Measuring sustainability through ecological sustainability and human sustainability: A machine learning approach - Nilashi, Mehrbakhsh; Rupani, Parveen Fatemeh; Rupani, Mohammad Mobin; Kamyab, Hesam; Shao, Weilan; Ahmadi, Hossein; Rashid, Tarik A.; Aljojo, Nahla (Journal of cleaner production, 2019)
Nowadays, sustainability is recognized as one of the most important development paradigms and included in the international and national strategies of almost all organizations. Sustainability assessment methods have been important for monitoring sustainability performance. These methods are developed to help decision-makers in their attempts to make society more sustainable. Various methods have been proposed for assessing the sustainability performance, however, this research is the first attempt to employ fuzzy clustering and supervised machine learning techniques to country sustainability assessment. This research tries to extend the previous sustainability assessment systems by the use of these techniques to reveal the relationships between human sustainability, ecological sustainability and overall sustainability performance by discovering the decision rules. The decision rules discovered from the Sustainability Assessment by Fuzzy Evaluation data of 128 countries are used to predict the country sustainability performance. The method proposed in this paper is flexible to accept large number indicators of sustainability to be used in the assessment of countries sustainability. The results of our analysis on countries sustainability data showed that the proposed method has potential to be used as a decision-making tool for sustainability assessment through a large set of indicators.