| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 897107 | Technological Forecasting and Social Change | 2011 | 14 Pages |
The building sector is regarded as having one of the highest benefit–cost ratios from greenhouse gas (GHG) emission reduction strategies. However, because of uncertainties around household behaviour patterns, it is very difficult to assess and compare the GHG reduction impacts of different intervention schemes for whole housing stock. Intervention schemes include policy instruments such as incentives or rebates for energy efficient appliances or renewable energy, and regulatory building code requirements for energy efficiency. This paper presents a decision support tool based on mathematical diffusion that evaluates the adoption levels of different schemes or pathways towards reducing GHG emissions in housing stock. It is an extension of the Bass diffusion model that accommodates financial and non-financial benefits, ceilings of adoption and interactions between intervention options. The model capability was tested using a case study of seven suburbs in Brisbane, Australia, comprising of 25,000 houses and units. Estimates of GHG emission reductions to 2019 of a household rebate scheme for solar panels and a rebate scheme for solar hot water compared to a base case of no rebates were presented and analysed. Modelling also allowed identification of important characteristics of adoption trends that could assist policy makers and industry to substantially improve the design of effective intervention options.
Research Highlights► Forecasting the uptake of intervention options, which aim to speed up the reduction of GHG emissions in housing stock. ► A decision support tool based on mathematical diffusion. ► Implementation and calibration of the diffusion model using a large case study of housing stock.
