modeling-carbon-credit-economics

Builds carbon credit models with offset generation analysis, verification costs, and market pricing dynamics for carbon-linked investments. Use when modeling carbon credits, analyzing offset economics, or evaluating carbon market exposure.

11 stars

Best use case

modeling-carbon-credit-economics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Builds carbon credit models with offset generation analysis, verification costs, and market pricing dynamics for carbon-linked investments. Use when modeling carbon credits, analyzing offset economics, or evaluating carbon market exposure.

Teams using modeling-carbon-credit-economics should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/modeling-carbon-credit-economics/SKILL.md --create-dirs "https://raw.githubusercontent.com/CaseMark/skills/main/skills/capital/modeling-carbon-credit-economics/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/modeling-carbon-credit-economics/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How modeling-carbon-credit-economics Compares

Feature / Agentmodeling-carbon-credit-economicsStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Builds carbon credit models with offset generation analysis, verification costs, and market pricing dynamics for carbon-linked investments. Use when modeling carbon credits, analyzing offset economics, or evaluating carbon market exposure.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Modeling Carbon Credit Economics

## When To Use

- Evaluating a carbon offset project (forestry, DAC, methane capture, cookstoves, etc.) as an investment or revenue stream
- Modeling credit generation volumes and timing for a specific project methodology
- Pricing carbon credits across compliance and voluntary markets
- Analyzing the cost stack from origination through verification and retirement
- Stress-testing carbon-linked investment returns under varying regulatory and market scenarios
- Comparing project types or registries on an economics basis

## Inputs To Gather

- **Project type and methodology** — REDD+, ARR, avoided methane, direct air capture, improved cookstoves, etc.
- **Registry and standard** — Verra (VCS), Gold Standard, ACR, CAR, or compliance programs (EU ETS, CCA, RGGI) [VERIFY registry-specific issuance rules]
- **Baseline and additionality documentation** — project design document (PDD), baseline emissions scenario
- **Land/asset parameters** — hectares, capacity, sequestration or avoidance rates per unit
- **Credit issuance schedule** — crediting period length, buffer pool contribution, expected vintage distribution
- **Cost inputs** — project development, MRV (monitoring, reporting, verification), registry fees, brokerage, legal
- **Market pricing data** — spot and forward prices by credit type, vintage, and co-benefit attributes
- **Regulatory context** — applicable compliance market rules, Article 6 corresponding adjustment status [VERIFY jurisdiction-specific compliance eligibility]
- **Buyer/offtake terms** — fixed-price ERPAs, spot sales, streaming arrangements, volume commitments

## Workflow

1. **Classify the credit type and market**
   - Determine whether credits are compliance-grade or voluntary-only
   - Identify the applicable methodology version and crediting period
   - Note any co-benefit certifications (CCB, SD VISta) that affect pricing premiums

2. **Model gross credit generation**
   - Build a year-by-year issuance schedule based on sequestration/avoidance curves
   - Apply methodology-specific decay, leakage, and permanence discount factors
   - Deduct buffer pool contributions (typically 10–40% for nature-based projects) [VERIFY buffer pool % by methodology]
   - Output: net annual credits available for sale (tCO₂e/year)

3. **Build the cost stack**
   - **Development costs** — feasibility, PDD preparation, legal structuring, community engagement
   - **MRV cycle costs** — remote sensing, field verification, third-party auditor fees (typically every 3–5 years)
   - **Registry and transaction fees** — issuance fees, transfer fees, retirement fees
   - **Ongoing management** — project monitoring, community benefit-sharing, insurance
   - **Brokerage and marketing** — intermediary commissions (5–15% on voluntary market sales)
   - Calculate all-in cost per credit ($/tCO₂e) on a levelized basis over the crediting period

4. **Model revenue and pricing dynamics**
   - Set base-case pricing by credit category (nature-based removal, avoidance, tech-based removal)
   - Apply vintage discounting — older vintages typically trade at a discount
   - Incorporate co-benefit premiums where applicable
   - Model offtake structure: percentage sold forward via ERPAs vs. spot exposure
   - Build price scenarios: bear (oversupply / integrity concerns), base, bull (Article 6 demand / corporate net-zero mandates)

5. **Calculate investment returns**
   - Project-level IRR and NPV at each price scenario
   - Cash flow waterfall: development → first issuance → steady-state → crediting period expiry
   - Breakeven credit price (the $/tCO₂e needed to achieve target return)
   - Payback period under base-case assumptions

6. **Run sensitivity and risk analysis**
   - Key variables to stress: credit price (±30%), issuance volume (±20%), verification cost escalation, buffer pool invalidation events
   - Regulatory risk: methodology invalidation, Article 6 corresponding adjustment requirements, registry policy changes [VERIFY current Article 6 implementation status]
   - Permanence risk: reversal events (fire, disease, land-use change) and insurance adequacy
   - Counterparty risk: ERPA buyer credit quality, volume shortfall penalties

7. **Document assumptions and deliver model**
   - Create an assumptions register with source citations for every input
   - Flag all [VERIFY] items for client or specialist confirmation
   - Provide scenario comparison summary table

## Output

The deliverable is a financial model (spreadsheet or structured output) containing:

- **Issuance schedule** — annual net credits by vintage over the crediting period
- **Cost model** — itemized and levelized cost per credit
- **Revenue model** — scenario-based revenue by year with offtake mix
- **Returns summary** — IRR, NPV, breakeven price, payback period per scenario
- **Sensitivity tables** — tornado chart inputs showing return sensitivity to key variables
- **Assumptions register** — every input sourced and flagged where verification is needed
- **Risk matrix** — regulatory, permanence, market, and counterparty risks with mitigation notes

## Quality Checks

- Verify that buffer pool deductions match the specific registry/methodology rules — do not use generic percentages without confirmation
- Confirm crediting period length aligns with the methodology version (e.g., VCS 7-year renewable vs. 10-year fixed) [VERIFY]
- Ensure cost-per-credit calculation includes all MRV cycles, not just the first verification
- Cross-check pricing assumptions against recent transaction data (e.g., Ecosystem Marketplace, S&P Platts assessments)
- Validate that IRR calculations properly reflect the J-curve: development spend precedes first issuance by 2–4 years
- Confirm that compliance-grade claims are backed by actual registry eligibility — do not assume voluntary credits qualify for compliance use
- Check that co-benefit premium assumptions are supported by observable market data, not aspirational pricing

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