Since over 70% of carbon emissions are from urban areas, it is of great importance to develop an effective measurement technique that can accurately monitor atmospheric CO2 in global urban areas. Remote sensing could be an effective way to achieve this goal. However, due to high aerosol loading in urban areas, there are large, inadequately resolved areas in the CO2 products acquired by passive remote sensing. China is planning to launch the Atmospheric Environment Monitoring Satellite (AEMS) equipped with a CO2-light detecting and ranging (LIDAR) system. This work conducted a feasibility study on obtaining city-scale column CO2 volume mixing ratios (XCO2) using the LIDAR measurements. A performance framework consisting of a sensor model, sampling model, and environmental model was proposed to fulfill our demand. We found that both the coverage and the accuracy of the LIDAR-derived city-scale XCO2 values were highly dependent on the orbit height. With an orbit height of 450 km, random errors of less than 0.3% are expected for all four metropolitan areas tested in this work. However, random errors of less than 0.3% were obtained in only two metropolitan areas with an orbit height of 705 km. Our simulations also showed that off-nadir sampling would improve the performance of a CO2-Integrated Path Differential Absorption (IPDA) LIDAR system operating in a 705 km orbit. These results indicate that an active remote sensing mission could help to effectively measure XCO2 values in urban areas. More detailed studies are needed to reveal the potential of such equipment for improving the verification of carbon emissions and the estimation of urban carbon fluxes.