How AI tools are being used to detect greenwashing

Can AI really pull back the emerald curtain to reveal the truth behind corporate green marketing claims? Let’s discover how sophisticated AI tools are unmasking the truth about greenwashing.

In today’s corporate landscape, many companies use buzzwords like “sustainability” and “environmental consciousness” to portray themselves as “eco-friendly”. However, not all companies are sincere in their claims, and greenwashing has become more prevalent.

Fortunately, the rise of Artificial Intelligence (AI) provides us with the tools to uncover these deceptions. AI, including machine learning (ML) algorithms, natural language processing (NLP), and big data analytics, allows us to examine the authenticity of corporate sustainability efforts. This blog post explores how AI tools are revolutionizing the detection and exposure of greenwashing, improving transparency, and holding corporations accountable.

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What is greenwashing?

The practice of greenwashing has been around for decades and is used by companies with good intentions and those seeking to make their products and services seem greener than they are. Read this blog post to learn more.  

The Cambridge Dictionary defines brainwashing as “behaviour or activities that make people believe that a company is doing more to protect the environment than it really is[2].”

Greenwashing also has several layers. For example, it occurs when a company claims that its product or service has environmental benefits but does not have any real proof to support its claims. Companies can also engage in greenwashing by using misleading images or words on packaging without any supporting evidence – such as including pictures of trees alongside text such as “100% natural”, even though there aren’t any trees involved with making the product!

Why is greenwashing a problem?

Greenwashing poses several significant issues, as it manipulates consumers and interferes with efforts toward genuine sustainability. Here’s why it’s problematic:

  1. Consumer Deception: Greenwashing misleads people into believing they’re making environmentally friendly choices when they may not be. This false impression prevents consumers from making genuinely informed decisions.
  2. Damages Trust: When companies engage in greenwashing, they risk damaging their reputation. If discovered, the resulting public backlash can lead to a significant loss of customer trust and loyalty, potentially harming their business in the long run.
  3. Hampers Real Sustainability: Greenwashing diverts resources away from real sustainability efforts. Instead of investing in truly green practices, companies spend money on making their existing practices seem more environmentally friendly than they are.
  4. Legal and Regulatory Consequences: Misleading advertising, such as greenwashing, can result in regulatory action and legal troubles. Authorities worldwide are cracking down on false environmental claims, with hefty fines and lawsuits a real risk for guilty companies.
  5. Undermines Authentic Efforts: Companies genuinely working toward sustainability may find their efforts undermined by greenwashing. Consumers, growing more sceptical due to prevalent greenwashing, might begin to doubt all green claims, even those of companies with genuine practices.
  6. Market Manipulation: By falsely advertising products as “eco-friendly”, greenwashing creates a distorted market. Consumers aiming to support green products are misled, which can hinder the development and growth of genuinely green products and technologies.

AI is emerging as an effective tool for detecting instances of greenwashing, especially for climate mitigation-related initiatives and potentially for consumers.

But how does this work?

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How and why was AI developed for unmasking greenwashing?

In 2016, Tide launched Purclean detergent, claiming it was 100% plant-based. However, an analysis revealed that it was only 75% plant-based and contained petroleum-based materials, making it misleading for consumers and an example of corporate greenwashing. A lawsuit followed these revelations.

To address this issue, an AI tool called ClimateBert[4] was developed to analyze corporate statements and reports, providing a more accurate assessment of climate-related performance. However, ClimateBert’s assessment of over 800 companies[3] found that their environmental performance falls short, partly due to a lack of scrutiny on greenwashing, selective disclosure, and voluntary reporting. ClimateBert is now trained on over 1.6 million climate-related paragraphs, crawled from sources such as common news, research articles, and company climate reporting[11].

Here are some of the AI greenwashing tools currently available:

  • Ping An in China is also using AI to assess corporate climate disclosure and detect greenwashing. Challenges remain in collecting meaningful data and evaluating environmental progress accurately.
  • Engager, created by Arcadian uses an algorithm to examine company documents such as sustainability reports, policies, press releases, regulatory filings and even executives’ voices on analyst calls.
  • Greenifs is a tool that detects greenwashing errors on social media, ensures compliance with green marketing guidelines.
  • NovA!, is a new tool unveiled by the Monetary Authority of Singapore (MAS) and is an AI technical platform designed to generate insights on financial risk. In the initial phase, NovA! will help FIs harness AI to assess companies’ environmental impact and identify emerging environmental risks.

How ethical AI can detect greenwashing

ML and NLP can significantly aid in detecting greenwashing by analyzing extensive data, finding hidden patterns, and addressing false “eco-friendly” advertising. Here are just some of the ways ethical AI can detect greenwashing:

  1. NLP compares corporate environmental claims with consumer feedback on social media.
  2. ML tracks carbon emissions and energy usage, suggesting environmental improvements.
  3. ML assesses supply chain impacts, aiding in decision-making.
  4. AI identifies new ESG product development, risk management, and investment opportunities.
  5. The creation of automated reports fosters transparency and sustainable practices.
  6. NLP evaluates a company’s claims and practices, pinpointing discrepancies.
  7. AI ensures regulatory compliance, detecting when environmental claims contradict performance.
  8. Social media analysis flags potential greenwashing in the face of scepticism or criticism.
  9. ML compares a company’s practices with industry benchmarks, identifying deviations as potential greenwashing.

The continuous evolution of AI technologies promises to further aid in combating greenwashing and promoting sustainability.

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The wider role of AI in sustainability

The capabilities of AI are like a secret weapon for marketers and consumers. It helps us catch greenwashing claims, pinpoint when our marketing efforts might be veering into greenwashing territory and detect products that we, as consumers, may want to buy.

A 2023 study by the Johannesburg Business School[1] delved into this very topic and unearthed some intriguing findings:

  • The Quest for detecting greenwashing with AI: using AI and ML to spot greenwashing is still relatively new and in the early stages of development. This is shown by the limited number of studies conducted so far and how recent they are. In contrast, AI and ML have been more widely explored in sustainability reporting, which has a longer history and more established research behind it.
  • Some AI and ML tools are already taking centre stage: Some AI and ML tools, like data mining, text mining, big data analysis, and good old artificial intelligence, are becoming increasingly important when studying greenwashing and sustainability reporting.
  • AI and ML are widely used in sustainability reporting: AI and ML help detect greenwashing by analyzing textual content and overall sentiment, comparing reports with industry benchmarks, recognizing suspicious patterns, assessing data quality, fact-checking information, and gauging stakeholder sentiment through public data analysis. These techniques can identify the overuse of green buzzwords without evidence, overly positive claims without validation, and inconsistencies with benchmark data, among other signs of greenwashing. The AI models need to be updated and retrained to detect evolving greenwashing practices accurately.
  • AI’s untapped potential in greenwashing: Surprisingly, AI and ML techniques haven’t yet been fully utilized in the study of greenwashing.

Although AI tools can efficiently detect potential greenwashing in marketing, they can scan a website for terms like “carbon neutral” or “recyclable,” marking them for further review. However, let’s not forget that a human would then assess these flagged terms and decide on the next steps, such as removing them from the company’s website.

Singapore sustainable copywriter

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Navigating the challenges in AI’s battle against greenwashing

Ensuring companies live up to their environmental promises can be tough. Two hurdles often stand in the way:

  • Gathering In-depth and Reliable Data: A significant challenge lies in the lack of comprehensive data about companies’ environmental performance. This data scarcity makes it hard for AI systems to spot greenwashing reliably. However, innovative technologies like Internet of Things sensors and blockchain show promise in this area. They have the potential to amass a wealth of information, such as real-time figures on energy use and waste generated during manufacturing.
  • Precise Measurement of Environmental Progress: Typical initiatives like tree planting may not accurately gauge environmental impact. Properly assessing factors like the potential for carbon sequestration and the types and locations of trees can be a labour-intensive and complex task.

There are still challenges to overcome, but we can anticipate that AI will play an increasingly important role in reducing greenwashing.


As AI continues to evolve, it will play an increasingly crucial role in exposing greenwashing and promoting authentic sustainability.  By leveraging ethical AI to its fullest potential, we can build a world where companies are held accountable to a higher standard. The AI-driven fight against greenwashing is just beginning. It’s time to shine a light on the truth and make every company walk the walk, not just talk the talk.

Contact me if you need help avoiding greenwashing in your company’s communications.

References and further reading

  1. Schweitzer, T., Stevens, T., & Bougard, F. (2020). AI techniques for detecting greenwashing in sustainability reports. IEEE Transactions on Sustainable Computing, 5(3), 265-274. doi:
  2. Cambridge Dictionary. (n.d.). Greenwashing. Retrieved from
  3. Owen, E. (2022, April 27). Greenwashing AI: How companies use artificial intelligence to deceive. Fast Company. Retrieved from
  4. Berg, A., Chen, X., & Han, D. (2021). ClimateBert: Pretraining of Large-Scale Climate Change Discourse for Modeling Climate Bias and Exploration. SSRN. Retrieved from
  5. Tan, G. (2019, March 29). How AI can help instos spot greenwashing. Asian Investor. Retrieved from
  6. PlanetHome. (n.d.). Home. Retrieved from
  7. Guest Post. (2022, March 28). AI and sustainability: The fight against greenwashing. UKTN (UK Tech News). Retrieved from
  8. Cojoianu, T., Hoepner, A., Ifrim, G., & Lin, Y. (n.d.). Greenwashing: Using AI to Detect Greenwashing. CPA Ireland. Retrieved from,-Andreas-Hoepner,-Georgiana-Ifrim,-Yanan-Lin.pdf?lang=en-IE
  9. Mohamed, A. S. (n.d.). Can AI Help Fight Greenwashing? LinkedIn. Retrieved from
  10. Thomas, T. (2022, February 2). When Artificial Intelligence Meets Greenwashing in Insurance. Clyde & Co. Retrieved from
  11. Ifrim, G., & Hoepner, A. (2022). Climate-related Greenwashing in ESG Communication. SSRN. Retrieved from

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