2013 APEC Typhoon Symposium (APTS)
Socio-Economic Session 3: Extreme Weather Risk Assessment
Using Prescriptive Decision-making Model to Estimate the
Economic Values of Meteorological Information Services for
Agriculture in Taiwan
Hen-I Lin, Ph.D.
Assistant Research Fellow
Chung-Hwa Institution for Economic Research (CIER)
Presented at 2013 APTS
Taipei
10.22. 2013
1
Acknowledgements
• This study is currently funded by a research project from the
Central Weather Bureau (CWB) of the Ministry of
Transportation and Communications (MOTC) in Taiwan.
• The conclusions and opinions are those of the authors and not
necessarily the sponsors.
2
Background Introduction
• To estimate the economic value of meteorological information
services provided by CWB in Taiwan, we have done an economic
valuation methodology review and built a model framework in
2012.
• This year, we target on the agriculture sector and analyze the
economic values of meteorological information.
• Searching for the end-to-end framework of meteorological
information services and developing the networks/ links between
users in many sectors in Taiwan and CWB is the major task
behind this project.
3
Methodology for Valuation
•
•
•
•
•
market prices
normative or prescriptive decision-making models
descriptive behavioral response studies
contingent valuation method (CVM)
others
4
Prescriptive Decision-Making Model Approach
5
（1) Analysis of Prescriptive Decision-Making Model
Step 1
Step 2
• Define the decision-making problem
• Monetize the impacts of the consequences in different scenarios
Step 3
• Assess the probabilities of each event and decision maker’s
action, and estimate the expected values of the payoffs in each
scenario
Step 4
• Use the estimates of the payoffs to infer the economic values of
the meteorological services
6
Step 1: Define the decision-making problem
Decision Making
Units (DMU)
• Farmers (producing rice, coarse
grain, special crops, ornamental
plants, vegetables, and fruits)
Events
• Typhoon, Extremely Heavy Rain,
Drought, and Freezing Alerts(<10℃)
Actions
• Take actions for prevention or do
nothing
Consequences
• Damage in farmed area
7
Step 2: Monetize the impacts of the consequences in different
scenarios
Payoff Matrix of Actions
Events
Actions
Event occurs
Nothing happened
Action for
prevention
R-C-(D-N)
R-C
Do nothing
R-D
R
R：maximum profit per hectare without damage, R≥0.
C：cost of prevention, C>0.
D：damage estimate of DMU, D ≥0.
N：gains from taking preventing measures, N>C>0.
8
Step 3: Estimate the expected values of the payoffs in
each scenario
 Expected value of decision with taking actions for prevention 𝜋 𝑆𝐴 |𝑃 ：
π 𝑆𝐴 |𝑃 = 𝑃 ∗ 𝑅 − 𝐶 − 𝐷 − 𝑁
+ 1−𝑃 ∗ 𝑅−𝐶
 Expected value of decision with doing nothing 𝜋 𝑆𝐴 |𝑃 ：
π 𝑆𝐴 |𝑃 = 𝑃 ∗ 𝑅 − 𝐷 + 1 − 𝑃 ∗ 𝑅
 Expected value of final decision 𝜋 ∗ ：
𝜋 ∗ ＝𝐸[ 𝑚𝑎𝑥 π 𝑆𝐴 |𝑃 , π 𝑆𝐴 |𝑃
]
SA: percentage of farmers who take actions for prevention
SA : percentage of farmers who do nothing
9
Step 4: Use the estimates of the payoffs to infer the
economic values of the meteorological services
• Expected payoff of action with subjective perception :Vn；
• Expected payoff of action with imperfect information : Vi；
• Expected payoff of action with perfect information : Vp；
• The information value with imperfect information（IVi）:
IVi = Vi − Vn
• The information value with perfect information（IVp）:
IVp = Vp − Vn
10
（2）variables and data (1/2)
variables
formulation
description
D
D=dam*R
dam: damaged proportion
R: maximum profit per hectare
without damage
R
R=(D/DA)* Area
DA: damaged area
Area: farmed area
N
C
N=a*D
C=PB*N
a: reduced damage in percentage
by taking prevention measures
PB: payoff break-even probability of
taking actions
11
（2）variables and data (2/2)
variables
Price
Area
DA
Dam
a
Descriptions
sources
Price of agricultural products in local
market（$NT/ha.）
Farmed area of agricultural products
（ha.）
Damaged area of agricultural products
（ha.）
Damaged proportion of agricultural
products （%）
reduced damage in percentage by taking
prevention measures（%）
percentage of farmers who take actions for
prevention（%）
Given Farmers’ subjective perception of
meteorological information
Given Probabilities of
imperfect information
forecast
with
Given Probabilities of forecast with perfect
information
Council of agriculture, Taiwan
Council of agriculture, Taiwan
Council of agriculture, Taiwan
Shen et al.（2005; 2006; 2007）
Shen et al.（2005; 2006; 2007）
Shen et al.（2005; 2006; 2007）
0.5
~N(0.5, 0.162)
~N(0.5, 0.162)
12
（3）Results（1/4）
Meteorological Information Value in 2011（in NT thousand dollars）
Agricultural
Products
Total
Information value w/
perfect information
Information value w/
imperfect information
22,346,489
8,016,727
6,107,297
2,698,277
50,083
-15,565
(3) Special crops
3,101,845
1,485,751
(4) Coarse grain
1,748,490
1,069,333
(5) Fruits
7,444,071
1,797,588
(6) Vegetables
3,894,702
981,342
(1) Rice
(2)Flowers(orname
ntal plants)
13
（3）Results（2/4）
Meteorological Information Value in 2010（in NT thousand dollars）
Agricultural
Products
Information value w/
perfect information
Information value w/
imperfect information
Total
32,758,696
11,279,265
(1) Rice
(2)Flowers(ornamen
tal plants)
(3) Special crops
10,513,510
4,502,560
1,062,245
-98,534
5,737,810
2,332,675
675,590
355,625
(5) Fruits
5,990,678
1,966,688
(6) Vegetables
8,778,864
2,220,250
(4) Coarse grain
14
（3）Results（3/4）
── Information Value Comparisons
( w/perfect information and w/ imperfect information in 2011)
Information value
(1,000 $NT/year)
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
Rice
Flowers
Special crops Coarse grain
Perfect information value
Fruits
Vegetables
Imperfect information value
15
（3）Results（3/4）
── Information Value Comparisons
( w/perfect information and w/ imperfect information in 2010)
Information value
(1,000 $NT/year)
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
Rice
Flowers
Special crops Coarse grain
Perfect information value
Fruits
Vegetables
Imperfect information value
16
Concluding Remarks
• For the six major agricultural product farmers, the overall estimated
economic values of meteorological information services by prescriptive
decision-making model:
Year 2010 - 32.8 billion NT dollars (1.09 billion US dollars) PF
11.3 billion NT dollars (0.37 billion US dollars) IF
Year 2011 - 22.3 billion NT dollars (0.74 billion US dollars) PF
8 billion NT dollars (0.27 billion US dollars) IF
PF: perfect information; IF: imperfect information
17
Further Works
• A national survey on registered farmers by using CVM method
• A case study on Agriculture Meteorological Information
System (AMIS) built by Taiwan Agriculture Research Institute
(TARI) of Council of Agriculture (COA)
18
Thanks for your attention!!
19