STRATEGIC ENVIRONMENTAL ASSESSMENT AS A MEANS OF PURSUING SUSTAINABILITY: TEN ADVANTAGES AND TEN CHALLENGES
Bibliographic record
Abstract
While strategic environmental assessment can be a powerful tool for fostering progress towards sustainability, effective implementation involves confronting a set of substantial challenges. This paper, based on Canadian and international literature and experience, outlines the ten most compelling advantages of strategic environmental assessment for sustainability and the ten main challenges faced in implementation. The ten advantages of the strategic environmental assessment for sustainability are that it • provides a process for integrated pursuit of sustainability objectives in policy making and planning; • operationalises sustainability principles; • improves the information base for policy making, planning and programme development; • is proactive and broad in ways that strengthen consideration of fundamental issues; • improves analysis of broad public purposes and alternatives; • facilitates proper attention to cumulative effects; • facilitates greater transparency and more effective public participation at the strategic level; • provides a framework for more effective and efficient project-level assessments; • provides a base for design and implementation of better projects where project-level assessment is not required; and • facilitates establishment of a more comprehensive overall system of sustainability application at all levels from the setting of decision objectives to the monitoring of implementations effects. The ten main challenges for effective implementation are • limited information and unavoidable uncertainties; • boundary-setting complexities; • primitive methodologies; • difficulties in defining the proper role of public participants and ensuring effective involvement; • co-ordination and integration of strategic assessment with assessment processes at other levels; • institutional resistance; • conflict between integrated assessment and bureaucratic fragmentation; • jurisdictional overlap; • limitations of the standard rational planning and policy making model; and • resistance to integration of strategic assessment in core decision making. The paper concludes with a discussion of the major implications.
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 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 | 0.090 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".