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

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.