A Conceptual Framework for Sustainable Pollution Control in Informal Economies with Generative AI

Abstract

Intangible environmental externalities in informal economies are hard to detect, attribute, and regulate because transaction records and evidentiary trails are fragmented. This conceptual paper reframes pollution control from improving model performance to designing institutions for verifiability and examines how generative AI (GAI) can both strengthen and undermine that verifiability. Integrating transaction-structure theory, institutional economics, and digital-governance research, we derive four propositions: (P1) standardized, interoperable evidence and hybrid auditing allow GAI to lower verification costs; (P2) opaque, multi-tier transactions and concentrated data control enable plausible falsification; (P3) detection reduces pollution only when linked to remediation through enforcement capacity; and (P4) incentives must reward verified, not merely claimed, circularity to deter greenwashing. We illustrate feasibility and boundary conditions through three precedents: Amazon’s unit-level identifiers and sustainability labeling, India’s CPCB extended producer responsibility portal for plastic packaging, and Brazil’s nationwide e-invoicing infrastructure (NF-e/SPED). The framework offers actionable design principles, testable hypotheses, and measurable indicators (evidence linkage, audit-log completeness, time-to-remediation) for future empirical work. The framework is intended to support analytic generalization for policy and practice across contexts.