A study published in the journal Nature Medicine evaluates the impact of climate change on mortality caused by heat and cold in 854 European cities between 2015 and 2099. It analyses different scenarios that take into account climate change, demographic developments and adaptation to heat.

Key findings:

• No heat adaptation Heat-related mortality is expected to overcomes the decline in cold-related mortality in all scenarios considered.
• Under the SSP3-7.0 scenario, which assumes the least effort in mitigation and adaptation, the total number of deaths related to climate change could increase by 49.9 % and accumulate 2,345,410 deaths between 2015 and 2099. Even with a high level of adaptation (50% risk reduction), this trend would not be reversed.
• Regional differences are significant: Northern European countries could see a slight decrease in mortality, while Mediterranean and Eastern Europe they are very vulnerable.
• The increase in heat-related mortality is associated with a steeper curve of the mortality-temperature curve at high temperatures. This means that an increase in temperature has a greater impact on mortality than a decrease in temperature.
• Heat adaptation can mitigate negative impacts, but large-scale adaptation (90% risk reduction) is needed to reverse the trend of increasing mortality, especially under the SSP3-7.0 scenario. Even with 50 % adaptation, mortality would still increase in the Mediterranean, Central Europe and the Balkans.
• Geographical differences are significant. For example, Malta shows the largest increase in mortality, while Ireland shows the lowest.
• The data indicate that strong mitigation policies aimed at reducing greenhouse gas emissions are keyto avoid widespread increases in mortality associated with climate change.

The document emphasizes that it is A comprehensive assessment of the impacts of climate change on health is needed and that mitigation and adaptation measures need to be taken to protect the health of the population.

Study methodology:

• The study uses data from 854 European cities with more than 50,000 inhabitants.
• They were used daily temperature data from 19 general circulation models (GCMs), demographic projections, and specific temperature-related mortality curves for each city and five age groups.
• To isolate the impacts of climate change, two sub-scenarios were compared: "full" (with climate and demographic changes) and "demographic change only" (with constant temperature distribution).
• They were also used Monte Carlo simulations to account for uncertainty in epidemiological analyses.

The study also considers factors such as population aging, which increases vulnerability to heat and cold, as well as the potential for adaptation through socio-economic factors. However, the consideration of adaptation was limited to general adaptation without geographical differences and specific driving forces. Spring

Glossary of key terms

• SSP (Shared Socioeconomic Pathways): Shared socio-economic pathways; a framework for socio-economic development scenarios used in modelling the future impact of climate change.
• GCM (General Circulation Model): General circulation model; a computer model that simulates the global climate system and is used to predict future climate conditions.
• ERF (Exposure-Response Function): Exposure response function; an epidemiological function that describes the relationship between exposure (e.g. temperature) and health outcome (e.g. mortality).
• MMT (Minimum Mortality Temperature): Minimum mortality temperature; the temperature at which mortality is lowest, and from which it increases at higher and lower temperatures.
• Net Effect: The balance between increases in heat-related mortality and decreases in cold-related mortality due to climate change.
• Adaptation: Measures that reduce the negative impacts of climate change. In this context, these are measures that reduce the negative impacts of heat on health.
• Mitigation: Measures to reduce greenhouse gas emissions and thereby slow down climate change.
• Monte Carlo Simulations: A computational technique that uses random numbers to model the probability of different outcomes in a process that cannot be easily predicted due to random variables. It is used in the study to model uncertainty in ERF estimates.
• Mortality: The number of deaths in a given population during a certain period.
• Temperature-related mortality: Mortality that is directly affected by exposure to extremely high or low temperatures.