|Main authors:||Berit Hasler, Fiona Nicholson, John Williams, Rachel Cassidy, Linda Tendler, Peter Lendertsee, Marije Hoogendoorn, Rikke Krogshave Laursen, Doan Nainngolan, Ingrid Nesheim|
|FAIRWAYiS Editor:||Jane Brandt|
|Source document:||»Hasler, B. et al. (2019) Assessment of costs and benefits for farmers, water companies and society from using Decision Support Tools. FAIRWAY Project Deliverable 5.3 49 pp|
As described in »Criteria for assessing the costs and benefits of using decision support tools, DSTs that measure benefits of water quality improvements for decision makers were chosen as examples of applications of benefit and ecosystem services assessments for decision support. The benefits are measured as the populations’ ( in catchments or country level) willingness to pay for water quality protection or improvements, and should be measured so that they can be compared to the costs to enable cost benefit analyses. DSTs were chosen that could measure a defined level of change; e.g. a water quality change from a baseline to a policy target (e.g. a limit value for groundwater pollution or a defined level of ecological or chemical status such as in the Water Framework Directive). For this type of valuation both cost-based and utility-based approaches exist.
One valuation approach which can be used as a DST is meta regression analysis which uses data from a large number of original studies on water quality valuation. Meta regression is an example of benefit transfer, i. e. use of original study results for transfer to other sites, where the results can be used for decision support. Meta regression analyses and functions have been established in order to create models for decision support, by using primary and original studies and data to perform more general and generic information on the value of an environmental improvement, and two examples are given to illustrate the use of this type of DST:
- One international groundwater valuation study applying meta analysis (Brouwer and Neverre, 2018).
- One Danish meta analysis based on valuation studies from the Nordic countries, valuing water quality improvements using the Water Framework Directive classification of ecological status.
These examples are presented in Section 1 below.
Ecosystem services mapping using valuation study results is another example of an decision support approach for assessments of the benefits of water quality improvements. This type of approach can be used to value a large range of benefits from the improvement of water quality. The ecosystem services mapping approaches are spatial, and can include information from the above mentioned meta regression analyses (Bateman et al., 2011). This type of approach also has the possibility of assessing coherence and conflicts between different policy objectives and related ecosystem services, such as the different services resulting from implementation of pesticide and nutrient policies.
Examples of mapping and asessments of ecosystem services as decision support are provided in Section 2 below
1. Examples of decision support tool using benefit transfer and meta regression analysis assessment of benefits
Meta regression analyses represent a robust type of benefit transfer method which can be used to estimate individuals’ or households’ willingness to pay (WTP) for environmental changes, e.g. in water quality, effects of groundwater protection etc. The modelling is built on data from existing studies that are already published, and by using regression the large variety of results and explanatory factors from the literature are combined to create a robust function that can be used for decision support. Metaregression models are typically estimated as log-linear models (log (WTP) which include constants and the explanatory variable. When used for decision support, this function is then populated with data from the policy area being considered.
The way it works is that a WTP function is estimated using data from the identified literature, and this function can then be applied to so-called policy sites by including important local values into the function, e.g. regarding demographic factors, average income level and natural conditions in the area. One of the inputs to the model is the change in environmental status of the water, e.g. from moderate to good, and the output of the model is the value of improving water quality from moderate to good condition.
Brouwer and Neverre (2018) made a global meta-analysis consisting of almost three decades of groundwater quality valuation studies, including uncertainty assessment in terms of the uncertainties linked to groundwater quality levels and groundwater contaminants. The functions they have developed are interesting as they have estimated separate meta-regression models for USA, Europe and the rest of the world. In a FAIRWAY context, the European metaregression model is most interesting, as this type of model is robust .The number of groundwater valuation studies has increaed a lot after the Groundwater Directive was agreed on in 2006, meaning that there exist good datasources to populate the regression model. The tests of the developed function indicate that it was very robust, and the model can therefore be used for decision support in European catchments, groundwater protection zones, by water works professionals and other decision makers.
Olsen et al. (2019) conducted a meta-regression analysis based on the primary valuation studies undertaken for water quality improvement in the Nordic countries. The main purpose of this study was to develop a meta-regression function that could be used for benefit transfer for decision support when assessing new policies and projects having impacts on water quality. The development of this model built on a review of the literature to identify all primary valuation studies that were relevant for water quality in the Nordic countries. A list of 50 potentially relevant studies was identified, and from this 34 studies were selected for further study. The criterion for choosing a study was that it should provide estimates of WTP for changes in water quality, but also include sufficient information on sociodemongraphic and environmental characteristics of the population studied in the primary study. Identification of about 100 variables from this literature study provided data for the meta regression; using these regressions a function describing a households’ WTP was derived. Using this meta regression therefore enables estimation WTP for water quality improvements at both household andcatchment level. It was found that improvement from ‘moderate’ to ‘good’ ecological status was more valuable than an improvement from ‘good’ to ‘high’ ecological status. In addition, a range of biophysical, sociodemographic as well as other study-specific variables significantly influenced WTP.
The metaregresion model developed by Olsen et al (2019) will be used for advising decision makers on the value of improving water quality, and the function is an input to the Ministry of Environment and Foods assessments of the economic effects of the Water Framework Directive.
2. Examples of decision support tools using spatial mapping of ecosystem services as decision support
Termansen et al. (2017) developed a tool for analysing changes in the provision of a range of different ecosystem services resulting from setting aside land in agricultural or forestry production. Water quality regulation is one of the services included in the tool, which is used for decision support by the Danish Ministry of Environment and Food. The focus of the development of the tool has been on the spatial analysis of synergies and trade-offs between services, such as water quality regulation, climate regulation, recreation, food provision, timber production and biodiversity. By focusing on synergies and potential conflicts the tool aims to support multi-objective land use planning. The tool is currently set up for the Limfjord Catchment, where the Aalborg case study in FAIRWAY is situated.
When modelling the ecosystem service water quality regulation (which is a regulating service) attention is paid to how the variation of hydrology and nitrogen retention in the catchment affects the nitrogen loads to Limfjorden. Three different land use change scenarios are modeled in the analysis reported in Termansen et al. (2017):
- Conversion of agricultural land to semi-natural areas;
- Conversion of agricultural land to forest land;
- Conversion of productive forest land to semi-natural un-managed forest land.
The results are illustrated in maps. An example illustrates this in Figure 5, which specifies the location of the areas selected for conversion.
The scenarios for agriculture are modelled for setting aside 1%, 3% and 5% of the agricultural land. The effect on water quality regulation is modelled as an effect of optimizing the land set aside with respect to achieving nitrogen load reductions. In addition, the effect on water quality is modelled when achievement of the other ecosystem services are optimized, one by one. Finally, a scenario was set up to maximise several services at a time. For this purpose the ecosystem services were measured in monetary terms. Maps are then used for illustration of each of the identified scenarios, as well as scenario-effect matrices in tables. The results of the assessment include the expected changes in ecosystem services and their values.
Water quality targets in the sub-sea regions, soil types in the area and crop composition were shown to be important determinants of which areas should be set aside, and the analysis also showed that synergies with other ecosystem services were limited.
The chosen method is static, and further work could focus on developing more dynamic approaches as the delivery of ecosystem services often will develop over time. An example is water quality regulation, including protection of groundwater and drinking water, where changes in nitrogen leaching will have an effect over time and not immediately as assumed in this model tool.
The tool will be set up for th whole ounry in 2020, and used for decision support with respect to decision on land-use and protection of land and water-bodies.
A similarision support system; UK NEA, (National Ecosystem Assessment )was developed in UK; supported by Defra. The UK NEA aimed at providing better understanding and improved quantification of the value of the natural environment at large. As part hereof the aim was to develop decision support tools that could be used by governmental agencies as well as other stakeholders from local to national level.
Decision support tools for assessments of benefits related to water quality protection include, amongts others, valuation approaches such as meta-regression analyses and more spatialy explicit ecosystem services value assessment tools. The examples given in this chapter show that
- There are good data to use for development of generic value functions. Two examples of a national and a global approach shows that this can be done at many spatial levels.
- There are also rich experiences on assessment tools that value the ecosystem services linked to water quaility improvements. The apporaches are developed in Denmark, but also other countries have developed more or less similar DST’s. In UK the National Eosystem Assessment (NEA) was constructed by a large number of researcher, and used for a large number of assessments of conflicts and synergies between the production of different types of ecosystem services.
Note: For full references to papers quoted in this article see