• DocumentCode
    3521998
  • Title

    Long-term Forecast of Chinese Urban Residential Average Per Capita and Stratum Subdivision Estimation

  • Author

    De-chen, YANG ; Xiao-dong, Yang

  • Author_Institution
    Sch. of Manage., Harbin Inst. of Technol.
  • fYear
    2006
  • fDate
    5-7 Oct. 2006
  • Firstpage
    2172
  • Lastpage
    2176
  • Abstract
    Applicable "multi-model weight number generating integration method" has been proposed based on the gray forecast model. Studied to 2050, the main conclusions of Chinese urban average per capita residence change tendency are the average per capita residence built-up area will achieve 27.16 m2 at the end of 2006; it will achieve 30.51 m 2 in 2010; 36.63 m2 in 2020 and 43.61 m2 in 2030. In 2040 it will be 46.71 m2 with a turning point. Hereafter the numerical value goes to sTab. Before 2025, non-city residence area has been counted is one of the main factors of average per capita residence increasing. Total amount of dismantle and build will meet its balance after 2045. After 2020, main character of city residential stratum might be summarized as "increase in upper layers and decrease in bottom layers"
  • Keywords
    economic forecasting; estimation theory; grey systems; Chinese urban residential average per capita; gray forecast model; long-term forecasting; multimodel weight number generating integration method; stratum subdivision estimation; Aerospace industry; Buildings; Cities and towns; Construction industry; Demand forecasting; Economic forecasting; Predictive models; Technology forecasting; Technology management; Turning; Grey forecast model; Multi-model weight number generating integration method; Residential average per capita; Stratum subdivision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2006. ICMSE '06. 2006 International Conference on
  • Conference_Location
    Lille
  • Print_ISBN
    7-5603-2355-3
  • Type

    conf

  • DOI
    10.1109/ICMSE.2006.314152
  • Filename
    4105256