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Record W4319017955 · doi:10.1007/978-3-031-15904-6_21

Strategy for Sustainable Urban Climate Mitigation: Kupang City Climate Risk Assessment

2023· book-chapter· en· W4319017955 on OpenAlexaff
Muhammad Ridwansyah, Christopher R. Bennett, Franky M.S. Telupere, Philiphi de Rozari, Fadwa Rhogib Asfahani, Utari N. Qalbi, Achmad F. Kanzil

Bibliographic record

VenueEnvironment & policy · 2023
Typebook-chapter
Languageen
FieldEngineering
TopicGeotechnical and construction materials studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClimate changeSanitationVulnerability (computing)BusinessEnvironmental planningGreenhouse gasUrbanizationMunicipal solid wasteNatural resource economicsGeographyEnvironmental resource managementEnvironmental scienceEnvironmental engineeringEconomic growthEngineeringEconomicsWaste management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.236
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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