Co-authors: Roy Brouwer, Tjasa Bole, Dolf de Groot, Salman Hussain, Onno Kuik, Alistair McVittie, Sander van der Ploeg, Peter Verburg, Alfred Wagtendonk

Scaling up ecosystem service values: methodology and applications. II – Value of changes in water quality

Luke Brander

Institute for Environmental Studies (IVM), VU University Amsterdam

Division of Environment, Hong Kong University of Science and Technology

Email: lukebrander@gmail.com

Outline

• Introduction• Methodology: meta-analysis and GIS• Water quality value data• Water quality value function• TEEB case study: global value of water quality changes• Conclusions and discussion

Introduction

• Need for value information at large spatial scales (e.g. river basin)

• CBA of investments in water quality improvements• Assess disproportionate costs• Value of water quality improvements varies with body

body, context and beneficiary characteristics

Proposed method for scaling up values

1. Construct database of primary value estimates2. Estimate a meta-analytic value function (including water

abundance variable)3. Construct database of water bodies using GIS4. Estimate site-specific values for changes in water

quality 5. Aggregate across relevant population and spatial level

Meta-analysis

SpatialData (GIS)

Estimatevalues

Valuation of changes in water quality

$/annum

Water quality

Marginal value curve

Q0Q1

P0

P1

Change in value

Water quality value data

• AquaMoney database of water quality values• 154 contingent valuation studies (1981 – 2006)• 54 with complete information for meta-analysis• 388 value estimates• Wide variety of descriptions of water quality

change standardised to 10-point water quality index

• Standardised values to WTP/household/year (USD 2007 prices)• Mean = 130 USD/household/year• Median = 78 USD/household/year

Location of water quality value study sites

Ecosystem services valued

Ecosystem service Number of estimates

Drinking water 17

Irrigation 3

Nature conservation 80

Fishing 151

Boating 128

Swimming 119

Walking 10

Other recreation 29

Health 4

Amenity 21

Non-use 275

Meta-analytic value function

• Dependent variable y: Annual WTP per household (USD 2007)

• Study characteristics Xsi:– Valuation method

• Water characteristics Xwi:– Baseline water quality– Change in water quality– Water body type

• Context characteristics Xci:– GCP per capita– Abundance of lakes and rivers within 10km radius– Accessibility index– Urban extent within 20km radius

uXbXbXbay CiCWiWSiSi )ln(

Meta-analytic value function

Variable name Beta Std. Error

Constant 4.898*** 0.314

WQI_CHANGE 0.081** 0.032

WQI_BASE -0.046 0.041

RIVER dummy -0.472*** 0.127

LAKE dummy -0.563*** 0.209

GCP 50km radius (ln) 0.103*** 0.036

URBAN 20km radius (ln) -0.106*** 0.030

WATER 10km radius (ln) -0.099*** 0.037

ACCESS INDEX -0.416** 0.204

N 388

TEEB case study: global value of water quality change

• TEEB Quantitative Assessment• Change in water quality 2000 - 2050• OECD baseline scenario of population and

development• IMAGE/GLOBIO model• Global coverage at 50km grid cell resolution • Nitrogen and phosphorous concentrations • Converted to 10-point water quality index• Large variation in positive and negative changes

in water quality

Changes in water quality 2000-2050

Region Mean Region Mean

Canada -1.26 Turkey -2.51

USA -0.25 Ukraine + -1.79

Mexico -0.47 Asia-Stan -1.87

Rest Central America

-1.26Russia +

-1.24

Brazil -0.82 Middle East -0.65

Rest South America -0.93 India + -2.25

Northern Africa -1.68 Korea -0.01

Western Africa -1.92 China + -1.22

Eastern Africa -2.30 South East Asia -1.32

Southern Africa -2.80 Indonesia + -1.99

Western Europe 0.91 Japan 1.30

Central Europe -0.22 Oceania -1.92

Changes in water quality 2000 - 2050

• Water quality changes combined with global map of lakes and rivers

• Global lakes and wetlands database GLWD (1x1km grid)

• 375,316 water bodies (lakes and rivers)• Site specific characteristics are substituted into

value function• Household WTP is aggregated across number of

households in 50km grid cell

Region Annual value Region Annual value

Canada -0.46 Turkey -0.96

USA -0.56 Ukraine + -1.07

Mexico -1.21 Asia-Stan -0.62

Central America -0.96 Russia + -1.32

Brazil -2.11 Middle East -0.91

Rest South America -2.43 India + -25.23

Northern Africa -1.90 Korea -0.22

Western Africa -8.65 China + -8.02

Eastern Africa -3.93 South East Asia -4.55

Southern Africa -3.75 Indonesia + -3.52

Western Europe 2.17 Japan 1.60

Central Europe -0.21 Oceania -0.53

World -69

Annual values in 2050 (billions USD 2007)

Discussion and conclusions

• Value transfer on a large scale– GIS to account for spatial variation– Scale, substitutes, and income effects

• Limitations:– Does not produce service specific values– Partial valuation: value data is focussed on

recreational uses– Partly accounts for changes in water quantity– Restricted measure of water quality– Difficult to identify relevant population for aggregation