ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability

TL;DR

ExioML is a pioneering dataset that integrates ecological economics and machine learning to evaluate sectoral sustainability, enabling advanced modeling for climate action and sustainable investments.

Contribution

This paper introduces ExioML, the first ML benchmark dataset for sustainability analysis based on the Environmental Extended Multi-Regional Input-Output framework.

Findings

Deep learning models outperform shallow models on the dataset

ExioML enables low-error predictions of greenhouse gas emissions

The dataset fosters collaboration between ML and ecological economics

Abstract

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.