Forecast model of agricultural circular economy development trend based on GP algorithm

Abstract

To thoroughly and objectively analyze the developmental status of the agricultural circular economy and accurately forecast its future trends, we delved into a predictive model utilizing the GP algorithm. Building upon this foundation, we formulated an evaluation system for the agricultural circular economy’s progression, taking into account economic and social development, resource reduction and investment, resource recycling, environmental impact, and population dynamics. To streamline the evaluation process, we employed the kernel principal component analysis method to condense the indicator system’s dimensions for agricultural circular economy development. The reduced dimensionality, representing the agricultural circular economy development index, served as input for the GP algorithm. Enhancements were made to the GP algorithm through a fixed structure, multipopulation, and coefficient climbing method. Ultimately, we applied the refined GP algorithm to anticipate the developmental trajectory of the agricultural circular economy. The findings suggest that the model presented in this article successfully forecasts the agricultural circular economy, holding significant implications for advancing its further development.