The role of economic freedom in achieving the environmental sustainability for the highest economic freedom countries: testing the environmental Kuznets curve hypothesis

doi:10.21203/rs.3.rs-2628897/v1

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The role of economic freedom in achieving the environmental sustainability for the highest economic freedom countries: testing the environmental Kuznets curve hypothesis

https://doi.org/10.21203/rs.3.rs-2628897/v1

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published 12 Aug, 2023

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In the study, the relationship between ecological footprint, economic freedom, renewable energy consumption and economic growth is analysed under the scope of environmental Kuznets curve (EKC) hypothesis. Here, fifteen countries with the highest economic freedom for the period 1996–2018 are examined i.e., Australia, Canada, Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Singapore, South Korea, Sweden, and Switzerland. The long-term relationship between the variables is examined using the panel cointegration test. According to the test results, it has been observed that the variables in the EKC model act together in the long run. According to the long term estimation results, it is seen that economic freedom decreases the ecological footprint, namely environmental degradation, in Canada, while it increases in Estonia. Furthermore, it is concluded that renewable energy reduces the ecological footprint in Australia, Denmark, Luxembourg, Norway, Singapore, and Switzerland. Nevertheless, it has been determined that the EKC hypothesis is valid for Canada, Denmark, and Singapore, but not for other countries.

Economic freedom

EKC hypothesis

Ecological Footprint

Economic growth

Renewable energy

Environmental Pollution

In the globalizing world, competition in economies brings along many problems. Environmental pollution is one of the most important one that brings with it global warming, depleted natural resources and increasing carbon emissions. Since this is a common problem of all countries, many international conferences related to the environment have been held, such as the climate policies of Kyoto (1997) and Paris (2015). For instance, with the Kyoto protocol, an international agreement has been made to set various targets for industrialized countries to reduce the emission of greenhouse gases. In this context, decisions have been taken such as supporting the use of renewable energy and protecting forests and natural water resources. This means that sustainable economic growth also brings environmental sustainability. In order to prevent this environmental degradation, researchers from many fields have tried to produce solutions with studies and still continue. Environmental pollution is not only represented by carbon emissions, but also ecological footprint is one of the important indicators. Created in the early 1990s by Wackernagel (1994) and Rees (1992), the ecological footprint is a key sustainability indicator used to measure our impact on the planet. The difference between the amount of footprint and the amount of biocapacity gives the ecological openness. While a sustainable environment can be explained by the reduction of ecological openness, this can be achieved by reducing the ecological footprint and increasing the biocapacity. In this context, ecological footprint is an important factor in determining environmental policies. When examining the causes of pollution and seeking solutions, not only economic growth, but also economic freedom, energy consumption, and macroeconomic variables that have an impact on economic growth should be analysed together with economic growth. There are several studies in the literature that link the relationship between efficient energy resource use and economic growth to environmental pollution. The increase in economic activities has increased energy consumption more, thus causing more emissions. On the other hand, it is argued that growth under the light of an innovative technology reduces environmental degradation. The effects of variables such as energy consumption and economic growth that may cause environmental degradation can be analysed under the scope of environmental Kuznets curve (EKC) hypothesis. Furthermore, the EKC hypothesis not only examines the relationship between economic growth and the environment, but also deals with issues such as financial growth, globalization, energy consumption, trade-openness. The EKC hypothesis is developed by Grossman and Krueger (1991) and advocates that economic growth, up to a certain income level, increases environmental pollution with the use of fossil fuels. However, when the threshold value is exceeded, the opposite is true and economic growth reduces environmental pollution. This is achieved through the use of renewable resources (Aydın 2019).

If, EKC hypothesis argues the increase in income decreases environmental pollution by exceeding the target, that is called the threshold value, the focus is on exceeding this target while examining the hypothesis. Therefore, the variables that will reduce the impact of income growth on environmental degradation should be well determined. Looking at the literature, it can be said that economic freedom is one of the variables that have a significant effect on environmental degradation due to income increase. Economic freedom is developed by Heritage Foundation and measured as annual indexes based on 12 quantitative and qualitative factors those are grouped in 4 main categories. The economic freedom index ranges from 0 to 100, with 0 the lowest and 100 the highest freedom score (Heritage Foundation 2022). With economic freedom, individuals have the right to control their own workforce and property, and they can also work, produce, consume, or invest as they please, and they are supported by governments to act freely in these areas. Economic freedom is closely related to issues such as greater prosperity, a healthier society, a cleaner environment, more wealth per capita, the development of humanity and democracy, and the eradication of poverty. According to many researchers, economic freedom makes markets more efficient and competitive, allowing resources to be used less and more effectively. It is hypothesized that economic freedom can reduce the ecological footprint, along with ecological regulations regarding the production of clean goods and services brought to efficient market consumers. On the contrary, some researchers argue that economic freedom encourages trade liberalization, and thus causing a high proportion of capital-intensive industries to turn to dirty industries. In addition, by stabilizing the macroeconomic environment in the long term, it creates a safer investment mechanism for investors and causes resource-intensive industry combinations, which can increase the ecological footprint.

In the study, 15 countries with the highest economic freedom are selected, and it is aimed to reveal the impact of economic freedom more clearly on the environment. The contribution of the study to the existing literature is that for the first time, this study investigated the impact of economic freedom on the ecological footprint under the scope of environmental Kuznets curve hypothesis for the 15 countries with the highest economic freedom. According to the author's best knowledge, this is the first study in this country group and with this method. The literature appears in the second part of the study. The third part includes data, model, and methodology. The part four represents the empirical results of the study and the final section includes the results of the study and policy recommendations.

In the last few years environmental challenges have caught the attention of researchers. All kinds of economic activities not only increase economic growth and welfare, but also decrease environmental quality by increasing the demand for infrastructures such as food, clean water and energy. In other words, it increases the ecological footprint. In the literature, it is stated that together with economic growth, economic freedom also has effects on environmental pollution. While some studies claim that economic freedom negatively affects environmental quality, others claim that resources are used more effectively by leading to efficiency and market competition.

Economic freedom has positive effects on environmental degradation from many different perspectives. Shahnazi and Shabani (2021) have discussed economic freedom from the perspective of efficiency and have argued that there are positive effects such as relative pricing, energy efficiency and improvement of ecological laws. They also say that it can increase the focus on reducing CO2 emissions and reduce freebies. Arrow et. al. (2004) has examined economic freedom from the perspective of stability and moderation. In this context, economic freedom can affect environmental quality through long-term investments in energy efficient companies and clean renewable energy sources. In addition, increasing competitiveness leads consumers to choose environmentally friendly goods. When the demand for a cleaner environment increase, companies often meet this demand by producing more customer friendly products. In the study of Joshi and Beck (2018), it is stated that economic freedom has a positive effect on CO2 emissions in OECD countries, while the existence of a negative effect in non-OECD countries is mentioned. The effects of openness to international goods markets on pollution is researched by Antweiler et. al. (2001) and stated that free trade has positive effects on the environment. Sub-Saharan countries between 2010 and 2016 has been analysed by Koçak et. al. (2019) and Baloch et. al. (2020) and in both studies, it is observed that economic freedom reduces environmental degradation. Also, economic freedom negatively affects the ecological footprint. Roy and Goll (2014) have revealed that economic freedom has positive effects on environmental quality for 57 countries selected from all continents. Bjørnskov (2020) has found that economic freedom has reduced greenhouse gas emissions within the effect of adopting renewable energy, green technological development, and innovation on 155 countries during the 1975-2015 period. Recently, the selected South Asian countries between 1981 and 2016 are studied by Jain and Kaur (2022). The results indicate that in countries with increased economic freedom, economic growth is faster, investment rates and income levels increase, poverty decreases faster and can provide better air quality by controlling carbon emissions. Between 2000 and 2017, Mamkhezri et. al. (2022) has investigated the effects of economic freedom on the ecological footprint of three landmass, those are cropped lands, forest products, and grazing lands, for 17 countries in the Asia-Pacific. The findings indicate that investment freedom decreases the cropped land and forest products footprint, while financial freedom decreases the forest products footprint and increases the grazing land footprint. Moreover, business freedom, property rights and tax burden rise the pressure on the environment and threaten environmental pollution. In addition, while the existence of the environmental Kuznets curve hypothesis is valid for forest products and grazing lands, the hypothesis is not valid for cropped lands.

There are researchers who argue that economic freedom has positive effects on environmental degradation, as well as those who argue that it has negative effects on pollution. The effects of economic freedom on many areas such as poverty, economic growth, environmental degradation, ecological footprint, fishing grounds footprint, capitalism, forest-products have been examined. It has been agreed with many studies that economic freedom brings economic growth and wealth and is seen that the free economy increases the growth more (see Mahmood et. al. 2022, Brkic et. al. 2020, Piatek et. al. 2013, Aisen and Jose Veiga 2010, Erdal 2009, De Haan and Jan129 Egberg 2000). However, it also drives demand for food, clean water, energy, railroads, farmland, and other infrastructure, increasing more resource-intensive use. Thus, the ecological footprint increases as a result of any government or private sector activities. Looking at the effect of the economic freedom index on the fishing grounds footprint, some studies show that economic freedom increases environmental destruction and disrupts the ecosystem cycle (see, Amin et. al. 2022, Karimi et. al. 2022, Longo et. al. 2015, Gould et. al. 2004). Carlsson and Lundström (2001) argue that greater economic freedom can harm the environment by increasing the number of companies and industries that produce more products and therefore pollutants. In addition, while economic freedom positively affects environmental degradation in developed countries, the opposite is observed in developing countries. Majeed et al. (2021), examining the indicators of economic freedom in Pakistan for the period 1990-2019, has concluded that it increases environmental pollution in the long run.

Alola et al. (2022a) have examined the effects of economic freedom on environmental sustainability in G-20 economies between 2000 and 2016, by using difference GMM and two-step system GMM estimations. Real wage and renewable energy consumption are chosen as explanatory data indicators. They observed that these indices harm environmental quality for developing and emerging economies, excluding countries advancing the 2030 Sustainable Development Goals (SDGs). The relationship between economic freedom, political risk, financial risk, and ecological footprint for 86 China's Belt and Road Initiative (BRI) countries has been examined by Ashraf (2022) between 1995 and 2020. The findings suggest that it requires focusing on strategies to minimize ecological degradation and prevent market failure. However, governments that increase economic freedom encourage the creation and growth of more companies. This, in turn, raises national economic conditions and increases ecological degradation.

Alola et. al. (2022b) has found that renewable energy contributes to environmental sustainability across countries by significantly reducing the ecological footprint in G7 countries. Furthermore, it has been indicated that economic freedom negatively affects environmental sustainability by rising the ecological footprint of countries.

Data and Model

The existence of the EKC hypothesis in this study for the 15 highest economic freedom countries is examined, namely Australia, Canada, Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Singapore, South Korea, Sweden, and Switzerland, for the period 1996–2018. The ecfo, efr, ren, gdp and gdpsq denote ecological footprint (per capita, global hectares per person), index of economic freedom, renewable energy consumption (per capita, total final consumption, ktoe), per capita GDP (constant 2010 US $) and square of GDP, respectively. The ecfo and efr data are taken from Global Footprint Network database and the Heritage Foundation, respectively. The ren, gdp and gdpsq data are taken from the World Bank database. All data in the study are converted into logarithmic form.

The following model is used to test the existence of the EKC hypothesis:

$$lnecf{o}_{it}={\beta }_{0}+{\beta }_{1}lnef{r}_{it}+{\beta }_{2}lnre{n}_{it}+{\beta }_{3}lngd{p}_{it}+{\beta }_{4}lngdps{q}_{it}+{\epsilon }_{it}$$

where, ${\beta }_{1}, { \beta }_{2}, {\beta }_{3} and {\beta }_{4}$are the coefficients of $lnef{r}_{it}, lnre{n}_{it}, lngd{p}_{it} and lngdps{q}_{it}$, respectively. ${\epsilon }_{it}$ is the error term. To determine the existence of the EKC hypothesis some conditions have to be satisfied. The first condition is the existence of the cointegration relationship and then the coefficient of lngdp should be positive while the coefficient of lngdpsq should be negative. Moreover, these coefficients should be statistically significant in Eq. (1). The turning point formula is as follows:

$${Y}^{*}={e}^{\frac{{\beta }_{3}}{2{\beta }_{4}}}$$

Methodology

Priori tests

Cross-section dependency is one of the first priori tests used in studies using panel data. For this reason, the first issue addressed in this study is the cross-section dependency. From this point of view, in the study, three different test statistics are calculated, and the cross-section dependence is tested. Along with the variables, the EKC model is also calculated. Cross-sectional dependence of Eq. (1) is tested with the test statistics of Breusch and Pagan (1980).

$$CD=T\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}{\widehat{\rho }}_{ij}^{2}$$

where ${\widehat{\rho }}_{ij}$ displays the correlation between errors calculated from Eq. (1). The test statistic below has been suggested by Pesaran (2004) due to the calculated test statistic in Eq. (3) might yield deviant results in large samples:

$$C{D}_{LM1}=\sqrt{\frac{1}{N(N-1)}}\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}(T{\widehat{\rho }}_{ij}^{2}-1)$$

The test statistic is stated above to make some adjustments to the test statistic in Eq. (3). Thus, cross-sectional dependency in large samples has become testable. On the other hand, if the time (T) is smaller than observation (N), then Eq. (4) is adjusted by Pesaran (2004). Afterwards, the obtained test statistic is as follows:

$$C{D}_{LM2}=\sqrt{\frac{2T}{N\left(N-1\right)}}\left(\sum _{i=1}^{N-1}\sum _{j=i+1}^{N}{\widehat{\rho }}_{ij}\right)$$

For three tests statistics the hypotheses are as follows:

${H}_{0}:$ there is no cross-sectional dependency

${H}_{1}:$ there is cross-sectional dependency.

Here, delta tests proposed by Pesaran and Yamagata (2008) are used as homogeneity tests. The test statistic for the first of the delta tests are as follows:

$$\widehat{\varDelta }=\sqrt{N}\left(\frac{{N}^{-1}\tilde{S}-k}{\sqrt{2k}}\right)$$

Here, S̃ is the changed statistics of Swamy (1970). The Delta adjusted test statistics are as follows:

$${\widehat{\varDelta }}_{adj}=\sqrt{N}\left(\frac{{N}^{-1}\tilde{S}-E({\tilde{z}}_{\widehat{it}}}{\sqrt{var\left({\tilde{z}}_{\widehat{it}}\right)}}\right)E\left({\tilde{z}}_{\widehat{it}}\right)=k, var\left({\tilde{z}}_{\widehat{it}}\right)$$

$$=\frac{2k(T-k-1)}{T+1}$$

For above test statistics the hypotheses to test slope homogeneity are as follows:

${H}_{0}:$ there are homogenous slopes

${H}_{1}:$ there are heterogeneous slopes.

Panel Unit Root Test

Increasing globalization trends in recent years increase the importance of cross-sectional dependency in economic research. In addition, the crises in the world economy and the transformations in the economic structures of countries make structural breaks extremely important. The most important issue for the reliability of structural break unit root tests is that the break dates, numbers and forms can be determined accurately beforehand. The difficulties that may arise here are tried to be overcome by Fourier unit root tests. Because this type of tests allows not only hard breaks but also gradual breaks (soft transitions). During the modeling of the test, it is not necessary to know the breakage form and dates beforehand.

For this reason, the long-run dynamic behaviour of per capita ecological footprint is determined via FPKSS test (Nazlioglu and Karul 2017) in this study. The difference of FPKPSS test from traditional panel unit root methods is that it utilizes Fourier functions to determine structural shifts. Also, unlike the others, the null hypothesis is stationary, while the alternative hypothesis is non-stationary. Furthermore, FPKPSS allows us to smoothly model structural changes and take into account country-specific heterogeneities dealing with cross-sectional dependency. FPKPSS test procedure has explained as follows:

$${y}_{it}={\alpha }_{i}\left(t\right)+{r}_{it}+{\lambda }_{i}{F}_{t}+{\epsilon }_{it}$$

$${r}_{it}={r}_{it-1}+{u}_{it}$$

Here, ${r}_{it}$ and ${F}_{t}$ denote a stochastic term, and unobserved common factor,respectively. ${\lambda }_{i}$ denotes the loading weights. The deterministic term of the Eq. (8) as a time-dependent function is denoted by ${\alpha }_{i}\left(t\right)$. ${\epsilon }_{it}$ and ${u}_{it}$ are independent and identically distributed (i.i.d.) across i and over t.

For KPSS stationary test the deterministic component is as follows:

$${\alpha }_{i}\left(t\right)={a}_{i}+{\gamma }_{1i}\text{sin}\left(\frac{2\pi kt}{T}\right)+{\gamma }_{2t}\text{c}\text{o}\text{s}\left(\frac{2\pi kt}{T}\right)$$

Here, ${\gamma }_{1i}$ indicates the amplitude of the changes, while ${\gamma }_{2i}$ indicates the displacement of the changes. Eq. (10) conjectures the time-varying intersection term, using the nonzero values of ${\gamma }_{1i}$ and ${\gamma }_{2i}$ to detect gradual changes in intersection. Furthermore, fluctuation of the slope of both intercept and time trend is allowed by FPKPSS test. A Fourier expansion has been improved to approach the non-linear trend function either with structural-breaks or another form of non-linearity by Jones and Enders (2014) as follows:

:${\alpha }_{i}\left(t\right)={a}_{i}+{b}_{i}t+{\gamma }_{1i}\text{sin}\left(\frac{2\pi kt}{T}\right)+{\gamma }_{2t}\text{c}\text{o}\text{s}\left(\frac{2\pi kt}{T}\right)$ (11)

The KPSS procedure of Becker et al. (2006) can estimate the country-specific statistics, with (k) is as follows:

$${\eta }_{i}\left(k\right)=1/{T}^{2}\sum _{t=1}^{T}\frac{{\tilde{S}}_{it}{\left(k\right)}^{2}}{{\tilde{\sigma }}_{\epsilon i}^{2}}$$

Here, ${\tilde{S}}_{it}\left(k\right)=\sum _{j=1}^{t}{\tilde{\epsilon }}_{ij}$ is a partial sum process obtained from the OLS errors in Eq. (8). Computation of the long-run variance of error term ${\epsilon }_{it}$ is denoted as ${\tilde{\sigma }}_{\epsilon i}^{2}$, which is specified as ${\sigma }_{\epsilon i}^{2}=\underset{T\to \infty }{\text{lim}}{T}^{-1}E\left({S}_{it}^{2}\right)$. The panel statistics $FP\left(k\right)$, which consists of the average of KPSS-based individual test statistics, can be calculated as follows:

$$FP\left(k\right)=\frac{1}{N}\sum _{i=1}^{N}{\eta }_{i}\left(k\right)$$

While $\xi \left(k\right)$ and $\zeta \left(k\right)$ are the mean and the variance, the final test statistics for panel stationarity test $FP\left(k\right)$ converges to the standard normal distribution.

$$FZ\left(k\right)=FPKPSS=\sqrt{N}\left(\frac{FP\left(k\right)-\xi \left(k\right)}{\zeta \left(k\right)}\right)\sim N\left(\text{0,1}\right).$$

Panel Cointegration Test:

Panel cointegration test:

While traditional cointegration tests do not consider cross-section dependency, recently developed cointegration tests take this into account. Two test statistics proposed by Westerlund (2008) for the panel cointegration test does consider the cross-sectional dependency:
$D{H}_{g}=\sum _{i=1}^{n}{\widehat{S}}_{i}{\left({\tilde{\varphi }}_{i}-{\widehat{\varphi }}_{i}\right)}^{2}\sum _{t=2}^{T}{\widehat{e}}_{it-1}^{2}$ (13)

$$D{H}_{p}={\widehat{S}}_{n}{\left(\tilde{\varphi }-\widehat{\varphi }\right)}^{2}\sum _{i=1}^{n}\sum _{t=2}^{T}{\widehat{e}}_{it-1}^{2}$$

Here, DHg is the panel statistic and obtained by adding the individual terms before they are place together. The average statistic is represented by DHp. Various terms are multiplied and then added together. Thus, this statistical value is created. According to Durbin Hausman test, the null hypothesis indicates the absence of cointegration. Conversely, the alternative hypothesis refers to the existence of cointegration in the panel.

Here, firstly, the cross-sectional dependency of the variables and the EKC model is checked. For this reason, The Breusch and Pagan (1980) LM, Pesaran (2004) CD, and Pesaran (2004) scaled LM tests are used. Table 1 displays the results of the cross-sectional dependency and indicate that the cross-section dependency of the variables and EKC model exist, according to these three tests. Therefore, unit root and co-integration tests that consider the cross-section dependency is used in the next part of the study.

Table 1

Cross-section dependency test results
Variables	$C{D}_{LM1}$	$C{D}_{LM2}$	$CD$
lnecfo	583.0663*	32.98971*	8.321658*
lnefr	913.5079*	55.79235*	25.55998*
lnren	1219.507*	76.90831*	32.05556*
lngdp	2170.967*	142.5652*	46.55152*
lngdpsq	2168.057*	142.3645*	46.51909*
Model	1171.283*	73.58051*	27.61621*
Note: * denotes the statistical significance at %1 level.

According to the results of homogeneity test shown in Table 2, the null hypothesis of homogeneous slopes is rejected. Thus, the model is heterogeneous.

Table 2

Slope Homogeneity test results
Slope Homogeneity	Statistics	p-value
$\widehat{\varDelta }$	7.588*	0.0000
${\widehat{\varDelta }}_{adj}$	8.762*	0.0000
Note: * denotes the rejection of null hypothesis at %1 level.

The unit root test results for the variables are displayed in Table 3. According to the results, all variables are stationary at level (I(0)) except the variable lnecfo that is stationary (I(1)) at first difference at 1% significance level. In order to EKC model, the cointegration between ecological footprint and economic freedom, renewable energy consumption, economic growth, and square of economic growth are analysed.

Table 3

FPKPSS stationary test results
Variables	Level	First differences
lnecfo	12.32 (2.22e-016)*	-1.562 (0.9408)
lnefr	-0.3049 (0.6198)
lnren	0.4527 (0.3254)
lngdp	1.032 (0.1511)
lngdpsq	1.083 (0.1394)
Note: * denotes the rejection of null hypothesis at %1 significance level.

The test results of panel cointegration displayed in Table 4 demonstrate that the EKC model is cointegrate. Here, the dependent variable ecological footprint and the other variables act together in the long-run. The augmented mean group (AMG) estimators have been utilized in order to estimate the long run coefficients of the EKC model.

Table 4

Panel cointegration test results
Test	Value	p-value
DHg	1.597***	0.055
DHp	1.350***	0.088
Note: *** denotes the rejection of the null hypothesis at 10% significance level.

The long-run coefficients of the EKC model and countries are displayed in Table 5. This study has examined the effect of explanatory variables on environmental pollution. In the light of this analysis the results are as follows by country:

Table 5

AMG test results: long-run estimates
Countries	lnefr	lnren	lngdp	lngdpsq	EKC
Australia	0.998	-0.472*	9.745	-0.441	X
Canada	-0.936**	-0.198	51.098*	-2.384**	✓
Denmark	-0.205	-0.244*	74.330**	-3.375**	✓
Estonia	1.953*	0.07	-7.401	0.429	X
Finland	0.409	-0.175	-38.53**	1.869**	X
Germany	0.241	0.044	19.672	-0.954	X
Latvia	-0.253	0.614	-6.470	0.411***	X
Lithuania	-0.086	-0.054	-3.849	0.252	X
Luxembourg	-0.771	-0.058**	25.959	-1.105	X
Netherlands	0.002	0.182	76.510	-3.555	X
Norway	-0.248	-1.29***	-97.383	4.378	X
Singapore	1.872	-0.21***	28.192*	-1.321*	✓
South Korea	3.664	0.3	18.361	-0.792	X
Sweden	0.256	0.333	-17.627	0.88	X
Switzerland	-0.227	-1.039*	4.779	-0.199	X
Panel	0.412	-0.146	9.159	-0.394	X
Note: , * and *** denote the rejection of the null hypothesis at 1%, 5%, and 10% significance level, respectively. ✓ and X denote the validity and non-validity of the EKC model, respectively.

The economic freedom is statistically significant for Canada and Estonia. While it has a positive effect on decreasing ecological footprint in Canada, it has an increasing effect on Estonia. Renewable energy consumption coefficients are also statistically significant for the countries Australia, Denmark, Luxembourg, Norway, Singapore, and Switzerland, and it has been observed that the variable reduces environmental pollution in all these countries.

The long-run coefficients are analysed under EKC hypothesis, and the coefficient of lngdp is positive while lngdpsq is negative for Canada, Denmark, and Singapore. The findings reveal that there is an existence of EKC hypothesis between ecological footprint and economic growth with the effect of economic freedom for Canada, Denmark, and Singapore. In other words, as GDP per capita increases in Canada, Denmark and Singapore, environmental degradation will increase accordingly, up to a certain milestone. While GDP per capita continues to rise, once the threshold is exceeded, it will begin to reduce environmental degradation.

In this study, the effect of economic freedom on environmental degradation is analysed for the 15 highest economic freedom countries between 1996 and 2018 under the scope of the EKC hypothesis. Firstly, the tests for cross sectional dependency are performed, and as a result of these tests the presence of cross-sectional dependency for models and series are stated. FPKPSS stationary test that consider the cross-sectional dependency is used to investigate the integration degrees of the series under the scope of the EKC model. Next, the long-term relationship between ecological footprint and the other variables are analysed using Durbin Hausman panel cointegration test. The results demonstrate that there is a cointegration in the countries used under the scope of EKC model. Finally, the long-run coefficients are estimated via AMG estimators to indicate the existence of the EKC hypothesis.

The obtained results, under the scope of the EKC hypothesis, indicate that there is an existence of EKC hypothesis for Canada, Denmark, and Singapore. The results demonstrate that economic growth in these countries decrease the environmental degradation. This means that the effects of environmental protection policies for Canada, Denmark and Singapore have appeared positively. However, the same is not the case for other countries, and environmental policies are insufficient to reduce environmental degradation.

Furthermore, the results between ecological footprint and explanatory variables are as follows: Considering the literature, it has been found that the renewable energy consumption reduces the ecological footprint and thus improves environmental sustainability. In this study, these claims are supported in this direction with the results found. Namely, there is a statistically significant relationship between ecological footprint and renewable energy consumption for Australia, Denmark, Luxembourg, Norway, Singapore, and Switzerland. According to the coefficients, the variable has a reducing effect on environmental degradation for all these countries.

The economic freedom is statistically significant for Canada and Estonia. Some studies have revealed that economic freedom has positive effects on ecological footprint, while others have revealed that it increases the ecological footprint. In this study, it is seen that economic freedom reduces the ecological footprint in Canada. In other words, economic freedom in Canada reduces environmental degradation. On the contrary, in Estonia, ecological freedom increases the country's ecological footprint, namely environmental degradation. In this context, according to American Heritage Foundation, the index of economic freedom is quite high in Estonia. According to Palm (2021), high economic freedom has led to economic success that has earned Estonia a place among the countries with the highest standard of living in the world and has helped it grow rapidly. However, this has led to problems related to environmental protection, such as the protection of various natural assets and the reduction of pollution. It is expected that the Estonian economy will reach a point where the use of natural resources must be adjusted. For this reason, it is recommended to impose restrictions on the use of resources.

Statements & Declarations

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Compliance with Ethical Standards

The author states that there is no conflict of interest. Research does not include Human Participants and/or Animals. Informed consent is not included in the study.

Consent to Participate

The author has agreed with the content and has given explicit consent to submit.

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The author has agreed with the content and has given explicit consent to submit.

Authors Contributions

The entire study was performed by Çisem Bektur, the only author in the study.

Funding

The authors did not receive support from any organization for the submitted work.

Competing Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Availability of data and materials

All data and materials as well as software application support the published claims and comply with field standards.

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published 12 Aug, 2023

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Editorial decision: Major Revision
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The role of economic freedom in achieving the environmental sustainability for the highest economic freedom countries: testing the environmental Kuznets curve hypothesis

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Journal Publication

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Abstract

Introduction

Literature Review

Data And Methodology

Data and Model

Methodology

Priori tests

Panel Unit Root Test

:\({\alpha }_{i}\left(t\right)={a}_{i}+{b}_{i}t+{\gamma }_{1i}\text{sin}\left(\frac{2\pi kt}{T}\right)+{\gamma }_{2t}\text{c}\text{o}\text{s}\left(\frac{2\pi kt}{T}\right)\) (11)

Panel Cointegration Test:

Empirical Results

Conclusion And Policy Implications

Declarations

References

Status:

Journal Publication

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Slope Homogeneity	Statistics	p-value
\(\widehat{\varDelta }\)	7.588*	0.0000
\({\widehat{\varDelta }}_{adj}\)	8.762*	0.0000
Note: * denotes the rejection of null hypothesis at %1 level.