A scenario-based approach for assessing the energy performance of urban development pathways
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ML-based analysis has shown that the development of efficient public transportation is dependent on both the extent of urban sprawl and the local development around transportation hubs.
“ML-based analysis has shown that the development of efficient public transportation is dependent on both the extent of urban sprawl and the local development around transportation hubs [547,737].”
From Tackling climate change with machine learning (2023) · cites this paper as [737]
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Tackling climate change with machine learning
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- Authors
- Silva, M., Leal, V., Oliveira, V., & Horta, I. M.
- Affiliations
- Universidade do Porto (PT)
- Corresponding author
- Silva, Mafalda
- Venue
- Sustainable Cities and Society
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 40
- Pages
- 372-382
- ISSN
- 2210-6707 (ISSN-L)
- OpenAlex
- W2793585826
- Cited by
- 53
- References
- 63
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 6 · 2023: 2 · 2022: 7 · 2021: 5 · 2020: 5 · 2019: 9 · 2018: 4
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Sustainable Cities and Society (publishedVersion)
- Funders
- Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção (SFRH/BD/52305/2013)
- Fundação para a Ciência e a Tecnologia (SFRH/BD/52305/2013)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- urban energy performance, scenario-based assessment, artificial neural networks, energy demand estimation, space heating, building cooling, transit-oriented development, infill development, multi-family housing, green infrastructure, urban form, Porto
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