PCAF Financed Emissions Calculators: Loans & Investments

Start here: pick your asset class

One standard, seven attribution formulas

These seven calculators look like seven methods. They are one method with seven parameterisations, and seeing that is the difference between maintaining seven mental models and maintaining one.

The attribution factor is the whole method — and its denominator changes per class

Every financed-emissions figure in this slice is the same product: an attribution factor multiplied by the emissions of the thing you financed. The attribution factor is your exposure over the financed entity’s total value. Your exposure — the outstanding amount, the market value of your holding — is rarely contentious; you know what you lent or hold.

The denominator is where the classes diverge, and it diverges because “total value” has no single meaning across a balance sheet. A listed company has an enterprise value including cash. An unlisted one does not, so PCAF substitutes book value. A building has a property value. A car has a vehicle value. A country has GDP. Same formula, five different answers to the same question — and each substitution carries its own error behaviour, which is what the per-class methodology pages exist to explain.

Why the denominator is frozen at origination for some classes and floats for others

This is the distinction that catches practitioners, because nothing in the formula signals it. For mortgages and commercial real estate, PCAF attributes on property value at origination — the denominator is fixed on the day the loan was written and never moves again. For listed equity and corporate bonds, attribution runs on EVIC, which is remeasured continuously and moves with the market.

The consequence is that two portfolios with identical emissions and identical exposures report differently depending on which classes they hold. A bank’s mortgage book produces a stable financed-emissions figure that changes only as the book changes. An asset manager’s equity portfolio produces a figure that moves when the market moves, with no change in any underlying company’s emissions at all. Neither is wrong. They are different denominators doing what they were specified to do.

Data quality 1–5: the score is a deliverable, not a diagnostic

Every class carries a five-level data quality scorecard, running from 1 for the strongest evidence to 5 for the weakest. For listed equity, a score of 1 means reported emissions with EVIC known and the emissions themselves verified; a 5 means the figure was estimated from little more than the sector the company sits in.

The scores are frequently misread as an internal diagnostic — something to check and improve before reporting. They are not. PCAF requires the score to be disclosed alongside the number, weighted across the portfolio, which means the score travels to your reader. A portfolio-weighted average of 4.2 is not a private note about data gaps; it is a published statement about how much confidence your own figure deserves. That reframing is what changes behaviour, because it makes data quality a reporting outcome rather than a housekeeping task.

What each calculator covers

Listed equity & corporate bonds — EVIC, and the denominator that moves

What drives this class is EVIC, and EVIC is the reason listed-equity financed emissions behave unlike anything else in the portfolio. Because it includes market capitalisation, the denominator rises in a bull market and falls in a crash — so identical holdings in identical companies produce a smaller financed-emissions figure when markets are up. Practitioners meeting this for the first time usually assume a calculation error.

The calculator resolves the attribution and derives the data quality score from what you supply: reported and verified emissions with a known EVIC earns a 1; physical-activity estimates sit in the middle; sector-average estimation lands at 5. Use it for the equity and bond book, and expect to explain the market-movement effect to whoever reads the output. Full method: PCAF listed equity and corporate bonds methodology.

Business loans & unlisted equity — no market cap, so book value

This is the largest class on most commercial banks’ balance sheets and the one where data quality is hardest to move. The denominator substitutes book value — equity plus debt from the borrower’s accounts — because there is no market price to reference. That substitution is defensible and it is also the source of most of the class’s error: book value and enterprise value are not the same thing, and the gap varies by sector.

The scoring ladder here runs from reported verified emissions down through revenue-based and asset-based estimation to sector averages. Most portfolios sit low on that ladder at first inventory, because SME borrowers do not report emissions and never have. Use the calculator to establish the baseline honestly rather than to chase a score you cannot yet support. Full method: PCAF business loans and unlisted equity methodology.

Commercial real estate — attribution on property value, emissions on the building

CRE breaks the pattern of the first two classes in a way worth stating plainly: the emissions you attribute are the building’s, not the borrower’s. A property company’s corporate footprint is irrelevant here — what matters is the energy the financed building consumes, which is why the data quality ladder runs on metered energy first and falls back to floor area and building type.

The denominator is property value at origination, frozen. Combined with building-level emissions, that makes CRE the class where retrofit shows up most directly: improve the building’s energy performance and the financed emissions fall, with no change to the denominator at all. Use the calculator for the CRE book and pair it with whatever stranding analysis your risk function runs — CRREM stranding pathways are the common reference. Full method: PCAF commercial real estate methodology.

Mortgages — origination value, and the LTV trap

Mortgages are CRE’s residential sibling and share its structure: attribution on property value at origination, emissions from the dwelling. What differs is the data. Residential metered energy is rarely available to a lender, so the ladder descends through EPC rating, floor area and dwelling type — and in the UK the calculator estimates building energy from national benchmarks when metered data is absent.

Full method: PCAF mortgages methodology.

Motor vehicle loans — the class where the asset drives itself

Motor vehicle loans attribute on vehicle value, but the emissions are use-phase — what the financed car actually emits being driven. That makes distance travelled the swing variable, and distance is the thing a lender almost never knows. The gap between a modelled average annual mileage and a real one is large enough to dominate the result.

The scorecard reflects exactly this: actual distance data scores well, modelled averages score poorly, and the fall-off is steep. Use the calculator for the vehicle book, and treat any mileage assumption as the number most worth improving. Full method: PCAF motor vehicle loans methodology.

Project finance — ring-fenced, so attribution is cleaner and data isn’t

Project finance is the tidiest attribution in the set. The asset is ring-fenced, the total project equity plus debt is documented in the financing agreement, and your share is unambiguous — no proxy, no substitution, no market movement. If the seven classes were ranked on denominator confidence, this one wins.

The difficulty moves to the numerator instead. A project’s emissions depend on what it does and how it runs, and a project financed at construction has no operating history to report. That is why the scoring ladder here separates verified operational data from modelled projections so sharply, and why a project-finance book’s data quality typically improves with asset age rather than with effort. Full method: PCAF project finance methodology.

Sovereign debt — production vs consumption, and the double-counting problem

Sovereign debt is the odd member of this slice and it is worth being direct about that. The attribution is a holding over PPP-adjusted GDP, and the emissions are a country’s national inventory. Nothing about that resembles lending to a company. It sits here because it is balance-sheet attribution — you hold the exposure and you attribute a share — but the conceptual family is different.

The other live decision is production versus consumption accounting: whether to attribute the emissions produced within a country’s borders or those embodied in what it consumes. The two give materially different answers for trade-exposed economies. The calculator handles the attribution and the inventory selection; the choice of basis is yours to state explicitly. Full method: PCAF sovereign debt methodology.

One adjacency worth knowing: PCAF’s Part A 3rd edition added sub-sovereign debt as a scored asset class, and the underlying scorecard exists in our reference data. There is no calculator for it yet.

The portfolio view: WACI, absolute, and what your target can cover

Seven per-class figures are not a portfolio answer. What you do with them depends on which question you are being asked.

Absolute financed emissions — the sum of the attributed tonnes — is the figure that belongs in a Scope 3 Category 15 disclosure, and the one a science-based target is set against. It is additive across the six corporate classes; it is not additive with sovereign, for the double-counting reason above.

Weighted average carbon intensity answers a different question: how carbon-intensive is the portfolio per unit of value, rather than how many tonnes does it finance. WACI is the metric most investor disclosures lead with because it is comparable across portfolios of different sizes, and it is the metric most likely to move for reasons that have nothing to do with emissions — a valuation shift changes it.

The data quality score is weighted across the portfolio too, and this is where the per-class scores stop being per-class. A portfolio dominated by business loans at score 4 and 5 will carry a weighted average that no amount of precision on the equity book can rescue. If you are deciding where to spend data effort, the weighting tells you: it is almost always the largest class at the worst score, not the class that is easiest to improve.

How the numbers are built

The attribution rules, scorecards and scope-coverage logic behind these seven calculators resolve from the same versioned MasterBrain rows the engines read at runtime. You can inspect the source data and its versioning at MasterBrain.

What these rows contain is worth being precise about, because it is not what the rest of our reference data contains. A PCAF row is a framework row: its value is a scoring rank, an attribution rule or a methodology marker, not a quantity of carbon. The row for a score of 1 on listed equity carries the value 1 and the unit “PCAF data quality score” — it means best-available-evidence, and it would be meaningless to add it to anything. That is why these rows are explicitly flagged as sitting outside the GHG Protocol scopes, and why an inventory total must filter them out rather than sum them in. The distinction matters here more than on most pages: a data quality score and an emission factor look identical in a database and mean nothing like the same thing.

The governing standard is the PCAF Global GHG Accounting and Reporting Standard, currently Part A 3rd edition and Part C 2nd edition, both published 2 December 2025. The 3rd edition was additive: PCAF confirmed that existing methodologies were not changed, so no data quality score was revalued — the scorecards were relabelled to renumbered tables and new asset classes were added alongside. If you built an inventory on the 2nd edition, your existing numbers stand. For the sovereign class, scenario work commonly references the NGFS climate scenarios, which we hold as reference data in their own right.

The honest limitation: PCAF gives you a defensible, comparable and auditable attribution. It does not give you a measured number. A score of 4 or 5 means the figure is an estimate built from sector averages, and no amount of methodological rigour converts an estimate into a measurement — which is precisely why the standard makes you publish the score next to the number.

Zoom out: facilitated, insurance and data quality

This slice covers what sits on your balance sheet. Three PCAF classes do not. Capital-markets facilitated emissions — underwriting and arranging rather than holding — are attributed on a different basis entirely and are counted at a fixed fraction rather than a full share. Insurance-associated emissions attribute on premiums, not exposure, which puts them outside the attribution logic described on this page. And the data quality scorecard has its own calculator, for portfolios that need the weighted score across mixed classes rather than per class.

Those three have their own calculators: reach them through the financed emissions hub, which covers all ten PCAF calculators and is the right starting point if you are not sure which attribution basis your exposure needs.

Next step

If you are building a first inventory, start with the class that dominates your balance sheet — business loans for most banks, listed equity for most asset managers — and accept the data quality score it produces rather than delaying for better data. If you already have a baseline, the weighted score tells you where the next unit of effort belongs.

Frequently asked questions