Search Results (267)

As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China’s energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China’s energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China’s energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market. Full article

The implications of digital finance for financial stability has come under serious scrutiny since the aftermath of the 2008 global financial crisis (GFC). Empirical evidence on this nexus are somewhat inconsistent and ambiguous. This study therefore attributes this puzzle to multiple structural breaks (MSBs) which were long neglected by previous studies. Consequently, this study aims to identify possible MSBs in the digital finance–stability nexus and examine if its impact is consistent/weakened in the presence of MSBs in a sample of 41 developing African economies for the 2004–2023 periods. Results from the PCA index generation report that instability is more susceptible to bank crisis/Z-score. Again, the panel extension of BP98 MSBs detection identified three breaks with their confidence intervals overlapping the periods of the 2006–2011 GFC/subprime mortgage crises, the 2012–2016 Br-exit referendum and the 2017–2021 COVID 19 pandemic/Ukraine war. The quantile regression methodology also shows that these breaks weaken the impact of digital finance (i.e., mobile banking and internet banking) on financial stability, particularly for economies at lower quantiles of financial stability but with marginal effects for economies at higher quantiles. The study concludes that digital finance can stabilize the financial system of developing economies when shocks from structural breaks are controlled. Therefore, the study contributes to knowledge by developing a new econometric model for BP98 panel extension of MSBs detection, calibrating an index for financial stability and detecting valid break dates for three major breaks. Structural and financial development through policy coordination to forestall the effects of structural breaks were recommended. Full article

Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article

This study aims to empirically examine the effects of CEO characteristics (gender, nationality, multiple directorships) on the carbon emissions of UK-listed firms. We focus on understanding how these factors influence carbon emissions across the overall sample and within specific industry sectors grounded on the upper echelons and stakeholder theories. We employed a quantitative research design using quantile regression analysis. Our dataset comprises 295 UK-listed firms from the STOXX 600 Index of European-listed companies, covering the period from 1999 to 2023. Data were sourced from BoardEx, Refinitiv DataStream, annual reports, and sustainability reports. Our results indicate that foreign CEOs are associated with higher carbon emissions across the overall sample of UK-listed firms, across the three levels of carbon emitters within the sensitive industries, and within low- and high-level emitters within the non-sensitive industries. CEOs with multiple directorships were found to have a significant association with higher carbon emissions, likely due to divided attention and obligations. As for the CEO gender, it is noteworthy that it has an insignificant effect on reducing carbon emissions in low emission companies within sensitive industries. In contrast, female CEOs were associated with lower carbon emissions in medium-emitting firms within non-sensitive industries. This study contributes to existing literature by employing sensitivity analysis (sensitive sectors and non-sensitive). The study also employs a novel econometric technique, quantile regression, which provides a comprehensive understanding of the relationship between independent and dependent variables across different points of the distribution. Full article

We establish a correspondence between Lorenz curves and Pickands dependence functions, thereby reframing the construction of any bivariate extreme-value copula as an inequality problem. We discuss the conditions under which a Lorenz curve generates a closed-form Pickands model, considerably expanding the small set of tractable parametrizations currently available. Furthermore, the Pickands measure-generating function M can be written explicitly in terms of the quantile function underlying the Lorenz curve, providing a constructive route to model specification. Finally, classical inequality indices like the Gini coincide with scale-free, rotation-invariant indices of global upper-tail dependence, thereby complementing local coefficients such as the upper tail dependence index ${\lambda }_U$. Full article

Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R² improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands. Full article

Research on the spread of dengue fever is typically measured periodically, producing longitudinally structured data. The varying coefficient model for longitudinal data allows the coefficient to vary as a smooth function of time. The data in this study have a longitudinal structure that offers a long-term presentation of dengue fever in Bandung City, Indonesia, influenced by a set of covariates that vary over time and space. The former are temperature, rainfall, and humidity, and the latter is residential location, such as vector index and population density. Considering space- and time-varying effects, a space-time varying coefficient model was proposed. The model parameters were estimated by minimizing the P-splines quantile objective function. The results implemented on the data show that the model and method satisfy the condition of the data, which means the coefficients vary over space and time. Based on the three quantile levels, each subdistrict in Bandung City has a different level of incidence rate category. Due to differences in covariate effects both over time and over space, Bandung City also exhibits a heterogeneous incidence rate pattern based on its three quantile levels. The result provides a quantile pattern that can be used as a guide for high-performance dengue fever classification. Full article

This study investigates formation homogeneous regions in regional flood frequency analysis (RFFA) and compares two RFFA methods, the quantile regression technique (QRT) and the index flood method (IFM). A total of 201 gauged stations from southeast Australia were adopted in this study. Multivariate statistical techniques were applied to form candidate regions. Also, regions are formed in the L-moments space (such as the L coefficient of variation (LCV) and L coefficient of skewness (LCS) of annual maximum flood data). Hosking and Wallis test statistics were used to find discordant sites and for testing the homogeneity of the assumed regions. No homogeneous regions were found in southeast Australia based on catchment characteristics data; however, homogeneous regions can be formed in the space of L-moments. It was found that regions formed in the L-moments space have little link with the catchment characteristics data space. The QRT provides more accurate flood quantile estimates than the IFM. Full article

An improved adaptive constant false alarm rate (CFAR) detector based on fuzzy theory is proposed to address the issue of poor detection performance and robustness of the variability index (VI) class CFAR detectors due to the misjudgment of the background environment and other reasons. The integration of the order statistic threshold adjustable detection algorithm (OSTA) into the adaptive CFAR detector has the potential to address the aforementioned issue. Therefore, in a clutter edge environment, the ratio of the means of the leading and lagging windows is calculated separately, and the differences between these mean ratios and predefined thresholds are used as inputs to the fuzzy inference machine, and the background clutter estimation of the OSTA is determined based on the fuzzy output, which can extend the range of values of the background clutter estimation, and improve the detection performance of the OSTA in this environment. The Kaigh–Lachenbruch quantile detection algorithm (KLQ) exhibits robust detection performance in multiple-target environments. Therefore, KLQ is used to detect targets in this environment, further improving the detection performance of the detector. The experimental results show that in multiple-target environments with an average misjudgment rate of 27.48%, the proposed detector increases the detection probability by 15.58% compared to the recently proposed variability index heterogeneous clutter estimate modified ordered statistics CFAR detector (VIHCEMOS-CFAR), and in a clutter edge environment, the false alarm rate of the proposed detector was reduced by approximately 89.64% compared to VIHCEMOS-CFAR. Additionally, the effectiveness of the proposed detector is also validated by real clutter data. It can be seen that the proposed adaptive CFAR detector has better robustness to the misjudgment of the background environment and better overall detection performance regardless of the environment. Full article

Climatic events are expected to significantly impact global agricultural production, with China being particularly vulnerable. Research in China emphasizes the urgent need for sustainable agricultural practices that address climate change, implement effective management strategies to mitigate the impacts of climatic events, and ensure food security. Therefore, this study examines the impact of climatic events on agricultural production optimization in Northeast China. To complete this objective, this study uses Method-of-Moments Quantile Regression (MM-QR) and data from 2003 to 2020. The main findings reveal that climatic factors, such as the Standardized Precipitation Index (SPI) and High-Temperature Days (HTDs), have a more pronounced effect on agricultural outcomes at higher production levels, particularly for larger producers. In addition, machinery power (TPAM) enhances productivity. Its role is more focused on risk mitigation than on expanding production. Insurance payouts (AIPE) increase grain production capacity at higher quantiles, while fertilizer use (FEU) has diminishing returns on capacity but encourages planting. Granger causality tests further demonstrate that management factors—such as machinery, irrigation, and insurance—play a more significant role in shaping agricultural outcomes than extreme climatic events. To improve agricultural sustainability in the context of climate change, policy recommendations include promoting climate-resilient crops, investing in smart irrigation systems, expanding affordable agricultural insurance, and encouraging sustainable fertilizer use through incentives and training. These strategies can help mitigate climate risks, enhance productivity, and reduce the environmental impact of agricultural activities. Full article

Sustainable agricultural development in China in the face of growing environmental concerns relies critically on how well regulatory policies strengthen agricultural resilience. This study aims to systematically investigate the impact of air pollution on agricultural economic resilience and its mechanisms of action and to explicitly assess the moderating role of environmental regulation. This study develops a thorough index system that evaluates agricultural economic resilience in three areas: risk resistance and recovery, adaptive adjustment capacity, and restructuring innovation. Panel data from 30 Chinese provinces from 2000 to 2023 is used to achieve this. The implications of air pollution and its diverse consequences on agricultural economic resilience are systematically assessed using a two-way fixed-effects and moderating-effects model. The following are the primary conclusions: First, air pollution has a significant negative impact on the economic resilience of agriculture. This conclusion holds after considering the endogeneity problem and a series of robustness tests, such as the exclusion of samples, random sampling, and quantile regression. Second, different dimensions of agricultural economic resilience, intensity levels, and economic growth phases influence how much air pollution reduces agricultural economic resilience. Notably, at various stages of economic growth, air pollution steadily weakens the economic resilience of agriculture. In particular, the impact is more pronounced in the post-financial-crisis phase of domestic demand expansion and the phase of financial clearing and high-quality development. According to a dimensional perspective, air pollution significantly reduces the farm sector’s capacity to endure and recover from dangers while also making adaptive modifications easier, and the impact on transformational innovation is not significant. In terms of intensity, in contrast to places with higher resilience, those with lower resilience are disproportionately more adversely affected by air pollution. Third, environmental control mitigates some of the detrimental effects of air pollution on agricultural economic resilience. Based on these results, this study calls for stricter air pollution control measures, strengthens environmental regulatory support for agricultural resilience, and demonstrates region-specific governance solutions to guarantee the stability and sustainability of the agricultural economic framework. 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

To efficiently delineate mineral-induced geochemical anomalies within the Hongyahuo area, principal component analysis (PCA), S-A multifractal modeling, and singularity modeling were employed to examine multi-element datasets derived from 641 soil samples collected from natural gully systems. The isometric log-ratio (ilr) transformation was implemented in conjunction with histogram and quantile-quantile plot analysis to assess and compare the multivariate statistical properties of elemental data across three formats—original, logarithmic, and ilr-transformed. The findings demonstrate the following: (1) following ilr transformation, the issue of data closure was resolved, resulting in elemental distributions more closely approximating normality; (2) PCA revealed two distinguishable elemental associations: PC1 corresponds to the Cu-Fe-Mn-Ni-Pb-Zn group, indicative of a medium- to high-temperature hydrothermal mineralization assemblage associated with Cu-Pb-Zn polymetallic mineralization linked to magmatic intrusion and fracture systems, signifying overprinted copper polymetallic mineralization events; PC2 reflects the Au-As-Sb elemental combination, associated with low-temperature hydrothermal processes indicative of Au-Sb mineralization; (3) the decomposition of the S-A model indicated that low-background and high-anomaly zones for PC1 are primarily situated within andesitic units, where nearby intermediate to felsic intrusions and structural fracture zones have likely served as sources for Cu-polymetallic mineralization; (4) the spatial distribution of the singularity index suggested that anomalous regions characterized by a PC1 singularity index α < 2 were relatively confined, offering strategic implications for mineral exploration targeting; and (5) when integrated with regional metallogenic background, three prospecting targets were identified, leading to the subsequent discovery of two copper ore bodies through anomaly validation. Therefore, this integrative analytical framework is demonstrated to be a robust approach for delineating geochemical anomalies. Full article

This study explores the nuanced relationship between biodiversity and sovereign credit ratings, underscoring the link between environmental sustainability and economic resilience. As credit rating methodologies increasingly incorporate Environmental, Social, and Governance (ESG) dimensions alongside traditional macroeconomic indicators, biodiversity has emerged as a vital factor influencing sovereign creditworthiness. Drawing on a panel dataset of 62 countries—representing 91% of the global GDP and 81% of the world’s greenhouse gas emissions—from 2001 to 2021, the research utilizes advanced econometric techniques, including the panel Generalized Method of Moments (GMM) and panel quantile regression. The GMM analysis indicates that higher biodiversity levels are generally associated with a decline in credit ratings. However, the quantile regression provides a more differentiated view, revealing that biodiversity’s impact varies by a country’s existing credit standing. Specifically, nations with lower credit ratings tend to benefit from richer biodiversity, while countries with higher credit ratings show a modest negative association—reflecting structural and institutional differences. Robustness checks confirm these results, highlighting the relevance of biodiversity indicators such as the Red List Index in credit evaluations. The findings support the integration of biodiversity into sovereign risk assessments to enhance the alignment of financial systems with long-term ecological and economic sustainability goals. Full article

This work introduces the use of Bayesian Neural Networks (BNNs) for inferring chlorophyll-a concentration ([CHL-a]) from remotely sensed data. BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. The performance of the proposed probabilistic model is compared to that of standard ocean color algorithms, namely ocean color 4 (OC4) and ocean color index (OCI). An extensive in situ bio-optical dataset was used to train and validate the ocean color models. In contrast to established methods, the BNN allows for enhanced modeling flexibility, where different variables that affect phytoplankton phenology or describe the state of the ocean can be used as additional input for enhanced performance. Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. Furthermore, the output probability distribution can be used for risk assessment and decision making through analyzing the quantiles and shape of the predicted distribution. Full article