The effect of energy poverty reduction and renewable energy use on CO2 emissions: empirical evidence from ASEAN countries

Access to affordable and clean energy and increasing CO₂ emissions are among the leading global challenges. Energy is one of the fundamental requirements of life, and, in turn, problems regarding access to energy include many negative educational, health, environmental, and economic implications for nations. On the other hand, increasing CO₂ emissions can negatively impact societies through global warming, climate change, and air pollution. For this reason, this research investigates the influence of energy poverty, the use of renewable energy, and per capita GDP on CO₂ emissions in the ASEAN states for the years 2000–2021 through causality and co-integration methods. The outcomes of the causality test uncover a significant effect of per capita GDP and energy poverty indicators on CO₂ emissions and a bidirectional causal relation between CO₂ emissions and the use of renewable energy. In addition, the outcomes of the co-integration analysis unveil that the effect of energy poverty indicators on CO₂ emissions differs among the ASEAN countries. In conclusion, advancement in access to energy through clean energy sources and energy-efficient technologies is beneficial for improvements in the environmental quality.

1 Introduction

CO₂ emissions are one of the significant drivers underlying worldwide climate change. In this regard, global CO₂ emissions from energy combustion and industrial processes increased from 2.0 Gt CO₂ in 1900 to 37.6 Gt CO₂ in 2024 (IEA, 2025a). This remarkable increase in global CO₂ emissions mainly results from the utilization of fossil fuels, industrial and agricultural production, and deforestation, and it accounts for global warming and climate change to a great extent (Nunes, 2023). In addition, climate change impacts human health, property, infrastructure, and agriculture, and its annual cost is predicted to be between 1.7 trillion US$ and 3.1 trillion US$ by 2050 (Bennett, 2023). Therefore, decreasing global CO₂ emissions is specified as one of the sub-targets of climate action [SDG (Sustainable Development Goal)-13], given its negative implications for the world (UN, 2025).

On the other hand, energy poverty is another global challenge that needs to be addressed. Nearly 750 million people do not have access to electricity in the world, and over 2 billion people use harmful cooking fuels such as wood, charcoal, agricultural waste, and animal dung, which can result in environmental and education problems, mental and physical health problems, and premature deaths (IEA, 2025b). In this context, access to affordable and clean energy is also specified as SDG-7 (UN, 2025). This research article aims to explore the influence of energy poverty reduction, together with the use of renewable energies and real GDP per capita, on CO₂ emissions.

Energy poverty can cause increases in CO₂ emissions through the utilization of low-quality fuels with higher CO₂ emissions and less energy-efficient heating systems and household appliances (Reyes et al., 2019; Wang et al., 2023). On the other hand, reductions in energy poverty through the use of renewable energy can decrease CO₂ emissions (Dong et al., 2021a). As a consequence, the effect of improvements in energy poverty on CO₂ emissions depends on the energy type and technology level utilized. At the same time, costs related to the increasing CO₂ emissions can encourage countries to decrease energy poverty by enhancing renewable energy production. Therefore, a bidirectional relationship between energy poverty and CO₂ emissions also appears to be possible.

In addition, the use of renewable energy is expected to significantly contribute to decreases in CO₂ emissions because major renewable sources, including water, wind, and sunlight, emit almost no greenhouse gases (Dong et al., 2021b). In a similar vein, increases in CO₂ emissions prompt countries to shift toward renewable energy production and consumption (Mukhtarov et al., 2023). As a consequence, a bidirectional causal relation between CO₂ emissions and the use of renewable energy is theoretically expected. Finally, the relation between economic development, as represented by per capita GDP, and the environment is usually analyzed in the context of the Environmental Kuznets Curve (EKC) hypothesis, which proposes an inverted U-shaped relation between economic development and environmental degradation. The EKC hypothesis suggests that environmental degradation is witnessed in the early stages of economic development, but environmental degradation decreases after some progress in economic development (Grossman and Krueger, 1995).

This study analyzes the connection between energy poverty reduction and CO₂ emissions in eight ASEAN (Association of Southeast Asian Nations) states reported in Table 1. The figures for per capita CO₂ emissions, access to clean fuels and technologies for cooking, access to electricity, and renewable energy use in the ASEAN countries for 2000 and 2021 are also provided in Table 1. In this regard, these ASEAN countries have experienced increases in per capita CO₂ emissions, but the largest increases in per capita CO₂ emissions have been in Lao PDR, Cambodia, Vietnam, and Myanmar. On the other hand, all ASEAN countries except Malaysia made progress in access to clean fuels and technologies for cooking, with Cambodia, Indonesia, Lao PDR, and Vietnam having achieved relatively more advancement. However, access to electricity increased in all ASEAN countries, and Cambodia, Lao PDR, Myanmar, and the Philippines experienced the largest increases in access to electricity. Finally, the rate of renewable energy use in the final energy consumption decreased in all ASEAN countries except Malaysia. In conclusion, Table 1 shows that all countries achieved increases in access to electricity and in access to clean fuels and technologies for cooking between 2000 and 2021; however, the ratio of renewable energy usage in total final energy consumption decreased during the same period. Therefore, energy poverty reduction in terms of access to electricity is substantially met by fossil fuels.

Table 1

Table 1. Energy access and CO₂ emissions in the ASEAN countries (2000 and 2021).

Increasing CO₂ emissions have become one of the major global challenges over the past 5 decades. Therefore, all countries, especially developed countries, and international organizations have endeavored to reduce CO₂ emissions. In this regard, energy poverty, which is the inability to access affordable and clean energy for basic needs such as lighting and cooking, can be a significant driver of CO₂ emissions in developing countries. Therefore, the objective of this study is to analyze the nexus between energy poverty reduction and CO₂ emission in eight ASEAN countries experiencing problems in accessing affordable and clean energy.

Access to affordable and clean energy was determined as one of the SDGs in 2015, given its significant negative health, environmental, economic, and social implications. Nevertheless, its environmental implications have not been sufficiently researched to date. For this reason, the effect of energy poverty, together with the control variables of renewable energy use and per capita GDP, on CO₂ emissions is analyzed in this research because Zhao et al. (2021), Zhang et al. (2022), and Li and Ma (2025) analyzed the environmental effects of energy poverty for China and Wang et al. (2024), Simionescu et al. (2024), Rao et al. (2024), Messie Pondie and Engwall (2025), Farooq et al. (2025), Butty et al. (2025), Tanveer et al. (2024), Wang and Qu. (2024), and Alnour et al. (2024) examined the connection between energy poverty indicators and CO₂ emissions for other countries. Therefore, this research would be one of the first research papers studying the nexus between energy poverty indicators and CO₂ emissions in the ASEAN states. Additionally, the methodological approach of this research allows for country-level analysis and a two-way analysis of the connection between energy poverty indicators and CO₂ emissions, unlike approaches based on regression often utilized by previous empirical studies.

Section 2 presents a summary of the empirical literature on energy poverty, the use of renewable energy, real GDP per capita, and CO₂ emissions, while Section 3 explains the dataset and methodology of this research. Section 4 conducts the econometric tests and discusses their results within the context of the related literature, and the research concludes with Section 5.

2 Literature overview

Energy poverty has been one of the main global challenges and has many economic, environmental, educational, and health implications for societies (Zhao et al., 2021; Katoch et al., 2024), but both the causes and consequences of energy poverty have not been adequately explored to date. Hence, this study concentrates on the connection between energy poverty indicators and CO₂ emissions in the selected ASEAN countries. In the literature, the studies analyzing the nexus between energy poverty and CO₂ emissions have usually employed regression and causality tests and remained inconclusive. On one hand, Zhao et al. (2021), Zhang et al. (2022), and Li and Ma (2025) discovered a positive influence of energy poverty on CO₂ emissions in China. Furthermore, Wang et al. (2024), Simionescu et al., (2024), Rao et al. (2024), Messie Pondie and Engwall (2025), Farooq et al. (2025), and Butty et al. (2025) found a positive relationship between energy poverty and CO₂ emissions for different regions of the world. On the other hand, Tanveer et al. (2024) uncovered a negative relation between energy poverty and ecological footprint for South Asian countries. Wang and Qu (2024) and Alnour et al. (2024) discovered mixed consequences on the nexus between energy poverty and diverse environmental indicators depending on the economic development levels and income inequality.

Zhao et al. (2021) analyzed the influence of energy poverty on CO₂ emissions in 30 Chinese provinces during 2002–2017 using dynamic regression analysis, and their results revealed that energy poverty had a positive impact on CO₂ emissions. In addition, they revealed a bilateral causality between energy poverty indicators and CO₂ emissions in the provinces with high levels of energy poverty and a unilateral causal relation from energy poverty indicators to CO₂ emissions. Zhang et al. (2022) also analyzed the connection between energy poverty indicators and carbon intensity in the Chinese construction industry during 2004–2016 using regression analysis and found a positive effect of energy poverty on carbon intensity. In addition, Li and Ma (2025) examined the link between energy poverty in rural areas and CO₂ emissions in a sample of 30 Chinese provinces between 2010 and 2021 through causality tests and dynamic regression. Their results also revealed a positive relation between rural energy poverty and CO₂ emissions and a bilateral causal nexus between the two variables.

Wang et al. (2024) explored the influence of improvements in energy poverty indicators on CO₂ emissions in the Belt and Road Initiative countries between 2010 and 2020 through regression analysis and revealed that improvements in energy poverty had a positive effect on CO₂ emissions. Simionescu et al. (2024) examined the link between energy poverty, the use of renewable energy, and CO₂ emissions in Central and Eastern European countries between 2005 and 2022 using regression analysis and found a positive influence of energy poverty indicators on CO₂ emissions. Rao et al. (2024) examined the relation between CO₂ emissions and energy poverty in South Asian countries using the quantile-on-quantile approach and found a positive impact of individuals without access to electricity on CO₂ emissions.

Furthermore, Messie Pondie and Engwall (2025) researched the influence of energy poverty on CO₂ emissions in sub-Saharan African countries during 2000–2021 using regression analysis and found a positive impact of both access to clean energy for cooking and access to electricity on CO₂ emissions. Farooq et al. (2025) also examined the effect of energy poverty, energy efficiency, and the use of renewable energy on CO₂ emissions in the BRICS states between 2000 and 2023 via dynamic common correlated effects and discovered that energy poverty increased CO₂ emissions in the long term. Finally, Butty et al. (2025) analyzed the relationship among energy poverty, gender inequality, GDP, and CO₂ emissions between 1996 and 2020 in Africa using the ARDL method and found that energy poverty increased CO₂ emissions.

However, Tanveer et al. (2024) analyzed the impact of energy poverty on the ecological footprint in South Asian countries during 2000–2021 using the pooled mean group estimator and found a negative relation between energy poverty and the ecological footprint. The negative effect indicated that the renewable energy transition had a negative impact on the ecological footprint. Furthermore, Wang and Qu (2024) examined the effect of energy poverty and income inequality on low-carbon transformation in 193 countries during 1990–2019 using dynamic regression analysis and revealed that access to electricity negatively impacted CO₂ emissions in countries with a Gini coefficient lower than 0.461, while access to electricity had a positive impact on CO₂ emissions in countries with a Gini coefficient higher than 0.461. Alnour et al. (2024) analyzed the effect of energy poverty on various ecological footprint components in sub-Saharan African countries using the method of moments quantile regression between 2000 and 2021. Their results revealed different effects of urban and rural energy poverty on ecological footprint components.

Renewable energy has been identified in the literature as one of the most effective instruments for reducing CO₂ emissions, but the causality between CO₂ emissions and renewable energy use can vary depending on countries’ socio-economic development levels. In this regard, almost all studies, such as Habimana Simbi et al. (2025), Otim et al. (2025), Almulhim et al. (2025), Sezgin et al. (2024), Saidi and Omri (2020), and Chen et al. (2019), exploring the effect of renewable energy usage on CO₂ emissions have found a negative influence of renewable energy use on CO₂ emissions. Furthermore, Habimana Simbi et al. (2025), Sezgin et al. (2024), Saidi and Omri (2020), and Chen et al. (2019) found a bilateral causal link between the use of renewable energy and CO₂ emissions, while Inglesi-Lotz and Dogan et al. (2018) uncovered a unidirectional causal relation from the usage of renewable energy to CO₂ emissions, and Otim et al. (2025) found a unidirectional causal relation from CO₂ emissions to the use of renewable energy.

Finally, research studies on the relationship between per capita GDP and various environmental indicators have revealed mixed results, in line with the suggestions of the EKC hypothesis. On one hand, Raihan and Tuspekova (2022), Onofrei et al. (2022), and Freire-González et al. (2024) uncovered a positive impact of per capita GDP on CO₂ emissions, while Zhigolli and Fetai (2024) and Adela et al. (2025) revealed a negative link between per capita GDP and CO₂ emissions. In addition, Salari et al. (2021) and Habimana Simbi et al. (2025) found an inverted-U-shaped relation between per capita GDP and CO₂ emissions. Dogan and Aslan (2017), Espoir et al. (2023), Mitic et al. (2023), and Habimana Simbi et al. (2025) uncovered a bilateral causal relation between per capita GDP and CO₂ emissions.

3 Data and methodology

This research article examines the link among indicators of energy poverty, the use of renewable energy, per capita GDP, and CO₂ emissions in eight ASEAN states for the years 2000–2021 through co-integration and causality methods. The variables of the empirical research are described in Table 2. CO₂ emissions are represented by CO₂ emissions per capita, and these data are acquired from Climate Watch (Climate Watch, 2025). In addition, energy poverty is represented by access to clean fuels and technologies for cooking (EPOV1) and access to electricity (EPOV2), both of which are calculated by the World Bank (World Bank, 2025a; World Bank, 2025b). On the other hand, renewable energy use (REU) and per capita GDP are represented, respectively, by renewable energy consumption and per capita GDP, and these data are obtained from the database of the World Bank (World Bank, 2025c; World Bank, 2025d).

Table 2

Table 2. Variables utilized in the econometric analyses.

The impact of the indicators of energy poverty, the use of renewable energy, and per capita GDP on CO₂ emissions in the ASEAN states is analyzed. The sample of the research comprises the ASEAN states of Vietnam, Thailand, the Philippines, Myanmar, Malaysia, Lao PDR, Indonesia, and Cambodia but excludes Brunei Darussalam and Singapore, because these countries already had 100% energy access between 2000 and 2021. The dataset’s period is limited to the years 2000–2021 because energy poverty indicators are available from 2000 onward and renewable energy data are available up to 2021. The co-integration test is run using Gauss 12.0; cross-sectional dependence (CD), homogeneity, stationarity, and causality analyses, together with augmented mean group (AMG) estimation, are carried out using Stata 17.0.

The main aim of this research paper is to analyze the interplay among CO₂ emissions, energy poverty indicators, the use of renewable energy, and per capita GDP. Thus, the two models in Equations 1, 2 are formed for the empirical analyses:

${CO2}_{it}={\alpha }_0+{\beta }_1{EPOV1}_{it}+{\beta }_2{REU}_{it}+{\beta }_3{PCGDP}_{it}+u_{it}\left(Model-1\right),(1)$

${CO2}_{it}={\alpha }_0+{\beta }_1{EPOV2}_{it}+{\beta }_2{REU}_{it}+{\beta }_3{PCGDP}_{it}+u_{it}\left(Model-2\right).(2)$

The mean values of CO₂, EPOV1, EPOV2, REU, and PCGDP introduced in Table 3 are 2.073 metric tons per capita, 43.382%, 80.211%, 41.882%, and 3,107.971 US$, respectively. However, energy poverty indicators (EPOV1 and EPOV2), the use of renewable energy (REU), and per capita GDP significantly differ among the ASEAN states, while CO₂ emissions show a moderate variation.

Table 3

Table 3. Summary statistics of the series.

In the context of the methodology, CD and heterogeneity tests are run using the LM and delta tests, respectively. Then, the unit root test is carried out by the CIPS test proposed by Pesaran (2007), given the presence of CD. In addition, the co-integration interplay among CO₂, EPOV1, EPOV2, REU, and PCGDP is analyzed using the LM co-integration test of Westerlund and Edgerton (2007) because this test yields reliable outcomes for small datasets and also accounts for CD and heterogeneity. Co-integration coefficients of the panel and each ASEAN members are estimated using the AMG estimator proposed by Eberhart and Bond (2009), since this estimator addresses CD, heterogeneity, and endogeneity problems (Eberhart and Bond, 2009). The AMG uses Equation 3 and accounts for CD by including a common dynamic effect from the pooled regression’s dummy coefficients with the first differences in the group regressions (Eberhart and Bond, 2009).

$y_{it}={\beta }_i^{\prime }{x}_{it}+u_{it}{u}_{it}={\alpha }_i+{\lambda }_i^{\prime }{f}_t+{\epsilon }_{it},(3)$

where the observable covariates ( $x_{it}$ ) and unobservables ( $u_{it}$ ) are specified as a combination of group effects ( ${\alpha }_i$ ), common factors ( $f_t$ ), and group factor loadings ( ${\lambda }_i^{\prime }$ ).

The causal relation among CO₂, EPOV1/EPOV2, REU, and PCGDP is examined using the JKS (Juodis–Karavias–Sarafidis) causality method (Juodis et al., 2021. The JKS causality method takes account of heterogeneity and is robust under CD availability (Juodis et al., 2021). Moreover, the test takes advantage of the half-panel jackknife (HPJ) approach proposed by Dhaene and Jochmans (2015) to decrease Nickell bias.

4 Results

In the results and discussion part of the research, the existence of CD and heterogeneity in the panel datasets is first explored using the LM and delta tests. Thus LM_adj., LM CD, and LM tests are carried out, and the H₀ hypothesis of CD independence is rejected based on the probability values in Table 4, which are less than 0.01. Then, the delta tests are implemented, and the H₀ hypothesis of homogeneity is rejected based on probability values of less than 0.02. The outcomes of the CD and heterogeneity tests confirm the presence of CD and heterogeneity among the series in both model-1 and model-2.

Table 4

Table 4. Outcomes of CD and delta tests.

The stationarity of the series utilized in the econometric analyses should be specified to select the appropriate causality and co-integration methods. For this reason, the CIPS unit root test is implemented to examine whether CO2, EPOV1, EPOV2, REU, and PCGDP have a unit root in the presence of CD, and the results are reported in Table 5. The results of the CIPS test reveal that CO2, EPOV1, EPOV2, REU, and PCGDP are non-stationary at level values, but these series become stationary following the first-differencing process.

Table 5

Table 5. Outcomes of the CIPS test.

The long-run interplay among the variables in the two models is analyzed using the LM bootstrap co-integration method, and test statistics along with bootstrap and asymptotic p-values are reported in Table 6. The asymptotic p-values indicate the rejection of the H₀ hypothesis of significant co-integration, while bootstrap p-values suggest the acceptance of the H₀ hypothesis. However, bootstrap p-values are taken into account due to the presence of CD. Therefore, utilization of the LM co-integration method with CD and heterogeneity increases the robustness of our outcomes.

Table 6

Table 6. Consequences of the LM co-integration method*.

The long-term coefficients of the explanatory variables in model-1 and model-2, shown in Table 7, are estimated using the AMG estimator. The coefficients of model-1 estimation reveal that increases in access to clean fuels and technologies for cooking (decreases in energy poverty) negatively impact CO₂ emissions in Indonesia and Thailand and positively impact CO₂ emissions in Cambodia, Lao PDR, and the Philippines. In addition, the use of renewable energy negatively affects CO₂ emissions at the panel level in the Philippines, Myanmar, Malaysia, Lao PDR, Indonesia, and Cambodia. Finally, per capita GDP positively impacts CO₂ emissions in Thailand, the Philippines, Lao PDR, Indonesia, and Cambodia.

Table 7

Table 7. Co-integration coefficients’ estimation (model-1 and model-2).

The results of model-2 estimation reveal that increases in access to electricity (in other words, decreases in energy poverty) negatively impact CO₂ emissions in Myanmar and positively impact CO₂ emissions in Malaysia. In addition, the use of renewable energy negatively impacts CO₂ emissions at the panel level in the Philippines, Myanmar, Malaysia, Lao PDR, Indonesia, and Cambodia. Finally, per capita GDP positively impacts CO₂ emissions in Cambodia, Indonesia, the Philippines, and Thailand.

The causal association among CO2, EPOV1, EPOV2, REU, and PCGDP is investigated using the JKS causality method, and the test results, shown in Table 8, indicate a unilateral causal relation from EPOV1 to CO2 and a bilateral causal interplay between EPOV2 and CO₂. In other words, both indicators of energy poverty have a significant influence on CO2 emissions. Furthermore, the findings also demonstrate a bidirectional causal interplay between REU and CO2 and a unilateral causal relation from PCGDP to CO2.

Table 8

Table 8. Outcomes of the JKS causality test.

5 Discussion

The effect of improvements in energy poverty on CO₂ emissions can vary depending on whether energy requirements are met by low-quality fuels and less energy-efficient devices or clean energy sources and energy-efficient devices. In a similar manner, our results also indicate that the impact of energy poverty indicators on CO₂ emissions differs among the ASEAN states. In this regard, increases in access to clean fuels and technologies for cooking negatively impact CO₂ emissions in Indonesia and Thailand and positively impact CO₂ emissions in Cambodia, Lao PDR, and the Philippines. Moreover, increases in access to electricity positively impact CO₂ emissions in Malaysia and Myanmar. The differences among the ASEAN states probably result from the fact that Indonesia and Thailand made significant improvements in access to clean fuels and technologies, while Cambodia, Lao PDR, Myanmar, and the Philippines achieved relatively greater improvements in access to electricity. Therefore, our outcomes are compatible with the relevant theoretical views. Furthermore, the related empirical literature also supports our mixed results. On one hand, Zhao et al. (2021), Zhang et al. (2022), Li and Ma (2025), Wang et al. (2024), Simionescu et al. (2024), Rao et al. (2024), Messie Pondie and Engwall (2025), Farooq et al. (2025), and Butty et al. (2025) found a positive relation between the energy poverty indicators and CO₂ emissions for different countries, while Tanveer et al. (2024) found a negative relation between energy poverty and ecological footprint. In addition, Wang and Qu (2024) and Alnour et al. (2024) reported mixed findings.

On the other hand, the use of renewable energy produces far fewer harmful emissions than fossil fuels and, thus, makes a crucial contribution to countries’ decarbonization efforts. Furthermore, volatility in fossil fuels’ prices, disruptions in fossil fuels’ supply, and energy security-related concerns encourage countries toward a renewable energy transition. In this regard, the negative influence of renewable energy use on CO₂ emissions in most of the ASEAN countries supports these theoretical views. Furthermore, the findings of Habimana Simbi et al. (2025), Otim et al. (2025), Almulhim et al. (2025), Sezgin et al., (2024), Saidi and Omri (2020), and Chen et al. (2019) also support our results to a great extent.

Finally, the influence of per capita GDP on CO₂ emissions differs based on the development levels of the countries within the scope of the EKC hypothesis. Our outcomes also support the EKC hypothesis and demonstrate that per capita GDP has a positive influence on CO₂ emissions in the developing economies of Cambodia, Lao PDR, the Philippines, Indonesia, and Thailand, which is compatible with the EKC hypothesis. Furthermore, related empirical studies such as Raihan and Tuspekova (2022), Onofrei et al. (2022), and Freire-González et al. (2024) also support our results.

In the empirical literature, the causality between energy poverty indicators and CO₂ emissions has been investigated by a few researchers. In this regard, Zhao et al. (2021) and Li and Ma (2025) revealed a bilateral causality between CO₂ emissions and energy poverty in China, while Zhao et al. (2021) found a unilateral causal relation from energy poverty to CO₂ emissions. As a consequence, our outcomes are consistent with the results of Zhao et al. (2021) and Li and Ma (2025). On the other hand, our two-way causal relation between the use of renewable energy and CO₂ emissions is compatible with the outcomes of Habimana Simbi et al. (2025), Sezgin et al., (2024), Saidi and Omri (2020), and Chen et al. (2019). Finally, a one-way causal nexus from per capita GDP to CO₂ emissions in the ASEAN economies differs from the results of Habimana Simbi et al. (2025), Espoir et al., (2023), Mitic et al. (2023), and Dogan and Aslan. (2017). This difference could be because the ASEAN countries are in the early stages of economic development, and one-way causality may turn into two-way causality over time as these countries make progress in their economic development.

6 Conclusion, limitations, policy implications, and future research directions

Energy poverty and environmental degradation have been two major global challenges and are also among the Sustainable Development Goals. Scholars have conducted a large number of empirical studies on the drivers and implications of environmental degradation. However, the causes and implications of energy poverty have not been adequately explored although energy is an inevitable input to all economic activities and a necessity for many human activities. Accordingly, this study focuses on the environmental implications of energy poverty alleviation in a sample of ASEAN countries.

The limitations of the study are as follows:

The study period covers the years between 2000 and 2021 because energy poverty indicators and the use of renewable energy for the ASEAN countries are available during the 2000–2021 period.

Brunei Darussalam and Singapore are excluded from the study sample due to the non-availability of energy poverty data for these countries.

The main focus of the study is to explore the environmental impacts of energy poverty along with renewable energy use and per capita GDP. Therefore, other potential drivers of the CO₂ emissions have been disregarded.

This study investigates the causal and long-term effects of energy poverty indicators, renewable energy use, and per capita GDP on CO₂ emissions using the JKS causality method and the LM bootstrap co-integration method. The findings of the JKS causality test reveal a unilateral causality from access to clean fuels and technologies for cooking and per capita GDP to CO₂ emissions and a bilateral causality among access to electricity, renewable energy use, and CO₂ emissions. Furthermore, the co-integration coefficients indicate a negative relationship between access to clean fuels and technologies for cooking and CO₂ emissions in Indonesia and Thailand and a positive relationship between access to clean fuels and technologies for cooking and CO₂ emissions in Cambodia, Lao PDR, and the Philippines. In addition, increases in access to electricity negatively impact CO₂ emissions in Myanmar and positively impact CO₂ emissions in Malaysia. Therefore, the effect of improvements in energy poverty by the ASEAN countries on CO₂ emissions is evaluated to be largely dependent on whether these countries meet energy needs through clean energy sources and energy-efficient technologies or through fossil fuels and energy-inefficient devices. In addition, usage of renewable energy is found to negatively affect CO₂ in most of the ASEAN countries, while per capita GDP positively impacts CO₂ emissions. The positive influence of renewable energy usage on CO₂ emissions mainly arises because these countries are already in the early phase of economic development.

In light of our outcomes and the related literature, the following policy recommendations are suggested for the ASEAN countries:

In the short term, the environmental awareness of individuals should be increased through education and training programs.

In the medium term, governmental policies should be designed to increase the share of renewable energy use in total energy consumption through institutional and financial incentives.

In the long term, educational programs should be designed to prioritize the development of green energy technologies.

Future empirical studies can examine the role of education and human capital in the link between energy poverty alleviation and the environment.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

BG: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review and editing. GS: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review and editing. AKÇ: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review and editing. YB: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review and editing.

Funding

The authors declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Generative AI was used in the creation of this manuscript.

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