A Review of the Importance of Window Behavior and Its Impact on Indoor Thermal Comfort for Sustainability

Abstract

Windows play a crucial role in maintaining indoor thermal comfort, influenced by occupant behavior, passive design strategies, and advanced technologies that contribute to sustainable building practices. Despite advancements in adaptive and occupant-centric design, critical gaps remain unresolved in understanding of multi-climate adaptability, the complex interrelation between window operation and occupant behavior, and the integration of occupant roles into energy-related strategies under emerging technologies. This scoping review synthesizes peer-reviewed studies to assess the importance of window design (geometry, glazing, shading), operational strategies (manual control to AI-driven systems), and technological approaches (passive to smart systems) on thermal comfort, energy performance, and occupant behavior. Using bibliometric and scientometric analyses, the review focuses on four primary research clusters: thermal comfort and occupant behavior, window operation strategies, their impact on energy performance, and sustainability, with an emphasis on emerging trends. The findings highlight that glazing technologies, shading systems, and operational choices have a significant impact on both comfort and energy efficiency. The study develops a framework linking thermal comfort to window operation, occupant behavior, and climate context while conceptualizing a comprehensive design matrix and outlining future research directions aligned with the Sustainable Development Goals (SDG 3: health and well-being, SDG 7: clean energy, and SDG 11: sustainable cities and communities).

1. Introduction

Buildings account for approximately 40% of total global energy consumption, with a significant portion dedicated to Heating, Ventilation, and Air Conditioning (HVAC) systems that regulate indoor environmental quality (IEQ) [1,2]. This demand is propelled by rapid urbanization and climate change, accentuating the urgent need for sustainable architectural practices linked to the Sustainable Development Goals (SDGs), notably SDG 3 (Health and Well-being), SDG 7 (Affordable and Clean Energy), and SDG 11 (Sustainable Cities and Communities) [3,4]. The study revealed that people spend more than 80% of their time indoors [5]. Indoor environments that embrace thermal comfort, air quality, and daylighting exert a significant impact on health, productivity, and overall well-being [6]. In this context, the window serves as a key architectural element in addressing the dual challenge of IEQ and energy efficiency, which is interlinked with the building’s physics, operations, environment, and socio-behavior aspects. While evaluating the interlinkage, previous studies have shown that window-related factors such as geometric design, glazing technologies, shading systems, and a range of control strategies—ranging from manual adjustments to artificial intelligence (AI)-driven systems—directly influence fundamental building physics, including thermal insulation, solar heat gain, airflow dynamics, and ventilation strategies like natural and mixed-mode airflow regimes [7,8,9,10,11,12]. Strategic window design significantly enhances natural ventilation performance [13], while optimized placement can substantially improve thermal comfort [14]. Even simple modifications to window parameters, such as size and orientation, have been shown to reduce air temperature by 2.5% and increase air velocity by a factor of six [15]. In the technological era, Computational Fluid Dynamics (CFD) and EnergyPlus simulations can help to enhance building performance for sustainable and occupant-focused building design [16].

Recent studies underline the importance of adaptive design strategies that enhance IEQ through robust occupant–window interactions, merging socio-behavioral insights with environmental responsiveness. For instance, entire openings coupled with wind from the south and southeast directions can improve the indoor environment for occupants engaged in various activities [12]. Similarly, passive strategies, particularly those focused on ventilation and roofing materials in sustainable systems, have been shown to improve thermal comfort by up to 16% in hot–humid climates, thereby reducing reliance on energy-intensive HVAC systems [17]. These findings align with the historical significance of passive design approaches, which have long been used to maintain thermal comfort in a cost-effective manner [18]. The strategic placement and orientation of windows, combined with intelligent control systems—such as AI-driven adjustments and occupant behavior—can enhance natural ventilation efficiency and thermal comfort across diverse climatic conditions and user preferences [5,19,20,21]. Moreover, integrating adaptive thermal comfort models with user-operated mechanisms, such as dynamic shading, can reduce HVAC energy consumption by up to 30% in office and residential buildings while enhancing thermal comfort across different climatic conditions [22,23]. Moreover, proper ventilation reduces indoor pollutant concentrations, thereby improving health outcomes while supporting energy savings [24]. This integration of passive design strategies with occupant activities positions the window as a dynamic interface that translates human behavior into sustainable performance, effectively supporting SDGs 3, 7, and 11 by promoting context-aware and occupant-centric architectural design.

Window operational behavior is influenced by five major factors: environmental, contextual, psychological, physiological, and social milieus [25]. The linkage between window behavior and energy performance highlights that understanding occupant behavior assists in developing design strategies to enhance the indoor environment. While existing studies emphasize context-specific benefits, there is limited field validation across diverse climatic zones, particularly in tropical and arid regions, where solar heat gain and ventilation dynamics vary significantly. In addition, the impact of geometric design parameters—such as window-to-wall ratios (WWRs), orientation, and shading integration—is often investigated in isolation rather than holistically, overlooking their combined effect on thermal comfort, particularly in relation to room-specific functions. Spaces with specific functions, such as kitchens, living rooms, and bedrooms, have distinct thermal, ventilation, and daylighting needs, yet most studies do not tailor window design or operation strategies according to room usage.

Despite the significant progress that has been made in adaptive thermal comfort and occupant-centric strategies, previous studies offer a limited understanding of the interconnection between window operation, design strategies, and occupant behavior linked to building energy performance and multi-climate adaptability for sustainability. This review provides four key contributions: (i) a foundational framework linking window behavior with thermal comfort, based on a rigorous literature review, (ii) a conceptual framework illustrating how occupant activities influence window operation with occupant-centric design, (iii) climate-specific window design strategies aimed at supporting the sustainable development goals, and (iv) identification of future research directions leveraging emerging technologies to promote healthy and energy-efficient indoor environments.

2. Methodology

This study adopted a scoping review protocol with bibliometric mapping to investigate the effects of window behavior on indoor thermal comfort, with a focus on behavioral patterns and methods. This approach was selected to provide a comprehensive overview of the current research landscape, identify key concepts, and map influential studies and authors. The review aimed to identify, scrutinize, and synthesize relevant peer-reviewed studies, ensuring a comprehensive assessment of the current research landscape. The Scopus database was selected for its extensive indexing of peer-reviewed literature across engineering, architecture, and environmental sciences. Its multidisciplinary scope makes it an ideal resource for retrieving studies pertinent to thermal comfort, ventilation strategies, window behavior, design strategies, and emerging trends.

The primary literature search was conducted in the Scopus database by targeting article titles, abstracts, and keywords. The search strategy was developed using four distinct keyword clusters. These clusters, shown in Table 1, were combined using Boolean operators (AND, OR) to capture a wide range of relevant studies, focusing on English-language articles. To ensure the quality and relevance of the reviewed literature, specific inclusion and exclusion criteria were applied. Inclusion criteria encompassed peer-reviewed journal articles, review papers, and book chapters published between 2000 and 2025 that focused on thermal comfort and window behavior in buildings. Exclusion criteria eliminated editorials, non-peer-reviewed sources, articles not in English, and studies unrelated to window behavior, ventilation, or thermal comfort.

Table 1. Keywords clusters and search strings used for the literature search in the Scopus database.

Clusters	Keywords
Thermal Comfort	“Thermal Comfort”, “Occupant Comfort”, “Human Thermal Comfort”, “Indoor Thermal Comfort”, “Indoor Environment”, “Indoor Environmental Quality”, “Indoor Temperature”
Ventilation & Windows	“Natural Ventilation”, “Passive Cooling”, “Passive Cooling Techniques”, “Windows”, “Smart Windows”, “Window-to-wall-Ratio”, “Window Operations”, “Window-Opening”, “Window-opening Behavior”
Computational Analysis	“Simulation”, “Computer Simulation”, “Building Simulation”, “Computational Fluid Dynamics”, “CFD”, “EnergyPlus”
Emerging Trends	“Sustainability”, “Sustainable Building”, “Sustainable Architecture”, “Green Building”, “Low-Energy Buildings”, “Intelligent Buildings”, “Bioclimatic Design”, “Carbon Reduction”, “Artificial Intelligence”, “Deep Learning”, “Machine Learning”

Table 1. Keywords clusters and search strings used for the literature search in the Scopus database.

Clusters	Keywords
Thermal Comfort	“Thermal Comfort”, “Occupant Comfort”, “Human Thermal Comfort”, “Indoor Thermal Comfort”, “Indoor Environment”, “Indoor Environmental Quality”, “Indoor Temperature”
Ventilation & Windows	“Natural Ventilation”, “Passive Cooling”, “Passive Cooling Techniques”, “Windows”, “Smart Windows”, “Window-to-wall-Ratio”, “Window Operations”, “Window-Opening”, “Window-opening Behavior”
Computational Analysis	“Simulation”, “Computer Simulation”, “Building Simulation”, “Computational Fluid Dynamics”, “CFD”, “EnergyPlus”
Emerging Trends	“Sustainability”, “Sustainable Building”, “Sustainable Architecture”, “Green Building”, “Low-Energy Buildings”, “Intelligent Buildings”, “Bioclimatic Design”, “Carbon Reduction”, “Artificial Intelligence”, “Deep Learning”, “Machine Learning”

The initial primary search in the Scopus database retrieved 225 articles, which formed the basis for the subsequent bibliometric analysis. A rigorous screening process based on relevance, methodological clarity, and data completeness refined this dataset. This first phase of screening yielded 29 studies from the original Scopus retrieval for detailed content analysis. To ensure comprehensive coverage of the literature, a secondary, targeted search was performed beyond Scopus. This broader approach was adopted because, given the multidisciplinary nature of the topic, several highly relevant studies were published in specialized journals and conference proceedings not indexed in Scopus. This second phase identified 83 additional studies. To maintain methodological rigor, all non-Scopus studies included were peer-reviewed and published in English, consistent with our inclusion criteria. The second phase of the search employed different search strings tailored to each section of the review and included a broader range of peer-reviewed English-language publications, such as journal articles, reviews, book chapters, conference papers, and manuals. The complete study selection process is illustrated in Figure 1 in the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) style flow diagram. Given that this study adopts a scoping review framework, its primary aim was to map the breadth and characteristics of the literature rather than assess study quality. Therefore, a formal quality or risk-of-bias assessment was not conducted, consistent with standard scoping review methodology.

3. Bibliometric and Scientometric Analysis

To provide a comprehensive understanding of the research landscape on the importance of window behavior and its impact on indoor thermal comfort, bibliometric and scientometric analyses were conducted using VOSviewer version 1.6.20. These methodologies facilitated the identification of key research themes, influential authors, global research collaboration patterns, and most cited works in this domain, thereby offering valuable insights into the evolution of research trends and interdisciplinary linkages.

3.1. Thematic Analysis Using Keyword Co-Occurrence

A keyword co-occurrence analysis was performed to identify key research themes within the study of window behavior and its impact on indoor thermal comfort. The analysis considered all keywords from 225 selected papers, establishing a minimum occurrence threshold of five. Out of 1931 keywords, 152 met the threshold, and the results, illustrated in the keyword co-occurrence map (Figure 2), revealed four principal research clusters: Thermal Comfort, Window Strategies and Window Operations, Computational Modeling, and Emerging Technologies as Design Strategies and Sustainability.

The Thermal Comfort cluster highlights the significance of cooling strategies in enhancing indoor environmental conditions. Dominant keywords such as “thermal comfort” (92 occurrences, total link strength 864), “air quality” (24 occurrences, total link strength 258), “daylighting” (13 occurrences, total link strength 92), “indoor thermal environments” (6 occurrences, total link strength 63), and “occupant behavior” (5 occurrences, total link strength 55) indicate a strong research focus on optimizing temperature, humidity, and air movement to improve occupant well-being. This cluster underscores the development of both passive and active cooling strategies aimed at improving indoor comfort.

The Ventilation Strategies and Window Operations cluster emphasizes the critical role of behavior in different climates. Keywords such as “natural ventilation” (54 occurrences, total link strength 452), “passive cooling” (16 occurrences, total link strength 147), “smart windows” (10 occurrences, total link strength 85), and “window-openings” (5 occurrences, total link strength 60) reflect an increasing research emphasis on airflow dynamics, temperature regulation, and energy efficiency through optimized window operations. Studies in this cluster investigate the impact of various window configurations, opening schedules, and occupant interaction patterns on indoor climate control.

The Computational Modeling cluster signifies the growing reliance on numerical simulations to assess window behavior and their effects on thermal comfort linked to building energy performance. Keywords such as “CFD” (29 occurrences, total link strength 287) and “EnergyPlus” (8 occurrences, total link strength 67) indicate a strong dependence on simulation tools and building performance modeling. Computational approaches in this domain facilitate the assessment of window operations under varying environmental conditions, optimizing both thermal comfort and energy efficiency through data-driven analyses.

The Emerging Technologies cluster represents an increasing interest in integrating advanced methodologies to optimize window performance. Keywords such as “intelligent buildings” (50 occurrences, total link strength 510), “sustainability” (29 occurrences, total link strength 864), “green buildings” (20 occurrences, total link strength 227), “sustainable buildings” (7 occurrences, total link strength 61), “machine learning” (22 occurrences, total link strength 235), and “bioclimatic design” (8 occurrences, total link strength 54) highlight the exploration of AI, predictive modeling, and sustainable design principles. The incorporation of AI and machine learning is expected to enhance adaptive window control strategies, fostering the development of innovative and effective energy-efficient building environments.

3.2. Key Contributors and Collaboration Networks

An author co-authorship analysis was conducted to identify influential contributors and collaborative networks in the study of window behavior and indoor thermal comfort. The analysis considered authors with a minimum of one publication and at least ten citations, ensuring the inclusion of impactful researchers. Out of 805 authors, 371 met the threshold, reflecting a relatively small yet highly influential group driving research advancements in this field.

Chen Xi and Yang Hongxing emerged as among most influential researchers, with five publications and 321 citations. Their extensive collaborations emphasized significant contributions to passive cooling strategies and computational modeling, particularly in advancing simulation-based approaches for optimizing indoor thermal comfort. Malkawi Ali and Wu Yupeng are also perceived as key contributors, with four publications and 562 and 361 citations, respectively, focusing on window performance, energy efficiency, and ventilation strategies. Additionally, Chen Yujiao, with three publications and 558 citations, demonstrated substantial contributions through data-driven methodologies and advanced modeling techniques, further enriching the understanding of window behavior and indoor thermal comfort.

The co-authorship network (Figure 3) illustrates key researchers and their collaborative alliance, revealing a well-structured research community where established scholars actively engage with emerging researchers. The largest connected network comprises 14 authors, demonstrating the extent of research collaboration within this domain. The presence of highly interconnected researchers underscores the dynamic nature of the field, where interdisciplinary partnerships facilitate methodological integration across computational modeling, field measurements, and AI-driven predictive modeling.

3.3. Global Research Collaboration

A country co-authorship analysis was performed to explore global collaboration patterns in window behavior research. The analysis, conducted using a minimum threshold of one publication and one citation per country, identified 62 qualifying countries out of 66. The results, visualized in the country co-authorship map (Figure 4), highlight strong international engagement and a well-connected research network in this domain.

China has emerged as the leading research hub, with 43 published papers, 932 citations, and a total link strength of 29, reflecting the country’s strong emphasis on building energy efficiency and occupant comfort. The United Kingdom has followed closely, with 28 publications, 1040 citations, and a total link strength of 28, demonstrating significant contributions to computational modeling and passive cooling strategies. The United States has played a pivotal role, with 23 papers, 1168 citations, and a total link strength of 14, emphasizing the data-driven optimization of window performance and emerging technologies for energy-efficient buildings.

Beyond individual country contributions, strong cross-border partnerships are evident, facilitating knowledge exchange and the development of universally applicable sustainable solutions. The increasing collaboration from different geographical regions underscores the importance of interdisciplinary joint efforts in advancing the field of indoor thermal comfort and window behavior.

3.4. Publication Trends over Time

An analysis of publication trends from 2000 to 2025, as illustrated in Figure 5, reveals a consistent increase in research activity within this domain. The early years, from 2000 to 2011, saw minimal publications, with a peak of two publications in 2004. A gradual increase began in 2012, followed by a sharp rise between 2015 and 2017, reaching 20 publications in 2017. Research output continued to grow exponentially from 2020 onward, with the highest number of 42 publications recorded in 2024. In early 2025, 21 publications have already been recorded, indicating sustained research momentum. The overall trend underscores the growing interest and advancements in window behavior research, particularly in recent years.

3.5. Prominent Journals

Research on window behavior and indoor thermal comfort is primarily disseminated through several high-impact journals, as illustrated in Figure 6. The analysis was based on citation counts, with the source-counting method applied. A minimum threshold of one document per source and at least one citation per source was set for inclusion. Out of 96 sources, 79 met these criteria, with the largest connected set comprising 23 sources.

Building and Environment follows closely with 11 publications, 465 citations, and a total link strength of 8. This journal is essential in advancing knowledge on IEQ, building physics, and the interactions between occupants and fenestration systems. It plays a critical role in bridging the gap between theoretical modeling and practical applications of window behavior in various climatic conditions.

Sustainability has published 18 papers with 184 citations and a total link strength of 5, highlighting its growing influence in exploring innovative approaches to optimize window design and operation. The presence of multiple publications in these journals underscores the interdisciplinary nature of the research, which integrates architecture, mechanical engineering, and computational modeling. The concentration of studies in these respected journals reflects the significance of window behavior research within the broader context of energy-efficient building design and indoor thermal comfort.

3.6. Influential Papers

A co-citation analysis was conducted to identify the most influential studies shaping the field of window behavior and indoor thermal comfort. This analysis was based on co-citation, with the unit of analysis set to cited authors. A minimum citation threshold of 10 was applied, and out of 15,578 sources, 398 met these criteria. Figure 7 illustrates the strong co-citation links among these studies, emphasizing their pivotal role in establishing the theoretical and empirical foundation for ongoing research in this domain.

Among most frequently co-cited works are those by Tianzhen Hong (91 citations, total link strength 9082) and Yin Zhang (82 citations, total link strength 5539). These studies have made significant contributions to the understanding of thermal comfort, building ventilation, and passive cooling strategies. Their findings have provided critical insights into occupant behavior, environmental interactions, and energy-efficient building design, shaping subsequent research directions. The frequent co-citation of these works suggests that they are essential references to explore window performance, computational modeling, and adaptive comfort research.

The identification of these foundational studies highlights the structured evolution of research in this field, demonstrating how past findings continue to influence contemporary advancements. The strong interlinkages between these works indicate a well-established knowledge base that supports ongoing innovations in improving indoor thermal comfort through optimized window operations.

4. Results and Discussions

The scoping review identifies six primary areas of discussion: (1) the historical importance of the relationship between thermal comfort and its indices, (2) the relationship between thermal comfort and window behavior, (3) the role of occupant behavior in shaping window operation and influencing design strategies; (4) the connection between window behavior and building energy performance; (5) the influence of climatic context and thermal comfort on building design strategies within a sustainability framework, and (6) the establishment of a comprehensive design matrix derived from influencing determinants with the aid of technology to achieve healthy indoor thermal comfort and sustainability.

4.1. Historical Significance of Thermal Comfort and Its Indices

The quantification of thermal comfort has long been a subject of research across various disciplines, including environmental engineering, human physiology, and architectural design. This research has led to the development of numerous thermal comfort indices, aiming to portray the interplay between environmental parameters, such as air temperature, humidity, air velocity, and radiant temperature, and human factors, including clothing insulation, metabolic rate, and behavioral adaptations. These indices have extended from simplistic environmental models to sophisticated frameworks that integrate physiological responses, adaptive behaviors, and real-time data.

Table 2. A chronological overview of key thermal comfort indices and their contributions to the development of human thermal comfort standards.

Period	Index	Developer(s)	Key Contribution/Improvement
1923	Effective Temperature (ET)	Houghten & Yaglou, cited in [26,27]	Combined air temperature, humidity and air velocity; Combined multiple environmental variables.
1927	Statistical Analysis of Numerical Values	Yaglou, cited in [28]	Empirical data; Enhancing systematic definitions of comfort zones.
1936	Equivalent Warmth	Bedford, cited in [29]	Combined air temperature, humidity, air velocity, clothing, skin temperature; Nomogram based scale.
1940s	Operative Temperature (OT)	Winslow et al. [30]	Combined air Temp and mean radiant temperature; Included radiant heat effects
1950s	Corrected Effective Temperature (CET)	Gagge et al. [31]	Combined air temperature, air velocity, mean radiant temperature; Expanded ET with radiant temperature
1950s	Predicted 4-Hour Sweat Rate	McArdle et al., cited in [32]	Combined metabolic rate, clothing, environmental conditions; Introduced physiological response (sweat rate)
1955	Heat Stress Index (HSI)	Belding & Hatch, cited in [33]	Linked metabolic rate to environmental stress; Quantified stress-load relationship
1970	PMV–PPD Model	Fanger [34]	Air Temp, MRT, Relative Humidity (RH), Air Velocity, Clothing, Activity; Comprehensive model combining 6 variables
1998	Adaptive Thermal Comfort (ATC)	de Dear & Brager [35]	Climate, Culture, Behavior; Addressed overestimation of discomfort in PMV;
2009	Universal Thermal Climate Index (UTCI)	Jendritzky et al. [36]	Wind, Solar Radiation, RH, Air Temp; Improved modeling for transient and outdoor environments
2010–Present	Emerging Trends (Personalized Models, Climate Specific Indices, Sustainability Integration)	Researchers [37,38]	Real-time physiological and environmental data; Personalization, real-time data integration; Links comfort metrics to passive cooling/heating strategies.

illustrates a chronological overview of key thermal comfort indices, highlighting their methodological basis, primary developers, and the key contributors and improvements made over time.

To systematically analyze the progression of thermal comfort indices, a taxonomy tree categorizing these indices by methodological approach is presented in Figure 8. This taxonomy distinguishes between indices based on environmental determinism (e.g., Effective Temperature, Operative Temperature), physiological response models (e.g., P4SR, HSI), human-centric adaptive models (e.g., Adaptive Thermal Comfort, Predicted Mean Vote (PMV)—Predicted Percentage of Dissatisfied (PPD)), and modern integrative approaches that incorporate real-time data and sustainability considerations (e.g., UTCI, emerging personalized models).

The conceptual framework illustrated in Figure 9 maps the evolution of thermal comfort research, highlighting key contributions and interlinkages between thermal comfort, air quality, window operations, and smart window technologies.

The study of thermal comfort was initiated by Kerka & Humphreys, cited in [39], who explored the relationship between odor and thermal conditions in indoor environments. Using sensory panels, their study measured the intensity of smoke and fume odors and found that odor strength diminished with increasing temperature for a constant partial vapor pressure and atmospheric humidity. This was followed by Woods, cited in [39], who established a linear relationship between air enthalpy and odor intensity, emphasizing the thermodynamic basis of indoor environmental perception.

Fanger [34] significantly advanced the field by defining thermal comfort as a state of equilibrium between material and energy. His work introduced the PMV model, which became the foundation for international standards such as ISO 7730 and ASHRAE Standard 55 [34]. Further advancing the field, Cain et al., cited in [40] investigated human adaptation to varying air components and found a general decrease in pollutant perception by approximately 2.5% per second until a threshold of 40% was reached. However, no significant differences were found in overall levels of perception, whereas Berglund & Cain [41] found that air acceptability remained stable across various humidity levels when the temperature was maintained at 24 °C during the initial hour of exposure.

Gunnarsen et al. [42] reported on adaptation effects, observing a diminished perception of odors over time. The ASHRAE [43] standard formalized thermal comfort parameters through a seven-point thermal sensitivity scale, incorporating metabolic rate and clothing insulation [43,44]. This comfort equation established a framework for identifying optimal conditions for varying activities and apparel combinations [45]. To strengthen the study, Brohus et al. [46] examined human airflow perception in stable thermal environments, whereas Fang et al. [39] demonstrated that initial air quality impressions significantly influence perception, even in pollutant-free environments. In the following decade, Hoes et al. [47] emphasized the critical role of occupant behavior as an inevitable aspect, influencing both comfort and energy efficiency, which was further supported by Fabi et al. [48], who highlighted the importance of window operation behaviors with occupants’ activities. Torabi Moghadam et al. [49] made a significant contribution by analyzing active and passive ventilation strategies, offering practical guidance for energy-efficient building design.

4.2. Coalition of Thermal Comfort with Window Behavior

Window behavior plays a crucial role in maintaining healthy thermal comfort in buildings, directly impacting the air change rate in indoor environments. Window behavior represents the behavior of occupants’ interaction with the window, including activities such as opening, closing, duration, and frequency of use. As illustrated in Figure 10, window behavior is strongly influenced by five primary determinants: environmental, contextual, physiological, psychological, and social factors. Environmental factors include meteorological variables such as relative humidity (RH), wind speed, rainfall, and solar radiation. Contextual factors encompass building features such as area, floor level, window orientation, and user type that affect window operation behavior. Physiological factors were associated with the comfort levels and health conditions that drive occupants’ choices regarding window use. Psychological factors pertain to individual preferences and behavioral habits that can influence how and when windows are operated. Social factors involve social norms and cultural influences that shape occupant behavior regarding window operation [25].

Numerous studies highlight that window operation, shading control, lighting adjustments, thermostat settings, and appliance usage, as part of window operation, have a direct impact on the air change rate in indoor environments [25]. As illustrated in Figure 10, Kim et al. [23] examined the nexus between indoor environmental factors (operative temperature and air velocity) and outdoor conditions (air temperature and wind gust intensity) regarding window-opening behavior. Their results showed that increased solar radiation elevated indoor wet-bulb globe temperatures, which subsequently influenced the probability of window-opening. Additionally, higher outdoor wind gusts produced improved indoor air velocity, thus reducing the likelihood of occupants choosing to open the window.

Furthermore, Yang et al. [5] developed a predictive model for window-opening behavior in residential buildings in China, employing multivariate analysis. They found that indoor temperature correlated positively with the window-opening, whereas indoor CO₂ concentration and outdoor RH were negatively interlinked [5]. Likewise, Fabi et al. [48] extended the investigation by demonstrating the dynamics of occupant behaviors linked to window operation and emphasized the importance of energy performance in residential buildings.

Underlying the dynamics of occupant behavior and building energy consumption, Torabi Moghadam et al. [49] analyzed active and passive scenarios, highlighting the importance of occupant engagement in the building control systems for energy outcomes. Similarly, Wang & Greenberg [24] examined the impact of window operations across natural ventilation, mixed-mode ventilation, and conventional Variable Air Volume (VAV) systems in a medium-sized office building. Their results, based on the adaptive comfort standard, confirmed potential HVAC energy savings of 17–47% by using mixed-mode ventilation during the summer months across four climate types (Mediterranean, Hot–Humid, Temperate, and Arid-Cold) in residential buildings. In a culturally specific context, Foruzanmehr [50] studied the loggia, a vernacular passive cooling system in Iranian architecture, through user perception. The findings highlight its strong effectiveness in facilitating passive ventilation and provide guidance for integrating traditional design practices into energy-efficient modern buildings [50]. In summary, combining passive strategies, mixed-mode ventilation, and dynamic shading with adaptive thermal comfort approaches can achieve energy savings of up to 47%, depending on the specific combination of design strategies employed.

Additionally, in Brazilian residential contexts, Sorgato et al. [51] evaluated the window-opening control and the thermal mass of buildings on HVAC energy consumption. The study summarized that buildings with moderate thermal inertia can achieve satisfactory user comfort, particularly when regulated ventilation control mechanisms are in place, thereby significantly curtailing HVAC energy requirements. Recent developments in thermal comfort research have integrated smart technologies into building systems. Suzuki et al. [52] demonstrated that electrochromic windows could reduce energy consumption by 6.1–8.6%, while Lee & Song [53] reported that smart window technologies can achieve energy savings of up to 20.5%. Fathi & Kavoosi [54] showed that integrating electrochromic windows with advanced glazing, building-integrated photovoltaics (BIPV), and building energy management systems (BEMS) could result in energy savings of up to 35.57%. Similarly, He et al. [55] found that smart window systems can achieve energy savings of up to 39%, underscoring the emergent importance of intelligent technologies for sustainable building design.

Recent findings suggest that specific factors, including building height, window area, floor level, occupant age, and behavior, have a significant influence on window-opening patterns. Al-Waheed Hawila et al. [25] identified 20 critical influencing drivers in window operation; however, many undefined factors remain in existing research regarding the relationships between these drivers and occupant behavior. Overall, as illustrated in Figure 10, window behavior is a complex, multi-factor phenomenon influenced by both exogenous and endogenous factors. The integration of advanced materials and intelligent technologies adds new dimensions to enhance thermal comfort, improving building energy performance through proper window operation. Meanwhile, occupants’ activities and behavior are integral to the window operation system.

4.3. Role of Occupants Behavior in Window Behavior and Design Strategies

Occupants and their activities have a stronger role in window-opening and closing behavior linked to building design and its energy performance. These activities have a significant impact on indoor environmental conditions, including thermal comfort, air quality, lighting, and noise, through the occupants’ control actions. Hoes et al. [47] explored the impact of occupant behavior on building performance and highlighted the importance of considering the dynamics of this behavior in the architectural building design process. Their study revealed that occupant activities can significantly impact environmental conditions within buildings and that user control actions are essential in maintaining comfort. Furthermore, simple design evaluations using numerical tools can provide valuable insights into how users interact with buildings. It is essential to understand the importance of accounting for both current and anticipated occupant behavior in simulation models to optimize overall building performance and project the thermal comfort level.

As illustrated in Figure 11, existing research has identified two main categories of drivers shaping occupant window-use patterns. Exogenous factors deal with outdoor environmental conditions and indoor air quality. Endogenous factors are associated with contextual elements, psychological influences, and personal preferences. These factors have a direct impact on occupant behavior and thermal comfort, influencing building energy performance with the use of appropriate building design strategies and materials to achieve sustainability. Underpinning this linkage, the previous studies demonstrated that outdoor temperature has a strong effect on occupant behavior on the frequency of window-openings [48,56,57]. Research showed that external temperature alone conveyed more than 70% of the variations in the number of ventilation and window opening, and 10% could be associated with wind speed [58]. Similarly, Wallace et al. [59], Herkel et al. [60]; Yun et al. [61] uncovered a strong seasonal effect impact on window opening and closing patterns.

Moreover, Ruan et al. [62] broadened the concept beyond the building level and investigated the role of occupant behavior for low-carbon-oriented community planning. It has been identified that the age of occupants may significantly affect dwelling time and air conditioner use. They highlighted that urban planning design parameters such as floor area ratio, building coverage ratio, and aspect ratio are essential to include in energy simulation models. The study also highlights that the building aspect ratio is more important than its height for space cooling and heating, with the optimal aspect ratio depending intensely on occupants’ characteristics and the type of HVAC system. Older occupants, who generally require more heating energy, are better suited to buildings with a lower aspect ratio. Likewise, communities with a district heating system and a decentralized cooling system need a lower aspect ratio than those with other types of HVAC systems. These findings, illustrated in Figure 12, offer valuable guidance for integrating occupant behavior into low-carbon residential community design.

The society is immensely integrated with the gizmo context, to visualize these impacts, Tang et al. [63] investigated the coordination between occupant behavior and energy-efficient technologies, introducing the concept of “technology-guided occupant behavior” to enhance building energy control systems. In a Hong Kong study, such guidance reduced central air-conditioning energy consumption by 23.5%, which accounts for the inconveniences to occupants due to the interference of these technologies. It has been demonstrated that technology is more effective in maintaining thermal comfort, which is closely tied to the occupant behavior. Similarly, Wang et al. [64] focused on Occupant-Centric Control (OCC) strategies for natural ventilation using AI-powered cameras and deep learning to capture real-time occupant profiles and window operations. Their findings showed heating energy savings of 0.6–29% and indoor comfort improvements of up to 58.8% compared to conventional strategies.

To advance new applications in the window operating system, window adjustment behavior (WAB) has been identified as a critical factor in predicting building energy consumption. Numerous studies have undertaken to develop a reliable WAB model that considers three key issues: reliance on an “average occupant” fashion, which overlooks variability of individual preferences, the need to assess how occupants respond to both environmental and non-environmental factors and the diversity of predictive modeling approaches, including probabilistic methods and machine learning techniques, each with distinct strengths and limitations in terms of adaptability, complexity, and explainability. In this context, Kim & Park [65] focused on explainable artificial intelligence (XAI) to quantitatively examine these issues. Their findings revealed that WAB was influenced by the following factors. First, different people have varying personal preferences for window operation, and employing a customized WAB model is preferable to a universal one. Second, the personal preferences of occupants on WAB cannot be labeled by a single environmental factor because WAB is responsive to environmental, psychological, social, and other undefined factors. Finally, the current complex black-box model can be further elaborated by applying XAI techniques to examine feature influence.

In addition, from an optimization perspective, Stavrakakis et al. [12] explored a computational method to optimize window design, achieving thermal comfort in naturally ventilated buildings. The employed methodology provides optimal window design guidelines for both single- and multiple activity levels. Similarly, from an architecture perspective, Barbosa et al. [66] studied semi-detached houses and showed that improving occupant behavior could lead to energy savings of 4–30%. Building simulations revealed that unoccupied rooms experienced a variation of up to 7% in discomfort hours, while lighting usage varied by approximately 4%. Solar orientation significantly impacted energy consumption patterns, with the north-facing orientation performing superiorly in 79% of cases evaluated, compared to south-facing orientations (21%).

Figure 12 illustrates that occupants’ activities have a stronger role in window behavior; however, as technology development and use of machine learning systems have enhanced WAB to improve thermal comfort. It has helped to improve building design perspective and strategy development for sustainability. Influencing policy frameworks and strategies is essential to align with the behavior of building occupants, which includes providing proper eco-feedback, facilitating social interaction, and incorporating gamification, whereas Paone & Bacher [67] emphasized that maintaining energy-efficient behavior without compromising the comfort of building occupants endures a challenge despite the development of new technologies and strategies. Therefore, understanding occupant-driven window operation and its impact on building energy savings is crucial for achieving holistic sustainability.

4.4. Impact of Window Behavior Building Energy Performance

Studies showed that the behavior of occupants can improve energy efficiency, and different design strategies can be used to enhance window behavior [67]. Hawila et al. [25] investigated occupants’ window-opening behavior in office buildings and found that the frequency of window-openings declined when outdoor temperatures exceeded 27 °C. In contrast, the probability of opening windows increases significantly at outdoor temperatures above 20 °C, while complementary studies employing artificial neural networks for natural ventilation control have indicated that the likelihood of window-opening becomes minimal when indoor temperatures drop below 24 °C. The preferred indoor temperature range for ventilation comfort in Japan was identified as 24–30 °C [68]. It has been demonstrated that window-opening behavior is strongly correlated with outdoor temperature to maintain indoor temperature, although it depends on the occupant’s preferences and climatic conditions.

Recent research highlights the substantial impact of occupant behavior on window operation patterns and overall building energy consumption across various climatic and building typologies. For instance, Yoon et al. [69] developed an innovative window scheduler algorithm that explored natural ventilation and thermal mass to optimize energy use and control smart home environments, demonstrating the ability to manage window operations and reduce energy consumption, maintaining indoor comfort. Similarly, El Khattabi et al. [70] analyzed the impact of shutter control in double-walled residential buildings using the NoMASS simulation framework. Their results showed that shutter operation reduced solar gains through windows by 13% and decreased occupant discomfort by 52% via enhanced natural ventilation. However, the NoMASS model elevated 65% of heating requirements compared to the Standard Fixed Scenario (SFS), exploring the trade-offs inherent in passive cooling strategies as illustrated in Figure 13.

Furthermore, the evolution of predictive analytics, employing deep neural networks, has enabled the forecasting of window-opening behavior and its impact on HVAC energy consumption. Engaging this model, Pandey & Dong [71] reported a 50% reduction in monthly cooling energy when the indoor setpoint temperature was increased from 20–27 °C in Sub-Saharan Africa. Similarly, their UK-based study achieved an accuracy of 77.8% in predicting window states. Furthermore, simulations conducted across five U.S. cities showed that mixed-mode ventilation strategies could achieve cooling energy savings ranging from 45 to 94%. Temperature thresholds have a significant influence on window operation patterns. It is also observed that window-openings become more frequent when outdoor temperatures exceed 12 °C, with prolonged openings above 28 °C. The probability of a bedroom window-openings crossing the Behavioral Likelihood Ratio (BLR) threshold of 0.50 lies within the 13–19 °C range, whereas living room windows reach this threshold between 26 °C and 27 °C. It states that predictive models are useful in different climatic contexts to visualize energy performance and thermal comfort.

Whereas, in the educational building of Budapest, Belafi et al. [72] found that window-opening behavior was driven more by occupant activities rather than environmental parameters. Their study highlighted that incorporating dynamic occupant behavior simulations and adopting behavioral profiles could reduce ventilation energy consumption by up to 26%. Furthermore, Naspi et al. [73] analyzed the probability of window-opening relative to indoor temperatures. While comparing standard versus behavioral ventilation patterns, the discrepancy was notably higher in north-facing oriented classrooms. Employing behavioral profiles has resulted in 28% energy savings through improved modeling. Roccotelli et al. [74] evaluated stochastic occupant behavior for building energy management, highlighting that passive strategies such as natural ventilation and solar shading could reduce energy consumption by an average of 42%. Additionally, Tien et al. [75] employed a deep learning approach for real-time occupancy and window activity detection, achieving 85.63% accuracy in occupant activity recognition and 92.20% for window operation detection. Their findings indicated potential HVAC energy savings of 2–6% through optimized occupancy and window management.

In addition, the comparative analyses between green-rated and non-rated buildings revealed that occupant behavior exerts a more pronounced impact on energy consumption in non-green buildings. In this concern, Almeida et al. [76] observed that occupants in non-rated buildings had an 11% higher impact on cooling energy use compared to occupants in green-rated counterparts. Occupants in non-rated buildings also exhibited a higher propensity to open and close windows—36% versus 51% in green buildings—contributing to an overall 6% higher energy consumption attributable to occupant behavior as shown in Figure 13.

Furthermore, Eco-feedback programs have been enhanced as behavioral interventions as a soft strategy. Paone & Bacher [67] reported energy savings of 7–15% as illustrated in Figure 13. Eco-feedback is an effective way to influence behavior, and gamification presents a new prospect of eliciting behavior change. The factors affecting human behavior are numerous, and multi-fold approaches are needed to provide new insights into the activity dynamics of occupants’ energy behavior. In addition, Paleni et al. [77] emphasized the importance of different occupant behavior groups based on socio-demographic and economic characteristics and their energy-use profiles to control and monitor building energy consumption, such as gender, age, and education, which also impact efficient behavior as a process. Eco-feedback is an effective way to influence behavior, and gamification presents a new prospect of eliciting behavioral change. The factors affecting human behavior are numerous, and multi-fold approaches are needed to provide new insights into the activity dynamics of occupants’ energy behavior. In addition, Paleni et al. [77] emphasized the importance of different occupant behavior groups based on socio-demographic and economic characteristics and their energy-use profiles to control and monitor building energy consumption, such as gender, age, and education, which also impact efficient behavior as a process. The incentives strategy is essential and effective in encouraging occupants to adopt energy-saving behaviors.

As illustrated in Figure 13, technology-guided and AI building modeling can improve energy performance up to 23–58%. Using different building design strategies (window optimization, double-glazed windows, mixed-mode ventilation, etc.) can improve energy savings by up to 30–70%. Currently, reviews claim to have insights into the multifaceted nature of occupant interactions with window, and their impact on building energy performance. It requires extensive research to adopt a holistic approach that addresses existing knowledge gaps and comprehensively examines the diverse range of factors interacting in different climatic contexts, influencing window operation and occupant behavior, ultimately leading to sustainable solutions.

4.5. Window Design Strategies and Thermal Comfort in Different Climatic Contexts Approaching Sustainability

Thermal comfort design strategies may vary depending on different climatic contexts and have differing impacts on a building’s energy performance. The evaluation and effectiveness of different bioclimatic design strategies on building energy performance in various climatic contexts, as well as their sustainability, are important to understand, especially in light of the crucial role of occupant behavior in window operation. This understanding is crucial for maximizing energy efficiency and promoting sustainability. In this review study, four major climatic zones—Mediterranean, Hot–Humid, Temperate and Arid—and cold regions are prioritized for a window design strategies study applied for sustainability thought.

In the Mediterranean climate, Elaouzy & EI Fadar [78] demonstrated that an increased WWR leads to a lower annual heating requirement but also a higher cooling demand due to increased solar heat gain. However, a more extended overhang depth can reduce the cooling load. The study also revealed that cooling loads increase and heating loads decrease with the augmentation of the solar heat gain coefficient (SHGC). Similarly, windows with a high SHGC are recommended for areas with a cold climate. The most effective strategy for increasing thermal performance in a building is the addition of thermal insulation. The study suggested that initiating new regulations and encouraging programs that provide efficient and advanced materials at lower prices can motivate occupants to adopt bio-climatic designs with a shorter payback period. This contributes to improving sustainability in mitigating climate change, resulting in significant cost savings.

Hany & Alaa [79] examined bioclimatic strategies in Alexandria, Egypt, focusing on architectural elements like low-pitched roofs and chimney vents, which improved thermal comfort by up to 13% in winter and 12.6% in summer, as illustrated in

Table 3. Window Design Strategies in different climatic contexts for Sustainability.

Climatic Context	Design Strategies Approach	Sustainability Concept
Mediterranean	The higher the WWR, the lower the annual heating requirements. Thermal insulation increases thermal preferences.
	Low-pitched roofs and top chimney elements can achieve reductions of 12.6% and 5% in summer, and 13% and 6.8% in winter.	Socio-economic impact study Bioclimatic design.
Hot–Humid	Window SHGC, window—to—ground ratio, external objective angle, and overhang projection—influencing factors.	Passive design approach
	Window-opening percentage—proper thermal comfort. WWR varies based on climatic conditions as design goals. Horizontal shading is more efficient than vertical louvres (South Direction). The optimal window-opening percentage for thermal comfort is around 0.2% (PPD).	WWR values can serve as guidelines for energy-efficient design.
Temperate and Arid	Balancing solar transmittance with advanced shading and glazing technologies is crucial for optimizing energy efficiency and comfort in future building design. Xenon is suitable for window insulation. City Information Model (CIM) can enhance urban planning in a holistic approach.	Multi-objective design focuses on optimizing a range of building parameters.
Cold Regions	Insulated walls, roofs, and building envelopes.	Local materials

. The results revealed that improvement on the passive design for cooling is more important compared to heating which allowed wind flow for maximum natural ventilation using chimneys and other similar elements. Similarly, the use of ventilated pitched roof spaces is more beneficial for cooling the house. The sun shading elements, such as pergolas and projected porches, in the proper facades and angles can take advantage of the sun protection elements as well as the architectural esthetics, preferably moveable and interchangeable shading elements to function in both summer and winter. The research also suggested that exploring the socio-economic impacts of implementing bioclimatic designs and investigating their scalability in urban contexts is crucial, as illustrated in Table 3 [79]. It is suggested that the socio-economic impacts on bioclimatic design implementation and the investigation of their scalability in urban contexts require further exploration.

In a hot–humid climate, Chen et al. [80] conducted a parametric study of passive design strategies in high-rise residential buildings, incorporating sensitivity analysis to consider multiple indoor environmental indices and impact factors related to natural ventilation. The results showed that the window SHGC, window-to-ground ratio, external obstruction angle, and overhang projection fraction are the most influential factors over the indoor illuminance level, operative temperature, humidity ratio, and stable comfort. Similarly, Alibaba [81] focused on optimizing the WWR and window-opening percentages for a naturally ventilated room in Famagusta, Cyprus. The study revealed that horizontal shading is more effective than vertical shading in reducing energy consumption and providing better solar radiation, especially during the summer months. The most optimal window-opening percentage for thermal comfort is approximately 20%. Both studies suggest that combining WWR, shading devices, and natural ventilation strategies is essential for designing energy-efficient buildings in hot–humid climates.

For temperate and arid zones, Miao et al. [82] revealed that Sustainable Architecture for Future Climates in Multi-Objective Design focuses on optimizing the parameters of building elements. The parameters, including cooling and heating setpoints, air change rates, shading device depths, window visible transmittance, and window types, are considered to balance energy consumption and comfort. These parameters are based on simulations using future weather data for the years 2020, 2050, and 2080. The results demonstrated a discernible trend towards increased cooling energy consumption and discomfort hours, likely due to rising temperatures, underscoring the need for climate-responsive building designs and efficient cooling systems. At the same time, the heat energy pattern suggests the need for adaptable heating solutions. It emphasized the importance of sustainable and resilient architectural practices that prioritize both energy efficiency and occupant comfort, responsive to climatic conditions. Balancing solar transmittance with advanced shading and glazing technologies is crucial in optimizing energy efficiency and comfort within a building.

As shown in Table 3, Harkouss et al. [83] explored the passive design optimization across in different cold, temperate, and hot climates through an implementation approach in four phases of building energy simulation; optimization; Multi-Criteria Decision Making (MCDM); sensitivity study; and finally, an adaptive comfort analysis. Their results indicated potential energy savings of up to 54% in cooling, 87% in heating, and 52% in lifecycle costs through optimized building envelope. Notably, severely cold regions require high insulation (walls, roofs, ground U-values ~0.2 W/m²K), while hot climates benefit from moderate insulation levels. Mixed climates require balanced insulation, allowing ground heat evacuation in summer. Kim et al. [84] outlined the optimized thermal comfort and sustainability approach through passive cooling and eco-friendly materials for indoor temperature reduction. Wood and red clay brick as building materials in building construction have the potential to mitigate the temperature effect with the provision of natural ventilation. The time it took for the wood to reach 26 °C from the initial experiment temperature of 30.7 °C was 9 h, while the time for the red clay brick was 8 h. Similarly, use of a lower window ventilation system resulted in a slower air exchange rate compared to a standard side window, indicating that both size and placement of the window play critical roles in improving effective natural ventilation.

New technologies are also shaping sustainable building design. Nguyen et al. [85] evaluated algae-based window systems, which can reduce energy costs by up to 12% compared to single glazing and mitigate up to 20% of overall energy consumption in buildings, such as the World Trade Center skyscraper. Public awareness and effective policies are crucial for the adoption of such innovation. Nadarajan & Kirubakaran [86] explored the potential of sustainable building materials to enhance thermal comfort in Indian residential buildings, with a focus on stabilized mud block-walled construction. Their study highlighted that mud block walls effectively minimize heat conductivity from the external environment to the interior, enabling self-cooling properties in buildings. The study employed CFD analysis in ANSYS to assess the suitability of various sustainable materials for walls, roofs, floors, and other building components to enhance comfort in housing and promote sustainable development in the rural building sector. A comparative analysis of model rural residential houses revealed that a home with burnt brick walls and another with mud block walls exhibited an average air temperature difference of 0.7 °C, a maximum local temperature difference of 6 °C, and an average wall temperature difference of 3 °C.

From another standpoint, Ebuy et al. [87] reviewed sustainability and highlighted the importance of life-cycle analysis from the construction to the design stage and energy consumption patterns. Menezes et al. [88] and Ledo et al. [89] found that discrepancies between actual and expected energy consumption in functional buildings, such as schools, offices, and universities, ranged from 60 to 85%. From the user-centered perspective, prior studies have consistently emphasized the significant influence of occupant behavior on building energy performance. Yan et al. [90] noted that occupant behavior can lead to differences in building energy performance as high as 300%. Similarly, Sonderegger et al. [91] cautioned that oversimplifying occupant behavior models can cause discrepancies in energy demand of up to 70%. Further, Erickson et al. [92] highlighted that incorporating real-time occupancy data into HVAC work schedules can yield annual energy savings of 42%. Yousefi et al. [93] observed that variations in window types could lead to differences of up to 20% in residential heating and cooling demands. To further illustrate the role of occupants, Gaetani et al. [94] demonstrated that refining occupant behavior models improved total electricity consumption predictions, reducing the average deviation from measurements from 22.9% to 1.7% between ideal and worst-case scenarios. Similarly, Klein et al. [95] highlighted that integrating modeling and simulation tools for occupant-driven controls—such as lighting, window, shading, and temperature set points- can enhance occupant comfort by approximately 5%.

Gladdys et al. [96] investigated the sustainable strategies for humanitarian housing in the Democratic Republic of the Congo and the Republic of Burundi, focusing on the role of material properties and design parameters in enhancing building energy performance. The study identified wall material reflectance and window-to-floor ratio (WFR) as critical factors influencing both daylight availability and thermal comfort. Findings revealed that a WFR of 20%, when coupled with properly oriented windows, facilitated uniform daylight distribution and controlled glare. The prototype shelter showed significant improvements in performance, including a 52% enhancement in material efficiency, a 37.15% increase in energy efficiency, and reduced CO₂ emissions (0.04 tons/month/house). Moreover, adobe walls contributed to a 20–24% improvement in thermal comfort during peak heat hours, while reflective materials and roof overhangs maintained indoor temperatures within the optimal range of 20–25 °C. The study underscored the importance of prioritizing locally available materials to ensure both environmental sustainability and contextual appropriateness.

Yousuf & Taleb [97] conducted a simulation-based study on UAE residential buildings, showing that the use of double and triple glazing significantly improved building performance. Double glazing reduced cooling loads by 8.63%, while triple glazing achieved a reduction of 13.22%, and solar heat gained by 16.7% and 28.3%, respectively. These measures also resulted in a 5.1% and 7.7% decrease in electricity consumption. Furthermore, the study emphasized compliance with the Pearl Rating System, which requires a minimum daylight factor of 2% or 200 lx for 20% of living space. In related work, Tzempelikos & Athienitis [98] demonstrated that shading devices can achieve up to a total of 50% energy savings, while preventing at least 12% of solar and heat gain Palmero-Marrero & Oliveira [99] further specified that vertical louvers are more effective for east and west façades, while horizontal louvers provide optimal performance on south façades. Further specified that vertical louvers are more effective for east and west façades, while horizontal louvers provide optimal performance on south façades. Ebrahimpour & Maerefat [100] demonstrated that external shading devices, such as overhangs and side fins, outperform advanced glazing solutions (e.g., double clear pane or low-E pane) for window in all orientations. Complementing these findings, Lai & Wang [101] and Zhu [102] highlighted that the integration of improved wall insulation and window shading could yield energy savings of approximately 11.31 to 11.55% in air-conditioned buildings. In addition to glazing and shading strategies, natural ventilation has been explored as a sustainable design approach. Cuce et al. [103] analyzed sustainable ventilation strategies in Chinese university buildings using CFD. The study found that natural ventilation could reduce annual cooling and heating energy use by up to 55% compared to mechanically ventilated buildings. Improved ventilation also boosted annual productivity by 3–18%. Even without solar chimneys, cross-ventilation via windward and leeward window provided effective airflow. However, the study also noted that passive cooling strategies may be less suitable in extreme climates or polluted environments, where hybrid ventilation systems—combining natural and mechanical methods—offer a more reliable solution. The study also acknowledged a key limitation of CFD models, as it does not account for conduction and radiation, limiting accurate simulation of combined ventilation effects.

Furthermore, Chen et al. [104] investigated the potential of natural ventilation for improving indoor thermal comfort and energy efficiency across various climatic contexts. Using Building Energy Simulation (BES) across 1854 locations in the 60 largest cities worldwide, they found that subtropical highland climates—such as in South-Central Mexico, Southwest China, and the Ethiopian Highlands—are most suitable for natural ventilation due to stable, mild weather. Mediterranean climates also showed good natural ventilation potential, while desert regions benefited from night-purge ventilation. In contrast, hot–humid regions like Singapore and Malaysia offered little to no natural ventilation potential. These findings provide a framework for developing climate-specific ventilation strategies. Building on this, Sokar et al. [105] examined natural ventilation and IEQ in hot semi-arid climates. The study highlighted the crucial role of dehumidification when humidity levels exceeded 70%, particularly during cool mornings. Results indicated that maintaining indoor humidity within the range of 30–70% and temperatures between 18 and 28 °C or 20 and 26 °C reduced annual thermal loads to about 22.3 kWh/m², compared to 56 kWh/m² in uncontrolled environments. Furthermore, the study emphasized the significant impact of occupant behavior on both IEQ and energy consumption, underscoring the need to incorporate user-centered parameters into climate-responsive ventilation strategies.

4.6. Comprehensive Design Matrix: Linking Window Behavior Determinants to Climate-Specific Strategies

Previous studies have reflected a rigorous examination of five key determinants and their impact on window behavior. These influencing determinants lead to the development of practical applications for window design, considering five key aspects: building design, planning, user-centric concept, personalized concept, and behavior-based policy interventions, as summarized in

Table 4. Studies addressing determinants influencing window behavior.

Determinants	Representative Studies	Practical Applications and Implications
Environmental	Thermal comfort linked to odor and fume (Kerka & Humphreys, cited in [39]); Temperature and air quality (Gunnarsen et al. [42]); Combined environmental variables (Houghten & Yaglou, as cited in [26,27]; Bedford as cited in [29]; Winslow et al. [30]); Indoor–outdoor environmental nexus (Kim et al. [23]); Natural ventilation reducing energy use (Chen et al. [80]; Wang et al. [64]); Humidity–temperature effects (Cain et al. as cited in [40]; Berglund & Cain [41]); Indoor air quality–window operation linkage (Yang et al. [106]).	Window sizing, glazing type, shading strategies, and automated window systems based on climate.
Contextual	Active vs. passive scenarios (Torabi Moghadam et al. [49]; Suzuki et al. [52]); Occupant behavior affecting thermal comfort and efficiency (Hoes et al. [47]); Function–wind relationship (Hawila et al. [25]); Occupant behavior influencing both comfort and energy (Tang et al. [63]; Fabi et al. [48]); Design parameters and materials (Ruan et al. [62]; Muroni et al. [94]; Sonderegger et al. [91]; Nguyen et al. [85]).	Passive Design Planning: Orientation, WWR, natural ventilation, and daylighting.
Psychological	Human perception in controlled environments (Brohus et al. as cited in [46]); Air quality perception (Fang et al. [39]); Window adjustment behavior (Kim & Park [65]); Subjective perceptions of energy (Nahmens et al. [56]); Anticipated future user behavior (Hoes et al. [47]).	Occupant-Centric Design: Catering to a user’s perceived needs rather than just objective measurements.
Physiological	Combined metabolic rate, clothing, and environment (McArdle et al., cited in [32]); Qualitative interpretations of behavior (Paone & Bacher [67]); Climate–culture–behavior link and PMV discomfort overestimation (de Dear & Brager [35]); Personalization with real-time data integration (Havenith et al. [37]; Rupp et al. [38]).	Personalized Solutions: Implementing thermal comfort systems that use real-time physiological data (e.g., wearables) to adjust micro-environments.
Social	Social parameters, interactions, and activities (Day et al. [107]).	Policy Interventions: Develop educational campaigns and policies to promote collective behaviors in shared spaces, enhancing energy efficiency and comfort.

. Policy interventions may include the educational system in an incentive-based system.

As illustrated in Figure 14, the five determinants (environmental, contextual, Physiological, psychological, and social) play a crucial role in developing design strategy solutions that yield healthy thermal comfort, higher building energy performance, and sustainability. Window design should be adapted to the specific requirements of different climatic zones, as each climate presents distinct challenges to achieving thermal comfort and minimizing energy consumption. Previous studies have explored a series of environmental determinants, and based on these findings, six major design solutions can be applied in different climatic zones. These solutions are particularly focused on environmental factors, with actual calculations of factors using current modern technology and numerical tools to achieve sustainability.

Design strategies and parameters like thermal mass, automated ventilation, algae-based window systems, mixed-mode ventilation, galvanizing technology, shading devices, passive design, WWR, window adjustment behavior, incentives as behavior transformation, and smart technology can serve as window design strategies. These solutions aim to achieve thermal comfort and promote sustainable building practices. As shown in Figure 14, these approaches can be applied across various climatic conditions, depending on their unique challenges and local resources. Additionally, these strategies can be enhanced through advanced AI and numerous numerical tools and simulations, as depicted in the figure.

For example, in a Mediterranean climate, moderate WWR can be used to balance winter solar gain with summer overhangs, as described in Section 4.5. At the same time, other factors, such as social and cultural considerations, support the use of shutters and balconies as contextually appropriate sun control solutions. Similarly, in a hot–humid climate, maximum natural ventilation can be achieved through operable window-openings to capture prevailing winds for cross ventilation, using overhangs to reduce solar radiation. Physiological adaptation may require frequent opportunities for high ventilation. Psychological factors (such as preference for daylight, but also the need to control glare) influence shading needs. Social factors (like privacy and personal boundaries) often call for lattice screens and shading devices. In cold climates, a large south-facing window is preferred to maximize passive solar gain. High-performance double or triple glazing with low U-values, combined with insulated shutters, helps maintain comfortable indoor temperatures in an energy-efficient building. Physiological factors make solar gain a priority, while psychological comfort requires access to winter daylight for good feelings and mood. Social factors, such as long indoor stays during winter, reinforce the importance of ample daylight and views. In a temperate climate, a proper balance of WWR, combined with a south-facing window with adjustable shading to allow winter solar gains and reduce summer overheating, is a preferred design solution. The primary window design parameters should include moderate U-value glazing, medium SHGC, flexible shading devices, and an operable air-tight window. In a temperate and arid climate, physiological comfort is improved by adaptive features such as manual control of shading and ventilation. Psychological factors, like a connection to greenery and seasonal daylight changes, align with biophilic window placement. Social practices, including regular seasonal use of outdoor and indoor spaces, support operability and transitional window systems.

Across all climatic zones, window design must consider environmental and contextual factors, as well as human-centered physiological, psychological, and social needs. The use of advanced technologies, including smart glazing, automated shading, and AI-driven control systems—combined with occupant engagement and behavioral adaptation—ensures that window systems effectively support thermal comfort, energy efficiency, and sustainable building outcomes.

5. Existing Research Trends and Methodological Limitations (Gaps)

As outlined in Section 2, a refined dataset of 112 studies formed the basis for analyzing methodological limitations and research gaps in the field of window technologies and occupant–window behavior. Building upon the findings presented in Section 4, this review highlights key research trends and persistent shortcomings. Although bibliometric and scientometric analyses indicate a rapidly expanding research field with increasing interdisciplinary collaboration, several imbalances and omissions constrain the generalizability and applicability of existing findings. The analysis of these 112 studies reveals distinct patterns in research approaches, limited climatic and contextual diversity, and insufficient focus on demographic and spatial considerations.

Approximately 32.14% of the reviewed studies relied solely on simulation tools such as EnergyPlus, CFD, and DesignBuilder to assess window performance and thermal comfort, while only 16.96% combined simulations with experiments. Additionally, 21.42% of studies accounted for algorithm-based approaches. This distribution reveals a clear imbalance, with most research relying on simulated or model-driven data. Although simulations and algorithms provide valuable control over variables and experimental flexibility, they often fail to capture the complexity and variability of real-world occupant–window interactions. The comparatively lower proportion of exclusive experimental studies (18.33%) and hybrid approaches highlights the need for more field-based validation and integration of empirical data into models to improve reliability and real-world applicability.

Out of all studies, 43.75% incorporated empirical behavioral data, yet only 3.57% combined such data with statistical prediction or analysis methods. This limited integration of behavioral insights into predictive models contributes to the persistent gap between modeled and observed window operation patterns.

Climate diversity represents another significant research gap, with fewer than 9.82% of studies validating findings across multiple climatic zones. While few studies focus on specific climates, such as hot–humid (e.g., Hong Kong and Sudan), or temperate regions (e.g., Romania and China), cross-climatic validation is rare. This limitation is also reflected in collaboration networks, showing minimal co-authorship between researchers working in different climatic regions, hindering the development of globally applicable window solutions.

Demographic variables, particularly gender, are significantly underexplored. Only 3.57% of research addresses gender-specific factors, despite well-documented differences in thermal comfort preferences—for instance, women generally prefer indoor temperatures 1–2 °C warmer than men. Similarly, the neurobehavioral approach emphasizes that productivity within indoor environments is influenced by occupant behavior patterns, and cognitive functions—a mental operation related to perception and thinking. These intangible factors, such as psychological milieus—perception, learning & thinking, and expression—play a crucial role in creating effective and productive environments and are indirectly affected by indoor thermal comfort [106,108,109,110,111]. In contrast, spatial-functional differentiation remains largely overlooked. Only 12.5% of studies specifically analyze rooms with unique thermal and ventilation demands, such as kitchens or utility areas. This oversight restricts the practical applicability of current models, particularly in multifunctional or high-load thermal zones.

Table 5. Quantified research gaps in thermal comfort and window behavior through bibliometric indicators.

Gap Category	Percentage of Studies Addressed (%)	Exemplar Studies
Simulation methods	32.14	[1,8,10,12,13,14,17,19,24,25,36,37,47,51,52,54,62,68,69,70,78,79,80,81,82,83,84,85,86,96,97,98,99,100,101,104,105]
Behavioral data incorporated with models	3.57	[5,35,56,72]
Multi-climate validation	9.82	[9,27,35,36,38,52,70,78,83,93,104]
Gender-specific analysis	3.57	[42,108,109,111]
Room-specific design	12.5	[3,7,11,12,14,15,16,71,81,82,83,86,105,112]

illustrates a quantitative overview of these methodological and contextual research gaps. It categorizes the gap into five domains: simulation reliance, behavioral data integration, climate diversity, gender-specific analysis, and room-specific design. Each category includes the percentage of studies addressing the gap and exemplar studies.

6. Research Gap, Limitations, and Future Direction

The previous section established the important relationship between thermal comfort and window behavior for sustainability from an occupant’s behavior perspective. It has established a comprehensive window design matrix influenced by determinants, aided by technological development, in four major climatic zones, to achieve indoor thermal comfort and sustainability. However, the current research in thermal comfort is extensive; it often lacks sufficient depth in specific domains. Additionally, this research has limitations in its database scope, as it excludes conference papers, theses, and dissertations, and therefore cannot capture the entire gray literature and non-English publications. The study has focused on only three major areas—window behavior, thermal comfort, and sustainability, with limited keywords. However, window behavior has a broad linkage with other arenas.

To address this gap and its limitations, this review paper proposes the possibility of future research expansion into specific sub-themes, including energy, indoor air quality, technology, and economic perspectives on window behavior, to enhance sustainability.

Table 6. Research Stream with Sub-theme and existing gaps in research.

Theme	Sub-Theme	Gaps and Future Research
Thermal Comfort	Energy perspective Indoor air quality	- Health perspective. - Function-based study, e.g., maternity hospital. - Geographical/climate-based study - Clean energy perspective. - Technology and occupant behavior integration - Big data analysis to guide the occupant’s behavior.
Window Behavior	Technology-linked design	- Occupancy-based activity. - Material/technology-driven design. - Window orientation based on different climates. - Culture, values, and norms perspective. - Behavioral perspective. - Context-based study. - Real-time behavior and more advanced machine-learning algorithm control strategies - Conflicts and complexities between building automation systems and multiple user preferences
Comfort and Sustainability	Economic Perspective	- Gender - Climate diversity. - Age group study - Linkage of AI and real-activity study.

provides a structured overview of key research themes, associated sub-themes, and existing gaps. It highlights that while energy and air quality perspectives have gained attention, the health dimension remains underexplored, particularly in sensitive environments such as hospitals. For instance, Niu et al. [112] demonstrated that window operation significantly impacts both energy consumption and airborne infection rates in a maternity hospital. This underscores the critical need to investigate window behavior not only from an energy standpoint but also from a functional and health-related perspective. In the context of window behavior, most studies have focused on predictive modeling based on factor analysis. However, a quantitative, standardized approach remains lacking. The research objectives—whether targeting “window-opening behavior” or “window status”—are often inconsistently defined, leading to confusion and limited comparability across studies.

Furthermore, studies rarely explore how technology-linked design can be adapted to cultural context, spatial orientation, or behavioral diversity. Integrating tools like artificial neural networks (ANN) can offer promising pathways to simulate real-time behavioral dynamics and inform algorithm-driven adaptive strategies. Moreover, integrating thermal comfort with economic and sustainability perspectives requires greater attention to socio-demographic variables, such as gender, age, and geography. These aspects remain underrepresented in existing literature, as do social-behavioral dynamics, which are crucial to understanding how building design, occupancy patterns, and user interactions collectively shape IEQ [107].

In summary, future research should adopt an interdisciplinary and context-sensitive approach, integrating technological innovations, AI applications, and social behavior modeling. Such integration will be essential for addressing the knowledge gaps identified in Table 5 and Table 6 and advancing a comprehensive understanding of the role of window behavior in sustainable and health-oriented building design, thereby contributing to the achievement of the SDGs.

7. Conclusions

Thermal comfort is a long-standing research topic. However, its association with occupant behavior and window configuration has received comparatively less attention in a synthesized manner. This scoping review addresses this gap by critically examining the role of window design strategies in bridging theoretical models with practical applications across diverse climatic contexts. This review provides future research directions for integrating green building policy guidelines, developing healthy building policies, and achieving energy-efficient buildings to achieve overarching sustainable development goals.

Window design strategies are shaped by multiple determinants, including environmental, contextual, psychological, physiological, and social factors. Recent advancements in AI and simulation tools have enhanced the accuracy of predicting occupant behavior and energy consumption, thereby contributing to sustainable building practices. The design approach is crucial in maintaining thermal comfort and determining the optimal placement of the window. For example, semi-detached homes with improved occupant behavior patterns demonstrate potential energy savings of 4–30%. Solar orientation exerts a significant influence on energy use across climate zones.

Several design elements—such as thermal mass, ventilation, mixed-mode systems, algae technology, advanced glazing (double or triple), and shading devices—substantially affect thermal comfort and overall building performance. Among these, shading systems play a central role. Evidence suggests that horizontal shading is more effective than vertical shading for reducing energy use and controlling solar radiation, particularly during summer months. Moreover, the optimal window-opening percentage for ensuring thermal comfort is approximately 20%. Studies emphasize that integrating WWR, shading devices, and natural ventilation is crucial for developing energy-efficient buildings that are suitable for multiple climates. Nonetheless, the performance of shading devices remains highly dependent on local climatic conditions, as elaborated in Section 4.5. Passive ventilation is highly recommended for combining traditional design principles with modern energy efficiency goals. Balancing solar transmittance with advanced shading and glazing technologies is vital for achieving both energy efficiency and occupant comfort. Furthermore, automated smart systems have proven to be more effective than manual strategies in regulating indoor thermal and visual comfort. Despite these technological advancements, window behavior remains strongly influenced by factors such as room height, as well as occupant characteristics, including gender, age, and activity level.

At the macro scale, urban planning and settlement design parameters also play a crucial role in advancing the concept of zero-carbon cities. Factors such as floor area ratio, building coverage ratio, and aspect ratio should be incorporated into energy simulation models to ensure comprehensive design strategies. Notably, building aspect ratio has a greater influence on space cooling and heating than building height, with optimal ratios depending largely on occupants’ characteristics and the type of HVAC system in use. Age-related differences are particularly significant; for instance, older occupants typically require more heating energy and benefit from buildings with lower aspect ratios. Consequently, building morphology and window design strategies are essential for enhancing building energy performance. The inclusion of behavioral models into bioclimatic design further enhances thermal comfort while supporting long-term sustainability.

The findings of this review also indicate that window and adaptive behavior (WAB) are extensively determined by personal preferences rather than a single environmental factor. WAB is shaped by a complex interplay of environmental, psychological, social, and undefined influences, making the adoption of customized WAB models preferable to universal frameworks. The application of XAI presents a promising pathway to understand the impact of these multifaceted determinants on window behavior. Incorporating AI and machine learning is therefore expected to advance adaptive window control strategies, supporting the development of innovative and energy-efficient building environments.

Finally, the study suggests that implementing new regulations and incentive programs, particularly those that promote the use of advanced materials at lower costs, can encourage the widespread adoption of bioclimatic design. Such measures would not only reduce payback periods for energy-efficient interventions but also contribute to national sustainability goals by mitigating the impacts of climate change and achieving significant cost savings.