A multi-objective optimization framework for sustainable transitions

TL;DR

This paper introduces a multi-objective optimization framework that models complex interdependencies among sustainability targets, helping policymakers evaluate holistic impacts and tradeoffs in sustainable transitions.

Contribution

It proposes a novel modeling framework inspired by NK fitness landscapes, integrating dynamic evolutionary algorithms and network analysis for holistic policy evaluation.

Findings

Increasing resources improves performance but with diminishing returns.

System sensitivity is mainly influenced by budget, network density, and policy efficacy.

The framework aids in identifying optimal policy allocations for sustainability.

Abstract

Achieving a just and sustainable transition requires the pursuit of multiple social and environmental targets. Two primary barriers impede this process: (1) targets are often in conflict with each other, and (2) policies aimed at these targets are commonly planned in isolation, neglecting complex interdependencies in the system. To address these challenges, we propose a general modeling framework that evaluates the holistic impact of policies and decision-making on sustainability targets while capturing system interdependencies in a policy-target network. Inspired by Kauffman's NK fitness landscape, our framework takes the form of a multi-objective optimization model that employs a dynamic evolutionary algorithm in conjunction with network analysis. Our algorithm accounts for tradeoffs between conflicting targets by dynamically reallocating resources to the most impactful and efficient policies. One key finding indicates that increasing resources generally enhances performance, but marginal gains stagnate at a point of diminishing returns. Sensitivity analysis reveals that the system is primarily driven by three factors: budget constraint, network density (interconnectivity), and policy efficacy. This study serves as a foundational step towards developing a decision-support tool that assists policymakers in achieving optimal outcomes for problems with a large number of dynamically interacting targets.