Urban Land Use Trend and Drivers over the Last Three Decades in Addis Ababa and Impacts to the Sustainable Land Management
Bibliographic record
Abstract
Developing countries are experiencing a fast urban expansion which is highly impacted the land use land cover, LULC, biodiversity, local climate, and socio economic conditions. Understanding of urban LULC and its consequences is imperative to explore the opportunities that the urban has for sustainable development. The purpose of this study is to examine the patterns of LULC change, the effects of unsustainable land use, and mitigation measures. The study employed mixed methods including Satellite image, GIS techniques, and social survey. To further refine the study, secondary data from both published and unpublished materials were also used. The transfer of green space to built-up regions during the past three decades is evidence that the patterns of land use changes have become unsustainable. From 1986 to 2017, there has been a substantial quantity of deforestation (4467 ha of forest lost), and reduction of grassland (6314 ha) while built-up land has gone up by 9876 ha. The city's inefficient plans, along with the growing population, are the primary causes of the unsustainable LU. The city has experienced negative effects from unsustainable land use namely: flooding (areas susceptible for flooding increased by 69.5%), urban heat islands (the land surface temperature has increased by 3.80C), and carbon sequestration (at least 616, 044 C not sequestered and so released). On the other hand, a number initiatives have been implemented, albeit sporadically, to improve sustainable land use. Thus there is a need for policy makers and urban land use mangers to take into account empirical knowledge while planning.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".