Prediction of carbon emissions from municipal solid waste treatment in 31 provinces and cities in China under the shared socio-economic pathways

Abstract

In order to reduce greenhouse gas (GHG) emissions from the municipal solid waste (MSW) sector, it is essential to describe its greenhouse gas emission patterns and propose appropriate mitigation measures. Therefore, this study predicts the greenhouse gas emissions of urban domestic waste treatment by combining the Intergovernmental Panel on Climate Change (IPCC), shared socio-economic pathways and in-depth learning model. Under the hypothetical policy scenario and the shared socio-economic pathways (SSPs), the greenhouse gas emissions from municipal solid waste treatment in 31 provinces and cities in China by 2030 are analyzed. Combined with the model comparison results, the bidirectional long and short-term memory neural network model has better prediction accuracy than the other models, with an average mean absolute percentage error of 9.68% for 31 provinces and cities, indicating good applicability. Scenario analysis shows that greenhouse gas emissions from municipal solid waste disposal can be significantly reduced only after the implementation of municipal solid waste classification. Scenario 3 (S3) of waste segregation will produce the most GHG emissions, and switching to Scenario 5 (S5) of waste segregation will produce significantly less GHG emissions. Finally, the paper makes policy recommendations on the priority areas of urban domestic waste classification, waste source classification and recycling, and the structure of urban municipal solid waste treatment.