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Dynamic baselines with blended BAU scenarios ensure conservative mitigation estimates for improved forest management accounting

Published Article

United States

Publication date: October 7, 2026

File format: URL

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Subject Tags

  • Forests
  • Natural climate solutions
  • Carbon markets

Abstract

Introduction: 

Improved forest management (IFM) is vital for climate change mitigation. Yet, precise carbon accounting to ensure credible attribution is a challenge. Current accounting protocols vary in their analytical approaches, leading to substantial differences in mitigation outcomes.

Methods:

We evaluated four common decision points in baseline design—modeled versus measured, static versus dynamic, matched versus random, and single versus blended business-as-usual (BAU) scenarios— across diverse regions and forest types in the eastern United States (US) using Forest Inventory and Analysis (FIA) and the Forest Vegetation Simulator (FVS).

Results:

Dynamic baselines that integrate blended BAUs produced, on average, 63% more conservative estimates of net climate mitigation benefits than static, single BAU models, highlighting how baseline design choices can substantially influence results, with estimated mitigation varying by more than fivefold across accounting approaches, regions, and forest types. Stand characteristics, such as stand density, also affected outcomes.

Conclusion:

Since the true BAU counterfactual cannot be directly observed, adopting dynamic baselines with blended BAU scenarios can reduce the risk of overestimation, strengthen the credibility of carbon accounting, support more informed investment in IFM projects, and reinforce the integrity of carbon accounting for natural climate solutions.

Citation

Chamberlain CJ, Riley L, Busby SU, Russell M, Austin KG, Campbell R, Gunn JS, Johnson T, Martin DM, Rifkin B, Shoch D and Belair EP (2026) Dynamic baselines with blended BAU scenarios ensure conservative mitigation estimates for improved forest management accounting. Front. For. Glob. Change 9:1940074. doi: 10.3389/ffgc.2026.1940074

TNC Authors