Environmental Quality Index in Indonesia: Economic Activities, Investment, Forest and Land Fire
Bibliographic record
Abstract
Abstract Rapid economic growth requires more activities that affect the environment negatively. The production process from economic activities yields goods and services and wastes. The waste can be contained by hazardous elements that can cause health problems and endanger the quality of the environment. Thus, the environmental quality should be maintained to create ideal conditions and minimize negative externalities. The issue regarding environmental quality induces some studies to develop policies on maintaining the environment’s quality. Studies on environmental quality are investigated not only by using a natural science perspective but also from social science, such as economics. Many studies have discussed environmental quality using different approaches from a social science perspective. However, only a few studies have covered Indonesia by province in the past five years. This study aims to estimate the determinants of the Environmental Quality Index in 34 provinces in Indonesia. The current research treats forest, land fire, and economic variables as independent variables, including Gross Domestic Regional Product (GDRP), provincial environmental budget, and investment. The secondary data are generated from Statistics Indonesia from 2016-2022. This study employs static panel regression with a Fixed-Effect model to estimate the data. The results revealed that forest and land fires and the provincial budget for the environment significantly affect the environmental quality index in Indonesia. This implies that budget allocation for environmental spending is one of Indonesia’s policies that control environmental quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".