Development of a system dynamics model for dioxin flow and its application to the energy sector
Bibliographic record
Abstract
Dioxin pollution has been problematic in Taiwan. Although the government has established emission standards and emission inventory to control dioxin pollution, such efforts only apply to pollution emissions; no attempt has been made to understand the flow of dioxins in different environmental media. In this study, the STELLA software was used to model the flow pattern of dioxins in various media. This model and the RAIDAR model established by the Canadian Environmental Model Research Center were used to simulate dioxin flow in Taiwan, and their results were compared with the measured values. The accuracies of the RAIDAR and STELLA models were 63.92% and 49.78%, respectively. This shows that the simulation with the STELLA model provided results closer to the measured values and that the error was less than ten times that of the RAIDAR model, indicating that the proposed model is predictive. In addition, we used the results of a system dynamics model for dioxin flow and an air resource co-benefits (ARCoB) model to apply the obtained results to the energy sector to quantify the co-benefits of reducing dioxin, greenhouse gas, and air pollutant emissions on the basis of the policy target for the year 2030. The total co-benefits of natural gas and renewable energy (RE) scenarios were US$9.63 billion and US$12.57 billion, respectively; the benefit-cost ratios were 2.89 and 20.67, respectively. The development of an RE policy as an alternative to a coal-fired power generation policy will contribute to the best co-benefits of integrated reductions and will also contribute to human health.
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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 | 0.000 | 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".