ai4data/datause-dataset-rechunked huggingface dataset:
As part of a regional Protection Profiling and Monitoring exercise, UNHCR collects and analyses on a quarterly basis data about the profiles, protection risks, and needs of refugees from Ukraine in Czechia, through individual interviews and focus group discussions with diverse groups. The key findings are highlighted below. #### Protection Risk I **Access to Temporary Protection (TP):** 98% of the respondents are holders of Temporary Protection in Czechia. Only 1% (five individuals) provided that their application for Temporary Protection was pending, whilst also 1% (four individuals) did not apply for Temporary Protection in Czechia. The reasons being, that they applied for refugee status, did not meet the eligibility criteria, did not stay in the country, or had since transitioned to a different legal status. Overall, access to Temporary Protection in Czechia continues to remain high. of respondents (55 individuals) reported facing some challenges with the Temporary ## 13% Protection application or extension process. In line with previous reports, the main challenges related to applying for Temporary Protection were long waiting times to receive a decision (44%), a lack of required documents (such as ID or proof of address) to complete the registration process (40%), and, despite its overall decrease in
###### **III.2 Country level analysis** The impacts of SLR on coastal wetlands were also estimated for individual countries. Table 2 summarizes our results by presenting the 10 most vulnerable countries. Estimates indicate large effects of SLR on coastal wetlands are much more concentrated in some countries than others. The 5 most vulnerable countries are: Viet Nam, Argentina, Iran, China and Mexico that represent 77% of the total freshwater marshes at risk from a 1 m SLR. Vietnam is by far the most vulnerable country with close to 65% of its freshwater marshes at risk. For vulnerable GLWD Coastal Wetlands, the top-ranked country China accounts for 56% of GLWD Coastal Wetlands” at risk. Of all vulnerable saline wetlands, Libya, Egypt, Mauritania, India and Argentina account for 93%, with Libya and Egypt representing 61%. Also within the regions, vulnerability is clearly far from balanced across countries. Viet Nam represents 85.1% of all vulnerable freshwater marshes and China represents 79.1% of all vulnerable GLWD Coastal Wetlands within EAP. Within MENA, 92.7% of all regional vulnerable saline wetlands are in Libya and Egypt. On the whole, our results suggest a significant asymmetry in the burden of SLR on wetlands: a small number of developing
**The World Bank** Costa Rica Results in Education (CORE) (P181174) in OECD countries, and few students enroll in tracks in high demand in the labor market, such as STEAM and digital skills. [13] Quality TVET could play a pivotal role in developing skills that are directly relevant to the labor market and in creating an adaptable workforce. 7. **Costa Rica is undergoing a TVET reform to better align it to the needs of the current and future labor market,** **emphasizing skills for the technology sector and green jobs** . The recently initiated reform includes efforts to revitalize entry into the labor market by targeting support for youth and adults. Participants complete their education certification, and a proposed program of micro-credentials promises further improvement on employability, seeking to align skills provision to labor demand through both the secondary level and vocational training courses and dual modality education offered by INA. As the global landscape shifts towards sustainability, there is likely to be a growing demand for a workforce equipped with expertise in green technologies, renewable energy, environmental sciences, and sustainable practices. Accessing skillsets in these areas requires strong foundational learning, cognitive and digital competencies. Possible green jobs in Costa Rica would
the increasing influx (Kemp and Kritzman 2008). In the past, Israel has recognised groups of refugees and asylum seekers as a gesture of goodwill. Notable examples of such ̳humanitarian anomalies‘ are the decision to grant refuge to a few hundred Vietnamese boat-people in the end of the 1970s and to admit a group of Muslim Bosnian refugees during the 1990s. (Ben-Dor and Adut 2003, 21-22; Markowitz 1996). Following its withdrawal from South Lebanon in 2000, Israel admitted nearly 6,000 members (and their families) of the South Lebanese Army, a sectarian militia who collaborated with Israel during its occupation of South Lebanon. Such precedents, coupled with public pressure, led former Prime Minister (PM) Olmert to grant an exceptional temporary residency to approximately 500 Darfurian asylum seekers in September 2007 (Mualem 2007). Israel historically handled asylum requests by outsourcing the process to UNHCR, using the assistance of the agency‘s honorary correspondent in Israel, which later became an official representative office (UNHCR 2007) It was only in 2001, 50 years 6 Driven by the horrors of the Holocaust in Europe and aiming to protect the Jewish refugees from World War II the young state of Israel was among the first 26 states who
sources and vintages of imagery. To guide the identification of slums, previous work has provided information on the appearance and geographical extent of slums in HCMC. Surveys of poverty in the city find the appearance of slums in HCMC to be characterized as densely built small households and shelters that have predominantly semi-permanent character (Habitat for Humanity 2008). In terms of geographic extent, many slums are located in certain districts (districts 2, 3, 4, 6, 8, 11, 12, Binh Thanh, Go Vap, Tan Phu) and along the Saigon River (e.g. Kenh Te, Rach Ben Nghe, Thi NgheNhieu Loc Canal, Kenh Doi, Thi Nghe Canal, Lo Gom, and Canala) (Horsley 2004; De Lay 2011; Habitat for Humanity 2008). Taking into account these spatial and geographic characteristics, the PUMA data set interprets Google Earth imagery to produce two layers of potential slum areas (PUMA 2013): areas with defined borders (polygon-data) and potential slum areas without (point-data) defined borders. In the latter case, we applied a circular buffer of 50 m around each
availability of fresh water and sediment, and the salinity of soil and groundwater (Belperio, 1993; Semeniuk, 1994; Blasco et al., 1996; Alongi, 2008). [8] Even though location-specific studies are needed to define the specific details, experts and scientists agree that adaptation of wetlands to future sea-level rise depends on its success in landward progression and is conditioned by the availability of adequate and suitable space for expansion/ migration, and a rate of sea level rise that is not greater than the rate at which wetlands can migrate. [9] This paper is an effort to quantify the coastal wetlands of different types [10] at risk from 1 m SLR [11] in 76 developing countries and territories in the five regions [12] of the world. Our estimates take into account the exposure of wetlands derived from the recent GLWD-3 database to 1 m SLR and the estimated capacity of the coastline to retreat and for coastal wetlands ecosystems to move (or migrate) inland as the coastline is receding. Attempts have also been made to estimate the economic loss, which may be associated with adversely impacted wetlands. Our estimates indicate that a 1 m rise in sea level would lead to a loss
associated with them will lose their services. Wetlands in WMP category 4 are at great risk, but may survive, depending on the effect of flooding behind the coastline. If the flooding is severe enough and persists long enough to seriously disrupt the trapping of the sediment or building upon the peat the sediment creates, the wetlands will be severely degraded and may perish. Hence, the wetlands in WMP categories 3 and 4 exposed to the inundation zone for 1 meter SLR are the estimates of wetlands at inundation risk in a changing climate. ##### **Results** For the 76 coastal developing countries and territories included in this analysis, estimates indicate more than 60% of freshwater marsh, GLWD Coastal Wetlands, and brackish/saline wetlands (henceforth saline wetlands for brevity) might be lost as a result of a 1m SLR. In terms of area estimates, this would translate to a loss of 16,492 square kilometer of freshwater marsh, 17,421 square kilometer of GLWD Coastal Wetlands and 10,969 square kilometer of saline wetlands. Among the four coastal wetland categories, only swamp forests appear less vulnerable to SLR and more capable of migrating as the coast line is receding and henceforth dropped from further analysis [19]
University of Kassel, Germany, and the World Wildlife Fund US (WWF-US), Washington DC, USA (Lehner and Döll, 2004). In the generation of the global map of lakes and wetlands from a grid at a spatial resolution of 30 seconds (approximately 1km by 1km at the equator), the GLWD-3 followed the definition of wetlands adopted at the Ramsar Convention, the International Union for Conservation of Nature. Our analysis focuses on freshwater marsh, swamp forests, GLWD Coastal Wetlands [14] and Brackish/ saline wetlands. _Response of wetlands to SLR_ . In order to assess the impact of SLR on wetlands and the potential for adaptation, the wetland migratory potential (WMP) characteristic in the Dynamic Interactive Vulnerability Assessment (DIVA) database from the DINAS-COAST project has been used (Vafeidis et al, 2008). Different types of wetlands are expected to have different migratory potential depending on their own natural characteristics as well as the characteristics of their surrounding environment. For example, it is expected that SLR will have its most pronounced effects on brackish and freshwater marshes in the coastal zone through alteration of hydrological regimes (Burkett and Kusler, 2000; Baldwin et al., 2001; Sun et al., 2002). Similarly, sea-level rise may not lead to loss
US$ per month, [14] significantly below that by non-agriculturalists (45 US$ per month), pure owners are better off than the rest in terms of total land owned (9.5 vs. 3.2 acres for owner-cum-occupants), the value of own land, livestock, and non-land assets (US$ 4,011, 968 and 1733 vs. 1651, 227, and 1252, respectively), and overall welfare as measured by per capita income (US$ 226 vs. 193). Third, while crop productivity [15] for pure occupants is not significantly different from that for pure owners (US$ 95.3 vs. US$ 96.7 per acre) it is below that achieved by owner-cum-occupants (US$ 110.8/ac.). While pure owners have access to more land than owner-cum-occupants, there is no significant difference in the amount of land cultivated by the two groups during the 2004/5 agricultural season. Pure occupants cultivated significantly less land than any of the two other groups and, as a result, derived a much larger share of their income (42%) from wage employment, as compared to mixed farmers (24%) and pure owners (30%). Finally, with only 16.5% of households and 9.5% of women knowing about the 1998 Land Act, legal awareness remains low. **3.2 Parcel level characteristics** Tables 2 and 3 provide evidence on physical
Chi Minh City In addition to the flood hazard maps developed for this study as described above, we use an additional set of maps produced specifically for HCMC. 2 These uncertainties largely arise from climate models; global climate models (GCMs) struggle to represent the physical processes that produce extreme rainfall. Indeed even in higher resolution regional climate models (RCMs), heavy rainfall events are poorly represented. As a result the modeled rainfall data must be ‘corrected’, in order to render it realistic. The fact that the underlying models themselves cannot represent flood driving rainfall means that there is little confidence in the projections that they produce. Moreover, at the national scale there is very little river gauge data available in Vietnam. Therefore rainfall-runoff models, required to transform rainfall projections into river discharge values, would be largely un-calibrated. This adds an additional source of significant modeling uncertainty to the model cascade. The combination of poorly represented extreme rainfall in climate models, coupled with uncalibrated rainfall-runoff models, would largely render any projections of future flood hazard impractical, owing to the significant uncertainties that arise.
the wealth of the farmers. If farmers become poorer because of bad rainfall, CRRA utility would suggest that they would be even more risk averse the next year as a second shock would cause greater disutility. I start by examining whether there is actual autocorrelation in the rainfall data. To test for autocorrelation, I create a panel of various rainfall indicators for the period 1961–2004 for each weather station. For each indicator, I run a regression of six lags of the variable on the current value, including weather
between 132 million and 587 million poor people are exposed to flood risks (depending on which poverty definition is used). About 1.2 billion flood-exposed people live in lower- and uppermiddle-income countries. Taking into account the income levels of flood exposed populations is particularly important, as income is a relatively reliable proxy for people’s ability to mitigate, withstand, cope with, and recover from floods. For instance, while a large share of the Dutch population lives in flood risk areas, large-scale investments in flood protection infrastructure have enabled them to mitigate risks. Similarly, flood exposed populations in Canada or Japan are more likely to have access to rapid government support systems in post-disaster situations compared to people in Malawi or Bangladesh. Thus, action to strengthen disaster prevention and recovery capacity is most urgently needed in the hotspots where poverty and flood exposure coincide. References Braese, J., S. De Vries Robbe, J. Rentschler, Jun. (2020). Coastal Development between Opportunity and Disaster Risk: A Multisectoral Risk Assessment for Vietnam. Policy Research Working Paper; No. 9352. World Bank, Washington Bright, E. A., Rose, A. N. & Urban, M. L. _Landscan 2015 High-Resolution Global Population Data Set_ . (Oak Ridge National Laboratory (ORNL), Oak Ridge, TN,
Economics of Disasters and Climate Change https://doi.org/10.1007/s41885-018-0035-4 # Exposure to Floods, Climate Change, and Poverty in Vietnam Mook Bangalore [1,2] & Andrew Smith [3] & Ted Veldkamp [4,5] Received: 16 February 2018 /Accepted: 4 October 2018/ # The Author(s) 2018 Abstract With 70% of its population living in coastal areas and low-lying deltas, Vietnam is highly exposed to riverine and coastal flooding. This paper conducts a Bstress-test^ and examines the exposure of the population and poor people in particular to current and future flooding in Vietnam and specifically in Ho Chi Minh City. We develop new high-resolution flood hazard maps at 90 m horizontal resolution, and combine this with spatially-explicit socioeconomic data on poverty at the country and city level, two datasets often kept separate. The nationallevel analysis finds that a third of today’s population is already exposed to a flood, which occurs once every 25 years, assuming no protection. For the same return period flood under current socioeconomic conditions, climate change may increase the number exposed to 38 to 46% of the population (an increase of 13–27% above current exposure), depending on the severity of sea level rise. While poor districts are not found to be more exposed to
**1. Introduction** Recurrent cyclonic storms in the Bay of Bengal inflict massive losses on the coastal regions of Bangladesh and India. Extensive research has investigated the incidence, power and impacts of cyclones in Bangladesh (Ali 1999; Dasgupta et al. 2014; Hoque 1992; Khalil 1992), India (Mishra 2014; Srivastava et al. 2000) and the Bay of Bengal more generally (Bhaskar Rao 2001; Dash et al. 2004; Mandke and Bhide 2003; Mooley and Mohile 1983; Mooley 1980; Muni Krishna 2009; Rao 2002; Yu and Wang 2009). This paper attempts to extend the previous work in several ways. First, we focus on the historical frequency and locations of cyclone strikes to inform household- and community-level research on adaptation to past and expected future cyclones. Previous studies of coastal community adaptation in the region have focused on responses to specific cyclones or generally-defined coastal hazards (Brouwer et al. 2007; Khalil 1993; Khan et al. 2015; Mallick et al. 2011; Mallick and Vogt 2013; Shameem and Momtaz 2014; Siddiqui et al. 2013; Sultana and Mallick 2015). With the notable exception of Dasgupta et al. (2016), these studies have seldom used household-level data to assess the impacts of past cyclones on communities and households, as well
of which will be supported by Jya Mbere II. 9. **Climate change poses risks to Rwanda’s positive economic outlook and refugees are particularly vulnerable** **to climate shocks** . Ranked 112 out of 185 countries in the 2021 Notre Dame-Global Adaptation Index (ND-GAIN), Rwanda is vulnerable to flooding, heat, drought, and landslides, which are expected to increase in frequency and severity. The country has already experienced a temperature increase of 1.4°C since 1970, higher than the global average; and an increase of up to 2.0°C is expected by the 2030s. The need for urgent action to address climate change impacts was highlighted by severe flooding and landslides in May 2023 that claimed many lives, destroyed homes and infrastructure and affected livelihoods. Rwanda’s high dependence on rain-fed agriculture, as well as the need to improve road networks, health facilities and water resource management all contribute to climate vulnerability. [10] The location of Rwanda’s refugee camps on rural hilltops makes them vulnerable to extreme weather events such as flooding and associated landslides. High population density in the camps and the limited capacity of refugees to adapt to the impacts of climate change exacerbates their vulnerability. Despite the protracted nature of displacement, refugee camps
(GSS). [2] Point coordinates (global positioning system [GPS]) for the surveyed DHS clusters [3] allow us to match all individuals to one or several mineral mines. We do this in two ways. First, we calculate distance spans from an exact mine location given by its GPS coordinates, and match surveyed individuals to mines. These are concentric circles with radiuses of 10, 20, and 30 kilometers (km), and so on, up to 100 km and beyond. In the baseline analysis where 2 The data was shared by Aragón and Rud (2013) 3 Both the DHS and GLSS enumeration area coordinates have a 1-5 km offset. The DHS clusters have up to 10km displacement in 1% of the cases. 7
is the World Bank's Country Policy and Institutional Assessment (CPIA) database, which rates environmental policies and institutions on a numerical scale of 1 (the lowest) to 6. For this exercise, we reverse the scaling (1 becomes the highest) and normalize the ratings so that countries with the greatest problems score 100. To proxy the scale of the problems confronted by environmental institutions, we compute the mean value of our five thematic indicators (global emissions, pollution, natural resource degradation, biodiversity threats, water-related problems). [5,6] To assure equal weighting 4 Our index of demand pressure also provides a useful proxy for economic damage from drought conditions. We are indebted to our colleagues in the Bank’s Middle East / North Africa region for this observation. 5 While the CPIA ratings provide useful information for comparing institutional needs, they are not sufficient for judging investment priorities because they do not account for differences in the scale of environmental problems faced by a country's institutions. If Brazil and Bhutan receive the same CPIA rating, for example, ignoring their scale difference will lead to assignment of identical lending in the optimization model. 6 We recognize that an equal-weighted index is only one of numerous plausible indicators
26.9 percent in Markazi, and 46 percent in Djibouti-Ville. [10] Livelihood opportunities in refugee villages are scarcer than in the capital, exacerbating food security challenges, and even highly educated refugees struggle to find employment, without a command of French. Refugees’ **access to finance** is limited by their lack of inclusion in the national ID system; underdeveloped credit markets; and limited awareness of their right to work. Their self-sufficiency is limited by a lack of access to vocational training, business development support services, and decent jobs [11] . [6 UNHCR (2023), Republic of Djibouti - Country Summary as at 30 June 2023](https://www.refworld.org/reference/countryrep/unhcr/2024/en/147860) 7 UNHCR (2025), Djibouti Education Statistics for Refugees; Ministry of Education and Vocational Training of the Republic of Djibouti (2024), [Annuaire Statistique 2023/2024; World Bank (2024),](http://www.education.gov.dj/index.php?option=com_k2&view=item&id=631:annuaire-statistique-2023-2024&Itemid=784&lang=en) [Djibouti: Giving Refugee Children a Chance to Go To School](https://www.worldbank.org/en/news/feature/2024/01/11/djibouti-giving-refugee-children-a-chance-to-go-to-school) 8 World Bank (2024), [Djibouti: Giving Refugee Children a Chance to Go To School](https://www.worldbank.org/en/news/feature/2024/01/11/djibouti-giving-refugee-children-a-chance-to-go-to-school) 9 The Ministry of Health and UNHCR signed a cooperation agreement in 2018 for the inclusion of refugees in the national health system 10 Government of Djibouti (2024), Stratégie Livelihoods et Inclusion Economique Djibouti 2024 – 2028 11 UNHCR, African Development Bank Group, IGAD, EAC (2024), Regional Report Draft:
in housing prices (Daniel, Florax, and Rietveld 2009). In Ho Chi Minh City, for instance, qualitative surveys suggest flood-prone areas can be much cheaper than non-flood-prone areas for the same quality of accommodation (World Bank and Australian AID 2014). Using a household survey in Dar es Salaam, Tanzania, Erman et al. (2019) find that flood-prone dwellings are valued at 30 percent less on average than safe ones. Reduced housing prices, then, make it possible for poor people to access housing opportunities that could be out of reach in the absence of risk (Husby et al. 2015). In developing countries with informal markets, land scarcity can be particularly acute and land markets function poorly (Durand-Lasserve, Selod, and Durand-Lasserve 2013). In these places, it may not be the prices that push poor people into risky places but simply the availability of land with appropriate access to jobs and services. Informal settlements are often located in hazard-prone locations such as on hillsides, close to riverbanks, or near open drains and sewers—Pune (India), Dhaka, Caracas, Rio de Janeiro, and Mumbai have many such settlements (Lall and Deichmann 2012; Lall, Lundberg, and Shalizi 2008). 3
farming, either for their own production or as hired workers or sharecroppers on host community land. 4 World Bank (2023). _World Development Report 2023: Migrants, Refugees, and Societies._ Washington, DC: World Bank at p. 208. 5 Poverty rates in refugee-hosting districts during the COVID-19 crisis ranged from 37.7 percent in rural areas to 20.5 percent in urban areas; the overall national poverty rate was 33.2 percent. Headcount poverty rates averaged 39.9 percent in the Northern Region, 26.6 percent in the Western Region, and 20.4 percent in the West Nile. (World Bank. 2022. _Uganda Poverty Assessment Overview – Strengthening Resilience to_ _Accelerate Poverty Reduction in Uganda)._ 6 A study commissioned by the Norwegian Refugee Council revealed that between 2016-2020, 40.3 percent of refugees self-settled in the urban centers of Adjumani, Koboko, Hoima and Mbarara. Steady flows have continued into these locations and Arua, another secondary city, over the last four years: see Office of the Prime Minister and UNHCR (2024) _Uganda Urban Response Strategy 2024-2029_ . 7 The definition used is canopy height >5m. Global Forest Watch 8 Compounded by the fact that refugee settlement population density is typically ten times the national average: World Agroforestry Center, 2024. 9 Gianvenuti, A.,