Ecological macroeconomic assessment of meeting a carbon budget without negative emissions
Bibliographic record
Abstract
Non-technical summary This paper expands the range of scenarios usually explored in integrated assessment models by exploring unconventional economic scenarios (steady-state and degrowth) and assuming no use of negative emissions. It is shown, using a mathematical model of climate and economy, that keeping cumulative emissions within the 1.5 degree carbon budget is possible under all growth assumptions, assuming a rapid electrification of end use and an immediate upscaling of renewable energy investments. Under business-as-usual investment assumptions no economic trajectory corresponds with emissions reductions consistent with the 1.5 degree carbon budget. Technical summary This paper presents a stock-flow consistent input–output integrated assessment model designed to explore the dual dynamics of transitioning to renewable energy while electrifying end use subject a carbon budget constraint. Unlike the majority of conventional integrated assessment model analyses, this paper does not assume the deployment of carbon dioxide removal and examines the role that alternative economic pathways (steady-states and degrowth) may play in achieving 1.5°C consistent emissions pathways. The model is internally calibrated based on a life-cycle energy return on investment scheme and the energy transition dynamics are captured via a dynamic input–output formulation. Renewable energy investment as a fraction of gross domestic product for successful emissions pathways reaches 5%. In terms of new capital requirements and investments, degrowth trajectories impose lower transition requirements than steady-state and growth trajectories. Social media summary We explore the role that steady-state and degrowth economic trajectories may play in emissions reductions consistent with a 1.5 degree world..
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".