Environmental Quality Index in Indonesia: Economic Activities, Investment, Forest and Land Fire
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it