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emission inventories to keep pace with the dynamic changes in city development and industrial activities,
while also ensuring that future developments align with the long-term sustainability objectives.

Comparison with EDGAR inventory
The comparison between the HEI-CD model and the widely used EDGAR inventory revealed both the
strengths and limitations of each approach. The EDGAR inventory, with its global coverage and long-term
data records, was invaluable for large-scale atmospheric modeling and international climate assessments,
making it particularly suitable for CO emission flux inversions at global or national levels. However, its
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relatively low spatial resolution (0.1° × 0.1°) limited the applicability for city-scale or high-resolution
inversions, where detailed spatial information was crucial for accurately mapping emissions and informing
policy decisions.

In contrast, the HEI-CD model offered a much higher spatial resolution (1 km × 1 km), which was better
suited for small-scale inversions, especially at the city scale. The improved accuracy in point source
information, as provided by the HEI-CD model, significantly enhanced the quality of inversion by more
precisely capturing the spatial variability of emissions. This high resolution enabled more accurate
identification of emission hotspots and a more detailed understanding of the spatial distribution of
emissions within the city, ultimately leading to reliable results in atmospheric modeling.

However, the higher resolution in the HEI-CD model requires detailed and often harder-to-obtain data, and
the reliance on proxy data can introduce uncertainties due to variations in data choice, quality and
availability. Nevertheless, for urban carbon monitoring and management, the HEI-CD model represents a
substantial improvement over broader-scale inventories like EDGAR, offering a tailored approach to local
emission reduction efforts.

Improvement relative to previous studies
This study achieved high-resolution gridded CO emission inventories by integrating multiple open data
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sources for spatial proxies. Unlike previous studies that relied on relatively homogeneous proxy data (e.g.,
[40]
[53]
nighttime lights or NDVI ), this study used diverse, sector-specific, high-resolution proxy data,
including population distribution, road networks, and industrial point sources, to describe city CO
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emissions with greater detail and accuracy.
Compared to global-scale inventories like EDGAR which was suited for broader scales, this inventory at the
city scale provided a detailed view of city emissions, improving hotspot identification, enhancing the ability
of CO emission flux inversion, and supporting better-informed policy decisions. Similarly, previous
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studies, like the Vulcan inventory by Gurney et al. (2009) and vehicle emission inventory by Cai et al.
[54]
[55]
(2020) and Sun et al. (2021) , either focus on specific sectors or lack the comprehensive integration of
[36]
sector-specific data that our study achieves. The sector-specific spatial allocation strategy employed here
reduces uncertainties by tailoring methods to the unique characteristics of each sector, providing a more
accurate foundation for CO monitoring and mitigation efforts.
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Study limitations and future directions
Despite the advancements presented in this study, several limitations warrant discussion. First, the reliance
on proxy data for non-point sources, such as residents and agriculture emissions, introduces uncertainty
into the inventory. While efforts have been made to minimize uncertainties through careful data selection
and processing, the inherent variability in proxy data quality remains challenging. Future research should
explore the integration of high-resolution and representative data sources, such as smart city infrastructure
data or detailed land-use records based on remote sensing technologies, to further refine the accuracy of
emission inventories.