Instructions Green Innovation and Creating Shared Value on Achievement of Environmental Development Pillar in Indonesian Energy Sector
Bibliographic record
Abstract
Indonesia's engagement in the Sustainable Development Goals (SDGs) has prompted the implementation of mandatory SDG support measures for companies.However, challenges remain in the energy sector due to limited understanding and participation.This study investigates the effects of Green Innovation and Creating Shared Value as independent variables on the dependent variable Environmental Development Pillar, focusing on energy sector companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2021.Content analysis was employed to extract information from corporate sustainability reports regarding SDG-related disclosure, specifically Goals 6, 11, 12, 13, 14, and 15.A purposive sampling technique yielded a sample of 66 energy sector companies, with 10 meeting the selection criteria.Multiple linear regression analysis, conducted using SPSS version 29, revealed a significant influence of Green Innovation and Creating Shared Value on the Environmental Development Pillar.This study suggests that energy sector companies should prioritize environmentally conscious and socially responsible policies and that the Indonesian government should assess the Environmental Development Pillar guidelines, adapting them to various corporate sectors within the country.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".