Uncertainties in deforestation emission baseline methodologies and implications for carbon markets

Area of Science:

• Environmental Science
• Climate Change Research
• Forestry Management

Background:

• Jurisdictional-scale avoided deforestation projects generate carbon credits.
• Accurate deforestation emission baselines are crucial for these projects' integrity.
• Current baselining methods face significant robustness challenges.

Purpose of the Study:

• To assess the variability, accuracy, and uncertainty of deforestation emission baselining methods.
• To identify key drivers of uncertainty in baseline estimations.
• To provide recommendations for improving baseline accuracy and carbon market trust.

Main Methods:

• Applied sensitivity and variable importance analysis to typically-used baselining methods and parameters.
• Analyzed data from 2,794 jurisdictions worldwide.
• Evaluated forecast errors and uncertainty levels across different methods and biomes.

Main Results:

• The median jurisdiction's deforestation emission baseline varied by 171% (90% range: 87%-440%) of its mean.
• Median forecast error was 0.778 times (90% range: 0.548-3.56) the actual deforestation rate.
• 68.0% of methods exceeded 15% uncertainty for the median jurisdiction; tropical and polar biomes showed higher uncertainties.

Conclusions:

• Deforestation projection approach strongly influences baseline uncertainty.
• Sensitivity analyses and ensemble approaches can reduce variability and bias.
• Improving baseline estimations is vital for enhancing carbon market integrity and trust.