Abstract
Abstract In this study, a synthetically simulated panel dataset (2015–2025) containing 11,000 observations was analyzed to examine theoretical expectations in the literature regarding the phenomenon of greenwashing. The primary objective of the analysis was to assess the methodological robustness of the proposed Greenwashing Index (GWI) and the associated panel econometric framework under a controlled data-generating process (DGP). An additional robustness specification excluding energy consumption from the index construction was also estimated to address potential component overlap. The fixed-effects model results indicate that the simulation design produces patterns consistent with theoretical expectations. Larger firms, proxied by market capitalization (β = 0.1973, p < 0.05), exhibit higher simulated greenwashing propensity, while a strong negative association with energy consumption (β = − 21.739, p < 0.001) reflects the imposed regulatory transparency constraints on energy-intensive sectors, serving as an internal consistency check. Sectoral results further confirm that Finance and Technology emerge as higher-risk sectors, as defined within the simulation design. Overall, the findings confirm that the framework successfully recovers the embedded structure, showing that the GWI responds systematically to financial, sectoral, and regional factors in a manner consistent with the theoretical structure embedded in the DGP. The contribution of the study lies in providing methodological validation and structural consistency evidence for the proposed index, rather than empirical inference, thereby establishing a foundation for future applications using real-world data.