Climate risk to rice labour

Code demo to go with Regional disparities and seasonal differences in climate risk to rice labour.

This code has a DOI .

Abstract

The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health and income of individuals, while also decreasing global economic productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term (p<<0.01, R2>0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress.

Getting started

The notebook has already been evaluated, so have a look reduced_example.

You can also run it on or on your own machine.

CMIP6 data is retreived from the Pangeo GCS.

A script is provided to setup the conda environment from inside the notebook if required.

Project Organization

├── README.md
├── environment.yml    <- Conda environment specification.
├── env.sh             <- Conda environment setup script.
│
├── notebooks          <- Jupyter notebooks.
│   └──reduced_example.ipynb <- Example of heat/labour analysis using climate data and crop calendars.
│   └──reduced_example.py    <- Script version of above notebook, used for clean version control.
├── src                <- Source code for use in this project.
│   ├──Labour.py       <- Formulae for assumptions about the effect of WBGT on labour.
│   └──RiceAtlas.py    <- Routine for loading RiceAtlas data.