Industrial Ecology Machine Learning Mapping

This Python module uses machine learning to map two classifications together, based on words similarity.

Uses in Industrial Ecology

Industrial Ecology is a field of study where a lot of disciplines co-exists and often interacts. Mapping is thus typically required in IE projects to connect two databases. This module can assist in mapping to

Sector classifications (NAICS, NACE, ISIC, etc.)
Product classifications (NAPCS, CPC, CPA, etc.)
Life Cycle Impact Assessment (LCIA) classifications (ReCiPe, EF3.0, IMPACT World+, CML, etc.)

Installation

Install necessary module with pip

pip install -r requirements.txt

Getting started

Choose a reference classification, a machine learning model and the amount of guess the algorithm will display.

self = ML_mapping.Mapping(reference_classification='exiobase',
                          transformer_model='all-MiniLM-L6-v2',
                          number_of_guesses=5)

Then enter the inputs you want to match to the reference classification, calculate similarity scores and format the results

self.match_inputs(['ADPE System Configuration','Chocolate','Renting a film'])
self.calculate_scores()
self.format_results()

The module returns:

Classifications

The work for the following classifications has already been done in this module

EIO/LCA databases
- ecoinvent 3.8
- ecoinvent 3.9
- openIO-Canada
- exiobase
- USEEIO 2.0
- GTAP 10
Elementary flows classifications
- ecoinvent 3.8
- ecoinvent 3.9
- IMPACT World+ 2.0
- USEtox 2
- EF 3.0
- EF 3.1
Product/Industry classifications
- IOCC
- NACE Rev.1.1
- NACE Rev.2
- CPA 2008
- CPA 2.1
- NAPCS 2017
- NAPCS 2022
- NAICS 2017
- NAICS 2022
- ISIC Rev.4
- CPC 2.1
- COICOP 2018

If the classification you want to match is not already covered, adding it is not complicated.

Credit