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Wang et al. Carbon Footprints 2024;3:14 https://dx.doi.org/10.20517/cf.2024.19 Page 11 of 19

particularly in the city center. Despite lower total emissions, the city center exhibits higher emission
intensity, indicating a larger number of emissions per unit area.

Spatial distribution characteristics of CO emissions by sector
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The spatial distribution characteristics of emissions from various sectors are presented in Figure 6.
Figure 6A-C depict the spatial distribution of point source emissions for industrial energy emissions,
industrial processing emissions, and service emissions, respectively. Due to the high density of point sources
in industrial energy and service emissions, these were subjected to gridding. The industrial energy emissions
comprise a total of 5,335 point source emissions. High-emission point sources are primarily concentrated in
the areas surrounding the city center. Shuangliu District has the highest number of point sources, totaling
646, followed by Pidu District with 598 and the adjacent Xindu District with 538. Analysis of industrial
energy emissions by district reveals that Jintang County, home to a power plant, has the highest emissions at
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3,727 kt·yr . In contrast, Shuangliu District, despite having the largest number of point sources, has
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emissions of only 627 kt·yr , ranking seventh. This indicates that the key determinant of regional industrial
energy emissions is not the number of emitting enterprises but the industrial structure of this region. In
contrast to industrial energy emissions, the number of point sources for industrial processing emissions is
significantly lower. There are five point sources from the cement production process, two from the
limestone production process, and four from the steel production process. The cement production process
is the largest contributor to emissions of this type. The top three emitters collectively contributed the
majority of emissions, with a total of 6,831 kt·yr . The emissions from the limestone production process are
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similar to those from the cement production process. However, due to the lower production volume of
limestone compared to cement, the emissions are significantly lower than that of cement. Service emissions
are more widely distributed, primarily concentrated in the city center and the central areas of the various
subordinate cities and counties. The total number of service point sources is approximately 66,000.

The spatial distribution characteristics of non-point source emissions, which are indicated in Figure 6D,
Figure 4E and F, mainly arise from residents, agriculture, and transportation emissions. The spatial
distribution of residents’ emissions [Figure 6D] is entirely dependent on the population distribution. High-
emission areas are concentrated in the city center, with the highest emission intensity reaching
8,655 kt·km ·yr . Agriculture emissions [Figure 6E] are predominantly found in the plain areas on the
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outskirts of the urban, mainly on the southern and eastern sides. The western region, affected by the
Qinghai-Tibet Plateau, is mostly mountainous terrain and unsuitable for agricultural cultivation, thus
resulting in virtually no agriculture emissions. Transportation emissions [Figure 6F], as a major non-point
source, are widely distributed, with the highest emission intensity reaching 13,964 kt·km ·yr . They are
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influenced by road network density and traffic volume. Highways have high traffic volume but lower road
network density, resulting in lower emission intensity compared to secondary roads with higher road
network density in the city center. The distribution characteristics conform to the pattern of high emission
intensity in the city center, radiating outward.

Estimation and analysis of proxy data uncertainty
Due to the lack of uncertainty estimation in the tabular inventory provided by CCCED, this study only
addressed uncertainties resulting from proxy data deficiencies. The primary focus focused on the
uncertainties associated with four major emission sectors: industrial energy, industrial processing,
transportation, and residents. For the first two sectors, the locations of high-emission point sources were
manually verified, ensuring the location information was absolutely accurate and resulting in the
uncertainty of 0 for industrial energy and industrial processing proxy data.