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Studi Potensi Serapan Karbon dan Nilai Ekonomi dari Inisiatif Penghijauan di PT PLN Indonesia Power Pangkalan Susu PGU

2025· article· id· W4410385955 on OpenAlexaff
Mazda Adli, Rahmi Utami, Yasmine Anggia Sari, Lies Setyowati, Randy Zulkarnain

Bibliographic record

VenueDampak · 2025
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPower (physics)ArtPhysics

Abstract

fetched live from OpenAlex

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

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.231
Teacher spread0.223 · 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 designObservational
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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