Predicting ecological footprint based on global macro indicators in G-20 countries using machine learning approaches

Paying attention to human activities in terms of land grazing infrastructure, crops, forest products, and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. For the present study, global database data were used. The ability of the penalized regression (RR) approaches (including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 countries over the past two decades (1999-2018) was illustrated and compared. For this purpose, 10-fold cross-validation was used to evaluate the predictive performance and determine the penalty parameter for PR models. According to the results, the predictive performance compared to linear regression improved somewhat using the penalized methods. Using the elastic net model, more global macro indices were selected than Lasso. Although Lasso selected only a few indicators, it had better predictive performance among PR ns models. In addition to relative improvement in the predictive performance of PR methods, their interest in selecting a subset of indicators by shrinking coefficients and creating a parsimonious model was evident and significant. As a result, PR methods would be preferred, using variable selection and interpretive considerations to predictive performance alone. On the other hand, ANN models with higher determination coefficients (R²) and lower RMSE values performed significantly better than PR and OLS and showed that they were more accurate in predicting EF. Therefore, ANN could provide considerable and appropriate predictions for EF indicators in the G-20 countries.