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Political economic analyses of recent social protection reforms in Asian, African or Latin American countries have increased throughout the last few years. Yet, most contributions focus on one social protection mechanism only and do not provide a comparative approach across policy areas. In addition, most studies are empirical studies, with no or very limited theoretical linkages. The paper aims to explain multiple trajectories of social protection reform processes looking at cash transfers and social health protection policies in Kenya. It develops a taxonomy and suggest a conceptual framework to assess and explain reform dynamics across different social protection pillars. In order to allow for a more differentiated typology and enable us to understand different reform dynamics, the article uses the approach on gradual institutional change. While existing approaches to institutional change mostly focus on institutional change prompted by exogenous shocks or environmental shifts, this approach takes account of both, exogenous and endogenous sources of change.
Die vorliegende Forschungsarbeit behandelt die Filtrierung von sozialen Medien durch die Content Moderatoren. Die Content Moderatoren sind Menschen, die unter schlechten Arbeitsbedingungen und hoher psychischer Belastung Plattformen wie Facebook tagtäglich von strafbaren Inhalten filtern. Durch dieses Löschregime werden zwar gewaltzeigende Inhalte gelöscht, aber auch aufklärende oder künstlerische Inhalte zensiert.
Mithilfe von durchgeführten Fokusgruppendiskussionen wurde der Einfluss der Gruppenzusammensetzung und Darbietung positiver und negativer Informationen auf die individuelle und eindimensionale Wahrnehmung der Teilnehmer bezüglich sozialer Medien, Content Moderatoren und Löschrichtlinien erforscht.
Die Ergebnisse zeigen, dass die Darbietung der Informationen keinen Einfluss auf die mehrdimensionale Wahrnehmung der Probanden hatte und sie unabhängig von der Gruppenzusammensetzung nonkonforme Meinungen vertraten. Trotz des erweiterten Wissenstandes und der entwickelten Alternativlösungen äußerten die meisten Probanden nicht die Absicht, ihr Nutzungsverhalten künftig zu ändern. Trotz der Annahme, dass die meisten Probanden eine eindimensionale Wahrnehmung sozialer Medien haben, zeigten die Ergebnisse, dass viele Probanden eine ähnlich positive und kritische Haltung gegenüber den Plattformen hatten. Darüber hinaus wird deutlich, dass es einen starken Forschungsbedarf in Bezug auf die Langzeitfolgen der Arbeit als Content Moderator und Auswirkungen von Zensur und Filtrierung auf die Nutzer sozialer Medien gibt.
Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic (PV) systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data.
Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data. Hence the generation of robust reviewed global irradiance data is an essential contribution for the energy transition.
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.
In recent years, there has been a growing interest in the start-up scene in sub-Saharan Africa. "Silicon Savannah" is today widely used to describe the thriving IT industry in and around Nairobi. Kenya's geographical advantage, its favorable economic reforms, and mature start-up ecosystem makes it stands out positively. Since a lot of hype exists around the start-up scene many investors are drawn to it, but in reality very few start-ups are investment-ready. The increasing start-up requirements and needs force incubators to diversify their offer. In contrast, to traditional incubators, an Innovation Hub (Hub) is characterized based on the concept of open innovation and collaboration. A Hub nurtures an enabling environment where a community of entrepreneurs can grow. At the same time, it serves as a nexus point for the local start-up community, investors, academia, technology companies and the wider private sector. It aims to create a structure where people serendipitously interact with others that they would not typically meet. Considering the great interest for and the large amounts of money invested in Hubs by governments, universities, private companies and other interested parties, not only researchers have been raising the question of the actual benefit of Hubs. This research study aims to investigate to what extent the support offered by the Hubs is tackling the challenges faced by start-ups in Nairobi, Kenya. The analysis can serve as a basis for identifying strength and weaknesses in the Hub models.
Nachhaltigkeitsökonomie
(2014)
Angewandte Makroökonomie
(2013)
Makroökonomische Ereignisse wie die Schuldenkrise, Rezession, Arbeitslosigkeit und Inflation haben nicht nur gesamtwirtschaftliche Konsequenzen, sondern auch vielfältige Berührungspunkte zum täglichen Leben. Diese Ereignisse sind häufig komplex und für den Einzelnen nicht immer leicht zu durchschauen. Um Studierende auf die globalen Herausforderungen von Wirtschaft, Gesellschaft und Umwelt vorzubereiten ist in diesem Lehrbuch explizit auch das Thema der nachhaltigen Entwicklung integriert. Außerdem werden die großen Themen der Makroökonomie teilweise gebündelt behandelt, um die vielfältigen Zusammenhänge zwischen den einzelnen Gebieten transparenter zu gestalten. Dies hat für Studierende und Lehrende u.a. den Vorteil, dass eine modulare Verwendung möglich ist.
Angewandte Makroökonomie
(2023)
In den Atmosphärenwissenschaften spielt die Strahlungsbilanz der Erde eine wichtige Rolle für unser Verständnis des Klimasystems. Hier liefern ausgereifte Satellitenprodukte dekadische Klimazeitreihen mit einer so hohen Genauigkeit, dass z.B. Änderungen im Zusammenhang mit dem Klimawandel detektiert werden können. Dies gilt insbesondere auch für die solaren Strahlungsflüsse an der Erdoberfläche. Beim Vergleich dieser Satellitenprodukte mit instantanen Beobachtungen der Strahlung am Erdboden sind jedoch oft erhebliche Abweichungen feststellbar, die hauptsächlich durch kleinskalige Variabilität in der räumlichen Struktur von Wolken und ihrer Strahlungswirkung verursacht werden. Hier ist auch zu bedenken, dass Bodenbeobachtungen fast einer Punktmessung entsprechen, während Satellitenpixel eine Fläche in der Größenordnung von Quadratkilometern abtasten.
Der Begriff der Nachhaltigkeit ist heute weit verbreitet, und seine Nutzung erstreckt sich auf alle Gesellschaftsbereiche. Als abstraktes Leitprinzip bleibt oftmals unklar, wie der Begriff definiert und ausgelegt wird. Seine Unbestimmtheit trägt zur Verwässerung und inflationären Verwendung bei. Erschwerend wirkt zusätzlich die unzureichende Trennung des politischen vom alltagssprachlichen Begriff der Nachhaltigkeit.
Green infrastructure improves environmental health in cities, benefits human health, and provides habitat for wildlife. Increasing urbanization has demanded the expansion of urban areas and transformation of existing cities. The adoption of compact design in urban planning is a recommended strategy to minimize environmental impacts; however, it may undermine green infrastructure networks within cities as it sets a battleground for urban space. Under this scenario, multifunctionality of green spaces is highly desirable but reconciling human needs and biodiversity conservation in a limited space is still a challenge. Through a systematic review, we first compiled urban green space's characteristics that affect mental health and urban wildlife support, and then identified potential synergies and trade-offs between these dimensions. A framework based on the One Health approach is proposed, synthesizing the interlinkages between green space quality, mental health, and wildlife support; providing a new holistic perspective on the topic. Looking at the human-wildlife-environment relationships simultaneously may contribute to practical guidance on more effective green space design and management that benefit all dimensions.