Karichalil (2024) proposed a four-theory, nine-proposition framework for AI adoption in municipal waste management without specifying a data-generating structure precise enough to say what evidence would count against it. This paper does not test that framework empirically. It formalizes the framework’s implied structural model as an explicit data-generating process, simulates a panel of 100 municipalities over ten years under that structure, and evaluates what a real empirical test would require and where it would be most exposed to failure. Method: two-way fixed-effects panel regression with municipality-clustered standard errors, evaluated against known simulation ground truth; a null-effects placebo check; single-parameter and global (variance-based) sensitivity analysis; an identification diagnostic for a time-invariant moderator; a selection-on-capability comparison decomposed into dose-composition and residual confounding components; and two theoretical extensions—a DOI-relative-advantage-by-capability interaction and a market access threshold specification—added to the model and tested for detectability. Findings: minimum detectable effects for the central sorting-technology claim fall below literature-consistent magnitudes for most feasible panel sizes (Table 2); a static difference-in-differences specification recovers only 45–67% of the true dose-weighted average treatment effect because the true effect accumulates with time since adoption, a bias only partially corrected by a dynamic event-study specification; letting adoption timing correlate with capability shifts the estimated effect by a further 65%, of which approximately 42% reflects a genuine change in realized treatment dose and 58% reflects residual confounding; a capability-persistence claim is underpowered (12–15%) at conventional sample sizes because within-municipality variation is only 5.4% of total variance in the relevant construct, a problem panel length resolves far more efficiently tha