Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation Article Swipe
H. K. Wang
,
Mianrong Zhang
,
Zheng Chen
,
Nan Shang
,
Shangheng Yao
,
Fushuan Wen
,
Junhua Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.09713
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.09713
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.09713
- https://arxiv.org/pdf/2408.09713
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403006699
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403006699Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.09713Digital Object Identifier
- Title
-
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented GenerationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-19Full publication date if available
- Authors
-
H. K. Wang, Mianrong Zhang, Zheng Chen, Nan Shang, Shangheng Yao, Fushuan Wen, Junhua ZhaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2408.09713Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2408.09713Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2408.09713Direct OA link when available
- Concepts
-
Carbon footprint, Footprint, Carbon accounting, Computer science, Environmental science, Accounting, Economics, Greenhouse gas, Geography, Geology, Oceanography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Traditional | 31 |
| abstract_inverted_index.accounting, | 119 |
| abstract_inverted_index.acquisition | 117 |
| abstract_inverted_index.cost-saving | 185 |
| abstract_inverted_index.deviations. | 166 |
| abstract_inverted_index.information | 71, 97, 116, 154, 160 |
| abstract_inverted_index.integrating | 51 |
| abstract_inverted_index.management, | 191 |
| abstract_inverted_index.outperforms | 146 |
| abstract_inverted_index.quantifying | 6 |
| abstract_inverted_index.traditional | 147 |
| abstract_inverted_index.Experimental | 128 |
| abstract_inverted_index.LLMs-RAG-CFA | 91, 144 |
| abstract_inverted_index.challenging. | 44 |
| abstract_inverted_index.economically | 168 |
| abstract_inverted_index.necessitates | 26 |
| abstract_inverted_index.professional | 96, 110 |
| abstract_inverted_index.capabilities, | 87 |
| abstract_inverted_index.environmental | 194 |
| abstract_inverted_index.photovoltaic, | 136 |
| abstract_inverted_index.professional, | 64 |
| abstract_inverted_index.significantly | 158 |
| abstract_inverted_index.understanding | 81 |
| abstract_inverted_index.carbon-related | 20 |
| abstract_inverted_index.cost-effective | 121 |
| abstract_inverted_index.near-real-time | 42 |
| abstract_inverted_index.neutrality.The | 13 |
| abstract_inverted_index.sustainability | 195 |
| abstract_inverted_index.industries(primary | 132 |
| abstract_inverted_index.cost-effectiveness, | 179 |
| abstract_inverted_index.retrieval-augmented | 57 |
| abstract_inverted_index.transformers)demonstrate | 141 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |