Abstract
This study is an attempt to comparatively analyze the impact of renewable energy sources on air quality represented by particulate matter 2.5 concentrations utilizing panel data of 60 countries which are divided into two sub-panels industrialized economies and emerging industrial economies over the period 2010–2019. The study adopts both demand- and supply-side approaches and hence renewable sources are handled in two different structures, i.e., renewable energy consumption and production. Empirical results from both demand- and supply-side regressions strongly confirm the positive impact of renewable sources on air quality in all country groups, meaning that higher renewable energy production and consumption bring about improvement in air quality. In addition, this positive impact of renewables on air quality turned out to be higher in emerging industrial economies than that in industrialized ones. To be more precise, as all control variables are considered, a 10% increase in the production of renewable energy sources brings about a 0.66% improvement in air quality in industrialized economies while its impact is a value of 1.33% in emerging industrial economies. On the other hand, a 10% increase in consumption of renewable energy sources leads to a 0.62% improvement in air quality in industrialized economies and a 1.97% improvement in emerging industrial economies. As for control variables, industrialization gives rise to an increase in air pollution in all country groups, whereas economic growth and trade openness function as favorable factors for air quality. Although population density improves air quality in industrialized economies, it is found as one of the main pollutant factors in emerging industrial economies. Overall results proved that renewable sources improve air quality by reducing particulate matter 2.5 concentrations. Therefore, these countries, especially emerging industrial economies, should replace primitive energy sources like fossil fuels with renewables to bring down environmental degradation up to a reasonable level and increasingly continue to invest in renewable energy domain to reach their environmental sustainability targets. The study also provides some additional policy implications.
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Data availability
The datasets used and/or analyzed in the study are available in the [Google-Drive] repository [https://docs.google.com/spreadsheets/d/15xS9uOc9bTFENIV0zHZK_xu_mtXwv7df/edit#gid=477358765]. Unreported additional results/statistics are also available from the author upon request.
Notes
PM2.5 concentrations (μg/m3) intervals corresponding to colors are as follows: blue (0–5), green (5.1–10), yellow (10.1–15), orange (15.1–25), red (25.1–35), purple (35.1–50), maroon (50+).
These studies generally focused on the relationship between environmental quality and economic growth and found evidence that environmental quality is associated with economic growth in a non-linear form. Accordingly, as economic growth increases initially, the environment deteriorates; however, as the economy continues to grow beyond the threshold value, environmental quality begins to recover. This inverted U-shaped relationship between economic growth and environmental degradation is called the environmental Kuznets curve.
IEs are Australia, Austria, Belarus, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea (Rep.), Lithuania, Luxembourg, Malaysia, Malta, the Netherlands, Norway, Portugal, Russia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the UK, and the USA.
EIEs are Argentina, Brazil, Bulgaria, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, Egypt, Greece, India, Indonesia, Kazakhstan, Latvia, Mauritius, Mexico, Peru, Poland, Romania, Saudi Arabia, Serbia, South Africa, Thailand, Tunisia, Türkiye, Ukraine, and Uruguay.
The null hypotheses for the LR test, F-test, and LM test suppose the non-presence of cross-section and/or time effects in the mixed-effects model, fixed-effects model, and random-effects model, respectively. In other words, if the null hypothesis is accepted due to the probability values above 0.05, it is inferred the absence of any cross-section and/or time effects, i.e., pooled model. Vice versa, if the probability values are found under 0.05, the null hypothesis is rejected, and then decided the presence of cross-section and/or time effects.
The null hypothesis of the Hausman test shows non-association between non-observable effects and explanatory variables. In this case, if the probability value of the Hausman test statistic is found above 0.05, the null hypothesis is accepted and went further with the random effects model in the estimation process. Otherwise, in the case of rejection of the null hypothesis, the fixed effects model is utilized in the estimation process since it exhibits a more fitting feature to data. Hausman test statistics here are estimated with option “sigmamore” which specifies that the covariance matrices are based on the estimated disturbance variance from the efficient estimator leading to a proper estimate.
The null hypothesis for the modified Wald test refers to no heteroscedasticity. If the probability values of test statistics are under 0.05, the null hypothesis is rejected, and then decided that the model comprises heteroscedasticity concerns.
The Durbin-Watson test is only applicable where there is a constant term in the model, as in our case. When the test statistic is lower than 2, it is concluded that there is positive autocorrelation in the model. On the other hand, Baltagi and Wu (1999) offered the LBI test for zero first-order serial correlation against positive or negative serial correlation. Unless its statistic value is 2, there is autocorrelation in the model.
The null hypothesis for the Pesaran CD test refers to no cross-section dependency. If the probability values of test statistics are under 0.05, the null hypothesis is rejected, and then decided that the model comprises cross-section dependency concerns. If the Frees’ test statistics are found bigger than their Q distributions, the presence of the cross-section dependency is verified. Pesaran’s CD test is applicable with both large t and large i, despite Frees’ test may be only used under the condition of i>t, as in our case (De Hoyos and Sarafidis 2006).
The fixed-b approach delivers approximations for test statistics depending on the choice of kernel and bandwidth needed to apply the robust standard errors, which is an advantage of this approach over the standard asymptotic approach of Driscoll and Kraay (1998). Besides, the fixed-b approach improves the asymptotic approximation which results in reducing size distortions (Bunzel 2006). Moreover, stationary is not required, in the case of fixed-t and large i (Vogelsang 2012), as in our case.
Another justification for why this method is chosen is that the size of the cross-section dimension in a finite sample does not have any feasibility constraints, even so, the i > t, as in our case.
To get robust and reliable results, the study prefers to estimate all the econometric model specifications hierarchically. The hierarchic estimation procedure refers to the step-by-step estimation technic in which firstly only the main independent variable (REC or REP) is regressed with the dependent variable (this is called model (1) in all tables), then other control variables are included in the estimation process, respectively (they are called model (2), model (3), model (4), and model (5) in all tables).
Das (2019) expressed that the FGLS estimator is approximately normally distributed and produces more consistent results as the sample size is larger
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The author would like to thank the editor and anonymous referees who significantly improved themanuscript with their crucial suggestions, as well as Prof. Mehmet Demiral at Niğde ÖmerHalisdemir University for his valuable opinion and suggestions.
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Each part of the manuscript including research planning, literature review, model structure, data collection, research method, analysis procedure, and discussion of the findings was performed by Emrah Eray Akça, and the whole manuscript was written and read by me.
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Akça, E.E. Do renewable energy sources improve air quality? Demand- and supply-side comparative evidence from industrialized and emerging industrial economies. Environ Sci Pollut Res 31, 293–311 (2024). https://doi.org/10.1007/s11356-023-30946-2
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DOI: https://doi.org/10.1007/s11356-023-30946-2