Local Government Forestry Expenditure and Forest Land Cover: A Preliminary Lesson from Decentralized Indonesia
Bibliographic record
Abstract
Even though government of Indonesia invests billions of rupiah to tackle deforestation, its effectiveness has been questionable. This study analyses changes in rates of forest cover in Indonesia and their association with forestry expenditures (FE) spent by the provincial governments. Based on 2007 to 2010 data, linear multiple regression results indicate that FE is not enough to tackle the negative change in forest land cover that could represent deforestation. Moreover, it was found that FE have negative association with forest land cover and therefore, can be associated to deforestation. This negative association remains when other factors that affect forest land cover such as wood extraction, agriculture outcome, forestry outcome, population growth and population density and initial environmental conditions have been controlled.AbstrakWalaupun pemerintah Indonesia telah menginvestasikan trilyunan rupiah untuk mengurangi deforestasi, akan tetapi efektifitasnya dipertanyakan. Penelitian ini menganalisa tingkat perubahan luasan tutupan hutan di Indonesia and asosiasinya dengan Pengeluaran Sektor Kehutanan (PSK) yang dibelanjakan oleh pemerintah provinsi. Berdasar data tahun 2007 sampai 2010, hasil regresi multiple linier mengindikasikan bahwa PSK tidak cukup untuk mengurangi akibat negatif dalam perubahan hutan dan lahan yang dapat mngakibatkan deforestasi. Lebih lanjut, ditemukan bahwa PSK berhubungan erat pengurangan luasan hutan, yang dapat dikaitkan dengan deforestasi. Asososiasi yang negatif ini tetap terjadi walaupun faktor lain yang memengaruhi tutupan hutan seperti penebangan kayu, hasil pertanian, hasil kehutanan, pertumbuhan populasi dan kepadatan populasi dan kondisi awal lingkungan telah dikontrol.Kata kunci: Deforestasi, Hutan Tropis; Populasi Penduduk; Tutupan Hutan; Pengeluaran untuk Reboisasi HutanJEL classifications: E62; Q58; H76
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".