Carbon data and its requirements in infrastructure-related GHG standards

Accurate carbon data is crucial for informed decision-making to achieve net-zero targets within the next several decades. However, data collection in the infrastructure sector faces significant challenges. Carbon data is either manually collected or extracted from design models, and carbon factors often come from secondary databases with varying boundaries and assumptions. Distributed infrastructure presents complex data management issues throughout its lifecycle, leading to uncertainty in accurately estimating emissions. Despite numerous guidelines and standards emerging since the 1990s, trustworthy data management remains nascent. This paper provides a thematic review of international, European and British standards for carbon data in distributed infrastructure, focusing on data categories, measurement methods, and sources. The standards broadly set out the boundaries of the assessment in terms of emission scopes and categories. While three scopes of emissions are often recognised, many standards do not yet require Scope 3 accounting. Embodied carbon is the current key focus whilst operational carbon is gaining more attention. The lifecycle analysis method is a dominating method for measuring lifecycle embodied emissions. Standards endeavour to direct the user to quality sources of activity data and emission factors; they also emphasise using primary activity data and specific emission factors from reliable sources and accurate measurement methods to enhance data trustworthiness. Developing a standardised carbon data collection methodology with a unified scheme, standard format, clear ontology, streamlined process, and transparent sharing protocol is essential and warrants further research. • Carbon data of distributed infrastructure assets face complexity and uncertainty. • This paper reviews carbon data requirements from standards and guidelines. • The core review themes are: data categories, measurement methods, and data sources. • Standards recommend specific data from trustworthy sources with reliable methods.