Purpose
Convert operational data into a defensible Scope 1/2/3 emissions estimate, then rank every available abatement lever by cost per tonne of CO2e avoided — so decision-makers spend capital on the cheapest reductions first and hit reduction targets at minimum cost. Replaces guesswork and consultant-dependent footprinting with a structured, repeatable calculation and a MACC-based investment sequence.
When to Use
Invoke this skill when the user asks to:
- Calculate or estimate a company's carbon footprint across Scope 1, 2, and 3
- Build an emissions reduction, decarbonization, or net-zero plan
- Compare the cost-effectiveness of different sustainability initiatives
- Decide which abatement projects to fund first within a fixed budget
- Prepare emissions baseline data for ESG, CSRD, or SBTi reporting
- Justify a sustainability capital request to a CFO or board
Inputs Required
Before starting, collect:
- Operational profile: Industry, headcount, number/type of facilities, and reporting year
- Energy use: Electricity consumed (kWh/year) and grid region; on-site fuel use (natural gas, diesel, propane — units/year)
- Fleet and direct combustion: Owned vehicles, machinery, refrigerants, and process emissions if any
- Scope 3 drivers: Annual spend or volume for purchased goods, business travel, freight, employee commuting, and waste — whatever data exists
- Reduction target: Target percentage and target year (e.g., 50% by 2030), or "no target set yet"
- Budget constraint: Available capital or annual budget for abatement, if known
- Discount/energy price assumptions: Local energy prices or "use regional defaults"
If Scope 3 data is partial, estimate using spend-based factors and flag the assumption. If no target is set, default to a 42% reduction by 2030 (SBTi 1.5°C-aligned trajectory) and note it.
Instructions
Step 1: Build the Emissions Inventory by Scope
Classify and quantify each source using the GHG Protocol structure. Show the calculation chain (activity data × emission factor) for transparency.
Scope 1 — Direct emissions
- On-site fuel combustion: fuel volume × fuel-specific factor (natural gas ≈ 0.0531 tCO2e/GJ; diesel ≈ 2.68 kgCO2e/L; propane ≈ 1.51 kgCO2e/L)
- Company fleet: distance or fuel × vehicle factor
- Fugitive emissions: refrigerant top-ups × GWP (e.g., R-410A GWP ≈ 2,088)
Scope 2 — Purchased energy
- Electricity: kWh × grid emission factor for the region (use location-based factor; note market-based if RECs/PPAs exist). Reference ranges: US avg ≈ 0.37 kgCO2e/kWh, EU avg ≈ 0.23, India ≈ 0.71, Nordics ≈ 0.03
- Purchased steam/heating/cooling if applicable
Scope 3 — Value chain emissions (15 categories; calculate those with data)
- Use spend-based factors when activity data is missing (e.g., purchased goods ≈ 0.3–0.8 kgCO2e per $ depending on category)
- Prioritize the typically dominant categories: purchased goods & services (Cat 1), capital goods (Cat 2), upstream transport (Cat 4), business travel (Cat 6), employee commuting (Cat 7), and use/end-of-life of sold products (Cat 11–12) where relevant
Output a total in tCO2e with the per-scope breakdown and a one-line note on data confidence (high/medium/estimated) for each scope.
Step 2: Identify Abatement Levers
For the organization's profile, generate the relevant set of reduction levers across these families. Only include levers the company can realistically pull.
- Energy efficiency: LED retrofit, HVAC optimization, building controls, equipment upgrades
- Electrification: heat pumps, fleet EV conversion, process electrification
- Clean power procurement: on-site solar, PPAs, RECs, green tariffs
- Operational/behavioral: load shifting, idle reduction, travel policy changes
- Scope 3 supplier engagement: supplier emission targets, low-carbon material substitution, logistics consolidation
- Offsets/removals: only as a residual lever, flagged as last-resort
Step 3: Calculate Cost Per Tonne for Each Lever (MACC Logic)
For each lever, compute:
- Abatement potential: tCO2e avoided per year
- Net annualized cost: (annualized capex + annual opex) − annual savings (energy cost avoided, etc.)
- Cost per tonne: net annualized cost ÷ tCO2e avoided
Negative cost-per-tonne levers (those that save money net of emissions) sit at the bottom of the curve and should be funded first. Include a simple payback period (years) for capex levers.
Step 4: Build the Marginal Abatement Cost Curve
Rank all levers from lowest to highest cost per tonne. Present as a table that functions as a MACC: each row is a lever, ordered by $/tCO2e, with abatement potential as the "width." Mark the cumulative abatement so the reader can see how far down the list they must go to hit the target.
Step 5: Sequence Against the Target and Budget
- Sum cumulative abatement until the reduction target is met; identify the marginal lever (the last one needed)
- If a budget constraint exists, flag which levers fit and which exceed it
- Highlight any "no-regret" moves (negative or near-zero cost-per-tonne)
- State the total estimated cost to hit the target and the residual emissions requiring offsets, if any
Step 6: Produce the Roadmap
Sequence implementation across three horizons:
- 0–12 months: no-regret and fast-payback levers
- 1–3 years: capex-heavy efficiency and electrification
- 3+ years: clean power procurement, deep Scope 3, residual removals
Output Format
- Structure: Emissions inventory table → abatement lever list → MACC ranking table → target sequencing summary → three-horizon roadmap
- Length: 1,000–1,400 words
- Format: Structured document with two tables (inventory, MACC) and bulleted roadmap; prose limited to summaries and assumptions
- Sections: Emissions Inventory (by scope) → Abatement Levers Identified → Marginal Abatement Cost Curve → Target & Budget Sequencing → Decarbonization Roadmap → Key Assumptions
- Tone: Analytical and decision-ready — written for a CFO and operations director who will allocate capital from it; quantified, no advocacy language
Quality Checklist
Before delivering output, verify:
- [ ] Every emission source shows its calculation chain (activity data × factor) — no unexplained totals
- [ ] Total emissions are broken out by Scope 1, 2, and 3 with a data-confidence note per scope
- [ ] Every abatement lever has an explicit cost per tonne (including negatives) and abatement potential in tCO2e
- [ ] The MACC table is sorted from lowest to highest $/tCO2e with cumulative abatement shown
- [ ] The sequencing section identifies the exact marginal lever that meets the stated reduction target
- [ ] All estimated or default factors are listed in the Key Assumptions section
Notes
- The cheapest tonnes are almost always energy efficiency and operational changes (negative cost) — fund these before any offset spend
- Scope 3 typically dominates total footprint (often 70–90%) but carries the lowest data confidence; estimate it rather than omit it, and flag it as the priority for data improvement
- Emission factors vary by region and year — treat the output as a defensible estimate for prioritization, not an audit-grade inventory, and recommend factor verification before external reporting
- Offsets should be presented only for residual emissions after abatement, never as a substitute for reduction levers that are cheaper per tonne
How to use this skill
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Installing in Claude Code:
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Using in Claude.ai, ChatGPT, Gemini, or other AI tools:
- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills