Using an artificial neural network for estimating sustainable development goals index

Abstract

Purpose

This paper aims to develop a simple model for estimating sustainable development goals index using the capabilities of artificial neural networks.

Design/methodology/approach

Sustainable development has three pillars, including social, economic and environmental pillars. Three clusters corresponding to the three pillars were created by extracting sub-indices of three 2018 global reports and performing cluster analysis on the correlation matrix of sub-indices. By setting the sustainable development goals index as the target variable and selecting one indicator from each cluster as input variables, 20 artificial neural networks were run 30 times.

Findings

Artificial neural networks with seven nodes in one hidden layer can estimate sustainable development goals index by using just three inputs, including ecosystem vitality, human capital and gross national income per capita. There is an excellent similarity (>95%) between the results of the artificial neural network and the sustainable development goals index.

Practical implications

Instead of calculating 232 indicators for determining the value of sustainable development goals index, it is possible to use only three sub-indices, but missing 5% of precision, by using the proposed artificial neural network model.

Originality/value

The study provides additional information on the estimating of sustainable development and proposes a new simple method for estimating the sustainable development goals index. It just uses three sub-indices, which can be retrieved from three global reports.

Citation

Mirghaderi, S.-H. (2020), "Using an artificial neural network for estimating sustainable development goals index", Management of Environmental Quality, Vol. 31 No. 4, pp. 1023-1037. https://doi.org/10.1108/MEQ-12-2019-0266