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Workshop: Tackling Climate Change with Machine Learning

Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit

Keisuke Takahata · Hiroshi Suetsugu · Keiichi Fukaya · Shinichiro Shirota


In forest carbon credit, the concept of dynamic (or ex-post) baseline has been discussed to overcome the criticism of junk carbon credit, while an ex-ante baseline is still necessary in terms of project ļ¬nance and risk assessment. We propose a Bayesian state-space SCM, which integrates both ex-ante and ex-post baseline estimation in a time-series causal inference framework. We apply the proposed model to a REDD+ project in Brazil, and show that it might have had a small, positive effect but had been over-credited and that the 90% predictive interval of the ex-ante baseline included the ex-post baseline, implying our ex-ante estimation can work effectively.

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