  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.
