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Water Resources Management

, Volume 28, Issue 10, pp 2967–2980 | Cite as

Urban Household Water Demand in Beijing by 2020: An Agent-Based Model

  • Xiao-Chen Yuan
  • Yi-Ming WeiEmail author
  • Su-Yan Pan
  • Ju-Liang Jin
Article

Abstract

Beijing is faced with severe water scarcity due to rapid socio-economic development and population expansion, and a guideline for water regulation has been released to control the volume of national water use. To cope with water shortage and meet regulation goal, it has great significance to study the variations of water demand. In this paper, an agent-based model named HWDP is developed for the prediction of urban household water demand in Beijing. The model involves stochastic behaviors and feedbacks caused by two agent roles which are government agent and household agent. The government agent adopts economic and propagandist means to make household agent optimize its water consumption. Additionally, the consumption is also affected by the basic water demand deduced from extended linear expenditure system. The results indicate that the total water demand of urban households in Beijing will increase to 317.5 million cubic meters by 2020, while the water price keeps growing at a low level. However, it would drop to 294.9 million cubic meters with high growth of water price and low increment in per capita disposable income. Finally, some policy recommendations on water regulation are made.

Keywords

Water demand Agent Extended linear expenditure system Genetic algorithm 

Notes

Acknowledgments

The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (NSFC) under Grant Nos. 71020107026, 51309072; National Basic Research Program of China under Grant No. 2012CB955704; S&T Program of MOST under Grant No. 2012BAC20B01; the Public Welfare Industry Funding for Research and Special Projects of Ministry of Water Resources of China (201301003). We thank editors of WRM and the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper according to which we improved the content.

References

  1. Ahmad S, Prashar D (2010) Evaluating municipal water conservation policies using a dynamic simulation model. Water Resour Manag 24(13):3371–3395CrossRefGoogle Scholar
  2. Altunkaynak A, Özger M, Çakmakci M (2005) Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resour Manag 19(5):641–654CrossRefGoogle Scholar
  3. Alvisi S, Franchini M, Marinelli A (2003) A stochastic model for representing drinking water demand at residential level. Water Resour Manag 17(3):197–222CrossRefGoogle Scholar
  4. Arbués F, García-Valiñas MÁ, Martínez-Espiñeira R (2003) Estimation of residential water demand: a state-of-the-art review. J Socio-Econ 32(1):81–102CrossRefGoogle Scholar
  5. Arbués F, Barberán R, Villanúa I (2004) Price impact on urban residential water demand: a dynamic panel data approach. Water Resour Res 40(11), W11402Google Scholar
  6. Arbués F, Villanúa I, Barberán R (2010) Household size and residential water demand: an empirical approach. Aust J Agric Resour Econ 54(1):61–80CrossRefGoogle Scholar
  7. Athanasiadis IN, Mentes AK, Mitkas PA, Mylopoulos YA (2005) A hybrid agent-based model for estimating residential water demand. Simulation 81(3):175–187CrossRefGoogle Scholar
  8. Babel MS, Gupta AD, Pradhan P (2007) A multivariate econometric approach for domestic water demand modeling: an application to Kathmandu, Nepal. Water Resour Manag 21(3):573–589CrossRefGoogle Scholar
  9. Balling RC, Gober P (2007) Climate variability and residential water use in the city of Phoenix, Arizona. J Appl Meteorol Climatol 46(7):1130–1137CrossRefGoogle Scholar
  10. Barthel R, Janisch S, Schwarz N et al (2008) An integrated modelling framework for simulating regional-scale actor responses to global change in the water domain. Environ Model Softw 23(9):1095–1121CrossRefGoogle Scholar
  11. Beijing Municipal Bureau of Statistics (2005) Beijing statistical yearbook 2005. China Statistics Press, Beijing (in Chinese)Google Scholar
  12. Beijing Municipal Bureau of Statistics (2006) Beijing statistical yearbook 2006. China Statistics Press, Beijing (in Chinese)Google Scholar
  13. Beijing Municipal Bureau of Statistics (2007) Beijing statistical yearbook 2007. China Statistics Press, Beijing (in Chinese)Google Scholar
  14. Beijing Municipal Bureau of Statistics (2008) Beijing statistical yearbook 2008. China Statistics Press, Beijing (in Chinese)Google Scholar
  15. Beijing Municipal Bureau of Statistics (2009) Beijing statistical yearbook 2009. China Statistics Press, Beijing (in Chinese)Google Scholar
  16. Beijing Municipal Bureau of Statistics (2010) Beijing statistical yearbook 2010. China Statistics Press, Beijing (in Chinese)Google Scholar
  17. Beijing Municipal Bureau of Statistics (2011) Beijing statistical yearbook 2011. China Statistics Press, Beijing (in Chinese)Google Scholar
  18. Beijing Municipal Bureau of Statistics (2012) Beijing statistical yearbook 2012. China Statistics Press, Beijing (in Chinese)Google Scholar
  19. Beijing Municipal Commission of Development and Reform (2011) The twelfth five-year plan for the conservation and utilization of water resources (in Chinese)Google Scholar
  20. Billings RB, Agthe DE (1998) State-space versus multiple regression for forecasting urban water demand. J Water Resour Plan Manag 124(2):113–117CrossRefGoogle Scholar
  21. Browne AL, Medd W, Anderson B (2013) Developing novel approaches to tracking domestic water demand under uncertainty-a reflection on the “Up Scaling” of social science approaches in the United Kingdom. Water Resour Manag 27(4):1013–1035CrossRefGoogle Scholar
  22. Campisi-Pinto S, Adamowski J, Oron G (2012) Forecasting urban water demand via wavelet-denoising and neural network models. Case study: city of Syracuse, Italy. Water Resour Manag 26(12):3539–3558CrossRefGoogle Scholar
  23. Chen H, Yang ZF (2009) Residential water demand model under block rate pricing: a case study of Beijing, China. Commun Nonlinear Sci Numer Simul 14(5):2462–2468CrossRefGoogle Scholar
  24. Chu JY, Wang C, Chen JN, Wang H (2009) Agent-based residential water use behavior simulation and policy implications: a case-study in Beijing city. Water Resour Manag 23(15):3267–3295CrossRefGoogle Scholar
  25. Danielson LE (1979) An analysis of residential demand for water using micro time-series data. Water Resour Res 15(4):763–767CrossRefGoogle Scholar
  26. Dharmaratna D, Harris E (2012) Estimating residential water demand using the Stone-Geary functional form: the case of Sri Lanka. Water Resour Manag 26(8):2283–2299CrossRefGoogle Scholar
  27. Espey M, Espey J, Shaw WD (1997) Price elasticity of residential demand for water: a meta-analysis. Water Resour Res 33(6):1369–1374CrossRefGoogle Scholar
  28. Firat M, Yurdusev MA, Turan ME (2009) Evaluation of artificial neural network techniques for municipal water consumption modeling. Water Resour Manag 23(4):617–632CrossRefGoogle Scholar
  29. Fox C, McIntosh BS, Jeffrey P (2009) Classifying households for water demand forecasting using physical property characteristics. Land Use Policy 26(3):558–568CrossRefGoogle Scholar
  30. Franczyk J, Chang H (2009) Spatial analysis of water use in Oregon, USA, 1985–2005. Water Resour Manag 23(4):755–774CrossRefGoogle Scholar
  31. Galán JM, López-Paredes A, del Olmo R (2009) An agent-based model for domestic water management in Valladolid metropolitan area. Water Resour Res 45, W05401Google Scholar
  32. Gaudin S (2006) Effect of price information on residential water demand. Appl Econ 38(4):383–393CrossRefGoogle Scholar
  33. Ghiassi M, Zimbra DK, Saidane H (2008) Urban water demand forecasting with a dynamic artificial neural network model. J Water Resour Plan Manag 134(2):138–146CrossRefGoogle Scholar
  34. Gilbert N (2007) Agent based models. Sage, LondonGoogle Scholar
  35. Holland JH (1995) Hidden order: How adaptation builds complexity. Addison-Wesley, ReadingGoogle Scholar
  36. House-Peters LA, Chang H (2011) Urban water demand modeling: review of concepts, methods, and organizing principles. Water Resour Res 47, W05401Google Scholar
  37. House-Peters L, Pratt B, Chang H (2010) Effects of urban spatial structure, sociodemographics, and climate on residential water consumption in Hillsboro, Oregon. J Am Water Resour Assoc 46(3):461–472Google Scholar
  38. Howe CW, Linaweaver FP (1967) The impact of price on residential water demand and its relation to system design and price structure. Water Resour Res 3(1):13–32CrossRefGoogle Scholar
  39. Jin JL, Wei YM (2008) Generalized intelligent assessment methods for complex systems and applications. Science Press, Beijing (in Chinese)Google Scholar
  40. Li YH, Wang DX (2008) Analysis of the urban household water demand function and evaluation of the water saving effect of water tariff. J China Inst Water Resour Hydropower Res 6(2):156–160 (in Chinese)Google Scholar
  41. Li XF, Liu GZ, He CZ (2001) Selection of forecast model for water consumption of Chengdu’s residents in the future. J Sichuan Univ (Eng Sci Ed) 33(6):104–107 (in Chinese)Google Scholar
  42. Lluch C (1973) The extended linear expenditure system. Eur Econ Rev 4(1):21–32CrossRefGoogle Scholar
  43. Lyman RA (1992) Peak and off-peak residential water demand. Water Resour Res 28(9):2159–2167CrossRefGoogle Scholar
  44. Mayer PW, DeOreo WB (1999) Residential end uses of water. AWWA Research Foundation, DenverGoogle Scholar
  45. Miller JH, Page SE (2007) Complex adaptive systems: An introduction to computational models of social life. Princeton University Press, PrincetonGoogle Scholar
  46. Milly PCD, Betancourt J, Falkenmark M et al (2008) Stationarity is dead: whither water management? Science 319:573–574CrossRefGoogle Scholar
  47. Praskievicz S, Chang H (2009) Identifying the relationships between urban water consumption and weather variables in Seoul, Korea. Phys Geogr 30(4):324–337CrossRefGoogle Scholar
  48. Qi C, Chang NB (2011) System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts. J Environ Manag 92(6):1628–1641CrossRefGoogle Scholar
  49. Ruth M, Bernier C, Jollands N, Golubiewski N (2007) Adaptation of urban water supply infrastructure to impacts from climate and socioeconomic changes: the case of Hamilton, New Zealand. Water Resour Manag 21(6):1031–1045CrossRefGoogle Scholar
  50. Schleich J, Hillenbrand T (2009) Determinants of residential water demand in Germany. Ecol Econ 68(6):1756–1769CrossRefGoogle Scholar
  51. Shandas V, Rao M, McGrath MM (2012) The implications of climate change on residential water use: a micro-scale analysis of Portland (OR), USA. J Water Clim Chang 3(3):225–238CrossRefGoogle Scholar
  52. Valkering P, Tabara JD, Wallman P, Offermans A (2009) Modelling cultural and behavioural change in water management: an integrated, agent based, gaming approach. Integr Assess 9(1):19–46Google Scholar
  53. Wu L, Zhou HC (2010) Urban water demand forecasting based on HP filter and fuzzy neural network. J Hydroinformatics 12(2):172–184CrossRefGoogle Scholar
  54. Zhou SL, McMahon TA, Walton A, Lewis J (2000) Forecasting daily urban water demand: a case study of Melbourne. J Hydrol 236(3–4):153–164CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Xiao-Chen Yuan
    • 1
    • 2
  • Yi-Ming Wei
    • 1
    • 2
    Email author
  • Su-Yan Pan
    • 1
    • 2
  • Ju-Liang Jin
    • 1
    • 3
  1. 1.Center for Energy and Environmental Policy ResearchBeijing Institute of Technology (BIT)BeijingChina
  2. 2.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina
  3. 3.School of Civil EngineeringHefei University of TechnologyHefeiChina

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