Determining the Relationship Between Economic Growth Carbon Emission and Energy Consumption Panel Co

Abstract

Keywords: AMG; Economic growth; G-20; global warming; granger causality; panel data

1. Introduction

The G20 is a group of 20 of the world’s leading countries in economic terms representing more than 80% of total gross domestic product (GDP), 80% of global investment, 75% of world trade and 66% of the world’s population (Paratama, 2023). 77% of the world’s total CO2 emissions (kt) are produced by G20 countries (World bank, 2024). Today’s increasing energy demand also gradually increases CO2 emissions which a greenhouse gas. Energy use and CO2 emissions in G20 countries, which have an important place in the world economy, naturally contribute to global warming. Therefore, it is important to investigate CO2 emissions and energy use in G20 countries. There are two ways to meet increasing energy demand. Either using fossil-based energy or using renewable energy sources. For this reason, countries’ energy production preferences may be based on fossil-based or renewable energy, sometimes out of necessity and sometimes to avoid costs. It

is important to see the change over the years in countries’ use of both fossil-based and renewable energy. It is also important to see the substitution of these energy types with each other over the years. Another issue is determining how these energy resources move with economic growth. The following graphs have been prepared to see the change in data on a country basis (Wold Bank, 2024). Figure 1 shows CO2 emissions (kt) in G20 countries. The highest CO2 emission belong to China. It is also noteworthy that CO2 emission are increasing in China. The country that produces the second highest CO2 emissions is the USA. However, US emission figures have not increased over the years examined have even started to decrease slightly in recent years. There is no significant change in the emission figures of other countries. Renewable energy consumption rates are shown in Figure 2. When consumption rates are examined, it is seen that renewable energy consumption has decreased

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Figure 2. Renewable energy consumption rate (%). significantly in Indonesia, India and China. Fluctuations in other countries do not appear to be very significant. The fossil fuel consumption rate is shown in Figure 3. When the rates are examined, it is seen that fossil fuel use has increased significantly in India and Indonesia between the years examined. Additionally, it is seen that there have been significant increases in Brazil and Japan after 2015. Figure 4 shows the GDP growth rates of G20 countries between years of 1990 and 2020. According to the figure,

1998, 2002, 2009 and 2020 are the years when serious declines occurred. These years generally represent crisis years whose effects appear with a one-year delay. These; The Asian crisis centered on Thailand in 1997 (Lane, 1999), the financial crisis centered on Turkey in 2001(Turan, 2011), the global economic crisis centered on the USA in 2008 (El-Erian, 2008), and the global contraction caused by Covid-19 in 2020.

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Sections

One of the aims of this study is to determine whether there is a relationship between the economic growth of countries and carbon dioxide emissions. To achieve this goal, G20 countries were chosen as the study area. In the literature, studies have been conducted to determine the relationship between carbon dioxide emissions and economic growth in various countries or groups of countries. Esso and Keho (2017) conducted energy consumption, economic growth and carbon emissions in selected African countries. The relationships between CO2 emissions, energy consumption and income were studied by Ajmi et al. (2015). In the article conducted by Zhang and Cheng (2009) energy consumption, carbon emissions and economic growth have been studied in China. Ang (2008.) examined economic development, pollutant emissions and energy consumption in Malaysia. Another aim of this study is to determine the direction and degree of the relationship between economic growth and energy consumption in G20 countries. To achieve this goal, the energy variable has been divided into renewable and fossil fuel energies. Thus, it has been possible to see the change in the consumption of renewable energies and fossil-based energies over the years. Many studies have been conducted in the literature on the relationship between energy variables and economic growth. The first article examining the causality relationship between energy consumption and GDP using data between 1947 and 1974 was written by Kraft and Kraft (1978). Energy imports are also primarily responsible for environmental degradation.

In this context, renewable energy resources are considered as an alternative to non-renewable resources in order to protect the natural environment (Yadav and Mahalik, 2024). The relationship between energy consumption and GDP is crucial for realizing their future development and growth objectives (Mishra et al., 2009). Economic growth is among the most significant issue to be considered in projecting changes in world energy consumption. Therefore, the investigation of the relationship between energy consumption and economic growth has received a great deal of attention during the past years (Omri et al., 2015). Bozoklu and Yilanci (2013) conducted a causal relationship between energy consumption and economic growth for 20 OECD countries. It was also aimed to determine whether there is cointegration between the variables in the study. For this aim, comments were made with the Augmented Average Group (AMG) results developed by (Eberhardt and Teal, 2010) and (Eberhardt and Bond, 2009) which show long-term relationships. It was also aimed in the study to determine the causality results developed by Dumitrescu and Hurlin (2012). Causality between variables was examined bidirectionally. Lee (2005) studied energy consumption and GDP in developing countries a cointegrated panel analysis. The impact of GDP growth, industrialization, energy use and urbanization on CO2 emissions in developing countries was revealed by Sikder et al. (2022) through the panel ARDL approach. The relationship between energy saving and sustainable economic growth was examined by Chang and Carbello (2011) in the example of Latin America and the Caribbean. Mozumder and Marathe (2007) studied

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causality relationship between electricity consumption and GDP in Bangladesh. Huang et al. (2008) examined the causal relationship between energy consumption and GDP growth with a dynamic panel data approach. Oh and Lee (2004) The causal relationship between energy consumption and GDP was re-examined for Korea between 1970 and 1999. Soytaş and Sarı (2009) in her article examined the long-term Granger causality relationship between economic growth, carbon dioxide emissions and energy consumption in Turkey by controlling for gross fixed capital formation and labor. Soytaş at al (2007) in their article they investigated the impact of energy consumption and production on carbon emissions in the United States. Relationship between Electrical Energy Consumption and GDP, causal link Researched for 17 countries in Latin America, Canada and USA. Rodríguez-Caballeroa and Ventosa-Santaulàriab (2016). Relationships between GDP and electricity consumption in 10 developing Asian countries are estimated using panel data procedures. Empirical results from a single data set show that there is a unidirectional short-term causality from economic growth to electricity consumption (Chen et al., 2007). Chu and Chang (2012) conducted nuclear energy consumption, oil consumption and economic growth in G-6 countries. Heil and Selden (1999) studied panel stationarity with structural breaks for carbon emissions and GDP. The relationships between energy consumption, pollution emission and economic growth in Nepal were studied by Bastola and Sapkota (2015). Temporal analysis of cross-country distribution patterns of carbon dioxide emissions and income was made by Coondoo and Dinda (2008). In the article written by Friedl and Getzner (2003), the relationship between economic development and carbon dioxide (CO2) emissions was investigated for Austria, a small, open and industrialized country. Halıcıoğlu (2009) conducted an econometric study on CO2 emissions, energy consumption, income and foreign trade in Turkey.

4. Result And Discussion

Table 4 shows the results of (Pesaran and Yamagata. 2008) slope heterogeneity and homogeneity test. The null hypothesis in the test is that the slope coefficients are homogeneous. According to the test results, the homogeneity of the slope coefficients was rejected at the 0.01 significance level and it was concluded that the slope coefficients were heterogeneous across all cross-sections. Breusch-Pagan LM, Pesaran scaled LM and Pesaran CD cross-section tests results are shown in Table 5 These tests developed by Breusch and Pagan (1980), Pesaran (2021). The null hypothesis that cross-sectional independence was rejected significance level at 0,01 for all variables. Test results show that there is cross-sectional dependence for all variables. CIPS unit root test (Pesaran, 2007) results are shown in the table 6. In the CIPS test, the null hypothesis is that it contains a unit root. The results were tested for models with constant and with constant and trend. Since the CIPS Table 4. Testing for slope heterogeneity and homogeneity Delta

* indicate significance level at 1%. parentheses indicate P value.

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statistical values ​​were found to be lower than the critical values ​​given at the bottom of the Table 6, the null hypothesis was accepted for all variables. It was found to be I(1) in all variables, both in constant and constant and trend models. Critical values at the 5% level are 2,21 for level and first difference with constant. Critical values at the 5% level are 2,73 for level and first difference with constant and trend. ECM test results are shown in Table 7. According to the results, the null hypothesis is rejected and it is decided that there is cointegration in all groups (Gt, Ga) and the entire panel (Pt, Pa). Once the existence of panel cointegration is determined, the panel estimators stage can be started. The results of Augmented Mean Group (AMG), the panel cointegration estimator, are as follows: Augmented Mean Group (AMG) results are shown in Table 8. According to Augmented Mean Group estimator (AMG) results, the existence of a positive relationship between CO2 use and growth in GDP is at the 0.001 Table 7. Error correction model (ECM) panel cointegration tests Statistic

significance level. The increase in CO2 use increases the growth in GDP. One unit increase in CO2 use causes a 0.88 unit increase in GDP growth. Sikder et al. (2022) stated by the short-term estimate found a positive relationship between GDP growth, CO2 emission. According to AMG results, there is no significant relationship between renewable energy consumption and GDP growth. In the research conducted by Demir and Görür (2020) in OECD countries, the following conclusion was reached; A one unit increase in renewable energy consumption created a 0.529 unit increase in GDP. In the panel results by Omri et al. (2015), the GDP variable was found to be significant at the 5% significance level and was determined to have a positive slope. It was determined that a 0.227% growth in the economy caused an increase in additional energy demand of 0.23%. According to AMG results, there is no significant relationship between fossil fuel energy consumption and GDP growth. Rahman and Velayutham (2020) indicated positive effects of capital on economic growth, which ultimately supports for having more capital stock in Pakistan and Sri Lanka. Economic growth also encouraged non-renewable energy usage in Pakistan. Table 9 shows the granger causality results. When the causality results are examined, it is seen that there is a Granger causality relationship from the independent variables to the dependent variable. Carbon dioxide use, renewable energy consumption and fossil fuel energy consumption are the causes of gross domestic product. In the study conducted by Chien and Hu (2008), the relationship

Table 8. Augmented mean group estimator (AMG) results Coefficient

Number of obs = 589 Wald chi2(3) = 13.47 Prob > chi2 = 0.0037 * indicate significance level at 1%. Variable 00000R_c refers to the common dynamic process

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Table 9. Causality test results Null hypothesis (H0): no causality

* indicate significance level at 1%. H0: x does not Granger-cause y. H1: x does Granger-cause y for at least one panelvar.

between economic growth and renewable energy may make renewable energy more economical in the countries examined. In the mentioned studies, the existence of a causal relationship between energy use and economic growth was found to be important (Asafu-Adjaye, 2000; Akinlo, 2008; Adewuyi, 2016). There is no Granger causality relationship from the dependent variable to the independent variables. Growth in GDP is not a cause of carbon dioxide use, renewable energy consumption and fossil fuel consumption.

5. Conclusion

In the study, GDP growth was taken into the model as the dependent variable. CO2 usage, renewable energy consumption and fossil fuel energy consumption were included in the model as independent variables. According to AMG results in the study, a positive relationship was found between CO2 use and GDP growth. According to the Granger causality results in the study, a unidirectional causality relationship was found from CO2 use to GDP growth. A unidirectional causality relationship was also found from renewable energy consumption and fossil fuel energy consumption to GDP growth.) The effect of CO2 emissions and energy consumption on social and economic variables was examined by Pehlivan, at al. (2020). Unidirectional causality was determined from CO2 emissions to health expenditures and GDP per capita. CO2 production is actually associated with growth in agriculture, industry and services sectors. There is a very strong relationship between energy use and agricultural productivity (Karkacier et, al., 2006). The increasing number of facilities in the industry both fuels energy demand and increases CO2 emissions along with production. As it is known, CO2 is a greenhouse gas. Greenhouse gases contained in fossil fuels mix with the atmosphere and cause global warming on earth. Global warming causes many negative situations such as irregular rainfall, drought, difficulties in accessing fresh water, and changes in living biology. Thus, life on earth is becoming increasingly threatened. With such studies, variables related to economic growth can be determined. These variables can be considered separately and useful suggestions can be made to policy makers. Nowadays, countries want to have better economic and

environmental living conditions. The best way to achieve this is to grow economically while also protecting the environment. These concepts are not rivals to each other. So there is no need to give up one to choose the other. The way to achieve balanced development is to protect both the environment we live in and the environment where future generations will live, without damaging the environmental structure along with economic growth. Policy makers have important duties in this regard. As stated in the introduction of the article, the two countries with the highest CO2 usage are the USA and China. This situation is due to both the dense population of these countries and the intense production demand. Policies should be implemented to guide all countries in the world, especially these countries, to meet their energy demands in their production and consumption with renewable energy sources instead of fossil fuels. Production with renewable energy sources will both reduce CO2 emissions and contribute to correcting the economic current balance of countries that are especially dependent on fossil fuel consumption by reducing the consumption of energy resources such as oil and natural gas. Authors’ Contributions: The authors contributed to the study equally. Declaration of Conflict of Interest: The authors declare that there is no conflict of interest.

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