@inproceedings{li-etal-2025-llm-driven,
    title = "{LLM}-Driven Estimation of Personal Carbon Footprint from Dialogues",
    author = "Li, Shuqin  and
      Du, Huifang  and
      Wang, Haofen",
    editor = "Dutia, Kalyan  and
      Henderson, Peter  and
      Leippold, Markus  and
      Manning, Christoper  and
      Morio, Gaku  and
      Muccione, Veruska  and
      Ni, Jingwei  and
      Schimanski, Tobias  and
      Stammbach, Dominik  and
      Singh, Alok  and
      Su, Alba (Ruiran)  and
      A. Vaghefi, Saeid",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
    month = jul,
    year = "2025",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.climatenlp-1.20/",
    pages = "278--287",
    ISBN = "979-8-89176-259-6",
    abstract = "Personal Carbon Footprint (PCF) Estimation is crucial for raising individual environmental awareness by linking daily activities to their environmental impact. However, existing tools are limited by fragmented scenarios and labor-intensive manual data entry. We present PCCT, an LLM-powered system that combines conversational understanding with emission knowledge grounding for PCF Estimation. We address two key challenges: (1) resolving incomplete activity information across turns through knowledge-guided and context-aware tracking, and (2) accurately mapping emission factors using multi-step LLM inference and vector-based similarity search. The system dynamically combines knowledge-guided activity extraction, and context-aware memory management, generating accurate carbon footprint estimates. We validate the effectiveness with the \textit{CarbonDialog-1K} benchmark, comprising 1,028 annotated user activity narratives. Experimental results demonstrate that our method outperforms baseline systems in accuracy, while subjective evaluations show superior appropriateness, usability, efficiency, and naturalness."
}