Integrated models for carbon emission prediction: Combining epidemiology models and statistical forecasting

The topic of our article is Integrated Models for Carbon Emission Prediction: Combining Epidemiology Models and Statistical Forecasting. In this work, we need to combine various epidemiological models from different perspectives, including evolution, application and statistics, to simulate and predict carbon emissions and obtain the results we need. The following content is about the results we have obtained, and its significance lies in predicting and analyzing when and how much carbon emissions will peak in various possible scenarios. Predict the most likely peak time and peak volume from a regional and industry perspective, providing decision-making basis and reference for scientifically and reasonably formulating emission reduction policies. Also, some application in real life of the integrated models is shown in this paper. In addition, the limitations and challenges we face are provided in the article, we are able to consider how to improve the prediction by analyzing these limitations. This article includes a case study about energy which allows us to better have a more comprehensive and vivid image and understanding of the models. All the actions in this work will be meaningful in our life because carbon play an important role in environment like greenhouse effect and biological carbon cycle.