Comparing the uncomparable? An investigation of car manufacturers' climate performance
Bibliographic record
Abstract
Abstract The objective of this study is to investigate the interfirm comparability of climate performance disclosed in the sustainability reports produced by car manufacturers that have been criticized over the past few years. The in‐depth content analysis of the data disclosed by 17 car manufacturers over the period of 2014 to 2017, covering the five main climate indicators from the Global Reporting Initiative standard, shows that it is impossible to make meaningful comparisons between companies' performance, regardless of the intrinsic reliability of the data disclosed. A detailed examination of the data obtained highlights the four main difficulties that prevent a rigorous and credible ranking of the climate performance disclosed in the sustainability reports: the fuzzy and eclectic measurement methods employed, the unclear and heterogeneous scope of measurement, the noncompliance and lack of standardization of the reported data, and the inconsistencies in and inappropriate contextualization of disclosed information. The use of three complementary theoretical lenses—functionalist, critical, and postmodern—allows for a better understanding of the reasons underlying these problems. Contributions to the literature are set out, particularly on the measurability and comparability of sustainability performance. The practical implications of the study and avenues for future research are also explained.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".