Simulation Analysis of Impact of Smart Grid and Renewable Energy on GHG Emission
Bibliographic record
Abstract
China once again promises to manage energy consumption and peak CO2 emission around 2030 in Paris Agreement in 2016 that expresses her ambition of mitigating emission. Using renewable energy to optimize energy structure is recognized as effective countermeasure to reduce GHG emission. Additionally, it is inevitable that improving energy efficiency is still core issue in energy usage. Smart Grid (SG) and renewable energy are collectively introduced in this research. Power supply and demand model is constructed to analyze the effect of SG and renewable energy on energy usage. Input-Output (I-O) simulation model is applied to make dynamic analysis based on extended I-O framework. Comprehensive model is constructed to evaluate the impact of SG and renewable energy on economic growth, energy usage and environmental improvement under different emission limitation. The proper policies covering carbon tax and subsidy are proposed to mitigate GHG emission, improve energy usage and optimize economic structure. Trade-off among economic growth, energy conversation and environmental improvement is realized in the study area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".