Mixed Land Use in Delhi: Impact on Infrastructure and Environment and Suggestions for Sustainable Planning
Bibliographic record
Abstract
Mixed-use has been a part of our cities since historic times where retail, workshop, etc. all co-existed with the residence. Industrialization and associated adverse impacts led to overtaking of mixed-use concept by zoning. However, the ills of segregation started showing in the cities, and urban planners, sociologists and anthropologists started advocating in favor of re-introducing mixed-use. Now it is being planned worldwide to attain vibrant and cohesive urban development. Despite the well-established benefits of mixed-use, the Master Plan of Delhi’s approach of increasing the ambit of ‘permitting’ mixed-use as against the strategy of ‘planned mixed-use development’ adopted in other global cities makes us question the likely impacts of this liberal shift. Thus the study aims at investigating the environmental implications of such mixed-use development in the city of Delhi. From the assessment of two case study areas, it can be inferred that the nature and magnitude of impact on a mixed-use area is determined by the type of mix and its intensity, for example, in Lajpat Nagar numerous retail establishment led to vehicle-related issues while in Naraina the issues were infrastructure-related due to type of activities. It was recommended that the policies guiding the mixed-use development are in dire need of a mechanism to assess the impacts, identify demand-supply gaps and future needs, and thereafter augment accordingly to mitigate the implications in a case-specific manner.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".