Barriers and Best Practices for the Circular Economy

Introduction We’re living in an exciting era. Rather than just another societal transition, we’re going through a fundamental societal transformation. Ecologist Joanne Macy calls this period ‘The Great Turning’: a period wherein we change from an industrial growth society into a life sustaining system’. Macy: “The most remarkable feature of this historical moment on Earth is not that we are on the way to destroying the world; we've actually been on the way for quite a while. It is that we are beginning to wake up, as from a millennia-long sleep, to a whole new relationship to our world, to ourselves and each other.” It is with these eyes that we have to see the rise of the Circular Economy. The Circular Economy is not just another trend in business; it’s the start of a completely new economic reality. The Circular Economy is the starting point for regenerative economics; for a new business-as-usual that - first and foremost - serves life and is based upon a fundamentally new value-paradigm. The future of success in business is about doing good for all stakeholders and creating benefit; not just profit. The Circular Economy demands next level thinking-and-doing in business, and there is no one more willing and able than the next generation of young professionals. It is therefore with great pride and pleasure that I present to you this publication of the SMO Promovendi. It offers fresh perspectives of a group of promising young scientists. All aspiring changemakers. It’s made with love and with the best of intentions; to help the Circular Economy forward

Analyzing small data sets using Bayesian estimation : The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors

Background: The analysis of small data sets in longitudinal studies can lead to power issues and often suffers from biased parameter values. These issues can be solved by using Bayesian estimation in conjunction with informative prior distributions. By means of a simulation study and an empirical example concerning posttraumatic stress symptoms (PTSS) following mechanical ventilation in burn survivors, we demonstrate the advantages and potential pitfalls of using Bayesian estimation. Methods: First, we show how to specify prior distributions and by means of a sensitivity analysis we demonstrate how to check the exact influence of the prior (mis-) specification. Thereafter, we show by means of a simulation the situations in which the Bayesian approach outperforms the default, maximum likelihood and approach. Finally, we re-analyze empirical data on burn survivors which provided preliminary evidence of an aversive influence of a period of mechanical ventilation on the course of PTSS following burns. Results: Not suprisingly, maximum likelihood estimation showed insufficient coverage as well as power with very small samples. Only when Bayesian analysis, in conjunction with informative priors, was used power increased to acceptable levels. As expected, we showed that the smaller the sample size the more the results rely on the prior specification. Conclusion: We show that two issues often encountered during analysis of small samples, power and biased parameters, can be solved by including prior information into Bayesian analysis. We argue that the use of informative priors should always be reported together with a sensitivity analysis