A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases

Khalil, Marwa; Pons-Valladares, Oriol; Bosch González, Montserrat

doi:10.3390/su172210086

Open AccessArticle

A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases

by

Marwa Khalil

^1,*,

Oriol Pons-Valladares

¹

and

Montserrat Bosch González

²

¹

Barcelona School of Architecture—ETSAB, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain

²

Barcelona School of Building Construction—EPSEB, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain

^*

Author to whom correspondence should be addressed.

Sustainability 2025, 17(22), 10086; https://doi.org/10.3390/su172210086

Submission received: 21 October 2025 / Revised: 5 November 2025 / Accepted: 6 November 2025 / Published: 11 November 2025

(This article belongs to the Special Issue Innovative Approaches in Construction: Between Circular Economy and Industrial Symbiosis)

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Abstract

Earthen heritage in desert oases reflects local identity, craftsmanship and traditional knowledge but is facing increasing threat of disappearance from material vulnerabilities, social abandonment and unsuitable interventions. This study develops and validates a novel decision-making tool to guide stakeholders in selecting the most suitable building technology for the adaptive reuse of earthen heritage complexes to ensure their long-term sustainability while maintaining their cultural and social values. The proposed methodology combines the Integrated Value Model for Sustainability Assessment (MIVES) and Delphi technique to evaluate the cultural, economic, environmental and social aspects. Quantitative and qualitative indicators were defined through literature review and weighted by experts in two rounds of Delphi to obtain comparable sustainability index for each building technology. The evaluation of economic and environmental aspects was based on literature data, while cultural and social aspects were assessed through a third round of Delphi with local participants. The tool was applied to the Adrere Amellal Ecolodge in Siwa Oasis, Egypt, comparing three building technologies: Karshif traditional earthen technique, commonly used red bricks and innovative 3D-printed saltblocks. Karshif achieved the highest sustainability index (0.77) due to its cultural values, social acceptance and environmental performance. The findings demonstrate the potential of traditional earthen techniques to ensure sustainable adaptive reuse, providing a replicable method for sustainable adaptive reuse of earthen heritage in desert oases in Egypt and worldwide.

Keywords:

vernacular architecture; heritage conservation; sustainability assessment; earthen building technologies; multi-criteria decision making (MCDM); MIVES; Delphi; Karshif

Graphical Abstract

1. Introduction

Earthen architecture is one of the oldest and most common building techniques in the world due to its affordability, simplicity, availability and environmental adaptability [1]. It is particularly prevalent in hot, arid climates because of its capacity to provide indoor thermal comfort and regulate moisture more than industrialized materials [2]. Adaptive reuse has emerged as a key conservation strategy, revitalizing earthen heritage buildings while preserving their authenticity and local identity [3]. It does not only safeguard the buildings’ structures but also their architectural, social, cultural and historical values [4].

In Egypt’s desert oases, earthen architecture is the core of their vernacular architecture, representing their local identity and inherited traditional knowledge. Siwa Oasis, in particular, is known for its traditional Karshif construction technique, dating back to the 12th century. Karshif is made from salt, clay, mud and other minerals and used for its availability, cost-effectiveness and excellent thermal insulation [5]. Earthen architecture has always been the most common building technology in desert oases all over the world due to its adaptability to climate conditions and integration with local context. The locally available material provides excellent thermal mass and regulated humidity, making buildings suitable to extreme high temperatures and dry weather [6,7]. In addition to its environmental performance, the traditional know-how of earthen techniques is an integral part of the culture, linking vernacular architecture to the local identity.

However, in such a fragile environment, earthen architecture, such as Karshif, faces increasing threats. Natural factors like underground water, growth of vegetation and presence of animals, temperature and humidity fluctuations and strong wind erosion cause wall cracking and loss of cohesion [8,9,10]. The socio-cultural pressures from modernizing traditional lifestyle have led to the abandonment of the material and replacing it with industrialized materials. In addition, this decay is further accelerated by interventions unsuitable to the material and original values of the buildings [11,12]. These combined shifts endanger the continuous transmission of local building know-how and the survival of earthen settlements in desert oases. Therefore, addressing these vulnerabilities through context-sensitive conservation strategies becomes essential for ensuring their sustainability.

Consequently, adaptive reuse has reintroduced Karshif as a suitable construction material for the revitalization of deteriorated complexes, such as Adrere Amellal Ecolodge, Albabenshal Ecolodge and Kenooz Shali Lodge [13]. International conservation guidelines emphasize that interventions on heritage buildings should preserve authenticity and significance by minimal and reversible changes. The 1931 Athens Charter encouraged minimizing the changes made to the conserved buildings [14]. This concept was further supported by the 1964 Venice Charter and the International Charter on Archaeological Heritage Management. Venice Charter supported the reuse of heritage buildings while highlighting the necessity of limiting their modification to the minimum and preserving their authenticity represented in their artistic and historic values [15]. Similarly, the 1999 Burra Charter highlights that the emphasis of the cultural significance of the place should be the priority and an essential guide of the amount of the changes that occur and that change should not affect the authentic and historical evidence of the place [16].

The pioneer Riegl (1903) was the first one to identify the cultural heritage values and divided them into memory values (age value, historical value and commemorative value) and current values (use value and art value) [17]. In this sense, adaptive reuse and the authenticity of the buildings cannot be separated. The adaptation of heritage buildings must ensure a balance between the efficiency of its new use, the preservation of its architectural components and the maintenance of its intangible cultural and social values. The most successful processes are the ones that add a new useful layer for the future while having a minimal effect on the asset and preserving its historic authentic qualities [18,19].

However, the adaptive reuse projects face many post-intervention challenges, such as physical degradation and the absence of global conservation strategies [20,21]. With the increasing interest in reuse projects, many multi-criteria decision-making (MCDM) tools have been developed to guide adaptive reuse strategies, prioritize renovation decisions, evaluate alternative solutions for reuse and assess building significance and suitability of function [22]. However, there remains a significant lack of systematic decision-making tools to assess the sustainability and cultural compatibility of building technologies used in adaptive reuse. Table 1 depicts a representative sample of them.

As summarized in Table 1, existing MCDM tools for adaptive reuse have addressed the economic, environmental, social and cultural aspects but in various ways. Several researchers, such as Wang & Zeng [23], Haroun et al. [25], Vehbi et al. [27], Giove et al. [31], Piñero et al. [33] and El Borolosy [35], included all four requirements but mainly focused on the evaluation of the new selected reuse rather than the material or the building technology used in the adaptive reuse project. In these studies, environmental criteria focused on the impact of the project on the site and its surroundings, while economic requirements highlighted the return on investment and profitability of the possible uses rather than the project costs. Other research included only selected aspects of the sustainability pillars; Ribera et al. [24], Cucco et al. [26] and Della Spina [29] have not included the environmental indicators, whereas Della Spina & Lanteri [36] have overlooked the cultural and social dimensions. Additional frameworks have used new categories not clearly related to the sustainability pillars, such as on-site/off-site aspects [30] or context quality and building flexibility [32]. Ghobadi & Sepasgozar [34] uniquely addressed the different reuse options for prefabricated timber buildings but excluded the cultural and heritage dimensions since their case studies were not heritage buildings.

To address these gaps, the present decision-making tool builds on these earlier MCDM tools while addressing their limitations by integrating cultural, economic, environmental and social requirements based on detailed literature review. The inclusion of these requirements extends previous MCDM tools and adapts them to the context of earthen heritage in desert oases. Therefore, the aim of this research is to contribute to supporting the long-term sustainability of earthen heritage and its maintenance in good condition for a long period of time. This target is to be achieved through the following specific objectives:

To develop a sustainability assessment tool that supports stakeholders, municipalities and the government in selecting the most suitable building technology for adaptive reuse of earthen building complexes, based on cultural, environmental, economic and social aspects.
To validate the tool by applying it to a selected case study, by comparing three building technologies and determining the most suitable option for its reuse.

2. Methodology

The development process of the proposed tool is structured into three main stages, as presented in Figure 1: (1) the selection of the case study and alternatives, (2) the development of the sustainability assessment tool, and (3) the sensitivity analysis to validate the most suitable building technology.

Stage 1: Case Study and Alternatives Selection

The first stage involves selecting both a representative case study and three building technology alternatives for testing the tool. The selection is based on the following criteria. The building chosen as a case study must:

Be a complex—either a large building or a group of small buildings—originally constructed using a traditional earthen technique.
Be located in an Egyptian desert oasis.
Have undergone adaptive reuse, particularly a transformation from residential use into an ecolodge.

The selected building technologies are chosen to represent three specific approaches:

An experimental or futuristic technique.
A currently common technique.
A traditional technique.

Stage 2: New Tool Development

Based on the analysis of existing tools (see Table 1), the second stage involves developing a novel Multi-Criteria Decision-Making (MCDM) tool. The proposed tool integrates the Integrated Value Model Sustainability Assessment (MIVES) and the Delphi structured communication technique, due to their ability to support agile, informed sustainability assessment and generate sustainability indexes [38].

MIVES is composed of four main steps:

P1—Requirements Tree Definition: The tree is a hierarchical structure used to decompose the problems into smaller hierarchical data. It comprises four main requirements—cultural, economic, environmental and social—each composed of criteria and indicators.

P2—Weights Assignment Using Delphi Technique: The Delphi process involves two anonymous rounds with 12 multidisciplinary experts selected based on predefined criteria with different weights (Appendix A). To reduce bias, some measures are taken, such as anonymous participation, alphabetical ordering of tree components and use of median absolute deviation (MAD) to assess consensus (<10%). In the first round, experts assign weights to requirements, criteria and indicators. In the second round, 5 of these 12 experts are invited to revise their weights that do not meet the consensus threshold.

P3—Construction of Value Functions: Value functions are used to convert indicator scores—quantitative or qualitative—into non-dimensional “values” on a scale from 0 to 1. These values normalize the data and allow comparison across different indicators and alternatives. A third Delphi round is established to evaluate social and cultural indicators. This round engaged 29 experts familiar with the Siwan culture, including a majority of local residents. In this round, the experts scored each indicator on a scale from 1 to 10, 1 being the lowest and 10 being the highest.

P4—Calculation of Sustainability Indexes and Selection of Best Alternative: Each alternative is evaluated across all indicators. Indicator value functions are multiplied by their respective weights, resulting in a global sustainability index for each building technology. The alternative with the highest sustainability index is considered the most sustainable.

Stage 3: Sensitivity analysis

To validate the robustness of the results, a sensitivity analysis is performed. Four extreme scenarios are suggested, where each requirement is given a higher percentage of 70%, while the remaining are given 30%. This helps identify the specific conditions where each alternative performs better than the other ones.

Development of the Proposed Tool

Stage 1: Case Study and Alternatives Selection

Based on the defined criteria, a general analysis of Egypt’s five main desert oases was conducted. Due to its accessibility and the availability of data, Siwa Oasis was selected as the test site for the developed tool. Out of its 90 ecolodges, Adrere Amelal Ecolodge—the oldest and most famous—was chosen for the first application.

Located in the northern Western Desert of Egypt, Siwa Oasis is renowned for its historical significance, natural beauty and unique cultural heritage [39], as shown in Figure 2. Siwa has preserved its traditional customs despite centuries of being a crossroad and home for many civilizations along desert trade routes [40]. Siwa’s natural landscape includes the Great Sand Sea, surrounding mountains, agricultural lands, palm groves, natural springs and salt lakes [41], as presented in Figure 3. While Karshif remains the traditional construction technique in Siwa, new constructions have witnessed the introduction of red bricks since the mid-20th century, alongside experimental innovations such as 3D-printed salt blocks that explore modern uses of local materials [42].

In 1998, a landmark initiative, the “Siwa Sustainable Development Initiative”, was launched by Environmental Quality International (EQI), a consulting company based in Cairo, Egypt [43]. It aimed to empower the local community and safeguard Siwa’s natural and cultural heritage. Three heritage complexes were restored and transformed into ecolodges under this initiative, all using Karshif traditional building technique [44]. Among them, Adrere Amellal Ecolodge became the initiative’s centerpiece, as depicted in Figure 4. Restored in 2000, it is located on the edge of Siwa Lake, at the foot of Gaafar Mountain, surrounded by seven salt lakes and dense groves of palm and olive trees [21]. The ecolodge had previously been a deteriorated residential complex built in Karshif. Without internet or electricity, the ecolodge embraces the philosophy of ecological integration, through location, material and daily operation [44,45]. Its self-sufficiency concept includes the cultivation of food, use of local materials and handmade furniture, natural water sourcing, a non-site waste management station and composting systems [46], as shown in Figure 5. Plans can be found in Appendix B.

To test the developed decision-making tool, three building technology alternatives were selected for evaluation, each representing a distinctive approach to construction, as previously explained:

3D-printed salt blocks (S1)—an experimental and futuristic technique that combines local material reuse with precision and flexibility.
Red bricks (S2)—a widely used, currently common material used for new buildings in Siwa oasis.
Karshif (S3)—the traditional Siwan technique already used in adaptive reuse.

Stage 2: Sustainability Assessment

P1—Requirements Tree

To evaluate the most sustainable building technology for adaptive reuse, four main aspects were considered: cultural (R1), economic (R2), environmental (R3) and social (R4). The selection of indicators was based on a comprehensive analysis of previous decision-making tools and a detailed review of literature related to these three alternatives. Indicators were chosen based on their relevance to adaptive reuse in heritage contexts, data availability and their ability to differentiate between alternatives. Table 2 shows the final requirements tree consists of 4 requirements, 10 criteria and 15 indicators and the performance of the three alternatives in each indicator.

The 15 selected indicators were evaluated through two different approaches based on their nature and data availability. Quantitative indicators (I4–I11), linked to economic and environmental aspects, were evaluated using quantitative data from the literature, international standards and prior case studies. Qualitative indicators (I1–I3 and I12–I15), reflecting cultural and social aspects, were assessed qualitatively using the third Delphi round.

P2—Weight Assignment Using Delphi Technique

The weights shown in Table 2 were assigned through two rounds of Delphi anonymous questionnaires to requirements, criteria and indicators. In the first round, 12 experts assigned weights to all levels of the tree: requirements, criteria and indicators. All weights were examined to ensure the maximum consensus threshold was achieved at 10%. In the second round, weights were revised using percentage scores. In order to meet the required consensus, 5 of these 12 experts were asked to re-evaluate their inputs for two specific indicators: I5—Access to the material and I6—Adaptive reuse time. Experts’ responses on requirements, criteria and indicators are shown in Appendix D.

P3—Construction of Value Functions

To normalize the different indicators into comparable units, value functions were constructed following MIVES methodology, based on guidelines from [62,63]. As previously mentioned, these functions transform the performance of each indicator into non-dimensional values between 0.00 and 1.00. Depending on the nature of the indicator, the function can be linear, convex, concave or S-shaped. The parameters defining each value function are further elaborated in Appendix E. As shown in Table 3, each indicator was assigned a value function, shape and tendency from the aforementioned. The value function of each indicator is illustrated in Appendix F.

P4—Calculation of Sustainability Indexes and Selection of Best Alternative

To calculate the sustainability index for each alternative, the process followed four main steps, explained in Appendix G. Some indicators—I5, access to the material; I8, CO₂ emissions; I9, % of recyclable waste; and I10, thermal conductivity—show limited variation among the alternatives in this study. By contrast, cultural indicators I1, I2 and I3 show the highest differentiation between alternatives, reflecting the great variability between the given values. While MIVES can be adapted to each case study, the developed model serves as a general framework applicable to future adaptive reuse projects in Siwa and other oases. Indicators that are not discriminative in this context may have different effects in other cases.

Table 4 presents the non-dimensional indicator values for the three building technology alternatives. The original quantified values, prior to applying value functions, are listed in Table 2.

Stage 3: Sensitivity Analysis

To evaluate the reliability of the decision-making process, a sensitivity analysis was conducted through the simulation of four additional extreme scenarios, complementing the base scenario analyzed in the case study (Scenario 1, see Section 4). In each of the new scenarios, one scenario—cultural, economic, environmental or social—was given the weight of 70%, while the remaining three requirements were each given 10%. This approach allowed for assessing the influence of each requirement on the sustainability ranking of the three building technologies.

3. Results

Based on the indicator value functions, Figure 6 depicts the resulting sustainability index for each building technology. According to this proposed model, Karshif achieved the highest sustainability index at 0.77, which makes it the most suitable building technology for the adaptive reuse of earthen complexes in the Siwa Oasis. On the other hand, 3D-printed salt blocks obtained the lowest index at 0.50, making them the weakest candidate for adaptive reuse. The key differentiators between the most and least sustainable options were the cultural (R1) and economic (R2) requirements, which had comparatively higher weights than the other requirements.

To further understand these results, Figure 7 illustrates the sustainability indexes of the four requirements.

Cultural requirement (R1) achieved the second-highest sustainability indexes across all alternatives. This is caused by the dependence of its indicators on Delphi responses from the local Siwan community, which tended to support Karshif.
Economic requirement (R2) resulted in the lowest values for both 3D-printed salt blocks (S1) and Karshif (S3), situating S1 as the lowest candidate in this aspect. Red bricks (S2), on the other hand, obtained the highest values in this requirement.
Environmental requirement (R3) produced the most uniform values between S1 and S3 but showed the lowest scores for (S2) due to its higher embodied energy and lower recyclability.
Social requirement (R4) presented the highest sustainability values for S2 and S3. Similar to R1, it benefited from the third Delphi round, which emphasized the knowledge, acceptance and perceived safety of the community—factors that significantly favored Karshif.

The integration of qualitative cultural and social indicators has significantly affected the final sustainability index. As shown in Table 5, the cultural (R1) and social (R4) requirements have had a larger impact on the sustainability indexes of S2 and S3. While Karshif (S3) performed moderately in quantitative aspects such as adaptive reuse time (I6) and maintenance frequency (I7), it had a strong performance in qualitative indicators—such as cultural identity and historical value (I3) and community acceptance (I14). In contrast, red bricks (S2) and 3D-printed salt blocks (S1) scored higher in quantitative environmental and economic indicators but had lower scores in social and economic aspects, which results in their overall lower index. Although the economic requirements (R2) are expected to have the highest share of the total index, it was the least influential requirement of the four due to the low weight given by the experts in the two DELPHI rounds. This demonstrates that the inclusion of qualitative indicators has enabled a more comprehensive overview of the alternatives, based on not only the measurable data but also the impact on the asset and the perception of the community.

Sensitivity Analysis

The results of the different scenarios of the sensitivity analysis, presented in Figure 8, are as follows:

Scenario 1—Base scenario: revealed Karshif as the most sustainable alternative at 0.77, followed by red bricks at 0.60 and finally 3D-printed salt blocks at 0.50.
Scenario 2—Cultural scenario: Karshif (S3) performed best with a sustainability index at 0.85, showing its agreement with local identity and heritage values. This supports previous findings on the central role of cultural identity and authenticity in guiding sustainable adaptive reuse [39].
Scenario 3—Economic scenario: red bricks (S2) achieved the highest score (0.65), highlighting their affordability and fast application. This result supports the significance of cost-efficiency and time in making sustainable adaptive reuse decisions [31].
Scenario 4—Environmental scenario: Karshif (S3) and 3D-printed salt blocks (S1) showed comparable results, with Karshif (S3) slightly outperforming at 0.70. Red bricks (S2) attained low scores due to its lower thermal performance and recyclability and higher embodied energy. Similarly, El-Mahdy et al. [42] reported that Karshif (S3) has the lowest embodied energy, followed by 3D-printed salt blocks (S1), with red bricks having the highest (S2).
Scenario 5—Social scenario: Karshif (S3) was the highest candidate at 0.86, driven by community acceptance, knowledge of the technique and feasibility for unskilled locals. This underlines the role of community participation in different stages of heritage conservation, from decision-making to implementation, as expressed by Hamada & Hamada [64].

4. Discussion

The application of the proposed methodology not only produced comparative sustainability indexes but also revealed how the assessed building technologies perform through cultural, economic, environmental and social aspects in a sensitive heritage context such as Siwa Oasis.

Karshif (S3) outperformed the other alternatives, primarily due to its strong performance in the cultural and social requirements. It performed well in indicators such as compatibility (I2), cultural identity and historical value (I3), and community acceptance (I14). It also achieved high scores in ease of construction (I12) and knowledge of the technique (I13), which reinforces its role in promoting community participation. These findings are consistent with conclusions by El Haridi et al. [65] and El-Shafie [66] that emphasized the ability of Karshif to preserve cultural identity in the desert environment of Siwa.

On the other hand, Karshif performed weakly in economic indicators, such as adaptive reuse time (I6) and maintenance frequency (I7) (view Table 2), indicating higher time and effort demand observed by Mohamed [44]. Despite the deep historic and social presence of Karshif, Mohamed [44] noted its low structural performance and suggested its integration with industrialized materials to improve its resistance. In the same way, Sameh et al. [67] and Rovero et al. [68] criticized its vulnerability to insects and water, which can lead to users’ discomfort and frequent need for maintenance. These show that, although Karshif performs well in cultural and social aspects, its durability limitations should be addressed to ensure its sustainability in the future.

Red bricks (S2) had high scores in economic and social requirements. They achieved the lowest life cycle cost (I4), shortest adaptive reuse time (I6) and minimal maintenance demand (I7), which makes them a cost-effective and rapid solution. Since they started to be commonly used in Siwa in themid-20thcentury, they had high scores in perceived safety (I15) and ease of construction (I12). However, they had lower scores in cultural and environmental indicators, such as compatibility (I2) and embodied energy (I11) (view Table 2). Many studies similarly criticized that industrial materials diminish heritage identity and have poor insulation and high embodied energy [46]. Moreover, Elalfy & Farrag [43] underlined the need to harmonize adaptive reuse strategies with original heritage materials.

Three-dimensional-printed salt blocks (S1) ranked the lowest of the three technologies, despite their environmental potential. They received average scores in environmental indicators such as CO₂ emissions (I8) and recyclability (I9), which was confirmed by El-Mahdy et al. [42], who validated the compressive strength and recyclability of the material. However, their low scores in cultural and social indicators such as community acceptance (I14), knowledge of the technique (I13) and compatibility (I2) indicated the unfamiliarity of the local community with digital fabrication. Many studies, such as Matos et al. [69], highlighted the innovation of salt-based 3D printing but mentioned their possible challenges, such as the limited social acceptance, loss of traditional labor and the need for new skills. In addition, Rückrich et al. [70] mentioned the remaining need to develop earth-based 3D printing in terms of material thermal performance and mechanical performances.

The analyzed results indicate the dominance of cultural indicators (compatibility (I2) and cultural identity and historical value (I3)) and social indicators (ease of construction (I12), knowledge of the technique (I13), acceptance (I14) and safety (I15)) over economic and environmental ones. This dominance reflects the specificity of sustainability assessment in heritage-sensitive contexts like Siwa, where social acceptance and local culture are essential for long-term sustainability. While economic feasibility impacts short-term maintenance of the reused assets, social and cultural factors determine if adaptive reuse interventions are embraced or abandoned by the community.

The results of this study expand the conclusions drawn from the analyzed MCDM frameworks for adaptive reuse. Although some models integrated the three sustainability pillars—economic, environmental and social—along with the cultural aspect, most of these studies focused on selecting new functions for heritage buildings, while overlooking the evaluation of the materials and construction technologies used, as seen in Wang and Zeng [23], Haroun et al. [25], Vehbi et al. [27] and Mohamed and Marzouk [21]. In contrast, this research uses the same criteria for the level of material and building technology level, while showing that cultural and social aspects can decisively impact the global sustainability index. This result supports Mohamed and Marzouk [21], where heritage values weighed 36.1% of total sustainability index, but disagrees with Chen et al. [28], where economic criteria were dominating and historical value had the lowest impacts. Furthermore, this study emphasizes community involvement and compatibility with cultural and heritage values, aligning with Della Spina [29], Ribera et al. [24] and Cucco et al. [26]. In addition, while recent MIVES applications (e.g., Boix Cots et al. [38]; Banirazi Motlagh et al. [59]) proved the capacity of the method to combine qualitative and quantitative indicators in solving complex problems, the current study extends its use in heritage conservation.

4.1. Comparative Position of the Proposed Method

In comparison with other MCDM approaches, the proposed MIVES-Delphi framework offers notable advantages. Early compensatory techniques such as WSM and WPM models [71,72] are simple and transparent but cannot handle complex multi-dimensional problems and can cause unrealistic results due to their extremist approach of prioritization of the alternatives. AHP and ANP [73,74] are commonly used to solve simple and complex problems due to their improved weighing process through pairwise comparisons (hierarchy and network relations), yet they can be sensitive to subjectivity, rank-reversal and lack of consideration of uncertainties in the results. Outranking methods such as ELECTRE and PROMETHEE [75,76] introduced non-compensatory logic in the comparison between alternatives but are time-consuming, require extensive pairwise comparisons and often fail to produce a single optimal solution. Distance-based models such as TOPSIS and VIKOR [77,78] are simple, programable and use clear mathematical methods but can be inconsistent and inadequate to use in real-life problems because it depends on the proportional distance to an ideal solution.

On the other hand, MIVES [62,79] overcomes many of the mentioned limitations through a value-function approach from Multi-Attribute Utility Theory [80], which transforms each indicator with different measurement units into dimensionless comparable values. This reduces compensability, which signifies weak performance of one indicator cannot be fully compensated by strong performance of another one. In addition, it minimizes instability and rank-reversal risks that could happen if something minor changes in the indicator scores. Despite MIVES evaluating each indicator individually using value functions, it uses the requirements tree, which is a clear and transparent hierarchy structure, to show the relationship between requirements, criteria and indicators.

Nevertheless, MIVES presents certain challenges such as time consumption, the high dependence on multidisciplinary expertise and the allowance of a percentage of compensability between indicators. However, these limitations are decreased in this study by the integration of the Delphi technique [81] incorporating experts’ knowledge and local perspectives. The Delphi technique was selected to complement the MIVES methodology for its suitability to complex decision-making problems [82,83]. Anonymous questionnaires allow experts’ opinions to be collected and reach consensus using anonymous questionnaires and without direct communication between participants or physical meeting. This saves time and cost, increases the reliability of the opinions and reduces potential bias and influence of others’ opinions [82,84,85]. However, the feedback between the Delphi rounds allows the experts to reconsider their opinions and to link together the areas of knowledge and the perspectives of other experts [48].

MIVES has proved its efficiency in many fields such as risk assessment, buildings, building elements and regulations [38]. According to Cárdenas-Gómez et al., it represents a guide to choose construction alternatives such as concrete columns [86] and wooden structures [87] and underground installations such as pipe systems. Moreover, it is used in the evaluation of post-disaster [88] and autonomous housing [58,89]. Overall, the proposed MIVES-Delphi framework is more stable and participatory than the other compensatory, outranking and distance-based MCDM methods, making it particularly suitable for evaluating building technologies in heritage-sensitive contexts, like Siwa. Advantages and disadvantages of most common MCDM methods are detailed in Appendix H.

4.2. Reflections from Expert Interviews

In addition to the quantitative results, during the two rounds of Delphi qualitative analysis, the experts provided insights that could add another layer of refinement to the tool. Some experts suggested additional indicators—such as reversibility, water use and initial cost—in the future. These indicators were not included in the model due to the need to choose the most influential and differentiating indicators, ensure the availability of the data for evaluation and avoid overlap among indicators. Nevertheless, this emphasizes the role of participatory process in the development of decision-making tools, which allows for continuous feedback and adaptation to different cases and contexts.

Experts strongly highlighted that public perception plays an essential role in adaptive reuse success. One expert stated, “Public perception is 100% of everything”, underlining the importance of local acceptance in material selection in adaptive reuse processes. According to Barakat [90] and Sarhan [91], community participation and perception are keys for Siwa’s sustainable development. Several experts also mentioned the complexity in quantifying social and cultural requirements, stating that community responses may be shaped by social or political factors. Some also suggested that “community acceptance” may belong more to cultural aspects rather than social values.

Thermal comfort emerged as another point of debate. It was argued that thermal comfort does not only depend on material properties but also on the construction technique (e.g., wall thickness). The importance of including operational energy consumption in future assessments was also highlighted. In the same way, many studies stated that Siwan traditional architecture uses passive cooling techniques to achieve indoor thermal comfort, which cannot be evaluated through thermal conductivity alone [67,92].

These insights show the strength of the proposed tool in assessing the sustainability of adaptive reuse of earthen heritage conservation. While each building technology offers strengths and weaknesses, the tool effectively investigated multi-dimensions of sustainability, including social and cultural aspects overlooked in other decision-making models. By including experts and community perspectives, the tool was proven reliable and applicable to historic oases like Siwa.

4.3. Limitations of the Study

Firstly, the MIVES model applies a weighted-sum aggregation, which allows full compensability among the indicators. That signifies that a low score of one indicator can be compensated by a high score in another, assuming a perfect substitutability between the indicators. Although this direct linear relation is commonly used in sustainability assessment for its simplicity and ease, it may not fully consider the impact of very low scores on the overall sustainability index. Future studies could explore alternative aggregation operators (e.g., geometric mean, OWA and partial compensation) to further verify the robustness of the results and reduce potential bias.

Although the sensitivity analysis method reveals the dominance of Karshif building technology in four of the five scenarios, it still represents a simplified robustness test. Future research can include advanced global techniques—such as Monte Carlo simulation, bootstrapping, or variance-based sensitivity analysis—to explore uncertainty across all the factors simultaneously and validate the model’s stability.

Another limitation of this study is its focus on a single geographical context—Egyptian desert oases—and a specific type of adaptive reuse project—from residential complexes to ecolodges. This narrowing of scope was necessary to ensure consistency of the methodology and comparability between case studies (context and climate conditions, local community, and building technology). The focus on the adaptive reuse of earthen heritage in Egypt is due to its great cultural significance and its long history. In addition, concentrating the analysis within the country allowed the conduction of direct field observations and on-site evaluation of social and cultural indicators, ensuring the reliability of data and contextual appropriateness. As previously mentioned, earthen heritage in Egypt is primarily concentrated in Egypt round the Nile Valley and in eastern and western deserts. Focusing on one of the two major zones was essential to define clear study boundaries. Furthermore, adaptive reuse of earthen heritage in Egypt has been commonly documented in New Gourna Village in Luxor and in Siwa Oasis in Western Desert. Siwa was then selected because it uniquely preserves Karshif heritage, which is largely undocumented in the international literature.

4.4. Implications for Adaptive Reuse Policies and Practices

This study proves that the long-term success of adaptive reuse depends not only on the technical performance of the selected building technologies but also on their social acceptance and cultural impact. In Siwa and other similar oases, factors such as community participation, preservation of heritage values and compatibility with original features of the assets have been the key strategies for sustaining adaptive reuse projects. These findings align with the lessons drawn from other desert oases—AlUla, Al-Turaif, KsarAït Ben Haddou and Old Sana’a—where projects that included the socio-economic development of the reused assets, generated new economic opportunities, involved local participation and respected original values revitalized the heritage buildings and achieved continuity of use by locals and visitors [93].

In the development of heritage management policies, authorities should institutionalize community engagement throughout all project phases, while respecting cultural values in parallel with environmental and economic considerations. Linking adaptive reuse projects to local livelihoods—through construction process, training programs, traditional crafts or guided tourism—ensures that conservation integrates traditional skills and crafts while aiming for the development of the locals and their welfare. In addition, the inclusion of locals results in a better sense of belonging and a greater will to preserve and maintain their heritage after its revitalization. In parallel, preserving traditional construction knowledge through workshops, training of local craftsmen and the promotion of hybrid techniques that include traditional materials with modern solutions can improve the durability and acceptance of heritage assets.

In practice, future adaptive reuse projects should use decision-support tools such as the proposed framework to translate local perception into measurable sustainability criteria and to ensure that the decision-making process is inclusive, transparent and participatory. Design decisions must adapt to cultural values, local identity and social patterns of heritage-sensitive contexts. By combining technical considerations with communities’ values and development, adaptive reuse can develop from being a short-term intervention into a durable strategy to extend the usability and cultural value of earthen heritage in desert oases.

5. Conclusions

This research introduced a novel decision-making tool to evaluate the sustainability of building technologies used in the adaptive reuse of earthen heritage conservation in desert oases. The tool was applied to Adrere Amellal Ecolodge in Siwa Oasis, comparing three alternatives—Karshif, red bricks, and 3D-printed salt blocks. The results demonstrated that traditional building technologies, such as Karshif, have a higher sustainability index than innovative building technologies when cultural and social aspects are considered due to their strong link to heritage values and local identity.

The proposed methodology is fully replicable; each of the three main stages—case study and alternatives selection, new tool development and sensitivity analysis—is clearly defined and can be directly applied in other contexts. Moreover, the five steps of the model development, from requirements tree definition to the calculation of sustainability index, can be reused and adapted in similar studies by modifying indicator weights or performances. While the tool was designed to be used for earthen heritage in desert oases, the structure and indicator choice can be adapted to other building technologies, typologies and climatic conditions.

The tool contributes to the field of heritage conservation and adaptive reuse of earthen heritage by its proven ability to integrate qualitative and quantitative indicators in a single sustainability index. It provides a structured method that reduces ambiguity, improves objectivity and results in balanced outcomes. By including expert knowledge and local perception in the decision-making process, the tool bridges the gap between involved stakeholders such as authorities, investors, architects, researchers and local residents.

Future research could further verify the applicability of the model by applying it to multiple case studies in Siwa and other oases, involving broader expert participation and considering the inclusion of new suggested indicators—such as reversibility, water use and operational energy consumption. In addition, exploration of hybrid building technologies—such as Karshif systems with structural reinforcements—could address durability and vulnerability weaknesses.

In conclusion, this study proposes a novel systematic decision-making tool to guide stakeholders in decision-making process of adaptive reuse projects. By integrating cultural, economic, environmental and social considerations, it supports future heritage conservation policies and adaptive reuse practices in desert oases around the world.

Author Contributions

Conceptualization, M.K., O.P.-V. and M.B.G.; methodology, M.K.; formal analysis, M.K.; resources, M.K., O.P.-V. and M.B.G.; data curation, M.K.; writing—original draft preparation, M.K.; writing—review and editing, O.P.-V. and M.B.G.; visualization, M.K.; supervision, O.P.-V. and M.B.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All the authors of this research have ensured compliance with the ethical principles of the institution to which they belong, as set out in the Code of Ethics of the Universitat Politècnica de Catalunya, the Code of Research Integrity of the Universitat Politècnica de Catalunya and the Ethics Education in Science: Statement by the ALLEA Permanent Working Group on Science and Ethics. Given the nature of this study, which analyzes the sustainability of rehabilitation projects in Egypt’s oases and does not involve individuals or personal data as such, it was considered unnecessary to seek the approval of an ethics committee. During the study, only the opinions of experts were collected in order to determine the prioritization of economic, social or environmental issues, and these opinions were treated in aggregate form without indicating the expert who provided them. Furthermore, these opinions do not form part of the main core of the study, only complement part of it.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The author would like to express sincere gratitude to the supervisors for their continuous guidance and valuable feedback throughout this research. Special thanks are extended to the experts and local community members who generously agreed to use their time and insights to contribute to the Delphi process and fieldwork. This research was made possible through the cooperation and hospitality of the residents of the Siwa Oasis.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

AHP	Analytic Hierarchy Process
ANP	Analytical Network Process
ELECTRE	ÉLimination Et Choix Traduisant la REalité (Elimination and Choice Translating Reality)
GPIV	Grey Proximity Indexed Value
MAVT	Multi-Attribute Value Theory
MCDM	Multi-Criteria Decision-Making
MIVES	Integrated Value Model for Sustainability Assessment (From the Spanish Modelo Integrado de Valor para una Evaluación Sostenible)
NAIADE	Novel Approach to Imprecise Assessment and Decision Environments
PROMETHEE	Preference Ranking Organization Method for Enrichment Evaluation
SECA	Simultaneous Evaluation of Criteria and Alternatives
TOPSIS	Technique for Order Preference by Similarity to Ideal Solution
VIKOR	Vise Kriterijumska Optimizacija I Kompromisno Resenje (Multi criteria Optimization and Compromise Solution)
WSM	Weighted Sum Model

Appendix A. Experts Selection

Table A1. Expert selection criteria.

Achievement or Experience	Points
Years of experience	2
Published papers about relevant topic	2
Published papers about one of the three case studies	4
MSc	2
PhD	4
Sustainability expert	2
Heritage conservation/ earth construction expert	4

Table A2. Experts’ information of Delphi round 1–2.

N	Country of Affiliation	Organization	Expertise Area	N	Country of Affiliation	Organization	Expertise Area
1	Ljubljana	University	Vernacular Architecture	7	Egypt	Company	Sustainable Architecture
2	US	University	Materials Engineering	8	UAE	Company	Sustainable Architecture
3	Italy	Company	Conservation of Cultural Heritage	9	Spain	University	Social and Cultural Anthropology
4	Egypt	University	Conservation of Cultural Heritage	10	Egypt	Company	Earthen Architecture
5	UK	Freelance	Conservation of Cultural Heritage	11	Spain	Research Center	Conservation of Cultural Heritage
6	Italy	University	Earthen Architecture	12	Portugal	University	Conservation of Earthen Heritage

Appendix B. Adrere Amellal Plans

Figure A1. Ground floor plan of building 1.

Figure A2. First floor plan of building 1.

Figure A3. Ground floor plan of building 2.

Figure A4. Ground floor plan of building 3.

Appendix C. Definition of Indicators

Cultural Requirement (R1):

I1—Public Benefit and Immortality Value: measures the extent to which the reused building serves as a common good for current and future generations.
I2—Compatibility: assesses the harmony between the intervention and the original identity and features of the building.
I3—Cultural Identity and Historical Value (I3): evaluates the degree of preservation of authenticity and historical integrity of the building.

Economic Requirement (R2):

I4—Life Cycle Cost (LCC): calculates total costs throughout the building’s lifespan, including production and construction (A1–A5 building life cycle stages) (view Appendix C, Table 1).
I5—Access to the Material: measures the distance to the nearest available source of the material.
I6—Adaptive Reuse Time: captures the duration of the adaptive reuse process using the specified technique.
I7—Maintenance Time: assesses the time required for periodic yearly maintenance using this technique.

Environmental Requirement (R3):

I8—CO₂ Emissions: quantifies carbon emissions generated during the adaptive reuse process, focusing on stages A1–A5 of the life cycle.
I9—% of Recyclable Waste: evaluates the proportion of material waste that can be reused after the end of the building’s life cycle.
I10—Thermal Conductivity: measures the insulation properties of the material, influencing energy efficiency and indoor thermal comfort.
I11—Embodied Energy Consumption: reflects the total energy required for material extraction, processing, transport and construction.

Social Requirement (R4):

I12—Ease of Construction: assesses the level of difficulty in applying this building technique by local laborers and unskilled workers.
I13—Knowledge of the Technique: evaluates the local community’s familiarity with this method.
I14—Acceptance: measures the extent of community approval of the use of this technique to the building.
I15—User Safety: reflects local perception of safety and reliability associated with this building technique.

Table A3. Building life cycle information stages and corresponding modules. Source: author based on [94].

Building Life Cycle Information
Building Life Cycle																Supplementary Information
Product			Construction		Use Stage							End of Life				Benefits and Loads beyond the System Boundary
A1	A2	A3	A4	A5	B1	B2	B3	B4	B5	B6	B7	C1	C2	C3	C4	D
Raw Materials Supply	Transport	Manufacturing	Transport	Construction	Use	Maintenance	Repair	Replacement	Refurbishment	Operational Energy Use	Operational Water Use	De-Construction—Demolition	Transport	Waste Processing	Disposal	Reuse– Recovery– Recycling– Potential

Appendix D. Delphi Responses

Table A4. Delphi responses on requirements’ weights. Source: author.

Requirements	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Expert 6	Expert 7	Expert 8	Expert 9	Expert 10	Expert 11	Expert 12	Median	Mean	MAD
R1—Cultural	30%	15%	30%	30%	30%	40%	25%	30%	15%	20%	20%	35%	30%	27%	5%
R2—Economic	20%	15%	10%	25%	25%	20%	20%	10%	35%	40%	30%	10%	20%	22%	7.5%
R3—Environmental	30%	35%	30%	35%	15%	20%	20%	30%	30%	30%	30%	25%	30%	28%	2%
R4—Social	20%	35%	30%	10%	30%	20%	35%	30%	20%	10%	20%	30%	25%	24%	5%

Table A5. Delphi responses on criteria weights. Source: author.

Criteria	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Expert 6	Expert 7	Expert 8	Expert 9	Expert 10	Expert 11	Expert 12	Median	Mean	MAD
C1—Effect on the community	80%	70%	60%	80%	80%	80%	70%	80%	80%	80%	40%	50%	80%	71%	0%
C2—Effect on the asset	20%	30%	40%	20%	20%	20%	30%	20%	20%	20%	60%	50%	20%	29%	0%
C3—Cost	70%	70%	40%	20%	40%	40%	40%	50%	50%	70%	70%	50%	50%	51%	10%
C4—Time	30%	30%	60%	80%	60%	60%	60%	50%	50%	30%	30%	50%	50%	49%	10%
C5—Emissions	20%	15%	5%	25%	20%	30%	20%	30%	30%	10%	45%	25%	23%	23%	7.5%
C6—Waste	20%	20%	45%	25%	30%	20%	30%	10%	25%	20%	30%	25%	25%	25%	5%
C7—Thermal comfort	30%	50%	25%	25%	40%	30%	15%	20%	20%	50%	10%	25%	25%	28%	5%
C8—Energy consumption	30%	15%	25%	25%	19%	20%	35%	40%	25%	30%	15%	25%	25%	25%	5%
C9—Community participation	50%	20%	30%	60%	60%	40%	40%	60%	30%	40%	40%	50%	40%	43%	10%
C10—Community perception	50%	80%	70%	40%	40%	60%	60%	40%	70%	60%	60%	50%	60%	57%	10%

Table A6. Delphi responses on indicators’ weights. Source: author.

Criteria	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Expert 6	Expert 7	Expert 8	Expert 9	Expert 10	Expert 11	Expert 12	Median	Mean	MAD
I1—Public benefit and immortality value	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	0%
I2—Compatibility	20%	65%	30%	20%	20%	20%	45%	30%	20%	20%	60%	40%	25%	33%	5%
I3—Cultural identity and historical value	80%	35%	70%	80%	80%	80%	55%	70%	80%	80%	40%	60%	75%	68%	5%
I4—LCC (Life Cycle Cost)	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	0%
I5—Access to the material	40%	50%	70%	30%	30%	40%	40%	60%	35%	50%	60%	50%	45%	46%	8%
I6—Adaptive reuse time	30%	15%	20%	30%	15%	30%	30%	10%	30%	25%	15%	20%	23%	23%	8%
I7—Maintenance time	30%	35%	10%	40%	55%	30%	30%	30%	35%	25%	25%	30%	30%	31%	5%
I8—CO₂ Emissions	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	0%
I9—% of recyclable waste	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	0%
I10—Thermal conductivity	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	0%
I11—Embodied energy consumption	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	100%	0%
I12—Ease of construction	30%	50%	40%	55%	40%	30%	40%	30%	70%	30%	35%	50%	40%	42%	10%
I13—Knowledge of the technique	70%	50%	60%	45%	60%	70%	60%	70%	30%	70%	65%	50%	60%	58%	10%
I14—Acceptance	50%	25%	40%	65%	30%	60%	50%	20%	30%	50%	50%	50%	50%	43%	10%
I15—Users’ safety	50%	75%	60%	35%	70%	40%	50%	80%	70%	50%	50%	50%	50%	57%	10%

Appendix E. Definition of Value Functions

Each function is defined by five main parameters, as presented in Equation (A1):

$K_{i}$ : ordinate of the inflection point.
$C_{i}$ : abscissa of the inflection point.
$X_{m i n}$ and $X_{m a x}$ : maximum and minimum satisfaction values of indicator’s performance.
X: performance of evaluated indicator which lies between $X_{i, m i n}$ and $X_{i, m a x}$ .
$P_{i}$ : value that indicates the shape of the value function.
If it is concave, then $P_{i}$ < 1.
If it is linear, then $P_{i} \approx$ 1.
If it is convex or S-shape, $P_{i}$ > 1

$V_{i} = B \cdot [1 - e^{- K_{i} \cdot {(\frac{|X - X_{m i n}|}{C_{i}})}^{P_{i}}}]$

(A1)

B is the factor that guarantees that the value function stays between the minimum and maximum values, which are 0.0 and 1.0, respectively. It is expressed by the following Equation (A2):

B = \frac{1}{[1 - e^{- K_{i} \cdot {(\frac{|X_{m a x} - X_{m i n}|}{C_{i}})}^{P_{i}}}]}

(A2)

Shapes of value functions can be:

Linear: Satisfaction increases proportionally with indicator value.
Convex: Greater importance is given to approaching maximum satisfaction rather than moving away from minimum satisfaction.
S-Shape: Used when alternatives cluster around the mid-range between the points of minimum and maximum satisfaction.

Indicators are also classified by tendency:

Increasing: Increase insatisfaction is directly proportional to increase inindicator value.
Decreasing: Increase insatisfaction is inversely proportional to increase inindicator value.

Appendix F. Value Functions of Indicators

Figure A5. Cultural indicators. Legend: Red dots are the three alternatives, White dots are the discrete points defining the function.

Figure A6. Economic indicators. Legend: Red dots are the three alternatives, White dots are the discrete points defining the function, Green dots are standard values based on experience of previous projects.

Figure A7. Environmental indicators. Legend: Red dots are the three alternatives, White dots are the discrete points defining the function, Green dots are standard values based on [95].

Figure A8. Social indicators. Legend: Red dots are the three alternatives, White dots are the discrete points defining the function.

Appendix G. Calculation of Sustainability Indexes

To calculate the sustainability index for each alternative, the process followed four main steps as follows:

Step 1—Indicator values were calculated using the value functions. For each indicator, the quantified value of the alternative on the x-axis was converted into a non-dimensional score on the y-axis based on the assigned function.

Step 2—Criteria values were calculated by multiplying the normalized values of the indicators within the same criterion by their given weights, as depicted in Equation (A3):

V_{C r i t e r i o n} = \sum_{i = 1}^{n} V_{i n d i c a t o r} \times W_{i}

(A3)

Step 3—Requirement values were calculated similarly by multiplying the sum of values of criteria within the same requirement by their given weights, as shown in Equation (A4):

V_{R e q u i r e m e n t} = \sum_{i = 1}^{n} V_{C r i t e r i o n} \times W_{i}

(A4)

Step 4—Finally, the Global Sustainability Index (GSI) for each alternative was calculated by summing the requirements values, multiplied by their given weights, as presented in Equation (A5):

{G S I}_{A l t e r n a t i v e} = \sum_{i = 1}^{n} R e q u i r e m e n t \times W_{i}

(A5)

Appendix H. Advantages and Disadvantages of Common MCDM Methods

Table A7. Most common advantages and disadvantages of each MCDM method. Source: author.

Method	Advantages	Disadvantages	References
WSM (SAW)	Simplicity and comprehensibility. Easy application by decision makers. Performance values in each criterion can compensate each other.	Inability to solve multi-dimensional problems because of the different units. Results are not always compatible with reality.	[96]
WPM	Ability to solve single and multi-dimensional decision-making problems. Use of relevant dimensionless values not actual values.	Use of an extremist approach in the prioritization of the alternatives and the consideration of only their maximum and minimum performance values.	[72,97]
AHP	Easy application. Adaptability to simple and complex decision-making problems. Useful to assign weights to define hierarchy of alternatives and criteria. Not requiring computational methods.	Prefers to be applied on from 5 to 9 elements. Inability to give accurate results with low number of elements. Subjectivity. Inconsideration of uncertainty in the results. Possibility of inconsistency due to the interdependent relation between criteria and alternatives. Subject to rank reversal.	[98,99,100]
Revised AHP(ANP)	Suitability to complex decision-making problems. Use of network structure instead of hierarchy, so interdependent relation between elements is not needed. Possibility for improvement of accuracy of results.	Time consuming because of the long time spent in brainstorming. Requires the presence of a large number of experts because of its complexity. Inconsideration of uncertainty in the results. Requires computational methods.	[98]
ELECTRE	Consideration of uncertainty percentage of the different criteria. Ability to include quantitative and qualitative types of criteria.	Time consuming. Use of a ranking approach causes inability to choose the optimal solution and inconsideration of the advantages and disadvantages of each alternative.	[98,100,101]
TOPSIS	Simplicity and programmability. Use of clear mathematical methods. Not affected by the number of elements. Use of all the received information to rank the alternatives and choose the ideal one.	Inconsideration of uncertainty. Results can be inconsistent. Deal with every element individually to evaluate its Euclidean distance from the ideal solution, therefore neglecting the relation between different elements.	[96,98,102,103]
VIKOR	Simplicity and use of clear mathematical methods. Not affected by the number of elements. Allowance of easy interaction. Permission of the interference of the decision maker to adjust criteria weights.	Inadequacy to use in real life problems because of its inability to consider vague information. Inability to solve controversial problems. Inconsideration of relation between different elements.	[97,104]
COPRAS	Time saving and requirement of simple computation. Use of importance of alternatives to rank them. Dealing with increasing and decreasing independently.	Reliance on the values and the number of decreasing criteria. Instability in terms of dealing with different information.	[97,105,106]
PROMETHEE-GAIA	Stability. Easy application because it requires few inputs and has its own criteria units. Ability to include quantitative and qualitative types of criteria. Can be used for real life application due to its convenience.	Complexity of the results in the case of including a large number of elements. Subject to rank reversal.	[98]
MIVES	Capability to carry out sustainability assessment. Use of value function approach and involvement of experts that increases objectivity of opinions. Recent consideration of uncertainty. Adaptability to different problem sizes. Proven to be the most suitable to decision-making problems in the building sector. Time disadvantage could be solved by optimization of the seven steps of the tool.	Time consuming because of the number of steps. Requirement of the presence and devotion of experts. Unsuitability for urgent decision-making problems that require a specific method. Inconsideration of relation between different elements in the decision tree.	[62]

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Figure 1. Overview of the three-stage process of the developed tool. Source: author.

Figure 2. Map of Egypt showing location of Siwa Oasis. Source: author based on https://www.ArcGIS.com powered by ESRI.

Figure 3. Aerial view of general landscape of Siwa Oasis. Photo by Marwa Mohammed, used with permission, 2023.

Figure 4. Exterior view of Adrere Amellal Ecolodge. Photo by Marwa Mohammed, used with permission, 2023.

Figure 5. Interior view of King Charles’s Suite in Adrere Amellal Ecolodge. Photo by Marwa Mohammed, used with permission, 2023.

Figure 6. Global sustainability index for each alternative. Legend: S1 is 3D-printed salt blocks, S2 is red bricks, and S3 is Karshif.

Figure 7. Sustainability indexes for the different requirements. Legend: S1 is 3D printed salt blocks; S2 is red bricks; and S3 is Karshif.

Figure 8. Sustainability indexes obtained for the different considered scenarios in the sensitivity analysis. Legend: S1 is 3D-printed salt blocks, S2 is red bricks, and S3 is Karshif.

Table 1. Representative existing multi-criteria decision-making (MCDM) tools for the adaptive reuse of heritage buildings.

Case Study	Objectives of Evaluation	Assessment Criteria	MCDM Method	References
Control Yuwan and Red House governmental buildings, Tapei City, Taiwan	Determine the highest and best use (HBU) for heritage buildings	Cultural, economic, architectural, environmental, social and continuity aspects	ANP and Delphi	[23]
Palazzo Genovese in Salerno, Italy		Social, cultural and economic aspects	AHP	[24]
Aziza Fahmy Palace, Alexandria, Egypt		Architectural character, heritage value, economic aspect, social impact and environmental performance		[25]
Villa Venusio, Mugnano di Napoli, Italy		Social, economic and cultural aspects		[26]
Old Administrative Hospital, Kyrenia, Cyprus		Cultural, economic, architectural, environmental, social and legal values		[27]
Sun Yat-Sen Historical Museum		Economic, social, environmental, architectural and historical aspects	Fuzzy Delphi	[28]
Palazzo Stella, Catanzaro, Italy	Determine the highest and best use of unused historical public heritage	Social, historical and cultural, economic andfinancial values	AHP	[29]
Castello Visconteo in Cusago, Lombardy, Italy	Evaluate different alternative scenarios for reuse of heritage buildings	Off-site aspects (design of public recreational spaces, compatibility of the function with the property, initial cost, etc.) On-site aspects (mixed new job opportunities, sustainable development goals (SDGs), involvement of the community, etc.)	WSM–NAIADE	[30]
Venice Arsenale, Italy	Evaluate sustainability of projects for economic reuse of historical buildings	Intrinsic, context sustainability and economic–financial feasibility	MAVT	[31]
Seven industrial buildings in Turin, Italy	Determine the best reuse of historic industrial buildings	Context quality, economic activity, building flexibility, pedestrian accessibility and preservation level	MAVT	[32]
Historic Centre of La Habana, Cuba	Prioritizing rehabilitation interventions of historic sites	Technical aspects (technical status, need for emergency and risks) Socio-cultural aspects (residents affected, non-residents affected and cultural value)	MIVES	[33]
Three heritage buildings reused as banks, Egypt	Evaluation of post-adaptive reuse	Architectural, functional, heritage, structural and sustainable values	Fuzzy Ensemble (Fuzzy Comprehensive Evaluation, TOPSIS, VIKOR, GPIV)	[21]
Alternatives for timber reuse	Assessing the reuse options for prefabricated timber buildings	Environmental, economic, social, technical and legal aspects	SECA + TOPSIS	[34]
The Palace of Prince Said Halim, Cairo, Egypt	Determining the most appropriate funding for the adaptive reuse of heritage buildings	Heritage, architectural, economic, social and environmental values	AHP	[35]
Noto–Pachino railway, Sicily, Italy	Assessing the adaptive reuse options for disused railways	Economic, environmental and urban planning criteria	PROMETHEE	[36]
Mansions in Aksaray, Turkey	Determining the sustainable and optimum model for adaptive reuse of heritage buildings	Building features, accessibility and environmental value	AHP	[37]

Table 2. Decision tree with its requirements, criteria, indicators and their units.

Requirements	Criteria	Indicators	Unit	3D-Printed Salt Blocks	Red Bricks	Karshif	References
R1—Cultural (26.7%)	C1—Effect on the community (71%)	I1—Public benefit and immortality value (100%)	Points	5.14	7.93	8.93	[17,23,47,48,49,50,51,52]
	C2—Effect on the asset (29%)	I2—Compatibility (32.5%)		5.34	6.83	9.21	[17,47]
		I3—Cultural identity and historical value (67.5%)		5.62	6.52	9.45	[17,53,54]
R2—Economic (21.7%)	C3—Cost (51%)	I4—LCC (Life Cycle Cost) (100%)	LE/m²	375,000,000	4,500,000	45,000,000	[26,55,56,57]
	C4—Time (49%)	I5—Access to the material (46.3%)	Km	18	370	21	[58]
		I6—Adaptive reuse time (22.5%)	Months	1666.67	555.57	1094.89
		I7—Maintenance time (31.3%)	Months	1	0.5	2
R3—Environmental (27.5%)	C5—Emissions (22.9%)	I8—CO₂ Emissions (100%)	kgCO₂/m²	1650	4,938,750	2550	[59]
	C6—Waste (25%)	I9—% of recyclable waste (100%)	%	13,500	10,500	13,500	[58]
	C7—Thermal comfort (27.9%)	I10—Thermal conductivity (100%)	W/(m²·K)	0.94	1.7	1.65
	C8—Energy consumption (24.2%)	I11—Embodied energy consumption (100%)	MJ/Kg	0.612	6.4	0	[59]
R4—Social (24.2%)	C9—Community participation (43.3%)	I12—Ease of construction (41.7%)	Points	5.10	7.76	6.62	[20,23,55,60,61]
		I13—Knowledge of the technique (58.3%)		5.38	6.93	7.66	[58]
	C10—Community perception (56.7%)	I14—Acceptance (43.3%)		5.07	7.31	7.79
		I15—User Safety (56.7%)		5.00	8.28	7.52

The explanation of the indicators is elaborated in Appendix C.

Table 3. Parameters of the indicators’ value functions.

Indicator	Unit	Shape	Tendency	Xmin	Xmax	C	K	P
I1—Public benefit and immortality value (100%)	Points	Linear	Increasing	0	10	5	0.01	1
I2—Compatibility (32.5%)				0	10	5	0.01	1
I3—Cultural identity and historical value (67.5%)				0	10	5	0.01	1
I4—LCC (Life Cycle Cost) (100%)	LE/m²	Convex	Decreasing	375,000,000	0	18,5250,000	0.0001	3
I5—Access to the material (46.3%)	Km			370	0	185	0.0001	3
I6—Adaptive reuse time (22.5%)	Days			1666	555	1110	0.0001	3
I7—Maintenance time (31.3%)	Months			2	0	1	0.0001	3
I8—CO2 Emissions (100%)	kgCO₂/m²			10,314,000	0	5,157,000	0.0001	3
I9—% of recyclable waste (100%)	%		Increasing	0	13,500	6750	0.0001	3
I10—Thermal conductivity (100%)	W/(m²·K)		Decreasing	2	0	1	0.0001	3
I11—Embodied energy consumption (100%)	MJ/Kg		Decreasing	6.4	0	3.2	0.0001	3
I12—Ease of construction (41.7%)	Points	S-Shape	Increasing	0	10	5	0.8	3
I13—Knowledge of the technique (58.3%)				0	10	5	0.8	3
I14—Acceptance (43.3%)				0	10	5	0.8	3
I15—User Safety (56.7%)				0	10	5	0.8	3

Table 4. Non-dimensional indicator satisfactions for the three assessed building technologies.

Requirements	Indicator	Non-Dimensional Values
Requirements	Indicator	3D-Printed Salt Blocks	Red Bricks	Karshif
R1—Cultural	I1—Public benefit and immortality value	0.52	0.79	0.89
	I2—Compatibility	0.54	0.69	0.91
	I3—Cultural identity and historical value	0.56	0.65	0.95
R2—Economic	I4—LCC (Life Cycle Cost)	0	0.98	0.68
	I5—Access to the material	0.86	0	0.84
	I6—Adaptive reuse time	0	1	0.14
	I7—Maintenance time	0.13	0.42	0
R3—Environmental	I8—CO₂ Emissions	1	0.08	1
	I9—% of recyclable waste	0.73	0.34	0.73
	I10—Thermal conductivity	0.15	0	0.01
	I11—Embodied energy consumption	0.74	0	1
R4—Social	I12—Ease of construction	0.57	0.95	0.85
	I13—Knowledge of the technique	0.63	0.88	0.95
	I14—Acceptance	0.57	0.92	0.95
	I15—User Safety	0.55	0.98	0.94
	Global Sustainability Index	0.50	0.60	0.77

Table 5. Contribution of each requirement to the final sustainability index.

Requirements	Alternatives
Requirements	3D-Printed Salt Blocks (S1)	Red Bricks (S2)	Karshif (S3)
R1—Cultural (26.7%)	0.14	0.20	0.24
R2—Economic (21.7%)	0.05	0.15	0.12
R3—Environmental (27.5%)	0.17	0.03	0.18
R4—Social (24.2%)	0.14	0.22	0.23
Global Sustainability Index	0.50	0.60	0.77

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Khalil, M.; Pons-Valladares, O.; Bosch González, M. A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases. Sustainability 2025, 17, 10086. https://doi.org/10.3390/su172210086

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Khalil M, Pons-Valladares O, Bosch González M. A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases. Sustainability. 2025; 17(22):10086. https://doi.org/10.3390/su172210086

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Khalil, Marwa, Oriol Pons-Valladares, and Montserrat Bosch González. 2025. "A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases" Sustainability 17, no. 22: 10086. https://doi.org/10.3390/su172210086

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Khalil, M., Pons-Valladares, O., & Bosch González, M. (2025). A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases. Sustainability, 17(22), 10086. https://doi.org/10.3390/su172210086

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A New Decision-Making Tool for Guiding the Sustainability of Adaptive Reuse of Earthen Heritage Complexes in Desert Oases

Abstract

1. Introduction

2. Methodology

Development of the Proposed Tool

3. Results

Sensitivity Analysis

4. Discussion

4.1. Comparative Position of the Proposed Method

4.2. Reflections from Expert Interviews

4.3. Limitations of the Study

4.4. Implications for Adaptive Reuse Policies and Practices

5. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

Conflicts of Interest

Abbreviations

Appendix A. Experts Selection

Appendix B. Adrere Amellal Plans

Appendix C. Definition of Indicators

Appendix D. Delphi Responses

Appendix E. Definition of Value Functions

Appendix F. Value Functions of Indicators

Appendix G. Calculation of Sustainability Indexes

Appendix H. Advantages and Disadvantages of Common MCDM Methods

References

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