This paper introduces the Animal Welfare and Policy Risk Index (AWPRI), a composite risk index covering 25 countries over the period 2004-2022 (N = 475 country-year observations). The AWPRI is constructed from 15 variables organised across three equal-weighted conceptual layers: Current Welfare State (L1), Policy Trajectory (L2), and Artificial Intelligence (AI) Amplification Risk (L3). Variables are normalised to [0, 1] using min-max scaling, with higher values denoting greater policy risk. The index is validated through k-means cluster analysis (k = 4; silhouette coefficient = 0.447), principal component analysis (PCA) of the 15-variable cross-section, and sensitivity analysis under \pm10 percentage-point layer weight perturbation (mean Spearman \r{ho} = 0.993, minimum 0.979; mean Adjusted Rand Index (ARI) = 0.684, range 0.477-1.000). Our Hausman specification test favours random-effects (RE) panel estimation (H = 2.55, p = 0.467). We use a difference-in-differences (DiD) design to exploit the 2019 AI governance risk classification divergence and find that countries identified as high-AI-governance-risk carry AWPRI scores 0.080 points higher than their low-risk counterparts, after controlling for country and year fixed effects (\b{eta} = 0.080, SE = 0.005, p < 0.001). The L3 layer records the highest mean score in the 2022 cross-section (0.552, SD = 0.175), significantly exceeding both L1 (Wilcoxon W= 102,651, p < 0.001) and L2 (W= 99,295, p < 0.001). China (0.802), Vietnam (0.612), and Thailand (0.586) record the highest composite risk scores in 2022; the United Kingdom (0.308) the lowest. AutoRegressive Integrated Moving Average (ARIMA)-based projections indicate that Thailand, Brazil, and Argentina face AWPRI risk deterioration by 2030. The AWPRI and its interactive visualisation are publicly accessible at https://awpri-dashboard.streamlit.app.
Parliaments do not constitute the true epicentre of policymaking in traditional consociational democracies like Belgium or the Netherlands. Historically, consen‐ sus seeking by the political elite has been a key remedy against the threat of immobilism and instability in these countries with deep-rooted cleavages based on religion, class and language (Lijphart, 1977). In Belgium, in particular, parlia‐ ment has been “the victim of the subtle equilibrium that is constantly needed for governing a divided society” (Deschouwer, 2009, p. 188). Major political conflicts have typically been appeased through reforms or pacts negotiated by (extra-par‐ liamentary) party leaders in more secluded environments rather than in the con‐ flictual parliamentary arena (Deschouwer, 1999; Dewachter, 2002). But also in the Netherlands, consociational logic long implied a “top-down approach to poli‐ tics” (Andeweg, 2019, p. 413) that included a depoliticisation of controversial issues and government’s right to govern without too much interference from par‐ liament (Koole, 2018; Lijphart, 1975).
AbstractThe COVID-19 pandemic has profoundly impacted politics, governance, and public policy, posing unprecedented challenges worldwide. This editorial introduces a Special Issue of Politics of the Low Countries that examines the political implications of the pandemic in the Low Countries and neighboring regions. Featuring four empirical studies, the issue explores analyzes democratic resilience, opposition dynamics, radical-right populist narratives, and gendered media portrayals during the crisis. Together, these contributions provide valuable insights into the complex interplay of governance, political culture, and public discourse in pandemic times.
Mona Bielig, Florian Kutzner, Sonja Klingert
et al.
Heating of buildings represents a significant share of the energy consumption in Europe. Smart thermostats that capitalize on the data-driven analysis of heating patterns in order to optimize heat supply are a very promising part of building energy management technology. However, factors driving their acceptance by building inhabitants are poorly understood although being a prerequisite for fully tapping on their potential. In order to understand the driving forces of technology adoption in this use case, a large survey (N = 2250) was conducted in five EU countries (Austria, Belgium, Estonia, Germany, Greece). For the data analysis structural equation modelling based on the Unified Theory of Acceptance and Use of Technology (UTAUT) was employed, which was extended by adding social beliefs, including descriptive social norms, collective efficacy, social identity and trust. As a result, performance expectancy, price value, and effort expectancy proved to be the most important predictors overall, with variations across countries. In sum, the adoption of smart thermostats appears more strongly associated with individual beliefs about their functioning, potentially reducing their adoption. At the end of the paper, implications for policy making and marketing of smart heating technologies are discussed.
Background Primary hyperparathyroidism (PHPT), the most common cause of hypercalcemia, is most often identified in postmenopausal women. The clinical presentation of PHPT has evolved over the past 40 years to include three distinct clinical phenotypes, each of which has been studied in detail and has led to evolving concepts about target organ involvement, natural history, and management. Methods In the present review, I provide an evidence-based summary of this disorder as it has been studied worldwide, citing key concepts and data that have helped to shape our concepts about this disease. Results PHPT is now recognized to include three clinical phenotypes: overt target organ involvement, mild asymptomatic hypercalcemia, and high PTH levels with persistently normal albumin-corrected and ionized serum calcium values. The factors that determine which of these clinical presentations is more likely to predominate in a given country include the extent to which biochemical screening is used, vitamin D deficiency is present, and whether parathyroid hormone levels are routinely measured in the evaluation of low bone density or frank osteoporosis. Guidelines for parathyroidectomy apply to all three clinical forms of the disease. If surgical guidelines are not met, parathyroidectomy can also be an appropriate option if no medical contraindications are present. If either the serum calcium or bone mineral density is of concern and surgery is not an option, pharmacological approaches are available and effective. Conclusions Advances in our knowledge of PHPT have guided new concepts in diagnosis and management.
The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GCC) countries in achieving economic diversification and social development, focusing on the Social Progress Index (SPI), which provides a broader measure of societal well-being beyond just economic growth. Using data from the World Bank, covering 2010 to 2023, the study employs the XGBoost machine learning model to forecast SPI values for the period of 2024 to 2026. Key components of the methodology include data preprocessing, feature selection, and the simulation of independent variables through ARIMA modeling. The results highlight significant improvements in education, healthcare, and women's rights, contributing to enhanced SPI performance across the GCC countries. However, notable challenges persist in areas like personal rights and inclusivity. The study further indicates that despite economic setbacks caused by global disruptions, including the COVID-19 pandemic and oil price volatility, GCC nations are expected to see steady improvements in their SPI scores through 2027. These findings underscore the critical importance of economic diversification, investment in human capital, and ongoing social reforms to reduce dependence on hydrocarbons and build knowledge-driven economies. This research offers valuable insights for policymakers aiming to strengthen both social and economic resilience in the region while advancing long-term sustainable development goals.
Wenlan Zhang, Miguel Nunez del Prado, Vincent Gauthier
et al.
The NetMob24 dataset offers a unique opportunity for researchers from a range of academic fields to access comprehensive spatiotemporal data sets spanning four countries (India, Mexico, Indonesia, and Colombia) over the course of two years (2019 and 2020). This dataset, developed in collaboration with Cuebiq (Also referred to as Spectus), comprises privacy-preserving aggregated data sets derived from mobile application (app) data collected from users who have voluntarily consented to anonymous data collection for research purposes. It is our hope that this reference dataset will foster the production of new research methods and the reproducibility of research outcomes.
This article examines how emerging economies use countercyclical monetary policies to manage economic crises and fluctuations in dominant currencies, such as the US dollar and the euro. Global economic cycles are marked by phases of expansion and recession, often exacerbated by major financial crises. These crises, such as those of 1997, 2008 and the disruption caused by the COVID-19 pandemic, have a particular impact on emerging economies due to their heightened vulnerability to foreign capital flows and exports.Counter-cyclical monetary policies, including interest rate adjustments, foreign exchange interventions and capital controls, are essential to stabilize these economies. These measures aim to mitigate the effects of economic shocks, maintain price stability and promote sustainable growth. This article presents a theoretical analysis of economic cycles and financial crises, highlighting the role of dominant currencies in global economic stability. Currencies such as the dollar and the euro strongly influence emerging economies, notably through exchange rate variations and international capital movements. Analysis of the monetary strategies of emerging economies, through case studies of Brazil, India and Nigeria, reveals how these countries use tools such as interest rates, foreign exchange interventions and capital controls to manage the impacts of crises and fluctuations in dominant currencies. The article also highlights the challenges and limitations faced by these countries, including structural and institutional constraints and the reactions of international financial markets.Finally, an econometric analysis using a Vector AutoRegression (VAR) model illustrates the impact of monetary policies on key economic variables, such as GDP, interest rates, inflation and exchange rates. The results show that emerging economies, although sensitive to external shocks, can adjust their policies to stabilize economic growth in the medium and long term.
Aggregate measures of family planning are used to monitor demand for and usage of contraceptive methods in populations globally, for example as part of the FP2030 initiative. Family planning measures for low- and middle-income countries are typically based on data collected through cross-sectional household surveys. Recently proposed measures account for sexual activity through assessment of the distribution of time-between-sex (TBS) in the population of interest. In this paper, we propose a statistical approach to estimate the distribution of TBS using data typically available in low- and middle-income countries, while addressing two major challenges. The first challenge is that timing of sex information is typically limited to women's time-since-last-sex (TSLS) data collected in the cross-sectional survey. In our proposed approach, we adopt the current duration method to estimate the distribution of TBS using the available TSLS data, from which the frequency of sex at the population level can be derived. Furthermore, the observed TSLS data are subject to reporting issues because they can be reported in different units and may be rounded off. To apply the current duration approach and account for these data reporting issues, we develop a flexible Bayesian model, and provide a detailed technical description of the proposed modeling approach.
Not only motivated by the fact that the publication of the GAFT first appeared 60 years ago in print we reconstruct its history and so show that it is no exaggeration to claim that it has appeared already 75 years ago!
High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries, few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 square kilometers, the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution.
RPYS is a bibliometric method originally introduced in order to reveal the historical roots of research topics or fields. RPYS does not identify the most highly cited papers of the publication set being studied (as is usually done by bibliometric analyses in research evaluation), but instead it indicates most frequently referenced publications - each within a specific reference publication year. In this study, we propose to use the method to identify important researchers, institutions and countries in the context of breakthrough research. To demonstrate our approach, we focus on research on physical modeling of Earth's climate and the prediction of global warming as an example. Klaus Hasselmann and Syukuro Manabe were both honored with the Nobel Prize in 2021 for their fundamental contributions to this research. Our results reveal that RPYS is able to identify most important researchers, institutions, and countries. For example, all the relevant authors' institutions are located in the USA. These institutions are either research centers of two US National Research Administrations (NASA and NOAA) or universities: the University of Arizona, Princeton University, the Massachusetts Institute of Technology (MIT), and the University of Stony Brook.