Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

Firdaus Ara Hussain; Mokbul Morshed Ahmad

doi:10.1155/2019/1587034

Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

Hussain, Firdaus Ara;Ahmad, Mokbul Morshed 2019-07-01 00:00:00 Hindawi Advances in Meteorology Volume 2019, Article ID 1587034, 16 pages https://doi.org/10.1155/2019/1587034 Research Article Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh Firdaus Ara Hussain and Mokbul Morshed Ahmad Regional and Rural Development Planning (RRDP), School of Environment, Resources and Development (SERD), Asian Institute of Technology, Klong Luang, Pathum !ani 12120, !ailand Correspondence should be addressed to Firdaus Ara Hussain; st116549@ait.asia Received 1 March 2019; Accepted 3 June 2019; Published 1 July 2019 Guest Editor: Bimal K. Paul Copyright © 2019 Firdaus Ara Hussain and Mokbul Morshed Ahmad. .is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Utilising climate funds properly to reduce the impact of potential risks of climate change at the local level is essential for successful adaptation to climate change. Climate change has been disrupting the lives of millions of households along the coastal region of Bangladesh. .e country has allocated support from its national funds and accessed international funds for the implementation of adaptation interventions. With the focus of the scientiﬁc community on climate ﬁnance mechanisms and governance at the global and the national level, there is a lacuna in empirical evidence of how climate ﬁnance aﬀects risk appraisal and engagement in adaptation measures at the local level. .is paper aims to examine how the support from climate ﬁnance aﬀects risk appraisal in terms of the perceived probability and severity and the factors which inﬂuence risk appraisal. A ﬁeld survey was conducted on 240 climate ﬁnance recipient households (CF HHs) and 120 nonclimate ﬁnance recipient households (non-CF HHs) in Galachipa Upazila of Patuakhali District in coastal Bangladesh. .e results indicate that both CF and non-CF HHs experience a high probability of facing climatic events in the future; however, CF HHs anticipated a higher severity of impacts of climatic events on diﬀerent dimensions of their households. With higher income and social capital, the overall risk appraisal decreases for CF HHs. CF HHs have higher engagement in adaptation measures and social groups and maintain alternative sources of income. Climate ﬁnance played a critical role in supporting households in understanding the risks that they were facing, assisting them in exploring as well as enhancing their engagement in adaptation options. Bangladesh, mainly by the ministries and their subordinated 1. Introduction departments and agencies, and do not consider the Risk appraisal at the household level plays a crucial role in household needs. .e implementation is left to the local the adaptation pathway and towards exploring available level. Hence, the approach is rather top-down. .is limits adaptation measures. Reasons for this are manifold, rec- consideration of local needs and participation within the ognising the centrality of the household unit in governing process. .is is also similar for adaptation projects, although responses to external stimuli as cited by Jones and Tanner climate change is localised and needs to consider localised [1]. Risk appraisal comprises of perceived probability, which adaptation responses. Local governments are less involved in is the person’s expectancy of being exposed to the threat, the planning process of the adaptation measures, which while perceived severity is the person’s appraisal of how results in the local needs being insuﬃciently considered [3]. harmful the consequences of the threat would be to things he Furthermore, localised encounters contrast signiﬁcantly or she values if the threat were actually to occur [2]. with the techno-scientiﬁc accounts through which scien- Households are concerned about how climatic events may tists, policymakers, and practitioners often conceptualise aﬀect their livelihoods and assets based on past experiences. climate change risks and operationalise responses [4]. However, project planning and budgetary allocation for the Customised adaptation interventions at the household level local level are considerably done at the national level in are also limited. In fact, households contribute from their 2 Advances in Meteorology To analyse the perception of risk, a ﬁeld survey was own resources to participate in the planned adaptation measures, increasing the overall adaptation cost for the conducted in Patuakhali, on the southwestern coast of Bangladesh, on the beneﬁciaries of a climate ﬁnance project aﬀected households. Indeed, many of the assets, capacities, and functions required to respond to climate risk are (referred to as CF Project) run by an international NGO. .e dictated by household-level dynamics [1]. Adaptation aim of the CF Project was to support the vulnerable com- funding is scarce and has to be used eﬀectively [5]. External munities against a changing and uncertain climate. .is assistance facilitates, secures, and improves the process of study limits itself to the adaptation interventions at the climate risk reduction [6]. household level due to the focus of the research on the eﬀect Some authors argue that the bottom-up approach preva- of climate ﬁnance on the risk appraisal at the household level. lent in development assistance brings ﬂexibility and innovation and that such an approach ﬁts well with the many motivations .e paper sets out to analyse how climate ﬁnancial makes a diﬀerence in the risk appraisal of households in for providing aid, with the diverse willingness and capabilities to contribute to development ﬁnance eﬀorts [7]. Local gov- comparison with households which do not receive support. One of the objectives of the study is to examine the inﬂu- ernments are the authorities who need to react at the earliest in case of any calamities or disasters. .e local governments in encing factors of risk appraisal, which were identiﬁed Bangladesh have the mandate of developing and maintaining through multiple regression analysis. .is research con- infrastructure and basic public services such as water, sani- tributes to the existing literature through a comparative tation, health, and educational facilities, which can be adapted analysis of risk appraisal and adaptation measures of CF and to make the localities resilient. In addition, although local non-CF households. To date, much of the adaptation lit- governments are often better informed on how to go about erature has been theoretical, reﬂecting the absence of em- pirical data from activities on the ground [11]. Eﬀective making development activities climate-adapted in a partici- patory manner with the involvement of local political leaders, utilisation of climate funding requires a critical analysis of where climate ﬁnance should be focused and which factors communities, practitioners, and authorities, they are often unable to respond as they are provided limited funding from inﬂuence the household in engaging in adaptation. Risk appraisals of climate change and inﬂuencing factors are the central level as they have little involvement in the budgetary decisions. .e insuﬃcient funding can be followed back to the critical to understanding adaptation behaviour [12, 13]. It planning and budgeting done at the national level, which often has become important to adaptation strategies because the does not take account of the local dimensions and cost esti- way individuals interpret their risks aﬀects what adaptation mates of climate change into the development planning. A behaviour they are likely to take [13]. more participatory bottom-up approach with the engagement of local governments and local people in the adaptation project 2. Literature Review planning, budgeting, and implementation process is needed. .e challenge, however, is to channel climate ﬁnance towards Climate change is disrupting the socioeconomic conditions adaptation measures, which ﬁt the local context and support of people, which are diﬃcult to overcome especially by the the most vulnerable and marginalised population. poor households. Natural calamities will reinforce preex- During the Conference of Parties (COP) 15 in 2011, the isting socioeconomic divide by damaging natural resources Green Climate Fund was agreed upon which oﬀers an equal that support the poor’s long-term livelihood prospects and funding window for adaptation and mitigation. However, destroying their current produce, and by repeatedly ren- almost a quarter of a century into climate change negoti- dering the poor homeless and destroying whatever little ations, an adequate system for deﬁning, categorising, material possessions they might have [14]. Risk appraisal is tracking, and evaluating climate change ﬁnance, is still derived from the premise that people comprehend their absent [7]. Despite the “polluter-pays-principle” at the cli- abilities and limitations. A large number of deﬁnitions, mate negotiations, the gap in the availability of climate ﬁ- frameworks, and approaches have been proposed for nance is prevalent. .e inadequacy of climate ﬁnance to explaining and quantifying risk [3, 13]. Nearly all studies on meet the adaptation needs will be analysed further through the eﬀects of personal experience on self-protective be- this study. haviour regarding natural hazards show preparedness in- At the national level, Bangladesh has taken initiatives to creasing with the severity of past damage [2]. ﬁnance climate change interventions from national and .e discourse around the provision of climate ﬁnance international ﬁnancial sources. By 2050, total investments of has been focused at the international level. Without eﬀorts to $5,516 million and $112 million in annual recurrent costs marry adaptation theory with real-world adaptation prac- will be needed to protect Bangladesh against climate change tices, the adaptation ﬁeld will continue to be siloed between [8]. .e country is not helpless, therefore, against coping theory and practice [11]. In the last 20 years, there has been with sea-level rise, but it might need ﬁnancial and technical an increase in bilateral and multilateral funds providing assistance with providing practical mitigation measures [9]. climate ﬁnance such as Global Environmental Facility Climate ﬁnance comes from diﬀerent sources such as from (GEF), Climate Investment Fund (CIF), Fast Start Finance, the national budget, Bangladesh Climate Change Trust Fund Adaptation Fund, and most recently, the Green Climate (BCCTF), Bangladesh Climate Change Resilience Fund Fund (GCF); each responds to needs that emerged at dif- (BCCRF), and bilateral donors through Fast Start Finance, ferent times. However, the proliferation of funds has led among others [10]. policymakers to question whether such a diverse landscape Advances in Meteorology 3 of funds can eﬀectively channel climate ﬁnance to support important additional component of an individual’s per- the necessary transformation to low-emission, climate-re- ceived risk and adaptation capacity [1]. For risk appraisal, the framework was operationalised further through quan- silient societies [15]. .e IPCC special report in 2018 argued that the world only had until 2030 to keep the global titative analysis. .e formula utilised was followed by this temperature increase at a maximum of 1.5 C if immediate study as well, with some modiﬁcations explained in the action is not taken [16]. .is collates with the Paris succeeding section, and is as follows: Agreement in 2015 with the goal of keeping the temperature risk appraisal � perceived probability × perceived severity. ° ° between 1.5 C and 2 C. (1) Adaptation ﬁnance is primarily allocated to multilateral entities and national governments, rather than local orga- .e concept of threat appraisal formulated by [13] in nisations [17]. BCCTF has allocated almost USD 400 million their conceptual framework is as follows: as of date for climate change. BCCRF, a multidonor trust risk appraisal � uncertainty × adverse consequences, fund, had committed USD188 million in grant funds to build resilience but was discontinued after 2016. .e Pilot (2) Program for Climate Resilience (PPCR) has allocated USD where uncertainty � perceived probability and adverse 110 million in grants (45%) and interest credits (55%). Until consequences � perceived severity. 2019, Bangladesh has received commitments of USD 88.13 million from the GCF through three approved adaptation Overall risk appraisal � 􏽘 uncertainty projects. .e funds are mostly in the form of loans or grants (3) i�1 when the funding is from international sources. × adverse consequences . .e climate-relevant budget data of ﬁve key ministries for climate change from FY 2014-15 to FY 2017-18 were analysed [18]. An increase is evident for the period 2014 to 4. Materials and Methods 2017, with a slight decrease in the FY 2017-18, as illustrated in Table 1. One of the barriers for shifting investments’ 4.1. Study Area. In South Asia, Bangladesh is the most allocation to green sectors and assets is the poor un- densely populated delta of the Ganges-Brahmaputra-Meghna derstanding of the relation between climate risks, the (GBM) Basin [19]. .e country drains out approximately economy, and ﬁnance [16]. 92.5% of the water that is generated in the GBM Basins (an An analysis of the budget allocation according to Ban- area of 175,106 ha) [20] to the Bay of Bengal. Bangladesh has gladesh Climate Change Strategy and Action Plan 19 coastal districts and with a coastal population of 50 million (BCCSAP) thematic areas for the four ﬁscal years 2014–2018 people, nearly about one-third of its total population. .e is presented in Table 2, which indicates that the majority of coastal area represents an area of 47,211 km equalling 32% of the budget allocation went towards adaptation. On the the country’s total geographical area [21]. .e coastal zone contrary, the budget allocation for mitigation and low has been aﬀected by 174 natural disasters during the period carbon development decreases from 1.74% in FY 2014-15 to 1974–2007 [22]. Current predictions claim that this coastal 1.16% in FY 2017-18 of the total climate-relevant budget area will be increasingly submerged up to 3 per cent by the [18]. 2030s, 6 per cent in the 2050s, and 13 per cent by 2080s as a result of the sea-level rise [21]. 3. Theoretical and Empirical Basis of Research Galachipa Upazila (a subdistrict/administrative region in Bangladesh) is one of the thirteen subdistricts under the For measuring risk appraisal to climate change, this paper Patuakhali district in the southwestern coast of Bangladesh. follows the Model of Private Proactive Adaptation to Cli- Galachipa, as shown in Figure 1, has been selected as the study mate Change (MPPACC) to examine why some people area as it had interventions under the CF Project concluded in exhibited adaptation behaviour while other people did not 2016. Galachipa is around 925 km large and is around [2]. .e MPPACC was based on the Protection Motivation 35–50 feet above the mean sea level. It has been aﬀected .eory, which was recognised as one of the four major by cyclones, tidal surges, coastal ﬂooding, thunderstorms, theories in psychological research conducted on health nor’westers, heavy/irregular rainfall, and salinity. Besides behaviour. these, river erosion is a severe problem in this area as the two (i) First, the perceived probability is the person’s ex- large rivers, Agunmukha and Tetulia, ﬂow on both sides of pectancy of being exposed to the threat (to use a Galachipa and several smaller rivers ﬂow cross-terrain natural-hazard example that a ﬂood reaches the through Galachipa. It has around 109 km of the embankment house in which a person lives). at diﬀerent points, with around 12 door sluice gates. (ii) Second, perceived severity is the person’s appraisal of how harmful the consequences of the threat would 4.2. Sampling. .e ﬁeld survey was conducted as a com- be to things he or she values if the threat were ac- parative study of the risk assessment of households who tually to occur. received support from the climate ﬁnance interventions (CF Grothmann and Patt’s framework was expanded further HHs hereafter) in relation to households who did not receive by Frank et al.’s study which identiﬁed social identity as an support from climate ﬁnance interventions (referred to as 4 Advances in Meteorology Table 1: Climate-relevant budget allocation in selected ministries from 2014 to 2018 [18]. Annual budget (amount in thousand taka) Budget description 2014-15 2015-16 2016-17 2017-18 Nondevelopment budget 270,827,806 300,456,768 309,209,969 358,797,697 Climate-relevant allocation 65,982,855 77,193,807 84,036,986 85,334,676 % of nondevelopment budget 24.36 25.69 27.18 23.78 Development budget 253,047,122 297,119,124 350,529,332 403,219,100 Climate-relevant allocation 28,066,412 46,513,505 53,701,881 61,001,430 % of development budget 11.09 15.65 15.32 15.13 Total budget 523,874,928 597,575,892 659,739,301 762,016,797 Climate-relevant allocation 94,049,267 123,707,312 137,738,867 146,336,106 % of total budget 17.95 20.70 20.88 19.20 % of GDP 0.62 0.71 0.70 0.66 Source: Climate Protection and Development: Budget Report, 2017-18. Table 2: Allocation in BCCSAP thematic areas in the selected ministry budget [18]. CC-relevant allocation (amount in thousand taka) BCCSAP themes 2014-15 2015-16 2016-17 2017-18 Food security social protection and health 13,304,421 16,146,944 16,678,265 17,353,924 % of total CC-relevant allocation 14.15 13.04 12.11 11.86 % of ministry budget 1.75 2.12 2.19 2.28 Comprehensive disaster management 20,687,257 30,318,387 29,434,227 34,671,966 % of total CC-relevant allocation 22.00 24.49 21.37 23.69 % of ministry budget 2.71 3.98 3.86 4.55 Climate resilient infrastructure 6,559,625 13,248,641 23,947,171 24,743,940 % of total CC-relevant allocation 6.97 10.70 17.39 16.91 % of ministry budget 0.86 1.74 3.14 3.25 Research and knowledge management 2,631,410 4,165,926 2,804,454 3,658,676 % of total CC-relevant allocation 2.80 3.37 2.04 2.50 % of ministry budget 0.35 0.55 0.37 0.48 Mitigation and low carbon development 1,639,602 1,653,730 1,613,205 1,699,220 % of total CC-relevant allocation 1.74 1.34 1.17 1.16 % of ministry budget 0.22 0.22 0.21 0.22 Capacity building and institutional strengthening 49,226,953 58,246,322 63,261,544 64,208,380 % of total CC-relevant allocation 52.34 47.06 45.93 43.88 % of ministry budget 6.46 7.64 8.30 8.43 Total CC relevance (Tk) 94,049,267 123,779,950 137,738,867 146,336,106 % of total budget for 6 ministries 17.95 20.71 20.88 19.20 Source: Climate Protection and Development, Budget Report, 2017-18. non-CF HHs). .e unit of analysis was at the household level. beneﬁciaries of the CF Project had been selected by the NGO Crucially, household-level assessments also oﬀer value in based on the criteria that the household did not have more capturing the interactions of individual-level decisions and than 10 decimal or 0.004 hectares of productive lands and had traits with broader social norms, behaviour, and institutions less than USD 62.5 in productive assets. Some other criteria that collectively aﬀect responses to climate hazards [1]. .e included the nonengagement of households in microcredit CF HHs functioned as the experimental group while the non- programmes or projects similar to the CF Project. .e last criteria were that each household had a monthly income of CF HHs were the control group. Other risk assessment studies opted for an experimental and control group, allowing for less than USD 62.5. A list of 489 CF HHs supported in the Galachipa Upazila comparison and reliability of results [23]. Also, both CF and non-CF HHs were of similar criteria and lived within similar was provided by the project managers of the CF Project to geographical conditions and allowed comparison. .e study the researcher. Based on the formula of Yamane (1967) for employed mixed sampling methods to select households. computing the size of the sample under equation (1), the From the 19 districts in the coast of Bangladesh, Patuakhali sample size of 220 CF HHs was estimated. Finally, a sample was chosen through purposive sampling. From within the of 240 CF HHs was collected through random sampling. .e seven subdistricts of Patuakhali, Galachipa was chosen list of sample beneﬁciary households then underwent ran- through purposive sampling as a CF Project was imple- dom sampling in MS Excel to determine which households menting adaptation activities at the household level. .e should be surveyed: Advances in Meteorology 5 Map of Galachipa Upazila KEY MAP LEGEND INDEX MAP District boundary Upazila boundary River Major road FGD location Coordinate system: GCS WGS 1984 Datum: WGS 1984 Units: degree RF: 1:350,000 0 2.25 4.5 9 13.5 18 Kilometers Figure 1: Galachipa Upazila. .e CF HHs received support for adaptation measures n � , (4) such as plinth raising, construction of new housing, 1 + Ne homestead gardening, agricultural support, and income where n is the sample size in each area, N is total numbers of diversiﬁcation activities such as livestock, small busi- households in an area, and e is the precision value, set as 10% nesses, small solar home systems, among others. A control (0.10). group from the same study area (indicated as non-CF HHs) 6 Advances in Meteorology probability was based on the likelihood of the household which did not receive support from climate ﬁnance was also surveyed to allow the comparison and reliability of results. facing these natural calamities in the future. A summation was drawn from all the perceived severity corresponding to Unlike the CF households, there was no existing name list for the non-CF HHs. .erefore, four focus group discus- a particular climatic event to obtain a single perceived sions (FGDs) were conducted in the study area to identify severity. .e perceived probability was then multiplied by those households, which met the same criteria for choosing the corresponding severity for the event to obtain the risk CF households of the households, those who had less than appraisal for that climatic event. .en, a summation was 10 decimal or 0.004 hectares land, had less than USD 62.5 in drawn to estimate an overall risk appraisal. .is overall risk productive assets, were not engaged in microcredit or appraisal was used as the dependent variable for the multiple linear regression model. Le Dang followed the similar projects, and had a monthly income below USD 62.5. Participants of the FGDs included Upazila members, multiple regression model risk appraisal � f (risk experi- ence, information, belief in climate change, trust in public members of the local disaster management committee, and local leaders who were able to identify around 240 adaptation, farm household characteristics, farm charac- teristics, and income). households in the study area, which met the selection criteria. .e names of the households obtained from the Drawing on dimensions developed by Le Dang et al., the FGDs were then entered into an Excel sheet. However, model was executed under the following function for this budget constraints in conducting the survey were con- research and with independent variables identiﬁed for ad- sidered while maintaining a comparable sample size. aptation eﬃcacy under the bivariate analysis such as x � HH .erefore, the number of non-CF HHs was limited to 120 size, x � income, and x � immobile assets: 2 3 households. .e percentage of simulated studies with an Y � b + b x + . . . + b x + . . . + ε, (5) opr 0 1 1 p p elevated eﬀect increases as control group size increases (30 participants: 85%; 60 participants: 92%; 100 participants: where b is the intercept, b to b are the regression co- 0 1 p 96%; and 200 participants: 99%) [24]. .rough random eﬃcients corresponding to the covariates x ,. . .., x , ε is 1 p sampling in MS Excel, a list of non-CF households of 120 the error term of the model, x � HH size, x � income, 1 2 HHs was shortlisted and surveyed. In case any of the x � immobile assets, x � productive assets (livestock, 3 4 households were unwilling to be surveyed or were un- etc.), x � mobile assets (rickshaw), x � participation in 5 6 available, the next household was chosen from the ran- social circle, x � information on climate change from domized list. NGO, x � support from social safety net, x � monetary/ 8 9 .e ﬁeld survey was conducted in April and May 2016 in-kind support from social circle (relatives, friends, using a structured questionnaire. Eight interviewers were neighbours, and self-help groups) for adaptation mea- chosen based on the experience of data collection and sures, x � ﬁnancial support from loans (microcredit, previous knowledge of the area, and they were provided with bank, cooperative, and mohajan) for adaptation re- a three-day training. Before the actual survey, the ques- sponses, x � ﬁnancial support from govt. for adaptation tionnaire was pretested on 15 households by the in- responses, x � ﬁnancial support from NGOs (cash, terviewers. .e interviewees were read out a uniform materials, labour costs, and advisory services) for adap- introduction on the purpose of the survey. .e interviews tation responses, x � self-ﬁnanced adaptation responses were conducted between one and a half hours to two hours. through selling assets, and x � self-ﬁnanced adaptation measures through income/savings/reducing expenditure on food, health, and education. 4.3. Variable Measurement. .e questionnaire included two main aspects of risk appraisal, namely, perceived probability and perceived severity. .e variables were chosen based on 4.4. Data Analysis Techniques. Statistical Analysis Software previous studies on risk appraisal [13, 25] and cross-checked (SAS) was used to analyse the household data including during pretesting. For perceived probability, the households descriptive statistics—chi-square, Pearson’s correlation co- 2 2 were asked about how likely they were to experience the eﬃcient, factor analysis, R and adjusted R , variance in- seven main climatic events in the future, as identiﬁed in the ﬂation factor, Durbin–Watson statistic, normality, and Patuakhali Disaster Management Plan 2014. .e scale homoscedasticity were used. ranged from 1 to 5 (1 � not likely; 2 � less likely; 3 � likely; To identify signiﬁcant variables, bivariate analysis was 4 � more likely; and 5 � very likely). For perceived severity, conducted on all relevant independent variables which the households were asked about how each of the climatic were important to the dependent variable, overall per- events could aﬀect diﬀerent features of their lives. Perceived ceived risk. .ose variables which had an association in the severity had the scales of 1 to 5 (1 � not aﬀected; 2 � less bivariate analysis were included under the independent aﬀected; 3 � aﬀected; 4 � more aﬀected; and 5 � highly variables for the multiple linear regression analysis in the aﬀected). model for this study. One regression model under equa- A number of methods, frameworks, and approaches tion (5) was ﬁtted, with CF and non-CF as the independent were applied for risk assessment [13, 26–29]. For this study, variables. By including CF and non-CF as independent it utilised the method by Le Dang et al., in which the risk variables, we can assess the impact that CF and non-CF appraisal was computed by multiplying perceived proba- have on the dependent variable for the overall perceived bility with perceived severity as given in equation (1). .e risk. Advances in Meteorology 7 erosion and who now work as day-labourers, i.e., without a 5. Results and Discussion stable or regular income. .is bias might have caused Both the climate and nonclimate ﬁnance households were barriers to climate ﬁnance reaching other households, who from Galachipa subdistrict and lived under similar condi- were also in dire conditions. tions. .is study examined the diﬀerences in the socio- Engaging in adaptation measures was made possible economic proﬁle of the CF HHs and the non-CF HHs through some households’ own ﬁnancial resources or ex- through chi-square test and corresponding P values, as ternal sources. Poor households, however, had fewer income shown in Table 3. .e adaptation measures engaged in by CF sources, often had to sell assets to sustain their family’s and non-CF HHs are given under Table 4. As shown in expenditure, and had limited access to ﬁnancial services. Table 3, more CF HHs lived outside or on the embankment, .ere exists growing evidence of the beneﬁcial impact of which may have been related to the loss of land due to river access to ﬁnancial services on all aspects of social and erosion. .ose without land inside the embankment economic outcomes at the household and ﬁrm level [30]. .e remained outside of the embankment mainly on govern- absence of ﬁnancial services makes diversiﬁcation of income ment land or the embankments. sources a livelihood strategy, as well as an adaptation re- Moreover, the housing structure of CF HHs was made of sponse for climate-aﬀected households. .e ﬁrst step is to less durable materials. .ere were also a higher number of stabilising their socioeconomic conditions. For this, it is wage labourers, traders, and ﬁshers in the CF HHs. About essential to understand how the poor farmers manage their 18% of CF HHs pursued a secondary source of income. .e cash ﬂows, their preferences, attitudes, and behaviours to average monthly income of CF households was higher at determine the scope of diversifying income sources as an USD 59.9 in comparison with USD 54.9 for non-CF HHs. eﬀective path out of poverty [30]. About 96.2% of the CF HHs participated in a social group Once climate funds reach the national level, a further such as self-help groups, producer groups, or cooperatives breakdown of the allocated amounts for hard and soft while only 16.7% of the non-CF HHs participated in some measures ensues. Studies have shown that 65% of climate form of social groups. More non-CF households received funding was allocated to infrastructure investments, in- support from the social safety net at 78.3% compared with cluding coastal protection measures for ﬂooding and erosion 63.8% of CF HHs receiving support. [11], that caused a signiﬁcant portion of climate ﬁnance to be Data showed that non-CF HHs participated in most of preplanned for infrastructures such as embankments. At the the 26 adaptation measures on self-initiatives while the CF local level, the projects with “soft” measures, such as those HHs received support in 14 adaptation measures, partially related to capacity building, policy reform, and planning and supported by the climate ﬁnance interventions. At an ag- management, are traditionally low cost [11] and in- gregated level, the main categories of adaptation measures all adequately meet the adaptation needs of the households. saw a higher engagement of CF HHs than non-CF HHs as Data generated from this study showed that the households shown in Table 4. Higher income levels were likely to sig- engaged in 24 adaptation measures whereas climate ﬁnance niﬁcantly increase the likelihood of planting trees and using had 14 adaptation measures available (Table 4). .us, it can supplementary irrigation as adaptation choices [29]. .e two be argued that climate ﬁnance was inadequate and the highest engagements for both groups were in housing and support received was beneﬁcial to a limited extent. livestock. .e socioeconomic proﬁle of the CF HHs showed .e perceived probability in this study is deﬁned as how that a signiﬁcant number lived outside or on the embank- likely households expect to face a climatic event in the future ment and needed support for housing. Livestock was per- on the basis that it will aﬀect a household’s ability to re- ceived as an instant cash source and was the preferred option bound, i.e., overcome the eﬀects of the climatic events. Risk for receiving external support as they could quickly experiences tend to induce people to think of the risks more reproduce. often, thereby increasing their risk appraisals [13]. Furthermore, households did not need a new skill set to maintain their livestock. Both groups participated least in 5.1. Perceived Probability. .e perceived probability, as agriculture and ﬁsheries. From the aggregated level, a sig- shown in Table 5, had the highest mean of 3.6667 and 3.9750, niﬁcant diﬀerence based on the chi-square and P value which ranged between likely and quite likely for both CF and between CF and non-CF HHs was seen for gardening, ag- non-CF HHs. .e P value for cyclone and river erosion ricultural land and crops, livestock, ﬁsheries, safe water, and showed a signiﬁcant correlation, however, with river erosion access to power and fuel resources. From the socioeconomic indicating a negative association. T-tests indicated that there proﬁle and focus group discussions, support was least given is indeed a signiﬁcant diﬀerence between the perceived to agriculture and crops as the CF HHs had lost their arable probability of climate ﬁnance and nonclimate ﬁnance and homestead land due to river erosion. Most of the households. Signiﬁcant diﬀerences between CF and non-CF households who had lost land and could no longer do HHs have also been observed based on the means of the CF farming worked as day-labourers. About 55.4% of the CF and non-CF HHs for all climatic events as indicated by the P HHs had the main source of income as day-labourers. .e value. occupation of a day-labour is also critical as they are out of work 6 to 8 months in the whole year. .is analysis is in- dicative of the selection bias of the climate ﬁnance project 5.2. Perceived Severity. .e perceived severity was mea- towards selecting households who had lost their land to river sured by multiple aspects of households that are aﬀected by 8 Advances in Meteorology Table 3: Summary of diﬀerences in socioeconomic proﬁles of climate ﬁnance (CF HHs) and nonclimate ﬁnance households (non-CF HHs). Findings (P value) CF households Non-CF households Household size No signiﬁcant diﬀerence 4.07 4.27 (i) .e lower level of illiteracy with (i) A higher level of illiteracy with 69.6% 75.8% Signiﬁcant diﬀerence at 10% Educational (ii) Higher completion of the primary (ii) Lower completion of primary conﬁdence interval attainment level with 26.3% level with 25.6% P value (0.062) (iii) Higher completion of the (iii) No completion of secondary secondary level with 4.2% level However, more men have land than Land-holding size No signiﬁcant diﬀerence women; both CF men and CF women have more land than non-CF women Signiﬁcant diﬀerence at 0.01% and 0.05% conﬁdence interval (i) More non-CF (11.7%) live free (i) 4.2% CF live free (ii) 0 non-CF rents (ii) Only 1 CF rents (iii) More non-CF (80%) own Tenancy P value (0.049) (iii) 85% own housing housing (iv) More (10%) inherited from (iv) Less (8.3%) inherited from parents parents (i) More CF (10.4%) live outside (i) Less non-CF (2.5%) live outside embankment embankment (ii) 7 CF live on embankment (ii) 0 non-CF live on embankment Location now P< 0.001 (iii) Less CF (74.6%) live inside (iii) More non-CF (94.2%) live inside embankment embankment (iv) More CF (12.1%) live upland (iv) Less non-CF (3.3%) live upland (i) More CF (3.8%) houses made of (i) Fewer non-CF (0.8%) houses mud made of mud (ii) More CF (22.9%) houses made of (ii) More non-CF (13.3%) houses Housing conditions leaves made of leaves Household construction material P (iii) Less CF (66.7%) houses made of (iii) More non-CF (83.3%) houses value (0.016) corrugated tin made of corrugated tin (iv) More CF (2.9%) houses made of (iv) Fewer non-CF (1.7%) houses brick and cement made of brick and cement (v) More CF (3.8%) houses made of (v) Fewer non-CF (0.8%) houses others (wicker) made of others (wicker) (i) Less CF (1.3%) use water (i) More non-CF (2.5%) use water puriﬁcation tablets puriﬁcation tablets (ii) More CF (2.5%) use ﬁltering (ii) 0 non-CF use ﬁltering systems systems (iii) More non-CF (23.3%) use Water puriﬁcation methods (iii) Less CF (21.7%) use boiling boiling P< 0.001 (iv) More CF (62.1%) use ﬁtkari (iv) Fewer non-CF (45%) use ﬁtkari (aluminium sulfate, also known as (aluminium sulfate, also known as alum) as others alum) as others (v) Less CF do not use any (v) More non-CF do not use any puriﬁcation method (12.5%) puriﬁcation method (29.2%) (i) Less non-CF wage labourer (i) More CF wage labourer (55.4%) (40.8%) Primary source of Signiﬁcant diﬀerence at 0.05% (ii) Less CF in service (6.3%) (ii) More non-CF in service (25.5%) income conﬁdence interval (iii) More CF in trade (8.8%) (iii) Fewer non-CF in trade (7.5%) (iv) More CF ﬁshermen (7.5%) (iv) Less non-CF ﬁshermen (5.8%) (i) More CF as pastoralist (12.5%) (i) Only 1 farmer, 1 domestic worker, Secondary source of Signiﬁcant diﬀerence at 0.01% and wage labourer (5.4) 1 begging non-CF income conﬁdence interval (ii) 71.7% CF does not have any (ii) 96.7% non-CF do not have any secondary source secondary source Average monthly Diﬀerence USD 59.9 USD 54.9 income Advances in Meteorology 9 Table 3: Continued. Findings (P value) CF households Non-CF households Signiﬁcant diﬀerence at 0.01% conﬁdence interval More CF (64.6%) participation in Less non-CF (1.7%) participation in Self-help groups P< 0.001 self-help group self-help group More CF (48.8%) participation in Less non-CF (0.8%) participation in Producer group P< 0.001 producer group producer group Membership in More CF (10.5%) participation in Less non-CF (0.8%) participation in social groups DMC P< 0.001 disaster management committee disaster management committee (DMC) (DMC) More CF (76.3%) participating in Less non-CF (16.7%) participating in Cooperatives P< 0.001 cooperatives cooperatives No membership in any social group Less CF (3.8%) not participating in More non-CF (83.3%) not P< 0.001 any social group participating in any social group Table 4: Percentage of climate ﬁnance (CF) and nonclimate ﬁnance (non-CF) households who engaged in the adaptation measures. Groups Group Adaptation measures Chi (P value) CF (%) Non-CF (%) ∗∗ Plinth raising and reinforcement of housing 78.8 64.2 8.82 (0.003) Housing Construction of new housing 70.8 51.7 13.76 (<0.001) ∗∗ Repair of damaged housing 28.8 31.7 0.33 (0.568) ∗∗ Homestead gardening 55.0 42.5 5.00 (0.025) Garden Community nursery 11.3 0.83 12.10 (<0.001) Social forestry 19.2 12.5 2.53 (0.112) ∗∗ Changed crop varieties 7.5 1.7 5.19 (0.023) Agricultural land and crops Changed crop patterns 7.1 1.7 4.70 (0.030) Changed irrigation management 3.8 0 6.57 (0.037) ∗∗ Poultry farming 85.4 72.5 8.71 (0.003) ∗∗ Duck farming 70.8 58.3 5.63 (0.018) ∗∗ Raised poultry housing 53.8 38.3 7.61 (0.006) ∗∗ Livestock Goat rearing 35.8 19.2 10.53 (<0.001) ∗∗ Cow rearing 75.8 45.8 32.01 (<0.001) ∗∗ Raised barn 42.9 12.5 33.59 (<0.001) ∗∗ Cow fattening 30.4 2.5 37.43 (<0.001) ∗∗ Fisheries Change in ﬁsh culture 7.9 2.5 4.09 (0.043) Installation of deep tube wells 54.6 40.8 6.05 (0.014) ∗∗ Safe water Elevated tube wells 32.5 30 0.23 (0.631) Water storage tanks 4.6 0 5.67 (0.017) Access to power and fuel sources Solar systems 60.8 50 3.84 (0.050) ∗∗ Rickshaw/.ela 5.8 9.2 1.38 (0.241) IGA .ree-wheeler 5 3.3 0.52 (0.469) ∗∗ Trading/small business 17.9 16.7 0.09 (0.769) ∗∗ ∗ Source: Survey conducted under this study. Adaptation measures fully supported by climate ﬁnance; partial support from climate ﬁnance. climatic events. To obtain a single perceived severity, all non-CF HHs. Signiﬁcant diﬀerences between CF and non- the perceived severities were aggregated corresponding to CF HHs have also been observed for the means of the CF a particular climatic event such as cyclone, storm surge, and non-CF HHs for all climatic events as indicated by the and river erosion, among others. As shown in Table 6, the P value. perceived severity to cyclones had the highest eﬀect on the diﬀerent dimensions of households such as housing, 5.3. Risk Appraisal and Overall Risk Appraisal. All the gardening, crops, and livestock and with a mean of 1.93 for the CF HHs and 1.66 for the non-CF HHs. .e P value for perceived severity for each of the climatic event was added to reach a single perceived severity, as presented in Ta- all values shows a signiﬁcant correlation, however, in a negative direction, derived from the t-test. .e t-tests also ble 7. .e perceived probability was multiplied by the corresponding severity for the event to obtain the risk indicated that there is indeed a signiﬁcant diﬀerence be- tween the perceived severity of climate ﬁnance and non- appraisal for that climatic event. After which, all the risk appraisals were added to estimate an overall risk appraisal climate ﬁnance households. CF HHs have a higher severity on the diﬀerent dimensions of the households compared to which indicated a signiﬁcant diﬀerence between the 10 Advances in Meteorology Table 5: Mean, standard deviation, and standard error of the perceived probability, i.e., likelihood of facing the climatic events in the future. CF/non-CF Mean Std. deviation T-test P value CF 3.667 1.541 Cyclone 1.963 0.051 Non-CF 3.975 1.331 CF 2.988 1.451 Storm surge 1.798 0.073 Non-CF 3.242 1.160 CF 3.346 1.281 Flooding −0.551 0.583 Non-CF 3.258 1.487 CF 2.829 1.818 River erosion −7.110 <0.001 Non-CF 1.558 1.477 CF 2.992 1.284 Irregular rains 0.795 0.428 Non-CF 3.108 1.327 CF 4.158 1.094 Nor’wester (Kalboishakhi) 0.502 0.616 Non-CF 4.225 1.233 CF 1.471 1.420 Salinity −3.271 <0.001 Non-CF 1.000 1.216 Table 6: Mean, standard deviation, and standard error of the perceived severity, i.e., the eﬀect of climatic events on diﬀerent dimensions of the households. CF/non-CF N Mean Std. deviation Std. error T-test P value CF 240 1.937 0.560 0.036 Cyclone −4.575 <0.001 Non-CF 120 1.663 0.522 0.048 CF 240 1.692 0.630 0.041 Storm surge −5.516 <0.001 Non-CF 120 1.373 0.447 0.041 CF 240 1.723 0.675 0.044 Flooding −4.380 <0.001 Non-CF 120 1.442 0.510 0.047 CF 240 0.892 0.697 0.045 River erosion −7.009 <0.001 Non-CF 120 0.418 0.553 0.051 CF 240 1.021 0.458 0.030 Irregular rains −3.056 0.002 Non-CF 120 0.883 0.371 0.034 CF 240 1.710 0.533 0.034 Nor’wester (Kalboishakhi) −4.384 <0.001 Non-CF 120 1.466 0.465 0.043 CF 240 0.540 0.471 0.030 Salinity −5.011 <0.001 Non-CF 120 0.315 0.362 0.033 Table 7: Mean, standard deviation, and standard error of the risk appraisal of the households. CF/non-CF N Mean Std. deviation Std. error T-test P value CF 240 7.332 3.805 0.246 Risk appraisal_Cyclone −1.448 0.149 Non-CF 120 6.773 3.259 0.296 CF 240 5.340 3.539 0.229 Risk appraisal_Storm surge −2.821 0.005 Non-CF 120 4.466 2.284 0.209 CF 240 6.114 3.408 0.220 Risk appraisal_Flood −2.909 0.004 Non-CF 120 5.057 3.151 0.289 CF 240 3.256 3.091 0.199 Risk appraisal_River erosion −8.207 <0.001 Non-CF 120 1.142 1.784 0.163 CF 240 3.250 2.127 0.137 Risk appraisal_Irregular rains −2.247 0.025 Non-CF 120 2.794 1.633 0.149 CF 240 7.165 3.097 0.200 Risk appraisal_Nor’wester −2.596 0.010 Non-CF 120 6.306 2.881 0.264 CF 240 1.092 1.399 0.090 Risk appraisal_Salinity −5.118 <0.001 Non-CF 120 0.501 0.791 0.072 overall risk appraisal of CF and non-CF HHs, where the dependent variable for the multiple linear regression CF HHs have a higher overall risk assessment than non- model to assess the impact of selected independent var- CF HHs. .is overall risk appraisal was used as the iables on it (Table 8). Advances in Meteorology 11 Table 8: Independent variables with details for the dependent variable (overall risk appraisal). Measurement/ Recoded for regression Types of Categories Independent variables Scale explanation analysis data Numbers of HH size Continuous Socioeconomic household members factors Total annual Income Continuous income in number Agricultural land in Immobile Assets (land) Continuous acre in number Assets Productive Assets (livestock, In number Continuous etc.) Mobile Assets In number Continuous Participation in self-help Involvement in groups, producer group, Participation 1 � yes; 0 � no 1 � yes; 0 � no Binary social groups DMC, cooperatives SISCH: information on climate change from social Information on Information circle, i.e., relatives/ 1 � yes; 0 � no 1 � yes; 0 � no Binary climate change received neighbours/friends/ community 1 � vulnerable group development (VGD) 2 � vulnerable group feeding (VGF) 3 � food for work (KABIKA) 4 � cash for work (KABITA) 5 � old age allowance Support received 6 � allowance for the Support from social safety net 1 � yes; 0 � no Binary from widowed, deserted, and destitute 7 � housing support 8 � test relief (TR) programme 9 � zakat in cash 10 � zakat in kindness 11 � scholarship 12 � others Monetary/in-kind support 1 � relatives from Social Circle (relatives, Support received 2 � friends 1 � yes for all options; friends, neighbours, self-help Binary from 3 � neighbours 0 � no for all options groups) for adaptation 4 � self-help groups responses External sources Financial support from loans 1 � microcredit of ﬁnance (microcredit, bank, Support received 2 � bank 1 � yes for all options; Binary cooperative, mohajan) for from 3 � cooperatives 0 � no for all options adaptation responses 4 � mohajan Financial support from govt. Support received 1 � yes; 0 � no 1 � yes; 0 � no Binary for adaptation responses from 1 � cash Financial support from NGOs 2 � materials (cash, materials, labour costs, Support received 1 � yes for all options; 3 � labour costs Binary advisory services) for from 0 � no for all options 4 � advisory services adaptation responses 5 � training 1 � land or building 2 � durable HH assets Self-ﬁnanced adaptation 3 � livestock 1 � yes for all options; Binary responses through selling 4 � mobile assets 0 � no for all options 5 � agricultural/ﬁsheries equipment 1 � reduced expenditure on food, health, and education Self-ﬁnanced adaptation 2 � relied on savings 1 � yes for all options; responses through reduced Binary 3 � paid from income 0 � no for all options household expenditure 4 � HH members took other employment 12 Advances in Meteorology Table 9: Summary of results from statistical tests for CF and non- 6. Multiple Linear Regression Model for CF models for overall risk appraisal. Overall Risk Appraisal CF HHs Non-CF HHs 6.1. Regression Model for Overall Risk Appraisal for CF and Number of observations 240 120 Non-CF HHs. From the model for overall risk appraisal for Model 10 8 CF HHs and non-CF HHs with 240 and 120 observations, Error 229 111 respectively, the summary of statistical tests and the re- Corrected total 239 119 gression analysis results are given in Tables 9 and 10. .e F value 15.16 6.30 P value <.001 <.001 models were statistically signiﬁcant at F � 15.16, P< 0.01 for Root MSE 52.470 41.537 CF HHs and F � 6.30, P< 0.01 for non-CF HHs, respectively. Dependent mean 142.163 112.350 Positive auto-collinearity was observed. As shown in Ta- Coeﬃcient of variance 36.908 36.971 ble 10, a highly signiﬁcant (P< 0.01) and negative re- R-square 0.398 0.312 lationship between overall risk appraisal and the Adj R-square 0.372 0.263 independent variables was observed for income and par- Durbin–Watson D 1.420 1.375 ticipation in a social circle for CF HHs, while a signiﬁcant and negative relationship was observed for non-CF HHs for income and information on climate change from the social .e CF HHs were trained through a household adap- circle. For CC HHs, positive and highly signiﬁcant associ- tation plan to interpret the exposure they were facing and ation (P< 0.01) between overall risk appraisal and the in- identify adaptation measures. .e climate ﬁnance project dependent variables was seen for household size, immobile organised exchanges between the households on the eﬀects assets, and support from the social safety net. Signiﬁcant on climate change on their households and the pre- relationship for CF HHs was observed between overall risk cautionary measures they take, thus building awareness appraisal productive assets and ﬁnancial support from within the wider social circle of the households. As argued by government. On the contrary, for non-CF HHs, highly Granderson, discourses play a signiﬁcant role in how climate signiﬁcant and positive relationship can be observed be- change and its risks are interpreted and made meaningful for tween overall risk assessment and immobile and productive communities [4]. Furthermore, climate ﬁnance supported assets and ﬁnancial support from NGOs, while the signiﬁ- the development of consensus and of a common un- cant and positive relationship is observed for social safety derstanding within the households on how to adapt. net. According to Granderson, responses required the adoption of a particular vision of the future, the course of action rather 7. Discussion than another [4], and an understanding of sharing common resources and labour between the households to adapt. 7.1. Climate Hazards, Risk Appraisal, and Role of the Climate Finance Project. Galachipa is vulnerable to natural hazards such as cyclones, storm surges, and river erosion, among 7.2. Household Size. .e estimated coeﬃcient for overall risk others. It was one of the hardest hit upazilas by the 2007 appraisal was statistically positive and highly signiﬁcant for super cyclone SIDR [31]. For the CF HHs, the analysis from household size, indicating that overall risk perception in- Table 5 indicated a signiﬁcant correlation between cyclone creases with more household members. From the household with perceived probability based on previous exposure of the proﬁle in Table 3, it can be seen that the average household households to cyclones and their perception that they will be size of the surveyed household is 4.07 and 4.27 for CF and aﬀected by cyclones in the future. Table 5 also shows that non-CF HHs, respectively, while the Upazila average is 4.5 there is a signiﬁcant diﬀerence between climate ﬁnance people per household. .e possible reasons of higher risk recipient households and nonrecipients in their correlation perception may be due to the awareness that evacuated between river erosion and perceived probability, indicating household members during climatic events leads to higher that CF HHs were severely aﬀected by river erosion, given consumption costs borne by members during and after that the CF HHs and non-CF HHs were taken from the same climatic eﬀects when resources and commodities are scarce study area. A possible reason is the staﬀ of the CF Project as well as adaptation costs in the future. may have had a selection bias towards selecting those households who have lost land to river erosion as beneﬁ- ciaries. Loss of places is a signiﬁcant risk from climate 7.3. Income. Table 10 shows that as income increased, the change for physical loss of land and resources [4]. Fur- overall risk perception of both the CF and non-CF house- thermore, Table 6 shows that CF HHs had a higher perceived holds decreased. Results from this research are consistent severity than non-CF HHs, which indicates that the CF HHs with the ﬁndings of Alauddin and Sarker that higher-income were sensitised by the climate ﬁnance project towards the households engaged in adaptive measures and undertook exposure to climatic events on their lives and livelihoods. more associated risks [32]. .e study also showed that risk Climate change and its risks can be understood through appraisals to production, physical health, and income di- memories of past weather, current experience, and future mensions received greater priority while farmers paid less attention to risks to happiness and social relationships [13]. imaginaries, which are attached to particular places and practices [4]. Sociodemographic characteristics like farm experience, Advances in Meteorology 13 Table 10: Multiple linear regression results for CF and non-CF models for overall risk appraisal. CF Non-CF Independent variables Parameter Standard error P value Parameter estimate Standard error P value estimate HH size 6.251 2.387 0.009 — — — Income −0.003 <0.001 0.002 −0.004 0.002 0.023 Immobile assets 8.169 1.438 <0.001 8.649 2.165 <0.001 Productive assets (livestock, etc.) 1.882 0.921 0.042 3.310 1.210 0.007 Participation in self-help groups, producer group, −14.806 4.029 <0.001 — — — DMC, cooperatives SISCH: information on climate change from social — — — −7.285 2.815 0.011 circle, i.e., relatives/neighbours/friends/community Support from social safety net 23.182 7.411 0.002 21.881 8.385 0.010 Monetary/in-kind support from social circle (relatives, friends, neighbours, self-help groups) −15.206 7.325 0.039 — — — for adaptation responses Financial support from govt. for adaptation 18.830 7.547 0.016 — — — responses Financial support from NGOs (cash, materials, labour costs, advisory services) for adaptation — — — 39.503 13.963 0.006 responses education, and income level are the most signiﬁcant factors risks through the assets that they can mobilise in the face of in increasing the likelihood of farmers’ adaptation practices shocks [34]. However, if immobile assets are aﬀected, then [33]. .e inverse association between income and overall the households lose their ability to cope with the eﬀects of climatic events. risk appraisal may be due to higher income; the households perceive less risk as they engage more in adaptation mea- However, Islam et al. argue that a household’s in- sures and ascertain that they are in a better position to deal volvement in a diverse set of income-generating livelihood with the impacts of climate change. activities or strategies reduces the vulnerability of the Findings from the study, as seen in Table 3, also revealed household [25]. Income-generating activities provide that the CF HHs had a higher average income than the non- households with additional income in addition to their main CF HHs and had an increase in assets and livestock received source of income and support in having savings and from the CF interventions. However, more assets expanded investing in building assets. Assets help in engaging in ac- the risk of losing the assets they have gained through the CF tivities to address the household vulnerability. While people intervention. Perceived probability, production, physical from diﬀerent occupations are aﬀected diﬀerently, farmers, health, and income are the essential dimensions farmers pastoralists, and ﬁshers are especially aﬀected by climate change as they rely on natural resources and are, at the same perceive to be threatened by climate change [13]. A negative and signiﬁcant association was observed between overall risk time, exposed to meteorological events as well. Most de- and income and involvement in social circles for overall risk cisions by farmers to adapt to climate change vary directly appraisal. .is may indicate that social circles aﬀect the with livestock ownership since it serves as a store of value spending behaviour of households towards engaging in and encourages adaptation to climate change [32]. adaptation measures, resulting in a decrease in income and a simultaneous decrease in overall risk appraisal. 7.5. Participation in Social Circles. As shown in Table 10, results demonstrate increased participation in social circles 7.4. Immobile and Productive Assets. Table 10 shows that an causes a highly signiﬁcant decrease in the overall risk ap- praisal of household, especially for the non-CF HHs. As increase in immobile assets, in terms of land, increases overall risk perception of CF and non-CF households alike. hypothesised by Le Dang et al., social discourse is Furthermore, Table 10 also indicates a positive and highly hypothesised to aﬀect risk perception and adaptation as- signiﬁcant association is observed between risk appraisal and sessment [13]. In fact, social capital facilitates access to a productive assets in terms of livestock, poultry, agricultural broader source of information [35]. .e involvement in the equipment, and ﬁsheries equipment for CF HHs, while for social circle gives the households a better support system to non-CF the association is highly signiﬁcant. A considerable gain information and even jointly face the diﬀerent climatic proportion of the households have been aﬀected by river events as social circles can act as sources of ﬁnancial support erosion and are landless; therefore, this explains the high- and even interpersonal relationships, such as kinship net- risk perception between overall risk perception and im- works, social obligations, trust, and reciprocity, mobilise mobile assets. Furthermore, Vatsa argues that assets play a capacity directly by enabling material responses to climate hazards or indirectly via institutional modiﬁcations [4]. critical role in risk situations, and households try to resist and cope with adverse consequences of disasters and other Islam et al. have derived similar ﬁndings [25]. .e non-CF 14 Advances in Meteorology aﬀord, households also could share common resources households’ ability to cope and adapt was constrained be- cause of their lack of participation in community organi- within their social circle to adapt, which did not cause them to incur additional costs and yet beneﬁt from them. Cor- sations or the absence of community organisations as a whole, indicating that social relationships received less at- respondingly, support from the social circle increases the tention [36] from non-CF HHs. eﬀectiveness of climate ﬁnance through enhancing the utilisation of the household’s own resources towards con- tributing towards adaptation needs of the social circle. 7.6. Information on Climate Change from the Social Circle. Access to information from the social circle has a signiﬁcant 8. Conclusions impact on the overall risk appraisal of non-CF HHs, as shown in Table 10. From Table 3, it is seen that around 83.3% .is study conducted a comparative analysis between CF of the non-CF HHs do not engage in any social groups. and non-CF HHs regarding the anticipated climatic events .erefore, information received from the social circle on and the severity of the events on their lives as well as factors climate change informed the households on the exposure of which inﬂuenced their risk appraisal. Both CF and non-CF climate change and could have prepared accordingly, thus HHs resided in the same geographical and meteorological decreasing their risk appraisal. Information and discussion, conditions and dealt with the same climatic events. Both therefore, can inﬂuence perception [13]. Information from groups anticipated climatic events such as river erosion and the social circle could also have given the non-CF house- cyclones, among others, occurring in the future. However, holds a better understanding of how to interpret the in- the ﬁndings of the study indicated that CF HHs expected formation and also explore new ways of adapting from their higher severity of climatic events on the various dimensions social circle, also contributing to a decrease in the overall risk of their households such as housing, income, crops, appraisal. However, other scholars have found that farmers equipment, among others. .is result suggests that the CF who believe that climate change is happening and inﬂu- HHs are more aware of the consequences of climate change encing their family’s lives perceive higher risks in most and therefore engaged in more adaptation measures than dimensions [13]. non-CF HHs. Barrett had similar ﬁndings in a study in Malawi of adaptation ﬁnance-assisted villages [6]. Programs that provide technical assistance or compensation to change 7.7. Support from Social Safety Nets and Financial Support practices may be a positive opportunity for agricultural from Government and NGOs. .e regression analysis of communities to address climate change and help oﬀset the social safety net programmes under Table 10 illustrated a transaction costs associated with changing practices [25]. positive and highly signiﬁcant relationship with overall risk .is analysis implies that climate ﬁnance support informed appraisal for CF HHs and a positive and signiﬁcant re- the households about the risks that they were facing, assisted lationship for non-CF HHs. Social protection or safety net them in exploring adaptation options and engaging in them. programs assist individuals, households, and communities Awareness and training sessions, climate information, in- in managing better a wide range of risks that leaves people creased accessibility to public extensions services, and ex- vulnerable [34]. One of the reasons for this could be that change within the social groups about the eﬀects of climate these programs deal with both the deprivation and vul- change aﬀect the potential severity or eﬀect of climate nerability of the poorest people; thus, when these pro- change on the households. grammes supported the households, they perceived Furthermore, several factors inﬂuenced the risk appraisal themselves being at risk. Similarly, the CF HHs perceive of the CF and non-CF HHs, respectively. .e socioeconomic signiﬁcant and positive risk, when they received support conditions of the households played a key role in the risk from the government, as the assistance is aimed towards appraisal. Particularly, household size increased the risk people who are at risk and need support. Furthermore, as assessment for CF HHs. While the increase in income caused seen in Table 10, a relationship between ﬁnancial support a decrease in the risk assessment, increase in assets caused an from NGOs for adaptation measures and overall risk ap- increase in the risk perception of the households. .ese praisal for non-CF HHs is evident. Possible reasons could be results could indicate that households do not have stable that when they receive the support for adaptation measures socioeconomic conditions yet. from government or NGOs, they believe that they are at risk. .e beneﬁts by strengthening the social circle of the CF HHs under the climate ﬁnance project became evident as 7.8. Monetary/In-Kind Support from Social Circle (Relatives, participation in the diﬀerent groups and the monetary/in- Friends, Neighbours, and Self-Help Groups) for Adaptation Kind support from the social circle resulted in a decrease in Measures. As seen in Table 10, a negative and highly sig- overall risk appraisal. On the contrary, Le Dang et al. argue niﬁcant relationship is evident between monetary support that social circles play a signiﬁcant role in facilitating de- and in-Kind support from social circle for the CF HHs and cisions on using adaptation measures based on information overall risk appraisal. Instead of being at risk of facing the obtained from friends, relatives, and neighbours, which eﬀects of climate change alone, the CF HHs could rely on increases the overall perceived risk [13]. getting support from their social circle. While risk appraisal Furthermore, awareness is not suﬃcient for households played a crucial role in motivating the household to explore to engage in adaptation measures as the households were adaptation options which are crucial and which they can extremely poor and lacked adequate resources to adapt. In Advances in Meteorology 15 households. Mechanisms to locally generate and integrate fact, households, which intend to reduce the risks associated with climate change and have the resources or access to customised and contextualised adaptation measures into planning processes should be studied further. How to resources needed to make the appropriate changes, are generally more resilient and have a greater capacity to adapt channel the climate ﬁnance into reaching the vulnerable [37]. Information variables can increase or decrease risk population in the coastal region of Bangladesh so that in- appraisals. .erefore, information from social circles is equality is not increased and marginalisation is avoided was signiﬁcant for non-CF HHs as they are less engaged in social not covered under this research and may be explored fur- circles and value the information on climate change ther. Finally, future research may be conducted on the costs received. of various adaptation actions together with a cost-beneﬁt analysis which may contribute towards the future adaptation Diﬀerent sources of external ﬁnance such as from the government, social safety net programmes, or ﬁnancial implementation at the local level. support from NGOs, all increased the risk appraisal of the households. One of the reasons could be that ﬁnance is Data Availability mainly provided to the households if they are at risk. Sec- .e data used to support the ﬁndings of this study are ondly, since the ﬁnancial support is limited, the households have to examine the dimensions that are at risk from climatic available from the corresponding author upon request. events, hence making them more aware of the risks, which increases their risk appraisal correspondingly. Furthermore, Disclosure this causes the households to explore adaptation options .is article is an outcome of the research study of Ms Firdaus which are crucial and which they can aﬀord to address those Ara Hussain for her PhD at the Asian Institute of dimensions of the households that are at risk. .us, risk Technology. appraisal may increase the eﬀective utilisation of external climate ﬁnance, and the households own resources for adaptation measures. Conflicts of Interest .is study tried to address the knowledge gap on the .e authors declare that there are no potential conﬂicts of eﬀect of climate ﬁnance on risk appraisal at the local level. interest concerning the research, authorship, and publica- .e ﬁndings of this research reinforce the pattern of the tion of this paper. inadequacy of climate ﬁnance to meet the local needs of the most vulnerable communities. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2019 Firdaus Ara Hussain and Mokbul Morshed Ahmad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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DOI: 10.1155/2019/1587034
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Abstract

Hindawi Advances in Meteorology Volume 2019, Article ID 1587034, 16 pages https://doi.org/10.1155/2019/1587034 Research Article Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh Firdaus Ara Hussain and Mokbul Morshed Ahmad Regional and Rural Development Planning (RRDP), School of Environment, Resources and Development (SERD), Asian Institute of Technology, Klong Luang, Pathum !ani 12120, !ailand Correspondence should be addressed to Firdaus Ara Hussain; st116549@ait.asia Received 1 March 2019; Accepted 3 June 2019; Published 1 July 2019 Guest Editor: Bimal K. Paul Copyright © 2019 Firdaus Ara Hussain and Mokbul Morshed Ahmad. .is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Utilising climate funds properly to reduce the impact of potential risks of climate change at the local level is essential for successful adaptation to climate change. Climate change has been disrupting the lives of millions of households along the coastal region of Bangladesh. .e country has allocated support from its national funds and accessed international funds for the implementation of adaptation interventions. With the focus of the scientiﬁc community on climate ﬁnance mechanisms and governance at the global and the national level, there is a lacuna in empirical evidence of how climate ﬁnance aﬀects risk appraisal and engagement in adaptation measures at the local level. .is paper aims to examine how the support from climate ﬁnance aﬀects risk appraisal in terms of the perceived probability and severity and the factors which inﬂuence risk appraisal. A ﬁeld survey was conducted on 240 climate ﬁnance recipient households (CF HHs) and 120 nonclimate ﬁnance recipient households (non-CF HHs) in Galachipa Upazila of Patuakhali District in coastal Bangladesh. .e results indicate that both CF and non-CF HHs experience a high probability of facing climatic events in the future; however, CF HHs anticipated a higher severity of impacts of climatic events on diﬀerent dimensions of their households. With higher income and social capital, the overall risk appraisal decreases for CF HHs. CF HHs have higher engagement in adaptation measures and social groups and maintain alternative sources of income. Climate ﬁnance played a critical role in supporting households in understanding the risks that they were facing, assisting them in exploring as well as enhancing their engagement in adaptation options. Bangladesh, mainly by the ministries and their subordinated 1. Introduction departments and agencies, and do not consider the Risk appraisal at the household level plays a crucial role in household needs. .e implementation is left to the local the adaptation pathway and towards exploring available level. Hence, the approach is rather top-down. .is limits adaptation measures. Reasons for this are manifold, rec- consideration of local needs and participation within the ognising the centrality of the household unit in governing process. .is is also similar for adaptation projects, although responses to external stimuli as cited by Jones and Tanner climate change is localised and needs to consider localised [1]. Risk appraisal comprises of perceived probability, which adaptation responses. Local governments are less involved in is the person’s expectancy of being exposed to the threat, the planning process of the adaptation measures, which while perceived severity is the person’s appraisal of how results in the local needs being insuﬃciently considered [3]. harmful the consequences of the threat would be to things he Furthermore, localised encounters contrast signiﬁcantly or she values if the threat were actually to occur [2]. with the techno-scientiﬁc accounts through which scien- Households are concerned about how climatic events may tists, policymakers, and practitioners often conceptualise aﬀect their livelihoods and assets based on past experiences. climate change risks and operationalise responses [4]. However, project planning and budgetary allocation for the Customised adaptation interventions at the household level local level are considerably done at the national level in are also limited. In fact, households contribute from their 2 Advances in Meteorology To analyse the perception of risk, a ﬁeld survey was own resources to participate in the planned adaptation measures, increasing the overall adaptation cost for the conducted in Patuakhali, on the southwestern coast of Bangladesh, on the beneﬁciaries of a climate ﬁnance project aﬀected households. Indeed, many of the assets, capacities, and functions required to respond to climate risk are (referred to as CF Project) run by an international NGO. .e dictated by household-level dynamics [1]. Adaptation aim of the CF Project was to support the vulnerable com- funding is scarce and has to be used eﬀectively [5]. External munities against a changing and uncertain climate. .is assistance facilitates, secures, and improves the process of study limits itself to the adaptation interventions at the climate risk reduction [6]. household level due to the focus of the research on the eﬀect Some authors argue that the bottom-up approach preva- of climate ﬁnance on the risk appraisal at the household level. lent in development assistance brings ﬂexibility and innovation and that such an approach ﬁts well with the many motivations .e paper sets out to analyse how climate ﬁnancial makes a diﬀerence in the risk appraisal of households in for providing aid, with the diverse willingness and capabilities to contribute to development ﬁnance eﬀorts [7]. Local gov- comparison with households which do not receive support. One of the objectives of the study is to examine the inﬂu- ernments are the authorities who need to react at the earliest in case of any calamities or disasters. .e local governments in encing factors of risk appraisal, which were identiﬁed Bangladesh have the mandate of developing and maintaining through multiple regression analysis. .is research con- infrastructure and basic public services such as water, sani- tributes to the existing literature through a comparative tation, health, and educational facilities, which can be adapted analysis of risk appraisal and adaptation measures of CF and to make the localities resilient. In addition, although local non-CF households. To date, much of the adaptation lit- governments are often better informed on how to go about erature has been theoretical, reﬂecting the absence of em- pirical data from activities on the ground [11]. Eﬀective making development activities climate-adapted in a partici- patory manner with the involvement of local political leaders, utilisation of climate funding requires a critical analysis of where climate ﬁnance should be focused and which factors communities, practitioners, and authorities, they are often unable to respond as they are provided limited funding from inﬂuence the household in engaging in adaptation. Risk appraisals of climate change and inﬂuencing factors are the central level as they have little involvement in the budgetary decisions. .e insuﬃcient funding can be followed back to the critical to understanding adaptation behaviour [12, 13]. It planning and budgeting done at the national level, which often has become important to adaptation strategies because the does not take account of the local dimensions and cost esti- way individuals interpret their risks aﬀects what adaptation mates of climate change into the development planning. A behaviour they are likely to take [13]. more participatory bottom-up approach with the engagement of local governments and local people in the adaptation project 2. Literature Review planning, budgeting, and implementation process is needed. .e challenge, however, is to channel climate ﬁnance towards Climate change is disrupting the socioeconomic conditions adaptation measures, which ﬁt the local context and support of people, which are diﬃcult to overcome especially by the the most vulnerable and marginalised population. poor households. Natural calamities will reinforce preex- During the Conference of Parties (COP) 15 in 2011, the isting socioeconomic divide by damaging natural resources Green Climate Fund was agreed upon which oﬀers an equal that support the poor’s long-term livelihood prospects and funding window for adaptation and mitigation. However, destroying their current produce, and by repeatedly ren- almost a quarter of a century into climate change negoti- dering the poor homeless and destroying whatever little ations, an adequate system for deﬁning, categorising, material possessions they might have [14]. Risk appraisal is tracking, and evaluating climate change ﬁnance, is still derived from the premise that people comprehend their absent [7]. Despite the “polluter-pays-principle” at the cli- abilities and limitations. A large number of deﬁnitions, mate negotiations, the gap in the availability of climate ﬁ- frameworks, and approaches have been proposed for nance is prevalent. .e inadequacy of climate ﬁnance to explaining and quantifying risk [3, 13]. Nearly all studies on meet the adaptation needs will be analysed further through the eﬀects of personal experience on self-protective be- this study. haviour regarding natural hazards show preparedness in- At the national level, Bangladesh has taken initiatives to creasing with the severity of past damage [2]. ﬁnance climate change interventions from national and .e discourse around the provision of climate ﬁnance international ﬁnancial sources. By 2050, total investments of has been focused at the international level. Without eﬀorts to $5,516 million and $112 million in annual recurrent costs marry adaptation theory with real-world adaptation prac- will be needed to protect Bangladesh against climate change tices, the adaptation ﬁeld will continue to be siloed between [8]. .e country is not helpless, therefore, against coping theory and practice [11]. In the last 20 years, there has been with sea-level rise, but it might need ﬁnancial and technical an increase in bilateral and multilateral funds providing assistance with providing practical mitigation measures [9]. climate ﬁnance such as Global Environmental Facility Climate ﬁnance comes from diﬀerent sources such as from (GEF), Climate Investment Fund (CIF), Fast Start Finance, the national budget, Bangladesh Climate Change Trust Fund Adaptation Fund, and most recently, the Green Climate (BCCTF), Bangladesh Climate Change Resilience Fund Fund (GCF); each responds to needs that emerged at dif- (BCCRF), and bilateral donors through Fast Start Finance, ferent times. However, the proliferation of funds has led among others [10]. policymakers to question whether such a diverse landscape Advances in Meteorology 3 of funds can eﬀectively channel climate ﬁnance to support important additional component of an individual’s per- the necessary transformation to low-emission, climate-re- ceived risk and adaptation capacity [1]. For risk appraisal, the framework was operationalised further through quan- silient societies [15]. .e IPCC special report in 2018 argued that the world only had until 2030 to keep the global titative analysis. .e formula utilised was followed by this temperature increase at a maximum of 1.5 C if immediate study as well, with some modiﬁcations explained in the action is not taken [16]. .is collates with the Paris succeeding section, and is as follows: Agreement in 2015 with the goal of keeping the temperature risk appraisal � perceived probability × perceived severity. ° ° between 1.5 C and 2 C. (1) Adaptation ﬁnance is primarily allocated to multilateral entities and national governments, rather than local orga- .e concept of threat appraisal formulated by [13] in nisations [17]. BCCTF has allocated almost USD 400 million their conceptual framework is as follows: as of date for climate change. BCCRF, a multidonor trust risk appraisal � uncertainty × adverse consequences, fund, had committed USD188 million in grant funds to build resilience but was discontinued after 2016. .e Pilot (2) Program for Climate Resilience (PPCR) has allocated USD where uncertainty � perceived probability and adverse 110 million in grants (45%) and interest credits (55%). Until consequences � perceived severity. 2019, Bangladesh has received commitments of USD 88.13 million from the GCF through three approved adaptation Overall risk appraisal � 􏽘 uncertainty projects. .e funds are mostly in the form of loans or grants (3) i�1 when the funding is from international sources. × adverse consequences . .e climate-relevant budget data of ﬁve key ministries for climate change from FY 2014-15 to FY 2017-18 were analysed [18]. An increase is evident for the period 2014 to 4. Materials and Methods 2017, with a slight decrease in the FY 2017-18, as illustrated in Table 1. One of the barriers for shifting investments’ 4.1. Study Area. In South Asia, Bangladesh is the most allocation to green sectors and assets is the poor un- densely populated delta of the Ganges-Brahmaputra-Meghna derstanding of the relation between climate risks, the (GBM) Basin [19]. .e country drains out approximately economy, and ﬁnance [16]. 92.5% of the water that is generated in the GBM Basins (an An analysis of the budget allocation according to Ban- area of 175,106 ha) [20] to the Bay of Bengal. Bangladesh has gladesh Climate Change Strategy and Action Plan 19 coastal districts and with a coastal population of 50 million (BCCSAP) thematic areas for the four ﬁscal years 2014–2018 people, nearly about one-third of its total population. .e is presented in Table 2, which indicates that the majority of coastal area represents an area of 47,211 km equalling 32% of the budget allocation went towards adaptation. On the the country’s total geographical area [21]. .e coastal zone contrary, the budget allocation for mitigation and low has been aﬀected by 174 natural disasters during the period carbon development decreases from 1.74% in FY 2014-15 to 1974–2007 [22]. Current predictions claim that this coastal 1.16% in FY 2017-18 of the total climate-relevant budget area will be increasingly submerged up to 3 per cent by the [18]. 2030s, 6 per cent in the 2050s, and 13 per cent by 2080s as a result of the sea-level rise [21]. 3. Theoretical and Empirical Basis of Research Galachipa Upazila (a subdistrict/administrative region in Bangladesh) is one of the thirteen subdistricts under the For measuring risk appraisal to climate change, this paper Patuakhali district in the southwestern coast of Bangladesh. follows the Model of Private Proactive Adaptation to Cli- Galachipa, as shown in Figure 1, has been selected as the study mate Change (MPPACC) to examine why some people area as it had interventions under the CF Project concluded in exhibited adaptation behaviour while other people did not 2016. Galachipa is around 925 km large and is around [2]. .e MPPACC was based on the Protection Motivation 35–50 feet above the mean sea level. It has been aﬀected .eory, which was recognised as one of the four major by cyclones, tidal surges, coastal ﬂooding, thunderstorms, theories in psychological research conducted on health nor’westers, heavy/irregular rainfall, and salinity. Besides behaviour. these, river erosion is a severe problem in this area as the two (i) First, the perceived probability is the person’s ex- large rivers, Agunmukha and Tetulia, ﬂow on both sides of pectancy of being exposed to the threat (to use a Galachipa and several smaller rivers ﬂow cross-terrain natural-hazard example that a ﬂood reaches the through Galachipa. It has around 109 km of the embankment house in which a person lives). at diﬀerent points, with around 12 door sluice gates. (ii) Second, perceived severity is the person’s appraisal of how harmful the consequences of the threat would 4.2. Sampling. .e ﬁeld survey was conducted as a com- be to things he or she values if the threat were ac- parative study of the risk assessment of households who tually to occur. received support from the climate ﬁnance interventions (CF Grothmann and Patt’s framework was expanded further HHs hereafter) in relation to households who did not receive by Frank et al.’s study which identiﬁed social identity as an support from climate ﬁnance interventions (referred to as 4 Advances in Meteorology Table 1: Climate-relevant budget allocation in selected ministries from 2014 to 2018 [18]. Annual budget (amount in thousand taka) Budget description 2014-15 2015-16 2016-17 2017-18 Nondevelopment budget 270,827,806 300,456,768 309,209,969 358,797,697 Climate-relevant allocation 65,982,855 77,193,807 84,036,986 85,334,676 % of nondevelopment budget 24.36 25.69 27.18 23.78 Development budget 253,047,122 297,119,124 350,529,332 403,219,100 Climate-relevant allocation 28,066,412 46,513,505 53,701,881 61,001,430 % of development budget 11.09 15.65 15.32 15.13 Total budget 523,874,928 597,575,892 659,739,301 762,016,797 Climate-relevant allocation 94,049,267 123,707,312 137,738,867 146,336,106 % of total budget 17.95 20.70 20.88 19.20 % of GDP 0.62 0.71 0.70 0.66 Source: Climate Protection and Development: Budget Report, 2017-18. Table 2: Allocation in BCCSAP thematic areas in the selected ministry budget [18]. CC-relevant allocation (amount in thousand taka) BCCSAP themes 2014-15 2015-16 2016-17 2017-18 Food security social protection and health 13,304,421 16,146,944 16,678,265 17,353,924 % of total CC-relevant allocation 14.15 13.04 12.11 11.86 % of ministry budget 1.75 2.12 2.19 2.28 Comprehensive disaster management 20,687,257 30,318,387 29,434,227 34,671,966 % of total CC-relevant allocation 22.00 24.49 21.37 23.69 % of ministry budget 2.71 3.98 3.86 4.55 Climate resilient infrastructure 6,559,625 13,248,641 23,947,171 24,743,940 % of total CC-relevant allocation 6.97 10.70 17.39 16.91 % of ministry budget 0.86 1.74 3.14 3.25 Research and knowledge management 2,631,410 4,165,926 2,804,454 3,658,676 % of total CC-relevant allocation 2.80 3.37 2.04 2.50 % of ministry budget 0.35 0.55 0.37 0.48 Mitigation and low carbon development 1,639,602 1,653,730 1,613,205 1,699,220 % of total CC-relevant allocation 1.74 1.34 1.17 1.16 % of ministry budget 0.22 0.22 0.21 0.22 Capacity building and institutional strengthening 49,226,953 58,246,322 63,261,544 64,208,380 % of total CC-relevant allocation 52.34 47.06 45.93 43.88 % of ministry budget 6.46 7.64 8.30 8.43 Total CC relevance (Tk) 94,049,267 123,779,950 137,738,867 146,336,106 % of total budget for 6 ministries 17.95 20.71 20.88 19.20 Source: Climate Protection and Development, Budget Report, 2017-18. non-CF HHs). .e unit of analysis was at the household level. beneﬁciaries of the CF Project had been selected by the NGO Crucially, household-level assessments also oﬀer value in based on the criteria that the household did not have more capturing the interactions of individual-level decisions and than 10 decimal or 0.004 hectares of productive lands and had traits with broader social norms, behaviour, and institutions less than USD 62.5 in productive assets. Some other criteria that collectively aﬀect responses to climate hazards [1]. .e included the nonengagement of households in microcredit CF HHs functioned as the experimental group while the non- programmes or projects similar to the CF Project. .e last criteria were that each household had a monthly income of CF HHs were the control group. Other risk assessment studies opted for an experimental and control group, allowing for less than USD 62.5. A list of 489 CF HHs supported in the Galachipa Upazila comparison and reliability of results [23]. Also, both CF and non-CF HHs were of similar criteria and lived within similar was provided by the project managers of the CF Project to geographical conditions and allowed comparison. .e study the researcher. Based on the formula of Yamane (1967) for employed mixed sampling methods to select households. computing the size of the sample under equation (1), the From the 19 districts in the coast of Bangladesh, Patuakhali sample size of 220 CF HHs was estimated. Finally, a sample was chosen through purposive sampling. From within the of 240 CF HHs was collected through random sampling. .e seven subdistricts of Patuakhali, Galachipa was chosen list of sample beneﬁciary households then underwent ran- through purposive sampling as a CF Project was imple- dom sampling in MS Excel to determine which households menting adaptation activities at the household level. .e should be surveyed: Advances in Meteorology 5 Map of Galachipa Upazila KEY MAP LEGEND INDEX MAP District boundary Upazila boundary River Major road FGD location Coordinate system: GCS WGS 1984 Datum: WGS 1984 Units: degree RF: 1:350,000 0 2.25 4.5 9 13.5 18 Kilometers Figure 1: Galachipa Upazila. .e CF HHs received support for adaptation measures n � , (4) such as plinth raising, construction of new housing, 1 + Ne homestead gardening, agricultural support, and income where n is the sample size in each area, N is total numbers of diversiﬁcation activities such as livestock, small busi- households in an area, and e is the precision value, set as 10% nesses, small solar home systems, among others. A control (0.10). group from the same study area (indicated as non-CF HHs) 6 Advances in Meteorology probability was based on the likelihood of the household which did not receive support from climate ﬁnance was also surveyed to allow the comparison and reliability of results. facing these natural calamities in the future. A summation was drawn from all the perceived severity corresponding to Unlike the CF households, there was no existing name list for the non-CF HHs. .erefore, four focus group discus- a particular climatic event to obtain a single perceived sions (FGDs) were conducted in the study area to identify severity. .e perceived probability was then multiplied by those households, which met the same criteria for choosing the corresponding severity for the event to obtain the risk CF households of the households, those who had less than appraisal for that climatic event. .en, a summation was 10 decimal or 0.004 hectares land, had less than USD 62.5 in drawn to estimate an overall risk appraisal. .is overall risk productive assets, were not engaged in microcredit or appraisal was used as the dependent variable for the multiple linear regression model. Le Dang followed the similar projects, and had a monthly income below USD 62.5. Participants of the FGDs included Upazila members, multiple regression model risk appraisal � f (risk experi- ence, information, belief in climate change, trust in public members of the local disaster management committee, and local leaders who were able to identify around 240 adaptation, farm household characteristics, farm charac- teristics, and income). households in the study area, which met the selection criteria. .e names of the households obtained from the Drawing on dimensions developed by Le Dang et al., the FGDs were then entered into an Excel sheet. However, model was executed under the following function for this budget constraints in conducting the survey were con- research and with independent variables identiﬁed for ad- sidered while maintaining a comparable sample size. aptation eﬃcacy under the bivariate analysis such as x � HH .erefore, the number of non-CF HHs was limited to 120 size, x � income, and x � immobile assets: 2 3 households. .e percentage of simulated studies with an Y � b + b x + . . . + b x + . . . + ε, (5) opr 0 1 1 p p elevated eﬀect increases as control group size increases (30 participants: 85%; 60 participants: 92%; 100 participants: where b is the intercept, b to b are the regression co- 0 1 p 96%; and 200 participants: 99%) [24]. .rough random eﬃcients corresponding to the covariates x ,. . .., x , ε is 1 p sampling in MS Excel, a list of non-CF households of 120 the error term of the model, x � HH size, x � income, 1 2 HHs was shortlisted and surveyed. In case any of the x � immobile assets, x � productive assets (livestock, 3 4 households were unwilling to be surveyed or were un- etc.), x � mobile assets (rickshaw), x � participation in 5 6 available, the next household was chosen from the ran- social circle, x � information on climate change from domized list. NGO, x � support from social safety net, x � monetary/ 8 9 .e ﬁeld survey was conducted in April and May 2016 in-kind support from social circle (relatives, friends, using a structured questionnaire. Eight interviewers were neighbours, and self-help groups) for adaptation mea- chosen based on the experience of data collection and sures, x � ﬁnancial support from loans (microcredit, previous knowledge of the area, and they were provided with bank, cooperative, and mohajan) for adaptation re- a three-day training. Before the actual survey, the ques- sponses, x � ﬁnancial support from govt. for adaptation tionnaire was pretested on 15 households by the in- responses, x � ﬁnancial support from NGOs (cash, terviewers. .e interviewees were read out a uniform materials, labour costs, and advisory services) for adap- introduction on the purpose of the survey. .e interviews tation responses, x � self-ﬁnanced adaptation responses were conducted between one and a half hours to two hours. through selling assets, and x � self-ﬁnanced adaptation measures through income/savings/reducing expenditure on food, health, and education. 4.3. Variable Measurement. .e questionnaire included two main aspects of risk appraisal, namely, perceived probability and perceived severity. .e variables were chosen based on 4.4. Data Analysis Techniques. Statistical Analysis Software previous studies on risk appraisal [13, 25] and cross-checked (SAS) was used to analyse the household data including during pretesting. For perceived probability, the households descriptive statistics—chi-square, Pearson’s correlation co- 2 2 were asked about how likely they were to experience the eﬃcient, factor analysis, R and adjusted R , variance in- seven main climatic events in the future, as identiﬁed in the ﬂation factor, Durbin–Watson statistic, normality, and Patuakhali Disaster Management Plan 2014. .e scale homoscedasticity were used. ranged from 1 to 5 (1 � not likely; 2 � less likely; 3 � likely; To identify signiﬁcant variables, bivariate analysis was 4 � more likely; and 5 � very likely). For perceived severity, conducted on all relevant independent variables which the households were asked about how each of the climatic were important to the dependent variable, overall per- events could aﬀect diﬀerent features of their lives. Perceived ceived risk. .ose variables which had an association in the severity had the scales of 1 to 5 (1 � not aﬀected; 2 � less bivariate analysis were included under the independent aﬀected; 3 � aﬀected; 4 � more aﬀected; and 5 � highly variables for the multiple linear regression analysis in the aﬀected). model for this study. One regression model under equa- A number of methods, frameworks, and approaches tion (5) was ﬁtted, with CF and non-CF as the independent were applied for risk assessment [13, 26–29]. For this study, variables. By including CF and non-CF as independent it utilised the method by Le Dang et al., in which the risk variables, we can assess the impact that CF and non-CF appraisal was computed by multiplying perceived proba- have on the dependent variable for the overall perceived bility with perceived severity as given in equation (1). .e risk. Advances in Meteorology 7 erosion and who now work as day-labourers, i.e., without a 5. Results and Discussion stable or regular income. .is bias might have caused Both the climate and nonclimate ﬁnance households were barriers to climate ﬁnance reaching other households, who from Galachipa subdistrict and lived under similar condi- were also in dire conditions. tions. .is study examined the diﬀerences in the socio- Engaging in adaptation measures was made possible economic proﬁle of the CF HHs and the non-CF HHs through some households’ own ﬁnancial resources or ex- through chi-square test and corresponding P values, as ternal sources. Poor households, however, had fewer income shown in Table 3. .e adaptation measures engaged in by CF sources, often had to sell assets to sustain their family’s and non-CF HHs are given under Table 4. As shown in expenditure, and had limited access to ﬁnancial services. Table 3, more CF HHs lived outside or on the embankment, .ere exists growing evidence of the beneﬁcial impact of which may have been related to the loss of land due to river access to ﬁnancial services on all aspects of social and erosion. .ose without land inside the embankment economic outcomes at the household and ﬁrm level [30]. .e remained outside of the embankment mainly on govern- absence of ﬁnancial services makes diversiﬁcation of income ment land or the embankments. sources a livelihood strategy, as well as an adaptation re- Moreover, the housing structure of CF HHs was made of sponse for climate-aﬀected households. .e ﬁrst step is to less durable materials. .ere were also a higher number of stabilising their socioeconomic conditions. For this, it is wage labourers, traders, and ﬁshers in the CF HHs. About essential to understand how the poor farmers manage their 18% of CF HHs pursued a secondary source of income. .e cash ﬂows, their preferences, attitudes, and behaviours to average monthly income of CF households was higher at determine the scope of diversifying income sources as an USD 59.9 in comparison with USD 54.9 for non-CF HHs. eﬀective path out of poverty [30]. About 96.2% of the CF HHs participated in a social group Once climate funds reach the national level, a further such as self-help groups, producer groups, or cooperatives breakdown of the allocated amounts for hard and soft while only 16.7% of the non-CF HHs participated in some measures ensues. Studies have shown that 65% of climate form of social groups. More non-CF households received funding was allocated to infrastructure investments, in- support from the social safety net at 78.3% compared with cluding coastal protection measures for ﬂooding and erosion 63.8% of CF HHs receiving support. [11], that caused a signiﬁcant portion of climate ﬁnance to be Data showed that non-CF HHs participated in most of preplanned for infrastructures such as embankments. At the the 26 adaptation measures on self-initiatives while the CF local level, the projects with “soft” measures, such as those HHs received support in 14 adaptation measures, partially related to capacity building, policy reform, and planning and supported by the climate ﬁnance interventions. At an ag- management, are traditionally low cost [11] and in- gregated level, the main categories of adaptation measures all adequately meet the adaptation needs of the households. saw a higher engagement of CF HHs than non-CF HHs as Data generated from this study showed that the households shown in Table 4. Higher income levels were likely to sig- engaged in 24 adaptation measures whereas climate ﬁnance niﬁcantly increase the likelihood of planting trees and using had 14 adaptation measures available (Table 4). .us, it can supplementary irrigation as adaptation choices [29]. .e two be argued that climate ﬁnance was inadequate and the highest engagements for both groups were in housing and support received was beneﬁcial to a limited extent. livestock. .e socioeconomic proﬁle of the CF HHs showed .e perceived probability in this study is deﬁned as how that a signiﬁcant number lived outside or on the embank- likely households expect to face a climatic event in the future ment and needed support for housing. Livestock was per- on the basis that it will aﬀect a household’s ability to re- ceived as an instant cash source and was the preferred option bound, i.e., overcome the eﬀects of the climatic events. Risk for receiving external support as they could quickly experiences tend to induce people to think of the risks more reproduce. often, thereby increasing their risk appraisals [13]. Furthermore, households did not need a new skill set to maintain their livestock. Both groups participated least in 5.1. Perceived Probability. .e perceived probability, as agriculture and ﬁsheries. From the aggregated level, a sig- shown in Table 5, had the highest mean of 3.6667 and 3.9750, niﬁcant diﬀerence based on the chi-square and P value which ranged between likely and quite likely for both CF and between CF and non-CF HHs was seen for gardening, ag- non-CF HHs. .e P value for cyclone and river erosion ricultural land and crops, livestock, ﬁsheries, safe water, and showed a signiﬁcant correlation, however, with river erosion access to power and fuel resources. From the socioeconomic indicating a negative association. T-tests indicated that there proﬁle and focus group discussions, support was least given is indeed a signiﬁcant diﬀerence between the perceived to agriculture and crops as the CF HHs had lost their arable probability of climate ﬁnance and nonclimate ﬁnance and homestead land due to river erosion. Most of the households. Signiﬁcant diﬀerences between CF and non-CF households who had lost land and could no longer do HHs have also been observed based on the means of the CF farming worked as day-labourers. About 55.4% of the CF and non-CF HHs for all climatic events as indicated by the P HHs had the main source of income as day-labourers. .e value. occupation of a day-labour is also critical as they are out of work 6 to 8 months in the whole year. .is analysis is in- dicative of the selection bias of the climate ﬁnance project 5.2. Perceived Severity. .e perceived severity was mea- towards selecting households who had lost their land to river sured by multiple aspects of households that are aﬀected by 8 Advances in Meteorology Table 3: Summary of diﬀerences in socioeconomic proﬁles of climate ﬁnance (CF HHs) and nonclimate ﬁnance households (non-CF HHs). Findings (P value) CF households Non-CF households Household size No signiﬁcant diﬀerence 4.07 4.27 (i) .e lower level of illiteracy with (i) A higher level of illiteracy with 69.6% 75.8% Signiﬁcant diﬀerence at 10% Educational (ii) Higher completion of the primary (ii) Lower completion of primary conﬁdence interval attainment level with 26.3% level with 25.6% P value (0.062) (iii) Higher completion of the (iii) No completion of secondary secondary level with 4.2% level However, more men have land than Land-holding size No signiﬁcant diﬀerence women; both CF men and CF women have more land than non-CF women Signiﬁcant diﬀerence at 0.01% and 0.05% conﬁdence interval (i) More non-CF (11.7%) live free (i) 4.2% CF live free (ii) 0 non-CF rents (ii) Only 1 CF rents (iii) More non-CF (80%) own Tenancy P value (0.049) (iii) 85% own housing housing (iv) More (10%) inherited from (iv) Less (8.3%) inherited from parents parents (i) More CF (10.4%) live outside (i) Less non-CF (2.5%) live outside embankment embankment (ii) 7 CF live on embankment (ii) 0 non-CF live on embankment Location now P< 0.001 (iii) Less CF (74.6%) live inside (iii) More non-CF (94.2%) live inside embankment embankment (iv) More CF (12.1%) live upland (iv) Less non-CF (3.3%) live upland (i) More CF (3.8%) houses made of (i) Fewer non-CF (0.8%) houses mud made of mud (ii) More CF (22.9%) houses made of (ii) More non-CF (13.3%) houses Housing conditions leaves made of leaves Household construction material P (iii) Less CF (66.7%) houses made of (iii) More non-CF (83.3%) houses value (0.016) corrugated tin made of corrugated tin (iv) More CF (2.9%) houses made of (iv) Fewer non-CF (1.7%) houses brick and cement made of brick and cement (v) More CF (3.8%) houses made of (v) Fewer non-CF (0.8%) houses others (wicker) made of others (wicker) (i) Less CF (1.3%) use water (i) More non-CF (2.5%) use water puriﬁcation tablets puriﬁcation tablets (ii) More CF (2.5%) use ﬁltering (ii) 0 non-CF use ﬁltering systems systems (iii) More non-CF (23.3%) use Water puriﬁcation methods (iii) Less CF (21.7%) use boiling boiling P< 0.001 (iv) More CF (62.1%) use ﬁtkari (iv) Fewer non-CF (45%) use ﬁtkari (aluminium sulfate, also known as (aluminium sulfate, also known as alum) as others alum) as others (v) Less CF do not use any (v) More non-CF do not use any puriﬁcation method (12.5%) puriﬁcation method (29.2%) (i) Less non-CF wage labourer (i) More CF wage labourer (55.4%) (40.8%) Primary source of Signiﬁcant diﬀerence at 0.05% (ii) Less CF in service (6.3%) (ii) More non-CF in service (25.5%) income conﬁdence interval (iii) More CF in trade (8.8%) (iii) Fewer non-CF in trade (7.5%) (iv) More CF ﬁshermen (7.5%) (iv) Less non-CF ﬁshermen (5.8%) (i) More CF as pastoralist (12.5%) (i) Only 1 farmer, 1 domestic worker, Secondary source of Signiﬁcant diﬀerence at 0.01% and wage labourer (5.4) 1 begging non-CF income conﬁdence interval (ii) 71.7% CF does not have any (ii) 96.7% non-CF do not have any secondary source secondary source Average monthly Diﬀerence USD 59.9 USD 54.9 income Advances in Meteorology 9 Table 3: Continued. Findings (P value) CF households Non-CF households Signiﬁcant diﬀerence at 0.01% conﬁdence interval More CF (64.6%) participation in Less non-CF (1.7%) participation in Self-help groups P< 0.001 self-help group self-help group More CF (48.8%) participation in Less non-CF (0.8%) participation in Producer group P< 0.001 producer group producer group Membership in More CF (10.5%) participation in Less non-CF (0.8%) participation in social groups DMC P< 0.001 disaster management committee disaster management committee (DMC) (DMC) More CF (76.3%) participating in Less non-CF (16.7%) participating in Cooperatives P< 0.001 cooperatives cooperatives No membership in any social group Less CF (3.8%) not participating in More non-CF (83.3%) not P< 0.001 any social group participating in any social group Table 4: Percentage of climate ﬁnance (CF) and nonclimate ﬁnance (non-CF) households who engaged in the adaptation measures. Groups Group Adaptation measures Chi (P value) CF (%) Non-CF (%) ∗∗ Plinth raising and reinforcement of housing 78.8 64.2 8.82 (0.003) Housing Construction of new housing 70.8 51.7 13.76 (<0.001) ∗∗ Repair of damaged housing 28.8 31.7 0.33 (0.568) ∗∗ Homestead gardening 55.0 42.5 5.00 (0.025) Garden Community nursery 11.3 0.83 12.10 (<0.001) Social forestry 19.2 12.5 2.53 (0.112) ∗∗ Changed crop varieties 7.5 1.7 5.19 (0.023) Agricultural land and crops Changed crop patterns 7.1 1.7 4.70 (0.030) Changed irrigation management 3.8 0 6.57 (0.037) ∗∗ Poultry farming 85.4 72.5 8.71 (0.003) ∗∗ Duck farming 70.8 58.3 5.63 (0.018) ∗∗ Raised poultry housing 53.8 38.3 7.61 (0.006) ∗∗ Livestock Goat rearing 35.8 19.2 10.53 (<0.001) ∗∗ Cow rearing 75.8 45.8 32.01 (<0.001) ∗∗ Raised barn 42.9 12.5 33.59 (<0.001) ∗∗ Cow fattening 30.4 2.5 37.43 (<0.001) ∗∗ Fisheries Change in ﬁsh culture 7.9 2.5 4.09 (0.043) Installation of deep tube wells 54.6 40.8 6.05 (0.014) ∗∗ Safe water Elevated tube wells 32.5 30 0.23 (0.631) Water storage tanks 4.6 0 5.67 (0.017) Access to power and fuel sources Solar systems 60.8 50 3.84 (0.050) ∗∗ Rickshaw/.ela 5.8 9.2 1.38 (0.241) IGA .ree-wheeler 5 3.3 0.52 (0.469) ∗∗ Trading/small business 17.9 16.7 0.09 (0.769) ∗∗ ∗ Source: Survey conducted under this study. Adaptation measures fully supported by climate ﬁnance; partial support from climate ﬁnance. climatic events. To obtain a single perceived severity, all non-CF HHs. Signiﬁcant diﬀerences between CF and non- the perceived severities were aggregated corresponding to CF HHs have also been observed for the means of the CF a particular climatic event such as cyclone, storm surge, and non-CF HHs for all climatic events as indicated by the and river erosion, among others. As shown in Table 6, the P value. perceived severity to cyclones had the highest eﬀect on the diﬀerent dimensions of households such as housing, 5.3. Risk Appraisal and Overall Risk Appraisal. All the gardening, crops, and livestock and with a mean of 1.93 for the CF HHs and 1.66 for the non-CF HHs. .e P value for perceived severity for each of the climatic event was added to reach a single perceived severity, as presented in Ta- all values shows a signiﬁcant correlation, however, in a negative direction, derived from the t-test. .e t-tests also ble 7. .e perceived probability was multiplied by the corresponding severity for the event to obtain the risk indicated that there is indeed a signiﬁcant diﬀerence be- tween the perceived severity of climate ﬁnance and non- appraisal for that climatic event. After which, all the risk appraisals were added to estimate an overall risk appraisal climate ﬁnance households. CF HHs have a higher severity on the diﬀerent dimensions of the households compared to which indicated a signiﬁcant diﬀerence between the 10 Advances in Meteorology Table 5: Mean, standard deviation, and standard error of the perceived probability, i.e., likelihood of facing the climatic events in the future. CF/non-CF Mean Std. deviation T-test P value CF 3.667 1.541 Cyclone 1.963 0.051 Non-CF 3.975 1.331 CF 2.988 1.451 Storm surge 1.798 0.073 Non-CF 3.242 1.160 CF 3.346 1.281 Flooding −0.551 0.583 Non-CF 3.258 1.487 CF 2.829 1.818 River erosion −7.110 <0.001 Non-CF 1.558 1.477 CF 2.992 1.284 Irregular rains 0.795 0.428 Non-CF 3.108 1.327 CF 4.158 1.094 Nor’wester (Kalboishakhi) 0.502 0.616 Non-CF 4.225 1.233 CF 1.471 1.420 Salinity −3.271 <0.001 Non-CF 1.000 1.216 Table 6: Mean, standard deviation, and standard error of the perceived severity, i.e., the eﬀect of climatic events on diﬀerent dimensions of the households. CF/non-CF N Mean Std. deviation Std. error T-test P value CF 240 1.937 0.560 0.036 Cyclone −4.575 <0.001 Non-CF 120 1.663 0.522 0.048 CF 240 1.692 0.630 0.041 Storm surge −5.516 <0.001 Non-CF 120 1.373 0.447 0.041 CF 240 1.723 0.675 0.044 Flooding −4.380 <0.001 Non-CF 120 1.442 0.510 0.047 CF 240 0.892 0.697 0.045 River erosion −7.009 <0.001 Non-CF 120 0.418 0.553 0.051 CF 240 1.021 0.458 0.030 Irregular rains −3.056 0.002 Non-CF 120 0.883 0.371 0.034 CF 240 1.710 0.533 0.034 Nor’wester (Kalboishakhi) −4.384 <0.001 Non-CF 120 1.466 0.465 0.043 CF 240 0.540 0.471 0.030 Salinity −5.011 <0.001 Non-CF 120 0.315 0.362 0.033 Table 7: Mean, standard deviation, and standard error of the risk appraisal of the households. CF/non-CF N Mean Std. deviation Std. error T-test P value CF 240 7.332 3.805 0.246 Risk appraisal_Cyclone −1.448 0.149 Non-CF 120 6.773 3.259 0.296 CF 240 5.340 3.539 0.229 Risk appraisal_Storm surge −2.821 0.005 Non-CF 120 4.466 2.284 0.209 CF 240 6.114 3.408 0.220 Risk appraisal_Flood −2.909 0.004 Non-CF 120 5.057 3.151 0.289 CF 240 3.256 3.091 0.199 Risk appraisal_River erosion −8.207 <0.001 Non-CF 120 1.142 1.784 0.163 CF 240 3.250 2.127 0.137 Risk appraisal_Irregular rains −2.247 0.025 Non-CF 120 2.794 1.633 0.149 CF 240 7.165 3.097 0.200 Risk appraisal_Nor’wester −2.596 0.010 Non-CF 120 6.306 2.881 0.264 CF 240 1.092 1.399 0.090 Risk appraisal_Salinity −5.118 <0.001 Non-CF 120 0.501 0.791 0.072 overall risk appraisal of CF and non-CF HHs, where the dependent variable for the multiple linear regression CF HHs have a higher overall risk assessment than non- model to assess the impact of selected independent var- CF HHs. .is overall risk appraisal was used as the iables on it (Table 8). Advances in Meteorology 11 Table 8: Independent variables with details for the dependent variable (overall risk appraisal). Measurement/ Recoded for regression Types of Categories Independent variables Scale explanation analysis data Numbers of HH size Continuous Socioeconomic household members factors Total annual Income Continuous income in number Agricultural land in Immobile Assets (land) Continuous acre in number Assets Productive Assets (livestock, In number Continuous etc.) Mobile Assets In number Continuous Participation in self-help Involvement in groups, producer group, Participation 1 � yes; 0 � no 1 � yes; 0 � no Binary social groups DMC, cooperatives SISCH: information on climate change from social Information on Information circle, i.e., relatives/ 1 � yes; 0 � no 1 � yes; 0 � no Binary climate change received neighbours/friends/ community 1 � vulnerable group development (VGD) 2 � vulnerable group feeding (VGF) 3 � food for work (KABIKA) 4 � cash for work (KABITA) 5 � old age allowance Support received 6 � allowance for the Support from social safety net 1 � yes; 0 � no Binary from widowed, deserted, and destitute 7 � housing support 8 � test relief (TR) programme 9 � zakat in cash 10 � zakat in kindness 11 � scholarship 12 � others Monetary/in-kind support 1 � relatives from Social Circle (relatives, Support received 2 � friends 1 � yes for all options; friends, neighbours, self-help Binary from 3 � neighbours 0 � no for all options groups) for adaptation 4 � self-help groups responses External sources Financial support from loans 1 � microcredit of ﬁnance (microcredit, bank, Support received 2 � bank 1 � yes for all options; Binary cooperative, mohajan) for from 3 � cooperatives 0 � no for all options adaptation responses 4 � mohajan Financial support from govt. Support received 1 � yes; 0 � no 1 � yes; 0 � no Binary for adaptation responses from 1 � cash Financial support from NGOs 2 � materials (cash, materials, labour costs, Support received 1 � yes for all options; 3 � labour costs Binary advisory services) for from 0 � no for all options 4 � advisory services adaptation responses 5 � training 1 � land or building 2 � durable HH assets Self-ﬁnanced adaptation 3 � livestock 1 � yes for all options; Binary responses through selling 4 � mobile assets 0 � no for all options 5 � agricultural/ﬁsheries equipment 1 � reduced expenditure on food, health, and education Self-ﬁnanced adaptation 2 � relied on savings 1 � yes for all options; responses through reduced Binary 3 � paid from income 0 � no for all options household expenditure 4 � HH members took other employment 12 Advances in Meteorology Table 9: Summary of results from statistical tests for CF and non- 6. Multiple Linear Regression Model for CF models for overall risk appraisal. Overall Risk Appraisal CF HHs Non-CF HHs 6.1. Regression Model for Overall Risk Appraisal for CF and Number of observations 240 120 Non-CF HHs. From the model for overall risk appraisal for Model 10 8 CF HHs and non-CF HHs with 240 and 120 observations, Error 229 111 respectively, the summary of statistical tests and the re- Corrected total 239 119 gression analysis results are given in Tables 9 and 10. .e F value 15.16 6.30 P value <.001 <.001 models were statistically signiﬁcant at F � 15.16, P< 0.01 for Root MSE 52.470 41.537 CF HHs and F � 6.30, P< 0.01 for non-CF HHs, respectively. Dependent mean 142.163 112.350 Positive auto-collinearity was observed. As shown in Ta- Coeﬃcient of variance 36.908 36.971 ble 10, a highly signiﬁcant (P< 0.01) and negative re- R-square 0.398 0.312 lationship between overall risk appraisal and the Adj R-square 0.372 0.263 independent variables was observed for income and par- Durbin–Watson D 1.420 1.375 ticipation in a social circle for CF HHs, while a signiﬁcant and negative relationship was observed for non-CF HHs for income and information on climate change from the social .e CF HHs were trained through a household adap- circle. For CC HHs, positive and highly signiﬁcant associ- tation plan to interpret the exposure they were facing and ation (P< 0.01) between overall risk appraisal and the in- identify adaptation measures. .e climate ﬁnance project dependent variables was seen for household size, immobile organised exchanges between the households on the eﬀects assets, and support from the social safety net. Signiﬁcant on climate change on their households and the pre- relationship for CF HHs was observed between overall risk cautionary measures they take, thus building awareness appraisal productive assets and ﬁnancial support from within the wider social circle of the households. As argued by government. On the contrary, for non-CF HHs, highly Granderson, discourses play a signiﬁcant role in how climate signiﬁcant and positive relationship can be observed be- change and its risks are interpreted and made meaningful for tween overall risk assessment and immobile and productive communities [4]. Furthermore, climate ﬁnance supported assets and ﬁnancial support from NGOs, while the signiﬁ- the development of consensus and of a common un- cant and positive relationship is observed for social safety derstanding within the households on how to adapt. net. According to Granderson, responses required the adoption of a particular vision of the future, the course of action rather 7. Discussion than another [4], and an understanding of sharing common resources and labour between the households to adapt. 7.1. Climate Hazards, Risk Appraisal, and Role of the Climate Finance Project. Galachipa is vulnerable to natural hazards such as cyclones, storm surges, and river erosion, among 7.2. Household Size. .e estimated coeﬃcient for overall risk others. It was one of the hardest hit upazilas by the 2007 appraisal was statistically positive and highly signiﬁcant for super cyclone SIDR [31]. For the CF HHs, the analysis from household size, indicating that overall risk perception in- Table 5 indicated a signiﬁcant correlation between cyclone creases with more household members. From the household with perceived probability based on previous exposure of the proﬁle in Table 3, it can be seen that the average household households to cyclones and their perception that they will be size of the surveyed household is 4.07 and 4.27 for CF and aﬀected by cyclones in the future. Table 5 also shows that non-CF HHs, respectively, while the Upazila average is 4.5 there is a signiﬁcant diﬀerence between climate ﬁnance people per household. .e possible reasons of higher risk recipient households and nonrecipients in their correlation perception may be due to the awareness that evacuated between river erosion and perceived probability, indicating household members during climatic events leads to higher that CF HHs were severely aﬀected by river erosion, given consumption costs borne by members during and after that the CF HHs and non-CF HHs were taken from the same climatic eﬀects when resources and commodities are scarce study area. A possible reason is the staﬀ of the CF Project as well as adaptation costs in the future. may have had a selection bias towards selecting those households who have lost land to river erosion as beneﬁ- ciaries. Loss of places is a signiﬁcant risk from climate 7.3. Income. Table 10 shows that as income increased, the change for physical loss of land and resources [4]. Fur- overall risk perception of both the CF and non-CF house- thermore, Table 6 shows that CF HHs had a higher perceived holds decreased. Results from this research are consistent severity than non-CF HHs, which indicates that the CF HHs with the ﬁndings of Alauddin and Sarker that higher-income were sensitised by the climate ﬁnance project towards the households engaged in adaptive measures and undertook exposure to climatic events on their lives and livelihoods. more associated risks [32]. .e study also showed that risk Climate change and its risks can be understood through appraisals to production, physical health, and income di- memories of past weather, current experience, and future mensions received greater priority while farmers paid less attention to risks to happiness and social relationships [13]. imaginaries, which are attached to particular places and practices [4]. Sociodemographic characteristics like farm experience, Advances in Meteorology 13 Table 10: Multiple linear regression results for CF and non-CF models for overall risk appraisal. CF Non-CF Independent variables Parameter Standard error P value Parameter estimate Standard error P value estimate HH size 6.251 2.387 0.009 — — — Income −0.003 <0.001 0.002 −0.004 0.002 0.023 Immobile assets 8.169 1.438 <0.001 8.649 2.165 <0.001 Productive assets (livestock, etc.) 1.882 0.921 0.042 3.310 1.210 0.007 Participation in self-help groups, producer group, −14.806 4.029 <0.001 — — — DMC, cooperatives SISCH: information on climate change from social — — — −7.285 2.815 0.011 circle, i.e., relatives/neighbours/friends/community Support from social safety net 23.182 7.411 0.002 21.881 8.385 0.010 Monetary/in-kind support from social circle (relatives, friends, neighbours, self-help groups) −15.206 7.325 0.039 — — — for adaptation responses Financial support from govt. for adaptation 18.830 7.547 0.016 — — — responses Financial support from NGOs (cash, materials, labour costs, advisory services) for adaptation — — — 39.503 13.963 0.006 responses education, and income level are the most signiﬁcant factors risks through the assets that they can mobilise in the face of in increasing the likelihood of farmers’ adaptation practices shocks [34]. However, if immobile assets are aﬀected, then [33]. .e inverse association between income and overall the households lose their ability to cope with the eﬀects of climatic events. risk appraisal may be due to higher income; the households perceive less risk as they engage more in adaptation mea- However, Islam et al. argue that a household’s in- sures and ascertain that they are in a better position to deal volvement in a diverse set of income-generating livelihood with the impacts of climate change. activities or strategies reduces the vulnerability of the Findings from the study, as seen in Table 3, also revealed household [25]. Income-generating activities provide that the CF HHs had a higher average income than the non- households with additional income in addition to their main CF HHs and had an increase in assets and livestock received source of income and support in having savings and from the CF interventions. However, more assets expanded investing in building assets. Assets help in engaging in ac- the risk of losing the assets they have gained through the CF tivities to address the household vulnerability. While people intervention. Perceived probability, production, physical from diﬀerent occupations are aﬀected diﬀerently, farmers, health, and income are the essential dimensions farmers pastoralists, and ﬁshers are especially aﬀected by climate change as they rely on natural resources and are, at the same perceive to be threatened by climate change [13]. A negative and signiﬁcant association was observed between overall risk time, exposed to meteorological events as well. Most de- and income and involvement in social circles for overall risk cisions by farmers to adapt to climate change vary directly appraisal. .is may indicate that social circles aﬀect the with livestock ownership since it serves as a store of value spending behaviour of households towards engaging in and encourages adaptation to climate change [32]. adaptation measures, resulting in a decrease in income and a simultaneous decrease in overall risk appraisal. 7.5. Participation in Social Circles. As shown in Table 10, results demonstrate increased participation in social circles 7.4. Immobile and Productive Assets. Table 10 shows that an causes a highly signiﬁcant decrease in the overall risk ap- praisal of household, especially for the non-CF HHs. As increase in immobile assets, in terms of land, increases overall risk perception of CF and non-CF households alike. hypothesised by Le Dang et al., social discourse is Furthermore, Table 10 also indicates a positive and highly hypothesised to aﬀect risk perception and adaptation as- signiﬁcant association is observed between risk appraisal and sessment [13]. In fact, social capital facilitates access to a productive assets in terms of livestock, poultry, agricultural broader source of information [35]. .e involvement in the equipment, and ﬁsheries equipment for CF HHs, while for social circle gives the households a better support system to non-CF the association is highly signiﬁcant. A considerable gain information and even jointly face the diﬀerent climatic proportion of the households have been aﬀected by river events as social circles can act as sources of ﬁnancial support erosion and are landless; therefore, this explains the high- and even interpersonal relationships, such as kinship net- risk perception between overall risk perception and im- works, social obligations, trust, and reciprocity, mobilise mobile assets. Furthermore, Vatsa argues that assets play a capacity directly by enabling material responses to climate hazards or indirectly via institutional modiﬁcations [4]. critical role in risk situations, and households try to resist and cope with adverse consequences of disasters and other Islam et al. have derived similar ﬁndings [25]. .e non-CF 14 Advances in Meteorology aﬀord, households also could share common resources households’ ability to cope and adapt was constrained be- cause of their lack of participation in community organi- within their social circle to adapt, which did not cause them to incur additional costs and yet beneﬁt from them. Cor- sations or the absence of community organisations as a whole, indicating that social relationships received less at- respondingly, support from the social circle increases the tention [36] from non-CF HHs. eﬀectiveness of climate ﬁnance through enhancing the utilisation of the household’s own resources towards con- tributing towards adaptation needs of the social circle. 7.6. Information on Climate Change from the Social Circle. Access to information from the social circle has a signiﬁcant 8. Conclusions impact on the overall risk appraisal of non-CF HHs, as shown in Table 10. From Table 3, it is seen that around 83.3% .is study conducted a comparative analysis between CF of the non-CF HHs do not engage in any social groups. and non-CF HHs regarding the anticipated climatic events .erefore, information received from the social circle on and the severity of the events on their lives as well as factors climate change informed the households on the exposure of which inﬂuenced their risk appraisal. Both CF and non-CF climate change and could have prepared accordingly, thus HHs resided in the same geographical and meteorological decreasing their risk appraisal. Information and discussion, conditions and dealt with the same climatic events. Both therefore, can inﬂuence perception [13]. Information from groups anticipated climatic events such as river erosion and the social circle could also have given the non-CF house- cyclones, among others, occurring in the future. However, holds a better understanding of how to interpret the in- the ﬁndings of the study indicated that CF HHs expected formation and also explore new ways of adapting from their higher severity of climatic events on the various dimensions social circle, also contributing to a decrease in the overall risk of their households such as housing, income, crops, appraisal. However, other scholars have found that farmers equipment, among others. .is result suggests that the CF who believe that climate change is happening and inﬂu- HHs are more aware of the consequences of climate change encing their family’s lives perceive higher risks in most and therefore engaged in more adaptation measures than dimensions [13]. non-CF HHs. Barrett had similar ﬁndings in a study in Malawi of adaptation ﬁnance-assisted villages [6]. Programs that provide technical assistance or compensation to change 7.7. Support from Social Safety Nets and Financial Support practices may be a positive opportunity for agricultural from Government and NGOs. .e regression analysis of communities to address climate change and help oﬀset the social safety net programmes under Table 10 illustrated a transaction costs associated with changing practices [25]. positive and highly signiﬁcant relationship with overall risk .is analysis implies that climate ﬁnance support informed appraisal for CF HHs and a positive and signiﬁcant re- the households about the risks that they were facing, assisted lationship for non-CF HHs. Social protection or safety net them in exploring adaptation options and engaging in them. programs assist individuals, households, and communities Awareness and training sessions, climate information, in- in managing better a wide range of risks that leaves people creased accessibility to public extensions services, and ex- vulnerable [34]. One of the reasons for this could be that change within the social groups about the eﬀects of climate these programs deal with both the deprivation and vul- change aﬀect the potential severity or eﬀect of climate nerability of the poorest people; thus, when these pro- change on the households. grammes supported the households, they perceived Furthermore, several factors inﬂuenced the risk appraisal themselves being at risk. Similarly, the CF HHs perceive of the CF and non-CF HHs, respectively. .e socioeconomic signiﬁcant and positive risk, when they received support conditions of the households played a key role in the risk from the government, as the assistance is aimed towards appraisal. Particularly, household size increased the risk people who are at risk and need support. Furthermore, as assessment for CF HHs. While the increase in income caused seen in Table 10, a relationship between ﬁnancial support a decrease in the risk assessment, increase in assets caused an from NGOs for adaptation measures and overall risk ap- increase in the risk perception of the households. .ese praisal for non-CF HHs is evident. Possible reasons could be results could indicate that households do not have stable that when they receive the support for adaptation measures socioeconomic conditions yet. from government or NGOs, they believe that they are at risk. .e beneﬁts by strengthening the social circle of the CF HHs under the climate ﬁnance project became evident as 7.8. Monetary/In-Kind Support from Social Circle (Relatives, participation in the diﬀerent groups and the monetary/in- Friends, Neighbours, and Self-Help Groups) for Adaptation Kind support from the social circle resulted in a decrease in Measures. As seen in Table 10, a negative and highly sig- overall risk appraisal. On the contrary, Le Dang et al. argue niﬁcant relationship is evident between monetary support that social circles play a signiﬁcant role in facilitating de- and in-Kind support from social circle for the CF HHs and cisions on using adaptation measures based on information overall risk appraisal. Instead of being at risk of facing the obtained from friends, relatives, and neighbours, which eﬀects of climate change alone, the CF HHs could rely on increases the overall perceived risk [13]. getting support from their social circle. While risk appraisal Furthermore, awareness is not suﬃcient for households played a crucial role in motivating the household to explore to engage in adaptation measures as the households were adaptation options which are crucial and which they can extremely poor and lacked adequate resources to adapt. In Advances in Meteorology 15 households. Mechanisms to locally generate and integrate fact, households, which intend to reduce the risks associated with climate change and have the resources or access to customised and contextualised adaptation measures into planning processes should be studied further. How to resources needed to make the appropriate changes, are generally more resilient and have a greater capacity to adapt channel the climate ﬁnance into reaching the vulnerable [37]. Information variables can increase or decrease risk population in the coastal region of Bangladesh so that in- appraisals. .erefore, information from social circles is equality is not increased and marginalisation is avoided was signiﬁcant for non-CF HHs as they are less engaged in social not covered under this research and may be explored fur- circles and value the information on climate change ther. Finally, future research may be conducted on the costs received. of various adaptation actions together with a cost-beneﬁt analysis which may contribute towards the future adaptation Diﬀerent sources of external ﬁnance such as from the government, social safety net programmes, or ﬁnancial implementation at the local level. support from NGOs, all increased the risk appraisal of the households. One of the reasons could be that ﬁnance is Data Availability mainly provided to the households if they are at risk. Sec- .e data used to support the ﬁndings of this study are ondly, since the ﬁnancial support is limited, the households have to examine the dimensions that are at risk from climatic available from the corresponding author upon request. events, hence making them more aware of the risks, which increases their risk appraisal correspondingly. Furthermore, Disclosure this causes the households to explore adaptation options .is article is an outcome of the research study of Ms Firdaus which are crucial and which they can aﬀord to address those Ara Hussain for her PhD at the Asian Institute of dimensions of the households that are at risk. .us, risk Technology. appraisal may increase the eﬀective utilisation of external climate ﬁnance, and the households own resources for adaptation measures. Conflicts of Interest .is study tried to address the knowledge gap on the .e authors declare that there are no potential conﬂicts of eﬀect of climate ﬁnance on risk appraisal at the local level. interest concerning the research, authorship, and publica- .e ﬁndings of this research reinforce the pattern of the tion of this paper. inadequacy of climate ﬁnance to meet the local needs of the most vulnerable communities. 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References

‘Subjective resilience’: using perceptions to quantify household resilience to climate extremes and disasters,

L. Jones and T. Tanner
Adaptive capacity and human cognition: the process of individual adaptation to climate change,

T. Grothmann and A. Patt
Actual vis-à-vis perceived risk of flood prone urban communities in Pakistan,

I. A. Rana and J. K. Routray
Making sense of climate change risks and responses at the community level: a cultural-political lens,

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Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

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Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

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