An Environmental Cost Value Model Based on Dynamic Neural Network Prediction

Abstract

Ecological environment, which human beings depend on for survival, constantly provides us with various benefits. The exploitation and use of land by human beings will cause damage to the ecological environment. More seriously, the loss caused by environmental damage is often not concluded in the cost. Therefore, it is necessary to establish a complete environmental cost assessment model. By analyzing the value of ecosystem service and combining with previous studies, we proposed a new model—“DIP- PRSC model”—for evaluating the environmental costs of land projects. This model creatively divides environmental costs into fixed costs and floating costs. We use 16 indicators to measure the cost of each part of the model. We applied the model to the construction of the Rondônia highway in the Brazilian to verify the validity of the model, using NAR neural network to predict the cost of each part of the DIP model, and put the predicted cost into the DIP model to calculate the total cost. The results show that the environmental cost of the land project will continue to rise in the next eight years.