Integrating Solar Energy in Urban Development: Strategies for Sustainable and Smart Cities
Bibliographic record
Abstract
The increasing pace of urbanization has intensified the global demand for clean and decentralized energy systems, placing solar energy at the forefront of sustainable urban transitions. While prior studies have separately explored photovoltaic (PV) technologies, urban form, or energy policy frameworks, few have synthesized these dimensions into an integrated roadmap for solar adoption in smart cities. This study addresses that gap by introducing the policy–technology–morphology nexus (PTMN), a novel conceptual model developed through the cross‐analysis of 120 peer‐reviewed studies and urban case implementations. The PTMN framework unifies three essential pillars: policy instruments (e.g., feed‐in tariffs, net metering), enabling technologies (e.g., AI‐based solar mapping, smart grids, battery optimization), and urban morphological variables (e.g., building density, orientation, and shading).Through comparative tables and geospatial insights, the review reveals that morphology‐sensitive design, when coupled with intelligent technologies and regulatory incentives, can enhance solar efficiency by up to 40% in selected cities such as Geneva, Stonehaven, and Shenzhen. Methodologically, the study integrates GIS‐based assessments, deep learning approaches, and system‐level classification typologies to map deployment scales, performance gaps, and policy relevance. Findings highlight the critical role of digital twins and smart storage integration in enabling equitable and scalable solar transitions. Limitations include the reliance on location‐specific data and the absence of multicity dynamic simulations. Future research should focus on enhancing AI‐driven predictive modeling for solar energy optimization, developing novel energy storage technologies, and fostering interdisciplinary collaborations among policymakers, engineers, and urban planners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".