Life Cycle Assessment of building stocks from urban to transnational scales: A review

Renewable and Sustainable Energy Reviews 74 (2017) 316–332

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Life Cycle Assessment of building stocks from urban to transnational scales: A review

MARK

⁎

Alessio Mastrucci , Antonino Marvuglia, Ulrich Leopold, Enrico Benetto Environmental Research and Innovation (ERIN) Department - Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, L4362 Esch-sur-Alzette, Luxembourg

A R T I C L E I N F O

A BS T RAC T

Keywords: Building stocks Energy consumption Bottom-up LCA GIS Decision support

Buildings are responsible for a large share of the energy consumption and environmental impacts worldwide. An assessment of the energy demand and environmental performance of building stocks at large spatial scales is increasingly needed for decision support in sustainable planning and policy making. While current bottom-up building stock models mainly focus on the operational energy use, a new research trend has introduced Life Cycle Assessment (LCA) for a more holistic environmental performance assessment including several stages of the building life cycle. This paper reviews a set of selected bottom-up LCA studies evaluating the environmental impact of building stocks at several scales, from urban to transnational. The selected studies were analyzed according to three common elements: building stock aggregation model, energy analysis and LCA. Two main stock aggregation approaches, archetypes and building-by-building, are applied to model individual buildings and extrapolate results at the stock level. Bottom-up energy models, either statistical or engineering-based, provide energy profiles for the operational phase of buildings. LCA is subsequently performed to assess the building environmental performance and support policy decisions. Current limitations and opportunities for the improvement of the LCA of large building stocks are finally addressed and discussed to support future works. Building stock aggregation models may be enhanced by addressing model calibration, integration with Geographic Information Systems (GIS) and stock dynamics models. Uncertainty propagation, sensitivity analysis and model simplification are recommended for energy models and LCA to bridge data gaps and reduce computationally intensive models. Future research is encouraged on the inclusion of climate change, evolution of the energy supply and integrated modeling for the LCA of building stocks.

1. Introduction Buildings are responsible for about 40% of the global energy use, 40% of the global resources use and one third of greenhouse gases emissions [1]. In the European Union (EU), the building sector accounts for 40% of the final energy consumption and 36% of the carbon emissions [2]. As a consequence, the EU set up a wide legislative framework to lower the energy consumption of buildings, including the Energy Efficiency Directive (2012/27/EU) [3] and the Directive on the Energy Performance of Buildings (2010/31/EU) [2]. Therefore, there is an increasing need for methodologies to evaluate the environmental impact of building stocks [4] and its potential reduction

to support decision in sustainable planning and policy making at different scales, from local to national. As the energy consumption during the operational phase is the largest responsible for environmental impacts of buildings [5], studies on large building stocks have mainly focused on energy-related aspects. A wide range of top-down and bottom-up building stock models has been developed to estimate the current energy profile and evaluate the potential for energy savings [6–8]. However, a thorough analysis of the options for transforming the building stock towards meeting the energy and carbon goals should take into account environmental impacts considering the entire life-cycle of the buildings. This is to avoid suboptimized development strategies when only emissions and impacts

Abbreviation: AP, Acidification potential; CF, Characterization factor; CED, Cumulative energy demand; C & DW, Construction and demolition waste; DHW, Domestic hot water; EP, Eutrophication potential; EU, European Union; FU, Functional unit; GIS, Geographic information system; GSA, Global sensitivity analysis; GWP, Global warming potential; IO-LCA, Input-output life cycle assessment; LCA, Life cycle assessment; LCCO2A, Life cycle carbon emission assessment; LCEA, Life cycle energy assessment; LCI, Life cycle inventory; LCIA, Life cycle impact assessment; MFA, Material flow analysis; OAT, One-at-a-time; ODP, Ozone depletion potential; POCP, Photochemical ozone creation potential ⁎ Correspondence to: International Institute for Applied Systems Analysis (IIASA) - Energy Program (ENE), Schlossplatz 1 - A-2361, Laxenburg, Austria. E-mail address: [email protected] (A. Mastrucci). http://dx.doi.org/10.1016/j.rser.2017.02.060 Received 27 September 2016; Received in revised form 26 December 2016; Accepted 13 February 2017 1364-0321/ © 2017 Published by Elsevier Ltd.

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retrieved from “findit.lu”1 and “Google Scholar”. The terms used for the research were: (life cycle OR LCA) AND (built environment OR building OR housing OR household) AND (stock OR city OR national). From an initial selection of more than 300 papers, subsequent filtering steps were applied to isolate papers focusing on the sustainability performance of buildings and using bottom-up approaches. A set of 15 representative papers (see Table 1) was retained for an in-depth comparative analysis, based on the completeness of the study, provision of results and type of paper. Only peer-reviewed journal papers were retained and gray literature was not taken into account. The included studies span the past ten years, from 2006 to 2015. Additional recent studies were retained to address and discuss specific aspects of the review and formulate future recommendations. The structure of the researches carried out and described in the selected papers follows the same outline in most of the cases. A recurrent set of three elements composing the outline was therefore identified (Fig. 1):

during the operational phase of buildings are considered [9]. Therefore, neglecting the embodied greenhouse gases emissions in retrofitting itself significantly increases the risk of over-representing the environmental contributions of renovation measures, which should be considered regardless of the operational energy saving capacities of the renovation [10]. Furthermore, the transition to low-energy buildings is leading to a decreased impact for the use stage compared to the other life cycle stages [11,12]. The analysis of environmental impacts addressing the entire life cycle of buildings has been extensively addressed at the scale of single buildings. Three streams of life-cycle studies on buildings have been identified [13]: Life Cycle Assessment (LCA) focuses on evaluating the total environmental impacts of buildings over their entire life cycles; Life Cycle Energy Assessment (LCEA) is used to evaluate the energy use as a resources input to a building over its total life cycle; Life Cycle Carbon Emissions Assessment (LCCO2A) addresses the evaluation of the CO2 emissions as an output over the whole life cycle of a building. An extensive literature on the LCA of buildings and building components exists and is the object of several reviews [5,13–17]. A new stream of studies has shifted the environmental evaluation of buildings to larger scales, ranging from the urban scale to the transnational scale. Urban environmental sustainability assessment is increasingly a part of urban planning, from the perspective of mitigating local and global impacts and for adapting to regional and global resource constraints [18]. This change of scale results in increased challenges due to the complexity of the system tackled, data quality and availability and requires new methodological developments to process building stock data in a systematic way. Additional challenges occur when shifting from the urban scale to larger scales due to increased number of buildings and complexity. Loiseau et al. [19,20], after analyzing several methodologies for the environmental evaluation of territories, selected LCA as a promising framework due to the ability to integrate the accounting of consumed resources and emissions as well as their potential environmental impacts. In recent times, some authors focused on the application of the LCA methodology for the evaluation of policies on the transformation of large building stocks in order to improve their environmental performance (see e.g. [10,21–26]). Several scales were addressed depending on the goal of the study, ranging from the urban scale to regional, national and even transnational scale. The use of life cycle product modeling techniques made it possible to develop flow-oriented approaches scalable from particular objects to urban fragments and national stocks and to link them to Life Cycle Inventory (LCI) data [27]. Consequential LCA, aiming at the description of changes in flows as a response to possible decisions [28], has the potential to be applied in this context. While the literature addressing the LCA of the built environment at the neighborhood scale has been recently reviewed by Lotteau et al. [29], to the best of our knowledge there is currently no review work on the LCA of buildings covering larger scales. This paper reviews the literature on LCA applied to the evaluation of the environmental performance of building stocks at a large scale and the effect of policies aiming at the mitigation of environmental impacts. The focus of this review is on bottom-up approaches starting with the analysis of individual buildings and subsequently extrapolating results to the entire building stock. The scales taken into consideration range from the urban scale to the transnational scale. Based on the reviewed studies, recommendations for the development of LCA methodologies for building stocks are finally proposed.

• • •

Building stock aggregation model Building stock energy model LCA

Building stock aggregation is used to describe the entire building stock under investigation starting from modeling individual stock components (buildings or technologies) and allowing for extrapolation of results. The stock aggregation model can be fed by several types of empirical data, such as Census data, Geographic Information Systems (GIS), building databases and subsequent statistical analyses and provides the main inputs for the energy analysis and LCA. The energy analysis provides the energy demand profile of buildings to feed the LCI for the use stage of buildings. A LCA is performed to assess the environmental performance of buildings and outputs are finally sent back to the stock aggregation model for extrapolation at the desired scale and further visualization and reporting. The set of three elements has been taken into account and carefully studied in each of the selected papers. Accordingly, results are reported in three distinct sections: Building stock aggregation (Section 3), Building stock energy analysis (Section 4) and LCA (Section 5). Limitations and opportunities were then addressed in the discussion (Section 6) and final conclusions and recommendations were drawn (Section 7). 3. Building stock aggregation A general classification of building stock models distinguishes topdown and bottom-up approaches [7,8]. Top-down approaches rely on describing the complete building stock at an aggregated level using macro-economic or other statistics. In contrast, bottom-up approaches analyze the performance of a set of individual stock components, such as buildings or technologies, and then extrapolate the results to the stock level. In order to perform a LCA at the building stock level in a bottom-up fashion, a building stock aggregation approach should be identified. Building stock aggregation refers to the performance evaluation of a building stock by environmental assessments of the components of the stock [39]. Two main approaches for stock aggregation were identified 1 “findit.lu” provides access to the following databases: Academic Search Premier, ACM Digital Library, AGRALIN - Union Catalog of Agricultural Libraries in the Netherlands, Allgemeines Künstlerlexikon/Artists of the World (AKL), Berkeley Electronic Press, bibnet.lu (catalogue collectif luxembourgeois), EbscoHost Mobile, EBSCOhost Web, Emerald Management Plus Collection, esp@cenet, IEEE Xplore, IEEE Xplore Mobile, MédiHAL - archive ouverte de photographies et d′images scientifiques, OLC-SSG Technik, OLC-SSG Technikgeschichte, ProQuest Dissertations & Theses A & I, Proquest Dissertations & Theses Open (PQDT Open), SAGE Journals Online, Science Citation Index Expanded (Web of Science), ScienceDirect, SCOPUS, SpringerLink, Wiley Online Library

2. Methodology Only papers focusing on building stocks at a large scale, from urban to transnational scale, and using the LCA methodology were retained for this review. The literature review was conducted on scientific papers

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Table 1 Overview of the reviewed studies. Reference

Geographical context

Nichols et al., [30] Norman et al., [31] Stephan et al., [21] Saner et al., [32] Saner et al., [22] Heeren et al., [23] Anderson et al., [33] Reyna et Chester, [34] Brown et al., [35] Famuyibo et al., [36] Moschetti et al., [37] Wang et al., [10] Pauliuk et al., [24] Yang and Kohler, [25] Nemry et al., [38,26] a

Scale

Building functions

Building type

Aggregation Approacha

Energy model

LC stages Product Construction

Use

End-oflife

Austin, USA Toronto, Canada Melbourne, Australia Wattwil, Switzerland Wattwil, Switzerland Zurich, Switzerland Munich, Germany

Neighbourhood Neighbourhood Neighbourhood

All buildings Residential Residential

Existing New New

A A A

Statistical Existing data Engineering

✓ ✓ ✓

✓ ✓ ✓

Small Municipality

Residential

Existing

B

Engineering

✓

✓

Small Municipality

Residential

Existing

B

Engineering

✓

✓

City Urban region

All buildings Residential

All buildings New

A A

Engineering Engineering

✓

✓ ✓

Los Angeles, USA

County

All buildings

Existing

A

–

✓

Sweden Ireland

National National

Residential Residential

Existing Existing

B A

Engineering Engineering

Italy

National

Residential

Existing

A

Engineering

✓

✓

✓

Sweden Norway China

National National National

Residential Residential Residential

Existing All buildings All buildings

A A A

Engineering Engineering Engineering

✓ ✓

✓ ✓ ✓

✓

Europe

Transnational

Residential

All buildings

A

Engineering

✓

✓

✓

✓

✓ ✓

Stock aggregation approach: A=Archetype; B=Building-by-building.

stock [23]. Up-scaling factors, representing the number of buildings per type or the floor area per type (e.g. [34]) can then be used to extrapolate results. Archetypes can be created from a range of sources, including expert opinion, top-down statistics on characteristics of the stock, empirical databases of the entire stock of buildings or wellclassified reference buildings [39]. Previous review studies about building stock energy modeling [7,8] have shown significant differences in the number of archetypes from few units to some thousands, depending on the adopted approach. The amount of simplification involves careful trade-offs between the complexity of building archetypes and their number [39]. In general it is beneficial to minimize the number of archetypes to facilitate scenario planning while avoiding over-simplifications. In the sample of selected LCA studies, the number of archetypes differs from 2 to 72 based on the scope of the study, building stock dimension and stock characterization approach. At the neighborhood and urban scale the number of archetypes is commonly small due to a more distinctive homogeneity of the stock. Some of the studies represent a neighborhood by the most representative building type. Anderson [33] identified three building types (a multi-family house, a row-house and a single-family house), one for each of the three locations considered (city center, city periphery and extra-urban ares) representing the predominant typology of each area. The use of only three archetypes is justified by the authors to maximize the impacts between locations taking into account the extremes only. Reyna et Chester [34] generated 42 prototype LCAs, normalized the environmental impacts per unit area and finally multiplied the results by the area of each building in the Assessor database for the County of Los Angeles [40]. At the national and transnational scale, a higher number of archetypes is often necessary to cover the range of different building typologies, construction characteristics and climatic zones. Famuyibo et al. [36] identified 13 archetypes after a statistical analysis [41] by the combination of construction details and household variables of energy use for the characterization of the Irish national building stock covering 65% of the residential buildings. Moschetti et al. [37] examined

Fig. 1. Generic methodology for the LCA of building stocks.

in the sample of selected studies (Table 2 and Fig. 2): archetypes and building-by-building approaches. In the archetypes approach, the global environmental performance is assessed by analyzing a subset of archetype buildings, each one representative of a specific building cohort, and then factoring the results in proportion to the total number (or other indicators) of such buildings in the stock. In the building-bybuilding approach, all individual buildings in the stock are analyzed and the global environmental performance is obtained by aggregating individual results at the stock level. We relate to this stream the sample approach described by Swan et al. [7] where a representative sample of real buildings is modeled - instead of the entire population - and weighting factors are then applied to get the results at the stock level. The following sections address the description of the two approaches (Section 3.1), the inclusion of spatial aspects (Section 3.2) and the accounting of stock dynamics (Section 3.3).

3.1. Stock aggregation approaches 3.1.1. Archetypes approach The archetype approach has been widely adopted for the building stock characterization oriented to the estimation of the energy performance. The archetypes technique is used to broadly classify the building stock according to age of construction, size, house type, etc. [7]. Each archetype represents a specific class of buildings, starting from which one can extrapolate the energy requirements to the entire 318

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No 19

✓

3.1.2. Building-by-building approach An alternative approach to the archetypes is represented by the building-by-building approach. In this approach, instead of modeling a limited number of archetype buildings and subsequently extrapolating results to the building stock, all buildings belonging to the stock are modeled one-by-one. The total performance of the building stock can therefore be obtained by summing-up the performance of individual buildings. In the sample of selected studies, only two papers by the same research group adopted a full building-by-building approach [32,22]. Instead of analyzing the final demand vector f of an average household, they assessed the final demand vectors fi for individual households i in a small municipality in Switzerland. The final demands can therefore be expressed as matrix F = (f1 |…|fi |…|fN ) for all N households of a region under investigation. Each building was modeled as a cube with a square base and the areas of main building components, namely roof, wall, floor and windows, were calculated from the area of the building surface using suitable computation methods and correction factors. The input data for the analysis of the environmental performance in the stock are not always available at the individual building level. For this reason, building characteristics can be attributed based on typical features of a certain building cohort, e.g. based on the type and period of construction, and subsequently associated with individual buildings. Saner et al. [32,22] assumed the parameters not available at the building level (renovation year of specific building parts, roof inclination, specific heat technology) as stochastic and they randomly assigned values among buildings based on statistical distributions. Other building-by-building models were developed at the urban scale to assess specific stages of the life cycle of buildings. García-Pérez [43] assessed the environmental impact of refurbishing building

Stock aggregation approach: A=Archetype; B=Building-by-building.

53 Transnational Nemry et al., [38,26]

A

Dynamic MFA using building demolition and renovation rates. Model based on population evolution, urbanization rate, need for building surface and comfort. ✓ ✓

archetype buildings for three different Italian locations belonging to three distinct climatic zones. Suitable U-values of building elements were set up according to the national regulation requirements. Wang et al. [10] modeled three building archetypes at the national scale in Sweden representing at the same time three representative building types and periods of construction claiming that buildings' main elements and service systems were similar. At the European level, Nemry et al. [26] identified a total of 72 building types including existing and new buildings, representing altogether 80% of the whole building stock in the EU-25 in terms of living area. Building archetypes can greatly differ regarding the level of details used for their description. Studies adopting pre-calculated values for the intensity of embedded or operational energy and environmental impacts (e.g. per unit of floor surface area) typically use a simplified description of archetypes, often limited to their floor surface area or main geometrical features (see e.g. [30,31,34]). On the other hand, some authors modeled archetypes up to the level of detail of individual building components (see e.g. [33,37,10,26]). This latter approach makes it possible to fully account for different building designs and future interventions on the stock. The level of detail is also strongly related to the type of energy model and LCA approach used for the analysis. For instance, engineering-based dynamic models (see Section 4.1) typically require a number of input parameters and consequently a high level of detail for the description of building archetypes. Advantages of the building archetypes technique include the capability to easily describe and analyze the stock and create new scenarios, improved understanding of how resources are and will be used and potential greater accuracy than top-dow approaches if high quality data and analysis is available [39]. At the same time, one of the main limitations in the archetypes approach concerns the process of classification and characterization of building archetypes which relies on many assumptions and remains rather arbitrary due to the lack of detailed information at the large scale. Attempts to move forward and include such a variability in the analysis were made by some authors limited to the use phase of buildings [42].

a

Model based on building turnover estimation. ✓ No Inventory, Visualization

3 – NA – 36 – NA NA Urban region County National National National National National National Anderson et al., [33] Reyna et Chester, [34] Brown et al., [35] Famuyibo et al., [36] Moschetti et al., [37] Wang et al., [10] Pauliuk et al., [24] Yang and Kohler, [25]

A A B A A A A A

– 42 NA 13 – 3 30 NA

✓ ✓

✓ Inventory, Visualization Inventory, Visualization ✓ ✓

Inventory ✓

NA 2 3 NA NA 5 NA – – NA NA 8 Neighbourhood Neighbourhood Neighbourhood Small Municipality Small Municipality City

New

Nichols et al., [30] Norman et al., [31] Stephan et al., [21] Saner et al., [32] Saner et al., [22] Heeren et al., [23]

A A A B B A

Stock dynamics Spatially explicit Existing

Aggregation Approacha Scale Reference

Table 2 Building stock aggregation approaches in the reviewed studies.

GIS integration

Temporal aspects Spatial aspects N. archetype buildings

Technique

Model based on retrofit diffusion factors, market diffusion rates and lifetimes of products.

A. Mastrucci et al.

319

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Fig. 2. Overview of the archetype and building-by-building approaches.

climatic zones and weighting the environmental impact results based on country-specific data on buildings and dwellings, including the segmentation into building types and age. The energy carrier mix was calculated from the weighted energy mixes of the individual countries. In other cases, the process of spatialization was carried out in a more refined way, commonly by using GIS data and processing. Having GIS data available, it is possible to make the association between buildings and archetypes and obtain geolocalized impact sources. This kind of spatial analysis was mostly carried out at the urban [30,32,22] or regional scale [34]. Nichols et al. [30] used GIS databases and data processing to estimate the amount of energy embedded in building materials at the city scale. Their estimation of total built areas are based on information on building types, base footprints and number of stories. Average rate of embodied energy per built area by building type are then assumed and multiplied by the total area to obtain global results. A similar approach was adopted in the study by Reyna and Chester [34] where a series of prototype LCAs depending on the building type and period of construction were normalized per unit area and multiplied by the area of each building provided by a local georeferenced database. Saner et al. [32,22] computed the surface area of individual geo-referenced buildings for the estimation of the energy demand at the municipality level. Integrating GIS with building stock energy analysis and LCA offers a series of advantages. The use of spatially-explicit data contributes to the refinement and enrichment of the building inventory, making it possible to explicitly consider spatial constraints, e.g. linked to resource supply, site-specific characteristics, current and future infrastructures and networks, suitability of renewable energy installations [45,22]. Geo-referencing energy demands and environmental impact sources further supports the identification of hot-spots and improvement potentials region-by-region. The geometry of buildings across the stock can be estimated in a more detailed way by GIS processing [43,44,12]. As current bottom-up models often rely on average intensities for the estimation of environmental requirements disregarding the three dimensional geometry of each building, it is therefore needed to advance bottom-up approaches at a high spatial resolution and include construction aspects, assemblies and materials [55]. Finally, GIS can be used to display results as spatial maps for improved communication [32,22,34]. In contrast, current limitations regard the integration of GIS with energy models and with LCA [56], in particular those at the territorial scale [20]. In addition, GIS applications are mainly limited to the urban scale, as revealed by the sample of reviewed studies. Application to the regional and national scale might be hampered by data availability, resolution and larger computation burdens.

façades for the city of Barcelona (Spain) using a building-by-building approach integrating LCA and GIS. Mastrucci et al. [44] developed a geospatial framework for the characterization of building material stocks and LCA of the end-of-life scenarios applied to a municipality in Luxembourg. At larger scales (e.g. national), developing a full building-bybuilding model might results as excessively demanding due to the large size of the stock and not feasible due to the lack of detailed data. Modeling a sample of representative real buildings in lieu of the full population becomes more convenient in this case. Environmental impacts are calculated for every building in the sample and up-scaling factors (e.g. number of buildings, floor surface, etc.) are then used to aggregate results at the stock level. This approach, also known as sample approach from previous studies [7], makes it possible to capture the wide building stock variability in the results. Brown et al. [35] developed a model of this type to assess the embodied greenhouse gas emissions from refurbishment of the Swedish residential stock. A sample of about 1400 single-family and multi-family houses was modeled to assess the effect of several refurbishment measures in achieving the target of 50% operational energy reduction and the consequent increase in embodied emissions. Up-scaling factors representing the number of buildings in the national stock represented by buildings in the sample were applied to aggregate results at the stock level. 3.2. GIS integration In recent times, a new trend started moving bottom-up building stock analysis towards more sophisticated, spatially differentiated models related to GIS [45]. A number of spatial building stock models were developed for the analysis of energy policy scenarios (see e.g. [46– 51]) and anthropogenic material stocks (see e.g. [52–54]) in urban contexts. An extension of spatial building stock models to LCA has been suggested to account for life-cycle environmental impacts of the development strategies [9]. While a part of the reviewed studies is limited to the analysis of buildings regardless their location, some others explicitly include a spatial dimension. In some cases, the spatialization is made by just attributing input data and performing calculation at the level of a specific geographical entity. For instance, Anderson et al. [33] analyzed the urban region of Munich by distinguishing three different locations (namely, the city center, the city periphery and the districts) and by attributing a specific archetype building to each of them. The analysis of environmental impacts for each of the three locations revealed that different strategies should be applied to each location. Nemry et al. [26] characterized the building stock in the EU-25 at the level of the countries by identifying archetypes for each of the three considered 320

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the building stock evolution and effects of policy decisions over time. At the city scale, Heeren et al. [23] modeled the evolution of the stock's energy demand over time by using diffusion factors for describing retrofit quality and rate. The energy demand for household appliances was also modeled over time by considering specific energy demand, market diffusion rate and product lifetime. Reyna et Chester [34] developed a methodology for estimating turnover rates based on buildings in the county of Los Angeles (USA). A doubly-constrained growth factor model was used to converge on a matrix-based solution of estimates of building turnover. Pauliuk et al. [24] used dynamic MFA to model the dynamics of the entire Norwegian dwelling stock for future prediction with externally given rates for demolition and renovation. The adopted approach is a combination of the “leaching model” and the “lifetime model”, where the former assumes that the annual outflow is proportional to the total stock, considered as homogeneous, and the latter makes use of predetermined lifetime distributions for the individual building cohorts. While the majority of the reviewed studies adopted a static approach, accounting for the temporal evolution of the stock appears as a crucial point for modeling future scenarios and supporting medium and long term projections. Nevertheless, uncertainty linked to future scenarios should be considered and carefully analyzed to ensure the robustness of results.

Fig. 3. Inclusion of building stock dynamics in the reviewed studies.

3.3. Stock dynamics A large amount of construction material is required in urban areas for the development and maintenance of buildings and infrastructure [52]. Considering the dynamics of building stock is therefore important to understand existing material cycles and investigate ways to make them more efficient [57]. One of the advantages of bottom-up models, is the capability of modeling the building stock dynamics, by accounting for the turn-over of older portion of the stock and changes in technologies [39]. The integration of material flow analysis (MFA) with LCA appears particularly interesting in this context. MFA models [58] have been widely applied to model the flows and stocks of resources over the time within a given system and are based on a mass balance of inputs, outputs and change in the stock. MFA methodologies at the national scale make use of conventionally accepted economic indicators like gross domestic product whereas at the regional and city scale the available data and boundary conditions can vary greatly making it difficult to establish any widely accepted MFA methodology [57]. In the sample of studies, the stock was modeled according to static or dynamic methods (Fig. 3). Static methods consider the building stock at a precise moment in time, either in the present or in a future state. In contrast, dynamic stock methods are able to capture the evolution of the building stock in time. Stock dynamic aspects taken into account were building demolitions, renovations and new constructions. Most of the studies adopted a static approach by focusing on the current state of the building stock [30–32,37] or comparing the current situation with an ideal future situation to show the potential for improving the environmental performance (e.g. calculate the theoretical effect of buildings' retrofit compared to the current situation) and the effect of policy decisions [21,22,33,36,10,26]. A few studies integrated dynamic stock models with LCA to study

4. Building stock energy analysis The goal of this step is the estimation of the energy demand of buildings at the stock level for providing further input to the LCI. A wide range of building stock models exist and were extensively reviewed elsewhere [7,8,59]. This section aims at describing the range of energy models adopted in the reviewed LCA studies and it is not intended to be a comprehensive review on building stock energy models. Due to the high level of disaggregation, the choice of a bottom-up energy modeling approach is common in the LCA of building stocks. The selected energy models encompass both engineering-based and statistical bottom-up approaches (Table 3), according to the definition given by Swan et al. [7], and are further detailed in the following sections. Most of the reviewed studies integrated a bottom-up energy model, with only a few exceptions: Reina et al. [34] did not consider the use stage phase of buildings; Norman et al. [31] made use of existing data about energy consumption of buildings. 4.1. Engineering-based approach Engineering-based approaches use information on the building

Table 3 Building stock energy models in the reviewed studies. Reference

Nichols et al., [30] Norman et al., 2006 [31] Saner et al., [32] Saner et al., [22] Heeren et al., [23] Anderson et al., [33] Brown et al., [35] Famuyibo et al., [36] Moschetti et al., [37] Wang et al., [10] Pauliuk et al., [24] Yang et Kohler, [25] Nemry et al., [38,26]

Energy model

End-uses

Approach

Type

Calculation time-step

Space heating

Space cooling

Statistical Existing data Engineering Engineering Engineering Engineering Engineering Engineering Engineering Engineering Engineering Engineering Engineering

Regression – Dynamic Dynamic Steady-state Steady-state Dynamic Steady-state Dynamic Steady-state Steady-state Steady-state Steady-state

NA Annual Hourly Hourly Monthly NA Hourly Annual Hourly Monthly Monthly Annual Monthly

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓

321

✓

✓

DHW

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Lighting, Appliances, others ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

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Only one of the selected studies [30] adopted a statistical approach for the prediction of the electricity and natural gas consumption of residential and commercial buildings in Austin (Texas, USA). The approach was based on ordinary least squares regression for the estimation of building energy consumption based on a series of building-specific variables, including home age, square footage, and indicators for urban versus suburban location, and single-family versus multi-family unit type. In contrast, statistical approaches and reference values are commonly adopted for the evaluation of DHW and electricity use (for appliances, lighting, etc.) [10,23,30–33,35,37]. None of the studies applied geostatistical approaches, such as kriging [64], for a spatial-dependent analysis of building energy consumption. This would help to improve statistical models by adding geospatial aspects.

Fig. 4. Goal, spatial scale and temporal horizon of building stock LCA studies (adapted from [39]).

5. Life cycle assessment

characteristics and end-uses to calculate the energy consumption based on heat transfer and thermodynamic principles. These models have the capability to model new technologies which have no historical consumption data, but assumptions on the behavior of occupants must be done [7]. Most of the reviewed studies adopted an engineering-based model to calculate the energy demand for space heating (Table 1). On the other hand, they mainly relied on statistical approaches or other existing data to estimate the energy demand for other end-uses, such as Domestic Hot Water (DHW), lighting and appliances. This can be explained as space heating demand is highly dependent on building physics while other final uses are highly related to the behavior of occupants. Engineering methods are also effective in predicting the effect of building refurbishment in terms of energy savings. Concerning the calculation time-step of the energy models, two basic methods exist: steady-state methods calculate the heat balance with long time steps (monthly to seasonal) and can take dynamic effects into account by empirical gain and loss utilization factors (quasisteady-state methods); dynamic methods calculate the heat balance with short times steps, for instance hourly, and take into account the effect of the mass of the building in storing and releasing heat [60]. The studies in the selected sample equally divide between steady-state and dynamic methods. As an example of steady-state methods, Stephan et al. [21] computed the thermal energy requirements based on static heat transfer equations that multiply the average heat transfer coefficient of the building (U-value) by its heat loss area and the heating or cooling degree-hours. Ventilation losses and gains were calculated by multiplying the average ventilation rate by the volume thermal capacity of air and the heating/cooling degree-hours (natural ventilation assumed).

This section presents a comparison between LCA studies for building stocks at large scales. The analysis was performed according to the four steps of the LCA [65]: goal and scope definition, life cycle inventory, life cycle impact assessment and interpretation. 5.1. Goal and scope definition 5.1.1. Goal and scope The reviewed studies have as a goal the evaluation of building stock sustainability in a life cycle perspective to support urban planning and policy making. The goal is generally related to the specific spatial scale and planning temporal horizons of the study. Here we include studies ranging from urban to transnational scales and having medium to long temporal horizons (Fig. 4). Three main streams of investigation have been identified (Table 4): 1. studies aiming at evaluating the environmental performance of the building stock in the current state to inform about issues and improvement opportunities [30–32,34,37]; 2. studies comparing the current situation with an ideal future situation to show the potential for improving the environmental performance (e.g. calculate the theoretical effect of retrofitting buildings compared to the current situation) and the effect of policy decisions [21,22,33,36,10,26]; 3. studies modeling the evolution of the building stock over time to build medium or long term scenarios and support decision in policy making [23–25]. Studies belonging to the first and second stream include all geographical scales, from the neighborhood to the urban, national and transnational scale. On the other hand, the third stream includes only studies at a large scale, from the city to the national scale. Regarding the geographical distribution of the studies, it is evident that most of the case studies are located in Europe and North America (Table 1). Bottom-up LCA studies at the large scale in other continents are currently limited, with a few exceptions for Australia [21,55], China [25] and Middle-East [66]. The selected studies differ not only for the geographical scale, but also for the accounted portion of building stock, in particular concerning building function and existing versus new constructions. Regarding the building function, all studies include residential buildings. A few studies include other building functions: office and commercial [30,25], offices and schools [23] or the full building stock [34]. Most of the studies deal with the existing building stock while others focus on new buildings only (Table 4). As to the future evolution, some of the studies assumed that new building are built according to the current construction practice [31,33,37]. Some other authors included in the analysis existing buildings and new constructions to provide a more comprehensive picture of the building stock and analyze its evolution in the future [23–26]. Refurbishment measures already implemented in the past [22–26] or to be implemented in the future [33,23,35,36,10,24] have been accounted for by some studies for a better depiction of the current or future state of the stock. Some of the

4.2. Statistical approach Statistical approaches rely on measured consumption records and techniques such as regression, conditional demand analysis and neural networks to model the energy consumption of buildings [7]. They represent an interesting option to avoid the burden associated to engineering-based approaches, provided that measured energy consumption data are available [61]. A number of statistical models were developed to predict the energy consumption of buildings at the large scale [7,8]. In particular, linear regression analysis has been largely used as it provides a good trade-off between accuracy, computational efficiency and ease of interpretation of the results [61–63]. Statistical approaches are capable of taking the actual occupants’ behavior into account, being based on measured consumption data. On the other hand, they are limited in predicting the impact of new technologies [7]. For this reason they are scarcely applied for prospective LCA studies requiring the modeling of future scenarios. Another barrier for their implementation is the availability of measured consumption data at the large scale. 322

Studies were grouped in three streams: 1=evaluation of the environmental impacts in the current state; 2=comparison with an ideal future state of the stock; 3=assessment of the evolution of the stock in medium-long term scenarios.

✓ ✓

✓ ✓ ✓ ✓

5.1.2. Functional unit The functional unit (FU) provides a quantification of the identified function of the studied system and constitutes a reference to which the inputs and outputs are related [65]. A range of FUs have been used, including absolute, spatial or per capita FU. FUs are not always explicitly specified in the studies. In general, the choice of the FU seems to be independent from the scale of the study but rather dependent on the objectives and on the presentation of results. Many authors used the living (or heated) floor area as the FU [36,26,25]. This choice allows the comparison of different residential development projects on a homogeneous basis [67]. Other authors used the gross floor surface unit, including unheated spaces, to ensure the comparability of LCA analyses [37]. For the evaluation of the embodied emission associated with refurbishment measures, a different approach consists in establishing a FU for each identified measure [35]. While the floor surface area unit is used for some of the measures (e.g. installation of mechanical ventilation), the building component surface area was adopted as a FU for measures related to the building envelope (e.g. wall insulation or window replacement). The total embodied emissions related to each refurbishment action were then calculated as the product of the quantity of the FU required and the embodied emissions for the relevant refurbishment measure. Per capita FUs, such as per inhabitant or per person, are adopted by many authors [23,30,33]. This choice makes it possible to compare the impact of different life cycle stages and sectors (e.g. building-transportation) [33]. In addition, per capita indicators allow for a more fair and reasonable comparison as the responsibility to comply with the environmental restrictions to our development process should be shared among the people, and not among the square meters [68]. An absolute FU was adopted by Saner et al. [22], defining their FU as providing a heated living space as well as hot water and electricity for each building in the municipality. Some authors used multiple FU to highlight their relevance and differences in results [31] or make them better comparable with other studies [21]. Norman et al. [31] illustrated that the choice of FU is highly relevant to a full understanding of urban density effects and critical when assessing relative embodied energy and GHG emissions in the context of different urban densities. Stephan et al. [21] used absolute, spatial or per capita FU for a better comparability of results with other studies. 5.1.3. System boundaries This section analyzes the life cycle stages included within the reviewed papers. According to the standard EN 15643-2 [69] three main stages are identified for the building life cycle (Fig. 5): the product stage and construction process; the use stage; the end-of-life stage. The reviewed studies are heterogeneous regarding the included or omitted

a

✓ ✓ ✓ ✓

✓ ✓ ✓ ✓

NA 50 50 NA 50 100 NA NA Depending on retrofit measures 50 50 NA NA 30 20–40 (residual life) 1 1 2 1 2 3 2 1 1 2 1 2 3 3 2 Nichols et al., [30] Norman et al., [31] Stephan et al., [21] Saner et al., [32] Saner et al., [22] Heeren et al., [23] Anderson et al., [33] Reyna and Chester, [34] Brown et al., [35] Famuyibo et al., [36] Moschetti et al., [37] Wang et al., [10] Pauliuk et al., [24] Yang and Kohler, [25] Nemry et al., [38,26]

Existing New New Existing Existing All buildings New Existing Existing Existing Existing Existing All buildings All buildings All buildings

Per capita Living area, per capita Absolute, land area, per capita Absolute Absolute Per capita Per capita Land area Depending on retrofit measures Heated floor area Gross floor area Retrofit area, heated floor area Heated floor area Heated floor area Reference floor area

✓ ✓

✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓ ✓ ✓

Building operation Construction Production

✓ ✓ ✓ ✓ ✓ ✓ ✓

✓

Maintenance Repair Refurbishment Use Product/Construction

Stages included Service life (years) FU Building type Goala Reference

Table 4 Overview of the LCA goal and scope phase in the reviewed studies.

✓

End-of-life

studies have an extended scope embracing a wider assessment of the urban settlement, including urban infrastructures [30,31,33,25] and mobility [30–33].

✓ ✓

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Fig. 5. Overview of the main life cycle stages considered by the reviewed studies.

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the results can change significantly replacing average data by (a set of) marginal suppliers and taking constrained suppliers into account, as shown by two recent studies [73,74]. A distinction between foreground and background inventory was considered in this review, referring respectively to the processes which are or are not under the control of the decision-maker for which an LCA is carried out [75]. The analysis of the foreground inventory was further detailed according to the main stages of building life cycle (Fig. 5): product-construction stage, use stage and end-of-life stage. In some of the reviewed studies, data sources are not specified and the distinction between primary and secondary data is not clear.

stages. Only four of the selected studies considered the three stages of the life cycle [33,37,24,26]. The production-construction stage is normally taken into account for new buildings. However, Reyna et Chester [34] evaluated the embedded environmental impact of existing buildings. Other authors, (e.g. Nemry et al. [38]) decided to omit the construction phase for existing buildings since they argue that it is not relevant for the identification of improvement options. All reviewed studies included the use stage, except the study by Reyna et Chester [34] whose focus is the embedded environmental impact of buildings. Contributors to the environmental impact of the use stage included in most of the studies are thermal energy requirements for heating and DHW. Many studies took into consideration the use of electricity for appliances and lighting, while only a few included space cooling [30,31,37] and drinking water [30]. The refurbishment stage is included by most of the studies to account for the impact of implementing operations to increase the energy performance of buildings. However, in the study by Reyna et Chester [34] about the building stock embedded environmental impact, refurbishments were excluded due to their uncertain impact on the embedded emissions profile. It is argued that retrofitting increases the life cycle emissions of the individual building, but not necessarily the embedded emissions profile of the urban building stock, due to the contemporary addition and removal of building material during this operation. In contrast, repairreplacement and maintenance are only considered by respectively [21,26] and [36,37]. The end-of-life stage was considered by only six of the reviewed studies [33,36,37,24,26]. The exclusion of this stage from other studies was motivated by the lack of available data and the relative insignificance of this stage to the overall buildings' life cycle [31], despite the considerable amount of waste produced [21].

5.2.1. Foreground inventory Product and construction stage. This stage includes the production stage and construction process. In this respect, detailed information about the building construction, materials used, material transportation distance, and end-of-life processes are required [33]. While the extraction of raw materials and product manufacturing is normally taken into account, only a part of the studies included transportation and construction. Input data for the life-cycle inventory were obtained from a range of sources: local GIS datasets and building registers [30,22], statistical and real estate market analysis [33], national building libraries [37], surveys [35,36], guidelines [10] and case studies [24]. In process-based LCAs, the life-cycle inventory is derived from the building stock characterization process. For the archetypes approach, the inventory is generated based on the archetype buildings by identifying typical characteristics. Nichols et al. [30] estimated the embodied energy for buildings based on a meta-analysis of detailed life-cycle assessment studies. Embodied energy estimated depending on the building types (per floor space unit) and various materials are combined with georeferenced measurements and GIS based calculations for neighborhood features (e.g. total square footage of residential space by home type). Results were normalized on an annual basis depending on the material lifespans assumptions. According to Heeren et al. [23], the flow of construction materials can be conveniently estimated using a component-based approach. Material flows were deduced after having identified the diffusion of different technologies and the retrofit rates for building components [23]. Anderson et al. [33] defined typical characteristics of residential buildings at three different locations in the area of Munich (Germany) through local statistical and real estate market analysis. In the study by Reyna et Chester [34] for Los Angeles (USA), material inventories are based on standard classifications and sample material information depending on the period of construction. Scaling factors were developed to account for increased material quantities required in seismic areas. At the national scale, Moschetti et al. [37] relied on Italian building libraries completed with literature data to identify the characteristics of building archetypes. Pauliuk et al. [24] referred to a specific case study for the detailed material and energy inventory of single-family houses in Norway and determined approximate results for other housing typologies on this basis. Alternatives to process-based LCA are represented by IO-LCA and hybrid LCA. Norman et al. [31] used a IO-LCA approach coupling national economic input-output accounts with environmental data for major industrial sectors to account for construction materials in the LCA of a neighborhood in the USA. One of the main advantages of this technique is that it takes into account economy-wide impacts and it is not constrained by arbitrary system boundaries. A hybrid approach was used in the study by Stephan et al. [21] to assess the energy embodied in building infrastructures. The authors claim that this technique is systemically complete and is able to provide the most comprehensive figures for embodied energy compared to process LCA or IO-LCA analysis. Transportation of construction materials is not frequently detailed. Moschetti [37] defined transportation means and distances to the

5.1.4. Service life of buildings Service life of buildings is necessary for the evaluation of building life cycles. Values vary across author and study for various reasons, ranging from differing economic life times of buildings and countries to non-technical and technical considerations. Different approaches were found in the reviewed studies. The simplest approach consists in attributing a fixed service life of buildings. For new buildings, typical chosen values range from 50 [31,37] to 100 years [21]. For existing building, a residual service life is considered instead. Values are heterogeneous in this case as well, 20– 40 years depending on the building type [26], 35 years [25] up to 50 years [36]. In reality, building lifetimes vary considerably, and scenarios using standard assumptions may have incorrect results [70]. In addition, the typical service life period is inappropriate for some materials, given their exposure and use, and the simplified descriptions do not fully represent maintenance, repair and replacement cycles [71]. Some of the reviewed studies overcame this issue by identifying different service lives for different building elements and materials [38] and for refurbishment measures [35]. 5.2. Life cycle inventory The LCI stage consists in the collection and inventory of inputs and outputs of energy and materials within the boundaries of the analyzed system. Two main approaches to data collection can be identified [29]: process-based LCA is a bottom-up technique modelling the system based on inventories at material and process level; input-output LCA (IO-LCA) is a top-down technique using economy or industry sector wide inventory data. Hybrid LCA is combination of the two techniques. Almost all the LCA studies currently available in the construction sector are based on an attributional approach. However, for the development of long-term future improvement scenarios a consequential approach would be more suitable [72]. For some building products 324

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Similarly to the findings of a recent review on the LCA of building refurbishment [79], most of the studies focused on energy refurbishment while a very few addressed the environmental impact of building system maintenance and reparations, including structures or finishing. In particular, maintenance was taken into account only by few of the reviewed studies [21,36,37,26]. A life span factor [37] or residual service life [26] was attributed to building materials to evaluate the number of replacements during the life span of buildings. End-of-life stage. The literature on the application of LCA to the end-of-life of buildings and construction and demolition waste (C & DW) treatment is quite large [80], however only few studies addressed this topic at the building stock level. From the perspective of “urban mining”, the building stock can be considered as a repository of natural resources [81]. An inventory of their quantity and dynamics is therefore needed to provide information on potential future production of C & DW and potential reduction of associated environmental impacts [53]. At the city scale, some recent studies addressed the characterization of materials embedded in existing buildings for the quantification of C & DW. Wu et al. [54] proposed a novel approach based on GIS for the quantification of demolition waste, from generation to final disposal, and formulation of corresponding management strategies. A GIS-based characterization of the existing material stock can further support the generation of LCI for the assessment of demolition waste strategies at the city scale [44]. Blengini and Garbarino [82] analyzed the environmental implications of the C & DW recycling chain in the administrative territory of the Province of Turin using GIS and site-specific data. The geographical coverage of market demand and use was evaluated under assumptions relevant to transportation distances, type and grade of recycled aggregates, as well as local availability of natural aggregates and geographical coverage of market demand. At the national scale, Moschetti et al. [37] modeled the end-of-life stage for the housing stock on the basis of the information available in previous studies. The processes taken into account are: initial selective dismantling stage regarding recyclable materials, such as windows, doors, aluminum; controlled demolition of the building by hydraulic hammers; demolition waste disposal, including transportation to treatment plants and final processing (e.g. recycling, landfill). The average distance from the treatment plant was fixed to 15 km for inert material and 20 km for recyclable materials. In their study on the European housing stock, Nemry et al. [38] accounted separately for the handling of wastes accumulated during the demolition of the original construction materials and during the refurbishment of the building. Different waste treatments were identified according to the material: landfill for inert matter (glass, concrete, stones, other minerals and construction waste), recycling (aluminium and steel) and incineration (for foam plastic, PVC and wood waste) with respective collection rates.

construction site through LCA guidelines [76]. Transportation of building materials was assumed by lorry and distances were differentiated between concrete based materials and wood (50 km) and all other materials (100 km). Nemry et al. [38] estimated the transportation distance for the European residential building stock by a distribution model for Germany based on two parameters, namely population density and the distance [77]. The resulting transportation distance for Europe resulted in 293 km. The construction phase was included only by few authors. Moschetti et al. [37] considered energy and diesel for the construction process, assessed and adapted with reference to literature data and associated materials with a cutting waste factor representing the percentage of cutting waste generated during the building construction processes. Pauliuk et al. [24] included in their analysis construction energy and groundwork. Use stage. The use stage includes operational energy and water use of buildings, but also maintenance, refurbishment, etc. Most of the authors focused on the operational energy use, in particular energy for space heating and DHW, due to its importance in the life cycle of conventional buildings [78,11]. Inventories for the energy use in buildings are typically obtained based on a range of energy models, as described in Section 4. In contrast, the use of water was rarely addressed in the sample of reviewed studies. Nichols et al. [30] assessed the water use in residential and commercial buildings by assuming aggregate estimates as detailed data are rarely available. Waste water use was assumed as 40% of fresh water use to include drain flows of indoor uses. Moschetti et al. [37] included the consumption of water consumed, limited to DHW requirement. The refurbishment stage was assessed by many of the studies, typically representing the measures to achieve thermal performance improvements of existing buildings for operational energy reduction (also known as retrofitting) [33,35–37,10,24,26]. In the LCA of buildings, energy refurbishment are commonly assessed by comparing the environmental impacts before and after refurbishment [79]. The analysis of sustainability improvements of retrofitting, particularly large-scale measures, should take into account the embodied energy and environmental impacts from the material productions and refurbishment implementation. This aspect is particularly important for the analysis of future sustainable refurbishment options, which is defined as considering both operational energy savings and corresponding embodied energy and environmental impacts [35]. Therefore, neglecting the embodied impact of refurbishment options will increase the risk of over-representing their energy-saving contributions [10]. The activities considered in the refurbishment stage include the provision of new materials, transportation and disposal of old materials. Building envelope refurbishment measures are among the options more frequently assessed, in particular external walls insulation, roof/ attics insulation and window replacement [33,35,36,10,24,26], ground floor and foundation insulation [35,36,10,24], air tightness improvement through sealants [36,10,26] and thermal bridges reduction [10,24]. The installation of mechanical supply/exhaust ventilation systems with air recovery was addressed by a few studies [35,10,24]. Some authors evaluated the improvement of heating and domestic hot water systems with regard to the replacement of traditional boilers with condensing boilers [36], installation of energy-efficient circulation pumps [10], low-flow domestic hot-water fittings [35], thermostatic radiator valves and radiator retrofit [35,10]. The behavior of occupants was also considered, limited to the reduction of the indoor set-point temperature [35]. Famuyibo et al [36] addressed the integration of renewable energy sources, in particular photovoltaic roof panels. Finally, lighting improvements were addressed as for the replacement of incandescent bulbs with compact fluorescent light bulbs [36] and the installation of sensor and controls [10]. Depending on the goal of the analysis, different refurbishment standards were considered, including current refurbishment standard required by regulations and future standard, such as passive building standards (see e.g. [36,24]).

5.2.2. Background inventory Background inventories, including processes not specific for the studied system, relied on international databases in most of the studies. Nine of the reviewed studies [10,22–25,32,33,35,37] used the Ecoinvent database [83], resulting in the most frequently used. The Gabi database was used by two studies [36,26]. A few studies reported about adaptation of the background inventory to the case study. One of the major aspects in background inventory is the account of energy mixes. For instance, Moschetti et al. [37] used the national Italian natural gas and electricity mix for their case study. Great level of uncertainty occurs for future energy mix scenarios. While some of the studies kept it constant over time [36,24], others adapted it for future predictions. Heeren et al. [23] modeled scenarios to represent future energy supply as a consequence to nuclear fade-out and integration of local renewable energy sources in Switzerland. The authors argue that this approach facilitates the evaluation of the effectiveness of newer and more efficient technologies 325

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studies). These two categories are often chosen as they relate to key drivers of current national and international policy making in the built environment [36]. Other categories, such as AP, Eutrophication Potential (EP), ODP and Photochemical Ozone Creation Potential (POCP) are recommended by the standard EN 15643-2 [69] and used only by two studies in the sample [37,26]. Saner et al. [22] are the only ones in the sample to have selected Particulate Matter Formation as an additional indicator due to expected trade-offs with climate change, especially in the context of using wood energy. Only a part of the studies specified the used LCIA method. ReCiPe and CML are the most frequently used methods in the reviewed studies. GWP is assessed in 33% of the studies [32,22,23,37,24] using the LCIA method ReCiPe [85] and in 27% of the studies [33,35,36,26] using the LCIA method CML 2001 [86] or CML2 baseline 2000 [87]. Other impact categories evaluated by ReCiPe included particulate matter formation [22], freshwater and marine EP, terrestrial AP and POCP [37]. Nemry et al. [26] used CML 2001 to evaluate ODP, AP, EP and POCP in addition to GWP. Cumulative Energy Demand (CED) was applied in 13% of the studies to calculate PE demand. Other authors [25] reported results as material mass flows, arguing that they can be used to a certain degree as a proxy for unknown environmental effects. The analysis emphasizes that the majority of the works focused on energy-related impact categories, namely GWP and PE. Other impact categories relevant to urban sustainability were rarely considered, including abiotic depletion potential and land use. Results were essentially provided at midpoint level. Most of the studies considered only one or two impact categories, while a wider range of impact categories is rarely taken into account. While using a single environmental indicator may ease the process of decision making, it may also induce information losses [88]. Conversely, a large set of indicators may result in a more difficult decision making because of the high number of parameters. Normalization and weighting are the optional steps of LCIA. In the normalization step all indicators are expressed by the same unit, relative to some reference information, for example the average environmental impact of a European citizen in one year [89]. Weighting implies the application of weighting factors to normalized results so to express the relative importance of each impact category and convert indicator results of different impact categories into more global issues of concern or a single score [89]. Among the reviewed studies, none applied neither normalization nor weighting. Normalization can contribute to a better understanding of the relative importance and magnitude of the category indicator results. However, normalized results may suffer bias due to uncertainties in emission data and characterization factors (CF) [90]. The choice of weighting can significantly affect the conclusions, for instance on which substances were most damaging [13]. In addition, general consensus on the approach to perform weighting for various environmental issues is missing [91,92].

and practices related to energy policies and prices. Saner et al. [22] developed an optimization model for the energy supply in a small municipality in Switzerland. The optimal scenario supposes a drastic shift of heat supply systems from a fossil fuel dominated portfolio to a portfolio consisting of mainly heat pump and woodchip incineration systems, entailing significant reduction of greenhouse gas and particulate emissions. Background datasets cover various supply technologies for each energy source. In the case of building-by-building approach data about the supply technology may be missing at the building level. Saner et al. [32] stochastically distributed supply technologies among buildings based on statistical distributions and linked them with the space heating and DHW demands per apartment. Other adaptations regarded the evaluation of transportation distances from the production to the building site [26] and from the demolition site to the disposal site [37]. 5.3. Life cycle impact assessment The Life Cycle Impact Assessment (LCIA) phase involves the association of inventory data with specific environmental impact categories and category indicators and calculation of the potential impacts generated on humans and ecosystems. Three mandatory elements are part of this phase [65]: selection of impact categories, category indicators and characterization models; assignment of LCI results (classification); calculation of category indicator results (characterization). Optional elements include normalization, grouping and weighting. A range of impact assessment methods exists in literature, including mid-point (problem-oriented) and end-point (damage oriented) LCIA methods. In mid-point methods, values at the beginning or middle of the environmental mechanism are used and impacts are classified according to impact categories, for instance global warming potential (GWP), acidification potential (AP), eutrophication potential (EP) and ozone depletion potential (ODP) [13]. In end-point methods, impacts are grouped into issues of general concern (called areas of protection) such as human health, natural environment and resources and can be expressed as a single score. Mid-point methods are widely and internationally recognized as fairly objective indicators but some of them might not be in agreement, making the decision process more difficult; in contrast, end-point methods involve both physical and social aspects and have collected less international consensus as they introduce subjective value choices and higher uncertainty [84]. Several impact categories and impact assessment methods were used in the reviewed studies. Mid-point indicators were mostly selected in the LCIA. Fig. 6 shows the share of different impact categories among the papers. GWP and energy use, in particular primary energy (PE) are the mostly used categories (respectively 93% and 80% of the

5.4. Interpretation In the interpretation stage, results of the LCI and LCIA are checked and summarized to provide conclusions and recommendations according to the goal and scope of the study. This section addresses the following aspects related to interpretation: contribution analysis, uncertainty and sensitivity analysis and spatial visualization of results. 5.4.1. Contribution analysis Many authors performed contribution analysis to study the influence of the different phases of the life cycle on results (Tables 5 and 6). Norman et al. [31] evaluated the relative contributions of specific building materials and residential services infrastructure to total material manufacturing-related greenhouse gases emissions and energy use. They concluded that the most important construction materials contributing to embodied manufacturing energy and green-

Fig. 6. Impact categories taken into account to evaluate the environmental impact of building stocks.

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population density and reducing the size of residential units provides reduction of both operational and embodied energy demand, thus resulting in per-capita energy savings throughout the building lifecycle. Saner et al. [32] performed uncertainty analysis on input parameters by using Monte Carlo simulation and identified the median and the 95%-density interval of impact results. The authors concluded that the distributions of impact results among the households remain constant over the whole range of uncertainty samples. In addition to uncertainty linked to input data, some authors highlighted the uncertainty related to LCA [33] and partial models such as energy consumption models [25]. Sensitivity analysis was mainly carried out for the assessment of scenarios describing the evaluation of the stock, effect of policies and refurbishment strategies [23,33,22,24,26].

Table 5 Results of contribution analyses on grenhouse gases emissions and GWP. Reference

Norman et al., [31] Saner et al., [32] Anderson et al., [33]

Nemry et al., [26]

Contribution of life cycle stages (%) Product-Construction

Use

End-oflife

∼10 5.8 10

– – 16 to 23 (electricity)

– – –

19 to 19.8

11 to 22 (space heating) 80.2 to 81.0

1.2 to 5.0

Table 6 Results of contribution analyses on PE. Reference

Stephan et al., [21] Moschetti et al., [37] Nemry et al., [26]

5.4.3. Geospatial visualization GIS offer the possibility of visualizing results in the form of spatially-explicit maps. Some of the reviewed studies showed energy demand and impact assessment results as maps, at the urban or regional scale. Reyna et al. [34] displayed energy use and GHG emissions of embedded material aggregated by census block for the county of Los Angeles. Saner et al. [32] presented the magnitude of household-specific consumption impacts at the place of residence of the respective households to identify areas of the municipality where high-impact households are located. Raster maps with a cell size of 100×100 m were used to show impact, averaged for the sake of data privacy protection. The annual life cycle GHG emissions related to different energy supply scenarios and optimal decisions for four possible refurbishment options (roof, wall, floor, and window refurbishments) were also shown at the municipality level [22]. Mapping the energy demand and environmental impact sources can contribute to a more effective and spatially explicit visualization of results and improved communication to stakeholders for planning and policy support.

Contribution of life cycle stages (%) Product-Construction

Use

End-of-life

15.3 to 39.4 29 11.5 to 18.9

28.7 to 52.5 75 81.1 to 88.5

– 4 −0.3 to −2.5

house gases are brick, windows, drywall, and structural concrete used for dwellings (60–70% of the total). According to their results, only 10% of the total energy use and greenhouse gas emissions are inputable to material production. In Stephan et al. [21], the contribution of embodied, operational and transport energy demands were analyzed. The share of embodied energy demand (including infrastructures) and operational energy demands resulted within the ranges of 15.3–39.4% and 28.7–52.5% of the total (including transports), showing the importance of all contributions. A similar analysis was performed by Anderson et al. [33]. Building embodied emissions summed up to approximately 10%, building operational electricity emissions account from 16% to 23% and operational heating emissions 11–22% of total emissions (transports included). Saner et al., [32] found that building infrastructure amounts on average only to 5.8% of total housing GWP impacts for a small municipality in Switzerland. In the study by Moschetti et al. [37] results concerning the total energy use showed that the use phase provided the main contribution (75% on average), while the pre-use and the end-of-life phases contributed with an average of 29% and 4% respectively. In Nemry [26] the end-of-life does not exceed 5% of the impacts from the use phase of existing buildings in most cases all over Europe. In general, the use phase dominates the environmental impacts for all building groups and zones. The construction phase can reach considerable shares (up to 50% in the case of single-family houses in South Europe). The end-of-life phase is of minor relevance for all zones and building groups (not exceeding 8% and in the majority of cases not more than 5% of the impacts).

6. Discussion 6.1. Building stock aggregation The selection of an efficient and reliable building stock aggregation model represents one of the key points in the LCA of large building stocks. The archetype approach is the most used in the reviewed studies. Advantages of this approach are given by the need of modeling only a limited number of archetypes representing the full building stock. This point is even more remarkable for LCA studies due to the large amount of input data requirement. This approach can be applied at every scale ranging from the urban to the transnational scale, being the principle of aggregation the same and adapting only the size of archetype building sets and the descriptors used. Limitations include the potential over-simplification of the large variety of building characteristics represented only by a restricted number of archetypes. Excluding from modeling building cohorts which represent a minor share in the stock might also results in hampered results. Lack of available comprehensive information about buildings has been also identified as an issue for the definition of building archetypes. Recently, some authors [42] proposed to face this issue by defining unknown or uncertain parameters in archetype descriptions as probability distributions and using measured energy data to update these distributions by Bayesian calibration. This methodology has shown significantly better fits compared to traditional deterministic archetype definitions. Another key point is the selection of the up-scaling factors. While using the floor surface to extrapolate results to the building stock scale is widely accepted, using the number of buildings might results in higher error due to the different size of buildings within the same category. Up-scaling factors should be therefore selected in accordance

5.4.2. Uncertainty propagation and stochastic sensitivity analysis Many authors stressed the issue of uncertainties associated to the LCA results of large building stocks [30,10,25]. However, uncertainty and variability in the input data and their propagation throughout the LCA were quantitatively evaluated only by few of the authors. Stephan et al. [21] identified suitable ranges around nominal values of input data based on the level of accuracy. The uncertainty on the embodied energy data is set to 20% and 50% for the process data and inputoutput data components respectively. Nichols and Kockelman [30] estimated the response to changes in the building environment such as population density, unit size and percentage of single-family houses on the life-cycle PE of buildings. Results demonstrated that increasing the 327

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further limits their credibility. While some studies proposed a validation at the full building stock level, more thorough validation would be needed at a more disaggregated level. Nevertheless, the limited availability of energy consumption measures currently restricts validation possibilities. The review results showed that both steady-state and dynamic models are equally employed. Steady-state models are easy to implement and computationally lighter. However, their results might not be completely accurate, especially for the estimation of cooling loads, due to their limited capacity of accounting for building dynamics [97]. While dynamic simulation tools might produce more accurate results, the ease of implementation and flexibility of steady-state calculations render their use preferable for an early stage assessment [98]. On the other hand, dynamic models are more adequate when an evaluation of the cooling loads or renewable energy implementation are demanded. Dynamic models are computationally more demanding and require a number of input parameters that are difficult to gather at a large scale. This issue is particularly important when considering buildingby-building approaches and in general when a conspicuous number of buildings should be modeled. Dynamic low order thermal network models (see for instance [99]), able to represent a thermal zone by thermal resistances and capacitances, may be suitable for this type of application due to their low computational costs. Surrogate models (see e.g. [100–102]), approximating the computer code with a statistical model, e.g. regression-based, may be developed to simplify complex energy models and reduce computation burdens. The choice of the energy model should be carefully made according to the goal and scope of the LCA study. The level of detail of archetypes should therefore correspond to the requirements of the respective standard for energy calculation [23].

with the FU of the study (see Section 6.3). The building-by-building approach allows for more refined building stock modeling at the cost of higher input data requirements and computational burdens. For these reasons, the use of this approach is currently limited to a few applications at the urban scale and conditioned by the availability of distributed building data. The building-bybuilding approach implies a series of advantages and limitations compared to the archetypes approach. Among the advantages, incorporating GIS in building stock modeling provides a link between the building statistics and the spatial location of the different building types within the urban setting [9]. This modeling approach has the advantages of performing improvement studies, for instance, for finding the optimal housing energy supply under regional supply constraints [32]. Disadvantages of this approach include the need for detailed data at the building level and higher computation time that could be a hurdle for big datasets. Nevertheless, the increasing availability of large datasets for buildings, both in 2D and 3D, can boost the development of building-by-building in the coming years. Three-dimensional city models may enhance the collection and storage of data, thanks to new standards like 3D CityGML [93]. While 3D CityGML models were already tested for calculating the energy demand of buildings (e.g. [51,94]), their extension to the LCA is foreseeable in the near future. The spatial dimension was taken into account by a limited number of studies. Even if a coupling between LCA and GIS has been already suggested in several fields, consensus is still missing and methodological advancements are needed [56]. Integrating LCA and GIS for building stock modeling is promising to generate new LCI data in an efficient way, take into account the spatial dimension [44] and consider spatial and local constraints. Visualization of results as maps further enhance the localization of hot-spots and support communication of results. While GIS has been mostly integrated at the urban scale (e.g. [32,34]) applications at larger scales might be more demanding in terms of data requirement and computation burden and need further investigation. Including the temporal evolution of the building stock, has proven to be efficient for shaping future scenarios in a dynamic way. Integrating dynamic MFA with building stock modeling appears as promising and should be further investigated by future research. A dynamic stock model allows for estimating the impact of new building technologies over time and their eventual contribution to energy security and climate change mitigation [24]. Future uncertainties should be nevertheless tackled and properly addressed for plausible results.

6.3. Life cycle assessment The extension of building stock energy analyses into a LCA perspective determines a higher level of complexity in terms of input data requirements and building stock characterization. Including the production, construction, maintenance, refurbishment and end-of-life stage requires taking into account building envelope materials and components, in addition to the technical systems and installation. Such requirements result in the need of an amount and quality of information substantially higher than in the case of exclusive energy analysis. The definition of the scope and system boundaries is a crucial point for the LCA of building stocks. While many of the reviewed studies used similar formulations as for the LCA of individual buildings, systemsbased consideration of interactions among urban form, buildings and the environment are critical to the development of strategies and policies to meet sustainability targets [103]. Further research is needed to address integrated model developments for building stocks, including the behavior of occupants and dynamics of the surrounding environment [103]. The integration with supply energy networks and renewable energy sources should be also further addressed [6]. The service life of buildings and building elements should be considered to properly assess environmental impacts along their whole life cycle. Previous studies [104,71,70] demonstrated that building lifetimes vary considerably and using standard assumptions for scenario development may entail biased results. Further investigation on the modeling of the service life in building stock models is therefore recommended. The choice of the FU is highly important in this context as highlighted by several authors. However, a range of FUs is currently being used, including absolute, spatial and per-capita FUs (Section 5.1.2), thus limiting the comparability of results. While a spatial FU appears more appropriate to compare the performance of different building types, regardless of the building size, it is less suitable to assess the global performance on a city or regional scale. A per-capita FU can be suggested to make a comparison between different cities,

6.2. Building stock energy analysis The energy models’ review highlighted a predominant use of bottom-up engineering-based over statistical approaches, especially for space heating demand evaluation. This is justified by the prevalent prospective nature of most of the reviewed LCA studies. Engineering-based models are more suitable for predicting the effect of refurbishment and other actions for improving the environmental performance of buildings. However, they are affected by a series of issues that should be carefully tackled to guarantee the reliability of the results. First of all, such models are based on a series of assumptions related to the current state of the building stock and occupants' behavior that should be carefully verified in order to obtain realistic results. Previous studies have shown significant deviations between calculated and measured energy consumption of buildings mainly due to the initial assumptions [95]. The use of stochastic instead of deterministic approaches for modeling unknown parameters might lead to improved results [32]. Uncertainties of model inputs, model parameters and modes structure should be also quantitatively evaluated so to provide more plausible results [96]. Validation of energy consumption results is rarely performed and 328

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potential that model simplification can provide when dealing with computationally expensive building stock LCAs.

regardless of their population density. More research on the implication of choosing different FUs would be therefore needed and objective criteria to support decision according to the goal of the study should be developed. In addition to FUs, transparent and congruent system boundaries are essential to allow the comparison of impacts of different measures and technologies [24] and specific attention should be paid to their definition. A significant effort is required for the identification of the foreground inventory in stock models. The level of detail in the description of the heating/cooling systems in the different buildings, their age, the materials and the building components used in each household is a determining factor for the type and quality of the results obtained. Concerning the background inventory, international commercial databases are used by most of the authors. Even if their validity is widely recognized and accepted, the applicability to specific local contexts should be addressed to ensure the reliability of results. Possibilities for adaptation to the local context include the modification of transportation distances, electricity mix and processes [80,37,26]. While some of the authors put such adaptations in place, in most cases this issue is not explicitly addressed. Accounting for the evolution of the energy systems and climate change should be further addressed by future research as they have a large influence on LCA results [74]. Most of the studies focused on a set of few, recurrent indicators for LCIA. The chosen impact categories are typically the energy-related ones, PE and GWP. However, these are not the only significant impact categories at the city or larger scales. The European standard EN 15643-2:2011 [69] further recommends a series of impact categories for the LCIA of buildings. Other authors included other impact categories for buildings, such as land use, water shortage, air pollution, traffic congestion, deterioration of ecological systems, and waste management [14]. Studies are ongoing to evaluate the relevance of additional impact categories for the building sector, including human and eco-toxicity and land use [105]. The normalization and weighting steps were commonly not addressed in the sample of reviewed studies. These steps would allow further comparison between impact categories and relevance to the addressed building stock. On the other side, they would be source of additional uncertainty. The uncertainty of model inputs, model parameters and modes structure is currently a major issue in the LCA of large building stocks. The issue is particularly relevant due to the number of missing information and assumptions to be made for large building stocks and potentially having important effect on results. This review highlighted that only few of the considered studies addressed this problem with quantitative approaches. While uncertainty propagation and sensitivity analysis were largely addressed when modeling the energy performance of individual buildings [106], few studies focused on the uncertainty of building stock energy models (e.g. [107–109]) and LCA of buildings (e.g. [110]). In the case of building stocks, characterization and modeling of input parameters uncertainties should be addressed with special care [111]. A recurrent approach for studying the sensitivity of input parameters on the variability in the results at this scale is the one-at-a-time (OAT) local sensitivity technique [112]. The advantage of using this technique is the ease of use and implementation. However, OAT does not allow for exploring the full variation space of input parameters and interaction effects [6]. The implementation of global sensitivity analysis (GSA) techniques is further recommended as they are more robust and provide more reliable results even if at the expenses of higher computation burden [113]. GSA been successfully used in the LCA of renewable energy pathways [114–116] and its application to the LCA of building stocks appears as a promising development. Where possible it is suggested to rely also on fast calculators, such as surrogate models. The advantage of surrogate models is that they can be used instead of complex and computationally expensive models in particular when running Monte Carlo based uncertainty propagation and stochastic sensitivity analyses. It will be therefore important to understand the

7. Conclusions and recommendations This review highlighted a number of current limitations and potential opportunities for the LCA of building stocks from the urban to the transnational scale. The building stock aggregation step can be improved by refinement of the archetypes and building-by-building techniques, integration of GIS and stock dynamics models. GIS integration is promising to explicitly consider spatial constraints and localize the hot-spots (e.g. areas with high potential of emissions reductions if refurbishment actions are put in place, areas with high material concentrations, etc.). Accounting for the temporal evolution of the stock has been demonstrated to be effective for evaluating the results of sustainable policies and future scenarios. Nevertheless, spatial uncertainties propagation and scaling errors, as well as uncertainty linked to future scenarios, e.g. linked climate change, should be properly tackled for more robust results. Future research is recommended on the calibration of building stock models, integration of GIS and 3D semantic models for improved description of the building stocks, integration of dynamic MFA for the inclusion of dynamic evolution. Engineering-based models are commonly applied to assess the energy demand of buildings stock in LCA studies. Current limitations include the lack of uncertainty propagation and stochastic sensitivity analysis. This can be achieved by developing an uncertainty propagation framework and where possible also rely on global sensitivity analysis and fast calculators, such as surrogate models, e.g. regression based models. Validation of energy consumption results is another key issue and should be addressed close to the building level, where possible, to improve the reliability of results. Recommendations for future research include the accounting of uncertainty and the development of surrogate models able to provide a fast and reliable prediction of energy consumption for buildings. In the LCA step, the transparent and clear definition of system boundaries, FUs and service life of buildings are still central questions. Operational developments are further needed for the integration of LCA, building stock aggregation and energy modeling. In particular, there is a potential for coupling LCA and GIS for spatially-explicit inventories and LCIA. The application of consequential LCA at the scale of the building stock should be also further investigated. The spectrum of indicators for LCIA should be not limited to GWP and PE but enlarged to other impact categories relevant for building stocks. Normalization and weighting of results is suggested to compare the importance of different impact categories. Uncertainty and sensitivity analysis are rarely performed and should be therefore addressed with proper methods. Finally, the visualization of results as maps offers the potential of improved communication with stakeholders. Future research is therefore recommended on uncertainty propagation, sensitivity analysis and development of surrogate LCA models. Modeling of future energy mix and inclusion of climate change for prospective scenarios should be also further investigated. Integrated LCA modeling for building stocks, including the behavior of occupants, interaction with the surrounding environment and integration with supply networks, should be further addressed by future research for a more comprehensive assessment able to effectively support sustainable policy development.

Acknowledgements This project is supported by the National Research Fund, Luxembourg, Grant agreements AFR - 7579115 “DAEDALUS”. 329

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