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Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicles (EVs)

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Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicles (EVs)

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Llopis-Albert, C.; Palacios Marqués, D.; Simón-Moya, V. (2021). Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicles (EVs). Technological Forecasting and Social Change. 169:1-7. https://doi.org/10.1016/j.techfore.2021.120843

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/184739

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Título: Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicles (EVs)
Autor: Llopis-Albert, Carlos Palacios Marqués, Daniel Simón-Moya, Virginia
Entidad UPV: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Universitat Politècnica de València. Departamento de Ingeniería Mecánica y de Materiales - Departament d'Enginyeria Mecànica i de Materials
Fecha difusión:
Resumen:
[EN] Facing global climate change challenges entails a sustainable development of transportation. Governments and automobile manufacturers are highly aware of how a large-scale deployment of Electric Vehicles (EVs) can ...[+]
Palabras clave: Fuzzy sets , Qualitative comparative analysis , Decision-making , Climate change , Greenhouse gas (GHG) emissions , Electric vehicles
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Technological Forecasting and Social Change. (issn: 0040-1625 )
DOI: 10.1016/j.techfore.2021.120843
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.techfore.2021.120843
Tipo: Artículo

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