A novel integrated fuzzy DEA–artificial intelligence approach for assessing environmental efficiency and predicting CO2 emissions

Abstract

Undesirable output of industrial economic activities—carbon dioxide (CO2) and other pollutants—has been become global concern because of their harmful effects on the climate, especially for environmentally sustainable production systems which attempts to generate less undesirable outputs, as well as achieve higher levels of production and economic growth. This study proposes a novel environmental efficiency data envelopment analysis (DEA) in conjunction with predicting artificial intelligence algorithms. The proposed model—fuzzy undesirable slacks-based measure DEA model (FUNSBM)—measures environmental efficiency in terms of the directional distance function and weak disposability, and its combined approaches (artificial neural network (ANN), ANN + particle swarm optimization (PSO) and artificial immune system (AIS)) predict optimal values of inefficient decision-making units (DMUs) so that they become more efficient considering the possible reduction of CO2 emissions in their production process. The FUNSBM model is applied to a dataset of 30 Iranian forest management units. The findings show that almost 47% DMUs are operating at low efficiency levels with a weak efficiency dispersion; however, these inefficient DMUs could improve their efficiency border via following the combined approaches. This analysis shows that the FUNSBM-AIS approach, by 53% reduction of CO2 emission, is the best approach to predict and/or control CO2 emission in optimal way while FUNSBM-ANN and FUNSBM-ANN + PSO are reduced CO2 emission by 15% and 32%, respectively. As the major conclusion, the FUNSBM-AIS approach exhibits a high degree of reliability to predict the lowest amount of CO2 emission and can help improve the inefficient DMUs by following their predicted optimal values.