Impacts of Energy Consumptions, Economic Growth, ICT, Military Expenditure, and Tourism on CO2 Emissions: Evidence from G20 Countries

doi:10.21203/rs.3.rs-1735421/v1

This study determines the effects of economic growth, energy consumption, information and communication technology (ICT), military expenditure, and tourism on CO2 emissions for the G20 countries by employing panel data from 1980 to 2019. In the presence of cross-sectional dependence and mixed order of integration, the study employs Westerlund co-integration test and panel ARDL approach to obtain reliable, consistent, and unbiased long-run associations among the intended variables. The empirical results of the analysis demonstrate that energy consumption, ICT and military expenditure increase CO2 emissions while economic growth mitigates CO2 emissions. However, tourism has an insignificant effect on CO2 emissions. Hence, the study recommends that policymakers should take appropriate actions to control CO2 emissions through environment-friendly strategies.

CO2 emissions

Economic growth

Energy consumption

ICT

Military expenditure

Tourism

Climate change and global warming have huge attention of the worldwide researcher. The temperature of the surface of the planet has been increasing day by day over the decades. It conveyed an upsurge of sea levels, an identical drop of snow and ice cover, annihilations of species, and many other serious environmental problems. The problems related to global warming appear due to the upturn absorption of greenhouse gases, precisely due to the increase in carbon dioxide (CO2) emissions in the world atmosphere, which comes from different sources (Kaygusuz, 2009). The researchers and specialists consider that the world economy is intensively influenced by global warming and climate change. To achieve sustainable development, international societies attempt to work together to find out a universal solution. To oppose global warming, international organizations such as the United Nations strive to moderate the negative influence of global warming and climate change by introducing obligatory intergovernmental contracts such as the Kyoto Protocol (Halicioglu, 2009). The United Nations agreed to both the "2030 Agenda for Sustainable Development" and in 2015 "Paris Climate Change Agreement." These agreements' most common issue is to bind the worldwide average temperature not more than 2 degrees centigrade above the pre-developed temperature. In recent times, the Intergovernmental Panel reports on Climate Change (IPCC) of the United Nations highlight that the global average temperature is rising should bound to 1.5 degrees centigrade and make this initiative succeed in producing coal-fire-based electricity should be terminated by 2050 (IPCC, 2011.). Human activities, technological progress, and the industrial revolution have contributed significantly to global environmental degradation by accumulating CO2 and other types of heat-trapping gases. The emission of different kinds of greenhouse gas (GHG) has increased the world's temperature and the greenhouse effect (EPA, 2017). It is investigated that the enlargement of the CO2 emissions is almost 40% more compared to the Pre-industrial era. Consequently, as a significant amount of heat absorbs by the CO2, the presence of additional CO2 has accelerated the amount of heat in the world's heat balance (Solomon, Manning, Marquis, & Qin, 2007; Stone et al., 2009). Another difficulty is that the existence of CO2 is much longer than any other vital heat-trapping gas. On the other hand, to vanish today's emitted CO2 required about a century, but approximately 800 years will exist around 20% of this CO2 from now (Forster et al., 2007). It is considered that at an early step in economic development, carbon emissions increase sharply. On the other hand, when economic development is raised at a certain level, the growth rate of carbon emissions will decline progressively. There is also a possibility of becoming an opposite economic development (Ru, Chen, & Dong, 2012).

Energy is considered the oxygen by which economic activity finds its life (Ahmad et al., 2016). Most countries consume a large amount of energy to achieve sustainable economic development, responsible for massive CO2 emissions. Climate change is influenced by energy consumption, accompanied by pollutants (Alkhathlan & Javid, 2013). Information and communication technology (ICT) is one of the trendiest issues in the present time; the structure of information and communication technologies has been developed dramatically. Technology helps in production growth and cost minimization and helps reduce energy consumption and carbon emission (Ahmed, Uddin, & Sohag, 2016). With the innovation of technology, powerful countries use it to produce advanced military equipment for the military sector. As a result, military expenditure has become one of the most identified and renowned sources of government expenditure. Most of the leading economic states expense a significant amount of money in the defense sector for the states' security and establish a dominating position globally. Launching modern biological and chemical weapons of mass destruction and testing nuclear-capable ballistic missiles ejaculate many pernicious elements that threaten global security and the environment. However, the security of a country is an inseparable issue for the tourism sector. Tourism is considered one of the largest emerging and potential industries for any economy, growing fast worldwide. The production of tourism activities requires a massive amount of energy which produces greenhouse gases (Cadarso, Gómez, López, & Tobarra, 2016). The increasing potentiality of tourism and tourism-oriented movements has become a severe issue and challenging for the environment. Environmental degradation is influenced by tourism, which is well documented. According to the World Tourism Organizations' 2008 report ((UNWTO), 2008.) about tourism and climate change suggested that it becomes a very urgent necessity to take essential strategies and policies to establish a sustainable tourism industry that will show responsiveness to the environment.

The continuous growth of carbon emissions in different countries due to the economic growth, energy consumption, ICT, military expenditure, and tourism of the emerging economy has gathered the world's attention. The process will be influenced by the policies to reduce carbon emissions. To make any policy regarding carbon emissions, it is essential to analyze the relationship between carbon emissions and its influencing factors at different stages. In these circumstances, a question arises about the association among the intended variables. That is, "Do the economic growth, energy consumption, ICT, military expenditure, and tourism influence CO2 emissions?" The investigation's emphasis on the G20 countries and the motivation of the study derived from the scanty literature on the link of military expenditure and CO2 emissions of G20 countries. Where the military expenditure of the counties is higher than any of the world and the Group of Twenty (G20) refers to the group of the state of the most advance and leading economies of the world which includes G7 countries of advanced economics (Canada, France, Germany, Italy, Japan, United Kingdom, United States), BRICS countries of emerging economics (Brazil, Russia, India, China, South Africa), seven dominant middle powers including MIKTA countries (Australia, Argentina, Indonesia, Mexico, Saudi Arabia, South Korea, Turkey), and European Union (EU). The G20 countries embody the world's two-third population. The G20 countries' economy represents around 90% of the world's GDP, and the energy consumption is approximately 77% of the world, with a renewable power capacity of about 81%. The G20 countries are also liable for around 85% of CO2 emissions of the world (PricewaterhouseCoopers(PwC), 2013).

This investigation contributes to the existing literature in many ways. First, it exemplifies a new notion by investigating the impact of military expenditure and others variables on CO2 emissions in the interior of a single structure. However, very scanty studies investigated this without considering various econometric issues like cross-sectional dependence and show controversial outcomes. But this study considers several econometric issues to find reliable outcomes. Second, the study will support the official authority to make efficient policy regarding the intended factors to make a vigorous ecosystem as G20 countries have highest volume of intended factors. Finally, there are no such previous studies, where these six variables employed simultaneously to examine the connection among those factors for the panel of G20 countries and this study will provide new evidence regarding these factors and top up the gap.

This study comprises five sections. The first section of this study is considered the introduction. The primary and general information about CO2 emissions, economic growth, energy consumption, ICT, military expenditure, and tourism are discussed in contests of the world's different countries. The second section of this study contains the literature reviews where the previously conducted studies and their finding related to the issue of this investigation are discussed. Section three represents the data analysis, model, and methodology of the study's investigations. The research applied several unit root tests, including 1st generation and 2nd generation, to escape spurious regression. The study uses Westerlund panel co-integration techniques which is appropriate in the presence of cross-sectional dependence. The investigation examined heterogeneous and cross-sectional dependence characteristics of the panel estimation procedures like Mean Group (MG), Pooled Mean Group (PMG), and Dynamic Fixed Effects (DFE), where most of the prior studies were limited to do so. Section four displays the results and discussions of the model and presents the interpretation of the empirical results. Section five specifies the conclusion, policy recommendation, and proposal for further studies.

The investigation explores the influence of economic growth, energy consumption, ICT, military expenditure, and tourism on CO2 emissions in the case of G20 countries. For consistency, the association between the explanatory variables and CO2 emissions is discussed individually in the following sections. The prior studies about the linkage among the intended variables deliberated and begin with economic growth and carbon emission by illustrating the current literature.

Economic growth and Carbon Emissions

The way through which economic growth influences carbon emissions is a controversial issue. The researcher involves in the debate with their decision about the link between economic growth on carbon emissions. The empirical analysis of Lau, Choong, & Eng (2014) employed data from 1984 to 2008 on Malaysia and claimed that economic growth stimulates carbon emissions positively which is statistically significant. The study of Ehigiamusoe (2020a) on 25 African countries by using data from 1980 to 2016 with the FMOLS approach represented that the real GDP has a positive and significant impact on CO2 emission. Ehigiamusoe & Lean (2019) analyzed the panel data of heterogeneous income groups of around 122 counties over the period of 1990–2014 with various estimation approaches, where the outcomes of DOLS showed that economic growth hastens the CO2 emissions in case of low and middle-income countries. However, for high-income countries, economic growth has a negative impact on CO2 emissions. Another investigation by Ehigiamusoe, Lean, Babalola, & Poon (2022) reported that CO2 emission is accelerated by GDP and that is statistically significant where the investigation was conducted on 31 African nations for the 1990–2019 period. The exploration of Aye & Edoja (2017) indicated that economic growth associated with carbon emission negatively in a lower progressive area, conversely, in the case of the higher progressive area it stimulates carbon emission positively and the results do not agree with the Environmental Kuznets Curve (EKC) hypothesis. The assessment was carried out with a panel of 31 developing countries. The experiment was accompanied by Magazzino (2016) employing the VAR model with data from 1971 to 2006 about 10 Middle East countries, to identify the inter-association among carbon emission, energy consumption, and economic growth. The outcomes of the experiment indicate that the economic growth of GCC countries affects carbon emissions negatively.

Energy Consumptions and Carbon Emissions

Most of the analysis about the interconnections between carbon emission and energy consumption emphasis total energy. Numerous factors affect energy consumption, but the concerning matter is how the environment is influenced by energy consumption. The development of a nation depends on the nation's ability to utilize energy resources efficiently. The researchers scrutinize the link of how energy consumption is related to carbon emissions, and the findings of their analysis varied from country to country and regime to regime. The analysis of Begum, Sohag, Abdullah, & Jaafar (2015) constructs a dynamic connection between carbon emission and energy consumption with the rejection of the EKC hypothesis in the case of Malaysia from 1970 to 1980. The findings specify that in the long run energy consumption is positively influences carbon emission. The study of Ehigiamusoe & Lean (2019) on heterogeneous income groups with different methods finds that energy consumption increases the CO2 emissions for low, middle, and high-income countries. Another investigation of EHIGIAMUSOE (2020b) on ASEAN + China with PMG estimation stated that energy consumption aggravates environmental degradation. In the case of African countries Ehigiamusoe et al. (2022) claimed that energy use intensifies the CO2 emissions, where the outcomes revealed from panel data of 1990–2019 by FMOLS approaches. According to the valuation of Waheed, Chang, Sarwar, & Chen (2018) using the ARDL model with annual data from 1990 to 2014 about Pakistan shows that the association between carbon emission and renewable energy consumption is negative and it is also claimed that carbon emission is reduced with the increase of renewable energy consumption. The study of Acheampong (2018) applies panel VAR and system GMM model to scrutinize the causal association among carbon emission, energy consumption, and economic growth of around 116 countries with data for 1990 to 2014. The outcomes describe mixed indications that; their association varies from regime to regime. Another investigation by Anwar, Sarwar, Amin, & Arshed (2019) examines 59 countries of different income groups for various factors by utilizing data from 1982 to 2015. The results designated that carbon emission and nuclear energy consumption have a bidirectional causality. Munir & Khan (2014) Argued that energy consumption affects carbon emission negatively and they find the outcome by applying the VEC model and Johansen Co-integration approach with data from 1980 to 2010 for Pakistan. According to Akin (2014; Esso & Keho (2016); Wang, Li, & Fang (2018), energy consumption negatively impacts carbon emissions amongst the various groups of countries with different income levels.

ICT and Carbon Emissions

Environmental quality and (ICT) information and communication technology is another hot issue of debate in the modern age. Modern innovations and technological progress are becoming a very concerning matter for climate change all over the world. In the present world, developed countries are innovating technology to save energy, including smart and green cities, updated industrial processes, and advanced and smart transportation systems. CO2 emissions are expected to alleviate with these developments (Lennerfors, Fors, & van Rooijen, 2015). The analysis of Higón, Gholami, & Shirazi (2017), with the panel of 142 economics by using data from 1995 to 2010, confirms that initially, ICT deteriorates the environmental quality, but after a specific time, updated ICT has contributed to decreasing and controlling of emission of CO2. The study of Batool et al. (2019) examines the link among carbon emission, ICT, economic growth, and energy consumptions for South Korea with data from 1973 to 2016. The results of the study designated that in the long run ICT diminishes environmental degradation. The study of Al-Mulali, Sheau-Ting, & Ozturk (2015) explored the heterogeneous connection for developing and developed countries amongst CO2 emission and internet retailing. The outcomes of the study claimed that CO2 emission is negatively influenced by internet users in developed countries, however, CO2 emission has an insignificant effect on internet retailing in the case of developing countries. Due to the slower speed of the internet in developing counties, there is a heterogonous effect. The empirical investigations of Santra (2017) about the BRICS countries indicated that technological innovation positively impacts carbon emissions. Another empirical study by Raheem, Tiwari, & Balsalobre-Lorente, (2020) explores the connection between CO2 emission and ICT in G7 countries and specifies that in the long run, CO2 emission is positively influenced by ICT. According to the investigations of Mensah et al. (2018), on OECD countries, of Santra (2017), on BRICS countries, of Shahbaz, Nasir, & Roubaud (2018), on France, of Jin, Duan, Shi, & Ju (2017), on China, of Dinda, (2018) on the US specified that to diminish carbon emission, technological innovations play a significant role. Identically, lots of advanced methods are innovated for the efficient utilization of energy. These modernized methods of using energy are friendly to the ecological system and environment in every country, and these approaches assist in reducing emissions of greenhouse gas.

Military Expenditure and Carbon Emissions

Environmental quality is affected by numerous factors. Recently, the researcher concentrated on military expenditure as one of the leading elements in the existing arms-dominated world. And one of the main focuses of modern technological innovations on military improvement. At present international military race is increasing day by day all over the world. The exploitation of various destructive tests and manufacture of military weapons generates many damaging elements that harm the environment's quality. In response to the issue, scholars investigate the association between military expenditures and the environment. The investigations of M. Bildirici (2017) assess the link between carbon emission and military expenditure for G7 countries by utilizing data from 1985 to2015 with the help of ARDL methods. The outcomes of the investigation indicated that carbon emission is positively influenced by the military expenditure of G7 countries, however causal analysis of the investigation shows that a unidirectional causal association between military spending and CO2 emission of G7 countries. Another investigation of M. E. Bildirici (2017) examines the long-run affiliation between environmental degradation and militarization by utilizing data from 1960 to 2013with the ARDL bond testing method. The results of the investigation indicated that carbon emissions are influenced by military spending positively in the case of the United States. It is considered that military expenditure is related to the activity of human beings and the movements of military personnel, extensive military equipment, & heavy-weighted military assets worldwide done by the sea, land, and air transportations. And those activities are responsible for a massive amount of pollution. And military campaigns need to consume enormous fuel in military ships, planes, and tanks (Jorgenson, Clark, & Kentor, 2010). According to the analysis of Ozcan & Apergis (2018), technological innovations created by states, militaries may be connected with declining emission methods. ICT industry developed by the innovations in the military sector. The analysis of (Bildirici, 2018) assesses the long-run association between CO2 emission and military expenditure by employing FMOLS and DOLS approaches with data from 1985 to 2015 for G7 countries. The findings of the analysis claimed that military spending and carbon emission has significant and positive associations.

Tourism and Carbon Emissions

Nowadays, tourism is becoming an identical factor for any economy all over the world. Tourism involves a country's infrastructural services, transportation services, and conditions of the resorts and restaurants for all of those required colossal energy consumption. And researchers and policymakers from around the world became worried about the potential link between tourism and the environment. According to the investigation of Miller, Rathouse, Scarles, Holmes, & Tribe (2010), it is difficult to make a sustainable tourism industry in the absence of awareness of tourists about the environment in the UK. Peeters & Dubois (2010), found that almost 4.4% of the world's carbon emissions are contributed by tourism, and most of these come from transportation. According to the exploration of Katircioglu (2014), carbon emission and tourism have a long-run association where environmental degradation refers to a proxy for Turkey. The investigation of Scott, Peeters, & Gössling (2010) reported that tourism has become a potential threat to the environment and it has badly influence on CO2 emission. The author also forecasted that in the coming days' tourism would be one of the most noticeable causes of carbon emission globally. Ehigiamusoe (2020c) indicated that environmental degradation is positively influenced by the tourism in case of African countries, where the study analyzed data from 1995–2016 with FMOLS methods. Another investigation by Dubois, Peeters, Ceron, & Gössling (2011) argued that tourism sectors could reduce carbon emissions, the consequences of alleviating global movement initiated by tourism by 2050. The authors also claimed that if we make a significant variation in the travel system, there is a possibility of diminishing carbon emissions in circumstances where the transportation system and tourism industry are connected to tourism which is essential to decrease emissions. Stefănica & Butnaru (2015), claimed that the tourism sector depends on environmental excellence, and tourism is affected by environmental factors like climate change, global warming, pollution, etc. Chen, Thapa, & Yan, (2018), Analyze the association among carbon emission, tourism, and economic growth in China from 2001 to 2015. The authors claimed that improvement in the tourism industry positively impacts CO2 emissions, and it is considered that transportations sectors are responsible for carbon emissions. The study of Paramati, Shahbaz, & Alam (2017), with panel data about the European Union, reported that the rise of the tourism industry could reduce carbon emissions.

However, the investigations of the recent studies indicate that there is no identical result about the association between CO2 emissions, economic growth, energy consumption, (ICT) information and communication technology, military expenditure, and tourism. Some of the investigations argued there are different shapes of their link. And some of them claimed that the connection between the dependent variable carbon emission and intended independent variables is positive, while other investigations contended that there is a negative relationship. And there are very few numbers of study which tries to investigate the effect of military expenditure and tourism on carbon emission. On the other hand, no previous research employed economic growth, energy consumption, ICT, military expenditure, tourism, and carbon emission simultaneously to investigate the interrelation among these variables. So, this empirical study tries to find reliable and consistent outcomes about the association among the intended variables.

3.1 Data and Sources

For the analysis of the investigation, we used panel data from 1980 to 2019 of G20 countries. All the data were collected from secondary sources and composed of World Bank Indicators (WDI). The data collected from WDI indicated a few missing values of those filled up by the interpolation method. We use one of the most popular methods of interpolation, Cubic Spline. For the investigation, we identified six variables like CO2 emissions, economic growth, energy consumption, ICT, military expenditure, and tourism. And the variables such as CO2 emissions indicated by the CO2 emissions (metric tons per capita), economic growth represented by GDP per capita (constant 2010 US$), energy consumptions denoted by energy use (kg of oil equivalent per capita), ICT measured by the composite index of weighted of [Individuals using the internet (% of the population), Mobile cellular subscriptions (per 100 people), Fixed telephone subscriptions (per 100 people)], military expenditure specified by the Military expenditure (% of GDP), tourism dignified by International tourism, number of arrivals. The list of the G20 countries are Argentina, Australia, Brazil, Canada, China, European Union, France, Germany, India, Indonesia, Italy, Japan, Mexico, Russia, Saudi Arabia, South Africa, South Korea, Turkey, United Kingdom, and the United States.

3.2 The model specification

The investigation intends to explore how environmental degradation is influenced by economic growth, energy consumption, (ICT), military expenditure, and tourism in G20 countries. Depending on panel data availability throughout 1980 to 2019, the study about the selected variables is done on G20 countries. The empirical analysis was performed throughout the following model:

Where,

LCO2 = Log of CO2 (Carbon dioxide emissions)

LGDP = Log of GDP (Gross domestic product per capita)

LEC = Log of EC (Energy consumptions)

LICT = Log of ICT (Information and communication technology)

LME = Log of ME (Military expenditures)

LTRSM = Log of TRSM (Tourism)

U = Disturbance term

β₀ is considered as intercept as well as the constant of this regression line, and β_1, β₂, β_3, and β₄ are considered as coefficients for explanatory variables, respectively. Where$\text{i}=\text{1,2},3, \dots \dots \text{N}$ represents the cross country and $\text{t}=\text{1,2},3, \dots \dots \text{T}$represents the time of the panel data.

3.3 Econometric Methodology

The analysis of this study is taking place by exploring the order of integration of the intended variables. Firstly, Levin-Lin Chu (LLC) and Im-Pesaran-Shin (IPS) (Im, Pesaran, & Shin, 2003; Levin, Lin, & Chu, 2002) first-generation unit root tests and secondly, cross-sectionally augmented Dickey-Fuller (CADF) Pesaran, (2007) second-generation unit root test. We have employed the cross-sectional dependence tests introduced by Pesaran, (2004). In the presence of cross-sectional dependence, we have also conducted panel co-integration test introduced by Westerlund (2007) to investigate the long-run association among the selected variables which is established on structural dynamics instead of the residual and large degree of heterogeneity is supported by it. The uniqueness of (Westerlund) over other co-integration tests is that it does not provide limitations for mutual elements in order to conserve the supremacy of the test. The most distinctive variation of homogeneous models with heterogeneous models is on (∝𝑖=∝₁, βi = β₁, δi = δ₁, θi = θ₁) for homogeneous models and (∝𝑖≠∝₁, βi ≠ β₁, δi ≠ δ₁, θi ≠ θ₁) for heterogeneous models. Former static models like GMM, pooled OLS, and instrumental variables are conducted for the assessment of homogeneous models and later dynamic models like (MG) mean group Pesaran & Smith, (1995), (PMG) pooled mean group, (AMG) augmented mean group, (DFE) dynamic fixed effect model (Eberhardt & Bond, 2009; Eberhardt & Teal, 2011) are employed to estimate the heterogeneous models. The process of estimating MG comprises two different stages. Firstly, time-series OLS assessed for every panel individually with intercept by the model to shelter the unnoticeable time-variant and fixed effect. Secondly, averaging distinctive slopes which are calculated with weights or without it. In the presence of dynamic features, when there is a large number of T and N with heterogeneous coefficients then the estimators of MG models show more consistency (Pesaran & Smith, 1995). The analysis conducted the Hausman test in order to recognize the most reliable and consistent estimators among the different MG estimators.

4.1 Descriptive Statistics

For the empirical investigation of the panel data throughout 1980 to 2019, here, the descriptive statistics of the study’s variables are represented by the following Table 1.

Table 1

Descriptive Statistics
Variables	CO2	EC	GDP	ICT	ME	TRSM
Mean	8.386303	3246.070	21335.22	37.22210	2.677417	35279184
Median	7.872910	3207.933	18849.33	28.43724	2.064213	17855855
Maximum	26.89927	8455.547	57071.17	94.20978	19.00435	5.20E + 08
Minimum	0.449267	286.1638	347.1201	-5.762218	0.400006	487250.0
Std. Dev.	5.661414	2071.843	15948.01	28.46858	2.431766	65893649
Skewness	0.730125	0.659061	0.349639	0.405980	3.335705	4.526609
Kurtosis	2.980525	2.784634	1.751606	1.765135	16.99136	25.73309
Jarque-Bera	71.09037	59.46092	68.24920	72.80570	8008.858	19958.47
Probability	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Sum	6709.042	2596856.	17068178	29777.68	2141.934	2.82E + 10
Sum Sq. Dev.	25609.23	3.43E + 09	2.03E + 11	647557.4	4724.874	3.47E + 18
Observations	800	800	800	800	800	800

The above Table 1 reveals the results of descriptive analysis where the mean value and standard deviation are shown, the highest mean value of the selected variables is tourism (TRSM), and the lowest mean value of the selected variables is military expenditure (ME), the outcomes of the standard deviation denoted that highest volatility for the tourism (TRSM) and lowest volatility for the military expenditure (ME). Besides these, the median value, maximum value, minimum value, skewness, kurtosis, and the values of Jarque-Bera, along with the probability value of Jarque-Bera, are also revealed. All of the variables are positively skewed. According to the Jarque-Bera test, the variables are not normally distributed.

4.2 Correlation Matrix

The analysis of the study identifies the correlation among the variables. Correlation measured the relationships among the variables of the investigation. The coefficient of correlation has a value between − 1 to + 1, where '-1', '+1', and' 0', indicating perfectly negative, positive, and no correlation between two variables, respectively. The '0 to + 1' and '-1 to 0' correspondently identify the positive and negative correlation range.

Table 2

Correlation Matrix
Variables	LCO2	LGDP	LEC	LICT	LME	LTRSM
LCO2	1.000
LGDP	0.768***	1.000
LEC	0.951***	0.848***	1.000
LICT	0.493***	0.599***	0.538***	1.000
LME	0.241***	0.028	0.203***	-0.098***	1.000
LTRSM	0.225***	0.334***	0.270***	0.389***	0.042	1.000
Notes: ^a , , and ** denote the level of significance at 10%, 5%, and 1%, respectively. ^b identifying that Correlation is statistically significance. All values are in logarithmic form.

Table 2 shows that the correlation among the variables is positive, except LICT with LME, they have an adverse association between them at 1% level of significance.

4.3 Cross-sectional Dependence Test

Table 3 demonstrations the outcomes of the cross-sectional dependence test displays in where it is noticed that all p-value related to the gross domestic product, energy consumption, (ICT), military expenditure, and tourism are less than 1% which means they are significant at 1% level of significance but carbon emission statistically insignificant, that is confirmed the existence of cross-sectional dependence in the panel data except carbon emissions.

Table 3

Results of Cross-sectional Dependence Test
Variable	CD-test	p-value
LCO2	-1.19	0.234
LGDP	63.25***	0.000
LEC	13.59***	0.000
LICT	75.98***	0.000
LME	36.38***	0.000
LTRSM	33.95***	0.000
Notes: and *are suggest the significant levels at 5% and 1%.

4.4 Panel Unit Root Results

The following Table 4 contains first-generation unit root tests (Im et al., 2003; Levin et al., 2002) and second-generation unit root tests CADF (Pesaran, 2007). According to the results of different unit root tests some variables are non-stationary at level but stationary at first difference and again some variables stationary at level but non-stationary at first difference. Some are stationary and non-stationary in both level and first difference. Finally, it can be concluded that all the unit root test methods conducted here indicated a mixed order of integration among the selected variables.

Table 4

Panel Unit Root Results
Variable	LLC (t*)		IPS (W-stat)		CADF
Variable	Level	First difference	Level	First difference	Level	First difference
LCO2	7.289	4.128	9.866	-0.921	9.630	4.865
LGDP	-3.229***	-16.902***	0.630	-15.324***	-2.404***	-6.570***
LEC	9.915	6.382	12.716	1.249	8.795	4.551
LICT	-4.269***	-0.437	-1.485*	-0.644	-4.021***	-4.823***
LME	-1.171	-3.202***	0.125	-10.938***	-2.120***	-6.846***
LTRSM	-1.981**	-9.975***	1.280	-9.239***	0.998	-8.940***
Notes: ^a , , and **are represents the significant levels at 10%, 5% and 1%

4.5 Westerlund (2007) Co-integration Test

Following Table 5 demonstrates the results of Westerlund, (2007) co-integration test with the existence of cross-sectional dependence. This indicates all robust p-values are significant with different significance levels and the null hypothesis is rejected. The outcomes of the test confirmed the co-integration among the CO2 emissions, gross domestic product, energy consumptions, ICT, military expenditure, and tourism.

Table 5

Westerlund's (2007) Co-integration Test Results
Statistic	Value	Z-value	P-value
Gt	-3.14***	-4.366	0.000
Ga	-15.565***	-2.466	0.007
Pt	-11.249**	-2.097	0.018
Pa	-15.462***	-4.873	0.000
Notes: and * indicate the levels of significance at 5% and 1%. H₀ is no co-integration.

4.6 Panel ARDL Model

The results of various unit root tests confirm the existence of a mixed order of integration among the variables. In the presence of the mixed order of integration with panel data, the panel ARDL estimation technique is most appropriate to investigate the relationship among the CO2 emissions and explanatory variables as it developed to deal with both mixed and same order of integrated variables. Following Table 6 displays the outcomes of the MG, PMG, and DFE estimators with the results of the Hausman test. Hausman test assists in recognizing the more consistent and efficient estimators among the MG, PMG, and DFE estimators. According to the Hausman test, PMG is more efficient than others. For long-run coefficients, the null hypothesis of the Hausman test is accepted, which is homogeneity restriction. It specifies that PMG is more efficient and reliable than the MG estimators. On the other hand, dealing with the outcomes of the DFE and PMG, the Hausman test also provides clear evidence in favor of PMG estimators over the DFE. The panel ARDL model assists in identifying long-run association along with the short-run connections among the intended variables. The significant and negative error correction coefficient of the regression model with a value less than − 2 indicates the presence of the long-run dynamic connection.

Table 6

Panel ARDL Models
Variables	MG			PMG			DFE
LCO2	Coefficient	Std. Err	P-value	Coefficient	Std. Err	P-value	Coefficient	Std. Err	P-value
Long-run
LGDP	-0.320*	0.179	0.073	-0.216***	0.031	0.000	-0.821***	0.281	0.004
LEC	1.800***	0.346	0.000	1.398***	0.037	0.000	2.419***	0.419	0.000
LICT	-0.046	0.050	0.358	0.030**	0.012	0.013	0.129*	0.078	0.098
LME	0.269	0.179	0.134	0.100***	0.025	0.000	0.386**	0.165	0.02
LTRSM	0.205**	0.089	0.022	-0.009	0.012	0.474	0.139*	0.076	0.069
Short-run
ECM	-0.496***	0.052	0.000	-0.312***	0.068	0.000	-0.079***	0.017	0.000
D1.LGDP	0.490***	0.083	0.000	0.504***	0.117	0.000	0.525***	0.075	0.000
D1.LEC	-0.049	0.147	0.738	0.275	0.228	.228	0.630***	0.043	0.000
D1.LICT	0.042	0.027	0.126	-0.034	0.026	0.188	-0.008	0.021	0.687
D1.LME	-0.074**	0.037	0.048	-0.031	0.026	0.239	0.0006	0.022	0.978
D1.LTRSM	-0.056**	0.028	0.045	0.038	0.045	0.393	-0.0007	0.018	0.967
_cons	-4.942***	0.935	.000	-2.225***	0.482	0.000	-0.986***	0.164	0.000
Hausman test mg or pmg				chi2(5) = 7.74			Prob > chi2 = 0.1714
Hausman test pmg or dfe				chi2(5) = 1.08			Prob > chi2 = 0.9559
Notes: ^a , , and **are indicate the levels of significance at 10%, 5%, and 1%. respectively ^b

The above Table 6 results recognized the negative and significant error correction terms with the essential standard range in the case of three estimators. However, this investigation is concentrated on the findings of the PMG estimators. The analysis identified that in the long-run carbon emissions is negatively influenced by economic growth and that is 1% rise in economic growth decreases the carbon emissions by 0.216%. But in the short-run carbon emission is positively influenced by economic growth and that is 1% rise in economic growth increases the carbon emissions by 0.504%. The outcomes are statistically significant. The findings are supported by (Aye & Edoja, 2017; Azam, Khan, Abdullah, & Qureshi, 2016; Kasperowicz, 2015). There is a negative association between economic growth and carbon emission because, in the long-run, development of an economy encouraged to the adoption of that method which is reduced carbon emissions without changing the production level. But in the short run, economic growth requires utilizing many factors to innovate new methods and to accelerate the growth and those factors are harmful to the environment and immediately affect the environment as a result it shows adverse relations. Again, in the long-run carbon emission is positively affected by the energy consumption that is 1% growth of energy consumption increasing the carbon emissions by 1.398%, which is statistically significant. The outcome is established by (Arouri, Youssef, M'henni, & Rault, 2012; Begum et al., 2015; Tiwari, 2011). One of the most significant amounts of carbon emissions in the world arises from the energy consumption of different sectors like the transportation sector, industrial sectors, and power generation sectors, this association is consistent with (Hossain, 2011; Ozturk & Acaravci, 2010). But in the short run, the association between energy consumption and carbon emission is insignificant and positive.

The outcomes designate that in the long-run, ICT affects the carbon emissions positively that is 1% developments of ICT increases the CO2 emissions by 0.030%, which is statistically significant. This outcome is recognized by (Asongu, 2018; Lee & Brahmasrene, 2014; Mirza, Ansar, Ullah, & Maqsood, 2020; Raheem et al., 2020). This finding suggests that ICT leads to the more significant usage of energy resources in rapidly developing economies and thus accelerates the environmental impacts in the countries. The high growth of ICT requires more energy consumption. The improvements in ICT and massive use of ICT products produce various detrimental elements, like electronic chips, rejected and discarded instruments, and electromagnetic radiation of ICT, which are detrimental to the environment. But in the short run, the association between ICT and carbon emissions is insignificant and negative. In the case of military expenditure, the outcomes confirm that carbon emissions are influenced by the military expenditure positively that is 1% rising of military expenditure increases the carbon emissions by 0.100%, which is statistically significant. This finding sustained with (M. Bildirici, 2017; Bildirici, 2018; M. E. Bildirici, 2017). As military activities consume a massive amount of fuel that is emitting a large number of climate-changing gases. Operating every military vehicle and the different fighter-bomber and various long-rang-bomber utilizes numerous such resources, which generates a massive amount of carbon dioxide. But in the short run, the connection between military expenditure and carbon emissions is insignificant and negative. The association between tourism and carbon emissions is insignificant and the outcome is in the line with (Al-Mulali, Fereidouni, & Mohammed, 2015; Liu, Kumail, Ali, & Sadiq, 2019), negative in the long-run and positive in the short run. As much as tourism industries developed the tourism spot improve by the different environment-friendly elements that reduce environmental pollution, initially, it may harm the environment, but this is very insignificant related to the other pollutant factors. However, most of the G20 countries have strict environmental rules and regulations to control the transportation industry. As a result, these countries' tourism industries have less possibility to influence the CO2 emissions.

This investigation intends to explore the association among the economic growth, energy consumption, ICT, military expenditure, tourism, and carbon emissions for G20 countries by utilizing the panel data from 1980 to 2019. It is conducted several empirical approaches to address different econometric issues including cross-sectional dependence. Westerlund co-integration test is used to test to recognize the long-run association among the intended variables. To estimate the parameters panel ARDL approaches were employed and the Hausman test identify PMG estimators are efficient. The outcomes of the analysis reported that carbon emission is reduced by economic growth in the long run and enhanced in the short run. Carbon emission is augmented by energy consumption in the long-run and insignificant in the short run. Expanding the ICT amplifies the carbon emission in the long run and is insignificant in the short run. Escalating the military expenditure boost carbon emission in the long run and is insignificant in the short run. In the case of tourism and carbon emission, the effect is insignificant in both the long-run and short-run due to strong eco-friendly rules.

Based on the analytical outcomes of the investigation, it can be recommended that the G20 countries' authority be more conscious about the environmental degradation and the quality of the environments as they are the largest in terms of the economy and size of the country. The authority of those countries should increase the awareness of their people about environmental quality and global climate change. And motivate the concerned people toward plantation along with the forest concerned division to improve forestation areas as it is essential to control the environmental quality. The outcomes of the empirical analysis designated that economic growth plays a crucial role in reducing CO2 emissions and environmental degradations in the long run. The improvement of economic growth assists in inventing new low-carbon producing methods that keep production the same in every section of economic development. So, it is crucial to maintain the policies and strategies regarding the increasing economic growth for the G20 countries. To control the environmental quality, climate change, and reduce CO2 emission, it is also essential to improve the strategies and principles with the requirements of the current world. It is suggested that, to revise the factors of economic growth, which are based on damaging environmental elements. The authority should continue to introduce modern and environmentally friendly sources to advance the economy. The analysis also specifies that energy consumption is the prime reason for the growing CO2 emissions and damaging environmental quality. In this regard, the authority of the G20 counties must take essentials to control environmental degradation. It is recommended to the policymakers to introduce energy sources that have minimum degradation of environmental quality. In case of the information and communication technology (ICT), the policymakers of the G20 countries should avoid those technologies which increase environmental pollution. The authorities of the countries should generate awareness about the cost and benefits of information and communication technology. The policymaker should focus on the invention of green technologies, which increases production ability and saves the earth from environmental pollution. The advancement of modern green technologies, which contribute to reducing energy consumption, should be taken into consideration. In the case of military expenditures, the authority of the G20 countries should reduce military expenditure. The policymaker should restrict the destructive nuclear weapons and control the harmful missile tests and using military equipment and weapons since those military activities generate numerous detrimental components which are harmful to environmental quality. However, the analysis claimed that tourism has an insignificant impact but reduces CO2 emissions. In future studies sample size, variables and approaches should be expanded where the category of the small and medium-sized countries can be included to observe worldwide conditions.

I, the undersigned, declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

Availability of data and materials: I declare that the collected data are original, and all data are collected by authors.

Conflicts of interest competing interest: The authors declare that there is no competing interest associated with this publication.

Funding: I declare that this research is not supported or funded by any organization or individual. The authors bear all the expenses of this study by themselves. So, this research is self-funded research of the authors.

Author’s contribution: All authors contributed to the study's conception and design. The manuscript has been read and approved by all named authors, and there are no other persons who satisfied the criteria for authorship but are not listed. The order of authors listed in the manuscript has been approved by all of us. The individual contribution of the authors is given herewith: Introduction, Literature review [Md. Harun Ur Rashid]. Data Collection, Data Analysis, Methodology [Myne Uddin]. Formatting Tables, Discussion, Conclusion [Shamsuddin Ahamad]. Review, Editing and Critically Revised the work [Kizito Uyi Ehigiamusoe]

Ethics Approval: I also confirm that due consideration has been given to the protection of intellectual property associated with this work. I further confirm that ethical considerations of all relevant bodies have been covered in this manuscript.

Acknowledgment: N/A

Consent for Publication: All participants have given their consent regarding publishing their statements and data.

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Impacts of Energy Consumptions, Economic Growth, ICT, Military Expenditure, and Tourism on CO2 Emissions: Evidence from G20 Countries

Status:

Version 1

Abstract

1. Introduction

2. Literature Review

3. Data, Model, and Methodology