Land-use decision-making processes have a long history of producing globally pervasive systemic equity and sustainability concerns. Quantitative, optimization-based planning approaches, e.g. Multi-Objective Land Allocation (MOLA), seemingly open the possibility to improve objectivity and transparency by explicitly evaluating planning priorities by the type, amount, and location of land uses. Here, we show that optimization-based planning approaches with generic planning criteria generate a series of unstable "flashpoints" whereby tiny changes in planning priorities produce large-scale changes in the amount of land use by type. We give quantitative arguments that the flashpoints we uncover in MOLA models are examples of a more general family of instabilities that occur whenever planning accounts for factors that coordinate use on- and between-sites, regardless of whether these planning factors are formulated explicitly or implicitly. We show that instabilities lead to regions of ambiguity in land-use type that we term "gray areas". By directly mapping gray areas between flashpoints, we show that quantitative methods retain utility by reducing combinatorially large spaces of possible land-use patterns to a small, characteristic set that can engage stakeholders to arrive at more efficient and just outcomes.

翻译：土地利用决策过程长期以来在全球范围内引发了普遍的系统性公平性与可持续性问题。基于量化的优化规划方法（例如多目标土地分配，MOLA）看似通过根据土地利用的类型、数量及位置明确评估规划优先级，为提升客观性和透明度开辟了可能性。然而，我们在此表明，采用通用规划准则的优化规划方法会引发一系列不稳定的"闪点"——即规划优先级的微小变化将导致各类土地利用面积产生大规模改变。我们通过定量论证指出，在MOLA模型中发现的这些闪点属于更普遍的稳定性问题表现形式，此类问题出现在规划需协调用地单元内部及跨单元间因素的任何场景中，无论这些规划因素是显式还是隐式设定。研究表明，这种不稳定性会导致土地利用类型出现被称为"灰色区域"的模糊地带。通过直接映射闪点之间的灰色区域，我们证明定量方法能够将组合爆炸式增长的土地利用模式可能空间缩减为小规模特征集，从而帮助利益相关方达成更高效、更公正的决策，这保留了其实际应用价值。