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arXiv · 2201.02433

Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations

Pierre Browne·Aranildo Lima·Rossella Arcucci·César Quilodrán-Casas
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Abstract

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

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Pierre Browne, Aranildo Lima, Rossella Arcucci, César Quilodrán-Casas. 2022-01-07. Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations. https://arxiv.org/abs/2201.02433

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