Avoiding investment in fossil fuel assets
Bibliographic record
Abstract
Abstract Reducing greenhouse gas emissions requires a transformation of capital assets in the economy, especially those for energy supply. This paper explores the hypothesis that economically efficient decarbonization occurs when the demand for fossil fuels declines at the same rate as their capital assets depreciate. In theory this means that new investments in fossil fuel assets are avoided, but without incurring stranded assets. We examine the practicality of this hypothesis using a biophysical economic model of the US energy supply system, with an example focused on impacts of electric vehicles on the petroleum supply chain. We specifically address two questions: (1) What rate of market penetration for electric vehicles is necessary to avoid investments in the petroleum‐related assets? (2) How do the costs of upstream capital assets change with the transformation to electric vehicles? High annual depreciation rates for oil refineries ( δ = 9.47%) and assets for crude oil extraction ( δ = 8.23%) have important impacts on results. To avoid new investment in oil refining assets through widespread electrification of light‐duty vehicles, the vehicle stock would need to be transformed in just 4 or 5 years. Under most scenarios, some petroleum pipelines will likely become stranded assets due to their low rate of depreciation ( δ = 2.48%). In some scenarios, additional investments in wind and solar power generation surpass oil and gas extraction for about 5 years during the transformation to electric vehicles. Once built, however, wind and solar capital assets last longer, as shown by their low rate of depreciation ( δ = 3.26%).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".