Greenwashing Intelligence Systems: Detecting ESG Narrative-Performance Gaps With Multimodal AI

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Demonstrates that multimodal AI can operationalize greenwashing detection at scale, with a validated framework (NAS/PI/GGS) and regression evidence linking gaps to market outcomes.

🏢実務担当者:Use GIS as an internal audit tool to compare your ESG narrative against verified performance data and satellite evidence; identify greenwashing risk before it triggers market penalties.

🏛政策担当者:Regulatory frameworks should incorporate externally verifiable metrics (like satellite-reported divergence) to strengthen greenwashing enforcement; the GGS could serve as a model indicator.

Corporate environmental, social, and governance (ESG) disclosures increasingly rely on persuasive sustainability narratives, yet investors, regulators, and civil society organizations often lack scalable tools to distinguish genuine environmental performance from rhetorical positioning. This study develops and validates a Greenwashing Intelligence System (GIS) that integrates six data modalities — ESG narrative text, verified emissions data, satellite and remote-sensing indicators, controversy and incident records, financial disclosures, and supply-chain risk signals — to construct two independent indices: a Narrative Ambition Score (NAS), derived from transformer-based analysis of sustainability disclosure text, and a Performance Index (PI), derived from verified and independently observable environmental performance data. The difference between these indices, the Greenwashing Gap Score (GGS = NAS – PI), is computed for a global panel of 4,642 public firms across five regions and six sectors over a 2019–2026 observation period. Firms are classified into four quadrants: Aligned Leaders (high NAS, high PI, 23.5% of sample), Greenwashing Risk (high NAS, low PI, 16.0%), Quiet Achievers (low NAS, high PI, 13.3%), and Disengaged (low NAS, low PI, 31.7%). Regression results show that GGS significantly predicts negative cumulative abnormal returns around disclosure events (β = –0.041, p < .001), elevated 24-month litigation risk (β = 0.0021, p < .001), and negative media sentiment shifts (β = –0.0089, p < .001), with these relationships substantially amplified when satellite-reported divergence (SRD) is high (GGS × SRD interaction significant across all outcomes, p < .001) — indicating that externally verifiable narrative-performance gaps carry the largest market and reputational consequences. Sector analysis reveals the largest gaps in Energy and Materials sectors, particularly for Scope 3 emissions claims. A validation study comparing GIS classifications against a 180-member expert panel shows substantial agreement (Cohen's κ = 0.65–0.78 across classification dimensions). A two-year disclosure-change pilot demonstrates that sharing GIS reports with firms reduces subsequent GGS, with the largest reductions (–9.7 points) among Greenwashing Risk firms receiving publicly benchmarked reports. The paper contributes the GIS architecture, the NAS/PI/GGS measurement framework, and a five-level ESG assurance maturity roadmap to ESG analytics, accounting information systems, and AI governance research, demonstrating that multimodal AI can operationalize sustainability assurance at scale.

• Zenodo https://zenodo.org/records/20920915first seen 2026-06-27 04:25:56