Evaluating national environmental sustainability: performance measures and influential factors for OECD-member countries featuring Canadian performance and policy implications
Bibliographic record
Abstract
This research reviews five studies that evaluate national environmental sustainability with composite indices; performs uncertainty and sensitivity analyses of techniques for building a composite index; completes principal components factor analysis to help build subindices measuring waste and pollution, sustainable energy, sustainable food, nature conservation, and sustainable cities (Due to its current importance, the greenhouse gases (GHG) indicator is included individually as another policy measure.); analyses factors that seem to influence performance: climate, population growth, population density, economic output, technological development, industrial structure, energy prices, environmental governance, pollution abatement and control expenditures, and environmental pricing; and explores Canadian policy implications of the results. The techniques to build composite indices include performance indicator selection, missing data treatment, normalisation technique, scale-effect adjustments, weights, and aggregation method. Scale-effect adjustments and normalisation method are significant sources of uncertainty inducing 68% of the observed variation in a country’s final rank at the 95% level of confidence. Choice of indicators also introduces substantial variation as well. To compensate for this variation, the current study recommends that a composite index should always be analysed with other policy subindices and individual indicators. Moreover, the connection between population and consumption indicates that per capita scale-effect adjustments should be used for certain indicators. Rather than ranking normalisation, studies should use a method that retains information from the raw indicator values. Multiple regression and cluster analyses indicate economic output, environmental governance, and energy prices are major influential factors, with energy prices the most important. It is statistically significant for five out of seven performance measures at the 95% level of confidence: 37% variance explained on the environmental sustainability performance composite indicator out of 73%, 55% (of 55%) on the waste and pollution subindex, 20% (of 70%) on the sustainable energy subindex, 5% (of 100%) on the sustainable cities subindex, and 55% (of 81%) on the GHG indicator. Energy prices are relevant to Canadian policy; increasing prices could substantially improve Canada’s performance. Policy makers should increase energy prices through a carbon pricing strategy that is congruent with the ecological fiscal reform advanced by the National Round Table on the Environment and the Economy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".