Environmental regulation and renewable energies: Evidence from generalized panel unconditional quantile regression

Rahmane, Amal; Abdelaoui, Okba; Djouadi, Issam Article Environmental regulation and renewable energies: Evidence from generalized panel unconditional quantile regression Central European Economic Journal (CEEJ) Provided in Cooperation with: Faculty of Economic Sciences, University of Warsaw Suggested Citation: Rahmane, Amal; Abdelaoui, Okba; Djouadi, Issam (2024) : Environmental regulation and renewable energies: Evidence from generalized panel unconditional quantile regression, Central European Economic Journal (CEEJ), ISSN 2543-6821, Sciendo, Warsaw, Vol. 11, Iss. 58, pp. 252-268, https://doi.org/10.2478/ceej-2024-0017 This Version is available at: https://hdl.handle.net/10419/324614 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ISSN: 2543-6821 (online) Journal homepage: http://ceej.wne.uw.edu.pl To cite this article Rahmane, A., Abdelaoui, O., Djouadi, I. (2024). Environmental Regulation and Renewable Energies: Evidence from Generalized Panel Unconditional Quantile Regression. Central European Economic Journal, 11(58), 252-268. DOI: 10.2478/ceej-2024-0017 To link to this article: https://doi.org/10.2478/ceej-2024-0017 Environmental Regulation and Renewable Energies: Evidence from Generalized Panel Unconditional Quantile Regression Amal Rahmane, Okba Abdelaoui, Issam Djouadi Open Access. © 2024 A. Rahmane, O. Abdelaoui, I. Djouadi, published by Sciendo. This work is licensed under the Creative Commons Attribution 4.0 International License. Amal Rahmane University of Mohamed Khider Biskra, Department of Economics, BP 145 RP, 07000 Biskra, Algeria corresponding author: [email protected] Okba Abdelaoui University of Eloued , Department of Commerce, PB 789 El Oued, Algeria, University of Ouargla, Laboratory of Requirements of the Promotion and Development of Emerging Economies in the Context of Integration into the Global Economy (LEPEM), Ave 1er Novembre 1954, Ouargla, Algeria Issam Djouadi Higher National School of Statistics and Applied Economics, Koléa University Center, Department of Applied Economics, 42400, Koléa, Tipaza, Algeria Environmental Regulation and Renewable Energies: Evidence from Generalized Panel Unconditional Quantile Regression Abstract This study aims to measure the impact of environmental regulation on the production of renewable energies in OECD countries from 1990 to 2021. Environmental policies stringency, environmental taxes, and CO2 emissions are variables indicating environmental regulation, which affect renewable energies production. The study relied on unconditional quantitative regression methods. The study found that strict environmental policies do not necessarily enhance renewable energy production in countries with high or low production. Moreover, environmental tax revenues have varying impacts on renewable energy production based on renewable energy production in each country. For countries with below-average levels of renewable energy (Q25), environmental taxes positively affect renewable energy production; however, in countries with high production levels (Q90), environmental taxes show a negative effect. Furthermore, CO2 emissions negatively affect the total production of renewable energy in all quantiles except Q50, whereas R&D spending positively affects renewable energies in all quantiles except Q75. The estimates also showed a significant negative effect of patents on the renewable energy production in quantile Q10. The results underscore the importance of flexibility and adaptability in environmental policies and taxes. Finally, the study indicates that policies must be dynamic and respond to the specificity of each stage of renewable energy development in the studied countries. Keywords stringency of environmental policies | environmental taxes | CO2 emissions | production of renewable energies | quantile regression JEL Codes C01, K32, Q42 1. Introduction The global energy crisis includes increasing growth in the global demand for energy, with limited reserves available from traditional sources, and the rise of prices since 2014. Another issue is the international interest in confronting the phenomenon of climate change. In this context, both local and international interest in discussing energy issues is compulsory in order to achieve sustainable development goals by shifting from an economy based on traditional energy to an economy based on a sustainable one. CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 254 Energy transition refers to the global energy sector’s shift from fossil-based systems of energy production and consumption—including oil, natural gas, and coal—to renewable energy sources such as wind, solar, and lithium-ion batteries (S&P Global, 2020). The energy transition is a continuous process requiring long-term energy strategies and planning with a country-tailored focus on applying appropriate energy technologies to reach net-zero emissions (United Nations Development Programme, 2023). In 2015, the United Nations Climate Change Conference (UNFCCC) in Paris adopted a transformative universal climate change agreement. This landmark agreement articulates the social and economic opportunities offered by low emissions and a climate-resilient future. It also articulates the intrinsic relationship between climate change action, sustainable development, and poverty eradication. The 2015 Paris Agreement represents a historic turning point as a global response to the need for urgent action at scale to mitigate climate change. At the same time, the UN 2030 Agenda and the Sustainable Development Goals (SDGs) call for action by all countries to improve people’s lives everywhere (United Nations, 2018, p. 3). Seventeen SDGs were announced, with goal number seven defining targets to “ensure access to affordable, reliable, sustainable and modern energy for all” (United Nations Economic Commission for Europe, n.d.). Both the Paris Agreement and the 2030 Agenda (with SDGs, particularly SDG 7) set clear directions and paths for humanity towards a development powered by clean energy. These agreements are based on the efficient use of resources and are defined by resilience to climate impacts (United Nations, 2018, p. 3). Energy transition is driven by a combination of factors, including environmental regulations. Environmental regulation refers to the imposition of limitations or responsibilities on individuals, corporations, and other entities to prevent environmental damage or improve degraded environments (McManus, 2009). Environmental regulations can have a significant influence on energy transition performance in several ways (Zou and Wang, 2023): • Stringent environmental regulations, such as emissions standards and renewable energy targets, can incentivize industries and individuals to transition towards cleaner energy sources. • Environmental regulations can drive technological advancements and innovation in the energy sector. • Clear and consistent environmental regulations provide stability and certainty for businesses and investors in the energy sector. • Environmental regulations can influence consumer behavior and energy consumption patterns. The present study sheds light on the relationship between environmental regulation and renewable energy production in OECD countries during 1990– 2021. The study relied on unconditional quantitative regression methods. To determine the effects of the independent variables on the distribution of the dependent variable, we used quantile regression, which allows for several effects of the independent variables on the dependent variable. We divide the article into multiple sections. First, the introduction addresses environmental regulation and renewable energies. In the second section, we review the literature. In the third section, we examine the existing body of literature. In the fourth section, we examine the results of the study. Lastly, we provide some concluding remarks. 2. Literature Review Many authors have tackled the relationship between environmental regulations and energy transition. According to the available literature, we can divide them into studies that found a negative relationship between the two variables and studies that found a positive relationship, i.e., environmental regulations can lead to an increase in renewable energy production or consumption. In addition, some studies have used renewable energy consumption; in contrast, others have used renewable energy production, represented by the number of patents in renewable energy or the renewable energy capacity. Concerning the first group of studies, Bashir et al. (2022) suggested that environmental regulations impede renewable energy consumption in OECD economies. In addition, Li et al. (2022) claimed that in BRICST (Brazil, Russia, India, China, South Africa, Turkey) economies, the environmental stringency CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 255 index contributed positively to renewable energy consumption with low consumption of renewable energy and vice versa. Likewise, Huang and Zou (2020) found that energy-specific environmental regulation has a significant positive impact on energy transition in China; however, this impact is weakened by high-energy intensity. Moreover, in several studies, environmental taxes have been found to harm the production of renewable energy (Bilan et al., 2022; Altay Topcu, 2023; Dogan et al., 2023). In addition, the research of Farhan Bashir et al. (2020) conducted on biofuel production and consumption indicated a significant impact of environmental taxation on the volumes of biofuel production and consumption. Similarly, the empirical results of the work of Regueiro-Ferreira and Cadaval Sampedro (2022) suggested that increasing environmental taxes hinders the deployment of renewable energy in EU countries. However, many studies found a positive relationship between renewable energy production or consumption and environmental regulations. For instance, Wissema and Dellink (2007) discovered that a carbon energy tax of 10–15 euros per tonne of CO2 could achieve a 25.8% reduction target in Ireland; simultaneously, it stimulates renewable energy use and reduces peat and coal use. In addition, Nesta et al. (2014) found that renewable energy policies are more effective in fostering green innovation in countries with liberalized energy markets. We also find that environmental policies are crucial only generating high-quality green patents, whereas competition enhances the generation of low-quality green patents. Moreover, Hille et al. (2020) concluded that more comprehensive portfolios of renewable energy support policies increase patenting in solar and wind-power-related technologies.In addition, Godawska and Wyrobek (2021) claimed that stringent environmental policies, eventually improve renewable energy production and the replacement of energy from fossil sources. Barnea et al. (2022) revealed that market-based instruments (MBIs) affect renewable energy production. Regulatory and market instruments, along with electoral democracy and government effectiveness, expand wind and solar electricity production. Furthermore, Zhang et al. (2022) found that economical environmental regulations have the greatest positive impact on sustainable growth for renewable energy enterprises. In contrast, resource endowment has a negative moderating effect on environmental regulations and growth. Similarly, Yang and Zhong (2022) concluded that green supervision and public regulations significantly enhance renewable energy investments in China, while green accounting regulations show no significant impact. Zhang and Chen (2022) also found a mutual promoting effect between renewable energy technological innovation and environmental regulation intensity. Additionally, the findings of Dzwigol et al. (2023) confirmed that environmental regulation has a mediating positive effect on interconnections among knowledge spillover, innovations, and renewable energy. Moreover, Zhao et al. (2022) showed that environmental regulation significantly contributes to renewable energy development. Liu et al. (2023b) exhibited that green energy investment, financial development, and environmental policy stringency eventually stimulate sustainable energy transition. Manifestly, the interaction between financial development and ecological regulations offers a comparatively stronger influence than their individual effects, implying that effective environmental regulations direct the movement of financial resources toward the renewable energy transition. Lastly, Ulfatun Najicha et al. (2023) concluded that transition management has an essential role in the shift towards renewable energy production. It involves proactive management and acceleration of transitions in the energy sector, focusing on securing energy justice. The just transition management framework combines transition management with the concept of “just transitions” in order to mitigate negative impacts on workers and communities in traditional energy production regions. This framework can help identify political barriers to transitions and achieve distributional, recognition, and procedural justice in the energy sector. Some works focused on the link between environmental regulations and renewable energy efficiency. For example, Zhang and Du (2022) claimed that environmental regulation directly improves the green energy efficiency of polluting industries and clean industries, and it has a positive intermediary role between technology and green energy efficiency in China. Similarly, Liu et al. (2023a) had multiple findings. First, there are four green energy efficiency enhancement paths: the government pressure type, the market mobilization type, the government-led public association type, and the multiple subject type. Second, the command-and-control type of environmental regulation enhances green energy efficiency in most CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 256 provinces of China. Third, the multiple subject type of enhancement paths can achieve higher green energy efficiency. Finally, Zou and Wang (2023) examined the relationship between environmental regulation and energy transition performance in China. The study employed statistical analysis techniques to analyze the correlation between environmental regulation stringency and various energy transition metrics, including renewable energy capacity, carbon intensity, and energy efficiency. The findings provided valuable insights into the role of environmental regulation in shaping China’s energy transition landscape. . To conclude, other recent studies are close to ours. We mention Hasan et al. (2023), who found that fossil fuel consumption leads to environmental deterioration, while renewable energy consumption, financial development, and trade openness enhance environmental quality. Additionally, the results support the validity of the EKC (Environmental Kuznets Curve) hypothesis for all BRICS countries (Brazil, Russia, India, China, and South Africa). In addition, Yang et al. (2023) employed the novel quantile-based econometrics approach of “Method of the Moments Quantile Regression” (MMQR), which provides the direction and magnitude of the asymmetric association of natural resources NTR, green finance GFN, green energy GEC, and economic growth GDP with the ecological footprint. This test’s results revealed that the NTR and GDP have a significantly positive influence, whereas GEN and GEC have significantly negative associations with an ecological footprint across all quantiles. This implied that green finance and green energy work as the solution, while natural resources and economic growth are key drivers of environmental degradation. Lastly, Igeland et al. (2024) indicated that economic policy uncertainty (EPU) positively impacts the returns of renewable stocks, attributing that to an increased engagement towards a renewable transition. 3. Study Variables and Model This study aims to estimate the impact of environmental regulation on the production of renewable energies to judge whether environmental regulation has contributed to maximizing the roles of innovation. Thus, it contributes to the creation of new investments in clean industries and environmentally friendly sectors, and it expands production in these sectors. This is done by considering the impact of environmental policy strength (EPS), environmental related tax revenue (ERTR), research and development expenditure (RDE), patents (patent applications, residents) (PAR), CO2 emissions (CO2EME), environmentally adjusted multifactor productivity growth (EAMPG), households and NPISHs (Non-profit institutions serving households) final consumption expenditure (FCE), and gross fixed capital formation (GFCF) on total renewable energy (TRE). Accordingly, the model is written as follows: TRE=f(EPS,ERTR,CO2EME,EAMPG,PAR,RDE,FCE,GFCF) The OECD database was relied on for study data related to the production of renewable energies, the stringency of environmental policies, environmental taxes, CO2EME , and EAMPG. We also relied on the World Bank database for the variables of family expenditure, fixed capital accumulation, research and development (R&D) expenditures, and patents. 4. Method and Tools Estimation has two strategies. The first strategy is static panel data analysis; in this strategy, we used the Fisher test to compare pooled and fixed effects, and in the next step, we used the Hausman test to distinguish fixed and random effects models. To assess the model’s statistical robustness, we used the Pesaran test to confirm that the residuals are not correlated at the cross-sectional level. Next, we tested heteroscedasticity using the modified Wald test. Additionally, to test autocorrelation, we applied the Wooldridge test. Conversely, if one of these three difficulties exists, the model must be estimated using feasible generalized least squares. The findings of the Fisher test reveal that the fixed effects (FE) model is more valid and effective than the pooled model. The Hausman test showed that the FE model is more effective than the random effects (RE) model; thus, we use it for the rest of the robustness tests. In the Pesaran CD test, residuals correlate at the cross-sectional level. Heteroskedasticity is seen in the modified Wald test. Furthermore, the Wooldridge test showed an autocorrelation of residuals. We must estimate FE using the feasible generalized least squares (FGLS) method. CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 257 The second method uses unconditional quantile regression. One of the main benefits of the ordinary least squares approach is its consistent estimation of the independent variables’ impact on the dependent variable. The law of iterative expectations converges the conditional mean E(Y/X) to the unconditional mean E(Y). Therefore, conventional least squares calculate the independent variables’ influence on the dependent variable’s mean value without considering how they affect one of its levels. Quantile regression is among the most important econometric methods that seek the effects of independent variables on the distribution of the dependent variable (Martínez-Zarzoso et al., 2019). In addition, the production of renewable energies and their levels differ between the countries under study. Therefore, research on the factors affecting the production of renewable energies requires that it be built based on quantile regression estimates to avoid problems of heterogeneity in the distribution of data. This type of regression is more robust than OLS (ordinary least squares) due to the problems of heterogeneity, outliers, and structural change. This allows us to draw conclusions about the influence of independent variables on the distribution of the dependent variable. Koenker and Bassett (1978) first introduced quantile regression, where the parameters of the model β _τ represent the marginal effect of the variable X on the quantile τ of the distribution Y conditional on the average values of all other variables. This type is called conditional quantile regression, which takes into account the distribution of independent variables. Therefore, conditional quantile regression provides us with the marginal effect of the variable X on the quantile τ of the distribution Y, taking into account all changes occurring in these independent variables. According to Firpo et al. (2009), conditional quantile regression does not answer the question that aims to search for the marginal effect of the variable X on the quantile τ of the distribution Y, with other factors remaining constant. In this context, Firpo et al. (2009) developed a method to search for the marginal effect of the variable X on the quantile τ of the distribution Y with other factors remaining constant, called unconditional quantile regression. In this method, we obtain the parameters of β _τ that correspond to the effect on the quantity τ of Y regardless of the changes occurring in the rest of the independent variables. The parameters are estimated based on the recentered influence function (RIF). This type of regression provides us with two types of parameters. The first parameter represents the marginal effect of the variable X on the quantile τ of the distribution Y. The second parameter represents the effects of overall changes in the distribution of independent variables (policy effect) on the quantile τ of the distribution Y based on a non-parametric approach. 5. Results and Discussion Table 1 provides detailed data on the main statistical descriptive measures, including the mean, standard deviation, and minimum and maximum values of dependent and independent variables across sample countries. Growth rate exhibited the highest volatility values per the standard deviation metrics, whereas the ERTR demonstrated the lowest values. Furthermore, based on the results of the Jarque–Bera test, it was determined that six of the variables exhibited a nonnormal distribution at a significance level of 5%, whereas two variables had a normal distribution. The findings from Table 2 show that there is no high correlation among the variables, indicating the absence of multicollinearity in the estimated model. The results of the static model estimation confirm the significant positive effect of stringent environmental policies, environmental tax revenues, CO2 emissions, patents, and investment in total renewable energies. In contrast, the outcomes of the static model estimation show the significant negative effect of both the growth rate and spending on R&D on total renewable energy. Estimates of unconditional quantile conventional standard errors show a significant positive effect of the stringency of environmental policies in the quantiles Q25, Q50, and Q75 related to the distribution of total renewable energies. This means that the stringency of environmental policies does not lead to an increase in the levels of renewable energies in countries characterized by high and weak levels of renewable energies. The results maintain their statistical significance with the estimates: bootstrapped standard errors. In contrast, these results keep their statistical significance with the Q75 quantile with both the cluster–robust standard errors and the cluster–bootstrapped standard errors. Additionally, the estimates reveal a significant positive effect of environmental tax revenues on total CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 258 renewable energies in Quantile Q25. It also shows the significant negative effect of environmental tax revenues on the total renewable energies in Quantile Q90. These results maintain their statistical significance with the estimates: bootstrapped standard errors in addition to the statistical significance of the negative impact of Q50 in these estimates. These results also maintain their statistical significance with the quantile Q25 with the cluster–robust standard errors. This indicates that environmental tax revenues contribute to increasing total renewable energies in countries with lower-than-average levels of renewable energies. However, they adversely affect total renewable energies in countries with medium and high levels. Table 1. Statistics Descriptive of Variables Study Variable Mean Std. dev. Min Max Jarque–Bera test p-value Total renewable energy 9.716629 1.999758 2.890372 15.08672 84.71 0.0000 Environmental policy stringency 2.03709 1.174578 05.055555 318.99 0.0000 Environmentally related tax revenue 6.581747 0.8795824 2.88759 8.155826 1224.68 0.0000 CO2 emissions 11.58532 1.558937 7.152589 15.56919 3.04 0.2184 Patent applications 7.402104 2.23668 0.8526029 12.86712 28.80 0.0000 Research and development expenditure 1.676507 0.9343578 0.25067 5.00197 16.47 0.0003 Growth rate 2.218023 4.115878 -14.62906 24.37045 801.61 0.0000 Gross fixed capital formation 25.00864 1.651858 20.30501 29.2283 3.56 0.1686 Observations: N =1056, n =33, T =32 Source: STATA 16.0 Table 2. Correlation Matrix Total renewable energy Environmental Policy Stringency Environmentally Related Tax Revenue CO2 emissions Patent applications R&D expenditure Growth rate Gross fixed capital formation Total renewable energy 1.0000 Environmental policy stringency 0.2096 1.0000 Environmentally related tax revenue –0.0489 0.3926 1.0000 CO2 emissions 0.4253 0.0516 –0.1258 1.0000 Patent applications 0.5782 0.2537 0.0420 0.5957 1.0000 R&D expenditure 0.3516 0.4917 0.3552 0.1887 0.5100 1.0000 Growth rate –0.0482 –0.1153 0.0676 –0.0168 –0.0567 –0.0056 1.0000 Gross fixed capital formation 0.6874 0.4009 0.0238 0.6294 0.8670 0.4277 –0.0520 1.0000 Source: STATA 16.0 CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 259 Furthermore, the estimates indicate a significant negative impact of CO2EMEs on the total production of renewable energy in all quantiles except Q50. These results maintain their statistical significance with the estimates: bootstrapped standard errors, which indicates that CO2EMEs weaken the levels of renewable energy production. In all countries other than those with intermediate levels, these results maintain their statistical significance with the Q10 quantile with the estimates: cluster–robust standard errors. In contrast, these results keep their statistical significance with Q90 with the cluster–robust standard errors and cluster–bootstrapped standard errors in most quantiles. Additionally, the estimates display a significant positive effect of spending on R&D on total renewable energies in all quantiles except Q75. These results maintain their statistical significance with the estimates: bootstrapped standard errors. This indicates that spending on R&D increases the levels of renewable energies in all countries except the ones with above-average levels. These results also maintain their statistical significance with Q90 with cluster–robust standard errors. In contrast, these results keep their statistical significance with Q25 with the estimates: cluster–robust standard errors and cluster–bootstrapped standard errors. Table 3. Total Renewable Energy Static Models Pooled Fixed Random GLS Environmental policy stringency –.273*** .152*** .145*** .056*** Environmentally related tax revenue –.161*** –.029 –.038 .08*** CO2 emissions –.045 –1.297*** –.805*** .203*** Patent applications –.208*** .026 .016 .196*** Research and development expenditure .428*** .433*** .443*** –.028*** Growth rate –.014 –.034*** –.031*** –.004*** Gross fixed capital formation 1.077*** .44*** .475*** .286*** _cons –14.236*** 12.773*** 6.329*** –1.848*** Observations 1056 1056 1056 1056 *** p<.01, ** p<.05, * p<.1 Source: Prepared by the authors based on STATA 16.0 Table 4. Total Renewable Energy Non-Robust Q10 Q25 Q50 Q75 Q90 Environmental policy stringency –.177 .177* .201*** .563*** –.016 Environmentally related tax revenue .184 .241*** –.086 –.013 –.159** CO2 emissions –2.585*** –.698** –.01 –1*** –2.162*** Patent applications –.779*** –.188 .115 .043 –.056 Research and development expenditure .862*** 1.074*** .196* –.148 .391*** Growth rate –.017 –.035*** –.045*** –.011 –.013 Gross fixed capital formation 2.201*** .658*** .182 –.65*** –.128 _cons –14.352* –2.208 4.678 37.747*** 40.951*** Observations 1056 1056 1056 1056 1056 *** p<.01, ** p<.05, * p<.1 Source: Prepared by the authors based on STATA 16.0 CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 266 Hasan, M., Wieloch, J., Ali, M., Zikovic, S., & Uddin, G. (2023). A New Answer to the Old Question of the Environmental Kuznets Curve (EKC). Does it Work for BRICS Countries? Resources Policy, 87, 104332. https://doi.org/10.1016/j.resourpol.2023.104332 He, X. 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Environmental Impact Assessment Review, 104, 107335. https://doi.org/10.1016/j.eiar.2023.107335 CEEJ • 11(58) • 2024 • pp. 252-268 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0017 268 Appendices Figure 1. Scatter graph between variables Source: STATA 16.0