Search Results (13,617)

Rapid growth in photovoltaic (PV) development has intensified competition between renewable energy expansion and land resources, highlighting the importance of integrating PV growth with spatial planning toward carbon neutrality. This study addresses the lack of township-level evidence by integrating spatio-temporal analysis, the Optimal Parameter Geodetector (OPGD), and the Patch-generating Land Use Simulation (PLUS) model to investigate the evolution, driving mechanisms, and future spatial patterns of PV development. Taking Xintai City as a case study, this research examines PV development from 2010 to 2022 and simulates future spatial patterns under natural development (ND) and carbon neutrality (CN) scenarios. Results show that PV development entered a rapid expansion stage after 2015, characterized by leapfrog growth and increasing spatial agglomeration. The centroid of PV patches shifted eastward, whereas the centroid of PV area migrated westward, indicating differentiated spatial evolution between the number and scale of PV facilities. PV development was jointly shaped by climatic conditions, socioeconomic factors, and land availability, with interactions among multiple factors substantially enhancing spatial heterogeneity. Scenario simulations revealed that PV development continued under both scenarios, while the CN scenario promoted more concentrated expansion in coal subsidence areas and inefficient industrial and mining lands, thereby helping alleviate potential conflicts with cultivated land protection. These findings highlight the importance of prioritizing low-conflict land resources and strengthening spatial coordination between renewable energy development and land management to support sustainable low-carbon development in resource-based cities. Full article

Geological conditions fundamentally constrain land-use suitability, terrain stability, and long-term sustainability, yet they remain underutilized in urban planning frameworks. This study presents a GIS-based geological suitability assessment model, tested on the City of Valjevo, Serbia, integrating lithological, structural, land use/land cover change and spatial data to guide urban development decisions. Seven lithological units—Quaternary deposits, lacustrine sediments, carbonate rocks, volcanic and pyroclastic rocks, ophiolites, ophiolitic mélanges, and Jadar Block sediments—were classified into four suitability classes based on lithological composition, structural characteristics, and rock mass behavior. An Integrated Geological Index (Igeo) was derived using an area-weighted approach across a regular hexagonal grid, enabling spatially explicit suitability mapping. Comparison with land use/land cover changes for 2012–2021 revealed a notable mismatch between geological suitability and observed development patterns: favorable and conditionally favorable terrains cover 72.5% of the study area but account for only 48.7% of recent urban expansion, while unfavorable terrains, occupying just 20.9% of the territory, absorbed 51.3% of new development. No expansion occurred within highly unfavorable terrains. These findings expose critical gaps in integrating geological criteria into planning practice and demonstrate a reproducible methodology for embedding geological suitability into sustainable urban development strategies across geologically heterogeneous regions. Full article

The Taklamakan Desert, the primary source of sand and dust in China, has a significant impact on PM_2.5 concentrations in the urban belt around the Tarim Basin. In this study, moderate-resolution imaging spectroradiometer (MODIS) images and machine learning algorithms were used to simulate PM_2.5 concentrations and analyze deaths from ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and stroke (STK) attributed to PM_2.5. From 2015 to 2024, the aerosol optical depth (AOD) showed a decreasing trend, and the spatial distribution pattern gradually increased from west to east. The counties and cities with higher average PM_2.5 concentrations during the 2024 dust-prone season were Awati County, Alar City, Moyu County, and Aksu City, with average concentrations of 89.7 μg/m³, 88.8 μg/m³, 86.0 μg/m³, and 84.1 μg/m³, respectively. PM_2.5 concentrations during the non-dust season were significantly lower (approximately 50%) than those during the dust-prone season. Among the four major fatal diseases, STK and IHD accounted for the majority of attributable deaths, with proportions exceeding 80% of the total. The results show that PM_2.5 concentrations can be accurately monitored over a large scale, addressing the shortage of fixed monitoring stations, and providing a theoretical basis for sustainable development of ecological environment, health-risk management and control in the urban belt around the Tarim Basin. Full article

Fine-scale site selection of urban parks serves as a core measure to guarantee spatial equity of public service spaces for residents and advance the sustainable development of urban ecological spaces. Among relevant tasks, balancing the supply and demand of green spaces stands out as an essential foundation for maintaining long-term stability of urban human well-being and ecosystems. Green-space supply is defined as the stock of various existing urban parks within the city, while green-space demand is quantified via grids generated based on residential communities in Changsha. Existing research on urban park site selection lacks a full-process coupled framework, fails to accommodate differentiated layout demands for multi-level parks, and struggles to reconcile the sustainable operation and long-term ecological empowerment of urban green-space systems. Taking the main urban area of Changsha as the research scope, this study divides the study area into grid units to analyze the spatial differentiation of green-space accessibility and identify service blind zones. The XGBoost model is adopted to predict areas suitable for green-space construction, and the NSGA-III algorithm is applied to realize collaborative multi-objective optimization covering service efficiency, ecological benefits, and land development costs. The results reveal that the 15-min walking coverage of community parks in central Changsha only reaches 57.29%. Respectively, 34.52% and 41.04% of residential communities record accessibility levels below the municipal average of urban parks and forest parks, with prominent shortages of green-space supply in peripheral urban areas. This study optimizes and screens twenty-eight candidate sites for community parks, twelve candidate sites for urban parks, and eight candidate sites for forest parks. The proposed scheme effectively narrows the gap in green-space accessibility across the whole city and coordinates ecological conservation with land development costs. Compared with research relying on a single model or two-stage coupling frameworks, this paper constructs a systematic workflow spanning supply–demand status assessment to multi-objective layout decision-making, enabling differentiated optimized layout of multi-tiered parks. The integrated framework effectively enhances the spatial resilience and resource utilization efficiency of urban green-space systems, facilitates high-quality and sustainable upgrading of urban living environments, and provides a referable innovative approach for multi-level urban park arrangement and refined multi-objective planning. Full article

This paper presents a resilience-driven probabilistic multiobjective framework for reactive power planning in islanded microgrids under extreme contingencies, explicitly integrating sustainability objectives and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed planning framework simultaneously optimizes technical reliability, economic viability, environmental sustainability, and social resilience using the IEEE 33-bus distribution system as a representative test network. Uncertainties associated with solar irradiance, wind speed, and load demand are modeled using the Two-Point Estimation Method (2PEM), while the Non-dominated Sorting Genetic Algorithm II (NSGA-II) determines Pareto optimal planning solutions for five reactive power support strategies. The results demonstrate that planning solutions optimized for grid-connected operation are not necessarily the most effective under islanded conditions. Within the adopted multi-criteria evaluation framework, the dedicated D-STATCOM strategy achieves the highest overall normalized performance, providing 87.2% load preservation, 93.7% critical-load protection, and an 8.7 h representative survival time, while reducing total load shedding to 12.8% and eliminating high-risk shedding events (>30%). Furthermore, it decreases event-related economic losses by more than 75% and achieves the lowest environmental impact, with a 62.5% reduction in life-cycle CO₂ emission intensity relative to the conventional grid baseline. A normalization sensitivity analysis confirms that the comparative ranking of the investigated strategies remains unchanged under alternative normalization methods, demonstrating the robustness of the proposed evaluation framework. From a sustainability perspective, the proposed framework contributes to SDG 7 (Affordable and Clean Energy) through reliable low-carbon microgrid operation, SDG 9 (Industry, Innovation and Infrastructure) through resilient power system planning, SDG 11 (Sustainable Cities and Communities) by enhancing the continuity of critical urban services, SDG 13 (Climate Action) through reduced life-cycle emissions, and SDG 8 (Decent Work and Economic Growth) by supporting local employment associated with distributed energy deployment. Full article

Accelerating the construction of an international, modern, and people-oriented city, Suzhou often faces bottlenecks in its peripheral communities. These open spaces suffer from rapid vitality decline, extensive governance, and weak resilience, which directly hinder residents’ daily interactions and community integration. Moving beyond subjective qualitative approaches, this study selects typical peripheral communities in Suzhou and employs a comprehensive methodology combining field surveys, space syntax analysis, and time-segmented spatial trajectory entropy measurement to quantitatively assess the “lack of vitality.” By quantifying spatial form, accessibility, and behavioral patterns, the study examines their coupling relationships with vitality, social interaction, and safety resilience. The results show that the spatial trajectory entropy model effectively captures the spatiotemporal movement patterns of crowds, addressing the gap in dynamic characterization left by traditional methods. Community vitality is significantly positively correlated with accessibility, interface continuity, and facility allocation. These spatial elements also play a crucial role in promoting social interaction and enhancing the sustainable use of spaces. Based on quantitative diagnosis, this paper proposes a governance framework of “digital empowerment–community co-governance–resilience enhancement,” transforming spatial weaknesses into targeted strategies for smart governance. This research provides quantitative analysis methods and localized implementation approaches for the refined governance and sustainable development of communities on the outskirts of cities like Suzhou. Full article

Connected, automated, shared, and electric (CASE) vehicle concepts are considered a promising approach for improving the sustainability of urban mobility by increasing vehicle utilization and reducing fleet size. While the energy consumption of conventional battery electric vehicles has been investigated extensively, the influence of fleet size, speed limits, and ambient temperature on the energy demand of CASE vehicle fleets has received little attention. This study presents a simulative consumption analysis of an all-electric CASE vehicle fleet based on the EDAG CityBot concept. A validated microscopic traffic simulation of the city center of Darmstadt, Germany, is coupled with a backward-facing powertrain model and detailed secondary consumer models to determine the total fleet energy consumption under varying operating conditions. The analysis considers fleet sizes between 20% and 100% of a reference fleet, together with a 17% fleet size scenario, which allows for the fulfillment of the urban mobility demand according to the vehicle system provider. Besides fleet size, three urban speed limit scenarios and five ambient temperature scenarios are evaluated. Among the investigated fleet size scenarios, the lowest mean fleet energy demand is observed at a fleet size of 20%, resulting from the opposing effects of increasing driving energy consumption and decreasing secondary consumer energy consumption. However, the difference between the 20% and 17% scenarios is not statistically significant. Furthermore, the study demonstrates that secondary consumers, particularly automated driving hardware and heating, ventilation and air conditioning systems, represent a major contribution to the total energy consumption of CASE vehicles and must therefore be considered in fleet-level energy analyses. Although an individual CASE vehicle exhibits higher average energy consumption than a conventional battery-electric vehicle, primarily due to its greater average weight and rolling resistance, an increase in utilization of more than 16% would be sufficient to offset this disadvantage. Full article

Generative artificial intelligence (AI) now allows a single founder, working with a cluster of AI agents as “digital employees,” to run a venture that once required a team. We call this form the one-person company (OPC) and treat it as a human–AI socio-technical system with a “1 + N + AI” architecture, whose viability depends on the density of the surrounding AI ecosystem. Anchored in a systematic review of 2452 studies reported under PRISMA 2020, we build a task-based model in which a founder allocates tasks across her own labor, hired labor, and AI agents; once the local AI ecosystem density crosses a threshold, one person can cover the whole value chain. The model yields three propositions on the level, heterogeneity, and cost channel of OPC entry, which we test on a panel of 35 major Chinese cities (2019–2024). A one-percent increase in a city’s AI enterprise stock raises OPC entry by about 1.06 percent, an estimate robust to a Bartik shift-share instrument; the effect concentrates in initially AI-sparse, ordinary, and central–western cities and strengthens with the tertiary-sector share, as the threshold model predicts. Because OPCs are asset-light and create knowledge-intensive work, AI ecosystem building emerges as a lever for inclusive, sustainable entrepreneurship. Full article

Rapid urban development has intensified conflicts between land development and ecological conservation, making spatially explicit land-use planning increasingly important for resource-depleted cities. This study develops a geoinformation-based decision-support framework for Huangshi, China, by integrating multi-scenario land-use modeling, production–living–ecological space analysis, landscape pattern assessment, and SDG 15 diagnostics. Four 2035 policy-oriented scenarios were compared: Business-as-Usual (BAU), Ecological Restoration Priority (ERP), Economic Development Priority (EDP), and Sustainable Development (SD). The results show that ERP delivers the strongest ecological performance, with ecological space reaching 46.47%, forest cover increasing from 35.40% to 36.80%, water area rising to 9.65%, net land degradation declining to −2.04%, and mean habitat quality reaching 0.484. SD provides a more balanced pathway, with ecological space of 44.80%, living space of 8.93%, a land-use stability rate of 96.36%, and a relatively low net degradation rate of 1.33%. BAU and EDP show higher ecological risks. The framework demonstrates how multi-source geospatial data and spatially explicit SDG diagnostics can support adaptive planning in resource-depleted cities. Full article

Riparian landscapes in peri-urban floodplains are important components of urban green infrastructure, but urbanization is eroding their capacity to support ecosystem services. This study examined the implications of riparian landscape change for ecosystem service capacity and developed spatially explicit recommendations to inform land use planning in Shanghai’s peri-urban fringe. A multi-scale landscape metric-based proxy assessment was applied to characterize changes in landscape composition and configuration at the regional and riparian buffer scales. From 2000 to 2020, the study area underwent pronounced land use change: the dominant land use type shifted from agricultural land (59.16%) to built-up land (67.69%), while 28% of water area and 57% of vegetated land were converted to other uses. Landscape metrics indicated a systemic shift toward fragmentation, characterized by higher patch density, smaller mean patch size, lower largest-patch and Contagion Indices, and simpler patch geometries. Changes within riparian buffers further indicated an overall decline in the potential capacity to provide provisioning, regulating, and cultural ecosystem services. By integrating landscape composition and configuration across spatial scales, the proposed framework links regional land use control with site-scale riparian design and provides an adaptable basis for sustaining ecosystem services in rapidly urbanizing floodplain and deltaic cities. Full article

Artificial Intelligence-driven detection mechanisms present both opportunities and challenges in modern systems, particularly within smart cities that rely on complex operational technologies. In the context of Saudi urban infrastructure, rapidly evolving and multidimensional cyber threats require advanced, energy-efficient security solutions. This research proposes an Artificial Intelligence-based Threat Detection Mechanism designed to proactively identify and mitigate cyber threats while maximizing energy efficiency and minimizing cost and system complexity. Purpose: The proposed theoretical framework focuses on securing sustainable smart cities by integrating Artificial Intelligence-based anomaly detection with quantum-enhanced algorithms to address high-dimensional and emerging cyber threats across interconnected urban systems. The Artificial Intelligence-based Threat Detection Mechanism enables early and proactive threat detection across sustainable smart city networks, including connections to external and global infrastructures, ensuring continuous monitoring, resilience, and service continuity. Methods: The methodology emphasizes the development of energy-efficient Artificial Intelligence models and quantum protocols, incorporating intelligent risk assessment, adaptive calibration, and automated response mechanisms. In addition, the framework introduces distributed security hubs to enhance cybersecurity robustness and scalability. Anticipated Results and Conclusions: Anticipated outcomes include improved security management policies, automated threat detection and response, and adaptive protection against evolving cyber risks. The proposed framework provides a scalable and cost-effective solution aligned with sustainability objectives. Ultimately, this research contributes a proactive and intelligent framework for securing smart city ecosystems, supporting long-term development goals and aligning with Saudi Vision 2030. Full article

The transport sector contributes nearly 20% of New Zealand’s total greenhouse gas emissions, making it crucial for interventions to meet the 2050 net-zero target. Transitioning to electric vehicles (EVs) presents a sustainable solution but poses challenges in electricity distribution due to unpredictable EV charging behaviours. This research addresses these challenges by developing three mathematical models that optimise EV charging patterns, manage power flow along distribution lines and incorporate battery storage systems. Using the Tāmaki area as a case study, the models analyse total energy demand and optimal battery storage size, revealing that a 3.49 MWh battery system could mitigate the projected 2040 peak daily grid energy demand of 541.5 MWh and avoid costly power line upgrades. The study also introduces a vehicle-to-grid (V2G) integration model, showcasing its potential to reduce grid dependence and improve energy utilisation. The findings provide critical insights for Auckland’s electricity distribution companies, supporting strategic asset upgrades and offering evidence-based guidance for government policies on EV adoption. In summary, this research provides innovative solutions for optimising EV charging infrastructure, benefiting utility companies and policymakers by informing data-driven decisions. The comprehensive approach, which includes power flow, battery storage, and V2G technology, presents a scalable framework for international cities facing similar challenges, promoting global sustainable transport solutions towards achieving international climate targets and sustainable urban development. Full article

Pluvial flooding is intensifying in fast-growing coastal cities as rapid urbanization increases impervious surfaces and reduces natural infiltration, challenging planners to identify scalable and site-level mitigation strategies. Although statutory setbacks around institutional buildings are widely mandated for ventilation, safety, and accessibility, their contribution to urban flood resilience remains largely unexplored. This study develops the Flood Setback Performance Index (FSPI), a dimensionless indicator that integrates setback permeability and area-weighted runoff coefficients to quantify the flood-mitigation potential of mandatory setbacks. The framework was demonstrated using eight government school campuses in the Chennai, Chengalpattu, and Thiruvallur regions, which are part of a flood-prone coastal metropolis in India, following the extreme rainfall event of December 2025. Flood conditions were verified through post-event field surveys and visual evidence, while setback characteristics were extracted from satellite imagery and classified into permeable and impervious surfaces. The permeable fraction of setbacks and the FSPI values exhibited a strong inverse relationship with flood severity (Spearman’s ρ = −0.87, exact permutation p = 0.011). Ordinal logistic regression confirmed this gradient (likelihood-ratio χ² = 9.3–10.3, p ≤ 0.002, McFadden’s R² = 0.54–0.60), and campus rankings were made under saturated runoff coefficients. The findings demonstrate that statutory setbacks can function as nature-based flood-mitigation infrastructure with measurable hydrological benefits. Beyond the case study, the FSPI provides a simple, transferable, evidence-based tool for screening institutional sites by flood-mitigation performance and prioritizing where adaptation measures, such as increasing permeable-surface cover within statutory setbacks are needed, supporting climate-resilient urban planning and providing a regulatory reform decision-support tool with three applications: prioritizing the campus-level retrofit of permeable surfaces; screening building applications against setback surface standards; and identifying shelter-designated schools that require drainage upgrades. It thereby supports climate-resilient urban planning, regulatory reform, and progress toward Sustainable Development Goals 11 and 13. Full article

Green Production and Lifestyles (GPL) are essential for advancing green development and sustainable development. However, limited attention has been paid to their integrated transformation and the combined effects of social governance-oriented and environmental technology-oriented policies. This study examines the effects of the Dual Pilot Policy (DP) of the National Civilized City Program and the Low-Carbon City Pilot Policy on urban GPL transition. Using balanced panel data from 276 prefecture-level cities in China during 2002–2023, we employ a staggered difference-in-differences (DID) model. The results show that the DP significantly increases the GPL index by 0.0367 units, equivalent to a 3.67% improvement on a 0–1 scale. The DP generates a stronger additional combined effect than individual policies, while “Civilized City first, followed by Low-Carbon City” produces a stronger effect. The effects are more pronounced in non-resource-based cities, non-old industrial base cities, cities outside the Yangtze River Basin, and coastal cities. Mediation analysis identifies government support, industrial structure upgrading, and public GPL awareness as important channels, while significant spatial spillover effects are also observed. These findings provide empirical evidence for optimizing policy combinations. Full article

Sustainable urban development is increasingly shaped by the joint evolution of social development, ecological conditions, and technological capacity. Understanding how these dimensions are associated with green economic efficiency (GEE) is therefore important for advancing urban green transition. This study examines GEE from a social–ecological–technological (S–E–T) systems perspective and incorporates spatial dependence into the empirical framework. Using panel data for 281 prefecture-level cities in China from 2011 to 2022, this study measures GEE with a Super-SBM model, characterizes its spatiotemporal evolution, and estimates a two-way fixed-effects spatial Durbin model to distinguish local and cross-city associations. The results show that: (1) urban GEE improved overall, while maintaining a clear southeast-high and northwest-low spatial gradient; (2) regional disparities widened over time, and efficiency transitions exhibited strong path dependence and spatial neighborhood effects; (3) the spatial Durbin model is preferred over the SAR and SEM alternatives, indicating that both local conditions and geographically connected cities should be considered; (4) green patenting shows the most stable positive association with GEE, both locally and across connected cities; and (5) population density and ecological conditions display differentiated local and cross-city associations. These findings show that combining the S–E–T framework with spatial panel analysis provides a systematic approach for understanding urban GEE and offers evidence for coordinated green development policies across connected cities. Full article