| Summary: | Transport emissions, including road, rail, air, and marine transportation, account for a large part of the overall emissions; hence, there is a need to review strategies for managing associated issues and coping with negative impacts. A simultaneous improvement in economic efficiency can help us achieve our desired objectives in the concerned context. Sharing economy, i.e., a peer-to-peer-based sharing of access to assets, can help reduce the total resources required and consequently reduce carbon footprints. In line with this objective, we propose an intelligent model to study carbon dioxide emissions from road transport using taxi trips in Dublin, Ireland. The proposed method is a hybrid unsupervised learning approach tailored for the particular structure of the problem. We present how an intelligent approach can be implemented to model CO2 emissions from road transport. The model categorizes taxis based on different features related to the emissions they release. Five clusters are detected, which can be attributed to varying levels of emissions. Accordingly, those vehicles labeled as the highest emitters can be targeted for further improvements in reducing CO2, i.e., replacing pollutant cars with electric cars or including them in the taxi fleet as sharing ones only.
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