Ecosystem services are the benefits that ecosystems provide to society and the economy. Ecosystem services are often not fully taken into account in policy decisions because they are not fully captured in commercial markets nor adequately quantified in terms comparable with commercial services traded in markets. Ecosystem service valuation can provide policy with additional insights as it allows policy appraisals to fully consider the costs and benefits associated with changes in the natural environment and highlights the implications for human wellbeing.
This manual focuses on pragmatic methods to value ecosystem services. The guidance and tool can help everyone who wants to quantify the socio-economic importance of ecosystems (land managers, land developers, national and local authorities, non-governmental organisations and active citizens).
This guidance provides methodologies to value the ecosystem goods and services of (semi)-natural land use, including forests and agricultural land use. We discuss qualitative, quantitative and monetary valuation methods for a range of ecosystem services.
For each ecosystem service we discuss the data requirements, the assumptions made to value ecosystem services, where to find the necessary input data, and finally illustrate the methods with an example. This information is also the basics for a webtool. The tool can be consulted on the internet via www.natuurwaardeverkenner.be. End-users are able to create and save scenarios, share scenarios with other registered users and consult public scenarios. Interactive discussions are stimulated through a discussion forum.
This guidance and tool are an update of a previous guidance document published in 2010. Compared to this previous document, the number of services is increased and some methods are improved. We also better account for uncertainty by using low and high estimates. In the next chapters of this summary we will explain which methods were applied in our study.
The manual and tool are not static. The list of ecosystem services that is valued in this manual is not complete as it was not possible to develop methodologies for all ecosystem services. The quantification and valuation functions that are presented are built on the current state of knowledge and data-availability, but can be improved in the future when new scientific insights emerge and /or better data are available. Suggestions from you as end user are always welcome.
It is important to understand that this study values a marginal change in ecosystem service provision, but does not measure the total value of certain ecosystems. The figures do inform policy makers about the impact on human welfare due to a project or policy initiative influencing existing ecosystems and the services they deliver.
The international classification of ecosystem services as provided in CICES version 4.3 is the starting point of this guidance document. When assessing the impact on ecosystem services it is essential to assess the whole bundle of relevant services. However, there is not the same amount of information available for all ecosystem services. State-of-the-art data and insights were gathered and used to develop the best possible valuation methodology. The total value of ecosystem services can be represented by a combination of monetary values, quantitative numbers and qualitative insights (and unknowns), with generally less information and insight being available at the monetary level, and a broader view at the qualitative level. This corresponds to the pyramid-approach as described in Kettunen et al. 2009.
We consider the following ecosystems: agriculture, grasslands, forest and woodland, heathland, coastal, inland wetlands and rivers and lakes. Marine ecosystems are not included in the tool. Agricultural land use is divided in cropland, meadows and orchards. Urban land use is a category to be used whenever a part of the case study exists of buildings, roads, residential area, industry…The manual and tool are not intended to value ecosystem services in this urban environment (e.g. green roofs, parks).
Qualitative scores express the importance of ecosystem services with a score from 1 to 10. These scores can be used for scoping. When the score is high (>6) it means that the case study is an important area for the delivery of that particular ecosystem service. It would be appropriate to look at these ecosystem services more into detail, especially when there is discussion amongst stakeholders.
Quantification functions express the importance of ecosystem services in physical terms (e.g. tonnes of C sequestration, amount of visits per year, ...). Quantification functions applied here take into account the main driving factors of the underlying ecological processes such as soil texture, groundwater level and vegetation type but still require little computation time. They build on region specific datasets (Flanders - Belgium) and studies (existing land-use/land-cover and soil map classifications) to increase the accuracy and transparency.
Monetary valuation expresses the importance in monetary terms (€) and makes it able to sum up all individual services to a total economic value. For the monetary valuation we use a range of methods depending on the service. If goods (products) delivered by ecosystems can be sold on a market which is the case for provisioning services as agricultural production and wood production, market prices are used. Regulating services are valued with stated preference methods, revealed preference methods (adjoining markets), marginal damage costs or marginal reduction costs. Cultural services bundle the value people put on nature from a recreational, spiritual or emotional view and can be monetised with revealed or stated preference methods. Supporting services are not valued separately to avoid double counting.
The values are only applicable under the assumptions made in the guidance. We incorporate spatial factors applicable to the Flemish situation. Therefore, using the estimations outside the region of Flanders will increase uncertainty. Choices made by the user need to be reported when using the results.
In a last chapter we explain how the results of the tool can be used in a social cost-benefit analysis taking into account economic growth and discounting over a certain time horizon.
This service includes the production of crops such as grains, vegetables and fruits which are the cultivated plants or agricultural products harvested by people for human or animal consumption. Agricultural services may under some schemes not be considered as ecosystem services but are referred to as environmental services. In this assessment, they are considered as ecosystem services. The main argument is that including provisioning services derived from agriculture or agro-ecosystems is essential in a trade-off analysis. Furthermore, agricultural systems comply in a strict sense with the definition of an ecosystem (Maes et al. 2011).
The quantification and monetary valuation are based on the amount of specific cultures (number of hectares) harvested in the study area. The monetary value is based on the standard gross margins (market prices – variable costs related to production excl. subsidies). For fodder production, the standard gross margins is indirectly derived from the standard gross margins of the livestock grown with this fodder.
Wood can be used for different purposes going from construction material, packaging, raw material and energy.
We use the study of Jansen et al (1996) and Moonen et al (2011) to estimate the potentially produced volume for specific forest types and soil conditions and apply a harvest factor (depending on the management regime) to know the actually harvested volumes. The harvested volumes are generated only for the eight most important commercial species in Flanders (Beech, Oak, Poplar, European Larch, Scots Pine, Corsican Pine, Spruce, Douglas fir, deciduous and coniferous). The soil type is based on the Belgian classification system (texture, drainage and profile, http://geo-vlaanderen.gisvlaanderen.be/geo-vlaanderen/bodemkaart/).
The monetary value is derived from the average market prices paid for wood harvested in public forests for the years 2009 to 2012 and does not take into account harvesting costs, as they are assumed to be neglectable.
The benefits coming from other provisioning services such as mushroom picking; reed are negligibly small for areas where biodiversity is the primary objective, because of two reasons. First, the quantities that can be extracted compared to commercial areas is minimal. Second, the market for specific products is relatively small or informal and harvesting costs can be substantial, which means that the added value is mostly low. In addition, Flemish nature law has strict limitations about harvesting from nature reserves.
Marine ecosystems and its importance for fish production is not included in this guidance document.
It is well documented that trees and vegetation can serve as effective sinks for air pollutants and thus contribute to air quality improvement and related public health benefits. As PM10 (particulate matter below 10 μg) is the most important pollutant, accounting for 70% of health impacts from environmental pollution in Flanders (MIRA-T 2009), the focus of this analysis is on PM10.
Although literature provides detailed studies for specific tree species at specific locations, the analysis of air quality impacts in integrated modelling is typically based on more generic indicators expressed as kg pollutant removal per ha for generic vegetation types (Hein 2011; Nowak et al. 2006; Tiwary et al. 2009). As there are no specific data available for Flanders, the estimates are based on removal factors for individual trees and shrubs from Oosterbaan et al. (2006) as low estimate and Oosterbaan et al. (2011) as high estimate. These removal factors (expressed in kg/ha) are in the same range (+/−50%) as in Hein (2011), Nowak (2006) or Tiwary (2009) for grasslands.
We extrapolated these numbers for missing ecosystems e.g. heathland = grassland.
Reducing concentrations of PM10 has important benefits for public health, especially related to cardiovascular and respiratory impacts. These impacts are typically valued with the avoided costs for health care and medicine, avoided loss of productivity at the work- place and at home and willingness to pay to avoid suffering and loss of life expectancy. In line with Nowak (2006), we assume that we can use valuation data for external health costs caused by emissions of PM10 from low stacks (e.g. buildings) as a proxy for the PM10 removal by trees and shrubs. The valuation data are based on results from air quality models for Flanders, dose–response functions (impact on health) and valuation data from European research projects (De Nocker et al. 2010). We further account for the size and origin of the particles. This results in a value of 54€/kg PM10.
Nature areas can contribute to the mitigation of noise from for example traffic. The effect of the soil and especially the vegetation is often underestimated in models for noise-simulation (Goossen and Langers 2003; Huisman 1990). The effect of soft soils is derived from Goossen and Langers (2003) and assumed to be a decrease of 3 decibel (dB(A)) per 100m in comparison with a hard surface. The effect of forests is derived from Huisman (1990) en De France et al. (2002). Huisman observed different noise mitigation effects in forests based on the frequency of the source, the soil characteristics, the meteorological effects and the noise penetration in the forest. He found an average decrease of 6–16 dB(A) for 100 to 300 m wide forests.
To monetise the impact on noise mitigation, we apply the hedonic property price method. This method examines the premium which people are prepared to pay in order to purchase houses in areas of higher environmental quality, e.g. quieter, less polluted neighbourhoods. Den Boer et al. (2008) indicate that the market value of properties decrease with 0.4% per dB(A) at lower noise levels (40 dB(A)) and 1.9% at higher noise levels (60 dB(A)). This information is applied in combination with area specific housing prices on the number of houses for which noise-levels are influenced by nearby forests.
Ecosystems can potentially store additional flood water and as such prevent flood events and damages elsewhere. They can also reduce the water current or the wave intensity which also has an impact on flood events.
Services related to disturbance prevention or moderation can reduce flood risk. The benefits of flood alleviation comprise the flood damage averted due to reducing the frequency of flooding or reducing the economic impact of flooding (less material and immaterial damages), or a combination of both.
The methodology for assessing the benefits of flood prevention includes an assessment of risk in terms of the probability or likelihood of future floods to be averted and a vulnerability assessment in terms of the damage that would be caused by the floods and therefore the economic saving to be gained by their reduction (FHRC 2010).
A wide range of methodologies is available to estimate the benefits of flood prevention. We refer to FHRC 2010 the benefits of flood and Coastal Risk management: a handbook of assessment techniques 2010 for a stepwise approach to assess the benefits of flood prevention. For Flanders, MOW-WL developed the LATIS-method to quantify and value avoided flood risks. Hydrologic models are used to create flood maps. The flood maps give information on the extent of the flood and the water depth for a given chance of occurrence (e.g. 1/1000 years). This is performed for different chances of occurrence. These maps are used as input for economic and human damage (potential casualties) estimations. The LATIS-method starts from a maximum damage calculation of an area depending on the land use and the replacement value (damage as if everything would be destroyed). Next, it estimates how much is actually damaged based on damage functions that indicate the percentage of the replacement value at risk as a function of the inundation depth. The total annual risk is equal to the probability of occurrence multiplied by the corresponding damage and this for the total range of possible occurrences. The benefit is equal to the reduced annual flood risk calculated by subtracting the annual flood risk of a scenario with and without the estuarine ecosystems.
It is not possible to translate the assessment methods into easily applicable indicators that can be applied in different estuaries.
Erosion is commonly defined as the displacement of solids (e.g. sediment and soil) and other particles by wind or water. Erosion is a natural process, but is heavily increased by specific types of land use, in particular by intensive and inappropriate land management practices such as deforestation, overgrazing, unmanaged construction activity and road-building.
Areas used for the production of agricultural crops generally experience a significant greater rate of erosion than areas under natural vegetation. This capacity of natural ecosystems to control soil erosion is based on the ability of vegetation (i.e. the root systems) to bind soil particles, thus preventing the fertile topsoil from being blown or washed away by water or wind.
Sedimentation is defined as the net retention of sediments carried in suspension by waters inundating the nature area.
We quantify the soil loss based on the RUSLE equation. No monetary valuation is done.
Carbon sequestration in soils is based on estimates from Meersmans et al. (2008). They performed a multiple regression approach to assess the spatial distribution of soil organic carbon (SOC) and its dependency on soil characteristics in Flanders, Belgium. Based on this regression model we determine a potential maximum carbon content for a given vegetation type, soil drainage class, and soil texture. The inputdata are based on the Belgian soil map. Changes in soil drainage and/or vegetation will change the potential maximum carbon content. The annual carbon sequestration potential is a percentage of the difference in potential carbon content and actual carbon content. This approach is process based and incorporates changes in potential storage and the associated temporal dynamics. Based on more recent research by Vos (2012) the results of Meersmans are corrected for the carbon storage in forests with 32%. Inland wetlands and lakes are not included in Meersmans et al. (2008). Here we use the method of Altor and Mitsch (2008).
The benefits of carbon sequestration are related to reducing the impact of global climate change. An avoided abatement cost approach uses the costs of mitigation measures for climate change as an indicator of the social value of carbon sequestration. Studies estimate these abatement costs from 10 to 60€/tonne CO2 for the year 2010, but these marginal abatement costs will increase in the coming decades as more expensive measures will be required to further limit CO2 emissions and to ensure that the 2 °C target is within reach (Kuik et al. 2009). As average value we use a lower value of 30€/tonne CO2 (100€/tonne C) and a higher value of 100€/tonne CO2 (366€/tonne C) for 2020 (De Nocker et al. 2010).
Carbon sequestration in biomass is limited to the uptake by forest ecosystems. The method for carbon uptake builds further on the method for wood production. The increment in biomass per ha per year is used to derive the annual carbon sequestration per ha per year using the species-specific carbon density (Van de Walle et al. 2005).
Similar to carbon sequestration in soil, we use a lower value of 30€/tonne CO2 (100€/tonne C) and a higher value of 100€/tonne CO2 (366€/tonne C)
The quantification of denitrification processes in wetland ecosystems and rivers is based on the formula for nitrogen removal in wetlands from Seitzinger et al. (2006). The main parameters are water depth, size of the area and average daily discharge, which determines the residence time and the nitrogen load entering the aquatic ecosystem. For terrestrial alluvial soils we multiply the maximum removal percentage with the feed range (these factors depend on soil moisture, silt content) and the nitrogen load, calculated by the nitrogen leaching of the surrounding land use.
The avoided abatement cost method is used to value nutrient removal, as costly abatement measures to obtain environmental goals can be avoided due to the natural denitrification. The specific value of an additional kg N removed by an ecosystem is derived from the marginal abatement costs of N removal, which were calculated for the Flemish river basin management plans as required for the European Water Framework Directive (Broekx et al. 2008). The costs of the measure with the highest marginal cost included in the programme of measures to reach water quality objectives are 74€/kg N. As this value is significantly higher than most figures in literature we use this as a high estimate and use averages from international literature as a low estimate (5€/kg N).
We associated the retention of nutrients in soils (or avoided nitrogen leaching) with the carbon content. This is considered as supporting service for soil quality. The nitrogen (N) and phosphorus (P) content of soils is derived from the carbon content. Based on analyses performed in Flanders, the C/N ratio varies between 10 and 30 depending on the land use. Based on Koerselman and Meuleman (1996), the average N/P ratio is set at 15.
The mobilisation of nutrients due to a change in land use can be seen as a negative benefit for water quality as part of the nutrients will be leaching to the groundwater. We don’t calculate this because we lack information on the amount of leaching and the time horizon of the leaching.
The avoided abatement cost method is used to value nutrient removal, as costly abatement measures to obtain environmental goals can be avoided because of the avoided leaching. The specific value of an additional kg N or P removed by an ecosystem is derived from the marginal abatement costs of N and P removal, which were calculated for the Flemish river basin management plans as required for the European Water Framework Directive (Broekx et al. 2008). The costs of the measure with the highest marginal cost included in the programme of measures to reach water quality objectives are 74€/kg N and 800€/kg P. Most measures have impact on both N and P, and it is therefore impossible to individually link avoided costs to separate pollutants. To avoid double counting, we estimate the value of nutrient retention for both pollutants but only apply the maximum value. As these values are significantly higher than most figures in literature we use these as a high estimate and use the average low figures from literature as a low estimate (5€/kg N and 80€/kg P).
Pollination by insects is essential for the cultivation of several crops (e.g.fruits). Based on the existing information, we cannot draw well-founded indicators for quantitative and monetary evaluation of specific projects. However, a qualitative analysis can be made. Analysis does show the potential importance of pollination for fruit and vegetables in Flanders. This benefit is closely linked to agricultural production.
The qualitative valuation is a simple approach based on the suitability of the ecosystem for harboring insects and the presence of crops dependent on pollination within 1 km of the area.
Given the lack of valuation studies for specific cultural services in Flanders, a stated preference study (choice experiment) surveying peoples willingness to pay for nature restoration, was performed to capture all cultural services in a single value function. This study is described in detail in Liekens et al. (2013). Additionally we also performed several smaller choice experiments in 2011 for specific areas and specific types of restoration.
In a choice experiment, respondents are presented with a number of alternatives from which they are asked to choose their most preferred option. The alternatives can be a good or service, but also policy alternatives or land use change scenarios. Each choice alternative is defined in terms of the same elements or so-called “attributes”, including a price, and has a unique combination of the levels of the attributes. Examples include varying levels in biodiversity (high-low), accessibility (accessible or not) and size of the area (between 1 and 200 ha). As respondents express their preferences by making choices between different alternatives, they trade off the different attributes and levels. A statistical function can then be estimated that links choice probabilities to the characteristics of the alternatives. The trade-off between price and other attributes is especially relevant, as this reflects how much a respondent is willing to pay (WTP) for a particular change in this attribute. This allows to determine marginal values for changes in the attributes and combinations of attributes.
The resulting valuation function was downscaled in a pragmatic way to take into account substitutes and the budget constraint of households when paying for several areas.
In the tool 4 different functions are present.
Function 1 is suitable to value a change from an agricultural land-use - without specific natural or landscape values - to a natural landscape. The characteristics influencing the value estimate, relate to the nature type, number of species, size, adjacent area, availability of walking trails and the distance to the respondents’ residence.
The tool calculates this for all households in specific distance ranges with a maximum of 50 km (maximum range questioned in the survey).
This function estimates the experiential and non-use value for the construction of small landscape elements (SLE) in a field or pasture with little scenic value. Results of a survey show that households are willing to pay for the construction of small landscape elements regardless of the type of SLE. Only for the recovery of sunken roads they have some extra preference. Households that are members of a nature or environmental organisation do have specific preferences for certain types: shelterbelts and hedges are valued higher than the construction of an orchard, planting pollarded or the restoration of a sunken road. The construction of ponds is valued less.
This function values the conversion of coniferous forest to deciduous forest or heathland. The results from three different surveys show that there is a positive willingness to pay for this replacement, but there is a higher preference for deciduous forest. The willingness to pay depends strongly on the proportion of the replacement in the overall surface of forest and heathland in the study area. If the variation in the study area becomes smaller because the coniferous forest almost completely disappears, the willingness to pay per ha decreases. This effect is stronger for a conversion to heathland than for a change towards deciduous forest.
WTP = (0.12 + 0.36*water quality from bad/moderate to moderate/good +0.12 * water quality good to very good + 0.037 * complexity bad to moderate + 0.010 * complexity moderate to good + 0.015*complexity good to very good + 0.18 * species bad to moderate + 0.16 * species moderate to good/very good) * 77.42 * length river improved in km*1000
+ (0.022 + 0.15 * water quality from bad/moderate to moderate/good +0.052 * water quality good to very good + 0.016* complexity bad to moderate + 0.0043 * complexity moderate to good + 0.0064 * complexity good to very good + 0.075 * species bad to moderate + 0.069 * species moderate to good/very good)* length river improved in km * 16.8% * number of households in the sub-basin
WTP > or equal to zero
WTP = (0,19 + 0,25 * water quality from bad/moderate to moderate/good + 0,17* water quality good to very good + 0,028 * complexity bad to moderate + 0,0062 * complexity moderate to good + 0,0092 * complexity good to very good + 0,25 * species bad to moderate + 0,095* species moderate to good/very good) * 125,08 * length river improved in km*1000
+ (0,094 + 0,23 * water quality from bad/moderate to moderate/good + 0,16 * water quality good to very good + 0,026 * complexity bad to moderate + 0,006 * complexity moderate to good + 0,008 * complexity good to very good + 0,22 * species bad to moderate + 0,087 * species moderate to good/very good)* length river improved in km * 16.8% * number of households in the sub-basin
WTP>or equal to zero
Important remark for applying the willingness to pay functions
The valuation functions are deduced from choice experiments where people judge specific scenarios. Such scenarios can only approximate a real project that is being studied with a CBA. The characteristics of these scenarios and choice sets have consequences for the applicability of the valuation function. The functions are applicable for Flanders. Benefit transfer to other regions is not recommended.
Recreation as estimated in this manual includes specific nature related activities, informal recreation and specific activities such as playing, fishing, hunting swimming, boating.
The quantification starts from different surveys of the number of visits that people in Flanders bring to green open space. The total number of visits is than spread over the supply of available green open space in Flanders on the basis of :
A) the characteristics of the study area itself: share of forest, nature and agriculture area; accessibility for recreation; size of the area
B) the characteristics of the surroundings: the share of forest, nature and agricultural area within the community itself and in the region (30 km)
Based on this information the tool estimates the total number of yearly visitors and how this number is influenced by changing land use.
Houses in the neighbourhood of green space have a higher market value than houses with a view on petrified space. Using this difference in value is also referred to as the hedonic pricing method. Based on a literature review in the Netherlands that found that the added value of a view on green space (nature, forest or agriculture) lies between 5% and 14% with an average of 9% (Ruijgrok, 2006), we assume an average benefit for an average house of 447 euro/year to 1252 euro/year.
We only take the houses into account that have a direct view on green space to avoid a large double counting with recreation.
There is much scientific evidence that green areas contribute to improving the physical and mental health of residents and people who visit these areas.
We base the quantification of this service on the results of a multi-year academic program in the Netherlands (Vitamin G - Maas, 2008). This study shows a positive correlation between the amount of green space within a 1 km radius of the property and a lower incidence of 18 out of a total of 24 specific diseases studied.
Economic valuation of health
effects includes three types of income (De Nocker et al, 2010 ) :
Less health costs: less spending on medications , hospital expenses , etc. To this end, data for Flanders and / or Belgium are used.
· Reduced loss of productivity : both in the workplace and home work , both based on Belgian / Flemish data .
· Less welfare loss by suffering (both own suffering and suffering by family members , ... ) : This is based on data from the European literature using stated preferences ( eg contingent valuation studies) These data are also used in European studies to support environmental policy.
To measure the reduction in "lost quality of life " DALYs can be valued at 93,000 euros per DALY ( Stassen , 2007; converted to price levels 2010). This number is based on a comparison of the economic data for different diseases with the corresponding DALYs.
The non-use value is an important aspect of the total economic value and consists of three components:
· “Bequest value" - values for maintaining or preserving an asset or resource, so that it is available for future generations.
· "Existence value"- values people attach to a certain ecosystem from knowing that a particular environmental resource, such as Antarctica, the Grand Canyon, endangered species, or any other organism or thing exists without visiting, using it.
· "Altruistic value" - the value for maintaining an asset or resource that is not used by the individual, so that others may make use of it. Its value arises from others' use of the asset or resource.
It is very difficult to distinguish between these three different motives to value nature and to provide specific methodologies. Here we artificially split the use and the non-use value based on the valuation function developed for the total cultural value and the total planned nature creation in the spatial planning of Flanders.
Some of the explaining information is not translated in this English summary as the ecosystem service was not quantified or the input data needed are very specific for the Flemish/Belgian case. Please consult the Dutch version to find more information on the topic you were looking for. More information on the classification of ecosystem services you also find at http://cices.eu/
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 The six nature types that have been used are: pioneer vegetation, estuary, natural grassland, forest, open water en inland wetlands, heath land and dunes.