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Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR
The spatialization of anthropogenic carbon emissions is of great significance for achieving the goal of "carbon peaking and neutrality" and promoting the development of carbon trading market. The SVR-ZSSR spatialization model is proposed to solve the problems of "insufficient local fe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894835/ https://www.ncbi.nlm.nih.gov/pubmed/36732578 http://dx.doi.org/10.1038/s41598-023-28462-x |
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author | Liu, Minghao Qi, Liai Chen, Haiyan Luo, Xiaolin Zhu, Xiaobo Chen, Chun |
author_facet | Liu, Minghao Qi, Liai Chen, Haiyan Luo, Xiaolin Zhu, Xiaobo Chen, Chun |
author_sort | Liu, Minghao |
collection | PubMed |
description | The spatialization of anthropogenic carbon emissions is of great significance for achieving the goal of "carbon peaking and neutrality" and promoting the development of carbon trading market. The SVR-ZSSR spatialization model is proposed to solve the problems of "insufficient local feature learning" and "scale dependence of driving factors" existing in the single model, and this model is applied to the study of the spatialization of carbon emissions in China in this paper. The results show that: (1) the simulation results of our proposed model show the distribution characteristics of "high in the East and low in the west"; On the micro-scale, the high carbon emission areas in the simulation results are all concentrated in the built-up land, while the carbon emission in the surrounding areas of the city is significantly lower than that in the center, which is similar to the spatial distribution trend of carbon emission in the existing database. (2) Compared with the results of SVR, the results of our proposed model increase the carbon emission ratio of built-up land in each province by 15.9% on average; Compared with the ODIAC database and SVR model, the carbon emission ratio of built-up land in Gansu Province, Qinghai Province, and other low-carbon emission areas has increased by about 25%. |
format | Online Article Text |
id | pubmed-9894835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98948352023-02-04 Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR Liu, Minghao Qi, Liai Chen, Haiyan Luo, Xiaolin Zhu, Xiaobo Chen, Chun Sci Rep Article The spatialization of anthropogenic carbon emissions is of great significance for achieving the goal of "carbon peaking and neutrality" and promoting the development of carbon trading market. The SVR-ZSSR spatialization model is proposed to solve the problems of "insufficient local feature learning" and "scale dependence of driving factors" existing in the single model, and this model is applied to the study of the spatialization of carbon emissions in China in this paper. The results show that: (1) the simulation results of our proposed model show the distribution characteristics of "high in the East and low in the west"; On the micro-scale, the high carbon emission areas in the simulation results are all concentrated in the built-up land, while the carbon emission in the surrounding areas of the city is significantly lower than that in the center, which is similar to the spatial distribution trend of carbon emission in the existing database. (2) Compared with the results of SVR, the results of our proposed model increase the carbon emission ratio of built-up land in each province by 15.9% on average; Compared with the ODIAC database and SVR model, the carbon emission ratio of built-up land in Gansu Province, Qinghai Province, and other low-carbon emission areas has increased by about 25%. Nature Publishing Group UK 2023-02-02 /pmc/articles/PMC9894835/ /pubmed/36732578 http://dx.doi.org/10.1038/s41598-023-28462-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Minghao Qi, Liai Chen, Haiyan Luo, Xiaolin Zhu, Xiaobo Chen, Chun Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR |
title | Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR |
title_full | Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR |
title_fullStr | Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR |
title_full_unstemmed | Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR |
title_short | Study on the spatialization of anthropogenic carbon emissions in China based on SVR-ZSSR |
title_sort | study on the spatialization of anthropogenic carbon emissions in china based on svr-zssr |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894835/ https://www.ncbi.nlm.nih.gov/pubmed/36732578 http://dx.doi.org/10.1038/s41598-023-28462-x |
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