Search Results (1,627)

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

5G technology was introduced in 2019 with the aim of transforming digital connectivity, enabling a new generation of communication capabilities, such as significantly faster mobile broadband, highly reliable low-latency links, and the capacity to support vast IoT deployments. However, the expected improvements promised by 5G technology do not seem to be reflected in actual usage. This study aims to address the issue of the real-world usage of 5G telecommunications networks and compare it with the theoretical specifications of the network as officially published by 3GPP. Specifically, the focus will be on the evaluation of the implementation of the 5G network in northwestern Greece, which operates in Non-Standalone (NSA) mode as of the date of this study’s completion. 5G Standalone (SA) networks were not available for public testing in this region during the data collection period. The analysis focuses on key performance indicators, including throughput, latency, stability, and coverage, to assess how effectively current deployments meet the expectations set by 5G standards. Results show that while 5G delivers notable improvements in peak data rates and latency, several practical limitations persist. NSA deployments remain constrained by their dependence on 4G infrastructure, resource sharing between LTE and 5G components affects performance under high-load conditions, and inconsistent coverage leads to significant variability in user experience. These findings highlight the gap between theoretical capabilities and operational performance, offering insights that can guide future network optimization and inform the transition toward 5G Standalone (SA) architectures. Full article

Driven by the digital transformation of territorial spatial governance, traditional urban planning is irreversibly shifting towards a data-driven empirical paradigm. However, constrained by mimetic isomorphism and path dependence, many geography-based regional universities remain trapped in an educational dilemma: they overemphasize morphological representation while marginalizing quantitative decision-making, fostering a structural mismatch between graduate competencies and industry demands. To explore a systematic pathway out of this dilemma, this study chronicles a three-year pedagogical intervention utilizing a mixed-methods design with a historical control cohort (N = 275) within the urban planning program of Gansu Agricultural University—a regional institution situated in a less-developed frontier where territorial renewal demands macro-spatial synthesis over aesthetic forms. The intervention strategically redefined the graduate competency profile as “spatial data analysts”, constructing a pedagogical model comprising foundational algorithmic training, cross-disciplinary faculty collaboration, and real-world Project-Based Learning (PBL), coupled with a restructured, evidence-based evaluation system. Longitudinal tracking and quantitative analyses indicate a structural alignment with elevated educational efficacy. At the macro level of employment trajectories, the proportion of graduates securing knowledge-intensive data positions experienced a structural shift, rising from a baseline of 14.5% to 42.5%, reflecting an enhanced capacity to capitalize on expanding societal demands. At the meso level of practical competence, the award rate in high-level professional competitions increased by 35.4%. At the micro cognitive level, the new evaluation mechanism is associated with a successful redirection of students’ cognitive resources toward algorithmic logic and policy translation (p < 0.001) while highly significantly enhancing their self-efficacy in tackling complex, wicked engineering problems (p < 0.001). Rather than isolating pure causal mechanics, this study interprets these systemic gains as a contextual realignment of academic supply. It provides a context-sensitive, reproducible methodological reference for cultivating professional distinctiveness and reshaping the spatial planning education system in the digital era. Full article

The global transition to a diversified renewable energy portfolio requires reliable assessment of predictable marine energy resources. This study develops a high-resolution, three-dimensional Regional Ocean Modeling System (ROMS) to quantitatively evaluate theoretical tidal current energy resources in the Bohai and Yellow Seas. The model, configured with fine-scale bathymetry and forced by harmonic tidal constituents, is validated against tide gauge and Acoustic Doppler Current Profiler (ADCP) observations. Multi-year simulations reveal pronounced spatial heterogeneity in tidal current energy distribution. Rather than treating resource assessment as a single power density mapping exercise, this study combines annual mean theoretical power density, peak theoretical power density, threshold-dependent effective flow duration, effective water depth, current directionality, and vertical velocity structure to characterize resource intensity, temporal persistence, and vertical deployability. The results identify distinct hydrodynamic resource regimes. High theoretical resource intensity is concentrated west of Laotieshan Cape and east of Chengshantou, where cumulative annual effective flow duration exceeds 5000 h and short-term instantaneous theoretical power density can reach approximately 10 kW/m² and 8 kW/m², respectively. These peak values indicate strong local tidal acceleration but should be interpreted together with annual mean power density and effective flow duration. In contrast, the northern Jiangsu coastal area exhibits lower peak intensity but relatively persistent moderate flow conditions. The results provide a hydrodynamic resource basis for preliminary site screening and for guiding subsequent turbine-performance, wake/array, environmental, grid accessibility, and techno-economic assessments. Full article

To reduce the generation of waste electrical and electronic equipment (WEEE), or electronic waste (hereafter referred to as E-waste), within urban waste streams, extended producer responsibility (EPR) has evolved into an important framework for E-waste management and circular economy policies worldwide over the past thirty years. This policy has received increasing attention because of concerns regarding environmental pollution and resource depletion, as E-waste may contain heavy metals, such as mercury, cadmium, and lead, as well as valuable metals, including gold, silver, platinum, palladium, copper, and aluminum. In the developed East Asia region, Japan, South Korea (hereafter abbreviated as Korea), and Taiwan are renowned for their electronics industries and share similar socioeconomic and environmental characteristics, such as high population density, dependence on imported resources, and comparable levels of per capita national income. This review paper first provides the brief information on precious and valuable base metals derived from E-waste in urban waste. Furthermore, it presents a brief overview of the legal systems for urban waste management and compares urban mining from E-waste in Japan, Korea, and Taiwan. In this regard, the policies, regulations, and achievements related to urban waste management and E-waste recycling in East Asia, especially in Taiwan, are summarized and linked to increasing recycling rates for urban waste, including E-waste. Finally, the paper also examines two leading case studies in Taiwan, which focus on the recovery of precious metals from information and communication technology (ICT) products and valuable base metals from home electronic appliances, respectively. Full article

The conventional “resources–energy–environment–economy” growth paradigm has imposed severe environmental pressures on China, including land desertification and smog pollution. In the context of carbon peaking and carbon neutrality, the synergistic advancement of pollution reduction, carbon mitigation, ecological expansion, and economic growth (PCEG) has become a critical development pathway. Drawing on Pareto improvement theory, this study applies a super-efficient slack-based measure (SBM) model to evaluate PCEG synergistic efficiency across 30 Chinese provinces from 2003 to 2020. We further investigate its temporal evolution, regional heterogeneity, and convergence characteristics. The empirical results reveal that (1) PCEG synergistic efficiency follows a U-shaped trajectory; (2) both technological change and efficiency change contribute positively to post-2018 recovery; (3) substantial regional heterogeneity and cross-regional overlap are observed, with intra-regional disparities playing an equally important role in shaping overall inequality as inter-regional differences; and (4) no σ-convergence is observed at the national or regional level; β-convergence is significant in the non-spatial setting but drops sharply once spatial dependence is incorporated, indicating that administrative barriers, market segmentation, and frictions in factor mobility hinder the convergence process. These results inform a policy mix that addresses within-region heterogeneity, sustains the post-2018 momentum of technological progress, and dismantles spatial barriers to factor mobility. Full article

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

Sustainable water resource management in arid and transboundary-dependent regions requires that hydrological assessment be integrated with institutional governance analysis. This study provides a comprehensive hydro-institutional evaluation of water sustainability in Kazakhstan using a multi-source empirical framework. The analysis is based on international and national datasets (FAO AQUASTAT, World Bank, national statistics for 2010–2024) and incorporates key indicators, including per capita renewable water resources, sectoral withdrawal structure, transboundary dependence, and water stress. In addition, a Water Sustainability Composite Index and a Regional Vulnerability Index were developed to capture system-wide sustainability and spatial heterogeneity. The results show that Kazakhstan possesses moderate renewable water availability (approximately 5411 m³ per capita per year), yet exhibits significant structural vulnerability due to high transboundary dependence (40.64%), dominant agricultural water use (≈57%), and infrastructure inefficiencies (25–35% losses). Regional analysis reveals substantial disparities, with southern irrigation-dependent regions demonstrating higher vulnerability compared to resource-abundant eastern basins. Elasticity analysis indicates that improvements in irrigation efficiency have a substantially greater impact on sustainability than equivalent changes in transboundary inflows, highlighting the dominant role of internal system performance. The findings suggest that water sustainability in Kazakhstan is primarily constrained by governance effectiveness and efficiency limitations rather than absolute resource scarcity. This study contributes to the literature by integrating quantitative hydrological indicators with institutional analysis through a composite modeling framework, demonstrating that internal system efficiency—particularly irrigation performance—has a significantly greater influence on sustainability outcomes than external hydrological variability. The proposed approach provides a transferable methodology for assessing water sustainability in semi-arid and transboundary contexts. Full article

Rapid urbanization and climate change are exerting unprecedented pressure on regional water resources, particularly in emerging megacities. This study examines the Xiong’an New Area (XNA) in the water-stressed North China Plain, where high-intensity urbanization coincides with rigorous ecological restoration mandates. To overcome the limitations of single-model assessments, a coupled SD–PLUS–InVEST framework was developed, integrating System Dynamics for socio-economic and policy drivers, Patch-Generating Land-Use Simulation for fine-scale urban expansion, and InVEST for hydrological process assessment. Projecting spatiotemporal water dynamics to 2035 under three Shared Socio-Economic Pathways (SSPs), results reveal that urbanization-driven water demand growth consistently outpaces climate-induced supply gains. While precipitation increases are projected to raise water yield by 8.91–19.58% by 2035, demand surges by up to ~26% under the extensive expansion scenario (SSP5–8.5), driven predominantly by impervious surface proliferation. External water transfers are projected to sustain 40–45% of total supply by 2035, yet this dependency introduces systemic vulnerabilities. Quantitative assessment further indicates severe spatiotemporal mismatches, with Seasonal Water Shortage Rates of 26.1–27.3% and a Spatial Mismatch Index rising from 0.44 to 0.98. These findings indicate that climate-driven precipitation increments alone cannot offset water deficits induced by unregulated urban sprawl, and that integrating strategic land-use planning, resilient infrastructure, and adaptive governance is essential for water security in rapidly developing regions. Full article

Under increasing land constraints and food security pressures, understanding the direction of agricultural technological change is essential for improving land use efficiency. This study investigates the regional disparities, distributional dynamics, and spatial convergence of biased technological change in Chinese agriculture. Dagum Gini decomposition is used to identify regional differences and their sources, kernel density estimation examines distributional dynamics, and spatial econometric models test convergence patterns. The results show that agricultural technological progress in China is predominantly biased toward labor and capital, with labor–land bias being the strongest and continuously increasing. This indicates a gradual shift from land-dependent growth toward more intensive use of non-land inputs under farmland constraints. Regional disparities in technological bias have widened over time, mainly driven by interregional differences and distributional overlap. Kernel density analysis reveals dynamic but largely non-polarized evolution, suggesting gradual adjustment rather than structural divergence. Although no σ-convergence is observed, both absolute and conditional β-convergence exist, with faster convergence in the labor–land dimension and under conditional settings. These findings imply that regions tend to adapt to land constraints through differentiated technological pathways, resulting in uneven improvements in land-related productivity. Overall, biased technological change plays an important role in shaping land use efficiency under resource constraints. The study provides evidence for understanding agricultural adaptation to land scarcity and offers implications for sustainable agricultural development, farmland protection, and long-term food security. Full article

This study investigates the development and current status of agritourism in Croatia, with a specific focus on Istria County, a region characterized by favorable Mediterranean climatic conditions and a long-standing tourism culture. The research aims to assess the structure and success factors of agritourism enterprises within the broader Croatian tourism market. An empirical field survey was conducted on a sample of 58 agritourism businesses operating in Istria County. The collected data were analyzed using descriptive statistical methods, and enterprises were segmented into three groups according to their length of business operation. The results reveal significant differences among the identified groups in terms of demographic and professional characteristics, including age, educational attainment, prior tourism experience, years in business, and annual tourist visits. The analysis further identifies three key dimensions influencing agritourism: future development (1), consisting of economic and social variables enhancing the business; limitations (2) of land, capital, and laws that can hinder their future; and state interventions (3), such as incentives and taxes created by state authorities. The findings suggest that the sustainable development of agritourism in Istria depends on coordinated policy support, effective utilization of farm, local nature, and heritage resources, as well as continuous improvement in service provision. This study contributes to a better understanding of agritourism dynamics in emerging rural tourism markets and provides a basis for future research and policy development. Full article

The optoelectronic information industry is a typical high-tech industry whose technological innovation capacity depends not only on internal industrial factors, such as R&D input, innovation output, technology commercialization, and economic performance, but also on support from the regional macro-level innovation environment. To comprehensively evaluate the technological innovation level of China’s optoelectronic information industry, this study constructs an evaluation framework that includes both industry-level indicators and macro-level innovation environment indicators, drawing on domestic and international innovation evaluation systems. Using the correlation coefficient method and the coefficient of variation method, 20 core evaluation indicators are selected from an initial pool of 48 candidate indicators. The composite index, coordination index, and coordinated development index are then used to measure the technological innovation level of the optoelectronic information industry in 20 typical Chinese provinces and municipalities from 2000 to 2022. In addition, scenario analysis, weight sensitivity analysis, global simulation, and segmented simulation are conducted to examine the relationship between the overall development level and the coordination structure among subsystems. The results show that the coordinated development level of technological innovation in China’s optoelectronic information industry exhibits an overall upward trend, with the national coordinated development index increasing from 0.132 in 2000 to 0.627 in 2022. However, regional disparities remain evident. Guangdong, Beijing, Zhejiang, Fujian, and Shandong have remained in the high-value group, whereas Liaoning, Hebei, Shaanxi, Jilin, and Guangxi have lagged behind. Further analysis indicates that the composite index mainly reflects the overall development level of the system, while the coordinated development index additionally characterizes the coordination structure among the input, output, benefit, and environmental support subsystems. It can help identify situations such as low-level equilibrium, structural imbalance, and cases where regions have similar development levels but different coordination states. The findings provide a reference for optimizing innovation resource allocation, identifying systemic weaknesses, and formulating differentiated innovation support policies for the optoelectronic information industry across regions. Full article

Climate change has become a global challenge that cannot be ignored in the pursuit of development. The implementation of the Low-Carbon City Pilot Policy (LCCPP) represents an approach to addressing environmental issues and promoting sustainable development. Investigating the impact of this policy on urban compactness (UC) is therefore crucial for advancing sustainable development. Using a staggered DID model with panel data from 108 cities in the Yangtze River Economic Belt (YREB) over the period 2008–2023, along with robustness checks and spatial analysis, this study evaluates LCCPP’s impact on UC. The main findings are as follows: (1) The LCCPP has a significant positive effect on UC. (2) The policy enhances UC primarily through two channels: intensifying infrastructure development and promoting green technology innovation. (3) The promoting effect of the LCCPP is stronger in cities located in the upper reaches of the Yangtze River, within urban agglomerations, and in non-resource-dependent cities. (4) The policy reduces UC in neighboring non-pilot cities, leading to an overall decline in regional compactness. This study provides an important reference for exploring pathways toward sustainable development. Full article

This study investigates how regulatory pressure and organizational capabilities influence innovation-enabled compliance and supply chain performance in Thailand’s plastic recycling sector. Drawing on institutional theory, the resource-based view, and dynamic capability perspectives, the study develops and empirically tests a conceptual model using partial least squares structural equation modeling (PLS-SEM). Data were collected from 300 respondents across 20 plastic recycling facilities in the Bangkok Metropolitan Region. The results show that Digital Innovation Capability (DIC) is the strongest predictor of legal compliance behavior (LCB), followed by Organizational Regulatory Readiness (ORR), Regulatory Enforcement Intensity (REI), and Compliance Process Maturity (CPM). In turn, LCB significantly enhances supply chain resilience (SCR). The findings further indicate that REI exerts both direct and indirect effects on SCR through LCB. Although REI demonstrates a significant direct effect on SCR, the indirect effect through LCB is comparatively weaker than that of Digital Innovation Capability (DIC). Nevertheless, the mediation effect remains supported based on bootstrapped confidence interval analysis. These findings suggest that regulatory pressure alone may encourage compliance at a formal level, but sustainable operational performance ultimately depends on the development of internal organizational and technological capabilities. Mediation analysis further confirms that LCB serves as a key mechanism linking organizational and technological capabilities to supply chain performance. Overall, the findings position compliance as an innovation-enabled and capability-driven mechanism that supports digital transformation, operational resilience, and sustainability within the circular economy. Full article