Unveiling the inverted N-shaped environmental Kuznets curve in OECD nations: an empirical analysis

doi:10.21203/rs.3.rs-4356060/v1

Download PDF

Research Article

Unveiling the inverted N-shaped environmental Kuznets curve in OECD nations: an empirical analysis

https://doi.org/10.21203/rs.3.rs-4356060/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

In the contemporary global context, economic growth has engendered various factors such as increased trade, industrialization and technological advancements, giving rise to negative externalities on a worldwide scale. Among these, environmental destruction stands as a significant global public bad resulting from these adverse effects. Balancing economic growth objectives while addressing environmental challenges has become a paramount issue for modern society. This study investigates the correlation between economic growth and environmental degradation across 33 OECD countries during the period 1995–2015, employing fixed effect with Driscoll-Kraay standard error estimation approach. The findings surprisingly revealed an inverted N-shaped relationship between economic growth and air pollution levels, contradicting the conventional Environmental Kuznets Curve theory. Our analysis also shows that rising renewable energy consumption helps reduce CO₂ emissions, while non-renewable energy consumption has the opposite effect.

Environmental quality

Externalities

Global public bad

EKC hypothesis

Renewable energy

OECD countries

Since the Industrial Revolution, human-caused releases of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) have become major drivers of global warming, making them central targets in international efforts to combat climate change (Jones et al. 2023). This has led to a strong focus on tracking each country's role in climate change as well as setting fair reduction goals (decarbonization commitments) for all nations. Global warming, as predicted by most climate models, is expected to persist in the near future (2021–2040), reaching 1.5°C even under the lowest Greenhouse Gases (GHG) emission scenario and this persistence is primarily driven by heightened cumulative CO₂ emissions (Lee et al. 2023). The urgency of the situation is underscored by recent data. The latest Intergovernmental Panel on Climate Change (IPCC) (2023) report reveals that atmospheric CO₂ concentrations reached record highs (410 parts per million) in 2019, the highest level in at least 2 million years. This highlights the necessity for more decisive actions to reduce fossil fuel consumption.

While certain international efforts focus on reducing emissions, a key challenge remains achieving sustainable economic development without compromising environmental well-being. Achieving this equilibrium requires a comprehensive understanding of the complex relationship between economic development and environmental destruction.

EKC hypothesis, building on the inverted U-shaped relationship between income inequality and economic growth identified by Simon Kuznets (1955), is one of the main conceptualizations dealing with the relationship between environment and economy. It suggests that as economies grow, environmental destruction initially worsens, but this trend reverses at higher development levels. Grossman and Krueger (1991), pioneers in studying the relationship between environmental pollution and economic growth, suggest that while various pollutants such as Sulphur Dioxide, Dark Matter and Suspended Particles increase with GDP per capita at lower national income levels, environmental pollution decreases at higher income levels. Since Grossman and Krueger's seminal work, numerous studies have focused on validating the EKC hypothesis, primarily investigating its applicability to the relationship between carbon emissions and income.

As previously noted, most EKC studies aim to confirm the inverted U-shape pattern. However, a subset of studies, inspired by works such as those by Grossman and Krueger (1995), Panayotou (1997), Dinda (2004), Galeotti and Lanza (2006), and Akbostancı et al. (2009), have incorporated a cubic term into their models. This term allows researchers to investigate the possibility of a reversal in the trend, where environmental degradation might increase again at very high-income levels, (Özokcu & Özdemir 2017). The research on (inverse) N-shaped EKC hypothesis remains limited, especially for OECD countries (Özokcu & Özdemir 2017; Ullah et al. 2024).

This research investigates the EKC hypothesis for 33 OECD countries from 1995 to 2015, analyzing the impact of economic growth on CO₂ emissions while considering the role of renewable and non-renewable energy consumption. Notably, this research departs from prior studies by incorporating a cubic term in the analysis to examine the potential for an (inverted) N-shaped relationship. Intriguingly, our findings challenge the conventional EKC theory by revealing an inverted N-shaped relationship between economic growth and air pollution levels. The implications of this inverted N-shaped relationship are profound, signaling that unchecked economic growth might not lead to automatic environmental improvement, even though growth may ultimately appear to improve the environment. Therefore, this study highlights the need for targeted and effective environmental policies to mitigate the adverse environmental impacts of economic development. Policymakers must recognize the complexities in addressing the challenges posed by varying levels of environmental degradation across different income brackets. Furthermore, testing this hypothesis and modeling the relationship between the environment and economic growth with OECD countries will be very instructive considering the distribution of economic power in the world economy. As a matter of fact, OECD countries, in addition to their economic power, may play a leading role in reaching effective solutions on a global scale with their advantages of international cooperation. In addition, OECD countries prioritize transitioning to renewable energy sources as a key strategy to combat global environmental issues like climate change, air and water pollution, and biodiversity loss. To assess the significance of this transition, this study utilizes renewable and non-renewable energy consumption as control variables. Lastly, we conducted estimations for two distinct dependent variables, namely total GHG emissions and CO₂ emissions, to validate the robustness of the study's findings.

The remainder of the research proceeds as follows. Section 2 highlights the relevant literature about the EKC for OECD countries. Section 3 details the data utilized, outlining sources and variables. Section 4 explains the chosen model and methodology, including the rationale and analytical techniques. Section 5 presents the key findings from the data analysis. Finally, Section 6 addresses the conclusions drawn from the research and explores potential policy implications.

EKC hypothesis is a popular concept tested by researchers. It suggests an inverted U-shaped relationship between economic growth and environmental degradation. Economic growth is first thought to increase pollution, but after reaching a certain income level, societies prioritize environment at higher income levels, leading to a decline in pollution. In other words, once the economy has accumulated sufficient capital stock, it is possible to restore environmental quality. In essence, the EKC argues that economic growth may eventually become a solution, not just a cause, of environmental pollution; therefore, policies that promote economic growth should not be sacrificed in the quest to reduce pollution (Dinda 2004). However, critics argue that the EKC is highly optimistic. They argue that there's no guarantee economic growth will give rise to environmental improvement, and it might even exacerbate ecological issues (Beckerman 1992; Arrow et al. 1995).

Building on Grossman and Krueger's (1991) work, researchers have estimated the EKC using different methodologies across various contexts (Panayotou 1993; Holtz-Eakin and Selden 1995; Cole et al. 1997; Galeotti and Lanza 1999; Magnani 2001; Hill and Magnani 2002; Vollebergh et al. 2005; Aldy 2005; Galeotti et al. 2006; Jalil and Mahmud 2009; Apergis and Payne 2010; Tamazian and Rao 2010; Ahmed and Long 2012; Öztürk and Acaravcı 2013; Shahbaz et al. 2013; Yavuz 2014lük and Mert 2015; Doğan and Şeker 2016; Sinha and Sen 2016; Nasreen et al. 2017; Gill et al. 2018). However, these studies lack consensus on the EKC's shape due to differences in model definitions, choice of explanatory variables, timeframes, and geographical scope. The outcomes of these investigations indicate a linearly increasing or decreasing relationship, as well as a U-shaped or inverted U-shaped pattern supporting the EKC hypothesis.

Extending the work of Grossman and Krueger (1995), some studies challenge the EKC by analysing economic growth in a cubic form, suggesting an N-shaped or inverted N-shaped relationship. The N-shaped relationship indicates that the decrease in environmental degradation with economic growth is a temporary phenomenon (Moomaw and Unruh 1997; Hill and Magnani 2002; Day and Grafton 2003; Akbostancı et al. 2009; Fodha and Zaghdoud 2010; He and Richard 2010; Fosten et al. 2012; Lorente and Álvarez-Herranz 2016). Meanwhile, inverted N-shape suggests that pollution initially falls but rises again with further income growth, eventually declining after a high-income threshold (Dijkgraaf and Vollebergh 2005; Vollebergh et al. 2005; Abdallah et al. 2013; Nasr et al. 2015; Yaduma et al. 2015; Dong et al. 2016; Abbasi et al. 2023; Fakher et al. 2023; Huang et al. 2023; Farooq et al. 2024).

In this study, we examine the (inverted) N-shaped EKC hypothesis within the context of OECD countries. We, therefore, present the findings from several selected studies focusing on OECD nations in the literature. Georgiev and Mihaylov (2015) investigated the validity of the EKC hypothesis across 30 OECD countries, using four local air pollutants, sulfur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO), volatile organic compounds (VOC) and two global pollutants (CO₂, GHG) as dependant variables. The findings varied depending on the dependent variables. Specifically, the curve for CO₂ and GHG emissions exhibited a linear increase. In contrast, SOx emissions yielded a U-shaped curve, while employing CO, VOC, and NOx variables resulted in an inverted U-shape.

Jebli et al. (2016) utilized both the FMOLS and DOLS methods across 25 OECD countries spanning the years 1980 to 2010. They confirmed the presence of an inverted U-shaped EKC hypothesis within the sample. In contrast, Özokcu and Özdemir (2017) analyzed 26 high-income OECD countries for the years 1980 and 2010 and found evidence against the EKC hypothesis. Their results suggest an inverted N-shaped curve, implying economic growth may not inherently decrease environmental damage.

Churchill et al. (2018) examined the EKC hypothesis for 20 OECD countries from 1870 to 2014. Their country-level analyses revealed diverse outcomes. While five countries (Finland, France, Spain, the UK, and the USA) exhibit an inverted U-shaped relationship, consistent with the EKC hypothesis, three countries (Australia, Canada, and Japan) show an N-shaped curve, and one (Denmark) reveal an inverted N-shaped curve. Notably, in 11 countries, no significant relationship was found between economic growth and environmental pollution.

Lau et al. (2019), using Panel GMM and FMOLS methods for 18 OECD countries, questioned the validity of the EKC hypothesis based on nuclear energy for the period 1995–2015. That is, in this study, the effects of electricity generation from nuclear sources, electricity generation from non-renewable sources and trade openness on CO2 emissions were investigated. Their results point to an inverted U curve to support the idea that electricity generated from nuclear sources leads to lower CO₂ emissions without delaying long-term growth in these countries. Ng et al. (2019) used the PMG, panel FMOLS, and panel DOLS approaches for 25 OECD countries. Their results support an inverted U-shaped relationship.

Leal and Marques (2020) examined the EKC hypothesis for the 20 highest CO₂-emitting OECD countries (1990–2016) by analysing different dimensions of globalization. They divided the countries into low- and high-globalization groups based on rankings. Their findings supported the EKC hypothesis only in highly globalized countries, suggesting that economic growth might improve environmental quality in more integrated economies. Conversely, the EKC didn't hold for low-globalization OECD countries. This study highlights the importance of considering different aspects of globalization when examining its link to environmental pollution.

The study by Isik et al. (2021) investigated the EKC hypothesis for 8 OECD countries, utilizing Driscoll-Kraay standard errors and the CCEMG estimator for robust analysis. They tested the EKC hypothesis using a different method. This method involved decomposing the per capita GDP series into periods of increase and decrease, focusing only on the economic growth phases in the model. They argued that this approach better reflects the core idea of the EKC hypothesis. The standard model, using undecomposed GDP data, supported the EKC hypothesis for four countries (Turkiye, Australia, Canada, and France). However, when they applied their method with decomposed GDP data, the EKC hypothesis was not supported in any country.

This study addresses a critical gap by investigating the validity of the inverted N-shaped EKC in OECD countries. While prior research has extensively examined the EKC hypothesis, limited studies have focused on OECD nations, particularly regarding the differential impacts of renewable and non-renewable energy consumption. Additionally, we employ two distinct environmental degradation measures to strengthen robustness of our analysis. This study aims to contribute to a deeper understanding of the complex relationship between environment and economic development in OECD nations, offering valuable insights for policymakers navigating the energy transition.

Data

This study applies a set of panel data for the 33 OECD countries covering the spanning from 1995 to 2015. All the data of this study were extracted from the World Development Indicators (WDI). Detailed information about the data used in the study is given in Table 1.

Table 1

Variable descriptions
Acronyms	Variables	Measurement units	Source
Dependant Variables
LNCO₂	CO₂ emissions	Metric tons per capita	The World Bank
LNGH	Total greenhouse gas emissions	kt of CO₂ equivalent	The World Bank
Explanatory Variable
LNGDP	GDP per capita	Constant 2017 international $	The World Bank
Control Variables
REN	Renewable energy consumption	% of total final energy consumption	The World Bank
NREN	Fossil fuel energy consumption	% of total	The World Bank

LNCO ₂ is the logarithm of per capita carbon dioxide emissions. Carbon dioxide emissions include carbon dioxide produced during the consumption of solid, liquid, and gaseous fuels and during gas flaring. LNGH shows the logarithm of total GHG emissions. Total GHG emissions refer to a sum in carbon dioxide equivalent, including anthropogenic sources of methane (CH₄), nitrous oxide (N₂O), and fluorinated gases (HFCs, PFCs, and SF₆), which exclude short-cycle biomass burning activities but include other sources of biomass combustion (e.g., forest fires, post-combustion decay, peat fires, and the decay of drained peatlands). LNGDP refers to the logarithmic transformation of GDP per capita, serving as a measure of income level. REN is the share of renewable energy obtained from natural resources such as solar, wind, hydroelectric, biomass, and geothermal in the total final energy consumption. NREN denotes fossil fuel energy consumption as a non-renewable energy source. It is the total energy consumption of a country as a percentage of the amount of energy obtained from fossil fuels such as coal, oil, and natural gas.

Our baseline model is specified as follow:

$E=f (Y,{Y}^{2}, {Y}^{3},Z$ (1))

Where E represents each of environmental damage indicators, Y denotes income, ${Y}^{2}$ denotes income squared, ${Y}^{3}$ denotes income cubed, and Z includes other explanatory variables that may influence environmental degradation. The empirical model assumes the following structure:

$${LNCO2}_{it}={\beta }_{0}+{\beta }_{1}{LNGDP}_{it}+{\beta }_{2}{LNGDP}_{it}^{2}+{\beta }_{3}{LNGDP}_{it}^{3}+{\beta }_{4}{REN}_{it}+{\beta }_{5}{NREN}_{it}+{\mu }_{it}+{\eta }_{t}+{\epsilon }_{it}$$

Where i is the cross-sectional country index and t is the time index. LNGDP, LNGDP², and LNGDP³ represent the log transformed per capita real GDP in linear, quadratic, and cubic form. REN is renewable energy consumption (% of total) and NREN is non-renewable energy consumption (% of total). Finally, µ captures state-specific effects, η represents time-specific effects, and ε is the error term. The sign of coefficient $\beta$ related to income, determines the specific shape of the EKC:

• β₁ = β₂ = β₃ = 0: There is no association between income growth and pollution levels. Changes in income have no impact on pollution in this scenario.
• β₁ > 0, β₂ = β₃ = 0: Pollution increases linearly with income growth. As income rises, pollution is expected to proportionally increase.
• β₁ < 0, β₂ = β₃ = 0: Pollution decreases linearly with income level. This suggests that stricter environmental regulations or technological advancements might be offsetting the negative impact of income growth on pollution.
• β₁ > 0, β₂ < 0, β₃ = 0: This represents the inverted U-shaped EKC. Initially, pollution increases with income growth (β₁ > 0). However, as income continues to rise (β₂ < 0), pollution starts to decrease.
• β₁ < 0, β₂ > 0, β₃ = 0: This represents a U-shaped. Initially, pollution might decrease with income growth (β₁ < 0) due to initial environmental improvements. However, as income continues to rise (β₂ > 0), pollution might begin to rise again, potentially due to unsustainable practices or limitations of initial improvements.
• β₁ > 0, β₂ < 0, β₃ > 0: This describes an N-shaped EKC. Similar to the inverted U-shape, pollution rises with income initially (β₁ > 0). However, the decline in pollution (β₂ < 0) is followed by another increase (β₃ > 0) at even higher income levels. This could stem from factors like increased consumption patterns or limitations of previous pollution control strategies.
• β₁ < 0, β₂ > 0, β₃ < 0: This represents an inverted N-shaped EKC. Here, This indicates an initial increase in pollution (β₁ < 0) with economic growth, followed by a decline (β₂ > 0) as income reaches a certain level, eventually leading to lower pollution at higher income levels (β₃ < 0). This pattern is a less common scenario, but it may potentially reflect specific policy changes or technological advancements at various development stages. The study summarizes the theoretical framework of the inverted-N shaped relationship among the variables in Fig. 1.

Empirical results and discussion

Table 2 presents the summary statistics for the chosen variables, while Table 3 depicts the correlation between independent and dependent variables. Notably, all variables exhibit positive correlations, yet renewable energy displays a negative correlation with both CO₂ and GHG emissions.

Table 2

Descriptive statistics
Variable	Obs	Mean	Std. Dev.	Min	Max
LNCO₂	693	2.083	.408	.994	3.019
LNGH	693	11.943	1.452	8.006	15.734
LNGDP	693	10.461	.384	9.425	11.18
REN	693	16.688	15.489	.44	77.76
NREN	693	74.037	19.86	10.255	98.526

Table 3

Matrix of correlations
Variables	(1)	(2)	(3)	(4)	(5)
(1) LNCO₂	1.000
(2) LNGH	0.282	1.000
(3) LNGDP	0.422	0.043	1.000
(4) REN	-0.244	-0.600	0.216	1.000
(5) NREN	0.137	0.592	-0.184	-0.735	1.000

To assess the presence of cross-sectional dependence (CSD) in our data, we employ the Pesaran (2004) test. Table 4 reports the results, where the null hypothesis of no CSD is rejected for all variables at a 1% significance level. This confirms the existence of cross-sectional dependence within the data series for all variables. The stationarity properties of the variables are examined using Pesaran's CADF test due to the presence of cross-sectional dependence. Table 5 reveals that all variables achieve stationarity only in their first-differenced form (i.e., they are integrated of order one I(1)).

Table 4

Pesaran (2004) cross-section dependence test
Variable	CD-test	p-value
LNCO2	32.414***	0.000
LNGH	15.701***	0.000
LNGDP	91.368***	0.000
REN	42.453***	0.000
NREN	39.038***	0.000
LNCO2	59.474***	0.000
, , and ** denote significance at 1%, 5%, and 10% levels, respectively

Table 5

Cross-sectionally dependent panel unit root test
Variable	Level		First difference
Variable	t-bar	p-value	t-bar	p-value
LNCO2	-1.369	0.990	-3.310***	0.000
LNGH	-1.200	1.000	-3.275***	0.000
LNGDP	-2.117**	0.017	-2.306***	0.001
REN	-1.381	0.988	-3.122***	0.000
NREN	-1.465	0.960	3.381***	0.000
, , and ** denote significance at 1%, 5%, and 10% levels, respectively

Additionally, the study conducted post-fixed effects estimations, including Wooldridge tests for assessing serial correlation among estimated parameters, and a modified Wald statistic to detect groupwise heteroscedasticity within the residuals of fixed effect regressions. The outcomes of these tests, displayed in Table 6, reveal the presence of both heteroscedasticity and autocorrelation within the dataset under examination in this study.

Table 6

Heteroskedasticity and autocorrelation tests
Modified Wald test for groupwise heteroskedasticity	chi2	P-value
Modified Wald test for groupwise heteroskedasticity	2345.01	0.0000
Wooldridge test for autocorrelation	F-test	P-value
Wooldridge test for autocorrelation	115.745	0.0000

Given the identified issues of cross-sectional dependence, heteroscedasticity, and autocorrelation within the dataset, it is imperative to use appropriate statistical techniques to address these challenges and ensure the validity of the study's findings. In this regard, the utilization of Driscoll-Kraay standard errors presents a compelling solution (Hoechle 2007).

Table 7 illustrates the regression results of Driscoll-Kraay standard errors with the fixed-effect (FE) and random-effect (RE) model. We employed the Hausman test before Driscoll and Kraay standard error calculations and reported probability Chi-square at the bottom of the table. The Hausman test with a probability value of 0.003 suggests significant bias in the random effects model. Therefore, we prefer the fixed effects model for more consistent estimates.

Regarding Driscoll-Kraay standard errors with the FE model result, all variables show statistical significance at the 1% level. The estimated coefficients for LNGDP, LNGDP², and LNGDP³ are (-102.739), (10.080), and (-0.328), respectively, providing empirical support for the hypothesis of an inverted N-shaped EKC in OECD sample of 33 countries. More specifically, pollution falls with economic development at the early stages. However, beyond a certain threshold of economic development, further increases in GDP per capita results in increased CO₂ emissions, reflecting the environmental costs associated with industrialization and rising energy demands (the scale effect). Eventually, at even more advanced stages of development, further economic growth correlates with a subsequent decline in pollution. This may be due to factors such as technological advancements, shifts towards cleaner energy sources (the technique effect), heightened environmental awareness and the implementation of environmental policies and regulations. Our findings support the earlier research of Özokcu and Özdemir (2017) but contradict Ullah et al. (2024) who reported an N-shaped curve for 17 OECD countries, and other studies finding an inverted U-shape for OECD countries (Churchill et al. 2018; Lau et al. 2019; Jebli et al. 2016).

Sinha et al. (2018) argue that simply looking at the signs of the coefficients without verifying the first and second order conditions of the mathematical model employed is not sufficient to confirm an (inverted) N-shaped EKC. Instead, two conditions must be met: (i) β₁, β₃ < 0, β₂ > 0, and (ii) β₂² – 3 β₁ β₃ > 0 (Sinha et al. 2018). The first condition ensures the downward and upward slopes of the curve, while the second condition guarantees the existence of real turning points. By applying these conditions and testing our model mathematically, we ensure the validity of the inverted N-shaped EKC finding in the dataset of 33 OECD countries.

The results reveal a negative and significant correlation between REN and carbon emissions. Specifically, a 1% increase in REN usage corresponds to a reduction of environmental degradation by -0.018% in OECD countries. This suggests that incorporating renewable energy sources helps mitigate emission levels, offering a valuable means to alleviate the ecological impact of human activities. This finding is further supported by the research of Dagar et al. (2022) and Destek and Sinha (2020) concerning OECD nations. Given these findings, it is advisable for OECD governments to actively promote greater adoption of renewable energy. Strategies may include increasing investment in the renewable energy sector and implementing measures to lower the cost of renewable energy, thereby stimulating demand. By undertaking such initiatives, governments can foster a more sustainable energy landscape while concurrently addressing environmental concerns. Unsurprisingly, a 1% rise in NREN consumption has a statistically significant positive impact on CO₂ emissions, confirming established literature that identifies NREN use as a major driver of emission levels. Additionally, the R² value in the results reveals that 72% of the variations in CO₂ emissions is explained by LNGDP, REN, and NREN.

Table 7

Estimation results of FE and RE with Driscoll-Kraay standard error approach
DV:LNCO₂	DK-FE			DK-RE
	Coef.	Std.Err.	Prob.	Coef.	Std.Err.	Prob.
LNGDP	-102.739***	18.633	0.000	-101.742***	18.144	0.000
LNGDP2	10.080***	1.811	0.000	9.981***	1.771	0.000
LNGDP3	-0.328***	0.059	0.000	-0.325***	0.058	0.000
REN	-0.018***	0.001	0.000	-0.018***	0.002	0.000
NREN	0.005***	0.001	0.000	0.005***	0.001	0.000
_cons	349.240***	63.955	0.000	345.889***	62.156	0.000
Prob > Chi2			0.003
R-squared	0.72			0.20
Observations	693			693
N	33			33
, , and ** denote significance at 1%, 5%, and 10% levels, respectively

To verify the reliability of our results, we used total GHG emissions as the dependent variable. The analysis details are presented in Table 8.

Table 8

Robustness check
DV:LNCO₂	DK-FE			DK-RE
	Coef.	Std.Err.	Prob.	Coef.	Std.Err.	Prob.
LNGDP	-78.807***	21.282	0.001	-79.864***	18.872	0.000
LNGDP2	7.664***	2.079	0.001	7.766***	1.838	0.000
LNGDP3	-0.247***	0.068	0.002	-0.250***	0.060	0.000
REN	-0.020***	0.002	0.000	-0.020***	0.001	0.000
NREN	0.003***	0.001	0.001	0.003***	0.001	0.004
_cons	280.658***	72.639	0.001	284.266***	64.568	0.000
Prob > Chi2			0.006
R-squared	0.57			0.42
Observations	693			693
N	33			33

*, **, and *** denote significance at 1%, 5%, and 10% levels, respectively.

Conducting the Hausman test prior to estimations, we find a p-value of (0.006), indicating preference for the fixed effects model. The R² value suggest 57% of the variability in CO2 emissions can be explained by the independent variables. Notably, all coefficients remain significant at the 1% level and reinforces the presence of the inverted N-shaped pattern that characterizes the relationship between economic development and emissions in OECD countries. Additionally, the signs of the coefficients for REN and NREN remain consistent with the main analysis. Reinforcing the robustness of our findings, the mathematical conditions for an inverted N-shaped EKC (ß₁ < 0, ß₃ < 0, ß₂ > 0, and ß₂² − 3ß₁ß₃ > 0) are again satisfied in the robustness check using total GHG emissions as dependent variable.

Environmental destruction has been of great interest to researchers and policymakers and has taken a central place on their agendas. Decades of research have studied the complex interplay of factors underlying global environmental problems. However, the quests for comprehensive solutions are still on-going. Especially with the continuous increase in industrial production, increasing gas emissions have led to a cross-border externality, leading to critical problems such as ozone depletion and climate change.

Escalating environmental concerns highlight the need for a deeper understanding of economic growth patterns in advanced economies, particularly OECD countries, whose dominance in the international arena grants them significant influence over environmental outcomes. This paper investigates the impact of economic growth on the environment within the framework of the EKC hypothesis, considering the role of both renewable and non-renewable energy consumption. Specifically, we aim to empirically validate the (inverted) N-shaped EKC hypothesis in 33 OECD economies using panel data analysis from 1995 to 2015.

The analysis yielded surprising findings that challenge the conventional EKC theory. According to the regression results, the income (GDP), the income squared (GDP²), and the income cubed (GDP³) have a negative, positive and negative impact on environmental destruction, respectively. In other words, the findings reveal an inverted N-shaped EKC curve, signalling that unchecked economic growth might not lead to automatic environmental improvement, even though growth may appear to improve the environment in the long run. We also find that incorporating renewable energy sources plays a vital role in mitigating emissions, offering a valuable means to alleviate the environmental pressures of human activities.

Overall, environmental problems represent a critical global threat that transcends national borders, generating negative externalities impacting both current and future generations. These multifaceted issues demand a comprehensive approach. Yet, global environmental challenges are the result of contributions from numerous countries, and no single nation acting alone can effectively resolve them. Therefore, combating environmental problems necessitates international cooperation and coordination, alongside well-designed national public policies. Furthermore, a shift towards cleaner energy alternatives, such as renewable sources like solar and wind power, is crucial to achieve sustainable economic development. Further studies could investigate the effectiveness of specific environmental policies in different contexts to provide evidence-based guidance for policymakers.

Ethical approval

This study did not require ethical approval as it did not involve human participants, animal subjects, or sensitive data.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Declarations

of conflicting interests

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author contributions

Abdulkadir BULUT: Supervision, Writing - original draft, review & editing, Validation, Resources.

Abbasi KR, Awan A, Bandyopadhyay A, Rej S, Banday TP (2023) Investigating the inverted N-shape EKC in the presence of renewable and nuclear energy in a global sample. Clean Technol Environ Policy 25(4):1179–1194
Abdallah KB, Belloumi M, De Wolf D (2013) Indicators for sustainable energy development: A multivariate cointegration and causality analysis from Tunisian road transport sector. Renew Sustain Energy Rev 25:34–43
Ahmed K, Long W (2012) Environmental Kuznets curve and Pakistan: an empirical analysis. Procedia Econ Finance 1:4–13
Akbostancı E, Türüt-Aşık S, Tunç Gİ (2009) The relationship between income and environment in Turkey: is there an environmental Kuznets curve? Energy policy 37(3):861–867
Aldy JE (2005) An environmental Kuznets curve analysis of US state-level carbon dioxide emissions. J Environ Dev 14(1):48–72
Apergis N, Payne JE (2010) The emissions, energy consumption, and growth nexus: evidence from the commonwealth of independent states. Energy policy 38(1):650–655
Arrow K, Bolin B, Costanza R, Dasgupta P, Folke C, Holling CS, Pimentel D (1995) Economic growth, carrying capacity, and the environment. Ecol Econ 15(2):91–95
Beckerman W (1992) Economic growth and the environment: Whose growth? Whose environment? World Dev 20(4):481–496
Bölük G, Mert M (2015) The renewable energy, growth and environmental Kuznets curve in Turkey: an ARDL approach. Renew Sustain Energy Rev 52:587–595
Churchill SA, Inekwe J, Ivanovski K, Smyth R (2018) The environmental Kuznets curve in the OECD: 1870–2014. Energy Econ 75:389–399
Cole MA, Rayner AJ, Bates JM (1997) The environmental Kuznets curve: an empirical analysis. Environ Dev Econ 2(4):401–416
Day KM, Grafton RQ (2003) Growth and the environment in Canada: an empirical analysis. Can J Agricultural Economics/Revue canadienne d'agroeconomie 51(2):197–216
Destek MA, Sinha A (2020) Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Co-operation and development countries. J Clean Prod 242:118537
Dijkgraaf E, Vollebergh HR (2005) A test for parameter homogeneity in CO₂ panel EKC estimations. Environ resource Econ 32(2):229–239
Dinda S (2004) Environmental Kuznets Curve Hypothesis: A Survey. Ecological Economics: 431–455
Doğan E, Şeker F (2016) The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries. Renew Sustain Energy Rev 60:1074–1085
Dong B, Wang F, Guo Y (2016) The global EKCs. Int Rev Econ Finance 43:210–221
Fakher HA, Ahmed Z, Acheampong AO, Nathaniel SP (2023) Renewable energy, nonrenewable energy, and environmental quality nexus: An investigation of the N-shaped Environmental Kuznets Curve based on six environmental indicators. Energy 263:125660
Farooq U, Bhanja N, Rather SR, Dar AB (2024) From pollution to prosperity: Using inverted N-shaped environmental Kuznets curve to predict India’s environmental improvement milestones. J Clean Prod 434:140175
Fodha M, Zaghdoud O (2010) Economic growth and pollutant emissions in Tunisia: an empirical analysis of the environmental Kuznets curve. Energy policy 38(2):1150–1156
Fosten J, Morley B, Taylor T (2012) Dynamic misspecification in the environmental Kuznets curve: evidence from CO₂ and SO₂ emissions in the United Kingdom. Ecol Econ 76:25–33
Galeotti M, Lanza A (1999) Richer and cleaner? A study on carbon dioxide emissions in developing countries. Energy policy 27(10):565–573
Galeotti M, Lanza A, Pauli F (2006) Reassessing the environmental Kuznets curve for CO₂ emissions: A robustness exercise. Ecol Econ 57(1):152–163
Georgiev E, Mihaylov E (2015) Economic growth and the environment: reassessing the environmental Kuznets Curve for air pollution emissions in OECD countries. Lett Spat Resource Sci 8(1):29–47
Gill AR, Viswanathan KK, Hassan S (2018) A test of environmental Kuznets curve (EKC) for carbon emission and potential of renewable energy to reduce greenhouse gases (GHG) in Malaysia. Environ Dev Sustain 20(3):1103–1114
Grossman GM, Krueger AB (1991) Environmental impacts of a North American free trade agreement
He J, Richard P (2010) Environmental Kuznets curve for CO₂ in Canada. Ecol Econ 69(5):1083–1093
Hill RJ, Magnani E (2002) An exploration of the conceptual and empirical basis of the environmental Kuznets curve. Aust Econ Pap 41(2):239–254
Hoechle D (2007) Robust standard errors for panel regressions with cross-sectional dependence. stata J 7(3):281–312
Holtz-Eakin D, Selden TM (1995) Stoking the fires? CO₂ emissions and economic growth. J Public Econ 57(1):85–101
Huang J, Zhang D, Zhang Z, Kong W, Yan J, Xia F (2023) Trapped in dilemma: Inverted N-shaped EKC evidence of economic growth and ecological land in a spatial spillover perspective. Appl Geogr 161:103145
Isik C, Ongan S, Ozdemir D, Ahmad M, Irfan M, Alvarado R, Ongan A (2021) The increases and decreases of the environment Kuznets curve (EKC) for 8 OECD countries. Environ Sci Pollut Res 28:28535–28543
Jalil A, Mahmud SF (2009) Environment Kuznets curve for CO₂ emissions: a cointegration analysis for China. Energy policy 37(12):5167–5172
Jebli MB, Youssef SB, Ozturk I (2016) Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries. Ecol Ind 60:824–883
Kaul I, Grunberg I, Stern M (1999) Global public goods New York-Oxford
Kuznets S (1955) Economic growth and income inequality. Am Econ Rev 45(1):1–28
Lau LS, Choong CK, Ng CF, Liew FM, Ching SL (2019) Is nuclear energy clean? Revisit of Environmental Kuznets Curve hypothesis in OECD countries. Econ Model 77:12–20
Leal PH, Marques AC (2020) Rediscovering the EKC hypothesis for the 20 highest CO₂ emitters among OECD countries by level of globalization. Int Econ 164:36–47
Lee H, Calvin K, Dasgupta D, Krinner G, Mukherji A, Thorne P, Park Y, IPCC (2023), 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC Geneva Switzerland
Lorente DB, Álvarez-Herranz A (2016) Economic growth and energy regulation in the environmental Kuznets curve. Environ Sci Pollut Res 23(16):16478–16494
Magnani E (2001) The Environmental Kuznets Curve: development path or policy result? Environ Model Softw 16(2):157–165
Moomaw WR, Unruh GC (1997) Are environmental Kuznets curves misleading us? The case of CO₂ emissions. Environ Dev Econ 2(4):451–463
Nasr AB, Gupta R, Sato JR (2015) Is there an environmental Kuznets curve for South Africa? A co-summability approach using a century of data. Energy Econ 52:136–141
Nasreen S, Anwar S, Ozturk I (2017) Financial stability, energy consumption and environmental quality: Evidence from South Asian economies. Renew Sustain Energy Rev 67:1105–1122
Naveed A, Ahmad N, Aghdam RF, Menegaki AN (2022) What have we learned from Environmental Kuznets Curve hypothesis? A citation-based systematic literature review and content analysis. Energy Strategy Reviews 44:100946
Ng CF, Choong CK, Ching SL, Lau LS (2019) The Impact of Electricity Production from Renewable and Non-Renewable Sources on CO₂ Emissions: Evidence from OECD Countries. Int J Bus Soc 20(1)
Özokcu S, Özdemir Ö (2017) Economic growth, energy, and environmental Kuznets curve. Renew Sustain Energy Rev 72:639–647
Öztürk I, Acaravci A (2013) The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey. Energy Econ 36:262–267
Panayotou T (1993) Empirical tests and policy analysis of environmental degradation at different stages of economic development, vol 992927783402676. International Labour Organization
Panayotou T (1997) Demystifying the environmental Kuznets curve: turning a black box into a policy tool. Environ Dev Econ 2(4):465–484
Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Cambridge Working Papers Economics 1240(1): 1
Shahbaz M, Mutascu M, Azim P (2013) Environmental Kuznets curve in Romania and the role of energy consumption. Renew Sustain Energy Rev 18:165–173
Sinha A, Sen S (2016) Atmospheric consequences of trade and human development: A case of BRIC countries. Atmospheric Pollution Res 7(6):980–989
Sinha A, Shahbaz M, Balsalobre D (2018) N-shaped environmental Kuznets curve: a note on validation and falsification
Tamazian A, Rao BB (2010) Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies. Energy Econ 32(1):137–145
Ullah A, Dogan M, Pervaiz A, Bukhari AA, Akkus HT, Dogan H (2024) The impact of digitalization, technological and financial innovation on environmental quality in OECD countries: Investigation of N-shaped EKC hypothesis. Technology in Society 102484
Vishal D, Khan MK, Alvarado R, Rehman A, Muhammad I, Adekoya OB, Shah F (2022) Impact of renewable energy consumption, financial development and natural resources on environmental degradation in OECD countries with dynamic panel data. Environ Sci Pollut Res 29(12):18202–18212
Vollebergh HR, Dijkgraaf E, Melenberg B (2005) Environmental Kuznets curves for CO, vol 2. heterogeneity versus homogeneity
Yaduma N, Kortelainen M, Wossink A (2015) The environmental Kuznets curve at different levels of economic development: a counterfactual quantile regression analysis for CO₂ emissions. J Environ Econ Policy 4(3):278–303
Yavuz NÇ (2014) CO₂ emission, energy consumption, and economic growth for Turkey: evidence from a cointegration test with a structural break. Energy Sources Part B: Econ Plann Policy 9(3):229–235

It is important to note that Grossman and Krueger (1991) did not analyze CO₂ emissions in their study due to data limitations at the time. However, many studies mistakenly assume that the authors include CO2 as a measure of environmental degradation in their analyses.
For a thorough examination, refer to the research paper "What have we learned from Environmental Kuznets Curve hypothesis? A citation-based systematic literature review and content analysis." by Naveed et al. (2022).
https://datatopics.worldbank.org/world-development-indicators/
ß₂² − 3ß₁ß₃ = (10.080)² − 3(-102.739)(-0.328)
= 0.511
Since 0.511 > 0, the second condition is also satisfied.

Download PDF

Version 1

posted

You are reading this latest preprint version

Unveiling the inverted N-shaped environmental Kuznets curve in OECD nations: an empirical analysis

Status:

Version 1

Abstract

Figures

Introduction

Selected Empirical Literature

Data, methodology, and empirical results

Data

Model and Methodology

Empirical results and discussion

Conclusion

Declarations

Ethical approval

Declarations

Funding

Author contributions

References

Footnotes

Status:

Version 1