Suchergebnisse

Despite its 'wordless' and hidden characteristics, it is within the everyday tasks, routines, and rhythms that consumption takes place, from getting up every morning, having breakfast, going to work or school, having lunch, going home, having dinner, reading a book, surfing the Internet, watching TV, and probably doing similar things again and again. This study examines this routinized daily use of time of employed individuals based on the 2014–2015 UK Time-Use Survey data. In doing this, we focus on individual's day-to-day activities and how they are routinized or how they are formed into stabilized practices. Starting from the definition of stable practices, we apply a relatively new method of social network analysis to visualize stable practices during workdays. We then analyse the cohesion between practices based on work hours and connections and coordination between practices. Our results suggest that work arrangements create stable practices that by themselves are stone pillars of daily routines. This implies that the removal (or 'unlocking') of stable practices during these time periods could produce some – albeit marginal – decongestion of routinized activities.

AbstractThis article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy‐making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.