Although human emissions of carbon dioxide (CO2) to the atmosphere are the main cause of global climate change, detailed and spatially-explicit estimates of such emissions are updated infrequently, typically lagging emissions by at least a year. However, with the rising ambition of climate policies and mitigation efforts,1,2 a reliable, spatially-explicit and up-to-date dataset of fossil CO2 emissions is becoming increasingly important. For example, such detailed data is necessary to link emissions to observable atmospheric concentration signals and constrain regional CO2 fluxes, and can help decision makers to more quickly assess both the effectiveness of policies and local priorities for further mitigation.3,4

Since the end of 2019, the COVID-19 pandemic has caused major disruptions of human activities and energy use. Governments around the world have imposed compulsory lockdowns that restrict in-person educational and commercial activities to reduce the spread of coronavirus. In turn, industries and factories reduced their activities and production, people s local and long distance mobility was reduced, and human activities were reduced on a large scale, resulting in a substantial decrease in fossil energy consumption and CO2 emissions, albeit with large regional differences.4-6 As lockdown restrictions have relaxed in many countries and economic activities have recovered in some sectors, the effect of the pandemic on CO2 emissions has weakened, even during large “second waves” of cases. A timely and finely-gridded emissions dataset enables quantitative analysis of how temporal and spatial changes in CO2 emissions in each country in response to emergencies (e.g. COVID-19) and other disturbances of human activities, and helps constraining predictions of future trends.

Existing datasets of global gridded (i.e. spatially-explicit) CO2 emissions include the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) that distributes national emission totals estimated by the Carbon Dioxide Information Analysis Center (CDIAC) in space, using a combination of geospatial proxies such as satellite observations of nighttime lights and geolocations of major power plants(CARMA list): ODIAC provides maps of monthly CO2 emissions on a 1 km grid for the period 2000 to 2019, as of today, including emissions from power plant, transportation, cement production/industrial facilities, and gas flares over land regions.7-9 Similarly, the Community Emissions Data System (CEDS) uses data from a number of existing inventories to provide a monthly gridded dataset of all emission species for the Climate Model Inter-comparison Program (CMIP6) over the period 1750 to 2014 at a resolution of up to 0.1°, including sectors of energy transformation and extraction, industry, residential, commercial, transportation, agriculture, solvent production and application, waste, shipping and “other”.10-13 Another prominent example is the Emission Database for Global Atmospheric Research (EDGAR). EDGAR estimates emissions based on national CO2 emissions reported by the Global Carbon Project (GCP) and emission factors, broken down to IPCC-relevant source-sector levels. EDGAR uses spatial geospatial proxies such as point and line source locations at a 0.1°×0.1° resolution for the period 1970 to 2019, including sectors of agriculture, power, transport, residential, industry, manufacturing, and a number of others.14-17 More recently, The Global Carbon Grid (http://gidmodel.org) establishes high-resolution maps of global CO2 emissions from fossil fuel combustion and cement production based on a framework that integrates multiple data flows including point sources, country-level sectoral activities and emissions, and transport emissions and distributions. The Global Carbon Grid v1.0 provides global 0.1°×0.1° CO2 emission maps of six source sectors, including power, industry, residential, transport, shipping, and aviation in 2019.18-20

Even the most current of the gridded CO2 emissions datasets described above lag emissions by a year or more and do not reflect sub-monthly temporal variations related to seasonality, weather, economic activities, or policies. Nassar et al. made a first attempt to further downscale these global datasets at the weekly and diurnal scale using static local temporal scaling factors.21 However, during a normal year, day-to-day variations are due mainly to weather impacting heating/cooling demands of residences and commercial buildings and the generation of renewable energy, as well as weekends and holidays. Since the pandemic began in early 2020, though, daily variations have been perturbed by a multitude of other factors, including lockdowns, industrial production drops and recoveries, and changes in human behavior. Timely and quantitative analysis on the effects of these COVID-related changes on CO2 emissions using tools such as inversion systems thus requires dynamic knowledge of global CO2 emissions. It was this need for data that led to our development of the Carbon Monitor, a near-real-time daily dataset of global CO2 emission at the national level (https://carbonmonitor.org).3,4 Chevallier et al. disaggregated the daily national Carbon Monitor totals on a worldwide uniform grid using satellite retrievals of a pollutant co-emitted with CO2 as a spatial proxy, without sectoral distinction.22 Here, we considerably refine the approach by downscaling the daily national emissions from Carbon Monitor into a 0.1° × 0.1° grid for each of the seven sectors (power, industry, residential, ground transportation, domestic aviation, international aviation, and international shipping), using sector-specific geospatial data from the Global Carbon Grid (GID) v1.0, the EDGARv5.0_FT2019 database for 2019, and NO2 retrievals from the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite to provide a new spatially-explicit dataset of daily global CO2 emissions covering the last two years since January 1, 2019, which we name GRACED. The first high-resolution near-real-time gridded fossil CO2 emission emissions GRACED we presented will facilitate the adaptive management of emissions and the implementation of climate policy, which is of great importance of achieving the carbon neutrality around the world.