Analysis of Green GDP Revolution Neural Network Prediction Model

Abstract

In recent decades, GDP, the core indicator of the national economy, has played a key role in measuring the state of the economy and the level of development. However, GDP falls short as a measure of economic health, failing to fully account for the interrelationships between resources, the environment and the economy. In order to more comprehensively reflect the balance between the economy and the environment, we have introduced green GDP as an important indicator to measure the degree of social and economic health. This paper discusses in depth the differences in the definition, understanding and calculation methods of GGDP. An in-depth comparison of GDP and GGDP is made based on the grey forecast model to analyze their differences in measuring socio-economic health. At the same time, in order to measure social and economic health status more accurately, this paper classifies green GDP based on feedforward neural network model theory and clustering algorithm, builds a neural network prediction model, analyzes the correlation degree between different factors and GGDP, and analyzes the influence degree of different factors in resources and environment on social and economic status and development level. Through reasonable summary and analysis of the research results, considering the state of social and economic health, this study provides useful reference suggestions for the future government to implement economic measures and policies, and has positive significance for promoting the innovation and sustainable development of economic indicators.