  To help systematically lower anthropogenic Greenhouse gas (GHG) emissions,
accurate and precise GHG emission prediction models have become a key focus of
the climate research. The appeal is that the predictive models will inform
policymakers, and hopefully, in turn, they will bring about systematic changes.
Since the transportation sector is constantly among the top GHG emission
contributors, especially in populated urban areas, substantial effort has been
going into building more accurate and informative GHG prediction models to help
create more sustainable urban environments. In this work, we seek to establish
a predictive framework of GHG emissions at the urban road segment or link level
of transportation networks. The key theme of the framework centers around model
interpretability and actionability for high-level decision-makers using
econometric Discrete Choice Modelling (DCM). We illustrate that DCM is capable
of predicting link-level GHG emission levels on urban road networks in a
parsimonious and effective manner. Our results show up to 85.4% prediction
accuracy in the DCM models' performances. We also argue that since the goal of
most GHG emission prediction models focuses on involving high-level
decision-makers to make changes and curb emissions, the DCM-based GHG emission
prediction framework is the most suitable framework.
