Ecosystem services linked to nature-based solutions for resilient and sustainable cities in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 |
| 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 it