A critical analysis of the 100 Smart Cities Mission (2015-2020): implications for urban governance and planning in India
Bibliographic record
Abstract
The 100 Smart Cities Mission (SCM), India's ongoing national urban renewal program, was launched in 2015.The Mission continues to face major funding and implementation challenges with only a meagre 18% of total projects standing completed as of 2020.Yet even this dismal rate of progress has not deterred the Government from announcing plans to renew the Mission, soon aiming to cover 4,000 cities and towns across India.At this critical juncture, it becomes important to critically re-examine the motivations furthering the 'smart city' obsession in India.This is even more urgent given that 'smart' policy goals are being pursued in a context where the basics-with regard to infrastructure provision, governance and institutional capacities are themselves seriously flawed.Cities that are nearly non-functional nurture 'smart' aspirations.The 100 SCM in India therefore stands on a weak regulatory edifice.The task at hand is to look beneath the optics and understand the fundamental questions-what really is the 'Smart City'?Why does it sell?And who really is the 'Smart City' for?This SRP explores these questions through a critical analysis of policy documents published by relevant Government ministries at the national and city levels respectively.The first part highlights the historic challenges in urban governance and planning; and the violence of urban living in India.The second part discusses the objectives and implementation of the 100 SCM along with its possible outcomes through a specific 'smart' road project in the city of Pune.In conclusion, the study establishes the 100 SCM as perpetuating and intensifying the long-standing culture of technocratic, exclusionary and privatized governance and planning in India.The optics of technology and competition under the 100 SCM lend an illusion of 'neutrality' that further depoliticizes fundamental challenges in local capacity building, autonomy and participation. RésuméLa 100 Smart Cities Mission (SCM), le programme national de rénovation urbaine en cours en Inde, a été lancée en 2015.La mission est encore confrontée à des défis majeurs de financement et de mise en œuvre, avec seulement 18 % du total des projets achevés en 2020.Pourtant, même ce taux de progression lamentable n'a pas dissuadé le gouvernement d'annoncer des plans de renouvellement de la Mission, dont l'objectif est de couvrir bientôt 4 000 villes et agglomérations à travers l'Inde.À ce moment critique, il devient important de réexaminer de manière critique les motivations qui poussent à l'obsession de la "ville intelligente" en Inde.C'est d'autant plus urgent que les objectifs politiques "intelligents" sont poursuivis dans un contexte où les éléments de base -en ce qui concerne la fourniture d'infrastructures, la gouvernance et les capacités institutionnelles -sont eux-mêmes sérieusement déficients.Les villes qui sont presque non fonctionnelles nourrissent des aspirations "intelligentes".Les 100 MCS en Inde reposent donc sur un édifice réglementaire faible.La tâche à accomplir consiste à regarder sous l'optique et à comprendre les questions fondamentales : qu'est-ce que la "ville intelligente" ?Pourquoi se vend-elle ?Et à qui s'adresse réellement la "ville intelligente"?This research has been possible because of the unending support, care and patience of my supervisor, Professor Madhav Badami.I am very grateful for his guidance, detailed suggestions and constant encouragement.Professor Badami also introduced me to Mr. Avinash Madhale, Program Officer at the Centre for Environment Education, Pune.His nuanced, on the ground insights into the history of sustainable transportation projects in Pune has been an integral part of this study.My sincere thanks to him.A moment also to express my deepest
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".