## 1 Introduction  
To address climate change, greenhouse gas inventories including both source emissions and sink removals have been exceedingly important for policymaking and for scientific studies. The primary greenhouse gas in the atmosphere affected by human activities is carbon dioxide (CO\({}_{2}\)), with a global averaged concentration of 407.8 \(\pm\) 0.1 parts per million (ppm) in 2018 [1]. Scientific reports such as the Global Carbon Budget 2019 [2] estimated sources and sinks of CO\({}_{2}\) globally. National greenhouse gas inventories have included annual totals and trends of CO\({}_{2}\) emissions and removals by sector. In addition to global and national inventories, the Integrated Global Greenhouse Gas Information System (IG\({}^{3}\)IS) advocates the necessity of advancing estimation and attribution of megacity GHG emissions to support the implementation of the ambition cycle and global stocktake in the Paris Agreement [3]. Cities are the focal points of climate action for the following reasons: more than half of the human population resides in urban areas [4], where cities and their affiliated power plants, airports and other infrastructure constitute the largest human-induced CO\({}_{2}\) emission sources, accounting for 50%-80% of the global total [3; 5; 6; 7]; also, urban populations will continue growing to 68% of the global total by 2050 [4], with expected rapid rises occurring particularly in Africa, Asia, and Latin America, presumably increasing CO\({}_{2}\) emissions markedly [8; 9]. Some city-scale anthropogenic CO\({}_{2}\) emission datasets already exist. Cities such as Los Angeles [10], Paris [11], and Tokyo [12], with abundant resources and the greatest commitments to addressing climate change, self-report their GHG annual emissions. Studies such as those by [PERSON] et al. [13] and [PERSON] et al. [14] developed urban CO\({}_{2}\) emission inventory frameworks to estimate anthropogenic CO\({}_{2}\) emissions. [PERSON] et al. [15] synthesized anthropogenic CO\({}_{2}\) emissions in 343 cities from both developed and developing countries. However, a few aspects remain to be improved to acquire comprehensive and consistent urban CO\({}_{2}\) emission datasets. Because of variations in local resources and accounting capacities, global CO\({}_{2}\) emissions data at the city scale are incomplete. Moreover, the representativeness of cities is biased [7], and some cities in the global south are struggling with data scarcity challenges such as inadequate information collection [8; 16; 17]. Moreover, urban GHG inventory methods are yet to be standardized [7; 16]. Because of inconsistent accounted emissions sources, inventory methods and incompatible boundaries [7; 9], the contents and quality of city-scale CO\({}_{2}\) emission data are often incoherent, making city-to-city comparisons difficult. Top-down approach might address the research challenges described above and update megacities' anthropogenic CO\({}_{2}\) emission datasets extensively. In the field of atmospheric sciences, the top-down approach refers to the back-casting of CO\({}_{2}\) flux based on atmospheric concentration data. In fact, verification of greenhouse gas inventories via the top-down approach (atmospheric measurement and inverse model) has been highlighted by the Intergovernmental Panel on Climate Change (IPCC)'s Task Force on National Greenhouse Gas Inventories (IPCC-TFI) in the latest refinement of its 2006 guidelines [18], because this method allows the exploitation of independent observation datasets. Top-down approach can be applied flexibly at various scales. In particular, advances taking place during the last decade of global atmospheric greenhouse gas concentration observations derived from satellite retrievals (GOSAT-1, OCO-2, TanSat, GOSAT-2, and OCO-3) generated CO\({}_{2}\) and methane (CH\({}_{4}\)) datasets in fine resolution to support such application of inverse models [19; 20; 21].