The circular economy (CE) is heralded as reducing material use and emissions while providing more jobs and growth. We explored this narrative in a series of expert workshops, basing ourselves on theories, methods and findings from science fields such as global environmental input-output analysis, business modelling, industrial organisation, innovation sciences and transition studies. Our findings indicate that this dominant narrative suffers from at least three inconvenient truths. First, CE can lead to loss of GDP. Each doubling of product lifetimes will halve the related industrial production, while the required design changes may cost little. Second, the same mechanism can create losses of production jobs. This may not be compensated by extra maintenance, repair or refurbishing activities. Finally, ‘Product-as-a-Service’ business models supported by platform technologies are crucial for a CE transition. But by transforming consumers from owners to users, they lose independence and do not share in any value enhancement of assets (e.g., houses). As shown by Uber and AirBNB, platforms tend to concentrate power and value with providers, dramatically affecting the distribution of wealth. The real win-win potential of circularity is that the same societal welfare may be achieved with less production and fewer working hours, resulting in more leisure time. But it is perfectly possible that powerful platform providers capture most added value and channel that to their elite owners, at the expense of the purchasing power of ordinary people working fewer hours. Similar undesirable distributional effects may occur at the global scale: the service economies in the Global North may benefit from the additional repair and refurbishment activities, while economies in the Global South that are more oriented towards primary production will see these activities shrink. It is essential that CE research comes to grips with such effects. Furthermore, governance approaches mitigating unfair distribution of power and value are hence essential for a successful circularity transition.
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28-07-2024Distribution centres are becoming more and more relevant for spatial planning, due to their rapidly increasing size and number. There is little literature, however, that provides a generalized analysis of the size and functional attributes of distribution centres, and none that discusses the relationships between these attributes. Our aim is to fill this gap by providing new evidence and analysis to understand this relationship. We make use of an extensive database of 2888 DCs in the Netherlands to develop a new typology of DCs based on the geographical location of DCs, their functional attributes and client sector characteristics. The analysis shows that the context in which medium sized DCs are operating is more heterogeneous than in the case of very large and small size DCs. This study is a first attempt to analyse this relationship between facility size and functions based on a rich and extensive dataset of large population of DCs. The results can serve as input for further quantitative statistical analysis and international comparison.
This paper studies the factors that drive distribution structure design (DSD), which includes the spatial layout of distribution channels and location choice of logistics facilities. We build on a generic framework from existing literature, which we validate and elaborate using interviews among industry practitioners. Empirical evidence was collected from 18 logistics experts and 33 decision-makers affiliated to shippers and logistics service providers from the fashion, consumer electronics and online retail sectors. It turns out that interviewees share similar rankings of main factors across industries, and even confirm factor weights from earlier research established using multi-criteria decision analysis, which would indicate that the framework is sector- neutral at the highest level. The importance attached to subfactors varies between sectors according to our expectations. We were able to identify 20 possible new influencing subfactors. The results may support managers in their decision-making process, and regional policy-makers with regard to spatial planning and regional marketing. The framework is a basis for researchers to help improve further quantitative DSD support models.
