Natural resources efficiency in terms of digital economy: Institutional efficiency and digital economy from the lens of natural resources

In response to the heightened emphasis on climate change and sustainable development, economies and various states are directing their attention toward optimizing resource usage and reducing dependency on traditional revenue streams. Consequently, institutional efficiency and digitalization have emerged as pivotal elements in this endeavor. Despite the significance of these factors, there is a noticeable dearth of comprehensive studies in existing literature. This study addresses this gap by delving into the intricate relationship between institutional efficiency and digitalization and their impact on resource utilization and tax revenues in economies. Therefore, this research scrutinizes the nexus of national resource tax (NRTX), institutional efficiency (INSTINDX), and economic progression. The research also included the role of digitalization (DIGITE), financial development (FD), and research and development expenditures (RDEXPND). The research used panel data from 2006 to 2017 on China's provincial GDP. The research also utilized novel approaches, i.e., novel MMQR (Methods of moments of quantile regression) for primary estimations. The research used first generation panel unit root methods while Pedroni and Kao residual test for long run cointegration. For the causality test, use the Pairwise Dumitrescu Hurlin panel causality test. The empirical outcomes reflect long-run equilibrium between the predicted and its covariates, while the variables are found static at the first difference in all 1st generation methods. The quantile regression estimations based on the method of moment reflected that NRTX established a positive influence on GDP across quantiles. The NRTX revenues established resource blessings for China's provincial GDP. Moreover, the outcomes of INSTINDX were found positive, but the outcomes are insignificant due to its negligence regarding environmental concerns. However, the outcomes of FD and RDEXPND are positive and strongly correlated at 1%, while DIGITE negatively influences GDP across quantiles. The robustness outcomes are found to be comparable and reliable through the BSQR approach. The causality analysis reflects that DIGITE, INSTINDX, and NRTX have bi-directional (i.e., feedback effect) while FD and RDEXPND have uni-directional causal connections. This research provides relevant policy implications regarding the influence of resource tax revenues and sustainable economic progression.