Global emission factor dataset for Scope 3 machine learning applications

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a standardized open dataset and baseline model for benchmarking emission factor prediction methods.

🏢実務担当者:Enables corporate sustainability teams to integrate GPU-accelerated emission factor calculations into Scope 3 estimation workflows.

🏛政策担当者:Demonstrates the value of open data infrastructure for climate disclosure standards and could inform data quality requirements.

The accurate and transparent estimation of greenhouse gas emissions is essential for corporate sustainability reporting and machine learning applications. Existing emission-factor datasets have restrictive licenses, insufficient spatiotemporal granularity, or outdated information, limiting their reproducibility and utility across disciplines. We present ExioML, an open-source dataset derived from Exiobase 3.8.2. It integrates environmentally extended multi-regional input-output tables with a graphics processing unit (GPU)-accelerated computational toolkit, facilitating compatibility with and extensibility to other datasets. ExioML encompasses sector-level emission factor data for 49 regions and 28 years from 1995 to 2022, structured into two aggregation schemes: a product-by-product format covering 200 categories, and an industry-by-industry format covering 163 categories. To validate dataset usability and establish a reproducible baseline, we define a regression task for predicting sectoral greenhouse gas emissions. The task is evaluated using tree-based and neural-network-based models, with mean squared error as the evaluation metric. ExioML provides openly accessible emission-factor tables and a reproducible baseline intended to support reuse and benchmarking across sustainability and machine-learning studies.

• semanticscholar https://doi.org/10.1038/s41597-026-06699-1first seen 2026-07-24 06:51:58