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Page 12 of 19 Wang et al. Carbon Footprints 2024;3:14 https://dx.doi.org/10.20517/cf.2024.19

Figure 6. Spatial allocation of emissions by each sector: (A) Industrial energy emission; (B) Industrial processing emission; (C) Service
emission; (D) Residents emission; (E) Agriculture emission; (F) Transportation emission.

The uncertainties for the two main non-point source emissions were estimated using the method described
in previous sections. They were aggregated to obtain absolute [Figure 7A] and relative [Figure 7B] proxy
data uncertainties. The analysis revealed a strong correlation between the spatial distribution of absolute
uncertainty and emissions, with higher uncertainty in high-emission areas and lower uncertainties in low-
emission areas. The highest uncertainty recorded was 712 t. The relative uncertainty distribution indicated
lower values in the city center and higher values in suburban areas. Overall, the uncertainty across the study
area was relatively low and fell within acceptable limits.

Comparison results and analysis of gridded emission inventories
The processed HEI-CD model was compared and analyzed against the widely used EDGAR inventory,
which was commonly applied in city-scale CO emission inversion. Due to the substantial variance in spatial
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resolution between the two inventories, with the HEI-CD model operating at a resolution of 1 km and
EDGAR inventory at 0.1° (approximately 9.6 km at Chengdu’s latitude), direct overlay comparison was
unfeasible. To facilitate comparison, the HEI-CD model would be upscaled and resampled onto grids with a
resolution of 0.1°, aligning with the spatial resolution of the EDGAR inventory. This process would yield a