The current study investigates the predictive ability of critical minerals of price returns of precious metals (gold, silver, platinum, and palladium) in presence of different degrees of climate policy uncertainty (CPU). Using a novel Multivariate Quantile-on-Quantile Causality (MQQC) model, we test the predictive dynamics, unconditional and CPU-conditional, in the entire joint return distribution continuum. Predictability, unconditionally, is localized in the tails, i.e. under extreme market conditions, mineral shocks have strong impact but under normal regimes, they have little impact. The tail dependence is indicative of co-production and industrial-demand relationships of silver, platinum, and palladium, but gold mostly maintains its safe-haven property. After the addition of CPU, the predictive effects are stretched out further to the middle of the distribution, indicating wider and more enduring spillovers. In the case of gold, CPU enhances the safety haven demand by augmenting the crucial mineral precious metal co-movements between regimes. In the case of silver, platinum and palladium, CPU increases industrial sensitivities relating to clean-energy use. These findings highlight the twofold contribution of the geological factors in conjunction with policy uncertainty towards price fluctuations, and significance of the findings on resource planning, governance and risk management.

Addressing the risks associated with local government debt is crucial for economic development and fiscal security. This paper analyzes the spatiotemporal distribution of municipal government debt risks using panel data from 271 prefecture-level cities in China from 2015 to 2021, employing Exploratory Spatial Data Analysis (ESDA) and the Spatial Durbin Model. The prime objective of this research is to analyze the spatiotemporal distribution of municipal government debt in different regions of China, including the central, western, eastern regions. Key findings include: (1) Local government debt risk exhibits a fluctuating upward trend characterized by significant regional, administrative, and debt-type disparities. (2) Risk levels in central and western regions have increased, while major urban agglomerations have maintained medium or lower risk levels. (3) Local government debt risk demonstrates significant global spatial correlation, with low-low (LL) agglomerations evolving from multi-centered to dual-centered distributions. (4) Notably, a 1 % increase in neighboring debt risk leads to a 0.2467 % rise in local debt risk. (5) Fiscal pressure, urbanization rates, and economic scale are primary drivers of local government debt risk, whereas industrial structure, land transfer income, and financial development serve to mitigate it. These findings underscore the intra-regional and inter-regional heterogeneity and geographical differences, providing valuable insights for managing municipal government debt risk.