Bibliographic record
Abstract
Little of the literature from economists on climate change has focused on how accelerating climate change will affect economic structure, and the consequences which follow. Here we analyze the medium- to long-term implications of global warming for global financial structure. Stern (2007) suggests that greenhouse gas emissions generated by human activities could lead to global temperature increases of between 1 and 5°C by 2050. This will result in a large increase in global risk of extreme weather events, changing crop yields, sea level rise, and adverse health effects. Part of this risk, while uncertain, is non-diversifiable; but the other part, while diversifiable, is difficult for conventional insurance arrangements to accommodate. No established pattern of location or feature specific risks exists that are comparable to annual mortality tables for life cover. Insurance arrangements regulated on a national basis pose further possible problems. The response, we suggest, will be global financial market innovation primarily in the area of insurance, but also in diversification of asset holdings. Pressures will also build for a very large increase in government provided insurance. We suggest in this paper that, even with modest climate changes of 1 to 2°C, global insurance markets will expand dramatically. Under more extreme climate change scenarios, the entire global financial structure will undergo major change, with a refocusing of major financial activity away from intermediation between borrowers and lenders and the facilitation of the accumulation of assets, towards a focus on insurance arrangements and the diversification of risks associated with climate change. This will likely involve new contingent instruments, new issuers and financial institutions, and extensive government involvement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".