Economy-Wide Impacts of Climate Change: A Joint Analysis for Sea Level Rise and Tourism
Bibliographic record
Abstract
While climate change impacts on human life have well defined and different origins, the interactions among the diverse impacts are not yet fully understood. Their final effects, however, especially those involving social-economic responses, are likely to play an important role. This paper is one of the first attempts to disentangle and highlight the role of these interactions. It focuses on the economic assessment of two specific climate change impacts: sea-level rise and changes in tourism flows. By using a Computable General Equilibrium (CGE) model the two impacts categories are first analysed separately and then jointly. Considered separately, in 2050, the forecasted 25 cm. of sea level rise imply a GDP loss ranging from (-) 0.1% in South East Asia to almost no loss in Canada, while redistribution of tourism flows - which in terms of arrivals favours Western Europe, Japan, Korea and Canada and penalises all the other world regions - triggers GDP losses ranging from (-) 0.5% in Small Island States to (-) 0.0004% in Canada. GDP gainers are Australia, New Zealand, Western Europe, Middle East and South Asia.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".