Integrated Multi-Domain Assessment of Build Environment Factors on Neighborhood Carbon Emissions: A Generative Simulation Approach in a Hot-Arid Climate
Abstract
Carbon emission at the urban neighborhood scale is significantly influenced by
the built environment (BE) factor. This impact is studied from multiple perspectives,
including energy demand, renewable energy generation and urban heat island (UHI)
effect. Although these domains have been widely investigated, their integrated
assessment remains limited, mainly due to the computational demand of urban-scale
simulation models and the lack of efficient workflow for sharing data among models.
The present study proposes a generative bottom-up framework for multi-domain
simulation that integrates physical simulation models in energy demand, photovoltaic
(PV) electricity generation and UHI domains in a unified workflow. In the next step, the
study generated and evaluated 442 neighborhood massing-scenarios based on eight
building typologies. The result shows that connected typologies, through reducing
operational energy demand, have a better overall performance than connected
typologies, while offering lower photovoltaic generation potential. Volume to surface
ratio and Height to Width factor emerged as the prevalent BE factor in many
performance metrics. The findings support block-scale urban design strategies favoring
larger parcels, higher site coverage ratio, a building height of six to seven stories and
connected building typologies to reduce neighborhood-scale carbon emissions.
the built environment (BE) factor. This impact is studied from multiple perspectives,
including energy demand, renewable energy generation and urban heat island (UHI)
effect. Although these domains have been widely investigated, their integrated
assessment remains limited, mainly due to the computational demand of urban-scale
simulation models and the lack of efficient workflow for sharing data among models.
The present study proposes a generative bottom-up framework for multi-domain
simulation that integrates physical simulation models in energy demand, photovoltaic
(PV) electricity generation and UHI domains in a unified workflow. In the next step, the
study generated and evaluated 442 neighborhood massing-scenarios based on eight
building typologies. The result shows that connected typologies, through reducing
operational energy demand, have a better overall performance than connected
typologies, while offering lower photovoltaic generation potential. Volume to surface
ratio and Height to Width factor emerged as the prevalent BE factor in many
performance metrics. The findings support block-scale urban design strategies favoring
larger parcels, higher site coverage ratio, a building height of six to seven stories and
connected building typologies to reduce neighborhood-scale carbon emissions.
