Greenwashing Practices Threat in Indonesian Land-Based Private Sector's Participation in Carbon Trading
Bibliographic record
Abstract
The land-based private sector is one of the key actors in implementing carbon trading in Indonesia.This study aims to explore the behavioural intention of the land-based private sector in participating in carbon trading, identify whether the intentions pose an immoral behavioural threat, and provide an alternative strategy to avoid the threat.Based on interviews with top-level executives of land-based private organisations, thematic analysis was used to analyse their behavioural intention in participating in carbon trading in line with the theory of planned behaviour.Furthermore, the legitimacy theory was applied to justify the emergence of the greenwashing threat and provide an alternative solution to avoid the threat.The results indicate that the land-based private sector intends to participate in carbon trading as the implementation offers new business opportunities to gain economic benefits.Despite resulting positive behaviour, the sector's participation poses an immoral threat to greenwashing behaviour in the implementation.Unethical behaviour emerges due to the large legitimacy gap or incompatibility between a business's actions and social pressures regarding what the actions should be.This study concludes that carbon performance disclosure should be mandated to all emitter corporations to avoid greenwashing threats in Indonesian carbon trading implementation, covering strict principle criteria.Also, the principle criteria for businesses' carbon disclosures must be validated thoroughly in the National Registry System process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".