LLM-Driven Estimation of Personal Carbon Footprint from Dialogues

AbstractPersonal 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 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.