Search

China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China's sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China's sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China's sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them. [ABSTRACT FROM AUTHOR]

This study examines the dynamics of return and volatility connectedness between the rare earth stock index and the indexes of clean energy, consumer electronics, telecommunications, healthcare equipment, and aerospace & defence. Using daily data from 25 March 2010 to 25 August 2020, a quantile-based connectedness approach is applied to uncover both average and tail-based connectedness while considering the full sample period and the COVID-19 pandemic days. The results suggest that the interdependence among these indexes changes dramatically at the lower and upper quantiles, suggesting a strong influence of extreme market scenarios on both returns and volatility connectedness dynamics. Higher integration of sectoral indexes is observed during 2010–2012 and the COVID-19 pandemic period. Health care and telecommunication indexes have been consistent transmitters of return and volatility spillovers to other indexes during the full sample period. Consumer electronics and clean technology indexes switch their roles from a net receiver to a net transmitter during pandemic days. The rare earth remains on the recipient's side consistently. The findings indicate that the ongoing U.S.–China trade embargo has not impacted the return and volatility dynamics of the five sectoral indexes superseding the demand-driven dynamics for rare earth. [ABSTRACT FROM AUTHOR]

Considering the constraints on urban land expansion, improving the efficiency of existing urban land becomes a key factor in promoting resource sustainability and environmental protection. This paper examines the impact mechanism and threshold effect of urban land use efficiency (ULUE) on resources and environment carrying capacity (RECC) using panel data covering 282 Chinese cities from 2007 to 2018. The empirical results show that: (1) ULUE enhances RECC in China, and the rationalization and advancement of industrial structure are important transmission mechanisms; (2) The impact of ULUE on RECC is heterogeneous, showing significant differences in different regions, periods, administrative levels, and resource endowments; (3) With industrial rationalization and industrial advancement as threshold variables, ULUE exhibits a "gradient-style" enhancement with significant threshold characteristics in improving RECC. These findings can provide practical guidance for various land policies. • Study the effect of urban land use efficiency on resources and environment carrying capacity. • The effective allocation and intense use of urban land can foster sustainable economic growth. • Urban land use efficiency enhances resources and environment carrying capacity. [ABSTRACT FROM AUTHOR]

This study investigates the impact of green finance (GF) and green innovation (GI) on corporate credit rating (CR) performance in Chinese A-share listed firms from 2018 to 2021. The least absolute shrinkage and selection operators (LASSOs) machine learning algorithms are first used to select the critical drivers of corporate credit performance. Then, we applied partialing-out LASSO linear regression (POLR) and double selection LASSO linear regression (DSLR) machine learning techniques to check the impact of GF and GI on CR. The main results reveal that a 1% increase in GF diminishes CR by 0.26%, whereas GI promotes CR performance by 0.15%. Moreover, the heterogeneity analysis reveals a more significant negative effect of GF on the CR performance of heavily polluting firms, non-state-owned enterprises, and firms in the Western region. The findings raise policies for managing green finance and encouraging green innovation formation, as well as addressing company heterogeneity to support sustainability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)