Evaluating spatial material distributions: adopting geospatial entropy definitions into resource management†

Human activity depends on resources that are often consumed without regard for their future availability. Consequently, resources in the form of raw materials and finished products are widely dispersed across society, creating energetic challenges for resource management, since the processes of procuring materials, purifying and processing them, distributing goods, and collecting waste all require significant amounts of energy. The costs and energy requirements for these activities depend on factors such as the mode of transportation and the distance travelled. Efficient transportation strategies can help reduce the negative environmental impact of human activities and ensure the sustainable use of resources for future generations. Quantifying the impact of this transport requires specific and expert logistics management knowledge. The current approach relies on information that is often not readily available, making it impractical and costly. Fast and quantitative methods to support decision making are especially needed when evaluating different potential circular economy (CE) strategies and business models that aim to reduce environmental impact by keeping materials at a high functionality level by closing material cycles (e.g., through reuse, reparation, refurbishment, remanufacturing, repurposing, recycling or material recovery). As a consequence, in this article, geospatial entropy definitions are studied as novel metrics to quantify the geospatial distribution of resources. The overall goal of this article is to review existing geospatial entropy definitions and evaluate their potential to be applied for assessing resource management strategies in view of a circular economy. In doing so, insight into the decision making behind the location of a value-added activity through a collection and processing of resources is gained, as well as how entropy can be used to support this. To achieve this, we analyse several definitions used in the field of urban sprawling, illustrate how they are calculated using conceptual examples, and translate these to relevant research questions for resource management. This analysis results in several promising definitions, which, in our view, are able to quantify the geospatial distribution of resources accurately. The resulting entropy value can then serve as a proxy for collection efforts. As a result, a viewpoint is presented on how these geospatial entropy definitions can support resource management decisions, such as the appraisal of resource/waste collection schemes and the location of processing and recycling facilities.