IPCC baseline scenarios have over-projected CO <sub>2</sub> emissions and economic growth
Bibliographic record
Abstract
Abstract Scenarios used by the Intergovernmental Panel on Climate Change (IPCC) are central to climate science and policy. Recent studies find that observed trends and International Energy Agency (IEA) projections of global CO 2 emissions have diverged from emission scenario outlooks widely employed in climate research. Here, we quantify the bases for this divergence, focusing on Kaya Identity factors: population, per-capita gross domestic product (GDP), energy intensity (energy consumption/GDP), and carbon intensity (CO 2 emissions/energy consumption). We compare 2005–2017 observations and IEA projections to 2040 of these variables, to ‘baseline’ scenario projections from the IPCC’s Fifth Assessment Report (AR5), and from the shared socioeconomic pathways (SSPs) used in the upcoming Sixth Assessment Report (AR6). We find that the historical divergence of observed CO 2 emissions from baseline scenario projections can be explained largely by slower-than-projected per-capita GDP growth—predating the COVID-19 crisis. We also find carbon intensity divergence from baselines in IEA’s projections to 2040. IEA projects less coal energy expansion than the baseline scenarios, with divergence expected to continue to 2100. Future economic growth is uncertain, but we show that past divergence from observations makes it unlikely that per-capita GDP growth will catch up to baselines before mid-century. Some experts hypothesize high enough economic growth rates to allow per-capita GDP growth to catch up to or exceed baseline scenarios by 2100. However, we argue that this magnitude of catch-up may be unlikely, in light of: headwinds such as aging and debt, the likelihood of unanticipated economic crises, the fact that past economic forecasts have tended to over-project, the aftermath of the current pandemic, and economic impacts of climate change unaccounted-for in the baseline scenarios. Our analyses inform the rapidly evolving discussions on climate and development futures, and on uses of scenarios in climate science and policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".