Driving Factors of CO2 Concentration in Mainland China Based on GWR
Considering that the complexity and dynamicity of CO2 emissions, the spatiotemporal distribution pattern of atmospheric CO2 and its drivers remain unclear. In this study, we used the Geographically Weighted Regression (GWR) method to analyze the comprehensive distribution of CO2 concentration in mai...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/43/e3sconf_icemee2023_04002.pdf |
| _version_ | 1797756182343974912 |
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| author | Renyang Qianqian Lian Yi Zhang Hu Gao Huichun |
| author_facet | Renyang Qianqian Lian Yi Zhang Hu Gao Huichun |
| author_sort | Renyang Qianqian |
| collection | DOAJ |
| description | Considering that the complexity and dynamicity of CO2 emissions, the spatiotemporal distribution pattern of atmospheric CO2 and its drivers remain unclear. In this study, we used the Geographically Weighted Regression (GWR) method to analyze the comprehensive distribution of CO2 concentration in mainland China from 2015 to 2019. We considered the relationship between nine factors, including natural and human activities, and CO2 concentration. To clarify the correlation between CO2 concentration and drivers, we utilized Pearson’s correlation coefficient. Then, the GWR analysis revealed the spatial heterogeneity across provinces, which reflects the extent to which impact factors influence CO2 concentrations. Finally, we analysed CO2 concentration spatiotemporal variation characteristics and predicted future trends of CO2 concentration in 31 provinces in China. According to our research, GDP has a major impact on CO2 growth, while natural factors have a minor influence on CO2 concentration. Our study found significant regional differences in the effects of combined variables on CO2 concentrations, with monthly rotational patterns temporally and clustering of high growth rates spatially and CO2 concentration in mainland China will continue to steadily increase. The findings of this research are crucial for China’s future energy low-carbon transition and policy-making. |
| first_indexed | 2024-03-12T17:57:50Z |
| format | Article |
| id | doaj.art-ca09df97ca334a72825ffdbe98990d28 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T17:57:50Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-ca09df97ca334a72825ffdbe98990d282023-08-02T13:19:07ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014060400210.1051/e3sconf/202340604002e3sconf_icemee2023_04002Driving Factors of CO2 Concentration in Mainland China Based on GWRRenyang Qianqian0Lian Yi1Zhang Hu2Gao Huichun3School of Geography and Environmental Sciences, Tianjin Normal UniversitySchool of Geography and Environmental Sciences, Tianjin Normal UniversitySchool of Geography and Environmental Sciences, Tianjin Normal UniversitySchool of Geography and Environmental Sciences, Tianjin Normal UniversityConsidering that the complexity and dynamicity of CO2 emissions, the spatiotemporal distribution pattern of atmospheric CO2 and its drivers remain unclear. In this study, we used the Geographically Weighted Regression (GWR) method to analyze the comprehensive distribution of CO2 concentration in mainland China from 2015 to 2019. We considered the relationship between nine factors, including natural and human activities, and CO2 concentration. To clarify the correlation between CO2 concentration and drivers, we utilized Pearson’s correlation coefficient. Then, the GWR analysis revealed the spatial heterogeneity across provinces, which reflects the extent to which impact factors influence CO2 concentrations. Finally, we analysed CO2 concentration spatiotemporal variation characteristics and predicted future trends of CO2 concentration in 31 provinces in China. According to our research, GDP has a major impact on CO2 growth, while natural factors have a minor influence on CO2 concentration. Our study found significant regional differences in the effects of combined variables on CO2 concentrations, with monthly rotational patterns temporally and clustering of high growth rates spatially and CO2 concentration in mainland China will continue to steadily increase. The findings of this research are crucial for China’s future energy low-carbon transition and policy-making.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/43/e3sconf_icemee2023_04002.pdf |
| spellingShingle | Renyang Qianqian Lian Yi Zhang Hu Gao Huichun Driving Factors of CO2 Concentration in Mainland China Based on GWR E3S Web of Conferences |
| title | Driving Factors of CO2 Concentration in Mainland China Based on GWR |
| title_full | Driving Factors of CO2 Concentration in Mainland China Based on GWR |
| title_fullStr | Driving Factors of CO2 Concentration in Mainland China Based on GWR |
| title_full_unstemmed | Driving Factors of CO2 Concentration in Mainland China Based on GWR |
| title_short | Driving Factors of CO2 Concentration in Mainland China Based on GWR |
| title_sort | driving factors of co2 concentration in mainland china based on gwr |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/43/e3sconf_icemee2023_04002.pdf |
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