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AI agent for carbon emission accounting and uncertainty analysis of Shanghai's electricity consumption
- Truvace record
- TRV-2026-2625raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- 7c14962817d1…check it
Accurate carbon emission accounting underpins decarbonization strategy development. However, the current manual methods for carbon emission accounting and uncertainty analysis are tedious, time-consuming, and costly. To solve these problems, an AI agent for carbon emission accounting consisting of reasoning-capable large language models (LLMs) and external tools was developed in this study. Using Shanghai's electricity consumption as a case study, the agent was adopted to quantify the electricity's carbon emissi…
The gain
In a Shanghai case study, a quality control framework of three LLMs scored higher accuracy than any single LLM at judging raw-data uncertainty across ten independent runs, automating a manual carbon accounting step.
What this doesn’t fix
The reliability and reproducibility results come from a single case study of Shanghai's electricity consumption, so performance elsewhere is untested.
Sources
1 on file- Journal of Cleaner Productiondoi:10.1016/j.jclepro.2026.148689
Cite this record
v1Truvace Impact Record TRV-2026-2625, v1: “AI agent for carbon emission accounting and uncertainty analysis of Shanghai's electricity consumption.” Truvace, 2026-11-12. /record/TRV-2026-2625 (accessed on the day you copy it). sha256 7c14962817d1cea1…
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