Laboratory of Forest Economics Biennial Workshop May 30 - June A Database for Non-Market Forest Values in Europe Ståle Navrud Department of Economics and Resource Management Norwegian University of Life Sciences (UMB), Ås

Contents Why do we need a European Database? Non-timber benefits (NTB) studies in EVRI database Benefit Transfer (BT) methods, protocol and data requirements Construction of a European database for non-timber benefits (NTB)

Why do we need a European Database? Cost-benefit analysis (CBA) requires economic values for non-timber benefits (NTB) / ecosystem services. Often no time and/or resources to perform new project specific valuation studies  Benefit transfer (BT) of use and non-use values BT = Transfer economic value of public good from study site (primary valuation study) to policy site; both benefits and costs transfer (i.e. rather call it “value transfer”) Four basic requirements for valid BT : 1) Complete, searchable and accessible database of domestic and foreign valuation studies  NTB database 2) Best practise criteria for assessing quality of primary valuation study (COST E45 Euroforex Revealed (RP) and Stated Preference (SP) Protocols) 3) Benefit transfer techniques 4) Best practise criteria for benefit transfer (COST E45 Euroforex BT protocol) and general BT guidelines e.g. Defra BT Guidelines (Eftec 2009)

NTB studies in the Environmental Valuation Reference Inventory (EVRI) database Web-based database; continiously updated 3240 (1966 studies in 2007) studies in total; 775 consider some aspect of forest (Full text search:”forest”); but not all relevant Search Protocol: - ” Land general” category does not contain ”Forest” - ”Plants” category have ”Trees” and ”Woodlands”, producing 482 hits (242 from Europe) RP and SP studies Reporting: - No specific format for reporting the results - Often lack methodological information necessary for juding quality for unit/function transfer and for variables in meta analysis

Benefit Transfer methods 1.Unit Value Transfer (from a ”similar” study) - Simple (naïve) unit value transfer - use value: Consumer surplus/activity day - non-use value: WTP/houshold/year - Unit value transfer with income adjustments - International transfer: PPP-adjusted exchange rates 2.Function transfer - Benefit function transfer (from a ”similar” study)  Meta-analysis (from many studies with different scope)

Unit value transfer with income adjustment Adjusted benefit estimate B p ' at the policy site: B p' = B s (Y p / Y s ) ß B s primary benefit estimate (e.g. WTP) from study site, Y s,Y p income levels at the study and policy site, respectively ßincome elasticity of WTP for environmental good

Benefit function (BF) and Meta analysis (MA) BF:WTP ij = b 0 + b 1 G j + b 2 H ij + e WTP ij = willingness-to-pay of household i at site j, G j = set of characteristics of environmental good at site j, H ij = set of characteristics of household i at site j MA:WTP s = b 0 + b 1 G j + b 2 H ij + b 2 C s + e WTP ij = mean willingness-to-pay/household of study s C s = set of methodological characteristics of study s n= number of studies (but also several estimates from each study)

Selected meta analyses of NTB Recreational use values (mainly TC) North American studies - Rosenberger and Loomis (2003) - Shrestha and Loomis (2003) European studies - Bateman and Jones (2003) (UK studies only) - Zandersen and Tol (2009) (9 European countries) - Scarpa et al (2006) conducted same CV study in 26 recreational forests in Ireland Use and non use values (mainly CV) - Lindhjem (2007) 30 studies in Norway, Sweden and Finland

Zandersen, M. and R.S.J. Tol (2009) A meta analysis of forest recreation values in Europe, JFE 15 (1-2), Meta-analysis of forest recreation in Europe based on studies that have applied the travel cost method covering 26 studies in nine countries since Meta-regression with an increasing number of variables where level I includes only data available from the studies, level II aggregate socio-economic variables and level III site-specific characteristics such as diversity, fraction of open land and location. Data shows that consumer surplus varies from €0.66 to €112 per trip; with a median of €4.52 per trip. Results of the model with the best overall summary indicate that the application of the individual travel cost method, inclusion of opportunity cost of time and average distance travelled lead to increasing benefits whereas the year of the study and estimations from theses and dissertations reduce welfare estimates. Including exogenous variables shows that site attributes, GDP per capita and population density play a significant role

Lindhem, H. (2007) : 20 years of Stated Preference Valuation of Nin-Timber Bemnefits from Fennoscandian Forests: A Meta Analysis. JFE, 12 (4); Stated preference (SP) surveys have been conducted to value non-timber benefits (NTBs) from forests in Norway, Sweden and Finland for about 20 years. The paper reviews the literature and summarises methodological traditions in SP research in the three countries. Second, a meta-regression analysis is conducted explaining systematic variation in Willingness-to-Pay (WTP). Two important conclusions emerge, with relevance for future research: (1) WTP is found to be insensitive to the size of the forest, casting doubt on the use of simplified WTP/ha measures for complex environmental goods; and (2) WTP tends to be higher if people are asked as individuals rather than on behalf of their household.

Benefit Transfer (BT) protocol and data requirements 1) Identify the change in the environmental good to be valued at the policy site (i)Type of environmental good (ii)Describe baseline, magnitude and direction of change in environmental quality 2) Identify the affected population at the policy site 3) Conduct a literature review to identify relevant primary studies (from EVRI database and/or specific database for NTB); preferably of the same category of affected population (local, regional, national)

BT protocol II 4) Assessing the relevance/similarity and quality of study site values for transfer (i) Scientific soundness; the transfer estimates are only as good as the methodology and assumptions employed in the original studies (ii) Relevance; primary studies should be similar and applicable to the “new” context (iii) Richness in detail; primary studies should provide a detailed dataset and accompanying information

BT protocol III 5) Select and summarize the data available from the study site(s) 6) Transfer value estimate from study site(s) to policy site (i) Determine transfer unit (use vs. non-use value) (ii) Determine transfer method for spatial transfer (unit transfer with income/PPP adj; and meta analysis) (iii) Determine transfer method for temporal transfer (CPI) 7) Calculating total benefits (or costs) - NTB= Mean WTP x ”affected population” 8) Assessment of uncertainty

Transfer Error (TE) Percent difference between the transferred (WTP T ) and policy site primary estimate (WTP P )

Criteria for Judging Similarity I)Characteristics of the good Similar good? (i.e. similar type forest, similar use and/or non-use value components; similar recreational activities, similar ecosystem services) Similar baseline, size and direction of change in the good valued? (To avoid scaling up and down values according to the size of the area, involving strict assumptions in terms of e.g. constant value per ha of use and/or non-use values; rather consider foreign study sites with nearly similar size than domestic study sites with a very different scale. The same applies to the baseline and the direction of the change. However, the general recommendation is to choose a domestic study site as close as possible geographically) Similar availability of substitute sites? (For use values: recreational sites; For non-use values: National parks and other preserved areas and the ecosystem services they contain) Similar forestry management regimes ?

Criteria for Judging Similarity (cont.) II) Population characteristics Similar average income level (and income distribution)? (If not, income adjustments should be made when performing the value transfer) Similar gender, age and educational composition? Similar size of affected population? Expected similar distance decay, if any, in non-use values? Similar rights to using forest areas for recreation? Similar attitudes to forest preservation? (attitudinal and cultural factors)

Four categories of ”Similarity” between Study site and Policy Site CategoryLevel of fit between study and policy sites Percentage transfer error 1Perfect Fit Acceptable fit Poor fit No fitDiscard study for this BT

Sensitivity analysis Sensitivity analyses should also be conducted for the size of the affected population the transferred unit value is multiplied with. If evidence of distance decay in WTP in the primary study that one think could be transferred to the policy site, sensitivity analysis with WTP and population estimates for each distance zone should be performed

Construction of a European database for NTB Evaluate NTB studies in EVRI - which studies; what information recorded Produce list of candidate studies to be entered in a new database Quality assessment of these candidate studies List of criteria/information needed for each study; see e.g 46 variables (of 45 studies ) in Elsasser, P; J. Meyerhoff; C. Montagné, and A. Stenger 2009: A bibliography and database on forest benefit valuation studies from Austria, France, Germany and Switzerland – A possible base for a concerted European approach J. of Forest Economics, 15 (1-2); Update criteria list according to requirements in the BT protocol (especially similarity criteria) European meta analyses also have databases with detailed description of studies - Lindhjem, H. (2007)- Database for Finland, Norway and Sweden - mainly non-use studies - Zandersen and Tol (2009) – Database for UK, Italy. Austria, Belgium, France, Germany, Spain, Denmark, Finland (Sweden) - recreational use values Two options: 1) Create a new database based on these existing databases (and EVRI records) 2 ) Establish agreement with Environment Canada (operating EVRI) to get access to EVRI for all European countries (now only France and UK), and revise/enter studies on NTB in EVRI that pass the quality check. In return; get a spreadsheet database containing all information on all studies on NTB in EVRI; including more detailed info on each study; according e.g. to the criteria suggested by Elsasser et al (2009) and criteria required for BT