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
翻译:从Kaya身份开始,我们使用Neoral CODE模型来预测国家一级与碳排放有关的若干指标的演变:人口、人均国内生产总值、国内生产总值的能源密集度、能源的碳密集度。我们将该模型与基线统计模型VAR进行了比较,并取得了良好的业绩。我们的结论是,这种机械学习方法可以用来产生广泛的成果,并给决策者提供相关的见解。