Open Access

As modern society progresses, waste treatment becomes a pressing issue. Not only are global waste amounts increasing, but there is also an unmet demand for sustainable materials (e.g. bioplastics). By identifying and developing processes, which efficiently treat waste while simultaneously generating sustainable materials, potentially both these issues might be alleviated. Following this line of thought, this dissertation focuses on procedures for treatment of the organic fraction of waste. Organic waste is a suitable starting material for microbial fermentation, where carbohydrates are converted to smaller molecules, such as ethanol, acetic acid, and lactic acid. Being the monomer of the thermoplastic poly-lactic acid, lactic acid is of particular interest with regard to bioplastics production and was selected as target compound for this dissertation. Organic waste acted as substrate for non-sterile batch and continuous fermentations. Fermentations were initiated with inoculum of Streptococcus sp. or with indigenous consortium alone. During batch mode, concentration, yield, and productivity reached maximum values of 50 g L−1, 63%, and 2.93 g L−1 h −1. During continuous operation at a dilution rate of 0.44 d−1, concentration and yield were increased to 69 g L−1 and 86%, respectively, while productivity was lowered to 1.27 g L−1 h −1 . To fully exploit the nutrients present in organic waste, phosphate recovery was analyzed using seashells as adsorbent. Furthermore, the pattern of the indigenous consortium was monitored. Evidently, a very efficient Enterococcus strain tended to dominate the indigenous consortium during fermentation. The isolation and cultivation of this consortium gave a very potent inoculum. In comparison to the non-inoculated fermentation of a different organic waste batch, addition of this inoculum lead to an improved fermentation performance. Lactic acid yield, concentration, and molar selectivity could be increased from 38% to 51%, 49 g L−1 to 65 g L−1, and 46% to 86%, respectively. Eventually, fermentation process data was used to perform techno-economic analysis proposing a waste treatment plant with different catchment area sizes ranging from 50,000 to 1,000,000 people. Economically profitable scenarios for both batch and continuous operation could be identified for a community with as few as 100,000 inhabitants. With the experimental data, as well as techno-economic calculations presented in this dissertation, a profound contribution to sustainable waste treatment and material production was made.

Online behaviors analysis consists of extracting patterns from server-logs. The works presented here were carried out within the “mBook” project which aimed to develop indicators of the quantity and quality of the learning process of pupils from their usage of an eponymous electronic textbook for History. In this thesis, we investigate several models that adopt different points of view on the data. The studied methods are either well established in the field of pattern mining or transferred from other fields of machine learning and data-mining. We improve the performance of archetypal analysis in large dimensions and apply it to unveil correlations between visibility time of particular objects in the e-textbook and pupils’ motivation. We present next two models based on mixtures of Markov chains. The first extracts users’weekly browsing patterns. The second is designed to process essions at a fine resolution, which is sine qua non to reveal the significance of scrolling behaviors. We also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, we establish a general framework for the study of similarity measures between spatio-temporal trajectories, for which the study of sessions is a particular case. Finally, we construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks. Keywords: online behaviors analysis, educational data mining, Markov models, archetypal analysis, spatio-temporal trajectories, neural network

Network analysis methods have long been used in the social sciences. About 25 years ago, these methods gained popularity in various other domains and many real-world phenomena have been modeled using networks. Well-known examples include (online) social networks, economic networks, web graphs, metabolic networks, infrastructure networks, and many more. Technological development made it possible to store and process data on a scale not imaginable decades ago — a development that also includes network data. A particular characteristic of network data is that, unlike standard data, the objects of interest, called nodes, have relationships to (possibly all) other objects in the network. Collecting empirical data is often complicated and cumbersome, hence, the observed data are typically incomplete and might also contain other types of errors. Because of the interdependent structure of network data, these errors have a severe impact on network analysis methods. This cumulative dissertation is about the impact of erroneous network data on centrality measures, which are methods to assess the position of an object, for example a person, with respect to all other objects in a network. Existing studies have shown that even small errors can substantially alter these positions. The impact of errors on centrality measures is typically quantified using a concept called robustness. The articles included in this dissertation contribute to a better understanding of the robustness of centrality measures in several aspects. It is argued why the robustness needs to be estimated and a new method is proposed. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can be used as a basis for decision making. The relationship between network properties and the robustness of centrality measures is analyzed. Experimental and analytical approaches show that centrality measures are often more robust in networks with a larger average degree. The study of the impact of non-random errors on the robustness suggests that centrality measures are often more robust if missing nodes are more likely to belong to the same community compared to missingness completely at random. For the development of imputation procedures based on machine learning techniques, a process for the evaluation of node embedding methods is proposed.

My dissertation embraces four empirical papers addressing socio-economic issues relevant to policy-makers and society as a whole. These papers cover important aspects of human life including health at birth, life satisfaction, unemployment periods and retirement decisions, and are intended to provide a contribution to the respective research areas. The analyses are carried out applying advanced econometric methods and are based on data sets consisting of survey data as well as administrative records. The joint paper with Alessandro Palma and Daniela Vuri "Prenatal Air Pollution Exposure and Neonatal Health" in Chapter 2 investigates the causal impact of prenatal exposure to air pollution on neonatal health in Italy in the 2000s combining detailed information on mother’s residential location from birth certificates with PM10 concentrations from air pollution monitors. Variation in local weekly rainfall is exploited as an instrumental variable for non-random air pollution exposure. Using quasi-experimental variation in rainfall shocks allows to identify the effect of PM10, ruling out potential bias due to confounder pollutants. The paper estimates the effect of exposure for both the entire pregnancy period and separately for each trimester to test whether the neonatal health effects are driven by pollution exposure during a particular gestation period. This information enhances our understanding of the mechanisms at work and help prevent pregnant mothers from most dangerous exposure periods. Additionally, the effects of prenatal exposure to PM10 are estimated by maternal labor market status and maternal education level to understand how the pollution burden is shared across different population groups. This decomposition allows to identify possible mechanisms through which environmental inequality reinforces the negative impact of early-life exposure to air pollution. This study finds that average PM10 and days with PM10 level above the hazard limit reduce birth weight, gestational age, and measures of overall newborn health. Effects are largest for third trimester exposure and for low-income and less educated mothers. These findings imply that further policy efforts are needed to fully protect fetuses from the adverse effects of air pollution and to mitigate the environmental inequality of health at birth. The joint paper with Christian Pfeifer "Life Satisfaction in Germany After Reunification: Additional Insights on the Pattern of Convergence" in Chapter 3 updates previous findings on the total East-West gap in overall life satisfaction and its trend by using data from the German Socio-Economic Panel for the years 1992 to 2013. Additionally, the effects are separately analyzed for men and women as well as for four birth cohorts. The results indicate that reported life satisfaction is, on average, significantly lower in East than in West German federal states and that part of the raw East-West gap is due to differences in household income and unemployment status. The conditional East-West gap decreased in the first years after the German reunification and remained quite stable and sizable since the mid-nineties. The results further indicate that gender differences are small. Finally, the East-West gap is significantly smaller and shows a trend towards convergence for younger birth cohorts. The joint paper with Christian Pfeifer "Unemployment Benefits Duration and Labor Market Outcomes: Evidence from a Natural Experiment in Germany" in Chapter 4 explores the effects 2 of a major reform of unemployment benefits in Germany on the labor market outcomes of individuals with some health impairment. The reform induced a substantial reduction in the potential duration of regular unemployment benefits for older workers. This work analyzes the reform in a wider framework of institutional interactions, which allows to distinguish between its intended and unintended effects. The results based on routine data collected by the German Statutory Pension Insurance and a Difference-in-Differences design provide causal evidence for a significant decrease in the number of days in unemployment benefits and increase in the number of days in employment. However, they also suggest a significant increase in the number of days in unemployment assistance, granted upon exhaustion of unemployment benefits. Transitions to unemployment assistance represent an unintended effect, limiting the success of a policy change that aims to increase labor supply via reductions in the generosity of the unemployment insurance system. The single-authored paper "How Older Workers Respond to Raised Early Retirement Age: Evidence from a Kink Design in Germany" in Chapter 5 explores how an increase in the early retirement age affects labor force participation of older workers. The analysis is based on a social security reform in Germany, which raised the early retirement age over several birth cohorts to boost employment of older people and ultimately alleviate the burden on the public pension system. Detailed administrative data from the Federal Employment Agency allow to distinguish between employment and unemployment as well as disability pensions and retirement benefits claims. Using a Regression Kink design in a quasi-experimental framework, I show that the raised early retirement age had positive employment effects and negative effects on retirement benefits claims. The reform did not affect unemployment benefits or disability pensions claims. My results also show that some population groups are more sensitive to a reduction in retirement options and more likely to seek benefits from other government programs. In this respect, I find that workers in manufacturing sector respond to the raised early retirement age by claiming benefits from the disability insurance program designed to compensate for reduced earnings capacity due to severe health problems. The treatment heterogeneity analysis further suggests that high-wage workers are more likely to delay exits from employment, which is in line with incentives but might also indicate an increased inequality within the affected birth cohorts induced by the reform. Finally, women seem to rely on alternative sources of income such as retirement benefits for women, or spouse's or partner's income not observed in the data. All things considered, workers did not adjust to the increased early retirement age by substituting early retirement with other government programs but rather responded to the reform in line with the policy intent. At the same time, the findings point to heterogeneous behavioral responses across different population groups. This implies that raising the early retirement age is an effective policy tool to increase employment only among older people who have the real choice to delay employment exits. Therefore, reforms that raise statutory ages should ensure social support for workers only marginally attached to the labor market or not able to work longer due to potential health problems or other circumstances.

This doctoral thesis contains four empirical studies analysing the personal accountability of prime ministers and the electoral presidentialisation of parliamentary elections in European democracies. It develops the concept of presidentialised prime ministerial accountability as a behavioural element in the chain of accountability in parliamentary systems. The ongoing presidentialisation of parliamentary elections, driven by changes in mass communication and erosion of societal cleavages, that fosters an increasing influence of prime ministers’ and other leading candidates’ personalities on vote choices, has called performance voting – and the resulting accountability mechanism of electoral punishment and reward of governing parties – into question. This thesis analyses whether performance voting can be extended to the personal level of parliamentary governments and asks whether voters hold prime ministers personally accountable for the performance of their government. Furthermore, it explores how voters change their opinion of prime ministers and how differences in party system stability and media freedom between Western and Central Eastern Europe contribute to higher electoral presidentialization in Central Eastern European parliamentary elections. This thesis relies on several national data sources: the ‘British Election Study’, the ‘German Longitudinal Election Study’ and other German election surveys, the ‘Danish Election Study’, as well as, data from the ‘Forschungsgruppe Wahlen’. In addition, it utilises cross-national data from the ‘Comparative Study of Electoral Systems’. The findings contribute to the ongoing scholarly debate on the issue of accountability and electoral presidentialisation in parliamentary systems by providing extensive evidence on prime ministerial accountability under presidentialised electoral behaviour. Keywords: presidentialisation; prime ministers; voting behavior; accountability; personalisation.