Flashpoints Signal Hidden Inherent Instabilities in Land-Use Planning

TL;DR

This paper reveals that optimization-based land-use planning models exhibit unstable 'flashpoints' where small priority changes cause large land-use shifts, leading to ambiguous 'gray areas' that can be managed for better stakeholder outcomes.

Contribution

It uncovers inherent instabilities in land-use planning models and proposes mapping gray areas to improve decision-making and stakeholder engagement.

Findings

Identification of unstable flashpoints in MOLA models

Gray areas correspond to regions of ambiguity in land-use decisions

Mapping gray areas helps reduce complexity and improve planning outcomes

Abstract

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.