Novata Emissions Estimator Methodology

What the Novata Emissions Estimator is

The Novata Emissions Estimator (NEE) produces estimated Scope 1, Scope 2 (location-based), and Scope 3 emissions for a company when directly measured data isn't available. It is a proxy-based estimate: it combines peer-reported benchmark data from the Novata platform with a few company characteristics you provide, and applies a transparent, deterministic matching process. Each estimate comes with a match-quality indicator and a confidence interval so you can see both how good the peer match is and how much uncertainty surrounds the figure.

The NEE is informed by GHG accounting principles and is aligned with guidance from the Partnership for Carbon Accounting Financials (PCAF), but it is an estimation approach — not a substitute for a company's own measured, GHG Protocol–aligned inventory. Estimates are always meant to be replaced by higher-quality, company-reported data as it becomes available.

For step-by-step instructions on running the tool in a report, see How do I use the Novata emissions estimator?

What you provide

The estimate is driven by four company inputs, entered in the company details tab of the report:

1. Annual revenue (in USD)
2. Full-time equivalent (FTE) employees
3. Industry, expressed as a SASB SICS code
4. Location (country)

How an estimate is calculated

At its core, the NEE multiplies your company's size by an emissions intensity drawn from comparable companies:

Estimated emissions (t CO₂e) = Annual revenue ($M) × Benchmark intensity (t CO₂e / $M)

The benchmark intensity is the median emissions intensity from the closest-matching peer benchmark, selected through the matching process described below.

Normalization: revenue first, then FTE

Benchmarks are normalized so companies of different sizes can be compared. The NEE prefers revenue-normalized benchmarks and falls back only when needed:

How the best benchmark is matched

Once your company characteristics are set, a waterfall algorithm finds the closest peer benchmark and assigns it a match level from 1 to 12 — lower is a better match. The level reflects three things: how specifically the industry matches, whether the geography matches, and whether the company-size (FTE) band matches.

Industry hierarchy. Benchmarks follow the SASB Sustainable Industry Classification System (SICS), from most to least specific: Industry → Sub-Sector → Sector. The system tries Industry first and only generalizes if no benchmark exists at that level.

Geography. Benchmarks are grouped into North America, Europe, and APAC, with a global ("ALL") benchmark used when no regional one is available. For example, a company headquartered in Brazil — a region without Novata benchmarks — would default to the global benchmark.

Company size. Benchmarks are segmented into FTE bands (under 100, 100–500, over 500), with a size-agnostic band used when size-specific data isn't available.

Within each hierarchy level, the system searches in order — exact FTE + exact region, exact FTE + ALL region, ALL FTE + exact region, ALL FTE + ALL region — and stops at the first match. That produces the 12 match levels:

The platform also uses colour to signal match quality at a glance: green for high (levels 1–4), yellow for medium (5–8), and orange for medium-low (9–12).

Worked example

Consider an Asset Management & Custody Activities company (SASB FN-AC), 180 FTEs, headquartered in the United Kingdom, with $50M in revenue. The matching process finds industry-level benchmarks for each scope, then applies the intensity to revenue:

Scope 2 reached Level 1 because Europe-specific data existed; Scopes 1 and 3 fell back to Level 2 where no Europe-specific benchmark was available.

Confidence intervals and rating

Every estimate carries an 80% confidence interval, calculated from the spread of the underlying benchmark distribution: the low bound uses the 10th-percentile intensity and the high bound uses the 90th-percentile intensity (each multiplied by revenue). From that spread, a confidence rating is derived:

Relative uncertainty is |high − low| ÷ (2 × estimate). Wider benchmark distributions produce lower confidence ratings.

High-emission industries: the EMRIO model

Novata's peer benchmarks cover most private-market industries, but a small number haven't reached the data thresholds needed to build a reliable benchmark. For 11 high-emission industries in the Extractives & Minerals Processing and Transportation sectors — such as Coal Operations, Metals & Mining, Construction Materials, Airlines, and Road Transportation — the NEE instead uses an Extended Multi-Regional Input-Output (EMRIO) model. EMRIO derives country-specific spend-based emission factors from OECD input-output tables (2023 release, reflecting 2020 economic structures), extended with environmental data from sources including EDGAR, FAOSTAT, UNFCCC, and EUROSTAT, and inflation-adjusted to 2025.

For these industries the system matches on industry and country only (not FTE size) and returns a single median intensity. Because EMRIO is built from macroeconomic models rather than company-level data, it does not carry percentile distributions — so confidence bands are not available for EMRIO-based estimates.

Which methodology should I select for the Scope 1 and Scope 3 questions?

When a report asks you to record the methodology behind an estimate, the NEE is a benchmark-derived proxy, so:

1. Scope 1 → "Other." The NEE is not a direct measurement tool, so "Direct" is not accurate; "Other" reflects that it is a benchmark-derived proxy.
2. Scope 3 → activity-based (defensibly "Other" / benchmark-derived proxy). The estimate uses revenue- or FTE-based benchmarks rather than a company's own spend.

One nuance worth setting with clients: the NEE produces a total Scope 3 estimate based on peer benchmarks — it does not break Scope 3 down into the 15 categories.

Using proxy estimates responsibly

Proxy estimates are a practical stand-in when measured data isn't available, and they are designed to be replaced as better data arrives. They are modeled approximations that may differ materially from a company's actual emissions, and they should not be the sole basis for regulatory reporting, disclosures, or financial decisions. Match levels and confidence indicators are directional context, not guarantees of accuracy. For the full terms, see Novata's benchmark disclaimer.

Related articles

1. How do I use the Novata emissions estimator?
2. Novata Proxy Estimator Methodology (the broader ESG-metric proxy system)
3. Using Proxy Data to Fill Gaps in Reporting
4. How Your Best-fit Benchmark is Determined
5. PCAF Data Quality Scores

Last updated: August 21, 2026