Cost-Benefit Analysis of Green Space Investment in Residential Areas of Dar es Salaam City, Tanzania
Bibliographic record
Abstract
Urban green spaces are increasingly recognized as alternative ameliorative methods to technical solutions abating cities' environmental problems like poor air quality, climate change impacts, and heat stress. However, the costs of development and maintenance of green spaces in terms of materials, labor, and time are not known. The main objective of the study was to assess the costs and benefits associated with green space investment in residential plots of Dar es Salaam City. The study employed in-depth interviews using structured questionnaires and document review. Results indicated that households incur an average cost of TZS 136,579 (USD 59) and a maximum of TZS 6,629,019 (USD 2,882) for establishing more than one home greenery type. The total net monetary benefit per household after all costs due to disservices have been accounted for was TZS 3,148,827 (USD 1,369) annually. Based on a cost-benefit analysis of home greenery, it was found that the benefit was 2.6 times as much as the investment cost thus suggesting that maintaining home greeneries is cost-effective and a worthwhile investment. The results may help in evaluating trades off between courses of action as well as a decision tool for the households when investing in green spaces. The study recommends that residents and City managers should invest in allotments, shade trees, and/or fruit trees, as they were found to have the highest benefits, monetary savings, and benefit-cost ratio. Moreover, to maximize monetary benefit from home greenery, residents should select the right type of green space followed by choosing the right plant species, identification of the right location within the residential plot for establishing green space, and adopting building designs that optimally support green space functioning.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".