Sustainability assessment: the state of the art in 2026
Bibliographic record
Abstract
Sustainability assessment (SA) has developed over the past three decades as a decision-support process explicitly directing choices towards sustainability. This paper provides an updated state-of-the-art review of SA in 2025, building on the 2012 assessment by Bond, Morrison-Saunders and Pope. We examine progress in theory, practice and process across selected jurisdictions, considering how SA has evolved conceptually and in application. While the volume of academic research on SA continues to grow, practical uptake remains uneven and context-dependent. Key innovations shaping the field include the embedding of sustainability provisions within Canada’s next-generation impact assessment framework, the incorporation of holistic assessment principles in Western Australia, the global adoption of the sustainable development goals (SDGs), and the rise of sustainable finance taxonomies. Together, these developments sharpen debates about definitions, scope, and trade-offs inherent in SA, highlighting both its strengths – such as integrated treatment of cumulative effects – and weaknesses, including costs, jurisdictional constraints, and challenges of transdisciplinarity. Opportunities exist to enhance SA through improved design, cross-jurisdictional cooperation, and clearer sustainability benchmarks, but threats remain from political pressures to streamline assessment and reduce costs. We conclude by reflecting on the contested future of SA and the need to communicate its value more effectively.
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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 | 0.038 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".