Corporate Social Responsibility or Government Regulation? Evidence on Oil Spill Prevention
Bibliographic record
Abstract
Major oil spills normally occur from oil pipelines and oil tankers that are under operational control of companies, namely, oil companies and tanker owners.There are two generic responses for changing the behavior of companies with regard to oil spill prevention: mandatory government regulation or voluntary initiatives often pursued under the banner of Corporate Social Responsibility (CSR).Here we investigate to what extent voluntary CSR initiatives can be effective in oil spill prevention.A global perspective on voluntary mechanisms is taken by looking at the progress of 20 oil and gas firms from around the world toward oil spill prevention, using the companies' 2010 sustainability reports for self-reported oil spill information.The analysis includes ten oil companies from OECD countries (including Exxon and Shell, among others) and 10 oil companies from non-OECD countries (including Brazil's Petrobras and Indian Oil, among others).The study finds that oil spill prevention has generally improved over recent decades.Government regulation played a significant part in these improvements whereas it is less clear to what extent CSR played a significant part in these improvements.Some of CSR's key limitations are highlighted.It is not suggested that CSR should be abandoned; however, new hybrid forms of regulation that combine voluntary and mandatory elements are advocated.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".