Target‐setting for ecological resilience: Are companies setting environmental sustainability targets in line with planetary thresholds?
Bibliographic record
Abstract
Abstract The purpose of this research is to explore the extent to which companies are setting organization‐centric versus resilience‐based environmental targets in their sustainability reports. We define ecological resilience through the planetary thresholds identified by the Planetary Boundaries (PB) framework. On this basis, we define resilience‐based targets as corporate environmental targets that are connected (quantitatively or qualitatively) to these thresholds. Sustainability reports issued by 50 sustainability leader firms in Canada were analyzed to identify environmental sustainability targets. These targets were classified as resilience‐based and organization‐centric based on their connection to the PB framework. A total of 303 targets were identified, distributed across eight different corporate performance areas. None of these targets was found to be quantitatively tied to any PB thresholds. A small number of targets did nevertheless make reference to the global/regional ecological processes that underpin some of the Boundaries. These targets made reference to only five of the nine Boundaries described by the framework. This study highlights the extent of organization‐centric environmental targets in corporate sustainability reports. The implications of setting such targets are discussed, along with the challenges of adopting resilience‐based targets. This study also discusses the reasons why companies may not be adopting a resilience‐based approach to set sustainability targets and measure performance, despite increasing calls from stakeholders to do so. On this basis, several recommendations are also provided for managers to guide resilience‐based target‐ and goal‐setting.
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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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".