Abstract: Under the dual impacts of the implementation of mandatory waste classification policies and COVID-19 prevention and control, the actual domestic waste removal and transportation volume in megacities has shown significant volatility and structural changes in recent years, posing challenges to traditional prediction methods. Taking Guangzhou, a megacity in the Pearl River Delta region, as an example, four key driving factors with strong anti-interference to historical data fluctuations were identified using the grey relational analysis method based on statistical data of domestic waste removal and transportation volume and related factors over the years. These factors were per capita consumption expenditure, per capita gross domestic product, permanent population, and urban built-up area. Meanwhile, to capture the impact of social events, mandatory domestic waste classification policy implementation and epidemic prevention and control were introduced as dummy variables into the multi-dimensional grey system prediction model GM(1,6). The model predicted the future waste removal and transportation volume under four scenarios: strong classification without epidemic, no classification without epidemic, strong classification with epidemic, and no classification with epidemic. The results showed that compared with GM(1,1) and GM(1,4) models, the GM(1,6) model had significantly improved accuracy and fitting effect. Mandatory classification policies and the epidemic have achieved obvious effects in reducing waste removal and transportation volume. Under the strong classification scenario, the growth rate of waste removal and transportation volume is more moderate, reaching 7.415 4 million tons by 2035, which is 9% lower than the 8.158 8 million tons under the no-classification scenario. This reflects the long-term effect of source reduction and effectively supports the system planning and governance strategies of domestic waste in megacities in the “waste classification era”.

Key words: domestic waste, removal volume, megacities, mandatory waste classification, epidemic, multi-dimensional grey model prediction