Estimating Global Anthropogenic CO<sub>2</sub> Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model

Estimating Global Anthropogenic CO<sub>2</sub> Gridded Emissions Using a Data-Driven Stacked Random Forest Regression Model

The accurate estimation of anthropogenic carbon emissions is of great significance for understanding the global carbon cycle and guides the setting and implementation of global climate policy and CO<sub>2</sub> emission-reduction goals. This study built a data-driven stacked random fores...

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Bibliographic Details
Main Authors: Yucong Zhang, Xinjie Liu, Liping Lei, Liangyun Liu
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Remote Sensing
Subjects:
anthropogenic CO<sub>2</sub> emission
ODIAC
XCO<sub>2</sub>
terrestrial carbon flux
data-driven
random forest regression
Online Access:https://www.mdpi.com/2072-4292/14/16/3899

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https://www.mdpi.com/2072-4292/14/16/3899

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