A new peer-reviewed paper proposes a responsible way to combine artificial intelligence with human expertise to help anticipate the long-term challenges facing Britain’s forests.
Published in Forestry: An International Journal of Forest Research, the paper by Sylva Foundation’s Gabriel Hemery and Gillian Petrokofsky builds on an earlier national horizon scan of issues likely to affect UK forest management over the next 50 years. That study identified catastrophic forest ecosystem collapse as the emerging issue with the greatest potential impact.
Forestry is particularly dependent on good long-term thinking. Trees planted today may grow for a century or more, while threats such as climate change, pests and diseases, biodiversity loss, and changing timber markets are developing rapidly. Yet conventional horizon-scanning exercises provide a snapshot based on the evidence and expert views available at one point in time.
The new paper asks whether artificial intelligence could help make foresight more continuous and responsive.
AI can rapidly scan large volumes of scientific, policy, environmental, and economic information, helping to detect emerging trends and weak signals. However, it cannot understand every local context, make ethical judgements, or decide what society should value. Human expertise therefore remains essential.
The authors propose a hybrid foresight framework with six iterative stages:
- framing the strategic questions;
- discovering emerging signals;
- engaging experts to interpret them;
- synthesising and prioritising the issues;
- exploring possible future scenarios; and
- learning and adapting as new evidence emerges.
In this model, AI broadens the evidence base and helps identify patterns, while people provide context, challenge assumptions, weigh competing priorities, and remain accountable for decisions. The aim is not to use AI to predict the future, but to help institutions prepare for several plausible futures.
The paper also introduces the concept of a UK Forestry Foresight Lab: a distributed network linking researchers, policymakers, practitioners, and woodland managers. It could support shared data, continuous horizon scanning, expert workshops, and early warning of emerging ecological and economic risks across the four nations of the UK.
Any such system would need careful governance. The paper proposes principles covering transparency, accountability, inclusion, data stewardship, environmental sustainability, and continued human oversight.
Ultimately, the future of forestry foresight lies neither in expert opinion nor artificial intelligence alone. Combining the reach and speed of AI with the experience, judgement, and imagination of people could help the forestry sector anticipate change earlier and make better-informed decisions for resilient forests.
Abstract
Forests occupy a pivotal position in addressing the intertwined crises of climate change, biodiversity loss, and sustainable resource use. Yet, the United Kingdom, one of Europe’s least-wooded and most nature-depleted nations, faces persistent barriers to resilient forest management: low forest cover, narrow species diversity, dependence on timber imports, and fragmented ownership. Building on a national horizon scan conducted in 2022, which identified ‘catastrophic forest ecosystem collapse’ as the most pressing emerging issue, this paper explores how artificial intelligence (AI) can strengthen foresight and policy coherence. Although large language models and other AI tools were technically available during the 2022 horizon-scanning exercise, their use in environmental foresight was not yet methodologically normalized or institutionally validated. The absence of AI from that process should therefore be understood less as an omission than as evidence of a rapidly changing methodological context. We propose an AI-assisted horizon-scanning framework that combines AI-enabled signal detection and synthesis with expert interpretation, deliberation and judgement through six iterative stages: framing, signal discovery, expert engagement, synthesis, simulation, and reflexive learning. The framework operationalizes principles of Responsible Research and Innovation and Responsible AI to ensure transparency, accountability, and ethical governance while addressing the ‘data deserts’, institutional fragmentation, and limited place-sensitive decision-making that currently limit the deployment of AI in the forestry sector. A concept for a UK Forestry Foresight Lab is advanced as a practical institutional mechanism linking government, research, and landowners through federated data commons. The paper outlines how ethically-governed AI can transform horizon scanning from a static expert exercise into a dynamic, adaptive and place-sensitive foresight system, supporting sustainable forest governance across the UK’s devolved administrations.
Hemery and Petrokofsky (2026)
Read the paper: Gabriel Hemery and Gillian Petrokofsky (2026) Hybrid foresight: integrating artificial intelligence and expert judgement to address strategic challenges in UK Forest resilience, Forestry: An International Journal of Forest Research, Volume 99, Issue 4, October 2026, cpag066, https://doi.org/10.1093/forestry/cpag066
