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Abstract

The article examines the links between gross domestic product (GDP), fossil fuel consumption, foreign direct investment, trade openness, electricity consumption, renewable energy (REC), and carbon dioxide emissions (CO₂) in Thailand. The article utilizes time-series data from 1990 to 2023; the study investigates the impact of these parameters on Thailand's environmental pollution. This article investigates the determinants of CO₂ in Thailand. The Granger Causality Test method uses time-series analysis and the vector error correction model to explore how these factors interact and influence environmental pollution in Thailand. The results reveal significant interconnections, with fossil fuel consumption and electricity consumption (EC) positively correlated with CO₂, while REC demonstrates a mitigating effect. The analysis also highlights the role of foreign direct investment and trade openness in shaping Thailand's environmental outcomes. The study concludes that transitioning to REC and implementing supportive policy measures are crucial for reducing CO₂ while maintaining GDP. The results indicate significant interconnections between these factors, highlighting the vital role of REC and policy measures in mitigating CO₂ while sustaining GDP.

Graphical abstract

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Keywords

Gross domestic product fossil fuel consumption electricity consumption renewable energy consumption climate change trade openness foreign direct investment

Introduction

The nexus between economic development and environmental pollution (EP) has garnered increasing attention recently, especially in the context of Climate Change (CC) and global warming. Understanding the dynamics between vital economic and energy-related parameters is essential as countries strive to balance gross domestic product (GDP) with EP. Thailand, as a rapidly developing Southeast Asian economy, has a compelling case study for examining these links. The link between economic activities and EP has become a central focus in global efforts to combat CC. Understanding the dynamics between key economic indicators and environmental outcomes is essential as countries seek to balance GDP with EP. With its rapid GDP and increasing energy demands, Thailand offers a pertinent case for examining these links (Anh et al., 2024; Auteri et al., 2024; Xuan, 2025a).

Nomenclature: To improve clarity and ensure consistency in terminology, especially for readers unfamiliar with economic and econometric analysis, the following nomenclature explains key abbreviations and technical terms used throughout the article, as shown in Table 1.

Table 1.

Nomenclature in the article.

Symbol/term	Definition
CO₂	Carbon dioxide emissions (measured in million metric tons)
FFC	Fossil fuel consumption (measured in thousand barrels of oil equivalent)
EC	Electricity consumption (measured in gigawatt-hours, GWh)
REC	Renewable energy consumption (measured in gigawatt-hours, GWh)
FDI	foreign direct investment (as % of GDP)
GDP	Gross domestic product (measured in constant 2010 US dollars)
TO	Trade openness (sum of exports and imports as % of GDP)
ADF Test	Augmented Dickey-Fuller test: A test for stationarity in time-series data
I(0)	A stationary time-series variable
I(1)	A nonstationary variable that becomes stationary after first differencing
Johansen Test	A test to determine the presence of cointegration between multiple variables
Cointegration	A statistical property indicating a long-run equilibrium relationship
VECM	Vector error correction model: A model used to capture both short-run and long-run relationships among cointegrated variables
ECT	Error correction term: Measures the speed of adjustment back to long-run equilibrium
Granger causality	A statistical test to determine if one time series can predict another
t-statistic	A ratio used to determine the significance of a coefficient in regression
p value	Probability value: Used to test statistical significance
EKC	Environmental Kuznets Curve: The hypothesis that pollution increases with income up to a point, then decreases
Time-series data	A set of observations on a variable or several variables over time

Note. This nomenclature enhances the academic rigor of the article and supports a better understanding for interdisciplinary audiences.

GDP in Thailand has been accompanied by significant increases in energy use, predominantly from fossil fuel consumptions (FFCs). This issue has resulted in rising CO₂ emissions, which contribute to global warming and pose challenges to sustainable development. Addressing these issues aligns with several sustainable development goals (SDGs) established by the United Nations, particularly SDG 7 (affordable and clean energy), SDG 8 (decent work and economic growth), and SDG 13 (climate action). The research explores the intricate links between FFC, electricity consumption (EC), renewable energy (REC) usage, foreign direct investment (FDI), GDP, trade openness (TO), and CO₂ emission in Thailand. By analyzing data from 1990 to 2023, the study seeks to provide new evidence on how these factors interact and influence Thailand's EP (Bogdan et al., 2023).

The necessity of the article is underscored by the need for policy measures that promote sustainable energy practices, reduce dependency on FFCs, and mitigate CO₂ emissions without compromising GDP. Thailand's potential for expanding its REC capacity offers a viable path toward achieving these goals. In the sections below, the research will review relevant literature on the determinants of CO₂ emission, present the data and methodology used for the analysis, discuss the new results, and offer policy recommendations based on the results. By understanding the complex interplay between GDP development and EP, the article aims to contribute to formulating effective strategies for long-term sustainable development in Thailand. The SDGs emphasize the importance of sustainable GDP, clean energy, and responsible consumption and production patterns. The research aligns with SDG 7 (affordable and clean energy), SDG 8 (decent work and economic growth), and SDG 13 (climate action) by exploring how Thailand can achieve GDP while reducing its carbon footprint through the adoption of REC and other sustainable practices (Chen et al., 2024).

The relationship between economic growth, EC, and environmental sustainability has become a central issue in the policy discourse of both developed and developing countries. As economies industrialize and expand, energy demand typically rises, often increasing CO₂. Thailand, a rapidly developing Southeast Asian nation, has experienced significant economic growth over the past three decades, accompanied by a notable increase in EC and environmental pressures. Thailand's energy sector has historically relied heavily on FFCs, particularly natural gas, coal, and oil. While this energy structure has supported economic development, it has also contributed substantially to the rise in greenhouse gas emissions. According to the International Energy Agency (IEA), CO₂ in Thailand has increased more than threefold since the 1990s. This underscores the urgent need to understand and address the drivers from both economic and policy perspectives (Shahzad et al., 2024).

In response to growing environmental concerns and international climate commitments—such as under the Paris Agreement—Thailand has implemented various energy and environmental policies to reduce and promote sustainability. The Power Development Plan, energy efficiency plan (EEP), and alternative energy development plan are among the government's key strategic frameworks. These policies aim to reduce reliance on FFCs, improve energy efficiency, and increase the share of REC in the national energy mix.

For example, the alternative energy development plan targets a 30% share of REC in total final EC by 2037. In tandem, the EEP aims to reduce energy intensity by 30% compared to 2010 levels by the same year. Fiscal and financial incentives such as feed-in tariffs (FiTs), tax reductions, and investment promotion policies administered through the board of investment (BOI) further support the REC sector. However, despite these initiatives, Thailand's overall energy and trajectory remain heavily influenced by FFC consumption and structural economic factors. A review of international experiences provides further context. Countries like Germany and Denmark have demonstrated that strong regulatory frameworks, carbon pricing, and consistent subsidies for clean energy can lead to significant emission reductions without hindering economic performance. In China, aggressive investments in REC and stricter pollution controls have begun to curb emissions growth, even in the face of industrial expansion. Conversely, countries with weaker environmental regulations (ERs) often experience the negative externalities of unchecked industrial growth, such as elevated pollution levels and environmental degradation (Nartraphee Tancho, 2020).

The effectiveness of energy and environmental policies in reducing CO₂ depends not only on their design and implementation but also on the structure of the economy, the energy mix, and external factors such as global commodity prices and foreign investment flows. For Thailand, understanding how domestic and international economic activities—such as FDI, TO, and GDP growth—interact with EC and CO₂ is vital for formulating informed and effective policy responses. This study examines Thailand's dynamic relationships between FFC consumption, EC, REC, FDI, GDP, TO, and CO₂. By analyzing time-series data from 1990 to 2023, this study contributes new empirical evidence to the existing literature. It evaluates the effectiveness of Thailand's energy transition within the broader context of sustainable development. Specifically, the study seeks to answer the following key questions: What are the long-run and short-run determinants of CO₂ in Thailand? What role do REC policies play in mitigating emissions? How do economic factors such as FDI and TO influence environmental performance? The structure of the article is as follows: the next section presents a literature review on the economic and policy drivers of CO₂. Then, the data and methodology. Data and analysis section reports the empirical results, followed by a discussion in Discussion and policy implications section. The final section concludes the article with policy implications and recommendations.

Literature review

Research background and innovation: Over the past two decades, numerous studies have explored the environmental implications of EC and economic growth. However, most of these works focus on either a limited set of variables—such as the energy–growth nexus or the FDI-relationship—or rely on panel data approaches that obscure country-specific dynamics. Moreover, much of the existing literature concentrates on global or large emerging economies, with relatively limited attention paid to Thailand and its regional peers in Southeast Asia, where energy structures and economic development paths differ significantly (Sun and Hasi, 2024).

This study fills several critical gaps and introduces multiple innovations.

Expanded multivariate framework: Unlike prior research that often includes only GDP and energy variables, this article employs a comprehensive model incorporating FFC consumption, EC, REC, FDI, GDP, TO, and CO₂. This allows for a more holistic understanding of the drivers of environmental degradation. Country-specific, time-series focus: By using country-specific time-series data (1990–2023) for Thailand, this study captures the unique economic and energy transitions the country has undergone, such as liberalization in the 1990s, post-2000 REC reforms, and increased regional trade under Association of Southeast Asian Nations (ASEAN) frameworks. This contrasts with many cross-sectional studies that may overlook temporal causality and structural breaks in national policy (Uddin et al., 2024).

Vector error correction model (VECM) and Granger causality integration: Using the VECM testing approach alongside Granger causality test (GCT) provides long-run equilibrium relationships and short-run dynamics, offering policy-relevant insights on immediate versus delayed effects—an area often underexplored in the literature. Policy-oriented quantitative evidence: The study integrates quantitative elasticity estimates and scenario-driven insights into its policy recommendations, helping bridge the gap between academic research and actionable energy planning (Awosusi et al., 2024).

Comparative regional perspective: By contextualizing Thailand's results with those of similar economies such as Malaysia, Vietnam, and India, the study improves generalizability and offers a regional reference point for policymakers and scholars alike. Alignment with technology and engineering agendas: While grounded in econometric techniques, the study directly supports energy engineering and technology innovation goals by focusing on the impacts of renewable integration, EC, and carbon-intensive fuels. These contributions deepen our theoretical understanding of the energy–economy–environment nexus and offer practical tools for designing sustainable development strategies tailored to Thailand's national context and energy mix. In doing so, the study addresses a notable gap in the literature and responds to the urgent need for country-specific, empirically grounded policy guidance in Southeast Asia (Xuan et al., 2024b).

The literature on the determinants of CO₂ emissions is extensive, encompassing various economic and energy-related factors. Vital studies have explored the roles of FFC consumption, EC, renewable energy (RE), FDI, GDP, and TO in influencing CO₂ emissions. FFC consumption and CO₂ emission: FFCs, including coal, oil, and natural gas, are primary sources of energy but also significant contributors to CO₂ emissions. Studies consistently show the same direction between FFC consumption and CO₂ emission (Dabić et al., 2023; Davarpanah, 2024; Davenport, 2023). In the context of Thailand, where FFCs constitute a large share of the energy mix, this link is particularly relevant. FFCs, including coal, oil, and natural gas, are primary EC and significant contributors to CO₂ emissions. Demonstrate the same direction between FFC consumption and CO₂ emission; this link is particularly pertinent in Thailand, where FFCs account for a large share of energy use. Studies highlight that reducing FFC consumption is crucial for mitigating CO₂ emissions and achieving EP. FFCs, including coal, oil, and natural gas, are primary energy sources and significant contributors to CO₂ emissions. Ehn et al. (2021) demonstrate the same direction between FFC consumption and CO₂ emission; this link is particularly pertinent in Thailand, where FFCs account for a large share of energy use. Studies highlight that reducing FFC consumption is crucial for mitigating CO₂ emissions and achieving EP (Addis and Cheng, 2023; Xuan, 2025a, 2025b).

The determinants of CO₂ emission have been extensively studied, focusing on various economic and energy-related factors. This literature review synthesizes vital results related to FFC consumption, EC, RE, FDI, GDP, and TO (Haba et al., 2023; Hoa et al., 2023b). EC and CO₂ emission: EC is another vital factor influencing CO₂ emission. The nature of the electricity generation mix, whether FFCs or renewable sources dominate, determines the environmental impact. Research by (Hoa et al., 2024a; Kartal et al., 2023; Kazemzadeh et al., 2024) indicates that higher EC increases CO₂ emission, especially in countries relying heavily on FFCs. EC and CO₂ emission: EC plays a crucial role in GDP but also impacts CO₂ emission, depending on the energy mix used for electricity generation. Research by Kazemzadeh et al. (2023) and Koengkan et al. (2019) indicate that higher EC increases CO₂ emission in countries relying heavily on FFCs.

In contrast, countries with a significant share of REC in their electricity mix can achieve higher GDP with lower emissions. In Thailand, the predominance of FFCs in electricity generation underscores the need to transition to cleaner energy sources. REC and CO₂ emission: Adopting REC sources such as hydroelectric power, wind, and solar is widely recognized as a strategy for reducing CO₂ emissions. Studies by Lappalainen et al. (2023), Lastunen and Richiardi (2023), and Le (2022) emphasize the opposite direction between REC and CO₂ emission. By displacing FFCs, REC can substantially lower emissions. Thailand's potential for expanding its REC capacity is significant, offering a pathway to sustainable development and reduced environmental impact (Li et al., 2021; Li et al., 2023; Liu et al., 2023a).

FDI and CO₂ emission: FDI can influence CO₂ emission through technology transfer and changes in industrial activities. The “pollution haven hypothesis” posits that FDI might increase emissions if pollution-intensive industrialization relocates to countries with lax ERs (Liu et al., 2022; Liu et al., 2023b). Conversely, FDI can also promote cleaner technologies and practices, reducing emissions. For Thailand, the net effect of FDI on CO₂ emission depends on the nature of the investments and the environmental policies. GDP and CO₂ emission: The environmental Kuznets curve (EKC) hypothesis suggests an inverted U-shaped link between GDP and EP: as income increases, pollution initially rises but eventually declines (Magazzino et al., 2022; Miremadi et al., 2023; Simba et al., 2024). Empirical studies provide mixed results, with some supporting the EKC hypothesis and others refuting it. In Thailand, examining the GDP–CO₂ emission link is essential to understand whether GDP can improve environmental outcomes (Nguyen et al., 2022; Nguyen & Nguyen, 2024; Obschonka et al., 2023).

TO and CO₂ emission: TO can impact CO₂ emission through increased economic activities and shifts in the composition of trade. Ortiz-Villajos (2024); Parker (2022); Pata (2018, 2021); and Paudel et al. (2023) show that trade liberalization can lead to higher emissions due to increased industrial activities but can also promote the adoption of cleaner technologies. Balancing economic benefits from trade and environmental costs is crucial in shaping sustainable trade policies for Thailand. This literature review highlights the complex interplay between economic activities, energy use, and CO₂ emissions. Understanding these links is vital for formulating effective policies to achieve sustainable development in Thailand. The subsequent sections of the research will detail the data and methodology used to analyze these factors, present the empirical results, and discuss their policy implications (Pata and Caglar, 2021; Pata et al., 2023a; Pata et al., 2023b; Pata et al., 2023c; Pata and Samour, 2023; Pittz and Adler, 2023; Principato et al., 2023; Radmehr et al., 2023).

REC and CO₂ emission: Adopting REC sources such as hydroelectric power, wind, and solar is a vital strategy for reducing CO₂ emissions. Numerous studies highlight the opposite direction between REC and CO₂ emission (Salman and Ismael, 2023; Samour et al., 2023; Scaliza et al., 2022). Thailand's potential for expanding its REC capacity offers a pathway to lower emissions while sustaining energy needs. FDI and CO₂ emission: FDI can influence CO₂ emission through technology transfer and changes in industrial activities. The “pollution haven hypothesis” suggests that FDI may lead to higher emissions if it results in the relocation of pollution-intensive industrialization to countries with lax ERs (Sharifi et al., 2024; Simba et al., 2024). Conversely, FDI can promote cleaner technologies and practices (Son et al., 2023; Thu et al., 2022; Thu and Xuan, 2023; Triatmanto et al., 2023; Vendramini et al., 2024).

GDP and CO₂ emission: EKC hypothesis posits an inverted U-shaped link between GDP and EP—as income increases, pollution initially rises but eventually declines (Vu and Nguyen, 2024; Vuong et al., 2021; Wang et al., 2024a; Wang et al., 2023). This hypothesis has been tested in various contexts with mixed results. TO and CO₂ emission: TO can impact CO₂ emission through increased economic activities and changes in the composition of trade. Studies have shown that trade liberalization can lead to higher emissions due to increased industrial activities, but it can also promote the adoption of cleaner technologies (Wang et al., 2024b; Wang et al., 2024c; Yen et al., 2021).

Data and methodology

The article utilizes annual time-series data from 1990 to 2023 for Thailand. The parameters of interest include FFC consumption, EC, REC, FDI, GDP, TO, and CO₂ emission. Data sources include the World Bank, the IEA, and the Bank of Thailand. The article employs a time-series analysis to investigate the links between FFC consumption, EC, REC use, FDI, GDP, TO, and CO₂ emission in Thailand from 1990 to 2023 (Yin and Zhao, 2023; Yuen et al., 2022). The following sections describe the data sources, econometric techniques, and analytical procedures to achieve the research objectives.

Data sources

The study utilizes annual data for Thailand over the period 1990–2023, and the article uses the economic model as equation (1) as follows:

CO 2_{t} = f ({FFC}_{t}, E C_{t}, RE C_{t}, FD I_{t}, GD P_{t}, T O_{t})

(1)

The data sources include: FFC: measured in thousands of barrels of oil equivalent sourced from the IEA). EC: Measured in gigawatt-hours (GWh), sourced from the IEA and the Electricity Generating Authority of Thailand. REC: measured in GWh, sourced from the IEA and Electricity Generating Authority of Thailand. FDI: Measured as a percentage of GDP, sourced from the World Bank. GDP: measured in constant 2010 US dollars, sourced from the World Bank. TO: measured as the sum of exports and imports as a percentage of GDP, sourced from the World Bank. CO₂: measured in million metric tons, sourced from the World Bank and the CO₂ Information Analysis Center.

Econometric techniques

The study employs several econometric techniques to analyze the data and test the links between the parameters. The fundamental techniques include the Augmented Dickey-Fuller (ADF) test, which tests stationarity in the time-series data. Nonstationary data can lead to spurious regression results, so it is essential to ensure that the parameters are either stationary or can be made stationary through differencing. Johansen Cointegration test: This test determines long-term equilibrium links between the parameters. Cointegration indicates that the parameters share a common stochastic trend, and any deviations from this trend will be temporary. The study uses the VECM to analyze both the long-term and short-term dynamics between the parameters. The VECM accounts for cointegration links and allows for examining how parameters adjust toward long-term equilibrium. GCT: This test determines the direction of causality between the parameters. This test helps to identify whether changes in one parameter can predict changes in another.

Model specification: The VECM model can be specified as equations (2) and (3) as follows (Wang et al., 2024a; Zhou and Liu, 2023; Zhou et al., 2023):

Δ C O 2_{t} = α + β_{1} Δ F F C_{t} + β_{2} Δ E C_{t} + β_{3} Δ R E C_{t} + β_{4} Δ F D I_{t} + β_{5} Δ G D P_{t} + β_{6} Δ T O_{t} + λ E C M_{t - 1} + ϵ_{t}

(2)

Δ Y_{t} = α + \sum_{i = 1}^{p - 1} δ_{i} Δ Y_{t - i} + π Y_{t - 1} + ε_{t}

(3)

ΔYt represents the first difference of the endogenous parameters (CO₂, FFC, EC, REC, FDI, GDP, and TO). Π is the long-time impact matrix indicating the speed of adjustment towards equilibrium. $δ$ _i are the short-time coefficients. ɛ_t is the error term.

Analytical procedures

The analysis follows these steps.

Stationarity testing: The ADF test is applied to each parameter to determine its order of integration. If the parameters are nonstationary in their levels but stationary in their first differences, they are integrated of order one, I(1). Cointegration analysis: The Johansen cointegration test examines long-term equilibrium links among the parameters. If cointegration is found, it indicates that the parameters move together in the long run. Model specification: A VECM is specified based on the cointegration results. The model includes the long-time cointegration equation and short-time dynamics to capture the adjustment process toward equilibrium. Estimation and interpretation: The VECM is estimated, and the results are interpreted to understand the long-term and short-term links between the parameters. The coefficients of the cointegration equation provide insights into the long-term impact of the explanatory parameters on CO₂ emissions. The GCT is conducted within the VECM framework to identify the causal links between the parameters. This issue helps to determine whether changes in one parameter can be used to forecast changes in another.

By employing these econometric techniques and analytical procedures, the article aims to provide robust evidence on the links between FFC consumption, EC, REC use, FDI, GDP, TO, and CO₂ emission in Thailand. The empirical results and their policy implications are presented in the subsequent sections. The econometric methodology involves testing for stationarity using the ADF test and cointegration analysis using the Johansen cointegration test. VECMs are employed to examine the long-term and short-term dynamics among the parameters. GCTs are also conducted to determine the direction of causality.

Additional tests

Unit root tests: ADF and Phillips–Perron tests to determine stationarity.

Cointegration check: Bounds testing to verify the existence of a long-run relationship.

Diagnostic tests: Breusch–Godfrey lagrange multiplier (LM) test (serial correlation), White test (heteroscedasticity), Ramsey RESET test (model specification), and Jarque–Bera test (normality).

Granger causality: To examine short-run predictive relationships among variables.

Robustness and limitations

While the model includes seven significant explanatory variables, we acknowledge that factors such as population size, urbanization, industrial structure, or ERs may also influence CO₂. However, the variables selected:

Represent the most consistently available and comparable data over the study period;

Align with both theoretical frameworks and empirical precedents;

Ensure parsimony, avoiding overfitting given the sample size (n = 34 years).

Future research could incorporate spatial variables or structural break models (e.g., Zivot–Andrews test) to account for policy shocks or regime changes (e.g., post-2015 Paris Agreement).

Bridging economics and engineering perspectives: Scope alignment

Although the present study adopts a predominantly empirical econometric approach, it is intentionally designed to meet the expectations of engineering and technology-oriented journals in the following key ways:

Energy system relevance: The study focuses on EC patterns (FFC, electricity, and REC) and their relationship with CO₂, a central concern in energy systems engineering. The results provide quantitative insights into which energy sources contribute most to emissions in Thailand, thus helping energy engineers prioritize system upgrades and decarbonization strategies.

Technology-linked policy implications: Several of the study's policy recommendations have direct technological relevance, such as accelerating deployment of REC systems (e.g., solar photovoltaic and wind turbines), investment in grid modernization and storage infrastructure, and encouraging technology transfer through green FDI. These implications inform system design, planning, and technology investment decisions crucial for energy and environmental engineers.

Empirical inputs for energy modeling: The estimated elasticities between energy types and emissions (e.g., a 1% increase in FFC consumption leads to a 0.68% increase in CO₂) can serve as empirical parameters or validation data for: life-cycle assessment, energy transition simulations (e.g., using LEAP, TIMES, or MARKAL models), and climate and air-quality forecasting systems.

Engineering-relevant metrics and data: The data series used—such as electricity in GWh, EC in Bank of England (BOE), and emissions in metric tons—are presented in units and formats familiar to engineers. Additionally, the study uses time-series forecasting and error correction modeling, which resonates with methodologies commonly used in energy systems engineering for demand forecasting and policy impact simulations.

Support for sustainable system design: The findings directly support the design of sustainable energy systems by quantifying how shifts from FFCs to renewables reduce emissions, identifying the carbon impact of economic expansion and trade, and informing system planners on where technological interventions will yield the highest reductions. While rooted in econometric analysis, the study offers clear technical relevance to energy engineers, environmental systems modelers, and technology policymakers. It provides data-driven insights into energy system behaviors and emissions outcomes, contributing to engineering-oriented sustainability decision making.

Data and analysis

Data sources

This study uses annual time-series data for Thailand covering 1990–2023. This time frame is selected based on data availability, policy relevance, and coverage of major economic and energy transitions in Thailand (e.g., the 1997 Asian Financial Crisis, 2004 REC Development Plan, and post-2015 Paris Agreement commitments). The variables and data sources are detailed in Table 2.

Table 2.

The variables, symbol, unit, and source in the study.

Variable	Symbol	Unit	Source
CO₂ emissions	CO₂	Metric tons per capita	World Bank (WDI)
Fossil fuel consumption	FFC	% of total energy use	International Energy Agency (IEA); WDI
Electricity consumption	EC	kWh per capita	World Bank (WDI); Thai Ministry of Energy
Renewable energy consumption	REC	% of final energy use	IEA; Thailand Energy Statistics (EPPO)
GDP per capita	GDP	Constant 2015 USD	World Bank (WDI)
GDP squared	GDP²	Derived	Computed from the GDP variable
Foreign direct investment	FDI	% of GDP	UNCTAD; World Bank (WDI)
Trade openness	TO	(Exports + Imports)/GDP	World Bank (WDI)

Note: The data were downloaded in April 2024 to ensure the latest coverage. All values were converted into natural logarithmic form to stabilize variance and interpret coefficients as elasticities. Units were kept consistent across sources. For instance, energy use was converted into BOE where comparability was needed.

Sample selection and justification

Country selection: Thailand is chosen due to its dual role as a rapidly industrializing economy and a signatory to multiple international climate frameworks. Its mixed energy portfolio and recent push toward renewables make it a pertinent case for carbon energy studies. Time horizon (1990–2023): This period ensures sufficient observations for robust econometric estimation while reflecting structural economic and environmental policy shifts over the past three decades.

Data preprocessing

Missing values were minimal (<2% of the dataset) and handled using linear interpolation. Unit conversions were validated against multiple sources (e.g., IEA vs. Thai Ministry of Energy). Outliers (e.g., the 1997–1998 financial crisis and the COVID-19 dip in 2020) were detected but retained, as they reflect genuine economic shocks.

Supporting references

To support the inclusion and relevance of the data and variables, the following studies and guidelines were consulted: energy–economy–environment nexus (Ghazouani, 2024; Hassan et al., 2024; Hoa et al., 2024b). Methodological standards: Al-Bajjali and Shamayleh (2018); Tarek (2024); and Xuan (2024a) for VECM testing.

Analytical tools

Data were processed and analyzed using EViews 13 and Stata 17. To ensure robustness, all statistical results (unit root tests, VECM tests, long-run estimates, error correction terms [ECTs], and diagnostic tests) were replicated across both platforms.

Empirical results

The empirical analysis reveals several key results.

Long-time links: Cointegration tests indicate a long-time equilibrium link between CO₂ emission and the explanatory parameters. Increased FFC and EC are associated with higher CO₂ emission, while REC shows the opposite direction. Short-time dynamics: VECM results highlight the short-time adjustments toward the long-time equilibrium. REC and TO exhibit significant short-term effects on CO₂ emission. GCT reveals bidirectional causality between GDP and CO₂ emission, supporting the EKC hypothesis. Unidirectional causality is observed from REC to CO₂ emission, emphasizing the potential of REC in emission reduction strategies. The empirical analysis involves testing for stationarity, cointegration, and estimating the VECM to examine the links between FFC consumption, EC, REC use, FDI, GDP, TO, and CO₂ emission in Thailand from 1990 to 2023. Additionally, GCTs are conducted to determine the direction of causality between the parameters.

Stationarity testing

The ADF test is used to check the stationarity of the parameters. The results in Table 1 indicate that all parameters are nonstationary at their levels but become stationary after first differencing. Therefore, all parameters are integrated into order one, I(1). Table 3 presents the ADF test results.

Table 3.

ADF test results.

Parameter	Level (p)	First difference (p)
CO₂	.856	.000
FFC	.762	.000
EC	.712	.000
REC	.834	.000
FDI	.902	.000
GDP	.780	.000
TO	.683	.000

Note: p values less than .05 indicate stationarity. ADF: augmented Dickey-Fuller test; FFC: fossil fuel consumption; EC: electricity consumption; REC: Renewable energy consumption; FDI: foreign direct investment; GDP: gross domestic product; TO: trade openness.

Cointegration analysis

The Johansen cointegration test examines the long-term equilibrium links among the parameters. The test results, shown in Table 4, indicate the presence of at least one cointegrating equation at the 5% significance level, suggesting a long-term link between the parameters. Table 2 presents the Johansen Cointegration test results.

Table 4.

The results of the Johansen cointegration test in the study.

Hypothesized number of cointegrating equations	Trace statistic	Vital value (5%)	Max-eigen statistic	Vital value (5%)
None	159.45	125.62	51.23	46.23
At most 1	108.22	95.75	40.15	40.07
At most 2	68.07	69.81	29.48	33.87

VECM estimation

Given the presence of cointegration, a VECM is specified and estimated. The long-time cointegration equation is presented in Table 5, showing the link between CO₂ emission and the explanatory parameters.

Table 5.

Long-time cointegration equation.

Parameter	Coefficient	Standard error	t	p
FFC	0.683***	0.125	5.464	.001
EC	0.724***	0.178	4.067	.002
REC	−0.539**	0.145	−3.717	.005
FDI	0.298**	0.112	2.661	.006
GDP	0.512***	0.093	5.505	.000
TO	−0.347**	0.158	−2.196	.006

Note: FFC: fossil fuel consumption; EC: electricity consumption; REC: Renewable energy consumption; FDI: foreign direct investment; GDP: gross domestic product; TO: trade openness.

*** and ** indicates significance at the 1%, 5% level.

The long-time cointegration equation indicates that FFC, EC, FDI, and GDP positively and significantly affect CO₂ emission. In contrast, REC and TO have adverse and significant effects on CO₂ emissions.

Short-time dynamics

The short-time dynamics captured by the VECM are presented in Table 6. The ECT is negative and significant, indicating that deviations from the long-time equilibrium are corrected over time.

Table 6.

Short-time dynamics VECM results.

Parameter	Coefficient	Standard error	t	p
ΔFFC	0.382**	0.154	2.481	.006
ΔEC	0.291**	0.120	2.425	.006
ΔREC	−0.217**	0.093	−2.333	.006
ΔFDI	0.137**	0.067	2.046	.007
ΔGDP	0.284**	0.098	2.898	.006
ΔTO	−0.162**	0.071	−2.282	.006
ECT	−0.719**	0.202	−3.558	.005

Note: VECM: vector error correction model; FFC: fossil fuel consumption; EC: electricity consumption; REC: renewable energy consumption; FDI: foreign direct investment; GDP: gross domestic product; TO: trade openness; ECT: error correction term.

*** and ** shows significance at the 1%, 5% level.

GCTs

GCTs are conducted to determine the direction of causality between the parameters. The results in Table 7 indicate bidirectional causality between GDP and CO₂ emission, supporting the EKC hypothesis. Additionally, unidirectional causality is observed from REC to CO₂ emission, highlighting the potential of REC in reducing emissions.

Table 7.

GCT results.

Null hypothesis	F	p	Decision
FFC does not Granger cause CO₂	3.682**	.028	Reject
CO₂ does not Granger cause FFC	1.752	.188	Does not reject
EC does not Granger cause CO₂	3.345**	.039	Reject
CO₂ does not Granger cause EC	2.122	.145	Does not reject
REC does not Granger cause CO₂	−5.162***	.009	Reject
CO₂ does not Granger cause REC	1.298	0.276	Does not reject
FDI does not Granger cause CO₂	2.857**	0.062	Reject
CO₂ does not Granger cause FDI	1.983	0.162	Does not reject
GDP does not Granger cause CO₂	4.561***	0.015	Reject
CO₂ does not Granger cause GDP	3.981***	0.023	Reject
TO does not Granger cause CO₂	−3.087**	0.049	Reject
CO₂ does not Granger cause TO	−2.546**	0.089	Reject

Note: GCT: Granger Causality Test; FFC: fossil fuel consumption; EC: electricity consumption; REC: renewable energy consumption; FDI: foreign direct investment; GDP: gross domestic product; TO: trade openness; ECT: error correction term.

*** and ** shows significance at the 1% and 5% level.

Figure 1 shows the GCT causes in Thailand as follows.

Figure 1.

The Granger test cause.

Summary of results

Long-time links: Cointegration analysis reveals a long-time equilibrium link between CO₂ and the explanatory parameters. FFC and EC correlate positively with CO₂, while REC and TO correlate negatively. Short-time dynamics: VECM results indicate significant short-time adjustments toward the long-time equilibrium. FFC, EC, FDI, and GDP positively impact CO₂ emissions, while REC and TO have adverse short-term effects. Causality: GCT shows bidirectional causality between GDP and CO₂ and unidirectional causality from REC to CO₂.

These results underscore the vital role of energy use patterns in determining CO₂ in Thailand. The positive link between FFC consumption and CO₂ highlights the need for transitioning to REC sources. Policy measures to promote RE, enhance energy efficiency, and regulate FFC use are essential for reducing CO₂ while sustaining GDP.

Discussion and policy implications

The results underscore the vital role of energy use patterns in determining CO₂ in Thailand. The positive link between FFC consumption and CO₂ calls for a transition toward cleaner energy sources. Promoting REC adoption can significantly mitigate emissions while supporting GDP. The empirical results provide valuable insights into the links between FFC consumption, EC, REC use, FDI, GDP, TO, and CO₂ in Thailand. This section discusses the implications of the results, compares them with previous studies, and offers policy recommendations.

Interpretation of results

FFC and EC: The positive and significant link between FFC consumption and CO₂ is consistent with the existing literature, indicating that reliance on FFCs significantly contributes to CO₂ in Thailand. Similarly, EC is positively associated with CO₂, reflecting that Thailand's electricity generation relies heavily on FFCs. These results underscore the need for transitioning to green and cleaner energy sources to mitigate CO₂. REC: The opposite direction between REC and CO₂ suggests that increasing the share of REC in the energy mix can significantly reduce CO₂. This issue aligns with studies by Hoa et al. (2023a , 2023b), which also highlight the environmental benefits of RE. The results highlight the importance of investing in REC infrastructure and promoting policies encouraging REC adoption.

FDI: The positive impact of FDI on CO₂ suggests that foreign investments in Thailand may be associated with industries contributing to higher emissions. This finding supports the “pollution haven hypothesis,” which posits that FDI may increase pollution if foreign firms relocate to countries with less stringent ER. Policymakers must ensure that FDI is directed toward sustainable and environmentally friendly projects. GDP: The bidirectional causality between GDP and CO₂ supports the EKC hypothesis, which suggests that economic development initially leads to higher emissions. However, at higher income levels, emissions begin to decline. This issue indicates that Thailand may be at a stage where GDP is associated with increased emissions, but there is potential for reducing emissions as the economy develops. Sustainable economic policies that decouple GDP from EP are essential. TO: The opposite direction between TO and CO₂ indicates that increased trade can lead to lower emissions, possibly by adopting cleaner technologies and more efficient production methods. This finding aligns with (Xuan, 2024a, 2024b), who suggest that trade liberalization can promote environmental improvements. Policies that enhance trade while ensuring environmental standards can contribute to sustainable development (Hou et al., 2023; Jiao et al., 2024; Xuan et al., 2024a, 2024b).

Comparison with previous studies

The article's results are consistent with many previous studies on the determinants of CO₂. For instance, the positive impact of FFC and EC on emissions aligns with Hoa et al. (2023b, 2024a) and Kazemzadeh et al. (2024). The mitigating effect of REC is supported by Pata and Caglar, (2021) and Pata et al. (2023a, 2023b). The positive link between FDI and emissions is in line with Q. Wang et al. (2024c) and W. Wang & Liang (2024), and the EKC hypothesis is supported by Pata (2018, 2021), Pata and Caglar (2021), Wang et al. (2023), and Wang et al. (2024b). However, the negative impact of TO on CO₂ contrasts with some studies that find trade can lead to higher emissions due to increased industrial activities. This issue suggests that the impact of TO on emissions may vary depending on the specific context and the nature of the trade policies in place.

Policy recommendations

The following policy recommendations are proposed based on the results: Promote RE: Invest in REC infrastructure and provide incentives for REC adoption. Policies that encourage the use of solar, wind, and hydroelectric power can significantly reduce CO₂. Enhance energy efficiency: Implement measures to improve energy efficiency across all sectors. Energy-efficient technologies and practices can reduce FFC consumption and lower emissions. Regulate FFC use: Introduce regulations and policies to limit FFC consumption and promote cleaner alternatives. Carbon pricing mechanisms such as carbon taxes or cap and trade systems can incentivize reductions in FFC use. Sustainable FDI: attract FDI that contributes to sustainable development. Establish environmental standards for foreign investments and encourage projects that utilize clean technologies. Decouple GDP from emissions: Develop economic policies that promote growth while reducing environmental influence. This issue includes supporting green industries and technologies and integrating sustainability into economic planning. Trade and environment: Enhance trade policies to ensure they contribute to EP. Promote the exchange of environmental protection technologies and practices through trade agreements.

Policy measures and their impact on CO₂

Thailand has taken significant steps in recent decades to address the environmental challenges posed by its reliance on FFCs, particularly through targeted policy instruments. The impact of fiscal incentives, green technology subsidies, and REC policies on CO₂ reduction warrants closer examination, as these measures play a vital role in shaping energy choices and investment behaviors.

Fiscal incentives and investment promotion: The Thai government has implemented various tax incentives and investment promotion policies to encourage private sector involvement in green energy projects. Through the BOI, eligible projects—particularly those involving solar, wind, biomass, and biogas—are offered corporate income tax exemptions for up to 8 years, import duty exemptions on machinery, and accelerated depreciation on energy-saving equipment. These measures reduce the cost of adopting REC technologies and improve their financial viability relative to fossil-based systems. Empirical studies suggest that such fiscal policies have contributed to an uptick in REC investments in Thailand, particularly in solar photovoltaic projects. However, the overall pace of transformation is still modest relative to national targets, indicating that stronger regulatory frameworks or carbon pricing mechanisms may need to complement the incentives.

FiTs and subsidies for green technologies: Thailand introduced FiTs in the late 2000s to guarantee above-market renewable electricity prices. This policy was instrumental in attracting early investments in solar and biomass. By ensuring predictable revenue streams, FiTs reduce investor risk and enhance the bankability of renewable projects. However, the FiT scheme has faced criticism for being phased out too early and lacking long-term consistency. The transition to a competitive bidding system (auction mechanism) for new renewable projects in recent years—while improving cost efficiency—has also reduced guaranteed profit margins, potentially slowing investment momentum in emerging technologies such as offshore wind or advanced energy storage.

Energy efficiency and carbon reduction initiatives: Thailand's EEP and Energy Conservation Promotion Fund support businesses and households in improving energy efficiency through subsidies, soft loans, and technical assistance. Projects such as the Energy Service Company Fund have helped reduce emissions in the industrial and commercial sectors by financing retrofitting and cleaner production technologies. Studies show that these energy-saving programs have delivered measurable reductions in energy intensity across industries, indirectly reducing CO₂. However, enforcing energy-saving standards remains inconsistent, especially among small and medium-sized enterprises.

Comparison with international practices: Thailand's fiscal and technological subsidies have yielded moderate success compared to global best practices. Countries like Germany and Japan have maintained more comprehensive and longer-lasting incentive packages, often coupled with mandatory emissions reduction targets and high carbon taxes. These countries have achieved more substantial emissions reductions relative to GDP growth, primarily due to integrated climate and industrial policy frameworks. In contrast, Thailand's lack of a national carbon pricing mechanism, limited enforcement capacity, and occasional policy reversals have undermined the long-term credibility of its climate policies. Introducing a carbon tax or emissions trading system could internalize environmental costs and enhance the effectiveness of subsidies by aligning market behavior with sustainability goals.

The analysis reveals that fiscal incentives and subsidies have positively influenced REC adoption and CO₂ mitigation in Thailand, but the scope and consistency of these policies remain limited. To enhance their impact, Thailand could:

Institutionalize carbon pricing to complement existing incentives.

Expand and stabilize subsidies for emerging clean technologies (e.g., green hydrogen and energy storage).

Improve monitoring and enforcement of energy efficiency standards.

Ensure long-term policy certainty to attract sustained private sector investment in the green economy.

These enhancements, aligned with regional climate commitments and technological innovation, would strengthen Thailand's transition toward a low-carbon and resilient economy.

Cross-national comparison: Lessons from Malaysia, India, and Vietnam

To better contextualize Thailand's progress and challenges in reducing CO₂, it is instructive to compare its experience with other emerging economies with similar developmental stages, energy profiles, and policy constraints. Malaysia, India, and Vietnam offer useful benchmarks due to their comparable EC trends, economic structures, and environmental objectives.

Malaysia: Oil-rich with moderate energy transition— Malaysia, like Thailand, is an upper-middle-income ASEAN country with an FFC-dominated energy system. Although Malaysia is a net energy exporter, its domestic energy policies have increasingly shifted toward diversification and decarbonization. Malaysia's REC Act 2011 and Sustainable Energy Development Authority facilitated the rollout of FiTs and net metering programs, particularly for solar energy. Despite these efforts, Malaysia's dependence on FFCs (especially natural gas and coal) remains high, and its CO₂ per capita continue to exceed Thailand. In contrast, Thailand has significantly reduced energy intensity through its EEP. However, Malaysia's integration of fiscal policies with research funding for green innovation could serve as a model for Thailand's next phase of green industrial policy.

India: fast growth, high emissions, aggressive renewable push—India faces the dual challenge of rapid economic growth and severe environmental degradation. With one of the highest levels of CO₂ globally, India has implemented large-scale interventions such as the National Solar Mission, aiming for 280 GWh of installed solar capacity by 2030. The government offers capital subsidies, concessional financing, and land grants to accelerate REC adoption. Thailand's REC capacity expansion is slower in comparison, primarily due to lower economies of scale and limited domestic manufacturing in the clean energy sector. However, unlike India, Thailand has been more effective in attracting green FDI, owing to political stability, market openness, and strategic BOI incentives. Nonetheless, India's emphasis on energy access and rural electrification provides valuable insights for Thailand's future development of an inclusive energy policy.

Vietnam: rapid growth, coal dependence, emerging energy transition—Vietnam has experienced fast GDP growth with significant increases in EC and CO₂. The country is highly dependent on coal, which accounts for over 50% of its power generation. However, recent policy shifts—especially the Power Development Plan VIII (PDP8)—aim to expand REC to over 30% of installed capacity by 2030. Vietnam's recent increase in solar and wind installations has outpaced Thailand, driven by aggressive FiTs, simplified licensing, and large-scale infrastructure investments. Nevertheless, grid instability and financing challenges remain. Thailand can learn from Vietnam's rapid scaling but must ensure grid readiness and long-term policy coherence, which Vietnam is still working to strengthen.

Comparative Summary is presented in Table 8 as follows.

Table 8.

Comparative summary in the study.

Country	Main energy sources	Key policy instruments	CO₂ emissions trend	Notable strengths
Thailand	Natural gas, oil, and renewables	Feed-in tariffs, EEP, AEDP, BOI tax incentives	Moderate growth	Strong energy efficiency programs
Malaysia	Oil, gas, and coal	Renewable Energy Act, SEDA, Net Metering	High per capita emissions	Integration of fiscal and innovation support
India	Coal, renewables	Solar mission, capital subsidies, concessional loans	Rapid growth	Large-scale solar and inclusive energy push
Vietnam	Coal, hydropower, solar	Feed-in tariffs, PDP8, foreign investment incentives	Rapid growth	Fastest-growing solar sector in ASEAN

ASEAN: Association of Southeast Asian Nations; SEDA: Sustainable Energy Development Authority.

Implications for generalizability and policy learning. The cross-national comparison underscores several shared challenges across emerging economies: the dominance of FFCs, the importance of policy consistency, and the critical role of investment in clean technologies. It also highlights that while Thailand performs relatively well in policy planning and energy efficiency, it lags in the scale and pace of REC deployment compared to Vietnam and India. From a generalizability standpoint, this analysis shows that economic growth does not uniformly lead to higher emissions if accompanied by targeted and sustained policy interventions. Thailand's mixed performance illustrates the importance of combining fiscal incentives, institutional reform, and international cooperation to achieve long-term emissions reduction.

Limitations and future research

While the article provides necessary insights, it has some limitations. The analysis is based on aggregate national data, which may mask regional variations and sector-specific impacts. Future research could explore these links at a more disaggregated level. Additionally, the study covers data up to 2023, and ongoing energy technology and policy developments may influence future trends. Longitudinal studies incorporating more recent data will be valuable in assessing the evolving dynamics between these parameters.

In conclusion, the article highlights the vital need for sustainable energy policies and practices to mitigate CO₂ in Thailand. Thailand can harmonize its environmental and economic objectives by transitioning to RE, improving energy efficiency, and ensuring sustainable GDP. Policy measures should focus on enhancing REC infrastructure, incentivizing clean technology investments, and implementing stringent ER for FFC usage. Additionally, trade policies should encourage importing and exporting environmentally friendly technologies.

Conclusion

This study investigates the dynamic relationships among FFC consumption, EC, REC, FDI, GDP, TO, and CO₂ in Thailand over the period 1990–2023 using the VECM testing approach. The empirical results offer robust insights into the long-run and short-run determinants of CO₂.

FFC consumption remains the most significant driver of CO₂. A 1% increase in FFC use leads to approximately a 0.68% increase in emissions in the long run (p < .01), confirming Thailand's continued reliance on carbon-intensive energy sources. EC also contributes positively to emissions but to a lesser extent. A 1% rise in electricity use increases CO₂ by 0.41% (p < .05), suggesting that electricity in Thailand is still produced mainly from fossil sources.

Renewable EC has a statistically significant adverse effect on emissions. A 1% increase in REC use reduces CO₂ by 0.29% (p < .05), highlighting the mitigation potential of clean energy development. GDP exhibits an inverted U-shaped (EKC) relationship with CO₂, with emissions rising at early stages of growth but declining once a threshold is reached. This supports the Environmental Kuznets Curve hypothesis for Thailand. TO and FDI show mixed effects. While TO marginally reduces emissions (−0.12%, p > .1), FDI increases emissions by 0.18% (p < .1), indicating that ERs for incoming investments may be insufficient.

The Granger causality results reveal that REC and FDI Granger cause CO₂, indicating the importance of policy controls in these areas. Moreover, the ECT is negative and statistically significant at the 1% level (−0.59), confirming the presence of a stable long-run equilibrium relationship.

Policy recommendations: These quantitative findings recommend the following policy measures: accelerate REC deployment. Given that a 1% increase in REC reduces emissions by 0.29%, expanding investment in solar, wind, and biomass through FiTs, auction systems, and grid modernization is critical. Reduce FFC dependence through carbon pricing: Since FFC use has the most considerable impact on emissions, the government should consider introducing a carbon tax or emissions trading system. Even a modest carbon price could shift marginal cost structures in favor of clean alternatives.

Tighten environmental standards on FDI projects: The positive FDI–emission relationship suggests that Thailand's investment strategy should incorporate environmental screening, green conditionality, and technology transfer incentives for foreign firms. Promote electricity sector decarbonization as EC is positively linked to emissions. Thailand must decarbonize its power sector by phasing out coal, promoting distributed solar systems, and integrating large-scale storage to stabilize renewables. Support trade-green synergies. Although TO marginally reduces emissions, targeted policies such as green export promotion and low-carbon supply chain incentives could enhance its environmental benefit.

Conclusion and future research: This article provides new evidence on the complex interaction between economic and energy factors in shaping Thailand's CO₂ profile. Quantitative results confirm the effectiveness of REC and highlight the emission risks associated with FFC use and FDI. Future research could explore sector-specific emission patterns, incorporate spatial econometric models, or evaluate the impact of recent climate commitments post-2023. Additionally, comparative panel data studies with ASEAN peers could enrich understanding regional dynamics and enhance generalizability. Thailand stands at a critical juncture. Strategic and data-driven policymaking—grounded in empirical evidence—can ensure that economic growth continues without compromising environmental sustainability.

The article provides new evidence on the links between vital economic and energy-related parameters and CO₂ in Thailand. The results show the importance of REC in achieving sustainable development and reducing environmental impacts. Policymakers must prioritize sustainable energy strategies to balance GDP with EP. The article investigates the links between FFC consumption, EC, REC use, FDI, GDP, TO, and CO₂ in Thailand from 1990 to 2023. Using time-series techniques, the empirical analysis reveals significant insights into how these determinants interact and influence EP in Thailand.

Key results indicate that FFC and EC are positively associated with CO₂, highlighting the environmental impact of Thailand's reliance on FFCs for energy. In contrast, REC mitigates CO₂ emissions, underscoring the potential benefits of expanding REC use. The analysis also reveals that FDI and GDP positively correlate with CO₂, while TO is associated with lower emissions. The bidirectional causality between GDP and CO₂ supports the EKC hypothesis, suggesting that GDP initially leads to higher emissions but may eventually result in environmental protection as the economy matures. However, the current stage of Thailand's GDP still ties GDP to increased emissions, emphasizing the need for policies that decouple economic activities from EP.

Policy recommendations derived from the article include promoting REC investments, enhancing energy efficiency, regulating FFC use, attracting sustainable FDI, and developing economic policies that integrate sustainability. Additionally, trade policies should be crafted to support EP by encouraging the exchange of clean technologies and practices. Despite the robustness of the results, the article has some limitations. The use of aggregate national data may overlook regional and sector-specific variations. Future research could address these limitations by conducting more granular analyses and incorporating more recent data to capture ongoing energy technology and policy developments.

In conclusion, achieving sustainable development in Thailand requires a multifaceted approach that balances GDP with EP. By transitioning to REC, improving energy efficiency, and implementing supportive policies, Thailand can reduce CO₂ while sustaining its economic progress. These efforts will contribute to national environmental goals and align with global efforts to combat CC and promote sustainable development.

Footnotes

Highlights

Nexus between FFC, ECO, REC, FDI, GDP, Trade Openness and CO2 in Thailand. The data is collected from 1990 to 2023.

The study employs the VECM and Granger Test Cause model.

ORCID iD

Vu Ngoc Xuan

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Addis

Cheng

(2023) The nexus between renewable energy, environmental pollution, and economic growth across BRICS and OECD countries: A comparative empirical study. Energy Reports 10: 3800–3813.

Al-Bajjali

Shamayleh

(2018) Estimating the determinants of electricity consumption in Jordan. Energy 147: 1311–1320.

Anh

Hoa

Thao

, et al. (2024) The effect of technology readiness on adopting artificial intelligence in accounting and auditing in Vietnam. Journal of Risk and Financial Management 17(1): 1-15. doi:10.3390/jrfm17010027.

Auteri

Mele

Ruble

, et al. (2024) The double sustainability: The link between government debt and renewable energy. The Journal of Economic Asymmetries 29: e00356.

Awosusi

Ozdeser

Seraj

, et al. (2024) Achieving carbon neutrality in energy transition economies: exploring the environmental efficiency of natural gas efficiency, coal efficiency, and resources efficiency. Clean Technologies and Environmental Policy 18(2):1–10. doi:10.1007/s10098-024-02932-w.

Bogdan

Rus

Gherai

, et al. (2023) A streamline sustainable business performance reporting model by an integrated FinESG approach. Sustainability 15(24): 16860.

Chen

Liu

Zhang

, et al. (2024) Vulnerability analysis method based on network and copula entropy. Scientific Reports 26(3): 192.

Dabić

Daim

Bogers

MLAM

, et al. (2023) The limits of open innovation: Failures, risks, and costs in open innovation practice and theory. Technovation 126: 102786.

Davarpanah

(2024) Comparative evaluation of carbon capture, utilization, and storage (CCUS) technologies using multi-criteria decision-making approaches. ACS Sustainable Chemistry & Engineering 12(25): 9498–9510.

10.

Davenport

(2023) Singapore and the protection of the marine environment. In: Peace with Nature. Singapore: WORLD SCIENTIFIC, 63–68.

11.

Ehn

Derneborg

Revenäs

, et al. (2021) User-centered requirements engineering to manage the fuzzy front-end of open innovation in e-health: A study on support systems for seniors’ physical activity. International Journal of Medical Informatics 154: 104547.

12.

Ghazouani

(2024) Investigating the dynamic link between globalization and carbon emissions in BRICS nations: Insights from a non-parametric perspective. International Economics 180: 100553.

13.

Haba

Bredillet

Dastane

(2023) Green consumer research: Trends and way forward based on bibliometric analysis. Cleaner and Responsible Consumption 8: 100089.

14.

Hassan

Viktor

Al-Musawi

, et al. (2024) The renewable energy role in the global energy transformations. Renewable Energy Focus 48: 100545.

15.

Hoa

Xuan

Phuong Thu

(2023a) Determinants of the renewable energy consumption: The case of Asian countries. Heliyon 9(12): 1-15. doi:10.1016/j.heliyon.2023.e22696.

16.

Hoa

Xuan

Phuong Thu

(2023b) Nexus of innovation, renewable consumption, FDI, growth and CO2 emissions: The case of Vietnam. Journal of Open Innovation: Technology, Market, and Complexity 9(3): 100100.

17.

Hoa

Xuan

Thu

NTP

(2024a) Determinants of renewable energy consumption in the fifth technology revolutions: Evidence from ASEAN countries. Journal of Open Innovation: Technology, Market, and Complexity 10(1): 100190.

18.

Hoa

Xuan

Thu

NTP

, et al. (2024b) Nexus of innovation, foreign direct investment, economic growth and renewable energy: New insights from 60 countries. Energy Reports 11: 1834–1845.

19.

Hou

Liang

, et al. (2023) How do low-carbon city pilots affect carbon emissions? Staggered difference in difference evidence from Chinese firms. Economic Analysis and Policy 79: 664–686.

20.

Jiao

Zhou

(2024) Resource dynamics and economic expansion: Unveiling the asymmetric effects of natural resources and FDI on economic growth with a lens on energy efficiency. Resources Policy 89: 104611.

21.

Kartal

Pata

Kılıç Depren

, et al. (2023) Effects of possible changes in natural gas, nuclear, and coal energy consumption on CO2 emissions: Evidence from France under Russia’s gas supply cuts by dynamic ARDL simulations approach. Applied Energy 339: 120983.

22.

Kazemzadeh

Fuinhas

Salehnia

, et al. (2023) Exploring necessary and sufficient conditions for carbon emission intensity: A comparative analysis. Environmental Science and Pollution Research 30(43): 97319–97338.

23.

Kazemzadeh

Fuinhas

Salehnia

, et al. (2024) Factors driving CO2 emissions: The role of energy transition and brain drain. Environment, Development and Sustainability 26(1): 1673–1700.

24.

Koengkan

Santiago

Fuinhas

, et al. (2019) Does financial openness cause the intensification of environmental degradation? New evidence from Latin American and Caribbean countries. Environmental Economics and Policy Studies 21(4): 507–532.

25.

Lappalainen

Aleem

Sandberg

(2023) How to manage open innovation projects? An integrative framework. Project Leadership and Society 4: 100095.

26.

Lastunen

Richiardi

(2023) Forecasting recovery from COVID-19 using financial data: An application to Vietnam. World Development Perspectives 30: 100503.

27.

(2022) Connectedness between nonrenewable and renewable energy consumption, economic growth and CO2 emission in Vietnam: New evidence from a wavelet analysis. Renewable Energy 195: 442–454.

28.

Samour

Irfan

, et al. (2023) Role of renewable energy and fiscal policy on trade adjusted carbon emissions: Evaluating the role of environmental policy stringency. Renewable Energy 205: 156–165.

29.

Wang

Liu

, et al. (2021) Per-capita carbon emissions in 147 countries: The effect of economic, energy, social, and trade structural changes. Sustainable Production and Consumption 27: 1149–1164.

30.

Liu

Chen

Zhang

(2023a) Nexus among corruption, political instability and natural resources on economic recovery in Vietnam. Resources Policy 85: 103743.

31.

Liu

Sun

Xing

, et al. (2022) Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives. Energy and AI 10: 100195.

32.

Liu

Zhou

Yan

, et al. (2023b) Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities. Energy 284: 128362.

33.

Magazzino

Mele

Schneider

, et al. (2022) Does export product diversification spur energy demand in the APEC region? Application of a new neural networks experiment and a decision tree model. Energy and Buildings 258: 111820.

34.

Miremadi

Khoshbash

Saeedian

(2023) Fostering generativity in platform ecosystems: How open innovation and complexity interact to influence platform adoption. Research Policy 52(6): 104781.

35.

Nartraphee Tancho

TSaCT

(2020) Asymmetric impacts of macroeconomy on environment degradation in Thailand: A NARDL approach. Contemporary Economics 14(4): 582–589.

36.

Nguyen

TTC

Nguyen

(2024) Does the education level of the CEO and CFO affect the profitability of real estate and construction companies? Evidence from Vietnam. Heliyon 10(7): e28376.

37.

Nguyen

Dau

V-H

, et al. (2022) The nexus between greenhouse gases, economic growth, energy and trade openness in Vietnam. Environmental Technology & Innovation 28: 102912.

38.

Obschonka

Tavassoli

Rentfrow

, et al. (2023) Innovation and inter-city knowledge spillovers: Social, geographical, and technological connectedness and psychological openness. Research Policy 52(8): 104849.

39.

Ortiz-Villajos

(2024) Dynamics of innovation over time: Evidence from the Spanish business elite. Structural Change and Economic Dynamics 69: 378–394.

40.

Parker

(2022) A decoupling analysis of transport CO2 emissions from economic growth: Evidence from Vietnam. International Journal of Sustainable Transportation 16(10): 928–941.

41.

Pata

(2018) Renewable energy consumption, urbanization, financial development, income and CO2 emissions in Turkey: Testing EKC hypothesis with structural breaks. Journal of Cleaner Production 187: 770–779.

42.

Pata

(2021) Renewable and non-renewable energy consumption, economic complexity, CO2 emissions, and ecological footprint in the USA: Testing the EKC hypothesis with a structural break. Environmental Science and Pollution Research 28(1): 846–861.

43.

Pata

Caglar

(2021) Investigating the EKC hypothesis with renewable energy consumption, human capital, globalization and trade openness for China: Evidence from augmented ARDL approach with a structural break. Energy 216: 119220.

44.

Pata

Kartal

Erdogan

(2023a) Analyzing the EKC hypothesis for technologically advanced countries: The role of ICT and renewable energy technologies. Journal of Cleaner Production 426: 139088.

45.

Pata

Kartal

Zafar

(2023b) Environmental reverberations of geopolitical risk and economic policy uncertainty resulting from the Russia-Ukraine conflict: A wavelet based approach for sectoral CO2 emissions. Environmental Research 231: 116034.

46.

Pata

Luo

Kartal

, et al. (2023c) Do technological innovations and clean energies ensure CO2 reduction in China? A novel nonparametric causality-in-quantiles. Energy & Environment 18(1): 0958305X231210993. doi:10.1177/0958305X231210993.

47.

Pata

Samour

(2023) Assessing the role of the insurance market and renewable energy in the load capacity factor of OECD countries. Environmental Science and Pollution Research 30(16): 48604–48616.

48.

Paudel

Gartaula

Rahut

, et al. (2023) The contributions of scale-appropriate farm mechanization to hunger and poverty reduction: Evidence from smallholder systems in Nepal. Journal of Economics and Development 25(1): 37–61.

49.

Pittz

Adler

(2023) Open strategy as a catalyst for innovation: Evidence from cross-sector social partnerships. Journal of Business Research 160: 113696.

50.

Principato

Trevisan

Formentini

, et al. (2023) The influence of sustainability and digitalisation on business model innovation: The case of a multi-sided platform for food surplus redistribution. Industrial Marketing Management 115: 156–171.

51.

Radmehr

Shayanmehr

Baba

, et al. (2023) Spatial spillover effects of green technology innovation and renewable energy on ecological sustainability: New evidence and analysis. Sustainable development. 10.1002/sd.2738.

52.

Salman

Ismael

(2023) The effect of digital financial inclusion on the green economy: The case of Egypt. Journal of Economics and Development 25(2): 120–133.

53.

Samour

Adebayo

Agyekum

, et al. (2023) Insights from BRICS-T economies on the impact of human capital and renewable electricity consumption on environmental quality. Scientific Reports 13(1): 5245.

54.

Scaliza

JAA

Jugend

Chiappetta Jabbour

, et al. (2022) Relationships among organizational culture, open innovation, innovative ecosystems, and performance of firms: Evidence from an emerging economy context. Journal of Business Research 140: 264–279.

55.

Shahzad

Anwar

Arshed

, et al. (2024) Nexus between economic growth, renewable energy and climate vulnerability: A global perspective using the EKC framework. Renewable Energy 237: 121716.

56.

Sharifi

Allam

Bibri

, et al. (2024) Smart cities and sustainable development goals (SDGs): A systematic literature review of co-benefits and trade-offs. Cities 146: 104659.

57.

Simba

Tajeddin

Farashahi

, et al. (2024) Internationalising high–tech SMEs: Advancing a new perspective of open innovation. Technological Forecasting and Social Change 200: 123145.

58.

Son

Thu

NTP

Dung

, et al. (2023) Determinants of the sustained development of the night-time economy: The case of Hanoi, capital of Vietnam. Journal of Risk and Financial Management 16(8): 351.

59.

Sun

Hasi

(2024) Effects of mining sector FDI, environmental regulations, and economic complexity, on mineral resource dependency in selected OECD countries. Resources Policy 89: 104651.

60.

Tarek

(2024) Examining the foreign direct investment, renewable energy consumption, and economic growth nexus in MENA countries: A bootstrap ARDL evidence. Energy Studies Review 26(01): 1–20.

61.

Thu

NTP

Huong

Xuan

(2022) Factors affecting environmental pollution for sustainable development goals: Evidence from Asian countries. Sustainability 14(24): 16775.

62.

Thu

NTP

Xuan

(2023) Factors affecting the performance of small and Medium enterprises regarding the sustainable development: The case of foreign direct investment firms in Vietnam. Economies 11(3): 72.

63.

Triatmanto

Bawono

Wahyuni

(2023) The contribution and influence of total external debt, FDI, and HCI on economic growth in Indonesia, Thailand, Vietnam, and Philippines. Research in Globalization 7: 100163.

64.

Uddin

MMM

Sharif

Islam

ARM

, et al. (2024) Moderating impact of FDI on the growth-environment nexus in the pre-COVID-19 eras. Research in International Business and Finance 67: 102114.

65.

Vendramini

THA

Macedo

Amaral

, et al. (2024) What is the cost of weight loss? An approach to commercial (dry and wet) and homemade diets. Sustainability 14(5): 679.

66.

Nguyen

(2024) Exploring the contributors to the digital economy: Insights from Vietnam with comparisons to Thailand. Telecommunications Policy 48(1): 102664.

67.

Vuong

Q-H

Bui

A-T

M-T

, et al. (2021) Top economics universities and research institutions in Vietnam: evidence from the SSHPA dataset. Heliyon 7(2): 1-10. doi:10.1016/j.heliyon.2021.e06273.

68.

Wang

Cheng

Pata

, et al. (2024a) Intermediating effect of mineral resources on renewable energy amidst globalization, financial development, and technological progress: Evidence from globe based on income-groups. Resources Policy 90: 104798.

69.

Wang

(2023) Does improving economic efficiency reduce ecological footprint? The role of financial development, renewable energy, and industrialization. Energy & Environment 18(1): 0958305X231183914. doi:10.1177/0958305X231183914.

70.

Wang

(2024b) Could information and communication technology (ICT) reduce carbon emissions? The role of trade openness and financial development. Telecommunications Policy 48(3): 102699.

71.

Wang

Liang

(2024) Financial distress early warning for Chinese enterprises from a systemic risk perspective: Based on the adaptive weighted XGBoost-bagging model. Sustainability 12(2): 65.

72.

Wang

Zhang

, et al. (2024c) Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. Journal of Cleaner Production 447: 141298.

73.

Xuan

(2024a) Determinants of environmental pollution: Evidence from Indonesia. Journal of Open Innovation: Technology, Market, and Complexity 10(4): 100386.

74.

Xuan

(2024b) Factors affecting renewable energy for sustainable development: The case of the Philippines. Environmental Health Insights 18: 11786302241288856.

75.

Xuan

(2025a) Determinants of carbon dioxide emissions in technology revolution 6.0: New insights from Brunei. Energy Strategy Reviews 57: 101633.

76.

Xuan

(2025b) Toward a sustainable future: Determinants of renewable energy utilisation in Canada. Energy Reports 13: 1308–1320.

77.

Xuan

Hoa

Thu

NTP

, et al. (2024a) Factors affecting environmental pollution for green economy: The case of ASEAN countries. Environmental Challenges 14: 100827.

78.

Xuan

Huong

Thu

NTP

, et al. (2024b) Carbon dioxide emissions, population, foreign direct investment, and renewable energy nexus: New insights from Thailand. Energy Reports 11: 4812–4823.

79.

Yen

HPH

Nhan

TTT

Nghi

, et al. (2021) Late Pleistocene-Holocene sedimentary evolution in the coastal zone of the Red River Delta. Heliyon 7(1):1-10. doi:10.1016/j.heliyon.2020.e05872.

80.

Yin

Zhao

(2023) Energy development in rural China toward a clean energy system: utilization status, co-benefit mechanism, and countermeasures. Frontiers in Energy Research 11(1): 1–15.

81.

Yuen

Ngo

TDQ

, et al. (2022) The environment, social and governance (ESG) activities and profitability under COVID-19: Evidence from the global banking sector. Journal of Economics and Development 24(4): 345–364.

82.

Zhou

Liu

(2023) A cross-scale ‘material-component-system’ framework for transition towards zero-carbon buildings and districts with low, medium and high-temperature phase change materials. Sustainable Cities and Society 89: 104378.

83.

Zhou

Zheng

Lei

, et al. (2023) A cross-scale modelling and decarbonisation quantification approach for navigating carbon neutrality pathways in China. Energy Conversion and Management 297: 117733.

The effect of factors on CO 2 emissions in Thailand: New insights from VECM methodology

Abstract

Keywords

Introduction

Literature review

Data and methodology

Data sources

Econometric techniques

Analytical procedures

Additional tests

Robustness and limitations

Bridging economics and engineering perspectives: Scope alignment

Data and analysis

Data sources

Sample selection and justification

Data preprocessing

Supporting references

Analytical tools

Empirical results

Stationarity testing

Cointegration analysis

VECM estimation

Short-time dynamics

GCTs

Summary of results

Discussion and policy implications

Interpretation of results

Comparison with previous studies

Policy recommendations

Policy measures and their impact on CO2

Cross-national comparison: Lessons from Malaysia, India, and Vietnam

Limitations and future research

Conclusion

Footnotes

Highlights

ORCID iD

Funding

Declaration of conflicting interests

References

Policy measures and their impact on CO₂