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Open Access

Peer-reviewed

Research Article

RETRACTED: Embracing the digital revolution: Examining the relationship between ICT adoption and carbon emissions in the Persian Gulf

Retraction

The PLOS One Editors retract this article [1] because it was identified as one of a series of submissions for which we have concerns about potential manipulation of the publication process, peer review integrity, and authorship. These concerns call into question the validity and provenance of the reported results. We regret that the issues were not identified prior to the article’s publication.

ZUR did not agree with the retraction. HS either did not respond directly or could not be reached.

23 Oct 2025: The PLOS One Editors (2025) Retraction: Embracing the digital revolution: Examining the relationship between ICT adoption and carbon emissions in the Persian Gulf. PLOS ONE 20(10): e0334865. https://doi.org/10.1371/journal.pone.0334865 View retraction

Abstract

In this digital age, promoting economic development through technology innovation and adoption has become a pressing matter, contributing to increased productivity and, in turn, carbon emissions. Consequently, this study employs a novel technique (Newey–West Standard Error Method, Technology Adaptation Model) to quantify information and communication technology (ICT) adoption rates as a proxy indicator for evaluating the Persian Gulf economy’s technology development. Moreover, this study investigates the evidence of the environmental Kuznets curve, with trade openness, technological adoption, and innovation as sustainable development controls. The findings reveal that two of three technological innovation instruments, fixed telephone, and internet subscriptions, increase carbon emissions. In contrast, mobile cellular subscriptions simultaneously reduce carbon emissions in the Persian Gulf. Furthermore, measures of technology adoption, high-technology exports, and electricity use contribute to the increase in carbon emissions. Trade openness also raises carbon emissions in the Persian Gulf. These findings suggest that policymakers must develop technological innovation and adoption strategies that effectively promote a greener environment.

Citation: Si H, Rahman ZU (2024) RETRACTED: Embracing the digital revolution: Examining the relationship between ICT adoption and carbon emissions in the Persian Gulf. PLoS ONE 19(6): e0304088. https://doi.org/10.1371/journal.pone.0304088

Editor: Mohammed Balfaqih, University of Jeddah, SAUDI ARABIA

Received: December 1, 2023; Accepted: May 3, 2024; Published: June 5, 2024

Copyright: © 2024 Si, Ur Rahman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: https://databank.worldbank.org/source/world-development-indicators.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Globally, all economies see a development frame with viability and sustainability as an operational aim. Achieving both rapid development and sustainability may be seen as two separate goals, significantly when technological advancement harms the environment [1]. It becomes more difficult for politicians, philosophers, and academics to create sustainable growth. Swift economic growth has been complemented by depletion of natural resources and a decreased environmental value during the last 30–35 years [2]. The boundaries between nature and society have blurred as the global economy has expanded. Researchers and government officials have been paying increasing attention to the green economy’s rapid rise [3]. Efficiency and power savings in the digital era are propelled by ICT. The ecological challenge of using technology sets is being addressed through technological innovation, specifically in the energy sector [4].

ICT is expected to reduce carbon dioxide emissions by 9 billion metric tons by 2020 [5]. Similar findings were made by Hasan et al. [6], who revealed that ICT has a positive effect on greenhouse gas emissions. On the contrary, ICT allegedly rises energy demand and greenhouse gas emissions [7]. Additionally, according to a study by Hailiang et al. [8], between 2007 and 2012, the EC of ICT products enlarged from 3.9 percent to 4.6 percent. ICT continues to have a significant influence on CO2 emissions consequently. ICT and carbon emissions are thus related, and this cannot be ignored.

Energy and carbon emissions have a complicated connection to ICT. The usage of online delivery as well as transportation solutions has the potential to lower energy consumption and carbon dioxide emissions as a result of IT [9]. They stated that through re-engineering the workplace and company model, ICT might continue to lower EC and carbon emissions. On the contrary, a large amount of communication technology equipment and electronic trash will harm the environment [10].

However, European electric battery cars reduce carbon emissions [11]. Energy sources and tax policies also reduce greenhouse gas and carbon emissions [12]. Subsidies and taxes in Europe affect carbon emissions in several ways, liable on the vehicle and country [13]. For instance, increasing tariff subsidies for renewable energy may contribute to reducing emissions [14]. Conversely, China benefits from carbon consumption contributions via oil production and carbon capture and storage programs [15]. Taxes on coal production are another option for reducing carbon emissions in China’s economy [16].

Transportation’s social cost of carbon emissions drops by magnitude [17]. ICT’s impact on energy usage and carbon emissions in several sectors. It is observed that ICT, on the one hand, consumes energy and emits carbon into the atmosphere. Still, it may cut carbon emissions and EC in other areas of the economy, such as transportation, intelligent buildings, and cybernetic work and knowledge. ICT and carbon emissions were inspected by Owuru et al. [18]. According to the opposing idea of how ICT influence carbon emissions, this study found an inverted relation between ICT and carbon emissions. The findings, derived from a sample of nations in emerging regions, may be extended to developed areas [19].

Tunisia is not considered to have an environmental Kuznets curve. The results also demonstrated that ICT did not affect greenhouse gas emissions [20]. The carbon emissions may be reduced via commerce and energy use, as well. The environmental performance Index was analyzed [21] and co-workers in Belt and Road nations by looking at the influence of ICT, power usage, EG, and FD; it revealed that concerning factors are interdependent and affiliated. It can potentially cut CO2 emissions and drastically enhance environmental quality for ICT. According to Ishida et al. [22], the long-term energy consumption elasticity of ICT investment in the Japanese economy is 0.155. In addition, he claims that although ICT investment may reduce energy use somewhat, it will not affect GDP [23] and affirmed an inverted U-shape interlink between ICT and CO2 emissions. Carbon dioxide emissions will only rise if information technology use exceeds the average level [24]. Academics have not come to a uniform opinion about the link between ICT and CO2 emissions. Rethinking the ascendancy of ICT on CO2 emissions using the most up-to-date methodologies and data is essential [25].

Academics have long been interested in how ICT may reduce CO2 emissions. Academics widely accept ICT as having a significant impact on other economic sectors because of their fast expansion and constant innovation. A new generation’s way of life and economic structure is being profoundly altered by advances in ICT [26]. It is possible to quantify a company’s internal communication technology, including its technical effectiveness, economic development, and alterations to its industrial structure. One possibility is to build an IT influence model with three levels: one for the ICT environment, another for the ICT services that are given, and a third for the development of digital activities [27,28]. Information technology is believed to have a third-order outcome on first- and second-order outcomes, which causes its dynamic impact.

Persian Gulf economies have been profoundly impacted by the fast growth of information and communications technology in recent years [29]. In the Arab states from the Persian Gulf, energy use and CO2 emissions are fascinating rising attention. In addition, the short-term uncertainty of economic policy is influenced by energy usage [30]. OECD was defeated by the economy as the world’s significant source of CO2 in 2013, according to International Energy Agency statistics and estimates, and this increasing trend will not be relieved until 2023. The present study on this topic is quite limited, with examples such as investigating the influence of Persian Gulf cooperation on regional economies and the link between economic development in Persian Gulf nations and CO2 emissions [31,32].

As a group, CO2 emissions and innovation for Persian Gulf nations are still poorly documented. Innovative solutions may help cut CO2 emissions in certain parts of the world, but not all. One study found that innovation brought down CEM in the G6 nations but raised excretion in the MENA and Persian Gulf regions, as suggested by Duada et al. [33]. Researchers in China’s industrial sector analyzed the footprint of environmental restrictions on technological innovation efficiency. They advised that environmental regulations should be published according to the technical innovation potential of each province [34,35]. Independent research might concentrate on generalizing this idea to the economies of the Persian Gulf countries. They have all grown tremendously in the last twenty years. On the other side, they had to deal with environmental challenges, including the production of greenhouse gases, mainly carbon. According to the Environmental Performance Index (EPI) [36], most Persian Gulf economies have more work to do to meet their environmental policy objectives. Because EPI’s latest index shows that UAE is 39, Kuwait is at 87, Bahrain is at 90, Saudi Arabia is 109, Qatar at 137, Oman at 149, and Iraq at 169th [37,38].

This study is important for examining how technology innovation and acceptability contribute to increasing environmental conservation efforts to achieve carbon neutrality in the Persian Gulf. The analysis assesses worldwide concerns including climate change and increasing carbon emissions by examining the impact of technical advancements and implementation tactics on sustainability. This project seeks to explore the potential of the Persian Gulf in reducing environmental hazards and promoting a sustainable, low-carbon future, particularly in relation to its robust economy. This study pursues to clarify the substantial influence of technological innovation and adoption on economic dynamics, environmental factors, and energy consumption. This paper provides insightful viewpoints on the efforts in the Persian Gulf region to tackle climate change and achieve carbon neutrality, highlighting its significant influence on the worldwide economy. In this research, we look at how technical innovation, acceptance, and their impact on the environment are related to the Environmental Kuznets hypothesis. In order to promote economic growth while maintaining the environment, reduce carbon emissions, and increase the use of energy, modern technology is crucial. The paper ends by analyzing the policy outcomes and highlighting the increasing necessity for fiscal actions, environmental taxes, and regulations to deal with changing environmental conditions.

This study focuses on technical innovation, technology adoption, high technologies, and trade openness. This study investigates if the use of technology contributes to achieving carbon neutrality. The third purpose is to establish a link between the characteristics that governments emphasize to encourage green innovation and accomplish carbon neutrality objectives. This project aims to establish the Environmental Kuznets Curve (EKC) to attain carbon neutrality in the Persian Gulf.

2. Review of literature

2.1. Technological innovation and carbon emissions

Malik et al. [39] analyze the impact of Internet prevalence on the connection between income and the environment, namely carbon emissions. An examination of data spanning from 1996 to 2021 across 115 countries with different GDP levels and Internet use showed a consistent EKC trend in both industrialized and developing nations. The research also discovered that increased Internet use reduces the income threshold at which emissions start to decrease. Bildirici et al. [40] analyzed 13 chosen G-20 nations using Quantile Panel Regressions and FMOLS to assess a model that challenges the nonexistence of EKC. Faisal et al. [41] studied the link between ICTs, such as Internet and mobile phone use, and the strength of CO2 emissions in China using quantile regression analysis. The data shows significant reductions in carbon emission intensity in the Chinese regions studied, linked to the rising use of mobile phones and Internet. In developing nations, economic advancement may occur without an increase in carbon emissions due to the use of ICTs. The results suggest that financial development and use of renewable energy have a limited effect and might explain, at least in part, the levels of carbon emissions and economic advancement. The study indicates that the renewable energy sector and economic development in MENA nations are now inadequate and have little influence on economic and environmental advancement. Assessing the current and future influence of increased fixed and mobile broadband penetration on CO2 emissions is essential, given the country’s digital transformation and the rise of digital ecosystems. Differences between industrialized and developing countries may lead to varying results in the research.

Tsimisaraka et al. [42] analyze the influence of ICT and several variables (trade, financial development, economic growth, and energy consumption) on carbon emissions in South and Southeast Asia between 1990 and 2014. They distinguish between developed and developing nations using cluster analysis. Studies show that ICT and energy use have a detrimental effect on the environment by increasing carbon emissions in all industries, indicating that ICT goods and services in these areas may lack energy efficiency. Financial expansion is linked to higher carbon emissions, suggesting that financial resources could not have been allocated to ecologically beneficial projects in both developing and developed countries. Financial improvement in industrialized countries leads to a decrease in carbon emissions. Research has shown that commerce, economic growth, financial advancement, and ICT together improve ecological conditions by reducing carbon emissions via a unidirectional causal link. The research confirms the validity of EKC. Adebayo et al. [43] examined the correlation among ICT, economic development, and carbon emissions in 9 nations of the Association of Southeast Asian Nations from 1991 to 2009. Using cointegration techniques and regression estimation methods, their study found that the spread of ICT results in increased economic growth and carbon emissions.

Irfan et al. [44] analyze how Internet use, economic growth, financial development, and trade openness affect CO2 emissions in EU nations. The pooled mean group estimator is used on panel data spanning from 2001 to 2014. The study shows that the Internet has a detrimental effect on the environment and leads to higher power consumption. Economic growth and financial development have a decreasing negative impact on carbon emissions. Ben et al. [45] examined the influence of ICTs on carbon emissions in 13 G-20 countries, including both developing and industrialized nations, over the period from 2000 to 2020. The research used Quantile Panel Regressions in combination with FMOLS technique. The results suggest that five factors have a favorable impact on reducing carbon emissions: energy prices, FDI, technology, innovation investment, and trade openness. On the other hand, some factors like financial growth negatively impact air quality. The analysis verifies the validity of the EKC hypothesis.

  1. H1: Does technological innovation influence carbon emissions.

2.2. Technology adoption and carbon emissions

Technical intensity in manufacturing on the quantity of dangerous carbon emissions generated throughout manufacturing operations. Current industrial plans in emerging countries prioritize encouraging investments in technology-driven manufacturing businesses. There is less empirical research about the ecological impacts of industrial enterprises operating at different levels of technology. We assess the carbon dioxide emissions from low-, medium-, and high-tech manufacturing in developing and emerging nations by examining the technology intensity of manufacturing value added and exports. The research analyses 56 developing and emerging countries from 1991 to 2022. A panel dataset derived from World Bank and UNIDO data is used for analysis, using the GMM estimation.

Technological progress is crucial for industrial development [46]. Developing nations must shift from low- to medium- and high-technology manufacturing to strengthen competitiveness, improve capabilities in items with growing global demand, and accomplish long-term structural transformation and economic development. Nations must keep abreast of technical breakthroughs and inventions to avoid being stuck in a ’middle-income technology trap’ and a more extensive middle-income trap [47]. Medium- and high-tech (MHT) manufacturing companies are often leaders in technological breakthroughs and play a key role in generating spillover effects and linkages. The manufacturing sectors of technology adoption produce complex items that are considered catalysts for growth, particularly via enhancing industrial competitiveness, expanding technology, investing in research and development, and participating in creative activities. The qualities may improve financial performance in the wider economy by establishing links to other sectors via backward and forward linkages, as well as producing positive externalities. Technology-based manufacturing has a substantial effect on a country’s economic value creation, product diversity, complexity, and enhancement of technical skills. High-technology-intensive enterprises tend to be more innovative, productive, provide better wages, and achieve more success compared to low-technology-intensive firms, as shown by several research [48]. technology adoption exports are more competitive in global commerce because of their efficient production and resource distribution, leading to a greater market size and more foreign investment [49]. The MHT manufacturing industry does not experience deindustrialization as low-tech manufacturing sectors do. Enhancing the technical quality of exports is seen as essential in the industrialization goals and initiatives of developing countries. Transitioning to sustainability via technological advancements the importance of MHT manufacturing sectors in reducing carbon emissions throughout industrialization is crucial in this context. Technology adoptions are recognized for their advanced scientific and technological expertise and significant investment in research and development, which may help advance sustainable production and consumption in the global economy. Developing nations are now focusing on fostering technology-intensive manufacturing via industrial strategies to stimulate sustainable economic development and prosperity in the green economy. Developing countries may attain sustainable economic development by using eco-friendly technological innovations to minimize carbon emissions in the industrialization process. Green technology is crucial for attaining sustainable economic growth paths [50]. Manufacturing enterprises in the MHT sector may help advance low-carbon development in developing nations. Businesses using modern high-tech methods are often eco-friendly and may produce lower carbon emissions compared to traditional low-tech production. High-technology-intensive manufacturing enterprises are expected to have lower pollution levels compared to medium-technology-intensive manufacturing sectors.

  1. H2: Does Technological adoption affect carbon emission.

2.3. Economic growth and carbon emissions

Onofrei et al. [51] established the causal association between GDP and CO2 emissions in several nations. In South Africa, GDP influences carbon emissions, but in Brazil, carbon emissions impact GDP. Nevertheless, there was no indication of causation in India and China. Namahoro et al. [52] identified evidence of one-way causality from economic development to carbon emissions in China when studying the correlation between carbon emissions and economic growth. Cai et al. [53] identified a prolonged causal association among economic growth, carbon emissions, and energy consumption in 24 African nations by the application of a panel ARDL approach. An investigation of the link between economic progress and CO2 emissions from 1980 to 2019 using the Granger causality method revealed evidence of a unidirectional causal relationship from economic growth to CO2 emissions. Gorus et al. [54] examined the correlation between carbon emissions and economic development in 12 Sub-Saharan African nations from 1971 to 2019. The research revealed that economic expansion leads to an increase in carbon emissions in the near run in Benin, Nigeria, Democratic Republic of Congo, Ghana, and Senegal. Reverse causation between carbon emissions and economic development was identified in Nigeria, Gabon, and Togo. Both causal relationships were identified between economic growth and carbon emissions in Nigeria in the near run and in Gabon and Congo in the long term. Ait Soussane et al. [55] analyzed the link between carbon emissions and economic progress in five West African countries: Senegal, Ghana, Burkina Faso, Nigeria, and Benin. These nations were selected on the basis of the uniformity and consistency of the data. This study attempts to create an algorithm for forecasting carbon emissions in Africa, a topic that has not been well explored in current literature focusing on the relationship between carbon emissions and economic progress. This study used a univariate forecasting method to simplify the process of measuring and evaluating the influence of causative factors on our primary dependent variables, due to the wide range of prediction tools for carbon emissions and their uses. Univariate forecasting has a long history. Ullah et al. [56] created an algorithm to forecast energy consumption in several U.S. sectors and assessed its prediction precision for commercial, industrial, residential, and transportation sectors. Liu et al. [57] used a neural network-based grey residual modification model to anticipate energy consumption and assessed the predictive precision of their model.

  1. H3: Does GDP affect carbon emissions.

The literature review analyses previous studies on technology adoption, technical innovation, GDP, trade openness, and their influence on carbon emissions. These investigations are frequently conducted autonomously and mainly concentrate on affluent nations. This article thoroughly examines how several factors collectively influence carbon emissions, with a specific focus on the Persian Gulf as a developing region. The study aims to analyze the correlation between technical breakthroughs, technological innovation, GDP, and carbon emissions using sophisticated empirical methods and a inclusive dataset covering a substantial timeframe. This study aims to rectify a notable gap in current literature.

This paper provides a thorough examination of various research on the adoption of technology, technical progress, GDP, and trade openness in relation to carbon emissions. The authors emphasized the need of investigating these issues in connection to carbon emissions. To improve the transparency of the research gap, researchers should clearly state the limitations of the current literature and highlight their distinct viewpoints. Highlighting the necessity of performing a thorough and cohesive analysis of these components is crucial, particularly in developing economies like the Persian Gulf region. Using advanced statistical methods and comprehensive historical data is essential for gaining a more accurate comprehension of the matter. The authors effectively communicate the urgent necessity of their attempt to tackle the various difficulties linked to carbon emissions through this method.

Consequently, several investigations have used two or three similar criteria. Carbon neutrality does not include technical innovation, technology adoption, or GDP, especially in poor countries with respect to carbon emissions. The study examined carbon neutrality in a developing country, specifically in terms of technology innovation and uptake. This study aims to evaluate the correlation between technological innovation, adoption, GDP, and carbon emissions. Data from 1990 to 2021 was collected for this purpose. Advanced econometric methods such as the technological adoption model, technological innovation model, and Newey West were employed to examine the influence of technological innovation, technical adoption, and GDP on carbon emissions.

3. Data collection and methodology

From the perspective of previous work, this study shows that technological innovation, technology adoption, and CO2 emissions are all linked in the Persian Gulf countries. For this purpose, the analysis relies on yearly data from 1990 to 2021 acquired from world data repositories. The variables are described and abbreviated in Table 1.

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Table 1. Abbreviation and variables details.

https://doi.org/10.1371/journal.pone.0304088.t001

Economic development is triggered by adding human capital and talent, according to Salam et al. [58], whereas CO2 emissions are triggered immediately by output levels [59]. ICT relies heavily on mobile phones and the internet, two of the most critical technologies [60]. The number of fixed and mobile cellular subscribers can be used to gauge the ICT level of an economy [61]. ICT may increase expertise and human capital, promoting economic growth. ICT also helps worldwide trade and investment [62].

On the other hand, TRD and value-added trade raise air pollution and CO2 emissions. A different study, however, demonstrates that free trade agreements decrease two-sided CO2 emissions [63]. In this way, the functional formula may be summarized as follows:

Technological Innovation Model 1

Technology Adoption Model 2

FTS per 100 persons are the instruments used in Eq (1) to measure the amount of technological innovation in the economy. While Eq (2) the export of high-tech goods and the amount of electricity used per capita are indicators of technology adoption. In contrast to CEM, GOP2 is the square of GDP, TRD is trade openness, I = Gulf countries, and "t" represents the time. GOP2 is the natural log of GDP.

α0 And α1 show the intercepts of Eqs (1) and (2), correspondingly, while α1 to α4 in Eq (1), and α6 to α9 in Eq (2), are the magnitudes of independent variable.

According to this research, technology innovation and adoption significantly impact the Persian Gulf’s CO2 emissions. Cross-sectional dependence must be considered throughout the calculation to estimate robust estimators [64]. The Driscoll–Kraay evaluating approach is used to assess the footprint of technological innovation and adoption on CO2 emissions in the Gulf economies. The DK regression approach is recommended by [65,66] for dealing with cross-sectional dependency difficulties of heteroskedasticity.

Other distinctive features include developing trustworthy estimators, handling missing data, and being suitable for short and long periods [67]. In addition, the DK regression approach makes robust estimates despite heteroskedasticity and spatial dependence. Thus, the DK approach has a linear functional form.

3

Eq (3) shows the “i” = Gulf nations, “t” is a period of the dataset, CEMit is a dependent variable, and X* is for control variables. The CD test elaborates that if CD is found in the dataset, then the panel unit root test will be irrelevant [68]. Consequently, a panel fixed effects regression and a Wald test were employed to analyze unit root. The following is an explanation of the fundamental unit root method used in panel fixed regression: 4 πit is the function of its lag πit-1 as shown in Eq (4); to determine the order of integration, the null hypothesis, φ = 1, will be evaluated using Wald statistics. The panel fixed effects regression results and the Wald statistic demonstrate that none of the investigated variables have a unit root.

4. Results and discussion

A panel dataset of Persian Gulf countries from 1990 to 2021 inspects the effect of technological innovation and adoption on CO2 emissions. Data availability indicates that the study’s time frame runs from 1990 to 2021. Seven leading developing economies around the globe make up the Persian Gulf. WDI (World Development Indicators) was used to get the data. In addition, as a further measure of environmental effect, CO2 emission was utilized [69]. CO2 emissions, according to Hafeez et al. [70], have a longer half-life in the atmosphere than other air pollution indicators.

Similarly, CO2 emissions from freight transportation are a significant source of greenhouse gas emissions [63]. The GOP of 2010 in USD was used to calculate the GDP of 2011. In this work, two metrics were used to quantify technological advancement: FTS and MCS per 100 persons. Regarding gauging the level of technology adoption, the HTX and EOC ratios of high-tech exports as a percentage of manufactured exports [62] were utilized. TRD is measured by comparing trade volume to GDP (as a percentage of GDP) [71].

It was possible to evaluate the elasticities of estimations and derive meaningful interpretations by applying the natural log alteration to the selected variables. The descriptive data of the Persian Gulf is presented in Tables 2 and 3 show the correlation matrix. The mean values of electric power consumption and openness to trade are greater than those of the other variables studied.

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Table 2. Descriptive statistics.

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Table 3. Matrix of correlations.

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Table 3 shows that compared to FTS, HTX, and MCS, EOC and TRD have a more significant positive correlation with CEM. GOP, on the other hand, has a negative relationship with CEM.

The statistics show that the Persian Gulf is among the worlds’ per capita polluted nations. Fig 1 elaborates a snapshot of each variable included in the panel. From the data perspective, United Arab Emirates is the Persian Gulf group leader in average per capita emission generation, followed by Oman, Saudi Arabia, Qatar, Kuwait, Iraq, and Bahrain (see Fig 2). However, Iraq has lower per capita emissions than the other Persian Gulf nations because of its high population. In addition, as seen in Fig 3, the Persian Gulf economies have recently adopted a new trend in technology adoption.

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Fig 1. Profile of each variables (Arab Countries from the Persian Gulf).

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Fig 2. A snapshot of Arab States from the Persian Gulf.

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Fig 3. A snapshot of technology innovation in the Arab States from the Persian Gulf.

https://doi.org/10.1371/journal.pone.0304088.g003

Statistics elaborate that the United Arab Emirates is the leader in adopting technology in the Arab States of the Persian Gulf and Saudi Arabia, followed by Iraq, Kuwait, Bahrain, Qatar, and Oman, respectively. However, in per capita electricity consumption, the dense nations of Bahrain lead all the other economies in the Persian Gulf. Qatar, Kuwait, United Arab Emirates, Saudi Arabia, Oman, and Iraq secure the last position in the significant electric power utilization per capita among the Arab States from the Persian Gulf.

Energy consumption and CO2 emissions are linked. Thus, we may assume that energy use raises the danger of environmental deterioration. As a result, ICT is helping to reduce greenhouse gas (GHG) emissions and restore environmental quality. Another area where Russia has a leg up on the competition is technological adoption (see Fig 4). Persian Gulf members Bahrain, Kuwait, Oman, Iraq, Saudi Arabia, Qatar, and the United Arab Emirates lead the way in the value addition of manufactured exports via cutting-edge technology (Fig 5).

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Fig 4. Technology adoption snapshot in the Arab States from the Persian Gulf.

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Fig 5. Technology adoption snapshot of Persian Gulf: High-technology exports.

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Before studying panel unit tests, preliminary testing is necessary for the cross-sectional dependence issue [72,73]. In Table 4 below, data from the CD tests are shown. The CD test shows the cross-sectional dependency between the input variables—FTS and MCS; GOP, TRD, HTX, EOC, and CEM in the Persian Gulf. However, CD tests reveal that the dataset on the cross-sectional dependence of the Persian Gulf economy is inaccurate. Consequently, root cause analysis cannot be performed on the panel dataset [74]. Therefore, panel fixed effects regression and Wald test-based unit root are employed to examine the panel dataset of the Persian Gulf in Table 5.

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Table 4. CD test.

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Table 5. Unit root.

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Table 6 displays data from the Newey-West standard error method and DK’s technical innovation estimators. The Newey–West standard error approach is shown in Table 6 at the bottom for robustness testing and strength estimation. There are two out of three technological innovation instruments, FTS and MCS, that have a considerable beneficial influence on CO2 emissions in the Persian Gulf, according to the estimations. It means that FTS and MCS heavily influence the amount of CO2 emitted. The estimates predict that a 1% increase in FTS and MCS in the Persian Gulf group will lead to a 0.604% and 0.082% rise in per capita CEM. As a crucial component of the digital economy, technology innovation produces jobs and enhances the quality of life [75]. On the other hand, business growth and production levels are significant sources of new CO2 emissions [76].

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Table 6. Technology innovation model.

https://doi.org/10.1371/journal.pone.0304088.t006

Even though it will reduce CEM per capita by 0.2355 percent, the effect is statistically negligible. In Persian Gulf countries, MCS cut CEM, which is corroborated by the findings of [77]. Interactive regressions show that ICT dramatically reduces CEM in the United Arab Emirates. The Persian Gulf countries are pursuing energy-efficient ICT to speed up the internet and reduce the energy needed to keep the environment in good condition. According to Chavanne et al. [78], ICT acts as a constructive tool for ecological quality by mitigating GHG emissions. According to Zhou et al. [79], CEM declines as ubiquitous networking technologies advance.

Furthermore, the Persian Gulf region’s CO2 emissions are significantly reduced due to greater trade openness. When countries open their markets, they tend to rely on more energy, which harms their environment. The U-Shape EKC theory is confirmed because of the study, and the EKC hypothesis is also tested in technological innovation models [80]. The U-Shape EKC theory predicts that CEM starts to rise in the final stages of economic expansion, which is consistent with the results [81]. Table 6 includes the results of the Newey–West regression, which confirms the accuracy of the DK regression’s values.

Table 7 displays information from the Newey-West standard error method and the DK technology adoption model. Environmental deterioration may occur due to excessive electricity consumption and it leads towards the consumption of energy sources [82], while more use of non-renewable energy sources causes increase in carbon emissions. Zhang et al. [83] investigated the impact of energy inequalities on environment and it has found that it also contribute to increased CEM in the BRI area [84]. There is support for the U-Shape EKC theory in the Persian Gulf area based on technological innovation and adoption models [85,86]. The Persian Gulf region’s CEM is impacted favorably by high-tech exports and electric power consumption, magnitudes of 0.712 and 0.921, respectively. The impact of energy use on CO2 emissions is thought to be more significant than the impact of high-tech exports.

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Table 7. Technology adoption model.

https://doi.org/10.1371/journal.pone.0304088.t007

Economic growth in the Persian Gulf countries reduces CO2 emissions statistically. However, CO2 emissions may rise at a later stage of development because of the substantial positive magnitude of GDP2. Testing the EKC hypothesis, the U-Shape EKC hypothesis was shown to be true in the technology adoption models. The U-Shape EKC theory suggests that GDP decreases CO2 emissions in the initial stages of growth; CO2 emissions begin to rise in later stages of development, per the findings of Liu et al. [68]. Hafeez et al. [84] evaluated China’s energy usage, trade openness, and income levels, finding that ecological degradation has been crucial in China owing to rapid expansion in the previous several decades.

Trade openness is also 0.661, which means that a one percent increase in TRD would raise the Persian Gulf CO2 emissions by 0.66% on average. The air pollution indicators rise due to value-added trade [67]. Energy consumption, economic development, and openness to international commerce all diminish burden on energy requirement after the verge income level for Persian Gulf nations [84]. Therefore, free trade agreements are more effective in reducing bilateral CO2 emissions [63]. The outcomes of the Newey–West regression may be seen at the bottom of Table 7 after the results of the DK regression.

In Table 8, high f-stat value (64.60) indicates high cross-sectional effect, while p-value (0.082) discloses that this effect is not significant. Various diagnostic tests are applied to determine the residual and coefficient diagnostic. The results of coefficient diagnostic (confidence ellipse and Wald Test) test has been shown in Table 9 and Fig 6. While residual diagnostic (cross-section heteroskedasticity) test results are shown in Table 10 supports the study.

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Fig 6. Confidence ellipse diagnostic test.

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Table 8. Test cross-section fixed effects.

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Table 9. Wald test.

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Table 10. Panel cross-section heteroskedasticity LR test.

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4.1. Discussion

It is acknowledged that technological innovation tremendously changes the face of the world, thus developed and emerging economies are pressing importance installation and adoption of technological innovation. So, we need to do something about carbon emissions and environmental destruction. To find out what factors contribute to reaching carbon neutrality, the present study used a panel data set covering the years 1990–2021.

Technological innovation is crucial for reducing carbon emissions by providing solutions that mitigate environmental harm. Innovation may greatly decrease carbon emissions in different sectors through the development of renewable energy, energy-efficient technologies, and sustainable practices [87]. The finding discloses that a 1% increase in technological adoption influence carbon emission significantly. The findings are supported by the work of [39,72,88].

Moreover, technological adoption can lead to more efficient and cleaner energy production, transportation, and industrial processes, thereby reducing carbon emissions. For instance, the adoption of renewable energy sources like solar and wind power, along with advancements in battery technology, can help decarbonize the energy sector [89]. The findings revealed that technological adoption significantly influence the carbon emission in the perspective of Persian Gulf. The results of current study is supported by the [90–92].

Higher economic activity usually results in increased carbon emissions because of heightened production and energy usage. Various factors like technology efficiency, governmental actions, economic structural upheavals, and changes in behavior may impact this connection [93]. Advancements in technology, efficient legislation, and transitioning to cleaner economic practices can separate economic expansion from carbon emissions. To achieve sustainable economic growth, it is essential to align economic advancement with environmental concerns and encourage the use of eco-friendly methods throughout various sectors of society [94]. The findings is supported by the work of [95–97].

5. Conclusions

The main goal of this paper is to describe the first approach to quantify ICT proposed in the literature, using the most recent dataset from 1990 to 2021 to link each Persian Gulf economy’s adoption rate of information technology as a replacement indicator for measuring ICT. In this research, the controls utilized to analyze the EKC evidence included technological innovation, technological adoption, and trade openness. The cross-sectional reliance among the Persian Gulf countries was evaluated using the CD tests. The unit root test via panel fixed effects regression was utilized to address the cross-sectional dependency. The results showed no unit root process for the research variables.

The resilient parameters for the technological innovation and adoption models were computed using the DK technique in the case of CD. The result estimates for technological innovation and adoption models were checked using the Newey-West standard error method. In technical innovation and adoption techniques for the Persian Gulf region, the empirical estimates support the U-shape EKC theory and provide insight into several intriguing findings. Fixed internet and phone subscriptions dramatically lower CO2 emissions, per technological innovation models. In contrast, mobile cellular subscriptions lower CO2 emissions in the economies of the Persian Gulf.

The Persian Gulf countries are focusing on ICT, using less energy to speed up the internet, and minimizing their energy use to save the environment. High-technology exports, indicators of technology adoption, and electric power consumption all show empirically significant positive effects on CO2 emissions for countries in the Persian Gulf. Due to trade openness, technology innovation and adoption models in the Persian Gulf contribute to CO2 emissions. GDP is a crucial element in the link between technology and CO2 emissions.

The results of this study suggest that ICT reforms may benefit the environment by encouraging technological innovation and adoption. Policymakers could encourage mobile cellular subscriptions to reduce CO2 emissions, address the positive impact of energy consumption on CO2 emissions, allocate renewable energy resources and energy conservation projects for improved environmental quality, and introduce eco-friendly technological advancements.

Findings from the Persian Gulf region may be generalized to the other regions using the same methodology for additional case studies. Additionally, heteroskedasticity and cross-sectional dependency must be considered in the study domain. Finally, when comparing the CO2 emissions of various technologies in future research, it is essential to consider each country’s energy matrix.

References

  1. 1. Hughes B.B., Narayan K., Enhancing integrated analysis of national and global goal pursuit by endogenizing economic productivity, PLoS One. (2021). pmid:33630871
  2. 2. Song Y., Yang T., Zhang M., Research on the impact of environmental regulation on enterprise technology innovation—an empirical analysis based on Chinese provincial panel data, Environ. Sci. Pollut. Res. (2019). pmid:31134544
  3. 3. Zhao S., Jiang Y., Wang S., Innovation stages, knowledge spillover, and green economy development: moderating role of absorptive capacity and environmental regulation, Environ. Sci. Pollut. Res. (2019). pmid:31256405
  4. 4. Al-Mulali U., Sheau-Ting L., Ozturk I., The global move toward Internet shopping and its influence on pollution: an empirical analysis, Environ. Sci. Pollut. Res. (2015). pmid:25631741
  5. 5. Asongu S., Akpan U.S., Isihak S.R., Determinants of foreign direct investment in fast-growing economies: evidence from the BRICS and MINT countries, Financ. Innov. 4 (2018) 1–17. https://doi.org/10.1186/S40854-018-0114-0/TABLES/9.
  6. 6. Hasan M.K., Habib A.A., Islam S., Balfaqih M., Alfawaz K.M., Singh D., Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations, Energies 2023, Vol. 16, Page 1140. 16 (2023) 1140. https://doi.org/10.3390/EN16031140.
  7. 7. Amin A., Altinoz B., Dogan E., Analyzing the determinants of carbon emissions from transportation in European countries: the role of renewable energy and urbanization, Clean Technol. Environ. Policy. 22 (2020) 1725–1734. https://doi.org/10.1007/S10098-020-01910-2/TABLES/5.
  8. 8. Hailiang Z., Chau K.Y., Waqas M., Does green finance and renewable energy promote tourism for sustainable development: Empirical evidence from China, Renew. Energy. 207 (2023) 660–671. https://doi.org/10.1016/j.renene.2023.03.032.
  9. 9. Li B., Amin A., Nureen N., Saqib N., Wang L.Y., Rehman M.A., Assessing factors influencing renewable energy deployment and the role of natural resources in MENA countries, Resour. Policy. 88 (2024) 104417. https://doi.org/10.1016/J.RESOURPOL.2023.104417.
  10. 10. Mngumi F., Shaorong S., Shair F., Waqas M., Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure, Environ. Sci. Pollut. Res. 1 (2022) 1–13. pmid:35386082
  11. 11. Houghton J.W., ICT and the environment in developing countries: A review of opportunities and developments, in: IFIP Adv. Inf. Commun. Technol., 2010. https://doi.org/10.1007/978-3-642-15479-9_23.
  12. 12. Yang L., Xu M., Yang Y., Fan J., Zhang X., Comparison of subsidy schemes for carbon capture utilization and storage (CCUS) investment based on real option approach: Evidence from China, Appl. Energy. (2019). https://doi.org/10.1016/j.apenergy.2019.113828.
  13. 13. Amin A., bte Mohamed Yusoff N.Y., Yousaf H., Peng S., Işık C., Akbar M., et al, The influence of renewable and non-renewable energy on carbon emissions in Pakistan: evidence from stochastic impacts by regression on population, affluence, and technology model, Front. Environ. Sci. 11 (2023) 1182055. https://doi.org/10.3389/FENVS.2023.1182055/BIBTEX.
  14. 14. Tang Y.M., Chau K.Y., Fatima A., Waqas M., Industry 4.0 technology and circular economy practices: business management strategies for environmental sustainability, Environ. Sci. Pollut. Res. (2022). pmid:35218493
  15. 15. Shepelev V., Glushkov A., Slobodin I., Balfaqih M., Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions, Math. 2023, Vol. 11, Page 3591. 11 (2023) 3591. https://doi.org/10.3390/MATH11163591.
  16. 16. Nocera S., Tonin S., A joint probability density function for reducing the uncertainty of marginal social cost of carbon evaluation in transport planning, Adv. Intell. Syst. Comput. (2014). https://doi.org/10.1007/978-3-319-04630-3_9.
  17. 17. Amin A., Ameer W., Yousaf H., Akbar M., Financial Development, Institutional Quality, and the Influence of Various Environmental Factors on Carbon Dioxide Emissions: Exploring the Nexus in China, Front. Environ. Sci. 9 (2022) 838714. https://doi.org/10.3389/FENVS.2021.838714/BIBTEX.
  18. 18. Owuru C., Osakwe J.O., Akinsola S., Utilisation of Information and Communication Technology for Environmental Sustainability: A Global Perspective, J. Inf. Syst. Informatics. (2021). https://doi.org/10.51519/journalisi.v3i3.163.
  19. 19. Añón Higón D., Gholami R., Shirazi F., ICT and environmental sustainability: A global perspective, Telemat. Informatics. (2017). https://doi.org/10.1016/j.tele.2017.01.001.
  20. 20. Amri F., Carbon dioxide emissions, total factor productivity, ICT, trade, financial development, and energy consumption: testing environmental Kuznets curve hypothesis for Tunisia, Environ. Sci. Pollut. Res. (2018). https://doi.org/10.1007/s11356-018-3331-1.
  21. 21. Weili L., Khan H., khan I., Han L., The impact of information and communication technology, financial development, and energy consumption on carbon dioxide emission: evidence from the Belt and Road countries, Environ. Sci. Pollut. Res. (2022) 1–16. pmid:34984617
  22. 22. Ishida H., The effect of ICT development on economic growth and energy consumption in Japan, Telemat. Informatics. (2015). https://doi.org/10.1016/j.tele.2014.04.003.
  23. 23. Caglar A.E., Mert M., Boluk G., Testing the role of information and communication technologies and renewable energy consumption in ecological footprint quality: Evidence from world top 10 pollutant footprint countries, J. Clean. Prod. 298 (2021) 126784. https://doi.org/10.1016/j.jclepro.2021.126784.
  24. 24. Majeed M.T., Information and communication technology (ICT) and environmental sustainability in developed and developing countries, Pakistan J. Commer. Soc. Sci. (2018).
  25. 25. Jere J.N., Ngidi N., A technology, organisation and environment framework analysis of information and communication technology adoption by small and medium enterprises in Pietermaritzburg, SA J. Inf. Manag. (2020). https://doi.org/10.4102/sajim.v22i1.1166.
  26. 26. Sharma G.D., Rahman M.M., Jain M., Chopra R., Nexus between energy consumption, information and communications technology, and economic growth: An enquiry into emerging Asian countries, J. Public Aff. (2021). https://doi.org/10.1002/pa.2172.
  27. 27. Amin A., Dogan E., The role of economic policy uncertainty in the energy-environment nexus for China: Evidence from the novel dynamic simulations method, J. Environ. Manage. 292 (2021) 112865. pmid:34051471
  28. 28. Abbas A.B., Almohammedi A.A., Balfaqih M., Darshi S., Conceptual Design of Wireless Smart Grid for the Optimization of Electric Transmission in Iraq, 2023 3rd Int. Conf. Comput. Inf. Technol. ICCIT 2023. (2023) 577–580. https://doi.org/10.1109/ICCIT58132.2023.10273892.
  29. 29. Wang J., Xu Y., Internet usage, human capital and CO2 emissions: A global perspective, Sustain. (2021). https://doi.org/10.3390/su13158268.
  30. 30. Adedoyin F.F., Zakari A., Energy consumption, economic expansion, and CO2 emission in the UK: The role of economic policy uncertainty, Sci. Total Environ. 738 (2020) 140014. pmid:32806391
  31. 31. Haseeb A., Xia E., Saud S., Ahmad A., Khurshid H., Does information and communication technologies improve environmental quality in the era of globalization? An empirical analysis, Environ. Sci. Pollut. Res. (2019). pmid:30710332
  32. 32. Biryukova O. V., Matiukhina A.I., ICT Services Trade in the BRICS Countries: Special and Common Features, J. Knowl. Econ. (2019). https://doi.org/10.1007/s13132-017-0517-6.
  33. 33. Dauda L., Long X., Mensah C.N., Salman M., The effects of economic growth and innovation on CO2 emissions in different regions, Environ. Sci. Pollut. Res. (2019). pmid:30919181
  34. 34. Li G., Li X., Wang N., Research on the influence of environmental regulation on technological innovation efficiency of manufacturing industry in China, Int. J. Environ. Sci. Technol. (2022). https://doi.org/10.1007/s13762-021-03412-0.
  35. 35. Zhu Wang, Qiu Zhu, Effects of Environmental Regulations on Technological Innovation Efficiency in China’s Industrial Enterprises: A Spatial Analysis, Sustainability. 11 (2019) 2186. https://doi.org/10.3390/su11072186.
  36. 36. Yale , Environmental Performance Index 2020, Yale Univ. (2020).
  37. 37. Yassin J., Aralas S., Basa D.E.K., Tourism density effect on environmental performance index: Evidence in asean countries, Appl. Environ. Res. (2021). https://doi.org/10.35762/AER.2021.43.1.7.
  38. 38. Olabemiwo F.A., Danmaliki G.I., Oyehan T.A., Tawabini B.S., Forecasting CO2 emissions in the Persian gulf states, Glob. J. Environ. Sci. Manag. (2017). https://doi.org/10.22034/gjesm.2017.03.01.001.
  39. 39. ur R S. Malik Z.U. Rahman M. Ibrahim , Validity of Environmental Kuznets Curve in the Malaysian Economy: A Fresh Evidence, J. Econ. Impact. (2022). https://doi.org/10.52223/jei4032214.
  40. 40. Bildirici M.E., Castanho R.A., Kayıkçı F., Genç S.Y., ICT, Energy Intensity, and CO2 Emission Nexus, Energies. (2022). https://doi.org/10.3390/en15134567.
  41. 41. Faisal F., Azizullah T. Tursoy, Pervaiz R, Does ICT lessen CO2 emissions for fast-emerging economies? An application of the heterogeneous panel estimations, Environ. Sci. Pollut. Res. (2020). pmid:31942718
  42. 42. Tsimisaraka R.S.M., Xiang L., Andrianarivo A.R.N.A., Josoa E.Z., Khan N., Hanif M.S., et al, Impact of Financial Inclusion, Globalization, Renewable Energy, ICT, and Economic Growth on CO2 Emission in OBOR Countries, Sustain. 2023, Vol. 15, Page 6534. 15 (2023) 6534. https://doi.org/10.3390/SU15086534.
  43. 43. Adebayo T.S., Agyekum E.B., Altuntaş M., Khudoyqulov S., Zawbaa H.M., Kamel S., Does information and communication technology impede environmental degradation? fresh insights from non-parametric approaches, Heliyon. (2022). pmid:35313485
  44. 44. Irfan M., Chen Z., Adebayo T.S., Al-Faryan M.A.S., Socio-economic and technological drivers of sustainability and resources management: Demonstrating the role of information and communications technology and financial development using advanced wavelet coherence approach, Resour. Policy. (2022). https://doi.org/10.1016/j.resourpol.2022.103038.
  45. 45. Ben Lahouel B., Taleb L., Ben Zaied Y., Managi S., Does ICT change the relationship between total factor productivity and CO2 emissions? Evidence based on a nonlinear model, Energy Econ. 101 (2021) 105406. https://doi.org/10.1016/j.eneco.2021.105406.
  46. 46. Razzaq A., Sharif A., Ahmad P., Jermsittiparsert K., Asymmetric role of tourism development and technology innovation on carbon dioxide emission reduction in the Chinese economy: Fresh insights from QARDL approach, Sustain. Dev. (2021). https://doi.org/10.1002/sd.2139.
  47. 47. Lau H.C., Decarbonization of ASEAN’s power sector: A holistic approach, Energy Reports. (2023). https://doi.org/10.1016/j.egyr.2022.11.209.
  48. 48. Ahmed N., Areche F.O., Nieto D.D.C., Borda R.F.C., Gonzales B.C., Senkus P., et al, Nexus between Cyclical Innovation in Green Technologies and CO2 Emissions in Nordic Countries: Consent toward Environmental Sustainability, Sustain. (2022). https://doi.org/10.3390/su141811768.
  49. 49. Peng G., Meng F., Ahmed Z., Ahmad M., Kurbonov K., Economic growth, technology, and CO2 emissions in BRICS: Investigating the non-linear impacts of economic complexity, Environ. Sci. Pollut. Res. (2022). pmid:35526204
  50. 50. da S C. Cachola M. Ciotta A. Azevedo dos Santos, Peyerl D, Deploying of the carbon capture technologies for CO2 emission mitigation in the industrial sectors, Carbon Capture Sci. Technol. (2023). https://doi.org/10.1016/j.ccst.2023.100102.
  51. 51. Onofrei M., Vatamanu A.F., Cigu E., The Relationship Between Economic Growth and CO2 Emissions in EU Countries: A Cointegration Analysis, Front. Environ. Sci. (2022). https://doi.org/10.3389/fenvs.2022.934885.
  52. 52. Namahoro J.P., Wu Q., Zhou N., Xue S., Impact of energy intensity, renewable energy, and economic growth on CO2 emissions: Evidence from Africa across regions and income levels, Renew. Sustain. Energy Rev. 147 (2021) 111233. https://doi.org/10.1016/j.rser.2021.111233.
  53. 53. Cai Y., Sam C.Y., Chang T., Nexus between clean energy consumption, economic growth and CO2 emissions, J. Clean. Prod. 182 (2018) 1001–1011. https://doi.org/10.1016/j.jclepro.2018.02.035.
  54. 54. Gorus M.S., Aydin M., The relationship between energy consumption, economic growth, and CO2 emission in MENA countries: Causality analysis in the frequency domain, Energy. (2019). https://doi.org/10.1016/j.energy.2018.11.139.
  55. 55. Ait Soussane J., Mansouri D., Mansouri Z., Impact of the divestment of Spanish FDI on economic growth of Morocco: an econometric analysis of 13 country-of-origin, J. Chinese Econ. Foreign Trade Stud. 16 (2023) 40–54. https://doi.org/10.1108/JCEFTS-04-2022-0024/FULL/XML.
  56. 56. Ullah I., Rehman A., Svobodova L., Akbar A., Shah M.H., Zeeshan M., et al, Investigating Relationships Between Tourism, Economic Growth, and CO2 Emissions in Brazil: An Application of the Nonlinear ARDL Approach, Front. Environ. Sci. (2022). https://doi.org/10.3389/fenvs.2022.843906.
  57. 57. Liu X., Latif K., Latif Z., Li N., Relationship between economic growth and CO2 emissions: does governance matter?, Environ. Sci. Pollut. Res. (2020). pmid:32152858
  58. 58. Salam S., Hafeez M., Mahmood M.T., Iqbal K., Akbar K., The Dynamic Relation between Technology Adoption, Technology Innovation, Human Capital and Economy: Comparison of Lower-Middle-Income Countries, Interdiscip. Descr. Complex Syst. (2019). https://doi.org/10.7906/indecs.17.1.15.
  59. 59. Hafeez M., Chunhui Y., Strohmaier D., Ahmed M., Jie L., Does finance affect environmental degradation: evidence from One Belt and One Road Initiative region?, Environ. Sci. Pollut. Res. (2018). pmid:29357077
  60. 60. Hu A.H., Huang L.H., Chang Y.L., Assessing corporate sustainability of the ICT sector in Taiwan on the basis of un Sustainable Development Goals, in: 2016 Electron. Goes Green 2016+, EGG 2016, 2017. https://doi.org/10.1109/EGG.2016.7829866.
  61. 61. Yi J., Dai S., Li L., Cheng J., How does digital economy development affect renewable energy innovation?, Renew. Sustain. Energy Rev. 192 (2024) 114221. https://doi.org/10.1016/J.RSER.2023.114221.
  62. 62. Ioannides Y.M., Overman H.G., Rossi-Hansberg E.A., Schmidheiny K., Effect of Information and Communication Technologies on Urban Structure, SSRN Electron. J. (2021). https://doi.org/10.2139/ssrn.998769.
  63. 63. Yao X., Yasmeen R., Li Y., Hafeez M., Padda I.U.H., Free trade agreements and environment for sustainable development: A gravity model analysis, Sustain. (2019). https://doi.org/10.3390/su11030597.
  64. 64. Hafeez M., Yuan C., Shahzad K., Aziz B., Iqbal K., Raza S., An empirical evaluation of financial development-carbon footprint nexus in One Belt and Road region, Environ. Sci. Pollut. Res. 26 (2019) 25026–25036. pmid:31250390
  65. 65. Özokcu S., Özdemir Ö., Economic growth, energy, and environmental Kuznets curve, Renew. Sustain. Energy Rev. (2017). https://doi.org/10.1016/j.rser.2017.01.059.
  66. 66. Sarkodie S.A., Strezov V., Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries, Sci. Total Environ. 646 (2019) 862–871. pmid:30064112
  67. 67. Pei Y., Huang T., You J., Nonparametric fixed effects model for panel data with locally stationary regressors, J. Econom. (2018). https://doi.org/10.1016/j.jeconom.2017.06.023.
  68. 68. Liu J., Yuan C., Hafeez M., Yuan Q., The relationship between environment and logistics performance: Evidence from Asian countries, J. Clean. Prod. (2018). https://doi.org/10.1016/j.jclepro.2018.08.310.
  69. 69. Hafeez M., Yuan C., Yuan Q., Zhuo Z., Stromaier D., Sultan Musaad O A., A global prospective of environmental degradations: economy and finance, Environ. Sci. Pollut. Res. (2019). pmid:31273666
  70. 70. Magazzino C., Mele M., Morelli G., Schneider N., The nexus between information technology and environmental pollution: Application of a new machine learning algorithm to OECD countries, Util. Policy. 72 (2021) 101256. https://doi.org/10.1016/J.JUP.2021.101256.
  71. 71. Yasmeen R., Li Y., Hafeez M., Ahmad H., The trade-environment nexus in light of governance: a global potential, Environ. Sci. Pollut. Res. (2018). pmid:30302733
  72. 72. Su C.W., Umar M., Khan Z., Does fiscal decentralization and eco-innovation promote renewable energy consumption? Analyzing the role of political risk, Sci. Total Environ. (2021). pmid:33181994
  73. 73. Pesaran M.H., General diagnostic tests for cross-sectional dependence in panels, Empir. Econ. 60 (2021) 13–50. https://doi.org/10.1007/s00181-020-01875-7.
  74. 74. Hai Ming L., Gang L., Hua H., Waqas M., Modeling the influencing factors of electronic word-of-mouth about CSR on social networking sites, Environ. Sci. Pollut. Res. (2022) 1–18. pmid:35501440
  75. 75. Du L., Razzaq A., Waqas M., The impact of COVID ‑ 19 on small ‑ and medium ‑ sized enterprises (SMEs): empirical evidence for green economic implications, Environ. Sci. Pollut. Res. (2022). pmid:35917071
  76. 76. Li M., Yao-Ping Peng M., Nazar R., Ngozi Adeleye B., Shang M., Waqas M., How Does Energy Efficiency Mitigate Carbon Emissions Without Reducing Economic Growth in Post COVID-19 Era, Front. Energy Res. 10 (2022) 1–14.
  77. 77. Asongu S.A., Le Roux S., Biekpe N., Enhancing ICT for environmental sustainability in sub-Saharan Africa, Technol. Forecast. Soc. Change. (2018). https://doi.org/10.1016/j.techfore.2017.09.022.
  78. 78. Chavanne X., Schinella S., Marquet D., Frangi J.P., Le Masson S., Electricity consumption of telecommunication equipment to achieve a telemeeting, Appl. Energy. (2015). https://doi.org/10.1016/j.apenergy.2014.10.027.
  79. 79. Zhou L., Ke Z., Waqas M., Beyond the Arena: How sports economics is advancing China’s sustainable development goals, Heliyon. (2023) e18074. pmid:37501984
  80. 80. You Z., Li L., Waqas M., How do information and communication technology, human capital and renewable energy affect CO2 emission; new insights from BRI countries, Heliyon. 0 (2024) e26481. pmid:38420430
  81. 81. Nasir M.H., Wen J., Nassani A.A., Haffar M., Igharo A.E., Musibau H.O., et al, Energy Security and Energy Poverty in Emerging Economies: A Step Towards Sustainable Energy Efficiency, Front. Energy Res. 10 (2022) 1–12. https://doi.org/10.3389/fenrg.2022.834614.
  82. 82. Cao Y.H., You J.X., Liu H.C., Optimal environmental regulation intensity of manufacturing technology innovation in view of pollution heterogeneity, Sustain. (2017). https://doi.org/10.3390/su9071240.
  83. 83. Zhang J., Alharthi M., Abbas Q., Li W., Mohsin M., Jamal K., et al, Reassessing the environmental kuznets curve in relation to energy efficiency and economic growth, Sustain. 12 (2020) 1–21. https://doi.org/10.3390/su12208346.
  84. 84. Hafeez M., Yuan C., Khelfaoui I., Musaad A.S.O., Akbar M.W., Jie L., Evaluating the energy consumption inequalities in the one belt and one road region: Implications for the environment, Energies. (2019). https://doi.org/10.3390/en12071358.
  85. 85. Zhu L., Optimization and Simulation for E-Commerce Supply Chain in the Internet of Things Environment, Complexity. (2020). https://doi.org/10.1155/2020/8821128.
  86. 86. Vera Y.E.G., Dufo-López R., Bernal-Agustín J.L., Energy management in microgrids with renewable energy sources: A literature review, Appl. Sci. (2019). https://doi.org/10.3390/app9183854.
  87. 87. Zhong S., Chen J., Ur Z., Faiz R., Quantifying digital economy and green initiatives for carbon neutrality targets: a Kilian bias ‑ adjusted bootstrap model evaluation of China economy, Environ. Sci. Pollut. Res. (2024). https://doi.org/10.1007/s11356-023-31445-0.
  88. 88. Su C.W., Xie Y., Shahab S., Faisal C.M.N., Hafeez M., Qamri G.M., Towards achieving sustainable development: Role of technology innovation, technology adoption and co2 emission for brics, Int. J. Environ. Res. Public Health. 18 (2021). pmid:33401479
  89. 89. Imran M., Rahman Z.U., Atta A., Afzal F., Ramzan S., Analyzing the Association Among Energy Consumption, CO2 Emission and Per Capita Economic Growth in the Perspective of Net Oil Importing Countries, 21 (2022) 243–253.
  90. 90. Ledna C., Muratori M., Brooker A., Wood E., Greene D., How to support EV adoption: Tradeoffs between charging infrastructure investments and vehicle subsidies in California, Energy Policy. (2022). https://doi.org/10.1016/j.enpol.2022.112931.
  91. 91. Rosa L., Gabrielli P., Achieving net-zero emissions in agriculture: a review, Environ. Res. Lett. (2023). https://doi.org/10.1088/1748-9326/acd5e8.
  92. 92. Chen M., Cui Y., Jiang S., Forsell N., Toward carbon neutrality before 2060: Trajectory and technical mitigation potential of non-CO2 greenhouse gas emissions from Chinese agriculture, J. Clean. Prod. (2022). https://doi.org/10.1016/j.jclepro.2022.133186.
  93. 93. Mirziyoyeva Z., Salahodjaev R., Renewable energy, GDP and CO2 emissions in high-globalized countries, Front. Energy Res. (2023). https://doi.org/10.3389/fenrg.2023.1123269.
  94. 94. Mai T.N., Renewable Energy, GDP (Gross Domestic Product), FDI (Foreign Direct Investment) and CO2 Emissions in Southeast Asia Countries, Int. J. Energy Econ. Policy. (2023). https://doi.org/10.32479/ijeep.14022.
  95. 95. Ahmed K., Perspective on China’s commitment to carbon neutrality under the innovation-energy-emissions nexus, J. Clean. Prod. (2023). https://doi.org/10.1016/j.jclepro.2023.136202.
  96. 96. Magazzino C., Toma P., Fusco G., Valente D., Petrosillo I., Renewable energy consumption, environmental degradation and economic growth: the greener the richer?, Ecol. Indic. (2022). https://doi.org/10.1016/j.ecolind.2022.108912.
  97. 97. Umar M., Ji X., Kirikkaleli D., Alola A.A., The imperativeness of environmental quality in the United States transportation sector amidst biomass-fossil energy consumption and growth, J. Clean. Prod. 285 (2021) 124863. https://doi.org/10.1016/j.jclepro.2020.124863.