Change estimates of carbon emissions for Functional Urban Areas(v1)

World Bank·January 12, 2025

Description

Annual change estimates for Functional Urban Areas (FUAs) from Schiavina et al. (2019) by statistical significance class, providing information that links grid cells in the World Bank’s global XCO2 database to IDs for FUAs and national administrative units.

Beginning in September 2014, the XCO2 database uses satellite data from NASA’s OCO (Orbiting Carbon Observatory)-2 satellite, which provides reliable information about global CO2 emissions at high levels of spatial resolution. OCO-2 follows a sun-synchronous near-polar orbit, crosses the equator in ascending mode around 1330 hours local time and has an observation repeat time of 16 days.

The database downloads monthly updates for georeferenced measures of XCO2—the column-averaged, dry-air mole fraction of the gas— and filters the data for atmospheric concentration anomalies that identify local emissions. The filter uses the methodology of Hakkarainen et al. (2019).  See Dasgupta, Lall and Wheeler (2023) for a detailed description.

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World Bank
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CreatedFebruary 20, 2023
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Change estimates of carbon emissions for Functional Urban Areas(v1)

World Bank·January 12, 2025

Description

Annual change estimates for Functional Urban Areas (FUAs) from Schiavina et al. (2019) by statistical significance class, providing information that links grid cells in the World Bank’s global XCO2 database to IDs for FUAs and national administrative units.

Beginning in September 2014, the XCO2 database uses satellite data from NASA’s OCO (Orbiting Carbon Observatory)-2 satellite, which provides reliable information about global CO2 emissions at high levels of spatial resolution. OCO-2 follows a sun-synchronous near-polar orbit, crosses the equator in ascending mode around 1330 hours local time and has an observation repeat time of 16 days.

The database downloads monthly updates for georeferenced measures of XCO2—the column-averaged, dry-air mole fraction of the gas— and filters the data for atmospheric concentration anomalies that identify local emissions. The filter uses the methodology of Hakkarainen et al. (2019).  See Dasgupta, Lall and Wheeler (2023) for a detailed description.

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