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Effects of information and communication technology and real income on CO2 emissions: The experience of countries along Belt and Road
Journal Pre-proofs Effects of information and communication technology real income, and CO2 emissions: The experience of countries along Belt and Road Danish Khan PII: DOI: Reference:
S0736-5853(19)30792-0 https://doi.org/10.1016/j.tele.2019.101300 TELE 101300
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Telematics and Informatics
Received Date: Revised Date: Accepted Date:
2 August 2019 11 September 2019 4 October 2019
Please cite this article as: Khan, D., Effects of information and communication technology real income, and CO2 emissions: The experience of countries along Belt and Road, Telematics and Informatics (2019), doi: https://doi.org/ 10.1016/j.tele.2019.101300
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Effects of information and communication technology real income, and CO2 emissions: The experience of countries along Belt and Road Danish*[email protected], [email protected] School of Trade and Economics, Guangdong University of foreign studies Guangzhou, 510006 China *Corresponding Author.
Highlights The nexus between ICT and CO2 emission is analyzed along Belt and Road countries. The innovative methodological approach is introduced. The generelized least-square and pooled mean group methods are used. The moderating effect of international trade and FDI deepens the positive impact of ICT on CO2 emissions. Promoting the ICT sector through international trade and foreign direct investment (FDI) is suggested.
Abstract In recent years, information and communication technology (ICT) and its impact on society are debated; however, little research has been conducted regarding the future environmental consequences of ICT in various countries. However, this study presented the empirical analysis of the relationship between ICT, real income, and 1
CO2 emissions while considering foreign direct investment and international trade. The study presents an innovative methodological approach by introducing the interaction of ICT with foreign direct investment and international trade. For empirical estimation, this study used the generalized least-square method in 59 countries along Belt and Road from 1990 to 2016. The results summarized that ICT mitigates the level of CO2 emissions in countries along Belt and Road. Further, the moderating effect of ICT and foreign direct investment reduces CO2 emissions and the interaction between ICT and international trade does the same. Based on the policy perspective, the countries along Belt and Road need to strategically focus on promoting trade and investment in the ICT sector and also on innovations to promote sustainable economic development.
Keywords:
Information
and
communication
technology;
CO2
emissions;
Moderating effect; Environmental Kuznets Curve; Generalized least-square approach
1. Introduction Over the past decades, the impact of information and communication technology (ICT) on society has become a hot issue of debate. The growing importance of ICT would reshape the society, and it would also result in effects on tomorrow’s society. 2
ICT is crucial for industrialization, ultimately influencing economic growth and environmental quality (Danish et al., 2018b). Also, ICT contributes both directly and indirectly to economic, social, and environmental aspects of Sustainable Development Goals (SDG). ICT is the basis for monitoring climate change, mitigating and adapting its effects and assisting in the transition to a green, circular economy.1 Regardless of the positive role of ICT in economic growth (Hong, 2017; Latif et al., 2018), however, little attention has been given to the future environmental consequences of ICT, such as satellites, mobile phones, or the Internet. All these play an essential role in addressing the major challenges regarding climate change and sustainable development. Moreover, in today’s digital era, it is critical to understand the carbon implications of ICT while addressing climate change challenges (Zhou et al., 2019). The nexuses between ICT and the environment are complicated: ICT influences the environment via three channels. First comes the use effect, which means that during the production of ICT equipment, processing, distribution, and installation waste significantly contribute to CO2 emissions (Shabani et al., 2019; Shahnazi and Shabani, 2020) The use effect mainly increases energy consumption and CO2 emissions (Park et al., 2018). Also, e-waste production and harmful ICT equipment, such as large global data centers and mobile data traffic use, pose a threat to environmental quality (Lennerfors et al., 2015). The production of ICT equipment generates about 2%–3% of the world CO2 emissions (Røpke and Haunstrup, 2012). The second channel through which ICT influences the environment is known as substitution effect, which is defined as the reorganization of the production process, including dematerialization (Danish et al., 2018b), decarbonization (Ozcan and 1ICTs,
Environmental Sustainability and Climate Change, https://www.itu.int/en/action/climate/Pages/default.aspx 3
Apergis, 2017), demobilization (Salahuddin et al., 2016), replacing physical goods with e-books, post mail with e-mail, and tele-conferences (Park et al., 2018); smart transport system (Danish et al., 2018a), GPS, intelligent traffic control system using cameras, and reducing outdoor activities (Shabani et al., 2019).
Also,
dematerialization and online distribution, transport and travel substitution, monitoring and management applications, and product stewardship and recycling together reduce energy efficiency and reduce CO2 emissions (Danish et al., 2018b). Third is the cost effect in which ICT increases demand for other goods and services due to a decrease in prices and an increase in the return of CO2 emissions (Shabani et al., 2019). Meanwhile, the development in ICT boosts trade activities and flow, facilitating new communication channels (Ozcan, 2018) and this may influence the quality of the environment. Also, ICT goods transfer through foreign direct investment (FDI), such as secondary electronic goods, moves from high-income countries to low-income countries and may produce electronic waste (e-waste); this may harm the environment However, the knowledge transfer through FDI, communication among people produces an awareness among people that leads to a reduction of CO2 emissions (Danish et al., 2018c). From the earlier discussion, the environment impact of overall ICT is ambiguous; also, the complex nature of ICT is similar in both developed and developing countries. No one knows exactly what role ICT plays in the future, and our decision today will affect the direction of sustainable development. Thus, investigating the influence of ICT on CO2 has been an area needing further investigation. Also, nowadays both developing and developed countries need to counter many environmental challenges, such as climate changes, to improve energy efficiency, waste management, water quality, and scarcity; to address pollution and loss of 4
natural habitat and biodiversity (Danish and Wang, 2019a). Moreover, the trend of ICT development has increased rapidly in all regions of the world, with a rapid increase in trade activities and FDI. Thus, there is a need to identify whether the rapid growth in ICT translates into CO2 emissions due to the rapid process of globalization that expands trade activities and FDI. Since there is an attempt (Asongu, 2018a) to investigate the relationship between CO2 emissions, ICT, and openness, this study is similar but it is focused on Belt and Road countries. Henceforth, future analysis can be conducted to tailor current environmental policies to cater to the future relationship and economic activities in the Belt and Road countries. Ultimately, continuous use of ICT will determine whether the population could lead better lives with good environmental quality. Therefore, this study explores how ICT (mobile use and Internet use) is helpful in tackling the environmental challenges in Belt and Road countries. Nevertheless, there is no recent literature investigating the link between ICT and CO2 emissions in the context of Belt and Road countries. The motivation behind countries along the Belt and Road is due to several reasons. First, Belt and Road countries interact with each other with regard to trade and FDI. The Belt and Road project is initiated by China for endorsing the collaboration of trade among 60 countries and beyond in Asia, Africa, and Europe (Liu and Hao, 2018). ICT enhances trade activities and helps in promoting importing, which is less significant than promoting exporting. So, the ICT connectivity is critical to provide a basic communication channel for global connectivity, trade, transport, infrastructure development, and socioeconomic collaboration among people, organizations, and countries along Belt and Road corridors (United Nations, 2017). To this end, investment in infrastructure is needed to develop energy, 5
particularly in central Asian and Southeast Asian countries. Besides, the collaborative investment in energy projects and trade in the Belt and Road initiative combines a range of regional hotspot issues with major international issues, including global warming. Second, the large-scale construction, operation, and maintenance of infrastructure, especially roads that consume large amounts of cement and steel, dams, bridges, and power plants, require large amounts of fossil energy consumption, resulting in huge CO2 emissions (Fan et al., 2017; Zhao et al., 2016). Accordingly, the curbing of CO2 emissions is critical and it is very hard to improve the quality of the environment during the implementation of “the Belt and Road” project (Zhang et al., 2017). Three additional features of the study are as follows: First; we use a large sample of the Belt and Road countries for the longest available dataset but include recent data (1990–2016). Second, we use two additional variables, international trade and FDI in the nexuses of ICT and the environment. Further, we introduce the interaction between ICT and the FDI, and between ICT and international trade, as they may improve the quality of the environment through a reduction of CO2 emissions. Third, the study employs the developed generalized least-square (GLS) method to serve the objectives of this study. The econometric approach has not been used in the literature to investigate the study variables in Belt and Road countries. This article consists of six main sections. Section 1 is an introductory one; Section 2 provides a detailed literature review; Section 3 highlights the materials and methods; Section 4 analyzes and explains the results; the results are discussed in Section 5; and finally, Section 6 provides a conclusion and policy recommendations. 2. Literature review 2.1. Economic growth and pollution: the environmental Kuznets curve 6
Economic growth directs that a country generates more goods and services, particularly with ICT use (Añón Higón et al., 2017), and economic growth tends to increase for ICT goods and services, which increases electricity consumption and CO2 emissions (Wan Lee et al., 2014). However, the environmental Kuznets curve (EKC) hypothesis states that initially pollution worsens with per capita increase, but eventually more income decreases pollution (Dinda, 2004; Grossman and Krueger, 1995). Economic growth influences the environment via three channels. First, income through the scale effect harms the environment, compromising economic structure and technological change (Danish et al., 2019a). Second, the composition effect minimizes the harmful effects of income through structural changes in the economy. Third, the effect reduces pollution due to adaptation of environmental friendly technologies and the implication of stringent environmental standards; this is known as the technique effect (Destek and Sarkodie, 2019). The domination of composition and technique effect over scale effect lead to the formation of an inverted U-shape relationship between per capita income and pollution. The EKC hypothesis was largely investigated; some scholars confirmed the existence of EKC (Danish et al., 2019b; Dehghan Shabani and Shahnazi, 2019; Pata, 2018), whereas some studies have rejected its presence (Amri, 2018; Belloumi et al., 2017).
2.2. ICT and environment The environmental impact of ICT has remained a hot issue of debate for the past one decade. The introduction of smart cities, transportation system, industrial process, and energy-saving gains on a global scale are expected to mitigate the level 7
of CO2 emissions (Lennerfors et al., 2015). ICT in various sectors of the economy, such as agriculture, power, energy, transport, agriculture, and the financial sector, also reduces CO2 emissions. Further, instead of employing labor at a large scale and mechanization in different sectors, the use of the labor-saving machine can be helpful in reducing CO2 emissions (Sadorsky, 2012; Salahuddin and Alam, 2015). The literature on the relationship between ICT and CO2 emissions is divided into two strands. In the first strand, the authors have concluded that ICT mitigates the level of CO2 emissions (Haseeb et al., 2019; Lu, 2018; Wang et al., 2015; Zhang and Liu, 2015). Moreover, Ozcan and Apergis (2017a) estimate the effect of Internet use on CO2 emissions in emerging countries. The results of a certain study recommend that Internet usage reduces the likelihood of pollution. In addition, Al-Mulali et al. (2015a) conducted a study regarding the impact of online shopping on CO2 emissions in both developing and developed countries. The empirical findings suggest that Internet usage reduces outdoor activities, which reduce energy consumption and CO2 emissions in developed countries. However, online shopping is ineffective in developing countries to provide the reason for the sluggish speed of the Internet. Recently, both Añón Higón et al., (2017) and (Shahnazi and Shabani, 2020) have discussed the inverted U-shaped relationship between ICT and CO2 emissions on a global scale and for Iran, respectively. They found that in the early stage of development of ICT, it worsens environmental quality; however, pollution is reduced with more development in the ICT sector. Meanwhile, the second strand of research dealt with the adverse impacts of that ICT on the environment (Lee et al., 2016). The ICT use stimulates the CO2 emissions (Salahuddin et al., 2016). Also, Lee and Brahmasrene (2014) prove that ICT contributes to both economic growth and CO2 emissions in ASEAN countries. 8
Also, Asongu (2018) employs the generalized methods of moments (GMM) model to examine the effect of ICT and openness on CO2 emissions in African countries. The empirical evidence argues that ICT diminishes the negative impact of globalization on CO2 emissions. Park et al. (2018) estimated the effect of ICT on CO2 emissions for European Union (EU) countries. The results of the pooled mean grouped (PMG) method confirm that ICT contributes to CO2 emissions. Recently, Danish et al. (2018a) examined the relationship between ICT and CO2 emissions by using a novel panel estimation approach for emerging countries. The innovative contribution of the study offers that the use of ICT along with a growing income is helpful in mitigating the level of pollution. However, Dehghan Shabani and Shahnazi (2019) found heterogenous effects of ICT on CO2 emissions in various economic sectors of Iran. But Zhou et al. (2019) found the negative role of ICT in pollution in a sectoral study conducted in China. In the discussion just referred to, no consensus regarding the role of ICT in the environment is developed. The relationship between ICT and CO2 emissions is unclear. Also, we have observed that the relationship between ICT and CO2 emissions is studied for both single countries and panels of countries but ignored in countries along Belt and Road. The results drawn regarding ICT–CO2 emissions nexus are inconclusive. Thus, there is a need to further investigate the relationship between ICT and CO2 emissions. Therefore, this study aims at identifying the key role of ICT in the CO2 emissions and at considering trade and FDI in the context of the Belt and Road initiative BRI countries. From the aforementioned studies, the authors know fully well that no prevailing study investigates the effect of ICT on CO2 emissions within the framework of the EKC hypothesis for Belt and Road countries, adding to the effect of international trade and FDI. Also, apart from other 9
studies, we added the interaction of trade ration and FDI with ICT (mobile and Internet penetration) that was ignored in the existing studies. These two are the reasons that mainly contribute to the literature. 3. Materials and Methods 3.1. Empirical Framework This empirical work links ICT, real income, and CO2 emissions while considering international trade and FDI. A CO2 emission is a dependent variable, and real income (Y) and ICT are independent variables. However, international trade and FDI are used as potential control variables to avoid specification bias. Consequently, the empirical specification in this study is developed consistently with recent ICT and CO2 literature and additional explanatory variables (Asongu et al., 2018). To capture the influence of ICT on CO2 emissions, the econometric model (Eq:1) is estimated in this study and is presented as follows:
Log (CO2 )it it 1 Log (Yit ) 2 Log (Yit 2 ) 3 Log ( ICTit ) 4 Log (TRit )
5 Log ( FDI it ) 0
(1)
where i and t indicate country and time, μ is stochastic error term, CO2 is carbon dioxide emissions per capita, Y is real income, ICT stands for information and telecommunications, TR is traded ration, and FDI represents a foreign direct investment. The sign of coefficient β1 is expected to be positive, and β2 is negative. In this case, β1>0 and β2<0 are an indication for the presence of the well-known EKC hypothesis. We have suggested a square model (model 1) that confronts CO2 emissions with income, including control variables, to confirm that the relationship between income and CO2 emissions is an inverted U-shaped pattern. The sign of β3 is unexpected, and either ICT has an adverse impact or it is beneficial for the environment. Several control variables are included in the model to 10
avoid specification bias. These included control variables spontaneously influence environmental quality. In the literature, it is observed that economic growth increases the level of CO2 emissions that pollute the environment (see (Danish et al., 2018c, 2017; Ozcan and Apergis, 2017)). Also, both FDI and trade have either a positive or negative effect on CO2 emissions. So, the coefficient of β4 and β5 would be either positive or negative. Eq: (2) is an extension of Eq: (1); in addition to the additional explanatory variable, the interaction between ICT and FDI (ICT*FDI) is introduced into the model. The interaction between ICT and FDI (ICT*FDI)it is included in the function of CO2 emissions to investigate whether rising FDI leads toward a high usage of information and telecommunication in various sectors to expand economic activities. The advancement of ICT with growing FDI may affect the environment either positively or negatively. A generalized least-square method (GLS) model was used during the period 1990–2016: Log (CO2 )it it 1 Log (Yit ) 2 Log (Yit 2 ) 3 Log ( ICTit ) 4 Log (TRit )
5 Log ( FDI it ) 6 ( ICTxFDI )it it
(2)
where (CO2)it emission is a measure of environmental degradation in a million tons per capita in the country i in year t. (ICT*FDI) is the interaction between ICT and FDI for country i and time t. Similarly, an increase in trade activities leads to more usage of ICT goods that may affect the environment. Log (CO2 )it it 1 Log (Yit ) 2 Log (Yit 2 ) 3 Log ( ICTit ) 4 Log (TRit )
5 Log ( FDI it ) 6 Log ( ICTxTR)it it
(3)
(ICT*TR)it is the interaction between ICT and trade for country i and time t. The interaction between ICT and trade (ICT*TR)it may transfer clean technology, and the advanced use of technology may increase energy efficiency that could be helpful 11
in reducing CO2 emissions. Also, ICT equipment’s transfer trade, production, and consumption of ICT equipment and electronic waste may increase the rate of CO2 emissions. That explains the impact of trade and FDI that bring in new ICT and the moderating effect on CO2 emissions. Based on the earlier discussion, adding control variables is motivated by the perception of the literature on CO2 emissions. 3.2. Econometric method This empirical study employs a generalized least-square (GLS) model for empirical estimation consistently with recent literature (Alvarez-Herranz et al., 2017). The GLS approach is used for estimation of the parameter in a regression model. The variables under consideration are mainly economic variables; therefore, there exists the possibility that correlation may exist between regressors and error terms. In this case, the ordinary least-square method results in biased and unreliable estimates. In the case of a regression model when there is a certain degree of correlation between regressors and error terms, the GLS is the most suitable tool for empirical investigation (Alvarez-Herranz et al., 2017). Further, the empirical work seeks to find the existence of specific individual effects of each country, which disturbs their decision. If these effects are ignored, the problem of omitted variables would arise and estimates will be inconsistent. Thus, the GLS approach is preferred for model estimation; it relaxes the assumption of auto-correlation and crosssectional heteroskedasticity. To validate the findings of the GLS method, we further employed a pooled mean group (PMG) estimator consistent with the recent literature (Danish and Wang, 2019b). This PMG approach allows dependencies across countries, suggesting that disturbances are independently distributed across units and over time with mean zero 12
and constant variances. Also, PMG is independent from the assumption of stationarity; therefore, an investigation of stationary level is necessary but the study variables must be cointegrated. 3.3. Data The analysis of this empirical work is based on a panel of 59 Belt and Road countries using annual time series data from 1990 to 2016. Data availability dictates the choice of both data and countries. The measurement of ICT variables is characterized into two methods: nonmonetary and monetary. Nonmonetary variables include fixed telephones and mobile telephones, as well as Internet broadband subscribers. Monetary variables are ICT investment or ICT capital stock (Shabani and Shahnazi, 2019). The limitation of monetary ICT variables is un-availability of data; therefore, in this study, nonmonetary ICT variables are selected. The Internet users and mobile users (per 100 people) are used as a proxy for ICT. The data for Internet users and mobile users is collected from the world development indicator (WDI); a database of the World Bank real income is calculated as real GDP per capita (constant 2010 USD). CO2 emission is taken as metric tons per capita. Trade ration is calculated as the sum of import (% of GDP) and export (% of GDP). Finally, FDI is measured as the inflow of foreign investment (% of GDP). The data for all the variables is collected from WDI. It is worth mentioning that data are optimized in that we apply the longest available dataset. Also, variables of this study are converted into a logarithmic form, so the reported coefficients are equivalent to elasticities of CO2 emissions regarding Y, the square of Y, ICT, trade, and FDI. The descriptive statistic and correlation are presented in Table 1A. 4. Empirical Results and Analysis
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The EKC presence is a long-run phenomenon that has a lot of demand for checking the unit root properties and cointegration of study variables as well to estimate the function of polynomial carbon emissions. In this situation, empirical investigation is more fruitful for policymakers in designing comprehensive environmental policies to combat climate change issues and for maintaining sustainable economic development. To this end, this study has applied the CIPS panel unit root test by Pesaran (2007), which is helpful in countering the issue of cross-sectional dependence. Since 59 Belt and Road countries are included in our sample, there is a probability of dependencies across countries and the results of CIPS are shown in Table 1. It should be noted that the variables under investigation are stationary after first difference, revealing that the order of integration is one I (1). As the integration order of study variables is one, it further calls for an investigation of cointegration among the variables under consideration in the study. We need to keep in mind the issue of cross-sectional dependence and heterogeneity that the study uses (Westerlund, 2007) to counter these issues, and the results are illustrated in Table 2. The Westerlund cointegration confirms that integration exists among CO2 emissions and their determinants. The integration order one and the confirmation of cointegration among study variables motivate us to estimate the relationship between CO2 emissions and their determinants. The suggested empirical model is estimated through a GLS specification while considering the existence of cross-section heteroskedasticity and autocorrelation. We estimate Equ. (1) to confirm the environmental Kuznets curve and Equ : (2) and Equ : (3) of the main model to capture the effect of the interaction terms between ICT and FDI (ICT*FDI) and the effect of the interaction terms between ICT and trade (ICT*TR) on CO2 emissions, respectively. There are two main sets of 14
specifications about the CO2 emissions that serve as the dependent variables. Each sub-specification is characterized by a number of mobile users (per 100 users) and a number of Internet users (per 100 users). The reported results in Table 3 with the specification of mobile use, the net effect of GDP, and the square of GDP on carbon emissions are positive and negative, respectively, in the three models (4th, 5th, and 6th columns). The corresponding negative marginal effect of GDP infers that the Belt and Road countries support the EKC hypothesis. The results respond to a confirmation of the inverted U-shape relationship corresponding to economic growth and CO2 emissions. Similar findings are produced for the specification of Internet use (1st, 2nd, and 3rd columns). The impact of the Internet user on CO2 emissions is also negative, which suggests that an increase in Internet users mitigate the level of CO2 emissions in Belt and Road countries. A similar result is inferred from both the specifications of ICT. Regarding the environmental impact of ICT by the specification, an increase in the number of mobile phone users reduces CO2 emissions; whereas the influence of Internet users on CO2 emissions is also negative, which suggests that an increase in Internet users mitigates the level of CO2 emissions in Belt and Road countries. A similar result is inferred from both the specifications of ICT. Similarly, the environmental impact of trade is insignificant in the specifications of both Internet use and mobile use. This postulates that trade does not affect environmental quality in Belt and Road countries. On the other hand, the environmental impact of FDI is negative but significant. Besides, another novel contribution is that the interaction between ICT and FDI (ICT*FDI) has a significant but negative impact on CO2 emissions. A similar result is found for the interaction effect of ICT and international trade (TR). 15
The results of PMG are reported in Table 4. The results from both PMG are strongly in line with those of GLS, which confirms the reliability and robustness of our empirical findings. The empirical results of the study are reliable and well established; thus, they can be used for policy purposes.
Further, to see the behavior of the standardized residual of CO2 emissions, we draw the residual plot (see Figure 1). For performing generalized least-square regression analyses, it is imperative to check the residual plots to validate the model. From the residual plot, it can be inferred that the residual is equally spread along a horizontal line without distinct patterns, which is an indication of linear relationships among analyzed variables. Therefore, the current model is the best way to understand the nature of data, and the plots show the goodness of fit of the model.
5. Discussion The positive and negative behavior of real income (Y) and square of income (Y2) is confirmed in the study. The corresponding negative marginal effect of GDP infers that the Belt and Road countries support the EKC hypothesis. The results correspond to a confirmation of the inverted U-shape relationship with economic growth and CO2 emissions. Similar findings are produced for the specification of Internet use (4th, 5th, and 6th columns). Regarding the role of ICT in CO2 emissions, the result found a positive role of ICT in the mitigation of CO2 emissions. State of the art agrees that the use of ICT 16
improves quality of the environment in Belt and Road countries. This implies that ICT in Belt and Road countries helps mitigate the level of CO2 emissions. This is corroborated with contributions to measuring, monitoring and managing, and enabling more efficient resource utilization and operation of infrastructure through dematerialization (Houghton, 2010); for example, from books to the e-book, paper mail to email, and newspaper to electronic paper, thus minimizing waste. Also, teleand video conference rather than traveling, staying and working at home instead of going to the workplace, ordering food online instead of cooking at home, and online shopping minimize outdoor activities, which can save fossil fuel consumed in vehicles and result in fewer carbon emissions. The intelligent transport system logistics and freight rationalization, smart buildings, and home automation are the effects that can help in improving energy efficiency, consequently mitigating the level of pollution. Another mechanism that contributes to the productive role of ICT is that the induction of ICT contributes to energy saving in some areas of life, which surpasses ICT-induced additional energy consumption in other areas. For example, the use of smaller ICT devices, laptops, smartphones, and others are energy efficient. It seems that the world is taking advantage of the technology spillover effect due to significant progress in innovation for the past three decades. Traditional industries switch to higher energy efficiency and low carbon economy due to ICT use. This can be further interpreted that ICT use in Belt and Road countries positively drives the quality of the environment and we should continue to use the policies that promote the use of ICT, as this will ensure sustainable economic development. The negative coefficient estimate of ICT is in line with (Ozcan and Apergis, 2017) for emerging economies and consistent with (Al-Mulali et al., 2015b) for developed countries. The results are also inconsistent with (Wan Lee and Brahmasrene, 2014) 17
for a panel of ASEAN countries and with (Salahuddin et al., 2016) for OECD countries. Meanwhile, the imapct of FDI on CO2 emissions is insignificant. However, the impact of international trade on CO2 emissions is both positive and significant, suggesting that it contributes to pollution at the international level. However, the innovative contribution of the study, the interaction between FDI and ICT, has a negative but significant impact on CO2 emissions in specifications of both Internet and mobile phone. A similar result is found for the moderating effect of trade between ICT and CO2 emissions. This means that both FDI and international trade deepen the positive role of ICT in CO2 emissions. This can be attributed to the fact that ICT equipment transfer through trade does not disrupt the environment but reduces CO2 emissions. Also, the process of production of ICT equipment is not energy intensive, and the import of ICT goods is not harmful to the environment. However, keeping in mind the future environmental consequences of ICT through FDI, Belt and Road countries should enforce high environmental standards that restrict the import of dirty goods and polluting technology. It makes sense, because Belt and Road countries, in particular, have made good progress in the inventing and use of new technologies, and the analyzed countries seem to take full advantage of technology spillover through trade and FDI. Another mechanism is that the Belt and Road countries are likely to produce exports and import nonenergy intensive and environmentally friendly goods. 6. Conclusion This work investigates the link between ICT, energy consumption, economic growth, and CO2 emissions while considering the role of international trade and FDI for a panel of 59 selected BRI countries from 1990 to 2016. The secondary objective 18
of the study is to test the EKC hypothesis on the significance of ICT. The generalized least-square method is applied. For robustness check, we divide the panel data into two panels. The empirical finding of the study suggests interesting findings. The findings infer that ICT use mitigates carbon emission level in the Belt and Road countries. Moreover, the novel contribution of the study, the interaction between ICT and trade, does not affect CO2 emissions in Belt and Road countries. Besides, the FDI mitigates the level of CO2 emissions. Also, EKC holds that the significance of ICT is verified in the Belt and Road countries. Prior studies mainly focused on the link between ICT and CO2 emissions that incorporate trade and FDI. However, those studies ignore the effect of interaction of FDI and international trade with ICT on CO2 emissions, which is mentioned in this study. The finding suggests that the moderating effect of FDI and international trade deepens the positive of role of ICT in CO2 emissions. The study results call for a suggestion for a crucial policy. The energy-efficient ICT devices (i.e., smart application) and other key inventions related to Internet use are required in Belt and Road countries to control CO2 emissions. Green energy projects, with the help of ICT, can reduce dependency on fossil fuel consumption. The main practical implication related to ICT use should be encouraged in every sector. However, the globalization of economies also results in environmental damage, so in the short-term sample countries need to strengthen their environmental standards and apply high dumping duties to polluted goods. The ICT equipment transfers through FDI and international trade are beneficial. Therefore, we urge the government of these countries to continue with investment regulations to enjoy the fruit of sustainable development through ICT.
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The current study also has some important limitations. At present, more robust and advanced econometric tools are applied to get reliable and unbiased estimates. In future, studies can be augmented to analyze a single-country analysis for the BRI economies using time series estimation tools. Further, checking the environmental impact of the index of all ICT measures is another possible avenue for future research. Also, the diffusion of ICT and its influence on the environment is another interesting area of future research.
Appendix Table 1A. Descriptive statistic and correlation matrix LOGCO2 LOGGDP LOGFDI LOGTR ICT internet ICT mobile Descriptive Statistic Mean 0.512044 3.655991 0.286011 1.876713 24.35320 2.384553 Median 0.638316 3.670707 0.407833 1.914059 15.44000 2.496641 Maximum 1.845939 4.861361 1.740962 2.645033 93.47830 15.20501 Minimum -1.470631 2.213845 -4.591014 -1.677797 0.000000 0.000000 Std. Dev. 0.605709 0.579549 0.658732 0.342749 24.70409 1.898877 Observations 1593 1593 1593 1593 1593 1593 Correlation matrix LOGCO2 1 LOGGDP 0.879892 1 LOGFDI 0.071675 0.059992 1 LOGTR 0.288458 0.295528 0.275258 1 ICT internet 0.340540 0.447461 0.059412 0.217221 1 ICT mobile 0.211274 0.260398 0.170470 0.160849 0.543995 1 Note: CO2 =carbon dioxide emissions; GDP =Gross domestic product proxy for economic growth; FDI= foreign direct investment; TR= trade ratio; ICT = Information and communications technology
20
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Figure 1. Graph of ICT (Internet use) for the standardized residual of dependent variable CO2
Table 1. CIPS panel unit root tests result Log (CO2) Log (Y) Log (Y2) Log (FDI) Log (TR) CIPS (at level) -1.979 -2.364 -2.366 -3.817 -2.392 CIPS (at first difference -4.483 -4.212 -4.108 -5.736 -4.839 Note: the critical values of CIPS are: ( -2.58) 10% ; (-2.65) 5% ; (-2.78) 1%
25
Log (ICT InterNet)
Log (ICT mobile)
-2.745 -4.690
-3.009 -4.388
Table 2. Results of Westerlund panel cointegration tests ICT InterNet use Statistic Value Z-value P-value Gt -3.894 -10.058 0.000 Ga -0.550 13.442 1.000 Pt -16.734 1.382 0.017 Pa -6.151 4.761 1.000 Note: *&*** refers to 1% and 10% respectively
Value -2.474 -3.733 -23.33 -3.472
ICTmobile use Z-value P-value 1.386 0.091 10.475 1.000 -4.721 0.000 7.230 1.000
Gt, Ga : Group statistic; Pt, Pa = Panel statistic
Table 3. Results of generalized least square model with correction of heteroscedasticity 1990-2016
Variables Constant Log (Y) Log (Y2) Log (FDI) Log (TR) Log (ICT InterNet)
ICT with InterNet penetration Model (1) (Model 2) Coefficient Coefficient [prob] [prob] a -6.228 -6.235 a [0.000] [0.000] 2.756 a 2.748 a [0.000] [0.000] -0.252 a -0.251 a [0.000] [0.000] -0.013 0.028 [0.223] [0.753] 0.080 a 0.084 a [0.000] [0.000] -0.097 a -0.064 b [0.002] [0.044]
(Model 3) Coefficient [prob] -6.209 a [0.000] 2.670 a [0.000] 0.240 a [0.000] -0.003 [0.737] 0.144 a [0.000] -0.071 a [0.000]
ICT with mobile use penetration Model (1) (Model 2) (Model 3) Coefficient Coefficient Coefficient [prob] [prob] [prob] a a -6.219 -6.154 -6.305 a [0.000] [0.000] [0.000] 2.804 a 2.689 a 2.802 a [0.000] [0.000] [0.000] -0.261 a -0.241 a -0.259 a [0.000] [0.000] [0.000] -0.012 -0.012 0.003 [0.267] [0.253] [0.786] 0.076 a 0.081 a 0.801 a [0.000] [0.000] [0.000] --
--
--
Log (ICT mobile)
--
--
--
-0.053 a [0.005]
-0.017 a [0.002]
-0.040 a [0.003]
Log (ICT InterNet x FDI)
--
-0.015 a [0.000]
--
--
--
--
Log (ICT InterNet x TR)
--
--
-0.041 a [0.000]
--
--
--
Log (ICT mobile x FDI)
--
--
--
--
-0.054 [0.000]
--
26
Log (ICT mobile x TR) Wald Chi [prob.] Observation
--
--
--
--
--
6521.76 6604.38 6619.49 6481.74 6642.35 [0.000] [0.000] [0.000] [0.000] [0.000] 1593 1593 1593 1593 1593 Note: P-values are given in [ ]; a & b show significance at the 1%, and 5% levels, respectively.
-0.021 a [0.007] 6518.38 [0.000] 1593
Table 4. Results of Pooled Mean Group (PMG) estimator ICT with InterNet penetration Model (1) (Model 2) Coefficient Coefficient [prob] [prob] a -0.630 -1.744 a [0.000] [0.002] 1.542 a 3.358 a [0.000] [0.000] -0.125 b -0.280 a [0.000] [0.018] -0.0624 -0.002 [0.537] [0.669] 0.262 a -0.565 b [0.000] [0.046] -0.0495 b -0.065 a [0.026] [0.000]
Variables Constant Log (Y) Log (Y2) Log (FDI) Log (TR) Log (ICT InterNet)
(Model 3) Coefficient [prob] -0.568 a [0.000] 1.286 a [0.000] -0.084 c [0.087] -0.039 [0.637] 0.183 a [0.000] -0.027 c [0.079]
ICT with mobile use penetration Model (1) (Model 2) (Model 3) Coefficient Coefficient Coefficient [prob] [prob] [prob] a a 3.295 -2.807 -1.107 a [0.000] [0.002] [0.000] 4.384 a 2.150 a 1.503 a [0.000] [0.000] [0.000] -0.596 a -0.188 a -0.084 a [0.000] [0.000] [0.014] 0.038 -0.052 -0.071 [0.924] [0.107] [0.362] -0.041 b -0.131 a -0.027 b [0.0682 [0.043] [0.000] --
--
--
Log (ICT mobile)
--
--
--
-0.053 a [0.000]
-0.080 a [0.000]
-0.061 a [0.001]
Log (ICT InterNet x FDI)
--
-0.013 a [0.000]
--
--
--
--
Log (ICT InterNet x TR)
--
--
-0.012 a [0.000]
--
--
--
Log (ICT mobile x FDI)
--
--
--
--
0.011 a [0.000]
--
Log (ICT mobile x TR)
--
--
--
--
--
Observation
1593 1593 1593 1593 1593 Note: P-values are given in [ ]; a, b, and c show significance at the 1%, 5%, and 10% levels, respectively.
27
0.071 a [0.000] 1593
S0736-5853(19)30792-0 https://doi.org/10.1016/j.tele.2019.101300 TELE 101300
To appear in:
Telematics and Informatics
Received Date: Revised Date: Accepted Date:
2 August 2019 11 September 2019 4 October 2019
Please cite this article as: Khan, D., Effects of information and communication technology real income, and CO2 emissions: The experience of countries along Belt and Road, Telematics and Informatics (2019), doi: https://doi.org/ 10.1016/j.tele.2019.101300
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Effects of information and communication technology real income, and CO2 emissions: The experience of countries along Belt and Road Danish*[email protected], [email protected] School of Trade and Economics, Guangdong University of foreign studies Guangzhou, 510006 China *Corresponding Author.
Highlights The nexus between ICT and CO2 emission is analyzed along Belt and Road countries. The innovative methodological approach is introduced. The generelized least-square and pooled mean group methods are used. The moderating effect of international trade and FDI deepens the positive impact of ICT on CO2 emissions. Promoting the ICT sector through international trade and foreign direct investment (FDI) is suggested.
Abstract In recent years, information and communication technology (ICT) and its impact on society are debated; however, little research has been conducted regarding the future environmental consequences of ICT in various countries. However, this study presented the empirical analysis of the relationship between ICT, real income, and 1
CO2 emissions while considering foreign direct investment and international trade. The study presents an innovative methodological approach by introducing the interaction of ICT with foreign direct investment and international trade. For empirical estimation, this study used the generalized least-square method in 59 countries along Belt and Road from 1990 to 2016. The results summarized that ICT mitigates the level of CO2 emissions in countries along Belt and Road. Further, the moderating effect of ICT and foreign direct investment reduces CO2 emissions and the interaction between ICT and international trade does the same. Based on the policy perspective, the countries along Belt and Road need to strategically focus on promoting trade and investment in the ICT sector and also on innovations to promote sustainable economic development.
Keywords:
Information
and
communication
technology;
CO2
emissions;
Moderating effect; Environmental Kuznets Curve; Generalized least-square approach
1. Introduction Over the past decades, the impact of information and communication technology (ICT) on society has become a hot issue of debate. The growing importance of ICT would reshape the society, and it would also result in effects on tomorrow’s society. 2
ICT is crucial for industrialization, ultimately influencing economic growth and environmental quality (Danish et al., 2018b). Also, ICT contributes both directly and indirectly to economic, social, and environmental aspects of Sustainable Development Goals (SDG). ICT is the basis for monitoring climate change, mitigating and adapting its effects and assisting in the transition to a green, circular economy.1 Regardless of the positive role of ICT in economic growth (Hong, 2017; Latif et al., 2018), however, little attention has been given to the future environmental consequences of ICT, such as satellites, mobile phones, or the Internet. All these play an essential role in addressing the major challenges regarding climate change and sustainable development. Moreover, in today’s digital era, it is critical to understand the carbon implications of ICT while addressing climate change challenges (Zhou et al., 2019). The nexuses between ICT and the environment are complicated: ICT influences the environment via three channels. First comes the use effect, which means that during the production of ICT equipment, processing, distribution, and installation waste significantly contribute to CO2 emissions (Shabani et al., 2019; Shahnazi and Shabani, 2020) The use effect mainly increases energy consumption and CO2 emissions (Park et al., 2018). Also, e-waste production and harmful ICT equipment, such as large global data centers and mobile data traffic use, pose a threat to environmental quality (Lennerfors et al., 2015). The production of ICT equipment generates about 2%–3% of the world CO2 emissions (Røpke and Haunstrup, 2012). The second channel through which ICT influences the environment is known as substitution effect, which is defined as the reorganization of the production process, including dematerialization (Danish et al., 2018b), decarbonization (Ozcan and 1ICTs,
Environmental Sustainability and Climate Change, https://www.itu.int/en/action/climate/Pages/default.aspx 3
Apergis, 2017), demobilization (Salahuddin et al., 2016), replacing physical goods with e-books, post mail with e-mail, and tele-conferences (Park et al., 2018); smart transport system (Danish et al., 2018a), GPS, intelligent traffic control system using cameras, and reducing outdoor activities (Shabani et al., 2019).
Also,
dematerialization and online distribution, transport and travel substitution, monitoring and management applications, and product stewardship and recycling together reduce energy efficiency and reduce CO2 emissions (Danish et al., 2018b). Third is the cost effect in which ICT increases demand for other goods and services due to a decrease in prices and an increase in the return of CO2 emissions (Shabani et al., 2019). Meanwhile, the development in ICT boosts trade activities and flow, facilitating new communication channels (Ozcan, 2018) and this may influence the quality of the environment. Also, ICT goods transfer through foreign direct investment (FDI), such as secondary electronic goods, moves from high-income countries to low-income countries and may produce electronic waste (e-waste); this may harm the environment However, the knowledge transfer through FDI, communication among people produces an awareness among people that leads to a reduction of CO2 emissions (Danish et al., 2018c). From the earlier discussion, the environment impact of overall ICT is ambiguous; also, the complex nature of ICT is similar in both developed and developing countries. No one knows exactly what role ICT plays in the future, and our decision today will affect the direction of sustainable development. Thus, investigating the influence of ICT on CO2 has been an area needing further investigation. Also, nowadays both developing and developed countries need to counter many environmental challenges, such as climate changes, to improve energy efficiency, waste management, water quality, and scarcity; to address pollution and loss of 4
natural habitat and biodiversity (Danish and Wang, 2019a). Moreover, the trend of ICT development has increased rapidly in all regions of the world, with a rapid increase in trade activities and FDI. Thus, there is a need to identify whether the rapid growth in ICT translates into CO2 emissions due to the rapid process of globalization that expands trade activities and FDI. Since there is an attempt (Asongu, 2018a) to investigate the relationship between CO2 emissions, ICT, and openness, this study is similar but it is focused on Belt and Road countries. Henceforth, future analysis can be conducted to tailor current environmental policies to cater to the future relationship and economic activities in the Belt and Road countries. Ultimately, continuous use of ICT will determine whether the population could lead better lives with good environmental quality. Therefore, this study explores how ICT (mobile use and Internet use) is helpful in tackling the environmental challenges in Belt and Road countries. Nevertheless, there is no recent literature investigating the link between ICT and CO2 emissions in the context of Belt and Road countries. The motivation behind countries along the Belt and Road is due to several reasons. First, Belt and Road countries interact with each other with regard to trade and FDI. The Belt and Road project is initiated by China for endorsing the collaboration of trade among 60 countries and beyond in Asia, Africa, and Europe (Liu and Hao, 2018). ICT enhances trade activities and helps in promoting importing, which is less significant than promoting exporting. So, the ICT connectivity is critical to provide a basic communication channel for global connectivity, trade, transport, infrastructure development, and socioeconomic collaboration among people, organizations, and countries along Belt and Road corridors (United Nations, 2017). To this end, investment in infrastructure is needed to develop energy, 5
particularly in central Asian and Southeast Asian countries. Besides, the collaborative investment in energy projects and trade in the Belt and Road initiative combines a range of regional hotspot issues with major international issues, including global warming. Second, the large-scale construction, operation, and maintenance of infrastructure, especially roads that consume large amounts of cement and steel, dams, bridges, and power plants, require large amounts of fossil energy consumption, resulting in huge CO2 emissions (Fan et al., 2017; Zhao et al., 2016). Accordingly, the curbing of CO2 emissions is critical and it is very hard to improve the quality of the environment during the implementation of “the Belt and Road” project (Zhang et al., 2017). Three additional features of the study are as follows: First; we use a large sample of the Belt and Road countries for the longest available dataset but include recent data (1990–2016). Second, we use two additional variables, international trade and FDI in the nexuses of ICT and the environment. Further, we introduce the interaction between ICT and the FDI, and between ICT and international trade, as they may improve the quality of the environment through a reduction of CO2 emissions. Third, the study employs the developed generalized least-square (GLS) method to serve the objectives of this study. The econometric approach has not been used in the literature to investigate the study variables in Belt and Road countries. This article consists of six main sections. Section 1 is an introductory one; Section 2 provides a detailed literature review; Section 3 highlights the materials and methods; Section 4 analyzes and explains the results; the results are discussed in Section 5; and finally, Section 6 provides a conclusion and policy recommendations. 2. Literature review 2.1. Economic growth and pollution: the environmental Kuznets curve 6
Economic growth directs that a country generates more goods and services, particularly with ICT use (Añón Higón et al., 2017), and economic growth tends to increase for ICT goods and services, which increases electricity consumption and CO2 emissions (Wan Lee et al., 2014). However, the environmental Kuznets curve (EKC) hypothesis states that initially pollution worsens with per capita increase, but eventually more income decreases pollution (Dinda, 2004; Grossman and Krueger, 1995). Economic growth influences the environment via three channels. First, income through the scale effect harms the environment, compromising economic structure and technological change (Danish et al., 2019a). Second, the composition effect minimizes the harmful effects of income through structural changes in the economy. Third, the effect reduces pollution due to adaptation of environmental friendly technologies and the implication of stringent environmental standards; this is known as the technique effect (Destek and Sarkodie, 2019). The domination of composition and technique effect over scale effect lead to the formation of an inverted U-shape relationship between per capita income and pollution. The EKC hypothesis was largely investigated; some scholars confirmed the existence of EKC (Danish et al., 2019b; Dehghan Shabani and Shahnazi, 2019; Pata, 2018), whereas some studies have rejected its presence (Amri, 2018; Belloumi et al., 2017).
2.2. ICT and environment The environmental impact of ICT has remained a hot issue of debate for the past one decade. The introduction of smart cities, transportation system, industrial process, and energy-saving gains on a global scale are expected to mitigate the level 7
of CO2 emissions (Lennerfors et al., 2015). ICT in various sectors of the economy, such as agriculture, power, energy, transport, agriculture, and the financial sector, also reduces CO2 emissions. Further, instead of employing labor at a large scale and mechanization in different sectors, the use of the labor-saving machine can be helpful in reducing CO2 emissions (Sadorsky, 2012; Salahuddin and Alam, 2015). The literature on the relationship between ICT and CO2 emissions is divided into two strands. In the first strand, the authors have concluded that ICT mitigates the level of CO2 emissions (Haseeb et al., 2019; Lu, 2018; Wang et al., 2015; Zhang and Liu, 2015). Moreover, Ozcan and Apergis (2017a) estimate the effect of Internet use on CO2 emissions in emerging countries. The results of a certain study recommend that Internet usage reduces the likelihood of pollution. In addition, Al-Mulali et al. (2015a) conducted a study regarding the impact of online shopping on CO2 emissions in both developing and developed countries. The empirical findings suggest that Internet usage reduces outdoor activities, which reduce energy consumption and CO2 emissions in developed countries. However, online shopping is ineffective in developing countries to provide the reason for the sluggish speed of the Internet. Recently, both Añón Higón et al., (2017) and (Shahnazi and Shabani, 2020) have discussed the inverted U-shaped relationship between ICT and CO2 emissions on a global scale and for Iran, respectively. They found that in the early stage of development of ICT, it worsens environmental quality; however, pollution is reduced with more development in the ICT sector. Meanwhile, the second strand of research dealt with the adverse impacts of that ICT on the environment (Lee et al., 2016). The ICT use stimulates the CO2 emissions (Salahuddin et al., 2016). Also, Lee and Brahmasrene (2014) prove that ICT contributes to both economic growth and CO2 emissions in ASEAN countries. 8
Also, Asongu (2018) employs the generalized methods of moments (GMM) model to examine the effect of ICT and openness on CO2 emissions in African countries. The empirical evidence argues that ICT diminishes the negative impact of globalization on CO2 emissions. Park et al. (2018) estimated the effect of ICT on CO2 emissions for European Union (EU) countries. The results of the pooled mean grouped (PMG) method confirm that ICT contributes to CO2 emissions. Recently, Danish et al. (2018a) examined the relationship between ICT and CO2 emissions by using a novel panel estimation approach for emerging countries. The innovative contribution of the study offers that the use of ICT along with a growing income is helpful in mitigating the level of pollution. However, Dehghan Shabani and Shahnazi (2019) found heterogenous effects of ICT on CO2 emissions in various economic sectors of Iran. But Zhou et al. (2019) found the negative role of ICT in pollution in a sectoral study conducted in China. In the discussion just referred to, no consensus regarding the role of ICT in the environment is developed. The relationship between ICT and CO2 emissions is unclear. Also, we have observed that the relationship between ICT and CO2 emissions is studied for both single countries and panels of countries but ignored in countries along Belt and Road. The results drawn regarding ICT–CO2 emissions nexus are inconclusive. Thus, there is a need to further investigate the relationship between ICT and CO2 emissions. Therefore, this study aims at identifying the key role of ICT in the CO2 emissions and at considering trade and FDI in the context of the Belt and Road initiative BRI countries. From the aforementioned studies, the authors know fully well that no prevailing study investigates the effect of ICT on CO2 emissions within the framework of the EKC hypothesis for Belt and Road countries, adding to the effect of international trade and FDI. Also, apart from other 9
studies, we added the interaction of trade ration and FDI with ICT (mobile and Internet penetration) that was ignored in the existing studies. These two are the reasons that mainly contribute to the literature. 3. Materials and Methods 3.1. Empirical Framework This empirical work links ICT, real income, and CO2 emissions while considering international trade and FDI. A CO2 emission is a dependent variable, and real income (Y) and ICT are independent variables. However, international trade and FDI are used as potential control variables to avoid specification bias. Consequently, the empirical specification in this study is developed consistently with recent ICT and CO2 literature and additional explanatory variables (Asongu et al., 2018). To capture the influence of ICT on CO2 emissions, the econometric model (Eq:1) is estimated in this study and is presented as follows:
Log (CO2 )it it 1 Log (Yit ) 2 Log (Yit 2 ) 3 Log ( ICTit ) 4 Log (TRit )
5 Log ( FDI it ) 0
(1)
where i and t indicate country and time, μ is stochastic error term, CO2 is carbon dioxide emissions per capita, Y is real income, ICT stands for information and telecommunications, TR is traded ration, and FDI represents a foreign direct investment. The sign of coefficient β1 is expected to be positive, and β2 is negative. In this case, β1>0 and β2<0 are an indication for the presence of the well-known EKC hypothesis. We have suggested a square model (model 1) that confronts CO2 emissions with income, including control variables, to confirm that the relationship between income and CO2 emissions is an inverted U-shaped pattern. The sign of β3 is unexpected, and either ICT has an adverse impact or it is beneficial for the environment. Several control variables are included in the model to 10
avoid specification bias. These included control variables spontaneously influence environmental quality. In the literature, it is observed that economic growth increases the level of CO2 emissions that pollute the environment (see (Danish et al., 2018c, 2017; Ozcan and Apergis, 2017)). Also, both FDI and trade have either a positive or negative effect on CO2 emissions. So, the coefficient of β4 and β5 would be either positive or negative. Eq: (2) is an extension of Eq: (1); in addition to the additional explanatory variable, the interaction between ICT and FDI (ICT*FDI) is introduced into the model. The interaction between ICT and FDI (ICT*FDI)it is included in the function of CO2 emissions to investigate whether rising FDI leads toward a high usage of information and telecommunication in various sectors to expand economic activities. The advancement of ICT with growing FDI may affect the environment either positively or negatively. A generalized least-square method (GLS) model was used during the period 1990–2016: Log (CO2 )it it 1 Log (Yit ) 2 Log (Yit 2 ) 3 Log ( ICTit ) 4 Log (TRit )
5 Log ( FDI it ) 6 ( ICTxFDI )it it
(2)
where (CO2)it emission is a measure of environmental degradation in a million tons per capita in the country i in year t. (ICT*FDI) is the interaction between ICT and FDI for country i and time t. Similarly, an increase in trade activities leads to more usage of ICT goods that may affect the environment. Log (CO2 )it it 1 Log (Yit ) 2 Log (Yit 2 ) 3 Log ( ICTit ) 4 Log (TRit )
5 Log ( FDI it ) 6 Log ( ICTxTR)it it
(3)
(ICT*TR)it is the interaction between ICT and trade for country i and time t. The interaction between ICT and trade (ICT*TR)it may transfer clean technology, and the advanced use of technology may increase energy efficiency that could be helpful 11
in reducing CO2 emissions. Also, ICT equipment’s transfer trade, production, and consumption of ICT equipment and electronic waste may increase the rate of CO2 emissions. That explains the impact of trade and FDI that bring in new ICT and the moderating effect on CO2 emissions. Based on the earlier discussion, adding control variables is motivated by the perception of the literature on CO2 emissions. 3.2. Econometric method This empirical study employs a generalized least-square (GLS) model for empirical estimation consistently with recent literature (Alvarez-Herranz et al., 2017). The GLS approach is used for estimation of the parameter in a regression model. The variables under consideration are mainly economic variables; therefore, there exists the possibility that correlation may exist between regressors and error terms. In this case, the ordinary least-square method results in biased and unreliable estimates. In the case of a regression model when there is a certain degree of correlation between regressors and error terms, the GLS is the most suitable tool for empirical investigation (Alvarez-Herranz et al., 2017). Further, the empirical work seeks to find the existence of specific individual effects of each country, which disturbs their decision. If these effects are ignored, the problem of omitted variables would arise and estimates will be inconsistent. Thus, the GLS approach is preferred for model estimation; it relaxes the assumption of auto-correlation and crosssectional heteroskedasticity. To validate the findings of the GLS method, we further employed a pooled mean group (PMG) estimator consistent with the recent literature (Danish and Wang, 2019b). This PMG approach allows dependencies across countries, suggesting that disturbances are independently distributed across units and over time with mean zero 12
and constant variances. Also, PMG is independent from the assumption of stationarity; therefore, an investigation of stationary level is necessary but the study variables must be cointegrated. 3.3. Data The analysis of this empirical work is based on a panel of 59 Belt and Road countries using annual time series data from 1990 to 2016. Data availability dictates the choice of both data and countries. The measurement of ICT variables is characterized into two methods: nonmonetary and monetary. Nonmonetary variables include fixed telephones and mobile telephones, as well as Internet broadband subscribers. Monetary variables are ICT investment or ICT capital stock (Shabani and Shahnazi, 2019). The limitation of monetary ICT variables is un-availability of data; therefore, in this study, nonmonetary ICT variables are selected. The Internet users and mobile users (per 100 people) are used as a proxy for ICT. The data for Internet users and mobile users is collected from the world development indicator (WDI); a database of the World Bank real income is calculated as real GDP per capita (constant 2010 USD). CO2 emission is taken as metric tons per capita. Trade ration is calculated as the sum of import (% of GDP) and export (% of GDP). Finally, FDI is measured as the inflow of foreign investment (% of GDP). The data for all the variables is collected from WDI. It is worth mentioning that data are optimized in that we apply the longest available dataset. Also, variables of this study are converted into a logarithmic form, so the reported coefficients are equivalent to elasticities of CO2 emissions regarding Y, the square of Y, ICT, trade, and FDI. The descriptive statistic and correlation are presented in Table 1A. 4. Empirical Results and Analysis
13
The EKC presence is a long-run phenomenon that has a lot of demand for checking the unit root properties and cointegration of study variables as well to estimate the function of polynomial carbon emissions. In this situation, empirical investigation is more fruitful for policymakers in designing comprehensive environmental policies to combat climate change issues and for maintaining sustainable economic development. To this end, this study has applied the CIPS panel unit root test by Pesaran (2007), which is helpful in countering the issue of cross-sectional dependence. Since 59 Belt and Road countries are included in our sample, there is a probability of dependencies across countries and the results of CIPS are shown in Table 1. It should be noted that the variables under investigation are stationary after first difference, revealing that the order of integration is one I (1). As the integration order of study variables is one, it further calls for an investigation of cointegration among the variables under consideration in the study. We need to keep in mind the issue of cross-sectional dependence and heterogeneity that the study uses (Westerlund, 2007) to counter these issues, and the results are illustrated in Table 2. The Westerlund cointegration confirms that integration exists among CO2 emissions and their determinants. The integration order one and the confirmation of cointegration among study variables motivate us to estimate the relationship between CO2 emissions and their determinants. The suggested empirical model is estimated through a GLS specification while considering the existence of cross-section heteroskedasticity and autocorrelation. We estimate Equ. (1) to confirm the environmental Kuznets curve and Equ : (2) and Equ : (3) of the main model to capture the effect of the interaction terms between ICT and FDI (ICT*FDI) and the effect of the interaction terms between ICT and trade (ICT*TR) on CO2 emissions, respectively. There are two main sets of 14
specifications about the CO2 emissions that serve as the dependent variables. Each sub-specification is characterized by a number of mobile users (per 100 users) and a number of Internet users (per 100 users). The reported results in Table 3 with the specification of mobile use, the net effect of GDP, and the square of GDP on carbon emissions are positive and negative, respectively, in the three models (4th, 5th, and 6th columns). The corresponding negative marginal effect of GDP infers that the Belt and Road countries support the EKC hypothesis. The results respond to a confirmation of the inverted U-shape relationship corresponding to economic growth and CO2 emissions. Similar findings are produced for the specification of Internet use (1st, 2nd, and 3rd columns). The impact of the Internet user on CO2 emissions is also negative, which suggests that an increase in Internet users mitigate the level of CO2 emissions in Belt and Road countries. A similar result is inferred from both the specifications of ICT. Regarding the environmental impact of ICT by the specification, an increase in the number of mobile phone users reduces CO2 emissions; whereas the influence of Internet users on CO2 emissions is also negative, which suggests that an increase in Internet users mitigates the level of CO2 emissions in Belt and Road countries. A similar result is inferred from both the specifications of ICT. Similarly, the environmental impact of trade is insignificant in the specifications of both Internet use and mobile use. This postulates that trade does not affect environmental quality in Belt and Road countries. On the other hand, the environmental impact of FDI is negative but significant. Besides, another novel contribution is that the interaction between ICT and FDI (ICT*FDI) has a significant but negative impact on CO2 emissions. A similar result is found for the interaction effect of ICT and international trade (TR). 15
The results of PMG are reported in Table 4. The results from both PMG are strongly in line with those of GLS, which confirms the reliability and robustness of our empirical findings. The empirical results of the study are reliable and well established; thus, they can be used for policy purposes.
Further, to see the behavior of the standardized residual of CO2 emissions, we draw the residual plot (see Figure 1). For performing generalized least-square regression analyses, it is imperative to check the residual plots to validate the model. From the residual plot, it can be inferred that the residual is equally spread along a horizontal line without distinct patterns, which is an indication of linear relationships among analyzed variables. Therefore, the current model is the best way to understand the nature of data, and the plots show the goodness of fit of the model.
5. Discussion The positive and negative behavior of real income (Y) and square of income (Y2) is confirmed in the study. The corresponding negative marginal effect of GDP infers that the Belt and Road countries support the EKC hypothesis. The results correspond to a confirmation of the inverted U-shape relationship with economic growth and CO2 emissions. Similar findings are produced for the specification of Internet use (4th, 5th, and 6th columns). Regarding the role of ICT in CO2 emissions, the result found a positive role of ICT in the mitigation of CO2 emissions. State of the art agrees that the use of ICT 16
improves quality of the environment in Belt and Road countries. This implies that ICT in Belt and Road countries helps mitigate the level of CO2 emissions. This is corroborated with contributions to measuring, monitoring and managing, and enabling more efficient resource utilization and operation of infrastructure through dematerialization (Houghton, 2010); for example, from books to the e-book, paper mail to email, and newspaper to electronic paper, thus minimizing waste. Also, teleand video conference rather than traveling, staying and working at home instead of going to the workplace, ordering food online instead of cooking at home, and online shopping minimize outdoor activities, which can save fossil fuel consumed in vehicles and result in fewer carbon emissions. The intelligent transport system logistics and freight rationalization, smart buildings, and home automation are the effects that can help in improving energy efficiency, consequently mitigating the level of pollution. Another mechanism that contributes to the productive role of ICT is that the induction of ICT contributes to energy saving in some areas of life, which surpasses ICT-induced additional energy consumption in other areas. For example, the use of smaller ICT devices, laptops, smartphones, and others are energy efficient. It seems that the world is taking advantage of the technology spillover effect due to significant progress in innovation for the past three decades. Traditional industries switch to higher energy efficiency and low carbon economy due to ICT use. This can be further interpreted that ICT use in Belt and Road countries positively drives the quality of the environment and we should continue to use the policies that promote the use of ICT, as this will ensure sustainable economic development. The negative coefficient estimate of ICT is in line with (Ozcan and Apergis, 2017) for emerging economies and consistent with (Al-Mulali et al., 2015b) for developed countries. The results are also inconsistent with (Wan Lee and Brahmasrene, 2014) 17
for a panel of ASEAN countries and with (Salahuddin et al., 2016) for OECD countries. Meanwhile, the imapct of FDI on CO2 emissions is insignificant. However, the impact of international trade on CO2 emissions is both positive and significant, suggesting that it contributes to pollution at the international level. However, the innovative contribution of the study, the interaction between FDI and ICT, has a negative but significant impact on CO2 emissions in specifications of both Internet and mobile phone. A similar result is found for the moderating effect of trade between ICT and CO2 emissions. This means that both FDI and international trade deepen the positive role of ICT in CO2 emissions. This can be attributed to the fact that ICT equipment transfer through trade does not disrupt the environment but reduces CO2 emissions. Also, the process of production of ICT equipment is not energy intensive, and the import of ICT goods is not harmful to the environment. However, keeping in mind the future environmental consequences of ICT through FDI, Belt and Road countries should enforce high environmental standards that restrict the import of dirty goods and polluting technology. It makes sense, because Belt and Road countries, in particular, have made good progress in the inventing and use of new technologies, and the analyzed countries seem to take full advantage of technology spillover through trade and FDI. Another mechanism is that the Belt and Road countries are likely to produce exports and import nonenergy intensive and environmentally friendly goods. 6. Conclusion This work investigates the link between ICT, energy consumption, economic growth, and CO2 emissions while considering the role of international trade and FDI for a panel of 59 selected BRI countries from 1990 to 2016. The secondary objective 18
of the study is to test the EKC hypothesis on the significance of ICT. The generalized least-square method is applied. For robustness check, we divide the panel data into two panels. The empirical finding of the study suggests interesting findings. The findings infer that ICT use mitigates carbon emission level in the Belt and Road countries. Moreover, the novel contribution of the study, the interaction between ICT and trade, does not affect CO2 emissions in Belt and Road countries. Besides, the FDI mitigates the level of CO2 emissions. Also, EKC holds that the significance of ICT is verified in the Belt and Road countries. Prior studies mainly focused on the link between ICT and CO2 emissions that incorporate trade and FDI. However, those studies ignore the effect of interaction of FDI and international trade with ICT on CO2 emissions, which is mentioned in this study. The finding suggests that the moderating effect of FDI and international trade deepens the positive of role of ICT in CO2 emissions. The study results call for a suggestion for a crucial policy. The energy-efficient ICT devices (i.e., smart application) and other key inventions related to Internet use are required in Belt and Road countries to control CO2 emissions. Green energy projects, with the help of ICT, can reduce dependency on fossil fuel consumption. The main practical implication related to ICT use should be encouraged in every sector. However, the globalization of economies also results in environmental damage, so in the short-term sample countries need to strengthen their environmental standards and apply high dumping duties to polluted goods. The ICT equipment transfers through FDI and international trade are beneficial. Therefore, we urge the government of these countries to continue with investment regulations to enjoy the fruit of sustainable development through ICT.
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The current study also has some important limitations. At present, more robust and advanced econometric tools are applied to get reliable and unbiased estimates. In future, studies can be augmented to analyze a single-country analysis for the BRI economies using time series estimation tools. Further, checking the environmental impact of the index of all ICT measures is another possible avenue for future research. Also, the diffusion of ICT and its influence on the environment is another interesting area of future research.
Appendix Table 1A. Descriptive statistic and correlation matrix LOGCO2 LOGGDP LOGFDI LOGTR ICT internet ICT mobile Descriptive Statistic Mean 0.512044 3.655991 0.286011 1.876713 24.35320 2.384553 Median 0.638316 3.670707 0.407833 1.914059 15.44000 2.496641 Maximum 1.845939 4.861361 1.740962 2.645033 93.47830 15.20501 Minimum -1.470631 2.213845 -4.591014 -1.677797 0.000000 0.000000 Std. Dev. 0.605709 0.579549 0.658732 0.342749 24.70409 1.898877 Observations 1593 1593 1593 1593 1593 1593 Correlation matrix LOGCO2 1 LOGGDP 0.879892 1 LOGFDI 0.071675 0.059992 1 LOGTR 0.288458 0.295528 0.275258 1 ICT internet 0.340540 0.447461 0.059412 0.217221 1 ICT mobile 0.211274 0.260398 0.170470 0.160849 0.543995 1 Note: CO2 =carbon dioxide emissions; GDP =Gross domestic product proxy for economic growth; FDI= foreign direct investment; TR= trade ratio; ICT = Information and communications technology
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Figure 1. Graph of ICT (Internet use) for the standardized residual of dependent variable CO2
Table 1. CIPS panel unit root tests result Log (CO2) Log (Y) Log (Y2) Log (FDI) Log (TR) CIPS (at level) -1.979 -2.364 -2.366 -3.817 -2.392 CIPS (at first difference -4.483 -4.212 -4.108 -5.736 -4.839 Note: the critical values of CIPS are: ( -2.58) 10% ; (-2.65) 5% ; (-2.78) 1%
25
Log (ICT InterNet)
Log (ICT mobile)
-2.745 -4.690
-3.009 -4.388
Table 2. Results of Westerlund panel cointegration tests ICT InterNet use Statistic Value Z-value P-value Gt -3.894 -10.058 0.000 Ga -0.550 13.442 1.000 Pt -16.734 1.382 0.017 Pa -6.151 4.761 1.000 Note: *&*** refers to 1% and 10% respectively
Value -2.474 -3.733 -23.33 -3.472
ICTmobile use Z-value P-value 1.386 0.091 10.475 1.000 -4.721 0.000 7.230 1.000
Gt, Ga : Group statistic; Pt, Pa = Panel statistic
Table 3. Results of generalized least square model with correction of heteroscedasticity 1990-2016
Variables Constant Log (Y) Log (Y2) Log (FDI) Log (TR) Log (ICT InterNet)
ICT with InterNet penetration Model (1) (Model 2) Coefficient Coefficient [prob] [prob] a -6.228 -6.235 a [0.000] [0.000] 2.756 a 2.748 a [0.000] [0.000] -0.252 a -0.251 a [0.000] [0.000] -0.013 0.028 [0.223] [0.753] 0.080 a 0.084 a [0.000] [0.000] -0.097 a -0.064 b [0.002] [0.044]
(Model 3) Coefficient [prob] -6.209 a [0.000] 2.670 a [0.000] 0.240 a [0.000] -0.003 [0.737] 0.144 a [0.000] -0.071 a [0.000]
ICT with mobile use penetration Model (1) (Model 2) (Model 3) Coefficient Coefficient Coefficient [prob] [prob] [prob] a a -6.219 -6.154 -6.305 a [0.000] [0.000] [0.000] 2.804 a 2.689 a 2.802 a [0.000] [0.000] [0.000] -0.261 a -0.241 a -0.259 a [0.000] [0.000] [0.000] -0.012 -0.012 0.003 [0.267] [0.253] [0.786] 0.076 a 0.081 a 0.801 a [0.000] [0.000] [0.000] --
--
--
Log (ICT mobile)
--
--
--
-0.053 a [0.005]
-0.017 a [0.002]
-0.040 a [0.003]
Log (ICT InterNet x FDI)
--
-0.015 a [0.000]
--
--
--
--
Log (ICT InterNet x TR)
--
--
-0.041 a [0.000]
--
--
--
Log (ICT mobile x FDI)
--
--
--
--
-0.054 [0.000]
--
26
Log (ICT mobile x TR) Wald Chi [prob.] Observation
--
--
--
--
--
6521.76 6604.38 6619.49 6481.74 6642.35 [0.000] [0.000] [0.000] [0.000] [0.000] 1593 1593 1593 1593 1593 Note: P-values are given in [ ]; a & b show significance at the 1%, and 5% levels, respectively.
-0.021 a [0.007] 6518.38 [0.000] 1593
Table 4. Results of Pooled Mean Group (PMG) estimator ICT with InterNet penetration Model (1) (Model 2) Coefficient Coefficient [prob] [prob] a -0.630 -1.744 a [0.000] [0.002] 1.542 a 3.358 a [0.000] [0.000] -0.125 b -0.280 a [0.000] [0.018] -0.0624 -0.002 [0.537] [0.669] 0.262 a -0.565 b [0.000] [0.046] -0.0495 b -0.065 a [0.026] [0.000]
Variables Constant Log (Y) Log (Y2) Log (FDI) Log (TR) Log (ICT InterNet)
(Model 3) Coefficient [prob] -0.568 a [0.000] 1.286 a [0.000] -0.084 c [0.087] -0.039 [0.637] 0.183 a [0.000] -0.027 c [0.079]
ICT with mobile use penetration Model (1) (Model 2) (Model 3) Coefficient Coefficient Coefficient [prob] [prob] [prob] a a 3.295 -2.807 -1.107 a [0.000] [0.002] [0.000] 4.384 a 2.150 a 1.503 a [0.000] [0.000] [0.000] -0.596 a -0.188 a -0.084 a [0.000] [0.000] [0.014] 0.038 -0.052 -0.071 [0.924] [0.107] [0.362] -0.041 b -0.131 a -0.027 b [0.0682 [0.043] [0.000] --
--
--
Log (ICT mobile)
--
--
--
-0.053 a [0.000]
-0.080 a [0.000]
-0.061 a [0.001]
Log (ICT InterNet x FDI)
--
-0.013 a [0.000]
--
--
--
--
Log (ICT InterNet x TR)
--
--
-0.012 a [0.000]
--
--
--
Log (ICT mobile x FDI)
--
--
--
--
0.011 a [0.000]
--
Log (ICT mobile x TR)
--
--
--
--
--
Observation
1593 1593 1593 1593 1593 Note: P-values are given in [ ]; a, b, and c show significance at the 1%, 5%, and 10% levels, respectively.
27
0.071 a [0.000] 1593