Forecasting Municipal Solid Waste Generation in Thailand with Grey Modelling

Abstract

Forecasting municipal solid waste generation is crucial in planning for effective and sustainable waste management. Where data on waste are limited, the grey model (GM) has proven to be a useful tool for forecasting. This study applied GM for forecasting municipal solid waste generation in Thailand up to 2030, based on a dataset from 2011-2018. Both univariate models and multivariate models with four influencing factors (population density, gross domestic product per capita, household expenditure, and household size) were tested. The GM (1,1)-0.1 and GM (1,3) provided the lowest prediction errors among all models. Based on these models, waste generation in 2030 was projected to be 84,070-95,728 tonnes/day (1.23-1.40 kg/capita/day), an approximately 10-25% increase compared to 2018. In a business-as-usual scenario, there would be 6,404,848 tonnes of improperly treated waste by 2030, resulting in greenhouse gas emissions from its disposal of up to 2,600 GgCO2e. This amount of waste is equivalent to 380 MWe of electricity; therefore, it should receive more attention. Results show that the improved management of improperly treated waste would help Thailand reach its waste-to-energy production target of 500 MW by 2036. Furthermore, diverting this portion of waste from open dump sites would directly reduce greenhouse gas emissions from the waste sector more than the set target of Thailand’s Nationally Determined Contribution Roadmap on Mitigation 2021-2030 (1,300 GgCO2e).

Keywords

• municipal solid waste
• forecasting model
• grey forecasting model
• greenhouse gas
• waste-to-energy