Assessing the Long-Term Energy Landscape Using a Global Energy Market Model
Bibliographic record
Abstract
Abstract The objective of this paper is to estimate the long-term energy mix – i.e. the combination of resources including solids, liquids and gases that will satisfy energy demand to the year 2040 – with a Global Energy Market model (GEM). The GEM provides a close match of the historical energy mix dating back to the year 1850 and is then used to make forecasts for the future. Originally developed in 2007, the GEM was used to project the energy mix to the year 2030. In the present paper, the validity of the original projection is tested against the most recently available data. The GEM estimates the fractional contribution of different primary energy sources to the global market. In total, there are six parameters that allow the GEM equation to give the best possible match of the historical energy mix. Using the estimated parameter values, the model can then be extended into the future, providing a reference case and alternative scenarios of the energy mix based on evolving unconventional, conventional and renewable resource quantities, costs, technologies, economic growth, population and policies. The original GEM findings from 2007 forecasted a "2030 1/3 forecast", indicating that solids, liquids and gases would each occupy a third of the energy market in the year 2030. After further disaggregating the categories, it was found that liquids, mostly, oil would experience a declining market share by 2030 while natural gas would see a rapid rise. The share solids, mostly coal, was relatively flat by that time. Our new results show continued penetration of natural gas in the energy mix – a result consistent with efforts to reduce carbon emissions. Our proposed paper is novel in that it uses the most recent statistics of the last 10 years on consumption of different energy sources to verify the accuracy of the original GEM projections carried out in 2007. Once the results are proven reasonable, new scenarios are developed with an extended time horizon to the year 2040.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".