Bibliographic record
Abstract
coal accounted for 30.3% of total global energy consumption in 2011, the highest share since 1969 (BP 2012a). Since coal on a per unit energy basis is the most prolific of the fossil fuels in terms of CO2 emissions, this fact alone underscores the magnitude of the challenge we face in addressing the climate issue. Emissions of CO2 from oil and natural gas, expressed on a per unit energy basis, amount to approximately 78% and 54% of those from coal. In 2010, 43% of global CO2 emissions were derived from coal, 36% from oil, and 20% from natural gas. China was responsible for 49.3% of total coal consumed worldwide in 2011. The United States (13.5%), India (7.9%), and Japan (3.2%) ranked 2 through 4. Consumption in the Asian Pacific region amounted to 71.2% of the global total in 2011, as compared to 14.3% in North America (the United States, Canada, and Mexico) and 13.4% in Europe and Eurasia (including the Russian Federation and Ukraine). Coal accounted for 70% of total energy use in China in 2011 (65.5% in 2013), as compared to 22% in the United States. Use of coal increased in China by 9.7% in 2011 relative to 2010. In contrast, consumption in the United States declined by 0.46% over the same period. BP (2012b) projects that demand for coal in OECD countries will decrease by 0.8% per year between 2011 and 2030. The projected falloff in OECD countries is offset by growth of 1.9% per year over the same time interval in non- OECD countries. China, in the BP projection, remains the world’s largest consumer of coal in 2030 (52% of total global consumption). The growth rate in China is expected to drop, though, from 9% per year over the decade 2000 to 2010, to 3.5% between 2010 and 2020, falling further to 0.4% between 2020 and 2030. The trend, as indicated in the BP analysis, reflects the assumption of a shift to less coal- intensive economic activities, combined with an improvement in overall energy efficiency. India is projected to surpass the United States in terms of total demand for coal by 2024.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.013 |
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".