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This research aims to empirically compare four sustainability assessment frameworks for agri-food systems.

February 12, 2026Sustainability0 citationsOpen Access

Empirical Comparison of Sustainability Evaluation Frameworks Applied to Agri-Food Systems

PRPablo RituayNational University Toribio Rodríguez de Mendoza MMMarilú MestanzaNational University Toribio Rodríguez de Mendoza CACarlos AldeaNational University Toribio Rodríguez de Mendoza

Key Points

This research aims to empirically compare four sustainability assessment frameworks for agri-food systems.
Compiling a corpus of 89 empirical applications from a Scopus search (2000–2025)
Coding studies using a standard rubric with normative, systemic, and procedural dimensions
Constructing dimension indices with polychoric PCA and rescaling to 0–1
Computing a global index as the mean of the three dimensions
MESMIS achieved the highest mean global index at 0.54
LCA led in the normative dimension with a score of 0.56
MESMIS excelled in systemic (0.64) and procedural (0.53) dimensions
SAFA showed a balanced profile with governance considerations
Findings reflect descriptive patterns rather than confirming method superiority

Abstract

This study aims to provide an empirical, decision-oriented comparison of four widely used sustainability assessment frameworks applied to agri-food systems (LCA, MESMIS, SAFA, and RISE). Using a Scopus-based search (2000–2025), we compiled a purposively balanced corpus of 89 empirical applications and coded each study with a standard rubric spanning normative, systemic, and procedural dimensions. Dimension indices were constructed using polychoric PCA (first component) and rescaled to 0–1, and a global index was computed as the mean of the three dimensions. Within the constructed corpus, MESMIS shows the highest mean global index (0.54), followed by LCA (0.46), SAFA (0.45), and RISE (0.32). LCA leads the normative dimension (0.56), while MESMIS leads the systemic (0.64) and procedural (0.53) dimensions; SAFA presents a balanced profile and explicitly incorporates governance considerations. Findings are interpreted as descriptive patterns within the constructed corpus rather than population-level estimates of inherent method superiority. We conclude that framework choice should be driven by evaluation purpose and information conditions and that hybrid approaches can combine complementary strengths across methods.

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