Hybrid life-cycle assessment for robust, best-practice carbon accounting

Abstract In order to meet internationally agreed targets for avoiding dangerous anthropogenic climate change, an absolute priority for global society is to rapidly stabilise and then reduce carbon dioxide emissions into the atmosphere. Any entity, be it individual, company, or nation state, is more able to reduce its carbon dioxide (and other greenhouse gas) emissions if these can be quantified and attributed and the effects of interventions estimated. The current state of product and supply chain carbon accounting methods does not consistently meet the standards required to tackle this global challenge. This study therefore aims to identify key methodological practices affecting the accuracy of carbon accounting models and in particular to assess the effects of the system boundaries they employ. Models currently available for estimating carbon emissions are either input-output based (using macro-economic analysis), process-based (using specific carbon emissions attributes through the life-cycle of a product, service or event), or a hybrid of the two. Here, a detailed comparison has been made between various input-output and process-based models and the results compared with those from a hybrid model that was taken to represent ‘best practice’ in carbon accounting. Key factors affecting accuracy were found to lie in: the detail of methodological decisions for input-output models, the economic region or regions upon which the model is based, and the quality, disaggregation and, especially for price-volatile products, the temporal alignment of the data. The relative significance of these factors is explored. For copper wire, a system boundary gap analysis was conducted on an industry-leading process-based model (GREET.net) compared with a complete system as described by the best performing input-output model. GREET.net was found to suffer a 60% truncation error. The copper wire example demonstrates the practicality of substituting process-based analysis into input-output based supply chain emissions assessments.