A fuel consumption-based method for developing local-specific CO₂ emission rate database using open-source big data

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.