|
|
Exploring price effects on the residential water conservation technology diffusion process: a case study of Tianjin city |
Junying CHU1, Hao WANG1, Can WANG2( ) |
1. State Key Laboratory of Simulation and Regulation of River Basin Water Cycle, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; 2. State Key Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China |
|
|
Abstract Reforms of the water pricing management system and the establishment of a flexible water pricing system are significant for cities in northern China to tackle their critical water issues. The WATAP (Water conservation Technology Adoption Processes) model is developed in order to capture the water conservation technology adoption process under different price scenarios with disaggregate water demands down to the end use level. This model is explicitly characterized by the technological selection process under maximum marginal benefit assumption by different categories of households. In particular, when households need to purchase water devices in the provision market with the consideration of complex factors such as the life span, investment and operating costs of the device, as well as the regulated water price by the government. Applied to Tianjin city, four scenarios of water price evolutions for a long-term perspective (from year 2011 to 2030) are considered, including BAU (Business As Usual), SP1 (Scenario of Price increase with constant annual rate), SP2 (Scenario of Price increase every four years) and SP3 (Scenario of Price increase with affordable constraint), considering many factors such as historic trends, affordability and incentives for conservation. Results show that on aggregate 2.3%, 11.0% and 18.2% of fresh water can be saved in the residential sector in scenario SP1, SP2 and SP3, respectively, compared with the BAU scenario in the year 2030. The water price signals can change the market shares of different water appliances, as well as the water end use structure of households, and ultimately improve water use efficiency. The WATAP model may potentially be a helpful tool to provide insights for policy makers on water conservation technology policy analysis and assessment.
|
Keywords
technology selection
model optimization
water price
scenario analysis
consumer behavior
|
Corresponding Author(s):
WANG Can,Email:canwang@tsinghua.edu.cn
|
Issue Date: 01 October 2013
|
|
1 |
Bradley R M. Forecasting domestic water use in rapidly urbanizing areas in Asia. Journal of Environmental Engineering , 2004, 4(4): 465–471 doi: 10.1061/(ASCE)0733-9372(2004)130:4(465)
|
2 |
Butler D, Memon F A. Water Demand Management. London: IWA Publishing, 2006
|
3 |
Seckler D. The New Era of Water Resources Management. Research Report 1 . Colombo: International Irrigation Management Institute (IIMI), 1996
|
4 |
Song X M, Kong F Z, Zhan C S. Assessment of water resources carrying capacity in Tianjin city of China. Water Resources Management , 2011, 25(3): 857–873 doi: 10.1007/s11269-010-9730-9
|
5 |
Bai X M, Imura H. Towards sustainable urban water resource management: a case study in Tianjin, China. Sustainable Development , 2001, 9(1): 24–35 doi: 10.1002/sd.149
|
6 |
Rogers E M. Diffusion of Innovations. New York: The Free Press, 1983
|
7 |
Attewell P. Technology diffusion and organizational learning: the case of business computing. Organization Science , 1992, 3(1): 1–19 doi: 10.1287/orsc.3.1.1
|
8 |
Caswell M, Zilberman D. The choices of irrigation technologies in California. American Journal of Agricultural Economics , 1985, 5: 223–234 doi: 10.2307/1240673
|
9 |
Bagheri A, Ghorbani A. Adoption and non-adoption of sprinkler irrigation technology in Ardabil province of Iran. African Journal of Agricultural Research , 2011, 6(5): 1085–1089 10.5897/AJAR09.380
|
10 |
Berger T. Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics , 2001, 25(2-3): 245–260 doi: 10.1111/j.1574-0862.2001.tb00205.x
|
11 |
Corral-Verdugoa V, Bechtelb R B, Fraijo-Singc B.Environmental beliefs and water conservation: an empirical study. Journal of Environmental Psychology , 2003, 23(3): 247–257 . doi: 10.1016/S0272-4944(02)00086-5
|
12 |
Randolph B, Troy P. Attitudes to conservation and water consumption. Environmental Science & Policy , 2008, 11(5): 441–455 doi: 10.1016/j.envsci.2008.03.003
|
13 |
Willis R M, Stewart R A, Panuwatwanich K, Williams P R, Hollingsworth A L. Quantifying the influence of environmental and water conservation attitudes on household end use water consumption. Journal of Environmental Management , 2011, 92(8): 1996–2009 doi: 10.1016/j.jenvman.2011.03.023 pmid:21486685
|
14 |
Arbuésa F, María ángeles García-Vali?asb M A, Martínez-Espi?eira R. Estimation of residential water demand: a state-of-the-art review. Journal of Socio-Economics , 2003, 21(1): 81–102 doi: 10.1016/S1053-5357(03)00005-2
|
15 |
Inman D, Jeffrey P. A review of residential demand-side management tool performance and influences on implementation effectiveness. Urban Water Journal , 2006, 3(3): 127–143 doi: 10.1080/15730620600961288
|
16 |
Chu J, Wang C, Chen J N, Wang H. Agent-based residential water use behavior simulation and policy implications: a case-study in Beijing city. Water Resources Management , 2009, 23(15): 3267–3295 doi: 10.1007/s11269-009-9433-2
|
17 |
Galán J M, del Olmo R, López-Paredes A. Diffusion of domestic water conservation technologies in an ABM-GIS integrated model. In: Corchado E, Abraham A, Pedrycz W, eds. Hybrid Artificial Intelligent Systems (HAIS) 2008, Lecture Notes in Computer Science (LNCS) 5271 . Berlin: Springer, 2008, 567–574 . doi: 10.1007/978-3-540-87656-4_70
|
18 |
Ahmad S, Prashar D. Evaluating municipal water conservation policies using a dynamic simulation model. Water Resources Management , 2010, 24(13): 3371–3395 doi: 10.1007/s11269-010-9611-2
|
19 |
Chu J Y, Chen J N. Potential Evaluation and Policy Analysis of Urban Water Conservation and Wastewater Reclamation in China. Beijing: Science Press, 2009 (In Chinese)
|
20 |
Jordan J L. Georgia Water Series Issue 4: Issues in Water Pricing. Athens University of Georgia , 1998, Faculty Series 98-16 . Available online at http://ageconsearch.umn.edu/bitstream/16652/1/fs9816.pdf (accessed July1, 2013)
|
21 |
Leydesdorff L. The complex dynamics of technological innovation: a comparison of models using cellular automata. Systems Research and Behavioral Science , 2002, 19(6): 563–575 doi: 10.1002/sres.482
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|