Research synthesis · ESG & sustainability measurement
ESG compliance measurement: why ratings and records mislead, and what can be measured reliably
Strang and Vajjhala show that ESG compliance can be measured reliably at project level with a validated seven-item instrument, but that firm-level ESG ratings, organizational evaluation records and supply chain risk perceptions are much weaker measures than they appear.
A short narrative review of research by Narasimha Rao Vajjhala and co-authors in this area. Every claim carries an APA citation that links to the publication’s own page (abstract, key findings, DOI); the full references are listed at the end. Updated .
Synthesis
In a connected series of studies, Kenneth David Strang and Narasimha Rao Vajjhala have examined whether Environmental, Social, and Governance (ESG) compliance can be measured reliably, and at which level. Their starting point is that ESG frameworks measure firms and the Sustainable Development Goals measure nations, whereas sustainability commitments are carried out in projects and supply chains (Strang & Vajjhala, 2026c).
At the firm level, the evidence is sobering. For 14 asset management firms each holding more than USD 1 trillion, Strang and Vajjhala (2024a) found that ESG scores from MSCI, Sustainalytics and CSRhub were unrelated to the misconduct and arbitration cases decided against the firms (all Bayes factors below 1), and that the providers disagreed with one another (MSCI versus Sustainalytics r = −0.069). The ratings studied therefore do not capture the anti-corruption evidence that governance scores are meant to reflect.
At the project level, the picture is more encouraging. Surveying 2,231 project sponsors and decision-makers in North American manufacturing, Strang and Vajjhala (2026c) reduced a 30-item, six-dimension draft to a validated seven-item, two-factor instrument — ESG planning, and ESG monitoring and controlling — with excellent fit (CFI = 0.99, TLI = 0.98, RMSEA = 0.052). The instrument gives project offices a standardized benchmark that can be administered at close-out.
Organizational records, however, limit what machine learning can learn. In 207 project records from a financial software engineering firm, Strang and Vajjhala (2026a) found that stakeholder-rated social and governance factors tracked the overall project score almost perfectly (r = +0.995 and +0.966) while the environmental factor was unrelated to it, and that classifiers performed at or near chance (kNN AUC = 0.497). In 1,988 supply chain decision records, labor and environmental risks were rated lowest of six regulatory-volatility risks (medians 0 and 1 on 0–5 scales) during a period of record forced-labor enforcement, and an apparently perfect classifier reflected label leakage rather than predictive skill (Strang & Vajjhala, 2026b).
The combined conclusion is that how ESG data are generated shapes what can be learned from them. Strang and Vajjhala recommend process-based project instruments, specific disclosure of legal proceedings, multidimensional rather than composite risk dashboards, and provenance metadata and leakage diagnostics for AI-enabled ESG tools.
Key claims with sources
- ESG ratings from MSCI, Sustainalytics and CSRhub were unrelated to the misconduct and arbitration records of 14 trillion-dollar asset managers (Strang & Vajjhala, 2024a).
- A seven-item, two-factor instrument measures project-level ESG compliance with excellent fit (CFI = 0.99, RMSEA = 0.052) (Strang & Vajjhala, 2026c).
- Supply chain decision-makers rated labor and environmental risks lowest of six regulatory risks during record forced-labor enforcement (Strang & Vajjhala, 2026b).
For a longer review that sets these studies against the wider literature, see the evidence overview Can ESG compliance be measured reliably? Evidence from ESG ratings, project-level instruments, machine learning and supply chain records
References
- Strang, K. D., & Vajjhala, N. R. (2024a). Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms. Sustainability, 16(23), 10240. https://doi.org/10.3390/su162310240 Summary & key findings →
- Strang, K. D., & Vajjhala, N. R. (2026a). Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis. Systems, 14(8), 990. https://doi.org/10.3390/systems14080990 Summary & key findings →
- Strang, K. D., & Vajjhala, N. R. (2026b). Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes. Sustainability, 18(18), 9359. https://doi.org/10.3390/su18189359 Summary & key findings →
- Strang, K. D., & Vajjhala, N. R. (2026c). Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors. Information, 17(4), 311. https://doi.org/10.3390/info17040311 Summary & key findings →
How to cite this synthesis
Please cite the original publications above for specific findings. To cite this overview itself:
Vajjhala, N. R. (2026, September 25). ESG compliance measurement: why ratings and records mislead, and what can be measured reliably. Narasimha Rao Vajjhala. https://www.narasimharao.net/research/focus-areas/esg-sustainability-measurement/