The Effect of Ecological Elasticity in Taiwan’s Carbon Reduction Policies: The STIRPAT Model
Bibliographic record
Abstract
<p>The challenges from the climate change and the global warming have become one of the most important issues to solve in the world. Under the Kyoto Protocol, countries which have signed the Kyoto Protocol have faced the pressure of reducing greenhouse gas emissions. The two main policies for reducing carbon dioxide are “carbon tax” and “carbon trading”. This research explores which policy will be more suitable for the society and economic environment of Taiwan. This research uses EIA database, the statistical data from the Taiwan Bureau of Energy, Ministry of Economic Affairs, and the data from AREMOS database from 1982 to 2010. The dependent variable is the emission of carbon dioxide, and the independent variables are premium diesel oil price index, population, GDP per capita and the squared term of GDP per capita. The research method is based on the Ordinary Least Squares to estimate the ecological elasticity in the STIRPAT model by analyzing the influence of the change of energy price to the change of the emission of carbon dioxide. From the empirical result, it was discovered that though the energy price and the emission of carbon dioxide was negatively correlated, the ecological elasticity was inelastic. As a result, carbon trading seems a more suitable policy for Taiwan.</p>
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".