230
Views
9
CrossRef citations to date
0
Altmetric
Original Articles

A method to estimate urban optimum population conditions: a case study of Xiamen, China

, &
Pages 324-328 | Published online: 21 Jul 2010
 

Abstract

Maintaining an optimum population is crucial for urban sustainable development. A happiness degree model was constructed to estimate the optimum population of Xiamen, southern China. Happiness degree was defined as the total satisfaction of households working and living in Xiamen in terms of possessing and/or consuming certain resources. The optimum city size was considered for a city in which the population has maximised the happiness degree. The resources considered in the assessment were those possessed or consumed by citizens: GDP, investment in education and in science and technology, water, electricity, housing, green land, transportation. The results show that the optimum population of Xiamen in 2007 would be about 1.66 million, i.e. the actual population of nearly 2.43 million is obviously too large. Insufficient electricity supply was the most significant constraint for Xiamen as a sustainable city.

Acknowledgements

This study was supported by the Chinese Academy of Sciences (CAS) (KZCX2-YW-450), the Ministry of Science and Technology of China (2009DFB90120), One Hundred Talents Program and the Academy-Locality Cooperation Program of CAS.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 235.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.