Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.704107
Title: Advancing sustainable nanotechnology with multiple criteria decision aiding
Author: Cinelli, Marco
ISNI:       0000 0004 6062 4501
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2016
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Abstract:
Nanotechnology is currently emerging as the next industrial revolution. It enables the production of goods (i.e. nanoproducts, NPs) with enhanced functionalities, which have nonetheless caused mounting concerns about the potential implications they can have on the environment, economy and society. This thesis employs Multiple Criteria Decision Aiding (MCDA), one form of decision support, to aid the sustainable development of nanotechnology. The first original contribution of this doctoral research is the development of a framework of sustainability assessment criteria for NPs, through a three-phase procedure based on the MCDA process, including a literature review, a pilot and a main survey. It lead to a comprehensive framework of 68 criteria, ranked according to their relative importance, allocated to six main domain areas: (i) economic performance; (ii) environmental impacts; (iii) environmental risk assessment; (iv) human health risk assessment; (v) social implications; and (vi) technical performance. All the criteria are reliable and can be used in real case studies to increase the knowledge about the sustainability of NPs. The second original contribution presented in this thesis is a robust model (DRSA-based model) based on green chemistry principles implementation for the classification of synthesis processes of nanomaterials in preference-ordered classes. This tool was developed through knowledge elicitation techniques based on coconstructive MCDA with the collaboration of two experts (the decision makers) in synthesis of nanomaterials. The robustness of the ensuing model was assessed (and confirmed) by means of another model developed ad hoc (ELECTRE-based model), structured on an MCDA method implementing a stochastic multiple criteria classification strategy. The results confirm that MCDA is an effective decision support approach to foster sustainable development of nanotechnology, providing that the analysts who apply it take these considerations into account. They must ensure that (1) multidisciplinary teams are created to perform comprehensive and credible sustainability evaluations; (2) problem structuring and model construction are as important as (if not more important) than the results (i.e. decision recommendations) themselves; (3) identification of the appropriate MCDA method depends on the problem at hand and not vice-versa; and (4) the credibility of the decision recommendations is subject to the preferences of the decision-makers. If these considerations are accounted for, the possibility of advancing nanotechnology on a sustainable path is very concrete and realistic.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.704107  DOI: Not available
Keywords: T Technology (General)
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