Reducing uncertainty in climate risk perception to enhance resilience: A data science approach using the World Risk Poll

Year
2025
Primary Supervisor
Mohammad Shamsudduha <m.shamsudduha@ucl.ac.uk>
Institution
University College London (Department of Risk and Disaster Reduction)
Academic Supervisors
Carina Fearnley <c.fearnley@ucl.ac.uk> (University College London, Science and Technology Studies)
Project Partners
Lloyd’s Register Foundation

Lloyd’s Register Foundation (LRF) is the industry partner (co-supervisor Nancy Hey). External collaborators may join the PhD advisory team from NGOs.

Background

The Lloyd’s Register Foundation’s (LRF) World Risk Poll is a unique global dataset capturing how people perceive and experience risks to their safety. This PhD project will analyse data from the 2019, 2021, and 2023 surveys (published in 2024) to examine how perceptions of climate change threats to communities and countries have evolved over time, particularly before and after the global COVID-19 pandemic.

Focusing on spatiotemporal and geographic dimensions, the research will conduct global-scale analyses to identify broad trends and undertake detailed national-scale studies to uncover specific drivers of climate change risk perception. A key focus will be on understanding differences between perceptions of climate risks and the reality of actual levels of climate-related hazards, such as extreme weather events, water and food security. By addressing these perception-reality gaps and the uncertainties they create, the project will provide critical insights to inform strategies for effective risk reduction and climate resilience-building.

The project will employ advanced data science methods and data visualisation tools to uncover these insights, supporting global efforts to reduce uncertainty and enhance responses to climate risks at global, national, and regional levels. Specific examples will be drawn from the LRF’s Ocean Stewardship Centres located in Global South nations, including Brazil, Kenya, Ghana, Bangladesh, India, the Philippines, and Indonesia. Additional relevant datasets and research will be utilised to compare and contrast findings.

PhD opportunity

This PhD project will address critical challenges in analysing large-scale survey data, leveraging the extensive Lloyd’s Register Foundation World Risk Poll dataset. It will explore advanced data science methods to extract meaningful insights from the multiple survey datapoints, focusing on reducing risks by understanding sources of uncertainty and enhancing climate resilience. A key emphasis will be placed on vulnerable nations, particularly in the Global South, where the impacts of climate change are most acute. The research will examine spatiotemporal and geographic dimensions, exploring the evolution of risk perceptions over time and across regions.

Key research challenges and questions include:

  • Temporal changes in risk perception: How have perceptions of climate change threats evolved between the 2019, 2021, and 2023 surveys, particularly in relation to extreme weather events and disasters? What influence has the COVID-19 pandemic had on public awareness and concern about climate risks?
  • Geographic variation in perception: How do perceptions of climate-related risks vary across regions and countries? What socio-economic, cultural, and environmental factors drive these differences, and how do they intersect with actual levels of climate hazards?
  • Alignment between perceived and actual risks: How closely do public perceptions of climate risks align with the actual exposure and vulnerability of communities and countries to climate hazards? What role does risk communication play in reducing uncertainty and enhancing climate resilience?
  • Data visualisation and communication: How can advanced data science techniques and visualisation tools be employed to effectively map and communicate the evolving perceptions of climate risks, making the findings accessible to policymakers and communities?

By addressing these questions, the project will provide valuable insights into global and national responses to climate threats, supporting efforts to build resilience through improved risk communication and public engagement and future World Risk Poll Survey strategies.

Other information

Applicant profile: The ideal applicant will have a strong academic background in a relevant discipline such as geography, environmental science, data science, or social sciences, with experience in quantitative and qualitative research methods. Proficiency in data analysis and visualisation, using tools such as R, Python, or Geographic Information System (GIS), is essential. Familiarity with climate risk or disaster reduction studies is highly desirable. Excellent analytical and communication skills, along with a passion for addressing global challenges in climate resilience and risk reduction, are crucial.

Further reading