon emissions, yet is heavily impacted by climate change as a country with over 90% of it classified as hyper-arid, arid, or semi-arid, ranking second to the Sahara Desert in terms of aridity ( World Bank Group, 2021). As a result, communities are prone to catastrophic events such as increased temperature, drought, and floods ( World Bank Group, 2021; Government of the Republic of Namibia, 2023). Namibia faces immense pressure to control its ecological footprint, especially in terms of land use, which is the highest emitter of greenhouse gases in the country. Economic activities such as exploration of natural resources are the highest contributor to land use and contribute 14.4% to GDP, more than 50% to export earnings, and 55.9% to government revenue ( Bank of Namibia, 2023; Chamber of Mines of Namibia, 2024). While, these resources are crucial for socioeconomic development, dependence on unsustainable economic activities has a consequential impact on the environment. Propelling growth through the extractive primary sector strains the environment ( Aladejare, 2022). As such, increased economic activities, such as the use of fossil fuels, exploration, and industrialization, contribute to global warming and environmental degradation ( Gutti, Aji and Magaji, 2012; Amer et al., 2024). This has prompted policymakers and governments to encourage sustainable land use management to protect the environment. Although resource rent and other economic activities are beneficial to the economy, they also pose a threat to the environment ( Sadaoui et al., 2024). In addition, the financial sector, as an enabling division of the economy with its intermediating role, plays a role in transferring and managing climatic risk; hence, the development of these sectors matters in finding a lasting solution.

Environmental degradation is a result of increased economic activities, production, and exploitation of natural resources. However, it is believed not to be a result of capitalism and economic growth alone but also involves the absence of effective institutional frameworks and policies to deal with greenhouse gas emissions and climate change ( Cohen, 2023). Thus, there are calls for institutional frameworks to address the global emission levels of greenhouse gases. According to Hussain and Dogan (2021); Warsame et al. (2022) indicate that institutional quality plays an important role in environmental sustainability. Song et al. (2024) emphasized that environmental quality can be preserved through the implementation of stringent environmental policies that reduce the use of non-renewable energy sources that burden the environment. Contrary, institutional quality contributes to poor environmental management depending on priority of the economy between economic growth and environmental sustainability (Handoyo, 2024). In Namibian, various laws have been implemented to avert environmental degradation, such as Article 95 of Namibia’s Constitution, Environmental Management Act 7 of 2007, the National Policy and Strategy on Climate Change 2011, and the Environmental Assessment Policy 1994. These policies are aimed at environmental protection and dealing with environmental degradation. The theory of Institutions suggests that the presence of institutional frameworks is not sufficient to ensure market functioning (North, 1990). Therefore, presence of institutional framework does not guarantee environmental sustainability. However, the Ecological modernisation theory highlights that financial markets are necessary to provide capital for green innovations, however it requires robust institutions that prioritise enforcement of environmental policies discourage carbon-intensive industrial expansion (Raihan and Sarker, 2026).

The advocacy for investment in green energy and technology to avert environmental degradation has been at the forefront of climate change talks at the United Nations Convention Conferences and Paris Agreement negotiations. Therefore, financial development is critical for investments in projects and technologies that preserve the environment ( Imran et al., 2023; Gul and Hussain, 2024). Kirikkaleli and Adebayo (2021) indicates that financial development is crucial for environmental sustainability, as it allows for a shift from traditional to more modern and sustainable practices (Raihan and Sarker, 2026). Given the importance of investment in clean energy, technology, and low-carbon development, Namibia has implemented initiatives such as the 2016 Green Climate Fund (GCF) through the Environmental Investment Fund established under Act 13 of 2001. The GCF has a portfolio of N$ 640 million secured for grant and readiness support by 2023. These funds aim to finance climate change mitigation programs that allow for overall economic development and environmental protection. Despite the legislative framework, and financial commitments, there remain implementation gaps. Therefore, while the country continues to attract FDI into the extractive sector, the allocation of capital toward environmentally friendly and climate-resilient infrastructure remains unclear.

While the literature acknowledges the impact of production and consumption on the environment, current views show that additional factors are responsible for climate change and environmental degradation. Despite the importance of environmental sustainability to our livelihoods, there is a lack of consensus on the influence of policies and finance on the environment. To address the gap, the study utilised the Auto-Regressive Distributed Lag (ARDL) simulation technique and the FMLOS, CCR for robustness to investigate the impact of financial sector development and institutional policies on environmental degradation in Namibia from 1990 to 2023. The study found that while financial sector development reduces environmental degradation in line with the ecological modernisation theory. Institutional quality exacerbates degradation of the environment, contrary, to the EKC hypothesis, therefore the results support the Treadmill of Production Theory, suggesting that Namibia’s institutional frameworks favour economic expansions than environmental protection. The findings provide insight into the effectiveness of institutional frameworks and financing. The study offers guidelines for a holistic approach to creating synergies between these variables to enhance environmental quality and sustainability. Additionally, the research assists policymakers in creating environmentally friendly policies that balances environmental sustainability and the pursuit for economic growth.

The rest of the paper is structured as follows: Section 2 presents the literature review, Section 3 discusses the data and methods, and Section 4 presents the empirical results. Section 5 presents the conclusions and recommendations.

This study used time-series data from 1990 to 2023 in Namibia to investigate the impact of institutional quality and financial development on environmental degradation. The data are sourced from World Bank Databases (WDI) and the Fraser Institute databases. This study used carbon dioxide (CO ₂) per capita emissions as a proxy for environmental degradation, as it is a major contributor to environmental change and has been widely used in empirical studies examining the relationship between economic growth, financial development, and environmental outcomes ( Sida, 2011; Doğan, Saboori and Can, 2019; Tran et al., 2023; Gul and Hussain, 2024). This study used CO ₂ as a proxy due to data constraints relating to broader environmental indicators such as ecological footprint and land degradation indices. Although Namibia’s major environmental challenges include desertification, land degradation, droughts, and ecosystem stress, consistent long term data for these indicators remain limited. Nevertheless, given Namibia’s dependence on mining, fossil fuel based energy consumption, and resource intensive economic activities, CO ₂ emissions remain a relevant and consistent measure of emission related environmental degradation. Furthermore, institutional quality was measured using the Worldwide Governance Indicators, which capture dimensions such as government effectiveness, regulatory quality, rule of law, and control of corruption, reflecting the role of governance in supporting environmental sustainability and policy implementation. The independent variables includes institutional quality (IQ) proxied by economic freedom index which assigns scores up 10; where 10 indicates high quality while 0 indicates low quality as adopted from Dube and Horvey (2023). The index includes various institutional quality characteristics such as rule of law, government size, regulatory efficiency, market openness. The index measures both government efficiency and enforcement of regulation in a resource dependent country where the government intervention in economic activities and resource management plays a role in environmental outcomes. Other proxies from World Governance Indicators (WGI) only starts from 1996, the use of EFI allows to capture post-independence and give which are commonly used have data limitations as such EFI allows a broader analysis. Credit to the private sector (%GDP) as an indicator of financial sector development (FSD), adopted from studies such as Amin et al. (2022), Awdeh (2022), and Khan, Weili and Khan (2022a). Other explanatory variables included renewable energy consumption (% of total usage) (REC), GDP per capita (constant 2015 US$) as proxy of economic growth (GDP), and trade openness (% of GDP) (TO). All the variables have been transformed to natural logarithm form for easy analysis. Given data constraints for REC the analysis are carried out on common sample. This study acknowledges that the relatively small sample size of approximately 31 to 34 observations may present limitations for time series estimation and statistical inference. Small sample sizes can reduce statistical power and increase the possibility of unstable coefficient estimates, particularly in models with multiple parameters such as the ARDL framework. However, the ARDL approach remains appropriate for this study because it is widely recognized for its suitability in small sample time series analysis and for variables integrated at different orders, provided none are integrated beyond I(1). Furthermore, diagnostic and stability tests were conducted to assess the reliability and robustness of the estimated model. The data and its sources are presented in Table 1. The model used in the study, informed/adapted from Amin et al. (2022) and modified, is presented as follows: ln CO 2 t = β 0 + β 1 lnIQ t + β 2 lnFSD t + β 3 lnTO t + β 4 lnREC t + β 5 GDP t + ϵ t (1)

Table 2 presents descriptive statistics of the variables used in this study. Due to data limitation for the Renewable energy consumption % of total electricity, the study used the common period of 31 observations.

To select suitable estimation techniques for the study, a unit root test was adopted, as economic and financial data are not stable and thus contain outliers. Economic and financial data tend to be non-stationary and can lead to spurious estimations. Therefore, data must be stationary to produce reliable results. In addition, it allows us to choose a suitable estimation approach based on the order of integrations at levels I(0), first difference I(1), or second difference I(2). The study used the Dickey-Fuller, augmented Dickey-Fuller, and Phillips–Perron (P–P) methods to ensure robustness of the results and allow the majority rule to apply where conflicting order of integration exists.

This study used ARDL estimation techniques to examine the relationship between NRR, FSD, IQ, and environmental degradation. ARDL is preferred because it allows the use of a mixed order of integration of either I(0) or I(1) ( Pesaran et al., 2001). In addition, ARDL is recommended because it reduces the issue of misspecification, spurious, and random errors that can occur because of non-stationary data ( Nkoro and Uko, 2016). Furthermore, ARDL allows for bound tests to assess whether cointegration exists as dependent and independent variables, even when integrated at different orders of integration ( Pesaran et al., 2001). Additionally, ARDL approach is suitable for small sample size, its flexible and unlike the Johansen and Juselius’s and the Engle and Granger’s cointegrations, as it allows for both long term and short term cointegrations to be regressed simultaneously ( Nkoro and Uko, 2016; M. H. Pesaran et al., 2001). To examine the existence of cointegration, this study proposes the following hypothesis: HO : β 1 i = β 2 i = β 3 i = β 4 i = β 5 i = 0 H 1 : β 1 i ≠ β 2 i ≠ β 3 i ≠ β 4 i ≠ β 5 i ≠ 0

Pesaran, Shin and Smith (2001) provides critical values to be tested against the F-statistic. Lower bound I(0) and upper bound I(1). Pesaran, Shin and Smith (2001) indicate that when the F-statistic is below the lower bound, the null hypothesis indicates that no cointegration exists, and when it falls between the upper and lower bounds, the results are inconclusive, as none of the hypotheses are accepted. However, when the F-statistic is larger than both critical values, the alternative hypothesis is accepted, which indicates the presence of cointegration ( Nkoro and Uko, 2016). As such, if there is evidence of cointegration (long-run relationship), the ARDL-EC model is used to estimate the long-run. The ARDL model used in the study is specified below: ∆ln CO 2 = a 0 + ∑ i = 1 n β 1 i ∆ ln CO 2 t − 1 + ∑ i = 1 n β 2 i ∆ lnIQ t − 1 + ∑ i = 1 n β 3 i ∆ lnFSD t − 1 + ∑ i = 1 n β 4 i ∆ lnTO t − 1 + ∑ i = 1 n β 5 i ∆ lnREC t − 1 + ∑ i = 1 n β 6 i ∆ lnGDP t − 1 + β 7 ln CO 2 t − 1 + β 8 lnIQ t − 1 + β 9 lnFSD t − 1 + β 10 lnTO t − 1 + β 11 lnREC t − 1 + β 12 lnGDP t − 1 + μECT t + ε t (2)

β ₁−β ₁₂ are short- and long-run parameters, ∆ is the difference operator, ε _t is the error term, and ECT is the Error Correction term. Given the dynamics of time-series data that tend to be serially correlated, this study has carried out serial correlation to ensure that the error terms are serially independent. In addition, the model was assessed for normality and if residuals were homoscedastic. To ensure that the model was stable, a stability test was performed using the CUSUM. The stability test is important in time series analysis, as we cannot predict the structural changes that may have occurred. The Ramsey RESETs test was used to assess the model specifications.

The study applied Fully Modified Ordinary Least Squares (FMOLS), Canonical Cointegration Regressions (CCR), and pairwise Granger causality tests to further assess the reliability of the ARDL model results. The FMOLS used in estimation of long run cointegration among integrated variables which is common in time series analysis (Phillips, 1995). As such, can produce reliable estimations when dealing with time series data which often exhibits non-stationarity. Additionally, FMOLS can address issues of serial correlation and endogeneity in error term (Phillips, 1995; Phillips and Hansen, 1990)s. Therefore, this makes the approach suitable for robustness check for ARDL results. CCR estimates the cointegrating vectors and can account for issues of endogeneity and serial correlation in the error terms as such ensure efficient and consistent estimation of cointegrating relationships (Park, 1992). Additionally, it can correct the asymptotic biasness that arises due to correlation between the regression and stochastics regressor errors. Therefore, together with the FMOLS, CCR was used as a robust estimator to validate the findings from the ARDL model.

This section discusses the empirical results, which include unit root, cointegration analysis using ARDL, and presentation and discussion of the results for both the short and long coefficients. It also includes diagnostic tests.

The essence of environmental sustainability and concerns regarding the impact of environmental degradation on humanity prompted the study to investigate Namibia. Namibia, like many developing countries, has the least carbon emissions, yet is heavily impacted by climate change as a country with over 90% of it classified as hyper-arid, arid, or semi-arid, ranking second to the Sahara Desert in terms of aridity ( World Bank Group, 2021). As a result, communities are prone to catastrophic events such as increased temperature, drought, and floods ( World Bank Group, 2021; Government of the Republic of Namibia, 2023). Therefore, this study examined the impact of financial sector development and institutional quality on environmental degradation. The following conclusions and recommendations were drawn: •

Institutional quality improvements contribute to significant increases in carbon emissions and environmental degradation, as the institutional frameworks prioritise rent-seeking and the pursuit of economic development over ecological sustainability. This focus often leads to increased emissions of greenhouse gas emissions and environmental neglect.

The findings suggest that current institutional frameworks exert the scale effect, where regulatory efficiency promotes carbon-intensive industrialisation and resource exploration. Therefore, for Namibia to reduce environmental degradations and achieve environmental sustainability, the country should undergo institutional reforms that ensure that the country moves away from growth focused policies towards balancing growth and environmental protection. The current policy frameworks on environmental protection have not been highly effective; the national laws prioritise industrialisation and economic expansion and which leads to pressure on the environment. Therefore, policymakers should strengthen institutional quality by promoting sustainable development which can reduce negative environmental impacts. Furthermore, based on the findings developing countries must incorporate financial globalization into environmental protection frameworks to adopt international green standards that penalise environmental destruction. Therefore, given the impact of institutional quality on the environment, there is a need to link environmental protection and investment law, to attract green economic activities that contribute to development and environmental sustainability. •

Financial sector development presents a positive impact on the environment. In both the short- and long-run financial development reduces environmental degradation, as such contribute to environmental sustainability.

Environmental sustainability relies heavily on access to finance for advanced technologies and means of production. Developing countries are most impacted by climate change; however, they have limited financial capacity. Thus, there is a concerted effort by developing countries to continue to persuade advanced economies to increase climate change financing as they lack domestic liquidity to fund climate mitigation projects, while they are the most impacted by environmental degradation. Thus, it is imperative that countries proactively invest in clean energy. Domestically government, through the central bank, should implement green credit guidelines providing preferencial climate loans with special conditions to commercial banks that provide financing for environmentally friendly technologies. Thus, by improving financial accessibility and inflow into clean energy investments can reduce dependence on non-renewable energy. The research, therefore, offers insight and contributes to policy and literature by providing a view on the importance of finance and institutional frameworks in environmental protection and the need to create a balance between economic development and environmental sustainability.

The current study focuses on Namibia; however, its implications can be applied to other contexts. Therefore, future studies should incorporate other measures of environmental degradation, such as the ecological footprint or other forms of greenhouse gases. Furthermore, institutional quality might not be adequately captured as the study used economic freedom index and not the measure by the World Governance, as such future study may use other measure of governance to measure institutional quality. Additionally, although the study used multiple econometric models, the complexity of the relationship may not be fully captured as such future studies can incorporate other control variables such as resources rent to assess whether the relationship might be different. Further studies can apply asymmetric relationships, as the impact may be influenced by changes in a country’s economic or political atmosphere.