Comparing Data-Derived and LLM-Elicited Indicator Weights for EU Circular Economy Assessment: An MCDA Study

Country rankings of circular economy (CE) performance depend on how indicators are weighted. This study compares data-derived and large language model (LLM)-elicited weights for 13 EU Circular Economy Monitoring Framework indicators across 27 EU countries in 2022. CRITIC, Entropy and MEREC provided data-derived weights, while Claude-sonnet-5 (Anthropic) and ChatGPT gpt-5.6-terra (OpenAI) generated pairwise comparisons using the Analytic Hierarchy Process (AHP) and expert personas. Countries were ranked using TOPSIS and MARCOS. LLM weights were tested through additional repetitions and prompt variations and compared with two published human expert panels. Under TOPSIS, pooled LLM and CRITIC weights produced broadly similar country rankings (Spearman’s ρ = 0.78), despite different indicator priorities. The LLM panel gave greater weight to material and consumption footprints. Rankings varied with weighting and normalization choices. Claude and ChatGPT produced different TOPSIS rankings, although removing persona instructions barely changed pooled rankings (ρ = 0.995); ChatGPT showed greater sensitivity (ρ = 0.872). Both published expert panels prioritized Competitiveness and Innovation, unlike the LLM panel. Rankings under TOPSIS and MARCOS were generally similar. These findings show that similar rankings can conceal different indicator priorities. LLM-elicited weights offer an additional assessment scenario, not a substitute for human expert judgment.