This article investigates the phenomenon of rebound effects in relation to a transition to a Circular Economy (CE) through qualitative inquiry. The aim is to gain insights in manifestations of rebound effects by studying the Dutch textile industry as it transitions to a circular system, and to develop appropriate mitigation strategies that can be applied to ensure an effective transition. The rebound effect, known originally from the energy efficiency literature, occurs when improvements in efficiency or other technological innovations fail to deliver on their environmental promise due to (behavioral) economic mechanisms. The presence of rebound in CE contexts can therefore lead to the structural overstatement of environmental benefits of certain innovations, which can influence reaching emission targets and the preference order of recycling. In this research, the CE rebound effect is investigated in the Dutch textile industry, which is identified as being vulnerable to rebound, yet with a positive potential to avoid it. The main findings include the very low awareness of this effect amongst key stakeholders, and the identification of specific and general instances of rebound effects in the investigated industry. In addition, the relation of these effects to Circular Business Models and CE strategies are investigated, and placed in a larger context in order to gain a more comprehensive understanding about the place and role of this effect in the transition. This concerns the necessity for a new approach to how design has been practiced traditionally, and the need to place transitional developments in a systems perspective. Propositions that serve as theory-building blocks are put forward and include suggestions for further research and recommendations about dealing with rebound effects and shaping an eco-effective transition. Thomas Siderius, Kim Poldner, Reconsidering the Circular Economy Rebound effect: Propositions from a case study of the Dutch Circular Textile Valley, Journal of Cleaner Production, Volume 293, 2021, 125996, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2021.125996.
BackgroundConfounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.MethodsA Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.ResultsThe simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.ConclusionIn logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
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Research demonstrated a large variety regarding effects of light (e.g. health, performance, or comfort effects). Since human health is related to each individual separately, the lighting conditions around these individuals should be analysed individually as well. This paper provides, based on a literature study, an overview identifying the currently used methodologies for measuring lighting conditions in light effect studies. 22 eligible articles were analysed and this resulted in two overview tables regarding the light measurement methodologies. In 70% of the papers, no measurement details were reported. In addition, light measurements were often averaged over time (in 84% of the papers) or location level (in 32% of the papers) whereas it is recommended to use continuous personal lighting conditions when light effects are being investigated. Conclusions drawn in light effect studies based on personal lighting conditions may be more trusting and valuable to be used as input for an effect-driven lighting control system.
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