Analysis on the Potential of Greenhouse Gas Emission Reduction in Henan’s Electricity Sector
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".