Abstract

Most developing countries, like Nepal, are expected to experience the greatest impact of climate change (CC) sooner and on a greater magnitude than other developed countries. Increase in the magnitude and frequency of extreme rainfall events is likely to increase the risk of flooding in rivers. The West Rapti River basin is one of the most flood prone and also one of the most dynamic and economically important basins of Nepal. This study elicits the willingness to pay (WTP) from the local people in the basin to reduce risks from possible floods due to CC. The WTP for flood mitigation in different flood hazard zones and flood scenarios were determined using referendum method and a face to face questionnaire survey. From a total of 720 households across all flood zones, a stratified randomly selected sample of 210 households was surveyed. The sample included households from a range of socio-economic backgrounds. The average WTP varied by flood hazard zone and within each zone, by CC-induced flood scenarios. The average WTP of respondents was highest for the critical flood prone zone, followed by moderate and low flood prone zones. Similarly, within each zone, the average WTP increased with increasing flood magnitudes due to CC. The variation of average WTP of respondents in different flood prone zones and scenarios indicate different levels of perceived severity. Moreover, the introduction of the concept of ‘man-day’ or ‘labour-day’ in WTP research is a novel and applicable methodological approach, particularly in the South Asian region. The findings of this study are useful for policy implications for the design of participatory flood management plans in the river basin.

INTRODUCTION

Most developing countries are in tropical or arid regions, and are expected to experience the greatest impact of climate change (CC) sooner and on a greater magnitude than temperate regions (FAO 2017). Nepal is a developing country lying at the central part of the Himalayan range (Maraseni et al. 2005, 2014). The average annual rainfall of the country is about 1,750 mm, ranging from more than 5,000 mm in the central part to less than 250 mm in the higher Himalaya in the north (Maraseni et al. 2014; Shrestha et al. 2014). Summer monsoon brings around 80% of the annual rainfall in Nepal, the rain being more intense in the east, declining westwards, while the winter rain falls heavily in the north-west and declines to the southeast. Higher intensity rainfall but fewer rainy days, with no significant change in the total amount of annual precipitation has been experienced in recent years (Botzen 2013; Devkota et al. 2016). Since there is a direct relationship between rainfall and river flow, the probability of occurrence of flooding events in the country has increased recently. The aim of this study is to determine the level of willingness to pay (WTP) for flood risk management under CC in the West Rapti River Basin in Nepal.

How people perceive CC directly influences their willingness to pay (WTP) to reduce the associated risks. Veronesi et al. (2014) and Zhai & Suzuki (2008) report that perceptions of CC with regard to long term changes in temperature and/or an increase in rainfall positively affect the WTP for the reduction of such risks. Similarly, taking predicted future average temperatures as an indicator of CC, Akter & Bennett (2011) show that individuals who accept these predictions are likely to exhibit a higher WTP for mitigation measures. Conversely, people who believe that temperatures are not rising globally or locally are less likely to be willing to pay for such preventive actions (Carlsson et al. 2012; Botzen & van den Bergh 2012). People who perceive long term changes in climate are more willing to pay to reduce the climate related risks than people that do not perceive any change (O'Neill et al. 2016).

Several studies (Hoffman & Spitzer 1993; Meyerhoff 2006) have helped identify different dimensions of WTP, especially when climate related damage is more likely expected to increase in the coming decades. Tol (2013) estimated that the global average costs of CC damage lie between 2% and 3% of global GDP. Application of economic instruments for environmental management has been dealt with by O'Connor (1996) and Osberghaus (2017). Liebe et al. (2011) and Lytle & Poff (2004) highlight the importance of WTP for disaster insurance so that insurers can assess the future profitability of offering coverage against damage caused by natural disasters. Insurance demand increases with increased flood probabilities due to CC and increased levels of expected flood damage (Wertenbroch & Skiera 2002; Zhai et al. 2006; Botzen et al. 2009; Lo 2013). In industrialized countries, the WTP for the (uninterrupted) provision of potable water to households or the willingness to accept interruption was elicited (Ghanbarpour et al. 2014). The study found a high WTP, not only to secure the households' own water provision, but also to maintain a good ecological status of the river. WTP is further constrained by household income and the disutility from flood risks measured through higher or lower flood damage costs and risk aversion according to people's attitude to flood protection (Lera-López et al. 2012). WTP for flood mitigation or amelioration among owners of houses, agricultural and forest lands varies according to individual location, dependency on natural resources, income and social situation (Hall et al. 2003; Maraseni et al. 2005; Maraseni & Xinquan 2011) and exposure to CC induced flood scenarios. However, the relationship between such flood risks and WTP for flood mitigation has not yet been examined at the community level of underdeveloped countries like Nepal.

It is therefore possible to evaluate the WTP for flood mitigation by the flood affected people under climatic uncertainty, so that an assessment can be made of the perception of the community about flood risk management. The results of this study, therefore, provide a basis for the appraisal of policy options, allocation of resources and monitoring performance of substantial government investment in flood management (Hensher et al. 2005). Such assessments are important to support policy makers' decisions on how to deal with emerging risks of CC in the water sector and where to set priorities.

THE STUDY AREA

The West Rapti River (WRR) originates in the Lesser Himalaya and flows through the Siwaliks and Terai plain of Nepal before joining the Ganga River in India. Geographically, the study area extends from 27°56′50″ to 28°02′30″ North latitudes and 81°45′00″ to 81°40′00″ East longitudes. The total catchment area within the Nepalese territory is 6,500 km2 and the elevation varies from about 131 m (at the Indian border) to 3,620 m above the sea level. About 45% of the basin consists of hills, whereas the remaining 55% is occupied by plains of the Nepal in Terai. The basin includes two districts of Nepal, namely, Dang and Banke (Figure 1). There are 39 Village Development Committees (VDCs) and two municipalities in Dang, whereas 46 VDCs and one municipality in Banke. Among these, only five VDCs (Gangapur, Kamdi, Holiya, Lalmatiya and Sohanpur) with 720 households are affected by floods (CBS 2012).

Figure 1

Location map of study area with flood severity zones (West Rapti River Basin of Nepal).

Figure 1

Location map of study area with flood severity zones (West Rapti River Basin of Nepal).

METHODOLOGY

The study area was classified into three different zones or strata based on the extent of previous flooding (i.e. depth of the inundation and the frequency of flooding). These are identified as: the critical flood prone zone (Zone 1); moderate flood prone zone (Zone 2); and low flood prone zone (Zone 3). The total household (HH) numbers in each zone were derived from the District Development Committee profile: 144 in the HH critical zone; 274 in the moderate zone; and 302 in the low flood prone zone. Initially, stratified random sampling with proportion to HH number was considered as a basis of allocating the sampling effort in each zones. However, for obvious reasons, the critical flood prone zone had more serious flood related problems than the other two zones. Therefore, more samples were taken from Zone 1 (38%) than from Zone 2 (30%) or Zone 3 (24%) (Table 4). In each of the zones, HHs were allocated a number and HHs to be sampled were selected randomly using a random number table so that each and every HH had an equal chance of being selected. Finally, out of 720 flood affected HHs, 210 HHs were selected to participate in the face-to-face semi-structured questionnaire survey. Prior to conducting the survey, the questionnaire was pre-tested with the focus groups and translated into Nepalese language.

In each HH, the head of the HH, who was deeply involved in all financial and flood related activities, was interviewed. If the head of a HH was not available, another senior member available at the house was interviewed. If none is available at the first attempt, some additional attempts (up to five) were made until someone was interviewed from the selected HH. Therefore, the response rate of selected HH was 100%.

Contingent valuation (CV) method extracted WTP for minimum water quality levels for boating, fishing or swimming in the United States. This was followed by a later large study concerning the value of water quality (Viscusi et al. 2008). A similar study was conducted by Roberts et al. (2008), examining uncertainty of people's WTP for flood risk management in developed countries. However, for developing countries, a different approach is more appropriate for measuring the WTP at the grass root level. In such countries, where most of the people are unemployed and, thus, their income levels are very low, using ‘man-day’ as a unit of WTP is a promising approach. In this study, the National Oceanic and Atmospheric Administration Panel (NOAA) guidelines (Arrow et al. 1993) were strictly followed and WTP for mitigating floods under different flood scenarios were estimated with the man-day approach.

Scenario development

The Government of Nepal (GoN) envisages managing the flood problem in the WRR as a priority basin (GoN/ADB 2016). This is expected to reduce: (1) the risk of casualties; (2) the loss of private properties such as houses, agricultural and forest lands, livestock, etc.; and (3) the loss and damage of public lands and infrastructure. By developing flood controlling mechanisms, the GoN plans to guarantee protection of lives, private and public goods, and property.

Four CC induced flood scenarios (in terms of inundation depth and water logging time were proposed: (I) the current flood scenario; (II) a flood scenario for 2030; (III) a flood scenario for 2070; and (IV) a flood scenario for 2100. Readers are referred to (Devkota 2014) for details. During the survey, all the four flood scenarios were shown on a map and also on a laptop to the community (Devkota et al. 2014). Information of various flood prone areas, depth and its frequencies were presented for different scenarios. Through this process, respondents were quickly able to calculate their damage costs corresponding to the flood scenarios and come up with their WTP accordingly. It is assumed that when respondents calculate the amount that they are willing to pay for flood mitigation under each scenario, they take into account the expected flood damage to their property. Respondents were informed of the importance this study could have to the government and other stakeholders for developing and implementing flood mitigation policies.

Pre-testing of questionnaire and its final setting

In order to produce the WTP of respondents, a bidding game method was used. During the reconnaissance survey, the questions were pre-tested and refined using words/language which was locally appropriate to the group of people being interviewed. The wording of the question was as follows:

Would you vote in favour of reducing your annual loss due to flood in terms of labour days each year to protect life and properties?

Yes / No

If ‘yes’, what would be the highest amount/labour days you would pay per year?

Flood scenariosScenario IScenario IIScenario IIIScenario IV
WTP in labour days         
Flood scenariosScenario IScenario IIScenario IIIScenario IV
WTP in labour days         

If ‘no’, why do you say ‘no’? What is the least amount/labour days you would pay?

Flood scenariosScenario IScenario IIScenario IIIScenario IV
WTP in labour days         
Flood scenariosScenario IScenario IIScenario IIIScenario IV
WTP in labour days         

Respondents’ characteristics

In order to triangulate the validity of the responses, WTP values were cross-checked with the estimated (by respondents) damage costs of flood to agricultural crops and livestock, and impacts on their total income, as was done in Maraseni et al. (2008). Appropriate statistical tests were conducted to see whether there were any statistically significant differences in WTPs associated with income and damage cost classes.

We were aware of flaws of WTP method. One of the major drawbacks of WTP method is that it seeks responses to a hypothetical question that do not involve cash outlays. Therefore, they may overstate their WTPs. In order to avoid this problem: (1) wording of questions were carefully chosen; (2) they were given an option of labour-day (man-day, instead of cash) as a means of WTP so that they know the real value of their response; and (3) they were reminded that their disposable incomes will decrease by the same amount. Moreover, the context and scenarios were briefed many times in a plain language until they clearly understood the issues and possible implications.

RESULTS

Willingness to pay by flood zones

Whether there are any differences in WTP between the respondents living in different flood prone areas is a key question for policy makers. In order to answer this question, the study area was divided into three flood severity zones (low, moderate and critical) and WTP were assessed for each zone and for all four flood scenarios.

As expected, the average WTP of respondents was highest within the critical flood prone zone and the lowest in the low flood prone zone (Table 1). Similarly, the average WTP of respondents increased from Scenario I to Scenario IV. The average WTP of respondents between the different zones were statistically significantly different (p < 0.05), as were WTPs within the flood hazard zones for the various scenarios (p < 0.05).

Table 1

Willingness to pay by flood zones

Flood prone areas (zones)WTP (man-days/yr) for Scenario IWTP (man-days/yr) for Scenario IIWTP (man-days/yr) for Scenario IIIWTP (man-days/yr) for Scenario IVF valueSignificance
Critical flood prone (Zone 1) (N = 54) 
 Mean 4.67 6.93 8.37 10.22 4.51 0.004 
 SD 1.74 2.77 3.21 3.47 
Moderate flood prone (Zone 2) (N = 83) 
 Mean 4.34 5.81 7.73 9.76 2.93 0.009 
 SD 1.73 2.18 2.59 3.27 
Low flood prone (Zone 3) (N = 73) 
 Mean 3.78 5.37 6.77 8.55 3.13 0.006 
 SD 1.44 2.26 2.68 3.22 
F value 4.85 6.92 5.39 4.56   
Significance (p-value) 0.009 0.001 0.005 0.012 
Total (N = 210) 
 Mean 4.23 5.94 7.56 9.46 
 SD 1.67 2.44 2.85 3.36 
Flood prone areas (zones)WTP (man-days/yr) for Scenario IWTP (man-days/yr) for Scenario IIWTP (man-days/yr) for Scenario IIIWTP (man-days/yr) for Scenario IVF valueSignificance
Critical flood prone (Zone 1) (N = 54) 
 Mean 4.67 6.93 8.37 10.22 4.51 0.004 
 SD 1.74 2.77 3.21 3.47 
Moderate flood prone (Zone 2) (N = 83) 
 Mean 4.34 5.81 7.73 9.76 2.93 0.009 
 SD 1.73 2.18 2.59 3.27 
Low flood prone (Zone 3) (N = 73) 
 Mean 3.78 5.37 6.77 8.55 3.13 0.006 
 SD 1.44 2.26 2.68 3.22 
F value 4.85 6.92 5.39 4.56   
Significance (p-value) 0.009 0.001 0.005 0.012 
Total (N = 210) 
 Mean 4.23 5.94 7.56 9.46 
 SD 1.67 2.44 2.85 3.36 

Scenario I = current flood situation; Scenario II = potential flood level for 2030; Scenario III = potential flood level for 2070; and Scenario IV = potential flood level for 2100.

Income of respondents by flood prone zones

WTP for flood mitigation actions could also be influenced by income level, especially those incomes which are directly affected by flooding. Farm and livestock are the major sources of income in the study area and are highly impacted by floods. Table 2 presents the survey results for average farm and livestock incomes within each flood zone. The highest average farm and livestock income was reported by respondents within the critical flood prone area, followed in turn by those in the moderate and low flood prone areas. This may, in part, be due to the larger size of farms and higher reliance on farming and livestock activities in the critical flood prone area compared to other areas.

Table 2

Farm and livestock income by flood zones

Flood prone areas (zones)Farm income (US$)Livestock income (US$)a
Critical flood prone (N = 54) 
 Mean 1,652 218 
 SD 952 244 
Moderate flood prone (N = 83) 
 Mean 1,228 211 
 SD 779 213 
Low flood prone (N = 73) 
 Mean 1,132 164 
 SD 538 206 
Total (N = 210) 
 Mean 1,328 197 
 SD 780 219 
Flood prone areas (zones)Farm income (US$)Livestock income (US$)a
Critical flood prone (N = 54) 
 Mean 1,652 218 
 SD 952 244 
Moderate flood prone (N = 83) 
 Mean 1,228 211 
 SD 779 213 
Low flood prone (N = 73) 
 Mean 1,132 164 
 SD 538 206 
Total (N = 210) 
 Mean 1,328 197 
 SD 780 219 

aNote: Conversion rate: US$1=NRs92 (Date 25/04/2014).

Average annual damage cost of respondents by flood zones

Another factor that critically affects the WTP of respondents is flood related damage costs already incurred (Zhai 2006). The WTP for different flood zones and average annual damage from 2010 to 2014 is shown in Table 3. As expected, the low flood prone zone has the lowest average annual damage per family (US$88) during that period. The average damage cost is higher in the moderately affected zone and highest in the critically affected zone. The mean WTP of these three zones are statistically significantly different (p < 0.000) at over 99.99% confidence level.

Table 3

Average annual damage in last 5 years by flood zones

Flood prone areas (zones)Average annual damage cost in last 5 years (US$)
Critical flood prone (N = 54) 
 Mean 147 
 SD 100 
Moderate flood prone (N = 83) 
 Mean 106 
 SD 75 
Low flood prone (N = 73) 
 Mean 88 
 SD 57 
Total (N = 210) 
 Mean 110 
 SD 80 
Significance (p-value) 0.000 
Flood prone areas (zones)Average annual damage cost in last 5 years (US$)
Critical flood prone (N = 54) 
 Mean 147 
 SD 100 
Moderate flood prone (N = 83) 
 Mean 106 
 SD 75 
Low flood prone (N = 73) 
 Mean 88 
 SD 57 
Total (N = 210) 
 Mean 110 
 SD 80 
Significance (p-value) 0.000 

Source: Field survey.

DISCUSSION

The application of WTP in terms of man-days or labour-days is rare in the developing world and is particularly novel to this South Asian Region. This is very useful for developing countries that is something they can contribute easily (culturally acceptable, too), where a man-day is very easy to explain and commonly understood. Total average annual household income of the study area is about US$1,328 (Table 4), while average annual livestock income is US$197. The total farm and livestock income is slightly higher than the national average (US$1,256). More than 50% of the flood plain residents included in the study appear to live below the poverty line (<US$2 per day) (Bhirthal & Digvijay 2012).

Table 4

Summary of willingness to pay

Flood prone areasSample size (210)Income (US$)
Flood damage (2010–2014) (US$)Willingness to pay (US$)
House hold no.FarmLivestockScenario IScenario IIScenario IIIScenario IV
Critical flood prone (Zone 1) 54 1,652 218 147 18 26 31 39 
Moderate flood prone zone (Zone 2) 83 1,228 211 106 16 22 23 37 
Low flood prone (Zone 3) 73 1,132 164 88 14 20 20 32 
Average   1,328 197 110 17 22 28 36 
Flood prone areasSample size (210)Income (US$)
Flood damage (2010–2014) (US$)Willingness to pay (US$)
House hold no.FarmLivestockScenario IScenario IIScenario IIIScenario IV
Critical flood prone (Zone 1) 54 1,652 218 147 18 26 31 39 
Moderate flood prone zone (Zone 2) 83 1,228 211 106 16 22 23 37 
Low flood prone (Zone 3) 73 1,132 164 88 14 20 20 32 
Average   1,328 197 110 17 22 28 36 

The average WTP of respondents decreased from the critical to low flood prone zones and, within each zone, WTP increased from Scenario I to Scenario IV. As noted, the average WTP for Scenario IV was the highest (equivalent to US$36), followed by Scenario III (US$28), Scenario II (US$22) and Scenario I (US$17). Considering the scale and magnitude of flood damage, these figures seem realistic. In addition, WTPs were positively correlated with livestock and agricultural crop incomes and incurred flood damage costs. Information about WTP for different zones and flood scenarios provide an evidence-base which will help decision makers to develop and implement robust and equitable flood mitigation plans and programs.

WTP varied with income level within each flood hazard zone. The relationship between income and average WTP for various scenarios was positively correlated with farm income and livestock income. The income from farms was found to be highest for the critical flood prone zone. People living in this zone have comparatively more land and irrigation is easier because of the proximity to the river. This increases the possibility of cultivating paddy, and therefore improving livelihoods. Moreover, two crops per year are possible due to the greater availability of water. Furthermore, the soil is also highly fertile in this zone due to regular deposition of alluvial soil by the river. Local people also can grow cash crops such as watermelon, cucumber, and pumpkin in the floodplain area. As the distance increases from the river, the land does not get irrigation facilities and thus rain fed agriculture is prominent in such areas. Also, rice production decreases, as do the chances of growing other cash crops. At the same time, cropping frequency also goes down.

In addition to flood damage cost, socio-economic factors such as age, gender and education level have influenced the level of WTPs. In summary, for all flood scenarios analysed: (1) mean WTPs were statististically significantly different between the different age groups (p ≤ 0.05) and mean WTP was highest for the 35 to 44 year age group; (2) mean WTP of female respondent was found higher than that of male respondents (p ≤ 0.05); and (3) average WTP of literate people was found higher than illiterate people (p ≤ 0.05). For detailed discussion of socioeconomic factors and their relationships with WTPs, please see Devkota (2014).

The average WTP of respondents was highest within the critical flood prone zone and the lowest in the low flood prone zone. Similarly, the average WTP of respondents increased from Scenario I to Scenario IV. The average WTP of respondents between the different zones were statistically significantly different (p < 0.05), as were WTPs within the flood hazard zones for the various scenarios (p > 0.05).

On average, the estimated flood damage cost in the study area was $US110 per household per year. Most flood damage costs were due to property and crop damage, followed by loss or damage to livestock. The results support the argument that the higher the damage, the greater the WTP, comparable to the results of Botzen et al. (2009) and Abbas et al. (2015). However, the damage cost could vary both spatially and temporally (O'Neill et al. 2015). Floods in a place with high value property and at the time of harvesting crops would incur more damage. Therefore, WTP estimates derived from perceived impacts of all potential costs are very useful for developing flood mitigating policies, programs and projects (Blocker & Rochford 1986; Morss et al. 2005; Brouwer et al. 2009). Further, comprehensive WTP studies considering direct costs of physical property as well as indirect costs associated with the psychology and livelihoods of the local people in the flood prone areas could be extremely useful for the planning and policy makers.

People were victims of floods for many years and how to resolve this issue was unanimous and indisputable interest of all respondents. Therefore, their response and support for this research was spontaneous and praiseworthy, and there was neither any political bias in this research nor were the respondents forced to elicit their response and WTPs.

CONCLUSION

Results of this study showed that income levels of the residents and the extent of flood damage costs influenced WTP for various flood scenarios. The average WTP value of respondents was greater in the critical flood prone zone than in moderate and less flood prone zones. This applied equally for all flood scenarios. Similarly, the average WTP of respondents increased from Scenario I to Scenario IV. This research has further explored and enriched the understanding of differential impacts of CC on floods from a hydro-economic perspective. Moreover, the introduction of the concept of ‘man-day’ or ‘labour-day’ in WTP research is a novel and applicable methodological approach, particularly in the South Asian region. Application of this methodology and the results of this study could be instrumental for the fair allocation of resources. Although a more comprehensive study is warranted, this finding provides a rational basis for the appraisal of policy options and resources allocation. Similarly, this assessment is likely to be one of the several sources of evidence that decision makers may employ while making difficult and often highly contested long term planning and flood risk management decisions.

ACKNOWLEDGEMENT

This study was funded by University of Southern Queensland, Australia. We are extremely grateful to all key informants and interviewees for their time for meetings, discussions and interviews. We cordially thank anonymous referees and our editor for their highly valuable suggestions, which were gratefully acknowledged.

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