Mixed prediction model of agricultural CO2 emissions based on machine learning

With the development of agriculture, agricultural CO₂ emissions account for an increasing share of global CO₂ emissions. However, there is still no uniform prediction standard for agricultural CO₂ emissions, so understanding and monitoring agricultural activities play a crucial role in the prediction of CO₂ emissions. In this paper, eight indicators affecting rural CO₂ emissions are selected to predict CO₂ emissions through ordinary linear regression, random forest regression, and GBDT prediction models, and experiments prove that the model in this paper still has good prediction results for complex data.