Estimating Sustainable Development Goals Index Using Global Knowledge Index: An Artificial Intelligence Approach

The Sustainable Development Goals (SDGs) Index has gained attention from policymakers and researchers as a framework for national development. With 230 indicators, calculating the SDGs index is challenging. However, a positive correlation between sustainability and knowledge offers a potential solution. This research aims to develop a simple model for estimating the SDGs index using variables extracted from the Global Knowledge Index (GKI). A hybrid algorithm of artificial neural network (ANN) and genetic algorithm was used to find and evaluate the inputs for the ANN. Findings revealed that the best subset of inputs is composed of six GKI variables, which include “university enrollment,” “student competency,” “research and development outputs,” “ICT subscriptions,” “financing and taxes,” and “health.” These inputs minimize estimation error, standard deviation, and input count. Testing revealed that two nodes in the hidden layer of ANN yielded a 97% correlation coefficient and an RMSE of 2. The ANN model achieved 94% accuracy with new data, confirming its effectiveness and sound goodness of fit. This paper proposes a roadmap for using artificial intelligence in sustainability, provides clear thought about the effect of knowledge on sustainable development, and creates a simplified tool for predicting the SDGs index.