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DitchCarbon vs Moody's ESG: a comparison for financial firms
Updated 2 September 2026. This comparison is for finance and sustainability teams at financial institutions weighing how to improve the data behind their portfolio emissions reporting. It is factual by design: both products are credible, and the right choice depends on which problem you are solving.
What is Moody's ESG?
Moody's ESG Solutions provides ESG ratings, climate risk scores and modelled emissions datasets for investment analysis, delivered mainly as data feeds and APIs into portfolio management and risk systems. Its top-down models estimate emissions from industry, geography and company-level economic data, which gives consistent coverage across large investment universes for portfolio footprinting and climate risk work. If you are evaluating it, Moody's own documentation is the right source for its current products.
What is DitchCarbon?
DitchCarbon provides verified emissions data for over 2 million organisations, built on primary emissions data wherever it exists, then turns it into action: portfolio and supplier engagement, benchmarking and target tracking, inside the tools finance and sustainability teams already run. It is a specialist Scope 3 carbon accounting platform, and for financial institutions the emphasis is counterparty coverage: private companies included, entity resolution against DUNS, LEI and ISIN identifiers, a source and change history on every figure, and coverage gaps shown, not hidden.
How do the two differ?
The honest distinction is reported against modelled. A modelled dataset assigns a figure to every counterparty from a top-down model, which is what makes portfolio-wide risk analysis possible, and it is also why the resulting PCAF data quality scores (DQ scores) sit at the proxy end of the scale. DitchCarbon is bought to move counterparties onto their own reported figures, which is primary data under the GHG Protocol and what improves a DQ distribution rather than reorganising it.
| Feature | DitchCarbon | Moody's ESG Solutions |
|---|---|---|
| Primary focus | Counterparty-level emissions data and engagement for supply chain and portfolio Scope 3 | ESG ratings, climate risk scores, and modelled data for investment analysis |
| Data source | Published disclosures and supplier-provided data, with a source and change history on every figure | Primarily modelled data, supplemented by public disclosures and company-reported information |
| Methodology | Bottom-up: each counterparty's own published or provided figures, with gaps shown | Top-down: models emissions based on industry, geography, and economic data |
| Core use case | Raising PCAF data quality and engaging counterparties, for Category 1 and Category 15 | Portfolio carbon footprinting and climate risk analysis across large universes |
| Delivery | Platform and integrations into the tools teams already run | Data feeds and APIs into portfolio and risk systems |
When is Moody's the right choice?
When the job is portfolio-wide analytics: consistent modelled coverage across thousands of holdings for footprinting, screening, benchmarking and climate risk models, delivered into the systems quantitative teams already use. For that job, breadth and consistency matter more than any single counterparty's reported figure, and a modelled dataset is built for exactly that.
When is DitchCarbon the right choice?
When the data is the problem. If your DQ distribution sits at 4 and 5, if private companies make up much of your book, or if your auditor is asking where the figures came from, the gap is not analytics. DitchCarbon moves counterparties onto their own reported figures, with the source attached, and fits the tools you already run, so improving the data does not require replacing the platform around it.
Can you use both?
Yes, and institutions do. A modelled dataset can serve the portfolio-wide risk view while DitchCarbon raises the quality of the reported figures underneath the disclosed number, starting with the counterparties that carry the most financed emissions. The two solve different problems, so running both is common rather than awkward.
See the comparison on your own book
The fastest test is your own portfolio. Request a walkthrough and we will show the DQ distribution on a sample of your counterparties.
Run the test on your own list.
Tell us what you're assessing and we'll show you the coverage we already hold, before you commit to anything.
