Analyzing Risks in Voluntary Forest Carbon Offsets Using Open Data: A Hybrid Framework Integrating Retrieval-Augmented Generation in LLMs and Geospatial Analytics
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xu-zx96-mcp-dusp-2025-thesis.pdf
Description
Thesis PDF
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1.83 MB
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Adobe PDF
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4f05768e2272a691913866442b356058
Author(s)
Xu, Ziqing (Becky)
Advisor(s)
D'Ignazio, Catherine
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
The credibility of voluntary carbon markets hinges on the quality of carbon offset projects, particularly in forestry and land-use sectors where claims of additionality and emissions reductions are often disputed. This paper introduces a novel, open-source approach to evaluating carbon offset projects by integrating open datasets, satellite-based remote sensing, and large language models (LLMs). Focusing on additionality and baseline integrity, the study examines existing challenges—including inflated baselines, inconsistent standards, leakage risks, and limited transparency—and proposes a system to automate early-stage project assessment. The platform combines AI-driven document analysis and geospatial data processing to evaluate risk factors such as additionality, leakage, and policy compliance, offering stakeholders an accessible, scalable tool to identify high-integrity carbon credits and mitigate greenwashing. This work aims to enhance transparency, accountability, and trust in the voluntary carbon market through data-driven, user-friendly decision support.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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