1
Bene i s om wa e ecosys em se ices in A ica and adap a ion o clima e 1
change. 2
Lae i ia Pe ino ia,*, Amaia de Ayalaa and Elena Ojeab
3
a Basque Cen e o Clima e Change (BC3), Sede Building 1, 1s loo , Scien i ic Campus o he Uni e si y 4
o he Basque Coun y, 48940 Leioa, Spain. 5
b Fu u e Oceans Lab, Uni e sidad de Vigo, Spain. 6
*Co esponding au ho a : Basque Cen e o Clima e Change (BC3), Building 1, 1s loo , Scien i ic7
Campus o he Uni e si y o he Basque Coun y, 48940 Leioa, Spain. Tel.: +34 944 014 690 8
E-mail add esses: l.pe ino[email p o ec ed]k (L. Pe ino i), amaia.deayala@bc3 esea ch.o g (A. de Ayala),9
elenaojea@u igo.es (E. Ojea) 10
11
Abs ac : The p esen s udy collec s o iginal mone a y es ima es o wa e ela ed ecosys em se ice 12
bene i s on he A ican con inen om 36 alua ion s udies. A da abase o 178 mone a y es ima es is 13
cons uc ed o conduc a me a-analysis ha , o he i s ime, digs in o wha ac o s d i e wa e ela ed 14
ecosys em se ices alues in A ica. We ind ha he se ice ype, biome and o he socioeconomic 15
a iables a e signi ican in explaining bene i s om wa e ela ed se ices. In o de o unde s and he 16
impo ance ha bene i s om wa e ela ed ecosys em se ices ha e o clima e change, we explo e 17
he ela ionship be ween hese bene i s and he coun ies ulne abili y and eadiness o adap o 18
clima e change. We ind ha coun ies ace syne gies and ade-o s in e ms o how aluable hei 19
wa e ela ed ecosys em se ices a e and hei po en ial ulne abili y and adap a ion capaci y. While 20
mo e ulne able coun ies a e associa ed wi h lowe bene i s om ecosys em se ices, coun ies wi h a 21
This documen is he Accep ed Manusc ip e sion o a Published Wo k ha appea ed in inal o m in:
Pe ino i, L.; de Ayala, A.; Ojea, E.. 2018. Bene i s F om Wa e Rela ed Ecosys em Se ices in A ica and Clima e Change. Ecological
Economics. 149. DOI (10.1016/j.ecolecon.2018.03.021).
© 2018 Else ie B.V. All igh s ese ed.
This manusc ip e sion is made a ailable unde he CC-BY-NC-ND 3.0 license h p://c ea i ecommons.o g/licenses/by-nc-nd/3.0/
2
highe eadiness o adap a e also associa ed wi h lowe ecosys em se ices alues. Resul s a e 22
discussed in ligh o na u al capi al accoun ing and ecosys em-based adap a ion. 23
Keywo ds: Adap a ion; A ica; Ecosys em Se ices; Me a-analysis; Na u al Capi al; ND-GAIN; Readiness; 24
Valua ion; Vulne abili y; Wa e . 25
JEL classi ica ion: N57: A ica • Oceania. O13: Ag icul u e • Na u al Resou ces • Ene gy • En i onmen • 26
O he P ima y P oduc s. Q57: Ecological Economics: Ecosys em Se ices • Biodi e si y Conse a ion • 27
Bioeconomics • Indus ial Ecology. Q54: Clima e • Na u al Disas e s • Global Wa ming.28
3
29
1 In oduc ion
30
The concep o ecosys em se ices (ES), unde s ood as he con ibu ion o he bene i s de i ed passi ely
31
o ac i ely om ecosys ems owa ds cu en and u u e human well-being (Fishe e al., 2009), has
32
gained inc easing ecogni ion in he las decade. Mains eamed by he Millennium Ecosys em
33
Assessmen (MA) P og am (2005), ES we e a he ocus o he Uni ed Na ions En i onmen P og amme
34
(UNEP) led s udy on The Economics o Ecosys ems and Biodi e si y (TEEB, see de G oo e al., 2012), and
35
a e s ill e ol ing unde he cu en ly de eloping In e go e nmen al Science-Policy Pla o m on
36
Biodi e si y and Ecosys em Se ices (IPBES) ini ia i e (Díaz e al., 2015). The conse a ion and
37
imp o emen o ecosys ems has been iden i ied as a cen al challenge o sus aining li elihoods o he
38
XXIs cen u y (Gleik e al., 2003; Gue y e al., 2015), and esea ch p og ams as well as conse a ion
39
ini ia i es ha e been launched a local, na ional and in e na ional le els (Díaz e al., 2015). In his
40
con ex , esea ch o syn he ize a ailable e idence on ES mone a y alues is o p ime impo ance, and
41
unde s anding wha d i es hese alues and how hey ela e o coun ies’ clima e ulne abili y can
42
p o ide policy guidance ega ding he po en ial o ES o clima e change adap a ion.
43
The p esen pape ocuses on wa e ela ed ES in A ica and hei links o clima e change ulne abili y
44
and adap a ion. Wa e - ela ed ES a e unde s ood as he se ices p o ided by biomes ha a e i e low
45
impac ing o i e low dependen (see he concep o na u al in as uc u e in Mul e al., 2017)
a
. In
46
o he wo ds, biomes ha impac o a e p edominan ly dependen on i e low, as opposed o being
47
p edominan ly ain ed, deli e wa e ela ed ES. This landscape app oach conside s biomes as he en y
48
poin o iden i y he se o ES p oduced. The wa e ela ed ES ca ego y d aws on he MA and TEEB
49
classi ica ions (MA, 2005; de G oo e al., 2012) and encompass mo e ES han hyd ological se ices
50
a
Fo mo e on his dis inc ion, please see he WISE UP p ojec h p://www.wa e andna u e.o g/ini ia i es/wise-clima e
4
(G izze i e al., 2016). Figu e 1 p esen s he biomes included in he p esen s udy which in e ac wi h
51
su ace i e low and p o ide wa e ela ed ES.
52
P e ious esea ch has paid a lo o a en ion o wa e ela ed ES in o he egions mainly due o he
53
de elopmen o Paymen o Ecosys em Se ices (Lele, 2009), bu no p e ious s udies ha e analysed he
54
alues o wa e ela ed ES in ela ion o clima e ulne abili y and adap a ion. In his pape , he ocus is
55
on he A ican con inen , o h ee main easons: 1) Ri e lows a e pi o al o he deli e y o ES c ucial o
56
millions o li elihoods (WWAP, 2016); 2) he A ican con inen p esen s in gene al a high clima e change
57
ulne abili y exace ba ing he need o immedia e policy solu ions (Wo ld Bank, 2007), and; 3) wa e
58
ela ed ES in A ica con inue o be inadequa ely in es iga ed wi h e y poo co e age (Lele, 2009).
59
Figu e 1: Wa e ela ed se ices om biomes linked o i e lows.
60
61
Sou ce: adap ed om Mul e al., 2017 and he MA, 2005.
62
5
Wa e ela ed ES a e a ec ed by a e y high a iabili y o all clima e and wa e esou ces cha ac e is ics
63
- in u n exace ba ed by clima e change (Fa ama zi e al., 2013; IPCC, 2014). Unde s anding he bene i s
64
o wa e ela ed se ices deli e y h ough economic alua ion and he ac o s ha a ec hese
65
economic bene i s can p o ide guidance o wa e esou ces managemen and clima e change
66
adap a ion.
67
A ica is no he con inen wi h he la ges ES alua ion li e a u e ( o de ails on ES alua ion me hods
68
see de G oo e al., 2012; Pascual e al., 2010). Only 19% o he alua ion s udies e e enced in TEEB a e
69
loca ed in A ica. Mos s udies a e loca ed in he Ame icas (33%) and Asia (26%) (based on Mc Vi ie and
70
Hussain, 2013). Mo eo e , he alua ion li e a u e in A ica is geog aphically dispa a e: Sou he n and
71
Eas e n A ica ga he he highes numbe o s udies while No h, Wes and cen al sub-Saha an A ica go
72
unde - ep esen ed. Valua ion s udies on wa e ela ed ES in A ica ep esen 28% o all wa e ela ed ES
73
alua ion s udies globally. The mos equen ly alued wa e ela ed ES a e aw ma e ials and ood
74
p o ision, mainly due o wo di e en easons: 1) hese se ices a e ela i ely easy o alue using he
75
di ec ma ke p icing me hod (Van de Ploeg e al., 2010) and; 2) dependence on p o isioning se ices is
76
high and p opo ionally la ge in A ican de eloping coun ies han in de eloped coun ies, hence an
77
ea ly ocus on es ima ing alues o his ype o se ice (Egoh e al., 2012; Mc Vi ie and Hussain, 2013).
78
Indeed, ES’ consump i e ou pu s (e.g. c ops, ish e c.) con ibu e o subsis ence li elihoods and
79
cons i u e a e y impo an sha e o households’ income in A ican de eloping coun ies, hus
80
pa icipa ing in po e y alle ia ion and educing ulne abili y o nega i e shocks (Egoh e al., 2012; Suich
81
e al., 2015).
82
The ole o ES in educing ulne abili y and in con ibu ing o adap a ion is pa icula ly impo an in he
83
ace o clima e change (Jones e al., 2012; Munang e al., 2013a). Adap a ion o clima e change can be
84
oo ed in ES sus ainabili y - known as ‘ecosys em based adap a ion’ (Ojea, 2015). I is de ined as an
85
6
app oach ha “ha ness he capaci y o na u e o bu e human communi ies agains he ad e se
86
impac s o clima e change h ough he sus ainable deli e y o ES” and is expec ed o p o ide cos -
87
e ec i e adap a ion esul ing in esilien socio-ecological sys ems (Jones e al., 2012). Such adap a ion
88
op ion is hailed as pa icula ly bene icial as ca bon seques e ing ecosys ems
b
such as o es s, we lands
89
and pea lands can con ibu e o achie ing mi iga ion a ge s se unde he 2015 Pa is ag eemen as well
90
as he sus ainable de elopmen goals o he Uni ed Na ions while deli e ing on adap a ion o clima e
91
change (Munang e al., 2013b). Ea ly e idence on ecosys em based adap a ion suppo s his is he case
92
(Doswald e al., 2014). Howe e , li le is known ye on he linkages be ween adap a ion and he alue o
93
ES a a egional scale (Ojea e al., 2015). Indeed, ecosys em-based adap a ion app oaches ha e no been
94
mains eamed ye , wi h only li le e idence in he li e a u e (Jones e al., 2012). Indeed, ES alua ions
95
a e mos ly conduc ed in isola ion o clima e change and adap a ion conside a ions. To ill his gap, one
96
easible app oach is o explo e o wha ex en wa e ela ed ES alues a e ela ed o highe (o lowe )
97
ulne abili y and highe (o lowe ) le e age o adap o clima e change in coun ies. The p esen pape
98
add esses hese ques ions o explo e he po en ial links be ween he alue o wa e ela ed ES and
99
coun ies ulne abili y and po en ial o adap o clima e change.
100
To do his, he pape syn hesises wa e ela ed ES alues elici ed o A ica in he las h ee decades
101
using a me a-analysis. Me a-analyses – he analysis o analyses as de ined by (Glass, 1976) - ha e been
102
inc easingly used in he ield o en i onmen al alua ion (B ande e al., 2006; Ghe mandi e al., 2008)
103
as i allows o a igo ous es ing o a cen al endency ac oss a la ge numbe o s udies while con olling
104
o he e ec o se e al pa ame e s (Nelson and Kennedy, 2009). In his con ex , a me a-analysis o
105
wa e ela ed ES alues is ca ied ou o: 1) p o ide a quan i a i e answe o wha ac o s d i e wa e
106
b
Recen e iew highligh s ha much o he claimed clima e egula ion bene i s o EbA, beyond ca bon seques e ing
ecosys ems, ela e o local empe a u e egula ion a he han mi iga ion (McVi ie e al., 2017).
7
ela ed ES alues in A ica and; 2) unde s and he ela ionship be ween clima e change ulne abili y and
107
eadiness o adap and he bene i s ob ained om ES.
108
Nex sec ion in oduces he me hodology, ou lines he da a selec ion, s anda diza ion and coding
109
ca ied ou in o de o pe o m he me a- eg ession. Sec ion 3 p esen s he model speci ica ion and
110
sec ion 4 i s associa ed esul s. Sec ion 5 discusses he esul implica ions be o e he concluding sec ion.
111
2 Me hodology
112
2.1 Exis ing me a-analyses o wa e - ela ed ES
113
S udies aimed a unde s anding he bene i s om ES ha e so a conduc ed me a-analyses ocused on
114
one ecosys em ype, such as co al ee s (B ande e al., 2007; Ghe mandi and Nunes, 2013), coas al and
115
ma ine ecosys ems (Liu and S e n, 2008), we lands (B ande e al., 2006; B ouwe e al., 1999;
116
Chaikumbung e al., 2016; Ghe mandi e al., 2008; Woodwa d and Wui, 2001), o es s (Ba io and
117
Lou ei o, 2010; Ojea e al., 2016), o mang o es (B ande e al., 2012). O he s udies ocus on one o a
118
bundle o ES o a speci ic ecosys em, such as ec ea ional se ices om o es s (Ojea e al., 2015;
119
Zande sen and Tol, 2009); wa e ES om o es s (Ojea e al., 2015; Ojea and Ma in-O ega, 2015);
120
egula ing se ices om we lands (B ande e al., 2013) and non-ca bon se ices om o es s (Ojea e
121
al., 2016). The geog aphic co e age o hese me a-analyses is sligh ly biased owa ds No h Ame ica,
122
especially i he s udy is ocused on we lands (Ghe mandi e al., 2008). Mos s udies ha e adop ed a
123
global co e age while a ew ha e speci ically ocussed on de eloping o eme ging economies (we lands
124
in de eloping coun ies in Chaikumbung e al., 2016; wa e and ec ea ion se ices om o es s in
125
cen al Ame ica in Ojea e al., 2015; and wa e se ices om o es s in cen al and sou h Ame ica in
126
Ojea and Ma in-O ega, 2015).
127
8
The p esen wo k is, o ou knowledge, he i s me a-analysis s udy on he economic alua ion o wa e
128
ela ed ES ocussed on he A ican con inen . Fo his, an o iginal da ase is cons uc ed based on
129
seconda y da a om published li e a u e, ga he ing in o ma ion on he ES, i s mone a y alue, and
130
addi ional socioeconomic a iables ollowing ou unde s anding o he con ex whe e he alues occu
131
(sec ion 2.2). A me a analy ical model is es ima ed (sec ion 3.3) o explain he obse ed a ia ions in
132
wa e ela ed ES economic alues while con olling o a se o s udy and con ex cha ac e is ics (S anley
133
e al., 2013).
134
2.2 Con ex o a iable selec ion
135
The selec ion o po en ial a iables a ec ing ES alues in he me a-analysis is guided by p e ious s udies
136
(e.g. B ande e al., 2012; Ghe mandi e al., 2008; Ojea e al., 2010; Richa dson and Loomis, 2009) and
137
he unde s anding o he sys em and p ocesses whe e he ES occu (Figu e 2). The wa e sys em ( he
138
biome) suppo s he deli e y o ES (ca ego ized as su ace a ea o p oduc ion
c
and ype o ES), which
139
yields a bene i o people ha can be measu ed in mone a y e ms and could po en ially depend on he
140
alua ion me hodology used and he au ho s’ amilia i y wi h he case s udy a ea. This mone a y o
141
economic alue is also dependen on he wide con ex whe e i occu s, and will be in luenced by
142
con ex a iables on a la ge scale, including socio economic and demog aphic ac o s (e.g. popula ion,
143
GDP, educa ion le el), biodi e si y ichness, and clima e change adap a ion eadiness and ulne abili y.
144
A he same ime, he ES economic alue also impac s he wa e sys em. In u n, i can ha e a eedback
145
e ec on he deli e y o ES (deple ion, o example) as well as on he con ex (e.g. educed po e y).
146
147
c
S anda diza ion by p oduc ion uni a ea is necessa y o allow compa abili y ac oss es ima es.
9
148
Figu e 2: Po en ial a iables a ec ing ES alues in he me a-analysis
149
150
P e ious me a-analy ical app oaches o ES suppo his easoning. These s udies include a iables
151
ela ed o he con ex , he s udy and he ecosys em, ha a e impac ing he economic alues o he ES -
152
he dependen a iable (B ande e al., 2013; Chaikumbung e al., 2016; Ojea e al., 2010; Ojea and
153
Ma in-O ega, 2015). The nex sub sec ion de ails he selec ion p ocess o he dependen a iable. The
154
ull lis o a iables and hei summa y s a is ics a e p esen ed in Table 1. In addi ion, a mo e de ailed
155
de ini ion o each a iable is gi en in Appendix 1.
156
2.3 Da abase building
157
A pee e iewed li e a u e sea ch was conduc ed h ough elec onic jou nal da abases including EVRI
d
,
158
SCIENCEDIRECT and Google Schola du ing he mon hs o Ma ch o Augus 2014 using all di e en
159
combina ions o he keywo ds “Economic Valua ion”, “A ica”, “Valua ion”, “Ecosys em” and
160
d
Accessible a h p://www.e i.ca/en
16
Table 1: Va iable desc ip ion and summa y s a is ics
233
Va iable
Type
Desc ip ion
Va iable
name
Coding
Numbe o
obse a ions
Mean
(S d. De .)
Range
[Min; Max]
Dependen a iable
lnVAL
Nume al
Na u al loga i hm o he ES alue in in e na ional $/ha.yea (2014 alue)
178
3.84
(3.00)
[-4.35; 11.35]
Explana o y a iables
S udy a iables
BIO
Dummy
Type o biome whe e he
se ice is p o ided
B_IWT
Inland we lands (=1)
64
0.36
(0.48)
[0; 1]
B_CWT
Coas al we lands (=1)
45
0.25
(0.44)
[0; 1]
B_FWT
F eshwa e g (=1)
20
0.11
(0.32)
[0; 1]
B_WDL
Woodlands (=1)
14
0.08
(0.23)
[0; 1]
B_TRO
T opical o es (=1)
27
0.15
(0.36)
[0; 1]
B_GRAS
G asslands (=1)
8
0.04
(0.21)
[0; 1]
SERV
Dummy
Type o ecosys em se ice
as pe he TEEB
classi ica ion
PROV
P o isioning (=1)
113
0.63
(0.48)
[0; 1]
REG
Regula ing (=1)
35
0.18
(0.39)
[0; 1]
SUPP
Suppo ing (=1)
15
0.08
(0.28)
[0; 1]
CULT
Cul u al (=1)
18
0.10
(0.30)
[0; 1]
logHA
Nume al
Log o he su ace a ea o
he ES in hec a es
logHA
178
10.35
(3.60)
[-.47h; 18.19]
METD
Dummy
O iginal alua ion me hod
used in he p ima y
alua ion
METD_M
Ma ke –based me hods: di ec ma ke
p ice, cos -based, ac o income (=1)
158
0.90
(0.31)
[0; 1]
METD_NM
Non-ma ke - based me hods: Con ingen
alua ion and a el cos (=1)
19
0.10
(0.31)
[0; 1]
g
F eshwa e biomes include i e s, lakes and loodplain in line wi h he ca ego isa ion o he TEEB (2010)
h
The nega i e alues a e due o he <1ha igu es o ce ain ES.
17
Va iable
Type
Desc ip ion
Va iable
name
Coding
Numbe o
obse a ions
Mean
(S d. De .)
Range
[Min; Max]
LEAD
Dummy
Whe he he lead au ho o
he s udy is based in a local
o in e na ional ins i u ion
loca ed in A ica.
LEAD
Fi s au ho based in A ica (=1)
o he (=0)
178
0.80
(0.40)
[0; 1]
Con ex a iables
Socio economic and demog aphic
PMRY_ENROL
Nume al
P ima y school en olmen a e, bo h sexes, in pe cen age (Wo ld Bank, 2015)
177
102.94
(17.13)
[30.61; 131.27]
GDP
Nume al
GDP pe capi a in housands o 2014 PPP USD (Wo ld Bank, 2015)
178
3.30
(3.27)
[0.61; 12.3]
POP_R
Nume al
Pe cen age o u al popula ion (Wo ld Bank, 2015)
178
74.86
(12.00)
[23.56; 88.17]
POVTY_R
Nume al
Ru al po e y headcoun a io a na ional po e y line in pe cen age (Wo ld Bank, 2015)
175
50.17
(19.09)
[22.4; 92.2]
Biodi e si y
GEF
Nume al
Composi e index by he Global En i onmen al Facili y o ela i e biodi e si y po en ial o
each coun y. (Global En i onmen al Facili y, 2015)
178
9.37
(6.67)
[0.31; 23.52]
Clima e change
VUL
Nume al
Composi e index sco ing he ulne abili y o each coun y o clima e change. (No e Dame
Uni e si y, Canada, 2016)
178
1.01
(0.024)
[0.98; 1.11]
READ
Nume al
Composi e index sco ing he eadiness o a coun y o le e age in es men in clima e change
adap a ion policies. (No e Dame Uni e si y, Canada, 2016)
178
0.99
(0.06)
[0.88; 1.09]
18
S udy-speci ic a iables include he me hodology applied in he o iginal alua ion exe cise and o he
234
cha ac e is ics o he case s udies. Biome (BIO) is based on wha is de ined in he o iginal publica ion
235
and can be an inland we land (B_IWT), a coas al we land (B_CWT), a eshwa e sys em i.e. i e , lake,
236
loodplains (B_FWT), woodlands (B_WDL), opical o es (B_TRO), o g assland (B_GRAS) (Table 1). The
237
numbe o obse a ions o e es ial and aqua ic ecosys ems is 49 and 129, espec i ely. Bo h ypes o
238
biomes p esen simila a e age alues pe hec a e wi h 2014 PPP USD 1,457 o e es ial and 1,469 o
239
aqua ic biomes. Ecosys em se ices (SERV) a e classi ied ollowing he MA and TEEB ca ego isa ion in o
240
p o isioning (PROV), egula ing (REG), habi a o suppo ing (SUPP) and cul u al (CULT) se ices (Table
241
1). The alua ion me hod (METD) can be ma ke -based (METD_M), i.e. di ec ma ke p icing, cos based
242
me hods, ac o income and p oduc ion unc ion; o non-ma ke based (METD_NM) i.e. con ingen
243
alua ion and a el cos . The su ace a ea is included in log- ans o med hec a es (logHA) and e e s o
244
he a ea o he ES p o ision. Finally, in o ma ion on he lead au ho is collec ed o iden i y any
245
“au ho ship e ec ” (B ouwe e al., 1999), eco ding i he lead au ho has an a ilia ion o a esea ch
246
cen e o an in e na ional o ganisa ion based in A ica (LEAD)
i
. The li e a u e shows mixed e idence on
247
au ho ship e ec s. On one side, one can expec ha i s au ho s a ilia ed o an ins i u ion loca ed in
248
A ica migh be mo e likely o epo highe ES alues, as hey may ha e be e knowledge o he local
249
con ex and o he communi ies whe e he ma ke and non-ma ke based alua ion me hods a e
250
implemen ed, he e o e p o iding mo e accu a e and comp ehensi e es ima es. On he o he side, in
251
he case o ma ke -based me hods, hese local au ho s may ha e access o ine scale ma ke da a ha
252
could lowe he es ima es (B ande e al., 2006), and ha e be e knowledge o cul u al and social no ms
253
ha may help design unbiased non-ma ke p e e ence elici a ion app oaches.
254
Con ex a iables ela ed o socio-economic ai s, biodi e si y le el, ulne abili y and adap a ion o
255
clima e change a he na ional le el a e also expec ed o in luence he alue o wa e ela ed ES (see
256
i
I is assumed ha a ilia ion be ween publica ion ime and esea ch ime has no changed.
19
Figu e 2) and we e included in he da ase . Each da a poin o he con ex a iables co esponds o he
257
s udy’s coun y and yea
j
. Fi s , socio-economic and demog aphic a iables such as GDP pe capi a
258
(GDP), educa ion le el as he pe cen age o he popula ion o o icial p ima y educa ion age en olled in
259
p ima y school (PMRY_ENROL), u al popula ion sha e exp essed as he pe cen age o popula ion li ing
260
in u al a eas (POP_R) and u al po e y, he pe cen age o u al popula ion li ing below he na ional
261
po e y lines (POVTY_R). The las wo a iables abo e a e a u al le el o e lec ha ES p o ision in he
262
da ase mainly occu s in u al a eas. All a iables ela e o a coun y’s de elopmen le els and can
263
po en ially explain da a he e ogenei y. Indeed, i can be expec ed ha mo e de eloped coun ies would
264
end o p esen highe ES alues as highligh ed in p e ious me a-analyses (Ba io and Lou ei o, 2010;
265
B ande e al., 2006; Ghe mandi e al., 2008; Ojea e al., 2010).
266
Second, a a iable e lec ing he coun y’s biodi e si y s a us is also included wi h he biodi e si y
267
ichness indica o elabo a ed by he Global En i onmen al Facili y (GEF). Indeed, biodi e si y
268
undamen ally unde pins ecosys ems, suppo ing hei capaci y o p o ide se ices o humans
269
(Ca dinale e al., 2012; Ojea e al., 2010). Highe biodi e si y le els a e associa ed wi h wa e ela ed ES
270
(Bal ane a e al., 2014). Howe e , gi en ha a single se ice may esul om mul iple unc ions, posi i e
271
and nega i e e ec s o biodi e si y ichness can coun e ac each o he and he esul ing ne e ec is s ill
272
unknown (Bal ane a e al., 2014). Less e idence is a ailable ega ding he e ec o biodi e si y on he
273
economic alue o hose ecosys em se ices and he p esen s udy wan s o con ibu e in his espec .
274
Thi d, clima e change indices de eloped by No e Dame Uni e si y
k
o ulne abili y o clima e change
275
and eadiness o adap a e also conside ed (VUL and READ). These indices a e included o explo e he
276
ex en o which ES alues a e ela ed o clima e change ulne abili y and po en ial adap a ion le e age
277
in s udy coun ies. I is expec ed ha highe ES alues a e associa ed wi h less ulne able and mo e
278
j
As an example, o a 2012 s udy in Uganda, GDP pe capi a and all o he con ex a iables will co espond o yea 2012 o Uganda.
k
ND Gain coun y index h p://index.gain.o g/
20
eady o adap coun ies, as a high alue ES can e lec he s a e o he ecosys ems and he associa ed
279
le el o bene i s socie y ecei es. Each index conside s se e al dimensions o a coun y’s ulne abili y
280
and eadiness (see Appendix 1). The adjus ed o GDP indices a e used, hey measu e he ac ual
281
pe o mance o he coun y compa ed o i s expec ed pe o mance gi en i s GDP. A de ailed
282
explana ion on all con ex a iables and hei sou ces is a ailable in Appendix 1. Ca e was aken when
283
selec ing he a iables o minimize po en ial collinea i y
l
. The es s o collinea i y p oduced a diagnos ic
284
o no co ela ion p oblem as he Va iance In la ion Fac o s (VIF) e u ned alues lowe han 6 o all
285
a iables
m
(Ojea e al., 2010). Co ela ion coe icien s be ween each a iable a e a ailable in Appendix 4.
286
3.2 Model speci ica ion
287
The dependen a iable in he models (
in
yln
) is a ec o o he wa e ela ed ES mone a y alues
288
con e ed o 2014 in e na ional US$ pe hec a e pe yea . I is exp essed in loga i hmic e ms (see Table
289
1) based on he analysis o he his og ams o he dependen a iable in log and non-log o m as well as
290
on he esul o he Box-Cox model es (Came on and T i edi, 2009, chap e 3)
n
. Semi-loga i hmic
291
eg ession is also he esul ing unc ional o m in p e ious me a-analyses o ES alues (Ba io and
292
Lou ei o, 2010; B ande e al., 2007; Johns on e al., 2005; Lindhjem, 2007; Liu and S e n, 2008;
293
Richa dson and Loomis, 2009; Rol e and B ouwe , 2012; Woodwa d and Wui, 2001). The explici
294
speci ica ion o he me a- eg ession model can be desc ibed as ollows:
295
,ln ,,,,, jicjicsjisji u X X y ++++=
(1)
296
l
Fo example, he adjus ed o GDP ND gain indices we e chosen o e he non-adjus ed ones o limi collinea i y.
m
Mean VIF o model 1 is 2.36 anging om 1.25 o 3.84 and 2.69 o model 2, anging om 1.36 o 4.95.
n
The Box-Cox es esul ed in a alue o – 1038 (
2
= 129.45) hence he null hypo hesis o no di e ence be ween semi-log and linea model
was ejec ed a a 1% signi icance le el (i.e. models a e signi ican ly di e en a 99% con idence le el in e ms o goodness o i ). In addi ion, we
ob ain an es ima e o
04.0=
, which gi es much g ea e suppo o a log-linea (o semi-log) model
)0( =
han he linea model
)1( =
(see
Came on and T i edi, 2009, chap e 3).
21
whe e
i
deno es each speci ic s udy
,N i )...,,2,1( =
j
e e s o he alue es ima e epo ed in he
297
s udy
)...,,2,1( i
M j =
,
is he usual cons an e m o in e cep and he
ec o s a e he
298
coe icien s o be es ima ed in he me a-analysis. Each
coe icien is associa ed o a ype o
299
explana o y a iable: ei he s udy speci ic (
s
X
) o con ex speci ic (
c
X
) (see Table 1). Whe e each
300
s udy
i
p o ides a single es ima e
,j
hen
1 Mi=
and
i
collapses in o
j
u
. Howe e , whe e a s udy
301
gi es mo e han one alue es ima e, i is necessa y o accoun o he common e o ac oss es ima es
302
)( j
u
and he indi idual-speci ic e ec o panel e o wi hin a s udy
).(i
303
3.3 Model es ima ion
304
The e a e se e al app oaches o es ima ing his model depending on assump ions ega ding he e o
305
a iance-co a iance ma ix (Lindhjem, 2007). Table 2 p esen s he di e en es ima o s used in ecen
306
me a-analysis li e a u e in en i onmen al economics. These include Weigh ed Leas Squa es (WLS),
307
Gene alized Leas Squa es (GLS), explici speci ica ions o panel models wi h ixed o andom e ec s,
308
and O dina y Leas Squa es (OLS) usually applied wi h Hube -Whi e adjus ed s anda d e o s clus e ed
309
by s udy. This las es ima o has been mos commonly used in he en i onmen al economics li e a u e
310
(see Table 2). Me a- eg ession models dealing speci ically wi h da a he e ogenei y, he e oscedas ici y
311
and co ela ed obse a ions a e desc ibed in Nelson and Kennedy (2009).
312
Table 2: Models es ima ed in me a-analysis s udies
313
Es ima ion echnique
S udy
OLS
B ande e al., 2012; Ghe mandi e al., 2008; Lindhjem,
2007; Liu and S e n, 2008; Loomis and Whi e, 1996; Ojea e
al., 2016, 2010; Richa dson and Loomis, 2009; Sh es ha and
Loomis, 2001
OLS wi h Hube –Whi e adjus ed SE
Ba io and Lou ei o, 2010; B ande e al., 2006; Ghe mandi
and Nunes, 2013; Johns on e al., 2003; Lindhjem, 2007;
Woodwa d and Wui, 2001; Zande sen and Tol, 2009
Weighed OLS wi h Hube Whi e
Ghe mandi and Nunes, 2013
Mul i-le el OLS
Ba eman and Jones, 2003; B ande e al., 2007; B ouwe e
22
al., 1999; Ghe mandi e al., 2008; Johns on e al., 2003
GLS
Ojea e al., 2015; Ojea and Lou ei o, 2011
Fixed GLS
Ojea and Ma in-O ega, 2015
RE GLS
Chaikumbung e al., 2016; Ojea and Lou ei o, 2011
GLS clus e SE
Chaikumbung e al., 2016
Weighed GLS wi h clus e SE
Chaikumbung e al., 2016; Johns on e al., 2003
No e: some s udies es ima e mo e han one model and hence a e epo ed mul iple imes. Gene alized Leas Squa e (GLS),
314
O dina y Leas Squa es (OLS), Fixed E ec s (FE), Random E ec s (RE), S anda d E o s (SE).
315
316
Since mos s udies in he da abase epo mo e han one mone a y alue es ima e - a panel o
317
obse a ions - es ima es om he same s udy a e likely o be co ela ed. The e o e he me a- eg ession
318
speci ica ion de ined in (1) can be es ima ed wi h da a-panel s uc u e (Nelson and Kennedy, 2009). The
319
app op ia eness o including he s udy speci ic e o e m
i
was es ed by applying he B eusch Pagan
320
Lag ange Mul iplie es o andom e ec s (To es-Reyna, 2007; Zande sen and Tol, 2009)
o
. The null
321
hypo hesis o no panel e ec was ejec ed a 5% signi icance le el (
2
alue o 6.92 wi h
322
P ob.>
2
= 0.0043). In addi ion, he Hausman es was used o de e mine whe he he andom e ec s
323
model (as opposed o he ixed e ec s one) is he co ec speci ica ion. This p ocedu e es s whe he a
324
signi ican co ela ion be ween unobse ed indi idual-speci ic andom e ec s (
i
) and he explana o y
325
a iables (
i
X
) exis s (Came on and T i edi, 2009, chap e 8; Woold idge, 2002, chap e 10). Unde he
326
null hypo hesis,
i
in (1) is pu ely andom, implying ha i is unco ela ed wi h eg esso s
i
X
in (1). The
327
Hausman speci ica ion es esul ed in a
2
alue o 11.46 wi h P ob. >
2
= 0.32, yielding o no ejec
328
he null hypo hesis o non-co ela ion a 5% signi icance le el, and he e o e suppo ing he adop ion o
329
a andom e ec s model. Clus e - obus s anda d e o s we e speci ied o he andom e ec s panel
330
da a models es ima ed in sec ion 4 (Came on and T i edi, 2009,chap e 8).
331
o
This es helps choosing be ween a andom e ec s eg ession and a simple OLS eg ession (To es-Reyna, 2007)
23
4 Resul s
332
To be e explain he a ia ions in he alue obse a ions and check o he obus ness o he esul s
333
ob ained, Model 1 and ex ended Model 2 wi h a ocus on clima e change ulne abili y and eadiness o
334
adap a e es ima ed. In addi ion, c oss-p oduc s o a iables a e compu ed o u he in e p e he
335
esul s (sec ion 4.2).
336
4.1 Model 1 and 2
337
Bo h models a e andom e ec s panel da a models wi h clus e - obus s anda d e o s and a e
338
es ima ed in STATA (V.14.1)
p
. The wo models pe o m well wi h easonable R squa e o his ype o
339
s udy
q
. The es ima ed coe icien s along wi h hei s anda d e o s and 95% con idence in e als a e
340
p esen ed in Table 3:
341
342
p
A GLS model co ec ed o he e oscedas ici y and an OLS wi h clus e obus s anda d e o s we e also es ima ed o bo h models (model 1
and model 2) and simila esul s we e ob ained in e ms o coe icien s signi icance and beha io .
q
The o e all R-sq is in line wi h p e ious published wo k using he same model (Ma mann e al., 2016) as well as wi h o he model esul s
(B ande e al., 2012, 2006; B ouwe e al., 1999; Chaikumbung e al., 2016; Ghe mandi e al., 2008; Ojea e al., 2010, 2015; Sh es ha and
Loomis, 2001; Woodwa d and Wui, 2001).
24
343
Table 3: Me a-analysis eg ession model 1 and 2 esul s.
344
The coe icien s o he dummy a iables can be in e p e ed as cons an p opo ional changes gi en an
345
absolu e change in he a iable.
346
Model 1
Model 2
Va iable
Coe icien
(S d. E o )
95% CI
Coe icien
(S d. E o )
95% CI
B_FWT
-1.086**
(0.376)
[-1.822 -0.349]
- 1.023**
(0.337)
[-1.684 -0.363]
PROV
-1.481*
(0.859)
[-3.165 0.204]
-1.461*
(0.868)
[-3.163 0.241]
REG
-0.166
(0.713)
[-1.564 1.232]
-0.215
(0.727)
[-1.639 1.210]
SUPP
-1.668*
(0.951)
[-3.531 0.196]
-1.810**
(0.914)
[-3.603 -0.019]
logHA
-0.357***
(0.084)
[-.523 -0.192]
-0.295***
(0.083)
[-0.458 -0.133]
METD_M
-0.617
(0.852)
[-2.288 1.053]
-0.587
(0.859)
[-2.271 1.097]
LEAD
1.949**
(0.817)
[0.349 3.550]
2.044**
(0.749)
[0.575 3.512]
PMRY_ENROL
-0.035
(0.031)
[-0.096 0.027]
-0.0342
(0.026)
[-0.085 0.017]
GDP
0.311*
(0.189)
[-0.060 0.681]
0.367**
(0.143)
[0.087 0.648]
POP_R
0.039
(0.044)
[-0.048 0.126]
0.0482
(0.040)
[-0.029 0 .126]
POVTY_R
-0.040**
(0.017)
[-0.074 -0.007]
-0.044***
(0.014)
[-0.071 -0.018]
GEF
-0.019
(0.075)
[-0.166 0.128]
-0.139*
(0.074)
[-0.284 0.005]
VUL
-46.302***
(12.166)
[-70.147 -22.458]
READ
-12.971**
(6.840)
[-26.377 0.435]
Cons an
9.985**
(3.930)
[2.282 17.688]
9.950**
(4.165)
[1.785 18.114]
Obse a ions
174
174
G oups
34
34
R-sq:
0.3917
0.4818
No e:
***, **, *: Signi icance a he 1%, 5% and 10% le els, espec i ely.
CI: Con idence In e al
O he combina ions o a iables we e ied bu ga e no signi ican esul
I he eg essions had included METD_NM ins ead o METD_M he coe icien s o his a iable would ha e been
he e e sed o he ones p esen ed he e i.e. 0.617 and 0.587 o Model 1 and 2, espec i ely.
25
Fo he s udy cha ac e is ics, eshwa e ecosys ems (B_FWT) esul ed in o a nega i e and signi ican
347
coe icien indica ing ha eshwa e ecosys ems ha e in gene al, lowe ES bene i s han o he ypes o
348
biomes in he da ase (g asslands, we lands, opical o es s and woodlands). P o isioning (PROV) and
349
habi a o suppo ing se ices (SUPP) display signi ican nega i e coe icien es ima es, wi h espec o
350
cul u al se ices as he omi ed a iable (CULT). This esul indica es ha p o isioning and habi a
351
se ices a e, in gene al, ela ed o lowe ES mone a y alue as compa ed o cul u al se ices. One
352
explana ion could be ha e enues om in e na ional ou ism can be subs an ially la ge han he
353
economic alue de i ed om gene ally low alue p o isioning goods (e.g. ish ca ch), as ob ained in
354
o he analyses (UNEP, 2010). Indeed, in e na ional use s may place highe alues han local use s on
355
se ices such as ou ism, bu lowe alues on egula ion se ices, while local use s may do he opposi e.
356
Mos o iginal s udies included in he da abase did no p o ide explici in o ma ion on end use s.
357
Howe e , i can be expec ed ha end use s o cul u al se ices a e mos o en o eign isi o s, who a e
358
weal hie han end use s o p o isioning and egula ing se ices, who a e mos ly local communi ies.
359
Ano he po en ial explana ion lies in he common use o he ma ke p ice alua ion me hod o
360
p o isioning se ices alua ion, which in he li e a u e is ecognized o p o iding sligh ly lowe
361
es ima es han o he me hodologies (e.g. B ande e al., 2006).
362
Rega ding he alua ion me hod o he p ima y s udies, ma ke -based alua ion me hods (METD_M)
363
seem no o be signi ican ly di e en om non-ma ke me hodologies in ou da ase . Howe e ,
364
en i onmen al alua ion li e a u e gene ally shows highe alues wi h non-ma ke alua ion echniques
365
han wi h ma ke -based alua ion me hods (B ande e al., 2006).
366
The coe icien o su ace a ea is also nega i e and signi ican , showing ha , on a e age, he la ge he
367
a ea whe e he ES is p oduced, he lowe he ma ginal bene i pe hec a e. This endency is in line wi h
368
o he s udies on en i onmen al alua ion and is due o dec easing ma ginal e u ns wi h size (B ande e
369
al., 2006; Chaikumbung e al., 2016; Ghe mandi e al., 2008).
370
32
esea ch should look in his di ec ion o explo e ade-o s be ween na u al and non-na u al capi al in
501
adap a ion.
502
Fu he esea ch should add ess wha d i es ES alues a he local scale by combining obse ed alues
503
wi h spa ial in o ma ion ha can explain a ia ion a a ine scale. This was no possible o explo ing
504
biodi e si y, adap a ion and ulne abili y o clima e change in he A ican case s udies. Bu i may be a
505
necessa y app oach o unde s and he speci ic dynamics o he se ice use s and p o ide s, he a ea o
506
he ecosys ems p oducing he se ices and po en ial seasonal a ia ions in he se ice p o ision ha
507
may ha e an e ec on hei alue.
508
33
Appendices
Appendix 1 – Lis o a iables
ACRONYM
VARIABLE
DESCRIPTION
UNIT
TYPE
VAL
VALUE
ES alue in 2014 pu chasing powe pa i y (PPP) $ pe hec a e (ha) and yea
2014 PPP $ pe
ha and yea
Quan i a i e
ECOLOGICAL VARIABLES
BIO
BIOME
Type o biome in which he se ice is p o ided
n.a.
Quali a i e
SERV
SERVICE
Type o ecosys em se ice conside ed as pe he TEEB classi ica ion in 4 ca ego ies: p o isioning
(PROV), egula ing (REG), suppo ing (SUPP) and cul u al (CULT)
Sou ce: h p://www. eebweb.o g/ esou ces/ecosys em-se ices/
n.a.
Quali a i e
STUDY VARIABLES
METD
METHOD
O iginal alua ion me hod used o ob aining he alue es ima e o he ES.
No e: Bene i ans e alua ion me hod was eplaced by he o iginal alua ion me hod o he o iginal
s udy o Seyam e al., (2001) and Tu pie e al.,(2000).
n.a.
Quali a i e
HA
SURFACE AREA IN HA
Su ace a ea in hec a es whe e he ES is deli e ed
Hec a es
Quan i a i e
LEAD
LEAD
Whe he he lead o he pape ( i s au ho ) is a ilia ed o ei he an o ganisa ion loca ed in A ica,
ei he an in e na ional o ganisa ion wi h o ices in A ica a he ime o publica ion.
n.a.
Quali a i e
SOCIO ECONOMIC INDICATORS
PMRY_ENROL
GROSS ENROLMENT
RATIO, PRIMARY BOTH
SEXES, PERCENTAGE
To al en olmen in p ima y educa ion, ega dless o age, exp essed as a pe cen age o he popula ion
o o icial p ima y educa ion age. The a io can exceed 100% due o he inclusion o o e -aged and
unde -aged s uden s because o ea ly o la e school en ance and g ade epe i ion.
No e: Da a is no always a ailable o he s udy yea . When his was he case he closes yea wi h
da a a ailable was en e ed.
Sou ce: Wo ld Bank indica o , can be accessed a
h p://da a.wo ldbank.o g/indica o /SE.SEC.ENRR/coun ies
Pe cen age
Quan i a i e
GDP
GDP PER CAPITA IN
THOUSANDS OF 2014 PPP
$
GDP pe capi a based on pu chasing powe pa i y (PPP). Da a a e in cu en in e na ional dolla s based
on he 2011 ICP ound.
No e: Fo yea 1982 in Zambia, da a was no a ailable in 2014 PPP. The cu en 2014 USD da a was
aken om he Wo ld Bank. Cu en 2014 USD is equi alen o PPP 2014 USD.
Sou ce: Wo ld Bank indica o , can be accessed a
h p://da a.wo ldbank.o g/indica o /NY.GDP.PCAP.PP.CD
2014 PPP USD
Quan i a i e
POP_R
PERCENTAGE OF RURAL
POPULATION
Ru al popula ion e e s o people li ing in u al a eas as de ined by na ional s a is ical o ices. I is
calcula ed as he di e ence be ween o al popula ion and u ban popula ion. Agg ega ion o u ban and
u al popula ion may no add up o o al popula ion because o di e en coun y co e ages.
Sou ce: Wo ld Bank indica o , can be accessed a h p://da a.wo ldbank.o g/indica o /SP.RUR.TOTL.ZS
Pe cen age
Quan i a i e
34
ACRONYM
VARIABLE
DESCRIPTION
UNIT
TYPE
VULNERABILITY/ ES RELIANCE INDICATORS
POVERTY INDICATOR
POVTY_R
RURAL POVERTY
HEADCOUNT RATIO AT
NATIONAL POVERTY LINE
IN PERCENTAGE
Ru al po e y headcoun a io is he pe cen age o he u al popula ion li ing below he na ional
po e y lines.
Sou ce: Wo ld Bank indica o , can be accessed a h p://da a.wo ldbank.o g/indica o /SI.POV.RUHC
Pe cen age
Quan i a i e
ENVIRONMENTAL INDICATOR
GEF
GEF BENEFITS INDEX FOR
BIODIVERSITY
GEF bene i s index o biodi e si y is a composi e index o ela i e biodi e si y po en ial o each
coun y based on he species ep esen ed in each coun y, hei h ea s a us, and he di e si y o
habi a ypes in each coun y. The index has been no malized so ha alues un om 0 (no
biodi e si y po en ial) o 100 (maximum biodi e si y po en ial)
Sou ce: Wo ld Bank indica o , can be accessed a
h ps://www. hege .o g/ge /si es/ hege .o g/ iles/documen s/GBI_Biodi e si y_0.pd
Index
Quan i a i e
CLIMATE CHANGE INDICES
VUL
VULNERABILITY INDEX
ADJUSTED FOR GDP
The adjus ed o GDP No e Dame Global Adap a ion Index (ND-GAIN) o ulne abili y is an index
assessing he ulne abili y o a coun y by conside ing six li e suppo ing sec o s: ood, wa e , heal h,
ES, human habi a , in as uc u e. Each sec o is ep esen ed by six indica o s ha span he h ee
c oss cu ing componen s o ulne abili y:
- exposu e o clima e ela ed haza ds;
- sensi i i y o ha sec o o clima e ela ed haza ds;
- adap i e capaci y o he sec o o cope wi h hese impac s
Index anges om - 0.989 o 0.222. Lowe sco es indica e lowe ulne abili y. We used he adjus ed
o GDP e sion o he index as he e is a co ela ion be ween he ND-Gain sco es and GDP pe capi a.
The adjus ed o GDP sco e is de ined as “ he dis ance o a coun y's measu ed ND-GAIN sco e and i s
expec ed alue based on he eg ession o ND-GAIN and GDP”.
Sou ce ND-GAIN websi e, can be accessed a h p://index.gain.o g/
Index
Quan i a i e
READ
READINESS INDEX
ADJUSTED FOR GDP
The adjus ed o GDP No e Dame Global Adap a ion Index (ND-GAIN) o adap a ion is an index
measu ing eadiness by conside ing a coun y's abili y o apply economic in es men s o adap a ion
ac ions. I conside s h ee componen s:
- economic eadiness;
- go e nance eadiness;
- social eadiness
Index anges om -0.387 o 1.228. A lowe sco e indica es a lowe pe o mance. We used he adjus ed
o GDP e sion o he index as he e is a co ela ion be ween he ND-Gain sco es and GDP pe capi a.
The adjus ed o GDP sco e is de ined as “ he dis ance o a coun y's measu ed ND-GAIN sco e and i s
expec ed alue based on he eg ession o ND-GAIN and GDP”.
Sou ce ND-GAIN websi e, can be accessed a h p://index.gain.o g/
No e: Educa ion in he index is he en olmen a e a e ia y school le el, no p ima y school like he
a iable we used in ou model.
Index
Quan i a i e
n.a: no applicable
35
Appendix 2 – S udies in he da abase
1. Acha aya, G. and Ba bie E.B., 2000, Valuing g oundwa e echa ge h ough ag icul u al p oduc ion in he Hadejia-Ngu u
we lands in no hen Nige ia. Ag icul u al Economics 22(3): 247-259.
2. Adekola, O., Mo ade S., de G oo R. and G elo F., 2008, The economic and li elihood alue o p o isioning se ices o Ga-
Mampa we land, Sou h A ica. In: 13 h IWRA Wo ld Wa e cong ess, 1 - 4 Sep embe , 2008, Mon pellie , F ance.
3. A n zen, J., 1998, Economic alua ion o communal angelands in Bo swana: a case s udy. IIED, London, UK.
4. Ba bie , E.B., Adams W.M. and Kimmage K., 1991, Economic alua ion o we land bene i s: he Hadejia-Jama loodplain,
Nige ia. IIED, London, UK.
5. Eme on L, 1994, An Economic Valua ion o he Cos s and Bene i s in he Lowe Tana Ca chmen Resul ing om Dam
Cons uc ion, A Repo P epa ed Fo Ac opolis Kenya Limi ed
6. Eme on, L., 1998, Djibou i biodi e si y - economic assessmen . IUCN, Gland, Swi ze land.
7. Eme on L., 1999, Balancing he oppo uni y cos s o wildli e conse a ion o communi ies a ound lake Mbu o Na ional Pa k,
Uganda, E alua ing Eden Se ies Discussion Pape No.5
8. Eme on L., 2002, The e u n o he wa e : Res o ing he Waza Logone loodplain in Came oon, IUCN We lands and Wa e
Resou ces P og amme
9. Eme on, L (ed), 2005, Values and ewa ds: coun ing and cap u ing ecosys em wa e se ices o sus ainable de elopmen .
IUCN Wa e , Na u e and Economics Technical Pape No. 1, IUCN — The Wo ld Conse a ion Union, Ecosys ems and
Li elihoods G oup Asia.
10. Eme on, L. and A. As a , 1998, E i ea biodi e si y - economic assessmen . IUCN, Gland, Swi ze land.
11. Eme on, L., Iyango L., Luwum P. and Malinga A., 1998, The p esen economic alue o Naki ubo U ban We land, Uganda.
Na ional We lands Conse a ion and Managemen P og amme; IUCN: Biodi e si y economics o Eas e n A ica.
12. Eme on, L. and Mu ami a E., 1999, Uganda biodi e si y - economic assessmen . P epa ed wi h Na ional En i onmen
Managemen Au ho i y, Kampala. IUCN, Gland, Swi ze land.
13. Ha ield R. Malle e King D., 2007, The economic alue o he moun ain go illa p o ec ed o es s - The Vi ungas and Bwindi
Impene able Na ional Pa k, IGCP
14. Hobe g J., 2011, Economic Analysis o Mang o e Fo es s: A case s udy in Gazi Bay, Kenya, UNEP epo
15. Kai o J, Wanji u C and Ochiewo J, 2009, Ne Pay: Economic analysis o a Replan ed Mang o e Plan a ion in Kenya, Jou nal o
Sus ainable Fo es y, 28:3,395 — 414
16. Kaku u W., Tu yahabwe N., and Mugisha J., 2013, To al Economic Value o We lands P oduc s and Se ices in Uganda, The
Scien i ic Wo ld Jou nal, Volume 2013
17. Ka anja, F., Eme on L., Ma umbo J. and Kaku u W., 2001, Assessmen o he economic alue o pallisa dis ic we lands,
Uganda. Biodi e si y Economics o Eas e n A ica & Uganda's Na ional We lands P og amme, IUCN Eas e n A ica
P og amme.
18. Kipkoech A., Mogaka H., Cheboiywo J. & Kima o D., 2011, The o al economic alue o Maasai Mau, T ans Ma a and Eas e n
Mau Fo es blocks o he Mau Fo es , Kenya, En i onmen al Resea ch and Policy Analysis
36
19. Lannas Ka h yn S. M. and Tu pie Jane K., 2009, Valuing he P o isioning Se ices o We lands: Con as ing a Ru al We land in
Leso ho wi h a Pe i-U ban We land in Sou h A ica, Ecology and Socie y 14(2): 19
20. Ly, O.K., Bishop J.T., Mo an D. and Dansohho M., 2006, Es ima ing he Value o Eco ou ism in he Djoudj Na ional Bi d Pa k in
Senegal. IUCN, Gland, Swi ze land, 34pp.
21. Naidoo, R. and W.L. Adamowicz (2005) Biodi e si y and Na u e-Based Tou ism a Fo es Rese es in Uganda, En i onmen
and De elopmen Economics 10(2): 158-178.
22. Na ud, S. and Munga ana E.D., 1994, En i onmen al alua ion in de eloping coun ies: The ec ea ional alue o wildli e
iewing. Ecological Economics 11(2): 135-151.
23. O’Fa ell P.J., W.J. De Lange a, D.C. Le Mai e a, B. Reye s a, J.N. Blignau b, S.J. Mil on c, D. A kinson d, B. Egoh e, A. Mahe y
a, C. Col in a, R.M. Cowling, 2011, The possibili ies and pi alls p esen ed by a p agma ic app oach o ecosys em se ice
alua ion in an a id biodi e si y ho spo , Jou nal o A id En i onmen s 75 - 612:623
24. Schaa sma, M., Mo se-Jones, S., Posen, P., Swe namR.D., Balm o d, A., Ba eman, I.J., Bu gess, N.D., Chamshama, S.A.O.,
Fishe , B., F eeman, T., Geo ey, V., G een, R.E., Hepelwa, A.S., He na´ ndez-Si en m, A., Hess, S., Kajembe, G.C.,
Kayha a a j, G., Kilonzo, M., Kulindwa, K, Lund, J.F., Mado e, S.S., Mbwamboq, L., Meilby, H., Ngaga, Y.M., Theilade, I.,
T eue, T., an Beuke ing, P., Vyamana, V.G. & Tu ne , R.K.. 2014. The impo ance o local o es bene i s: Economic
alua ion o Non-Timbe Fo es P oduc s in he Eas e n A c Moun ains in Tanzania. Global En i onmen al Change, Volume
24, Pages 295-305
25. Seyam, I.M., Hoeks a A.Y., Ngabi ano G.S. and Sa enije H.H.G., 2001, The alue o eshwa e we lands in he Zambezi basin.
Value o Wa e Resea ch Repo Se ies No. 7, IHE Del , The Ne he lands.
26. Spu geon, J., 2002, Socio-economic assessmen and economic alua ion o Egyp ’s mang o es, FAO epo
27. Tu pie, J., 2000, The use and alue o na u al esou ces o he Ru iji Floodplain and Del a, Tanzania. Ru iji En i onmen al
Managemen P ojec , Technical epo No. 17.
28. Tu pie, J.K. (2003) The exis ence alue o biodi e si y in Sou h A ica: how in e es , expe ience, knowledge, income and
pe cei ed le el o h ea in luence local willingness o pay. Ecological Economics 46(1-2): 199-216.
29. Tu pie J., 2005, The Valua ion o Ripa ian Fishe ies in Sou he n and Eas e n A ica, T opical i e ishe ies alua ion:
backg ound pape s o a global syn hesis by Neiland, A. E.; Béné, C. 2006. T opical i e ishe ies alua ion: A global syn hesis
and c i ical e iew. Colombo, S i Lanka: In e na ional Wa e Managemen Ins i u e. 45 pp. (Comp ehensi e Assessmen o
Wa e Managemen in Ag icul u e Resea ch Repo 15)
30. Tu pie, J. e al. 2010. Es ima ion o he Wa e Quali y Amelio a ion Value o We lands A Case S udy o he Wes e n Cape,
Sou h A ica
31. Tu pie J., Ba nes J., A n zen J., Nhe e a B., Lange G., Baleseng Buzwani B., 2006, Economic alue o he Oka ango del a,
Bo swana, and implica ions o managemen , Repo
32. Tu pie, J.K., B.J. Heyden ych and S.J. Lambe h, 2003. Economic alue o e es ial and ma ine biodi e si y in he Cape
Flo is ic Region: implica ions o de ining e ec i e and socially op imal conse a ion s a egies. Biol. Conse a ion 112: 233-
251.
33. Tu pie, J., Joube A., 2000, Es ima ing po en ial impac s o a change in i e quali y on he ou ism alue o K uge Na ional
Pa k: An applica ion o a el cos , con ingen and conjoin alua ion me hods, Wa e SA, ol 7-3
34. Tu pie, J., Smi h B., Eme on L. and Ba nes J., 1999, Economic alue o he Zambezi Basin We lands. Zambezi Basin We lands
conse a ion and esou ce u iliza ion p ojec . IUCN Regional O ice o Sou he n A ica.
37
35. Tu pie, J., Day E., Ross-Gillespie V., Louw A.., 2010, Es ima ion o he Wa e Quali y Amelio a ion Value o We lands A Case
S udy o he Wes e n Cape, Sou h A ica, En i onmen o De elopmen Discussion Pape Se ies 10-15
36. Wasswa H., Mugagga F., Kakembo V., 2013, Economic Implica ions o We land Con e sion o Local People’s Li elihoods: The
Case o Kampala- Mukono Co ido (KMC) We lands in Uganda, Academia Jou nal o En i onmen al Sciences 1(4): 066-077
38
Appendix 3 – C oss abula ion o he alue o wa e ela ed ES and biomes in 2014 PPP USD
Biome ES
sub ype
Clima e
egula ion
Aes he ics
E osion
p e en ion
Ex eme
e en
p o ec ion
Food
Genepool
Medicinal
esou ces
Nu se y
Pollina ion
Raw
ma e ials
Rec ea ion
Soil e ili y
Was e
media ion
F eshwa e
p o ision
Inland
We lands
239
(375)
2,304
(3,058)
10,735
(10,785)
444
(908)
235
(491)
97
16
912
(2,112)
518
(648)
3,862
512
(1,111)
Coas al
We lands
476
(270)
6,541
1,775
(2,865)
271
(247)
5
28,406
(49,043)
191
(451)
337
(203)
F eshwa e
52
(68)
2
(4)
622
(582)
43
(53)
Woodlands
43
(50)
2
(1)
1
254
(574)
T opical
o es
42
(33)
287
9
(16)
20
(13)
39
69
(76)
1,260
(1,488)
150
233
(319)
G asslands
3
(0.2)
0.1
(0.1)
1,012
5,552
(7,478)
51,552
39
Appendix 4 – Co ela ion ma ix
The igu es in he ma ix co espond o he co ela ions signi ican a he 5% le el.
40
Re e ences
Bal ane a, P., Siddique, I., Dee, L., Paque e, A., Isbell, F., Gonzalez, A., By nes, J., O’Conno , M.I., Hunga e,
B.A., G i in, J.N., 2014. Linking Biodi e si y and Ecosys em Se ices: Cu en Unce ain ies and he
Necessa y Nex S eps. BioScience 64, 49–57. h ps://doi.o g/10.1093/biosci/bi 003
Ba io, M., Lou ei o, M.L., 2010. A me a-analysis o con ingen alua ion o es s udies. Ecol. Econ. 69, 1023–
1030. h ps://doi.o g/10.1016/j.ecolecon.2009.11.016
Ba eman, I.J., Jones, A., 2003. Con as ing con en ional wi h mul i-le el modeling app oaches o me a-
analysis: expec a ion consis ency in UK woodland ec ea ion alues. Land Econ. 79, 235–258.
B ande , L., B ouwe , R., Wag endonk, A., 2013. Economic alua ion o egula ing se ices p o ided by
we lands in ag icul u al landscapes: A me a-analysis. Ecol. Eng. 56, 89–96.
h ps://doi.o g/10.1016/j.ecoleng.2012.12.104
B ande , L., J. Wag endonk, A., S. Hussain, S., McVi ie, A., Ve bu g, P.H., de G oo , R.S., an de Ploeg, S.,
2012. Ecosys em se ice alues o mang o es in Sou heas Asia: A me a-analysis and alue ans e
applica ion. Ecosys . Se . 1, 62–69. h ps://doi.o g/10.1016/j.ecose .2012.06.003
B ande , L.M., Flo ax, R.J.G.M., Ve maa , J.E., 2006. The Empi ics o We land Valua ion: A Comp ehensi e
Summa y and a Me a-Analysis o he Li e a u e. En i on. Resou . Econ. 33, 223–250.
h ps://doi.o g/10.1007/s10640-005-3104-4
B ande , L.M., Van Beuke ing, P., Cesa , H.S.J., 2007. The ec ea ional alue o co al ee s: A me a-analysis.
Ecol. Econ. 63, 209–218. h ps://doi.o g/10.1016/j.ecolecon.2006.11.002
B ouwe , R., Lang o d, I.H., Ba eman, I.J., Tu ne , R.K., 1999. A me a-analysis o we land con ingen alua ion
s udies. Reg. En i on. Change 1, 47–57.
Bu cha , S.H.M., Walpole, M., Collen, B., an S ien, A., Scha lemann, J.P.W., Almond, R.E.A., Baillie, J.E.M.,
Bomha d, B., B own, C., B uno, J., Ca pen e , K.E., Ca , G.M., Chanson, J., Chene y, A.M., Csi ke, J.,
Da idson, N.C., Den ene , F., Fos e , M., Galli, A., Galloway, J.N., Geno esi, P., G ego y, R.D., Hockings,
M., Kapos, V., Lama que, J.-F., Le e ing on, F., Loh, J., McGeoch, M.A., McRae, L., Minasyan, A.,
He nández Mo cillo, M., Old ield, T.E.E., Pauly, D., Quade , S., Re enga, C., Saue , J.R., Skolnik, B.,
Spea , D., S anwell-Smi h, D., S ua , S.N., Symes, A., Tie ney, M., Ty ell, T.D., Vié, J.-C., Wa son, R.,
2010. Global biodi e si y: indica o s o ecen declines. Science 328, 1164–1168.
h ps://doi.o g/10.1126/science.1187512
Came on, C., T i edi, P., 2009. Mic oeconome ics Using S a a. S a a P ess Publica ion.
Ca dinale, B.J., Du y, J.E., Gonzalez, A., Hoope , D.U., Pe ings, C., Venail, P., Na wani, A., Mace, G.M., Tilman,
D., Wa dle, D.A., Kinzig, A.P., Daily, G.C., Lo eau, M., G ace, J.B., La igaude ie, A., S i as a a, D.S.,
Naeem, S., 2012. Biodi e si y loss and i s impac on humani y. Na u e 486, 59–67.
h ps://doi.o g/10.1038/na u e11148
Chaikumbung, M., Doucouliagos, H., Sca bo ough, H., 2016. The economic alue o we lands in de eloping
coun ies: A me a- eg ession analysis. Ecol. Econ. 124, 164–174.
h ps://doi.o g/10.1016/j.ecolecon.2016.01.022
Daily, G.C., Polasky, S., Golds ein, J., Ka ei a, P.M., Mooney, H.A., Pejcha , L., Ricke s, T.H., Salzman, J.,
Shallenbe ge , R., 2009. Ecosys em se ices in decision making: ime o deli e . F on . Ecol. En i on. 7,
21–28. h ps://doi.o g/10.1890/080025
de Ayala, A., Ma iel, P., Meye ho , J., 2014. T ans e ing landscape alues using disc e e choice expe imen s:
Is me a-analysis an op ion? Econ. Ag a . Recu . Na . 14, 103–128.
h ps://doi.o g/10.7201/ea n.2014.01.06
41
de G oo , R., B ande , L., an de Ploeg, S., Cos anza, R., Be na d, F., B aa , L., Ch is ie, M., C ossman, N.,
Ghe mandi, A., Hein, L., Hussain, S., Kuma , P., McVi ie, A., Po ela, R., Rod iguez, L.C., en B ink, P.,
an Beuke ing, P., 2012. Global es ima es o he alue o ecosys ems and hei se ices in mone a y
uni s. Ecosys . Se . 1, 50–61. h ps://doi.o g/10.1016/j.ecose .2012.07.005
Díaz, S., Demissew, S., Ca abias, J., Joly, C., Lonsdale, M., Ash, N., La igaude ie, A., Adhika i, J.R., A ico, S., Báldi,
A., o he s, 2015. The IPBES Concep ual F amewo k—connec ing na u e and people. Cu . Opin.
En i on. Sus ain. 14, 1–16. h ps://doi.o g/10.1016/j.cosus .2014.11.002
Doswald, N., Mun oe, R., Roe, D., Giuliani, A., Cas elli, I., S ephens, J., Mölle , I., Spence , T., Vi a, B., Reid, H.,
2014. E ec i eness o ecosys em-based app oaches o adap a ion: e iew o he e idence-base. Clim.
De . 6, 185–201. h ps://doi.o g/10.1080/17565529.2013.867247
Egoh, B.N., O’Fa ell, P.J., Cha e , A., Josephine Gu ney, L., Koellne , T., Nibam Abi, H., Egoh, M., Willemen, L.,
2012. An A ican accoun o ecosys em se ice p o ision: Use, h ea s and policy op ions o
sus ainable li elihoods. Ecosys . Se . 2, 71–81. h ps://doi.o g/10.1016/j.ecose .2012.09.004
Eme on, L., 2014. Valuing and in es ing in ecosys ems as de elopmen in as uc u e: economic analysis o
op ions o clima e-compa ible de elopmen in coas al zones o Kenya & S i Lanka, iCoas epo . LTS
In e na ional, Edinbu gh, UK.
Fa ama zi, M., Abbaspou , K.C., Ash a Vaghe i, S., Fa zaneh, M.R., Zehnde , A.J.B., S ini asan, R., Yang, H.,
2013. Modeling impac s o clima e change on eshwa e a ailabili y in A ica. J. Hyd ol. 480, 85–101.
h ps://doi.o g/10.1016/j.jhyd ol.2012.12.016
Fishe , B., Tu ne , R.K., Mo ling, P., 2009. De ining and classi ying ecosys em se ices o decision making. Ecol.
Econ. 68, 643–653. h ps://doi.o g/10.1016/j.ecolecon.2008.09.014
Ghe mandi, A., Nunes, P.A.L.D., 2013. A global map o coas al ec ea ion alues: Resul s om a spa ially
explici me a-analysis. Ecol. Econ. 86, 1–15. h ps://doi.o g/10.1016/j.ecolecon.2012.11.006
Ghe mandi, A., Van den Be gh, J.C., B ande , L.M., De G oo , H.L., Nunes, P.A., 2008. The economic alue o
we land conse a ion and c ea ion: A me a-analysis.
Glass, G., 1976. P ima y, Seconda y, and Me a-Analysis o esea ch. Am. Educ. Res. Assoc. 5, 3–8.
Gleick, P.H., 2003. Wa e use. Annu. Re . En i on. Resou . 28, 275–314.
h ps://doi.o g/10.1146/annu e .ene gy.28.040202.122849
G izze i, B., Lanzano a, D., Lique e, C., Reynaud, A., Ca doso, A.C., 2016. Assessing wa e ecosys em se ices
o wa e esou ce managemen . En i on. Sci. Policy 61, 194–203.
h ps://doi.o g/10.1016/j.en sci.2016.04.008
Gue y, A.D., Polasky, S., Lubchenco, J., Chaplin-K ame , R., Daily, G.C., G i in, R., Ruckelshaus, M., Ba eman,
I.J., Du aiappah, A., Elmq is , T., Feldman, M.W., Folke, C., Hoeks a, J., Ka ei a, P.M., Keele , B.L., Li,
S., McKenzie, E., Ouyang, Z., Reye s, B., Ricke s, T.H., Rocks öm, J., Tallis, H., Vi a, B., 2015. Na u al
capi al and ecosys em se ices in o ming decisions: F om p omise o p ac ice. P oc. Na l. Acad. Sci.
112, 7348–7355. h ps://doi.o g/10.1073/pnas.1503751112
IPCC (Ed.), 2014. Clima e change 2014: impac s, adap a ion, and ulne abili y: Wo king G oup II con ibu ion
o he i h assessmen epo o he In e go e nmen al Panel on Clima e Change. Camb idge
Uni e si y P ess, New Yo k, NY.
Johns on, R.J., Besedin, E.Y., Io anna, R., Mille , C.J., Wa dwell, R.F., Ranson, M.H., 2005. Sys ema ic Va ia ion
in Willingness o Pay o Aqua ic Resou ce Imp o emen s and Implica ions o Bene i T ans e : A
Me a-Analysis. Can. J. Ag ic. Econ. Can. Ag oeconomie 53, 221–248. h ps://doi.o g/10.1111/j.1744-
7976.2005.04018.x
Johns on, R.J., Besedin, E.Y., Wa dwell, R.F., 2003. Modeling ela ionships be ween use and nonuse alues o
su ace wa e quali y: A me a-analysis: NONUSE VALUE META-ANALYSIS. Wa e Resou . Res. 39, n/a-
n/a. h ps://doi.o g/10.1029/2003WR002649
Jones, H.P., Hole, D.G., Za ale a, E.S., 2012. Ha nessing na u e o help people adap o clima e change. Na .
Clim. Change 2, 504–509. h ps://doi.o g/10.1038/nclima e1463
