Australian Experience with ‘New’ Environmental Policy Instruments: The Greenhouse Challenge and Greenhouse Friendly Programs
Bibliographic record
Abstract
In association with international moves to address the impacts of global climate change some governments including those in the EU, US, Canada and Australia have taken steps to reduce greenhouse gas emissions via ‘new’ environmental policy instruments (NEPIs) (e.g. voluntary agreements, emissions trading and eco-labelling). This has been in response to the Framework Convention on Climate Change and in anticipation of the Kyoto Protocol coming into force. This paper focuses on Australian experience with two particular NEPIs: The Greenhouse Challenge and Greenhouse Friendly programs. Empirical evidence on the evolution and effectiveness of these programs is related to theoretical discussion on the role of NEPIs in industrial transformation, social learning and sustainability The success or effectiveness of these greenhouse NEPIs appears to be dependent on industry motivations and incentives for participation, the implementing agency's procedures and the design of the process for collaboration and information sharing between government and industry.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".