What would it cost to collect supplier emissions data manually?

The default plan for supplier emissions data is not a platform. It is a spreadsheet, a determined analyst, and the belief that reading sustainability reports is free. It is not free. It is a headcount decision, and this post prices it.

One scenario runs through this whole series, and it is an assumption of ours rather than a sourced figure: a UK organisation with 10,000 suppliers in its accounts payable export, already committed to Scope 3, reporting annually. The 10,000 is the purchase ledger rather than an engagement list: most teams actively engage a few hundred suppliers, but Scope 3 Category 1 brings the whole ledger into the inventory, so the whole ledger is what has to be accounted for. Suppliers are the worked case here; the arithmetic is the same for portfolio companies. Change the numbers and the totals move with them, which is why the working is shown.

What does the manual work actually involve?

Per supplier, per year: work out which legal entity the name on the invoice actually is, and where it sits in a corporate group. Find whatever that entity has published, in annual reports, regulatory filings and sustainability statements, on no common schedule and in no common format. Extract the figures per scope and per year. Record whether each scope was third-party assured. Note which method produced the figure, because that is what an auditor asks for. Where nothing has been published, choose and apply a factor instead, and write down that you did. Then repeat next year, because companies restate and republish.

None of those steps is difficult. The cost is that there are 10,000 rows.

How many people does your supplier list take?

No published figure exists for how long this work takes per supplier, so here is our assumption, with the reasoning. Some suppliers take ten minutes, because nothing is published and a factor is applied. Some take half a day, because the disclosure exists in a 200-page report in another language and restates last year. Averaged across the file, we assume 2 hours per supplier per year as the central case, with 1 and 4 hours either side, and a working year of 1,650 hours (220 days at 7.5 hours). None of those numbers is sourced; all are stated so you can substitute your own. The table runs them across list sizes, priced at a data analyst's published cost from the section below.

Costs are salary plus employer National Insurance at the published figures below, rounded to the nearest £100. The smaller rows are pro-rata and assume the work fits into spare capacity, which flatters them slightly; the 10,000 row uses whole analysts.

Read the central column downwards and the shape of the decision appears. At a few hundred suppliers the work is a slice of one job. Somewhere between 600 and 1,000 suppliers, on the central assumption, it crosses a full analyst, which in practice is where the business case gets written, because the cost stops hiding inside someone else's role. At 10,000 it is a department. The assumption drives everything, which is why it is stated: halve it or double it and the crossover moves with it.

What does that team cost a year?

The salary inputs behind those figures are published. Robert Half's 2026 UK Salary Guide puts a data analyst's median base salary at £48,250, base only, excluding bonus and benefits. Employer Class 1 National Insurance is 15% on earnings above a £5,000 threshold for 2025 to 2026, per HMRC's published rates. So the twelve analysts of the 10,000-supplier central case cost £579,000 in salary and £77,850 in employer NI: £656,850 a year, before pension, recruitment, equipment, management time and workspace, none of which has a single published multiplier.

Two honest corrections, both upward. Somebody senior has to check the judgement calls, because deciding whether a restated figure supersedes the one in your baseline is not an entry-level task, and that reviewer is not in the twelve. And the survey evidence says the work is harder than it looks: the Science Based Targets initiative's corporate survey with BCG, February 2023, from 230 responding organisations, found 85% saw data access as a barrier to a usable baseline and 59% named limited time and resource, and 94% were relying on secondary data. The teams reporting those barriers were already doing this work.

Why does the work not shrink in year two?

Because the inputs keep moving. Companies publish, restate and republish every year, so the reading starts again. The free factor sets are reissued annually, and the GHG Protocol Corporate Standard obliges base year recalculation for changes in calculation methodology or improvements in the accuracy of emission factors or activity data that have a significant impact. Improving your own spreadsheet triggers that obligation. In practice it happens constantly: the same SBTi survey found 70% of organisations had re-baselined in the last five years, half of those, so 35% of all respondents, for methodological reasons.

Manual is not a one-off cost with a maintenance tail. It is the maintenance, done by hand, forever.

What if you only care about a few hundred suppliers?

Then the cash case for manual is genuinely good, and the table further up shows it: at 200 suppliers on the central assumption, the labour is about £13,300 a year, a slice of one job rather than a hire, and this page is not going to pretend otherwise.

What a small team buys instead is everything a spreadsheet cannot produce at any size: a source document and change history behind every figure, assurance status recorded per scope, restatements handled rather than overwritten, a calculation that has been independently verified rather than taken on trust, and a refresh that does not depend on one person still being in the job. That is the difference between a number and a number an auditor can follow. The other thing it buys is the time itself. No organisation hires 0.2 of an analyst, so on a small list the work lands on a sustainability or procurement lead whose year was budgeted for something else, and the real cost is whatever they stopped doing to chase PDFs. DitchCarbon is bought at that size for the evidence and the hours rather than the headcount: the Portal calculator is verified to ISO 14064-3, limited assurance, by UL Solutions, renewed annually, which is the part no afternoon a quarter can replicate.

Why does the whole file matter if you only engage a few hundred?

Because the few hundred are an output, not a scope. Scope 3 Category 1 covers purchased goods and services as a whole, so the published inventory has to account for the full purchase ledger, and an auditor's questions land on the full ledger too. Engagement then concentrates where the emissions are, which is exactly how it should work: you find the 300 that matter by measuring the 10,000, not instead of it. The lines you never engage still sit in the published number, and if they are carried on sector averages, that is where the data quality questions come from. DitchCarbon covers the whole file so the engagement list can stay short: the disclosed lines carry their published figures from the start, and the rest show as a ranked gap rather than a silent one.

When is manual the right answer?

With a short, stable list. Fifty suppliers, most of them large listed companies with assured reports, is a spreadsheet and an afternoon a quarter, and buying a platform for it would be the wrong call. The manual option fails on volume, not on principle, and 10,000 rows is volume.

What is the alternative to reading 10,000 reports?

Buying the reading already done. DitchCarbon provides verified emissions data for over 2 million organisations, so procurement, sustainability and finance teams can measure and act on supply chain and portfolio emissions from one source. The disclosed part of a supplier list is already matched and scored on day one, before anyone sends a request; where a figure needs improving, the request goes out prepopulated with what that organisation has already published; and where nothing has been disclosed, a generic factor closes the inventory and the gap is shown rather than hidden. A recent deployment reached about 60% of a large supplier base within 2 weeks, which at 2 hours a row is roughly 12,000 hours of the manual plan.

One last thing to be straight about: every calculation on this page prices a single output, obtaining defensible data. It puts no value on what the data is for. The analysis that finds the hotspots, supplier engagement that runs at list scale rather than one inbox at a time, and the year-on-year view of whether anything changed: a manual build produces none of those at any headcount, and they are the part of DitchCarbon a labour comparison cannot see.

To test it against your own file, send a sample of your supplier list to the coverage check and see how much of it already carries a matched, scored figure.

This post is part of a series pricing the alternatives to buying supplier emissions data, all on one shared scenario: consultants every cycle, the spend-based minimum, doing nothing, and building the platform in house.

Last reviewed August 2026.