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Housing energy efficiency has a critical role in greenhouse gas and carbon emission mitigation. Previous studies have seldom considered the interrelationships among the different architectural aspects, i.e. human, building, environment, and climate in impacting housing energy efficiency. Given this gap, a literature review was conducted to explain the conceptual platform of the study. Then detailed cross-comparisons among five recent studies were made to develop the variable setting of the study. The study then addressed the implications of artificial intelligence in a multi-criteria assessment of housing energy efficiency. In conclusion, the overall improvement of housing energy consumption depends on a balanced interplay between the different architectural aspects, with an especial emphasis on the role of occupants’ socio-demographic characteristics. To achieve this goal it is necessary to develop a smart multi-criteria energy efficiency assessment tool, which also assists in putting passive climatic principles in practice, and in reducing fossil fuel dependency.
Department of Architecture, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Elyas Jahanshahi
Department of Architecture, Mashhad Branch, Islamic Azad University, Mashhad, Iran
*Address all correspondence to: sja.1075@gmail.com
1. Introduction
Energy efficiency is a key concept in mitigating climate change and global warming. Among different industries, the housing sector has a significant share of energy consumption.
In some developed countries, e.g. USA, UK, and Australia, the government and housing authorities aim for zero-carbon housing. To achieve this goal, a variety of sustainability action plans, e.g. building codes, measures, regulations, technologies, and initiatives, have been developed (e.g. [1, 2, 3, 4, 5, 6]). The implementation of such action plans has significantly impacted the housing market and the perceptions of buyers in these countries, and hence has significantly impacted the attention of different stakeholders, e.g. architects, designers, and builders towards the running costs and environmental impacts of housing energy consumption.
One critical challenge, however, in improving the EE is the lack of proper attention to the impacts of the interplay between human factors, building characteristics, environmental dimensions, and climatic information, which may even result in relatively higher energy consumption of a residential building, even though it may built based on sustainability codes and principles [7, 8, 9, 10].
A recent study indicated that providing comprehensive sustainability information plays a crucial role for promoting sustainable housing [11]. The provision of comprehensive information improves public knowledge and awareness about the different aspects of sustainability, thereby has the potential to encourage people’s positive perceptions, attitudes, and behaviours [10]. EE at the building scale may not result in EE during the operation phase because of human behaviours, which are influenced by socio-demographic characteristics and neighbourhood environmental conditions [8, 9]. Gower [8] indicated that EE at the building scale may not adequately address occupants’ demands and desires at the unit scale, potentially may worsening their energy vulnerability. She believed that this happened because of their socio-demographic characteristics and the impact of some secondary environmental factors, and therefore, it is highly important to proactively alleviate energy vulnerability. Some studies [12, 13] have recently developed multi-criteria housing energy-efficiency (HEE) assessment platforms. The main gap in such research is the lack of attention to the human aspect of the equation and the substantial roles of people’s perceptions and attitudes reflected in their socio-demographic characteristics.
The dispersion of housing sustainability information and the lack of proper attention to the interplay between the different architectural aspects, especially in relation to human factors, detract from the outcomes of the implemented EE action plans. This would in turn prevent occupants from meeting their needs rooted in their perceptions and attitudes, which would also negatively impact their energy consumption behaviours.
Affluent people are urban populations with more energy consumption and heat generation than vulnerable low-income people regardless of the differences in urban environmental conditions and housing quality of these two population groups [9]. Gower [8] indicated that although the currently implemented energy efficiency codes provide EE at building scale, the energy performance at the unit scale might be differ during the operation phase due to the differences in occupants’ socio-demographic characteristics and urban environment dimensions (e.g. orientation, density, sun radiation, wind direction, vegetation). Such conditions eventually impact the opportunity for proper utilisation of passive climatic principles and the dependency on active energy solutions, which can compromise the health and well-being of vulnerable occupants [8].
The currently implemented measures have been more applicable and beneficial for affluent people, whereas vulnerable people who are compromised by the impacts of environmental challenges and experience higher discomfort and health-related problems from environmental pollution and UHI are at high-risk groups [8]. The conducted studies revealed that poor building condition is highly critical in increasing energy consumption and heat transfer of housing, and good design quality with the emphasis on the passive climatic principles is more impactful in dealing with these deficiencies than using energy-efficient technologies [7, 8]. Accordingly, good design quality is a highly important factor in dealing with energy vulnerability and in moving towards energy justice [8]. Good design quality depends not only on low-cost, simply available measures suitable for ordinary people at building scale but also on careful consideration of the congruity between occupants’ needs, housing unit characteristics, and environmental dimensions [14].
Given this, achieving EE in housing is more impactful when it aligns with occupants’ expectations and priorities, which is addressed as a serious scientific gap in this study. A potential solution to bridge this gap is to develop a concrete explanation of the interrelationships between the different architectural aspects of HEE, i.e. Human characteristics, building characteristics, environmental characteristics, and climate information.
In other words, given the necessity of achieving zero carbon housings studying the interplay between the different architectural aspects of housing EE would provide a good opportunity for the representation of occupant’ concerns, worries, and needs; and the explanation of the differences between different population groups in relation to their different socio-demographic characteristics, neighbourhood environmental dimensions, and physical attributes of dwelling units. Such a study provides a suitable platform to assist the different people to meet their demands and desires based on their perceptions, attitudes, and affordability, and hence enhances their proactive participation and pro-environmental behaviours.
This paper includes two different stages, first identifying the research background and variable setting in relation to the different architectural aspects of housing EE, and then developing the conceptual and methodological platform for a smart multi-criteria energy efficiency assessment, as will be discussed in the next sections.
An overview of the literature shows that the implemented energy efficiency (EE) measures have not been able to reduce the overall energy consumption (e.g. [15, 16, 17]). Partial improvements in EE without paying proper attention to all aspects of the concept often result in greater use of services and products or the emergence of new expectations and demands [18]. Comprehensive and integrated improvements of the entire system [19] are essential for the overall EE. To achieve this goal, some recent research works has addressed energy sufficiency, conservation, and saving through the enhancement of all energy-related aspects of the system [17, 20].
A balanced enhancement of the whole energy-related aspects of a system has the potential to provide a practical platform for the reduction of overall energy consumption of the system, and that is why some studies stress the necessity of developing a hybrid approach that is a combination of energy efficiency and energy, sufficiency, or a combination of bottom-up human-centric and top-down techno-centric approaches [16, 21, 22, 23, 24]. This hybrid approach provides a suitable platform for a balanced interplay between the different energy-related aspects of the system, with especial emphasis on the human side of the equation, and hence results in people’s positive perceptions, attitudes, and sense of control, promoting their active participation and pro-environmental behaviours [25, 26].
Given the share of housing industry as the main source of energy consumption in many countries, and the role of the different architectural aspects, e.g., people’s characteristics, building characteristics, and environmental dimensions in the energy performance of the industry [7, 9, 27, 28], it is highly critical to develop such a blended approach for residential buildings.
In some countries, e.g., the UK and Australia, there is a vast amount of information in the area of housing sustainability, and hence there is a proper level of public and general knowledge and awareness. As a result, people have gained positive attitudes towards sustainable housing [29]. This would ensure the implementation of housing sustainability codes, initiatives, and regulations in the housing sector. For instance, the implementation of building sustainability codes makes it possible for all new houses to meet the requirements of sustainability in the UK and Australia [29, 30]. In Australia, a variety of governmental initiatives that aim to encourage the energy efficiency of existing houses, e.g., solar panels and insulation initiatives have been also developed [30]. A few websites, e.g. Your Home, Home Energy Use, and Energy Efficient Home Design have also been developed to provide energy efficiency information, recommendations, and tips [31, 32, 33]. These online information provision platforms aim at providing some tips and recommendations, which help occupants to improve their housing energy efficiency.
The snowballing effects of the abovementioned measures play a critical role in invariably improving the Australian people’s knowledge and awareness about housing sustainability. Judge et al. [30] revealed that a strong and serious predictor of intention to purchase a sustainable dwelling in Australia is related to some other subjective norms; e.g. people’s attempts to promote environmental values and to be ‘green consumers’. A wide range of empirical evidence in Australia shows that people hold positive norms and attitudes towards sustainability of their dwelling units so that a dwelling unit with a higher level of energy efficiency has a higher market value (e.g. [6, 30, 34, 35, 36]).
Despite these efforts, there is an abundance of evidence that shows a steady increase in the overall energy consumption of the housing sector [29, 37, 38], rooted in the current direction of housing development and household behaviours and daily life activities [18, 28, 29, 37, 38]. Rashed et al. [11] indicated that the sustainability improvement of the building industry remains suboptimal, and stressed the lack of comprehensive, easy access, simple, and integrated sustainability information as the main reason of unsatisfactory progress of sustainable housing. This means that although the EE of housing is technologically and physically achieved at the building scale, there are serious insufficiencies in EE at the unit scale [8], because people are not able to find an energy-efficient house in relevance to their demands, needs, desires, and expectations; hence, the occupied house is not able to meet their needs. This would in turn put serious pressures, e.g., comfort, health, and well-being, on the households, resulting in inappropriate energy consumption habits and detracting from the housing energy performance [7].
Therefore, a good housing energy performance depends on the provision of accountability and ambition in energy conservation/saving of dwelling units via a balanced interplay between different sides, of housing energy consumption e.g., human, building, and environment. To achieve this goal, developing more sensitive and profound measures, i.e., online databases, rating tools, and evaluation instruments that makes it possible to identify the interplay between the different architectural aspects of HEE is highly essential. Such a decision-making tool gives the occupants the chance to achieve more congruent housing in relevance to their perceptions, affordability, demands, and desires, resulting in broad and proactive participation.
Given this, the overall increase in energy consumption is indeed a sign of serious insufficiency in the housing market, which is mainly related to the lack of comprehensive and integrated information platforms. The platform assists the public in assessing the level of congruity of energy-related features of residential units with their needs and desires, which enriches the leverage of energy codes, enhances the energy performance of units, and improves people’s energy consumption habits and activities [14, 28]. Therefore, enhancing HEE would depend seriously on the provision of suitable assessment opportunities that make it possible to evaluate the interplay among the different architectural aspects related to the energy performance of housing.
Regarding the necessity of a balanced interplay between the different architectural aspects in achieving housing EE, the next step is to develop the variable setting of the study in relation the different architectural aspects of housing. For this purpose, the following paragraphs are dedicated to have a look at some empirical studies conducted on the interrelationships among the different architectural aspect in impacting HEE.
Looking at environmental equity and energy justice, environmental challenges impact at-risk vulnerable people more seriously [7]. Hence, it remains very important to pay proper attention to these population groups in the development of sustainability measures to provide environmental equity in the mitigation programmes [7]. Regarding the inequitable distribution of environmental challenges in different urban districts, the lack of attention to the different architectural aspects of housing EE has resulted in serious environmental inequity. For instance, among the different vulnerable groups, low-income people living in poor housing conditions are the most at-risk, and they require particular attention [8]. A number of recent studies have stressed the impacts of environmental challenges on low-income occupants living in poor and low-standard housing with high thermal transfer and energy waste. Such conditions put higher pressures on their life conditions and compromise their health, well-being, and comfort (e.g. [7, 8, 9]). In contrast, affluent people have the chance to simply consume higher amounts of energy regardless of their opportunity to live in better urban environmental conditions with good housing design quality [8].
Referring to energy justice, Gower [8] stated that low-income people experience higher pressure and discomfort; because the energy costs seriously restrict their ability to consume the proper amount of energy. She indicated that poor and low-standard physical conditions of dwelling units of these population groups not only result in high thermal transfer and energy waste but also amplify the negative impacts of environmental challenges. Antonopoulos et al. [9] stressed that affluent people consume higher amounts of energy, whereas vulnerable low-income occupants are not able to consume a proper amount of energy because of the related costs and expenditures. They indicated that vulnerable occupants confront a high level of energy waste because of the high thermal transfer in their housing units. This occurs because they live in relatively low-standard, high-density, small, and poorly planned and designed houses, while living in warmer urban districts with poor environmental conditions, which increase their energy needs and requirements [9].
Sari [10] addressed the effect of different climatic dimensions, e.g., wind speed, wind direction, temperature, ventilation, and humidity, on building energy consumption. She also stated that some human factors e.g. information, communication, awareness, and education, would assist in improving energy consumption behaviours, e.g., relaxing in shade rather than simply using air- conditioning. Asadpoor et al. [14] stressed that the occupants’ perception of housing EE depend on their bioclimatic preferences and sense of control, which impact their perceptions and attitudes towards different energy-related housing attributes. Rashed et al. [11] indicated that the availability of reliable, simple, and informative information in relation to the different architectural aspects of housing sustainability for different groups of occupants has essential role in promoting housing sustainability. Table 1 addresses the different architectural aspects of HEE, e.g., building characteristics, occupant characteristics, urban environmental dimensions, and climatic data, alongside the various variables related to each aspect mentioned by these recently conducted studies, including Rashed et al. [11], EPA [7], Sari [10], Gower [8], and Antonopoulos et al. [9]. The table encompasses 105 variables categorised into four aspects factors, i.e. human characteristics, building characteristics, environmental characteristics (site level and neighbourhood), and climatic data.
These research works together have stressed the provision of a multi-criteria information platform about HEE, especially in relevance to occupants’ socio-demographic characteristics as a crucial requirement of housing EE. The provision of such a platform depends on a concrete explanation of the interrelationships among occupants’ socio-demographic characteristics, building physical characteristics, energy consumption, urban environmental characteristics, and climatic data. Therefore, to improve housing EE, developing a more sensitive and innovative information platform relevant to the occupants’ perceptions of EE is substantially necessary. Such an information provision would be beneficial not only for the engagement of occupants in adjusting, modifying, changing, and retrofitting their housing units in relation to their energy needs and demands but also for the enhancement of their EE consumption habits and behaviours by improving their sense of control, pro-active participation and pro-environmental behaviours [39, 40, 41]. Such a database would also provide a suitable platform for housing authorities to enhance housing EE in relevance to the different groups of occupants.
The Paris Climatic Agreement stressed that long-term sustainable development depends deeply on the development of a blended hybrid approach, which is a combination of bottom-up flexibility and top-down fixed rules [21]. Moser and Uzzell [22] stated that a combination of up-down needs-based approach with top-down right-based approach is essential to properly respond to environmental challenges through pro-environmental behaviours, proactive participation, and people’s involvement in decision-making. Biloria [16] also indicated that in achieving the expected EE goals, the integration of techno-centric and human-centric approaches is highly necessary. Orr [42] believed that paying attention to technological aspects is just the beginning, and real long-term sustainability depends on paying proper attention to all aspects of environmental sustainability. He recommended ecological (earth-centered) approach instead of the current technological approach. Given the concept of energy equity, Gower [8] also stressed that it is necessary to proactively alleviate energy vulnerability by considering the different aspects of energy consumption, e.g. human factors, environmental factors, and building characteristics. Such a comprehensive, hybrid, and proactive approach is necessary for a balanced interplay between the different aspects, especially in relation to human factors. This would, in turn, promote pro-environmental behaviours, and hence enhance the overall outcomes of the implemented measures by encouraging individual and collective environmentally friendly behaviours [41, 43].
Such a multi-criteria approach in the area of HEE makes it possible to combine efficiency and sufficiency (energy conservation/saving/consumption reduction) concepts, and hence to reduce the overall energy use in housing industry [15, 16]. Energy conservation/saving depends mainly on the reduction of the overall energy demands [15], which is strongly related to pro-environmental behaviours rooted in the enhancement of positive perceptions and attitudes among different groups of people [22, 24]. Table 2 represents an overall picture of the hybrid multi-criteria approach addressed by various scholars and researchers, e.g. Orr, [42] Moser and Uzzell [22], Centre for Climate Change and Energy Solutions (C2ES) [21], Biloria, [16], Antonopoulos et al. [9], EPA [7], Sari [10], Gower [8].
Study
Year
Hybrid approaches to EE
Conceptual platform
Orr
2002
Ecological (earth-centred) approach with technological approach
Radical reconceptualization of Sustainability: An intense consciousness of land The careful meshing of human purposes with the larger patterns and flows of the natural world. An integrative principle requiring a radical reconceptualization.
Moser & Uzzell
2003
Bottom-up right-based approach with up-down needs- based approach
Pro-environmental behaviours: Proactive participation, Self-determination, transparency, accountability, informed community, involvement of people in decision-making
C2ES
2015
Bottom-up flexibility with top-down fixed rules
Long-term sustainable development: Climate change mitigation, Accountability and ambitious through a broad participation.
Antonopoulos, et al.
2019
The interplay between the different aspects in impacting EE
Environmental inequity: To assist vulnerable groups by considering their limitations and affordability and the impacts of climatic conditions on their health and wellbeing.
Biloria
2020
Human-centric approach with techno-centric approach
Emphatic city: A combination of efficiency and sufficiency concepts. Energy saving and conservation
EPA
2021
The interplay between the different aspects in impacting EE
Environmental equity: the impact of buildings and urban environmental characteristics on changing neighbourhods climatic conditions and its impacts on vulnerable people
Sari
2021
The impact of building, urban planning and people information, awareness, education and communication on energy consumption and UHI mitigation.
Changing land cover, poorly designed building, thermal discomfort, and lack of awareness, the increase in energy consumption and UHI.
Gower
2021
To proactively alleviate the energy vulnerability by taking into account the different aspects impacting energy consumption in the predesign and design stages.
Energy justice: Building physical condition, Design quality, Passive climatic control, building regulations to protect vulnerable people, energy efficiency at building scale vs. residential scale, the different energy performance of different apartments in relation to the units physical dimensions and occupants’ characteristics.
Table 2.
Hybrid multicriteria approach to HEE reflected in several recent studies.
According to Table 2, all these researchers, scholars, and experts have addressed the interrelationships between different aspects, although their conceptual platforms, assumptions, and contributions are different. They emphasised balanced interrelationships between various aspects as the key-determinant of environmental equity, energy justice, pro-environmental behaviours, proactive participation, assistance to vulnerable urban populations, and proper responses to a variety of ecological deficiencies. A reliable informational platform about the interrelationships between various aspects is, therefore, essential to enhance public awareness, and to help them meet their needs while considering ecological concerns and requirements.
These recently conducted studies provide a suitable platform for developing a hybrid multi-criteria approach to HEE (Figure 1). Given the figure, such an approach depends initially on a sensitive and careful explanation of the interplay between the different architectural aspects, i.e., human characteristics, building characteristics, environmental characteristics, and climatic information, in impacting housing energy performance. The explanation of the interplay between the different aspects assists in the reduction of the overall energy consumption by improving the congruity of housing energy-related attributes with occupants’ socio-demographic characteristics. Such a reliable information platform is beneficial not only for putting passive climatic principles in practice more effectively and reducing fossil fuel dependency but also for encouraging pro-environmental behaviours, which lead to an overall energy consumption reduction in housing sector.
Figure 1.
Interplay between the different architectural aspects incorporated in the hybrid multi-criteria approach to HEE.
This research was developed on the premise that identification of the interplay between the different architectural aspects is highly critical for housing EE. To meet this aim, this research develops a multi-method approach through a combination of qualitative and quantitative methodologies by adopting different data collection methods and analysis techniques, which are discussed in this section [44].
Given the complexity of the interrelationships among the different architectural aspects in impacting housing EE, it is also necessary to address the implication of AI in explaining these interrelationships, with especial emphasis on the role occupants’ socio-demographic characteristics. There is an abundance of evidence that shows the impact of physical characteristics on housing EE, and people’s perceptions (e.g. [38, 45, 46, 47]). Environmental physical characteristics convey a wide range of information to people that can have significant impacts on their perceptions, affecting their daily life activities and choice behaviours [22]. Therefore, it is initially necessary to objectively explain people’s perceptions of housing physical characteristics.
One of the first attempts to develop a concrete approach to environmental perceptions is environmental image (EI) [48]. The concept of EI has been developed by various researchers and scholars [22] and has been recently employed in many empirical studies to objectively address human environmental perceptions (e.g. [38, 49, 50, 51]). Such studies, despite differences in their research areas, theoretical platform, and assumptions, have also provided a suitable platform to objectively explain the interrelationships between different aspects e.g. human, environment, building, and climate, in responding to the different environmental challenges.
These studies employed photo-based methodologies to explain people’s environmental perceptions [38, 49], some of which developed AI platforms using deep learning [50, 51]. Their efforts are based on the identification of people’s perceptions of environmental attributes through a semantic representation of the attributes presented in different photos using deep learning methodologies. The semantic representation is derived from the content of the environmental attributes in the photos, as determined from people’s expressions of the meanings and messages conveyed by these attributes. Identifying people’s expressions of the meanings and messages conveyed by the attributes, alongside semiotic interpretations of the attributes meanings results in identification of a number of semantic categories of the attributes [50]. Such a semantic categorisation of the environmental attributes provides a proper platform for determining the multifunctional and multidimensional content of environments and the similarities and differences among them. The interpretations of the interrelationships among different semantic categories, and similarities in their functions and meanings lead to identification of a number of high-level attribute-based factors, explaining people’s environmental perceptions. Analyses at this level provide a proper platform for a semantic categorisation of the different environmental photos, and then make it possible to develop an AI platform for the prediction of meaning and functionality of other photos from unknown environments using deep learning methodology [50, 51].
Sallesses et al. [52] also employed the concept of environmental image to develop a GIS-based platform for mapping people’s equity and safety perceptions using geo-tagged urban photos. This is a step forward to develop an AI platform for mapping people’s EI in different urban areas and neigbourhoods. Most recently, Takagi and Nishimato [53] also developed an AI platform to predict people’s thoughts through people’s expressions about various photos. Looking at such research works is a promising indication of the potential implications of AI in predicting people’s environmental perceptions.
In response to the gap addressed by this study, the next step is to transfer the concept of EI into the area of housing EE. Back to the abovementioned studies, EI is a suitable conceptual platform for explaining residents’ perceptions of housing EE [46, 54]. Accordingly, energy efficient housing image (EEHI) would have the potential to concretely explain occupants’ perceptions of housing EE, and to identify the determinants of EEH perceptions [54]. Therefore, EEHI provides a suitable platform for determining the of people’s perceptions of the interrelationships between building characteristics, environmental characteristics, and climatic data in impacting HEE.
EEHI depends on a clear representation of HEE perceptions derived from occupants’ assessment of the role of different energy-related attributes/factors on housing EE and a specific explanation of the rationales of their assessments [46]. The assessment can be made with a photo-based methodology, which would end up in developing a deep learning methodology for the prediction of occupants’ perceptions of HEE for unknown housing photos. The employment of geo-tagged photos would make it also be possible to map occupants’ perceptions of HEE in the different urban areas and neighbourhoods in a GIS-based database.
This study emphasises the external aspects of residential buildings, i.e., architectural composition principles and envelope physical features, to determine occupants’ HEE perceptions. External aspects are the most salient aspects of buildings, which have significant impacts on people’s perceptions, have critical impacts on the energy performance of a dwelling unit [46], and as mentioned above, many studies have recently addressed the proper potential of photos from sceneries, open spaces, exterior environments, and building outside views in developing an AI platform.
The third step is to identify the interrelationships among the different architectural aspects of housing EE, e.g., housing characteristics, occupants’ characteristics, environmental characteristics, and climatic information. Hsu [55] developed an AI platform to predict the level of housing energy efficiency from outside by employing Google Street view photos, aerial photos, and satellite-based measurements of building heat loss [55]. The study of Gower [8] and Antonopoulos, et al. [9] also employed primary and secondary real data to address the interrelationships between the different architectural aspects in impacting housing EE.
In the study conducted by Antonopoulos et al. [9], variables were selected from the literature. Data were collected from various reliable sources, e.g. electric and gas bills at the block group and individual household levels, as well as national census data. The researchers then applied several inferential analysis techniques, i.e. pearson’s correlation, linear regression, spatial autocorrelation, and spatial regression model [9]. Gower [8] developed a case study method and conducted content analysis by observing design and planning documents alongside the actual spatial design impacts. Her study developed a coding technique for the qualitative rating of the variables, which classified them into three scales, i.e. optimal, moderate, and poor.
Some efforts have also been made to develop an AI platform for multi-criteria HEE evaluations based on real and stated housing data [13, 14]. Accordingly, two types of data were collected, including secondary data, e.g. online databases and websites, national census, as well as design and planning documents, and primary data, e.g. field surveys, observations, and experimental measurements, stimulation software, and satellite-based measurements [8, 9, 10, 11, 12, 13]. The analyses of the collected data have provided a suitable platform for addressing the interrelationships among the different architectural aspects in impacting housing EE. The development of a smart multi-criteria database would be a product of the analyses, which has the potential to address the interplay among the different architectural aspects of housing EE.
Putting these three steps together provides a reliable platform for developing an AI-based multi-criteria decision making in the area of HEE. The platform would also provide a balanced interplay between different aspects of housing EE, i.e., building characteristics, human characteristics, environmental characteristics, and climatic information from a building’s outside view. The AI platform is a GIS-based database with a set of various primary and secondary data relevant to building, environment, climate, and occupants, with geo-tagged photos of housing outside views, which provides comprehensive and integrated housing energy information for different occupants in relation to their socio-demographic characteristics.
This AI multi-criteria platform is beneficial for the assessment of the different architectural aspects of housing EE in relevance to occupants’ perceptions. It provides specific and comprehensive EE recommendations, tips, and suggestions tailored to different occupants based on their socio-demographic characteristics. The platform assists in modifying, changing, adjusting, and retrofitting dwelling units based on occupants’ socio-demographic characteristics, improving sense of control, proactive participations, and pro-environmental behaviours, and finally enhancing energy equity and justice. Furthermore, this research is beneficial for the different stakeholders in the housing industry, e.g. government, planners, architects, and designers, as it helps enhance the overall HEE by enabling them to enhance the energy-related attributes based on occupants’ perceptions while considering the influence of their socio-demographic characteristics in the multi-aspects equation of housing EE.
Regarding the main objective of this study which is to explain the implications of AI in a multi-criteria energy efficiency assessment of housing based on occupants’ socio-demographic characteristics, this paper initially made a literature review to explain the research background. The conducted review stressed the importance of a balanced interplay between the different architectural aspects in providing housing EE. Then the research made a cross comparison among five recently conducted studies, i.e. Rashed et al. [11], EPA [7], Sari [10], and Gower [8] to develop the variable setting of the study in relation to the different architectural aspects which has significant impacts on housing EE. The cross comparison resulted in identification of 105 variables in 4 areas including environment, building, human, and climatic data (Table 1).
The paper then developed the conceptual platform of the research by looking at some scholars’ and experts’ empirical and conceptual research works, i.e. (C2ES) [21], Moser and Uzzell [22], Biloria, [16], Antonopoulos et al. [9], EPA [7], Sari [10], and Gower [8] (Table 2). Despite differences in the research areas, theoretical platforms, and assumptions, these research works all together stressed the critical roles of the interrelationships among the different aspects in properly responding to environmental challenges. They indeed stressed a hybrid approach which is a combination of techno-centric and human-centric approaches as the main solution for the environmental challenges. Transferring this approach to housing, the overall energy consumption reduction depends on a balanced interplay among the different architectural aspects, by special emphasis on the occupants’ socio-demographic characteristics (Figure 1), which enhances sense of control and pro-environmental behaviours. This approach also assists in reducing fossil fuel dependency, and in putting passive climatic principles in practice more effectively.
The last section of the paper was dedicated to the explanation of methodological platform of the study, which was made in three steps. The first step explained how to represent people’s environmental perceptions through the concept of EI based on previous studies (e.g. [22, 38, 49, 50, 51, 52, 53]). This concept was then transferred to EEHI, by looking at some other studies (e.g. [46, 54]). The third step considered some other research works (e.g. [8, 9, 10, 11, 12, 13, 55]) and explained the methods and techniques that can be adapted in the identification of the interrelationships among different architectural aspects in impacting HEE. Putting all three parts together provides a proper platform to develop a smart multi-criteria EE assessment platform for housing based on occupants’ perceptions and attitudes expressed through their socio-demographic characteristics. In the future, conducting the operation phase of the research with a proper sampling design will make it possible to develop a smart multi-criteria EE evaluation platform for housing based on occupants’ socio-demographic characteristics.
The authors would like to express their highest appreciation and gratitude to Islamic Azad University – Mashhad Branch, Iran for all the supports provided for this research.
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Written By
Sayyed Javad Asadpoor and Elyas Jahanshahi
Submitted: 09 July 2024Reviewed: 15 July 2024Published: 23 October 2024
Open access peer-reviewed chapter
