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carbon-accounting-check

Sanity-check a greenhouse gas inventory before it goes into a report or gets audited. Use when asked to review a carbon footprint, check a GHG inventory, validate scope 1/2/3 numbers, explain a year-over-year emissions change, or prepare emissions data for assurance. Produces a boundary review, data-quality assessment, emission-factor sensitivity list, YoY bridge, and a fix-before-publishing list.

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Carbon Accounting Check Skill

A GHG inventory fails audit (and credibility) on boundaries, factors, and data quality — rarely on arithmetic. This skill runs a structured sanity pass over an inventory so the weak points are found internally, not by an assurer or a journalist. It reviews rigor; it does not certify compliance.

What This Skill Produces

  • A scope 1/2/3 boundary review with gaps and out-of-boundary items named
  • A data-quality tier map (measured / calculated / estimated / proxy) per emission source
  • The top emission-factor sensitivities — where a factor choice swings the total
  • A year-over-year bridge separating real reduction from methodology or boundary change
  • Double-counting flags and a prioritized fix list

Required Inputs

Ask for these if not provided; if the brief is thin, proceed with clearly labelled assumptions rather than refusing:

  • Inventory data — emissions by scope and category, with units (tCO2e), and the reporting year
  • Organizational boundary — operational control, financial control, or equity share, and which entities are in/out
  • Scope 2 method(s) — location-based, market-based, or both; contractual instruments held (RECs, GOs, PPAs)
  • Emission factor sources — which factor sets and vintages were used per category
  • Data provenance — per major source: meter/invoice, activity-data calculation, estimate, or spend-based proxy
  • Prior-year inventory (optional) — needed for the YoY bridge
  • Offsets or removals held (optional) — reviewed separately, never netted

Review Framework

Walk the inventory in this order:

1. Boundary. Confirm the consolidation approach is stated and applied consistently. For scope 3, check all 15 GHG Protocol categories were screened — a category may be excluded only with a stated reason and a rough size estimate.

2. Scope 2 duality. Both market- and location-based figures should exist. Flag market-based claims without matching contractual instruments (vintage, geography, retirement).

3. Data-quality tiers. Assign each material source a tier and estimate what share of the total sits in each:

TierBasisTypical uncertainty
MeasuredMeters, fuel invoices, utility billsLow
CalculatedActivity data × published factorLow–medium
EstimatedExtrapolation, averages, occupancy modelsMedium–high
ProxySpend-based, industry-average intensityHigh — directional only

Flag any headline claim (e.g. "-12% YoY") that rests mostly on proxy-tier data.

4. Factor sensitivity. Identify the 3–5 sources where switching to an equally defensible factor set or vintage moves the total by >2%. Recompute or estimate the swing.

5. YoY bridge. Decompose the change into: activity change, factor/vintage updates, boundary changes, methodology changes, and data-quality improvements. Only the first is a real emissions trend.

6. Double-counting traps. Check the classics: fleet fuel in both scope 1 and scope 3 category 6; electricity in scope 2 and again via spend-based scope 3; parent and subsidiary both claiming the same site; RECs claimed against grid averages already lowered by those RECs; leased assets counted by both lessor and lessee.

Output Format

GHG inventory check: [organization, reporting year]

1. Summary verdict — publishable as-is / publishable with caveats / fix first, with the two or three decisive reasons.

2. Boundary review — consolidation approach, scope 3 category screening table (included / excluded + reason + size estimate), gaps.

3. Data-quality map — table of major sources × tier × share of total, with the weakest material sources called out.

4. Factor sensitivities — the top swings, each with source, alternative factor, and estimated delta.

5. YoY bridge — waterfall from prior year to current year with each driver quantified or labelled as an estimate.

6. Flags and fixes — numbered list, each with severity (blocks publication / caveat needed / improve next cycle) and a concrete fix.

Include this line in the artifact: "Verify boundary, methodology, and disclosure choices against the applicable standard (e.g. GHG Protocol, ISO 14064-1) and regulation with your compliance team and assurer."

Quality Checks

  • Consolidation approach is stated and every in/out entity decision is visible
  • All 15 scope 3 categories are screened; exclusions have a reason and a size estimate
  • Both scope 2 methods are reported, or the absence of one is flagged
  • Every material source has a data-quality tier; the share of total per tier is stated
  • The YoY bridge separates real activity change from methodology and boundary effects
  • Each flag has a severity and a concrete fix, not just an observation
  • Assumptions made from a thin brief are labelled as assumptions

Anti-Patterns

  • Do not net offsets or removals against gross emissions in the headline number — report gross, then offsets separately
  • Do not present a market-based scope 2 figure without naming the contractual instruments behind it
  • Do not let a YoY "reduction" stand if it is driven by a factor-vintage update or boundary change — say so
  • Do not treat spend-based proxy data as if it supports precise claims — flag it as directional
  • Do not silently drop scope 3 categories — every exclusion needs a reason and a size estimate
  • Do not certify compliance — this is a rigor review; assurance and legal sign-off stay with the professionals

Based On

GHG Protocol Corporate Standard and Scope 2/Scope 3 guidance practice (boundaries, dual reporting, data-quality tiers, recalculation policy).

Example Trigger Phrases

  • "Review a carbon footprint."
  • "Check a GHG inventory."
  • "Validate scope 1/2/3 numbers."
  • "Explain a year-over-year emissions change."
  • "Prepare emissions data for assurance."

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