The values of ecosystem services provided by lakes and reser- voirs are examined through a meta-analysis on an expanded world- wide database. The study ...
Valuing Ecosystem Services Provided by Lakes: Insights from a Meta-Analysis∗ By The values of ecosystem services provided by lakes and reservoirs are examined through a meta-analysis on an expanded worldwide database. The study assesses the socio-economic values attributable to the hydrological, biogeochemical and ecological functions provided by lakes and reservoirs (either natural or artificial). Based on the most extensive global database of non-market and market valuations of ecosystem services provided by artificial and natural lakes, we provide an estimation of the average value of a lake per household (84 USD$2010 per respondent and per year) and we offer some insights on how people value eleven different lake ecosystem services. A particular a high valuation is found for lake amenities whereas a low value is documented for the spiritual or symbolic appreciation of the lake. An interesting result is the fact that some interactions between ecosystem services appear to be significant for explaining lake values. This reflects some tradeoffs, synergies and antagonisms between ecosystem services. As a result, the value for a specific lake ecosystem service is shown to depend upon other ecosystem services provided by this lake. Keywords: Meta-Analysis, Lake, Ecosystem services, Environmental Valuation
I.
Introduction
Lakes are one of the most important source of water available for human and economic use. It is considered that at the world level 90% of liquid water is con∗
This work is a part of the FP7 European Mars project. 1
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tained in natural and artificial lakes and, according to Shiklomanov and Rodda (2003), the estimated area of all lakes in the world is about 2 million km2 representing about 1.5% of the total land area.1 Lakes (and more generally freshwater resources) provide many services. Some of them are directly valued by humans (increased water quantity, reduced damage due to flooding) whereas others benefit mainly to environment (reduced erosion, improved habitat for species). Since most of these services are not traded on markets, their economic valuation is not straightforward. As a result a wide non-market valuation literature has developed in the last decades and numerous lake valuation studies have been performed.2 Due to the wide range of valuation methods, characteristics of lakes and value estimates, it is very difficult to assess whether any systematic trends can be distilled from this literature and to shed light on what factors determine a lake’s value.3 Trying to identify if there exists an unobserved valuation function that determines a lake’s value given its physical, economic and geographic characteristics is the main objective of our paper. We propose here to conduct a meta-analysis on the value of ecosystem services provided by lakes. The term meta-analysis was coined by Glass (1976) to refer to “the statistical analysis of a large collection of analysis results from individual studies for the purpose of integrating the findings” (p. 3).4 This approach allows us to synthesize information regarding to the value of ecosystem services from selected studies in a systematic way, and to test some hypotheses on the determinants of these estimates. In the field of economic valuation of environmental resources, several metaanalyses published are related to water resources. These meta-analyses include for wetlands (Brouwer, Langford, Bateman, and Turner 1999, Brander, Florax, 1 This percentage varies highly according to the country considered, up to 8.6 and 9.4% for Sweden and for Finland, respectively. 2 See Artell (2014), Abbott and Klaiber (2013) or Abidoye, Herriges, and Tobias (2012) for some recent examples of this literature. 3 A similar point has been made by Woodward and Wui (2001) for wetlands. 4 Originally used in experimental medical treatment and psychotherapy, meta-analyzes have been playing an increasingly important role in environmental economics research since the beginning of the 1990s, Brouwer, Langford, Bateman, and Turner (1999).
3
and Vermaat 2006, Ghermandi, van den Bergh, Brander, de Groot, and Nunes 2010, Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk 2012, Eppink, Brander, and Wagtendonk 2014), coastal recreation (Ghermandi and Nunes 2013), coral reef recreation (Brander, Beukering, and Cesar 2007, Londo˜ no and Johnston 2012), lake amenities (Braden, Feng, Freitas, and Won 2010), aquatic resources (Johnston, Besedin, Iovanna, Miller, Wardwell, and Ranson 2005, Johnston, Ranson, Besedin, and Helm 2006, Moeltner, Boyle, and Paterson 2007, Johnston and Thomassin 2010), water quality (Van Houtven, Powers, and Pattanayak 2007) and flood risk (Daniel, Florax, and Rietveld 2009). To our best knowledge, our meta-analysis is the first one focusing on ecosystem services provided specifically by lakes. We argue that the results of a meta-analysis on ecosystem services provided by lakes might be useful for several reasons. First, as explained above there remain substantial debates on the economic value of lakes. Understanding of the physical, economic and geographic characteristics of lakes impact upon their economic value may inform decisions related to their use, conservation or restoration. Second, it is not clear if the relationships obtained with the existing meta-analyses for other water bodies (rivers, wetlands, coastal water) may by used for lakes especially because, since some services provided by lakes are quite specific, ecosystem economic values may differ according to the water body considered.5 The analysis in this paper relies on the most extensive global database of nonmarket and market valuations of ecosystem services provided by artificial and natural lakes.6 In total, we identified and reviewed over 300 publications related to 5 Magat, Huber, Viscusi, and Bell (2000) develop a framework for valuing river water quality improvement. They find that the mean valuation of people for a lake water quality improvement is roughly twice as valuable as a similar improvement in river water quality, implying that far more people were willing to pay large amounts to improve lakes over rivers. Working on water quality of water bodies in the United States, Viscusi, Huber, and Bell (2008) find that people have a significant preference for lake improvements over river improvements, a result compatible with a higher valuation attributed to lakes. Using an hedonic price approach, Sander and Polasky (2009) reports significant higher amenity values for houses located at proximity of a lake, compared to a proximity of a river. 6 Including artificial lakes (dams and reservoirs) is important due to their environmental impacts. In their mapping of the world’s reservoirs and dams, Lehner, Liermann, Revenga, V¨ or¨ osmarty, Fekete, Crouzet, D¨ oll, Endejan, Frenken, Magome, Nilsson, Robertson, R¨ odel, Sindorf, and Wisser (2011) indicate that 7.6% of the world’s rivers with average flows above 1 cubic meter per second are affected by a
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valuations of ecosystem services provided by lakes. Among them, we selected values from a subset a little bit more than 100 of these studies that were sufficiently comparable for inclusion in a meta-analysis. We then identify and quantitatively evaluate the role of income effects, substitution effects, return to scale, population density, biodiversity and geo-climatic conditions in the formation of lake ecosystem values. Our result show promise for benefit transfer because they suggest that it may be possible to reliably predict the value of ecosystem services provided by lakes based on their physical, economic and geographic characteristics. This opens the door to some value upscaling approaches as the ones proposed by Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk (2012) or Ghermandi and Nunes (2013).
II.
Ecosystem services provided by lakes and reservoirs
This section outlines the definition and typology of lakes and reservoirs used in this article, the functions that are utilized by humans, and the valuation methods that are applied to value various lake and wetland services. This section also discusses the heterogeneity of lake value estimates.
A.
Defining lakes
One may think that defining lakes is an easy task but it is not in practice. Quite simply, lakes are bodies of water that occupy depressions on land surface. There is however no universally accepted definition of a lake. The International Glossary of Hydrology briefly defines a lake as an “inland body of water of considerable size”.7 In the European Water Framework, a lake is defined as a “body of standing inland surface water” and this will be the definition we will refer to in the rest of cumulative upstream reservoir capacity that exceeds 2% of their annual flow. 7 The International Glossary of Hydrology is a joint publication of the United Nations Educational, Scientific and Cultural Organization and the World Meteorological Organization. It is available at http://webworld.unesco.org/water/ihp/db/glossary/glu/aglu.htm.
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this paper.8 We will include both natural and artificial lakes. We will also consider bodies of water in dams and reservoirs. Indeed, since dams and reservoirs provide several services including regulation of river flows, water storage, flood control, irrigation of agricultural lands, navigation and electricity, they may have specific economic values.9 On the other hand, dams and reservoirs can induce substantial costs to human societies (population displacement, loss of land) but also to environment, see Lehner, Liermann, Revenga, V¨or¨osmarty, Fekete, Crouzet, D¨oll, Endejan, Frenken, Magome, Nilsson, Robertson, R¨odel, Sindorf, and Wisser (2011). Flow regulation from dams and reservoirs has been shown to lead to numerous physical and ecological impacts on freshwater ecosystems and on their dependent species. The fragmentation of aquatic habitats, which limits the movement of species but also the delivery of nutrients and sediments downstream, is another important adverse ecological consequence of dams and reservoirs. It has also been debated recently debated whether dams and reservoirs used for hydroelectric generation are merely in-stream water users or whether they also consume water in the sense of taking away water from water bodies.10 Those detrimental impact of dams and reservoir might result is some negative value premia put by people on these water bodies. This is an issue we will investigate in the meta-analysis. Due to their specificities, we will exclude from the scope of our analysis wetlands.11 The interested reader mays refer to (Brouwer, Langford, Bateman, and 8 One of the most elaborated definition of lakes has been provided by Kuusisto (1985) as “a depression or a group of depressions partly or fully filled by water, all parts of the water body have the same surface, excluding temporary variability, caused by wind or ice, the ratio between in-flow and volume is small enough to let most of the suspended, inflowing material to form bottom sediments, and the surface area exceeds a given minimum value.” 9 Mekonnen and Hoekstra (2012) indicate that hydropower accounts for about 16% of the world’s electricity supply and that about 30–40% of irrigated land worldwide relies on water stored behind dams. 10 Working on 35 hydropower plants representing 8% of the global installed hydroelectric capacity, Mekonnen and Hoekstra (2012) suggests that hydropower is in fact a large consumptive user of water. The amount of water lost through evaporation annually from the selected reservoirs is equivalent to 10% of the global blue water footprint related to crop production. 11 The International Glossary of Hydrology defines wetlands as area of marsh, fen, peatland or water – whether natural or artificial, permanent or temporary – with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which does not exceed six metres at low tide. Wetlands play a specific role for instance in abating nitrogen load from agricultural sources or in
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Turner 1999, Brander, Florax, and Vermaat 2006, Ghermandi, van den Bergh, Brander, de Groot, and Nunes 2010, Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk 2012, Eppink, Brander, and Wagtendonk 2014) for some meta-analyses of values generated by wetlands, in different part of the world. B.
Identifying, measuring and valuing ecosystem services provided by lakes
In Table 1, we proposed a classification of ecosystem services provided by lakes. We also provide the methods most commonly used in their valuation. The classification of ecosystem services has been developed by the Joint Research Center of the European Commission specifically for lakes within the FP7 European MARS project. The conceptual framework is based on the CICES v4.3 and has been tested in several pilot studies, including one on freshwater ecosystems.12
flood protection. 12 Several classification of ecosystem services have been proposed, the most well-known can be found in Costanza, d’Arge, de Groot, Farberk, Grasso, Hannon, Limburg, Naeem, O’Neill, Paruelo, Raskin, Suttonkk, and van den Belt (1997), de Groot, Wilson, and Boumans (2002), TEEB (2010), Haines-Young and Potschin (2011). Some classifications are dedicated to aquatic ecosystem services, see for instance Brander, Florax, and Vermaat (2006) for wetlands.
Provisioning Provisioning Provisioning Provisioning Provisioning
12345-
Cultural Cultural Cultural Extra abiotic Extra abiotic
15- Recreation 16- Intellectual and aesthetic appreciation 17- Spiritual and symbolic appreciation
18- Raw abiotic materials 19- Abiotic energy sources
Direct Direct
Direct Non-use Non-use
Indirect Indirect Indirect Indirect Indirect Indirect Indirect Indirect Indirect
Direct Direct Direct Direct Direct
Value type
MP MP
CV
CV
CV
PF, MP PF, MP
CV, TC, DC, HP CV, DC CV, TC, DC
RC, RC RC RC, RC RC, RC RC, RC,
MP, RC MP, CV MP, RC MP,PF RC
Valuation methodb
extraction of sand & gravel hydropower generation
swimming, recreational fishing, sightseeing matter for research, artistic representations existence of emblematic species
excess nitrogen removal by microorganisms deposition of NOx on vegetal leaves vegetation controlling soil erosion vegetation acting as barrier for the water flow habitats use as a nursery natural predation of diseases and parasites rich soil formation in flood plains carbon accumulation in sediments maintenance of humidity patterns
fish catch water for domestic uses algae as fertilisers water for industrial or agricultural uses wood from riparian zones
Examples of economic good provided
Note: The classification of ecosystem services has been developed specifically for lakes within the FP7 European MARS and proposed by the Joint Research Center of the European Commission. a : Provisioning, Regulation and maintenance, Cultural, Extra abiotic services. b : Contingent value (CV), Hedonic price (HP), Market price (MP), production function (PF), Replacement cost (RC), travel costs (TC).
Regulation Regulation Regulation Regulation Regulation Regulation Regulation Regulation Regulation
6- Water purification 7- Air quality regulation 8- Erosion prevention 9- Flood protection 10- Maintaining populations and habitats 11- Pest and disease control 12- Soil formation and composition 13- Carbon sequestration 14- Local climate regulation
Fisheries and aquaculture Water for drinking Raw (biotic) materials Water for non-drinking purposes Raw materials for energy
Categorya
Ecosystem services
Table 1—Lake ecosystem services, type of value, and commonly applied valuation methods
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As noted above, lake provide a wide range of vital ecosystem services, which have an equally wide range of value.13 Economists usually decompose the total economic value of ecosystems into direct use, indirect use and nonuse values. Direct use values refer to consumptive and non-consumptive uses that entail direct physical interaction with the lakes and their services such as outputs of fish, fuel wood, recreation, and transport. Indirect use values include regulatory ecological functions, which lead to indirect benefits such as flood control, storm protection, nutrient retention, nursery grounds for different species, and erosion control. Nonuse values include existence and bequest values of lakes.
Methods for valuing ecosystem services vary depending on the nature of the service, and belong to two main categories namely revealed preference and stated preference methods. Revealed-preference methods exploit the relationship between some forms of observed individual behavior (e.g., visiting a lake) and associated environmental attributes (e.g., water quality of the lake) to estimate value. The revealed preference approaches include market price (MP), production function (PF), hedonic pricing (HP), travel cost (TC), replacement cost (RC), and damage cost avoidance (DC). On contrary, stated preference methods use survey questions to have respondents explicitly or implicitly state their preferences and values for a specific good. Within this category scholars usually make the distinction between contingent value (CV) and discrete choices (DC). The choice of valuation method matters and depends upon the context. For instance, revealed preference cannot be used to estimate nonuse values. While stated preference techniques can, in principle, be used to value any type of ecosystem service, in practice there may be cognitive limitations to stating preferences.
13 Ecosystem services have been defined as the direct and indirect contributions of ecosystems to human well-being in TEEB (2010). The existing literature of ecosystem services provided by lakes has been recently summarized in Schallenberg, de Winton, Verburg, Kelly, Hamill, Hamilton, Dymond, et al. (2013).
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C.
A global assessment of lake ecosystem services
We survey now the existing literature having addressed ecosystem services provided by lakes and reservoir, following the structure of the classification proposed in Table 1. When no information is available specifically for lakes and reservoir, we discuss the ecosystem services for inland waters.
Provisioning services. — Provisioning services are the products provided by
ecosystems, of which freshwater and food are two of the most important.
The Fisheries and aquaculture service corresponds to the ability of an ecosystem to support fish supply. In 2012, it was estimated that 33.8% of world fisheries and aquaculture production comes from inland water (FAO 2014), and this percentage has steadily increased over the last years from 28.4% in 2007. No official publication allows to make the distinction between fish an aquaculture produced from lakes and from rivers. Fish and fishery products are of great importance for since they represent an important source of protein and essential micronutrients for human consumption.14 A specific characteristic of this service is its highly uneven distribution at the global level. Indeed, 90% of the inland fish catch worldwide is concentrated in Africa and Asia where the direct dependence on inland fisheries and human well-being is the highest, see Dugan, Delaporte, Andrew, O’Keefe, and Welcomme (2010). The Water for drinking service corresponds the ability of an ecosystem to provide water for domestic use. Inland surface water sources account for a substantial part for providing this service. Using a global-scale assessment model, Doll, Hoffmann-Dobrev, Portmann, Siebert, Eicker, Rodell, Strassberg, and Scanlon (2012) indicate for instance that 64% of the water used by domestic users worldwide comes from surface sources. We may expect that lake will play an even 14 According to FAO (2012), fish accounted for 16.6% of the world population’s intake of animal protein in 2009 and 6.5% of all protein consumed.
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more important role in the future in securing drinking water supply. Indeed some global projection models such as Hejazi, Edmonds, Chaturvedi, Davies, and Eom (2013) predict an increase of municipal water withdrawals from 466 km3 year−1 in 2005 to 1098 km3 year−1 in 2100. The Raw (biotic) materials service is the capacity for an ecosystem to sustain the production of biotic resources such as wood and strong fibers (for building), biochemicals or biodynamic compounds (latex, gums, oils, waxes, tannins, dyes, hormones, etc.) for all kinds of industrial purposes. Lakes produce a large amount of biotic resources such as algae (which can be used as fertilisers) or vegetal compounds (which can be used as cosmetics). The Water for non-drinking purposes service corresponds to the provision of water for industrial or agricultural uses. The Raw materials for energy service include the supply in fuelwood from riparian zones.
Regulation & Maintenance services. — This category encompasses all benefits
obtained from the regulation of ecosystem processes.
The Water purification service corresponds to the fact that some ecosystems may allow the sedimentation (retention) of some soil particles. Run-off from city streets and agricultural fields contain various pollutants such as oil, pesticides, and fertilizer as well as excess soil. These pollutants are absorbed by the plants and broken down by plants and bacteria to less harmful substances. Pollutants attached to suspended soil particles are filtered out by grasses and other plants and deposited in lakes. This process helps improve water quality. It has been shown that lakes and reservoirs can contribute substantially to river network nitrogen retention (Harrison, Maranger, Alexander, Giblin, Jacinthe, Mayorga, Seitzinger, Sobota, and Wollheim 2009, Powers, Robertson, and Stanley 2013). Powers, Robertson, and Stanley (2013) present also evidence of long-term retention of
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phosphorous by lakes and reservoirs. The Air quality regulation service corresponds to the fact that lakes extract chemicals from the atmosphere, influencing many aspects of air quality. The Erosion prevention service corresponds to the fact that the vegetative cover on lake banks plays an important role in soil retention and the prevention of landslides. Soil erosion is the most widespread form of soil degradation. 12% of land area is globally affected by erosion which represents 1094 million ha (Mha). Flood protection. For both flood formation and the occurrence of droughts, the storage and retention of water in lakes or reservoirs is of high importance. Analysing disasters having impacted population over the period 1975–2001, Jonkman (2005) concludes that floods were the most frequently occurring, followed by windstorms. While some other disasters are more significant with respect to numbers of killed (especially droughts and earthquakes), floods by far affect the most persons, in total almost 2.2 billion over the considered period. The Maintaining population and habitats service is the fact that an ecosystem provides living space for all wild plant and animal species. The Soil formation and composition service corresponds to rock weathering and organic matter accumulation leading to the formation of productive soils. Carbon sequestration is the process of capture and long-term storage of atmospheric carbon dioxide. It has been estimated that, on a global basis, lakes and reservoirs sequester around 20% of the carbon transferred from land, reducing carbon losses from inland waters to the atmosphere by around one-third (Tranvik, Downing, Cotner, Loiselle, Striegl, Ballatore, Dillon, Finlay, Fortino, Knoll, et al. 2009). Therefore, lakes can perform an important ecosystem service in reducing the effect of climate warming. It has been however recently shown that inland waters can be substantial sources of carbon dioxide and methane emissions (Bastviken, Tranvik, Downing, Crill, and Enrich-Prast 2011). Accordingly, the terrestrial green house gas sink may be smaller than currently believed. Typically, effects of lakes and reservoirs within river networks have been expressed as
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changes in flux magnitude, but changes in flux variability may also occur. This has recently been shown through decreased intra-annual variability of stream dissolved organic carbon fluxes downstream of natural lakes (Goodman, Baker, and Wurtsbaugh 2011). The Local climate regulation service corresponds to the fact that an ecosystem may affect climate at the regional scale. Inland waters affect climate at the regional scale through exchange of heat and water with the atmosphere (Krinner 2003). Inland waters tend to humidify the atmosphere, especially in summer, and may modify the pattern of precipitations. Inland waters also regulate local temperatures by absorbing heat in summer time and releasing it in winter (Hardin and Jensen 2007).
Cultural services. — These are the nonmaterial benefits people obtain from
ecosystems in particular through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences. Cultural ecosystem services are among the most challenging of services to address since they comprise complex ecological and social properties and interactions.
Extra abiotic environmental services. — Abiotic resources are all products
not from living plants and animals, like minerals, fossil fuels, windand. In building our classification, we have decided to consider two types of abiotic services (raw abiotic materials and abiotic energy sources) due to their importance when considering.15
Raw abiotic materials include mainly extraction of sand & gravel. Globally, between 47 and 59 billion tonnes of material is mined every year, of which sand 15 Some previous ecosystem service classification have excluded abiotic resources based on the ground that they were usually non-renewable and/or they cannot be attributed to specific ecosystems (de Groot, Wilson, and Boumans 2002).
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and gravel, hereafter known as aggregates, account for both the largest share (from 68% to 85%) and the fastest extraction increase (Krausmann, Gingrich, Eisenmenger, Erb, Haberl, and Fischer-Kowalski 2009). Abiotic energy sources corresponds to the production of renewable abiotic energy. Mekonnen and Hoekstra (2012) indicate that hydropower accounts for about 16% of the world’s electricity supply. D.
Valuating ecosystem services provided by lakes at the large scale
Research on the monetary valuation of ecosystem services dates back to the early 1960s but received wide attention with the publication of Costanza, d’Arge, de Groot, Farberk, Grasso, Hannon, Limburg, Naeem, O’Neill, Paruelo, Raskin, Suttonkk, and van den Belt (1997). Surprisingly, whereas an important number of valuation case studies have been published for lakes ecosystem services (see following sections), it is quite difficult find some aggregated values at world-wide level. There are however a few exceptions. Costanza, d’Arge, de Groot, Farberk, Grasso, Hannon, Limburg, Naeem, O’Neill, Paruelo, Raskin, Suttonkk, and van den Belt (1997) have estimated the worldwide economic value of 17 ecosystem services for 16 biomes. They report worldwide average value for lake and rivers equal to $8,498 per hectare and per year, 64% of this value being provided by the water regulation service. Another exception is TEEB (2010). Appendix C of this book gives the results of an analysis of 11 main biomes/ecosystem-complexes (i.e. open ocean, coral reefs, coastal systems, coastal wetlands, inland wetlands, rivers & lakes, tropical forests, temperate & boreal forests, woodlands, grasslands and polar & high mountain systems) and collate their monetary values from different socio-economic contexts across the world. For the rivers & lakes biomes, the total monetary value of the potential sustainable use of all services varies between 1.779 and 13.488 Int.$/ha/year-2007 value. One should however point out that this average value has been computed based on only 12 points taken from 6 distinct studies.
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III.
Meta-database
We propose to use a meta-analysis as a means to estimate benefit functions that synthesize information from multiple primary studies having valuated ecosystem services provided by lakes and reservoirs. We will focus our attention on cultural services, and within this category more specifically on recreational services. A.
Search protocol
The scientific references have been selected through systematic searches of the keywords Valuation and Lake, Value and Lake, Willingness to pay or WTP and Lake, Stated preferences and Lake, on various search engines and on the web sites of major publishers of academic journals (Scopus, Science Direct, Wiley, Web of knowledge, RepEc, AgEconSearch, etc.). Similar searches were also conducted on databases specialized in environmental valuation.16 Lastly, the grey literature was searched using various search engine including Google Scholar and Science.gov.17 In all, the literature search process took about six months (December 2013 – May 2014). A three-step procedure has been implemented for each search. Based on the abstract, studies have been first classified into three categories namely irrelevant (studies without any reference to one or several lakes or those which did not report any economic valuation results), potentially relevant and relevant. Irrelevant studies where disregarded at this first step. Second, further investigations were then conducted on potential relevant studies in order to reclassify them either as irrelevant or relevant. Third, all studies considered as relevant were downloaded and an additional screening process was conducted to decide if they had to be included or not in the final database.18 16 We have in particular considered the Environmental Valuation Reference Inventory, the database of valuation studies in Southeast Asia, the Nordic Environmental Valuation Database and the Greek Environmental Valuation Database. 17 This is important to reduce the influence of a potential publication bias in the metaregression analysis but it implies further search efforts. 18 As an example we provide some information on the search with SCOPUS, the largest abstract and
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Figure 1. Type of study in the meta-analysis
Book
2
Journal
92
Other
1
Phd Thesis
2
Report
11
Working paper
5
0
20
40
60 frequency
80
100
The selection procedure led us to retain 101 studies. A vast majority of the database is made of peer-reviewed articles (86 studies), the second category the most represented being institutional reports (9 studies). Studies are quite recent on average. Among the 98 studies of the database, 39 have been published after 2010, 41 between 2000 and 2010 and the remaining before 2000. All continents are represented in our database, with an over-representation of North-America. North-America ranks first with 61 studies (60 studies deal with United States). The second continent the most represented is Europe with 20 studies. As already mentioned, United States are by far the country for which we have the most of studies. This may result from a selection bias since our systematic searches for lake valuation study has been done in English. It may also reflect the fact that hedonic price approaches have been extensively used in this country for valuing housing amenities. We will come back to this issue of citation database of research literature. The first stage of the search resulted in selecting 95 studies (the domain was restricted to documents in economics or in Social Sciences). Based on the abstract, 44 were classified as irrelevant, 13 as potentially relevant and 38 as relevant. After having downloaded the 13 potentially relevant studies, only 1 was reclassified from potentially to relevant. The 45 relevant articles were then downloaded and carefully examined. Following this third screening step, only 31 studies from the SCOPUS search have been kept and included in the final database. A similar method has been used for other search engines.
16
Figure 2. Repartition of studies per country Armenia Australia Cameroon Canada Chile China Czech Republic England Estonia Ethiopia Finland France Germany Greece India Italy Japan Netherlands New Zealand Norway Poland Scotland Turkey United States
1 3 1 3 1 10 1 1 1 2 1 2 2 1 3 1 3 2 3 3 1 1 2 64
0
20
40
60
frequency
Figure 3. Repartition of observations per country Armenia Australia Cameroon Canada Chile China Czech Republic England Estonia Ethiopia Finland France Germany Greece India Italy Japan Netherlands New Zealand Norway Poland Scotland Turkey United States
4 14 1 18 8 19 3 6 1 3 5 15 8 9 10 2 19 10 25 55 2 1 26 365
0
100
200 frequency
300
400
17
sample representativeness in the discussion section. A given study main report multiple lake values, either because several lakes are considered or because of use of several valuation methods or scenarios. Due to multiple values per studies, we have then 563 observations (i.e. lake value) in our final sample. This represents on average a little bit more than 5.5 observations per study. Again, United States rank first with 338 observations. They are followed by Norway (54 observations), Finland (23 observations) and China (22 observations). B.
Description of water bodies
One critical issue when conducting a meta-analysis is the high level of heterogeneity and the potentially non-comparability of studies pooled in the metadata. As a good practice, studies included in the meta-analysis should satisfy a criterion of minimal consistency for the dependent variable across observations, (Smith and Pattanayak 2002). This commodity consistency criterion requires in particular a minimal level of uniformity for the definition of the good that is valued. In our metadata, the way lakes have been defined varies significantly from one primary study to another. For instance, some studies refer to a particular lake whereas others consider all water bodies in a given area. Some studies focus on artificial lakes whereas other deal with natural ones. All these lake characteristics should be introduced as moderators in the meta-analysis in order to insure a minimal level of uniformity for the good valued. In a vast majority of cases, values are reported for a specific lake. We have in our meta-database 170 distinct lakes for which, on average, a little bit more than 3 values are reported. This means that we have mainly in our database a collection of local valuation studies, which is relevant from the point of view of conducting a meta-analysis. Lake can be either natural or artificial, and this distinction may matter since ecosystem services differ according to this two categories of lakes. Most of the
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Figure 4. Location and number of observations per lake
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Obs per study
!
16 - 22
Num
!
23 - 29
!
1-4
!
30 - 37
!
5-9
!
38 - 45
!
10 - 15
!
46 - 52
lake values have been obtained for natural lakes (405 observations). An artificial lake is considered for 126 observations whereas for 33 observations both natural and artificial lakes are included in the valuation exercise (which is conducted in that case either at a regional or a national level). When restricting our sample to local lake valuation studies, the average area of each lake is equal to 5014 km2, varying from 0.02 km2 (Raintree Ranch lake in Arizona, United States) to 58000 km2 (lake Michigan, United States). The median lake area is 33 km2 which suggests a very skewed distribution of the lakes in our sample. If we exclude Lake Michigan for the sample (Lake Michigan is by
19
far the greatest lake in our sample in terms of area), the average area drops to 722 km2, and to 146 km2 if other Great Lakes are taken out. In Figure 5, we gives the distribution of observations per lake area (restricting our sample to local lakes).
Figure 5. Repartition of observations per area of the lake
< 1 km2
57
[1,20[ km2
188
[20,1000[ km2
200
>1000 km2
184
0
50
100 frequency
150
200
It should be noticed that, when possible, lakes in our database have been georeferenced with ArcGIS. This will allow us to add some spatially explicit context variables (such as anthropogenic pressure, climate conditions or GDP in the considered area) to be accounted for in the meta-regression model. Spatiallyaugmented data have been used for a meta-analysis in particular by (Ghermandi, van den Bergh, Brander, de Groot, and Nunes 2010, Ghermandi and Nunes 2013). In addition and in order to get additional information on lakes in our database (type of use, shape, shoreline length, etc.), we have complemented our data with lake attributes taken from two global lake and reservoir databases, namely the Global Lakes and Wetlands Database (GLWD)19 and the Global Reservoir and 19 The GLWD compiles worldwide data on lakes and reservoirs with an extensive list of attribute for each. We have used the first level (GLWD–1) which comprises the 3067 largest lakes (area greater than 50 km2) and 654 largest reservoirs (storage capacity greater than 0.5 km3) worldwide. The GLWD is jointly developed by WWF and the Center for Environmental Systems Research, University of Kassel in Germany.
20
Dam Database (GRanD)20 C.
Valuation methods
An additional consideration for any meta-analysis of nonmarket values is the degree of consistency in welfare measures (Johnston and Rosenberger 2010).21 In our case, lake value measures have been obtained from the primary studies through various valuation methods and analytical techniques including travel cost, choice experiment, contingent valuation and hedonic price methods. This raises some welfare consistency concern since the measures obtained may not rely on the same theoretical construct.22 One way to address this issue consists in pooling estimates drawn from the numerous methods (contingent valuation, travel cost, choice experiment, net factor income, productivity, gross revenue methods, etc.) into a single meta-database and including dummy variable for the used methods as moderators in the metaanalysis, see for example (Brander, Beukering, and Cesar 2007). This approaches still raises some welfare inconsistency concerns, see Nelson and Kennedy (2009). Another solution adopted by Londo˜ no and Johnston (2012) consists in excluding from the analysis any study which does not comply with a strict application of the welfare consistency criterion. One drawback of this solution is to exclude de facto all studies based on methodologies that do not generate well-defined welfare measures (e.g., replacement costs, gross revenues, etc.). There might be also a selection bias issue if the excluded studies present some systematic characteristics related to lake values. A third solution consists in estimating separated meta-regressions for lake values obtained by valuation methods based on differ20 The GRanD compiles reservoirs with a storage capacity of more than 0.1 km3. it contains 6.862 spatially explicit records of reservoirs with their respected dams and gives information on their storage volume. The development of the GRanD database has been coordinated by the Global Water System Project, University of Bonn in Germany. 21 Welfare consistency requires that welfare measures represent the same theoretical construct (Smith and Pattanayak 2002). 22 It is for instance well-known that contingent valuation and travel cost methods provide Hicksian and Marshallian welfare measures, respectively, so that pooling across these study types violates the strictest form of welfare consistency (Smith and Pattanayak 2002).
21
ent theoretical constructs. This approach however implies to have a sufficient number of observations for each sub-sample considered. In what follows, we will implement these three strategies to address this welfare consistency issue. In Figure 6, we have plotted the distribution of observations per type of valuation method. Figure 6. Type of valuation method
CV
175
ChoiceExp
84
HedonicPrice
224
Other
22
TravelCost
124
0
50
100
150 frequency
200
250
Four main valuation methods have been used namely contingent valuation (CV), choice experiment (CE), hedonic prices (HP) and travel costs (TC). A fifth category (Other) has been added, which basically includes a small number of studies having used some mixed protocols. With 236 and 153 observations respectively, hedonic price and contingent valuation are the two methods which have been the most often used by scholars for valuing lake’s ecosystem services. Travel costs and choice experiment rank third and fourth, respectively. We do not expect a particular impact of using a specific method since the existing literature does not report any systematic bias which could be associated with using a specific valuation method. For a given valuation method, lake value may differ according to the format used. For instance, among contingent valuations, open-ended elicitation formats are more liable to free-riding behavior, which may lead to understatement of lake
22
values. Such value estimates likely lie below those obtained with other elicitation formats such as payment card and dichotomous choice. Among the 153 observations based on contingent valuations, 72 use a dichotomous choice format (either single or double-bounded), 44 a payment card format, 31 an open-ended format and 6 an iterative bidding approach. The valuation format will be included as a moderator in the meta-analysis. D.
Ecosystem services provided by lakes
Different ecosystem services have been valued in the literature we have surveyed, although not all of the services identified in Table 1 have been valued (e.g., carbon sequestration or erosion prevention). In total, we have gathered some economic values for 11 different ecosystem services provided by lakes appearing in our database, see Figure 7. Figure 7. Lake ecosystem services in the valuation studies ESS_Flood
13
ESS_DrinkWater
26
ESS_Fishing
240
ESS_Swiming
158
ESS_Boating
174
ESS_Camping
33
ESS_Sightseeing
145
ESS_UnspecRec
199
ESS_Amenity
228
ESS_PopHabitat
179
ESS_Spiritual
20
0
50
100
150 frequency
200
250
For each lake valuation study (or for each observation in case of multiple observations par study) we have identified the ecosystem services provided by the considered lake. They belong to three categories of ecosystem services (provisioning services, regulation and maintenance services, cultural services ).
23
We have only 22 observations of economic values for provisioning services and all these observations correspond to the “water for drinking service”. We have 174 observations of economic values for regulation and maintenance services. The majority (163 observations) refers to the “maintaining populations and habitats” services (ESS P opHabita), whereas the remaining observations deal with the “flood protection” service (ESS F lood). Not surprisingly, the vast majority of ecosystem services for which a lake value is associated with corresponds to the cultural service category. In order to reflect the distinctions that are generally made between cultural services of lakes in the valuation literature, we have categorized these services in our database slightly differently from the list in Table 1. In particular, the “recreation service” has been split into several sub-services (e.g., fishing, boating, swimming, camping, sightseeing and unspecified recreational service). In addition, the “Amenity” sub-service has been created for valuation studies based on the hedonic price approach.23 Among the cultural service category, the “amenity service” ranks first (244 observations for ESS Amenity) followed by the different recreational services such as “fishing” (192 observations for ESS F ishing) or “boating” (144 observations for ESS Boating). Some studies value only one particular lake ecosystem, but a significant number of them provides values for two or more services, Figure 8. The number of ecosystem services valued in each study varies from 1 to 7, with an average a little bit higher than 2. This raises an interesting identification issue since in most cases a direct mapping between a particular service and its associated economic value does not exist. This identification issue might be particularly relevant to address in case of complementarity or substitutability relationships among ser23 As explained in (Lansford and Jones 1995), an hedonic study of shoreline and “near-the-lake” properties capture an important component of the recreational and “amenity” (aesthetic) values that are provided by the existence of such a lake. There is however no direct mapping between these amenities and the cultural service category as defined in Table 1. In fact to obtain the total recreational and aesthetic value, other components must be added to the value of amenities. These include the value to persons living outside the immediate area who travel to the lake to enjoy its benefits and components for existence, bequest, and option value by those who never visit the lake yet believe it to be beneficial.
24
Figure 8. Number of ecosystem services valued in each study
1
383
2
69
3
19
4
38
5
47
6
61
7
12
0
100
200 frequency
300
400
vices. Indeed, in all previous meta-analysis on water ecosystem services, it has been assumed that the economic value of a water body is a linear function of the ecosystem services provided by a lake.24 We argue that such a specification could be questioned in case of trade-offs, synergies and antagonisms between ecosystem services. Since there are complex relationships among ecosystem services (Fu, Su, Wei, Willett, L, and Liu 2011, Raudsepp-Hearne, Peterson, and Bennett 2010)25 , the value for a specific ecosystem services might depend upon the other ecosystem services provided by a lake. Not introducing interactions across ecosystem services may then lead to biased estimates in the meta-analysis. It also raises some concerns with respect to using a “value catalog approach” for doing some transfer of values for ecosystem services. 24 In existing meta-analyses, ecosystem services are accounted for by a set of binary variables indicating the ecosystem services valued (Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk 2012). In their meta-analysis of values of natural and human-made wetlands, (Ghermandi, van den Bergh, Brander, de Groot, and Nunes 2010) estimate an extended model that includes a series of cross-effect variables. These variables capture the relationship between the provision of a specific wetland service and the type of wetland that provides it, but not the relationships among services. 25 In their analysis of provision of multiple ecosystem services across landscapes, Raudsepp-Hearne, Peterson, and Bennett (2010) report that among the 66 possible pairs of ecosystem services they have considered, 34 pairs have appeared to be significantly correlated either positively (synergies and complementarities) or negatively (tradeoffs). At the landscape scale, they typically observe a pattern of tradeoffs between provisioning ecosystem services and both regulating and cultural ecosystem services. On contrary they document synergies across regulating ecosystem services, all regulating ecosystem services being positively correlated with each other.
25
Figure 9. Lake ecosystem services by valuation methods CV ESS_Flood ESS_DrinkWater ESS_Fishing ESS_Swiming ESS_Boating ESS_Camping ESS_Sightseeing ESS_UnspecRec ESS_Amenity ESS_PopHabitat ESS_Spiritual
ChoiceExp
2 22 114 85 107 25 85 110 2 92 13
ESS_Flood ESS_DrinkWater ESS_Fishing ESS_Swiming ESS_Boating ESS_Camping ESS_Sightseeing ESS_UnspecRec ESS_Amenity ESS_PopHabitat ESS_Spiritual
0 4
ESS_Flood ESS_DrinkWater ESS_Fishing ESS_Swiming ESS_Boating ESS_Camping ESS_Sightseeing ESS_UnspecRec ESS_Amenity ESS_PopHabitat ESS_Spiritual
0 0
36 41 36 2 35 44 7 54 0
HedonicPrice ESS_Flood ESS_DrinkWater ESS_Fishing ESS_Swiming ESS_Boating ESS_Camping ESS_Sightseeing ESS_UnspecRec ESS_Amenity ESS_PopHabitat ESS_Spiritual
TravelCost
11 0 1 1 2 0 1 2 219 2 3
0
50
100
150
200
70 30 28 5 23 41 0 31 4
0
50
100
150
200
frequency Graphs by ValuMethod2
Finally, it should be mentioned that, for some lake ecosystem services, the economic values have been obtained using a single valuation method whereas, for others, they have been derived from multiple methods. For instance, the amenity service has been valued mainly using an hedonic price approach whereas the contingent valuation approaches have been mainly implemented for the “water for drinking” service. On contrary the “fishing service” has been valued using contingent valuation, choice experiment and travel cost approaches. This will allow us to assess if a particular valuation method results in a systematic bias for valuing an ecosystem services. E.
Reconciliating lake values
Lake/reservoir values have been reported in the literature in many different metrics (i.e. willingness to pay per unit of area or volume, marginal values, capitalized value), using different currencies and for different period of time. In order to enable a comparison across studies all these values must be standardized.
26
As explained by Ghermandi et al. (2010) or by Londo˜ no and Johnston (2012), the standardization of different and heterogenous metrics used to value ecosystem services is a difficult and controversial task. We explain here how ecosystem services values from the original studies have been normalized.
Accounting for heterogeneity in space and in time . — In our meta-database,
lake/reservoir values have been obtained for different countries (21 countries) and for different period of time (from 1957 to 2012). This requires some normalization procedures. First, to account for differences in purchasing power among countries, a purchasing power parity indexes has to be used. Following Ghermandi et al. (2010), differences in purchasing power among countries have been accounted for by using the purchasing power parity (PPP) index provided by the PennWorld Table. As a result, all currency have been converted in USD PPP. Second, the problem of having different years of observation is usually solved by using appropriate price deflators, see Ghermandi and Nunes (2013) for a recent example. Values reported for price levels other than 2010 have been converted using national customer price indexes (CPI) provided by International Monetary Fund (World Economic Outlook 2014). As a result, all ecosystem services values are computed on an annual basis and they have been expressed in 2010 US$. Similar transformations have been done for the other economic variables to be used in the meta-regression (household income for example).
Applying the commodity and welfare consistency principles. — Based on
the requirement to work on estimates satisfying both the property of commodity consistency for the dependent variable across observations and the property of consistency of the welfare measure, we have decided to split our meta-dataset into two sub-samples. The first sub-sample will include all observations for which lake values have been obtained by an hedonic price approach. The second subsample include all other remaining observations.
27
Normalized lake value for hedonic price studies. — Restricting the sub-
sample to hedonic price studies is a way to satisfy the requirement of working on quite homogenous data in terms of good valued and welfare measure. Indeed, the good which is valued is well identified (a property sold on a market) and, under some assumptions on the functioning of the property market, the implicit marginal price obtained from hedonic price studies is directly related to a measure of consumer welfare. One specific issue with hedonic price studies is that they give a capitalized value whereas the other valuation methods typically provide a value estimate per unit of time. Additional data management is then required for making estimates obtained with these different methods more comparable. In our case, the capitalized values obtained from hedonic price studies have been annualized assuming constant value per year, using the 30-year fixed mortgage rate as a discount factor (for the year of the study) and considering a 30-year time horizon.26 In our meta-analysis all values obtained from hedonic price studies have then been normalized and expressed in monetary units per sold property and per year.
Normalized lake value for other valuation studies. — Lake values reported
in studies which are not based on an hedonic price approach are expressed in very different metrics including monetary units per unit of lake area per unit of time or monetary value per household/person/trip per unit of time. Rationalizing the use of a normalized is quite difficult is this case. Some previous studies have used a normalized value expressed in monetary units per unit of area per unit of time Woodward and Wui (2001), Ghermandi et al. (2010), Brander et al. (2012), Ghermandi and Nunes (2013). When an aggregated value for the investigated ecosystems is provided in the primary study, 26 A similar procedure has been used by Woodward and Wui (2001) or Ghermandi and Nunes (2013) using the discount factors provided directly in the studies (or a 6% rate in the two studies in Woodward and Wui (2001) that did not state any discount rate). In the empirical analysis we will test the robustness of our annualization procedure, in particular by considering other time horizons (20 or 25 years).
28
such a normalization procedure is easy to implement.27 When no aggregated value is provided, the study must be disregarded. This is why some meta-analysis of ecosystem service values have excluded studies in which values are estimated per unit of area (Londo˜ no and Johnston 2012). In that case values are usually expressed in monetary units per visit per unit of time (Brander, Beukering, and Cesar 2007, Johnston and Rosenberger 2010) or in monetary units per household/respondent per unit of time (Brouwer, Langford, Bateman, and Turner 1999, Johnston, Besedin, Iovanna, Miller, Wardwell, and Ranson 2005, Johnston, Ranson, Besedin, and Helm 2006, Londo˜ no and Johnston 2012, Ge, Kling, and Herriges 2013). We have opted for this later normalization procedure and expressed all the values from primary studies in monetary units per household/respondent per year. In some cases, the reported primary study results needed to be adapted to fit the required format. For example, values per person/visit were to be transformed into values per person per year using data on number of visit/duration of visit. Such adjustments are required to reconcile variable definitions across sites (commodity consistency requirement), and are nearly universal in valuation meta-analyses (Johnston and Rosenberger 2010, Nelson and Kennedy 2009). As discussed above, it is clear that not all values reported within this category rely on the same theoretical construct. This is for instance the case for contingent valuation and travel cost methods which provide Hicksian and Marshallian welfare measures. The welfare requirement issue will be discussed in the empirical analysis.
A preliminary view of lake values. — For studies using an hedonic price
approach, we find a mean value of a lake equal to 769 USD$2010 per property per year. The median value is 215 USD$2010 per property per year, showing that the 27 It simply requires to divide the total value of the ecosystem by its area. Notice however that the methods for computing the aggregated value of ecosystems might differ from one primary study to another. This may raise some concerns with respect to the commodity consistency requirement.
29
distribution of values is skewed with a long tail of high values. For other studies (i.e studies not relying on an hedonic price approach), we find an annual value of a lake equal to 348 USD$2010 per respondent and per year with a median value equal to 106 USD$2010 per respondent and per year. A first result is that hedonic price studies result in significantly higher lake values, compared to studies using other valuation methods. It might be interesting to compare the mean lake values obtained from our metadatabase with those obtained form similar/comparable meta-analysis. Brouwer, Langford, Bateman, and Turner (1999) have conducted a meta-analysis for the use and non-use values generated by wetlands across North America and Europe. On average, the values we find for ecosystem services provided by lakes are higher than the ones reported by Brouwer, Langford, Bateman, and Turner (1999) for wetlands. Their average willingness to pay for wetland function preservation found in all studies taken together is 134 USD$2010 per respondent and per year.28 The median is considerably lower, namely 74 USD$2010 per respondent and per year. We discuss now the breakdown of lake values according to a number of possible explanatory factors. Mean lake values have been calculated (1) by countries, (2) by lake size classes and (3) by ecosystem services. Results are presented separately for studies using an hedonic price approach and for studies using another type of valuation method. Figure 10 presents the mean annual value of lakes per country (in USD$2010 per property per year for studies using an hedonic price approach and in USD$2010 per respondent per year for studies using another type of valuation method). When considering hedonic price studies, United States rank first with a mean annual value of lakes per property equal to 816 USD$2010. The following countries in terms of values are Finland, Ireland Canada and Netherlands with 552, 543, 387 28 The willingness to pay in Brouwer, Langford, Bateman, and Turner (1999) is expressed in International Monetary Fund’s Special Drawing Rights for 1995. It has been converted in USD$2010 using an appropriate discount factor (US CPI).
30
Figure 10. Mean annual value of lakes per country and per valuation method
Armenia Australia Cameroon Canada Chile China Czech Republic England Estonia Ethiopia Finland France Germany Greece India Italy Japan Netherlands New Zealand Norway Poland Scotland Turkey United States 0
200
400 600 2010 USD PPP
LakeValueHedonic
800
LakeValueOther
and 284 USD$2010, respectively. When considering studies using another type of valuation method, Switzerland ranks first with a mean annual value of lakes per respondent equal to 765 USD$2010. The following countries are France, United States and Australia. For countries where lake values are available both with an hedonic price approach and with another valuation approach (i.e. China, England, Finland, Netherlands, United States), we observed some significant differences across lake values by method of valuation. This indicates that the valuation method used in the primary study is likely to have an impact. Another lake characteristic that we may expect to determine its value is its size (area). There is no clear a priori expectation of the sign of this relationship given on the one hand that there may be diminishing marginal returns to most lake services as lake size increases, but on the other hand some ecological functions require minimum thresholds of habitat area which suggests that lake values may increase with size, see (Brander, Florax, and Vermaat 2006). For hedonic price studies, no monotonic relationship seems to emerge between the lake
31
Figure 11. Mean annual value of lakes per size of lake and per valuation method
< 1 km2
[1,20[ km2
[20,1000[ km2
>1000 km2
0
500
1,000 2010 USD PPP
LakeValueHedonic
1,500
2,000
LakeValueOther
value per respondent and its size. This is consistent with previous findings having found constant returns to scale with respect to size for some ecosystem service values. Indeed, both Brander, Florax, and Vermaat (2006) and Woodward and Wui (2001) conclude that the economic value of wetland services is not significantly influenced by the size (area) of wetlands, i.e. that wetland values exhibit constant returns to scale. When considering studies using a valuation method different from the hedonic price one, the picture is quite different. We find in that case a positive relationship between the size of the lake and its value. This may indicate the presence of increasing return to scale but the result may also be related to the fact that the biggest lakes in our meta-database are located in the United States (lake Michigan, lake Erie and lake Ontario), a country for which we would expect a priori high values for lake ecosystem services. In Figure 12 we have split the annual value of lakes according to the presence or not of a specific ecosystem services (and still according to the valuation method used in the primary study). Values reported in this Figure should not be interpreted as the value for the the considered service since each lake in our metadatabase provides on average more than one service. This figure calls for a few comments. In our meta-database, cultural ecosystem services provided by lakes
32
Figure 12. Mean annual value of lakes per ecosystem services and per valuation method Flood
0
1,000
2,000 3,000 2010 USD PPP
Boating
4,000
0
1,000
DrinkWater
0
1,000
2,000 3,000 2010 USD PPP
2,000 3,000 2010 USD PPP
Amenity
4,000
0
Camping
4,000
0
1,000
Fishing
2,000 3,000 2010 USD PPP
1,000
2,000 3,000 2010 USD PPP
4,000
0
0
1,000
Swiming
0
1,000
2,000 3,000 2010 USD PPP
1,000
Sightseeing
4,000
2,000 3,000 2010 USD PPP
0
1,000
2,000 3,000 2010 USD PPP
4,000
2,000 3,000 2010 USD PPP
4,000
Spiritual
4,000
UnspecRec
4,000
2,000 3,000 2010 USD PPP
PopHabitat
0 0
1,000
4,000
1,000
2,000 3,000 2010 USD PPP
LakeValueHedonic
4,000
LakeValueOther
33
are highly valued. This is particularly true for the “spiritual and symbolic appreciation” and for the “amenity” services. Interestingly, the two regulation and maintenance services in our meta-database (i.e. “flood protection” and “maintaining populations and habitats”. this is quite consistent with previous findings on wetland ecosystem services. Indeed, in their meta-analysis of values for wetland ecosystem services, Brouwer, Langford, Bateman, and Turner (1999) report that the wetland function which generates the highest value is flood control, followed by wildlife habitat provision and landscape structural diversity. More recently, Brander, Florax, and Vermaat (2006) also report high values for biodiversity, amenity and flood protection services of wetlands.29 IV.
Meta-analysis specification and results
The above analysis of the available data in the lake valuation literature does not allow for interactions between the various potential explanatory variables. In order to attain marginal effects – given the interference of potentially relevant intervening characteristics – we will use a meta-regression analysis to assess the relative importance of all potentially relevant factors simultaneously. A.
Non-independence of estimates from primary studies
The non-independence of estimates from primary studies has been recognized has a crucial methodological issue in the meta-analysis literature, Nelson and Kennedy (2009). There are two main reasons why primary estimates may not be independent of one another. The most common one is the use by researchers of multiple estimates from the same primary study, which implies within-study autocorrelation. Within-study correlation is usually not the most difficult problem to solve. One simple way to address this issue is the use of regression weighting observations (generalized 29 Brander, Florax, and Vermaat (2006) indicate for instance an average annual value equal to 17000 US$ for the biodiversity service of wetlands.
34
least-squares) in which each study in the data set receives equal weight, instead of each observation as in ordinary least squares (Ghermandi, van den Bergh, Brander, de Groot, and Nunes 2010). Another common treatment consists in selecting a single observation per primary study. In their review of meta-analysis in environmental economics, Nelson and Kennedy (2009) indicate that the most common treatment for data dependencies or correlation is the use of a single observation per primary study (30 studies over a total of 140 studies reviewed). A second reason is that the primary studies (from which primary estimates are taken from) may not be independent of each other, which implies between-study autocorrelation. Nelson and Kennedy (2009) mentions several potential sources for between-study correlation: (1) some primary studies may utilize the same data sources or may have conducted on the same area, (2) the analyst may apply a similar adjustment to the primary data, (3) some primary studies may share an unobservable characteristic such as similar management of an environmental commodity at different locations, or (4) several primary studies may share an observable characteristic, such as an identical functional form, omission of a key explanatory variable, or data drawn from the same study location. There are various ways to address the issue of between-study correlated observations. A first solution consists in using specific panel data models, see Rosenberger and Loomis (2000). A second approaches is to rely on a multilevel modelling approach (MLM) which allows the regression coefficients to vary randomly across groups, creating composite errors see Brouwer, Langford, Bateman, and Turner (1999), Bateman and Jones (2003), Londo˜ no and Johnston (2012) among others. It should be stressed that the random-effect model for panel data matches the multilevel model with random intercept commonly used in this literature. Its estimation via panel-data software produces results that are virtually identical to its estimation using hierarchical/multilevel software, Nelson and Kennedy (2009). This is the approach we have chosen to follows here.
35
B.
Empirical specification and estimation strategy
The dependent variable in our regression equation is is the natural logarithm lake values in USD per year in 2010 prices, labelled ln y. The explanatory variables are grouped in different matrices that include the ecosystem services provided by the lake (with potential interactions across ecosystem services) in ES, the water body characteristics in X b (i.e., type of water body, size of water body, etc.), the study characteristics in X s (i.e., survey method, payment vehicle, elicitation format, etc.) and context-specific explanatory variables in X c . There are two popular panel-data models which can be used for estimating the meta-regression model, e.g. the fixed-effect model and the random-effects model. The crucial difference between these two models lies on the assumptions used to define the error variance. In the fixed-effect model it is assumed that all studies included in the meta-analysis share a common true effect size, differences in observed effects arise only due to sampling error. However because studies commonly differ in implementation and underlying population, among others, the assumption of the fixed-effect model is often implausible. The random-effects model allows the true effect size to differ from study to study and this is the approach we have chosen to follows here. The base meta-analytical regression model is specified as follows:
(1)
ln yij = α + γESij + β b Xijb + β s Xijs + β c Xijc + µi + ij
where the subscript i takes values from 1 to the number of studies and subscript j takes values from 1 to the number of observations, α is the constant term, µj is an error term at the second (study) level, ij is an error term at the first (observation) level and the vectors β b , β s , β c , γ contain coefficients to be estimated by the model on explanatory variables in X b , X s , X c , ES, respectively. We assume that µj and ij follow a normal distribution with means equal to zero and that they are uncorrelated, so that it is sufficient to estimate their variances, σ 2µ and
36
σ 2 , respectively. The first group of moderators, ES, consists in a set of dummy variables representing all ecosystem services provided by the lake under consideration (Flood, DrinkWater, Fishing, Swimming, Boating, Camping, Sightseeing, UnspecRec, Amenity, PopHabitat, Spiritual).
We also include a set of dummy variables
when two ecosystem services are jointly provided by the lake (P opHabitat × F ishing, P opHabitat×Swimming, P opHabitat×Boating, P opHabitat×Sightseeing). The second set of moderators, X b , include some characteristics of the water body. Natural is a dummy variable equal to 1 if the water body is natural. In order to capture a size effect, a set of dummy variables depending on the water area of the lake has been included. The reference category is lake with a water area lower than 1 km2. The third group of moderator variables, X s , control for specific characteristics of the primary studies. As a proxy for quality of the study, we include a dummy variable (P eerReviewed) which takes the value of one to distinguish studies that have been published in refereed journals from those published as book chapters, dissertations, working papers or conference proceedings. We also introduce a set of dummy variables depending upon the valuation method used in the primary study (ChoiceExp, HedonicP rice, T ravelCost, Other), the reference category being contingent valuation. The last group of moderator variables X c , includes some context variable which were not directly available from the primary studies. GDP capita gives the GDP per capita based on annual and country-level figures provided by the World Bank. Three variables have been introduced to capture water scarcity within the river basin of each study site (Water Stress, Water Variability and Drought Index). These variables are based from the GIS datasets developed within the Aqueduc project. In addition we make use of a GIS to compute a spatially explicit variable (Lakeabundance) representing lake and wetland abundance for each primary study site. Using the Global Lakes and Wetlands Database we compute the lake
37
and wetland abundance as the share of lake and wetland area within a 100 km radius. This variable is intended to capture the availability of substitute or complementary sites within the vicinity of each study site. C.
Meta-regression results
As a starting point of the analysis, we provide in Table 2 some estimates of the meta-analytical models using random-effects approach, for primary studies which have not used an hedonic price approach (i.e for primary studies relying on choice experiments, contingent valuation, travel cost and other methods).
Constant 3.97*** 0.11 3.08*** 0.20 R-squared 0.00 0.26 N 385.00 385.00 ***, **, * for significant at the 1, 5, 10 percent level, respectively.
Context variables GDP capita Water stress Water variability Drought index Lake abundance 3.56*** 0.29 385.00
0.26
0.67 0.68 0.63 0.68
1.45 0.56 0.28 0.38 0.38 0.41 0.51 0.23 0.65 0.37 0.49
1.03 0.93 1.20** 1.56**
2.07*** 0.46 384.00
0.64
0.26 . 0.43 0.23 0.27
0.27 0.50 0.52 0.50
0.63 0.63 0.58 0.66
1.32 0.53 0.26 0.34 0.34 0.37 0.48 0.21 0.64 0.36 0.47
Model 4 Std. err.
0.15 1.02* -0.10 -0.56 -0.25 0.30 1.09** -0.19 4.49*** -1.77*** -0.11
Coeff.
-0.73*** 0.00 -1.61*** 0.44* 1.68***
0.06 0.80 0.47 1.18*
-0.49 1.64*** 0.19 -0.49 0.22 0.25 0.91* -0.14 4.19*** -1.65*** -0.68
Model 3 Std. err.
Characteristics of the study ChoiceExp HedonicPrice Other TravelCost PeerReview
1.45 0.52 0.23 0.30 0.31 0.39 0.36 0.22 0.65 0.26 0.49
Coeff.
-0.99*** 1.13** 0.69 0.92*
0.06 1.23** 0.58** -0.08 0.44 0.30 1.59*** -0.18 4.59*** -0.60** -0.52
Model 2 Coeff. Std. err.
Characteristics of the water body Natural [1, 20[ km2 [20, 1000[ km2 > 1000 km2
Ecosystem service interactions PopHabitat × Fishing PopHabitat × Swimming PopHabitat × Boating PopHabitat × Sightseeing
Ecosystem services Flood DrinkWater Fishing Swimming Boating Camping Sightseeing UnspecRec Amenity PopHabitat Spiritual
Model 1 Coeff. Std. err.
0.10 0.52 384.00
0.30*** 0.05** -0.14 0.00 7.09***
-0.80*** 0.00 -1.80*** 0.39* 1.06***
-1.24*** -0.17 -0.09 0.42
1.55** 1.24** 0.68 0.71
1.25
0.10 0.03 0.44 0.02 1.44
0.25 . 0.47 0.23 0.29
0.28 0.55 0.51 0.49
0.62 0.60 0.58 0.65
1.28 0.56 0.26 0.34 0.33 0.36 0.49 0.21 0.64 0.35 0.49
Model 5 Std. err.
1.27 1.03* -0.30 -0.13 -0.26 0.32 0.78 0.13 3.52*** -1.54*** 0.22
Coeff.
Table 2—Estimates of the meta-analytical with random-effects – Non hedonic price studies
38
39
The results obtained for different specifications of the basic metaregression model described in Equation (1) are presented in Table 2. For all estimates in Table 2, a series of diagnostic tests were performed. Model 1 does not include any explanatory variable. In Model 2, only dummy variables for the ecosystem services provided are included. The set of explanatory variable is augmented in Model 3 by added some variables describing interaction across ecosystem services. In Model 4 we add some variables describing the water body and the study from which values are obtained. Lastly, context variables are included in Model 5. After having conducted some specification tests, we estimate the basic metaregression model described in Equation (1) using a loglinear specification. The first model provides an estimation of the average value of a lake per respondent (84 USD$2010 per respondent and per year). The second model provides some indications on how people value lake ecosystem services. Interestingly, six ecosystem services (among the eleven specified) appear to be significant namely drinking water, fishing, sightseeing, amenity, maintenance of populations and habitats and spiritual or symbolic appreciation. A particular high value is found for amenity (+51.4 USD$2010 per respondent and per year) whereas a low value is documented for spiritual or symbolic appreciation. An interesting result from Model 3 and 4 is the fact that some interactions between ecosystem services appears to be significant. In Model 3, the interaction between the “maintenance of populations and habitats” service and the “recreational sightseeing” service appears to be significant at 10% with a positive sign, which suggests some complementarities among services. This means that there is a particularly high value for a lake jointly providing these two services. More specifically, the added value of a lake jointly providing these two service is estimated to be 15.5 USD$2010 per respondent and per year. This is not surprising given the potentially high level of complementarity between a providing a high level of maintenance of populations and habitats, and the provision of a
40
recreational service like sightseeing. In Model 4, we also document some complementarity between the “maintenance of populations and habitats” service and the “recreational swimming” service. In model 4, we have added some characteristics describing the type of water body (natural versus artificial, size of the water body) and the study from which a value is obtained (valuation methods). We do not find any specific premium for natural lakes compared to artificial ones. Although some artificial lakes may have been constructed without fully replacing natural lakes ecological functions or without fully supporting recreational activities or aesthetic services, it appears that they are valued by people in the same way as natural lake are. We also find a significant impact of the valuation method used to obtain lake values. In particular, the value obtained with a travel cost approach results in a higher lake value (2.8 USD$2010 per respondent and per year). The impact of the valuation method has already been document in some previous meta-analysis of ecosystem services value, but with contrasting results. For instance for value of wetlands, (Woodward and Wui 2001) have found positive and significant coefficients for replacement cost and hedonic pricing, while (Brander, Florax, and Vermaat 2006) have reported a positive coefficient for contingent valuation. Next, in Table 3, we provide some estimates of the meta-analytical models using random-effects approach, for primary studies having used an hedonic price approach.
Constant 5.62*** 0.26 9.29*** 2.30 R-squared N 214.00 214.00 ***, **, * for significant at the 1, 5, 10 percent level, respectively.
Context variables GDP capita Water stress Water variability Drought index Lake abundance
214.00
9.29***
2.30
.
1.35 5.72 3.62 2.27 3.04 1.55
12.43**
-3.81*** 0.00 -4.71 -4.46* -5.90* 1.65
205.00
0.00
.
. 2.50 . . 0.72
0.48 0.68 0.52 0.63
5.99
1.48 . 3.26 2.38 3.17 1.70
Model 4 Std. err.
0.00 10.53*** 0.00 0.00 -0.69
0.00
-3.28** -9.69* 5.39 -3.60 -4.23 1.88
Coeff.
Characteristics of the study ChoiceExp HedonicPrice Other TravelCost PeerReview
1.35 5.72 3.62 2.27 3.04 1.55
Model 3 Coeff. Std. err.
-0.22 0.63 -0.15 0.84
-3.28** -9.69* 5.39 -3.60 -4.23 1.88
Model 2 Coeff. Std. err.
Characteristics of the water body Natural [1, 20[ km2 [20, 1000[ km2 > 1000 km2
Ecosystem service interactions PopHabitat × Boating
Ecosystem services Flood Fishing Boating Amenity PopHabitat Spiritual
Model 1 Coeff. Std. err.
205.00
0.00
0.84 0.21 -2.85* 0.00 7.50***
0.00 2.69 0.00 0.00 -0.44
-0.75 -0.74 -0.54 0.58
11.58**
.
0.62 0.57 1.60 0.05 2.54
. 7.07 . . 0.55
0.50 0.73 0.56 0.67
5.81
1.16 . 3.13 2.43 3.09 1.38
Model 5 Std. err.
-3.30*** 0.00 -4.69 -3.77 -4.89 1.65
Coeff.
Table 3—Estimates of the meta-analytical with random-effects – Hedonic price studies
41
42
D.
Valuing some hypothetical lakes
Table 4—Valuation of hypothetical lakes Lake A
Lake B
Ecosystem services provided Flood DrinkWater × Fishing Swimming Boating Camping Sightseeing UnspecRec Amenity PopHabitat × Spiritual Lake characteristics Natural × [20, 1000[ km2 ×
Lake C
×
×
×
×
×
× × ×
× ×
× ×
Value of the lake 295.9* 223.6* 340.4* *(in USD$2010 per respondent and per year.
In this paragraph, we demonstrate how the results of the meta-analysis may be used to provide some predictions for the value of some hypothetical lakes. We will consider three different lakes for which we would like to predict the value (with a travel costs approach). Lake A is a natural lake with a total area belonging to the second size class ([20, 1000[ km2). This lake is supposed to provide two ecosystem services namely drinking water and maintenance of populations and habitats. Applying the valuation function from Model 4 in Table 2, the predicted value of this lake is estimated to be 295.6 USD$2010 per household and per year. Next, we consider another lake (lake B) similar to lake A except that instead of providing the “maintenance of populations and habitats” service, it provide two recreational services namely “sightseeing” and “swimming”. Applying again the valuation function from Model 4 in Table 2, we obtain a predicted value for this lake equal to 223.6 USD$2010 per household and per year. Comparing lake A and B value, we can conclude that the loss of the “maintenance of populations and
43
habitats” service is not fully compensated by the gain of the “sightseeing” and “swimming” recreational services, for the lake characteristics we have consider here. This very simple simulation exercise allows to quantity the relative benefit of each ecosystem service. If we add the “camping” service to lake B (i.e we pass from lake B to lake C), then the predicted value is estimated to be 340.4 USD$2010 per household and per year. Allowing people to benefit from the three recreational services “sightseeing”, “swimming” and “camping” cancels out the loss of welfare resulting from loosing the “maintenance of populations and habitats”. V.
Conclusion
Today, while there is now widespread recognition that lakes provide valuable ecological services, there remain substantial debates on their economic value. As a result, lake ecosystems are often undervalued in decisions related to their use, conservation or restoration. In this paper, estimates for values attached to different ecosystem services provided by lakes and reservoirs have been compared and synthesised in a metaanalysis. The meta-analysis provides insights into the factors that have to be considered when attempting to transfer lake values on the basis of the valuation studies. We have provided some preliminary estimates first using OLS. We provide an estimation of the average value of a lake per household (84 USD$2010 per respondent and per year) and we offer some insights on how people value different lake ecosystem services. A particular high value is found for lake amenity whereas a low value is documented for spiritual or symbolic appreciation of the lake. An interesting result is the fact that some interactions between ecosystem services appear to be significant for enplaning lake values. This traduces some trade-offs, synergies and antagonisms between ecosystem services as already documented by (Fu, Su, Wei, Willett, L, and Liu 2011, Raudsepp-Hearne, Peterson, and Bennett 2010) for landscape. We then show that the value of a
44
specific lake ecosystem service might depend upon other ecosystem services provided by this lake. Not introducing the potential interactions across ecosystem services may then lead to biased estimates of ecosystem service values. The work presented in this paper is in progress. More variables describing lake’s characteristics and methodologies used in the primary studies should be introduced. A more substantial comparison of our meta-regression results and the existing meta-analyses in this field is planned to be carried out to extend the paper (in particular by including the different lake values we have mentioned in the descriptive part of the paper). Another step in the analysis presented here will be to test the performance of in and out of sample transfers based on the estimated meta-regression model. This implies that additional control factors should be included for the countries and regions in which the valuation studies were conducted in the spirit of what has been done by (Brander, Bruer, Gerdes, Ghermandi, Kuik, Markandya, Navrud, Nunes, Schaafsma, Vos, and Wagtendonk 2012) for wetlands (Ghermandi and Nunes 2013) for costal areas.
45
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