First, few studies have looked at the possible trade–offs and synergies between ecological sustainability and fiscal decentralization in the EU. This disparity makes it more difficult to comprehend the best practices and regulations for distributing funds to assist ecological projects at the municipal/local or regional level. Second, there are significant gaps in the literature on ecological sustainability and environmental policy stringency in the EU. Studies explicitly examining environmental policy stringency’s effects on ecological sustainability in the EU are scarce, even though it is widely acknowledged as a key force behind sustainable development. This disconnect makes it more challenging to fully utilize environmental policy stringency to solve environmental issues and advance sustainable practices. Third, the MMQR econometric technique is employed as opposed to other methods utilized by prior studies, such as NARDL, CS–ARDL, ARDL, FMOLS, and DOLS.

This section clarifies the theoretical foundation of this analysis, unveiling the interplay between fiscal decentralization, environmental policy stringency, import diversification, expenditure on environmental protection, and twin indicators of environmental sustainability. Fiscal decentralization refers to redefining the competencies of the central and subnational governments in conducting fiscal policy. More specifically, it is the process of shifting the expenditure and revenue–based responsibilities from central to the lower authorities. As expenditure and revenue decentralization are the two dissimilar indicators of fiscal decentralization, their distinct interplay with environmental sustainability is expected. Herein, the evidence on the fiscal decentralization–environment nexus is divided into two contrasting mechanisms, namely, “race to the top” and “race to the bottom.” The first strand of evidence induces a favourable environmental impact under the “race to the top” mechanism, unveiling that growing fiscal decentralization permits local authorities to rigorously follow–up dirty industries and, forcefully displace them abroad, when necessary. Local governments are imposing more stringent environmental regulations rather than relaxing them, and thus provoke a betterment of environmental sustainability. Fiscal decentralization empowers local authorities to consider the unique characteristics and diversities of the particular region in formulating more efficient environmental policies. The “race to the top” also supposes that local authorities act more effectively than central officials in organizing public services free from negative externalities. In essence, local officials may acknowledge the necessity to maximize the welfare of the population by optimizing the environmental advantages of fiscal decentralization. On the contrary, “race to the bottom” mechanism suggests that local officials might prioritize the improvement of economic well–being over environmental sustainability. A growing fiscal decentralization can induce competition, pushing local officials to relax environmental standards to attract foreign investors, which may exacerbate environmental sustainability. Such practices may attract investments in dirty industries that rely on unclean energy sources, translating into environmental deterioration. Following the “race to the bottom” mechanism, a growing fiscal decentralization is regarded as a key driver of environmental challenges.

Trading activities are the essential determinants of environmental sustainability, as these are among the most substantial emitters of anthropogenic gases. Considering their vitality, trade factors (trade openness, import and export diversification) sparked growing attention from researchers. The reason for this is that trade activities boost energy use and may potentially cause appreciably greater environmental pressure (Sadiq et al., 2022; Zhou et al., 2024). Trading activities increase the variety of goods available and help strengthen the relationships between distant countries. However, one should not overlook the environmental impact of trade activities. Among trade factors, particular emphasis is placed on import diversification, as it is not only the critical determinant of economic upturn but also represents a prominent instigator of pollution. Import diversification measures the extent to which the pattern of imports of an economy deviates from the rest of the world. The sign of the environmental impact of import diversification unriddles the basic economic country conditions. Specifically, introductory stages of growth may be accompanied by dirty industries that consume more energy, reflecting an upswing in pollution. However, at later stages of growth, nations may be provoked to transition the industry to a low–carbon state. The movement towards lower carbon industries directs advanced nations to shift towards green energy and to specialize in the manufacturing of eco–friendly products. To accelerate environmental sustainability, advanced nations introduce modern technology and innovations in production activities. Notwithstanding the efforts of advanced nations to make their economies cleaner, it may be recalled that these nations mobilize significant funds to implement their environmental policies. Accordingly, advanced nations prioritize the low–carbon manufacturing over dirty industries, inclining towards the import of eco–unfriendly products from developing nations. Given these arguments, the beneficial environmental impacts of import diversification is likely to occur in advanced nations. On the other hand, developing countries might be importing energy–intensive intermediate products and machinery, causing an escalation of pollutant emissions. The ultimate objective of developed countries is to reduce energy intensity in favour of energy efficiency via the channel of technological advancement. As developed countries are among the most technologically advanced nations, they are attempting to enhance technological capacity through technology transfer. Moreover, the developing countries’ existing technology might be modernized via the means of imported technology to sustain the environmental quality. The advantageous environmental impact of import diversification is prominently associated with the imposition of stringent environmental policies. If there is a lack of environmental regulations, the advantageous environmental impact of import diversification will fade out.

Central and subnational officials develop policies to impose a higher price on environmentally harmful behaviour, regarded as stringent environmental policies. These policies unveil the degree to which environmental rules, laws, and regulations peg a price on eco–unfriendly conduct. Thus, the purpose of stringent environmental regulation is to make unsustainable production and consumption unaffordable, affirming the behavioural changes in both the business sector and households. As such, rigorous environmental policy paves the way towards anthropogenic emissions mitigation for a sustainable future. It also encourages the business sector to use modern machinery that curtails pollutants and affirms eco–innovations. By fostering low–carbon machinery, stringent environmental policies can alleviate the harmful effects of pollutants. Consequently, these can promote the shift from unsustainable consumption and production towards an environmentally beneficial one. An effective environmental policy benefits the environment in two ways; firstly, it fosters green innovation, and secondly, it enhances eco–friendly products. Herein, rigorous environmental standards are not only effective in instigating the development of low–carbon technologies but also in preventing the utilization of eco–unfriendly raw materials and intermediate products. On the flip side, the business sector might be unwilling to invest in green machinery as it may cause extra costs. Acknowledging that the implementation of rigorous environmental legislation is expensive, the business sector may rather shift their carbon–intensive production to developing countries that impose less strict environmental standards. However, environmental awareness strengthens as income rises, insinuating that after reaching a threshold level of income, developing nations will enforce environmental regulation to boost green production and consumption trajectories. Herein, in the introductory stages of growth, environmental regulations may be too weak to impose an environmental betterment effect, but at later stages, these regulations may be effective in amplifying environmental sustainability.

The Gross Domestic Product (GDP) incorporates consumption, government spending, and the difference between exports and imports (net exports) of a country, among other economic factors. Consumption is an essential component of GDP, and its rising trend may explain the intensification of environmental pressure. The expected harmful environmental impact of GDP, especially in the early stages of growth, can be justified in the sense that this study incorporates countries that have a sharp economic acceleration. The economic upturn is accompanied by rising energy demand, with fossil fuels being the most sizeable source of energy in the EU. Although countries have inaugurated various policies to curb environmental deterioration, they still import emissions via the channels of trade and consumption. Government expenditure on environmental protection is among the essential instruments of environmental policy in the EU and is assumed to curb anthropogenic emissions. It can be defined as government spending dedicated towards pollution mitigation, maintenance of biodiversity, and waste reduction. Environmental protection expenditure prompts the industrial sector to affirm eco–innovations and low–carbon technology that will help in attaining the targets of energy efficiency. The advantageous environmental impact of environmental protection expenditure can be gauged on the ground that it encourages producers to shift towards the production of eco–friendly commodities. In addition, environmental protection expenditure may help the business sector to manage waste more efficiently and to combat pollution (Yıldız, 2025). On the flip side, expenditures for environmental protection impose additional costs, boost burdens, and present a new set of enterprise challenges. Environmental policy stringency is a vital tool that may be employed directly or indirectly to curtail environmental issues. Indeed, the alliance of stringent environmental rules and strong institutions contends the propulsive force of environmental sustainability (Imran et al., 2024; Wahab et al., 2024). This is because strict environmental regulations aspire to repair the negative environmental externalities, including soil, water, and air pollution. Empowering subnational governments with stringent environmental rules, laws, and regulations is expected to reduce the adverse ecological impact of fiscal decentralization (Zhang and Xiang, 2023). This can be explained on the ground that in authorities with stricter environmental regulations, public spending may be directed to support renewable energy solutions and green innovations, and reduce tax burdens for eco–friendly enterprises. The imposition of efficient environmental policies might affirm subnational governments to invest in pollution treatment and pollution prevention to induce the modification in the production process towards green and eco–friendly manufacturing. As a result, rigorous environmental policies oil the wheel of environmental sustainability, advocating the opinion that subnational governments can ameliorate the environmental cost of economic upturn and harmonize economic and environmental objectives. In this context, this study prompts the assessment of the moderating role of environmental policy stringency in eliminating the harmful effects of fiscal decentralization on energy and carbon efficiency as proxies for environmental sustainability. These two proxies are selected to capture not only the environmental but also the economic facet of environmental sustainability. Energy efficiency (units of output produced per unit of energy used) is accommodated to comprehensively assess the environmental sustainability of the EU in the sense that lower energy efficiency means higher energy intensity (price of turning energy into output) and vice versa. Carbon efficiency (units of output produced per unit of carbon dioxide emissions) prompts that a cut in carbon efficiency often enlarges carbon–intensity (carbon dioxide emissions per unit of output), being a threat to the environmental sustainability of the EU.

Based on the aforementioned theoretical interplays between variables, this study makes use of the esteemed EKC (Environmental Kuznets Curve) framework to assess the associations between fiscal decentralization, environmental policy stringency, import diversification, economic upturn, environmental protection expenditure, and environmental sustainability (Bergougui and Satrovic, 2025; Villanthenkodath et al., 2024; Musah et al., 2024). This empirical setting examines whether environmental policy stringency helps in mitigating environmental deterioration and is specified as follows (Equation 1): ENST it = f UPT it , UPT 2 it , FIS it , STR it , IDIV it , GEP it (1)

We have transformed panel data into logarithmic form to cope with outliers. Accordingly, the logarithm function is applied to Equation 1, to specify a log-linear regression form that is seen below (Equation 2): LENST it = ω 0 + ω 1 L UPT it + ω 2 L UPT 2 it + ω 3 L FIS it + ω 4 L STR it + ω 5 L IDIV it + ω 6 L GEP it + ε it (2)

In the above equation, ω 1 − ω 6 indicate regression parameters, i denotes the analytical laboratory (European Union countries), and t is the notation for the selected time interval (1995–2020). The intercept is reflected by ω 0 . The twin proxies for environmental sustainability (ENST) namely, energy efficiency–EEFF (gross domestic product (GDP)/total energy supply) and carbon efficiency–CEFF (GDP/energy–related CO2 emissions) as proposed by Satrovic et al. (2024), are the function of economic upturn (UPT), economic upturn squared (UPT2), fiscal decentralization (FIS), environmental policy stringency (STR), import diversification (IDIV) and government expenditure on environmental protection (GEP) as suggested by Fan et al. (2022), Gariba et al. (2024), Li et al. (2023), Wang et al. (2024), Zhang and Xiang (2023). L refers to the natural logarithm, while ε it depicts the error term. This study adopts three proxies to capture the EU’s respective expenditure decentralization (FEXP), revenue decentralization (FREV), and the composite indicator that integrates these two dimensions (Ji et al., 2021; Khan et al., 2021).

As a dependent variable, this study adopts energy efficiency to capture both economic and environmental patterns of environmental sustainability (Ma et al., 2022). For the sake of robustness, an alternative dependent variable is accommodated, namely, carbon intensity. Previous empirical evidence mainly opted for greenhouse gas emissions or ecological footprint to measure environmental sustainability, overlooking the economic perspective (Villanthenkodath et al., 2024; Musah et al., 2024). To close this gap, this study sets out to answer the question of how selected independent variables interrelate with units of output produced per unit of energy used or units of output produced per unit of carbon dioxide emissions. As far as the independent variables are considered, the interplay between economic upturn and the environment is frequently assessed in mainstream studies under the shadow of the EKC. The authenticity of inverted U–type nonlinear behaviour that verifies the EKC phenomenon is validated if economic upturn yields worsening environmental impact in the introductory stages of growth, whereas later stages set out the advantageous environmental impact of economic upturn as discovered by Ansari (2022), Musah et al. (2024). The environmental impact of fiscal decentralization is getting far more attention in empirical studies with inconclusive findings. According to the literature opting for the “race to the top” pattern, fiscal decentralization is beneficial for environmental sustainability in a manner that subnational governments are carefully monitoring heavy pollution businesses to direct them towards greener production processes (Fang and Fang, 2023; Hu et al., 2023; Liu et al., 2022). On the other hand, “race to the bottom” specification indicates that local governments favour economic upturn over environmental protection, causing a relaxation of environmental standards that give rise to environmental concerns (Sun et al., 2023; Udeagha and Ngepah, 2023b; Zhang and Xiang, 2023). Along these lines, fiscal decentralization might aggravate or amplify the environmental sustainability of the EU (i.e., ∂ E N S T i t ∂ F I S i t < 0 or ∂ E N S T i t ∂ F I S i t > 0 ). An imposition of strict environmental policies fosters the business sector to implement green technologies that aggravate anthropogenic emissions. By favouring green and disregarding heavy polluting machinery, environmental policy stringency may affirm environmental sustainability (Dai and Du, 2023; Wang et al., 2022; Xie et al., 2023). However, environmental protection is not free and may induce extra costs, fostering companies to move their production to locations with less tight environmental standards (Liu et al., 2023). Therefore, it is projected that more rigorous environmental policies may either enhance or curtail environmental sustainability (i.e., ∂ E N S T i t ∂ S T R i t > 0 or ∂ E N S T i t ∂ S T R i t < 0 ). Acknowledging the responsibility they have for environmental sustainability, developed countries are implementing various policies to tackle environmental concerns and to use resources from nature in a more responsible manner. One of the reasons is that environmentally unfriendly behaviour is very expensive in developed countries. To avoid additional burdens, producers from developing countries very often move their production sites to locations with relaxed environmental standards. Through imports, developed countries meet their demand for carbon intensive products, generating an adverse environmental impact (Doğan et al., 2022; Udeagha and Ngepah, 2023b). However, developing countries are importing modern technology from the advanced nations, which might aid in their energy efficiency and reduce energy–related greenhouse gas emissions. The import of advanced technologies may foster developing countries to improve their current production process and to adopt eco-friendly products (Meng et al., 2022; Wang et al., 2024). Given these arguments, it is anticipated that environmental sustainability improvement or deterioration effect may be attributable to import diversification (i.e., ∂ E N S T i t ∂ I D I V i t > 0 or ∂ E N S T i t ∂ I D I V i t < 0 ). By subsidizing green technology and inducing technological innovations, government expenditure on environmental protection may be affirmative in ensuring the sustainable development of the EU (Aydin et al., 2023; Carmona et al., 2023; Fan et al., 2022). However, a rising cost of environmental protection may discourage the transition towards eco–production and consumption, posing a severe threat to a sustainable future (Caglar and Yavuz, 2023; Feng et al., 2023). In this vein, environmental protection expenditure may either amplify or curb environmental sustainability (i.e., ∂ E N S T i t ∂ G E P i t > 0 or ∂ E N S T i t ∂ G E P i t < 0 ).

This study aims to assess the moderating role of environmental policy stringency in curbing the disadvantageous environmental impact of fiscal decentralization in the EU countries. Therefore, the model specified in Equation 2 is augmented to capture the joint effect of three indicators of fiscal decentralization and environmental policy stringency on environmental sustainability as shown below (Equation 3): LENST it = ω 0 + ω 1 L UPT it + ω 2 L UPT 2 it + ω 3 L FIS it + ω 4 L Mod it + ω 5 L IDIV it + ω 6 L GEP it + ε it (3)

Based on Equation 3, Mod refers to the combined effect of FIS and environmental policy stringency. Given the potential of stringent environmental policies to foster subnational governments to assist the business sector in a transition towards cleaner production, it is projected that the coefficient of Mod will be positive (i.e., ∂ E N S T i t ∂ S T R i t * F I S i t > 0 ) enabling environmental policy stringency to curtail the environmental damaging effect of FIS as insinuated by Zhang and Xiang (2023).

This study incorporates seven EU members (Austria, Belgium, Germany, Hungary, the Netherlands, Spain, and Sweden) in the period from 1995 to 2020. Sample period selection was primarily based on the data availability to ensure a balanced panel data set. Notably, the data for import diversification index were available from 1995, whereas the data on environmental policy stringency were not available after 2020. It is worth noting that fiscal decentralization proxies were available in a balanced form for Austria, Belgium, Canada, Estonia, Georgia, Germany, Hungary, Japan, Latvia, Netherlands, Peru, Spain, Sweden, Switzerland, and the United Kingdom. As the majority of the countries are members of the EU, it justifies the selection of this bloc to analyze the driving forces of energy and carbon efficiency. The selected countries unveil a substantial progress in STR (OECD, 2022) in the sample period (i.e., Austria from 1.61 to 3.31, Belgium from 1.11 to 3.44, Germany from 1.50 to 3.47, Hungary from 0.53 to 2.81, Netherlands from 1.44 to 3.47, Spain from 1.11 to 2.50, and Sweden from 1.25 to 3.83). Moreover, the selected countries contain the value of carbon efficiency far above the OECD average of 5.50 in 2020, whereas all countries but Belgium report the value of energy efficiency to outpace the OECD average of 11,283.13 US$ (2015) per tonne of oil equivalent in 2020 (OECD, 2023). In addition, viable reasons to select these countries can be summarized as: they enacted an environmental policy framework that comprises the world’s most rigorous environmental standards and regulations. Next, even though fiscal decentralization processes vary between the selected nations, these are strongly decentralized countries regarded as developed. EU members allocate sizable funds to comply with strict environmental standards, and thus stimulate specialization in manufacturing of green products, whereas the demand for eco–unfriendly products is met from imports. Lastly, the environmental sustainability of the EU is strongly attributable to trade factors as these boost energy demand, which may aggravate environmental harm. Table 1 covers the description of the chosen variables and their measurement units.

Energy efficiency has been utilized as a primary dependent variable to adapt economic and environmental features of environmental sustainability. Energy efficiency surpasses the traditional environmental indicators (i.e., greenhouse gas emissions or ecological footprint) based on a single environmental dimension, as it opts to cover economic components as well. It is calculated as units of output produced/energy used, and is taken from the statistics collected by the Organisation for Economic Co–operation and Development (OECD, 2023). This study employs the alternative dependent variable for the sake of robustness measured by dividing units of output produced with carbon dioxide emissions gathered from (OECD, 2023). The independent variable economic upturn is the fundamental agent that enlarges pollutant emissions, resulting in environmental harm (Ahmad and Satrovic, 2024). Gross domestic product per capita in constant 2015 US$ compiled from the (World Bank, 2023) is chosen to measure the economic upturn of EU members. This study encompasses three essential proxies for fiscal decentralization, namely, expenditure decentralization, revenue decentralization, and the composite fiscal decentralization index. The data on fiscal decentralization were gathered from the International Monetary Fund (IMF, 2021). Furthermore, environmental standards are regulations that might be the ultimate agents in ascertaining the environmental sustainability of the EU. To capture the vitality of the environmental impact of these regulations, our study opts for the environmental policy stringency index collected from the OECD Environmental Policy Stringency Index database (OECD, 2022). Import diversification might also be harmful to environmental sustainability as it strongly interrelates with energy use. Herein, to unveil the environmental impact of import diversification, this study used the diversification index of imports gleaned from the United Nations Conference on Trade and Development (UNCTAD, 2023). Expenditure on environmental protection is used to probe the environmental impact of government spending. The data on GEP is gathered from the statistics gathered by the International Monetary Fund (IMF, 2023). The information on summary statistics is detailed in Table 2.

According to outcomes presented in Table 2, all average values are above zero in the sample of seven EU members. Economic upturn yields the highest standard deviation, with the diversification index of imports displaying a minimum. Kurtosis coefficients slightly reject the notion of normal distribution, where energy efficiency, economic upturn, expenditure decentralization, and environmental policy stringency unveil the negative skewness. Other panel data are opting for positive skewness. In the sample of seven EU nations, Austria portrays the highest average energy efficiency, with Sweden spotlighting the minimum value. However, Sweden seems to have the highest average carbon efficiency, with Belgium yielding minimum mean carbon efficiency. Although Sweden reported the highest average expenditure decentralization, the maximum average revenue decentralization is calculated for Germany. Hungary is characterized by the maximum average import diversification, whereas Germany seems to have the minimum average import diversification. On average, Sweden is the most stringent economy in terms of environmental policies, with Spain having less rigorous environmental regulations. Although Sweden seems to have the least average government spending on environmental protection, the Netherlands displays the highest score.

Before calculating the regression coefficients, it is of principal significance to deploy various econometric techniques. As the EU has established economic integration, the member states are substantially interrelated being subject to global events prone to cause cross–section dependency (CRDP). Disregarding the presence of cross-sectional dependence in panel data may entail biased findings. Aiming to unveil the presence of CRDP concern, this study opts for the test by (Pesaran, 2004) delineated in the form of an equation as shown below (Equation 4): CRDP = 2 T N N − 1 ∑ i = 1 N − 1 ∑ j = i + 1 N ρ ^ i j (4)

The analytical period is denoted as T , while the analytical laboratory comprising seven EU members is denoted as N . Correlation is abbreviated by ρ ^ i j being mathematically expressed as (Equation 5): ρ ^ i j = ρ ^ j i = ∑ t = 1 T ε i t ε j t ∑ t = 1 T ε i t 2 1 2 ∑ t = 1 T ε j t 2 1 2 (5)

Additionally, the slope heterogeneity test by Pesaran and Yamagata (2008) is performed to verify the existence of slope heterogeneity (SLPHR) issues to provide accurate and unbiased empirical outcomes. The following equations serve to mathematically formulate delta tilde value ( ∆ ^ S L P H R ) (Equation 6): ∆ ^ S L P H R = N · 2 k · N − 1 · S ^ − k (6)

While Equation 7 mathematically expresses adjusted delta tilde value ( ∆ ^ ^ S L P H R ) as shown below (Equation 7): ∆ ^ ^ S L P H R = N · 2 k · T − k − 1 T + 1 − 1 / 2 · N − 1 · S ^ − 2 k (7)

Based on the abovementioned equations, S ^ depicts Swamy’s statistics and k denotes the independent variables. Assessing the pattern of slope property will assist in deciding on the necessity to involve or subtract heterogeneous slopes. The delta tilde and adjusted delta tilde values will approve or disapprove H0, which assumes homogeneous slopes over the selected sample of EU members. The endorsement of CRDP and SLPHR concerns in panel data opts for the second generation unit root tests that produce firmer results in comparison with first generation tests. For this reason, our study incorporates Pesaran (2007)’s unit root (SGUR) test illustrated in general form as follows (Equation 8): ∆ C D i t = ρ i + ρ i C D i t − 1 + ρ i X t − 1 + ρ i T + ∑ j = 1 n ρ i j ∆ C D i t − j + θ i t (8)

In Equation 8, cross–sectional averages are expressed by CD as below (Equation 9): C D i t = ρ 1 L U P T i t ¯ + ρ 2 L U P T 2 i t ¯ + ρ 3 L F I S i t ¯ + ρ 4 L S T R i t ¯ + ρ 5 L I D I V i t ¯ + ρ 6 L G E P i t ¯ (9)

Further, Equation 10 specifies the SGUR test is as follows: S G U R = N − 1 ∑ i = 1 n C A D F (10) where C A D F depicts the cross–sectional augmented Dickey–Fuller. The null hypothesis predicts the non-stationarity properties of the selected variables, whereas the alternative hypothesis promotes stationarity. In the next step, this study evaluates the long-term interrelationship between environmental sustainability and its fundamental determinants exposed in detail in Table 1. To avoid the distortion of unbiased findings, this study is based on the Westerlund (2007) cointegration test that reflects the four test statistics, namely, Gt, Ga, Pt and Pa advocating no cointegration under the null hypothesis. The guiding equations for these statistics are shown below (Equations 11–14): G t = 1 / N ∑ i = 1 N α i ′ S E α i ′ (11) G a = 1 / N ∑ i = 1 N T α i ′ α i ′ 1 (12) P t = a ′ S E a ′ (13) P a = T a ′ (14)

In Equations 11–14, a ′ symbolizes the error correction term, G t and G a are mean group statistics; P t and P a reflect panel statistics.

In the next stage, the impacts of the independent variables on environmental sustainability are assessed by deploying the Method of the Moments Quantile Regression (MMQR) by Machado and Santos Silva (2019). The selection of the MMQR model is justified since it tackles various concerns associated with panel data, including endogeneity constraints. An additional advantage of the MMQR econometric technique is its ability to assess non–linear relationships. It does not only capture linear interconnections but also reflects non–linear enabling the estimation against different quantiles of environmental sustainability. As such, it provides more appropriate results in comparison with traditional techniques that only capture mean effects of economic upturn, fiscal decentralization, environmental policy stringency, import diversification, and GEP on dependent variables (Satrovic et al., 2025b). In addition, MMQR gets through the variables that do not follow the normality pattern. An additional motivation for selecting MMQR as an estimation strategy in the present study is that it accommodates the fixed effects and is adequate for assessing the heterogeneous estimates across low, middle, and upper quantiles of ENST. It also furnishes trustworthy outcomes in the case of outliers and location asymmetries. Following (Dai and Du, 2023; Fang and Fang, 2023; Hu et al., 2023; Lingyan et al., 2022), the present study adopts the MMQR procedure that can be formalized as (Equation 15): Q Y i t τ X i t = α τ ′ X i t + β i , i = 1 , … , N , t = 1 , … , T (15) where Y i t are the indicators of environmental sustainability, X i t showcased L U P T , L U P T 2 , L F I S , L S T R , L I D I V , L G E P , a τ are unidentified parameters, β i represent individual effect. Fundamental function form of our models is shown below (Model 1 – Equation 16; Model 2 – Equation 17; Model 3 – Equation 18; Model 4 – Equation 19; Model 5 – Equation 20; Model 6 – Equation 21): Q L E E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F I N D i t + a 4 τ L S T R i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (16) Q L E E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F E X P i t + a 4 τ L S T R i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (17) Q L E E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F R E V i t + a 4 τ L S T R i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (18) Q L E E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F I N D i t + a 4 τ L M o d 1 i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (19) Q L E E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F E X P i t + a 4 τ L M o d 2 i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (20) Q L E E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F R E V i t + a 4 τ L M o d 3 i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (21) where Mod1 = STR*FIND; Mod2 = STR*FEXP; Mod3 = STR*FREV.

The Driscoll–Kraay estimator (RDIS) is used to check for robustness and to juxtapose our baseline outcomes (MMQR technique) with those of RDIS. The motivation behind selecting the RDIS estimation strategy is that it allows for CRDP and heteroscedasticity issues. Moreover, it addresses the autocorrelation issue and allows for country–specific diversity. Given the appreciable heterogeneity among EU members, it is of vital importance to check the robustness of MMQR outcomes by adopting an estimator resistant to country–specific heterogeneity. In this vein, the present study opts for the RDIS as an ideal strategy for robustness checks by Essayem et al. (2024), Hossain et al. (2024), Islam et al. (2025). As a final step, the present study dives into the causal linkage of independent variables with ENST. Since CRDP and SLPHR issues are likely to occur, the present study opts for the Dumitrescu and Hurlin (2012)– DH test to safeguard the valid empirical outcomes. Unlike the traditional Granger causality test, DH yields trustworthy outcomes in the case of heterogeneity pattern.

Moreover, our study includes an additional proxy for environmental sustainability, i.e., carbon efficiency to look into the robustness of our models from the angle of alternative dependent variable. The amended model specifications are illustrated as under (Model 7 – Equation 22; Model 8 – Equation 23; Model 9 – Equation 24; Model 10 – Equation 25; Model 11 – Equation 26; Model 12 – Equation 27): Q L C E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F I N D i t + a 4 τ L S T R i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (22) Q L C E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F E X P i t + a 4 τ L S T R i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (23) Q L C E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F R E V i t + a 4 τ L S T R i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (24) Q L C E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F I N D i t + a 4 τ L M o d 1 i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (25) Q L C E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F E X P i t + a 4 τ L M o d 2 i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (26) Q L C E F F τ X i t = a 1 τ L U P T i t + a 2 τ L U P T 2 i t + a 3 τ L F R E V i t + a 4 τ L M o d 3 i t + a 5 τ L I D I V i t + a 6 τ L G E P i t + β i (27)

Based on Equations 22–27, CEFF stands for carbon efficiency.

To fulfil the targets of SDGs, in particular, SDG7 and SDG13, EU members should consolidate fiscal decentralization initiatives and environmental policy stringency to ensure the achievement of ecological priorities. This consolidation is an attestation to sustainable development practices because it can contribute to economic progress, as well as improve the well–being of the society (Ge et al., 2024). Tang and Imran (2024) also highlighted the importance of governance in solving problems related to the environment. Thus, some outstanding recommendations and implications are as follows.

1. Clear and strict environmental regulations should be implemented, with quantifiable goals, legally enforceable standards, and efficient enforcement systems in place for all sectors. This will serve as a safeguard against the race to the bottom situation. In addition, this can assist member states in designing better environmental policies to their unique circumstances, local preferences, and economic structures.

In summary, although the political landscape of the selected EU economies may slightly differ, these policies can still be applicable to the member states because they are highly interdependent and they have some fundamental things in common, such as a single market, single currency, common policies, supranational institutions, and shared sovereignty. However, one must also take into account that the results of this research might slightly differ if individual member states have been investigated. Thus, this can be investigated further.

Although this study is timely and will be of great benefit to the EU in terms of policy formulation, it is limited to examining how fiscal decentralization and environmental policy stringency are used to achieve ecological sustainability in the EU. Ecological sustainability may, however, be influenced by a wide range of additional elements, including geopolitical risks and institutional quality. Including geopolitical risk in further studies will help to determine the EU’s policy priorities and the contextualization of domestic policies. In addition, including institutional quality deepens the understanding of policy effectiveness. Lastly, future studies could adopt a further theoretical approach that goes beyond the context of the EU and employ advanced econometric approaches.