Strategy for Sustainable Urban Climate Mitigation: Kupang City Climate Risk Assessment
Bibliographic record
Abstract
Abstract Kupang City is one of the cities in Indonesia that is vulnerable to disasters caused by climate change, mainly prolonged dry season, strong winds, and increasing GHG emissions. These disasters will significantly affect all aspects of life, such as ecosystems, property, and infrastructure. This vulnerability is worsened by increasing urbanization, which creates additional risks for many people. This also can be explained by the growing number of motorized vehicles, which caused an increase in NO 2 (Nitrogen Dioxide). Data shows that a higher concentration of NO 2 was found on roadsides, amounting to 22,16 μg/m 3 . Therefore, in this study, the geographical, demographic, and socioeconomic characteristics of Kupang City were analyzed to find the shortcomings and the challenges faced by Kupang City in order to implement policies related to climate risk reduction. This study aims to investigate the status of the current phenomenon by using descriptive design. Based on the literature analysis, it was found seven priority sectors, which considered able to resolve the disasters and challenges caused by climate change. The seven sectors are climate change adaptation and disaster risk reduction, water and sanitation, energy and transportation, solid waste management/municipal waste, sustainable use of resources, GHG emission inventory, and financing.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".