Technical Papers
Jul 7, 2015

Evaluating and Forecasting the Drivers of Water Use in a City: Model Development and a Case from Beijing

Publication: Journal of Water Resources Planning and Management
Volume 142, Issue 1

Abstract

Comprehensive studies examining the driving forces of water use and evaluating their historical and future effects are paramount for cities concerned about water shortages. Conventional studies using input-output-structural decomposition analysis (IO-SDA) identify aggregated factors (technology, industrial structure, and production scale), which are not detailed, concrete, and controllable enough for systematic water management into the future. Some recent strategies adding additional factors could be further improved. This study focuses on decomposing the factor of production scale into more detailed drivers (population, consumption pattern, and per capita consumption volume). This was achieved by extending the IO-SDA model with an impact = population × affluence × technology (IPAT) analysis. The resultant IO-IPAT-SDA approach developed a compartmentalized decomposition for water use drivers and evaluated their impacts on water use changes by different final users. This model can be used in scenario forecasting to evaluate drivers and potential effects into the future. As a test case, the study assessed the driving factors of water use in Beijing from 1987 to 2020 and found that Beijing’s production-related water use per year decreased from 3.92 to 2.68×109m3 during 1987–2010, since water use efficiency gains offset potential water use increases by other drivers. If Beijing maintains the current development patterns, production-related water use would increase another 51.1% by 2020. Forecasting scenarios show that relying on improvements in water use efficiency alone will not stabilize Beijing’s future water use, but a comprehensive control of various drivers could maintain sustainable levels of water usage.

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Acknowledgments

We thank the National Natural Science Foundation of China (No. 51439001), the National Science Foundation for Innovative Research Group (No. 51421065), and the International Science & Technology Cooperation Program of China (No. 2011DFA72420) for their financial support.

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Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 142Issue 1January 2016

History

Received: Jul 29, 2014
Accepted: Mar 25, 2015
Published online: Jul 7, 2015
Discussion open until: Dec 7, 2015
Published in print: Jan 1, 2016

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Yuan Zhi
Ph.D. Student, State Key Laboratory of Water Environmental Simulation, School of Environment, Beijing Normal Univ., No. 19 Xinjiekouwai St., Beijing 100875, China.
Zhifeng Yang [email protected]
Professor. State Key Laboratory of Water Environmental Simulation, School of Environment, Beijing Normal Univ., No. 19 Xinjiekouwai St., Beijing 100875, China (corresponding author). E-mail: [email protected]
Xin-An Yin, Ph.D.
State Key Laboratory of Water Environmental Simulation, School of Environment, Beijing Normal Univ., No. 19 Xinjiekouwai St., Beijing 100875, China.
Paul B. Hamilton
Senior Research Assistant, Research and Collections Division, Canadian Museum of Nature, P.O. Box 3443, Station D, Ottawa, ON, Canada K1P 6P4.
Lijia Zhang
Ph.D. Student, State Key Laboratory of Water Environmental Simulation, School of Environment, Beijing Normal Univ., No. 19 Xinjiekouwai St., Beijing 100875, China.

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