Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
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Abstract (arXiv)
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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References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018Neural Ordinary Differential Equationscited by 3,779
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
- 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 231
- 2019Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasetscited by 69
- 2019Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matterscited by 10
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