THE IMPACT OF NON-FOSSIL ENERGY CONSUMPTION ON ENVIRONMENTAL QUALITY

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Master thesis II in Economics, 15 hp Spring term 2021

THE IMPACT OF NON-FOSSIL

ENERGY CONSUMPTION ON

ENVIRONMENTAL QUALITY

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This study aims to investigate the impact of non-fossil energy consumption on environmental quality. The sample includes 36 OECD countries for the period 1993-2016. This study investigates environmental quality by analyzing the ecological footprint and its six components of cropland, forest land, grazing land, fishing grounds, built-up land, and carbon footprint. The ecological footprints’ six components underline a relationship between human demands and the biological supply of earth resources. This relationship is of major concern due to the hazardous development in global warming and climate change. Further, this study investigates the presence of the environmental Kuznets curve (EKC) hypothesis in the sample of OECD countries. The main results from the Generalized Method of Moments and Fixed Effects estimators revealed that increased use of non-fossil energy consumption reduces the carbon footprint, while it increases the environmental damage on grazing land and fishing grounds. Moreover, the EKC hypothesis was only confirmed for grazing land and fishing grounds but was not confirmed for carbon, built-up land, forest land, and cropland. The results of this study have several policy implications that are further discussed.

1. Introduction

Over the last few decades, the increase in countries’ energy use and human exploitation of goods and services has had a substantial impact on the ecosystem. Human livelihood relies on the supply of land, food, fresh water, and multiple other important ecosystem services. However, increasing human consumption of energy is causing the pressing issue of environmental degradation. The rising global average temperature is directly linked to the increasing carbon dioxide (CO2) emissions, foremost a

consequence of fossil fuel consumption. This development has multiple severe consequences, such as continued degradation of both permafrost and coastal, increased wildfire, decreased food security, decreased water availability, and vegetation loss (IPCC, 2019).

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different impacts on the environmental quality that needs to be considered when developing sustainability policies and transitioning from fossil energy use.

The literature on the determinants of environmental quality become a popular subject of research over the last decade (e.g. Apergis et al., 2010; Bowden & Payne, 2010; Jaunky, 2011; Apergis & Payne, 2012; Hamit-Haggar, 2012; Salim & Rafiq, 2012; Farhani & Shahbaz, 2014; Alper & Oguz, 2016; Sinha et al., 2018). Most of the previous literature on the determinants of environmental quality investigates the impact of economic development and/or energy consumption on CO2 emissions.

However, Ulucak & Apergis (2018) argue that the literature thus fails to consider other important indicators of environmental degradation, such as soil, forestry, mining, and oil stocks. Instead, the ecological footprint is used as an environmental indicator (e.g. Al-Mulali et al., 2016; Ozturk et al., 2016; Charfeddine & Mrabet, 2017; Destek & Sarkodie, 2019; Ozcan et al., 2019; Zafar et al., 2019) that is considered a more comprehensive measure of environmental degradation (Bilgili & Ulucak, 2018; Sarkodie, 2018; Wang & Dong, 2019). The ecological footprint measures the impact of human activities on the earth’s available resources: the larger the country’s footprint, the greater is the country’s environmental degradation (Global Footprint Network, 2020a).

However, the ecological footprint is a comprehensive measure that is aggregated from the environmental measures of cropland, forest land, grazing land, fishing grounds, built-up land, and carbon. While previous literature investigates the aggregate ecological footprint measure, the author of this study believes that the implementation of an aggregated environmental measure may not be very informative for decision-makers and policy implications. For example, a country may know that its ecological footprint is large, but since the ecological footprint is a comprehensive measure, it is not obvious which parts of the environment that exhibits degradation. It is therefore important to disaggregate the ecological footprint and examine its components since each component has unique characteristics. By doing so, this study can offer a deeper understanding of the impact of non-fossil energy consumption on different aspects of the environment. Further, policy implications to mitigate environmental degradation may differ depending on the desired outcome. For example, carbon emissions may be reduced by a carbon tax, but the same carbon tax may not help to reduce the negative impacts on fishing grounds. Instead, improving fish migration at hydropower dams, or implementation of more fish-friendly hydropower may be a way to protect the fishing grounds.

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environmental quality, however, the possible negative impacts of non-fossil energy on biodiversity and ecosystem process may have been undervalued in countries’ transitioning towards sustainable development (UNEP, 2011). The non-fossil disaggregation of energy is implemented before in the energy economics literature (Asafu-Adjaye et al., 2016; Zhou et al., 2020). However, to the best of the author’s knowledge, it has not been implemented when investigating ecological footprints.

The increase in energy use that has been witnessed in many countries is linked to economic development. This brings attention to the impact of economic development on environmental pressure, a relationship that is examined in the environmental Kuznets curve (EKC) hypothesis. The EKC hypothesis is an inverted U-shaped curve, obtained from plotting GDP per capita on the horizontal axis, and an index of environmental degradation per capita on the vertical axis. This obtained relationship shows that when countries are in the early stages of economic development, the environmental degradation increases until GDP reaches a certain level, then it turns to decrease as GDP continues to grow. The seminal paper on EKC was Grossman & Krueger (1991). They found that economic development impacts environmental quality with three different effects: scale effect, structural effect, and technological effect. The optimistic view emphasizes that GDP growth does not necessarily conflict with environmental sustainability. This perspective tells that technological progress will make way for green alternatives of consumption and production, eliminating the harmful climate change in the long run. However, the EKC literature is divided, as some argue that the relationship bears a very naïve hypothesis (Ozturk et al., 2016; Churchill et al., 2018)

A majority of the previous literature on the relationship between economic development, energy, and environmental degradation found that economic development and energy consumption are the main contributors to increasing CO2 emissions (e.g. Soytas et al., 2007; Halicioglu, 2009; Soytas &

Sari, 2009; Zhang & Cheng, 2009; Lean & Smyth, 2010; Hamit-Haggar, 2012; Al-Mulali et al., 2015a, 2015b; Kasman & Duman, 2015; Ozturk & Al-Mulali, 2015a; Salahuddin et al., 2015; Saidi & Mbarek, 2017; Sinha et al., 2018; Apergis et al., 2018). Furthermore, CO2 emissions were also

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The literature on environmental quality has been extended by several studies that investigate the impact of renewable energy consumption on CO2 emission. However, there is a lack of studies that

examines the impact of non-fossil energy consumption on other important environmental services, that are essential for the ecosystem. Therefore, this study seeks to fill this gap in the energy economics literature by utilizing the ecological footprint as a proxy for environmental quality, and investigate the following question: What is the impact of non-fossil energy consumption on environmental quality? The sample in this study includes 36 OECD countries over the period 1993-2016. The investigation will look at the aggregated ecological footprint measure, along with each of its six components. Examining each component will enable meaningful insight on the impact of non-fossil energy on the different environmental measures of cropland, forest land, grazing land, fishing grounds, built-up land, and carbon footprints. Further, this study will be testing the quadratic EKC relationship between economic development and ecological footprints.

To account for unobserved heterogeneity and endogeneity bias, this study implements the System Generalized Method of Moment (GMM) estimator. Further, the Fixed Effects estimator was employed to achieve robustness in the empirical results. The Fixed Effects estimator is a popular model that can eliminate the problems of omitted variable bias that do not change over time. However, the GMM estimator is utilized due to its specific properties that provide consistent results in the presence of endogeneity bias, an area in which the fixed effects are weak. Further, the GMM estimator is consistent under weak distributional assumptions and is overall considered a more robust method for estimating parameters, compared to the Fixed Effects estimator.

In brief, the results of this study show that increased use of non-fossil energy consumption reduces the carbon footprint, while it increases the environmental degradation on grazing land and fishing grounds. This result is in line with a majority of the previous literature on the impact of renewable energy on CO2 emissions. Moreover, the EKC hypothesis was only confirmed for grazing land and

fishing grounds, it was not confirmed for carbon, built-up land, forest land, and cropland,

The remainder of the study is organized as follows. Section 2 briefly presents the concept of the ecological footprint. Section 3 presents a review of the empirical literature of interest regarding the research question in this paper. Section 4 presents the data and empirical methodology. Section 5 and 6 provide the results and the conclusion along with policy implications.

2. The ecological footprint

The concept of the ecological footprint was laid out by Rees (1992) and has since been employed

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up land, forest area, and carbon demand on land. More precisely, it measures how fast countries consume and generate waste, compared to how fast nature can absorb the waste and generate new resources (Global Footprint Network, 2021a). This means the higher the ecological footprint is, the higher is the environmental pressure for the country. Therefore, it is important for countries’ to have a footprint smaller than the biological capacity. The biological capacity is the ability of an ecosystem to produce useful biological materials and absorb CO2 emissions. When the ecological footprint

exceeds the biological capacity there is an ecological overuse, which is not a sustainable condition in the long run. When examining OECD countries’ on an average aggregate level, the ecological footprint in 2016 was 5.27 global hectares per capita, and the OECD countries’ average biological capacity was 2.75 global hectares per capita. This leads to an average ecological deficit of 2.52 global hectares per capita in OECD countries. Hence, OECD countries’ population’s demand on the ecosystem exceeds the capacity of the ecosystem by far, this development is not sustainable and has to be reversed.

In line with previous literature, this study utilizes the ecological footprint of consumption per capita. The ecological footprint of a country is simply the sum of the six categories that make up the ecological footprint, divided by its population. The six categories of productive surfaces areas are calculated individually and then aggregated to get the ecological footprint of consumption, according to the following formula,

𝐸𝐹_𝐶 = 𝐸𝐹_𝑃+ 𝐸𝐹_𝐼− 𝐸𝐹_𝐸 (1)

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Where 𝐸𝐹_𝐶 depicts the ecological footprint of consumption, 𝐸𝐹_𝑃 is the ecological footprint of production, 𝐸𝐹_𝐼 represents the ecological footprint of imported commodity flows, and 𝐸𝐹_𝐸 is the ecological footprint generated by exported commodity flows. Moreover, the ecological footprint tracks environmental degradation with a more comprehensive measure of ecosystem services and natural resources rather than studying only atmospheric pollution of CO2 emissions.

In terms of ecological footprint by land type, Fig. 1 displays the sample of OECD countries investigated in this paper. There are some trends in the per-capita ecological footprint for OECD countries over this period. (1) It has experienced an increasing trend from 1993 to 2007, with exception of a slight decrease in the early 2000s, then steadily decreasing in recent nine years. (2) The carbon footprint is by far the largest contributor with a share of approximately 65 percent of the total ecological footprint. Below follows an explanation of the six components of the ecological footprint, as explained by Global Footprint Network (2021b).

2.1. Cropland

Cropland is the most bioproductive land area and is used to produce food and fiber for human consumption, feed for livestock, oil crops, and rubber. The calculation includes crop products allocated to livestock and aquaculture feed mixes and those used for fibers and materials.

2.2. Forest land

Forest land is calculated from the amount of lumber, pulp, timber products, and fuelwood consumed by a country every year.

2.3. Grazing land

Grazing land is used to raise livestock for meat, dairy, hide, and wool products. This component is calculated by comparing the amount of livestock feed available in a country with the amount of feed required for all livestock in that year, with the remainder of feed demand assumed to arise from grazing land.

2.4. Fishing grounds

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2.5. Built-up land

The footprint of built-up land is calculated from the area of land covered by human infrastructure - transportation, housing, industrial structures, and reservoirs for hydropower. Built-up land may occupy what would previously have been cropland.

2.6. Carbon

The carbon footprint represents the CO2 emissions associated with burning fossil fuels. The

carbon footprint also includes embodied carbon in imported goods. It is represented by the area necessary to sequester these CO2 emissions. It is calculated as the amount of forest land needed to

absorb these carbon dioxide emissions.

3. Literature review

The energy economics literature on the determinants of environmental degradation can be divided into two main sections. The first section includes all the literature investigating the environmental Kuznets curve (EKC). The second section includes the empirical research on the income-energy nexus, a subject that more recently also investigates the environmental aspect. Previous literature has shown that increasing CO2 emissions leads to harmful levels of pollution.

Further, the literature has also suggested that increasing the use of renewable energy decreases CO2

emissions. However, the possible negative impacts of non-fossil and renewable energy on biodiversity and ecosystem process are often excluded in the previous literature. The previous literature is described in more detail below.

3.1. The environmental Kuznets curve

The EKC is the inverted U-shaped curve obtained from plotting income per capita on the horizontal axis and environmental degradation per capita on the vertical axis. This relationship can be explained as countries’ initial phase of economic development is commonly characterized by increasing consumption of fossil fuels. This is because the energy consumption of fossil fuels requires less technological development than non-fossil alternatives. The countries environmental quality then deteriorates until income per capita reaches a certain level, then it turns, and the environmental degradation decrease as income increases. However, environmental degradation can be measured by many different proxies, with CO2 emissions being the most popular one today.

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basins. They found evidence that the environmental quality indicators deteriorate in the countries initial phase of economic growth, followed by a subsequent phase of improvement. Selden & Song (1994) also confirm the EKC inverted U-shape relationship when investigating pollution and economic development.

However, the evidence for EKC formation is mixed among studies that investigate the global pollutants of CO2 emissions. Several studies find that CO2 emissions exhibit a monotonical increase

when GDP per capita increases (e.g. Forrest, 1995; Stern et al., 1996; Ekins, 1997). Moreover, the early literature on EKC is argued to be econometrically weak due to problems of important statistical properties such as serial dependence, stochastic trends, and multicollinearity (Stern, 2004). Still, while more recent studies on EKC are more statistically robust, the results are nevertheless mixed and seem to depend on various factors, such as disaggregation of countries, time-periods, econometric methodology, and proxy for environmental degradation (e.g. Jaunky, 2011; Arouri et al., 2012; Hamit-Haggar, 2012; Kasman & Duman, 2015; Tutulmaz, 2015).

Following up on more recent studies in the literature on the EKC relationship, there is a number that investigates OECD countries. Cho et al. (2014), Dogan & Seker (2016), Bilgili et al. (2016), and Jebli et al. (2016) all use CO2 and/or total greenhouse gas (GHG) emissions to analyze the EKC

relationship. They all find the support of the EKC relationship when investigating OECD countries. Churchill et al. (2018) investigates CO2 emissions in 20 OECD countries and finds mixed evidence

for the EKC relationship depending on countries.

The empirical results from the studies that employ the ecological footprint measure of environmental quality when investigating the EKC relationship are also mixed. Bagliani et al. (2008), Caviglia-Harris et al. (2009), and Wang et al. (2013) found no evidence in support of the EKC relationship when investigating large datasets. While other studies using the ecological footprint proxy confirm the inverted U-shaped EKC relationship (e.g. Charfeddine & Mrabet, 2017; Bilgili & Ulucak, 2018; Destek & Sarkodie, 2019). Al-Mulali et al. (2015a) and Ozturk et al. (2016) categorized countries by their world bank income classification and found the support of EKC among the upper-middle and high-income countries, but not for low and lower-middle-income countries.

3.2. Income, energy, and environmental quality

The bulk of the empirical literature on environmental quality and its determinants focuses on a single environmental indicator, CO2 emissions. When examining the causation of CO2 emissions,

Apergis et al. (2010) focused on 19 developed and developing countries for the period 1984-2007. Their short- and long-run estimates suggest that nuclear energy consumption is important for reducing CO2 emissions while renewable energy consumption does not reduce CO2 emissions. Salim & Rafiq

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when investigating six major emerging economies covering the period 1980-2006. Farhani & Shahbaz (2014) explore the relationship between renewable and non-renewable electricity consumption, GDP, and CO2 emissions for 10 the Middle East and North African (MENA) countries

for the period 1980-2009. The estimations imply that renewable and non-renewable electricity consumption increases CO2 emission, while income exhibits the quadratic EKC relationship. Kasman

& Duman (2015) use panel unit root and panel cointegration methods to investigate CO2 emissions

for new EU member and candidate countries over the period 1992-2010. Their results imply that GDP, energy consumption, urbanization, and trade openness increase CO2 emissions in the long run. Sinha et al. (2018) analyze the Next 11 countries from 1990 to 2016. The impacts of renewable energy on GDP and CO2 emissions are uncertain due to the low adoption of renewables. However, fossil

fuels should be replaced to mitigate CO2 emissions.

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3.3. Contributions of this paper

This paper contributes to the previous empirical literature in two ways. (1) This paper employs energy disaggregated in non-fossil energy consumption when investigating ecological footprints. To the best of the author’s knowledge, non-fossil energy consumption has not been implemented in previous literature that examines ecological footprints. (2) This paper employs the ecological footprint as a proxy for environmental degradation, which has been done in previous literature. However, this paper extends the previous literature by disaggregating the ecological footprint and investigate each of the six components carbon, built-up land, fishing grounds, grazing land, forest land, and cropland as dependent variables. To the best of the author’s knowledge, there is no previous literature that disaggregates the ecological footprint and examines each of its six components. Investigating the six components of the ecological footprint is important since each component has unique characteristics, hence, the efforts to reduce environmental degradation will differ depending on the desired result. For example, hydropower is globally the largest contributor of renewable energy, that mitigates harmful levels of pollution. However, there is substantial evidence that numerous fish populations are exhibiting a dramatic decline. Hence, many countries are concerned with fish conservation, which can be threatened by hydropower production. Therefore, these countries may be better off by implementing other sources of energy production than hydropower.

4. Methodology and data

4.1. Methodology

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will contain unobserved characteristics. This will most likely lead to a correlation between the explanatory variables and the error term, causing problems of endogeneity (Greene, 2000). Endogeneity is a bias that can lead to inconsistent estimates. This, in turn, may result in incorrect interpretations and conclusions (Ullah et al., 2018).

Therefore, this study tests the robustness of the model by including the explanatory variables fossil energy consumption, total labor force, trade openness, and biocapacity. These variables are suggested to be determinants of environmental quality in previous literature. To test the robustness of the model, each explanatory variable is added systematically, one variable per model. The estimation results from this are reported in Table A.1 and Table A.2 in Appendix 1. When comparing the extended models with the basic approach taken in this study, the estimation results remain robust. Hence, the basic model is used to investigate the study question: What is the impact of non-fossil energy consumption on environmental quality? To investigate this, the following logarithmic equations are used:

𝐸𝐹_𝑖𝑡 = 𝛿𝐸𝐹_{𝑖,𝑡−1}+ 𝐗_𝑖𝑡′ 𝛽 + η_𝑖 + 𝜀_𝑖𝑡 (2) 𝐶_𝑖𝑡 = 𝛿𝐶_{𝑖,𝑡−1}+ 𝐗_𝑖𝑡′ 𝛽 + η_𝑖+ 𝜀_𝑖𝑡 (3) 𝐵𝑈𝐿𝑖𝑡 = 𝛿𝐵𝑈𝐿𝑖,𝑡−1+ 𝐗𝑖𝑡′ 𝛽 + η𝑖+ 𝜀𝑖𝑡 (4) 𝐹𝐺𝑖𝑡 = 𝛿𝐹𝐺𝑖,𝑡−1+ 𝐗𝑖𝑡′ 𝛽 + η𝑖+ 𝜀𝑖𝑡 (5) 𝐺𝐿_𝑖𝑡 = 𝛿𝐺𝐿_{𝑖,𝑡−1}+ 𝐗_𝑖𝑡′ 𝛽 + η_𝑖+ 𝜀_𝑖𝑡 (6) 𝐹𝐿_𝑖𝑡 = 𝛿𝐹𝐿_{𝑖,𝑡−1}+ 𝐗_𝑖𝑡′ 𝛽 + η_𝑖 + 𝜀_𝑖𝑡 (7) 𝐶𝐿_𝑖𝑡 = 𝛿𝐶𝐿_{𝑖,𝑡−1}+ 𝐗_𝑖𝑡′ 𝛽 + η_𝑖 + 𝜀_𝑖𝑡 (8)

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is a vector of dependent variables, including GDP per capita, the square of GDP per capita, and non-fossil energy consumption.

The panel equations (2) - (8) are the system-GMM equations that contain dynamic effects (𝑦_{𝑖,𝑡−1}). The dynamic effects of the system-GMM are suitable for panel data where the cause and effect relationship is dynamic over time. Previous literature has shown that there can be a time delay in the impact of energy consumption on environmental quality (Bretschger & Smulders, 2018). For example, the non-fossil energy consumption for the current year may be affecting next year’s grazing land footprint. Or, the non-fossil energy consumption for the current year may be affecting next year’s fishing grounds footprint, and so forth. The GMM controls for this by using lagged values of the dependent variable as instruments. Moreover, if the lagged dependent variable 𝑦_{𝑖,𝑡−1} in equations (2) – (8) is excluded from the equations, then we can apply the Fixed Effects or Random Effects estimators. The Random Effects estimator is preferred under the assumption that the individual effects η_𝑖 are uncorrelated with the independent variables 𝐗_𝑖𝑡′ . Meanwhile, the Fixed Effects estimator allows for correlations between η_𝑖 and 𝐗_𝑖𝑡′ . To distinguish between these two models the Hausman (1978) test is used.

Furthermore, if the variables on the right-hand side are correlated with the disturbance term, a condition that refers to endogeneity bias, then the results from the Random Effects and Fixed Effects estimators will be biased and unpredictable. To solve this potential problem, this study utilizes the system-GMM estimator. The system-GMM estimator addresses the problems of endogeneity bias by using a system that includes lagged differences and lagged levels of variables as instruments to dispose of the effect of correlation between the independent variables and the error term. Hence, the GMM model can obtain parameter estimators that are unbiased and consistent in the presence of different sources of endogeneity. It is suitable in models with finite-dimensional (T) and large cross-sectional units (N) (Arellano & Bover, 1995; Blundell & Bond, 1998).

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which significantly reduces these biases by extending the instruments for the level equations with lagged first-differences of the series (Blundell & Bond, 2000).

Furthermore, to examine the validity of the GMM lagged levels of variables, that are used as instruments’, this study utilizes the Sargan (1988) test along with Arellano & Bond (1991) test for first and second-order serial correlation in the first differenced errors. If the first-order serial correlation test is negative and significant, while the null hypothesis of the second-order serial correlation test is not rejected, the disturbance 𝜀_𝑖𝑡 is not serially correlated, and the underlying GMM assumptions are validated. The Sargan test is essentially a Chi-square test that determines whether the residuals are correlated with the instruments. If we cannot reject the null hypothesis of the Sargan test, there is no indication of miss-specified instruments; therefore, the instruments are valid (Arellano & Bond, 1991). Moreover, the number of instruments should not exceed the number of groups of observations, to avoid overfitting endogenous variables (Roodman, 2009).

Further, this study also investigates the EKC relationship in the sample of OECD countries. Methodology-wise, most of the previous EKC literature follows a quadratic model to test the EKC hypothesis given in Eq. (9).

𝐸 = 𝑓(𝑌, 𝑌2_{, 𝑍)} ₍₉₎

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Where 𝐸, 𝑌, 𝑌2_{, and} _{𝑍 represent environmental quality, economic development, economic}

development squared, and other explanatory variables affecting the environmental quality, respectively. Further, the previous literature on environmental quality found that economic development and energy consumption are the main determinants of environmental quality. Therefore, this study employs the adapted form of the following model to test the EKC hypothesis.

𝐸𝐹 = 𝑓(𝑌, 𝑌2, 𝑁𝐹𝐸) (10)

Where 𝐸𝐹 denotes the ecological footprint components carbon, built-up land, fishing grounds, grazing land, forest land, and cropland as dependent variables. 𝑌, 𝑌2, and 𝑁𝐹𝐸 represent real GDP, real GDP squared, and non-fossil energy consumption, respectively. The Eq. (10) is re-written as Eq. (2) – (8). Hence, this study is able to investigate the EKC relationship between economic development and environmental quality, and the impact of non-fossil energy on environmental quality in the same framework. If this study finds that the slope coefficient for the GDP is positive and significant, and the slope coefficient of the GDP square is negative and significant, an inverted U-shaped relationship between GDP and ecological footprint will be concluded, which indicates the existence of the EKC relationship.

Fig. 2 shows the per capita ecological footprint plotted against the per capita GDP for the sample of OECD countries investigated in this study. Where each dot represents a single observation, and countries are separated by color. The figure shows that the sample follows a pattern similar to the inverted U-shaped EKC relationship. The expected result from this study is that increasing the use of non-fossil energy decreases carbon footprints. This result would be in line with a majority of the previous literature on the impact of renewable energy on CO2 emissions. It is also expected that

increasing the use of non-fossil energy increases environmental degradation on fishing grounds, cropland, and grazing land. Fishing grounds are expected to be harmed by increasing non-fossil energy due to the contradiction between fish conservation and the implementation of hydropower. Further, increasing the use of non-fossil energy is expected to be harmful to cropland and grazing land since development in non-fossil energy may increase global competition for land.

4.2. Data sources and descriptive statistics

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productive space. For example, cropland to grow potatoes, or forest to produce timber. All of these materials and wastes are then individually translated into an equivalent number of global hectares. To achieve this, an amount of material consumed per capita (tonnes per year) is divided by the yield of the specific land or sea area (annual tonnes per hectare) from which it was harvested, or where its waste material was absorbed. The number of hectares that result from this calculation is converted to global hectares by yield and equivalence factors. A person’s total ecological footprint is the sum of the global hectares needed to support that person. The ecological footprint of a country is simply the sum of the ecological footprint of all the residents of that country. This paper reports each country’s ecological footprint consumption per capita, which is the productive area needed to provide for that country’s population’s consumption. Further, the ecological footprint is added up from each of its components and is essentially the sum of cropland, grazing land, fishing grounds, built-up land, forest area, and CO2 emissions (Global Footprint Network, 2021c). The ecological footprint is obtained

from the Global Footprint Network (2021d) database. Further description of the ecological footprint and its components is available in Section 2.

Data on real GDP per capita (constant 2010 US$) were collected from the World Bank World Development Indicators (WDI) (2021). The non-fossil energy consumption was derived from the Energy Information Administrative (EIA) (2021). The EIA does not publish data disaggregated into non-fossil energy consumption. Further, the data availability on non-fossil energies are mixed. For example, solar and hydropower consumption are not published. Therefore, the non-fossil energy consumption was obtained in the same way as Asafu-Adjaye et al. (2016), with non-fossil energy consumption derived as total energy consumption less fossil energy consumption.

Table 1 presents descriptive statistics of the variables in this study, over the period 1993 - 2016. Table 1 displays the unit, mean, median, min, max, standard deviation, and the number of observations of each variable before the logarithmic transformation. The GDP per capita uncovers the gap in the variable between the OECD countries in the sample. In 2016, Colombia is the country with the lowest GDP per capita, with 7 633 US$, and Luxembourg is the country with the highest GDP per capita, with 110 162 US$. Looking at Fig. 2, Luxembourg is the blue dots in the upper right corner. From the plot, it is visible that Luxembourg by far has the highest GDP per capita and the highest ecological footprint per capita. It is also visible in Fig. 2 that Luxembourg unveils an inverted U-shaped relationship between GDP per capita and ecological footprint per capita, which indicates that the EKC relationship is confirmed for the country.

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energy consumption is 17.7 trillion British thermal units (BTU), however, the median is 0.32 trillion BTU. Hence, the non-fossil energy consumption has a left-skewed distribution with most of the observations in the lower values. Further, in 2016, the ten countries with the lowest non-fossil energy consumption, as a share of total energy consumption, had an average share of 7.6 percent. While the ten countries with the highest non-fossil energy consumption, as a share of total energy consumption, had an average of 44.8 percent. This is interesting as it shows the gap between OECD countries in their transition from fossil to non-fossil energy consumption. All OECD countries are, according to the requirements of becoming an OECD country, democratic with open, transparent, and free-market economies. However, there is still a big difference in the countries’ consumption of fossil and non-fossil energy.

Table 1

Summary statistics OECD countries, 1993-2016

Variables Unit Mean Median Min Max Std. dev Obs.

GDP per capita Constant US$, in 2010 prices 33275.07 32777.82 4708.31 111968.3 21556.78 860 Non-fossil energy consumption Trillion British thermal units 1.1664 0.3236 0 17.5148 2.5822 864 Ecological footprint per capita Global hectares 5.7395 5.4901 1.8027 17.7229 2.2228 864 Carbon footprint per capita Global hectares 3.5522 3.4512 0.2047 13.0302 1.9115 864 Built-up land footprint

per capita

Global hectares

0.0848 0.0679 0.0169 0.2244 0.0474 864 Fishing grounds

footprint per capita

Global hectares

0.2389 0.1186 0.0006 3.6142 0.4215 864 Grazing land footprint

per capita

Global hectares

0.3077 0.2719 0.0246 1.3822 0.1984 864 Forest land footprint

per capita Global hectares 0.7330 0.5817 0.0973 3.3716 0.5401 864 Cropland footprint per capita Global hectares 0.8585 0.8651 0.1196 1.6513 0.2434 864 5. Empirical Results

The regression results from estimating Equation (2) – (8) by Fixed Effects and one-step system-GMM estimator for OECD countries, over the period 1993-2016 are reported in Tables 2 and 3, respectively.1_{The results of the Hausman test in Table 2 supports the use of the Fixed Effects}

________

1_{The command “pgmm” and “plm” from the plm package are used in R for the system-GMM and Fixed Effects}

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estimator across all models, except for Eq. (3), where Random Effects are utilized. The one-step system-GMM estimator is reported in Table 3, instead of the two-step GMM estimator, since downward bias in the asymptotic standard errors is likely in the two-step GMM estimator (Blundell & Bond, 1998; Hoeffler, 2002). When examining the GMM assumptions in Table 3, the AR (1) test supports a negative and significant first-order correlation of the first-difference residuals, while the AR (2) rejects the existence of second-order correlation. This is the case for all estimations. These results support the hypothesis of serially uncorrelated level disturbances, validating the GMM instruments. Further, the number of countries is larger than the number of instruments in all estimations. The Sargan test p-value is close to one, which supports that the moment conditions in the model are valid. However, the Sargan test requires that the error terms are independently and identically distributed, which is not assumed to be the case in this study. Therefore, the Sargan test is not meaningful in this context. Instead, GMM modeling assumptions are examined with the AR (1) and AR (2) tests.

The analysis starts with analyzing the impact of non-fossil energy consumption on the ecological footprint with the Fixed Effects estimator in Table 2. Eq. (2) is the baseline regression that examines the ecological footprint as the dependent variable. The results from the estimation suggest that non-fossil energy consumption has a negative impact on ecological footprints at a 1% significance level. Eq. (3) examine the carbon footprint as the dependent variable. The estimation results show that non-fossil energy consumption has a negative impact on carbon, at a 1% significance level. The Fixed Effects estimation results in Eq. (3) show that a 1% increase in non-fossil energy consumption, ceteris paribus, decreases the carbon footprint by 0.082%. Further, Eq. (4), (6), and (7) expands the analysis by examining the dependent variables of built-up land, grazing land, and forest land, respectively. Regarding the impact of non-fossil energy consumption, all models suggest that non-fossil energy

Table 2

The Fixed Effects regression results

Dependent variables Independent variables Ecological footprint (Eq. (2)) Carbon (Eq. (3)) Built-up land (Eq. (4)) Fishing grounds (Eq. (5)) Grazing land (Eq. (6)) Forest land (Eq. (7)) Cropland (Eq. (8)) Non-fossil energy cons. ⎼ 0.072*** (0.011) ⎼ 0.082*** (0.021) 0.008 (0.017) ⎼ 0.103*** (0.033) 0.034 (0.030) ⎼ 0.057* (0.029) ⎼ 0.069*** (0.021) GDP per capita 1.118*** (0.066) 0.516*** (0.127) 0.964*** (0.104) 0.784*** (0.201) 1.510*** (0.177) 2.114*** (0.175) 0.278** (0.126) GDP2_{per capita} _{⎼ 0.360}_*** (0.029) 0.018 (0.021) ⎼ 0.272*** (0.045) ⎼ 0.425*** (0.088) ⎼ 0.646*** (0.078) ⎼ 0.742*** (0.077) ⎼ 0.104* (0.055) Hausman p-value 0.000 0.613 0.000 0.000 0.000 0.000 0.001 R2 _0.375 _0.301 _0.229 _0.081 _0.099 _0.230 _0.089

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consumption has no significant impact on the dependent variables, at a 5% significance level. Moreover, Eq. (5) and (8) investigates fishing grounds and cropland as dependent variables, respectively. The results show that non-fossil energy consumption has a negative impact on both fishing grounds and cropland, at a 1% significance level.

All equations in Table 2 include the square of real GDP per capita and investigates the existence of EKC. The results of the baseline regression in Eq. (2), with the ecological footprint as the dependent variable, show that GDP is positive and significant, while GDP square is negative and significant, at a 1% significance level. Hence, an inverted U-shaped relationship between real GDP and ecological footprint is found, which indicates the existence of the EKC formation in the sample. This result is consistent through Eq. (4) – (7), with the dependent variables built-up land, fishing grounds, grazing land, and forest land, respectively. Eq. (8) investigates cropland as dependent and does not confirm the EKC formation, on a 5% significance level. Further, The EKC formation is not supported in Eq. (3), which examines the carbon footprint as the dependent variable.

Table 3 displays the results obtained from the system-GMM regression. The baseline regression in Eq. (2) suggest that non-fossil energy consumption has a negative impact on ecological footprints, at a 5% significance level. The negative impact of non-fossil energy remains when investigating the carbon footprint in Eq. (3), at a 5% significance level. The GMM estimation results in Eq. (3) suggests that a 1% increase in non-fossil energy consumption, ceteris paribus, decreases the carbon footprint by 0.065%. However, Eq. (4) – (8) does not support that non-fossil energy has a negative impact on the dependent variables of built-up land, fishing grounds, grazing land, forest land, and cropland, respectively. Eq. (5) and (6) suggest that non-fossil energy has a positive impact on fishing grounds and grazing land, on a 5% significance level. Further, Eq. (4), (7), and (8), show that non-fossil energy has no significant impact on built-up land, forest land, and cropland.

The system-GMM estimation results in Table 3 confirm the existence of EKC in the baseline regression in Eq. (2). Further, Eq. (5) and (6) also show that GDP is positive, while GDP square is negative, confirming the EKC relationship for fishing grounds and grazing land, at a 5% significance level. However, Eq. (3) – (4), and Eq. (7) – (8) show no support for EKC formation when investigating carbon, built-up land, forest land, and cropland, at a 5% significance level.

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estimation results suggest that GDP square is negative and significant when investigating built-up land and forest land, while the GMM estimation results show that GDP square has no significant impact on built-up land and forest land, on a 5% significance level. The author of this study believes that the system-GMM is the more robust estimator for analyzing the data in this study, as compared to the Fixed Effects estimator, which is motivated in Section 4. Therefore, the conclusions in this study will rely on the GMM estimation results.

Moreover, the estimation results in Table 2 and Table 3 show that non-fossil energy has a negative impact on the carbon footprint, on a 5% significance level. Both estimations also suggest that non-fossil energy has no significant impact on built-up land and forest land, on a 5% significance level. The Fixed Effects estimation results suggests that non-fossil energy has a negative impact on fishing grounds, while the GMM results show the opposite. The Fixed Effects results show that non-fossil energy has no significant impact on grazing land, while the GMM suggests that non-non-fossil energy has a positive significant impact on grazing land, on a 1% significance level. Further, when

Table 3

The system-GMM regression results

Dependent variables Independent variables Ecological

footprint (Eq. (2)) Carbon (Eq. (3)) Built-up land (Eq. (4)) Fishing grounds (Eq. (5)) Grazing land (Eq. (6)) Forest land (Eq. (7)) Cropland (Eq. (8)) Non-fossil energy cons. ⎼ 0.030**

(0.014) ⎼ 0.065** (0.032) 0.004 (0.013) 0.073** (0.038) 0.070*** (0.025) 0.020 (0.024) ⎼ 0.017 (0.051) GDP per capita 0.233*** (0.046) 0.281** (0.129) 0.075** (0.037) 0.076** (0.031) 0.142** (0.060) 0.067* (0.039) 0.467** (0.200) GDP2_{per capita} _{⎼ 0.026}_*** (0.010) 0.024 (0.015) ⎼ 0.014 (0.008) ⎼ 0.047** (0.020) ⎼ 0.038*** (0.014) ⎼ 0.028* (0.014) ⎼ 0.030 (0.046) Ecological footprint (-1) 0.408*** (0.093) Carbon (-1) 0.513*** (0.180) Built-up land (-1) 0.885*** (0.052) Fishing grounds (-1) 0.892*** (0.042) Grazing land (-1) 0.807*** (0.030) Forest land (-1) 0.853*** (0.060) Cropland (-1) 0.329 (0.244)

Sargan test (p-value) 0.99 0.99 0.99 0.99 0.99 0.99 0.99

AR (1) test ⎼ 3.67*** ⎼ 3.03*** ⎼ 3.61*** ⎼ 3.34*** ⎼ 3.45*** ⎼ 2.09** ⎼ 4.67***

AR (2) test 1.44 1.12 2.17* 0.68 ⎼ 0.31 1.51 ⎼ 1.01

Instruments 35 35 35 35 35 35 35

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examining cropland, the Fixed Effects show that non-fossil energy has a negative impact on cropland, while the GMM suggests that non-fossil energy has no significant impact on cropland. To summarize, the GMM estimation method suggests that non-fossil energy has a negative significant impact on the carbon footprint. When investigating built-up land, forest land, and cropland, the GMM results show no significant impact of non-fossil energy. Lastly, the GMM results show that non-fossil energy has a positive significant impact on fishing grounds and grazing land.

The results in this study are interesting to compare with previous studies on ecological footprints. However, there is no other study known to the author of this paper that investigates the impact of non-fossil energy on ecological footprints. Further, there is no previous study that investigates each of the six components of the ecological footprint as the dependent variable. However, many investigate the aggregate ecological footprint as a proxy for environmental quality. Charfeddine & Mrabet (2017), Destek & Sarkodie (2019), and Zafar et al. (2019) also found a positive impact of GDP on ecological footprints. The EKC relationship between GDP and ecological footprint that is found in this study is consistent with Al-Mulali et al. (2015a), who found the support of EKC formation in upper-middle and high-income countries but not in low and lower-middle countries. Charfeddine & Mrabet (2017), and Baglini et al. (2008) also found the support of EKC formation between GDP and ecological footprints. While Destek et al. (2018) and Al-Mulali et al. (2016) found no support of EKC in their samples. Further, Destek et al. (2018) found that renewable energy decreases ecological footprints, while Al-Mulali et al. (2016) found the opposite.

The results from the Fixed Effects and system-GMM estimations in this study are not consistent in all equations when investigating the impact of non-fossil energy on ecological footprints. However, the more robust system-GMM estimation results suggest that non-fossil energy has a significant negative impact on carbon, while the other components show either a positive significant impact or no significant impact. From these results, it is evident that substituting fossil with non-fossil energy diminishes environmental pressure from carbon. However, an increase in non-fossil energy will increase environmental pressure on fishing grounds and grazing land. Moreover, the estimation results on the real GDP square in this study supports the existence of EKC for fishing grounds and grazing land. While the remaining equations found no evidence for EKC formation.

6. Conclusion

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grounds, built-up land, and carbon as dependent variables. The independent variables were non-fossil energy, real GDP, and real GDP square. The sample included 36 OECD countries for the period 1993-2016.

The regression results from the Fixed Effects and system-GMM estimators show that increased use of non-fossil energy consumption reduces environmental degradation by its negative effect on the carbon footprint. This result is expected and consistent with a majority of the previous literature on the impact of renewable energy on CO2 emissions (Bigli et al., 2016; Apergis et al., 2018). Hence,

if countries aim to decrease their environmental pressure from carbon, it is recommended that decision-makers should introduce taxes on carbon and policy that subsidy the non-fossil energies of nuclear, biomass, hydropower, geothermal, wind, and solar. Previous literature has shown that there is substitutability between fossil and fossil energy (Xie et al., 2017). Therefore, increasing non-fossil energy consumption decreases the dependency on non-fossil energy consumption, which reduces environmental pressure from carbon.

However, increasing non-fossil energy consumption also has negative impacts on biodiversity as non-fossil energy is found to increase environmental pressure on grazing land and fishing grounds. The increase in environmental pressure on fishing grounds from non-fossil energy that is found in this study may be a consequence of the contradiction between fish conservation and hydropower production. Hydropower is the largest source of renewable energy in the world, however, there is substantial evidence that dams for hydropower production have negative impacts on migratory fish populations. Unfortunately, an unavoidable consequence of hydropower plants is the worldwide decline of many fish species (Young et al., 2011; Brown et al., 2013; Agostinho et al., 2016). The results in this study suggest that increasing use of non-fossil energy increases environmental degradation on fishing grounds, this result is in line with this study’s expectations and previous literature on the subject (Young et al., 2011; Brown et al., 2013; Agostinho et al., 2016).

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Further, it should be noted that the effect of the estimated impact of non-fossil energy consumption on environmental quality is relatively small, as compared to the effect of real GDP. This result is expected and can be explained by the energy mix, as a great majority of OECD countries’ energy consumption comes from fossil energy. In 2016, the OECD countries’ average share of non-fossil energy consumption, as a share of total energy consumption, was 23.3 percent. The remaining 76.7 percent of total energy consumption comes from fossil energy sources. However, this does not diminish the importance of the policy implications in this study, since the demand for non-fossil energy is steadily growing. Further, this study investigates the consumption of non-fossil energy, instead of the production of non-fossil energy. This may bring complexity when interpreting the results of this study. For example, if a country does not produce any energy, and therefore, only imports energy, the country’s energy consumption will have implications on the environmental quality in the energy-producing country. However, this is probably not a big issue as most countries are energy producers. Still, the estimation results in this study should be interpreted with this in mind. Furthermore, the EKC inverted U-shaped relationship is confirmed for grazing land and fishing grounds in the investigated OECD countries. This result reveals that countries’ environmental pressure on grazing land and fishing grounds is increasing in the initial phase of growth, but as the countries grow richer they will decrease their environmental degradation on grazing land and fishing grounds. The countries investigated in this paper are overall categorized to be among the more developed ones, which may explain the EKC formation found in this study. Developed countries may have access to technologies that improve energy efficiency and enables more environmentally friendly energy to be produced. These technologies are not generally accessible for less developed countries due to their high cost. However, The EKC is not confirmed for the remaining environmental measures of carbon, built-up land, forest land, and cropland. Therefore, the results from this study show that continued economic development has a substantial negative impact on biodiversity and ecosystem processes in the ecological footprints of carbon, built-up land, forest land, and cropland. This calls for further investigation on the subject. As economic development enables better standards of life through improved health, education, technology, infrastructure, and poverty reduction, sacrificing economic development may not be a good policy despite its negative impact on environmental quality. Instead, it is recommended that the investigated countries put efforts into energy-saving projects to improve energy efficiency and aim resources at innovating cleaner technologies in the power sector. This will aid the countries’ transformation towards sustainable growth.

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is that non-fossil energy has both positive and negative impacts on ecological footprints, depending on which environmental aspect you investigate. This study is by no means providing the defining answers to the impact of non-fossil energy consumption and economic development on environmental quality. Instead, the results from this study underline the importance of acknowledging the complexity of the global sustainable development issue. This study does not question the present transitioning towards non-fossil energy consumption, as the development in non-fossil energies has been shown to have positive environmental benefits. However, the results from this study suggest that there are some negative impacts on biodiversity due to the development of non-fossil energy, and this needs to be carefully calculated in policymaking. Further, human wellbeing depends on environmental diversity and resources such as farming, fishing, forestry, and means of transportation. Therefore, this study suggests that future avenues of research on the impact of non-fossil energy consumption on environmental quality should investigate the human wellbeing implications that arise when developing non-fossil energy production. This would be an interesting pathway in extending the previous literature.

Appendix 1

Table A.1

The Fixed Effects regression results, the dependent variable is ecological footprints per capita.

Variables Model I Model II Model III Model IV Model V Model VI Model VII Non-fossil energy consumption ⎼ 0.062*** (0.012) ⎼ 0.117*** (0.011) ⎼ 0.072*** (0.011) ⎼ 0.027*** (0.010) ⎼ 0.026*** (0.010) ⎼ 0.022** (0.010) ⎼ 0.020** (0.010) GDP per capita 0.392*** (0.020) 1.117*** (0.066) 1.447*** (0.063) 1.193*** (0.135) 1.243*** (0.133) 1.007*** (0.159) GDP2_{per capita} _{⎼ 0.360}_*** (0.029) ⎼ 0.568*** (0.029) ⎼ 0.429*** (0.072) ⎼ 0.416*** (0.070) ⎼ 0.304*** (0.082) Fossil energy consumption 0.398*** (0.027) 0.411*** (0.028) 0.383*** (0.028) 0.376*** (0.028) Labor force ⎼ 0.230** (0.108) ⎼ 0.221** (0.106) ⎼ 0.234** (0.106) Trade openness ⎼ 0.114*** (0.022) ⎼ 0.114*** (0.022)

Biocap per capita 0.171***

(0.064) Hausman test

(p-value)

0.085 0.000 0.000 0.000 0.000 0.000 0.000

R2 _{⎼ 0.011} _0.249 _0.375 _0.512 _0.514 _0.530 _0.534

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Table A.2

The system-GMM regression results, the dependent variable is ecological footprints per capita.

Variables Model I Model II Model III Model IV Model V Model VI Model VII Ecological footprint (-1) 0.966*** (0.010) 0.763*** (0.082) 0.293*** (0.091) 0.311*** (0.075) 0.275*** (0.073) 0.322*** (0.074) 0.335*** (0.068) Non-fossil energy consumption ⎼ 0.005** (0.002) ⎼ 0.019* (0.011) ⎼ 0.038** (0.016) ⎼ 0.035** (0.016) ⎼ 0.032** (0.016) ⎼ 0.038** (0.018) ⎼ 0.034** (0.016) GDP per capita 0.087** (0.037) 0.0288*** (0.051) 0.399*** (0.068) 0.655*** (0.175) 0.589*** (0.183) 0.613*** (0.171) GDP2_{per capita} _{⎼ 0.025}_** (0.010) ⎼ 0.114*** (0.035) ⎼ 0.252*** (0.088) ⎼ 0.208** (0.089) ⎼ 0.226*** (0.082) Fossil energy consumption 0.174*** (0.059) 0.187*** (0.059) 0.121** (0.060) 0.132** (0.053) Labor force 0.260 (0.190) 0.280 (0221) 0.293 (0.201) Trade openness 0.054 (0.095) 0.013 (0.055)

Biocap per capita 0.015

(0.052)

Sargan test (p-value) 0.99 0.99 0.99 0.99 0.99 0.99 0.99

AR (1) test ⎼ 3.46*** ⎼ 4.17*** ⎼ 3.91*** ⎼ 4.18*** ⎼ 4.17*** ⎼ 3.90*** ⎼ 3.97***

AR (2) test 1.667* 1.737* 1.332 1.492 1.473 1.553 1.555

Instruments 35 35 35 35 35 35 35

Notes: All variables are in natural logarithms. "(-1)" reports the first-order lag of the dependent variable. The estimations are obtained using the Blundell & Bond (1998) and Arellano & Bover (1995) system-GMM one-step estimation approach. AR (1) and AR (2) are the first and second-order tests of serial correlation, respectively. Heteroscedasticity robust standard errors in parenthesis. Windmeijer's (2005) corrected standard errors are employed. ***, ** and * represent the statistical significance at 1%, 5% and 10% levels, respectively.

Table A.3

List of countries

Australia Greece New Zealand

Austria Hungary Norway

Belgium Ireland Poland

Canada Israel Portugal

Chile Italy Slovak Republic

Colombia Japan Slovenia

Czech Republic Korea, Republic of Spain

Denmark Latvia Sweden

Estonia Lithuania Switzerland

Finland Luxembourg Turkey

France Mexico United Kingdom

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