Can Today's and Tomorrow's World Uniformly Gain from Carbon Taxation?
Bibliographic record
Abstract
Climate change will impact current and future generations in different regions very differently. This paper develops a large-scale, annually calibrated, multi-region, overlapping generations model of climate change to study its heterogeneous effects across space and time. We model the relationship between carbon emissions and the global average temperature based on the latest climate science. Predicated average global temperature is used to determine, via pattern-scaling, region-specific temperatures and damages. Our main focus is determining the carbon policy that delivers present and future mankind the highest uniform percentage welfare gains – arguably the policy with the highest chance of global adoption. Damages from climate change are positive for all regions apart from Russia and Canada, with India and South Asia Pacific suffering the most. The optimal policy is implemented via a time-varying global carbon tax plus region- and generation-specific net transfers. Uniform welfare improving carbon policy can materially limit global emissions, dramatically shorten the use of fossil fuels, and raise the welfare of all current and future agents by over four percent. Unfortunately, the pursuit of carbon policy by individual regions, even large ones, makes only a limited difference. However, coalitions of regions, particularly ones including China, can materially limit carbon emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".