Investigating the Role of Information and Communication Technologies,Economic Growth,and Foreign Direct Investment in the Mitigation of Ecological Damages for Achieving Sustainable Development Goals

Abstract

At the present time, information and communication technology (ICT) has played a vital role in socio-economic development such as economic growth, literacy, life expectancy, and employment levels in societies, however, such development has come with various environmental damages perspectives. This study scrutinizes the impact of ICT, economic growth, and foreign direct investment (FDI) on carbon dioxide (CO₂) emissions in the 44 One Belt and Road Initiative (OBRI) countries split into sub-region from 1991 to 2019. This study applied various econometrics approaches such as cross-sectional dependence, second-generation unit root, and Westerlund panel cointegration techniques are executed to analyze the panel data set. The full modified ordinary lease square and dynamic ordinary lease square estimators are applied to investigate the long–term influence of ICT development, GDP (economic growth), and FDI on CO₂ emissions. The empirical analysis was performed at a disaggregated level to assess the possible environmental influences across the OBRI countries. Overall, the results reported that broadband and mobile development have an adverse effect on CO₂ emissions. The finding further reveals that the broadband indicator negatively affects CO₂ emissions in all OBRI regions except South Asia. Similarly, the mobile use indicator protects the environmental quality in all OBRI regions except MENA (Middle East and North Africa) and Central Asia. Regarding country-wise analysis, broadband has alleviated the pollution level in 21 countries, while mobile has alleviated it in 15 countries. Moreover, economic growth is responsible to increase pollution levels in all panels and regions except Europe. Besides, the results highlight that higher FDI reduces environmental pollution whereby, the pollution halo hypothesis is supported to hold for all OBRI panels and regions except MENA countries. Based on the empirical findings, the policymakers and governments of these economies should design policies to grow smarter cities, transportation systems, electrical grids, industrial processes, and energy-saving production through ICT development on a macro level.

Keywords

carbon dioxide emissions information and communication technology development foreign direct investment economic growth full modified ordinary lease square one belt and road initiative countries

Introduction

The significant impact of information and communication technology (ICT) on the socioeconomic development of society has been an extensively debated topic over the last couple of decades. In the current era of economic integration, a rapid increase in ICT sector development contributes to all divisions of an economy, however, its environmental penalties cannot be overlooked. Moreover, ICT is a vital indicator in the development of the industrialization process through a significant upsurge in the utilization of energy resources which negatively damages the environment (Dogan & Turkekul, 2016). In spite of being causative to gross domestic product (GDP), the quick alteration to the industrialization has carried environmental contamination and sternly influence the health and morals of human beings. It is the foundation for assessing climate variation, adapting and mitigating its impacts, and helping in the conversion to an eco-friendly nation (Zhang & Liu, 2015; Faisal et al., 2020; Mirza et al., 2020). Irrespective of the effective applications of ICT development in a country very slight devotion has been given to the upcoming environmental penalties of ICT for instance, the internet, mobile phones, satellites, etc. All these technologies play a vital part in handling the main challenges of Sustainable Development Goals (SDGs) regarding climate variation and global warming. The 17 United Nation Sustainable Development Goals (SDGs) seek to mobilize worldwide efforts around a common set of goals and targets and establish the priorities and aspirations for global sustainable development for the year 2030. Within the constraints of the planet, the SDGs encourage governments, businesses, and members of civil society all over the world to act to eradicate poverty, create a life of dignity and opportunity for all people, and ensure environmental sustainability (Mirza et al., 2020). Additionally, in the modern era of digitalization, it is important to recognize the carbon inferences of ICT while addressing the challenges of climate change (Jiang et al., 2022; Wang et al., 2015; Lu, 2018).

The linkages between environmental quality and ICT are very complicated (Danish, 2019; Asongu, 2018). A series of related literature postulates that ICT development affects environmental quality through three different channels. First, the “use effect” means that during the ICT equipment production, distribution, processing, and installation waste considerably contributes to environmental pollution. Likewise, hazardous ICT equipment and e-waste output, for instance, mobile data traffic use and large global data centers pose a risk to environmental quality (Ibrahim & Waziri, 2020). It is observed that approximately 2–3% of global carbon dioxide (CO₂) emission is generated by the production of ICT equipment (Danish, 2019). Second, the “substitution effect” depicts that ICT sector development influences the environmental quality through reorganization of the production process including, dematerialization, demobilization, decarbonization reducing outdoor activities, smart transport systems, quick traffic control structure using closed-circuit television (CCTV) cameras, replacement of physical goods with e-books, teleconferences, and post mail with email (Ozcan & Apergis, 2018; Park et al., 2018; Salahuddin et al., 2016; Usman & Radulescu, 2022). Likewise, online distribution and dematerialization, management and monitoring applications, travel and transport substitution, recycling, and product stewardship together diminish energy consumption and carbon dioxide emissions. Third, the “cost-effective” explores that ICT development increases demand other goods due to a reduction in prices consequently increasing environmental pollution. In the meantime, ICT development increase trade flow and other economic activities, enabling new communication networks and this may affect environmental quality. In this context, OBRI CO₂ emissions (metric tons per capita) increased from 476.5 to 497.4 from 1991 to 2019 (Figure 1).

Figure 1.

CO₂ emissions (metric tons per capita) in OBRI over the tested period.

It is observed that the impact of ICT sector development on environmental excellence is not only vague and indefinite but multifaceted in nature for both developed and developing nations is also unclear how the decisions about the implementation and deployment of ICT will affect the excellence of the environment which leads to distinction in the economic development sustainability. For that reason, exploring the impact of ICT sector development on environmental excellence is a hot issue that needs to be addressed on urgent bases. Additionally, global warming and climate variations have now a universal challenge that interrupts the economies and people’s health severely. Some other global environmental challenges should be addressed on early bases such as upgrading in energy efficiency, loss of natural habitat, waste management, biodiversity, water superiority, and CO₂ emissions. The fundamental objective of this current paper is to scrutinize the impacts of ICT development on environmental excellence from the viewpoint of belt and road initiative (OBRI) economies. It links ICT and environmental quality while considering GDP, and foreign direct investment (FDI) as control variables. Sketchily, this study will try to explore how the internet and mobile use are useful in tackling the environmental quality issue in the OBRI economies.

The importance of including FDI is that along with the quick upsurge in FDI and trade actions, the tendency of ICT sector growth has enlarged speedily across the world. So, there is a necessity to discover the answer to the question; does ICT recover or worsen environmental excellence? Moreover, there are several reasons behind the selection of OBRI economies for this study. First, these economies interact with each other based on FDI, GDP growth, and trade openness. The Belt and Road project is started by China for validating the association of trade among 60 economies beyond Europe, Africa, and Asia continents. Moreover, ICT plays a vital role in economic integrations from side to side of FDI and helps in promoting trade collaboration. Hence, ICT has a significant and constructive influence on communication channels for infrastructure development, trade transport, investment flows, global connectivity, and socio-economic partnership among companies, firms, people, and economies along Belt and Road corridors (Oliveira et al., 2020; Qiang et al., 2022). Second, the maintenance of infrastructure, operation, and large-scale construction, especially roads that consume a big amount of steel, cement, power plants, bridges, and dams, need a big volume of fossil energy utilization that leads to higher environmental pollution levels (Danish, 2019). As a result, abating environmental pollution is crucial and it is very complicated to protect environmental quality during the execution of the “Belt and Road” project. Other distinctive features of this study include the use of a large sample of OBRIcountries for the available dataset, the introduction of some important indicators related to economic development like GDP growth, and FDI, and the use of a full modified ordinary least-square (FMOLS), and dynamic ordinary least-square (DOLS) models for the empirical estimation.

The rest of the paper is planned as follows. The bellow sections provide the litrature review, data and empirical framework, econometric strategy, results and discussion, and finally concludes the study with suitable policy recommendations.

Literature Review

The nexus between ICT development and environmental quality has been documented in a plethora of studies. In order to explain the nexus between numerous variables of this study, the literature has been divided into different categories. There are a plethora of studies available in the literature which explores the association between ICT development, GDP, and environmental dilapidation. In this regard, many researchers applied different econometric approaches to discover the long-term and short-term nexus between these variables (Ibrahim & Waziri, 2020). According to Higón et al. (2017), GDP directs that an economy produces more products and services, mostly with the use of ICT, and economic growth leads to an increase in ICT goods and services which enhances the utilization of electricity and ultimately CO₂ emissions. Furthermore, Asongu (2018) examined the link between ICT, carbon emissions, and trade openness in the case of Africa. This study determined that ICT growth can help in lowering the possible negative impact of the globalization process on the environment. Another research conducted by Zhang and Liu (2015) revealed that ICT industries diminished environmental contamination in China for the period of 2000–2010. For OECD economies, Salahuddin et al. (2016) analyzed the influence of ICT development on environmental contamination using the pool mean group (PMG) approach. The conclusions approve the long-term association among the candidate variables. Also, this study postulated that GDP, financial development, and trade significantly diminish the environmental contamination in the panel of OECD nations. Similarly, Shahiduzzaman and Alam (2014) explored the nexuses between GDP growth, and ICT development and extend the empirical scrutiny with the association between GDP and non-ICT products for the Australian economy. The empirical evidence confirmed the causalities discover from ICT development to GDP growth and non-ICT products to GDP growth. Moreover, Binuyo (2015) found similar results and confirmed the positive impact of ICT development on GDP growth in the long run.

In addition, Batool et al., (2022) explored the linkages between ICT development and CO₂ emissions in selected developing countries of East and South Asia over the period from 1985 to 2020. The PMG estimator findings suggest that ICT and financial development (FD) positively contribute to the dilapidation of environment excellence in the long term, while their influence on environmental contamination is insignificant in the short term. Khan et al., (2022) conclusions recommend that ICT and effective governance are the crucial indicators to reduce environmental contamination. Danish (2019) described that the introduction of innovative industrial processes, transportation systems, smart cities, and energy-saving output on a universal scale is predictable to alleviate environmental contamination. Similarly, Lennerfors et al. (2015) claimed that ICT is a crucial factor for CO₂ emissions mitigation in many sectors of an economy like finance, industrial, transport, energy, and agriculture sectors. Instead of using workers on a large scale in different sectors, the usage of labor-saving machines can be supportive in improving environmental quality. Furthermore, Nchofoung & Asongu, 2022 noted that ICT sector development has a positive and significant effect on sustainable development in the case of a global sample. Moreover, (Zafar et al., 2022) conclusions recommend that ICT and financial development upsurge environmental excellence.

Some researchers divided the association between ICT and environmental excellence into two different strands. First-strand of the literature concluded that ICT development alleviates environmental pollution (Zhang & Liu, 2015; Wang et al., 2015; Lu, 2018). Consistent with this argument, Ozcan and Apergis (2018) analyzed the influence of internet use on environmental contamination and determined that the use of the internet plays a key role in the reduction of pollution levels in developing countries. Similarly, Al-Mulali et al., (2015) reported that online shopping (a proxy for ICT) has a positive inspiration on environmental excellence because internet usage lowers outdoor actions that decrease energy utilization. In contrast, the second strand of literature highlights the adverse influences of ICT development on environmental excellence. In this regard, Lee et al. (2016) described that ICT usage stimulates environmental contamination. However, Lee and Brahmasrene (2014) confirmed that ICT contributes to both CO₂ emissions and GDP growth in ASEAN economies. Similarly, Asongu (2018) studied the African nations and verified the positive association between ICT and environmental contamination. Likewise, in Europe, Park et al., (2018) confirmed that ICT contributes to environmental pollution through a significant increase in CO₂ emissions levels in the long run.

For the economic growth and environmental degradation relationship, it is observed in many studies that this relationship is nonlinear in nature. In this regard, a well-known theory Environmental Kuznets curve (EKC) developed by Grossman & Kruger (1995) exists in the literature. The EKC hypothesis postulates that initially, environmental quality effects are initially low at the lower economic growth levels. However, pollution increases as economic growth proceed in an economy. Further, at higher economic growth levels, economies are able through structural transformation to substitute towards agricultural and industrial technologies that are less damaging to the environmental quality. Literature reports that GDP growth affects the environmental quality through three different channels. First, income through “scale effect” depicts that the environmental damages, compromising technological change and economic structure. Second, the “composition effect” lower down the hazardous impacts of income through the structural change in a country. Third, the “technique effect” that shows the significant deficiency in pollution levels due to the implication of stringent environmental standards and adaptation of eco-friendly technologies. Danish (2019) described that the domination of technique and composition effect over scale effect leads to the establishment of an inverted U-shaped connection between environmental degradation and per capita income.

A seminal contribution is made by Dogan and Turkekul (2016) who examined the legitimacy of EKC for the US economy. Using data from 1960 to 2010, this study concluded that the findings are not supporting the inverted U-shaped relationship between economic growth and environmental damage. Additionally, this study established bidirectional causal linkages between GDP and environmental quality via using Granger causality. On the other hand, Dogan et al. (2017) study explored the linkages between GDP growth and CO₂ for OECD economies and confirmed the long-term association between understudy variables under the EKC framework. Massagony and Budiono (2022) study explored the linkages between GDP and CO₂ emissions for Indonesia’s economy and found that the EKC hypothesis is not valid for CO₂ emissions in Indonesia. However, the EKC is valid in the PIIGS region (Balsalobre-Lorente et al., 2022).

It is observed that there are still many deficiencies that prevail in the existing literature. First of all, earlier studies are mainly based on individually developed economies like Australia, the EU, the UK, and the USA or disaggregated developing economies using cross-sectional or time-series datasets. Other than the environmental quality challenges are faced by all economies together with developed and developing as well. For that reason, the authors need a panel data analysis as it can capture more accurate information and reduce estimation bias. Second, the majority of the earlier studies emphasized the influence of ICT development on electricity consumption or energy efficiency, and less emphasis on the direct impact of ICT sector development on environmental quality. Furthermore, most of the earlier studies have applied conventional/traditional regression techniques to analyze the impact of ICT development on environmental pollution. Therefore, the findings based on this study are vulnerable.

A review of previous literature points out that there is no clear consensus related to the impact of ICT development on environmental quality. Numerous researchers used various sets of variables and different kinds of econometrical models to explore the ICT-environment nexus. However, the authors observed that the study on the ICT-environment nexus is limited for developed and developing economies. In this pursuit, there is no serious attempt has been made yet with the perspective of countries along with the Belt and Road project. This study is an attempt to seal these gaps in the literature. The inconclusive relation between ICT and CO₂ emissions and no empirical study on Belt and Road economies motivated us to re-investigate this relationship with an effective set of series and appropriate econometrical techniques.

Data, Empirical Framework, and Methods

Data Specifications

The data employed in this study of 44 countries split into sub-region of OBRI economies over the time period from 1991 to 2019. The list of a selected panel of OBRI countries is given in Table 1. The authors extracted all data from World Bank and the definition of variables is given in Table 2. The mobile and internet users (per 100 people) are employed as a proxy for ICT. Data on OBRI economies are missing a few years, therefore, the missing data are generated by using the linear interpolation methods. On average, CO₂ emissions of OBRI countries are 322,562.1 Kilotons. The OBRI countries have broadband at 5.88% and mobile cellular at 57.96%. Broadband and mobile cellular have also a positive correlation with carbon emissions.

Table 1.

List of a Selected Panel of OBRI Countries.

South Asia	East and South East Asia	MENA	Europe	Central Asia
Sri Lanka	Singapore	Iraq	Azerbaijan	Kazakhstan
India	Mongolia	Saudi Arabia	Bulgaria	Uzbekistan
Bangladesh	Malaysia	Yemen, Rep.	Hungary	Tajikistan
Pakistan	Vietnam	Egypt, Arab Rep.	Slovak Republic	Kyrgyz Republic
	Korea, Rep.	Morocco	Ukraine
	Thailand	Iran, Islamic Rep.	Czech Republic
	Philippines	Jordan	Romania
	Indonesia	Israel	Latvia
	China	Tunisia	Estonia
	Russian Federation		Slovenia
			Georgia
			Lithuania
			Croatia
			Moldova
			Belarus
			Poland
			Armenia

OBRI = One belt and road initiative.

Table 2.

Description of Variables.

Variable	Symbol	Measure	Source
Carbon dioxide emissions	CO₂	Carbon dioxide emissions (Kilotons)	WDI (2021)
Fixed broadband	Broadband	Fixed broadband subscriptions (per 100 people)	WDI (2021)
Mobile cellular	Mobile	Mobile cellular subscriptions (per 100 people)	WDI (2021)
GDP per capita	GDP	GDP per capita (constant 2010 US$)	WDI (2021)
Foreign direct investment	FDI	Foreign direct investment, net inflows (% of GDP)	WDI (2021)

Empirical Framework

This study investigates the relationship between ICT development and environmental quality while, considering GDP growth, and FDI as control variables. CO₂ emission is a dependent variable, and fixed broadband and mobile cellular are independent variables for empirical analysis. However, GDP and FDI are used as control variables to avoid model specification bias. The empirical model specification of this study is followed by Asongu (2018) and Danish (2019). To capture the impact of ICT development on environmental quality, the econometric model of this study is presented as follows

{C O}_{2_{i t}} = β_{0} + β_{1} ({B r o a d b a n d}_{i t}) + β_{2} ({M o b i l e}_{i t}) + β_{3} ({G D P}_{i t}) + β_{4} ({F D I}_{i t}) + e_{i t}

(1)

where i and t indicate the country and period, μ denotes the error term, CO₂ shows carbon dioxide emissions, Mobile indicates mobile cellular and broadband is fixed broadband services (proxy of ICT development), GDP illustrates a gross domestic product, and FDI represents a foreign direct investment. The sign of coefficients,

β_{1}

and

β_{2}

are expected to be negative. It is detected that ICT indicators decrease the level of CO₂ emissions (Ozcan & Apergis, 2018; Danish, 2019). Also, both GDP and FDI have either negative or positive results on CO₂ emissions. So, the estimates of

β_{3}

and

β_{4}

would be either negative or positive, respectively.

Methods

Cross-Sectional Dependency

The possible cross-sectional dependency (CSD) across panel countries is an essential assumption considered in panel data models. The CSD emanates due to ICT development, GDP growth, and FDI, GLOB, economic integration, externalities (spatial or spillover), unknown common shocks, and undefined residual dependency. The presence of CSD support in the error term is acquired from the model analyzed by Pesaran (2007). The CSD equation is given as

C D = \sqrt{\frac{2 T}{N (N - 1)}} (\sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} {\hat{ρ}}_{i j})

(2)

Panel Unit Root Test

To investigate the panel data’s stationarity properties in the presence of CSD, a unit root test that can control for CSD can generate reliable results. The first-generation unit root tests lack this property, as these tests assume the panel cross-sections to be homogeneous and independent, leading to biased and misleading results (Chandio et al., 2022). The unit root tests of the second-generation kind are thus selected to capture the order of integration for the study variables. Pesaran (2007) recommended two tests, the cross-section augmented Dickey-Fuller (CADF) and the cross-section Im-Pesaran and Shin (CIPS) to check series stationarity. Based on the assumption of heterogeneity, both CADF and CIPS can generate more robust and consistent results in CSD presence. The equation for the CADF is written as

∆ y_{i t} = α_{i} + π_{i} y_{i, t - 1} + φ_{i} {\bar{y}}_{t - 1} + \sum_{l = 0}^{p} \emptyset_{i l} ∆ {\bar{y}}_{t - 1} + \sum_{l = 1}^{p} γ_{i l} ∆ {\bar{y}}_{i, t - 1} + \in_{i t}

(3)

In equation (3),

{\bar{y}}_{t - 1}

and

∆ {\bar{y}}_{t - 1}

is averages for lagged and 1^st difference of each series. From CADF, CIPS statistics are attained and given as

C I P S = \frac{1}{N} \sum_{i = 1}^{N} {C A D F}_{i}

(4)

where

{C A D F}_{i}

represents the “cross-sectional augmented dickey fuller test” and N is the number of observations.

Panel Cointegration Test

The panel cointegration tests are applied when the variables are not level stationery. Hence, the present study employs the panel cointegration technique of Westerlund (2007) to explore the variables’ long-run interactions based on error correction. There are no common factor limitations imposed by the four simple structural-based Westerlund panel cointegration tests and are robust against the panel’s CSD. The appearance of the Westerlund cointegration approach is as follows

α_{i} (L) ∆ y_{i t} = δ_{1 i} + δ_{2 i} t + α_{i} (y_{i, t - 1} - β_{1}^{'} x_{i t} + λ_{i} (L^{'}) v_{i t} + e_{i t})

(5)

In this equation (5),

β_{i}

is an error correction coefficient, and

α_{i}

is the vector of the cointegration association between x and y.

Long-Run Cointegration Test

This study uses FMOLS and DOLS estimation tactics to estimate the impact of ICT, GDP, and FDI on CO₂ emissions. The variables under consideration are primarily macroeconomic variables and they have a normal relationship between regressors and error terms. In this way, the ordinary least-square (OLS) approach gives unreliable and biased estimates. Regarding the regression model, when there is a certain degree of association between regressors and error terms, the FMOLS is a suitable method for empirical analysis (Faisal et al., 2020). Further, panel data has the problems of endogenous variables, which disturbs their decision and estimates. If these effects are ignored in the model, the omitted variables problem would arise and estimates would be inconsistent. To confirm the findings of the FMOLS method, Also, FMOLS and DOLS estimators have eliminated the possible effects of serial correlation in the error term and endogeneity, which can increase the accuracy of the panel econometric model. The mathematical procedure of FMOLS evaluations is stated in equation (6) as follows

{\hat{β}}_{G F M}^{*} = N^{- 1} \sum_{n = 1}^{N} {\hat{β}}_{F M, n}^{*}

(6)

where

{\hat{β}}_{G F M, n}^{*}

represents the coefficient of FMOLS test for all cross-sections n and the concerned t-statistic coefficient that can be expressed in equation (7) as follows

t_{{\hat{β}}_{G F M}^{*}} = N^{- 1 / 2} \sum_{n = 1}^{N} t_{{\hat{β}}_{F M, n}^{*}}

(7)

The descriptive statistics for the selected series are provided in Table 3. On the basis of the descriptive information statistics, the CO₂ emissions have the uppermost average value (24.19,275), at the same time as the CO₂ emissions have the lowest average value (7.53,965), and the mean value of CO₂ emissions is 11.03,077. The second-highest after Mobile in the model. Similarly, the mean value of broadband, mobile, GDP, and FDI are 5.88,048, 57.9609, 8.34,428, and 3.87,813, respectively. Table 3 presents the correlation analysis of all the selected variables CO₂ emissions, Broadband, Mobile, GDP, and FDI. Moreover, FDI is adversely interrelated with CO₂ emissions. Furthermore, the variance inflation factor (VIF) analysis discloses that there are no multicollinearity concerns in the data since the VIF figures for the independent variables.

Table 3.

OBRI Countries Descriptive Statistics and Correlation Matrix.

	CO₂	Broadband	Mobile	GDP	FDI
Descriptive statistics
Observations	1276	1276	1276	1276	1276
Mean	11.03077	5.88048	57.9609	8.34428	3.87813
Std. Dev.	1.68813	9.39234	55.1107	1.10933	5.8768
Minimum	7.53965	0.00001	0	5.90519	−41.508
Maximum	16.34418	42.76435	191.032	10.9865	55.0759
VIF		2.44	2.08	1.6	1.04
(1/VIF)		0.410449	0.48178	0.62444	0.96477
Correlation matrix
CO₂	1
Broadband	0.0525	1
Mobile	0.0976*	0.7106*	1
GDP	0.1790*	0.5970*	0.4917*	1
FDI	−0.1426*	0.0604*	0.1327*	0.1469*	1

Notes. *represents 1% level of significance. CO₂ = Carbon dioxide; FDI = Foreign direct investment.

Empirical Results and Discussion

Cross-Sectional Dependency Test

In order to examine the link between ICT and CO₂ emissions in the panel heterogeneous, it is important to recognize the CSD among the candidate variables. Therefore, this study evaluates the CSD issue by using the Pesaran (2007) CSD test. For this purpose, the outcomes of the CSD test outcomes are reported in Table 4. The CSD test statistics suggest that the null hypothesis of cross-section independence is rejected at a 1% significance level. Alternatively, this depicts a presence of CSD among the panel and sub-region of OBRI countries. These outcomes are also considered in the next phase of panel data estimation.

Table 4.

CSD Test Results.

Variables	CO₂	Broadband	Mobile	GDP	FDI
OBRI
CSD-stats.	5.823***	19.69***	30.85***	19.12***	27.78***
Off-diagonal	0.573	0.620	0.360	0.700	0.279
South Asia
CSD-stats.	0.235	1.108	6.517***	−3.242***	2.058***
Off-diagonal	0.468	0.480	0.262	0.537	0.172
East and South East Asia
CSD-stats.	−1.531	1.669*	3.430***	1.546	5.489
Off-diagonal	0.434	0.407	0.506	0.336	0.321
MENA
CSD-stats.	4.476***	7.300***	7.010***	−1.683	7.842***
Off-diagonal	0.373	0.511	0.435	0.555	0.298
Europe
CSD-stats.	15.77***	25.98***	13.78***	6.734***	8.923***
Off-diagonal	0.475	0.532	0.378	0.465	0.254
Central Asia
CSD-stats.	2.189**	−1.278*	5.098***	2.876***	1.986***
Off-diagonal	0.452	0.390	0.456	0.363	0.198

Note. Null hypothesis: no cross-sectional dependence, and ***p < .01, **p < .05, *p < .1 denote the level of significance. CSD = Cross-sectional dependency; OBRI = One belt and road initiative.

Panel Unit Root Tests

In order to get reliable and robust consequences, it is important to check the first level of stationarity of macroeconomic variables. In literature, numerous panel unit root tests are available, and these are separated into two groups. The first group of tests called the first-generation unit root tests can provide low stationary power because this test could not address the issue of CD. To tackle this issue, the second-generation stationarity tests are most effective by solving the CD issue in the panel data analysis. After confirming the significant CD, this study employs the second-generation panel unit root test to analyze the integration order. To do this, this study employed the CIPS and CADF unit root tests that allow heterogeneity and CD. The results of CIPS and CADF unit root are reported in Table 5. The results of the CIPS unit root test propose that Broadband, Mobile, and GDP are stationary at their first difference. This indicates that the order of integration for the Broadband, Mobile, and GDP variables is deliberated to be I(1). Although CO₂ emissions and FDI are stationary at the level, implies that the integration order of CO₂ emissions and FDI is considered to be I(0). Similar outcomes have been found in CADF stationary test. A detailed description of both test results for the panel and sub-regions of OBRI is reported in Table 5.

Table 5.

Panel Unit Root Statistics.

Variables	CIPS			CADF
Variables	Level	1^st Diff.	Decision	Level	1^st Diff.	Decision
OBRI Countries
CO₂	−1.588*		1 (1)	−1.924*		1 (0)
Broadband	0.376	−2.242**	1 (1)	2.550	7.824***	1 (0)
Mobile	−0.971	−2.618***	1 (1)	10.61	−9.033***	1 (0)
GDP	−1.584	−3.078***	1 (1)	4.124	−19.87***	1 (1)
FDI	−2.539***		1 (0)	−10.63***		1 (0)
South Asian countries
CO₂	−1.536	−3.868***	1 (1)	0.386	3.980***	1 (1)
Broadband	0.475	−2.284**	1 (1)	1.740	3.909**	1 (1)
Mobile	−0.110	−2.879***	1 (1)	1.380	5.987***	1 (1)
GDP	0.403	−2.912***	1 (1)	2.180	3.094***	1 (1)
FDI	−3.714***		1 (0)	−2.707**		1 (0)
East and South East Asian countries
CO₂	−1.958**		1 (1)	−3.092***		1 (0)
Broadband	0.136	−2.628***	1 (1)	4.498	−4.248***	1 (0)
Mobile	−1.088	−2.833***	1 (1)	4.498	−4.248***	1 (0)
GDP	−1.615	−2.971***	1 (1)	1.160	−8.426***	1 (1)
FDI	−2.531***		1 (0)	−4.445***		1 (0)
MENA countries
CO₂	−2.005**		1 (1)	0.231	−16.77***	1 (1)
Broadband	−0.481	−2.785***	1 (1)	4.457	−3.744***	1 (1)
Mobile	−0.991	−2.582***	1 (1)	4.707	−3.437***	1 (1)
GDP	−1.059	−3.570***	1 (1)	0.687	−11.33***	1 (1)
FDI	−2.497***		1 (0)	−4.604***		1 (0)
European countries
CO₂	−0.677	−3.916***	1 (1)	−1.115	−21.04***	1 (1)
Broadband	−1.075	−2.752***	1 (0)	0.341	−3.156***	1 (0)
Mobile	−1.076	−2.714***	1 (1)	4.822	−5.030***	1 (0)
GDP	−2.045***		1 (1)	1.849	−13.13***	1 (1)
FDI	−2.747***		1 (0)	−7.013***		1 (0)
Central Asian countries
CO₂	−1.703	−3.718***	1 (1)	0.130	−9.047***	1 (1)
Broadband	−1.767	−2.624***	1 (1)	3.307	−4.772***	1 (0)
Mobile	−1.337	−3.370***	1 (1)	4.279	−3.518***	1 (0)
GDP	2.654***		1 (1)	3.570	−4.875***	1 (1)
FDI	−3.017***		1 (0)	4.533***		1 (0)

CO₂ = Carbon dioxide; FDI = Foreign direct investment; OBRI = One belt and road initiative.

Panel Cointegration Test

Further, this study employs a second-generation panel cointegration test that has the ability to account for the CSD. In this regard, Westerlund (2007) developed a test for long-run cointegration which can handle the issue of CSD. Westerlund cointegration test is applied to create bootstrapped critical values at different levels of alpha. The results of OBRI regions suggest that the null hypothesis of no cointegration is rejected. This shows that CO₂ emissions, ICT, Broadband, Mobile, GDP, and FDI are strongly associated in the long run. The detailed results of the panel and sub-regions of OBRI countries are reported in Table 6.

Table 6.

Results of Westerlund Cointegration Test.

Regions	Gt	Ga	Pt	Pa
OBRI	−3.84***	−5.57	−22.6***	−11.7
South Asia	−3.74***	−8.05	−16.4***	−13.4
East and South East Asia	−3.74**	−5.58	−19.28***	−8.37
MENA	−5.68***	−3.69	−10.7***	−8.76
Europe	−3.66***	−6.66	−14.2***	−13.8
Central Asia	−4.16***	−2.71	−14.6***	−6.28

Note. Null hypothesis: no co-integration, level of significance is ***p < .01, **p < .05, *p < .1. OBRI = One belt and road initiative.

Regional Long-Run Elasticity/Coefficients

The evidence of panel cointegration permits us to regulate the long-run coefficients of the model variables. Therefore, this study employs the FMOLS and DOLS estimators, because it eliminates the problems of serial correlation in the error term and endogeneity. As shown in Table 7, the long-run result of the coefficients of broadband is negative and significant in OBRI and sub-regions of OBRI (East and South East Asia, MENA, Europe, and Central Asia). These findings are in concordance with the study of Al-Mulali et al. (2015) for developed countries, Ozcan and Apergis (2018) for emerging economies, and Li et al. (2022) for MINT economies. These economies used efficient resources through e-books, emails, electronic paper and which results in reduced overall CO₂ emissions. In these economies, individuals mostly rely on video conferences rather than traveling, however, they are working and staying at home instead of going to the workplace. They minimize pollution by ordering food and shopping online instead of cooking at home and minimizing outdoor activities, which can save energy consumed in vehicles and consequently, it diminishes carbon emissions. Although broadband has a positive impact on CO₂ emissions in South Asia according to both FMOLS and DOLS approaches, this implies that broadband users have a positive impact on CO₂ emissions and are not passively influenced by the internet. This also implies that the internet is not exerting a positive role in these countries which is polluting the environment. Excess use of broadband is a waste of resources and it cannot contribute to environmental quality. Broadband increases pollution by the usage of electricity consumption, although it decreases energy consumption. The eco-friendly ICT equipment is used very rarely for environmental quality.

Table 7.

Results of FMOLS and DOLS Estimates (Region-wise).

	OBRI		South Asia		East and South East Asia		MENA		Europe		Central Asia
	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS
Broadband	−0.21***	−0.14*	0.05***	0.53***	−0.02***	−0.11***	−0.03***	−1.49***	−0.01***	−0.01***	−2.32	−4.14***
Broadband	(2.93)	(1.73)	(6.19)	(7.62)	(6.83)	(6.95)	(6.92)	(17.1)	(7.52)	(19.3)	(1.29)	(11.7)
Mobile	−0.01	−0.01***	−0.02***	−0.01***	−0.02***	−0.01**	0.03***	0.01***	−0.02	−0.01***	0.01	0.01***
Mobile	(0.53)	(10.3)	(15.1)	(18.5)	(3.99)	(2.31)	(11.6)	(3.44)	(1.13)	(6.13)	(0.05)	(11.8)
GDP	0.71***	1.07***	1.98***	2.58***	1.28***	1.44***	0.96***	1.94***	−0.13*	−0.17***	0.65***	0.52***
GDP	(11.1)	(7.48)	(18.6)	(6.82)	(19.2)	(5.40)	(6.45)	(5.63)	(1.77)	(8.68)	(20.6)	(7.28)
FDI	−0.01***	−0.02***	−0.01***	−0.12	−0.01***	−0.02***	0.01*	0.05***	−0.01***	−0.01***	−0.01***	−0.05***
FDI	(5.69)	(9.42)	(9.97)	(0.93)	(8.41)	(7.08)	(1.67)	(10.3)	(6.39)	(4.21)	(4.88)	(5.08)

Note. t-statistics are in presented in parentheses, and ***p < 0.01, **p < 0.05, *p < 0.1. FMOLS = Full modified ordinary lease square; FDI = Foreign direct investment; OBRI = One belt and road initiative; DOLS = Dynamic ordinary lease square.

However, mobile cellular has a negative impact on CO₂ emissions in OBRI panel, South Asia, East and South East Asia, Europe, and Central Asia. This means that with the high usage of internet services along with mobile networks, these nations are producing energy-efficient ICT equipment, which furthermore improves energy utilization and eventually improves environmental excellence. This negative effect of mobile in this study is consistent with the findings of Lu (2018) for Asian countries. In this respect, mobile and internet have continued to improve the environment in the Asia region, because China, Japan, South Korea, and India are the new centers of eco-friendly manufacturing in the Asia region. In short, all ICT-related alterations in daily life outcome in less energy consumption and then very fewer carbon emissions. Although mobile has a positive influence on environmental contamination in the MENA region, it infers that mobile is not used in positive activities for the environment. They create environmental pollution through mobile information. Though, it can be understood that the coefficient of broadband is greater as compared to mobile, which specifies that the role of mobile in declining environmental excellence still needs public awareness in order to make it eco-friendly. Overall, FMOLS and DOLS have similar findings in direction but different in magnitude.

Additionally, GDP has an adverse impact on environmental contamination only in the Europe continent. This proposes that a 1% upsurge in GDP can lessen environmental contamination by 0.13% in FMOLS and 0.17% in DOLS. Although Salahuddin et al. (2016) also described the same conclusions for OECD nations. This outcome is similar to fast-emerging economies (Faisal et al., 2020) as it helps in producing more domestic output without hindering the environment. However, the domestic production of South Asia, East and South East Asia, MENA, and Central Asia are not environmentally clean, they produce more pollution for these economies. Similarly, the coefficient of FDI is a positive and statistically significant effect on environmental contamination only in the MENA region. The possible reason is that FDI leads to a further upsurge in energy utilization, which is the main reason for environmental contamination in MENA countries. This depicts that the pollution haven hypothesis prevails in this region. However, all other regions of OBRI have negative effects of FDI on pollution in South Asia, East and South East Asia, Europe, and Central Asia. This shows that the pollution halo hypothesis exists in these regions. Furthermore, the robustness of our empirical estimations has been verified by using the DOLS. The signs of Broadband, Mobile, GDP, and FDI remain robust in DOLS maintaining a similar level of significance.

Country-Wise Analysis

In order to determine a country-wise analysis, this study has found the coefficient estimates of the long run of each country in the OBRI countries. For this purpose, the FMOLS method has been employed. The long-run evaluations on environmental contamination regarding country-wise analysis are presented in Table 8. The described coefficient of broadband with respect to CO₂ emissions is negative and statistically significant in 21 economies like Bangladesh, Pakistan, China, Indonesia, Malaysia, Vietnam, Korea, Egypt, Morocco, Israel, Bulgaria, Croatia, Czech Republic, Hungary, Romania, Slovak Republic, Slovenia, Ukraine, Kazakhstan, Tajikistan, and Uzbekistan. The results of India, Sri Lanka, Mongolia, Iraq, Jordan, Saudi Arabia, Tunisia, Armenia, Azerbaijan, Belarus, Estonia, Georgia, Latvia, Lithuania, Poland, and the Kyrgyz Republic confirm the positive and significant impact of broadband on environmental contamination. The result is also in line with earlier studies such as Danish (2019).

Table 8.

FMOLS Estimates (Country-wise).

Country	Broadband		Mobile		GDP		FDI
Country	Coefficient	t-stats.	Coefficient	t-stats.	Coefficient	t-stats.	Coefficient	t-stats.
Bangladesh	−0.07***	7.22	−0.01***	8.15	2.48***	31.4	0.06**	2.33
India	0.33***	12.6	0.001***	7.70	0.83***	78.7	0.05***	22.2
Pakistan	−0.13*	1.84	0.003**	2.02	2.26***	14.0	−0.01	0.85
Sri Lanka	0.06***	9.39	−0.01***	16.5	2.03***	36.9	−0.05***	3.76
China	−0.01***	3.60	−0.01***	6.94	0.42***	8.03	0.05***	5.19
Mongolia	0.06***	8.69	0.002***	5.06	0.27**	2.57	0.02***	3.16
Indonesia	−0.12***	13.3	0.004***	3.69	1.92***	29.8	−0.04***	12.6
Malaysia	−0.04***	15.1	0.01***	21.3	0.93***	30.6	−0.01***	6.09
Philippines	−0.06	1.22	−0.004	1.19	1.82***	4.32	−0.01	0.48
Singapore	0.01	0.51	−0.01***	6.81	1.53***	7.09	−0.02***	6.22
Thailand	0.02	0.01	−0.005***	5.14	1.97***	18.9	0.02***	6.02
Vietnam	−0.04***	6.04	0.006***	7.04	2.03***	14.5	−0.01***	6.98
Korea, Rep.	−0.01***	5.53	−0.002***	8.48	1.32***	14.8	−0.01	0.73
Russia	0.03	0.83	0.001***	13.5	0.58***	25.9	−0.02***	7.87
Egypt	−0.01***	3.48	0.002***	4.50	1.92***	79.6	0.02***	5.90
Iran	0.01	0.77	0.003	0.02	1.87***	3.52	0.05	0.85
Iraq	0.01***	2.62	0.004***	4.73	0.09	1.03	−0.07***	5.90
Jordan	0.07***	11.2	0.005***	11.2	0.09	1.05	0.01***	7.01
Saudi Arabia	0.02***	9.14	0.003***	14.6	0.12	0.59	−0.01**	2.27
Yemen, Rep.	0.18	1.51	0.01***	4.12	1.11***	6.87	−0.01	1.64
Morocco	−0.01***	4.43	0.002***	4.71	1.55***	51.1	0.02***	2.58
Tunisia	0.02***	12.2	0.001***	5.54	1.04***	38.1	0.01	1.07
Israel	−0.01***	8.87	−0.001***	15.0	0.85***	15.4	0.03	0.43
Armenia	0.01***	3.17	−0.001**	2.55	0.31***	16.3	0.02**	2.30
Azerbaijan	0.02***	22.4	−0.01***	28.0	0.54***	15.4	−0.01***	20.1
Belarus	0.04*	1.81	0.002***	2.83	−0.14**	2.13	−0.02***	3.29
Bulgaria	−0.01***	4.16	0.003***	2.86	0.37	2.89	0.02	0.87
Croatia	−0.01***	34.1	0.002***	5.82	0.56**	2.22	0.01***	5.43
Czech Republic	−0.01***	10.8	0.02**	2.33	−0.09	1.40	−0.01***	5.27
Estonia	0.01***	15.6	0.03	1.10	−0.45***	7.09	0.02***	5.56
Georgia	0.01***	2.85	−0.04***	9.56	0.11***	2.81	−0.03***	16.4
Hungary	−0.01***	29.5	−0.05***	2.67	−0.04	1.34	0.02***	9.30
Latvia	0.04***	4.20	−0.002***	14.6	−0.74***	3.87	0.03***	13.4
Lithuania	0.01***	12.3	−0.03***	6.79	−0.51***	8.37	0.02***	11.8
Moldova	0.01	0.54	−0.01***	2.88	−1.64***	7.20	−0.06***	4.87
Poland	0.01***	17.1	0.04	0.99	−0.41***	7.13	0.01***	8.30
Romania	−0.02***	4.15	0.05	0.16	0.09	0.46	−0.01**	2.06
Slovak Republic	−0.01***	22.4	0.05***	8.99	−0.24***	11.7	0.04***	5.26
Slovenia	−0.01***	39.4	−0.07***	11.5	0.96***	11.6	−0.01***	11.9
Ukraine	−0.03***	16.3	0.001***	14.1	0.81***	6.68	−0.02***	7.82
Kazakhstan	−0.01***	3.70	0.03***	3.91	0.52***	11.7	−0.03***	14.7
Kyrgyz Republic	0.05***	8.35	0.04	0.61	0.94***	7.93	0.02	1.03
Tajikistan	−2.31***	4.85	0.05	1.36	0.95***	9.00	0.03	0.58
Uzbekistan	−0.01**	2.38	0.06***	4.76	0.19***	2.68	0.03***	3.33

Note. t-stats are in parentheses ***p < .01, **p < .05, *p < .1. FDI = Foreign direct investment.

However, on the other hand, mobile subscription has a negative impact on CO₂ emissions for Bangladesh, Sri Lanka, China, Singapore, Thailand, Azerbaijan, Korea, Israel, Armenia, Azerbaijan, Georgia, Hungary, Latvia, Lithuania, Moldova, and Slovenia. The coefficient estimate of mobile subscription with CO₂ emissions is positive for countries like India, Pakistan, Mongolia, Indonesia, Malaysia, Vietnam, Russia, Egypt, Iraq, Jordan, Saudi Arabia, Yemen, Morocco, Tunisia, Belarus, Bulgaria, Croatia, Czech Republic, Slovak Republic, Ukraine, Kazakhstan, and Uzbekistan. Regarding control variables, GDP has a positive significant effect on CO₂ emissions for 30 out of 45 economies, this suggests every economy still consumes large amounts of fossil fuel to accelerate a country’s economic growth, in adverse, it enhances the CO₂ emissions. This also indicates that GDP increases the economic size and dirty economic activities, which creates more pollution in an economy while GDP has a negative significant effect on carbon emissions in 7 economies. Similarly, a positive and significant impact of FDI on CO₂ emissions is observed in 19 economies and also proved the “Pollution haven hypothesis” but a negative impact of FDI on CO₂ emissions has been observed in 17 economies. These findings confirm the “Pollution halo hypothesis.” These economies allow only green FDI that is a more positive effect on environmental quality. Another channel of FDI is mainly improving the level of technological innovation by inducing social, human, and physical capital, thus affecting environmental pollution. The detailed empirical findings are reported in Table 8.

Conclusion and Policy Implications

This study examines the impact of ICT development, and CO₂ emissions in OBRI countries, over the period from 1991 to 2019. The CSD tests, CIPS, CADF unit root tests, Westerlund panel cointegration test, and the FMOLS, and DOLS are applied for empirical analysis. For robustness check, this study divides the OBRI countries' data into regional sub-samples. This study uses broadband and mobile as proxies of ICT development. The findings of this study show that broadband and mobile have a negative effect on carbon emissions in OBRI panel economies. Furthermore, broadband and mobile subscription negatively affect the carbon emissions in sub-regions of OBRI countries such as, East and South East Asia and Europe continents. Besides, broadband (mobile) has a positive (negative) effect on CO₂ emissions in MENA and Central Asia countries. Although the adverse result is found in South Asia, mobile subscription alleviates the level of carbon emissions but broadband enhances environmental pollution. Regarding country-wise analysis, broadband has a negative significant impact on carbon emissions in 21 countries while broadband has a positive significant impact on carbon emissions in 16 countries. However, mobile subscription has a negative impact on carbon emissions in 16 countries. The coefficient estimate of mobile with CO₂ emissions is positive for 22 countries.

The study results call for a suggestion for some crucial policies. Energy-saving ICT devices (i.e., mobile, broadband, and smart application) are necessarily required in OBRI countries to alleviate CO₂ emissions. Clean energy projects, with the assistance of ICT, authorities can decrease dependence on fossil fuel consumption in OBRI. The major findings suggest that ICT equipment utilization should be stimulated in all sectors of the economy. The ICT equipment that can be transferred in the course of FDI is beneficial. Moreover, OBRI countries can upgrade their industrial structure and increase green productivity through ICT development. The policymakers and governments of these economies should design policies to grow smarter cities, transportation systems, electrical grids, industrial processes, and energy-saving production through ICT development on a macro level. Government authorities and policymakers should inspire firms, individuals, and industries to move into the registration of brand names when they have been competent to invent climate alleviation processes, products, and services, as well as technology. The industrial sector would be stimulated by environmental-related technologies, and better production methods, among others which could earn more from the trademark. Moreover, the foreign trade strategies might be redesigned to accommodate revolutionizes in the energy strategies, and the foreign trade policies will be intended for increasing ecological excellence, such as the countries will be pitiful towards cleaner and green trade strategies. This calls for the bloc to make use of sustainable energy sources such as wind and solar. The study also recommends that the government sets an emission cap and strictly implements them and one exceed the cap, the government may put a heavy fine. Furthermore, the OBRI nations should also consider improving rural areas to avoid the urbanization problem that increases carbon emissions. All these initiatives can help the countries to speed up the achievement of SDGs.

One of the important limitations of this study is that this study could not find the composite (interaction) effect of globalization and ICT on environmental pollution. The authors of this study left this question for future research. In the future, a similar study can be designed to analyze a single-country analysis for the OBRI economies by performing the advanced nonlinear and dynamic ARDL time series methods.

Footnotes

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Hainan Provincial Social Science Project 2022 (HNSK (QN) 22-60).

ORCID iDs

Atif Jahanger

Muhammad Usman

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