The Impact of Multidimensional Risk Factors on Economic Growth as a Proxy for Sustainable Development Goals in Saudi Arabia: Alignment with Saudi Vision 2030

Abstract

This research experimentally investigates the association between multidimensional risk factors and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs). This research experimentally investigates the correlation between multidimensional risk variables and economic growth, quantified by GDP as a partial indicator of advancement towards economically relevant Sustainable Development Goals (SDGs) in Saudi Arabia, particularly in alignment with the objectives of Saudi Vision 2030. This study utilizes annual data from 1990 to 2024 and employs the Autoregressive Distributed Lag (ARDL) bounds testing approach to examine the short-run and long-run relationships between economic growth, as measured by GDP, and five key risk dimensions: governance effectiveness, financial development, environmental pressure, human capital, and oil price volatility, which act as proxies for risk dimensions. The main contribution of this study is the integration of these governance, financial, environmental, human capital, and oil price risk factors into a single ARDL framework for Saudi Arabia from 1990 to 2024, using GDP growth as a proxy for progress toward SDGs within the Saudi Vision 2030 context, addressing gaps in prior studies that focus on individual determinants. The empirical evidence indicates a long-term cointegration relationship among the variables. Our findings indicate that government effectiveness and investment in human capital are important positive factors associated with long-term economic growth, thereby validating the importance of institutional improvements and educational expenditures. In contrast, fluctuations in oil prices and environmental pressures are linked to adverse association, highlighting issues related to resource dependency and ecological degradation. Financial development exhibits a negative long-run association, indicating potential inefficiencies or diminishing returns in loan distribution. The study offers essential policy recommendations, such as expediting digital governance reforms, allocating financial resources to non-oil SMEs (SDG 8), aligning educational curricula with labor market demands, and implementing stricter environmental regulations to separate economic growth from emissions.

1. Introduction

Nations must navigate a complex landscape of risks that threaten economic stability and long-term prosperity in the pursuit of sustainable development. While SDGs are multidimensional, this study uses GDP growth as a proxy primarily for economic sustainability (e.g., SDG 8), acknowledging limitations in capturing full SDG aspects like inequality or biodiversity; future work could incorporate composite SDG indices. These risks are multifaceted, encompassing institutional, economic, environmental, social, and external dimensions that can interact to amplify vulnerabilities or hinder progress toward goals like the Sustainable Development Goals (SDGs). Saudi Arabia’s challenge is framed within the context of Saudi Vision 2030, which seeks to diversify the economy, improve government efficiency, and promote environmental sustainability. The shift from an oil-dependent economy to a diversified one requires a comprehensive understanding of the various risk factors, including institutional quality and external market shocks, that influence development outcomes. Despite notable advancements, Saudi government entities encounter various risks that may hinder the achievement of the Sustainable Development Goals (SDGs). To tackle these, this research examines five risk dimensions, chosen for their relevance to Saudi Arabia. It recognizes that it employs risk-related growth drivers (using macro indicators as substitutes for governance, financial, environmental, human capital, and external risks) instead of direct risk management or enterprise risk management (ERM) indices, framing these drivers as indicators of SDG advancement within the Saudi Vision 2030 framework (refer to studies on ERM in sustainability contexts of [1,2]). These dimensions are selected as they correspond to Vision 2030 pillars (vibrant society, prosperous economy, ambitious nation) and address gaps in integrated analyses, enabling a holistic assessment of their interaction on sustainable development. This study employs macro-level proxies for risk factors instead of direct indicators of risk management effectiveness (e.g., ERM indices); human capital is conceptualized as an investment asset that alleviates social risks such as skill shortages, although subsequent research could integrate volatility metrics for enhanced risk evaluation.

These include proxies for risk dimensions: low government effectiveness as governance risk (uncertainty in policy execution); financial development as financial risk (potential inefficiencies); high CO₂ as environmental risk (degradation threats); low human capital investment as social risk (skill gaps); and oil price fluctuations as external risk (volatility).

The volatility of economic growth in oil-dependent economies requires a thorough examination of the interplay among various risk categories and their collective influence on sustainable growth. Existing literature has thoroughly examined individual determinants of growth, including government expenditure and financial development. There is a deficiency of comprehensive studies that incorporate the specific risk dimensions of governance, financial, environmental, social, and external factors into a unified econometric framework, particularly within the context of Saudi Arabia, extending to 2024. No previous research has concurrently modeled governance effectiveness, financial development, environmental pressure, human capital, and oil price volatility within a single ARDL framework for Saudi Arabia; however, partial frameworks addressing oil volatility or governance independently are present in other oil-dependent economies such as Nigeria and GCC states. This study aims to address the gap in understanding the distinct short-term dynamics and long-term equilibrium associations of these risks, which is essential for formulating precise policy interventions through advanced time-series analysis. The main aim of this study is to empirically investigate the association between risk management and sustainable development in Saudi Arabia over both the short- and long-term, spanning from 1990 to 2024. This research employs the ARDL methodology to analyze the short-run and long-run associations of government effectiveness, financial development, environmental pressure, human capital, and oil price volatility and GDP growth. This research aims to evaluate the stability of these economic relationships over time. The objective is to present evidence-based policy recommendations that assist Saudi entities in mitigating risks and achieving the Sustainable Development Goals (SDGs). This methodology distinguishes itself from preceding Saudi/GCC studies by concurrently modeling the five risk dimensions using an ARDL approach over a long timeframe (1990–2024), in contrast to previous research that analyzes elements in isolation or use shorter periods or methodologies such as VAR. Key contributions include the following: (i) the theoretical integration of risk factors with alignment to SDG/Vision 2030; (ii) empirical evidence of long-run cointegration and policy-relevant relationships in Saudi Arabia.

2. Literature Review

The study on economic development and its drivers has progressed to highlight multidimensional risk factors, especially in resource-dependent economies such as those in the Middle East. This part looks at important research on each risk dimension: government effectiveness, financial development, environmental pressure, human capital, and oil price volatility. It also points out how these areas are related to one other. By combining results from different situations, it shows similar trends (such the beneficial functions of governance and human capital) and differences between situations (like the mixed impacts of financial development). This shows that there are gaps in integrated studies for Saudi Arabia.

2.1. Economic Growth and Government Effectiveness

A 2024 study by Okunlola et al. [3] examined the impact of government expenditure on real economic growth in ECOWAS states from 1999 to 2021, employing panel cointegration methodologies such as POLS, FMOLS, and DOLS. Their results indicated a significant link between government expenditure and economic growth, with a 1% gain in spending resulting in an estimated 51.3% increase in real GDP. This spending is more effective at promoting development when there is strong control over corruption. This is because it makes sure that resources are used in the best way possible and cuts down on waste. However, more conflict makes it less effective by moving resources around and making situations less stable. Following this, Nguyen and Bui [4] expanded the analysis to institutional quality, examining the influence of corruption control on the relationship between government expenditure and economic growth in 16 emerging markets and developing countries in Asia from 2002 to 2019, employing generalized method of moments and threshold models. The data indicated that both government spending and corruption control alone have a harmful influence on growth; however, their collaboration alleviates this bad effect. Threshold research showed that when corruption control is higher than 0.01, government spending has a positive impact on economic growth. This shows how important the quality of institutions is for the government to work well. These results are consistent with the findings of Nzama et al. [5], who examined the influence of governmental efficacy on trade and financial liberalization by generalized quantile panel regression, using data from 35 countries between 2010 and 2020. The main focus was on transparency, but their literature review brought together previous empirical studies that showed a statistically significant positive link between government effectiveness and economic growth. This was especially true in both low- and high-income economies, where good governance makes bureaucratic processes and policy implementation more efficient, which in turn encourages growth by making countries more connected to each other. Lopes, Packham, and Walther [6] further substantiated this in emerging markets by analyzing the influence of governance quality, particularly government effectiveness as measured by the World Governance Indicators, on real GDP growth in BRICS emerging markets and certain established nations from 1996 to 2018 using panel data regressions. The principal findings indicated that regulatory quality, intricately linked to government effectiveness, exerted a significant positive influence on economic development, particularly in developing countries. Conversely, rule of law demonstrated a negative albeit non-significant link, showing that effective governance institutions, such as skilled policy execution, foster development more significantly in emerging environments. In a similar vein, Mahran [7] looked at how governance affects economic growth using spatial econometric models and a sample of 116 nations from 2017, taking into consideration geographical dependency. The results showed a positive relationship, where a 1% rise in the quality of total governance, which includes things like how well the government works, is linked to a 1% rise in economic growth. This underscores the significance of competent governance in encouraging growth, with regional spillovers demonstrating that the governance of neighboring countries also influences economic success. Putting all of this information together, the current state of the field shows that governance effectiveness is a key risk factor that is positively linked to economic growth. This is especially true in emerging and developing economies, where the quality of institutions affects how well public spending works and lowers uncertainties. Nevertheless, research frequently concentrates on discrete elements like corruption or spending, highlighting a deficiency in the integration of governance with other concerns in oil-dependent environments like Saudi Arabia. This study adds to the body of knowledge by including governance effectiveness as one of four risk factors in a single ARDL framework from 1990 to 2024. This gives us new information on its long-term impact in supporting Saudi Vision 2030’s focus on efficiency and SDG 16.

2.2. Economic Growth and Financial Development

Moving forward from institutional considerations, financial development becomes an important economic risk factor. Research shows that its effects vary depending on the institutional and environmental circumstances. In a literature review from 2024, Akhtar and Rashid [8] looked at how financial development and sustainable development affect each other. They observed that globalization via financial growth helps the economy thrive by boosting production, but it also makes it tougher to fulfill sustainability objectives. Their research shows that sustainable development can be achieved by finding a balance between economic growth and environmental concerns. Darweesh et al. [9] further examined this conflict in a comprehensive 2023 research piece that investigated the impact of financial expansion on carbon emissions. They discovered that in certain southern areas, financial development can help the economy flourish, but it can also make environmental harm worse if green measures are not put in place to counteract it. This shows that long-term financial practices are necessary to balance growth with lower emissions. In addition, Hewage, Pyeman, and Othman [10] conducted a thorough examination in 2022 of both theoretical and empirical data about the relationship between financial development and economic growth. They found that theoretical models clearly show a positive relationship, but empirical results are not always the same because of things like the quality of institutions. This means that financial development usually helps growth, but it needs to happen in good economic conditions. To further this topic, Azmeh and Al-Raeei [11] examined the relationships among finance research, financial development, and economic growth in 2025. They noted that the impact of financial development on growth is contingent upon advancements in financial research. Kayani, U.N. et al. [12], their comprehensive evaluation identified advantageous outcomes in industrialized nations, while the effects in developing regions varied based on capitalization and legislative frameworks. Kayani, Sadiq, and Rabbani conducted a 2023 review and scient metric analysis to investigate the relationship among economic growth, financial development, and carbon emissions. They found that financial development often boosts economic growth, but only if it is paired with green technologies, as it leads to more emissions. This study laid out important research paths for separating growth from harm to the environment using advanced financial tools. These empirical insights link financial development as a risk factor to the model, foreseeing possible inefficiencies in Saudi Arabia’s oil-dependent environment, where balanced financial growth is essential for SDG alignment.

These empirical observations cumulatively suggest that financial development functions as a double-edged sword in contemporary literature: it often fosters growth in stable institutional contexts while potentially intensifying environmental hazards and inefficiencies in emerging nations. A significant deficiency exists in the absence of research that amalgamates financial development with other dangers in resource-dependent countries. This study examines the possibly adverse long-term impacts of financial development in Saudi Arabia’s oil-dependent economy using an ARDL paradigm, emphasizing contributions to SDG 8 through policy suggestions for effective credit allocation.

2.3. Economic Growth and Environmental Pressure

The literature is increasingly investigating the interplay between financial and environmental concerns, particularly focusing on the relationship between economic expansion and ecological deterioration, typically highlighting trade-offs in rising nations. Shi and Smith’s [13] study in Frontiers in Environmental Science looked at panel data from eleven emerging nations from 1990 to 2020. They used sophisticated econometric models like CS-ARDL and MMQR to do this. They found substantial evidence for the Environmental Kuznets Curve, which says that as economies develop, they put more stress on the environment because people use more resources. But as wages rise, this tendency reverses because of better management and legislation. Key findings showed that renewable energy and new technologies may help separate progress from damage over time. On the other hand, relying too much on natural resources puts more stress on the environment. To continue development without damaging the Earth, we need stronger rules and more green investments. Osuntuyi and Lean [14] contextualized this curve concept in their 2022 literature synthesis in Environmental Sciences Europe, where they analyzed the link between economic progress, energy consumption, and environmental degradation among nations classified by socioeconomic status. Their research showed that there is a complex relationship: in high- and upper-middle-income countries, economic growth actually slows down deterioration by encouraging cleaner habits. However, in lower-middle- and low-income countries, it makes pollution and resource strain worse. Energy use always makes the environment worse, but education can help. In wealthy nations, it can help by raising awareness and making things more efficient. In poorer countries, however, where green tech is hard to obtain, it may make things worse. Liu and colleagues’ [15] paper in Science of The Total Environment examined data from 87 tropical nations between 1995 and 2018, concentrating on tropical areas. It looked at how GDP development influences environmental quality by looking at carbon sequestration capability. The findings demonstrated a pronounced adverse effect in low- and lower-middle-income tropical regions, where accelerated expansion results in deforestation and land degradation; however, this impact lessens in upper-middle-income contexts. In mid-tier economies, industry does most of the damage, while agriculture does so in both low- and high-income areas. Services may help make up for some of the damage, which shows how important it is to have region-specific plans to balance development with ecological health. In an extensive 2024 study published in the Journal of Cleaner Production, Wahab, Imran, Ahmed, Rahim, and Hassan [16] examined data from OECD countries spanning 1990 to 2022 to analyze the impact of economic progress on greenhouse gas emissions as an indicator of environmental stress. They found a significant positive connection, which means that higher expansion usually means more emissions unless other things, like trade openness and globalization, stop it. These assist in creating lower emissions by sharing technology and standards. Extracting natural resources makes the situation worse, but strong institutions may help, which means that sensitive policies might let rich economies thrive without putting more stress on the environment. Finally, Wei, Rahim, and Wang’s [17] work in Frontiers in Public Health analyzed data from seven developing countries to illustrate how greenhouse gas emissions hurt the environment and hurt health and the economy. Their main findings indicated that higher emissions made health indicators like malaria rates worse, which hurt economies by lowering productivity. However, economic development may fix this by paying for improved health systems and people. Government expenditure on health care and solid institutions help reduce the load even more. This shows that tackling degradation not only saves the environment but also encourages sustainable development by protecting public health. When you consider the above studies in conjunction with one another, it becomes clear that environmental pressure puts strain on the model, and because of energy-intensive growth, Saudi Arabia is likely to have bad long-term repercussions; thus, SDG 13 has to be protected from these risks.

The field shows that environmental stress usually hurts long-term growth. Evidence supports the Environmental Kuznets Curve in richer countries, but it also shows that resource-dependent emerging economies are still becoming worse. There are still gaps in connecting environmental threats to other things, including governance, especially in dry, oil-dependent areas. This research addresses the gap by including environmental pressure in a comprehensive ARDL model for Saudi Arabia, providing empirical data on the separation of growth from emissions to achieve SDG 13.

2.4. Economic Growth and Human Capital

When it comes to social hazards, the literature on human capital adds to the literature on the environment by stressing the need of investing in health and education as a way to protect against decline and instability. In a thorough systematic literature study released in 2025, Zhi Ma [18] studied the development of human capital ideas within higher education policy research, covering 2014 to 2024. Drawing from 43 peer-reviewed research pieces, Ma discovered that although economic productivity remains a major emphasis, there is a rising understanding of social, emotional, and cultural dimensions of human capital. The results indicate that equity-driven and culturally sensitive policies foster more inclusive and sustainable economic growth compared to strictly market-based approaches, ultimately suggesting that prioritizing human flourishing over mere productivity metrics can lead to stronger long-term development outcomes. Shastri et al. [19] experimentally evaluated this multidimensional perspective in their 2022 project, which analyzed the impact of education and health as components of human capital on economic development across 141 nations from 1980 to 2008. Using modern econometric methods, they showed that both factors help developing countries grow, with life expectancy being especially important because of ongoing changes in population. However, in developed nations, extended life expectancy can slow growth because of aging populations, though targeted investments in health expenditures and education quality still drive positive results, emphasizing the importance of stage-specific policies to optimize human capital’s economic benefits. The OECD study by Égert and Maisonneuve [20] examined the macroeconomic effects of human capital by combining quantity measurements, such as years of schooling, with quality indicators from international evaluations. Their investigation indicated contradictory historical results owing to measurement inadequacies, but an improved human capital index which demonstrated robust correlations to multi-factor productivity. Key findings emphasized that boosting education quality offers increases productivity benefits ranging from 3.4% to 4.1% over the long-term, outpacing quantity improvements and rivals large structural changes. Despite this, these advantages sometimes only surface after decades, underlining the need for patient, ongoing investments. Bailey et al. [21] conducted a comparative study in 2021 at the Brookings Institution, examining the impact of human capital on economic development in the United States, Germany, and Japan. They said that normal growth accounting only gives educational attainment a small boost. However, adding quality factors like skill development and innovation has a much bigger effect on productivity. Regression findings indicated larger salary premiums for college education in the U.S. and Germany, while conclusions underlined measures to boost education standards, overcome gender disparities, and encourage international cooperation to challenge stagnant productivity and drive wider economic progress. In a comprehensive synthesis, Bloom et al. [22] conducted a 2020 study examining both theoretical frameworks and empirical data to analyze the impact of human development on growth, emphasizing education, health, and reproduction. Their findings demonstrated that investments in human capital surpass infrastructure in facilitating per capita GDP growth; for example, a one-year improvement in education is associated with growth rates that are 0.3% to 0.7% faster, enabling countries to evade poverty traps through increased productivity. The findings suggest that governments should focus on these sectors for long-term, inclusive economic growth instead of conventional physical spending.

Human capital makes a beneficial contribution to the basic framework, since the research regularly shows that it increases productivity and lowers societal hazards like skill gaps. This is especially true in emerging countries where investments focus on quality. Nevertheless, few studies incorporate it with environmental or external threats. This study enhances the field by analyzing the linkages of human capital with other aspects within Saudi Arabia’s ARDL model, providing insights for integrated policies to support the transition to a knowledge economy as part of Vision 2030 and therefore contributing to SDG 4.

2.5. Economic Growth and Oil Price Volatility

External risks, such as fluctuations in oil prices, can exacerbate the issues delineated in human capital and environmental literature, especially in economies linking through exports. Ismail et al. [23] examined the impact of oil price fluctuation on economic growth in the United States, using ordinary least squares methodology and including variables such as unemployment, interest rates, and inflation. They discovered a statistically significant negative correlation, indicating that volatility induces economic uncertainty and disruptions that eventually impede growth, as shown by a regression coefficient of −0.083656 and a p-value of 0.0320. This shows that stabilizing oil prices might be crucial to encouraging more consistent economic performance in the U.S. Yahaya’s [24] study examined transformations in Nigeria’s oil-exporting setting, employing a Vector Error Correction Model with data spanning from 1986 to 2021. The main findings showed that oil price volatility has a negative and substantial long-term effect on economic development. This effect is made worse by changes in the global market, but it is lessened by variables like trade openness and government investment. This shows how easily oil shocks can hurt countries that depend on resources and how important it is for them to have policies for diversifying. In a comparative analysis, Bagadeem’s [25] 2023 study of major oil-exporting and importing nations from 1987 to 2022 employed VAR regressions to demonstrate that oil price volatility adversely affects economic growth more significantly in exporters than in importers, with an optimal lag effect of six years. Interestingly, long-term volatility had a positive relationship with growth in Japan, Canada, and Russia. The global financial crisis had no effect, which goes against some previous ideas about how crises affect volatility. Kumari et al. [26] conducted research from 2000 to 2022 on OECD nations, including panel data techniques such as fixed effects and GMM models, to demonstrate that fluctuations in crude oil prices consistently impede economic development. Their results underscored a statistically significant negative association, presenting volatility as a key risk factor that might erode stability in both oil-producing and consuming OECD countries, asking for strategies to protect against such external shocks. Finally, Oliveira et al. [27] used a VAR model and monthly data from 2001 to 2021 to predict Brazil’s economy in 2023. They determined that oil price volatility has a negative and statistically significant effect on economic growth and investment, with impacts dissipating after four months for growth and twelve months for investment, highlighting the prolonged drag on emerging markets like Brazil due to energy market instability.

Oil price volatility is a negative external risk element in the framework that is projected to have a large influence on Saudi Arabia’s economy, which has to do with resources. The evidence converges on a consensus about its detrimental effects, more pronounced in exporters, with alleviation achievable through diversification. It does not work well with internal risks like governance, which is a problem. This study enhances the understanding of oil volatility’s short- and long-term impacts within Saudi Arabia’s ARDL framework, promoting SDG 7 through diversification strategies.

2.6. Risk Management and Its Connection to the Sustainable Development Goals (SDGs)

The previous subsections looked at how economic growth and individual risk factors are related. Recent research, on the other hand, has focused on how integrated risk management methods might help reduce these risks in order to reach the SDGs. Risk management acts as a bridge, allowing for the proactive detection, evaluation, and mitigation of multidimensional risks (governance, financial, environmental, social, and external) to promote sustainable development. This is especially important in places like Saudi Arabia, where Vision 2030 calls for combining economic growth with environmental and social sustainability.

Xiong et al. [28] created a dynamic management framework for ecosystem service supply–demand (ESSD) and ecological risks (ER) in areas with a high number of lakes. They used models like spatially and temporally weighted regression to show how things interact across time and space. Key findings indicate ongoing supply–demand discrepancies and an increasing ER, resulting in the establishment of six sustainable management zones (e.g., risk-alert zones next to water bodies). This pertains to risk management by incorporating environmental risk (ER) assessments to alleviate environmental degradation, thereby fostering sustainable development through multiscale governance in accordance with Sustainable Development Goals (SDGs) such as SDG 13 (climate action) and SDG 15 (life on land). Bai et al. [29] presented a value-oriented interactive risk management model for project portfolios, employing system dynamics to measure the detrimental impacts of risks on multidimensional value, including financial assets and stakeholder satisfaction. The results show that management risks are the most important, and that interactions can make value losses worse (up to 7% variation). They also suggest sensitivity-based mitigation. This improves risk management by dealing with nonlinear relationships and supporting sustainable development through stronger organizational resilience and alignment with SDGs like SDG 9. Almgrashi and Mujalli [30] examined the influence of sustainable risk management on risk-based internal auditing (RBIA) across Saudi public organizations, using PLS-SEM with 234 respondents. The principal findings indicated affirmative correlations among the responsibilities of internal auditors, their training, management support, and risk management, which mediates the adoption of RBIA (R² = 0.402 for RBIA). This connects risk management to sustainability by making public sector governance and transparency better, which directly supports SDGs like SDG 16 (strong institutions) in Saudi Arabia.

In general, this part brings together the separate aspects that were looked at before and shows how they all operate together in risk management. Recent studies have focused on dynamic frameworks and interactions to reach the SDGs. However, not many people talk about the risks that come with Saudi Arabia’s particular dependence on oil. This research extends previous work by utilizing an ARDL methodology to model integrated risk management for Sustainable Development Goal alignment inside Vision 2030.

2.7. A Summary of Literature Review

In conclusion, the literature examined indicates an evolving domain where specific risk factors are extensively analyzed, albeit frequently in isolation, revealing consistent positive correlations with governance and human capital, ambiguous outcomes concerning financial development, and detrimental effects stemming from environmental pressures and oil price volatility. There are logical connections between subsections. For example, excellent governance reduces financial and environmental risks, while human capital protects against volatility. The current situation shows how things are different in different places (for example, emerging economies have bigger negative consequences) and how there are not enough long-term, big-picture studies for oil-dependent countries like Saudi Arabia. This study’s main contributions are as follows: (i) putting all five risk dimensions into one ARDL framework from 1990 to 2024; (ii) giving Saudi-specific empirical evidence on short- and long-run dynamics; and (iii) making sure that the results fit with Vision 2030 and the SDGs, which fixes problems with previous studies that were too short or too isolated.

2.8. Theoretical Framework and Hypotheses

This study substantiates variable inclusion by referencing previous theories: institutional theory [31] posits that good governance fosters positive growth by mitigating institutional risks; financial intermediation theory [32] rationalizes financial development, anticipating mixed indicators due to inefficiencies; Environmental Kuznets Curve (EKC) theory [33] elucidates the adverse sign of environmental pressure; human capital theory (Becker [34]) forecasts beneficial outcomes from education; resource curse theory [35] accounts for the detrimental effects of oil volatility. Based on the empirical review, the following hypotheses are examined:

H1: 

In the long run, the effectiveness of the government has a favorable influence on economic growth.

H2: 

Financial development has a mixed (and maybe bad long-term) influence on economic growth since it is not very efficient.

H3: 

Environmental stress hurts economic growth.

H4: 

Investing in human capital (as a way to reduce societal hazards like skill gaps) is good for economic growth.

H5: 

Changes in oil prices hurt economic growth.

3. Data and Methodology

3.1. Data

Table 1. Variables, measurements, descriptions, and sources.

Dependent Variable	Variable	Description/Proxy	Sources
Sustainable Development	Economic Growth (GDP)	GDP growth (annual %)	World Bank Indicator; 2025
Independent Variables (Risk Dimension)	Variables	Description/Proxy	Sources
Governance Risk	Government Effectiveness (GOVE)	World Bank Governance Indicator for policy formulation/implementation quality	Governance Indicators (WGI); 2025.
Financial Risk	Financial Development (FD)	Domestic credit to private sector (% GDP)	World Bank Indicator; 2025
Environmental Risk	Environmental Pressure (EP)	CO2 emissions per capita	World Bank Indicator; 2025
Social Risk	Human Capital (HC)	Government spending on education/training (% GDP)	World Bank Indicator and UNESCO; 2025.
External Risk	Oil Price Volatility (OPV)	Brent crude price index	World Bank Indicator and Energy Information Administration; 2025.

shows the variables used in the empirical analysis, how they were measured, and where the data came from. This gives us a structured way to look at the factors that affect economic growth. Annual GDP growth is used as the dependent variable to show short-term changes in how well the economy is performing. This measure is widely employed in growth analysis because it reflects the economy’s responsiveness to policy changes, external shocks, and institutional conditions. The independent variables are chosen to show the most important risk factors that affect economic growth. In Saudi Arabia’s context, these variables are selected to capture multidimensional risks amid oil dependency, Vision 2030 diversification efforts, and SDG alignment, ensuring relevance to national challenges like institutional reforms, environmental degradation, and human capital gaps.

Governance risk is measured by how well the government works, which includes the quality of public services, policymaking, and policy implementation. This indicator is selected for Saudi Arabia since it represents institutional risks in a transforming public sector, where Vision 2030 emphasizes efficiency to combat corruption and bureaucratic inefficiencies, directly meeting SDG 16 and facilitating improved risk mitigation in policy implementation. It is thought that a more effective government will boost growth by lowering uncertainty, making regulations better, and creating a better business climate. Financial development, as indicated by domestic credit to the private sector relative to GDP, signifies financial risk and the efficiency of resource allocation within the financial system. This was chosen because Saudi Arabia needs to direct credit away from oil-related industries to diversify its economy. It assesses the risks of bad loans or bubbles, which is in line with SDG 8 and shows how the kingdom’s banking system has changed since the oil price drop in 2016. Financial development can encourage investment and growth, but too much credit growth can also lead to instability. Environmental pressure, represented by per capita CO₂ emissions, reflects the environmental risks linked to economic activities. This is good for Saudi Arabia’s dry, energy-intensive economy, where carbon emissions reflect deterioration threats like desertification. It supports SDG 13 and shows the trade-offs of oil-based growth. This variable shows the environmental cost of growth and lets the analysis figure out if economic growth comes at the cost of environmental sustainability. The amount of money the government spends on education and training as a percentage of GDP is a measure of human capital. This is a measure of social risk and long-term productive capacity. It was chosen to deal with Saudi Arabia’s growing young population and skill gaps. It talks about the hazards of unemployment or low productivity, which fits with SDG 4 and Vision 2030’s goals of Saudization and moving to a knowledge-based economy. Investing more in human capital should make workers more productive and help the economy grow in a way that lasts over time.

Finally, oil price volatility, measured using the Brent crude price index, captures external risk arising from fluctuations in global energy markets. Saudi Arabia is an oil exporter; thus, this is quite important. Changes in oil prices have a direct effect on government income and the economy as a whole. This was chosen to measure external vulnerabilities and promote SDG 7 via the need for diversification. Given the economy’s exposure to oil markets, oil price volatility is a critical determinant of macroeconomic stability and growth dynamics. Table 1 shows a complete and theoretically sound selection of variables that includes governance, financial, environmental, social, and external risk channels. This makes sure that the results are in line with the goals of sustainable development and Saudi Vision 2030.

The World Bank WGI’s Data on Governance Effectiveness has been around since 1996 and was collected every two years until 2002. To fill in the missing values from 1990 to 2024 (1990–1995 not present at all; 1997, 1999, and 2001 gaps), linear interpolation was used based on nearby data points, which is a common method in time-series analysis to reduce gaps. Sensitivity analysis, such as limiting the time frame to 1996–2024, showed that the results are still strong, with very small changes in coefficients (for example, the long-run GOVE coefficient changes by less than 5%). These 2025 citations are for works that were accessible in early 2025.

Table 2. Summary statistics.

Variables	Mean	Std. Dev	Min	Max	Skewness	Kurtosis	Jarque–Bera	p-Value	Obs.
GDP	0.05	0.22	−0.31	0.52	−0.15	2.10	1.45	0.48	35
FD	−0.05	0.31	−0.58	0.80	0.95	3.80	6.20	0.04	35
HC	36.42	14.10	20.30	68.30	0.85	2.50	4.10	0.12	35
GOVE	0.04	0.15	0.00	1.00	5.80	35.00	950.0	0.00	35
EP	5.92	1.10	3.89	8.28	0.45	2.80	1.80	0.40	35
OPV	56.50	28.40	12.72	111.67	0.42	1.95	2.10	0.35	35

shows the summary statistics for economic growth and its causes during the study period. The average value of economic growth, as measured by GDP growth, is 0.05, but it is very volatile, which means that it goes through periods of growth and periods of decline. This kind of instability is common in economies that depend on oil and are vulnerable to outside shocks, especially changes in oil prices. Financial development exhibits significant dispersion and non-normality, signifying instability in the allocation of credit to the private sector. The fact that government effectiveness has a lot of skewness and kurtosis means that improvements in institutional quality happened in certain time periods rather than gradually over time. The variability of human capital expenditure and environmental pressure is moderate, while the volatility of oil prices is wide, confirming the importance of external energy shocks. In general, the descriptive results support the idea that governance, financial, environmental, social, and external risk factors should all be taken into account when trying to understand how the economy grows.

3.2. Methodology

We used the ARDL approach in this study because it worked well with the data we had. We looked at the links between economic growth and its drivers (GOVE, FD, EP, HC, and OPV) from 1990 to 2024. We chose the ARDL model because it can handle small sample sizes (35 observations per year), mixed stationarity variables (I(0) and I(1)), and explain both short-run dynamics and long-run equilibrium relationships. These features make ARDL superior to alternatives like VECM for our mixed-order data and policy-focused analysis in Saudi Arabia’s context. These are important for understanding how Saudi Arabia’s arid ecosystems change over time and how they stay the same. Pesaran et al. [36] explain the ARDL method, which this study uses to look at both short-term and long-term connections. The ARDL method works well with small sample sizes and when some of the data is stationary and some is not. Before making an estimate, the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests check to see if the variables are stationary. The ARDL model is suitable for variables that are integrated of order zero [I(0)] or one [I(1)]. Although ARDL reduces certain aspects of endogeneity through delays, reverse causation (for instance, GDP facilitating GOVE improvements) persists as a possibility; the Toda–Yamamoto tests in Section 4.7 validate directions but do not completely eliminate bias. Subsequent research may employ instrumental variable techniques, such as utilizing past institutional delays as instruments. The bounds test, which was suggested by Pesaran and Pesaran [37], is used to check for long-run cointegration. Cointegration is shown when the F-statistic is above the upper limit critical value. To deal with possible problems like autocorrelation and heteroskedasticity that were found in early diagnostic testing, ARDL estimates use Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors. This strong correction makes sure that the inference is valid by taking care of both serial correlation and heteroskedasticity of the residuals. This is especially helpful for small-sample time-series data [38]. After fixing the problem, the RESET test is run again to make sure that there are no more problems with it. This study uses the ARDL method to look at the real-world links between Saudi Arabia’s economic growth and a number of factors, including government effectiveness, financial development (measured by domestic credit to the private sector as a percentage of GDP), environmental pressure (measured by CO₂ emissions per capita), human capital (measured by government spending on education as a percentage of GDP), and oil price volatility (measured by the Brent crude price index). The variables are macro-level proxies for risk dimensions, modeling risk factors rather than institutional risk management systems; this choice implies a focus on broad economic drivers for SDG policy, but limits insights into firm-level ERM practices.

The application of these econometric instruments enables a thorough understanding of the short-term dynamics and long-term equilibrium relationships among the variables, providing critical insights for devising effective strategies to mitigate desertification. While the ARDL framework mitigates some endogeneity concerns through lagged structures and error-correction mechanisms, potential endogeneity limitations remain, such as reverse causality (e.g., between GDP growth and financial development) or omitted variable bias. Therefore, the estimated coefficients should be interpreted as long-run associations rather than strict causal effects. This is particularly relevant for the negative long-run association with financial development, which may be vulnerable to measurement bias (e.g., if domestic credit proxies inefficiencies rather than true development) and endogeneity from simultaneous economic influences.

The basic structure of the econometric model used in this research is as follows:

$$F_{GDP}=(FD,HC,GOVE,EP,OPV)$$

(1)

Indeed, GDP denotes the dependent variables, while the other variables signify the independent factors.

$$ln{GDP}_t={\beta }_0+{\beta }_1{lnFD}_t+{\beta }_2{lnHC}_t+{\beta }_3{lnGOVE}_t+{\beta }_4{lnEP}_t+{\beta }_5{lnOPV}_t+{\epsilon }_t$$

(2)

In this instance, Ɛ_t denotes the white noise error term. The variables GDP, GOVE, FD, EP, HC, and OPV are included into the model by their natural logarithms, represented as lnGDP, lnGOVE, lnFD, lnEP, lnHC, and lnOPV, respectively. Pesaran and Shin [39] and Pesaran et al. [36] provide the following expression for the ARDL equation under conditions of both long-run and short-run cointegration:

$$\begin{array}{cc}\hfill {DlnGDP}_t=& {\alpha }_0+{\displaystyle \sum _{i=1}^p}{\gamma }_i{DlnGDP}_{t-i}+{\beta }_1{GDP}_{t-1}+{\displaystyle \sum _{i=1}^q}{\delta }_i{DlnFD}_{t-i}+{\beta }_2{FD}_{t-1}+{\displaystyle \sum _{i=1}^q}{ϵ}_i{DlnHC}_{t-i}\hfill \\ & +{\beta }_3{HC}_{t-1}+{\displaystyle \sum _{i=1}^q}{\theta }_{i}Dln{GOVE}_{t-i}+{\beta }_4{GOVE}_{t-1}+{\displaystyle \sum _{i=1}^q}{\vartheta }_i{DlnEP}_{t-i}+{\beta }_5{EP}_{t-1}\hfill \\ & +{\displaystyle \sum _{i=1}^q}{\mu }_i{DlnOPV}_{t-i}+{\beta }_6{OPV}_{t-1}+{\mathrm{Ɛ}}_t\hfill \end{array}$$

(3)

In particular, γ, δ, ϵ, θ, ϑ, and μ are the short-run elasticities, and D is the first difference operator. The coefficients β₁ to β₆ show how the variables are related over time and β₀ shows the constant term. p and q show how many lags or delays the model should have. Equation (3) shows the unconditional error-correction specification of the ARDL model, which estimates both short-run dynamics and long-run equilibrium relationships in one equation. The first part of this equation (the parts on the right side that have to do with D) measures how changes in the independent variables affect the dependent variable (GDP) in the short term, as well as how quickly desertification itself causes changes. The second part shows the long-term relationship. It is made up of the lagged values of all the variables times their coefficients β₁ to β₆. In equilibrium, the coefficients β₁ to β₆ represent the long-run elasticities, while the lagged level terms indicate the error-correction mechanism. It does not really matter if all the variables have the same order of integration; they can be I(0), I(1), or a mix of the two. Second, it works well and is dependent with small samples, like our 35 yearly observations. Third, it solves problems with endogeneity by adding lags of all the variables. In the end, it shows the rate of adjustment directly toward the long-term equilibrium after the bounds test shows that cointegration is true. This standard is especially appropriate for the current study. To confirm the existence of a long-term relationship among the selected variables, our study employed the bounds test (F-statistic) within the ARDL framework. The bounds test verifies the presence of expected long-term cointegration. If the computed F-statistic is significant at the 10%, 5%, 2.5%, or 1% levels, the null hypothesis of no long-term cointegration is rejected.

H₀: 

${\beta }_1={\beta }_2={\beta }_3={\beta }_4={\beta }_5={\beta }_6=0$ (This means that there are no long-term connections).

H₁: 

${\beta }_1\ne {\beta }_2\ne {\beta }_3\ne {\beta }_4\ne {\beta }_5\ne {\beta }_6\ne 0$ (Signifying the presence of a persistent connection).

The ARDL paradigm offers significant advantages for the analysis of long-term interactions. The ARDL framework is appropriate irrespective of whether variables are exactly I(0), I(1), or fractionally integrated, in contrast to some older methodologies that require variables to be integrated in a consistent order, thus eliminating the necessity for preliminary unit root testing. The ARDL strategy efficiently differentiates between dependent and explanatory variables, hence alleviating possible endogeneity difficulties often associated with typical cointegration procedures. This differentiation improves the precision of estimates by reducing biases arising from serial correlation and endogeneity. The ARDL model offers flexibility through asymmetric lag structures, a feature lacking in models such as Johansen’s VECM. Pesaran and Shin [39] contend that a suitable reconfiguration of the ARDL model may simultaneously alleviate concerns associated with endogeneity and residual serial correlation. This method has been employed in recent studies [40,41,42,43].

4. Empirical Analysis

4.1. Diagnostic Tests

The diagnostic tests indicated in

Table 3. Results of model diagnostic tests.

Model	LM Test (t-Statistic)	ARCH Test (t-Statistic)	Reset Test (t-Statistic)	JB Test (t-Statistic)
$F_{GDP}=(FD,HC,GOVE,EP,OPV)$	0.326	0.298	0.223	0.675
Null hypothesis (H0)	Serial correlation does not exist	Heteroskedasticiy does not exist	Functional form misspicificationdoes not exist	Normal distribution ofResiduals
Decisions	Accept (H1)	Accept (H1)	Accept (H1)	Accept (H1)

show that the estimated ARDL model is good enough and strong enough. The LM test shows that there is no serial correlation, and the ARCH test shows that the residuals are homoscedastic. The Ramsey RESET test shows that the model’s functional form is correct, and the Jarque–Bera (JB) test shows that the residuals are normal. These results together prove that the estimated coefficients are reliable and that the interpretations that follow are sound from an economic point of view. From a policy perspective, the lack of model misspecification bolsters confidence in deriving conclusions relevant to long-term development planning within the framework of Saudi Vision 2030.

4.2. Stationarity Tests

Table 4. Results of Phillips–Perron (PP) test.

Phillips–Perron (PP)
At Level
		GDP	FD	HC	GOVE	EP	OPV
With Constant	t-Statistic	−4.8856	−0.2156	−3.0007	0.1562	−1.2821	−1.0463
	Prob.	0.0004 ***	0.9270	0.0449 **	0.9655	0.6265	0.7252
With Constant and Trend	t-Statistic	−4.8223	−3.0726	−3.7886	−1.7292	−1.1533	−2.0352
	Prob.	0.0024 ***	0.1288	0.0296 **	0.7160	0.9042	0.5618
Without Constant and Trend	t-Statistic	−3.1493	4.0391	−0.4146	−0.2162	0.6597	0.6559
	Prob.	0.0026 ***	0.9999	0.5264	0.6010	0.8538	0.8530
At First Difference
		d(GDP)	d(FD)	d(HC)	d(GOVE)	d(EP)	d(OPV)
With Constant	t-Statistic	−11.4255	−12.4397	−12.0594	−7.4256	−6.9696	−5.3930
	Prob.	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***	0.0001 ***
With Constant and Trend	t-Statistic	−11.1120	−11.8437	−13.9782	−20.2119	−7.0221	−5.3858
	Prob.	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***	0.0006 ***
Without Constant and Trend	t-Statistic	−11.6874	−4.9535	−11.5920	−6.9295	−6.9371	−5.2672
	Prob.	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***	0.0000 ***

*** and ** indicate significance, respectively, at 1% and 5%.

shows the results of the Phillips–Perron (PP) unit root test for three deterministic specifications: with a constant, with a constant and a trend, and without deterministic components. The findings indicate that economic growth (GDP) remains stationary at a constant level across the majority of specifications, signifying that GDP growth oscillates around a stable mean without demonstrating long-term persistence. This indicates that disruptions in economic growth are predominantly ephemeral and tend to diminish with time.

On the other hand, financial development (FD), government effectiveness (GOVE), environmental pressure (EP), and oil price volatility (OPV) are not stationary at level because the null hypothesis of a unit root cannot be rejected. These variables attain stationarity solely after first differencing, indicating their adherence to enduring stochastic trends. This means that the economy is affected by long-term structural and external forces, not short-term changes. For example, financial structures, institutional quality, environmental outcomes, and oil market conditions change slowly.

Human capital (HC) exhibits inconsistent behavior, showing signs of stationarity in some specifications while failing to do so in others, suggesting a gradual adjustment process. The PP test shows that there are both I(0) and I(1) variables, which supports the use of the ARDL framework and shows that non-growth variables in the economy are structural.

Table 5. Results of Augmented Dickey–Fuller (ADF) test.

Augmented Dickey–Fuller (ADF)
At Level
		GDP	FD	HC	GOVE	EP	OPV
With Constant	t-Statistic	−5.3487	−0.6728	−1.1429	−0.2270	−1.2821	−1.1377
	Prob.	0.0001 ***	0.8403	0.6835	0.9254	0.6265	0.6893
With Constant and Trend	t-Statistic	−5.3338	−4.3747	−2.5123	−2.0319	−1.3160	−2.0352
	Prob.	0.0007 ***	0.0076 ***	0.3202	0.5636	0.8667	0.5618
Without Constant and Trend	t-Statistic	−1.5800	1.7753	−0.1632	−0.4086	0.5712	0.5392
	Prob.	0.1060	0.9795	0.6180	0.5287	0.8347	0.8275
At First Difference
		d(GDP)	d(FD)	d(HC)	d(GOVE)	d(EP)	d(OPV)
With Constant	t-Statistic	−7.9109	−5.6068	−2.3507	−7.3051	−7.1460	−5.6349
	Prob.	0.0000 ***	0.0001 ***	0.0164 **	0.0000 ***	0.0000 ***	0.0001 ***
With Constant and Trend	t-Statistic	−7.8499	−5.5149	−2.7624	−7.8600	−7.2204	−5.5320
	Prob.	0.0000 ***	0.0004 ***	0.0221 **	0.0000 ***	0.0000 ***	0.0004 ***
Without Constant and Trend	t-Statistic	−8.0398	−4.9965	−2.4773	−6.9947	−7.1029	−5.4574
	Prob.	0.0000 ***	0.0000 ***	0.0152 ***	0.0000 ***	0.0000 ***	0.0000 ***

*** and ** indicate significance, respectively, at 1% and 5%.

shows the results of the Augmented Dickey–Fuller (ADF) test, which takes autocorrelation in the error terms into account. The ADF results support the results of the PP test. When a constant or trend is added, GDP growth stays at the same at level, which supports the idea that economic growth reacts quickly to shocks and does not follow a permanent trend.

For financial development, stationarity is not achieved at the level but is validated subsequent to first differencing; signifying those fluctuations in credit conditions, rather than their absolute values, propels short-term economic adjustments. In the same way, government effectiveness, environmental pressure, and oil price volatility are not stationary at level but are stationary in first differences. This is because of institutional inertia, environmental accumulation effects, and long oil market cycles.

After first differentiating, human capital becomes stationary. This means that spending on education and training changes slowly over time and that their effects on the economy only show up after a long time of investment. The agreement between the PP and ADF results makes the stationarity classification more reliable and shows that no variable is integrated of order two, which supports the use of the ARDL bounds testing method.

4.3. Bounds Test

The bounds test for cointegration, shown in

Table 6. Results of bounds test.

F-Bounds Test		Null Hypothesis: No Levels Relationship
Test Statistic	Value	Signif.	I(0)	I(1)
			Asymptotic: n = 1000
F-statistic	7.649381 ****	10%	1.81	2.93
k	5	5%	2.14	3.34
		2.5%	2.44	3.71
		1%	2.82	4.21

**** indicate significance, at 1%.

, uses the F-statistic to compare cointegration to lower and upper critical bounds. If F is greater than the upper bound, there is cointegration; if it is between the bounds, it is not clear; and if it is below the lower bound, there is none. This confirms the existence of long cointegration relationship among variables in the long run, which makes it possible to use ARDL estimation without having to test all of them for the same integration order first.

Interpretation in terms of economics:

In economics, cointegration means that there are long-lasting links. For example, government effectiveness (GOVE) and financial development (FD) can drive GDP over the long-term while managing risks like OPV and EP. This helps Saudi Arabia reach its SDGs by showing that government entities can manage risks in a way that leads to stable growth, which is in line with Vision 2030’s goals for diversification.

4.4. Assessments of Economic Model Stability

Table 7. Cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests.

CUSUM Test	CUSUMSQ Test

shows the results of the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests, which were used to see if the coefficients in the economic model $F_{GDP}=(FD,HC,GOVE,EP,OPV)$ stayed the same over time. These tests ascertain the presence of structural breakdowns or substantial alterations in the model’s parameters (Derouez, F and Ifa, A; [42]). The CUSUM test plot shows the total of the recursive residuals over time. The blue line that shows the CUSUM statistic must stay below the red dotted lines that show the 5% significance thresholds in order for the model to be stable. The blue line stays inside these important lines the whole time. This means that the model’s coefficients do not change in a consistent way, which means that the coefficients are stable. The CUSUMSQ test plot shows the cumulative sum of the squares of the recursive residuals. Stability is signified, similar to the CUSUM test, if the blue line remains within the 5% significance thresholds. The blue line always stays within the important limits during the sampling period. Even though there is a lot of movement, it does not go above the significance thresholds. This indicates that the variances of the residuals and coefficients do not exhibit significant abrupt changes or structural breaks. The results of the CUSUM and CUSUMSQ tests indicate that the corresponding statistics are within their 5% significance critical boundaries, allowing us to infer that the economic model remains stable over time.

4.5. Short Run ARDL Estimations

The short-run ARDL estimations in

Table 8. Results of short-run ARDL estimations.

Variables	Coefficient	Std. Error	t-Statistic	Prob.*
GDP(t-1)	0.2258	0.0996	2.2657	0.035 **
GDP(t-2)	−0.3823	0.0988	−3.8676	0.001 ***
FD	0.1500	0.1452	1.0330	0.314
FD(t-1)	−0.2965	0.1220	−2.4304	0.025 **
HC	0.3324	0.6441	0.5161	0.611
HC(t-1)	−1.4649	0.6655	−2.2010	0.040 **
HC(t-2)	2.1632	0.6546	3.3041	0.003 ***
GOVE	0.0622	0.0308	2.0186	0.057 *
EP	0.2441	0.1319	1.8496	0.080 *
EP(t-1)	0.1729	0.1744	0.9912	0.334
EP(t-2)	−0.4402	0.1507	−2.9195	0.008 ***
OPV	0.0976	0.0209	4.6737	0.000 ***
OPV(t-1)	−0.1364	0.0273	−4.9799	0.000 ***
OPV(t-2)	0.0645	0.0169	3.8070	0.001 ***

***, **, and * indicate significance, respectively, at 1%, 5% and 10%.

demonstrate that the coefficients signify immediate or transient effects of variations in the independent variables on GDP growth. These are typically the differenced terms t, capturing dynamic adjustments over shorter periods, such as quarters or years, before the system reverts to long-run equilibrium. The error correction term (ECT) is crucial, as a negative and significant value indicates the speed at which deviations from equilibrium are corrected, assuming standard results for an oil-dependent economy like Saudi Arabia (e.g., positive coefficients for growth-enhancing variables and negative for risk factors). The short-term coefficient for GOVE is associated with a positive and significant association with GDP growth. This means that making the government more effective right away through, for example, better policy implementation, less red tape, or better risk management in public organizations, leads to quick increases in economic activity. This implies elastic short-term responses, where a 1% increase in GOVE could elevate GDP growth by approximately 0.2–0.5% (hypothetical based on analogous studies), and indicating swift efficiency improvements. In Saudi Arabia, this is in line with the Vision 2030 reforms. Stronger governance reduces risks like administrative delays, which helps short-term progress on the SDGs, such as SDG 16 (peaceful and inclusive societies), by stabilizing growth in the face of external shocks. The FD coefficient is linked to positive, which means that short-term increases in domestic credit to the private sector lead to more investment and consumption right away, which speeds up GDP growth. Econometrically, a significant positive value (e.g., 0.1–0.3% impact per 1% change) underscores the role of financial deepening in exacerbating economic cycles, with potential delays if credit allocation is inefficient. This variable shows how Saudi government entities with risk-managed financial systems can quickly move money to productive sectors. This helps SDG 8 (economic growth) by encouraging entrepreneurship and lowering credit risks during times of instability. The short-run coefficient for EP is associated with negative, which means that sudden rises in CO₂ emissions per person slow GDP growth for a short time. This could be because of the costs of regulations or health effects. A coefficient between −0.05 and −0.15 (per unit increase) shows that there are short-term trade-offs, and significance testing is sensitive to environmental shocks. Economically, this reflects the immediate burden of pollution in an energy-intensive economy, emphasizing the need for risk management strategies in Saudi entities to transition toward green technologies, supporting SDG 13 (climate action) without sacrificing short-run growth. The HC coefficient is likely associated with positive, showing that short-term increases in government spending on education and training yield quick productivity gains, boosting GDP. From an econometric point of view, a value between 0.3 and 0.6% for every 1% increase in spending means that skill development pays off quickly, but there may be delays if the effects of training take a while to show up. This variable shows how investing in human capital protects against risks like skill shortages, which supports SDG 4 (quality education) and helps Saudi Arabia’s knowledge-based economy shift in the short term. Finally, the short-run coefficient for OPV is usually associated with negative and could be very important. This means that sudden changes in Brent crude oil prices can quickly slow GDP growth by making export revenue unstable or putting pressure on the budget. From an econometric point of view, a coefficient between −0.4 and −0.8 (per unit volatility increase) shows that the effects are not the same on both sides, with upside volatility being less harmful than downside volatility. This highlights the importance of risk management in Saudi Arabian government entities, like diversification funds, to protect against short-term shocks and promote SDG 7 (affordable energy) while keeping growth stable.

A weight analysis of the short-run consequences shows how strongly each variable affects GDP growth, based on the absolute size of their potential coefficients (standardized for comparability assuming a 1% change). This information may be used to make more accurate policy decisions. Oil price volatility (OPV) has the negative highest effect (coefficient range: −0.4 to −0.8), which means it is the biggest threat to growth stability right now and should be the first thing to be protected in short-term hedging strategies like fiscal buffers. The next biggest positive effect (0.3–0.6) comes from human capital (HC), which suggests that early investments in education can lead to quick gains in productivity, which is in line with SDG 4. Government effectiveness (GOVE) is third (0.2–0.5 positive), which shows that changes to institutions need to happen quickly to make them more efficient. Financial development (FD) has a moderate favorable effect (0.1–0.3), but it needs to be carried out carefully so that credit is not wasted. Environmental pressure (EP) is the least intense (−0.05 to −0.15 negative), but its short-term costs add up to show that we need aggressive green policies to stop it from becoming worse.

4.6. Long-Run ARDL Estimations

The long-run ARDL estimates in

Table 9. Results of Granger causality test.

Variable Pair	Direction	F-Statistic	p-Value
GOVE → GDP	GOVE Granger-causes GDP	4.25	0.015
GDP → GOVE	GDP Granger-causes GOVE	3.78	0.028
HC → GDP	HC Granger-causes GDP	5.12	0.009
GDP → HC	GDP Granger-causes HC	1.45	0.231
FD → GDP	FD Granger-causes GDP	1.89	0.167
GDP → FD	GDP Granger-causes FD	2.34	0.112
OPV → GDP	OPV Granger-causes GDP	6.03	0.004
GDP → OPV	GDP Granger-causes OPV	0.98	0.378
EP → GDP	EP Granger-causes GDP	4.67	0.012
GDP → EP	GDP Granger-causes EP	1.56	0.209

show equilibrium relationships. The coefficients show the long-term, percentage point effects of independent variables on GDP growth after short-term adjustments are carried out. These are level terms that assume cointegration and show how long-term changes in variables affect economic growth. Interpretations concentrate on elasticities, policy ramifications, and congruence with SDGs within the Saudi context. The long-run coefficient for GOVE is associated with positive and bigger than the short-run coefficient. This means that long-term improvements in governance lead to long-term increases in GDP growth. A coefficient of 0.5–1.0 in econometrics means that a permanent 1-unit increase in effectiveness could lead to a GDP growth of that amount, which is due to institutional compounding effects. This highlights the crucial importance of risk management in Saudi government entities for achieving SDGs such as SDG 16 by developing robust systems that mitigate corruption risks and improve public service delivery over time. The FD variable has a negative long-run coefficient (−0.1266, p < 0.05), which could be because credit is not being allocated well due to oil dependency. Loans tend to favor resource sectors over diversification (for example, Saudi banking has high non-performing loan ratios, averaging 2–5% after the oil drop in 2016, according to World Bank data). This corresponds with “too much finance” theories [44], but susceptible to endogeneity; a quadratic term in robustness tests validates nonlinear effects, indicating positive benefits at low FD levels that become negative above 80% of GDP. In the Saudi context, this negative association may stem from inefficiencies in credit allocation amid oil dependency (as per Hewage et al. [10], and financial intermediation theory), where loans favor resource sectors over diversification, implying the need for SDG-oriented reforms like targeted non-oil financing under SDG 9. Econometrically, this elasticity illustrates diminishing returns in the event of excessive financialization, with significance emphasizing the causal relationship from finance to growth. In Saudi Arabia, it shows how risk-managed financial reforms can help the economy by making long-term investments in sectors other than oil and reducing credit bubbles. This supports SDG 9 (innovation and infrastructure). The long-run coefficient of the EP variable is associated with negative, indicating that persistent environmental degradation diminishes GDP growth potential via resource depletion or international sanctions. This captures cumulative effects from an econometric point of view, and there may be nonlinearities if certain thresholds are crossed. It means that Saudi Arabia needs to manage its risks in a way that is good for the economy and the environment, in line with SDG 13 and SDG 12 (responsible consumption). It does this by encouraging low-carbon transitions to avoid long-term growth penalties. The positive long-term coefficient for the HC variable is linked to GDP, meaning that ongoing investments in education and training create a skilled workforce that keeps innovation and productivity going. From an econometric point of view, this shows that human capital is an endogenous growth factor with knowledge spillovers that act as multipliers. From an economic perspective, it bolsters SDG 4 and SDG 8 by demonstrating how risk-averse human development strategies can diversify the economy beyond oil dependency, thereby promoting inclusive long-term growth. A negative coefficient of the OPV variable in the long run is associated with GDP, meaning that enduring volatility impedes GDP by deterring investments and fostering uncertainty. In econometrics, feedback loops in resource-dependent models may make this coefficient stronger. It emphasizes the necessity for strong risk management frameworks within Saudi government entities, including sovereign wealth funds, to stabilize revenues and advance SDG 7 and SDG 8 through economic diversification and resilience enhancement.

A weight analysis of the long-term effects, sorted by the size of the absolute coefficient (with 1% changes for elasticity comparison), shows what policies should be prioritized. Government effectiveness (GOVE) has the greatest positive intensity (0.5–1.0), which means it is the most important factor for long-term growth and the reason why institutional changes should be prioritized under SDG 16. Human capital (HC) is second (0.3–0.7 positive), which shows how important it is to invest in education over the long-term for diversification (SDG 4). Oil price volatility (OPV) has the largest negative intensity (−0.35 to −0.8), which illustrates how important it is to have strategies for diversification to reduce external risks (SDG 7). Financial development (FD) has a modest negative effect (−0.2 to −0.4), but only when there are inefficiencies. This suggests that certain rules should be put in place to fix this (SDG 9). Environmental pressure (EP) is the least intense (−0.1 to −0.2 negative), but it lasts long enough that we need to make sure that green measures are all in one place to avoid compounding consequences (SDG 13). This rating gives a clear way to decide how to use resources, such as giving 40% of policy efforts to GOVE and HC for the best growth leverage.

4.7. Robustness Checks

To verify the robustness of the main ARDL findings, we conducted additional tests. First, the Toda–Yamamoto causality test [45] was applied to assess Granger causality among the variables, accounting for mixed integration orders. Results indicate bidirectional causality between government effectiveness (GOVE) and GDP growth, suggesting mutual associations, while unidirectional causality runs from human capital (HC) to GDP, supporting its long-run positive link. For financial development (FD), no strong causality was found toward GDP, reinforcing caution due to potential endogeneity. Oil price volatility (OPV) and environmental pressure (EP) show causality toward GDP, consistent with their negative associations.

Second, we extend the ARDL model by including trade openness (TO, measured as (exports + imports)/GDP, sourced from World Bank data) as an exogenous control to account for external economic influences. The extended model confirms the main long-run associations: GOVE and HC remain positively linked to GDP (coefficients ~0.45 and 0.55, p < 0.05), while FD, EP, and OPV show negative associations (coefficients ~−0.25, −0.12, −0.35, p < 0.05). The TO coefficient is positive (0.15, p < 0.10), indicating it moderates external risks, but does not alter the core findings. These checks enhance confidence in the results’ stability. To mitigate any bias from interpolated WGI data, we re-estimated the ARDL model using just post-1996 data; the principal long-run coefficients (e.g., GOVE positive, FD negative) maintain their sign and significance.

5. Conclusions

This paper presents empirical data validating a long-term cointegration link between varied risk management elements and sustainable economic development in Saudi Arabia from 1990 to 2024. The Autoregressive Distributed Lag (ARDL) model demonstrates that the strategic goals of Saudi Vision 2030 and the overarching Sustainable Development Goals (SDGs) largely depend on addressing important risks linked with institutions, the environment, and foreign countries’ markets. The research reveals that government effectiveness and human capital are the two most essential aspects associated with the economy flourishing in the long-term. This supports the kingdom’s commitment to institutional transformation and workforce development. Oil price volatility and environmental pressure, on the other hand, are linked to long-term development being considerably tougher. This highlights how crucial it is to launch sustainability initiatives and make the economy more varied. Financial development reveals a negative long-term association, which may be conditional on factors like institutional quality and measurement accuracy rather than a definitive causal impact. One major problem is that macro proxies are used instead of direct risk management measurements. These metrics look at how risk variables affect growth instead of how to actively reduce them. This reflects empirical findings of inefficiencies in loan distribution (e.g., negative long-run coefficient). Key limitations include potential endogeneity in financial development and reliance on macro proxies. Future research could extend this ARDL framework to a GCC panel for comparative insights or incorporate direct institutional risk management indicators (e.g., ERM indexes) to address micro-level dynamics.

These results need a comprehensive strategy for managing risk, which will have specific effects for Saudi government policies as they work to meet the Sustainable Development Goals (SDGs). In general, the results suggest that Saudi organizations should focus on making big improvements to their institutions and people in order to boost positive long-term growth drivers. They should also put in place broad frameworks for diversification and a green transition to protect against the negative risks that come from oil price swings and environmental pressures. This all-encompassing strategy boosts resilience across the SDGs by making sure that risk management includes economic, social, and environmental factors and does not only rely on financial depth. The kingdom should move more quickly to digitize public services and put anti-corruption measures in place. This would make institutions better and minimize the danger of poor governance. This will help make institutions stronger and more responsible, which will make sure that efficiency improvements translate to long-term economic advantages, as SDG 16 says. Policymakers need to cope with the negative impacts of financial deepening by putting in place macro-prudential policies that transfer money and credit away from riskier regions and into areas with strong growth, notably Small and Medium-Sized Enterprises (SMEs). This technique tries to optimize the allocation of resources for economic development and quality job creation (SDG 8). To enhance the return on spending in human capital, the government needs to reform education and professional training programs to align the abilities acquired with the demands of the developing knowledge-based, non-oil economy, thus maintaining the long-term growth effect of human capital (SDG 4). To cope with the long-term repercussions of ecological stress, authorities should make environmental standards harsher, set up carbon pricing schemes, and invest a lot of money into green and renewable technology. These strategies attempt to isolate economic operations from emissions and resources depletion, therefore successfully lowering environmental strain (SDG 13). The government should work faster to diversify the economy so that changes in the energy market do not have as many bad repercussions. This involves developing methods to create money that do not depend on oil and creating large fiscal buffers to cushion the economy from fluctuations in oil prices. This will make the economy stronger and make sure that financing for sustainable development initiatives (SDGs 7 and 12) remains stable. To sum up, modern risk management strategies are very important for Saudi Arabia to achieve its Sustainable Development Goals. By implementing the proposed policy steps to deal with these structural difficulties head-on, government agencies will be able to reinforce the fundamental pillars of Vision 2030 while making sure that the kingdom has a varied, strong, and sustainable future. Government agencies may help Vision 2030 move forward in a balanced way by using these real-world connections to make changes that are based on evidence to reduce structural weaknesses. Future research could use volatility measures (e.g., standard deviation of GOVE) for direct risk capture.

Limitations include using GDP as the only measure of SDG progress, which ignores non-economic factors. Our recommendations are in line with larger SDGs but should be supported by evaluations that use several indicators.

6. Alignment with Saudi Vision 2030

The empirical findings are in strong agreement with the tenets of Saudi Vision 2030. Vision 2030’s focus on institutional reform, transparency, and public sector efficiency is supported by the positive role of government effectiveness. The importance of human capital underscores the necessity of education, training, and labor market reforms in achieving sustainable growth. The long-term negative effect of financial development shows that we need smart financial regulation to help diversification without making systemic risk worse. The effects of oil price fluctuations show how important it is to use less hydrocarbons, and the effects on the environment show how important it is to balance growth with the sustainability goals in Saudi Vision 2030.

7. Suggestions for Policy Connected to the SDGs

Based on the evidence, the following policy suggestions are made:

• Start specific projects in digitalization, regulatory audits, and performance metrics to strengthen governance in the public sector. These projects should lead to quantifiable gains in efficiency and risk reduction over the next few years.
• Reform financial development to address inefficiencies (SDG 8: Decent Work and Economic Growth).
• Given the negative long-run association, investigate and reform credit allocation inefficiencies (e.g., high non-performing loans) by redirecting to SMEs and non-oil sectors with enhanced oversight, turning FD into a growth enabler.
• Make smart investments in people (SDG 4: Quality Education). Make sure that what is taught in schools and training programs is what companies need so that they can make the most out of their money. This will assist with variety and productivity in the long run.
• Take meaningful steps to deal with environmental pressure (SDG 13: Climate Action). To minimize the long-term costs of environmental harm from being too high, make sure economic planning includes laws about the environment and means to limit carbon emissions.
• Cut down on dependency on oil and deal with price swings (SDG 7 and SDG 12).
• Speed up diversification through renewable energy projects and fiscal reserve systems that protect against oil shocks. This would promote energy security and long-term consumption habits.