Abstract This study examines the potential effects of skilled labor emigration on Bhutan’s economy using an extended computable general equilibrium (CGE) model. Numerical simulation analysis is undertaken to examine the impacts of an ongoing increase in skilled emigrants on the economy and welfare of Bhutan. The simulation results paint a somewhat pessimistic picture, showing that a rise in foreign wages would lead to a significant outflow of skilled labor, causing a substantial supply shortage in the domestic skilled labor market and a decline in all sectors - particularly in the manufacturing, services, and public sectors. Moreover, the increased inflow of remittances would exacerbate the negative impacts through the Dutch disease. The overall outcome is a decline in GDP by 0.2% from the base. To counter this adverse economic impact, this study proposes policy interventions by improving total factor productivity (TFP) or labor productivity in the domestic industries. Specifically, it is unveiled that reform initiatives that can enhance productivity gains beyond 0.3% in TFP or 0.4% in labor productivity across the sectors would contribute positively to GDP growth and curtail the brain drain from the country.
Cristina Sin, Orlanda Tavares, Elina Apsite-Berina
et al.
Abstract This systematic literature review summarises the state-of-the-art evidence on the impact of COVID-19 on the integration of international students in their host countries and institutions. Conducted between January and May 2022, it analyses the responses to COVID-19 of the key actors involved in international student mobility: national/regional authorities, higher education institutions, and students. Findings reveal that governmental action and institutional measures were decisive in shaping international students’ integration experiences. Regarding governmental action, criticism of the policies adopted by Australia and the USA in relation to immigration and/or support stand out, in contrast to policies adopted by the Canadian authorities. Higher education institutions played an important role in mitigating the negative effects of COVID-19 on international students’ integration. These targeted different needs– material, well-being, and social– through different types of support: logistical and financial support, psychological support, and the provision of platforms for ongoing social interaction and exchange. Most studies, however, focus on the students themselves, the challenges they faced during the pandemic and their coping strategies. Common to international students’ lived experience was (dis)connectedness, with the following themes emerging as obstacles to their social and cultural integration: distress during lockdown periods, disruption of their social life and support networks, mental health issues, discrimination and racialised prejudice, and language barriers. The review concludes by proposing recommendations and by identifying avenues for future research.
This article provides a systematic overview of the academic literature on the impact of ″refugee shocks“ – the sudden arrival of large numbers of refugees – on host countries. A scoping review was conducted using Google Scholar in September 2022 to describe the literature, drawing on 4,576 effects from 123 quantitative studies with no restrictions on countries, year of publication, type of publication, or the reported topics. This broad scope acknowledges that refugee shocks potentially affect many areas of life. A synthesis was carried out by aggregating and using regression models. We find an increase in studies on refugee shocks after 2015 and that the most commonly studied shocks took place in the Middle East and Europe. About two-thirds of the effects concern economic outcomes in the host country, while few cover health or environmental outcomes. Across topics, about half of the analyses indicate no statistically significant effect. Studies generally report normatively positive effects on education and generally negative effects on wages and employment in the host country. Refugee shocks tend to be associated with an increase in votes for the radical right. Future studies should address refugee shocks beyond the Western countries that are studied most closely and focus on understanding the dynamics of how different actors react to the arrival of refugees.
Urban groups. The city. Urban sociology, City population. Including children in cities, immigration
Accurately measuring street dimensions is essential to evaluating how their design influences both travel behavior and safety. However, gathering street-level information at city scale with precision is difficult given the quantity and complexity of urban intersections. To address this challenge in the context of pedestrian crossings - a crucial component of walkability - we introduce a scalable and accurate method for automatically measuring crossing distance at both marked and unmarked crosswalks, applied to America's 100 largest cities. First, OpenStreetMap coordinates were used to retrieve satellite imagery of intersections throughout each city, totaling roughly three million images. Next, Meta's Segment Anything Model was trained on a manually-labelled subset of these images to differentiate drivable from non-drivable surfaces (i.e., roads vs. sidewalks). Third, all available crossing edges from OpenStreetMap were extracted. Finally, crossing edges were overlaid on the segmented intersection images, and a grow-cut algorithm was applied to connect each edge to its adjacent non-drivable surface (e.g., sidewalk, private property, etc.), thus enabling the calculation of crossing distance. This achieved 93 percent accuracy in measuring crossing distance, with a median absolute error of 2 feet 3 inches (0.69 meters), when compared to manually-verified data for an entire city. Across the 100 largest US cities, median crossing distance ranges from 32 feet to 78 feet (9.8 to 23.8m), with detectable regional patterns. Median crossing distance also displays a positive relationship with cities' year of incorporation, illustrating in a novel way how American cities increasingly emphasize wider (and more car-centric) streets.
Kunihiko Fujiwara, Ryuta Tsurumi, Tomoki Kiyono
et al.
Three-dimensional urban environment simulation is a powerful tool for informed urban planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To address this challenge, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model generation and urban environment simulation for cities worldwide. VoxCity's `generator' subpackage automatically downloads building heights, tree canopy heights, land cover, and terrain elevation within a specified target area, and voxelizes buildings, trees, land cover, and terrain to generate an integrated voxel city model. The `simulator' subpackage enables users to conduct environmental simulations, including solar radiation and view index analyses. Users can export the generated models using several file formats compatible with external software, such as ENVI-met (INX), Blender, and Rhino (OBJ). We generated 3D city models for eight global cities, and demonstrated the calculation of solar irradiance, sky view index, and green view index. We also showcased microclimate simulation and 3D rendering visualization through ENVI-met and Rhino, respectively, through the file export function. Additionally, we reviewed openly available geospatial data to create guidelines to help users choose appropriate data sources depending on their target areas and purposes. VoxCity can significantly reduce the effort and time required for 3D city model preparation and promote the utilization of urban environment simulations. This contributes to more informed urban and architectural design that considers environmental impacts, and in turn, fosters sustainable and livable cities. VoxCity is released openly at https://github.com/kunifujiwara/VoxCity.
Urban economic vitality is a crucial indicator of a city's long-term growth potential, comprising key metrics such as the annual number of new companies and the population employed. However, modeling urban economic vitality remains challenging. This study develops ECO-GROW, a multi-graph framework modeling China's inter-city networks (2005-2021) to generate urban embeddings that model urban economic vitality. Traditional approaches relying on static city-level aggregates fail to capture a fundamental dynamic: the developmental trajectory of one city today may mirror that of its structurally similar counterparts tomorrow. ECO-GROW overcomes this limitation by integrating industrial linkages, POI similarities, migration similarities and temporal network evolution over 15 years. The framework combines a Dynamic Top-K GCN to adaptively select influential inter-city connections and an adaptive Graph Scorer mechanism to dynamically weight cross-regional impacts. Additionally, the model incorporates a link prediction task based on Barabasi Proximity, optimizing the graph representation. Experimental results demonstrate ECO-GROW's superior accuracy in predicting entrepreneurial activities and employment trends compared to conventional models. By open-sourcing our code, we enable government agencies and public sector organizations to leverage big data analytics for evidence-based urban planning, economic policy formulation, and resource allocation decisions that benefit society at large.
Jhoni Warmansyah, Restu Yuningsih, Meliana Sari
et al.
This study aims to evaluate the impact of early childhood executive function on emotional dysregulation. Participants were parents of children aged 5 to 6. Primary data was acquired by sending surveys via a Google Form. A correlational quantitative research approach was used. The research findings indicate that there is a relationship between executive function and emotional dysregulation in early childhood, particularly in Tanah Datar Regency. With a hypothesis test, the obtained value of r is 0.156, while the critical value (rtabel) for a sample size of 162 individuals is 0.148 at a significance level of 5%. The correlation test using SPSS 21 yields a significance value of 0.161, indicating a strong correlation between the two variables. The results showed that children's emotional dysregulation was highly influenced by their level of executive function in early childhood. This study emphasizes the need of recognizing and fostering executive function in early children to promote healthy emotional regulation, providing parents with vital insights into understanding and supporting their children's emotional development.
Education, City population. Including children in cities, immigration
chandra apriyansyah, Sofia Hartati, Fasli Jalal
et al.
This study aims to observe how the Integrative Holistic Early Childhood Development Program is implemented in ECCE units. The survey's conclusion that Bogor Regency's use of Integrative Holistic ECCE is still subpar served as the impetus for this investigation. This study uses a descriptive, qualitative research design. The PAUD unit in Bogor Regency hosted the study for three months, from March to May 2024. This study includes principals and instructors as subjects. Qualitative descriptive analysis is the data analysis method that is applied. Data reduction, data visualization, conclusion drafting, and data verification are among the stages of data analysis. Using triangulation, the data's veracity is verified. According to the study's findings, principals and teachers have a positive attitude toward PAUD HI, employ effective strategies or methods in their programs, and work together with the government, non-governmental organizations, parents, and the private sector. However, one of the main challenges in putting PAUD HI programs into practice in ECCE units is the lack of funding, facilities, and teaching staff, as well as a lack of resources for the learning environment and parent involvement. The study's conclusion The Integrative Holistic Early Childhood Development (PAUD HI) program faces a number of obstacles during implementation. To overcome these obstacles, the community, government, and educational institutions must work together well, actively involve parents, improve infrastructure and human resources, and conduct periodic evaluations.
Education, City population. Including children in cities, immigration
Despite representing the second-largest immigrant group in Germany, Polish immigrants and their descendants are understudied and have often been described as ‘invisible’ as they have a reputation of ‘becoming German’ quickly and unproblematically. Challenging this notion and considering the prevalence of anti-Eastern European racism in the German context, this study analyses interviews with 22 highly educated Germans of Polish descent, focusing on how interviewees talked about being German and/or Polish and their experiences of stigmatisation and discrimination, in both their childhood and teenage years and as adults. In so doing, the study contributes to the literature on how the ethnic and national identities of white descendants of immigrants are related to experiences of exclusion. Specifically, some interviewees (Type 1) said that they felt only German (and not Polish) and denied experiencing stigmatisation or discrimination in their present lives. Other interviewees (Type 2) embraced a symbolic Polish ethnicity while framing exclusionary experiences as a thing of the past. Type 3 interviewees reported a process of re-ethnicisation, arguably enabled by the absence of exclusionary experiences in their present lives. Finally, there were interviewees (Type 4) who reported embracing their Polish identity, which led to experiences of stigmatisation in certain contexts.
Colonies and colonization. Emigration and immigration. International migration, City population. Including children in cities, immigration
Ayad Ghany Ismaeel, S. J. Jereesha Mary, C. Anitha
et al.
Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large scale data sets.
Comparative analyses frequently examine respondents’ self-rated health (SRH), assuming that it is a valid and comparable measure of generic health. However, given SRH’s vagueness, this assumption is questionable due to (1) manifold non-health influences, such as personal characteristics including optimism, interviewer effects on the rating, and cultural contexts, as well as (2) potential gender, age- or country-specific expectations for one’s health or frames of reference. Conceptually, two major components of SRH can be distinguished: latent health and reporting behavior. While latent health exclusively refers to objective health status, reporting behavior collectively refers to non-health characteristics (NH) affecting SRH. The present paper is primarily concerned with the latter and aims to identify whether and how NH bias SRH, including possible differences by gender, age, and country of residence.
The presented analyses are based on data from 16,183 participants in five countries drawn from the fifth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE). Latent health is controlled via a wide array of health indicators and the residuals are examined with a model covering NH from three different sources: the interviewer, the respondent, and the country of residence. To identify subgroup-specific response behaviors, all analyses are carried out separately by gender, three age groups (50-64, 65-79, and 80+ years), and country of residence.
The analyses uncovered influences of – among others–the interviewer’s SRH, the respondent’s life satisfaction, and the country of residence on SRH, while other factors differed by subgroup. The amount of explained variance due to such reporting behavior (with a mean of seven percent) can be deemed meaningful, considering that controlling for latent health already explains around half of SRH’s variance. The greatest source of non-health influences was respondent characteristics, with the interviewer and country having smaller effects.
These results illustrate the importance of taking NH into account when using SRH measures. Future research on complementing SRH with factual questions in survey design is advisable.
* This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.
Urban groups. The city. Urban sociology, City population. Including children in cities, immigration
Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being. China has initiated a range of urban greening policies over the past decades, however, monitoring their impact on urban tree dynamics at a national scale has proven challenging. In this study, we deployed nano-satellites to quantify urban tree coverage in all major Chinese cities larger than 50 km2 in 2010 and 2019. Our findings indicate that approximately 6000 km2 (11%) of urban areas were covered by trees in 2019, and 76% of these cities experienced an increase in tree cover compared to 2010. Notably, the increase in tree cover in mega-cities such as Beijing, and Shanghai was approximately twice as large as in most other cities (7.69% vs 3.94%). The study employs a data-driven approach towards assessing urban tree cover changes in relation to greening policies, showing clear signs of tree cover increases but also suggesting an uneven implementation primarily benefiting a few mega-cities.
The rise of Internet of things (IoT) technology has revolutionized urban living, offering immense potential for smart cities in which smart home, smart infrastructure, and smart industry are essential aspects that contribute to the development of intelligent urban ecosystems. The integration of smart home technology raises concerns regarding data privacy and security, while smart infrastructure implementation demands robust networking and interoperability solutions. Simultaneously, deploying IoT in industrial settings faces challenges related to scalability, standardization, and data management. This research paper offers a systematic literature review of published research in the field of IoT in smart cities including 55 relevant primary studies that have been published in reputable journals and conferences. This extensive literature review explores and evaluates various aspects of smart home, smart infrastructure, and smart industry and the challenges like security and privacy, smart sensors, interoperability and standardization. We provide a unified perspective, as we seek to enhance the efficiency and effectiveness of smart cities while overcoming security concerns. It then explores their potential for collective integration and impact on the development of smart cities. Furthermore, this study addresses the challenges associated with each component individually and explores their combined impact on enhancing urban efficiency and sustainability. Through a comprehensive analysis of security concerns, this research successfully integrates these IoT components in a unified approach, presenting a holistic framework for building smart cities of the future. Integrating smart home, smart infrastructure, and smart industry, this research highlights the significance of an integrated approach in developing smart cities.
Md Abu Sayed, Md Maksudur Rahman, Moinul Islam Zaber
et al.
Analysis of traffic pattern recognition and traffic congestion expansion in real time are one of the exciting and challenging tasks which help the government to build a robust and sustainable traffic management system specially in a densely populated city like Dhaka. In this paper, we analyze the traffic intensity for small areas which are also known as junction points or corridors. We describe Dhaka city traffic expansion from a congestion point by using gravity model. However, we process real-time traffic data of Dhaka city rather than depend on survey and interview. We exactly show that traffic expansion of Dhaka city exactly follows gravity model. Expansion of traffic from a congestion point spreads out rapidly to its neighbor and impact of congested point decreases as the distance increases from that congested point. This analysis will help the government making a planned urbanized Dhaka city in order to reduce traffic jam.
Cities are becoming smarter and more resilient by integrating urban infrastructure with information technology. However, concerns grow that smart cities might reverse progress on civil liberties when sensing, profiling, and predicting citizen activities; undermining citizen autonomy in connectivity, mobility, and energy consumption; and deprivatizing digital infrastructure. In response, cities need to deploy technical breakthroughs, such as privacy-enhancing technologies, cohort modelling, and fair and explainable machine learning. However, as throwing technologies at cities cannot always address civil liberty concerns, cities must ensure transparency and foster citizen participation to win public trust about the way resilience and liberties are balanced.
Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.