Sustainable operations management and climate change issues across the globe
Bibliographic record
Abstract
Sustainable operations management is an ever-developing field: The process of making operations management truly sustainable can never have a well-defined endpoint because it is a continuous process. The widespread concern over global warming puts pressure on companies to reduce carbon emissions and become green. The global development to biodiversity, ecosystem, climate condition, environment, ecology, etc., today, the world is heavily burdened with high carbon emissions and environmental pollution. Global warming has been a reality since the 1800s. Therefore, we have to address the climate change issues first. Finland was the first country in the world to adopt a carbon tax, and Germany adopted a feed-in-tariff law to subsidize the generation of renewable energy. Subsequent carbon taxes were adopted in Norway (1991), Sweden (1991), Denmark (1992), Ireland (2008), Japan (2012), France (2014), and Canada (2018). A few developed countries have imposed tax on carbon emission. For example, headline carbon tax rates are $139 per tonne of carbon dioxide (CO2) in Sweden, $55 in France, $29 in Denmark, and $3 in Japan. Therefore, all countries across the world must ensure the reduction of carbon emissions to zero by 2050. In Bangladesh, we need to establish an institution immediately. The ratio of large three to employee in a green factory shall be (1.38:1). Through semi-structured interviews with green factory experts, I have found that green factories, green technology, green production process, clean air, clean energy, green banking, sustainable financing are urgently required for ensuring sustainable operations management in Bangladesh.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".