Acta Polytechnica CTU Proceedings https://doi.org/10.14311/APP.2022.38.0044 Acta Polytechnica CTU Proceedings 38:44–49, 2022 © 2022 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague APPLYING LIFE CYCLE ASSESSMENT WITH MINIMAL INFORMATION TO SUPPORT EARLY-STAGE MATERIAL SELECTION Matthew Roberts∗, Valeria Cascione, Stephen Allen, Barrie Dams, Daniel Maskell, David Coley University of Bath, Faculty of Engineering and Design, Department of Architecture and Civil Engineering, Claverton Down, BA2 7AY Bath, United Kingdom ∗ corresponding author: mar90@bath.ac.uk Abstract. Traditional life cycle assessment (LCA) is too data intensive and time consuming to be used during typical building design processes. Conducting an LCA during the building design process therefore requires simplifications and assumptions. Such “screening LCAs” are quicker and can be used with less data but introduce greater uncertainty. Unfortunately, uncertainty is not reflected in standard deterministic LCA calculations, which produce single-point values in LCA results. Thus, in this study, data quality scoring has been incorporated into a screening LCA to produce probabilistic predictions of environmental performance based on limited data. The approach has been applied during the design process of a bio-based wall panel designed for a circular economy. A combination of ecoinvent and material data sheets were used to analyse a wide range of novel bio-based insulation materials. The screening LCA analysed global warming potential and identified a short-list of promising materials that were then subjected to a detailed LCA for further consideration in the design. The method uses publicly available information and can be applied at material or building-element level. The method thus helps designers estimate environmental impacts without hindering the design process. Keywords: Life cycle assessment, bio-based materials, circular economy, uncertainty. 1. Introduction The timing of emissions associated with construction is significant when considering how to mitigate cli- mate change [1]. The production of materials, needed for building construction and renovation, contributed 11 % of global energy-related CO2 emissions in 2017 [2]. Unlike operational impacts, which occur over an ex- tended period, upfront embodied impacts have already occurred by the time a building has been constructed and, therefore, represent the immediate impacts of a building [3]. Decisions that occur during the de- sign process greatly influence the magnitude of these embodied impacts. Within the UK, the building de- sign process is standardised by the Royal Institute of British Architects (RIBA) Plan of Work [4]. The RIBA Plan of Work classifies the design process into 8 stages, including: two pre-design stages, three design stages and construction, handover, and use stages [4]. As the design progresses, the ability to influence the design with ease diminishes [5]. This is due to the elimination of design variants and the selection of char- acteristics and materials. Early-stage design decisions have a large influence on the overall environmental performance of a design. When appropriate consid- erations are taken, the design process can be guided to low-impact solutions that produce the most en- vironmentally beneficial outcome [1, 6]. Frequently, designers rely on past experience to evaluate between alternatives [7]. This approach does not guarantee that the best solution is taken and can perpetuate the selection of sub-optimal design solutions; thus, produc- ing environmental impacts that would be avoidable. Life cycle assessment (LCA) is an internationally recognized means of assessing the environmental im- pacts that occur throughout all stages of a product’s, or system’s, life cycle [8]. When implemented effec- tively, LCA can be used to support the selection of en- vironmentally beneficial solutions [9]. Unfortunately, LCA is typically perceived to be too time consuming and data intensive to be compatible with the nuances of early-stage design [10, 11]. Therefore, LCA is often only implemented late in the design process when little is expected to change [12, 13]. This relegates LCA to being an accountancy tool that, generally, is used for green building certification schemes [14, 15]. Screening LCAs (SLCAs) have emerged as a means of providing a relatively quick assessment [16]. SLCAs are, however, characterised by elevated levels of uncer- tainty that can devalue their conclusions [17]. SLCAs can be used to simplify the level of detail in the life cy- cle inventory and the scope of the impact assessment method [18]. The challenges of assessing materials, or design variants, in the design process are ampli- fied when unconventional materials or materials with limited information are considered. The use of bio-based materials to substitute tradi- tional, more impactful, materials has been proposed as one means of lowering the impacts attributed to ma- terial use in the built environment [19]. Bio-based ma- 44 https://doi.org/10.14311/APP.2022.38.0044 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en vol. 38/2022 Applying life cycle assessment with minimal information . . . terials sequester atmospheric carbon as they grow and, typically, have low processing impacts when compared to other construction materials [20]. The sequestered carbon is stored within the bio-based materials un- til end-of-life when a proportion of the sequestered carbon is emitted to atmosphere [20, 21]. The use of bio-based materials provides additional benefits within the context of a circular economy [22]. The concept of a circular economy is focused on the reten- tion of materials in the value chain to minimise raw material extraction and eliminate the production of waste [23]. For bio-based materials that are designed to be circular, the sequestered carbon is stored within the material for the duration that it is retained in the value chain [20, 21]. Therefore, the more circu- lar a bio-based product is, the longer the release of sequestered carbon is postponed. The present study uses data quality to incorporate uncertainty into a SLCA to improve the confidence in the conclusions that can be drawn from this type of assessment. The approach has been applied to the design of a circular economy bio-based external wall assembly. This study has been conducted to demon- strate how LCA can be used as a decision support tool while dealing with inadequate levels of information needed for a traditional LCA. 2. Methods The assessment discussed in this paper is categorised by two distinct phases: (1) an initial SLCA; (2) a de- tailed LCA of specific materials identified from the SLCA. The SLCA was done when there was a lack of available data for the assessed materials, a com- mon obstacle faced during the design process, whilst the detailed LCA was performed for materials once whole processes and required data was available. The goal of the SLCA was to identify which materials should be subjected to a detailed LCA based on what their environmental performance is likely to be. The SLCA did not try to quantify the exact environmen- tal impacts of each material. Data quality has been considered in the SLCA to visualise the uncertainty associated with the calculated impacts. The SLCA was limited to raw material supply (A1) and prod- uct stage transport (A2) [24] due to the availability of information for the assessed materials. Product stage manufacturing (A3) was not included within the scope of the SLCA due to the lack of available data for the assessed materials. The materials were compared based on their anticipated ranges of impacts. After the SLCA, two materials were identified and were subjected to a detailed LCA for A1–A3. 2.1. Data Quality For the purposes of this SLCA, only publicly accessible information was used. The use of publicly accessible information was chosen to enable life cycle thinking to support the design process even if information is sub- optimal and/or has significant gaps. A combination of publicly available material specifications and articles were used for this study. The use of sub-optimal data introduces various sources of uncertainty and makes data quality very relevant to the types of conclusions that can be made. Data quality assessment provide a means to visualise and communicate uncertainty, aiding the process of comparing multiple materials when the level of detail for each varies. The ecoinvent 3.0 pedigree matrix, as presented in Ciroth et al. [25], has been used to assign data quality indicators for each of the assessed materials based on reliability; completeness; tempo- ral correlation; geographic correlation; and, further technological correlation. The pedigree matrix is used to assign a value between 1–5, with 1 having the least uncertainty and 5 having the most uncertainty, to each indicator that reflects how well the informa- tion represents the assessed system. The data quality indicators are converted into uncertainty factors fol- lowing Table 10.5 from Weidema et al. [26]. These uncertainty factors have been combined with a basic uncertainty of 0.04 (Table 10.3, Weidema et al. [26]) to get the standard deviation for each material by using Equation (1). The basic uncertainty is used to capture the variances associated with representing the values as a normal distribution [26]. It is important to note that Equation (1) functions under the assump- tion that each variance is normally distributed and independent. σ = √√√√ 6∑ n=1 σ2 n, (1) where σ2 1 represents the basic uncertainty and σ2 2−6 represent the variance for each indicator score 2.2. Impact Assessment The EuGeos 15804+A2 v4.1, an extension to the ecoin- vent version 3.6 database, has been used to deter- mine the environmental impacts of the constituent components of each assessed material. However, any database that includes detailed environmental impacts for individual materials could be used to conduct a similar assessment. For this study, the global warm- ing potential evaluated over a 100-year time horizon (GWP100) has been used to compare the materials against one another. The total GWP100, including biogenic carbon storage, is reported for the SLCA results of each assessed material. 3. Case Study The presented approach has been applied to the se- lection of insulation materials for the design of an ex- ternal wall assembly. The design of the wall assem- bly in question is described in Cascione et al. [35]. A SLCA was conducted to compare multiple materi- als under consideration for improved design iterations and identify materials that would likely provide the most favourable environmental performance based on 45 M. Roberts, V. Cascione, S. Allen et al. Acta Polytechnica CTU Proceedings Material Density [kg/m3] λ [W/mk] Constituent Material Breakdown References Mycelium 95 0.08 Pleurotus Ostreatus, straw, flour, corn, wheat [27], [28] Recycled Cotton 20 0.039 Cotton, recycled fibre, polymer binder [29] Compressed Reed 275 0.052 Typha, magnesite [30] Agriculture Fibre 30 0.038 Fibres (cotton, flax, hemp), polyethylene (PE) binder, fungicide [31] Cellulose Wadding 45 0.04 Cellulose wadding, hemp, PE binder [31] Grass Fibre 40 0.04 Grass fibre, recycled fibres, polyester [32] Flax 23 0.035 Flax, polyester binder, salts [33] Sheep’s Wool 25 0.035 Sheep’s wool, bicomponent polyester [34] Table 1. Key characteristics for the assessed bio-based insulation materials. Material Reliability Completeness Temporal Correlation Geographic Correlation Further Technological Correlation Standard Deviation (σ) Mycelium 0.002 0.002 0 2.5 · 10−5 0.008 0.228 Recycled Cotton 0.0006 0 0.0002 2.5 · 10−5 0.008 0.221 Compressed Reed 0.002 0.002 0.002 2.5 · 10−5 0.008 0.232 Agriculture Fibre 0.0006 0.0006 0.0002 2.5 · 10−5 0.008 0.222 Cellulose Wadding 0.0006 0.0001 0 2.5 · 10−5 0.04 0.284 Grass Fibre 0 0 0 2.5 · 10−5 0.0006 0.202 Flax 0 0.0001 0 2.5 · 10−5 0.0006 0.202 Sheep’s Wool 0.0006 0.0006 0.0002 2.5 · 10−5 0.008 0.221 Table 2. Assigned variances (σ2) based on pedigree matrix scores with calculated standard deviation. a limited amount of information. The density, ther- mal conductivity (λ) and material breakdown of each assessed insulation are included in Table 1. The ref- erences included in Table 1 were used to gather the constituent material composition information needed to conduct the SLCA. As the study considers novel bio- based materials, there were no environmental product declarations (EPDs) available. EPDs are typically prepared for established and mass-produced materi- als to describe the environmental impacts that are anticipated to occur throughout the material’s life cycle. The SLCA was carried out for all materials un- der consideration for future design iterations. Data quality indicator scores were assigned, following the pedigree matrix [25], based on the quality of informa- tion available for each material. These data quality scores were used to assign variances for each data quality indicator which were then combined with the basic uncertainty to acquire the standard deviation based on Equation (1). Table 2 summarises the as- signed variances and standard deviations for each of the assessed materials. Based off the pedigree matrix method used, higher variances are associated with more uncertain results and correspond to higher stan- dard deviations. Variances with an assigned value of 0 demonstrate the highest level of confidence in the information used to conduct the assessment. 4. Results The assessment has been conducted in two parts to capture the thermal resistance and assembly thickness of the first design iteration [35]. The first comparison is conducted for the desired R-value of 6.3 m2K/W and an unrestricted insulation thickness. Table 3 presents the expected ranges of A1–A2 GWP100 for each material when the desired R-value is achieved. Most materials meet the desired R-value with a thick- ness of ∼300 mm. Mycelium and compressed reed insulation require thicknesses of 510 mm and 365 mm, respectively, to provide the desired thermal resistance. It is important to note that the required thickness of mycelium will have further implications on the wall panel design as it would require additional stud framing and fasteners to house the insulation, thus increasing material usage. These knock-on impacts of increased insulation thicknesses are not discussed within the scope of this paper. A second comparison was completed with an in- sulation thickness limited to a maximum of 300 mm, matching the wall assembly thickness of the first de- sign iteration [35]. The ranges of expected A1–A2 Total GWP100, depicted by probability density func- 46 vol. 38/2022 Applying life cycle assessment with minimal information . . . Figure 1. SLCA (A1–A2) Total GWP100 results for 300 mm insulation thickness (R-values vary). Material Required Thickness [mm] A1–A2 Total GWP100 [kg CO2e] -3 Standard Deviations Median Value +3 Standard Deviations Mycelium 510 -416.1 -351.1 -286.1 Recycled Cotton 300 23.2 36.5 49.8 Compressed Reed 365 -68.8 123.0 314.7 Agriculture Fibre 300 -90.0 -70.0 -50.0 Cellulose Wadding 310 -32.3 6.1 44.4 Grass Fibre 310 -52.1 -27.9 -3.7 Flax 280 -134.8 -120.8 -106.9 Sheep’s Wool 280 -0.3 16.3 32.9 Table 3. Expected SLCA GWP100 ranges and required thickness for thermal resistance of 6.3 m2K/W. tions, are presented in Figure 1 for each material. Under the 300 mm wall thickness, mycelium and the compressed reed insulations did not meet the required thermal resistance as they were only able to reach thermal resistances of 4 m2K/W and 5.3 m2K/W, re- spectively. 5. Discussion Table 3 and Figure 1 clearly convey how the certainty of GWP100 results is affected by variance in the under- lying data quality of each material. This is much more transparent than the typical approach of presenting single-point estimates and rankings, which implies all values are equally certain. A negative total GWP100 indicates that the amount of sequestered carbon out- weighs the fossil impacts of acquiring the constituent materials in A1–A2. The Total GWP100 for mycelium and compressed reed display the greatest difference when their expected ranges are compared between Table 3 and Figure 1. Based on Table 3 and Figure 1, the materials that are most likely to provide environmentally beneficial results are mycelium and the flax based insulations. The distributions for agricultural fibre mix and grass fibre insulations have some overlap when compared at a 300 mm thickness, but the expected ranges for both are separate from other considered materials. The expected range for the cellulose wadding insulation encompasses that of the sheep’s wool insulation in its entirety. From the SLCA, it would be impractical to make conclusions between these materials due to the significant overlaps present in their respective distribu- tions. The compressed reed insulation presents a very wide range of possible values, most of which are at the highest end of GWP results. The compressed reed insulation should not be included within the design considerations based on the information available. 5.1. Comparison against Detailed LCA Following the SLCA, a detailed LCA was conducted for A1–A3 for both the mycelium and flax based insu- lations. A combination of manufacturer information, material specifications and published literature was used to complete the detailed assessment. The full life cycle inventories (LCIs) for the detailed LCAs of mycelium and flax based insulations are included within the supplemental materials of the study by Cascione et al. [36]. The results for the SLCA are 47 M. Roberts, V. Cascione, S. Allen et al. Acta Polytechnica CTU Proceedings Material Required Thickness [mm] SLCA [A1-A2] Total GWP100 -3 σ Median +3 σ [A1-A3] GWP100 Total Biogenic Fossil LULUC Mycelium 510 -446.1 -348.5 -250.9 -181.2 -738.8 557.6 0.89 Flax 280 -131.4 -120.3 -109.2 -105.5 -155.4 50.0 0.06 Table 4. Comparison between SLCA (A1–A2) and detailed LCA (A1–A3) results in kg CO2e. compared against that of the detailed LCA in Table 4. The values presented in Table 4 represent the environ- mental impact needed to produce enough material to meet the desired thermal resistance of 6.3 m2K/W. To meet the desired thermal resistance of 6.3 m2K/W, 510 mm of mycelium or 280 mm of flax would be required as previously mentioned. The re- sults from the detailed LCA are higher than expected SLCA ranges presented in Figure 1 due to the inclu- sion of process impacts needed in the manufacturing (A3) life cycle stage. Since the sequestered carbon may be emitted at end-of-life, it is important to con- sider the Fossil GWP100 as it indicates an immediate emission of greenhouse gases. In order to reduce the environmental impacts of the built environment, ma- terials with minimal Fossil GWP100 impacts should be prioritised. As shown in Table 4, the Fossil GWP100 impacts for mycelium are 557.6 kg CO2e for the re- quired thickness due to the energy required during the manufacturing processes [37]. The Fossil GWP100 for the mycelium insulation is 11 times higher than the Fossil GWP100 for the flax based insulation. Based on the detailed LCA, the flax insulation provides a high performing bio-based insulation alternative with min- imal A1–A3 Fossil GWP100 impacts and therefore would be more environmentally favourable than the mycelium insulation. 6. Conclusions The use of LCA during early-stage material selec- tion is often hindered by insufficient information and constrained project timelines. The use of screening LCAs (SLCAs) can reduce the time needed to conduct an assessment but can introduce an increased level of uncertainty. In this study, a pedigree matrix ap- proach is used in a SLCA methodology to calculate a probability distribution for global warming potential and estimate the uncertainty caused by variations in data quality. This is more transparent than the typi- cal approach of presenting single-point estimates and rankings, which implies all values are equally certain. The approach has been applied during the design pro- cess of a bio-based wall panel for a circular economy. This was done to identify insulation materials that were likely to result in a low environmental impact while mitigating challenges associated with informa- tion gaps during early design material selection. The SLCA only considered A1–A2. There is scope to in- corporate additional life cycle considerations into the assessment, including transportation, product lifes- pan, and end-of-life. The inclusion of uncertainty highlighted that there was no clear ranking among some materials since their probability distributions of global warming potential exhibited significant over- laps. For the materials subjected to the detailed LCA, the SLCA scope (A1–A2) gave a reasonable indication of A1–A3 impacts for flax based insulation, since it did not have large processing impacts in A3. The mycelium insulation was found to have significant A3 impacts. SLCAs enable a multitude of materials to be considered prior to the completion of a detailed LCA for materials that perform favourably in the SLCA. Acknowledgements This work was completed as part of the Circular Bio- based Construction Industry (CBCI) project funded by the European Union Regional Development Interreg 2 Seas Mers Zeen (2S05-036). The work was supported by a Leveraged University Research Studentship in affiliation with the UK Engineering and Physical Science Research Council funded Active Building Centre [EP/N020863/1]. References [1] L. Strain. Time value of carbon. Carbon Leadership Forum, Seattle, 2017. [2] International Energy Agency. Material efficiency in clean energy transitions. 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