Emission-GPT: A domain-specific language model agent for knowledge retrieval, emission inventory and data analysis
This addresses inefficiencies in accessing and compiling emissions data for non-experts, facilitating research and management in air quality and climate change, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of fragmented and specialized emission-related knowledge by introducing Emission-GPT, a domain-specific language model agent that integrates a curated knowledge base to support accurate question answering and interactive data analysis, demonstrated in a case study in Guangdong Province to extract insights like point source distributions from raw data.
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.