Discovering hidden knowledge in carbon emissions data: A multilayer network approach

In this paper, we construct the first human carbon emissions network which connects more than a thousand geographical locations based on their daily carbon emissions. We use this network to enable a data-driven analysis for a myriad of scientific knowledge discovery tasks. Specifically, we demonstrate that our carbon emissions network is strongly correlated with oil prices and socio-economic events like regional wars and financial crises. Further, we propose the first multilayer network approach that couples carbon emissions with climate (temperature) anomalies and identifies climate anomaly outlier locations across 60 years of documented carbon emissions data; these outlier locations, despite having different emission trends, experience similar temperature anomalies. Overall, we demonstrate how using network science as a key data analysis technique can reveal a treasure trove of knowledge hidden beneath the carbon emissions data.