Search Results (867)

Under the strategic context of building a transportation powerhouse in China, the transportation sector faces the dual challenge of reducing emissions while improving efficiency. This study constructs a two-dimensional regional classification framework based on the “economic-carbon” dimension and systematically investigates the coordinated evolution of the development level (TD) and carbon emission intensity (TCEI) of the transportation systems in 31 provinces of China from 2014 to 2023, using methods such as entropy weight TOPSIS, the coupling coordination degree (CCD) model, kernel density estimation (KDE), spatial autocorrelation analysis, and the XGBoost-SHAP explainable machine learning framework based on transfer learning. The study finds that (1) TD shows a fluctuating upward trend, while TCEI continues to decline, with regional imbalances; (2) in terms of time, CCD shows a general upward trend with an N-shaped evolution; spatially, CCD presents a pattern of stronger coordination in the east and weaker in the west, with sustained regional heterogeneity, forming a development pattern of “Region I leading, Region II breaking through, Region III maintaining, Region IV catching up”; and (3) regarding the driving factors, freight volume, transport industry output value, and passenger turnover are the core driving factors of CCD, with significant regional heterogeneity in their mechanisms. This study provides a systematic analytical framework and differentiated policy tools for promoting coordinated regional development of green transportation. Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

As a key component of ecosystems, the synergistic relationship between wetland carbon storage and habitat quality is vital for maintaining ecological functions, and its evolution is profoundly influence by changes in wetlands. This study focuses on wetlands in western Jilin Province. Based on four sets of land use data from 2010 to 2023 and utilizing the InVEST model, combined with methods such as spatial autocorrelation, the Coupled Coordination Degree Model, and the GeoDetector, the study analyzed the co-variation of carbon storage and habitat quality, as well as their response to landscape patterns. The study found that between 2010 and 2023, the wetland area increased by a net 858.13 km², and landscape fragmentation was generally alleviated, although local connectivity continued to degrade. Regional carbon storage increased by 68.1%, totaling 7.43 × 10⁶ Mg, while the habitat quality index exhibited high spatiotemporal stability, fluctuating marginally between 0.609 and 0.621. Spatially, high-value areas remained primarily concentrated within nature reserves. Results of bivariate spatial autocorrelation analysis revealed a strengthening of spatial positive autocorrelation between carbon storage and habitat quality, with Moran’s I increasing from 0.410 to 0.501. The coupled coordination degree model further confirmed that the level of synergy between the two services exhibited a pattern of higher values in the north and lower values in the south, and that areas of high coordination expanded significantly outward following restoration projects. GeoDetector analysis indicates that the largest patch index is the core factor driving the synergistic development of ecosystem services. The results also suggest that the integrity of core wetland patches and a heterogeneous landscape pattern can promote the synergistic improvement of carbon storage and habitat quality through boundary effects and habitat complementarity. Full article

Using panel data from 11 prefecture-level cities in Zhejiang Province (2014–2023), this study applies the entropy method, spatial autocorrelation analysis, and an obstacle-factor diagnosis model to examine the spatiotemporal evolution, regional disparities, and constraints on urban–rural integration. The results show a steady upward trend in urban–rural integration alongside significant regional disparities. This reveals a complex pattern marked by the coexistence of convergence and divergence. Spatially, a clear “northeast–high, southwest–low” pattern is observed, with local adjustments within a stable framework, reflecting a “stable core and entrenched low-value areas.” Spatial agglomeration is characterized by “dual-core agglomeration with a predominantly non-significant periphery,” dominated by homogeneous “high–high” and “low–low” clusters, with no statistically significant spatial outliers. Obstacle factor diagnosis indicates markedly uneven constraining effects across subsystems, with spatial integration exhibiting the highest degree of obstacles. The composition of primary obstacle factors is highly stable, and obstacle structures differ significantly across city tiers. These findings elucidate the spatiotemporal evolution and core constraints of urban–rural integration in Zhejiang, offering a theoretical and decision-making basis for advancing high-quality urban–rural integration in the region. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

The primary processing of high-resolution precipitation records (5 min and shorter) is crucial for constructing dimensionless design hyetographs and identifying design-critical precipitation scenarios for urban drainage systems. A key step in this process is separating continuous precipitation records into individual precipitation events, typically based on minimum inter-event time (MIT) and precipitation amount thresholds. This separation directly influences the subsequent analysis steps and the accuracy of the design hyetographs. Building upon this foundation, this study systematically analyses how different MIT determination methods influence the construction of dimensionless Huff hyetographs in a moderately humid continental climate. Three approaches for defining MIT were examined: a fixed MIT method (1–12 h), an autocorrelation-based method (AC), and a kernel density estimation approach (KDE). The analysis also considers the effects of minimum precipitation thresholds (P = 1, 3, and 5 mm) and precipitation duration classes (all durations and short-duration events with $T\le 2$ h), utilising a continuous 10-year series of 5 min precipitation data. The results demonstrate that the choice of MIT substantially affects the identified precipitation events, duration, total amount, and the median Huff curve’s shape, especially for precipitation types with early and late maximum intensity. Specifically, increasing MIT values produces longer and deeper events with steeper Huff curves, while precipitation thresholds mainly filter weaker events rather than impacting peak intensities. The AC method yields results similar to larger fixed MIT values (≈6–9 h), whereas the KDE method corresponds to shorter separations (≈1–3 h). To unify the assessment of design relevance, a composite design index combining Huff curve slope and short-term peak intensities was introduced. Analysis shows that short-duration convective precipitation with an early maximum is the most critical design scenario. However, late-maximum events (events in which peak intensity occurs in the fourth quartile of storm duration, Type 4) can become equally critical when longer MIT values or autocorrelation-based separation are applied. These findings underscore the importance of a transparent and methodologically consistent definition of precipitation event separation criteria when using dimensionless hyetographs in urban drainage design. Full article

To investigate the crack evolution mechanisms and early-warning precursors of excavation-induced rockbursts, unloading rockburst simulation tests were conducted on granite using a true triaxial testing machine. Analysis of tensile and shear crack development shows that tensile cracking dominates the early stage, with the proportion of tensile cracks exceeding 50% (N_tr > 50%), whereas shear failure becomes predominant near final rupture, with the proportion of shear cracks exceeding 50% (N_sr > 50%). Based on this, the tensile–shear ratio (TSR) is proposed to quantify the dynamic evolution of both crack types. In the present tests, a sustained TSR below 1 was observed during the transition from tensile- to shear-dominated failure, suggesting that it may be a potential precursor to imminent rockburst under the current experimental conditions. According to critical slowing down (CSD) theory, both the autocorrelation coefficient and variance of acoustic emission (AE) parameters increase significantly prior to failure. In contrast, TSR shows earlier identifiable changes and is therefore more suitable for early-stage warning, whereas CSD indicators provide clearer signals as the system approaches failure. Additionally, granite exhibits a rapidly fluctuating decline in the AE b-value prior to failure, and the precursor points identified by TSR and CSD consistently fall within the b-value decreasing interval before peak stress. These results suggest that integrating TSR and CSD indicators may be useful for staged AE-based rockburst monitoring and early warning in deep underground engineering. Full article

This study addresses the challenge of early-stage recognition of control chart patterns in statistical process control, which is critical for timely detection of process abnormalities in real-time manufacturing environments. Unlike most existing approaches that focus on fully developed patterns, this work targets partially developed patterns within a fixed observation window to enable proactive intervention. A multi-layer perceptron (MLP) classifier was developed using statistical features, and a structured design of experiments (DOE) approach was employed to optimize both the feature set and network parameters. Simulated X-bar chart data representing six pattern types were used, and candidate features were systematically evaluated using fractional factorial design. The results identified an effective feature subset consisting of autocorrelation, mean, mean square value, standard deviation, slope, and cumulative sum. The optimized MLP achieved an offline accuracy of approximately 86%, while online implementation yielded an overall accuracy of 70.6% with acceptable error rates and average run length performance (ARL₀ = 207.3, ARL_I = 10.9). The findings demonstrate that, despite greater difficulty in online recognition, the proposed approach provides a practical and interpretable solution for early detection in quality control systems. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

The Galápagos Islands, located in the eastern equatorial Pacific approximately 1000 km west of mainland Ecuador, are highly sensitive to the El Niño–Southern Oscillation. However, the mechanisms linking sea surface temperature (SST) variability to daily rainfall extremes remain poorly understood. Focusing on Santa Cruz Island, one of the main islands of the archipelago, we analyzed the response of daily rainfall to four El Niño events (1991–1992, 1997–1998, 2015–2016 and 2023–2024) and their relationship with SST spatial patterns. Our approach followed three steps: (1) Daily rainfall observations were classified using percentile thresholds; (2) SST spatial clusters were identified using Local Indicators of Spatial Association (LISA), which explicitly incorporates spatial autocorrelation to distinguish warm and cold SST spatial clusters; and (3) SST cluster metrics (mean temperature, spatial extent, and persistence) were extracted and related to rainfall intensification. Results show that El Niño can increase daily extreme rainfall (>P95) in frequency and in totals, with the strongest and most persistent signal during 1997–1998; in contrast, the 2015–2016 event, despite being classified as very strong by the Oceanic Niño Index (ONI), exhibited a limited and short-lived >P95 rainfall response in Santa Cruz. The link between SST clusters and extreme rainfall strengthened during El Niño (r from ~0.40 to 0.70). Correspondingly, SST clusters underwent significant spatial reorganization in their extent and persistence. Contrasts were most evident in the central–southern domain, where 1997–1998 showed strong warm incursion and persistent ≥28 °C coverage, while 2015–2016 remained more spatially constrained and less coherent. The area where clusters reached mean SST ≥ 28 °C became widespread in 1997–1998 (98.55%), whereas it remained more localized in 1991–1992 (30.28%), 2015–2016 (27.02%), and 2023–2024 (26.55%) and was absent in neutral years (0%). Persistent warm-cluster coverage increased from neutral conditions (38.53%) in 1991–1992 (47.49%), 1997–1998 (53.42%), and 2023–2024 (42.97%), but was lower in 2015–2016 (34.53%). Overall, these results provide a process-oriented link between SST cluster organization and event-to-event differences in Galápagos rainfall extremes, highlighting the value of local SST metrics beyond basin-scale ENSO indices. Full article

Urban lake scenic areas serve as crucial ecological barriers but face acute conflicts between expansion and conservation. Existing research has often overlooked microscale landscape fragmentation and its associated ecological effects. Focusing on the Wuhan East Lake ecotourism scenic area (Wuhan East Lake), this study investigated the spatiotemporal impacts of micro-scale land-use transitions on ecosystem service value (ESV). To evaluate the historical evolution of ESV from 2010 to 2024, an improved equivalent factor method was coupled with a patch-generating land-use simulation (PLUS) model. Spatial autocorrelation and landscape pattern metrics were then incorporated to diagnose structural degradation and establish a foundation for simulating the four development scenarios for 2035. Results demonstrate that sporadic construction expansion led to a decline in total ESV from 2.445 to 2.216 billion CNY, driving a pronounced “core-hot vs. edge-cold” spatial disparity. Among future projections, the Sustainable Development pathway emerges as optimal, effectively balancing economic demands with the need to minimize ecological fragmentation. Ultimately, this study contributes to the literature by integrating microscale landscape fragmentation analysis with a PLUS-based multi-scenario simulation to provide a refined understanding of ecosystem service dynamics in urban lake systems, thereby offering a scientific reference for resilient spatial planning and policymaking. Full article

Accurate estimation of the mean-reversion speed $\alpha$ in the AR(1) process $X_{t+1}=(1-\alpha )X_t+{\epsilon }_t$ is central to energy-commodity modelling. Classical estimators such as GARCH, jump-diffusion, and regime-switching produce model-conditioned estimates by embedding $\alpha$ within distributional assumptions, so that different model choices yield different $\widehat{\alpha }$ values from the same series without a principled criterion to adjudicate. We propose a distribution-free neural estimator based on a Temporal Convolutional Network (TCN) trained on synthetic AR(1) series with Sinh-ArcSinh (SAS) innovations. Distribution-free here means that no parametric family is assumed for the innovation distribution at inference time: the estimator imposes no distributional hypothesis when processing a new series. The SAS family serves as a training vehicle—not a model for the real data—chosen for its ability to span a broad range of tail weights and asymmetry profiles. The theoretical foundation is spectral invariance: the Yule–Walker equations establish that the autocorrelation structure ${\rho }_k={(1-\alpha )}^k$ depends on $\alpha$ alone, provided innovations are uncorrelated across lags—a condition satisfied not only by i.i.d. innovations but also by conditionally heteroscedastic processes such as GARCH. The TCN therefore generalises to volatility-clustering environments without modification, learning to extract $\alpha$ from temporal dependence alone, independently of the marginal innovation distribution and of the temporal variance structure. On held-out test series the estimator outperforms all classical competitors, with the advantage growing monotonically with non-Gaussianity. A robustness analysis on three out-of-distribution innovation families and on AR(1)-GARCH(1,1) processes empirically validates the spectral invariance guarantee across both marginal and temporal variance structure, including near-integrated GARCH processes where innovation kurtosis far exceeds the training range. The distribution-free $\widehat{\alpha }$ enables a two-stage pipeline in which $\alpha$ and the innovation distribution are characterised independently—a decoupling structurally impossible in classical likelihood-based approaches. Once trained, the TCN acts as a universal mean-reversion estimator applicable to any price series without re-fitting. Applied to four energy markets—Italian natural gas (PSV price), Italian electricity (PUN price), US Henry Hub, and US PJM West Hub—spanning log-return kurtosis from near-Gaussian to strongly heavy-tailed, the TCN yields robust, distribution-free estimates of mean-reversion speed. Full article

Hydrological memory reflects the persistence of hydrological processes and plays an important role in understanding basin regime dynamics under changing climatic conditions. This study investigates the temporal stability of hydrological memory in the ten largest European basins: Volga, Danube, Dnieper, Don, Northern Dvina, Pechora, Neva, Rhine, Vistula, and Elbe. The analysis used rolling cross-correlation (CCF) and auto-correlation (ACF) functions calculated with a 50-month moving window to assess temporal changes in hydrological dependence structures. Additionally, an Instability Index was applied to quantify the variability of hydrological memory over time. The results indicate that the strongest correlations occur mainly at lag 0 and ±1, suggesting a relatively short hydrological memory in most basins. The lowest Instability Index was observed in the Volga basin, whereas the highest values were recorded in the Danube and Rhine basins. Full article

Rail trespassing remains a persistent safety challenge at the system level in the United States, with a 24% increase in incidents within the last decade (2016–2025). Identifying hotspots proactively is difficult due to limited incident data and strong spatial dependencies within the built environment. This study thus creates a ZIP-code–level geospatial analytics framework to identify current and emerging trespassing hotspots across North Carolina by combining land-use composition, rail exposure metrics, and historical Federal Railroad Administration (FRA) trespassing records. Geospatial layers were integrated within a GIS workflow to derive attributes such as rail miles, grade crossings, population density, and land-use types. Exploratory spatial analysis showed significant clustering of trespassing incidents, with Global Moran’s I indicating positive spatial autocorrelation across multiple neighborhood sizes. Permutation z-scores confirmed non-random hotspot formation along major rail corridors. A k-means clustering method also identified four structural risk environments, and a Composite Risk Index (CRI) was developed from weighted, standardized exposure and land-use variables to quantify latent risk, independent of raw casualty counts. Results indicate that clusters characterized by higher rail infrastructure exposure and mixed land-use environments exhibit the highest CRI values and elevated hotspot probabilities. In contrast, clusters with limited rail infrastructure, including predominantly commercial and rural ZIP codes, show substantially lower risk levels. The findings highlight that trespassing risk is more strongly associated with structural exposure conditions than with isolated historical incident counts. The resulting risk surfaces and hotspots provide an interpretable and scalable framework for statewide safety planning, early hotspot detection, and targeted interventions by transportation agencies. Full article