Factors Influencing Carbon Management Accounting Adoption in Indonesia
Bibliographic record
Abstract
This study aims to analyze the effects of green strategy, green social capital, and environmental consciousness on carbon management accounting adoption with green culture as a moderating variable.The study uses a quantitative approach by distributing questionnaires to 340 respondents in middle-up management positions in listed and non-listed companies on the Indonesian Stock Exchange.Multiple linear regression analysis was used with the Smart PLS & SPSS statistical tool.This research shows that green strategy has a significant positive effect and green social capital has a significant positive effect on carbon management accounting adoption.Meanwhile, environmental consciousness has no significant effect and green culture cannot moderate the relationship between green strategy, green social capital, and environmental consciousness on carbon management accounting adoption.The contribution of this research provides four new dimensions and 17 new indicators in measuring the adoption of carbon management accounting according to the Indonesian context.Also, this proves that carbon management accounting adoption needs to be supported by the implementation of a green strategy and the development of human resources who are always willing to share knowledge related to climate change issues as evidence to stakeholders of the efforts of corporations to support reducing greenhouse gas emissions.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".