susierwu/ML_mapLCI

Mapping product to current life cycle inventory (LCI) database with Machine Learning (ML)

Adapting from Amazon Science:Carbon assessment with machine learning and please follow its installation instruction

git clone https://github.com/amazon-science/carbon-assessment-with-ml.git
cd carbon-assessment-with-ml
pip install -r requirements.txt
pip install -e .

Currently available LCI database to be mapped:

• EIDB folder: ecoinvent database (EIDB): you can customize which version (cut-off, APOS, consequential) to map within the notebook (no impact score provided)
• FederalCommons folder: Selected US Federal Commons LCI databases (no impact score provided), incl:
  • University of Washington Design for Environment Laboratory/Field Crop Production - 'UW_DfE_crop'
  • National Renewable Energy Laboratory/USLCI_2023_Q1_v1 - 'USLCI'
  • Federal Highway Administration/MTU Asphalt Pavement Framework - 'Hwy_pavement'
• AGRIBALYSE folder: for mapping AGRIBALYSE_v3.1: farm-gate as well as ready-to-eat food product, with user-selected impact categories (IC) LCIA scores extracted and plotted

For more elementary flow (EF), LCI, industry classification mapping, please visit CIRAIG_IE_mapping