Search Results (357)

This study examines the dynamic interactions between precious metals (gold and silver) and cryptocurrencies (Bitcoin) in the context of banking systemic risk, by identifying the main banking factors of systemic vulnerability. The MES method was employed to estimate systemic risk, the DCC-GARCH model to assess dependency dynamics, and the VAR model to investigate causal relationships and impulse response functions. Empirical evidence shows that banking systemic risk has a unidirectional influence on gold and silver prices, reinforcing their role as safe-haven assets in times of financial stress. However, Bitcoin’s volatility dynamics amplify banking systemic risk, indicating that fluctuations in cryptocurrencies can increase financial uncertainty and affect the stability of the banking system. The findings of this study have several important implications for systemic risk management, portfolio diversification, and the assessment of the significant role of alternative assets in financial stability. Full article

Due to its extensive industrial applications and high market prices, as well as low mining yield, the recovery of rhodium from various secondary resources is becoming increasingly urgent for addressing its supply issues. Generally, rhodium is extracted last from the leaching solutions containing other platinum group metals and base metals. The lengthy processing flow led to the inevitable yield loss of rhodium. Compared to the conventional extraction process, selective preferential separation and extraction of rhodium are of great significance for achieving its high economic value and efficient recovery. However, selective preferential separation and extraction of rhodium have to face many difficulties, such as its kinetically inert properties, being prone to hydration and hydrolysis reactions, etc. This paper reviews various promising improvements and new technologies for selective preferential separation and extraction of rhodium from mixed metal solutions, including precipitation, liquid–liquid extraction, adsorption and other emerging technologies. The advantages and disadvantages of those reported technologies were evaluated. It is pointed out that the selective preferential adsorption of rhodium based on molecular recognition and ion imprinting is a promising rhodium recovery technology, which is economical and consistent with the concept of green chemistry. Full article

Rhodium is a high-value strategic platinum-group metal extensively applied in automotive exhaust purification, fine chemicals, glass production and high-temperature materials. Restricted by uneven primary resource distribution and volatile market prices, recovering rhodium from secondary resources has become increasingly critical. Solvent extraction is regarded as a promising technology for continuous and selective separation of rhodium, yet direct extraction of Rh(III) from chloride media faces severe industrial limitations. These bottlenecks are mainly attributed to diversified chloro-aqua complexes, kinetic inertness of low-spin Rh(III), strong hydration capacity and polynuclear species generation, while solution aging and inconsistent thermodynamic-experimental results further complicate extraction behaviors. This review systematically summarizes recent advances in rhodium solvent extraction from chloride media, correlating aqueous speciation regulation, activation chemistry, extractant molecular structure and extraction-stripping mechanisms. Special emphasis is placed on SnCl₂-, ascorbic acid-, trichloroacetic acid- and malonate-assisted activation systems, as well as amine-, phosphorus-, sulfur-based, synergistic, ionic-liquid and deep-eutectic-solvent extractants. Key factors affecting extraction efficiency, distribution ratio, selectivity and stripping performance are clarified, and current challenges are outlined. Future research should focus on quantitative speciation analysis, in situ mechanistic characterization, targeted extractant design, and integrated evaluation of extraction, stripping, recyclability, cost and real-feed adaptability, so as to provide theoretical support for efficient and clean rhodium recovery. Full article

This paper develops a novel coupled model to predict the thermal behavior of a high-temperature fast heat storage unit, integrating Power-to-Heat technology with steam generation. A phase change material (PCM) made of a ZnAl₆ metal alloy is used for heat storage. Electricity is used to charge the battery, and the stored energy is used to produce superheated steam during discharge. The coupled model was based on a 3D multiphase CFD model of the heat storage unit and a 1D multiphase water boiling model implemented in Python language. The CFD model solves the transient conservation equations of mass, momentum, and energy using the enthalpy–porosity method to describe phase change, while heat transfer to water is represented by a coupled 1D boiling model. The paper also presents a preliminary design, a computational strategy, and boundary conditions for the operating modes, providing an analytical foundation for detailed engineering, production, and implementation in real-world industrial environments. The presented results confirmed the correct operation of the model and enabled the evaluation of system performance, discharge behavior, and validation of the geometric assumptions required to achieve the target steam parameters. The proposed modular design allows for system scalability, while the entire system is a response to the daily variability of electricity prices resulting from periodic reductions in demand and overproduction of electricity from renewable sources. Estimated thermal behavior of the thermal storage unit for the discharging scenario allows reaching constant output power at the level of 200 kW for 85 min. Integration with a cooling reduction station allows constant system power output to be maintained by increasing the mass flow rate as the steam parameters decrease from over 400 °C to 200 °C with a lowering state of charge. Full article

Estimating long-term prices for base metals is central to the financial viability of mining investments, yet prices remain highly volatile and difficult to forecast. This study systematizes the determinants of base metal prices and evaluates their empirical influence using daily and weekly data from the London Metal Exchange (LME) for aluminium, copper, nickel, and zinc between April 2005 and May 2017. In this context, the study aims to identify and evaluate the key economic, financial, and physical drivers of base metal prices, with particular emphasis on distinguishing between short-run predictive factors and long-run equilibrium determinants. After aligning metal prices with candidate explanatory variables, linear associations are quantified through Pearson correlations and alternative functional forms are explored for price modelling, including linear, log-linear, and selected nonlinear transformations. The methodology is complemented with econometric diagnostics. Explanatory variables are grouped into four categories: (i) supply–demand metrics (inventories, production–consumption balances, sales aggregates, and LME position data), (ii) business cycle and income proxies (global GDP growth, China Caixin PMI, the U.S. S&P 500 index, and China steel rebar futures), (iii) investment variables (cross-metal prices and Brent crude), and (iv) monetary indicators (U.S. and the U.S. 10-year yield). Results show that short-run price movements are mainly driven by business cycle indicators and inventory dynamics, while long-run trends reflect structural supply conditions. Monetary variables generate temporary price impulses, and prices tend to lead speculative positioning rather than the reverse. Full article

This study examines the dynamic interlinkages among energy, food, and metal commodity markets under geopolitical tensions using daily data from January 2022 to July 2025. The empirical framework integrates correlation analysis, Granger causality tests, and a Vector Error Correction Model (VECM) to capture both short- and long-run transmission mechanisms, with robustness assessed through impulse response functions, forecast error variance decomposition, and a Diebold–Yilmaz connectedness analysis across three structurally distinct geopolitical event windows. The results reveal asymmetric and sector-specific transmission patterns in which geopolitical risk significantly influences key commodity prices—particularly WTI crude oil, wheat, copper, and aluminium—confirming its role as a primary external shock driver. WTI emerges as the dominant transmitter of shocks, while industrial metals exhibit strong internal connectedness. Critically, gold’s role proves to be conditional and context-dependent: within an integrated energy–food–metal network under geopolitical stress, it functions primarily as a net receiver and passive absorber of macroeconomic uncertainty rather than as a systemic transmitter, a finding that complements, rather than contradicts, its established safe-haven role in financial asset pricing frameworks. These findings are subject to limitations, including reliance on futures price data and a linear VECM framework that may not fully capture nonlinear or regime-dependent dynamics. Full article

The increasing frequency and severity of extreme droughts caused by climate change has emerged as a key risk factor exerting complex effects on the overall national economy through a structure of interconnected industries. The Water Input–Output Linear Programming (WIOLP) model was applied to data from 2015 to 2018 to quantitatively assess the effects of drought-induced water use constraints on production and socioeconomic potential losses. By modeling scenarios in which water use decreased by 10% from 100%, changes in the gross output, the value added, the socioeconomic potential loss, and the shadow price by industry were evaluated. Results showed that socioeconomic potential losses increased nonlinearly, with maximum potential losses of 311,118 billion Korean Won (KRW) in 2015 and 355,260 billion KRW in 2018. The shadow price rose from 7311 to 73,186 KRW/m³ in 2015 and from 3291 to 89,586 KRW/m³ in 2018, confirming that the marginal productivity of water increased exponentially under stricter constraints. Industry-level analysis revealed the largest losses in high water use industries (e.g., agriculture, forestry, fisheries, chemicals, and non-metals), whereas electricity, electronics, and machinery sectors maintained relatively stable production. This study demonstrates that the WIOLP model can empirically analyze nonlinear economic ripple effects under resource constraints, overcoming limitations of conventional input–output and computable general equilibrium models. Full article

This study employs a TVP-VAR-BK-DY framework to examine volatility spillovers between China’s financial markets and strategic metal assets. To capture retail investor sentiment, we construct a sentiment index using an LLM knowledge distillation framework. Building on this index, the analysis further incorporates economic policy uncertainty to investigate the joint effects of retail investor sentiment and economic policy uncertainty on cross-market volatility spillovers. The results show that: (1) Price movements in certain assets exhibit leading effects, while metals with stronger financial characteristics generate more pronounced spillover effects. (2) The spillover structure between China’s financial markets and strategic metal assets displays substantial heterogeneity across time horizons and frequency bands. In the 1–5-day frequency band, the stock market serves as a net transmitter of volatility to the banking sector, gold, and copper. In the frequency band exceeding five days, these three assets exert reverse net spillover effects on the stock market. (3) The effects of retail investor sentiment and economic policy uncertainty on volatility spillovers differ significantly. The impact of retail investor sentiment is primarily concentrated in the 1–5-day frequency band, whereas economic policy uncertainty exhibits significant spillover effects in the frequency band exceeding six months. Full article

This research examines the clustering structure and volatility spillover among steel-related products in monthly data from July 2004 to September 2025. Using various clustering methods, K-means, hierarchical techniques and market network analysis with correlations, four distinct marketing clusters have been identified: (1) US (United States) steel products, (2) global cyclical raw materials, (3) US iron ore market, and (4) global base metals. The overall volatility spillover index stands at 15.39%, exhibiting significant dynamics that vary over time, driven by major economic events, including the 2008 global financial crisis, the 2015 Chinese currency devaluation, the COVID-19 outbreak, the 2022 Ukrainian conflict, and the 2025 Trump trade tariffs. The primary driver of volatility in global trade is US carbon steel wire prices, while the largest net recipient of volatility shocks is the global copper price. These findings have key implications for understanding the global interconnectedness of steel markets in the current context. Full article

Commodity and carbon markets are central to natural resource allocation, energy security, and the effectiveness of carbon-pricing policies, yet their risk linkages can intensify sharply during crises. This study examines nonlinear, tail-dependent volatility spillovers across strategically important resource markets using a Quantile-on-Quantile connectedness framework. We employ weekly observed data from 3 January 2010 to 27 April 2025 for eleven futures markets spanning metals (copper, silver, gold), energy (WTI crude oil, heating oil, natural gas, gasoline), agricultural commodities (sugar, coffee, corn), and carbon emissions. Volatility is measured using GARCH-based estimates and embedded in quantile VAR dynamics to map state-contingent shock transmission across the distribution. The results indicate strong asymmetries: connectedness rises markedly in tail regimes and attains its highest levels during the COVID-19 pandemic and the Russia–Ukraine war, relative to the 2015–2016 energy market adjustment. Heating oil, gold, and natural gas frequently act as key volatility transmitters, while the carbon market shifts from a peripheral receiver to a more integrated and sometimes systemic node within the broader commodity risk network. The findings indicate that carbon-price risk propagates through resource markets in a regime-dependent manner, with implications for stress testing, tail-sensitive hedging, and the coordination of resource and climate policy under turbulent market states. Full article

Catalysis has entered everyday life through a range of technological processes that rely on different catalytic systems. The increasing demand for such systems requires rationalization of the use of their expensive components, such as noble-metal catalysts. As such, a catalyst with low noble-metal concentration, in which each one of the noble atoms is active, would reach the lowest price possible. Nevertheless, no clear reactivity descriptors have been outlined for this type of low-coordinated supported atom. Using DFT calculations, we consider three diverse systems as models of single-atom catalysts. We investigate monomers and bimetallic dimers of Ru, Rh, Pd, Ir, and Pt on MgO(001), Cu adatom on thin Mo(001)-supported films (NaF, MgO, and ScN), and single Pt adatoms on oxidized graphene surfaces. The reactivity of these metal atoms was probed by CO. In each case, we see the interaction through the donation–backdonation mechanism. In some cases, CO adsorption energies can be linked to the position of the d-band center and the adatom’s charge. A higher-lying d-band center and less-charged, supported single atoms bind CO more weakly. Also, in some cases, metal atoms that are less strongly bound to the substrate bind CO more strongly. The results suggest that the identification of common activity descriptor(s) for single metal atoms on foreign supports is a difficult task with no unique solution. However, it is also suggested that the stability of adatoms and strong anchoring to the support are prerequisites for the application of descriptor-based search to novel single-atom catalysts. Full article

Integrating sustainability into luxury products poses fundamental challenges when brands introduce alternative materials made from recycled content that lack the intrinsic value of precious metals. This study investigates consumer perceptions and willingness to pay more for luxury jewelry made from alternative recycled materials among 357 consumers in the Bangkok Metropolitan Region. The conceptual framework examined five value dimensions (self-expression value, aesthetic value, social value, perceived natural rarity, and perceived sustainability) with pro-environmental self-identity as a moderating variable. The model explains 59.2% of the variance in willingness to pay more. Results confirm significant effects of all five dimensions, with aesthetic value as the strongest predictor. Pro-environmental self-identity significantly moderates the relationship between perceived sustainability and willingness to pay more. Despite high levels of sustainability awareness, the results reveal an attitude–behavior gap: environmental concern does not automatically translate into greater spending on sustainable luxury jewelry. This research contributes to the literature on sustainable luxury consumption by clarifying the relative importance of value dimensions and highlighting the conditional role of consumer identity in shaping the acceptance of price premiums. Full article

The growing volume of waste electrical and electronic equipment presents both an environmental challenge and an opportunity for recovering critical raw materials embedded in discarded products. While recycling technologies are advancing, effective recovery remains strongly constrained by upstream collection systems, particularly in urban contexts subject to uncertainty, capacity limits, and regulatory constraints. This paper examines WEEE collection as a key lever for supporting sustainable critical-metal recovery in Europe. Methodologically, the study combines a Scopus-based bibliometric mapping and an institutional analysis of EU collection arrangements with the development of a robust multi-period mixed-integer linear programming model. After analysing organisational and regulatory arrangements in Poland and Portugal as illustrative cases, the paper introduces the Robust Multi-Period WEEE Allocation and Rare Metal Accumulation Problem (MP-WARMAP). The model integrates uncertain WEEE availability, intertemporal logistics planning, threshold-based rare-metal accumulation with endogenous sale timing, and a binding transport-related emission cap. Computational experiments show that robustness against inflow uncertainty can be achieved at a relatively low economic cost, that emission regulation exhibits a feasibility-threshold effect, and that capacity constraints may dominate price signals in determining recovery timing. The results highlight the importance of collection-system design and operational feasibility for improving the recovery of critical materials from urban WEEE streams. Full article

Rechargeable zinc–air batteries (ZABs) are still impeded by the intrinsically sluggish kinetics of oxygen reduction and evolution reactions (ORR/OER) and by the instability or prohibitive price of state-of-the-art noble metal catalysts. Metal–organic frameworks (MOFs) have recently emerged as versatile sacrificial templates for next-generation air–cathode electrocatalysts. By programming pyrolytic or chemical conversion pathways, MOFs can be quantitatively transformed into hierarchically porous, heteroatom-doped carbon scaffolds that embed uniform metal, alloy, or metal-oxide nanodomains. The resulting architectures couple metallic conductivity with molecular-scale active site tunability, delivering exceptional ORR/OER activity, stability, and mass transport properties. This review critically examines the most recent advances in MOF-derived electrocatalysts for ZABs, establishing quantitative structure–composition–performance relationships across mono-, bi-, and multi-metallic systems. Emphasis is placed on deciphering how framework topology, metal–ligand coordination, and post-synthetic parameters dictate the density, electronic structure, and accessibility of surface-active moieties during catalyst evolution. We further dissect engineering strategies that enhance intrinsic activity via electronic modulation, bolster durability through encapsulation effects, and optimize hierarchical porosity for rapid O₂/water transport. This article concludes by outlining unresolved challenges and future research directions, including atomically precise active site construction, multi-scale compositional control, long-term reversibility under realistic ZABs cycles, scalable and green synthesis, providing a roadmap for translating MOF-derived catalysts from laboratory curiosities to commercially viable air–cathode materials. Full article