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.

翻译：可持续发展是当今世界面临的最普遍挑战之一，其关键在于社会经济增长与环境可持续性之间的平衡。我们提出了监测和量化共享社会经济路径（SSPs）下可持续发展进程的方法，包括基于数学推导的评分算法和机器学习方法。这些方法整合了社会经济与环境数据集，以生成一种可解释的SSP一致性指标。初步研究显示出令人鼓舞的结果，为将不同方法应用于全球可持续发展监测奠定了基础。