Comparative analysis of local adaptation processes in the future across peri-urban India to support transformations to sustainability
Bibliographic record
Abstract
Peri-urban transformations in emerging economies like India demand scientific attention given their impact on global environmental change processes. Some studies examine past or ongoing peri-urban adaptation processes, but insight into future adaptation needs and aspirations of peri-urban communities is lacking. Also, it is unknown how the high degree of informality that characterizes peri-urban areas, interacts with formal institutions to shape or enable more sustainable adaptation pathways. This study addresses these scientific gaps, using an existing typology of adaptation processes to investigate plausible future adaptation pathways in three peri-urban villages in India, near Pune, Hyderabad, and Kolkata cities. On-site field research followed by a Delphi-study were used to develop normative adaptation pathways for livelihood and household water use with local actors. The pathways represent development trajectories and adaptation strategies over the next 15 years in the livelihood and household water sectors. Pathways data was thereafter analyzed and compared in terms of drivers of vulnerability and opportunity, adaptation processes, and formal and informal institutions. Our ex-ante study identifies general and context specific drivers of vulnerability and opportunity shaping different peri-urban transformations. Results reveal similarities in future drivers, whose impact on peri-urban livelihoods and household water is context dependent. This comparative analysis contributes a deeper understanding of future adaptation needs by highlighting patterns in locally preferred adaptation processes for different drivers and water-use sectors. This normative understanding reveals preferences of local communities who are otherwise marginalized from decision-making arenas. A combination of adaptation processes will be needed to respond to the various drivers, only some of which are achievable through informal institutions. Formal government intervention will be essential for stimulating innovation, intensification, and revitalization forms of adaptation. Institutional adjustments will be key to shaping local agency and future adaptive capacity away from a business-as-usual trajectory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".