Climate change, social inequality & psychosocial wellbeing with emerging digital data – a multidisciplinary network between UK and South Korea

Description

Climate change-related inequalities in psychosocial wellbeing are relatively difficult to analyse directly because they are typically subjective experiences and feelings at the individual level.

Recent developments in social data science and machine learning techniques mean the disproportionate effect of climate change on different vulnerable groups can be measured and tracked using emerging sources of digital data. These new data sources contain patterns of various human behaviours in vulnerable people, which can be utilised to infer individual differences in psychosocial wellbeing.

This project aimed to build a cross-national network of multidisciplinary researchers between the UK and the Korea Advanced Institute of Science and Technology (KAIST) in South Korea, to investigate the effect of climate change on psychosocial wellbeing inequalities with emerging sources of digital data.

Research overview

The objective of this project was to investigate the effect of climate change on psychosocial wellbeing inequalities with emerging sources of digital data, simulating large research projects and fostering long-term collaborations.

Key findings

We developed a research agenda that considers climate change and psychosocial wellbeing inequality within the same framework, identified priority research questions, and conducted interdisciplinary and collaborative research on larger projects globally.

Through the scoping study and testing in specific research projects, we have:

1. Investigated appropriate machine learning methods for effectively predicting multiple inequality outcomes from unstructured data, and evaluated the transferability of prediction models developed in data-rich settings to cases with limited data.

2. Evaluated the effectiveness and importance of climate-related features in the prediction of economic indicators.

The project raised awareness of climate change's influence on psychosocial wellbeing, and highlighted issues where emerging digital data and machine learning may improve modelling and predicting such influence.

With the support of this grant, we actively engaged and built collaborations and partnerships with policymakers, local authorities, and industrial and business partners such as the Leeds City Council, the Met Office, and regional companies. New and advanced data analytics techniques are being developed as part of those collaboration projects.

The research outputs have the potential to inform the development of new machine learning algorithms and data analysis techniques that can effectively predict multiple inequality outcomes from unstructured data.

Publications and outputs

Conference presentation

• J. Jeon, “Forecasting the Market Size of Industries using Time Series of Patents", the International Symposium of Forecasters, University of Virginia, US, 2023
• J. Liang, X. Wei, B. Summers, “A method to tabular data to images for convolutional neural networks”, the International Symposium on Forecasting, Oxford, UK, 2022

Other

• “Digital footprints for climate change insight”, University of Leeds – “Further Together” campaign

Contact

Dr Xingjie Wei

Visit the website

This project was funded by the Economic and Social Research Council.