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How systematic is the environmental sustainability index 2002 as a tool for grouping countries in terms of their environmental sustainability?

机译:2002年环境可持续性指数作为系统对国家进行环境可持续性分组的工具有多系统?

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With the aim of exploring the evolution of the Environmental Sustainability Index (ESI, 2000-2005), the earliest complete version of the index - namely, the ESI 2002 - is examined. In this piece of research, the investigation is specifically focused on the clustering of the participating countries into groups that demonstrate distinct ES-related characteristics, as these have been put forward and described in the primary relevant literature. The main two directions of this study cover (a) whether (and how accurately) the proposed clustering can be replicated, and (b) to what extent the reported clusters are consistent, i.e. how well the underlying groupings can be generalised to other (novel) countries. To this end, a variety of parametric and non-parametric clustering techniques - which tackle the problems of cluster creation as well as of cluster prediction - are applied, initially to the entire dataset and, subsequently to subsets of the dataset that are systematically derived via cross-validation. While self-organising maps are revealed as the most appropriate technique for recreating the clusters put forward in the relevant literature, probabilistic neural networks constitute the most accurate methodology for cluster prediction. The results concerning the overall performance attained by these techniques demonstrate that, although the means of creating the original (reported) clusters cannot be derived using either parametric or non-parametric methods, the generalisation/prediction potential of the proposed clustering is satisfactory, especially when using artificial neural network-based techniques.
机译:为了探索环境可持续性指数(ESI,2000-2005)的演变,研究了该指数的最早完整版本(即ESI 2002)。在这项研究中,调查专门针对参与国的聚类,这些聚类表现出与ES相关的独特特征,因为这些特征已在主要相关文献中提出并描述。本研究的两个主要方向包括:(a)是否可以(以及如何准确地)复制建议的聚类,以及(b)所报告的聚类在多大程度上保持一致,即,基础聚类可以很好地推广到其他类别(新颖)。 )国家。为此,首先将各种参数和非参数聚类技术(用于解决聚类创建以及聚类预测的问题)应用于整个数据集,然后应用于通过数据集系统导出的数据集的子集。交叉验证。虽然自组织图被揭示为重建相关文献中提出的聚类的最合适技术,但概率神经网络构成了最准确的聚类预测方法。通过这些技术获得的有关总体性能的结果表明,尽管无法使用参数或非参数方法来推导创建原始(报告的)聚类的方法,但建议聚类的泛化/预测潜力是令人满意的,尤其是当使用基于人工神经网络的技术。

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