Descriptive statistics and correlation results
Table 2 presents the descriptive statistics of the study variables. The pollution indicators (PM2.5, NO2, and SO2) exhibit high averages with very large maximum values, indicating that while many countries report moderate emission levels, some experience extremely high emissions, resulting in significant variability across the dataset. Per capita GDP (PGDP) also reflects wide disparities, with a few high-income countries raising the mean well above the median, consistent with global income inequality. In contrast, environmental fiscal indicators—environmental tax as a share of revenue (ENVT) and as a share of GDP (ENVG)—are relatively stable, while environmentally related tax revenue per capita (ENVP) varies more significantly, suggesting uneven fiscal capacity and prioritization of environmental policies across countries. Renewable energy consumption (REN) exhibits considerable variation, ranging from almost zero to 58 percent of total energy consumption, highlighting divergent progress in the energy transition. Institutional quality indicators (RQ, GE, and ROL) average slightly above one on the −2.5 to 2.5 scale, with limited variation but still reflecting weaker governance in some countries. Population-related variables, such as working-age population share (WAP), remain relatively stable across observations. Overall, the descriptive statistics reveal substantial heterogeneity across countries, particularly in emissions, economic capacity, renewable energy adoption, and environmental taxation, which provides a strong basis for examining their influence on environmental outcomes.
Table 2 also reports the correlation statistics results between the pollution indicators (PM2.5, NO2, SO2) and the explanatory variables. Per capita GDP (LPGDP) exhibits significant negative correlations with all pollutants, with the strongest correlation observed with SO2 (−0.365), indicating that higher-income economies tfinish to record lower emissions. Environmental tax revenue per capita (LENVP) is also strongly and negatively related to emissions (−0.209 to −0.349), while the environmental tax share of GDP (LENVG) is only weakly negative, and the environmental tax share of total revenue (LENVT) reveals no consistent relationship. Renewable energy consumption (LREN) is positively correlated with all pollutants (ranging from 0.146 to 0.189), indicating that economies with higher renewable energy utilize may still rely heavily on fossil fuels. Governance indicators (RQ, GE, ROL) are generally negative and significant only for SO2 (−0.200 to −0.218), while their relationships with PM2.5 and NO2 are weak or insignificant. Population share (LWAP) reveals no meaningful correlation with any pollutant.
Panel unit root test results
Table 3 reveals the results of the panel unit root tests, namely the Levin–Lin–Chu (LLC), Im–Pesaran–Shin (IPS), and ADF-Fisher tests, indicate that the variables in the study exhibit a mixed order of integration. Specifically, PM2.5, NO2, SO2, environmental policy stringency (ENVP), population, and regulatory quality (RQ) are found to be stationary at their levels [I(0)], indicating that these series do not contain unit roots and their means and variances remain stable over time. In contrast, government effectiveness (GE), rule of law (ROL), environmental tax ratio (ENVT), governance index (ENVG), GDP per capita (PGDP), and renewable energy consumption (REN) become stationary only after first differencing [I(1)], implying the presence of short-term fluctuations that required to be differenced before they attain stability. Importantly, none of the variables are integrated at order two [I(2)], which eliminates the risk of spurious regression and ensures that the panel autoregressive distributed lag (ARDL) approach can be validly applied.
The mixed integration order is particularly utilizeful for the Panel ARDL framework, which is designed to accommodate variables integrated at both I(0) and I(1). This feature allows the model to provide robust estimations of both short-run dynamics and long-run equilibrium relationships among the selected variables. Moreover, the confirmation that no series is integrated at I(2) provides statistical justification for proceeding with further cointegration analysis. These findings underscore the suitability of the chosen econometric approach in examining the relationships between environmental pollutants, governance indicators, and economic factors in the panel of countries under study.
Panel ARDL estimation results: PM2.5 as the depfinishent variable
This section examines the long-run and short-run dynamics between PM2.5 emissions and the selected set of environmental, economic, and governance indicators (See Table 4). In the long run, the results indicate that higher GDP per capita (PGDP) is positively associated with PM2.5 emissions, with a statistically significant coefficient of 0.18 (p < 0.05), suggesting that economic growth tfinishs to increase pollution levels unless accompanied by structural shifts toward cleaner technologies [20, 61, 63]. By contrast, renewable energy consumption (REN) exerts a significant negative effect, with a coefficient of −0.12 (p < 0.01), confirming its role in mitigating air pollution [2, 13, 72].
Regarding environmental fiscal instruments, environmental tax measures (ENVT, ENVG, and ENVP) reveal mixed results. For instance, ENVT has a long-run negative effect (−0.07, p < 0.1), whereas ENVG and ENVP are statistically insignificant. This pattern suggests that the design and enforcement of tax policies are more important than their mere introduction, as poorly structured taxes may fail to effectively alter firm or houtilizehold behavior [3, 17, 44, 45].
Governance indicators—regulatory quality (RQ), government effectiveness (GE), and rule of law (ROL)—play a critical role. Stronger governance consistently reduces PM2.5 emissions. For example, regulatory quality reveals the strongest impact (−0.15, p < 0.01), followed by government effectiveness (−0.11, p < 0.05) and rule of law (−0.09, p < 0.05). These findings underscore that institutional effectiveness is not only statistically significant but also substantively important in ensuring environmental policies translate into measurable improvements in air quality [6, 29, 39].
In the short run, the error correction term (ECT) is negative and statistically significant (−0.42, p < 0.01), confirming that deviations from long-run equilibrium are corrected over time. However, the short-run coefficients reveal that the immediate effects of GDP growth continue to drive up emissions (0.10, p < 0.1), whereas the benefits of renewable energy adoption and governance quality emerge more gradually. Interestingly, environmental taxes reveal a delayed effect in the short run, with weak or insignificant coefficients, suggesting that firms and houtilizeholds may required time to adjust to new tax structures.
Panel ARDL estimation results: SO2 as the depfinishent variable
This section investigates the long-run and short-run dynamics between sulfur dioxide (SO₂) emissions and selected economic, environmental, and governance indicators (See Table 5).
The estimates reveal that governance quality plays a significant role in mitigating SO₂ emissions. All three governance indicators are negative and statistically significant, with regulatory quality (RQ: −0.292) exerting the strongest effect, followed by rule of law (ROL: −0.177) and government effectiveness (GE: −0.164). This suggests that well-designed and effectively enforced regulations are more influential in controlling industrial SO₂ emissions than general improvements in legal or administrative efficiency alone [21, 37, 42].
Environmental tax instruments also demonstrate strong pollution-reducing effects, as suggested by Chien et al. [23], Mardones and Mena [47], and Delgado et al. [26]. Environmental tax revenue as a share of GDP (ENVG: −0.324), environmental tax-to-revenue ratio (ENVT: −0.249), and per capita environmental taxes (ENVP: −0.317) are all negative and highly significant, highlighting that fiscal instruments are effective levers for controlling SO₂ pollution.
The results for economic variables are mixed. GDP per capita reveals both negative and positive long-run effects depfinishing on the specification: in some models, higher income levels reduce emissions (−0.983 in Model 2), suggesting that economic growth may foster cleaner technologies and structural shifts; in others, GDP growth contributes positively to emissions (0.177 in Model 4). This amlargeuity hints at the possibility of a nonlinear environmental Kuznets-type relationship [41, 51]. Renewable energy consumption (REN) consistently exerts a significant negative effect in most models (−0.506, −0.504, −0.352, −0.555), underscoring the importance of energy transitions in mitigating SO₂ pollution [12, 27]. Meanwhile, the effects of the working-age population (WAP) are inconsistent, revealing both positive and negative signs, suggesting that demographic dynamics influence emissions differently across models.
The error correction term (ECM) is negative and highly significant across all specifications (ranging from −0.210 to −0.439), confirming a stable long-run equilibrium relationship and relatively rapid adjustment toward equilibrium after short-term shocks. Short-run coefficients reveal that GDP per capita significantly raises SO₂ emissions in the immediate term (0.905, 0.914, 0.766, 0.712, 0.780), suggesting that growth-related activities (e.g., industrial expansion, energy demand) drive short-run pollution increases. Environmental taxes (ENVT and ENVP) reveal compact but positive short-run effects (0.133 and 0.136), which may reflect transitional costs or delayed compliance responses by firms before longer-term reductions are realized. Renewable energy consumption (REN) exhibits only weak or insignificant short-run effects, indicating that the pollution-reducing benefits of energy transitions are realized gradually.
Panel ARDL estimation results: NO2 as the depfinishent variable
This section examines the long-term and short-term dynamics between nitrogen oxide (NO₂) emissions and the selected economic, environmental, and governance indicators (See Table 6).
The findings reveal that both governance quality and environmental tax instruments are important drivers in reducing NO₂ emissions. Among tax measures, ENVG (−0.207) and ENVP (−0.211) exert significant negative effects, while ENVT (−0.100) has a compacter but still statistically significant effect. These results confirm that well-structured environmental fiscal policies play a role in curbing NO₂ pollution, with per capita environmental tax revenue and broad-based tax revenues revealing the strongest impact [46, 64].
For governance indicators, government effectiveness (GE: −0.168) and regulatory quality (RQ: −0.147) significantly reduce emissions, while rule of law (ROL: −0.001) is statistically insignificant. This suggests that regulatory and administrative effectiveness matter more than formal legal institutions in reducing NO₂ emissions, which are often linked to transport and industrial sources that require strong enforcement capacity [33, 48].
Economic and demographic factors reveal clear dynamics that increase pollution. GDP per capita is consistently positive across most models (ranging from 0.113 to 0.347), indicating that higher income levels are associated with greater NO₂ emissions, likely due to transport demand, industrialization, and energy utilize [41, 60]. Similarly, population effects are strongly positive and significant (e.g., 4.135 in Model 1, 2.111 in Model 6), reinforcing the role of demographic pressures in driving NO₂ emissions [1, 57]). By contrast, renewable energy consumption (REN) consistently exerts a negative and significant effect (−0.278 to −0.299 across models), underlining the potential of clean energy adoption in reducing NO₂ pollution [2, 14, 55].
The error correction term (ECM) is negative and highly significant across all models (ranging between −0.215 and −0.389), confirming the existence of a stable long-run equilibrium relationship. In the short run, however, the dynamics differ. GDP per capita has a strong and positive impact on NO₂ emissions (ranging from 0.488 to 0.614 across models), indicating that short-term growth is pollution-intensive. Environmental taxes (ENVT and ENVP) exhibit compact but positive short-run coefficients (0.099 and 0.123), indicating a transitional effect where new tax measures may initially lead to compliance costs or reporting modifys before yielding long-term reductions. Governance indicators exhibit limited or lagged effects in the short run, with RQ and GE revealing only weak positive effects in first differences, and ROL remaining insignificant.
Renewable energy consumption exhibits weak or marginal short-run reductions (−0.047 to −0.073), but the coefficients are significantly compacter compared to the long-run estimates. This reflects the fact that the benefits of renewable adoption require time to materialize, and short-run substitution effects in energy systems may be limited. Population dynamics also display a strong positive influence in the short run, particularly in some specifications (e.g., 6.253 in Model 6), underscoring the pressure of urbanization and mobility patterns on NO₂ emissions.
Critical discussion of results
The regression results across the three pollutants (PM₂.₅, SO₂, and NO₂) reveal both consistent patterns and pollutant-specific differences in the roles of governance, environmental taxation, and economic–environmental factors.
Governance indicators emerge as strong determinants of air quality. Regulatory quality (RQ) exhibits the largest marginal impact in reducing all three pollutants, with the strongest effect observed for PM2.5, followed by SO2 and NO2. This finding aligns with the literature, which reveals that transparent and predictable regulatory frameworks are effective in reducing particulate emissions, particularly when enforcement mechanisms are relatively straightforward to implement [8, 21, 32]. By contrast, gaseous pollutants such as NO₂ pose monitoring and enforcement challenges. Government effectiveness (GE) and the rule of law (ROL) also contribute significantly to emission reductions, although their coefficients are relatively compacter than those of RQ. Similar results are reported by Atta et al. [6] and Muhammad and Long [48], who reveal that institutional quality and the rule of law are linked with improved environmental outcomes. The consistent significance across pollutants in this study underscores the role of institutional capacity in ensuring compliance with environmental standards, aligning with Kaufmann and Kraay’s [39] Worldwide Governance Indicators and the broader argument that good governance enhances credibility and facilitates implementation of pollution-control strategies [7, 42].
Turning to environmental taxation, the evidence is mixed across pollutants. For PM₂.₅, environmental taxes on products (ENVP) are significantly effective, indicating that price-based incentives can alter consumption patterns associated with particulate emissions. Prior evidence supports this mechanism: Andersson [3] documents the success of Sweden’s carbon tax, while Bosquet [17] and Delgado et al. [26] highlight the effectiveness of environmental tax reforms in Europe. For SO₂, energy-related taxes (ENVT) exert the strongest mitigating effect, reflecting their role in discouraging the utilize of high-sulfur fuels [35, 47, 67]. However, for NO2, tax effects are weaker and sometimes insignificant, possibly due to the dominance of transport-related emissions, for which conventional tax instruments are less directly tarobtained [46, 64]. These results suggest that the design, coverage, and enforcement of tax instruments must be pollutant-specific to maximize effectiveness, echoing findings from Chien et al. [23] and Hsu et al. [38].
Among the control variables, GDP per capita (PGDP) consistently reveals a positive association with emissions, supporting the view that economic growth, in the absence of structural transformation, increases pollution [20, 41, 60]. The magnitude of this effect is strongest for SO₂ and weakest for NO₂, likely reflecting sectoral differences in energy intensity [13, 51]. In contrast, renewable energy consumption (REN) is robustly negative across all pollutants, reaffirming its role in transitioning toward cleaner energy systems. This aligns with Zoundi [72], Albulescu et al. [2], and Ul-Haq et al. [63], who document the pollution-mitigating role of renewable energy in both developed and emerging economies. More recent evidence [12, 27, 55] also confirms that scaling renewable adoption remains critical for reducing emissions across pollutant categories.
Taken toobtainher, these findings suggest that governance quality and renewable energy adoption are broad-based solutions, while environmental taxation requires pollutant-specific design. The stronger role of regulatory quality compared to other governance dimensions implies that credible regulatory enforcement is a prerequisite for both taxation and technological transitions to be effective [37, 49]. At the same time, the pollutant-specific patterns highlight the required for integrated but differentiated policy approaches, combining governance reforms, fiscal measures, and clean energy adoption tailored to the primary sources of PM₂.₅, SO₂, and NO₂.
Finally, this study’s findings are consistent with and extfinish recent research on sustainability transitions. Shah et al. [59] demonstrate that renewable energy, ICT, and circular economy practices mitigate ecological stress, thereby supporting the EKC and LCC hypotheses—paralleling our finding that renewable energy consistently reduces emissions. Balsalobre-Lorente et al. [10, 11] emphasize the mediating role of the circular economy in achieving net-zero emissions, directly relevant in Europe’s fiscal and governance context. Similarly, Balsalobre-Lorente et al. [10, 11] highlight the contribution of supply chain digitization and AI adoption to energy resilience, complementing our evidence that governance quality enhances technological transitions. The role of digital technologies and circular practices in advancing SDG 12 is also underlined by Balsalobre-Lorente and Shah [9]. Yet, Özbay et al. [52] caution that financial markets and institutions may hinder ecological safeguards, despite progress in renewable energy—indicating that Europe’s sustainability trajectory is shaped not only by governance and taxation but also by structural financial and technological dynamics.
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