Report

RP 3035 final report: modelling uptake of water conservation and efficiency measures in Sydney

7 Feb 2019
DOI

http://doi.org/10.25916/5ce8dc21981b0
Description

The Modelling the Uptake of Water Conservation and Efficiency Measures in Sydney report team worked with Sydney Water’s WaterFix Program to validate their agent based, decision-making model aimed at encouraging consumers to participate in water saving programs.

Customer behaviour plus the development and adoption of water efficient appliances were identified as the two biggest uncertainties in estimating long-term demand for water. The team used data from the WaterFix program collected during the Millennium Drought (2001-2009) and applied it to their model, replicating Sydney householder decision-making at the time. The modelling is hoped to contribute to future water supply resilience for Sydney at a time of declining dam inflows and increasing population.

The project has demonstrated the benefits of Agent-based Model (ABM) in understanding the implications of different policy setting in increasing the adoption of water conservation actions. In comparison to more traditional water demand models, the ABM can explore system leverage points and scenarios, as well as describing the complex behavioural responses across the community. The ABM approach is not as data intensive as other water demand modelling approaches so it can allow for rapid prototyping to assess water conservation policy options.

The approach demonstrated in this project can provide a useful approach for the water sector in targeting investment in their water conservation programs by improving institutional capacity to evaluate likely impacts of different policy options. The emergence of big data, from the widespread uptake of smart meters and the ‘internet-of-things’, creates an opportunity to develop ABMs that simulate individual responses to water conservation programs.

Publication Details
Identifiers: 
doi: 
10.25916/5ce8dc21981b0
Language: 
English
Peer Reviewed: 
Yes
Published year only: 
2019
5
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