Subscribe to our blog!
We're all becoming increasingly aware of the environment, including companies. In fact, many organizations have engaged in improving their carbon footprints to preserve the Earth for the future generation. However, some businesses and companies looking to boost their eco-friendly image without committing to serious changes have been associated with greenwashing practices. As a result, false advertising, misleading initiatives, and groundless claims have increased green investors' exposure to risks emerging from potential lawsuits from activist groups, image deterioration, and some heavy loss in assets invested.
It’s still challenging to produce an accurate assessment of environmental, social, and governance (ESG) factors, which gives companies the opportunity to cover or hide ineffective and fake green initiatives. Therefore, early environmental risk detection is crucial for mitigating greenwashing risks.
As greenwashing practices become more common, activist investors, experts, journalists, and even the general public are spreading awareness of the issue by the day by making use of social media, news outlets, forums, and blogs, among other means.
Artificial intelligence (AI), and in particular, natural language processing (NLP), has proven to be effective in the early detection of greenwashing by analyzing vast amounts of qualitative data publicly available on the web.
At SESAMm, we apply our NLP capabilities to identify companies likely to engage in greenwashing practices by analyzing text in billions of web-based articles. By combining AI with one of the largest data lakes in the world, reliable, timely, and comprehensive insights can be systematically crafted to detect greenwashing or identify related risks.
Examples of greenwashing mentions over time.
The following two cases focused on this very topic:
Coca-Cola has been called out many times for its greenwashing practices, as they’ve been trying to gain attention from eco-conscious individuals who enjoy drinking soda. In 2021, the Earth Island Institute sued the company for its deceptive sustainable marketing. Despite advertising ecological awareness, Coca-Cola remains one of the world's largest plastic waste producers, with around 2.9 million tons of plastic per year. According to our ESG reports, these accusations of greenwashing appear to be correlated with spikes in negative sentiment related to environmental risks and controversies.
Coca-Cola greenwashing controversies and scandals.
On the flip side, we analyzed and derived ESG insights for SaaS company Salesforce. The company has shown low environmental risks compared to the high volume of mentions related to sustainable initiatives. Same for greenwashing mentions, which are almost non-existent when compared with environmental Initiatives.
On this note, the company developed Net Zero Cloud, an automatized calculator of individuals' and organizations' carbon footprint, which allows them to control and monitor their effect on the environment. As shown in the graph below, this has had a very positive effect on consumer sentiment.
Environmental risks and environmental initiatives correlation for Salesforce.
In sum, certain companies advertise their sustainability and green initiatives while, in reality, they are greenwashing practices, as evidenced by our analysis using SESAMm's NLP and ESG reports. By leveraging these technologies, investors and companies can reliably ensure the credibility of ecological strategies set up to limit climate change and preserve the environment for future generations.
It’s important to raise awareness on the issue of global warming and the negative effects it generates. We should also take advantage of the technological progress to diminish greenwashing practices and false marketing messages conveyed by these companies. In this case, SESAMm uses AI and machine learning to study billions of articles and online sources to generate quantitative signals and dashboards to help firms gain more insight on ESG risks and initiatives implemented by companies of all sizes.