Mapping vulnerability to climate change and its repercussions on human health in Pakistan
Bibliographic record
Abstract
BACKGROUND: Pakistan is highly vulnerable to climate change due to its geographic location, high dependence on agriculture and water resources, low adaptive capacity of its people, and weak system of emergency preparedness. This paper is the first ever attempt to rank the agro-ecological zones in Pakistan according to their vulnerability to climate change and to identify the potential health repercussions of each manifestation of climate change in the context of Pakistan. METHODS: A climate change vulnerability index is constructed as an un-weighted average of three sub-indices measuring (a) the ecological exposure of each region to climate change, (b) sensitivity of the population to climate change and (c) the adaptive capacity of the population inhabiting a particular region. The regions are ranked according to the value of this index and its components. Since health is one of the most important dimensions of human wellbeing, this paper also identifies the potential health repercussions of each manifestations of climate change and links it with the key manifestations of climate change in the context of Pakistan. RESULTS: The results indicate that Balochistan is the most vulnerable region with high sensitivity and low adaptive capacity followed by low-intensity Punjab (mostly consisting of South Punjab) and Cotton/Wheat Sindh. The health risks that each of these regions face depend upon the type of threat that they face from climate change. Greater incidence of flooding, which may occur due to climate variability, poses the risk of diarrhoea and gastroenteritis; skin and eye Infections; acute respiratory infections; and malaria. Exposure to drought poses the potential health risks in the form of food insecurity and malnutrition; anaemia; night blindness; and scurvy. Increases in temperature pose health risks of heat stroke; malaria; dengue; respiratory diseases; and cardiovascular diseases. CONCLUSION: The study concludes that geographical zones that are more exposed to climate change in ecological and geographic terms- such as Balochistan, Low-Intensity Punjab, and Cotton-Wheat Sindh -also happen to be the most deprived regions in Pakistan in terms of socio-economic indicators, suggesting that the government needs to direct its efforts to the socio-economic uplift of these lagging regions to reduce their vulnerability to the adverse effects of climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".