Is the environmental Kuznets Curve in Europe related to the per-capita ecological footprint or CO2 emissions?

Ecological Indicators 113 (2020) 106187

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Review

Is the environmental Kuznets Curve in Europe related to the per-capita ecological footprint or CO2 emissions? Halil Altıntaş, Yacouba Kassouri

T

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Affiliation Erciyes University, Faculty of Economics and Administrative Sciences, Department of Economics, Kayseri 38039, Turkey

A R T I C LE I N FO

A B S T R A C T

Keywords: Ecological footprint CO2 emissions Economic growth Renewable energy Fossil fuels consumption Environmental Kuznets Curve (EKC)

The nexus between environment-economic development has been investigated for a long time. Many empirical studies have measured environmental degradation by CO2 emissions and ignored the possibility that the use of such metric as environmental degradation indicator may be invalid in some cases when it comes to resource stocks. This paper focuses on two indicators of environmental degradation including ecological footprint (EF) and CO2 emissions as target variables to provide new insights into the ongoing discussions of whether the environmental Kuznets Curve (EKC) hypothesis is related to the indicators of environmental pressure used. Estimating a heterogeneous panel model with data on 14 European countries over the period 1990–2014, we provide evidence for the sensitivity of the EKC hypothesis to the type of environmental degradation proxy used. Furthermore, we provide new insights regarding the relevance of EF as an appropriate environmental tool that fits the EKC prediction in contrast to CO2 emissions. Regarding the explanatory variables, the results show that renewable energy is an environmentally friendly source while fossil fuels contribute to environmental degradation. The inclusion of renewable energy and fossil fuel does not alter the behavior of economic growth in all environmental degradation indicators. The empirical results demonstrate the need to implement environmental management policies that encourage the production/supply of renewable energy and to reduce reliance on fossil fuel consumption. This paper is expected to provide policy makers with a set of policy proposals to achieve sustainable environmental and economic development.

1. Introduction According to the 2019 Eurostat report, the European Union (EU, hereafter) has registered important progress towards most of the sustainable development goals (European Commission, 2019). However, member states still face persistent environmental problems such as poor urban air quality, unsatisfactory waste treatment and declining habitats. Against these environmental problems, a common strategy for greening European economies in a variety of sectors has been placed at the forefront. Despite resource efficiency strategies such as Europe 2020 (European Commission, 2010), resource efficiency roadmap (European Commission, 2011), and the Waste Framework directive including end of waste criteria (Europeon Parliament, 2008) being implemented in the region, achieving a sustainable decrease in pollution emissions continues to be an issue in the Union (Hennig, 2017). For an effective response to these environmental challenges, it is critical to address them from a holistic perspective. This is intended to act as a remainder to member States to continue to grow within limits and stay below critical environmental thresholds. This will enhance

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energy and resource efficiency and prevent the depletion of biodiversity and ecosystem. With this background, understanding how economic growth patterns affect environmental degradation is critical for implementing efficient and effective environmental management policies. The environmental aspect of economic growth has been extensively investigated through the EKC hypothesis (Danish et al., 2019; Mikayilov et al., 2018; Ulucak and Bilgili, 2018; Xie et al., 2019). However, the EKC hypothesis roughly illustrates the fact that during the early stage of economic growth, environmental degradation increases but then, at a given level of income (turning point), the trend reverses and economic growth reduces environmental deterioration. Typically, there is a plethora of applied research that examines the validity of the EKC hypothesis for both single and multi-countries which provide mixed evidence (Kaika and Zervas, 2013). A survey of the existing literature (Table 1) reveals four prominent features. First, the EKC hypothesis is tested empirically on various pollutants (CO2 emissions/other greenhouse gas emissions) and different control variables have been used to model the environmental degradation-economic growth nexus (human capital, trade openness,

Corresponding author. E-mail address: [email protected] (Y. Kassouri).

https://doi.org/10.1016/j.ecolind.2020.106187 Received 2 September 2019; Received in revised form 30 January 2020; Accepted 3 February 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

2 1997–2001; 1995–200 1960–2003 1995–2010 1990–2008 1992–2010 1980–2012

EU-15, EU-28 France EU-27 EU countries 16 countries EU-15

European countries France

41 European countries

EU-15

EU-15

EU-26

Azomahou et al. (2006) Akbostancı et al. (2009) Markandya, Golub, and PedrosoGalinato (2006) Mazzanti and Zoboli (2009) Iwata et al. (2010) Lapinskienė et al. (2014) López-Menéndez et al. (2014) Kasman and Duman (2015) Dogan and Seker (2016)

Al-Mulali et al. (2015) Can and Gozgor (2017)

Abid (2017)

Armeanu et al. (2018)

Destek et al. (2018)

Aydin et al. (2019)

PSTR

Mixed results

No EKC

MG-FMOLS, MG-DOLS, and DCCEMG

No EKC

Panel GMM

EKC (Inverted U-shaped)

No EKC EKC (Inverted U-shaped)

Panel cointegration and FMOLS DOLS

OLS

No EKC EKC (Inverted U-shaped) Mixed results Mixed results EKC (Inverted U-shaped) EKC (Inverted U-shaped)

No EKC No EKC EKC (Inverted U-shaped)

EKC (Inverted U-shaped)

EKC (Inverted U-shaped)

Neutrality hypothesis Growth hypothesis

Conservation hypothesis Neutrality hypothesis Growth hypothesis Neutrality hypothesis Feedback hypothesis Neutrality hypothesis Conservation hypothesis Conservation hypothesis

Conclusion

Panel Fixed effect and random effect ARDL OLS OLS and Panel Fixed effect FMOLS Panel DOLS

Computable general equilibrium model pooled cross-section, fixed-effects and random effects Nonparametric panel approach Cointegration techniques Panel Fixed effect and random effect

Causality Cointegration and causality Causality Causality Cointegration and causality ARDL ARDL ARDL and Johansen-Juselius maximum Likelihood Bootstrap panel Causality Wavelet approach

Method(s)

Acronym list: AMG: Augmented Mean Group; ARDL: Autoregressive-Distributed Lag model; CUP-FM: Continuously Updated Fully Modified; CUP-BC: Continuously Updated Bias Corrected; DCCE-MG: Dynamic Common Correlated Effects-Mean Group; FMOLS: Fully Modified Ordinary Least Squares; GMM: Generalized Method of Moments; MG-DOLS: Mean Group-Dynamic Ordinary Least Squares; MG-FMOLS: Mean Group- Fully Modified Ordinary Least Squares; OLS: Ordinary Least Squares; PSTR: Panel Smooth Transition Regression; VECM: Vector Error Correction Mechanism.

1990–2013

1980–2013

1980–2013

1990–2011

1990–2013 1964–2014

Waste indicator and, GDP CO2, GDP, Urbanization, and trade GHG and real GDP CO2, Real GDP, renewable energy and non-renewable energy GDP, trade, urbanization, CO2, and energy consumption CO2, Real GDP, Renewable energy, and non-renewable energy consumption CO2, Real GDP, Trade, Urbanization, and renewable energy GDP, energy consumption, economic complexity index, and carbon emissions CO2, GDP, GFCF, Public expenditure, trade openness, and institutions GDP, trade openness, energy consumption, renewable and non-renewable energy consumption Ecological Footprint, GDP, trade openness, energy consumption, renewable and non-renewable energy consumption Ecological Footprint, GDP

Sulfur dioxide, Oxides of nitrogen, Carbon monoxide, and per capita GDP CO2 emissions and GDP per capita CO2 emissions, PM10, SO2 and per capita income SO2 and real per capita GDP

Selden and Song (1994)

1977–1984 Data is averaged for the periods 1973–1975; 1979–1981; and 1982–1984 1960–1996 1968–2003 and 1992–2001 1870–2001

Grossman and Krueger (1991)

Energy consumption and GNP Energy consumption, economic growth, and employment Energy use and GDP GDP, capital, energy use, labor force and CO2 emissions Energy consumption and GNP CO2, energy consumption, income, and foreign trade CO2, Energy consumption, employment, and economic growth CO2, Energy consumption, and economic growth

Urban areas in 42 countries Low, Middle and High income 100 countries 58 provinces in Turkey EU-12

17 Emerging countries USA

Ozcan and Ozturk (2019) Bilgili et al. (2019)

1947–1974 1974–1990 1947–1990 1960–2004 1970–2006 1975–2005 1968–2005 1980–2009

Variables used

Renewable energy consumption and economic growth Biofuels, solar, wind, geothermal, wood, waste and Industrial production Air quality and economic growth

USA USA USA USA Turkey China Turkey Malaysia

Kraft and Kraft (1979) Yu and Jin (1992) Stern (1993) Soytas et al. (2007) Erdal et al. (2008) Jalil and Mahmud (2009) Ozturk and Acaravci (2010) Saboori and Sulaiman (2013)

Data

1990–2016 January 1989-November 2016

Country

Author (s)

Table 1 Relationship between economic growth and environmental quality.

H. Altıntaş and Y. Kassouri

Ecological Indicators 113 (2020) 106187

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H. Altıntaş and Y. Kassouri

environmental challenges faced by the member states deserve further investigation, which motivates the focus on EU countries. In addition, the focus on EU countries is relevant because the EU has set several targets to achieve sustainable economic development while maintaining environmental conservation through promoting green economy. The remainder of this paper is organized as follows: Section 2 “Previous studies” explains relevant empirical literature, Section 3 “Data and Model specification” describes the data and model, Section 4 “Estimation methodology” provides the methodology, Section 5 “Empirical Results” describes the empirical results, Section 6 “Discussion points and policy implications” further discusses the results and provides some policy implications, and, the “Conclusion”, Section 7, yields the highlights of this research.

energy consumption, among others). Despite the use of various pollutant indicators and control variables through multivariate or bivariate regression models, empirical evidence has remained inconclusive. Secondly, literature on the EKC hypothesis usually focuses on CO2 emissions, which may lead to spurious estimates due to the fact that the mix of effluent has shifted from sulfur and nitrogen oxides to carbon dioxide and solid waste, causing aggregate carbon emissions to display an upward trend (Ulucak and Bilgili, 2018). The fact that CO2 emissions tend to be upward trended poses a problem for those studies that focus on CO2 emissions in their analysis of the EKC hypothesis as it can bias the estimates. Following this insight, economists should investigate the EKC hypothesis by considering a more inclusive environmental degradation indicator such as the per capita EF. Thirdly, literature relies on different econometric approaches stemming from early studies using time series models to recent empirical studies based on panel data regression models. Some scholars have presented the merits and demerits of different econometric approaches used in EKC literature (Narayan et al., 2016). Econometric specifications of the EKC model used in the current literature usually are not always robust to omitted variables bias and ignore the possible cross-country heterogeneity and cross-sectional dependencies between countries, which may cause large loss in efficiency and invalidates panel tests. Building on these insights, a rigorous econometric approach to test the EKC hypothesis should be able to deal with these different issues. Finally, to our knowledge only a few previous studies (Bello et al., 2018; Charfeddine and Mrabet, 2017; Mrabet and Alsamara, 2017) have compared the per capita EF indicator with CO2 emissions in exploring the EKC hypothesis for a single country. Consequently, when estimating the impact of economic development on environment, these authors rely on time series approaches, which is not desirable and can be wasteful because it does not explore all available information in the data (Westerlund and Sharma, 2019). The present paper can be seen as a response to these shortcomings in current literature. By considering the current stage of knowledge about the EKC model, our aim is to understand better the EKC hypothesis in Europe from the EF and CO2 emissions perspectives. This study contributes to EKC literature along the following dimensions. Firstly, in contrast to previous studies, our model investigates the impact of renewable energy deployment, fossil fuels consumption, economic growth on environmental degradation indicators within the same framework, especially for European countries. Including renewable energy and fossil fuels consumption enables us to deal with possible omitted variable bias existing in the basic EKC model specification. Secondly, in addition to the carbon dioxide emissions proxy widely used in existing literature, in the present paper we also used the newly introduced measure, namely per capita ecological footprint in order to cover various aspects of environmental degradation such as cropland footprint, carbon footprint, grazing land footprint, built-up footprint, fishing ground, in contrast to conventional greenhouse gas emissions indicators. By doing so, we provided some evidence on whether the EKC relationship is pollutant dependent or not. Thirdly, our econometric approach controls for several factors which may lead to spurious inference if ignored. Indeed, through the joint climate agenda in the EU region, it is highly expected that an individual member state’s action is likely to influence all other member states. In addition, there exists huge diversity across member states in their path of environmental degradation and pollution. As a result, our econometric approach must control for such heterogeneity across countries and potential cross-sectional dependencies in our panel data model. We rationalized these types of biases which may arise in our panel dataset in order to achieve reliable empirical outcomes. The motivation of this paper is that the available literature usually considers the traditional measure of environmental pollution (carbon dioxide emissions) and, regrettably, offers fewer insights for EU countries in analyzing the EKC hypothesis for ecological footprint. Given the pivotal role of the EU in spearheading global climate action, the

2. Previous studies Since the influential work of Kraft and Kraft (1979), the energygrowth-environmental degradation nexus has been at the center of research interest, and empirical studies have proliferated through extensive modelling approaches. However, rare studies have considered the role of ecological footprint in the energy-growth-environmental degradation nexus in the case of European countries. Studies on the relationship between energy consumption, economic growth and environmental degradation can be classified in two groups. The first strand of literature examines the nexus between energy consumption and economic growth through causality analysis. In energy economics literature, the energy consumption-economic growth nexus was investigated within the scope of four hypotheses: neutrality hypothesis, growth hypothesis, conservation hypothesis and feedback hypothesis. The neutrality hypothesis posits that there is no causal relationship between energy consumption and economic growth implying that energy consumption does not affect economic growth. The growth hypothesis is valid when the causal relationship runs from energy consumption to economic growth. In such cases, energy consumption has a positive impact on economic growth indicating that adverse energy shocks adversely affect economic growth. In the conservation hypothesis scope, there is causal relationship from economic growth to energy consumption. In this context, economic growth is critical for energy consumption. Finally, when there is a bidirectional relationship between energy consumption and economic growth, then the feedback hypothesis is valid. Following this hypothesis, energy shocks decrease economic growth and economic recession negatively affects energy consumption. Most of the studies review use causality analysis to explore the causal relationship between variables. For instance, Kraft and Kraft (1979) employed Granger causality analysis to examine the causal relationship between economic growth and gross energy consumption for the United States over the period 1947–1974 and found the conservation hypothesis valid for the United States. Employing a similar approach, Yu and Jin (1992) reached similar conclusion for the US over the period 1947–1990. In contrast, Stern (1993) reported evidence in favor of the growth hypothesis in the US over 1947–1990. Erdal, Erdal, and Esengün (2008) employed cointegration and causality techniques to investigate causal relationship between energy consumption and economic growth over the period from 1970 to 2006 and found that the feedback hypothesis is valid in Turkey. Soytas et al. (2007) studied the effect of energy consumption and economic growth on CO2 emissions in the United States. Following the Granger causality approach, they showed that there is no causal relationship between energy consumption and economic growth, supporting the neutrality hypothesis. Similar results have been reached by Jalil and Mahmud (2009) in the case of China. Ozturk and Acaravci (2010) examined the causal relationship between economic growth, carbon emissions, energy consumption and employment ratio in Turkey and found that neither carbon emissions nor energy consumption caused real GDP per capita indicating that the neutrality hypothesis is valid in the Turkish case. Saboori and Sulaiman (2013) used ARDL 3

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the EKC hypothesis was valid for the European Union (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom) between 1980 and 2012. In the same vein, Kasman and Duman (2015) studied the relationship between energy consumption, carbon emissions, economic growth, trade openness, and urbanization for a panel of new EU members (Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Iceland, Latvia, Lithuania, Macedonia, Malta, Poland, Romania, the Slovak Republic, Slovenia, and Turkey) over the period 1992–2010. Based on panel FMOLS estimation and panel Granger causality tests, they documented the existence of the EKC hypothesis. Can and Gozgor (2017) studied the impact of economic development on carbon emissions in France over the period 1964–2014 and found that the EKC hypothesis is valid in France. In addition, their results highlighted the positive impact of energy consumption on CO2 emissions. Al-Mulali et al. (2015) employed panel data techniques to study the relationship between economic growth, urbanization, trade openness, financial development, renewable energy, and pollution in 23 selected European countries from 1990 to 2013. Abid (2017) tested the hypothesis of the EKC with a sample of 58 Middle East and African countries and 41 European Union countries for the period 1990 to 2011, the author empirical approach rested on the use of panel GMM. The author found a monotonically increasing relationship between CO2 emissions and GDP in both EU and Middle East and African regions. Considering a panel of 28 EU countries, Armeanu et al. (2018) investigated the validity of the EKC hypothesis during the period 1990–2014. Their results showed evidence in favor of the EKC hypothesis in the 28 EU countries. Destek et al. (2018) used a broader measure of environmental degradation to revisit the EKC relationship between environmental degradation in 15 EU countries from 1980 to 2013. Their empirical results indicated that the standard EKC hypothesis is not valid across European countries. Recently, Aydin et al. (2019) analyzed the EKC hypothesis using the recently advanced panel smooth transition regression (PSTR) model for 26 European countries over the period 1990–2013. The empirical results differ according to the subcomponents of the ecological used in the study. Three main considerations can be extracted from above studies. Firstly, EKC literature is extensive with no consistent conclusions. Some scholars reported inverted U-shaped relationship between economic growth and environmental degradation while some other scholars provided evidence against. Secondly, CO2 emissions are widely used as the measure of environmental degradation in the extant literature. Thirdly, there is dearth of studies considering human ecological footprint as an environmental indicator for estimating EKC hypothesis. Fourthly, there are limited numbers of studies investigating the impacts of economic growth, renewable energy supply and fossil fuels consumption on environmental degradation indicators. Our paper contributes to the existing literature in investigating the existence of the EKC in EU through the estimation of the quadratic ecological functions controlling the role of renewable energy and fossil fuels consumption in the same framework. The quadratic model tests the U-shaped relationship between EF-economic growth nexus.

methodology, Johansen-Juselius maximum likelihood approach, and Granger causality technique to explore the relationship between environmental degradation, economic growth, and energy consumption in Malaysia. They revealed causality running from economic growth to energy consumption, confirming the conservation hypothesis. In recent research, Ozcan and Ozturk (2019) employed a bootstrap panel causality test to examine renewable energy consumption-economic growth nexus in emerging countries and highlighted that there is no causality between renewable energy consumption and economic growth in nearly all emerging countries except for Poland. Their results provided evidence in favor of the neutrality hypothesis in nearly all emerging economies. Bilgili et al. (2019) employing a continuous wavelet approach through disaggregated data reported that renewable energy positively affects industrial production, supporting the growth hypothesis. The main consideration that may be extracted from the previous studies is that the literature on energy consumption-economic growth is extensive with different results depending upon the region or period considered and the econometric approach. The second strand of research looked at the energy consumptioneconomic growth-environmental quality nexus by providing evidence in favor or against the energy-EKC hypothesis. Since the pioneering paper by Grossman and Krueger (1991), who initially introduced Kuznets curve into the research of the relationship between economic development and environmental degradation, researchers have paid greater interest to the economic growth-pollution nexus in energy economic literature. The economic growth-environmental degradation nexus was investigated within the scope of the EKC hypothesis. Selden and Song (1994) used a cross-national panel data to examine the inverted-U relationship between pollution (nitrogen oxides and carbon monoxide) and economic development in low, middle, and high-income countries. Employing pooled cross-section, fixed-effects and random effects estimates, they found that per-capita emissions of all pollutants exhibit inverted-U relationships with per capita GDP. Azomahou et al. (2006) rejected the EKC hypothesis using kernel regression methods during the period 1960–1966 for a panel of 100 countries. Akbostancı et al. (2009) employed time series cointegration and panel data pooled EGLS methodologies to explore the relationship between three types of pollutants (namely CO2, SO2 and PM10) and per capita income in Turkey over 1968–2003 and 1992–2001. Their empirical results provided evidence against the inverted U-shaped relationship between environmental degradation and income. Markandya et al. (2006) studied the relationship between per capita GDP and SO2 sulfur emissions for 12 Western European countries from 1850 to 2001. The authors revealed the existence of the EKC relationship between SO2 and economic growth. Mazzanti and Zoboli (2009) focused on waste indicators and investigated the relationship between waste and economic growth through the waste Kuznets curve (WKC) functional form. Their results strongly rejected the prediction of the WKC trend between various waste indicators and economic growth between EU countries over the period 1995–2000. Iwata et al. (2010) explored the relationship between per capita GDP, trade openness, urbanization, and carbon emissions in France for the 1960–2013 period. Their results provided evidence supporting the EKC hypothesis. López-Menéndez et al. (2014) employed OLS and panel fixed effect approaches for a panel data of 27 EU countries for the period 1996–2010. According to the empirical findings, only four countries exhibited an inverted U-shaped, while 11 countries followed an increasing pattern; 9 showed a decreasing path and the remaining 3 countries showed a U-shape curve path. In the same vein, Lapinskienė et al. (2014) investigated whether the inverted U-shaped EKC nexus between greenhouse gases and GDP holds true for 29 European countries in the period of 1995–2010. The authors found different patterns in the relationship between greenhouse gases and GDP. This mixed evidence is partly due to several factors including economic factors, environmental policies, and the level of income. Based on panel cointegration and DOLS estimations, Dogan and Seker (2016) showed that

3. Data and model specification 3.1. The data This paper used annual panel data for the period 1990–2014 for 14 EU countries: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, Switzerland, and the United Kingdom. These EU-14 countries agreed to ratify both international and regional targets to increase the share of renewable energy and to reduce their levels of pollution. For instance, EU member states committed to decreasing their emissions to 20% below 1990 levels by 2020, which is also the basis for the EU international commitments under the Kyoto Protocol’s scope. Based on the aforementioned 4

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Table 2 Detail of variables. Variable name

Definition

Per capita ecological footprint (EF)

The amount of biologically productive land needed to meet human needs and to absorb carbon dioxide emissions. It is measured in global hectares (gha) Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. It is measured in metric tons per capita The value of GDP divided by population (constant 2010 US dollar) The square of per-capita GDP The contribution of renewables to total primary energy supply. It includes the primary energy equivalent of hydro (excluding pumped storage), geothermal, solar, wind, tide and wave sources. Energy derived from solid biofuels, bio gasoline, biodiesels, other liquid biofuels, biogases, and the renewable fraction of municipal waste are also included. This indicator is measured in thousands of toe (tons of oil equivalent) Fossil fuel comprises coal, oil, petroleum, and natural gas products (% of total energy)

CO2 emissions per capita (CE) Per capita real GDP (Y) Square of GDP (Y2) Renewable Energy (RE)

Fossil fuels consumption (FO)

Notes: we have converted the variables used in our study into natural logarithms when conducting our empirical analysis.

growth rate of carbon emissions, which is even lower than the average growth rate of their per capita ecological footprint. Moreover, Norway and Sweden have the highest renewable energy supply with an annual growth rate of 4.07% and 3.65%, respectively. Belgium, the Netherlands, and the UK have the least renewable energy supply in our sample. Turning to fossil fuel consumption, Ireland is the highest fossil fuels consumer with an average growth rate of 5.02%, while Italy is the lowest fossil fuels consumer with an average growth rate of 3.84%. Based on the annual average growth rate for the whole panel, one can draw the profile of EU countries as follows: (i) compared to CO2 emissions, the annual average growth rate of per capita ecological footprint is higher than that of CO2 for all the countries in our sample; (ii) the annual average growth rate of renewable energy supply (2.22%) is lower than that of fossil fuels consumption (4.33%). EU countries are characterized by lower annual average growth rate of CO2 compared to EF growth rate and higher fossil fuels consumption growth rate compared to that renewable energy supply. From the correlation matrix (Table 4), the per capita ecological footprint (lnEF) is negatively correlated to CO2 emissions per capita (lnCE) at the 1% level of significance. The correlation matrix also reveals that the per capita EF is positively correlated with real per capita and negatively correlated with the square of GDP at the 1% level of significance. We reported a positive influence of fossil fuels consumption (lnFO) on the per capita ecological footprint (lnEF) at 1% level of significance, while the negative correlation between (lnRE) and (lnEF) is statistically insignificant. Moreover, per capita CO2 emissions is negatively correlated to real per capita GDP (lnY), the square of GDP (lnY) ^2 and (lnRE) are at 1% level of significance, except (lnFO) which is positive and not significant. Overall, the correlation matrix only describes a preliminary relationship between variables.

influences, we focus on EU member states whose renewable energy pathway is crucial for meeting global and regional climate and renewable energy goals. To investigate the EKC hypothesis, our empirical study considered annual data collected from various sources. The data on the supply of renewable energy was sourced from OECD, real per capita GDP and fossil fuel consumption were obtained from the World Development Indicators. The human ecological footprint was obtained from the Global Footprint Network. These variables were collected from 1990 to 2014, thus, our sample period includes the adoption of global action on climate change (the UNFCC convention in Rio, 1992; the Kyoto Protocol in 1997; The EU-ETS in 2005). In Table 2, a summary of the variables as well as their descriptions are seen. Table 2 provides the readers with clear information about the variables used and their units of measurement. Table 3 depicts the annual average growth rate by country of all the variables over the period from 1990 to 2014. Denmark ranks among the EU countries with the highest per capita ecological footprint growth rate (2.065%) followed by Belgium (1.984%), with Italy and France having the least per capita ecological footprint during this period. Despite the various environmental and climate actions implemented in Denmark, the ecological footprint per household is relatively higher compared to other EU countries. According to the 2019 World Wildlife Fund (WWF) Living Planet Report, Denmark appears to have a relatively higher ecological footprint mainly due to the unsustainable food production, consumption, and farming practices in the country. Concerning CO2 emissions, Ireland has the highest carbon dioxide emissions per capita with an annual growth rate of 1.08%, followed by Denmark and Italy with an annual growth rate of 0.98% and 0.94%, respectively. In addition, France and Sweden have the lowest average

Table 3 Annual average growth rate of the per capita ecological footprint, CO2 emissions, per capita real GDP, square of GDP, Renewable energy, and fossil fuels consumption.

Austria Belgium Denmark Finland France Germany Ireland Italy The Netherlands Norway Spain Sweden Switzerland The UK Total

lnEF

lnCE

lnY

(lnY)^2

lnRE

lnFO

1.756467 1.984112 2.065815 1.864997 1.676809 1.714261 1.754375 1.668644 1.840425 1.860183 1.603036 1.848939 1.714215 1.735132 1.791958

0.7568278 0.670035 0.9853169 0.5340521 0.3642739 0.9013288 1.087005 0.9409495 0.8301613 0.4561283 0.8510776 0.0265678 0.45793 0.9015655 0.6973728

10.63995 10.59112 10.89968 10.59137 10.53906 10.55303 10.59317 10.45543 10.70617 11.28595 10.22632 10.7121 11.13266 10.47355 10.6714

113.223 112.1835 118.8132 112.2051 111.0796 111.3758 112.2982 109.3202 114.6386 127.3895 104.5931 114.771 123.942 109.712 113.9675

3.294898 0.8168495 2.536777 3.414151 2.334441 1.567862 1.093261 1.937662 0.811134 4.070652 2.299155 3.655585 2.957365 0.4090725 2.22849

4.437507 4.905355 4.411958 4.225136 3.927279 4.111313 5.023505 3.846869 4.796717 4.249947 3.921682 4.325579 4.568376 3.954816 4.336146

Note: Table 3 provides the annual average growth rate by country of all variables considered in our study over the period 1990–2014. 5

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level of emissions and renewable energy levels. Following the discussions above, in the first step we tested the cross-section dependence and slope heterogeneity across countries. Secondly, the cross-section dependence results told us the appropriate panel unit root tests to follow in our sample. For instance, the second-generation panel unit root tests, which account for cross-section dependence, was preferred over conventional panel unit roots if cross section dependence was detected in our sample. Thirdly, if variables are integrated of the same order, we employed the panel cointegration test to identify the existence of a long-run relationship (Westerlund, 2007). Fourthly, we estimated the long-run parameters relying on econometric approaches able to deal with the above-mentioned issues. In this study, we relied on two econometric approaches, notably the Interactive Fixed Effect model advanced by Bai (2009) and the Dynamic Common Correlated Effects (DCCE) developed by Chudik and Pesaran (2015). Lastly, the direction of a causal relationship was investigated by using the heterogeneous non-linear panel causality developed by Dumitrescu and Hurlin (2012).

Table 4 Correlation Matrix.

lnEF lnCE lnY ln(Y2) lnRE lnFO

lnEF

lnCE

lnY

(lnY)2

lnRE

lnFO

1 −0.00705 0.315*** −0.310*** −0.0264 0.346***

1 −0.363*** −0.362*** −0.626*** 0.0353

1 1.000*** 0.502*** 0.406***

1 0.505*** 0.400***

1 −0.103

1

Note: * p < 0.05,

**

p < 0.01,

***

p < 0.001.

3.2. The model Following the studies by Charfeddine (2017); Mrabet and Alsamara (2017) and Chen et al. (2019), the general form of the empirical model used in this study is written as below:

ED = f (Y , Y 2, RE , FO)

(1) 4.1. Cross-section dependence and slope heterogeneity

where ED refers to indicators of environmental degradation which is captured in our paper by the per capita ecological footprint (EF) and CO2 emissions (CE). In order to provide further insights and clear the conflictual debate in the literature, we made use of these two proxies of environmental degradation proposed in literature. As described in the previous section, RE and FO refer to the renewable energy supply and fossil fuels consumption variables. By taking the natural logarithmic forms of all the variables, we can specify the model used in this paper as below:

lnEDit = β0 + β1 lnYit + β2 (lnYit )2 + β3 lnREit + β4 lnFOit + εit

As briefly outlined in the previous section, EU countries may display strong interdependencies mainly due to the implementation of common policies for cutting carbon emissions in the region such as the EST legislation. It was important to control the interdependencies that might be present in our panel dataset, otherwise, results might lead to substantial bias (Breusch and Pagan, 1980) and Pesaran (2004). Accordingly, we empirically tested for cross-sectional dependence by following the methodologies developed by Breush and Pagan (1980) and Pesaran (2004). Later, we paid closer attention to the homogeneity of the slope following Hashem Pesaran and Yamagata (2008) in order to capture the heterogeneous dimension of the member states. To preserve space, we reproduced only the cross-section dependence tests proposed by Pesaran (2004) and Pesaran et al. (2008), and later we describe the slope homogeneity tests introduced by Hashem Pesaran and Yamagata (2008). Pesaran (2004) developed a more general cross-sectional dependence (CD) test that is valid for panel where T→∞ and N→∞.

(2)

where i = 1, 2, …, N for individuals (e.g., member states in our case); t = 1, 2, …, T for time. According to the EKC hypothesis, the signs of β1 and β2 are expected to be positive and negative, respectively. The effects of renewable energy and fossil fuels consumption are determined by β3 and β4 , respectively. As a result, statistically significant and negative β3 indicates that the development of renewable energy, as clean energy, contributes to environmental quality while statistically significant and positive β4 implies that fossil fuel consumption deteriorates the environmental quality. Empirically, the relationship between environmental degradation indicators and economic growth is estimated as follows:

Model 1: lnEFit = β0 + β1 lnYit + β2 (lnYit )2 + εit

(3)

Model 2: lnCEit = ζ 0 + ζ1 lnYit + ζ2 (lnYit )2 + uit

(4)

N −1

CD =

(5)

Model 4: lnCEit = Γ0 + Γ1lnYit + Γ2 (lnYit

⎜

⎟

N

⎞ ρiĵ N (0, 1) ⎟ j=i+1 ⎠

∑

(7)

Pesaran (2004) showed that the CD test is robust for heterogeneous dynamic models including multiple breaks in slope coefficients and/or error variances. The CD test has an important drawback, namely it will lack power under certain situations where the population average pairwise correlations are zero, although the individual population pair-wise correlations are non-zero (Pesaran et al., 2008, p 106). Pesaran et al. (2008) proposed bias-adjusted version of the LM tests by using the exact mean and variance of the LM statistic, which is consistent even when the cross section mean of the factor loading is near zero, under which the CD test is not. The bias adjusted LM statistic was calculated as follows:

Model 3: lnEFit = γ0 + γ1 lnYit + γ2 (lnYit )2 + γ3 lnREit + γ4 lnFOit + vit )2

2T ⎛ ⎞ ⎛∑ ⎜ − N N ( 1) ⎝ ⎠ ⎝ i=1

+ Γ3 lnREit + Γ4 lnFOit + τit (6)

Following these four models, we aim at analyzing the sensitivity of the EKC model to various econometric specifications as well as the role of renewable energy and fossil fuel consumption in the environmenteconomic growth nexus. The following section discusses the estimation strategy adopted in this study.

LMadj =

2 ⎛ ⎞ ⎝ N (N − 1) ⎠ ⎜

⎟

N −1

N

∑ ∑ i=1 j=i+1

ρiĵ

(T − k ) ρij2̂ − μTij 2 vTij

N (0, 1) (8) 2 vTij

are respecwhere k represents the number of regressors, μTij and tively the exact mean and variance of (T − k ) ρij2̂ (Pesaran et al., 2008 , p 108). Testing for slope homogeneity Hashem Pesaran and Yamagata (2008) proposed the so-called delta test, under the null hypothesis of slope homogeneity. H0: βi = β, for all i against the alternative hypothesis of heterogeneity, H1: βi ≠ βj, for a non-zero fraction of pair wise slopes for i ≠ j. This test is valid for (N, T)→∞ without any restrictions on the relative expansion of N and T when the error terms are normally distributed. The first step is to calculate the following

4. Estimation methodology When working with macro-panel data, there are two important issues to consider. The first issue is cross-section dependence which is likely to arise in our panel data due to the higher degree of integration (economic, political and financial integration) across member states. Pesaran (2004) reported the substantial bias that may arise when crosssection dependence is ignored. The second issue is the slope heterogeneity in panel data. The homogeneity assumption may not be always valid across our sample of countries as member states differ in their 6

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cointegration could be analyzed in a more efficient way by applying the Westerlund (2007) cointegration test. Westerlund (2007) constructed four tests of the null hypothesis of no cointegration. Two statistics (Group-τ and Group-α) test if the panel is cointegrated as a whole or not, while Panel-τ and Panel-α statistics test the existence of at least one unit that is cointegrated. Westerlund (2007) accounting for cross-sectional dependence and non-strictly exogenous regressors developed his tests based on an error correction model as follows:

modified version of Swamy, 1970 test:

~ s =

N

∑ (βi − βWFE ), i=1

x ′i Mτ x i   (βi − βWFE ) σi2̂

(9)

where β i is the pooled OLS and βWFE is the weighted fixed effect pooled estimator, Mτ is an identity matrix, and σi2̂ is the estimator of error variance. Pesaran and Yamagata proposed a standardized version of Swamy’s test for panel data models where the cross-section dimension (N) could be relative to the time to the time series dimension (T). The proposed test denoted by Δadj is valid for small samples:

Δadj =

N

− ⎛ N−1s − E (z it ) ⎞ − ⎟ ⎜ var (z it ) ⎠ ⎝

(16)

δi (L) z it = δi (z it − 1 − γ1' x i, t − 1) + βi (L)'uit + εit

(10)

(17)

−

−

Westerlund (2007) obtain the error correction model by substituting (15) in (16) and obtained

4.2. CADF panel unit root tests

δi (L)Δyit = θ0i + θ1i t + δi (yit − 1 − γ1' x i, t − 1) + βi (L)'uit + εit

After obtaining confirmation of cross-section dependences and heterogeneity in our panel dataset, we had to check these stylized facts (cross-section dependences and heterogeneity) by applying a robust panel unit root estimation technique. Pesaran (2007) suggested the adoption of Dickey Fuller based panel unit root tests to account for cross-sectional dependence and heterogeneity as well as breaks in the series. Additionally, the tests proposed by Pesaran (2007) display better statistical properties over conventional stationarity tests (Levin, Lin and Chu, (LLC); Im, Pesaran and Shin, (IPS)), which motivated our choice. The cross-sectionally augmented dickey fuller (CADF) proposed by Pesaran (2007) can be expressed as follows:

yi, t = (1 − δi ) μi + δi yi, t − 1 + vi, t

(11)

Δyi, t = αi + βi yi, t − 1 + γi ft + ei, t

Pτ =

Δyi, t = ai + bi yi, t − 1 +

+

− Di Δyt

+ εi, t

δ

i=1

δ î

⋯⋯,Gα =

1 N

N

Tδ

∑ δ (1)i i=1

i

(19)

Most of the literature on the estimation of the regression equation with factor error structure falls into two categories. The first method is the common correlated effects approach of Pesaran (2006). Since this influential paper, the common correlated effects (CCE) model was widely used to estimate the common factor in the error structure. Pesaran (2006) proposed a common correlated effect estimator for linear static panel data models with homogeneous/heterogeneous coefficients. This estimator does not attempt to estimate the factor components (common factors and factor loadings) and instead it uses the cross-sectional averages of the dependent variables and regressors as proxies for the common factors, which can be strongly critical in econometric settings. The second method is the Interactive fixed effects (IFE) method by Bai (2009). This method does estimate jointly the factor component and the regression coefficients through an iterative process. This method has the added benefit that it allows cross-sectional and time effects to enter the models multiplicatively and then, can capture unobserved heterogeneity in contrast to traditional ones with individual and time effects. However, we made use of the IFE estimator because it (i) takes account nonlinear cross-country heterogeneity which is of great importance in this study since it has been shown that the environment-economic growth nexus varies across units and over time. It was therefore important to take account such cross-country heterogeneity otherwise our empirical results could be severely biased; (ii) accommodates strong cross-sectional error dependencies as strong cross section dependence is an important concern in this panel data;

(13)

where y is the proposed proxy of the unobservable common factor proposed by Pesaran (2007) to remove the cross-section dependence due to a common shock which affect all the units similarly. Pesaran (2007) derived a cross-sectional augmented version of the IPS-test N '= 1

N

∑ σ î

4.4. Bai (2009) interactive fixed effects

−

∑ CADFi

δ̂ 1 ⋯⋯Pα = T δ ,̂ andGτ = σ δ̂ ̂ N

yi are the usual Newey and ui ω yi where ω ui and ω In which, δi (1) = ω West (1994) long-run variances estimators. Westerlund (2007) suggested using the bootstrap approach to account for cross-sectional dependence.

(12)

αi = (1 − δi ) μi , βi = −(1 − δi ) and Δyi, t = yi, t − yi, t − 1. The null hypothesis of unit root is given by H0: βi = 0 for all i against the alternative hypothesis H1: βi < 0 for i = 1, 2,….N1, βi = 0 for i = N1 + 1, N1 + 2, ⋯⋯N . The following CADF regression was used in Pesaran (2007) to test the above hypothesis. − D0 yt − 1

(18)

where the deterministic components are given by θ0i = δi (1) α1i − δi α 0i + δi α1i and θ1i = −δi α1i . Based on Eq. (18) the null hypothesis of no cointegration can be written as H0: δi = 0,for all i. The alternative hypothesis for the two panel statistics is H1: δi = δ < 0, for all i. In this case, the existence of a common factor δ is assumed for all units. Thus, if H0 is rejected then that implies the whole panel is cointegrated. For the two group statistics, the alternative hypothesis H1: δi < 0 for at least one-unit is i. According to this hypothesis, there is no common parameter for the error correction model. Westerlund (2007) panel statistics and group mean statistics are:

where vi, t = γi ft + ei, t , with ft the observed common factor, ei, t is the individual-specific error. To account for unit root hypotheses Eq. (11) can be specified as:

1 N

(15)

x it = x it − 1 + uit where i = 1….….N and t = 1,........T with z it specified as

where E (z it ) = k and var (z it ) = 2 k(T − k − 1)/(T + 1).

CIPS =

yit = α 0i + α1i t + z it

(14)

where the critical values are given in Pesaran (2007). Due to the presence of the common factor, the CADFi statistic will be not cross-sectionally independent. Pesaran (2007) CADF unit root tests have good properties and can account for serial correlation. 4.3. Westerlund (2007) panel cointegration As briefly discussed above in the prior section, we tested the longterm relationship between variables by employing the error correction based cointegration test Westerlund (2007), which accounts easily for cross-section dependences. Westerlund’s test was preferred because it controls nuisance arising from endogeneity of the regressors. and has good statistical power compared to the cointegration based on dynamic tests (Pedroni tests, DOLS). As a result, in our panel dataset, 7

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N T principal component analysis, where ({Γ} i i = 1, {Ft }t = 1) are estimated by minimizing the following SSR with a commonly used normalization T T −1 ∑t = 1 Ft Ft' = Ir :

(iii) estimates the coefficients of the regression and the factor structure under genuine interact effects and offers more consistent estimates of the coefficients than other methods. The estimation strategy followed in this study deals with these three features (heterogeneity, cross-section dependence and multifactor error structure) and provides the best available choice in terms of efficiency and consistency to model the relationship between environmental degradation and economic growth in Europe. Moreover, the IFE model by Bai (2009) highly fits the purpose of this study and, as a result, we estimate the following equation:

Yi, t = X 'i, t β + ϕi + st + ei, t

SSR2 =

SSR =

(20)

Dumitrescu and Hurlin (2012) extended the traditional Granger (1969) causality test between time series to detect causality in panel data. Dumitrescu and Hurlin (2012) (hereafter, DH) panel causality test is applicable in macro panel (T > N) and requires variables to be stationary at the same level (i.e. I(1)). DH considers the following regression: K

yi, t = αi +

N

T

yi(1) ,t

− Xi', t β − ϕi − st )2

+

∑ ψik xi,t−k + εit k=1

(25)

where yi, t and x i, t are two stationary variables for country i at time t. K depicts the lag order and it is assumed to be the same across all the panel countries, while parameters θi and ψi differ across units. DH (2012) defined the null hypothesis of the absence of causal relationship between variables for all the cross-section as:

Ho: ψi1 = ⋯=ψik = 0 ∀ i = 1, ⋯..,N

(26)

The null hypothesis is known as the Homogenous Non-Causality hypothesis. Against the alternative hypothesis that there can be causal relationship between variables for some units, which is written as:

H1: ψi1 = ⋯=ψik = 0 ∀ i = 1, ⋯..,N1

ψi1 ≠ 0or⋯orψik ≠ 0 ∀ i = N1 + 1, ⋯..,N The alternative hypothesis or the Heterogeneous Non-causality hypothesis indicates that causality is heterogeneous. DH (2012) computes the average individual Wald statistics associated with the null hypothesis (WNHnc , T ) for units i = 1, ⋯..,N . The average Wald statistic is given by: N −1 WNHnc ∑ Wi,T ,T = N

T

∑i =1 ∑t=1 (yi(1) ,t

K

∑ θik yi,t−k k=1

(22)

i=1

(27)

Where Wi, T are the individual Wald statistics for the ith country corresponding to the null hypothesis of homogeneous non-causality relationship between variables. However, this test detects causality at − the country level. This harmonized statistic (Z ) when T → ∞ and N → ∞ follows a standard normal distribution and can be written as follows:

In Equation (22), Bai (2009) presented two different ways to estimate the factor structure and the regression coefficients: (i) if the factor structure Γ'i Ft is known, we can estimate the regression coefficients by minimizing the following SSR:

SSR1 =

(24)

4.5. Dumitrescu and Hurlin (2012) panel causality tests

(21)

∑i =1 ∑t=1 (Yi,t − Xi',t β − ϕi − st − Γ'i Ft )2

− Γ'i Ft )2

Bai (2009) suggested initiating iteration between estimating one given the other based on SSR1 and SSR2 until the difference in the sum of squared residuals becomes less than a pre-specified threshold set at 10−9 Bai (2009). Additionally, the bias corrected estimators were obtained to correct for time series correlation and heteroskedasticity in ei, t by increasing the number factors until ei, t becomes independent and identically distributed (i.i.d).

where, Ft is an r × 1 vector of unobserved time-specific common shocks, Γi is an r × 1 vector of factor loadings to capture unit-specific reactions to the common shocks, ui, t is an idiosyncratic error. Ft and Γi are all unobserved components.1 For instance, Ft may represent a vector of common shocks such as weather shocks/oil shocks which have heterogeneous effects on each cross-sectional unit via Γi ; as a result, environmental degradation is likely to be affected by both observable and nonobservable factors. Under interactive effects, environmental degradation can be affected by multiple unobservable individual factors and more than one common shock, making the IFE approach more flexible and efficient than the traditional approach which includes dummy variables to control for the unobserved factor components in the data. Therefore, the unobserved factors cause the same statistical bias as the omitted variables bias in the OLS estimate of β from Eq. (20). It is important to control for this bias by following the factor structure model developed in Equation (21). This was done by simultaneously estimating the factor structure and the regression coefficients in an iterative way. The idea behind this estimation technique is that, given the factor structure, the regression coefficients are estimated using OLS after removing the factor structure from the data by performing the principal component analysis on the regression residuals2. Bai (2009) proposed to estimate consistently both the factor process and the regressions coefficients by minimizing the sum of squared residuals (SSR): N

T

' yi(2) , t = Yi, t − Xi, t β − ϕi − st

where Yi, t is the environmental degradation indicator in country i and period t, Yi, t represents two environmental degradation measures (i) per capita human ecological footprint, (ii) CO2 emissions per capita. Xi, t is a vector of regressors including renewable energy and fossil fuel consumption. Country and period fixed effects are represented by ϕi and st . β is a vector of predictive slopes. In our specification, β was not allowed to vary across countries while at the same time we allowed ample heterogeneity through ei, t (Westerlund and Sharma, 2019). Following Bai (2009), we used the factor model specification to facilitate the control of common factors and factor loadings:

ei, t = Γ'i Ft + ui, t

N

∑i =1 ∑t=1 (yi(2) ,t

(23)

−

Γi' Ft .

= Yit − where (ii) if the regression coefficients are known, then we can use the

Z =

N . (WNHnc , T − M ) → N (0, 1) 2K

(28)

Also, the harmonized statistic (Z ) for a fixed T dimension with T> 5 + 3 again follows a normal distribution:

1 Statistically, Ft corresponds to the principal components of the idiosyncratic error uit (see Bai 2009 for more details). 2 Bai (2009) proposed two different ways to initiate the iteration: (i) with the OLS estimates of the regression coefficients without necessarily controlling for the factor structure, and (ii) with the principal component estimates of the factors from the data, ignoring regressors. Finally, one can compare which estimator provides the lowest final sum of squared residuals.

Z=

N T − 3K − 5 T − 3K − 3 ⎤ ∗ ∗ ⎡ ∗ WNHnc , T − K → N (0, 1) 2K T − 2K − 3 ⎣ T − 3K − 1 ⎦ (29)

Using Monte Carlo simulations, the DH (2012) reported that these tests have good finite sample properties, even when both T and N are 8

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Notes: a, b and c denote 1%, 5% and 10% significance level, respectively. Numbers in parentheses are p-value.

UK); iv) The square of GDP is not stationary at level for the countries under consideration and is stationary at first difference for (Finland, France, Switzerland and UK); v) renewable energy is stationary at level for three countries out of fourteen (Austria, Sweden and Switzerland) and it reaches stationarity at first difference for Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands and, Spain; vi) fossil fuel consumption is stationary at level for Finland, France, Germany, Spain, Switzerland, and the UK while it becomes stationary at first difference for Belgium, Denmark, Ireland, the Netherlands, Norway, and Sweden. The empirical results for the CIPS-Statistics indicate that per capita real GDP, square GDP, renewable energy, and fossil fuel contain no unit root at first difference, implying that the variables are integrated of order 1 (I(1)). In addition, ecological footprint and carbon emissions indicators do not contain unit root at level. This empirical finding provides additional evidence on the stationarity properties of ecological footprint and CO2 emissions (Ulucak and Lin, 2017; Yilanci et al., 2019). In other words, shocks in the ecological footprint (CO2 emissions) for EU countries would have transitory effects on the path of EF and CO2 emissions.

small.

5.3. Westerlund (2007) ECM bootstrap cointegration results

5. Empirical results

This section checks whether there is a cointegration relationship between variables in model 3 & 4 by following the Westerlund (2007) ECM cointegration approach. The Westerlund (2007) cointegration tests are consistent with our data since it controls for the cross-sectional dependency in the data set. Table 7 highlights the results of the cointegration tests by including constant and trend in the analysis. As seen, Group and Panel tests statistics are able to reject the null hypothesis of no cointegration with bootstrapped p-values at the 1% and 5% level of significance. For instance, the cointegration vector is confirmed for model 3 by Group_t , Panel_t and by Panel_a at 5% significance level. Resulting output implies that there is cointegration relationship between lnEF, lnY, (lnY)2, lnRE and lnFO. Similarly, the cointegration vector is certified for model 4 by Group_t at 1% level of significance, and Panel_t at 5% level of significance. Therefore, the null hypothesis of no cointegration is strongly rejected indicating the presence of cointegration between lnCE, lnY, (lnY)2, lnRE and lnFO in EU countries for 1990–2014. Since variables are cointegrated, the next step consists of investigating the long-run elasticities.

Table 5 Cross section dependence and slope homogeneity tests. Variables LM-test P-value CDLM-test P-value CD-test p-value LM adj P-value

lnEF

lnCE a

378.718 (0.000) 21.327a (0.000) −3.003a (0.001) 8.543a (0.000)

(lnY)^2

lnY a

a

409.690 (0.000) 23.623a (0.000) −1.882b (0.030) 26.979a (0.000)

498.558 (0.000) 30.210a (0.000) −2.091b (0.018) 32.334a (0.000)

lnRE a

499.461 (0.000) 30.277a (0.000) −2.102b (0.018) 32.230a (0.000)

lnFO a

408.382 (0.000) 23.526a (0.000) −1.431c (0.076) 29.628a (0.000)

654.685a (0.000) 41.783a (0.000) −2.649a (0.004) 18.302a (0.000)

Slope Homogeneity Tests

Statistic P-value

Δ

Δadj

−4.288a (0.000)

−4.889a (0.000)

5.1. Cross-section dependence and slope homogeneity results In this section of the paper, the results of cross-sectional dependence and slope homogeneity tests are described. These tests have been implemented as described in Section 4. Beginning with the findings of cross-sectional dependence depicted in Table 5, empirical outcomes confirm our initial intuitions since the different tests considered were able to reject strongly the null hypothesis of cross-sectional independence for variables under consideration. This output implied that there is considerable degree of cross-correlation in our variables. Table 5 also presents, the estimates of the slope homogeneity tests based on the Δ tests proposed by Hashem Pesaran and Yamagata (2008). The estimates from the slope homogeneity tests reject the null hypothesis of slope homogeneity supporting thus specific cross-country heterogeneity across member states. Given the above findings, it was important to launch second generation estimation techniques to account for both cross-sectional dependence and country specific heterogeneity in the data. Consequently, we launched the cross-sectionally ADF unit root test (CADF) advanced by Pesaran (2007) to consider the cross-sectional dependency and slope heterogeneity issues.

5.4. Interactive fixed effects (IFE) estimation results Table 8 presents the estimation outputs through IFE results for various specifications. As discussed above, if the EKC hypothesis is valid, then the estimated coefficients of lnY and (lnY)^2 are expected to be positive and negative, respectively for the environmental degradation indices. We first estimated model 1 & 2 without including any control variables and thereafter to make sure that our results were free from misspecification problems, we included renewable energy and fossil fuel consumption as potential control variables. It has been established that these variables are able to modify the environment-economic growth nexus (Chen et al., 2019; Zaidi et al., 2019). We explained in the Introduction section, why one would expect positive and negative signs for lnY and (lnY)2 in the EKC specification. For the sake of readability, it is essential here, to mention the expected signs of renewable energy and fossil fuel consumption.4 Renewable energy is expected to have negative influence on environmental degradation indices, since the EU countries’ target is to enhance their environmental quality through the expansion of renewable sources. Fossil fuel consumption is expected to have a positive impact on environmental degradation because fossil-based energy sources release more waste and

5.2. CADF panel unit root results As pointed out by Bilgili et al. (2017), the cross-sectionally augmented Dickey Fuller (CADF) unit root test as developed by Pesaran (2007) has the unique advantage to simultaneously account for crosssectional dependency and heterogeneity among countries.3 The results of the CADF unit root test are presented in Table 6, as we notice that i) the ecological footprint is stationary at level for Austria, Germany, and Norway and it becomes stationary at the first difference for nearly all the countries; ii) C02 emissions is stationary at level for the whole sample, except for Austria, Germany, Ireland, Italy, the Netherlands, and, Norway. In addition, by using the first difference, C02 emissions become stationary for the whole sample. iii) GDP is not stationary at level for the whole sample while it is stationary at first difference for four out of fourteen countries (Finland, France, Switzerland and the 3 The first generation may lead to spurious outcomes as these tests have lower power under the presence of cross-sectional dependence in the series. Therefore, we followed the second-generation panel unit root tests (CADF and CIPS) to examine the stationarity properties of our variables.

4 One might refer to Section 1 for more information about the definitions of these variables.

9

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Table 6 Panel CADF unit root tests. Country ΔlnEF

lnEF Austria Belgium Denmark Finland France Germany Ireland Italy Netherlands Norway Spain Sweden Switzerland UK CIPS-Stat

−3.84 −3.17 −2.70 −1.45 −3.11 −3.95c −2.39 −3.05 −2.89 −3.99c −2.40 −2.63 −2.98 −2.64 −2.94c c

−5.07 −5.27a −4.78a −3.72c −4.59a −6.73a −3.28 −3.96c −4.68a −5.92a −3.55c −3.84c −3.76c −3.59c −4.47a a

lnCE

ΔlnCE

−3.27 −4.52b −6.25a −4.30b −3.70c −3.20 −3.13 −3.52 −2.56 −3.36 −4.44b −6.83a −5.74a −3.84c −4.19a

−7.79 −7.76a −9.41a −8.24a −4.76a −4.72a −5.62a −5.90a −4.66a −5.15a −5.60a −6.19a −5.26a −5.15a −6.16a a

lnY

ΔlnY

(lnY)^2

Δ (lnY)^2

lnRE

−0.86 −1.04 −1.90 −2.73 −2.64 −1.60 −2.35 −2.60 −2.45 −1.93 −2.20 −2.07 −2.87 −2.50 −2.13

−2.83 −2.05 −3.36 −3.72c −3.81c −2.23 −2.64 −3.53 −3.49 −2.85 −2.24 −3.21 −5.27a −5.17a −3.25a

−0.85 −1.03 −1.95 −2.70 −2.62 −1.58 −2.35 −2.60 −2.44 −1.99 −2.20 −2.06 −2.89 −2.53 −2.12

−1.81 −2.02 −3.33 −3.71c −3.81c −2.23 −2.65 −3.54 −3.49 −2.86 −2.40 −3.21 −5.27a −5.17a −3.25c

−3.83 −3.02 −3.07 −2.58 −3.33 −3.07 −2.41 −2.65 −2.85 −3.21 −3.10 −4.74c −4.06b −3.37 −3.23

ΔlnRE c

−7.27 −5.62a −5.54a −5.30a −4.96b −4,46b −3.91c −3.71c −3.87c −2.62 −4.36b −3.28 −5.49a −4.99b −4.66a a

lnFO

ΔlnFO

−2.00 −3.38 −1.92 −4.82b −5.57a −5.04a −3.46 −2.50 −2.78 −3.33 −3.92c −3.11 −4.16b −3.60c −3.54

−3.39 −5.04a −3.77c −5.65a −6.68a −6.38a −4.50b −3.13 −3.95c −5.42a −5.48a −5.26a −4.93b −5.14a −4.91a

Notes: a, b and c denote 1%, 5% and 10% significance level, respectively. 1%, 5%, and 10% critical values for individual units are − 4.97, −3.99, and − 3.55, respectively. 1%, 5%, and 10% critical values for the whole panel are − 3.15, −2.92, −2.74, respectively. Critical values were obtained from Pesaran (2007). Δ denotes the first difference operator. All the variables were tested with intercept and trend.

basic models used in our analysis to test the classical EKC hypothesis based on EF and CO2 emissions, respectively. In these specifications, we estimated the effects of per capita real GDP (lnY) and the quadratic term of per capita real GDP (lnY)2 on the per capita human ecological footprint (lnEF) and CO2 emissions per capita (lnCE). [D-CCE(2)] & [DCCE(6)] were used as alternative approaches to check the robustness of our baseline results. For more reliability of previous model estimates, control variables-namely renewable energy and fossil fuel-were subsequently added into [IFE(3)] & [IFE(7)], respectively. In order to check the sensitivity of the [IFE(3)] & [IFE(7)] outcomes, we estimated D-CCE (4) & D-CCE(8), respectively. This study relied on the dynamic common correlated effects (D-CCE) approach developed by Chudik and Pesaran (2015) for several reasons. First, it resolved cross-sectional dependencies issues in the data by taking the cross-sectional averages and lagged cross-sectional averages of dependent variables. Secondly, the DCCE approach was robust to omit variable bias and bi-directional feedback effects between environmental degradation and its determinants. Thirdly, the D-CCE approach allows for heterogeneity in the

Table 7 ECM cointegration results. Error Correction

Value

Asymptotic p-value

Bootstrap p-value

Group_t Group_a Panel_t Panel_a

−11.273b −1.183 −8.158b −4.146b

0.000 0.118 0.000 0.000

0.049 0.340 0.011 0.038

Group_t Group_a Panel_t Panel_a

−19.427a −0.705 −6.657b −2.602

0.000 0.240 0.000 0.005

0.003 0.704 0.037 0.278

Model 3

Model 4

Note: a , b illustrates statistical significance at the 1% and 5% level. The null hypothesis is of no cointegration between variables and the alternative hypothesis is cointegration. Optimal leads/lags were selected by AIC. Bootstrap was based on 1000 replications. Model 3&4 are described in Section 3.2 and stand for the relationship between lnEF, lnY, (lnY)^2, lnRE, lnFO, and, lnCE, lnY, (lnY)^2, lnRE, lnFO, respectively. Table 8 IFE and D-CCE results. Variables

lnEF IFE(1)

lnCE D-CCE(2)

IFE(3)

L.lnEF

D-CCE(4)

IFE(5)

D-CCE(6)

−2.366 (0.229) 0.107 (0.252)

−0.590a (0.000) −150.81a (0.000) 7.003a (0.000)

(lnY)^2 lnRE lnFO

8.538a (0.001) −0.350a (0.002)

82.415a (0.008) −3.846a (0.007)

D-CCE(8)

−4.210c (0.056) 0.191c (0.066) −0.038 (0.168) 0.096c (0.092)

−1.004a (0.000) −87.489c (0.054) 3.984c (0.054) −0.164 (0.175) 0.005 (0.978)

b

L.lnCE lnY

IFE(7)

−1.028 (0.044)

−0.623 (0.000)

a

5.977b (0.018) −0.236b (0.046) −0.089a (0.000) 0.215a (0.000)

153.052c (0.095) −7.253c (0.097) −0.633b (0.010) −0.296 (0.808)

Notes: IFE refers to the Interactive Fixed Effects estimator following Bai (2009). The IFE model includes fixed effect and a factor model in individual effects. In addition, time effects of dimension 2 were considered. The maximum number of factor was set to 2. D-CCE refers to the dynamic common correlated effects estimator developed by Chudik and Pesaran (2015). L.lnEF and L.lnCE refer to lagged Ecological footprint and lagged CO2 emissions, respectively. P-values for coefficients are in parentheses. a, b, c represent significance level at 1%, 5% and 10% respectively. Numbers in parentheses are p-value.

parameters through the mean group method proposed by (Eberhardt and Presbitero, 2015). The results of the four models are displayed in Table 8 and are discussed below.

emissions. This expectation was confirmed by Chen et al. (2019). For a deep understanding of the EKC model, we estimated a basic EKC model without control variables. [IFE(1)] & [IFE(5)] were the 10

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Looking at the Granger causality results in Table 9, there is significant causality running from economic growth (lnY) to ecological footprint (lnEF) at the 1% significance level. Such causal relationship between (lnY) and (lnEF) was found by Uddin et al. (2017) and Ozcan et al. (2019). As for the ecological footprint (lnEF) and fossil fuels consumption (ln FO) relationship, unidirectional causality running from (lnFO) to (lnEF) was found at the 1% significance level. Moreover, increases in fossil fuels consumption has an influence on environmental quality, which is consistent with the argument made by Dogan and Seker (2016). As for renewable energy, the results describe a bidirectional causality for (lnEF) & (lnRE) at 1% level of significance. The feedback relationship between (lnEF) & (lnRE) implies that EU member states should further promote renewable energy to safeguard the environment. For the sake of robustness, similar tests were performed using carbon dioxide emissions (lnCE) as a proxy of environmental degradation. Similar causal direction was found when we replace (lnEF) with ln(CE), indicating that causality results are not sensitive to the environmental degradation indicators used. Overall, there is one-way causality running from economic growth to ecological footprint. There is evidence of feedback effect between renewable energy and measures of environmental degradation. Furthermore, there is one-way causality from fossil fuel consumption to environmental degradation indicators.

Our results in [IFE(1)] approved the EKC phenomena for European countries as lnY and (lnY)2 are positively and negatively related to lnEF, while the empirical results in [IFE(5)] disconfirmed the EKC phenomena as the coefficients of lnY are negative and the coefficient of the quadratic term is positive. In other words, our baseline results indicated the existence of an inverted U-shaped nexus when considering the ecological footprint as proxy of environmental degradation. However, the U-shaped nexus held when we considered CO2 emissions as an environmental degradation indicator [IFE(1)]. As discussed above, it is essential to check the robustness of baseline findings through other econometric techniques. To achieve this objective, the latest technique developed by Chudik and Pesaran (2015) was performed. In [D-CCE(2)] & [D-CCE(6)] estimations, results show that (i) lagged lnEF (L.lnEF) and lnCE (L.lnCE) were found statistically significant and have a negative relationship with lnEF and lnCE at 1% level of significance, respectively. This empirical result highlights an inertia effect that legitimates the dynamic common correlated effects specification. Similar results were also reported by Jorgenson et al. (2017) and Ghazali and Ali (2019); (ii) interestingly, [D-CCE(2)] results approved the EKC hypothesis with ecological footprint as dependent variable, while [D-CCE (6)] results suggested that the EKC does not hold for CO2 emissions. In the next step, we asked whether empirical results regarding the EKC hypothesis depend on the model specification considered. We reexamined the EKC hypothesis by including renewable energy and fossil fuel consumption variables in our baseline specification. Moreover, the results were clear and showed very little differences when renewable energy and fossil fuel consumptions were considered. We got similar results regarding the validity of the EKC hypothesis with EF [IFE (3)], while the U-shaped nexus was confirmed when employing CO2 emissions as a proxy of environmental degradation [IFE (7)]. It is also worth noting that these results held when employing the DCCE approach. Concerning control variables, the effect of renewable energy supply was found to be negative and statistically significant at 1% level of significance. This result holds only for EF as a dependent variable in [IFE(3)] and [D-CCE(4)], while the effect of RE is negative and not significant in other cases. The main implication of this finding is that renewable energy supply is likely to improve environmental sustainability, a finding which is consistent with (Akadiri et al., 2019; Alola et al., 2019; Balcilar et al., 2019; Bekun et al., 2019; Destek and Sarkodie, 2019). Fossil fuel consumption was found to be positively correlated with environmental degradation indices and statistically significant at traditional levels. Our results are consistent with (Ghazali and Ali, 2019; Hanif, 2017; Hanif et al., 2019).

6. Discussion points and policy implications The present study aims to provide new insights on several critical evaluations of the evidence supporting EKC literature. Knowledge of the EKC prediction is critical to address environmental and development challenges faced by countries. Evidence for the EKC hypothesis is very mixed (Caviglia-Harris et al., 2009; Dinda, 2004; Pablo-Romero et al., 2017; Stern, 2004) and several factors have been advanced to explain this inconsistency, including the functional form (monotonic, quadratic or cubic behaviors); the pollutants (CO2 emissions, waste emissions, or other solid pollutants), and the econometric techniques used (time series or panel approach). One of the major gaps that exists in the EKC literature is the scarcity of empirical study able to overcome these shortcomings. This inquiry makes up this empirical gap by reinvestigating the EKC hypothesis from the ecological footprint and CO2 emissions perspective in 14 EU countries covering the period 1990–2014. We responded to the following empirical problems in EKC literature. First, the issue of model specification (functional form) was addressed by providing evidence for a quadratic specification without control variables and later, we considered the role of RE and FO in the environment-growth nexus. By doing so, this study provides readers with a clear picture on the correct specification of the functional form of the EKC model. Secondly, we addressed the issues of assessing the EKC hypothesis following the EF versus CO2 emissions perspective. Such characterization of the EKC has

5.5. Dimitrescu-Hurlin (2012) panel Granger causality estimation results In this section, we present the Dumitrescu and Hurlin (2012) panel’s Granger causality results and show the heterogeneous causality results. Table 9 exhibits the panel Granger causality. Table 9 Dumitrescu and Hurlin (2012) Panel Granger causality. Panel

Null Hypothesis (No causality)

lnEF → lnY

lnY → lnEF

lnEF → lnRE

lnRE → lnEF

lnEF → lnFO

lnFO → lnEF

Z-bar p-value

1.464 (0.143)

5.094a (0.000)

2.992a (0.002)

8.316a (0.000)

1.257 (0.208)

5.041a (0.000)

Panel

Null Hypothesis (No causality)

lnY → lnCE

lnCE → lnRE

lnRE → lnCE

lnCE → lnFO

lnFO → lnCE

a

a

a

1.815 (0.200)

8.687a (0.000)

lnCE → lnY Z-bar p-value

2.128 (0.122)

5.094 (0.000)

6.751 (0.000)

Notes: Z-bar Statistic; Wald Statistics. The values in parentheses are p-values. parentheses are p-value.

6.450 (0.000) a

,

11

b

and

c

denote significance level at 1%, 5% and 10% respectively. Numbers in

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an unsustainable development path. To achieve sustainable environmental quality, it is mandatory and vital for member states to spend more budget on research and development to adopt environment-friendly energy sources. Accordingly, renewable sources should be explored and used on large scale as an alternative to fossil energy. Fossil fuels consumption should be gradually substituted by RE across countries.

not been explored rigorously in EKC literature. Empirically, there is no clear-cut answer about the relevance of indicators of environmental degradation in understanding the EKC model. Thirdly, we use econometric approaches that allow for (i) the possibility of cross-sectional error dependencies, which could arise due to omitted common effects; and (ii) the presence of cross-country heterogeneity, which suggests that the effects of growth on environmental degradation varies across countries. Such heterogeneity is expected in our panel framework and may lead to inconsistent estimates if ignored. Our empirical results using second generation unit root tests showed that our measure of environmental degradation is stationary at levels. Therefore, this study contributes to the ongoing debate about the stochastic properties of ecological indicators by examining the stationarity of EF and CO2 emissions. The results of this study match those of Belbute and Pereira (2017); Ozcan et al. (2019); Tiwari et al. (2016) and Yilanci et al. (2019). The major implication of this finding is that shocks to EF and CO2 emissions have temporary impact suggesting that the behaviors of these environmental indicators can be predicted based on historical movements. In addition, our study revealed long-run relationship between environmental degradation indicators, economic growth, the quadratic term of economic growth, fossil fuels and renewable energy. From a policy analysis perspective, it is useful to estimate parameters (elasticities) of this long-run relationship. Our results also provided strong evidence for the validity of the EKC hypothesis for EF, while a U-shaped behavior is obtained in the CO2 emissions specification. This evidence for the EKC hypothesis confirms several results (Baek and Kim, 2013; Mrabet and Alsamara, 2017; Ulucak and Bilgili, 2018). Our empirical results that invalidate the EKC hypothesis when we use carbon dioxide emissions as an indicator of environmental degradation is consistent with Akbostancı et al. (2009) and Ozturk and Acaravci (2010). Based on these findings, we can establish several key implications to sustain economic growth and improve environmental quality in Europe. First, member States should adopt new policies to sustain economic development while preserving nature. However, policy to reduce ecological degradation must be extended to other components of ecological footprints such as built-up land, carbon absorption land, cropland, fishing grounds, forest area and grazing land. As stated by Aydin et al. (2019), the behavior of the EKC relationship varies across each component of the ecological footprint. Since EF is found to be environmentally relevant in capturing the EKC relationship, it is essential to integrate this metric into the development plans across member States. There is a need to construct a more comprehensive indicator able to capture both the level of economic development and the regenerative biocapacity of every country in order to evaluate a better sustainable development trajectory of the countries. Given that carbon dioxide emissions and economic growth followed a U-shaped nexus in Europe, it is important for the member states to follow a comprehensive economic growth path able to balance between CO2 emissions and economic development. As evidenced in this study, the rapid degradation of the EU’s environment has been exacerbated by massive consumption of fossil fuels to boost economic growth. Indeed, a majority of environmental challenges across member states is due to regional dependence on conventional fossil energy, which causes both environmental challenges and energy insecurity risk in Europe. This study spotlights the problems of dependence on imported fossil fuels on environmental degradation in the EU. It is imperative that policy makers reduce their dependence on fossil fuels and promote the use and consumption of renewable sources able to foster an environmentally friendly economic development. Furthermore, policy makers should focus on new types of technologies that are more efficient and less oil-based in order to achieve their renewable energy targets. The negative influence of fossil fuels on environmental degradation indicates the need to urge EU countries to start internalizing this negative effect of fossil fuels, otherwise, environmental degradation may sharp cut economic activities mainly due to the uncertainty associated with fossil fuel prices, which may lead to

7. Conclusion During the last few decades, the objective of preserving the regenerative biocapacity for future generations has led stakeholders and scholars to seek new ways to live that sustain economic development without damaging certain ecosystem functions like climate regulation, biodiversity, land availability, and air and water quality which are quite essential for humankind survival and cannot be replaced. Thus, in order to make economic development sustainable, policy makers should clearly understand and be able to predict the environmental pressure that is due to progress of economic development. As discussed previously, there is a huge amount of literature on the EKC hypothesis with mixed empirical results. This inquiry is unique in that it addresses major empirical issues, notably sensitivity of the EKC hypothesis on indicators of environmental degradation, econometric and model specification problems, and the robustness of the EKC model raised in the literature testing this hypothesis. Our study aimed to assess the EKC hypothesis following the ecological footprint versus CO2 emissions perspective with a data set of 14 EU countries covering the period 1990–2014. The European countries used are Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, Switzerland, and the United Kingdom. To do so, we followed the second-generation panel estimation approach by conducting cross-sectional dependence and slope heterogeneity tests. Then, we performed panel unit root followed by cointegration tests. Finally, we estimated the long-run parameters as well as the causal relationship among variables. By taking account several criticisms raised against the EKC hypothesis in the literature, this paper provides new insights on the environment-growth nexus. Specifically, we questioned whether the EKC hypothesis was valid for each environmental pressure indicator and discussed the sensitivity of the outcomes to the econometric methods and functional form used to test the hypothesis. By doing so, the empirical results of this study tackled relevant criticisms toward the EKC prediction raised in literature. The empirical results of this study verify the environmental convergence hypothesis for our measure of environmental degradation, namely EF and CO2 emissions. This outcome provides some evidence for the elaboration of a common environmental policy across member states in the Union in order to achieve major environmental targets such as clean environment, preservation of biodiversity and sustainable development. In addition, rank analyses showed that a long run relation occurs between the variables indicating the need to estimate parameters (elasticities) of this long-run relationship. By following efficient and consistent estimators, we can summarize our findings as follows: (i) The EKC hypothesis is very sensitive to the indicators of environmental degradation used. Although, EF and CO2 emissions are environmental degradation indicators, we found that only the EF indicator is compatible with the EKC hypothesis. In other words, the results conclude that the presence of an inverted U-shaped relationship was found between EF and growth, supporting the EKC hypothesis, whereas a U-shaped nexus perfectly describes the relationship between CO2 emissions and growth in Europe. The main implication of this finding is that EU countries should follow appropriate energy policies to decline the growth rate of CO2 emissions. In addition, the empirical prediction of the EKC hypothesis for EF may take longer to happen without sound policies 12

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to reduce demand for renewable natural capital, preserve the biodiversity and speed the transition to alternative energy sources. (ii) Our empirical results showed that fossil fuels consumption and renewable energy do not alter the relationship between environmental pressure indicators and economic growth. Indeed, the consideration of these variables highlights the robustness of the EKC model in the functional form used. Across various functional forms, the results support the EKC for EF and accept the U-shaped nexus for CO2 emissions. Furthermore, RE significantly decreases EF while the effects of RE on CO2 emissions is negative but not statistically significant. We found that fossil fuel consumption positively affects environmental degradation indicators, even though the results are not consistent across various specifications. (iii) The panel Granger causality results validated the feedback hypothesis between RE and ED. There is also one-way causality running from economic growth to environmental degradation indicators and from fossil fuels to environmental degradation indicators. These empirical results support the argument that the current pattern of economic growth in Europe stimulate the consumption of fossil fuels, thus increasing environmental pressures. It appears crucial to set new energy policies able to cut the levels of fossil fuels in order to improve environmental quality. Energy systems in Europe should focus on reducing the share of energy consumption from fossil fuel sources and place more emphasis on investment and research in the deployment of renewable sources.

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