Page 51 - Read Online

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

strategies at the city scale. CO emission flux inversion relies on gridded emission inventories as prior
2
information. These inventories provide the spatial resolution required to relate emissions to atmospheric
concentrations using atmospheric transport models [9,25] . Large errors in the spatial distribution and sectoral
categorization of these prior inventories would cause significant biases in the posterior estimates, which can
further mislead the interpretation of the posterior results. Therefore, a gridded emission inventory that
represents our best knowledge of the spatial distribution is required for successfully deriving city-scale
carbon emissions from a carbon monitoring network.

The Emission Database for Global Atmospheric Research (EDGAR) global emission inventory is often used
as prior information due to its unique comprehensive global inventory, detailed spatial information at a fine
resolution of 0.1° × 0.1°, and extensive data on greenhouse gas (GHG) emissions from energy production,
industrial processes, waste disposal, and biomass burning since 1970 [26-28] . These high-resolution data are
important for accurately representing the spatial distribution of emissions in global atmospheric models and
[29]
inversion studies . However, several studies have demonstrated that EDGAR inventory, due to its biases in
spatial information and emission estimates, could introduce substantial errors into posterior results when
used as prior information. Hu et al. (2022) highlighted that the accuracy of power plant locations in the
prior information has a substantial impact on the estimated city CO emissions flux in the study on CO
2
2
emissions in Nanchang, Jiangxi Province, China . Maasakkers et al. (2019) discussed the impact of using
[30]
EDGAR v4.3.2 inventory as prior emission inventories on inversion results of methane emissions using
Greenhouse gases observing satellite (GOSAT) data . Their study highlighted the discrepancies between
[31]
prior inventory results, particularly noting the overestimation of methane emissions in certain regions,
especially in China (coal emissions) and in the Middle East (oil and gas emissions) . Lyon et al. (2015)
[31]
demonstrated that the methane emission inventory constructed in that study for the Barnett Shale region
exhibited significantly higher accuracy and detail compared to the EDGAR inventory . Further, the low
[32]
resolution of such inventories failed to capture the intricate pattern of CO emissions in city areas, leading
2
to significant uncertainties in spatial distribution at the city scale [33,34] . Large-scale inventories, such as
EDGAR inventory, typically use national emission factors and activity data. However, they often lack
essential spatial information and rely instead on proxy data, such as GDP or population data, to represent
emission distribution. This approach could result in significant discrepancies in capturing the actual spatial
variability of emissions within cities. Compared to large-scale, low-resolution inventories, previous studies
about high-resolution emission inventories tailored for city-scale or region-scale also had shortcomings.
High spatial resolution is a key factor influencing the accuracy of emission inversion results . Utilizing
[35]
high-resolution proxy data for the direct spatial allocation of emissions is a common practice for the
compilation of gridded emission inventories. However, most existing studies on gridded emission
inventories have not comprehensively compiled emissions across all sectors, often focusing on key sectors
such as industrial point sources and transportation sources [36-38] for GHG or air pollutant emissions.
Additionally, several studies have relied on relatively homogeneous proxy data, primarily including
nighttime light data, population distribution, or GDP distribution [39,40] . This limitation has hindered the use
of the most appropriate proxy data for spatial allocation for each sector, thereby increasing the uncertainty
of gridded emission inventories.

To address the shortcomings of existing inventories, this study focused on the development of a
comprehensive, multi-sector gridded CO emission inventory for Chengdu at a 1 km resolution based on
2
multiple open data sources. By integrating diverse proxy data with spatial allocation algorithms, this
research offered a more refined description of spatial distribution characteristics of city CO emissions and
2
provided a robust data foundation for city-scale carbon flux inversions. Moreover, this study contributed to
the broader discourse on carbon management by highlighting the importance of high-resolution emission
inventories in supporting local and regional climate policies.