ADDRESSING AND COMMUNICATING CLIMATE CHANGE AND ITS UNCERTAINTIES IN PROJECT ENVIRONMENTAL IMPACT ASSESSMENTS
Bibliographic record
Abstract
While climate change has become an important concern at both regional and global levels, its inherent uncertainties have often been cited as the main reason for delaying many actions to mitigate its potential impacts. Reviews of environmental assessments (EAs) have shown that impacts from climate change have been inadequately addressed within them and that the corresponding uncertainties have been addressed even more poorly. This paper describes several basic approaches for addressing and analysing climate change within the EAs of individual projects with a focus on its uncertainties. Subsequently, the paper describes how the results from this analysis can be effectively and comprehensively communicated to the EA's disparate set of technical and non-technical decision-makers and stakeholders. Based upon this overall approach, the paper proposes a general set of guidelines that enables proponents to incorporate climate change and its uncertainties into project EAs.
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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 | 0.142 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".