Optimal Placement of Nature-Based Solutions for Urban Challenges

TL;DR

The paper tackles urban resilience to heat and air pollution by optimizing the placement of Nature-Based Solutions (NBSs) with a Mixed-Integer Linear Programming (MILP) framework. It introduces convolution-based kernel effects, clustering to model contiguous NBS deployments, and fairness constraints to ensure equitable access, while balancing environmental benefits and budget. The approach is validated on Italian city cases with 4 NBS types and 5 urban-challenge measures, demonstrating reductions in peak and average UC indicators and improved equity, supported by public code and data. The work offers a practical decision-support tool for planners to maximize environmental, social, and economic gains from urban greening initiatives.

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.

Figures (10)

• Figure 1: Three examples of NBSs.
• Figure 2: The fundamental components of our approach include pre-processing and optimal NBS planning phases. During the pre-processing phase, we collect all required data to stack a detailed set of matrices representing the present use case, alongside kernels for assessing the NBSs' impact. The subsequent phase involves the MILP formulation, which integrates convolution, fairness, clustering, and physical constraints. The output of this phase is a list of newly placed NBSs and the corresponding improvements in UC measures.
• Figure 3: The proposed convolution approach to assess the influence of NBSs on the urban grid. The kernel employed to assess the NBS influence on the map is depicted in Figure \ref{['fig:kernel']}. Figure \ref{['fig:newGreen_val']} represents a sample implementation of a new NBS (the green cells corresponding to the 1 entries). The kernel is applied to the newly installed NBSs as shown in Figure \ref{['fig:Kernel_on_map']}. This convolution is applied to evaluate the reduction of the current temperature whose heatmap and associated values are depicted in Figure \ref{['fig:original']}. As a result, the temperature decreases by the value depicted in Figure \ref{['fig:reduction_value']}. Figure \ref{['fig:results']} presents the updated heatmap obtained by subtracting the values in Figure \ref{['fig:reduction_value']} from that of the initial heatmap in Figure \ref{['fig:original']}. Before the NBS installation (Figure \ref{['fig:original']}), the highest peak temperature was $33^{\circ}$. After the installation (Figure \ref{['fig:results']}), it is reduced to $27^{\circ}$.
• Figure 4: Comparison between the fraction of the budget (in percentage) allocated to each NBS. The reported value represents the average over all the instances of the same size.
• Figure 5: Average variations of peaks and average values of four Urban Challenges, respectively (a) MaxTemp variation, (b) MinTemp variation, (c) $\textrm{PM}_{10}$ variation, and (d) $\textrm{PM}_{2.5}$ variation. The variations correspond to reductions of the relative unit (i.e., [°C] for temperature and [ppm] for $\textrm{PM}_{10}$, and $\textrm{PM}_{2.5}$).