论文标题
使用神经普通微分方程通过KAYA身份进行预测排放
Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations
论文作者
论文摘要
从Kaya身份开始，我们使用神经模型模型来预测与碳排放相关的几种指标的演变，在一个国家 /地区：人口，人均GDP，GDP的能量强度，能量的碳强度。我们将模型与基线统计模型-VAR进行了比较，并获得了良好的性能。我们得出的结论是，这种机器学习方法可用于产生广泛的结果，并为决策者提供相关的见解。
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
