How green can it be? A methodology for calculating green roof retrofit potential in Valencia
[EN] Cities are accountable for more than 70% of global CO2 emissions and consume around 65% of the world¿s energy. In the pathway towards urban decarbonisation, nature-based solutions rise as a promising opportunity. They improve a city¿s resilience, stabilise temperatures and capture CO2, both mit...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/214097 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/214097 |
| Access Level: | acceso abierto |
| Palabra clave: | Greenhouse gas sequestration Urban decarbonisation Nature-based solutions Green roof Artificial vision Case study INGENIERIA ELECTRICA PROYECTOS DE INGENIERIA MAQUINAS Y MOTORES TERMICOS |
| Sumario: | [EN] Cities are accountable for more than 70% of global CO2 emissions and consume around 65% of the world¿s energy. In the pathway towards urban decarbonisation, nature-based solutions rise as a promising opportunity. They improve a city¿s resilience, stabilise temperatures and capture CO2, both mitigating climate change effects and adapting to them. This paper presents a novel methodology to assess the potential effects of green roof retrofit in urban areas, selecting suitable buildings for greening and estimating their decarbonising capacity. The proposed method combines the use of Geographic Information Systems (GIS) and artificial vision algorithms to select the roofs. Once selected, direct carbon sequestration and energetic savings are estimated based on empirical results. This methodology is successfully applied to the L¿Illa Perduda neighbourhood of Valencia (Spain) as a case study. The GIS analysis shows that about 50% of the roofs¿ surface could be greened, directly absorbing around 350 tCO2 yr¿1 and reducing the energetic emissions of the neighbourhood in about 100 tCO2 yr¿1 by improving the insulating envelope of the buildings. The artificial vision computation selected 38% of the surface of the neighbourhood residential buildings. |
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