Profitability and environmental sustainability: analyzing the energy sector's role in Saudi Arabia's sustainable development Open Access

Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy), SDG 8 (Decent Work and Economic Growth) and SDG 13 (Climate Action), place the energy sector at the center of global sustainability debates (United Nations, 2015). Saudi Arabia, as the world's leading oil producer, faces the pressing challenge of reconciling profitability with environmental responsibility while advancing its Vision 2030 reforms (Khoirunnisa & Nurhaliza, 2024). This paper positions itself within this global and national context by examining how profitability drivers in the Saudi energy sector interact with emissions reduction and energy efficiency. By doing so, it addresses the broader sustainability challenge of decarbonizing while maintaining economic growth, offering empirical evidence that is both sector-specific and policy-relevant.

Saudi Arabia's energy sector plays a critical role in the nation's economic landscape, heavily influenced by oil revenues that drive profitability, but simultaneously pose significant environmental challenges. According to Climate Action Tracker (2023), Saudi Arabia's efforts to decarbonize its economy are currently insufficient, with projected emissions expected to rise significantly by 2030. This situation highlights a key tension between the energy sector's profitability and the urgent need for substantial reductions in carbon emissions. Despite the ambitious renewable energy targets outlined in Saudi Arabia's Vision 2030, actual progress has been minimal, suggesting that the profitability from fossil fuel operations continues to dominate over investments in sustainable alternatives (GlobalData, 2023).

Furthermore, Saudi Arabia's heavy reliance on oil revenues constrains its efforts toward economic diversification, creating a conflict in which profitability from oil extraction limits incentives for reducing carbon emissions (CMS Law, 2023). This dynamic is crucial for understanding how energy sector profitability influences environmental sustainability. While Saudi Arabia intends to obtain 50% of its electrical power from renewable sources by 2030, investment levels have yet to meet these targets due to the prevailing dominance of oil in the energy supply (International Trade Administration, 2023). Additionally, Saudi Arabia has implemented a Circular Carbon Economy (CCE) strategy to integrate sustainability into its economic planning while maintaining profitability in the energy sector. This framework represents a strategic shift that could influence future profitability dynamics within the sector (SCAVO, 2023).

Several studies have explored the link between energy sector dynamics and environmental outcomes, particularly in the context of carbon emissions and energy efficiency (Alkhathlan & Javid, 2013). However, the specific interaction between profitability and environmental performance in Saudi Arabia remains understudied. The current study seeks to fill that gap by investigating the relationship between profitability and environmental sustainability metrics such as carbon emissions, renewable energy investments and energy efficiency.

Despite the critical role of Saudi Arabia's energy sector in driving economic growth, its heavy reliance on fossil fuels poses significant environmental challenges, particularly in balancing profitability with emission reduction. This creates the central research problem addressed in this paper: how can energy sector profitability align with environmental sustainability within the framework of Saudi Arabia's Vision 2030? Accordingly, the study aims to (1) analyze the effect of profitability on environmental outcomes, specifically carbon emissions and energy efficiency; (2) assess the role of macroeconomic and structural factors in shaping this relationship; and (3) generate insights to support policy and managerial decision-making. The novelty of this research lies in its dual focus on profitability and sustainability in the Saudi context, offering sector-specific evidence that contributes to global discussions on sustainable energy transition.

In order to address the research gap, our paper seeks to achieve three key objectives:

1. To assess the relationship between energy sector profitability and carbon emissions in Saudi Arabia.

2. To examine how energy sector profitability influences overall energy efficiency.

3. To explore the role of economic factors (GDP growth and population size) as control variables in moderating the impact of energy sector profitability on environmental sustainability.

The relationship between energy sector profitability and environmental sustainability has attracted considerable interest in academic and policy-making spheres, particularly in oil-dependent nations. While existing research provides useful evidence on the connections between energy use, economic growth, and environmental outcomes, the profitability dimension remains relatively underexplored and often treated only indirectly. To enhance clarity, the literature can be organized into two broad themes: (1) international evidence and (2) Saudi Arabia–specific evidence, followed by a synthesis of the identified research gap. Presenting the review in this way also allows the findings to be summarized in a SmartArt chart (Figure 1), which highlights global studies, Saudi-specific studies and the gap that motivates the present research.

A paper published by Al-Ajlan, Al-Ibrahim, Abdulkhaleq, and Alghamdi (2006) analyzes the main challenges facing Saudi Arabia's electrical power sector in implementing sustainable development. It also reviews energy conservation efforts in developed countries and highlights Saudi Arabia's current programs to raise awareness and encourage participation. Although valuable for understanding policy directions, the study does not integrate the profitability aspect of the energy sector, making it less relevant to the specific research question pursued here. It proposes strategies and policy measures for energy conservation in Saudi Arabia (Al-Ajlan et al., 2026).

Apergis and Payne examined renewable energy use and economic growth. They focused on a sample of 22 OECD countries (the Organization for Economic Cooperation and Development) from 1985 to 2005. They found that there were positive and significant relationships between real GDP, renewable energy use, gross fixed capital formation and the labor force in long-run equilibrium. Furthermore, in both the short and long term, Granger-connection tests showed a bidirectional causality between renewable energy usage and economic development. The study revealed a significant relationship between renewable energy use and economic growth in OECD countries, highlighting the positive impacts of renewable energy on the economy. However, their analysis does not incorporate profitability in the energy sector, nor does it explore how financial performance may shape renewable energy investment or efficiency improvements in oil-dependent countries such as Saudi Arabia (Sadorsky, 2010; Apergis & Payne, 2010).

Sadorsky looked at the influence of financial development on energy consumption. The study focused on 22 of the emerging countries from 1990 to 2006. The author used generalized method of moments estimation methods. The study found a significant and positive relationship between financial development, measured by the stock market variables such as market capitalization, value traded, turnover and energy consumption. This highlights the connection between finance and energy, but the study neither isolates the role of profitability in the energy sector nor investigates the environmental consequences of increased consumption. This leaves a conceptual and empirical gap regarding how profitability may contribute to or hinder sustainability efforts in the energy industry (Sadorsky, 2010; Apergis & Payne, 2010).

Alkathlan and Javid investigated the association among economic growth in the economy, emissions of carbon and consumption of energy at both aggregate and disaggregate levels in Saudi Arabia. They found that emissions of carbon increased alongside the growth in income per capita in Saudi Arabia. Also, in the gas consumption model, they found that the possibility that CO2's income elasticity was negative. In contrast, the oil consumption model showed a positive CO₂'s income elasticity. Their research revealed that gas and oil were more polluting than electricity. While this provides important context for Saudi Arabia's energy and environmental profile, it does not assess the link between profitability and these outcomes, leaving unanswered whether financial performance in the energy sector exacerbates or alleviates pollution (Alkhathlan & Javid, 2013).

Al-mulali, Lee, Mohammed, and Sheau-Ting (2013) examined the long-term, bidirectional link between consumption of energy, emissions of carbon and growth in economics. They focused on Latin American and Caribbean nations from 1980 to 2008. The findings indicated that 60% of the sample studied showed a positive two-way interaction between these variables, while the remaining sample presented mixed results. In order to reduce energy waste, the study suggested boosting energy efficiency, growing the proportion of green energy and encouraging energy conservation based on its findings. Although the study provides valuable evidence on dynamics in developing countries, it cannot be directly applied to Saudi Arabia's case, where sectoral profitability and dependence on oil revenues strongly condition environmental performance (Al-mulali et al., 2013).

A study by Alshuwaikhat and Mohammed (2017) explores the sustainability content of Saudi Arabia's 2030 Vision and National Transformation Program (NTP). Using the Sustainable Society Index (SSI), they assess the alignment of these initiatives with sustainability goals. The paper concludes that the success of the 2030 Vision depends on stakeholder involvement and effective progress assessment mechanisms for sustainability. However, profitability in the energy sector is not considered, despite its central role in financing Vision 2030 initiatives (Alshuwaikhat & Mohammed, 2017).

Chao-Qun-Ma conducted a study examining the impact of foreign direct investment, economic growth and energy intensity on carbon emissions in China's manufacturing industry from 2000 to 2013. The study found that foreign direct investment and economic growth significantly increased emissions, particularly in high-emission provinces. Moreover, it found that reducing energy intensity did not lower emissions due to the energy rebound effect (Ma et al., 2019). While insightful for China's industrial sector, these results cannot be transferred to Saudi Arabia without adjustment, as the profitability of the energy sector plays a unique and decisive role in shaping environmental outcomes.

Anis Omri explored the factors affecting environmental sustainability in Saudi Arabia, focusing on whether the EKC exists and if financial development, FDI and trade openness improve the environment. Findings show that (1) income, financial development, FDI and trade worsen environmental degradation; (2) the EKC holds true for Saudi Arabia; and (3) environmental degradation is sensitive to these factors (Omri, Euchi, Hasaballah, & Al-Tit, 2019). Although significant, the study does not analyze profitability within the energy sector as an explanatory factor, leaving open questions about how financial performance interacts with these broader economic drivers.

A study by Amran, Amran, Alyousef, and Alabduljabbar (2020) aims to review the current status, growth, potential, resources, sustainability performance and future prospects of renewable and sustainable energy (RnSE) technologies in KSA according to Saudi Vision (2030). The usage of RnSE resources reduces the reliance on oil and natural gas and introduces RnSE from clean and maintainable resources for the Saudi national economy (Amran et al., 2020). This aligns with Saudi Arabia's policy direction, but the profitability of the energy sector and its influence on supporting or delaying renewable adoption is not incorporated.

In summary, although extensive research has examined the relationships between energy consumption, economic growth and environmental sustainability, there remains a substantial gap in understanding the role of profitability as a determinant of sustainability outcomes. Prior studies on Saudi Arabia focus largely on growth, trade, or policy frameworks but have not assessed whether energy sector profitability drives or constrains efforts to reduce emissions and improve efficiency. This study addresses that gap by analyzing the effect of profitability on carbon emissions and energy efficiency in Saudi Arabia. Figure 1 presents this review in SmartArt format, grouping international evidence, Saudi-specific evidence and the identified research gap. The following section introduces the theoretical framework underpinning this analysis.

A strong theoretical foundation is essential to understand the mechanisms through which energy sector profitability may influence environmental sustainability. This study is guided by agency theory and stakeholder theory. Each offers a distinct perspective on how financial performance can shape environmental outcomes, and together they provide a comprehensive conceptual framework.

Agency theory highlights the potential conflict of interest between managers and shareholders (Jensen & Meckling, 1976). In the energy sector, managers may pursue short-term profit maximization to demonstrate immediate financial success, potentially at the expense of long-term sustainability. For example, profitability could be diverted toward expanding conventional fossil fuel production rather than investing in cleaner technologies. However, when corporate governance mechanisms are effective, agency theory suggests that profitability can instead be aligned with shareholder and societal interests, supporting transparency, accountability and sustainability initiatives. This perspective emphasizes the role of governance in determining whether profitability enhances or undermines environmental outcomes.

Stakeholder theory extends the analysis beyond shareholders, recognizing that firms operate within a network of stakeholders, including governments, communities, investors and consumers (Freeman, 1984). In this view, profitability strengthens a company's capacity to address stakeholder demands, such as reducing emissions or improving efficiency. In resource-dependent economies like Saudi Arabia, stakeholders increasingly expect energy companies to contribute to national sustainability goals, including Vision 2030. Profitability provides the means to meet these expectations, but failing to balance profit with stakeholder concerns risks reputational damage and social disapproval. Stakeholder theory thus explains the social pressures that link financial performance with sustainability actions.

In combination, agency theory and stakeholder theory provide a comprehensive lens for understanding the role of profitability in shaping environmental outcomes. Agency theory highlights how profitability may either be used responsibly under sound governance or diverted toward short-term gains that undermine sustainability. Stakeholder theory emphasizes that profitability equips firms with the capacity to respond to growing societal and institutional demands for accountability, particularly in contexts like Saudi Arabia, where sustainability is embedded in national strategies such as Vision 2030. By integrating these perspectives, this study positions profitability as a critical factor that can either advance or hinder progress toward environmental sustainability, depending on governance quality and stakeholder pressures.

Previous studies established a relationship between profitability and environmental sustainability in the energy sector, revealing various effects on emissions and efficiency. However, there is a need to investigate how these dynamics apply specifically to Saudi Arabia's energy sector. Building on agency theory and stakeholder theory, this study conceptualizes profitability as both a potential driver and constraint of sustainability. These theoretical perspectives provide the foundation for testing the following hypotheses:

1. H1: Higher energy sector profitability leads to lower carbon emissions.

2. H2: Higher energy sector profitability improves energy efficiency.

In this study, we structured the methodology in a systematic manner (Figure 2). We first selected the sample and identified the data sources. Next, we defined and transformed the variables to prepare them for analysis. We then applied appropriate econometric techniques to test the hypotheses, employing regression models to examine the relationships among variables.

In our study, we aimed to collect data spanning from 1990 to 2023 for all variables involved in the analysis. However, due to limitations in data availability, we were able to gather complete data for the period between 2010 and 2020, with some variables extending until 2022 or 2023. The time span of the available data is still sufficient to perform a comprehensive statistical analysis, even though the lack of previous data may limit the long-term trend analysis. Since much of the literature and policy shifts have occurred in the past decade, the available data reflects a period of significant change in the energy sector, particularly regarding profitability, environmental impact and technological advancements (Appendix I).

This study applies a static ordinary least squares (OLS) regression model. The dataset covers annual observations from 2010 to 2023, which provides a moderate time horizon but not a long enough series to robustly estimate lag-dependent models such as ARDL, VAR, or VECM that require larger samples to ensure stability in lag selection and parameter estimation. This study's research model, which focuses on two important dependent variables, energy intensity (X2) and carbon emissions (X1), examines the relationship between the profitability of the energy sector and environmental sustainability. Independent variables, including energy productivity, oil revenue, GDP growth and total population, are employed to evaluate their impact on these environmental indicators. The model utilizes log-transformed and differenced data to mitigate non-stationarity, hence providing reliable outcomes. The research seeks to quantify the impact of profitability-driven factors in the energy sector on emissions and energy efficiency by conducting distinct OLS regression models for each dependent variable, thereby elucidating the relationship between economic growth and environmental results. Our study model adheres to the equation stated below to substantiate the proposed hypotheses;

where X1 and X2 serve as the dependent variables, representing the metrics of Environmental Sustainability. Y1 and Y2 serve as the independent variable presenting for Profitability. C1 and C2 represent GDP growth and Population size, used as control variables. α indicates the intercept, β₁signifies the coefficient for Energy Productivity, γ₁ and γ₂ indicate the coefficients for GDP growth rate and population growth rate respectively, and ε represents the error term. “t” in all equations is the notation of time period. Figure 3 presents the research model.

Table 1 outlines the operational definitions of the key variables used in the analysis. We took dependent variables as proxies for Environmental Sustainability. Our independent variables are proxies for Profitability. This study also dealt with two control variables, GDP Growth and Population Growth.

To address the varying measurement scales of the variables in this study, we transformed the data series into logarithmic form. This transformation allows for a more standardized comparison of relationships among variables, reducing the influence of extreme values and stabilizing variance (Introductory Econometrics, n.d.) (Appendix II).

In Table 2, the descriptive statistics of the log-transformed variables reveal essential characteristics of the dataset (Cooksey, 2020). The positive mean and median values for all variables except logY2 indicate increasing trends over time. The exception is logY2, which shows negative mean and median values, implying that, on average, the values for this variable were below zero, indicating a decrease or negative trend during the study period. The negative skewness values indicate that the distributions of these variables are slightly left-skewed. logX1 shows moderate skewness, suggesting a stronger left tail, while logX2, logC1 and logC2 have mild skewness, indicating near-symmetry but with a small bias toward lower values. LogY1 shows moderate positive skewness, suggesting that the distribution is slightly skewed to the right. logY2, with a skewness of 0.15, exhibits very mild positive skewness, indicating a nearly symmetric distribution with only a slight right tail. The kurtosis values of logX1, logX2, logY1 and logC1 being above 2 suggest that these variables have leptokurtic distributions. This means their distributions have heavier tails and a sharper peak compared to a normal distribution. Whereas the kurtosis values of logY2 and logC2 being below 2 suggest that these variables have platykurtic distributions. This means their distributions are flatter than a normal distribution, with thinner tails and fewer extreme values. The Jarque-Bera test values for logX1 (1.67), logX2 (0.61), logY1 (0.69), logY2 (1.16), logC1 (0.60) and logC2 (1.16) are all below the critical value of approximately 5.99 for a 5% significance level. This means that for each of these variables, the JB test fails to reject the null hypothesis of normality. This supports the use of parametric statistical methods for further analysis.

We applied augmented Dickey-Fuller (ADF) test and checked stationarity in our time series data (Appendix II) before running correlation or regression in order to ensure stable statistical properties, preventing spurious results (Bierens & Guo, 1993). The stationarity analysis (Appendix III) indicates mixed orders of integration across the variables (Table 3). Appendix IV shows the transformation of the series according to the unit root test results.

To ensure stationarity, we transformed our variables according to the results of Table 3. Hereafter we applied correlation among transformed variables, which allows us to assess the initial relationships and strength between the dependent and independent variables (Rezaee, Aliabadi, Dorestani, & Rezaee, 2020).

Table 4 shows results of correlation analysis. LogX1(2) and logC1(0), logX2(1) and logY1(1), logX2(1) and logY2(1) are showing moderate positive correlation. This indicates that as GDP grows, CO₂ emissions also tend to rise, explaining that increased economic activities lead to higher energy consumption and emissions. Similarly, as energy productivity increases, energy intensity tends to increase as well, which reflects that improvement in energy efficiency leads to more optimized energy use relative to economic output. Also, as oil revenue increases, energy intensity increases significantly, proving that higher oil revenues can lead to increased energy production and consumption, driving up energy intensity.

LogX1(2) and logY2(1), logX1(2) and logC2(2), logX2(1) and logY2(1), logX2(1) and logC1(0) are showing negative correlations. When oil revenue increases, CO₂ emissions decrease. This could reflect more efficient energy production methods or an increase in alternative energy investments as oil revenues rise. Higher population growth does not lead to higher CO₂ emissions. This might reflect improvements in energy efficiency or a shift towards cleaner energy as the population grows. When oil revenues and GDP increase, energy intensity decreases. The reason could be more efficient use of energy when oil revenues and GDP are higher, likely because of investments in more efficient technologies or processes.

By using ordinary least squares (OLS) regression, we can quantify the effect of independent variables, such as profitability indicators, on environmental outcomes like carbon emissions and energy efficiency (Burton, 2021).

In Table 5, we presented results of OLS regression for both dependent variables. When X1 (CO₂ Emissions) is dependent variable, energy productivity (Y1) and GDP growth (C1) have a statistically significant impact (p<5%) on CO₂ emissions, indicating a meaningful relationship. The model explains 82% of the variation in CO₂ emissions (R² = 0.82), and the adjusted R² of 0.68 shows that even after adjusting for the number of predictors, the model remains a good fit. The p-value of the F-statistic is significant, showing that the model as a whole is statistically significant. Durbin-Watson statistic (2.03) indicates no autocorrelation in the residuals, meaning the OLS assumptions hold.

When X2 (Energy Intensity) is the dependent variable, Oil revenue (Y2) is statistically significant, indicating that it has a strong effect on energy intensity. The model explains 80% of the variation in energy intensity (R² = 0.80), and the adjusted R² of 0.65 suggests a good overall fit for the model. P-value of F-statistics is 0.05, indicating that the model as a whole is statistically significant. Durbin-Watson statistic (1.58) is slightly below 2; this suggests mild positive autocorrelation. However, it is not severe enough to raise significant concerns about model validity.

Both models demonstrate strong fits, with significant predictors and high R² values, indicating that the relationships between the variables are well captured. The results are reliable, and further diagnostic tests may not be necessary given the robustness of the findings.

By calculating VIF, we quantified the degree of multicollinearity among our independent variables (Thompson, Kim, Aloe, & Becker, 2017).

In Table 6, the centered VIF values for all independent variables being less than 2 in both models (with X1 and X2 as dependent variables) indicate that there is no significant multicollinearity among the predictors. This means that the independent variables are not strongly correlated with each other, and their effects on the dependent variables (CO₂ emissions and energy intensity) can be reliably estimated without inflated variances.

Saudi Arabia's energy sector must reconcile profitability with environmental performance under Vision 2030. We operationalize profitability with two sector-level proxies: energy productivity (logY1, TES/GDP) and oil-revenue share in total fiscal revenues (logY2). We assess environmental outcomes through CO₂ emissions (logX1) and energy intensity (logX2, energy consumption per unit of GDP), controlling for GDP growth (logC1) and population (logC2). The core problem is whether profitability, measured as productivity gains and fiscal strength from hydrocarbons, supports decarbonization (lower CO₂) and more efficient energy use (lower intensity). The emissions model exhibits a strong fit (R² ≈ 0.82, Adj. R² = 0.68; DW = 2.03; p < 0.05) (Figure 4). We find a significant association between higher energy productivity (Y1) and lower CO₂ emissions, consistent with a technical-efficiency channel: when the economy produces more output per unit of energy supplied, the carbon burden per unit of activity declines. GDP growth (C1) is also statistically significant and, in line with scale effects, is associated with higher emissions, underscoring that expansion in aggregate activity can offset efficiency gains. Oil-revenue share (Y2) and population (C2) are not significant in this model, suggesting that fiscal dependence on oil and demographic scale, by themselves, do not explain year-to-year emissions variation once productivity and growth are accounted for. The energy-efficiency model also shows good explanatory power (R² = 0.80; Adj. R² = 0.65; p = 0.051; DW = 1.58) (Figure 4). Here, oil-revenue share (Y2) is significantly associated with lower energy intensity, indicating that periods with stronger hydrocarbon revenues coincide with improvements in efficiency—plausibly through budgetary space for maintenance, fuel-mix optimization, or incremental technology upgrades. By contrast, energy productivity (Y1), GDP growth (C1) and population (C2) are not statistically significant in the intensity model once Y2 is included, implying that fiscal capacity rather than macro scale or productivity levels primarily tracks efficiency changes over the sample. This study shows that profitability in the Saudi energy sector influences environmental outcomes through two different channels. First, higher energy productivity (Y1) helps reduce CO₂ emissions by making energy use more efficient relative to output. Second, higher oil-revenue share (Y2) is linked to improvements in energy efficiency, as strong revenues provide resources for efficiency-related investments. These findings highlight that profitability does not affect all environmental outcomes in the same way: what drives lower emissions is not necessarily what improves efficiency. This distinction adds new evidence to the debate on whether profitability and sustainability can align in oil-dependent economies. The results suggest that policy should combine productivity-based reforms with revenue-driven efficiency programs to balance growth and sustainability.

1. Independent variables: Energy Productivity (Y1): The relationship between energy productivity and carbon emissions is statistically significant, indicating that higher energy productivity leads to a reduction in carbon emissions. Oil Revenue (Y2): The relationship between oil revenue and carbon emissions is not significant. This indicates that the profitability generated from oil revenues does not have a direct impact on reducing carbon emissions in this context.

2. Control variables: GDP Growth (C1): The positive and significant relationship between GDP growth and carbon emissions suggests that increased economic activities lead to higher energy consumption and emissions. Population Growth (C2): The relationship between population growth and carbon emissions is insignificant, indicating that population changes in this context do not directly influence emissions, possibly because population growth alone does not account for the energy sector's emissions dynamics.

3. Result: Hypothesis H1 is partially supported.

1. Independent variables: Energy Productivity (Y1): The relationship between energy productivity and energy intensity is not significant, suggesting that energy productivity improvements do not necessarily result in better energy efficiency. Oil Revenue (Y2): Oil revenue (Y2) shows a significant positive relationship with energy efficiency, indicating that higher profitability improves energy intensity. This could be because the sector invests more in technologies or practices that optimize energy use as profitability rises.

2. Control variables: GDP Growth (C1): The relationship between GDP growth and energy efficiency is not significant, suggesting that economic growth alone is not a strong predictor of changes in energy efficiency in the energy sector. Population Growth (C2): The relationship between population growth and energy intensity is also insignificant, indicating that population growth does not directly impact energy efficiency in this context.

3. Hypothesis H2 is partially supported.

By evaluating how energy sector profitability influences carbon emissions and energy efficiency, this work provides actionable guidance to align financial outcomes with broader societal and environmental objectives. Enhanced energy efficiency can reduce operational costs and emissions, paralleling findings that energy-efficient practices drive cost savings and competitiveness (Bensouda, Benali, & Zizi, 2024). This also resonates with principles of creating shared value, where corporate financial performance and societal well-being go hand in hand (Menghwar & Daood, 2021). Within Saudi Arabia's national context, this study supports the Vision 2030 goal of transitioning to a more sustainable, diversified economy, with its implications helping shape energy policy and corporate behavior in alignment with SDG 7, SDG 13 and equitable economic development (Thomas, 2025).

This study explored the relationship between energy sector profitability and environmental sustainability in Saudi Arabia, focusing on two key aspects: carbon emissions and energy efficiency. Using Ordinary Least Squares (OLS) regression models, we analyzed the impact of various financial and demographic variables on these environmental indicators. The results indicate that energy productivity and GDP growth significantly reduce carbon emissions, supporting the notion that higher economic activity and energy sector efficiency can contribute to environmental sustainability. However, oil revenue and population growth were found to have no significant effect on emissions.

On the other hand, oil revenue showed a strong, positive impact on energy efficiency, suggesting that higher sectoral profitability, driven by oil revenues, improves energy use in relation to economic output. Other factors like energy productivity, GDP growth and population had no significant influence on energy efficiency.

The findings of this study demonstrate that certain aspects of energy sector profitability can enhance environmental sustainability, particularly through improvements in energy efficiency. However, the role of oil revenues in reducing carbon emissions appears limited. This highlights the importance of integrating both profitability and environmental considerations in policy frameworks to achieve long-term sustainability in Saudi Arabia's energy sector. Some actionable policy directions are as follows:

1. Since energy productivity was found to be a significant driver of environmental sustainability, policymakers should prioritize technological innovation, efficiency-enhancing measures and incentives for firms adopting energy-efficient practices.

2. The continued reliance on oil revenues highlights the need for fiscal strategies that channel a portion of oil rents into renewable energy projects and low-carbon technologies. This can reduce dependence on fossil fuels while maintaining profitability.

3. Support circular carbon economy (CCE) strategies. Strengthening implementation of Saudi Arabia's CCE framework can balance profitability and emissions reduction by integrating carbon capture, recycling and reuse into sector operations.

4. Encourage corporate ESG (environmental, social and governance) integration. Regulatory authorities could require companies listed on Tadawul to publish ESG disclosures linked to profitability indicators, ensuring greater transparency and accountability.

5. Policies should continue to tie profitability to sustainability goals, ensuring that profitability-driven growth contributes to Vision 2030's renewable energy targets and to global SDG commitments (SDG 7, 8 and 13).

While this study contributes valuable insights into the profitability–sustainability nexus of Saudi Arabia's energy sector, it remains bounded by its specific focus on profitability drivers, emissions and energy efficiency within the Saudi context. Future research can expand this scope by integrating wider sustainability dimensions explored in global studies. For instance, ecological footprint and natural resource rents (Latin America), agricultural productivity and poverty alleviation (China), and efficiency variations across agro-climatic zones (India) provide cross-country evidence that can strengthen comparative analyses. Similarly, insights from financial development for energy access in the MENA region and corporate SDG adoption studies in India highlight important pathways where financial systems and governance practices intersect with environmental outcomes. Finally, empirical works on balancing environmental degradation and sustainable development in China underscore the global relevance of reconciling profitability with sustainability. Incorporating these dimensions in future studies will bridge gaps in literature and create more robust, comparative frameworks that link sectoral profitability with broader Sustainability and Development Goals.

The supplementary material for this article can be found online.