Challenges in using environmental indicators for measuring sustainability practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it