Abstract

The Sustainable Development Goals Index is an important index for measuring the movements toward sustainable goals. However, many indicators are needed for computing the index. This chapter aims to operationally show that for tackling the problem of the high number of indicators, artificial intelligence techniques may provide contributions. This chapter uses a combination of two famous techniques, including artificial neural networks and genetic algorithms. So, 288 indicators of 127 countries from 7 global reports were extracted, and the collinear and ineffective ones were removed. Finally, 90 indicators remained. A combination of genetic algorithms and artificial neural networks tried to find the best subset of remained indicators that provide a simple system for predicting Sustainable Development Goals Index. The results revealed that artificial neural networks with just four nodes and indicators include “Deaths from infectious diseases,” “ICT use,” “Expenditure on education,” and “Assessment in reading, mathematics, and science” can predict sustainable development index with an accuracy rate of 97%. This chapter also validates the role of innovation in meeting Sustainable Development Goals (SDGs) and uncovers the insignificant role of environmental indicators in the Sustainable Development Goals Index.