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.

可持续全球发展是当前世界面临的最主要挑战之一，平衡社会经济增长与环境可持续性，我们提出了一种监测和量化共享社会经济路径（SSPs）上可持续发展的方法，包括数学推导的评分算法和机器学习方法，将社会经济和环境数据整合以产生可解释的 SSP 对齐度量。初步研究展示了有希望的结果，为应用不同方法监测可持续全球发展奠定了基础。