A neuro-computational intelligence analysis of the ecological footprint of nations

The per capita ecological footprint (EF) is one of the most-widely recognized measures of environmental sustainability. It seeks to quantify the Earth’s biological capacity required to support human activity. This study uses three neuro-computational methodologies: multi-layer perceptron neural network (MLP), probabilistic neural network (PNN) and generalized regression neural network (GRNN) to predict and classify the EF of 140 nations. Accuracy indices are used to assess the prediction and classification accuracy of the three methodologies. The study shows that neuro-computational models outperform traditional statistical techniques such as regression analysis and discriminant analysis in predicting and classifying per capita EF due to their robustness and flexibility of modeling algorithms.