Sustainable Energy Industry Systems in the United States and Canada Demonstrating the Value of Solar-to-X
Bibliographic record
Abstract
The transition to highly sustainable energy industry systems is being driven by significant growth in solar photovoltaics (PV). Despite targets to reach net-zero emissions by 2050, fossil fuels still dominate the energy industry systems in the USA and Canada. Transition pathways are developed and analyzed comparing a complete defossilization of both energy and nonenergy demands with business-as-usual conditions based on government projections. The results demonstrate the benefits of transitioning to 100% renewable energy for all sectors, as excess low-cost electricity from solar PV can be used for power-to-X solutions to produce electricity-based fuels, chemicals, and materials. By 2050, the power sector will only consume 20% of generated electricity, with the remaining used to electrify the heat, transport, and industry sectors. Thus, 86% of all primary energy in the system comes from renewable electricity, as total electricity generation increases from 4394 TWh in 2020 to 20 795 TWh in 2050. Solar PV reaches 78% of all electricity generation, leading to 10.6 TW of installed capacity. The full energy industry sector transition leads to reductions in both levelized cost of electricity (LCOE) and levelized cost of final energy (LCOFE). The LCOE sees massive reductions from 72 €/MWh in 2020 to 25 €/MWh in 2050, and the LCOFE decreases from the current 50 to 41 €/MWh in 2050. The strong operational synergies between solar PV and flexible electrolysis enable a transition pathway that demonstrates the viability of a Power-to-X Economy in achieving climate targets of net-zero emissions. The high share of solar PV indicates a Solar-to-X Economy characteristic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".