  Sustainable global development is one of the most prevalent challenges facing
the world today, hinging on the equilibrium between socioeconomic growth and
environmental sustainability. We propose approaches to monitor and quantify
sustainable development along the Shared Socioeconomic Pathways (SSPs),
including mathematically derived scoring algorithms, and machine learning
methods. These integrate socioeconomic and environmental datasets, to produce
an interpretable metric for SSP alignment. An initial study demonstrates
promising results, laying the groundwork for the application of different
methods to the monitoring of sustainable global development.
