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Forecasting the mineral resource rent through the inclusion of economy, environment and energy: Advanced machine learning and deep learning techniques

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  • Sarwar, Suleman
  • Aziz, Ghazala
  • Waheed, Rida
  • Morales, Lucía

Abstract

Accurate forecasting of mineral resource rent is essential for economic planning, revenue management, and proper investment decisions. The current study is an effort to forecast the mineral resource rent in China through the help of machine learning and deep learning techniques. We have used advanced artificial intelligence (AI) and hybrid AI techniques for estimations, which address the denoising method. Later, we compared the AI and hybrid AI techniques to explore the higher predictability of mineral resource rent. The results have confirmed that hybrid AI models are significant, whereas, among these models recurrent neural network based discrete wavelet Transform model is the best technique. Moreover, economic growth, environment and renewable energy are essential determinants of mineral resource rent. Specifically, environmental policies, renewable energy, and mining positively impact mineral resource rent. Whereas, economic growth and producer price index impacts mineral resource rent negatively. The findings are useful for policy makers while drawing the resource related policies.

Suggested Citation

  • Sarwar, Suleman & Aziz, Ghazala & Waheed, Rida & Morales, Lucía, 2024. "Forecasting the mineral resource rent through the inclusion of economy, environment and energy: Advanced machine learning and deep learning techniques," Resources Policy, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:jrpoli:v:90:y:2024:i:c:s0301420724000965
    DOI: 10.1016/j.resourpol.2024.104729
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