https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-021-26212-z/MediaObjects/41467_2021_26212_MOESM1_ESM.pdf

Table of Contents

1. Model framework

The overall model framework is shown in Supplementary Figure 1. It is comprised of a building demand module, a material demand module, and a material supply module. Each module is described in turn.

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Supplementary Figure 1. The conceptual framework of the GloBUME model. This dynamic GloBUME model (global dynamic building materials use and embodied GHG emissions model) consists of three layers, i.e. a building demand layer, a material demand layer, and a material supply layer. Two rectangles
in the center represent stocks of floor area and materials respectively, ovals denote flows, hexagons are
the drivers or determinants. Colors indicate drivers given by each scenario, driven by related strategies
illustrated in the rounded rectangles filled with the same colors. Data and codes details are freely
available from the link: https://doi.org/10.5281/zenodo.5171943.

Building demand layer

In the building demand layer, the dynamic building stock and flows are calculated by the following equations:
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Equation (1) describes the fundamental balance between stocks and flows of building floor area, where A(𝑡) denotes the building demand in year 𝑡, and is the key driver of the model, determined by population, denoted by 𝑃(𝑡), and floor area per capita, denoted by A𝑝(𝑡), as shown in Equation (2) and Supplementary Figure 1. A𝑖𝑛(𝑡) represents the building inflow in year t and is determined by the increase in building demand, calculated by 𝐴(𝑡)− 𝐴(𝑡 −1), and the building outflow, represented by A𝑜𝑢𝑡(𝑡). The building outflow in year 𝑡, defined as the demolition of buildings which were built in different years before year 𝑡, is determined by the cohort and the building’s lifetime distribution function.

Is it possible to involve stock changes caused by climate change factors, flood or other potential factors instead demolition only?

As shown in equation (3), 𝑡′(𝑡′< 𝑡) represents the year in which the building was constructed, and 𝐿(𝑡,𝑡′) denotes the probability that the buildings built in year 𝑡′ will be demolished in year 𝑡. We assume buildings’ lifetime follows the Weibull distribution (韦伯分配)and use the equation (4) to calculate 𝐿(𝑡,𝑡′), where 𝜆(𝑡) and 𝑘(𝑡) represent the scale parameter and the shape parameter in year 𝑡 , respectively. Note that the functional form for the lifetime distributions can have a considerable impact on the calculation of outflow and inflow.1 Other functional forms, e.g., normal2, Log-normal3, and Gamma functions4 have also used in material related dynamic analysis. However, the Weibull distribution is the most common in the literature, and is therefore the distribution for which the most reliable data are available5.

This study covers 4 types of residential buildings (detached houses, semi-detached houses, apartments, and high-rise buildings) in 2 areas (urban, and rural) and 4 types of commercial buildings (offices, retails & warehouses, hotels & restaurants, and Other commercial buildings) in 26 global regions. The data on population are derived from the integrated assessment model of IMAGE6. The population share by building types are derived from Marinova et. al (2019)7. The floor area per capita in each region differs by scenario and is discussed in the Scenario generation section (see below). The region classification is shown in Region classification section (see below).

Note that although we mainly consider social-economic factors (i.e., population and floor area per person) as the driver of the global demand in building stocks. There are other factors that will impact future building stocks and could be modelled in future work (using different modelling approaches). For example, the increasing frequency and severity of climate changes may create extra building demand as a complement for those damaged by natural disasters8 , or abandoned in areas declared uninhabitable9.

Material demand layer

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Materials inflow in year 𝑡 , defined as 𝑀𝑖𝑛(𝑡) , is calculated by multiplying building inflow with materials use intensity per floor area in year 𝑡, denoted as 𝑀𝐼(𝑡). We include seven major building materials, i.e. steel, concrete, brick, aluminium, copper, wood, and glass. Detailed material density data is included in the Scenario generation section (see below).
As shown in equation (6), the materials outflow in year 𝑡, denoted by 𝑀𝑜𝑢𝑡(𝑡), is determined by the cumulative sum of the floor area of demolished buildings in year 𝑡 (i.e. 𝐿(𝑡,𝑡′) ∗ A𝑖𝑛(𝑡′)), and the material intensity in year 𝑡′(i.e. 𝑀𝐼(𝑡′)), when the demolished buildings were built.

Material supply layer

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Materials demand can be met by primary production, secondary production, or reuse and their emission
intensities differ significantly (cradle-to-gate emission intensity is 0 for reused materials). In equation (7), 𝑃𝑀𝑖𝑛(𝑡) and 𝑆𝑀𝑖𝑛(𝑡) denote the quantities of materials supplied by primary and secondary production respectively, while 𝑃𝐸𝐼(𝑡) and 𝑆𝐸𝐼(𝑡) represent the GHG emission intensity of the cradle-to-gate life cycle primary and secondary material production, respectively. 𝑃𝑀𝑖𝑛(𝑡) and 𝑆𝑀𝑖𝑛(𝑡) can be calculated by equation (8) and (9) respectively, where 𝑅𝑈𝑟(𝑡) and 𝑅𝐶𝑟(𝑡) represent the reuse and recycling rate of building materials, i.e., the percentage of post-consumer material scraps that are collected for reuse or to produce secondary materials, respectively. Note that here are some inherent assumptions: 1) we only consider the recycling and reuse of the newly generated material outflows, which means the historical scraps would not be considered for recycling and reuse in the future; 2) we only consider the recycling and reuse of material outflows within the same region, area, and building type, which means cross-regional scraps, for example, are not considered for recycling and reuse; 3) we assume that the secondary materials provide the same service as the primary materials, that is, a 1:1 substitution between primary and secondary materials in mass terms.

If we can handle the above limitations in our new research? Extending the boundary of secondary production materials?


Under these assumptions, our circularity model should be taken as a simplified approach for short-term scenario analysis where material inflows are generally larger than outflows. However, in the longer-term (i.e. beyond 2060) when material outflows largely outstrip inflows, it could be important to consider the potential of using historical scrap reserves and scraps moving across regions/areas/building types. 𝑃𝐸𝐼(𝑡) and 𝑆𝐸𝐼(𝑡) are calculated using the life cycle assessment (LCA) model based on the life cycle inventory database eco-invent, with emission intensities for unit processes specified per world region, and which we further specified for future moments in time (more information about the LCA model can be found in section Scenario evaluation in this document).

2. Region and income group classification

Supplementary Table 1 below shows the 26 world regions in this research and the countries or sub-regions included in each world region. We further classify the 26 regions into 3 income groups, i.e., high-income, upper-middle-income, and low- & lower-middle-income groups according to the World Bank Atlas classification (see Supplementary Table 2)10. Note that we report results for the low-income and lower-middle-income regions together for ease of inspection.

Supplementary Table 1. The region classification used in the model6
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Supplementary Table 2. Income groups and included regions according to World Bank’s classfication11.
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3. Scenario generation

Our model described above provides an approach of scenario explorations based on customized development trends. Note that our investigations here represent what-if scenarios rather than an accurate prediction of the future. We follow Deetman et al., 20205, in assuming that some variables (e.g., building lifespan, and material intensity) have remained constant in the pre-2020 period based on 2020 levels (for more details see Deetman et al., 20205). For variables where historical data is available (e.g., for population, and GDP) year-specific data was used. For the future, the Baseline scenario represents a reference case where the historical trends in the building sectors around the world largely continue. The High Efficiency scenario represents a deep emission mitigation scenario where the seven strategies will be simultaneously adopted. Please see below for the full details of the strategies in each scenario.

M1-More intensive use

More intensive use represents the potential to decouple the housing demand (per capita floor area) from economic development. In the Baseline scenario, the residential floor area per capita sees increasing trends, in line with the IMAGE-SSP2 baseline scenario6 (IMAGE data is allocated to the four building types according to Marinova et al. 20197). The commercial floor area per capita towards 2050 is collected from Deetman et al. (2020)[^5] and then extended to 2060 using the same regression model as that used in the original study5. In the High Efficiency scenario, we adopt the assumption from the literature that sees a potential for 20% reduction12 from the Baseline values by 2050. It is noteworthy that in the High Efficiency scenario, in general, the floor area per capita will still increase (at a lower speed than in the Baseline scenario) in the rapidly urbanizing regions but slightly decrease in the highly urbanized regions. As such, the floor space gap between the rich and poorer regions will be narrowed. In future work, it may be worth exploring a more realistic and region-specific implementation of a more-intensive-use scenario, for example by adjusting the per capita floorspace differently for urban and rural areas. This would ideally account for the various drivers of floorspace demand trends (such as high demand & prices in densely populated areas) and make sure that assumptions are not at odds with achieving decent living standards or the Sustainable Development Goals13.

M2-Lifetime extension

Short-lived buildings lead to frequent demolition of old buildings and construction of new buildings. The construction activities are associated with large amounts of building materials and GHG emissions. Therefore, prolonging service life of buildings can reduce the construction of new buildings and therefore avoid materials consumption and emissions1415. Today, the service life of buildings is fairly short and sees a potential of up to 90% extension by 205012. Based on the literature1516, we assume that the service life of buildings follows the Weibull distribution. For residential buildings, we derive Weibull parameters
(shape and scale) from Deetman et al. (2020)5, which differ by region and the building type where possible (see details in Table S.3). We then classify the 26 regions into three groups according to the average lifetime of the buildings in each region (Supplementary Table 6). For commercial buildings, we employ a shape parameter of 1.44 and a scale parameter of 49.6 on the global average, according to the same study5.

Under the Baseline scenario, we assume that the lifetime of buildings will stay at the current level. In the High Efficiency scenario, we assume a lifetime extension by 30%, 60%, and 90% by 2050 for residential buildings newly constructed in the regions classified as having long-, medium-, and short-lifetimes, respectively (see Supplementary Table 4). Similar assumptions are found in the literature12, which assumed a 90% lifetime extension by 2050 in several countries. We assume a lifetime extension by 60% for newly constructed commercial buildings by 2050.

Supplementary Table 3. Weibull parameters used for modelling the lifetime of residential buildings5
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Supplementary Table 4. Region classification according to residential building lifetime.
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M3-Lightweight design

Today’s construction practices often overuse materials due to less efficient design or a relatively low physical strength of primary materials1718. There is a potential for lightweighting through more advanced design strategies including novel structural design19, typology optimization20, additive construction (such as 3D printing)21, and high strength steel and aluminium utilization22. For the Baseline scenario, we assume that material use intensity will stay at the current level. We used the material density of steel, concrete, copper, aluminium, glass, and wood by building type per region from Marinova et. al7 and Deetman et. al5. Given that the material intensity for brick was missing in these works we used values based on multiple other peer-reviewed studies (see Supplementary Table 5). Since China is the largest brick producer (producing over 60% of the world total23), we also derive a specific brick dataset for China. Given the lack of data, we applied a global average for other regions. In the High Efficiency scenario, we assume that the material intensities of steel and aluminium will linearly decrease in the future reaching a reduction of 19% 1718 by 2050. A reduction by 10% is assumed for concrete. The assumptions are somewhat more conservative than some national scenarios1224 to account for the fact that there may be regulatory resistance and delays across the world. The intensities of other materials are assumed to remain constant.

Supplementary Table 5. The brick intensity of buildings.
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M4-Material substitution

Timber houses are generally considered an environmentally friendly substitution for steel and concrete structures for two reasons: firstly, using timber can avoid considerable emissions during material production processes39; secondly, timber can serve as long-term carbon storage if sources sustainably40. However, there are also concerns surrounding land-use change to manage forests and related biodiversity loss41, especially in rapidly growing Asian and African regions. In the Baseline scenario, we assume no
additional timber substitution compared to the current level. In the High Efficiency scenario, we assume that a 10% higher timber substitution (following a liner increase as of 2020) is achieved in new buildings by 2050. That is, a fraction (10% in 2050, for example) of new buildings are designed as wood-based while other buildings conform to the Baseline material intensity (or the light-weighted intensity when light-weight design is applied). Globally a 10% timber substitution by 2050 has been investigated as a reasonable strategy given forest sustainability and capacity to manufacture mass-timber products42, despite being more conservative compared to other scenarios in the literature which see 50% or even 90% scenarios1242. We derive the material intensity of the typical timber building systems from several studies424344(see Supplementary Table 6). We mainly consider the change in the intensity of steel, concrete and wood. Other materials (e.g., gypsum, glass) are relatively insignificant and are therefore not considered. Similar with Churkina et al., 202042, we do not consider timber-based substitution for ‘High-rise’ buildings due to regulation and technological concerns. In future work, it may be interesting to apply different substitution strategies across regions and building types, especially when exploring more ambitious substitution scenarios12.Carbon storage is not considered in this study.

M5-More recovery

An increased recovery (recycling and reuse) of post-consumer scraps is considered as a material efficiency strategy. Currently, large amounts of steel, aluminium, copper, and wood are widely recycled while bulk nonmetallic materials are mostly downcycled to coarse aggregates14. To investigate the development of the recycling rate of different materials, we collect evidence from a series of studies. A recent study reports that the current recycling rate of steel in the construction industry is around 82%45. This is compatible with the assumed range of 75–91% by Krausmann et al46, who also assumed a recycling rate of 65% in 1900. Accordingly, we adopt a linear development of the steel recycling rate from 65% in (and prior to) 1900 to 82% in 2020. For copper recycling, according to the studies mentioned above, we assume a linear increase from 10%46 in (and prior to) 1900 to 60%45 in 2020. Globally the building aluminium recycling rate is already high at around 85%47. GARC (Global Aluminium Recycling Committee) estimated the time-series evolution of the aluminium recycling rate in the building and construction sector, seeing an increase from 20% in 1950 to 89% in 200948. Based on these estimates, we adopt 20% in (and prior to) 1950 to 89% in 2020, following a linear increase.

In the Baseline scenario, we assume that the adopted recycling rate of steel, copper, and aluminium in 2020 will be constant until 2060. Evidence shows that the recycling rate of steel, copper, and aluminium has a potential to reach 90%49:, 93%45, and 95%48, respectively. In the High Efficiency scenario, we assume a linear increase from 2020 values to the potential maximum recycling rates from the literature by 2050 and they are held constant afterwards. See a summary in TableS.7.

There is a potential for increasing the reuse of building components through increased prefabrication and modular construction design12. Based on case studies, we assume that up to 15%18 of steel and concrete could be reused with these approaches. Both assumptions are somewhat conservative compared to the recent scenario analysis for several countries12, yet were chosen considering the varying technology and regulation development patterns across global regions. In the Baseline scenario, no reuse is adopted for any material. In the High efficiency scenario, we assume a linear increase to 15% reuse for concrete and steel between 2020 and 2050 and hold constant afterwards. Note that an inherent assumption is that reused components will meet the function needed during the service life of the building. While this may be quite reasonable in our scenarios up to 2060, it may not be so for later in the century. Split lifespans of the buildings and reused components may be considered when exploring scenarios of longer time horizon, e.g., towards and beyond 2100.

Supplementary Table 7. Historical and projected maximum recycling rate of building materials
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M6-Energy transition

The trend in decarbonization of the global energy system may have significant impacts on the GHG emissions of materials production50. Among all energy types, the electricity system is expected to see profound changes in the coming decades5051.
We derive two global electricity mix scenarios from the Integrated Assessment Model of IMAGE6, as developed by Mendoza Beltran et al. (2018)51 and Cox et al. (2018)52. These electricity scenarios have incorporated consistent techno-socio-economic development in both energy demand (e.g., population size, income-level, and lifestyle) and energy supply (e.g., costs of competing electricity generating technologies) determinants51. Specifically, we apply the electricity scenarios that are compatible with SSP2-baseline and SSP2-2.6 pathways to our Baseline and High Efficiency scenarios, respectively. The SSP2-2.6 corresponds to a strong mitigation scenario aligned to the RCP2.6 (Representative Concentration Pathway), and thus realizes a radiative forcing of 2.6 W/m2 in 2100 (approximately 450 ppm CO2eq.)53. Biogenic carbon flows are not included in the IPCC GWP method used, which means that no credit is given to BECCS. As such, our evaluation of the emission reduction from the electricity transition may represent a lower bound. Also note that we do not take into account the transition in heat and other fuel types due to a lack of data. 2020 is set as the base year for all electricity transition scenarios. That is to say the electricity system changes apply only after 2020.

M7-Production routes and efficiency improvement

The literature indicates considerable potentials for improving the efficiency of material production processes or switching to different material production routes in the future5054. Such developments can reduce energy requirements of future material supply and thus also associated emissions5054.

For the Baseline scenario, we assume the production efficiency and the production routes will not change in the future. For the High Efficiency scenario, we consider scenarios for two developments in this study. These are energy efficiency improvements and a change in market share of the primary copper production routes. First, energy efficiency increases are modeled for the production of steel, aluminium, and copper. For steel making, an improvement of 1.5% per year is assumed based on the study by Van der Voet et al.
(2018)50 who extrapolated historical data from World Steel Association54 into the future. For aluminium production, we assume a yearly energy efficiency increase of 0.5% for aluminium smelting applying the results by Van der Voet et al. (2018)50 who used data from World Aluminium55. Efficiency improvements are not considered for aluminium smelting, since historical data does not reveal specific trends in this case. For primary copper production, we assume a decrease of electricity and natural gas requirements of 1.77% and 1.5% per year based on an exponential regression model by Harpprecht et al. (2021)56 and Kuipers et al. (2018)57 who apply data from Kulczycka et al. (2016)58. Note that 2020 is set as the base year for all energy efficiency increase scenarios and the efficiency changes apply only after 2020. For the development of production routes, we investigate the market shares of hydrometallurgical and pyrometallurgical primary copper production based on Harpprecht et al. (2021)56 (Supplementary Table8.).

Supplementary Table 8. Assumed market share of two primary copper production routes.
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Ore grade decline

There has been a secular ore grade declined over the last century for copper59:, nickel60, zinc61, and lead61 due to large amounts of metal mining across the globe. A lower ore grade means that more ore needs to be processed to produce the same amount of metals 59. This leads to increased energy requirements and higher emission intensities of primary metal supply50. Since copper, nickel, zinc, and lead are amongst the top seven metals consumed globally62, their impacts play a considerable role in emission intensities of numerous production processes in the downstream supply chain45. To include their impacts on the building material production in the future, we consider ore grade decline scenarios and incorporate them equally into the Baseline and High Efficiency scenarios.

We derive metal-specific scenarios of future ore grades between 2020 and 2050 from Harpprecht et al. (2021)56, which are then extrapolated into 2060 following the same trends drawn from the literature. Specifically, for copper, the ore grade scenarios are regionalized and derived from Northey et al. (2014)59, who considered historic data on copper mining, global copper resources, various deposit types, as well as future copper demand and supply. For nickel, zinc, and lead, our ore grade scenarios are based on
metal-specific models by Van der Voet et al. (2018)50, who derived them from historical ore grade development data6061.

Time series data

For most variables, the year-to-year values are linearly interpolated between the known years and linearly extrapolated from 2050 to 2060 where applicable. However, for the change in floor area per capita driven by more intensive use, we use a cubic spline interpolation instead of linear interpolation to avoid sudden drops in the initial years, which may be caused by discontinuities in the first and second derivatives of the time-series curve63. To do this, we use the historical values of floor area per capita in 2010, 2015, 2018, 2019, 2020, and the assumed value in 2050 to interpolate and extrapolate unknown values (see an example in Supplementary Figure 2).

Note that recycling and reuse rates and materials reduction rates by lightweight design are assumed to be at the maximum value they can reach in each scenario by 2050 and remain unchanged after 2050.

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Supplementary Figure 2. An example of cubic spline interpolation for the floor area per capita in Canadian urban residential buildings during 2021-2060. This figure represents the High Efficiency scenario, where the value in 2050 sees a 20% reduction from the Baseline scenario. The blue dots represent the known values in the year 2010, 2015, 2018, 2019, 2020, and 2050.

4. Scenario evaluation

4.1 Evaluation of materials demand by scenario

Using Python, we develop a cohort-based and stock-driven dynamic building materials model to evaluate materials demand in the building sector. On this basis we assess the effect of four strategies (M1-M4, see above) on building materials demand towards 2060. A detailed introduction of the dynamic model can be found in the Model framework section above and the Methods section in the main text.

4.2 GHG emission intensity of building materials by scenario

We evaluate the GHG emission intensity of each building material using a prospective LCA model as described below. The scope is the crade-to-gate production system of per kg of each building material. We use the ecoinvent 3.6 cut-off database64 as the life cycle inventory database because of its global coverage and regional resolution. The ecoinvent processes used are shown in Supplementary Table 9.

Supplementary Table 9. The process names refer to the ecoinvent 3.6 cut-off database.
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Notes: a. For simplicity, low-alloyed steel is used in consistent with ref.50. In practice, steel products are used in different alloys. This model can be improved when market share of alloy types is available.
b. Based on the literature review, we assume that the wood products in buildings are mainly supplied by
softwood beam and hardwood (beam and board) and the ratio is 0.53:0.29:0.1839. The impact of the ratio
assumption on the LCA result is minor because these wooden product types share similar GHGs intensities. For the secondary wood production, based on the wood cascading study, we assume that the raw wood materials are directly replaced by waste wood65.
c. There are two major routes for post-consumer aluminium recycling: scrap remelting for wrought alloy
and refining for cast alloy. Based on related studies48, 66, we assume that 61% of scraps are to be remelted and 39% to be refined.

In general, the regional mapping in ecoinvent has a different resolution with that in IMAGE. According to the approach used in Mendoza Beltran et al. (2020)51, for ecoinvent regions that involve more than one region in IMAGE, we apply the LCA results for the larger region to all smaller regions included. For IMAGE regions involving more than one ecoinvent region, we use an average of the ecoinvent data. As an example, Supplementary Table 10 shows the regional match for the primary copper case.

Supplementary Table 10. The regional mapping between IMAGE and ecoinvent for primary copper production.
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For the impact category of GHG emission, we use the Global Warming Potential per substance from the IPCC 2013, with a time horizon of 100 years67. We use the Activity Browser (AB) software68 to conduct the prospective LCA calculation because of its high-efficiency at scenario-based modeling. To model the development of GHG emission intensity of different unit processes in the different scenarios, we need to incorporate the relevant scenario data into the LCI database of ecoinvent. For the Baseline scenario, we incorporate the ore grade decline data into the ecoinvent database. For the High Efficiency scenario, data on ore grade decline, energy transition, efficiency improvement, and production routes development are incorporated into the ecoinvent database. To do this, we translate the scenario data of each parameter into the presamples structure69 required for the prospective LCA calculation in the Activity Browser68. In practice this means that the scenario data of each parameter is specified in excel files using the approach developed by Steubing et al. (2020)68. We also conduct a ‘Contribution analysis’ using the Activity Browser to split the contribution of CO2 and non-CO2 emissions to the total GHG emission.

5. Additional figures and tables

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Supplementary Figure 3. Building material outflow by magnitude during 1980-2020 (left axis) and recycling input rate (the ratio of available recycled metals from outflows to annual inflows, for steel, copper and aluminium in dashed line) (right axis).

Supplementary Table 11. The eight regions classification.
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6. Uncertainty and sensitivity analysis

The aim of this research is to evaluate long-term materials and emission trends across different regions and income groups. We then investigate the emission mitigation potentials for key material efficiency strategies. We are not able to explicitly evaluate the uncertainties across all datasets due to data limitations. However, we conduct two analyses to understand where major uncertainties may arise so as to facilitate improvements for future work.

First, we compare several primary variables in the Baseline scenario with other literature (Supplementary Table 12). For example, we employ the population trend of IMAGE-SSP2 as a representative of a middle-of-the-road path, which is comparable to the recent scenario explored by Vollset et al., (2020)70(with a slightly smaller estimate than the UNDP Medium variant estimate, 201971). The residential floor area per capita used in this study is slightly larger than other assumptions in the literature42,72. One important area of uncertainty is in commercial floor area which we derive from the time-series estimates by Deetman et al. (2020)5, originally based on a Navigant Global Building Stock Database73 (including service-related floor space in 231 countries in 2017). Similarly, large uncertainties are involved in the average lifetime assumptions of regional buildings. It is difficult to compare the lifetime assumptions between this study and the literature due to data limitations. In the future more detailed data along with time-series data (especially those related to the commercial buildings) are needed to improve the robustness of our analysis.

Second, we employ a sensitivity analysis to show how the High efficiency scenario is influenced by different assumptions on several variables: more intensive use, lightweight design, lifetime extension, material substitution, and more recovery. We assume a sensitivity range for each variable (see Supplementary Table 12). Note that for light weight design, increased recycling and reuse, the High efficiency scenario values are already the projected maximum from the literature. As shown in Supplementary Figure 4, emissions are sensitive to assumptions on more intensive use, whereas different assumptions on other material efficiency strategies tend not to have significant impacts on GHGs. This also indicates the importance of the consumption-based changes (leading to lower floor area per person) to emissions mitigation in the building material sector.

Supplementary Table 12. Comparison of several variables between this study and other literature.
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Supplementary Figure 4. The impact of different assumptions of several material efficiency strategies on GHG emissions. The green lines in all panels represent the High efficiency scenario. More details are available in Supplementary Table 13.

Supplementary Table 13. Sensitivity ranges.
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