A Research on National Sustainability Evaluation Model
- 10.2991/lemcs-15.2015.100How to use a DOI?
- Mechine Learning; K-means; Logistic Regression; Neural Network; Sustainability
By applying an objective method to evaluate its sustainability, a certain country could make proper plans and policies for further development. However, subjective and complicated problems have been found in the current methods and index systems. Therefore, researchers set up a composite model that it can evaluate the sustainability for a certain country in a more objective way, in comparison with other methods. Researchers propose a method for evaluating sustainability for a certain country, which solves problems concerning strong subjectivity and complicity in current models. Researchers choose 17 representative core indicators based on the index system of UNCSD and divide them into two catalogues -- Nature Indicators and Operate Indicators. First, Means Clustering Algorithm of k-means (an unsupervised learning method) divides the data into three categories. Then, researchers obtain those indicators, using regression analysis, and build an objective evaluation model. When researchers make policies for a country to allocate resources reasonably, researchers maximize the improvement of the ability of sustainable development based on “along the gradient direction ascend the fastest”. In this paper, researchers conduct simulations experiment, using data of 96 countries in the World Bank. After analyzing the deviations and sensitivity of the model, the theoretical results are verified experimentally.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ge Fan AU - Wei Peng AU - Shan Sun AU - Peiwen Li PY - 2015/07 DA - 2015/07 TI - A Research on National Sustainability Evaluation Model BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 524 EP - 529 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.100 DO - 10.2991/lemcs-15.2015.100 ID - Fan2015/07 ER -