What is Greenwashing? And How to Use Analytics to Detect It?
Greenwashing is the practice of making misleading claims about the environmental benefits of a product or a service to communicate a false image of sustainability.
How can we use analytics help the world fight greenwashing?
This act of embellishment or hiding falsehood has become a common challenge as companies seek the attention of environmentally conscious consumers.
In this article, we will delve into greenwashing to explain its manifestations.
We will use case studies to show how to use data analytics to detect and prevent these unethical practices.
Summary
I. Understanding Greenwashing
1. What is Greenwashing?
2. Examples of Greenwashing
3. Greenwashing x Data Analytics
II. Data Analytics for Greenwashing Detection
1. The difficult task of detection
2. Natural Language Processing (NLP)
3. Change Point Analysis
4. Regression Analysis
5. Network Analysis
III. ConclusionUnderstanding Greenwashing
I discovered Greenwashing when I conducted my first supply chain sustainability project.
As a supply chain solution manager, my task was to estimate the environmental footprint of our customers' Logistics operations.
How can a company selling disposable plastic products can claim to be carbon neutral?
It was surprising to see the claims of some of their competitors, considering that they were producing and selling similar products.
This article aims to show you how analytics tools can help you detect this kind of false claims.
What is Greenwashing?
Greenwashing is a portmanteau word of ‘green' and ‘whitewashing'.
Organizations use this dishonest practice to create a false impression of environmental responsibility.
The objective is to capitalize on customers' and investors' growing demand for eco-friendly products.
The most common forms of greenwashing include,
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Vagueness: undefined terms such as ‘eco-friendly' or ‘all-natural' used without clear definitions or evidence. For example, a company labels a product as ‘100% natural' without disclosing that the natural materials were unsustainably sourced.
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Irrelevance: highlighting an eco-friendly attribute that is either unimportant or unrelated to the product's environmental impact. For example, a company emphasises that its product is ‘CFC-free' while Chlorofluorocarbons have been banned for decades.
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Hidden trade-offs: promoting one environmentally friendly aspect of a product while ignoring other significant impacts For example, a paper company promotes its use of recycled paper without mentioning energy consumption and carbon emissions for production and logistics.
When you see an advertisement for a naturally sourced recycled t-shirt, consider:
- The amount of energy, electricity and water used to source these "natural raw materials."
- The additional CO2 emissions and waste generated by the recycling process
With life cycle assessment (LCA), you have a data-driven method to evaluate these impacts by considering the entire product life cycle and avoiding this trap.
The idea is to estimate the environmental impact of sourcing, producing and using a specific product or service.
This requires collecting and processing data from multiple sources using Business Intelligence tools.
