The Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dataset designed for sustainability analysis, aimed at lowering barriers and fostering collaboration between Machine Learning and Ecological Economics research. A crucial greenhouse gas emission regression task was conducted to evaluate sectoral sustainability and demonstrate the usability of the dataset. We compared the performance of traditional shallow models with deep learning models, utilizing a diverse Factor Accounting table and incorporating various categorical and numerical features. Our findings reveal that ExioML, with its high usability, enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future Machine Learning research. Through ExioML, we aim to build a foundational dataset supporting various Machine Learning applications and promote climate actions and sustainable investment decisions.

该研究介绍了ExioML，这是第一个专为可持续性分析设计的机器学习基准数据集，与传统浅层模型相比，深度学习模型在用于评估行业可持续性方面表现出较低的均方误差。通过ExioML，我们旨在构建支持多种机器学习应用的基础数据集，并促进气候行动和可持续投资决策。

ExioML：全球部门可持续发展的机器学习生态经济数据集