The impact of energy efficiency and renewable energy consumption on carbon emissions in G7 countries

ABSTRACT

This study analyzes the impact of energy efficiency and renewable energy use on carbon emissions in G7 countries. The period examined covers the years 1971–2023. There are two important results. The first is the cointegration relationship between the variables. Accordingly, there is a long-run cointegration relationship between energy efficiency, renewable energy use and carbon emissions. The second is the direction and strength of this long-run relationship. When energy efficiency increases, carbon emissions decrease. This result is consistent with general expectations. On the other hand, there is a positive relationship between renewable energy consumption and carbon emissions. This result is not consistent with general expectations. The existence of a positive relationship between renewable energy consumption and carbon emissions is an issue that needs to be discussed.

KEYWORDS:

• Energy efficiency
• renewable energy consumption
• carbon emissions

Introduction

The carbon footprint was first defined in 1992 by Australian environmental scientists Mathis Wackernagel and William Rees. Based on the concept of ecological footprint, Wackernagel and Rees (1998) developed a method to measure the damage caused by human activities to the environment. According to this method, ‘the carbon footprint is a measure of the damage caused by human activities to the environment in terms of the amount of greenhouse gases produced, measured in units of carbon dioxide’. In the study, the ecological footprint is defined as the amount of biologically productive land needed to produce the natural resources consumed by a given population in a given period of time. It concludes that the rate at which humans consume natural resources is faster than the rate at which the Earth replenishes its biologically productive land. World Bank (2022) argues that the carbon footprint is an important economic and financial indicator. Reducing the carbon footprint helps to reduce the costs of climate change, adapt to climate change, reduce its costs, encourage investment in renewable energy and other low-carbon technologies, increase return on investment, reduce risks and achieve cost savings. According to McKinsey & Company (2021), reducing carbon footprint for businesses is a significant business opportunity. Reducing carbon footprint helps businesses increase their return on investment, reduce risks and achieve cost savings. The Climate Group (2022) study examines the potential impacts of climate change on the financial system. The study argues that climate change could increase volatility in financial markets, raise insurance industry costs and negatively affect investments. The findings suggest that climate change poses a significant risk to the financial system. In another study, the International Monetary Fund (2021) examines the potential impacts of climate change on the global economy. According to this study, climate change could negatively affect the global economy and slow economic growth. According to the Intergovernmental Panel on Climate Change (IPCC 2021), G7 countries are responsible for about 40% of global greenhouse gas emissions. Therefore, reducing their carbon footprint will play an important role in combating climate change. Efforts by G7 countries to reduce their carbon footprint can make significant progress in combating climate change. Their success will help reduce global greenhouse gas emissions and mitigate the impacts of climate change.

In The G7 Climate Declaration, the G7 countries declared their commitment to achieve a net zero emissions target by 2050. To achieve this goal, the G7 countries committed to implementing various policies and measures to significantly reduce greenhouse gas emissions. Fulfilling the commitments in the declaration will help reduce global greenhouse gas emissions. Furthermore, these commitments accelerate the transition to clean energy and protect communities vulnerable to the impacts of climate change. According to the Energy Market Regulatory Authority (2023) and the International Renewable Energy Agency (2022), renewable energy is using energy from natural sources such as sun, wind, water, geothermal and biomass. Energy efficiency means using energy more efficiently. Both play a role in reducing energy consumption and, thus, CO2 emissions. The general consensus is that energy efficiency and renewable energy consumption play an important role in reducing carbon emissions. However, it is also noted that this relationship is complex and may vary depending on country, time and other factors.

In conclusion, G7 countries are the world’s largest industrialised economies. In industrialised countries like G7, energy consumption and fossil fuel use are high. This situation leads to high carbon emissions. G7 countries are taking various steps to address climate change and reduce carbon emissions. However, the implementation of these steps is both slow and insufficient. The general opinion is that international cooperation is necessary.

Using the Panel VAR Analysis approach, this study analyzes the impact of energy efficiency and renewable energy consumption on carbon emissions in G7 countries.

Literature review

The literature review consists of two parts. The first part summarises the studies in line with general expectations. The second part summarises studies that are not in line with general expectations.

General expectations-consistent literature

Kelly (2006) examines the role of energy efficiency in reducing carbon dioxide emissions (CO2) in Scotland and the United Kingdom. The study’s findings show that energy efficiency plays an important role in reducing carbon dioxide emissions in Scotland and the UK. It also finds that energy efficiency plays a more important role in reducing carbon dioxide emissions in Scotland than in the UK. This is because Scotland has an energy-intensive economy and has a higher potential for energy efficiency. The study results suggest that Scotland and the UK should focus on increasing energy efficiency to reduce carbon dioxide emissions. Blesl et al. (2007) evaluates the impact of energy efficiency standards on CO2 emissions in Germany. The study’s findings show that energy efficiency standards play an important role in reducing CO2 emissions. The sectors where energy efficiency standards have the greatest impact are the building sector and the transportation sector. It also finds that the economic cost of energy efficiency standards is low. The implementation of energy efficiency standards will not negatively affect Germany’s economic growth. The study results suggest that energy efficiency standards will help Germany reduce CO2 emissions and contribute to the fight against climate change. Saygin et al. (2013) model the future CO2 reduction potential of energy efficiency and carbon capture and storage technologies in Dutch industry. The study’s findings show that energy efficiency and carbon capture and storage technologies can significantly reduce CO2 emissions in Dutch industry. It also finds that energy efficiency and carbon capture and storage technologies will have different impacts in different sectors of Dutch industry. The results of the study suggest that the Dutch government and industry should aim to reduce CO2 emissions in Dutch industry by investing in energy efficiency and carbon capture and storage technologies. Özbuğday and Erbas (2015) examine the effectiveness of energy efficiency and renewable energy in reducing CO2 emissions in the long run in developed and developing countries. The study’s findings show that energy efficiency and renewable energy play an important role in reducing CO2 emissions. As energy efficiency increases, less energy is consumed, leading to reduced CO2 emissions. The study’s findings show that energy efficiency has a positive and significant impact on reducing CO2 emissions in the long run. Joo et al. (2015) examine the relationship between energy consumption, CO2 emissions and economic growth in Chile. The study’s findings show that energy consumption and CO2 emissions have a positive relationship with economic growth in Chile. The study finds that as energy consumption and CO2 emissions increase, economic growth also increases. It also found that in the early stages of economic growth, energy consumption and CO2 emissions affect economic growth more. The results of the study suggest that to reduce energy consumption and CO2 emissions in Chile, economic growth should be managed sustainably. Heryadi and Hartono (2017) examine the relationship between energy efficiency, renewable energy use and carbon dioxide emissions in G20 countries. The study shows that energy efficiency and renewable energy use reduce carbon dioxide emissions. The study results suggest that G20 countries should focus on increasing energy efficiency and promoting the use of renewable energy to reduce carbon dioxide emissions. Jebli and Youssef (2017). examine the role of renewable energy and agriculture in reducing CO2 emissions in North African countries. The study’s findings show that renewable energy and agriculture effectively reduce CO2 emissions. Renewable energy sources have less environmental impact than energy sources based on fossil fuels. On the other hand, agriculture helps reduce the concentration of greenhouse gases in the atmosphere by absorbing CO2. It also shows that renewable energy and agriculture are more effective when practiced together. The study results show that North African countries should invest in renewable energy and support agriculture. Wongsapai et al. (2016) analyse the impact of Thailand’s long-term low-carbon energy efficiency and renewable energy plan on energy and CO2 emissions. The study’s findings show that the energy efficiency and renewable energy plan will reduce energy demand and lower CO2 emissions. The study results show that the energy efficiency and renewable energy plan will help Thailand achieve its energy and climate goals. Alberini et al. (2018) examine the preferences of households in Italy and the Czech Republic towards energy efficiency and renewable energy and their willingness to pay to reduce CO2 emissions. Households in Italy and the Czech Republic have a positive attitude towards energy efficiency and renewable energy. They support energy efficiency and renewable energy investments and are willing to pay to reduce CO2 emissions. Policies for energy efficiency and renewable energy are important to reduce CO2 emissions and combat global climate change. Busu (2019) aims to measure renewable energy efficiency and its impact on CO2 emissions at the European Union level. The findings of the study found that renewable energy efficiency has increased in the EU, and this has a positive impact on reducing CO2 emissions. The study results show that renewable and energy efficiency are important in reducing CO2 emissions. Le Quéré et al. (2019) examine the factors that cause the decline in CO2 emissions in 18 advanced economies. According to the study, increased energy efficiency reduces CO2 emissions by reducing energy consumption. The increase in renewable energy reduces CO2 emissions by reducing the fossil fuel-based energy production demand. Slowing economic growth reduces CO2 emissions by reducing energy consumption. The study results suggest that governments and policymakers should focus on improving energy efficiency, promoting the use of renewable energy, and sustainably managing economic growth to reduce CO2 emissions. Ben Jebli et al. (2019) examine the dynamic link between renewable energy, tourism, CO2 emissions, economic growth, foreign direct investment and trade in 22 Central and South American countries. The study shows that renewable energy, tourism, FDI, and trade help reduce CO2 emissions. Economic growth, on the other hand, increases CO2 emissions. It also shows a positive relationship between renewable energy, tourism and FDI. This suggests that renewable energy, tourism and FDI are mutually supportive. The study results suggest that Central and South American countries should invest in renewable energy, promote tourism and create an attractive environment for FDI to reduce CO2 emissions and increase environmental sustainability. Akram, et al. (2020a) examine the impact of energy efficiency and renewable energy on CO2 emissions in developing countries. The study’s findings show that energy efficiency and renewable energy play an important role in reducing CO2 emissions. However, the impact of these two variables on CO2 emissions varies across different countries. The impact of energy efficiency on CO2 emissions is stronger in developing countries. This is because the energy use of developing countries is less efficient. Improving energy efficiency in developing countries is one of the most effective ways to reduce CO2 emissions. Akram, et al. (2020b) examine the asymmetric effects of energy efficiency and renewable energy on CO2 emissions in BRICS countries. The study’s findings show that energy efficiency and renewable energy have asymmetric effects on CO2 emissions. Positive shocks, i.e. increases in energy efficiency and renewable energy, are more effective in reducing CO2 emissions. Negative shocks, i.e. decreases in energy efficiency and renewable energy, are more effective in increasing CO2 emissions. The study results show that energy efficiency and renewable energy play an important role in reducing CO2 emissions. Xiaosan et al. (2021) evaluate the impact of hydropower and renewable energy generation on carbon dioxide emissions in China. The study’s findings show that hydropower and renewable energy generation reduce carbon dioxide emissions. It also found that green innovation reduces carbon dioxide emissions. The study results suggest that China should increase hydropower and renewable energy production to reduce carbon dioxide emissions. Ali et al. (2021) examine the role of combustible renewables and waste on CO2 emissions and economic growth in selected European countries. The findings of the study show that combustible renewables and waste reduce CO2 emissions and increase economic growth. However, the magnitude of this effect varies across countries. The study shows that the impact of combustible renewable energy and waste on CO2 emissions and economic growth increases over time. The study results suggest that European countries can reduce CO2 emissions and increase economic growth by increasing investments in renewable energy and waste management. Lei et al. (2022) examine the dynamic link between energy efficiency, renewable energy consumption and CO2 emissions in China. The study’s findings show that energy efficiency and renewable energy consumption play an important role in reducing CO2 emissions. Accordingly, energy efficiency and renewable energy consumption have a positive effect on reducing CO2 emissions in the long and short run. The study results show that policies towards energy efficiency and renewable energy can play an important role in reducing CO2 emissions. Awan et al. (2022) examine the relationship between energy efficiency and CO2 emissions in 107 countries. The study’s findings show that energy efficiency has a negative and significant effect on CO2 emissions. This effect is particularly strong in countries with low CO2 emissions. This finding suggests that energy efficiency measures can be effective in reducing CO2 emissions, especially in developing countries. Moreover, renewable energy consumption negatively and significantly affects CO2 emissions. This finding suggests that the use of renewable energy sources is effective in reducing CO2 emissions. The study results show that energy efficiency and renewable energy play an important role in reducing CO2 emissions. Shahbaz et al. (2022) examine the role of renewable energy and energy efficiency in developing economies on the path to cleaner consumption and production patterns. The study’s findings show that energy efficiency and renewable energy play an important role in reducing carbon emissions in developing countries. However, the impact of these two variables on carbon emissions varies across different countries. The impact of renewable energy on carbon emissions is also stronger in developing countries. This is due to the higher use of fossil fuels in developing countries. Increasing the use of renewable energy by reducing fossil fuel use in developing countries is another effective way to reduce carbon emissions. The study results show that policies towards energy efficiency and renewable energy are important for developing countries. Mirza et al. (2022) examine energy efficiency’s impact on CO2 emissions in developing countries. The study’s findings show that energy efficiency has a negative and significant effect on CO2 emissions. This effect is particularly strong in countries with low CO2 emissions. This finding suggests that energy efficiency measures can be effective in reducing CO2 emissions, especially in developing countries. It also finds that renewable energy consumption has a negative and significant effect on CO2 emissions. The study results show that energy efficiency and renewable energy play an important role in reducing CO2 emissions. Aguir Bargaoui (2022) examines the impact of energy efficiency and renewable energies on environmental quality in OECD countries. The study’s findings show that energy efficiency and renewable energies positively impact environmental quality. Increased energy efficiency and renewable energies improve air quality by reducing CO2 emissions. Moreover, energy efficiency and renewable energies help protect water resources and soil quality by reducing water and soil pollution. The study results suggest that OECD countries should invest in energy efficiency and renewable energy to improve environmental quality. Akram et al. (2022) examine the dynamic relationship between energy efficiency, renewable energy, economic growth and carbon dioxide emissions in Mexico, Indonesia, Nigeria and Turkey (MINT countries). The study’s findings show that energy efficiency and renewable energy reduce carbon dioxide emissions, while economic growth increases carbon dioxide emissions. The study results suggest that MINT countries should focus on increasing energy efficiency and promoting the use of renewable energy to reduce carbon dioxide emissions. Khezri et al. (2022) examine the relationship between economic complexity and the effects of renewable energies on CO2 emissions in 29 Asia-Pacific countries. The findings of the study show that there is a positive relationship between economic complexity and CO2 emissions. It is observed that as economic complexity increases, CO2 emissions also increase. However, as the use of renewable energy increases, the relationship between economic complexity and CO2 emissions weakens. The study results show that economic complexity can increase CO2 emissions, but renewable energies can reduce this effect. Alola et al. (2023) examine the role of renewable and non-renewable energy efficiency in achieving environmental sustainability in India. The study’s findings show that renewable energy and non-renewable energy efficiency play an important role in achieving environmental sustainability. Increased consumption of renewable energy and increased non-renewable energy efficiency lead to a reduction in carbon emissions. In contrast, financial development and trade have a negative impact on environmental sustainability. The study results show that India must invest in renewable energy and increase non-renewable energy efficiency to ensure environmental sustainability. Hassan et al. (2022) examine whether energy efficiency is a source of low-carbon energy sources in 16 high-income OECD economies. The study’s findings show that energy efficiency is a source of low-carbon energy sources. As energy efficiency increases, energy consumption and greenhouse gas emissions decrease. Moreover, as energy efficiency increases, energy demand decreases. This, in turn, encourages the use of renewable energy sources to become more economical and encourage their use. The study results show that energy efficiency plays an important role in the transition to a low-carbon energy system. Batool et al. (2022) analyse the relationship between information and communication technology (ICT), renewable energy, financial development and CO2 emissions in developing countries of East and South Asia. The study’s findings show that ICT and financial development increase CO2 emissions in the long run. This is because ICT and financial development increase economic growth, which in turn increases energy consumption. On the other hand, the use of renewable energy reduces CO2 emissions both in the short and long run. The study results suggest that developing countries of East and South Asia should invest in renewable energy to reduce CO2 emissions. Jahanger et al. (2023) examine the contribution of technology and renewable energy to energy efficiency and carbon neutrality in the top ten manufacturing countries. The study’s findings show that the consumption of technology and renewable energy positively contributes to energy efficiency and carbon emissions. Technology increases energy efficiency by reducing energy consumption and lowering energy intensity. Renewable energy contributes to carbon emissions by reducing the use of fossil fuels and lowering carbon emissions. It also finds that the contribution of technology and renewable energy consumption to energy efficiency and carbon emissions differs from sector to sector. The study results suggest governments and policymakers should aim to increase energy efficiency and minimise carbon emissions by investing in technology and renewable energy. Naimoglu and Akal (2023) examine the relationship between energy technology, energy efficiency, renewable energy and the environment in Turkey. The study’s findings show that the development of energy technology contributes to the increase in energy efficiency and the use of renewable energy. This leads to a decrease in negative environmental impacts. The study shows that the relationship between energy technology, energy efficiency and, renewable energy and the environment in Turkey has changed over time. With the development of energy technology, energy efficiency and the use of renewable energy have also increased. The study results show that energy technology, energy efficiency and renewable energy play an important role in reducing environmental impacts in Turkey. Wang et al. (2023) investigate which of energy efficiency, renewable energy and natural gas is the most effective option to prevent environmental degradation. The study’s findings show that the most effective option is energy efficiency. Improving energy efficiency helps prevent environmental degradation by reducing energy consumption and greenhouse gas emissions. The study shows that renewable energy sources also effectively prevent environmental degradation. Renewable energy sources have fewer negative environmental impacts than energy sources based on fossil fuels. The study results show that energy efficiency is the most effective option to prevent environmental degradation. Zhou et al. (2023) examine the impact of energy efficiency on carbon emissions in China. The study’s findings show that energy efficiency effectively reduces carbon emissions. It also shows that the impact of energy efficiency on carbon emissions increases over time. The study results suggest that the Chinese government should reduce carbon emissions by developing policies and programs to improve energy efficiency. In their 2023 study, Aghabalayev and Ahmad (2023) investigate whether innovations in ocean energy generation technologies in G7 countries (G7 Leaders 2023), particularly the use of resources such as wave and tidal energy, reduce carbon dioxide emissions. The study also explores the role of international collaboration, green technology development, and trade and monetary policies in this impact. Innovations in ocean energy technologies play an important role in reducing carbon dioxide emissions in G7 countries. International collaboration helps to reduce emissions by promoting green technology development. Expansionary monetary policies (low interest rates) and expansionary trade policies (low tariffs) can increase emissions, while contractionary monetary policies and contractionary trade policies can reduce emissions. Gross domestic product (GDP) per capita has an emissions-increasing effect. In their 2023 study, You et al. (2023) investigate the potential effects of innovative solar energy technologies on carbon dioxide emissions in China. The study finds that innovations in photovoltaic technology have a positive effect on reducing China’s carbon dioxide emissions. Innovative technology can lead to more efficient energy production, reduced losses, and reduced reliance on fossil fuel sources. Innovations in the production stage lead to the development of lower-cost and environmentally friendly panels, while innovations in the distribution and transmission stages reduce emissions by reducing grid management and energy losses.

The overall conclusion is that energy efficiency and renewable energy consumption reduce CO2 emissions.

General expectations-inconsistent literature

Herring (2006) critically examines the environmental and economic impacts of energy efficiency. The study emphasises that while energy efficiency has environmental and economic benefits, it also has some limitations. The findings of the study show that energy efficiency can reduce energy consumption, but it cannot completely eliminate energy demand. This is not enough to solve the problem of energy resource depletion. Energy efficiency can reduce energy costs, but it cannot prevent energy prices from increasing. Energy efficiency can encourage the use of renewable energy sources, but it cannot completely eliminate dependence on fossil fuels. Fossil fuels remain an important component of the energy sector. Accordingly, energy efficiency policies should be developed, taking into account the limitations and potential risks of energy efficiency. Sheinbaum et al. (2011) examine changes in energy consumption and related CO2 emissions in five Latin American countries. The study’s findings show that although significant reductions in energy intensity have been achieved, none of the five countries has significantly reduced CO2 emissions. This is due to the continuous increase in energy consumption and the high share of fossil fuels in energy consumption. The study results suggest that Latin American countries should focus more on improving energy efficiency and promoting renewable energy sources. Bilgili et al. (2016) examined the relationship between renewable energy consumption, CO2 emissions and economic growth in OECD countries. The study’s findings show a positive relationship between renewable energy consumption and CO2 emissions in the long run. However, this relationship may be negative in the short run. The study results show that CO2 emissions can occur in the production and distribution stages of renewable energy, the increased use of renewable energy can increase economic growth and thus energy demand, and the use of fossil fuels will not be completely eliminated. Jebli et al. (2020) examine the relationship between renewable energy consumption, CO2 emissions and value-added in countries with different income levels. The findings of the study show that renewable energy consumption reduces CO2 emissions. However, the magnitude of this effect depends on the income level of the country. In low-income countries, renewable energy consumption reduces CO2 emissions the most. As the income level of countries increases, the effect of renewable energy consumption on CO2 emissions decreases. The study results show that low-income countries can reduce CO2 emissions by investing in renewable energy. However, in high-income countries, the impact of investing in renewable energy on reducing CO2 emissions is less. Wang et al. (2022) assess the impact of renewable energy generation, energy efficiency and cleaner energy on economic growth, carbon emissions and the impact of technological innovation in seven emerging economies (Brazil, China, India, Indonesia, Mexico, Russia and Saudi Arabia). The study’s findings show that renewable energy generation, energy efficiency and cleaner energy have a positive impact on economic growth, a negative impact on carbon emissions and no significant impact on technological innovation. Moreover, the study finds that renewable energy generation and energy efficiency have complementary effects. The study results suggest that the seven emerging economies should invest in renewable energy generation and energy efficiency to achieve the goals of economic growth, environmental protection and technological innovation. Rehman et al. (2023) examine how globalisation and renewable energy use play a role in increasing global CO2 emissions. The findings of the study show that globalisation increases CO2 emissions. This is because globalisation increases trade and investment, which in turn increases energy consumption. It also shows that the use of renewable energy reduces CO2 emissions. However, renewable energy use cannot fully meet the increased energy consumption due to globalisation. The results of the study show that it is important to understand the complex relationship between globalisation and renewable energy use.

The overall conclusion is that renewable energy consumption can have a negative impact on CO2 emissions.

Data and methodology

The main objective of this study is to analyse the impact of energy efficiency and renewable energy consumption on carbon emissions in G7 countries in a panel data environment. Annual data are used in the study, and the period analysed covers the years 1971–2023. 2023 data are based on estimated calculations. The data used in the study were obtained from the International Energy Agency.

Descriptive statistics of the variables used in the study are given in Table 1.

Table 1. Descriptive statistics.

	Total Carbon Emissions (Million Tons) Log	Energy Efficiency (Billion BTU/Billion Dollars) Log	Renewable Energy (Billion BTU) Log
Mean	7.253237	5.533483	3.119908
Median	7.410347	5.655992	3.148453
Maximum	8.712760	6.507278	3.632309
Minimum	5.598422	3.688879	2.484907
Std. Dev.	0.826030	0.582794	0.259901
Observations	371	371	371

The expression ‘Billion BTU/Billion Dollars’ in Table 1 is a unit of measurement used to compare a country’s energy consumption and economic productivity. One billion BTU stands for one billion British Thermal Units and is a unit of energy measurement. This ratio shows how efficient a country’s energy consumption is relative to its economic productivity. A higher ratio means that the country uses its energy more efficiently. A low ratio means that the country uses its energy less efficiently.

Limitations of the study

As stated in Altin (2023, 6), ‘in both time series and panel data, unit root tests are not performed if the number of observations of the units is well below 30. In time series analyses, for observation numbers below 30, not only the unit root but also the analysis is not performed. However, in panel data analysis, even if the number of observations of the units is well below 30, forecasts and other analyses are made, but the unit root test is not required’.

At the beginning of the study, the second-generation unit root tests and cross-sectional dependency tests were examined for each series. The series were found to have both unit roots and cross-sectional dependency problems. At this stage, the model was defined as Panel VAR. The variables designed according to the PVAR equation were converted to the form of Panel Vector Error Correction model (PVECM). Then, Johansen Pedroni and Johansen KAO, which explain the cointegration relationship in the Panel VECM model, were used. In the last stage, the Panel Dynamic Least Squares estimation model was selected. In addition, the lag number of the series was calculated. Various information criteria are used to determine the number of lags. The model with the lowest information criterion value is considered to be the best model. However, determining the optimal number of lags does not guarantee the accuracy of the model. In this study, using annual data, lags of 1,2,3,4 and 5 years are used. In this framework, the optimal number of lags for testing the long-term relationship between variables is found to be 1, while the optimal number of lags for estimating the direction of the long-term relationship between variables is found to be 2.

Panel VAR

In this section of the study, brief explanations on Panel VAR Tests are provided.

Pedroni residual cointegration

The Pedroni Residual Cointegration Test, proposed by Pedroni (1999, 2004), is used to test the long-run relationship between two variables in panel data sets. The test analyzes the relationship between the series of two variables based on the relationship between the error terms in the series. In other words, it determines the presence or absence of cointegration by testing whether the error term has a unit root.

Pedroni uses seven test statistics. Four of these test statistics are defined as panel statistics and three as group statistics Pedroni (1999, 660–661). Panel statistics are Panel v, Panel Philips-Perron type rho, Panel Philips-Perron type t and Panel ADF type t statistics. Group statistics are Group Philips-Perron type rho, Group Philips-Perron type t and Group ADF type t statistics.

The panel regression equation can be written as follows EViews (2022, 1261):

(1) $y_{\mathit{it}}={\alpha }_i+{\delta }_{\mathit{it}}+{\beta }_{1\mathit{i}}{\chi }_{1\mathit{i},\mathit{t}}+{\beta }_{2\mathit{i}}{\chi }_{2\mathit{i},\mathit{t}}+\dots +{\beta }_{\mathit{Mi}}{\chi }_{\mathit{Mi},\mathit{t}}+e_{\mathit{it}}$(1)

$\mathit{t}=1,\dots ,\mathit{T};\mathit{i}=1,\dots ,\mathit{N};\mathit{m}=1,\dots ,\mathit{M}$

In Equation (1)(1) $y_{\mathit{it}}={\alpha }_i+{\delta }_{\mathit{it}}+{\beta }_{1\mathit{i}}{\chi }_{1\mathit{i},\mathit{t}}+{\beta }_{2\mathit{i}}{\chi }_{2\mathit{i},\mathit{t}}+\dots +{\beta }_{\mathit{Mi}}{\chi }_{\mathit{Mi},\mathit{t}}+e_{\mathit{it}}$(1) , M is the coefficient of the regression variables. The model can be interpreted for N different equations. Are the coefficients of the cointegration slopes. And ${\chi }_{\mathit{it}}$variables are cointegrated at the I(1) level. ${\alpha }_i$and ${\delta }_i$parameters denote fixed effects and linear trends.

Kao Residual Cointegration

Kao Residual Cointegration Test, proposed by Kao (1999), is used to test the long-run relationship between two variables in panel data sets. The test analyzes the relationship between the series of two variables based on the relationship between the error terms in the series. It tests whether the error term has a unit root.

The Kao Residual Cointegration Test is similar to the Pedroni Residual Cointegration Test but with some differences. The Residual Cointegration Test is more powerful than the Pedroni Residual Cointegration Test. This means that the Kao Residual Cointegration test can detect the presence of cointegration more reliably.

The panel regression equation can be written as follows EViews (2022, 1262):

(2) $y_{\mathit{it}}={\alpha }_i+\mathit{\beta }{\chi }_{\mathit{it}}+e_{\mathit{it}}$(2)

(3) $y_{\mathit{it}}=y_{\mathit{it}-1}+u_{\mathit{i},\mathit{t}}$(3)

(4) ${\chi }_{\mathit{it}}={\chi }_{\mathit{it}-1}+{\epsilon }_{\mathit{it}}$(4)

$\mathit{t}=1,\dots ,\mathit{T};\mathit{i}=1,\dots ,\mathit{N}$

In Equation (2)(2) $y_{\mathit{it}}={\alpha }_i+\mathit{\beta }{\chi }_{\mathit{it}}+e_{\mathit{it}}$(2) , it is assumed that $y_{\mathit{it}}$ and ${\chi }_{\mathit{it}}$ variables are stationary at I(1) level but cointegration is not realized. However, Kao (1999) investigates the cointegration relationship between the $e_{\mathit{it}}$ series by using DF and ADF unit root tests for the series, defending the $z_{\mathit{it}}=\left\{{\mu }_i\right\}$ equation.

Panel dynamic least squares (DOLS)

The Panel Dynamic Least Squares (DOLS) test was proposed by James H. Stock and Mark W. Watson in a paper published in 1993. Stock and Watson (1993) developed the DOLS test to estimate the direction and strength of the long-run relationship between two variables in panel data sets. The test uses an error correction model and is therefore considered to be powerful for estimating the direction and strength of the long-run relationship.

The panel regression equation can be written as follows EViews (2022, 304):

(5) $y_i=X_t^{\prime }\mathit{\beta }+D_{1t}^{\prime }{\gamma }_1+\sum _{\mathit{j}=-\mathit{q}}^r\mathrm{\Delta }{X}_{t+j}^{\prime }\mathit{\delta }+u_{1\mathit{t}}$(5)

After adding $\mathit{q}$lagged and $\mathit{r}$ forward transformations of the differentiated regressors to the model, and under the assumption of eliminating long-run correlation between $u_1$ and $u_2$ error terms, the parameters $\mathit{\theta }=({\beta }^{\prime },{\gamma }^{\prime }{)}^{\prime }$ p are obtained by least squares estimation using equation (5)(5) $y_i=X_t^{\prime }\mathit{\beta }+D_{1t}^{\prime }{\gamma }_1+\sum _{\mathit{j}=-\mathit{q}}^r\mathrm{\Delta }{X}_{t+j}^{\prime }\mathit{\delta }+u_{1\mathit{t}}$(5)

Empirical results

Cross section dependency

In this part of the study, cross-sectional dependency tests were examined for each series.

Hypothesis tests are formed as H0: There is no cross-sectional dependence and H1: There is cross-sectional dependence.

According to Table 2, prob. Since its value is less than 0.05 significance level, H0 hypothesis is rejected. In other words, panel series are cross-section dependent. Then, since there is cross-sectional dependence, the model was defined as Panel VAR and the model was estimated.

Table 2. Cross-section dependence test.

Test	Statistic	d.f.	Prob.
Series: DLOGENERGYEFFICIENCY
Breusch-Pagan LM	326.0149	21	0.0000
Pesaran scaled LM	47.06483		0.0000
Bias-corrected scaled LM	46.99620		0.0000
Pesaran CD	9.252629		0.0000
Series: DLOGRENEWABLEENERGY
Breusch-Pagan LM	1036.780	21	0.0000
Pesaran scaled LM	156.7382		0.0000
Bias-corrected scaled LM	156.6696		0.0000
Pesaran CD	32.18818		0.0000
Series: DLOGTOTALCARBONEMISSIONS
Breusch-Pagan LM	880.0598	21	0.0000
Pesaran scaled LM	132.5558		0.0000
Bias-corrected scaled LM	132.4872		0.0000
Pesaran CD	29.48903		0.0000

Cointegration

In the first stage, a cointegration relationship between the corrected series was examined according to the Panel VAR analysis.

Hypothesis tests are formed as H0: There is no cointegration and H1: There is cointegration.

Table 3 shows the results of the Pedroni Residual Cointegration Test.

Table 3. Pedroni residual cointegration test.

				Weighted
		Statistic	Prob.	Statistic	Prob.
Panel v-Statistic		−0.282456	0.0012	2.977455	0.0015
Panel rho-Statistic		−1.973479	0.0042	−5.955061	0.0000
Panel PP-Statistic		−3.155625	0.0008	−6.779768	0.0000
Panel ADF-Statistic		−0.875575	0.0006	−5.007095	0.0000
Group rho-Statistic		−8.163334	0.0000
Group PP-Statistic		−10.51427	0.0000
Group ADF-Statistic		−11.45021	0.0000

According to Table 3, since the prob. values are less than 0.05 significance level, the null hypothesis H0 is rejected. Thus, it is revealed that there is a cointegrated relationship between the panel series. In other words, there is a long-run relationship between the panel series.

Table 4 shows the results of the Kao Residual Cointegration Test.

Table 4. Kao residual cointegration test.

			t-Statistic	Prob.
ADF			−3.921380	0.0000
Residual variance			0.000662
HAC variance			0.001258

According to Table 4, since the prob. value is less than 0.05 significance level, the null hypothesis H0 is rejected. Similarly, it is once again demonstrated that there is a cointegrated relationship between the panel series.

Finally, the direction and the strength of the long-run relationship between the series in the panel data environment are analysed using the Panel Dynamic Least Squares approach.

Table 5 shows the results of the Panel Dynamic Least Squares Test.

Table 5. Panel dynamic least squares.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
LOGRENEWABLEENERGY	1.419063	0.093541	15.17052	0.0000
LOGENERGYEFFICIENCY	−0.736920	0.136536	−5.397253	0.0000
R-squared	0.999872
Adjusted R-squared	0.999843
S.E. of regression	0.009982
Long-run variance	0.000249

According to Table 5, there is a negative relationship between energy efficiency and carbon emissions. A 1% increase in energy efficiency leads to a 0.73% decrease in carbon emissions. In other words, when energy efficiency increases, carbon emissions decrease. This finding is in line with general expectations. As energy efficiency increases, less energy is consumed. Less energy consumption reduces CO2 emissions. In contrast, there is a positive relationship between renewable energy consumption and carbon emissions. A 1% increase in renewable energy use increases carbon emissions by 1.41%. In other words, when renewable energy use increases, carbon emissions also increase. This finding is not consistent with general expectations. The general expectation is that renewable energy consumption reduces CO2 emissions. Because renewable energy sources emit less CO2 emissions than fossil fuels.

Conclusions and policy implications

This study examines the impact of energy efficiency and renewable energy use on carbon emissions in G7 countries. There are two important results. The first is the cointegration relationship between the variables. Accordingly, there is a long-run cointegration relationship between energy efficiency, renewable energy use and carbon emissions. The second is the direction and strength of this long-run relationship. Accordingly, carbon emissions decrease when energy efficiency increases. This result is fully consistent with the literature in line with general expectations. On the other hand, there is a positive relationship between renewable energy consumption and carbon emissions. This result is consistent with Bilgili et al. (2016), Jebli et al. (2020), Wang et al. (2022) and Rehman et al. (2023), which are not consistent with general expectations.

The existence of a positive relationship between renewable energy consumption and carbon emissions is an important issue that needs to be discussed. As stated earlier, a negative relationship between renewable energy consumption and carbon emissions is expected. On the contrary, the existence of a positive relationship can be explained in two ways. The first is a theoretical explanation. Accordingly, the technology used in renewable energy investments and maintenance processes can produce carbon emissions. Second, it can be explained technically. Accordingly, changes in the estimation models used can lead to different results. In addition, since the renewable energy consumption and carbon emissions series exhibit a positive upward trend feature in the fifty-two-year period under investigation, it does not seem very possible to capture a negative correlation between the two series. On the other hand, since the increase in energy efficiency means less energy use, it has been much easier to capture a negative correlation between the two series.

On the other hand, CO2 emissions are the main cause of climate change with global warming. Developing policies and measures to reduce CO2 emissions helps to mitigate the effects of climate change. The common point of the reviewed studies is the following: ‘To increase energy efficiency, energy efficiency standards should be set and implemented. Tax breaks and other financial incentives should be provided to encourage energy efficiency investments. To expand renewable energy consumption, investments should be made in renewable energy projects, and electricity generated from renewable energy sources should be guaranteed for purchase. In addition, costs for the use of fossil fuels should be increased’.

My suggestion is to set an upper limit for the carbon emissions produced by each unit. Below this upper limit, there are tax advantages; above the upper limit, there are significant tax liabilities. This would involve the creation of an environment where consensus is achieved both on a global scale and on a societal scale.

In conclusion, the analysis of CO2 emissions is an important tool in the fight against climate change. They are important for developing policies and measures to reduce greenhouse gas emissions and mitigate the impacts of climate change.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Hakan Altın

Hakan Altın was born ın Ankara. He completed hıs undergraduate educat’on ın Anadolu Unıversıty Departmant of Econonomıcs ın 1996. He completed hıs postgraduate educatıon at Ankara Unıversıty wıth a wıthstandıng degree ın 2010 and recıeved hıs Ph.D ın Busıness Admınıstratıon. He ıs currently workıng as a Professor Doctor ın the faculty of Economıcs and Admınıstratıve Scıences, Departmant of Busıness Admınıstratıon at Aksaray Unıversıty. The author has penned numerıos artıcle and book chapter ın fınance.