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Record W4406809502 · doi:10.18280/ijsdp.200117

Evaluation of Household Carbon Absorption for Greenhouse Modeling in East Lombok Regency

2025· article· en· W4406809502 on OpenAlexvenueno aff
Armin Subhani, Baiq Liana Widiyanti

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2025
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasAbsorption (acoustics)Carbon fibersGreenhouseEnvironmental scienceEnvironmental engineeringMaterials scienceGeologyAgronomyComposite material

Abstract

fetched live from OpenAlex

Attention and efforts to reduce greenhouse gases including carbon dioxide is increasing, and begun to be socialized at household level.This study aims to calculate total carbon emissions from household activities that use energy, for evaluation the carbon absorption.The research method was a qualitative survey using questionnaires and field observations from 380 residents spread over 7 villages in Selong sub-district, East Lombok Regency, West Nusa Tenggara Province.Complementary data obtained from literature studies regarding the provisions and rules used.The findings show that indirect emissions originating from electricity use dominate with 69% (481.91638ton CO2/year), which shows very high dependency.The general type of settlement was a mixture of residential and economic activities, which easy to find small shops, service kiosks such as laundry, computer and internet rental, grocery, rented and boarding houses that causes high demand for electricity.Electrical energy comes from diesel power plants that still use coal and other fossil fuels in the production process.Evaluation of emission absorption shows unbalance.It is necessary to save energy usage and also make efforts to find and utilize renewable energy sources and designing scenarios to optimize open spaces, both by increasing the number and model themselves.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.176
GPT teacher head0.380
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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