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Documentation Index

Fetch the complete documentation index at: https://docs.mangrovesystems.com/llms.txt

Use this file to discover all available pages before exploring further.

What you’ll learn in this lesson:
  • Understand uncertainty propagation and how it affects reported values
  • Work with system ledgers for embodied emissions and renewable instruments
  • Handle methodology versioning and updates in existing projects
  • Design multi-project configurations for complex operations
This lesson covers techniques you’ll encounter in complex production projects — situations where the basic patterns from earlier modules need to be extended or adapted.

Uncertainty propagation

Carbon accounting methodologies often require reporting uncertainty ranges alongside point estimates. Uncertainty propagates through calculations — if your input measurement has ±5% uncertainty, the output will also have uncertainty.

How uncertainty compounds

When you multiply values with uncertainty, the relative uncertainties add (approximately):
  • Mass: ±5% uncertainty
  • Carbon content: ±5% uncertainty
  • Result: approximately ±7.1% uncertainty (√(5² + 5²))
Different methodologies have different rules for uncertainty propagation. Some use simple addition of relative uncertainties, others use root-sum-of-squares (RSS), and some specify fixed deductions. Always follow the methodology’s approach.

Implementing uncertainty in Mangrove

You can model uncertainty as:
  • Separate calculation branches — one for the point estimate, one for the upper bound, one for the lower bound
  • Deduction factors — a methodology-specified discount (e.g., report the 95% lower confidence bound instead of the mean)
  • Keisan expressions — for complex uncertainty formulas
If the methodology requires a conservative approach (report at 95% confidence):
conservative_output = point_estimate × (1 - uncertainty_deduction)
Where uncertainty_deduction is a static input defined by the methodology (e.g., 0.05 for 5% deduction).

System ledgers

Beyond production ledgers that track physical material, some projects need system ledgers for tracking non-physical quantities:
Tracks the amortized carbon cost of facilities and equipment.
  • Credits: Annual amortization amount (e.g., 50 tCO2e/year from facility construction)
  • Debits: Monthly allocation to production batches
  • Purpose: Ensures embodied emissions are spread evenly and don’t distort individual batch calculations
This is configured as a separate ledger in the mass balance section, linked to the production model’s embodied emissions node.For a complete guide, see Embodied Emissions.
System ledgers follow the same mass balance principle as production ledgers — what goes in must come out or remain in inventory. This prevents double-counting of embodied emissions or renewable instruments.

Methodology versioning

Methodologies evolve — emission factors get updated, calculation requirements change, new sources must be included. Your project needs to handle these transitions.

Key challenges

ChallengeImpact
Updated emission factorsHistoric batches used old factors; new batches should use new ones
New calculation requirementsNew model nodes needed without breaking existing batches
Changed reporting boundariesMore or fewer emission sources included

Handling version transitions

1

Create a new model version

In the Model Editor, create a new version of the model with the updated calculations. Previous versions remain linked to historic batches — they aren’t affected by the change.
2

Update static inputs

Change emission factors and methodology parameters in the resource library. Set effective dates if the platform supports it, so historic and future calculations use the correct values.
3

Document the change

Record what changed, when, and why. This is critical for verification — auditors will want to see that the transition was handled correctly.
4

Test the transition

Generate batches for a period that spans the transition date. Verify that batches before the change use the old methodology and batches after use the new one.
Never modify a model version that’s already linked to generated batches. Always create a new version. Modifying an existing version could retroactively change historic batch outputs and break the audit trail.

Multi-project configurations

Some operations span multiple projects — for example, a company with three biochar facilities, each configured as a separate Mangrove project.

When to use multiple projects

  • Different facilities with different operational parameters
  • Different methodologies or registries
  • Separate reporting requirements
  • Independent mass balance tracking

Shared components

Even with separate projects, some components can be shared:
ComponentCan Be Shared?How
Emission factorsYesSame static inputs across projects
Event type definitionsNoEach project has its own event types
Model logicPartiallySimilar structure but separate instances
Feedstock typesYesCommon feedstock definitions
Reporting templatesYesStandardized report formats
Multi-project configurations are an advanced topic. For most implementations, start with a single project and only split into multiple projects when the operational differences genuinely require it. See the Guide to Production Accountingfor project configuration guidance.

Check your understanding

Modifying an existing model version would retroactively change historic batch outputs, breaking the audit trail. Verifiers expect that generated batches reflect the calculations as they were at the time of generation. Always create a new model version for methodology updates — this preserves historic batches while applying new logic to future ones.
A system ledger tracks non-physical quantities like embodied emissions or renewable energy certificates. You’d use one when: (1) the emission source isn’t tied to a specific production event (like annual facility construction costs amortized monthly), or (2) you need to track instruments like RECs to avoid double-counting when offsetting grid emissions across batches.

Next, prepare your project for production deployment in Lesson 6.3: Production Readiness.