System boundaries
In this study, the substitution impact was assessed by estimating DFs for comparable house types under future scenarios assuming reduced energy sector emissions and increased recycling of construction products. Given the relatively long timeframe required for meeting such targets, the temporal scope of the study was 2020–2050. As the future scenarios contain inevitable uncertainties, we used stochastic simulation approach to determine the DFs by including a range of possible scenarios related to decarbonization of the energy sector and recycling rates of discarded construction products. This assessment is limited to product level DFs, and therefore excludes biogenic carbon emissions and removals as well as market dynamics (Fig. 1).
The impacts of the maintenance and demolition of buildings were not considered. Also, it was assumed that the maintenance and energy use of the alternative house types were identical, as the compared buildings have the same functionality. Using discarded wood as energy can replace fossil energy and provide additional substitution impacts (e.g., [27]). However, determining the emissions of discarded wood from material recycling instead of energy is a complex undertaking. The use of discarded wood for energy can be less favorable in terms of GHG emissions reduction than recycling [5]. Importantly, for wood buildings built today, the possible end-of-life substitution impacts would only be gained well past 2050, by which point the energy sector is expected to have radically reduced its average emissions, leading to smaller relevancy of end-of-life energy recovery of wood products. Therefore, plausible end-of-life substitution credits were left outside the system boundaries.
Alternative house types
The DFs for construction were assessed using scientific articles and reports from the Nordic countries with similar climate conditions and construction regulations. Three studies report sufficient data on the life cycle inventories and impact assessment of functionally equivalent house types: Tettey et al. [30], Ruuska and Häkkinen et al. [25] and Peñaloza et al. [23]. As it was assumed that the use-phase of house types was similar in terms of energy use and maintenance, only the GHG emissions of manufacturing construction materials were considered.
Tettey et al. [30] assessed a six-storey building with a frame of prefabricated concrete, prefabricated modular timber or cross laminated timber (CLT) elements, designed to meet the Swedish passive house criteria. Ruuska and Häkkinen [25] evaluated a five-storey residential model building with a concrete, CLT or timber frame. The volume of the building was 6108 m3, with a respective floor area of 1813 m2. Additional file 1 was requested from the authors of the original research paper to complete the calculations for all construction components such as the external walls, roofs, and base floors (see Additional file 1: Tables S1–S3). These components were then calibrated to match with the total mass and volume of the buildings. A study by Peñaloza et al. [23] included data on multi-family houses: three types of timber-based multi-family dwellings (prefabricated volume elements, massive elements, and column-beam); and three types of multi-family dwellings with a concrete structure.
Wood-frame multi-story construction is still in its infancy, with the market share remaining at a few percentage even in the Nordic regions where it has been promoted for decades [32]. So far, the house types described by Tettey et al. [30] and Ruuska and Häkkinen [25] appear to be the most typical structures or are considered to increase significantly in the future [9]. However, the exact market shares are unknown and they remain subject to speculation for future scenarios.
GHG emissions of construction products, and impacts of future decarbonization and recycling scenarios
The GHG emissions of houses were aggregated using data on material use and GHG emissions of construction products. The GHG emissions of construction products were projected for the period 2020–2050 and were aggregated for each year by considering the decarbonization of the energy sector as well as the impacts of recycling construction products. Data on the GHG emissions of the production of construction products were acquired from various sources, such as the ecoinvent database and GHG emissions reports (see more details in Additional file 1: Table S4).
A set of “what if” scenarios were assumed to estimate the GHG emissions of construction materials in the future. For the sake of simplicity, the GHG emissions of energy production were assumed to decrease in a linear trend between 2020 and 2050. The range of emission reductions varied from 40 to 80% from the level in 2020. The shares of energy emissions for various construction materials were obtained from the ecoinvent 3.0 database (see Additional file 1: Table S7). The calcination of cement was included by assuming that emissions of concrete would decrease 45–75% in 2050 from the level in 2020 [18, 37]. A linear decrease in annual calcination emission reduction was assumed, with an uncertainty range.
There are no binding targets on the recycling rates of different construction materials. As such data were not available, alternative approaches were applied to assess the recycling potential of construction materials in the future scenarios. Data on the recycling rates of construction products were taken from Finnish reports on recycling targets for various materials (see Additional file 1: Table S8).
The process of recycling construction materials, such as transportation to the recycling center and the crushing of concrete, requires some energy input. Consequently, recycling causes GHG emissions, although the amount is typically less than that caused by using virgin raw materials. Data on GHG emissions of recycling construction materials of non-wood origin were taken from Turner et al. [33]. The energy emissions of recycling are also assumed to decrease in the future because of the decarbonization of the energy sector.
The GHG emissions of construction products were aggregated according to Eq. 1:
$$GHG_{it} = \left( {1 - REC_{it} } \right)*\left( {1 - EN_{i2020} } \right)*GHG_{i2020} + \left( {1 - REC_{it} } \right)*\left( {EN_{i2020} *ENER_{i} *GHG_{i2020} } \right) + REC_{it} *ENER_{i} *REM_{i2020}$$
(1)
where, \(REC_{it}\) = Share of recycled products of material i in year t, \(EN_{i2020}\)= Share of energy emissions of material i in 2020, \(GHG_{i2020}\)= GHG emissions product i in 2020, \(ENER_{i}\)= Share of energy emissions in year t compared to emissions in 2020, \(REM_{i2020}\)=GHG emissions of recycling of material i in 2020. The emissions were aggregated as kg CO2 eg/kg of a construction material.
Calculation of displacement factors
DFs were calculated for each house type from 2020 to 2050 according to Eq. 2, as provided in Sathre and O’Connor [27]
$$DF = \frac{GHG\, nonwood - GHG \, wood}{{WUwood - WUnonwood}}$$
(2)
where GHGnonwood and GHGwood include aggregated GHG emissions of the required construction materials, and WUwood and WUnonwood include wood use as biogenic carbon contained in the respective house types. GHGnon-wood and GHGwood were aggregated by multiplying the required raw materials with the carbon footprint of each raw material. WUwood and WUnonwood were calculated assuming that the carbon content of wood-based raw materials is 50%. As buildings based on mineral materials in most cases also include some wood, WUnonwood is typically not zero, although the values are much smaller than in wood-framed buildings (see Additional file 1: Tables S1–S3). However, the DFs are calculated only for substitution cases where a wood-based design replaces a non-wood-based design, because substitution between wood-based products ought to only be considered in the process of upscaling the substitution impacts from a product level to a market level through weighted DFs for intermediate wood-based products [13]. Because of the decarbonization of the energy sector and recycling of construction raw materials, GHGnonwood and GHGwood are assumed to decrease whereas WUwood and WUnonwood remain at the same level. The DFs applied in this study imply that there are no emission leakages, i.e., a unit emission reduction at the building level equals a unit emission reduction to the atmosphere.
Simulacion 4.0. Excel add in was used to simulate the DFs in 10-year intervals from 2020 to 2050 for three scenarios: the first with decarbonization of the energy sector and the recycling of discarded construction products; the second including only decarbonization of the energy sector; and the third including only recycling. 1000 simulations were carried out for each scenario. Because of decarbonization and recycling GHG emissions of the construction raw materials are expected to decrease in the future. Furthermore, GHG emissions of construction materials in this study are based on different data sources (see Additional file 1: Tables S5–S8). By including several comparable data sources, we ensured that the variation in GHG emissions of construction products is included as one source of uncertainty along with decarbonization of the energy sector and recycling. For all three variables a uniform distribution was assumed.
Sensitivity analyses
As concrete, steel and wood are abundantly used in construction, the GHG emissions of these raw materials are assumed to have a substantial impact on the total GHG emissions of a building and on the substitution impacts of wood construction. To assess the sensitivity of the results on the GHG emissions of the presumed most influential raw materials, three alternative scenarios were implemented.
In ‘zero fossil GHG emissions wood’ scenario, it was assumed that the emissions of all wood-based products would be zero in 2050 [3]. This could be achieved by using an even higher rate of bio-based residues or alternative renewable energy sources to cover the operational energy demand of mills. In this scenario, the reduction in emissions is assumed to be caused by increasing the circular use of wood products and by decarbonization of the energy sector. In this setup, the carbon footprints of all wood-based raw materials were zero in 2050. For the sake of simplicity, a linear decrease in emissions from 2020 to 2050 was assumed. In ‘zero GHG fossil emissions concrete’ scenario, it was assumed that the cement production will reach carbon neutrality by 2050, as envisioned by Cembureau [6]. The target could be reached through alternative clinkers, increased efficiency of production, the use of alternative fuels, better transport efficiency, and breakthrough technologies, such as carbon capture. As cement is the main component causing GHG emissions in concrete production, it was assumed that because of the carbon neutrality of cement, in 2050 the carbon footprint of concrete will be zero. In ‘zero fossil GHG emissions steel’ scenario, it was assumed that the steel production will reach carbon neutrality by 2050 by using, e.g., top gas recycling blast furnaces, carbon capture and storage, and the substitution of pulverized coal injection with biomass [31]. For all three zero fossil GHG emission scenarios DFs were aggregated using the same approach (see Section “Calculation of displacement factors”).