Decoupling carbon emissions from economic development is a critical strategy for achieving dual-carbon goals. However， the instability of decoupling states can easily trap regions into a "double crisis" characterized by both increased carbon emissions and reduced economic effectiveness （strong negative decoupling） and high carbon growth with low effectiveness gains （growth-negative decoupling）. In this study， spatio-temporal evolution characteristics of the carbon emission decoupling status of China's 30 provinces （municipalities， autonomous regions） were analyzed from 2010 to 2021 by using the Tapio model. An evaluation model was constructed for the transformation of carbon emission decoupling crises and chain reaction features of improvements in the decoupling state were analyzed. The findings revealed that： ① Although China's overall decoupling process showed improvement， it exposed systemic risks associated with high-carbon dependency models. Some regions had successfully broken through path lock-ins via crisis-driven mechanisms， creating demonstrative effects of "low-carbon breakthroughs" and validating the feasibility of crisis-driven transformations. ② The effectiveness of transforming carbon emission decoupling crises faces a "halfway dilemma" （40%-50% conversion rate）， reflecting persistent resistance from traditional developmental inertia as well as policy response disparities underlying regional differentiation. ③ The "chain leapfrogging" characteristic of decoupling states indicates that crisis transformation possesses dynamic cumulative effects of "risk deconstruction-element repositioning-development transition." ④ The core driving role of energy intensity and the positive role of carbon emissions of per energy consumption highlight the dual path of driving crisis transformation， short-term dependence on intensity regulation may exacerbate volatility risks， and efficiency improvement is the systematic solution to resolve the "emission-growth" contradiction. Short-term reliance on intensity control may exacerbate volatility risks； however， enhancing efficiency remains the systematic solution for resolving the contradiction between emission reduction and growth.

Purpose The objective of this study is to empirically examine the impact of financial innovation on bank risk-taking in the MENA region, while analyzing how this effect is influenced by different macroeconomic conditions. Design/methodology/approach Using a panel dataset covering 19 MENA countries from 2000 to 2021, the study employs robust econometric techniques, including ordinary least squares (OLS), fixed-effects (FE) and instrumental variable (IV) regressions, to examine the direct and conditional effects of financial innovation on bank risk. Financial innovation is measured using on patents and FinTech indicators, while macroeconomic conditions are captured through inflation, GDP volatility and financial liberalization. Findings The study shows that financial innovation reduces banks’ insolvency and earnings volatility risks, but simultaneously increases liquidity and funding mismatch risks. Furthermore, we show that the relationship between financial innovation and bank risk is highly dependent on GDP volatility, inflation and capital controls, such that the actual sign of the marginal effect changes. Financial openness also mitigates this relationship, but not enough to reverse the sign. Originality/value This article makes new contributions to the literature on the MENA region concerning the relationship between financial innovation and bank risk, and formulates recommendations for the MENA banks and policymakers.

Research aims: This study examines whether Bitcoin can serve as a safe-haven asset amid global market uncertainty during the 2022–2025 period, characterized by geopolitical tensions, post-pandemic inflation, and heightened financial volatility.Design/Methodology/Approach: The study employs a quantitative approach using daily data on Bitcoin, gold, oil, the S&P 500 index, and the Volatility Index (VIX) from January 2022 to June 2025. All variables are transformed into logarithmic returns and analyzed using an ARCH model to capture time-varying volatility and assess the influence of global market factors on Bitcoin returns..Research findings: The empirical results indicate that the VIX has a statistically significant negative effect on Bitcoin returns, implying that rising global uncertainty weakens rather than strengthens Bitcoin’s value. The S&P 500 exerts a significant positive influence, showing that Bitcoin moves pro-cyclically with equity markets and behaves like a risky asset. Oil prices have no significant impact, while gold returns exhibit a significant but unstable co-movement, lacking consistent value preservation. Overall, these findings reject Bitcoin’s safe-haven role and characterize it as a speculative digital asset with high sensitivity to stock market dynamics.Theoretical contribution/Originality: This study contributes to the safe-haven and digital finance literature by providing recent empirical evidence that distinguishes Bitcoin from genuine safe-haven assets. Grounded in formal safe-haven theory and volatility dynamics, it challenges the “digital gold” narrative and clarifies the boundary between high-risk digital assets and traditional safe havens.Practitioner/Policy implication: For investors, the results of this study confirm the need for caution in treating Bitcoin as a portfolio diversification instrument, as its behavior is more like that of a risky asset than a hedge asset. For Policymakers and regulators, these results show the importance of public education regarding Bitcoin's volatility risks and its limitations as a safe haven.

This paper examines bank failures during the subprime mortgage crisis, emphasizing sibling dynamics within multi-bank holding companies (MBHCs). While traditional risk indicators effectively predict failures for one bank holding companies (OBHCs), they exhibit limited explanatory power for MBHCs, where internal capital markets and interdependencies across affiliates shape risk outcomes. We extend the standard failure framework by incorporating group-level characteristics that capture sibling network structure and the distribution of risk across affiliates. Using pre-crisis data from 2006 to 2007, we show that group structure significantly influences failure risk. Larger sibling networks reduce individual bank failure risk through diversification, while greater size dispersion across affiliates increases vulnerability by constraining internal resource allocation. Beyond these aggregate effects, we introduce a weakest link approach that identifies the most distressed affiliate based on extreme tail risk in capitalization, asset quality, liquidity, earnings, and income volatility, capturing organizational fragility that aggregate measures miss. Concentrated vulnerabilities at a single affiliate significantly amplify failure risk throughout the holding company, even after controlling for traditional bank-level fundamentals and parent-level characteristics. These findings, derived from the 2007–2010 crisis, a severe stress test of holding company structures, identify organizational dynamics: resource competition among siblings and concentrated vulnerabilities at the weakest affiliate. Supervisory frameworks should explicitly account for within-group interdependencies rather than relying solely on individual bank metrics or aggregate indicators when monitoring bank holding company structures.

This study aims to analyze and compare the implementation of growth investing and value investing strategies in the Indonesian capital market. The research approach used is a qualitative comparative study method. Data were obtained through in-depth interviews with investors, capital market analysts, and academics, supported by literature and documentation in the form of issuer financial reports and publications from the Indonesia Stock Exchange. Informant selection was conducted using purposive sampling, while data analysis used the Miles and Huberman interactive analysis model, which includes data reduction, data presentation, and conclusion drawing. The results show that both growth investing and value investing are applied in the Indonesian capital market with various adjustments to the volatile market characteristics dominated by retail investors. Growth investing focuses more on a company's future growth potential and offers the opportunity for high returns, but is accompanied by significant volatility risks. Meanwhile, value investing emphasizes finding stocks that promise to be below their intrinsic value with a relatively lower level of risk, although requiring a longer investment period. This study also found that no investment strategy is absolutely superior, as the effectiveness of each strategy is strongly influenced by market conditions, investor characteristics, and investment objectives and horizons. This study concludes that understanding the characteristics of the Indonesian capital market and implementing investment strategies are crucial factors for investors in optimizing portfolio performance. These findings are expected to contribute theoretically to the study of investment strategies and serve as a practical reference for capital market investors in Indonesia.

The widespread adoption of electricity market trading platforms has enhanced the standardization and transparency of trading processes. As markets become more liberalized, regulatory policies are phasing out protective electricity pricing mechanisms, leaving retailers exposed to price volatility risks. In response, demand for risk management tools has grown significantly. Futures contracts serve as a core instrument for managing risks in the energy sector. This paper proposes a futures-based risk hedging model grounded in electricity price forecasting. A price prediction model is constructed using historical data from electricity markets and energy futures, with SHAP values used to analyze the transmission effects of energy futures prices on monthly electricity trading prices. The Monte Carlo simulation method, combined with a t-GARCH model, is applied to calculate CVaR and determine optimal portfolio weights for futures products. This approach captures the volatility clustering and fat-tailed characteristics typical of energy futures returns. To validate the model’s effectiveness, an empirical analysis is conducted using actual market data. By forecasting electricity price trends and formulating futures strategies, the study evaluates the hedging and profitability performance of futures trading under different market conditions. Results show that the proposed model effectively mitigates risks in volatile market environments.

Abstract This paper investigates the impact of volatility on expected corporate bond returns in China by using transactional data from 2010 to 2022. Portfolio analysis and Fama‐MacBeth regression show that volatility has a negative impact on expected corporate bond returns. After controlling for credit rating, maturity, liquidity, stock volatility and risk exposures, the volatility effect remains significant. By incorporating a new volatility factor and the bond market factor into the term‐default two‐factor bond pricing model of Fama and French ( Journal of Financial Economics , 1993, 33, 3), we construct a new four‐factor pricing model of corporate bonds. The proposed model captures the premium of volatility risk well and makes a significant marginal contribution to explaining the excess returns of corporate bonds. In addition, we find that volatility has a predictive effect on the default of corporate bonds.

ABSTRACT Lithium‐ion batteries (LIBs) are the most widely used commercial rechargeable batteries, but the stable supply of key raw materials such as lithium, nickel, and cobalt faces challenges. Sodium‐ion batteries (SIBs) are considered as potential alternatives and complements to LIBs due to similar working principles and the abundance of sodium resources. Layered oxide cathode materials (LOCMs) are recognized as one of the most promising practical cathodes for SIBs because of mature synthesis technology and satisfactory energy density. However, the use of nickel in LOCMs for SIBs has raised concerns about environmental pollution during nickel production and the risk of price volatility stemming from the widespread application of high‐nickel LOCMs for LIBs. Therefore, developing low‐cost nickel‐free LOCMs is crucial for enhancing the environmental friendliness and cost advantages of SIBs. For low‐cost LOCMs, this review discusses the feasibility of replacing Ni 2+ /Ni 4+ with Fe 3+ /Fe 4+ and Mn 3+ /Mn 4+ for charge compensation in SIBs, and summarizes the resulting critical scientific challenges (Fe migration, Mn dissolution, Jahn‐Teller effect, Na deficiency, and thermal instability). Economic efficiency assessment based on cost and electrochemical properties indicates that low‐cost LOCMs exhibit the highest cost‐performance ratio. Finally, to accelerate the commercialization of cost‐effective SIBs technologies, this review outlines promising development pathways of low‐cost LOCMs.

Financialisation has an increasing influence on the functioning of non-financial enterprises. It is therefore important to examine whether and to what extent food sector enterprises are subject to the process of financialisation. The research objective was to determine the level of financialisation of food industry enterprises in Poland in relation to the whole industry sector. To achieve this objective, the following research hypothesis was formulated: the process of financialisation of food industry enterprises proceeds similarly to the analogous process undergoing in industrial enterprises but varies across different sectors of the food industry. The research was conducted on the basis of statistical data from Statistics Poland (SP) published in various statistical studies. Financial data from 2010 to 2023 were analysed. For this purpose, research tools used in the paper are referred to in the literature as measures of the level of financialisation, so-called balance sheet indicators. The main limitation of the research is that the results can only be applied to countries with similar economic conditions, especially post-communist countries, and that balance sheet indicators are used to measure financialisation, which, although widely used, are limited in their effectiveness because they focus only on balance sheet data. The results support the research hypothesis. The companies in the analysed industries are characterised by a low level of financialisation. The process of financialisation of food industry companies is similar to the one in industrial companies and is more intense in beverage production than in other food industry sectors. There is room for a sustainable financing policy. The results indicate that there is room for higher financing of food industry enterprises in Poland, but excessive financing may lead to excessive concentration and monopolisation of enterprises and even to speculation on agricultural markets. To maintain financial stability, it will be important to pursue a stable monetary policy, limit the risk of food price volatility, improve communication and coordination in international monetary policy, and increase national food self-sufficiency. This study fills a research gap in understanding the process of financialisation, assessing its degree of advancement and diversity in the main sectors of food processing enterprises.

Fund similarity is important for investors when constructing diversified portfolios. Because mutual funds do not always adhere closely to their stated investment policies, investors may unintentionally hold funds with overlapping exposures, leading to reduced diversification and instead causing “diworsification”, which is an investment term for when too much complexity leads to worse results. As a result, various quantitative methods have been proposed in the literature to investigate fund similarity, primarily using portfolio holdings. Recently, machine learning tools such as clustering and graph theory have been introduced to capture fund similarity. This paper builds on this literature by applying bipartite graphs and node2vec embeddings to a comprehensive dataset that covers 3874 funds over a nearly 6-year period. Our empirical evidence suggests that, bipartiteness is not preserved for non-index (active) funds. Furthermore, while graph embeddings yield higher similarity scores than holding-based measures, they do not necessarily outperform holding-based similarity in explaining returns. These findings suggest that graph-based embeddings capture structural relationships among funds that are distinct from direct portfolio overlap but are not sufficient substitutes when similarity is evaluated solely through returns. As a result, we recommend a more comprehensive similarity measure that includes important risk metrics such as volatility risk, liquidity risk, and systemic risk.

Forecasting domestic energy needs is crucial to ensure a reliable and affordable energy supply, which is vital for maintaining economic stability and promoting growth. It also aids in planning and managing resources efficiently, reducing the risk of energy shortages and price volatility. The traditional literature presents numerous deep learning-inspired energy forecasting frameworks. However, this field may still be limited by various issues, as most frameworks focus solely on predicting short-term or long-term energy consumption. The research studies that have attempted to predict both long-term and short-term energy simultaneously may have failed to achieve optimal results concurrently, in terms of both high accuracy and low error. This research work presents a novel hybrid framework that combines long short-term memory (LSTM), convolutional neural network (CNN), and bi-directional long short-term memory (Bi-LSTM) in a distributed federated learning setup. This framework constitutes a simple yet effective predictive model designed to simultaneously forecast both immediate (short-term) and sustained (long-term) energy consumption. It harnesses the capability of CNN for local and spatial feature extraction. Subsequently, LSTM and Bi-LSTM are utilized for capturing current, past, and future contexts. The proposed model achieved an mean absolute percentage error (MAPE) score of 1.39. It achieves high accuracies in the simultaneous prediction of short and long-term energy, outperforming similar techniques in the literature for hourly, daily, weekly, and monthly energy consumption, with minimal computational costs.

External debt sustainability in Egypt under exchange rate volatility risks

ABSTRACT This study explores the use of ozone (O3) as an oxidizing agent to enhance the selective flotation of copper (Cu) and arsenic (As) sulfide minerals. Bench-scale flotation experiments were conducted on arsenic-rich copper ores at pH 3,7, and 12, using three gas regimes: air, pure O3, and O3–air mixture. The optimal separation was achieved under acidic conditions (pH 3) with pure ozone, obtaining a Cu recovery of 92.2% and a concentrate grade of 16,245 ppm, while reducing As recovery to 17.9% and its content in the concentrate to 76 ppm. X-ray diffraction analysis confirmed the oxidation of enargite (Cu3AsS4) into scorodite (FeAsO4), indicating arsenic transformation from the more soluble and toxic As(III) to the stable As(V) form. Flotation kinetics showed that the best Cu-grade (21,308 ppm) was achieved within the first 3 minutes, after which the grade declined and As content increased. These results demonstrate that O3 flotation promotes arsenic partitioning into tailings while maintaining high copper recovery in the concentrate. The formation of scorodite enhances the environmental stability of arsenic residues. Therefore, ozone-assisted flotation under acidic conditions offers a promising and sustainable alternative for the processing of complex Cu – As ores, reducing the risk of arsenic volatilization in downstream operations.

ABSTRACT This research delves into the connection between national policy measures and economic success as measured by the stock market, examining the link between the all-share index and specific factors such as corruption control, government efficiency, political stability, lack of violence, and citizen participation and accountability in BRICS. Stock market index data were collected to fit and forecast government initiatives and yield volatility to quantify the risk value of the green financial market. The findings reveal apparent distinguishing features between the volatility risks of various financial markets, the green stock market exhibits 40% higher volatility than the green bond market, with policy interventions reducing crisis-period volatility by 18 to 22%. GARCH-LSTM hybrid models have the potential to enhance the accuracy of predictions considerably. To overcome traditional modelling limitations, a progressive fuzzy-GARCH modelling technique will be developed to predict returns on stock market assets. Highlighted an integrated fuzzy-GARCH-LSTM approach demonstrates superior accuracy, the hybrids achieve 30% better volatility clustering capture and 25% faster adaptation to regime shifts, validated by robust cross-testing. This integration provides actionable insights for managing risks in the green financial market, leading to a higher degree of volatility persistence in emerging markets.

A reminder that, in first-best equilibria, risk premia are not directly spanned by any of uncertainty risk, volatility risk, or aggregate risk

Extreme volatility risk dynamic diffusion in financial market based on a new VEBN framework

Purpose This research reveals the effect of gender on corporate risk management. Female involvement in business and management has been a hot topic in recent years. The authors use emerging market data to determine the effects of female chairpersons and general managers on risk management and corporate governance. In particular, the authors consider the combination of different genders of the chairperson and general manager positions and test their contributions to risk control. This study aims to contribute to the academic community while making policy contributions and providing gender equality opportunities for female involvement at the management level. Design/methodology/approach This research uses empirical methods to show the effects of different chairperson and general manager gender combinations, with firm share crash risk and volatility used as risk indicators. This study considers several specific issues, such as bank–firm relationships and a firm’s political connections, which may increase the degree of risk-taking incentives at the firm’s management level. It also further explores the effect of female involvement as the chairperson and the general manager in those specific situations. Findings The results show that when the chairperson and the general manager are both male, the firm has significantly greater risk. Once a female assumes the chairperson or general manager position, the regression coefficient of gender becomes statistically insignificant, indicating lower risk level. When a firm has a director or manager who has expertise in bank relationships, a female general manager could reduce the degree of risk. A similar situation occurs when a firm has political connections, and the female chairperson can better monitor and supervise the firm and reduce the risk of share price crashes and volatility. Originality/value This research, unlike most past research, uses manager and chairperson combinations rather than merely female involvement to demonstrate that females at the management level could alleviate a firm’s degree of corporate risk. The effects of the female chairperson, who plays the monitoring role, and the female general manager, who operates the firm, are separated and analyzed. Furthermore, it extends such influences to contribution when the chairperson and general managers are of different genders.

ABSTRACT This study investigates the influence of idiosyncratic volatility, volatility risk, and climate risk on Greek corporate green bond returns and pricing. While existing literature often examines these risks in isolation or within traditional bond markets, our research focuses specifically on green bonds, an underexplored asset class. We first analyze the effect of idiosyncratic volatility on green bond returns. More importantly, we develop a novel five‐factor pricing model for green corporate bonds, integrating a volatility factor, a climate risk factor, and a bond market factor into a standard bond pricing framework. To account for varying macroeconomic conditions, particularly the pronounced inflationary cycles in Greece, we apply Markov Switching Regression. Our findings reveal that the impact of idiosyncratic volatility on Greek green bond returns is contingent on inflationary pressures. Furthermore, our five‐factor model demonstrates that under inflationary regimes, increased climate risk negatively affects green corporate bond valuations, while volatility risk becomes insignificant. Conversely, during non‐inflationary periods, investors respond positively to increasing climate risk and decreasing volatility risk. The proposed model effectively captures volatility and climate risk premia, offering a significant contribution to explaining the excess returns of green corporate bonds.

Solid electrolytes are seen as a safer alternative to the conventional liquid electrolytes due to having significantly lower flammability and volatility risks. Metal-sulfur batteries are researched due to their improved energy density that can potentially power larger vehicles than those that current Li-ion batteries can power. A longstanding challenge with metal-sulfur batteries with liquid electrolytes is the polysulfide shuttle effect where reaction intermediate metal sulfides (MxSy) dissolve into the electrolyte, “shuttle” away from the cathode, and cause side reactions and anode passivation, thus severely diminishing the electrochemical capacity of the cell upon cycling. This issue is not fully resolved with the implementation of solid polymer electrolytes due to the high solubility of polysulfides in polar polymer electrolytes. We propose that a polysulfide-rejecting interlayer can be implemented at the polymer electrolyte/cathode interface. However, polysulfide speciation and transport are not yet well-understood in all-solid polymer-based systems. In this work, we present ex-situ methodology for investigating the effectiveness of polymeric polysulfide-rejecting interlayers for all-solid metal-sulfur batteries with polymer electrolytes. These interlayers ideally allow migration of active cations while rejecting the negatively charged polysulfides. We report on the polysulfide rejection capability as a function of tethered salt material and composition, duration of use, and applied potential. The degree of polysulfide rejection by the interlayer is determined both qualitatively and quantitatively, where the quantitative studies are achieved using elemental analysis on lithium and sulfur.

ABSTRACT Using a novel news‐based climate policy uncertainty (GCPU) index, we empirically investigate its impact on commodity market volatility risk. Our findings reveal the implicit cost of policy chaos, showing that GCPU significantly amplifies commodity futures volatility, especially following major climate policy events. Channel analyses indicate that GCPU affects volatility through mechanisms such as inventory scarcity, speculative activity, and shifts in investor attention. Furthermore, employing the network connectedness framework, we trace the dynamic risk spillovers of GCPU. We find that while systemic spillovers moderate over time, pronounced heterogeneity remains across sectors and contracts: agriculture and metals display persistently higher exposure, whereas the muted aggregate effect for energy is due to offsetting dynamics at the futures level. Taken together, these results reconcile regression evidence with spillover analysis and offer important implications for risk management.