Limitations of Greenhouse Gas Mitigation Technologies Set by Rapid Growth and Energy Cannibalism
Bibliographic record
Abstract
As the unacceptable results of continued fossil fuel combustion on climate change become ever clearer, a need to dramatically reduce greenhouse gas (GHG) emissions by aggressive energy conservation and immediate transitioning global civilization to alternative energy sources has become evident. Many energy technologies are capable of displacing significant volumes of fossil fuels. Unfortunately, neither the enormous scale of the current fossil fuel energy system nor the necessary growth rate of these technologies is well understood within the limits imposed by the net energy produced for a growing industry. This technical limitation is known as energy cannibalism and refers to an effect where rapid growth of an entire energy producing or energy efficiency industry creates a need for energy that uses (or cannibalizes) the energy of existing power plants or production plants. Thus during rapid growth, the industry as a whole produces no net energy because new energy (or conserved energy) is used to fuel the embodied energy of future power plants or production facilities. Such life cycle analysis is also valid for GHG emissions. All current technologies are dependent to some degree on fossil fuel energy and thus also contribute to emissions. This paper expands earlier work to generalize the GHG emission neutral growth rate limitation imposed by energy cannibalism to any renewable energy technology or any energy efficiency technology. Conclusions and recommendations are made from the analysis to assist decision makers in optimizing deployment of technologies on large scales to reduce GHG emissions to safe levels without overshoot.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".