Improving existing methods for calculating embodied carbon emissions in trade through feature discovery: an information theoretic approach

The continued societal and ecological risks posed by climate change have spurred renewed interest in quantitative tools that can improve policy aimed at climate mitigation. In 2008, international trade accounted for up to 26\% of global anthropogenic emissions, and therefore trade has garnered increased attention from policymakers seeking carbon mitigation. The concept of embodied carbon emissions in trade (EET) quantifies overall carbon emitted in the production and transport of goods for the purposes of trade. EET in theory could prove an indispensable tool to climate-concerned policymakers, but current implementations and data availability limit EET calculation to annual snapshots that extend to the year 2015 at the latest. Existing methods for calculating EET therefore are only useful for retroactive analyses at the moment. This thesis attempts to discover effective surrogate variables which can model embodied emissions in trade on a monthly basis and up to the present day. Pearson correlation and information theoretic measures mutual information and transfer entropy were applied to capture nonlinear and dynamical relationships between variables. Further, modern continuous integration, continuous delivery (CI/CD) software was applied to deploy an interactive, online tool for customization and extension of the work presented in this thesis