Evaluating the impact of REDD+ Carbon Credit Projects using Satellite Data

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

Required:

Strong R coding skills
Causal inference/econometric training

Desired:

Background working with spatial data
Knowledge of machine learning methods
Interest in environmental applications