Plant species selection by hybrid multiple-attribute decision-making model for promoting green mining in the Sungun copper mine, Iran

Adopting the most suitable plant species selection is a multi-dimensional problem. Many parameters affect judges' decisions. Accordingly, the present study aimed to develop a multi-attribute platform for plant species selection consisting of parameters such as aesthetic outlook, resistance in front of insects, plant disease resistance, economic efficiency, pollution prevention, erosion reduction, and growth rate. The plant species selection was performed according to the primary factors. Along with the priorities mentioned above, a multi-attribute decision-making (MADM) model was presented to define the selected species based on the secondary factors. This study used two methods (Entropy and AHP) to attribute weighting because plant species selection is highly case sensitive, and global weighting was fundamental. Therefore, attribute weighting was calculated by two objective and subjective methods, respectively. Then, the ELECTRE method was applied for ranking plant species in acidic and alkaline soil types in the Sungun copper mine of Iran. This case study results showed that Acer campestre, Robinia pseudoacacia, Juniperus communis, Betula pendula, Ulmus minor, and Rhus coriria had more priority in acidic soil type, respectively. Similarly, Juglans regia was the best type for alkaline soil, and either Ficus carica or Fraxinus excelsior is located in the following ranking. When the number of possible options was more significant, the outranking result taken by the ELECTRE method was more reliable.