Search Results (9,526)

Against the escalating challenges of global climate change and intensifying resource-environment constraints, exploring the green effects of industrial spatial organization has become crucial. Utilizing panel data from the Yangtze River Delta cities spanning 2011–2023, this study empirically examines the nonlinear impact of manufacturing-producer services co-agglomeration on urban green efficiency. The results reveal a significant U-shaped relationship: co-agglomeration initially suppresses efficiency due to coordination costs and congestion effects, but after crossing a specific threshold, the resulting scale economies and knowledge spillovers dominate and begin to promote green enhancement. Mechanism tests indicate that industrial upgrading serves as a direct mediating channel, while the mediating effect of green technological innovation exhibits a time lag. Further heterogeneity analysis shows that this U-shaped pattern is particularly pronounced in cities with low agglomeration levels, those not designated as low-carbon pilots, and non-resource-based cities. This study uncovers the nonlinear dynamics and key boundary conditions of the green effects arising from industrial co-agglomeration, providing an empirical basis for implementing differentiated regional spatial coordination policies. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

As global production networks become increasingly regionalized, diversified, and resilience-oriented, metropolitan areas (MAs) have emerged as important spatial platforms for industrial development. This study examines whether China’s national-level metropolitan area integration policies promote urban industrial structure upgrading and, if so, through which channels. We first develop a set of conceptual mechanisms and hypotheses, and then test them using panel data for 281 prefecture-level cities in China from 2012 to 2022. A staggered difference-in-differences (DID) model, complemented by a series of robustness checks, is employed to identify the policy effects. The baseline estimates indicate that the industrial structure of MA member cities is, on average, about 2.43 percentage points more advanced than that of non-MA cities. Mechanism analysis shows that the policies foster urban industrial upgrading through unified market formation, technological improvement, and optimization of factor endowments. However, the policies have only a very limited impact on breakthroughs in cutting-edge or frontier technologies. Based on these findings, we propose targeted policy recommendations to address the identified shortcomings. Full article

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors. Full article

Sustainable urban mobility necessitates traffic regimes that enhance operational efficiency and improve traffic safety and flow stability; the rise in intelligent connected vehicles (ICVs) provides a salient mechanism to meet this imperative. This paper aims to investigate the mixed traffic flow characteristics in an intelligent connected environment, using one-way single-lane, double-lane, and three-lane straight highways as modeling objects. Combining the different driving characteristics of human-driven vehicles (HDVs) and ICVs, a single-lane mixed traffic flow model and a multi-lane mixed traffic flow model are established based on the intelligent driver model (IDM) and flexible symmetric two-lane cellular automata model (FSTCAM). The mixed traffic flow in the intelligent connected environment is then simulated using MATLAB R2021a. The research results indicate that the integration of ICVs can improve the speed, flow, and critical density of traffic flow. The increase in the proportion of ICVs can reduce the congestion ratio and speed difference between front and rear vehicles at the same density. As the proportion of ICVs increases, the frequency of lane-changing for HDVs gradually increases, while the frequency of lane-changing for ICVs gradually decreases. The overall lane-changing frequency shows a trend of first increasing and then decreasing. In addition, with the continuous infiltration of ICVs, the area of road congestion gradually decreases, and congestion is significantly alleviated. The speed fluctuation of following vehicles gradually decreases. When the infiltration rate reaches a high level, vehicles travel at a stable speed and remain in a relatively steady state. The findings substantiate the potential of ICV-enabled operations to advance efficiency-oriented and stability-enhancing urban mobility and to inform evidence-based traffic management and policy design. Full article

Ice-and-snow tourism cities face pronounced seasonal fluctuations that place strong pressure on urban accommodation systems. Understanding the spatial distribution, accessibility, and service capacity of hotels is therefore critical for sustainable tourism management in cold-region cities. Taking Harbin, China, as a representative winter tourism destination, this study develops a GIS-based spatial analytical framework to examine the spatial organization and service performance of star-rated hotels. Using data from 553 three-star and above hotels, combined with questionnaire survey data (N = 224), we apply the Nearest Neighbor Index (NNI), Kernel Density Estimation (KDE), and raster-based cost-distance accessibility analysis to identify spatial clustering patterns, accessibility differentiation, and mismatches between hotel supply and peak seasonal demand. We find that available hotel rooms can only meet about 60% of peak-season demand, indicating a severe capacity deficit. The results reveal a clear core–periphery spatial structure of star-rated hotels, significant accessibility disparities among hotel categories, and a pronounced mismatch between accommodation capacity and tourism demand during peak winter seasons. Peripheral areas exhibit limited accessibility and insufficient service capacity, while central districts experience high concentration and pressure. These findings highlight the importance of integrating spatial equity and seasonal demand considerations into accommodation planning and infrastructure optimization, providing policy-relevant insights for sustainable tourism development in cold-region cities. Full article

This study investigates the sustainability, resilience, and institutional performance of urban governance systems by operationalizing key thermodynamic principles energy, exergy, entropy, equilibrium, open systems, and irreversibility within a political and behavioral systems framework. Urban political systems are conceptualized as open, non-equilibrium systems, characterized by continuous flows of resources, information, and institutional feedback across metropolitan governance structures. Within this model, energy represents systemic inputs to urban governance, exergy denotes usable governing capacity at the city and metropolitan scale, and entropy reflects levels of institutional disorder, inefficiency, and systemic degradation affecting urban sustainability. The study first formulates a conceptual analytical model defining urban political entropy and systemic exergy as measurable variables associated with institutional stability, crisis-management capability, adaptability, and reform potential in urban and metropolitan governance. It then conducts a comparative empirical analysis of Germany, Türkiye, China, and South Africa using normalized indicators derived from international datasets for 2023, with particular attention to their implications for urban governance capacity and city-level institutional performance. These indicators are employed to construct proxy measures for the Exergy Efficiency Ratio, Societal and Institutional Entropy, and overall urban governance capacity. The comparative results reveal that open and decentralized governance systems tend to maintain higher exergy efficiency and lower entropy levels at the urban scale, whereas highly centralized systems, although effective in resource mobilization, tend to accumulate greater systemic entropy over time. Transitional governance systems exhibit hybrid and fluctuating thermodynamic characteristics in their urban institutional structures. The findings empirically support the Thermodynamic Model of Political Systems and demonstrate its utility as a predictive and diagnostic framework for evaluating urban institutional efficiency, resilience, and sustainability. By quantifying political energy flows and entropy dynamics within urban governance systems, this study contributes to the development of integrated systems thermodynamics of cities and provides a robust analytical foundation for sustainable urban governance, institutional reform, and long-term strategic policy design Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

This article examines the role of coworking and flexible workspaces in promoting sustainable spatial development in the non-metropolitan areas of Bulgaria. A mixed-method approach was applied, combining inventory enumeration, spatial classification, SDG-based sustainability assessment, and qualitative coding (open, axial, selective). A total of 74 coworking and flexible workspaces were identified across the six national planning regions, evaluated according to six analytical criteria (accessibility, seasonality, specialization, municipal administrative district, urban planning zone, building function) and assessed against five SDG-aligned dimensions (SDG 8, 9, 11, 12, 13). The results reveal uneven territorial distribution, strong concentration in major cities outside the capital, and emerging sustainable models in peripheral areas. Comparative SDG scoring and typological interpretation demonstrate three recurring models—Sustainable Reuse, Nature-Oriented, and Innovative/Experimental—each associated with distinct spatial and environmental characteristics. A metropolitan benchmarking exercise further contextualizes the strongest sustainability profiles. Based on these findings, a conceptual sustainable coworking model is developed for a nationally significant spa and climatic resort, illustrating how coworking can address regional disparities, support green transition policies, and reinforce territorial cohesion. The article concludes by outlining research directions related to digitalization, circular construction, environmental performance indicators, and feasibility assessments for non-metropolitan coworking development. Full article

The study investigates the presence of emerging contaminants in a river within a watershed located in the Brazilian semiarid region, specifically within the Caatinga biome, emphasizing the importance of environmental monitoring in areas that have historically been underrepresented in scientific research. The analysis focused on the associations between microplastics and pharmaceutical compounds, demonstrating that the discharge of untreated domestic effluents and the low efficiency of sanitation systems increase water resource contamination and threaten water security. The interdependence between these variables underscores the need for integrated public policies for waste management, complemented by environmental education strategies and technological innovations. The work makes an unprecedented contribution to expanding knowledge about emerging pollutants in semiarid environments, highlighting the urgency of holistic approaches, continuous monitoring, and strengthening environmental governance to ensure the sustainability and resilience of ecosystems like the Caatinga in the face of the challenges posed by global environmental change, urban growth, and those outlined in the Sustainable Development Goals. Full article

Reducing energy intensity is critical for combating climate change, yet current progress remains insufficient to meet international targets. Green-oriented policy narratives hold significant potential for mitigating energy intensity, but existing research lacks regional-level quantitative analysis. This study examines how green-oriented policy narratives influence urban energy intensity. We analyze textual data from Chinese provincial Party newspapers using large language models and LDA topic modeling to measure narrative-related variables, then combine these measures with panel data from 288 Chinese cities spanning 2010–2022. The findings reveal that green-oriented policy narrative exposure significantly reduces urban energy intensity through promoting green credit development and stimulating green innovation, with the negative effect strengthening as the prominence of the public and narrativity of narratives increase. Heterogeneity analysis further shows that narrative effectiveness is amplified in cities with higher internet penetration and marketization levels. This study broadens research on energy intensity determinants beyond traditional policy instruments, extends green-oriented narrative effects from the micro to macro level, and offers insights for leveraging narratives and contextual conditions to promote energy conservation. Full article

This study investigates the factors influencing the expansion of organic farming in Europe between 2000 and 2022. Driven by consumer demand and EU support through the Common Agricultural Policy, organic farming has grown significantly. The research uses panel data and linear regression to assess the impact of socio-economic, agronomic, and environmental variables, including GDP, HDI, population density, education, broadband access, pesticide use, and biodiversity indicators. Data sources include FAOSTAT, FiBL, Eurostat, and the World Bank. The analysis also incorporates crop-specific organic farming data and environmental metrics such as ammonia emissions. The results show that expansion is shaped simultaneously by environmental pressures and socio-economic conditions: greater pesticide use, larger land availability, higher human development, and agricultural employment support organic adoption, while intensive livestock-related emissions and indicators of urbanization, such as broadband access, tend to constrain it. Full article

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems. Full article

Chennai’s transport sector is undergoing a structural transition as the city seeks to accommodate rapidly growing travel demand while reducing energy consumption and emissions. This study develops a city-scale system dynamics model using STELLA to simulate long-term transitions in bus and Intermediate Public Transport (IPT) systems over the period 2011–2038. Four policy scenarios—Do Minimum, Partial, Desirable, and Ideal—are evaluated to examine how fleet expansion, propulsion technology substitution, and service restructuring influence urban transport energy sustainability. The model integrates demographic growth, service-level fleet benchmarks, and multiple propulsion pathways, including diesel, CNG, LPG, bio-CNG, hydrogen, and battery- and solar-electric technologies. Full article

This study investigates the factors influencing Chinese K-12 teachers’ adoption of generative artificial intelligence (GenAI) for instructional purposes by extending the Technology Acceptance Model (TAM) with pedagogical beliefs, perceived intelligence, perceived ethical risks, GenAI anxiety, and demographic moderators. Drawing on a theory-driven framework, survey data were collected from 218 in-service teachers across K-12 schools in China. The respondents were predominantly from urban schools and most had prior GenAI use experience. Eight latent constructs and fourteen hypotheses were tested using structural equation modeling and multi-group analysis. Results show that perceived usefulness and perceived ease of use are the strongest predictors of teachers’ intention to adopt GenAI. Constructivist pedagogical beliefs positively predict both perceived usefulness and intention, whereas transmissive beliefs negatively predict intention. Perceived intelligence exerts strong positive effects on perceived usefulness and ease of use but has no direct effect on intention. Perceived ethical risks significantly heighten GenAI anxiety, yet neither directly reduces adoption intention. Gender, teaching stage, and educational background further moderate key relationships, revealing heterogeneous adoption mechanisms across teacher subgroups. The study extends TAM for the GenAI era and highlights the need for professional development and policy initiatives that simultaneously strengthen perceived usefulness and ease of use, engage with pedagogical beliefs, and address ethical and emotional concerns in context-sensitive ways. Full article