Declining lake ice in response to climate change can impact spending for local communities
Bibliographic record
Abstract
Lake ice is an important socio-economic resource that is threatened by climate change. The cover and duration of lake ice are expected to decline as air temperatures warm in the coming decades, disrupting a previously reliable source of income for many activities dependent on lake ice. The economic consequences of climate-induced lake ice loss remain unexplored, creating a significant research gap. The purpose of this study was to quantify the monetary spending associated with lake ice and how climate change may impact that value. Using a series of General Circulation Models (GCMs), greenhouse gas emissions scenarios, and models for lake ice cover, we predicted changes in lake ice by the end of the 21st century for the Northern Hemisphere. We also synthesized examples of spending associated with lake ice activities and discussed the potential implications expected with declining ice cover. We found that lake ice will decrease in area by 44,000-177,000 km2 and shorten in duration by 13-43 days by 2100. Using 31 examples of revenue from lake ice, we found that lake ice generates spending of over USD 2.04 billion to local communities and economies. We also found that countries predicted to experience the greatest ice loss by the end of the century are those that currently have the largest GDP, highest greenhouse gas emissions, and are most dependent on freshwater withdrawal. Our findings confirm predicted losses in lake ice that are expected because of climate change and quantify some of the potential consequences for local communities. Here we highlight lake ice as another casualty of human-caused climate change that will have profound socio-economic implications.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".