Search Results (73)

Saudi Vision 2030 seeks to improve social sustainability in the Kingdom through comprehensive economic and social reforms. This study investigates whether these reforms are associated with changes in subjective well-being (SWB), commonly understood as individuals’ experienced quality of life. Using Gallup World Poll data from 2013 to 2023 on Cantril ladder scores and affect balance in Saudi Arabia, we find increases in SWB following the implementation of Vision 2030 reforms, particularly for the affective component of SWB. While positive changes in SWB are observed across the entire population, the gains in affective well-being are especially pronounced among women. Blinder–Oaxaca decompositions show that improvements are associated with enhanced community basics, followed by greater income sufficiency, improved perceptions of social life, and an increased sense of personal freedom. Full article

This paper examines how artificial intelligence (AI) can be strategically deployed to enhance urban planning and environmental livability in Riyadh by generating data-driven, people-centric design interventions. Unlike previous studies that concentrate primarily on visualization, this research proposes an integrative appraisal framework that combines AI-generated design with site-specific environmental data and native vegetation typologies. This study was conducted across key jurisdictional areas including the Northern Ring Road, King Abdullah Road, Al Rabwa, Al-Malaz, Al-Suwaidi, Al-Batha, and King Fahd Road. Using AI tools, urban scenarios were developed to incorporate expanded pedestrian pathways (up to 3.5 m), dedicated bicycle lanes (up to 3.0 m), and ecologically adaptive green buffer zones featuring native drought-resistant species such as Date Palm, Acacia, and Sidr. The quantitative analysis of post-intervention outcomes revealed surface temperature reductions of 3.2–4.5 °C and significant improvements in urban esthetics, walkability, and perceived safety—measured on a five-point Likert scale with 80–100% increases in user satisfaction. Species selection was validated for ecological adaptability, minimal maintenance needs, and compatibility with Riyadh’s sandy soils. This study directly supports the Kingdom of Saudi Arabia’s Vision 2030 by demonstrating how emerging technologies like AI can drive smart, sustainable urban transformation. It aligns with Vision 2030’s urban development goals under the Quality-of-Life Program and environmental sustainability pillar, promoting healthier, more connected cities with elevated livability standards. The research not only delivers practical design recommendations for planners seeking to embed sustainability and digital innovation in Saudi urbanism but also addresses real-world constraints such as budgetary limitations and infrastructure integration. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

The penetration of renewable energy, especially solar and wind, is increasing globally to promote a sustainable environment. However, in the Middle East, this momentum is slower compared to other regions, primarily due to abundant local fossil fuel reserves and historically low energy prices. This trend is shifting, with several countries, including the Kingdom of Saudi Arabia (KSA), setting ambitious goals. Specifically, KSA’s Vision 2030 aims to generate 50% of its energy from renewable sources by 2030. Due to favorable conditions for solar and wind, various mega-projects have either been completed or are underway in KSA. This study analyzes the potential and reliability impact of these projects on the power system through a three-step process. In the first step, all major projects are identified, and data related to these projects, such as global horizontal irradiance, wind speed, temperature, and other relevant parameters, are collected. In the second step, these data are used to estimate the solar and wind potential at various sites, along with annual averages and seasonal averages for different extreme seasons, such as winter and summer. Finally, in the third step, a reliability assessment of power generation is conducted to evaluate the adequacy of renewable projects within the national power grid. This study addresses a gap in the literature by providing a region-specific reliability analysis using actual project data from KSA, which remains underexplored in existing research. Sequential Monte Carlo simulations are employed, and various reliability indices, including Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Supplied per Interruption (ENSINT), and Demand Not Supplied per Interruption (DNSINT) are analyzed. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability, with improvements observed across all reliability indices, demonstrating the viability of meeting Vision 2030 targets. Full article

The use of solar photovoltaic systems for power generation requires efficient battery energy storage systems to ensure a steady and constant supply for self-sufficient power generation and off-grid areas. “Vision 2030” is Saudi Arabia’s strategy for reducing the country’s dependence on oil by 50% through investment in clean, renewable resources by 2030. This paper reviews the latest advancements in battery technologies designed for solar photovoltaic panels through a detailed comparative analysis of performance, energy storage capacity, efficiency, lifespan, cost, safety, and environmental impact for residential applications in the Kingdom of Saudi Arabia and those available in the United States of America. The performance of the advanced lithium-ion battery technology available in the USA surpasses that in the Kingdom of Saudi Arabia. The findings underscore the need for investments by the Kingdom of Saudi Arabia in advanced battery manufacturing technologies to improve the availability of different battery types and capacities and achieve the objectives outlined in the Kingdom of Saudi Arabia’s Vision 2030. Full article

Saudi Arabia has emerged as a key middle power, leveraging niche diplomacy to expand its global influence. This study examines how the Kingdom strategically employs mediation, religious diplomacy, humanitarian aid, energy leadership, and sports diplomacy to shape regional and international dynamics. Unlike great powers, middle powers focus on specialized areas where they hold a comparative advantage, allowing them to exert influence despite material constraints. Saudi Arabia’s diplomatic recalibration aligns with its Vision 2030 agenda, prioritizing economic diversification, regional stability, and multilateral engagement. Saudi Arabia enhances its geopolitical significance by positioning itself as a mediator in regional conflicts, reinforcing its religious leadership, investing in sports diplomacy, and maintaining energy dominance. This study contributes to understanding middle power strategies in international relations, highlighting Saudi Arabia’s role as a stabilizing force in an evolving global order. Full article

As Saudi Arabia accelerates its Vision 2030 agenda, sustainable urban development has emerged as a critical pillar for economic diversification and climate resilience. This study investigates the role of green municipal bonds (GMBs) as a catalytic financing tool to address funding gaps in low-carbon infrastructure and renewable energy projects within the Kingdom’s arid, fossil-fuel-dependent context. Employing a mixed-methods approach—combining qualitative case studies of global best practices (e.g., Gothenburg, Cape Town) and quantitative analysis of Saudi municipal financial data—we evaluate the feasibility of GMBs in bridging fiscal shortfalls while aligning with environmental, social, and governance (ESG) criteria. The research introduces a novel conceptual framework that integrates regulatory harmonization, stakeholder coordination, and Shariah-compliant financial mechanisms, tailored to Saudi Arabia’s socio-economic and climatic realities. Key findings reveal that GMBs could cover 40% of municipal revenue gaps, attract global ESG investors, and reduce carbon emissions by 30% through projects such as NEOM’s renewable grids and Riyadh’s urban greening initiatives. By addressing underexplored intersections of fossil-fuel transitions, arid-climate governance, and Islamic finance, this study advances sustainable urban scholarship and offers actionable policy recommendations, including a phased roadmap for GMB adoption and the establishment of a Saudi Green Bond Taskforce. The results position Saudi Arabia as a regional leader in climate-resilient finance, providing replicable insights for resource-dependent economies pursuing carbon neutrality. Full article

This paper investigates the application of artificial intelligence (AI) in forecasting Saudi Arabia’s non-oil export trajectories, contributing to the Kingdom’s Vision 2030 objectives for economic diversification. A suite of machine learning models, including LSTM, Transformer variants, Ensemble Stacking, XGBRegressor, and Random Forest, was applied to historical export and GDP data. Among them, the Advanced Transformer model, configured with an increased attention head size, achieved the highest accuracy (MAPE: 0.73%), effectively capturing complex temporal dependencies. The Non-Linear Blending Ensemble, integrating Random Forest, XGBRegressor, and AdaBoost, also performed robustly (MAPE: 1.23%), demonstrating the benefit of leveraging heterogeneous learners. While the Temporal Fusion Transformer (TFT) provided a useful macroeconomic context through GDP integration, its relatively higher error (MAPE: 5.48%) highlighted the challenges of incorporating aggregate indicators into forecasting pipelines. Explainable AI tools, including SHAP analysis and Partial Dependence Plots (PDPs), revealed that recent export lags (lag1, lag2, lag3, and lag10) were the most influential features, offering critical transparency into model behavior. These findings reinforce the promise of interpretable AI-powered forecasting frameworks in delivering actionable, data-informed insights to support strategic economic planning. Full article

The construction sector in the Kingdom of Saudi Arabia has shown notable growth and progress as seen by the transformation of outdated neighborhoods into modern smart infrastructure that aligns with the 2030 vision of the nation. As a result of this development trend, which is led by the Jeddah region, many older buildings are being demolished, and a significant amount of concrete is being produced. Utilizing recycled aggregates from construction and demolition waste has been recognized as an effective approach to addressing the current environmental problems that our world faces. An appropriate process of treatment techniques must be incorporated into the production of recycled aggregate in order to enhance its properties and optimize its use. The purpose of this study is to determine the potential benefits of an alkaline Ca(OH)₂ activated by fly ash Class F (FA_F) for various concrete mixes composed using recycled concrete aggregate (RCA). Seven mixes were developed to determine the effects of several factors on the properties of recycled concrete aggregate (RCA), including the type of recycled aggregate, the activator used, and the impact of the filler material. The study assessed how different forms of recycled aggregate affected physical characteristics. The compressive strength, flexural strength, indirect tensile strength, and pull-out strength of the concrete mixes was measured. The aggregate water absorption of Jeddah concrete aggregate (JRCA) and variable concrete aggregate (VRCA) was evaluated. Activated JRCA water absorption drops to 1.7% while inactivated JRCA water absorption was 3.95%. The AIV values of JRCA rise to 18.4 while the AIV for NA is 13. The same trend complies with the findings, which demonstrated that the strength performance and other characteristics of the various concrete mixes under study were significantly impacted by the type of recycled aggregate utilized and the mix design. Among these, Jeddah recycled concrete aggregate (JRCA) showed promising results that could lead to a wider use of (JRCA) in civil engineering applications. Full article

This study investigates the effect of using renewable energy resources on sustainable development in Saudi Arabia. The research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM), with data collected from 180 respondents with expertise in energy and sustainability, using quantitative research design. The results suggest that hydropower and wind energy significantly contribute to sustainable development in the economic, environmental, and social dimensions. Despite this, the scalability of hydropower is restricted by Saudi Arabia’s geographical and resource limitations. On the other hand, wind energy has become a realistic alternative, especially along the Red Sea coast, where there are consistent wind speeds. Despite the Kingdom’s high levels of solar insolation, solar energy is highlighted more for its potential than it is for its currently measurable impact. Interestingly, the contributions from the three renewable energy sources were not statistically different from each other, suggesting the need to interpret the findings with care and to recognize the specific sustainability tradeoffs of different energy sources. Hydropower is applicable within the Kingdom’s geographical and resource context, but solar and wind energies are scalable and contextually relevant solutions. Therefore, the findings of this study emphasize the strategic value of a balanced and diversified renewable energy strategy to help Saudi Arabia to achieve its Vision 2030 goals and foster sustainable development. Full article

Background: Due to technological advancements, the demand for easily accessible and convenient healthcare services is rising globally. Thus, telehealth is gaining momentum that was previously unheard of. The Kingdom of Saudi Arabia (KSA) actively embraces digital innovation in the healthcare industry through its ambitious Vision 2030 initiative. This study aimed to assess the perceived usability of telehealth for personalized healthcare among the KSA adult population in the post-COVID-19 era. Methods: This cross-sectional study used a convenience sample of 975 adults from the general population in KSA. A digital survey was used for data collection through Survey Monkey software. It contained two sections: personal and health data and the Telehealth Usability Questionnaire (TUQ). The data was collected over three months (July–September 2024) and investigated using the multinomial logistic regression analysis. Results: This study showed that 59.8% of the participants have initiated telehealth service use before the COVID-19 pandemic, and the most frequently used services are the issuance of sick leave (30.6%), tele-prescription (29.0%), virtual consultation (20.0%), tele-mental health services (19.4%), telemonitoring (18.6), and tele-referral (18.2%). A high total telehealth usability score was observed among 60.4% of the participants: 72.7% perceive telehealth as highly useful, 76.4% perceive it as easy to use, 60.1% have high satisfaction and intention for future use, 57.4% perceive high interface quality, and 51.8% perceive a high interaction quality. Conversely, 45.8% perceive low reliability of the telehealth system. Multinomial logistic regression showed that low education (AOR = 2.04; 95% CI = 1.16–4.85) and using virtual consultation (AOR = 0.98; 95% CI = 0.38–0.99) were predictors of low telehealth usability. However, being female (AOR = 1.67; 95% CI = 1.27–2.55), being in higher education (AOR = 1.47; 95% CI = 1.02–2.34), and living in the central KSA region (AOR = 1.37; 95% CI = 1.10–2.23) were predictors of high telehealth usability. Working status, income, and presence of chronic disease were not statistically significant predictors. Conclusions: Telehealth services have been highly usable in KSA even before the COVID-19 pandemic, with commonly used asynchronous services. Many social, technological, and system-related factors could affect the user experience and system reliability. Hence, telehealth developments are recommended to overcome such barriers, with future initiatives focusing on the flexibility and convenience of telehealth systems. Full article

Saudi Vision 2030 is a game-changer for all aspects of the economy, including healthcare. This article provides a comprehensive overview of healthcare financing in the Kingdom of Saudi Arabia (KSA). It identifies key healthcare financing challenges that must be addressed to achieve the initiative’s envisioned health system goals. The review also examines and demonstrates how healthcare funds in the KSA are allocated among different healthcare services, to offer a perspective on resource use efficiency at various healthcare levels. This research used a mixed-method design which includes a literature review and secondary data analysis. A literature review was conducted aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. The secondary data were gathered from the reports and websites of government agencies, international organizations, and non-governmental organizations. Despite implementing significant reforms in its healthcare system, the share of private healthcare expenditure in total healthcare spending has seen only marginal growth. The current healthcare financing system appears insufficient to adequately support the chronically ill and the poor. There is a significant imbalance in the allocation of government budgets between hospitals and primary care, with four-fifths of financial resources directed towards hospital services. The Ministry of Health’s budget allocation prioritizes personnel compensation, potentially reducing the available budget for medicines and other essential healthcare supplies. Ongoing reforms in the health sector, including privatization, public–private partnership initiatives, and the government’s commitment to developing a robust primary healthcare network, are expected to play a significant role in controlling rapidly increasing public healthcare expenditures in Saudi Arabia. Full article

Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness about the disease. Diabetes impacts the human body in various ways, one of the most serious being diabetic retinopathy (DR), which can result in severely reduced vision or even blindness if left untreated. Therefore, an effective early detection and diagnosis system is essential. As part of the Kingdom of Saudi Arabia’s Vision 2030 initiative, which emphasizes the importance of digital transformation in the healthcare sector, it is vital to equip healthcare professionals with effective tools for diagnosing DR. This not only ensures high-quality patient care but also results in cost savings and contributes to the kingdom’s economic growth, as the traditional process of diagnosing diabetic retinopathy can be both time-consuming and expensive. Methods—Artificial intelligence (AI), particularly deep learning, has played an important role in various areas of human life, especially in healthcare. This study leverages AI technology, specifically deep learning, to achieve two primary objectives: binary classification to determine whether a patient has DR, and multi-class classification to identify the stage of DR accurately and in a timely manner. The proposed model utilizes six pre-trained convolutional neural networks (CNNs): EfficientNetB3, EfficientNetV2B1, RegNetX008, RegNetX080, RegNetY006, and RegNetY008. In our study, we conducted two experiments. In the first experiment, we trained and evaluated different models using fundus images from the publicly available APTOS dataset. Results—The RegNetX080 model achieved 98.6% accuracy in binary classification, while the EfficientNetB3 model achieved 85.1% accuracy in multi-classification, respectively. For the second experiment, we trained the models using the APTOS dataset and evaluated them using fundus images from Al-Saif Medical Center in Saudi Arabia. In this experiment, EfficientNetB3 achieved 98.2% accuracy in binary classification and EfficientNetV2B1 achieved 84.4% accuracy in multi-classification, respectively. Conclusions—These results indicate the potential of AI technology for early and accurate detection and classification of DR. The study is a potential contribution towards improved healthcare and clinical decision support for an early detection of DR in Saudi Arabia. Full article

Riyadh, the capital of the Kingdom of Saudi Arabia, is now presenting itself as one of the most attractive capitals in the Middle East, with a number of ambitious expansion projects that aim to develop the city and align its urban development with the goals of Vision 2030. This urban renaissance requires researchers to adopt evaluation approaches (BSAMs) based on comprehensive sustainability criteria represented by environmental and cultural sustainability, community engagement, and economic feasibility. This research relies on the ETE methodology to determine evaluation criteria and their weights, which is a systematic and interactive method of prediction based on the opinion of a group of experts, or what is known as the Delphi method. Experts answered questionnaires to determine the weights of the criteria in three rounds where they received an anonymous summary of the experts’ predictions from the previous round with the reasons they provided for their judgments. The responses were then analyzed to identify recurring and converging themes and contradictions using the top-of-priority similarity to ideal solution (TOPSIS) technique, thus deriving an integrated evaluation model. The model was applied to evaluate architectural practices in Riyadh through three major projects: the King Abdullah Petroleum Studies and Research Center, the King Abdullah Financial District, and the King Abdullah Financial District Metro Station. Data sources included comprehensive site visits, detailed project documentation, and expert evaluation through structured questionnaires to gain a realistic view of attitudes towards architectural needs and sustainability. This adds to the knowledge on how globalization interacts with the urban renewal landscape in Riyadh and encourages us to continue proposing effective evaluation models by drawing attention to the multidimensional nature of sustainability. This in turn points to the need for continuous re-evaluation of architectural activities in Riyadh through project evaluation results that attest to their compatibility with international standards and local cultural contexts. Overall, the proposed evaluation model has proven successful in testing projects at the local level by providing a sustainable framework. The results showed that projects adhere to varying levels of sustainability requirements, but, more importantly, these evaluation models were developed to rationalize accelerated construction processes. Full article

Our investigation, bolstered by the robust gravity trade model and panel data econometric technique, underscores the pivotal factors that influence trade interactions between Saudi Arabia and the BRICS nations—Brazil, Russia, India, China, and South Africa. The study, spanning from 1998 to 2023, delves into key economic metrics such as the gross domestic product, exchange rate fluctuations, inflationary trends, political conditions, and trade deals. We employ a range of econometric strategies, including pooled Ordinary Least Squares (OLS) and fixed effects models, to reveal that the GDP of BRICS states consistently and significantly impacts trade volumes. Specifically, a 1% increase in the GDP of partner countries correlates with a 0.37% rise in trade volume within the pooled OLS model. This effect amplifies to 1.43% when adjusting for temporal and country-specific factors in the fixed effects, underscoring the importance of accommodating unobserved heterogeneity, which refers to the unmeasured factors that can influence the relationship between GDP and trade volume. The political stability of BRICS nations mitigates transactional risks and promotes more stable trade relationships, thereby enhancing trade flows. Fluctuations in exchange rates exert positive and significant effects. This indicates that a more robust Saudi Riyal, an essential policy instrument, can enhance trade by increasing the competitiveness of Saudi exports. This study demonstrates that economic magnitude, political stability, and exchange rates affect Saudi Arabia’s trade with BRICS nations. These results bolster the Kingdom’s Vision 2030 objectives for economic diversification. This research advocates for stable political climates and strategic trade agreements to enhance trade relations. This study asserts that this approach will guarantee sustainable growth and diminish the Kingdom’s reliance on oil exports, instilling optimism in the Saudi economy. Full article