Abstract
In recent years, with a rise in climate change, the notion of natural resource managementhas received much attention worldwide. The heavy reliance of both developed and developing economies on mineral resource management (MRM) is an important aspect of the debate.Meanwhile, advances in artificial intelligence (AI)-based financial management (FM) have transformed the day-to-day operations and management prospects of ecological resources. The present research examines the relationship between AI-based FMand MRM.The study has been done for the economy of the United States of America (USA) and the time ranges from the year 1980–2020.Firstly, the stationarity of the variables are checked using the Augmented Dickey-Fuller (ADF),Phillips-Perron (PP)and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests.The findings reveal that all the variables follow I(1). The BDS testing is employed for checking the non-linearity among the variables under consideration. The results verify the presence of nonlinearity. Then, the non-linear autoregressive distributed lag (NARDL) approach is used to find the asymmetric effect of FM on mineral resource rent(MRR). The findings provide evidence of an asymmetric relationship between FM and MRR. Such that the positive shock of FM is negatively related to MRR while a negative shock is positively related to MRR. Based on findings, the study suggests that the USA economy must redesign its mineral resource strategy to establish AI-based financial systems for improving MRM by assuring worker safety and efficiency in the mining industry, resulting in long-term growth.
•Advances in AI-based financial management (FM) have transformed the day-to-day operations of economic actors.•This research study examines the relationship between FM and mineral resource management (MRR).•The study has been done for the economy of the United States (US) covering timespan 1980 to 2017.•The Non-Linear Autoregressive Distributed Lag Approach provides evidence of an asymmetric relationship between FM and MRR.