Introduction
Environmental sustainability is often viewed as a technological challenge.
Yet many sustainability problems are fundamentally problems of information and attention. Individuals, firms, and governments make decisions under limited cognitive resources, while environmental issues compete for attention with many other economic, social, and political concerns.
At the same time, Artificial Intelligence is rapidly transforming the information environment. Search engines, recommender systems, and generative AI increasingly determine which information people encounter, which issues become salient, and how individuals allocate their limited attention. AI therefore has the potential not only to inform decisions, but also to shape the agenda of public debate and collective action.
This project investigates the role of AI as an allocator of attention in the context of environmental sustainability. The central premise is that attention is a scarce resource. Climate change, biodiversity loss, energy transition, and other sustainability challenges compete for attention with inflation, migration, public health, geopolitical conflicts, and countless other concerns. By influencing which information receives visibility and which remains ignored, AI may fundamentally alter how societies perceive environmental risks and respond to them.
The project seeks to understand how AI-mediated information environments affect attention allocation, belief formation, and collective decision-making. It will explore whether AI can help societies focus on long-term sustainability challenges or whether algorithmic systems inadvertently contribute to short-termism, fragmentation of attention, and disagreement over environmental priorities.
Research Questions
Potential research questions include:
How do AI systems influence the salience of environmental issues?
Can AI help overcome attention constraints that hinder effective responses to climate and sustainability challenges?
How does algorithmic personalisation affect the allocation of attention across competing societal concerns?
Under what conditions does AI promote consensus regarding environmental priorities, and when does it amplify disagreement?
How should AI systems be designed when attention itself is viewed as a scarce societal resource?
Research Approach
The precise direction of the PhD will be developed jointly with the student. Possible approaches include:
Formal economic models of attention allocation, information acquisition, and collective decision-making;
Behavioural and experimental studies examining how AI-generated information affects attention and choices;
Natural Language Processing (NLP) and machine learning methods to measure the salience of environmental issues in large-scale digital communication data;
Empirical analysis of online information environments, social media, news platforms, and AI-generated content.
The project is inherently interdisciplinary and lies at the intersection of economics, artificial intelligence, behavioural science, sustainability, and public policy.
Expected Contributions
The project will contribute to emerging research on the societal consequences of AI by placing attention allocation at the centre of the analysis. It will generate new theoretical and empirical insights into how AI shapes the visibility of environmental challenges, influences collective responses to sustainability problems, and affects the capacity of societies to pursue long-term environmental goals.
References:
Acemoglu, D. and Johnson, S. (2023) Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. New York: PublicAffairs.
Agrawal, A. et al. (2018) Prediction Machines: The Simple Economics of Artificial Intelligence. Boston, MA: Harvard Business Review Press.
Fore, A., et al. (2024) ‘Unlearning Climate Misinformation in Large Language Models’, arXiv preprint arXiv:2405.19563.
Ianni A. et al (2026) 'On the Heresthetics of Salience: Competing over Voters' Attention', downloadable at http://www.personal.soton.ac.uk/ianni/
Varian, H.R. (2019) ‘Artificial Intelligence, Economics, and Industrial Organization’, in Agrawal, A., Gans, J. and Goldfarb, A. (eds.) The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press, pp. 399–419.
Zanartu, F., Otmakhova, Y., Cook, J. and Frermann, L. (2024) ‘Generative Debunking of Climate Misinformation’, arXiv preprint arXiv:2407.05599.