Search Results (844)

Urban spatial justice is a critical issue in the context of rapid urbanization. Improving public well-being depends on the efficient use of park green space (PGS) resources. This study evaluates the spatial distribution equity and social equity of PGS in Jinan City, Shandong Province, China, with the aim of optimizing their spatial layout, mitigating poor accessibility due to uneven spatial distribution, and improving the quality of life for all inhabitants. Firstly, based on Sustainable Development Goal 11 (SDG11), we constructed an urban sustainable development index system to quantify residents’ demand levels. The supply level was measured through three dimensions: quantity, quality, and accessibility of PGS utilizing multi-source geospatial data. A coupling coordination degree model (CCDM) was employed to analyze the supply-demand equilibrium. Secondly, Lorenz curves and Gini coefficients were utilized to evaluate the equity of PGS resource distribution to disadvantaged populations. Finally, a k-means clustering algorithm found the best sites for additional parks in low-accessibility regions. The results show that southern areas—that is; those south of the Yellow River—showed greater supply-demand equilibrium than northern ones. With a Gini index for PGS services aimed at vulnerable populations of 0.35, the citywide social level distribution appeared to be relatively balanced. This paper suggests an evaluation technique to support fair resource allocation, establishing a dual-perspective evaluation framework (spatial and social equality) and giving a scientific basis for PGS planning in Jinan. Full article

Existing drainage systems have limited capacity to mitigate future climate change-induced flooding problems effectively. However, some studies have evaluated the effectiveness of integrating Water-Sensitive Urban Design (WSUD) with existing drainage systems in mitigating flooding in tropical regions. This study examined the performance of drainage systems and integrated WSUD options under current and future climate scenarios in a sub-catchment of Saltwater Creek, a tropical catchment located in Cairns, Australia. A combination of one-dimensional (1D) and two-dimensional (1D2D) runoff generation and routing models (RORB, storm injector, and MIKE+) is used for simulating runoff and inundation. Several types of WSUDs are tested alongside different climate change scenarios to assess the impact of WSUD in flood mitigation. The results indicate that the existing grey infrastructure is insufficient to address the anticipated increase in precipitation intensity and the resulting flooding caused by climate change in the Engineers Park sub-catchment. Under future climate change scenarios, moderate rainfall events contribute to a 25% increase in peak flow (95% confidence interval = [1.5%, 0.8%]) and total runoff volume (95% confidence interval = [1.05%, 6.5%]), as per the Representative Concentration Pathway 8.5 in the 2090 scenario. Integrating WSUD with existing grey infrastructure positively contributed to reducing the flooded area by 18–54% under RCP 8.5 in 2090. However, the efficiency of these combined systems is governed by several factors such as rainfall characteristics, the climate change scenario, rain barrel and porous pavement systems, and the size and physical characteristics of the study area. In the tropics, the flooding problem is estimated to increase under future climatic conditions, and the integration of WSUD with grey infrastructure can play a positive role in reducing floods and their impacts. However, careful interpretation of results is required with an additional assessment clarifying how these systems perform in large catchments and their economic viability for extensive applications. Full article

County economies are the cornerstone of China’s economic and social development but face challenges such as a singular industrial structure and the outflow of production factors. As an important policy tool for rural revitalization, the impact mechanism of National-Level Modern Agricultural Industrial Parks (NMAIPs) on county economies remains inadequately explored. This study aims to quantify the dynamic economic effects of the NMAIP policy through rigorous empirical analysis and elucidate the core pathways driving county economic growth. Based on panel data from 44 counties in six central Chinese provinces from 2014 to 2024, this study employs a Multi-Period Difference-in-Differences (DID) model and finds a significant one-year lag effect of the NMAIP policy: in the year following park establishment, county GDP increased by an average of 8.5%, and this positive effect persisted until the fourth year but showed a trend of marginal diminution. Pathway analysis reveals that agricultural scale expansion (measured by gross output value of agriculture, forestry, animal husbandry, and fishery) and production efficiency improvement (measured by the ratio of output value to agricultural expenditure) are the core driving mechanisms, accounting for 48% and 35% of the total effect, respectively. In contrast, the mediating roles of industrial integration (comprehensive index) and industrial structure upgrading (share of agricultural services) were not statistically significant in the short run. The policy lag primarily arises from the conversion cycle of infrastructure investment to economic output, while pathway differences are closely related to the maturity of the county’s agricultural industrial chain and resource allocation efficiency. This study provides robust empirical evidence for optimizing the timing and pathways of the NMAIP policy design: policy effect evaluations require a 1–2 year “window period”; resources should be prioritized for projects that can rapidly enhance scale and efficiency (e.g., scaled planting, technology-driven efficiency gains), laying a solid agricultural foundation before gradually fostering industrial integration. This aligns with the spirit of “avoiding industrial hollowing-out” proposed in the 2024 Central “Thousand Villages Project” and provides the Chinese experience for the policy evaluation and path selection of global agricultural parks. Full article

Digital twins play a crucial role in linking data with practical solutions. They convert raw measurements into actionable insights, enabling spatial planning that addresses environmental challenges and meets the needs of local communities. This paper presents the development of a digital geospatial twin for a residential district in Burgas, the largest port city on Bulgaria’s southern Black Sea coast. The aim is to provide up-to-date geospatial data quickly and efficiently, and to merge available data into a single, accurate model. This model is used to test three scenarios for revitalizing coastal functions and improving a waterfront urban park in collaboration with stakeholders. The methodology combines aerial photogrammetry, ground-based mobile laser scanning (MLS), and airborne laser scanning (ALS), allowing for robust 3D modeling and terrain reconstruction across different land cover conditions. The current topography, areas at risk from geological hazards, and the vegetation structure with detailed attribute data for each tree are analyzed. These data are used to evaluate the strengths and limitations of the site concerning the desired functionality of the waterfront, considering urban priorities, community needs, and the necessity of addressing contemporary climate challenges. The carbon storage potential under various development scenarios is assessed. Through effective visualization and communication with residents and professional stakeholders, collaborative development processes have been facilitated through a series of workshops focused on coastal transformation. The results aim to support the design of climate-neutral urban solutions that mitigate natural risks without compromising the area’s essential functions, such as residential living and recreation. Full article

Previous landscape design mostly relies on general standards, failing to fully consider gender differences in landscape visual perception, with relevant research still needing further exploration. This study takes Beijing’s Beihai Park as the research object, using five types of on-site-collected photos (water landscape, plant landscape, architectural landscape, path landscape, and square landscape) as stimuli. Twenty males and twenty females participated in an eye-tracking experiment and a questionnaire survey to analyze gender differences in the visual perception of these five landscapes. The results show the following: (1) females show a “core–radiation” pattern, focusing on mid-short vision and environmental details; males focus on distant views and functional areas. (2) Females have slightly higher APD and fixation counts, with stronger cognitive/emotional fluctuations; males have longer total fixation time and more sustained attention. (3) Males prefer architectural/square landscapes, emphasizing functionality; females favor water/plant landscapes, prioritizing emotional connection with nature. (4) The total fixation time significantly impacts subjective evaluations; the average fixation duration is gender-neutral but uniquely affects evaluations of certain landscape types. This study has guiding significance for enhancing park landscapes’ inclusiveness and attractiveness, promoting different genders’ participation and satisfaction, and boosting space vitality and utilization efficiency. Full article

Existing non-intrusive load monitoring (NILM) methods predominantly rely on centralized models, which introduce privacy vulnerabilities and lack scalability in large industrial park scenarios equipped with distributed energy resources. To address this issue, a Federated Temporal Pattern-based NILM framework (FedTP-NILM) is proposed. It aims to ensure data privacy while enabling efficient load monitoring in distributed and heterogeneous environments, thereby extending the applicability of NILM technology in large-scale industrial park scenarios. First, a federated aggregation method is proposed, which integrates the FedYogi optimization algorithm with a secret sharing mechanism to enable the secure aggregation of local data. Second, a pyramid neural network architecture is presented to capture complex temporal dependencies in load identification tasks. It integrates temporal encoding, pooling, and decoding modules, along with an enhanced feature extractor, to better learn and distinguish multi-scale temporal patterns. In addition, a hybrid data augmentation strategy is proposed to expand the distribution range of samples by adding noise and linear mixing. Finally, experimental results validate the effectiveness of the proposed federated learning framework, demonstrating superior performance in both distributed energy device identification and privacy preservation. Full article

To address the challenges of power generation rights trading and profit distribution in the integrated energy system of multi-park combined cooling, heating, and power (CCHP) with new energy grid integration, we constructed a hierarchical game model involving multi-energy system aggregators. By having aggregators price electricity, heat, cold, and carbon costs, the model establishes a hierarchical game framework with the linkage of the four prices (electricity, heat, cold, and carbon), achieving inter-park peer-to-peer (P2P) multi-energy dynamic price matching for the first time. It aims to coordinate distribution network dispatching, renewable energy, energy storage, gas turbine units, demand response, cooling–heating–power coupling, and inter-park P2P multi-energy interaction. With the goal of optimizing the profits of integrated energy aggregators, a hierarchical game mechanism is established, which integrates power generation rights trading models and incentive-based demand response. The upper layer of this mechanism is the profit function of integrated energy aggregators, while the lower layer is the cost function of park microgrid alliances. A hierarchical game mechanism with Two-Level Optimization, integrating the Adaptive Disturbance Quantum Particle Swarm Optimization (ADQPSO) algorithm and the branch and bound method (ADQPSO-Driven Branch and Bound Two-Level Optimization), is used to determine dynamic prices, thereby realizing dynamic matching of energy supply and demand and cross-park collaborative optimal allocation. Under the hierarchical game mechanism, the convergence speed of the ADQPSO-driven branch and bound method is 40% faster than that of traditional methods, and the optimization profit accuracy is improved by 1.59%. Moreover, compared with a single mechanism, the hierarchical game mechanism (Scenario 4) increases profits by 17.17%. This study provides technical support for the efficient operation of new energy grid integration and the achievement of “dual-carbon” goals. Full article

The rapid clustering of Chemical Industrial Parks (CIPs) has escalated the risk of cascading disasters (e.g., toxic leaks and explosions), underscoring the need for resilient emergency logistics systems. However, traditional two-stage optimization models often yield suboptimal outcomes due to decoupled facility location and routing decisions. To address this issue, we propose a unified mixed-integer nonlinear programming (MINLP) model that integrates site selection and routing decisions in a single framework. The model accounts for multi-source supply allocation, enforces minimum safety distance constraints, and incorporates heterogeneous economic factors (e.g., regional land costs) to ensure risk-aware, cost-efficient planning. Two deployment scenarios are considered: (1) incremental augmentation of an existing emergency network and (2) full network reconstruction after a systemic failure. Simulations on a regional CIP cluster (2400 × 2400 km) were conducted to validate the model. The integrated approach reduced facility and operational costs by 9.77% (USD 13.68 million saved) in the incremental scenario and achieved a 15.10% (USD 21.13 million saved) total cost reduction over decoupled planning in the reconstruction scenario while maintaining an 8 km minimum safety distance. This integrated approach can enhance cost-effectiveness and strengthen the resilience of high-risk industrial emergency response networks. Overall, the proposed modeling framework, which integrates spatial constraints, time-sensitive supply mechanisms, and disruption risk considerations, is not only tailored for hazardous chemical zones but also exhibits strong potential for adaptation to a variety of high-risk scenarios, such as natural disasters, industrial accidents, or critical infrastructure failures. Full article

With the rapid expansion of Wind Parks (WPs), Wind Turbine Clutter (WTC) has become a significant challenge due to the interference it causes with data from next-generation Doppler weather radars. Traditional clutter detection methods struggle to strike a balance between detection accuracy and efficiency. This study proposes a deep learning model named WTC-MobResNet, which integrates the architectures of MobileNet and ResNet and is specifically designed for WTC detection tasks. The model combines the lightweight characteristics of MobileNet with the residual learning capabilities of ResNet, enabling efficient extraction of WTC features from weather radar echo data and achieving precise identification of WTC. The experimental results demonstrate that the proposed model achieves an ACC of 98.21%, a PRE of 97.52%, a POD of 98.99%, and an F1 score of 98.25%, outperforming several existing deep learning models in both detection accuracy and false alarm control. These results confirm the potential of WTC-MobResNet for real-world operational applications. Full article

The revitalization of post-industrial areas has emerged as a critical strategy for sustainable urban development, achieving a balance between economic, social, and environmental priorities. This study assesses the transformative capacity of revitalization strategies by conducting a comprehensive case analysis of “Radex Park Marywilska” in Warsaw, Poland. The analysis quantifies the benefits of revitalization in comparison to demolition and new construction methodologies. An examination of the revitalization initiative demonstrates that it yielded a total of PLN 41.15 million in benefits, with PLN 28.13 million attributed to direct cost savings and another PLN 13.02 million resulting from environmental improvements. In practical terms, this equates to a return of PLN 1.93 for every PLN 1 invested—a notably efficient outcome. The project transformed four industrial buildings, significantly increasing usable space in some (e.g., Building L1 by 345% and K1 by 21.6%) while slightly reducing it in others (B1 by 4.7% and I1 by 10.5%). From an environmental impact perspective, the success was staggering: 48,217 tons of carbon dioxide emissions were prevented, and 72,315 tons of building waste were diverted from landfills. To these figures, the study further adds a return in economic activity, the generation of new jobs, and improvement in local infrastructure. The retrofitting of historical buildings to contemporary standards has encountered numerous challenges; nonetheless, the implementation of circular economy principles has succeeded in negating such challenges. Generally, the results show economic, environmental, and social benefits of revitalization projects compared to new, greenfield projects. The case study provides valuable lessons to policymakers and urban planners, rendering adaptive reuse a fundamental approach in achieving sustainable urban development. Full article

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R² = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ²/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%. Full article

Electric bikes (e-bikes) offer a low-carbon, space-efficient solution for campus mobility, yet their sustainable potential is increasingly challenged by patterns of non-compliant use, including speeding, informal parking, and unauthorized charging. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to examine the cognitive and contextual factors that shape such behaviors among university students. Drawing on a survey of 408 e-bike users and structural equation modeling, the results show that non-compliance is primarily driven by perceived usefulness, ease of action, and behavioral feasibility, with affective and normative factors playing indirect, reinforcing roles. Importantly, actual behavior is influenced not only by intention but also by students’ perceived capacity to act within low-enforcement environments. These findings highlight the need to align behavioral perceptions with sustainability goals. The study contributes to sustainable mobility governance by clarifying key psychological pathways and offering targeted insights for designing perception-sensitive interventions in campus transport systems. Furthermore, by promoting compliance-oriented campus mobility, this research highlights a pathway toward enhancing the resilience of transport systems through behavioral adaptation within semi-regulated environments. Full article

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

Rapid urbanization and demographic shifts present significant challenges to spatial justice in green space provision. Traditional static assessments have become increasingly inadequate for guiding park planning, which now requires a dynamic, future-oriented analytical approach. To address this gap, this study incorporates population dynamics into urban park planning by developing a dynamic evaluation framework for park accessibility. Building on the Gaussian-based two-step floating catchment area (Ga2SFCA) method, we propose the human-population-projection-Ga2SFCA (HPP-Ga2SFCA) model, which integrates population forecasts to assess park service efficiency under future demographic pressures. Using neighborhood-committee-level census data from 2000 to 2020 and detailed park spatial data, we identified five types of population change and forecast demographic distributions for both short- and long-term scenarios. Our findings indicate population decline in the urban core and outer suburbs, with growth concentrated in the transitional inner-suburban zones. Long-term projections suggest that 66% of communities will experience population growth, whereas short-term forecasts indicate a decline in 52%. Static models overestimate park accessibility by approximately 40%. In contrast, our dynamic model reveals that accessibility is overestimated in 71% and underestimated in 7% of the city, highlighting a potential mismatch between future population demand and current park supply. This study offers a forward-looking planning framework that enhances the responsiveness of park systems to demographic change and supports the development of more equitable, adaptive green space strategies. Full article

Urban parks play an essential role in mitigating the urban heat island (UHI) effect driven by urbanization. A rigorous understanding of the cooling effects of urban parks can support urban planning efforts aimed at mitigating the UHI effect and enhancing urban sustainability. However, previous research has primarily focused on the maximum cooling impact, often overlooking the accumulative effects arising from spatial continuity. The present study fills this gap by investigating 74 urban parks located in the central area of Jinan and constructing a comprehensive cooling evaluation framework through two dimensions: maximum impact (Park Cooling Area, PCA; Park Cooling Efficiency, PCE) and cumulative impact (Park Cooling Intensity, PCI; Park Cooling Gradient, PCG). We further systematically examined the influence of park attributes and the surrounding urban structures on these metrics. The findings indicate that urban parks, as a whole, significantly contribute to lowering the ambient temperatures in their vicinity: 62.3% are located in surface temperature cold spots, reducing ambient temperatures by up to 7.77 °C. However, cooling intensity, range, and efficiency vary significantly across parks, with an average PCI of 0.0280, PCG of 0.99 °C, PCA of 46.00 ha, and PCE of 5.34. For maximum impact, PCA is jointly determined by park area, boundary length, and shape complexity, while smaller parks generally exhibit higher PCE—reflecting diminished cooling efficiency at excessive scales. For cumulative impact, building density and spatial enclosure degree surrounding parks critically regulate PCI and PCG by influencing cool-air aggregation and diffusion. Based on these findings, this study classified urban parks according to their cooling characteristics, clarified the functional differences among different park types, and proposed targeted recommendations. Full article