AI turns the tide on greenwashing
The A3CG framework helps separate fact from fiction, training AI to cut through vague corporate lingo and spot greenwashing camouflaged in polished sustainability claims.
We’ve all seen it: skincare packaging proclaiming “100% recycled materials”, a supermarket markets itself as “committed to sustainable sourcing”, an MNC announces on LinkedIn its bold plans to slash carbon emissions. Such statements suggest a world in which sustainability is front and centre. But more often than not, they’re carefully worded promises that raise more questions than they answer.
Sustainability is indubitably crucial to securing a liveable future. Yet in the corporate world, it has too often devolved into a buzzword deployed for optics than for impact, especially in formal reports companies produce to signal progress and satisfy investor or customer expectations.
This practice, known as “greenwashing” (where companies mislead, exaggerate or fabricate claims about their sustainability efforts), obscures genuine progress and chips away at public trust. One study led by the United Nations found that 60% of sustainability claims by European fashion giants were “unsubstantiated” and “misleading.” In a global review, four in ten of websites appeared to be using tactics that could be considered misleading. With environmental, social and governance (ESG) investing now measured in the tens of trillions, trust is a currency companies increasingly misuse to inflate their perceived value.
AI has begun to reshape how corporate sustainability reports are processed. Natural language processing (NLP), a branch of AI that extracts insights from text, is capable of evaluating such reports at a speed and scale virtually unattainable by manual, human processing. However, even cutting-edge NLP methods run into bottlenecks. In particular, they often fail to tell the difference between substantive commitments and astutely wordsmithed but misleading claims.
A team led by Assistant Professor Gianmarco Mengaldo from the Department of Mechanical Engineering, College of Design and Engineering, National University of Singapore, has developed an approach to tackle this challenge. Presented at The 63rd Annual Meeting of the Association for Computational Linguistics, the researchers created a dataset and evaluation framework called A3CG (AspectAction Analysis with Cross-Category Generalisation).
“A3CG is like a training ground designed specifically to test and improve AI’s ability to extract and interpret sustainability claims,” explains Asst Prof Mengaldo.
To build the dataset, the team annotated real-world sustainability statements by linking each sustainability “aspect” to the type of “action” taken. The aspect is the specific sustainability goal or issue — say, waste reduction or carbon emissions targets. The action is what the company claims to do about it, categorised as either “implemented,” “planning,” or “indeterminate.” It is the last category,
Issue 06 | Aug 2025
Forging New Frontiers
indeterminate, that often flags greenwashing. Vague, hedging statements — “we may consider,” or “where possible” — can be caught and isolated, allowing investors, consumers and regulators to scrutinise more effectively.
In the team’s study, they put forward a company’s claim: “Where possible, we have implemented sustainable measures to monitor our water consumption and increase water efficiency.” The statement may read fine at first glance. But A3CG picks up the qualifier “where possible,” tagging it as “indeterminate,” highlighting the uncertainty and lack of firm commitment.
“Systematically surfacing these intentionally worded subtleties allows us to provide a clearer view into corporate sustainability that is less vulnerable to clever PR and more closely aligned to measurable outcomes,” adds Asst Prof Mengaldo.
Flushing out the fluff
A strength of the A3CG framework is its adaptability when companies pivot their sustainability narratives, especially when it’s done purposefully to their benefit. For instance, if a company shifts focus from well-monitored categories (such as carbon emissions) to newer or less transparent ones (such as biodiversity or cybersecurity), traditional NLP methods might miss the greenwashing hid in unfamiliar territories. The team’s framework is designed specifically to test for “cross-category generalisation,” allowing AI systems to stay effective even when confronted with entirely new or unexpected sustainability themes.
“A3CG helps make AI systems more capable at understanding greenwashing tactics, potentially improving the reliability of ESG analysis at scale.”
“A3CG helps make AI systems more capable at understanding greenwashing tactics, potentially improving the reliability of ESG analysis at scale,” adds Asst Prof Mengaldo. “Regulators and rating agencies gain a clearer way to assess sustainability disclosures, while investors and watchdog groups can more readily flag questionable claims. For researchers, it sets a benchmark for developing more linguistically attuned NLP models in this domain.”
Using their dataset, the researchers put various state-of-the-art AI models to the test. They found that supervised models, particularly those trained with contrastive learning (which teases apart subtle semantic nuances), outperformed large language models like GPT-4 or Claude 3.5. These supervised models were better at identifying recurring patterns in the language of sustainability reports.
Nevertheless, the team also found that while supervised models are good at spotting suspicious syntax, they struggled with completely novel sustainability themes outside their training scope. On the other hand, large language models handled unfamiliar vocabulary better but falter in the subtle pragmatics of language, especially in highly nuanced cases where tone and phrasing convey strategic ambiguity or cautious non-commitment.
“This just means there’s more work to be done,” adds Asst Prof Mengaldo. “For example, our team is working towards understanding how changes in the narrative of sustainability reports over time may be linked to greenwashing practices.” Issue 06 | Aug 2025