Economic Value of Ecosystem Disservices of Green Spaces found in Residential Plots of Dar es Salaam City
Bibliographic record
Abstract
Most studies have reported benefits of green spaces to households but few studies have been reported on negative effects (disservices) as well as their economic cost. Understanding ecosystem disservices from home greenery is important for health, safety and security of urban environment. The current paper reports on a study on economic value of green spaces including aesthetics, health, safety and security, physical, social and economic disservices. The study employed focus group discussion and in-depth interviews using structured questionnaire. Results indicate that, 65% of the respondents face the aforementioned disservices. Disservices which are aesthetic in nature were found to be faced by majority followed by health and physical disservices. The study has shown that households spend an average of TZS 60,691 (USD 26) per year on prevention and control of aesthetic and health disservices. In totality, valuation of ecosystem disservices from home greeneries has revealed that a household can incur an average total cost of TZS 116,817 (USD 50) per year. At City level, the total disservice cost is estimated to be TZS 106 billion (USD 45,415,595) per year. Disservices affect 5% of the annual household income on preventing and controlling their impacts. The study recommends that departments responsible for handling environmental management issues should recognize the value of green space and integrate aesthetic factors into their planning and budgeting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".