An Improved Integrated Model for Solid Waste Management
Date Issued
2009
Date
2009
Author(s)
Su, Jun-Pin
Abstract
Both the quantity and quality of municipal solid waste (MSW) have changed with time. The problems of municipal solid waste management (MSWM) become multiple. While many technologies and models on waste management have been developed, more and more attentions have been paid to whether these tools are appropriate to solve increasingly complex problems. The core concern of this research is to deal with the issues of how to plan a sustainable waste management policy for Taiwan for the purpose of approaching.n improved integrated model for solid waste management has been provided in this study. This model has considered and integrated the environmental, social, and technical aspects of waste policies as well as the characteristic of potential conflicts. Compared to other models, this model has three characteristics as follows. First, fuzzy MCDM has been used in this model as a basic framework to build the policy impact potential analysis (PIPA) method to assist in the assessment of influences from social conflict toward each case. Second, the SOM neural network is combined with multiple regression for estimating MSW based on a regional database to assist in the transfer of regional experience. Third, in order to enhance integration, the mid- and long-term planning is also incorporated in the model.his study tried to set up a policy decision-making support system. The system can provide a long-term forecast function, an model on transfer of regional experiences, and an analysis of potential policy impacts. This study examined the practicability of the model by testing case studies, including “fly ash regeneration in Taipei city”, “regional PAYT policy assessment in Taoyuan county”, and “mid-term and long-term regional waste treatment policies in Taipei county”.he results of the case studies showed that policy impact potential analysis (PIPA) can be valuable to the decision-making support system. It can provide decision-makers with guidelines of communication when other information was not available. The SOM neural network and the multiple regression for estimating MSW was useful for the transfer of regional experience if the regional database was sufficient. The integrated long-term model has also been demonstrated to be one of the models to idenfify the key points of long-term waste management. This study used Stella as tool for building the improved Decision Support System of Sustainable Waste Management.
Subjects
Multiple Criteria Decision Making
Fuzzy Analytic Hierarchy Process (Fuzzy AHP)
PAYT
SOM neural network
waste management
decision support system
Type
thesis
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