ESG data quality in multitier supply networks: Graph-based provenance and reconciliation methods

Environmental, Social, and Governance (ESG) reporting in global supply chains is increasingly scrutinized for accuracy, comparability, and accountability. However, the quality of ESG data is undermined by opacity in multitier supplier relationships, fragmented data collection practices, and inconsistent standards. This paper proposes a graph-based framework for improving ESG data quality across multitier supply networks, integrating provenance tracking, reconciliation algorithms, and cross-tier validation protocols. Using mixed-methods analysis combining case studies from manufacturing, energy, and consumer goods sectors with computational experiments, the study demonstrates that graph-based provenance can significantly reduce data inconsistencies while improving auditability. The research contributes to ESG reporting scholarship by aligning supply network complexity with computational approaches, and to practice by providing managers and regulators with actionable methods to reconcile ESG disclosures across tiers. Findings reveal that firms deploying graph-based reconciliation improve verifiability of ESG metrics by up to 32% compared to conventional spreadsheet- or silo-based approaches. Implications span corporate governance, sustainable procurement, and regulatory compliance, highlighting graph-driven provenance as a scalable strategy for robust ESG reporting.