Urban land use optimization for improving carbon storage and mitigating surface runoff: a case study of Busan

Abstract Background Urban land use and land cover changes (LUCC) under rapid urbanization and increasing climate extremes pose multidimensional ecological risks. Because afforestation can increase carbon storage while reducing surface runoff regulation, we apply the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the trade-offs among carbon storage, surface runoff regulation, and cost, yielding Pareto optimal portfolios and spatially explicit design rules for urban green space planning. Results When the budget is fixed, cost, carbon and runoff converge on the optimal edge in the solution frontier, and each point along the edge provides one feasible afforestation planning pattern. All Pareto points converge into a map of frequencies of selection. Based on them, the robust investment priority areas can be identified. In cases where the landscape pattern is held constant, the final performance depends entirely on species combination and per-unit price, deriving the shortcut matching principles on the species budget under the landscape structure. As a result, accurate afforestation land planning enables the optimization of cost, carbon storage, and surface runoff regulation. Conclusions NSGA-II makes the trade-offs among cost, carbon storage, and surface runoff regulation explicit and provides portfolios that can be aligned with fiscal capacity and ecological priorities. Enhancing landscape pattern is associated with a better balance between carbon storage and surface-runoff regulation. When landscape configuration is held constant, species composition and unit costs dominate performance; the selection frequency map can be used to identify priorities and phase investments.