Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities
Abstract The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84212-7 |
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| author | Xue Zhao Bilin Shao Jia Su Ning Tian |
| author_facet | Xue Zhao Bilin Shao Jia Su Ning Tian |
| author_sort | Xue Zhao |
| collection | DOAJ |
| description | Abstract The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity. Extreme gradient boosting (XGBoost) multiscale geographically weighted regression models(MGWR) and SHAP theory from game theory were employed to deeply investigate the disparities in relevance, spatial heterogeneity, and multiscale fluctuations of carbon emissions and air pollution. The main results are summarized as follows: (1) Between 2015 and 2018, CO2 emissions exhibited significant fluctuations, while SO2 and PM2.5 concentrations decreased markedly. (2) The XGBoost-SHAP model identified seven key driving factors, demonstrating high precision, the SHAP model is used to explain the model and reveal the influence of driving factors on carbon emissions. (3) The concentrations of CO2, SO2, and PM2.5 were positively correlated, the influence of each factor exhibited significant spatiotemporal differences, with varying directions of fluctuation across different regions. Thus, the symbiotic relationship between carbon emissions and air pollutants can inform decision-making for regional planning and sustainable urban development. |
| format | Article |
| id | doaj-art-d515d1b85b494189975de1773c52d2ad |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-d515d1b85b494189975de1773c52d2ad2025-01-26T12:24:05ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-84212-7Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese citiesXue Zhao0Bilin Shao1Jia Su2Ning Tian3School of Management, Xi’an University of Architecture and TechnologySchool of Management, Xi’an University of Architecture and TechnologySchool of Management, Xi’an University of Architecture and TechnologySchool of Management, Xi’an University of Architecture and TechnologyAbstract The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity. Extreme gradient boosting (XGBoost) multiscale geographically weighted regression models(MGWR) and SHAP theory from game theory were employed to deeply investigate the disparities in relevance, spatial heterogeneity, and multiscale fluctuations of carbon emissions and air pollution. The main results are summarized as follows: (1) Between 2015 and 2018, CO2 emissions exhibited significant fluctuations, while SO2 and PM2.5 concentrations decreased markedly. (2) The XGBoost-SHAP model identified seven key driving factors, demonstrating high precision, the SHAP model is used to explain the model and reveal the influence of driving factors on carbon emissions. (3) The concentrations of CO2, SO2, and PM2.5 were positively correlated, the influence of each factor exhibited significant spatiotemporal differences, with varying directions of fluctuation across different regions. Thus, the symbiotic relationship between carbon emissions and air pollutants can inform decision-making for regional planning and sustainable urban development.https://doi.org/10.1038/s41598-024-84212-7CitiesCarbon emissionsAir pollutantsCoevolutionXGBoost-SHAPMultiscale geographically weighted regression models |
| spellingShingle | Xue Zhao Bilin Shao Jia Su Ning Tian Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities Scientific Reports Cities Carbon emissions Air pollutants Coevolution XGBoost-SHAP Multiscale geographically weighted regression models |
| title | Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities |
| title_full | Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities |
| title_fullStr | Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities |
| title_full_unstemmed | Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities |
| title_short | Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities |
| title_sort | exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in chinese cities |
| topic | Cities Carbon emissions Air pollutants Coevolution XGBoost-SHAP Multiscale geographically weighted regression models |
| url | https://doi.org/10.1038/s41598-024-84212-7 |
| work_keys_str_mv | AT xuezhao exploringsynergisticevolutionofcarbonemissionsandairpollutantsandspatiotemporalheterogeneityofinfluencingfactorsinchinesecities AT bilinshao exploringsynergisticevolutionofcarbonemissionsandairpollutantsandspatiotemporalheterogeneityofinfluencingfactorsinchinesecities AT jiasu exploringsynergisticevolutionofcarbonemissionsandairpollutantsandspatiotemporalheterogeneityofinfluencingfactorsinchinesecities AT ningtian exploringsynergisticevolutionofcarbonemissionsandairpollutantsandspatiotemporalheterogeneityofinfluencingfactorsinchinesecities |