Studi Potensi Serapan Karbon dan Nilai Ekonomi dari Inisiatif Penghijauan di PT PLN Indonesia Power Pangkalan Susu PGU
Bibliographic record
Abstract
Pemanasan global menjadi permasalahan krusial yang berdampak terhadap lingkungan, terutama disebabkanolehemisi gas rumah kaca (GRK) dari sektor energi. PT PLN Indonesia Power Pangkalan Susu PGU, sebagai pembangkit listrik berbahan bakar batubara, bertanggung jawab mengurangi dampak lingkungan dari operasional PLTU. Penelitian ini bertujuan mengevaluasi potensi serapan karbon dari program penghijauan PT PLN Indonesia Power Pangkalan Susu PGU serta nilai ekonominya. Metode yang digunakan adalah persamaan allometrik untuk mengukur serapan karbon dan perhitungan nilai ekonomi dengan metode proxy good technique. Penelitian ini mengidentifikasi potensi serapan karbon dan nilai ekonomi dari program penghijauan di lokasi operasional (8,57 ha) dan lokasimangrove (2 ha) di Desa Pasar Rawa. Hasil penelitian menunjukkan total serapan karbon mencapai 1.231,06 tonCO2 per tahun dengan nilai ekonomi sebesar 12.310,68 USD dolar atau sekitar Rp201.895.152,00 per tahun, berdasarkanharga pasar karbon sebesar 10 dolar per ton CO2. Meskipun memberikan manfaat lingkungan dan nilai ekonomi, programini masih defisit dalam memenuhi persetujuan teknis batas atas emisi (PTBAE) yang ditetapkan oleh KementerianESDM. Untuk memenuhi target emisi pada tahun 2024, diperlukan pengurangan sebesar 353.744,58 ton CO2e yangmembutuhkan lebih luas lagi lahan penghijauan. Kata Kunci: emisi gas rumah kaca, nilai ekonomi karbon, pemanasan global, penghijauan, serapan karbon
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".