Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs

Abstract

The relationship of mineral resources rent with economic performance, greener energy, and environmental policy is crucial because it could impact how welfare and eco-friendly are treated in a nation. This paper employed machine learning (ML) and Artificial Neural Networks (ANN) to predict minerals resourcesthrougheconomic growth, the producer price index, market prices, the environmental policy stringency index (EPSI), and greener energy in China. The ML and ANN outputs showed how responsive quarterly minerals resource rent variations are too dynamic shifts in economic performance and renewable energy use. The more critical prediction accuracy of ML experiments compared to ANN experiments is highlighted by data from mean absolute percentage, mean square, root mean square, and root mean absolute errors, as well as the coefficient of determination. Though China is abundant in natural resources, there is a need to employ these resources efficiently to achieve long-term eco-friendly performance. In addition to comparing the results of ML and ANN, it also provided causality conclusions and policy implications.