{"ID":2864201,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02359","arxiv_id":"2510.02359","title":"Emission-GPT: A domain-specific language model agent for knowledge retrieval, emission inventory and data analysis","abstract":"Improving air quality and addressing climate change relies on accurate understanding and analysis of air pollutant and greenhouse gas emissions. However, emission-related knowledge is often fragmented and highly specialized, while existing methods for accessing and compiling emissions data remain inefficient. These issues hinder the ability of non-experts to interpret emissions information, posing challenges to research and management. To address this, we present Emission-GPT, a knowledge-enhanced large language model agent tailored for the atmospheric emissions domain. Built on a curated knowledge base of over 10,000 documents (including standards, reports, guidebooks, and peer-reviewed literature), Emission-GPT integrates prompt engineering and question completion to support accurate domain-specific question answering. Emission-GPT also enables users to interactively analyze emissions data via natural language, such as querying and visualizing inventories, analyzing source contributions, and recommending emission factors for user-defined scenarios. A case study in Guangdong Province demonstrates that Emission-GPT can extract key insights--such as point source distributions and sectoral trends--directly from raw data with simple prompts. Its modular and extensible architecture facilitates automation of traditionally manual workflows, positioning Emission-GPT as a foundational tool for next-generation emission inventory development and scenario-based assessment.","short_abstract":"Improving air quality and addressing climate change relies on accurate understanding and analysis of air pollutant and greenhouse gas emissions. However, emission-related knowledge is often fragmented and highly specialized, while existing methods for accessing and compiling emissions data remain inefficient. These iss...","url_abs":"https://arxiv.org/abs/2510.02359","url_pdf":"https://arxiv.org/pdf/2510.02359v1","authors":"[\"Jiashu Ye\",\"Tong Wu\",\"Weiwen Chen\",\"Hao Zhang\",\"Zeteng Lin\",\"Xingxing Li\",\"Shujuan Weng\",\"Manni Zhu\",\"Xin Yuan\",\"Xinlong Hong\",\"Jingjie Li\",\"Junyu Zheng\",\"Zhijiong Huang\",\"Jing Tang\"]","published":"2025-09-28T07:50:05Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}