Spatial clustering of waste reuse in a circular economy: A spatial autocorrelation analysis on locations of waste reuse in the Netherlands using global and local Moran’s I

Abstract

In recent years, implementing a circular economy in cities has been considered by policy makers as a potential solution for achieving sustainability. Existing literature on circular cities is mainly focused on two perspectives: urban governance and urban metabolism. Both these perspectives, to some extent, miss an understanding of space. A spatial perspective is important because circular activities, such as the recycling, reuse, or storage of materials, require space and have a location. It is therefore useful to understand where circular activities are located, and how they are affected by their location and surrounding geography. This study therefore aims to understand the existing state of waste reuse activities in the Netherlands from a spatial perspective, by analyzing the degree, scale, and locations of spatial clusters of waste reuse. This was done by measuring the spatial autocorrelation of waste reuse locations using global and local Moran’s I, with waste reuse data from the national waste registry of the Netherlands. The analysis was done for 10 material types: minerals, plastic, wood and paper, fertilizer, food, machinery and electronics, metal, mixed construction materials, glass, and textile. It was found that all materials except for glass and textiles formed spatial clusters. By varying the grid cell sizes used for data aggregation, it was found that different materials had different “best fit” cell sizes where spatial clustering was the strongest. The best fit cell size is ∼7 km for materials associated with construction and agricultural industries, and ∼20–25 km for plastic and metals.The best fit cell sizes indicate the average distance of companies from each other within clusters, and suggest a suitable spatial resolution at which the material can be understood. Hotspot maps were also produced for each material to show where reuse activities are most spatially concentrated.