Analisis Pengurangan Emisi CO2 Melalui Manajemen Penggunaan Listrik dan Ketersediaan Ruang Terbuka Hijau di Gedung Perkantoran Pemerintah Kota Surabaya
Bibliographic record
Abstract
Penggunaan listrik pada aktivitas dalam gedung dapat menyumbang emisi gas rumah kaca khususnya CO2. Pada penelitian ini dikaji pengurangan emisi CO2 melalui manajemen penggunaan listrik dan ketersediaan ruang terbuka hijau (RTH) di Gedung Jimerto Pemerintah Kota Surabaya. Sampling penggunaan listrik dilakukan dengan mengukur penerangan indoor, penggunaan AC, dan komputer pada ruang yang sama. Metode BEE Code of Lighting digunakan untuk pengukuran penerangan, sedangkan metode observasi langsung dilakukan untuk manajemen penggunaan listrik dan RTH eksisting. Emisi CO2 dari penggunaan daya listrik dihitung dengan faktor emisi sesuai dengan ketentuan Surat Kementrian ESDM Dirjen Ketenagalistrikan Nomor 1281/05/600.4/2012. Hasil penelitian menunjukkan bahwa emisi yang dihasilkan sebesar 1.966,266 ton CO2/tahun. Pengurangan emisi dengan manajemen penggunaan listrik eksisting diperkirakan dapat mengurangi emisi sebesar 31,302 ton CO2/tahun. Apabila dilakukan penggantian peralatan listrik pengurangan emisinya menjadi 251,271 ton CO2/tahun. Sedangkan RTH yang dibutuhkan untuk memenuhi ketentuan minimal adalah sebesar 325,3 m2 dan ini sebanding dengan penyerapan CO2 sebesar 1,789 ton CO2/tahun.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".