Two multicriteria-sorting methods that generalize the relational paradigm have been recently presented in the literature. One uses objects representative of classes, the other uses objects in the limiting boundaries of classes; both can use either a reflexive or an asymmetric preference relation. However, defining the parameters of relation-based methods is not straightforward. The present work operationalizes those methods with a methodology that takes examples provided by the decision-maker and, using an accuracy measure that specifically fits the characteristics of the methods, exploits an evolutionary algorithm to determine the parameters that best reproduce such examples. The assessment of the proposal showed that (i) it can achieve considerably high levels of out-of-sample effectiveness with only a few decision examples; (ii) the inference process is more effective learning the parameters of the method based on representative objects; (iii) it tends to be more effective with a reflexive relation; (iv) the effectiveness decreases while increasing the number of classes, which is not always the case when increasing the number of criteria. Theoretical properties of the proposed methodology will be investigated in future works.

The transition toward circular economy (CE) systems is essential for improving re-source efficiency and sustainability performance in industrial production. However, small and medium-sized enterprises (SMEs) face structural barriers that limit the adoption of cir-cular practices and business model innovation. This study examines the systemic drivers shaping circular transitions in timber-based SMEs within an industrial cluster in northern Mexico. The research integrates the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) structural analysis with the Circular Business Model Canvas (CBMC) to examine influence–dependence relationships among key barriers and their im-plications for business model transformation. Empirical data were collected from 32 SMEs using structured surveys and expert consultation. Results identifies financial constraints, technological limitations, and weak collaboration networks as dominant systemic drivers. The CBMC assessment reveals an average implementation level of 45%, with high variabil-ity across firms (31%–99%), indicating fragmented and early-stage circular transition pat-terns. By linking structural diagnostics with business model components, the study identi-fies strategic leverage points and intervention pathways. The findings contribute to CE re-search by providing a systematic, replicable analytical framework and offering practical insights for policymakers and industry stakeholders seeking to accelerate circular bioecon-omy transitions in SME-based industrial clusters.

The transition toward circular economy (CE) systems is essential for improving resource efficiency and sustainability performance in industrial production. However, small and medium-sized enterprises (SMEs) face structural barriers that limit the adoption of circular practices and business model innovation. This study examines the systemic drivers shaping circular transitions in timber-based SMEs within an industrial cluster in northern Mexico. The research integrates the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) structural analysis with the Circular Business Model Canvas (CBMC) to analyze influence–dependence relationships among key barriers and their implications for business model transformation. Empirical data were collected from 32 SMEs using structured surveys and expert consultation. The results suggest that financial constraints, technological limitations, and weak collaboration networks act as dominant systemic drivers. The CBMC assessment indicates an average implementation level of 45%, with high variability across firms (31–99%), reflecting fragmented and early-stage circular transition patterns. By linking structural diagnostics with business model components, the study identifies strategic leverage points and potential intervention pathways. The findings contribute to CE research by providing a systematic and replicable analytical framework, as well as insights for understanding circular bioeconomy transitions in SME-based industrial clusters.

The circular economy (CE) has emerged as a central paradigm for advancing sustainable development; however, most bibliometric studies rely on single-database approaches, potentially generating partial and structurally biased representations of the field. This study addresses this limitation by developing a multi-database bibliometric framework integrating Web of Science (WoS) and Scopus to support more robust, evidence-based circular economy solutions. A total of 17,836 records from Scopus and 13,552 from WoS (2015–2024) were analyzed using the Bibliometrix R-package, combining performance analysis, science mapping, and conceptual structure techniques. The results reveal the existence of dual and complementary knowledge structures: WoS predominantly captures theory-driven, policy-oriented, and strategic research, whereas Scopus expands techno-industrial, application-oriented, and innovation-driven domains. This study demonstrates that database selection functions as an epistemic mechanism shaping knowledge structures, introducing systematic biases when single-source approaches are used. By bridging conceptual and technological knowledge domains, the proposed framework enhances the design, evaluation, and scalability of circular strategies, particularly when aligned with life cycle-based approaches (LCA, S-LCA, and LCSA) and cleaner production systems. These findings contribute to sustainability science by reframing bibliometric structures as operational enablers of solution development, providing a robust decision-support perspective for sustainability transitions, climate change mitigation, and circular economy implementation across diverse sectors.