Urban experience is shaped not only by form and function but by the body’s continuous negotiation with its surroundings. Drawing on recent research in neuroscience and environmental psychology, this paper argues that open public spaces—plazas, sidewalks, shared streets, and civic thresholds—operate as multisensory environments that directly influence stress regulation, attention, emotional safety, and social connection. Extending neuroarchitecture to the scale of the city, the study examines how light, sound, shade, texture, rhythm, enclosure, and biophilic presence modulate the nervous system and contribute to urban well-being.

Using a mixed methodology that includes literature review, phenomenological observation, perceptual mapping, and design-research case studies, the paper analyzes how small-scale atmospheric cues support or hinder perceptual ease. Contemporary works by Petra Blaisse, Kazuyo Sejima, and Frida Escobedo demonstrate how softness, shadow, porosity, and movement can generate environments of perceptual clarity and emotional grounding. Four practice-based design projects—the Living Chair, Urban Biombo, the UPM Library threshold, and the Double City neighborhood—further show how neuroarchitectural principles translate across scales, from furniture to the urban fabric.

The paper proposes a vocabulary for neuro-urban design—including Urban Salons, Sensory Thresholds, Biophilic Patches, Microzones of Illumination, Acoustic Refuges, Human-Sound Zones, Sensory Corridors, and Domestic Urbanity—to articulate how atmospheric conditions shape inclusive and emotionally supportive urban spaces. By foregrounding the unity of body and mind, the research reframes urban design as an act of care, emphasizing that well-being emerges not only from infrastructure but from the perceptual and emotional qualities that make everyday environments habitable.

As housing affordability deteriorates and housing instability increasingly manifests as a public health crisis, governments worldwide are searching for governance models capable of delivering deeply affordable housing at scale while improving social outcomes. In Canada, the emerging Public–Private–Philanthropic–Provider (P⁴) model represents a significant institutional innovation: a collaborative governance framework that aligns government, private-sector expertise, philanthropic capital, and nonprofit housing providers to produce supportive housing designed for long-term stability, health, and social inclusion.

Despite extensive scholarship on public–private partnerships, little research has examined governance models in which philanthropic actors and nonprofit service providers function as co-equal institutional partners shaping both housing delivery and the built environment. This paper addresses that gap by evaluating the P⁴ model through a mixed-methods analysis combining policy review, financial and operational data, tenancy outcomes, and architectural program assessment.

The study focuses on Calgary’s Resolve Campaign and the stewardship model of HomeSpace Society, where more than 1,850 units of permanent supportive housing have been delivered and over $120 million in cross-sector capital mobilized. Reported outcomes include tenancy retention rates exceeding 85 percent at twenty-four months, a 40 percent reduction in emergency-room utilization, and a 54 percent decline in police interactions among residents. Spatial analysis further demonstrates that supportive housing within the P⁴ system incorporates integrated service environments—including on-site clinical rooms, communal kitchens, counseling offices, and adaptable community spaces—illustrating how governance alignment directly informs architectural form.

By defining the “Provider” as nonprofit housing organizations that combine property stewardship with embedded social services, the P⁴ model clarifies the relationship between institutional design and spatial design. Comparative reflection on more fragmented public–private partnership systems highlights P⁴’s distinctive capacity to produce stable, health-supportive housing ecosystems. The paper argues that P⁴ reframes architecture as civic infrastructure—where governance, design, and care converge—offering a transferable framework for cities seeking durable, equity-centered housing solutions.

Solar photovoltaic (PV) generation now lies at the heart of the most decarbonization plans. Policy and decision makers need more than energy forecasts, like clear estimates of the annual economic value of each plant in particular market context. This study examined a harmonized solar-to-grid dataset from the U.S. Department of Energy for distributed photovoltaic systems. The study applies machine learning methods to model and interpret the economic value of distributed photovoltaic systems using two value-based performance metrics: Energy_Value_MWh and Total_Value_MWh. By framing PV systems as socio-technical infrastructure, the analysis integrates physical generation characteristics with temporal and regional market context. Three regression models: Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost) are evaluated using operational, temporal, and spatial features, including annual generation, installed capacity, year, balancing authority, and geographic identifiers. Model performance is assessed through coefficient of determination (R²), root mean square error (RMSE) and predicted-versus-actual visual analysis.

Results indicate that ensemble tree-based models substantially outperform SVR across both targets. Random Forest achieves the strongest overall performance, with R² values exceeding 0.88 and consistently lower prediction errors, while XGBoost demonstrates comparable accuracy with slightly higher dispersion at extreme values. Feature importance analysis reveals that temporal evolution and regional grid structures dominate PV economic outcomes, whereas physical generation alone plays a secondary role. These findings highlight the importance of contextual and system-level factors in PV valuation and demonstrate the potential of machine learning as a planning-oriented tool for evaluating renewable energy performance within architectural and urban energy systems. The proposed framework supports more informed decision-making in PV design, deployment, and policy evaluation across diverse built environments.