1. Bayesian state-space synthetic control method for deforestation baseline estimation for forest carbon credits
- Author
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Keisuke Takahata, Hiroshi Suetsugu, Keiichi Fukaya, and Shinichiro Shirota
- Subjects
Bayesian modeling ,causal inference ,state-space modeling ,carbon credit ,REDD+ ,Environmental sciences ,GE1-350 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Carbon credits from the reducing emissions from deforestation and degradation (REDD+) projects have been criticized for issuing junk carbon credits due to invalid ex-ante baselines. Recently, the concept of ex-post baseline has been discussed to overcome the criticism, while ex-ante baseline is still necessary for project financing and risk assessment. To address this issue, we propose a Bayesian state-space model that integrates ex-ante baseline projection and ex-post dynamic baseline updating in a unified manner. Our approach provides a tool for appropriate risk assessment and performance evaluation of REDD+ projects. We apply the proposed model to a REDD+ project in Brazil and show that it may have had a small, positive effect but has been overcredited. We also demonstrate that the 90% predictive interval of the ex-ante baseline includes the ex-post baseline, implying that our ex-ante estimation can work effectively.
- Published
- 2024
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