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

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.

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References

  1. Ahmad S, Prashar D (2010) Evaluating municipal water conservation policies using a dynamic simulation model. Water Resour Manag 24(13):3371–3395

    Article  Google 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):641–654

    Article  Google 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):197–222

    Article  Google 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):81–102

    Article  Google 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), W11402

    Google 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):61–80

    Article  Google Scholar 

  7. Athanasiadis IN, Mentes AK, Mitkas PA, Mylopoulos YA (2005) A hybrid agent-based model for estimating residential water demand. Simulation 81(3):175–187

    Article  Google 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):573–589

    Article  Google 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):1130–1137

    Article  Google 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):1095–1121

    Article  Google 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):113–117

    Article  Google 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):1013–1035

    Article  Google 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):3539–3558

    Article  Google 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):2462–2468

    Article  Google 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):3267–3295

    Article  Google Scholar 

  25. Danielson LE (1979) An analysis of residential demand for water using micro time-series data. Water Resour Res 15(4):763–767

    Article  Google 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):2283–2299

    Article  Google Scholar 

  27. Espey M, Espey J, Shaw WD (1997) Price elasticity of residential demand for water: a meta-analysis. Water Resour Res 33(6):1369–1374

    Article  Google 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):617–632

    Article  Google Scholar 

  29. Fox C, McIntosh BS, Jeffrey P (2009) Classifying households for water demand forecasting using physical property characteristics. Land Use Policy 26(3):558–568

    Article  Google Scholar 

  30. Franczyk J, Chang H (2009) Spatial analysis of water use in Oregon, USA, 1985–2005. Water Resour Manag 23(4):755–774

    Article  Google 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, W05401

    Google Scholar 

  32. Gaudin S (2006) Effect of price information on residential water demand. Appl Econ 38(4):383–393

    Article  Google 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):138–146

    Article  Google Scholar 

  34. Gilbert N (2007) Agent based models. Sage, London

    Google Scholar 

  35. Holland JH (1995) Hidden order: How adaptation builds complexity. Addison-Wesley, Reading

    Google Scholar 

  36. House-Peters LA, Chang H (2011) Urban water demand modeling: review of concepts, methods, and organizing principles. Water Resour Res 47, W05401

    Google 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):461–472

    Google 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):13–32

    Article  Google 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):21–32

    Article  Google Scholar 

  43. Lyman RA (1992) Peak and off-peak residential water demand. Water Resour Res 28(9):2159–2167

    Article  Google Scholar 

  44. Mayer PW, DeOreo WB (1999) Residential end uses of water. AWWA Research Foundation, Denver

    Google Scholar 

  45. Miller JH, Page SE (2007) Complex adaptive systems: An introduction to computational models of social life. Princeton University Press, Princeton

    Google Scholar 

  46. Milly PCD, Betancourt J, Falkenmark M et al (2008) Stationarity is dead: whither water management? Science 319:573–574

    Article  Google Scholar 

  47. Praskievicz S, Chang H (2009) Identifying the relationships between urban water consumption and weather variables in Seoul, Korea. Phys Geogr 30(4):324–337

    Article  Google 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):1628–1641

    Article  Google 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):1031–1045

    Article  Google Scholar 

  50. Schleich J, Hillenbrand T (2009) Determinants of residential water demand in Germany. Ecol Econ 68(6):1756–1769

    Article  Google 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):225–238

    Article  Google 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):19–46

    Google Scholar 

  53. Wu L, Zhou HC (2010) Urban water demand forecasting based on HP filter and fuzzy neural network. J Hydroinformatics 12(2):172–184

    Article  Google 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):153–164

    Article  Google Scholar 

Download references

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.

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Correspondence to Yi-Ming Wei.

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Yuan, X., Wei, Y., Pan, S. et al. Urban Household Water Demand in Beijing by 2020: An Agent-Based Model. Water Resour Manage 28, 2967–2980 (2014). https://doi.org/10.1007/s11269-014-0649-4

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Keywords

  • Water demand
  • Agent
  • Extended linear expenditure system
  • Genetic algorithm