The effect of energy prices, energy losses, and renewable energy use on CO₂ emissions in energy-importing developing economies in the presence of an environmental Kuznets curve

Abstract

In this study, energy losses, energy prices, and the relationship between green energy and environmental quality are investigated for 15 energy-importing emerging economies. In addition, the validity of the environmental Kuznets curve is tested in this study. Autoregressive Distributed Lag (ARDL) approach based on panel dataset, related intermediate estimators including PMG, MG, and DFE were used as a method. In addition, FMOLS and DOLS estimators were used for robustness testing in the study. According to empirical findings, the environmental Kuznets curve is valid in energy-importing emerging economies. In addition, green energy use and energy prices have a reducing effect on CO₂ emissions. However, energy losses increase CO₂ emissions. While the long-term results of the variables were similar, the short-term results were mixed. This situation is attributed to different economic growths in energy-importing developing economies, the share of energy resources in total energy resources, and energy-efficient technologies in the energy field. The fact that these variables have never been investigated for this economy group makes the study different.

Graphical Abstract

Introduction

Since the 19th century, energy input has been a cornerstone of economic development throughout the world. It is an integral component of industrial growth and has led to an expansion of global capital (Al-Mulali and Ozturk 2016). Over the past 30 years, the world's GDP has grown dramatically, rising 136.03% in 2019 compared to 1990, which has caused total energy use to increase by 65.79% in the same timeframe (World Bank 2022). However, fossil fuels make up a large portion of the world's total energy resources, with an 80.88% share in 2019. This high rate of fossil fuel usage has caused the world's CO₂ emissions to skyrocket by 63.92% since 1990. Despite modern advancements, renewable energy has only accounted for 4.72% of total energy use in 2019, and nuclear energy 5.02% (IEA 2022). The utilization of non-renewable resources like fossil fuels is especially high in developing countries that make up the majority of the world, which is a great source of concern in terms of environmental degradation and climate change. This is due to their lack of technology in the field of energy production, leading to higher losses during the production, transmission, and transportation of energy, in addition to a greater reliance on these resources for economic growth. Consequently, it is impossible for these countries to completely abandon the use of fossil fuels in the near future. Therefore, it is essential for policymakers and researchers to address this issue with the utmost seriousness and continue to research the topic in the coming years.

The share of developing economies in global energy consumption is significant. In 2019, China and India alone accounted for nearly 30% of total energy consumption (IEA 2022). However, price fluctuations for energy, especially for fossil fuels, can put these economies in a difficult situation. This is because rising energy prices can be a significant problem for energy security, economic growth, and sustainable development. The countries most vulnerable to this issue are emerging economies that import high amounts of energy, despite their potential for economic and social growth. These economies are unable to meet their demands with their resources and rely on foreign energy. As a result, high energy prices indicate a scarcity of energy. This situation incentivizes the substitution of expensive energy sources with cheaper alternatives, which can affect the energy supply (Naimoglu and Akal 2021). Although price fluctuations in energy can lead to several challenges, it may also benefit environmental quality. Higher energy prices will lead to lower energy consumption, reducing emissions of CO₂. Since approximately 81% of global energy use in 2019 came from fossil fuels, pollution can be reduced (IEA 2022).

Using renewable energy sources has been taken into account as an effective way to reduce environmental pollution. Renewable energy is a source of clean, sustainable energy that is better for the environment than fossil fuels, which produce pollutants when burned. From 1990–2019, the usage of fossil fuels increased by an average of 1.74% each year, while nuclear energy had an average increase rate of 1.13%. However, renewable energy was the energy source with the highest growth rate at 3.98%, according to the International Energy Agency (2022). Although renewable energy is not yet at the desired level, having the fastest growth rate is key to lowering the impact of pollution on the environment. Renewable energy is also essential for helping global economies, especially developing countries that rely on energy imports. Renewable energy sources do not produce secondary waste, providing countries with energy security and meeting their current and future economic and social needs (Owusu and Asumadu-Sarkodie 2016).

The other side of the EKC hypothesis has not been explored in terms of the energy losses that take place during the production, transmission, and transportation of energy. These losses can have an immense impact on environmental quality. Developing economies are more susceptible to these losses due to their lack of technology in the energy sector. Furthermore, energy losses are a waste of resources, as they do not generate any output and only increase costs. Additionally, a higher share of fossil fuels in developing economies' energy resources leads to increased environmental degradation due to increased energy losses. This, in turn, leads to an increased demand for fossil fuels, energy demand, foreign dependency on energy, foreign exchange needs, and current account deficits (Naimoglu 2021; Naimoglu and Ozel 2022; Naimoglu and Akal 2022). Therefore, it is important to reduce energy losses to have a sustainable energy supply and maintain high environmental quality.

It is hypothesized that the importer of renewable energy can affect CO₂ emissions in developing economies in several ways. Firstly, the increased availability of renewable energy sources can reduce the amount of energy derived from burning fossil fuels, which is the primary source of CO₂ emissions in the majority of developing countries (Adebayo et al. 2023; Caglar and Askin 2023; Li 2023; Karaaslan and Camkaya 2022). Secondly, the introduction of renewable energy sources can help to reduce the cost of energy, making it more affordable for individuals and businesses in developing countries, which in turn reduces the amount of energy consumed and reduces related CO₂ emissions (Li et al. 2023; Zhang et al. 2023; Yan et al. 2023a; Razzaq et al. 2023). Finally, the increased use of renewable energy sources can help to create jobs (Candra et al. 2023; Hanna et al. 2023; Wang et al. 2023) and stimulate economic growth in developing countries, which can also help to reduce CO₂ emissions (Sadiq et al. 2023; Nahrin et al. 2023; Adebayo et al. 2023; Mukhtarov et al. 2023). Therefore, the use of renewable energy is important in combating CO₂ emissions in energy-importing emerging economies. On the other hand, Energy losses affect CO₂ emissions in energy importing developing economies. This is because energy losses reduce the efficiency of energy use, meaning that more energy is required to produce the same amount of goods and services (Allouhi et al. 2023; Yan et al. 2023b; Cheng et al. 2023). This increased energy use can lead to increased emissions of CO₂. Moreover, due to the limited resources and infrastructure of many importing developing economies, they are often unable to invest in efficient energy production and use, leading to higher levels of energy losses and associated emissions (Mukwarami et al. 2023; Singh et al. 2023; Ortiz 2023). Therefore, energy losses are important for energy-importing emerging economies. Additionally, in energy-importing developing economies, energy prices have a significant impact on CO₂ emissions. In general, when energy prices increase, the cost of energy production is higher, and the demand for energy decreases, leading to lower CO₂ emissions (Adebayo et al. 2023; Lei et al. 2023; Mukhtarov et al. 2023). On the other hand, when energy prices decrease, the cost of energy production is lower, and the demand for energy increases, leading to higher CO₂ emissions (Rasheed et al. 2022; Borzuei et al. 2022). This suggests that energy prices can effectively be used to reduce CO₂ emissions in energy-importing developing economies.

This article endeavors to explore the connection between energy prices, energy losses, renewable energy utilization, and CO₂ emissions for 15 developing economies that are energy-importing. It additionally examines if the EKC hypothesis applies to these nations. This research adds to the existing literature in various manners. Firstly, in 2019, the GDP of these 15 energy-importing countries had grown 460.09% since 1990, and their energy imports had risen 670.65% (World Bank 2022; IEA 2022). At the same time, the share of fossil fuels in the total energy resources of these economies was 81.93% (coal 47.28%, oil 23.26%, and natural gas 11.39%), while the share of renewable energy was 5.84% (hydro 3.22% and wind, solar and others 2.62%) (IEA 2022). Therefore, these nations are highly reliant on foreign energy sources, have high levels of energy losses, and very little renewable energy use—which makes them a unique case in the study of energy. To date, no research in the literature has considered the combination of these three factors for this particular country group. Secondly, These 15 economies are countries that make a significant contribution to global energy use due to their high use of fossil fuels. These economies accounted for 39.29% of global fossil fuels (68.51% of coal, 29.21% of oil, and 11.39% of natural gas) in 2019 and 43.67 of global CO₂ emissions from fossil fuel use. While these countries have only a small percentage of the least polluting natural gas and renewable energy, they are highly dependent on the most polluting fossil fuel, coal. As a result, any improvement in energy resources for these 15 economies will have a significant positive impact on the global environment. Third is the share of these economies in the use of clean energy, which can be an alternative to fossil fuels. For 2019, the share of renewable energy in the total energy resources of these economies is 5.84%, while the share of nuclear energy is 2.64% (IEA 2022). Therefore, alternative cleaner energy sources are quite low for the high share of fossil fuels these economies have. In addition, considering that not every economy can use nuclear energy (Bangladesh, Chile, Peru, Philippines, Thailand, and Turkey do not use nuclear energy among these 15 economies), the use of renewable energy is a very important alternative in terms of environmental quality. Fourth, these economies have a lot of potential for economic growth. According to the World Bank (2022), the average global GDP growth rate between 1990 and 2019 was 3.01%, while the rate in these economies was 6.12%. This makes them the driving force of the global economy with their high growth rates. In that case, the shift of the high growth rates achieved by these economies to the improvements in the energy field, which has the highest cost in production, will lead to both a decrease in energy losses by using energy more efficiently and an improvement in environmental quality with clean energy investments. Finally, the ARDL model estimated from the panel data set including 15 energy-importing countries for the timeframe of 1990–2019 was tested for its reliability with the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) estimators.

In the following section, econometric analysis methods and findings are given after the literature research on the subject. Finally, the study is concluded by presenting the results and recommendations.

An overview of the literature within the framework of the use of renewable energy, energy losses, energy prices, economic growth, and the environment

In the literature, the EKC hypothesis is generally investigated for the relationship between economic growth and the environment. In this study, in parallel with the literature, the relationship between economic growth and the environment is investigated. On the other hand, while investigating the EKC hypothesis, energy prices, energy losses, and renewable energy usage variables are also used. For this, the literature has been examined under four chapters. First, the relationship between economic growth and the environment was examined. Then, the relationship between energy prices and clean energy use with the environment was investigated. Finally, the possible effects of energy losses on the environment were investigated. In the literature, there are not many studies investigating the relationship between energy losses and energy prices and the environment. Therefore, the studies examined in these chapters are examined in more detail.

The relationship between economic growth and environment

In the literature, the number of studies investigating the relationship between economic growth and the environment for the EKC hypothesis has begun to increase. For example, in the study by Bandyopadhyay and Rej (2021), it was found that the EKC hypothesis is valid for India. The Indian economy is an important economy among emerging market economies. The reason for this is that the Indian economy has grown above the global economic growth and is among the wheel economies of global economic growth. Increasing economic growth in this economy causes the use of efficient technologies in the field of energy to increase. In addition, considering that the Indian economy is an energy importing economy, increasing high growth rates increase the use of renewable energy and reflect positively on the energy input that brings the most cost in production. This situation causes both the efficient use of energy and the increase in the use of clean energy. Therefore, increased growth increases the environmental destruction in India up to a certain level. However, it causes less CO₂ emissions later on. Similarly, in the study by (Akadiri et al. 2021) for BRICS countries, in the study by Adebayo (2021) for Indonesia, in the study by Jian et al. (2022) for West African countries, in the study by Ali et al. (2021) for Pakistan, in the study by Genc et al. (2022) for Turkey, Kilinc-Ata and Likhachev (2022) found that the EKC hypothesis was valid for Russia and Zeraibi et al. (2022) for the Chinese economy. Therefore, in these studies, economic growth increases environmental pollution up to a certain point and then causes environmental degradation to increase gradually. Awan et al. (2022a) for the overall level of economy in 10 developing countries, Awan et al. (2022b) for the transport sector in 33 high-income countries, and Awan et al. (2022c) obtained that the EKC is valid at the general economy level for 107 countries.

Renewable energy use and environmental relationship

There are many studies in the literature investigating the relationship between the use of renewable energy and the environment. For example, Lee (2013) has obtained that the increase in the use of renewable energy for G20 countries is important for economic development by reducing foreign dependency in the field of energy. It has been achieved that the increases in economic development increase the clean energy investments and decrease the use of fossil energy. Therefore, increased use of renewable energy is important to reduce CO₂ emissions. Shafiei and Salim (2014) confirmed the EKC hypothesis for OECD countries. However, the increasing use of renewable energy in OECD countries reduces CO₂ emissions. Similarly, Jebli et al. (2016) found that the EKC hypothesis was valid for 25 OECD countries. In addition, the use of renewable energy increases the environmental quality. On the other hand, Dogan and Seker (2016) obtained that energy consumption reduces environmental damage for EU countries. Besides, Jebli and Youssef (2017) obtained that the use of renewable energy, in the long run, increases CO₂ emissions for the North African country. Because the highest share in the total energy resources of these countries belongs to fossil fuels. Therefore, since the majority of the increasing energy demand is met by fossil fuels, the use of renewable energy increases CO₂ emissions with higher fossil fuel use. Moreover, while examining the 15 countries that consume the most renewable energy, Saidi and Omri (2020) found that the use of renewable energy has an increasing effect on economic growth. In addition, the use of renewable energy has reduced CO₂ emissions, helping these countries achieve clean growth. Ulucak and Khan (2020) found that the EKC hypothesis is valid for the BRICS countries. Environmental degradation in BRICS countries is above the world average. Therefore, improvements to be made to the environmental quality of these countries will positively affect the global environmental quality. For this, it has been obtained that renewable energy is an important alternative energy for environmental degradation in these countries. Similarly, Ahmad et al. (2021) obtained that for 11 developing economies, renewable energy is important for human health, sustainable development, and sustainable energy supply. In addition, using renewable energy is an important policy tool to reduce CO₂ emissions in these economies. Awan et al. (2022a) for 10 developing countries between 1996 and 2015 and Awan et al. (2022c), on the other hand, found that the increase in the use of renewable energy for 107 countries between 1996 and 2014 increased the environmental quality. Therefore, except for a few EKC-related exceptions, increased use of renewable energy reduces environmental pollution.

Energy prices and the environment relationship

Amano (1990) investigated the relationship between energy prices and CO₂ emissions in the 1990s. The findings have found that rising real energy prices and energy savings from continued improvements in energy-saving technologies will likely offset emissions levels in developed countries. However, he stated that total world emissions will continue to grow. Sun et al. (2016) conducted research on energy pricing reform and energy efficiency in China for the automobile market. Empirical results show that gasoline pricing reform has reduced energy use in the auto industry in China. The reason for this is the efficient use of energy as well as its efficient use. However, the policy of increasing energy prices remained very modest to achieve the desired target. If energy prices rise in the long run, consumers may choose to drive less. However, considering more policy measures to improve fuel efficiency in the auto market and other sectors in China will further reduce fuel-related environmental pollution. Ullah et al. (2019) investigated the factors behind increased environmental pollution as a result of electricity intensity in Pakistan. The results of the study showed that electricity prices did not have a significant effect on density changes. However, it has been shown that oil and natural gas prices have a significant effect on electricity density by providing more efficient use of energy. This is an important policy for environmental quality. Zeng et al. (2014) investigated the results of promoting energy-efficient products for the benefit of the public in 2012. In this way, energy will be used efficiently and environmental damage will be reduced. China's average electricity price has increased steadily from 0.262 RMB/kWh in 1996 to 0.51 RMB/kWh in 2007 and thereafter. Therefore, the continuous increase in China's electricity price is found to be one of the reasons explaining that the majority of Chinese consumers buy energy-efficient appliances. Considering that fossil fuels have the highest share in China's energy resources, energy prices are important for environmental improvement. Therefore, energy prices generally have a positive effect on environmental quality by reducing energy use as well as causing efficient and efficient use of energy.

Energy losses and the relationship with the environment

It is expected to increase the environmental damage as the increased energy losses do not turn into any production and increase the energy costs more. More than 80% of global energy in 2019 is fossil fuels (IEA 2022). Therefore, one of the most important reasons affecting the increase in energy demand and thus fossil fuel demand is the losses experienced during the production, transmission, and transportation of energy. The determination of energy policies in all processes such as energy production, distribution, and consumption and the selection of technologies compatible with these policies constitute an important problem (Ogul 2005). According to 2013 data, the ratio of energy loss from the grid to gross generation is 18% ($2 billion). In addition, while the annual average consumption per capita in the world is 2.326 Kwh/person, it is 1.509 Kwh/person on average in Turkey, including leakages and losses (Ogul 2005). Therefore, the cost of rising energy prices also increases. Considering the emerging energy importing economies, total energy losses were 33.58% in Bangladesh in 1990, 27.58% in India in 2000, and 16.22% in Brazil in 2019 (IEA 2022). Losses in energy use are a very important problem that does not produce any output, that has to be endured for growth and that should be focused on among the serious problems that countries experience in energy, and it is necessary to reduce losses. Because the energy source with the highest share of energy importing emerging economies is a fossil fuel. Therefore, increasing energy losses cause more fossil fuel use, resulting in more cost and more CO₂ emissions (Naimoglu 2021; Naimoglu and Ozel 2022).

A summary of the literature related to the study is reviewed and summarized in Table 1.

Table 1.

The table of literature

Researcher(s)	Time period	Countries	Method(s)	Variables	Results
Bandyopadhyay and Rej (2021)	1978–2019	Indıa	ARDL	CO2, GDP, FDI, TRD, NEC	EKC hypothesis was not valid
Akadiri et al. 2021	1995–2018	BRICS countries	ARDL	CO2, GDP, EFREE, COAC, GASC, OILC, HDI	EKC hypothesis was valid
Adebayo (2021)	1980–2016	Indonesia	ARDL	CO2, GDP, TRD, EC	EKC hypothesis was valid
Jian et al. (2022)	1990–2018	West African countries	AMG, CCEMG	CO2, GDP, REC, URB, IND	EKC hypothesis was not valid
Ali et al. (2021)	1975–2014	Pakistan	ARDL	CO2, ED, NREC, FDI	EKC hypothesis was valid
Genc et al. (2022)	1980–2015	Türkiye	ARDL	CO2, GDP, EC, VOL	EKC hypothesis was valid
Kilinc-Ata and Likhachev (2022)	1990–2020	Russia	ARDL	CO2, GDP, EC, TRD, FD, POP	EKC hypothesis was valid
Zeraibi et al. (2022)	1980–2018	China	ARDL	CO2, GDP, M2	EKC hypothesis was not valid
Awan et al. (2022a)	1996–2015	10 developing countries	The novel Method of Moments Quantile Regression	CO2, GDP, REC, ETH, FDI, URB	EKC hypothesis was not valid, increased use of renewable energy is important to reduce CO2 emissions
Awan et al. (2022b)	1996–2014	33 high-income countries	FMOLS, DOLS	TCO2, GDP, URB, PTNT	The validity of an N-shape EKC curve for the transport sector
Awan et al. (2022c)	1996–2014	General economy level for 107 countries	Panel quantile regression (PQR) approach	CO2, GDP, EI, REC, NREC	EKC hypothesis was not valid, the impact of renewable and nonrenewable energy consumption on environmental degradation was significantly negative and positive across all quantiles
Lee (2013)	1971–2009	G20 countries	fixed effects models	CO2, GDP, FDI, REC	increased use of renewable energy is important to reduce CO2 emissions
Shafiei and Salim (2014)	1980–2011	OECD countries	Fisher-type Johansen panel cointegration test, panel least squares method	CO2, GDP, IND, PD, URB	EKC hypothesis was valid, increased use of renewable energy is important to reduce CO2 emissions
Jebli et al. (2016)	1980–2010	25 OECD countries	FMOLS, DOLS	CO2, GDP, REC, NREC, EXP, IMP	EKC hypothesis was valid, increasing non-renewable energy increases CO2 emissions, increased use of renewable energy is important to reduce CO2 emissions
Dogan and seker (2016)	1980–2012	EU countries	DOLS	CO2, GDP, REC, NREC, TRD	EKC hypothesis was valid, increasing non-renewable energy increases CO2 emissions, increased use of renewable energy is important to reduce CO2 emissions
Jebli and Youssef (2017)	1980–2011	North African country	FMOLS, DOLS	CO2, GDP, AVA	An increase in GDP or renewable energy consumption increases CO2 emissions
Saidi and Omri (2020)	1990–2014	15 major renewable energyconsuming countries	VECM, FMOLS	CO2, GDP, LBR, GCFC, REC, TRD, URB	increased use of renewable energy is important to reduce CO2 emissions
Ulucak and Khan (2020)	1992–2016	BRICS countries	FMOLS, DOLS	EFC, GDP, REC, UDB, NRR	EKC hypothesis was valid, increased use of renewable energy is important to reduce EFC
Ahmad et al. (2021)	2004–2017	31 Chinese provinces	AMG	EEI, GDP, NREC, URB	EKC hypothesis was not valid
Amano (1990)	1990–2000	World	Evaluation analysis	CO2, PRC	Rising energy prices reduce CO2 emissions
Sun et al. (2016)	2008–2013	China	Impact analysis	EPC, EE	Rising energy prices increase the efficient use of energy and reduce CO2 emissions
Ullah et al. (2019)	1972–2012	Pakistan	Index Decomposition Analysis	SO, PEC	Increasing oil and natural gas prices have a positive effect on the more efficient use of energy and the quality of the environment
Zeng et al. (2014)	June 2012	10 cities with different socioeconomic status in China	Survey	Likert variables	Increasing energy prices increase the use of efficient technologies and positively affect environmental quality
(Ogul 2005)	1980–2022	Türkiye	Regresyon	PG, GDP, POP	Energy losses affect energy consumption, and energy consumption negatively affects environmental quality
Naimoglu 2021	1990–2018	Germany	FMOLS, CCR	REC, LOS, GDP	Energy losses cause more fossil fuel use, increasing CO2 emissions
Naimoglu and Ozel 2022	1990–2018	16 emerging economies	CCEMG, AMG	EI, COAC, OILC, GASC, HDRC, ELEC, LOS	Energy losses increase the energy density and negatively affect the environmental quality

EFC: Ecological Footprint, COAC: Coal consumption, OILC: Oil consumption, GASC: Natural gas consumption, PEC: primary electricity consumption, REC: Renewable energy consumption, NREC: Non- renewable energy consumption, HDRC: Hydro, ELEC: Electricity, TRD: Trade, SO: sectoral output, FD: Financial development, ED: Economic development, FDI: Foreign Direct Investment, POP: Population, URB: Urbanization, GFCF: Gross fixed capital formation, LBR: Labor force, NRR: Natural Resource Rent, EPRC: Energy pricing reform, EFREE: Economic Freedom, HDI: Human Development Index, IND: Industrialization, EC: Energy Consumption, VOL: volatility of economic growth, M2: Money Supply, ETH: Internet penetration, TCO₂: Transport sector based CO₂ emissions, PTNT: Patents residents, EI, Birim enerji kullanımı başına GSYİH, PD: population density, EXP: per capita real exports, IMP: per capita real imports, AVA: agricultural value added, EEI: environmental emissions index, PG: Production Gap, LOS: Energy Losses

Methodology, data and empirical results

Data

In the world economic report published by IMF in 2015, Argentina, Bangladesh, Brazil, Bulgaria, Chile, China, Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Romania, Russia, South Africa, Thailand Turkey, Ukraine, and Venezuela are classified as emerging economies (IMF 2015). Among these economies, Argentina, Bangladesh, Brazil, Bulgaria, Chile, China, Hungary, India, Mexico, Pakistan, Peru, Philippines, Romania, Thailand, Turkey, and Ukraine are energy-importing countries. However, Ukraine could not be included in the study due to data constraints. Therefore, in this study, a set of panel data was used for 15 energy importers and emerging economies from 1990 to 2019. The Autoregressive Distributed Lag (ARDL) model was used as the main approach for panel data analysis. Thus, the effects of renewable energy consumption, energy prices, and energy losses on CO₂ emissions are investigated. CO₂ emissions per capita used in the model (metric tons), REN per capita use of renewable energy (total of all renewable energy except hydro (Koe)), LOS per capita energy losses (all energy losses during the generation, transmission, and transportation of energy (koe)), GDP is GDP per capita (constant 2015 US$). Finally, PRC represents real energy prices. If the Brent price is preferred for energy price, this situation does not differ between countries. Therefore, the real energy price index was preferred for each country. For this, first of all, the annual energy prices of Brent Petrol were obtained from Brent Petrol's address. Then, these prices were converted into real terms by using annual consumer price indices from inflationdata.com. Then, Official exchange rate (LCU per US Dollar, period average) data for each country was collected from the World Bank. Finally, these data were multiplied by each other and in this way, the real energy price index for the countries was formed (Antonietti and Fontini 2019).

Balanced panel analysis was used in this study using 30 years of data for 15 countries. Before proceeding to the analysis, firstly, summary information for the variables is given in Table 2. When Table 2 is examined, only the CO₂ emission has a negative average value. The smallest volatility is GDP. The highest average and highest volatility are GDP².

Table 2.

Definition of variables, descriptive statistics and source

Variables	Description	Source	Mean	Std. Dev	Max	Min
CO2	Log (Carbon dioxide emissions per capita (metric tons))	IEA	-5.686	0.399	-5.086	-6.990
GDP	Log (GDP per capita (constant 2015 US$))	WDI	3.622	0.397	4.177	2.709
GDP2	Log (GDP per capita (constant 2015 US$))2	WDI	13.273	2.766	17.450	7.341
LOS	Log (Energy losses per capita (koe))	IEA	1.665	0.547	2.997	0.620
PRC	Log (Real energy prices index (Inflation-adjusted Brent price x average nominal exchange rate w.r.t. USD))	BP statistical review Inflationdata.com, WDI	2.682	1.116	4.717	-2.932
REN	Log (Renewable energy use per capita (all except hydro) (koe))	IEA	0.665	1.190	3.470	-2.533

The time (T) is 30 and the unit (N) is 15 for all variables. NT is 450

The flow chart of the panel data analysis

The research procedure to be used for the study is as follows.

$$\genfrac{}{}{0.0pt}{}{\mathit{Unitroot}}{\mathit{test}}\Rightarrow \genfrac{}{}{0.0pt}{}{\mathit{Stationarityat}}{I\left(0\right)orI\left(1\right)}\Rightarrow \genfrac{}{}{0.0pt}{}{\begin{array}{c}CO2\to I\left(1\right)\\ GDP\to I\left(1\right)\end{array}}{\begin{array}{c}{\mathit{GDP}}^2\to I\left(1\right)\\ PRC\to I\left(0\right)\\ LOS\to I\left(1\right)\\ REN\to I(1)\end{array}}\Rightarrow \genfrac{}{}{0.0pt}{}{\begin{array}{c}\mathit{ARDLapproachesto}\\ co-integration\\ PMG\end{array}}{\begin{array}{c}\mathit{MG}\\ DFE\end{array}}\Rightarrow \genfrac{}{}{0.0pt}{}{\begin{array}{c}\mathit{Panel}\\ short-run\end{array}}{\begin{array}{c}long-run\\ coefficients\end{array}}\Rightarrow \genfrac{}{}{0.0pt}{}{\begin{array}{c}Mode{l}^{{}^{\prime }}s\\ robustnesscheck\end{array}}{\mathit{DOLSandFMOLS}}$$

Unit Root Test

In this section, the degree of stationarity of the variables will be investigated. For this, Im et al. (2003) (IPS) stationarity test will be used. The approach of this test is similar to the standard Dickey-Fuller (DF). First, we describe the standard ADF regression with individual country effects and without time patterns. But here, the same unit root tests are not used for every country. In addition, the average of the groups is carried out similarly in ADF statistics. In addition, the IPS test can perform better for a small time and unit observations.

Dynamic Model

In this study, the relationship between environmental quality and clean energy use, energy prices, and energy losses was examined. While investigating this relationship, appropriate tools, and appropriate contexts were used. In addition to these, the dynamic panel method was used to take into account heterogeneous data. Three estimators are used for Autoregressive Distributed Lag (ARDL, p, q) according to the characteristics of the data. Primarily Pesaran and Shin (1995) and Pesaran et al. (1999), MG and PMG estimators introduced to the literature were preferred. On the other hand, DFE estimation is made together with MG and PMG. For this, the ARDL model given below is used (Loayza and Ranciere 2006). Pesaran et al. (1999) argued that panel ARDL estimators can produce reliable estimates independent of the potential internality problem since the lags of the dependent and independent variables are included in the model. Therefore, the ARDL model is a good method for problems caused by possible sincerity problems.

$$\mathrm{\Delta }{\left(y_i\right)}_t=\sum _{j=1}^{p-1}{\gamma }_j^i\mathrm{\Delta }{\left(y_i\right)}_{t-j}+\sum _{j=0}^{q-1}{\delta }_j^i\mathrm{\Delta }{\left(X_i\right)}_{t-j}+{\phi }^i\left[{\left(y_i\right)}_{t-1},-,\left\{{\beta }_0^i,+,{\beta }_1^i,{\left(X_i\right)}_{t-1}\right\}\right]{\epsilon }_{\mathit{it}}\cdots$$

where y represents the dependent variable CO₂. X explanatory variables represent renewable energy, energy prices, and energy losses. γ and δ are short-run coefficients. β represents the long-run coefficients. In addition, φ is the rate of return of the deviations to equilibrium. […] indicates a long-term growth recession. In addition, the expression given above can be calculated for PMG, MG, and DFE. However, long-term dynamic adjustments are needed for these calculations. It is also important to consider the heterogeneity feature here (Demetriades and Hook Law 2006).

When the studies by Johansen (1995) and Phillips and Hansen (1990) are examined, the degrees of stability must be the same to examine the long-term relationship. However, PMG and MG estimators are newly developed estimators. These estimators have many benefits over other approaches. First, the variables do not have the same degree of rigidity as being stationary. This also applies to small units and time dimensions. On the other hand, these estimators can calculate the short- and long-term results simultaneously. On the other hand, while using Engle and Granger's (1987) test, an internality problem may arise, this problem can be eliminated with ARDL methods.

Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effect (DFE) estimator

In this study, the PMG estimator will be preferred as the estimation method. There are some important reasons for this. First, the short-run coefficients are heterogeneous. In addition, it can give a speed of return to the balance in the long run. On the other hand, the long-run coefficients are homogeneous. If the ECT coefficient obtained for PMG is obtained according to the theoretical expectation, effectiveness, consistency, and validity will be ensured. Thus, the long-term relationship between the variables is obtained. Efficiency is provided for PMG if the series is uncorrelated. for this, the independent variables are considered exogenous. In cases where these conditions are dependent (p) and independent (q), ARDL(p,q) delays are included and the ECT form is fulfilled. In addition, the T and N dimensions in the study are suitable for the dynamic model. Heterogeneity and bias problems can be eliminated for analyzes to be made in this way. According to Teal and Eberhardt (2007), it is important to consider heterogeneity to better understand the growth process. Therefore, the PMG estimator is preferred.

On the other hand, as a second estimator, the MG estimator, which was introduced to the literature by Pesaran and Shin (1995), is used. In MG, regression is made for each unit. Thus, the coefficients for each unit are obtained. However, these coefficients are coefficients with unweighted averages. In addition, the short- and long-term coefficients obtained while estimating MG have heterogeneous properties. On the other hand, T and N in the study are sufficient for the validity and consistency assumptions of the MG estimator.

On the other hand, DFE is the third estimator to be used. The DFE estimator is similar to the PMG estimator. However, the short-run coefficients for DFE are also homogeneous. In addition, while running the DFE model, there is a simultaneous equation bias due to the internality between ut and the lagged dependent variable. Hausman test can be used to calculate the degree of internality (Baltagi et al. 2000).

Thus, while all coefficients are heterogeneous for the MG estimator, only the short-run coefficients are heterogeneous in PMG. For DFE, all coefficients are homogeneous. Which of these estimators is more efficient is decided by the Hausman test. The basic hypothesis for the Hausman test between PMG and MG is that the PMG estimator is more efficient. Similarly, for MG and DFE, the main hypothesis is that the DFE estimator is more efficient. In addition, between PMG and DFE, PMG is preferred. This is because the PMG estimator dominates the DFE estimator. In the other case, MG is preferred.

Empirical results and discussion

Descriptive statistics and correlation matrix outcomes

The terminology of the data set to be used in this study is given in Table 3. When Table 3 is examined, the minimum value for CO₂ emission, which is the measure of environmental quality, is -6,990, the maximum value is -5,086 and the average is -5,534. This shows that for the examined countries, there has been a certain level of increase and decrease in CO₂ emissions in the examined periods. Indeed, in addition to the characteristics of every country, countries that are still highly dependent on polluted energy sources have a significant percentage of CO₂ and are among the countries with increasing environmental degradation. A value of -0.970 indicates that CO₂ is negatively skewed. Considering that most of the sample countries are energy-importing countries, it implies that these countries are witnessing reduced CO₂ emissions due to the reduction of energy use by being significantly affected by energy prices or the crises experienced. A standard deviation of 0.399, well above the mean value of -5,685, indicates significant variation between the countries studied. A kurtosis value of 3.494 indicates that it peaks above CO2. The GDP of recorded income in these sampled countries has an average of 3,761, a maximum of 4,177, and a minimum of 2,709. This indicates that these countries have high incomes among developing countries, so they were classified as emerging economies in the IMF's 2015 world report. The average value of renewable energy use and energy prices is 0.665 and 2.682, the maximum value is 3.470 and 4.717, and the minimum value is -2.533 and -2.932, respectively. When these indicators are examined, besides the negative renewable energy profile of most countries examined, it shows the changes in energy prices that will shake their economies significantly. Although energy losses do not give a more negative image such as energy prices and renewable energy profile for the countries examined, it shows that the technological level in these countries is insufficient and energy is not used efficiently.

Table 3.

Descriptive Statistics

	Mean	Median	Max	Min	Std. Dev	Skewness	Kurtosis	Jarque–Bera	Probability
CO2	-5.685	-5.534	-5.086	-6.990	0.399	-0.970	3.494	75.165***	0.000
GDP	3.622	3.761	4.177	2.709	0.397	-0.700	2.292	46.110***	0.000
LOS	1.664	1.568	2.997	0.620	0.547	0.566	2.583	27.292***	0.000
PRC	2.682	2.831	4.717	-2.932	1.116	-1.077	5.665	220.165***	0.000
REN	0.665	0.421	3.470	-2.533	1.190	0.047	2.429	6.276**	0.043

^*** and ** show significance at 1% and %10

Table 4 shows the correlation matrix of the variables. Table 4 shows that the explanatory variables have a weak correlation with the dependent variable. The low correlation coefficient indicates an excellent fit for co-modeling the variables in examining their effects on CO₂ emissions in selected countries. In addition, since the explanatory variables do not have a high correlation above the generally accepted standard level, it excludes the existence of the multicollinearity problem. Therefore, this indicates that the study will produce strong predictive results.

Table 4.

Correlation Matrix

Probability	GDP	LOS	PRC	REN
GDP	1
	(––-) [––-]
LOS	-0.090	1
	(-1.910) [0.057]	(––-) [––-]
PRC	-0.137	-0.109	1
	(-2.920) [0.004]	(-2.320) [0.021]	(––-) [––-]
REN	0.068	0.409	0.035	1
	(1.437) [0.152]	(9.485) [0.000]	(0.751) [0.453]	(––-) [––-]

Upper values represent the correlation power, p-values are presented in [] and t-statistics are in ()

Panel unit root test

In the study, PMG was preferred to investigate the short-long-term relationship between the independent variables and the dependent variable. For the asymptotic properties of the parameters to be estimated for PMG, it is important to determine to what degree the series are stationary. Therefore, the stationarity of the variables will be investigated first. For this, IPS and Maddala-Wu stationarity tests will be used. Unit root test was performed for all series to be used in the study and the results are shown in Table 5. According to Table 5 test results, the PRC variable is stationary in its level value. However, all other variables have unit roots in their level values. On the other hand, according to both stationarity results, it is seen that all variables become stationary after taking the first difference. Therefore, the integration degrees of the series to be used in the study are mixed. In that case, Panel ARDL can be used for the study (Behera and Mishra 2020).

Table 5.

Unit root test results

	Level		1ST DIF
	Im, Peseran & Shin (2003)	Maddala and Wu (1999)	Im, Peseran & Shin (2003)	Maddala and Wu (1999)
CO2	0.052	32.228	-13.461***	209.425***
GDP	6.335	17.168	-8.866***	139.857***
GDP2	7.181	14.656	-8.595***	135.424***
LOS	0.595	33.101	-14.748***	238.964***
PRC	-3.261***	79.950***	-11.708***	182.361***
REN	2.827	26.697	-8.498***	132.012***

 ∗  ∗  ∗ , ∗  ∗ , and ∗ are significance levels at the 1%, 5%, and 10% level, respectively

PMG, MG and DFE Estimation

In this section PMG, MG, and DFE estimators are run. The choice between them is made by the Hausman test. For preference, the Hausman test is performed between PMG and MG. Then, the Hausman test is performed between DFE and MG. If the Hausman test statistical value is less than the critical values or the probability value is greater than 0.05, the PMG estimator is more efficient than the MG estimator. On the other hand, if the Hausman statistical value is less than the critical values or the probability value is greater than 0.05, the DFE estimator is more efficient than the MG estimator. In this case, the PMG estimator dominates the DFE estimator and it is decided that the effective estimator is PMG (Mehmood et al. 2014). Otherwise, the efficient estimator is the MG estimator.

Hausman test results are given in Table 6. According to the Hausman test results between MG and PMG, PMG is more efficient. On the other hand, DFE is more efficient according to the Hausman test results between MG and DFE. Also, PMG will be used for estimation as PMG dominates DFE.

Table 6.

Results of PMG, MG, and DFE

	PMG		MG		DFE
Dep.Var: EF	Long-Term	Short-Term	Long-Term	Short-Term	Long-Term	Short-Term
GDP	5.284*** (0.866)		-12.924 (30.888)		1.753** (0.740)
GDP2	-0.601*** (0.107)		1.606 (3.982)		-0.163 (0.105)
PRC	-0.182*** (0.018)		-0.039 (0.042)		-0.039** (0.020)
LOS	0.178*** (0.056)		0.098 (0.074)		0.279*** (0.082)
REN	-0.116*** (0.020)		-0.023 (0.028)		-0.029* (0.016)
ECT		-0.139*** (0.047)		-0.583*** (0.062)		-0.126*** (0.021)
∆GDP		-8.769 (6.763)		-12.289 (9.066)		1.057 (0.888)
∆GDP2		1.277 (0.940)		1.603 (1.363)		-0.534 (0.118)
∆PRC		0.008 (0.008)		0.011 (0.008)		0.008 (0.006)
∆LOS		0.044 (0.030)		0.038 (0.032)		0.007 (0.020)
∆REN		0.024* (0.014)		0.002 (0.023)		0.005 (0.003)
Constant		-2.290*** (0.770)		-19.727 (16.760)		-1.285*** (0.277)
Hausman Test	χh2 (5) = 3.57 Proba = 0.6130				χh2 (5) = 3.91 Probb = 0.5626
Observation	450		450		450

 ∗  ∗  ∗ , ∗  ∗ , and ∗ are significance levels at the 1%, 5%, and 10% level, respectively. The values in parentheses show the standard error values of the estimators

According to the PMG results, all variables are statistically significant. According to empirical findings, it is energy prices that reduce CO₂ emissions the most. However, energy losses increase CO₂ emissions. On the other hand, renewable energy reduces environmental degradation. Considering the size of the variables, a 1% increase in energy losses causes an increase of 0.18% in CO₂ emissions in the long run. In addition, a 1% increase in energy prices and renewable energy use causes a 0.18% and 0.12% decrease in CO₂ emissions, respectively, in the long run. Therefore, the increase in energy prices in energy-importing developing economies is an important policy to reduce CO₂ emissions. Because the increase in energy prices increases environmental costs. However, this policy may also restrict or reduce economic growth by causing production to decrease. Renewable energy is important for reducing CO₂ emissions. In addition, considering that developing economies are energy importers, renewable energy offers very important opportunities for sustainable energy supply and sustainable growth. In addition, emerging energy-importing economies do not have sufficient technology in the field of energy. Therefore, significant energy losses occur during the production, transmission, and transportation of energy. On the other hand, since fossil fuels have the highest share in the energy use of these economies, increasing energy losses increase the use of fossil fuels, and increasing energy losses increase CO₂ emissions. In addition, increasing energy losses do not turn into any output, thus bringing a serious cost. These losses cause an increase in energy imports and an increase in the use of fossil fuels. This situation increases environmental degradation as it causes more fossil fuel consumption. These results Anwar et al. (2021), Pata (2021), Li and Haneklaus (2022), and Mujtaba et al. (2022) state that the increase in fossil fuel use increases CO₂ emissions. In addition, Bhattacharya et al. (2017), Destek et al. (2018), Wang et al. (2021), Zafar et al. (2022), and Gyimah et al. (2022) supports the results that the use of renewable energy reduces CO₂ emissions. It is also consistent with the results of the study by Shan et al. (2021), Zhang et al. (2021), and Umar et al. (2021) state that the increase in energy prices improves environmental quality.

On the other hand, according to all estimation results, the sign of the square of economic growth is negative and statistically significant. Therefore, the EKC hypothesis is valid for energy-importing emerging economies. This shows that CO₂ emissions first increase and then decrease with economic growth. This result is consistent with the results of studies by (Sarkodie 2018; Isik et al. 2019; Adebayo 2021 and Pata and Samour 2022). However, these results are inconsistent with results for China in the study by Yilanci and Pata (2020) and for low-income countries in the study by Al-Mulali et al. (2015) and the study by BIRCST countries (Dogan et al. 2020). This may be due to economic complexity instead of economic growth and the use of ecological footprint instead of CO₂ emissions.

Short-term parameters for PMG are heterogeneous. The ECT coefficient was obtained by the theoretical expectation. That is, the ECT coefficient is -0.139 ∈ (-1,0) and is statistically significant. According to the ECT coefficient, the variables move together in the long run. In addition, ECT (-0.139), which shows the rate of return to equilibrium in the long run, indicates that approximately 0.14% of a variant in the t-1 period will return to equilibrium in the t period.

At the same time, according to the results of Table 6, all other variables, except renewable energy, increase CO₂ emissions in the short run. However, except for renewable energy use, other variables were found to be statistically insignificant.

The short-run coefficients were estimated for each country and are given in Table 7. Country characteristics can be investigated further by comparing the findings obtained in the short term.

Table 7.

Individual PMG short-term results

	ECT	GDP	GDP2	PRC	LOS	REN	C
Argentina	-0.015	-1.372	0.248	0.010	-0.005	-0.006	-0.263
	(0.013)	(5.000)	(0.620)	(0.014)	(0.032)	(0.005)	(0.227)
Bangladesh	-0.113	22.180**	-3.345**	-0.007	0.005	0.012	-1.887
	(0.087)	(9.568)	(1.503)	(0.035)	(0.063)	(0.048)	(1.472)
Brazil	-0.022*	-28.117*	3.779*	-0.007	-0.031	-0.002	-0.378*
	(0.011)	(15.352)	(1.968)	(0.009)	(0.110)	(0.005)	(0.195)
Bulgaria	-0.055	-0.671	0.216	0.031**	0.271*	0.005	-0.916
	(0.048)	(8.852)	(1.211)	(0.012)	(0.151)	(0.007)	(0.792)
Chile	-0.034	-11.422	1.546	-0.049	-0.050	0.014	-0.552
	(0.031)	(8.476)	(1.087)	(0.046)	(0.055)	(0.012)	(0.507)
China	-0.171***	0.835	0.041	0.005	0.297*	0.061***	-2.818***
	(0.051)	(1.712)	(0.255)	(0.025)	(0.156)	(0.021)	(0.816)
Hungary	-0.619***	-0.638	0.130	0.057***	-0.077	0.194**	-10.078***
	(0.088)	(5.054)	(0.642)	(0.022)	(0.058)	(0.084)	(1.699)
India	0.109*	-3.337	0.635	0.046**	-0.026	0.032	1.756*
	(0.061)	(3.142)	(0.534)	(0.021)	(0.097)	(0.041)	(0.985)
Mexico	-0.224*	-81.186	10.400	-0.049	0.120	0.007	-3.705**
	(0.116)	(71.610)	(9.138)	(0.077)	(0.351)	(0.154)	(1.898)
Pakistan	-0.403***	-48.323***	8.062***	0.041**	-0.085***	-0.003	-6.617***
	(0.075)	(11.694)	(1.919)	(0.021)	(0.025)	(0.032)	(1.554)
Peru	-0.100	-7.123	1.075	0.009	-0.024	0.004	-1.705
	(0.080)	(8.159)	(1.138)	(0.034)	(0.060)	(0.008)	(1.355)
Philippines	-0.285***	17.889*	-2.661*	0.023	0.119	-0.019	-4.659***
	(0.080)	(10.004)	(1.501)	(0.039)	(0.081)	(0.104)	(1.340)
Romania	-0.091	2.060	-0.148	0.006	0.017	0.052**	-1.553
	(0.098)	(5.593)	(0.741)	(0.032)	(0.045)	(0.022)	(1.624)
Thailand	-0.065**	4.093	-0.442	-0.004	0.010	0.025*	-1.079**
	(0.027)	(5.772)	(0.807)	(0.021)	(0.036)	(0.013)	(0.441)
Turkey	0.007	3.602	-0.377	0.009	0.125	-0.023	0.113
	(0.029)	(7.416)	(0.961)	(0.030)	(0.186)	(0.141)	(0.483)

 ∗  ∗  ∗ , ∗  ∗ , and ∗ are significance levels at the 1%, 5%, and 10% levels, respectively. The values in parentheses show the standard error values of the estimators

When Table 7 is examined, it is seen that renewable energy use, energy prices, and energy losses have different effects on CO₂ emissions in many countries. The increase in CO₂ emissions of renewable energy in some countries can be associated with the increased use of fossil fuels. Because the share of fossil fuels is very high in energy-importing emerging economies. Therefore, it can be associated with an increase in CO₂ emissions, since the increasing energy demand causes an increase in the use of fossil fuels, which have a high share. On the other hand, increasing energy losses reduce CO₂ emissions in some countries. Emerging economies have high growth figures. It has to use energy while performing these growths. Energy losses that have to be endured during energy use may occur. Therefore, the increase in energy losses that have to be endured causes growth. This situation increases the use of energy-efficient technologies by causing more capital. Therefore, this situation causes the same output to be obtained with less energy. Therefore, increases in energy losses during efficient use of energy reduce CO₂ emissions (Akal 2015, 2016). Moreover, increases in energy prices have an increasing effect on CO₂ emissions. Since high growth in emerging economies requires high amounts of energy, it is realized with high energy imports in these economies. However, some countries are caught in the production ambition to compete. This situation may not affect the amount of energy used much, even if energy prices increase. Therefore, rising energy prices may cause an increase in CO₂ emissions in some economies. On the contrary, the use of renewable energy and increases in energy prices reduce CO₂ emissions. Energy losses increase CO₂ emissions.

Robustness test

FMOLS and DOLS robustness tests are also used in this section to increase the reliability of the results of the PMG estimator. DOLS is an efficient estimator if the variables have different degrees of stationarity. In addition, another reason for using the DOLS estimator is that it can take into account the internality problem. it can also give effective results for the study data. DOLS not only resolves endogeneity but also corrects serial correlation through different antecedents and lags (Sulaiman and Abdul-Rahim 2020: 37,703).

When Table 8 is examined, DOLS results showed similar results to PMG results. For DOLS, energy prices and the use of renewable energy reduce CO₂ emissions. Energy losses increase environmental degradation. However, the use of renewable energy was found to be statistically insignificant. On the other hand, when using heterogeneous cointegrated panel data, the FMOLS estimator can be preferred for robustness testing by Pedroni (2001) criteria.

Table 8.

FMOLS and DOLS results

	FMOLS		DOLS
	Coefficient	Std. Error	Coefficient	Std. Error
GDP	1.976***	0.098	1.325***	0.451
GDP2	-0.213***	0.014	-0.136**	0.062
PRC	-0.028***	0.003	-0.031***	0.008
LOS	0.347***	0.011	0.334***	0.035
REN	-0.008***	0.003	-00.004	0.007

 ∗  ∗  ∗ , ∗  ∗ , and ∗ are significance levels at the 1%, 5%, and 10% levels, respectively. Values in parentheses show the standard error values of the estimators

There are important reasons for choosing the FMOLS estimator in this study. First, this estimator is asymptotically unbiased and efficient. It is also an important estimator to eliminate the internality problem that may arise. On the other hand, it can ensure that the errors are heterogeneous with coefficients in the long run. In addition to the above, FMOLS results are in line with theoretical expectations. When FMOLS results are examined, all variables are statistically significant. On the other hand, FMOLS results are similar to PMG and DOLS results. According to FMOLS results, energy prices and the use of renewable energy increase environmental quality. However, it is not enough to combat CO₂ emissions. Because the share of use of clean energy is still very low despite today's modern technologies. In addition, emerging energy-importing economies can be significantly affected by fluctuations in energy prices. This situation may cause serious fragility in the macro indicators of these economies. However, increases in energy prices bring more costs to these economies. Therefore, higher energy prices cause a higher current account deficit as well as the need for more foreign exchange for these economies. In addition, rising energy prices can sometimes cause the production of these economies to decrease. In this way, the energy demanded can also be reduced. On the other hand, energy losses can cause huge problems for these economies. Because energy losses do not have any output equivalent. Therefore, increasing energy losses cause more energy demand. This situation leads to a higher increase in the use of fossil fuels, which have a high share. The use of renewable energy offers important opportunities for environmental quality. Higher use of renewable energy may result in less fossil fuel use. In addition, increasing renewable energy will increase the resilience of these economies against fluctuations in energy prices.

Conclusion and Policy Implications

Despite today's advanced technologies, 81% of the world's total energy needs in 2019 were still met by fossil fuels. In addition, 62.90% of global electricity usage was supplied by fossil fuels. This indicates that most of the energy requirements worldwide are usually supplied by fossil energy sources, which leads to serious pollution. Moreover, the increasing economic and population growth increases the demand for fossil fuels even further. The losses in global electricity production, transmission, and transportation in 2019 were 7.44%, creating an even higher energy demand and thus for fossil fuels. The use of these fuels leads to a much higher amount of CO₂ emissions, causing harm to the environment. This situation has been made even more important by the COVID-19 pandemic, making it more imperative than ever to work towards a more livable, healthier, and cleaner world.

Emerging economies are a significant economic group, distinguished by their high growth rates amongst developing countries. Of particular importance are energy-importing economies, as they are unable to provide the energy needed for their development with their resources. In addition, these economies generally lack the technology necessary for efficient energy usage, resulting in higher energy losses than average. As a consequence, the energy wasted does not translate into any output and incurs additional costs, further increasing energy demand. This in turn makes these countries more reliant on outside sources for their energy, along with a higher current account deficit and a more fragile economy. Fluctuations in energy prices are also a critical factor for emerging energy-importing economies. Increasing prices can reduce CO₂ emissions by reducing energy consumption, but this may be at the cost of reduced production. On the other hand, the use of renewable energy presents these countries with great opportunities. Investing in renewable energy sources will reduce energy losses and make them less vulnerable to energy price changes. This would lead to a freer, more independent economy with less dependence on foreign sources. For this purpose, this study examines the effects of these variables on CO₂ emissions for the period 1990–2019 for 15 emerging economies. Also, the validity of the EKC hypothesis for these economies is tested.

In this study, a large data set and a variety of analytical techniques have been used to determine the relationship between the Environmental Kuznets Curve (EKC) hypothesis and environmental pressure indicators for emerging economies. The use of panel data and country-specific techniques, as well as robustness checks, have enabled the results to be more robust and comprehensive than those of other studies. Empirical findings have shown similar results in the long term. In the long run, energy prices have the most reducing effect on CO₂ emissions in energy-importing emerging economies. In addition, the use of renewable energy is very important for these economies to increase the quality of the environment. This situation creates important opportunities for further work in this country group.

In energy-importing emerging economies, increasing energy prices reduce energy consumption, thus reducing the use of fossil fuels and reducing CO₂ emissions. However, increasing energy prices is a sensitive and serious policy. This is because, since these economies are energy importers, rising energy prices can seriously affect macro indicators. Increasing energy prices in these economies may cause a reduction in CO₂ emissions by reducing energy consumption, as well as causing a reduction in production by creating a reduction effect. Downsizing is very costly for economies. This is an undesirable situation. Because economies compete to have high growth. The desired growth here is to be sustainable and to be provided with sustainable energy. However, energy prices are an important factor in reducing CO₂ emissions in developing countries, as they provide an incentive for businesses and households to move away from fossil fuels and towards cleaner, renewable energy sources. Higher energy prices increase the cost of using fossil fuels, making them less attractive and pushing consumers and businesses to switch to lower-carbon sources. This can help to reduce greenhouse gas emissions and reduce the impacts of climate change. Additionally, higher energy prices can encourage investment in renewable energy sources like wind and solar, helping to create jobs and further reduce emissions.

Renewable energy offers very important opportunities for this situation. Moreover, it is relatively easier for these countries to increase the use of renewable energy and to benefit more from this energy source compared to other countries. Because the annual average growth rates of these economies are much higher than the world average. These economies are the wheels of the global economy with these high growths. These high growths cause a capital increase for the host economy. This may lead to further technological developments. Increasing technological development causes more production and more competition. This situation can increase the use of energy-efficient technologies by reflecting on the energy input that brings the highest cost in production. Therefore, it causes less energy use for the same output. Also, increased capital input can increase renewable energy investments. Increasing the use of renewable energy results in less external costs and less environmental pollution. Therefore, policymakers have important duty to increase the use of renewable energy in energy-importing emerging economies. First of all, incentive programs should be created. Governments in developing countries can create incentive programs to encourage the uptake of renewable energy. This could include tax credits, grants, and subsidies for those who invest in renewable energy systems. On the other hand, investment can be made in education and training. Governments can also invest in education and training programs to help people learn about renewable energy and the benefits it brings. This will help to create awareness and inspire people to make the switch to renewable energy. It would also be beneficial to increase Access to finance. Financing is a major barrier for many people in developing countries when it comes to investing in renewable energy. Governments can help by providing access to financing or programs that make renewable energy more affordable. On the other hand, it is necessary to establish Policies and Regulations. Governments can also establish policies and regulations that promote the use of renewable energy. This could include renewable energy targets, laws that require the use of renewable energy, and other regulations that make it easier to invest in renewable energy. Finally, it is important to develop the energy infrastructure. Developing the necessary infrastructure is essential for the uptake of renewable energy. Governments can invest in infrastructure such as transmission lines and distribution networks to help make renewable energy more accessible.

The study's results showed that higher energy losses were linked to greater environmental damage. This is especially unfavorable for developing countries that rely on importing energy, as it makes them dependent on foreign sources, requires more nonrenewable fuels, and leads to extra costs and higher CO₂ emissions. Policymakers have important duties to reduce energy losses. First, it will be important to implement energy efficiency policies. Governments of emerging economies should work to implement energy efficiency policies that provide incentives for businesses and citizens to reduce energy loss. This could include regulations on energy-efficient appliances, standards on building insulation, and tax credits for investing in renewable energy sources. On the other hand, electricity distribution networks need to be improved. Emerging economies should invest in improving their electricity distribution networks. This could involve replacing outdated infrastructure, upgrading existing systems, and installing smart grids that are better able to detect and respond to energy losses. In addition, energy monitoring systems need to be strengthened. To reduce energy losses, emerging economies should strengthen their energy monitoring systems. This could involve the installation of sensors and meters that measure energy consumption and detect any abnormal loss. It will also be useful in promoting energy-saving practices. Emerging economies should also encourage citizens and businesses to practice energy conservation. This could include providing incentives for businesses to reduce energy consumption, implementing public awareness campaigns about energy conservation, and offering rewards for citizens who practice energy-saving behaviors. These policies will lead to less energy demand by reducing energy losses in energy-importing emerging economies. This will reduce CO₂ emissions.

On the other hand, the relationship between economic growth and pollution in energy-importing emerging economies has been shown to follow an inverted U-shape, confirming the EKC hypothesis. This implies that while pollution increases at the start of the economy's high growth, the rate of increase eventually slows until it reaches a turning point and then rapid economic growth reduces the number of polluting emissions. Evidence to support this was found in the form of statistically significant results in FMOLS, DOLS, and Panel ARDL methods. Further, the effects of renewable energy and energy prices on CO₂ emissions were observed to have similar signs and magnitudes in Panel ARDL, FMOLS, and DOLS estimators.

Finally, this research provides some important insights for emerging economies that rely on energy imports. By understanding the dynamic connections between the factors involved, these countries can work towards reducing energy losses, using energy more efficiently and effectively to prevent a drop in production, and promoting growth through clean energy sources. This will help to gradually reduce CO₂ emissions. To maintain environmental quality, a key goal should be to invest more in renewable energy sources. Additionally, a carbon pricing strategy should be the foundation of any climate policy that encourages green energy development.

In this study, the use of social and political variables, as well as general economic variables, is important for the multifaceted investigation of the EKC hypothesis. In addition, the use of economic complexity and ecological footprint values instead of economic growth and CO₂ emissions will also contribute to the EKC literature. Furthermore, it is important to analyze this issue from a sectoral perspective, as it will provide more detailed information.

Author contribution

MN performed material preparation, data collection, analysis, writing, and other contributions.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The datasets analyzed during the current study are available in the BP Database, World Bank Data, and IEA repository (https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html, https://data.worldbank.org/, https://www.iea.org/data-and-statistics/data-sets/?filter=balances%2Fstatistics).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author declares no competing interest.