Siting a municipal solid waste disposal facility, Part II: The effects of external criteria on the final decision

Abstract

The procedure of a multi-criteria decision analysis supported by the geographic information systems was applied to the site selection process of a planning municipal solid waste management practice based on twelve different scenarios. The scenarios included two different decision tree modes and two different weighting models for three different area requirements. The suitability rankings of the suitable sites obtained from the application of the decision procedure for the scenarios were assessed by a factorial experimental design concerning the effect of some external criteria on the final decision of the site selection process. The external criteria used in the factorial experimental design were defined as "Risk perception and approval of stakeholders" and "Visibility". The effects of the presence of these criteria in the decision trees were evaluated in detail. For a quantitative expression of the differentiations observed in the suitability rankings, the ranking data were subjected to ANOVA test after a normalization process. Then the results of these tests were evaluated by Tukey test to measure the effects of external criteria on the final decision. The results of Tukey tests indicated that the involvement of the external criteria into the decision trees produced statistically meaningful differentiations in the suitability rankings. Since the external criteria could cause considerable external costs during the operation of the disposal facilities, the presence of these criteria in the decision tree in addition to the other criteria related to environmental and legislative requisites could prevent subsequent external costs in the first place.

System tradeoffs in siting a solar photovoltaic material recovery infrastructure

“…Conveyor belts are assumed to be used to feed into size reduction and spinning processes as described in literature (Bohland et al, 1997;Hu et al, 2002). We estimate the equipment cost is $1500 and $9000 for a conveyor system and forklift based on data from equipment manufacturers (Delgado et al, 2008;Ekmekçio glu et al, 2010;Gorsevski et al, 2012;Korucu and Karademir, 2013;Sharifi et al, 2009;Sumathi et al, 2008;Toyota Industrial Equipment, 2014;Grainger Inc, 2014). The variable cost is $2.5 per ton for the forklift based on Choi and Fthenakis (2010).…”

“…Geographic information systems (GIS) literature has proposed several land suitability models to site a hazardous waste facility or landfill (Delgado et al, 2008;Ekmekçio glu et al, 2010;Gorsevski et al, 2012;Korucu and Karademir, 2013;Lin and Kao, 1999;Sharifi et al, 2009;Sumathi et al, 2008). Collectively, previous GIS-based models demonstrate the important tradeoffs of including qualitative and quantitative criteria for waste facility siting.…”

Landfill Site Selection Using GIS and Multi-Criteria Decision-Making AHP and SAW Methods: A Case Study in Sulaimaniyah Governorate, Iraq

“…In order to achieve the research objective, thirteen important criteria that have influenced the environment and waste management have been considered as a data set for the decision model, two techniques of multi-criteria decision-making (MCDM) have been used in this research, which are analytic hierarchy process (AHP) and simple additive weighting (SAW) methods to assess the weights of prospective variables for selecting landfill sites [10] [11] [12]. Geographic Information System (GIS) with AHP and SAW methods are the most common techniques in MCDM with a high capacity to manage complicated problems with large data during the decision-making process [13] [14] [15].…”

The use of grey-based methods in multi-criteria decision analysis for the evaluation of sustainable energy systems: A review

“…To address this constraint, grey relational methods have been applied in several research works in the energy-environment field [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. This method is used for sustainability assessment of energy systems [20,24], selecting best technology [21,23,27], deciding optimum system option to produce cooling, heating and power [17][18][19]28,30], planning system management [31], or predict the yield of a system [22]. The application areas have common features of linguistic variables, long-term projects, technical demands, conflicting objectives and uncertainty.…”