Dataset Information

Carbon Monitor Cities near-real-time daily estimates of CO₂ emissions from 1500 cities worldwide.

ABSTRACT: Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO₂) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries.

SUBMITTER: Huo D

PROVIDER: S-EPMC9434530 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Carbon Monitor Cities near-real-time daily estimates of CO<sub>2</sub> emissions from 1500 cities worldwide.

Huo Da D Huang Xiaoting X Dou Xinyu X Ciais Philippe P Li Yun Y Deng Zhu Z Wang Yilong Y Cui Duo D Benkhelifa Fouzi F Sun Taochun T Zhu Biqing B Roest Geoffrey G Gurney Kevin R KR Ke Piyu P Guo Rui R Lu Chenxi C Lin Xiaojuan X Lovell Arminel A Appleby Kyra K DeCola Philip L PL Davis Steven J SJ Liu Zhu Z

Scientific data 20220901 1

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO<sub>2</sub>) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to impro ...[more]

PMID: 36050332

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Project description:BackgroundSatellite imagery will offer unparalleled global spatial coverage at high-resolution for long term cost-effective monitoring of CO2 concentration plumes generated by emission hotspots. CO2 emissions can then be estimated from the magnitude of these plumes. In this paper, we assimilate pseudo-observations in a global atmospheric inversion system to assess the performance of a constellation of one to four sun-synchronous low-Earth orbit (LEO) imagers to monitor anthropogenic CO2 emissions. The constellation of imagers follows the specifications from the European Spatial Agency (ESA) for the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) concept for a future operational mission dedicated to the monitoring of anthropogenic CO2 emissions. This study assesses the uncertainties in the inversion estimates of emissions ("posterior uncertainties").ResultsThe posterior uncertainties of emissions for individual cities and power plants are estimated for the 3 h before satellite overpasses, and extrapolated at annual scale assuming temporal auto-correlations in the uncertainties in the emission products that are used as a prior knowledge on the emissions by the Bayesian framework of the inversion. The results indicate that (i) the number of satellites has a proportional impact on the number of 3 h time windows for which emissions are constrained to better than 20%, but it has a small impact on the posterior uncertainties in annual emissions; (ii) having one satellite with wide swath would provide full images of the XCO2 plumes, and is more beneficial than having two satellites with half the width of reference swath; and (iii) an increase in the precision of XCO2 retrievals from 0.7 ppm to 0.35 ppm has a marginal impact on the emission monitoring performance.ConclusionsFor all constellation configurations, only the cities and power plants with an annual emission higher than 0.5 MtC per year can have at least one 8:30-11:30 time window during one year when the emissions can be constrained to better than 20%. The potential of satellite imagers to constrain annual emissions not only depend on the design of the imagers, but also strongly depend on the temporal error structure in the prior uncertainties, which is needed to be objectively assessed in the bottom-up emission maps.

Dataset Information

Carbon Monitor Cities near-real-time daily estimates of CO₂ emissions from 1500 cities worldwide.

Publications

Carbon Monitor Cities near-real-time daily estimates of CO<sub>2</sub> emissions from 1500 cities worldwide.

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Dataset Information

Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide.

Publications

Carbon Monitor Cities near-real-time daily estimates of CO<sub>2</sub> emissions from 1500 cities worldwide.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Carbon Monitor Cities near-real-time daily estimates of CO₂ emissions from 1500 cities worldwide.