The 2022 China report of the Lancet Countdown on health and climate change: leveraging climate actions for healthy ageing
Bibliographic record
Abstract
A health-friendly, climate resilient, and carbon-neutral pathway would deliver major benefits to people's health and wellbeing in China, especially for older populations, while simultaneously promoting high-quality development in the long run. \n \nThis report is the third China Lancet Countdown report, led by the Lancet Countdown Regional Centre based in Tsinghua University. With the contributions of 73 experts from 23 leading institutions, both within China and globally, this report tracks progress through 27 indicators in the following five domains: (1) climate change impacts, exposure, and vulnerability; (2) adaptation, planning, and resilience for health; (3) mitigation actions and health co-benefits; (4) economics and finance; and (5) public and political engagement. From 2021 to 2022, two new indicators have been added, and methods have been improved for many indicators. Specifically, one of the new indicators measures how heat affects the hours that are safe for outdoor exercise, an indicator of particular relevance given the boom in national sports triggered by the summer and winter Olympics. Findings in this report, which coincide with the UN Framework Convention on Climate Change 27th Conference of the Parties (COP27) hosted in Egypt (where much attention is being focused on adaptation for clinically vulnerable populations), expose the urgency for accelerated adaptation and mitigation efforts to minimise the health impacts of the increasing climate change hazards in China.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".