Short answer: a drinks producer’s carbon splits into Scope 1 (on-site fuel), Scope 2 (purchased energy) and Scope 3 (packaging, transport, supply) — and Scope 3 is usually the biggest and the hardest. Build the inventory on real meters and supplier data; use AI to fill gaps and generative AI to draft the disclosure, never the numbers.

You cannot manage what you have not counted, and for drinks the count is dominated by glass, cans and freight, not the brewhouse. A credible carbon inventory is the foundation of every other sustainability claim.

Related: carbon accounting for breweries · avoiding greenwashing with AI.

Building a carbon inventory1Collectenergy, materials, freight2Mapto Scope 1/2/33Factoremission factors4Modelfill the gaps5ReportUK/EU/US/India
From scattered data to a Scope 1/2/3 inventory you can defend.

Measure first, model second

Pull energy meters (Scope 1/2) and material and freight records (Scope 3) into one place, with activity data per unit produced. Scope 3 is where the carbon and the measurement difficulty both concentrate.

Where AI and data cut carbon emissions

ML and data engineering reconcile messy supplier and logistics data, apply emission factors consistently, and estimate gaps where primary data is missing — flagging which assumptions move the total most.

Where generative AI (Claude, ChatGPT) helps

A Claude or ChatGPT copilot drafts the CSRD, SECR or voluntary disclosure narrative and answers ‘what drove our Scope 3 last year?’ — but the figures must trace to data, and a person owns the disclosure. The rule holds: it drafts and explains, a person verifies anything that reaches a regulator.

The rules, region by region

Across regions the levers are the same but the rules differ: the UK (SECR energy/carbon reporting, packaging EPR), the EU (CSRD, the EU ETS, and the Packaging and Packaging Waste Regulation), the USA (EPA water and Energy Star, state programmes like California’s, and TTB for labelling), and India (the Bureau of Energy Efficiency’s PAT scheme and CPCB effluent norms). Measure to your own meters first; map to whichever framework applies.

Where the emissions sit — by scopeScope 1 — direct fuel & processScope 2 — purchased energyScope 3 — packaging, transport, supply (largest)
Most of a drinks producer's footprint is Scope 3 (packaging, transport, supply) — measure it, don't guess it.

Where it breaks

Scope 3 relies on estimates and supplier data of varying quality, so the number carries uncertainty — be transparent about method, and never let generative AI invent a figure or a reduction you cannot substantiate.

The bottom line

Carbon accounting is data engineering before it is climate science: collect, map to scope, factor, and disclose. AI fills gaps and drafts the report; the credibility lives in the measured data.

Frequently asked questions

How can data and AI cut carbon emissions? ML and data engineering reconcile messy supplier and logistics data, apply emission factors consistently, and estimate gaps where primary data is missing — flagging which assumptions move the total most.

Where do Claude and ChatGPT fit in sustainability? A Claude or ChatGPT copilot drafts the CSRD, SECR or voluntary disclosure narrative and answers ‘what drove our Scope 3 last year?’ — but the figures must trace to data, and a person owns the disclosure.

What is the biggest source of carbon for a brewery or winery? Usually Scope 3 — packaging (especially glass) and transport — not the energy used to make the drink. That is why carbon accounting that stops at Scope 1 and 2 misses most of the footprint.

Part of the ESG Analytics for Beverage track.