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- Title
A fuel consumption-based method for developing local-specific CO<sub>2</sub> emission rate database using open-source big data.
- Authors
Li, Linheng; Wang, Can; Gan, Jing; Zhang, Dapeng
- Abstract
Emission data collection has always been a significant burden and challenge for Chinese counties to develop a CO2 emission inventory. This paper proposed a fuel consumption-based method to develop a local-specific CO2 emission rate database for Chinese counties using only open-source big data. Localized vehicle fuel consumption data is obtained through natural language processing (NLP) algorithm and large language model (LLM). The emission rates derived by our proposed method are consistent with field test results in literature. Besides, the CO2 emission estimation results using local-specific traffic activity data indicate that our method could effectively improve the accuracy of vehicle emission assessment. Compared with conventional method, the novel approach proposed in this paper can provide a pathway for convenient, universal, and cost-saving assessment for local scale CO2 emission rates. With this method, it is possible to formulate a local-specific CO2 emission database in various Chinese counties using only open-access big data.
- Subjects
DATABASES; BIG data; LANGUAGE models; NATURAL language processing; ENERGY consumption; NONRELATIONAL databases
- Publication
Journal of Big Data, 2024, Vol 11, Issue 1, p1
- ISSN
2196-1115
- Publication type
Article
- DOI
10.1186/s40537-024-00932-7