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| Verfasst von: | Zhang, Da [VerfasserIn]  |
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| | Wang, Qingyi [VerfasserIn] |
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| | Song, Shaojie [VerfasserIn] |
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| | Chen, Simiao [VerfasserIn] |
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| | Li, Mingwei [VerfasserIn] |
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| | Shen, Lu [VerfasserIn] |
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| | Zheng, Siqi [VerfasserIn] |
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| | Cai, Bofeng [VerfasserIn] |
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| | Wang, Shenhao [VerfasserIn] |
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| | Zheng, Haotian [VerfasserIn] |
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| Titel: | Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition |
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| Verf.angabe: | Da Zhang, Qingyi Wang, Shaojie Song, Simiao Chen, Mingwei Li, Lu Shen, Siqi Zheng, Bofeng Cai, Shenhao Wang, and Haotian Zheng |
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| E-Jahr: | 2023 |
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| Jahr: | 15 September 2023 |
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| Umfang: | 16 S. |
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| Fussnoten: | Gesehen am 23.01.2024 |
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| Titel Quelle: | Enthalten in: iScience |
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| Ort Quelle: | Amsterdam : Elsevier, 2018 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 26(2023), 9, Artikel-ID 107652, Seite 1-16 |
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| ISSN Quelle: | 2589-0042 |
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| Abstract: | Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system. |
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| DOI: | doi:10.1016/j.isci.2023.107652 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.isci.2023.107652 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S2589004223017297 |
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| | DOI: https://doi.org/10.1016/j.isci.2023.107652 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Atmospheric chemistry |
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| | Atmospheric science |
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| | Energy sustainability |
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| | Machine learning |
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| K10plus-PPN: | 1878714406 |
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| Verknüpfungen: | → Zeitschrift |
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Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition / Zhang, Da [VerfasserIn]; 15 September 2023 (Online-Ressource)
