basics of forest economicsbeahrselp.berkeley.edu/wp-content/uploads/2017.06... · basics of forest...
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Basics of Forest Economics
J. Keith GillessDean & Professor of Forest Economics
6/12/17
COLLEGE OF
Natural ResourcesUNIVERSITY OF CALIFORNIA, BERKELEY
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Alternative Systems
• Even-Aged: Managing forests composed of stands of trees in which the age of the trees is relatively uniform – harvesting usually by clearcutting
• Uneven-Aged: Managing forests where three or more age classes are present in all stands – harvesting usually by single-tree selection
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Even-Aged Forest Landscape(Note spatial pattern)
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Uneven-Aged Forest Stand(Note structural diversity)
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Uneven-Aged Forest Stand(Note species diversity)
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Decision Making Tools
• Financial Analysis• Linear Programming• Integer Programming• Dynamic Programming• Simulation Modeling
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Key Economic Decisions InUneven-Aged Forest Management
• Cutting cycle (how long between entry)• Diameter distribution (Inverse “J”)• Operational costs for roads/harvest setup• Regeneration
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Key Economic Decisions InEven-Aged Forest Management
• Rotation (how long to grow)• Planting density• Thinnings (timing and intensity)• How much land to clearcut at different
points in time
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Key Constraints InForest Management
• Resource:Land, seedlings, labor, budget
• Environmental:Minimum amounts of habitat Maximum sediment loads
• Economic:Minimum harvest or revenue flows
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Linear Programming• General approach for modeling problems that
can be expressed as the maximization or minimization of a linear function of a set of decision variables, subject to a set of linear constraints on those variables
• Applications:o Harvest schedulingo Personnel managemento Project Management
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Example: “Poet’s Problem”• Records indicate that managing red pine earns
$90/ha/yr, compared to $120 for hardwoods• Owns 40 ha of red pine and 50 ha of hardwoods• Managing red pine takes 2 days/ha/yr,
compared to 3 days for hardwoods• Doesn’t want to work more than 180 days per
year managing forest (needs time to write)• Wants to maximize return from managing forest
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Mathematical Formulation• Objective
Maximize annual revenue• Decision VariablesX1 = ha of red pine to manageX2 = ha of northern hardwoods to manage
• ConstraintsLaborLand
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Linear Programming Model
0,200300
000,40200100300
:subject to5.1min
21
2
1
21
21
21
³££
³+³+
+=
XXXX
XXXX
XXZ
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Graphical Solution
D
C
B A
X2
X1 0
20
30
40 0
50 0 0
60 0 0 0 10
10
20
30
40
50
0
21 120907600 XXZ +==
21 120903600 XXZ +==
21 120901800 XXZ +==
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Spreadsheet Model
12345678910111213141516
A B C D E F GPOET PROBLEM
Red pine HardwoodsManaged area 40 33.333333
(ha) (ha) ResourcesTotal available
Red pine land 1 40 <= 40 (ha)Hardwoods land 1 33 <= 50 (ha)
Poet's time 2 3 180 <= 180 (d/y)Total
Returns 90 120 7,600 Max($/ha/y) ($/ha/y) ($/y)
Key FormulasCell Formula Copied toD6 =SUMPRODUCT(B6:C6,B$3:C$3) D6:D8D10 =SUMPRODUCT(B10:C10,B$3:C$3)
Resources required
Objective function
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Integer Programming Models
• Useful when some decision variables are binary, i.e., yes or no
• Applications in forestry:o Design of road networkso Allocation of capital to indivisible projectso Modeling adjacency rules
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Dynamic Programming
• Useful for problems where multistage decisions are linked temporally or physically
• Examples:o Thinning decisionso How to buck a tree into logso How to rip or cross cut a board
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Example: Thinning Timing & Intensity
30 m3
E
150 m3 5
180 m3 5 220 m3
5
240 m3 5
250 m3 0 5
0 0
0
10 m3
0 0 000
20 m3 000
40 m3
20 m3
40 m3 50 m3
30 m3
A
B C D
F G H L
M
Initial stand
Stage 1 (first thinning)
Stage 2 (second thinning)
Stage 3 (Final harvest)
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Solution Algorithm
• Starting at the “end” of the network, decide what would be the best thing to do given the “state” of the system from that point forward
• Recursive equation:
)](*),([max)(* 1 jVjiriV tjt ++=
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Dynamic Programming (Crosscut Saws)
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Simulation Modeling
• Useful when “optimality” is difficult to define but you can quantify the relationships between key variables
• Allows for experimentation with a system that would be too costly or risky, to do in the real world
• Less threatening to decision makers
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Applications of Simulation Modeling in Forestry
• Population modeling:o Survival analysis (for endangered species)o Predator/Preyo Fisheries
• Watershed management• Fire behavior & suppression
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Interdisciplinary Isn’t Rocket Science – It’s Harder:
Biologists vs. Economists
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Biologist’s Perspective
• From a purely biological perspective culmination of mean annual increment (MAI) maximizes the total production from the stand
MAI = Volume per unit area/age• MAI increases, then decreases with age• This is NOT what economists would
almost ever recommend
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Economist’s Perspective
• In the absence of significant price differentials for quality, the economic rotation is ALWAYS shorter than the biological rotation
• This follows from the logistic growth curve over time for trees and discounting
• It is further reduced by considering that delaying harvest delays ALL FUTURE HARVESTS (Faustmann)
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Complicating Factors
• Harvesting system costs have fixed and variable components
• The price of wood is highly stochastic• Quality differentials may be important in
some species• Social acceptance varies for even-aged
and uneven-aged forestry• Aesthetic value of forest generally
positively correlated with age
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Complicating Factors (con’t)
• Biodiversity value depends on landscape considerations, not particular stands
• Economic agent may be an integrated forest owner/wood processor – capital costs may need to be serviced on mill investment
• Risk factors (fire, disease, regulatory)• Result ~ Most industrial forests are now
owned by third parties in NA & the EU
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Sources of Inefficiency
• Externalities (+/-) are ubiquitous & few mechanisms have been internalizedo E.g., sediment, cumulative impacts
• Incentives are often “perverse”oConcessionaires contracts are often too short
to benefit from conservationo Tax & titling structures often encourage
deforestation• Transboundary problems are common