This report describes the Utrecht regio with regard to sustainability and circular business models.
This Whitepaper presents the essence of research into existing and emerging circular business models (CBMs). This results in the identification of seven basic types of CBM, divided into three groups that together form a classification. This Whitepaper consists of three parts. ? The first part discusses the background and explains the circular economy (CE), the connection with sustainability, business models and an overview of circular business models. ? In the second part, an overview is given of the developed classification of CBM, and each basic type is described based on its characteristics. This has resulted in seven knowledge maps. Finally, the last two, more future-oriented models are further explained and illustrated. ? The third part looks back briefly at the reliability of the classification made and then at the aspects of change management in working on and with a CBM.
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ObjectiveTo compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios.Study design and settingWe performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis.ResultsApplying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings.ConclusionIn case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences.
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