The impact of climate change on agricultural labour productivity: implications for human mobility and poverty
Bibliographic record
Abstract
BACKGROUND AND AIM: Working in hot or cold environments causes discomfort, fatigue, and cognitive impairment, raising the risk for health complications. The present study developed a new model to estimate the impact of ambient conditions on labour productivity based on field data and applied this model to predict the welfare implications of climate change by estimating the labour productivity change between the years 2000 and 2040. METHODS: In total, we monitored 1,260 hours of work performed by 194 (men=123; women=71) experienced and acclimatized agriculture workers from 10 nationalities. Time-motion analysis using video recordings was used to extract detailed information on each worker’s activities during their work shift. Sine orthogonal distance regression was used to generate the labor loss functions for WBGT and air temperature. Using this model, we projected the welfare implications across the globe of climate change by estimating the labour productivity change between the years 2000 and 2040, using an extended unified general equilibrium framework combining labour mobility and trade interactions between locations. RESULTS: Our findings reveal an inverted U-shaped relationship with the highest labour productivity observed at 15 °C WBGT or ambient temperature (R2 0.95-0.98). By applying this model to project global welfare implications, we found that the ongoing climate change is expected to impair agricultural labour productivity, promoting significant labour mobility and wealth redistribution across the globe. In contrast to cold regions, which are projected to have average gains up to 6.3%, regions located close to the equator, where poverty is widespread, will face average losses up to 1.2% in productivity and wealth. CONCLUSIONS: Our projections show larger labour productivity losses in countries where poverty is widespread and the economy is heavily dependent on the agricultural sector. This creates concerns over whether the 1st Sustainable Development Goal involving eradication of poverty can be achieved by 2030.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".