Challenges in using environmental indicators for measuring sustainability practices
Bibliographic record
Abstract
Many businesses are pursuing sustainability for a variety of reasons, ranging from increased financial competitiveness to meeting upcoming regulatory initiatives. Choosing the appropriate indicators to measure environmental progress, however, is a critical challenge. Using data from the automotive industry, this paper illustrates how indicators can be incorrectly selected, misused, or misinterpreted, resulting in misleading conclusions. Such issues are especially critical when using indicators in emerging tools, such as life cycle analysis, to assess the impacts posed by alternative designs. Furthermore, incorporating the impacts represented by the indicators into the decision-making process can be problematic, since these indicators will be used to assess the success or failure of design changes. The automotive sector is an ideal example because it has implemented a variety of measures to meet its environmental challenges: numerous indicators and decision approaches have been developed for or adapted to the industry and it has had to address important issues, such as the use of normalized metrics and appropriate weighting schemes. Key words: indicators, automotive industry, sustainability, environment, life cycle assessment, decision making.
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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 | 0.188 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".