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Area of Science:

  • Environmental Science and Economics
  • Data Science and Machine Learning

Background:

  • Accurate greenhouse gas (GHG) emission estimation is crucial for corporate sustainability and machine learning.
  • Existing emission factor datasets suffer from licensing restrictions, poor spatiotemporal detail, and outdated information, hindering reproducibility.
  • There is a need for an open-source, granular, and up-to-date dataset for GHG emission factors.

Purpose of the Study:

  • To introduce ExioML, an open-source dataset for greenhouse gas emission factors.
  • To provide a computationally efficient and extensible toolkit for data integration and analysis.
  • To establish a reproducible baseline for benchmarking sustainability and machine learning models.

Main Methods:

  • ExioML is derived from Exiobase 3.8.2, integrating multi-regional input-output tables with a GPU-accelerated toolkit.
  • The dataset includes sector-level emission factors for 49 regions across 28 years (1995-2022).
  • Data is structured in product-by-product (200 categories) and industry-by-industry (163 categories) formats.

Main Results:

  • ExioML provides openly accessible emission factor tables with enhanced spatiotemporal granularity.
  • A regression task for predicting sectoral GHG emissions was defined and evaluated using machine learning models.
  • The dataset facilitates compatibility and extensibility with other datasets and computational tools.

Conclusions:

  • ExioML offers a valuable, open-source resource for accurate and transparent GHG emission estimation.
  • The dataset and associated toolkit support reproducibility and benchmarking in sustainability and machine learning research.
  • ExioML aims to advance the utility and accessibility of emission factor data across disciplines.