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Page 4 of 19 Wang et al. Carbon Footprints 2024;3:14 https://dx.doi.org/10.20517/cf.2024.19

MATERIAL AND METHODS
Study area
Chengdu, the capital of Sichuan Province in southwestern China [Figure 1], is strategically located in the
fertile Chengdu Plain, surrounded by mountains. With a population of over 20 million, it ranks among
China's largest and most vibrant cities. While renowned for its advanced high-tech industries, Chengdu also
hosts some high-emission sectors, such as cement manufacturing. Its extensive road network enhances
regional connectivity and logistics but also contributes to significant traffic-related carbon emissions.

Data sources and data collection
As the foundation of this research, the data collection and screening process was particularly indispensable.
To ensure the smooth progress of subsequent research, data collection was divided into two parts: tabular
CO emission inventory and spatial proxy data. Additionally, only publicly available data were collected to
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facilitate the future dissemination and application of this methodology. Figure 2 illustrates the datasets
required for this study, along with the corresponding spatial proxy data used for the spatial allocation of
emission quantities across various sectors.

The tabular CO emission inventory was sourced from the “China City Carbon Dioxide Emission Dataset
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(CCCED)” , which provides CO emissions from various sectors, including agriculture, services, industry,
[41]
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residential, and transportation. We selected appropriate spatial proxy data that match the characteristics of
each sector for emission allocation. Detailed corresponding proxy data of each sector are listed in Table 1.
Due to the lack of a distinct classification of industrial energy and industrial processing, the emissions for
individual industry sectors could not be represented accurately, resulting in significant uncertainty in point
source emissions. Consequently, it was necessary to reclassify the emission sectors. Detailed emission data
processing and reclassification can be found in Supplementary Material Section 1. After reclassification, CO
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emissions by each sector area are shown in Figure 3, where dashed circular arcs represent point source
emissions, and solid circular arcs indicate non-point sources. According to CCCED, the total CO emissions
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of Chengdu in 2020 were 4.277 million tons. The transportation sector was the largest contributor, with
emissions reaching 15.47 million tons, representing 36.2% of the total emissions. Due to the electrification
transition of China’s railways transportation, emissions could be considered negligible. Air transportation
emissions were estimated based on aviation fuel consumption; however, this method could not account for
the fuel consumption for flights within Chengdu’s jurisdiction. Therefore, this study did not analyze air
transportation emissions. The second and third largest emission sources were the industrial energy and
industrial processing sectors, with emissions of 11.82 and 8.22 million tons, respectively, accounting for
27.6% and 19.2% of the total emissions.

The selection of proxy data was influenced by the ease of data acquisition, the correlation between the proxy
data and the emission sources, and the accuracy of the proxy data. The proxy data for point source
emissions mainly included latitude and longitude coordinates and emission scale of emitting enterprises
and service points of interest (POI). For non-point source emissions, the data included population
distribution, land cover, and road networks.

As the proxy data of point sources, the location information of the enterprises and service POI were
obtained from the API interface of the online map platform. Industrial point sources, as the major emission
sources, included a total of 49,350 industrial POIs from the online map platform. These data contained
three key attributes: name, type, and geographic coordinates. Subsequently, the registration information for