Prediction of CO2 Emissions Related to Energy Consumption for Rural Governance

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 consum...

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Bibliographic Details
Published inSustainability Vol. 15; no. 24; p. 16750
Main Authors Yu, Xitao, Cheng, Jianhong, Li, Liqiong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 12.12.2023
Subjects
Agricultural production
Carbon sequestration
Computer centers
Economic development
Economic growth
Electronic government
Energy consumption
Industrial development
Quality of service
Rural areas
Service centers
Sustainable development
China
Henan China
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su152416750