ALGORITHMIC GOVERNANCE AND CROSS-JURISDICTIONAL REGULATORY INTELLIGENCE: DOCTRINAL, ARCHITECTURAL, AND EMPIRICAL FOUNDATIONS

Abstract

This study develops a jurisprudential, algorithmic, and systems framework for automated regulatory intelligence within the structurally fragmented administrative environment of the United States. Mechanical and HVAC regulation is used as a test domain because a single field operation may simultaneously implicate federal environmental rules, state licensing regimes, county or municipal permitting, locally adopted building codes, technician credentials, and address-specific inspection procedures. Comparative analysis of Illinois, Texas, North Carolina, and Arizona therefore treats the United States not merely as a geographical setting, but as an empirical governance laboratory for examining how artificial intelligence can mediate rules that vary by authority, location, and time. The analysis addresses three related problems. First, Home Rule, Dillon's Rule, and state preemption distribute local regulatory authority differently across the four states, creating materially different operational pathways (Baker & Rodriguez, 2009; Briffault, 2018; Briffault et al., 2020; Diller, 2017; Mead, 2019; Richardson, 2011; Stahl, 2024). Second, the federal HFC phasedown and EPA Technology Transitions program interact with state and local code-adoption cycles; this interaction may create sequencing gaps or administrative uncertainty, but it should not be characterized as a universal direct conflict or as a federal mandate to use one specific A2L refrigerant (U.S. EPA, 2026a, 2026b). Third, digitally generated compliance records require provenance, temporal reconstruction, qualified certification, and notice procedures if they are later offered under Federal Rules of Evidence 901 or 902(13)/(14); cryptographic hashing alone does not establish admissibility (Krishnamurthy, 2025; U.S. Courts, 2017). By combining constitutional and administrative analysis, point-in-polygon jurisdictional resolution, temporal knowledge graphs, constrained generative AI, human-in-the-loop governance, and cryptographic provenance, the study proposes a scalable research architecture rather than claiming a completed production platform. The U.S. HVAC case also provides a bridge to Metaverse governance: it demonstrates how physical territory can first become machine-readable, then algorithmically mediated, and comparable to an electronic jurisdiction in which digital space, identity, activity, and time determine the applicable governance regime (Kostenko et al., 2022, 2024, 2025).