## 2 Theoretical Framework
### Methods Combined in the MCDM Model  
Evaluating, ranking, and identifying practices of supply chains oriented toward the preservation of biodiversity, according to the requirements of different participants and a large number of conflicting criteria, represent a problem whose solution requires the application of MCDM methods. There are studies in the literature that have used MCDM methods to solve similar problems (e.g., [33]). This paper contributes to this field by proposing a new hybrid grey BWM-grey ADAM MCDM model. The BWM [34] is a pairwise comparison method and is used to rank elements based on their relative importance or the ranker's preference. This method is used when different criteria are available to evaluate the different elements, and the goal is to select the best option or to rank the options according to the ranker's preference. In the literature, it is most often used to determine the weights of criteria in more complex MCDM models that combine several methods (e.g., [35]). When applying this method, respondents rank the elements according to their importance or preference; that is, they rate them as \"best\" and \"worst\" relative to the others, which enables a clear distinction between the most desirable and least desirable options. The main advantages of the method are precision, simplicity, transparency, and efficiency [36]. This method is characterized by high precision, i.e., sensitivity to small differences in preferences, which means that it can effectively identify subtle differences in the ranking of elements. The BWM method is often simpler to apply compared to other MCDM methods, such as the Analytic Hierarchy Process (AHP) or Analytic Network Process (ANP) techniques, because it requires a significantly smaller number of comparisons of decision elements. This method provides transparent insight into the respondents' preferences, as the best and worst elements are identified, which facilitates the interpretation of the results. In addition, the BWM method can quickly generate relevant information about preferences, which makes it efficient in situations where a quick decision needs to be made. The method also has some drawbacks. It may be less suitable for complex MCDM problems because it focuses on the direct ranking of elements rather than their mutual interaction. However, if there are no prominent mutual influences between the elements, as is the case with the problem in this study, this drawback is negligible. Another disadvantage is subjectivity. Like other multi-criteria decision-making methods, the BWM can be subject to subjective interpretations and biases, especially if respondents are insufficiently informed or have different perspectives. Tomitigate this shortcoming, in this paper, a grey extension of the BWM method was used, which considers the uncertainty and ambiguity in the judgments of the decision-makers. Apart from the advantages mentioned, the BWM method was used in the proposed model because of its proven applicability for solving problems in various fields and the possibility of effectively combining it with other MCDM methods. Particularly, the grey BWM method has previously been used to obtain criteria weights in hybrid MCDM models [37]. In recent years, the method has been used independently or in combination with other methods, in a conventional form or in a fuzzy, grey, or rough set environment, to solve problems such as the ranking of cloud services [35], the evaluation of business strategies in biomedical waste management [38], the performance evaluation of retail warehouses [39], the evaluation of sustainable energy systems in smart cities [40], the evaluation of agri-food circular-economy-based business models [41], supplier selection [42], etc. The ADAM method [41] is a geometric MCDM method because ranking is based on determining the values of the volumes of complex polyhedra formed in three-dimensional space by the evaluation vectors of the alternatives according to the criteria and the evaluation of the weights of those criteria.