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The sectoral approach has traditionally served as the principal tool for analyzing regional specialization; however, it does not reveal the functional content of employment or the distribution of tasks within economic activities. This article assesses the degree of correspondence between the functional structure of employment and the sectoral structure of output across 17 harmonized regions of Kazakhstan, a resource-dependent post-Soviet economy, over the period 2019–2024. Regional functional profiles are constructed within the task-based framework and comprise four aggregate task categories: non-routine cognitive (NRC), routine cognitive (RC), routine manual (RM), and non-routine manual (NRM). The sectoral structure is represented by the distribution of nominal output across 47 sectors. Inter-regional differences are measured using Jensen–Shannon distance. The empirical analysis is conducted on a dyadic panel of 816 observations, controlling for region size, urbanization, gross regional product per capita, extractive specialization, geographic distance, and time effects. Primary statistical inference relies on the Freedman–Lane permutation procedure within multiple regression quadratic assignment procedure (MRQAP). The results indicate a positive but moderate association between the two structures. The standardized coefficient on functional distance is 0.300 (MRQAP p = 0.001). Including the task variable raises R² by 5.5 percentage points. The finding remains robust under most alternative distance metrics, different sets of control variables, and sequential exclusion of individual regions. However, the association weakens noticeably when the three largest cities—concentrating a substantial share of non-routine cognitive tasks—are excluded simultaneously. Within-region estimates of correspondence for 2019–2024 are considerably less precise. Pair fixed-effects models and correlations based on net change and cumulative path length do not reveal a systematic contemporaneous relationship between changes in functional and sectoral structures over the observed period. The six-year observation window does not allow the longer-term pattern of their coevolution or possible lagged adjustment to be determined. Overall, functional and sectoral structures are related but not interchangeable. The results provide statistically supported evidence of a positive inter-regional association, although its strength varies by distance metrics and regional subsamples. For regional policy, the findings underscore the value of combining sectoral specialization indicators with information on the characteristics of jobs and accumulated labor competencies. Full article

To address the challenges of information fusion and prediction for highly non-stationary, noisy, and lag-responsive heterogeneous time-series data from multi-source environmental sensing, this paper proposes a novel adaptive hybrid framework, SAPSO-VMD-GRU. First, the framework employs Variational Mode Decomposition (VMD) to decouple the target sequence into trend, periodic, and random components to reduce complexity and filter noise. Then, Lagged Cross-Correlation Analysis (LCCA) is introduced to quantify the time-lagged correlation between the target components and variables such as rainfall and multi-depth soil temperature and moisture, eliminating redundant features to achieve deep fusion of multi-source information. This paper designs an adaptive particle swarm optimization algorithm, SAPSO, by integrating improved Circle chaotic initialization, Sa-function-based nonlinear inertia weight, and a two-stage Cauchy mutation strategy. SAPSO is used to adaptively determine the VMD parameters and the key GRU hyperparameters in different modeling stages. Experiments based on the Bayintun landslide dataset show that, by utilizing the past 5 days of multi-source historical data, including GNSS displacement, rainfall, and lagged soil moisture and temperature, as inputs to forecast the next-day displacement, the proposed framework achieved $R^2$ values above 0.95 on the chronological hold-out validation subset at all three GNSS monitoring stations. These results indicate that the proposed framework can effectively capture lagged triggering effects and improve displacement prediction accuracy under complex, noisy, and non-stationary monitoring conditions. Full article

The rapid diffusion of generative AI is profoundly transforming knowledge-intensive work and reshaping how employees innovate. Although prior research has highlighted the positive role of generative AI in improving work efficiency and performance, the underlying mechanisms through which it promotes employee creative behavior remain insufficiently understood. Drawing on Social Cognitive Theory and Psychological Safety Theory, this study develops a moderated dual-path model to examine how generative AI use influences employee creative behavior through creative self-efficacy and psychological safety. Using a three-wave time-lagged survey design, data were collected from 297 employees in highly digitalized industries. The results indicate that generative AI use is positively associated with subsequent self-reported employee creative behavior. Both creative self-efficacy and psychological safety significantly mediate this relationship. Furthermore, digital leadership strengthens the positive effects of generative AI use on creative self-efficacy and psychological safety, thereby further enhancing its indirect effects on employee creative behavior. The findings not only extend the literature on generative AI and employee creative behavior but also deepen the theoretical understanding of AI empowerment mechanisms by identifying two critical pathways: a capability-enhancement pathway and a risk-reduction pathway. This study provides important theoretical insights and practical implications for organizations seeking to effectively leverage generative AI to foster employee creative behavior in the context of digital transformation. Full article

Using balanced panel data for 30 Chinese provinces from 2014 to 2023, this study examines the relationship between digital village development and input-related agricultural carbon emissions in China. A composite digital village index is constructed from eight standardised indicators, and PCA diagnostics together with a PCA-based alternative index are used to assess index validity and sensitivity. Two-way fixed-effects models are used for empirical estimation. The results show that digital village development is significantly associated with lower agricultural carbon emissions after controlling for economic, fiscal, technological, regulatory, provincial, and temporal factors. The finding remains robust when using a rural e-commerce proxy and alternative specification checks. The lagged digital index has a negative coefficient but is less precisely estimated after the first-year observations are excluded. Heterogeneity analysis shows that the association is stronger in provinces with higher rural educational attainment, in Central China, and in major grain-producing areas, while the coefficient for grain-balanced areas is negative but not statistically significant. Mechanisation-based subgroup results further show that the negative association is more pronounced where agricultural mechanisation is relatively advanced. These findings suggest that rural digitalisation can support a low-carbon agricultural transition, particularly when digital infrastructure is transformed into effective industrial applications, public services, human capital development, and improved production organisation. This study focuses on input-related and field-operation-related agricultural carbon emissions rather than a complete agricultural greenhouse gas inventory including methane, nitrous oxide, and manure management and land use change emissions. Full article

This study investigates the relationship between economic growth, trade openness, energy intensity, and carbon dioxide (CO₂) emissions in South Africa within the Environmental Kuznets Curve (EKC) framework over the period 1970–2022. Using quarterly time series data and the Autoregressive Distributed Lag (ARDL) modelling approach, the study examines both the short-term and long-term dynamics between economic activity and environmental degradation. Descriptive statistics, correlation analysis, unit root tests, ARDL bounds testing, error-correction modelling, Granger causality analysis, and diagnostic tests were employed to ensure robust empirical results. The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests indicate that all variables are integrated of order one, I(1), thereby satisfying the conditions for ARDL estimation. The ARDL bounds test confirms the existence of a long-term cointegrating relationship among carbon emissions, economic growth, trade openness, and energy intensity. The long-term results reveal a statistically significant negative coefficient for economic growth and a positive coefficient for the squared income term, indicating a U-shaped relationship between income and carbon emissions. Consequently, the conventional Environmental Kuznets Curve hypothesis is not supported for South Africa. The findings suggest that economic growth initially reduces environmental degradation; however, beyond a certain income threshold, further economic expansion increases carbon emissions. Trade openness and energy intensity exert positive and statistically significant effects on carbon emissions in the long run, implying that increased integration into global markets and continued dependence on energy-intensive production contribute to environmental degradation. The Error-Correction Model (ECM) reveals a negative and highly significant adjustment coefficient (−0.928), indicating that approximately 92.8% of short-term disequilibrium is corrected within one period. Granger causality results further show a unidirectional causal relationship running from trade openness to carbon emissions, while no significant causal relationship is found between economic growth and carbon emissions. The study concludes that economic growth alone is insufficient to achieve environmental sustainability in South Africa. Policy efforts should therefore focus on promoting renewable energy adoption, improving energy efficiency, strengthening environmental regulations, encouraging cleaner production technologies, and integrating environmental considerations into trade and industrial policies. These measures are essential for achieving sustainable economic development while meeting national climate-change-mitigation objectives. Full article

Lakes on the Tibetan Plateau are sensitive indicators of climate change. Their water storage variations play an important role in regional hydrological processes and ecological security. This study is based on multi-source remote sensing and meteorological data from 1988 to 2025. Lake area was extracted using the MNDWI and the Otsu threshold method. HYDROWEB water level data were used to establish an area-water level relationship. This relationship was then applied to reconstruct a long-term water level time-series and estimate changes in lake water storage. GRACE/GRACE-FO data and meteorological observations were further analyzed to identify the driving factors. The results show that Siling Co experienced a persistent expansion over the study period, with the lake area increasing by 805.83 km², water level rising by 14.33 m, and water storage increasing by 30.42 km³. Correlation analysis indicates that air temperature, precipitation, and evaporation jointly influenced lake water storage variations. Among these factors, precipitation plays a relatively more important role. During 2002–2019, lake water storage changes (LWSC) were highly consistent with terrestrial water storage (TWS) variations. However, TWS anomalies lagged approximately one year behind LWSC. These findings improve the understanding of the long-term hydrological responses of Siling Co to climate change. They also provide a scientific basis for water resource management and infrastructure planning in the region. Full article

Urban ecological resilience is essential for mitigating environmental risks, supporting green urban transitions, and promoting sustainable development. However, existing studies have mainly explained urban ecological resilience through linear and single-factor approaches, paying insufficient attention to the configurational mechanisms through which technological, financial, institutional, and structural conditions jointly shape resilience outcomes. Drawing on resilience theory, socio-ecological system theory, and synergetics, this study constructs a “risk identification–resource mobilization–institutional adaptation–structural carrying capacity” analytical framework. Using 249 prefecture-level cities in China as cases, we apply fuzzy-set qualitative comparative analysis (fsQCA) to examine how six antecedent conditions—FinTech, green finance, environmental regulation intensity, economic development, industrial structure, and urbanization—combine to generate high and non-high urban ecological resilience. The antecedent conditions are measured in 2021, while urban ecological resilience is measured in 2023 to capture the lagged effects of resilience-building conditions. The results show that no single condition constitutes a necessary condition for high urban ecological resilience, indicating that resilience is shaped by multi-condition conjunctions rather than isolated determinants. The sufficiency analysis identifies two pathways leading to high urban ecological resilience. Both pathways share FinTech, economic development, and urbanization as common foundational conditions, while green finance and industrial structure play complementary roles under different urban contexts. Specifically, the first pathway reflects a “risk identification–resource mobilization–structural carrying capacity” mechanism, whereas the second pathway reflects a “risk identification–structural carrying capacity” mechanism. In contrast, five configurations lead to non-high urban ecological resilience, demonstrating clear causal asymmetry. Non-high resilience is mainly associated with the joint absence of technological feedback, resource mobilization, and spatial carrying capacity, rather than being a simple reversal of the high-resilience pathways. These findings suggest that urban ecological resilience should be promoted through differentiated, combination-based strategies tailored to cities’ technological, financial, institutional, and structural endowments. Full article

As mineral resource extraction progressively extends to greater depths, the complex deep underground environment poses severe corrosion-induced deterioration risks to anchoring structures such as rock bolts. Anchorage failure has thus become a critical safety concern constraining the stability of deep roadways. To address the failure mechanisms of anchoring systems under multi-physical field coupling effects, this study conducts numerical simulations of multi-field corrosion processes and ultrasonic nondestructive testing (NDT) based on a numerical modeling platform. The influence of temperature on corrosion rate and current density is systematically analyzed, and interface response characteristics are extracted and interpreted for defects of varying dimensions. A spatial complementary mechanism under different corrosion defect configurations is revealed, and a health diagnosis system incorporating multiple critical indicators is established. The results indicate that elevated temperature significantly accelerates bolt corrosion: the rise in temperature shifts the equilibrium potential negatively and exponentially increases the reaction rate constant, both of which synergistically promote anodic dissolution. In ultrasonic testing, monitoring points along the main axis are positioned within the transmission-focused zone, where defects induce acoustic wave diffraction and superposition such that even minor defects cause a multiplication of the dominant frequency. Lateral monitoring points lie in the reflection–interference zone, where small defects preferentially attenuate energy, while larger defects manifest as amplitude reduction and first-arrival wave lag; all characteristic indices increase monotonically with defect size. Based on the numerical simulation outcomes, a four-level grading diagnosis standard and a “bottom–lateral” detection scheme are proposed as simulation-based reference indicators. The model effectively reproduces both corrosion deterioration and acoustic wave propagation characteristics, thereby providing a quantitative basis for the assessment of anchoring structures in high-temperature deep underground environments. Full article

Climate change increasingly threatens agricultural productivity in climate-sensitive regions, yet evidence on how temperature and precipitation shape wheat yields over both short- and long-term horizons remains limited in Central Asia. This study investigates the dynamic relationship between temperature, precipitation, and wheat yield in Uzbekistan using annual time-series data for 1992–2023. An Autoregressive Distributed Lag (ARDL) bounds-testing approach was applied, with stationarity confirmed via ADF and Phillips–Perron tests and model stability verified through CUSUM and standard diagnostic tests. The results show a statistically significant positive long-run association between temperature and wheat yield, with a 1% increase in temperature linked to a 2.62% yield increase, alongside a negative, one-year-lagged effect of precipitation, indicating that excessive rainfall in the prior season penalizes subsequent yields. The estimated Error Correction Term (−0.234) indicates a comparatively slow adjustment to climatic shocks, equivalent to roughly 4.3 years under this specification. These findings show that temperature and lagged precipitation are statistically associated with wheat yield dynamics in Uzbekistan, though the temperature effect should be interpreted alongside concurrent irrigation expansion and agricultural modernization rather than as an isolated climatic benefit. The results offer an empirical basis for climate-adaptive agricultural planning in Uzbekistan, including drainage management and adjusted sowing calendars. Full article

Short-video addiction has emerged as a growing behavioral concern in the context of the widespread use of platforms such as Douyin and TikTok, posing potential risks to young adults’ psychological well-being. Psychological resilience and rejection sensitivity are considered important psychological characteristics that may function as protective and risk factors, respectively, in the development of addictive media use. However, the longitudinal interplay among these variables remains insufficiently understood. This study examined the longitudinal relationships among psychological resilience, rejection sensitivity, and short-video addiction over time among university students. A two-wave longitudinal design with a six-month interval was conducted among 1394 Chinese university students. Participants completed validated measures assessing short-video addiction, psychological resilience (CD-RISC-10), and rejection sensitivity (RSQ). Cross-lagged panel modeling (CLPM) was employed to investigate the bidirectional predictive associations among these variables while controlling for autoregressive effects. All three constructs demonstrated substantial temporal stability across the two measurement waves. Cross-lagged analyses indicated that higher psychological resilience at Time 1 predicted lower rejection sensitivity and lower short-video addiction at Time 2, whereas higher rejection sensitivity at Time 1 predicted lower psychological resilience and higher short-video addiction over time. Although these longitudinal associations were statistically significant, their effect sizes were modest. These findings suggest that psychological resilience and rejection sensitivity may represent protective and vulnerability-related psychological characteristics associated with short-video addiction over time. The findings support the I-PACE model by highlighting the dynamic and longitudinal associations among psychological resources, interpersonal vulnerability, and addictive digital behaviors. Interventions aimed at strengthening psychological resilience and reducing rejection sensitivity may contribute to preventing short-video addiction and promoting healthier digital engagement among university students. Full article

Mature waterflood reservoirs contain long injection–production histories, yet routine surveillance remains challenged by delayed producer responses and interference from multiple injectors. Existing diagnostic plots, CRM-type analyses, and data-driven models are useful, but they often require preselected patterns, aggregate long-period behavior, or provide limited event-level traceability. This study presents an event-window evidence-fusion workflow (EWEF) for screening candidate injection–production responses without treating the screened objects as causal proof. EWEF uses material injection-rate perturbations as observational triggers and constructs auditable event-window evidence for candidate injector–producer objects. The workflow includes dynamic-record checking, injection-event extraction, candidate-pair construction, pre/post response-window quantification, lag scanning of oil-rate and water-cut responses, static-prior scoring from geometry and transmissibility proxies, relative multi-injector attribution, and rule-model disagreement routing. A transparent rule layer assigns engineering review classes, and a random-forest model trained on rule-derived weak labels serves only as a consistency-review gate. The workflow was applied to three anonymized field waterflood samples comprising 54 dynamic wells, 153 injection perturbation events, 297 candidate injector–producer evidence objects, and 30 final review-list objects. In the weak-label consistency model, lagged oil-rate correlation, water-cut shift, oil-rate shift, static prior, and water-cut lag correlation were recurrent influential input variables. In one representative event, the six-month post-event window showed that oil rate increased by 5.49 m³/d, whereas water cut decreased by 0.52 percentage points relative to the pre-event baseline. Lag scanning yielded modest correlations, with maximum absolute values of approximately 0.34; these correlations are treated as descriptive screening evidence rather than as statistically confirmed connectivity indicators. EWEF therefore provides a traceable workflow for screening and prioritizing field-review candidates. It does not establish causal interwell connectivity. Confirmation requires independent tracer, pressure, intervention, logging, or simulation evidence, and field-specific calibration remains necessary. Full article

Microbial degumming offers an eco-friendly alternative to chemical methods for ramie fiber production, but industrial application is constrained by low efficiency stemming from limited mechanistic insight. This study systematically investigates the process using Pectobacterium carotovorum HG-49. Strain HG-49 showed a lag phase of 0–4 h, a logarithmic phase of 6–10 h, and peak biomass at 12 h. Pectin (97.05%) and water-soluble substances (98.45%) were nearly fully removed, whereas hemicellulose removal was only 73.54%, rendering it the primary residual gum component. Pectinase activity peaked at 120.75 U/mL, while mannanase (35.85 U/mL) and xylanase (30.20 U/mL) reached roughly one-quarter of that level; cellulase activity remained minimal. Scanning electron microscopy (SEM) indicated that 6–12 h constituted the main gum degradation phase. Fourier transform infrared spectroscopy (FTIR) and micro-FTIR showed progressive decreases in pectin, hemicellulose, and lignin absorption peaks with degumming. X-ray diffraction (XRD) revealed increased crystallinity from 72.07% to 80.02%, and thermogravimetric analysis (TGA) showed elevated degradation temperature from 417 °C to 435 °C. Collectively, these data confirm progressive removal of gummy substances and enhanced cellulose purity. Transcriptomic profiling further revealed that low abundance and reduced expression of hemicellulases significantly limited degumming performance. Therefore, enhancing efficiency should focus on: supplementing pectin-rich substrates to accelerate bacterial proliferation and enzyme production, broadening the hemicellulase spectrum and enhancing catalytic activities and establishing effective pretreatment protocols for ramie bast. These findings provide a theoretical foundation for improving microbial degumming efficiency and advancing industrial feasibility. Full article

Artificial intelligence (AI) is increasingly regarded as a key instrument for sustainability governance, but its role may differ across risk domains. Rather than functioning solely as an anticipatory tool, AI may also develop reactively in response to risk shocks. Using monthly Chinese data from January 2013 to December 2023, this study examines the dual role of AI in sustainability governance by analyzing its time–frequency relationships with climate-related government response and infectious disease-related market attention. The continuous wavelet transform and partial wavelet coherence are employed, with the World Uncertainty Index controlled. The results show that AI-sector development is positively associated with both risk-related signals, but the lead–lag patterns differ substantially. In the climate-response domain, the relationship shifts from risk-signal precedence in 2015–2016 to AI-sector precedence in short-term bands after 2020, suggesting that AI-related capacity may have become increasingly embedded in anticipatory climate governance. In contrast, infectious disease-related attention mainly precedes AI-sector movements, especially during 2017–2018 and the COVID-19 period, indicating a more reactive role of AI in public health risk contexts. These findings do not provide causal evidence that AI directly reduces physical climate risks or epidemiological burdens. Instead, they reveal the dual role of AI as both a response to sustainability-related risk shocks and a potential contributor to forward-looking governance capacity. This study contributes to AI and sustainability governance research by clarifying the conditions and boundaries under which AI shifts from reactive crisis response toward anticipatory risk preparedness. Full article

Accurate forecasting of country-level temperature anomalies is increasingly important for climate monitoring, policy planning, and environmental risk assessment. However, the trade-off between predictive performance, model complexity, and computational cost remains insufficiently explored, particularly across multiple countries using compact and interpretable feature representations. This study presents a comprehensive comparative evaluation of eight forecasting approaches for annual temperature anomaly prediction using country-level observations from the FAOSTAT Temperature Change dataset. The evaluated methods comprise a Persistence baseline, Ordinary Least Squares (OLS), Ridge regression, Support Vector Regression (SVR), Random Forest, a multilayer perceptron (MLP), and the classical time-series models ARIMA and ETS. Annual temperature anomalies were modeled using lagged observations, a temporal trend, and a trailing moving average under a temporally ordered 80/20 train–test split. Model performance was assessed using RMSE, MAE, $R^2$, per-country win-rate, computational runtime, and pairwise statistical comparisons based on the Wilcoxon signed-rank test with Holm correction. Hyperparameters were optimized through expanding-window temporal cross-validation, and an ablation study was conducted to quantify feature contributions. Results indicate that the ETS model achieved the best overall predictive performance, obtaining the lowest median RMSE (0.3388 °C), the lowest MAE (0.2792 °C), and the highest per-country win-rate (40.07%). ARIMA provided competitive forecasting accuracy but incurred substantially higher computational cost, whereas OLS and Ridge offered an attractive compromise between predictive performance, robustness, interpretability, and computational efficiency. In contrast, the more flexible machine learning models (SVR, Random Forest, and MLP) did not consistently outperform the simpler approaches despite their higher complexity. Overall, the results demonstrate that classical statistical forecasting methods remain highly competitive for annual country-level temperature anomaly prediction and that increasing model complexity does not necessarily translate into improved predictive performance. Full article

Autonomous vehicles (AVs) are moving from pilots to regular urban service, yet the speed of large-scale implementation remains uncertain. While prior research emphasizes technological feasibility and adoption, less attention has been paid to the socio-technical dynamics that constrain deployment. This study examines how unintended consequences shape the pace of AV implementation in cities. Drawing on a mixed-methods design combining a structured scoping review with 18 expert interviews, interrelated dynamics are identified across institutional, behavioral, economic-platform, spatial, and normative-societal domains. The findings indicate that implementation speed is not determined by technology alone but emerges from reinforcing feedback loops that generate systemic frictions, including governance lag, demand rebound, spatial bottlenecks, and legitimacy challenges. The study advances a systems-oriented framework that conceptualizes implementation speed as an emergent property of socio-technical dynamics, highlighting the importance of adaptive and anticipatory governance for sustainable urban mobility transitions. Full article