Digital economy and environmental sustainability: How does ICT affect ecological footprint and what is the role of economic complexity?

doi:10.21203/rs.3.rs-1593054/v1

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Digital economy and environmental sustainability: How does ICT affect ecological footprint and what is the role of economic complexity?

https://doi.org/10.21203/rs.3.rs-1593054/v1

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In the current era of digital economy, the role of information communication and technology and economic complexity are important for controlling environmental unsustainability and formulating policies to deal with ecological concerns. To inspects the impact of ICT and economic complexity on ecological footprint in G-seven (digital economies) countries from 2001–2018, in the current study we perform first generation as well as second generation unit root testing methods. Findings of both Pesaran’s and B&P’s cross-sectional dependence testing approaches confirms the presence of cross-sectional dependence across all G-seven economies. The empirical findings of cointegration (Pedroni and Kao) tests verify and stable long-run association between ecological footprint, ICT-import, ICT-export, economic complexity, economic growth and other control grouped variables. The empirical evidence obtained from Fully-Modified OLS model suggests that ICT export economic complexity, and economic growth enhance the intensity of ecological footprint, while ICT import, research and development (RD), and trade are helpful to reducing ecological footprint in G-seven economies. These empirical findings obtained from Fully-modified OLS models are also verified by Pooled Mean Group-ARDL(PMG-ARDL) methodologies and confirm that there is no inconsistency in the results. On the basis of these results, some policy implications for ecological footprint, ICT, and economic complexity are discussed.

Digital economy

ICT

ECI

G-seven

economic growth

The challenge of climate changes and environmental degradation are not national issues now, but these are global challenge and cross the national border (Khan et al., 2022b). Issues related to climate change has been top priorities of researchers because it is a greatest challenge facing by developing as well as developing economies (Benzie and Persson, 2019). All developing and developed economies are facing changing weather patterns and sea level which badly interrupt people lives and structure of economies. According to the Inter-governmental Panel of Climate Change (IPCC), the release of greenhouse gas (GHGS) and climate change is increasingly considered to be a serious environmental problem, which has a long-term adverse impact on wellbeing. The increase in greenhouse gas in the atmosphere is the most important determinant of climate change and global warming (Pachauri et al., 2014). As stated by National Aeronautics and Space Administration (NASA), rise of GHGS emissions (i.e., per-capita ecological footprints and carbon emissions), the global warming has augmented by 1.6-degree Fahrenheit (⁰F) since industrial revolution, which is very alarming situation for life in this planet. The United States-Environmental Protection Agency (US-EPA) stated that since 1990 to 2010, the global emissions increased by 40%, reaching nearly 46 billion tons. These extreme climate change results global warming and extreme weather-related events for instance heavy rainfall, heatwaves, hot spell, and drought repeatedly seen in the last few years. These climatological changes significantly influence our ecosystem and human lives (Baloch et al., 2020). To handle these severe issues of climate change and environmental degradation, numerous global agreements and treaties came to pass such as United Nations Framework Convention on Climate Change-1992 (UNFCCC-1992), Kyoto Protocol-1997, and Paris Climate Agreement-2015 (PCA-2015). These agreements based on to cope the growth of global warming below 2-degrees centigrade (⁰C), while to accomplish this goal, many developed and developing nations are singed to obstruct inefficient energy structure. Nevertheless, regardless of these accords, the global warming is on at highest peak and carbon footprints is still growing at the rate of 2.8% in 2018 (Khan et al., 2022b). Growing carbon footprints compelled the social scientists, researchers, and environment economists to identify the factors influencing ecological unsustainability.

Although, the achievement of the highest economic growth is the end goal of any country, while developed countries are mostly concerned about their environmental sustainability (Khan et al., 2022a). In contrast, several less developed economies ignoring the problem of environmental issues while achieving their desirable target of economic growth. Consequently, the vulnerable and more impoverished people of the society might be suffering more. Hence, international cooperation and global community in undertaking these indefinite problems could help developing economies to grow in more maintainable means (Usman et al., 2022). Recently, in many studies the effect of economic growth on carbon emissions has been inspected, while these studies also investigated Environmental Kuznets Curve Heidari et al. (2015) for ASEAN economies, Malik et al. (2020) for Pakistan, and Khan et al. (2022a) for G-seven economies.

The developed economies such as G-seven group is leading and massive increase in economic growth during the recent decade. As a matter of fact, G-seven economies hold about 60% of global wealth ($320000 billion dollars) (World Bank, 2021). In contrast, developing economies are going to emerging economies by improving their human capital, energy supply structure, infrastructure, and economic growth. Among them, the economic production and financial output of China and India were about $30000 billion that were a far more than as compared to United States ($19400 billion dollars) and all European Union (EU) countries ($21000 billion dollars) in 2017-18 (Shahzad et al., 2021). The enormous economic growth in these countries leads to a massive increase the demand for energy consumption. As International Energy Agency (IEA) stated that all G-seven economies and few emerging economies are biggest carbon emitter. For example, in list G-seven group, USA on top (produce 4800 metric ton), Japan (1100 metric ton), Germany (718.9 metric ton) Canada (548 metric ton), France (306.09 metric ton), UK (359 metric ton), and Italy (321.5 metric ton) are the highest deficit of ecological footprint and largest carbon producer countries (IEA, 2020). Contrary to the background framework of the study, the current research proposes to examine the impact of information and communication technologies (ICT) and economic complexity on ecological footprint in G-seven economies.

In the current time of digitalization, the contributing role of ICT on environmental quality and economic growth has been discussed by numerous environmental economists. In the path economic growth, ICT contributes to economic growth in real term in the following three ways. First, the emerging of ICT cuts the total factor costs (TFC) by providing well direction of communication with lower transaction costs. For instance, Online business, online banking, online buying and selling, E-commerce, and numerous applications of smartphones (Datta* and Agarwal, 2004). Second, correspondingly transforms of financial assists and facilitate stockholders and boosts monetary insertion. Third, it changes and improve industrial structure by developing advance production methods (Andrianaivo and Kpodar, 2012). In contrast, the environmental significances of ICT have been largely considered in the environmental economics literature. Many researchers argued that the development of ICT does more hazardous constructive to our ecosystem. Thus, arguments require of more empirical studies om the relationship between ICT and environmental quality. The advance era of ICT began in early 1990s that increasingly reshaped all characteristics of human wants and their well-being. The ICT gave the impression to human an innovative strong point with the actual potential to grow the forthcoming life by transporting society and culture’s closers, overcome the restriction between countries, and encourage business activities for instance trade openness, economic complexity, and foreign direct investment (Ahmed and Le, 2021; Ahmed et al., 2021; Arushanyan et al., 2014). Indisputably, the emerging of advance ICT system could bring more aptitude for enhancing per capita income of the people by endorsing new business markets, cumulative revenue, and, stabilize pricing unemployment, and costs of transaction (Chen et al., 2019; Huang and Khan, 2022).

However, the contributing role of ICT is not limited to real economic growth; the enormous infrastructure development related to ICT and other macro and micro uprising can also control the environmental performance by means of the cost’s upshots, consumption, and their substations. Primarily, technological infrastructure based on ICT improve utilization of energy consumption, while collecting, installation of machineries, and distribution of these machines and equipment generates toxins which degrade the ecological system (Ahmed and Le, 2021; Chen et al., 2019; Huang and Khan, 2022; Park et al., 2018). The production and consumptions of ICT are mostly depending on unfavorable resources such as, by manufacturing of computer system uses about one thousand gist’s, while those manufacturing machineries are harshly unfavorable for our ecosystem; hence, as stated by “Global E-sustainability Initiative (GESI), between 2.5–3.5% of global carbon emissions produced by the manufacture of ICT devices (GeSI, 2020; Shabani and Shahnazi, 2019). In addition, ICT can also control the climatic pathway of substitution effect (SF) and consumption effect (CE), which comprise dematerialization, demobilization, and carbon decarbonization exercises (Avom et al., 2020). Nevertheless, the SE reduces the consumptions of non-renewable energy and therefore pollution level by substituting the conservative and conventional commodity to effective commodities, particularly in the current New Corona Virus 2019 (COVID-19) time (Ahmed et al., 2021; Alola et al., 2021). For example, the consumption of electronic books (e-books) and emails have reduced the aggregate demand for conventional books and letters. Likewise, virtual and business meetings, online classes (education/schooling), e-banking (online banking), e-shopping, and e-commerce significantly overcome physical appearance decreased the traveling expenditures (Ulucak and Khan, 2020). Similarly, the up-to-date development in transport system (GPS system etc.,) have enhanced the traffic flows and sequences by falling overcrowding level that steadily carbon emissions. (Avom et al., 2020). The emerging of ICT has miscellaneous consequences on environmental quality (carbon emissions, ecological footprint, and greenhouse gases). On one the hand, the development of ICT (digital sector) is directly linked to the establishment of advance, renewable, and cleaner energy and decreases of unsustainable utilization (misuse) of productions units. On the other hand, ICT also leads to modern device and energy structure with less low power-dependent which further leads to diminishes the dependency on non-renewable energy consumptions and fossil fuels, and therefore protect environmental sustainability.

In current prevailing economic structure, ECI has been considered a widespread attention all the way through many social and environmental scientist, because it is also a most consistent and valid indicator of economic growth, like ICT (Hausmann and Hidalgo, 2011). On the one hand, ECI upsurges the range and verity of the products while accelerate the further production and investment, which increase the energy consumption that led to increase pollution and environmental unsustainability. On the other hand, ECI has more capacity to maintain the sustainability of the environment as this comprises research and development (R&D), more ICT related machines and equipment, renewable and cleaner technology, and more environmentally-friendly productions. (Neagu and Teodoru, 2019).

In the context of above discussed arguments, the current study investigates the impact of information communication and technologies (ICT) and economic complexity (ECI) (along with economic growth, research & development, foreign direct investment, trade ration and other important control variables) on ecological footprint (EcoFP). To do so, we used Fully-Modified Ordinary Least Square (FM-OLS) and Pooled Mean Group- Autoregressive Distributive Lag (PMG-ARDL) regressions models over the time spanning from 2001 to 2018 for G-seven group of countries (G-seven: Canada, France, Germany, Italy, Japan, United Kingdom, United States). These investigated group of countries was originally founded in early1970s while now they well economically developed and technological advance nation. In early 2018, these economies collectively comprised approximately 60% of the global wealth for a total of $317000 billion UD dollars, while their share of nominal GDP is about 45.8%, Purchasing Power Parity based GDP is approximately 31%, and 770 million people or 10% of world’s population (Khan et al., 2020a). So, it can be reasonably expected to profound empirical evidence on the theoretical framework of EKC-hypothesis as we as other important environment influencing factors in these regions.

The current research add to the existing literature in four ways: (1st ), as far as we know, there is hardly any study that has examined the relationship between ICT, ECI, and EcoFP for G-seven economies. (2nd ) As compared to other papers in the existing literature, which incorporated conventional proxies of ICT, ECI, environmental quality, economic growth, particularly, in this study we inspects that how ICT (two types of ICTs; ICT-exported good and ICT-imported goods), and most reliable and established economic complexity index (based on countries’ industrial structure and knowledge based production structure) on the EcoFP (based on carbon uptake land, grazing land, cropland, forest land, fishing ground, and built-up land). (3rd ), Besides the wide-ranging proxies of environmental degradation (EcoFP) and digitalization and modernization (ICT and ECI) and other comprehensive measures of economic indicators, the current study used FM-OLS, Dynamic-OLS (D-OLS), and Pooled Mean Group-Autoregressive Distributive Lag (PMG-ARDL) regression models obtained the robustness and validity of the results. (4th ) after obtaining the empirical findings, we suggest few productive and important policy recommendations for policymakers that might contribute to achieve the target of SDGs.

The remaining structure of the paper is as follows Section 2 and 3 outline literature review and its summary in tabulated form. Section 4, presents data and model specification, while section 5, discuss empirical results obtained from specified models. Section 6 provides discussions part of the study and policy recommendation.

In the existing environmental economics literature, there have been numerous empirical studies related to environmental Kuznets curve (EKC) since the early work of Grossman and Krueger (1991), where this model has been verified in many developing as well as in developed countries through applying miscellaneous econometric methods and techniques. The relationship between economic growth and environment has been investigated by applying times-series and panel data with different others important control variables that contains international trade/trade openness (Khan et al., 2020a), foreign direct investment (Pao and Tsai, 2011), economic development and tourism management (Zaman et al., 2016), demand and price of energy (renewable and non-renewable energy) (Richmond and Kaufmann, 2006) innovation and renewable energy (Khan et al., 2020b), ICT and real income (Danish, 2019), ICT and human capital (Haini, 2021), ICT and innovation (Nguyen et al., 2020). While many empirical studies incorporated ecological pressure measures like biodiversity and deforestation, while some of these results studies based on carbon emissions to assess the actual quality of the environment. Nevertheless, as the carbon dioxide emission might not be a good measure for the exploitation of our ecosystem and cave, while concentrating only on greenhouse gas emissions declines the validity of findings.

In the recent existing literature of environmental economics, some empirical studies have utilized EcoFP as a measure environmental sustainability. EcoFP, supports us to well recognize both direct and indirect effect consumptions and production on the environment (Khan and Yahong, 2022; Khan et al., 2022a; Khan et al., 2022b). After the empirical work of Rees and Wackernagel (1996), EcoFP (as a measure of environmental quality) has been utilized in many empirical papers related environmental economics. This measure of environmental sustainability has becoming novel scientific evidence of ecology while it measures the quality of environment in the framework of six productive surface areas’ components (Huang et al., 2022). Few empirical studies examining the long-run association between EcoFP and economic growth for different countries. Economic growth effect environmental sustainability through few technical channels; For instance, Baloch et al. (2019) revealed that real income growth within the Scale-effect (S-E) deteriorate environmental sustainability, changing technological structure and compromising economy structure. Secondly, the Composition-Effect (C-E) reduces the detrimental effects of real income growth via economic structure change. Lastly, the Techniques-Effect (T-E) increase environmental sustainability due to environmentally-friendly technologies and implementation of mitigation policy (Destek and Sarkodie, 2019). The dominant role of C-E and T-E over S-E lead to the establishment of an inverted-U or EKC-hypothesis between economic growth and environmental quality. This phenomenon is examined again and again; while some studies tested and verified the establishment of EKC-hypothesis (Khan et al., 2022a) for G-seven economies, (Baloch and Wang, 2019) for BRICS countries, and (Huang et al., 2022) for E-seven and G-seven groups of countries, however, the empirical work of (Amri et al., 2019) in the context of Tunisian economy (Alshehry and Belloumi, 2017) for Saudi Arabia have fail to confirmed the EKC-hypothesis between economic growth and Environment.

However, in this era of digitalization, the significance of ICT and ECI on development has been broadly deliberated in last few years. According to Schumpeter (1942) and initial definition, the process of industrialization, will substitute the low and incompetent economic sectors (private or business sector) with advance and modern sector. Therefore, the emerging of ICT and ECI contributes positively in the process of sustainable economic growth and development. These two important measures play its positive role to development of economies in three main ways. (i) they minimize the production costs via providing low or no costs of transaction and better system for communication (e.g., online buying and selling or E-commerce) Ozcan and Apergis (2018). (ii) they expand the business sector (both agriculture sector and industrial sector) with more skillful and knowledgeable production. (iii) ICT and ECI transformed all resources especially, financial resources (such as irrevocable guarantees, irrevocable lines of credit, and liquid assists) to more active stakeholders and encourage financial inclusion (Moyer and Hughes, 2012). Nevertheless, the impact of ICT and ECI on environment have been broadly argued in the existing literature of environmental economics. But some studies stated that the emerging of ICT and ECI is more detrimental than other machines and equipment. Therefore, this leads us to further analyze the relationship between ICT, ECI, and EcoFP.

The emerging and advancement of ICT progress have different effect on climate change and our ecosystem. It is associated with the formation of environmental-friendly good and services and consumption of cleaner energy. Furthermore, technological advancement is opening up new apparatus with low non-renewable energy consumptions and power supply units which based on renewable energy consumption, therefore mitigate environmental pollution. The association between ICT and EcoFP can be study from different viewpoints; real income (Danish, 2019) human capital (Haini, 2021), and economic development (Huang et al., 2022). In contrast, it can also be known as propellent for environmental unsustainability, economic growth, and industrialization progress. Nevertheless, in the empirical work of few researchers revealed that the relationship between ICT and environmental pollution is significantly positive than that of financial development in the context of highly developed economies (Raheem et al., 2020).

Apart from ICT, measure of economic complexity, ECI is positive related with environmental degradation (i.e., ecological footprint) (Khan et al., 2022a). According to definition of Hidalgo and Hausmann (2009), ECI is country’s economic structure changes, technological-rigorous export, skills and knowledge-based production structure in the direction of particular energy utilization pattern. Undeniably, ECI through skills and education sector plays a very vital role to encourage business activities and improve mitigation process. For example, Dinda (2018) for Unites states, Huang et al. (2022) for E-seven and G-seven economies, Jin et al. (2017) for Chinese economy, Mensah et al. (2018) for OECD group of countries, and Santra (2017) for BRICS countries reported that ECI has negative relationship with environmental unsustainability. The work of Doğan et al. (2019) examined the dynamic relationship between ECI and ecological pollution for lower, middle, and high-income countries. The empirical findings of the study suggested that ECI and environmental unsustainability has positive relationship in lower and middle-income economies, while economic complexity increase environmental sustainability in higher-income countries. Similarly, Chu (2021) examines the relationship between ECI and environmental pollution by using panel data of 118 economies. The findings tested and confirmed EKC-hypothesis in investigated region. Using the panel data with different econometric approaches Dong et al. (2020) evaluated the emission mitigation policy force on China. Likewise, the results revealed that emissions mitigation SDGs-13 decrease diversification in Chinese economy. Moreover, Romero and Gramkow (2021) used 67 countries data to investigate the association between greenhouse gases emissions and ECI by using different panel techniques. The empirical results suggests that ECI depress the level of greenhouse gases and also suggested that advanced and complicated production level has to impend to overthrow environmental pollution. Besides, Pata (2021) and Shahzad et al. (2021) scrutinized the dynamic effect of ECI and demand for energy consumption on EcoFP for USA and revealed that increasing demand for energy consumption positively associated with the EcoFP. Moreover, Neagu and Teodoru (2019) stated that the impact of ECI on ecological degradation is more than that of internal and external trade which ultimately supports to achieve the UN target of SDGs, and the role of ECI and consumptions demand for renewable energy reduces greenhouse gases in the context of developed economies. The empirical findings further revealed that ECI mitigate ecological unsustainability even in the long-run.

In the light of above-mentioned discussion and related literatures listed in Table 1, we observed that ICT and ECI both have the ability that can increase the quality of environment or increase environmental unsustainability depending on their level of diffusion in an economy, time period of analysis, and data and method that are incorporated. Best to our knowledge, only few empirical researches scrutinized the impact of ICT and ECI on EcoFP in the context of developed countries, especially, G-seven economies. To the possible extent, the existing literature have overlooked some important environment influencing factors, such as Research and Development (RD) that might be some foundation of misspecification and biasness. In addition, as for as our knowledge, the current research is the first study of its kind that examines the linkages between ICT, ECI, and, environmental degradation in G-seven economies over the period of 2001–2018. Our study aims to fill the existing research gap in the literature. The empirical findings obtained from the analysis of the study would deliver initial caveats for policymakers of targeted region.

Table 1

Literature review’s summary in tabulated form
Authors	Regions	Time periods	Methodology	Results
Relationship between ICT and environmental unsustainability
(Ahmed et al., 2021)	Caribbean and Latin American economies	1995 to 2017	Continuously upgraded and fully modified (CUP-FM) and Biased corrected (CUP-BC) models	The results obtained from the models suggest that ICT increase environmental sustainability by reducing carbon emission.
(Ahmed and Le, 2021)	Six ASEAN economies	1995 to 2017	Generalized method of moment (GMM), CUP-FM and CUP-BC models	ICT decrease the intensity of carbon emissions in ASEAN economies
(Amri et al., 2019)	Tunisian economy	1975 to 2014	Autoregressive Distributed Lag (ARDL)	The suggested empirical evidence indicates statistically insignificant relationship between ICT and carbon emission in investigated region.
(Asongu et al., 2017)	Forty-four Sub-Sharan African economies	2000 to 2012	GMM	There is negative relationship between ICT and carbon emissions in targeted region.
(Avom et al., 2020)	Twenty-one Sub-Saharan African economies	1996 to 2014	Panel Corrected Standard Error (PSCE) and Feasible Generalized Leas Square (FGLS)	ICT, energy consumptions, and carbon emissions have positive relationship in Sub-Saharan African countries
(Chen et al., 2019)	Chinese provinces	2001 to 2016	Panel quantile regression model (PQR)	All measures of ICT support to decrease carbon emission in Chinese economy
(Park et al., 2018)	European Union (EU)	2001 to 2014	ARDL-Pooled Mean Group (ARDL-PMG)	The use of internet (proxy for ICT) declines environmental sustainability. The findings also suggest that EU economies did not achieve the goal of green economy.
(Shabani and Shahnazi, 2019)	Iran	2002 to 2013	Dynamic OLS	The findings suggest different results, such as, ICT in services and transportation sector has negative effect on environmental degradation while in the industrial sector ICT and carbon emissions has positive relationship
(Ulucak and Khan, 2020)	BRICS countries	1990 to 2015	CUP-BC and CUP-FM	The development of ICT significantly reduces carbon emissions in investigated region
(Usman et al., 2021)	Nine Asian countries	1990 to 2018	ARDL	The emergence of ICT in economy significantly influence carbon emissions in all countries.
Relationship between ECI and environmental unsustainability
(Balsalobre-Lorente et al., 2022)	Portugal, Ireland, Italy, Greece, and Spain (PIIGS) economies	1990 to 2019	Dynamic OLS	The empirical findings suggest the nonlinear relationship between ECI and carbon emission. This relationship supports EKC-hypothesis and N-shaped relationships in the long-run.
(Can and Gozgor, 2017)	France	1964 to 2014	Dynamic OLS	The results show that ECI overturns the intensity of carbon emissions in France. The empirical evidence also support the existence of U-shaped association between variables
(Chu, 2021)	One hundred and eighteen developed and developing economies	2002 to 2014	System-GMM	ECI has significantly positive effect on carbon emissions in investigated region
(Doğan et al., 2019)	Twenty-eight OECD economies	1990 to 2014	Augmented Mean Group (AMG), Common correlated AMG (CCEMG), ARDL, Dynamic OLS, and FM-OLS	ECI reduce environmental unsustainability and can help to mitigate environmental effects
(Khan et al., 2022a)	G-seven economies	1996 to 2019	Fully modified ordinary least square (FM-OLS) and Dynamic OLS (DOLS)	The linear impact of ECI decline environmental sustainability, while the non-linear (ECI²) support the presence of EKC-hypothesis
(Neagu and Teodoru, 2019)	EU	1995 to 2016	FM-OLS and DOLS	ECI significantly effect greenhouses green emissions in EU economies for the long-run.
(Shahzad et al., 2021)	United States of America	1965 (Quarter-1) to 2017 (Quarter-4)	Quantile autoregression distributed lag (QARDL) model	ECI significantly increase EcoFP in USA.

Model Specification, Data And Method

We targeted G-seven economies (developed) over the period of 2001 to 2018 (18 years). According to Global Footprint Network (GFN) these countries are in the list of top 10 countries with high EcoFP (GFN, 2018). The data for EcoFP are obtained from official website of GFN (2021) database. As a dependent variable per capita EcoFP used as a proxy for environmental sustainability. Regrading independent variables of our models, we used as many as possible main as well as control variables (see Table 2). We used two types of ICTs (ICT_exp and ICT_imp), ICPexp measure the percentage of total ICT related exported goods, ICTimp measure the percentage of total ICT related imported goods, ECI presents country’s composition expression of the production process by inclusion the knowledge on their diversity (goods exports), economic growth measure as per capita GDP (US dollars). Likewise, R&D measures the expenditure on research and development programs (% of GDP), FDI, represents the net inflow of foreign direct investment to investigated region. Similarly, access to clean energy and technology is percentage of total population, trade ratio taken in the share of GDP, and population is taken as how many people living per square kilometer of land area.

Table 2

Acronym, Measuring unit, nature, and source of the variables
Variables	Abbreviations	Measures	nature	Source
Ecological footprint	EcoFP	Per capita ecological footprint (in global hectares)	Main	Global Footprint Network (GFN) https://www.footprintnetwork.org/
Informational communication and technology (export)	ICTexp	Export of ICT-related goods	Main	World Development Indicators (WDI) https://databank.worldbank.org/
Informational communication and technology (import)	ICTimp	Import of ICT-related goods	Main	WDI https://databank.worldbank.org/
Economic Complexity (index)	ECI	The production composition face of a country by installing and inclusion the information of their variety of range (the number of exported product)	Main	ALTAS of Economic Complexity https://atlas.cid.harvard.edu/rankings
Economic growth	GDPpc	Gross domestic products per person (measure in current price of US dollar)	Main	WDI https://databank.worldbank.org/
Foreign Direct Investment	FDI	Net inflow of FDI (percentage of GDP)	Control	WDI https://databank.worldbank.org/
Research and Development	RD	Expenditure on research and development (percentage of GDP)	Control	WDI https://databank.worldbank.org/
Trade Ratio	TRA	Trade ratio in percentage of GDP	Control	WDI https://databank.worldbank.org/
Population	POP	People living per square kilometer of land area (population density)	Control	WDI https://databank.worldbank.org/

Econometric Process

The current study analyzes the relationship between ICT, ECI, and EcoFP i.e., whether and how ICT reduce ecological pollution in G-seven countries and what is the possible role of economic complexity in this process. In addition, this study also used some control group factors so that, it would not omit any important factor. Also, all possible variables used in the regressions are converted to logarithmic form to verify homogeneousness in our series. Following the study objective, our EcoFP model as follows.

$yit=\beta 0+Xit\beta 1it+έit+$ (Equ. 1)

In the above equation, y represents dependent and X represents includes independent variables (including main and control variables). In additions ‘i’ and ‘t’ denotes cross-sections (countries) and time (years), correspondingly, while έ represents stochastic error term.

By considering these variable, our study further proceeds for the specification of the model following Khan et al. (2022), we extant the specification of model with log relationship as follows (see Equ. 2);

lnEcoFP_it = B₀ + B₁lnICT^exp_it + B₂lnICT^imp_it + B₃lnECI_it + B₄lnGDP_it + B_nlnX_it +$έit$ (Equ. 2)

Since all variables (dependent and indument variables) in Equ. 2 are presented in log form, the estimated parameters in log form represents long-run responsiveness (long-run elasticities).

Regrading estimation process, firstly, the current study investigates the under-examine panel data of G-seven economies for the cross-sectional dependency, which is a very common matter in panel data. To this end, in this study we have applied Pesaran et al. (2004) CD test and Breusch and Pagan (1980) dependency tests. Additionally, the panel unit root (stationarity tests) have been estimated for level I(0) as well as for 1st difference I(1). Since, the presence of cross-sectional dependency in a series’, we used second-generation stationarity tests. The generalized equational form of first-generation unit root test is presented in Equ. 3 as follow;

ΔY _it = ρ_iYt−1 + ξ_itλ+е_it Equ (3)

Y _it = (1-ρ_i)µ_i + δ_iY_it-₁+ε_it Equ (4)

In Equ 3 and 4, i is different cross sections (countries), t is time specific time periods (years), ξ_it and µ_i represents the deterministic component, while е_it express the process of stationary series. Regarding second-generation (CIPS) panel unit root testing approach, this paper also suggests a simple stationary test in the occurrence of cross-sectional dependency. The generalized form of CIPS panel unit root test is presented above in Equ. 5.

Panel Co-integration Approache

The existing of co-integrational association among variables can be explored by using Kao panel co-integration and Engle-Granger based Pedroni test. Based on these tests, the long-run relationship confirms the existence of co-integration, therefore we applied FM-OLS techniques. The Equ. 6 provides the general form of the model:

Ǎ_FM−OLS$\left({\sum }_{i=1}^{N}{й}_{22i}^{-1}\sum _{t=1}^{T}{\left({x}_{it}-{ x̄}_{i}\right)}^{2}\right)$⁻¹ ${\sum }_{i=1}^{N}{й}_{11i}^{-1}{й}_{22i}^{-1}\left({\sum }_{t=1}^{N}({x}_{it}-{ x̄}_{i}\right){y}^{*}-T{Ê}_{i}$ Equ (6)

In above equation,

Y ^* = (yit - ȳ) – $\left(\frac{{й}_{21i}}{{й}_{22i}}\right)\varDelta$x_it + $\left(\frac{{й}_{21i}-{й}_{221}}{{й}_{22i}}\right)$β(x_it - x̄_i) Equ. (7)

${Ê}_{i}$ $\cong$Ѓ₂₁ + ${{\Omega }}_{21i}^{0}$ – $\left(\frac{{й}_{21i}}{{й}_{22i}}\right)$ (Ѓ₂₁₊${ {\Omega }}_{21i}^{0}$) Equ (8)

Thus, y_it and x_it represents dependent and independent variables, moreover, with partition covariance, й is the triangle of lower-case matrix. Similarly, asymptotic covariance matrix for co-integration analysis is denoted by Ω, while Ѓ presents dynamic covariance in above equations. Furthermore, in the analysis of current study, we also developed the Pooled Mean Group-ARDL (PMG-ARDL) model that allow us for the estimation of both short and long-run coefficients. In the existing literature, many studies suggest several advantages of PMG-ARDL model over FM-OLS model that can be traced by (Afshan and Yaqoob, 2022; Naseer et al., 2022; Zeng and Yue, 2022).

Summary statistic

Table 3, displays descriptive statistics of all possible variables used in regression models. Apparently, dependent and all independent variables provides skewness (positive and negative) and kurtosis; consequently, these skewness and kurtosis deviating us from normal distribution (bell-shaped) and leading us to asymmetric distribution. Moreover, the null hypothesis of JB or Jarque-Bera normality test for mostly all variables except ln ICT^exp are rejected even 10%, 5%, and at 1% level. Particularly, lnICTimp, lnGDPpc lnECI, lnFDI, lnPOP, lnTRA, lnPOP, lnINF, and lnRD are negatively skewed while the remaining variables (such as lnEcoFP and lnICTexp) get positive skewness and therefore heavy-tailed distribution.

The phenomenon heavy-tailed distribution also leads us to apply most valid and suitable panel techniques to empirically observed the association between ICTexp, ICTimp, GDP, ECI, and EcoFP (along with other control variables). As discussed above we use two latest econometric panel techniques namely “FM-OLS” and “PMG-ARDL” to actually reflect the net effect of all independent variables on dependent variable.

Table 3

summary statistics of the variables
Variables	lnEcoFP	lnICT^exp	lnICT^imp	lnGDP_pc	lnECI	lnFDI	lnPOP	lnTRA	lnRD
Mean	1.777	1.713	2.207	10.580	0.439	0.296	4.541	3.892	0.739
Max	2.324	3.013	2.749	11.052	1.022	2.478	5.861	4.482	1.223
Min	1.430	0.512	1.569	9.927	-0.616	-6.393	1.241	2.973	0.039
S.D.	0.261	0.671	0.318	0.213	0.390	1.236	1.500	0.393	0.325
Skew	0.691	0.100	-0.093	-0.726	-1.089	-2.130	-1.274	-0.689	-0.422
Kurt	2.109	2.111	2.032	3.693	3.814	10.796	3.240	2.338	2.198
JB	14.20	4.353	5.100	13.600	28.419	397.97	34.39	12.292	7.061
Prob	0.000	0.113	0.078	0.001	0.000	0.000	0.000	0.002	0.029
Note: the calculations based on log values, the average value of the variable denoted by Mean, Max is maximum value, Min is minimum, S.D represents standard deviation of variables, Skew and Kurt indicates skewness and kurtosis in variables, JB and Prob represents Jarque & Bera tests and its probability values.

Cross-sectional Dependence Tests

Similarly, in Table 4, The empirical findings of cross-sectional Pesaran et al. (2004) and B-P tests suggests statistically significant results of test statistics, which is evident of that all cross-sections (countries) are economically and socially independent. The test statistics of both the tests for cross-sectional dependence reject the null-hypothesis for all variables used in our regression models.

Table 4

Cross-sectional dependence
Variables	Pesaran CD	B-P LM	H₀
lnEcoFP	15.41***	242.94***	Rejected
lnICT^exp	18.92***	325.50***	Rejected
lnICT^imp	14.05***	205.97***	Rejected
lnGDP_pc	14.88***	233.47***	Rejected
lnECI	12.44***	177.70***	Rejected
lnFDI	2.20**	28.49	Rejected at CD
lnPOP	4.85***	208.22***	Rejected
lnTRA	8.08***	209.89***	Rejected
lnRD	5.09***	202.06***	Rejected

(2 Asterisks) denotes 5% and * (3 Asterisks) denotes 1% level of significance

Panel unit root tests

In order to check the stationarity level and order of integration of all series, we utilized IPS and CIPS panel unit root tests as shown in Table 5. The IPS is the first-generation stationarity test while the CIPS is second-generation test. The empirical results of both the tests for stationarity tests suggest that all variables seem to have integrated with different order. However, both the test for stationarity check offers same findings, the findings of CIPS test for heterogenous panel are very significant than other tests because it adopts cross-sectional dependency caused by a single common factor.

Table 5

Panel Unit root tests
Variable	Level		First difference		Order
Variable	No trend	With trend	No trend	With trend
IPS (2003)
lnEcoFP	2.8165	-0.4742	-4.0519***	-3.0111***	1st difference
lnICT^exp	0.4289	2.1736	-3.2769***	-2.9242***	1st difference
lnICT^imp	-1.1060	-0.7257	-5.7259***	-4.4426***	1st difference
lnGDP_pc	-3.9456***	-1.8660**	-	-	Level
lnECI	-1.3804*	1.1144	-4.2139***	-3.5384***	1st difference
lnFDI	-2.4363***	-2.3832* **	-	-	Level
lnPOP	0.0863	2.5328	0.1925	-1.3963**	1st difference
lnTRA	-0.6490	-0.9442	-4.8942	-3.8293	1st difference
lnINF	-2.3324	-1.7257	-3.0346***	-2.8874***	1st difference
lnRD	2.1801	-1.4298*	-3.2647***	-1.4815**	1st difference
Cross-sectional IPS (CIPS)
lnEcoFP	-2.361**	-2.129	-	-	Level
lnICT^exp	-1.934	-2.254	-3.886***	-3.950***	1st difference
lnICT^imp	-2.365**	-2.744	-	-	Level
lnGDP_pc	-1.399	-1.998	-3.101***	-3.028***	1st difference
lnECI	-2.721***	-4.269	-	-	Level
lnFDI	-3.232***	-3.292**	-	-	Level
lnPOP	-0.867	-0.987	-3.077**	-1.357	1st difference
lnTRA	-1.248	-1.612	-2.483**	-2.942***	1st difference
lnRD	-2.160	-2.359	-4.133***	-4.141***	1st difference
* (1 Asterisks) denotes 10%, (2 Asterisks) denotes 5%, and * (3 Asterisks) denotes 1% level of significance

Panel Cointegration Tests

On the basis of unit root tests, this study further proceeds to scrutinize the long-run linkages between dependent and independent used in our regression models. To this end, two cointegration tests has been applied namely “Pedroni” and “Kao” cointegration tests. The Table 6 suggests that three test statistics (such as., Panel v-stat, Panel PP-stat, and Panel ADF-stat) within dimension and two test statistics (such as., group PP stat and Group ADF stat) between dimension are statistically significant under the methodology of Pedroni panel cointegration test. Likewise, the result of Kao panel cointegration test also suggests statistically significant value under the test statistic of ADF approach. Therefore, the statistically significant results of both the cointegration tests propose the presence of long-run association among the variables, which is help us to employ FM-OLS and AMG-ARDL methodologies.

Table 6

Cointegration tests
Pedroni			Kao
Dimensions			ADF
Statistics	Within-Dim	Between-Dim	t-Statistics	Prob.
Panel v-stat	1.691**	-	-3.7063***	0.0001
Panel PP-stat	-2.616***	-	-	-
Panel ADF-stat	-2.758***	-	-	-
Group PP-stat	-	-4.885***	-	-
Group ADF-stat	-	-3.858***	-	-

(2 Asterisks) denotes 5%, and * (3 Asterisks) denotes 1% level of significance

FM-OLS panel model.

Table 7 after verifying the existence of long-run relationship between the dependent and independent variables it is therefore required to estimates the long-run panel cointegration model. FM-OLS econometric technique is used to assist the aim of the study. In models FM-OLS I to FM-OLS III, we reported all the empirical results obtained from FM-OLS technique. The estimated models (FM-OLS) support the hypothesized association of ICT export, ICT import, GDP per capita, economic complexity, and other control group variables with ecological footprints. The statistically significant and negative coefficient of ICT export (lnICTexp), research and development (lnRD), tarde (lnTRA), and (population) lnPOP suggest that an upsurge in these factors reduce environmental degradation in terms of ecological footprint (lnEcoFP) in G-seven economies. On the other hand, the statistically significant and positive coefficients of ICT imports (lnICTimp), GDP per capita (lnGDPpc), and economic complexity (ECI) imply that an increase in these factors increase EcoFP in investigated regions. Precisely, the long run responsiveness (elasticities) of ICT export, ICT import, GDP per capita, economic complexity, research and development, trade, and population are 0.091%, -0.079%, 0.193%, 0.071, -0.771%, -0.092%, and − 2.044% respectively (FM-OLS, column III). Furthermore, Table 6 has reporting more interesting empirical findings.

Frist, the coefficient of ICT export is positively and statistically significant with Eco-FP. Averagely, a 0.232% (FM-OLS I), 0.260% (FM-OLS II), and 0.091% (FM-OLS III) increase in EcoFP is caused by ICT export in the long-run. It is worth mentioning that producing extra ICT related heavy machines, equipment, and infrastructure and exporting of these goods are unfriendly to environmental quality indicating that it is increasingly EcoFP. Therefore, our study suggests that ICTexport and its expansion might not be detected as a reasonable clarification in handling with environmental issues (Alataş, 2021). This empirical results in line with empirical work of (Avom et al., 2020) for twenty-one Sub-Saharan African countries, and (Raheem et al., 2020) for G-seven economies. Our findings reveals that although the production of ICT related goods for exports has high production costs, overall, it contributes in environmental degradation. Particularly, it suggests that the governments and concern policymakers of G-seven economies should further upgrade their machinery for the production of ICT-related goods to achieve high and sustainable economic growth and development. Speciously,

On the other hand, ICT import statistically significant and negatively effect EcoFP in the long-run. Precisely, a 0.162% (FM-OLS 1), 0.046% (FM-OLS II), and 0.079% (FM-OLS) decrease in EcoFP caused by ICT import in the long-run. These specific empirical results can be defendable by understanding the contributing role of the SE that is positively associated with environmental sustainability, in terms of dematerialization, demobilization, and decarbonization activities. The substantial improvement in ICT sector especially in ICT import encourage in the implicit activities, for example, online business and academic conferences, online businesses, electronic books, e-commerce and e-banking, decrease the aggregate demand for non-renewable energy and therefore decrease environmental unsustainability. The enhancement of SE in mitigating EcoFP has previously ignored the effect of consumption and cost effect. The empirical findings of this study are in line with the work of many researchers, who suggest that the ICT import-based SE contributing to reduce emission levels by reducing the aggregate demand for energy consumption, encourage resource competence, and technological development in ICT sector (Ahmad et al., 2016; Ulucak and Khan, 2020). The empirical results of all the panel models (FM-OLS I, II, and III) demonstrate the existence of big inconsistency between ICTexport and ICTimport (positive and negative) in the long run. Therefore, all countries from G-seven region should plan and formulate their policies related to ICT according to prevailing domestic economic situation. In this regard, G-seven region are considerately need to increase private-public awareness about environmental issues by to utilizing extra resources in their ICT sectors.

Regarding economic complexity (ECI), a 0.271% (FM-OLS I), 0.273% (FM-OLS), and 0.071% increase EcoFP is caused by ECI in G-seven economies for the long-run. This empirical results of ECI is in line with work of Khan et al., (2022) for G-seven region, Neagu and Teodoru (2019) for EU economies, and Pata (2021) for USA. According to them, ECI has positively affect the production greenhouse gasses emission, representing a haughty risk-interfering emissions which export product’s complexity adds to rising EcoFP. Therefore, to construct complex product’s exports, these economies work hard to produce premium and heavy products with misuse of environmental resources. Consequently, the prevailing technological system, and high aggregate demand of non-renewable energy consumption degrading environmental quality in investigated region.

Table 7
Estimations under the FM-OLS methodology
variables	FM-OLS I	FM-OLS II	FM-OLS III
lnICTExp	0.232 ***(0.024)	0.260*** (0.021)	0.091*** (0.011)
lnICTImp	-0.162***(0.046)	-0.172*** (0.038)	-0.079*** (0.018)
lnGDPpc	0.130***(0.035)	0.132*** (0.029)	0.193*** (0.013)
lnECI	0.271***(0.069)	0.273*** (0.057)	0.071*** (0.027)
lnFDI	-0.002 (0.004)	-0.005 (0.003)	-0.001 (0.002)
lnRD	-0.670*** (0.073)	-0.779*** (0.069)	-0.771*** (0.031)
lnTRA	-	0.160*** (0.042)	-0.092*** (0.010)
lnPOP	-	-	2.044*** (0.076)
Models’ statistics
R2	0.955199	0.956982	0.979361
Adjusted-R2	0.949599	0.951095	0.976287
*** (3 Asterisks) denotes 1% level of significance (the values in parenthesis represents standard error of the coefficient)

When it comes to economic growth, the results suggest that economic growth (lnGDPpc) is significantly contributes to environmental degradation with a of 0.130% (FM-OLS I), 0.132% (FM-OLS II), and 0.193% (FM-OLS III) increase in EcoFP caused by GDPpc growth or economic growth. The coefficients of GDPpc are statistically significant in all models (FM-OLS I, II, and III) as shown in Table 6. Likewise, GDP per capita is a best proxy for measuring economic of any country because it add up many components of the economy, such as consumptions (C) + investment (I) + Government expenditures (G) + net export (X-M). Best to authors’ once the level of income upsurge in many developed and developing countries, it is most likely, not only individual (households) and firms but government will increase spending (consuming), causing EcoFP to increase and therefore environmental pollution (Khan et al., 2022a).

Regarding expenditures on research and development (RD) suggest significantly negative effect on EcoFP in G-seven economies for the long run. In Table 6, on average, a 0.670% (FM-OLS I), 0.779% (FM-OLS II), and 0.771% (FM-OLS III) decrease in EcoFP is caused by expenditure on RD. particularly, research and development expenditure related to environment support policymakers and government of these countries to recognize environmental influencing factors that are unfriendly to environment. In addition, expenditures on RD can also help to find other substitutes which can be utilized to control the increasing demand of energy-intensive products. Furthermore, the government spending on RD help to classify the tools and machineries that the industries can install as substitutes to energy-intensive tools and machineries and use environmental-friendly energies to improve environmental sustainability (Safi et al., 2021). These empirical results are in line with work of Fernández et al. (2018) for EU, USA, and China, Nguyen et al. (2020) for 13 G-20 economies, and Zhang et al. (2015) for Chinese industries

Table 8

Estimations under PMG-ADRL methodology
Variables	PMG I	PMG II	PMG III
lnICTExp	0.582***(0.071)	0.694*** (0.043)	0.689*** (0.055)
lnICTImp	-0.250***(0.105)	-0.475*** (0.086)	-0.375*** (0.088)
lnGDPpc	0.166***(0.025)	0.227*** (0.030)	0.237*** (0.050)
lnECI	0.416***(0.128)	0.461*** (0.111)	0.221*** (0.064)
lnFDI	0.081* (0.043)	0.050** (0.001)	0.018 (0.002)
lnRD	-0.177*** (0.078)	-0.011*** (0.044)	-0.588*** (0.065)
lnTRA	-	0.068 (0.058)	0.188*** (0.028)
lnPOP	-	-	2.210*** (0.153)
Models’ statistics
Dep: lags	1 (fixed)	1 (fixed)	1 (fixed)
* (1 Asterisks) denotes 10%, (2 Asterisks) denotes 5%, and * (3 Asterisks) denotes 1% level of significance (the values in parenthesis represents standard error of the coefficient)

In turn of control variables, foreign direct investment (FDI), all models (FM-OLS I, II, and III) of Table 6, there is an statistically insignificant relationship between and FDI and EcoFP even at 10% level in the long-run. These empirical results are consistent with empirical work of (Khan and Yahong, 2021) for Pakistan. Likewise, the coefficient of trade ratio (lnTRA) is statistically insignificant in model 2 (FM-OLS II), while became significant in model 3 (FM-OLS III) with negative sign, indicating that 0.092% decrease in EcoFP caused by 1% increase in trade. Similarly, population has a direct association with EcoFP. More precisely, a 1% increase in population will leads to increase in EcoFP by 2.044% in G-seven economies for the long-run equilibrium. These findings are in line with empirical work of Khan and Yahong (2021) for Pakistan, Khan and Yahong (2022), and Khan et al. (2022b) for eighteen Asian emerging and less developed economies. It is also worth mentioning that this study applied the PMG-ARDL econometric technique to verify the validity and accuracy of our main models (FM-OLS I, II, and III) and check the predictability of all variables used in regression models. The empirical findings in Table 8, obtained from PMG-ARDL (PMG I, II, and III) are in line with the findings of FM-OLS models which verify the robustness of our results. PMG-ARDL methodology consider cross-sectional dependency between different cross sections (here countries) and heterogeneity through the parameter estimates for the short-run. In additions, this econometric technique also enables the long-run causality inferences and short-run causality inference which can be drawn irrespective whether the series (variables) are integrated with first difference or at level. In the existing environmental economics literature Mensah et al. (2018) for African countries, Banday and Aneja (2018) and Raheem et al. (2020) for G-seven countries used the similar application of PMG-ARDL.

In this study we critically analyzed existing papers in the environmental economics literature in which many researchers used carbon emissions as a proxy for environmental unsustainability, which cannot reflect the major parts of ecosystem. By taking this into account, in this study we analyze the linkages between information, communications and technology, economic complexity, and ecological footprint (along with other control variables such as; economic growth, FDI, research and development, trade ratio, and population) in G-seven countries. The G-seven countries (Canada, France, Germany, Italy, Japan, UK, and USA) are producing more emissions which put pressure on the environmental quality. Using different econometric methods such as panel unit root/stationarity tests (both first and second-generation), panel cointegration tests (Pedroni and Kao), and panel regression analysis (FM-OLS and PMG-ARDL), the empirical evidence obtained from this study concludes that ICT export increases the EcoFP in the long-run for G-seven economies while ICT import contribute to reduce EcoFP in these regions. Furthermore, the empirical findings conclude that economic complexity increases environmental sustainability and playing more detrimental role to increase EcoFP in all cross-sections. However, the elasticities of coefficients are divers for all regression models used in this study. In addition, economic growth or per capita GDP also playing harmful role by reducing environmental sustainability, whereas, FDI does not contributed to effect EcoFP in the long-run. As expected, research and development significantly reduce ecological destructions by spending on environmental related research and development programs. On the other hand, trade playing a vital role to reduce environmental pollution in the long-run; whereas population played a very destructive role to degrade environmental quality in G-seven region.

The specific policy implications and discovers can be summarized from the empirical finds of this study as follows: (I) the development of ICT import in the G-seven economies appears to help maintain environmental sustainability since the empirical results of our regression models have given a significantly positive of ICT import on EcoFP. This empirical evidence suggests that the governments and policymakers of these regions should implement policy to further encourage the penetration of ICT sector to maintain environmental sustainability. (II) the empirical results for economic complexity and economic growth, have a positively effect EcoFP, regardless the existence of EKC. This infers that industrial structure and economic activities are in selected G-seven economies are exploiting environmental sustainability. Therefore, the concern authorities should consider economic activities and complex structure of industries while implementing environmental sustainability policies. (III) Finance ministries of G-seven countries governments should increase fiscal budget for environmental related research and development by revising and reconsider the environmental protection policies to mitigate environmental unsustainability. (IV) G-seven countries should reevaluate the tariff policy that can further encourage environmental-friendly trade activities and discourage unproductive and destructive trade activities. Purposely, the role of institutions is significant which can enable policymakers to formulate productive policies, so that they can control environmental unsustainability in the future.

In order to strengthen the existing qualitative and quantitative information, the research gap can be highlight on the light of the following caveats and limitation. First, the current study emphases on the relationship between ICT, ECI, and EcoFP, therefore, it would be of more robust and interesting if future researchers inspect the relationship between ICT and EcoFP on a micro level (firms or industrial level). Specifically, analyzing the relationship between ICT and environmental sustainability in different firm and industries in the G-seven countries. Second, by using another econometric method (alternative methodologies) to estimate whether the empirical findings of this study are in line with the empirical examination within the homogenous (standardized) panel and country specific analysis. Finally, the current study also leaves the gap for future researchers to fill this gap by investigating other developed or emerging economies.

Data availability statement:

The material and data that supports the empirical findings of this study are accessible at: https://datacatalog.worldbank.org/dataset/world-developmentindicators

https://www.footprintnetwork.org/our-work/ecological-footprint/

https://atlas.cid.harvard.edu/rankings

Authors’ contribution:

Asif Khan: Performed Conceptualization, Methodology, Formal analysis, Investigation, Writing (original draft), and Software coding. Dr. Wu Ximei performed Validation, Software coding, Resource Review & editing, discussed the results and supervised manuscript status.

Funding

Not applicable

Declarations

Ethical approval: Not applicable

Consent to practice: Not applicable

Consent to publish: Not applicable

Competing interests

The authors declare that they have no competing interests

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Digital economy and environmental sustainability: How does ICT affect ecological footprint and what is the role of economic complexity?

Status:

Version 1

Abstract

Introduction

Review Of The Literature

Model Specification, Data And Method

Econometric Process

Panel Co-integration Approache

Empirical Results

Summary statistic

Cross-sectional Dependence Tests

(2 Asterisks) denotes 5% and * (3 Asterisks) denotes 1% level of significance

Panel unit root tests

Panel Cointegration Tests

(2 Asterisks) denotes 5%, and * (3 Asterisks) denotes 1% level of significance

Conclusion And Policy Recommendation

Declarations

References

Status:

Version 1

Digital economy and environmental sustainability: How does ICT affect ecological footprint and what is the role of economic complexity?

Status:

Version 1

Abstract

Introduction

Review Of The Literature

Model Specification, Data And Method

Econometric Process

Panel Co-integration Approache

Empirical Results

Summary statistic

Cross-sectional Dependence Tests

** (2 Asterisks) denotes 5% and *** (3 Asterisks) denotes 1% level of significance

Panel unit root tests

Panel Cointegration Tests

** (2 Asterisks) denotes 5%, and *** (3 Asterisks) denotes 1% level of significance

Conclusion And Policy Recommendation

Declarations

References

Status:

Version 1

(2 Asterisks) denotes 5% and * (3 Asterisks) denotes 1% level of significance

(2 Asterisks) denotes 5%, and * (3 Asterisks) denotes 1% level of significance