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To help systematically lower anthropogenic Greenhouse gas (GHG) emissions,accurate and precise GHG emission prediction models have become a key focus ofthe climate research. The appeal is that the predictive models will informpolicymakers, and hopefully, in turn, they will bring about systematic changes.Since the transportation sector is constantly among the top GHG emissioncontributors, especially in populated urban areas, substantial effort has beengoing into building more accurate and informative GHG prediction models to helpcreate more sustainable urban environments. In this work, we seek to establisha predictive framework of GHG emissions at the urban road segment or link levelof transportation networks. The key theme of the framework centers around modelinterpretability and actionability for high-level decision-makers usingeconometric Discrete Choice Modelling (DCM). We illustrate that DCM is capableof predicting link-level GHG emission levels on urban road networks in aparsimonious and effective manner. Our results show up to 85.4% predictionaccuracy in the DCM models' performances. We also argue that since the goal ofmost GHG emission prediction models focuses on involving high-leveldecision-makers to make changes and curb emissions, the DCM-based GHG emissionprediction framework is the most suitable framework.

Interpretable and Actionable Vehicular Greenhouse Gas Emission Prediction at Road link-level