Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways
This work addresses the problem of tracking sustainable development for global policymakers and researchers, but it appears incremental as it builds on existing SSP frameworks without claiming major breakthroughs.
The paper tackles the challenge of monitoring sustainable global development by proposing approaches to quantify alignment with Shared Socioeconomic Pathways, using scoring algorithms and machine learning methods that integrate socioeconomic and environmental datasets, with an initial study showing promising results.
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.