Using a Balanced Scorecard Approach to Measure Environmental Performance: A Proposed Model
Bibliographic record
Abstract
Environmental aspects have been recognized by today’s organizations as the most important components of value creation that would contribute to the achievement of the goals and success in the future. The purpose of this study is to propose an Environmental Balanced Scorecard (EBSC) model to evaluate environmental performance in business organizations. It also aims to illustrate how the environmental performance aspects can integrate into the Balanced Scorecard (BSC). To achieve the goals of the study, the descriptive analytical approach was adopted for its suitability for the purpose of the study. An EBSC model was developed to evaluate environmental performance with proposed four perspectives and environmental strategic objectives within each perspective. The four perspectives are the customer, internal process, learning and growth and financial. The proposed model will help managers not only to evaluate the environmental performance, but also to plan, manage and control organization’s environmental activities. In addition, it can serve as a template for the organizations which aims to create environmental awareness and pursue environmental sustainability.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".