**1. Introduction**

One of the biggest environmental challenges impacting the planet and humanity is climate change. To handle this challenge, various international initiatives aiming to rethink the energy-energy security-climate change nexus have been taken. Among them, the 2021 OECD International Program for Action on Climate (IPAC), the Earthshot Prize, and the 2020 European Green Deal (EGD) support countries progress toward net-zero greenhouse gas (GHG) emissions and a more resilient economy by 2050. They propose a mixt of channels and ambitious goals to mitigate climate change and provide more certainty to policymakers and investors regarding their future decisions on CO2 emission levels. These emissions must be consistent with the EU's/ OECD's goals of being climate-neutral by 2050 such as: reduce the GHG to at least 55% below 1990 levels by 2030; increase the share for renewable energy to more than 32%, improve energy efficiency at about 33%, sustain people and companies to find solutions to help fight climate change and so on.

But these challenges will not be easy to keep given that OECD or EU countries still rely on fossil fuels (such as coal, oil, gas, and petroleum products) for about

80% of their energy supply, notably for industry and transport that are the largest emitters of GHGs. New OECD data indicate that total fossil fuel support in 44 OECD and G20 economies rose by 10% in 2019 to 178 billion USD, ending a 5 year descending trend and undermining global efforts to mitigate climate change [1, 2].

In this context, the question that rises is whether economic growth leads inevitably to more pollution. Until the 1980s, the Club of Rome view (highlighting a negative impact of economic growth on the environment and natural resources) and the Dasgupta and Heal [3]'s view (pointing out a complementary relationship between economic growth and environmental improvement) have been the main approaches of the applied economic analysis. Since 1990s, the Environmental Kuznets Curve (EKC) penciled by Baloch and Wang [4] comes to the scene by suggesting an inverse U-shaped curve between GHG and income. Four main aspects explain it. *The first one* is income elasticity of environment demand. This means that as income increases, people are incited to consume more healthy products and pay more attention to the environmental quality while government is expected to put in place stricter rules on environment protection to improve it. *The second one* is the effect of economic scale, technology, and structure. The economic scale reflects the situation in which more economic growth means more energy consumption from nonrenewable sources and, as a result, more pollution [4]. The structural effect implies that structural changes may improve environment with economic development. Finally, the technology effect suggests that old pollution machines should be replaced by clean techniques of production, which are expected to positively affect the environmental quality. Overall, the EKC indicates that, at the beginning stage of development of a country, the negative scale prevails and the pollution increases (see, [5]). Once the income reaches a certain threshold, the structural and technological effect exceeds the scale effect leading to a fall of pollution levels. *The third aspect* refers to the international trade, which is a key factor of EKC validity. Trade has a dual, twin influence on the environment quality: a negative one through the scale effect or a positive one via the technological effect (see, [5]). *The last aspect* is that of FDI, which has two possible impacts on the quality of environment, too. On the one hand, the developing countries are tempted to lower their claim on the environment protection to attract more foreign investors, which aggravates environmental pollution; on the other hand, by getting technology by FDI from developed countries, most emerging countries help improve environment.

The empirical literature on the relationship between economic growth and environmental quality is considerable (e.g., [6–15]) and concentrates three main strands of studies. The first group of studies focuses on the growth-pollution nexus by testing the EKC validity with different time series and panel data models (e.g., [16–18]). The second group of studies focuses on the link between the economic growth and energy consumption from (non)renewable sources given that the GHG emissions are produced by fossil fuels (e.g., [19–23]). The last group of studies combines the two approaches by analyzing the link between energy consumption from renewable and nonrenewable sources, economic growth, and environmental quality [24]. Compared with the previous one, this wave of studies addresses concerns regarding biasness caused by omission of variables. By integrating variables on international trade, urbanization degree, foreign direct investment, and so on, one can assess the linear and nonlinear relationship between economic growth, pollution, and energy consumption with time-series and/or panel data. Overall, empirical studies conducted until now show diverging results on the growth-pollution nexus and explain this gap

### *The Environmental Kuznets Curve: Empirical Evidence from OECD Countries DOI: http://dx.doi.org/10.5772/intechopen.108631*

in findings by the use of various methods (with time series or panel data), length of periods, and/or the nature of country sample.

Regarding the OECD studies, some of them seek to empirically estimate the nonlinear relationships between income level and environmental degradation and dynamic causalities between environmental pollution and economic growth, along with other key explanatory macroeconomic indicators (e.g., [11, 25–27]). But the attempt to validate the EKC hypothesis still gives ambiguous outcomes. Frodyma [26] shows that the EKC models fail to explain the relationship between income and production-based emissions (or consumption-based emissions) when using an ARDL testing bound approach (due to the heterogeneity of the panel sample considered) over the 1970–2017 period. At the opposite side, Benjebli et al. [25] identifies an inverted U-shaped EKC curve for 25 OECD for the period 1980–2010 based on fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimates. Differently, Madaleno and Moutinho [11] focuses on 15 EU countries (old members of OECD) and 12 Emerging EU countries over the 2008–2018 period. With dynamic fixed effects (DFE) and DOLS estimates, they identify a U-shaped EKC curve. Likewise, Churchill et al. [28] finds evidence that supports the EKC hypothesis in nine of the 20 OECD countries over the period 1870–2014 (a long-time horizon of 144 years). The EKC hypothesis is validated for Australia, Canada, Denmark, Finland, France, Japan, Spain, United Kingdom, and the United States, albeit with different turning points for income per capita. These findings are generally comparable to other empirical studies, which identified an EKC curve for OECD with panel data over shorter periods. For example, Dijkgraaf and Vollebergh [29] finds that 11 out of 22 OECD countries have a statistically significant turning point and verify an inverted Ushaped EKC pattern. Likewise, Apergis [30] and Shafiei and Salim [31] find evidence supporting an inverted EKC relationship for some OECD countries as well.

Another group of studies go further and compute the turning points in income per capita leading to lower emissions per capita. Galeotti [32] finds for OECD over the period 1960–1997 a turning point ranging between 15599.9\$ and 21185.83\$ (in 1990 US dollars). In the same vein, Churchill et al. [28] identifies greater turning points ranging from \$19 978 to \$84001, depending on whether or not financial development variable is included in the equation's model. Based on a panel smooth transition approach over the 2000–2018 period, for 75 countries (including some OCDE countries), Tatoglu and Polat [14] finds a turning point between \$42053 and \$42057. Also, Sun et al. [27] uses the Common Correlated Effect Mean Group (CCEMG) and Augmented Mean Group (AMG) methods for the period 1992–2015 and shows that growth output has significant positive effect on environmental pollution. The estimated turning points are greater ranging from 1479029.30 to 3277677.02. As we can observe, the EKC empirical evidence is still controversial, and there is no consensus on the income level at which environmental degradation begins diminishing.

This paper will contribute to the last strand of the empirical literature by providing evidence on the validity of the EKC curve for 34 OECD countries [33] and by identifying threshold levels of GDP per capita leading to lower emissions per capita for these countries. But, unlike previous studies, it will point out the key role of energy from renewable sources.

Therefore, the paper contributes to the existing empirical literature in the following ways. First, contrary to the most part of studies focusing on the role of energy consumption from fossil fuels in validating the EKC curve, the paper embodies indicators on energy from renewable sources, since OECD countries are considered as regions with a less energy-intensive economic structure and a more advanced

development in renewable sources than emerging ones (e.g., [34]). Second, the paper includes a heterogeneous sample of countries with respect to their energy consumption mix over the period 1997–2015, which allow capturing disparities, if any, in the value of the threshold above and below which the effect of economic activity on environmental pollution may differ within this group of countries. The threshold is identified with a recent empirical model by Fouquau et al. [35] and González et al. [36] a panel smooth transition model (PSTR), which is the best model for tackling the heterogeneity in the effect of economic activity on the environmental degradation. Third, the paper includes two types of indicators for energy from non-fossil fuels: the renewable energy consumption and the electricity production from renewable sources per capita, excluding hydroelectric (kW h), the last being used for robustness purposes. Fourth, the findings provide further support to economic approaches investigating the renewable EKC curve (R-EKC curve). However, they go further by pointing out the non-monotonous relationship between environmental degradation and economic activity in combination (or not) with the renewable energy use, FDI, and urbanization degree. To my best knowledge, this aspect of the R-EKC curve has never been explored before with a PSTR approach. Finally, the paper provides useful insights for policymakers in supporting green economy to achieve the 2030 Agenda for Sustainable Development (via the IPAC and the EGD international initiatives).

The results show evidence for an inverted U-shaped relation between income and environmental pollution for OECD-34. However, the overall effect of output on the environmental degradation (through carbon dioxide emissions) is positive meaning that technology, preference, and environmental investments in clean energy are not sufficient developed in the energy consumption mix. The turning point or the income level in inflexion of R-EKC ranges between 10774.1 and 44967.7 dollars *per head* when the indicator of gas pollutant is the carbon dioxide emissions (such as in [17, 18, 27]).

The rest of the paper is structured as follows. Section 2 describes the data and presents the PSTR methodology. Section 3 provides the empirical results and discussion. The final section concludes.
