Agricultural sustainability assessment has become increasingly complex in the context of the low-carbon transition, as agricultural systems are expected to balance economic viability, environmental protection, and social considerations under conditions of uncertainty and conflicting objectives. Traditional assessment methods that rely on single indicators or deterministic evaluation methods do not provide decision support, particularly in situations where trade-offs and qualitative judgments are critical. Thus, there is a need for clearly outlined decision-support frameworks that cover different dimensions of sustainability and incorporate varying degrees of uncertainty. This study seeks to develop a conceptual methodological framework design for agricultural sustainability assessment that combines the Fuzzy Best-Worst method (Fuzzy-BWM) with the MULTIMORA approach. This framework integrates Fuzzy-BWM with decision-support structures to address each of the evaluation dimensions and to incorporate multi-perspective assessments of alternatives. This study prioritizes the integration of concepts and the complementarity of methodologies of the multi-criteria decision-making approaches over empirical application or numerical validation. The sequence-based assessment logic within the proposed framework separates the definition of the criterion, the weighting of the criterion, and the multi-dimensional assessment. While MULTIMORA provides stable and interpretable rankings across various evaluation perspectives, Fuzzy-BWM captures the expert assessment and the linguistic uncertainty pertaining to the importance of the criteria. This functional separation improves transparency and avoids the mixing of the assessment of uncertainty and the assessment of relative performance. From the perspective of agricultural decision-making under the low-carbon transition, the framework provides a reference model that is flexible and adaptable, and facilitates learning and dialogue processes more than prescriptive processes. Empirical application and validation are identified as important directions for future research.
Štreimikis et al. (Thu,) studied this question.