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Municipal solid waste management (MSWM) has become a crucial issue worldwide, not only because of the increase of waste generation, but also due to its improper disposal. Improper management of MSW, typically through landfills and open dumping, hascaused serious environmental impacts.To evaluate the most appropriate MSWM system, several models have been developed to support a decision making process. These models were mostly practiced for large cities, while only a few were found for medium and small cities. There is no single optimal MSWM system that can be applied to both large and small cities since they have different characteristics. It is, therefore, vital to evaluate the most suitable MSWM system for medium and small cities, as decisions made at an early stage can have significant impacts to MSWM in laterphases as cities grow. Selecting the most effective MSWM requires consideration of various parameters. For instance, it is complicated for developing countries, such as Thailand, due to the lack of reliable data, including thestatus of MSWM, characteristics of technologies used and applied, and present and future MSW generation and composition.Firstly, this study aims to assess the status of MSWM systems in Thailandto clarify existing decision making processes and to propose appropriate alternatives. The implementationof waste-to-energy (WTE) and other treatment systems invested and operated by the private sector were also comprehensively assessed. Due to the lack of data on MSW treatment systems in medium and small cities, this assessment was carriedout at the country level and was used to represent alternative treatment systems for medium and small cities. Secondly, this study attempts to fill a gap where traditional forecasting models of MSW quantity (i.e., regression analysis, time-series analysis, and econometric models) are challenging to implement given their data requirements. The innovative models, grey models (GM), are developed by using limited data and consider influencing factors to achieve greatest accuracy. In most developing countries, it is difficult to collect reliable data on MSW quantities in large, medium and small cities. Therefore, this study utilized time-series data of MSW collected countrywide for developing alternative models. Despite sources of MSW generation identified in various sectors (residential, commercial, institution, and municipal services), a fewstudies investigated factors affecting MSW generated from only two sectors (residential and commercial). Hence, this study considers these factors in developing MSW forecasting models to address the lack of data. Thirdly, based on the outcomes obtained above, the MSWM systems for medium and small cities were evaluated involving relevant participatory stakeholders. Given the lack of data, this study focuses on front-end and end-pipe treatment systems,excluding separation at source and collection systems. Group meetings or workshops were not conducted in this study due to the unavailability of stakeholders at the top of each organizationThe assessment of MSWM systems was carried out based on a review of available literature, interviews,andsitevisits focusing on existinggeneral MSW management systems and WTE treatment systems, including consideration of pre-treatment, treatment and disposal systems. This studyassessedthe details of WTE systems:anaerobic digestion (AD), incineration (IC), gasification (GF), landfill gas (LFG) and refuse derived fuel (MBT-RDF). Results show that the current annual amount of MSW generatedtreated is2.54 Mt (9.5% of total 26.8 Mt generatedin 2013), which generates66 MW of electricity,as well as 78.26 ktoe of heat. It is estimated that about 8.17 Mt of MSW (30.5%) will be treated,319 MW of electricitygenerated,as well as 78.26 ktoe of heat,when the plants presently under construction and planning start operation. Eight systems consisting of three general MSWM systems iv(composting (MBT-CP), recycling (MT-Re) and landfill (LF)) and five WTE treatment systems (AD, IC, GF, MT-RDF and LFG) were proposed as MSWM alternatives for further decision making process.A methodological framework for MSW forecasting was developed considering traditional and innovative grey models. The forecasting framework could be distinguished as having four main steps: i) identifying and selecting influencing factors, ii) developing alternative models, iii) verifying models, and iv) forecasting the amount of MSW quantity at country level with prediction intervals. Fifteenmodels were developed by using time series data from 2000 to 2012. Results show that GMwith convolution integral,GMC (1, 5), is the best fitmodel and used to forecast MSW collectedwith the least error of 1.16% MAPE. This model indicates that the amount of MSW collected would increase by 1.40% per year,which is in the range from 43,435{u2013}44,994 t/d in 2013 to 47,735{u2013}49,293 t/d in 2020, and 55,177{u2013}56,735 t/d in 2030. The increase of MSW collected may reflect representative factors of the commercial sector (population density, urbanization and proportion employment) rather than that of the residential sector (household size). It was also observed that demographic factors are more important than socio-economic factors.The evaluation of MSWM systems was conducted by using the Analytical Hierarchy Process (AHP) method through assigning weights in pair-wise comparison matrices by 11 stakeholders from five groups of organization such as governmental, academic, technical, local authority and NGOs. This study identified four main criteria(environmental, social, economic and technical), and 12 sub-criteria(diversion from landfill, GHG emissions, environmental impacts, capital cost, O&M cost, revenue & benefit, public acceptance, creation of jobs, simplicity, maturity, and local equipment)to be simultaneously considered for selecting the most suitable MSWMsystems among eight alternatives mentioned above. Results indicate that stakeholderspreferenced the environmental aspect as being the most important, followed by social consideration,whereaseconomic and technical aspects wereconsidered equal for both medium and small cities. It can be concluded that in the context of Thailand, stakeholders focus more on strong sustainability. The preferences of stakeholders on sub-criteria show that public acceptance is the most important, followed by environmental impacts and diversion from landfill. As per this study, the most suitable MSWM system is the mechanical biological treatment combined with composting (MBT-CP) for medium city and mechanical treatment combined with RDF (MT-RDF) for small city, while both landfilling and landfill gas were identified as the worst options. Stakeholders prefer the waste-to-resource (WTR) scheme than waste-to-energy (WTE) to achieve sustainable MSW management in Thailand.This studypresentsa systematic methodology for MSW forecasting that addresses the limited availability of data in a developing country. It also offerswider application and encouragement toresearchers in other developing countries and fields of study. The evaluation of MSWM systemsthat engage relevant stakeholders and thus reflecttheir preferences,can assist decision makers and local authorities in tacklingthe issue of MSWM in other regions of the study area. |
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