Search Results (1,700)

Financial crises are usually identified through drawdowns, volatility, and changes in returns, but these indicators do not directly describe whether the recurrence structure of market behaviour changes during a shock. This study tests Laminarity, a Recurrence Quantification Analysis measure derived from vertical structures in recurrence plots, as a nonlinear diagnostic of persistence and market-regime structure during the COVID-19 market shock. Daily data for the Dow Jones Industrial Average, S&P 500, and NASDAQ Composite from 2018 to 2022 are analysed using adjusted prices and log returns. Rolling-window Recurrence Quantification Analysis is applied across alternative window lengths and recurrence thresholds, testing crisis-responsive and longer robustness windows, as well as sparse, intermediate, and denser recurrence definitions. Drawdown and rolling volatility are used as descriptive benchmarks for cumulative loss and fluctuation intensity over the same stress episode. The results show that conventional indicators identify the COVID-19 shock clearly. Price-based Laminarity generally increases during the stress period, consistent with a more persistent crisis trajectory in price levels. Return-based Laminarity is more heterogeneous, with some specifications showing Laminarity loss and others increases. The findings do not support Laminarity as a universal crisis-warning signal, but as a parameter-sensitive diagnostic of recurrence structure, especially when interpreted alongside related RQA metrics. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

Social media has become a significant factor in unhealthy consumption behaviors among adolescents, given the prevalent use of mobile phones and the internet. This study investigates the association between social media exposure and adolescents’ sugar-sweetened beverage (SSB) preferences, as well as their consumption behavior. This study included 1517 adolescents across Henan Province, China, in 2025. We employ a mixed logit model, a hurdle model, and an Ordinary Least Squares (OLS) model to assess the association of social media exposure with adolescents’ SSB preferences and consumption behavior. The findings indicate that social media exposure is positively associated with adolescents’ overall preference for SSB products. Specifically, it is associated with a higher preference for carbonated drinks and beverages containing sweeteners and a lower preference for juice. Furthermore, the association between social media exposure and SSB preferences differs between urban and rural adolescents. Rural adolescents exposed to social media tend to show a lower willingness to forgo SSB options, whereas urban adolescents exposed to social media tend to show less sensitivity to price attributes. Additionally, social media exposure is positively associated with both the selection and consumption of SSBs among adolescents, which in turn are linked to health concerns such as overweight and obesity. Full article

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

In past decades, the macroeconomic stability of India has been tested repeatedly by major global disruptions, including oil price shocks, the 2008 global financial crisis and the COVID-19 pandemic. Analysing how macroeconomic variables respond to these shocks is essential for evaluating external vulnerability and policy resilience in emerging economies. Our study provides a comprehensive empirical investigation of the dynamic responses of wholesale price inflation, industrial output, oil prices and exchange rates in India by employing monthly data from January 1993 to December 2024. To examine long-run equilibrium relationships along with short-run adjustment dynamics, the present study employs co-integration analysis within a Vector Error Correction Model (VECM) framework. Further, we applied impulse response functions and forecast error variance decomposition to track volatility spillover mechanisms. Quantile regression and ARCH–GARCH models were further estimated to account for distributional heterogeneity and time-varying volatility. The findings of our study suggested stable long-run linkages among the selected variables, where oil price shocks emerged as a key external source of macroeconomic fluctuations. Short-run dynamics suggested that shocks in oil prices are transmitted primarily through inflation and exchange rate channels and then affect industrial output. Distributional estimates revealed the effects were stronger during stress periods, indicating tail risks that were not captured by the mean-based models. Lastly, volatility analysis confirmed persistent clustering, especially during phases of crisis. Overall, the findings suggest that India’s macroeconomic system remains externally sensitive, with adjustment mechanisms that operate gradually but come under strain during global disruptions. These results underscore the importance of energy risk management and crisis-responsive macroeconomic stabilisation policies. Full article

Islanded renewable microgrids must balance power internally, so day-ahead dispatch is affected by wind and photovoltaic variability, battery state-of-charge (SOC) dynamics, demand-response (DR) participation, and emissions from dispatchable generation. This paper proposes a low-carbon economic dispatch model for an islanded photovoltaic–wind-turbine–battery-energy-storage–dispatchable-generator–demand-response (PV-WT-BESS-DG-DR) microgrid. The objective includes fuel, operation and maintenance, BESS degradation, renewable curtailment, load shedding, DR compensation, and carbon-emission costs. A repair-based constraint-handling strategy keeps the search space continuous while enforcing power balance, DG ramping, BESS operating and SOC limits, terminal SOC, and DR constraints. An improved whale optimization algorithm (WOA) is then developed with three modules: diversity enhancement, exploration–exploitation balancing, and local escape and refinement. The method is assessed through base-case dispatch, benchmark comparison, strategy comparison, ablation tests, and sensitivity analysis. In 30 independent runs, the proposed method achieves a mean cost of 2662.96 CNY/day, 4.07% lower than standard WOA, and reduces the standard deviation by 79.72%. Wilcoxon and Friedman tests confirm significant differences from the benchmark algorithms. Sensitivity tests show that higher BESS degradation coefficients and carbon prices increase the accounting cost but do not change the qualitative feasibility of the deterministic dispatch framework. Full article

Empty-vehicle repositioning is a persistent challenge in long-haul road freight because carriers must reduce empty mileage without sacrificing service reliability under lead times, appointment windows, and uncertain load realization. This paper formulates empty-vehicle repositioning on freight corridors as a stochastic control problem with explicit space–time feasibility and a stated within-epoch event order. Lead times couple current dispatch decisions to future capacity, pickup windows impose reachability constraints, and stochastic match feasibility captures information and market frictions. We develop dynamic lower bounds from time-expanded relaxations, showing that dual prices of inventory-balance constraints can be interpreted as space–time scarcity values. We further introduce an order-dependent nested friction decomposition that separates excess empty movement into spatial imbalance, temporal mismatch induced by lead times and time windows, and information frictions. Guided by this structure, we propose price-guided rolling-horizon and generalized-cost policies and evaluate them on synthetic corridor experiments organized around the three friction families. The results reveal service–empty-mileage trade-offs, a pronounced knee in the Pareto frontier, lower service loss under widened tight pickup windows, and strong sensitivity to match feasibility. The PG-RH policy reduces empty-distance exposure and total cost relative to static balancing in the main scenarios while maintaining comparable, but not uniformly dominant, service performance. The framework provides a diagnostic basis for identifying the sources of deadhead and for designing operational interventions that reduce empty mileage without undermining reliability. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

Achieving sustainable development requires decoupling economic growth from fossil fuel dependence—a challenge that places carbon pricing at the intersection of environmental policy, economic efficiency, and social equity. Carbon taxation is widely regarded among economists as the most cost-effective instrument for reducing greenhouse gas emissions, yet the United States has not adopted a federal carbon price. This study examines the macroeconomic and sectoral consequences of a hypothetical federal carbon tax using the Standard GTAPv7 computable general equilibrium model calibrated to GTAP Database version 12 (2023). A tax rate of 27.7% is derived from the Regional Greenhouse Gas Initiative (RGGI) average auction price of USD 12.81/t CO₂ for 2023—the lowest among active U.S. state carbon programs—and applied as a production tax shock to the fossil fuel sector. Simulations at the California (USD 32.93/t CO₂) and Washington state (USD 53.10/t CO₂) prices provide sensitivity bounds. Under the baseline scenario, U.S. real GDP falls by 0.09%, unskilled employment declines by 0.17%, and fossil fuel production and exports contract sharply. Outside the fossil fuel complex, most sectors record output and export gains, and total U.S. net exports improve by 0.33 percentage points. Bilateral GDP spillovers across eighteen trading partners range from −0.17% (South Korea) to −0.01% (Australia), principally through fossil fuel trade exposure. The results demonstrate that a federal carbon tax at the RGGI price can achieve meaningful emissions reduction at a contained macroeconomic cost, supporting the environmental pillar of sustainability. The concentration of adjustment burdens on unskilled workers highlights the social sustainability challenge of ensuring a just transition. The production reallocation from fossil-intensive to non-fossil sectors is consistent with the circular economy framework and contributes to long-run economic sustainability by reducing dependence on finite, non-renewable resources. Revenue recycling, just-transition provisions, and carbon border adjustment are identified as complementary policy instruments essential for aligning carbon taxation with the integrated environmental, economic, and social dimensions of sustainable development. Full article

Electric vehicle (EV) charging behaviors exhibit significant heterogeneity in terms of price sensitivity, time-of-day preference, and weekend charging habits, creating challenges for charging demand prediction and service management. To address this issue, this paper proposes a three-variable charging response framework that jointly considers electricity price, time-of-day preference, and weekend preference. Using real charging-order data from a public charging platform, four behavioral parameters, namely baseline charging demand (Q₀), price sensitivity (α), time preference (β), and weekend preference (γ), are estimated through nonlinear least squares (NLS). Based on the extracted parameter vectors, K-means clustering is employed to identify five representative user groups: Commuting-Dominant, elastic energy-saving, Weekend-Switching, Night-Preferential, and discount-sensitive users. The results reveal substantial behavioral heterogeneity among users. To validate the proposed framework, both parameter interpretability analysis and benchmark comparisons are conducted. Compared with the best baseline model, the proposed method reduces the test RMSE from 11.5 kWh to 8.3 kWh (27.8%), decreases the test MAPE from 25.3% to 18.7% (26.1%), and improves the test R² from 0.70 to 0.80. The proposed framework provides an interpretable approach for EV charging behavior modeling and user segmentation, offering practical support for differentiated pricing, charging demand management, and intelligent charging service operation. Full article

The blockchain-based Automated Market Maker (AMM) mechanism establishes a multilateral trading market for multi-source homogeneous data assets. Its advantage lies in realizing algorithmic dynamic pricing and automated circulation through decentralized liquidity pools, effectively avoiding the single-point failure issues and pricing inefficiencies associated with traditional centralized platforms, while significantly improving the trading efficiency and value conversion potential of data assets. However, in high-frequency, large-scale, multilateral data trading scenarios, these AMM liquidity pools face intensified Impermanent Loss (IL) that cannot be easily addressed by conventional risk mitigation approaches, necessitating domain-specific tailored solutions. To address this issue, our study proposes a blockchain on-chain liquidity pool-based data trading market model. Through mathematical modeling and simulation experiments, we quantify how trader behavioral characteristics, including price sensitivity differentials, heterogeneous trading frequencies, and trading size variations, impact the value of AMM liquidity pool. On this basis, we propose a Behavior-Aware Real-time Trading-driven Impermanent Loss optimization method (BART-IL), which uses multi-factor scoring to dynamically sequence trades, generating low-impermanent-loss execution paths to mitigate risks for Liquidity Providers (LPs). Experimental results demonstrate that BART-IL reduces IL for LPs, capping maximum loss at 25.6% in large-scale trading scenarios and achieving over 40% loss reduction in high-frequency-dominant markets. Accordingly, the method substantially lowers the overall risk of data trading. This research addresses the adaptability bottleneck of AMM mechanisms for non-standard assets. By integrating innovations in mechanism design and algorithm optimization, we construct a low-cost blockchain-based decentralized data trading framework with enhanced fairness, offering important implications for ensuring the robustness and attractiveness of data trading platforms. Full article

Understanding how consumers perceive the value of travel packages is essential for pricing and product design. Grounded in behavioral economics frameworks, such as Prospect Theory and Mental Accounting, this study analyses satisfaction across three progressive travel packages before and after explicit price disclosure, exploring multi-attribute service valuation and the moderating influence of traveller profiles. Using a quantitative approach with 387 higher education participants, expected satisfaction was measured through a two-phase price disclosure design. Inferential statistical analyses revealed that satisfaction levels decreased significantly for all packages once prices were revealed, with the sharpest decline occurring in the highly comprehensive, all-inclusive option, validating a psychological threshold of value saturation. Packages comprising only essential elements (flights, accommodation with breakfast and insurance) yielded the highest consistent post-price satisfaction, with these core structural components identified as the absolute most valued attributes. Findings suggest that explicit price disclosure acts as a negative moderator of expected satisfaction, triggering an immediate psychological pain of paying, particularly among independent travellers who exhibit higher price sensitivity and remain more analytical of bundled configurations than users of physical travel agencies. This study provides a framework for stakeholders to avoid over-bundling and optimize product efficiency. Furthermore, it contributes to academic discourse on generational consumer behaviour by highlighting how individual travel organization profiles within an emerging European cohort shape the perceived utility and fairness of tourism pricing. Full article

Sustainable industrial transition requires productivity evidence that separates real efficiency improvement from scale expansion, capital deepening, and reporting change. This study develops a reproducible frontier-productivity diagnostic for provincial leading industry policy, using official 2013–2023 sector panels for 23 two-digit manufacturing sectors in Shandong Province and a matched 2019–2023 benchmark against Jiangsu. The framework combines input-oriented Banker–Charnes–Cooper (BCC) data envelopment analysis (DEA), Simar–Wilson bootstrap bias correction, Malmquist total factor productivity change (TFPCH) decomposition, producer price index (PPI) deflation diagnostics, scale-productivity classification, and targeted sensitivity tests. Bootstrap correction lowers mean BCC efficiency from 0.77 to 0.69 in Shandong and from 0.79 to 0.70 in Jiangsu. Uniform provincial PPI deflation leaves constant-returns-to-scale (CRS) Malmquist estimates almost unchanged, whereas asymmetric deflation creates measurable sensitivity. Direct sector-cluster resampling places Shandong’s aggregate TFPCH at 1.016 with a 95% interval of 0.995–1.045, supporting a near-stationary interpretation rather than a broad upgrading surge; Jiangsu’s corresponding estimate is 0.976 with a 95% interval of 0.955–0.997. The study does not measure environmental performance directly. It shows how frontier-productivity evidence should be stress-tested and paired with environmental indicators before it is used in sustainability-oriented industrial policy. Full article

This study examines the dynamic relationships among oil prices, monetary conditions, and nominal GDP growth in Saudi Arabia, with particular attention to short-run adjustment and long-run equilibrium patterns in an oil-dependent economy operating under a fixed exchange-rate regime. Rather than identifying structural monetary policy shocks, the study focuses on reduced-form dynamic associations between market-based monetary indicators, oil-price movements, and nominal economic activity. Using a high-frequency monthly dataset covering key macroeconomic variables, the analysis employs the Autoregressive Distributed Lag (ARDL) framework to estimate both short-run dynamics and long-run equilibrium relationships. An Error Correction Model (ECM) is used to capture the speed of adjustment toward equilibrium, while Granger causality tests assess short-term predictive linkages. The empirical results reveal that monetary indicators, particularly interest rates and money supply, exhibit lagged and non-monotonic associations with nominal GDP growth, reflecting delayed transmission under exchange-rate constraints. Oil-price movements emerge as a dominant driver, showing strong contemporaneous and lagged associations with growth, whereas inflation and exchange-rate movements display limited short-run predictive relevance. The ECM results indicate relatively rapid convergence toward long-run equilibrium, suggesting efficient adjustment dynamics. Granger causality findings further confirm the short-term predictive content of key macroeconomic variables. By integrating high-frequency data with ARDL–ECM estimation, VAR-based robustness checks, and sensitivity analysis, the study provides evidence on how oil-price movements, liquidity conditions, and interest-rate dynamics jointly shape growth fluctuations in Saudi Arabia. Full article

Hydrogen production from waste plastics is emerging as a potential low-carbon pathway that integrates waste management with energy production. This study develops an integrated socio-technical framework combining a comparative assessment of thermochemical conversion pathways with market acceptance analysis based on survey data (n = 162). The results show that acceptance is mainly driven by trust (β = 0.47) and environmental perception (β = 0.32), while price sensitivity has a negative effect (β = −0.21). Awareness does not significantly affect acceptance (β = 0.08). The model explains 48% of the variance (R² = 0.48), and a strong correlation is observed between trust and acceptance (r = 0.68). These results show that technological performance alone is insufficient; consumer perception and economic factors play an equally important role, highlighting the need for integrated socio-technical approaches in low-carbon energy systems. Full article