Search Results (260)

In gas-condensate reservoirs, the phase behavior of reservoir fluids is inherently dynamic during pressure depletion. When the rate of external pressure decline exceeds the intrinsic relaxation rate governing phase equilibrium, the system deviates from thermodynamic equilibrium and exhibits pronounced non-equilibrium effects. These transient behaviors significantly influence fluid properties; meanwhile, conventional equilibrium models neglect phase transition lag, resulting in inaccurate phase behavior and biased production predictions. In this study, a non-equilibrium dynamic phase transition model is developed to quantitatively couple the pressure depletion rate with the relaxation kinetics of the system. This model, established based on controlled non-equilibrium phase transition experiments performed on the condensate-gas fluid investigated in this work, provides an analytical framework for describing the temporal evolution of phase behavior under dynamic conditions. Model validation through integrated experimental measurements and numerical simulations shows good agreement between calculated and measured results for the studied condensate-gas system, with average relative errors below 5%. Results reveal that accelerated pressure depletion strengthens non-equilibrium effects. At a rate of 15 MPa/h, the relative volume and retrograde condensate saturation decrease by 9.09% and 5.38%, respectively, while condensate recovery improves by 13.85%. Moreover, the characteristic relaxation time toward equilibrium exhibits a strong dependence on the depletion rate, increasing as the depletion rate rises. This work provides an experimentally constrained analytical framework for describing rate-dependent non-equilibrium phase behavior during pressure depletion and for interpreting its impact on condensate recovery in the specific condensate-gas system studied. Although the governing framework may be transferable to other rate-sensitive hydrocarbon systems after fluid-specific recalibration, the parameterized analytical model and validation presented in this study are limited to the investigated condensate-gas fluid, and its applicability to other hydrocarbon fluid types remains to be evaluated in future studies. Full article

Against the backdrop of China’s innovation-driven development strategy, innovation diffusion is a key stage through which firm-level innovation outcomes generate broader economic value. However, this process is often constrained by financing pressure, information asymmetry, and uncertainty in external evaluation. This study examines whether and how bank–firm common ownership, as an ownership-based financial linkage between banks and firms, affects corporate innovation diffusion. Using data on Chinese A-share non-financial listed companies from 2010 to 2023, this paper finds that bank–firm common ownership significantly promotes corporate innovation diffusion. The results remain robust after alternative variable measurements, a higher identification threshold for bank–firm common ownership, lagged explanatory variables, instrumental-variable estimation and propensity score matching. Further mechanism tests show that bank–firm common ownership promotes innovation diffusion mainly through two risk-related channels: liquidity-risk buffering and information-risk reduction. First, it improves firms’ access to commercial credit financing, thereby strengthening their liquidity-risk buffering capacity and helping them withstand financing pressure during the innovation diffusion process. Second, it improves firms’ information disclosure, thereby reducing information asymmetry and external evaluation uncertainty surrounding innovation activities. Further analysis shows that the positive effect of bank–firm common ownership on innovation diffusion is more pronounced among state-owned enterprises and firms with stronger market positions. This study enriches the literature on financial linkages and corporate innovation diffusion, and provides evidence on how bank–firm ownership ties can support innovation diffusion through liquidity-risk buffering and information-risk reduction. Full article

Based on panel data of Chinese A-share listed firms from 2011 to 2023 (29,868 firm-year observations in total), this paper explores the environmental spillover relationship between regional digital financial inclusion (a proxy for the external digital financial ecosystem) and corporate financial investment efficiency. To identify causal effects, we adopt firm fixed effects and three strategies to mitigate endogeneity, namely, interactive fixed effects, lagged terms of regional digital financial inclusion, and instrumental variable estimation. The results suggest that regional digital financial inclusion, when interpreted as an environmental spillover from the external digital financial ecosystem, is associated with curbed inefficient financial investment and thus with improved investment efficiency. This effect operates through three channels: easing financing constraints, improving managerial sentiment, and accelerating digital transformation. Moreover, the positive effect is statistically significant and concentrates among non-state-owned enterprises, firms in eastern China, and sectors with limited traditional financial access (e.g., manufacturing and low-contact industries). Different from prior studies focusing on real investment efficiency, this paper enriches the literature on regional digital financial inclusion from an environmental spillover perspective. It also offers policy implications for fostering sustainable economic growth, strengthening the resilience of the real economy, and improving capital allocation efficiency. Full article

This paper documents a fundamental sectoral divergence in capital–employment relationships using UK quarterly data (2014Q1–2024Q4, N = 44). While manufacturing automation studies consistently find negative employment effects, we show that information-intensive service sectors (SIC J: Information and Communication; K: Financial and Insurance; M: Professional/Scientific/Technical) exhibit robust positive co-movement between capital formation and employment. Structural vector autoregression analysis reveals persistent positive employment responses following capital shocks, with effects peaking at 5–6 quarters and remaining significant through 10 quarters. This pattern holds across eight alternative specifications with varying lag structure, variable ordering, and subsample periods. Granger causality tests reveal bidirectional temporal relationships (capital → employment: F = 3.932, p = 0.028; employment → capital: F = 5.659, p = 0.007), indicating joint determination from anticipated demand growth rather than unidirectional technology-driven dynamics. This finding—while complicating causal interpretation—strengthens the contribution by providing honest empirical characterization of coordination mechanisms in information-intensive sectors. Our capital formation proxy measures all investment in AI-intensive sectors (buildings, equipment, conventional IT, emerging AI systems) rather than AI expenditure specifically, creating measurement ambiguity we acknowledge transparently. The sectoral focus (J+K+M sectors with 22–34% AI adoption rates exceeding the 15% economy-wide average) provides indicative evidence that patterns relate to advanced technology deployment, but measurement breadth prevents definitive AI-specific conclusions. The contribution lies not in establishing AI-specific causality—which aggregate time-series methods cannot achieve—but in documenting robust sectoral heterogeneity using methodology comparable to manufacturing displacement studies. The positive association in information-intensive services contrasts sharply with manufacturing’s negative relationship, suggesting technology–employment dynamics vary fundamentally across sectors with different task structures. Three limitations constrain interpretation: (i) recursive identification cannot definitively rule out common demand shocks, (ii) the 44-quarter sample provides limited statistical power for precise magnitude estimation, and (iii) external validity to other countries, time periods, or service sectors remains uncertain. The findings motivate sector-specific rather than economy-wide technology policy approaches, recognizing that extrapolating manufacturing evidence to service-dominated economies may systematically mischaracterize employment dynamics. Full article

Transport infrastructure is widely viewed as a key lever for integrating lagging rural regions into broader economic systems. Western China, marked by vast territory, complex topography, and historically severe spatial market frictions, offers a particularly informative setting for examining this question within the sustainable rural development agenda. Exploiting the staggered rollout of China’s National Highway Expansion Program across 276 prefectures from 2003 to 2018, we combine high-frequency wholesale prices for 93 agricultural commodities, geocoded expressway network data, and the China Family Panel Studies. A staggered difference-in-differences design is supplemented by a time-varying minimum spanning tree instrument capturing network-efficiency considerations, alongside event-study and recently developed robust estimators for staggered treatments. Two-stage least squares estimates indicate that expressway connection raises the agricultural price integration index by 0.071, reduces within-prefecture price volatility by approximately 0.040 (about 13% of baseline), raises agricultural household income per capita by roughly 16%, and improves the household food-security index by 0.571 points. Event-study results show no pre-trends, with effects materializing over three to four years post-connection. Mechanism analysis highlights expanded market linkages, and the gains are stronger in nationally designated poverty counties and prefectures with rugged terrain. Partial-equilibrium welfare accounting implies annual gains of roughly USD 4.92 billion, and unconditional quantile regressions reveal a progressive distribution across farm incomes. These findings underscore the role of transport infrastructure in alleviating spatial frictions, integrating lagging regions, and advancing sustainable rural development while warranting careful attention to the environmental externalities of large-scale infrastructure. Full article

This paper studies the topic that has been rather less explored until now—the internal diversification of trading. Unlike looking at aggregate measures of financial development such as market capitalization and liquidity, the study focuses on trading diversification, defined as the portion of trading volume attributed to firms other than the ten most actively traded (VTX). The empirical analysis is based on the World Bank’s Global Financial Development database. It covers an unbalanced cross-country dataset of 2004–2021. Due to limited data availability, the resulting database became smaller and has an unbalanced panel structure. Four main independent variables in the core regression specification are related to financial structure (bank deposits) and financial integration (remittances, international public debt), as well as external measures of financial development (market capitalization, excluding firms within VTX). A broad range of control variables are introduced into the model to account for macroeconomic conditions, financial development, market size, liquidity, and participation. Lagged regressors are introduced to address persistence, delays, and potential endogeneity issues. The methodology relies on panel data econometrics, hierarchical clustering, and machine learning. The findings show that market structure and remittances positively affect trading diversification, whereas banks’ dominance and international public debt contribute to its concentration. The results persist across alternative specifications and robustness tests. The country-level analysis shows a core–periphery pattern, while machine learning demonstrates the critical importance of market structure. Full article

Background: Providing effective peer feedback is a challenge in nursing education. While Generative AI (GenAI) can assist, students often struggle with the task. Metacognitive scaffolding may help guide students through this complex process. Aim: This study aimed to evaluate the effects of a metacognitive scaffolding-based GenAI-assisted peer feedback provision (MGPFP) approach on nursing students’ feedback engagement and behavioral patterns. Methods: A quasi-experimental study was conducted with 71 nursing students. The experimental group (n = 35) used the MGPFP approach, while the control group (n = 36) used a standard GenAI-assisted approach without scaffolding. A Mann–Whitney U test was used to compare feedback engagement. Lag sequential analysis was used to examine feedback giving behavior patterns based on coded video data. Results: The experimental group reported significantly higher engagement than the control group across four dimensions: behavioral, cognitive, social, and emotional engagement. The experimental group generated 5219 coded behaviors, while the control group generated 1861. In the experimental group, common behaviors included referring external resources (19.58%), comparing and making judgements (17.80%), and recognizing the purpose (15.77%). Non-feedback behaviors were much higher in the control group (2.69%). Lag sequential analysis identified 17 significant sequences in the experimental group and 14 in the control group. Conclusions: Integrating metacognitive scaffolding into GenAI-assisted peer feedback can improve nursing students’ engagement and promote more productive and structured feedback behaviors. This approach is a valuable strategy for enhancing the quality of peer feedback in nursing education. Full article

Road infrastructure projects are frequently affected by cost overruns driven by volatility in critical construction inputs and by the uneven association between external market shocks and material price movements. However, existing studies still provide limited evidence on how comparative forecasting, temporal price-signal diagnostics and probabilistic simulation can be integrated into a contingency-oriented decision framework. This study examines how construction input price forecasting and probabilistic simulation can inform contingency estimation in a road infrastructure case study. The empirical application is based on a Peruvian bridge project and combines benchmark-oriented forecasting using Bi-GRU and Random Walk models, descriptive temporal diagnostics based on lead–lag assessment and rolling-correlation analysis, and Monte Carlo simulation. Monthly series for structural steel, construction steel, cement, and diesel were transformed into log-returns and evaluated under a strict chronological design, while oil, the exchange rate, and the consumer price index were incorporated as exogenous variables. The Random Walk model produced lower forecasting errors for most inputs, achieving lower RMSE values in seven of the eight input-period comparisons; Bi-GRU outperformed it only for diesel in the test subset, with a 7.24% lower RMSE. From a project cost-risk perspective, the P95 contingency was estimated at 3.92% under Bi-GRU and 3.96% under Random Walk, indicating a similar upper-percentile contingency envelope under both forecasting specifications. The findings support contingency as a confidence-based budgeting decision rather than a fixed percentage. Full article

This paper examines how remittances-to-GDP are conditionally associated with GDP growth upswings and downturns in four lower-middle-income countries (LMICs) in mainland Southeast Asia—Cambodia, Laos, Myanmar, and Vietnam (CLMV)—over 2000–2021, conditional on other external inflows including foreign direct investment (FDI), official development assistance (ODA), and trade openness. Employing a nonlinear Autoregressive Distributed Lag (N-ARDL) model with a Dynamic Fixed Effects (DFE) estimator, this study estimates short- and long-run regime-differentiated associations between GDP growth regimes and remittances to GDP, controlling for foreign direct investment (FDI), official development assistance (ODA), and trade openness. GDP growth is decomposed into above- and below-median regimes, allowing the model to examine whether remittance dynamics differ across growth upswings and downturns. Panel estimates are complemented with dynamic multipliers that trace conditional adjustment paths over different horizons. The results reveal a high-growth-driven regime pattern rather than formal statistical evidence of unequal high- and low-growth coefficients. In the long run, above-median growth significantly reduces remittances to GDP (${\widehat{\theta }}_1=-0.130$, very strong evidence), consistent with the household insurance motive; below-median growth has no significant long-run association (${\widehat{\theta }}_2=-0.127$, no evidence). In the short run, above-median growth is positively associated with remittances (${\widehat{\tilde{\beta }}}_1^{+}=0.033$, very strong evidence), while below-median growth again shows no significant short-run response (${\widehat{\tilde{\beta }}}_1^{-}=0.051$, no evidence). Formal Wald tests do not reject equality between the high- and low-growth coefficients in either horizon; therefore, the findings should be interpreted as a regime-differentiated significance pattern within a nonlinear specification, not as formal proof of coefficient asymmetry. Taken together, these responses are consistent with a one-sided counter-cyclical interpretation of remittances: remittances to GDP decline when domestic growth is above the median, while no significant adjustment is observed during below-median growth episodes. The pattern documented here is therefore driven by the high-growth regime and should not be read as evidence of an active counter-cyclical surge during downturns. Trade openness and ODA exhibit significant positive short-run co-movement with remittances, whereas FDI shows a strong positive long-run association with remittances to GDP. The novelty of this study lies in providing new panel evidence on regime-differentiated remittance–growth associations for CLMV within a nonlinear N-ARDL and dynamic multiplier framework, while transparently reporting that formal Wald tests do not reject equality between high- and low-growth coefficients. Policy implications center on facilitating reliable remittance channels—reducing transfer costs and expanding financial inclusion—without assuming that remittance inflows automatically rise during downturns. Full article

This study investigates the short-term and long-term impacts of gross domestic product (GDP), inflation, foreign capital flows, trade balance and interest rate on stock market performance in Saudi Arabia for the period 1990–2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) are employed to empirically examine the short-run and long-run relationships. The ARDL-ECM technique is effective for analyzing cointegration and assessing adjustment processes. Additionally, impulse response function (IRF) analysis based on the vector autoregression (VAR) model, estimated using these macroeconomic indicators, is applied in this paper. This study provides novel insights and addresses emerging gaps in the literature concerning Saudi Arabia as a developing economy. The long-term relationship in the bounds test results confirms its existence. In the long run, inflation and interest rate exert a statistically significant negative effect on stock market performance, while the trade balance has a significant positive impact. GDP and foreign capital inflows do not exhibit statistically significant long-run effects. Short-run dynamics indicate persistence in stock market performance along with significant effects from inflation and interest rate changes, while GDP and foreign capital inflows remain statistically insignificant in the long-run scenario. Forecast error variance decomposition (FEVD) results show that approximately 68.5% of the variation in market performance is explained by its own shocks, followed by foreign capital flows (16.3%) and inflation (8.4%). While foreign capital flow does not exhibit statistical significance in the ARDL long-run estimates, its contribution in variance decomposition highlights its role as an important source of external shocks. These findings are relevant to various stakeholders, including investors and policymakers. Additionally, policy emphasis should be placed on controlling inflation and maintaining stable interest rates while improving trade balance conditions. Although foreign capital flow does not show a direct long-run effect, its role in influencing market variability suggests the need for a stable and well-regulated investment environment. Full article

This study examines the Xinjiang Uygur Autonomous Region as a critical case study, constructing comprehensive evaluation frameworks for both ecological environment and tourism economy. We calculate the integrated development levels of both systems from 2010 to 2024, employing entropy weighting to derive composite development indices, Coupling Coordination Degree modeling to quantify the intensity and quality of system interactions, Relative Development Degree modeling to characterize coordination typologies and developmental asymmetries, and Grey Relational Analysis to identify key driving factors. Our findings reveal that although the coupling coordination of Xinjiang’s tourism–ecological system has transitioned from “mild imbalance” to “marginal coordination”, the system exhibits pronounced vulnerability and persistent “tourism-lag” dynamics. To effectively leverage the current “strategic window” of ecological surplus, we propose a multi-dimensional transformation pathway: (1) enhancing digital resilience through intelligent monitoring systems to mitigate external mobility shocks; (2) optimizing spatial connectivity via a “fast transit, slow travel” infrastructural paradigm; (3) institutionalizing micro-scale ecological governance to position oasis cities as sustainable “ecological gateways”; and (4) catalyzing deep cultural-tourism integration, shifting from scale-driven sightseeing to value-driven Silk Road heritage experiences. These pathways furnish a clear blueprint for Xinjiang to achieve high-quality, sustainable regional tourism development while maintaining its strategic positioning as a northwestern ecological security barrier. Full article

This study examines the dynamic impact of geopolitical risk (GPR) and renewable energy consumption (RENE) on energy security in Egypt from 2000 to 2023. Given the increasing regional instability and Egypt’s strategic pivot toward a green economy, this research employs the Autoregressive Distributed Lag (ARDL) bounds testing approach, which is robust for small sample sizes and mixed integration levels. The empirical results provide preliminary evidence of a long-run negative relationship between geopolitical risk and energy security (coefficient: −11.92), suggesting that external political shocks may act as a deterrent to energy stability. Conversely, renewable energy is found to exert an indicative positive influence (coefficient: +1.17) on the energy security index. Notably, these long-run coefficients are significant at the 10% level, implying that while these variables represent emerging structural trends, they remain sensitive to high regional volatility and the evolving nature of the Egyptian energy sector. Diagnostic tests, including Jarque–Bera (0.92) and Breusch–Pagan–Godfrey (0.94) tests, support the model’s reliability, while CUSUM and CUSUMSQ tests indicate general parameter stability. The study suggests that while renewable energy integration shows potential for enhancing resilience, its current scale may not yet be sufficient to fully counterbalance the potential pressures of geopolitical shocks. Policy implications point toward the strategic value of “geopolitical hedging” through continued green investment, the expansion of strategic reserves, and the adoption of de-risking financial instruments like Green Sukuk to support long-term energy sovereignty as a precautionary measure. Full article

The carbon allowance price series exhibits nonlinearity, non-stationarity, and high noise due to multiple factors. Accurate forecasting is crucial to the stability of the carbon market and to resource allocation. This paper proposes a forecasting framework using multi-scale decomposition and a TCN–LSTM hybrid model. First, the original carbon allowance price series is decomposed using CEEMDAN optimized by PSO. Then, VMD performs secondary decomposition of complex components based on sample entropy. Next, transfer entropy identifies causal relationships between each component and the original series, enabling reconstruction based on causality. Finally, a TCN–LSTM model uses reconstructed sequences to forecast carbon prices. The method achieves high-precision short-term forecasts using only the carbon allowance price series, avoiding reliance on external variables. Empirical results on the Hubei carbon market show an optimal lag of 3, with R² = 0.8873, outperforming the single LSTM and TCN models and achieving a lower RMSE. The forecast using January–March 2026 data shows stable carbon prices with slight fluctuations. This study provides a reliable method for data-constrained short-term carbon price forecasting, supporting decision-making and policy assessment. Full article

Digital technologies are profoundly reshaping the landscape of rural industrial development. Existing studies have yet to systematically reveal their impact mechanisms on modern rural industrial systems, with limited attention paid to the dual mechanisms of demand and supply. Based on panel data from 30 provinces (2011–2022), this study empirically examines the influence of digital technologies on modern rural industrial system development and its transmission pathways. The results demonstrate that digital technologies significantly promote the development of modern rural industrial systems, exhibiting a time-lag effect. On the demand side, growth of rural and urban consumption serves as crucial transmission channels, while on the supply side, effects manifest through agricultural agglomeration, manufacturing agglomeration, and producer services agglomeration. Heterogeneity analysis reveals that grain-deficit regions are primarily driven by internal demand mechanisms, whereas consumption-balanced areas show significant external demand effects. Topographically flat regions with robust industrial foundations exhibit distinct responses to internal and external supply mechanisms, respectively. This research deepens the understanding of how digital technologies influence modern rural industrial systems and provides practical insights for their systematic development. Full article

Amid the ongoing transformation of global climate governance, climate policy uncertainty has emerged as an increasingly important factor influencing both energy and agricultural commodity markets, with direct implications for energy and food security. Using monthly data from 2008 to 2025, this study applies the TVP-VAR-DY and TVP-VAR-BK frameworks, together with complex network analysis, to investigate spillover dynamics among climate policy uncertainty, energy, and agricultural markets from both time-varying and frequency-based perspectives. The results show that spillover effects evolve substantially over time and become more pronounced during periods of major external shocks, particularly under the influence of short-run factors. Notably, the transmission effect of climate policy uncertainty is stronger for bioenergy-related agricultural commodities, especially soybeans and corn. While the agricultural market exhibits strong internal connectedness, cross-market risk transmission is heterogeneous across commodities, with corn remaining a relatively stable net transmitter of risk. By contrast, crude oil generally acts as a net receiver, whereas climate policy uncertainty behaves as a net receiver in the short run but gradually shifts into a net transmitter over the medium and long term, suggesting a lagged transmission pattern. Robustness checks based on alternative lag lengths, forecast horizons, and CPU proxies confirm that the main connectedness structure is stable and not driven by specific parameter choices. These findings provide useful evidence for policymakers seeking to improve the stability and transparency of climate policy and mitigate cross-market risk, while also offering practical guidance for investors in portfolio allocation and hedging against policy-induced volatility. Full article