A tale of two cities: evidence from the Global South on established versus emerging cities’ approaches to adaptive and sustainable water governance
Bibliographic record
Abstract
The call for adaptive governance approaches to guide the sustainable transformation of urban water management systems is growing amongst scholars and policy professionals. Responding to this call, the Global North (GN) has focused significant evidence-based research on issues of scale, capacity, and institutional arrangements to support such transformations, whereas evidence from the Global South remains nascent. This paper contributes to the growing body of knowledge from the Global South, discussing how adaptive governance operates under different local contexts and conditions. Following empirical investigations in two cities in Bangladesh, which involved 58 semi-structured interviews, 17 oral histories, and secondary data analysis, and drawing on the adaptive capacity and attributes framework, we examined how scale, capacity, and institutional hybridization might deliver the conditions necessary for guiding a sustainable transformation in water governance. The research revealed that a large-scale urban system such as Dhaka is currently experiencing “lock-in” due to ongoing investments in large-scale infrastructure, inappropriate transfer of technology from GN contexts, bureaucratic complexity, and general resistance to change. In contrast, the relatively smaller urban system represented by the secondary city Mymensingh was found to be more open, flexible, showcasing key enabling factors that might support sustainable growth. Overall, this study sheds light on the role of adaptive governance in the context of system scales and capacity (i.e., institutional / organizational / individual) and reveals how capacity development is linked to key enabling attributes including multi-level and polycentric institutions, participatory approaches, networking, bridging organizations, and leadership. Collectively these findings offer insights into how adaptive attributes can inform sustainable transformation processes
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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 | 0.000 | 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 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".