2020•EPiC series in built environmentOpen access

The Relationship of Embodied Energy and Cost of Buildings and Building Materials

Manish Dixit, Sarel Lavy, Parag Vijay Abdagiri

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Abstract

Buildings consume nearly half of global energy each year in their construction and operation as embodied and operational energy releasing approximately 40% of global carbon emission. Embodied energy (EE) is consumed indirectly through the use of construction materials, assemblies, and equipment, and directly in construction processes and related transportation. Operational energy (OE) is consumed in building air-conditioning, heating, lighting, and powering equipment. Both EE and OE must be minimized to lower this huge energy footprint of buildings. To decrease EE, a complete and accurate EE assessment is essential, which, however, is a quite data-intensive and time-consuming process. EE is conventionally computed using process- and input-output (IO)-based methods. Hybrid approaches that combine the two methods are also used to compute EE. In an IO-based method, macroeconomic data is translated into energy flows, which indicates a potential relationship between energy and economic flows, and consequently between EE and cost. In this paper, we investigated the EE-cost relationship at the building and construction material levels and found a strong positive correlation between the EE and cost of the study buildings. The results indicate a need to further analyze this relationship through regression analysis to see if EE can be predicted from cost data.

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Buildings consume nearly half of global energy each year in their construction and operation as embodied and operational energy releasing approximately 40% of global carbon emission. Embodied energy (EE) is consumed indirectly through the use of construction materials, assemblies, and equipment, and directly in construction processes and related transportation. Operational energy (OE) is consumed in building air-conditioning, heating, lighting, and powering equipment. Both EE and OE must be minimized to lower this huge energy footprint of buildings. To decrease EE, a complete and accurate EE assessment is essential, which, however, is a quite data-intensive and time-consuming process. EE is conventionally computed using process- and input-output (IO)-based methods. Hybrid approaches that combine the two methods are also used to compute EE. In an IO-based method, macroeconomic data is translated into energy flows, which indicates a potential relationship between energy and economic flows, and consequently between EE and cost. In this paper, we investigated the EE-cost relationship at the building and construction material levels and found a strong positive correlation between the EE and cost of the study buildings. The results indicate a need to further analyze this relationship through regression analysis to see if EE can be predicted from cost data.

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Available abstract

Buildings consume nearly half of global energy each year in their construction and operation as embodied and operational energy releasing approximately 40% of global carbon emission. Embodied energy (EE) is consumed indirectly through the use of construction materials, assemblies, and equipment, and directly in construction processes and related transportation. Operational energy (OE) is consumed in building air-conditioning, heating, lighting, and powering equipment. Both EE and OE must be minimized to lower this huge energy footprint of buildings. To decrease EE, a complete and accurate EE assessment is essential, which, however, is a quite data-intensive and time-consuming process. EE is conventionally computed using process- and input-output (IO)-based methods. Hybrid approaches that combine the two methods are also used to compute EE. In an IO-based method, macroeconomic data is translated into energy flows, which indicates a potential relationship between energy and economic flows, and consequently between EE and cost. In this paper, we investigated the EE-cost relationship at the building and construction material levels and found a strong positive correlation between the EE and cost of the study buildings. The results indicate a need to further analyze this relationship through regression analysis to see if EE can be predicted from cost data.

Key concepts: Embodied energy, Carbon footprint, Footprint, Process (computing), Energy (signal processing), Ecological footprint, Energy consumption, Computer science

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