Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-Research on ARIMA-BP Neural Network Algorithm

Abstract: China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions.