Claudiu Tiberiu
email: claudiual@yahoo.com

Albulescu Ab

Maria-Elena Boatcă-Barabaș

Andra Diaconescu

Claudiu Tiberiu Albulescu
email: claudiu.albulescu@upt.ro

The asymmetric effect of environmental policy stringency on CO2 emissions in OECD countries

Keywords: CO2 emissions, environmental policies, environmental Kuznets curve, pollution haven hypothesis, panel quantiles regression Q43, Q56, F21

This paper uses a quantile fixed-effect panel data approach to investigate how environmental policy stringency affects CO2 emissions in a set of 32 OECD countries from 1990 to 2015. This approach allows us to identify the asymmetric impact of policy stringency on emissions, considering the emission level recorded in each analysed country. More precisely, we posit that the effectiveness of environmental regulations and policies is influenced by the air pollution level. Our results show that an increase in policy stringency has a negative impact on emissions. As a new contribution, we show that environmental stringency has a more powerful impact in the countries with lower level of carbon emissions. This result is also recorded for the subset of EU member countries of the OECD. Moreover, we show that policy stringency measures only become effective after the implementation of the Kyoto agreement.

Finally, the policy stringency effect is stronger for EU countries at high risk of missing the 20-20-20 target in terms of greenhouse gas emissions.

Introduction

Environmental degradation in general, and the increase in greenhouse gas emissions in particular, requires urgent action and policy measures to fight against global warming. Although the European Union (EU) recorded an emission reduction by 24% between 1990 and 20191 , at the global level, emission levels are constantly rising. Most emissions are generated by the energy sector, which accounts, according to the International Energy Agency (IEA), for more than 80% of CO2 emissions (IEA 2019). In finding solutions to a low-carbon economy and increasing energy security, the guidelines of the Organization for Economic Cooperation and Development (OECD) for over 60 years has aimed at increasing and improving the quality of the environment, bringing to the fore stringent actions for climate change awareness. 2 In fact, promoting green innovation and regulating emissions through carbon pricing are the two fundamental driving forces of climate change policies on carbon abatement [START_REF] Hashmi | Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation[END_REF]. Regarding the environmental policies and regulations of carbon emissions, environmental taxes and environmental policy stringency represent the main instruments used by authorities to fight against climate change (Wolde-Rufael and Mulat-Weldemeskel 2021).

Only a few empirical works, however, have assessed the efficiency of these instruments in moving down the emission level (e.g. [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF][START_REF] Hashmi | Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation[END_REF][START_REF] Ma | How government regulation impacts on energy and CO2 emissions performance in China's mining industry[END_REF][START_REF] Niedertscheider | Austrian climate policies and GHG-emissions since 1990: What is the role of climate policy integration?[END_REF][START_REF] Wenbo | Assessing the efficiency of China's environmental regulation on carbon emissions based on Tapio decoupling models and GMM models[END_REF]Zhao et al. 2015). A portion of these works (Wang et al. 2020a; Wolde-Rufael and Mulat-Weldemeskel 2021) investigate the effectiveness of environmental policy stringency in reducing CO2 emissions. Stringency is associated with a specific pollution price imposed by environmental policies. The OECD computes a specific environmental policy stringency index (EPSI), combining market-based (taxation, trading schemes, deposit and refund schemes, etc.) with non-market-based policies (standards and limitations, research and development expenditures, etc.). 3 While Wang et al. (2020a) investigated the EPSI's impact on air pollution, with a focus on different dimensions of air pollution in 23 OECD countries from 1990 to 2015, more recently Wolde-Rufael and [START_REF] Wolde-Rufael | Do environmental taxes and environmental stringency policies reduce CO2 emissions? Evidence from 7 emerging economies[END_REF] have tested the U-shaped relationship between CO2 emissions and EPSI for a set of seven emerging economies.

We add to this narrow strand of the literature, and we analyse the non-linear and asymmetric effect of environmental policy stringency on carbon emissions in a set of 32 countries, using OECD data and the fixed-effect quantile panel data approach by [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF].

Our empirical investigation covers the period from 1990 to 2015.

Consequently, we contribute to the existing literature as follows. First, we argue that the environmental policy stringency effect on CO2 emissions is influenced by the emission level recorded in each analysed country. This way, we can shed light on the mixed findings reported so far in the literature, showing either a significant (e.g. Zhao et al. 2015) or an insignificant (e.g. [START_REF] Niedertscheider | Austrian climate policies and GHG-emissions since 1990: What is the role of climate policy integration?[END_REF]) impact of environmental regulation on carbon emissions. Indeed, the impact of policy stringency on CO2 emissions is asymmetric, being influenced by the pollution level. This is because policy measures should target specific pollution sources and their magnitude should be correlated with the pollution intensity. However, we cannot say a priori whether the increase in policy stringency will lead to a stronger reduction in environmental degradation in the countries with lower or higher pollution levels. On the one hand, if the emission levels record higher dynamics (the case of emerging economies), the authorities will be forced to adopt more restrictive policy measures to prevent environmental degradation. On the other hand, the authorities from the countries which are exposed to additional environmental constraints as regional targets or policies (the EU case), even in the presence of negative dynamics of carbon emissions, might be forced to take action and enhance the policy stringency. If this is the case, the environmental regulations have a more powerful impact in the countries with lower levels of carbon emissions. [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF] approach will allow us to verify and test these hypotheses. As far as we know, this is the first paper which resorts to a fixed-effect quantile panel data approach to investigate the relationship between environmental policy stringency and CO2 emissions.

Second, we perform a series of analyses to see whether the asymmetric effect of EPSI on CO2 emissions also manifest inside the EU group of OECD member countries or whether this effect is stronger after the adoption of the Kyoto protocol. For example, [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF] has shown that the entry into force of the Kyoto protocol has had no significant effect on carbon emissions in EU countries, whereas the "Climate and Energy Package 2020" adopted in 2009 has had only a marginal effect on CO2 emissions.

Third, we perform a deeper analysis of the EU sample of OECD member states, and we split the group into two parts, considering the countries at risk and not at risk of achieving the 20-20-20 target. We posit that countries that are in a position to miss the target over the analysed time horizon will take more vigorous actions to prevent environmental degradation. Finally, in line with previous papers on this topic, we check the environmental Kuznets curve (EKC) and the pollution haven hypotheses(PHH). To this end, similar to [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF] and [START_REF] Apergis | Testing environmental Kuznets curve hypothesis in Asian countries[END_REF], we address the endogeneity effect between emissions and foreign direct investment (FDI). Moreover, we posit that the endogeneity effect might also manifest between carbon emissions and environmental policy stringency. Indeed, if carbon emissions are high, this element could force the authorities to impose more stringent policy measures.

To preview our findings, we show that policy stringency has a negative impact on CO2 emissions. That is, more restrictive environmental policies and measures contribute to a reduction in emissions. Interestingly, we document that environmental regulations and restrictions have a stronger impact on carbon emissions in countries with a lower level of emissions (i.e. EU countries). Our results thus confirm the findings by [START_REF] Hashmi | Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation[END_REF] and Wang et al. (2020a), showing the negative impact of EPSI on carbon emissions in OECD countries. However, as a new contribution, we show that the impact is asymmetric and more powerful for lower quantiles, that is, for countries with a lower level of emissions. This result proves the efficiency of EU environmental regulations compared to the effect of similar regulations in other OECD member states.

The rest of the paper presents a review of the literature (Section 2), the data and methodology (Section 3), the main empirical findings (Section 4) and the robustness checks (Section 5). The last section concludes with the asymmetric impact of environmental policy stringency on carbon emissions.

Literature review

Acknowledgment of environmental degradation brought stringent actions from different countries around the world that introduced policy stringency instruments (e.g. environmental taxes, trading schemes, emission standards, etc.) in response to environmental challenges [START_REF] Machiba | Eco-innovation for enabling resource efficiency and green growth: Development of an analytical framework and preliminary analysis of industry and policy practices[END_REF]. One of the strictness measures was developed by [START_REF] Botta | Measuring environmental policy stringency in OECD countries: A composite index approach[END_REF] through the environmental policy stringency index, distinguishing between market-based instruments and non-market ones. EPSI focuses on climate and air policies in key upstream sectors and combines information on selected environmental policies to create a composite measure of relative policy across countries over a period of time, which represents its main advantage (for additional explanations, please refer to [START_REF] Botta | Measuring environmental policy stringency in OECD countries: A composite index approach[END_REF]. The use of EPSI is based on different assumptions. The first assumption is that the omission of certain types of instruments creates heterogeneity between countries, which makes comparison of their stringency across time and eventually across countries problematic. Thus, in the recent research, there are some interpretations of EPSI in terms of multidimensionality, sampling, identification (and enforcement) and lack of data (Brunel and Levinson 2013;[START_REF] Koźluk | Environmental policies and productivity growth: A critical review of empirical findings[END_REF].

The second assumption can prove that the stringency of environmental policies encourages green innovation and works to set international standards to improve economic performance.

In this line, the research conducted by [START_REF] Ahmed | Environmental policy stringency, related technological change and emissions inventory in 20 OECD countries[END_REF], [START_REF] Martínez-Zarzoso | Does environmental policy stringency foster innovation and productivity in OECD countries?[END_REF], Wang et al. (2020a[START_REF] Wang | Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach[END_REF], [START_REF] Ouyang | Environmental regulation, economic growth and air pollution: Panel threshold analysis for OECD countries[END_REF] resorted to policy instruments for innovation, productivity, technological innovation and green growth.

The applicability of EPSI has therefore caught the attention of researchers and was employed in investigating the connection between climate change and economic growth (e.g.

De [START_REF] De Angelis | Climate change and economic growth: The role of environmental policy stringency[END_REF] or in assessing the impact of environmental regulations in reducing greenhouse gases (e.g. [START_REF] Ahmed | A predictive analysis of CO2 emissions, environmental policy stringency, and economic growth in China[END_REF]. However, recent literature strongly emphasises that the unavailability of a reliable measure of environmental policy strictness generates an asymmetric response to environmental issues among countries [START_REF] Galeotti | Measuring environmental policy stringency: Approaches, validity, and impact on environmental innovation and energy efficiency[END_REF]. Therefore, the OECD measure of policy stringency represents a reliable indicator to perform cross-country analyses. Figure 1 describes the construction logic of the EPSI index.

Fig. 1 The OECD Environmental Policy Stringency Index Source: https://www.oecd.org/economy/greeneco/how-stringent-are-environmental-policies.htm

The effectiveness of environmental regulations and policies

The role of global political debates on environmental degradation is to search for solutions by identifying the best policy mechanisms for a sustainable environment. Following these political debates, some research has arisen that is closely linked to environmental policies [START_REF] Boyd | Energy taxation as a policy instrument to reduce CO2 emissions: A net benefit analysis[END_REF][START_REF] Ma | How government regulation impacts on energy and CO2 emissions performance in China's mining industry[END_REF][START_REF] Simões | Cost of energy and environmental policy in Portuguese CO2 abatement-scenario analysis to 2020[END_REF][START_REF] Yin | The effects of environmental regulation and technical progress on CO2 Kuznets curve: An evidence from China[END_REF]Zhao et al. 2015), economic growth modelling [START_REF] Pao | Modeling the CO2 emissions, energy use, and economic growth in Russia[END_REF], the impact of pollution on the environment and energy consumption [START_REF] Benvenutti | The impact of CO2 mitigation policies on light vehicle fleet in Brazil[END_REF][START_REF] Li | The aggregate effect of air pollution regulation on CO2 mitigation in China's manufacturing industry: An econometric analysis[END_REF][START_REF] Mao | Achieving CO2 emission reduction and the cobenefits of local air pollution abatement in the transportation sector of China[END_REF].

The effectiveness of environmental regulations might be explained by two driving forces, namely the political willingness to achieve the environmental targets and the trade-off between economic and environmental short-run objectives.

First, the implementation of environmental policies is closely linked to the political parties in power. For example, Asafu-Adjaye and Mahadevan (2013) underlined the fact that a focus of the Australian government on distortionary state and federal taxes, such as income tax, savings tax, property tax and indirect taxes, may negate the efficiency gains of the emissions trading scheme (ETS). In Austria, [START_REF] Niedertscheider | Austrian climate policies and GHG-emissions since 1990: What is the role of climate policy integration?[END_REF] indicated some concerns regarding the future of climate policy integration (CPI) due to the perpetuation of the conflict "economic growth versus climate protection" by political parties. The authors showed that political parties are closely connected to the business environment, which negatively affects the implementation of CO2 emission decisions and policies. Further, policy adjustments also influence the impact of economic and social factors on CO2 emissions [START_REF] Huo | Analysis of influencing factors of CO2 emissions in Xinjiang under the context of different policies[END_REF].

Second, the economic interest of some countries might affect the effectiveness of their environmental regulations. This is the case for emerging market economies, which record high economic growth rates. In this context, one book in the literature under review addressed the case of China, analysing the effectiveness of environmental regulations in specific industries or its overall effect on climate change. In this line, [START_REF] Ma | How government regulation impacts on energy and CO2 emissions performance in China's mining industry[END_REF] analysed whether government regulations influence the efficiency of the mining sector in China and identify heterogeneity in the effect of regulations according to property type. The authors stated that the absence of an effective incentive, a monitoring mechanism and a deviation from the profit maximisation objective hinders eco-efficient performance. They proposed market-oriented regulatory instruments, such as tax breaks and subsidies, for private enterprises, and for state-owned enterprises, command-and-control instruments combined with appropriate target-based regulations. Similarly, [START_REF] Xu | The impacts on CO2 emission reduction and haze by coal resource tax reform based on dynamic CGE model[END_REF] have pointed out that coal tax policy reform is conducive to promoting reductions in emissions and environmental benefits, but the impact is different when different tax rates are used. The 5% tax rate on coal resources can effectively inhibit excessive resource consumption, improve resource efficiency and reduce environmental damage, which can be a reasonable choice to reduce CO2 emissions in China. [START_REF] Wenbo | Assessing the efficiency of China's environmental regulation on carbon emissions based on Tapio decoupling models and GMM models[END_REF] analysed in their turn the relationship between CO2 emissions and energy consumption in 30 regions of China between 2004-2015, using Tapio decoupling models, GMM differential methods and peak forecasting models. They controlled for the role of environmental regulations and discovered an inverted U-shaped relationship between environmental regulations and CO2 emissions. In the same spirit, Zhao et al. ( 2015) conducted an empirical study on 137 power plants in China and performed a micro-level analysis of the role of environmental regulations.

They concluded that market-based regulations and government subsidies help to reduce the CO2 emissions of China's power plants. In contrast, [START_REF] Cheng | The emissions reduction effect and technical progress effect of environmental regulation policy tools[END_REF] showed that command-andcontrol policy tools are conducive to emissions reduction, but their effects on technical progress are not significant; market-based policy tools are conducive to technical progress, but their effects on emissions reduction are relatively weak.

A different approach has been taken by [START_REF] Mao | Achieving CO2 emission reduction and the cobenefits of local air pollution abatement in the transportation sector of China[END_REF], who compared the effectiveness of various policy instruments, such as CO2 tax, fuel tax and energy tax, on influencing carbon emissions. Among these instruments, energy and fuel taxes are the two most promising instruments for CO2 emission intensity reduction, and subsidies are the least promising options.

The authors also show that CO2 tax could be an effective policy tool. With a focus on three types of environmental policies in China (energy structure policies, energy efficiency improving policies and production scale policies), [START_REF] Li | Assessing the policy impacts on non-ferrous metals industry's CO2 reduction: Evidence from China[END_REF] demonstrated that CO2 reduction benefits a lot from cleaner electricity generation. The connection between environmental policies and CO2 emissions was recorded for all categories of policies.

Other similar studies have focused on Chile [START_REF] Mardones | Effectiveness of a CO2 tax on industrial emissions[END_REF], Malaysia [START_REF] Yahoo | Employing a CGE model in analyzing the environmental and economy-wide impacts of CO2 emission abatement policies in Malaysia[END_REF] or BRICS countries as a group [START_REF] Adedoyin | Modelling coal rent, economic growth and CO2 emissions: Does regulatory quality matter in BRICS economies?[END_REF], and have underlined the importance of environmental taxes, abatement policies and coal rents on the reduction of CO2 emissions. Table A1 (Appendix), presents a comparison of early studies focusing on the effectiveness of environmental regulations and policies.

Environmental policy stringency and CO2 emissions in OECD countries

As previously mentioned, a reliable approach to identify the effectiveness of environmental regulations and policies on environmental pollution is to consider an aggregate policy stringency index. OECD countries have worked together to establish policy measures to reduce the impact of economic activities that have negative effects on society and environment [START_REF] Johnstone | Environmental policy stringency and technological innovation: Evidence from survey data and patent counts[END_REF]. One measure was the construction of EPSI in 2014. Since then, several recent empirical papers have addressed the non-linear effect of environmental regulations on carbon emissions. 2019) found an inverted U-shaped relationship between the environmental policy stringency index and PM2.5 emissions for 30 OECD countries. A similar result was reported by Wolde-Rufael and Mulat-Weldemeskel (2021), who documented the existence of an inverted U-shaped relationship between CO2 emissions and EPSI, suggesting that it takes time for environmental policy stringency measures to be effective. Finally, [START_REF] Wang | Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach[END_REF] analysed the strictness of environmental regulatory policies and measured the growth of ecological productivity using an extended SBM-DDF approach. The authors validated the idea that environmental policy has a positive impact on increasing ecological productivity to a certain level of strictness.

Most of these recent papers posit that the relationship between EPSI and environmental pollution is non-linear. Nevertheless, none of these papers investigated the asymmetric response of CO2 missions to the EPSI level. To overcome this limitation, we proceed to a panel fixed regression in quantiles to see the effectiveness of policy stringency in low, medium and high polluter OECD countries.

Data and methodology

Data

We use annual data from 1990 to 2015 (OECD statistics) to analyse the emissionsenvironmental regulation nexus, with a focus on 32 countries (Table A2-Appendix). EPSI represents our variable of interest, and it is used as a proxy for environmental regulations. These data are available until 2015 only. However, the time span we analysed is long enough to identify the effect of worldwide major changes in terms of environmental policies and regulations at a global level (e.g. the entry into force of the Kyoto protocol) or the effect of environmental and climate change policies at the EU level (e.g. 20-20-20 targets) on carbon emissions.

In line with previous papers assessing the impact of environmental regulations on air pollution levels (e.g. [START_REF] Albulescu | FDI, income, and environmental pollution in Latin America: Replication and extension using panel quantiles regression analysis[END_REF][START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF][START_REF] Ma | How government regulation impacts on energy and CO2 emissions performance in China's mining industry[END_REF][START_REF] Niedertscheider | Austrian climate policies and GHG-emissions since 1990: What is the role of climate policy integration?[END_REF][START_REF] Sapkota | Foreign direct investment, income, and environmental pollution in developing countries: Panel data analysis of Latin America[END_REF][START_REF] Wenbo | Assessing the efficiency of China's environmental regulation on carbon emissions based on Tapio decoupling models and GMM models[END_REF]Zhao et al. 2015), we focus on the CO2 emissions (metric tons per capita). These data are extracted from the World Development Indicators database (World Bank). The explanatory variables come from the same database and are represented by the level of GDP per capita, expressed in natural log (which allows us to test the EKC hypothesis), the net inflows of FDI as a percentage of GDP (necessary to test the PHH), the energy use (kg of oil equivalent per capita in natural log) and the renewable energy consumption as a percentage of total final energy consumption.

A series of other control variables have been advanced in the literature to check the determinants of CO2 emissions (i.e. human capital, gross fixed capital formation, population density, unemployment rate, etc.). However, as [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF] have shown, the impact of these variables is either reduced or insignificant. Therefore, we have focused on the main drivers of carbon emissions, which are the energy consumption and economic growth levels while placing special emphasis on the role of environmental regulations and policies. We also address the potential impact of using renewable energy on CO2 emissions. Most previous studies have shown that this impact is marginal (e.g. Aliprandi et al. 2016;[START_REF] Chen | CO2 emissions, economic growth, renewable and nonrenewable energy production and foreign trade in China[END_REF][START_REF] Dong | CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions[END_REF], although the use of renewables represents a promising solution to control and mitigate air pollution levels. The descriptive statistics of our variables are presented in

Methodology

We ran a panel regression model in quantiles to account for non-linearities and asymmetry in the tested relationship, and we used [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF] approach with fixed effects to account for unobserved covariates. The general equation we tested is (we used the first lag of explanatory variables to avoid any endogeneity bias):

𝑐𝑜 2𝑖𝑡 = 𝛼 0 + 𝛼 1 𝑒𝑝𝑠𝑖 𝑖𝑡-1 + 𝛼 2 𝑍 𝑖𝑡-1 + 𝜇 𝑖 + 𝛾 𝑡 + 𝜀 𝑖𝑡 , (1)
where 𝑐𝑜 2𝑖𝑡 are the carbon emissions (metric tons per capita) of the country 𝑖 in the year 𝑡; 𝛼 0 represents the intercept; 𝑒𝑝𝑠𝑖 𝑖𝑡-1 is the environmental policy stringency index; 𝑍 𝑖𝑡-1 is the vector of control variables represented by GDP per capita, FDI, energy use (kg of oil equivalent per capita) and the renewable energy consumption (% of total final energy consumption); 𝜇 𝑖 are the time-invariant firm specific effects, 𝛾 𝑡 are the time-specific effects and 𝜀 𝑖𝑡 are the error terms.

A series of panel quantile fixed-effects models are proposed in the literature (e.g. Canay 2011; [START_REF] Galvao | Quantile regression for dynamic panel data with fixed effects[END_REF][START_REF] Koenker | Quantile regression for longitudinal data[END_REF][START_REF] Lamarche | Robust penalized quantile regression estimation for panel data[END_REF][START_REF] Rosen | Set identification via quantile restrictions in short panels[END_REF]. These models might be described as follows:

𝑌 𝑖𝑡 = 𝑋 𝑖𝑡 ′ 𝜃(𝑈 𝑖𝑡 ) + 𝛼 𝑖 , (1)
where 𝑡 = 1, . . . , 𝑇; 𝑖 = 1, . . . , 𝑛; 𝑌 𝑖𝑡 and 𝑋 𝑖𝑡 represent observable variables; 𝑈 𝑖𝑡 is an unobservable component; 𝑋 𝑖𝑡 ′ includes a constant term and 𝜃(𝜏) is the parameter of interest.

If the function 𝜏 → 𝑋 ′ 𝜃(𝜏) is increasing in 𝜏 ∈ (0,1), for an observable 𝛼 𝑖 , we have

𝑃[𝑌 𝑖𝑡 ≤ 𝑋 𝑖𝑡 ′ 𝜃(𝑈 𝑖𝑡 ) + 𝛼 𝑖 |𝑋 𝑖 , 𝛼 𝑖 ] = 𝜏, (2)
where 𝑈 𝑖𝑡 ~𝑈[0,1], conditional on 𝑋 𝑖 = (𝑋 𝑖1 ′ , . . . , 𝑋 𝑖𝑇 ′ )′ and 𝛼 𝑖 .

For all these models, the challenge is to correctly identify 𝜃(𝜏). If 𝑄 𝑌 (𝜏|𝑋) is the 𝜏quantile of a random variable 𝑌 conditional on 𝑋 and 𝑒 𝑖𝑡 (𝜏) ≡ 𝑋 𝑖𝑡 ′ [𝜃(𝑈 𝑖𝑡 ) -𝜃(𝜏)], equation (

becomes

𝑌 𝑖𝑡 = 𝑋 𝑖𝑡 ′ 𝜃(𝑈 𝑖𝑡 ) + 𝛼 𝑖 + 𝑒 𝑖𝑡 (𝜏). (3)
By assuming that 𝛼 𝑖 is a location shift, [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF] proved that 𝜃(𝜏) is identified for 𝑇 ≥ 2 under independence restrictions and existence of moments. In fact, [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF] assumed that only 𝜃(𝜏) and 𝑒 𝑖𝑡 (𝜏) depend on 𝜏 and transforms equation (1) as follows:

𝑌 𝑖𝑡 = 𝑋 𝑖𝑡 ′ 𝜃𝜇 + 𝛼 𝑖 + 𝑢 𝑖𝑡 , 𝐸(𝑢 𝑖𝑡 |𝑋 𝑖 , 𝛼 𝑖 ) = 0. (4)
This transformation represents the novelty of [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF]

where

𝔼 𝑛𝑇 (•) ≡ (𝑛𝑇) -1 ∑ ∑ (•) 𝑛 𝑖=1 𝑇 𝑡=1
.

Results

The main empirical results are presented in Table 2. Firstly, we noticed that the impact of EPSI on carbon emission was negative and very significant, except for the extreme upper quantiles. This means that an increase of environmental policy stringency results in a decrease in CO2 emissions. The findings are therefore consistent with those reported by [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF] for the EU countries and by Zhao et al. (2015) for China, but they contradict those reported by [START_REF] Niedertscheider | Austrian climate policies and GHG-emissions since 1990: What is the role of climate policy integration?[END_REF] for Australia. As a new contribution, we have shown that environmental stringency has a more powerful impact in the countries with lower levels of carbon emissions (that is, for the lower quantiles) compared to the high polluters. In line with most previous papers on the topic, we noted the importance of energy use for CO2 emissions, and we validated the EKC hypothesis at all quantiles, except for the extreme upper quantiles. The effect of inward FDI was insignificant, a result that explains the mixed findings previously reported by the literature. For example, [START_REF] Sapkota | Foreign direct investment, income, and environmental pollution in developing countries: Panel data analysis of Latin America[END_REF] and [START_REF] Albulescu | FDI, income, and environmental pollution in Latin America: Replication and extension using panel quantiles regression analysis[END_REF] found evidence for the PHH in the case of Latin American countries, whereas [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF] documented opposite findings for EU countries and validated the pollution halo hypothesis.

The impact of the share of renewables in total electricity consumption had a negative impact on emissions, although the effect was reduced. This impact is stronger for lower quantiles, that is, for lower emissions levels. Our results validate most of the previous findings underlining the positive role of renewables in the reduction of carbon emissions (e.g. [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF]Aliprandi et al. 2016;[START_REF] Chen | CO2 emissions, economic growth, renewable and nonrenewable energy production and foreign trade in China[END_REF][START_REF] Dong | CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions[END_REF][START_REF] Inglesi-Lotz | The role of renewable versus nonrenewable energy to the level of CO2 emissions a panel analysis of sub-Saharan Africa's big 10 electricity generators[END_REF][START_REF] Sinha | Estimation of environmental Kuznets Curve for CO2 emission: Role of renewable energy generation in India[END_REF][START_REF] Zoundi | CO2 emissions, renewable energy and the environmental Kuznets Curve, a panel cointegration approach[END_REF].

Our sample contains data on a large set of OECD countries but also information about carbon emissions in several emerging countries. In addition, several international treaties were concluded to fight against climate change during the analysed timespan. Therefore, to check the robustness of our findings, we first assessed the impact of the entry into force of Kyoto protocol, namely in 2005. To this end, we worked on two subsamples covering the periods 1990 to 2004 and 2004 to 2015. Second, we focused on the EU countries sample. It is well known that the EU acts as an international leader in fighting against greenhouse gas (GHG) emissions.

The EU established precise targets in terms of GHG emissions, renewable energy, and energy efficiency to be achieved in 2020 (the so-called 20-20-20 targets). However, the progressive monitoring of reaching those targets showed that several countries were in difficulty and risked missing the targets. For example, during the 2016 monitoring of the European Commission4 , in the case of four EU countries (Austria, Belgium, Denmark and Ireland), the projected progress underlined some issues in terms of GHG emissions targets. Consequently, we also compared the impact of environmental policy stringency for EU countries not at risk of achieving the 20-20-20 target in terms of GHG emissions with those at risk of missing the targets.

Robustness check

The impact of the Kyoto protocol's entry into force

The Kyoto protocol was adopted in 2003, and its effective entry into force happened in 2005. Consequently, we first looked at the pre-Kyoto period and surprisingly noticed that the sign of EPSI was positive and significant, meaning that previous environmental regulations were completely inefficient in fighting against climate change. The results reported in Table 3 show that only energy consumption and the use of renewables had an effective impact on CO2 emissions. of the Kyoto protocol enhanced the environmental policy stringency, with a negative impact on carbon emissions. Again, the impact was higher for lower polluter countries. (5.133) (10.33) Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 296 observations; (iv) EPSIenvironmental policy stringency index, GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

These results partially contradict the findings reported by [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF], who documented a significant role of EU environmental regulations in carbon emissions decreasing, but no significant effects for the entry into force of the Kyoto protocol. The discrepancies might be explained by the fact that the Kyoto protocol has not determined an abrupt decline in CO2 emissions and the dummy variable used in [START_REF] Albulescu | CO2 emissions, renewable energy production and environmental regulation in the EU countries[END_REF] was not efficient in this case.

Moreover, the country sample was different. However, in line with this study, we confirmed the EKC hypothesis and the role of renewables for reducing carbon emissions.

A focus on the EU situation

To get further insight into the role of environmental regulations on carbon emissions, we focused on EU countries (Table 5). Our sample included 18 EU countries with different levels of economic development and energy consumption. These findings are very similar to those reported in Table 2, proving their robustness. However, the EKC hypothesis was only documented for the middle quantiles in this case. In addition, for the EU countries, the effect of environmental policy stringency was higher for the upper quantiles associated with higher polluters. A similar robustness check was conducted for the OECD countries sample (Table A3 -Appendix) and confirmed the main results. The last set of estimations compared the two EU countries subsamples: not at risk and at risk of achieving the 20-20-20 target in terms of GHG emissions. It is expected that countries with a higher risk of missing the targets will enhance policy stringency in order to achieve the targets for 2020. Indeed, if we compare the results from Tables 6 and7, we observe that EPSI had a negative impact on CO2 emissions in both cases, whereas the effect was stronger for the countries at risk of achieving the targets. There is mixed evidence regarding the EKC hypothesis, while the FDI impact was insignificant. For both samples, energy use played a major role in carbon emissions, and the renewables contributed to the reduction in CO2 emissions.

Conclusions and policy implications

Environmental degradation requires immediate action. Actions can be grouped into two main categories, namely green innovation and adequate environmental regulations and climate policies necessary to reduce pollution levels.

Most of the existing empirical works have focused on air pollution and the role of environmental regulations, such as standards, taxes or penalties. However, only a few works have addressed the effectiveness of environmental regulations, with mixed findings. A focus on the impact of policy stringency is almost non-existent. In this context, our paper contributes to the literature by assessing the asymmetric and non-linear impact of environmental policy stringency on CO2 emissions in a set of OECD countries. To this end, we rely on [START_REF] Canay | A simple approach to quantile regression for panel data[END_REF] panel regression in quantiles.

Our main findings show that environmental policy stringency has led to a decrease in CO2 emissions in OECD countries. However, as expected, this effect has been asymmetric.

Likewise, we show that EPSI has a stronger impact on emissions in countries with lower levels of carbon emissions (that is, at lower quantiles). We also document the importance of energy use and revenue on CO2 emissions levels. However, our findings invalidate the PHH and show that the impact of the share of renewables in the total electricity consumption has only a marginal negative impact on emissions.

The robustness check we performed for a set of 18 EU countries validated the main findings of the paper. Further, additional tests indicated that EPSI negatively impacts the emissions level only during the implementation of the Kyoto protocol, whereas in the pre-Kyoto period, the environmental regulations were completely inefficient. Moreover, policy stringency measures are more effective in EU countries at higher risk of missing the 20-20-20 target in terms of GHG emissions.

Our findings have different implications for policymakers. First, policy stringency measures should be correlated with emissions levels to become effective. EU countries have succeeded in lowering air pollution levels by imposing higher levels of stringency and clear emission targets. If the risk of missing the target is high, environmental policy stringency should increase. Second, in order to record a negative trend in carbon emissions on a global level, emerging economies should follow the EU model. Energy consumption still represents the main driver of emissions, whereas the use of renewables has only a marginal effect on air pollution levels.

The limitation of this papers resides in the data availability, which did not allow us to investigate the recent period. However, the analysis can be developed by making a comparison between market-based and non-market-based policies or by assessing the effectiveness of specific regulations used for environmental protection.

  Likewise, the paper byÁlvarez et al. (2017) explores the impact of improvements in energy innovation-an EPSI component-on GHG emissions in 28 OECD countries for the period of 1990-2014. They showed that the effect of energy innovation on environmental correction requires a significant time lag. Similar,[START_REF] Hashmi | Dynamic relationship among environmental regulation, innovation, CO2 emissions, population, and economic growth in OECD countries: A panel investigation[END_REF] examined the effects of environmental regulation and innovation on the carbon emission reduction of OECD countries during the period 1999-2014 and aimed to optimise market-based instruments, such as patents and carbon pricing, for the implementation of efficient and cost-effective climate change policies. At the same time, Ahmed (2020) has argued that stringent environmental policies coupled with environmentally friendly innovation are the impetus for sustainable development. A different approach was taken by Ahmed and Ahmed (2018) who considered environmental policy stringency and economic activity as controlling variables in forecasting CO2 emissions in China. Sadik-Zada and Ferrari (2020) employed in their turn the EPSI to test the EKH and PHH in a set of 26 OECD countries over the period 1995 to 2011. More recently, Ouyang et al. (

Table 1 .Table 1 .

 11 Descriptive statistics CO2-carbon emission, EPSI-Environmental Policy Stringency Index, GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

	Variables	Observations	Mean	Std. Dev.	Min	Max
	CO2	823	8.283	4.116	0.709	20.17
	EPSI	776	1.584	0.963	0.208	4.133
	GDP	824	9.944	1.063	6.355	11.42
	GDP 2	824	100.0	19.73	40.38	130.5
	FDI	823	3.776	7.396	-15.83	86.58
	EU	826	8.040	0.649	5.858	9.042
	RTEC	832	16.97	15.84	0.441	61.37
	Notes:

  √𝑛𝑇) is obtained, with 𝛼 ̂𝑖 ≡ 𝐸 𝑇 [𝑌 𝑖𝑡 -𝑋 𝑖𝑡 ′ 𝜃 ̂𝜇]. In the second step, the author defines 𝑌 ̂𝑖 ≡ 𝑌 𝑖𝑡 -𝛼 ̂𝑖, whereas 𝜃 ̂𝜇 becomes

	𝜃 ̂𝜇 ≡ argmin 𝜃∈Θ	𝔼 𝑛𝑇 [𝜌 𝜏 (𝑌 ̂𝑖𝑡 -𝑋 𝑖𝑡 ′ 𝜃 ̂𝜇],

approach, which allows the author to compute the two-step estimator 𝜃 ̂𝜇. In the first step, a consistent estimator of 𝛼 𝑖 (√𝑇) and 𝜃𝜇 (

Table 2 .

 2 Panel conditional quantile regression-entire sample

	Quantiles		Lower				Middle			Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65	0.75	0.85	0.95
	EPSI -0.248*** -0.297*** -0.219*** -0.214*** -0.212*** -0.202*** -0.196*** -0.107*** -0.098**	0.178**
		(0.079)	(0.065)	(0.044)	(0.029)	(0.028)	(0.033)	(0.038)	(0.041)	(0.045)	(0.070)
	GDP	4.866*** 3.357*** 2.331*** 2.378*** 2.266*** 2.565*** 2.086*** 1.754*** 1.199** 1.038
		(0.963)	(0.785)	(0.539)	(0.354)	(0.337)	(0.397)	(0.461)	(0.501)	(0.546)	(0.850)
	GDP 2 -0.292*** -0.206*** -0.151*** -0.155*** -0.149*** -0.166*** -0.142*** -0.128*** -0.100*** -0.094**
		(0.051)	(0.042)	(0.029)	(0.019)	(0.018)	(0.021)	(0.025)	(0.027)	(0.029)	(0.045)
	FDI	0.011	0.011	0.005	0.004	0.004	0.001	0.000	-0.004	-0.003	0.006
		(0.009)	(0.008)	(0.005)	(0.003)	(0.003)	(0.004)	(0.004)	(0.005)	(0.005)	(0.008)
	EU	2.896*** 3.010*** 3.064*** 3.236*** 3.277*** 3.295*** 3.382*** 3.555*** 3.616*** 3.866***
		(0.180)	(0.146)	(0.101)	(0.066)	(0.063)	(0.074)	(0.086)	(0.094)	(0.102)	(0.159)
	RTEC -0.042*** -0.048*** -0.048*** -0.046*** -0.044*** -0.040*** -0.040*** -0.038*** -0.037*** -0.029***
		(0.005)	(0.004)	(0.003)	(0.002)	(0.002)	(0.002)	(0.002)	(0.003)	(0.003)	(0.004)
	𝛼 0	-34.12*** -27.92*** -23.55*** -24.82*** -24.56*** -26.00*** -24.15*** -23.57*** -21.26*** -22.35***
		(4.432)	(3.615)	(2.482)	(1.632)	(1.552)	(1.826)	(2.121)	(2.307)	(2.514)	(3.914)
	Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 754 observations; (iv) EPSI-
	environmental policy stringency index, GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows,
	EU-energy use, RTEC-renewable to total electricity consumption.

Table 3 .

 3 Panel conditional quantile regression-entire sample before Kyoto protocol GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

	Quantiles		Lower				Middle			Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65	0.75	0.85	0.95
	EPSI	0.085	0.193** 0.124*	0.163** 0.122** 0.092	0.230*** 0.355*** 0.403*** 0.495***
		(0.182)	(0.088)	(0.072)	(0.066)	(0.061)	(0.058)	(0.062)	(0.077)	(0.108)	(0.182)
	GDP	-0.505	-0.616	-0.704	-0.483	-0.579	-0.315	-0.045	-0.099	-0.191	0.422
		(1.235)	(0.596)	(0.488)	(0.446)	(0.417)	(0.393)	(0.420)	(0.522)	(0.735)	(1.234)
	GDP 2	0.023	0.034	0.037	0.024	0.032	0.018	0.001	0.003	0.007	-0.037
		(0.067)	(0.032)	(0.027)	(0.024)	(0.023)	(0.021)	(0.023)	(0.028)	(0.040)	(0.067)
	FDI	0.012	0.011	0.007	-0.002	-0.004	-0.001	0.001	-0.001	0.010	0.033*
		(0.018)	(0.009)	(0.007)	(0.006)	(0.006)	(0.006)	(0.006)	(0.008)	(0.011)	(0.018)
	EU	0.666*** 0.575*** 0.762*** 0.842*** 0.788*** 0.782*** 0.859*** 0.907*** 0.991*** 1.318***
		(0.242)	(0.117)	(0.096)	(0.088)	(0.082)	(0.077)	(0.082)	(0.102)	(0.144)	(0.242)
	RTEC	0.019*** 0.011*** 0.013*** 0.015*** 0.012*** 0.011*** 0.014*** 0.015*** 0.014*** 0.016**
		(0.007)	(0.003)	(0.003)	(0.002)	(0.002)	(0.002)	(0.002)	(0.003)	(0.004)	(0.007)
	𝛼 0	4.331	5.560** 4.848** 3.463*	4.268** 3.245*	1.564	1.491	1.429	-2.790
		(5.667)	(2.734)	(2.240)	(2.048)	(1.913)	(1.801)	(1.928)	(2.394)	(3.374)	(5.660)
	Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 455 observations; (iv) EPSI-
	environmental policy stringency index,

Table 4 ,

 4 however, presents a different situation. It clearly shows that the entry into force

Table 4 .

 4 Panel conditional quantile regression-entire sample after Kyoto protocol

	Quantiles		Lower				Middle			Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65	0.75	0.85	0.95
	EPSI -0.414*** -0.357*** -0.472*** -0.426*** -0.425*** -0.457*** -0.455*** -0.364*** -0.335*** -0.295*
		(0.113)	(0.103)	(0.087)	(0.065)	(0.060)	(0.061)	(0.077)	(0.078)	(0.088)	(0.177)
	GDP	13.11*** 13.08*** 12.05*** 11.98*** 11.65*** 11.03*** 10.45*** 10.52*** 9.799*** 8.935***
		(1.414)	(1.296)	(1.086)	(0.820)	(0.754)	(0.770)	(0.970)	(0.978)	(1.100)	(2.215)
	GDP	-0.679*** -0.674*** -0.618*** -0.617*** -0.597*** -0.566*** -0.537*** -0.544*** -0.505*** -0.443***
		(0.074)	(0.068)	(0.057)	(0.043)	(0.039)	(0.040)	(0.051)	(0.051)	(0.057)	(0.116)
	FDI	0.010	0.006	0.005	0.003	0.000	-0.001	-0.004	-0.005	-0.007	-0.015
		(0.008)	(0.008)	(0.007)	(0.005)	(0.005)	(0.005)	(0.006)	(0.006)	(0.007)	(0.013)
	EU	-0.012	0.080	0.221	0.312** 0.316*** 0.432*** 0.504*** 0.639*** 0.647*** 0.411
		(0.220)	(0.201)	(0.169)	(0.127)	(0.117)	(0.120)	(0.151)	(0.152)	(0.171)	(0.344)
	RTEC -0.060*** -0.061*** -0.064*** -0.063*** -0.064*** -0.065*** -0.065*** -0.061*** -0.063*** -0.067***
		(0.006)	(0.006)	(0.005)	(0.004)	(0.003)	(0.003)	(0.004)	(0.004)	(0.005)	(0.010)
	𝛼 0	-53.16*** -53.83*** -49.83*** -49.81*** -48.43*** -46.14*** -43.57*** -44.73*** -41.36*** -36.56***
		(6.602)	(6.048)	(5.067)	(3.830)	(3.521)	(3.596)	(4.526)	(4.565)

Table 5 .

 5 Panel conditional quantile regression-EU countries sample GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

	Quantiles		Lower				Middle			Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65	0.75	0.85	0.95
	EPSI -0.261*** -0.264*** -0.189*** -0.231*** -0.239*** -0.269*** -0.294*** -0.302*** -0.307*** -0.238***
		(0.075)	(0.059)	(0.046)	(0.036)	(0.039)	(0.040)	(0.039)	(0.045)	(0.056)	(0.086)
	GDP	0.977	3.241	3.739	4.390** 4.665** 3.132	3.860*	2.976	3.338	-3.101
		(4.282)	(3.348)	(2.618)	(2.044)	(2.227)	(2.289)	(2.223)	(2.581)	(3.193)	(4.871)
	GDP 2 -0.055	-0.169	-0.199	-0.228** -0.241** -0.161	-0.196*	-0.151	-0.167	0.161
		(0.215)	(0.168)	(0.131)	(0.103)	(0.112)	(0.115)	(0.112)	(0.130)	(0.160)	(0.245)
	FDI	-0.005	-0.006	-0.009** -0.003	-0.004	-0.004	-0.005	-0.006	-0.008*	-0.010
		(0.007)	(0.005)	(0.004)	(0.003)	(0.004)	(0.004)	(0.004)	(0.004)	(0.005)	(0.008)
	EU	3.772*** 3.917*** 4.219*** 4.140*** 4.190*** 4.033*** 4.099*** 4.238*** 4.255*** 4.406***
		(0.235)	(0.184)	(0.144)	(0.112)	(0.122)	(0.126)	(0.122)	(0.142)	(0.175)	(0.267)
	RTEC -0.123*** -0.118*** -0.117*** -0.114*** -0.112*** -0.107*** -0.106*** -0.103*** -0.103*** -0.094***
		(0.006)	(0.005)	(0.004)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.004)	(0.007)
	𝛼 0	-25.61	-37.73** -42.09*** -44.96*** -46.73*** -38.00*** -42.15*** -38.84*** -40.88** -10.56
		(21.70)	(16.97)	(13.27)	(10.36)	(11.28)	(11.60)	(11.27)	(13.08)	(16.18)	(24.69)
	Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 410 observations; (iv) EPSI-
	environmental policy stringency index,

Table 6 .

 6 Panel conditional quantile regression-EU countries sample not at risk of achieving the 20-20-20 target in terms of GHG emissions, as projected in 2016 GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

	Quantiles		Lower				Middle			Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65	0.75	0.85	0.95
	EPSI -0.303*** -0.241*** -0.194*** -0.176*** -0.173*** -0.187*** -0.208*** -0.195*** -0.140** -0.098
		(0.073)	(0.071)	(0.052)	(0.040)	(0.038)	(0.039)	(0.039)	(0.048)	(0.058)	(0.084)
	GDP	-7.974*	-3.111	1.261	1.494	4.036*	4.450*	5.121** 6.959** 7.097*	9.542*
		(4.631)	(4.519)	(3.322)	(2.536)	(2.400)	(2.517)	(2.501)	(3.075)	(3.676)	(5.385)
	GDP 2	0.379	0.129	-0.102	-0.113	-0.238*	-0.259** -0.294** -0.388** -0.397** -0.523*
		(0.235)	(0.229)	(0.168)	(0.129)	(0.122)	(0.128)	(0.127)	(0.156)	(0.186)	(0.273)
	FDI	0.004	0.001	-0.001	-0.002	-0.003	-0.004	-0.005	-0.006	-0.008	-0.009
		(0.007)	(0.007)	(0.005)	(0.004)	(0.004)	(0.004)	(0.004)	(0.005)	(0.006)	(0.008)
	EU	3.511*** 3.707*** 4.073*** 4.105*** 4.029*** 4.043*** 4.060*** 4.133*** 4.352*** 4.751***
		(0.239)	(0.233)	(0.171)	(0.131)	(0.124)	(0.130)	(0.129)	(0.158)	(0.189)	(0.278)
	RTEC -0.084*** -0.085*** -0.082*** -0.082*** -0.079*** -0.075*** -0.072*** -0.067*** -0.065*** -0.056***
		(0.005)	(0.005)	(0.004)	(0.003)	(0.003)	(0.003)	(0.003)	(0.004)	(0.004)	(0.006)
	𝛼 0	21.92	-3.020	-26.434	-27.88** -40.00*** -42.15*** -45.36*** -54.86*** -57.01*** -72.03***
		(23.62)	(23.05)	(16.94)	(12.93)	(12.24)	(12.84)	(12.75)	(15.68)	(18.75)	(27.47)
	Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 318 observations; (iv) EPSI-
	environmental policy stringency index,

Table 7 .

 7 Panel conditional quantile regression-EU countries sample at risk of achieving the 20-20-20 target in terms of GHG emissions, as projected in 2016 GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

	Quantiles		Lower				Middle			Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65	0.75	0.85	0.95
	EPSI -0.339** -0.444*	-0.597*** -0.647*** -0.620*** -0.613*** -0.579*** -0.466*** -0.358	-0.647
		(0.145)	(0.234)	(0.149)	(0.129)	(0.126)	(0.158)	(0.176)	(0.176)	(0.266)	(0.596)
	GDP	1.119	87.51*	75.84*** 66.36*** 77.79*** 63.27** 62.69*	69.65** 69.61	34.68
		(27.91)	(45.25)	(28.68)	(25.00)	(24.27)	(30.57)	(33.97)	(33.92)	(51.43)	(115.1)
	GDP 2 -0.109	-4.165*	-3.594*** -3.141*** -3.679*** -2.990** -2.966*	-3.284** -3.285	-1.555
		(1.322)	(2.143)	(1.358)	(1.184)	(1.150)	(1.448)	(1.609)	(1.606)	(2.436)	(5.451)
	FDI	-0.000	-0.017	-0.017** -0.013** -0.011*	-0.010	-0.006	-0.012	-0.022*	-0.031
		(0.007)	(0.011)	(0.007)	(0.006)	(0.006)	(0.007)	(0.008)	(0.008)	(0.012)	(0.028)
	EU	7.458*** 6.805*** 7.073*** 7.201*** 7.045*** 7.079*** 7.043*** 7.102*** 7.658*** 7.850***
		(0.387)	(0.627)	(0.397)	(0.346)	(0.336)	(0.423)	(0.470)	(0.470)	(0.712)	(1.594)
	RTEC -0.121*** -0.133*** -0.124*** -0.121*** -0.115*** -0.113*** -0.111*** -0.124*** -0.134*** -0.127***
		(0.010)	(0.015)	(0.010)	(0.009)	(0.008)	(0.010)	(0.012)	(0.012)	(0.018)	(0.039)
	𝛼 0	-50.56	-504.1** -446.6*** -397.9*** -457.2*** -381.1** -377.4** -415.7** -419.6	-244.6
		(145.5)	(235.9)	(149.5)	(130.4)	(126.6)	(159.4)	(177.1)	(176.9)	(268.2)	(600.2)
	Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 92 observations; (iv) EPSI-
	environmental policy stringency index,

Table A3 .

 A3 Panel conditional quantile regression-OECD countries sample EPSI -0.162** -0.183*** -0.128*** -0.120*** -0.123*** -0.096*** -0.091*** -0.054 .104*** -0.113*** -0.113*** -0.111*** -0.108*** -0.106*** -0.103*** -0.102*** -0.099*** -0.096*** Notes: (i) *** p < 0.01, ** p < 0.05, * p < 0.1; (ii) standard error in parentheses; (iii) 611 observations; (iv) EPSIenvironmental policy stringency index, GDP-GDP per capita, GDP 2 -squared GDP per capita, FDI-net FDI inflows, EU-energy use, RTEC-renewable to total electricity consumption.

	615
	Quantiles		Lower			Middle				Upper
		0.05	0.15	0.25	0.35	0.45	0.55	0.65		0.75	0.85	0.95
											-0.057	0.144
		(0.076)	(0.062)	(0.043)	(0.029)	(0.028)	(0.032)	(0.035)	(0.038)	(0.049)	(0.106)
	GDP	5.773	-0.196	-1.931	-1.720	-0.842	-0.792	-0.312		-0.908	-2.037	-6.224
		(3.754)	(3.042)	(2.122)	(1.420)	(1.383)	(1.575)	(1.728)	(1.848)	(2.402)	(5.198)
	GDP 2 -0.319*	-0.021	0.066	0.053	0.008	0.006	-0.021		0.009	0.070	0.273
		(0.186)	(0.151)	(0.105)	(0.070)	(0.069)	(0.078)	(0.086)	(0.092)	(0.119)	(0.258)
	FDI	0.008	0.002	0.004	0.002	0.001	-0.001	-0.002		-0.004	-0.007	0.006
		(0.009)	(0.007)	(0.005)	(0.003)	(0.003)	(0.004)	(0.004)	(0.004)	(0.006)	(0.012)
	EU	3.374*** 3.622*** 3.764*** 3.846*** 3.961*** 4.008*** 4.113*** 4.166*** 4.091*** 4.305***
		(0.201)	(0.163)	(0.114)	(0.076)	(0.074)	(0.084)	(0.093)	(0.099)	(0.129)	(0.278)
	countries (0.005) -43.42** -15.11 RTEC -0(0.006) 𝛼 0 (18.63) (15.10)	1990-2017 (0.003) -7.486 -8.880 (0.002) (10.53) (7.05)	Static panel data models and dynamic GMM models (0.002) (0.002) -14.00** -14.56* -17.49** -14.81 Mixed evidence on the role of (0.003) (0.003) (0.004) (0.008) environmental regulations in -8.869 10.91 influencing the reduction of CO2 emissions in EU countries (6.868) (7.823) (8.579) (9.178) (11.92) (25.81)
	Galeotti et al.	19 OECD	1995-	Computing different		Indicators based on pollution
	(2020)		countries	2009		indicators of			abatement give rise to
						environmental policy		significantly different results
						stringency			than emission-based indicators or
									composite indexes.
	Hashmi and	29 OECD	1999-	Stochastic impacts by		An increase in environmentally
	Alam (2019)	countries	2014		regression on population,	friendly patents and in
						affluence, regulation and	environmental tax revenue per
						technology (STIRPART)	capita reduces carbon emissions.
						framework, GMM models
	Johnstone et	77 countries	2001-	Panel data, two-stage		Positive role of both general
	al. (2012)		2007		models			innovative	capacity	and
									environmental policy stringency
									on		environment-related
									innovation.
	Ouyang et al.	30 OECD	1998-	Panel threshold model to	This study has revealed the
	(2019)		countries	2015		explore the non-linear		existence of an inverted U-
						effects of environmental	shaped relationship between
						regulations on PM2.5		environmental regulations and
						pollutions			PM2.5 emissions.
	Sadik-Zada,	26 OECD	1995-	Variational model of		Different thresholds of
	and Ferrari	countries	2011		environmental		environmental degradation,
	(2020)					degradation, pooled mean	strong and robust confirmation of
						group (PMG) estimator	the pollution haven conjecture
	Wang et al.	25 OECD	1990-	Panel data, system		Environmental policy strictness
	(2020a)		countries	2015		generalised moments		has a significant effect on CO2,
						(SYS-GMM)		NOx, and SOx emissions,
									whereas the impact on PM2.5
									emissions and PM2.5 exposure is
									very weak.
	Wolde-		Czech	1994-	Panel data, augmented		Inverted U-shaped relationship
	Rufael and	Republic,	2015		mean group (AMG)		between CO2 emissions and
	Mulat-		Greece,			estimator			environmental policy stringency
	Weldemeskel	Hungary,
	(2021)		Korea,
			Poland,
			South Africa,
			and Turkey

https://ec.europa.eu/clima/policies/strategies/progress_en

According to[START_REF] Sadik-Zada | Environmental policy stringency, technical progress and pollution haven hypothesis[END_REF], over 90% of the population in OECD countries are aware of climate change, and over 60% of them believe that climate change poses a serious threat to sustainable livelihoods and international security.

https://www.oecd.org/environment/how-stringent-are-environmental-policies.htm

https://www.eea.europa.eu/themes/climate/trends-and-projections-in-europe/trends-and-projections-in-europe-2016/1-overall-progress-towards-the

Acknowledgements

This work was supported by a Grant of the Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI, Project Number PN-III-P1-1.1-TE-2019-0436.

This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI, project number PN-III-P1-1.1-TE-2019-0436.

Appendix

Notes: * The EU 20-20-20 GHC emission target at risk of not being achieved, as projected in 2015 (https://www.eea.europa.eu/themes/climate/trends-and-projectionsin-europe/ trends-and-projections-in-europe-2016/1-overall-progress-towards-the); ** As of 1 January 2021, the United Kingdom has left the EU.