Auctions play a vital role in modern commerce by offering a transparent, structured, and competitive method for trading goods, services, and data. However, traditional in-person auctions are limited in terms of accessibility, convenience, security, and efficiency. However existing online auction platforms, while addressing some of these limitations, still face challenges such as limited transparency, centralized control, and insufficient security and privacy protections. Moreover these issues become extremely critical in case of sensitive applications like healthcare, defense and finance. To address these challenges, this article proposes a three-phase trading framework. In the first phase, data generation, anonymization, and storage are performed. In the second phase, an ensemble learning-based price forecasting approach is employed to estimate the asking and bidding prices, which depend on the volume and type of data. Finally, in the third phase, a Monte Carlo-inspired auction-based Non-Fungible Token (NFT) trading mechanism (MCiANT) is incorporated to enable efficient trading between buyers and sellers. The efficacy of the proposed MCiANT framework is compared with three distinct auction algorithms: the Vickrey auction, the Markov-Inspired Stationary Distribution Auction (MISD), and the Two-Phase English–Dutch Hybrid Auction (TPEDHA). The results demonstrate that the MCiANT framework significantly outperforms the others, achieving success-rate improvements of 5%, 5%, and 1% over the Vickrey, MISD, and TPEDHA auctions, respectively. Furthermore, the proposed framework is evaluated using health data by measuring anonymization time, encryption time, InterPlanetary File System (IPFS) upload time, and I/O performance.
As the climate crisis intensifies and urban populations swell, megacities face compounding threats from carbon emissions, urban heat islands (UHIs), and ecosystem degradation. While nature-based solutions (NbS) offer a promising response through ecological restoration and carbon sequestration, current NbS deployments are often fragmented, non-adaptive, and lack quantitative optimization. This research presents a cutting-edge, artificial intelligence (AI)-driven architecture that operationalizes NbS through a scalable, data-intensive framework. It integrates deep learning (DL) for satellite-derived land classification, graph neural networks (GNNs) for spatial co-benefit mapping, and reinforcement learning (RL) with dynamic reward weighting to optimize intervention strategies in real time. Life cycle assessment (LCA) and ecosystem service valuation modules are embedded to ensure holistic, cross-sectoral impacts. The architecture is deployed in a high-resolution case study of Dhaka, Bangladesh, a climate-vulnerable megacity, achieving over 8,500 metric tons of modeled annual carbon sequestration, 2.1°C reduction in UHI intensity, and quantifiable gains in urban biodiversity and flood mitigation. The system ingests multi-source data, including Sentinel-2, LiDAR, and CMIP6 climate projections, while leveraging federated learning to ensure decentralized, privacy-preserving optimization across municipal zones. A carbon market compatibility layer, aligned with Verra, UN-REDD+, and Article 6 frameworks, enables eligibility for climate finance and offsets. The approach also integrates social equity metrics and indigenous ecological knowledge to prioritize interventions in marginalized zones. This work delivers a first-of-its-kind decision-support platform for AI-optimized NbS that is globally scalable, policy-aligned, and climate-finance ready. It represents a paradigm shift from heuristic-based planning to algorithmically adaptive ecosystem engineering, accelerating progress toward net-zero emissions, SDG convergence, and resilient urban futures. The framework is poised to inform urban sustainability strategies worldwide, offering a replicable model for AI-governed environmental transformation in the age of planetary emergency.
The release of the Fourth Policy Document on Spatial Planning in 1988 was the start of a new and highly dynamic age of spatial development in The Netherlands. The policy document itself embodied a major reorientation of the national spatial policy strategy. Development of the economy and infrastructure became the new goals of spatial policy, and thereby replaced the focus upon the public housing sector of the years before. The national airport Schiphol and the port of Rotterdam both expanded and became important focal points for the Dutch economy, new transport infrastructure including High Speed Railway was planned. In the cities, dilapidated districts were transformed into new urban residential areas and new suburban districts were built close to the cities. In the countryside many projects were started in order to transform agrarian land into ‘new nature’. On top of this, the Dutch spatial planning system itself faced a partial ‘regime shift’. Spatial development projects became more market-based instead of financed by public resources. But at the same time, the national government kept its central position in the planning system. Only fifteen years later, at the beginning of the new millennium, decentralization of spatial planning towards regional and local government became a major trend. This paper will focus upon the spatial transformation of the Netherlands during the 25 years after the release of the Fourth Policy Document on spatial planning. In order to assess the influence of the national spatial policy, I will give a brief review of the Fourth Policy Document. But the changes in the spatial policy strategy of the Fourth Policy Document did not came out of the blue. They were both result of and response to political and economic trends. Therefore, I will start with two major and interrelated trends: the urban crisis and globalization.
OBJECTIVE: To illustrate the ability of hierarchical Bayesian spatio-temporal models in capturing different geo-temporal structures in order to explain hospital risk variations using three different conditions: Percutaneous Coronary Intervention (PCI), Colectomy in Colorectal Cancer (CCC) and Chronic Obstructive Pulmonary Disease (COPD). RESEARCH DESIGN: This is an observational population-based spatio-temporal study, from 2002 to 2013, with a two-level geographical structure, Autonomous Communities (AC) and Health Care Areas (HA). SETTING: The Spanish National Health System, a quasi-federal structure with 17 regional governments (AC) with full responsibility in planning and financing, and 203 HA providing hospital and primary care to a defined population. METHODS: A poisson-log normal mixed model in the Bayesian framework was fitted using the INLA efficient estimation procedure. MEASURES: The spatio-temporal hospitalization relative risks, the evolution of their variation, and the relative contribution (fraction of variation) of each of the model components (AC, HA, year and interaction AC-year). RESULTS: Following PCI-CCC-CODP order, the three conditions show differences in the initial hospitalization rates (from 4 to 21 per 10,000 person-years) and in their trends (upward, inverted V shape, downward). Most of the risk variation is captured by phenomena occurring at the HA level (fraction variance: 51.6, 54.7 and 56.9%). At AC level, the risk of PCI hospitalization follow a heterogeneous ascending dynamic (interaction AC-year: 17.7%), whereas in COPD the AC role is more homogenous and important (37%). CONCLUSIONS: In a system where the decisions loci are differentiated, the spatio-temporal modeling allows to assess the dynamic relative role of different levels of decision and their influence on health outcomes.