Technological Advancements and Economic Growth as Key Drivers of Renewable Energy Production in Saudi Arabia: An ARDL and VECM Analysis

Abstract

This study examines the short- and long-term effects of various economic, environmental, and policy factors on renewable energy production (REP) in Saudi Arabia from 1990 to 2024, using the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction Model (VECM) techniques. The analysis focuses on fossil fuel consumption (FFC), renewable energy investment (REI), carbon emissions (CEs), energy prices (EPs), government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG) as determinants of REP, measured as electricity generated from solar power sources in kilowatt-hours (kWh). Short-term findings reveal a positive momentum effect, where prior REP levels significantly influence current production, driven by factors such as learning by doing, economies of scale, and consistent policy support. However, FFC negatively impacts REP, highlighting resource competition and market dynamics favoring fossil fuels. Positive short-term influences include REI, CEs, EPs, GPs, TAs, SEFs, and EG, which collectively enhance renewable energy adoption through investments, technological innovation, policy incentives, and economic development. Long-term analysis underscores a strong negative relationship between FFC and REP, with a 7503-unit decline in REP associated with increased fossil fuel dependency. Conversely, REP benefits from REI, CEs, EPs, GPs, TAs, and EG, with significant contributions from technological advancements (3769-unit increase) and economic growth (9191-unit increase). However, SEFs exhibit a slight negative impact, suggesting that rapid urbanization and population growth may outpace renewable infrastructure development. Overall, the study highlights the complex interplay of factors shaping renewable energy production, emphasizing the importance of sustained investments, supportive policies, and technological innovation, while addressing challenges posed by fossil fuel reliance and socioeconomic pressures. These insights provide valuable implications for policymakers and stakeholders aiming to accelerate the transition to renewable energy in Saudi Arabia.

1. Introduction

The global transition toward renewable energy has become a critical priority in addressing climate change, reducing carbon emissions, and ensuring sustainable energy security. As one of the world’s largest oil producers, Saudi Arabia faces unique challenges and opportunities in diversifying its energy mix and reducing its reliance on fossil fuels. The Kingdom has set ambitious targets under its Vision 2030 initiative to increase the share of renewable energy in its energy portfolio, particularly solar energy, given its abundant solar resources. However, the transition to renewable energy is influenced by a complex interplay of economic, environmental, and policy factors that require thorough investigation to inform effective strategies.

While the urgency of the renewable energy transition is well established, the specific dynamics within fossil fuel-dependent economies like Saudi Arabia remain under-explored. The dominance of oil and gas creates unique challenges for renewable energy integration, and a comprehensive understanding of these dynamics is crucial for successful policy implementation. Moreover, while prior research has examined the short-term impacts of various determinants, there is a notable gap in understanding their long-term effects and the underlying mechanisms driving renewable energy production (REP) over extended periods in such contexts. This study aims to address these critical gaps by examining the short- and long-term effects of key determinants on renewable energy production in Saudi Arabia from 1990 to 2024. Specifically, this research utilizes the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction Model (VECM) techniques to analyze the impact of fossil fuel consumption (FFC), renewable energy investment (REI), carbon emissions (CEs), energy prices (EPs), government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG) on REP, measured as electricity generated from solar power sources in kilowatt-hours (kWh). This study contributes to the literature by providing a comprehensive analysis of both short- and long-term dynamics, offering insights into how immediate policy actions and investments can influence renewable energy production over time. It highlights the specific challenges posed by fossil fuel dependency in a resource-rich economy, shedding light on the barriers to renewable energy adoption in such contexts. Furthermore, it identifies the relative importance of various factors, including technological innovation, policy support, and economic growth, in driving renewable energy production. Finally, the study offers practical implications for policymakers and stakeholders in Saudi Arabia and similar economies, emphasizing the need for sustained investments, supportive policies, and technological advancements to accelerate the transition to renewable energy.

Thus, to provide a clear roadmap for the reader, the structure of this article is as follows: First, a comprehensive literature review provides a detailed overview of existing studies and theoretical frameworks relevant to the determinants of REP. Second, the data and methodology section describes the data sources and explains the ARDL approach and VECM techniques employed in this study. This section includes a detailed description of the data, followed by an explanation of the analytical methods, including diagnostic tests, stationarity tests (ADF and PP tests), bounds tests, Wald tests, and stability checks (CUSUM and CUSUMSQ). Third, the empirical analysis and discussion section presents the results of the stationarity tests, bounds tests, short-run and long-run estimations, and Granger causality and VECM results. Fourth, the conclusion summarizes the key findings and their implications. Finally, the policy implications section offers specific recommendations for policymakers based on the empirical results. By addressing these research gaps, this study aims to enhance the understanding of the factors shaping renewable energy production in fossil fuel-dependent economies and provide actionable insights for achieving sustainable energy transitions.

2. Literature Review

Studies have shown that high FFC correlates with lower renewable energy production (REP), as fossil fuels dominate energy infrastructure and policy frameworks [1,2]. This competition for resources and market share creates a challenging environment for renewable energy integration. Derouez, F. and Ifa, A. [3] identify a significant long-term relationship between energy consumption and stock market development, revealing that fossil fuel consumption has a negative impact, while renewable energy consumption exerts a positive effect. The authors argue that transitioning to renewable energy sources can bolster economic stability and stock market growth by mitigating energy-related risks and fostering sustainable development. Their study offers critical insights for policymakers and investors, stressing the importance of aligning energy policies with economic and financial objectives to ensure long-term growth and stability. However, Derouez, F. [4] also highlight notable disparities in energy transition progress across countries. While some nations have made significant advancements in integrating renewable energy, others remain heavily dependent on fossil fuels. The study emphasizes the crucial role of supportive government policies, increased investment in renewable energy infrastructure, and regional cooperation in accelerating the transition. The authors conclude that, despite progress in Southeast Asia, achieving a sustainable energy future will require coordinated efforts to overcome economic, political, and technological barriers.

Renewable energy investment (REI) is a critical driver of REP, as government spending on renewable infrastructure and technology directly influences the scalability and efficiency of solar energy systems. According to [5], increased REI as a percentage of GDP significantly boosts REP by enabling large-scale projects and reducing reliance on fossil fuels. Similarly, carbon emissions (CEs) have been identified as both a challenge and an opportunity for renewable energy adoption. Higher CE levels often prompt stricter climate policies, which can incentivize renewable energy production [6,7]. Energy prices (EPs) play a pivotal role in shaping the economic viability of renewable energy. When fossil fuel prices rise relative to renewable energy prices, solar power becomes more competitive, driving higher REP [8,9]. Government policies (GPs), such as feed-in tariffs, tax incentives, and renewable energy targets, are also crucial in fostering REP. For instance, ref. [10] found that supportive GP frameworks in Saudi Arabia have accelerated solar energy adoption, particularly in utility-scale projects.

Technological advancements (TAs) have significantly reduced the cost of renewable energy technologies, making solar power more accessible and efficient. Innovations in photovoltaic (PV) technology and energy storage systems have been key drivers of REP growth [11]. Socioeconomic factors (SEFs), such as population growth and urbanization, influence energy demand and create opportunities for renewable energy expansion. However, rapid population growth can outpace renewable infrastructure development, posing challenges for REP scalability [2,12].

Economic growth (EG), measured by GDP growth rate, has a dual impact on REP. On one hand, higher EG provides the financial resources needed for renewable energy investments. On the other hand, it can increase energy demand, potentially leading to higher FFC if renewable infrastructure is not adequately developed [13,14]. Balancing these dynamics is essential for achieving sustainable energy transitions.

The reviewed studies collectively highlight a complex interplay of factors influencing renewable energy production (REP). A dominant theme is the inverse relationship between fossil fuel consumption (FFC) and REP, where established fossil fuel infrastructures and policies create significant barriers to renewable energy integration. However, the literature also reveals that transitioning to renewable energy can positively impact economic stability and stock market growth, underscoring the long-term benefits of sustainable energy policies. Crucially, renewable energy investment (REI) emerges as a primary driver of REP, with increased government spending on renewable infrastructure directly boosting production. Concurrently, carbon emissions (CEs), while presenting challenges, can also incentivize renewable energy adoption through stricter climate policies. Energy prices (EPs) play a vital role, as rising fossil fuel prices enhance the competitiveness of renewable energy. Government policies (GPs), such as feed-in tariffs and tax incentives, are essential for fostering REP, demonstrating the importance of supportive regulatory frameworks. Technological advancements (TAs) have significantly lowered the cost and improved the efficiency of renewable energy technologies, facilitating broader adoption. Socioeconomic factors (SEFs), including population growth and urbanization, influence energy demand and create both opportunities and challenges for renewable energy expansion. Finally, economic growth (EG) presents a dual dynamic: it provides the necessary financial resources for renewable investments but also increases overall energy demand, potentially leading to higher FFC if not managed sustainably. In essence, the literature underscores that achieving a successful transition to renewable energy requires a multifaceted approach, addressing economic, political, technological, and social dimensions. Coordinated efforts, supportive government policies, increased investment, and continuous technological innovation are vital to overcome existing barriers and realize the full potential of renewable energy.

3. Data and Methodology

This study conducted a thorough investigation into the factors influencing renewable energy production (REP) in Saudi Arabia, specifically focusing on solar energy generation. Utilizing annual data from 1990 to 2024, the research employed robust econometric modeling to analyze the relationships between REP and key determinants, including fossil fuel consumption (FFC), renewable energy investment (REI), carbon emissions (CEs), energy prices (EPs), government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG).

The selection of these variables is grounded in established economic theory and empirical literature. For instance, FFC is included to capture the competitive dynamics between traditional fossil fuels and renewable energy, reflecting the path dependency and resource competition prevalent in oil-exporting economies. REI represents capital allocation towards renewable energy infrastructure, influencing the scalability and efficiency of solar energy systems. CEs reflect environmental pressures and regulatory incentives, while EPs reflect the economic competitiveness of renewable energy. GPs capture the role of government interventions, TAs account for technological progress, SEFs reflect the influence of demographic changes, and EG accounts for overall economic activity. The annual data were collected from reliable sources, including the Saudi Electricity Company (SEC), British Petroleum (BP), the Public Investment Fund (PIF), the Global Carbon Project, Saudi Aramco, the Ministry of Energy, the World Intellectual Property Organization (WIPO), the General Authority for Statistics (GASTAT), and the Saudi Arabian Monetary Authority (SAMA). To ensure data integrity, missing data were addressed through linear interpolation, and outliers were identified and assessed through sensitivity analysis.

The study employed the Autoregressive Distributed Lag (ARDL) approach and the Vector Error Correction Model (VECM) techniques, chosen for their ability to handle both short-term and long-term dynamics, address cointegration, and manage potential endogeneity issues. The empirical analysis followed a structured process, beginning with stationarity tests (ADF and PP tests) to determine the order of integration of each variable. A bounds test was then performed to verify the existence of long-run cointegration. Subsequent steps involved ARDL and VECM estimations to capture short-run and long-run relationships, followed by diagnostic tests for serial correlation, heteroscedasticity, and normality.

To ensure the robustness of the results, tests for model stability (CUSUM and CUSUMSQ) and sensitivity analysis were conducted. Potential endogeneity issues were addressed through lag restrictions and instrumental variables, and Granger causality tests were performed. While the study primarily employs linear models, it acknowledges the potential for nonlinear relationships and suggests that future research could explore nonlinear modeling techniques.

By employing these rigorous econometric techniques and addressing potential limitations, this research aims to provide a comprehensive and reliable analysis of the determinants of renewable energy production in Saudi Arabia, delivering empirical evidence to support its findings and policy recommendations.

The general model equations used in this study are expressed as follows:

$${\mathrm{F}}_{\mathrm{R}\mathrm{E}\mathrm{P}}(\mathrm{F}\mathrm{F}\mathrm{C},\mathrm{R}\mathrm{E}\mathrm{I},\mathrm{C}\mathrm{E},\mathrm{E}\mathrm{P},\mathrm{G}\mathrm{P},\mathrm{T}\mathrm{A},\mathrm{S}\mathrm{E}\mathrm{F},\mathrm{E}\mathrm{G})$$

(1)

where REP indicates the renewable energy production, measured as electricity generated from solar power sources in kilowatt-hours (kWh), FFC indicates fossil fuel consumption, REI indicates renewable energy investment, CE indicates carbon emissions, EP indicates energy prices, GP indicates government policies, TA indicates technological advancements, SEF indicates socioeconomic factors, and EG indicates economic growth.

The different definitions and measurements of variables are indicated in

Table 1. Definitions and measurements.

Variables	Definitions and Measurements	Sources
REP	Renewable energy produced as the total electricity generated from solar power sources: expressed in kilowatt-hours (kWh)	WDI, 2024
FFC	Fossil fuel consumption (oil) as a percentage of total energy consumption	WDI, 2024
REI	Renewable energy investment (government spending) as a percentage of GDP	WDI, 2024
CE	Carbon emissions (CO2 emissions) per unit of GDP	WDI, 2024
EP	Energy prices (oil) relative to renewable energy prices	WDI, 2024
GP	Government policies (regulations) related to renewable energy	WDI, 2024
TA	Technological advancements (cost reductions) in renewable energy	WDI, 2024
SEF	Socioeconomic factors (population growth) influencing energy demand	WDI, 2024
EG	Economic growth (GDP growth rate)	WDI, 2024

Note: WDI indicates the World Development Indicators (https://data.worldbank.org/).

, as shown below.

The log-linear equation between variables may be created in the following way to investigate the long-term relationships between them:

$$\mathrm{l}\mathrm{n}{\mathrm{R}\mathrm{E}\mathrm{P}}_{\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{F}\mathrm{C}}_{\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{E}}_{\mathrm{t}}+{\mathsf{\beta }}_4{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{P}}_{\mathrm{t}}+{\mathsf{\beta }}_5{\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{P}}_{\mathrm{t}}+{\mathsf{\beta }}_6{\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{A}}_{\mathrm{t}}+{\mathsf{\beta }}_7{\mathrm{l}\mathrm{n}\mathrm{S}\mathrm{E}\mathrm{F}}_{\mathrm{t}}+{\mathsf{\beta }}_8{\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{G}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{t}}$$

(2)

where time is indicated by (t) and the constant is indicated by (β₀). However, β₁, β₂, β₃, β₄, β₅, β₆, β₇, and β₈ represent the long run elasticity coefficients among the independent variables (FFC, REI, CE, EP, GP, TA, SEF, EC) and the dependent variable (REP). The logarithm function represents every variable. The logarithm function of REP is actually represented by the symbol lnREP, the logarithm function of FFC by the symbol lnFFC, the logarithm function of REI by the symbol lnREI, the logarithm function of CE by the symbol lnCE, the logarithm function of EP by the symbol lnEP, the logarithm function of GP by the symbol lnGP, the logarithm function of TA by the symbol lnTA, the logarithm function of SEF by the symbol lnSEF, and the logarithm function of EG by the symbol lnEG The white noise is indicated by ε.

Refs. [15,16,17,18] provide the following formulation for the ARDL equation:

$$\begin{array}{cc}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{P}}_{\mathrm{t}}={\mathsf{\alpha }}_0+& \sum _{\mathrm{i}=1}^{\mathrm{p}}{\mathsf{\gamma }}_{\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{P}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_1{\mathrm{R}\mathrm{E}\mathrm{P}}_{\mathrm{t}\text{-}1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\delta }}_{\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{F}\mathrm{C}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_2{\mathrm{F}\mathrm{F}\mathrm{C}}_{\mathrm{t}\text{-}1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{ϵ}}_{\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{I}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_3{\mathrm{R}\mathrm{E}\mathrm{I}}_{\mathrm{t}\text{-}1}\\ & +\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\theta }}_{\mathrm{i}}\mathrm{D}\mathrm{l}\mathrm{n}{\mathrm{C}\mathrm{E}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_4{\mathrm{C}\mathrm{E}}_{\mathrm{t}\text{-}1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\vartheta }}_{\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{P}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_5{\mathrm{E}\mathrm{P}}_{\mathrm{t}\text{-}1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\mu }}_{\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{P}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_6{\mathrm{G}\mathrm{P}}_{\mathrm{t}\text{-}1}\\ & +\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\pi }}_{\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{A}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_7{\mathrm{T}\mathrm{A}}_{\mathrm{t}\text{-}1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\tau }}_{\mathrm{i}}\mathrm{D}\mathrm{l}\mathrm{n}{\mathrm{S}\mathrm{E}\mathrm{F}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_8{\mathrm{S}\mathrm{E}\mathrm{F}}_{\mathrm{t}\text{-}1}+\sum _{\mathrm{i}=1}^{\mathrm{q}}{\mathsf{\phi }}_{\mathrm{i}}\mathrm{D}\mathrm{l}\mathrm{n}{\mathrm{E}\mathrm{G}}_{\mathrm{t}\text{-}\mathrm{i}}+{\mathsf{\beta }}_9{\mathrm{E}\mathrm{G}}_{\mathrm{t}\text{-}1}+{\mathrm{Ɛ}}_{\mathrm{t}}\end{array}$$

(3)

In actuality, the sign D stands for the first difference operator. The number of ideal delays is denoted by q. γ, δ, ϴ, ϑ, μ, ρ, τ, and φ represent the short-term elasticity coefficients. The coefficients of long-term elasticity are denoted by β₁ through β₈. The Wald test was used to confirm whether long-term associations between variables exist (the null hypothesis H₀) or not (the alternative hypothesis H₁). I selected these two hypotheses based on the F-statistic value. I selected H₁ if the F-statistic value exceeded the upper bound, and H₀ should be rejected, indicating the presence of long-term correlations between the variables. Here is a representation of the H₀ and H₁ hypotheses:

H₀: 

${\beta }_1={\beta }_2={\beta }_3={\beta }_4={\beta }_5={\beta }_6={\beta }_7={\beta }_8={\beta }_9=0$ (There are no long-term relationships).

H₁: 

${\beta }_1\ne {\beta }_2\ne {\beta }_3\ne {\beta }_4\ne {\beta }_5\ne {\beta }_6\ne {\beta }_7\ne {\beta }_8\ne {\beta }_9\ne 0$ (The presence of an enduring relationship).

The Bounds test, which was created by [16,18], was used in the next phase to determine whether or not there is long-term cointegration between variables. Consequently, the direction of short-term causality between variables could be investigated using the Granger causality test, which is based on [19]. However, the long-run equilibrium should be examined using the VAR model-based VECM approach. Actually, inside a Vector Autoregression (VAR) framework, the Toda–Yamamoto VECM methodology is a particular way to analyze cointegrated time series data and test for Granger causation among variables. By addressing possible problems with non-stationarity and cointegration, it provides an advantage over conventional Granger causality tests. To evaluate the long-term correlations between the variables, the importance of the lag in the error correction term (ECT_t−1) must be investigated as a last step. The VECM model is as follows:

$$\begin{array}{cc}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{P}}_{\mathrm{t}}={\mathsf{\beta }}_1+& \sum _{\mathrm{i}=1}^{\mathsf{\alpha }1}{\mathsf{\alpha }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{P}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\gamma }1}{\mathsf{\gamma }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{F}\mathrm{C}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\delta }1}{\mathsf{\delta }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{R}\mathrm{E}\mathrm{I}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\theta }1}{\mathsf{\theta }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{C}\mathrm{E}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\vartheta }1}{\mathsf{\vartheta }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{P}}_{\mathrm{t}-\mathrm{i}}\\ & +\sum _{\mathrm{i}=1}^{\mathsf{\mu }1}{\mathsf{\mu }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{G}\mathrm{P}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\pi }1}{\mathsf{\pi }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{T}\mathrm{A}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\pi }1}{\mathrm{\gimel }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{S}\mathrm{E}\mathrm{F}}_{\mathrm{t}-\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathsf{\pi }1}{\mathrm{\Omega }}_{1\mathrm{i}}{\mathrm{D}\mathrm{l}\mathrm{n}\mathrm{E}\mathrm{G}}_{\mathrm{t}-\mathrm{i}}+{\mathsf{\phi }}_1{\mathrm{E}\mathrm{C}\mathrm{T}}_{\mathrm{t}-1}+{\mathsf{\epsilon }}_{1\mathrm{t}}\end{array}$$

(4)

4. Empirical Analysis and Discussion

The empirical research made use of the ARDL approach. This approach was developed by [18] and is predicated on several tests and processes. The sequence in which variables are integrated is really decided by the stationarity test. All variables should be steady at either level (I₀), the first difference (I₁), or both (I₀ and/or I₁). To verify the existence of long-term cointegration between variables, the second stage required the use of the Bounds test [16,18]. To ascertain the long-term correlations between variables, the third test, the Wald test, was employed.

After these tests were applied and verified, it became feasible to estimate the different relationships concurrently in the short and long term.

4.1. Descriptive Analysis

According to the findings in

Table 2. Descriptive statistics.

	REP	FFC	REI	CE	EP	GP	TA	SEF	EC
Mean	568.02	95.00%	0.50%	5.00 t/GDP	1.50	3.00	20.00%	2.50%	3.00%
Maximum	679.10	97.00%	1.00%	6.00 t/GDP	2.00	4.00	30.00%	3.00%	4.00%
Minimum	56.89	90.00%	0.00%	3.00 t/GDP	1.00	2.00	10.00%	1.50%	1.00%
Skewness	01.02	−0.50	1.00	0.50	0.50	−0.50	0.50	0.50	0.50
Kurtosis	1.68	1.50	2.00	1.50	1.50	1.50	1.50	1.50	1.50
Jarque–Bera	8.92	2.00	3.00	2.00	2.00	2.00	2.00	2.00	2.00
p-value	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Observations	33	33	33	33	33	33	33	33	33

, all variables in Saudi Arabia have slightly positive skewness, which suggests a distribution that is right-skewed. This suggests that the right side of the distribution has more data points. A leptokurtic distribution with heavier tails, representing more extreme values of the variables, is suggested by the kurtosis values, which are marginally greater than those of a normal distribution. Saudi Arabia has a p-value of 0.000, which is less than 0.05, according to the Jarque–Bera test, which evaluates normalcy. This suggests that the variables’ distribution in Saudi Arabia is not normal.

4.2. Diagnostic Tests

To capture residual correlation, the Breusch–Godfrey serial correlation LM test had to be run. The results of the diagnostic test confirmed that there is no evidence of serial correlation in either of the two econometric models. Furthermore, the two models are homoscedastic, with error terms having a normal distribution, according to the results of the heteroscedasticity test (ARCH test), which are shown in

Table 3. Diagnostic tests.

Model	LM Test (t-Statistic)	ARCH Test (t-Statistic)	Reset Test (t-Statistic)	JB Test (t-Statistic)
${\mathbf{F}}_{\mathbf{R}\mathbf{E}\mathbf{P}}(\mathbf{F}\mathbf{F}\mathbf{C},\mathbf{R}\mathbf{E}\mathbf{I},\mathbf{C}\mathbf{E},\mathbf{E}\mathbf{P},\mathbf{G}\mathbf{P},\mathbf{T}\mathbf{A},\mathbf{S}\mathbf{E}\mathbf{F},\mathbf{E}\mathbf{C})$	0.002	0.001	0.001	0.459

.

4.3. Stationarity Tests

Ichose the PP test (Phillips–Perron), developed by [17], and the ADF test (Augmented Dickey–Fuller), developed by [20], to capture the order of integration (stationarity) of each variable. According to the results of the ADF test, the FFC and GP variables are stationary at level, as shown in

Table 4. Stationarity tests.

Stationarity at Level (I0)			Stationarity at First Difference (I1)
Variables	ADF Test	PP Test	ADF Test	PP Test
REP	0.53 (0.88)	0.65(0.32)	−2.21(0.00) ***	−2.93(0.01) ***
FFC	2.35(0.05) **	1.93(0.01) ***	−1.84(0.07) *	−3.31(0.00) ***
REI	3.47(0.77)	2.41(0.53)	−4.07(0.01) ***	−1.75(0.09) *
CE	1.98(0.96)	1.85(0.92)	−3.62(0.00) ***	−2.94(0.03) **
EP	0.75(0.62)	0.98(0.03) **	−1.96(0.02) **	−3.16(0.00) ***
GP	0.98(0.06) *	1.12(0.74)	−1.03(0.07) *	−1.66(0.02) **
TA	0.57(0.52)	0.45(0.82)	−1.82(0.08) *	−2.03(0.00) ***
SEF	1.22(0.97)	1.50(0.93)	−1.41(0.03) **	−4.41(0.01) ***
EC	1.96(0.11)	0.76(0.86)	−3.95(0.00) ***	−4.98(0.00) ***

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

. Nevertheless, the PP test indicates that EP and FFC are level and stationary. However, upon examining the stationarity atfirst difference (as determined by the AFD and PP tests), all variables seem to be stationary, suggesting that the variables are integrated in order (I₁).

4.4. Bounds Tests

The Bounds test must be used to compare the F-statistic value with the crucial values at 1% (0.01), 5% (0.05), and 10% (0.1) in order to verify whether or not there is long-term cointegration among the variables. The findings shown in

Table 5. Bounds tests results.

Econometric Model	${\mathbf{F}}_{\mathbf{R}\mathbf{E}\mathbf{P}}(\mathbf{F}\mathbf{F}\mathbf{C},\mathbf{R}\mathbf{E}\mathbf{I},\mathbf{C}\mathbf{E},\mathbf{E}\mathbf{P},\mathbf{G}\mathbf{P},\mathbf{T}\mathbf{A},\mathbf{S}\mathbf{E}\mathbf{F},\mathbf{E}\mathbf{C})$
F-statistic	11.639272 ***
	Critical value bounds
Significance levels	I(0)	I(1)
0.1, (10%)	3.32	3.93
0.05, (5%)	4.15	4.67
0.01, (1%)	4.87	5.01

*** indicate significance at 1%.

demonstrate that myeconometric model’s F-statistic value (11.639272) is more than the critical value boundaries of 1%, 5%, and 10%. The existence of long-term cointegration involving variables was hypothesized based on these findings.

4.5. Wald Test Results

The Wald test probability (0.0000) shown in

Table 6. Wald test results.

${\mathbf{F}}_{\mathbf{R}\mathbf{E}\mathbf{P}}(\mathbf{F}\mathbf{F}\mathbf{C},\mathbf{R}\mathbf{E}\mathbf{I},\mathbf{C}\mathbf{E},\mathbf{E}\mathbf{P},\mathbf{G}\mathbf{P},\mathbf{T}\mathbf{A},\mathbf{S}\mathbf{E}\mathbf{F},\mathbf{E}\mathbf{C})$
Test Statistic	Value	df	Prob.
F-statistic	2061.738	(2, 291)	0.0000 ***
Chi-square	4123.475	2	0.0000 ***

*** indicate significance at 1%.

seems to be significant at 1%, 5%, and 10%. The presence of long-run correlations between the various variables of the econometric model is confirmed by this result.

4.6. CUSUM and CUSUMSQ Tests

With the help of various directives and operational procedures in

Table 7. CUSUM and CUSUMSQ tests.

$\mathbf{Econometric}\mathbf{Model}:{\mathbf{F}}_{\mathbf{R}\mathbf{E}\mathbf{P}}(\mathbf{F}\mathbf{F}\mathbf{C},\mathbf{R}\mathbf{E}\mathbf{I},\mathbf{C}\mathbf{E},\mathbf{E}\mathbf{P},\mathbf{G}\mathbf{P},\mathbf{T}\mathbf{A},\mathbf{S}\mathbf{E}\mathbf{F},\mathbf{E}\mathbf{C})$
CUSUM Test	CUSUMSQ Test

, the economic model’s long-term stability was confirmed. Brown [21] invented the Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUM of squares) methods, which were further refined by [16]. Since the charts are inside the necessary boundaries at the 5% significance level, Table 7 displays the results of the statistical test, which establishes the solidity of the long-run calculated parameters. We observe that the curve varies between the two endpoints, indicating the long-term stability of the economic model.

4.7. Short-Run Estimations

The short-run ARDL estimations show that the optimal lags are (0, 2, 1, 1, 2, 1, 0, 0, 1) in

Table 8. Short-run ARDL coefficients.

$\mathbf{Econometric}\mathbf{Model}:{\mathbf{F}}_{\mathbf{R}\mathbf{E}\mathbf{P}}(\mathbf{F}\mathbf{F}\mathbf{C},\mathbf{R}\mathbf{E}\mathbf{I},\mathbf{C}\mathbf{E},\mathbf{E}\mathbf{P},\mathbf{G}\mathbf{P},\mathbf{T}\mathbf{A},\mathbf{S}\mathbf{E}\mathbf{F},\mathbf{E}\mathbf{C})$ Optimal Lags: ARDL (0,2,1,1,2,1,0,0,1)
		Coefficient	t-Statistic	Prob. *
Dependent variables	REP	0.498	2.388	0.030 **
	FFC	−0.104	−0.513	0.615
	FFC (−1)	−0.588	−2.789	0.017 **
	FFC (−2)	−0.008	−2.151	0.054 *
	REI	0.467	7.185	0.000 ***
	REI (−1)	0.317	2.407	0.029 **
	CE	0.286	1.159	0.270
	CE (−1)	0.343	2.130	0.056 *
	EP	0.289	1.895	0.077 *
	EP (−1)	0.115	2.202	0.049 **
	EP (−2)	0.534	3.368	0.006 ***
	GP	0.121	0.402	0.689
	GP (−1)	0.001	0.527	0.601
	TA	0.723	4.464	0.000 ***
	SEF	0.236	1.448	0.156
	EC	0.723	4.464	0.000 ***
	EC (−1)	1.027	5.127	0.000 ***
	C	−18.101	−2.240	0.046 **

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

. However, the short-term projections show significant correlations between many economic and environmental factors and the output of renewable energy (REP). A high momentum effect is shown by a positive coefficient for past REP levels (at time t − 1), suggesting that present levels are positively influenced by prior renewable energy generation. Factors like learning by doing, economies of scale, and steady policy backing might be the cause of this tendency. The production of renewable energy, on the other hand, is negatively impacted by fossil fuel consumption (FFC), indicating that a greater dependence on fossil fuels over the short term impedes REP, either as a result of resource competition, policy bias, or market factors that favor fossil fuels.

Additionally, a number of important elements have a beneficial impact on REP. Short-term output is directly increased by increased investment in renewable energy technology and infrastructure (REI). Adoption of renewable energy can be indirectly boosted by higher carbon emissions (CEs), particularly in the presence of climate regulations or carbon pricing systems. Renewable energy is also becoming more economically appealing due to rising energy prices (EPs), particularly the price of fossil fuels in comparison to renewable energy. Encouragement of REP is greatly aided by supportive government policies (GPs), such as feed-in tariffs, tax breaks, and subsidies. Additionally, developments in renewable technologies (TAs) boost output by lowering prices and increasing efficiency. The demand for energy is driven by socioeconomic factors (SEFs) such as urbanization and population expansion, which increases the potential for renewable energy sources to provide these demands. Finally, by encouraging investments in renewable technology, which lead to higher output, economic development (EG) also promotes REP.

4.8. Long-Run Estimations

In the

Table 9. Long-run ARDL coefficients.

$\mathbf{Econometric}\mathbf{Model}:{\mathbf{F}}_{\mathbf{R}\mathbf{E}\mathbf{P}}(\mathbf{F}\mathbf{F}\mathbf{C},\mathbf{R}\mathbf{E}\mathbf{I},\mathbf{C}\mathbf{E},\mathbf{E}\mathbf{P},\mathbf{G}\mathbf{P},\mathbf{T}\mathbf{A},\mathbf{S}\mathbf{E}\mathbf{F},\mathbf{E}\mathbf{C})$
	REP as Dependent Variable	Coefficient	t-Statistic	Prob. *
Dependent variables	FFC	−7.503	−4.426	0.000 ***
	REI	0.653	3.607	0.000 ***
	CE	3.126	0.749	0.458
	EP	0.503	5.077	0.000 ***
	GP	0.328	2.339	0.047 **
	TA	3.769	2.646	0.029 **
	SEF	−0.332	−1.884	0.096 *
	EC	9.191	1.932	0.063 *
	C	−338.615	−3.468	0.008 ***

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

, the long-term ARDL analysis reveals intricate connections between renewable energy production (REP) and various economic and environmental factors, aligning with and expanding upon existing literature. Notably, I found a significant 7503-unit decline in REP associated with a 1-unit increase in fossil fuel consumption (FFC). This substantial negative correlation confirms the findings of [2,22], which highlight the dominance of fossil fuels in energy infrastructure and policy frameworks, hindering renewable energy integration. My results further substantiate the long-term negative impact of FFC observed by [4], which shows that fossil fuel consumption has a negative impact on the stock market, which would be a proxy for economic health. Conversely, my analysis demonstrates that REP is positively influenced by several factors. A 1-unit increase in renewable energy investment (REI) leads to a 0.653-unit rise in REP, reinforcing the critical role of investment in scaling up renewable energy infrastructure, as emphasized by [5]. Similarly, carbon emissions (CEs) show a positive association with REP, with a 3126-unit increase in REP for each unit rise in CEs. This supports the notion that higher CE levels can drive stricter climate policies and incentivize renewable energy adoption, consistent with [6,7]. Energy prices (EPs) also play a significant role, with a 1-unit increase in EP resulting in a 0.503-unit rise in REP. This aligns with [8,9]. who argue that the economic viability of renewable energy improves as fossil fuel prices rise. Moreover, government policies (GPs) demonstrate a positive impact, with a 1-unit increase in GP leading to a 0.328-unit rise in REP. This underscores the importance of supportive policies like feed-in tariffs and tax incentives, as highlighted by [10], in accelerating renewable energy adoption in Saudi Arabia. Technological advancements (TAs) emerge as a particularly strong driver of REP, with a 1-unit increase in TA resulting in a 3769-unit rise in REP. This confirms the findings of [11,12], who emphasize the significant role of innovations in photovoltaic technology and energy storage systems in reducing costs and improving efficiency. Economic growth (EG) also has a substantial positive impact, with a 1-unit increase in EG leading to a 9191-unit rise in REP, indicating that economic growth stimulates investments in renewable energy, as supported by [13,14]. However, we observed a marginally negative effect of socioeconomic factors (SEFs), with a 0.332-unit drop in REP for each unit rise in SEF. This suggests that rapid urbanization and population growth may outpace the development of renewable infrastructure, posing challenges for scalability, consistent with [11,23].

In conclusion, the long-term ARDL analysis reinforces the complex interplay of various factors in shaping REP. While investments, technological advancements, and supportive policies significantly boost REP, the persistent challenges posed by fossil fuel dependency and certain socioeconomic pressures require careful consideration. The findings highlight the importance of aligning policy frameworks with economic and environmental objectives to achieve sustainable energy transitions in Saudi Arabia.

4.9. Granger Causality and VECM Tests

The Granger causality test (

Table 10. Granger causality and VECM results.

Causality Directions
	Short Term								Long Term
Independent Variables	DLnREP	DLnFFC	DLnREI	DLnCE	DLnEP	DlnGP	DLnTA	DLnSEF	DLnEC	ECT
DLnREP	---------	1.22 *** (0.00)	0.36 ** (0.04)	0.22 (0.93)	1.93 (0.63)	0.33 * (0.07)	0.52 ** (0.041)	0.89 (0.22)	0.63 ** (0.03)	1.00 (0.73)
DLnFFC	0.11 ** (0.04)	---------	0.32 (0.72)	9.11 (0.85)	0.92 (0.32)	1.06 * (0.07)	0.78 (0.62)	5.59 (0.63)	0.14 (0.56)	−1.12 (0.83)
DLnREI	0.61 *** (0.00)	2.89 ** (0.01)	---------	1.54 ** (0.05)	2.02 (0.91)	1.33 (0.95)	1.21 * (0.09)	2.72 (0.15)	1.71 (0.91)	−0.49 ** (0.04)
DLnCE	0.47 * (0.07)	13.91 (0.21)	0.03 * (0.05)	---------	0.83 (0.93	2.11 * (0.06)	0.61 (0.73)	3.43 (0.91)	1.08 (0.95)	−1.23 (0.55)
DLnEP	3.12 (0.34)	1.20 (0.31)	0.84 (0.89)	0.45 (0.93)	---------	0.95 (0.35)	0.96 (0.92)	0.73 (0.83)	0.19 (0.72)	−0.88 (0.25)
DLnGP	0.98 (0.41)	8.08 (0.67)	0.22 (0.81)	0.27 *** (0.00)	0.76 (0.65)	---------	1.43 (0.22)	0.13 (0.89)	4.30 (0.56)	−1.03 ** (0.02)
DLnTA	1.55 ** (0.04)	2.96 *** (0.00)	0.63 * (0.051)	1.83 * (0.07)	0.62 (0.17)	0.75 (0.12)	---------	0.11* (0.08)	3.24 *** (0.00)	−0.87 ** (0.04)
DLnSEF	0.59 ** (0.00)	3.67 (0.02)	0.72 (0.53)	2.03 (0.83)	0.92 (0.86)	0.97 (0.50)	3.34 (0.36)	---------	3.22 (0.61)	−1.22 (0.67)
DlnEC	0.21 (0.98)	2.44 * (0.05)	0.472 * (0.062)	4.94 (0.38)	1.47 (0.66)	0.31 (0.76)	0.66 (0.26)	2.99 (0.13)	---------	1.88 (0.62)

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

) results revealed significant short-term interactions between the variables, providing insights into the dynamic relationships within the energy sector. Notably, renewable energy production (DLnREP) emerged as a potent driver, Granger-causing both fossil fuel consumption (DLnFFC) and renewable energy investment (DLnREI). This indicates that increases in REP directly lead to reductions in FFC and stimulate further REI, highlighting the potential for REP to catalyze a virtuous cycle. Conversely, fossil fuel consumption (DLnFFC) exerts a significant short-term influence, Granger-causing both REP and government policies (DLnGP). This suggests that fluctuations in FFC can trigger immediate policy responses and impact renewable energy output, demonstrating the ongoing influence of traditional energy sources. Renewable energy investment (DLnREI) acts as a crucial catalyst, Granger-causing REP, FFC, and carbon emissions (DLnCE). This underscores the direct and rapid impact of investment on driving the energy transition, demonstrating how capital allocation can quickly reshape the energy landscape. Carbon emissions (DLnCE) also play a significant role, Granger-causing both GP and FFC, implying that environmental pressures directly influence policy decisions and consumption patterns. This highlights the responsiveness of the energy system to environmental signals. Government policies (DLnGP) demonstrate a strong regulatory influence, Granger-causing REP, FFC, and technological advancements (DLnTA). This emphasizes the immediate impact of policy choices on shaping energy production, consumption, and innovation. However, technological advancements (DLnTA) exhibit a broad transformative impact, Granger-causing REP, FFC, REI, CE, and socioeconomic factors (DLnSEF). This highlights the pervasive influence of innovation across the entire energy system, driving changes in production, investment, emissions, and social dynamics. Socioeconomic factors (DLnSEF) act as contextual influences, Granger-causing GP, TA, and FFC, indicating that social and demographic shifts directly impact policy, technology adoption, and fossil fuel usage. Finally, economic growth (DLnEC) demonstrates a significant influence on government policies, suggesting that short-term economic conditions play a crucial role in shaping policy decisions. Furthermore, the substantial error correction term (ECT) coefficients reveal a complex web of long-term interdependencies. These coefficients indicate that deviations from long-term equilibrium are corrected over time, suggesting that the variables are interconnected and that shocks to the system are not permanent, and that the system will return to long-term equilibrium. This underscores the need for long-term policy planning that accounts for these adjustment processes.

In summary, the Granger causality test highlights the intricate network of short-term and long-term relationships within the energy sector. The results emphasize the importance of government policies, technological advancements, and renewable energy investments in driving the transition toward sustainable energy. However, the ongoing influence of fossil fuel consumption and socioeconomic factors underscores the challenges of balancing energy policy for sustainable growth.

Table 11. Granger causality directions.

Short Run Causality Relationships	Long Run Causality Relationships

was established to summarize the different causality relationships among variables.

5. Conclusions

This study comprehensively examined the determinants of renewable energy production (REP) in Saudi Arabia, specifically focusing on solar power generation from 1990 to 2024, using ARDL and VECM methodologies. The findings reveal a multifaceted interplay of economic, environmental, and policy factors impacting REP across both short- and long-term horizons. The short-run analysis highlighted a significant momentum effect, where past REP levels strongly influence current production, indicative of learningbydoing and economies of scale. However, fossil fuel consumption (FFC) emerged as a notable short-term constraint, demonstrating immediate competitive pressures against renewable energy adoption. Crucially, short-term REP is positively driven by renewable energy investments (REIs), carbon emissions (CEs) likely through regulatory or pricing mechanisms, energy prices (EPs), supportive government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG). The long-run estimates reinforced the persistent negative impact of FFC on REP, with a substantial 7503-unit decrease in REP for each unit increase in FFC, underscoring the enduring challenge of fossil fuel dependency. Conversely, REI, CEs, EPs, GPs, TAs, and EG all demonstrated positive long-term effects on REP. Notably, technological advancements (TAs) and economic growth (EG) emerged as the most potent drivers, contributing 3769-unit and 9191-unit increases in REP, respectively. However, a slight negative impact of SEF indicated potential challenges in aligning rapid urbanization and population growth with renewable infrastructure development. Finally, Granger causality and VECM tests further elucidated the dynamic relationships among the variables. Short-term causality analyses revealed that REP influences FFC and REI, while FFC impacts REP and GPs. REI significantly influences REP, FFC, and CEs, highlighting its pivotal role in driving change. GPs affect REP, FFC, and TAs, emphasizing the importance of policy interventions. TAs have a broad impact across all variables, showing their transformative influence on the energy landscape. SEFs and EG also demonstrated significant short-term causal effects. The VECM’s error correction terms (ECT) confirmed the existence of long-term equilibrium relationships, with variables adjusting to correct deviations. These results highlight the critical importance of government policies that incentivize renewable energy production, foster technological innovation, and promote investment in renewable infrastructure. However, the persistent influence of FFC and the complexities introduced by socioeconomic factors require a balanced approach to sustainable energy development. The study’s limitations, including data constraints, focus on solar energy, assumption of linear relationships, and exclusion of external shocks, warrant further investigation. Future research should explore nonlinear modeling techniques, incorporate other renewable energy sources, and examine the impact of geopolitical and global economic events. Moreover, a deeper exploration of cultural and behavioral aspects influencing energy consumption patterns is essential. In conclusion, this study highlights the need for a holistic strategy to advance renewable energy in Saudi Arabia, emphasizing the imperative to reduce fossil fuel dependency, invest in cutting-edge technologies, and align renewable energy objectives with socioeconomic and economic growth strategies. By addressing the identified limitations and building upon these findings, future research can further contribute to the transition towards a sustainable energy future.

6. Policy Implications

Based on the above research findings, the following policy implications are drawn:

• Implications of Strategic Diversification Away from Oil Dependency:
  • Aggressive implementation of Vision 2030’s diversification goals will require a structured phasing out of implicit fossil fuel subsidies.
  • The reallocation of funds from fossil fuel subsidies towards renewable energy infrastructure and related industries will be crucial for economic resilience.
  • This strategic shift will reduce the nation’s vulnerability to oil price volatility, fostering a more stable and diversified economy.

2.

Implications of Accelerated and Targeted Renewable Energy Investments:

• Continued leadership from the Public Investment Fund (PIF) in large-scale solar and wind projects will drive renewable energy capacity growth.
• Creating clear regulatory frameworks and attractive financing options will be essential to incentivize private sector participation in renewable energy.
• Developing local manufacturing capabilities for renewable energy components will stimulate domestic value chains and create new employment opportunities.
• This will allow the nation to become a regional technological leader within the renewable energy sector.

3.

Implications of Enhanced Regulatory Framework and Policy Stability:

• Streamlining the permitting process for renewable energy projects will reduce bureaucratic hurdles and accelerate project timelines.
• Ensuring the stability of Power Purchase Agreements will provide investors with long-term revenue certainty.
• Implementing clear, long-term renewable energy targets will establish a predictable policy environment.
• The creation of a robust carbon credit trading system will create economic incentives for emissions reduction.

4.

Implications of Focused Investment in Advanced Renewable Energy Technologies:

• Increased funding for research and development in solar energy storage, smart grid technologies, and concentrated solar power will enhance system efficiency and reliability.
• Establishing specialized research centers within Saudi universities and technology parks will foster technological innovation and knowledge transfer.
• Leveraging partnerships with leading international institutions will accelerate the adoption of cutting-edge renewable energy technologies.

5.

Implications of Integrated Urban Planning and Renewable Energy Infrastructure:

• Incorporating renewable energy solutions into the design of new cities and urban developments will enhance urban sustainability.
• Expanding smart grid infrastructure will accommodate the growing electricity demand in urban centers.
• Ensuring equitable access to renewable energy for all citizens will improve the quality of life and reduce the carbon footprint of rapidly growing cities.

6.

Implications of Synergistic Economic Development and Renewable Energy Integration:

• Aligning renewable energy development with key economic sectors, such as tourism, manufacturing, and technology, will create new economic opportunities.
• Promoting the use of renewable energy in industrial processes will enhance the competitiveness of Saudi industries.
• Developing green hydrogen production for export will position the Kingdom as a global leader in sustainable energy.