Despite clear signs that the impacts of climate change are escalating, the global response has been inadequate. Traditional scientific efforts have fallen short of providing knowledge and tools that have been broadly applied in decision-making, and innovative approaches to knowledge translation are needed. To catalyse climate action in Europe to protect public health, we need new knowledge, data, and tools on the relationships between changes in environmental hazards caused by climate change, ecosystems, and human health; the health co-benefits of climate action; the role of health evidence in decision making; and the societal implications of climate change for health systems.
The effectiveness of adaptation strategies and monitoring of mitigation can be improved through innovative surveillance and forecasting tools. There is a need for innovative tools to identify, monitor, forecast and predict impacts of climate change induced environmental hazards on human health. Bottom-up, citizen-generated data can shed unique light on perceptions regarding health implications of climate change and the social acceptability of mitigation actions. Within CATALYSE, this project develops an innovative surveillance tool to monitor perceived attribution of health and wellbeing to climate change induced hazards.
In order to understand perceived attribution, we are building models that identify causal relationships in language. Causality is central to human reasoning and decision-making processes. By analysing and comprehending causal relationships expressed in language, we can gain deeper insights into the underlying thought processes and motivations behind actions, policies, and arguments. Additionally, understanding causal language can help improve communication strategies by enabling more persuasive and clear communication of cause-and-effect relationships. This can be particularly important in the climate change and health discourse, where effective communication of policy rationales can influence public opinion and support for specific policies.