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Page 16 of 19 Wang et al. Carbon Footprints 2024;3:14 https://dx.doi.org/10.20517/cf.2024.19

Another limitation was the exclusion of certain low-emission sources, such as aviation and some industries,
due to data availability constraints. This omission means that the inventory may not fully capture all city
emissions, potentially leading to an underestimation of the total carbon footprint. To address this, future
studies should aim to include a broader range of emission sources by leveraging new data collection
methods or collaborating with industry stakeholders to gain access to proprietary data.

Moreover, the emission allocation for individual point sources was based on weight data such as a
company's registered or paid-in capital. While this approach allowed for the distribution of emission data, it
introduced uncertainty regarding the actual emissions from each point source. This method could not
accurately reflect the specific operational details of each company, thus reducing the precision of point
source emissions in the inventory. Although this study achieved better point source identification than
large-scale inventories, there were still cases where companies were missing from the API interface of the
online map platform. In future work, it will be necessary to cross-check results from online map platforms
with enterprise information enquiry systems to minimize the omission of point sources and improve the
completeness of the inventory.

Looking forward, there is a clear need for the development of validating systems and automated methods
for updating the inventory. The dynamic nature of city environments, characterized by ongoing industrial
activities, transportation network changes, and population shifts, necessitates the ability to frequently
update emission inventories. Automation could involve the use of machine learning algorithms to
continuously refine spatial allocation models or the deployment of sensor networks to provide real-time
emission data.

In addition to automation, the validation systems of the emission inventory should become a focal point in
future work. This involves using the compiled gridded emission inventory as a prior inventory in
atmospheric transport models to simulate CO concentrations. By combining simulated concentrations with
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observed concentrations, a posterior inventory that better reflects actual emission distributions can be
inversed through a Bayesian inversion framework. Such comparisons will allow for the identification of
biases and discrepancies in the prior inventory, leading to accurate and optimized emission estimates. This
continuous loop of validation and optimization is critical for maintaining the relevance and accuracy of
inventories, ensuring that it effectively supports urban carbon reduction policies.

These advancements, both in automation and validation, will be crucial in enhancing the utility of the
inventory in cities like Chengdu toward their carbon neutrality goals. As city environments continue to
evolve, the ability to dynamically update and validate emission inventories will be essential for effective
carbon management.

CONCLUSION
This study developed a detailed gridded CO emission inventory for Chengdu, which is tailored for city-
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scale CO emission flux inversions. By integrating multiple open data sources and employing a spatial
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allocation algorithm, the inventory provided a detailed and accurate spatial description of CO emissions
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across various sectors. The analysis identified key high-emission areas, particularly in the densely populated
city center, the industrially active northwest region, and the energy-intensive eastern area, collectively
accounting for a significant portion of total emissions.