The International Demand Management Framework Stage 1

Publisher:
Institute for Sustainable Futures, UTS
Publication Type:
Report
Citation:
2006, pp. 1 - 29
Issue Date:
2006-01
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This report forms part of a larger study (Stage 1 of the International Demand Management Framework (IDMF)) which has been undertaken under the auspices of the International Water Association Task Force 7 of the Specialist Group Efficient Operation and Management. Current practice often utilises litres per capita per day (LCD) to describe and forecast water demand; however this practice has been found to be limited for planning purposes within water utilities. In its place, an emerging way forward is based on disaggregation of demand and robust comparison of both demand and supply options to improve reliability. Disaggregation of demand into sectors and end uses allows accurate forecasting of demand and strategic design of demand management options which may be used in complement to supply options. The findings indicate that Canal de Isabel II has completed excellent work in certain areas, such as drought and risk management, management of water losses, knowledge of supply and distribution system, and sector and end use data collection. There remains significant opportunity for Canal de Isabel II to incorporate other improvements toward best practice, including the following: ·approach the planning process in a coherent way that considers both demand and supply options and works through a logical sequence of steps ·utilise in-depth knowledge of sector and end-uses to strategically identify and design demand management options ·compare demand and supply options using a consistent economic analysis so that the solutions with the lowest cost to society can be selected and implemented ·involve a larger group of stakeholders at appropriate points in the planning process ·conduct pilot and implementation of chosen demand management options to initiate on-going learning about what works and doesn't in the local context & ·monitor and evaluate pilot and implementation programs using robust statistical methods.
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