Estimating carbon footprints from large scale financial transaction data

Financial transactions are increasingly used by consumer apps and financial service providers to estimate consumption-based carbon emissions. This approach promises a low-resource, ultra-fast, and highly scalable approach to measuring emissions at different levels of potential policy intervention-spanning the national, subnational, local, and individual level. Despite this potential, there is a lack of research exploring the validity of this approach to carbon profiling. Here we address this oversight in three ways. First, we provide a step-by-step description of our approach toward estimating carbon footprints from micro-level transaction data generated by more than 100,000 customers of a large retail bank in the United Kingdom. Second, we quantitatively compare emission estimates obtained from transaction data with those calculated from a more standard data source used in carbon profiling, the largest household expenditure survey in the United Kingdom. Third, we offer a detailed qualitative comparison of the advantages and disadvantages of transactions versus alternative data sources (such as survey data), across key dimensions including data availability, data quality, and data detail. We find that financial transactions offer a credible alternative to survey-based sources and, if made more widely accessible, could provide important advantages for profiling emissions. These include objective, micro-level data on consumption behaviors, larger sample sizes, and longitudinal, frequent data capture.

Automated summary

Financial transactions are increasingly used to estimate carbon emissions. This approach offers a low-resource and scalable way to measure emissions at different levels of policy intervention. We provide a step-by-step description of our approach, compare it with standard data sources, and highlight the advantages of using transaction data.