In this project we will gather information on existing and previously completed carbon credit projects registered under the REDD+ framework and evaluate how much carbon was conserved by applying a variety of causal inference methods, including matching, diff-in-diff, synthetic controls, and others to understand:
- How much carbon was truly conserved compared to how much was claimed by the project.
- How different methods and assumptions influence the generated estimates.
The overall goal of the project is to work toward a general methodology that can be applied to improve future carbon credit projects.
Requisite Skills and Qualifications:
- Strong R coding skills
- Causal inference/econometric training
- Background working with spatial data
- Knowledge of machine learning methods
- Interest in environmental applications