Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition

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 r...

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Bibliographic Details
Main Authors: Zhang, Da, Wang, Qingyi, Song, Shaojie, Chen, Simiao, Li, Mingwei, Shen, Lu, Zheng, Siqi, Cai, Bofeng, Wang, Shenhao, Zheng, Haotian
Other Authors: Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change
Format: Article
Language:English
Published: Elsevier BV 2024
Online Access:https://hdl.handle.net/1721.1/156543

Internet

https://hdl.handle.net/1721.1/156543

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