This working paper examines the concept of metabolism and its potential as a critical analytical lens to study the contemporary city from a political perspective. The paper illustrates how the metabolism concept has been used historically, both as a metaphor to describe the technological, social, political and economic dimensions of human-environment relations, and as a concrete analytical tool to quantify and better understand how flows of matter and energy shape the territorial and spatial configurations of cityscapes. Drawing on the example of the urban water metabolism of the Greater Accra Metropolitan Area (GAMA), it is argued that contemporary approaches to metabolic analysis should be extended in two ways to increase the integrative potential of the urban water metabolism concept. On the one hand, the paper demonstrates that a political ecology approach is particularly well-suited to illuminate the contested production of urban environments and move beyond a narrow technical, managerial and state- centric focus in research on urban metabolic relations. On the other hand, the paper advocates for an approach to metabolic analysis that views the urban environment not simply as a relatively static exteriority that is produced by dynamic flows of matter, energy and information, but rather as a dynamic, nested and co-evolutionary network of complex biosocial and material relations, which in itself shapes how various metabolisms interact across scales. The paper then concludes by briefly discussing how a combination of metabolic analysis and political ecology research can inform urban water governance. In sum, the paper emphasizes the need for metabolic analysis to remain open to a plurality of different knowledge forms and perspectives, and to remain attentive to the inherently political nature of material and technological phenomena in order to allow for mutually beneficial exchanges between various scholarly communities.

Background Identifying pain-related response patterns and understanding functional mechanisms of symptom formation and recovery are important for improving treatment. Objectives We aimed to replicate pain-related avoidance-endurance response patterns associated with the Fear-Avoidance Model, and its extension, the Avoidance-Endurance Model, and examined their differences in secondary measures of stress, action control (i.e., dispositional action vs. state orientation), coping, and health. Methods Latent profile analysis (LPA) was conducted on self-report data from 536 patients with chronic non-specific low back pain at the beginning of an inpatient rehabilitation program. Measures of stress (i.e., pain, life stress) and action control were analyzed as covariates regarding their influence on the formation of different pain response profiles. Measures of coping and health were examined as dependent variables. Results Partially in line with our assumptions, we found three pain response profiles of distress-avoidance, eustress-endurance, and low-endurance responses that are depending on the level of perceived stress and action control. Distress-avoidance responders emerged as the most burdened, dysfunctional patient group concerning measures of stress, action control, maladaptive coping, and health. Eustress-endurance responders showed one of the highest levels of action versus state orientation, as well as the highest levels of adaptive coping and physical activity. Low-endurance responders reported lower levels of stress as well as equal levels of action versus state orientation, maladaptive coping, and health compared to eustress-endurance responders; however, equally low levels of adaptive coping and physical activity compared to distress-avoidance responders. Conclusions Apart from the partially supported assumptions of the Fear-Avoidance and Avoidance-Endurance Model, perceived stress and dispositional action versus state orientation may play a crucial role in the formation of pain-related avoidance-endurance response patterns that vary in degree of adaptiveness. Results suggest tailoring interventions based on behavioral and functional analysis of pain responses in order to more effectively improve patients quality of life.

In politics and economics, and thus in the official statistics, the precise estimation of indicators for small regions or parts of populations, the so-called Small Areas or domains, is discussed intensively. The design-based estimation methods currently used are mainly based on asymptotic properties and are thus reliable for large sample sizes. With small sample sizes, however, this design based considerations often do not apply, which is why special model-based estimation methods have been developed for this case - the Small Area methods. While these may be biased, they often have a smaller mean squared error (MSE) as the unbiased design based estimators. In this work both classic design-based estimation methods and model-based estimation methods are presented and compared. The focus lies on the suitability of the various methods for their use in official statistics. First theory and algorithms suitable for the required statistical models are presented, which are the basis for the subsequent model-based estimators. Sampling designs are then presented apt for Small Area applications. Based on these fundamentals, both design-based estimators and as well model-based estimation methods are developed. Particular consideration is given in this case to the area-level empirical best predictor for binomial variables. Numerical and Monte Carlo estimation methods are proposed and compared for this analytically unsolvable estimator. Furthermore, MSE estimation methods are proposed and compared. A very popular and flexible resampling method that is widely used in the field of Small Area Statistics, is the parametric bootstrap. One major drawback of this method is its high computational intensity. To mitigate this disadvantage, a variance reduction method for parametric bootstrap is proposed. On the basis of theoretical considerations the enormous potential of this proposal is proved. A Monte Carlo simulation study shows the immense variance reduction that can be achieved with this method in realistic scenarios. This can be up to 90%. This actually enables the use of parametric bootstrap in applications in official statistics. Finally, the presented estimation methods in a large Monte Carlo simulation study in a specific application for the Swiss structural survey are examined. Here problems are discussed, which are of high relevance for official statistics. These are in particular: (a) How small can the areas be without leading to inappropriate or to high precision estimates? (b) Are the accuracy specifications for the Small Area estimators reliable enough to use it for publication? (c) Do very small areas infer in the modeling of the variables of interest? Could they cause thus a deterioration of the estimates of larger and therefore more important areas? (d) How can covariates, which are in different levels of aggregation be used in an appropriate way to improve the estimates. The data basis is the Swiss census of 2001. The main results are that in the author- view, the use of small area estimators for the production of estimates for areas with very small sample sizes is advisable in spite of the modeling effort. The MSE estimates provide a useful measure of precision, but do not reach in all Small Areas the level of reliability of the variance estimates for design-based estimators.

Food waste is the origin of major social and environmental issues. In industrial societies, domestic households are the biggest contributors to this problem. But why do people waste food although they buy and value it? Answering this question is mandatory to design effective interventions against food waste. So far, however, many interventions have not been based on theoretical knowledge. Integrating food waste literature and ambivalence research, we propose that domestic food waste can be understood via the concept of ambivalence—the simultaneous presence of positive and negative associations towards the same attitude object. In support of this notion, we demonstrated in three pre-registered experiments that people experienced ambivalence towards non-perishable food products with expired best before dates. The experience of ambivalence was in turn associated with an increased willingness to waste food. However, two informational interventions aiming to prevent people from experiencing ambivalence did not work as intended (Experiment 3). We hope that the outlined conceptualization inspires theory-driven research on why and when people dispose of food and on how to design effective interventions.

The cumulative and bidirectional groundwater-surface water (GW-SW) interaction along a stream is defined as hydrological turnover (HT) influencing solute transport and source water composition. However, HT proves to be highly variable, producing spatial exchange patterns influenced by local groundwater, geology, and topography. Hence, identifying factors controlling HT poses a challenge. We studied spatiotemporal HT variability at two reaches of a third order tributary of the river Mosel, Germany. Additionally, we sampled for silica concentrations in the stream and in the near-stream groundwater. Thus, creating snapshots of the boundary layer between ground- and surface water where HT occurs, driven by mixing processes in the hyporheic zone. We utilize an enhanced hydrograph separation method, unveiling reach differences in storage drainage based on aquifer dimension and connectivity. The data shows a site-specific negative correlation of HT with discharge, while hydraulic gradients correlate with HT only at the reach with faster catchment drainage behavior. Examining silica concentrations between stream and wells shows that silica variation increases significantly with the decrease of HT under low flow conditions at the slower draining reach. At the fast draining reach this relationship is seasonal. In Summary, our results show that stream discharge shapes the influence of HT on solute transport. Yet, reach drainage behavior shapes seasonal states of groundwater storages and can be an additional control of HT. Hence, concentration change of pollutants could be masked by HT. Thus, our findings contribute to the understanding of HT variability along streams and its ability of influencing physico-chemical stream water composition.