Is Corruption Bad for Environmental Sustainability? A Cross-National Analysis.
Bibliographic record
Abstract
This paper explores the hypothesis that higher levels of corruption are detrimental to environmental sustainability. It does this by employing the Environmental Sustainability Index (ESI) and its component variables and indicators as promoted by the World Economic Forum and the Corruption Perception Index (CPI) created by Transparency International (TI). Both the CPI and ESI were shown to be statistically significantly related to income (proxied as GDP/capita) such that environmental sustainability declined with decreasing income while corruption worsened. The ESI for 2002 was also divided into indicators representing pressure, state, impact and response (i.e., the PSIR framework), and each of these were regressed onto a 'residual CPI' (CPI of 2002 with the influence of income removed). The results suggest that for the most part the pressure, state and impact indicators of the ESI are not correlated with 'residual' CPI. The only statistically significant relationships with 'residual CPI' were for those of the response indicators of the ESI, although even here the R2 values were low (< 20%). Corruption was found to reduce any positive contribution from the response indicators towards environmental sustainability. However, great care needs to be taken when drawing conclusions from the sort of highly aggregated (spatially as well as mathematically) indices as the ESI and CPI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".