The rising burden of disease counts as one of the most salient concerns of a warming climate. These risks are especially serious in populous, rapidly growing urban landscapes of low-income, tropical countries. Surat, located on the banks of the River Tapi, has temperature and humidity patterns that can be climatologically described as ideal mosquitogenic conditions. Its flat terrain, long history of riverine flooding, and routine water logging during monsoons makes it especially prone to endemic vector borne diseases and morbidity during the peak rainy season. In the past, a large share of malarial cases within India, and Gujarat state in particular, were reported from Surat. In recent times however, government interventions with respect to the introduction of numerous public health initiatives has led to a plateauing of the number of cases reported. This deceleration in cases reported has occurred despite an increase in population over time and expansion of city limits in 2006. Climate change induced probable increases in temperatures and rainfall would arguably add to the aggregate malarial risk within the city. This paper attempts to develop an urban climate impact assessment model with a focus on public health. Using past data on disease cases, climate trajectories (temperature, precipitation) malarial risk is projected. This health risk is then monetized to help establish the burden of malaria to be faced by the city from an economic point of view. If viewed from a different angle, this estimated monetized value of health risk is also the disease burden that could be avoided due to possible health interventions (adaptation strategies). To compare against these, health intervention costs of a public programme undertaken by the government and households at a micro disease-treatment level is undertaken as an illustrative example of how the costs of prevention may compare to the benefits of prevented disease to assess the economic benefits of adaptation. We find that in a conservative estimate, against an investment of Rs. 8 million in programme and prevention costs, Surat saved Rs. 11.1 million in economic costs (loss of work-days, reduced income and productivity, and treatment costs, suggesting that there is an immediate economic case for adaptation in the face of a warming climate.
Public Health Climate Governance Economic Modelling