2.1. Different energy uses in Malaysian electricity supply company

Malaysia’s electricity supply sector operates within a complex energy landscape, historically dominated by fossil fuels, primarily coal and natural gas, due to their cost effectiveness and reliability [18]. However, recent efforts have focused on diversifying the energy mix to include renewables such as hydroelectric, solar, wind, biomass, and biogas, aiming to enhance energy security and reduce environmental impacts [7, 19].

Fossil fuel consumption, especially coal, remains a major contributor to greenhouse gas emissions, posing significant environmental and health risks [19]. Although these fuels facilitate economic development, they accelerate climate change through global warming and air pollution. In contrast, renewable energy sources emit minimal greenhouse gases and support long term sustainability [20]. Malaysia’s commitments under the Paris Agreement and its Net Zero 2050 target further underscore the urgency of this transition.

Besides coal, gas, and renewables, oil continues to serve as a supplementary energy source, particularly in remote areas using gas turbines. To enhance system resilience, the sector is also exploring innovative solutions such as liquefied natural gas (LNG) imports and integrated gasification combined cycle (IGCC) technologies

Moreover, initiatives such as the adoption of smart grid technologies and demand side management by electricity providers like Tenaga Nasional Berhad (TNB) aim to optimise energy efficiency and reduce system losses [21]. These strategies, supported by policy incentives and growing public private partnerships, reflect Malaysia’s broader alignment with environmental, social, and governance (ESG) standards [22].

Correspondingly, national energy plans have been revised to reflect these ambitions, particularly in response to international climate commitments such as the COP26 Glasgow Pact, which called for the phasing down of coal and fossil fuel subsidies. In this context, Malaysia aims to increase the share of renewable energy to 31 percent of its electricity generation mix by 2025, as outlined in the 2022–2040 energy roadmap. This shift is particularly critical given Malaysia’s heavy reliance on coal and natural gas, both of which are finite and environmentally detrimental. Although the country possesses substantial natural gas reserves, it faces the risk of becoming a net importer due to rising demand and resource depletion. Therefore, the transition towards renewables is not just an environmental imperative but an economic necessity [23].

Despite these ambitions, the practical energy landscape paints a more concerning picture. According to the International Atomic Energy Agency (2025), carbon emissions from coal have risen dramatically, from 5.93 Mt in 2000 to 88.86 Mt in 2022, signalling that Malaysia’s dependence on coal remains a major barrier to decarbonisation. Carbon emissions from natural gas have fluctuated, but the overall emissions from natural gas are at a higher level (see Fig 1). This paradox highlights the gap between policy intent and implementation. While Southeast Asian nations are increasingly embracing renewables due to their geographic advantages, Malaysia’s emissions profile suggests that energy consumption is still a dominant force in power generation.

Given this scenario, scholars have increasingly recognised the importance of examining coal and natural gas consumption as proxies for overall energy usage in studies of carbon emissions. However, a critical gap remains: there is a noticeable lack of empirical research specifically investigating the relationship between energy consumption, particularly coal usage and natural gas, and carbon emissions within the context of Malaysian electricity supply companies. Furthermore, environmental governance, a core pillar of ESG, demands that companies not only comply with sustainability regulations but also actively pursue strategies to reduce carbon emissions. In this light, energy producers such as TNB bear a significant responsibility.

At present, TNB Genco holds the largest market share in Malaysia’s power generation sector, with a total contracted capacity of 13.76 GW [24]. Data suggest that TNB is one of the primary contributors to carbon emissions through its energy consumption patterns, especially in coal and natural gas – based power generation. Therefore, against this backdrop, it is imperative to conduct an empirical investigation on the impact of energy consumption on carbon emissions in the electricity production sector. Such research will not only fill a vital gap in the literature but also offer practical insights for improving environmental governance and advancing ESG performance among energy providers in Malaysia.

2.2. ESG practices in the electricity supply company of Malaysia

Malaysia’s electricity supply sector continues to depend heavily on fossil fuels, especially coal and natural gas, to meet growing energy needs. While these sources help ensure energy security, their environmental consequences are severe. The burning of fossil fuels releases large amounts of greenhouse gases, which contribute to climate change, air and water pollution, and the degradation of natural ecosystems). In addition, the processes of extracting and transporting these fuels often disrupt local environments and threaten biodiversity.

In response to these challenges, global and national policies have increasingly promoted the transition to cleaner and more sustainable energy sources. Malaysia’s participation in the Paris Agreement ([25] along with its [26] National Energy Policy (2022 reflects a commitment to reducing environmental damage and improving sustainability. The International Renewable Energy Agency (2023) further emphasises that sustainability should be considered throughout the entire energy lifecycle, from production to end use.

ESG practices go beyond environmental efforts and also address social responsibility and good governance. When implemented effectively, ESG strategies help companies improve operational efficiency, build stakeholder trust, and attract responsible investors [27]. Malaysia’s regulatory bodies, such as the Securities Commission and Bursa Malaysia, have introduced ESG guidelines that encourage companies to follow clear sustainability standards and enhance reporting practices [28].

Long term sustainability requires a shift in how companies operate and plan their energy use. This includes moving away from fossil fuels, increasing investment in renewable energy, and prioritising energy conservation. These actions are essential not only for reducing emissions but also for strengthening the resilience of the electricity supply sector [29]. Moreover, collaboration with stakeholders such as policymakers, civil society, and local communities ensures that ESG practices align with wider development goals [30].

Amid this evolving energy landscape, Malaysia’s main electricity provider, which owns nearly 51% of Malaysia’s power generation market share, Tenaga Nasional Berhad (TNB), has begun integrating Environmental, Social, and Governance (ESG) practices into its strategy. These include investments in renewable energy, setting emission reduction targets, and improving energy efficiency [31, 9]. In alignment with Malaysia’s net zero ambition, Tenaga Nasional Berhad (TNB) has committed to achieving net zero carbon emissions by the year 2050. This target forms part of TNB’s broader sustainability pathway, announced in 2021. As part of its immediate efforts, the company aims to reduce Scope 1 carbon emission intensity by 5 percent annually. TNB is confident in surpassing its interim target of a 35 percent reduction in carbon emission intensity by 2035, with a projected overall reduction of 46.47 percent by 2050.

To support this transition, TNB is working to secure up to 5GW of renewable energy (RE) capacity in Malaysia. This will be achieved through various national initiatives including the Large Scale Solar 5 (LSS5), the Corporate Renewable Energy Supply Scheme (CRESS), and the Corporate Green Power Programme (CGPP) [12]. Moreover, as part of its commitment to environmental stewardship, TNB launched the My Brighter Green Programme in conjunction with its 74th anniversary. Under this initiative, a total of 78,100 trees were planted across 42 designated areas nationwide, covering approximately 174 acres. This effort is estimated to have sequestered around 3,100 tonnes of CO₂ equivalent (tCO₂e), contributing meaningfully to natural carbon offsetting. Additionally, TNB introduced Project Dragon in June 2024 to explore the feasibility of carbon capture technologies. The project focuses on integrating Carbon Capture and Utilisation (CCU) technology at the 2000MW Jimah East Power (JEP) coal power plant. The pilot aims to capture up to 5,000 kg of CO₂ annually, with potential applications through biological or hydrogenation pathways. This represents a significant step toward technological innovation in emission reduction.

Together, these ESG initiatives reflect TNB’s holistic approach to sustainability, combining operational transformation, renewable energy expansion, and nature-based solutions. However, despite these efforts, the company remains heavily dependent on coal and natural gas for electricity generation (Figs 2 and 3). As such, the visibility and effectiveness of its ESG initiatives remain limited. A meaningful shift towards environmental sustainability requires a substantial reduction in reliance on fossil fuels. Achieving this goal depends on the development and implementation of robust policies that genuinely promote ESG integration within the energy sector.

Therefore, understanding the relationship between energy consumption and environmental sustainability is essential for evaluating the extent to which ESG principles are being upheld in Malaysia’s electricity sector. By examining how different energy sources, particularly the continued reliance on coal and gas versus the integration of renewable energy, affect environmental outcomes such as carbon emissions, we can assess whether ESG commitments are translating into measurable environmental benefits. This analysis also helps determine if corporate sustainability strategies align with national and global climate goals, and whether current energy practices support or hinder the sector’s progress toward achieving long term ESG targets. A deeper exploration of this relationship is crucial for supporting more effective policy design and guiding the country’s transition towards a cleaner, more sustainable, and environmentally responsible energy future. This study aims to contribute to this area by providing empirical insights into how energy use patterns impact environmental outcomes in the context of ESG practices.

2.3. Energy consumption and environmental sustainability nexus in Malaysia’s electricity supply sector

Environmental sustainability has become increasingly central to corporate agendas, particularly in Malaysia’s electricity supply sector. As the nation pursues economic development, balancing energy demands with environmental preservation has become a key priority. In this context, Environmental, Social, and Governance (ESG) practices offer a framework for embedding sustainability into corporate strategies and operations.

While there is growing recognition of ESG in the electricity sector, empirical research directly linking energy consumption to environmental sustainability remains limited. Existing studies tend to emphasise the potential of ESG practices without providing robust empirical validation. For example, Ismail et al. (2020) highlighted the role of ESG in managing environmental risks, reducing resource consumption, and building stakeholder trust. Similarly, [9] stressed the importance of transparency in ESG reporting, which allows companies to monitor environmental performance and implement corrective actions. [32] emphasised the value of multi-stakeholder engagement, noting that alignment with societal expectations is vital for inclusive and sustainable development.

Several studies have examined the electricity sector’s role in Malaysia’s carbon reduction efforts. [33] explored the impact of fuel mix changes on emissions and found that despite shifts towards hydro and coal, emissions remained high due to ongoing fossil fuel reliance. [34] pointed to slow decarbonisation in the power sector, driven by rising energy demand and high renewable energy costs. They argued that national energy policy has prioritised energy security over climate goals, and recommended greater efficiency, emissions trading, and adoption of international best practices to meet the 2030 carbon reduction target. [35] found a strong positive correlation between energy consumption and carbon emissions, driven by economic and population growth. Their study identified the electricity and transport sectors as major emission sources and recommended mechanisms like the Feed-in Tariff (FiT) to promote renewable energy and energy efficiency.

Moreover, [36] evaluated three alternative power generation scenarios using the LEAP model, demonstrating that renewable energy could meet future demand and reduce emissions. They called for policymakers to integrate such scenarios into national energy planning. Based on the existing plans and projections of Malaysia, the power sector is expected to increase its renewable energy capacity to 31% by 2025 and 40% in 2035, reducing the carbon intensity to GDP of the sector by 60% by as early as 2030, compared to the year 2000 baseline [37]. Despite these efforts, fossil fuels continue to dominate Malaysia’s electricity generation. As of 2022, about 80 percent of electricity came from coal (46.8 percent) and natural gas (International Energy Agency, 2025).

With growing electricity demand fuelled by economic expansion, the environmental consequences are intensifying. According to the Ministry of Energy, Science, Technology, Environment, and Climate Change (MESTECC), managing carbon emissions will be difficult without major energy efficiency reforms. The Twelfth Malaysia Plan (RMK 12) outlines a national commitment to net zero emissions by 2050. A joint study by WWF Malaysia, the Boston Consulting Group (BCG), and the government found Malaysia has been a net emitter since 2004, with energy sector decarbonisation identified as a key priority [38, 39].

However, despite policy-level initiatives, there remains a critical research gap regarding the role of individual electricity companies, particularly TNB, in either driving or mitigating emissions. Most existing studies focus on national trends and policy analysis, often overlooking company-level dynamics and the environmental impacts of energy consumption by major producers. Therefore, it is essential to examine how TNB’s energy consumption patterns influence environmental sustainability and whether alignment with ESG principles can meaningfully reduce emissions. This study seeks to fill this gap by investigating the environmental implications of energy use by TNB. It also aims to provide insights for policymakers and stakeholders to craft more effective ESG strategies, supporting Malaysia’s transition to a sustainable energy future.

The study hypothesizes that non renewable energy consumption, specifically coal and natural gas, negatively impacts environmental sustainability by increasing carbon emissions. This is supported by graphical evidence (Figs 2 and 3) showing a linear increase in emissions alongside rising non-renewable energy use, as well as literature from Sharif et al. [40] (2019)), Ali et al. [41] (2022), and [42] Ou et al. (2011) highlighting the adverse effects of energy consumption on sustainability. The low adoption of renewable energy in Malaysia limits its mitigating potential, emphasizing the need to focus on reducing non-renewable energy consumption to improve environmental sustainability.Therefore, the hypothesis is:

H₁: Non renewable energy consumption reduces environmental sustainability

3.1. Data and source

The time series data was collected from the report of the Tenaga Nasional Berhad (TNB) Sustainability Reports from 2016 to 2021 for all variables, since this company is one of the main market share holders in electricity production. At present, TNB Genco owns nearly 51% of Malaysia’s power generation market share with a total contracted capacity of 13.76GW [12]. The focused variables included environmental sustainability (CO2) = total direct and indirect GHG emissions Million tCO2e-yearly, COL = Coal consumption GJ (Gigajoule or GJ equals one billion joules) and GAS = Natural Gas consumption GJ. Besides, this study also considers some control variables, i.e., GCAP = Generating Capacity Total MW (Megawatts), and USER = Total customer accounts, to provide a comprehensive evaluation.

3.2. Empirical equations

(1)

(2)

Where,

Environment Sustainability (CO2) = Total direct and indirect GHG emissions Million tCO2e -Yearly (the proxy variable of environmental sustainability to uphold ESG practice). COL = Coal consumption GJ, GAS = Natural Gas consumption GJ (Gigajoule or GJ equals one billion joules), GCAP = Generating Capacity Total MW (Megawatts), USER = Total customer accounts, and ε = error correction terms.

3.3. Econometric approach

This study employs a number of econometric approaches, i.e., the Prais-Winsten regression and the Ordinary Least Square (OLS) regression, for a number of reasons. Our data is a short time series dataset; therefore, we must use an approach which can produce strong estimations from the small dataset. [43] Beck and Katz (1996) argue that the Prais-Winsten regression approach is highly efficient in handling short time series data. Second, this method considers standard error terms in estimation. Third, if there are heteroscedasticity and autocorrelation issues in the data, this method is still able to avoid these disturbances [44]. It can also take into consideration any serial correlation that may exist in a time series dataset, although the process is more complicated. Transforming a data series to address first-order autoregressive (AR1) correlation problems and making the residual stationery, using a generalized linear square (GLS) regression function and producing more accurate and dependable forecasts. This method also provides “d” statistics, and the value ought to be between 0 and 4, where 0 denotes a complete negative correlation, 2 shows no autocorrelation, and a value going in the direction of 4 implies a positive correlation. This study also utilizes the OLS method to check the robustness.

4.1. Descriptive statistics and correlation

The descriptive statistics presented in Table 1 offer an overview of the dataset’s key variables. The average carbon dioxide emissions (CO2) (the proxy variable of environmental sustainability to uphold ESG practice) are approximately 5.47, with a standard deviation of around 0.18. The range of CO2 values spans from a minimum of 5.27 to a maximum of 5.76. For the variable “coal,” the average is 8.59, with a standard deviation of about 0.06, ranging from 8.51 to 8.66. The variable “gas” has an average value of 8.25, a standard deviation of roughly 0.04, and values ranging from 8.21 to 8.31. In the case of the “user” variable, the average is 6.96, with a standard deviation of approximately 0.02 and values ranging from 6.93 to 6.99. Lastly, the variable “gcap” has an average value of 4.09, a standard deviation of about 0.05, and values ranging from 4.03 to 4.15. These statistics provide a summary of the central tendency and dispersion of each variable.

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Table 1. Descriptive Statistics.

https://doi.org/10.1371/journal.pone.0327744.t001

Table 2 shows a correlation matrix, which provides insights into the relationships between variables. For example, CO2 emissions (the proxy variable of environmental sustainability to uphold ESG practice) are positively correlated with coal and gas, negatively correlated with USER, and strongly negatively correlated with electric generation capacity. These correlations help understand how changes in one variable may be associated with changes in others.

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Table 2. Correlation Matrix.

https://doi.org/10.1371/journal.pone.0327744.t002

Table 3 provides values of the multicollinearity test by the Variance Inflation Factor (VIF) test. The values of multicollinearity are mostly low to moderate. At the same time, the Table 2 a few of the independent variables show high correlations which often raise the issue of multicollinearity. However, before the VIF test, we transform the data to logarithmic form which actually occasionally helps in addressing moderate multicollinearity problems. And Table 4 shows mostly low multicollinearity which barely affects the reliability of the estimations of the study.

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Table 3. Multicollinearity Test.

https://doi.org/10.1371/journal.pone.0327744.t003

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Table 4. Model Set Test for Prais–Winsten AR(1) Regression.

https://doi.org/10.1371/journal.pone.0327744.t004

4.2. Main results

Table 4 provides statistical information regarding the model fit of the Prais–Winsten AR(1) regression according to equation (1). The value of the F-statistic is very large (“> 99999.00”), indicating high statistical significance of the overall model. The value of R-squared is 0.97, which indicates a perfect fit, suggesting that the model explains all the variability in the dependent variable. The adjusted R-squared penalizes the model for including irrelevant predictors. Like R-squared, it is 0.96, indicating a perfect fit. In summary, the model appears to have a perfect fit (R-squared and Adj R-squared), and the F-statistic is highly significant, suggesting that the regression model is a good fit for the data.

Table 5 provides the results of the study from the Prais–Winsten AR(1) Regression. The coefficient of coal is 0.670, indicating that a one-unit increase in the use of coal as a raw material for electricity regeneration is associated with an increase of 0.670 units of CO2 emissions (the proxy variable of environmental sustainability to uphold ESG practice). The coefficient is highly significant at a 1% significance level. This indicates that the use of coal as a raw material by electricity production companies in Malaysia significantly contributes to CO2 emissions. Though Malaysia highly prioritizes environmental conservation and a low carbon emissions policy, the electricity production sector still has not yet implemented the low carbon emissions policy, especially coal use, due to time constraints. This result is highly in line with the number of prior findings [45, 46].

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Table 5. Prais–Winsten AR(1) Regression with Iterated Estimates.

https://doi.org/10.1371/journal.pone.0327744.t005

Table 5 also shows the role of gas used as the raw material in electricity production companies in Malaysia. The coefficient of gas use is 1.195, suggesting that a one-unit increase in the use of gas is associated with an increase of 1.195 units of CO2 emissions. Besides, the coefficient of gas use is strongly significant at a 1% significance level. This result is consistent with a number of previous studies [47]. These studies also report that the use of natural gas is also highly responsible for generating huge amounts of CO2, in addition to coal, to produce electricity. Though coal and natural gas are highly responsible for CO2 emissions in Malaysia, the amount of emissions is decreasing day by day, which we can find in Fig 1 due to the government’s promise to reduce the CO2 emissions.

At the same time, the government of Malaysia has also taken a number of initiatives to minimize the dependence on coal and gas to generate electricity. First, the government formulated low carbon emissions policies, Vision 2030, which will help the country minimize the use of minerals in electricity generation. Second, focus more on renewable energy sectors, i.e., solar, wind, and biomass, to diversify its energy mix and reduce dependence on fossil fuels. Following the new energy use policy, the electricity production sector of Malaysia is also diversifying their production. The electricity production sector also put ESG practices in place, and they have strong internal regulatory cells to maintain social and environmental responsibilities and contribute to sustainable development [48]. Third, the sector also promotes green technologies and green energy to reduce greenhouse gas emissions and uphold ESG procedures.

The table also provides the coefficients for the control variables. The coefficient of Gcap (electricity generation capacity) is −0.882, indicating that a one-unit increase in “Gcap” is associated with a decrease of 0.882 units of CO2 emissions. This is a very interesting result; if the current electricity demand rises and the sector increases the production but for the new production, the CO2 emissions will decrease instead of rising. This means the newly adopted low CO2 policies, renewable energy sources, and green technologies started to contribute to reducing CO2 emissions and strengthening ESG practices. However, some studies come up with the same findings [49].

Finally, the coefficient of User (number of users) is 1.266, suggesting that, holding other variables constant, a one-unit increase in the “User” is associated with an increase of 1.266 units of CO2 emissions. This is a bit alarming; if the use of electricity rises, the CO2 emissions also rise. It may be brought on by the recent intensive industrialization and the introduction of various internet-based devices. As a result, the industry that produces electricity must turn to gas and coal for further production because the amount of energy produced from renewable sources is insufficient to fulfil demand. While coal and gas still significantly contribute to CO2 emissions, this could be the reason for the significant increase of CO2 from one unit increase of User.

The Prais-Winsten AR(1) regression also provides the Durbin–Watson statistic along with the coefficients in Table 6. The Durbin–Watson statistic (original) is 3.333, and the transformed statistic is 1.001. These statistics detect the presence of autocorrelation in the residuals. A value close to 2 suggests no autocorrelation. In this case, the transformed statistic is very close to 2, indicating minimal autocorrelation. Overall, the model fits the data well, with statistically significant coefficients and a low Durbin–Watson statistic suggesting minimal autocorrelation in the residuals. This indicates that the method has the capacity to tackle the abnormal strength of the estimation.

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Table 6. Results from Ordinary Least Squares (OLS).

https://doi.org/10.1371/journal.pone.0327744.t006

4.3. Robustness check with the Ordinary Least Square (OLS)

Table 6 presents the results from the OLS regression analysis followed by equation (2) as a robustness check. The coefficients represent that the use of coal and gas is associated with a significant increase in CO2 emissions. The results of both the variables from the OLS estimation are consistent with the results from the results of the Prais–Winsten regression presented in Table 4. At the same time, the electricity generation capacity (cap) and the number of users (user) exhibit significant negative and positive impacts on CO2 emissions, respectively, which is also harmonious with the results of Table 6.