WIT Press


Determinants Of Domestic Water Demand For The Beijing Region

Price

Free (open access)

Paper DOI

10.2495/WRM070111

Volume

103

Pages

10

Published

2007

Size

509 kb

Author(s)

D. Karimanzira, M. Jacobi & C. Ament

Abstract

The analysis of demand for water, including realistically forecasting future levels of demand, is an important and critical step in the economic analysis of water supply projects. The results of demand analysis will enable to determine the service levels to be provided, determine the size and timing of investments, estimate the financial and economic benefits of projects, and assess the ability and willingness to pay of the project beneficiaries. Furthermore, the surveys carried out during the demand assessment will provide data on cost savings, willingness to pay, income and other data needed for economic analysis. In this paper, methods of statistical analysis (correlation, regression, etc) will be used to determine the factors, which influence water demand. Each model region may have its own set determinants for domestic water demand and the importance of a given factor may vary from one region to another. Therefore, this paper focuses on the major determinants of domestic water demand for the Beijing region. Several models analyzing the determinants were compared. The models based on a feasible generalized least squares (FGLS) analysis. Keywords: decision support system, water demand models, statistical analysis, correlation and significance tests, regression analysis. 1 Background Increasing population growth and the associated process of urbanization in the semi arid city of Beijing, China requires a reliable source of water. Although the city currently has an inexpensive and abundant supply of water, it is imperative that the city faces the challenge associated with providing safe drinking water. This work is part of the Chinese – German joint project \“Towards Water-

Keywords

decision support system, water demand models, statistical analysis, correlation and significance tests, regression analysis.