Low-emission pathways in 11 major economies: comparison of cost-optimal pathways and Paris climate proposals
Bibliographic record
Abstract
In order to evaluate the effectiveness of climate policy, it is important to understand emission trends and policies at the national level. The 2015 Paris Agreement includes (Intended) Nationally Determined Contributions, so-called (I)NDCs, outlining the contribution of different Parties to the overall target of the agreement to limit global mean temperature increase to well below 2 °C. Here, we assess emission trajectories and the energy system transition of 11 major economies (in the remainder: countries) projected by integrated assessment models (IAMs) for baseline and cost-optimal 450 ppm CO2 eq mitigation scenarios and compare the results with the (I)NDCs. Limiting global temperature increase to below 2 °C implies a substantial reduction of the estimated available carbon budget for each country. The national carbon budgets between 2010 and 2100 showed reductions between the baseline and the 2 °C consistent mitigation scenario ranging from 52% in South Korea to 95% in Brazil. While in the baseline scenario, the share of low-carbon primary energy sources is projected to remain around 15% (with Brazil being a notable exception, reaching 30%); in the mitigation scenarios, the share of low-carbon energy is projected to increase to over 50% in 2050 in nearly all countries, with the EU, Japan and Canada reaching the largest shares. Comparison with the (I)NDCs shows that in Brazil, Canada, the EU, Mexico (conditional target), South Korea and the USA, the emission reduction targets of the NDCs are closer to the mitigation requirement of the 2 °C scenario; in other countries, however, there is still a large gap. The national detail of the indicators adds to the literature on low-carbon emission pathways, assists the assessment of the Paris Agreement and provides support to national policymakers to identify focus areas for climate policy in the coming years.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".