Transcript Valuing a Global Public Good: International Expert Delphi

Ståle Navrud
School of Economics and Business
Norwegian University of Life Sciences (NMBU), Ås, Norway
This represents joint work with the following:
 Jon Strand, Environment and Energy Team, The World Bank
 Richard Carson, University of California, San Diego
 Ariel Ortiz-Bobea, Cornell University
 Jeff Vincent, Duke University
Background:
World Bank project to value Amazon forest losses
 The Amazon rainforest is a global public good – of value to
the entire world
 Three main components to the value of protecting the
Amazon:
1) Values accruing to the local and regional population
2) Carbon values (global)
3) “Other global values”: How much are people
outside of the Amazon region willing to forego
(willing to pay) in order to preserve the Amazon
rainforest?
Background (cont.)
 Delphi Contingent Valuation (CV) survey – A tool for
global value transfer (Benefit transfer) ?
 A set of Delphi Contingent Valuation exercises, where
216 experts from 37 countries provided their best guess
of estimates for average population Willingness-To-Pay
(WTP) related to protecting the Amazon, if CV surveys
(described in detail in a web survey to the experts) had
been conducted in their own countries
Background (cont.)
 The Delphi exercises might not provide fully correct
values in terms of levels of population WTP, as experts
may have limited direct information about these
values in their country.
 The Delphi exercises might, in particular, indicate
useful relationships between national income per
capita and WTP
Key drivers of Amazon forest losses in
years to come:
1.
2.
3.
Man-made deforestation
Increased frequency of forest fires
Forest drying out, including “dieback” (transformation of
forest into savannah)
2-3 may follow from interactions between man-made factors
and climate change.
Whatever reasons, we seek a measure of the value of such
losses for populations outside of the Amazon region.
Basic features of our Delphi CV exercises:
 Valuation experts faced with a CV survey to be done in their country,




in a manner indicated below, and asked to indicate what they believe
would be its outcome.
Experts are asked to provide four numbers:
Mean, and median, WTP, for each of two separate plans for
protecting the Amazon rainforest:
Plan A: No further forest loss by 2050
(most ambitious protection plan)
Plan B: 12% of current forest lost by 2050
(less ambitious protection plan)
 Business-as-usual (BAU) alternative (no plan):
30% of the current Amazon forest lost by 2050.
 Survey in 2 rounds: experts were allowed to adjust their answers in
round 2 after learning about round 1 averages in their region.
Size of the Amazon rainforest area is comparable to that of the
continental U.S.
Plan A
Preserve current area (2012)
No plan
Plan B
Source: Centro de Sensoriamento Remoto/UFMG, Brazil http://www.csr.ufmg.br/
Experts participating in the surveys:
 Europe (48 experts from 21 countries):
 Austria, Belgium, Croatia, Czech Republic, Denmark, Finland,
France, Germany, Greece, Hungary, Italy, Netherlands, Norway,
Poland, Portugal, Republic of Ireland, Romania, Spain,
Sweden, Switzerland, UK
 Asia (70 experts from 12 countries):
 Bangladesh, Cambodia, China , India , Indonesia, Malaysia,
Nepal, Pakistan, Philippines, Sri Lanka, Thailand, Vietnam
 North America (82 experts from 2 countries):
 United States, Canada
 Oceania (16 experts from 2 countries):
 Australia, New Zealand
In toal: 216 experts from 37 countries.
Payment mechanism
 Households asked to make an annual payment per
household to support Plan A, and Plan B:
WTP in terms of a national tax collected in each country,
and submitted to an international Amazon protection fund.
 Key factors:
(1) per household rather than individual WTP
(2) annual for all future years rather than a one-time payment
(3) payment coercive (tax) rather than voluntary
(4) payment card (amounts shown from 0 to 1000 €, or $1500)
Examples of threathened mammal species in the Amazon (to be
shown in population surveys):
Survey results
Main results from the Delphi surveys for Plan A (most comprehensive plan). US$ per
household per year experts’ countries. R1 = Round 1, R2 = Round 2.
WTP
measure
North
America
(82)
Oceania (16)
Europe (49)
Asia (70)
Mean, R1
Mean, R2
Median, R1
Median, R2
89.2
71.1
51.1
41.1
45.6
39.3
25.6
22.9
46.6
42.8
25.6
22.6
27.8
16.1
25.2
13.8
Low/Lowermiddleincome Asia
(46)
22.5
11.0
22.3
11.9
Uppermiddle
income Asia
(24)
38.0
25.8
30.8
17.5
Low/Lowermiddleincome Asia
(46)
17.1
8.1
15.6
8.3
Uppermiddleincome Asia
(24)
25.0
17.6
18.0
10.9
Main results Plan B
WTP
measure
North
America
(82)
Oceania (16)
Europe (48)
Asia (70)
Mean, R1
Mean, R2
Median, R1
Median, R2
56.9
47.5
33.1
28.4
28.8
25.0
15.9
13.9
39.9
32.4
19.5
16.5
19.9
11.3
16.4
9.2
Plan A, Round 1: by country
Plan A, Round 2: by country
Changes in mean WTP from round 1 to round 2, Plans A and B, in total and by main
region. US$ per expert. Numbers of experts in parentheses.
Plan
Region
All experts
Plan A
All experts
Europe
Asia
North America
All experts
Europe
Asia
North America
-12.6 (216)
-7.1 (48)
-11.7 (70)
-18.0 (82)
-7.4 (216)
-4.0 (48)
-8.5 (70)
-9.4 (82)
Plan B
Downward
revisions only
-49.5 (65)
-19.7 (20)
-40.4 (21)
-94.1 (20)
-31.1 (61)
-15.6 (15)
-23.3 (26)
-65.5 (16)
Upward revisions
only
12.8 (40)
12.9 (4)
2.2 (12)
19.3 (22)
8.4 (35)
7.9 (5)
1.6 (7)
10.8 (22)
Elasticities of experts’ estimated WTP with respect to per-capita national GDP (regular
and PPP-adjusted), in total and by region, and by calculation, raw data estimates
Type of GDP
measure
included
Regular GDP
PPP adjusted
GDP
Group included
Plan A, R1
Plan A, R2
Plan B, R1
Plan B, R2
Full sample
Asia only
Europe only
Full sample
Asia only
Europe only
0.67
0.66
0.65
0.92
0.82
1.42
0.64
0.64
0.76
0.87
0.80
1.53
0.61
0.54
0.67
0.83
0.71
1.43
0.64
0.52
0.78
0.87
0.66
1.56
Country income elasticities and other coefficients, found from weighted regression
models
Type of
relationship
Corrected for
plan/region
Corrected for
additional
variables
Variable
GDP
Dummy Plan
A
Asia dummy
NA dummy
Oceania
dummy
GDP
Individual predictions, weighted
by inverse of expert number per
country
Mean
Median
0.729
0.775
0.280
0.293
0.030
0.575
-0.234
0.369
0.757
-0.298
0.847
0.872
Country means
Mean
0.940
0.302
Median
0.996
0.317
0.608
0.595
-0.137
0.894
0.664
-0.148
Not difficult at all (0) to Very difficult (10)
Conclusions
 Experts are not likely to precisely identify national
population WTP, but may have a “feel” for it,
perhaps mostly in relation to other populations.
 Individual experts’ answers are quite variable, but
there are strong patterns across all experts as a
group; perhaps most so for average assessed WTP
related to average income (Elasticity = 1 with
respect to PPP-adjusted per-capita national
incomes).
Conclusions (cont.)
 Expert valuations may in this way provide clues to
relative values across different populations, thus
making value transfer possible from populations where
values are measured to those where they are not.
 Testing accuracy of CV Delphi exercises:
Population valuation surveys, using joint CV and Choice
Experiment (CE) formats (internet-based in OECD
countries). The first survey, in U.S. and Canada, has
already been completed, in 2014 (results are not ready).
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