The Economic Value of
Mangroves: A Meta-Analysis
Marwa E. Salem D. Evan Mercer
Introduction
• Mangrove forests
• Ecosystem services provided by mangroves
- Support fisheries
- Nursery/ breeding grounds
- Raw materials and timber
- Recreation and tourism
- Coastal protection
- Erosion control
- Carbon sequestration, nutrient retention
• Threats to mangroves due to the non-market nature of
ecosystem goods and services leading to undervaluation.
• Numerous studies conducted to estimate economic value of
mangrove forests.
• Studies have covered a wide range of ecosystem services,
geographical locations, and methods of valuation.
• Importance of synthesizing results of literature:
• Understand factors affecting valuation
• Benefits transfer Motivation:
• Importance of examining factors that affect the annual per hectare
values of mangroves.
• Advantages of meta-regression analyses (MRA) (overcomes
researcher subjectivity, superior summarization and interpretation,
problems of primary studies).
Contribution:
• The first paper to undertake a mangrove-specific meta-analysis.
Types of ecosystem services
• Direct use values: fisheries, timber, recreation
• Indirect use values: ecological functions (storm
protection, erosion control).
• Nonuse values: existence, bequest
Valuation Methods
•  Production function (PF): change in CS and PS
-  Static, dynamic, other
•  Market prices (MP): total revenue
•  Net factor income (NFI): change in PS
•  Contingent valuation method (CVM): change in compensating and
equivalent surplus •  Travel cost (TC): change in CS •  Replacement cost (RC)
Literature
•  Studies have compiled data about mangrove values without conducting
regressions (Hamilton and Snedaker, 1984; Ronnback, 1999).
•  MRA undertaken for wetlands of all types (Brower et al. 1997;
Woodward and Wui, 2001; Brander et al., 2006; Chen, 2010). Their
main findings are:
-  Area has a negative effect on values (decreasing returns to scale)
-  GDP per capita has a positive effect
-  CVM and RC methods provide the highest estimates
-  Materials provide low values, water quality generates high values
Data
• 44 studies and 149 observations
• Values are highly left-skewed (mean higher than
median) • Total economic value $2,772 - $80,334 US$ ha−1yr−1
(mean = $28,662, median = $3,847)
Figure 1. Distribution of observations by continent
Figure 2. Distribution of mangrove valuations by type of service
(in US$ ha−1yr−1)
Table 1: Summary statistics for mangrove valuations by type of service (in US$
ha−1yr−1).
Service
Obs.
Mean
Median
Fisheries
51
23,613
627
Forestry
35
38,115
576
Coastal protection
29
3,116
3,604
Recreation & tourism
14
37,927
1,079
Nutrient retention
1
44
-
Carbon sequestration
7
967
211
Nonuse
6
17,373
15,212
Biodiversity
1
52
-
Water and air purification/ waste assimilation
4
4,748
5,801
Traditional uses
1
114
-
Total
149
Figure 3. Distribution of mangrove valuations by method of
valuation (in US$ ha−1 yr−1).
Table 2: Summary statistics for mangrove valuations by valuation method (in
US$ ha−1yr−1).
Method
Obs.
Mean
Median
2
2,975
2,975
10
209
53
4
257,905
236,037
Market prices
62
31,990
768
Net factor income
28
1,545
342
Replacement cost
32
3,390
3,889
Contingent valuation
10
10,691
1,082
1
8,094
Static PF
Dynamic PF
Other regressions
Travel cost
Total
149
Methodology:
Estimating a weighted, robust regression model with and
without interaction terms:
ln(y) = c + X m β m + X v β v + X d β d + µ
where y is annual per hectare value of mangroves
c is the constant term
€
β vectors represent the vectors of coefficients of the respective X
matrices Xm is the matrix of mangrove characteristics (area, local, location,
protected, service)
Xv is the matrix of study characteristics (marginal value, valuation
method)
Xd is per capita GDP
μ is the vector of residuals
Table 3: Estimation results.
Variable
w/o interaction
w/ interaction
Marginal value
−1.066 **
−1.274***
Static PF
-0.437
-0.328
Dynamic PF
1.148 *
1.344 **
Other regressions
3.705 ***
2.880 ***
NFI
−0.618 *
−0.614 **
RC
-0.791
3.103 ***
CV
-2.421
4.199 ***
Log (area)
-0.0774
-0.018
Global
0.674 *
-0.278
Asia (excl. Thailand)
−0.833 *
-0.0462
Middle East & Africa
1.043
2.175 ***
Americas
-0.581
0.197
Other continent
0.977
0.941
Protected
0.845 **
0.520 *
Forestry
-0.455
0.294
Recreation
-0.263
-0.00449
Coastal protection
2.059 **
−5.492 ***
Carbon sequestration
1.342 **
−3.123 ***
Nonuse
5.809 **
6.403 **
Water & air quality
3.027 **
7.869
Robust standard errors are between parenthesis and the asterisks *,**,*** depict significance at the 10%, 5% and 1% levels, respectively.
Table 3: Estimation results (cont.)
Variable
Model 1
Model 2
Log (GDP)
0.866 ***
0.792 ***
Forestry_GDP per capita
−9.72 × 10−5 **
Recreation_GDP per capita
−2.07 × 10−5
Coastal protection_GDP per capita
0.000563 ***
Carbon sequestration_GDP per capita
0.000288 ***
Nonuse_GDP per capita
−0.00119 ***
Water & air quality _GDP per capita
-0.00204
Constant
-0.0787
-0.0881
No. of observations
143
142
Adjusted R2
0.6
0.7
F
45.85***
59.45***
Robust standard errors are between parenthesis and the asterisks *,**,*** depict significance at the 10%, 5% and 1% levels, respectively.
Results
•  The CVM and RC methods generate the highest estimates.
•  GDP per capita has a positive effect.
•  Decreasing returns to scale (MV < AV, area has a negative effect).
•  The Middle East and Africa have the highest values (Brander et al.,
2006).
•  Protected sites have higher values.
•  Water and air quality and nonuse values are higher than average, while
carbon sequestration and coastal protection are lower.
•  Ecological services are more highly valued in higher GDP per capita
countries while materials are more valued in lower GDP per capita
countries.
Benefit transfer
• Two measures of forecast performance
-  In-sample forecast: Mean absolute percentage error (MAPE):
mean of |(yobs − yest)/yobs|
‣ Result = 40% (Model 1), 35% (Model 2)
-  Out-of-sample MAPE
‣ Result = 48.8% (Model 1), 54% (Model 2)
Recommendations
• Primary studies should report:
-  Physical quantities of quantifiable goods
-  Ecological health status of mangroves
-  Form of management of fisheries and forests
Thank you