A framework for building sustainability assessment for developing countries using F-Delphi: Moroccan housing case study

Academic Editor: Manuela Almeida Received: 27 August 2025 Revised: 8 October 2025 Accepted: 11 October 2025 Published: 21 October 2025 Citation: Rharbi, N.; García Martínez, A.; El Asli, A.; Oulmouden, S.; Mastouri, H. A Framework for Building Sustainability Assessment for Developing Countries Using F-Delphi: Moroccan Housing Case Study. Sustainability 2025,17, 9338. https://doi.org/10.3390/su17209338 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article A Framework for Building Sustainability Assessment for Developing Countries Using F-Delphi: Moroccan Housing Case Study Noussaiba Rharbi 1,* , Antonio García Martínez 2, Abdelghani El Asli 3, Safae Oulmouden 1 and Hicham Mastouri 4 1School of Architecture, Planning and Design, Mohammed VI Polytechnic University, Benguerir 43150, Morocco 2Instituto Universitario de Arquitectura y Ciencias de la Construcción (IUACC), Escuela Técnica Superior de Arquitectura, Universidad de Sevilla, 41012 Seville, Spain 3School of Science & Engineering, Al Akhawayn University, Ifrane 53000, Morocco 4Energy and Water Research Center, College of Chemical Sciences and Engineering, Mohammed VI Polytechnic University, Benguerir 43150, Morocco *Correspondence: noussaiba.r[email protected] Abstract International building sustainability assessment tools (BSATs) offer a comprehensive framework for assessing environmental, economic, and social sustainability. However, these tools cannot fill the gap between their standards and the regional needs of developing countries such as Morocco. This paper presents a new framework to assess the sustainability of buildings in Morocco. The methodology proposed is the Fuzzy Delphi method to minimize the list of indicators with the help of 14 local experts and give an appropriate weight to the indicators and sub-indicators. The two-round analysis found a balanced weighting for the environmental, economic, and social dimensions, with the social pillar ranked highest in importance. A hierarchical framework of six consensus-based categories and 63 sub-indicators was developed. Consensus was measured using the dispersion threshold approach ≤ 0.2. The results show that waste and pollution (0.80), adaptability and resilience (0.78) , and resources (0.75) are prioritized over the innovation category. Notably, sewage management, water reuse, and public infrastructure emerged as critical sub-indicators. A comparative evaluation against local BSATs from the region—Ethiopia, Sub-Saharan Africa, Saudi Arabia, and Oman—revealed convergence in core indicators like energy and water, yet divergence in economic and resilience criteria, reflecting regional specificities. This work contributes to the literature by presenting a validated, expert-driven assessment tool that aligns with local needs, offering a practical basis for national green certification and sustainable housing policy in Morocco and similar contexts. Keywords: Fuzzy Delphi; building sustainability assessment; green rating systems; developing countries; Morocco 1. Introduction Existing building sustainability assessment tools (BSATs) are either international, such as LEED, BREEAM, and WELL [ 1 ], or nationally focused, e.g., VERDE, ESTIDAMA [2,3] . The African continent represents a challenging context into which to implement the international exigencies [ 4 ]. The national ones benefit the specific needs of the targeted country or region. Thus, African countries need a specific BSAT that responds to the region’s Sustainability 2025,17, 9338 https://doi.org/10.3390/su17209338 Sustainability 2025,17, 9338 2 of 24 problems [ 5 ]. The majority of BASTs are issued from developing countries and give priority to environmental categories such as land sustainability [ 6 ], while categories related to culture, governance, and socio-economic issues are not present or prominent [ 7 ]. The focus on energy and environmental issues is necessary in the African context but is not the most important indicator of African dwellings [ 8 ]. Safety, amenities, and application of local regulations are still issues for the majority of African countries, especially Morocco [5]. Facing local challenges, developing countries tend to create a BSAT that is appropriate to local needs [ 9 ]. ESTIDAMA, as an example of a local BSAT in the UAE, is tolerant towards renewable energy and water reduction in comparison to the exigencies of LEED and BREEAM [ 10 ]. This tolerance is reflected in the weighting system and points attribution that reflect the local tolerances. The presence of the national BSAT does not change the need for further studies that target its shortcomings in other respects, such as heritage buildings’ assessment [ 11 , 12 ]. In other cases, countries tend to adapt existing international BSATs to their region, such as VERDE adapting SBTool to the Spanish context [ 13 ], or Green Star South Africa’s adaptation from Australia [ 14 ]. Adaptation of an international SBAT or the development of a national one requires the involvement of local experts using multi-criteria decision-making (MCDM) tools [15]. The African continent has few BSATs developed locally, such as the Ethiopian tool directed to developing countries [ 8 , 16 ]. This uses the FAHP method to collect the weight from experts. It contributes 15% to sustainable management and 14% to waste and pollution as well as cost and economy. Another BSAT was developed for Sub-Saharan countries using direct weighting from expert surveys and interviews [ 4 ]. It allocates 29% to sustainable construction practices, 17% to indoor environmental quality, and 13% to energy. The challenge of these studies is the completeness of the weighting process using local experts. The lack of knowledge about BSATs among architects and engineers can hinder the process or prolong it [ 17 ]. In the case of the MENA region, studies in Saudi Arabia [ 6 ] and Oman [ 7 ] have used the Delphi and AHP methods. Oman’s local certification attributes 12% to indoor environmental quality and 10% to critical water resources; in the same way, Saudi Arabia’s system gives importance to water efficiency, energy, and indoor environmental quality. Overall, there are weight differences between local systems, as they reflect the local challenges. Even with the similarities between Moroccan conditions and MENA countries as well as the African continent, some aspects reflect local construction, regional climate conditions, and local policies that need to be addressed in a local sustainability assessment framework [ 5 , 18 ]. Hence, there is a need for a clear methodology adopted in the Moroccan context that prioritizes its local policies and needs. To date, Morocco does not have a dedicated building sustainability assessment tool. Existing international frameworks such as LEED, BREEAM, or DGNB are not fully suited to the Moroccan context, as they give limited attention to local challenges such as water scarcity, informal urbanization, cultural practices, and inadequate waste infrastructure [ 5 ]. This study, therefore, introduces the first Moroccan BSAT, developed through expert consensus using the Fuzzy Delphi method. The originality of this research lies in its adaptation of proven international structures to Moroccan priorities, while ensuring local validity through expert elicitation. In doing so, it not only fills a national gap but also contributes to the broader discussion on tailoring sustainability assessment frameworks for developing countries. This study develops a local grading system for Moroccan dwellings using Moroccan experts. It establishes a hierarchy for lists of categories, indicators, and sub-indicators gathered across existing BSATs and the prior literature. This study aims to address the gap between international certifications’ exigencies and African housing conditions. It puts together a simplified system that focuses on the national goals for construction. This paper Sustainability 2025,17, 9338 3 of 24 is divided into five major sections: The first part is an introduction to the problem of the subject and explains the need for local BSATs. The second part is a literature review of the previous studies, discussing different local BSATs as well as the methods to develop a BSAT. The third part describes the methodology followed in this research. The fourth part is the presentation of the framework and the discussion of the results, as well as benchmarking the framework with international and local BSATs. The final part is the conclusion, presenting the main challenges facing local BSATs in Morocco. 2. Literature Review The development of local BSATs requires gathering local experts’ opinions on the subject. Multi-criteria decision-making (MCDM) methodologies are used following several steps: indicator identification, categorization, weighting, and normalization of values to obtain a score [ 15 ]. Various MCDM methodologies are identified in the literature, such as Delphi, Exploratory Factor Analysis (EFA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Analytic Hierarchy Process (AHP). Delphi is used to identify the hierarchy among categories, indicators, and sub-indicators [ 6 , 19 ]. The AHP is chosen for accurate weighting, as it proposes a pairwise assessment [ 20 ]. Various studies combine both methodologies for accurate weighting. Fuzzy methods are adopted to provide more accuracy and greater reasoning [21,22]. Expert surveys and interviews provide useful practical insights but are often affected by subjective bias and limited generalizability (Table 1) [ 23 ]. The Delphi method is widely used to develop structured expert consensus, yet it is slow and depends heavily on the availability of experts. Exploratory Factor Analysis effectively uncovers hidden patterns and variables but requires large, high-quality datasets to ensure accuracy. Multi-criteria decision-making tools like TOPSIS offer quick and clear rankings of options but can be sensitive to weight assignments and might miss qualitative stakeholder perspectives. The AHP is valued for its systematic approach to complex decisions, although its reliability may decline as the number of criteria grows, due to possible inconsistencies. The Fuzzy AHP builds on the AHP by including uncertainty, enhancing decision robustness in ambiguous situations, but it requires advanced technical understanding. Overall, the table highlights the trade-offs among qualitative insights, computational complexity, and adaptability when choosing methods for sustainability assessment frameworks. Table 1. Strengths and limitations of multi-criteria decision-making methods used in sustainability assessment. Methods Context Area Addressed Problems Sources Strength Limitation Expert Survey and Interview United Kingdom Sustainable Building Sustainability assessment framework for housing regeneration [24] -Provides practical insights from stakeholders -Subject to bias and limited generalizability Delphi Malaysia Building Engineering Assessment schemes for use in non-domestic buildings for refurbishment [17]-Achieves expert consensus systematically -Experts’ input -Time-consuming -Dependent on experts Saudi Arabia Sustainable Building A scheme for sustainable building assessment [6] Exploratory Factor Analysis UK Building Engineering Potential impediments to sustainable structural retrofit [25]-Identifies latent variables from data -Requires large, high-quality datasets Sustainability 2025,17, 9338 4 of 24 Table 1. Cont. Methods Context Area Addressed Problems Sources Strength Limitation Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Spain Building Engineering Framework of renovation for residential buildings [26]-Ranks alternatives clearly and efficiently -Effective in multi-criteria decisions -Sensitive to weight and scale choices -May overlook stakeholder nuances China Building Engineering Renovation of green building scheme [27] Analytic Hierarchy Process (AHP) Italy Building Engineering Seismic retrofitting scenarios for a one-story building [28]-Simple and structured comparison method -Breaks down complex decisions hierarchically -Inconsistencies in judgments can affect output -Becomes unwieldy with many criteria India Urbanism Assess the regional level of sustainability [20] Fuzzy AHP Ethiopia Building Sustainability Building a sustainability assessment system (BSAS) for the least developed countries [8]-Handles uncertainty in decision-making -Requires advanced expertise and interpretation A review of leading international sustainability assessment tools, including BREEAM [ 29 ], LEED [ 30 ], DGNB [ 31 ], VERDE [ 32 ], WELL [ 33 ], and ESTIDAMA [ 3 ], served as the foundation for structuring the BSAT hierarchy proposed in this study. These frameworks consistently emphasize a core set of categories, which informed the selection of the five main domains in the BSAT: site, resources, quality of life, waste and pollution, and innovation. The “Site” category is widely addressed in systems such as LEED, BREEAM, and ESTIDAMA, underscoring issues of land use, heritage conservation, and transport integration, which are particularly relevant in Morocco due to rapid urbanization. The “Resources” category, comprising energy, water, and materials, is a central component across all reviewed tools, reflecting global concerns around efficiency and scarcity, which are especially pressing in the Moroccan context. “Quality of Life” draws on WELL’s occupant-centered focus and the user comfort indicators present in DGNB and BREEAM, emphasizing thermal, acoustic, and daylight performance. “Waste and Pollution” are prioritized in LEED, DGNB, and ESTIDAMA, aligning with Morocco’s urgent need to strengthen its waste infrastructure and pollution management. “Innovation,” recognized as a bonus or credit category in LEED and ESTIDAMA, was included to highlight context-specific, forward-thinking strategies such as passive cooling and digitalization. This hierarchical structure thus adapts proven international practice to national priorities, ensuring both global coherence and local relevance. Building on this structural foundation, the next critical step in developing a robust BSAT is the identification and validation of an updated and context-sensitive list of indicators. Most sustainability assessment tools adopt a hierarchical framework comprising categories, indicators, and sub-indicators, typically aligned with the environmental, economic, and social pillars of sustainability. Certification tools used to give the environmental pillar more importance [ 15 ]. The presence of indicators from the three pillars serves as a reliability test for building performance [ 34 ]. Categories can vary according to local policies and national perspectives [ 15 ]. The most cited indicators in the literature are impact assessment, resources, waste, output flaws [ 35 ], materials, energy, and indoor environment quality [ 15 ]. There is no definite list or hierarchy for indicators and sub-indicators. Yang et al. cited 83 sub-indicators for energy indicators gathered in the literature and SBATs using a participatory expert survey [ 36 ]. Piparsania and Kalita followed the same method to determine social and cultural building assessment indicators [37]. 3. Methodology This study seeks to develop a BSAT model appropriate to the Moroccan context using the F-Delphi method. The experts helped develop a hierarchy list including categories, criteria, and sub-criteria, using several rounds of surveys. The developed Moroccan BSAT Sustainability 2025,17, 9338 5 of 24 was compared to five local BSATs in the existing literature used in the African context and the MENA region [4,6–8], to ensure its validity and alignment with the regional context. There are several methodologies in the literature that have been used to develop a sustainability assessment framework for buildings. To find the appropriate methodology, face-to-face interviews and pre-survey tests were established in the School of Architecture, Planning, and Design, with academic professionals permitted to test the methodologies as well as the answer rate, helping to distinguish which MCDM methodologies can be applied to the Moroccan context. The pre-tests, answered by 14 different professors and doctoral students, showcased the following: - Experts find difficulties in rating pairwise questions; this hinders the application of the AHP method. - Low response rate for surveys exceeding 40 questions: The list of categories, indicators, and sub-indicators proposed in the surveys cannot exceed a certain amount, so as to limit the number of questions. - Preference towards online systems rather than face-to-face interviews: The response rate is higher in online surveys. This preliminary study helped shape the survey questions as well as the choice of the Delphi method as an assessment methodology for this case. 3.1. F-Delphi The Delphi methodology is an MCDM approach that uses expert panel knowledge to obtain an objective opinion on an issue [ 38 , 39 ]. It is effective to obtain knowledge on issues using local experts from different areas [ 40 ]. The method consists of having a group of experts (expert panels) and obtaining their opinions about the issue in several rounds. This helps converge these opinions into statistical results [ 41 ]. The method has three pillars: anonymous response, iteration and controlled feedback, and statistical group response [ 6 ]. The anonymous pillar ensures the objectivity of the experts’ opinions and that they do not influence each other’s responses. The iteration ensures multiple rounds, with postanalysis results presented to the experts after each round. This operation helps in reaching a common ground among the experts. Statistical responses quantify the experts’ opinions using a scale. The results measure the median tendencies and the dispersion level [17]. Fuzzy theory was first developed to ensure the adequacy between the human scale nuances and numerical values [ 42 ]. Fuzzy theory adds adequacy to MCDM methodologies and is widely used for subjects regarding sustainability and refining the number of criteria [ 43 ]. The uncertainty of experts’ evaluation between scale numbers is reflected by a triangular fuzzy number (TFN). Its function is defined by a triplet of real numbers (a, b, c), and a ≤ b ≤ c, where a is the lowest value that b can take, and c is the highest value [40,44,45]. The triangle membership is defined as follows (Equation (1)): µN(x)=            0, x <a (x−a) (b−a), a ≤x≤b (c−x) (c−b), b ≤x≤c 0, x >c (1) where a is the lower bound, b is the peak (where membership = 1, meaning “fully belongs” to the fuzzy set), c is the upper bound (where the function starts decreasing), and µ N(x) is the membership degree of x (how much x belongs to the fuzzy set). ∼ N1(+) ∼ N2= (a1, b1, c1)(+)(a2, b2, c2) = (a1+a2, b1+b2, c1+c2)(2) Sustainability 2025,17, 9338 6 of 24 ∼ N1(−) ∼ N2= (a1, b1, c1)(−)(a2, b2, c2) = (a1−a2, b1−b2, c1−c2)(3) ∼ N1(×) ∼ N2= (a1, b1, c1)(×)(a2, b2, c2) = (a1a2, b1b2, c1c2)(4) ∼ N1(÷) ∼ N2 = (a1, b1, c1)(÷)(a2, b2, c2) = (a1 a2,b1 b2,c1 c2)(5) The BSAT developed using Delphi and Fuzzy Delphi gives different perspectives on the strengths and weaknesses of this method. The steps followed for this study are as follows: Step 1: Identification of key parameters to assess buildings’ sustainability from the literature; a list of set categories (C 1 , C 2. . . ), indicators (I 1 , I 2. . . ), and sub-indicators ( S1, S2. . . ). Step 2: Preparation of a questionnaire for the experts containing the list for BSA. Step 3: The mean values reflecting the weighting of categories, indicators, and subindicators are calculated after aggregation and defuzzification. 3.1.1. Category Identification The list of indicators found in existing BSATs, such as LEED, BREEAM, ESTIDAMA, HQE, and WELL, was added to the indicator list extracted from the literature and combined within 5 main categories. These categories represent the Moroccan future perspective: site, life quality, resources, waste and pollution, and innovation (Table 2). The following methodological rules governed the inclusion and organization of indicators and subindicators: Table 2. Gathered initial main list of BSA categories, indicators, and sub-indicators (Round 1). Category Indicator Sub-Indicator Description [C1] Site [I1] Environmental Impact Assessment [S1] Biodiversity assessment Evaluation of the variety of plant and animal species in the area and the building’s impact on them. [S2] Pollution assessment of the site Analysis of air, water, and soil contamination levels of the site. [S3] Flood risk design Measures to mitigate flood risks through effective design. [I2] Site Selection [S4] Historical site conservation Measures to protect and preserve culturally or historically significant sites. [S5] Fertile land, contaminated land Assessment of soil quality for agricultural use and identification of polluted areas. [S6] Orientation Evaluation of site orientation for optimal environmental and energy performance. [S7] Land use, waste land reuse Planning for efficient land use and repurposing of abandoned or unused sites. [I3] Construction [S8] Waste management (transport, CO2 pollution) Management of construction waste and reduction in carbon emissions. [S9] Quality assessment (foundation, earthquake regulation) Evaluation of structural stability and compliance with seismic regulations. [S10] Maintenance assessment (service years, replacement, repainting, etc.) Estimation of building lifespan and prediction of maintenance needs over time. [S11] Cost assessment (site, building MAD) An analysis of financial costs for site preparation and building construction, life-cycle cost. [S12] Usage of existing infrastructure Maximize the use of available infrastructure to minimize new construction. [I4] Urban Harmony [S13] Compliance with urban standards (shape, color) with regulations (RTCM, etc.) Adherence to urban design guidelines and national regulatory codes. [S14] Compliance with local forms and practices Alignment with local architectural styles and cultural practices. [S15] Architect commissioning Hiring qualified architects for project design and planning. [S16] Landscape design Planning and designing outdoor spaces to enhance aesthetics and usability. Sustainability 2025,17, 9338 7 of 24 Table 2. Cont. Category Indicator Sub-Indicator Description [I5] Transportation [S17] Public transport (stops), alternative transport Integration of public transit systems and promotion of alternative transport options. [S18] Road safety Measures to ensure the safety of road users, including pedestrians. [S19] Pedestrian roads (walkability, signage) Design of pedestrian-friendly pathways with clear signage. [S20] Parking spots Adequate provision of parking spaces for vehicles. [S21] Amenities (distances to mosques, souks, schools, hospitals, etc.) Proximity to essential services and community facilities. [C2] Quality of Life [I6] Occupant Education Level [S22] Household education level (high school, bachelor) Assessment of education levels within households. [S23] Community awareness (local sustainable practices) Evaluation of community engagement in sustainable practices. [S24] Conformity to local regulations (RTCM) Compliance with local laws and building regulations. [I7] Economic Comfort [S25] Household management cost (rent, mortgage, bills) Analysis of financial burdens related to housing and utilities. [S26] Yearly income (above/below average) Comparison of household income to national or regional averages. [I8] Occupants’ Well-Being (Health) [S27] Interior circulation, programs, movement encouragement Design features and programs promoting physical activity and mobility. [S28] Diets, health conditions Consideration of dietary habits and prevalent health issues in the design. [I9] Indoor Comfort [S29] Thermal comfort (temp, PMV) Evaluation of indoor temperature and thermal comfort for occupants. [S30] Lighting (daylight, interior lighting, lux) Assessment of natural and artificial lighting quality. [S31] Acoustics (db) Measurement of sound levels to ensure minimal noise disturbance. [S32] Air quality (PPM, CO2, etc.) Analysis of indoor air pollutants and ventilation efficiency. [I10] Outdoor Comfort [S33] Safety (overlooking facades, safe parks courtyard, emergency services, etc.) Safety measures for outdoor spaces, including visibility and emergency access. [S34] Privacy conservation in design Architectural designs that preserve occupant privacy. [S35] Accessibility of the building Features that ensure easy access for all individuals, including those with disabilities. [S36] Lighting (neighborhood lighting, etc.) Adequate outdoor lighting for safety and aesthetics. [S37] Fitness amenities, children’s playgrounds Provision of recreational areas for fitness and children’s play. [C3] Resources [I11] Energy [S38] Primary operational energy (kWh) Measurement of energy consumption during building operations. [S39] Conformity to local regulations (RTCM), envelope conformity Compliance with energy-related local regulations, (thermal Moroccan regulation baseline). [S40] Renewable energy integration (PV), wind energy (kWh) Use of renewable energy sources such as solar and wind power. [I12] Water [S41] Household water usage (m3)Monitoring and managing water consumption in households. [S42] Rainwater management (design pathways for freshwater conservation and reuse) Systems for collecting and reusing rainwater to conserve resources. [S43] Water source Evaluation of water supply sources for sustainability. [S44] Water quality Assessment of water purity and safety for use. [I13] Materials [S45] Material quality (robustness, sustainability) Selection of durable and eco-friendly construction materials. [S46] Safety (no hazardous materials, emissions while used) Use of materials that do not release harmful substances during use. [S47] Reusability (EOL) Design of materials for reuse or recycling at the end of their life cycle. [C4] Waste and Pollution [I14] Buildings’ Pollution [S48] Life-cycle assessment (GWP, AP, EP, etc.) An analysis of environmental impacts throughout the building’s life cycle. [S49] Waste management (waste usage) Systems for managing and reducing waste generation. [S50] Sewage system management Processes to treat and manage wastewater effectively. Sustainability 2025,17, 9338 8 of 24 Table 2. Cont. Category Indicator Sub-Indicator Description [C5] Innovation [I15] Building Design Innovation [S51] Integration of passive solutions, awards Incorporation of passive design strategies and recognition for innovation. [S52] New technologies integrated (quality-of-life improvement) Adoption of advanced technologies to enhance quality of life. [I16] Exemplary Overall Performance [S53] Baseline conformity Adherence to standard benchmarks for performance. [S54] Minimal resource usage Strategies to minimize the use of natural and human resources. 1. Consensus-Based Selection: Any indicator found in at least two internationally recognized sustainability assessment systems, such as BREEAM, LEED, DGNB, VERDE, WELL, or ESTIDAMA, was retained in the BSAT framework to ensure consistency with global standards and reflect international consensus on core sustainability priorities. 2. Indicator Mapping: Sub-indicators with conceptual similarities but derived from differently named categories across tools were grouped under a common indicator in the BSAT structure. For example, energy-related sub-indicators from both DGNB and LEED were consolidated under a single “Energy” indicator, ensuring semantic alignment and thematic coherence. 3. Contextual Addition: Where an indicator and its sub-indicators were not explicitly repeated across tools (and therefore not retained under Rule 1 or 2) but were deemed contextually significant for Morocco, due to climatic, regulatory, or socio-economic factors, they were included as standalone indicators. This step ensured that the BSAT hierarchy addressed local needs while maintaining methodological rigor. 3.1.2. Appointment of a Panel of Experts There is no fixed number of experts in MCDM methods in general and in the case of the Delphi method specifically [ 46 ]. If the number of experts is too small, the results may be subject to bias. Conversely, a larger number of experts increases the risk of a low response rate. Therefore, it is generally recommended to maintain the number of experts between 3 and 50 to balance these factors [ 17 , 40 ]. In this study, 14 experts answered the survey in Round 1, and 11 in Round 2. The selection of experts followed the criteria of advanced degrees and years of expertise [ 47 ]. Experts were recruited through professional networks, academic partnerships, and invitations sent to relevant ministries and professional associations. A total of 28 experts were invited, of whom 14 agreed to participate (response rate of 50% in Round 1 and 39% in Round 2). Eligibility criteria included holding at least a master’s degree or equivalent professional qualification, a minimum of two years of professional experience in the fields of architecture, engineering, urbanism, or environmental policy, and active engagement with Moroccan construction or sustainability practice. The final panel included participants with between 2 and 30 years of practice (median: 15 years), ensuring both early-career and senior expertise (Figure 1). No financial incentives were provided; participation was entirely voluntary. The panel composition was 70% from industry and professional practice (civil servants, private companies, independent architects, or certification bodies) and 30% from academia (Table 3). Geographically, most of the experts were concentrated in urban centers such as Rabat and Casablanca, mainly targeting the cities. Sustainability 2025,17, 9338 9 of 24 3 2 3 1 2 +60 < 30 30–39 40–49 50–59 5 6 Female Male 1 6 3 1 Casablanca–Settat Rabat–Salé–Kénitra Rabat–Salé– Kénitra+Casablanca– Settat Fès–Meknès Figure 1. Demographic data of experts, including age range, gender, and region of practice. Table 3. Experts’ background fields. Professional Field Experts Number Academia Industry Stakeholders Architecture/Urban Planning 404 Building Economy 2 2 0 Civil Engineering 4 1 3 Environment 1 0 1 3.1.3. Survey Development The survey was designed in four main sections: The first part collected the demographic data of the experts, revealing their educational field and years of expertise. The second part was for the categories section (C1, C2 . . . ), the third was for indicators (I1, I2 . . . ), and the last part was for sub-indicators (S1, S2 . . . ). The survey allowed the experts to rate each hierarchy element and provided space to add an explanation, along with more elements that were implemented in further rounds. The experts were presented with the list of categories, indicators, and sub-indicators to evaluate their importance and impact in the Moroccan context. Then, their evaluation was translated into quantitative scores, as explained in Section 3.1. The experts gave their answers, choosing from linguistic values varying from unimportant to very highly important (Table 4). The mean results were calculated and presented again in the following round. This study conducted two rounds of Delphi surveys. Each round’s results were analyzed and represented by the experts anonymously. Due to the restriction of gathering all of the different experts, the process was carried out entirely by e-mail. This process allowed the experts to review the results of the first rounds before assessing them again until there was consensus. Table 4. Linguistic scale for Fuzzy Delphi approach. Linguistic Variable Description Corresponding TFN Inadequate (Very Unimportant) No need; the category/indicator/subindicator is not important to assess Moroccan building sustainability (0, 0, 0.25) Not Important Minor importance for assessment (0, 0.25, 0.5) Important Medium importance; it can impact the assessment (0.25, 0.5, 0.75) Highly Important Important for the assessment (0.5, 0.75, 1) Very Highly Important Indispensable to assess Moroccan buildings’ sustainability (0.75, 1, 1) Sustainability 2025,17, 9338 16 of 24 contexts. The developed framework offers a balanced weighting and appropriate indicators that align with similar local BSATs in the same region. Applicability and Limitations This study develops a Moroccan building sustainability assessment tool (BSAT) tailored to urban residential contexts, offering policymakers and practitioners a framework aligned with national priorities such as waste management, water resilience, and quality of life. By combining international best practices with locally relevant sub-indicators, the tool provides a practical basis for certification schemes and urban planning policies. However, some limitations remain. The panel was concentrated in major cities, which limits generalizability to rural areas. In addition, the framework is yet to be tested on actual housing projects. To move from framework development to practical application, it is essential to establish clear scoring implementation paths, defining thresholds, crediting systems, and rating levels, and to conduct pilot applications on real residential projects. Such pilots would validate usability, support calibration of weights and thresholds, and offer municipalities and developers a roadmap for operational adoption. Future work should pilot the framework in practice and update the indicators over time to reflect evolving Moroccan priorities. 5. Conclusions This study developed and validated a local BSAT using Morocco as a case study. It used an F-Delphi process to structure a weighting system. The framework reached a consensus across six categories: site, resources, quality of life, waste and pollution, adaptability and resilience, and innovation. The established hierarchy of categories, indicators, and subindicators is the result of the second-round survey, reflecting the growing attention of the local experts to emerging challenges such as resilience and post-crisis functionality. The framework balances building-related and occupant-related indicators, with comparable average weights, reinforcing the dual importance of environmental performance and user-centered livability. In terms of pillars, the social dimension is ranked the highest, followed by the environmental pillar, whereas the economic pillar is weighted less heavily. The results align with local policies and present challenges in terms of social and environmental issues. The waste and pollution category has the highest weight, underscoring a strong consensus on the importance of environmental impacts on the local context for developing countries. In contrast, innovation has the lowest score, indicating its lower urgency in a similar context. The MBSAT aligns with the existing African and Middle-Eastern local BSATs. It prioritizes water, energy, and material efficiency, consistent with resource-scarce and arid geographies. The witnessed divergence in innovation and economic indicators reflects Moroccan local strategies, as the experts’ weightings align with local challenges. Ultimately, the Moroccan BSAT contributes to the growing body of localized sustainability assessment tools by offering a methodologically robust, consensus-driven framework tailored to national priorities. It provides a structured basis for promoting environmentally sound, socially integrated, and context-appropriate residential development. Future work should explore the dynamic integration of resilience and innovation, as well as field validation through case studies, to support more adaptive and forward-looking sustainability planning in Morocco and comparable regions. Author Contributions: Conceptualization, N.R. and A.E.A.; methodology, N.R. and A.G.M.; validation, A.G.M., A.E.A. and H.M.; resources, H.M.; data curation, N.R. and S.O.; writing—original draft preparation, N.R.; writing—review and editing, A.G.M., A.E.A. and H.M.; visualization, N.R. and S.O.; supervision, A.G.M., A.E.A. and H.M. All authors have read and agreed to the published version of the manuscript. Sustainability 2025,17, 9338 17 of 24 Funding: This research received no external funding. Institutional Review Board Statement: This study is waived for ethical review. According to Moroccan Law No. 28-13 on the Protection of Persons Participating in Biomedical Research, this study falls outside the scope of mandatory ethical. Informed Consent Statement: Informed consent for participation was obtained from all subjects involved in the study. Data Availability Statement: Dataset available on request from the authors. Acknowledgments: This work was performed in the frame of the PPlaME project. The authors would like to thank the financial support of the Ministry of Higher Education, Scientific Research and Innovation, Morocco, as well as the OCP Foundation for the financial support of PPlaME through the APRD20 program. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations BSA Building sustainability assessment BSAT Building sustainability assessment tool MENA Middle East and North Africa LEED Leadership in Energy and Environmental Design BREEAM Building Research Establishment Environmental Assessment Method MCDM Multi-criteria decision-making MBSAT Moroccan building sustainability assessment tool HQE Haute Qualité Environmental Appendix A. Detailed Results for Indicators and Sub-Indicators Table A1. MBSAT weights for the indicators. Indicators Defuzzification Normalization Site selection 0.754 0.046 Construction 0.826 0.050 Urban planning 0.769 0.047 Transport 0.727 0.044 Environmental Impact Assessment 0.814 0.050 Energy 0.886 0.054 Water 0.902 0.055 Materials 0.784 0.048 Economic comfort 0.667 0.041 Occupants’ well-being (health) 0.856 0.052 Indoor comfort 0.856 0.052 Outdoor comfort 0.814 0.050 Building pollution 0.856 0.052 Waste management 0.958 0.058 Waste water management 0.958 0.058 Environmental resilience 0.871 0.053 Thermal resilience 0.841 0.051 Social resilience 0.682 0.041 Innovation in building design 0.856 0.052 Exemplary overall performance 0.754 0.046 Sustainability 2025,17, 9338 18 of 24 Table A2. MBSAT weights for the sub-indicators. Normalized sub-indicator weights were computed globally across all 66 sub-indicators so that their total equaled 1. Sub-Indicators Defuzzification Normalization Biodiversity assessment 0.769 0.045 Site pollution assessment 0.886 0.052 Design for flood risk 0.845 0.049 Historical site and heritage conservation 0.784 0.046 Fertile land, contaminated land 0.830 0.049 Orientation 0.697 0.041 Compliance to urban standards (shape, color) and to regulations (RTCM, etc.) 0.769 0.045 Compliance with local forms, practices 0.769 0.045 Architect commissioning 0.784 0.046 Landscape design 0.758 0.044 Waste management (transport, CO2pollution) 0.814 0.048 Quality assessment (foundation, earthquake regulation) 0.830 0.049 Maintenance assessment (service years, replacement, repainting, etc.) 0.856 0.050 Cost assessment (site, building life-cycle “MAD”) 0.856 0.050 Usage of existing infrastructure 0.784 0.046 Public transport (stops), alternative transport 0.845 0.049 Road safety 0.886 0.052 Pedestrian roads (walkability, signage) 0.902 0.053 Parking spots 0.799 0.047 Amenities (distances to mosques, souks, schools, hospitals, etc.) 0.886 0.052 Conformity to local regulation (RTCM) 0.902 0.053 Household management cost (rent, mortgage, bills) 0.693 0.041 Yearly income (above/below average) 0.708 0.042 Optimization of investment cost and life-cycle cost 0.799 0.047 Interior circulation, programs, and movement encouragement 0.754 0.044 Diets, health conditions 0.754 0.044 Hedonic site value (env. quality, scenic views, etc.) 0.769 0.045 Thermal comfort (temp, PMV) 0.799 0.047 Lighting (daylight, interior lighting, lux) 0.871 0.051 acoustics (db) 0.814 0.048 Air quality (PPM, CO2, etc.) 0.871 0.046 Neighborhood safety (overlooking facades, safe parks, courtyard, emergency services, etc.) 0.886 0.043 Privacy conservation in design 0.799 0.046 Accessibility to the building 0.769 0.044 Lighting (neighborhood lighting) 0.902 0.048 Fitness amenities, children’s playgrounds 0.769 0.052 Primary energy (kWh) in the operational stage 0.784 0.046 Conformity to local regulation (RTCM), envelope conformity 0.739 0.056 Renewable energy integration PV, wind energy (kWh) 0.784 0.046 Electricity network coverage 0.754 0.044 Household water usage (m3)0.814 0.048 Rainwater management (design pathways for freshwater conservation and reuse) 0.886 0.052 Water source (grid, well, etc.) 0.784 0.046 Water quality 0.958 0.056 Material quality (robustness, sustainability) 0.784 0.046 Safety (no hazardous materials, emissions while used) 0.814 0.048 Reusability (end of life) 0.784 0.046 Life-cycle assessment (global warming potential, AP, EP, etc.) 0.739 0.043 Waste management (usage waste) 0.830 0.049 Sewage system management 0.902 0.053 Sustainability 2025,17, 9338 19 of 24 Table A2. Cont. Sub-Indicators Defuzzification Normalization Integration of passive solutions, awards 0.693 0.041 New technologies integrated (quality-of-life improvement) 0.799 0.047 Construction digitalization (digital twin, BIM) 0.758 0.044 Baseline conformity 0.754 0.044 Minimal resource usage 0.727 0.043 Risk to occupants and facilities from flooding 0.784 0.046 Stormwater retention capacity on site 0.856 0.050 Capacity for rainwater collection and storage for non-potable uses 0.886 0.052 Use of vegetation to improve microclimate and cooling during summer 0.871 0.051 Heat island effect 0.856 0.050 Capacity for post-disaster use 0.826 0.048 Community integration and shared spaces 0.856 0.050 Access to critical infrastructure during crisis 0.886 0.052 Appendix B. Worked Example (Step-by-Step) This appendix shows step-by-step how one indicator/category (site) was processed from raw expert responses to the final normalized weight. All numbers are taken from the worked-example table provided in the manuscript. Appendix B.1. Linguistic Scale →Triangular Fuzzy Numbers (TFNs) The Likert responses (0–4) were mapped to triangular fuzzy numbers (TFNs) as follows (used throughout the study): •0 (Very unimportant) →TFN = (0, 0, 0.25); •1 (Not important) →TFN = (0, 0.25, 0.5); •2 (Important) →TFN = (0.25, 0.5, 0.75); •3 (Highly important) →TFN = (0.5, 0.75, 1); •4 (Very highly important) →TFN = (0.75, 1, 1). Table A3. Worked example of Round 2 calculations for the site category: raw expert ratings were converted into TFNs, aggregated, consensus-checked via dispersion (distance < 0.2), and defuzzified to obtain the final score (0.723). ID Site T F N Average Distance from Consensus Min Mean Max Defuzzification 4 3 0.5 0.75 1 0.75 0.01 0.25 0.77 1.00 0.723 5 2 0.25 0.5 0.75 0.5 0.24 7 4 0.75 1 1 0.92 0.17 8 3 0.5 0.75 1 0.75 0.01 9 3 0.5 0.75 1 0.75 0.01 11 4 0.75 1 1 0.92 0.17 12 4 0.75 1 1 0.92 0.17 13 3 0.5 0.75 1 0.75 0.01 14 2 0.25 0.5 0.75 0.50 0.24 15 2 0.25 0.5 0.75 0.50 0.24 16 4 0.75 1 1 0.92 0.17 Category average 0.74 0.13 Consensus achieved Sustainability 2025,17, 9338 20 of 24 Appendix B.2. Aggregate TFNs (Pointwise Mean) Experts’ TFNs were aggregated by averaging the lower (a), modal (b), and upper (c) bounds separately: a=1 n n ∑ i=1 ai;b=1 n n ∑ i=1 bi;c=1 n n ∑ i=1 ci; Using the TFNs above: •Sum of a (lower bounds) = 5.75 →a≈0.523; •Sum of b (modal values) = 8.50 →b≈0.773; •Sum of c (upper bounds) = 10.25 →c≈0.932. Appendix B.3. Defuzzification (Crisp Score) We converted the aggregated TFNs to a single crisp value using the commonly used centroid-based formula: D=a+4b+c 6 Substituting the aggregated bounds: DSite = 0.7576. Appendix B.4. Consensus Check (Dispersion/Distance Metric) To test whether the experts reached consensus on this item, we computed two straightforward metrics that were reported in the manuscript: (a) Expert centroid (per-expert) Each expert’s TFN centroid (simple average of a, b, and c) was computed as follows: Centroidi=ai+bi+ci 3 The aggregated centroid (mean of per-expert centroids) equals Centroid =1 n∑ i Centroidi=0.74 This value corresponds closely to (a - + b - + c - )/3 and matches the category average reported in the worked example (Table A3). (b) Distance from consensus (mean absolute deviation of centroids) For each expert i, Distancei=centroidi−centroid The mean distance across experts is Distance =1 n∑ i distance =0.13 A consensus threshold of 0.20 was adopted (as specified in the methods). Since distance - = 0.13 < 0.20, the site category was considered to have reached consensus in the panel; this is reported in the worked-example table as “Consensus achieved”. Appendix B.5. Normalization (Sum-to-One) To convert the defuzzified scores into normalized weights that sum to 1, we compute wi=di ∑n j=1dj Sustainability 2025,17, 9338 21 of 24 di= Defuzzified score of category; n= Number of categories; wi= Normalized weight (sums to 1 across all categories); NSite = 0.758/4.65 ≈0.163. This normalized value (0.163) matches the normalized weight reported for the site category in Round 2 (Table 5). Appendix C. The Movement Between Rounds for Categories 0.120 0.130 0.140 0.150 0.160 0.170 0.180 0.190 0.200 Site Quality of life Resources Waste and pollution Innovation Adaptability and resilience Normalised weight Round 1 Round 2 Figure A1. Slope graph indicating weight shifts between Round 1 and Round 2 across the categories. Appendix D. Local BSAT Comparison To enable a meaningful comparison across regional BSATs, categories were harmonized with differing taxonomies. For example, “Indoor Environmental Quality” in the Sub-Saharan BSAT and “Health and Well-being” in the Ethiopian BSAT were both classified under the “Quality of Life” category, while “Pollution” and “Waste Management” were grouped under “Waste and Pollution.” Indicators not shared across frameworks, such as “Exemplary overall performance” in the Moroccan BSAT, were retained separately to preserve national specificity. No rescaling of the Moroccan results was performed, since the weights were directly derived from expert Delphi judgments. For the other schemes, published weights were normalized to sum to 1 within each framework, to allow comparability. Missing categories were treated transparently as “not applicable” and are left blank in the table; no artificial redistribution was applied. The provenance of all external weights is documented in the cited primary sources, ensuring traceability and transparency. It is acknowledged that methods, category definitions, and derivation procedures differ among tools. Accordingly, this comparison does not claim strict equivalence but, rather, highlights relative emphases and contextual divergences, thereby situating the Moroccan BSAT within broader regional sustainability trends. Sustainability 2025,17, 9338 22 of 24 Table A4. Comparative analysis of the local BSATs developed in the Middle East and Africa. All published weights were normalized by the original authors; where reported in percentages, they were converted to decimals for consistency. Blank cells indicate categories not addressed by the respective framework. Normalized Weighting Categories Indicators Moroccan BSAT (Current Study) Omani BSAT [7] Sub-Saharan BSAT [4] Ethiopian BSAT [8] Saudi Arabian BSAT [6] Site Site selection 0.046 0.062 0.066 0.083 Construction 0.050 0.090 0.286 0.089 Urban planning 0.047 0.086 Transport 0.044 0.066 0.103 Environmental Impact Assessment 0.050 Resources Energy 0.054 0.104 0.133 0.091 0.101 Water 0.055 0.099 0.075 0.067 0.104 Materials 0.048 0.087 0.095 0.096 0.083 Quality of Life Economic comfort 0.041 0.078 0.136 0.080 Occupants’ well-being (health) 0.052 Indoor comfort 0.052 0.117 0.174 0.098 Outdoor comfort 0.050 Waste and Pollution Building pollution 0.052 0.097 Waste management 0.058 0.080 0.144 0.090 Waste water management 0.058 Adaptability and Resilience Environmental resilience 0.053 0.108 0.150 Thermal resilience 0.051 Social resilience 0.041 0.081 0.045 0.088 Innovation Innovation in building design 0.052 0.080 0.100 Exemplary overall performance 0.046 References 1. 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