{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162071"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162071","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Analyzing Risks in Voluntary Forest Carbon Offsets Using Open Data: A Hybrid Framework Integrating Retrieval-Augmented Generation in LLMs and Geospatial Analytics","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). 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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. 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