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A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals
by
Jiang, Fei
, Lv, Guoyuan
, Feng, Shuzhuang
, Zhang, Lingyu
, Ju, Weimin
, Wang, Hengmao
, Mao, Yu
in
Bayesian analysis
/ Bayesian inversion method
/ Carbon
/ Carbon cycle
/ Carbon dioxide
/ Emissions
/ Emissions control
/ fossil fuel carbon emissions
/ Fossil fuels
/ Growing season
/ Inversions
/ Machine learning
/ Methods
/ OCO-2 XCO2 retrievals
/ Optimization
/ Regional analysis
/ Regional development
/ Regions
/ Satellite observation
/ Spatial distribution
/ Terrestrial ecosystems
/ Uncertainty
2025
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A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals
by
Jiang, Fei
, Lv, Guoyuan
, Feng, Shuzhuang
, Zhang, Lingyu
, Ju, Weimin
, Wang, Hengmao
, Mao, Yu
in
Bayesian analysis
/ Bayesian inversion method
/ Carbon
/ Carbon cycle
/ Carbon dioxide
/ Emissions
/ Emissions control
/ fossil fuel carbon emissions
/ Fossil fuels
/ Growing season
/ Inversions
/ Machine learning
/ Methods
/ OCO-2 XCO2 retrievals
/ Optimization
/ Regional analysis
/ Regional development
/ Regions
/ Satellite observation
/ Spatial distribution
/ Terrestrial ecosystems
/ Uncertainty
2025
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Do you wish to request the book?
A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals
by
Jiang, Fei
, Lv, Guoyuan
, Feng, Shuzhuang
, Zhang, Lingyu
, Ju, Weimin
, Wang, Hengmao
, Mao, Yu
in
Bayesian analysis
/ Bayesian inversion method
/ Carbon
/ Carbon cycle
/ Carbon dioxide
/ Emissions
/ Emissions control
/ fossil fuel carbon emissions
/ Fossil fuels
/ Growing season
/ Inversions
/ Machine learning
/ Methods
/ OCO-2 XCO2 retrievals
/ Optimization
/ Regional analysis
/ Regional development
/ Regions
/ Satellite observation
/ Spatial distribution
/ Terrestrial ecosystems
/ Uncertainty
2025
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A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals
Journal Article
A Top-Down Method for Estimating Regional Fossil Fuel Carbon Emissions Based on Satellite XCO2 Retrievals
2025
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Overview
Satellite XCO2 retrievals have been widely used in estimating fossil fuel carbon (FFC) emissions at point and urban scales. However, at the regional scale, it remains a significant challenge. Furthermore, current global and regional atmospheric inversions often overlook the uncertainties associated with FFC emissions. To meet the needs of the global carbon stocktake, we developed an inversion method based on Bayesian statistical theory and OCO-2 satellite XCO2 observations to optimize FFC emissions alongside terrestrial ecosystem carbon fluxes (NEE). The methodology’s core is to distinguish the contributions of NEE and FFC to the observed concentrations using their different spatial distributions. We designed an observing system simulation experiment to invert the 2016 FFC emissions. The results showed that posterior FFC emissions were significantly optimized during the non-growing seasons in the regions with high emissions, with the optimization effect diminishing as emissions shrank. Average FFC emissions uncertainty reductions are in the range of 13–82% in the non-growing season for the eight largest emitting regions globally. By assuming the same uncertainty reduction for FFC emissions in both the growing and non-growing seasons, we can optimize annual emissions for high-emission areas. We believe this study provides a new idea for the inversion of FFC emissions at the regional scale, which is important for achieving the goal of carbon neutrality.
Publisher
MDPI AG
Subject
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