Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting

Abstract

In the developing countries, the inadequacy of basic waste data is a significant obstacle for municipal solid waste management. To evaluate an effective waste management plan, identification of influencing socio-economic factors and projection of municipal solid waste generation (MSWG) plays a crucial role. Yet, several forecasting methods have been utilized to quantify future MSWG. In this study, we investigated the influencing socio-economic factors for MSWG in China using the fuzzy logic method, and a short-term forecasting of MSWG was conducted using multi-model approach. The Grey (1, 1), linear regression, and artificial neural network (ANN) models were evaluated for short-term forecasting. The factor analysis results show that urban population growth is the most influencing socio-economic factor for MSWG, and the influence of GDP on waste generation is not so obvious. Afterward, the multi-model forecasting results indicate an increasing trend of MSWG. Based on the absolute percentage error, root mean-squared error, mean absolute error, and coefficient of determination (R2), ANN found as the most acceptable model to forecast MSWG in China. Hence, the forecasting by ANN model illustrates that MSWG in China will be 24666.65 (104 tons) by 2030.