Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network

Abstract A gridded social-economic data is essential for geoscience analysis and multidisciplinary application. Spatial allocation of carbon dioxide statistics data is an important issue in the context of global climate change, which involves the carbon emissions accounting and decomposition of resp...

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Autores principales:	Jianbin Tao, XiangBing Kong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34820a4c387d4bca88736e735c10fa55
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spelling	oai:doaj.org-article:34820a4c387d4bca88736e735c10fa552021-12-02T18:02:23ZSpatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network10.1038/s41598-021-93456-62045-2322https://doaj.org/article/34820a4c387d4bca88736e735c10fa552021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93456-6https://doaj.org/toc/2045-2322Abstract A gridded social-economic data is essential for geoscience analysis and multidisciplinary application. Spatial allocation of carbon dioxide statistics data is an important issue in the context of global climate change, which involves the carbon emissions accounting and decomposition of responsibility for carbon emission reductions. In this research a new spatial allocation method for non-point source anthropogenic carbon dioxide emissions (ACDE) fusing multi-source data using Bayesian Network (BN) was introduced. In addition to common-used DMSP (Defense Meteorological Satellite Program), PD (population density) and GDP (Gross Domestic Production) data, the land cover and vegetation data was imported into the model as prior knowledge to optimize the model fitting. The prior knowledge here was based on the understanding that ACDE was dominated by human activities and has strong correlations with land cover and vegetation conditions. A 1 km gridded ACDE map integrated emissions form point-source and non-point source was generated and validated. The model predicts ACDE with high accuracies and great improvement can be observed when fusing land cover and vegetation as prior knowledge. The model can achieve successful statistics data downscaling on national scale provided adequate sample data are available, offering a novel method for ACDE accounting in China.Jianbin TaoXiangBing KongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianbin Tao XiangBing Kong Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network
description	Abstract A gridded social-economic data is essential for geoscience analysis and multidisciplinary application. Spatial allocation of carbon dioxide statistics data is an important issue in the context of global climate change, which involves the carbon emissions accounting and decomposition of responsibility for carbon emission reductions. In this research a new spatial allocation method for non-point source anthropogenic carbon dioxide emissions (ACDE) fusing multi-source data using Bayesian Network (BN) was introduced. In addition to common-used DMSP (Defense Meteorological Satellite Program), PD (population density) and GDP (Gross Domestic Production) data, the land cover and vegetation data was imported into the model as prior knowledge to optimize the model fitting. The prior knowledge here was based on the understanding that ACDE was dominated by human activities and has strong correlations with land cover and vegetation conditions. A 1 km gridded ACDE map integrated emissions form point-source and non-point source was generated and validated. The model predicts ACDE with high accuracies and great improvement can be observed when fusing land cover and vegetation as prior knowledge. The model can achieve successful statistics data downscaling on national scale provided adequate sample data are available, offering a novel method for ACDE accounting in China.
format	article
author	Jianbin Tao XiangBing Kong
author_facet	Jianbin Tao XiangBing Kong
author_sort	Jianbin Tao
title	Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network
title_short	Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network
title_full	Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network
title_fullStr	Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network
title_full_unstemmed	Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network
title_sort	spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on bayesian network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/34820a4c387d4bca88736e735c10fa55
work_keys_str_mv	AT jianbintao spatialallocationofanthropogeniccarbondioxideemissionstatisticsdatafusingmultisourcedatabasedonbayesiannetwork AT xiangbingkong spatialallocationofanthropogeniccarbondioxideemissionstatisticsdatafusingmultisourcedatabasedonbayesiannetwork
_version_	1718378919617363968

Spatial allocation of anthropogenic carbon dioxide emission statistics data fusing multi-source data based on Bayesian network

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