Emmi combines reported emissions data with machine learning to deliver Scope 1, 2, and 3 coverage across public equities, fixed income, private equity, private debt, property, infrastructure, and unlimited custom coverage.
Emmi draws on verified reported emissions from public companies as its primary source. Where reported data isn't available, machine learning models trained on 10,000+ public companies calculate company-level emissions, capturing the non-linear relationships between sector, geography, and scale that factor-based approaches miss.
UP TO
MORE ACCURATE
In peer-reviewed research, the machine learning approach behind Emmi's models was up to 30% more accurate than traditional revenue-based emission factors.
A financed emissions figure built partly on disclosure and partly on estimates is only useful if you can tell which is which, and how confident the estimate is.
Every data point carries a PCAF data quality score, on the same 1 to 5 scale your reporting already uses. Scores aggregate up to sector, asset class, and investment manager level, so you can see where the weakest data sits in your portfolio rather than reading a single blended figure.
Emmi calculates financed emissions across all asset classes. For private market assets with limited disclosure, machine learning models can generate emissions estimates from three inputs: industry, location, and one financial metric, such as revenue or total assets.
Private markets
Fixed income
Private equity
Property
Infrastructure
Any company or asset (custom coverage)
Scope 1, 2 and 3 in tCO2e, at asset level
Attributable and total financed emissions, aggregated to fund, asset class, and total holdings
EVIC-based attribution factor, the PCAF-aligned basis for ownership share
PCAF data quality score on every data point
Absolute emissions scale with the size of a holding, so a large low-carbon position can look worse than a small high-carbon one. Intensity metrics normalize for size, which is what makes companies comparable against each other and against their sector.
The global warming pathway a company or fund is currently on, placed on a 1.5°C to 4°C spectrum using IPCC AR6 carbon budgets scaled to company size.
The global warming pathway a company or fund is currently on, placed on a 1.5°C to 4°C spectrum using IPCC AR6 carbon budgets scaled to company size.
The global warming pathway a company or fund is currently on, placed on a 1.5°C to 4°C spectrum using IPCC AR6 carbon budgets scaled to company size.
Holdings-weighted intensity. The standard portfolio metric from TCFD, still widely used in disclosure.
Emissions per unit of revenue, measuring the carbon efficiency of operations.
Emissions per unit of enterprise value including cash, using the same EVIC denominator PCAF uses for attribution.
Emissions and transition risk don't stay fixed. Targets show where a company says it's headed. Forecasts show where its actual behavior suggests it's headed. Both extend the baseline picture with a forward view, not just where things stand today.
Emmi research published in PLOS Climate found companies under-report Scope 3 emissions by up to 44%. The missing emissions run into billions of tonnes, concentrated in the most material categories. Emmi's machine learning approach is designed to correct for this.
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