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Aggregating midpoint-indicator results from Environmental Product Declarations for comprehensive evaluations of products' profiles, through the Dominance-based Rough Set Approach: An application in the Durum-Wheat Pasta Sector


[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in

Environmental Impact Assessment Review

Journal year: 2024 | Journal volume: vol. 106

Article type

scientific article

Publication language


  • Pasta life cycle
  • Environmental Product Declaration
  • Dominance-based Rough Set Approach
  • Midpoint indicators
  • Classification problem
  • Decision rules

EN To-date there is an unsolved methodological problem for the type-III labelling scheme, namely the Environmental Product Declaration (EPD), that essentially derives from the midpoint approach used for the Life Cycle Assessment (LCA) which the EPD itself is based upon. As a matter of fact, in the current EPD system, it is impossible to perform univocal aggregated evaluations of the product's profile, due to the different equivalent unit-of-measures which the midpoint impacts are expressed with, and to the absence of a scientifically-based, objective scheme for aggregation and weighing. The authors wanted to undertake this challenge with the aim of enhancing the effectiveness and utility of EPDs to guide managers, consumers and other stakeholders in the promotion, distribution, and consumption of sustainable products, in the context of green public procurement. To achieve this goal for the case of durum-wheat pasta, the EPD was complemented by the authors with the Dominance-based Rough Set Approach (DRSA). In result, an aggregate preference model considering all impact categories was obtained, thereby leading to comprehensive comparisons of complete environmental profile of products. The model has the form of a set of minimal decision rules, that can be used by producers for understanding the environmental performance level of their pasta products, and subsequently making strategic decisions for its improvement. Seven of those rules were determined from DRSA application, that regarded grain production and milling, package manufacturing, and a comprehensive downstream phase that included the disposal of the packaging system, and the pasta cooking with water waste disposal. In addition to this, those rules primarily considered the following midpoint categories within the ensemble of those provided by the EPD system for pasta products: acidification, eutrophication, global warming, and mineral element depletion. All of them were certain rules: three of the “at least” type (two at least good, and one at least medium); and four of the “at most” type (one at most bad, and three at most medium). By referring the given PCR-based LCA results to the threshold values of performance on criteria present in the premise of these rules, producers can know in advance the quality class (bad, medium, or good) which their pasta products belong to. In case of a negative evaluation, they can try to improve the value of some crucial indicators before applying for obtaining the EPD, and so enhance the environmental profile of their products. The authors believe that their study advances knowledge and enriches the literature concerning the application of multiple criteria decision analysis (MCDA), wherein DRSA stands out as an outstanding example. This contribution is particularly significant in enhancing and valorising sustainability assessment tools and certification schemes. Under this perspective, the study documented that leveraging DRSA to supplement the EPD system, for the particular case of pasta products, can contribute directly and indirectly to achieving Sustainable Development Goals (SDGs) and, so, to advancing environmental sustainability within the food sector.

Pages (from - to)

107492-1 - 107492-14





Article Number: 107492

Ministry points / journal


Impact Factor

9,8 [List 2023]

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