Towards inclusive and sustainable strategies in smart cities: A comparative analysis of Zurich, Oslo, and Copenhagen
Bibliographic record
Abstract
Urbanisation has increased the need for sustainable urban development by positioning smart cities as critical frameworks for addressing environmental, economic, and social challenges. This study evaluates the sustainability strategies of three leading smart cities Zurich, Oslo, and Copenhagen by examining their environmental footprint, energy consumption, waste management, and air quality. The study uses a PRISMA-based systematic literature review to put together evidence from peer-reviewed articles published between 2017 and 2024. The Critical Appraisal Skills Program (CASP) was used to assess the quality of the articles. The study reveals that smart mobility and waste-to-energy systems drive Zurich’s strengths in urban densification and public transportation, Oslo’s leadership in renewable energy and electric mobility, and Copenhagen’s ambitious carbon–neutral initiatives. Despite these achievements, challenges such as high implementation costs, slow technological adoption, and social equity issues persist, emphasising the complexity of achieving inclusive and sustainable urban evolution. To address these challenges, this study recommends increasing public participation through inclusive urban planning and digital platforms, strengthening policy frameworks, and funding for sustainability projects, and investing in data collection technologies to monitor real-time environmental impacts. Furthermore, fostering cross-city collaboration and addressing energy consumption challenges associated with AI and IoT are essential for scaling successful models globally. These insights offer actionable guidance for policymakers and urban planners to improve sustainability strategies and ensure long-term benefits.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".