2023/12/07 by Michelle W. L. Wan, Jeffrey N. Clark, Wan, Michelle W. L. +7
Decision Sciences · Environmental Science · #Complex Systems and Decision Making #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sustainability and Ecological Systems Analysis
paper · pdf · doi:10.48550/arxiv.2312.04416
openalex publication_date 2023/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.