Scope

In 2022, greenwashing is at the top of the social agenda by companies, and there is no sign of relinquishing its position. Greenwashing happens when companies over-report positive data about their sustainability efforts while downplaying the negative impacts of their operations. The reason for this is the subjective nature of most ESG (Environmental, Social, and Governance) information. Public companies are required to report on their sustainability efforts. However, when investors and ESG rating providers focus on self-reported data (ESG reports and marketing communications), they often receive a distorted and overly positive picture of the company’s ESG performance. Because of this information asymmetry, even investors who want to invest sustainably can be misled in their investment decisions.

Data from external media can help address this problem. To a large extent, third-party content providers have no interest in promoting a particular company’s sustainability efforts – so by considering data from a variety of media outlets, we can form a more objective and even critical picture of a company.

To get an overview of greenwashing, you can consult this website: https://www.clientearth.org/projects/the-greenwashing-files/.

Aim

In this task, we want to:

• • Better understand the nature of greenwashing through large-scale text analysis

  • Detect gaps that are indicative of greenwashing when comparing company communication and external data

  • Analyze whether specific ESG topics (modeled in terms of the Sustainable Development Goals) are more prone to greenwashing 

Organizers

• Dr. Janna Lipenkova, CEO, Equintel GmbH, Germany, janna.lipenkova@anacode.de 

• Susie Xi Rao, Ph.D. candidate / Researcher at ETH Zurich, srao@ethz.ch 

• Dr. Guang Lu, Lecturer for Data Science, Lucerne University of Applied Sciences and Arts, guang.lu@hslu.ch 

Task

To conduct this task, we provide participants with a comprehensive ESG dataset covering companyESG reports as well as public media targeting a wide range of different stakeholders (including investors, NGOs, regulators, society, etc.). The task is to develop approaches to identify gaps and inconsistencies between company-reported data and “external” data that may indicate greenwashing. This can be done at the level of ESG sentiment, the three ESG pillars or more fine-grained ESG topics. A combined approach that considers sentiment across ESG pillars and topics is also possible.

This is an application-oriented task. While there are no specific requirements for the NLP algorithms to be used, we suggest to focus on the following three aspects:

• •  Greenwashing takes place at the information level. Using a large dataset, we want to better understand the nature of greenwashing and try to quantify the degree.

  •  We want to prototype NLP approaches to detect greenwashing using public documents reflecting different stakeholders.

  •  We want to visualize potential indicators of greenwashing as well as the confidence of these indicators.

Important dates

• April 12th: Organizers release data sample. (8 weeks before the actual workshop)

• June 1st: Teams submit test set results.

• June 12th: Workshop day

Participant

General audience and Bachelor/Master students at Universities (of Applied Sciences).

References

[1] Naderer, Brigitte, Desirée Schmuck, and Jörg Matthes. ‘2.3 Greenwashing: Disinformation through Green Advertising. Commercial communication in the digital age: Information or disinformation 105 (2017): 120. 

[2] SESAMm: How Organizations Are Using NLP To Detect Greenwashing, 2022, retrieved on April 12, 2023. 

[3] Noyes, Lydia et al. A Guide to Greenwashing and How to Spot It, 2022, retrieved on April 12, 2023. 

[4] Nemes, Noémi et al. An Integrated Framework to Assess Greenwashing, Sustainability 14(8), 2022. 

[5] The Greenwashing Files, retrieved on April 12, 2023.