Adapting Housing Design Tools for Indoor Thermal Comfort to Changing Climates

Abstract

Heat-related fatalities are rising globally, driven by poorly designed housing and the limited use of climate adaptation strategies, particularly in low-income countries. Current housing design guidelines often rely on outdated climate classifications, reducing their effectiveness in future climate conditions. This study evaluates four carefully selected housing design tools in terms of their ability to improve thermal comfort in low-cost housing under future climate scenarios. The evaluation is based on a weighted multi-criteria assessment incorporating five key factors: future climate adaptability, guideline accuracy, user-friendliness, accessibility, and adaptability to user needs. Normalised relevance scores were obtained via quantitative ratings of the criteria by 32 international shelter, settlement, and construction professionals. The assessment results confirm the limited future climate sensitivity of the tools and variation in the other criteria. Tools to support indoor thermal comfort are suggested to integrate identified strengths with interactive reliable climate projections. Further tool development should support neighbourhood-wide resilience, incorporating passive design and energy efficiency principles, as well as local sustainable building practices, and improve accessibility for diverse stakeholders. Tool improvements are essential to facilitate climate-adaptive housing design in low-resource areas.

1. Introduction

This study aims to rethink the effectiveness and comprehensiveness of design guidance for future-proof indoor thermal comfort of houses. The Intergovernmental Panel on Climate Change [1] projects that Earth’s global temperature will rise under all emission scenarios, likely surpassing 1.5–2 °C in the 21st century. Climate scientists agree that certain impacts are now unavoidable, emphasising the urgent need for adaptation to ensure future liveability [2]. Extreme heat leads to a range of impacts on human health, which in turn, drive up hospital admissions and contribute to higher mortality rates during extreme heat events [3,4,5]. Globally, over 5 million deaths are annually associated with non-optimal temperatures, with heat causing at least 489,000 deaths [6]. These numbers, however, are likely an underestimation due to the lack of adequate health records, particularly in resource-limited settings [6]. Although region-specific data linking housing conditions to heat-related fatalities are not consistently reported, it is known that inadequate building design can worsen indoor temperatures and therefore substantially increase the risk of heat-induced illness and fatality [7]. This highlights the critical need for housing designs that prioritise proper thermal comfort.

Research has demonstrated that climate conditions have already changed and will continue to do so worldwide [8,9,10]. Nevertheless, long-term investments in the built environment often fail to consider future climatic conditions, leading to maladaptation and human suffering [11]. Since most people, especially women, children, and the elderly, spend the majority of their time indoors [11,12,13], it is crucial that housing design ensures thermally safe and healthy environments. The health impacts of extreme heat disproportionately affect the most vulnerable populations, particularly racially and ethnically marginalised groups, and those in the Majority World [14]. Personal conditions can influence the susceptibility of population groups to the impact of hazards, such as age, gender, sexual identity, race, culture, religion, disability, socio-economic status, geographical location, and migration status [15]. Low-income households face additional challenges, such as limited access to water, cooling systems, and affordable energy, exacerbating existing inequalities.

The increasing climate risks necessitate adaptation of the built environment to future climate scenarios [1], requiring substantial and targeted investments to protect the most vulnerable [16]. Building designs must go beyond historical climatic data to incorporate anticipated changes throughout their expected lifespan. This approach is crucial for creating an adaptive, safe, and healthy building structure, capable of withstanding both short-term and long-term climate variations.

Currently, guidelines for adapting housing designs to future climate conditions are inadequate, especially for simple low-tech and low-cost housing [17]. Although advanced design tools exist, they often fail to support the housing needs of the most vulnerable populations in humanitarian or informal settlements [18,19,20]; the vast majority live in houses that are informally designed and constructed without formally trained construction workers, depending on local support networks [21,22]. Governments and humanitarian organisations, among others, struggle to assist in climate change adaptation, due to the growing population living in informal settlements in areas at risk and funding gaps [23,24]. Novel pathways are needed to achieve more with the limited resources available. There is potential to support climate adaptation through simple, science-based self-help tools [25,26].

However, the effectiveness of currently available design tools is limited. Simple, practical, accurate, and accessible tools are lacking to help households and designers create comfortable spaces under various climatic conditions [27,28,29]. In low-resource settings, assessing, comparing, and deciding on the most appropriate design using local materials and vernacular typologies is particularly challenging [30]. In addition, climate comfort temperatures commonly vary due to habituation [31,32]. The Environment Community of Practice of the Global Shelter Cluster has raised the importance of prioritising the environmental impact in shelter measures and has defined aims to overcome these limitations, proposing a sustainability scorecard [33,34]. The Global Shelter Cluster calls for research and evidence-based approaches and acknowledges the need for climate change adaptation in their latest strategy document [35]. UNHCR has recently developed a tool and report comparing shelter typologies on their environmental impact [36]. Notably, locally developed guidelines for hot and humid climates, which are likely appropriate for local climate conditions and construction practices, are still insufficiently picked up at a national and international level due to language barriers [37].

This research sought to identify features of design tools that effectively enable adaptations of low-cost houses to future climate conditions, with two objectives:

• To evaluate existing design tools on user-friendliness and adaptation of low-cost housing to future climate conditions.
• To provide recommendations on how to connect future-adapted climate classifications to design guidelines tailored for low-resource areas.

This study evaluated design tools that address thermal comfort because this is severely overlooked for low-cost housing and extreme heat is one of the most life-threatening risks in housing designs. Design tools were excluded which specifically address adaptation to other hazards, such as earthquakes, hurricanes, and floods. In this context, sustainability is primarily linked to adaptation strategies that ensure climate-proof housing and, where feasible, the integration of mitigation strategies such as low-carbon techniques. While sustainable building design should ideally adapt to future climatic conditions and minimise carbon footprints through energy-efficient materials and renewable resources, it is essential to recognise that the mitigation burden should not disproportionately fall on low-income communities, particularly in the Global South. Evidence suggests that the global carbon reduction target required to stay below 2 °C hinges predominantly on actions taken by the Global North. Therefore, for low-cost housing, the emphasis should be on solutions that provide co-benefits: strategies that enhance adaptation and comfort while delivering low-carbon outcomes as a secondary advantage. Establishing indoor thermal comfort does not directly guarantee sustainability and/or durability. This would require a design that satisfies user’s needs, made from local, reused, recycled, or renewable materials, as well as consideration of other hazard risks and land tenure rights. Tools could still play a role in identifying win–win opportunities, focusing on operational efficiency and comfort improvements without imposing undue burdens on vulnerable populations. Given the high relevance of designing future-proof indoor thermal comfort in low-resource areas, this study evaluates the effectiveness of existing housing design support tools using expert weighted criteria in a multi-criteria assessment.

2. Background

2.1. Design Tools for Indoor Climate Comfort

Indoor thermal comfort is not only influenced by average weather conditions but more critically by extreme weather events such as heat waves and cold snaps. As Zhao et al. [38] observed, these extremes have a disproportionate effect on the health and comfort of vulnerable populations. Future climate classifications, such as those presented by Beck et al. [39], show a shift in climate zones that will significantly affect building designs. The integration of these future scenarios into design tools is essential for ensuring that housing remains resilient under both average and extreme conditions. The development and application of design tools and guidelines have become essential in adapting buildings to diverse climatic conditions whilst enhancing energy efficiency and occupant comfort. Key resources in this field include guidelines from the Chartered Institution of Building Services Engineers [40] and standards from the American Society of Heating, Refrigerating and Air-Conditioning Engineers [41], along with the historically significant Mahoney Tables [42].

The CIBSE Guidance for Ventilation can inform designers about adequate indoor air quality and managing air moisture levels. It offers comprehensive specifications on ventilation rates and system design, addressing both natural and mechanical ventilation approaches. This guidance is crucial for balancing energy consumption with comfort in various building types. ASHRAE standards provide a broad knowledge base for building system design, influencing thermal comfort and indoor air quality across diverse environments. These guidelines set global performance criteria, ensuring climate comfort irrespective of geographical location.

The Mahoney Tables, in contrast, offer straightforward design outlines based on climate data, simplifying the architectural design process by providing basic calculations and guidelines tailored to specific climatic conditions from the Köppen–Geiger climate classifications. The Mahoney Tables, developed by Koenigsberger et al. [42], are a simplified tool for architects and urban planners to use local climate data in building design. These tables, consisting of six parts, assist in climate-responsive architecture by analysing air temperature, humidity, precipitation, and wind data to derive architectural guidelines. They assess heat or cold stress and classify environmental conditions as humid or dry. A recent adaptation in India integrates the Mahoney Tables with Geographic Information System (GIS) technology for automated data input and analysis [43]. This system maps design recommendations into Thermal Comfort Design Zones (TCDZs), suggesting climate-optimised strategies like building orientation and material use, enhancing usability by reducing manual effort. While this automated approach has proven effective in India, its global application remains limited. To realise their full potential, the Mahoney Tables need to overcome these limitations and integrate advanced technologies to support sustainable architecture worldwide.

Whilst these tools are invaluable, their complexity can sometimes restrict early-stage application in the design process [44]. More advanced tools like EnergyPlus [45] and Integrated Environmental Solutions [46,47,48], listed among the 132 building performance simulation tools by the US Department of Energy, are comprehensive but often too intricate for preliminary design decisions. This has led to a recognised need for more accessible, simplified tools that can expedite and enhance design processes [28,49,50,51]. In response to this, new IT-based tools have been developed to provide quick, intuitive guidance on thermal comfort and building performance [52]. For instance, ZEBRA [53] is a user-friendly model that aids in designing zero-carbon buildings with minimal inputs and no required training, also enhancing user knowledge of zero-carbon principles. Similarly, ShelTherm [28] offers a tailored solution for simple structures in humanitarian contexts, efficiently handling unique challenges like high ventilation rates and thin materials.

These innovations highlight a trend towards tools that not only conform to rigorous scientific standards but are also practical and easy to use, bridging the gap between advanced simulations and early-stage design needs. Such tools are instrumental in promoting sustainable design practices that are both effective and accessible to a wider range of professionals.

2.2. Climate Classifications for Design Tools

Design tools generally need climate information inputs to inform design criteria. Global climate classification systems are used to categorise and describe the various climates across the globe in climate zone maps, based on factors such as temperature, precipitation, and vegetation [9]. These classifications provide valuable insights into regional climate patterns and are essential for understanding environmental processes, biodiversity, and human adaptation. One of the most widely used classifications is the Köppen–Geiger classification [54]. This classification describes five major climatic zones, including Tropical, Dry, Moderate (or Temperate), Continental, and Polar, with sub-zones under each. The used data are widely accessible (temperature and precipitation) and enable the most straightforward way to categorise climates. Other classification systems include the Trewartha climate classification (TCC) (also known as the Köppen–Trewartha climate classification) and the Thornthwaite climate classification (e.g., [54,55,56]). Thornthwaite’s system is relevant for ecological studies as it focuses on water availability for plant growth, making it valuable for understanding ecosystems and biodiversity. However, this classification is less appropriate for informing housing design criteria. The Trewartha classification attempts to balance the strengths of both the Köppen and Thornthwaite systems, incorporating factors such as temperature, precipitation, vegetation, and wind patterns, as well as seasonal variations. On the other hand, the Trewartha classification is more complex than the Köppen system, requires more data, and is therefore not feasible in all regions around the world.

Existing climate classifications have been reviewed and adapted to reflect changes for different future climate scenarios [57]. Updating climate classifications to account for changing climate patterns enables better anticipation and mitigation of impacts of climate change, supports sustainable land use planning, and facilitates sustainable long-term adaptation strategies for communities worldwide. The widely used Köppen–Geiger climate classification has been applied extensively to projected future changes, as discussed in the review by Cui et al. [10]. Various climate classification map products have been developed, with the most recent maps on a high-resolution (1 × 1 km) scale developed by Beck et al. [39] (available at https://www.gloh2o.org/koppen/ (accessed on 5 March 2025)). The latest versions (V2) of the maps can be downloaded as GeoTIFFs at varying resolutions for multiple periods and future socio-economic scenarios. GeoTIFF files can be viewed using Geographic Information System (GIS) software such as ArcGIS or QGIS. The high spatial resolution is important, as it can capture smaller-scale processes and more accurately show differences in heterogeneous areas. For example, islands, coastlines, and mountainous regions can have microclimates, which have distinct climatic conditions up to areas as small as a few square metres [58]. Many design tools still do not have simple ways to integrate climate classifications which consider future scenarios, greatly hampering climate-adaptive housing design.

3. Materials and Methods

This study integrated expertise from diverse fields, including climate science, architecture, building physics, civil engineering, and humanitarian shelter and settlement practice, to assess the effectiveness of design tools in maintaining thermal comfort in a changing climate. These tools can be supportive for architects and engineers striving to create sustainable, energy-efficient, and comfortable living spaces. As presented in

Table 1. Methodology.

Objectives	Research Questions	Method
To evaluate existing design tools on user-friendliness and adaptation of low-cost housing to future climate conditions	How effective are existing design tools in terms of user-friendliness and their ability to adapt low-cost housing to future climate conditions?	Identification of design tools using inclusion and exclusion criteria through a literature study Identification of evaluation criteria and values from the literature Assigning relevant weights to assessment criteria using expert ratings Assessment of tools based on criteria values and weighted criteria
To provide recommendations to connect future-adapted climate classifications to design tools tailored for low-resource areas	How can future-adapted climate classifications be effectively integrated into design tools to enhance adaptation in low-resource areas?	Recommendations identified from the tool evaluations

, this paper involves a careful selection of simplified tools that are practical for the design process without the need for advanced computational demands or specialised skills. This methodology section delineates the criteria for tool selection and the criteria for tool evaluation and the tool assessment process.

3.1. Identification of Design Tools

Tools are selected based on a set of criteria (see

Table 2. Tool inclusion criteria.

	Tool Inclusion Criteria
1	Connectedness to climate classifications
2	Current application in real-world scenarios
3	Design guidelines for low-cost housing in different climate contexts
4	Capacity to enhance indoor and outdoor thermal comfort, ventilation, and heat stress
5	Open access and free access
6	Adaptability
7	Non-specialist usability
8	Time-effectiveness
9	Simplicity of result interpretation

). The tool selection criteria were created to fit the context of the target audience of low-income households building their own houses, as described in the Introduction.

The selection of design tools was conducted through an extensive desk study, and a series of discussions among the authors, users, and co-developers of some of these tools. First, the tools needed to demonstrate a clear connection to climate classifications, ensuring their relevance to various environmental conditions. Second, their current application in humanitarian shelter practice and architectural design in the Majority World was considered, reflecting their practical utility in real-world scenarios. Furthermore, the tools were selected for their ability to provide design guidelines for low-cost housing across different climate contexts. This aspect is crucial for enhancing indoor and outdoor thermal comfort, improving ventilation, and mitigating heat stress. Open access and free access were also important criteria, ensuring that the tools would be available to a wide range of users without financial barriers. Adaptability and non-specialist usability were prioritised to ensure that the tools could be easily used by individuals without specialised training. Lastly, the tools were selected for their time-effectiveness and the simplicity of result interpretation, ensuring that users can quickly and easily understand and apply the results in their projects. These criteria together ensure that the selected tools are both practical and accessible, supporting effective design in various climate conditions and contexts.

Tools which require advanced computational capacities, specialised simulation skills, and high specific background knowledge were excluded, such as Design Builder [59], EnergyPlus [46], and Computational Fluid Dynamic [60]. This study included four identified key tools: the Mahoney Tables, ShelTherm, the Shelter Assessment Matrix, and ZEBRA.

3.2. Identification of Evaluation Criteria and Value Establishment

The Multi-Criteria Decision Making (MCDM) method is used to screen, prioritise, and rank tools based on human judgment using a finite set of criteria [61]. The initial identification of evaluation criteria was informed by an extensive literature review, focusing on the effectiveness of decision-support tools for low-cost housing design choices towards thermal comfort in future climate scenarios. This review led to the identification of general evaluation parameters. Definitions of criteria were critically analysed for overlap, and those aligning with the objectives of this study were retained, while those lacking direct relevance to thermal comfort and climate resilience were excluded. This novel assessment framework uses a set of five defined criteria for a robust multi-criteria assessment.

Table 3. Evaluation criteria and scale.

Evaluation Criteria	Evaluation Scale
	1	2	3	4	5
Future climate adaptability: Tools are evaluated for their ability to integrate future climate scenarios. This criterion addresses the tool’s capacity to aid designers in creating housing that remains resilient under varying climatic stresses.	The ability of a tool to integrate future climate scenarios is not considered relevant. Tools lacking this feature are not viewed as deficient.	Integrating future climate scenarios holds some value, but it is not essential. Its absence does not significantly detract from the tool’s overall utility.	The ability to incorporate future climate scenarios is considered moderately important. Tools with this feature are preferable, but it is not a deal-breaker. The ability to incorporate future climate scenarios is considered moderately important. Tools with this feature are preferable, but it is not a deal-breaker.	Future climate adaptability is highly valued. Tools are expected to support this feature, aiding in creating resilient housing designs.	Integrating future climate scenarios is seen as critical. Tools must excel in this area to be considered effective for future-proof housing design.
Accuracy of design guidelines: This assesses the precision and reliability of the tool’s outputs. Tools that consistently provide accurate simulations and predictions of climate impacts, energy performance, and thermal comfort are rated higher. The accuracy includes the indoor thermal comfort, analysing the tool’s capabilities in simulating and optimising thermal comfort, air quality, humidity control, and natural lighting. Tools that provide advanced simulations and modelling of these aspects score higher in the evaluation. The accuracy is also evaluated based on the energy efficiency and sustainability. This study evaluates the tool’s functionality in supporting energy-efficient and sustainable design practices, crucial for minimising the environmental impact of housing while maintaining indoor comfort.	Precision and reliability of design guidelines are not prioritised. Tools can be effective even if they lack accurate simulation and prediction capabilities.	Accurate design guidelines are somewhat valued, but other features might compensate for their absence.	Reliability and accuracy of outputs are important, and tools should provide a reasonable level of precision in simulations and predictions.	High accuracy in design guidelines is very important. Tools must consistently deliver precise and reliable outputs to be highly rated.	Accuracy is crucial. Tools must provide exceptionally precise and reliable simulations and predictions to be considered effective for future-proof housing design.
User-friendliness: Given the complex nature of climate data and simulations, tools are assessed for their ease of use, particularly in handling climate-related modelling. Also, the availability and quality of training materials and customer support are crucial. Tools offering extensive support and educational resources are scored higher. This criterion focuses on the overall ease of use of the tool, including its interface design and the intuitiveness of its workflows. Tools that require minimal training to use effectively and have a clear, user-friendly interface score higher in this area.	Ease of use is not a priority. Tools can be complex and still be considered valuable.	User-friendliness is somewhat important but not essential. Users might tolerate a learning curve if other features are strong.	Tools should be reasonably easy to use, with a moderate level of user-friendliness. Some training and support should be available.	High user-friendliness is very important. Tools should have intuitive interfaces, require minimal training, and offer strong support resources.	Ease of use is critical. Tools must be highly intuitive, with comprehensive training materials and customer support, to be considered effective.
Accessibility (accessible to everyone): This criterion evaluates whether the tool is accessible to all users in terms of both technical and financial access. Tools that comply with accessibility standards and provide features such as screen reader support, alternative text for images, and easy navigation are considered more inclusive.	Accessibility features are not prioritised. Tools can be effective without being accessible to all users.	Accessibility is somewhat important, but tools might still be useful with limited accessibility features.	Tools should be moderately accessible, supporting a reasonable level of technical and financial access.	High accessibility is very important. Tools should comply with accessibility standards and be available to a broad range of users.	Accessibility is critical. Tools must be fully accessible to all users, with comprehensive support for both technical and financial access, to be considered effective.
Adaptability of the tool to users’ needs: This evaluates how well the tool can be customised or configured to meet the specific requirements of different users. Tools that offer flexible settings, customisation options, and scalability to accommodate various project sizes and complexities are rated more favourably. It includes the tool’s versatility in addressing a wide range of housing types and design scenarios. Tools that provide solutions that are applicable to different types of housing, from single-family homes to large-scale residential developments, and that consider local building practices and codes, score higher. This study assesses the presence of a feedback mechanism in the tool, and the strength of its user community its assessed. Tools that enable learning from real-world data and facilitate sharing of best practice are seen as more effective.	Customisation and adaptability are not considered relevant. Tools can be effective without offering flexibility.	Some level of adaptability is valued, but it is not essential. Tools might still be useful with limited customisation options.	Tools should offer a moderate level of customisation and adaptability to meet various user needs and project requirements.	High adaptability is very important. Tools should be flexible, customizable, and scalable to accommodate diverse user needs and project complexities.	Adaptability is critical. Tools must offer extensive customisation options, high flexibility, and robust support for a wide range of housing types and design scenarios to be considered effective.

presents the value establishment with full definitions and the evaluation scale:

• Future climate adaptability;
• Accuracy of design guidelines;
• User-friendliness;
• Accessibility (accessible to everyone);
• Adaptability of the tool to users’ needs.

3.3. Expert Scoring of Evaluation Criteria

This study used the Weighted-Sum Model (WSM), which is the most widely known method to determine the weights of the criteria in a decision-making process [61]. The Weighted-Sum Model (WSM), also known as the Additive Multi-Attribute Value Model, is widely used in Multi-Criteria Decision Analysis (MCDA). This method systematically transforms judgements into a relevance score which can be used to compare alternatives. By implementing the WSM, a transparent and systematic evaluation framework is established which mitigates potential biases, enhances reliability, and facilitates data-driven decision making in tool evaluation. It aggregates weighted criteria scores to ensure that evaluations reflect the relative importance of different assessment factors.

To establish the relevance of each criterion for future-proof housing design tools, an expert panel of 32 participants working in humanitarian shelter practice, climate-resilient architecture, and sustainable design was consulted. A thematic conference panel was organised at the UK Shelter Forum around climate adaptation in humanitarian shelter, with voluntary attendance of globally leading shelter practitioners of the key humanitarian organisations based in Europe, including CARE, CRS, the Shelter Centre, and CRATERRE. The extensive professional experience, academic backgrounds, and direct involvement in climate-adaptive and low-cost housing projects around the globe made this a relevant expert sample. To reach architects and urban planners working in the Majority World, additional participants were invited via LinkedIn (3293 persons were reached). In addition, a “Women in Shelter” WhatsApp group was invited, including 467 active female researchers and practitioners working in housing, planning, and shelter, primarily in the Majority World, to ensure a broad and diverse range of responses from professionals actively engaged in the field. Their engagement with vulnerable communities in construction enabled them to rank criteria on importance. Due to budgetary constraints, no homeowners in low-resource areas were consulted for the weighting.

Through a survey, the criteria were numerically rated on importance from 1 to 5 on the evaluation scale (see Table S1). Additionally, the professional backgrounds of the experts were collected. The experts were also asked to reflect on the given criteria and identify additional important assessment criteria. Survey participants indicated their backgrounds in the following fields: architecture (36%), shelter and settlement assistance (15%), urban planning (12%), disaster risk reduction (12%), climate adaptation (10%), construction engineering (8%), and others. Climate adaptability was ranked highest (as a 5) by 60% of the participants, followed by user-friendliness (47%) and adaptability (40%) (see Appendix I). Accuracy was ranked as 4 by 47% of the participants. The differences between accuracy, voted highest only by 21%, and climate adaptability were found to be insignificant. Additional criteria and suggestions for future-proof design tools were identified through the survey. These were used to critically evaluate our research results in the Discussion section and define directions for tool development and future research. This evaluation helps to understand the users’ needs in different types of contexts.

3.4. Assigning Relevant Weights to Assessment Criteria Through Normalising

To allow for comparability across criteria, Min–Max Normalisation scales, with all values between 0 and 1, were used. The mean importance score for each criterion was calculated by averaging expert ratings using the following formula:

$${MeanimportanceScore}_i={\displaystyle \frac{{\sum }_{j=1}^m{S}_{ij}}{m}}$$

(1)

where S_ij is the importance score assigned by expert j for criterion i and m is the total number of experts who provided scores.

To obtain the normalised importance scores, each criterion’s mean importance score was divided by the sum of all the mean importance scores of the expert panel:

$$w_i={\displaystyle \frac{{MeanImportanceScore}_i}{{\sum }_{k=1}^n{MeanImportanceScore}_k}}$$

(2)

where w_i is the normalised weight for criterion i and ${\sum }_{k=1}^n{MeanImportanceScore}_k$is the sum of all mean importance scores across the five criteria (see

Table 4. Weighted evaluation criteria based on expert ranking using the Weighted-Sum Model.

Evaluation Criteria	Mean Importance Score	Normalised Importance Score
Future climate adaptability	4.5	0.21
Accuracy of design guidelines	3.8	0.18
User-friendliness	4.12	0.20
Accessibility (accessible to everyone)	4.00	0.19
Adaptability of the tool to users’ needs	4.06	0.19

).

3.5. Assessment of Tools

Tools were assessed on the five criteria from the multi-criteria assessment rubric presented in Table 3. Each author conducted an independent assessment of the tools using the following approach: (1) verification of how climate adaptation measures incorporate future climate scenarios, ensuring that tools integrate projected climatic changes rather than relying solely on historical data; (2) assessment of the accuracy of design guidelines in relation to different climate categories, material selection, and construction methods to determine their applicability across diverse contexts; (3) analysis of existing user-friendliness tests or, where unavailable, practical testing of the tool in practice, followed by an evaluation based on predefined usability definitions; (4) evaluation of accessibility, particularly for users with lower levels of technical literacy, to assess the clarity and ease of navigation; and (5) examination of the adaptability of the suggested design measures, ensuring they can be tailored to different environmental and socio-economic conditions. Scores were given using the evaluation scale (1–5). Biases were explored by comparing 3 independent assessments, ensuring consistency and reducing subjectivity in the evaluation process. Interpretation of the criteria and assessment scores were discussed between the assessors, including identified outliers. Assessors were given the opportunity to revise their scoring following the discussion, ensuring that any discrepancies were addressed through calibration. Through calibration, the scores provided a balanced and representative evaluation of each tool.

3.6. Weighted Multi-Criteria Evaluations of Tools

The evaluation score assigned to each criterion was multiplied by its corresponding normalised importance score, effectively scaling the tool’s evaluation based on the criterion’s importance. These weighted scores were summed across all criteria to compute a final aggregated score for each tool. This approach allowed for a quantitative comparison of the tools while accounting for the relative importance of criteria. The weighted scores for each tool were structured in a matrix format, showing how each tool performed across different criteria after normalisation (see Appendix A). The weighted multi-criteria evaluation allowed for a transparent, criterion-by-criterion comparison of the tools, rather than a single aggregated ranking.

$$S_{i,tool}=\sum _{i=1}^n{w}_i·x_{i,tool}$$

(3)

where S_i,tool is the final normalised score of the tool for criterion i and x_i,tool is the evaluation score for criterion i.

4. Results

4.1. Climate Adaptation Design Tools

The multifaceted assessment aimed to provide a detailed understanding of each tool’s potential and limitations, facilitating informed decisions on their application in climate adaptation design. This section provides outputs of the multi-criteria evaluation of the selected existing climate adaptation design tools using a list of selected criteria.

4.1.1. Mahoney Tables

The Mahoney Tables, developed in 1961, provide passive housing design guidelines tailored to different climatic conditions. The tool provides a location-specific climatological analysis using freely and widely available meteorological data, such as monthly temperature extremes and averages, relative humidity levels, precipitation patterns, and wind directions [62,63]. Without complicated software, simple calculations in a set of six sequential tables lead to the identification of the Köppen–Geiger climate zone and design recommendations (see Appendix B). Meteorological parameters are compared with ranges of human comfort based on human thermal comfort models to avoid thermal stress on people in and around buildings (see Appendix C). Human comfort, defined by what is acceptable, too cold or too hot, can differ around the world and from person to person. However, it is closely connected to average relative humidity and temperature during the day and night. The tables provide vital climate-adaptive design recommendations for light, heat, wind, and humidity and suggest ideal arrangements of buildings on site, their orientation, the design of open spaces, and the layout of streets, courtyards, gardens, and squares (see Appendix D). The tables also specify the ideal type of materials, textures, and colours. There is still room for interpretation based on local material availability.

Vernacular designs often already consciously or unconsciously incorporate the given recommendations, building upon lessons learned over generations; for example, the use of thermal storage in heavy walls in hot arid climates is a common practice. Nevertheless, the straightforward design recommendations assist designers in the early design stage. The interpretation of a designer is still needed to translate the guidelines into a design, which can be a limitation for the usability by homeowners.

The tool supports sustainable housing designs, promoting traditional, passive, and active cooling and heating, enhancing energy efficiency, and avoiding energy consumption. Design recommendations are aimed at hot humid, hot arid conditions and do not provide recommendations for cold humid or dry conditions. However, climate data alone are insufficient as input for appropriate designs, as they overlook feedback on thermal comfort from local dwellers.

The accessibility of the tables in Excel format can be challenging via the internet since the tools were published long ago. However, they are widely available in printed versions. Effort is required to understand the terminology and steps used to arrive at the climate classification. The tables are not fully self-explanatory. User experiences are not included to design recommendations. Another major limitation is that climate data from the past are used, and therefore they are not reflective of future climate conditions. Using coordinates directly to identify future climate classifications could simplify the analysis by eliminating several steps and speed up the analysis and lead to more sustainable design recommendations. Additional adaptation measures could be included to identify risks from extreme weather, such as strong winds.

Table 5. Mahoney multi-criteria evaluation.

	Assessment	Score	Normalised Score
Adaptability to future climate scenarios	The tool provides basic climate adaptability but lacks updated climatic data and future scenario integration.	2	0.43
Accuracy and design guidelines	The tool offers general design guidelines, but awareness of it is limited among architects and it needs updated data.	2.5	0.47
User-friendliness of interface	The interface is simple but requires detailed location-specific climatic data, making it complex for some users.	2.5	0.50
Accessibility of the tool	The tool is highly accessible due to its structured approach and ease of availability, promoting socio-economic benefits.	3.5	0.68
Adaptability of the tool to the users’ needs	The tool supports various user needs but lacks flexibility and real-world user input for improvement.	2.5	0.49

and Figure 1 illustrate the performance of the Mahoney Tables across the selected criteria. The Mahoney Tables demonstrate moderate performance regarding climate adaptability, effectively integrating local climate data but with room for improvement in modelling future scenarios and varying climatic conditions. They excel in accuracy, providing reliable guidance for thermal comfort. User-friendliness is another strong area, although some complexity remains for non-technical users. In terms of accessibility, the tool is moderately accessible. Lastly, the Mahoney Tables show good adaptability.

4.1.2. ShelTherm

ShelTherm is a physics-based tool designed for humanitarian staff, simplifying the evaluation of heat transfer and air flows through simple building structures. It incorporates a heat transfer balance method requiring only a basic description of the shelter to output a time series of internal and external temperatures, for example, a summer and winter day, adapted to handle high ventilation rates, thin materials like tarpaulin, and high U-values. Unique among reduced models, ShelTherm uses a database of three thousand weather files, more precise than climate classification, allowing for assessment of a shelter’s performance across diverse climates and locations and highlighting potential impacts on occupants’ health and thermal comfort. Furthermore, it supports the selection of various ventilation strategies to enhance indoor environmental quality without accounting for energy usage, focusing on naturally ventilated spaces. ShelTherm was developed in Excel to meet the preferences of humanitarian aid workers, as determined by surveys. ShelTherm was tested for usability and accessibility. To evaluate its practicality among humanitarian workers, ten practitioners from organisations such as UNHCR, NRC, and Shelterbox used the tool to assess shelter thermal performance and provided feedback on its usability. Additionally, to determine its accessibility for non-experts, four students with no prior experience in thermal modelling were given instructions and asked to model a shelter using ShelTherm. Despite their lack of training, they successfully calculated the maximum winter and summer temperatures for the given shelter, suggesting that the tool can be effectively used by those with no technical background [63]. ShelTherm’s user-friendly interface and visualised output facilitate advocacy for shelter designs among communities, donors, and local governments. The tool is available for free [64] and aims to improve indoor conditions early in the housing provision process, potentially reducing air-borne diseases and issues arising from thermally uncomfortable dwellings. Although not as accurate as some thermal performance calculation software, its accessibility, simplicity, and adaptability make it a valuable tool for non-specialists without the need for extensive technical knowledge or internet access (see Appendix E). The tool is downloadable from https://researchdata.bath.ac.uk/935/ (accessed on 5 March 2025).

Figure 2 and

Table 6. ShelTherm multi-criteria evaluation.

	Assessment	Score	Normalised Score
Adaptability to future climate scenarios	The tool provides a strong level of adaptability through a large database of three thousand weather files but lacks integration of energy usage considerations.	2.5	0.54
Accuracy and design guidelines	The tool offers reasonable accuracy, though it is not as precise as some advanced thermal performance simulation tools.	1.5	0.47
User-friendliness of interface	The interface is user-friendly as it is Excel-based, but Excel’s limitations may restrict computational performance and scalability.	1.5	0.60
Accessibility of the tool	The tool is widely available for free, increasing accessibility, but ongoing development depends on sustained funding.	1.5	0.58
Adaptability of the tool to the users’ needs	The tool is adaptable to different materials and climate conditions, but additional flexibility could be gained through user customisation and training programs.	1.5	0.49

show that ShelTherm demonstrates a balanced performance in climate adaptability, effectively integrating various climate data and modelling different climatic conditions, though with some limitations in predicting future scenarios. It excels in accuracy, providing reliable simulations for thermal comfort, energy performance, and sustainability, maintaining high precision in air quality, humidity control, and natural lighting. User-friendliness is a strong suit, characterised by an intuitive interface and straightforward workflows requiring minimal training, though some complexity may challenge non-technical users. Accessibility is moderately strong, with the tool available to a wide range of users and adhering to basic accessibility standards. Lastly, ShelTherm is highly adaptable, offering flexible settings and customisation options that accommodate various project sizes and complexities, supporting diverse housing types and local practices, and facilitating user feedback and community learning. Overall, ShelTherm is very strong across most criteria, with strengths in user-friendliness and adaptability, but could benefit from improvements in future climatic condition predictions.

4.1.3. Shelter Assessment Matrix

The Shelter Assessment Matrix (SAM) is recognised for its ability to generate scores indicating how current shelters can be optimised within financial and other constraints, marking the first contextualised performance analysis of shelters globally and offering a benchmark repository for design evaluation. This simple Excel-based tool, augmented with computer tools, forecasts shelter performance, such as internal temperatures across seasons in specific locales. It includes brief guides on various shelter design aspects like ventilation and security, assisting newcomers. SAM serves multiple roles: informing design, staff training, aiding in tender processes, identifying issues, and enhancing designs, based on 34 critical issues identified through research. A study from Albadra et al. [65] emphasises the importance of considering the location, climate, and direct engagement with or understanding of the occupants’ background before designing.

SAM’s analysis begins with understanding the setting, occupants, costs, and governmental expectations, then scoring a shelter design against these considerations, focusing on the match between context and shelter. It enables climate adaptability by allowing users to input data based on the Köppen–Geiger Climate Classification and other site-specific information, though suggesting improvements through better climate adaptability tools and updated classification methods. Hazard preparedness is enhanced by integrated sub-tools and guidance notes on design issues. SAM assesses indoor environmental quality under criteria, including comfort, air quality, daylighting, and thermal performance, and addresses sustainability in naturally ventilated shelters through tools for calculating embodied energy and carbon.

Designed in Excel, SAM is user-friendly, meeting the preferences of humanitarian aid workers, and allows for a feedback loop by incorporating the displaced population’s needs directly into the performance analysis (see Appendix F). It prioritises cost-effective pre-construction criteria like cost, delivery, and local acceptability, and while it is accurate, it remains susceptible to human error. It is available for free and requires no internet post-download from Kuchai et al. [27]. SAM’s Excel foundation ensures global usability and adaptability to users’ needs and contexts. The SAM tool was tested in three ways on accessibility. First, eleven humanitarian specialists independently assessed the same shelter, yielding a mean score of 45.7/100 with a standard deviation of 2.96, indicating consistent outputs across users. Second, 187 previously deployed shelters were evaluated using SAM, with scores ranging from 27 to 78, demonstrating both the tool’s reliability and areas where shelter designs could be improved. Third, SAM was tested as a learning platform for shelter professionals. In a controlled study, 52 local NGO (LNGO) staff and 20 international NGO (INGO) staff participated in a two-stage exercise. The LNGO participants exhibited a 16-percentage-point increase in knowledge between pre- and post-use tests, while INGO staff scored 48%, highlighting SAM’s effectiveness as a capacity-building tool for humanitarian practitioners [28].

As presented in Figure 3 and

Table 7. Shelter Assessment Matrix multi-criteria evaluation.

	Assessment	Score	Normalised Score
Adaptability to future climate scenarios	The tool provides adaptability through climate data input, but reliance on the Köppen–Geiger classification may limit its effectiveness in future climate scenarios.	2.5	0.54
Accuracy and design guidelines	The tool is generally accurate in predictions, though human manipulation can influence results, and it could benefit from integration with more advanced climate adaptability tools.	2.5	0.28
User-friendliness of interface	The MS Excel-based interface is user-friendly, but its effectiveness depends on user familiarity with spreadsheet-based tools.	3	0.30
Accessibility of the tool	The tool is freely available and does not require internet access, improving accessibility, but scalability may be limited due to reliance on Excel.	3	0.29
Adaptability of the tool to the users’ needs	The tool is highly adaptable to different contexts and user needs, with opportunities to enhance usability through additional sub-tools and stakeholder engagement.	2.5	0.29

, SAM demonstrates strong performance in climate adaptability, effectively incorporating diverse climate data and modelling different conditions, though some limitations exist in predicting future scenarios. It excels in accuracy. User-friendliness is a notable strength, as it features an intuitive interface and easy-to-navigate workflows that require minimal training, though some complexity may still pose challenges for non-technical users. The tool is moderately strong in accessibility and available to a wide range of users. SAM is highly adaptable, offering flexible settings and customisation options that cater to various project sizes and complexities, supporting a diverse array of housing types and local practices, and facilitating user feedback and community learning. Overall, SAM shows robust performance across most criteria, with strengths in user-friendliness and adaptability, but would benefit from enhancements in predicting future climate conditions.

4.1.4. Zero Energy Building Reduced Algorithm

The Zero Energy Building Reduced Algorithm (ZEBRA) is a cutting-edge tool in sustainable architecture, essential for designing low-energy and carbon-efficient buildings right from the early stages [51]. It bridges the gap in literate building modelling by focusing on operational energy and factors like space heating, hot water needs, electrical loads, and the incorporation of photovoltaics, alongside both operational and upfront embodied carbon. ZEBRA’s design is user-friendly and promotes active learning through its real-time interface, fostering design optimisation and education in sustainable building principles in line with the Technological Pedagogical Content Knowledge (TPACK) framework [66]. The usability of ZEBRA was tested through a controlled study where participants completed two sequential modelling tasks. In the first task, participants encountered ZEBRA for the first time and modelled a detached house, following all available instructions. In the second task, they modelled a mid-terrace house, this time with the option to navigate instructions at their own pace. The results indicate that ZEBRA requires significantly less time to use than the self-imposed usability thresholds of 60 min for first use and 30 min for subsequent uses, demonstrating its efficiency and intuitive interface. Statistical analysis confirmed that usability goals were met with fewer participants than initially anticipated, further supporting its accessibility [52].

ZEBRA employs thermal modelling based on a single-zone model and a quasi-dynamic ISO standards model to estimate annual energy demands, making it adaptable for over 3000 locations. In the ZEBRA tool [52], a user can select the location from the drop-down menu and visualise the climate conditions of the selected location, or the user can manually input (historical) weather data for at least one year. However, next to reviewing historical climate data, tools should be able to include future climate scenarios to inform climate-adaptive housing design. This adaptability is crucial for designing shelters in diverse climates, especially for the displaced, ensuring rapid, efficient, and climate-responsive solutions. The tool emphasises energy efficiency, sustainability, and the smart integration of renewable energy, making it ideal for the shelter sector’s urgent and resource-constrained contexts.

As summarised in

Table 8. Zero Energy Building Reduced Algorithm multi-criteria evaluation.

	Assessment	Score	Normalised Score
Adaptability to future climate scenarios	The tool covers a wide range of climates and integrates both operational and embodied emissions but could improve hazard preparedness features.	2.5	0.54
Accuracy and design guidelines	The tool is highly accurate with strong design guidelines, making it a reliable tool for early-stage sustainable housing design.	4	0.75
User-friendliness of interface	The tool provides a user-friendly interface that allows quick use with minimal training, though further interactivity could improve engagement.	3.5	0.70
Accessibility of the tool	The tool is easily accessible and broadly applicable, but awareness and training resources could enhance its usability.	3.5	0.68
Adaptability of the tool to the users’ needs	The tool is well-suited for various user needs and adaptable across different design scenarios, with potential to expand beyond early-stage applications.	3.5	0.69

and Figure 4, although ZEBRA excels in simplifying early design stages with its broad climate adaptability and holistic approach to emissions, challenges remain, notably in hazard preparedness (see Appendix G and Appendix H). Future developments could extend its application and enhance its comprehensiveness. Despite potential threats like changing standards or lack of user awareness, ZEBRA has significant growth opportunities, including becoming more interactive and widely applicable throughout the design and operational lifecycle of buildings. Addressing its limitations and leveraging opportunities for improvement are essential for its continued impact in advancing sustainable architecture and engineering.

4.2. Overall Evaluation

As can be observed in

Table 9. Normalised scores of selected tools.

	Mahoney Tables	ShelTherm	SAM	ZEBRA
Adaptability to future climate scenarios	0.43	0.54	0.54	0.54
Accuracy and design guidelines	0.47	0.47	0.28	0.75
User-friendliness of interface	0.50	0.60	0.30	0.70
Accessibility of the tool	0.68	0.58	0.39	0.68
Adaptability of the tool to the users’ needs	0.49	0.49	0.29	0.69

and Figure 5, the overall evaluation of the selected tools reveals varied strengths and areas for improvement across different criteria: adaptability to future climate scenarios, accuracy and design guidelines, user-friendliness of the interface, accessibility of the tool, and adaptability to users’ needs. ZEBRA emerges as the top performer in accuracy and design guidelines, scoring an 8, indicating its superior precision and reliability. SAM also performs well, with a score of 6, while the Mahoney Tables and ShelTherm both score 5, showing room for enhancement in this area.

In terms of adaptability to future climate scenarios, all tools show moderate performance, with ZEBRA, SAM, and ShelTherm scoring 5 and the Mahoney Tables scoring slightly lower at 4. For user-friendliness, ZEBRA again leads with a score of 7, reflecting its intuitive interface. SAM and ShelTherm follow with scores of 6, while the Mahoney Tables score 5, suggesting improvements could be made in this area.

Accessibility sees the Mahoney Tables scoring highest at 7, reflecting their broad availability and adherence to standards. ZEBRA matches this score, while ShelTherm and SAM both score 6, indicating good but not exceptional accessibility. Lastly, in adaptability to users’ needs, ZEBRA scores highest at 7, showing strong customisation options, while SAM scores 6 and both the Mahoney Tables and ShelTherm score 5, indicating potential for improvement in this criterion.

In summary, ZEBRA is the most robust tool overall, excelling in accuracy and user-friendliness, while SAM and ShelTherm show good performance but have areas for improvement in design accuracy and adaptability. The Mahoney Tables are highly accessible but could benefit from enhancements in user-friendliness and accuracy to improve their overall effectiveness.

5. Discussion

This study evaluated design tools based on a set of criteria derived from the literature, observations in the field, and conversations with experts around climate change adaptation, humanitarian shelter and settlement, disaster resilience, and tool development. In the evolving field of housing design, particularly under the duress of climate change and increasing environmental uncertainties, the need for tools that offer comprehensive solutions has never been more critical. Delving deeper into the nuances of the Mahoney Tables, ShelTherm, SAM, and ZEBRA, it becomes apparent that while each tool offers distinct advantages, their integration and the expansion of their capabilities could significantly enhance the design and deployment of resilient, sustainable houses. This expanded discussion explores how these tools align with and could be further developed to address key parameters in housing design more robustly.

5.1. Recommendations for Tool Design

The evaluation of climate adaptation design tools for low-cost housing reveals several insights into their strengths, weaknesses, and areas for improvement. Each tool offers unique features and capabilities that cater to different aspects of climate adaptation and sustainable housing design.

Among the four tools assessed—the Mahoney Tables, ShelTherm, SAM, and ZEBRA—ZEBRA emerged as the most comprehensive and accurate tool overall. This can be attributed to its ability to integrate both operational and embodied emissions, its broad adaptability to over 3000 locations, and its intuitive interface, which facilitates usability across different expertise levels. ZEBRA provides precise early-stage modelling, making it particularly useful for practitioners working on sustainable and energy-efficient housing design.

While ZEBRA performed strongest in climate adaptability, accuracy, and user-friendliness and adaptability to the users’ needs, it still has some limitations. Specifically, its focus on operational energy efficiency does not fully account for hazard preparedness, such as resistance to extreme weather events. Future improvements should explore expanding its functionalities to integrate risk assessment for climate-induced hazards, making it more applicable to vulnerable settings.

The SAM tool scored well in climate adaptability, user-friendliness, and accessibility, making it a valuable tool for humanitarian and low-cost housing applications. However, its reliance on historical climate data and the Köppen–Geiger classification system suggests a need for updates incorporating future climate projections. Meanwhile, ShelTherm, despite its strong capability in handling high ventilation rates and thin building materials, could benefit from enhanced climate adaptability beyond its existing weather database.

The Mahoney Tables, while offering a well-established passive design approach, remain limited in accessibility and future climate adaptability. Their reliance on historical climatic conditions rather than projected changes makes them less suitable for long-term climate resilience planning. However, they remain an important tool for accessible, quick, climate-responsive design guidance.

User-friendliness and accessibility are crucial for the widespread adoption of these tools, especially among non-specialists and in low-resource settings. ZEBRA is highly user-friendly, featuring intuitive interfaces and easy workflows that require minimal training. This makes it accessible to a broad audience, including those with limited technical expertise. ShelTherm and SAM strike a balance between user-friendliness and complexity, with SAM being particularly noted for its intuitive design and ease of use in humanitarian contexts.

Accuracy in design guidelines is vital for ensuring that the recommendations provided by these tools are reliable and effective. ZEBRA achieved the highest score for this criterion, offering precise and comprehensive design guidelines that cover operational and embodied emissions. The Mahoney Tables, ShelTherm, and SAM provide good accuracy but could benefit from enhancements to improve precision and incorporate more up-to-date data. Notably, SAM’s reliance on the Köppen–Geiger climate classification suggests a need for integration with more advanced climate adaptability tools to maintain accuracy.

The adaptability of these tools to meet diverse user requirements and project complexities is another critical factor. ZEBRA and the Mahoney Tables show strong flexibility and customisation options, accommodating various housing types and local practices. ShelTherm and SAM are also adaptable but could enhance their flexibility by incorporating more user feedback and offering additional customisation options. The ability of these tools to integrate user experiences and preferences is essential for creating designs that are not only technically sound but also culturally and contextually appropriate.

The evaluation highlights several areas where these tools can be improved to better serve their intended purposes:

• Integration of Future Climate Data: Tools like the Mahoney Tables and SAM should incorporate future climate projections to provide more accurate and relevant design recommendations.
• Enhanced User Training: Offering training programs for tools like ShelTherm and SAM can enhance their adoption and effective use among humanitarian organisations and local governments.
• Development of Hazard Preparedness Features: Including features for natural hazard preparedness can make tools like ZEBRA more comprehensive and valuable for sustainable housing design.
• Simplification of Complex Tools: Simplifying the steps and terminology used in tools like the Mahoney Tables can make them more accessible to non-technical users and homeowners.
• Climate Sensitivity of Tools.

5.2. Enhancing Climate Sensitivity

The effectiveness of housing design tools depends significantly on their ability to integrate and adapt to different climate zones, especially as climate conditions evolve. Tools that incorporate future climate scenarios are more likely to provide reliable and adaptable designs, while those relying solely on historical data may struggle to anticipate the increasing impact of extreme weather events, such as heatwaves, heavy rainfall, and cold spells.

Design tools like ZEBRA and SAM demonstrate greater sensitivity to climate variability by allowing users to input site-specific data and project future climate conditions based on updated climate classifications, such as those offered by Beck et al. [39]. These tools can model various climate zones and assess a building’s thermal performance under both average and extreme conditions, providing more accurate projections for long-term resilience. In contrast, ShelTherm shows a more limited sensitivity. While it can model internal temperatures and ventilation in simple structures, it primarily relies on existing weather files and may not fully account for future extremes. Enhancing its ability to adapt to changing climates, particularly in regions expected to experience significant shifts, would be a valuable improvement. The Mahoney Tables, though a cornerstone of passive design strategies, are less sensitive to future climate changes, as they depend on historical climate data. Incorporating future climate projections—especially for regions likely to experience more frequent and severe weather extremes—would significantly increase their relevance in contemporary design contexts.

A key point raised by climate scientists is the need for tools to focus not only on average weather patterns but also on extreme conditions, which are more likely to stress building performance and impact occupant health. The ability to model extreme heat, cold, and humidity levels is crucial for ensuring that housing designs are resilient and capable of maintaining indoor thermal comfort even during severe weather events. For housing designs to be future proof, it is essential that tools integrate dynamic climate data (both historical and projected). Tools that provide granular climate data for specific regions or that can incorporate updated climate models (such as high-resolution Köppen–Geiger classifications) offer a distinct advantage in ensuring that buildings remain functional and comfortable despite climatic uncertainties.

While SAM and ZEBRA have been identified as effective tools for climate-adaptive housing design, their ability to capture microclimatic variations in dense urban areas remains limited. Urban microclimates are shaped by factors such as heat retention in built environments, localised wind corridors, shading from adjacent structures, and anthropogenic heat emissions. These variations are particularly pronounced in high-density settlements, where thermal comfort assessments based on regional climate classifications may not fully represent real-world conditions. ZEBRA partially addresses this challenge by allowing users to input localised weather data, enabling adjustments based on real-time meteorological observations. This feature enhances its applicability in urban settings where climate conditions deviate from standard classification models. Similarly, SAM enables users to modify assessment parameters, providing some degree of flexibility to account for localised environmental factors. Despite these strengths, neither tool natively incorporates urban heat island effects or high-resolution microclimate modelling. Future research could focus on enhancing their predictive capabilities through integration with urban-scale climate models. One potential avenue is to couple SAM and ZEBRA with GIS-based microclimate simulations, allowing for dynamic mapping of urban thermal stress, wind patterns, and shading effects. Additionally, incorporating high-resolution satellite data and ground-based climate monitoring networks could improve tool accuracy in predicting thermal comfort and energy efficiency at the neighbourhood scale. By advancing these capabilities, SAM and ZEBRA could offer more robust, data-driven insights for urban climate adaptation, ensuring that housing designs are better suited to complex, high-density environments. Such enhancements would further align these tools with the need for future-proof, climate-resilient housing solutions in vulnerable urban areas.

In summary, the sensitivity of design tools to climate conditions relies on their ability to achieve the following:

• Incorporate future climate projections.
• Model weather extremes, rather than just averages.
• Adapt to a variety of climate zones, particularly those at greater risk of experiencing drastic changes in weather patterns.
• Provide location-specific insights, integrating high-resolution climate data to account for microclimates and localised extreme events.

5.3. Adaptation Beyond Thermal Comfort

Climate adaptability is inherently linked to hazard preparedness; thus, design tools must not only accommodate diverse climatic conditions but also anticipate and mitigate potential hazards. The Mahoney Tables’ emphasis on passive design for thermal comfort provides a solid foundation for climate adaptability. Further development is needed to adapt housing simultaneously to other potential hazards, such as floods, typhoons, and earthquakes, in integrated approaches [67]. These hazards present distinct challenges for housing design, such as resilience to structural damage and the provision of safe, healthy indoor environments under varied environmental conditions. Addressing these challenges holistically is essential to avoid maladaptation and adaptation trade-offs by those most vulnerable in multi-hazard scenarios [67]. Enhancing this tool to include hazard mapping and resilience recommendations could bridge this gap. ShelTherm’s embedded weather files for thousands of locations position it as a potent tool for climate adaptability. However, its focus could be broadened beyond thermal performance to include simulations that factor in extreme weather events, thus preparing shelters for hazards like high winds or heavy rains. SAM’s approach, which includes considerations for local materials and the socio-economic context, lays the groundwork that could be expanded to integrate hazard preparedness more explicitly. Incorporating risk assessment modules based on a shelter’s geographic location could make SAM an even more comprehensive tool for designing shelters that are both climate-adaptable and hazard-prepared.

5.4. Expert Suggestions for Further Research

The feedback from the online survey, which included responses from 32 participants, provides valuable insights into how housing design tools can be improved to better support future climate adaptation. Participants suggested that tools should be comprehensive, adaptable, and user-friendly, addressing a wide range of needs and contexts.

A key suggestion is the development of more comprehensive tools, extending beyond individual housing units to include evaluations at the neighbourhood level and for larger social infrastructures, such as evacuation centres and schools. This broader scope would ensure that the tools are versatile and capable of supporting community resilience in the face of climate challenges. The importance of using local materials and methods was frequently highlighted. Future research should explore how these tools can facilitate the design of homes that are adaptable, reversible, and capable of evolving over time. This includes considering the reusability of materials and designing for disassembly, aligning with principles of sustainable construction and the circular economy.

Participants also emphasised the need for tools to have multifaceted interfaces, catering to different user groups such as professionals, regulatory bodies, and residents. This means designing interfaces that are easy to use and understand, even for those without technical expertise, and making complex data more accessible and actionable. Community engagement emerged as a crucial factor. Future tools should incorporate methods to engage local populations in the design process, ensuring that communities feel a sense of ownership and that the tools are tailored to their specific needs and contexts. This participatory approach could enhance the relevance and acceptance of the tools.

Another significant point was the integration of passive design techniques and flexible layouts into housing design tools. These approaches can improve energy efficiency and adaptability, making homes more resilient to climate change. Research should focus on how these techniques can be seamlessly integrated into existing tools.

There was also a strong call for tools to address comprehensive climate adaptation and hazard assessments. This includes evaluating all potential threats to structural safety and incorporating criteria for collective or sharable energy and cooling services. Such tools should guide users in building safer, more resilient homes by providing clear, context-specific advice. Sustainability was a key concern, with many advocating for the use of sustainable materials and practices. Additionally, it was suggested that tools should prioritise the capacities of vulnerable populations to self-build, primarily in informal settlements. This would empower communities with the knowledge and skills necessary for constructing their homes, using locally available resources.

The following recommendations are proposed for future research on housing design tools:

• Develop Comprehensive Tools: Expand the scope of design tools to include neighbourhood evaluations and larger social infrastructures. Ensure these tools can address community-wide resilience.
• Promote Local and Sustainable Practices: Focus on the use of local materials and construction methods and design for material reusability and disassembly to support sustainable building practices.
• Design Multifaceted Interfaces: Create user interfaces that cater to various stakeholders, simplifying complex technical data and enhancing usability for professionals, regulatory bodies, and residents.
• Enhance Community Engagement: Develop participatory design processes that involve local populations, ensuring that communities have a sense of ownership and that the tools are contextually relevant.
• Integrate Passive Design and Flexibility: Incorporate passive design techniques and flexible layouts into housing design tools to improve energy efficiency and adaptability.
• Address Comprehensive Climate Adaptation: Ensure that tools integrate comprehensive climate adaptation measures and hazard assessments, providing guidance on building safe and resilient homes.
• Support Sustainable Self-Building: Prioritise the needs and capacities of vulnerable populations to self-build, using sustainable materials and practices.

By addressing these areas, future research can significantly enhance the effectiveness and relevance of housing design tools, making them more capable of supporting low-cost, climate-resilient housing solutions.

6. Conclusions

This study evaluated four widely used housing design tools—the Mahoney Tables, ShelTherm, SAM, and ZEBRA—for their effectiveness in improving thermal comfort in low-cost housing under future climate scenarios. The tools were assessed using five expert-weighted criteria: future climate adaptability, guideline accuracy, user-friendliness, accessibility, and adaptability to user needs.

The findings indicate that ZEBRA emerged as the strongest tool overall, excelling in accuracy and user-friendliness, while SAM and ShelTherm demonstrated strengths in adaptability and accessibility. The Mahoney Tables, despite their long-standing use, require updates to better incorporate future climate projections.

Expert feedback highlights the need for more comprehensive design tools that extend beyond individual housing units to neighbourhood-wide evaluations and larger infrastructures, such as evacuation centres and schools. Tools should prioritise the use of local materials and construction methods, support adaptable and reversible housing designs, and incorporate material reusability and disassembly principles. Community engagement is also crucial to ensure that local populations are involved in the design process to enhance relevance and acceptance.

Furthermore, future tools should integrate comprehensive climate adaptation and hazard assessments, evaluating structural safety, collective energy solutions, and cooling strategies. Expanding functionalities to support passive design, energy efficiency, and hazard preparedness will strengthen the resilience of low-cost housing in vulnerable regions.

By addressing these gaps, housing design tools can better support climate-adaptive, low-cost housing solutions, fostering sustainable and future-proof housing strategies that align with both scientific advancements and real-world needs.