1 Helsinki University of Technology Systems Analysis Laboratory Selecting Forest Sites for Voluntary Conservation in Finland Antti Punkka and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology P.O. Box 1100, TKK, Finland

Helsinki University of Technology Systems Analysis Laboratory 2 INFORMS International, Hong Kong 2006 Outline n Pilot projects for voluntary forest conservation in Finland n Decision analytic observations about pilot projects –Site selection procedures –Decision support models for sites’ biodiversity n How Robust Portfolio Modeling (RPM, Liesiö et al. 2006) can be used in the evaluation and selection of forest sites?

Helsinki University of Technology Systems Analysis Laboratory 3 INFORMS International, Hong Kong 2006 Voluntary Conservation in Finland n Five pilot projects in METSO program ( ) –Objective to protect forest biodiversity in Finland –Habitat-oriented instead of species-oriented –Led by two ministries in cooperation n Voluntary conservation in pilot projects –Fixed-term deals (usually 10 years) against monetary compensation n Finland: population 5.3M, area km 2 –Cf. Hong Kong: population 7M, area 1100 km 2  A lot of forest (76 % of area), a lot of private land-owners n Our task is to evaluate pilot projects from a decision analytic perspective and give recommendations for future –Funding Ministry of Agriculture and Forestry

Helsinki University of Technology Systems Analysis Laboratory 4 INFORMS International, Hong Kong 2006 Selection of Conservation Sites n Mix of resource allocation and multicriteria decision-making: n How to model the biodiversity of the resulting portfolio (network)? –Additivity of value functions »Network’s value with regard to sites? »Sites’ values with regard to criterion-specific values? Which sites of different costs should be selected with regard to multiple criteria, subject to a limited budget?

Helsinki University of Technology Systems Analysis Laboratory 5 INFORMS International, Hong Kong 2006 DA / Optimization Methods in Reserve Site Selection n Several optimization models with one criterion –Maximize # of species subject to a limited # of sites –Minimize # of sites such that predefined species occur on these sites n Potentially optimal networks + SMART/MOP (Memtsas 2003) –SMART and multiobjective programming (distance from utopian vector) to compare potentially optimal networks –Sensitivity analysis on weights n Pareto optimal networks + modified AHP (Moffett et al. 2006) –Modified AHP to compare Pareto optimal networks (approximation) –Sensitivity analysis on weights

Helsinki University of Technology Systems Analysis Laboratory 6 INFORMS International, Hong Kong 2006 Pilot Projects in Finland n Five pilots –In the biggest pilot, some euros have been spent annually since 2003 –Average monetary compensation about 200 euros / ha / year Land-owner’s expression of interest some information on the site’s conservation values Evaluation of the site estimation of biodiversity values (compensation estimate) Land-owner’s offer assistance provided (second evaluation) Negotiations, decision examination of one or several sites No dealDeal

Helsinki University of Technology Systems Analysis Laboratory 7 INFORMS International, Hong Kong 2006 Selection Procedures in Pilot Projects n Site-by-site selection: candidates are accepted or discarded soon after evaluation and offer time n Portfolio selection: selection is made at a later date from a group of many site candidates time expression of interestevaluationspecification of offerdecision

Helsinki University of Technology Systems Analysis Laboratory 8 INFORMS International, Hong Kong 2006 Decision Analysis in Voluntary Conservation ÊDesign of a decision analytic selection procedure: “Site-by-site” or through portfolio analysis? … or something between these? ËEvaluation of sites –Accuracy of data / evaluations ÌModeling of sites’ conservation values –Decision support ÍSelection of sites

Helsinki University of Technology Systems Analysis Laboratory 9 INFORMS International, Hong Kong 2006 Differences between Selection Procedures (1/2) n Number of evaluations –Costly n Target of choosing the best site network –Spatial aspects n Decision delay n Information about unselected (but feasible) sites –Candidates’ prevailing biodiversity values

Helsinki University of Technology Systems Analysis Laboratory 10 INFORMS International, Hong Kong 2006 Differences between Selection Procedures (2/2) n Portfolio selection tends to be more cost-effective than site- by-site selection if: n Site-specific cost of evaluation is not very high n The share of infeasible site candidates is not very high n The budget is not too small

Helsinki University of Technology Systems Analysis Laboratory 11 INFORMS International, Hong Kong 2006 Multi-Criteria Modeling in Pilot Projects n Multi-criteria methods used to 1.Form compensation estimates for forest owners 2.Evaluate site candidates 3.Support selection n Additive models based on several conservation values –Area, dead wood, distance to other conservation sites, rare species regarded as criteria –Weights w i represent relative importance of criteria

Helsinki University of Technology Systems Analysis Laboratory 12 INFORMS International, Hong Kong 2006 increase of 1 m 3 interval in evaluation value m 3 /ha Deficiencies in Pilot Projects’ Multi-Criteria Models n Lack of sensitivity analysis –Use of point estimates for scores and weights leads to a single overall value for a site n Piecewise constant value functions n Network requirements not explicitly accounted for –E.g. the total area of selected sites must be at least 250 ha Figure: valuation of logs

Helsinki University of Technology Systems Analysis Laboratory 13 INFORMS International, Hong Kong 2006 Preference Programming: Incomplete Information n Site characteristics –The volume of dead wood on site x is between 8 and 11 m 3 n Relative importance of criteria –E.g. Salo and Hämäläinen (2001), Salo and Punkka (2005) –Area is more important than landscape values –Dead wood is the most important criterion –If the maximum value w.r.t. area is 20, max value w.r.t. burned wood is between 80 and 120

Helsinki University of Technology Systems Analysis Laboratory 14 INFORMS International, Hong Kong 2006 Feasible Weights and Scores n In the absence of information feasible criterion weights and scores belong to n Incomplete information (linear constraints) leads to subsets n Information set

Helsinki University of Technology Systems Analysis Laboratory 15 INFORMS International, Hong Kong 2006 Supporting Site Network Selection with RPM n Incomplete information n Subset of sites = a site network = a portfolio p n Select a feasible site network p to maximize overall value with budget B –Additive, consistent with value tree analysis

Helsinki University of Technology Systems Analysis Laboratory 16 INFORMS International, Hong Kong 2006 Comparing Site Networks: Dominance Relation n No unique overall values  no unique optimal portfolio usually n Portfolios compared through dominance relation Portfolio p is dominated, if there exists another portfolio p’ s.t. 1. V(p’,w,v N )  V(p, w,v N ) for all 2. exists for which V(p’,w,v N ) > V(p,w,v N )

Helsinki University of Technology Systems Analysis Laboratory 17 INFORMS International, Hong Kong 2006 Non-Dominated Portfolios n Portfolios that are not dominated by any other portfolio n Figure: n = 2, fixed scores –w 1 within the interval [0.4, 0.7] –p 1 dominates p 2 –p 1 and p 3 non-dominated n Non-dominated portfolios of interest –No other feasible portfolio has greater overall value across the information set –Non-dominated portfolios with information S’  S are a subset of non-dominated portfolios with S –Not necessarily potentially optimal V w1w w2w

Helsinki University of Technology Systems Analysis Laboratory 18 INFORMS International, Hong Kong 2006 RPM – Site Oriented Analysis n Sites that belong to every non-dominated site network: Core sites –If excluded, the selected network is dominated  include n Sites that do not belong to any non-dominated site network Exterior sites –If included, the selected network is dominated  exclude n Borderline sites belong to some but not all non-dominated networks n Core index of site –Share of non-dominated portfolios in which a site is included (CI=0%-100%)

Helsinki University of Technology Systems Analysis Laboratory 19 INFORMS International, Hong Kong 2006 Approach to promote robustness through incomplete information (integrated sensitivity analysis). Accounts for group statements RPM Framework Decision rules, e.g. minimax regret Narrower intervals Stricter weights Score intervals Loose weight statements Large number of site candidates. Evaluated w.r.t. multiple criteria. Border line sites “uncertain zone”  Focus Exterior sites “Robust zone”  Discard Core sites “Robust zone”  Choose Core Border Exterior Negotiation. Manual iteration. Heuristic rules. Selected Not selected Gradual selection: Transparency w.r.t. individual sites Tentative conclusions at any stage of the process

Helsinki University of Technology Systems Analysis Laboratory 20 INFORMS International, Hong Kong 2006 Example: Sensitivity of Recommendations (1/3) n Incomplete ordinal information –Importance-order of criteria groups (6) known –No stance is taken on the order of importance within the groups –Criteria with same w* form a group n 20, 15 and 10 % intervals –E.g. with 10 % interval the weight of old aspens (0.120) is allowed to vary within [0.9 x 0.120, 1.1 x 0.120] = [0.108, 0.132] n Data –Real data on 27 selected sites with criterion-specific values (non-normalized) –Weights (w i *) and scores derived from criterion-specific values –Budget 50 % of sum of offers

Helsinki University of Technology Systems Analysis Laboratory 21 INFORMS International, Hong Kong 2006 Example: Sensitivity of Recommendations (2/3) n Effect of weight perturbation

Helsinki University of Technology Systems Analysis Laboratory 22 INFORMS International, Hong Kong 2006 Example: Sensitivity of Recommendations (3/3) n Differences between ND networks with 10 % intervals –Examine site candidates in more detail »Spatial aspects? –Choose sites with highest core index (6/7) »ND #3, ND #4 and ND #6 become ”infeasible” –Decision rules (Salo and Hämäläinen 2001) recommend network ”ND #6” »Precise weights w* lead to solution ”ND #7”

Helsinki University of Technology Systems Analysis Laboratory 23 INFORMS International, Hong Kong 2006 Possibilities of RPM in Reserve Site Selection ÊDesign of DA selection procedure: “Site-by-site” or portfolio? –Synergies and network requirements can be explicitly included ËEvaluation of sites –Incomplete information on sites’ characteristics –Information on how further evalution efforts should be focused effectively ÌModeling of sites’ conservation values –Generic model –Additive models widely used and easy to understand –Incomplete information on weights ÍSelection of sites –A priori sensitivity analysis –Several robust decision recommendations

Helsinki University of Technology Systems Analysis Laboratory 24 INFORMS International, Hong Kong 2006 References »Liesiö, J., Mild, P., Salo, A., (2005). Preference Programming for Robust Portfolio Modeling and Project Selection, European Journal of Operational Research, (to appear). »Memtsas, D., (2003). Multiobjective Programming Methods in the Reserve Selection Problem, European Journal of Operational Research, Vol. 150, pp. 640–652. »Moffett, A., Dyer, J. S., Sarkar, S. (2006). Integrating Biodiversity Representation with Multiple Criteria in North-Central Namibia Using Non- Dominated Alternatives and a Modified Analytic Hierarchy Process. Biological Conservation, Vol. 129, pp. 181–191. »Salo, A., Hämäläinen R. P. (2001). Preference Ratios in Multiattribute Evaluation (PRIME) – Elicitation and Decision Procedures under Incomplete Information. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, vol. 31, s. 533–545. »Salo, A., Punkka, A., (2005). Rank Inclusion in Criteria Hierarchies, European Journal of Operational Research, Vol. 163, pp. 338–356.