Border adjustments under unilateral carbon pricing: the case of Australian carbon tax
Bibliographic record
Abstract
In the absence of a global agreement to reduce emissions, Australia adopted a carbon tax unilaterally to curb its own emissions. During the debate prior to passing the carbon tax legislation in 2011, there were concerns about the challenge that Australia’s emissions-intensive and trade-exposed (EITE) industries may face in terms of decreasing international competitiveness due to the unilateral nature of the tax and hence the potential for carbon leakage. In order to address these concerns, this paper explores possible border adjustment measures (BAMs) to complement the domestic carbon regulation in Australia using the multi-sector computable general equilibrium approach. We consider four border adjustments: border adjustments on imports based on domestic emissions; border adjustments on exports via a rebate for exports; a domestic production rebate; and full border adjustment on both exports and imports. We compare the numerical simulation results of these scenarios with a no border adjustments scenario from the standpoint of welfare, international competitiveness and carbon leakage. The key finding is that BAMs have a very small impact on the overall economy and on EITE sectors. In other words, the different BAMs have minimal impact on the outcomes of carbon pricing policy. This finding is consistent with studies for EU, USA, Canada and other countries. Hence, we conclude that the border adjustments are ineffective instruments to safeguard EITE industries in Australia.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".