Prediction of CO2 Emissions Related to Energy Consumption for Rural Governance

Abstract

In the context of rural revitalization, many industries have begun to shift towards rural areas. Industrial agglomeration not only brings economic growth to rural areas, but also increases local carbon emissions. This is particularly evident in some industrialized rural areas with high energy consumption. To accurately implement rural environmental governance, this study selected population, energy consumption, coal proportion, urbanization rate, and other factors as the influencing factors of carbon emissions. The grey correlation analysis method was used to obtain the correlation coefficient of the influencing factors. Then, the relationship between carbon emissions and economic growth, energy consumption, and other influencing factors was analyzed from multiple perspectives. In addition, this study constructed an energy consumption carbon emission prediction model based on deep learning networks, aiming to provide reference data for rural greenhouse gas emission reduction. These results confirmed that the correlation coefficients of the influencing factors of carbon emissions were all higher than 0.6, indicating that their carbon emissions were highly correlated. These test results on the dataset confirm that the RMSE values of the proposed model are all around 0.89, indicating its good prediction accuracy. Therefore, the proposed carbon emission prediction model can provide scientific and reasonable reference data for rural air governance.