The Ability of Sustainable Development Research Based on Statistics Synthetic Appraisal Theory
Haiyi Sun, Ning Li, Jinbao Wang, Hongmei Yan
Available Online April 2016.
- https://doi.org/10.2991/emim-16.2016.92How to use a DOI?
- Sustainable development; Statistics synthetic; The Principal Component Analysis(PCA); Analytic Hierarchy Process (AHP); Cluster Analysis
- In this paper, we make mathematical modeling and analysis on the sustainable development. In order to assess countries with different degree of sustainable development, we adapt the Principal Component Analysis(PCA) to collect a dozen factors which may affect the sustainable development for data analysis. For example, we filter 11 sets of data from 1990 to 2014 in the United States. Then we use the factor of principal component analysis to decide the solution layer of Analytic Hierarchy Process (AHP) and the rule layer. Our purpose is to use the specific value and weight of United States ( 11 factors* 25 years ) dates to get calculation formula of level indicators of sustainable development. Then we calculate level indicators of 14 countries with different degree of the sustainable development, and use these data for clustering analysis. At last, we get five clustering center,we divide the sustainable development indicators into five kinds of indicators: unsustainable, low level middle level, good level and very good level of sustainability. Therefore, according to the levels of these indicators and classification, we have distinguished more sustainable countries from less sustainable ones.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Haiyi Sun AU - Ning Li AU - Jinbao Wang AU - Hongmei Yan PY - 2016/04 DA - 2016/04 TI - The Ability of Sustainable Development Research Based on Statistics Synthetic Appraisal Theory BT - 6th International Conference on Electronic, Mechanical, Information and Management Society PB - Atlantis Press SP - 427 EP - 432 SN - 2352-538X UR - https://doi.org/10.2991/emim-16.2016.92 DO - https://doi.org/10.2991/emim-16.2016.92 ID - Sun2016/04 ER -