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Selecting Forest Sites for Voluntary Conservation in Finland


Selecting Forest Sites for Voluntary Conservation in Finland Antti Punkka and Ahti Salo Systems Analysis Laboratory Helsinki University of Technology – PowerPoint PPT presentation

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Title: Selecting Forest Sites for Voluntary Conservation in Finland

Selecting Forest Sites for Voluntary Conservation
in Finland
  • Antti Punkka and Ahti Salo
  • Systems Analysis Laboratory
  • Helsinki University of Technology
  • P.O. Box 1100, 02015 TKK, Finland
  • http//

  • Pilot projects for voluntary forest conservation
    in Finland
  • Decision analytic observations about pilot
  • Site selection procedures
  • Decision support models for sites biodiversity
  • How Robust Portfolio Modeling (RPM, Liesiö et al.
    2006) can be used in the evaluation and selection
    of forest sites?

Voluntary Conservation in Finland
  • Five pilot projects in METSO program (2003-2007)
  • Objective to protect forest biodiversity in
  • Habitat-oriented instead of species-oriented
  • Led by two ministries in cooperation
  • Voluntary conservation in pilot projects
  • Fixed-term deals (usually 10 years) against
    monetary compensation
  • Finland population 5.3M, area 338000 km2
  • Cf. Hong Kong population 7M, area 1100 km2
  • A lot of forest (76 of area), a lot of private
  • Our task is to evaluate pilot projects from a
    decision analytic perspective and give
    recommendations for future
  • Funding Ministry of Agriculture and Forestry

Selection of Conservation Sites
  • Mix of resource allocation and multicriteria
  • How to model the biodiversity of the resulting
    portfolio (network)?
  • Additivity of value functions
  • Networks value with regard to sites?
  • Sites values with regard to criterion-specific

Which sites of different costs should be selected
with regard to multiple criteria, subject to a
limited budget?
DA / Optimization Methods in Reserve Site
  • Several optimization models with one criterion
  • Maximize of species subject to a limited of
  • Minimize of sites such that predefined species
    occur on these sites
  • Potentially optimal networks SMART/MOP (Memtsas
  • SMART and multiobjective programming (distance
    from utopian vector) to compare potentially
    optimal networks
  • Sensitivity analysis on weights
  • Pareto optimal networks modified AHP (Moffett
    et al. 2006)
  • Modified AHP to compare Pareto optimal networks
  • Sensitivity analysis on weights

Pilot Projects in Finland
  • Five pilots
  • In the biggest pilot, some 400000 euros have been
    spent annually since 2003
  • Average monetary compensation about 200 euros /
    ha / year
  • Land-owners
  • expression of interest
  • some information
  • on the sites
  • conservation
  • values
  • Evaluation
  • of the site
  • estimation of
  • biodiversity values
  • (compensation
  • estimate)
  • Land-owners
  • offer
  • assistance
  • provided
  • (second
  • evaluation)
  • Negotiations,
  • decision
  • examination of
  • one or several
  • sites

No deal
Selection Procedures in Pilot Projects
  • Site-by-site selection candidates are accepted
    or discarded soon after evaluation and offer
  • Portfolio selection selection is made at a later
    date from a group of many site candidates

expression of interest
specification of offer
Decision Analysis in Voluntary Conservation
  • Design of a decision analytic selection
    procedure Site-by-site or through portfolio
  • or something between these?
  • Evaluation of sites
  • Accuracy of data / evaluations
  • Modeling of sites conservation values
  • Decision support
  • Selection of sites

Differences between Selection Procedures (1/2)
  • Number of evaluations
  • Costly
  • Target of choosing the best site network
  • Spatial aspects
  • Decision delay
  • Information about unselected (but feasible) sites
  • Candidates prevailing biodiversity values

Differences between Selection Procedures (2/2)
  • Portfolio selection tends to be more
    cost-effective than site-by-site selection if
  • Site-specific cost of evaluation is not very high
  • The share of infeasible site candidates is not
    very high
  • The budget is not too small

Multi-Criteria Modeling in Pilot Projects
  • Multi-criteria methods used to
  • Form compensation estimates for forest owners
  • Evaluate site candidates
  • Support selection
  • Additive models based on several conservation
  • Area, dead wood, distance to other conservation
    sites, rare species regarded as criteria
  • Weights wi represent relative importance of

Deficiencies in Pilot Projects Multi-Criteria
  • Lack of sensitivity analysis
  • Use of point estimates for scores and weights
    leads to a single overall value for a site
  • Piecewise constant value functions
  • Network requirements not explicitly accounted for
  • E.g. the total area of selected sites must be at
    least 250 ha

Figure valuation of logs
Preference Programming Incomplete Information
  • Site characteristics
  • The volume of dead wood on site x is between 8
    and 11 m3
  • Relative importance of criteria
  • E.g. Salo and Hämäläinen (2001), Salo and Punkka
  • 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

Feasible Weights and Scores
  • In the absence of information feasible criterion
    weights and scores belong to
  • Incomplete information (linear constraints) leads
    to subsets
  • Information set

Supporting Site Network Selection with RPM
  • Incomplete information
  • Subset of sites a site network a portfolio p
  • Select a feasible site network p to maximize
    overall value with budget B
  • Additive, consistent with value tree analysis

Comparing Site Networks Dominance Relation
  • No unique overall values ? no unique optimal
    portfolio usually
  • Portfolios compared through dominance relation

Non-Dominated Portfolios
  • Portfolios that are not dominated by any other
  • Figure n 2, fixed scores
  • w1 within the interval 0.4, 0.7
  • p1 dominates p2
  • p1 and p3 non-dominated
  • 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

RPM Site Oriented Analysis
  • Sites that belong to every non-dominated site
  • Core sites
  • If excluded, the selected network is dominated ?
  • Sites that do not belong to any non-dominated
    site network
  • Exterior sites
  • If included, the selected network is dominated ?
  • Borderline sites belong to some but not all
    non-dominated networks
  • Core index of site
  • Share of non-dominated portfolios in which a site
    is included (CI0-100)

RPM Framework
Decision rules, e.g. minimax regret
Large numberof site candidates. Evaluated w.r.t.
multiple criteria.
Core sites Robust zone ? Choose
  • Border line sitesuncertain zone
  • Focus

Score intervals Loose weight statements
Narrower intervals Stricter weights
Not selected
Exterior sitesRobust zone ? Discard
Negotiation. Manual iteration. Heuristic rules.
Approach to promote robustness through incomplete
information (integrated sensitivity
analysis). Accounts for group statements
Example Sensitivity of Recommendations (1/3)
  • 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
  • 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
  • Data
  • Real data on 27 selected sites with
    criterion-specific values (non-normalized)
  • Weights (wi) and scores derived from
    criterion-specific values
  • Budget 50 of sum of offers

Example Sensitivity of Recommendations (2/3)
  • Effect of weight perturbation

Example Sensitivity of Recommendations (3/3)
  • Differences between ND networks with 10
  • 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

Possibilities of RPM in Reserve Site Selection
  • Design of DA selection procedure Site-by-site
    or portfolio?
  • Synergies and network requirements can be
    explicitly included
  • Evaluation of sites
  • Incomplete information on sites characteristics
  • Information on how further evalution efforts
    should be focused effectively
  • Modeling of sites conservation values
  • Generic model
  • Additive models widely used and easy to
  • Incomplete information on weights
  • Selection of sites
  • A priori sensitivity analysis
  • Several robust decision recommendations

  • 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. 640652.
  • 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. 181191.
  • 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. 533545.
  • Salo, A., Punkka, A., (2005). Rank Inclusion in
    Criteria Hierarchies, European Journal of
    Operational Research, Vol. 163, pp. 338356.
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