Selecting Forest Sites for Voluntary Conservation in Finland

1
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//www.sal.tkk.fi/
• forename.surname_at_tkk.fi

2
Outline

• Pilot projects for voluntary forest conservation
  in Finland
• Decision analytic observations about pilot
  projects
• 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?

3
Voluntary Conservation in Finland

• Five pilot projects in METSO program (2003-2007)
• Objective to protect forest biodiversity in
  Finland
• 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
  land-owners
• Our task is to evaluate pilot projects from a
  decision analytic perspective and give
  recommendations for future
• Funding Ministry of Agriculture and Forestry

4
Selection of Conservation Sites

• Mix of resource allocation and multicriteria
  decision-making
• 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
  values?

Which sites of different costs should be selected
with regard to multiple criteria, subject to a
limited budget?
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DA / Optimization Methods in Reserve Site
Selection

• 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
• Potentially optimal networks SMART/MOP (Memtsas
  2003)
• 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
  (approximation)
• Sensitivity analysis on weights

6
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
Deal
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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

time
expression of interest
evaluation
specification of offer
decision
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Decision Analysis in Voluntary Conservation

• Design of a decision analytic selection
  procedure Site-by-site or through portfolio
  analysis?
• or something between these?
• Evaluation of sites
• Accuracy of data / evaluations
• Modeling of sites conservation values
• Decision support
• Selection of sites

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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

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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

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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
  values
• Area, dead wood, distance to other conservation
  sites, rare species regarded as criteria
• Weights wi represent relative importance of
  criteria

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Deficiencies in Pilot Projects Multi-Criteria
Models

• 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
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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
  (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

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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

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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

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Comparing Site Networks Dominance Relation

• No unique overall values ? no unique optimal
  portfolio usually
• Portfolios compared through dominance relation

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Non-Dominated Portfolios

• Portfolios that are not dominated by any other
  portfolio
• 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

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RPM Site Oriented Analysis

• Sites that belong to every non-dominated site
  network
• Core sites
• If excluded, the selected network is dominated ?
  include
• Sites that do not belong to any non-dominated
  site network
• Exterior sites
• If included, the selected network is dominated ?
  exclude
• 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)

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

• Border line sitesuncertain zone
• Focus

Core
Score intervals Loose weight statements
Narrower intervals Stricter weights
Border
Not selected
Exterior
Exterior sitesRobust zone ? Discard
Negotiation. Manual iteration. Heuristic rules.
Approach to promote robustness through incomplete
information (integrated sensitivity
analysis). Accounts for group statements
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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

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Example Sensitivity of Recommendations (2/3)

• Effect of weight perturbation

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Example Sensitivity of Recommendations (3/3)

• 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

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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
  understand
• Incomplete information on weights
• Selection of sites
• A priori sensitivity analysis
• Several robust decision recommendations

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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. 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.