SupTech and Greenwashing in European Banking: A Causal and Nonlinear Heterogeneous Analysis Using Synthetic Control and Causal Random Forest

This paper examines whether the adoption of Supervisory Technology (SupTech) is associated with a reduction in greenwashing in the European banking sector over the period 2014–2025. Using a progressive empirical framework combining the Synthetic Control Method (SCM), a split-sample SCM, an OLS regression of the SCM-estimated treatment effect, and a Causal Random Forest (CRL) via T-Learner applied to a panel of European banks, we provide evidence consistent with a meaningful reduction in greenwashing associated with SupTech adoption, which is robust across multiple identification and validation strategies. The split-sample SCM and OLS analyses reveal that this effect is amplified by higher capital adequacy, genuine ESG engagement, and stricter regulatory environments, while larger banks exhibit a systematically attenuated response. Contrary to the complementarity hypothesis, RegTech does not reinforce SupTech’s disciplining effect; instead, the evidence points to a substitution mechanism whereby banks with developed internal compliance infrastructure derive limited marginal benefit from external supervisory technology. The Causal Random Forest analysis provides evidence of a statistically significant and stable average treatment effect and indicates that bank digital maturity and FinTech adoption are the most consistent drivers of SupTech’s effectiveness. Policy simulations show that improving digital maturity, rather than RegTech endowment, yields the largest additional greenwashing-reduction gains. These findings suggest that SupTech acts as a credibilization mechanism whose effectiveness depends on the stringency of the external regulatory architecture and the digital absorptive capacity of supervised institutions. External validity to less harmonized regulatory environments remains an open empirical question.