Search Results (415)

Increasing external shocks have made the structural stability and vulnerability of the global corn trade network (GCTN) increasingly prominent. Based on country-level bilateral corn trade data, this study constructs a directed and weighted GCTN for each of 2004, 2014, and 2024. By integrating complex network indicators with a non-persistent attack simulation method, this study systematically examines the structural evolution and vulnerability changes in the GCTN at both the network and node levels. The results show that the GCTN has continuously expanded and is undergoing a structural transition from a random-like network toward a scale-free network. The United States has consistently remained the dominant supply center, while the export capacities of Ukraine and Brazil have increased substantially. Japan, South Korea, and Mexico have remained major demand centers, whereas China has shifted from a major exporter to a major importer. The regionalization of the strongest-link network has continued to strengthen. In 2024, the network exhibited higher clustering, greater path diversity, and improved transmission efficiency. Even after the failure of a relatively high proportion of nodes, the remaining network was still able to maintain or even improve its functions, indicating enhanced structural redundancy and stronger short-term resistance to local node disruptions. Although the number of vulnerable countries has declined, 13 countries remain persistently vulnerable. These findings provide empirical evidence for improving agricultural trade resilience and food security governance. Full article

Compared with conventional trade networks, the global coal and oil value network provides deeper insights into the competitive capabilities of various countries in the industry. Hence, measuring the resilience of value networks can effectively reflect the authentic recuperative state of energy sectors across diverse nations amid external shocks, which is highly important for maintaining global energy security. This paper, therefore, presents a directed, weighted global coal and oil value network to measure its evolutionary characteristics over the past 24 years. Then, the network’s resilience and the influence of key countries are evaluated through simulated attacks. The main conclusions are as follows. In terms of network characteristics, countries such as Saudi Arabia and Israel, which hold central positions in the coal and oil trade network, do not stand out in the value network. Meanwhile, although Russia ranks second on the value flow scale, it ranks only 15th in comprehensive node importance. Additionally, based on the resilience measurement, the global coal and oil value network has shown positive resilience. Lastly, the impact of attacks on the United States and Russia on the network has exhibited a trend of initial decline followed by an increase. The relevant findings of this study can provide long-term practical references and ideological insights for the governance of global energy security and the formulation of energy transition policies. Full article

Gold’s price reflects currency, opportunity-cost, and safe-haven channels whose strength shifts across regimes, motivating an empirical, data-driven forecasting approach. This study develops a monthly gold price forecasting system for ASM sales-timing decisions in Peru (January 2020–June 2026) using macro-financial predictors including a geopolitical risk index and three U.S. monetary indicators, none of which were Granger-causal and were therefore excluded from the production set. After confirming non-stationarity and Johansen cointegration (four vectors), thirty-two model-feature-set combinations, including Elastic Net, Bayesian Ridge, and a PCA factor, were compared under strict temporal validation with bounded hyperparameter search. The selected model, Ridge regression on the CONTROL feature set, achieved a cross-validation MAPE of 2.29% and test MAPE of 3.62% (official)/3.15% (extended sensitivity window). It was benchmarked against random walk, historical mean, and exponential smoothing and evaluated via the Diebold–Mariano, Clark–West, encompassing, and Model Confidence Set tests (low-power caveats given the small sample). A dual-horizon Monte Carlo simulation, robust to heavy-tailed shocks, projected USD 4482/oz (December 2026) and USD 5106/oz (December 2027). A sales-timing backtest showed a statistically significant result (−0.67%) versus a passive strategy, indicating calibrated price information alone does not yet yield a reliable trading edge, supporting the model’s role as decision support rather than an autonomous trading signal. Full article

Smart cities increasingly depend on chip-centered digital infrastructure, whose resilience is closely linked to the stability of upstream semiconductor manufacturing equipment supply. Disruptions in lithography equipment trade may propagate downstream through the semiconductor supply chain and affect the security and continuity of urban digital systems. Existing studies on semiconductor trade networks have paid insufficient attention to the timing of long-term structural shifts and the mechanisms through which external shocks reshape network organization. Using UN Comtrade data on lithography-equipment-related semiconductor manufacturing equipment from 2010 to 2024, this study develops an integrated topological-spatial analytical framework based on complex network analysis. The framework combines change-point detection, community evolution analysis, node roles and critical channel identification. The results show that the global lithography equipment trade network is increasingly characterized by the concentration of key nodes and critical channels, community differentiation in trade relations, and dependence on cross-community linkages. These findings provide both a complementary perspective and a methodological reference for understanding upstream structural risks in chip-centered urban digital systems. Full article

Copper is a strategically important commodity whose price dynamics are increasingly affected by structural changes, geopolitical shocks, and the global energy transition. These conditions create substantial challenges for forecasting models and provide a useful setting for evaluating the practical value of machine learning predictions. This study compares statistical and artificial intelligence-based forecasting models for copper price prediction under different market regimes and structural break conditions. Model performance is assessed using a multi-dimensional evaluation framework that combines statistical accuracy (MAPE), dynamic pattern reproduction (Taylor diagrams and time-lagged cross-correlation analysis), and the economic performance of forecast-driven trading strategies. The results reveal a consistent error–profit paradox: models with the highest statistical forecasting accuracy do not necessarily generate the best trading outcomes. In several cases, models with larger prediction errors achieve superior economic performance because they capture directional market dynamics more effectively. The analyses further show that structural breaks substantially alter model rankings and predictive usefulness, highlighting the importance of regime-aware evaluation. These findings suggest that forecast accuracy alone provides an incomplete assessment of model quality in financial and commodity forecasting applications. The study contributes to machine learning evaluation research by proposing an integrated framework that jointly considers predictive accuracy, temporal dynamics, model robustness, and economic utility, thereby offering a more comprehensive approach to assessing forecasting systems in real-world decision-making environments. Full article

The increasingly rapid process of de-dollarization in the context of growing geopolitical fragmentation has significantly altered the monetary dynamics of the global economy, thereby posing important questions about economic resilience and sustainable development in the emerging world. This research investigates de-dollarization-related macro-financial conditions and their association with economic resilience in the BRICS+ countries during the time span of 2000–2024, considering macroeconomic, financial, and environmental aspects. In the empirical analysis, economic resilience is operationalized through annual GDP growth, which is used as an indicator of the macroeconomic performance dimension of resilience. Due to the absence of a consistent direct de-dollarization index for all BRICS+ economies over 2000–2024, de-dollarization is captured through exchange-rate dynamics and related external monetary-financial indicators, including the official exchange rate, current account balance, total reserves, and trade openness. By employing annual panel data and the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) method to address cross-sectional dependence and heterogeneity, the study identifies the structural changes linked to major global shocks and currency shifts. The results show that de-dollarization-related macro-financial conditions have differential long-run associations with resilience, depending on the macroeconomic stability and financial development of the countries. The findings indicate that exchange-rate dynamics and external financial conditions play an important role in shaping the macroeconomic resilience of BRICS+ economies. Environmental outcomes are found to be important determinants of long-run resilience, suggesting that sustainability-driven structural changes improve resilience during the process of global monetary shifts. The results indicate that resilience during global realignment goes beyond financial diversification and is linked to the sustainability frameworks. Full article

This study investigates and explores the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using the Family GARCH (fGARCH) model and the First-Order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. Currency volatility across the globe, uncertainties, and instability in emerging markets have become increasingly consequential for trade flows, investment allocation, and macroeconomic management. The ZAR/USD serves as a benchmark of South Africa’s economic wealth and vulnerability to external shocks and is one of the most valued, significant, and heavily traded pairings of emerging market currencies. Simple standard GARCH (sGARCH) is one of the most useful models for exchange rate volatility; however, the sGARCH model has some limitations: it fails to accommodate or allow the long memory effects, skewness distribution, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by using the fGARCH model, which includes the most popular GARCH models and Beta-Skew-T-EGARCH for daily ZAR/USD returns ranging from 5 January 2000 to 1 October 2024. Five innovation distributions are used for evaluation and comparison under fGARCH and sGARCH, namely generalised hyperbolic (GH), generalised error (GED), skewed Student’s t (SSTD), skewed generalised error (SGED), and Student’s t (STD), with model fitness criteria assessed using the Shibata criterion (SIC), Hannan–Quinn criterion (HQ), Bayesian information criterion (BIC), and Akaike information criterion (AIC), choosing the specification with the lowest overall penalty. It is found that the fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming sGARCH. The study also includes an analysis among covariates, which are day, month, trend, oil, and platinum; the trend variable is a statistically significant predictor, with p = 0.007, showing a positive influence on ZAR/USD volatility. The Beta-Skew-T-EGARCH model with two components divides volatility into long-run and short-run components, which is found to deliver a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (−748.464826). The two components confirm that the model captures declining conditional volatility, whereas the one-component model sustains persistence in the evaluated estimates. Full article

Global forest-product trade faces severe disruptions from external shocks, necessitating robust forecasting and risk assessment tools. To capture the complex, nonlinear dynamics of the forest bioeconomy, this study constructs a comprehensive panel covering 229 countries and five product categories from 1995 to 2023 using FAOSTAT and World Bank data. We propose Forest-TAN, a novel multi-horizon temporal attention network that integrates static country–product feature embeddings with causal temporal convolution and multi-head attention. This architecture allows for the joint forecasting of exports at t + 1, t + 3, and t + 5 horizons. Comprehensive evaluations against statistical and advanced deep learning baselines (including ARIMA, XGBoost, and Transformer models) demonstrate that Forest-TAN significantly mitigates long-memory decay and cross-horizon error accumulation, achieving the lowest forecasting errors (RMSE) across all horizons. Furthermore, based on these forward-looking forecasts, we construct the Forest Bioeconomy Trade Resilience Index (FBTRI). Explainability analysis using SHAP and FBTRI assessments reveal that high-income countries and high-value-added processed products possess substantial systemic advantages in shock absorption and trade resilience. Ultimately, this research provides an interpretable, quantitative framework for global trade risk early warning, resource constraint evaluation, and dynamic scenario simulation. Full article

The service sector plays a central role in steering the economic structure toward greener development, and service trade provides an important route for achieving pollution reduction and carbon reduction. Drawing on a city-level panel of 278 Chinese cities from 2011 to 2022, this study uses Staggered DID and Spatial DID models to evaluate the direct and spatial spillover effects of the Service Trade Innovative Development Pilot Policy (STIDPP) on pollution reduction and carbon reduction. The results show that: (1) The STIDPP significantly promotes pollution reduction and carbon reduction, and this finding remains robust after placebo tests, PSM-DID estimation, checks addressing heterogeneous treatment effects, and controls for other policy shocks. (2) The mechanism analysis shows that the STIDPP mainly promotes pollution reduction and carbon reduction through industrial structure upgrading, green technological innovation, environmental governance investment, and public environmental concern. (3) The positive effects of the STIDPP are more pronounced in cities with higher levels of economic development, larger city size, and service-oriented industrial structures, but its promoting effect on carbon-reduction efficiency is relatively weaker in regions with stronger environmental regulation. (4) The STIDPP generates significant spatial spillover effects on pollution reduction and carbon reduction. As geographic distance increases, these effects shift from negative to positive and eventually become statistically insignificant. This study extends the research on the environmental effects of service trade and provides theoretical support and practical guidance for developing a green service trade system and enhancing the synergy between pollution reduction and carbon reduction. Full article

DeFi perpetual futures have expanded from crypto-native instruments to tokenised equities and commodities, yet the economics of these instruments remain poorly understood. We study 17 assets—5 crypto coins, 8 tokenised equities, and 4 tokenised commodities—on three DeFi perpetual platforms (Hyperliquid, EdgeX, Lighter) over July 2025 to February 2026. Applying a rolling 3-day t-test to identify abnormal trading volume without a predetermined event calendar, we document 1797 statistically significant volume anomalies. DeFi perpetual volume is driven primarily by macroeconomic and policy shocks (ADA $t=+628$ on the U.S. Crypto Strategic Reserve announcement; 15 of 17 assets simultaneously anomalous during January 2026 mega-cap earnings), asset-class-specific catalysts, and a recurring 24/7 market-structure effect tied to weekends and U.S. holidays. Price tracking accuracy reveals a sharp maturity gradient: crypto coin perpetuals exhibit near-perfect price tracking ($\rho \ge 0.999$) and strong TradFi volume co-movement ($\rho \left(0\right)\in [0.72,0.83]$), while equity perpetuals show weaker integration and commodity perpetuals range from adequate (oil, gold) to unreliable (natural gas). We conclude that crypto DeFi perpetuals constitute credible synthetic economic claims on underlying assets, while equity and commodity perpetuals remain at an early developmental stage. Integration with traditional financial markets is well-established for crypto coin perpetuals; for equity and commodity perpetuals, the evidence is preliminary, given short observation windows, and further research with longer time series is needed before definitive conclusions can be drawn. Full article

The classical monetary trilemma implies a binding trade-off among exchange rate stability, capital mobility, and monetary autonomy. Yet, emerging market economies increasingly operate hybrid policy configurations that depart systematically from the trilemma’s corner solutions. This paper proposes a continuous, time-varying measure of such departures—the Hybridity of Regime Index (HRI)—extracted via a dynamic factor model from sub-indices capturing exchange rate hybridity, capital account segmentation, and effective monetary autonomy for a balanced panel of thirty emerging markets over the period 2005–2024. The analysis yields four principal findings. First, a secular increase in average regime hybridity is observed, with a marked acceleration following the financial fragmentation shocks of 2022. Second, moderate hybridity is associated with attenuated output and inflation volatility, and local projections show that high-HRI economies experience milder output contractions in the immediate aftermath of global financial shocks. Third, panel threshold regressions identify an endogenous HRI level beyond which the stabilizing effect reverses: further hybridity amplifies macroeconomic volatility and erodes reserve adequacy. Fourth, the post-2022 geopolitical fragmentation of the international monetary system has amplified the pre-existing trend toward hybridity, with sanction-affected economies exhibiting discontinuous jumps in HRI that push them into the high-vulnerability regime. This paper characterizes this non-linear pattern as a resilience–vulnerability nexus and discusses its implications for early warning indicators and for the assessment of policy responses to the fragmentation of the international monetary system. Full article

This study employed a different type of wavelet approach to investigate the dynamic interdependence among exchange rate volatility, stock indices, and energy commodity markets. Using daily data covering the period from 2005 to October 2025 on energy commodities, stock index, and foreign exchange rates of selected BRICS economies. We include both crude oil and Brent oil, along with natural gas, aiming to capture not only the time–frequency interconnectedness among these assets but also to gain deeper insights into cross-commodity correlations across various energy sectors, thereby clarifying whether economic shocks in these markets are localized or globalized. Assessing data volatility, it can be observed that exchange rates and stock indexes tend to be less volatile than oil markets. The coherence zone indicates that foreign currency significantly influences the interdependence between the Bovespa and Brent oil prices. While rising oil prices can generate inflationary pressures, for an oil-exporting nation like Brazil, the favorable impacts on exports and the trade balance often led to an appreciation of the Brazilian Real (BRL) against the US dollar. Full article

Agricultural Global Value Chains (GVCs) link input suppliers, primary production, processing, and consumption across borders but are increasingly exposed to upstream disruptions. This study develops a network-based Leontief framework to analyze international trade in agricultural GVCs, explicitly modeling fixed-proportions technologies, intermediate input dependence, trade costs, and capacity constraints. It traces how final demand and supply-side shocks propagate through multi-country input–output networks, affecting both quantities and prices. A stylized numerical illustration motivated by war-related disruptions in Ukraine demonstrates how export constraints, trade frictions, and fertilizer shortages can be represented within the proposed framework. The illustrative exercise shows how nonlinear downstream effects may arise mechanically within a fixed-coefficient production network when upstream constraints bind. Fertilizer availability is treated as a potential amplification channel rather than as an empirically estimated determinant of output losses. Full article

Nickel has become a strategic mineral for the energy transition, yet its supply chain is increasingly shaped by a compound risk regime involving resource nationalism, processing concentration, geopolitical compliance rules, carbon-footprint requirements, and commodity-market volatility. This study develops NiChain-LLM-ABM, a large-language-model-empowered agent-based model for simulating nickel supply chain resilience under semantically rich policy shocks. The framework uses a policy semantic parsing module to transform official policy texts into structured shock parameters, a multi-agent strategy generation module to represent adaptive decisions by seven agent classes, a calibrated supply chain network module to simulate material, financial, and information flows, and a four-dimensional resilience assessment module. The model is anchored in observed nickel production, price, trade, and technology data from USGS, IEA, UN Comtrade, LME, and official legal sources, and its scenario outputs are generated through 100 Monte Carlo replications over 2025–2035. Results show that the baseline Comprehensive Resilience Index (CRI) declines from 0.620 in 2025 to 0.547 in 2035. Indonesian policy tightening causes the sharpest near-term deterioration, with CRI falling to 0.445 in 2028 and the simulated supply deficit reaching 24.5 kt Ni equivalent. A geopolitical compliance shock produces the lowest terminal resilience (CRI = 0.472 in 2035). A green-compliance scenario is disruptive in the short run but exceeds the baseline by 2035, while a coordinated policy portfolio raises the terminal CRI to 0.744, a 36.0% improvement over the baseline. Compared with a conventional rule-based ABM, the LLM-ABM reduces extreme-event backcasting error by 57%, improves policy-response fidelity by 53%, and more than doubles agent heterogeneity differentiation. The results support portfolio-based critical-mineral governance combining strategic reserves, overseas equity investment, recycling, technology substitution, and international cooperation. Full article

Piezoelectric pressure sensors for dynamic monitoring face a trade-off between charge output and measurement range, and existing high-sensitivity designs are largely confined to narrow ranges. This study presents a composite layered piezoelectric pressure sensor in which a 316L stainless-steel diaphragm drives a centrally suspended PZT-5H wafer supported by a perforated alumina gasket, with the wafer thickness and cavity radius optimized under a 10 MPa full-scale stress constraint. Over 0–10 MPa, quasi-static calibration gave a highly repeatable quadratic pressure–charge relationship ($R^2=0.99995$) with a maximum residual below 1% FS. The sensitivity is pressure-dependent: the secant sensitivity increased monotonically from 3.16 pC/kPa at 1 MPa to 5.36 pC/kPa at 10 MPa, reflecting a stress-stiffening response rather than a measurement tolerance band. The output deviation remained within 3% from 25 °C to 150 °C. Shock-tube testing yielded a resonance of ∼50 kHz and a mutually consistent 10–90% leading-edge interval of 10.12 $\mathsf{\mu }$s. Combining high charge sensitivity over a wide 0–10 MPa range with a fast transient response and stable operation up to 150 °C, the proposed sensor is suited to dynamic pressure-pulsation monitoring in fluid-power and thermal and power-plant fluid systems. Full article