Adapting the low carbon cities framework for Saudi Arabia: a comparative assessment of Riyadh and Jeddah under Vision 2030 and implications for sustainable social housing

Introduction: Rapid urbanization and extreme aridity in Gulf cities pose unique decarbonization challenges that generic global sustainability frameworks fail to address, particularly in guiding the development of sustainable, resilient social housing in Saudi Arabia’s major metropolitan regions. This study addresses the need for a context-sensitive performance metric that links city-scale infrastructure to the affordability and resilience of housing communities.

Methods: We developed and applied the Low Carbon Cities Framework for Saudi Arabia (LCCF-SA)—a localized adaptation of Malaysia’s Low Carbon Cities Framework tailored to the Kingdom’s reliance on energy-intensive desalination and extreme cooling demand. The framework was calibrated through a two-round Delphi process involving 15 experts to align indicator weights with Vision 2030 priorities. It was compared between Riyadh and Jeddah across 16 indicators in four domains (Urban Environment, Transportation, Infrastructure, Buildings), operationalized using 2018–2024 municipal, utility, and national datasets, along with GIS-based spatial analysis. Indicator values were transformed to a 0–100 scale using min–max normalization, aggregated into domain and composite city scores with Delphi-derived weights, and tested through alternative weighting scenarios to assess the robustness of the Riyadh–Jeddah comparison.

Results: Findings reveal a distinct performance divergence: Riyadh consistently attains higher domain and overall LCCF-SA scores than Jeddah, outperforming it in all 16 LCCF-SA indicators, driven by centralized governance and large-scale investments in metro transit, green infrastructure, and utility upgrades. Jeddah, by contrast, is constrained by coastal flood vulnerabilities, higher non-revenue water, and more fragmented planning. However, both cities exhibit systemic underperformance in residential retrofitting (<5%) and in active mobility, creating an “affordability trap” in which car dependency, inefficient building envelopes, and desalination-intensive water supply significantly inflate long-term housing operating costs for low-income residents.

Discussion: The analysis demonstrates that city-scale infrastructure performance is a non-negotiable prerequisite for housing resilience and long-term affordability. The study makes three key contributions: (i) it provides the first systematically localized low-carbon framework for hyper-arid, desalination-dependent cities in the Gulf; (ii) it reframes low-carbon metrics as “affordability indices” that link transport, energy, and water performance directly to the economic viability of social housing; and (iii) it offers a transparent, replicable site-selection and benchmarking tool to prioritize social housing projects in resilient, transit-connected, and infrastructure-efficient urban systems.

1 Introduction

Cities globally account for nearly 75% of greenhouse gas emissions, underscoring their central role in climate mitigation and sustainable development (McPherson and Clarke, 2024; Walker et al., 2025). In Saudi Arabia, approximately 85% of the population lives in urban areas that generate nearly 70% of national emissions (Alhowaish, 2025; Alqahtany, 2025). Rapid urban growth, car dependency, extreme heat, energy-intensive cooling, and desalination-based water supply place extraordinary pressure on infrastructure and natural resources. These challenges make urban decarbonization central to achieving national climate commitments and to safeguarding the long-term affordability and livability of housing, particularly for lower-income groups. Against this backdrop, social and subsidized housing becomes one of the sectors where macro-level decarbonization deficits are most acutely translated into household-level vulnerability.

Saudi Arabia’s Vision 2030 establishes an ambitious agenda for environmental sustainability, livability, and the transition to a low-carbon economy, supported by major initiatives such as the Saudi Green Initiative, Green Riyadh, and the National Renewable Energy Program (Vision, 2030, 2025; Royal Commission for Riyadh City, 2025). While these initiatives outline national priorities, translating them into operational city-level strategies requires robust, context-sensitive assessment frameworks—particularly in hyper-arid and desalination-dependent environments where global models often fail. Moreover, existing monitoring efforts rarely function as decision-support tools for national housing programs or municipal social-housing strategies, limiting their usefulness for aligning urban infrastructure investments with housing policy under Vision 2030.

Existing sustainability frameworks—UN-Habitat’s City Prosperity Index, the Green City Index, LEED for Cities—offer structured monitoring systems (Kakavand et al., 2025; Salati et al., 2022; Saraswat et al., 2025) but lack sensitivity to extreme climatic conditions, desalination-driven water systems, institutional fragmentation, and socio-cultural mobility patterns characterizing Gulf cities (Almheiri et al., 2024). Several studies highlight the poor transferability of such frameworks to the Middle East, where cooling dominates residential energy use and water scarcity intersects with carbon-intensive desalination (Helmi et al., 2021; Abubakar and Alshammari, 2023; Alawadi et al., 2024). The absence of calibrated tools hampers cities’ ability to track progress, prioritize investments, and connect climate action to planning decisions aligned with Vision 2030. In particular, it obscures how city-scale deficits in transport, energy, water, and green infrastructure cascade into higher operating costs and climate risks for residents of social and subsidized housing projects.

To address this gap, this study adapts Malaysia’s Low Carbon Cities Framework (LCCF) (KeTTHA, 2017) into a Saudi-specific version—LCCF-SA—reflecting the Kingdom’s environmental constraints, policy targets, and socio-technical systems. Beyond recasting tropical indicators (e.g., rainfall harvesting, biodiversity indices), the adaptation integrates metrics essential to Gulf cities: desalination energy intensity, non-revenue water, passive cooling, urban morphology, shading infrastructure, flood resilience, and building retrofitting rates. These dimensions are structured into 16 indicators across four domains—Urban Environment, Transportation, Infrastructure, and Buildings—that together provide a composite diagnostic of city-scale low-carbon performance and its implications for social housing.

Sustainable and resilient social housing has become a regional priority, as Gulf countries face rising energy demand, affordability challenges, and climate-related exposure in housing developments (Fereidani et al., 2021; Al-Ansari and AlKhaled, 2023; Aldhaher and Selçuk, 2024; Alkaabi et al., 2025). Despite large-scale government investment, the sector remains constrained by.

• Thermally inefficient building envelopes

• Minimal retrofitting of existing stock

• Limited integration of passive cooling and shading

• Peripheral locations poorly served by transit

• Fragmented governance and delivery systems

In this context, the study argues that low-carbon performance and long-term affordability are closely intertwined in Gulf cities. Electricity for cooling and fuel for private transport constitute a substantial share of household expenditure. Hence, deficits in transit provision, grid efficiency, water security, and urban microclimate directly undermine the economic sustainability of social housing. A nominally “affordable” unit can become costly to occupy when embedded in car-dependent, heat-exposed, and energy-inefficient urban systems. By analyzing city-scale gaps across the four LCCF-SA domains, the framework identifies structural conditions that shape operating costs and climate risks for social housing residents and highlights where urban systems require upgrading to support more sustainable housing outcomes.

Accordingly, this study asks:

How can Saudi cities—here represented by Riyadh and Jeddah—be systematically assessed and compared using a localized low-carbon framework that reflects hyper-arid environmental constraints and Vision 2030 priorities—and what do these diagnostics imply for sustainable and resilient social-housing development in the Arabian Gulf?

This study makes three key contributions.

1. Framework innovation

Develops the LCCF-SA, the first systematically localized version of the Low Carbon Cities Framework for hyper-arid, desalination-dependent contexts in Saudi Arabia and, by extension, the wider Gulf region.

2. Empirical benchmarking

Applies the LCCF-SA to Riyadh and Jeddah, producing the first comparative, indicator-based assessment of the low-carbon performance of Saudi cities using a transparent set of 16 indicators, explicit thresholds, and Delphi-derived weights.

3. Social-housing relevance

Demonstrates how LCCF-SA provides diagnostic insights directly applicable to social-housing planning, designing sustainable, resilient social-housing processes and products in the Gulf region. This link between LCCF-SA scores and housing strategies addresses a gap in social housing and low-carbon urbanism literature.

2 Literature review

Urban decarbonization has transitioned from a peripheral environmental concern to a central pillar of global development policy (Almheiri et al., 2024; Binu et al., 2025), with cities now recognized as the primary battlegrounds for achieving the Paris Agreement targets. Given that cities consume over two-thirds of the world’s energy and account for more than 70% of global CO2 emissions, the “urban turn” in climate policy is well-founded (Boeri et al., 2021; Creutzig et al., 2024; Beretta and Bracchi, 2025). This review, therefore, focuses on four strands of predominantly peer-reviewed literature that inform the design of LCCF-SA and the choice of its 16 indicators and four domains: (1) the contextual limitations of global urban sustainability assessments; (2) the methodological challenges of adapting Low Carbon City (LCC) models to arid environments; (3) the structural inertia of mobility systems in the Gulf; and (4) the emerging discourse on sustainability and resilience within Gulf social housing. Each strand is explicitly linked to one or more domains and indicators in the framework to strengthen the theoretical–empirical integration.

2.1 Global urban sustainability frameworks and contextual limitations

Over the past two decades, a proliferation of urban sustainability assessment tools has emerged to monitor environmental performance, guide policy, and benchmark city competitiveness. Prominent instruments such as UN-Habitat’s City Prosperity Index, the European Green City Index, and LEED for Cities provide standardized metrics for evaluating urban systems (Dashko et al., 2024). These frameworks generally operate by aggregating quantitative indicators—such as public transit density, green space per capita, and waste diversion rates—into composite indices that supposedly enable global comparisons.

However, a growing body of critical scholarship questions the “universalist” assumptions underpinning these tools. Research focusing on the MENA region argues that frameworks developed in temperate, water-rich contexts often fail to capture the specific environmental vulnerabilities of hyper-arid cities (Haou et al., 2025; Mosher et al., 2025; Saraswat et al., 2025). For example, standard metrics rewarding maximizing green cover can inadvertently encourage unsustainable irrigation practices in water-scarce regions. Similarly, global frameworks often overlook the carbon intensity of water production (desalination) and the severity of cooling loads, which are the dominant drivers of energy consumption in the Gulf. Furthermore, these tools frequently presume a level of institutional integration and data availability that does not exist in rapidly developing contexts, where governance is often fragmented between municipal and national entities. Without rigorous localization, imported frameworks risk becoming “box-ticking” exercises that produce misleading diagnostics and fail to address the root causes of regional unsustainability.

These critiques directly motivate the development of a localized framework that recalibrates global indicator sets for hyper-arid, desalination-dependent conditions and fragmented governance structures. In practical terms, they underpin the decision to adapt indicators across all four LCCF-SA domains rather than uncritically importing global benchmarks. Building on these contextual limitations, the following discussion turns to Low Carbon City models, particularly the Malaysian LCCF, as a starting point for such adaptation.

2.2 Low-carbon city models and the imperative of adaptation

The Low Carbon City (LCC) concept seeks to decouple urbanization from carbon emissions through integrated planning, renewable energy adoption, and behavioral change. Among operational LCC models, Malaysia’s Low Carbon Cities Framework (LCCF) stands out for its systematic, calculator-based approach that quantifies carbon abatement across four domains: Urban Environment, Transportation, Infrastructure, and Buildings (Juhari et al., 2019; Jamaluddin et al., 2023). Its modular structure offers a pragmatic blueprint for developing nations seeking to operationalize their Nationally Determined Contributions (NDCs).

Despite its structural utility, the direct transfer of the LCCF to the Arabian Peninsula is complicated by distinct socio-technical and climatic realities. It is highlighted that tropical indicators embedded in the original LCCF—such as rainfall-harvesting potential, biomass diversity, and passive ventilation strategies for humid climates—are fundamentally mismatched with the Gulf’s hyper-arid conditions (Al-Zu’bi et al., 2024). Crucially, the Water-Energy Nexus operates differently in the two contexts: in Malaysia, water supply is relatively low-carbon; in Saudi Arabia, it is energy-intensive due to reliance on thermal desalination. Empirical studies indicate that residential cooling and desalination combined account for the vast majority of the Kingdom’s domestic energy consumption (Du et al., 2024; Alzahrani and Tawfik, 2025). Therefore, an adapted framework must re-weight indicators to prioritize cooling efficiency, water conservation, and the reduction of “embodied carbon” in water supply—dimensions that are peripheral in tropical frameworks but central to urban survival in the Gulf (Yusuf and Lytras, 2023).

These insights directly inform the structural backbone of LCCF-SA: it justifies retaining the four original domains while redefining and reweighting indicators for desalination intensity, non-revenue water, renewable energy share, and residential energy intensity within the Infrastructure and Buildings domains. It also underpins the decision to embed explicit benchmarks for cooling demand and water–energy efficiency in the Saudi adaptation, moving beyond generic mitigation targets. Having established why and how a global LCC framework needs to be adapted, the following discussion narrows the focus to one of the most challenging domains for Gulf decarbonization—transport and mobility.

2.3 Transportation planning and mobility transitions in Gulf cities

The transport sector in Gulf Cooperation Council (GCC) cities presents a unique decarbonization challenge, characterized by some of the highest rates of private vehicle ownership and per capita transport emissions globally. The literature attributes this to a combination of dispersed, low-density urban morphology (“sprawl”), cheap fuel subsidies, and a historical underinvestment in public mass transit systems (Almatar, 2022; Almatar 2023; Almatar 2024a; Almatar 2024b). This car-centric urban form has created a path dependency that is difficult to reverse; even with new investments, public transport adoption remains low (Hegazy and Mahboob, 2024).

Moreover, the literature on active mobility in arid climates underscores that “walkability” in the Gulf is not merely a function of sidewalks and connectivity, but of thermal comfort. In cities where summer temperatures regularly exceed 45 °C, the “first and last mile” problem becomes a physiological barrier to transit usage. Recent studies on Riyadh suggest that without substantial investment in shading infrastructure, thermal comfort management, and mixed-use zoning, capital investments in metro systems may fail to achieve targeted ridership shifts (Alanazi, 2023; Alasmari and Alarabi, 2023; Alturif and Saleh, 2023; Mazzetto et al., 2025). Consequently, assessing low-carbon mobility in this context requires indicators that capture not only the presence of infrastructure but also the viability of non-car modes under extreme climatic stress.

These insights directly inform the Transportation domain of LCCF-SA, which operationalizes low-carbon mobility through indicators on transit accessibility, private-vehicle modal share, walkability and cycling infrastructure, and EV readiness. By grounding each of these indicators in the Gulf mobility literature, the framework explicitly links transport decarbonization to household mobility costs and social-housing location decisions. The following subsection extends this logic from mobility and urban form to the specific ways in which these structural conditions shape the sustainability and resilience of social and subsidized housing in the region.

2.4 Social housing in the Arabian Gulf: Sustainability and resilience gaps

Social and subsidized housing programs are central to the social contract in Gulf states, with governments delivering vast quantities of housing units to meet rapid demographic growth. However, the focus on quantitative delivery targets has often come at the expense of environmental performance and qualitative resilience. Recent reviews of social housing stock in Saudi Arabia, the UAE, and Kuwait identify systemic sustainability gaps: thermally inefficient envelopes that exacerbate energy poverty; a lack of passive design strategies (e.g., courtyards, orientation); and the siting of projects in peripheral locations disconnected from urban services (Mnea and Zairul, 2022; Al-Ansari and AlKhaled, 2023; Elmenghawi and Cazacova, 2023; Carlucci et al., 2024; Alkaabi et al., 2025).

A critical gap in the existing literature is the lack of connection between city-scale performance and housing-scale outcomes. Most studies evaluate social housing in isolation—focusing on architectural metrics like U-values—while ignoring the broader urban systems that determine a household’s carbon footprint and resilience. For instance, a “green” building located in a flood-prone, car-dependent district offers little genuine resilience to its low-income inhabitants. The literature increasingly calls for “integrated assessment frameworks” that can link macro-level indicators (transit access, grid carbon intensity, flood risk) to micro-level housing planning. This study responds to that call, positing that a localized city framework (LCCF-SA) can serve as a critical diagnostic tool to ensure that future social housing is not only affordable to construct but also sustainable for habitation.

In operational terms, this body of work on Gulf social housing underpins the inclusion of indicators such as flood resilience, wastewater coverage, retrofit rates, passive cooling and shading, and green-building certification in the Infrastructure and Buildings domains of LCCF-SA, explicitly tying city-level performance to housing-scale vulnerability and operating costs. By integrating these housing-relevant concerns into a city-scale low-carbon framework, the study addresses the documented disconnect between urban sustainability indices and social-housing realities, providing the conceptual bridge that the remainder of the paper empirically tests for Riyadh and Jeddah.

3 Materials and methods

3.1 Research design and case selection

To evaluate the robustness and transferability of the proposed framework, this study employs a “most-different systems” comparative design. This approach selects cases that share a common national policy context (Saudi Vision 2030) but differ significantly in their structural, geographic, and governance characteristics. Riyadh and Jeddah—the Kingdom’s two largest metropolitan regions—were selected to represent these contrasting urban typologies. Riyadh serves as the archetype of a centrally governed, inland metropolis defined by planned mega-projects and high-intensity investment. In contrast, Jeddah represents a coastal, organically evolved urban form characterized by historical underinvestment, fragmented governance, and acute exposure to climate risks, including heat stress and flooding.

By testing the LCCF-SA across these divergent contexts, the study aims to determine whether the framework is biased toward a specific urban morphology or if it serves as a universally applicable diagnostic tool. The analysis draws on a multi-source dataset comprising municipal strategic reports, GIS spatial layers, utility-sector performance data from the Saudi Electricity Company (SEC) and the Saudi Water Authority (SWA), and verified policy targets from the Saudi Green Initiative (SGI) and the Quality of Life (QoL) Program.

Supplementary Table SA1 consolidates datasets—covering the institution, time span, and spatial resolution—most core indicators focus on 2018–2024, with 3-year rolling means to smooth anomalies. The analysis mainly operates at the city scale but uses district-level GIS layers and raster products at 30 m where available. Missing data were handled with rules: small gaps (≤1 year) interpolated; key variables absent at neighborhood level replaced with proxies like vehicle registrations and building permits. Quantitative figures were cross-checked with municipal and national reports, sector strategies, and Vision 2030 documents; discrepancies over 5%–10% prompted further validation, with final figures documented in Supplementary Appendix A.

3.2 Adaptation of the low carbon cities framework (LCCF-SA)

The recalibration of Malaysia’s LCCF into the LCCF-SA involved a four-stage process to ensure it met hyper-arid climate and Gulf social housing resilience needs.

First, an indicator screening and substitution process was conducted to remove tropical-centric metrics (e.g., rainfall harvesting, biodiversity indices) that are ill-suited to the Arabian Peninsula. These were systematically replaced with indicators reflecting the region’s critical water–energy nexus and cooling dominance. Key additions included desalination energy intensity, non-revenue water (NRW), urban heat island (UHI) mitigation, and passive cooling performance. Crucially, indicators with direct implications for social housing—such as residential retrofitting rates, transit accessibility in low-income districts, and building envelope efficiency—were explicitly retained and enhanced to align with this study’s focus.

The resulting LCCF-SA Evaluation Matrix is presented in full in Table 1, which extends the original Malaysian framework by documenting, for each indicator: (i) the original LCCF indicator label; (ii) the adapted Saudi indicator definition; (iii) the corresponding domain (Urban Environment, Transportation, Infrastructure, Buildings); (iv) the assigned weight (%); (v) the benchmark or threshold value, with its primary source; (vi) alignment with Vision 2030 programs and national strategies; and (vii) the specific relevance to social-housing affordability and resilience. This matrix is complemented by Supplementary Supplementary Appendix A, which provides the precise computational formula and unit for each indicator, ensuring full replicability (Supplementary Table SA1) and the primary data source and any proxy or assumption used (Supplementary Table SA2).

Table 1

Table 1. Adapted low carbon cities framework for Saudi Arabia (LCCF-SA).

Second, a two-round Delphi method was employed to establish context-sensitive weighting. A panel of 15 experts were selected purposively according to three criteria: (i) at least 10 years of professional experience in urban planning, infrastructure, energy, water, or housing; (ii) direct involvement in Saudi or GCC projects; and (iii) representation from key institutional domains (academia, municipal government, national utilities/regulators, and development/housing entities). The final panel included six academics (urban planning, environmental engineering, housing policy), four municipal and regional planners, three representatives from utilities (electricity, water, wastewater), and two senior professionals from housing and real-estate development companies.

In Round 1, each expert rated all candidate indicators on a 0–10 scale across four criteria and could suggest additional indicators or rephrasing. These ratings were then averaged to determine an initial importance score for each indicator per criterion. In Round 2, experts received anonymized feedback showing the distribution (median and interquartile range) of their peers’ Round-1 scores and were prompted to adjust their ratings. Convergence was monitored by tracking changes in medians and ensuring the interquartile range for each indicator decreased below ±2 points on the 0–10 scale for overall importance. Final weights for indicators were calculated by normalizing the mean importance scores so their sum within each domain was 100% and then rescaling domain totals to match the predetermined domain weights. This two-step process is detailed in Supplementary Appendix B.

Third, benchmark thresholds were calibrated through triangulation of international best practices (WHO, IEA), GCC regional targets, and local regulatory standards (e.g., the Saudi Building Code and Mostadam rating system). Where immediate compliance with global standards (e.g., WHO green space targets) was deemed unfeasible, hybrid thresholds were adopted to reflect realistic phasing aligned with National Transformation Program timelines. Wherever possible, thresholds are drawn from peer-reviewed sources or authoritative institutional reports rather than ad hoc web materials.

Each indicator was associated with a specific computational formula and unit of analysis, documented in Supplementary Appendix A. For example, green space per capita (m²/person) is calculated as total area of publicly accessible parks and green corridors divided by the resident population; non-revenue water (NRW) is calculated as the difference between system input volume and billed authorized consumption, divided by system input volume; and residential energy intensity is expressed as annual electricity consumption in kWh divided by total residential floor area (m²) for the city. Where raw data were only available at coarser scales (e.g., regional energy statistics), per capita or per-m² values were estimated using GASTAT population and floor-area figures and then allocated to Riyadh and Jeddah through proportional scaling, as detailed in Table 1 and Supplementary Appendix A.

Fourth, the framework was embedded from a socio-technical perspective. Beyond physical infrastructure, the LCCF-SA integrates behavioral and governance dimensions, such as the enforcement capacity of building codes, cultural readiness for public transport adoption, and municipal coordination in flood response. This ensures a practical connection to social housing, with each domain of LCCF-SA mapped to key social-housing implications as shown in Supplementary Table SA2: Urban Environment indicators are linked to outdoor thermal comfort, exposure to heat islands, and quality of public open space around housing projects; Transportation indicators affect household mobility costs, forced car ownership, and accessibility of jobs and services for social-housing residents; Infrastructure indicators condition the reliability, carbon intensity, and tariff structure of water and energy services; and Buildings indicators directly influence indoor thermal comfort, energy bills, retrofit needs, and the long-term physical resilience of social-housing units. This mapping clarifies, at the methodological level, how city-scale low-carbon performance is expected to translate into affordability and resilience outcomes, which are analyzed in detail in the Results and Discussion sections.

3.3 Scoring, normalization, and aggregation

For each indicator i and city c, a raw value $X_{i,c}$ was first computed according to the formulas in Supplementary Appendix A. Where higher raw values represent better performance (e.g., green space per capita, renewable-energy share), normalized scores $S_{i,c}$ on a 0–100 scale were obtained via a standard min–max transformation based on the lower and upper thresholds $X_{i,\min }$ and $X_{i,\max }$ adopted for LCCF-SA:

$S_{i,c}=\frac{X_{i,c}-X_{i,\min }}{X_{i,\max }-X_{i,\min }}\times 100$

For indicators where lower values indicate better performance (e.g., non-revenue water, private-vehicle modal share, energy intensity), an inverse scoring was applied to ensure that higher normalized scores always represent better performance:

$S_{i,c}=\frac{X_{i,\max }-X_{i,c}}{X_{i,\max }-X_{i,\min }}\times 100$

Threshold ranges $\left[X_{i,\min },X_{i,\max }\right]$ were defined using a combination of globally recognized benchmarks (e.g., WHO green-space guidelines, IEA retrofit targets), regional GCC experience, and realistic medium-term targets from Saudi strategies; when global standards were considered unattainable in the short term, the upper bound was set to an ambitious but context-feasible value (e.g., intermediate green-space or renewable-energy targets), as documented in Table 1 and Supplementary Appendix B.

Qualitative indicators were scored on a 0–10 rubric that captured the maturity of policies, implementation mechanisms, and enforcement. For example, for flood-resilience governance, a score of 0 corresponded to no dedicated strategy or responsible entity; 3–4 indicated the presence of basic plans with limited implementation; 7–8 reflected established multi-agency protocols and ongoing infrastructure programs; and 9–10 denoted comprehensive, regularly updated plans with demonstrated performance during recent events. For passive cooling and shading compliance in residential design, 0 indicated no consideration of orientation, shading, or envelope performance in regulations; 5 indicated the presence of standards with partial enforcement; and 10 reflected mandatory, systematically enforced requirements embedded in building codes and design review for mass housing projects. These 0–10 qualitative scores were then linearly rescaled to 0–100 by multiplying by 10, yielding normalized qualitative scores comparable to quantitative ones.

Within each domain, the normalized scores were combined using the Delphi-derived indicator weights $w_i$. The domain score for domain d in city c, $D_{d,c}$, was computed as:

$D_{d,c}={\sum }_{i\in d}{w}_i{S}_{i,c}$

where the weights $w_i$ sum to 1 within each domain. Composite LCCF-SA scores for each city, $L_c$, were then obtained by aggregating the four domain scores using the agreed domain weights $W_d$ (Transportation 0.35, Infrastructure 0.25, Buildings 0.25, Urban Environment 0.15):

$L_c={\sum }_d{W}_d{D}_{d,c}$

Supplementary Appendix B provides the intermediate domain scores and final composite LCCF-SA scores, enabling readers to follow the complete calculation chain from raw data to index outputs.

3.4 Sensitivity analysis

To examine the robustness of the comparative results to alternative weighting schemes, a structured sensitivity analysis was conducted using three scenarios:

1. Baseline (Delphi-based) scenario: domain and indicator weights as derived from the expert panel (Transportation 35%, Infrastructure 25%, Buildings 25%, Urban Environment 15).

2. Equal-weight scenario: all four domains assigned equal weight (25% each), with indicator weights within each domain rescaled to sum to 1 while preserving their relative proportions.

3. Mobility- and buildings-centric scenario: Transportation and Buildings domains both increased to 30% each, with Infrastructure reduced to 20% and Urban Environment to 20%; indicator weights within each domain were again rescaled proportionally.

For each scenario, normalized indicator scores $S_{i,c}$ remained unchanged. In contrast, domain weights $W_d$ and, in the equal-weight case, domain-level rescaling was adjusted as described above. Domain scores $D_{d,c}$ and composite scores $L_c$ were recalculated using the formulas in Section 3.3. The results, summarized in Supplementary Table C1, show that although absolute composite scores vary modestly across scenarios, the ranking of cities (Riyadh consistently outperforming Jeddah) and the identification of weakest domains (Transportation and Buildings for both cities) remain stable. This robustness supports the conclusion that the diagnostic patterns reported in the Results section are not artefacts of a particular weighting configuration but reflect underlying structural differences in low-carbon performance between the two cities.

4 Case study

The selection of Riyadh and Jeddah as case studies follows a comparative, contrastive logic designed to test the adaptability and diagnostic capacity of the LCCF-SA framework.

4.1 Rationale for case selection

• Riyadh, the capital, is marked by centralized governance, large planned flagship projects (e.g., Riyadh Metro, Green Riyadh, King Salman Park), and a strong focus on Vision 2030. Its quick modernization and government-led investments foster a top-down implementation approach.

• Jeddah, by contrast, is a historic coastal city with a more organic urban layout, legacy infrastructure issues, and vulnerability to climate risks such as flooding. Governance has historically been more fragmented, with inconsistent investment in public infrastructure and lower performance in waste, water, and transportation sectors.

The contrasting profiles of these two cities establish a “most different systems” approach: Riyadh exemplifies a highly resourced, centrally managed environment, while Jeddah reflects decentralized, historically underfunded urban governance. Applying LCCF-SA to both ensures that the framework is not biased toward a specific governance style or urban type.

4.2 Comparative analytical logic

Comparative design extends beyond simple benchmarking by incorporating explanatory factors such as institutional, socio-cultural, and spatial factors.

• Governance capacity: Riyadh benefits from centralized authority and direct alignment with Vision 2030 initiatives, which speeds up sustainability progress. Jeddah’s weaker institutional coordination partially explains lower scores in infrastructure and transport.

• Urban morphology: Riyadh’s flat inland terrain allows for extensive greening and engineered stormwater systems. Jeddah’s dense historic fabric and coastal location increase climate risks and make infrastructure upgrades more challenging.

• Socio-cultural behavior: Car dependency is deeply rooted in both cities, but the lack of high-capacity mass transit in Jeddah strengthens behavioral inertia. Cultural preferences for private transportation, along with climatic conditions that make walking difficult, explain the slow progress toward sustainable transport.

• Investment pathways: Riyadh’s megaproject-led strategy (metro, parks, renewable energy pilots) contrasts with Jeddah’s gradual, reactive investments, reflecting institutional and financial asymmetries.

Embedding these explanatory variables enables comparative analysis to generate causal insights rather than merely descriptive contrasts. At the same time, the case-study design operates primarily at the city scale rather than the neighborhood scale, which constitutes an important limitation given the focus on social housing. Aggregated indicators cannot fully capture intra-urban disparities in exposure to heat islands, flood risk, or transit deserts, nor can they represent the micro-level vulnerabilities of specific social-housing estates. We therefore treat the present Riyadh–Jeddah comparison as a necessary first diagnostic layer; a clear avenue for future research is to downscale the LCCF-SA to selected social-housing neighborhoods in both cities, combining district-level indicators with parcel- and building-level data to more precisely evaluate the spatial distribution of low-carbon performance and housing risk.

5 Findings and results

The LCCF-SA was applied to Riyadh and Jeddah, generating a comparative diagnostic across 16 sub-indicators within four domains: Urban Environment, Transportation, Infrastructure, and Buildings. Table 2 reports the raw values for each indicator, Table 3 presents the corresponding normalized scores on a 0–100 scale, and Table 4 summarizes the resulting weighted domain scores and overall composite LCCF-SA indices for both cities.

Table 2

Table 2. LCCF-SA indicators: raw values for Riyadh and Jeddah.

Table 3

Table 3. LCCF-SA indicators: normalized scores (0–100) for Riyadh and Jeddah.

Table 4

Table 4. Weighted domain scores and composite LCCF-SA scores.

5.1 Urban environment

The urban environment domain, which evaluates green space, microclimate regulation, and environmental quality, reveals the sharpest contrast in policy implementation between the two cities. As shown in Table 2, Riyadh provides approximately 7.0 m² of public green space per capita, compared with 3.0 m² in Jeddah, resulting in normalized scores of 67 and 22, respectively (Table 3).

Green Space and Urban Heat Island Mitigation: Riyadh achieves a significantly higher score for green space accessibility, primarily due to the aggressive implementation of the “Green Riyadh” initiative, which aims to plant 7.5 million trees and develop integrated green corridors (Addas, 2025). In contrast, Jeddah performs moderately; its green infrastructure remains fragmented, restricted mainly to the Corniche and scattered neighborhood parks, and lacks the connectivity required for systemic cooling (Gadhi et al., 2024). For social housing, this divergence is critical. The deficit of green infrastructure in Jeddah disproportionately affects lower-income districts, where the lack of vegetative shading exacerbates the Urban Heat Island (UHI) effect. As a result, social-housing units in these areas are exposed to higher thermal loads and greater reliance on mechanical cooling, increasing household energy expenditures. In this respect, the difference in normalized shaded public realm coverage scores (64 for Riyadh versus 36 for Jeddah; Table 3) indicates that social-housing compounds in Jeddah are far more likely to depend on continuous air-conditioning, with higher monthly electricity bills and reduced usability of outdoor communal spaces.

Air Quality and Waste Management: While both cities grapple with regional dust events, Riyadh demonstrates superior performance in managing the Air Quality Index (AQI) due to stricter industrial zoning and recent mobility emission controls (Alajizah and Altuwaijri, 2024; Aloshan and Aldali, 2025). Jeddah’s score is suppressed by emissions from its port activities and industrial clusters, which frequently overlap with affordable housing zones (Aldosari and Alaboud, 2023; Shaltout et al., 2024). Similarly, recycling participation remains structurally low in both cities due to cultural and infrastructural inertia. However, the high residential density inherent in social housing projects presents an unexploited opportunity to implement community-based waste segregation schemes, which could significantly improve these scores if institutionalized. These environmental performance gaps—especially in AQI and recycling scores (Table 3)—translate into elevated health risks and service-quality deficits in lower-income districts, underscoring the need to prioritize social-housing projects as pilot sites for green-space provision and neighborhood-scale waste systems, as further detailed in Table 5.

Table 5

Table 5. LCCF-SA indicators and social housing leverage points.

5.2 Transportation

Transportation indicators reveal the most significant sustainability gaps, reflecting entrenched car dependency and limited uptake of alternatives. As shown in Table 4, the Transportation domain records the lowest scores in both cities; although Riyadh performs relatively better, both fall well below benchmark thresholds.

Modal Share and Transit Accessibility: Riyadh significantly outperforms Jeddah in this domain, driven by the operationalization of the Riyadh Metro and the expansion of its bus network. Table 3 shows a normalized transit-accessibility score of 55 for Riyadh, compared with 20 for Jeddah. In practical terms, approximately 55% of Riyadh’s residents live within 800 m of high-capacity or structured public transport, compared with 20% in Jeddah (Table 2). These investments are beginning to reshape the city’s accessibility landscape, creating transit corridors that are viable for non-car users (Mazzetto et al., 2025). Jeddah, lacking a high-capacity rail system, continues to rely heavily on private vehicles and informal transit services (Alotaibi et al., 2025). This disparity creates a “mobility inequality” for social housing residents. In Jeddah, the lack of reliable mass transit forces lower-income households to own private vehicles, thereby increasing their cost of living. Conversely, future social housing developments in Riyadh, located within the Metro catchment area, stand to benefit from substantial reductions in household carbon footprints and mobility costs. These patterns are reflected in the high normalized private-vehicle modal-share scores (30 for Riyadh and 10 for Jeddah, where lower scores are better), indicating that current site-selection practices for social housing are effectively locking households into long-term expenditures on fuel, parking, and vehicle ownership.

Active Mobility and EV Readiness: Both cities score poorly on walkability due to extreme summer temperatures and historically car-centric street design, although Riyadh’s recent investments in shaded pedestrian spines (e.g., the Sports Boulevard) provide a blueprint for improvement (Muhsen et al., 2025). Regarding future-readiness, Riyadh leads in deploying electric vehicle (EV) infrastructure through public-private partnerships. For the social housing sector, the current lack of EV readiness represents a risk of future obsolescence; housing projects built today without charging infrastructure will face costly retrofitting burdens as the Kingdom transitions toward electrified transport. The low normalized scores for walkability (45 in Riyadh, 25 in Jeddah) and EV readiness (55 versus 20; Table 3) suggest that, unless social-housing estates incorporate shaded micro-mobility corridors and at least basic EV-ready parking, they will rapidly diverge from national mobility transition pathways, creating additional retrofit liabilities for MOMRAH, ROSHN, and municipal authorities.

5.3 Infrastructure

The infrastructure domain assesses the resilience and efficiency of the utility networks that support housing development. As indicated in Table 4, this domain is a relative strength for Riyadh compared with Jeddah, although both cities still fall short of Vision 2030 and wider GCC best-practice benchmarks.

Water-Energy Nexus and Desalination: Both cities carry a heavy carbon burden due to reliance on energy-intensive desalination. However, Riyadh demonstrates superior performance in reducing Non-Revenue Water (NRW) and detecting leaks (Al-Zu’bi et al., 2024). NRW values of roughly 18% in Riyadh and 30% in Jeddah (Table 2) correspond to normalized scores of 55 and 25, respectively (Table 3). Jeddah’s aging network infrastructure results in higher water losses, threatening supply reliability in peripheral neighborhoods where social housing is often located (Almulhim and Abubakar, 2023; Attia, 2021). Furthermore, reducing per capita water consumption through efficient fixtures in social housing is identified as a high-leverage strategy to lower the “embodied carbon” of water supply in both cities. From a social-housing perspective, Jeddah’s relatively lower scores in NRW and per capita water consumption compared with Riyadh (Table 3) indicate that low-income districts face a higher probability of intermittent supply, service interruptions, and environmental-health risks. These conditions increase vulnerability and undermine the long-term livability and asset value of subsidized housing.

Flood Resilience and Wastewater: A critical resilience gap emerges in the flood risk assessment. Riyadh benefits from a more robust stormwater drainage network (Gbran and Alzamil, 2025), whereas Jeddah scores significantly lower due to its coastal topography, high water table, and history of severe flash floods (Dakhil et al., 2025). This finding is particularly urgent for social housing planning; low-cost housing is frequently pushed to low-lying or infrastructure-poor lands. The LCCF-SA diagnostics suggest that without significant investment in nature-based drainage and stormwater systems, social housing in Jeddah will remain physically vulnerable to climate-induced flooding. The differential flood-resilience scores (70 for Riyadh versus 30 for Jeddah; Table 3) therefore translate into differing expected frequencies of damage to social-housing assets and household displacement, underscoring the need to embed Infrastructure-domain metrics into site-selection and project-screening processes, as reflected in Table 5.

5.4 Buildings

The buildings domain reflects the energy performance of the residential stock and highlights a significant barrier to decarbonization: the retrofit gap. As summarized in Table 3, this is among the weakest domains in both cities, with normalized scores for energy intensity, retrofitting, passive cooling, and green-building penetration consistently below 50.

Energy Intensity and Passive Cooling: Riyadh’s building stock performs better on energy intensity metrics, aided by the stricter enforcement of the Saudi Building Code (SBC 601/602) in new developments (Naveed and Jaradat, 2025). Jeddah’s stock, on average, is older and exhibits higher cooling demand intensity (Rababa and Asfour, 2024). Indicative residential EUIs of around 170 kWh/m²/year in Riyadh and 210 kWh/m²/year in Jeddah (Table 2) translate into normalized scores of 55 and 35, respectively (Table 3). At the same time, both cities perform poorly on passive-cooling integration. Reliance on mechanical air-conditioning rather than vernacular strategies—such as deep shading, optimized orientation, and cross-ventilation—creates a long-term “lock-in” of high energy demand. For social housing, enhancing passive design is not merely desirable but economically essential. For a typical 100 m² social-housing unit, reducing EUI from 320 to 250 kWh/m²/year would yield annual savings of roughly 7,000 kWh, corresponding to several hundred SAR per year at current tariffs. Such reductions directly mitigate energy-poverty risks and improve the financial resilience of low-income households.

The Retrofitting Crisis: The most alarming finding across both cities is the negligible rate of residential retrofitting (<5%) (Rababa and Asfour, 2024; Rodrigues et al., 2025). While new “green” construction (Mostadam-certified) is emerging in Riyadh, the vast majority of existing housing stock remains thermally inefficient. This represents a critical policy blind spot. Low-income households are unlikely to retrofit voluntarily due to upfront costs. Consequently, a targeted, state-supported retrofitting program for existing social-housing stock is indispensable if Saudi cities are to reduce peak cooling loads, improve thermal comfort, and contain subsidy pressures. Normalized retrofit-rate scores of 18 for Riyadh and 12 for Jeddah (Table 3) place both cities well below the >2% annual retrofit trajectory recommended by international agencies. Unless MOMRAH, ROSHN, and municipal authorities establish dedicated retrofit pipelines for aging social-housing estates, these low scores will translate into decades of elevated cooling demand, avoidable public expenditure on subsidies, and missed opportunities to deliver equitable decarbonization.

5.5 Overall performance summary

The comparative analysis indicates that Riyadh outperforms Jeddah in all 16 LCCF-SA indicators. As shown in Table 4, Riyadh’s composite LCCF-SA score is substantially higher, with particularly pronounced advantages in the Transportation and Infrastructure domains. However, both cities still fall short of ideal low-carbon benchmarks. Riyadh’s relative lead is primarily attributable to stronger centralized planning capacity, substantial capital investment in mass transit (notably the Metro), and ambitious urban greening programs. By contrast, Jeddah’s weaker performance is more structural, reflecting legacy infrastructure deficits, heightened exposure to coastal climate risks, and the postponement or delayed delivery of major transit projects.

At the same time, the analysis reveals a set of shared vulnerabilities. Both cities perform poorly in the Buildings domain and on Active Mobility indicators. These deficits, especially the very low retrofitting rates and the hostile pedestrian environment, undermine the long-term sustainability of social housing in both contexts. While Riyadh currently offers a more enabling environment for low-carbon social housing, the results underscore that both cities require urgent shifts in housing typologies, neighborhood design, and public-realm quality to meet Vision 2030 targets.

Figure 1 illustrates Riyadh’s broader alignment with Vision 2030 objectives across the four domains, while also showing that it continues to lag behind international best practice. Figure 2 highlights three key areas of overlapping weakness in both cities:

1. Transportation (modal share, non-motorized mobility, EV adoption)

2. Buildings (retrofit rates, energy use, green-building certifications)

3. Renewable-energy integration within urban infrastructure systems

Figure 1

Figure 1. Radar chart of LCCF-SA domain scores (Riyadh vs. Jeddah).

Figure 2

Figure 2. Heatmap of sub-indicator performance (Riyadh vs. Jeddah).

Collectively, these findings establish an initial benchmark for monitoring Saudi cities’ low-carbon transition trajectories. They also provide the quantitative foundation for the social-housing implications matrix presented in Section 6.6 (Table 5), which translates domain- and indicator-level performance into specific design, siting, and governance strategies for MOMRAH, ROSHN, the Royal Commission for Riyadh City, and Jeddah Municipality.

6 Discussion

This study interprets the LCCF-SA results through five analytical lenses: the systemic divergence in city performance trajectories; the structural inertia within the transportation and building sectors; the critical interdependencies of the infrastructure–mobility–housing nexus; the comparative governance dynamics driving implementation; and the framework’s transferability to the wider Gulf region. The final subsection synthesizes these findings into actionable strategies for the design, planning, and governance of sustainable social housing in the Arabian Gulf.

6.1 Differential performance and urban trajectories

The comparative bifurcation between Riyadh and Jeddah reflects two distinct urban development trajectories within the Kingdom, offering a “tale of two cities” that informs national policy. In Riyadh, the combination of a unified development authority (RCRC), large-scale investment in the Metro and green infrastructure, and relatively higher scores in Transportation, Infrastructure, and Urban Environment creates a hosting environment in which new social-housing projects can be more readily integrated into an emerging low-carbon urban structure. Riyadh’s superior performance is not merely a function of capital investment but of a deliberate, centrally managed urban restructuring. The presence of a unified development authority, the Royal Commission for Riyadh City (RCRC), has enabled the synchronized execution of mega-projects (Metro, Green Riyadh) and the consistent enforcement of urban codes. This “top-down” transformation has allowed Riyadh to leapfrog developmental stages, rapidly integrating high-performance infrastructure into its urban fabric.

Conversely, Jeddah’s performance mirrors a legacy of “organic” growth, fragmented governance, and historical underinvestment in core utility networks. As a coastal gateway with a complex history of unplanned settlements and informal growth, Jeddah faces a steeper retrofit challenge. Its lower scores in flood resilience and infrastructure are symptomatic of an urban form that expanded faster than its support systems could adapt. The LCCF-SA results, therefore, imply that a nominally similar social-housing unit has very different long-term sustainability prospects depending on whether it is embedded in Riyadh’s progressively decarbonizing infrastructure or in Jeddah’s more carbon-intensive, flood-prone, and car-dependent context. This suggests that achieving national housing goals requires city-specific strategies—consolidation and resilience for Jeddah, and transit-oriented expansion for Riyadh.

6.2 The structural inertia of mobility and buildings

Across both cities, the LCCF-SA identifies the Transportation and Buildings domains as the weakest performing sectors, revealing a profound “carbon lock-in.” Transportation and Buildings record the lowest domain scores for both Riyadh and Jeddah, consistent with the indicator-level gaps documented for modal share, walkability, retrofit rates, and residential energy intensity. These sectors are characterized by high path dependency; the physical stock (roads, villas) changes slowly, creating a structural inertia that resists rapid decarbonization. The extreme reliance on private vehicles (car modal shares around 80%–90%) and the prevalence of thermally inefficient building envelopes are legacies of an era defined by subsidized energy and low-density planning guidelines.

As argued in the Introduction, these sectors are also key drivers of household expenditure, so their low scores confirm that city-scale low-carbon performance is tightly linked to long-term affordability for social-housing residents. While the energy grid can be decarbonized upstream (supply side), reducing demand in these sectors requires behavioral change and physical retrofitting, which are far more difficult to implement.

The LCCF-SA results imply that the current model of social housing provision—which often prioritizes unit delivery speed over urban integration and passive design—risks perpetuating this lock-in. Without intervention, new social housing will simply add to the stock of inefficient, car-dependent assets, effectively exchanging immediate housing access for long-term energy poverty. Thus, the low scores for Buildings and Transportation can be read as early warning signals that social housing, if poorly sited and designed, may become a structural driver of both emissions and affordability stress.

6.3 The infrastructure–mobility–housing nexus

The diagnostics confirm that low-carbon pathways in arid environments are inseparable from complex nexus interdependencies. The LCCF-SA results demonstrate that water, energy, and land use cannot be managed in silos. For instance, the water efficiency data suggests that reducing the carbon footprint of housing requires not just water-saving fixtures, but a reduction in the “embodied energy” of the desalinated water supplied to the neighborhood. In the Gulf, water is energy; therefore, water conservation in social housing is a direct decarbonization strategy.

Similarly, the land-use/transport nexus is evident in the disparity between transit accessibility and housing location. Indicators for transit accessibility, modal split, and flood risk jointly highlight the risks of placing social-housing estates in peripheral, low-lying districts that lack reliable public transport and resilient stormwater infrastructure. Jeddah’s low scores in flood resilience and transit access indicate that siting new social housing in peripheral, low-lying areas without antecedent investment in stormwater drainage or bus corridors constitutes a planning failure. This “spatial mismatch” exacerbates vulnerability. The framework highlights that sustainable social housing cannot be achieved through architectural interventions alone; it requires a multi-scalar approach that aligns housing location with resilient infrastructure corridors. A high-performance building in a disconnected, flood-prone district is not a sustainable asset; it is a stranded asset. By explicitly combining Infrastructure and Transportation domain scores with Buildings indicators, LCCF-SA provides a quantitative basis for identifying such stranded-risk configurations before projects are approved.

6.4 Governance capacity and institutional readiness

The variance in scores underscores the pivotal role of governance capacity in sustainability transitions. Riyadh’s ability to implement the Saudi Green Initiative targets highlights the value of “institutional readiness”—the capacity of local government to enforce codes, coordinate between utility providers, and manage complex projects. The alignment between the RCRC and national Vision 2030 goals has created a streamlined implementation environment where green building codes (Mostadam) and transit-oriented development (TOD) guidelines are increasingly normative.

In contrast, Jeddah’s fragmented institutional landscape, split between the Municipality, various developmental entities, and legacy service providers, poses a risk to the long-term resilience of social housing. Lower performance in areas such as flood resilience, waste management, and green-space provision suggests that “soft” infrastructure (governance) is as critical as “hard” infrastructure (pipes and rails). These governance-related indicators are partly based on expert judgment within the Delphi process and qualitative scoring rubric; while this allows institutional factors to be incorporated into LCCF-SA, it also underscores the need for future work to collect more systematic empirical evidence on enforcement levels and inter-agency coordination. For social housing sustainability, this implies that institutional reform must accompany construction. Ensuring the sustainability of new housing communities requires streamlined permitting for retrofits, rigorous inspection regimes to ensure code compliance, and apparent jurisdictional authority for maintaining neighborhood drainage and public spaces.

6.5 Transferability to the wider Gulf context

While explicitly calibrated for Saudi Arabia, the LCCF-SA addresses a socio-technical typology that is ubiquitous across the Gulf Cooperation Council (GCC). Cities like Doha, Kuwait City, Manama, and Muscat share the same fundamental challenges identified in this study: extreme hyper-aridity, near-total dependence on desalination, low-density urban sprawl, and a social contract heavily reliant on state-led housing provision. Existing global frameworks often fail to capture the nuance of these shared challenges, penalizing Gulf cities for climatic factors beyond their control while missing the specific opportunities for decarbonization within the water-energy nexus.

Consequently, the LCCF-SA serves as a replicable diagnostic model for the wider region. It offers a standardized method for Gulf capitals to benchmark their “hosting environments” for national housing programs, moving beyond generic global indices to metrics that capture the specific vulnerabilities of arid urbanization. Because the framework is organized into four domains and 16 indicators that can be re-scored with local data, it can be applied in other Gulf cities without changing its basic structure, while still allowing indicator thresholds to be re-tuned to national targets and regulatory standards. By linking desalination intensity, passive cooling, and retrofit rates to urban performance, the framework provides a common language for GCC planners. It facilitates cross-border knowledge sharing, allowing housing ministries across the region to compare not only the number of units delivered but also the infrastructural efficiency and climate resilience of the communities they are building.

6.6 Implications for sustainable and resilient social housing in the Arabian Gulf

City-scale low-carbon performance in Riyadh and Jeddah is not only an environmental metric but a structural determinant of long-term affordability and resilience in social housing. Energy-intensive cooling, car-dependent mobility, and exposure to flood and heat risks all translate into recurring costs, damage, and service disruptions that disproportionately affect low-income households. The LCCF-SA results, therefore, provide a diagnostic bridge between macro-level urban systems and micro-level housing design, siting, and management choices.

To translate this diagnostic into practice, Table 5 summarizes how key LCCF-SA indicators function as “leverage points” for social housing policy—linking performance gaps between Riyadh and Jeddah to concrete design strategies and clearly identified lead actors.

6.6.1 LCCF-SA indicators and social housing leverage points

Table 5 presents, for a subset of high-leverage indicators, the observed performance gap, the direct effect on social-housing households, recommended design or planning responses, and the institutional lead best positioned to act (e.g., MOMRAH, ROSHN, RCRC, Jeddah Municipality, SEC, SWA). This moves beyond a generic discussion of “co-benefits” toward an operational agenda that ministries and developers can directly integrate into program guidelines and project briefs.

6.6.2 Illustrative affordability impacts

To clarify the magnitude of these leverage points, a simple quantitative illustration can be provided. Assuming a typical 100 m² social-housing apartment, a reduction in residential energy use intensity from 320 kWh/m²/year (representative of older Jeddah stock) to 250 kWh/m²/year (achievable through envelope upgrades and passive design) corresponds to a reduction of 7,000 kWh per year. At a conservative residential tariff of 0.18 SAR/kWh, this translates into approximately 1,260 SAR/year (about 105 SAR/month) in electricity savings for a single household. For a 1,000-unit social-housing estate, this would imply potential aggregate annual savings on the order of 1.26 million SAR, alongside reduced peak-load pressure on the grid.

Similarly, if improved transit accessibility and TOD-based siting reduce average car-commuting distance by even 10 km/day per household, at a fuel cost of roughly 0.5–0.6 SAR/km (including maintenance), the monthly saving per household can easily exceed 150–200 SAR. These stylized calculations underscore how improvements in LCCF-SA scores translate into tangible affordability gains, not just abstract environmental benefits. Assumptions and detailed calculations can be reported in a brief methodological note or appendix.

6.6.3 Policy pathways and institutional roles

The LCCF-SA results suggest a clear division of labor among key national and local actors:

• MOMRAH–Integrate LCCF-SA thresholds into national social-housing guidelines, particularly for site selection (flood risk, transit access, green space), minimum building-performance standards, and mandatory passive design measures.

• ROSHN and the National Housing Company (NHC) – Align catalogues of standard housing typologies with LCCF-SA building and infrastructure indicators; prioritize estates in higher-performing LCCF-SA cells and co-design retrofit programs for existing projects.

• Royal Commission for Riyadh City (RCRC) – Use LCCF-SA scores to steer the spatial allocation of future residential projects around metro corridors, green-space networks, and blue–green infrastructure in Riyadh.

• Jeddah Municipality–Leverage LCCF-SA to identify critical infrastructure gaps (flood protection, water losses, missing green networks) in districts earmarked for social housing and integrate remedial investments into capital-investment plans.

• SEC, SWA, and the Saudi Energy Efficiency Center–Coordinate energy and water-efficiency programs targeting social-housing districts that show low scores on EUI, NRW, and retrofit indicators.

6.6.4 An LCCF-SA–based site-selection protocol

To operationalize these pathways, a simple site-selection protocol can be embedded into the workflow of MOMRAH, ROSHN, and municipal partners:

1. Pre-screening: Use LCCF-SA spatial layers (Infrastructure, Transportation, Urban Environment) to exclude parcels with high flood risk, very low transit accessibility, and extremely low green-space provision from the initial pool of candidate social-housing sites.

2. Multi-criteria scoring: For remaining sites, calculate a composite “social-housing suitability score” based on a subset of LCCF-SA indicators (e.g., flood risk, transit accessibility, walkability, UHI mitigation, infrastructure reliability).

3. Thresholding: Set minimum LCCF-SA thresholds (e.g., a required domain score for Transportation and Infrastructure) that candidate sites must meet or be able to reach through planned investments within a defined period.

4. Design integration: Require project briefs and design competitions to demonstrate how building forms, passive design, shading, and micro-mobility networks will improve local LCCF-SA scores over time.

5. Monitoring and feedback: After occupation, periodically re-assess LCCF-SA indicators at the neighborhood scale to track whether social-housing projects are delivering the expected reductions in energy use, mobility costs, and climate risk exposure; feed these results back into future site selection and design guidelines.

By embedding this protocol into standard housing-program procedures, LCCF-SA evolves from a one-off research tool into a practical decision-support instrument that directly shapes where and how social housing is built—and retrofitted—in Saudi cities.

7 Conclusion

This study establishes the Low Carbon Cities Framework for Saudi Arabia (LCCF-SA) as a critical methodological advancement for urban sustainability assessment in the Arabian Gulf. By rigorously recalibrating Malaysia’s LCCF to account for the hyper-arid, desalination-dependent, and cooling-dominated conditions of the region, the research provides a diagnostic tool that is theoretically sound and practically aligned with the transformation goals of Saudi Vision 2030. Unlike generic global indices, the LCCF-SA captures the specific socio-technical realities of the Gulf, moving beyond “green” aesthetics to measure the structural drivers of urban carbon emissions.

The comparative application of the framework reveals a distinct performance divergence between the Kingdom’s two largest metropolises. Riyadh outperforms Jeddah in all 16 indicators, a result driven by centralized institutional capacity (RCRC), massive capital investment in mass transit (Riyadh Metro), and the aggressive implementation of green infrastructure. Conversely, Jeddah’s performance is constrained by legacy infrastructure deficits, coastal climate vulnerabilities, and a fragmented governance landscape that has historically delayed critical drainage and transit projects. These findings underscore that national decarbonization targets cannot be achieved through uniform strategies; they require city-specific pathways that address Riyadh’s need for transit-oriented densification and Jeddah’s urgent need for infrastructural resilience and retrofitting.

A significant finding across both cities is the systemic underperformance of the Transportation and Buildings domains, where car dependency, low retrofit rates, and weak passive cooling design significantly increase household emissions and energy burdens. These sectoral gaps have immediate implications for sustainable and resilient social-housing development, which is central to national goals across Saudi Arabia and the Gulf. By reframing low-carbon metrics as affordability metrics, this study demonstrates that the “green” transition is essential for the economic viability of social housing. Low-carbon mobility and high-efficiency envelopes are the only safeguards against rising living costs for beneficiaries. By linking urban sustainability indicators to neighborhood-scale and housing-level outcomes—such as thermal comfort, energy affordability, exposure to urban heat islands, and access to low-carbon mobility—the LCCF-SA provides a powerful evidence base for designing next-generation social-housing programs that are climate-resilient, energy-efficient, and socially inclusive.

Theoretically, the framework’s explicit integration of the water–energy nexus—specifically desalination intensity and stormwater resilience—represents a significant contribution to the literature on urban sustainability in arid regions. It shifts the focus from “rainfall harvesting” (a tropical priority) to “cooling demand management” and “embodied water energy,” offering a transferable template for other Gulf capitals such as Doha, Muscat, and Manama. This allows policymakers to transition from broad strategic ambitions to measurable, actionable interventions that directly improve the habitability of urban environments.

At the same time, the study has several limitations that should be acknowledged explicitly. First, data constraints required the use of proxy indicators for key variables such as modal split, walkability, and retrofit rates, particularly in Jeddah; while carefully justified, these proxies inevitably introduce uncertainty into the resulting scores. Second, the analysis is conducted at the city scale, meaning that intra-urban disparities and neighborhood-level vulnerabilities—especially within specific social-housing estates—are only indirectly captured by the LCCF-SA indicators. Third, although the Delphi process involved 15 experienced experts from multiple sectors, the weighting scheme would benefit from future rounds with a larger, more diverse panel, as well as more formal statistical measures of consensus and uncertainty. Finally, the study does not yet incorporate confidence intervals or probabilistic sensitivity analysis around indicator scores and composite indices, which would further strengthen the robustness of the comparative results.

These limitations point directly to a future research agenda. Neighborhood-scale applications of LCCF-SA within selected social housing districts in Riyadh and Jeddah would provide a finer-grained understanding of microclimatic conditions, household behavior, and infrastructure reliability. They would validate the framework’s diagnostic power at the scale at which residents experience risk and affordability pressures. More detailed cost–benefit modelling, incorporating both capital and lifecycle operating costs, could quantify the economic returns of improving specific indicators (e.g., retrofit rates, transit accessibility), and integrate these into national housing-program budgeting. Finally, integrating advanced uncertainty analysis—such as Monte Carlo simulation of indicator ranges and alternative weighting scenarios—would provide policymakers with confidence bounds around LCCF-SA scores and rankings, further enhancing the framework’s value as a decision-support tool.

In conclusion, the LCCF-SA offers a robust, replicable, and policy-ready mechanism for advancing low-carbon urban development in Saudi Arabia. By bridging the gap between city-scale infrastructure planning and neighborhood-scale housing design, the framework supports the Ministry of Municipal and Rural Affairs and Housing (MOMRAH) and developers in delivering the next-generation of Gulf cities. It confirms that sustainable social housing is not an isolated architectural product, but an integrated component of a resilient urban system. As the framework is progressively applied at finer spatial scales and coupled with more detailed affordability and resilience metrics, it can evolve into a core reference for aligning Saudi Arabia’s social-housing programs with its long-term climate and quality-of-life commitments under Vision 2030.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author contributions

IH: Conceptualization, Investigation, Supervision, Project administration, Resources, Methodology, Writing – review and editing. AI: Supervision, Methodology, Writing – original draft, Formal Analysis, Investigation, Resources, Data curation.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia, under grant no. (IPP:424-137-2025).

Acknowledgements

The authors gratefully acknowledge the technical and financial support of the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. Proofreading and language editing.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbuil.2025.1751703/full#supplementary-material

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