Analysis on the Potential of Greenhouse Gas Emission Reduction in Henan’s Electricity Sector
Why this work is in the frame
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Bibliographic record
Abstract
Henan Province, located in the middle of China, is the typical case for a power system predominantly on fossil fuel and electricity sector, which is also the main emission source in Henan Province. In order to evaluate the potential for greenhouse gas (GHG) emission reduction of the electricity sector in Henan Province , this article analyses different development scenarios based on the “Long-range Energy Alternative Planning System” (LEAP) model to simulate diversification development patterns. Results showed that there is a potential reduction in GHG emission in the Henan’s electricity sector. The government should design and implement different emphasis in different terms. For instance, we founded that the greenhouse gas emission are decreased considerably in technology priority scenario (8.7 MtCO2) and energy structure optimization scenario (30.30 MtCO2)compared with baseline scenario before 2020, in terms of emission intensity per power unit, during 2005-2020, technology priority scenario, energy structure optimization scenario, and baseline scenario descend by 16.1%, 19.1%, 14.2%, respectively. Ultimately, it gives some policy advice to the power industry in Henan province, the advanced generated technologies will be employed to reduce the greenhouse gas emissions greatly before 2015; however, renewable energy and energy structure adjustment will play the dominant role in reducing the greenhouse gas emissions in the long term. It is also suggested to develop carbon tax and “Clean Development Mechanism” (CDM) projects in Henan Province, such as renewable CDM projects, Methane recovery CDM projects, waste heat/gas/pressure recovery CDM projects, to contribute to the reduction of greenhouse gas emission in Henan’s electricity sector.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it