Editorial: Assessing greenhouse gas emissions at city and regional levels: challenges and methods

Accurate quantification of greenhouse gas (GHG; i.e., CO2, and N2O) emissions 40 climate-change mitigation and evidence-based policymaking, and is therefore eseential to the 41 development of effective mitigation (Dogniaux et al., 2025). However, multi-decadal 42 records of global atmospheric GHG concentrations (https://gml.noaa.gov/aggi/) indicate persistent 43 and substantial knowledge gaps in constraining emissions and sinks, particularly over emission 44 hotspots, from regional to global scales. Although a wide range of instruments and approaches has 45 been deployed to quantify GHG fluxes at field and landscape scales, observed concentration 46 dynamics-which represent aggregated signals over multiple spatial extents-continue to reveal 47 considerable uncertainty and potential bias in emission estimates across scales, especially for 48 hotspots spanning urban areas and broader regional domains (Hu et al., 2023). One reason is that 49 city-and regional-scale assessments remain comparatively underdeveloped, in part due to limited 50 access to comprehensive observations and scalable methodologies. Addressing this gap will require 51 advances in both data acquisition and methodological innovation to enable a more integrated 52 understanding of emissions. Recent advances in atmospheric inversion frameworks and eddy-53 covariance observations make substantial progress, yet further work is needed to identify and 54 quantify the key drivers of emissions, particularly in densely populated urban environments and 55 heterogeneous, resource-diverse regions. The Research Topic "Assessing greenhouse gas emissions 56 at city and regional levels: challenges and methods" bring together timely contributions that 57 examine how complementary approaches can be applied and integrated to quantify GHG emissions 58 with reduced uncertainty, especially over emission hotspots. Collectively, these studies underscore 59 significant implication: integrating multiple data sources and combining "top-down" and "bottom-60 up" approaches can effectively reduce uncertainty and provide actionable evidence to support policy 61