Baseline Setting for Nature-Based Carbon Projects

Updated 2026-11-02 · 3 min read · By the akar.earth specialist team

Carbon project baseline explained: static, dynamic and jurisdictional baselines for ARR, REDD+, IFM and blue carbon, with Indonesian data and pitfalls.

A carbon project baseline is the scenario of what emissions or removals would have occurred without the project. Every credit is the gap between the baseline and what the project actually achieves, after leakage, uncertainty and buffer deductions, so the baseline is the single biggest driver of how many credits a nature-based project earns and how credible they are.

Baseline approaches have shifted sharply in recent years. Forecasts chosen by developers are giving way to measured, dynamic and jurisdictional baselines.

Three families of baselines

Baselines by project type

ARR. The baseline is the vegetation change that would happen without planting. Under VM0047’s area-based approach, a dynamic performance benchmark compares the change in a vegetative stocking index in project plots with matched control plots, using remote sensing. This sets both additionality and the crediting baseline at each verification. Natural regrowth on control land lowers credits.

REDD+. The baseline is deforestation and degradation that would have happened. Under VM0048, Verra sets jurisdictional activity data and allocates a share to each project using a risk map. Read jurisdictional allocation.

IFM. The baseline is the forest management that would have occurred, either from plans or matched inventory data.

Blue carbon and peat. The baseline describes continued degradation, drainage or erosion, with limits on how long soil carbon losses can continue.

How to set a baseline step by step

1. Read the methodology. Identify which baseline approach it requires and what data it needs.
2. Define the project area and strata.
3. Gather historical data. Land cover change maps, forest inventories, permits, plans.
4. Identify agents and drivers. Who changes land, and why.
5. Select reference or control areas that match the project on key characteristics, where required.
6. Build the baseline model following the methodology. Keep every input sourced.
7. Test conservativeness. Run sensitivity analysis on key assumptions.
8. Plan reassessment. Know when and how the baseline will be revisited.

Indonesian data sources

• National land cover and deforestation data published by the forestry ministry, consistent with the national forest reference level.
• Global datasets such as tree cover loss maps, used carefully because definitions differ.
• Concession and permit maps for planned deforestation or logging baselines. Read PBPH licences.
• Peat maps and hydrology data for peat baselines.
• Field plots for carbon stocks in each stratum.

Consistency with national data matters more each year. Indonesia’s FOLU Net Sink 2030 target and national reference level frame how authorities view project baselines. Read FOLU Net Sink 2030.

Pitfalls verifiers flag

• Reference regions that do not match the project on access, slope, tenure or drivers.
• Historical periods chosen to maximise deforestation rates.
• Planned deforestation baselines without evidence that conversion was legal and likely.
• Control plots in areas affected by the project.
• Baseline inputs without sources.
• Ignoring natural regeneration in ARR.

Baseline reassessment

Baselines are not permanent. Standards require periodic reassessment, and methodologies may change in the meantime. At reassessment a project must apply the rules in force, which can lower future credits. Build this into financial models. Keep historical data and models archived so that the reassessment can show what changed and why. A project that has collected good plot and land cover data throughout its life is in a much stronger position at reassessment than one that relied on defaults. Discuss expected reassessment outcomes with buyers before signing long-term offtake agreements.

Common baseline mistakes in Indonesian ARR

Two mistakes recur in Indonesian ARR. First, ignoring natural regrowth on fallow or abandoned land, which a dynamic benchmark will capture anyway. Second, treating every non-forest parcel the same, when some are on track to become oil palm and others to regrow. Stratify by realistic land-use trajectory.

Why baselines drive price

The REDD+ baseline controversy showed that baselines decide whether credits are trusted. Rating agencies weigh baseline risk heavily, and BeZero’s ratings show prices rise about 40% per rating band. A conservative, transparent baseline earns fewer credits but often more revenue per credit. Read additionality.

Our carbon modelling service builds baselines that follow methodology rules and national data. See the standards hub.