Dynamic Assessment of Construction Materials in Urban Building Stocks

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Critical Review

Dynamic assessment of construction materials in urban building stocks – A critical review Verena Göswein, José Silvestre, Guillaume Habert, and Fausto Freire Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b01952 • Publication Date (Web): 25 Jul 2019 Downloaded from pubs.acs.org on July 28, 2019

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Environmental Science & Technology

Dynamic assessment of construction materials in urban building stocks – A critical review Authors: Verena Göswein 1*, José Dinis Silvestre1, Guillaume Habert 2, Fausto Freire3 1

CERIS, Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal 2 Chair of Sustainable Construction, IBI, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland 3 ADAI-LAETA, Department of Mechanical Engineering, University of Coimbra, Polo II Campus, R. Luís Reis Santos, 3030-788 Coimbra, Portugal * Corresponding author

Abstract: There is a lack of understanding on the different types of dynamics of building stocks, in real life and in models. Moreover, there is now a particular interest in the embodied impacts of construction materials, since with the increasing efficiency of buildings operation, embodied impacts gain more importance in the overall building life cycle. This critical review wants to advance the understanding on the type of dynamics, methods and tools used. The well-known IPAT equation is adapted for building stocks and three dynamics are defined: spatial, evolutionary temporal and spatial-cohort dynamic. A framework is defined that can help researchers choose a method, tool and dynamics of input parameters depending on their research goal, case study and data. Moreover, generally valid conclusions are drawn, including: MFA is useful to model spatially dynamic material flows; GIS is needed to include spatial dynamics. Retrofit, compared to construction and demolition, is understudied and usually analyzed through top-down methods. Material Intensity and Emission Intensity are rarely modeled in a dynamic way. Overall, scholars seem to perform each time more data intensive and complex studies tailored to a specific case study. However, there are big differences in the quality depending on the dynamic of input parameters. Keywords: temporal, spatial, IPAT, Material Flow Analysis (MFA), Life Cycle Assessment (LCA), city

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1 Introduction Most of the world’s population is living in urban areas 1 while the built environment is a massive consumer of materials 2. During the last century overall global material consumption approximately multiplied tenfold, but consumption of construction minerals multiplied by a factor of 423, showing a positive feedback-loop between socio-technical system evolution and its construction material requirement. Urban Metabolism (UM) describes the city through its systematic and related processes. It estimates metabolic ﬂows (water, energy, material, and nutrients) and eﬃciencies to diagnose resources’ utilization and the associated environmental problems4. Future urbanization will require large amounts of material 5 and now is the time to assess sustainable alternatives. A recent report on the weight of cities 6 concludes that urban resource flows are key to promoting a transition from resource-intensive UM towards alternatives that manage resources more sensibly. Urban building stocks have been the focus of many fields of studies, from social sciences 7, to geography8, to mathematics9, ranging from theoretical, conceptual, and methodological to cartographic dimensions. Cities can be analyzed from many different angles. It is therefore important to clarify the terminology before diving into the literature review. The terminology described here builds on the assumption that we are living in the age of the Anthropocene, an epoch that is characterized by human imprint on the global environment 10,11. The urban building stock can be seen as a resource with a designated value that, besides its composition, state and short-term dynamic, is defined also through the long-term development and historical dynamic12. The “building stock” in “urban building stock” can be defined as all buildings, belonging to the housing and the non-housing sector. Infrastructures such as roads, water and energy distribution systems are usually excluded from urban stock models although their contribution in terms of material requirement is not negligible as it can represent half of the material consumption 13. The “urban”, however, does not entail a straightforward meaning14. Bourdic et al.9 argued that, in order to understand the urban, one most analyze the urban: they advocated for the use of the city or district scale, being the most comprehensive, as well as for the neighborhood scale, allowing a more complex analysis. We follow these authors recommendation and included studies in this critical review ranging from neighborhood to district scale. Urban building stock research can be categorized into different methodological approaches. Many authors only split into two categories: top-down and bottom-up accounting15,16. However, the present review follows the definition of four categories by Tanikawa et al.17 since these authors argue that this division more adequately reflects the characteristics of UM18 from a socioeconomic point of view. The four categories are: (1) Bottom-up accounting places the end-use object, i.e. buildings and their inventory at the model core. (2) Top-down accounting employs material inflow statistics and input-output tables. It is often used in UM studies analyzing various types of flows. (3) Demand-driven modeling can be similar to both, bottom-up or top-down accounting in terms of method and data. However, instead of historical statistics, it utilizes socioeconomic indicators to model the demand of buildings and therefore embedded materials. A (4) remote sensing approach can be considered a bottom-up accounting exercise that uses e.g. lidar data to sample building stocks. It is mostly used in data scarce environments to estimate material stocks. Even though there is overlapping between these four approaches, Huang et al.19 recognized that GIS and remote sensing are increasingly important to estimate material stocks of infrastructure. Moreover, Augiseau and Barles 16 distinguished in their review of material stock studies only between top down and bottom up but discussed in detail the importance ACS Paragon Plus Environment

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of models that are input flow driven, i.e. demand driven. Other authors that follow the same definition of four categories are Arora et al.20 and Dombi et al.21,22.Many existing tools quantify and analyze the effects of urban systems. The most important ones for urban building stocks within the UM discourse are Material Flow Analysis (MFA), Life Cycle Assessment (LCA) and Geo-Information Systems (GIS). MFA is a tool that is used to analyze flows through, in and out of a system, respecting principles of mass conservation 23. LCA supports the quantitative assessment of environmental impacts of goods and processes, usually from “cradle to grave”. It is a tool to understand the environmental consequences of human actions24 and can be applied to all scales 25. GIS is powerful to link a variety of data 26. It is the modern tool for data inventory and mapping and therefore inherent to urban studies. Moreover, GIS can be used to characterize life cycle impact data 27,28. Many studies have performed MFA or LCA of buildings as a mere accounting exercise 29. However, during recent years there is a growing trend to accommodate for spatial and temporal dynamics in the analysis and interpretation of urban building stocks 30. Dematerialization is considered a key factor in urban planning and can only be achieved through identifying drivers and characteristics of UM31. Yet, just using less material might not be sufficient and we need to rethink the use of materials by rematerializing construction 32, e.g. considering the local availability of materials 33, mining the cities 34,35, and inducing a systematic shift that is required for a circular economy36. In any case, a better understanding of the dynamics of the built environment is needed to govern building stocks towards a more environmentally sustainable state 13. Liu et al.37 clearly highlighted the importance of understanding the dynamics of material stocks by stating that “a retrospect on the quantity, quality, and patterns of […] material stocks is a prerequisite for projecting future […] demand, identifying urban mining potentials […], and informing sustainable urbanization strategies.” Müller et al.38 analyzed direct and indirect emissions of infrastructure and showed the great potential of urban planning to achieve an improved UM. Even though the average demolition age of buildings has decreased from over 200 years to 70 years, as shown for Zurich 12, it is still a long lifespan. During this time, changes in usage pattern 39, in external conditions such as increased temperatures due to climate change40, or in new building regulations to account for global climate goals 41 can occur. Often retrofit scenarios are unknown. Therefore, buildings should be understood as service providers with different future scenarios that ask for a dynamic understanding42. Based on the literature review of urban building stocks, we propose to divide their dynamics into: i) spatial, ii) evolutionary temporal and iii) spatial-cohort dynamics, as illustrated in Figure 1.

Figure 1: Illustrative description of three different types of dynamics



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The first type, spatial dynamic, reflects the spatial differences and changes that are inherent to cities43. The urban fabric, including building and construction types, varies from one point to another, and together with its user and environment, its consumption profile also varies 44. Both, input, mainly existing building stock and user behavior45, and output, energy, material consumption and emissions 46, of an urban model must exhibit spatial dynamics if achieving to be close to reality. To understand the second type of dynamic imagine we take one snapshot of an urban area now and one in the future to compare the changes between the two regarding number of inhabitants or buildings. We might even be able to observe a change in type of new construction between the two time steps if we compare two snapshots that are decades apart. We call the underlying processes of this “evolutionary temporal dynamic”. The third type deals with the composition of one snapshot, examining the building stock as a sort of jigsaw puzzle. Each piece stands for buildings from the same construction period, and has its own history that describes, for example, the energy intensity that was needed at the time of production of the building’s materials. We call this third type “spatial-cohort dynamic”. With the increasing efficiency of buildings operation, the embodied impacts gain more importance in the overall life cycle of the building47. While operational and embodied energy are comparably well studied, there is still a knowledge gap on material stocks and flows and their embodied impacts48. Various scholars have recommended to mature the field of Industrial Ecology49 to achieve urban sustainability by linking resource stocks and flows with environmental impacts50, thus coupling UM with LCA 51,52. Centered on this challenge, there are a number of recent reviews applied to urban building stocks. On the one hand, there are reviews that deal with the challenge of applying LCA to urban areas. Worth mentioning here are Lotteau et al.’s53 review of LCA at the neighborhood scale, compiling studies on the quantification of emissions at the typical operational scale for urban development. The authors focus on the common steps included in LCA studies, comparing for example system boundaries, functional unit, Life Cycle (LC) inventory and impact assessment, and included process-based, Input-Output and hybrid-LCA. They concluded that not many studies have been published on the subject yet. In addition, they recommended to include temporal evolution of neighborhoods as “complex objects”. Mastrucci et al.54 published a similar review of building stock LCA but included studies from the urban to the transnational scale, focusing on bottom-up building stock models. The authors advocate for performing LCA instead of only energy analysis and concluded that future research should include geolocalization and sensitivity analysis for potential technology changes. On the other hand, there are studies pushing the research field of material stock and flow analysis. Augiseau’s and Barles’s16 review of non-metallic construction stocks and flows studies, provides an overview of the actual results of the reviewed studies and highlights the importance of buildings as research objects for material stocks in the built environment. They advocate for the formulation of a conceptual framework despite the differences in data availability and data quality, but were only able to identify some common principles for definitions (of inflows, outflows, and stock), indicators and methodological approaches. Müller et al.15 reviewed dynamic MFA studies for metal stocks and flows, comparing foremost the methodological complexities of MFA. They divided the modeling approaches into retrospective and prospective and found that it is an extreme simplification to assume highly sensitive model parameters, such as lifetime distribution parameters, constant. The findings of these previous literature reviews draw attention to the lack of understanding of dynamics of material stocks and flows, especially for service units with long life spans such as buildings. For this reason, we propose to review studies at the urban scale of construction ACS Paragon Plus Environment

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material stocks, including flows and embodied emissions, regarding the modeling approaches used to consider dynamics in their model input parameters and results. We want to answer the following research questions: 1) What are the different dynamics of construction materials on urban building stocks and how can they be considered in an analysis of environmental sustainability? 2) What is the “best” method and tool to analyze specific dynamics of construction materials at an urban scale?

2 Materials and Methods 2.1 Adapting the IPAT equation for building stock research To describe the impact of human activity on the environment, the IPAT equation (Impact is equal to human Population times Affluence times Technology) was proposed 55. The IPAT equation is at the center of Industrial Ecology. It helps understanding emissions of a socio technical system and supports efforts to employ technology to gain environmental benefits 56. The Kaya identity57 as an adaptation of IPAT focuses on Greenhouse Gas (GHG) emissions. It characterizes the technology variable more specifically by stating that the total emission level of GHG carbon dioxide can be expressed as the product of: human population, Gross Domestic Product (GDP) per capita, energy intensity (per unit of GDP), and carbon intensity (emissions per unit of energy consumed). The concepts of IPAT and Kaya are also at the core of building stock research. Mavromatidis et al.58 adapted the Kaya identity to estimate GHG emissions from buildings by multiplying the total floor area of buildings by the energy intensity and carbon intensity, i.e. the authors substituted human population multiplied by affluence with the building floor area. However, this is then only applicable to certain models that are used to estimate GHG emissions of bottom-up building stock models that directly provide the floor area of buildings. Since this critical review includes studies of all four methods of building stock research 17, it seems preferable to be in line with the Kaya identity by keeping all four parameters to analyze the dynamics of buildings (Population, Affluence, Material Intensity, Emission Intensity). This review considers the term “P” as a more general term of Population (than can be people or buildings). As customary, “A” for Affluence refers to GDP per capita. However, here it is considered as a social indicator for the human need for shelter through per capita floor area (PCFA), or by disaggregating it into floor area per building multiplied by average number of building inhabitants 59. Hu et al.60 found that, for Beijing, PCFA is strongly correlated to Gross Regional Product (GRP) per capita. Some scholars have suggested to extent the right side of I=PAT with behavior “B” to I=PBAT to account for personal choices for per capita impacts in the calculation of environmental loads 61. We decided to aggregate “A” for GDP per capita and “B” for floor area per GDP into PCFA since the inclusion of “B” has strongly been criticized as “A” is intimately related with “B”62. In line with the Kaya identity, we propose Equation (1)to analyze dynamics of building stock models: 𝐼𝑚𝑝𝑎𝑐𝑡 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 ∗ 𝐴𝑓𝑓𝑙𝑢𝑒𝑛𝑐𝑒 ∗ 𝑀𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ∗ 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦

41 42 43 44 45

(1)

It is possible to exclude Emission Intensity from Equation (1). Then material quantities, instead of emissions, are obtained as Impact. However, the relation between materials and emissions is in fact more complex since embodied emissions should be accounted for 51. Nevertheless, depending on the study and on its system boundaries, it can be a good proxy.



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Moreover, since the beginning of IPAT’s mathematical formulation, which erupted from a heated scientific discussion in the 1970s 56, there are doubts on the independency of its variables. IPAT is not a formal mathematical model but a useful framework to reveal the driving forces of environmental impacts 61. However, the disentanglement of variables remains partly unsolved 63,64. It was stated that especially population acts as a multiplier of both consumption and environmental impacts 55. This observation clearly stems from real-life examples. However, for the purpose of this critical review it is necessary to omit the interdependencies of the variables since we are reviewing models and want to shed light on the dynamic of input and output of models. Therefore, a clear differentiation and analysis of each variable is required. For studies that employ top-down MFA, the different factors of Equation (1) are not as straightforward to detect, for example for demand driven building stock models. However, all studies included in this review analyze cities in the age of the Anthropocene, which can be translated to human impact on the environment, also applicable for top-down MFA. The aim of studying the built environment is understanding its dynamics. To obtain dynamic results on the “left side” of Equation (1), at least one of the parameters on the “right side” needs to incorporate such dynamic. The definition of possible dynamic parameters can be found in Supporting Information (SI I). A more detailed description of dynamics of the input parameters of IPAT follows later in this article.

2.2 Selection of articles This review includes recent articles (published in or after 2008) fulfilling the following criteria:  Analysis of building stock,  Scale within neighborhood to district,  Quantification of construction materials in terms of amount, embodied energy or emissions, and  Spatial and/or evolutionary temporal dynamics in minimum one input parameter, as shown in Equation (1), to yield dynamic results (while spatial-cohort dynamics alone in input parameters do not yield dynamic results). Twenty-eight articles were found (see SI I for more information). The articles were separated by tools used, which are LCA, MFA, GIS or any combination of those. Each article was then analyzed regarding characteristics under study as shown in Table 1.

Table 1: Overview of studied criteria in this review

Criteria Reference/Publication Case study Scale Intervention Study object Tool Method Data intensity Research goal

Description Author and year Location Scale of case study (from Neighborhood to District Scale) New construction, Demolition, and/or retrofit Material stocks, Material flows, Environmental impacts LCA, MFA, and/or GIS Bottom-up accounting, Top-down, Demand-driven, remotesensing approach Low, medium, high, very high Material demand (past/present), future demand, decoupling, waste, resource planning, recyclability (close the loop),


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material choice, intervention strategies, density, morphology, appropriateness of method Interested stakeholder Policy makers, urban planners, architects, engineers, manufacturers, investors, researchers Dynamics of input Spatial and/or evolutionary temporal and/or spatial-cohort parameters dynamics of population, affluence, material and impact intensity Dynamics of result Spatial and/or evolutionary temporal and/or spatial-cohort dynamics of material or emission quantification 1 2 3 4 5 6 7 8 9 10

3 Methodological approaches to capture different dynamics This section is divided into an overview of reviewed studies, a brief description of study objects and the main findings regarding dynamics of input parameters. Table 2 shows an overview of the reviewed studies, including a description of the studied criteria as listed in Table 1. It is important to note that some of the reviewed studies, i.e. Barles65, Browne et al.66 and Rosado et al.67, were not intended as studies of building stocks by the authors. However, they do include buildings as part of their study object. Furthermore, they fulfill the other criteria for this review and are therefore included. The criteria research goal and stakeholders are not included in Table 2 but discussed in section 4.1. A complete overview of all criteria for all reviewed articles can be found in SI II.



Table 2: Characterization of the reviewed studies.

Material intensity

x

x

s

c

s

s

Browne et al. 2011 66

Limerick, Ireland

x

x

x

x

MS

x

x

t

t

t

t

Condeixa et al. 2017 68

Rio de Janeiro, Brasil

MS

x

x

s

c

c

s

Rosado et al. 2014 67

Lisbon Metropolitan Area, Portugal

x

MS

x

x

t

s+t

s

s+t

Cheng et al. 2018 69 García-Torres et al. 2017

70

x x

x

x

x

Impacts

Affluence

MS

Emission intensity

Population

Remote Sensing Approach

x

MFA&GIS

x

MFA&LCA&GIS

x

MFA&LCA

MFA

x

LCA&GIS

Paris (3 regional levels), France

high

Barles 2009 65

Very high

Case study location

low

Author and year

medium

MS = Material stocks MF = Material flows Em = Emissions

Top-down Accounting

Input parameter dynamic Result dynamic

Retrofit

Demand-Driven modeling

Method

Bottom-up Accounting

Tools

Demolition

Building stock intervention Data intensity Study object

New Construction

1

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Taipei City, Taiwan

x

x

MS

x

x

s+t

s+t

i

s+t

Tacna, Peru

x

x

MS

x

x

s

s

c

s

MS

x

x

s+t

s

i

s+t

MS

x

s

s

s+i

s

MS

x

x

s+t

s+t

s+i

s+t

MS

x

x

s+t

c

s

s+t

Kleemann et al. 2016

71

Vienna, Austria

Kleemann et al. 2017

72

Vienna, Austria

Tanikawa and Hashimoto 2009 31

Salford Quays and Trafford area in Manchester, UK Wakayama City Center, Japan

Wu et al. 2016 73

Nan Shan district, Shenzhen, China

x

x x

x

x x

x

x

x

x


x


Melbourne, Australia

Heeren et al. 2013 77

Zurich, Switzerland

x

Nichols and Kockelman 2014 78

Westlake, Anderson Mill, Hyde Park, Riverside in Austin, Texas

x

Albelwi et al. 2017 79

Riyadh, Saudi Arabia

x

x

Göswein et al. 2018 80

Johannesburg, South Africa

x

x

x

x

x

MF

x

x

MF

x

x

Em

x

t

i

t

s+t

s+t

i

s+t

t

s+t

i

s+t

s

s

t+i

s+t

t

s+t

i

c

s+t

x

s

s

i

c

s

x

t

t

c

t

t

c

c

s

c

s

x x

x

x

x

x

Impacts

Stephan and Athanassiadis 2017 48

x

x

Emission intensity

All Cities in Switzerland

MF

x

Material intensity

Heeren and Hellweg 2018 76

x

x

Affluence

Melbourne, Australia

Population

Stephan and Athanassiadis 2018 75

x

t

x

MF



x

Top-down Accounting

x


Kildare County, Ireland


Roy et al. 2015 74

x

MFA&GIS

x

x

Method

MFA&LCA&GIS

x

MFA

Shanghai, China

MF

LCA&GIS

Han et al. 2018 30

x

Tools


x

high

x

Very high

Beijing, China

low

Case study location

Hu et al. 2010 60

medium

Demolition

Author and year

Retrofit

New Construction


MFA&LCA

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x

Em

x

x

Em

x

x

Em

x

x

t

t

c

t

t

x

Em

x

x

t

t

c

c

t


x


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Zernez, Switzerland

Harter et al. 2017 83

Stöckach in Stuttgart, Germany

Seo et al. 2018 84

Greater Melbourne Area, Australia

Mastrucci et al. 2017 85

Esch-sur-Alzette, Luxemburg

x

x

Reyna and Chester 2014

Los Angeles, USA

x

x

Stockholm Royal Seaport, Sweden

x

x

88

x

x

Em

x

x x

c

t

i

t

t

s

s+t

i

c

s+t

s+t

t

c

c

s+t

s

s

s+i

s

s

x

Em

x

x

x

Em

x

x

Em

x

x

s

s

i

c

s

x

Em

x

x

s

s+i

s+i

c

s

x

Em

x

s+t

t+i

c

t

s+t

Em

x

s+t

s

s+i

t

s+t

x

x

x

x


Top-down Accounting



MFA&GIS

MFA&LCA&GIS

MFA&LCA

MFA

LCA&GIS


high

low

medium

Very high

Em

x

x

Barcelona, Spain

87

Retrofit x

García-Pérez et al. 2018 86

Shahrokni et al. 2015

1 2 3

x

Impacts

Geyer et al. 2017 82

x

Emission intensity

Wyndham in Melbourne, Australia


Material intensity

Stephan et al. 2013 81

Method

Affluence

Case study location

Tools

Population

Author and year

Demolition

New Construction


x

x x

Note to Table 2: Cells that represent the same type of dynamic are colored as following: “s” for spatial in blue, “t” for evolutionary temporal in pink, “i” for spatial-cohort in orange, “s+t” in green, “s+i” in purple, “t+i” in yellow, and “c” for constant in grey.


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3.1 Study interests and goals From an ecological economics perspective, there are three main natural resource input categories for economic activities: energy flows, material flows and land use 89. This review focuses on the second category, materials and their embodied emissions. The physical account of the third category, land use, is not specifically reviewed but included through the tool used, in case land cover data is analyzed with GIS, or through the building stock model, in case of input-output tables when land use data is allocated to economic sectors. We consciously decided to not review studies of the first category since operational energy modeling is a complex research field of its own 90,91 and therefore out of scope for the present review. However, some of the reviewed studies also analyze operational energy. We found that the different articles related to the second category, construction material, have three types of study objects. These are: (i) material stocks, (ii) material flows, and (iii) emissions. (i) and (ii) are not interchangeable with MFA as described above, since the latter is used to analyze the throughput of process chains 92. There is a fine line between (i) and (ii): they refer to the objective of “understanding the state and changes of stocks and flows”93, respectively. While (i) focuses on the accumulation of materials, (ii) focuses on the source, pathway and destination of the materials, which are determined by the drivers of the stock 94. Examples for the drivers of the stock that justify the classification of the study object under (ii) are urban growth or demolition behavior that cause flows (as done by Hu et al.60) or infrastructure development (as done by Han et al.30). It was noticed that some authors of the articles collected for this review claimed to analyze material flows, when in fact they were analyzing material stocks, such as done by Condeixa et al.68. The authors simply accounted for materials captured in the residential building stock in one year. Objective (iii) refers to embodied energy and to emissions, occurring throughout the life cycle of the building stock under study. The conventional life cycle stages are divided into product, construction process, use and end of life stage 95. The differences and main findings with regard to these objectives are summarized in SI I.

3.2 Dynamics of the input parameters We reviewed the different studies regarding the type of dynamics they considered in the analyses of their case studies and interpretation of results to answer the first research question “What are the different dynamics of studies of construction materials on urban building stocks and how can they be considered in an analysis of environmental sustainability?”. For this purpose, we employed the adapted IPAT equation as described in section2.1. The following paragraphs are divided by parameter of the adapted IPAT equation as shown in Equation(1). For each parameter, the possible dynamics, including advantages and difficulties found, are discussed. Three different types of singular dynamics were already described: spatial (s), evolutionary temporal (t), and spatial-cohort (i). In addition, any combination of these three dynamics is possible. Figure 2 shows how these dynamics, as characteristics of the input parameters of Equation (1), contribute to a dynamic impact result. For example, if the Affluence variable considers spatial differences in the total amount or in density, it yields a spatially dynamic result. To obtain a dynamic result, a minimum of one dynamic input parameter must be considered but, of course, more than one input parameter can be dynamic. For example, the result is temporally dynamic if only one, or both of the input parameters, Population and Affluence, are evolutionary temporally dynamic. Due to its character spatial-cohort dynamic input alone cannot yield a dynamic result.



Figure 2: Schematic overview of dynamics of input parameters and their impact on the dynamics of results

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The variable Population can only be s or t, but not i. This is because Population describes a sole number, buildings or people, but is not linked to the history of the place. This in turn can be observed in the composition of one snapshot, which would be reflected in the temporal dynamic i. s of Population can be considered through accounting for differences of people or buildings at lower scales than the case study scale. To give an example, Seo et al.84 accounted for different population size per local government for the analysis of the entire Greater Melbourne Area. An example for the temporal dynamic t of Population is the human population growth in Zurich until 2050 as seen in Heeren et al.77. Emission Intensity can only be s or t but not i because the evolution of technology over time is not reflected in the building history, through i, but is an exogenous variable. 3.2.1 Dynamics of the population The parameter Population can be either number of people (six studies) or number of buildings (19 studies). Three of the reviewed studies, Barles 65, Browne et al.66 and Rosado et al.67, use a different approach that is top-down accounting for estimating material stocks through input output data. Here, the number of buildings is not directly expressed but estimated through the imported and extracted materials used in the construction sector. Ten studies48,65,68,70,72,75,82,84–86 considered Population in a s dynamic matter. Eight studies60,66,67,74,76,77,79,80 considered it t. Eight studies30,31,69,71,73,83,87,88 considered it as s+t. Two studies78,81 considered the variable Population as constant. The consideration of a temporal dynamic for Population, that can only be t as described in the introduction to section 3.2, can be based on actual past values and/or future forecasts. The values for temporal evolution of human population are usually obtained through census data, while building population evolution can be obtained through various sources, such as statistics or GIS databases. Similar observations were made for the spatial dynamics s of Population. All studies that consider s for Population, are based on buildings, and not human population. The reason seems to be that reviewed studies focus on building stocks. The application of GIS provides the actual spatial distribution, mostly building by building as done for example by Geyer et al.82 and Harter et al.83. It can also include its change over time, such as seen in Kleemann et al.71 to estimate past building material stocks from 1918 to 2013, or in Han et al.30 for the infrastructure development from 1980 to 2010. For spatial dynamics of building ACS Paragon Plus Environment

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population, which refers to the understanding of distribution of buildings in space, a GIS is recommended but not necessarily needed, which is shown by the s dynamic studies of Barles65 and Condeixa et al.68. The first one used data on local extraction and imports at different scales (city, suburb, and region), and the second one analyzed the distribution of different building types within five different urban zones of Rio de Janeiro. Certainly, this breakdown is less detailed than a building-by-building analysis. However, it does not require the use of GIS, which could be a restrain due to the lack of knowledge or software, and provides initial conclusions at scales that allow policy and action making. For spatial dynamics of human population, census data at a scale smaller than the case study scale, such as the building bl ock or neighborhood scale, could be employed, but this type of analysis was not found within the reviewed articles. 3.2.2 Dynamics of affluence Affluence as understood in the present review is not an expression of economic welfare but a social indicator. It refers to per capita floor area (PCFA), if need be, multiplied by inhabitants per building, or directly refers to floor area per building. Four of the reviewed studies used a constant assumption for Affluence: Condeixa et al.68, Nichols and Kockelman 78 and Wu et al.73 did not account for new construction but only made an inventory of the current building stock. In addition, Barles65 did not consider Affluence as a dynamic input by ignoring any correlation with end-users or buildings. Eight studies considered Affluence as s. All of those depended on GIS. Five studies48,71,75,85,88 accounted for the actual building footprint to estimate total floor area. Others accounted for s by: using data for average floor area per person per local government area 84; building on census data coupled with housing types to estimate size and volume of potential debris 70; or employing image matching to estimate volume of demolished buildings72. Seven studies60,66,77,79–81,83 considered affluence as t: through census data for historic numbers of people or average residents per building, and scenario analysis to analyze different future possibilities of PCFA growth 60,79,80; or by employing growth rates of constructed floor area 77,83 or reduction scenarios 81, or an annual change in domestic material output per capita 66. Reyna and Chester87 considered t+i through prototype categories based on statistics and included construction period to estimate square footages. They employed a statistics-based growth model for their building typologies. García-Pérez et al.86 considered s+i by accounting for the actual size per building, and analyzing per capita floor area, linking it to urban morphology. The seven remaining studies 30,31,67,69,74,76,82 considered Affluence as s+t by combining before mentioned techniques. 3.2.3 Dynamics of material intensity The Material Intensity parameter usually refers to the quantity of construction material per floor area. It is often subdivided by type of material, e.g. concrete as seen in Hu et al.60. Other interpretations of the parameter are material per average building type (e.g. Albelwi et al.79) or per industrial sector65,67. Seven studies68,70,77,79,80,83,87 considered constant values for Material Intensity through one set of building typologies. Ten reviewed studies modeled Material Intensity as i, with the construction period defining the Material Intensity of buildings30,48,69,72,85 . Geyer et al.82 used a similar approach. However, their study focus was on retrofit strategies for energy and emission reduction. Instead of assigning material intensity, they assigned energy consumption per construction period, therefore modeling it as i. Stephan et al.81 developed housing typology scenarios that propose denser built up area and more efficient use of materials. Therefore, the Material Intensity can be decreased. Hu et al.60 employed scenario analysis to estimate changes of concrete use intensity in future housing. Other scholars used a probabilistic approach to assign Material Intensity76, or applied a trend curve to model the change of Material Intensity over time74. ACS Paragon Plus Environment


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Four studies modeled Material Intensity as s: Barles65 modeled raw material and waste intensity, amongst others for the construction sector, for three regional levels of Paris. Similarly, Rosado et al.67 modeled it amongst other, for the construction sector, for all municipalities of the Lisbon Metropolitan area. Nichols and Kockelman 78 modeled Material Intensity per building typology for different neighborhoods. Wu et al.73 defined different structure types depending on the use of the building, which was related to the location in the city. Browne et al.66 modeled t for Material Intensity by considering the total waste per category from 1992-1997. Stephan and Athanassiadis75 considered the parameter as t+i. The Material Intensity of buildings depends on the construction period of the building and it is analyzed through forecasts for material replacement per construction product based on their defined lifetime estimate. The remaining five studies modeled s+i for Material Intensity. Kleemann et al.71, Seo et al.84, and Tanikawa and Hashimoto 31 included the Material Intensity of building typologies based on construction year (for i) and location (for s). The location scales considered were the actual location 71, the local government84 and the country of where the case studies were located 31. García-Pérez et al.86 characterized the type of construction per urban morphology (for s) and improvement of retrofitted façade per building cohort that they defined based on construction period (for i). Shahrokni et al.88 is the most accurate using real time data, therefore modeling material and energy demand dynamic in space and time. 3.2.4 Dynamics of emission intensity Only fourteen out of the twenty-eight reviewed studies include a quantification of emissions. This is reflected in the definition of the study object, either material flows, material stocks or emissions. From the fourteen studies that do include a quantification of emissions, eight48,76,78,80,82,83,85,86 assume a constant value for the amount of emission per quantity of material, such as kg of CO2 equivalents per kg of cement to estimate embodied GHG emissions. Seo et al.84 is the only study that accounts for spatial dynamics of Emission Intensity through energy and carbon intensity per local government. Five studies 77,79,81,87,88 considered Emission Intensity as t, meaning that they accounted for a decrease of energy or emissions per unit of construction material thanks to technology innovation. Albelwi et al.79 analyzed scenarios for future energy mixes with changing GHG emission factors. Heeren et al.77 analyzed scenarios for changes in energy demand and energy supply. Stephan et al.81 looked at the evolution scenarios of energy production and efficiency of systems during the next century. Reyna and Chester87 considers past innovation in production efficiency for steel, concrete and aluminum. Shahrokni et al.’s88 study is based on real time data.

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4 Discussion

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4.1 Research goals and interested stakeholders

The second research question is more complex to answer: What is the “best” method and tool to analyze specific dynamics of construction materials at an urban scale? The following paragraphs try to answer this question through a discussion and critical refl ection of approaches from the studied literature. Depending on the dynamic of the input parameters, the dynamics of the resulting study output are controlled, which was shown in Figure 2. However, not only the input parameters’ dynamic defines the results as will be discussed in the following paragraphs.

The overarching research goals were synthesized and are listed in Figure 3. The same figure connects these topics with interested stakeholders of the built environment. These links are ACS Paragon Plus Environment

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based on the reviewed literature and common sense. They are not exclusive but highlight the main interests. These are: past material demand or present consumption, potential future material consumption, decoupling material consumption or waste generation from economic activity, waste, resource planning to tackle resource depletion, recyclability of materials, material choice, intervention strategies such as new construction, demolition and renovation of existing buildings, density and morphology of urban areas, and a p ure methodological interest in the appropriateness of the model. Besides the actual users, who are commonly not the target audience of scientific studies and their outcomes, the main stakeholders of the built environment are policy-makers, urban planners, architects, civil and environmental engineers, manufacturers of construction products, academic researchers and real estate investors. By linking the research goals to the interested stakeholders, we can draw following conclusions on how the reviewed studies employ tools, methods and data to inform stakeho lders on the different research goals. An overview of the analysis of stakeholders and research goals can be found in SI II.

Figure 3: Stakeholders and their interest in the main research goals as found in the reviewed articles

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Focusing on the stakeholders that have active and direct influence on our urban world: policy makers and urban planners, we find that the main topics of their interest are future demand, resource planning and intervention strategies, as well as possible decoupling effects and waste. None of the reviewed studies addressed future demand, resource planning and intervention strategies at the same time. Especially resource planning seems to be disconnected from the other topics. If looking at the resource planning studies from a life cycle perspective, it seems that they evolve from the demolition of the building. They can be divided into those that are purely interested in analyzing past and present material consumption74,87, as to establish a resource cadaster71; those wanting to shed light on spatio(temporal) patterns of demolition waste30,70; and those pushing towards a circular economy, trying to understand how to “close the loop” 48,69,73,85. However, it might be interesting and relevant for policymakers and urban planners to have studies that analyze demolition at an urban scale as to spatialize material use and resources, while simultaneously looking at future housing demand and renovation need in cities. Only then concrete recommendations could be given as to how to optimize future material use. Regarding the methodological approach used, we can assume that urban planners find bottom-up models more useful since they allow understanding the ACS Paragon Plus Environment


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impact of their planning and designing actions at a small scale. Policy-makers probably prefer top-down, and especially demand-driven models since they provide streamlined results and allow for immediate big scale conclusions, e.g. which intervention strategy consumes more material or which material causes more emissions for the whole city. Other authors96 have highlighted the importance of urban structure for the emission of GHG in cities. To inform urban planners and architects regarding the design of cities this knowledge is crucial. However, only one86 of the reviewed studies analyzed urban morphology and only five others30,65,78,81,87 at least analyzed the density and composition of the area under study to connect it to material consumption or cause of emission.

4.2 Methods and tools and corresponding data We learned from the literature that bottom-up models are aiming to inform resource planning30,48,69–71,73,85 through uncovering materials stocks, or want to elucidate recyclability31,48,69,73,75,85,88 or waste potential 30,31,70,73,85 usually of construction practices and building materials97. Han et al.30 for example employed a building stock model that allows following the development of infrastructure, providing important findings for urban planners. Bottom-up models can be very data intensive, as five30,31,48,71,75 out of six30,31,48,71,75,76 high data intensity studies use bottom up models. Top-down models are useful to analyze past and present material consumption and demand. These models often allow to compare different scales of urban areas to uncover the most resource intensive areas, e.g. Barles65 compared the dense center of Paris with its surroundings to compare centers of production and consumption. All of the top-down models reviewed 65–68,78,81,84,87 are classified as requesting medium data intensity. Demand-driven models want to forecast the future and often try to suggest sustainable pathways through the analysis of intervention strategies. Heeren and Hellweg76 for example, were able to show the potential of wood-based construction in Switzerland through their demand-driven building stock model with high data intensity. Only one study72 included in this review used a remote sensing based method and it is therefore difficult to draw general conclusions. However, Kleemann et al.’s72 study deals with the amount and composition of demolition waste. Their methodological choice puts the spatial dimension of demolition waste at the focus of their study, requiring medium data intensity. The two studies82,88 classified as requesting very high data intensity are each a special case. Because of the required data, the models are not easily translatable to other case studies. Four31,48,75,76 out of six30,31,48,71,75,76 studies classified as requesting high data intensity analyze recyclability and its potential to contribute to a circular economy. Three 79,80,98 out of four68,79,80,98 studies using low data intensity deal with the topic of future demand. On one hand, this is not surprising since we simply do not know what the future holds. On the other hand, exactly for this reason a more complex but disentangled data structure could provide useful findings. All GIS studies needed at least medium data intensity. All studies that analyze intervention strategies76,78–80,82–84,86 employ LCA. This research development is favorable since it tries to incorporate a life cycle perspective in the analysis. Studies that only employ MFA 65–68,98 without combining it with another tool apparently do not need highly intensive data. All five65– 68,98 reviewed MFA studies have either low or medium data intensity. Nine48,65–67,70,72,77,82,85 of all reviewed studies highlight their methodological contribution. From those nine, four48,70,72,85 studies employed a combined MFA and GIS approach, and six48,65,66,70,72,85 studies research waste, resource planning and/or recyclability. Therefore, these seem to be currently the trending topics in research. Moreover, the review showed that sensitivity analysis and uncertainty analysis are often neglected in research studies. Müller et al.15 found that only 37% of their reviewed studies of ACS Paragon Plus Environment

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metal stocks and flows analyzed the sensitivity of their model parameters. Instead, many authors resort to scenario analysis to account for future trajectories as done by fourteen reviewed authors70,73,74,76,77,79–86,98. Only few actually perform some kind of sensitivity30,76– 78,86,87 and uncertainty analysis30,48,69,71,72,75,76,81,83,87 . From those who do talk about uncertainty most do it qualitatively, e.g Heeren and Hellweg discuss sources of data uncertainty, but they do not quantify their parameters uncertainty. A quantification of uncertainty is only included in three studies71,81,87.

4.3 Obtaining dynamic results A Sankey diagram was developed to summarize the main influencing factors for the reviewed studies (Figure 4). Seven articles study all three types of construction interventions: new construction (“+” symbol), demolition (“x” symbol) and retrofit (“o” symbol). Two articles only studied new construction, five studies only demolition, six studies retrofit, and the remaining eight articles studied new construction and demolition. Most studies were performed (not necessarily fulfilling all these criteria): at the city scale; with combined MFA and GIS; by employing bottom-up accounting as a method; in a way that produce results that are dynamic both in time and in space. 4.3.1 The importance of the scale The present review includes a characterization of the scale of each case study. However, it needs to be noted that, depending on the analysis and available data, the results are also valid at a different scale. Some of the studies provide results that are representative only at the same scale of the case study. Generally, these are studies employing top-down accounting. However, through the inclusion of spatial dynamics in their input parameters, some authors also allow for results that are meaningful on a smaller scale than the case study scale. For example, Rosado et al.67 analyzed the case study of Lisbon Metropolitan Area but, through accounting for the domestic material consumption at the municipal level, the study also draws conclusion at the Municipality scale. Other reviewed studies that employ a top-down accounting method but provide results at a smaller scale than their case study are: Barles65 that analyze three different levels of his case study - Paris; Condeixa et al.68 that analyze sub regions in their case study - Rio de Janeiro. Furthermore, Reyna and Chester87 studied Los Angeles County but their model is based on data with unique use codes in the Los Angeles Assessor database and therefore provides results at this scale. Seo et al.84 analyzed the Greater Melbourne Area but collected data and presented results for all local governmental regions. In comparison, studies employing bottom-up accounting are always providing results at a scale smaller than the case study scale, due to the nature of the accounting method. Depending on the data availability and quality, this can go down until the building scale (e.g. Geyer et al.82). The Sankey diagram (Figure 4) highlights that the studies that are more complex in terms of tools used, meaning combining MFA with LCA and GIS, have mostly “emissions” as their study object. From these studies, Reyna and Chester’s87 study of Los Angeles was found to make use of these three tools in a smart way. It helps to draw attention to lock-in effects (potential solutions in the future are not possible due to decisions made in the past) that otherwise are not much discussed in the literature. Their data collection and definition of building “prototypes” relies mainly on statistical databases. One can discuss the accuracy of the building typologies used but the detail seems to be sufficient to support the authors’ hypothesis and findings (for more information on building stock modeling and data requirements refer to Monteiro et al.99). Maps were used to illustrate per land area where materials, energy use and GHG emissions occurs. Through a temporal analysis, the authors uncovered building construction trends that can help inform improvement strategies on the ACS Paragon Plus Environment


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long-term. The study results are dynamic both in space and in time. By looking closer at the input parameters used (see Table 2), it becomes clear that the results dynamics have multiple layers thanks to the model being fed with various dynamics for building Population, prototypes (Affluence), and even for Emission Intensity, the latter by accounting for a past evolution in efficiency. The only input parameter that they assumed constant was Material Intensity. The scale of case study also seems important for the study object “emissions”. From the articles reviewed that belong to that study object, eight out of nine were performed at the neighborhood or city scale. The first idea would be to justify this choice of scale with the data complexity of bottom up models that seem best suited for such objective. However, Table 2 shows the exact method for each of those studies, which are ranging from top-down, bottomup to demand driven accounting. There is no apparent reason in terms of data collection and model set-up to restrict the analysis for these methods to the neighborhood or city scale. Usually the environmental assessment is performed through a LCA to account for LC impacts or at least to explicitly define the system boundaries and account for impacts of specific life cycle stages. 4.3.2 Choosing tools and methods Regarding the method used, the Sankey diagram (Figure 4) highlights that bottom-up accounting seems to be the preferred choice (used in 13 studies). However, there are big qualitative differences between the incorporated spatial and spatial-cohort dynamics of such models. This ranges from Wu et al.’s73 to Geyer et al.’s82. Even though both studies captured spatial and temporal dynamic results, the first one only defined four residential building typologies, differentiated by structure type, while the latter one established a data intensive bottom-up model that relies on specific data for each building within their case study location - Zernez. Demand-driven models, used in six articles60,74,76,79,80,83 , seems especially well suited to capture temporal dynamics. Nevertheless, three74,76,83 of these studies also include spatial dynamic input parameters, either for Population or Affluence. However, the temporal dynamics are here always estimates for new construction, without exploring them in more detail. The only study72 included in this review that used a remote sensing approach, generating results that are dynamic in space but not in time because the authors analyzed demolished buildings only during one year. Heeren and Hellweg’s76 study of the Swiss building stock emphasizes that there is not necessarily a need to obtain results at a building level. The authors used a probability function to assign building typologies, allowing drawing conclusions on groups no smaller than 400 buildings. This is detailed enough to inform a community government to define strategies. Both, Reyna and Chester 87 and Heeren and Hellweg76, developed complex methodologies based on the combination of MFA, LCA and GIS. The obtained results are representative on a scale that is small enough to inform policy makers. However, to propose concrete strategies, may it be for new construction or retrofit interventions, other methodologies seem better suited. García-Pérez et al.86 combined MFA, LCA and GIS at the urban scale, and used spatially dynamic input for building Population, Affluence and Material Intensity. Their results allow conclusions regarding choice of material per building and overall impacts for the city. Their methodology provides results for the current state of Barcelona to inform not only policy makers but also architects and engineers. However, their methodology does not include temporal evolution of the city to account for future changes in use or densification. For such purposes, a more restrained use of tools seems appropriate. Cheng et al.69 focused on the quantification of materials and combined MFA and ACS Paragon Plus Environment

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GIS tools. The authors used spatial dynamics for all input parameters combined with certain temporal dynamics. The spatial and temporal identification of material hotspots in the building stock can serve urban mining strategies. An informative study on the quantification of emissions is the one by Stephan and Athanassiadis 48, advancing the building stock model through highly spatial dynamic Population and Affluence parameters. Even though allowing conclusions at the building scale, an advance of their study would be to account for spatial and temporal dynamics of Material Intensity and Emission Intensity. This was only done by Seo et al.84, who used a very limited building stock model based on two typologies, and by Shahrokni et al.88. The latter being different from all the other studies because the authors proposed to use real time data to get immediate feedback on material and energy flows, wanting to inform the end user and to contribute to a reduction of consumption. The idea is interesting but depends greatly on the availability of data and only seems feasible in new urban areas, such as their case study - the Royal Seaport in Stockholm, which allows the implementation of information and communication technologies. Regarding case study, urban areas in both developed and emerging countries are represented. What is mostly missing in the selected articles is the representation of informal settlements as part of the built environment (except for Göswein et al.80), even though this topic seems to be gaining momentum especially in South Africa 100–102. Regarding study object it was found that the quantification of material stocks is interesting for demolition as a poten tial construction intervention, while new construction is the intervention that is most studied.



1

2 3

Figure 4: Sankey diagram highlighting the most important characteristics of the included studies

4


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4.4 Framework to choose the right method and tool The choice of methods and tools depends on the research goals and available data, but also on the scale of the case study and on the target audience. This critical review of twenty-eight major publications on dynamics of urban building stocks allows formulating a framework, as shown in Table 3, which includes recommendations for researching construction materials in urban building stocks. These recommendations are based on the reviewed studies but are not exclusive, as every new study of urban building stocks will provide more information on the choice and set-up of methodologies. The studies were filtered by research objective and separated by data intensity used. This allows for recommendations of tools and methods. Moreover, the quality of dynamics of input parameters were synthesized based on what the reviewed studies proved feasible in terms of modeling. Overall, a bottom-up building stock model requires higher data intensity than the other methodological approaches. It also allows for a combination of temporal and spatial dynamics, especially for Population and Affluence. For pure temporal dynamics modeling, a top-down model is useful. Pure spatial dynamics can be modeled either with GIS, or by collecting input data and running the analysis for different locations or scales of the same location. To model spatial-cohort dynamics of Emission Intensity demand-driven models are useful. To do the same for Material Intensity, bottom-up models can be recommended. It can be advantageous to focus on only one type of dynamic, spatial or temporal results, to facilitate the data collection, the interpretation and communication of results. Table 3 suggests that it is recommendable to use GIS to obtain results that are dynamic in space, and to use MFA to obtain results that are dynamic in time. Overall, studies that provide results that are dynamic in space and time are the most complex, requiring usually high data intensity. Therefore, they can be considered the most interesting from a pure modeling point of view. Scholars highlighted their contribution to methodological advances for almost any combination of tools, methods, and data. Three 30,69,87 of the reviewed studies that include a high number of dynamics, both in space in time, used GIS. The usefulness of GIS also becomes clear when recalling the stakeholders and their interests (Figure 3). For urban planners of course GIS is an integral part of their work. However, GIS cannot only be recommended to achieve spatially dynamic results but also as a powerful tool to communicate results to policy makers and users of the built environment. The three 30,69,87 mentioned studies also have in common that they were developed at the city scale. None of them analyzed renovation as a construction intervention. To be more precise the framework is intended to be used as follows: (i) Identify the research goal; (ii) select a case study and define the scale, type of intervention and study object; (iii) check the required data intensity and available data. Then (iv) verify with the help of the framework which dynamics are recommended for each input parameter and consequently (v) which methods and tools are recommended.



Table 3: Framework for the study of dynamics of building stocks depending on research goal and data intensity

x Resource planning Recyclability Material choice Intervention strategies

Density Morphology Appropriateness of method

3 4 5

x x x x x

x x x x x x x x

x

x

x x x

x x x

x x x x

x x x x

x x

x

x x x x x x

x x

x

x

x x

x x

x x

x

x x x x x

i s i s i s s

MFA MFA, GIS MFA, LCA MFA, GIS MFA MFA, GIS MFA MFA, GIS MFA, GIS MFA, GIS MFA, GIS MFA, GIS, (LCA) MFA, LCA, GIS MFA, LCA LCA, GIS MFA, LCA, GIS MFA MFA, GIS

Top-down Bottom-up Demand-driven Bottom-up Top-down Bottom-up Top-down Remote sensing Bottom-up Top-down Bottom-up Bottom-up Bottom-up Demand-driven Top-down Bottom-up Top-down Bottom-up

Depending on the dynamics of input parameters, the Results dynamic of result is defined as shown in Figure 2.

x x

x

s s i t

Dynamics

Method

x x x x x x x x x x x x

x

s i i t+i s i t i i s i i s+i t s+i i s s+i

Tool

Waste

x

Emission Intensity

x

t s+t Medium s+t s+t High t t Low - medium s+t s+t High t s+t Medium s+t s+t High t t Low - medium s s Medium s+t s+t Medium - high s+t t+i Medium s+t s+t Medium - high s+t s+t Medium - very high s s+i Medium - high t t Low - medium s s Medium s s+t High s s Medium s s+i Medium - high any combination

Recommended

Material Intensity

x

x

Affluence

Decoupling

x

Dynamics of input parameters

Population

x

Available

Data intensity

Material stocks

x

Emissions

Retrofit

x

Demolition

Future demand

District

x

Municipality

x

City

x

Neighborhood

x

Research goal Material demand (past/present)

Material flows

Type of Study Intervention object

Scale

Construction

1 2

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Note to Table 3: Acronyms for the dynamics of input parameters as follows: “s” for spatial in blue, “t” for evolutionary temporal in pink, “i” for spatial -cohort in orange, “s+t” in green, “s+i” in purple, “t+i” in yellow


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5 Conclusions and future perspectives This review has organized and deeply analyzed the major publications on the dynamics of urban building stocks. The categorization into three types of dynamics based on the IPAT equation helps to decompose model parameters and allows formulating a framework. The framework contributes to the body of knowledge by synthesizing tools, methods and input data to model the built environment dynamically depending on the defined research go al, addressed stakeholders, case study scale, study object and type of intervention. It gives recommendations on the type of input parameter dynamic, method and tool. It should be understood as a flexible guide for researchers when defining their methodology. The recommendations have limitations and should be further validated. Besides the framework, the following, more general, conclusions can be drawn: The review of dynamics of input parameters for the selected studies shows that Population is the best represented and always contributes to the dynamic of results. Similarly, Affluence is also well represented. In contrast, the two technology parameters Material Intensity and Emission Intensity do not always contribute to a dynamic result. This might be because future technology innovation is more difficult to forecast than population growth or their lifestyle as expressed in Affluence. Moreover:  There is no clear indication based on the scale of the case study;  LCA is the tool of choice to analyze environmental impacts, at all scales;  MFA is the tool of choice to better model and understand material flows, and to obtain results that are dynamic in space;  GIS is also the tool of choice to obtain results that are dynamic in space;  Retrofit seems understudied compared to new construction and demolition. The two studies selected here that include retrofit used top-down accounting;  For the sole analysis of demolition, remote sensing based on change detection data was shown to be an effective method;  Commonly, the input parameters Population and Affluence can be modeled dynamically in space and in time, while Material Intensity and Emission Intensity are lacking behind in terms of dynamic modeling. Dietz and Rosa 63 used IPAT to identify key drivers of society’s CO2 emissions. They identified population growth as the main driver but difficult to control short-term. Affluence’s effect on C02 was found to decline at the highest level of GDP, but for most nations this is out of reach. They concluded that the technology parameter is the clue to control impacts. Drawing on their work and based on this review, we can conclude that Material Intensity and Emission Intensity are the key drivers for building stock related impacts. Cities are complex structures that constantly change, both in space and in time. The inclusion and precise documentation of dynamic parameters in scientific models is crucial, especially when trying to forecast developments and wanting to provide policy-makers and urban planners with pathways to a more sustainable future. It is known that to accommodate the common wave of urbanization we need to dematerialize, even though some would argue that the solution lies within rematerializing construction. Fortunately, there are already many existing possible sustainable alternatives. However, the challenge remains within linking research to action-making. This can only be done by firstly, including the most important dynamics of the built environment in our models depending on the case study, and by secondly, formulating research with a target audience in mind. Both aspects should be better researched. Using GIS for the analysis as well as for visualizing results for communication ACS Paragon Plus Environment


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purposes can be recommended. Moreover, stakeholder analysis and multi-criteria decision analysis seem to be promising to improve studies of the built environment.


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Acknowledgements The first author would like to thank the Portuguese Foundation for Science and Technology (FCT) for making this work possible by awarding her with the doctoral scholarship in Eco Construction and Rehabilitation with the reference number PD/BD/127854/2016. The authors gratefully acknowledge the support of CERIS from IST, and of Giulia Celentano from ETH Zurich for the groundwork of the Sankey diagram.

Supporting Information SI I: Description of the procedure of selection of articles, definition of the three types of dynamics for each of the adapted IPAT equation variables, and in-detail discussion of the study object of each reviewed article. SI II: Overview of all reviewed criteria of the selected articles in table format.



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Figure 1. Illustrative description of three different types of dynamics



Figure 2. Schematic overview of dynamics of input parameters and their impact on the dynamics of results


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Figure 3. Stakeholders and their interest in the main research goals as found in the reviewed articles



Figure 4. Sankey diagram highlighting the most important characteristics of the included studies


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Dynamic Assessment of Construction Materials in Urban Building Stocks

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