Ozone Depletion Substances (ODS) Emission Analysis from the Life Cycle of Chemical Substances and Electricity Used in Potable Water Production in Malaysia
Bibliographic record
Abstract
Malaysia is a country that is very committed in ensuring a constant development in a sustainable way by creating a balance between economy, social and environment. It can be proven as Malaysia is ranked in a very good position in Environmental Sustainability Index. But this ranking should be a guideline to ensure the pockets of weaknesses in executing sustainable development in this country should be filled especially in effectively managing the environment. Ozone Depletion Substances (ODS) emission needed an environmental management method that is capable to identify the cause of this problem in order to the right action could be taken in place to mitigate the problem of ozone depletion. Event though drastic measures were taken in this country such as the ban of Halon gas use in fire control sector as a signatory to the Montreal Protocol 1989, it does not mean that this measure is enough to stop ODS from being emitted to the air. The use of Life Cycle Assessment (LCA) in a water treatment system proves that this method is capable to identify substances that emit ODS. Chemicals and electricity used in the water treatment is found to emits 8 types of ODS and Methane, bromotrifluoro-, Halon 1301 is contributed the most compared to the other 7 types. Aluminium sulphate (alum) is substance that contributed the most Methane, bromotrifluoro- and Halon 1301 in the atmosphere. Life cycle analysis conducted to identify the cause of ODS emission in Alum found that electricity generation using coal and fossil fuel contributed the highest ODS emission. Electricity generation through hydroelectric is found not to emit any ODS at all. The advantage of LCA in identifying weaknesses and shortcomings of a product should not be taken lightly by Malaysia. Malaysia should use LCA as an effective environmental management method that indirectly secures Malaysia's current ranking to a better position in the future. © 2010, INSInet Publication.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".