ECTF Methodology

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Why This Did We Do It This Way?

The Problem With Most Sustainability Ratings

Most tools that rate companies on sustainability are measuring the wrong thing.

They track what companies do. These include: emissions output, water usage, supply chain audits. Those things matter. But they miss whether companies are being honest about what those numbers actually mean.

A company can have a robust recycling program and still bury the part where its absolute emissions went up 34% since it made its biggest climate pledge. A company can have third-party auditors sign off on a report while keeping the most damaging data in a separate document that its own marketing team never mentions.

That's not a performance problem. That's a communication problem. Almost no one is measuring it.

Existing rating systems:

  • MSCI: one of the largest financial research firms in the world; its ESG ratings are widely used by investors but evaluate business risk, not communication honesty.

  • Sustainalytics: rates companies on how well they manage ESG risks; used heavily by institutional investors, not consumers.

  • CDP: a nonprofit that scores companies on climate disclosure; measures whether companies report, not whether what they report is accurate

  • SBTi: validates whether a company's climate targets align with climate science; confirms target setting, not whether companies communicate those targets honestly

    • These evaluate environmental performance. None evaluate whether companies communicate that performance honestly. That is the gap I built the Illumination Index to fill.

What the Illumination Index Measures

The Illumination Index scores companies on one specific question: does how a company talks about its sustainability efforts match what its own data shows?

This is not about whether the company is green. This about is simply about honesty and transparency.

I built it across eleven indicators in two categories:

Each indicator scores 0, 1, or 2. The scores add up to a composite out of 22. The higher the score, the more communicative failure the framework detected, and the more “present” each indicator is found.

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Part A (Greenwashing Risk). This looks at the language companies use. Is it vague? Do the bold claims on Instagram trace back to anything in the actual report? Are they talking mostly about future goals while quietly burying current performance?

Part B (Credibility Gap). This looks at whether the numbers add up. Are emissions actually going down, or just being measured differently? Is the net zero claim backed by real offsets or is it a promise buried in fine print for 2050?

What I looked at for every company

Before scoring anything, I assembled the same evidence set for each company:

  • Their most recent sustainability or ESG report

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  • Their own website and social media being Instagram, LinkedIn, the consumer-facing sustainability pages

  • Science Based Targets initiative (SBTi) database records, an independent body that validates whether a company's climate targets are actually aligned with science

  • Net zero trackers and carbon disclosure records from third parties

  • Any regulatory findings, ad bans, or legal proceedings in the public record

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Substantially reliable. Recoded seven companies five days apart, without reference to original scores. This framework is replicable.

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What the Illumination Index Illuminates

Greenwashing persists partly because the communications profession hasn't built precise tools to label it. Most accountability frameworks live in finance and environmental science. Analysts and researchers write most of them, not communicators. The Illumination Index is an attempt to bring that accountability into communications research, where the actual language choices are made.

Applying the framework across seven companies surfaced a consistent pattern: the companies with the most sophisticated verification infrastructure also made the most strategic omission choices. Credibility and transparency are not the same thing. The profession needs instruments that measure both separately.

If I were to expand this project, I would increase the sample size, introduce a second coder to establish a better system for inter-rater reliability instead of myself several days later, and develop a standardized intake process that could be operationalized as an AI assisted scoring pipeline that would be coded with the Illumination Index serving as the evaluation logic layer.

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Photo by Hush Naidoo Jade

Photo by Alessandro Bianchi