The Role of Digitalization in Facilitating Renewable Energy Transition and Reducing Greenhouse Gas Emissions in Thailand

Abstract

The study investigates the dual transitions of digitalization and renewable energy in Thailand to see if digital expansion facilitates the transition to renewable energy sources and greenhouse gas (GHG) mitigation. The study utilized data from the World Bank between 2000 and 2023 to reconstruct models for autoregressive distributed lag (ARDL) analysis for short-run and long-run dynamics under ecological modernization and technology diffusion theories. Contrary to expected synergies, empirical results revealed that a developing economy would find an ‘investment trade-off’ instead. Digitalization showed no significant immediate impact on renewable energy production; however, it exerted a significant negative lagged effect (coefficient = −0.593), suggesting that digital and energy infrastructures compete for limited financial resources. It was found that there is a 6.3% increase in greenhouse gas emissions for every 1% increase in internet usage. Thus, these results challenge the belief that increased internet usage will help improve the environment. Absent proper supportive policies about both digitalization and green transitions, such as investing in plants and machinery towards digitalization rather than green technology, the pacing effects of digitalization will affect the goals of converting to clean energy. This requires a policy coordination approach to ensure that funds earmarked for green infrastructure are safeguarded.

1. Introduction

The very processes of building, changing, and evolving toward low-carbon economies alongside the imminent threats of climate change are driving all countries today [1]. So, at the level of technology, they have dual transformation in energy systems and the development of technology infrastructures. Digitalization is among the new words that are gaining entry regarding promising alternatives to fossil fuels, especially in the electricity sector that is not limited to hydropower uses. Thriving on renewable energy sources such as solar, wind, and biomass in delivering its services, it dominated the discussions during the year 2021 [2]; in bringing this scenario into existence, the dual transformation force is the ability to change energy consumer patterns with increasing system efficacy [3]. Dimian et al. [4] and Jaroenwanit et al. [5] established that internet connectivity, information technology, characteristics of information systems, and AI-supported changes contribute to the digitalization path.

Thailand leads Southeast Asia at the forefront of this development. The notable effort is the national ‘Thailand 4.0’ strategy. Officially implemented to help free the country from the trap of being labeled a middle-income country, Thailand 4.0 is an economic strategy designed to change the country’s focus from heavy industry (Thailand 3.0) to an economy based on value and innovation. It operates as the ‘National Digital Blueprint,’ whose goal is to advance the nation into a high-income, innovation-driven economy while being in sync with the United Nations Sustainable Development Goals (SDGs) [6]. Digital technology capabilities are considered a component of this agenda, leveraging innovation, creativity, and technology as key drivers for economic prosperity, social well-being, and environmental sustainability. This agenda focuses on building strong digital infrastructure, creating digital IDs, and encouraging e-government solutions [7]. The aspect of environmental sustainability in ‘Thailand 4.0’ is the focus of this study; it is a commitment that focuses on transitioning to cleaner energy through key strategies such as renewable energy, energy efficiency, reduced greenhouse gases, and smart grid technology [8].

From the view of the Thailand 4.0 initiative, although digitalization and renewable energy are often studied separately, they are interrelated. Bartczak [9] argues otherwise; rather, renewable energy resources may be used, incorporated, or otherwise optimized through digital technologies. Digital technologies enable real-time energy management, predictive analytics, distributed energy systems, and a smart grid infrastructure. On the other hand, according to Scharl and Praktiknjo [10], the digital connections will elevate people’s awareness of environmental issues, likely to trigger behavior changes in energy consumption and, to a greater extent, policy changes. Digitization, as already discernible in developing economies like Thailand, offers the prospect of modernization of energy systems, integration of renewable energy sources, and an approach to a low-carbon economy in the future.

Introspectively, the last two decades have seen increased internet penetration in Thailand. This fact obviously makes the digital ecological transition grounded in Thailand [11,12]. Furthermore, very little empirical research examines digitalization with clean energy development, while most of it treats the whole environment in developing countries like Thailand. The majority of research either deals with impact analysis that relates digital transformation to productivity or only accounts for carbon emission rates, with very few political adoption levels characteristic of developing countries. The limits on studies in this field mean that they tend to assess whether and how the digital component interacts with renewable energy development; hence, the use of this technology affects emissions reductions by adoption. For instance, most recent studies show that digital investment and further diffusion of ICT could foster green energy consumption and enhance sustainable performance [13,14,15]. However, most of these analyses have been conducted in advanced economies or G20 countries.

Regarding the numerous writings developed on this ongoing empirical literature, the majority centers on direct load impacts from renewables with respect to carbon emissions [16] or economic issues concerning that technology [17]. The role that digital infrastructure plays in facilitating green transitions is masked to some extent. Dimian et al. [4] would argue that, in Europe and other OECD countries, carbon emissions through digitalization could be enhanced because of the effectiveness improvement or expansion of the reservation of energy from renewable sources. But findings like these cannot be as highly applicable to Thailand since differences will arise in technology maturity and policy framework [18].

Theoretically, this creates a critical disconnect. Ecological modernization theory suggests digital change can help the environment by making things more efficient and using fewer materials. This concept is largely based on data from developed nations. It remains uncertain whether technology diffusion will show similar results in countries such as Thailand. Resource conflicts could also land on the con side of environmental gains in terms of new tech. These issues still need answers. While recent literature has profoundly explored techno-economic modeling for integrated energy systems and flexible operations [19,20,21], macro-frameworks oftentimes fail to address the ‘developmental friction’ characteristics of emerging market frameworks. Specifically, how the dual transition competes for limited financial resources. Therefore, there is a lack of empirical evidence on whether the theoretical synergies proposed by engineering models actually materialize in the historical data of developing economies like Thailand.

The literature on Thailand has centered much on policy toward the promotion of renewable energy and energy security issues [22]. Although it is critically assessed, there seems to be, unfortunately, insufficient quantitative evidence for internet penetration or digitalization to raise renewable electricity generation per head, yielding less greenhouse gases. This lends itself to the research, which investigates the nexus between digitalization and renewable energy transition in Thailand, coupled with GHG reductions. The study is guided by two objectives: (1) to investigate the relationship between internet usage and electricity produced from renewable sources; (2) to evaluate the direct and indirect impacts of digitalization on GHG emissions through electricity generated from renewable sources.

The huge relevance of the research to energy policies and policies relating to digital development in Thailand, considering the interrelationship of internet penetration with renewable energy and emissions reduction, will therefore require emphasis. The investments can also show if they match the country’s environmental sustainability plan. This is key since Thailand 4.0 is pushing for both cleaner energy and digital changes at the same time. Most places in South Asia are the final rungs on the global ladder of sustainability research, but this study crosses that barrier of regional legitimacy. Hence, research will now design a region-wide insight that will benchmark against neighboring countries engaged in similar development programs. In terms of academia, this would provide relevance to the study under the ecological modernization theory (EMT) and the technology diffusion theory adopted in the research. The study would elaborate on how a tech system could meet environment-oriented as well as economy-oriented ends.

After the Introduction, the remainder of the paper contains the following: a literature review introduces the principal theories of ecological modernization and technology diffusion that underpin the theoretical foundation of this study and reviews the empirical literature concerning digitalization, renewable energy, and emissions to identify the research gap. The Research Methodology section outlines the conceptual framework and hypotheses, details the secondary data sources and variable measurements, and explains the application of the autoregressive distributed lag (ARDL) model for analysis. The Results section presents the empirical findings, including descriptive statistics, correlation analysis, stationarity tests, and the outcomes of the ARDL model estimations for testing the study’s hypotheses. Thereafter, the Discussion interprets these findings against the prevailing literature in their specific short- and long-run dynamics and finishes with an exposition on the theoretical and managerial implications. Finally, the Conclusion wraps up with the important findings and states what the policy recommendations should be, with regard to a coordinated digital and energy strategy, plus some limitations of the study and directions for future research, including sector-specific and more regional comparative analyses.

Novelty and Scientific Contribution

This study has distinct contributions to knowledge; the first is the identification of a significant geographical gap. The article, therefore, adds to the debate concerning the applicability of the EMT in the Global South. This study contributes some findings from Thailand, while the majority of the digitalization environment nexus literature has revolved around advanced G20 economies with mature technological infrastructures. Also, this study represents the rapidly developing ASEAN economy of Thailand as one that is daringly balancing a transition on two fronts (digital and green) under the ‘Thailand 4.0’ initiative and, therefore, serves as a setting to experimentally validate ecological modernization theory (EMT) outside the Global North.

Second, this study, instead of cross-sectional studies that do not consider the time dimension, adopts the autoregressive distributed lag (ARDL) model, which is a preferred technique that allows one to differentiate the short-run and long-run dynamics. It shows that digital expansion does not transform itself into renewable energy growth instantly. Third, the study reveals a peculiar mechanism of ‘investment trade-off.’ The evidence suggests that, in economies under development, digitalization competes for limited financial resources with renewable energy projects, a subtle point often overlooked by studies that assume automaticity, not synergies, among ICT and green energy.

2. Materials and Methods

2.1. Theoretical Basis

The ecological modernization theory (EMT) and technology diffusion theory were the two theories on which the research literature review was based. A holistic framework under which these two theories are offered for studying digitalization includes two aspects: developing renewable energy and reducing emissions.

2.1.1. The Ecological Modernization Theory

The EMT was developed in the 1980s to account for perceived contradictions between the demands of industrial capitalism and those of environmental sustainability. The basis of this theory is that technological advancement, market forces, and institutional reforms can produce improvements in nature [23]. This is one avenue for addressing environmental problems: not closing down industries. But this could also be achieved through the modernization of production and consumption lifestyles by cleaner technologies and smarter governance. Hence, this theory supports the idea that digitalization, through internet expansion and ICT integration, can be a key enabler of ecological transformation. This could be achieved by optimizing resource use, improving energy efficiency, and supporting the integration of renewable energy sources [24,25]. From the perspective of this study, the EMT provides a rationale for exploring how Thailand’s investment in digital infrastructure may influence renewable energy and address GHG emissions.

The theory relies on three interdependent hypotheses that are relevant to the current study: (a). Decoupling: Negative environmental impacts may arise that could never have been perceived as decoupled from economic growth through efficiency improvements and innovations. (b). Technological Optimism: Science and technology do not breach ecological balances but rather they are often the most important avenues to solving clean production and resource optimization, especially in the area of digitalization. (c). Market Internalization: Environmental factors are included within economic calculus; ‘green’ finally becomes profitable through efficiency improvements.

Looking from this research perspective, EMT provides the foundational basis for the implementation of the Thailand 4.0 initiative. Its relevance to the study is that the agenda drives the advancement of policy and governance for investments into the necessary digital infrastructure for adopting this ecological restructuring scheme (internet expansion and ICT). Thailand utilizes digital grids and performance monitoring to achieve EMT goals concerning embracing ‘ecological transformation,’ which decreases net GHG emissions without curtailing future developments in the economy.

2.1.2. Technology Diffusion Theory

Rogers [26] disseminated the understanding of diffusion of technology as diffusion of innovations in his literature. This paper is about the diffusion between individuals and organizations. The process itself can be categorized into four elements: innovation itself, the nature of communication, social influence, and adoption, the combination of which determining how far a new technology would break into the visible spectrum. According to Comin and Mestieri [27], if broadly circulated, digital infrastructure has the capacity of reducing uncertainty and cost as well as creating social norms for sustainable energy consumption. This theory underlies this argument by making reference to internet penetration depth and its unintended effect of hastening the use of renewable energy and abatement of emissions by way of knowledge spillovers, real-time monitoring, and tight coupling of energy systems according to Kraemer-Mbula et al. [28].

Table 1. Adaptation of theories in related literature.

Theory	Key Focus/Tenet	Application in Previous Studies	Relevance to Current Study
Ecological Modernization Theory (EMT)	Technological innovation as a solution to environmental degradation.	Used to link trade and energy efficiency to emissions reductions.	Justifies digitalization as a tool for decoupling economic growth from GHG emissions in Thailand.
Technology Diffusion Theory (TDT)	The process by which innovation spreads via communication channels over time.	Applied to explain ICT utilization and green energy consumption.	Explains the lag and transmission mechanism through which internet usage converts into tangible renewable energy adoption.

synthesizes the two theories, juxtaposing their tenets, applications in previous studies, and how they are relevant to this research.

Although macro- and micro-factors explain even the theoretical lens of why digital transformation leads to sustainability via modernization, the micro-level mechanisms under which these technologies diffuse across a developing country are left unexplained by EMT. Therefore, one will have to combine TDT with the above framework to understand the adoption process. TDT explains how the ‘innovation’ (digital infrastructure) diffuses through communication channels to influence the ‘adoption’ of renewable energy systems. Hence, on one hand, the EMT predicts that the outcome is reduced emissions, and on the other hand, the TDT explains the transmission mechanism (internet penetration enabling green tech adoption).

2.2. Empirical Literature

2.2.1. Digitalization and Renewable Energy

The force of digitalization is significant in modern economies, which leads to major transformations within most sectors and also transforms energy systems; at its core, three main components are recognized: the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. Combined with cloud computing, these instruments enhance the level of efficiency of operation execution, infrastructure monitoring, and decision making. In the case of developing economies, this transition provides an opportunity to modernize the energy system, introduce renewable energy sources, and approach the world of a low-carbon economy [3,29].

Researchers have discovered that including digital solutions, including smart grids and AI, accelerates the process of decarbonization; according to Maksymova and Kuryliak [30], it is evident that its effects are perceived in energy-intensive industries. Their publications suggest the acceptance that digitalization enhances the use of renewable energy and improves the efficiency of resource use and emissions reductions [31,32]. Thus, all these signs suggest that digital technologies are actually the game changers in the entire power generation scenario, with extensive effects on the advancement of renewable energy use. All these are tools available today in smart grids for real-time analysis are aimed at production and national integration of solar and wind power into local grids [33,34].

Current research has improved our understanding of these mechanisms. Studies on integrated energy systems [19,20,21], for example, have shown how flexible, low-carbon operations can use power-to-gas and carbon capture to manage the unstable nature of renewable energy. Although engineering research may validate these systems, there is not much real-world information on whether developing countries find these technologies economically viable. The economic issue of how digital infrastructure competes with energy infrastructure for early financing has not received much research in Southeast Asia. Research has shown that the two indicators are also highly positively correlated in terms of penetration of the internet and consumption of renewable energy. An example of such a result is a study by Ömür and Erkasap [35], which revealed the positive relationships between digital infrastructure, ICT utilization, and investments and consumptions of green energy (for both developed and emerging countries). Developing countries usually have poor infrastructure and, hence, these technologies are particularly essential to them.

Mavlutova et al. [14] have mentioned that the reduction of costs and remote energy management systems can assist renewable technologies in overcoming the restrictions placed by the digital infrastructure. In addition, according to Pan et al. [29], with new developments in IoT systems, control of energy distribution from renewable energy sources, such as micro-grids powered by solar energy, will increase. Hence, access and reliability are expected to improve.

2.2.2. Digital Technology and Emission Reduction

Literature attests to different viewpoints on the effect of electronic technology on emissions of greenhouse gases. Thus, it can be regarded as a two-edged factor driving change in the environment. One side of the divide, Öztürk [36] states that systems become optimized and waste is minimized, thereby allowing the energy contribution to be efficient and emitting less. However, on the other hand, it can increase energy demand and e-waste if not managed sustainably. However, it is noteworthy that the majority of studies indicate that the net effect of digitalization, especially in synergy with renewable energy, is a reduction of emissions. Kabeyi and Olanrewaju [33] show that, in developing countries, digital transformation supports cleaner energy transitions and reduces GHG emissions. This information has the following effects: fossil fuel systems are to be replaced with renewable substitutes. Differently, through digital access processes, citizens gain greater possibilities for engagement in low-carbon practices and innovation; Waris et al. [18] also extend the causation analysis about people reducing emissions, especially with respect to the diffusion of renewable energy.

Öztürk [36] in a follow-up study examined emissions concerning low- and middle-income economies. The findings demonstrated that, when renewable energy is adopted, it acts as a mediator between digitalization and carbon emissions. Such highly penetrative countries are hypothesized to use renewable energy for more emission reductions as compared to the least digitally progressive countries. Such mediation fits very well into the ecological modernization theory framework, which states that strategic technological development enhances performance in environmental conditions.

Even though both the European and American contexts are situated well to provide strong evidence for the findings, evidence articulated for Thailand or any other Southeast Asian country is minimal. To cite an example, Xolmurotov et al. [37] stated that localized figures for data and digital literacy would be required to guarantee sustainability through digitalization instead of increasing inequality or emissions; according to Labaran et al. [38], digital infrastructure and technologies can facilitate low-carbon technology and better environmental governance in terms of resource-scarce areas. The authors have made an effort to study the policy frameworks with respect to renewable energy and digital technologies, keeping in mind studies focused on Thailand [22]; however, they have failed to link the aspects of digitalization with renewable energy and emissions metrics, a gap addressed by this study.

2.3. Conceptual Framework and Hypotheses

Borrowing from the theoretical frameworks and empirical literature, the conceptual framework of this study was developed. The study operationalizes these constructs through four distinct variables. First, digitalization is represented by ‘internet usage,’ chosen because ubiquitous connectivity is the fundamental prerequisite for deploying the IoT and AI-driven energy systems discussed in the literature.

For this analysis, renewable energy excludes hydroelectric power. The fact that such exclusion happens is because of the older infrastructures that are mostly large hydropower projects. Thailand 4.0 aims to establish an environment that supports more modern and scalable energy sources like solar, wind, and biomass. Excluding hydro allows the framework to isolate the impact that digitalization creates in the developing green technologies as opposed to old baseload capacity. Current green technologies, such as solar, wind, and biomass, are highly relevant as emerging renewable energy sources. However, the analysis excludes hydroelectric power. The older hydraulic infrastructure is distinct from these technologies, as it is under the Thailand 4.0 plan. Electricity access is included as a control variable in the model. The hypotheses of the study are also provided, showing the relationships between the variables (see Figure 1).

Conceptually linking digitalization as a major driving force of change with renewable energy under the postulations of ecological modernization theory would create solid theoretical support for this proposition. For proponents of this theory, the channels of technological innovation and market processes that once seemed destructive to the environment are now emerging as principal drivers for ecological modernization and sustainable development [39]. Here, digitalization is perceived as an enabling technology for overcoming some barriers relative to the renewable integration possibilities in Thailand’s energy sector [40]; hence, access to the internet is a proxy. Smart grid technology enables electricity delivery through an internet connection, thereby allowing real-time monitoring, balancing of demand and supply, and flexibility to schedule generation according to these intermittent energy sources on such vast scales owing to its intermittent nature imposed by solar and wind in this regard [41].

While the visual framework aligns with the outcome-oriented predictions of the ecological modernization theory (EMT), which posits that technological progress drives ecological sustainability, the structural linkages (H1 and H3) are fundamentally grounded in technology diffusion theory (TDT). We argue that a separate graphical representation for TDT is unnecessary because the theory is intrinsic to the mechanism of the framework rather than its outcome. Specifically, the independent variable, ‘Digitalization (Internet Usage),’ serves as the proxy for the diffusion channel described by Rogers [26]. The arrows connecting digitalization to renewable energy and GHG emissions represent the ‘communication channels’ through which the innovation (digital tech) diffuses into the energy sector. So, on the one hand, the framework considers both theories: EMT suggests the type of relationship (positive environment impact), and TDT explains how to model the diffusion mechanism (digital adoption across energy systems).

Other digitalization dimensions of predictive maintenance for renewable energy infrastructure would comprise IoT sensor technology, processing advanced data analytics to promote better energy efficiency, as well as the flexibility of the grid [24]. This digital structure turns out to reduce the perception of risk as well as operational costs to what would have been really hard in bringing up renewables, under pressure for investment and adoption of it. Diffusion theory on technology classified such diffusion technologies as a positive feedback mechanism in which digital awareness and high accessibility continue to grow, strongly augmenting awareness of clean energy solutions [42,43]. Given that, ultimately, the increasing internet penetration will not merely go with increasing capacity for renewable energy but also catalyze the modernization of operations and strengthening of the management paradigms within energy systems. This relationship represents the first stage of the mediated pathway. We posit that digital diffusion acts as the antecedent for modernizing energy production. Hypothesis 1 is proposed based on these arguments:

H1: 

Digitalization (internet usage) has a positive effect on the adoption of renewable energy sources.

Dominant among reasons to affirm that renewable energy generation constitutes an effective measure to abate greenhouse gas emissions is evidence strongly emphasized in energy and environmental sciences [44]. Conspicuously, that direct negative relationship establishes a path for societies to consider entering into an ecological modernization by decoupling economic development from ecological harm [45]. The rationale is very simple: electricity produced from renewable sources—solar, wind, and biomass—would replace an equivalent amount of electricity produced from fossil fuels, the main anthropogenic source of CO₂ and other strong GHGs [46]. Clearly, the substitution effect would have reduced power sector emissions in the most straightforward and certain sense, considering that, in many circumstances, this may have been the most impactful sector on the national carbon footprint. Thailand is working toward changing its energy mix, in the sense of moving away from its historical reliance on gas and coal, by means of the ‘Thailand 4.0’ program [47]. According to Acosta [48] and Ye et al. [49], the same scalable renewables would be a standby option of all feasible alternatives to fulfilling Thailand’s international commitments on climate change. Taking renewable electricity expansion not only in terms of infrastructure and economy but also as a very substantive key intervening variable transposing policy and technology investment into real-world ecological gains brings us to this position. This completes the indirect mechanism, linking the technological shift to environmental outcomes. These views strengthen the argument on which hypothesis 2 is based:

H2: 

Renewable energy generation has a reducing effect on GHG emissions.

Digitalization is expected to bring about a reduction in greenhouse gas emissions (GHG) in a direct and independent way [50]. This is one of the tenets of the ecological modernization theory, which posits that the environment benefits from technological improvements, rather than exclusively cleaner production [51,52]. Directly through economy-wide efficiency, therefore, digitalization reduces emissions; through access over the digital lines, dematerialization becomes established, i.e., it allows virtual meetings, working from home, and e-commerce instead of carbon-intensive transport by physical space and infrastructure [53]. The applications of ‘smart cities’ are, in fact, dependent upon ubiquitous internet coverage; they facilitate traffic from sources to sinks to minimize idling and thus direct emissions reductions in the transport sector by optimizing operational public transport [54]. These mechanisms show how digital transformation modifies in practice the consumption, operational logistics, and resource management patterns to drastically bring down the carbon intensity of economic activity regardless of the energy mix. What it means in the end is establishing a digital infrastructure in Thailand, cobenefiting the environment by being a short-term factor of a more intelligent and efficient economic system; thus, making digitalization a really standalone, really powerful tool for emission reduction [55,56]. Hypothesis 3 was proposed based on the literature discussed. Distinct from the energy production pathway, this hypothesis captures the direct efficiency effect of the digital economy (e.g., dematerialization and virtual logistics):

H3: 

Digitalization exerts a direct negative impact on GHG emissions, independent of energy production.

Finally, we introduce electricity access as a control pathway to account for the baseline carbon intensity of the expanding national grid. In many emerging economies, to achieve universal electricity access, an absolute necessity for socioeconomic development, the past has seen the almost unrestrained expansion of fossil-fuel-based power generation [57]. Initially, therefore, increased electrification can go with increased GHG emissions since a larger populace accesses energy via a grid that is still reliant on coal or natural gas [58]. Such a situation produces a paradox whereby an important development goal temporarily works against environmental ideals. Testing this relationship is important in the case of Thailand, where electrification has progressed significantly [59]. Access to electricity is not in itself causing environmental harm, but the carbon intensity of the generation mix is responsible for the negative impact [60]. This thus establishes a condition for a positive correlation to show that grid extension must be separated from fossil fuels, leading to the conclusion that only clean electrification is synonymous with sustainable development. Hence, the access itself is not an unequivocal good but a condition to be managed for its negative environmental effects along with efforts to decarbonize the power sector [61,62]. The authors proposed the following hypothesis:

H4: 

Access to electricity has a direct positive relationship with GHG emissions during the electrification phase.

2.4. Research Methodology

2.4.1. Research Design

This research applied a quantitative approach using a time series econometrics model to establish a relationship between digitalization, renewable electricity generation, and greenhouse gas (GHG) emissions levels in Thailand. Basically, the study was directed at establishing the relationship between internet use and electricity generation from renewable sources, coupled with how these feedbacks extend to per capita GHG emissions. Therefore, this design justifies the objective testing and inference validity goals from empirical evidence. The study utilized annual time series data from 2000 to 2023. The data was sourced from the World Bank’s World Development Indicators (WDI) database. The data source is selected because of its global credibility, consistent methodology, and long historical coverage, enabling robust time series analysis. The variables used in the data, their measurement, and the type of variable are summarized in

Table 2. Variables, WDI Codes and their descriptions.

Variable	Measurement Unit	WDI Indicator Code	Description and Coverage
GHG Emissions (Dependent)	Metric tons of CO2 equivalent per capita	EN.GHG.ALL.PC.CE.AR5	Total greenhouse gas emissions from all sectors (energy, agriculture, waste, industrial processes), normalized by population.
Digitalization (Independent)	Percentage of individuals using the internet	IT.NET.USER.ZS	Proxy for digital diffusion. Captures digitalization (internet usage) levels, though it does not directly measure the depth of industrial ICT, IoT, or AI deployment.
Renewable Electricity (Mediator)	Percentage of total electricity output (excluding hydro)	EG.ELC.RNWX.ZS	Share of electricity generated from renewable sources including geothermal, solar, wind, tides, biomass, and biofuels; excludes hydroelectric power.
Access to Electricity (Control)	Percentage of population	EG.ELC.ACCS.ZS	Proportion of the population with access to electricity through national grid connections or off-grid sources.

Adapted from World Bank WDI [63].

.

2.4.2. Data Treatment and Limitations

Prior to analysis, all variables were transformed into natural logarithms (Ln) to minimize heteroskedasticity and normalize the scale differences between percentage and per capita units. The study relies on ‘individuals using the internet’ as the proxy for digitalization. We accept the vast assumption that this demand-side yardstick may not fully encapsulate the details of industrial Industry 4.0 adoption (e.g., specific investments in gridlike AI or IoT sensors). Nevertheless, we now consider it the best aggregate indicator for longitudinal estimates because there are no detailed, consistent industrial data before 2015 for Thailand. Specific periodic data from the year 2000 until 2023 have been included in the final dataset; at just 24 data points measuring year against an analysis of the ARDL model, the maximum lag of this model can only be set to 1 so as to maintain adequate degrees of freedom. This helps avoid shrinking the sample size too much when estimating the model. The CUSUM test was used to check structural stability. The data suggests the absence of a structural break, supporting the possibility that the coefficients are unreliable.

2.4.3. Data Analysis

The data analysis was conducted using statistical econometrics models. Before the empirical analysis to evaluate the objectives and hypotheses of the study was conducted, the initial empirical evaluation of the data was performed. The first preliminary analysis was the descriptive statistics of the data to evaluate aspects such as mean, range, variance, and standard deviation. The study also depicted the graphical representation of the data to determine the trend behavior over time. Several diagnostic tests were conducted. The first is the test for stationarity using the augmented Dickey–Fuller (ADF) test [64,65]. The other test is the cointegration test. In this test, if variables are integrated of order I(1) [66], the Johansen cointegration test is applied to assess the presence of a long-run equilibrium relationship among them [67].

The study adopted the autoregressive distributed lag (ARDL) model. The ARDL model estimates both short-run and long-run relationships among the study variables [68,69]. The bounds test was used to test if there are long-run or short-run effects between the variables [70].

The first objective investigated the relationship between internet usage and electricity production from renewable sources. The following ARDL model was adopted:

$${RE}_t=\alpha +{\sum }_{i=1}^p{\beta }_i {RE}_{t-i}+{\sum }_{j=0}^q{\gamma }_j {Internet}_{t-j}+{\epsilon }_t$$

(1)

where:

• RE_t = Dependent variable (renewable electricity generation) at time t.
• ${\mathit{Internet}}_{t-j}$ = Independent variables (internet usage).
• β_i and γ_j = Short-run dynamic coefficients.
• α = Intercept.
• ε_t = White noise error term.

Since this study employed publicly available secondary data, human respondents were not involved. In this case, the ethical considerations took into consideration proper citation of data sources, transparency of methodology, and storage and handling of data consistent with academic integrity.

$${\mathrm{G}\mathrm{H}\mathrm{G}}_t=\alpha +{\sum }_{i=1}^p{\varnothing }_i {GHG}_{t-1}+{\sum }_{j=0}^{q_1}{\beta }_j {RE}_{t-j}+{\sum }_{k=0}^{q_2}{\gamma }_k {Internet}_{t-k}+{\sum }_{l=0}^{q_3}{\delta }_i Access$$

(2)

where:

• GHG_t = Dependent variable (GHG emissions per capita).
• RE = Renewable electricity generation (% of total, excl. hydro).
• Internet = Internet usage (% of population).
• Access = Access to electricity (% of population).
• $\varnothing , \beta , \gamma , \delta$ = Short-run dynamic coefficients for each variable.
• P, q1, q2, q3 = Optimal lag orders selected via SBIC.
• ${\epsilon }_{\mathrm{t}}$ = Error term.

The analysis includes the following models:

• Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests for unit root and stationarity.
• Johansen cointegration test to determine long-run relationships.
• Autoregressive distributed lag (ARDL) model to assess both short-run and long-run dynamics.

As stated earlier, econometrics statistical methods were adopted (ADF test, PP, Johansen cointegration test, ARDL model, and ARDL bounds test); Stata software v15 was used to conduct the analysis. For the data processing and visualization, Excel software was used.

2.4.4. Model Specifications and Robustness

To meet statistical needs, a cautious estimation approach was used, given the annual limits found in the time series data (N = 24). The ARDL bounds test is chosen to provide the main estimation for the first stage because of the efficiency of the ARDL model in small samples with mixed orders of integration (I0/I1). The cointegration Johansen test is thereafter employed as an analogy of robustness. Selection of the lag was really tight under the ban of the Schwarz Bayesian information criterion (SBIC) that limited maximum lagging to just 1. This method addresses the risk of overparameterization, which is common in short time series data. Solid estimates of past elasticities from this method have a clear, defined nature. This method in econometrics does not include random uncertainty and dynamic capacity sizing, which are important for the technical planning of grids; some recent engineering papers, for example, studies on energy storage systems with uncertainty, deal with this issue [71]. Therefore, our results should be interpreted as macro-policy signals rather than technical grid dimensioning parameters.

3. Results

The first analysis of the study was descriptive statistics. The results in

Table 3. Descriptive statistics of study variables (2000–2023).

	Access to Electricity	Greenhouse Gas Emissions	Renewable Sources of Electricity	Internet Usage (Digitalization)
Mean	96.80	5.34	9.03	36.14
Std. Dev	4.65	0.46	7.98	27.80
Minimum	82.10	4.23	0.80	3.69
25%	93.13	5.14	1.78	15.56
50%	99.35	5.51	7.61	25.06
75%	99.90	5.68	17.51	55.84
Maximum	100.00	5.82	21.84	89.54
Range	17.90	1.59	21.04	85.85
Variance	21.58	0.22	63.71	773.10
Skewness	−1.69	−1.16	0.46	0.81
Kurtosis	2.88	0.39	−1.51	−0.64
Median	99.35	5.51	7.61	25.06
Mode	99.90	4.23	0.80	3.69
IQR	6.80	0.54	15.73	40.28
CV	4.80	8.69	88.35	76.93

Adapted from World Bank WDI [63].

of the descriptive statistics provide valuable insight into the distribution and variability of the key variables. Regarding the access to electricity as a % of the population, the mean access to electricity is 96.80%. This indicates widespread electrification across Thailand. Additionally, the minimum value is 82.10%, and the maximum is 100%, showing significant progress toward full coverage. The standard deviation is low at 4.65, with a negative skewness (−1.69), suggesting most values are concentrated near the upper end. Regarding the greenhouse gas emissions per capita (t CO2e/capita), the results showed that the mean is 5.34, with a relatively narrow range (4.23 to 5.82) and low variability (std = 0.46). Also, the coefficient of variation (CV) was found to be 8.69%, reflecting consistency in emissions trends per capita.

For electricity production from renewable sources (excluding hydroelectric), the results showed a mean of 9.03%. There is a broad range from 0.80% to 21.84%, which shows a clear, rapid development in renewable electricity. The standard deviation is 7.98, and the CV is quite high at 88.35%, indicating substantial variation over time. On the other hand, the average internet usage (% of population) showed a significant level of 36.14%. The trend indicates the gradual involvement of people in digital activities. Usage values were found within a minimum and maximum of 3.69% and 89.54%, respectively, with a very high standard deviation of 27.80-SD, CV = 76.93. The high variations can be interpreted to mean that there was a rapid expansion of these values in recent years. One could conclude from this statistical outcome that: (1) electricity access and greenhouse gas emissions have a realistic level of stability and approach saturation; (2) internet usage and renewable electricity production are changing dynamically. This indicates an evolutionary trend of digitalization and renewable energy efforts in the emerging environmental outcomes of Thailand.

The graphical representation (Figure 2) of the trend shows that access to electricity increased significantly between 2000 and 2010, reaching a level of around 98%. Then the trend remained significantly flat. The greenhouse gas emission (metric tons of CO₂ equivalent per capita) increased, displaying an increasing trend over time, particularly between 2000 and 2012. The trend reduced after 2013, depicting the country’s effort to reduce greenhouse gas emissions. The same trend was also observed regarding electricity access and internet usage/access. NB: Std. Dev = Standard Deviation; IQR = Interquartile Range.

3.1. Correlation Analysis

Digitization has an excellent relation to renewable electricity access (r = 0.9291, p < 0.01). Increased consumption or production using renewable energy has to go along with improvement in internet usage through means of digitalization, which follows the very expectation that access and innovation in technology are likely to drive clean transitions, maybe through better monitoring of the grid, smart technologies, or monitoring systems of energy. Strong and positive was the relationship of greenhouse gas emissions with access to electricity (r = 0.912 **), while moderate with digitalization (internet usage) (r = 0.7247 ***). Thus, this would mean an increase in electricity- and digital-infrastructure-generated energy, historically known as the highest emitters due to their fossil fuel dependency. A contradictory urgency is forged, where, above all, clean energy access should accompany digital growth. Among the most interesting findings was that, while renewable electricity and greenhouse gas emissions had a moderately significant relationship (0.7495 ***), they were instead anticipated to follow a generally negative relationship. The renewable energy sector has thus been driven by existing levels of emissions, as is characteristic of any developing and transitioning economy. That said, one conclusion that can be drawn is that, just as digitalization and access to electricity are propelling development, they too are correlated with increased emission levels; hence the need to prop up clean energy transitions and promote green technology. These are summarized in

Table 4. Correlation matrix of log-transformed variables (2000–2023).

	(1)	(2)	(3)	(4)
Access to electricity (1)	1
Greenhouse gas emissions (2)	0.912 ***	1
Renewable electricity generation (3)	0.6556 ***	0.7495 ***	1
Internet usage (digitalization) (4)	0.6530 ***	0.7247 ***	0.9291 ***	1

Note: *** = p < 0.01.

.

3.2. Empirical Tests

In this section, an analysis is conducted to evaluate the hypothesis of the study, following the objectives of the study. Before the analysis, the variables were transformed and linearized by taking their logs. The stationarity tests were conducted (

Table 5. Stationarity tests of the variables.

	ADF Test Statistic	1% Critical Value	5% Critical Value	10% Critical Value	p-Value	Stationarity
Ln(Access to electricity)	−4.585	−3.75	−3	−2.63	0.0001	At level
Ln(Greenhouse gas emissions)	−3.526	−3.75	−3	−2.63	0.0045	At level
Ln(Renewable electricity generation)	−0.948	−3.75	−3	−2.63	0.7721	Non-stationary at level
Ln(Internet usage—digitalization)	−3.672	−3.75	−3	−2.63	0.0042	At level
d_Ln(Renewable sources electricity)	−5.374	−3.75	−3	−2.63	0.0000	At first difference

), considering that the data were time series. The results indicated that greenhouse gas emissions, access to electricity, and internet usage (digitalization) were stationary at a level. However, renewable electricity becomes stationary after first differencing. These results support further analysis using techniques appropriate for a mix of I(0) and I(1) variables, such as the ARDL model.

In addition to the stationarity tests, the optimal lag length was evaluated using all four criteria. For all variables, FPE, AIC, HQIC, and SBIC, the optimal lag length for all the variables is specified in

Table 6. Optimal lag length evaluation.

Variable	Optimal Lag
In(Access to electricity)	4
In(Greenhouse gas emissions)	1
Ln(Renewable electricity generation)	1
In(Internet usage—digitalization)	1
d_Ln(Renewable sources electricity)	1

. The first objective of the study was to investigate the relationship between internet usage (as a proxy for digitalization) and electricity production from renewable sources. The short-run model first conducted was statistically significant, F(3, 19) = 141.70, p < 0.001, accounting for approximately 95.72% of the variance in renewable electricity generation. It was observed that the lagged value of the past renewable electricity generation was a strong and positive predictor, B = 0.809, p < 0.001. This meant high persistence over time. However, the short-term or immediate effect of internet usage (digitalization) on renewable energy production (electricity) was not significant (B = −0.409, p = 0.623).

Additionally, the lagged effect of internet usage (digitalization) was negative and statistically significant (B = −0.593, p = 0.042) (see

Table 7. Empirical results for Model One.

	Coefficient	Std. Error	t	p-Value
${\mathit{Renewable} \mathit{Energy}}_{t-1}$	0.809	0.181	4.48	<0.001
${\mathit{Internet} \mathit{access} \left(\mathit{Digitalization}\right)}_{t-j}$	−0.409	0.818	−0.50	0.623
${\mathit{Internet} \mathit{access}}_{t-j}$ L1.	−0.593	0.647	−2.82	0.042
Intercept	1.092	0.718	1.13	0.899

Note. Dependent variable: Renewable Energy Electricity Model: ARDL(1,1). F(3, 19) = 141.70, p < 0.001, R² = 0.957, Adjusted R² = 0.951, Root MSE = 258.

). This suggests that changes in internet usage in the prior year are associated with a subsequent decrease in renewable electricity generation. As a result, H1 was not supported. This could be explained by the possibility of infrastructural or resource allocation trade-offs over time, considering that Thailand is still a developing nation.

The study went ahead to conduct the bounds test to determine whether there was any long-run relationship between internet usage (as a proxy for digitalization) and electricity production from renewable sources. The results revealed that the F-statistic (F = 1.308) was below the lower bound of the critical values at all conventional significance levels, and the t-statistic (t = −1.055) is above the I(0) critical bounds (

Table 8. Long-run relationship test.

Significance Level	F-Stat I(0)	F-Stat I(1)	t-Stat I(0)	t-Stat I(1)
10%	4.04	4.78	−2.57	−2.91
5%	4.94	5.73	−2.86	−3.22
2.50%	5.77	6.68	−3.13	−3.5
1%	6.84	7.84	−3.43	−3.82
Test Statistic	Value	Decision
F-statistic	1.308	No long-run relationship
t-statistic	−1.055	No long-run relationship

). Hence, there was no long-run relationship between the two.

Model Diagnostics and Specification Note: Since the bounds test indicated no cointegration (no long-run equilibrium relationship), an error correction model (ECM) was not estimated. The definition is restricted to semi-dynamics and lagged distributed effects. Diagnostics upscale this specification into the short run: the Breusch–Godfrey LM test indicated a lack of serial correlation, while Breusch–Pagan showed no evidence of heteroskedasticity. This very much narrows the CUSUM plot into very critical bounds at a 5% level of significance, substantiating its parameter stability. The direct and indirect impacts of the processes from the digital sector on GHG emissions during renewable energy production were investigated in the next analysis under objective two and presented in

Table 9. Digitization and GHG Emissions (Model Two).

	Coefficient	Std. Error	t	p-Value
GHG (L1)	0.468	0.176	2.66	0.018
Renewable electricity generation	−0.006	0.0185	−0.35	0.731
Renewable electricity generation (L1)	0.0249	0.0211	1.18	0.255
Internet usage (digitalization)	0.0631	0.0660	−0.96	0.354
Internet usage (digitalization) (L1)	−0.042	0.056	0.760	0.460
Access to electricity	0.050	0.29	0.17	0.864
Access to electricity (L1)	0.544	0.222	2.45	0.027
Constant	−1.768	1.399	−1.26	0.226

Note: The dependent variable is greenhouse gas emissions per capita (GHG). Model 2 includes lagged and current values of renewable electricity generation, internet usage (digitalization), and access to electricity. The model is statistically significant: F(7, 15) = 49.37, p < 0.001. R² = 0.958; Adjusted R² = 0.939; Root MSE = 0.197.

.

The results revealed that the current level of internet usage was positive and had a non-significant association with GHG emissions (β = 0.468, p < 0.018); this suggested that there is moderate short-run persistence in GHG levels and it is attributed to the persistence of past emissions (GHG L1). The effect of renewable energy on GHG emissions revealed that the immediate effect was negative and insignificant (β = −0.006, p < 0.731). It implied that a 1% increase in renewable electricity generation is associated with a 0.6% reduction in GHG emissions, which invalidates H2. However, the lagged effect of renewable energy was negligible and insignificant. It meant that increased renewable energy in the previous year was not associated with higher GHG emissions in the current year. Regarding the impact of digitalization, the results revealed that the current level of internet usage was positively associated with GHG emissions (β = 0.0631). This specific coefficient was statistically insignificant (p = 0.354); the direction of the relationship contradicts the expectations of immediate environmental benefits. It meant that a 1% increase in internet usage is associated with a 6.3% increase in GHG emissions in the short run, rather than a reduction, hence H3 was not supported. It implied that greater digital access may contribute to slight reductions in emissions in the short term. The lagged effect of digitalization (internet usage) (L1) was also negative but not statistically significant (β = −0.042, p < 0.460). This meant that a 1% increase in past internet usage leads to a 4.2% reduction in GHG emissions. This indicates that, while the sign turns negative over time, there is no statistical evidence that digitalization has yet led to a significant reduction in emissions. Lastly, the effect of access to electricity on GHG emissions revealed that the short-term current effect of access to electricity was a significant positive lagged effect (B = 0.050, p = 0.864). However, the effect of lagged access to electricity was positive and significant. It implied that a 1% increase in past access to electricity leads to a 0.54% increase in GHG emissions (B = 0.0544, p = 0.027); this supported H4, confirming that historical grid expansion in Thailand has been carbon-intensive.

4. Discussion

Considering the global and national transition toward renewable energy, environmental conservation, and technological advancement, this study sought to establish the role of digitalization in facilitating renewable energy transition and reducing greenhouse gas emissions in Thailand. The first objective was aimed at evaluating the relationship between digitalization, as measured via internet usage, and electricity production from renewable energy sources (excluding hydroelectric) in Thailand. The study found that the short-run effect of internet usage (digitalization) on renewable energy production was not statistically significant. This suggests that short-term increases in internet usage do not yield instant changes in renewable electricity output. Several factors could justify these observations; first is the infrastructure-intensive and delayed-return nature of renewable energy deployment.

Compelling results were observed for the lagged effect of digitalization (with internet usage) therein: negative and statistically significant (B = −0.593, p = 0.042). The findings refuted the first hypothesis of the study (H1), which had posited a positive association between digitalization and renewable energy generation. The overriding reason for the observation could be trade-offs on a national investment priority scale. This is especially true for countries with emerging economies, such as Thailand, where these fields compete for limited public funding [18]. There is a potential for increased demand for energy that could go hand in hand with digitalization. In the short run, as demand for electric energy increases, the share of renewable energy will decrease in proportion to such demand. The energy demand that would have been met through fossil-fueled grids in rapidly developing infrastructures like data centers and telecommunication networks increases. This difference grows and offers an economy for our energy needs and the speed at which renewable alternatives are emerging [72]. According to Öztürk et al. [36], a somewhat similar trend occurs in emerging economies, where the environmental benefits of ICTs depend on coinvestment in green energy infrastructure. Pan et al.’s [29] Asian findings imply that the renewable systems will demonstrate efficiency improvement only with adequate regulatory framework directions, incentives included, for the adoption of clean energy technologies.

The second objective of this study was to assess how digitalization, specifically internet use, directly affects greenhouse gas emissions. It also looked at the indirect impact of digitalization through its influence on renewable electricity production. Contrary to the expectations of EMT—which suggests digitalization leads to dematerialization and efficiency—the results revealed a positive association between internet usage and GHG emissions. Specifically, a 1% increase in internet usage is associated with a 6.3% increase in GHG emissions in the short run. Hypothesis 3 thereby gets disconfirmed with this evidence, and evidently, Thailand’s present digitization ‘scale effect’ (additional energy input due to devices, servers, and networks) overwhelms the ‘technique effect’ (efficiency gains one could achieve from teleworking and smart logistics).

While earlier literature suggested that digitalization reduces emissions by favoring e-services and automation, these benefits are only realized if the underlying power grid is decarbonized. In Thailand, the grid is still relying on fossil fuel resources; thus, the digital expansion is merely increasing the carbon footprint of the energy mix and not eventually lowering it. It complements the already existing literature highlighting renewables as the means of environmental benefits in low- and middle-income countries [18,73]. But no lag effect was found, so such long-term emissions reductions could not be attributed to former renewable energy growth; perhaps because of delays in the project, grid integration restrictions, or fossil backup systems.

Furthermore, the study found that renewable energy production had a negligible and insignificant immediate effect on GHG emissions (β = −0.006). This suggests that the current scale of renewable integration is insufficient to offset the rising emissions driven by digital consumption. This reinforces the ‘developmental friction’ argument: the transition is not yet decoupled from carbon intensity. Thus, the above findings show that control variables past electricity access are strongly related to changing emissions (β = 0.544). Furthermore, it indicates that the emissions associated with electrification and digital access in Thailand are still linked to carbon-heavy generation, just as in other developing economies, where grid expansion follows emission peaks before mature renewable penetration.

Digitalization is theoretically enriching for future prospects of working from home, electronic services, and smart logistics that will reduce carbon intensity [4,14]. The context herein is essentially true with respect to the ecological modernization theory (EMT), wherein technology is supposed to manage resources towards environmental balance [25]. The case in Thailand, however, is contrary to the expectations of the theory. Internet penetration has a positive correlation with GHG emissions, acting as an environmental lever. This situation is particularly true in most developing economies where fossil sources primarily support grid expansion, so that electrification and digitalization will increase emissions before renewable penetration matures [19,72].

The contribution made by this study is one of significant originality and novelty to the existing literature by focusing on a critical yet understudied nexus within the context of a specific developing economy. Although a number of studies exist on the digitalization–environment nexus, they mainly deal with advanced or G20 economies, which substantially differ in terms of technological maturity and policy frameworks. By focusing on Thailand, a fast-growing ASEAN economy undergoing active dual digital and green transitions under its Thailand 4.0 vision, this research attempts to fill the gap with a context-specific and empirical investigation.

This study is unique in its methodological approach by applying the ARDL model, which exclusively specifies the individual short-run and lagged dynamic relationships between the internet-induced digitalization, renewable electricity generation, and GHG emissions. However, normally this dimension of time is neglected in a cross-sectional analysis; the greatest originality stems from the finding of a possible short-run trade-off with implications for investment, such that expansion in the digital sphere may temporarily siphon off resources, thereby inhibiting renewable capacity: grassroots intelligence for policy guidance in developing countries hindered by resource scarcity. The current research suggests that expansion in digitalization is associated with increased pollution. As the demand for energy is outpacing the rate at which improvements in energy efficiency are being considered, there is a push for all kinds of sources of energy. It puts forward a theory about a bigger, often missed way that digital tech can cut carbon use, applying this specifically to ecological modernization theory. Overall, this study offers a unique evidence-based framework that transgresses simplistic presumptions of synergy that lead to empirically sound analysis of complex, often contradictory realities of orchestrating concurrent transitions toward digitalization and sustainable energy in an emerging economy.

4.1. Theoretical Implications

Theoretically, these findings challenge the universality of the ecological modernization theory (EMT) when applied to the Global South. According to EMT, innovative technology will automatically bring about ecological sustainability; the present study finds an obvious obstruction or hurdle within this path—the investment gap. The study upholds TDT regarding the diffusion of digital tools but reveals that this does not imply that the anticipated environmental safeguards will automatically engage as predicted by the EMT. The originality of this research is founded on the empirical identification of relevant short-run investment trade-off concepts. It differs from cross-sectional studies, assuming synergy because this time series indicates that investment in digital expansion may be potentially resource-siphoning in the short term, thus impairing capacity for renewables. New evidence is provided to open yet another empirical avenue that will not stop at the simplistic narrations; the complexities of trying to orchestrate concurrent transitions in an emerging economy are highlighted.

4.2. Policy and Managerial Implications

Classifying digital adoption based only on internet use, ignoring other aspects of digitalization, is a simple way to measure how advanced technology use is. To address the empirical ‘investment trade-off’ identified in this study, where digital and energy infrastructures compete for capital, the current policy approach must evolve. We propose three concrete policy levers for Thai institutions:

• Integrated Incentive Structuring (Board of Investment—BOI): The BOI now has different tax breaks for digital improvements (A1/A2) and renewable energy projects. Our research shows that this split makes resource issues worse. We advise the BOI to create a ‘dual-transition’ incentive. The program entails granting larger tax benefits on corporate income tax for manufacturing companies investing in Industry-4.0-enabled digital enhancement, provided they implement local renewable energy resources, for example, rooftop solar facilities with Internet of Things monitoring.
• Ring-Fencing Infrastructure Funds: To mitigate the negative lagged effect of digitalization on renewable capacity, the Ministry of Digital Economy and Society (MDES) and the Ministry of Energy must coordinate infrastructure budgeting. As an example, the ‘Digital Fund’ budget for smart city expansion should feature a specific section for ‘Green Power Provision.’ Renewable energy storage should be incorporated into new data centers and 5G base stations in addition to the proposal. This will lessen the burden of fossil-fuel-based energy generation on the national grid.
• Demand Response Mechanisms: The results support the view that increased efficiency brought about by digitalization leads to further reductions in GHG emissions, yardstick implementations rather than the scaling up of renewable generation. It is assumed that the Energy Regulatory Commission requires demand response directly for that program to succeed. Rather than waiting for a new plant to come online, the ERC sandbox program should reward industries for their use of existing digital connections to automate load shedding during peak hours and cash in on the gains in efficiencies found in the model results from model 2.

5. Conclusions

The purpose of this research is to examine digitization and renewable energy in Thailand, especially with regard to mitigating greenhouse gas emissions. Empirical analysis comes up with a rather complicated reality: dual transition is currently an investment trade-off in place of quick synergy. Regarding lagged effects, digitalization exerted a significant adverse effect on the production of renewable energy. Simply put, ICT expansion in itself does not automatically drive green energy growth; instead, it appears to discourage energy investment by competing for limited financial resources in the developing context of Thailand.

Digitalization would not deliver on its promise of environmental benefits at all. Emissions were not reduced; rather, the internet appeared to show a positive association of a 6.3% increase in the levels of greenhouse gases, offering instead the insight that energy demand for digital infrastructure presently outweighs its efficiency gains. This leads to the argument that the addition of what may be considered a decoupling ‘milestone’ must still be achieved by the study. However, policy recommendations should shift attention toward energy synergies through the integration of incentive structures to optimize renewable energy installation with digital progress. Sustainable electrification requires a shift toward low-carbon generation systems to prevent digital access from driving up emissions. Development investment arrangements for digital infrastructure in the future shall be necessarily tied to energy transition targets (e.g., ring-fenced green funding). This study provides valuable information using the ARDL method, which works well with smaller datasets. Future research should focus on specific sectors like farming or transportation. Comparing different areas within Southeast Asia would show better practical ways to treat investment issues found in this study.

Limitations of the Study

The study has some strong points but also has some methodological limitations, one of which is that it relies on annual macro-level data for sample size (N = 23), which fits perfectly in ARDL bounds testing but does not apply very well in using other fine-grained machine learning validation techniques. Such restrictions have limited the analysis of shorter time frames pre- and post-COVID-19 or applications of machine learning that would need large validation sets. It was also confirmed from the test that parameters showed stability and consistency with several CUSUM tests, and such external forces as the 2011 floods or the 2020 pandemic did not create any structural break in the model. Also, using ‘internet usage’ as the sole proxy for digitalization may not fully capture the complexity of industrial Industry 4.0 adoption, such as specific AI or IoT investments in the energy grid. The findings about ‘investment trade-off’ might only be applicable to developing countries, like Thailand, that are constrained by resource limitations. It is possible they will not easily apply to developed nations that have well-established capital markets.