The impact of information and communication technology (ict) on environmental sustainability and the potential of applying ict to promote green and sustainable development in vietnam

Dissertation submitted in partial fulfillment of the Requirement for the MSc in Finance FINANCE DISSERTATION ON THE IMPACT OF INFORMATION AND COMMUNICATION TECHNOLOGY ICT ON ENVIRONME

INTRODUCTION

Background of the Study

1 Overview of global environmental challenges and sustainability

In recent decades, environmental issues have become increasingly important globally due to their extensive impact and the socioeconomic factors involved Initially, the focus was on pollution stemming from industrialization and urbanization, particularly air and water contamination in the U.S during the 20th century By the 1960s, the scope of awareness broadened to include soil erosion, pesticide contamination, deforestation, and declining wildlife, highlighted by environmental scientists, activists, and policymakers These concerns have now converged into the overarching issue of environmental degradation.

Key events such as the first Earth Day in 1970 and the 1972 United Nations Conference on the Human Environment in Stockholm were instrumental in elevating the concept of

The 1992 United Nations Conference on Environment and Development in Rio de Janeiro marked a pivotal moment for international environmental quality, coinciding with the rise of influential "Green Parties" in Europe This event highlighted the growing awareness of environmental issues among citizens and governments globally Today, human-induced climate change remains a critical priority on the global policy agenda, reflecting the ongoing commitment to addressing environmental challenges (Dunlap & Jorgenson, 2012).

Environmental problems are commonly acknowledged but often misunderstood A clearer perspective can be gained by applying fundamental ecological principles, as ecologists note that the environment provides essential "goods and services" to humanity (de Groot et al., 2002) These services can be categorized into three main functions that sustain both human populations and other species (Dunlap & Catton Jr, 2002).

Firstly, the environment supplies essential resources for life, including clean air, water, food, shelter, and natural resources used in industrial economies This role, known as the

The "sustenance base" acts as a vital "supply depot," offering both renewable resources like water and non-renewable resources such as fossil fuels However, the overexploitation of these resources can result in significant shortages and potential scarcities, highlighting the need for sustainable management practices.

Humans generate a vast amount of waste, far surpassing any other species, necessitating the environment to function as a "sink" for recycling or neutralizing these byproducts When waste production, including urban sewage and industrial emissions, exceeds the environment's ability to manage it, pollution of air and water becomes unavoidable (Piccolo et al., 2022).

Finally, like all species, humans require a place to live, and the environment provides this

The term "habitat" encompasses the places where I live, work, play, and travel, including homes, factories, shopping centers, transportation systems, and recreational areas One of the essential functions of the environment is to offer "living space" for human populations However, when this space is overused, it can lead to issues such as overcrowding and overpopulation, both in urban areas and globally (Piccolo et al., 2022).

Environmental issues such as pollution, resource depletion, and overcrowding arise when humans exceed the natural limits of the environment The environment must fulfill multiple functions simultaneously, and overexploitation of one aspect can hinder its ability to support others For example, using land for waste disposal can compromise its suitability for residential use Additionally, hazardous waste from landfills can contaminate soil and water, making areas unfit for drinking water or agriculture The conversion of farmland and forests into residential areas diminishes the land's ability to produce food and timber while disrupting wildlife habitats.

Understanding the environment's roles as a supply depot, living space, and waste repository is crucial for grasping the evolution of environmental issues The 1973-1974 energy crisis underscored the industrial world's reliance on finite fossil fuels Additionally, problems like deforestation, biodiversity loss, ozone depletion, and climate change stem from industrialization and heightened resource consumption These challenges highlight the global nature of environmental problems, where exceeding an ecosystem's capacity in one area can disrupt its overall functionality and hinder its ability to perform other essential roles.

Human overexploitation of natural resources has led to significant ecological crises, exemplified by the near destruction of the Aral Sea from industrial pollution and agricultural water diversion Alarmingly, ecologists and climatologists warn that ongoing dependence on fossil fuels may instigate global climate change, resulting in unpredictable and possibly irreversible ecological impacts that threaten both human populations and biodiversity (Matson et al., 2010).

The "ecological footprint" measures humanity's total impact on the global ecosystem, including resource consumption, waste generation, and land use Research indicates that current human populations and lifestyles are unsustainable, leading to the depletion of non-renewable resources such as fossil fuels, shortages of renewable resources like freshwater and forests, and rising pollution levels, especially hazardous waste.

In other words, the growing human population is exceeding the global ecosystem's

Historically, "limits to growth" emphasized the depletion of food supplies and natural resources like oil Today, "ecological limits" highlight the Earth's finite capacity to support essential functions without compromising its own health A key ecological limit is the atmosphere's limited ability to absorb greenhouse gas emissions, which poses a significant challenge in preventing global warming.

Global environmental challenges reveal the unequal contributions of different societies to current ecological issues Analysts note that the ecological footprints of poorer nations and their citizens are significantly smaller compared to those of wealthier nations.

Understanding the three functions of the environment is essential for clarifying ecological issues and revealing how these challenges are increasingly produced within a globalized economic framework.

In the global economy, resource extraction and waste management are often separated from processing and disposal sites, leading to significant ecological degradation in poorer nations This imbalance is perpetuated by wealthy nations that dominate the global economy, which results in detrimental economic relationships Additionally, poorer countries are particularly susceptible to global issues such as climate change, primarily driven by the actions of wealthier nations.

Environmental social scientists are increasingly utilizing comparative and cross-national studies to analyze the global generation and impact of ecological issues Many of these studies are guided by World Systems Analysis (WSA), a comprehensive framework for understanding the global economy Initially, WSA-based research predicted greenhouse gas emissions based on a nation's position within the world system—whether core, semi-peripheral, or peripheral Over time, scholars have expanded on Stephen Bunker’s foundational work to investigate the transnational flow of ecological goods, creating advanced models of "ecologically unequal exchange" that differentiate between the three fundamental roles of the environment.

2009) The United Nations Commission on Sustainable Development (UNCSD) devised a framework of monitoring the various sustainability indicators for assessing the performance of government towards sustainable development goals (Labuschagne et al.,

The structure of framework comprises four dimensions viz social, environment, economic and institutional and it is broken down into 38 sub-indicators and 15 main indicators (Fig

1) and has also formulated sustainability metrics covering three dimensions environment, economic and social which are further sub-divided into set of indicators (Labuschagne et al., 2005)

Figure 1: The United Nations Commission for Sustainable Development (UNCSD) theme indicator framework

Research Questions

The primary challenge is the ambiguity surrounding the role of Information and Communication Technology (ICT) in promoting environmental sustainability, leading to uncertainty about its actual impact This lack of clarity prompts three related questions that need to be addressed.

1 How does environmental sustainability occur? What are the specific factors influencing environmental sustainability?

2 How does ICT impact environmental sustainability?

3 What are the potential applications of ICT for green development in Vietnam?

To address these questions, I propose that a thorough examination is necessary, focusing on the following areas:

1 Mechanisms of Environmental Sustainability: Understanding the specific factors and processes that contribute to environmental sustainability, including the roles of technology, policy, and societal behavior

2 Impact of ICT on Environmental Sustainability: Analyzing how ICT influences environmental sustainability, both positively and negatively, by examining its effects on energy consumption, resource use, and emissions

3 Potential ICT Applications for Green Development in Vietnam: Identifying and evaluating the potential applications of ICT that can promote green development in

Vietnam, including smart cities, sustainable transportation, and energy -efficient systems

This approach will provide a comprehensive understanding of the relationship between ICT and environmental sustainability, particularly in the context of Vietnam

This article primarily addresses the second and third questions while briefly discussing the debate surrounding Environmental Sustainability theories Drawing on the work of Sein and Soeftestad (2002), it utilizes a human development perspective as a holistic indicator of national development.

LITERATURE REVIEW

Concepts of environmental sustainability and ICT

Numerous studies have investigated the relationship between Information and Communication Technology (ICT) and environmental quality, yielding mixed empirical results Research typically approaches this relationship from three angles, with the first focusing on the direct impact of ICT on carbon emissions Notably, Lee and Brahmasrene (2014) were among the first to empirically analyze this connection in ASEAN countries from 1991 to 2009, revealing a significant positive correlation between ICT penetration, CO2 emissions, and economic growth.

Salahuddin et al (2016) conducted a study on the impact of internet usage and economic growth on CO2 emissions in OECD countries from 1991 to 2012, utilizing the pooled mean group estimator Their findings indicate that internet usage positively affects CO2 emissions; however, the small coefficient suggests that its rapid growth is not a significant environmental concern in this region Furthermore, the study reveals that economic growth does not exert notable short- or long-term effects on CO2 emissions.

Research by Al-Mulali et al (2015) indicates that internet retailing significantly contributes to increased CO2 emissions in developed nations Conversely, Danish et al (2019) found that information and communication technology (ICT) helps reduce emissions in high- and middle-income countries, but has the opposite effect in low-income countries, where energy consumption consistently raises emissions across all income levels They also noted an inverted U-shaped relationship between economic growth and emissions Additionally, Park et al (2018) revealed that in select EU countries, factors such as ICT penetration, financial development, economic growth, and trade openness lead to reduced CO2 emissions, whereas electricity consumption tends to increase them Furthermore, ệzcan and Apergis (2018) explored the impact of ICT on CO2 emissions within their study sample.

From 1990 to 2015, a study of 20 emerging countries indicated that increased internet usage significantly contributes to reducing CO2 emissions Tsaurai and Chimbo (2019) highlighted that investments in Information and Communication Technology (ICT) have been instrumental in decreasing air pollution in emerging markets In contrast, Amri et al (2019) identified a negative correlation between ICT and CO2 emissions in Tunisia, although this relationship was not statistically significant.

The second perspective in the literature examines the non-linear effects of increasing ICT penetration on the environment, illustrating an inverted U-shaped curve akin to the Environmental Kuznets Curve This indicates that while environmental degradation initially rises with ICT adoption up to a certain threshold, further increases in ICT usage eventually lead to a reduction in environmental harm, suggesting that greater ICT penetration contributes to long-term environmental benefits.

Higón et al (2017) identified an inverted U-shaped relationship between ICT and CO2 emissions across 142 countries from 1995 to 2010, indicating that developed nations have reached a level of ICT that facilitates emission reductions, unlike their developing counterparts In a similar vein, Asongu et al (2018) revealed that in 44 Sub-Saharan African countries between 2000 and 2012, increased internet adoption correlates with higher per capita CO2 emissions, while greater mobile phone usage is associated with a decrease in emissions from liquid fuel consumption.

The impact of macroeconomic factors on the relationship between Information and Communication Technology (ICT) and environmental quality is significant Research by Asongu (2018) indicates that ICT diffusion, when paired with trade openness, leads to a reduction in CO2 emissions across 44 Sub-Saharan African countries from 2000 to 2012 Additionally, Khan et al (2018) demonstrate that while both ICT and economic growth tend to increase CO2 emissions, their interaction has the potential to mitigate these emissions, suggesting that advancements in ICT can contribute positively to environmental quality.

Information and Communication Technology (ICT) plays a dual role in sustainability, offering both benefits and challenges On one hand, it aids in reducing CO2 emissions through enhanced process optimization, increased energy efficiency, and the development of smarter cities (Akande et al., 2019) On the other hand, the rising demand for ICT leads to higher electricity consumption, which in turn contributes to CO2 emissions (Dabbous, 2018) Additionally, the lifecycle of ICT products—including their production, use, and disposal—can negatively impact the environment, despite their economic advantages.

The impact of Information and Communication Technology (ICT) on CO2 emissions is multifaceted, encompassing direct, indirect, and rebound effects Direct effects arise from increased energy consumption during the production, use, and disposal of ICT products, accounting for 2-3% of global CO2 emissions (Danish, 2019) In contrast, indirect effects, such as enhanced digital communication and remote work, can lead to significant reductions in waste and carbon emissions, often surpassing the negative impacts of direct effects (Houghton, 2010) However, rebound effects can diminish these benefits, as efficiency gains from ICT may lead to higher overall resource consumption, particularly in developing countries with rising energy demands Consequently, the environmental implications of ICT are complex and warrant further research (Houghton, 2010).

Theories and models related to ICT and sustainable development

In the early 1990s, Grossman and Krueger (1991) presented evidence for the Environmental Kuznets Curve (EKC) hypothesis, which indicates that environmental quality initially deteriorates with rising per capita income but improves after reaching a certain income threshold.

The Environmental Kuznets Curve (EKC) illustrates an inverted U-shape, driven by three key effects: scale, composition, and technique The scale effect reveals that economic growth leads to increased energy consumption and emissions In contrast, the composition effect indicates a transition towards cleaner service sectors, enhancing environmental quality Additionally, the technique effect emphasizes that economic growth encourages the adoption of cleaner technologies, which mitigates pollution even as output rises Ultimately, the positive impacts of the composition and technique effects surpass the negative scale effect, resulting in improved environmental outcomes as economies develop.

The EKC hypothesis has been widely tested with mixed results For example, Jebli et al

(2016) confirmed an EKC for 25 OECD countries, while Nuroğlu and Kunst (2017) found no EKC in developing countries, and Ng et al (2020) showed moderate support for the EKC in 76 countries

In EKC studies, CO2 emissions are often utilized as indicators of environmental degradation, but alternative measures exist Almeida et al (2017) investigated the EKC hypothesis across 152 countries using a composite ecological index rather than solely focusing on CO2 emissions Their findings did not support the EKC hypothesis, highlighting that economic growth alone is inadequate for enhancing environmental quality.

Impact of ICT development on GHG emissions and sustainability

The impact of information and communication technologies (ICT) on CO2 emissions varies by a country's development stage and economic structure ICT can both positively and negatively affect the environment

In developed countries, information and communication technology (ICT) drives economic growth, trade, and job creation, which may initially lead to increased CO2 emissions However, the Environmental Kuznets Curve suggests that as these economies mature, emissions may eventually decline In contrast, developing countries experience growth through ICT, but the impact on emissions can vary, potentially offering long-term benefits for CO2 reduction Research by Higón et al (2017) indicates an inverted U-shaped relationship between ICT and CO2 emissions in developing nations, with a critical threshold at USD 21,991 per capita, below which ICT can negatively affect the environment, and above which it may help reduce emissions For developed countries, the relationship remains linear and positive, with advanced ICT levels contributing to lower emissions Additionally, Lee and Brahmasrene (2014) found that in ASEAN countries like Vietnam, while ICT promotes economic growth, it also raises CO2 emissions These findings underscore the importance of tailored research on individual countries' positions on the Environmental Kuznets Curve and highlight the necessity for developing nations to proactively address the environmental impacts of ICT.

Researchers categorize the impact of ICT development on global warming into two primary groups: the direct effects, which encompass emissions generated by the ICT industry itself, and the indirect effects, which refer to emissions produced by other industries that utilize ICT technologies.

Hilty and Bieser (2017) analyzed the impact of information and communication technology (ICT) on greenhouse gas (GHG) emissions in Switzerland, categorizing the effects into direct and indirect They found that direct emissions arise primarily from the production and disposal of ICT devices, with end-user devices responsible for 67% of these emissions Conversely, indirect effects include GHG reductions in other sectors facilitated by ICT applications Their research indicates that transitioning from stationary to mobile devices can lead to lower emissions, although the shorter lifespan of mobile devices presents significant challenges.

The indirect impact of ICT applications is analyzed across ten sectors with significant greenhouse gas (GHG) abatement potential, including smart logistics, traffic control, smart buildings, smart energy, e-learning, e-commerce, e-work, e-banking, connected private transportation, and e-health These ICT applications not only demonstrate the potential for reducing GHG emissions but also offer financial advantages to users The assessment is based on an analytic framework established by GeSI in the SMARTer 2030 report.

The indirect effects of ICT can be up to three times more impactful than direct ones In Switzerland, the potential of ICT to reduce emissions is particularly significant in the transport, building, and energy sectors Nevertheless, establishing a secure legal and business framework to safeguard data continues to pose a major challenge.

Several important lessons can be drawn from these studies:

Further empirical studies are essential to clarify the relationship between ICT development and emissions in Vietnam If this relationship follows a U-shape and Vietnam is currently in the initial phase of the curve, it is crucial for the country to allocate additional resources to mitigate the adverse environmental impacts of ICT.

Hilty and Bieser (2017) highlight that while their study focuses on Switzerland, its key findings are relevant to other countries like Vietnam A crucial recommendation is to transition from stationary equipment, which contributes significantly to energy waste and emissions, to more efficient portable devices However, this shift must be accompanied by efforts to educate the public on the importance of extending the life cycle of mobile devices.

To enhance transparency and accountability, it is essential for businesses to adhere to policies mandating the disclosure of their actual emissions in line with international standards Implementing these standards will enable policymakers to effectively benchmark Vietnam’s emissions against those of other nations, facilitating the identification of potential strengths and areas for improvement in the country's environmental performance.

The primary obstacle to ICT development is the absence of a strong legal framework for information security, which impacts numerous countries Vietnam can benefit from studying international best practices and implementing pilot policies in the health and education sectors, which experienced significant ICT advancements during the COVID-19 pandemic.

GHG emission and ICT development in Vietnam

To combat climate change and safeguard public well-being, Vietnam has implemented various policies and measures aimed at reducing greenhouse gas emissions, with a focus on achieving national green development.

Vietnam has demonstrated its commitment to climate change by signing the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and ratifying it in 1994, as well as signing and ratifying the Kyoto Protocol in 1998 and 2002, respectively In September 2015, Vietnam submitted its Intended Nationally Determined Contribution (INDC) to the UNFCCC, which was later updated to the Nationally Determined Contribution (NDC) after signing the Paris Agreement in 2016 The NDC outlines Vietnam's commitment to reduce greenhouse gas emissions by 8% by 2030 compared to the Business as Usual (BAU) scenario, with a potential increase to 25% reduction contingent upon sufficient international support.

To develop a sustainable and environmentally friendly economy, reducing GHG emission, the government has set out polices like encouraging a transition towards high technology

1 VIETNAM'S INDC.pdf (unfccc.int)

The INDC program was introduced by the UNFCCC during the 19th Climate Change Convention (COP19) in Poland in 2013, urging all nations to create their own Intended Nationally Determined Contributions (INDCs) that outline strategies for reducing greenhouse gas emissions Vietnam has taken proactive steps to lower its GHG emissions as part of its commitment to stabilizing CO2 equivalent emissions.

The Paris Agreement, approved by 196 parties at the UNFCCC's 21st Conference in December 2015, aims to foster a green low-carbon economy It emphasizes improving energy production and consumption efficiency through energy-saving and renewable technologies, enhancing public transportation systems, and developing sustainable, high-tech agriculture.

- The National Socio-Economic Development Strategy for 2011-2020 and the Socio- Economic Development Plan for 2015- 2020 4

- The 2008 National Target Programme to Respond to Climate Change (NTP -RCC) 5

The 2010 Law on Economical and Efficient Use of Energy aims to enhance energy efficiency across various sectors, including construction, public transportation, public lighting, agriculture, industrial production, and household consumption.

- The National Climate Change Strategy to 2050 7 ;

- Decision No 1775/QĐ-TTg in 2012 by the Prime Minister on “Management of GHG emissions; management of carbon credit trading activities to the world market” 8 ;

- Resolution No 24-NQ/TW in 2013 by the Central Party Committee on “Pro-actively responding to climate change, enhancing natural resource management and environmental protection” 9 ;

- The 2020 Law on Environment Protection (Chapter VII Adaptation to Climate Change) 10 ;

- The 2015 Renewable Energy Development Strategy to 2030, which sets out the target to reduce GHG emission from energy sector by 5% in 2020, 25% in 2030 and 45% in

2050 as compared to usual scenario 11 ;

- Decision No 2053/QĐ-TTg by the Prime Minister in October 2016 on the Plan to implement the Paris Agreement on Climate Change 12 ;

- The 2019 National Target Programme on Efficient Energy Usage in 2019 -2030 13 Particularly, the National Green Growth Strategy 14 and its following Action plan 15 for 2021

4 Resolution No 136/NQ-CP on Sep, 25, 2020 by the Government

5 Decision No 158/2008/QD-TTg on Dec, 02, 2008 by the Prime Minister.

6 Law No 50/2010/QH12 on Jun, 17, 2010 by the National Assembly

7 Decision No 896/QD-TTg on Jul, 26, 2022 by the Prime Minister.

8 Decision No.: 1775/QD-TTg on Nov, 21, 2012 by the Prime Minister

9 Resolution No.: 24-NQ/TW on Jun, 03, 2013 by Central Executive Committee

10 Law No 72/2020/QH14 on Nov, 17, 2020 by the National Assembly

11 Decision No 2068/QD-TTg on Nov, 25, 2015 by the Prime Minister.

12 Decision No: 2053/QĐ-TTg on Oct, 28, 2016 by the Prime Minister.

13 Decision No.: 280/QD-TTg on Mar, 13, 2019 by the Prime Minister.

14 Decision No.: 1658/QD-TTg on Oct, 01, 2021 by the Prime Minister.

15 Decision No 882/QD-TTg on Jul, 22, 2022 by the Prime Minister.

– 2030 period, with a vision by 2050 were approved by the Prime Minister in 2021 and

In 2022, ambitious targets for green development were established, aiming to significantly reduce greenhouse gas (GHG) emission intensity per GDP The objective is to achieve at least a 15% reduction by 2030 compared to 2014 levels, with a long-term goal of a 30% reduction by 2050 Additionally, key targets for greening economic sectors include reducing average primary energy consumption per GDP by 1.0 - 1.5% annually from 2021 to 2030, achieving a 15 - 20% share of renewable energy in the total primary energy supply, ensuring the digital economy contributes 30% to GDP, maintaining a forest coverage rate of 42%, and implementing advanced, water-saving irrigation methods on at least 30% of irrigated dry crop areas Furthermore, efforts to promote sustainable consumption and greener lifestyles are also prioritized to support these environmental goals.

By 2030, the goals include ensuring that 95% of urban solid waste is collected and treated in compliance with established standards, while limiting direct landfill disposal to just 10% of the total urban solid waste collected Additionally, over 50% of wastewater in grade II or higher urban areas must be collected and treated, with a target of 20% for other urban areas Furthermore, the aim is to achieve public transport utilization rates of at least 20% in special cities and 5% in other designated areas.

To promote sustainable urban development, it is essential that at least 15% of buses in special cities and 10% of new buses in grade I urban areas utilize clean energy Additionally, achieving a minimum of 35% green public procurement within total public procurement is crucial Furthermore, at least 10 cities should approve and implement comprehensive plans aimed at fostering green urban growth towards the establishment of sustainable smart cities.

To meet its objectives, the Strategy outlines key measures such as enhancing energy efficiency, minimizing energy consumption across manufacturing, transportation, and commerce, and decreasing reliance on fossil fuels It also advocates for the adoption of renewable and low-emission energy sources, the development of sustainable and organic agriculture, and the promotion of green industries.

Vietnam has successfully implemented various environmental projects aimed at reducing greenhouse gas (GHG) emissions, supported by foreign partners According to Vietnam's Intended Nationally Determined Contributions (INDC) report from 2015, the country had registered 254 Clean Development Mechanism (CDM) projects by June 2015 This achievement places Vietnam fourth globally in terms of the number of CDM projects, contributing to a significant reduction of approximately 137.4 million CO2 equivalent during the credit period.

Vietnam has a total of 254 projects, with energy projects making up 87.6%, waste treatment at 10.2%, reforestation and afforestation at 0.4%, and other projects comprising 1.8% As of now, the country has received over 12 million Certified Emission Reductions (CERs) credits from the Executive Board (EB), positioning Vietnam as the eleventh-ranked nation globally in this regard.

Viet Nam is actively developing and preparing to implement Nationally Appropriate Mitigation Actions (NAMAs) under its Nationally Determined Contributions (NDC) Additionally, the country is focused on registering and executing carbon credit projects in accordance with the Verified Carbon Standard (VCS) and the Gold Standard (GS).

In reality, the CO2e emission of Vietnam is as follow:

Table 1: Vietnam’s GHG emission in 1994-2014

(Source: Ministry of Natural Resources and Environment 17 )

*: Industrial Processes and Product Use;

**: Land Use, Land Use Change and Forestry;

The energy sector is the largest contributor to Vietnam's greenhouse gas (GHG) emissions, primarily due to fuel combustion in energy production, manufacturing, construction, and transportation, along with fugitive emissions from coal mining and the oil and natural gas industries Following energy, agriculture is the second-largest emitter, with emissions stemming from enteric fermentation, manure management, rice cultivation, agricultural soils, and the burning of savannas and agricultural residues Additionally, water emissions arise from solid waste landfills, industrial and domestic wastewater, human waste, and waste incineration.

16 Vietnam Ranked Fourth in the World for Clean Development Mechanism Projects Quantity (vneec.gov.vn)

17 Viet Nam National Communication (NC) NC 3 | UNFCCC on Mar, 2019

Figure 3: Vietnam’s GHG emission in 1994-2014

(Source: Ministry of Natural Resources and Environment)

Figure 4: CO2 emission to GDP ratio of East Asian countries

Vietnam's 2015 INDC report highlights that a significant challenge in reducing greenhouse gas emissions lies in the implementation of technologies, particularly within the agriculture sector.

The ICT sector is governed by several key laws and regulations, including the 2005 Law on E-Transaction, the 2006 Law on Information Technology, the 2009 Law on Telecommunications, and the 2018 Law on Cyber Information Security.

To develop ICT, in 2014, the Politburo issued Resolution No 36-NQ/TW and in 2015, the

18 Audinet et al (2016) “Exploring a Low-Carbon Development Path for Vietnam” World Bank Report January 2016

19 Law No 24/2018/QH14 on Jun, 12, 2018 by the National Assembly

Energy IPPU Agriculture LULUCF Waste

Government issued Resolution No 26/NQ-CP, setting the goal of Viet Nam to become an advanced country in ICT 20

RESEARCH METHODOLOGY

Research method approach

The impact of Information and Communication Technology (ICT) on environmental quality is multifaceted Investment in ICT infrastructure can lead to positive environmental outcomes, particularly by improving energy efficiency This study estimates a reduced form equation that connects CO2 emissions with ICT, income per capita, and other relevant variables, as highlighted by Grossman and Krueger.

1991) Following Grossman and Krueger (1991), we model the environmental Kuznets curve (EKC) as follows:

LogCO2it = β0 + β1Log GDPCit + β2Log GDPC 2 it + 𝜀it (1)

In the Environmental Kuznets Curve (EKC) framework, environmental degradation is primarily influenced by GDP per capita, exhibiting an inverted U-shaped relationship However, factors such as information and communication technology (ICT), energy consumption (EC), tourism (TM), and trade openness (TO) also play significant roles in affecting environmental degradation Consequently, the relationship between GDP per capita and environmental degradation can be broadened to incorporate these additional variables.

LogCO2it = β0 + β1LogGDPCit + β2LogGDPC 2 it + β3ICTit + β4LogECit + β5LogTMit + β6LogTOit + 𝜀it (2)

Equation (2) presents the model used for the panel data analysis, where all variables, except the information and communication technology (ICT) index, are converted into natural logarithms to effectively capture their elasticities, as noted by Shahbaz et al (2013).

The analysis employs an unbalanced panel dataset encompassing 138 countries from 1990 to 2020, including 48 developed and 90 developing nations The model is specifically estimated for both developing and developed countries, utilizing data sourced from the World Development Indicators (WDI) 2020.

This study utilizes panel data analysis for its flexibility in estimation, incorporating tests for cross-sectional dependence (CSD) Various estimation techniques are employed, including pooled ordinary least squares (POLS), fixed-effects model (FEM), and generalized method of moments (GMM) with panel-corrected standard errors to effectively address the challenges posed by cross-sectional dependence in the data, as highlighted in the works of Driscoll & Kraay (1998), Hoechle (2007), and Reed & Ye.

2011) The GMM model includes a lagged dependent variable as a regressor, which captures the persistence of the dependent variable over time as follows:

LogCO2it = β0 + β1LogCO2i(t-1) + β2LogGDPCit + β3LogGDPC 2 it + β4ICTit + β5LogECit + β6LogTMit + β7LogTOit + 𝜀it (3)

Equation (3) illustrates that CO2 emissions are influenced by current factors such as GDP, energy consumption, and ICT, as well as historical CO2 emission levels This approach effectively captures the temporal dynamics of environmental degradation.

When selecting an appropriate estimator, it is crucial to consider the presence of unobserved heterogeneity, as Ordinary Least Squares (OLS) may yield biased results In such cases, either a random effects or fixed effects model is preferable for achieving consistent outcomes However, if country-specific effects are correlated with the explanatory variables, random effects estimates will be both biased and inconsistent; they will remain consistent only if individual effects are independent of the regressors To assess the suitability of the fixed effects model, the Hausman test, established by Hausman in 1978, is employed.

Cross-sectional dependence is essential to analyze, as a country's environmental behavior, especially regarding CO2 emissions, is often influenced by the actions of other nations Utilizing Breusch and Pagan’s test for cross-sectional dependence, I found evidence to reject the null hypothesis of independent residuals across all models estimated with standard fixed effects (Pesaran, 2020) Therefore, I applied Driscoll and Kraay (1998) standard errors, which are robust to heteroscedasticity and various forms of cross-sectional and temporal dependence (Hoechle, 2007).

The Hausman test indicates that when the p-value is below 0.05, the null hypothesis is rejected in favor of the alternative hypothesis, suggesting the fixed-effects model is more suitable Consequently, I selected between the random effects model (REM) and the fixed effects model (FEM) for developed countries, developing countries, and the overall sample, consistently favoring the fixed-effects model in all scenarios.

To tackle cross-sectional dependence in the data, this study employs POLS, FEM, and GMM estimation techniques alongside panel-corrected standard errors, ensuring robust results By utilizing panel-corrected standard errors, the covariance matrix estimator remains consistent across the cross-sectional dimension, as highlighted by Hoechle (2007) As a result, this methodology produces reliable estimation outcomes.

Data Collection Methods

The empirical analysis is based on data from various platforms that assess multiple ICT measures, including telephone usage, broadband access, telecommunication infrastructure, online services, and e-government Specifically, the study utilizes "fixed telephone subscriptions (per 100 people)" to measure telephone usage and "fixed broadband subscriptions (per 100 people)" to evaluate broadband access, with both indicators sourced from the World Development Indicators (WDI) 2020.

The telecommunication infrastructure index is derived from the average of five standardized indicators, including internet users, mobile and fixed broadband subscriptions, fixed telephone lines, and mobile subscriptions Additionally, the online service index evaluates countries based on their online service offerings, while the e-government index combines three normalized scores to reflect telecommunication infrastructure, human capital, and the quality and scope of online services.

This study utilizes data from the World Development Indicators (WDI) 2020, encompassing a timeframe from 1990 to 2020 across a sample of 138 countries, selected based on the availability of pertinent data A detailed list of the countries included in the analysis can be found in Appendix 1.

Table 3 outlines the descriptions, data sources, and definitions of the variables utilized in our analysis, with all data sourced from the World Development Indicators (WDI 2020).

Table 3: Description of the variables

Variable Definition of the variable Unit of measurement Data source

Carbon dioxide “These emissions originate from the use of fossil fuels and the production process.”

In the past three months, individuals have increasingly utilized the internet across a variety of devices, including computers, digital TVs, mobile phones, gaming consoles, and personal digital assistants.

“It includes the total of postpaid subscriptions and active prepaid accounts utilized within the last 3 months.” Per 100 people WDI

“Fixed telephone subscriptions include active comparison voice-over-IP (VoIP) subscriptions, fixed telephone lines, fixed wireless local loop subscriptions, and fixed public payphones”

“Fixed broadband includes cable modem, DSL, fiber-to- the-home/building, other wired broadband subscriptions, satellite, and domestic fixed wireless broadband”

GDP per capita is calculated by dividing the gross domestic product by the midyear population, reflecting the average economic output per person It represents the total value of goods and services produced across all economic sectors, accounting for taxes on products while excluding subsidies not included in product value.

Primary energy consumption is calculated by summing local production, imports, and stock changes, while subtracting exports and internationally supplied fuels This metric reflects the energy consumed before it is transformed into final-use fuels.

Kg of oil equivalent per capita

Trade openness “The total of exports and imports of goods and services expressed as a percentage of GDP” % of GDP WDI

Visitors' expenses in foreign countries encompass payments made to international carriers for transport This includes expenditures by residents on same-day trips abroad, unless categorized differently The data is reported in current U.S dollars.

The ICT index is calculated using four key components: internet users, mobile subscriptions, telephone subscriptions, and fixed broadband subscriptions, utilizing Principal Component Analysis (PCA) This index serves as a measure of the technological infrastructure essential for the collection, processing, and transmission of information through technology.

Constructed by the present study

The study analyzes CO2 emissions measured in metric tons as the dependent variable, while ICT adoption is assessed through the percentage of internet users, fixed broadband subscriptions, fixed telephone subscriptions, and mobile phone subscriptions per 100 people Economic growth is represented by per capita GDP, and additional variables such as energy consumption, trade openness as a percentage of GDP, and tourism are incorporated to mitigate omitted variable bias For detailed definitions of these variables, refer to Table 3.

(Source: Analysis by the author)

FIXED_BROADBAND INTERNET MOBILE TELEPHONE

(Source: Analysis by the author)

(Source: Analysis by the author)

Correlation FIXED_BROABAND INTERNET MOBILE TELEPHONE

An ICT index is developed through Principal Component Analysis (PCA), incorporating four sub-components: internet users, mobile subscriptions, telephone subscriptions, and fixed broadband subscriptions, to mitigate multicollinearity issues (Dabbous, 2018) PCA is a robust statistical method that reduces dimensionality, enhances interpretability, and minimizes information loss by generating uncorrelated variables while preserving data variation This technique, widely used in ICT research (Dabbous, 2018; Akande et al., 2019), involves assessing linear relationships via Pearson correlation coefficients, followed by the calculation of eigenvectors and eigenvalues to identify principal components The resulting ICT index is constructed using these four indicators, with the computed eigenvectors detailed in Table 4 (see Excel file for data).

Table 5 reveals a strong correlation between fixed broadband and internet usage, with the internet also showing a significant positive correlation with mobile and telephone services In contrast, fixed broadband displays minimal correlation with larger ICT, while mobile and telephone services tend to support the growth of larger ICT.

(Source: Analysis by the author)

Number Value Difference Proportion Cumulative

Variable PC 1 PC 2 PC 3 PC 4

FIXED_ BROADBAND INTERNET MOBILE TELEPHONE

The rising usage of the internet and mobile and fixed broadband services highlights the enhanced quality of life in developed countries, underscoring the critical role of Information and Communication Technology (ICT) in modern society.

Figure 6: Variable Loading Plots Output

(Source: Analysis by the author)

(Source: Analysis by the author)

Figure 7 displays a line graph illustrating the differences between successive eigenvalues, with a horizontal line representing the average of these differences This approach aims to retain only those eigenvalues that exceed this average threshold Notably, only the first eigenvalue (Fixed_Broadband) and the third eigenvalue (Mobile) meet this criterion.

In the loading plot illustrated in Figure 6, the angle between the vectors indicates the correlation between the associated original variables Notably, Telephone and Mobile exhibit a moderate positive correlation, while Fixed_Broadband and Internet show a similar relationship, albeit to a lesser extent.

Empirical results and discussion

The author conducted the following analysis based on data from 138 countries (detailed data in excel file) in Table 10:

Pooled OLS (see Appendix 2, Table 10.1 for Developed countries, Table 10.5 for Developing countries and Table 10.9 for Global)

Fixed effects (FEM) estimation (see Appendix 2, Table 10.2 for Developed countries, Table 10.6 for Developing countries and Table 10.10 for Global)

Generalized method of moments (GMM) estimation (see Appendix 2, Table 10.4 for Developed countries, Table 10.8 for Developing countries and Table 10.12 for Global)

Table 10: Panel analysis of developed countries, developing countries, and global sample

(Source: Analysis by the author)

Variable Developed countries Developing countries Global - Full sample

POLS FEM GMM POLS FEM GMM POLS FEM GMM

Notes: T-statistics is given in parentheses Levels of statistically significant: *p < 0.10, **p < 0.05, ***p < 0.01

Analysis of ICT's Impact on Environmental Sustainability

The study utilizes Pooled Ordinary Least Squares (POLS), Fixed Effects Model (FEM), and Generalized Method of Moments (GMM) techniques for analysis Table 10 presents the panel estimation results, detailing findings for developed countries in columns 1 to 3, developing countries in columns 4 to 6, and the overall sample in column 7.

Impact of GDP per Capita on CO2 Emissions

Table 10 reveals a significant positive relationship between GDP per capita and CO2 emissions across all income groups, with a stronger effect observed in developing countries In developed nations, the GMM coefficient of 1594.61, significant at the 1% level (t-value: 58.58), indicates that rising GDP per capita correlates with increased CO2 emissions This suggests that initial economic growth tends to elevate CO2 emissions in developed countries.

As nations boost their economic output, they often experience higher CO2 emissions, resulting in environmental degradation This correlation between Gross Domestic Product per Capita (GDPC) and CO2 emissions is primarily due to the scale effect, where increased economic activities lead to greater energy consumption and, consequently, higher emissions (Grossman and Krueger, 1991; Dinda, 2004) Additionally, the growth of the industrial sector and the increased reliance on fossil fuels play a significant role in escalating CO2 emissions (Sohag et al., 2017) Overall, these findings reinforce the idea that economic growth, as reflected in rising GDPC, contributes to environmental harm (Jebli et al., 2016; Lu, 2018).

The analysis reveals a positive relationship between GDP per capita and CO2 emissions, while the squared term indicates a significant negative impact across all income groups, with a GMM coefficient of -6101.29, significant at the 1% level (t-value: -1156.28) This supports the Environmental Kuznets Curve (EKC) hypothesis, which posits that CO2 emissions rise with economic growth until a certain GDP per capita threshold is reached, after which they decline (Grossman and Krueger, 1991) This phenomenon may be attributed to the technique effect, where the adoption of advanced, eco-friendly technologies allows for increased output with reduced emissions Consequently, as technological advancements improve resource allocation, pollution levels decrease alongside national economic growth (Dinda, 2004).

Economic development often involves a shift from the primary agricultural sector to the more energy-intensive industrial sector, resulting in increased emissions However, as countries progress, they typically transition to the service sector, which is less energy-intensive and generates lower emissions compared to the industrial sector (Sohag et al., 2017).

Role of ICT in CO2 Emissions

Information and Communication Technology (ICT) has a varied impact on CO2 emissions between developed and developing countries In developed nations, ICT significantly lowers CO2 emissions, as evidenced by negative coefficients in both Fixed Effects Model (FEM) and Generalized Method of Moments (GMM) analyses Notably, the GMM model shows a coefficient of -7318.11, which is highly significant at the 1% level (t-value: -61.39) This reduction is likely attributed to the implementation of energy-efficient technologies and digital innovations that enhance energy efficiency, minimize industrial waste, and encourage sustainable practices (Ezcan and Apergis, 2018).

In developing countries, the advancement of Information and Communication Technology (ICT) significantly contributes to increased CO2 emissions, with a GMM coefficient of 7708.02, which is highly significant at the 1% level (t-value: 1649.49) This trend is likely attributed to the adoption of less energy-efficient technologies, restricted access to green ICT infrastructure, and a rising demand for electricity as ICT continues to grow (Acheampong et al., 2023; Farhani and Ozturk, 2015).

Impact of Energy Consumption on CO2 Emissions

Since the Industrial Revolution, the global economy's reliance on fossil fuels has driven economic growth, resulting in significant greenhouse gas emissions Increased energy consumption is closely linked to environmental degradation, as demonstrated by a strong positive correlation between energy use and CO2 emissions In developed countries, the GMM coefficient is 0.75, while in developing nations, it rises to 8.39, highlighting the disparity in energy consumption's impact on emissions across different regions Overall, the coefficients for all countries combined are 1.998, indicating a global trend in the relationship between energy consumption and environmental impact.

Increased energy consumption is linked to higher CO2 emissions, reinforcing the idea that greater energy use contributes to environmental degradation Guo et al (2018) highlighted that major industries in China rely heavily on fossil energy, resulting in significant CO2 emissions and prompting increased energy use and emissions across other sectors.

Energy consumption significantly contributes to CO2 emissions in both developed (DCs) and less-developed countries (LDCs), though the effects are more pronounced in LDCs This disparity arises from DCs utilizing advanced technologies, enhancing energy efficiency, transitioning to less energy-intensive sectors, and implementing stricter energy policies In contrast, LDCs often rely on outdated and energy-intensive practices, resulting in greater environmental damage Additionally, factors such as economic size, industrialization, transport distances, market dynamics, and coal dependency further influence CO2 emissions (Leal et al., 2019).

Impact of Tourism on CO2 Emissions

Tourism positively impacts CO2 emissions in various country groups, particularly in developed nations, where the GMM coefficient for tourism is 2.2 with a t-value of 250.6, indicating a significant correlation This suggests that the growth of the tourism sector in developed countries leads to higher carbon emissions, primarily due to the substantial amount of international and domestic travel that relies on energy-intensive transportation, especially air travel Additionally, the expansion of tourism infrastructure, including hotels and resorts, tends to increase energy consumption, particularly in areas dependent on fossil fuels.

In developing countries, the GMM coefficient for tourism is 2.74, demonstrating a significant positive correlation between tourism and CO2 emissions, which is stronger than in developed nations This relationship stems from factors such as limited access to sustainable technologies, a heavy reliance on tourism for economic growth, rapid infrastructure development without environmental safeguards, and inefficient transportation systems that utilize older, less eco-friendly vehicles.

The global analysis reveals a GMM coefficient of 2.11 for tourism, accompanied by a t-value of 836.91, highlighting the significant positive correlation between tourism and CO2 emissions This aligns with previous research indicating that tourism is a key factor in global greenhouse gas emissions, primarily due to the sector's rapid growth and reliance on carbon-intensive transportation methods (Gửssling et al., 2015; Peeters and Dubois, 2010).

These results are consistent with other studies in the literature For example, Gửssling et al

(2015) highlight that tourism contributes significantly to global greenhouse gas emissions, with the sector accounting for approximately 8% of global emissions The study by Lenzen et al

A study conducted in 2018 revealed that emissions linked to tourism are rising more rapidly than those from other sectors, largely due to the increase in international travel and changing consumption patterns This underscores the necessity of implementing sustainable practices and green technologies to balance the economic advantages of tourism with its environmental repercussions.

Impact of Trade Openness on CO2 Emissions

Trade openness significantly impacts CO2 emissions differently in developed and developing countries In developing nations, there is a strong positive correlation between trade openness and CO2 emissions, with a GMM coefficient for trade at 456.75 and a t-value of 3718.14 This indicates that the push for increased production for export contributes to higher pollution levels, primarily due to a focus on emission-intensive goods, insufficient capital and human resources, and dependence on outdated, polluting technologies (Nguyen et al.).

2017) Furthermore, Farhani and Ozturk (2015) argue that weaker environmental regulations in developing countries result in higher emissions with increased trade openness

POTENTIAL OF APPLYING ICT/ INDUSTRY 4.0 TO PROMOTE

E-Health

In Vietnam, a lower middle-income country, the rapidly growing population aged over 65 is contributing to an increase in lifestyle diseases such as diabetes and high blood pressure, as well as age-related conditions like dementia and arthritis Additionally, healthcare services in remote and impoverished areas remain inadequate Consequently, ensuring access to quality and regular healthcare is becoming crucial The implementation of E-Health presents a promising solution to address these healthcare challenges.

E-Health, as defined by the World Health Organization (WHO), refers to the effective and secure application of Information and Communication Technology (ICT) in health and related areas, encompassing health services, surveillance, literature, education, and research By leveraging ICT, E-Health offers comprehensive solutions to address healthcare system challenges and enhance the availability and quality of health services.

In a 2015 survey conducted by the Global Observatory for E-Health, the World Health Organization found that 58% of the 125 respondent countries had established an E-Health strategy Additionally, 66% reported having a health information system (HIS) policy, while 87% indicated the presence of one or more national mHealth (mobile health) initiatives.

E-Health works by making use of data technology to provide reflective health analysis and facilitating a seamless flow of information across different stakeholders (such as healthcare professionals, patients, etc.)

Three key disruptive technologies are transforming health services: first, "Health in Our Hands," which involves wearable smart devices, such as smartphones and smartwatches, paired with biosensor technology to monitor patients' health, like tracking glucose levels for diabetes management Second, "Data Enabling Health" leverages big data analytics for automatic processing, enhancing healthcare delivery and patient outcomes.

The global diffusion of eHealth is crucial for achieving universal health coverage, as highlighted in the WHO report Key advancements include DNA and pathogen sequencing, which play a significant role in identifying genes linked to hereditary disorders and detecting new mutations With projected reductions in the costs of DNA sequencing by 2030, access to these essential health technologies will become more affordable for a broader population, enhancing self-directed health recommendations and improving overall health outcomes.

In addition to the three primary technologies, other ICT applications in health services enhance remote interactions among practitioners and between health professionals and patients through videoconferencing and online platforms These technologies enable electronic data storage, making information more portable and secure, and contribute to the development of health databases that improve system efficiencies Furthermore, they provide robotic assistance during surgeries, showcasing their significant impact on modern healthcare.

Information and Communication Technology (ICT) offers innovative and effective solutions for accessing, communicating, and storing health information By bridging the information gap in the healthcare sector, ICT enhances healthcare access, promotes preventive care and early diagnosis, improves self-health management, and facilitates fully personalized treatment plans.

The implementation of E-Health significantly minimizes outpatient visits, leading to reduced travel, lower fuel consumption, and decreased greenhouse gas emissions Additionally, it lessens paper usage for data storage and diminishes reliance on physical healthcare facilities, ultimately freeing up space for alternative uses while enhancing healthcare services for individuals.

The SMARTer2030 report from 2015 forecasts that by 2030, the implementation of E-Health will significantly reduce travel and healthcare construction, leading to savings of 1.7 billion liters of fuel and a reduction of 0.205 Gt CO2e emissions Additionally, E-Health is projected to save over USD 66 billion by optimizing urban space, freeing up 271.4 million square meters for alternative uses, and enhancing healthcare services for 1.6 billion individuals.

Figure 9: Environmental, economic and social impacts of E-Health

The National Health Services of England's Foundation Trusts utilize a mobile E-Health solution that empowers staff to deliver comprehensive mental health and addiction services This innovative tool allows clinicians to access patient records, view schedules, and update notes while on the move, ensuring they have the latest information at their fingertips, whether in the office, on the go, or at a patient's home By streamlining existing processes, the mobile solution minimizes the need for staff to return to base, ultimately saving time, reducing paper usage, and cutting costs, which enables healthcare providers to dedicate more time to patient care.

Vietnam has actively applied science and technology in healthcare services By the end of

In 2019, the Ministry of Health approved the Electronic Health Record (EHR) Implementation Plan, aiming to ensure that by 2025, 95% of the population will have regularly updated electronic health records linked to health facilities In June 2020, the Ministry launched a Project on Remote Medical Examination for 2020-2025, designed to connect central hospitals and local healthcare providers for telehealth consultations, imaging diagnosis, surgery, and skills training This initiative, alongside Viettel Group's telehealth platform deployment in major hospitals, aims to enhance medical examination and treatment quality across the healthcare system efficiently and cost-effectively It also enables residents in rural and mountainous areas to access high-quality healthcare services without the need to travel to urban centers, thereby alleviating the burden on central hospitals.

E-Learning

The demand for education among Vietnamese people is rapidly increasing, as it is viewed as a pathway out of poverty, a means to secure income opportunities, and a way to enhance quality of life With the growing middle class in Vietnam, the need for education continues to rise, even as the costs of university and college education escalate In this context, E-Learning emerges as a viable solution to address the increasing demand for education while also helping to reduce overall costs, including environmental impacts.

E-Learning is conducted via smart devices and broadband internet E-Learning solutions

27 Decision No 5349/QĐ-BYT on Nov, 12, 2019

Decision No 2628/QĐ-BYT, issued on June 22, 2020, highlights the transformative impact of open community platforms, gamification, and virtual reality, combined with data analytics, in creating accessible online courses and E-Learning applications E-Learning enhances education by making it more accessible, affordable, personalized, self-directed, and engaging for learners For educators, digital resources facilitate the development of effective teaching methods, enable remote instruction, foster collaboration with peers, and allow for customized curricula that meet student needs Additionally, virtual communication platforms promote cross-disciplinary and inter-institutional research, transcending geographical and linguistic barriers (Khazanchi et al., 2022).

The SMARTer 2030 report highlights that E-Learning could reduce global CO2 emissions by 0.1Gt annually by 2030, primarily benefiting the US, China, and India, which account for 85% of this potential due to their large populations and the significant travel distances for students To fully realize this potential, substantial improvements in infrastructure and technology are necessary, including access to high-speed broadband (over 50 Mbps) and smart devices Additionally, E-Learning can save 91 million tons of paper, 5 billion liters of fuel, and USD 1.181 billion in student expenses, with corporate learning through MOOCs potentially cutting training costs by at least 50% Furthermore, advancements in E-Learning could provide educational opportunities for nearly 450 million students on virtual platforms.

Figure 10: Environmental, economic and social impacts of E-learning

For example, Microsoft’s Skype in the Classroom is a free global community connecting students, guest speakers and more than 100,000 teachers from 235 different countries in

The program supports 66 different languages, creating a collaborative learning environment that introduces students to diverse cultures, languages, and concepts remotely By utilizing innovative online tools such as collaboration, games, and guest speakers, teachers can inspire the next generation of global citizens One highlight of this program is the engaging game called Mystery Skype, which fosters interactive learning experiences.

The COVID-19 pandemic has significantly increased the popularity of online learning in Vietnam, transitioning it from primarily unofficial courses to a more formalized approach In response, the Ministry of Education and Training is currently drafting a Circular to regulate online teaching in schools and educational institutions, aiming to establish a legal framework for this mode of education This Circular is anticipated to outline regulations regarding online teaching formats, technical infrastructure requirements, documentation, and the assessment and accreditation of online learning outcomes.

Smart Energy

Vietnam is currently grappling with a significant energy resource shortage, stemming from inadequate energy production and a flawed transmission and distribution system This has led to frequent electricity outages, negatively impacting both daily life and economic activities Access to electricity remains challenging for those in remote areas, while the development and utilization of renewable energy are still in their infancy Meanwhile, conventional energy sources continue to exacerbate climate change, as the energy sector is the largest contributor to Vietnam's greenhouse gas emissions Therefore, implementing a comprehensive Smart Energy plan is essential to address these pressing issues effectively.

The Smart Energy Networks of Denmark highlight that a smart energy system, centered around a smart grid, is a cost-effective, sustainable, and secure solution This system prioritizes renewable energy while integrating and coordinating energy production, infrastructure, and consumption through energy services, active users, and enabling technologies (Lund et al., 2022).

Smart energy, or the smart grid, primarily relies on three key technologies: Microgrids, Smart Meters, and Demand Response Systems Microgrids are decentralized electricity networks that can function independently or alongside the main grid, enhancing energy efficiency and reliability, particularly in remote areas and during outages, with 1,900 systems currently operational or in development in the US (Lund et al., 2022) Smart meters empower consumers by providing real-time energy usage data, fostering transparency and energy efficiency while enabling dynamic pricing models that help manage demand and alleviate system load The UK has set a goal to install smart meters in all homes by 2020 (Lund et al., 2022).

Energy storage solutions play a crucial role in optimizing renewable energy sources such as wind and solar These systems store excess energy in batteries for later use, significantly improving efficiency during unfavorable weather conditions (Lund et al., 2022).

Smart Energy technologies can significantly decrease dependence on centralized transmission and distribution systems, promote the integration of renewable energy sources, and enhance the efficiency of energy distribution and consumption This approach fosters a more balanced relationship between energy supply and demand, contributing to the development of a reliable, resilient, and secure energy infrastructure.

The global production and consumption of energy is the leading cause of air pollution, but the implementation of smart energy solutions can significantly reduce greenhouse gas emissions The SMARTer 2030 report estimates that smart energy could lead to a global emission reduction of 1.8 Gt CO2e, with an additional 1.6 Gt CO2e cut from the energy sector due to decreased energy production The US and China account for nearly 75% of this potential reduction, attributed to their large energy footprints Improved demand management and the integration of renewable energy could lower energy production by 20%, saving 6.3 billion MWh, while enhancing grid efficiency could reduce energy loss during distribution by 5% Furthermore, smart energy initiatives are expected to make energy generation and distribution more cost-effective, eliminating the need for over 700,000 km of new grid infrastructure This evolution could generate USD 2.1 billion in additional revenue for the ICT sector and USD 811.3 billion for renewable energy companies Additionally, smart energy promotes universal access to affordable energy and enhances energy security.

Figure 11: Environmental, economic and social impacts of Smart Energy

Microsoft eSmart Systems exemplifies the application of Smart Energy through its cloud-based automated energy management system This innovative solution utilizes sensors, smart meters, and advanced software to enable utility companies to leverage data for forecasting consumption, predicting maintenance needs, minimizing outages, and monitoring assets, ultimately enhancing energy efficiency (Ahmad and Zhang, 2021).

China Southern Power Grid, which supplies electricity to 230 million people in China, has collaborated with Huawei to implement a 4G TD-LTE-enabled smart grid This advanced grid features automatic distribution, measurement, video surveillance, and emergency communication capabilities As a result, the ICT-enabled grid achieves approximately a 5% reduction in energy consumption, emissions, and operational costs (Ahmad and Zhang, 2021).

Smart Buildings

In rapidly urbanizing countries like Vietnam, the increasing population drives a rising demand for housing and commercial real estate However, the construction and operation of these buildings are resource- and energy-intensive, with buildings accounting for approximately 30% of global energy consumption, according to an Accenture study on smart building solutions Consequently, smart buildings present a promising solution to address these challenges.

Smart buildings, encompassing smart homes and offices, leverage technology to enhance performance by facilitating information exchange between systems Key features of smart buildings include interconnected systems, sensor utilization, automation, and data management These structures can be likened to living organisms, interconnected through intelligent and adaptable software, ensuring efficient operation and responsiveness to changing conditions.

Smart buildings have two main components: smart meters (previously discussed in Smart

29 https://www.accenture.com/gb-en/case-studies/sustainability/smart-buildings

Smart appliances, including automated heating, cooling, ventilation, and lighting control systems, enhance energy efficiency by utilizing motion and light sensors These devices conserve energy by turning off lights when sufficient daylight is available and adjusting air conditioning settings when spaces are unoccupied.

Recent advancements in smart appliances enable users to control functions such as lighting and temperature remotely via smartphone apps and dashboards These innovations facilitate early fault detection, alerting users when an appliance is left on or requires repair Additionally, integration with personal calendars allows appliances to automatically adjust based on user schedules, enhancing convenience and efficiency.

Smart meters and appliances provide valuable data for operational analysis, enhancing building performance and influencing user behavior By offering real-time energy usage insights, these technologies encourage users to minimize consumption Additionally, smart buildings can optimize systems, such as adjusting air conditioning based on learned patterns and prioritizing electrical loads They also have the capability to integrate with local smart grids, facilitating on-site renewable energy generation and allowing for the sale of excess energy back to the grid (Dakheel et al., 2020).

Smart building solutions significantly boost asset reliability and performance, providing users with enhanced control and insights while conserving resources, energy, time, and costs These solutions improve both living and working environments, optimize space utilization, and minimize environmental impact Additionally, the data generated from these systems supports urban planners, utility companies, and architects in analyzing demand patterns and lowering expenses (Dakheel et al., 2020).

The SMARTer 2030 report predicts that by 2030, Smart Buildings will significantly reduce carbon emissions by 2.0Gt CO2, save 5 billion MWh of energy, conserve 300 billion liters of water, and generate USD 360 billion in cost savings Additionally, these innovative structures will enhance quality of life by customizing living spaces to meet individual needs, reducing expenses, and minimizing time spent on maintenance and repairs.

Figure 12: Environmental, economic and social impacts of Smart Building

For example, Microsoft evaluated smart building applications from three vendors across

The technology firm has implemented smart building solutions across 13 of its 118-building campus, requiring an initial investment of less than 10% of its annual energy costs, with a payback period of under two years By analyzing millions of daily data points, the company is enhancing building management, significantly lowering energy consumption, and aiming for net-zero emissions by 2025.

Germany is leading the way in smart meter implementation, with the federal government supporting a large-scale rollout facilitated by Deutsche Telekom These smart meters enable consumers to visualize their real-time energy usage, promoting a better understanding of their consumption patterns By adopting smart meters, households can potentially decrease their electricity usage by up to 8% The SMARTer Report 2030 estimates that installing smart meters in 7.8 million German households by 2020 could result in an annual reduction of up to 1.2 million metric tons of CO2e emissions.

In 2014, Date-City, Japan, implemented Fujitsu’s Enetune-BEMS, a cloud-based Energy Management System designed to enhance energy efficiency in public facilities This system enables centralized management, integration, and visualization of energy use across 45 public facilities and schools, facilitating demand management and automated control of energy consumption By streamlining information sharing between public officials and citizens, Enetune-BEMS empowers the local government to adopt energy conservation measures, reduce power consumption during peak times, and lower CO2e emissions, ultimately promoting sustainable energy practices in the city.

Smart mobility

Transportation and logistics are vital drivers of developing and emerging economies like Vietnam However, existing infrastructure is increasingly proved to be insufficient to cater

30 https://www.accenture.com/gb-en/case-studies/sustainability/smart-buildings

The Smart Meter Rollout in Germany and Europe addresses the increasing demand for efficient transportation of people and goods, which often leads to traffic congestion In Vietnam, logistics operations are characterized by fragmentation and inefficiency, resulting in unused capacity throughout the supply chain Moreover, the transportation sector significantly contributes to fuel consumption, pollution, and emissions Therefore, implementing smart mobility solutions can effectively tackle these challenges.

Smart mobility is the integration of different modes of transportation and infrastructure to make traveling safer, cleaner, and more efficient

Smart Mobility and Logistics fall into three categories: Connected Private Transportation,

Traffic Control and Optimization and Smart Logistics (Porru et al., 2020)

Traffic Control and Optimization leverage advanced data analysis, smart sensors, and intelligent infrastructure to enhance the efficiency of traffic, driving, and parking These technologies, including driverless vehicles, improve communication and navigation, ultimately promoting individual safety By utilizing data, they provide valuable insights for drivers, such as optimal routes to avoid congestion and the nearest available parking spots (Porru et al., 2020).

Connected Private Transportation refers to the integration of individuals and vehicles with shared origins or destinations, facilitated by smartphone-enabled car-sharing and carpool platforms These technologies enable travelers to connect at designated meeting points, promoting shared travel experiences By optimizing the use of existing vehicles, this approach helps alleviate traffic congestion, decrease fuel consumption and emissions, and ultimately saves time and money for users.

Smart Logistics is about connecting vehicles, products, and load units, thereby improving route and load optimization and reducing the amount of waste in the system

ICT-enabled solutions, including advanced data analytics, telematics, and sensor technology, empower logistics companies to enhance the flexibility and efficiency of freight transport across road, air, rail, and marine channels By integrating the dispatching office with the entire fleet, individual vehicles, routes, load units, and specific products, these technologies optimize logistics operations.

Fleet management and route optimization tools enhance operational efficiency and planning by minimizing costly redundancies, empty runs, and accidents Additionally, connected devices utilizing blockchain technology facilitate real-time tracking and monitoring of items, enabling flexible rerouting during transit to ensure optimal delivery.

The SMARTer 2030 report indicates that Smart Mobility could save 2.6Gt CO2e in emissions, and when factoring in reduced travel from shifts in health, education, work, and commerce, an additional 1.0Gt CO2e reduction can be achieved The United States, China, and India lead in potential emissions abatement through traffic control and optimization, with China alone representing nearly 50% of the abatement potential for Smart Logistics.

ICT-enabled mobility solutions also provide:

By 2030, traffic control and optimization could lead to a significant reduction of 236 billion liters of fuel, while connected private transportation might save an additional 220 billion liters Furthermore, the implementation of smart logistics solutions is projected to contribute to fuel savings of 267 billion liters and a reduction of 3.8 billion kg of wood, highlighting the potential for substantial environmental benefits through improved transportation efficiency.

Traffic control and optimization could lead to approximately $409 billion in avoided costs, while connected private transport may contribute around $611 billion in savings Additionally, the implementation of various smart logistics processes and methods could add about $174 billion in economic value Overall, these innovations could result in a total of $1 trillion in avoided costs and economic benefits.

Around 42 billion hours saved in 2030: Efficient traffic management solutions and high- quality navigation systems could save around 42 billion hours by 2030 As a result of car sharing, 135 million cars could be taken off the road by 2030 And for society at large, Smart Logistics solutions could significantly reduce negative externalities like noise, traffic congestion, and health and safety risks, leading to a safer, cleaner, and more peaceful urban environment

Figure 13: Environmental, economic and social impacts of Smart Logistics

For example, working with Microsoft, city-owned bus operator Helsingin Bussiliikenne

Oy (HelB) in Helsinki, Finland, enhanced its data warehouse solution to gather and analyze data from bus sensors, aiming to lower fuel consumption, boost driver performance, and create smoother, safer bus rides With over 4 million data points generated daily, the system tracks fuel usage, acceleration, speed, engine temperature, brake performance, and GPS location across the operator's 400 buses This meticulous analysis led to a 5% reduction in fuel consumption fleet-wide, contributing to a decreased carbon footprint for the city Furthermore, the public transport data solution enables companies to monitor driving behavior and incidents, sharing insights with drivers to enhance their comfort and safety, resulting in a 7% increase in driver satisfaction Additionally, the system facilitates monitoring of mechanical conditions, allowing for early identification of issues and predictive maintenance of vehicles.

BLS Trucking, a leading independent delivery service in the Midwestern US, faced challenges such as rising fuel costs, vehicle theft, and logistical inefficiencies To combat these issues, they implemented Verizon’s Network Fleet, a wireless fleet management system that integrates diagnostic monitoring with GPS technology This system allowed BLS to effectively track and monitor their fleet, providing insights into vehicle location, idle time, and fuel consumption As a result, BLS saved approximately $188,000 in fuel expenses in the first year by reducing unauthorized usage and idle time Additionally, the Network Fleet system facilitated predictive maintenance, minimized breakdowns and repair costs, deterred theft, and safeguarded drivers against wrongful claims.

Smart parking solutions offer real-time information on parking availability and average parking duration, enabling cities to optimize parking restrictions and enhance customer satisfaction Currently, around 7,000 parking spaces are free, but without smart guidance, an additional 12,000 spaces will be required by 2020 Implementing this ICT-enabled smart parking system could save the city at least £105 million Beyond economic advantages, this solution also reduces traffic congestion by 50%, leading to decreased fuel consumption and lower vehicle emissions.

32 Using smart data to improve Helsinki’s bus system (cgi.com)

Smart Manufacturing

Manufacturing plays a crucial role in Vietnam's economy and export activities, remaining a top priority in the country's development strategy However, this sector significantly consumes energy, water, and resources, while also contributing to various forms of pollution Therefore, it is essential to explore modern manufacturing methods that leverage advancements from the fourth industrial revolution.

Smart Manufacturing leverages advanced communication systems to enhance traditional manufacturing processes, resulting in increased flexibility, efficiency, and responsiveness By integrating technologies such as cyber-physical systems, industrial IoT, cloud computing, data analytics, 3D printing, drones, robotics, and augmented reality, factories become self-organized, decentralized, and highly connected This technological synergy enables optimized production, virtual manufacturing, and customer-centric approaches, while fostering a circular supply chain that allows for the tagging, tracking, and tracing of products throughout their lifecycle Consequently, Smart Manufacturing facilitates on-demand production, ensuring immediate adaptability to market changes and significantly reducing energy and resource consumption.

Despite technological barriers limiting the widespread adoption of Smart Manufacturing globally, recent Accenture surveys reveal that just over one-third of companies are utilizing automation technology However, the potential of Smart Manufacturing to drive significant advancements in production and fundamentally transform our economy and overall well-being is undeniable.

According to the SMARTer 2030 report, Smart Manufacturing can significantly reduce CO2 emissions by 2.7 gigatons (GT), while also offering potential energy savings of 4.2 billion megawatt-hours (MWh) and water savings of 81 billion liters Additionally, the implementation of automated and self-maintained smart processes is projected to yield cost savings of $349 billion.

Cyber-Physical Systems (CPS) are collaborative computational entities that maintain a strong connection with the physical world and its ongoing processes They simultaneously provide and utilize data-accessing and data-processing services available on the internet.

Figure 14: Environmental, economic and social impacts of Smart Manufacturing

The Fujitsu Virtual Product Simulator (FJVPS) simplifies the design process by utilizing 3D-CAD data, enabling even novices to create prototypes, identify faults, and enhance design quality during the initial stages of product development This innovative tool significantly improves quality by detecting 50-80% of design errors, reduces development time, and lowers costs by 50%, while also decreasing required man-hours by 30-40%.

Huawei's smart string inverters, utilized in a photovoltaic (PV) plant in Germany, exemplify advanced solar technology These inverters leverage a 4G LTE wireless network, enabling remote monitoring and enhanced data analysis, which empowers site owners with improved control over their solar operations Notably, they are more cost-effective than traditional inverters and excel in challenging weather conditions, including extreme heat and heavy rain This ICT-enabled solution has resulted in a 5% increase in energy yield and a remarkable 50% boost in maintenance efficiency for the German PV plant.

Smart Agriculture

Agriculture in Vietnam contributes 15% to the nation's GDP and 7.8% to its exports, yet it is responsible for 31.6% of the country's greenhouse gas emissions, ranking it as the second highest emitting sector Furthermore, the adoption of modern agricultural practices is essential for enhancing productivity and sustainability.

34 Smart Manufacturing Solution FJVPS : Fujitsu Global

Huawei has emerged as the leading string inverter brand, achieving over 100GW of global deployment in its 10-year Smart PV journey However, agricultural practices can negatively impact land, biodiversity, and local water resources Additionally, the rise of climate change-related extreme weather events poses significant risks to global food production, highlighting the urgent need for resilient and environmentally sustainable agricultural methods.

Smart Agriculture leverages advanced technologies such as genomic sequencing, satellite and drone imaging, and machine connectivity to enhance farming practices By utilizing sensors and automation for irrigation, fertilization, and environmental control, farmers can achieve greater precision and sustainability These innovations enable accurate assessments of water and fertilizer requirements, while real-time weather updates empower proactive decision-making, ultimately leading to more productive agricultural outcomes (Jararweh et al., 2023).

Not all Smart Agriculture technologies are commercially available, with a 2012 Accenture study for Vodafone's Connected Agriculture report indicating that most applications are only partially implemented, primarily in developed countries and by large farms Consequently, the majority of smallholders still lack access to these advanced agricultural technologies.

The SMARTer 2030 report projects that by 2030, Smart Agriculture could prevent 2.0 gigatons of CO2e emissions annually Additionally, these innovative solutions are expected to generate an extra USD 2 billion in revenue for companies, increase farmers' average income by USD 300, and yield USD 110 billion in cost savings through reduced water consumption.

Figure 15: Environmental, economic and social impacts of Smart Agriculture

36 Achievements of the agriculture industry – a year summation – General Statistics Office of Vietnam (gso.gov.vn)

37 Connected Agriculture : The role of mobile in driving efficiency and sustainability in agriculture | E-Agriculture (fao.org)

The Aeon Agri Create project, in collaboration with Fujitsu, leverages Akisai Cloud Computing for efficient farm management Utilizing smart devices and GPS technology, the system monitors crop health, input usage, and operational costs, resulting in a 33% increase in Japanese mustard spinach yields under optimal conditions This innovative approach saves up to 80% of time and supports 3,000 subcontracted farmers Additionally, Fujitsu’s Akisai solution facilitates year-round crop production and the cultivation of pesticide-free vegetables by effectively managing environmental conditions.

E- Business

Business, encompassing sectors like commerce and finance, is a cornerstone of any economy E-Business significantly enhances productivity and stimulates economic growth while minimizing environmental impact through the use of virtual platforms This broad field includes e-commerce, e-banking, and e-work, showcasing its diverse applications and benefits.

E-commerce involves online shopping with technologies like virtual assistants, 3D printing, and smart logistics to create a seamless, customer-focused experience

E-banking means using electronic delivery channels like internet or mobile banking, including the use of mobile money/digital wallet to provide a more productive and inclusive financial services to people

E-Work, also known as telework, is the use of cloud-based platforms, virtual business meeting, etc to facilitate and speed up daily office work between co -workers in different locations (Gupta and Sharma, 2003)

According to SMARTer 2030 report, by 2030, E-Business will lead to a reduction of 0.6Gt CO2e and a saving of 165 billion liters of fuel, 388,854 tons of paper and 105 billion working hours

Figure 16: Environmental, economic and social impacts of E-Business

Deutsche Telekom's Dynamic Workplace is a user-friendly, cloud-based solution that enables access to applications via a web browser on any device, including notebooks, tablets, and smartphones Delivered securely from a private cloud, it allows employees to conveniently access files and software from anywhere, at any time Featuring a simple user interface and a self-service portal with integrated workflows, the Dynamic Workplace solution helps companies reduce average commuting time by 56 hours annually, resulting in savings of over €250 per employee each year.

The calculation of impact

To calculate the environmental, economic, and social benefits of the application of ICT/Industry 4.0 in these 8 sectors, the SMARTer Report 2030 calculated based on inputs as below:

▪ Liters of fuel saved thanks to lower need to come directly 38

▪ Reduction of cars demand from connected private transportation

▪ Kg of wood saved from pallets from Smart Logistics

▪ Liter of water saved from being wasted from Smart Buildings

▪ Km of grid which are not necessary from Smart Energy

▪ Increase in renewable energy share

(Saving amount = Without ICT amount × Impact adoption rate)

▪ ICT revenues from connecting the unconnected, including:

38 Without ICT fuel consumption equals to Average distance to hospital, schools, etc × Number of reduced travel turn × Fuel used for each km https://journals.sagepub.com/doi/full/10.1177/09544070231185609

- Annual device cost (smart phone, smart tablet cost)

- Annual expenditure of connecting the unconnected (only the connecting the unconnected revenue due to the application of above-mentioned technology are considered)

- Translation of environment result into economic revenue (using current prices of fuel, energy, water, and paper)

▪ Average annual farmer income increases from Smart Agriculture

▪ Average worker productivity increase per hour from E-Work

▪ Additional loading rate and reduction of empty run from Smart Logistics

▪ Average income increases thanks to obtaining E-degree

▪ Number of people gaining ICT access

▪ Number of people with access to E-Health

To prevent double counting, it is essential to exclude the space savings and travel reductions associated with e-health, e-learning, and e-business from the total square meters saved in smart buildings, as well as the total travel cut attributed to smart mobility.

The rebound effect refers to the phenomenon where enhanced productivity in the production of goods or services leads to an increase in their consumption (Madlener & Alcott, 2009) According to the SMARTer 2030 report, the rebound effect significantly impacts CO2e abatement estimates.

▪ Potential rebound effect for E-Work: 27.4%

▪ Potential rebound effect for Smart Logistics: 20%

▪ Potential rebound effect for E-Health, E-commerce, E-Banking, E-Learning and Connected Private Transportation: 7%

▪ Potential rebound effect for Smart Agriculture, Smart Manufacturing, Smart Energy, Smart Building and Traffic Control & optimization: 10%

However, the Report has not reflected the energy and material input necessary for the production and the use of these technologies.

CONCLUSION AND POLICY RECOMMENDATIONS

This study explores the potential of Information and Communication Technology (ICT) in reducing emissions and fostering sustainable development in Vietnam It comprises four key components: an analysis of the relationship between ICT and emissions, an overview of the current emissions landscape and ICT advancements in Vietnam, an evaluation of ICT's potential to mitigate emissions across eight major sectors, and policy recommendations aimed at leveraging ICT developments for emission reduction in the country.

This study explores the relationship between ICT development, economic growth, and emissions, highlighting its significance for sustainable development It emphasizes that developing countries must collaborate with development partners to reduce emissions from ICT applications in their early stages Furthermore, to enhance the impact of ICT on emission reduction, Vietnam should establish a robust legal framework for information security and safety This framework is essential for building public trust, encouraging individuals to share personal information and databases, which is crucial for the broader adoption of ICT technologies.

The second part of the article provides an overview of Vietnam's current efforts and policies aimed at reducing emissions and advancing ICT development In recent years, the Vietnamese Government has actively promoted green development by participating in the Paris Agreement on Climate Change and implementing various strategies for green growth Recognizing ICT as essential for transforming its growth model and enhancing labor productivity and economic competitiveness, Vietnam has formulated numerous policies, particularly focusing on the application of Industry 4.0 technologies across all sectors to facilitate digital transformation However, a clear connection between green growth strategies and ICT development policies remains elusive, and the lack of awareness regarding ICT's environmental impact may hinder optimal investment and distract from solutions for sustainable development This oversight suggests that the government could strengthen its preferential policies for ICT investment if it fully acknowledges the environmental benefits of these technologies, highlighting a critical policy gap that Vietnam must address in the future.

The study examines the potential for reducing emissions and the economic and social advantages of ICT technologies across eight sectors, utilizing the assessment framework from the GeSI SMARTer 2030 Report (2015) outlined in Appendix 3 This approach will enable Vietnam to establish a stronger foundation for identifying effective policies and sectors for emission reduction through the implementation of ICT technologies.

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Table 11: List of developed and developing countries (138 countries)

Source: World Development Indicators (WDI)

Australia Finland Latvia Saudi Arabia

Canada Iceland New Zealand Sweden

Croatia Israel Oman Trinidad and Tobago

Cyprus Italy Poland United Arab Emirates

Czech Republic Japan Portugal United Kingdom

Denmark North Korea Qatar United States

Albania Egypt Madagascar Solomon Islands

Algeria El Salvador Malawi South Africa

Angola Eritrea Malaysia Sri Lanka

Argentina Ethiopia Mali Saint Lucia

Armenia Fiji Mauritius Saint Vincent and the

Costa Rica Kazakhstan Paraguay Yemen

Table 9 1: Hausman Test results for Developed countries

Correlated Random Effects - Hausman Test

Test cross-section random effects

Test Summary Chi-Sq Statistic Chi-Sq d.f Prob

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob GDPC

0.1498 0.4747 0.0018 0.0655 0.0000 0.0003 Cross-section random effects test equation:

Variable Coefficient Std Error t-Statistic Prob

Cross-section fixed (dummy variables)

Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat

Table 9 2: Hausman Test results for Developing countries

Correlated Random Effects - Hausman Test

Test cross-section random effects

Test Summary Chi-Sq Statistic Chi-Sq d.f Prob

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob

0.0007 0.5300 0.6131 0.1340 0.0001 0.0800 Cross-section random effects test equation:

Variable Coefficient Std Error t-Statistic Prob

Cross-section fixed (dummy variables)

Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter

Table 9 3: Hausman Test results for Global – Full sample

Correlated Random Effects - Hausman Test Equation:

Test cross-section random effects

Test Summary Chi-Sq Statistic Chi-Sq d.f Prob

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob GDPC

0.0005 0.9128 0.1804 0.2575 0.0000 0.0037 Cross-section random effects test equation: Dependent

Variable Coefficient Std Error t-Statistic Prob

Cross-section fixed (dummy variables)

Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter

II Tables for Hypothesis testing

(Source: Analysis by the author)

Table 10 1: Pooled least squares estimation

Variable Coefficient Std Error t-Statistic Prob

S.E of regression 532699.5 Akaike info criterion 29.21400

Sum squared resid 4.20E+14 Schwarz criterion 29.23895

Log likelihood -21728.21 Hannan-Quinn criter 29.22330

Variable Coefficient Std Error t-Statistic Prob

C 228069.5 7069.970 32.25891 0.0000 GDPC 51.86431 508.7857 0.101937 0.9188 GDPC^2 -58.67573 44.97020 -1.304769 0.1922 ICT -5741.619 1514.300 -3.791600 0.0002 ENERGY 1.289443 0.753172 1.712018 0.0871 TOURISM 1.06E-06 1.47E-07 7.220830 0.0000 TRADE 149.9432 74.34873 2.016756 0.0439

Effects Specification Cross-section fixed (dummy variables)

R-squared 0.993490 Mean dependent var 249352.4 Adjusted R-squared 0.993249 S.D dependent var 764957.2 S.E of regression 62850.67 Akaike info criterion 24.97053 Sum squared resid 5.66E+12 Schwarz criterion 25.16305 Log likelihood -18524.07 Hannan-Quinn criter 25.04228 F-statistic 4129.079 Durbin-Watson stat 0.177525 Prob(F-statistic) 0.000000

Method: Panel EGLS (Cross-section random effects)

Swamy and Arora estimator of component variances

Variable Coefficient Std Error t-Statistic Prob

C 228979.3 61300.52 3.735356 0.0002 GDPC 63.11924 508.7257 0.124073 0.9013 GDPC^2 -58.38667 44.96838 -1.298394 0.1944 ICT -5545.416 1512.995 -3.665190 0.0003 ENERGY 1.335404 0.752758 1.774015 0.0763 TOURISM 1.12E-06 1.47E-07 7.639565 0.0000 TRADE 129.7808 74.13914 1.750504 0.0802

R-squared 0.047217 Mean dependent var 6669.585 Adjusted R-squared 0.043357 S.D dependent var 66483.47 S.E of regression 65026.24 Sum squared resid 6.26E+12 F-statistic 12.23228 Durbin-Watson stat 0.160645 Prob(F-statistic) 0.000000

0.034931 Mean dependent var 249352.4 8.40E+14 Durbin-Watson stat 0.001198

Table 10 4: Generalized Method of Moments (GMM)

Method: Panel Generalized Method of Moments

White period (period correlation) instrument weighting matrix

White period (cross-section cluster) standard errors & covariance (d.f corrected)

Standard error and t-statistic probabilities adjusted for clustering

Constant added to instrument list

Variable Coefficient Std Error t-Statistic Prob CO2(-1) 0.952894 0.000281 3387.434 0.0000 GDPC 1594.611 27.21935 58.58371 0.0000 GDPC^2 -15.10985 1.330298 -11.35825 0.0000 ICT -7318.112 119.2091 -61.38888 0.0000 ENERGY 0.752406 0.120720 6.232675 0.0000 TOURISM 2.20E-07 8.79E-10 250.6034 0.0000 TRADE 240.7885 7.137296 33.73665 0.0000 Effects Specification

Cross-section fixed (first differences)

Mean dependent var -665.1183 S.D dependent var 27064.73 S.E of regression 33184.72 Sum squared resid 1.53E+12

Table 10 5: Pooled least squares estimation

Variable Coefficient Std Error t-Statistic Prob

The analysis reveals several key statistical findings: the coefficient for C is 225570.2, indicating a strong relationship with the dependent variable, while GDPC shows a positive effect with a coefficient of 10515.86 The ICT variable also demonstrates significant influence, with a coefficient of 110032.3, and ENERGY's coefficient stands at 123.4862, highlighting its relevance Conversely, TRADE presents a negative coefficient of -2332.613, suggesting an adverse impact The model's R-squared value is 0.087357, indicating that approximately 8.74% of the variance in the dependent variable is explained by the model The F-statistic is 44.39748 with a corresponding p-value of 0.000000, suggesting that the overall model is statistically significant The Durbin-Watson statistic of 0.020908 indicates potential autocorrelation in the residuals.

Variable Coefficient Std Error t-Statistic Prob

C 212896.4 22833.75 9.323760 0.0000 GDPC -330.9227 1079.200 -0.306637 0.7591 GDPC^2 -1.998099 25.39930 -0.078668 0.9373 ICT 127416.7 9978.497 12.76913 0.0000 ENERGY 69.83095 12.97406 5.382352 0.0000 TOURISM -1.42E-05 2.35E-06 -6.069161 0.0000 TRADE -116.3474 321.6486 -0.361722 0.7176

Effects Specification Cross-section fixed (dummy variables)

R-squared 0.813119 Mean dependent var 158322.4 Adjusted R-squared 0.806528 S.D dependent var 761479.3 S.E of regression 334939.7 Akaike info criterion 28.31509 Sum squared resid 3.02E+14 Schwarz criterion 28.51927 Log likelihood -39403.55 Hannan-Quinn criter 28.38880 F-statistic 123.3848 Durbin-Watson stat 0.024672 Prob(F-statistic) 0.000000

Method: Panel EGLS (Cross-section random effects)

Swamy and Arora estimator of component variances

Variable Coefficient Std Error t-Statistic Prob

C 215183.9 66427.60 3.239375 0.0012 GDPC -191.0498 1078.415 -0.177158 0.8594 GDPC^2 -2.483043 25.38756 -0.097806 0.9221 ICT 127932.1 9926.316 12.88817 0.0000 ENERGY 72.81352 12.82052 5.679452 0.0000 TOURISM -1.34E-05 2.34E-06 -5.738435 0.0000 TRADE -205.4857 317.5927 -0.647010 0.5177

R-squared 0.058194 Mean dependent var 15989.22 Adjusted R-squared 0.056164 S.D dependent var 346491.8 S.E of regression 336621.1 Sum squared resid 3.15E+14 F-statistic 28.66031 Durbin-Watson stat 0.023457 Prob(F-statistic) 0.000000

0.019100 Mean dependent var 158322.4 1.59E+15 Durbin-Watson stat 0.004663

Table 10 8: Generalized Method of Moments (GMM)

Method: Panel Generalized Method of Moments

White period (period correlation) instrument weighting matrix

White period (cross-section cluster) standard errors & covariance (d.f corrected)

Standard error and t-statistic probabilities adjusted for clustering

Constant added to instrument list

Variable Coefficient Std Error t-Statistic Prob CO2(-1) 1.003708 3.44E-06 291378.8 0.0000 GDPC 1730.139 0.476986 3627.228 0.0000 GDPC^2 -18.07233 0.012739 -1418.652 0.0000 ICT 7708.024 4.672968 1649.492 0.0000 ENERGY 8.391560 0.006678 1256.627 0.0000 TOURISM 2.74E-07 1.22E-10 2244.624 0.0000 TRADE 456.7504 0.122844 3718.142 0.0000 Effects Specification

Cross-section fixed (first differences)

Mean dependent var 4743.419 S.D dependent var 43978.68 S.E of regression 29363.74 Sum squared resid 2.24E+12

Table 10 9: Pooled least squares estimation

Variable Coefficient Std Error t-Statistic Prob

The analysis reveals significant insights into the variables affecting the model, with energy showing a notable coefficient of 11.57098 and a p-value of 0.0038, indicating its strong influence In contrast, tourism has a minimal impact with a coefficient of 2.56E-05 and a p-value of 0.0000, suggesting it is statistically significant Trade, however, presents a negative coefficient of -1765.633 and a p-value of 0.0000, highlighting its detrimental effect The model's R-squared value of 0.215835 indicates that approximately 21.58% of the variance in the dependent variable is explained by these predictors Additionally, the adjusted R-squared is 0.214733, with a standard deviation of the dependent variable at 763833.1 The regression's standard error is 676872.8, and the F-statistic of 195.9261, with a corresponding probability of 0.000000, confirms the overall significance of the model The Durbin-Watson statistic is 0.035249, suggesting potential autocorrelation in the residuals.

Effects Specification Cross-section fixed (dummy variables)

R-squared 0.870602 Mean dependent var 189985.0 Adjusted R-squared 0.866126 S.D dependent var 763833.1 S.E of regression 279477.5 Akaike info criterion 27.95231 Sum squared resid 3.23E+14 Schwarz criterion 28.16644 Log likelihood -59646.00 Hannan-Quinn criter 28.02795 F-statistic 194.5029 Durbin-Watson stat 0.021460 Prob(F-statistic) 0.000000

Method: Panel EGLS (Cross-section random effects)

Swamy and Arora estimator of component variances

Variable Coefficient Std Error t-Statistic Prob

C 190574.7 49130.18 3.878975 0.0001 GDPC -368.4403 829.4350 -0.444206 0.6569 GDPC^2 -4.664227 21.01772 -0.221919 0.8244 ICT 36366.33 4814.741 7.553123 0.0000 ENERGY 12.55929 3.110524 4.037676 0.0001 TOURISM 1.87E-07 6.14E-07 0.304542 0.7607 TRADE -324.1303 199.6884 -1.623180 0.1046

R-squared 0.014707 Mean dependent var 17400.03 Adjusted R-squared 0.013323 S.D dependent var 284327.5 S.E of regression 282427.1 Sum squared resid 3.41E+14 F-statistic 10.62522 Durbin-Watson stat 0.020084 Prob(F-statistic) 0.000000

0.027187 Mean dependent var 189985.0 2.43E+15 Durbin-Watson stat 0.002819

Table 10 12: Generalized Method of Moments (GMM)

Method: Panel Generalized Method of Moments

White period (period correlation) instrument weighting matrix

White period (cross-section cluster) standard errors & covariance (d.f corrected)

Standard error and t-statistic probabilities adjusted for clustering

Constant added to instrument list

Variable Coefficient Std Error t-Statistic Prob CO2(-1) 1.007678 7.27E-06 138701.5 0.0000 GDPC 2963.160 0.526313 5630.037 0.0000 GDPC^2 -21.01980 0.036198 -580.6956 0.0000 ICT -6101.294 5.276634 -1156.285 0.0000 ENERGY 1.998618 0.002354 849.0832 0.0000 TOURISM 2.11E-07 2.52E-10 836.9162 0.0000 TRADE 659.5562 0.307491 2144.960 0.0000 Effects Specification

Cross-section fixed (first differences)

Mean dependent var 2862.189 S.D dependent var 39019.45 S.E of regression 34351.12 Sum squared resid 4.71E+12

Table 12: Making Principal Component (ICT)

(Source: Analysis by the author)

Details in excel file data

Following SMARTer 2030 Report’s calculation method, this study develops its own calculation which specifies needed input information to calculate potential emission reduction in 8 sectors when applying ICT technologies as follows:

- Reducing travel to healthcare: Telepresence can reduce 38% number of outpatient attendances

- Less usage of healthcare facilities: e-health can reduce 36% number of healthcare facilities usages

Adoption rate by 2030 in non-OECD countries: 65%

Average distance to healthcare facilities (km) (5)

Number of outpatient attendances (number) (6)

Reduction in number of outpatients (percentage) (7)

Average consumption/km (liters/km) (8)

Average emission/liters (emission/liters)

Number of healthcare facilities (10) Average emission/hospitals (11) Reduction in healthcare facilities usage (percentage) (12)

Students attend class online -> Reduction in transport use -> CO2 abatement Students who adopt E-Learning will travel 30% less Adoption rate by 2030 in non-OECD countries: 35%

E-Learning Fall in transport use secondary education

Average distance to secondary school (km) Number of secondary students

Fall in transport use higher education

Average distance to higher school (km) Number of higher students

Fall in transport use company training

Average distance to company training facilities (km) Number of company training people

Adoption rate by 2030 in non-OECD countries: 15%

- Decreasing in energy production (can be reduced 20%): smart metering allows better demand management; more efficient devices and changes in personal behaviour

- Decreasing in energy lost (can be reduced 5%): reducing energy lost during distribution; new material can provide more efficient energy storage

Total energy production Average emission per energy production unit

Total energy lost Average emission per energy production unit

Calculating based on the growth of the share of renewable energy in total energy production

Adoption rate by 2030 in non-OECD countries: 45%

- Reducing energy use (65% reduction): smart sensors can help farmers to control energy use and machinery

- Reducing usage of fertilizers (65% reduction): smart sensors to understand conditions for farming activities

- Reducing enteric fermentation (65% reduction): farmers can monitor livestock food available and diet to reduce methane emissions from enteric fermentation (which contributes 30% total methane emissions related human activities)

- Rice cultivation (40% reduction): farmers using sensors can be able to control the concentration of methanotrophs in the waterbed needed for rice cultivation

- Food waste (20% reduction): better production management, connection and transparency within supply chain

Reduce energy use Total energy use in agriculture

Emission per energy use unit

Usage of fertilizers Total amount of fertilizers uses in agriculture

Emission per fertilizers use Enteric fermentation

Manure management Rice cultivation Total rice cultivation

Emission per rice cultivation units Food waste Total emission due to food production

30% all food produced is wasted

Adoption rate by 2030 in non-OECD countries: 20%

- Reduction in the energy consumption from households: maximum 40% due to better energy management, automatic default, building supervision and control

- Reduction in the energy consumption from commercial building: maximum 45%

Reduction in the energy consumption from households

Total household building Average emissions/household

Reduction in the energy consumption from commercial building

Total commercial building Average emissions/commercial building

Adoption rate by 2030 in non-OECD countries: 40%

- Ride sharing: Using car sharing platforms and apps can reduce 30% total km traveled

- Car production: Total number of cars produced will be reduced 15%

- Car sharing: Total cars circulating will be reduced by 15% However, km covered by each car will be increased 20% Thus, total impact will come from multiplying two assumptions

Ride sharing Total km traveled

Average emissions/km Car production Total number of cars produced

Adoption rate by 2030 in non-OECD countries: 10%

- Efficient routes: GPS, fast parking platform and Smart driving technologies can reduce distance travelled by 25% and fuel consumption by 30% Total impact will be 7,5%

- Efficient vehicles: will have lower consumption than normal vehicles, thus, reducing fuel consumption by 30%

- Efficient public transport: improvements in transparency in public transport system can attract people using this method and reduce kilometers made by private vehicles by 25%

Efficient routes Total distance travelled (km)

Average emission/km Total fuel consumption Average emission/liters Efficient vehicles Total liters of fuel consumption

Average emission/liter Efficient public transport Total km made by private vehicles

Adoption rate by 2030 in non-OECD countries: 75%

Smart logistics enables route optimization, maximizes vehicle capacity, and promotes logistics sharing, leading to significant reductions in freight transportation This innovative approach can decrease road freight by up to 30% (ton-km), air freight by 20% (ton-km), maritime freight by 20% (ton-km), and train freight by 25% (ton-km).

Road freight Total ton-km by road (weight in tons of material transported x number of km driven) Average emission/ton-km by road

Air freight Total ton-km by plane (weight in tons of material transported x number of km driven) Average emission/ton-km by plane

Maritime freight Total ton-km by ship (weight in tons of material transported x number of km driven) Average emission/ton-km by ship

Train freight Total ton-km by train (weight in tons of material transported x number of km driven) Average emission/ton-km by train