DETECTING EVIDENCE OF NON-COMPLIANCE IN SELF-REPORTED POLLUTION EMISSIONS DATA: AN APPLICATION OF BENFORD'S LAW

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dc.creatorDumas, Christopher F.
dc.creatorDevine, John H.
dc.date2017-04-01T16:52:39Z
dc.date.accessioned2026-07-09T03:36:44Z
dc.descriptionThe paper introduces Digital Frequency Analysis (DFA) based on Benford's Law as a new technique for detecting non-compliance in self-reported pollution emissions data. Public accounting firms are currently adopting DFA to detect fraud in financial data. We argue that DFA can be employed by environmental regulators to detect fraud in self-reported pollution emissions data. The theory of Benford's Law is reviewed, and statistical justifications for its potentially widespread applicability are presented. Several common DFA tests are described and applied to North Carolina air pollution emissions data in an empirical example.
dc.identifierdoi:10.22004/ag.econ.21740
dc.identifierhttps://ageconsearch.umn.edu/record/21740/files/sp00du02.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/21740
dc.identifier.urihttp://hdl.handle.net/123456789/536502
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/21740
dc.titleDETECTING EVIDENCE OF NON-COMPLIANCE IN SELF-REPORTED POLLUTION EMISSIONS DATA: AN APPLICATION OF BENFORD'S LAW
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