2026-09-18T18:17:20Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1620712025-07-30T03:07:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

D'Ignazio, Catherine Xu, Ziqing (Becky) Massachusetts Institute of Technology. Department of Urban Studies and Planning 2025-07-29T17:16:14Z 2025-07-29T17:16:14Z 2025-05 2025-06-05T13:42:58.198Z https://hdl.handle.net/1721.1/162071 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. M.C.P. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Analyzing Risks in Voluntary Forest Carbon Offsets Using Open Data: A Hybrid Framework Integrating Retrieval-Augmented Generation in LLMs and Geospatial Analytics Thesis application/pdf Master Master in City Planning Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f92a8e26-8db8-46d3-a204-6a1bde96552b"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Analyzing Risks in Voluntary Forest Carbon Offsets Using Open Data: A Hybrid Framework Integrating Retrieval-Augmented Generation in LLMs and Geospatial Analytics</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Xu, Ziqing (Becky)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>