Search Results (2,735)

This study examines whether institutional quality moderates the relationship between trade openness and renewable energy transition across thirteen emerging economies over the period 2007–2024. Using a panel framework, the analysis employs Feasible Generalized Least Squares (FGLS), Driscoll–Kraay standard errors, and Method of Moments Quantile Regression (MMQR) to capture both average and distribution-specific effects. The results show that gross fixed capital formation and institutional quality are among the most consistent positive determinants of renewable energy consumption, whereas urbanization and technological innovation, proxied by high-technology exports, exert predominantly negative effects. The effect of trade openness is heterogeneous across the distribution of renewable energy consumption, shifting from positive at lower quantiles to negative at higher quantiles. Most importantly, the interaction between trade openness and institutional quality is positive and statistically significant, with its effect becoming stronger toward the upper quantiles. This finding suggests that stronger institutions enhance countries’ capacities to translate the benefits of international trade, including technology diffusion, investment, and knowledge spillovers, into renewable energy development. The study therefore contributes to the literature by providing quantile-based evidence that the trade openness–renewable energy relationship is conditional on institutional quality and the stage of renewable energy transition. The findings suggest that trade liberalization and institutional strengthening should be pursued as complementary policy strategies to accelerate renewable energy transition in emerging economies. Full article

Energy transition is central to both economic development and climate-change mitigation and has become a shared global challenge. Given the close relationship between conventional energy prices and the development of the new-energy industry, this study investigates systemic risk spillovers among three crude-oil futures, two natural-gas futures, and five Chinese new-energy sector indices. We employ a quantile time-frequency-connectedness framework and an out-of-sample-validated link-prediction model to assess both realized spillovers and potential changes in the network structure. The results reveal that network connectedness is time-varying and asymmetric across quantiles, with short-horizon connectedness accounting for the majority of average system-wide connectedness. Overall connectedness also increases markedly during major crisis episodes. INE crude-oil futures and both natural-gas futures are net receivers of shocks, whereas WTI and Brent crude-oil futures consistently act as net transmitters, with Brent playing the dominant role under extreme market conditions. As the investment horizon lengthens, the solar sector shifts from a net risk receiver to a net risk transmitter. In the predicted network, the solar sector emerges as the market most likely to initiate new short-term spillover links. This finding reflects a prospective, model-implied tendency rather than a causal relationship. These findings offer useful implications for energy market policy, portfolio risk management, and investment decisions involving new-energy companies. Full article

Mountain rural regions face persistent challenges related to agricultural fragmentation, demographic decline, and limited market integration. Although farm diversification is widely promoted as a strategy for rural development, the mechanisms through which diversified rural resources generate economic value remain insufficiently understood. This study advances a Tourism Translation perspective, conceptualizing agritourism integration as the mechanism linking farm diversification and economic performance. Using survey data covering 63% of the identified rural household-business population across eleven mountain villages in Northern Albania, the study examines the relationships among farm diversification, tourism integration, knowledge and capacity development, territorial constraints, and economic performance. The analysis combines composite indices, principal component analysis, mediation modelling, quantile regression, threshold analysis, and robustness tests. The findings indicate no significant direct association between farm diversification and economic performance, while tourism integration is positively associated with economic performance. The mediation results reveal a significant but negative indirect pathway, suggesting that diversification and tourism integration may represent distinct rather than automatically sequential upgrading strategies. Additional exploratory analyses indicate potential heterogeneity and non-linearity in the TII–EPI association. Knowledge and capacity development facilitate tourism integration, while territorial constraints influence participation in tourism-oriented activities. The study contributes to the rural tourism and agritourism literature by providing a mechanism-based explanation of economic upgrading and positioning tourism integration as a core process within multifunctional mountain rural systems. Full article

Predictive maintenance of railway wheelsets requires remaining useful life (RUL) estimates and treatment of model limitations. This study develops a Gamma-process-informed XGBoost surrogate for an ONCF fleet of 444 vehicles, 3552 candidate wheel positions, and 14 inspection campaigns. Flange width (Fw), flange height (Fh), and flange steepness (qR) were measured using a CALIPRI C42 gauge. After date normalisation and series-level IQR filtering, 3296 candidate series remained per indicator; Gamma eligibility retained 2366 Fw, 1628 Fh, and 2445 qR series. RUL quantiles were evaluated from the discrete Gamma first-passage distribution, with Monte Carlo used only for convergence checking. Vehicle-level median RUL was 13 months for Fw, 3 months for Fh, and 7 months for qR. RUL below six months occurred for 25/402, 283/344, and 100/404 vehicles, respectively. Under vehicle-grouped five-fold cross-validation, surrogate-label reconstruction yielded Pearson r values of 0.968, 0.759, and 0.964, with MAEs of 4.16, 1.67, and 2.82 months. Ablation confirmed strong target-feature coupling, while calibration and bootstrap diagnostics revealed substantial uncertainty. The framework supports model-conditional fleet screening, not validated real-world RUL forecasting or automatic maintenance decisions, until prospective temporal validation links scores to maintenance outcomes. Full article

The inherent uncertainty of concrete dam systems, together with the complex influence of environmental loads and measurement noise, makes it difficult for traditional deterministic point prediction models to provide reliable deformation forecasts. In particular, the prediction performance of conventional models is highly dependent on parameter settings, while the uncertainty and potential deviation of future displacement responses are often not fully quantified. To address these limitations, this study proposes an intelligent data-driven prediction framework for dam displacement based on the integration of convolutional neural networks and long short-term memory networks. In the proposed framework, convolutional neural networks are used to extract local feature information from monitoring data, while long short-term memory networks are employed to capture temporal dependencies in displacement sequences. The Black-winged Kite Algorithm is introduced to optimize the key parameters of the integrated multi-level network, thereby improving the accuracy and robustness of point prediction. Furthermore, quantile regression is embedded into the optimized learning framework to construct an interval prediction model for dam deformation, enabling the conditional predictive uncertainty associated with displacement evolution to be quantitatively characterized. The engineering case study and comparative analyses with other models demonstrate that the proposed model achieves improved prediction performance for the investigated monitoring point. The interval prediction results further show that, for the investigated dam and monitoring point, the proposed framework can effectively characterize conditional predictive uncertainty and provide additional information for deformation interpretation and safety assessment. Further studies involving additional monitoring points and dams are required to assess its broader applicability. Full article

This study adopts the contingent valuation method (CVM), Logit model, and quantile regression to evaluate the compensation level of public welfare forests and identify the influencing factors of compensation standards from the perspective of forest farmers’ willingness to accept compensation. The results show that the forest farmers’ expected compensation standard ranges from 1034 to 1128 CNY·ha⁻¹·year⁻¹, more than three times the current official compensation standard. Based on forest farmers’ willingness to accept, the current forest ecological compensation level only reaches 30.6%–33.4% of the expected standard, which is insufficient to mobilize the enthusiasm of forest farmers for forest management and protection. Therefore, it is necessary to further raise the public welfare forest compensation standard and implement diversified compensation measures that adapt to the differentiated demands of forest farmers. In addition, analysis of zero-willingness samples reveals that poor policy cognition is the main reason for zero compensation bids among partial respondents, rather than the overall sample. A better understanding of ecological compensation policies can significantly promote farmers’ willingness to accept compensation, suggesting that targeted policy publicity should be strengthened to improve forest protection awareness and policy satisfaction among forest farmers. Furthermore, household age, annual household income, forestland area, forest management cost, and policy understanding degree are core factors affecting compensation willingness. Quantile regression results indicate that these influencing factors show heterogeneous effects at different compensation levels, which provides evidence for the formulation of differentiated compensation schemes. The robustness test and heterogeneity analysis further confirm the reliability of the research conclusions. This study provides a practical reference for governments to optimize forest ecological compensation policies, balance household welfare, and promote sustainable forest resource protection. Full article

This paper examines directional quantile dependence from green bonds, clean energy markets, and carbon markets to the return states of five literature-classified green cryptocurrencies. Using daily returns from 25 September 2019 to 23 May 2025, we estimate static cross-quantilograms on a $19\times 19$ quantile grid at lags 1, 5, and 22, 500-observation rolling cross-quantilograms, bootstrap surface tests, and green-specificity comparisons with five cryptocurrencies that used proof-of-work (PoW) consensus throughout the comparison sample. Point estimates display heterogeneous short-run patterns in selected green-bond and carbon-market pairs, but none of the 45 forward surfaces rejects the omnibus null at the 5% level. Rolling estimates vary across windows and tail cutoffs. The largest raw green-group contrast occurs for carbon quota prices at lag five, although its time-series bootstrap interval includes zero and factor-adjusted tests do not detect systematic green-minus-PoW separation. Descriptive quantile-on-quantile connectedness estimates are higher at extreme quantiles than at the median–median state. Overall, the evidence is more consistent with broad cryptocurrency-market conditions than with a uniform dependence pattern associated with the environmental label. Full article

This study presents an experimentally validated MuJoCo-based sim-to-real reinforcement learning framework for transition control among all eight equilibrium points (EPs) of a physical triple inverted pendulum (TIP). A multibody model is constructed from the apparatus properties, and uncertain actuation, damping, and friction parameters are identified by matching measured and simulated trajectories using the covariance matrix adaptation evolution strategy. The resulting effective parameter sets are sampled during training using truncated quantile critics to reduce policy dependence on a single model. The trained policies are transferred to the physical TIP without additional tuning or calibration. In the EP0-to-EP7 experiment, the policy trained without parameter identification failed in all ten trials, whereas the policy trained with the identified parameter sets succeeded in all ten. In the sequential experiment, all eight transitions were completed in each of ten trials, yielding 80 successful transitions with no observed failures while satisfying the cart travel and tangential force limits. These results demonstrate sim-to-real transition control across all eight EPs without state resets or precomputed trajectories. Full article

Financial returns have heavy tails and nonzero skewness. Machine learning risk models typically return isolated quantiles or rest on thin-tailed laws. We introduce a skewed, heavy-tailed distribution that is as easy to use as the normal and that converts any machine learning forecast of conditional moments into a full density. The law splices the left half of one logistic density onto the right half of another. A prescribed mean, variance, and skewness map into its three parameters by elementary algebra. Value at Risk (VaR), Expected Shortfall (ES), optimal holdings, and risk premia then have closed-form expressions. The attainable third-moment interval is wider than that of the smooth half-normal law and even a small departure from symmetry already moves the implied tails away from the Gaussian benchmark. The logistic base has a kurtosis of $4.2$ and above, so tail-risk estimates are more conservative than those of thin-tailed alternatives. Gradient-boosted trees predict the conditional mean, volatility, and skewness that enter the closed-form formulas. The resulting one-day-ahead VaR and ES forecasts are well calibrated and pass standard coverage tests. Unlike quantile-based machine learning forecasts, they deliver the entire conditional density in analytic form. Exponentially weighted moving average moments, fed through the same formulas, already give accurate ES forecasts. An application to stock-index, commodity, and foreign-exchange returns shows that the distribution tracks sample asymmetry and tail behavior. A three-moment calibration matches mean, variance, and skewness. The implied kurtosis is that of the logistic base and is not a free parameter. Full article

Background: The uric acid-to-magnesium (UA/Mg) ratio and frontal QRS-T angle have been associated with coronary artery disease, but their relationships with angiographic coronary disease severity in acute coronary syndrome (ACS) remain incompletely characterized. We investigated their associations with the Gensini score in patients with ACS. Methods: This single-center, retrospective observational study included 146 patients with ACS undergoing coronary angiography, including 85 with ST-segment elevation myocardial infarction (STEMI) and 61 with non-ST-segment elevation myocardial infarction (NSTEMI). The primary adjusted analysis evaluated the continuous Gensini score after natural logarithmic transformation using multivariable linear regression with HC3 robust standard errors. Median quantile regression was performed as a sensitivity analysis. A Gensini score ≥ 60 was evaluated as a secondary exploratory binary endpoint using a parsimonious multivariable logistic regression model, with bootstrap internal validation. Results: The UA/Mg ratio (ρ = 0.332; p < 0.001), uric acid-to-HDL cholesterol ratio (UHR) (ρ = 0.232; p = 0.005), and frontal QRS-T angle (ρ = 0.481; p < 0.001) correlated positively with the Gensini score. In the primary adjusted analysis, both the UA/Mg ratio (β = 0.294 per 1-SD increase; p < 0.001) and frontal QRS-T angle (β = 0.225 per 1-SD increase; p < 0.001) were independently associated with higher log-transformed Gensini scores. These associations remained significant in median quantile regression and after additional adjustment for C-reactive protein. Modeling uric acid and magnesium separately provided better model fit than use of the UA/Mg ratio. In the secondary exploratory analysis of a Gensini score ≥ 60, the UA/Mg ratio (OR = 1.86 per 1-SD increase; 95% CI: 1.22–2.95; p = 0.005) and frontal QRS-T angle (OR = 1.54 per 1-SD increase; 95% CI: 1.05–2.30; p = 0.030) remained independently associated with the endpoint. The parsimonious model had an apparent AUC of 0.787 and an optimism-corrected AUC of 0.765 after bootstrap internal validation. Conclusions: Higher UA/Mg ratio and wider frontal QRS-T angle were independently associated with a higher log-transformed Gensini score in the primary continuous-outcome analysis in patients with ACS. However, the UA/Mg ratio did not demonstrate statistical superiority over its individual components, and the secondary binary model showed only moderate discrimination after internal validation. These findings should be considered exploratory and require prospective external validation. Full article

Land subsidence amplifies the impacts of sea-level rise (SLR) in low-lying deltas, yet some Coastal Vulnerability Index (CVI) assessments omit this or rely on global estimates, drastically understating deltaic vulnerability. This study presents the first CVI assessment along Ghana’s coast to integrate validated, spatially resolved land subsidence derived from Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR). Nine geological, geomorphological, hydrodynamic, and anthropogenic variables were quantified across 72 contiguous grid cells spanning ~150 km of the Volta Delta’s coastline and ranked on a 1–5 vulnerability scale. Three composite indices were computed using a common quantile distribution: an index excluding subsidence (CVI_-sub), an index with a spatially uniform regional vertical land motion (CVI_+(u-sub)), and an index incorporating spatially resolved local InSAR subsidence (CVI_+(v-sub)). High to very high vulnerability ranks rose from 28% of cells without subsidence, through 44% with the regional estimate, to 75% with the local InSAR; every grid cell recorded a higher CVI once subsidence was incorporated (Wilcoxon signed-rank test, p < 0.001). Vulnerability peaked along the Keta and Songor lagoonal margins. Omitting measured subsidence understates deltaic vulnerability, and this approach offers a transferable method for data-sparse deltas. Full article

Background. Human populations are exposed to complex chemical mixtures, making interaction detection a central challenge in environmental epidemiology. We benchmarked methods for prediction and recovery of interaction structure. Methods. Eight approaches—main-effects Lasso (glmnet_main), interaction Lasso (glmnet_int), hierNet, Random Forests, Bayesian Kernel Machine Regression (BKMR), quantile g-computation (qgcomp), weighted quantile sum regression (gWQS) and SuperLearner—were evaluated across eight linear/nonlinear, additive/interaction, continuous/binary data-generating processes (500 replicates each). Every method completed in all 500 replicates of all eight scenarios. Prediction was assessed on held-out test data using observed-outcome and oracle-referenced metrics; interaction detection was assessed against three known pairwise interactions among 45 candidate pairs, using both hard selection and a threshold-free ranking criterion. BKMR was evaluated at 2000 versus 25,000 MCMC iterations with multi-chain convergence diagnostics. Sensitivity analyses varied sample size, exposure correlation, signal strength, and interaction form. BKMR was also applied illustratively to six metals and prevalent diabetes in NHANES. Results. In additive settings, observed-outcome prediction was similar across methods, but oracle-referenced continuous-outcome error differed by up to six-fold. With interactions, interaction-aware methods clearly outperformed additive-only approaches on the continuous oracle-referenced metrics: in LMI, the oracle MSE was 1.57 for hierNet and 1.87 for glmnet_int against 3.80 for glmnet_main and 4.94 for qgcomp. glmnet_int and hierNet showed comparable sensitivity; hierNet had a modestly lower mean per-replicate false discovery proportion in paired comparisons, while pooled false discovery favored hierNet in the continuous scenarios and glmnet_int in the binary ones; pooled false discovery rates were 0.79 to 0.82 in every interaction scenario, so roughly four in five selected pairs were false. In the scenarios without true interactions, the pooled false discovery rate was exactly 1. Under threshold-free ranking, BKMR was competitive with the penalized methods (pair-ranking AUC: 0.758 to 0.781 across the four interaction scenarios). BKMR’s apparent instability at 2000 iterations reflected inadequate sampling: 93% of monitored parameters had a Gelman–Rubin statistic above 1.1 and the minimum effective sample size was 7.5, whereas at 25,000 iterations the median statistic was 1.02 and the oracle MSE in LMI fell from 6.38 to 2.08. In NHANES, lead, manganese, and iron had the highest posterior inclusion probabilities, with predominantly nonlinear exposure–response functions. Conclusions. Method choice matters most when interactions are present. Interaction-aware methods are preferable when joint effects are relevant, selected interactions require replication given the high false discovery burden, and BKMR comparisons should report sampling budgets and convergence diagnostics rather than treating a short chain as characteristic of the method. Full article

Financial networks are typically summarised by a single average-regime connectedness estimate that treats transmission as symmetric across calm and turbulent markets. Using a quantile vector autoregression on nine European equity indices from 2000 to 2026, we show that crash-regime connectedness is not an episodic crisis response but a persistent premium over the normal regime, holding steady across nearly six thousand rolling windows. We introduce Geopolitical Risk Realised Volatility, a within-month measure of geopolitical risk dispersion distinct from its level, and show that it predicts a delayed, statistically robust decoupling of tail connectedness, modest in magnitude and specific to the crash regime, that adds information beyond GPR Act’s level alone. A quantile-specific structural break test shows that the Brexit referendum permanently shifted the United Kingdom’s net shock-transmission position within the European equity network. These shocks affect connectedness only in the crash regime, a pattern an average-regime estimate does not capture. Full article

This study forecasts cumulative log returns at horizons of one to twenty trading days and reconstructs future adjusted prices by exponentiating those return forecasts; it does not optimize a price-level loss. This task is difficult because financial returns are nonstationary, heavy-tailed, horizon-dependent, and subject to rapidly changing volatility. We propose FreqCast, which combines a market-conditioned spectral decomposition, a structured state-space branch for the component designated low-frequency, causal multiscale encoders for the components designated intermediate- and high-frequency, and a horizon-conditioned reliability gate. The gate uses expert representations, predictive scale, and cross-expert disagreement to fuse four cumulative-return estimates. The joint objective covers point loss, an auxiliary directional score, Laplace likelihood, ordered quantile loss, decomposition regularization, and horizon coherence. Experiments use eight large U.S. stocks and a single 2021–2024 test interval. Within that restricted benchmark, the reported point estimates favor FreqCast over the included baselines and show horizon- and volatility-dependent expert allocation. Our main contribution is the coordinated frequency-dependent assignment and reliability fusion of heterogeneous forecasting mechanisms, while broad market robustness and statistical superiority remain to be established. Full article

The Solvency II regulatory framework (Directive 2009/138/EC) defines the Solvency Capital Requirement (Solvency Capital Requirement, SCR) using a value-at-risk (VaR) approach, with a confidence level of 99.5% and a one-year time horizon. This structure makes regulatory capital extremely sensitive to the characteristics of the right tail of the loss distribution and, consequently, to the effectiveness of risk transfer mechanisms. This study analyzes the impact of the five main types of reinsurance contracts quota share, surplus, quota–surplus, excess-of-loss (XoL), and stop-loss on the transformation of the net loss distribution and the resulting dynamics of the SCR. The empirical analysis is based on a computational experiment using real data from a ten-year insurance portfolio covering the period 2016–2025. The results show that the use of reinsurance leads to a reduction in the total capital requirement in the range of 18.4–23.4% on an annual basis, with the effect exhibiting an approximately linear relationship with the size of the cession quota. The stratified comparative analysis conducted identifies significant differences in the effectiveness of individual contract structures with regard to the reduction of tail risk. In particular, XoL contracts demonstrate the strongest effect on the extreme quantiles of the loss distribution, with a reduction reaching −52.5% at the 99.5% VaR level and −68.6% at the 99.9% VaR level. In contrast, quota-share contracts result in a practically proportional scaling of risk, characterized by a symmetric reduction of approximately −40% across all confidence levels. The results further show that multi-tiered reinsurance programs combining quota share, excess, catastrophe XoL, and stop-loss components provide the highest degree of capital relief, reaching 48.8%, which indicates the presence of significant nonlinear diversification and complementarity effects among the individual risk transfer mechanisms. A waterfall decomposition is applied to identify the main factors determining the difference between the standard formula and the internal model. The analysis finds that the dominant drivers of the observed capital relief are the effect of precise risk calibration (on average −8.3%) and the effect of diversification (−4.7%). These results underscore the importance of adequately modeling the interdependencies among risk modules and the limitations of standardized regulatory parameterizations. In addition, a “wrong-way risk” stress scenario is developed, involving the simultaneous occurrence of a catastrophic risk and the insolvency of two key reinsurers. Under this scenario, the effectiveness of risk transfer is reduced to −27.3%, and the solvency ratio falls below the minimum capital requirement (12.5%). This result empirically confirms the cautious regulatory stance of the European Insurance and Occupational Pensions Authority regarding the limited recognition of capital reliefs that do not demonstrate resilience under extreme stress conditions. This study provides a quantitatively grounded framework for optimizing reinsurance programs under the Solvency II regime. The main conclusion is that capital efficiency is not a function of a single “optimal” reinsurance contract, but rather results from the strategic combination of various reinsurance mechanisms capable of simultaneously reducing tail risk, improving diversification, and limiting vulnerability to systemic stress events. Full article