Impacts of Technology Uncertainty on Energy Use, Emission and Abatement Cost in USA: Simulation results from Environment Canada’s Integrated Assessment Model
Bibliographic record
Abstract
To what extent could various technological advancements in the coming decades potentially help greenhouse gas mitigation in the U.S.? What could the potential contribution of end-use technology and other key clean electric energy technologies such as CCS, Nuclear power, wind & solar, and biomass be? This paper presents simulation results from an Integrated Assessment Model that suggest that, in the absence of policy measures, even under the most optimistic state of technology development and deployment scenarios, the U.S. energy system would still be dominated by fossil fuels and GHG emissions would increase significantly between 2010 and 2050. A pessimistic scenario in end-use technology would result in increased electric and non-electric energy use and GHG emissions compared to the advanced technology scenario, while a pessimistic scenario in any one of the four clean technologies examined would result in reduced electric and non-electric energy use and a small increase in GHG emissions. However, if all technologies are in pessimistic status, GHG emissions would increase significantly as more fossil fuels would be used in the energy system. Technology alone cannot achieve the abatement levels required. A market-based policy targeting the reduction of U.S. GHG emissions to 50% below 2005 levels by 2050 would result in dramatic decrease in coal-fired generation. With abatement policies in place, favorable technology scenarios reduce abatement costs and facilitate the energy system transition from fossil fuels to clean energy.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".