Project description
Examining population dynamics under global climate change
The impacts of climate change on health, wellbeing and livelihoods are already being felt. It is thus reasonable to assume that climate change can influence demographic processes, through influencing fertility, mortality and migration, the three key demographic outcomes driving population change. However, until now, global population projections have not considered the possible effects of climate change on population trends. The EU-funded POPCLIMA project will comprehensively address this research gap by studying how climate change influences demographic outcomes as well as identifying the mechanisms, examining the differential impacts on subgroups of populations, and forecasting future population dynamics under climate change. Results will help the scientific community build more realistic scenarios about population trends under the rapid pace of climate change.
Objective
This study is the first to comprehensively address the impacts of climate change (CC) on population trends. Existing studies on population and CC focus either on the effects of population growth on CC or on identification of populations at risks to climatic hazards. There is virtually no literature on the mechanisms and the extent to which CC affects and will affect demographic outcomes. It is even not clear whether CC may increase or decrease fertility, mortality and migration. Being the first study to comprehensively and systematically address this issue, this project is very timely given that CC impacts have already been felt and are forecast to be much stronger in the future.
The project has four main objectives: 1) to study the way (direction and extent) in which CC influences demographic outcomes; 2) to examine the differential impacts of CC on subgroups of populations; 3) to identify the mechanisms through which CC influences demographic outcomes; 4) to forecast future population dynamics under climate change.
The project will analyse fertility, mortality and migration separately, using a variety of methodologies and datasets. Results from these analyses will then be used to inform the population projections under future CC scenarios. Our methodological approach is innovative. We will utilise and combine geo-referenced climate, demographic and socioeconomic data from different data sources (surveys and social media data) at the individual-, regional and country-level. Structural equation models are employed to identify the causal pathways and machine learning method is used to handle large-scale data.
Results will be particularly important for: 1) helping the scientific community in building more realistic scenarios about populations trends under the rapid pace of CC; 2) informing the international debate over the social costs of CC; and 3) providing a set of estimations useful to design better social and environmental policies.
Fields of science
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
40126 Bologna
Italy