Identifying and mapping local contributions of carbon emissions from urban motor and metro transports: A weighted multiproxy allocating approach

Regional allocation of transport carbon emissions is increasingly important to meet global or national CO₂ reduction targets. Limited by scaling and data uncertainties, geographical allocation of carbon-emitting responsibilities or burdens at local or fine scales has not been well documented. In this regard, after estimating total carbon emissions from urban motor and metro transports, we proposed a multiproxy allocation system that included a series of transport-related demand and supply indicators. On the basis of urban high-resolution data, magnitudes of gridded proxies in fine scales were aggregated into each local administrative region (subdistrict and town) by a bottom-up approach. Then, weights of these indicators were calculated through an integration of Grey Relational Analysis with Fuzzy Logic. Finally, using the practical scenario of Wuhan (China), we allocated total carbon-emitting quantities from the Wuhan metropolis down to local units by using a top-down approach. Local carbon-emitting contributions and their variations were further identified and mapped from total, per capita, and per unit perspectives. We have not only shown this allocative approach to be effective and applicable, but have also depicted spatially similar patterns and evolutions under the three carbon-emitting indicators. These depictions include local inequality and polarization, core-peripheral structure, place-dependence on initial location, and spatial locking-in effect and diffusive trends along metro lines. Additionally, spatial differences between the per capita carbon emission and the others are revealed as well.