A Heterogeneity Study on the Impact of Sustainable Economic Growth on Environmental Pollution: The Case of Shandong Province

Abstract

Abstract Sustainable economic development is often accompanied by increasingly prominent environmental problems, and it is important to explore the relationship between economic growth and environmental pollution. This paper combines the BP neural network algorithm in machine learning with traditional econometric methods to study the relationship between environmental quality and economic growth, using a fixed-effects panel model to quantify the relationship between environmental pollution and economic growth, as well as generalized method of moments (GMM) estimation to address the endogeneity of the variables, and the theory of the Environmental Kuznets Curve (EKC) to observe polynomials in the shape of the fitted curve and determine the location of the inflection point, and analyzed that wastewater discharge is expected to increase again in the coming years. Then, a BP neural network evaluation model was established to obtain the environmental quality evaluation value of Shandong Province. Finally, the spatial correlation and differences between GDP and environmental quality in Shandong Province are investigated using Moran's index and Dagum Gini coefficient, and the relationship between them is explored. On this basis, targeted recommendations for economic development and environmental governance in Shandong Province are made in terms of adjusting industrial structure, strengthening coordinated regional development, implementing differentiated environmental protection policies, and strengthening environmental monitoring and enforcement. Our study contributes to a deeper understanding of the relationship between economic growth and environmental pollution and provides important insights for policy formulation.