ArticleEnvironmental science and pollution research international2022
Designing a fuzzy decision support framework for assessing solid waste management in the South European region.
Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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- Erratum issued
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4 authors.
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Abstract
The environmental planning of cities and rural regions is associated with monitoring the performance of several services, including solid waste management. This study proposes a new framework for the comparative assessment of the performance of integrated municipal solid waste management. The framework includes the fuzzy MACBETH multi-criteria decision-making model used to investigate the uncertainties and inefficiencies associated with solid waste management systems. The model consists of all major stages in solid waste management and its environmental impact. The applicability of the model was examined in the South European region. According to the global fuzzy values of the criteria weights, the most influential reported criteria were GHG emission (0.113,0.157,0.202), the waste generation growth rate (0.034,0.063,0.141), and waste generation (0.034,0.054,0.127). The other results indicated that Italy (47.26) and France (42.67) had shown a better performance, only to be followed by Spain (37.68), whereas Greece (15.77) and Portugal (12.85) had received the lowest score. In the context of promoting the circular economy in Europe, having a higher recycling rate and less landfilling is beneficial for Greece and Portugal. In addition to this, all these countries should make efforts on decoupling the waste generation-GDP correlation. Furthermore, the applicability of the model depends on an appropriate scale and criteria. The model can be replicated to other developed societies with a few modifications. However, it is necessary to modify the criteria for assessing developing societies based on local conditions.
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