Smart Cities and Sustainability: A Set of Vertical Solutions for Managing Resources
Bibliographic record
Abstract
The Smart City vision can be viewed as a “system of systems”, where all systems within it are interconnected, in constant communication with each other in real time, exchanging information, and making smart decisions all in a sustainable and highly efficient model. Two decades ago, the Smart City concept was born to address emerging city sustainability issues and was mainly focused on energy efficiency and greenhouse gas emissions reduction. More recently the term was attached to the role of ICT infrastructure. This paper aims to clarify interrelations between the Smart City concept and fostering the sustainable development of cities. The paper is based on an analytical study of the main characteristics and systems of a Smart City, emphasizing the significant role of Future Internet in the development of Smart Cities. The first section is a short introduction to challenges and drivers for a Smart City. Sections two and three discuss the technological context of Future Internet and the expected impact of Internet-of-Things, sensors, tags, and cloud computing on Smart Cities. The next two sections analyze the main Smart City Systems and approaches for managing them. Moreover, sections six and seven analyze two of the top performing Smart Cities in Europe and also address the UAE 2021 Vision in order to assert the environmental impacts that occur as a result of transforming into a Smart City. This paper concludes with a common framework for transforming cities into smart ones, which depends on the nature, circumstances, and resources of each city.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".