Co-governance for green infrastructure preservation: Collaborative strategies in customary land tenure cities of Sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) cities often struggle with the degradation of natural Green Infrastructure (GI), especially in cities where customary land tenure is more prevalent. Contrary to the prevailing narrative that traditional authorities are primarily responsible for this decline, this paper applies the collaborative governance theory to demonstrate the prospects of preserving GI in SSA cities through state-traditional institutional co-governance initiatives. Data for the paper was obtained from the Environmental Protection Agency, the Manhyia Palace and key paramouncies, and Metropolitan, Municipal and District Assemblies in Kumasi in Ghana. Corresponding spatial data was gathered from satellite images on eight GI in Kumasi. Analysis of the spatial data revealed that prior to the co-governance arrangements, the selected GI were depleting at an annual rate of 4.7 % between 2003 and 2013, and 5.4 % between 2013 and 2019 mainly due to encroachment by grey land uses. Five years after the initiative (2019–2023), the annual rate of decline reduced to 0.9 %, with a total of 20.36 km 2 of GI preserved. Drawing from this analysis, we assert that co-governing GI by both state and traditional institutions, as emphasized by the collaborative governance theory, is a viable strategy for preserving GI in cities that are characterised by organic and informal development patterns, often spurred by customary land tenure arrangements. • Customary land tenure influences GI governance in SSA cities. • Co-governance reduced GI depletion in Kumasi from 5.4 % to 0.9 % annually. • Traditional and state institutions collaboratively preserved over 20 km 2 of GI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".