Starting from the Kaya identity, we used a Neural ODE model to predict the evolution of several indicators related to carbon emissions, on a country-level: population, GDP per capita, energy intensity of GDP, carbon intensity of energy. We compared the model with a baseline statistical model - VAR - and obtained good performances. We conclude that this machine-learning approach can be used to produce a wide range of results and give relevant insight to policymakers
@article{arxiv.2201.02433,
title = {Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations},
author = {Pierre Browne and Aranildo Lima and Rossella Arcucci and César Quilodrán-Casas},
journal= {arXiv preprint arXiv:2201.02433},
year = {2022}
}
Comments
5 pages, 2 figures, Tackling Climate Change with Machine Learning workshop at ICML 2021