Desenvolvimento de um método de avaliação de impacto para ACV social (Social Metric for Life Cycle - Smile)
The assessment of potential social impacts related to the life cycle of products (SLCIA) is still an aspect under discussion in social life cycle assessment (S-LCA). Thus, different approaches are employed in the S-LCIA methods, segmented into two main classifications (Type I and Type II). As discus...
| Autor principal: | Araujo, Jaylton Bonacina de |
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| Formato: | Tese |
| Idioma: | Português |
| Publicado em: |
Universidade Tecnológica Federal do Paraná
2021
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| Assuntos: | |
| Acesso em linha: |
http://repositorio.utfpr.edu.br/jspui/handle/1/26473 |
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| Resumo: |
The assessment of potential social impacts related to the life cycle of products (SLCIA) is still an aspect under discussion in social life cycle assessment (S-LCA). Thus, different approaches are employed in the S-LCIA methods, segmented into two main classifications (Type I and Type II). As discussed in the literature, the methods of both approaches have certain limitations. For the Type I methods, there are limitations concerning the use of indicators closer to the end of the cause-effect chain, and the relevance of certain indicators for the representation of social impacts since the causal relationships are not clear. For the Type II methods, fewer subcategories are considered than in Type I and the lack of clarity that some methods bring regarding how impact pathways are defined. Given this context, this thesis aimed to develop a Type II method, with pathways built from the subcategories and that would allow obtaining characterization factors for S-LCIA. Therefore, to fulfill the proposed aim, this thesis was developed in three steps: I) a review of the state of the art of Type II methods and their relationship with the Sustainable Development Goals (SDGs); II) a review of the applicability of multivariate analysis techniques (MAT) for the development of Type II methods; III) development of a Type II method using generic data at the country level representing subcategories and MAT to obtain the impact pathways and characterization model. In step I, 20 methods were analyzed, being identified that the targets related to ten SDGs are contemplated in the social cause-effect chains, highlighting SDG 3 (Good health and well-being), which presented connections with 17 methods. As new possibilities, it was identified that several SDG targets can be implemented in the cause-effect chains, either through new methods that include issues still little explored in the context of S-LCA or as a reference for the definition of new S-LCA subcategories. In step II, seven MAT were analyzed with concerning their characteristics and applicability to support the development of Type II methods. Thus, it was identified that exploratory factor analysis (EFA) has potential application for identifying impact pathways. Additionally, confirmatory and predictive techniques such as regression and structural equation modeling (CB-SEM and PLS-SEM) can support the confirmation of impact pathways and the development of characterization models. Finally, in step III, the SmiLe method was presented, developed to identify potential impact pathways from correlations between indicators representing the subcategories and their effects on the endpoint category “human health”. As techniques for estimation, exploratory factor analysis and structural equation modeling (CB-SEM and PLS-SEM) were used in the exploratory, confirmatory, and predictive steps. As obtained results, from the analysis of 21 indicators, representing 15 subcategories, it was possible to identify and estimate two impact pathways, “Economy and competitiveness” and “Access to water, sanitation and conflict prevention”, which presented a strong relationship with the human health endpoint category (represented by life expectancy at birth), as well as the elaboration of a characterization model. |
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