Wagne , Simon; Thiam, Sophie; Dossoumou, Nadège I. P.; Daou, Da id
A icle — Published Ve sion
Wha In luences he Demand o a Po en ial Flood Insu ance P oduc
in an A ea wi h Low P e ious Exposu e o Insu ance? – A Case S udy in
he Wes A ican Lowe Mono Ri e Basin (LMRB)
Economics o Disas e s and Clima e Change
P o ided in Coope a ion wi h:
Sp inge Na u e
Sugges ed Ci a ion: Wagne , Simon; Thiam, Sophie; Dossoumou, Nadège I. P.; Daou, Da id (2023) :
Wha In luences he Demand o a Po en ial Flood Insu ance P oduc in an A ea wi h Low P e ious
Exposu e o Insu ance? – A Case S udy in he Wes A ican Lowe Mono Ri e Basin (LMRB),
Economics o Disas e s and Clima e Change, ISSN 2511-1299, Sp inge In e na ional Publishing,
Cham, Vol. 8, Iss. 1, pp. 1-32,
h ps://doi.o g/10.1007/s41885-023-00138-w
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/318348
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h p://c ea i ecommons.o g/licenses/by/4.0/
Vol.:(0123456789)
Economics o Disas e s and Clima e Change (2024) 8:1–32
h ps://doi.o g/10.1007/s41885-023-00138-w
1 3
RESEARCH
Wha In luences heDemand o aPo en ial Flood Insu ance
P oduc inanA ea wi hLow P e ious Exposu e oInsu ance?
– ACase S udy in heWes A ican Lowe Mono Ri e Basin
(LMRB)
SimonWagne 1,2· SophieThiam3· NadègeI.P.Dossoumou4· Da idDaou2
Recei ed: 12 Augus 2023 / Accep ed: 19 No embe 2023 / Published online: 18 Decembe 2023
© The Au ho (s) 2023
Abs ac
Floods po ay a se e e p oblem in he i e ine a eas o Wes A ica while mo e equen
and in ense hea y p ecipi a ion e en s a e p ojec ed unde clima ic change scena ios.
Al eady, loods cause mani old impac s, lea ing he popula ion o cope wi h he inan-
cial impac s o loods h ough hei own means. As o mal isk ans e mechanisms (e.g.,
insu ance) a e no ye widely a ailable o he popula ion, e o s o inc ease hei acces-
sibili y a e being in ensi ied. Howe e , s udies assessing lood insu ance demand cu en ly
mos ly ocus on egions wi h mo e es ablished ma ke s. Also, hey a e majo ly applying
con en ional s a is ical modeling app oaches ha conside only a small numbe o pa am-
e e s. Con a ily, his s udy aims o p o ide an app oach o assessing lood insu ance
in a con ex o low p e ious exposu e o such p oduc s, o allow o a be e conside a-
ion o he esea ch con ex . The e o e, a pa ame e selec ion amewo k is p o ided and
machine lea ning and deep lea ning models a e applied o selec ed pa ame e s om an
exis ing household su ey da a se . In addi ion, he deep lea ning sequen ial neu al ne -
wo ks ou pe o med all machine lea ning models achie ing an accu acy be ween 93.5—
100% depending on he loss unc ion and op imize used. The isk o be co e ed, insu ance
pe cep ion, no access o any sou ce, access o suppo om communi y solida i y unds,
access o go e nmen al suppo , o d awing upon own esou ces o inancial coping, inan-
cial eco e y ime, lack o means and p io i izing mo e essen ial needs eme ged as impo -
an model pa ame e s in esea ching insu ance demand. Fu u e oll-ou campaigns could
conside he pa ame e s poin ed ou by his s udy.
Keywo ds Floods· Machine lea ning· Deep lea ning· Willingness o insu e· Togo·
Benin
In oduc ion
O e he pas decades, he e ha e been obse a ions o an inc easing end o hyd ological
ex emes (i.e. maximum peak discha ge) in Wes A ica, leading o an inc ease o disas-
ous lood e en s in a eas loca ed in p oximi y o la ge i e s (Ranasinghe e al. 2021).
Ex ended au ho in o ma ion a ailable on he las page o he a icle
2
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Mo eo e , while o e all p ecipi a ion is p ojec ed o dec ease in Wes A ica, hea y p e-
cipi a ion e en s a e expec ed o occu mo e equen ly and in ensi ely acco ding o sce-
na ios conside ing medium o high emission le els, which leads o accumula ed hyd o-
clima ic s ess h ough d ough and lood e en s in he egion (T isos e al. 2022; Gio gi
e al. 2019). Al eady, loods cause a wide a ie y o impac s in Wes A ica, such as dam-
aged buildings, dis up ion o li elihoods, damaged goods, a ali ies, displacemen , sick-
ness and sp eading o diseases, damaged in as uc u e and c op damage (Wagne e al.
2021; A iyie e al. 2018; B isibe and Pepple 2018; Addo and Danso 2017; Ahadzie e al.
2016; Ene e e al. 2016; Adewole e al. 2015; Adelekan and F egene 2015; Codjoe e al.
2014). Wi h ega ds o he inancial implica ions o lood impac s in he Lowe Mono Ri e
Basin (LMRB) in pa icula , i was ound ha loods egula ly a ec households inancially
h ough ag icul u al (los in es men s h ough loss and des uc ion o c ops and plan a-
ions, loss o li es ock), ma e ial ( epai and eplacemen cos o damage o des uc ion o
esiden ial houses and pe sonal ma e ial belongings), heal h (sickness and subsequen pay-
men o medical ca e), and comme cial/ ade impac s (los income om damaged s o ed
p oduc s o sale, lack o ma ke access, and a ec ed ma ke places) (Wagne e al. 2022).
While mu ual suppo among a ec ed households, especially in he phases o esponse and
econs uc ion (especially hos ing lood ic ims and helping neighbo s o ebuild) (Lamond
e al. 2019; Amoako e al. 2019; Ahadzie e al. 2016; Codjoe and Issah 2016; Adelekan and
Asiyanbi 2016), seems o be e y p e alen in he Wes A ican egion, he e appea s o be
a lack o isk ans e ins umen s ha a e designed o add ess he inancial consequences
o loods (Wagne e al. 2021). Thus, people in he egion equen ly eso o in o mal
mechanisms ha a e no o iginally designa ed o alle ia ing he di e se inancial implica-
ions o lood impac s, which se s households back in hei inancial achie emen s (Wagne
e al. 2022; Boubaca e al. 2017; Addo and Danso 2017).
Mo eo e , he equency and se e i y o lood impac le els in he LMRB equi e mo e
conce ed isk educ ion ac i i ies be o e es ablishing isk ans e mechanisms, such as
insu ance, ha enable sp eading he isk o inancial losses ac oss a la ge pool o bene i-
cia ies (Wagne e al. 2022). Also, whe he insu ance is an app op ia e isk managemen
ool in de eloping economies o no emains a con es ed issue (Pill 2022; Mechle and
Deubelli 2021; Dehm 2020; Linne oo h-Baye e al. 2019; Schä e e al. 2019; Gewi zman
e al. 2018). While he e a e inc eased e o s o aise insu ance pene a ion and insu ance
co e age agains clima e- ela ed ex eme e en s in de eloping economies (InsuResilience
Global Pa ne ship 2021), insu ance p o ec ion agains lood impac s emains di icul o
be es ablished, e en globally (Lége 2022; Flood Resilience Ini ia i e 2020; Lloyd’s 2018).
In addi ion, much o he esea ch on he up ake o o willingness o pay o lood insu ance
ocusses on he Asian, No h Ame ican and Eu opean egion, in which he es ablishmen
o lood insu ance in he ma ke and amilia i y wi h such p oduc s a e e y di e en om
he Wes A ican egion. Aside om a ew s udies (Be g e al. 2022; Oduniyi e al. 2020;
Na ud and Vondolia 2020; Adzawla e al. 2019), his opic has no been widely esea ched
in he A ican con ex . Also, insu ance pene a ion on he A ican con inen in gene al
is only hal o he global a e age while also he a e age p emiums pe pe son a e ele en
imes lowe (Bagus e al. 2020). Thus, o be e in o m u u e oll-ou campaigns o lood
insu ance p oduc s i is impo an o esea ch he pa ame e s ha a e associa ed wi h insu -
ance ake-up in se ings whe e a la ge numbe o people a isk ha e no ye been insu ance
cus ome s, such as he LMRB.
Mos s udies esea ching he willingness o insu e (WTI) agains loods/willingness o
pay (WTP) ely on pa ame e selec ion di ec ly based on li e a u e and subsequen ly apply
eg ession me hods (Ne usil e al. 2021; Robinson and Bo zen 2019; Reynaud e al. 2018;
3
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Fahad and Jing 2018; Tu ne e al. 2014; Bo zen e al. 2013, Bo zen and an den Be gh
2012), ha usually only conside a low numbe o pa ame e s. Con a ily, i p esen s a
challenge o de i e such pa ame e s om a conside able body o s udies o he Wes A i-
can egion, due o he limi ed numbe o a ailable publica ions om his a ea. Thus, es ab-
lished amewo ks o easons o pa ame e inclusion om o he con ex s migh no be he
bes i ing o his esea ch con ex . To add ess his gap, his s udy in es iga es he ollow-
ing cen al esea ch ques ion: Which pa ame e s in luence he decision-making p ocess o
households o ake up a po en ial insu ance p oduc agains lood damages in a se ing wi h
low p e ious exposu e o such p oduc s, such as he LMRB?
Cons ained by he limi ed li e a u e base o he Wes A ican egion, his s udy ini-
ially e iews li e a u e on WTI agains loods/WTP o lood insu ance on a global scale.
Based on his body o li e a u e, a amewo k is de eloped ha summa izes six hema ic
a eas o pa ame e s (subjec i e pe cep ion o lood isk, objec i e lood isk, in e ac ions
wi h insu ance ins i u ions, In e ac ion wi h o he ins i u ions & social en i onmen , a ib-
u es o HH/indi iduals, asse s o be po en ially insu ed) o guide which ac o s a e in luen-
ial on he demand o insu ance in he esea ch se ing. To s uc u e he pa ame e selec-
ion, ea u e columns o he en i e da a se we e ini ially assessed o he en i e da a se .
Then, he emaining pa ame e s we e ca ego ized in o he six hema ic a eas o he ame-
wo k. Mo eo e , he g ouped pa ame e s we e assessed h ough pai plo s and a hea map
co ela ion ma ix. As a inal s ep o e i ica ion, c oss abs we e used o assessing he co -
ela ion be ween he pa ame e s and he ou pu alue. This da a-d i en pa ame e selec ion
app oach is deemed sui able o his s udy due o esea ching a con ex in which people
a isk ha e no been widely exposed o insu ance p oduc s. Subsequen ly, on he basis o
he selec ed pa ame e s, machine lea ning and deep lea ning models a e ained ha se e
in explaining he obse ed demand o a po en ial lood insu ance p oduc in he esea ch
a ea.
Backg ound
Insu ance andRisk T ans e o Floods inTogo andBenin
Cu en ly, insu ance p oduc s agains he impac s o loods a e no widely o e ed on
a household le el in Togo and Benin. The insu ance indus y is mos ly cen e ed a ound
mo o cycle/ca insu ance and less on na u al haza ds (Me on 2019). In addi ion, he e a e
e o s in Benin o es ablish heal h insu ance in pilo communi ies ee o cha ge o i s
bene icia ies in he i s h ee yea s (Go e nmen o he Republic o Benin 2021). Wi h
ega ds o loods, calls o a easibili y assessmen o a lood insu ance sys em h ough a
na ional insu ance und a e e en da ing back o a leas 2011, as s a ed in a pos -disas e
needs assessmen o he 2010 loods (Go e nmen o he Republic o Benin 2011). Also,
he Togolese go e nmen exp essed a s ong in e es in easibili y s udies o an ag icul-
u al insu ance sys em wi hin i s Na ional Adap a ion Plan (Go e nmen o he Republic
o Togo 2017). In addi ion, in 2018 Togo was chosen by he pan-A ican isk pool mecha-
nism A ican Risk Capaci y (ARC) o se e as a pilo coun y o he implemen a ion o a
lood insu ance scheme (Akoda 2018). Howe e , no in o ma ion on i s cu en s a us could
be ound, and he mos ecen a ailable epo o he Togolese Republic only con ains
in o ma ion o he e en o d ough (A ican Risk Capaci y 2021b), simila ly o Benin
(A ican Risk Capaci y 2021a). Mo eo e , he Beninese go e nmen also s a ed a p ac ical
4
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
absence o an insu ance sys em o clima e- ela ed impac s, such as loods, d ough s, wind
s o ms, o hea wa es, despi e hei po en ially high impac on he coun y’s g oss domes ic
p oduc (Go e nmen o he Republic o Benin 2020). Rega ding he LMRB in pa icula ,
a ecen s udy poin s ou a s ong need o isk- educing lood adap a ion measu es and
ha a con en ional, ma ke -based lood insu ance app oach could be imp ac ical due o he
high se e i y and equency le els o epo ed lood impac s om a household pe spec i e
(Wagne e al. 2022). As a consequence, his s udy aims o show ele an insigh s in o he
po en ial lood insu ance ma ke , o he case ha isk- educing lood adap a ion measu es
a e success ully implemen ed in he LMRB. Mo eo e , he esea ch p o ides insigh o
insu e s o see i hey could help o opening a ma ke o hemsel es by con ibu ing o
in es ing in o lood adap a ion measu es in he a ea. Finally, his esea ch could bene i
he p e iously men ioned endea o s o es ablishing lood insu ance ha a e al eady aking
place and suppo hei po en ial ollou campaigns.
S udies Resea ching heDemand o Flood Insu ance
Va ious s udies on he demand o insu ance and hei in luen ial ac o s ha e been pub-
lished in he pas yea s unde he ields o willingness o pay (WTP) o willingness o
insu e (WTI). Whe eas he o me s ide is mainly ocusing on calcula ing a p emium
ha po en ial insu ance clien s a e willing o pay, he la e usually esea ches he gene al
in e es le el among a ge ed g oups. The la e aspec also po ays he main ocus o his
s udy. Howe e , only a small numbe has esea ched he in luen ial ac o s on demand
o lood insu ance in he A ican con ex (Be g e al. 2022; Oduniyi e al. 2020; Na ud
and Vondolia 2020; Adzawla e al. 2019). The majo sha e o s udies om ha s ide o
esea ch ocused on he Asian (Hossain e al. 2022, Senapa i 2020a, b, Liu e al. 2019,
Dewi e al. 2018, Reynaud e al. 2018, Sidi e al. 2018, Fahad and Jing 2018, A shad e al.
2016, Ren and Wang 2016, Abbas e al. 2015, Aliagha e al. 2015, Aliagha e al. 2014,
Tu ne e  al. 2014, Hung 2009), No h Ame ican (Da ling on and Yiannakoulias 2022;
Huang and Lubell 2022; Ne usil e al. 2021; This le hwai e e al. 2020; A eya e al. 2015;
Oulahen 2015; Kousky 2011; B owne and Hoy 2000) o Eu opean con ex s (Osbe ghaus
and Rei 2021; Robinson and Bo zen 2020, 2019; Bo zen e al. 2013; Sei e e al. 2013,
Bo zen and an den Be gh 2012) – a eas in which lood insu ance sys ems and insu ance
in gene al a e mo e widely es ablished. In s udies om his s ide o esea ch, he in luen-
ial ac o s men ioned ha e o en been g ouped in o di e en ca ego ies o p o ide be e
o ien a ion o esea che s in he selec ion o ele an pa ame e s (summa ized in Table1).
Fo example, Sei e e al. (2013) s a e he in luence o pe cep ions o lood isks (subjec-
i e iews), expe iences wi h lood impac s (objec i e iews) as well as ac o s ela ing o
in e ac ions wi h disas e assis ance om ins i u ions (humani a ian/public compensa ion).
Simila ly, Ne usil e al. (2021) also poin ou he impo ance o ac o s exp essing subjec-
i e and objec i e iews on lood isk, while adding he cha ac e is ics o esiden ial houses
(asse s) and demog aphic cha ac e is ics o he esponden s (a ibu es o HH/indi idual).
Aliagha e al. (2014) as well aise he in luence o objec i e and subjec i e iews on lood
isk and socio-economic/demog aphic ac o s. To achie e i s objec i e, his s udy compiles
u he in luen ial ac o s om u he WTP/WTI s udies om a global scope/ a ious geo-
g aphical con ex s and g ouped hem as well in o dis inc ca ego ies while d awing upon
and complemen ing he sugges ed ca ego ies om he p e iously men ioned s udies. In
ha way, a amewo k o suppo he selec ion o in luen ial ac o s was c ea ed o his
s udy (Fig.1).

5
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 1 Summa y o pa ame e s men ioned in WTP/WTI s udies
Ca ego y Thema ic a ea Pa ame e Re e ences Compa able pa ame e in
su ey da a se
Flood isk “Subjec i e” pe cep ion o lood isk Flood isk pe cep ion (Hossain e al. 2022, Reynaud e al. 2018, Oulahen 2015, Sei e e al. 2013, Bo zen and
an den Be gh 2012, Hung 2009)
Yes
Recen ly) expe ienced lood e en s (Osbe ghaus and Rei 2021; Senapa i 2020a; Liu e al. 2019; Adzawla e al. 2019; Fahad
and Jing 2018; Ren and Wang 2016; A eya e al. 2015; Aliagha e al. 2014; Tu ne
e al. 2014; Hung 2009; B owne and Hoy 2000)
Yes
Pe cep ion on clima e change (Adzawla e al. 2019; Oulahen 2015, Bo zen and an den Be gh 2012)Yes
Awa eness (Senapa i 2020b)Yes
An icipa ed wo y and eg e abou uninsu ed losses (Robinson and Bo zen 2020, 2019)Yes
The obse a ion o o he ’s losses (Tu ne e al. 2014)Yes
“Objec i e” Flood Risk (Ex e nally de ined) le el o lood isk (Huang and Lubell 2022; Ne usil e al. 2021; Kousky 2011)Yes
P oximi y o i e s (Sidi e al. 2018, Bo zen and an den Be gh 2012, Kousky 2011) Indi ec ly con ained in o he
pa ame e o lood isk
Li ing in a low lying a ea (Bo zen and an den Be gh 2012) Indi ec ly con ained in o he
pa ame e o lood isk
House ele a ion (Aliagha e al. 2015)Yes
Expe ienced lood impac s (Hossain e al. 2022, Osbe ghaus and Rei 2021, Paopid e al. 2020, Senapa i 2020a, Liu
e al. 2019, Fahad and Jing 2018, Reynaud e al. 2018, A shad e al. 2016, Oulahen
2015, A eya e al. 2015, Tu ne e al. 2014, Sei e e al. 2013, Hung 2009, B owne
and Hoy 2000)
Yes
Flood dep h and du a ion (Paopid e al. 2020, Aliagha e al. 2015)Yes
P esence o o he isk- educ ion measu es/le ee
p o ec ion
(Hossain e al. 2022; This le hwai e e al. 2020; Kousky 2011)Yes
6
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 1 (con inued)
Ca ego y Thema ic a ea Pa ame e Re e ences Compa able pa ame e in
su ey da a se
In e ac ion In e ac ion wi h insu ance ins i u ions P ice o insu ance (Na ud and Vondolia 2020; Reynaud e al. 2018; B owne and Hoy 2000)No
Mul i-yea insu ance policies/billing equency (Reynaud e al. 2018; Bo zen e al. 2013)No
The amoun o e ed in he insu ance con ac (Senapa i 2020a; Reynaud e al. 2018)No
T us in insu e s (Sidi e al. 2018; Reynaud e al. 2018; Aliagha e al. 2014)Yes
Types o isk co e ed (Reynaud e al. 2018)Yes
P e ious insu ance pu chase (Senapa i 2020a)Yes
Insu ance p o ide (Reynaud e al. 2018)Yes
Pe cep ion o e ec i eness o insu ance (Abbas e al. 2015)Yes
Awa eness o insu ance (unde s anding) (Oduniyi e al. 2020; Senapa i 2020b)Yes
In e ac ion wi h o he ins i u ions &
social en i onmen
Pe cei ed esponsibili y o p e en ing damage (Oulahen 2015)Yes
Humani a ian/public compensa ion (Sei e e al. 2013, Bo zen and an den Be gh 2012)Yes
Flood isk communica ion (Bo zen e al. 2013)Yes
Flood p edic ion (wa ning) (Sidi e al. 2018)Yes
Access o in o ma ion and ex ension se ices (Hossain e al. 2022; Adzawla e al. 2019)Yes
Membe ship in a me ’s g oups (Hossain e al. 2022; Adzawla e al. 2019)Yes
Pe cep ion owa ds go e nmen e o in handling
lood
(Sidi e al. 2018)Yes
Risk sha ing be ween agen s (Be g e al. 2022)Yes
Social in luence (Lo 2013)No
7
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 1 (con inued)
Ca ego y Thema ic a ea Pa ame e Re e ences Compa able pa ame e in
su ey da a se
A ibu es A ibu es (o HH/indi idual) Income (Dewi e al. 2018, Sidi e al. 2018; A shad e al. 2016; Ren and Wang 2016; Aliagha
e al. 2015, 2014; Abbas e al. 2015; Kousky 2011; Hung 2009; B owne and Hoy
2000)
Yes
Educa ion (Oduniyi e al. 2020; Adzawla e al. 2019; Sidi e al. 2018; A eya e al. 2015)Yes
Age (Oduniyi e al. 2020; A eya e al. 2015; Abbas e al. 2015)Yes
E hnici y (A eya e al. 2015)Yes
A i udes owa ds isk aking (e.g., isk a e se) (Hossain e al. 2022; Reynaud e al. 2018, Bo zen and an den Be gh 2012)Yes
In e nal locus o con ol (Robinson and Bo zen 2020)Yes
Abili y o pay (Fahad and Jing 2018; A shad e al. 2016)Yes
Al e na i e income sou ces (non-ag icul u al) (Hossain e al. 2022; Adzawla e al. 2019; Abbas e al. 2015)Yes
P e e ence unce ain y (Hung 2009)Yes
Conse a ism (Hung 2009)No
Fa me ’s expe ience (Oduniyi e al. 2020)Yes
Ma i al s a us (Oduniyi e al. 2020)Yes
HH dependen s (Oduniyi e al. 2020)Yes
Remi ances (Adzawla e al. 2019)Yes
Ha ing he loca ion o he house in an a luen a ea (Adzawla e al. 2019)No
8
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 1 (con inued)
Ca ego y Thema ic a ea Pa ame e Re e ences Compa able pa ame e in
su ey da a se
Po en ial asse s o be insu ed House p ice/dwelling alue (Da ling on and Yiannakoulias 2022, Paopid e al. 2020, Kousky 2011)No
Amoun o land owned (Kousky 2011)Yes
Land s a us (owne ship) (Dewi e al. 2018, Abbas e al. 2015)Yes
Fa m ypology (Fahad and Jing 2018; A shad e al. 2016)Yes
Cul i a ed land size (Senapa i 2020a)No
Fa m size (Dewi e al. 2018)No
Seed p ices (Senapa i 2020a)No
Fe ilize p ices (Senapa i 2020a)No
Expendi u e o a me (Dewi e al. 2018)No
House condi ions (Hung 2009)Yes
Comme cial p oduc ion (Adzawla e al. 2019)No
15
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 2 (con inued)
Pa ame e s Responses F equency Pe cen age
Addi ional sou ces o income (mul iple esponses possible) Raising ca le 213 28.6
Fishing 86 11.6
Hun ing 7 0.9
Local indus ies 188 25.3
Manu ac u ing indus ies 14 1.9
Cons uc ion and public wo ks 13 1.7
Comme ce, ca e ing and accomoda ion 182 24.5
T anspo and communica ion 26 3.5
Banks and insu ance 1 0.1
No esponse 91 12.2
Cu en ly owning any o m o insu ance Yes 17 2.3
No 727 97.7
To al 744 100
P e iously owned insu ance bu e mina ed he con ac Yes 8 1.1
No 736 98.9
To al 744 100

16
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Da a P epa a ion andVa iable Selec ion
Ini ially, da a had o be sepa a ed in o ca ego ical and nume ical pa ame e s while clean-
ing he da a and emo ing NaN (No a Numbe ) alues. The la e was necessa y since
he p esence o NaN alues will s op he calcula ion o i ing he model i no emo ed,
bu will also gene a e NaN alues a e calcula ion. Fo he c ea ion o he model, one-ho
encoding was used o he ca ego ical pa ame e s ( ans o ma ion in o bina y 0–1 pa am-
e e s) and s anda d scaling o he nume ical da a (disca ding mean and scaling acco ding
o a iance o he uni ) o be able o c ea e a p ocesso o he model.
The p ocess o pa ame e selec ion is illus a ed in Fig. 4. In o de o begin he ini-
ial selec ion o ele an pa ame e s, ea u e columns we e assessed based on he p- alue
and (Spea man) co ela ion alue o unco e he ela ionships be ween pa ame e s. This
s eps allowed o a educ ion o he ini ially mo e han 400 pa ame e s o a ound 100. The
emaining pa ame e s we e hen g ouped by opic in o he six a eas o he amewo k p e-
sen ed in Fig.1. Then, pai plo s (showcasing pai wise bi a ia e dis ibu ions) and a (Pea -
son) co ela ion hea map we e gene a ed o u he acili a e he selec ion o in luen ial
pa ame e s. Based on he hea map co ela ion ma ix, i was decided o use he pa ame e s
wi h low co ela ion alues while dis ega ding he o he s, as he high co ela ion pa am-
e e s can be connec ed and ela ed in wo ways: i he alues o co ela ion a e highe
han + 0.5, hen hese pa ame e s a e di ec ly co ela ed and i less han -0.5 hen hey a e
in e sely co ela ed, which means i one pa ame e ends o inc ease, hen he connec ed
one dec ease o nega i e alues while i inc eases o posi i e alues. Fo addi ional e i-
ica ion, c oss- abula ions ha illus a e he co ela ions be ween he pa ame e s and he
ou pu pa ame e we e used be o e u he s eps we e conduc ed in he analysis. Mo eo e ,
i allowed o deciding which pa ame e s o e ain o d op.
Compa ison o Machine Lea ning Models
Machine lea ning models we e es ed by using he Sciki -lea n sklea n package. Fo all
models, he da a was spli in o aining (67%) and es da a (33%). The i s model was
he mul inomial logis ic eg ession model, and is conside ed a supe ised lea ning ech-
nique. This echnique se es o p edic i an objec belongs o a ce ain class by p o iding
111
285
50 33 17
11
96
34
90
17
0
50
100
150
200
250
300
Ve y likely Likely Indi e en Unlikely Ve y unlikely
Togo (n=496)Benin (n=248)
Fig. 3 Dis ibu ion o esponses wi hin ou come a iable (likelihood o pu chase o a po en ial lood insu -
ance p oduc )
17
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
a p obabili y on a ange be ween 0 and 1 (James e al. 2021). Fu he mo e, he His og am-
based G adien Boos ing classi ie model was applied, which conside s g adien alues
ob ained by p io upda e s eps om mo ing in o he s eepes di ec ion o descen (Feng
e  al. 2018). Also hype pa ame e uning and g idsea ch we e applied o his classi ie ,
which howe e did no lead o a sa is ac o y imp o emen o he model accu acy. Finally,
addi ional machine lea ning es s we e applied by using decision ees, a me hod d awing
upon he Gini-Index (James e al. 2021). In addi ion, bagging was applied o he decision
ees o lowe he a iance in he p edic ion unc ion, as well a andom o es model, d aw-
ing upon an assembly o a ious decision ees (Has ie e al. 2009).
Deep Lea ning Model (Sequen ial Neu al Ne wo k)
In o de o a emp achie ing be e esul s han he ones ob ained om mo e con en-
ional machine lea ning app oaches (see 3.2.2), his s udy added a deep lea ning (DL)
model (sequen ial neu al ne wo k model) o he analysis using bo h he Tenso Flow and
Ke as packages. Sequen ial models a e pa o a i icial neu al ne wo ks, which usu-
ally consis o se e al laye s (inpu laye , hidden laye s, and ou pu laye ) ha each a e
equipped wi h se e al nodes/neu ons, con aining ac i a ion unc ions, ha a e con-
nec ed h ough weigh ed connec ions be ween he laye s (Jung 2022; James e al. 2021).
In gene al, a sequen ial model p ocesses he inpu ed da a in a one-di ec ional, linea
sequence om he inpu laye , passing h ough he hidden laye s, and a i ing a he ou -
pu laye (Cholle 2021). Usually, DL app oaches a e chosen in cases whe e ex emely
la ge da a se s a e p ocessed and when he possibili y o in e p e he model does no
play and impo an ole (James e al. 2021). S ill, his s udy applied his app oach o
cla i y i a DL model would imp o e he accu acy o p edic ion. Wi h ega ds o he
la ge amoun o ca ego ical da a, ha we e encoded, i also helped o conside a la ge
Fig. 4 Selec ion p ocess o he inal se o model pa ame e s
18
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
amoun o a ailable da a. To analyze nume ical and ca ego ical ea u es in a combined
manne in his DL model, ea u e columns we e de ined by using a Dense Fea u es laye
and using i as an inpu in o he Ke as model. The sequen ial model buil o his s udy
uses he Relu (Rec i ied Linea Uni ) ac i a ion unc ion o he inpu laye , no allow-
ing ac i a ion o he neu on i inpu alues a e below 0 (James e al. 2021), and a So -
max unc ion o he ou pu laye , which is bes sui ed i a ca ego ical ou pu is desi ed
(Klimo e al. 2021). Each neu on o he inpu laye ecei es a a iable o he da ase and
passes ha in o ma ion o ano he neu on, which leads o a highe numbe o neu ons
wi h a highe numbe o a iables. This model con ains 256 neu ons. Besides, he So -
max laye mus ha e he same numbe o nodes as he ou pu laye , which is i e in he
case o his model (Fig.5). The ac i a ion laye is ac ually he nonlinea unc ion and
i ans o ms he alues o he i s hidden laye in o weigh ed sums o he nex laye . In
addi ion, he Adam as op imize wi h a c oss en opy and 200 epochs was applied o
i ing he model. To compa e his model, a second DL model was gene a ed con ain-
ing 50 neu ons, he he_uni o m unc ion as ke nel ini ialize , d awing samples om a
unca ed no mal dis ibu ion cen ed on 0 and he s ochas ic g adien descen (SGD)
op imize .
Sequen ial models bea he disad an age ha hey only allow o p o ide inpu in o
he model only once a he beginning, in con as o unc ional models in which lay-
e s can be connec ed o one ano he in a mul i-di ec ional way, allowing o eed-back
loops (Cholle 2021). Ye , sequen ial models s ill be e allow o a conside a ion o
a la ge numbe o inpu pa ame e s in compa ison o a con en ional eg ession model
app oaches, as cu en ly widely used in he ield o WTP/WTI. In addi ion, in compa i-
son o con en ional ML app oaches a neu al ne wo k can lea n om he da a in a be e
and mo e complex way and e en wo k wi h uns uc u ed da a (Janiesch e  al. 2021)
and hus be e e lec he esea ch con ex . This conside a ion was o high impo ance
o his esea ch p ojec o no di ec ly in e indings and assump ions om s udies in
egions wi h mo e es ablished insu ance ma ke s. Ins ead his s udy wan s o conside a
wide ange o pa ame e s o be e ep esen he in e es le els o a popula ion ha has
no been widely exposed o he usage o such p oduc s be o e.
Fig. 5 Applica ion o So max on he DL model ou pu laye
19
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Resul s
Selec ed Rele an Pa ame e s Acco ding oPai plo s, Co ela ion Ma ix andC oss
Tabs
Fo pa ame e selec ion, ea u e columns o he en i e da a se we e ini ially assessed o
he en i e da a se . Then, he emaining pa ame e s we e ca ego ized in o he six hema ic
a eas o he amewo k (Fig.1). Mo eo e , he g ouped pa ame e s we e assessed h ough
pai plo s and a hea map co ela ion ma ix. As a inal s ep o e i ica ion, c oss abs we e
used o assessing he co ela ion be ween he pa ame e s and he ou pu alue. The el-
e an pa ame e s e lec ed all six hema ic a eas o he p esen ed amewo k on in luen ial
ac o s on insu ance demand. As isualized in Table1, pa ame e s on po en ial asse s o be
co e ed we e only spa sely ep esen ed in his da a se , which can be seen as he eason o
hem only appea ing once in he inal selec ed se o pa ame e s.
Finally, 38 pa ame e s (including one ou pu pa ame e ) make up he inal se o selec ed
pa ame e s (Table3). The selec ed pa ame e s o he model co e ed he ollowing ca ego-
ies o pa ame e s om he amewo k: Pe cep ion on clima e change; Flood isk pe cep-
ion; Expe ienced lood impac s; (Ex e nally de ined) le el o lood isk; Awa eness o
insu ance (unde s anding); T us in insu e s; Pe cep ion o e ec i eness o insu ance; P e-
ious insu ance pu chase; Insu ance p o ide ; Types o isk co e ed; Pe cei ed esponsi-
bili y o p e en ing damage; Humani a ian/public compensa ion; Membe ship in a me ’s
g oups; Risk sha ing be ween agen s; Income; Ma i al s a us; Abili y o pay; P e e ence
unce ain y; Land s a us (owne ship).
Model Accu acies
All models we e applied o h ee sepa a e da a se s each, namely one o e all da a se con-
aining submissions om bo h Togo and Benin (n = 744) as well as wo subse s om Togo
(n = 496) and Benin (n = 248) exclusi ely. Ini ially, six machine lea ning models we e un
on he da a se s and compa ed by hei model accu acy. The applied model ypes o he
classi ica ion a e logis ic eg ession, a his og am-based g adien boos ing classi ie , an
op imized his og am-based g adien boos ing classi ie , decision ees, a bagging ees clas-
si ie , and a andom o es classi ie . Mo eo e , a sequen ial neu al ne wo k was applied o
he da a se s o compa e i a DL model would yield highe accu acies han he con en ional
ML models.
As illus a ed in Table4, almos all models (excep o he op imized his og am-based
g adien boos ing classi ie ) e u ned he highes accu acies o he Togo subse . The logis-
ic eg ession classi ie e u ned an accu acy o 54.0% (s d = 0.029) o he combined da a
se , 48.0% (s d = 0.0042) o he Benin subse , and 61.7% (s d = 0.049) o he Togo sub-
se . O e all, his classi ie he e o e anked among he ones wi h he weakes pe o mances
o he con en ional ML models. The his og am-based g adien boos ing classi ie achie ed
64.0% (s d = 0.00) o he combined da a se , 55.5% (s d = 0.00) o he Benin subse ,
and 65.3% (s d = 0.00) o he Togo subse . Thus, i anked among he be e pe o m-
ing con en ional ML models, especially o he combined da a se and he Benin subse .
The model was e en imp o ed u he h ough hype pa ame e uning and applying g id
sea ch. The model hen achie ed 67.0% (s d = 0.00) accu acy o he combined da a se ,
58% pe cen (s d = 0.00) o he Benin subse , which we e he highes o all con en ional
20
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 3 Summa y o included model pa ame e s o assessing he demand o lood insu ance
Ca ego y Thema ic a ea Associa ed ca ego y o pa ame e s om amewo k Desc ip ion o selec ed pa ame e s om he su ey da a se
Flood isk “Subjec i e” pe cep ion o lood isk Pe cep ion on clima e change In e iewee hea d o clima e change be o e
Flood isk pe cep ion Pe cei ed likelihood o u u e looding
“Objec i e” Flood Risk Expe ienced lood impac s Financial eco e y ime om comme cial impac s
F equency o comme cial impac s (pas 20yea s)
In ensi y o comme cial impac s (pas 20yea s)
Financial eco e y ime om all ou impac ypes combined
F equency o all ou impac ypes combined (pas 20yea s)
Se e i y o all ou impac ypes combined (pas 20yea s)
(Ex e nally de ined) le el o lood isk Flood isk zone based on dis ance o he i e , ele a ion, and epo s o lood a ec edness
In e ac ion In e ac ion wi h insu ance ins i u ions Awa eness o insu ance (unde s anding) Unde s anding o how insu ance wo ks
No p e ious insu ance pu chase due o lack o in o ma ion
T us in insu e s Le el o us ha insu ance companies will deli e payou as p omised
No p e ious insu ance pu chase due o gene al lack o us in companies
Pe cep ion o e ec i eness o insu ance Insu ance as an ins umen only sui ed o he needs o weal hy people
No p e ious insu ance pu chase due o oo much pape wo k
P e ious insu ance pu chase Household has access o insu ance in case o expe iencing lood impac s
Insu ance p o ide No insu ance p o ide /p oduc s p esen in he a ea
Types o isk co e ed Desi ed isk o be co e ed in po en ial lood insu ance p oduc
In e ac ion wi h o he ins i u ions & social
en i onmen
Pe cei ed esponsibili y o p e en ing damage Desi ing o ha e access o emi ances o deal wi h lood impac s
Humani a ian/public compensa ion Household has access o go e nmen al suppo in case o expe iencing lood impac s
Household has access o NGO suppo in case o expe iencing lood impac s
Membe ship in a me ’s g oups Household has access o suppo om coope a i es in case o expe iencing lood impac s
Risk sha ing be ween agen s Household is membe o a sa ings g oup
Household has access o c edi s om banks in case o expe iencing lood impac s
Household d aws upon hei own esou ces in case o expe iencing lood impac s
Household has access o suppo om communi y solida i y unds in case o expe iencing lood impac s
Household has access o c edi s om sa ings g oups in case o expe iencing lood impac s
Household has access o c edi s om a p i a e lende in case o expe iencing lood impac s
Household has no access o any p e iously men ioned sou ce in case o expe iencing lood impac s
Household has no bough any insu ance p e iously because hey had access o o he mechanisms o co e -
age

21
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 3 (con inued)
Ca ego y Thema ic a ea Associa ed ca ego y o pa ame e s om amewo k Desc ip ion o selec ed pa ame e s om he su ey da a se
A ibu es A ibu es (o HH/indi idual) Income Household income pe yea
Ma i al s a us Household is emale-headed
Abili y o pay Fea ha insu ance pu chase will a ec mo e essen ial needs o he household o be co e ed
Household has no bough any insu ance be o e due o lack o means
P e e ence unce ain y Household has no bough any insu ance be o e due o no being in e es ed in he opic
Unce ain y on he eason why no insu ance has been pu chased be o e
Asse s o be co e ed Land s a us (owne ship) Household is owne o he house hey a e li ing in
22
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Table 4 Model accu acies o ML/DL models applied o he selec ed pa ame e s
Con en ional Machine lea ning Deep lea ning
Logis ic Reg es-
sion
His og am-based
G adien Boos ing
Classi ie
Op imized
His og am-based
G adien Boos ing
Classi ie
Decision T ees Bagging ees
classi ie
Random Fo es
Classi ie
Sequen ial
Neu al Ne -
wo k
Fi s model
Sequen ial Neu al
Ne wo k second
model
Accu acy bo h
coun ies
(n = 744)
0.540 ± 0.029 0.64 ± 0.000 0.67 ± 0.000 0.437 ± 0.034 0.612 ± 0.045 0.636 ± 0.035 1 ± 5.67 × 10–5 0.9350 ± 0.2329
Accu acy Benin
subse
(n = 248)
0.480 ± 0.0042 0.550 ± 0.000 0.58 ± 0.000 0.476 ± 0.051 0.552 ± 0.035 0.585 ± 0.048 1 ± 0.0013 0.9756 ± 0.1614
Accu acy Togo
subse
(n = 496)
0.617 ± 0.049 0.653 ± 0.000 0.69 ± 0.000 0.534 ± 0.049 0.704 ± 0.041 0.716 ± 0.051 1 ± 8.17 × 10–5 0.9512 ± 0.1291
23
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
ML models, and 69% (s d = 0.00) o he Togo subse . Mo eo e , a decision ee clas-
si ie was applied, which me ely eached 43.7% (p = 0.034) o he combined da a se ,
47.6% (s d = 0.051) o he Benin subse , and 53.4% (s d = 0.049) o he Togo subse . As
a consequence, his classi ie achie ed he lowes accu acies among all con en ional ML
models. Howe e , i was imp o ed by applying bagging o hen each 61.2% (s d = 0.043)
o he combined da a se , 55.2% (s d = 0.035), and e en 70.4% (s d = 0.041) o he
Togo subse . Finally, as he las con en ional ML model, a andom o es classi ie was
applied achie ing 63.6% (s d = 0.035) o he combined da a se , 58.5% (s d = 0.048) o
he Benin subse , and e en 71.6% (s d = 0.051) o he Togo subse . These esul s clea ly
show ha he da ase s o Togo ende ed he highes accu acies. The la e is due o he ac
ha he e is highe co ela ion in he answe s p o ided by esponden s in Togo.
Since he accu acies o he con en ional ML models did no yield highe accu acies
(o e 75–80%), wo sequen ial neu al ne wo ks om he ealm o DL we e applied as a
compa ison. The i s sequen ial neu al ne wo k model e u ned 100.0% o accu acy o he
combined da a se , as well as o he Benin and Togo subse s. As a consequence, i yielded
he bes pe o mance by a in compa ison o he applied con en ional ML models. This
inding eme ged somewha su p ising, since deep lea ning is a he ecommended o da a
se s ha a e much la ge han he su ey da a se . The second model howe e exhibi ed a
sligh ly lowe accu acy wi h 93.5% o he combined da a se , 97.6% o he Benin subse
and 95.12% o he Togo subse . A mo e de ailed o e iew on he loss, p ecision, F1 sco e
and ecall a e p o ided in Annex 1 as well as a con usion ma ix in Annex 2 in he supple-
men a y in o ma ion o his a icle.
Con ibu ion o Pa ame e s oP edic ing Likelihoods o Insu ance Pu chase
in heDeep Lea ning Model
Fo he sequen ial neu al ne wo k model an o e iew o he mos impo an pa ame e s
based on he ea u e impo ance alue was gene a ed (Fig.6). The ea u e impo ance alue
exp esses he le el o in luence o a pa ame e on he ou pu a iable o he model (likeli-
hood o insu ance pu chase). When iden i ying he mos impo an ea u es, a subse o
ele an ea u es can be selec ed o use in building a model. The e o e, he dimensionali y
is educed as well as noise in he da a. Mo eo e , he model in e p e abili y is imp o ed in
ha way. The selec ion o ea u e impo ance u he mo e assis s in educing he numbe
o pa ame e s, he e o e educing he da a and dec easing he ime needed o ob ain he
esul s. The ea u e impo ance alues we e gene a ed o he combined da a se o bo h
coun ies, as well as o he Togo and Benin subse s. In gene al, i can be obse ed ha he
ea u e impo ance a ies in pa s o a la ge ex en ac oss he pa ame e s o he indi idual
da a se s.
Wi h ega ds o he pa ame e ca ego ies ou lined by he amewo k p esen ed in he
s udy, in e ac ion- ela ed pa ame e s we e he mos impo an ca ego y o pa ame e s by
a . Impo an pa ame e s ela ed o he hema ic a ea o in e ac ion wi h insu ance ins i u-
ions we e he desi ed isk (ag icul u al, ma e ial, heal h, o comme cial impac s) o be
co e ed in po en ial lood insu ance p oduc (Togo). Also, he deg ee o which insu ance
was pe cei ed as an ins umen only sui ed o he needs o weal hy people (all) exhibi ed
a high ea u e impo ance. In addi ion, pa ame e s ela ing he in e ac ion wi h o he ins i-
u ions and he social en i onmen eme ged as he hema ic a ea wi h he mos nume ous
impo an alues. Fea u e impo ance was high when a household had no access o any
sou ce men ioned in he ques ionnai e o inancial coping in case o expe iencing lood
24
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
070605040302010
Likelihood o u u e looding (pe cep ion)
Clima e change knowledge
Financial eco e y ime (all)
F equency o impac s (all)
Se e i y o impac s (all)
Financial eco e y ime (comme cial)
F equency o impac s (comme cial)
In ensi y o impac s (comme cial)
Flood isk le el
Le el o comp ehension
Lack o in o ma ion
T us in se ice deli e y
Lack o us in companies
Pe cep ion o insu ance
Expec ed pape wo k
Access o insu ance
Lack o p esence
Risk ype co e ed by insu ance
Remi ances (desi ed o coping)
Access o go e nmen al suppo
Access o NGO suppo
Access o coope a i es
Household sa ings g oup membe
Access o c edi s (bank)
Coping wi h own means
Access o communi y solida i y unds
Access o c edi s (sa ings g oups)
Access o c edi s (p i a e lende )
No access o any sou ce
Access o o he sou ces
Income pe yea
Female-headed household
P io i izing mo e essen ial needs
Lack o means
Lack o in e es
Unce ain y
House owne ship
“Subje
c i e”
pe cep
ion o
lood
isk “Objec i e” Flood Risk
In e ac ion wi h insu ance
ins i u ions
In e ac ion wi h o he ins i u ions & social
en i onmen
A ibu es (o
HH/indi idual)
Ass
e s
o
be
co
e e
d
Flood iskIn e ac ionA ibu es
Fea u e impo ance all Fea u e impo ance Togo Fea u e impo ance Benin
Fig. 6 Fea u e impo ance o pa ame e s in he sequen ial neu al ne wo k model
31
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Oulahen G (2015) Flood insu ance in Canada: implica ions o lood managemen and esiden ial ulne -
abili y o lood haza ds. En i on Manage 55:603–615. h ps:// doi. o g/ 10. 1007/ s00267- 014- 0416-6
Paopid S, Tang J, Leelawa N (eds) (2020) Willingness o pay o lood insu ance: a case s udy in Phang
Khon, Sakon Nakhon P o ince, Thailand. IOP Con e ence Se ies: Ea h and En i onmen al Science
Pa koo EN, Thiam S, Adjonou K, Kokou K, Ve leysdonk S, Adounkpe JG, Villamo GB (2022) Com-
pa ing expe and local communi y pe spec i es on lood managemen in he lowe Mono Ri e
Ca chmen , Togo and Benin. Wa e 14:1536. h ps:// doi. o g/ 10. 3390/ w1410 1536
Pill M (2022) Towa ds a unding mechanism o loss and damage om clima e change impac s. Clim
Risk Manag 35:100391. h ps:// doi. o g/ 10. 1016/j. c m. 2021. 100391
Ranasinghe R, Ruane AC, Vau a d R, A nell N, Coppola E, C uz FA, Dessai S, Islam AS, Rahimi M,
Ruiz D (2021) Clima e Change In o ma ion o Regional Impac and o Risk Assessmen . In: Mas-
son-Delmo e V, Zhai P, Pi ani A, Conno s SL, Péan C, Be ge S, Caud N, Chen Y, Gold a b L,
Gomis MI, Huang M, Lei zell K, Lonnoy E, Ma hews J, Maycock TK, Wa e ield T, Yelekçi O,
Yu R, Zhou B (eds) Clima e Change 2021: The Physical Science Basis. Con ibu ion o Wo king
G oup I o he Six h Assessmen Repo o he In e go e nmen al Panel on Clima e Change. Cam-
b idge Uni e si y P ess, Camb idge
Ren J, Wang HH (2016) Ru al homeowne s’ willingness o buy lood insu ance. Eme g Ma k Financ
T ade 52:1156–1166. h ps:// doi. o g/ 10. 1080/ 15404 96X. 2015. 11348 67
Reynaud A, Nguyen M-H, Aube C (2018) Is he e a demand o lood insu ance in Vie nam? Resul s om a
choice expe imen . En i on Econ Policy S ud 20:593–617. h ps:// doi. o g/ 10. 1007/ s10018- 017- 0207-4
Robinson PJ, Bo zen WJW (2019) De e minan s o p obabili y neglec and isk a i udes o disas e isk: an
online expe imen al s udy o lood insu ance demand among homeowne s. Risk Anal 39:2514–2527.
h ps:// doi. o g/ 10. 1111/ isa. 13361
Robinson PJ, Bo zen W (2020) Flood insu ance demand and p obabili y weigh ing: he in luences o eg e ,
wo y, locus o con ol and he h eshold o conce n heu is ic. Wa e Resou Econ 30:100144. h ps://
doi. o g/ 10. 1016/j. w e. 2019. 100144
Schä e L, Wa ne K, K e S (2019) Explo ing and managing adap a ion on ie s wi h clima e isk insu -
ance. In: Mechle R, Bouwe LM, Schinko T, Su minski S, Linne oo h-Baye J (eds) Loss and damage
om clima e change. Sp inge In e na ional Publishing, Cham, pp 317–341
Sei e I, Bo zen WJW, K eibich H, Ae s JCJH (2013) In luence o lood isk cha ac e is ics on lood
insu ance demand: a compa ison be ween Ge many and he Ne he lands. Na Haza ds Ea h Sys Sci
13:1691–1705. h ps:// doi. o g/ 10. 5194/ nhess- 13- 1691- 2013
Senapa i AK (2020b) Insu ing agains clima ic shocks: E idence on a m households’ willingness o pay
o ain all insu ance p oduc in u al India. In J Disas e Risk Reduc 42:101351. h ps:// doi. o g/ 10.
1016/j. ijd . 2019. 101351
Senapa i AK (2020a) Do a me s alue insu ance agains ex eme d ough s and loods? E idence om Odi-
sha, India. Glob Bus Re : 097215092095761. h ps:// doi. o g/ 10. 1177/ 09721 50920 957616
Sidi P, Mama MB, Sukono, Supian S, Pu a AS (2018) Demand analysis o lood insu ance by using logis-
ic eg ession model and gene ic algo i hm. IOP Con Se Ma e Sci Eng 332:12053. h ps:// doi. o g/ 10.
1088/ 1757- 899X/ 332/1/ 012053
This le hwai e J, Hens a D, B own C, Sco D (2020) Ba ie s o insu ance as a lood isk managemen ool:
e idence om a su ey o p ope y owne s. In J Disas e Risk Sci 11:263–273. h ps:// doi. o g/ 10.
1007/ s13753- 020- 00272-z
T isos CH, Adelekan IO, To in E, Ayanlade A, E i e J, Gemeda A, Kalaba K, Lenna d C, Masao C, Mgaya
Y (2022) A ica. In: Pö ne H-O, Robe s DC, Tigno M, Poloczanska ES, Min enbeck K, Aleg ía A,
C aig M, Langsdo S, Löschke S, Mölle V, Okem A, Rama B (eds) Clima e Change 2022: Impac s,
Adap a ion and Vulne abili y. Con ibu ion o Wo king G oup II o he Six h Assessmen Repo o he
In e go e nmen al Panel on Clima e Change. Camb idge Uni e si y P ess, Camb idge, pp 1285–1455
Tu ne G, Said F, A zal U (2014) Mic oinsu ance demand a e a a e lood e en : e idence om a ield
expe imen in Pakis an. Gene a Pap Risk Insu Issues P ac 39:201–223. h ps:// doi. o g/ 10. 1057/ gpp.
2014.8
Wagne S, Sou igne M, Walz Y, Balogun K, Komi K, K e S, Rhyne J (2021) When does isk become
esidual? A sys ema ic e iew o esea ch on lood isk managemen in Wes A ica. Reg En i on
Change 21:84. h ps:// doi. o g/ 10. 1007/ s10113- 021- 01826-7
Wagne S, Thiam S, Dossoumou NIP, Hagenloche M, Sou igne M, Rhyne J (2022) Reco e ing om
inancial implica ions o lood impac s— he ole o isk ans e in he Wes A ican con ex . Sus ain-
abili y 14:8433. h ps:// doi. o g/ 10. 3390/ su141 48433
Wanyan R, Yang L, Pu M, Zhao T, Zeng L (2022) The nexus be ween ai pollu ion and li e insu ance
demand in china: e idence om deep machine lea ning. In: Sun X, Zhang X, Xia Z, Be ino E (eds)

32
Economics o Disas e s and Clima e Change (2024) 8:1–32
1 3
Ad ances in a i icial in elligence and secu i y, ol 1586. Sp inge In e na ional Publishing, Cham, pp
524–539
Publishe ’s No e Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in published maps and
ins i u ional a ilia ions.
Au ho s and A ilia ions
SimonWagne 1,2· SophieThiam3· NadègeI.P.Dossoumou4· Da idDaou2
* Simon Wagne
s6siw[email p o ec ed]
1 Ag icul u al Facul y, Uni e si y o Bonn, Meckenheime Allee 174, 53115Bonn, Ge many
2 Uni ed Na ions Uni e si y – Ins i u e o En i onmen andHuman Secu i y (UNU-EHS), UN
Campus Pla z de Ve ein en Na ionen 1, D-53113Bonn, Ge many
3 Cen e o De elopmen Resea ch (ZEF) - Zen um ü En wicklungs o schung, Gensche allee 3,
53113Bonn, Ge many
4 Wes A ican Science Se ice Cen e onClima e Change andAdap ed Land Use (WASCAL) B.P.,
1515Lomé, Togo