Environmental Policies and Countermeasures for the Phase-Out of Ozone-Depleting Substances (ODSs) over the Last 30 Years: A Case Study in Taiwan
Bibliographic record
Abstract
It is well established that the reaction cycles involving some halogenated alkanes (so-called ozone-depleting substances—ODSs) contribute to the depletion of ozone in the stratosphere, prompting the Montreal Protocol (initially signed in 1987), and later amendments. The Protocol called for the scheduled phase-out of ODSs, including chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), carbon tetrachloride (CCl4), halon, methyl chloroform (CH3CCl3), methyl chloride (CH3Cl), and even hydrofluorocarbons (HFCs). In view of the urgent importance of ozone layer protection to the global ecological environment, the Taiwanese government has taken regulatory actions to reduce ODS consumption since 1993, through the joint venture of the central competent authorities. Under the government’s regulatory requirements, and the industry’s efforts to adopt both alternatives to ODSs and abatement technologies, the phase-out of some ODSs (i.e., CFCs, CCl4, halon, and CH3CCl3) was achieved prior to 2010. The consumption of HCFCs and methyl chloride has significantly declined over the past three decades (1993–2022). However, HFC emissions indicated a V-type variation during this period. Due to local production and extensive use of HFCs in Taiwan, the country’s emissions increased from 663 kilotons of carbon dioxide equivalents (CO2eq) in 1993 to 2330 kilotons of CO2eq in 2001, and then decreased to 373 kilotons of CO2eq in 2011. Since then, the emissions of HFCs largely used as the alternatives to ODSs showed an upward trend, increasing to 1555 kilotons of CO2eq in 2022. To be in compliance with the Kigali Amendment (KA-2015) to the Montreal Protocol for mitigating global warming, the Taiwanese government has taken regulatory actions to reduce the consumption of some HFC substances with high global warming potential (GWP) under the authorization of the Climate Change Response Act in 2023, aiming at an 80% reduction by 2045 of the baseline consumption in 2024.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".