Urban facilities management: A systemic process for achieving urban sustainability
Bibliographic record
Abstract
With cities at the forefront of the challenge of achieving global sustainability, a key concern for urban management bodies is to identify ways to promote sustainable development at the urban and sub-urban level. Assessment mechanisms have dominated this field for the past two decades, and sustainable community assessment tools (SCATs) are fast becoming the principal framework adopted by urban planners and developers to drive sustainability. This paper investigates the efficacy and applicability of this approach to urban sustainability. The study aims to establish that the deployment of a management platform, founded in the principles of facilities management (FM), can provide better mechanisms to facilitate the process of achieving urban sustainability. The data were collected by means of survey interviews with key stakeholders who consisted of two main categories: participants from private and public sector engaged in the management and development of sustainable cities. Secondly, developers of the tools were interviewed. The tools evaluated include: LEED for Neighbourhood Development (LEED-ND), BREEAM Communities (BREEAM-C), CASBEE for Urban Development (CASBEE-UD), and Green Star Communities (GSC). It was found that the prescriptive and outcomes-based nature of assessment tools do not adequately accommodate institutional and social imperatives of urban sustainability. Additionally, a need for more a robust procedural framework to manage relationships between the various relevant professionals and interest groups was highlighted. This would provide a unified method to facilitate the achievement of urban sustainability. The paper concludes that urban sustainability needs to draw upon the management principles of FM to facilitate more comprehensive development and assessment relevant to the needs of a specific locale. Without a process-oriented method such as this, cities will continue to fall short of their sustainable imperatives.
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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 | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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".