How well are corporate sustainability goals designed? A global assessment of corporate commitments to water, ecosystems, climate, and materials and waste management
Bibliographic record
Abstract
Amid rising global challenges, corporate sustainability commitments are under increasing scrutiny. This study offers a data-driven analysis of how companies develop sustainability goals and how these align with seven transformative criteria. We used a global dataset of 818 goals from 534 companies, evaluated through the Embedding Project’s third-party Sustainability Goal Assessment framework, to assess the prevalence and quality of commitments. We compared how frequently aspects like system resilience, strategic impact or commitment transparency were included in commitments across different company types and geographically. Climate-related goals were most common and tended to receive higher scores for quality, mainly due to alignment with established reporting frameworks, but often fell short on systemic change, transparency, and implementation clarity. Ecosystem and water-related goals, though less frequent, were more likely to aim for strategic impacts and systemic change reflecting the role of multi-stakeholder coordination and strategic alignment. Commitments were scored as higher quality on average in the communication service sector compared to industrials, information technology and consumer discretionary sectors and in cases of private ownership structures. Relational network analysis revealed dependencies among criteria, highlighting that transparency’s plays a central role in goal quality and the importance of actionable plans. Our findings suggest that achieving improvements in the quality of sustainability commitments requires corporate stakeholders to define measurable, time-bound sustainability goals, which extend accountability across the value chain, and integrate transparent reporting, and have incentives for systemic change. Policymakers can support this by standardizing terminology, creating fiscal incentives, and ensuring stable, long-term regulations that mandate transparency and full life cycle accountability.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".