DOI: 10.3390/accountaudit2040020 ISSN: 3042-6618

Financial Materiality and Environmental Enforcement Disclosure Detection

Suying Song, Yichuan Wang

This study links U.S. Environmental Protection Agency (EPA) enforcement settlements to U.S. Securities and Exchange Commission (SEC) filings and examines the association between observable financial burden and disclosure detection. The sample comprises 163 settlement matters involving 127 registrants during 2010–2025, with 96 disclosure matches and 67 protocol-based non-detections. The standardized financial coefficient in the full-sample joint model is 0.963 (95% profile interval 0.428 to 1.578). This positive association is not independently confirmed within the frozen-protocol cohort; among its 87 matters, including 20 disclosure matches, the coefficient is 0.342 (95% interval −0.569 to 1.283; p = 0.457). The company fixed-effect linear probability estimate is also imprecise (0.033; p = 0.823). The other 76 matters entered the legacy cohort after disclosure had already been found. Regulatory salience estimates are imprecise in joint models. Two independent human coders checked the same 40 selected matters; agreement is reported by sampling stratum, with remaining disagreements concentrated in restored legacy cases. Excluding all 12 restored legacy matters retains a positive full-sample financial association. The study provides a traceable EPA-SEC matching workflow, explicit disclosure-detection states and documented measurement uncertainty. Cohort selection and detection uncertainty condition interpretation of the full-sample estimates.

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