China’s future emission reduction challenge and implications for global climate policy
Bibliographic record
Abstract
In December 2015, China joined 190 plus nations at Paris in committing to the goal of limiting the rise in global average temperature to ‘well below’ 2°C. Carbon budget analysis indicates that goal will require not only that the European Union and US reduce their emissions by greater than 80% by 2050, but that China at least comes close to doing so as well, if any budget is to be left over for the rest of the world (RoW). Given that RoW emissions are, and will come from, low-income and emerging nations, China’s emission reduction potential is of no small consequence. In this paper, we use the Kaya identity to back out changes in the drivers of CO2 emissions, including gross domestic product (GDP), energy intensity (E/GDP) and the carbon content of energy (C/E), the latter two calculated to be consistent with China’s long-term GDP growth rate forecasts and specified 2050 CO2 emission reduction targets. Our results suggest that even achieving China’s highly optimistic renewable energy targets will be very far from sufficient to reduce China’s CO2 emissions from 9.1 Gt it emitted in 2015 to much below 3 Gt by 2050. Even reducing its emissions to 5 Gt will be challenging, yet this falls far short of what is needed if the world is to meet its ‘well below’ 2°C commitment.Key policy insights Under the Paris Agreement there is great pressure on China to very substantially reduce its emissions by 2050.While China has attached great importance to renewables and nuclear energy development, even achieving the most optimistic targets would not be sufficient to reduce China’s emissions from 9.1 Gt in 2015 to much below 3 Gt by 2050.China’s emission reduction potential falls far short of what is needed if the world is to meet its Paris ‘well below’ 2°C commitment, even if the EU and US reduce their emissions to zero by 2050.Emission cuts consistent with the Paris Agreement will require that China and the world give much greater weight to advancing research and development of scalable low-, zero- and negative-carbon sources and technologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".