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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".