Thermal comfort in social housing in Ecuador: do free-running buildings work in current and future climates?

Academic Editors: Igor Martek and Mehdi Amirkhani Received: 19 May 2025 Revised: 2 June 2025 Accepted: 10 June 2025 Published: 12 June 2025 Citation: Delgado-Gutierrez, E.; Rubio-Bellido, C.; Canivell, J. Thermal Comfort in Social Housing in Ecuador: Do Free-Running Buildings Work in Current and Future Climates? Buildings 2025,15, 2018. https:// doi.org/10.3390/buildings15122018 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Thermal Comfort in Social Housing in Ecuador: Do Free-Running Buildings Work in Current and Future Climates? Evelyn Delgado-Gutierrez , Carlos Rubio-Bellido * and Jacinto Canivell Higher Technical School of Building Engineering, University of Seville, Seville 41012, Spain; [email protected] (E.D.-G.); [email protected] (J.C.) *Correspondence: carlosr[email protected] Abstract: Ecuador faces a significant housing deficit, prompting government policies aimed at improving access to social housing for vulnerable families. Despite its relatively small geographic size, the country exhibits substantial climatic diversity, encompassing ten distinct Köppen–Geiger climate zones. These range from tropical rainforests to highaltitude Andean regions, each requiring specific housing strategies. However, social housing units are typically designed using a standardized model that disregards regional climatic variations, leading to suboptimal thermal performance and energy inefficiencies. This study evaluates the thermal comfort performance of standardized free-running social housing across six distinct cantons, using the ASHRAE 55-2020 adaptive comfort model. Dynamic simulations were conducted for both current climatic conditions and future scenarios for 2050 and 2100, employing tools such as Meteonorm 8.1 (for weather data), EnergyPlus 9.4.0, and DesignBuilder 7.0 (for thermal modeling). The findings reveal significant differences in indoor comfort levels among identical housing units due to localized climate conditions. Notably, high-altitude regions showed improved thermal performance under future scenarios, whereas coastal lowland areas experienced increased discomfort. These results underscore the urgent need for climate-responsive, adaptive housing designs tailored to local climatic realities across all regions of Ecuador. Keywords: free-running building; thermal comfort; social housing; climate change; building simulation 1. Introduction In 2020, the global urban population exceeded 4.4 billion, with more than 75% residing in urban centers of less developed regions. An estimated 1 billion people live in informal settlements or inadequate housing conditions [ 1 ]. While this issue predominantly affects low-income regions in Asia and Africa [ 2 , 3 ], Latin America also experiences a considerable housing deficit, exacerbated by ongoing migration trends [ 4 ]. The region’s urban population is projected to reach 100 million by 2025 [ 5 ], increasing the demand for housing and exacerbating the number of households living in inadequate conditions. This housing deficit is not only quantitative but also qualitative. Many dwellings fail to meet minimum standards of safety and habitability, contributing significantly to housing inadequacy [ 6 , 7 ]. In Ecuador, housing adequacy is assessed based on factors such as access to basic services, construction materials, and overcrowding [ 8 , 9 ]. Although public housing initiatives aim to reduce the quantitative shortfall, they often overlook qualitative dimensions like thermal comfort and energy efficiency [10]. Social housing plays a crucial role in mitigating the housing deficit. However, standardized designs often neglect local climatic conditions, resulting in thermally inefficient Buildings 2025,15, 2018 https://doi.org/10.3390/buildings15122018 Buildings 2025,15, 2018 2 of 17 buildings. Thermal comfort—a key determinant of indoor environmental quality and occupant well-being—is influenced by variables such as metabolic rate and clothing insulation [ 11 – 15 ]. The ASHRAE Standard 55 [ 16 ] defines the following acceptable indoor temperature ranges: 23–26 ◦ C in summer and 20–24 ◦ C in winter, with recommended humidity levels to ensure comfort and health. Consequently, indoor thermal comfort has become a widely studied topic [17–21]. Thermal comfort assessment commonly employs two approaches: the Predicted Mean Vote (PMV) and adaptive models [ 14 ]. PMV estimates thermal neutrality based on a controlled set of environmental and personal factors [ 22 ], while adaptive models account for occupants’ capacity to adjust to temperature variations over time. These adaptive models are particularly relevant in regions with low seasonal temperature variability, such as Ecuador. Thermally comfortable indoor environments are essential for preventing health risks such as heat stress and cardiovascular conditions [ 15 , 23 ]. In Ecuador, the housing deficit in 2020 was 13.92%, according to the National Institute of Statistics and Census (INEC) [ 24 , 25 ]. In response, the government launched initiatives like the “Casa para Todos” program [ 26 ], aiming to construct 220,900 housing units between 2019 and 2021 [27]. Ecuador’s diverse geography and climate pose distinct challenges for social housing design. The country comprises four major regions—the Coast, Highlands, Amazon, and Galápagos—and spans eight Köppen–Geiger climate zones [ 28 ]. Despite this variability, Ecuador experiences only two primary seasons—dry and rainy—with relatively stable temperatures throughout the year [ 29 , 30 ]. These characteristics make adaptive comfort models particularly suitable for the national context. Administratively, Ecuador is divided into 24 provinces, which are further subdivided into cantons and parishes [31]. Previous studies support the application of adaptive models in Ecuador due to the country’s unique climatic and geographic conditions and the population’s demonstrated capacity for acclimatization [ 32 ]. However, their use remains limited, as most research on adaptive comfort has focused on the Mediterranean, Southern European, and selected Asian and American regions. [33–41]. In recent years, adaptive thermal comfort models have become valuable tools for improving indoor environmental quality and energy performance. Their implementation has expanded across tropical climates like those in Malaysia and Mexico [ 42 , 43 ], temperate zones in Europe and India [ 44 ], and has informed the development of national comfort standards. Recent refinements include adjustments for temporal dynamics and occupant behavior, highlighting the models’ versatility in supporting sustainable building practices [45–48]. Given Ecuador’s minimal seasonal temperature variation, it presents an optimal context for applying adaptive thermal comfort models. Simulation tools are essential for evaluating climatic responses and thermal behavior in different housing typologies [ 49 – 52 ]. This study evaluates and compares the thermal comfort performance of two identical free-running social housing units—part of an existing government prototype—across six Ecuadorian climate zones under both current and projected climate scenarios. It seeks to answer the following questions (RQs): • RQ1: How do two identical homes perform thermally across Ecuador’s diverse climate zones? •RQ2: Can consistent behavioral patterns be identified across different climates? •RQ3: How do performances vary under three climate change scenarios? Simulations were conducted for six representative locations to analyze both present and future indoor thermal performance. The methodology and data sources are presented in the following section. Buildings 2025,15, 2018 3 of 17 The remainder of the paper is organized as follows: Section 2outlines the methodology and climate scenarios used. Section 3presents the simulation results and discusses their implications. Section 4concludes the study by summarizing key findings and proposing recommendations for future housing design in similar climatic contexts. 2. Methodology This study comprises four main phases: (i) defining the building model, (ii) setting simulation parameters, (iii) selecting the adaptive comfort model, and (iv) acquiring and processing climatic data. Each phase is described below. 2.1. Case Study The selected case is a standardized multi-family block from Ecuador’s “Casa para Todos” housing program, which aimed to build 220,900 units between 2018 and 2021 [26,27]. The “4D block” typology includes four 52 m 2 apartments—two per floor (Figure 1). Each unit contains two bedrooms, one bathroom, a kitchen, and a living–dining area, with a 2.55 m floor-to-ceiling height. Ground-floor units are adapted for individuals with reduced mobility. (a) (b) Figure 1. (a) Ground floor distribution. (b) Upper floor distribution. Information obtained from the Ministry of Housing and Urban Development and edited by the authors. Construction specifications are as follows: •Walls: 10 cm reinforced concrete with plaster and paint; U-value: 2.695 W/m2·K; •Floor: 10 cm concrete with ceramic finish; U-value: 3.15 W/m2·K; •Intermediate slab: 15 cm concrete with ceramic finish; •Roof: Metal frame with 5 mm polyurethane panel; U-value: 3.13 W/m2·K; •Windows: 4 mm single-glazed glass in aluminum frames; U-value: 5.70 W/m2·K; •Doors: Interior wood, exterior metal. Two units were selected for simulation: Living A (ground floor) and Living B (upper floor), based on high daytime occupancy patterns (Figure 1). The block’s south-facing orientation was preserved. Due to Ecuador’s equatorial location, orientation was not expected to significantly influence thermal performance [30]. Buildings 2025,15, 2018 4 of 17 2.2. Parametric Thermal Simulations Thermal simulations were conducted using DesignBuilder v7 [ 53 ], a validated dynamic simulation interface for EnergyPlus, to assess indoor thermal behavior based on the described envelope and occupancy conditions. Inputs were derived from national household profiles [54], assuming a four-member family per unit (Figure 2). Figure 2. Occupancy parameters used. All simulations modeled naturally ventilated conditions—windows opened from 09:30 to 17:30 daily. No mechanical heating or cooling systems were included, reflecting the original design specifications. A total of 42 annual simulations were performed: seven per location, representing present-day conditions and future climate scenarios under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 for the years 2050 and 2100. The models were exported in IDF format for EnergyPlus processing. The results included hourly operative temperatures, later compared against comfort thresholds from the adaptive model. 2.3. Adaptive Thermal Comfort Model from ASHRAE 55-2020 To evaluate thermal comfort, this study adopted the adaptive model outlined in ASHRAE Standard 55-2020 [ 55 ], which is appropriate for free-running buildings and climates with low thermal amplitude—such as those found in Ecuador. The standard defines two levels of acceptability: 80% and 90%. The 80% acceptability range was selected to reflect a broader range of occupant comfort. The model relates indoor comfort limits to the prevailing mean outdoor temperature (tpma(out)) (Equation (1)), which accounts for short-term outdoor temperature trends. For equatorial climates, ASHRAE recommends an exponential running mean calculated as follows: tpma(out)=(1−α)· n ∑ d=1α(i−1)·Text,d[◦C](1) where α = 0.9 and ( Text,d ) represents the daily mean external temperature on day d. This formulation emphasizes recent weather conditions while smoothing day-to-day fluctuations. Using this value, the operative temperature comfort limits are calculated as follows: Upper limit (80% acceptability)=0.31·tpma(out)+21.3 [◦C](2) Lower limit (80% acceptability)=0.31·tpma(out)+14.3 [◦C](3) These limits apply when (tpma(out) lies between 10 ◦ C and 33.5 ◦ C. Values outside this range fall outside the scope of the model (Figure 3). The operative temperature output from simulations was evaluated against these dynamic thresholds. Buildings 2025,15, 2018 5 of 17 Figure 3. Upper and lower limits considered in the adaptive comfort model with 80% acceptability. 2.4. Thermal Comfort Assessment Thermal performance was assessed by comparing simulated hourly operative temperatures with the calculated adaptive comfort range for each location and climate scenario. This comparison produced the Percentage of Time Within the Adaptive Acceptability Model (PDAAM), defined as Equation (4): PDAAM =∑8760 i=1di 8760 di=1i f 33.5 ≥tpma(out)≥10 (4) where di = 1 if the hourly operative temperature is within the comfort range and 0 otherwise. This metric indicates the fraction of the year during which indoor conditions meet adaptive comfort criteria. The analysis was conducted separately for both apartments (Living A and Living B), across all six locations and seven climate datasets (current and projected scenarios for 2050 and 2100 under RCPs 2.6, 4.5, and 8.5). The results were aggregated to identify patterns in comfort performance and vulnerability to future climatic changes. 2.5. Climate Data Six locations representing distinct Köppen–Geiger zones were selected, each with over 50,000 inhabitants [ 56 ]. The location of the localities is shown in the Figure 4. The climate zones analyzed are the following: • Cfb: Quito. Oceanic climate, characterized by cool summers and cold or mild winters; • Af: Nueva Loja. Humid tropical or jungle climate, with high temperatures and rain throughout the year; • Aw: Esmeraldas. Tropical savanna, with warm temperatures year-round and a defined dry season; • Am: Santo Domingo. Tropical monsoon climate, featuring warm temperatures with alternating dry and wet seasons; • BWh: Santa Elena. Hot desert climate, with mild winters and significant diurnal temperature variation; Buildings 2025,15, 2018 6 of 17 •BSh: Portoviejo. Semi-arid hot climate, with mild winters and warm summers; • Climate files were generated via METEONORM using stochastic extrapolation [ 57 , 58 ]. Figure 4. The regions of Ecuador and the location of the 6 localities used in this study. Seven datasets per location covered current and future RCP scenarios (2.6, 4.5, and 8.5) for 2050 and 2100 [59]. Temperature ranges of each locality are shown in Figure 5. Figure 5. Maximum and minimum monthly temperatures ( ◦ C). Single lines correspond to maximum temperatures and dotted lines to minimum temperatures of each location. Buildings 2025,15, 2018 7 of 17 3. Results and Discussion The thermal behavior of the analyzed units reveals significant differences in performance between ground-floor (Living A) and upper-floor (Living B) spaces under various climate scenarios and locations. For clarity, the operative temperature profiles were converted into degree-hours outside the adaptive comfort range, following Equation (4), and represented in point diagrams (see Appendix A). This representation accounts for all 8760 h of the year for each scenario and dwelling. These results are consistent with prior research evaluating adaptive comfort in Ecuador [ 60 ], highlighting how the relative thermal stability of many Ecuadorian climate zones throughout the year supports the application of broader adaptive setpoints. This approach contrasts with fixed temperature thresholds, offering potential energy savings by reducing reliance on mechanical heating and cooling systems, as supported by previous studies in diverse climate contexts [11,18,36–38,61–73]. Figure 6shows the degree-hours outside adaptive limits for each dwelling and scenario. Overall, Living A demonstrates better performance than Living B across most locations and time horizons, particularly in warmer regions. A recurring pattern is the presence of degree-hours below the lower comfort limit in all locations, while upper-limit exceedances are generally more prominent in Living B. Notably, only Quito recorded cooling demand in Living A, while Living B presented upper-limit exceedances in all scenarios and cities. (a) Figure 6. Cont. Buildings 2025,15, 2018 8 of 17 (b) Figure 6. Degree hours ( ◦ C) outside the upper (a) and lower limits (b) for each dwelling under current climate conditions and projections to 2050 and 2100 (RCP 2.6, RCP 4.5, and RCP 8.5). Quito (Cfb climate): Temperature variability between floors is relatively small. Both rooms register significant hours below the adaptive lower limit: 55.79% in Living A and 41.30% in Living B under current conditions (Table 1). Overheating is rare in Living A but increases notably in Living B under extreme scenarios, reaching 89.85% in 2050 (RCP 8.5) and 85.25% in 2100 (RCP 2.6). Portoviejo (Aw climate): Living B exceeds upper comfort limits under RCPs 4.5 and 8.5 by 2050 and 2100. While Living A remains mostly within acceptable ranges, it registers 206 degree-hours below the lower threshold under RCP 8.5 (2050). Living B experiences no hours below the lower limit in any scenario. Santo Domingo (Am climate): Degree-hours below lower limits are more frequent in Living A, with peaks of 818 ◦ C (current), 470 ◦C (2050, RCP 8.5), and 531 ◦C (2100, RCP 2.6). Overheating is concentrated in Living B, surpassing 4000 degree-hours in 2050 under RCPs 2.6 and 4.5. Santa Elena (BWh climate): Living A records negligible hours below lower limits, with a minor exception in 2050 (RCP 2.6). However, it shows increasing upper-limit exceedances across scenarios. In contrast, Living B remains relatively stable, staying mostly within comfort bounds. Esmeraldas (Af climate): While both dwellings register degree-hours below the lower limit, these increase substantially under the current and 2100 (RCP 2.6) scenarios, reaching 919 ◦ C and 2597 ◦ C, respectively. Overheating is a major concern for Living B, with nearly 6000 h above comfort limits in 2100 under RCP 8.5. Nueva Loja (BSh climate): Buildings 2025,15, 2018 9 of 17 Degree-hours below lower limits appear only in 2050 (RCP 8.5), with 99 ◦ C for Living A and 6473 ◦C for Living B. Overheating occurs exclusively in Living B under 2100 (RCP 8.5), whereas Living A remains within the adaptive limits throughout all scenarios. Table 1. Values within the adaptive comfort limits obtained by both dwellings in each climate scenario. Lighter shades indicate higher percentages of comfort (better performance), while darker shades reflect lower comfort levels. Living A Scenario Portoviejo Quito Santa Elena Esmeraldas Nueva Loja Santo Domingo 2020 98.85% 44.21% 99.33% 89.51% 95.96% 90.66% 2050 RCP 2.6 99.12% 52.69% 95.43% 99.34% 99.59% 100.00% 2050 RCP 4.5 99.82% 54.04% 99.70% 99.82% 98.20% 100.00% 2050 RCP 8.5 97.65% 94.11% 99.91% 99.62% 98.87% 94.63% 2100 RCP 2.6 98.95% 93.53% 99.97% 70.35% 97.16% 93.94% 2100 RCP 4.5 99.74% 62.81% 99.95% 99.25% 99.52% 97.05% 2100 RCP 8.5 100.00% 75.98% 100.00% 99.97% 100.00% 99.34% Living B Scenario Portoviejo Quito Santa Elena Esmeraldas Nueva Loja Santo Domingo 2020 69.78% 58.44% 60.96% 83.04% 78.90% 88.24% 2050 RCP 2.6 66.23% 67.57% 71.56% 60.53% 60.84% 44.58% 2050 RCP 4.5 49.94% 70.58% 52.72% 49.54% 66.83% 49.45% 2050 RCP 8.5 69.98% 10.15% 49.32% 56.70% 26.11% 82.42% 2100 RCP 2.6 64.55% 14.75% 45.40% 89.03% 71.42% 83.86% 2100 RCP 4.5 57.10% 78.73% 44.03% 57.31% 54.36% 79.50% 2100 RCP 8.5 36.05% 88.26% 25.72% 31.51% 29.94% 63.16% Table 1synthesizes the PDAAM values for both units across scenarios, now visualized using a grayscale gradient for clearer interpretation. Lighter shades indicate higher percentages of comfort (better performance), while darker shades reflect lower comfort levels. This approach responds to reviewer feedback aiming to improve visual accessibility and consistency. A consistent pattern emerges: ground-floor dwellings (Living A) maintain PDAAM values above 90% in most cases, except in colder climates like Quito. Conversely, upperfloor units (Living B) show reduced comfort performance in all cities, although Santo Domingo exhibits comparatively moderate results. The differences between Quito (average 14.16 ◦ C) and Esmeraldas (average 24.77 ◦ C) underscore the climatic contrasts driving these outcomes. As illustrated in Figure 7, Esmeraldas exhibits consistent differences between units, with Living A generally staying within comfort limits. Meanwhile, in Quito, both units frequently fall outside acceptable ranges—above and below—demonstrating the limitations of passive design in cooler highland conditions. 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