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|Title:||A framework of evolution and optimization of regional population distribution for sustainable development in China||Authors:||Lu, Chen||Advisors:||Shen, Qiping Geoffrey (BRE)||Keywords:||Population -- Economic aspects -- China
Sustainable development -- China.
|Issue Date:||2015||Publisher:||The Hong Kong Polytechnic University||Abstract:||Economic globalization promotes urbanization especially in developing countries. In China, regional economic integration has become a basic principle of social and economic development which could promote entire national economy. With the rapid development of urbanization, regional population distribution changes correspondingly. Excessive population agglomeration brings about problems like environmental disruption and resource waste, while too small population size is difficult to support economic development. In China, a big problem government is facing for years is population planning with sustainability considerations for social and economic development at regional level. Therefore, how to provide effective support for the regional population distribution is a necessary and significant research question. As documented in literature, there are three major gaps in this research area. First, the studies on population density functions are mostly limited on the mathematical expression, whereas, innovation of population density model taking environmental factors into account at regional level is rare. Second, research about evolution and optimization of population distribution at regional level has yet to be developed. Third, an optimization model providing quantitative support to formulate policy control for sustainable regional planning is urgent to be investigated. In order to fill these research gaps and answer the research question, this research develops a framework of evolution and optimization of regional population distribution for sustainable development. Five main objectives of this research are achieved and they are (1) to investigate the evolution mechanism of regional population distribution through qualitative and quantitative method from the perspective of sustainable development, (2) to identify the factors of population growth for sustainable development, (3) to set up an evolution model at regional level by identifying the determinants of density gradient, then, (4) to develop an optimization model for sustainable regional population redistribution and (5) to validate the integrated framework of evolutionand optimization for sustainable development. In the process of achieving these objectives, four research methods are used in this research: document analysis, analysis and induction, modeling and case study. Document analysis, analysis and induction and modeling are used to achieve Objective 1 and Objective 2 Evolution Mechanism; modeling are conducted to achieve Objective 3 Evolution Model; analysis and induction and modeling are employed to achieve Objective 4 Optimization Model; case study is used to achieve Objective 5 Framework Validation. The research conclusions include major findings from the document analysis, analysis and induction, modeling and case study. Ultimately, the results of this validation process prove that the framework developed in the research can provide theoretical and technical support for regional planning about population distribution by facilitating planners to understand evolution mechanism and conduct optimization. This research has contributed to the knowledge by investigating evolution mechanism and setting up optimization model of regional population distribution for sustainable development. Taking sustainability into account, it not only provides an improved research about how the regional population distribution evolves, but also provides effective solutions for unreasonable distribution. The main contributions of this research are: (1) it contributes to the research of regional population distribution from the perspective of sustainable development; (2) it reveals the driving force of urban population growth by identifying the influencing factors; (3) it improves the traditional population density model; (4) it develops an optimization model that helps reveal the proper population distribution for sustainable development.||Description:||PolyU Library Call No.: [THS] LG51 .H577P BRE 2015 Lu
209 pages :illustrations (some color)
|URI:||http://hdl.handle.net/10397/35467||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of Oct 14, 2018
Citations as of Oct 14, 2018
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