Business Strategy of Climate Change: Empirical Study of the Steel Industry Sector
Bibliographic record
Abstract
There is a broad scientific consensus that our plant is warming and that post industrial revolution human activities are contributing significantly to the process. If global warming continues at the present and projected pace, it will cause significant damages to the global eco-system upon which humans are dependent. There is also a consensus that in order to limit the temperature rise to 2 degrees Celsius and prevent risky anthropogenic interference with the climate system, it is critical to stabilize carbon dioxide (CO2) concentration at no more than 550 parts per million (ppm). \n\nThe latest report published by the Intergovernmental Panel on Climate Change (IPCC) in 2007 calls for the developed countries to reduce their CO2 emissions 60% by 2050 relative to the present level and for the developing countries to control their emissions starting around 2030.\n\nAs the first tangible step to cope with global climate changes, countries adopted the UN brokered, ‘Kyoto Protocol,’ that was developed in 1997. Under the Protocol, the European Union (EU) and Japan established targets to reduce greenhouse gas (GHG) emissions by 8% and 6% respectively between 2008 and 2012 relative to 1990. The developing countries including China, Brazil and India ratified the Protocol without specific emission reduction targets. Two countries with the highest CO2 emission releases per capita, the United States and Australia did not ratify the Protocol (In December 2007, however, Australia ratified the Protocol as soon as Rudd was elected as the prime minister). Intensive discussion has begun among policymakers and policy-minded social scientists with regard to how to include these countries in the post Kyoto regime. At Conference of the Parties (COP) 13 which took place in Bali, Indonesia in December 2007, the negotiators attempted to agree on the schedule for the discussion of the post Kyoto regime. As we know, the Kyoto Protocol is only the starting point of the forthcoming marathon-like multilateral negotiations.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".