MUSE-RASA captures human dimension in climate-energy-economic models via global geoAI-ML agent datasets

This article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build th...

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Detalles Bibliográficos
Autores principales: Moya, Diego, Copara, Dennis, Olivo, Alexis, Castro, Christian, Giarola, Sara, Hawkes, Adam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570386/
https://www.ncbi.nlm.nih.gov/pubmed/37828067
http://dx.doi.org/10.1038/s41597-023-02529-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10570386/
https://www.ncbi.nlm.nih.gov/pubmed/37828067
http://dx.doi.org/10.1038/s41597-023-02529-w

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