165 research outputs found
Replication Data for: The Effect of Austerity Packages on Government Popularity during the Great Recession
No full textDuring the Great Recession, governments across the continent implemented austerity policies. A large literature claims that such policies are surprisingly popular and have few electoral costs. This article revisits this question by studying the popularity of governments during the economic crisis. The authors assemble a pooled time-series data set for monthly support for ruling parties from fifteen European countries and treat austerity packages as intervention variables to the underlying popularity series. Using time-series analysis, this permits the careful tracking of the impact of austerity packages over time. The main empirical contributions are twofold. First, the study shows that, on average, austerity packages hurt incumbent parties in opinion polls. Secondly, it demonstrates that the magnitude of this electoral punishment is contingent on the economic and political context: in instances of rising unemployment, the involvement of external creditors and high protest intensity, the cumulative impact of austerity on government popularity becomes considerable
Calculation template for the unit-scale framework for designing step-pool sequences
No full textThis is the calculation template for the manuscript titled "A unit-scale framework for designing step-pool sequences" by Chendi Zhang, Marwan A. Hassan, Matteo Saletti, André E. Zimmermann, Mengzhen Xu and Zhaoyin Wang. The design method described in the manuscript is specifically for river restoration using artificial step-pool sequence (Zhang et al., 2018, 2020; Zimmermann et al., 2020). Version 1.0 of the calculation template was applied to a total of 21 artificial step-pools built in the Maso di Spinelle River in Italy (Lenzi 2002; Lenzi and Comiti 2003; Comiti et al. 2009). A natural step-pool sequence including 20 units in the Erlenback in Switzerland (Golly et al., 2019) was used in the Version 2.0 of the template. The instructions are included in the file. With changes in the inputs, this template can also be used for other cases where design for artificial step-pools is needed
Fish migration modeling and habitat assessment in a complex fluvial system
No full textFish migration patterns are driven by hydrodynamic factors, which are essential in aquatic ecology. This study investigated the hydrodynamic drivers of Gymnocypris przewalskii fish migration in two distinct river reaches—a straight reach (SR) and a confluence reach (CR)— in the area of Qinghai Lake, China, using a 3D numerical model, fish density field data, and four predictive models. Thirteen hydrodynamic factors, with a focus on water depth and velocity, were analyzed to identify their influence on fish migration. It was found that in the SR, linear factors of flow velocity and turbulent kinetic energy were most influential, while in the CR, nonlinear factors of water temperature and vortex intensity dominated. For CR, fish migration patterns are also important nonlinear factors. Methods that accurately reveal fish migration patterns, such as Random Forest, offer higher precision for habitat assessment. Our research also shows that fish swimming ability can, to some extent, reflect migration direction. Combining fish swimming ability with traditional linear habitat assessment methods can improve the adaptability of these methods in complex fluvial system. Based on our research findings, we propose a new workflow for fish habitat assessment that integrates both linear and nonlinear predictive methods. This framework provides valuable insights for enhancing fish conservation strategies in various fluvial systems
What happened to Putin’s friends? The radical right’s reaction to the Russian invasion on social media
No full textThe Ukrainian crisis has significantly shifted public opinion against Russia and Putin, placing politicians with prior Russian ties in a precarious situation. This paper tracks how parties that had some affinity to Putin have pivoted after the outbreak of war. Through computational text analysis of a decade of Facebook posts from 11 European radical right parties, we investigate their stance evolution towards Russia and their strategic management of public sentiment and Russian relationships. The results show that most radical right parties, after the invasion, neither tried to remain pro-Russia nor focussed their attention on shifting their prior position. Instead, they engaged in blurring the issue, diverting attention away from the war and using the events in Ukraine to assert their anti-EU positions
The effect of mandatory GHG Disclosure regulation on GHG disclosure quality, corporate financial and environmental performance : a UK study
Full text linkAs one of the main sources of greenhouse gas (GHG) emissions, firms must take primary responsibility for emission reduction. The major motivation of this study is that the government, as a legislative, may supervise and control corporate emissions by implementing regulations. The implementation of mandatory disclosure policies demonstrates this. This research examines whether the Companies (Directors Reporting) and Limited Liability Partnerships (Energy and Carbon Reporting) Regulations 2018 (the 2018 regulations) in the UK have a substantial influence on the corporate GHG information disclosure quality (IDQ). Besides, under the mandatory disclosure context, the impact of changes in corporate GHG IDQ on company financial performance (FP) and environmental performance (EP) is also explored.In this study, content analysis and quantitative research methods are combined. Content analysis is used to examine reports of firms and build an index framework of enterprise GHG disclosure content, which provides data sources for GHG IDQ. Quantitative research contains three models to explore the influence of the 2018 regulations on the GHG IDQ and the impact of IDQ on corporate FP and EP. For the purpose of this research, the annual reports, financial indicators, and GHG emission data of the Financial Times Stock Exchange 350 Index (FTSE350) listed companies are gathered. Based on the institutional theory, it is proposed that there is a positive correlation between the release of the 2018 regulations and the quality of corporate GHG information disclosure. The time-fixed and individual-fixed ordinary least squares (OLS) model is used to examine the influence of the 2018 regulations on the company GHG IDQ. The findings provide evidence that the 2018 regulations will positively affect the GHG IDQ. Similarly, based on agency theory, stakeholder theory, voluntary disclosure theory, legitimacy theory and signaling theory, it is proposed that under the influence of mandatory disclosure regulations, there is a positive correlation between corporate GHG disclosure and corporate FP and EP. This research utilizes panel data and the OLS interaction model to test hypotheses that corporate GHG IDQ positively affects their FP and EP. The results reveal that when corporate environmental IDQ progressively increases, company FP gradually improves, and GHG emissions decline. The findings of this study give investors, managers, regulators, and sustainability groups updated policy implications and new perspectives
Political multiplier effects of austerity : explaining the contention in different arenas under the great recession
Full text linkDefence date: 09 February 2021Examining Board: Professor Hanspeter Kriesi (European University Institute); Professor Philipp Genschel (European University Institute); Professor Thomas Sattler (University of Geneva); Professor Stefaan Walgrave (University of Antwerp)What are the political impacts of austerity policies? This dissertation sheds light on this question by offering five independent but interrelated empirical contributions that seek to understand and explain variegated societal and political resistance and consequences to austerity policies in the wake of the global financial crisis. The first account studies the impact of austerity policy announcements in the electoral arena. The results of time series analysis show that, on average, austerity packages hurt incumbent parties in opinion polls and secondly the magnitude of this electoral punishment is contingent on the economic and political context: in instances of rising unemployment, the involvement of external creditors, and high protest intensity, the cumulative impact of austerity on government popularity becomes considerable. The second study, focusing on the protest arena, demonstrates that austerity also drives people to the streets to voice their discontent. The findings of dynamic fixed-effects models demonstrate that people reacted more vehemently to earlier austerity policies while had gradually become disillusioned and no longer mobilised against later ones. Besides, the effect is larger when austerity is accompanied by rising objective and subjective economic grievances, the involvement of external actors, and a higher level of the previous mobilisation. To further understand the why austerity leads to protest, the third study explores the relationship between austerity and economic and political grievances, as well as the joint role of the two types of grievances for the determination of the mobilisation of protest. The fourth study links the consequences of fiscal austerity on electoral and protest politics. Relying on an original dataset containing data of protest event, electoral outcomes and detailed taxation and expenditure data in 30 European countries from 2000 to 2015, the study shows that citizens dislike large deficits and government debt, but they also resist austerity and punish the government, either at polls or in the streets or both, depending on the specific composition of austerity packages and the party colour of the incumbents. For the last study, I zoom in on the interactions between the governments and their challengers in reaction to austerity proposals by examining contentious episodes that have been unleashed by the governments’ austerity proposals. The results of a panel vector autoregression analysis reveal that the relationship of contentious interactions between actors and government popularity is not uni-directional but endogenous, and each plays a critical and interdependent role in the system in shaping the dynamics of the contentious policymaking process. In synthesis, the dissertation endeavours to investigate the political resistance against austerity in two important theoretical arenas. The central argument of this dissertation is that austerity does induce resistance from the citizens, both at polls and in the streets. Moreover, the magnitude of the political impact of austerity depends on other economic, social and political factors.Chapter 2 ‘The Effect of Austerity Packages on Government Popularity' of the PhD thesis draws upon an earlier version published as an article 'The effect of austerity packages on government popularity during the great recession' (2021) in the journal ‘British journal of political science’Chapter 4 ‘Austerity, economic and political grievances and protest' of the PhD thesis draws upon an earlier version published as chapter 'Economic grievances, political grievances, and protest' (2020) in the book ‘Contention in times of crisis : recession and political protest in thirty European countries
Multimodal human brain connectivity analysis based on graph theory
No full textBillions of people worldwide are affected by neurological disorders. Recent studies indicate that many neurological disorders can be described as dysconnectivity syndromes, and associated with changes in the brain networks prior to the development of clinical symptoms. This thesis presents contributions towards improving brain connectivity analysis based on graph theory representation of the human brain network. We propose novel multimodal techniques to analyze brain imaging data to better understand its structure, function and connectivity, i.e., brain connectomics. Our first contribution is towards improving parcellation, \ie brain network node definition, in terms of reproducibility, functional homogeneity, leftout data likelihood and overlaps with cytoarchitecture, by utilizing the neighbourhood information and multi-modality integration techniques. Specifically, we embed neighborhood connectivity information into the affinity matrix for parcellation to ameliorate the adverse effects of noise. We further integrate the connectivity information from both anatomical and functional modalities based on adaptive weighting for an improved parcellation. Our second contribution is to propose noise reduction techniques for brain network edge definition. We propose a matrix completion based technique to combat false negatives by recovering missing connections. We also present a local thresholding method which can address the regional bias issue when suppressing the false positives in connectivity estimates. Our third contribution is to improve the brain subnetwork extraction by using multi-pronged graphical metric guided methods. We propose a connection-fingerprint based modularity reinforcement model which reflects the putative modular structure of a brain graph. Inspired by the brain subnetwork's biological nature, we propose a provincial hub guided feedback optimization model for more reproducible subnetwork extraction. Our fourth contribution is to develop multimodal integration techniques to further improve brain subnetwork extraction. We propose a provincial hub guided subnetwork extraction model to fuse anatomical and functional data by propagating the modular structure information across different modalities. We further propose to fuse the task and rest functional data based on hypergraphs for non-overlapping and overlapping subnetwork extraction. Our results collectively indicate that combing multimodal information and applying graphical metric guided strategies outperform classical unimodal brain connectivity analysis methods. The resulting methods could provide important insights into cognitive and clinical neuroscience.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat
The Streets Speak: Unravelling the Impact of Austerity on Public Protests during the the Great Recession
No full textThis paper examines the impact of austerity policy announcements on protest mobilisation in 16 European countries during the Great Recession. It argues that austerity policies politicised grievances and enabled blame attribution, while institutional constraints and protest fatigue dampened reactions to later policies. Using monthly protest event data and systematically coded austerity policy announcements, the study utilises an interrupted time series design to analyse austerity announcements as shocks to protest levels. The findings indicate that earlier austerity announcements significantly increased economic protest levels, while later announcements had no effect or even decreased protest. Furthermore, the impact of austerity on protest was conditioned by economic and political contexts. Austerity had a larger effect when accompanied by rising unemployment, worsening household finances, external actor involvement, and higher prior protest levels. The study contributes to understanding varied public reactions to austerity and the dynamics between economic crisis, government policies, and contentious politics
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