Prediction of Regional Carbon Emissions Based on ARIMA Model and Kaya Model

Research on regional carbon neutrality targets and path planning is the foundation for achieving China's carbon neutrality goals and promoting sustainable development. By studying and analyzing the carbon emissions, economic structure, energy consumption, and other factors of specific regions or areas, a carbon reduction path and action plan suitable for the region can be formulated to guide the region in adjusting its energy structure, improving energy utilization efficiency, and developing low-carbon industries. This paper uses statistical methods combined with data science to conduct time series analysis and regression analysis based on the characteristics of the data provided in order to establish a predictive model for regional carbon emissions, economic, population, and energy consumption. The Autoregressive Integrated Moving Average Model (ARIMA) time series model is used to predict the trend of population, economy, energy consumption, and a multiple regression model between energy consumption, population, and GDP is established based on the Kaya model.