- Extreme weather poses numerous risks to India's economy and society, but there currently exist no comprehensive assessments of these risks.
- Our approach uses outcomes data (on mortality rates, work hours, agricultural yields, conflict incidence, and crime rates) from around the world to flexibly estimate how sensitive a location is to temperature based on its adaptive capacities.
- By pairing these results with high-resolution seasonal climate forecasts, we can predict future climate-induced mortality, reduction in work hours, reduction in crop yields, and conflict and crime incidence.
- For example, a single day at 32°C would raise mortality rates by 0.66 deaths per 100,000 in the year 2010. Taking a population in that year as 1.23 billion people, this would result in 8,124 extra deaths in India for that day.
- A day where the maximum temperature is 37°C throughout India is associated with 101 million lost work hours nationwide -- that's over 12.5 million full-time equivalent workers, assuming an eight-hour workday.
Extreme weather poses numerous risks to India's economy and society, and future climate change is expected to amplify these risks. However, at present there exist no comprehensive assessments of how either current weather shocks or future climate change might affect India's economic sectors. Furthermore, location-specific information on climate vulnerabilities within India is completely lacking, leaving both government and private actors ill-equipped to make informed decisions.
In this project, we develop India-wide, spatially-disaggregated estimates of temperature sensitivities for a range of socio-economically relevant outcomes (i.e. human mortality, labour supply, agricultural yields, crime rates, and conflict incidence). Our approach uses outcomes data (on mortality rates, work hours, agricultural yields, conflict incidence, and crime rates) from around the world to flexibly estimate how sensitive a location is to temperature based on its adaptive capacities, determined using measures of physical and socioeconomic characteristics such as long-run climate, income per capita, prevalence of irrigation, and how urban the location is. We generate high-resolution datasets of these measures and thereby extrapolate locally-relevant temperature sensitivities throughout India.
Results from a sister project focused on Africa are available here.