Modeling the Economic Value of Green Spaces in Residential Areas of Dar Es Salaam City, Tanzania
Bibliographic record
Abstract
The economic value of green spaces in residential areas has been advocated to raise the land and property values. However, the establishment of the economic value of green spaces need many data such that it is costly and needs expertise. The objective of this study was to develop a user-friendly mathematical model that employ few data to determine the economic value of green spaces in residential areas in Dar es Salaam City. The study was cross sectional and employed structured questionnaires as a data collection method. Linear regression model was developed by relating economic values of green spaces to various environmental and socio-economic conditions of households. The study revealed that the net economic value of home greenery was significantly influenced by income of the households, area covered, age of the respondents and green space type. The average net benefit calculated from the model (1,317USD per household per year) was not far from the normal arithmetic mean calculated through traditional methods (1,369USD per household per year). This implies that the model can best predict the overall mean of the net benefit by 96.2%. It is therefore, recommended that the model can be used to estimate the economic value of ecosystem services from home greenery, property value and can guide the establishment of compensation for households due to green space availability.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".