Causal effect of carbon footprint calculators

Abstract

This master thesis aims to answer whether theory on causality and multivariate time series are relevant tools for questions that might arise in the context of different tracking apps. The context is the mobile application Svalna, which is a research-based carbon calculator designed to help people track and reduce their emissions. It has been shown that information provision can impact behavior, so the central question is whether using the Svalna application impacts the users consumption. I introduce a statistical approach to analyse multivariate time series like those gathered through Svalna. I create a data generation model to test the suggested statistical model. As an intermediate check, the model is used to evaluate a data set from Svalnas users. I conclude that the mechanisms of the developed models function in well-behaved data and the model should be seen as a intermediate step towards a model to analyze real data from Svalna. I think it is a useful approach that can contribute to understanding behavioural change and contribute to better app design.