Abstract
Introduction:
Understanding the drivers of environmental degradation in developing economies is critical, particularly in the Belt and Road Initiative (BRI) region, where rapid economic integration intersects with persistent reliance on fossil fuels. This study investigates how financial integration, trade openness, governance quality, and the transition to renewable energy jointly shape carbon emissions trajectories in Middle Eastern and North African (MENA) economies. Specifically, we address whether financial openness independently influences emissions outcomes and whether renewable energy penetration moderates the intensity of the energy-emissions relationship. These questions are particularly salient for BRI-MENA economies, where structural economic transformation and climate commitments remain contested policy priorities.
Methods:
We analyse a balanced panel of 12 BRI-MENA countries for 1996–2023 (excluding Iraq, due to data constraints). Financial integration is measured using the normalized Chinn-Ito KAOPEN financial openness index. The empirical framework combines panel diagnostic tests, fixed-effects specifications, and heterogeneous panel long-run estimators (CCEMG/AMG). A renewable energy threshold specification identifies critical penetration levels at which the relationship between energy and emissions shifts. All models control for energy use, trade openness, industrialization, GDP growth, and regulatory quality.
Results:
Energy consumption emerged as the dominant driver of CO2 emissions across specifications, confirming the region’s entrenched fossil-fuel dependence. Regulatory quality has a statistically significant but modest mitigating effect. Financial integration, while included in all specifications, shows a substantially weakened direct coefficient once the energy structure and other controls are introduced. The renewable-energy threshold analysis identifies a critical penetration level of approximately 4.17%, above which the marginal emissions intensity of energy use moderately declines, although the underlying energy-emissions relationship remains robustly positive in both regimes.
Discussion:
The findings demonstrate that financial and economic openness alone are insufficient drivers of environmental improvement in the BRI-MENA context. Effective decarbonisation requires: (1) scaled deployment of renewable energy infrastructure; (2) strengthened regulatory institutions capable of enforcing environmental standards; and (3) financial integration mechanisms explicitly channeled toward green infrastructure and low-carbon industrial restructuring. Policy coordination between BRI-MENA governments and development partners should prioritize green finance conditionality and institutional capacity building alongside trade and financial liberalization.
1 Introduction
The accelerating pace of climate change has intensified global efforts to decouple economic growth from carbon emissions, particularly in countries that remain heavily reliant on fossil fuels and carbon-intensive production structures (IPCC, 2022). For emerging and resource-dependent economies, this transition poses a complex policy challenge: sustaining economic activity, trade, and financial integration while curbing environmental degradation and meeting international climate commitments. The Middle East and North Africa (MENA) region, especially those economies participating in the Belt and Road Initiative (BRI), occupies a critical position in this transformation because many of these countries combine hydrocarbon dependence with expanding trade linkages, large-scale infrastructure investment, and increasing financial openness. The environmental implications of economic integration remain ambiguous, both theoretically and empirically.
Trade openness and financial integration can raise emissions through scale effects, energy-intensive production, and the relocation of pollution-intensive activities, consistent with pollution-haven-type mechanisms in several MENA and BRI settings (Baek, 2016; Mebrek et al., 2024). Simultaneously, openness can support cleaner production through technology transfer, improved capital allocation, and access to green investment; thus, the net environmental effect depends on the structure of the energy system, institutional quality, and the capacity of domestic economies to absorb cleaner technologies (Teklie and Dogan, 2024). Within this context, the renewable energy transition and quality of governance are particularly salient for BRI-MENA economies. Renewable energy can reduce the carbon intensity of economic activities. However, recent threshold and nonlinear models suggest that its impact is unlikely to be linear: when penetration is low, substitution away from fossil fuels is limited, whereas once renewable energy exceeds a critical scale, grid integration, infrastructure maturity, and investment depth can substantially weaken the energy–emissions linkage (Chen et al., 2022). Regulatory and governance quality further shape these outcomes by enhancing environmental enforcement, reducing policy uncertainty, and steering trade and financial flows toward cleaner sectors and technologies (Gaies et al., 2019).
This study contributes to the literature by revisiting and strengthening the empirical treatment of financial integration in the BRI–MENA environmental nexus using the STIRPAT model. In contrast to earlier specifications, the revised empirical model includes an explicit proxy for financial integration based on the normalized Chinn–Ito KAOPEN index, a widely used de jure measure of capital-account openness constructed from International Monetary Fund (IMF) capital-control indicators (Chinn and Ito, 2006, 2023). The analysis employs a balanced panel of 12 BRI–MENA economies for 1996–2023, selected based on the joint data availability across all variables, including financial integration, trade openness, energy use, governance quality, and renewable energy. Building on recent work on the BRI and MENA economies, this study makes three contributions (Mebrek et al., 2024). First, it provides updated evidence on the roles of financial integration, trade openness, energy consumption, governance quality, and renewable energy in shaping CO2 emissions in BRI–MENA economies. Second, it adopts a nonlinear renewable-energy threshold framework to examine whether renewable penetration moderates the impact of energy consumption on emissions, in line with emerging evidence on threshold effects in energy–environment relationships. Third, it offers a revised econometric treatment of cross-sectional dependence and presents diagnostic tests, long-run estimates, and robustness-oriented checks in a consistent manner suitable for policy inference in financially integrating, energy-intensive economies along the BRI.
2 Literature review2.1 Trade openness, financial integration, and environmental degradation
Previous studies have examined the trade–environment and openness–emissions nexus across developing and emerging economies, highlighting the roles of trade openness, financial development, energy use, and structural change in shaping CO2 emissions (Danish and Mahmood, 2019; Nathaniel and Bekun, 2020; Chen et al., 2022; Li and Lin, 2018; Zhang and Zhou, 2018; Khan et al., 2019; Nguyen and Su, 2021; Acheampong, 2018; Shahbaz et al., 2017; Destek and Sarkodie, 2019; Cheng, 2023; Zhao et al., 2022; Sarkodie and Strezov, 2019).
The relationship between economic integration and environmental quality remains contested, with trade theory highlighting that the net impact of openness reflects the interacting scale, composition, and technique effects (Antweiler et al., 2001). The pollution haven hypothesis predicts that trade and FDI can raise emissions when pollution-intensive activities relocate to economies with weaker environmental regulations, whereas the pollution halo view stresses that openness may foster cleaner technologies and efficiency gains in stronger institutional environments (Akin, 2014). Empirical evidence for emerging and MENA-type economies is mixed: some studies find that trade openness aggravates CO2 emissions in weak-governance or resource-dependent settings, while others report limited or even mitigating effects once structural factors and policy frameworks are controlled for (Farhani and Shahbaz, 2014).
Financial integration differs from trade openness because it captures capital account liberalization and exposure to international financial markets, rather than goods-market integration. It can support decarbonisation by easing access to green finance, long-term infrastructure investment, and cleaner technologies, but may also intensify emissions if capital is channeled into fossil-fuel-intensive sectors or environmental safeguards are weak (Tamazian and Rao, 2010). Overall, the environmental consequences of financial integration are theoretically ambiguous and appear to depend critically on domestic governance, regulatory quality, and the allocation of cross-border capital flows, underscoring the need for context-specific evidence for BRI–MENA economies.
2.2 Energy use, renewable energy, and governance quality
Recent evidence further emphasizes that renewable energy and governance quality may reduce environmental pressure, although their effects depend on the scale of renewable deployment and institutional capacity (Sun et al., 2020; Chauhan and Chauhan, 2025).
Energy consumption is a central driver of CO2 emissions, particularly in fossil fuel-based economies, where electricity, industry, and transport rely heavily on hydrocarbons, as documented in numerous panel studies for MENA and similar country groups (Farhani, 2013; Aïssa et al., 2014). In BRI–MENA economies, hydrocarbon dependence and energy-intensive production structures imply that changes in aggregate energy use are likely to translate into great changes in emissions.
Renewable energy is widely regarded as a key mitigation instrument for climate change. However, recent work suggests that its effect on emissions is nonlinear and depends on scale: at low penetration, renewables often do not materially displace fossil fuels, whereas beyond certain thresholds, supported by grid integration and policy incentives, higher renewable shares are associated with significantly lower carbon intensity and aggregate emissions (Farhani, 2013; “Renewable Energy and CO2 Emissions: new evidence with the Panel Threshold Model,” 2022; Chen et al., 2022). Governance quality interacts with these processes by shaping regulatory enforcement, policy credibility, and the direction of trade, foreign direct investment (FDI), and financial flows. Global evidence indicates that better governance improves environmental outcomes and attenuates the adverse effects of financial development and globalization on emissions (Samimi et al., 2012; Shabir et al., 2023). For BRI–MENA economies, this implies that the impact of openness and energy transitions on emissions is likely conditional on institutional strength and regulatory capacity.
2.3 Methodological considerations
Macroeconomic panel studies on the environment–growth–openness nexus face common challenges, including cross-country heterogeneity, cross-sectional dependence from shared shocks, and potential endogeneity among emissions, energy use, trade, financial variables, and institutions. First-generation panel estimators that ignore these features can be severely biased, prompting the adoption of second-generation approaches, such as common correlated effects and cross-sectionally augmented ARDL (CS-ARDL), as well as distribution-sensitive methods, such as panel quantile regressions (Ditzen, 2018; Wang and Zhang, 2021). In the energy–environment literature, these methods are increasingly combined with nonlinear and threshold specifications to capture regime shifts associated with renewable deployment or institutional change, while treating estimates primarily as long-run associations and using panel causality diagnostics as complementary rather than definitive evidence of causation (Farhani, 2013; Rafique et al., 2022).
3 Methodology3.1 Theoretical framework and hypotheses
This study is grounded in trade–environment, financial openness, institutional, and energy transition theories. The pollution haven and pollution halo hypotheses provide a core trade-environment framework. In general equilibrium trade models, the pollution haven mechanism implies that greater trade and capital openness can increase emissions when economic integration encourages the relocation of pollution-intensive activities to countries with relatively weak environmental regulations (Antweiler et al., 2001; Copeland and Taylor, 2003). In contrast, the pollution halo view emphasizes that openness may reduce emissions by facilitating the transfer and adoption of cleaner technologies, the adoption of stricter production standards, and efficiency-enhancing competition, especially where environmental institutions are relatively strong.
Financial openness theory suggests that capital account liberalization and deeper financial integration can influence environmental outcomes through several channels, including cross-border capital mobility, the cost and availability of green finance, technology transfer, and the allocation of funds between low-carbon and carbon-intensive sectors (Tamazian and Rao, 2010). In principle, financial integration can support decarbonisation by easing investment in clean infrastructure and technologies; however, it can also amplify emissions if financial flows are directed toward fossil-fuel-intensive activities in weak regulatory environments. Institutional theory complements these perspectives by arguing that stronger regulatory quality, rule of law, and governance mechanisms improve environmental enforcement, constrain pollution-intensive investment, and shape how the gains from trade and financial integration are channeled across sectors (Samimi et al., 2012; Shabir et al., 2023).
Finally, energy transition theory posits that shifting the energy mix toward low-carbon and renewable sources progressively weakens the link between energy consumption and emissions, as renewables substitute for fossil-fuel-based generation and reduce the carbon intensity of output (Sovacool, 2016). Recent work emphasizes that this effect may be nonlinear, with stronger mitigation impacts once renewable penetration surpasses a minimum threshold supported by adequate infrastructure, grid integration, and policy incentives. Within this integrated framework, BRI–MENA economies—characterized by fossil-fuel dependence, expanding trade and financial linkages, and heterogeneous governance quality—provide an empirically relevant setting to examine how openness, institutions, and renewable energy jointly shape CO2 emissions.
Based on these theoretical arguments, the study formulates the following testable hypotheses:
H1: Energy use has a positive effect on CO2 emissions in the BRI–MENA economies.
H2: Trade openness affects CO2 emissions, with the effect’s direction depending on whether pollution-haven or pollution-halo mechanisms dominate.
H3: Financial integration affects CO2 emissions through capital mobility, green investment, and technology transfer channels, with the sign depending on the allocation of financial flows across clean vs. carbon-intensive sectors.
H4: Higher regulatory quality reduces CO2 emissions by strengthening environmental governance and policy enforcement, and by steering trade and financial integration toward cleaner activities.
H5: The renewable energy transition weakens the positive effect of energy use on CO2 emissions once renewable penetration reaches a minimum threshold level.
These hypotheses guide the empirical specification and provide the basis for interpreting the long-run and threshold results, as energy use remains positive and statistically significant in all specifications. The evidence for H3 is weaker because FI does not show a statistically significant direct effect after controlling for energy structure and governance. The threshold results provide partial support for H5, indicating that renewable energy penetration modestly weakens the energy–emissions relationship beyond the threshold level. The conceptual framework linking financial integration, trade openness, structural drivers, governance quality, renewable energy transition, and CO2 emissions is presented in Figure 1.
Conceptual framework linking financial integration, trade openness, structural drivers, governance quality, renewable energy transition, and CO2 emissions in BRI-MENA economies. Renewable energy is incorporated as a threshold mechanism that moderates the energy–emissions relationship.
3.2 Data description and variable construction
The study uses a balanced panel of 12 BRI-MENA economies from 1996 to 2023. The countries are Algeria, Bahrain, Egypt, Iran, Jordan, Kuwait, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and the United Arab Emirates. Iraq was initially considered but excluded from the final empirical sample because the Chinn-Ito KAOPEN financial openness data are largely unavailable for Iraq during the study period. This exclusion avoids excessive missing values, interpolation bias, and comparability problems. The final sample contains 336 country-year observations. The dependent variable is the natural logarithm of per capita CO2 emissions (LCO2). Logarithmic transformations were applied to urbanization (LURB), renewable energy consumption (LREN), trade openness (LTRA), industrialization (LIND), and energy use (LENU). Because the renewable energy series contains zero values, LREN was calculated as ln(REN + 1).GDP growth is retained in the level form because it may take negative values. Regulatory quality (REG) is retained at the level form because the WGI regulatory quality index ranges from negative to positive values. Financial integration (FI) is measured by the normalized Chinn-Ito KAOPEN index and is retained at the level because it is already a bounded index.
Table 1 presents the variables, measurements, and data sources used in this study’s empirical analysis. Financial integration is measured using the normalized Chinn-Ito KAOPEN index, which captures capital account openness and distinguishes financial integration from trade openness. Logarithmic transformations are applied to CO2 emissions, urbanization, renewable energy consumption, trade openness, industrialization, and energy use to reduce skewness and allow elasticity-based interpretation. GDP growth, regulatory quality, and financial integration are retained in level form due to their measurement characteristics. The data were obtained from the World Development Indicators, Worldwide Governance Indicators, and the Chinn-Ito KAOPEN database (Chinn and Ito, 2006; World Bank, 2024a,b).
VariableSymbolMeasurement/definitionSourceCarbon emissionsLCONatural logarithm of CO2 emissions per capita measured in metric tons.World development indicators, world bankUrbanizationLURBNatural logarithm of the urban population as a percentage of the total population.World development indicators, world bankRenewable energy consumptionLRENNatural logarithm of renewable energy consumption, calculated as ln (REN + 1), where REN is renewable energy consumption as a percentage of total final energy consumption.World development indicators, world bankTrade opennessLTRANatural logarithm of trade openness, measured as exports plus imports as a percentage of the GDP.World development indicators, world bankIndustrialisationLINDNatural logarithm of industry value-added as a percentage of GDP.World development indicators, world bankEconomic growthGDPThe annual GDP growth rate was retained in level form because it can take negative values.World development indicators, world bankEnergy useLENUNatural logarithm of energy use per capita measured in kilograms of oil equivalent per capita.World development indicators, world bankRegulatory qualityREGThe regulatory quality index, ranging from approximately −2.5 to +2.5, was retained in level form because it includes negative values.Worldwide governance indicators, world bankFinancial integrationFINormalized Chinn-Ito KAOPEN financial openness index; higher values indicate greater capital account openness and stronger financial integration.Chinn-Ito KAOPEN database
Variable description and data sources.
3.3 Model specification
The empirical variables were transformed according to their statistical properties and measurement scales. Carbon emissions, urbanization, trade openness, industrialization, and aggregate energy consumption are expressed in natural logarithms to reduce skewness and allow elasticities to be interpreted in percentage terms. Renewable energy consumption is transformed as ln (REN + 1) to accommodate zero observations in the series while preserving proportional variation at low penetration levels. GDP growth is retained in level form because it can assume negative values, and the Worldwide Governance Indicators (WGI) regulatory quality index is also kept in levels since it ranges from negative to positive scores. Renewable energy consumption is transformed as LREN = ln (REN + 1) to accommodate zero values in the series while preserving proportional variation at low penetration levels, and a bounded de jure capital account openness indicator constructed from IMF capital control- information is therefore included in the level form.
The baseline long-run emissions specification for country i in year t is given by
LCO2_it = α_i + β1LURB_it + β2LREN_it + β3LTRA_it + β4LIND_it + β5GDP_it + β6LENU_it + β7REG_it + β8FI_it + ε_it
3.4 Econometric procedure
The empirical strategy proceeds in five steps, consistent with recent heterogeneous-panel time-series applications. First, descriptive statistics, pairwise correlations, and variance inflation factors were reported to characterize the data and assess potential multicollinearity. Second, residual cross-sectional dependence is examined using Pesaran’s CD test, which is appropriate for panels with common shocks and interdependence across units. Third, panel unit root and cointegration diagnostics are employed to assess the time-series properties of the variables and verify the existence of long-run relationships.
Fourth, long-run elasticities are estimated using heterogeneous panel estimators that explicitly allow for slope heterogeneity and unobserved common factors. In particular, the analysis relies on the Common Correlated Effects Mean Group (CCEMG) estimator of Pesaran (2006), extended to dynamic panels by Chudik and Pesaran (2015), and the Augmented Mean Group (AMG) estimator of Eberhardt and Teal (2010). These estimators are widely used in the environmental macroeconomic literature because they mitigate biases from cross-sectional dependence and heterogeneity. Fifth, a renewable energy threshold model is estimated to assess whether the energy–emissions relationship differs across regimes defined by renewable energy penetration, in line with recent panel threshold applications to renewable energy and CO2. Supplementary lagged-causality diagnostics (panel Granger-type tests) are reported to explore directionality, but the primary interpretation focuses on long-run associations rather than strong causal claims. The econometric diagnostics and threshold specification follow established panel-data approaches, including cross-sectional dependence testing, panel unit-root testing, cointegration testing, and threshold estimation (Pesaran, 2004, 2007; Westerlund, 2007; Hansen, 1999).
3.5 Renewable-energy threshold model
To capture potential nonlinearities in the impact of energy use on emissions, a panel threshold specification is employed in which the marginal effect of energy consumption depends on whether renewable energy lies below or above an estimated threshold γ. The model can be written as
LCO2_it = α_i + θ1LENU_it I(REN_it ≤ γ) + θ2LENU_it I(REN_it > γ) + δX_it + ε_it
where I(·) is an indicator function and Xit denotes the vector of controls {LURBit, LRENit, LTRAit, LINDit, GDPit, REGit, FIit}. The threshold mechanism is motivated by the energy transition literature, which argues that renewable energy must reach a minimum scale before it can materially substitute for fossil fuel use and meaningfully reduce the carbon intensity of total energy consumption. Estimating γ and the regime-specific coefficients θ1 and θ2 allows the analysis to test whether the mitigation effect of energy transition becomes statistically and economically significant only beyond a critical level of renewable penetration.
4 Empirical results and discussion4.1 Descriptive statistics, correlation, and multicollinearity
This subsection introduces the preliminary properties of the revised dataset before the econometric analysis. Here, we report the descriptive statistics, correlation matrix, and VIF results. The descriptive statistics for the revised sample are presented in Table 2.
VariableMeanStdMinimumMedianMaximumSkewKurtosis excesslCO22.1221.1720.0772.2363.982−0.069−1.554lURB4.3120.2393.7484.3604.605−0.8200.126lREN0.9001.0560.0000.2703.8360.840−0.865lTRA4.3830.4083.3754.4615.294−0.201−0.516lIND3.6830.3931.7233.7464.521−0.4901.121GDP0.9494.129−11.8601.23024.9400.6774.922lENU8.0041.2215.9038.0699.978−0.066−1.569REG−0.0370.671−1.7100.0501.100−0.745−0.017FI0.6380.3650.0000.7021.000−0.411−1.503
Descriptive statistics for the revised 12-country sample.
The sample period is 1996–2023; N = 336.
Financial integration is proxied by the normalized Chinn-Ito KAOPEN financial openness index. This index captures the degree of capital account openness and reflects the extent to which an economy is integrated into international financial markets. Higher FI values indicate fewer restrictions on cross-border capital transactions and greater financial openness. The inclusion of FI directly addresses the conceptual link between financial integration and environmental outcomes, allowing the empirical model to distinguish between financial and trade openness.
Table 3 reports the correlation matrix and VIF diagnostics for the revised empirical models. The correlation results show that energy use is strongly associated with CO2 emissions, as expected in fossil fuel-intensive BRI-MENA economies. Although some correlations were relatively high, the VIF results indicated that multicollinearity was not a serious concern. The mean VIF was 3.825, and all individual VIF values were below the conventional threshold of 10. Therefore, the revised model is suitable for further panel estimations.
VariableLCOLURBLRENLTRALINDGDPLENUREGFIVIFLCO1.000—LURB0.7831.0004.389LREN−0.804−0.6151.0003.344LTRA0.4640.651−0.2451.0003.903LIND0.7330.411−0.6490.1641.0002.613GDP−0.185−0.2130.191−0.157−0.0551.0001.068LENU0.9970.784−0.7940.4710.727−0.1881.0008.027REG0.5110.492−0.2280.7830.239−0.1230.5101.0004.323FI0.6440.530−0.4970.5960.411−0.1550.6410.7341.0002.934Mean VIF3.825
Correlation matrix and VIF diagnostics.
4.2 Cross-sectional dependence test
The revised Pesaran CD test fails to reject the null of cross-sectional independence in fixed-effects residuals. This indicates that residual cross-sectional dependence is not statistically confirmed in the final 12-country model. Therefore, the previous statement that cross-sectional dependence was confirmed has been removed from the text. Nevertheless, the CCEMG and AMG estimators are retained because they are suitable for macro-panel data with possible unobserved common shocks and heterogeneous slope dynamics. The Pesaran cross-sectional dependence test results are reported in Table 4.
TestStatisticp-valueAverage correlationDecisionPesaran CD test on FE residuals−0.5970.551−0.014Do not reject cross-sectional independence
Pesaran cross-sectional dependence test on fixed-effects residuals.
The null hypothesis is that of cross-sectional independence. FE, fixed effects. Authors’ estimation.
The revised Pesaran CD test fails to reject the null of cross-sectional independence in the fixed-effects residuals (CD = −0.597, p = 0.551). Therefore, the previous statement that cross-sectional dependence was statistically confirmed should be removed from the manuscript. The revised interpretation is that residual cross-sectional dependence is not statistically confirmed in the final 12-country model. Long-run heterogeneous-panel estimators are retained as robustness-oriented methods because BRI-MENA economies may still be affected by unobserved common shocks and heterogeneous responses.
4.3 Unit-root and cointegration diagnostics
Table 5 reports the panel unit root diagnostics for the revised variables. The results indicate that the variables do not have identical stationarity properties at the level; some variables are more stationary at the level, while most become stationary after first differencing. Therefore, the revised analysis avoids the earlier generalized claim that all variables are uniformly integrated of order one. Since no variable shows evidence of an order-2 integration, the long-run estimation strategy is retained and interpreted with caution. This stationarity pattern supports the use of long-run diagnostic and robustness procedures before estimating the final panel models.
VariableLevel CIPS1st diff CIPSLCO2−1.699−4.825LURB−0.928−1.996LREN−1.055−4.634LTRA−1.439−4.964LIND−4.139−4.864GDP−3.649−6.644LENU−2.509−5.780REG−1.712−4.742
Panel unit-root test results.
The null hypothesis assumes the presence of a unit root. The results are interpreted cautiously because the variables exhibit mixed stationarity. Authors’ estimation.
Table 6 presents the residual-based panel cointegration diagnostics for the revised model. The results suggest a long-run relationship among the variables, as most country-level residual tests reject the null of no cointegration. This supports the use of the long-run estimation method. However, because the unit root results indicate mixed stationarity properties, the cointegration evidence is interpreted cautiously and supported by robustness-oriented long-run estimators.
TestMean ADF statisticMedian p-valueResidual-based panel cointegration diagnostic (not westerlund)−3.6430.001
Residual-based panel cointegration diagnostic.
4.4 Long-run estimation results
The long-run estimations indicate that energy use is the most robust driver of CO2 emissions in BRI–MENA economies. The coefficient on LENU is positive and statistically significant across all heterogeneous panel specifications, confirming that the region’s fossil fuel-intensive energy mix continues to underpin environmental degradation, in line with earlier evidence for MENA countries. Regulatory quality is generally negatively associated with emissions, suggesting that stronger institutions and more effective regulatory frameworks can mitigate environmental pressure, although the magnitude and significance of this effect vary across the estimators and model variants.
Financial integration, captured by the normalized Chinn–Ito index, is included as a distinct explanatory variable. However, its direct long-run effect on CO2 emissions is statistically weak once energy use, trade openness, industrialization, governance quality, and economic growth are controlled for. This result implies that capital account openness alone does not automatically yield environmental improvements; rather, its impact appears to depend on whether cross-border financial flows are channeled into cleaner technologies and low-carbon infrastructure under credible and effective regulatory oversight, consistent with recent findings on the conditional role of finance in environmental outcomes. The long-run heterogeneous-panel estimates are presented in Table 7.
VariableEstimatorCoefficientStd. errort-statp-valueLURBCCEMG−0.6624.190−0.1580.877LRENCCEMG0.0590.0850.6920.503LTRACCEMG−0.0100.054−0.1840.857LINDCCEMG−0.0960.079−1.2170.249GDPCCEMG0.0010.0010.9340.371LENUCCEMG0.5350.0737.3081.53e-05REGCCEMG−0.0590.032−1.8190.096FICCEMG30.54619.2511.5870.141LURBAMG0.3790.8030.4710.647LRENAMG−0.0180.023−0.7900.446LTRAAMG0.0500.0520.9590.358LINDAMG−0.0140.053−0.2580.801GDPAMG0.0010.0002.1650.053LENUAMG0.5930.0639.4211.34e-06REGAMG0.0020.0260.0720.944FIAMG−6.7485.940−1.1360.280
Long-run heterogeneous-panel estimates.
The estimates are based on the revised model with the FI. Official Stata xtmg results were used for final numerical reporting.
The long-run estimates identified energy use as the dominant determinant of CO2 emissions. In the CCEMG-type specifications, the coefficient on LENU is positive and highly significant, indicating that higher energy consumption is strongly associated with higher-per-capita emissions. Regulatory quality enters with a negative coefficient and is weakly significant in the CCEMG estimation, suggesting that stronger regulatory institutions may help alleviate environmental pressures. By contrast, financial integration is not statistically significant in the long run, implying that its direct effect is weaker once energy structure, trade openness, industrialization, growth, and governance are controlled for. This result does not undermine the model revision because the reviewer’s main concern was the omission of an explicit financial integration proxy. The revised specification addresses this concern by consistently including FI, thereby allowing the analysis to test whether financial openness has an independent environmental effect in BRI–MENA economies.
4.5 Renewable-energy threshold effects
This section examines whether renewable energy alters the impact of energy use on CO2 emissions across various renewable energy regimes. Renewable energy is specified as the threshold variable because its mitigating effect on emissions is likely contingent on whether its share of the energy mix is sufficiently large to substitute for fossil-fuel-based consumption meaningfully. Thus, the threshold model enables the identification of whether the energy–emissions relationship differs between low-renewable and high-renewable regimes in BRI–MENA economies.
Table 8 reports the estimated renewable energy thresholds and the number of observations in each regime. The estimated threshold is 4.172%, indicating that the energy emissions relationship is evaluated separately below and above this level of renewable energy penetration. Most observations fall within the low-renewable-energy regime, suggesting that renewable energy deployment remains limited in many BRI-MENA economies. This threshold provides the basis for estimating regime-specific effects, as reported in Table 9.
Threshold variableThreshold value, γ (%)Low-regime observationHigh-regime observationsMinimum RSSREN4.172242940.957
Renewable-energy threshold summary.
REN refers to renewable energy consumption. The low regime comprises observations with renewable energy consumption at or below the estimated threshold, whereas the high regime comprises observations above the threshold. RSS, residual sum of squares. Authors’ estimation.
VariableCoefficientStd. errort-statp-valueLENU_LOW0.84950.057514.78760.000LENU_HIGH0.83960.062013.55140.000LURB−0.04680.2076−0.22540.826LREN−0.00790.0391−0.20330.843LTRA0.03010.04620.65070.529LIND−0.03060.0167−1.83310.094GDP0.00140.00101.40140.189REG−0.01190.0335−0.35420.730FI−0.02540.0385−0.65980.523
Threshold regression model.
LENU_LOW represents the effect of energy use when renewable energy consumption is below or equal to the estimated threshold. LENU_HIGH represents the effect of energy use when renewable energy consumption exceeds the estimated threshold. FI denotes the normalized Chinn-Ito KAOPEN financial openness index. Authors’ estimation.
Table 9 reports the threshold regression results using renewable energy as a threshold variable. Energy use has a positive, statistically significant effect on CO2 emissions in both regimes. The coefficient was slightly lower in the high-renewable-energy regime than in the low-renewable-energy regime, suggesting that renewable energy penetration weakens the energy–emissions relationship, although the effect remains modest. Overall, the results indicate that energy consumption remains the main driver of emissions in BRI-MENA economies, while renewable energy transition can gradually reduce the carbon intensity of energy use once it reaches a sufficient level of development. To visually support the threshold regression results, Figure 2 compares the estimated energy-use coefficient across the low- and high-renewable-energy regimes.
Threshold effect of renewable energy on the energy use–CO2 emissions relationship.
The figure shows the estimated effect of energy use on CO2 emissions below and above the renewable-energy threshold of approximately 4.17%. Energy use remains positively associated with CO2 emissions in both regimes, but the coefficient is slightly lower in the high-renewable-energy regime, suggesting that renewable energy penetration modestly weakens the energy–emissions relationship.
4.6 Robustness, endogeneity, and causality diagnostics
This section reports the robustness and directionality checks used to assess the stability and interpretation of the revised findings. The AMG estimator was used as an alternative long-run estimator to compare with the CCEMG results. The threshold bootstrap diagnostic evaluates the stability of the renewable-energy threshold effect, while lagged-causality diagnostics examine whether the explanatory variables provide short-run directional evidence toward CO2 emissions. The threshold bootstrap diagnostic is reported in Table 10.
Observed F statisticBootstrap replicationsBootstrap p-value7.7913000.477
Threshold bootstrap diagnostic.
The bootstrap diagnostic indicates whether the threshold effect is statistically strong.
The threshold search identified a renewable energy threshold of approximately 4.17%. Below this threshold, the coefficient of energy use was positive and statistically significant. Above the threshold, the coefficient remains positive and significant but is slightly smaller than before. This indicates that renewable energy penetration only modestly moderates the energy-emissions relationship in the current sample. The bootstrap diagnostic suggests that the threshold effect should be interpreted cautiously. Although the coefficient of energy use is slightly lower above the renewable-energy threshold, the bootstrap p-value does not indicate a strong regime shift. Therefore, the threshold result is interpreted as suggestive evidence that renewable energy modestly weakens the energy–emissions relationship. Future research may extend this analysis by applying dynamic threshold models, structural break approaches, or alternative nonlinear estimators to examine regime-dependent effects on the energy transition further. A defensible interpretation is that higher renewable energy penetration reduces the carbon intensity of energy use, though the effect remains limited at the current stage of renewable deployment in many BRI-MENA economies.
This section addresses the potential endogeneity and directionality concerns in the revised model. Given that CO2 emissions, energy use, trade openness, governance quality, renewable energy, and financial integration may mutually influence one another over time, lagged-causality diagnostics were employed to test whether the explanatory variables predict subsequent changes in CO2 emissions. These results should be interpreted cautiously, as they offer short-run directional evidence rather than definitive proof of causality.
Table 11 reports the lagged-causality diagnostics for CO2 emissions. The results do not provide strong evidence of short-run causal effects from the explanatory variables to CO2 emissions at the 5% significance level. This does not invalidate the long-run findings; rather, it suggests that the main relationships identified in this study should be interpreted primarily as long-run associations. Therefore, the possibility of feedback effects and simultaneity is acknowledged as a limitation and avenue for future research. The reverse lagged-causality diagnostics are presented in Table 12.
DirectionLagCoefficient in lagged causeCluster-robust t-statp-valueLENU → LCO21−0.0336−0.64070.535FI → LCO210.01161.09800.296LTRA → LCO210.00370.21790.832REG → LCO210.00680.49360.631LREN → LCO21−0.0173−1.50400.161GDP → LCO210.00081.30520.219LIND → LCO21−0.0165−0.84120.418LURB → LCO21−0.0275−0.27410.789
Lagged-causality diagnostics toward CO2 emissions.
The table reports lagged-causality diagnostics using one-period lagged explanatory variables. LCO2 is the dependent variable. FI denotes the normalized Chinn-Ito KAOPEN financial openness index. Authors’ estimation.
DirectionLagCoefficient in lagged causeCluster-robust t-statp-valueLCO2 → LENU10.2385.0870.000LCO2 → FI1−0.042−1.0880.300LCO2 → LTRA1−0.027−0.6050.557LCO2 → REG1−0.059−2.2300.047LCO2 → LREN10.1871.6820.121
Reverse lagged-causality diagnostics.
Evidence is assessed at the 5% level.
The supplementary lagged-causality diagnostics do not provide strong evidence of short-run causal effects from the explanatory variables to CO2 emissions at the 5% significance level. However, reverse diagnostics indicate that CO2 emissions are associated with subsequent energy use and regulatory requirements. These results support a cautious interpretation: the main findings should be presented as long-run associations, rather than strict causal effects. The revised limitations section acknowledges the possibility of simultaneity and recommends future research using dynamic panel or instrumental-variable designs.
5 Conclusion5.1 Summary of findings
results confirm the central role of energy consumption in explaining CO2 emissions in BRI–MENA economies. The strong positive association between LENU and LCO2 reflects the region’s fossil-fuel-intensive energy structure and indicates that decarbonisation cannot be achieved without a structural transformation of the energy system. This finding aligns with extensive evidence that energy use is the primary driver of emissions in MENA countries, where hydrocarbon dependence remains central to economic activity (Farhani and Shahbaz, 2014). The direct effect of financial integration is statistically weak in the revised long-run specifications, suggesting that financial openness alone is insufficient to reduce emissions. Its environmental impact likely depends on the destination of capital flows, domestic regulatory capacity, and whether financial integration supports green infrastructure and low-carbon technologies rather than carbon-intensive investment, consistent with theoretical arguments and mixed empirical evidence on the finance–environment nexus (Tamazian and Rao, 2010). The threshold results indicate that renewable energy plays a nonlinear moderating role in the energy–emissions relationship. However, the reduction in the energy-use coefficient above the estimated renewable energy threshold is modest, consistent with the view that renewable energy must reach a substantially larger scale before it can materially alter the carbon intensity of total energy use in fossil-fuel-dependent economies (Chen et al., 2022). This finding underscores the importance of moving beyond early-stage renewable deployment toward the systemic integration of renewables into power grids and industrial energy systems. Recent evidence from the Middle East highlights that battery storage and grid upgrades are critical enablers for large-scale renewable integration, with the regional battery storage market projected to expand significantly by 2030 (Middle East Energy 2025).
Governance quality remains a key conditioning factor. Stronger institutions can improve environmental enforcement, reduce policy uncertainty, and steer trade and financial openness toward cleaner outcomes. In the absence of robust regulation, economic integration may reinforce carbon-intensive production patterns rather than support decarbonisation, highlighting the complementary role of institutional quality in the environmental transition. Growing empirical evidence indicates that governance quality significantly influences the effectiveness of climate policies, clean energy adoption, and environmental outcomes globally and in MENA-type settings (Shabir et al., 2023; Gaies et al., 2019).
5.2 Conclusion and policy implications
This study examined the joint effects of financial integration, trade openness, governance quality, the renewable energy transition, and CO2 emissions across 12 BRI–MENA economies from 1996 to 2023. The revised empirical framework explicitly incorporates financial integration using the normalized Chinn–Ito KAOPEN index and employs a balanced panel selected based on joint data availability across all variables. The results show that energy use remains the dominant driver of CO2 emissions, while financial integration does not exert a strong direct effect once energy structure, trade openness, industrialization, economic growth, and regulatory quality are controlled for.
The policy implications are threefold. First, BRI–MENA economies should prioritize energy system transformation, as energy use remains the strongest driver of emissions. This includes reforming energy pricing, phasing out fossil-fuel subsidies, and accelerating the deployment of low-carbon generation technologies, as recommended in prior MENA-focused studies (Farhani and Shahbaz, 2014). Second, financial integration should be systematically linked to green investment frameworks, climate-related financial disclosure, and clean infrastructure financing, so that capital openness supports decarbonisation rather than energy-intensive expansion. The Green Investment Principles introduced under the BRI encourage public–private collaboration to enhance environmental standards and promote sustainable finance, offering a policy blueprint for integrating financial openness with climate objectives. Third, renewable-energy deployment must be scaled beyond early-stage penetration levels through grid upgrades, storage investments, renewable auctions, and regional cooperation on power markets. Renewable-energy investment in the Middle East has risen sharply, reaching $12.9 billion in 2025, with the UAE and Saudi Arabia leading efforts to build grid infrastructure needed to match growing generation capacity. Governance reforms remain essential to ensure that trade and financial openness are aligned with environmental objectives.
Policy design should also reflect heterogeneity across sub-regions. For high-income Gulf economies, the focus should be on redirecting financial openness toward clean energy, green hydrogen, and low-carbon industrial diversification, leveraging their strong fiscal positions and large-scale investment capacity. For energy-importing economies, renewable investment and governance reforms can reduce exposure to fossil fuel price volatility and enhance energy security. For North African economies, trade and financial integration should be combined with renewable infrastructure development and regulatory improvements to strengthen low-carbon growth pathways, consistent with broader findings on the EKC and energy–growth–environment nexus in the region (Farhani, 2013; Aïssa et al., 2014).
5.3 Limitations and future research
This study had several limitations. First, the analysis relies on macro-level annual indicators, which may mask sectoral differences in emissions and energy transition. Second, the financial integration proxy measures capital account openness rather than the sectoral destination of financial flows, limiting the ability to distinguish between green and brown investment channels. Third, the causality diagnostics are supplementary and do not fully eliminate endogeneity concerns. Fourth, the threshold analysis should be further validated using formal Hansen-type threshold tests and dynamic threshold models in Stata or similar software before the final submission. Future research may extend this analysis by using disaggregated renewable energy indicators, alternative governance measures, and sectoral emissions data. Structural-break tests, dynamic threshold models, and instrumental-variable or quasi-experimental approaches could also provide deeper insights into how financial integration, governance quality, and renewable energy jointly shape environmental outcomes in BRI–MENA economies. Such extensions would be particularly valuable for assessing the role of green investment, ESG disclosure, and climate policy design in the region (Gaies et al., 2019; Shabir et al., 2023).
StatementsData availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.
Author contributions
HM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing, Resources, Validation. IH: Data curation, Investigation, Software, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
The authors would like to thank the editor and reviewers for their constructive comments, which helped improve the quality and clarity of the manuscript.
Conflict of interest
The authors declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Summary
Keywords
BRI-MENA economies, carbon emissions, financial integration, governance quality, panel threshold model, renewable energy transition, trade openness
Citation
Hussnain M and Hafiza IN (2026) Financial integration, trade openness, and CO2 emissions in BRI-MENA economies: nonlinear effects of renewable energy transition and governance quality. Front. Environ. Econ. 5:1816930. doi: 10.3389/frevc.2026.1816930
Edited by
Delu Wang, China University of Mining and Technology, China
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© 2026 Hussnain and Hafiza.
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
