Modelling building stock energy use and carbon emission scenarios

Abstract

Purpose

A systematic approach is needed to engage a broad range of stakeholders to reduce greenhouse gas (GHG) emissions from energy use in the building sector. The purpose of this paper is to develop a systems‐based bottom‐up approach for this purpose, and to demonstrate its application in a case study of office building stock in the State of New South Wales (NSW) in Australia.

Design/methodology/approach

A conceptual framework for the general method is developed based on a cross‐typology matrix of energy consumption and supply on the one hand, and intervention schemes or policy instruments on the other. This is then tested and demonstrated using a case study of commercial office building stock, with building energy demand calculated using a validated computer energy simulation tool for a representative set of office buildings within a local government area (LGA). The energy consumption and associated GHG emissions are then aggregated up from LGA to the whole State. The impact projections of different intervention schemes are compared and mapped spatially across the State.

Findings

Results have demonstrated the significance and capability of the proposed approach, in allowing quantitative comparisons of the relative effectiveness of a specific set of regulatory, technical and behavioral scenario settings on the spatial distribution and trends in energy consumption and GHG emissions of the NSW office building stock to 2020.

Research limitations/implications

Further case studies involving mixed building types and specific building types with greater granularity of modelling details, energy use and supply options, and spatial resolution are needed.

Originality/value

The structured cross‐typology approach is a novel contribution to bottom‐up modelling approaches to designing and/or assessing the effectiveness of specific policy instruments, alone or in combination. It will enable policy makers, property portfolio owners, utility companies and building tenants to assess the broader impacts of their specific actions towards a common goal.

Citation

Foliente, G. and Seo, S. (2012), "Modelling building stock energy use and carbon emission scenarios", Smart and Sustainable Built Environment, Vol. 1 No. 2, pp. 118-138. https://doi.org/10.1108/20466091211260578