Optimal Placement of Nature-Based Solutions for Urban Challenges

TL;DR

This paper presents a MILP model for optimally placing nature-based solutions in urban areas to maximize environmental and social benefits, addressing challenges like heat islands and air quality.

Contribution

It introduces a novel optimization framework integrating multiple factors for NBS placement, enhancing urban planning strategies.

Findings

Effective in improving ground temperature and air quality

Demonstrated through multiple case studies

Supports informed decision-making for urban greening

Abstract

Increased urbanization and climate change intensify urban heat islands and degrade air quality, making current mitigation strategies insufficient. Nature-based solutions (NBSs), such as parks, green walls, roofs, and street trees, offer a promising means to regulate urban temperatures and enhance air quality. However, determining their optimal placement to maximize environmental benefits remains a pressing challenge. Leveraging Operational Research (OR) tools, we propose a Mixed-Integer Linear Programming (MILP) model that integrates multiple factors, including urban challenges, physical constraints, clustering techniques, convolution theory, and fairness considerations. This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces. Through several case study analyses, we demonstrate the effectiveness of our approach in improving environmental and social indicators. This research holds implications for policy and practice, empowering urban planners and policymakers to make informed decisions regarding NBS implementation. Such decisions ensure that investments in urban greening yield maximum environmental, social, and economic benefits.