ADD-TREES: AI-elevated Decision-support via Digital Twins for Restoring and Enhancing Ecosystem Services

The UK's ambitions to achieve Net Zero by 2050 depend critically on large-scale greenhouse gas removal (GGR) that can offset emissions from difficult-to-decarbonise sectors. Capturing carbon in growing trees represents the only GGR technology that can be scaled up immediately and at relatively low cost to meet that requirement. As such, in the Environment Act (2021) the UK government committed to ambitious and legally-binding targets for the rapid expansion of UK woodland. Over the next few years, significant decisions must be made regarding where to plant half a million hectares of trees, decisions that will shape the UK countryside for generations to come.

Deciding where to plant trees, which species to plant and when to plant them is complicated. How much GGR a particular woodland expansion strategy realises depends on a myriad of factors including how planting impacts on soil carbon stocks, how different tree species respond to spatially-varying environmental conditions under a changing climate and the vulnerability of planted trees to pests and disease. To complicate things further, in most cases new woodlands will be established on farmland. So planting comes at the cost of lost food production. That is important not only to landowners who are unlikely to consider planting trees unless compensated for lost farm income but also to policy makers who may have concerns over UK food security. Moreover, land use underpins a variety of important ecosystem services. Decisions over where to plant trees has significant implications for, amongst other things, flood mitigation, water quality, pollination, biodiversity and human health.

The capacity to unravel that complexity and inform decision making, exists in the sophisticated science and socio-economic models developed by the academic community. Those models can simulate tree growth and GGR across the UK under climate change. They can estimate farm income changes from tree-planting and predict uptake of policy packages incentivising such land use change. They can even identify the impacts of tree planting on the flows of a whole array of ecosystems services. Unfortunately, these state-of-the-art models may take days to run and require expertise and specialist software that is simply not available to the diverse collection of policy makers and land managers engaged in tree-planting decisions. The central objective of this project is to bridge that gap, leveraging AI technologies to provide bespoke, AI-generated decision support tools that synthesise and present the information contained within state-of-the-art models in ways that can properly inform policy and planting decisions.

Delivering this vision requires the embedding of existing AI technologies into the models themselves, allowing those models to be automatically scaled to the spatial and temporal resolution that best suits some particular decision problem. In addition, AI methods will be used to automatically build and link fast-running emulators of those scientific models. Powering decision-support tools with this AI-generated, fast-running modelling capacity will provide users with unprecedented capabilities to explore in real time tree-planting decisions and their numerous consequences. We will deliver co-designed tools to our project partners at Defra, National Trust, Forestry England, the Ministry of Defence, the National Forest Company, Network Rail and Woodland Trust. Moreover, our AI methods will ensure that this modelling technology is accessible to all landowners and policy makers engaged in tree-planting decisions. Configured through simple interfaces, the AI will assemble bespoke decision-support tools shaped and scaled to the exact decision needs of any user. Through this project the knowledge embedded in the science community's latest modelling and data will be transferred into the hands of the users that will shape the UK's Net Zero contribution from trees.