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Environmental life cycle assessment (LCA) is increasingly being used to evaluate infrastructure products and to inform their funding, design and construction. As such, recognition of study limitations and consideration of uncertainty are needed; however, most infrastructure LCAs still report deterministic values. Compared to other LCA subfields, infrastructure LCA has developed relatively recently and lags in adopting uncertainty analysis. This paper presents four broad categories of infrastructure LCA uncertainty. These contain 11 drivers focusing on differences between infrastructure and manufactured products. Identified categories and drivers are: application of ISO 14040/14044 standards (functional unit, reference flow, boundaries of analysis); spatiotemporal realities underlying physical construction (geography, local context, manufacturing time); nature of the construction industry (repetition of production, scale, and division of responsibilities); and characteristics of infrastructure projects (agglomeration of other products, and recurring embodied energy). Infrastructure products are typically large, one-off projects with no two being exactly alike in terms of form, function, temporal or spatial context. As a result, strong variability between products is the norm and much of the uncertainty is irreducible. Given the inability to make significant changes to an infrastructure project ex-post and the unique nature of infrastructure, ex-ante analysis is of particular importance. This paper articulates the key drivers of infrastructure specific LCA uncertainty laying the foundation for future refinement of uncertainty consideration for infrastructure. As LCA becomes an increasingly influential tool in decision making for infrastructure, uncertainty analysis must be standard practice, or we risk undermining the fundamental goal of reduced real-world negative environmental impacts.

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The building sector is a voracious consumer of primary materials. However, the study of building material use and associated impacts is challenged by the paucity of publicly available data in the field and the heterogeneity of data organization and classification between published studies. This paper makes two main contributions. First, we propose and demonstrate a building material data structure adapted from UniFormat and MasterFormat, two widely used construction classification systems in North America. Second, the dataset included provides fine grained material data for 70 buildings in North America. The dataset was developed by collecting design or construction drawings for the studied buildings and performing material takeoffs based on these drawings. The ontology is based on UniFormat and MasterFormat to facilitate interoperability with existing construction management practices, and to suggest a standardized structure for future material intensity studies. The data structure supports investigation into how form and building design are driving material use, opportunities to reduce construction material consumption and better understanding of how materials are used in buildings.

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