5.1 Goal and scope of studies
The results of the analysis of the goal and scope of the investigated studies are presented in the subsequent paragraphs, focusing on spatial and temporal aspects. The understanding of spatial and temporal dynamics related to the evolution of building stocks builds on the research presented by Mastrucci et al. [12] and Göswein et al. [13] in their respective reviews. In our analysis, we differentiate modelling of the building stock and modelling at the building level. Building stock modelling is conducted at the scale of the investigated building stock, with the relevant timeframe of the study and information on building types considered in the model, whereas building information modelling relates to the building life cycle and parts and the energy end uses considered. In line with the European standard for environmental life cycle assessment of construction products and buildings (EN 15804, EN 15978), we documented the consideration of life cycle stages and their respective modules, namely: the product stage (modules A1-A3); the construction process stage (A4, A5); the use stage, differentiated into modules related to embodied impacts (B1-B5) and impacts from operational energy and water use (B6, B7); and the end-of-life stage (C1-C4). We also documented the handling of benefits and loads outside the system boundary (module D), as these are related to end-of-life recycling and potential reuse.
Figure 4 provides an overview of the spatial and temporal scope of the building stocks analysed in the investigated studies. We found that the majority of the studies in our final sample (13 of 22) investigate building stocks in a European context. Other regional contexts found are Australia and Oceania (4), the Greater Middle East (4) and North America (1). Most studies in our final sample investigate building stocks at a national scale (10), although the scale of building stocks studied ranges from the transnational (3) to the regional (1), urban (6) and portfolio scales (2). In terms of temporal scope, i.e., the timeframe of the study, most studies focus on prospective analyses and study building stock development and related environmental impacts based on stock development projections for several years, decades, or sometimes centuries into the future. Ten out of the 22 studies use a timeframe between 2013–2055 with a building stock reference study period (RSP) ranging from 25–40 years. Three studies deploy an extended timeframe of 84 to 200 years, taking 2016 to 2018 as their starting points. Five studies offer a snapshot of the state of the building stock in 1–4 specific years, with 2005 to 2013 as the reference years. The types of buildings investigated in the studies are mostly residential buildings (17 of 22), with two of these studies specializing in single-family houses (SFHs) and one on multi-family houses (MFHs). Two studies focus on other building types, such as office or university buildings. Three studies assess a full building stock covering various building types, including and beyond residential and office buildings. The majority of the studies focus on the retrofitting of existing buildings (15), taking different building types into consideration. Twelve out of the 22 studies in our sample take a life cycle approach to investigating both embodied and operational environmental impacts, while a total of ten studies focus exclusively on either embodied (5) or operational (5) impacts. While we documented the consideration of all life cycle stages and modules as defined in EN 15978, we here present the two general categories: embodied, i.e., material-related, impacts and operational impacts, i.e., those related to building energy and water use. The comprehensive analysis of the life cycle scope of the studies is presented in Supplementary Information Table S3.
Upon analysing the individual studies shown in Fig. 4, we found that the studies mainly investigate prospective timeframes of 25–35 years, i.e., reaching up to (or close to) 2050 and hence enabling an assessment of environmental targets and mitigation pathways up to mid-century. Only few studies project further into the future. Pittau et al. [22] apply an extensive temporal scope of 200 years to study refurbishment strategies for the European residential building stock with bio-based materials. Their study projects embodied GHG emissions as well as potential carbon storage from the use of different conventional and bio-based materials for a timeframe of 2018 until 2217. Pénaloza et al. [31] and Stephan and Athanassiadis [37] deploy timeframes of 100 and 84 years, respectively. Some studies analyse environmental impacts related to the building stock in a given year, e.g., the life cycle-based environmental impacts of the European building stock in 2010 [30]. One study conducts a retrospective analysis of the environmental impacts of building stocks in the 20th century based on the timeframe of 1940–1990 in a region of Canada [17]. In other cases, the expected environmental impacts of end-of-life treatment of materials are investigated for an urban building stock in Luxembourg (reference year 2012) [36] and Australia (reference year 2005) [32]. For three studies, we were unable to clearly identify the reference year and/or the duration. Additional information and details on the spatial and temporal scope of the individual studies together with a characterization of their modelling approaches are presented in the following sections and available in the Supplementary Data file.
The analysis of the spatial and temporal scope at the building level, i.e., life cycle stages, building parts and energy end uses considered in the different studies, revealed that none of the studies provide a complete assessment covering all these aspects in a comprehensive manner. Only two studies cover the full building life cycle, i.e., cradle to grave, without limitations [24, 30]. Six studies approach a cradle-to-grave scope but show limitations due to the exclusion of some life cycle modules. The remaining studies have different scopes, with some focusing on cradle-to-gate or cradle-to-site, in-use or end-of-use life cycle modules. In the case of four studies, we could not clearly identify the life cycle stages covered from the documentation provided in the papers. An overview of the temporal scope regarding life cycle modules considered in the individual studies is available in Supplementary Information Table S3. An elaborate description and discussion of the building parts as well as the energy end uses covered in the investigated studies is provided in Sect. 5.3.2 Material modelling and Sect. 5.3.3 Energy modelling, respectively.
5.2 Dynamic aspects and scenario variables
The definition of static and dynamic aspects in the LCA of buildings stocks follows the definition of Mastrucci et al. in their related review [12], where ‘static’ means the assessment of the situation at a given time or the comparison of two distinct states in time. In contrast, ‘dynamic’ modelling considers the evolution of the building stock over time. The review of Göswein et al. [13] suggested a further differentiation of dynamic aspects considering spatial, (evolutionary) temporal, and spatial-cohort dynamics. In this study, we adopt a combined approach considering three types of dynamics. First, spatial dynamics, as the spatial differentiation in the baseline model, e.g., of building characteristics in different geographical or climatic areas. Second, temporal dynamics, as consideration of changes over time, e.g., changes to demand for space, user behaviour, and technological innovation in energy or material production. Third, spatial-temporal dynamics, as changes over both space and time, e.g., diversification of properties for some buildings (spatial dynamic) over time (temporal dynamic) due to retrofit activities or changes in use.
Table 1 presents an overview of the dynamic aspects identified in the final sample of studies. The table is structured to cluster cases based on related steps in environmental modelling, i.e., modelling of foreground inventory, background inventory, or impact assessment. We define the foreground inventory as the definition of types and quantities of buildings, elements and systems as well as construction materials and operational energy and water along the life cycle of the buildings. The background inventory relates to the conditions of material and energy related processes and the use of respective LCA data for modelling the life cycle of materials and energy flows (as specified in foreground inventory), e.g., production of 1 kg of material type X, or 1 kWh of energy type Y.
Table 1
Examples of dynamic aspects identified in the final sample of studies.
Dynamic aspects in modelling steps | Types of dynamics |
Step | Aspects | Examples | Spatial | Temporal | Spatial-temporal |
Foreground inventory | Demand for space, occupant behaviour and comfort | Population projections; Typology of family; Floor area per capita; Presence time of occupants; Thermal comfort levels | Building stock classification in different climatic zones or geoclusters [Almeida 2017, Lavagna 2018, Pittau 2019, Ghose 2020] | Projections for population and floor area demand [Penaloza 2018, Ghose et al 2020, Mastrucci et al 2020]; | Demand projections and sensitivity of floor area per capita [Heeren 2019]; Building stock size and number of buildings to be renovated [Pittau 2019]; User behaviour and related energy demand reduction scenarios [Pombo 2019]; Sensitivity of dwelling size development [Penaloza 2018]; Influence of changes to cooling and heating set point, as well as illuminance [Ghose 2020] |
Building typologies and archetype definitions | Number and definition of representative buildings; Differentiation of archetypes; Changes in culture of design and construction | Differentiation of building types and construction period [Mastrucci 2017, Stephan 2017, Zygmunt 2019, Heeren 2019, Mastrucci 2020, Ghose 2020]; Considering building quality and refurbishment needs [Gabrielli 2019] | Future need for detached housing based on population assumptions [Chandrakumar 2020]; Future changes to market share of different building types (SFH, MFH) in new buildings [Penaloza 2018]; Changes in renewal rates, building materials and energy standards, building technology upgrades [Heeren 2019]; | Scenarios for energy retrofit (e.g. energy system switch to renewable energy, improved energy performance of the building envelope) [Almeida 2017, Seo 2018, Gabrielli 2019, Österbring 2019, Nageli, 2019, Drouilles, 2019, Pombo 2019, Zygmunt 2019, Feng 2020] |
Building elements and materials, technical systems and energy use | Material choice; uptake of technological innovation; degradation of technical performance of materials or systems; Integration of renewable energy systems | - | Investigation of future recycling rate scenarios [Mastrucci, 2017]; Material flow and embodied impacts of different future renovation scenarios [Mastrucci 2020, Pittau 2019]; Scenarios for future changes to rate of timber typologies, low-impact concrete in new buildings [Penaloza 2018] | Localized yearly material output from building stock turnover based on number of new construction, rebuilt or refurbished buildings [Heeren 2019] |
Background inventory | Material production, processing, end-of-life | Increased recycling content of new construction materials; Changes in recycling technologies and energy recovery practices; | - | Changes to energy mix for material production [Penaloza 2018] | - |
Energy mix scenarios | Changes to energy mix (electricity, heat), e.g. due to national policies and regulations or market mechanisms | - | Energy consumption and future electricity mix changes [Chandrakumar et al 2020]; Electricity grid evolution [Ghose et al 2020] | - |
Impact assessment | Carbon uptake and emissions | Carbon uptake during growth of bio-based materials; Biogenic carbon storage in construction products; Biogenic carbon emissions at the end of life of products; | Influence of biogenic carbon consideration for different building types [Chandrakumar 2020] | Dynamic impact assessment based on time-dependendant impact aggregation matrix and carbon storage from bio-based materials [Pittau 2019]; analysis of dynamic carbon footprint methodology [Mastrucci et al 2020] | - |
Characterization and weighting | Temporal changes to characterization and/or weighting factors; Regionalisation of impact factors based on process location; | - | - | - |
In the final sample for this review, half of the studies present a static assessment of the building stock. This means, for example, that for the energy modelling, the heating degree days (HDD) are considered constant and energy mixes are kept unchanged throughout the study timeframe. Some static models, however, include scenarios or variations to improve the model accuracy by considering, e.g., different renovation scenarios or varying energy mixes [17, 20, 32]. In their scenario analyses, some studies focus on different renovation scenarios [19, 21, 29, 34, 38], while others vary energy mixes to study their influence on the environmental impacts during operation [16, 18, 31]. Five studies include dynamic climate or weather data [24, 29, 35, 38, 39]. It should be noted that the four studies of Krarti et al. [28, 29, 34, 40] are, for the most part, based on a common methodology. In terms of temporal dynamics, two studies explicitly present the change in environmental impacts over time [19, 22], and four studies present the change in material flows [18, 22, 26, 31]. Finally, individual studies focus on other dynamic aspects, e.g., the influence of changing recycling rates [36], of changing financial investment capacity per year [21], and of population growth [18] and related trends in the use of floor area per capita [26].
5.3 Inventory modelling and analysis
The subsequent sections present our findings regarding inventory modelling and analysis for three main modules of environmental building stock models: (a) building stock aggregation, (b) material modelling, and (c) energy modelling. We also present an analysis of the various data sources used in the reviewed literature. An extended overview of the data sources as well as tools and software used in the reviewed studies for the modelling of these aspects are provided in Supplementary Information Table S4 and in the Supplementary Data file.
5.3.1 Building stock characterization and aggregation
5.3.1.1 Modelling
Figure 5 shows six features of the different building stock aggregation approaches and their interrelationship: the object of assessment (existing building stock, new buildings, retrofits, new buildings and retrofits, or materials and urban mining), the scale of the stock (building, urban, regional, national, or transnational), the number of buildings in the actual stock, the number of archetype variations used for modelling (as well as segmentation criteria applied to differentiate archetype variants), the aggregation approach (archetype, building by building, or sample) and life cycle scope (embodied, operational, or both). First, as noted earlier, it is clear that almost half of the studies focus on the national level (10), just over a quarter on the urban scale (6) and the remainder on either the transnational (4), regional (1) or building portfolio (2) scales. Second, although the number of buildings in the stock is not specified in six of the papers, the literature review shows that a larger scale of the stock tends to correspond to a larger number of buildings included in the stock. However, studies with a stock model size of over 1 million buildings, for example, may cover urban, national or transnational scales. Third, from the perspective of the connection between the number of buildings in the stock and the number of archetype variants, studies including a larger number of buildings (more than 10,000) always use the archetype approach. However, the segmentation criteria applied to differentiate buildings in the stock, as well as the overall number of archetype variants used for modelling, vary widely (from 1 to 1,680) and do not necessarily correspond to the size of the stock under investigation. Fourth, the archetype approach is used in two-thirds of the studies, while only one study makes use of a sample method, and six studies implement a building-by-building model. Investigating the combination of characteristics in the individual studies, we observed that all studies taking a building-by-building approach are on a relatively small scale, covering a maximum of 10,000 buildings (or they do not report the number of buildings involved), and are conducted at the building portfolio, urban or national scale. The sample method is used in one study of multi-family houses at a national scale.
Figure 5 further shows the segmentation criteria used to differentiate the archetypes and number of variants modelled for each criterion. We find that the building type (e.g., residential, office) or building typology (e.g., different types of residential buildings [SFH, MFH]) are the most common criteria. Studies further use the construction period, location or climate conditions as well as material scenarios (for both new construction and retrofit studies) to differentiate the archetypes. Overall, we find that several studies use a remarkably small number of archetype variants in their modelling of building stocks with several thousand buildings, which at times raises questions of the representativeness of the chosen archetype(s). At the same time, we find studies combining several criteria to generate several hundred archetype variants to model their building stock [18, 22].
An overview of the building (stock) characteristics and their use for building stock characterization and clustering in the different studies is presented in Supplementary Information Table S5. We found that construction year and building type are the most used criteria for building stock characterization and clustering. These two characteristics commonly appear both individually and in combination. This combination is followed by the combination of the size and climate characteristics, which appears in every study clustering based on size and in three of the six studies [18, 22, 23, 30, 33, 41] using climate zones as a clustering criterion. The combination of building typology and renovation state/need was identified three times.
5.3.1.2 Data sources
A variety of data sources were identified to obtain relevant information on building stock composition and characterization and to aggregate the building stock models. For studies not including a building-by-building case study analysis, we obtained building information by consulting various data sources to fill gaps in the data. Ten out of the 22 studies use statistical data on demographics and building information. These statistical data include national open source datasets [24, 26, 37] and generalized datasets regarding building parameters such as type of use or geometry [19–22]. Five papers fill gaps using data from other studies on building stock levels to estimate the climate impact [16], determine quantities of building materials [31, 36] or apply the methodology developed in a previous paper [23] (by the same author). Another five studies use geographical information systems (GIS) data to identify building geometry and fills gaps in building use data [19–21, 26, 36, 37]. Seven studies make use of building (stock) databases such as, among others, TABULA [22, 30] to establish building stock compositions, retrofit steps and timing [19, 26, 36, 37] or archetype definitions. In addition, probabilistic approaches [19] and existing studies at the building level [17] are used. An overview of data sources used for the inventory modelling in different studies is presented in Supplementary Information Table S4 and in the Supplementary Data file.
5.3.2 Material modelling
5.3.2.1 Modelling
This section presents an investigation of the material inventory modelling and analyses, as identified in the reviewed literature. Overall, we identified that most studies follow the element method; i.e., they model material inventories in a hierarchical way based on building elements and their material composition. Strong variation was identified, however, in the context of the model resolution and level of detail in the hierarchical models. In Fig. 6, we thus analysed the resolution of both the modelling and the reporting of material-related impacts, based on the levels of modelling used in the Belgian building and building stock LCA tool in [42] and referring to the levels of investigation as outlined in the European EF guidelines and their application to the building level in the PEF4Buildings pilot project [43].
We found that 14 studies model the stock from the level of building elements, which are aggregated to represent buildings and the building stock. Few studies apply modelling levels below the element level. Sub-element-level modelling is used in three studies and construction material-level modelling in five; only one study includes in its inventory the modelling of raw materials (i.e., the use of raw materials in construction products, e.g., according to the Eurostat raw material categories of biomass, metal ores, non-metallic minerals, and fossil energy materials).
The results are expressed mostly at the building and building stock levels. In general, we observed a lack of results presented at a higher resolution than the element level and a lack of environmental hotspot analyses across different levels, which are crucial for the environmental optimization of buildings, construction products and related supply chains. The topic of hotspots in terms of environmental indicators is discussed further in Sect. 5.4.
Figure 7 provides further information on the scope of the reviewed studies in terms of building parts included in the assessment as well as on the level of detail for material modelling and results presentation. We found that the studies investigate different objects, with a variety of building parts covered in the assessments. While most of the studies likely make use of quantity information and background data at the material level, many of them do not explicitly show this in the documentation of their modelling approach or the life cycle inventories presented. Analysing the most complete studies from this perspective, we found seven studies with a comprehensive scope [17, 20, 24, 28, 30, 44], i.e., covering building parts related to structural systems, envelopes and façades as well as internal elements and technical systems (some with limitations). The objects of assessment range from the current state of the existing building stock or investigation of retrofit measures to material-focused and urban mining studies. We found that four of the seven studies [17, 24, 28, 44] are modelled at the element level and present the results at the building level. Of these, the sub-element level is utilized by one study [17]. For another three studies, we also identified modelling at the sub-element level [21] with the results presented at the building cluster level [20, 30]. While some studies display a comprehensive focus on the building elements covered, others focus exclusively on one part of the building system. The latter is the case, for example, for retrofit studies focusing on the building envelope.
In terms of modelling material-related inventories and the presentation of related environmental impact results, we identified a wide variety of approaches. We found that in general, various levels of detail are used for modelling when an archetype-based aggregation approach is deployed. Multiple levels of modelling are utilized in four archetype-based studies [16, 21, 22, 31]. While also differing in their use of modelling levels, studies applying a building-by-building approach tend to utilize more levels of material modelling and more often use these levels to present the results in terms of both material inventories and environmental impacts. Utilization of several levels of modelling is presented in four out of the five building-by-building studies [19, 26, 36, 37].
From an overall perspective, while some studies were identified that either displayed a comprehensive scope in terms of building elements [17, 20, 24, 28, 30, 44] or offered a comparably detailed modelling of material-related inventories [16, 22, 26, 31, 36, 37], none of the studies could be identified as providing both at the same time. However, considering the variety of combinations found in terms of objects of assessment, building part scopes, levels of material modelling and aggregation approaches, lessons can be learned from these existing approaches to advance building stock modelling practices to achieve comprehensive and detailed modelling of material-related environmental impacts.
5.3.2.2 Data sources
We found a variety of material-related data sources. Several studies utilize statistical data from building material and element databases such as the KBOB database for Switzerland [24] or, in the Australian context, data from the national bureau of statistics [32] or the dedicated CLUE building material database [37]. Several studies base their analysis of material-related impacts on previous studies at the building stock level (as in [18], which is largely based on previous research by the same authors [45]) or, more commonly, at the building level. Prior studies at the building level are frequently utilized, based on LCA data from BRANZ for New Zealand in the case of [16], material data on representative buildings for Luxembourg in [36], or data and reports from related research projects such as IEA EBC Annex 56 (“Cost-Effective Energy and Carbon Emissions Optimization in Building Renovation”) in [33]. Databases on building or building stock data are used in several studies, especially in the European context. Such studies use data from projects such as TABULA, EPISCOPE, ENTRANZE, ODYSSEE or the European Building Stock Observatory [22, 30] as well as building data from national or local building libraries [19, 36]. Other data used include material-related data from building energy performance certificates [21] or material-relevant information from building energy cost data for maintenance and building services [20]. Regulations and standards are utilized to obtain information to model end-of-life scenarios and impact coefficients [36] or building envelopes according to existing thermal performance requirements [19]. We further identified the use of probabilistic approaches for projecting element service life and the timing of replacements [26] as well as for modelling past renovations and future renovation needs with statistics-based random distributions [19]. A complete overview of data sources used for inventory modelling in different studies is presented in Supplementary Information Table S4 and the Supplementary Data file.
5.3.3 Energy modelling
5.3.3.1 Modelling
This section presents a detailed investigation of the energy modelling and analyses identified in the reviewed literature.
Figure 8 shows the different approaches used to model the operational energy demand and related environmental impacts. Based on the objects of assessment, we documented the scope of energy end uses considered in the modelling. The energy end uses documented are heating (H), cooling (C), ventilation (V), domestic hot water (W), lighting (L), and other appliances (A). We observed two studies covering the full scope in terms of energy end uses (i.e., HCVWLA). These studies base their modelling on a monthly or yearly resolution of energy use. No studies combining a comprehensive end-use scope with the highest (hourly) resolution in energy modelling could be identified. We found eight studies that employ dynamic energy simulation and six that employ steady or quasi-steady modelling. When a steady simulation is applied, the resolution time step is yearly or monthly, whereas dynamic simulation is always conducted with an hourly time step resolution. We identified only three papers applying a statistical approach, and in these cases, a yearly resolution is utilized.
Among the papers with bottom-up statistical models, [25] is the only one that develops a multiple linear regression equation to estimate building energy demand. One study uses a top-down approach to calculate the energy consumption of each archetype in the baseline scenario [30]. As a further development of this study, Allacker et al. [46] perform dynamic energy simulation on the archetypes defined in Lavagna et al. [30] to assess the life cycle environmental burdens and benefits of some eco-innovation measures. Chandrakumar et al. [16] use a statistical approach to calculate the variations in energy sources in the future. Seo, Foliente and Ren [32] utilize statistical data to obtain the energy consumption of the construction sector and to quantify the embodied environmental impacts of retrofitting. Furthermore, they model heating and cooling energy requirements using an energy simulation tool to calculate the benefits of improved energy performance in residential buildings.
5.3.3.2 Data sources
The analysis of the data sources for the energy modelling showed that statistical data and regulations and standards are the most used sources.
Statistical data are utilized for different objectives, e.g., to acquire the national energy consumption of the construction sector [32], to obtain the share of energy carriers in households [27] or to cover heating and domestic hot water demand by type of building and construction period [24]. Statistical data are combined with individual national energy mixes to define the energy consumption of each archetype in Lavagna et al. [30]. National and international regulations and standards are mostly used as a reference to calculate energy demand through simulations [20, 23, 25, 27, 33]. Standards and regulations are considered to develop energy performance scenarios in two studies [24, 32]. Two studies define the geometric characteristics of buildings using GIS data provided by city planning offices and apply these data for energy calculations [20, 21]. The same two studies also utilize building energy performance certificates (EPCs) as a data source for information on heating and ventilation systems, the average number of apartments, building height and measured energy use [20, 21].
5.3.4 LCA scenarios
LCA studies typically use scenario modelling to assess the influence of certain parameters on the overall results. The literature review checked for which parameters such scenario analysis was performed, identifying the following parameters: estimated service life (ESL) and reference study period (RSP), recycled content versus primary material use, refurbishment scenarios, and end-of-life scenarios.
In ten studies, special attention is paid to specifying the ESL. In three studies [17, 18, 21], the ESL is defined by building element, while one study defines an overall RSP of 100 years for the building itself [30]. Four studies state the RSP, varying from 25 years [32] to 60 years [22, 23] after renovation, at the building level only. Pittau et al. [22] and Pombo et al. [23] further conduct a sensitivity analysis to examine a shorter ESL of building elements and buildings to evaluate the influence of ESL on the overall results. Only one study includes a sensitivity analysis to test the impacts and implications of substituting primary material with recycled content [26].
Various different renovation measures are investigated in seven of the studies in our sample [17–19, 23, 26, 27, 33]. One study investigates the financial implications of renovation activities at the urban scale for different renovation measures by studying the maximum renovation rate for a certain measure as a function of households’ investment capacity [21].
In the majority of the papers, end-of-life scenarios are not included. Pittau et al. [22] include three disposal scenarios. In the paper of Mastrucci et al. [36], the business-as-usual scenario is compared to a second scenario where an increased down-cycling rate for inner waste is assumed. The study of Heeren et al. [26] includes a sensitivity analysis for recycling and reuse scenarios for concrete, wood and thermal insulation.
In conclusion, only a small number of studies include different LCA scenarios in their modelling and analysis of the building stock in terms of material- and energy-related impacts. Furthermore, the LCA scenarios are critically investigated through sensitivity analyses in only a few of the studies.
5.4 Life cycle impact assessment
This section reviews the life cycle impact assessment (LCIA) methods and related environmental indicators adopted by the selected studies. Our analysis refers to the LCIA methods and environmental indicators suggested in the European Committee for Standardization (CEN) standards (EN 15804, EN 15978). The methods and environmental indicators analysed in the reviewed studies are available in Supplementary Information Table S6.
First, we found that LCIA methods are rarely mentioned in the papers, even though various environmental indicators are assessed. All studies in our final sample assess environmental indicators of global warming potential (GWP). Out of the sample, 16 studies focus exclusively on this indicator. After GWP, the most common environmental indicators are acidification potential (AP), ozone depletion potential (ODP), eutrophication potential (EP) and photochemical ozone creation potential (POCP). The categories of water resource depletion and land use are considered by only two studies. The review of the EU policy documents, however, revealed that the importance of land use and land use change is increasing, especially under the Biodiversity Strategy. A building stock impact assessment should hence include land use indicators to support EU policy goals. Finally, we noted that in current studies, normalization and weighting are rarely implemented. Normalization is applied in only two studies [21, 36], but no details on the method for normalization are provided. One study [30] uses the International Reference Life Cycle Data System (ILCD) method to normalize and weight the results.
5.5 Strengths and limitations of studies
As a final step towards identifying the potential of existing building stock modelling approaches to support policy as well as the required directions that such approaches should take to provide this support, we reviewed the strengths and limitations of the studies in our sample. An overview of these strengths and limitations, either as stated by the authors of the respective studies or as identified through our review, is presented in Supplementary Information Table S7. A summary of the findings is provided in this section.
Scenarios involving energy efficiency and refurbishments frequently report the potential for improvement in the considered studies. Six studies [16, 18, 19, 26, 27, 31] go even further by examining exogenous factors driving stock development, such as projected population growth and future evolution of the energy mix or the renovation rate. Seven studies [17, 21, 25, 28, 29, 32, 33, 40] furthermore include cost analyses measuring the energy or impact improvements of projects against the payback time, related job creation or investment capacity, which are mentioned as topics for future work in two studies [18, 20].
Not only the number of investigated impacts but also the level of detail in the assessments vary. One way of achieving a higher level of detail is by utilizing digital building stock information tools like GIS or building information modelling (BIM), which are implemented in four studies [17, 19, 26, 36] and stated as a requirement for future research in three more papers [18, 36, 37]. A higher level of detail is also seen in six studies [18, 21, 23, 27, 30, 36] considering multiple environmental indicators. This is in contrast to the other studies, which include only CO2 or GHG emissions, although the reliance on these indicators is criticized internally in some cases [23, 25, 30]. Only two papers [24, 30] achieve a full life cycle analysis by considering all life cycle stages. Three papers go beyond an environmental impact assessment by including policy impacts [21], mobility [24] or a social LCA [23]. The need for a higher level of detail regarding the spatiotemporal aspects applied in LCA is expressed in five papers [19, 21, 26, 31, 36].
Finally, to validate the quality of the results, almost half of the studies [16, 18, 19, 22, 25, 26, 30, 31] include an uncertainty analysis where the influence of different model parameters is considered to evaluate the susceptibility to variation in outcomes. Only one study presents an uncertainty analysis [25] including the use of a probability distribution, although three other studies mention the need to conduct uncertainty analyses [24, 31, 36].
5.6 Typology of methodological approaches
This section provides an overview of the various methodological approaches and their characteristics. The description of the modelling approaches and methodological modules is a synthesis of the methodological frameworks presented in the reviewed studies. The modules are based on previous studies, e.g., IEA EBC Annex 31 [11] and the review by Mastrucci et al. [12], who defined the building aggregation model, energy model, and LCA model as relevant components of stock modelling. We expand the definition to six basic modules common to most of the studies, as presented in Fig. 9: i) building stock characterization; ii) material modelling; iii) energy modelling; iv) life cycle impact assessment; v) building stock aggregation; and vi) visualization and reporting. These represent the basic blocks of models for the environmental assessment of building stocks, as identified in our review.
Building stock characterization relates to the characterization and clustering of the building stock and the definition of representative buildings or archetypes or, where applicable, data processing for a building-by-building aggregation approach. Materials modelling relates to the modelling of building components and materials based on thermal, physical and other characteristics and with related calculation of replacements according to the ESL of building elements. Depending on the modelling approach, quantification of construction materials may be conducted for building elements (e.g., for application in both building-by-building and archetype-based models) or for full buildings only (e.g., for use in archetype-based aggregation). Energy modelling refers to the determination of operational energy demand. The data and tools applied in this module strongly depend on the type of study and the energy modelling method applied (e.g., dynamic or quasi-steady approaches), which are discussed in Sect. 5.3.3 Energy modelling. LCIA relates to the quantification of environmental impacts through the application of impact coefficients. The studies in our sample vary strongly in terms of the scope of the life cycle stages and environmental indicators covered. Hence, the data and tools used for LCIA also depend on the modelling approach and range from a simple application of impact coefficients (i.e., multiplication of material quantities with the respective coefficients, e.g., kilos of CO2eq per kilo of material) to the use of comprehensive LCIA methods with an extended set of environmental indicators and dedicated LCA software. Building stock aggregation is the process of aggregating the information obtained through materials and energy modelling and scaling up to the level of a building stock. In most studies, especially those using an archetype-based aggregation approach, this step is carried out after the LCIA. However, especially for studies using a building-by-building aggregation approach, impact assessment is often conducted after materials and energy information have been aggregated and scaled up to the level of the building stock. The important factor here is the ability of the models to maintain the level of detail in life cycle inventories and impact assessment as they scale up to the level of the building stock. Eventually, visualization and reporting are conducted to interpret and communicate the results of the study and report the findings accordingly. Depending on the type of study, different tools and visualization options – ranging from bar charts or heat maps to show the contribution of different life cycle stages or building parts to spatialized visualizations of assessment results on geographical maps – may be utilized.
For some studies, we could observe the use of a central database or a common data environment to manage the large variety and quantity of data to be processed in complex building stock models. Furthermore, the data processed in the respective modules may follow one definition or contain multiple alternative solutions, as is the case in investigations of different scenarios, e.g., for different retrofit strategies in material modelling or climate change scenarios in energy modelling.
Considering these six modules, we distinguished the stock modelling approaches into four types: type A, product life cycle approach; type B, materials and flows focus; type C, building energy simulation; and type D, cost-benefit analysis. Based on their specific perspectives and focuses, modelling approaches were found to fit one type or a combination of several types. Figure 10 presents the four modelling approaches, indicating the common modules, the modules that are core to the approach, and the limitations or strengths (highlights) of the approach.
Table 2 provides a detailed description of the characteristics identified for the different modelling approaches. A detailed description of an exemplary modelling approach is available in Supplementary Information Figure S2.
Table 2
Description of modelling approach scope and system boundaries as well as tools and software for modelling.
| Modelling approaches and their focus |
Description | A: Product life cycle | B: Materials and flows | C: Energy simulation | D: Cost-benefit |
Summary | | | | |
Context | Background in environmental LCA of products. Detailed models of the building across its life cycle, scaled to stock using archetypes. Often combined with energy models (type C) | Background in industrial ecology and urban metabolism research. Customized approaches for extending material flow analysis (MFA) with LCA, use of GIS | Background in energy modelling, dynamic simulation of buildings. Mostly applied for analysing stock refurbishment. Often combined with cost-benefit models (type D) | Models focusing on identifying the financial cost-benefit optimum for retrofit measures applied at the stock level. Often combined with energy models (type C) |
Strengths | Full life cycle scope (cradle to grave) Hotspot analysis at building level (parts, materials, life cycle stages) | Flexibility regarding spatiotemporal scope, level of modelling detail Scalable across stock sizes (if data available) | Support for dynamic energy simulation of future climate scenarios | Support for identification of cost-effective solutions (some studies include other market-related aspects) |
Limitations | Aggregation based mostly on a limited number of archetypes, with exceptions | Energy modelling often simplified or not included at all | Limited to operation or in-use phase, focused mostly on refurbishment | Aggregation based on an often-limited number of archetypes |
Scope, system boundaries, indicators |
Building stock scale | Stocks from regional to transnational scale, no urban scale studies Timeframe 30–50 years | Stocks from urban to transnational scale Timeframe 25–200 years | Stocks mostly on national scale Timeframe 4–35 years | Stocks from portfolio to national scale Timeframe 20–35 years |
Life cycle stages | Cradle to grave (full life cycle) | Various: Cradle to grave (with limitations), production, end of use | Operation, in use (including materials replacement, refurbishment) |
Building parts, materials | Various: Potential to cover all building parts (e.g., structure, envelope, finishes, technical systems) | Focus on envelope (insulation, windows) and technical systems (heating, cooling, domestic hot water [DHW], photovoltaic [PV]) |
Energy end uses | Comprehensive scope (heating, cooling, DHW, lighting, appliances) | Various: Often no operational energy use considered, as focus on materials | Comprehensive scope (see type A). Only type considering ventilation | Comprehensive scope (see type A, some combinations with type C) |
Impact assessment, indicators | Various: Most studies focus on GHG-related indicators, independent of model type; some present comprehensive scope (e.g., CEN indicator set). Extending list of environmental indicators seems feasible for most approaches | Only GHG-related indicators (e.g., GWP) |
Modelling, data and tools |
General framework, data and tools | - | Use of tools for scripting (e.g., R, Python) and database management (e.g., PostgreSQL) | - | Often combined with dynamic energy models (type C) |
Building stock characterization | Statistical data (national statistics, energy performance, TABULA, EPISCOPE) | Statistical data (geoclusters), GIS tools (e.g., PostGIS, QGIS, GRASS GIS) for building by building | Statistical data (climate zone, energy performance) | Statistical data, characteristics of buildings in portfolio |
Building stock aggregation | Archetype only | Both archetype and building by building | Archetype mostly, sample method | Archetype mostly, building by building |
Materials modelling | Based on plans, documentation. Some use of BIM models to obtain building material information, quantities (e.g., Autodesk Revit) | Element composition based on statistics (material use) and functional requirements (regulations) | Often combined with life cycle models (type A) | Focus on operation, only one study considers embodied impacts of retrofitting |
Energy modelling | Based on statistics or quasi-steady approach. Often combined with energy models (type C) | Quasi-steady approach mostly. Some use of dedicated tools for building energy use modelling (e.g., ECCABS model) | Only type using dynamic energy simulation (e.g., DesignBuilder, EnergyPlus), as well as quasi-steady approach | Quasi-steady and/or dynamic energy modelling tools (e.g., AccuRate, EnergyPlus) |
Life cycle impact assessment | Use of dedicated LCA tools (e.g., SimaPro) and databases (e.g., ecoinvent) | Script-based LCA tools (e.g., Brightway2, SimaPro) | No pattern identified | Common use of impact coefficients |
To relate the identified modelling approaches to the goal and scope of the studies, their life cycle inventory modelling and analysis, we present a compilation of the data extracted on the spatiotemporal scope of studies, their aggregation method and the methodological approach in Fig. 11.