PLANNING FOR CLIMATE CHANGE-INDUCED DISPLACEMENT: SOCIAL INTEGRATION, UNCERTAINTY, DECENTRALIZATION, ADAPTABILITY, AND FAIRNESS

Abstract

The looming climate crisis is a significant driver of the displacement of communities. The adverse effects of slow-onset climate change are anticipated to strike people worldwide, causing displacements in significant quantities. These displacements will lead to large-scale movements from high-risk and less resilient areas to safer or more resilient ones, creating a relocation problem: where people should go and when. This is a problem with distinctive characteristics that has received limited attention in the field of operations research. The complexities of the problem, including the need for long-term planning, uncertainties about the future, the involvement of multiple stakeholders, diverse populations, and different locations experiencing varying levels of climate change impacts, call for unique ways. It requires taking up approaches that can assist in developing urgently needed high-level relocation plans to manage climate change-induced movements in a timely manner and with a long-term outlook while using the resources effectively and protecting the peace, well-being, and dignity of displaced people and receiving communities. This dissertation presents a comprehensive proposition for high-level and long-term relocation planning amidst the escalating climate crisis, contributing to the field of humanitarian operations research for societal good. It comprises three studies designed to assist decision-makers in preparing for future actions at the strategic level, even those that may unfold years from now. Each study addresses a challenge associated with optimizing high-level relocation planning in response to climate change-induced forced displacement: future uncertainties, decentralized systems, and ensuring fairness. The first study proposes a two-stage stochastic programming model that optimizes relocation decisions under demand uncertainty with a focus on societal integration outcomes based on diversity indicators. The second study introduces a consensus-driven decentralized optimization framework that balances global utilitarian goals with local interests inherent to relocation planning utilizing a combination of altruistic and self-centered models, negotiations, and a bi-level optimization model that considers culture-based social integration, irregular movements, the associated costs of social conflicts, and fairness among different origins. The third and final study investigates the fairness of destination selection and flow assignment decisions within the context of the high-level relocation problem, providing a comparative analysis of multiple fairness metrics from the perspectives of various stakeholders and considering different principles.