Input: 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

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Input: ###### **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

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Input: **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

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Input: 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

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Input: 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

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Input: 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

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Input: 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]

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Input: 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

Dynamic Interactive Vulnerability Assessment (DIVA)

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Input: 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

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Input: 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.

flood hazard maps
maps produced specifically for HCMC

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Input: 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

various rainfall indicators
rainfall data

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Input: 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,

Landscan 2015 High-Resolution Global Population Data Set

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Input: 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

spatially-explicit socioeconomic data on poverty

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Input: **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

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Input: 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

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Input: (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

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Input: 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

Country Policy and Institutional Assessment (CPIA) database

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Input: 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:

Djibouti Education Statistics for Refugees

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Input: 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

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Input: 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.,

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Input: Economics of Disasters and Climate Change and can be identified from the results of this paper. An important constraint is that for such development to be feasible, they should be paired with transportation investments, which maintain access to the city center where opportunities are presented (World Bank 2015). Despite the potential of risk-sensitive land-use planning, resettlement is the major ex-ante hazard adaptation mechanism employed in Vietnam currently, especially in the Mekong Delta. While such policies can reduce exposure, policy design is critical to ensure the livelihoods of the poor are supported. For instance, surveys in Tan Chau district suggests the resettlement policy enacted in 2002 may have made households worse-off: inadequate financing resulted in households paying for their new settlements out-of-pocket; many households who were farmers and fishers did not have adequate land, transportation and market access, and inadequate livelihood support was provided to them (World Bank 2016). Where resettlement policies are enacted, it is imperative that such policies are paired with livelihood and financing support. Beyond strategies to reduce exposure, other policy options to reduce vulnerability to improve households’ ability to adapt may warrant increased attention. Strategies such as government subsidies for household-level flood protection (like raising of floors),

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Input: To show the robustness of these results, we have tried three alternative strategies for each of the outcomes. In row 2, we drop the sample that lives 20 to 40 km away, since they might be affected by the mine; in row 3, we drop the sample that was surveyed two years prior to mine opening; in row 4, we add closest mine fixed effects; and in row 5 we cluster on the closest mine. The coefficients do not change much, even if some magnitudes become bigger and the estimates more significant. However, as in the results using DHS data, these estimates are not precisely measured – few are statistically significant because the standard errors appear large. Women are 7.4 to 10.4 percentage points more likely to work in service or sales if they live close to a mine (depending on the estimations in panel A columns 4, only one statistically significant estimate). Women close to mines are 2.5 to 2.6 percentage points more likely to work in mining (only one statistically significant estimate). Men, on the other hand, (results shown in panel B of table 12), are significantly more likely to work in mining, and insignificantly less in agriculture

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Input: Middle East & North Africa Latin America & the Caribbean South Asia Middle East & North Africa Latin America & the Caribbean East Asia & the Pacific South Asia Middle East & North Africa Latin America & the Caribbean **Figure 2. Distribution of lost wetlands by types of wetlands across regions, for a 1m SLR** We have also presented the percentage of wetland types at risk by region in Figure 2, which is revealing. Once again, it is apparent that among the various regions, EAP faces the greatest risk of overall loss of wetlands: for a 1 m SLR scenario, 61% and 71% of vulnerable freshwater marsh and GLWD Coastal Wetlands are in EAP. On the other hand, 65% of saline wetlands at risk is in MENA. After EAP, MENA represents the second largest proportion of GLWD Coastal Wetlands at risk (15%). LAC also represents a significant share (18%) of vulnerable freshwater marshes. 12

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Input: The second distinctive feature of this exercise is its incorporation of compatible data from different sources. For the period since 1960, we use georeferenced track information on major cyclones striking Bangladesh from the Bangladesh Meteorological Department (BMD). Equivalent information for the Indian coastal region comes from the India Meteorological Department (IMD). For cyclones prior to 1960, we add information from the IBTrACS database maintained by the Global Data Center for Meteorology, operated by the US National Oceanic and Atmospheric Administration. The IBTrACS data for the Indian Ocean have been provided by meteorological institutions in the region. We also use them for a few post-1960 cyclones that are not included in the data available to us from BMD and IMD. We ensure cross-source compatibility by using WMO standards for the two commonly-available measures of storm strength: maximum wind speed (measured in knots (kt)) and radial distance from a storm’s center to its zone of maximum wind speed. We employ a standard IMD storm classification based on maximum wind speed intervals in kt: (Cyclonic Storm (CS) [34-47 kt]; Severe Cyclonic Storm (SCS) [48-63]; Very Severe Cyclonic Storm (VSCS) [64-119] and Super Cyclonic Storm (SuCS) [120+]. We have excluded all storms rated as

georeferenced track information on major cyclones striking Bangladesh

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Input: of forced displacement, covering refugees and host communities. **III.** **PROJECT IMPLEMENTATION** **A.** **Institutional and Implementation Arrangements** 55. **The project will retain the same arrangements as Phase I**, but with enhanced coordination and collaboration mechanisms under OPM’s DoR. OPM will implement DRDIP II at the national level through a PIST, which will provide technical guidance and support to implementing districts and communities. The PIST will comprise technical experts who will be recruited or seconded from government agencies. Additional expertise can be contracted where necessary. In line with GoU objectives to streamline implementation units within IAs, the PIST will share some technical specialists to support DRDIP II and NUSAF IV. This will reduce costs and enhance coordination on the ground. 56. **The PIST will work in collaboration with OPM structures and under the guidance of an inter-agency PSC**, which will include representatives from the following agencies: Ministry of Finance, Planning, and Economic Development (MoFPED), IG, Ministry of Energy and Mineral Development, Ministry of Works and Transport, Ministry of Water and Environment, Ministry of Local Government, Ministry of Gender, Labor, and Social Development, Ministry of Education and Sports, Ministry of Health, National Environment Management Authority, Private Sector Foundation of Uganda, DoR, CRRF Secretariat,

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Input: reflect the same rank-order as the lending opportunities in Appendix 1. **9. Interpretation of Results: Ethiopia vs. Nigeria** We provide an illustrative interpretation of our results by comparing the cases of Ethiopia and Nigeria in Table 9.1. Both have been among the Bank’s top borrowers in Sub-Saharan Africa: From 1998-2003, Nigeria borrowed $912 million and Ethiopia borrowed $1,381 million. Among the 48 Sub-Saharan countries, Nigeria’s overall environmental problem indicator ranks first and Ethiopia’s second. Both countries are in the midrange for the OED project success rate (45% for Nigeria; 65% for Ethiopia). After adjusting for success rates, Nigeria’s optimal lending is $144 million and Ethiopia’s is $128 million. 25

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Input: assumptions of coastal protection, comparison of the prior research with our estimates proved to be difficult. ##### **Conclusion** Coastal wetlands will decline with rising sea level. In this paper, we have quantified the vulnerable freshwater marsh, swamp forest, GLWD Coastal Wetlands, and brackish/saline wetlands taking into account the exposure of wetlands to 1 m SLR and the estimated capacity of the coastline to retreat and for coastal wetlands ecosystems to migrate inland as the 25 Anthoff et al. (2010) has not reported exposure estimates of coastal wetlands to SLR. 20

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Input: fuel combustion (CO2), land-use change (CO2) and other sources (methane (CH4), nitrous oxide (N20), hydrofluorocarbons (HFC’s), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)). We draw our emissions estimates from the World Resources Institute’s Climate Analysis and Indicators database. [3] Our estimate of pollution damage is total DALY (disability-adjusted life year) losses from air and water pollution. We draw our DALY estimates from recent research 2 The supporting database and an accompanying atlas can be downloaded from the Environment Department (lnweb18.worldbank.org/ESSD/envext.nsf/41ByDocName/Environment), or from the Development Research Group (www.worldbank.org/nipr). 3 The World Resources Institute’s Climate Analysis and Indicators database is available online at http://cait.wri.org. 4

Climate Analysis and Indicators database

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Input: **The World Bank** Costa Rica Results in Education (CORE) (P181174) days after the end of each semester. The IFRs will contain: a statement of sources and uses of funds (with expenditures classified by category of expenditures, source of funds) and cash balance; a statement of budget execution per component and subcomponent; and a reconciliation of the Designated Account. The formats of the IFRs and annual financial reports will be included in the POM. **Flow of Funds- Disbursement.** 6. The disbursement methods that may be used for the withdrawal of the proceeds of the loan will include reimbursement; advance; and direct payment. Under the advance method, the project will have access to a Designated Account (DA) to be used exclusively for deposits of loan proceeds in the Central Bank of Costa Rica for eligible expenditures. The deposits into the DA will flow to a specific account into the Borrower ́s Treasury Single Account - TSA ( _Sistema de Cuenta del Sector Publico, SCSP_, by its initials in Spanish), assigned to the Project and managed by MEP. As needed, the PIU will request the MoF to transfer funds from the DA to the project operational account which will be opened in the

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Input: age migrant married cohabiting divorced fertility woman partner active*mine 0.263 0.028 0.025 0.018 -0.003 0.030 -0.036 -0.003 (0.510) (0.042) (0.027) (0.029) (0.017) (0.115) (0.031) (0.030) _Note:_ Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age (not column 1), and years of education (not columns 6 and 7). Active is active status of mine in the survey year. *** p<0.01, **p<0.05, *p<0.1. **5.1 Employment outcomes** Using the difference-in-differences approach (equation 1), we estimate results on occupation, child health, and inequality. First, panel A of table 4 indicates that women in active mining communities ( _active*mine_ ) are insignificantly more likely to work in service and sales and less in agriculture, and 1.7 percentage points less likely to work as professionals (statistically significant). There is no change in the likelihood that she is not working. These 5 categories stem from the same occupational variable in the DHS data, and are mutually exclusive. The surveyed individual is told to report their main occupation. The coefficients can therefore be interpreted as relative increases of each specific sector. Women are more likely to earn cash for work, and the likelihood increases

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Input: future ice-sheet instability and rapid sea-level rise. _Science_, 311,1747-1750. Pfeffer, W. T., Harper, J.T., and S. O’Neel. 2008: Kinematic constraints on glacier contributions to 21st-century sea-level rise. _Science_, 321, 1340-1343. Rahmsdorf, S. 2007. A semi-empirical approach to projecting future sea-level rise. _Science_, 308, 368-370. Semeniuk, V. 1994. Predicting the effect of sea-level rise on mangroves in Northwestern Australia. _Journal of Coastal Research_, 10, 1050-1076. Sun,G., McNulty, S.G., Amatya, D.M., Skaggs, R.W., Swift, L.W., Shepard, P., and H. Riekerk, 2002. A comparison of watershed hydrology of coastal forested wetlands and the mountainous uplands in the Southern US. _Journal of Hydrology,_ 263, 92-104. Titus, J.G. 1988. _Sea Level Rise and Wetland Loss: An Overview_ . [http://epa.gov/climatechange/effects/downloads/toc_wet_chap1.pdf](http://epa.gov/climatechange/effects/downloads/toc_wet_chap1.pdf) Tol, R.S.J. 2007. The Double Trade-off between Adaptation and Mitigation for Sea Level Rise: An Application of FUND. Mitigation Adaptation Strategy Global Change. 12:741-753. Vafeidis, A.T.; Nicholls, R.J.; McFadden L.; Tol, R.S.J.; Hinkel, J.; Spencer, T.; Grashoff, P.S.; Boot, G., and Klein, R.J.T. (2008) A new global coastal database for impact and vulnerability analysis to sea-level rise. Journal of Coastal Research, 24(4), 917–924. 24

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Input: to implement the selected subprojects, together with capacity-building measures. 32. **The Phase II design responds to lessons learned from Phase I as follows** : (a) some activities, such as drip irrigation systems and fish ponds, are too technically complex for communities to manage, so they will be dropped; (b) due to sustainability concerns about community-managed woodlots, tree planting activities will only support pre-existing groups with proven management capacity; (c) IP contract payments will be restructured to provide incentives for quality and to ensure they facilitate community-led implementation rather than implement activities themselves; (d) the subproject budget ceiling will be increased to reflect inflation and ensure sufficient scale to generate environmental impacts; and (e) due to the major investments in electricity provision and clean cooking under the World Bank-financed Electricity Access Scale-up Project (P166685), including in RHDs, DRDIP II will discontinue support for access to renewable energy, except for the provision of renewable energy and/or improved cooking systems for public institutions, which will be incorporated into Component 1. Component 2 will be organized around two sub-components, as described below. 33. _Sub-component 2.1: Restoration of Degraded Ecosystems (US$20 million)._ The DRDIP II Environmental and Social Assessment (ESA) carried out in 2024 identified

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Input: large-scale operations may be geographically correlated. Assuming that the start of a large scale mine does not affect the likelihood or viability of artisanal and small-scale mining, it is not a threat to our identifying assumptions. However, should ASM respond to large-scale activities, either by increasing or decreasing activity in the close geographic area, we will end up estimating the impact of these sectors jointly. In a later stage, should the opportunity arise, we encourage researchers to try to disentangle the effects of small-scale and large-scale mining. **3 Data** To conduct this analysis, we combine different data sources using spatial analysis. The main mining data is a dataset from InterraRMG covering all large-scale mines in Ghana, explained in more detail in section 3.1. This dataset is linked to survey data from the DHS and GLSS, using spatial information. Geographical coordinates of enumeration areas in GLSS are from Ghana Statistical Services (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

survey data from the DHS and GLSS
dataset from InterraRMG

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Input: weather station in this .75°×.75° grid, the observation is given a weight of 1.5. If there is a rainfall station within the .25°×.25°grid, the observation is given a weight of 2. The weighted results do not differ significantly from the unweighted results. 12 Note that while it is reasonable to think that village-specific characteristics (such as village size) may have an effect on village-level insurance take-up, village-level covariates are not included in the regression. When the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly. Also, most village-level characteristics had insignificant coefficients, with the exception that a more literate population was correlated with higher take-up. Since village-level coefficients were only available for around 50 percent of the villages, these variables are not included in the main specifications.

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Input: analyses to improve their capacity to develop climateresilient business plans. Component 3 will also expand adaptation capacity by improving drainage and water harvesting to better manage flooding and landslide risks. It will also enhance septage management to reduce the risk of soil contamination and associated health risks in the event of flooding. 73. **Citizen Engagement (CE)** . The project design builds on existing district planning mechanisms. Component 1 subprojects will be identified through the regular DDS process, while strengthening stakeholder engagement through additional consultations with refugees, host community members and humanitarian partners. CE will be measured through results indicators that track whether Jya Mbere II investments align with the needs and priorities of beneficiaries. A new feature in Phase II will be Iterative Beneficiary Monitoring (IBM), whereby a large sample of project beneficiaries will be surveyed by phone to track, among other things, satisfaction with citizen engagement processes and the project as a whole. MINEMA conducted public and stakeholder consultations across the project target areas to gather feedback on the proposed activities, address concerns, and explore ideas to mitigate potential adverse impacts on socio-economic activities and livelihoods. The project’s stakeholder engagement plan, built on inclusive consultations, incorporates a comprehensive grievance

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Input: Policy Research Working Paper 8316 ### **Abstract** Recurrent cyclonic storms in the Bay of Bengal inflict mas sive losses on the coastal regions of Bangladesh and India. Information on occurrences and severities of cyclones is necessary for understanding household and community responses to cyclone risks. This paper constructs a georeferenced panel database that can be used to obtain such information for Bangladesh, West Bengal, and Odisha. Cyclone strike locations and impact zones are analyzed for several historical periods between 1877 and 2016. The findings indicate that although the median location has shifted eastward, there is a marked variability in location, especially after 1960. Impacts also have varied considerably within and across zones over time, with the highest-impact zones in northern Odisha and the Sundarbans region of West Bengal. The pronounced spatial and temporal variation in cyclone impacts will provide robust controls for comparative research on household and community adaptation to cyclones in the study region. The methodology developed in the paper is general and could be expanded to an arbitrarily large set of coastal locations. This paper is a product of the Environment and Energy Team, Development Research Group and the Environment and Natural Resources Global Practice Group. It is

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Input: Japan (HIC) India (LMIC) Germany (HIC) Netherlands (HIC) Vietnam (LMIC) Indonesia (UMIC) Thailand (UMIC) Pakistan (LMIC) South Korea (HIC) 0 500 1000 1500 2000 2500 USD millions _Figure 13. Top 10 countries: Economic value at risk, computed as exposure headcount multiplied by_ _subnational income per capita. Note: no subnational income data are available for several countries,_ _including China._ 17

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Input: quarter confirm that 38% of those having to leave their accommodation was on account of the accommodation closing, compared to only 22% in the last quarter of 2023. ## 39% Consider accommodation an urgent need facilities across seven regions [1] highlighted the challenges in accessing sustainable and affordable housing, including the psychological distress of a relocation to rented housing by some Ukrainian refugees. This was found to be a particular concern amongst elderly persons, persons with disabilities, single mothers, persons with serious medical conditions and their carers. Many expressed reluctance to relocate, citing fatigue and fear of the unknown; some also appreciated their current living conditions and sense of community support, which they prefer over individual housing. The motivation and/or possibility of refugees to relocate to standard housing varied significantly based on income and employment status, Czech language skills, vulnerabilities related to disabilities, age, family size and support from government services or informal networks. The focus group discussions also underscored the precarious financial situation of many refugees, including those engaging in low-paid work below their qualifications, which creates a barrier for relocations to rented housing. 78% of respondents mentioned that they would not be able to cover costs of rented

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Input: 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 of present coastal wetland stocks of 60% or more, depending on wetland type. Human activity is generally 8 Further development in coastal areas may also close off wetland migration. 9 Historically, mangroves have shown considerable resilience to adapt to fluctuations in sea-level rise (Alongi, 2008; Erwin, 2009; Gilman et al., 2006). 10 Coastal wetlands in this analysis are defined as the following wetland types in a low elevation (with elevation 10 m or less from sea level) zone: freshwater marsh, swamp forest, GLWD coastal wetlands and brackish/saline wetlands, as delineated in the Global Lakes and Wetlands (GLWD-3) database. See the data section for details. 11 The IPCC’s Fourth Assessment Report (AR4) projects increased SLR between 0.18m and 0.59m across various emission scenarios over the next 100 years. However, this range has been criticized by many experts as being too conservative and not sufficiently reflective of the large uncertainty pertaining to

Global Lakes and Wetlands (GLWD-3) database

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Input: 0.127 0.186 0.162 0.154 Organization done by village leaders 0.264 0.237 0.276 0.222 0.165 0.261 0.285 Organization done by village representatives 0.059 0.059 0.053 0.089 0.064 0.061 0.053 Organization done by village assembly 0.121 0.112 0.138 0.111 0.119 0.130 0.132 Organization done by village econ. organizations 0.083 0.059 0.099 0.096 0.064 0.096 0.093 Organization done by township or above 0.472 0.533 0.434 0.481 0.587 0.452 0.437 Actual measurement done by village leaders 0.085 0.086 0.093 0.059 0.111 0.078 0.080 Actual measurement by village representatives 0.554 0.517 0.583 0.615 0.407 0.609 0.567 Actual measurement done by village assembly 0.042 0.033 0.040 0.022 0.056 0.026 0.053 Measurement by village econ. organizations 0.227 0.192 0.252 0.267 0.241 0.209 0.247 Actual measurement done by township or above 0.093 0.172 0.033 0.037 0.185 0.078 0.053 **Disputes** Any disputes encountered 0.549 0.187 0.497 0.348 0.226 0.250 0.300 ... if yes, no. of disputes 14.850 6.429 10.987 9.745 6.292 9.759 8.111 ... disputes could not be resolved by village 1.248 0.074 0.724 0.894 0.042 1.690 0.933 leaders **Results** Contract now longer than 30 years 0.987 0.993 0.980 0.977 1.000 0.983 0.993 Contract now permanent 0.717 0.792 0.497 0.481 0.848 0.861 0.860 _Source:_ Own computation from 2014 Chengdu

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Input: Economics of Disasters and Climate Change There is a significant amount of uncertainty with regard to how much sea level will rise. For that reason we model three future climate scenarios per return period: a low, medium, and high scenario (Table 1), using estimates from the IPCC (IPCC 2014; IPCC 2007). For the fluvial and pluvial hazards, future climate scenarios were not explicitly simulated owing to the complexity and considerable uncertainties that arise (Smith et al. 2014). [2] Although robust modeling of the magnitude of future extreme rainfall is not yet possible, heavy rainfall is expected to increase in a warmer climate, owing to the increased water holding capacity of the atmosphere. Therefore instead of a direct modeling approach, future climate scenarios were inferred by taking flood hazard maps derived under current climate conditions for different return periods, and using them as a proxy for future climate scenarios. The return period hazard maps used for each of the future scenarios are outlined in Table 2. Although simplistic, this method allows areas that may be impacted by increasing riverine and extreme rainfall driven flooding to be identified. Clearly there are some significant assumptions and uncertainties arising from this method. However, given

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Input: financed under DRDIP II and ensure they are aligned with and incorporated in the District Development Plan. 59. **DRDIP II will be implemented using the CDD approach, whereby communities identify, prioritize, and** **implement subprojects themselves,** guided by the Sub-County Implementation Support Team, Sub-County Executive Committee, Sub-County Technical Planning Committee, Parish Development Committee, and Assistant Commandant. At the community level, implementation will be carried out by committees that the project will establish and train, including a Community Project Management Committee (CPMC), Community Procurement Committee (CPC), Community Monitoring Group, Grievance Redress Committee (GRC), and Refugee Welfare Committee (RWC) in the settlements. The project will ensure gender equity in the membership of the various committees. Implementing Partners (component 2) and TSPs (component 3) will be hired to support community capacity for subproject identification, planning and implementation. A Community Operations Manual will be incorporated into the PIM to detail all steps in the CDD process. 60. **The Inspectorate of Government (IG) will remain engaged in implementation arrangements** at the national and local government level to strengthen transparency and accountability and to prevent and respond to corruption. **B. Results Monitoring, Evaluation, and Verification Arrangements** 61. **M&E System.** OPM will have overall responsibility for M&E, including

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Input: **The World Bank** Socio-economic Inclusion of Refugees & Host Communities in Rwanda Project Phase II (P509677) 33. _Sub-component 2(c): Partial Credit Guarantee Scheme (US$860,000)._ Despite the financing provided through the MG scheme, many financial institutions in Rwanda remain hesitant to lend to creditworthy refugees due to the lack of an established credit history and insufficient collateral. The GoR’s Business Development Fund (BDF) has a partial credit guarantee (PCG) scheme to respond to the collateral challenge. While refugees are eligible for the scheme, few either access it or are aware that it exists. Building on the experience of the ongoing Access to Finance for Recovery and Resilience project (P175273) in Rwanda, which is partly implemented by BDF, the sub-component will support and facilitate the provision of individual PCGs to refugees to offset PFI collateral requirements or portfolio guarantees to cover credit losses associated with loans made to refugees. 34. **BDF will implement the sub-component.** A full Financial Intermediary Assessment of BDF was conducted as part of project preparation, full details of which will be included in the Project Implementation Manual. [22] Given that capital needs will be provided by BDF, funding from Jya Mbere II will only be required for BDF’s

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Input: # **PROTECTION BRIEF** **CZECHIA** ### JULY- SEPTEMBER 2024 ## Operational Context Based on the number of Ukrainian refugees who applied for an extension of Temporary Protection in 2024 (320,000), the figures of Temporary Protection holders in Czechia, including new arrivals, were updated to **384,558** people as of **29 September 2024**, compared to 373,745 at the end of 2023 (source: Ministry of the Interior). This report presents an overview of refugees’ profiles as well as main findings regarding protection risks and priority needs of refugees from Ukraine in Czechia. The brief is based on the analysis of **438 Protection Monitoring interviews** conducted with refugees from Ukraine in all the 14 regions as well as findings from focus group discussions in the third quarter of 2024. ## Key Figures 384,558 71% Temporary Protection holders in Czechia as of 29 September 2024 [1] Temporary Protection holders in Czechia of Temporary Protection holders are women as of 29 September 2024 [1] and children [2] ## 98% 34% of Protection Monitoring respondents are Temporary Protection holders ## 34% of Protection Monitoring respondents are of respondents consider accommodation as Temporary Protection holders their highest priority need ## 67% 29% ## 29% of working age respondents are

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Input: UNHCR Czechia continued to conduct Protection Monitoring while also consulting different groups of refugees though focus group discussions on protection risks and priority needs. Specific challenges in accessing rights were reported. The key findings are highlighted below. ### Protection Risk I The number of respondents reporting a lack of critical documentation increased from 28% to 32% in the first quarter of 2024 compared to the last quarter of 2023. The increase was mainly in relation to obtaining or renewing biometric passports (85%) and internal passports (19%), which impacted the possibility to move freely, to access basic services as well as work and education. Various factors hinder refugees’ access to documentation, including associated processing fees, long waiting periods, unavailability of services or lack of information on procedures. 10% of the respondents also reported facing challenges in registering births, deaths, marriages, and divorces, and obtaining corresponding documents. UNHCR / March 2024 2

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Input: 1.7 **Labor inputs** Male family labor dummy 68.6 71.0 63.4 67.4 69.8 64.3 Female family labor dummy 92.1 92.0 92.3 94.1 94.3 93.7 Hired labor dummy 30.2 30.5 29.7 27.7 25.0 31.1 Exchange labor dummy 17.6 16.9 19.3 16.4 13.6 20.1 Number of male family labor days per acre 20.4 19.7 21.8 20.2 19.2 21.4 Number of female family labor days per acre 44.5 40.1 54.7 48.9 42.4 57.2 Total family labor days per acre 64.9 59.8 76.5 69.0 61.6 78.6 Total exchange labor days per acre 2.7 2.4 3.3 2.5 1.6 3.6 Total family and exchange labor days per acre 68.1 63.0 79.8 72.3 64.6 82.3 Hired labor days per acre 3.2 3.2 3.4 3.3 3.0 3.7 Total labor days per acre 70.8 65.4 83.2 74.8 66.2 85.8 Number of observations (parcels) 18220 12658 5562 6628 3720 2908 _Source:_ Own computation from 2005/06 UNHS III _Note:_ Unit of observation is a parcel (under crop) in a given season. Thus the same parcel could provide two observations if cultivated in both seasons in the reference period (July 2004 – June 2005). 19

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Input: from the national level to higher local governments (districts, cities, urban centers), to lower local governments (sub-counties), and to the communities. The FM assessment indicated that the arrangements satisfy the World Bank’s minimum requirements under Investment Project Financing (IPF) policies and directives. The FM residual risk rating is Substantial. 73. **Successful implementation of the project requires strong FM arrangements at the national, district, urban** **center (local government), and community levels.** CAOs and Town/City Clerks and CFOs are the key players for districts and urban centers in managing and disbursing funds to communities and ensuring accountability of project resources. DRDIP I established robust accountability mechanisms and operated a ring-fenced FM system. Phase II will use the same systems, including the existing MIS to record project data, processes, accounting, and reporting. Key risks identified Page 21

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Input: comments. All remaining errors are my own. A supplemental appendix to this article [is available at https://academic.oup.com/wber.](https://academic.oup.com/wber) 1 CIA World Factbook: India; Indiastat.com. © The Author 2016. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All [rights reserved. For Permissions, please e-mail: journals.permissions@oup.com](mailto:journals.permissions@oup.com)

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Input: infiltrators‖ ( _mistaneniavoda_ ) in an effort to associate refugees with a threat to the employment prospects of Israelis. As one academic interviewee observed, the ongoing primacy of this term demonstrates ―the success of agents within the [governmental] institutions to inject this discourse and create a sort of panic‖ characterised by a ―takeaway feeling – they will come here, take what we have, and change this place‖. This ―takeaway feeling‖ partly explains the urgency of an MK who suggested various security technologies that allowed Israel to act in a middle zone between violence and doing nothing: ―...preventing entrance, a fence, guard dogs, sensors...arrivals will not be answered and received, we will not let them in‖...‖Between killing and not acting there is prevention, [and the state should act upon it] if Israel wishes to survive‖. However, any discussion which touches upon security-related issues must also consider the close triangular relationships between African refugees, Palestinian refugees and Israeli security considerations. In the Israeli context, the term ̳refugee‘ is traditionally associated with two types of refugee: Jewish holocaust survivors who fled Europe and the Palestinian refugees who were displaced as a result of the 1948 War. The state fears that recognizing African asylum

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Input: accordance with the Ministry of Interior’s decision. So far, authorities have registered and issued 30,000 cards, but their resources have quickly depleted and they are running out of resources to continue registration. UNHCR has been requested to support this initiative, as resource shortages risk preventing further registration, exposing refugees to potential deportation and increased GBV risks. Approximately 40,000 vulnerable individuals will be directly impacted, including women, children, and other vulnerable groups among the Sudanese refugee population. **Shelter and Core Relief Items** Overcrowded and inadequate shelters heighten the risk of disease transmission and compromise the safety and dignity of refugees, with current shelter solutions falling below minimum humanitarian standards. In-kind support will be provided to Sudanese refugees with heightened risk and vulnerability if funding is available. **Education** Only one-third of primary school-aged children and 5% of secondary school-aged children in Mbera refugee camp are enrolled due to lack of financial means, documentation, and schools. The situation is equally dire outside of the camp in Hodh Chargui, where the enrolment rate is only 6%. School kits, capacity building and salaries for teachers, and support to transition from the Malian to Mauritanian curriculum will all increase access to education for some 45,000 refugee

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Input: limited understanding of the importance of climate adaptation for their livelihoods and there is a need for better government aid after flood events (Le Dang et al. 2014; McElwee et al. 2017). In urban areas, private adaptive capacity may be even lower, resulting in an increasing need for urban planning to incorporate climate risk dimensions (Liao et al. 2016). While adaptation is not a focus of this paper, the uncertain and growing impacts of flood risk found in this paper indicate more attention is warranted to design effective adaptation policies. Data To examine population and poverty-specific exposure to floods, we employ spatial data defining flood hazard and a number of socioeconomic characteristics representing poverty and population density. Flood Hazard Data Flood Hazard Maps for Vietnam Developed for this Study For this study, we developed flood hazard maps representing riverine, flash-flood and coastal flood hazards for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc-seconds, (~ 90 m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model

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Input: The audit reports for 2022/2023 that had an unqualified (clean) audit opinion were for Isingiro, Kamwenge, and Obongi districts. 2023/2024 audit reports were not available on the Auditor General’s website. The audit reports for 2023/2024 that had an unqualified (clean) audit opinion were for Adjumani, Kikuube, Kiryandongo, Koboko, Kyegegwa, Lamwo, Madi Okollo, Obongi, Terego, Yumbe, Amuru, Bundibugyo, Kanungu, Kisoro districts. Others were Arua City, Gulu City, Kitgum Municipality, Koboko Municipality, and Mbarara City. In the case of Ntoroko, this was due to understating value-added tax receivables, unrecognized prepayments to contractors, and not recognizing advance payments. Page 22

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Input: _Figure 21. Share of total population that is exposed to significant flood risk and living in poverty at_ _$5.50/day_ **Relationship between population exposure and other variables.** The relationship between poverty and flood risk exposure is driven by multiple partly opposing forces. As section 2 has outlined, country-level evidence has demonstrated that areas with higher flood risks (such as coastal zones) can offer economic opportunities, thus resulting in lower poverty rates; yet at the local level (e.g. within coastal towns), lowincome households can be driven into low-cost and high-risk neighborhoods. This means that – depending on the resolution of analysis and country-specific geographic and economic characteristics – the relationship between poverty and flood exposure could be either positive or negative. The global-level data used in this study only offer a limited view on this question. Figure 22 illustrates the relationship between average income levels and flood exposure rates for the 2,260 sub-national regions for which data are available. The figure suggests that regions with high flood-exposure tend to have lower income levels on average, only dedicated country-level analysis can assess this question conclusively – for instance based on household survey data. 22

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Input: South Asia Middle East & North Africa Latin America & the Caribbean **Figure 3. Regional distribution of economic losses** ###### **III.4 Prior research on impacts of SLR on coastal wetlands** The papers most immediately related to this analysis are those of Nicholls at al. (1999), Nicholls (2004), McFadden et al. (2007) with estimates of exposure of coastal wetlands to SLR, and Tol (2007), Anthoff et al. (2010) on valuation of wetland losses due to SLR. It should be noted that Nicholls (1999) and Nicholls (2004) are based on wetland losses derived from the Global Vulnerability Analysis (Hoozemans et al. 1993) [23] - “a first-order perspective on wetland loss rates with incomplete coverage and wetland losses controlled only by tidal range and accommodation space” - as pointed out by McFadden et al. (2007). Nicholls et al. (1999) estimated that a 38 cm rise in global sea level from 1990 to the 2080s will lead to an approximate 22% loss of the coastal wetlands, [24] and 46% of the coastal wetlands would be lost if the sea level rises by 1 m. Nicholls (2004) further estimated losses of wetlands under various greenhouse emissions scenarios and found that a 34 cm rise in

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Input: through the project grievance redress mechanism that are resolved (Percentage). **Economic and Financial Analysis** 69. **Results of Economic Analysis without Greenhouse Gas (GHG) Accounting.** The results of the economic analysis indicate that DRDIP II is economically viable, as it is projected to yield a baseline Economic Internal Rate of Return (EIRR) of 22 percent, which is above the prevailing discount rate of 16.7 percent adopted for economic analysis. The Economic Net Present Value (NPV) is estimated at US$62.7 million, equivalent to UGX 231 billion. The benefit-cost ratio (BCR) is estimated at 1.12 percent. 70. **Results of Economic Analysis, with GHG Accounting.** The overall economic analysis has been rerun to assess the economic viability of the project taking into account benefits from reduction of carbon gas emissions. The GHG accounting analysis done using the Environmental Externalities Accounting Tool (EX-ACT) shows that over 20 years, the project will result in a carbon emission savings of 2,056,927 tCO2e (102,846 tCO2e per year). The savings in GHG emissions further enhance the viability of the project, as they contribute to overall net revenue and ultimately to the EIRR. After considering all project costs and benefits from reduction of carbon gas emissions, the overall EIRR is

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Input: financial statements which will be audited under terms of reference and by an external firm, both acceptable to the World Bank, will be produced for the project. Basic arrangements to ensure control, recording, and reporting are further described in Annex 1. 52. **Procurement** . Procurement for the Project will be carried out in accordance with the World Bank’s Procurement Regulations for IPF Borrowers for Goods, Works, Non-Consulting and Consulting Services, dated September 2023. The Project will also be subject to the World Bank’s Anticorruption Guidelines, dated October 15, 2006 (revised July 2016). The World Bank's Standard Procurement Documents will be used in World Bank-financed Open International Competitive Procurement. The World Bank completed a procurement capacity assessment of MEP in April 2024. The assessment reviewed the organizational structure for implementing the Project and assessed the experience of the existing staff. A Project Procurement Strategy for Development (PPSD) was prepared by the executing agency, which describes how procurement in this Project will support the PDOs and deliver value for money under a risk-based approach. The PPSD will provide adequate supporting market analysis for the selection methods detailed in the Procurement Plan. Mandatory 29 MEP implemented the Equity and Efficiency of Education Project

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Input: across competing priorities and are fully aligned with the Local Area Development plan for Meheba prepared by Kalumbila Town Council. The project’s three components will contribute to the building of a future policy framework and an operational model for use of WHR resources that could be extended to assist refugees and host communities in other parts of the country in potential future phases of the project. **Component 1. Strengthening the Enabling Environment (US$7 million)** 24. The objective of this component is two-fold. First, it is to build an enabling legal framework for refugees related to documentation, basic services, and livelihoods and self-reliance. Secondly, it aims to support the rollout of new national ID cards to refugees in all three of the refugee hosting district. _Subcomponent 1.1. Building an enabling legal framework (US$1 million)_ 25. In November 2023, the Cabinet approved Zambia’s new National Refugee Policy. The GRZ has set five key objectives for its revised Refugee Policy: (a) strengthened refugee protection, (b) improved access to basic services, (c) better livelihoods and self-reliance, (d) progress on long term solutions including local integration, and (e) enhanced government coordination of refugees and their inclusion in national development processes. Under this subcomponent, a national

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Input: Single Account (TSA) to make payments. Subproject payments will be made to a community bank account in a commercial bank. The CFO of the local government will authorize the opening of community bank accounts in local governments and monitor payments made through the account to ensure they are used for project purposes. Each community will have a Community Project Management Committee, and the authorized signatories for their bank account will be the Chair, Secretary, and Treasurer. All payments by districts, cities, and urban centers will be accessible online in the IFMIS by MoFPED and OPM (that will have read on rights authorized by MoFPED) for monitoring, accountability, and audit purposes. **Figure 3: Funds flow overview** 63. **The report-based disbursement method will be used by OPM and IG to receive advances from the** **World Bank.** Advances to project’s Designated Accounts will be based on six-month cash flow forecasts supported by the Page 17

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Input: observed in PISA data. Looking at graduates of pre-university technical education (mainly technical stream secondary education) one finds an interesting pattern comparing three specializations (Figure 1b). Gender disparities are high in Electronics and Automation, less in Software Development and were recently eliminated in Network and Database Design. The final example of Network and Database Design in Figure 1b shows that gender disparity can be overcome in a short period of time. Two recent studies provide a detailed general analysis of policy options. [18] For STEAM courses, the causes relate to: (i) enjoyment of reading and preference for mathematics; (ii) self-efficacy or belief in own capabilities, often reinforced by teachers; (iii) parental support or lack thereof due to low socioeconomic status; (iv) teacher preparation to deal effectively with diversity. 16 Source: Government Budget for 2024 and MEP School Census data from 2023. 17 Program-Based budgeting at a government-wide level is being implemented under the Fiscal Management Improvement Project (P172352, Loan 9075-CR), known as _Hacienda Digital_ . Investing in readiness to implement program-based budgeting at MEP, the biggest Ministry (in terms of budget and staff) is a priority for the Government of Costa Rica. 18 [Desigualdades por género en Primaria y Secundaria,

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Input: of different tenure in cases where the same crop was grown on owned and occupied ones, we drop parcels if, in the same household, there is another parcel of different tenure devoted to the same crop, either as a pure or mixed stand. This implies that, for the case of output, our sample contains owner-cum-occupants who cultivated different types of crops on at least one of their owned and their occupied parcels. [12] **3. Data and descriptive evidence** Descriptive evidence from a large and nationally representative household survey at household and parcel levels illustrates the large number of owner-cum-occupants who differ from pure owners and, to a lesser degree, occupants, in few of the variables of interest. At the same time, it points towards presence of systematic differences in investment between owned and occupied parcels which provide the motivation for more detailed econometric analysis. **3.1 Data sources and household characteristics** The data for our study come from the 2005/2006 Uganda National Household Survey (UNHS), fieldwork for which was conducted by the Uganda Bureau of Statistics from May 2005 to April 2006. The survey collected information at the community, household, and parcel level for about 7,500 households in 753 EAs including

2005/2006 Uganda National Household Survey

large and nationally representative household survey

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