Problem
Research questions and friction points this paper is trying to address.
Optimizing land-use allocation for climate impact
Evaluating carbon emissions via surrogate modeling
Discovering location-specific land-use policies evolutionarily
Discovering Effective Policies for Land-Use Planning
🤖 AI Summary
This study addresses climate change mitigation by optimizing land-use configurations to enhance terrestrial carbon balance. We propose a novel framework integrating surrogate modeling with multi-objective evolutionary optimization: leveraging historical land-use data from LUH2, we couple the BLUE carbon accounting model to construct an efficient surrogate model; subsequently, we implement evolutionary search on the Project Resilience platform to automatically generate the Pareto-optimal trade-off frontier between regional carbon sequestration benefits and land-cover change intensity. This work represents the first systematic integration of carbon-dynamic land-use surrogate modeling and evolutionary multi-objective optimization, enabling customizable and scalable decision support for climate-resilient planning. Experimental validation demonstrates significant advantages in spatial adaptability, computational efficiency, and policy interpretability—facilitating actionable, science-informed land-use policy design under climate uncertainty.
📝 Abstract
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses neuroevolution for land-use policy discovery
Integrates surrogate modeling for carbon impact evaluation
Generates location-specific Pareto fronts for planning
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