ai4data/datause-dataset-rechunked huggingface dataset:
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),
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
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
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
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,
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
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
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
**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
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
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
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
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
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.
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
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
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
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
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
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
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
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