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Page 6 of 19 Wang et al. Carbon Footprints 2024;3:14 https://dx.doi.org/10.20517/cf.2024.19

Figure 3. Reclassified CO emissions by sectors, including 6 categories and 17 types.
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[42]
these POIs was retrieved from TianYanCha , an enterprise information query platform. The detailed
retrieval process is shown in Supplementary Material Section 2. A total of 5,335 emission point sources from
relevant enterprises were obtained by screening the business scope of the enterprises.

The proxy data required for the spatial allocation of non-point source emissions [Table 2] encompassed
population counts, nighttime light data, road networks, road traffic volume, waterways, ship tracking
[43]
intelligence, and land cover. Population counts were downloaded from WorldPop Hub with a spatial
resolution of 100 m. Nighttime light data were obtained from VIIRS Nighttime Light . The road networks
[44]
[45]
and waterways obtained from OpenStreetMap (OSM) were used to calculate “road density” and
“waterways density”. Road traffic volume, obtained according to standards [46,47] , was applied to calculate
[48]
traffic density within each grid. Waterways supplemented with ship tracking intelligence could identify
the shipping routes accurately. Land cover data utilized for the spatial allocation of agricultural emissions
[49]
were sourced from NASA MODIS production . The detailed proxy data are shown in
Supplementary Material Section 3.

Spatial allocation
The spatial allocation of CO was based on the ArcGIS platform. Firstly, grids with a spatial resolution of
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1 km were established over the study area. This resolution was selected to balance detail and computational
efficiency, providing sufficient spatial accuracy to describe the heterogeneity in emissions while remaining
manageable in terms of computational load. Observations from satellite imagery further supported this
choice, confirming that the top 500 point sources, which account for 89.59% of total emissions,
predominantly occupied areas smaller than 1 km , with only a few extremely high-emission sources
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exceeding this size. Then, the data extraction function of ArcGIS was used to extract proxy data including
population, road network, latitude and longitude of emission enterprises. Subsequently, the data statistics
and management functions of ArcGIS were applied to allocate the emission from several sectors to the
corresponding target grids in the following ways. By implementing these detailed procedures, we ensured a
precise and systematic allocation of CO across the study area, enhancing the accuracy and reliability of the
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grided emission inventory.