Assessing climate induced resettlement impacts on livelihood vulnerability in flood-prone areas of Punjab, Pakistan; an application of livelihood vulnerability index

Assessing climate induced resettlement impacts on livelihood vulnerability in flood-prone areas of Punjab, Pakistan; an application of livelihood vulnerability index | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing climate induced resettlement impacts on livelihood vulnerability in flood-prone areas of Punjab, Pakistan; an application of livelihood vulnerability index Dilshad Ahmad, Muhammad Afzal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3901129/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Population living in climate induced disaster vulnerable areas can mitigate risks by preventive resettlement strategies. However, prior to having resettlement risks and particular resettles livelihood impacts it is necessary to investigate those communities whose living have transformed through climate persuaded resettlement. Objective of this research work is to examine prior resettlement and after resettlement climate-based livelihood vulnerability variations of resettled two model villages flood prone community of Muzaffargarh. Livelihood vulnerability changes of resettled households were investigated by application of Livelihood vulnerability index that covers seven major components exposure, finance, water, health, social networks, livelihood strategy and sociodemographic profile. In this study data was collected by well-developed questionnaire from 241 households’ heads which resettled in two model villages from twelve flood prone union council areas. Data collected by direct interaction with respondents where questionnaire consists on some significant perspectives regarding resettlers subsidies receipts, physical conditions, job status, income aspect, socioeconomic perspective and damages of flood disasters prior and after resettlement. Livelihood vulnerability index each indicator values prior and after resettlement were calculated to determine in what way altered household’s livelihood after resettlement. Estimated outcomes of study indicated that vulnerability of health, water, livelihood strategy and exposure components were significantly declined when household moved to less flood prone areas owing to resettlement in well-construction model villages associated with government subsidies. On the other hand, some major components like finance and social networking becomes higher vulnerable owing to loss in economic activity and kinship which were deep rooted in original communities of households. In these resettled areas, proactive stance of concerned authorities or institutions and policy makers need to implement with compacted strategies to reduce financial risks and job vulnerabilities to develop sustainable livelihood of resettled households. Climate-induced resettlement Flood disaster Livelihood Vulnerability Index Punjab Pakistan Figures Figure 1 Figure 2 Figure 3 1. Introduction Climatic conditions sharp changes have raised geophysical and meteorological dynamics which subsequently enhanced disasters occurrence of landslides, floods, earthquakes and wildfire (Senko et al., 2022; UNDRR 1 , 2022; Qamer et al., 2023). Calamities without political aspects, economic scenarios, geographical limits, cultural boundaries and social perspective sequentially influenced by life style choices, risk exposure and geographical locations (Kimaro et al., 2018; Ahmad and Afzal, 2021; Kreft et al., 2021). In worldwide situation, substantial human cost and economic losses sequentially confronted by global community due to severe and recurrent incidence of about main natural hazards such as floods (Daniell et al., 2016; Ahmad et al., 2019; UNDP 2 , 2023). In past couple of decades, fast climatic variation has estimated to increase temperature, reducing humidity, enlarged heat stress and necessity of water concerning global population, crops and ecosystem (Wilkinson et al., 2016; Sillmann et al., 2022; UNICEF 3 , 2023). In 2022, severe climatic dynamics like long time heat wave expansion changing aspects were measured with massive monsoon associated uncommon high rainfall produced worsen situation of flooding and landslides in India, Pakistan and Afghanistan (UNDRR, 2022). Moreover, assessed lower than average rainfall and exaggerated drought circumstances in some regions such as western USA, South Africa and Horn of Africa (Munpa et al., 2022). In summer 2022 noticed heatwaves and drought in frequent areas of Western Europe and China where rivers and lakes shrinking preceding to regressive higher than average standing in later year (Fenta et al., 2019; UNDRR, 2022). Increasing risk or emerging recently drought generally measured in Mongolia, China, western Russia, most plains of Canada and South America that estimated to enlarge dry circumstances up to end of current year (Barrett et al., 2019; UNDRR, 2022). In last twenty years global extreme climatic events dramatically increased as these disaster events were estimated 6681 in 2000 to 2019 as compared to 3656 in 1980 to 1999. Storms and floods considered most frequent disasters where from 2000 to 2019 storms increased 1457 to 2034 while floods raised 1389 to 3254 (Mazhin et al., 2021). The region of South Asia, during past couple of decades faced hasty incidence of enlarged flood disasters which is expected to raise in strength in future (Opiyo et al., 2015; Abbas et al., 2017; Mahmood et al., 2020). Most populated and emerging economies of the region Bangladesh, China, India and Pakistan stated disasters supermarkets because of repeated suspectable flood disasters (Gorst et al., 2015; Ahmad and Afzal, 2020; UNICEF, 2023). In most worldwide climate change influenced countries as Pakistan was considered 29 th higher vulnerable country in 2009-2010, 16 th in 2010-2011while categorized 5 th most disasters susceptible country in 2021 (Kret et al., 2017; Teo et al., 2018; Schilling et al., 2020; UNDP, 2023). Pakistan’s 76 th years historical era from 1947 to 2023 confronted sequence of major and minor flood disasters while 2010 flood was recorded most distressing flood disaster (Kirsch et al., 2012; NDMA 4 , 2012; UNDP, 2023). Flood disaster of 2010 severely affected 45 out of 135 districts and its spam sustained nearly six months which reasoned 9.7 billion US dollars economic losses and devastation of more than 160,000km 2 residential and cropped areas (Kirsch et al., 2012; NDMA, 2017; Ahmad et al., 2019; Rasul et al., 2021). In flood affected area destroyed 1.1 million houses, damaged 436 healthcare centers and higher than 20 million population was severely affected (Kirsch et al., 2012; UNDRR, 2022; UNDP, 2023). Vulnerability of groups and individuals subject to other disasters and climate change is affected by amalgamation of composite economic, social, environmental and political determinants acting at different levels. In outcome scenario, some marginalized groups, households and individuals possible to be disproportionally influenced through climate change adversative effects which will raises regional inequality (IPCC, 2022). Broader development and socioeconomic state framework cause numerous low-income populations less strong to many climate influences particularly in areas of high inequality and vulnerability (IPCC, 2022). It is urgent to address how to reduce and approach disaster risks that are unevenly distributed within groups of low income in susceptible living situation. In disaster risk reduction context, it is necessary to efficiently take precautionary actions and manage risks to lessen population vulnerability. Preventive resettlement is considered one significant measure which is likely to lessen losses of infrastructure, assets, life risks, eliminate cost of reconstruction and emergency responses. Furthermore, precautionary resettlement must be last possibility at what time it appears incredible to mitigate risk aspects related with natural disasters which becomes impossible to control (Correa, 2011; Ahmad et al., 2023). In literature, natural disasters specifically flood hazards deliberated with numerous features such as flood disasters assessment and adaptation management strategies (Gardner and Dekens, 2007; Webster et al., 2011; Kellens et al., 2013; Arnall, 2014;Bukhari and Rizvi, 2015; Tullos et al., 2016; Mavhura et al., 2017; Wymann et al., 2017; Shah et al., 2018; Glago, 2019: Akukwe, 2019; Khosravi et al., 2019; Ahmad et al., 2020; Soulibouth, 2021; Kam et al., 2021; Devi, 2022; Senko et al., 2022), floods influences on flood-prone inhabitants resources losses (Douglas et al., 2010; Bremond, 2013; Durodola, 2019; Balgah et al., 2019; Englhardt et al., 2019; Gould,2020; Nguyen et al., 2020; Atanga and Tankpa, 2021; Bernier et al., 2021;; Bhamani, 2022; Ishaque et al., 2022; Khayyam and Munir, 2022; Qamer et al., 2023), flood hazards and riverine communities livelihood vulnerabilities (Florsheim et al., 2008; McMichael and Lindgren,2011; Hewitt and Mehta, 2012; Prasad et al., 2016; Bansal et al., 2017; Sam et al., 2017; Cramer et al., 2018;Ahmadalipour et al., 2019; Tong and Ebi, 2019; Simpson et al., 2021; Yang et al., 2021; Alcántara-Ayala et al., 2022; Lydie, 2022; Yaseen et al., 2023), flood hazards effects on urban regional destruction (Du et al., 2015; Stoffel etl., 2016; Gigović et al., 2017;Johann and Leismann, 2017; Dou et al., 2018;Park and Lee, 2019; Ballesteros-Cánovas et al., 2019; De Silva et al., 2020; Liu et al., 2021; Cea and Costabile, 2022; Ridha et al., 2022), flood disaster risk and non-mountainous area livelihood losses (Rafiq and Blaschke, 2012; Ballesteros-Cánovas et al., 2015;Niedźwiedź et al., 2015; Bodoque et al., 2016; Kundzewicz et al., 2017; Steeb et al., 2017; Mazzorana et al., 2018;Ruiz-Villanueva et al., 2018; Talbot et al., 2018; Sepehri et al., 2019; Chen et al., 2020; Rahman et al.,2021; Zheng et al., 2021; Qie et al., 2022; Matsa and Mupepi, 2022; Wang et al., 2023), flood risk vulnerability and mitigation strategies in Pakistan (Mustafa, 1998; Shaw, 2015; Shah et al., 2017; Abbas et al., 2018; Jamshed et al., 2019; Nazeer and Bork,2019; Ahmad and Afzal, 2020; Hamidi et al., 2020; Ahmad and Afzal, 2021; Hussain et al., 2021; Khan et al., 2021; Rana et al., 2021; Ullah et al., 2021). Some numerous studies focused resettlement as mitigation measure to flood risks (Barnett et al., 2008; Correa, 2011; Claudianos, 2014; Patel et al., 2015; McNamara et al., 2018; Cernea, 2021; IPCC, 2022). In flooding areas, community’s resettlement considered significant risk mitigation strategy regarding flood disasters while such strategy has some resettled communities’ livelihood vulnerabilities. Subsequently, in overall perspective the question regarding whether resettlement must be applied as adoption climate change strategy still not settled. In highlighting such issue more research work that indicates which scopes of livelihood vulnerability deteriorated or improved owing to climate-based resettlement aspect and in in what way considerably is required. Furthermore, in literature perspective previous research work have measured household’s livelihood after resettlement while did not investigated in what way household’s livelihood changed after and before resettlement. In general perspective, mostly research work focused low income households which having higher vulnerability prior to resettlement where after resettlement there may possibility of deterioration or improvement in some capacities of households. Having appropriate understanding of these changes and contradictory association in capitals it is compulsory to investigate livelihood vulnerability after and before of resettlement. In Pakistan perspective, resettlement aspect scarcely discussed like just post disaster resettlement while no other dimension like livelihood vulnerability of resettled population not properly elaborated. In findings out this research gap this study focused the influence on livelihood vulnerability in comparing prior resettlement and after resettled population. Objective of this research work is to investigate the which livelihood vulnerability dimensions deteriorated or improved owing to climate-induced flood prone population resettlement in Muzaffargarh model villages. This study is categorized in to six sections as introduction indicated in first section, second section illustrated conceptual framework while material and metrology explained in third section. Results and discussion highlighted in fourth section, fifth section elaborated discussion while last section explained conclusion and suggestions of the study. 2. Conceptual framework More significant terminology vulnerability can well-defined such as level of system unable to cope or susceptible to climate change adverse effects including climate extremes and variability. Regarding IPCC vulnerability is function of rate of climate variation, magnitude and character by through systems adaptive capacity, sensitivity and exposed (IPCC, 2022). Hence adaptive capacity, sensitivity and exposure considered three vulnerability dimensions. Degree and nature to which structure is exposed to substantial climatic variation known as exposure. Level through structure is influenced either beneficially or adversely through climate-induced stimuli indicated as sensitivity. Capacity of structure to manage climate change to moderate potential damages by coping with consequences or having advantage of opportunities considered as adaptive capacity (IPCC, 2022). Residents movement of climate induced resettlement inhabited from hazardous sites to safer areas anticipated to reducing exposure dimension of vulnerability. Yet, resettlement can outcomes the negative influences on inhabitants regarding social disarticulation, mortality, morbidity, common property access losses, food insecurity, marginalization, joblessness, homelessness and landlessness which are closely associated with adaptive capacity and sensitivity of inhabitants (Cernea, 2000). However, hypothesis have developed as due to climate-induced resettlement the vulnerability reduced in term of exposure while vulnerability increases in term of adaptive capacity and exposure. In literature, some significant studies focused the climate change negative impact on people’s livelihood specifically having poverty perspective (Thornton et al., 2007; Nyong, 2009; Pouliotte et al., 2009). In global perspective various set of indicators have applied and developed for understanding the impact such as Climate Change Social Vulnerability (CCSV), Social Vulnerability Index (SVI), Livelihood Effect Index (LEI), Livelihood Vulnerability Index (LVI) and Climate Vulnerability (CV) (Cutter, 1996; Smit and Pilifosova, 2003; Vincent, 2004; Smit and Wandel, 2006; Hahn et al., 2009; Urothody and Larsen, 2010). Sustainable Livelihood Approach (SLA) which formulated by United Kingdom Department for International Development which mostly applied for assessing capability of household to withstand shocks (Chambers and Conway, 1992). In estimating various influences of climate change on inhabited population LVI was developed. Assessing exposure to climate change and natural hazards multiple indicators mostly used such as economic and social household characteristics which influences their capability to adaptations, current food, health and water resources feathers which regulate sensitivity climate dynamics affects. Furthermore, whole index scores of sectoral indexes can be detached to recognize possible areas for additional involvement. In LVI major seven components are assigned to contributing factors of vulnerability defined by the IPCC sensitivity, adaptive capacity and exposure (Hahn et al.,2009). In significant literature research work LVI applied regarding climate change influence on poor households. In some studies climate change influence of various communities was estimated indicated various levels of vulnerabilities in term of social networks, adaptive capacity, health, water and food regarding different regions (Shah et al., 2013; Adu et al., 2018). This study research work hypothesis based on IPCC vulnerability framework having objective to estimate climate change-based livelihood vulnerability of community as used LVI to investigating livelihood vulnerability changes after and before of resettlement status. In this research work flood prone inhabited population resettlement of original communities two model villages of PDMA and local NGOs livelihood vulnerability was investigated. LVI was originally developed by Hahn et al., (2009) to investigating the livelihood vulnerability of rural hazards prone communities (Hahn et al., 2009). In literature perspective, some significant research work applied LVI approach to estimating vulnerability levels in comparing various communities, regions and determinant basis regarding various determinants such as adaptive capacity, health, social networks, water and flood (Shah et al., 2013; Adue et al., 2018). Mostly existing literature focused hazards prone areas and inhabited farming community to estimating livelihood vulnerability by application LVI approach (Nikuze et al., 2019; Das et al., 2020; Ahmad et al., 2023). In accommodating rural context like other scholars this research work used modified indicators which previously developed by Hahn et al., (2009). LVI sub-component and major components as used in this research work indicated in table 1. Major component food was replaced with finance owing to using household based monetary units where its sub-components were also replaced on household basis and regarding questionnaire aspect. In such perspective like in livelihood relevant indicators were focused as sources provides resources of income beyond agriculture which provide main livelihood as considered vulnerable to climate change. In this research work those indicators were applied which support income sources when flood hazards demolish their farming earning sources. 3. Materials and methods 3.1 Selection of study area and sampling design Administratively Pakistan consists of four provinces Khyber Pakhtunkhwa, Baluchistan, Sindh and Punjab whereas on few rational basis Punjab mainly preferred for this study. On initial basis, Punjab was engrossed in arrears to record flood-prone vulnerable long history with highest floods of 1976, 1982, 1988, 2005. 2006, 2007, 2010, 2011, 2012, 1013, 2014, 2022 and 2023 (NDMA, 2023). On some significant contribution 52% population, ¼ area and 53% agricultural GDP 5 of the country this province has major importance (GOP, 2022). Major area proportion of province comprises on fertile lands wherever recurrent curving of rivers reasons repeated flooding because of speedy glacier melting and erratic rains due to severe climate dynamics (NDMA, 2020; PDMA 6 Punjab, 2021). Intentionally favored southern Punjab region because of repeated confronting rising floods risks by means of regional life-threatening site of curving two most important rivers principally Indus and Chenab rivers (PDMA Punjab, 2020). Chenab and Indus both rivers largely flow throughout the year within various areas of southern region in both eastern and western sides. Mostly in summer rainy season overflow of rivers causes riverbank erosion, embankment and riverine flooding which reasons to raising river neighboring population floods vulnerability (BOS 7 , Punjab, 2020; PBS 8 , 2021). Out of thirty-six fourteen districts in province considered high flood risk 9 vulnerable districts about higher risk of consecutive floods occurrence and disasters destruction (BOS Punjab, 2020; PDMA Punjab, 2021). In high flood risk fourteen districts Muzaffargarh recognized most vulnerable owing to its critical location and facing major flood risk destruction where figure 1 indicated the study area. [Figure 1] In measuring association regarding frequent occurrence of flood disasters and its losses Muzaffargarh more favored to focused which is situated in higher vulnerable and flood-prone area of Punjab. In 2010, higher harshness of flood disaster instigated main destruction, washed away roads, schools, destroyed standing crops, homesteads, livestock, irrigation channels and human fatalities. Majority households in Muzaffargarh are involved in farming applies which cultivate food and cash crops to endure their livelihood necessities. Muzaffargarh administratively considered in four tehsils Alipur, Jatoi, Muzaffargarh and Kot addu comprises area of 8249 km 2 , population of 4.32 million and 93 union councils (BOS Punjab, 2020; GOP, 2022). The region of Muzaffargarh consists on life-threatening site within two major rivers Indus river flowing on Western side and flowing Chenab river on eastern bank raising vulnerability riverbank erosion and river embankment due to consecutive flooding (PBS, 2021). Climatic circumstances of research area represented 127mm average rainfall, 54 ° C(129 ° F) hot summer and 1 ° C(30°F) mild winter (PMD 10 , 2021). In current eras, flood disasters raising and recurrent harshness provoked region regarding frequently shifting riverbank erosion, river embankment, recurrent floods and increasing erratic rains. Disasters prompted severe misery of human fatalities, treasured lush land losses, infrastructure demolition, crops and livestock ruthless costs (BOS Punjab, 2020; PDMA Punjab, 2021). Repeated everchanging riverbank because of recurring disasters with core resources devastation as inhabited population livelihood becomes more severe. On provincial basis Muzaffargarh well-thought-out low socioeconomic district associated to lower cultural, economic and social dimensions in social progress index (BOS Punjab, 2020) where shifting of riverbank course as showed below in figure 2. [Figure 2] 3.2 Sampling procedure and data collection Flood disaster of 2010 considered more severe regarding resources destruction, displacement of million peoples and human fatalities in historical background of country disasters. In post flood scenario some significant projects such as model villages were established with in the areas to resettle flood affected displaced population. In selection of model villages there were utilized two significant measures firstly most severely flood affected district of province and secondly provincial area where completed most resettled projects. In this perspective, Muzaffargarh most flood affected district of province and two resettled model villages PDMA and NGOs projects from district Muzaffargarh were randomly selected. Basti Meera Mullan model village from PDMA and Ittehad model village completed by cooperated based local NGOs Engro Foundation. Government based institution PDMA established model village Basti Meera Mullan to resettlement of flood displaced population which is located East 71̊6ʹ25.81ʺ and North 29̊51ʹ46.09ʺ near Muzaffargarh urban town Khangarh (GOP, 2022). This model village consists of 106 household unit with inhabited population of almost more than 750 peoples and located neighboring of Chenab river within range of 5KM riverbank area. In the newly constructed model village heath centers, vocational training, primary schools, mosque and veterinary hospital were established. Cooperate based local NGOs Engro Foundation developed Ittehad model village which is located at East 70̊58ʹ29.77ʺ and North 30̊38ʹ25.64ʺ tehsil Kotaddu rural town Ehsanpur. This model village located in West in neighboring of Indus river almost 11 km away from riverbank of Indus river (GOP, 2022). This model village consists of 166 housing units, inhabited population of almost 1100 peoples, having area of 23.5 acres with basic facilities of mosque, school and basic health unit (PDMA, 2022). In sampling procedure, constructed overall 272 houses by PDMA and local NGOs in both study areas of the district where maximum households were tried to connected regarding to having residence and availability on the spot. In both study areas regarding data collection 248 households were willing to participate and having availability on spot were selected for this study. Out of overall collected data of 248 households 7 households’ data were skipped due to some major errors in responses and mistakes so 241 household’s data considered accurate for estimation usage. In both model villages from data of 241 households, 149 household’s data were collected from Ittehad model village and 92 household’s data was collected from Basti Meera Mullan model village. In September 2022, prior to questionnaire survey two focus group discussion were conducted as ten resettled households from each area to having appropriate understanding regarding environmental surrounding current status of resettled households. Focus group discussion collected information was applied to developing the questionnaire. Questionnaire survey was managed from October to December 2022 in both model villages where eight enumerators from local University were informed in one day training and engaged in data collection. Households those who experienced the flood disaster of 2010 were purposively selected and registered in the resettled list of PDMA and local NGOs. Household heads whether male or female were major respondents of the study. In overcoming intent questionnaire misinterpretation, each household was visited by the enumerator and filled questionnaire face to face with respondents meeting at farm, house and community meeting. Respondents household floods coping strategies, characteristics and personal information such as living environment, assets, transportation, government subsidy receipts, social network, finance, health and occupational status. For easy understanding and appropriate response from respondents’ questions were asked in local language Sariki and Punjabi. Questionnaire responses were normalized which related to sub-components of LVI 11 as illustrated in table 1 according to equation 1 for after and before of resettlement and means calculation. Samples t-test paired used for the mean difference in after and before calculated resettlement. Furthermore, regarding each sub-component in equation 2 major components values were calculated for both after and before resettlement. Lastly, regarding to equation 3 values of LVI after resettlement were calculated. 3.3 Methods of data analysis 3.3.1 calculation of LVI method On the basis of various scales each variable was calculated as in the procedure of evaluated when necessary to normalize variables. In this research work normalize Eq. 1 was applied for livelihood vulnerability index (LVI) regarding to significant perspective of literature (Hahn et al., 2009 ). Original sub-component denoted as S with sub-component maximum S max and minimum S min values as using data these values were determined. Human Development Index formula of life expectancy is based regarding this index which is calculated on difference basis of minimum life expectancy of pre-selected and life expectancy on actual basis with the pre-defined life expectancy in maximum and minimum range (Hahn et al., 2009 ). $${Index}_{s } = \frac{{S}_{ }-{S}_{min }}{{S}_{max }-{S}_{min }} \left(1\right)$$ Subsequently the standardized each sub-component and using average regarding each major component calculation as given in the Eq. 2. $$M=\frac{\sum _{i=1}^{n}{index}_{si }}{n} \left(2\right)$$ In above mentioned Eq. 2, among major seven components M is one of them which indicating sub-components as index si by indexed i which each major component makeup and each major component with sub-component numbers as n. In calculating each major component average Livelihood Vulnerability Index is estimated as given in the Eq. 3. $$LVI=\frac{\sum _{i=1}^{7}{{W}_{Mi }M}_{i }}{\sum _{i=1}^{7}{W}_{Mi }} \left(3\right)$$ In above mentioned equation among seven components M i is one of them where sub-component numbers which determined with the makeup major each component denoted as W Mi . Each major component weight W Mi is determined by sub-component numbers which comprises such major components and ensure included as all sub-components equally contributed in the whole LVI (Sullivan et al., 2002 ). The value of LVI determines situation regarding vulnerability of household livelihood as lower value of LVI illustrates lower household vulnerability livelihood and less risky situation while higher value of LVI indicates higher household vulnerability livelihood and critical and risky conditions. 4. Results In table 2, respondents feathers are summarized indicating as majority 89% respondents were male where 63% respondents categorized in age of 30 to 50 years while overall respondents having average age of 46.9 years. In schooling perspective majority respondents 36% higher schooling, 27% middle schooling, 24% primary schooling 5% above graduate while 8% having no schooling. Majority respondents 77% inhabited in original area from 16 years or more than more than 20 years where mostly inhabitants 53% resettled in 2015. Mostly respondents resettled almost in 87.6 months and while majority respondents resettled and having ownership certificates of houses. [Table 2] Households resettlement livelihood vulnerability changes status prior and after resettlement illustrated in the table 3. Households number members before resettlement and after resettlement have decreased 5.69 to 5.27 because of maximum family size in the area is almost seven. After resettlement unemployment status has raised as 18.3% inhabitants have lost their lives which was 11% prior resettlement. In occupational status fishing, retail market farming practices have decreased while construction, industry employ, transportation and hoteling employee’s status has raised. Decline in occupational status of farming, fishing and retail market is that resettle far from the original locality. In this table variation in household’s employment status is denoted as significant raised indicated in labor workers while decline in farming and fishing because far resettlement from farming areas and river. Resettlement caused major decline in the income PKRs 45697 to PKRs 31,984 and expenditures PKRs 43,961 to PKRs 30,825 because mostly resettled inhabitants lost their occupation due flood destruction and have to search for new directions of employment, faced significant lost in income so squeezed their expenditure regarding decline in income. Household head commuting mode have changed as increase in motorcycle, minibus and bus usage have increase due to increase in distance of resettlement in model village. After resettlement average time of traveling have increased from 21.59 minutes to 38.47 minutes where average cost of travelling have also raised from PKRs 147.96 to PKRs 256.81 as indicated in the table 3. [Table 3] Sub-components and major components livelihood vulnerability index values also composite LVI and major components estimates of prior and after resettlement illustrated in the table 4. In respondent’s socio-demographic profile almost 59% household have lower education than high schooling as such schooling status in lower than national average schooling rate. In study area almost 47% peoples have dependency which consists of adults above 60 years and child under 16 years whereas 11% households’ heads consist on females. Households livelihood economic stability after and during of climate-based disaster considered as livelihood strategy. Resettled households having no regular wages indicated the worsen scenario from 0.511 to 0.549 while having no level of significance (p<0.05) while those households having income ratio without government subsidies illustrated the significant improvement such as the scores from 0.655 to 0.337 with level of significance. In general perspective, after resettlement households received multiple government subsidies such as utility bills subsidies, Sehat cards, flood relief packages, disaster donations and NGOs reliefs. In obtaining disasters relief households were registered as provincial based rural low-income communities. Aggregate score of livelihood strategy showed improved status as 0.583 to 0.443 such improvement is based on household income ratio without subsidies of government. [Table 4] In community social networks have numerous characters such as mutual assistance, sharing and borrowing food (Beegom, 2014). Households having no relatives in neighborhood score have increased from 0.236 to 0.398 estimated with level of significance while the significant reasoning regarding such perspective is that during resettlement mostly relative were separated. The reason behind this situation is that limited number of household’s units were build where limited number affected household were settled while others relatives were resettled on somewhere other locations or relatives so families were separated. Households having no friends in neighboring indicated the deteriorated perspective as score from 0.179 to 0.186 because resettlement caused separation in original family’s settlements. In overall aspect of relatives and friend’s resettlement reduced social network of resettled households. Households perspective those are not part of any formal neighborhood association and communities have shown improved due to reduced score of 0.481 to 0.394 it showing that after resettlement most of the households have joined local communities and association rather than prior scenario of resettlement. Social networks overall scores after resettlement have increased from 0.298 to 0.326 showing as reduction in overall network of resettled population due to separation of families and friends. In health perspective, household’s family members suffer in chronic diseases showing increasing score from 0.093 to 0.146 indicating the rising perspective of chronic disease while such variation considered unaffected through length of time and after resettlement. After resettlement sanitary toilets inside houses showed significant improvement as declining from 0.178 to 0.000 illustrating as significant household 23% prior to resettlement used mutually toilets and some significant number of households having no toilet and used open spaces for their needs. In model villages each household having own toilet facility which is hygienically necessary for protecting some severe health issues. Some significant improvement was also estimated regarding the households having no medical expenditures subsidies about the score pattern from 0.897 to 0.799 showing the increase in households’ number who obtaining medical expenditure subsidies as increasing number of households obtaining medical service financial access. In overall health perspective significant improvement was estimated regarding the increasing score from 0.389 to 0.324. In drinking water perspective, some significant improvements were estimated with prominent decreasing score from 0.094 to 0.000. Such estimated score highlighted as after resettlement mostly households have access of drinking water on their own resettled home handpump access while prior to resettled perspective majority households used sharing handpump water access. In these outcomes resettled households in the model villages having ease access of drinking water within their homes. In financial perspective regarding resettled household’s unemployment scenario becomes to worsen off as increasing score from 0.159 to 0.267. Prior to resettlement 27 households were unemployed while after resettlement unemployed household increased to 44 where increasing unemployed households were mostly having occupation of farming, fishing and casual labors. Some significant reasons regarding increasing unemployment in resettled areas is new neighboring with no initial community interactions, increasing distance from previous working locations and rising traveling cost due to distance factors. After resettlement worsen situation was estimated regarding household’s income and expenditure equation due to increasing worsen score from 0.261 to 0.399 illustrating significant decline in household income and proportional reducing expenditure patterns. Prior and after resettlement household monthly income equation was from PKRs 45,697 to 31,984 while expenditure equation was from 31,984 to 30,825. The significant decline in income pattern was firstly resources destruction due to flood disaster and then resettlement displacement of location caused change in workplace, some occupational variations and some numbers of family member faced becomes to unemployed. The severe influence in decline income caused reducing expenditure pattern of community as household’s livelihood becomes more limited to basic subsistence. In exposure perspective, prior and after resettlement number of times households were warned to evacuate owing to floods showed significant betterment regarding the score decline from 0.089 to 0.006. In prior the resettlement households were received warning almost more than four times in a year while after resettlement having just almost once in the year because after resettlement having more secure area which is less prone than the original region. Households home having no second floor showing some improvements as declining value in obtained scores as from 0.398 to 0.183 indicating as after resettlement mostly households lives on or above second flood so having more secure perspective of homes rather prior to resettlement. Households having non-solid homes prior resettlement indicated significant improvement as showing significant decline score from 0.876 to 0.000 as highlighting that after resettlement mostly households having solid homes build with bricks and concrete replace with wood and mud homes having most secure structure rather than prior resettlement structure. In exposure perspective significant improvement was estimated with overall declining exposure score from 0.454 to 0.063. Exposure, health, livelihood strategy, finance, water, social networks and sociodemographic profile are seven major components livelihood vulnerability index as their estimated scores illustrated in figure 3. Worsen perspective was estimated regarding major components score of finance, water and social networks while improved score estimated related to exposure, health and livelihood strategy where the overall calculated score of LVI was 0.287. [Figure 3] Now the estimation was made whether each subcomponents LVI variation score was influenced by length of time which passed after the resettlement of households. In estimation procedure, overall respondents 241 were categorized in to two groups on the basis of length of time as 118 respondents those resettle within five years and those 123 respondents those resettled six years or more than six years. In perspective of time length from seven major component the five most relevant major components were chosen and ten subcomponents of these five major components selected for estimation as illustrated in table 5. In these two groups, each subcomponent score of prior resettlements was subtracted from score after resettlement while their mean value score each subcomponent was calculated as indicated in table 5. In such perspective, while the score value after resettlement minus score value before resettlement indicating negative it highlights as after resettlement respondents have become less vulnerable. In these two groups value of subcomponent is compared while value of longer group estimated small rather than shorter group it highlights as more decrease in vulnerability of longer group rather than specific subcomponents. Lastly, t-test was conducted regarding score mean value about each subcomponent in both groups as illustrated in table 5. Estimates indicated as terms like “households percentage having no subsidies of medical expenditure” “households percentage having no active participation in neighborhood association or formal communities” “households percentage having no relative in neighborhood” “households income ratio having no subsidies from government” the scores mean differences prior and after resettlement were different significantly at level of 5% in these both groups. [Table 5] In perspective of household’s percentage having no regular wages estimated as longer group considered significant reduction in vulnerability rather than shorter groups indicating as increasing time after resettlement raised resettles connections and information with local community as more likely to adjust regular wages jobs. Households income ratio having no government subsidies estimated the significant reduction in vulnerability in longer groups in contrast to shorter groups because long time to resettlement having more information and access capable them to having more access of government subsidies. In the scenario household’s percentage having no relatives in neighborhood raised longer group vulnerability in contrast to shorter group because with time passing movement of relative to other location causes to increase vulnerability. In aspect of household’s percentage have no active participation in neighborhood association and formal communities estimated the decline in longer group vulnerability rather than shorter group because more communication and interactions of longer groups with local organizations provides more updated information which reduces vulnerability status. Regarding household’s percentage having no subsidies of medical expenditures estimated as longer group more reduces vulnerability in contrast to shorter group the reason of from long time living becomes more familiar with information to access and capable to obtain medical subsidies. In these two groups no significant differences were estimated regarding the “income-expenditure monthly ratio”, “households percentage having unemployed heads” “regular basis drinking water access” “households percentage having family member chronic illness” “households percentage having no friends in neighborhood” indicating as no effect of decrease or increase in such subcomponents vulnerability. In estimates of table 5 and 4 considering decreased overtime occurred regarding the after-relocation vulnerability decreased in access medical expenditure subsidies, formal communities’ participation and non-subsidized ratio of household income while on other end worsen scenario developed overtime regarding after relocation increased vulnerability in kinship relationship. In monthly income-expenditure ratio and household unemployed indicated the relocation deterioration aspect as overtime seem no improvement. 5. Discussion In this research work investigated LVI variation prior and after climate induced resettlement by the two model villages resettlement in Punjab, Pakistan. Estimates indicated exposure one major component of LVI as exposure vulnerability was enormously reduced highlighting significant achievement in resettlement project. Exposure vulnerability reduction is because of model villages were constructed solidly and resettlement of households in the less flood prone and risky area as resettlement positive effect on condition of houses has demonstrated positively as findings are in line with the studies of (Zhang and Lu, 2016 ; Herath et al., 2017 ). In few LVI major components vulnerability becomes worsen off while in some major components it becomes to decreased. In health one major component of LVI where decreased aspect of vulnerability was estimated. Households maximum access of medical expenditure subsides and sanitary toilets main provision considered significant to reducing health vulnerability. Resettled household were priority focused regarding government subsidies because of their climate based more vulnerable situations and after resettlement household’s regular income increased which reason of more stable conditions of households and reduced vulnerability status. LVI major components vulnerability reduction is reasoned on resettlement households associated subsidies and resettlement to less flood prone areas. Increasing vulnerability tendency regarding social network of household was experienced in this research work as households living in resettled area while having no relatives in neighboring facing increased vulnerability. However, after resettlement number of households decreased those having no active part of associations in local neighboring or community as increasing association membership and kinship indicating solid social network in contrast to original communities’ perspective. These findings are in line with the studies of (Nikuze et al., 2019 ; Ahmad et al., 2023 ) indicating as resettled households remain fail to continuing pre-located social network. In LVI major components water indicated the improved perspective as after resettlement households having easy access of drinking water with availability of each home handpump access in model village while having to sharing sources of drinking water in pre-relocations. These findings are in contrast with the study of (Sholihah and Shaojun, 2018 ; Ahmad and Afzal, 2020 ). Resettled households of model villages having proper structure regarding access of drinking water and appropriate management of sewerage as such provisions having significant impact on resettlements (Herath et al., 2017 ; Nikuze et al., 2019 ). Financial burden has increased due to appropriate formalization in resettled areas with well-established and improved structure which is necessary for wellbeing of households. Income to expenditure ratio increase and rising household heads after resettlement have enhanced the vulnerability financial component as these findings are similar with the studies of (Sholihah and Shaojun, 2018 ; Nikuze et al., 2019 ). Increasing in households’ expenditures ratio regarding income perspective was because after resettlement households faced declining in income and rising unemployment regardless of increasing the government subsidies. Resettled households have to lost or change their jobs, reduced income having to change occupation owing to new neighboring and communities which raised their financial vulnerability and its related issues. In resettlement perspective, some major component of LVI indicated worsen scenario such as social disarticulations, marginalization, joblessness and homelessness as these findings are similar with the studies of (Cernea, 2000 ; Patel et al., 2015 ; Ahmad et al., 2023 ). Resettlement provides opportunities and formalize housing for displaced households in attaining access of government subsidies. Resettled households’ lives become to formalize which reduces LVI of resettled peoples regarding livelihood strategy and exposure while due to loss of economic activity and kinship other vulnerabilities of original communities raises. Resettled households’ length of stay also effects on LVI which was estimated regarding its relevant subcomponents. Medical expenditure subsidies access, formal communities’ participation, non-subsidized income of household and availability of regular wages estimates indicated as longer stay of households in resettled area reduces vulnerability in these four subcomponents of LVI. In contrast, households lost their kinship relation as more time passes from relocation area. Furthermore, sure perspective as neighborhood friendship, unemployment status, income expenditure ratio and chronic illness indicated no significant relation with longer the time passes as duration of resettlement having impact on these vulnerabilities. 6. Conclusion and suggestions Climate-induced flood disasters communities’ resettlement in model villages caused reducing vulnerability regarding health, water, adaptive strategy and exposure while worsened vulnerability perspective in finance and social networks. Flood prone resettled community of model villages livelihood vulnerability was better-quality because of allocated preferentially government subsidies to resettlement households. Outcomes of this study have some various aspects such as resettlement raised community access of government subsidies while these peoples suffer economically as increasing expenditure ratio to income and losing their jobs. In mitigating negative impact of resettlement government subsidies considered more prominent while residents life satisfaction level having more significance regarding current jobs and income pattern in new community. Furthermore, in estimated outcomes it was denoted as after relocation some significant vulnerability components variated overtime. These such type of study outcomes suggests as livelihood vulnerability assessment must assume endlessly instead of just once in time after relocation. This research work examined the climate induced case resettlement in Muzaffargarh district two model villages of Punjab, Pakistan. Numerous significant outcomes of this research work are similar with literature regarding other regions, highlighting several similarities about climate induce impacts on resettlement regarding livelihood vulnerability crossways of various regions. The significant novelty in this research work of Muzaffargarh preferentially resettled households’ subsidies allocated and regarding their formalization. Such subsidies impact on livelihood vulnerability resettled households expected to similar regarding other regions in this perspective such findings may have suggestions for relevant officials in various geographical background. Concerned policy makers and authorities must formulate policies and implement compacted strategies for increasing employment in neighboring of resettled areas communities for sustainable livelihood of resettled households. Declarations Ethical Approval Ethical approval taken from the COMSATS University Vehari campus, ethical approval committee Consent to Participate Not applicable Consent to Publish Not applicable Authors Contributions DA analyzed data, methodology, results and discussion, conclusion and suggestions and manuscript write up whereas both DA and MA finalized and proof read the manuscript and both authors read and approved the final manuscript. Funding This study has no funding from any institution or any donor agency. Competing Interests The authors declare that they have no competing interest. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Abbas, A., Amjath-Babu, T. S., Kächele, H., Usman, M., Amjed Iqbal, M., Arshad, M., ... & Müller, K. (2018). 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Flood risk and household losses: Empirical findings from a rural community in Khyber Pakhtunkhwa, Pakistan. International Journal of Disaster Risk Reduction , 96 , 103930. Zhang, C., & Lu, B. (2016). Residential satisfaction in traditional and redeveloped inner city neighborhood: A tale of two neighborhoods in Beijing. Travel Behaviour and Society , 5 , 23-36. Zheng, G., Allen, S. K., Bao, A., Ballesteros-Cánovas, J. A., Huss, M., Zhang, G., ... & Stoffel, M. (2021). Increasing risk of glacial lake outburst floods from future Third Pole deglaciation. Nature Climate Change , 11 (5), 411-417. Footnotes United Nations Disaster Risk Reduction United Nations Development Programme United Nations Children Funds Provincial Disaster Management Authority Gross Domestic Product Provincial Disaster Management Authority Bureau of Statistics Pakistan Bureau of Statistics Districts categorized according to flood vulnerability disaster high, medium, low https://pdma.punjab.gov.pk/system/files/vnl.JPG Pakistan Metrological Department Livelihood Vulnerability Index Tables Table 1 Livelihood vulnerability index indicators, sub-components and major components applied in the study IPCC Framework Study major components Description Study sub-components Sources of components Exposure Exposure Floods exposure In a year how many times households received flood evacuation warning after or prior resettlement Shah et al., (2013), Nikuze, (2019), Houng, (2019), Al Manum, (2023) Housing exposure to floods Households percentage whose homes does not have second floods, Shah et al., (2013) Non-solid material ratio in houses structure (woods and bricks =1, concrete and bricks=1) Shah et al., (2013), Huong, (2019) Sensitivity Finance Availability of finance for food Household percentage having unemployed heads Nikuze, (2019) Households monthly income and expenditures Yokoyama et al., (2023) Water Availability of water Drinking water availability payments Yokoyama et al., (2023) Health Status of health Households members percentage having chronic illness Hahn et al., (2009), Shah et al., (2013), Das et al., (2020), Al Manum, (2023) Medical service access Households percentage having no toilet access inside home Pendey et al., (2018), Giri, (2021) Household percentage having no medical expenditure subsidies Yokoyama et al., (2023) Adaptive capacity Social demographic profile Households capability to response or prepare emergencies Households percentage with heads of households having lower schooling (less than high school education) Hahn et al., (2009), Shah et al., (2013), Pandey et al., (2018), Giri et al., (2021), Al Manum, (2023) Dependency ratio Hahn et al., (2009), Shah et al., (2013), Al Manum, (2023) Households percentage having female-heads households Hahn et al., (2009), Shah et al., (2013), Pandey et al., (2018), Das et al., (2020), Giri et al., (2021) Livelihood strategy Household income stability Households percentage having not regular wage Nikuze, (2019) Households ratio having income without government subsidies Yokoyama et al., (2023) Social network Social network strength which is beneficial for responding or preparing emergency Household percentage having no relative in neighborhood Nikuze, (2019), Al Manum, (2023) Households percentage having no friend in neighborhood Nikuze, (2019), Al Manum, (2023) Households percentage having no association with formal neighborhood or communities Liu et al., (2018), Das et al., (2020), Giri, (2021) Table 2 Attributes of targeted households Households attributes Household head gender status Male 89% Female 11% Household head age group in years Below 20 years 6% 20 to 30 years 19% 30 to 40 years 38% 40 to 50 years 25% Above 60 years 12 Household head average age 46.9 years Household head educational status No schooling 8% Up to elementary schooling 24% Middle schooling 27% High schooling 36% College graduates and above 5% Prior to resettlement inhabited how long in original community Less than 6 years 9% 6 to 10 years 6% 11 to 15 years 17% 16 to 20 years 34% More than 20 years 43% Resettlement year 2014 47% 2015 53% Average number of months to resettlement 87.6 month Status of home ownership prior to resettlement Rent 9% Free rent 36% Own without certificate 11% Own with certificate 44% Table 3 Household status changes after and prior resettlement Status of household Prior resettlement After resettlement Family members average numbers 5.69 5.27 Household heads occupation percentage Fishing 2.3% 1.9% Casual/construction workers 3.9% 5.7% Retail market 3% 1.8% Industry and manufacturing employees 3.1% 3.6% Driving in transportation 3.7% 4.1% Government employees 2.6% 2.6% Restaurant, hotel employees 2% 5% Farming practices occupation 68.4% 57% Unemployed 11% 18.3% Employment status in percentage Laborer/casual workers etc 20.6% 33.7% Farming and fishing 70.7% 58.9% Self-employed 3.% 1.8% Government/Company employees 5.7% 6.2% Monthly average household income in PKRs 45,697 31,984 Monthly average household expenditure in PKRs 43,961 30,825 Household head commuting mode in percentage Walking 23.9% 19.8% Usage of bicycle 9.6% 7.3% Usage of motorcycle 47.5% 50.89% Usage of car 3.1% 2.6% Minibus 11.6% 13.9% Bus 4.3% 5.51% Average household head commuting time one way (minutes) 21.59 38.47 Average household head commuting cost one way (in PKRs) 147.96 256.81 Table 4 Standardized index sub-component, major components and overall LVI after and prior resettlement Study major components Study sub-components Prior resettlement Before resettlement t-test p-value Respondents socio-demographic profile 0.398 Households head percentage low education level (lower than higher schooling) 0.590 Ratio of dependency 0.469 Female household head percentage 0.114 Livelihood strategy 0.583 0.443 Households percentage without regular wage 0.511 0.549 0.04 Households income ratio without government subsidies 0.655 0.337 0.01 Social networks 0.298 0.326 Households percentage that do not having relatives in neighborhood 0.236 0.398 0.02 Households percentage that do not having friends in neighborhood 0.179 0.186 0.31 Households percentage which are not part of formal neighborhood associations and communities 0.481 0.394 0.01 Health 0.389 0.324 Households percentage where family members having chronic illness 0.093 0.146 0.00 Households percentage having no sanitary toilet inside home 0.178 0.000 0.01 Households percentage having no medical expenditure subsidies 0.897 0.799 0.02 Water 0.094 0.000 Households having issues of drinking water 0.094 0.000 0.01 Finance 0.210 0.333 Households percentage those heads as unemployed 0.159 0.267 0.01 Expenditure-income monthly ratio 0.261 0.399 0.00 Exposure 0.454 0.063 How many times (numbers of days) in a year household received flood evacuation warning prior or after resettlement 0.089 0.006 0.01 Households percentage where homes having no second flood 0.398 0.183 0.00 Houses percentage not built with solid material (concrete) 0.876 0.001 0.00 LVI * value 0.287 * Livelihood Vulnerability Index Table 5 Livelihood vulnerability index sub-components standardized indexed average difference in prior and after resettlement Study major components Study sub-components Mean index differences prior and after resettlement t-test p-value Resettlement within 5 years Resettlement within 6 years Livelihood strategy Households percentage without regular wages 0.067 -0.004 0.029 Households ratio having income without government subsidies -0.183 -0.399 0.001 Social networks Households percentage which having no relatives in neighboring 0.136 0.301 0.000 Household percentage which do not having friends in neighborhood -0.014 0.011 0.147 Households percentage which do not take part in neighborhood association or formal communities -0.043 -0.267 0.001 Health Households percentage which having family members suffer in chronic illness 0.018 0.069 0.046 Households percentage having no subsidies of medical expenditure -0.016 -0.126 0.003 Water Households having issues of obtaining drinking water 0.031 0.043 0.153 Finance Households percentage those heads is unemployed 0.148 0.087 0.098 Expenditure-income monthly ratio 0.143 0.126 0.131 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Introduction","content":"\u003cp\u003eClimatic conditions sharp changes have raised geophysical and meteorological dynamics which subsequently enhanced disasters occurrence of landslides, floods, earthquakes and wildfire (Senko et al., 2022;\u0026nbsp;UNDRR\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e, 2022; Qamer et al., 2023). Calamities without political aspects, economic scenarios, geographical limits, cultural boundaries and social perspective sequentially influenced by life style choices, risk exposure and geographical locations (Kimaro et al., 2018; Ahmad and Afzal, 2021; Kreft et al., 2021). In worldwide situation, substantial human cost and economic losses sequentially confronted by global community due to severe and recurrent incidence of about main natural hazards such as floods (Daniell et al., 2016; Ahmad et al., 2019; UNDP\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e, 2023). In past couple of decades, fast climatic variation has estimated to increase temperature, reducing humidity, enlarged heat stress and necessity of water concerning global population, crops and ecosystem (Wilkinson et al., 2016; Sillmann et al., 2022; UNICEF\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e, 2023). In 2022, severe climatic dynamics like long time heat wave expansion changing aspects were measured with massive monsoon associated uncommon high rainfall produced worsen situation of flooding and landslides in India, Pakistan and Afghanistan (UNDRR, 2022). Moreover, assessed lower than average rainfall and exaggerated drought circumstances in some regions such as western USA, South Africa and Horn of Africa (Munpa et al., 2022). In summer 2022 noticed heatwaves and drought in frequent areas of Western Europe and China where rivers and lakes shrinking preceding to regressive higher than average standing in later year (Fenta et al., 2019; UNDRR, 2022). Increasing risk or emerging recently drought generally measured in Mongolia, China, western Russia, most plains of Canada and South America that estimated to enlarge dry circumstances up to end of current year (Barrett et al., 2019; UNDRR, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn last twenty years global extreme climatic events dramatically increased as these disaster events were estimated 6681 in 2000 to 2019 as compared to 3656 in 1980 to 1999. Storms and floods considered most frequent disasters where from 2000 to 2019 storms increased 1457 to 2034 while floods raised 1389 to 3254 (Mazhin et al., 2021). The region of South Asia, during past couple of decades faced hasty incidence of enlarged flood disasters which is expected to raise in strength in future (Opiyo et al., 2015; Abbas et al., 2017; Mahmood et al., 2020). Most populated and emerging economies of the region Bangladesh, China, India and Pakistan stated disasters supermarkets because of repeated suspectable flood disasters (Gorst et al., 2015; Ahmad and Afzal, 2020; UNICEF, 2023). In most worldwide climate change influenced countries as Pakistan was considered 29\u003csup\u003eth\u003c/sup\u003e higher vulnerable country in 2009-2010, 16\u003csup\u003eth\u003c/sup\u003e in 2010-2011while categorized 5\u003csup\u003eth\u003c/sup\u003e most disasters susceptible country in 2021 (Kret et al., 2017; Teo et al., 2018; Schilling et al., 2020; UNDP, 2023). Pakistan\u0026rsquo;s 76\u003csup\u003eth\u003c/sup\u003e years historical era from 1947 to 2023 confronted sequence of major and minor flood disasters while 2010 flood was recorded most distressing flood disaster (Kirsch et al., 2012; NDMA\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e, 2012; UNDP, 2023). Flood disaster of 2010 severely affected 45 out of 135 districts and its spam sustained nearly six months which reasoned 9.7 billion US dollars economic losses and devastation of more than 160,000km\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eresidential and cropped areas (Kirsch et al., 2012; NDMA, 2017; Ahmad et al., 2019; Rasul et al., 2021). In flood affected area destroyed 1.1 million houses, damaged 436 healthcare centers and higher than 20 million population was severely affected (Kirsch et al., 2012; UNDRR, 2022; UNDP, 2023).\u003c/p\u003e\n\u003cp\u003eVulnerability of groups and individuals subject to other disasters and climate change is affected by amalgamation of composite economic, social, environmental and political determinants acting at different levels. In outcome scenario, some marginalized groups, households and individuals possible to be disproportionally influenced through climate change adversative effects which will raises regional inequality (IPCC, 2022). Broader development and socioeconomic state framework cause numerous low-income populations less strong to many climate influences particularly in areas of high inequality and vulnerability (IPCC, 2022). It is urgent to address how to reduce and approach disaster risks that are unevenly distributed within groups of low income in susceptible living situation. In disaster risk reduction context, it is necessary to efficiently take precautionary actions and manage risks to lessen population vulnerability. Preventive resettlement is considered one significant measure which is likely to lessen losses of infrastructure, assets, life risks, eliminate cost of reconstruction and emergency responses. Furthermore, precautionary resettlement must be last possibility at what time it appears incredible to mitigate risk aspects related with natural disasters which becomes impossible to control (Correa, 2011; Ahmad et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn literature, natural disasters specifically flood hazards deliberated with numerous features such as flood disasters assessment and adaptation management strategies (Gardner and Dekens, 2007; Webster et al., 2011; Kellens et al., 2013; Arnall, 2014;Bukhari and Rizvi, 2015; Tullos et al., 2016; Mavhura et al., 2017; Wymann et al., 2017; Shah et al., 2018; Glago, 2019: Akukwe, 2019; Khosravi et al., 2019; Ahmad et al., 2020; Soulibouth, 2021; Kam et al., 2021; Devi, 2022; Senko et al., 2022), floods influences on flood-prone inhabitants resources losses (Douglas et al., 2010; Bremond, 2013; Durodola, 2019; Balgah et al., 2019; Englhardt et al., 2019; Gould,2020; Nguyen et al., 2020; Atanga and Tankpa, 2021; Bernier et al., 2021;; Bhamani, 2022; Ishaque et al., 2022; Khayyam and Munir, 2022; Qamer et al., 2023), flood hazards and riverine communities livelihood vulnerabilities (Florsheim et al., 2008; McMichael and Lindgren,2011; Hewitt and Mehta, 2012; Prasad et al., 2016; Bansal et al., 2017; Sam et al., 2017; Cramer et al., 2018;Ahmadalipour et al., 2019; Tong and Ebi, 2019; Simpson et al., 2021; Yang et al., 2021; Alc\u0026aacute;ntara-Ayala et al., 2022; Lydie, 2022; Yaseen et al., 2023), flood hazards effects on urban regional destruction (Du et al., 2015; Stoffel etl., 2016; Gigović et al., 2017;Johann and Leismann, 2017; Dou et al., 2018;Park and Lee, 2019; Ballesteros-C\u0026aacute;novas et al., 2019; De Silva et al., 2020; Liu et al., 2021; Cea and Costabile, 2022; Ridha et al., 2022), flood disaster risk and non-mountainous area livelihood losses (Rafiq and Blaschke, 2012; Ballesteros-C\u0026aacute;novas et al., 2015;Niedźwiedź et al., 2015; Bodoque et al., 2016; Kundzewicz et al., 2017; Steeb et al., 2017; Mazzorana et al., 2018;Ruiz-Villanueva et al., 2018; Talbot et al., 2018; Sepehri et al., 2019; Chen et al., 2020; Rahman et al.,2021; Zheng et al., 2021; Qie et al., 2022; Matsa and Mupepi, 2022; Wang et al., 2023), flood risk vulnerability and mitigation strategies in Pakistan (Mustafa, 1998; Shaw, 2015; Shah et al., 2017; Abbas et al., 2018; Jamshed et al., 2019; Nazeer and Bork,2019; Ahmad and Afzal, 2020; Hamidi et al., 2020; Ahmad and Afzal, 2021; Hussain et al., 2021; Khan et al., 2021; Rana et al., 2021; Ullah et al., 2021). Some numerous studies focused resettlement as mitigation measure to flood risks (Barnett et al., 2008; Correa, 2011; Claudianos, 2014; Patel et al., 2015; McNamara et al., 2018; \u0026nbsp;Cernea, 2021; IPCC, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn flooding areas, community\u0026rsquo;s resettlement considered significant risk mitigation strategy regarding flood disasters while such strategy has some resettled communities\u0026rsquo; livelihood vulnerabilities. Subsequently, in overall perspective the question regarding whether resettlement must be applied as adoption climate change strategy still not settled. In highlighting such issue more research work that indicates which scopes of livelihood vulnerability deteriorated or improved owing to climate-based resettlement aspect and in in what way considerably is required. Furthermore, in literature perspective previous research work have measured household\u0026rsquo;s livelihood after resettlement while did not investigated in what way household\u0026rsquo;s livelihood changed after and before resettlement. In general perspective, mostly research work focused low income households which having higher vulnerability prior to resettlement where after resettlement there may possibility of deterioration or improvement in some capacities of households. Having appropriate understanding of these changes and contradictory association in capitals it is compulsory to investigate livelihood vulnerability after and before of resettlement. In Pakistan perspective, resettlement aspect scarcely discussed like just post disaster resettlement while no other dimension like livelihood vulnerability of resettled population not properly elaborated. In findings out this research gap this study focused the influence on livelihood vulnerability in comparing prior resettlement and after resettled population. Objective of this research work is to investigate the which livelihood vulnerability dimensions deteriorated or improved owing to climate-induced flood prone population resettlement in Muzaffargarh model villages. This study is categorized in to six sections as introduction indicated in first section, second section illustrated conceptual framework while material and metrology explained in third section. Results and discussion highlighted in fourth section, fifth section elaborated discussion while last section explained conclusion and suggestions of the study. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"2. Conceptual framework","content":"\u003cp\u003eMore significant terminology vulnerability can well-defined such as level of system unable to cope or susceptible to climate change adverse effects including climate extremes and variability. Regarding IPCC vulnerability is function of rate of climate variation, magnitude and character by through systems adaptive capacity, sensitivity and exposed (IPCC, 2022). Hence adaptive capacity, sensitivity and exposure considered three vulnerability dimensions. Degree and nature to which structure is exposed to substantial climatic variation known as exposure. Level through structure is influenced either beneficially or adversely through climate-induced stimuli indicated as sensitivity. Capacity of structure to manage climate change to moderate potential damages by coping with consequences or having advantage of opportunities considered as adaptive capacity (IPCC, 2022). Residents movement of climate induced resettlement inhabited from hazardous sites to safer areas anticipated to reducing exposure dimension of vulnerability. Yet, resettlement can outcomes the negative influences on inhabitants regarding social disarticulation, mortality, morbidity, common property access losses, food insecurity, marginalization, joblessness, homelessness and landlessness which are closely associated with adaptive capacity and sensitivity of inhabitants (Cernea, 2000). However, hypothesis have developed as due to climate-induced resettlement the vulnerability reduced in term of exposure while vulnerability increases in term of adaptive capacity and exposure. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn literature, some significant studies focused the climate change negative impact on people\u0026rsquo;s livelihood specifically having poverty perspective (Thornton et al., 2007; Nyong, 2009; Pouliotte et al., 2009). In global perspective various set of indicators have applied and developed for understanding the impact such as Climate Change Social Vulnerability (CCSV), Social Vulnerability Index (SVI), Livelihood Effect Index (LEI), Livelihood Vulnerability Index (LVI) and Climate Vulnerability (CV) (Cutter, 1996; Smit and Pilifosova, 2003; Vincent, 2004; Smit and Wandel, 2006; Hahn et al., 2009; Urothody and Larsen, 2010). Sustainable Livelihood Approach (SLA) which formulated by United Kingdom Department for International Development which mostly applied for assessing capability of household to withstand shocks (Chambers and Conway, 1992). In estimating various influences of climate change on inhabited population LVI was developed. Assessing exposure to climate change and natural hazards multiple indicators mostly used such as economic and social household characteristics which influences their capability to adaptations, current food, health and water resources feathers which regulate sensitivity climate dynamics affects. Furthermore, whole index scores of sectoral indexes can be detached to recognize possible areas for additional involvement. In LVI major seven components are assigned to contributing factors of vulnerability defined by the IPCC sensitivity, adaptive capacity and exposure (Hahn et al.,2009). In significant literature research work LVI applied regarding climate change influence on poor households. In some studies climate change influence of various communities was estimated indicated various levels of vulnerabilities in term of social networks, adaptive capacity, health, water and food regarding different regions (Shah et al., 2013; Adu et al., 2018). This study research work hypothesis based on IPCC vulnerability framework having objective to estimate climate change-based livelihood vulnerability of community as used LVI to investigating livelihood vulnerability changes after and before of resettlement status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this research work flood prone inhabited population resettlement of original communities two model villages of PDMA and local NGOs livelihood vulnerability was investigated. LVI was originally developed by Hahn et al., (2009) to investigating the livelihood vulnerability of rural hazards prone communities (Hahn et al., 2009). In literature perspective, some significant research work applied LVI approach to estimating vulnerability levels in comparing various communities, regions and determinant basis regarding various determinants such as adaptive capacity, health, social networks, water and flood (Shah et al., 2013; Adue et al., 2018). Mostly existing literature focused hazards prone areas and inhabited farming community to estimating livelihood vulnerability by application LVI approach (Nikuze et al., 2019; Das et al., 2020; Ahmad et al., 2023). In accommodating rural context like other scholars this research work used modified indicators which previously developed by Hahn et al., (2009). LVI sub-component and major components as used in this research work indicated in table 1. Major component food was replaced with finance owing to using household based monetary units where its sub-components were also replaced on household basis and regarding questionnaire aspect. In such perspective like in livelihood relevant indicators were focused as sources provides resources of income beyond agriculture which provide main livelihood as considered vulnerable to climate change. In this research work those indicators were applied which support income sources when flood hazards demolish their farming earning sources.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Selection of study area and sampling design\u003c/h2\u003e\n \u003cp\u003eAdministratively Pakistan consists of four provinces Khyber Pakhtunkhwa, Baluchistan, Sindh and Punjab whereas on few rational basis Punjab mainly preferred for this study. On initial basis, Punjab was engrossed in arrears to record flood-prone vulnerable long history with highest floods of 1976, 1982, 1988, 2005. 2006, 2007, 2010, 2011, 2012, 1013, 2014, 2022 and 2023 (NDMA, 2023). On some significant contribution 52% population, \u0026frac14; area and 53% agricultural GDP\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e5\u003c/sup\u003e of the country this province has major importance (GOP, 2022). Major area proportion of province comprises on fertile lands wherever recurrent curving of rivers reasons repeated flooding because of speedy glacier melting and erratic rains due to severe climate dynamics (NDMA, 2020; PDMA\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e6\u003c/sup\u003e Punjab, 2021). Intentionally favored southern Punjab region because of repeated confronting rising floods risks by means of regional life-threatening site of curving two most important rivers principally Indus and Chenab rivers (PDMA Punjab, 2020). Chenab and Indus both rivers largely flow throughout the year within various areas of southern region in both eastern and western sides. Mostly in summer rainy season overflow of rivers causes riverbank erosion, embankment and riverine flooding which reasons to raising river neighboring population floods vulnerability (BOS\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e7\u003c/sup\u003e, Punjab, 2020; PBS\u003ca href=\"#_ftn4\" name=\"_ftnref4\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e8\u003c/sup\u003e, 2021). Out of thirty-six fourteen districts in province considered high flood risk\u003ca href=\"#_ftn5\" name=\"_ftnref5\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e9\u003c/sup\u003e vulnerable districts about higher risk of consecutive floods occurrence and disasters destruction (BOS Punjab, 2020; PDMA Punjab, 2021). In high flood risk fourteen districts Muzaffargarh recognized most vulnerable owing to its critical location and facing major flood risk destruction where figure 1 indicated the study area. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[Figure 1]\u003c/p\u003e\n \u003cp\u003eIn measuring association regarding frequent occurrence of flood disasters and its losses Muzaffargarh more favored to focused which is situated in higher vulnerable and flood-prone area of Punjab. In 2010, higher harshness of flood disaster instigated main destruction, washed away roads, schools, destroyed standing crops, homesteads, livestock, irrigation channels and human fatalities. Majority households in Muzaffargarh are involved in farming applies which cultivate food and cash crops to endure their livelihood necessities. Muzaffargarh administratively considered in four tehsils Alipur, Jatoi, Muzaffargarh and Kot addu comprises area of 8249 km\u003csup\u003e2\u003c/sup\u003e, population of 4.32 million and 93 union councils (BOS Punjab, 2020; GOP, 2022). The region of Muzaffargarh consists on life-threatening site within two major rivers Indus river flowing on Western side and flowing Chenab river on eastern bank raising vulnerability riverbank erosion and river embankment due to consecutive flooding (PBS, 2021). Climatic circumstances of research area represented 127mm average rainfall, 54\u003csup\u003e\u0026deg;\u003c/sup\u003eC(129\u003csup\u003e\u0026deg;\u003c/sup\u003eF) hot summer and 1\u003csup\u003e\u0026deg;\u003c/sup\u003eC(30\u0026deg;F) mild winter (PMD\u003ca href=\"#_ftn6\" name=\"_ftnref6\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e10\u003c/sup\u003e, 2021). In current eras, flood disasters raising and recurrent harshness provoked region regarding frequently shifting riverbank erosion, river embankment, recurrent floods and increasing erratic rains. Disasters prompted severe misery of human fatalities, treasured lush land losses, infrastructure demolition, crops and livestock ruthless costs (BOS Punjab, 2020; PDMA Punjab, 2021). Repeated everchanging riverbank because of recurring disasters with core resources devastation as inhabited population livelihood becomes more severe. On provincial basis Muzaffargarh well-thought-out low socioeconomic district associated to lower cultural, economic and social dimensions in social progress index (BOS Punjab, 2020) where shifting of riverbank course as showed below in figure 2.\u003c/p\u003e\n \u003cp\u003e[Figure 2]\u003c/p\u003e\n \u003ch2\u003e3.2 Sampling procedure and data collection\u003c/h2\u003e\n \u003cp\u003eFlood disaster of 2010 considered more severe regarding resources destruction, displacement of million peoples and human fatalities in historical background of country disasters. In post flood scenario some significant projects such as model villages were established with in the areas to resettle flood affected displaced population. In selection of model villages there were utilized two significant measures firstly most severely flood affected district of province and secondly provincial area where completed most resettled projects. In this perspective, Muzaffargarh most flood affected district of province and two resettled model villages PDMA and NGOs projects from district Muzaffargarh were randomly selected. Basti Meera Mullan model village from PDMA and Ittehad model village completed by cooperated based local NGOs Engro Foundation. Government based institution PDMA established model village Basti Meera Mullan to resettlement of flood displaced population which is located East 71̊6ʹ25.81ʺ and North 29̊51ʹ46.09ʺ near Muzaffargarh urban town Khangarh (GOP, 2022). This model village consists of 106 household unit with inhabited population of almost more than 750 peoples and located neighboring of Chenab river within range of 5KM riverbank area. In the newly constructed model village heath centers, vocational training, primary schools, mosque and veterinary hospital were established. Cooperate based local NGOs Engro Foundation developed Ittehad model village which is located at East 70̊58ʹ29.77ʺ and North 30̊38ʹ25.64ʺ tehsil Kotaddu rural town Ehsanpur. This model village located in West in neighboring of Indus river almost 11 km away from riverbank of Indus river (GOP, 2022). This model village consists of 166 housing units, inhabited population of almost 1100 peoples, having area of 23.5 acres with basic facilities of mosque, school and basic health unit (PDMA, 2022). \u0026nbsp;\u003c/p\u003eIn sampling procedure, constructed overall 272 houses by PDMA and local NGOs in both study areas of the district where maximum households were tried to connected regarding to having residence and availability on the spot. In both study areas regarding data collection 248 households were willing to participate and having availability on spot were selected for this study. Out of overall collected data of 248 households 7 households\u0026rsquo; data were skipped due to some major errors in responses and mistakes so 241 household\u0026rsquo;s data considered accurate for estimation usage. In both model villages from data of 241 households, 149 household\u0026rsquo;s data were collected from Ittehad model village and 92 household\u0026rsquo;s data was collected from Basti Meera Mullan model village. In September 2022, prior to questionnaire survey two focus group discussion were conducted as ten resettled households from each area to having appropriate understanding regarding environmental surrounding current status of resettled households. Focus group discussion collected information was applied to developing the questionnaire. Questionnaire survey was managed from October to December 2022 in both model villages where eight enumerators from local University were informed in one day training and engaged in data collection. Households those who experienced the flood disaster of 2010 were purposively selected and registered in the resettled list of PDMA and local NGOs. Household heads whether male or female were major respondents of the study. In overcoming intent questionnaire misinterpretation, each household was visited by the enumerator and filled questionnaire face to face with respondents meeting at farm, house and community meeting. Respondents household floods coping strategies, characteristics and personal information such as living environment, assets, transportation, government subsidy receipts, social network, finance, health and occupational status. For easy understanding and appropriate response from respondents\u0026rsquo; questions were asked in local language Sariki and Punjabi. Questionnaire responses were normalized which related to sub-components of LVI\u003ca href=\"#_ftn7\" name=\"_ftnref7\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e11\u003c/sup\u003e as illustrated in table 1 according to equation 1 for after and before of resettlement and means calculation. Samples t-test paired used for the mean difference in after and before calculated resettlement. Furthermore, regarding each sub-component in equation 2 major components values were calculated for both after and before resettlement. Lastly, regarding to equation 3 values of LVI after resettlement were calculated. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Methods of data analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 calculation of LVI method\u003c/h2\u003e\n \u003cp\u003eOn the basis of various scales each variable was calculated as in the procedure of evaluated when necessary to normalize variables. In this research work normalize Eq. 1 was applied for livelihood vulnerability index (LVI) regarding to significant perspective of literature (Hahn et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Original sub-component denoted as S with sub-component maximum S\u003csub\u003emax\u003c/sub\u003e and minimum S\u003csub\u003emin\u003c/sub\u003e values as using data these values were determined. Human Development Index formula of life expectancy is based regarding this index which is calculated on difference basis of minimum life expectancy of pre-selected and life expectancy on actual basis with the pre-defined life expectancy in maximum and minimum range (Hahn et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${Index}_{s } = \\frac{{S}_{ }-{S}_{min }}{{S}_{max }-{S}_{min }} \\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eSubsequently the standardized each sub-component and using average regarding each major component calculation as given in the Eq.\u0026nbsp;2.\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$M=\\frac{\\sum _{i=1}^{n}{index}_{si }}{n} \\left(2\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn above mentioned Eq.\u0026nbsp;2, among major seven components M is one of them which indicating sub-components as index\u003csub\u003esi\u003c/sub\u003e by indexed i which each major component makeup and each major component with sub-component numbers as n. In calculating each major component average Livelihood Vulnerability Index is estimated as given in the Eq. 3.\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$LVI=\\frac{\\sum _{i=1}^{7}{{W}_{Mi }M}_{i }}{\\sum _{i=1}^{7}{W}_{Mi }} \\left(3\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn above mentioned equation among seven components M\u003csub\u003ei\u003c/sub\u003e is one of them where sub-component numbers which determined with the makeup major each component denoted as W\u003csub\u003eMi\u003c/sub\u003e. Each major component weight W\u003csub\u003eMi\u003c/sub\u003e is determined by sub-component numbers which comprises such major components and ensure included as all sub-components equally contributed in the whole LVI (Sullivan et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). The value of LVI determines situation regarding vulnerability of household livelihood as lower value of LVI illustrates lower household vulnerability livelihood and less risky situation while higher value of LVI indicates higher household vulnerability livelihood and critical and risky conditions.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eIn table 2, respondents feathers are summarized indicating as majority 89% respondents were male where 63% respondents categorized in age of 30 to 50 years while overall respondents having average age of 46.9 years. In schooling perspective majority respondents 36% higher schooling, 27% middle schooling, 24% primary schooling 5% above graduate while 8% having no schooling. Majority respondents 77% inhabited in original area from 16 years or more than more than 20 years where mostly inhabitants 53% resettled in 2015. Mostly respondents resettled almost in 87.6 months and while majority respondents resettled and having ownership certificates of houses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 2]\u003c/p\u003e\n\u003cp\u003eHouseholds resettlement livelihood vulnerability changes status prior and after resettlement illustrated in the table 3. Households number members before resettlement and after resettlement have decreased 5.69 to 5.27 because of maximum family size in the area is almost seven. After resettlement unemployment status has raised as 18.3% inhabitants have lost their lives which was 11% prior resettlement. In occupational status fishing, retail market farming practices have decreased while construction, industry employ, transportation and hoteling employee\u0026rsquo;s status has raised. Decline in occupational status of farming, fishing and retail market is that resettle far from the original locality. In this table variation in household\u0026rsquo;s employment status is denoted as significant raised indicated in labor workers while decline in farming and fishing because far resettlement from farming areas and river. Resettlement caused major decline in the income PKRs 45697 to PKRs 31,984 and expenditures PKRs 43,961 to PKRs 30,825 because mostly resettled inhabitants lost their occupation due flood destruction and have to search for new directions of employment, faced significant lost in income so squeezed their expenditure regarding decline in income. Household head commuting mode have changed as increase in motorcycle, minibus and bus usage have increase due to increase in distance of resettlement in model village. After resettlement average time of traveling have increased from 21.59 minutes to 38.47 minutes where average cost of travelling have also raised from PKRs 147.96 to PKRs 256.81 as indicated in the table 3.\u003c/p\u003e\n\u003cp\u003e[Table 3]\u003c/p\u003e\n\u003cp\u003eSub-components and major components livelihood vulnerability index values also composite LVI and major components estimates of prior and after resettlement illustrated in the table 4. In respondent\u0026rsquo;s socio-demographic profile almost 59% household have lower education than high schooling as such schooling status in lower than national average schooling rate. \u0026nbsp;In study area almost 47% peoples have dependency which consists of adults above 60 years and child under 16 years whereas 11% households\u0026rsquo; heads consist on females. Households livelihood economic stability after and during of climate-based disaster considered as livelihood strategy. Resettled households having no regular wages indicated the worsen scenario from 0.511 to 0.549 while having no level of significance (p\u0026lt;0.05) while those households having income ratio without government subsidies illustrated the significant improvement such as the scores from 0.655 to 0.337 with level of significance. In general perspective, after resettlement households received multiple government subsidies such as utility bills subsidies, Sehat cards, flood relief packages, disaster donations and NGOs reliefs. In obtaining disasters relief households were registered as provincial based rural low-income communities. Aggregate score of livelihood strategy showed improved status as 0.583 to 0.443 such improvement is based on household income ratio without subsidies of government.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 4]\u003c/p\u003e\n\u003cp\u003eIn community social networks have numerous characters such as mutual assistance, sharing and borrowing food (Beegom, 2014). Households having no relatives in neighborhood score have increased from 0.236 to 0.398 estimated with level of significance while the significant reasoning regarding such perspective is that during resettlement mostly relative were separated. The reason behind this situation is that limited number of household\u0026rsquo;s units were build where limited number affected household were settled while others relatives were resettled on somewhere other locations or relatives so families were separated. Households having no friends in neighboring indicated the deteriorated perspective as score from 0.179 to 0.186 because resettlement caused separation in original family\u0026rsquo;s settlements. In overall aspect of relatives and friend\u0026rsquo;s resettlement reduced social network of resettled households. Households perspective those are not part of any formal neighborhood association and communities have shown improved due to reduced score of 0.481 to 0.394 it showing that after resettlement most of the households have joined local communities and association rather than prior scenario of resettlement. Social networks overall scores after resettlement have increased from 0.298 to 0.326 showing as reduction in overall network of resettled population due to separation of families and friends. In health perspective, household\u0026rsquo;s family members suffer in chronic diseases showing increasing score from 0.093 to 0.146 indicating the rising perspective of chronic disease while such variation considered unaffected through length of time and after resettlement. After resettlement sanitary toilets inside houses showed significant improvement as declining from 0.178 to 0.000 illustrating as significant household 23% prior to resettlement used mutually toilets and some significant number of households having no toilet and used open spaces for their needs. In model villages each household having own toilet facility which is hygienically necessary for protecting some severe health issues. Some significant improvement was also estimated regarding the households having no medical expenditures subsidies about the score pattern from 0.897 to 0.799 showing the increase in households\u0026rsquo; number who obtaining medical expenditure subsidies as increasing number of households obtaining medical service financial access. In overall health perspective significant improvement was estimated regarding the increasing score from 0.389 to 0.324.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn drinking water perspective, some significant improvements were estimated with prominent decreasing score from 0.094 to 0.000. Such estimated score highlighted as after resettlement mostly households have access of drinking water on their own resettled home handpump access while prior to resettled perspective majority households used sharing handpump water access. In these outcomes resettled households in the model villages having ease access of drinking water within their homes. In financial perspective regarding resettled household\u0026rsquo;s unemployment scenario becomes to worsen off as increasing score from 0.159 to 0.267. Prior to resettlement 27 households were unemployed while after resettlement unemployed household increased to 44 where increasing unemployed households were mostly having occupation of farming, fishing and casual labors. Some significant reasons regarding increasing unemployment in resettled areas is new neighboring with no initial community interactions, increasing distance from previous working locations and rising traveling cost due to distance factors. After resettlement worsen situation was estimated regarding household\u0026rsquo;s income and expenditure equation due to increasing worsen score from 0.261 to 0.399 illustrating significant decline in household income and proportional reducing expenditure patterns. Prior and after resettlement household monthly income equation was from PKRs 45,697 to 31,984 while expenditure equation was from 31,984 to 30,825. The significant decline in income pattern was firstly resources destruction due to flood disaster and then resettlement displacement of location caused change in workplace, some occupational variations and some numbers of family member faced becomes to unemployed. The severe influence in decline income caused reducing expenditure pattern of community as household\u0026rsquo;s livelihood becomes more limited to basic subsistence. In exposure perspective, prior and after resettlement number of times households were warned to evacuate owing to floods showed significant betterment regarding the score decline from 0.089 to 0.006. In prior the resettlement households were received warning almost more than four times in a year while after resettlement having just almost once in the year because after resettlement having more secure area which is less prone than the original region. Households home having no second floor showing some improvements as declining value in obtained scores as from 0.398 to 0.183 indicating as after resettlement mostly households lives on or above second flood so having more secure perspective of homes rather prior to resettlement. Households having non-solid homes prior resettlement indicated significant improvement as showing significant decline score from 0.876 to 0.000 as highlighting that after resettlement mostly households having solid homes build with bricks and concrete replace with wood and mud homes having most secure structure rather than prior resettlement structure. In exposure perspective significant improvement was estimated with overall declining exposure score from 0.454 to 0.063. Exposure, health, livelihood strategy, finance, water, social networks and sociodemographic profile are seven major components livelihood vulnerability index as their estimated scores illustrated in figure 3. Worsen perspective was estimated regarding major components score of finance, water and social networks while improved score estimated related to exposure, health and livelihood strategy where the overall calculated score of LVI was 0.287.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Figure 3]\u003c/p\u003e\n\u003cp\u003eNow the estimation was made whether each subcomponents LVI variation score was influenced by length of time which passed after the resettlement of households. In estimation procedure, overall respondents 241 were categorized in to two groups on the basis of length of time as 118 respondents those resettle within five years and those 123 respondents those resettled six years or more than six years. In perspective of time length from seven major component the five most relevant major components were chosen and ten subcomponents of these five major components selected for estimation as illustrated in table 5. In these two groups, each subcomponent score of prior resettlements was subtracted from score after resettlement while their mean value score each subcomponent was calculated as indicated in table 5. In such perspective, while the score value after resettlement minus score value before resettlement indicating negative it highlights as after resettlement respondents have become less vulnerable. In these two groups value of subcomponent is compared while value of longer group estimated small rather than shorter group it highlights as more decrease in vulnerability of longer group rather than specific subcomponents. Lastly, t-test was conducted regarding score mean value about each subcomponent in both groups as illustrated in table 5. Estimates indicated as terms like \u0026ldquo;households percentage having no subsidies of medical expenditure\u0026rdquo; \u0026ldquo;households percentage having no active participation in neighborhood association or formal communities\u0026rdquo; \u0026ldquo;households percentage having no relative in neighborhood\u0026rdquo; \u0026ldquo;households income ratio having no subsidies from government\u0026rdquo; the scores mean differences prior and after resettlement were different significantly at level of 5% in these both groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Table 5]\u003c/p\u003e\n\u003cp\u003eIn perspective of household\u0026rsquo;s percentage having no regular wages estimated as longer group considered significant reduction in vulnerability rather than shorter groups indicating as increasing time after resettlement raised resettles connections and information with local community as more likely to adjust regular wages jobs. Households income ratio having no government subsidies estimated the significant reduction in vulnerability in longer groups in contrast to shorter groups because long time to resettlement having more information and access capable them to having more access of government subsidies. In the scenario household\u0026rsquo;s percentage having no relatives in neighborhood raised longer group vulnerability in contrast to shorter group because with time passing movement of relative to other location causes to increase vulnerability. In aspect of household\u0026rsquo;s percentage have no active participation in neighborhood association and formal communities estimated the decline in longer group vulnerability rather than shorter group because more communication and interactions of longer groups with local organizations provides more updated information which reduces vulnerability status. Regarding household\u0026rsquo;s percentage having no subsidies of medical expenditures estimated as longer group more reduces vulnerability in contrast to shorter group the reason of from long time living becomes more familiar with information to access and capable to obtain medical subsidies. In these two groups no significant differences were estimated regarding the \u0026ldquo;income-expenditure monthly ratio\u0026rdquo;, \u0026ldquo;households percentage having unemployed heads\u0026rdquo; \u0026ldquo;regular basis drinking water access\u0026rdquo; \u0026ldquo;households percentage having family member chronic illness\u0026rdquo; \u0026ldquo;households percentage having no friends in neighborhood\u0026rdquo; indicating as no effect of decrease or increase in such subcomponents vulnerability. In estimates of table 5 and 4 considering decreased overtime occurred regarding the after-relocation vulnerability decreased in access medical expenditure subsidies, formal communities\u0026rsquo; participation and non-subsidized ratio of household income while on other end worsen scenario developed overtime regarding after relocation increased vulnerability in kinship relationship. In monthly income-expenditure ratio and household unemployed indicated the relocation deterioration aspect as overtime seem no improvement.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eIn this research work investigated LVI variation prior and after climate induced resettlement by the two model villages resettlement in Punjab, Pakistan. Estimates indicated exposure one major component of LVI as exposure vulnerability was enormously reduced highlighting significant achievement in resettlement project. Exposure vulnerability reduction is because of model villages were constructed solidly and resettlement of households in the less flood prone and risky area as resettlement positive effect on condition of houses has demonstrated positively as findings are in line with the studies of (Zhang and Lu, \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Herath et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In few LVI major components vulnerability becomes worsen off while in some major components it becomes to decreased. In health one major component of LVI where decreased aspect of vulnerability was estimated. Households maximum access of medical expenditure subsides and sanitary toilets main provision considered significant to reducing health vulnerability. Resettled household were priority focused regarding government subsidies because of their climate based more vulnerable situations and after resettlement household\u0026rsquo;s regular income increased which reason of more stable conditions of households and reduced vulnerability status. LVI major components vulnerability reduction is reasoned on resettlement households associated subsidies and resettlement to less flood prone areas. Increasing vulnerability tendency regarding social network of household was experienced in this research work as households living in resettled area while having no relatives in neighboring facing increased vulnerability. However, after resettlement number of households decreased those having no active part of associations in local neighboring or community as increasing association membership and kinship indicating solid social network in contrast to original communities\u0026rsquo; perspective. These findings are in line with the studies of (Nikuze et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicating as resettled households remain fail to continuing pre-located social network.\u003c/p\u003e \u003cp\u003eIn LVI major components water indicated the improved perspective as after resettlement households having easy access of drinking water with availability of each home handpump access in model village while having to sharing sources of drinking water in pre-relocations. These findings are in contrast with the study of (Sholihah and Shaojun, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ahmad and Afzal, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Resettled households of model villages having proper structure regarding access of drinking water and appropriate management of sewerage as such provisions having significant impact on resettlements (Herath et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nikuze et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Financial burden has increased due to appropriate formalization in resettled areas with well-established and improved structure which is necessary for wellbeing of households. Income to expenditure ratio increase and rising household heads after resettlement have enhanced the vulnerability financial component as these findings are similar with the studies of (Sholihah and Shaojun, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nikuze et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Increasing in households\u0026rsquo; expenditures ratio regarding income perspective was because after resettlement households faced declining in income and rising unemployment regardless of increasing the government subsidies. Resettled households have to lost or change their jobs, reduced income having to change occupation owing to new neighboring and communities which raised their financial vulnerability and its related issues. In resettlement perspective, some major component of LVI indicated worsen scenario such as social disarticulations, marginalization, joblessness and homelessness as these findings are similar with the studies of (Cernea, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Resettlement provides opportunities and formalize housing for displaced households in attaining access of government subsidies. Resettled households\u0026rsquo; lives become to formalize which reduces LVI of resettled peoples regarding livelihood strategy and exposure while due to loss of economic activity and kinship other vulnerabilities of original communities raises. Resettled households\u0026rsquo; length of stay also effects on LVI which was estimated regarding its relevant subcomponents. Medical expenditure subsidies access, formal communities\u0026rsquo; participation, non-subsidized income of household and availability of regular wages estimates indicated as longer stay of households in resettled area reduces vulnerability in these four subcomponents of LVI. In contrast, households lost their kinship relation as more time passes from relocation area. Furthermore, sure perspective as neighborhood friendship, unemployment status, income expenditure ratio and chronic illness indicated no significant relation with longer the time passes as duration of resettlement having impact on these vulnerabilities.\u003c/p\u003e"},{"header":"6. Conclusion and suggestions","content":"\u003cp\u003eClimate-induced flood disasters communities\u0026rsquo; resettlement in model villages caused reducing vulnerability regarding health, water, adaptive strategy and exposure while worsened vulnerability perspective in finance and social networks. Flood prone resettled community of model villages livelihood vulnerability was better-quality because of allocated preferentially government subsidies to resettlement households. Outcomes of this study have some various aspects such as resettlement raised community access of government subsidies while these peoples suffer economically as increasing expenditure ratio to income and losing their jobs. In mitigating negative impact of resettlement government subsidies considered more prominent while residents life satisfaction level having more significance regarding current jobs and income pattern in new community. Furthermore, in estimated outcomes it was denoted as after relocation some significant vulnerability components variated overtime. These such type of study outcomes suggests as livelihood vulnerability assessment must assume endlessly instead of just once in time after relocation.\u003c/p\u003e \u003cp\u003eThis research work examined the climate induced case resettlement in Muzaffargarh district two model villages of Punjab, Pakistan. Numerous significant outcomes of this research work are similar with literature regarding other regions, highlighting several similarities about climate induce impacts on resettlement regarding livelihood vulnerability crossways of various regions. The significant novelty in this research work of Muzaffargarh preferentially resettled households\u0026rsquo; subsidies allocated and regarding their formalization. Such subsidies impact on livelihood vulnerability resettled households expected to similar regarding other regions in this perspective such findings may have suggestions for relevant officials in various geographical background. Concerned policy makers and authorities must formulate policies and implement compacted strategies for increasing employment in neighboring of resettled areas communities for sustainable livelihood of resettled households.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval\u003cbr\u003e Ethical approval taken from the COMSATS University Vehari campus, ethical approval committee \u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent to Publish\u003cbr\u003e Not applicable\u003c/p\u003e\n\u003cp\u003eAuthors Contributions\u003cbr\u003e DA analyzed data, methodology, results and discussion, conclusion and suggestions and manuscript write up whereas both DA and MA finalized and proof read the manuscript and both authors read and approved the final manuscript. \u003c/p\u003e\n\u003cp\u003eFunding\u003cbr\u003e This study has no funding from any institution or any donor agency. \u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest. \u003cbr\u003e \u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas, A., Amjath-Babu, T. 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Increasing risk of glacial lake outburst floods from future Third Pole deglaciation. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(5), 411-417.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e United Nations Disaster Risk Reduction\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e United Nations Development Programme\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e United Nations Children Funds\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Provincial Disaster Management Authority\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Gross Domestic Product\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Provincial Disaster Management Authority\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Bureau of Statistics\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Pakistan Bureau of Statistics\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Districts categorized according to flood vulnerability disaster high, medium, low \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pdma.punjab.gov.pk/system/files/vnl.JPG\u003c/span\u003e\u003cspan address=\"https://pdma.punjab.gov.pk/system/files/vnl.JPG\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Pakistan Metrological Department\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Livelihood Vulnerability Index\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Livelihood vulnerability index indicators, sub-components and major components applied in the study\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.414578587699317%\" valign=\"top\"\u003e\n \u003cp\u003eIPCC Framework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.084282460136674%\" valign=\"top\"\u003e\n \u003cp\u003eStudy major components\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.50113895216401%\" valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.954441913439634%\" valign=\"top\"\u003e\n \u003cp\u003eStudy sub-components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.045558086560366%\" valign=\"top\"\u003e\n \u003cp\u003eSources of components\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.414578587699317%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.084282460136674%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.50113895216401%\" valign=\"top\"\u003e\n \u003cp\u003eFloods exposure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.954441913439634%\" valign=\"top\"\u003e\n \u003cp\u003eIn a year how many times households received flood evacuation warning after or prior resettlement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.045558086560366%\" valign=\"top\"\u003e\n \u003cp\u003eShah et al., (2013), Nikuze, (2019), Houng, (2019), Al Manum, (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.079159935379643%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousing exposure to floods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.48788368336026%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage whose homes does not have second floods, \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.432956381260098%\" valign=\"top\"\u003e\n \u003cp\u003eShah et al., (2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eNon-solid material ratio in houses structure (woods and bricks =1, concrete and bricks=1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eShah et al., (2013), Huong, (2019)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.414578587699317%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eSensitivity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.084282460136674%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFinance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.50113895216401%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAvailability of finance for food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.954441913439634%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold percentage having unemployed heads\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.045558086560366%\" valign=\"top\"\u003e\n \u003cp\u003eNikuze, (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds monthly income and expenditures\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eYokoyama et al., (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.50585175552666%\" valign=\"top\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.407022106631988%\" valign=\"top\"\u003e\n \u003cp\u003eAvailability of water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.200260078023405%\" valign=\"top\"\u003e\n \u003cp\u003eDrinking water availability payments\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.886866059817944%\" valign=\"top\"\u003e\n \u003cp\u003eYokoyama et al., (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.50585175552666%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.407022106631988%\" valign=\"top\"\u003e\n \u003cp\u003eStatus of health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.200260078023405%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds members percentage having chronic illness \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.886866059817944%\" valign=\"top\"\u003e\n \u003cp\u003eHahn et al., (2009), Shah et al., (2013), Das et al., (2020), Al Manum, (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.079159935379643%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMedical service access\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.48788368336026%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having no toilet access inside home\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.432956381260098%\" valign=\"top\"\u003e\n \u003cp\u003ePendey et al., (2018), Giri, (2021) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold percentage having no medical expenditure subsidies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eYokoyama et al., (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.414578587699317%\" rowspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eAdaptive capacity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.084282460136674%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSocial demographic profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.50113895216401%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds capability to response or prepare emergencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.954441913439634%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage with heads of households having lower schooling (less than high school education)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.045558086560366%\" valign=\"top\"\u003e\n \u003cp\u003eHahn et al., (2009), Shah et al., (2013), Pandey et al., (2018), Giri et al., (2021), Al Manum, (2023)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eDependency ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eHahn et al., (2009), Shah et al., (2013), Al Manum, (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having female-heads households\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eHahn et al., (2009), Shah et al., (2013), Pandey et al., (2018), Das et al., (2020), Giri et al., (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.50585175552666%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLivelihood strategy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.407022106631988%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousehold income stability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.200260078023405%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having not regular wage \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.886866059817944%\" valign=\"top\"\u003e\n \u003cp\u003eNikuze, (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds ratio having income without government subsidies \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eYokoyama et al., (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.50585175552666%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSocial network \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.407022106631988%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSocial network strength which is beneficial for responding or preparing emergency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.200260078023405%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold percentage having no relative in neighborhood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.886866059817944%\" valign=\"top\"\u003e\n \u003cp\u003eNikuze, (2019), Al Manum, (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having no friend in neighborhood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eNikuze, (2019), Al Manum, (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"59.90888382687927%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having no association with formal neighborhood or communities \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.09111617312073%\" valign=\"top\"\u003e\n \u003cp\u003eLiu et al., (2018), Das et al., (2020), Giri, (2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Attributes of targeted households\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds attributes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHousehold head gender status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eHousehold head age group in years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBelow 20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e20 to 30 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e30 to 40 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e40 to 50 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAbove 60 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold head average age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e46.9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eHousehold head educational status\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eNo schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eUp to elementary schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eHigh schooling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCollege graduates and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePrior to resettlement inhabited how long in original community\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eLess than 6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e6 to 10 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e11 to 15 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e16 to 20 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e34%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMore than 20 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eResettlement year\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eAverage number of months to resettlement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e87.6 month\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eStatus of home ownership prior to resettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eRent\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eFree rent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e36%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eOwn without certificate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eOwn with certificate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3 Household status changes after and prior resettlement\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" valign=\"top\"\u003e\n \u003cp\u003eStatus of household\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003ePrior resettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003eAfter resettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" valign=\"top\"\u003e\n \u003cp\u003eFamily members average numbers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" rowspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003eHousehold heads occupation percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003eFishing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e1.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eCasual/construction workers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eRetail market\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eIndustry and manufacturing employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eDriving in transportation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eGovernment employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eRestaurant, hotel employees \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eFarming practices occupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e68.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eUnemployed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eEmployment status in percentage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003eLaborer/casual workers etc\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e20.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e33.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eFarming and fishing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e70.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e58.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eSelf-employed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e1.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eGovernment/Company employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly average household income in PKRs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e45,697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e31,984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" valign=\"top\"\u003e\n \u003cp\u003eMonthly average household expenditure in PKRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e43,961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e30,825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eHousehold head commuting mode in percentage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003eWalking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e23.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e19.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eUsage of bicycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e9.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e7.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eUsage of motorcycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e47.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e50.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eUsage of car\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eMinibus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e11.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.30769230769231%\" valign=\"top\"\u003e\n \u003cp\u003eBus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.84615384615385%\" valign=\"top\"\u003e\n \u003cp\u003e5.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" valign=\"top\"\u003e\n \u003cp\u003eAverage household head commuting time one way (minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e21.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e38.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.052332195676904%\" valign=\"top\"\u003e\n \u003cp\u003eAverage household head commuting cost one way (in PKRs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.890784982935152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e147.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.028441410693972%\" valign=\"top\"\u003e\n \u003cp\u003e256.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 Standardized index sub-component, major components and overall LVI after and prior resettlement\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" valign=\"top\"\u003e\n \u003cp\u003eStudy major components\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003eStudy sub-components\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003ePrior resettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003eBefore resettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003et-test p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eRespondents socio-demographic profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds head percentage low education level (lower than higher schooling)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eRatio of dependency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eFemale household head percentage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLivelihood strategy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage without regular wage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds income ratio without government subsidies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSocial networks\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage that do not having relatives in neighborhood \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage that do not having friends in neighborhood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage which are not part of formal neighborhood associations and communities\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHealth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage where family members having chronic illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having no sanitary toilet inside home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having no medical expenditure subsidies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds having issues of drinking water\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eFinance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage those heads as unemployed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eExpenditure-income monthly ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHow many times (numbers of days) in a year household received flood evacuation warning prior or after resettlement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage where homes having no second flood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.645116918844565%\" valign=\"top\"\u003e\n \u003cp\u003eHouses percentage not built with solid material (concrete)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.680880330123797%\" valign=\"top\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.15680880330124%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5171939477304%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.198177676537586%\" valign=\"top\"\u003e\n \u003cp\u003eLVI\u003csup\u003e*\u003c/sup\u003e value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.41913439635535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.984054669703873%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.034168564920273%\" valign=\"top\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.364464692482915%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eLivelihood Vulnerability Index\u003c/p\u003e\n\u003cp\u003eTable 5 Livelihood vulnerability index sub-components standardized indexed average difference in prior and after resettlement\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.464692482915718%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eStudy major components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.58542141230068%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eStudy sub-components\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.26879271070615%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMean index differences prior and after resettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681093394077449%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003et-test\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eResettlement within 5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eResettlement within 6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.464692482915718%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLivelihood strategy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.58542141230068%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage without regular wages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681093394077449%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.94141145139814%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds ratio having income without government subsidies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.318242343541945%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.464692482915718%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSocial networks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.58542141230068%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage which having no relatives in neighboring\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681093394077449%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.94141145139814%\" valign=\"top\"\u003e\n \u003cp\u003eHousehold percentage which do not having friends in neighborhood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.318242343541945%\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.94141145139814%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage which do not take part in neighborhood association or formal communities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.318242343541945%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.464692482915718%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHealth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.58542141230068%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage which having family members suffer in chronic illness \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681093394077449%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.94141145139814%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage having no subsidies of medical expenditure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.318242343541945%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.464692482915718%\" valign=\"top\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.58542141230068%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds having issues of obtaining drinking water \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681093394077449%\" valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.464692482915718%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFinance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.58542141230068%\" valign=\"top\"\u003e\n \u003cp\u003eHouseholds percentage those heads is unemployed \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.134396355353076%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.681093394077449%\" valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.94141145139814%\" valign=\"top\"\u003e\n \u003cp\u003eExpenditure-income monthly ratio\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.37017310252996%\" valign=\"top\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.318242343541945%\" valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate-induced resettlement, Flood disaster, Livelihood Vulnerability Index, Punjab, Pakistan","lastPublishedDoi":"10.21203/rs.3.rs-3901129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3901129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePopulation living in climate induced disaster vulnerable areas can mitigate risks by preventive resettlement strategies. However, prior to having resettlement risks and particular resettles livelihood impacts it is necessary to investigate those communities whose living have transformed through climate persuaded resettlement. Objective of this research work is to examine prior resettlement and after resettlement climate-based livelihood vulnerability variations of resettled two model villages flood prone community of Muzaffargarh. Livelihood vulnerability changes of resettled households were investigated by application of Livelihood vulnerability index that covers seven major components exposure, finance, water, health, social networks, livelihood strategy and sociodemographic profile. In this study data was collected by well-developed questionnaire from 241 households\u0026rsquo; heads which resettled in two model villages from twelve flood prone union council areas. Data collected by direct interaction with respondents where questionnaire consists on some significant perspectives regarding resettlers subsidies receipts, physical conditions, job status, income aspect, socioeconomic perspective and damages of flood disasters prior and after resettlement. Livelihood vulnerability index each indicator values prior and after resettlement were calculated to determine in what way altered household\u0026rsquo;s livelihood after resettlement. Estimated outcomes of study indicated that vulnerability of health, water, livelihood strategy and exposure components were significantly declined when household moved to less flood prone areas owing to resettlement in well-construction model villages associated with government subsidies. On the other hand, some major components like finance and social networking becomes higher vulnerable owing to loss in economic activity and kinship which were deep rooted in original communities of households. In these resettled areas, proactive stance of concerned authorities or institutions and policy makers need to implement with compacted strategies to reduce financial risks and job vulnerabilities to develop sustainable livelihood of resettled households.\u003c/p\u003e","manuscriptTitle":"Assessing climate induced resettlement impacts on livelihood vulnerability in flood-prone areas of Punjab, Pakistan; an application of livelihood vulnerability index","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 11:52:02","doi":"10.21203/rs.3.rs-3901129/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0239ed1-eac4-4649-beb1-2d1dc15f283e","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T20:03:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 11:52:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3901129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3901129","identity":"rs-3901129","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}