Rebound policy in the Paris Agreement: instrument comparison and climate-club revenue offsets
Bibliographic record
Abstract
It is argued that without an international climate treaty, or with a soft treaty in the form of voluntary pledges, well-intended local and national climate strategies will seriously rebound in terms of energy use and CO2 emissions. I unravel the complexity of rebound in an international context, for which purpose the distinction between national and international rebound is introduced. The main climate–energy strategies and policies are assessed based on their capacity to control rebound, with carbon pricing – especially cap-and-trade – appearing to be the best approach. It is argued that motivations for further negotiations on amendments to the recent Paris climate agreement, namely on national policy coordination, can be strengthened by rebound concerns. As part of this, a new suggestion is to complement carbon equalization tariffs on carbon-intensive goods imported from countries with weak or non-existent climate policies with ‘revenue-recycling offsets’. These offsets would ease the political way to implementing carbon equalization tariffs, in turn creating pressure on countries to sign an effective climate treaty – a requirement to achieve consistent and serious carbon pricing worldwide. To support these arguments, the empirical patterns of rebound estimates are broadly outlined.Policy relevanceRebound is given scant attention in both IPCC documents and UNFCCC climate summits, where climate agreements are negotiated. This article argues that without an international climate treaty, or with a soft treaty in the form of voluntary pledges, as characterizes the recent Paris climate agreement, well-intended local and national climate strategies will seriously rebound in terms of CO2 emissions. This means that the motivations for climate negotiations can be strengthened by drawing attention to rebound concerns. The major current climate–energy strategies and policies are assessed on the basis of their capacity to control rebound, with carbon pricing – notably cap-and-trade – revealed to be the best approach. Further analysis and discussion of this is welcome, especially as rebound is neglected in the comparisons of carbon tax and emissions trading solutions to global warming. In particular, rebound should be added to the set of core criteria for judging and comparing instruments of climate policy. In addition, a new suggestion offered is to complement a climate club with carbon equalization tariffs on carbon-intensive goods imported from countries lacking strong climate policy with ‘revenue-recycling offsets’. This would make the implementation of such tariffs more politically feasible as well as creating pressure on countries to sign an effective climate treaty.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".