Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation
<|reference_start|>Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation: Carbon footprint accounting is crucial for quantifying greenhouse gas emissions and achieving carbon neutrality.The dynamic nature of processes, accounting rules, carbon-related policies, and energy supply structures necessitates real-time updates of CFA. Traditional life cycle assessment methods rely heavily on human expertise, making near-real-time updates challenging. This paper introduces a novel approach integrating large language models (LLMs) with retrieval-augmented generation technology to enhance the real-time, professional, and economical aspects of carbon footprint information retrieval and analysis. By leveraging LLMs' logical and language understanding abilities and RAG's efficient retrieval capabilities, the proposed method LLMs-RAG-CFA can retrieve more relevant professional information to assist LLMs, enhancing the model's generative abilities. This method offers broad professional coverage, efficient real-time carbon footprint information acquisition and accounting, and cost-effective automation without frequent LLMs' parameter updates. Experimental results across five industries(primary aluminum, lithium battery, photovoltaic, new energy vehicles, and transformers)demonstrate that the LLMs-RAG-CFA method outperforms traditional methods and other LLMs, achieving higher information retrieval rates and significantly lower information deviations and carbon footprint accounting deviations. The economically viable design utilizes RAG technology to balance real-time updates with cost-effectiveness, providing an efficient, reliable, and cost-saving solution for real-time carbon emission management, thereby enhancing environmental sustainability practices.<|reference_end|>
@article{wang2024carbon,
    title={Carbon Footprint Accounting Driven by Large Language Models and
  Retrieval-augmented Generation},
    author={Haijin Wang, Mianrong Zhang, Zheng Chen, Nan Shang, Shangheng Yao,
  Fushuan Wen, Junhua Zhao},
    journal={arXiv preprint arXiv:2408.09713},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09713},
    primaryClass={cs.IR}
}