Evaluation of greenhouse gas emissions from municipal solid waste management and forecasting by multivariate grey model

Abstract

Thailand's waste problem has accumulated for a long time due to inadequate waste management. Much of Thailand's waste management is conducted by municipalities which continue to mainly use landfill method causing problems in terms of space management. If it is not managed well, it can result in many consequences ranging from overflowing waste, fire in landfills, and methane generated from landfills which is a key greenhouse gas (GHG) contributing to global warming and climate change. For this reason, the government has made solving waste problems a national agenda and formulated a national strategy to reduce GHG emissions to 20-25 percent by 2030. Although the policy has been clearly formulated, it is still unable to address these problems since the solution is inconsistent with the reality. One of the causes of these problems is the failure of inaccurate forecasts. Therefore, this research aims to evaluate GHG emissions from municipal waste management and to develop a multivariate gray model for forecasting waste and GHG emissions from municipal waste management that will occur in the future and for using the results to develop a suitable waste management method to reduce GHG emissions in a concrete way. The study evaluated GHG emissions from 90 municipal waste management sites, comprising 10 city municipalities, 55 town municipalities, and 25 subdistrict municipalities, based on the calculation method from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Intergovernmental Panel on Climate Change: The IPCC. Primary data used were field visits through the project of Thailand Greenhouse Gas Management Organization (Public Organization) or TGO to ask for information on the amount of waste and the management model in all 90 municipalities. Secondary data were such as information on the composition of the waste based on the Pollution Control Department and studies from textbooks, manuals, or research published in academic journals. For the development of a model for forecasting the amount of waste and GHGs from municipal waste management using a multivariate gray model, factors affecting the amount of waste generated from interviews with 90 municipalities were analyzed to make the model consistent with the context of the municipality. Data from Nakhon Ratchasima City Municipality, Cha-am Town Municipality and Ban Klang Subdistrict Municipality were used to develop a prototype model for municipalities of similar sizes. The data used to develop the model were from two parts: 1) municipality, ie. the amount of waste, the population density and household size, and 2) National Statistical Office, i.e. the household expenditure, Gross Provincial Product and the proportion of labor. In addition, greenhouse gas reduction guidelines have been analyzed to provide guidelines for municipalities to use in their decision-making in selecting technologies that are appropriate for their contexts by referring to the method in calculating the amount of GHG reduction from Thailand Voluntary Emission Reduction Program (T-VER), solid waste management, sewage and waste materials project developed by TGO, which consists of five methods: 1) MSW Incineration, 2) production of compost or soil amendments from organic waste, 3) RDF production from MSW, 4) methane capture from anaerobic organic waste treatment for utilization and 5) methane recovery from MSW management for utilization or flaring. The findings showed that the city municipality had the highest - lowest GHG emissions of 147,826.98 - 2,493.67 tonCO2eq. The town municipality had the highest - lowest GHG emissions of 43,767.61 - 540.77 tonCO2eq. The subdistrict municipality had the highest - lowest GHG emissions of 11,435.99 - 14.98 tonCO2eq. The average GHG emissions of the city, town, and sub-district municipality were 45,012.52, 11,806.99 and 2,273.60 tonCO2eq, respectively. Waste management by landfill is not only the most popular method, but it is also the waste management method with the highest amount of GHG emissions. For waste management with different types of waste separation, in addition to reducing the amount of waste that goes to landfill, it can also help reduce the amount of GHG emissions. For the development of a model to predict the amount of waste and GHGs from municipal waste management in the future, the amount of waste generated affected the amount of GHGs. If the amount of waste increased, it also increased the amount of GHGs. The factors affecting forecasting varied depending on the nature of the data and the context of the municipality. GM (1,3) was the most suitable model for the city municipality. GM (1,4) was the most suitable model for town and subdistrict municipalities since it had the least MAPE value. The factor affecting the forecast of city municipality the most was gross provincial product, followed by population density. The factor affecting the forecast of town municipality the most was population density, followed by proportion of employment and household expenditure, respectively. The factor affecting the forecast of the subdistrict municipality the most was gross provincial product, followed by population density and household size, respectively. According to the analysis of the amount of greenhouse gas reduction from waste utilization in accordance with Thailand's policy using the methodology of Thailand Voluntary Emission Reduction Program, solid waste management, sewage and waste materials project developed by TGO, if the municipality maximizes the use of waste by collects CH4 from a landfill for electricity generation, using tree branches/leaves for composting, using organic waste to produce biogas and using plastic and paper waste to make RDF, it will reduce the amount of GHG emissions 1,016,094 tonCO2eq