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Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR.
Estimating the CO2 emissions of Chinese cities from 2011 to 2020 based on SPNN-GNNWR.
- Source :
-
Environmental Research . Feb2023, Vol. 218, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Global warming is a serious threat to human survival and health. Facing increasing global warming, the issue of CO 2 emissions has attracted more attention. China is a major contributor of anthropogenic CO 2 emissions and so it is essential to accurately estimate China's CO 2 emissions and analyze their changing characteristics. This study recalculates CO 2 emissions from Chinese cities from 2011 to 2020 using the SPNN-GNNWR model and multiple factors to reduce the uncertainty in emission estimates. The SPNN-GNNWR model has excellent predictions (R2: 0.925, 10-fold CV R2: 0.822) when cross-validation is used. The results indicate that the total CO2 emissions in China calculated by the model are close to those accounted for by other authorities in the world, with the total CO 2 emissions increasing from 9.122 billion tonnes in 2011 to 9.912 billion tonnes in 2020. The city with the largest increase in CO 2 emissions is Tianjin, and the city with the largest decrease is Beijing. The study also reveals the regional differences in CO 2 emissions in Chinese mainland, including emissions, emission intensity and per capita emissions. Capturing and understanding the emissions and the related socioeconomic characteristics of different cities can help to develop effective emission mitigation strategies. • We estimate the CO 2 emissions of cities in Chinese mainland from 2011 to 2020. • SPNN-GNNWR shows great prediction performance (R2:0.925, CV R2:0.822). • The emission intensity and per capita emissions are characterized by geographical distribution. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CARBON emissions
*GLOBAL warming
Subjects
Details
- Language :
- English
- ISSN :
- 00139351
- Volume :
- 218
- Database :
- Academic Search Index
- Journal :
- Environmental Research
- Publication Type :
- Academic Journal
- Accession number :
- 161307111
- Full Text :
- https://doi.org/10.1016/j.envres.2022.115060