Integrasi Akuntansi Lingkungan dalam Sistem Pengendalian Manajemen: Tinjauan Kritis terhadap Praktik Greenwashing dan Legitimasi
Bibliographic record
Abstract
Tuntutan keberlanjutan mendorong organisasi untuk memposisikan akuntansi lingkungan sebagai bagian integral dari praktik manajemen berkelanjutan, bukan sekadar alat pelaporan. Namun, masih terdapat kesenjangan antara adopsi akuntansi lingkungan dan perubahan substantif dalam pengambilan keputusan manajerial. Penelitian ini menganalisis secara kritis peran akuntansi lingkungan dengan mengintegrasikan perspektif Environmental Management Accounting (EMA), Management Control Systems (MCS), teori legitimasi, dan greenwashing. Metode kualitatif deskriptif melalui studi literatur mengungkap bahwa akuntansi lingkungan memiliki potensi transformasional yang kondisional, tergantung pada integrasinya ke dalam sistem pengendalian dan proses strategis. EMA berperan dalam internalisasi biaya lingkungan, namun berpotensi menjadi simbolik tanpa integrasi MCS. Akuntansi lingkungan juga berfungsi sebagai mekanisme legitimasi yang ambivalen, dapat mendorong akuntabilitas maupun memicu greenwashing. Novelty penelitian terletak pada pendekatan integratif yang memposisikan akuntansi lingkungan sebagai praktik manajerial-institusional yang terikat pada sistem pengendalian dan dinamika legitimasi.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".