Urban energy use modeling methods and tools: A review and an outlook

Abbasabadi, Narjes; Ashayeri, Mehdi

doi:10.1016/j.buildenv.2019.106270

Cited by 119 publications

(51 citation statements)

References 98 publications

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“…An extended state of the art is available in the Supplementary Material (section SM1). Statistical, engineering, hybrid [7][8][9][10][11][12].…”

Section: State Of the Artmentioning

confidence: 99%

“…Existing models mostly focused on operational energy, but overlooked broader life-cycle impacts [3,10,[15][16][17].…”

Section: State Of the Artmentioning

confidence: 99%

“…Building stock energy models estimate the energy demand and the technical and economic effects of carbon mitigation strategies to support policies at sectoral level [7,8,10]. Building stock models can be broadly divided into top-down and bottom-up.…”

Section: Building Stock Modellingmentioning

confidence: 99%

“…They are in the need of reliable tools to assess the effect of policies on the carbon mitigation of the building sector. In recent times, significant efforts have been put on the development of building stock energy models to assess energy demand and energy saving potentials of building renovations [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Omitting these stages might result in overestimating the potential environmental benefits of renovation [16], especially for scenarios based on deeper interventions. Therefore, a holistic assessment of energy demand, resources durability and environmental performance of building stocks at larger spatio-temporal scales is increasingly needed for decision support in sustainable urban planning [10,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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A spatio-temporal life cycle assessment framework for building renovation scenarios at the urban scale

Mastrucci

Marvuglia

Benetto

et al. 2020

Renewable and Sustainable Energy Reviews

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Reducing the energy consumption of buildings is a priority for carbon emissions mitigation in urban areas. Building stock energy models have been developed to support decisions of public authorities in renovation strategies. However, the burdens of renovation interventions and their temporal distribution are mostly overlooked, leading to potential overestimation of environmental benefits. Life Cycle Assessment (LCA) provides a holistic estimation of environmental impacts, but further developments are needed to correctly consider spatio-temporal aspects.We propose a spatio-temporal LCA framework to assess renovation scenarios of urban housing stocks, integrating: 1) a geospatial building-by-building stock model, 2) energy demand modelling, 3) product-based LCA, and 4) a scenario generator.Temporal aspects are considered both in the lifecycle inventory and the lifecycle impact assessment phases, by accounting for the evolution of the existing housing stock and applying time-adjusted carbon footprint calculation.We apply the framework for the carbon footprint assessment of housing renovation in Esch-sur-Alzette (Luxembourg). Results show that the renovation stage represents 4% to 16% of the carbon footprint in the residual service life of existing buildings, respectively after conventional or advanced renovations. Under current renovation rates, the carbon footprint reduction would be limited to 3-4% by 2030. Pushing renovation rates to 3%, enables carbon reductions up to 28% by 2030 when combined with advanced renovations. Carbon reductions in the operational stage of buildings are offset by 8-9% due to the impacts of renovation. Using time-adjusted emissions, results in higher weight for the renovation stage and slightly lower benefits for renovation.

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“…An extended state of the art is available in the Supplementary Material (section SM1). Statistical, engineering, hybrid [7][8][9][10][11][12].…”

Section: State Of the Artmentioning

confidence: 99%

“…Existing models mostly focused on operational energy, but overlooked broader life-cycle impacts [3,10,[15][16][17].…”

Section: State Of the Artmentioning

confidence: 99%