Search Results (12,177)

Under the combined pressures of dual carbon policy constraints, the integration of intelligent construction technologies, and intensifying market competition, the development of a scientific and robust performance evaluation system has become essential for building construction enterprises seeking to enhance their core competitiveness. Traditional evaluation methods, however, often suffer from incomplete indicator systems and limited capability in addressing high-dimensional and nonlinear problems, rendering them inadequate for the evolving demands of the industry. To address these challenges, this study proposes a performance evaluation model for building construction enterprises based on the least squares support vector machine (LSSVM), optimized by an improved Pied Kingfisher Optimizer (IPKO). Drawing on environment–behavior theory, the model incorporates three environmental and ten behavioral factors. To overcome the limitations of the original PKO algorithm—namely, insufficient exploration capability and weak local search—the exploration phase of PKO is integrated with that of the Marine Predators Algorithm. Empirical results demonstrate that: (1) the proposed IPKO outperforms Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), Ospery Optimization Algorithm (OOA), and the original PKO in most benchmark functions; (2) the ReliefF feature selection algorithm improves the model’s test set accuracy by approximately 2.18%; and (3) the IPKO-LSSVM model achieves 6.53%, 4.16%, and 6.74% higher prediction accuracy than Backpropagation Neural Networks (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), respectively. These findings highlight the model’s effectiveness in addressing small-sample, high-dimensional, and nonlinear problems, offering a scientifically sound and practical tool for performance evaluation in building construction enterprises. Full article

The rapid digitalization of cities is reshaping urban planning practices; however, significant gaps persist between technological investments and institutional governance capacity, particularly in transition economies. This study investigates how digital tools can be systematically embedded within planning processes to improve decision-making quality, coordination, and administrative efficiency. Drawing on urban governance theory and an empirical implementation study conducted in Yerevan, Armenia (population 1.1 million) between 2019 and 2023, the paper develops and operationalizes a multi-layer governance framework that aligns digital instruments—including geospatial information systems, performance dashboards, and decision-support platforms—with strategic, tactical, and operational levels of city management. The framework is evaluated through institutional analysis of municipal policy documents, planning databases, and semi-structured interviews with planning officials. The results reveal substantial governance barriers, including data fragmentation, organizational silos, and limited digital capacity. Framework-based implementation produced measurable improvements: planning decision cycles shortened by 43%, GIS utilization increased from 18% to 68% of eligible projects, inter-agency data sharing rose sixfold, and annual cost savings of approximately $1.2 million were achieved through reduced duplication and faster approvals. By combining conceptual design with empirical validation, the study advances digital urban governance research and offers a transferable, evidence-based model for implementing resilient and efficient data-driven planning systems in resource-constrained contexts. Full article

The transition to net-zero energy systems requires operationally grounded decision-making frameworks that integrate technology performance, infrastructure readiness, and policy constraints at local scale. Food transportation represents a high-emission and operationally critical component of regional energy and supply chain systems, particularly in food-producing regions. This study proposes a preliminary Local Area Energy Planning (LAEP) framework to support operational decision-making for the decarbonisation of food transportation, using Lincolnshire, UK, as a case study. The framework evaluates alternative freight transport technologies—battery electric vehicles (BEVs), hydrogen fuel cell electric vehicles (HFCEVs), battery electric road systems (BERS), and conventional internal combustion engine vehicles—across energy efficiency, CO₂ emissions, infrastructure requirements, and cost implications. Secondary data from national statistics, regional planning documents, and peer-reviewed literature are analysed using comparative quantitative and qualitative assessment methods. Results indicate that BEVs currently offer the most energy-efficient and cost-effective solution for short-haul and last-mile food logistics, achieving overall efficiencies of approximately 77–82% with zero tailpipe emissions. HFCEVs and BERS present potential long-term operational advantages for heavy-duty and long-haul freight, but remain constrained by high infrastructure investment, energy conversion losses, and system-level costs. The findings highlight the importance of phased technology adoption, renewable energy integration, and infrastructure prioritisation to enable sustainable energy operations in freight transport systems. By embedding technology comparison within a place-based planning framework, this study contributes actionable insights for local authorities, logistics operators, and policymakers seeking to support operational decision-making in sustainable energy systems. The proposed LAEP framework is transferable to other food-producing regions aiming to decarbonise freight transportation while maintaining operational efficiency. Full article

Background: Public satisfaction with public transport infrastructure is a factor in the social legitimacy of infrastructure investment policies. Methods: This study analyzes the determinants of citizen satisfaction with toll roads in Mexico using a hierarchical regression model applied to a nationally representative survey. Results: Satisfaction does not depend primarily on sociodemographic factors, but rather on users’ overall perception of the quality, safety, and management of the road system as a whole. Furthermore, the pattern of predictors varies according to usage experience, suggesting that satisfaction is influenced by different factors among users and non-users of these facilities. These findings support a contextual evaluation model, in which citizen assessments are based more on systemic interpretations than on isolated experiences. Conclusions: The study has direct implications for public policy design and infrastructure management in contexts where the use of toll roads responds to structural constraints rather than voluntary decisions. Although the study focuses on the Mexican case, its contributions offer useful interpretative insights for other countries with similar challenges in terms of mobility and institutional legitimacy. Full article

The China–Pakistan Economic Corridor (CPEC) represents a transformative transport infrastructure initiative with the potential to reshape tourism in South Asia. However, the behavioral mechanisms through which corridor development translate into travel willingness remain insufficiently understood, particularly between domestic and cross-border tourism. This study investigated the determinants of domestic tourism willingness within Pakistan and cross-border tourism willingness toward China using a stated preference survey of 441 Pakistani respondents collected through an online questionnaire. To balance behavioral interpretation and predictive performance, this study integrated ordinal logistic regression (OLR) with multiple machine learning classifiers. The results revealed clear behavioral asymmetries between domestic and cross-border tourism decisions. Domestic tourism willingness was primarily driven by attitudinal evaluations, particularly perceived desirability, pleasantness, and comfort of travel along the CPEC. In contrast, cross-border tourism willingness was more strongly constrained by knowledge-related and institutional factors, including awareness of visa procedures, accommodation arrangements, and destination information. Comparative performance analysis indicated that machine learning models outperformed ordinal logistic regression, improving predictive accuracy by approximately 12.6 percentage points for domestic tourism (93.6% vs. 81.0%) and 1.7 percentage points for cross-border tourism (81.1% vs. 79.4%). These findings demonstrate that corridor-induced tourism demand is governed by distinct behavioral mechanisms across domestic and international contexts, highlighting the need for differentiated tourism development strategies. From a policy perspective, the results suggest that domestic tourism development along the CPEC should prioritize experiential quality and travel comfort, whereas cross-border tourism promotion should focus on reducing informational and procedural barriers such as visa knowledge, accommodation awareness, and travel facilitation. Full article

Background: Mental health needs are rising globally, while workforce shortages constrain access to timely care. Israel launched formal training for Psychiatric–Mental Health Nurse Practitioners (PMHNPs) in 2023 as part of broader efforts to strengthen the public mental health system. This narrative review provides a focused synthesis of international and Israeli literature on PMHNP roles, models of practice, outcomes, and implementation considerations relevant to the Israeli context. Methods: We conducted a narrative, non-systematic literature review of international and Israeli literature on Psychiatric–Mental Health Nurse Practitioners (PMHNPs). Searches were conducted in PubMed/MEDLINE, CINAHL, PsycINFO, and Scopus (January 2000–December 2024), alongside targeted policy and regulatory documents. Eligible sources addressed NP/PMHNP roles, scope of practice, clinical and service outcomes, implementation processes, workforce implications, or policy considerations in high-income health systems. Findings were synthesized thematically. Results: Across the reviewed literature, particularly in primary care and community-based settings, PMHNP/NP-delivered care was generally associated with comparable outcomes on selected quality and safety indicators, alongside improved accessibility, continuity, and high patient satisfaction. Successful implementation depended on regulatory clarity, organizational readiness, interprofessional collaboration, and the development of a clear professional identity. In Israel, the role is emerging within a cautious regulatory framework and may face early barriers related to role ambiguity, variable organizational support, and limited stakeholder awareness. Conclusions: PMHNP implementation may offer an important strategy for strengthening mental health service capacity in Israel. However, the extent of its contribution will depend on regulatory clarity, organizational support, implementation quality, and future empirical evaluation in the Israeli context. Full article

Historically, the electricity sector in Greece was based on local lignite, which provided a stable and affordable base for electricity production. However, current European policy directions, including decarbonization and climate neutrality by 2050, have accelerated the transformation of traditional energy models, resulting in a gradual phasing-out of fossil fuels and an increasing integration of Renewable Energy Sources (RES). In line with EU policy priorities and in light of the new dynamics shaped by the EU Emissions Trading System (EU ETS), lignite gradually became unprofitable for the national economy, leading the Greek government to announce an accelerated lignite phase-out plan. However, the phase-out of domestic lignite, although consistent with climate objectives, rapidly increased the country’s energy dependency on natural gas and its exposure to natural gas price volatility. At the same time, increased investment in solar and wind technologies has reshaped the electricity mix; yet market design, limited system flexibility and inadequate infrastructure and storage capacity have not allowed the full utilization of RES benefits. This structural gap, in turn, raises critical questions about resilience and affordability. The paper provides evidence on these issues and offers a critical evaluation of the decarbonization pathway that has reshaped the country’s energy dependency. Full article

Microplastics have become a ubiquitous environmental contaminant in natural waters, raising significant concerns regarding aquatic ecosystem health and potential human exposure. A comprehensive synthesis of current knowledge on microplastic pollution in freshwater and marine systems is presented, focusing on sources, distribution patterns, environmental behavior, and associated risks. In freshwater environments, microplastic inputs are closely linked to human activities and land use, with wastewater treatment plant effluent, urban runoff, and agricultural drainage serving as major pathways. In marine systems, microplastics undergo dynamic transport influenced by particle properties, hydrodynamic conditions, and biological interactions such as biofouling and aggregation, leading to widespread distribution from coastal zones to deep sea sediments. Importantly, the role of the freshwater–estuarine–marine continuum is emphasized, highlighting the coupled processes of transport, retention, and remobilisation that govern the spatiotemporal distribution and ultimate fate of microplastics across interconnected aquatic systems. Toxicological effects on aquatic organisms are further examined, particularly immunotoxicity and neurotoxicity, alongside potential human health risks via ingestion, inhalation, and dermal exposure. Attention is drawn to the discrepancy between experimental exposure conditions and environmentally relevant concentrations, which constrains robust risk assessment. Current mitigation strategies, including source reduction, wastewater treatment upgrades, transport interception, and degradation technologies, are critically evaluated in terms of effectiveness and limitations. A clear distinction is made between apparent removal and actual degradation, with further consideration of the environmental implications associated with sludge retention and degradation byproducts. Finally, key research priorities are identified, including the need for standardized detection methods, improved exposure assessment, development of environmentally benign alternatives, and strengthened policy-driven source control. These insights provide a basis for advancing sustainable management strategies for microplastic pollution in natural waters. Full article

The watershed boundaries in arid and semi-arid regions are critical zones where ecological vulnerability and socio-economic development are in severe conflict. The upper and middle reaches of the Yellow River basin are a typical example of this dilemma. Intensive land use and human developmental interventions in this region have severely disrupted the integrity and balance of the ecosystem. While spatially designated, networked conservation areas can effectively promote the integrity and balance of regional ecosystems, these areas may fail to capture dynamic changes in vulnerability. This study develops a “functional diagnosis-structural diagnosis-integrated optimization” framework. It integrates various scenarios to diagnose vulnerability under uncertainty and identifies bottlenecks in ecological networks. For functional diagnosis, the coupling of the sensitivity–resilience–pressure (SRP) model and the Ordered Weighted Averaging (OWA) algorithm accurately locates vulnerable areas within the regional ecosystem. In terms of structural diagnosis, the Morphological Spatial Pattern Analysis (MSPA), Minimum Cumulative Resistance model (MCR), and Circuit Theory are integrated to identify structural bottlenecks. The main findings of this study are as follows: (1) Functional Diagnosis: The coupling of SRP and OWA reveals the non-linear vulnerability responses to policy preferences and identifies areas that consistently exhibit functional vulnerability across different scenarios. (2) Structural Diagnosis: The circuit theory combined with MSPA and MCR analysis identifies 72 ecological pinch points. These bottlenecks represent the weakest structural nodes crucial for maintaining regional ecological robustness. (3) Coupled Delineation and Differentiated Restoration Strategies: High vulnerability areas identified by SRP and consistently vulnerable areas identified by OWA are combined to delineate four distinct ecological restoration units: Alpine Fragile Matrix Unit, Loess Hilly Soil Conservation Unit, Anthropogenic Pressure Pinch Point Unit, Key Structural Stepping Stone Unit. Differentiated ecological restoration strategies are proposed based on the varying sensitivity, resilience, and pressure characteristics of these units. The “functional-structural” coupled ecological vulnerability evaluation framework can precisely identify vulnerable areas. The delineated restoration units and their corresponding restoration strategies provide reference and supplementation for the protected areas system, offering transferable tools for enhancing regional ecological efficiency. Full article

Parking management in mid-sized Latin American cities is often limited by weak enforcement, scarce off-street supply, and widespread irregular parking. This study uses a stated preference experiment to analyze parking choices among 227 drivers in Loja, Ecuador. Six choice tasks evaluated four alternatives—regulated on-street, private off-street, irregular parking, and leaving the vehicle at home—based on cost, walking distance, search time, availability, expected fines, and security. Multinomial logit (MNL) and mixed logit (ML) models were estimated, including income- and gender-based segmentations. Results show that cost (β = −0.332, p < 0.01) and walking distance (β = −0.0026, p < 0.001) are the primary determinants of formal parking choice. The willingness to pay to avoid 100 m of walking is USD 0.77 per 2-h period. Low-income users are 4.8 times more sensitive to cost. Mixed logit results reveal significant heterogeneity in preferences for cost, search time, and enforcement sensitivity. Policy simulations indicate that increasing enforcement (70% probability, USD 250 fine) reduces illegal parking demand by 93%, while lowering regulated tariffs to USD 0.50 raises its share by 4.2 percentage points. These findings support sustainable mobility policies by promoting efficient parking management, reducing illegal parking, and improving equitable access to urban space. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

The reuse of treated wastewater for agricultural irrigation is increasingly considered a strategic response to water scarcity and climate change, particularly in Mediterranean regions. This study examines the local feasibility and social acceptance of water reuse within the framework of Regulation (EU) 2020/741, focusing on its implementation in Italy. The research combines policy analysis, technical assessment of effluent quality from the GIDA wastewater treatment plant (Prato, Tuscany), GIS-based spatial evaluation, and a mixed-method survey of local agri-food producers. Results show substantial compliance with EU minimum quality requirements, alongside additional constraints arising from national regulatory thresholds. Survey findings reveal cautious but tangible openness among farmers toward reclaimed water use, particularly in response to increasing climate-related pressures. The case of Prato is further analysed within the Prato Circular City and local food policy frameworks, highlighting the role of participatory governance and multi-actor engagement in supporting reuse initiatives. The study contributes empirical evidence on the interaction between EU regulation, national implementation measures, and local socio-institutional conditions shaping peri-urban water reuse systems. Furthermore, it serves as a preliminary framework for future economic feasibility studies and the subsequent regulatory and permitting phases required to operationalize this practice. Full article

Peatlands cover approximately 10% (640,000 ha) of Latvia’s territory, of which about 51,000 ha is officially classified as degraded due to peat extraction and related activities. This study assesses the current status of peat extraction site recultivation in Latvia and evaluates future after-use requirements under contrasting policy pathways using a review of scientific literature, project reports, national statistics, and updated peat extraction licence records. A simple allocation model was applied to estimate recultivation trajectories for the nationally defined degraded peatland area under two scenarios: (i) a licence-expiry baseline scenario and (ii) an accelerated immediate-stop-peat-mining scenario. The results show that full recultivation would require average annual efforts of approximately 1500 ha yr⁻¹ under the baseline scenario and around 2000 ha yr⁻¹ under the accelerated scenario. Although European Union-funded projects and corporate initiatives have demonstrated the potential of rewetting, paludiculture, and renewable energy integration, only a limited number of sites have been officially recognised as fully recultivated or restored. Because ecological recovery of peatland functions may take decades, administrative closure alone does not guarantee climate or biodiversity benefits. A phased recultivation strategy linked to licence expiry and prioritising degraded and self-regenerating sites emerges as the most pragmatic pathway for Latvia, balancing European Union climate objectives, institutional capacity, and socio-economic constraints. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article