Ecosystem services linked to nature-based solutions for resilient and sustainable cities in India
Bibliographic record
Abstract
Rampant urbanization and undervaluing of the natural ecosystem have detrimental impacts on urban spaces – increased flooding risk, increased air and water pollution, water stress, resource inefficiency, loss of biodiversity, and increased risk of ill health. Climate change further exacerbates the adverse impacts of urbanization. Despite the importance of the natural ecosystem, the blue and green spaces of the cities in India have drastically decreased. The present study highlights the degrading natural ecosystem, the negative impacts, and the need for resilience in Indian cities. Eco-centric approaches like nature-based solutions (NBS) are closely related to sustainability and resilience, offering a more efficient and cost-effective approach to urban development than traditional approaches. The paper explores the concept of NBS, focusing on ecosystem services as a ‘living’ and ‘adaptable’ tool to make cities resilient and sustainable with many regional implementations. It also focuses on the role of NBS in achieving the United Nations’ Sustainable Development Goals (SDGs). The paper critically analyses the five notable NBS projects from different countries (USA, Canada, The Netherlands, China, and Australia) and further addresses the viabilities for NBS intervention in Indian cities. It is observed that the successful adaptation of NBS in urban development necessitates eco-centric policies, collaborative research, adaptive management practices, community engagement, and a strong emphasis on a multi-benefit approach. A proactive focus on ecosystem services is strongly recommended for Indian cities, which includes raising an understanding of the value of nature, introducing NBS at the planning stage, and encouraging investment in ecosystem-based approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".