Search Results (77)

The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development. Full article

This study evaluates three pairs trading strategies—the distance method (DM), mispricing index (MPI) copula, and mixed copula—across the Chinese equity market from 2005 to 2024, incorporating time-varying transaction costs. To enhance computational efficiency, a novel two-step methodology is proposed that first selects candidate pairs based on the sum of squared differences and then applies copula models to capture nonlinear and asymmetric dependence structures between stocks. Pre-cost monthly excess returns are 84, 30, and 25 basis points, respectively, dropping to 81, 23, and 15 basis points post-costs. While the DM consistently delivers higher returns, copula strategies offer advantages in stability and resilience, especially in volatile markets. The Student-t copula proves particularly effective in capturing dependence structures with fat tails and asymmetric correlations. Although copula methods face challenges such as unconverged trades—instances where spreads fail to revert within the trading horizon—they nonetheless highlight the diversification and risk mitigation potential of advanced dependence-based approaches. Enhancing trade convergence and controlling downside risk could further improve copula strategy performance. Overall, the results highlight the diversification and risk mitigation potential of advanced copula-based pairs trading models under dynamic market conditions. Full article

The transition of South Africa from coal-dependent energy systems to renewable energy alternatives presents economic and environmental trade-off complexities that require empirical investigation. This study employed threshold-switching dynamic models, NARDL analysis, and threshold Granger causality tests to investigate nonlinear relationships between renewable energy generation, economic growth, and carbon dioxide emissions in South Africa from 1980 to 2023. The threshold-switching dynamic models revealed critical structural breakpoints: a 56.4% renewable energy threshold for carbon dioxide emissions reduction, a 397.9% trade openness threshold for economic growth optimisation, and a 385.32% trade openness threshold for coal consumption transitions. The NARDL bounds test confirmed asymmetric effects in the carbon dioxide emissions and renewable energy relationship. The threshold Granger causality test established significant unidirectional causality from renewable energy to carbon dioxide emissions, economic growth to carbon dioxide emissions, and bidirectional causality between coal consumption and trade openness. However, renewable energy demonstrated no significant causal relationship with economic growth, contradicting traditional growth-led energy hypotheses. This study concluded that South Africa’s energy transition demonstrates distinct regime-dependent characteristics, with renewable energy deployment requiring critical mass thresholds to generate meaningful environmental benefits. The study recommended that optimal trade integration and renewable energy thresholds could fundamentally transform the economy’s carbon intensity while maintaining sustainable growth patterns. Full article

This study employs the panel quantile regression (QR) technique to evaluate whether globalization threshold conditions are essential for achieving effective economic growth, utilizing data from 47 Sub-Saharan African (SSA) countries for the period from 2000 to 2021. The bootstrap simultaneous conditional QR analysis was conducted using the fixed-effects panel QR approach. The study findings revealed that the globalization thresholds at which the total effect of globalization as a percentage of global integration changes from negative to positive are 3.82% and 4.36%, respectively. Furthermore, the critical mass of FDI and trade thresholds at which the total effects of FDI and trade, as a percentage of knowledge spillovers, change from negative to positive is 4.66% and 2.19%, respectively. Conversely, these results revealed an asymmetric relationship between globalization and growth among SSA countries. Therefore, these triggers and globalization thresholds serve as essential conditions and catalysts that will foster economic development in SSA economies. The results also indicate significant effects of globalization thresholds on economic growth among the SSA countries. Regarding policy relevance, these findings are also crucial for policymakers when they are developing strategies that will promote equal opportunity and balance development in the region through knowledge spillovers and improvements in global integration. Full article

This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet’s Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems. Full article

Food irradiation is gaining popularity worldwide due to its potential to extend shelf life, improve hygienic quality, and meet trade requirements. The electron paramagnetic resonance (EPR) method is a reliable and sensitive technique for detecting untreated and irradiated foods. This study investigated the effectiveness of EPR in identifying irradiated meat and seafood containing bones. Beef, lamb, chicken, and various fish were irradiated with electron beams at different doses and analysed using an EPR spectrometer. During irradiation, the food samples were surrounded by small ice bags to prevent autodegradation of cells and nuclei. After the irradiation process, the samples were stored at −20 °C. For EPR signal recording, the flesh, connective tissues, and bone marrow were removed from the bone samples, which were then oven-dried at 50 °C. The EPR spectra were recorded using an X-band EPR analyzer. Unirradiated and irradiated samples were identified based on the nature of the EPR signals as well as the g-values of symmetric and asymmetric signals. The study found that the EPR method is effective in distinguishing between unirradiated and irradiated bone-containing foods across nearly all applied radiation doses. The peak-to-peak amplitude of the EPR signals increased with increasing radiation doses. It was observed that unirradiated bone samples showed low-intensity symmetrical signals, while irradiated samples showed typical asymmetric signals. Overall, the study demonstrated that the EPR method is a reliable and sensitive technique for identifying irradiated foods containing bones and can be used for the control, regulation, and proper surveillance of food irradiation. Full article

This study investigates the underexplored relationship between the shadow economy and environmental degradation and governance within the European Union, focusing on CO₂ and GHG emissions, and climate-related natural disasters, from 2012 to 2021. Employing both panel data econometrics and Elastic Net regularisation, the analysis reveals asymmetric effects: while a larger shadow economy is associated with lower reported GHG emissions, likely due to underreporting or less energy-intensive activities, it simultaneously increases vulnerability to climate-induced disasters. Furthermore, environmental taxes, although effective in mitigating emissions, show limited impact on disaster frequency, suggesting that fiscal instruments alone may be insufficient to foster climate resilience. Economic prosperity correlates with higher emissions and greater climate risk, highlighting a trade-off between growth and sustainability. The findings underscore the necessity of integrating informal economic activities into environmental governance frameworks, particularly in the context of the European Green Deal. Recognising and regulating the environmental footprint of the shadow economy is essential for achieving comprehensive and equitable climate goals. Future research should explore the role of institutional quality and fiscal transparency in moderating the environmental effects of informality. Overall, this study calls for a rethinking of climate policies to include both the formal and informal dimensions of economic activity. Full article

Amid escalating environmental crises—ranging from biodiversity loss to climate instability—the circular economy has emerged as a promising pathway to align economic growth with ecological limits. The objective of this study is to examine the asymmetric impact of a novel composite circular economy index (CEI)—constructed via entropy weighting—on the load capacity factor (LCF), a holistic sustainability metric, across 27 EU member states over 2010–2023. Employing the method of moments quantile regression (MMQR) and controlling for GDP, foreign direct investment, trade openness, employment, and population growth, the main findings indicate pronounced heterogeneity: positive CEI shocks yield a 1.219 percent increase in LCF at the 90th quantile versus just 0.229 percent at the 10th, revealing a “sustainability premium” for high-performing economies, while negative shocks inflict a −5.253 percent decline at the 90th quantile, exposing their greater vulnerability. Low-LCF countries, by contrast, display relative resilience to downturns, likely due to less entrenched circular systems. Panel Granger causality tests further reveal bidirectional feedback loops between LCF and economic growth, investment, and labor markets, alongside a unidirectional effect from trade openness to enhanced sustainability. These insights carry clear policy implications: high-LCF nations require safeguards against circularity backsliding, whereas low-LCF members need capacity-building to convert latent resilience into sustained gains—together forming a nuanced blueprint for achieving the EU’s 2050 climate-neutrality ambitions. Full article

As critical strategic mineral resources underpinning high-tech industries and national defense security, rare earth elements have become a central focus of international competition, with their global industrial chain configuration deeply intertwined with geopolitical dynamics. Leveraging a novel multilateral database encompassing 140 countries’ geopolitical relationships and rare earth trade flows (2001–2023), this study employs social network analysis and temporal exponential random graph models (TERGMs) to decode structural interdependencies across upstream mineral concentrates, midstream smelting, and downstream permanent magnet sectors. Empirical results show that topological density trajectories reveal intensified network coupling, with upstream/downstream sectors demonstrating strong clustering. Geopolitical cooperation and conflict exert differential impacts along the value chain: downstream trade exhibits heightened sensitivity to cooperative effects, whereas midstream trade suffers the most pronounced obstruction from conflicts. Cooperation fosters long-term trade relationships, whereas conflicts primarily impose short-term suppression. In addition, centrality metrics reveal asymmetric mechanisms. Each unit increase in cooperation degree centrality amplifies the mid/downstream trade by 3.29 times, whereas conflict centrality depresses the midstream trade by 4.76%. The eigenvector centrality of cooperation hub nations enhances the midstream trade probability by 5.37-fold per unit gain, in contrast with the 25.09% midstream trade erosion from conflict-prone nations’ centrality increments. These insights provide implications for mitigating geopolitical risks and achieving sustainable governance in key mineral resource supply chains. Full article

The results reveal significant evidence of herding in the overall, bearish, and extended crisis market phases during extreme downturns, while the magnitude of market returns in the tail distribution is considered. Asymmetric herding behavior is more pronounced and prevalent, conditioned by market dimensions like return direction, trading volume, and volatility, with CSSD proving more effective than CSAD in detecting asymmetric patterns. Notably, herding strongly appears in the COVID-19 market during periods of abnormally high market volatility, reflecting heightened market sentiment. Applying Dow Theory to delineate bull and bear market phases significantly improved the methodological complexity and analytical depth related to herding behavior. These findings suggest policy implications for regulators and market participants in minimizing herding effects to create an efficient market environment through enhanced market surveillance, improved investor education, and the use of advanced technologies. Full article

Renewable energy has emerged as a transformative and essential alternative in the global energy sector. Many countries are striving to achieve the Sustainable Development Goals (SDGs) established by the United Nations for 2030, particularly the goal of ensuring that all individuals have access to clean and affordable energy. This paper re-examines the impact of economic growth (EG), trade openness (TO), exchange rates (ER), foreign direct investment (FDI), green finance (GF), and oil prices (OL) on renewable energy consumption (REC) across 14 Southern African countries: South Africa, Botswana, Lesotho, Namibia, Tanzania, Madagascar, Mauritius, Kenya, the Comoros, Zambia, Eswatini, Rwanda, Angola, and Mozambique, during the period of 2000 to 2022. This study employed cointegration and unit root tests, as well as the RALS-EG and MMQR models, to estimate the long-run relationships among the variables. The results reveal that renewable energy consumption is positively and directly related to economic growth, trade openness, exchange rates, green finance, and foreign direct investment across all quantiles (q05–q95), with no evidence of asymmetric effects. These findings suggest that economic growth, green finance, and foreign direct investment are crucial for fostering renewable energy innovation in Southern African countries. Policymakers are encouraged to prioritize strategies that enhance these factors as a foundation for achieving sustainable energy solutions. Full article

Research on the impact of fiscal policy and foreign direct investment (FDI) on environmental quality has yielded conflicting results on their effects on carbon dioxide emissions. To further explore the asymmetric influences of these two critical factors on environmental quality, we employed a nonlinear ARDL approach to examine how fiscal policy (GOEX), FDI inflows, and other drivers of CO₂ emissions, such as trade openness, financial development, and economic growth, have affected environmental quality in Vietnam from 1990 to 2022. Our findings indicate that a positive shock in GOEX results in decreased emissions, whereas a negative shock in GOEX leads to increased emissions, challenging previous research that suggests that higher expenditures typically harm the environment. We also observe that positive changes in FDI result in higher CO₂ emissions, whereas negative FDI shifts have no significant impact. Additionally, our study reveals that trade openness improves environmental conditions, whereas economic growth and financial development contribute to increased CO₂ emissions. The responses of CO₂ emissions to the asymmetric effects of fiscal policy, FDI inflows, and other determinants in the short term last in the long term. These insights are valuable for policymakers in developing environmental sustainability strategies to mitigate climate change by addressing fiscal policies and other determinants of CO₂ emissions. Full article

This study focuses on the effect of financial development, natural resource rent, human development, and technological innovation on the ecological and carbon footprints of the G-10 countries between 1990 and 2022. This study also considers the impact of globalization, trade openness, urbanization, and renewable energy on environmental degradation. The study uses Kao and Westerlund DH cointegration tests, FMOLS and DOLS estimators, and panel Fisher and Hatemi-J asymmetric causality tests to provide reliable results. Long-run estimates confirm an inverted U-shaped linkage between financial development and ecological and carbon footprints. Natural resource rent and technological innovation increase ecological and carbon footprints, while human development decreases them. Furthermore, globalization, trade openness, and renewable energy contribute to environmental quality, while urbanization increases environmental degradation. The Fisher test findings reveal that financial development, natural resource rent, human development, and technological innovation have a causal link with the ecological and carbon footprint. The results of the Hatemi-J test show that the negative shocks observed in the ecological and carbon footprint are affected by both negative and positive shocks in financial development, natural resource rent, and technological innovation. Moreover, positive and negative shocks in human development are the main drivers of negative shocks in the carbon footprint, while positive shocks in human development lead to negative shocks in the ecological footprint. Full article