modeling increased supply and emerging technologies in the ...€¦ · •transfer station: takes...

Modeling Increased Supply and Emerging Technologies in the Forest Products

Industry in the Pacific Northwest

Photo credit: Dan Carer/DJC Oregon

Presented by Mindy S. Crandall Western Forest Economists 49th Meeting

Missoula, Montana May 19, 2014

Mindy Crandall, Darius Adams, Claire Montgomery

Why biomass?

• Some forests are at high risk of fire or need restoration

• The material is of low or no value

• Emerging technologies can:

– Motivate forest restoration (particularly on public lands)

– Be a use that is local and/or not as volatile

– Provide jobs in hard-hit rural communities, in harvesting and processing

The ideal use of biomass will improve conditions ecologically, economically, and socially, for both forest

and human community health

Northwest Advanced Renewables Alliance (NARA)

• Large grant funded by USDA’s AFRI

• Goal: to assess the feasibility of utilizing woody biomass to produce aviation bio-fuel and co-products

• Basic model: single large facility

• Collaborators in several states, universities, industry organizations

• Most of the resources are going to the development of the conversion technology

• OSU’s role is to assess the supply chain of the feedstock material: logistics, feasibility, and cost

Traditional and Emerging Markets for wood products (adapted from David Smith, 2010)

• Turning woody biomass into jet fuel: – Is it feasible?

How much would it cost to supply biomass for fuel production? Can what is needed be supplied without subsidies?

• Using woody biomass as a rural development tool: – Is there potential?

How feasible is local processing and small-scale demand for biomass?

What’s the opportunity cost of using biomass in this way? Would it require subsidies?

– Can we help rural communities? What might be the potential impacts on specific rural communities?

Models used

• Base Market Model – Timber harvest for sawlogs drives market model and chip production – Two control variables – Delivers sawlogs from known forest sites to known sawmills and veneer/plywood

mills – Chips can be sold for exogenous price from the harvest site – Capacity is adjusted endogenously

• NARA Model – Chips are either sold for pulp & paper from the harvest site, or shipped to a large

central refinery for an exogenous price

• Depot Model – Three control variables – Chips are either sold as before, or shipped to an intermediate processing facility – Capacity for both mills and intermediate processing facilities is adjusted

endogenously – Optimal investment in intermediate processing includes location of facility

Market Model

Mills demand (buy) logs and trade off sources to minimize cost Landowners supply sawlogs and trade off destinations to maximize returns

Refinery

Logs

Chips

Depot

Mill

Mill

Forest Stand

Forest Stand

Logs

Logs

Logs

What is a depot?

• An intermediate processing facility located in rural areas

• Provides employment in critical areas and enhances the feasibility of a bio-refinery

• Can be located at an existing mill site, a former mill site, or be a new development

• Transfer station: takes material from the woods, screens it, transfers to rail freight

FIA Inventory Information

Estimates of factor demand elasticities for plywood and lumber

Management Regimes (MIC)

Logging & Hauling Logging & Hauling Cost (per volume)

between FIA

Distance between FIA

plots and destinations

Model Base, NARA,

Depot

Mill location and capacity

Potential Isobutanol Potential Isobutanol production locations

Consumer + Consumer + Producer

Surplus ($)

Biomass supply

curve (tons)

Lumber supplied

(mbf)

Mill Longevity

Potential intermediate Potential intermediate processing locations

Inputs Outputs

III. Current Research

Inventory & Forest

Inventory & Structure

Control variables for Depot Model

are the acres of inventory unit n assigned to management prescription j

is the units of new capital stock (capacity expansion) for mill m purchased in time t

is the units of investment in new intermediate processing facilities for facility z in time t

Depot Model

max𝑋𝑛𝑗 ,𝐼𝑚𝑡 ,𝐹𝑧𝑡

𝑃𝑡

𝑠 𝑞,𝐾𝑚𝑡 𝑑𝑞 − 𝑘𝐾𝑚𝑡 − 𝑢𝐼𝑚𝑡 − 𝑋𝑛𝑗𝑚 𝐷𝑛𝑚𝑠 𝑁

𝑛=1𝑄𝑚𝑡𝑠

𝑞=0 𝑀

𝑚=1

(1 + 𝑟)𝑡 +

𝑋𝑛𝑗 𝐶𝑛𝑡𝑁𝑛=1

(1 + 𝑟)𝑡

𝑇−1

𝑡=0

+ 𝑃𝑡

𝑏 ,𝐻𝐼𝐺𝐻𝑄𝑎𝑡𝑏 + 𝑃𝑡

𝑏 ,𝐿𝑂𝑊𝑄𝐿𝑂𝐶𝐴𝐿𝑡𝑏 𝑍

𝑧=1 − ℎ𝑧𝐻𝑧𝑡 − 𝑙𝑧𝐹𝑧𝑡 − 𝑋𝑛𝑗 𝐷𝑛𝑧𝑏 − 𝑣𝑎𝑧𝑄𝑎𝑡

𝑏𝑁𝑛=1

(1 + 𝑟)𝑡

+ 𝑃𝑡𝑏 ,𝐿𝑂𝑊𝑄𝑎𝑡

𝑏 − 𝑋𝑛𝑗 𝐷𝑛𝑎𝑏 𝑁

𝑛=1

(1 + 𝑟)𝑡 +

(𝑄𝑇𝑠𝑃𝑇

𝑠 𝑞,𝐾𝑇 − 𝐶𝑇𝑠𝑄𝑇

𝑠) + (𝑄𝑇𝑏𝑃𝑇

𝑏 − 𝐶𝑇𝑏𝑄𝑡

𝑏)

𝑟(1 + 𝑟)𝑇

0

200

400

600

800

1000

1200

40 45 50 55

Bio

mas

s Sh

ipp

ed

( a

vera

ge 1

00

0 g

t/ye

ar)

Biomass Price Offered at Depot $/gt

Refinery Deliveries, Eugene

PRELIMINARY RESULTS – SUBJECT TO CHANGE

0

20

40

60

80

100

120

140

160

180

40 45 50 55

Bio

mas

s Sh

ipp

ed

( a

vera

ge 1

00

0 g

t/ye

ar)


Depot Establishment Cost $5/gt

Albany

Coos Bay

Grants Pass

Roseburg

Siletz

Vernonia


0

20

40

60

80

100

120

140

160

180

40 45 50 55

Bio

mas

s Sh

ipp

ed

( a

vera

ge 1

00

0 g

t/ye

ar)



Albany

Coos Bay

Grants Pass

Roseburg

Siletz

Vernonia


0

20

40

60

80

100

120

140

160

180

40 45 50 55

Bio

mas

s Sh

ipp

ed

( a

vera

ge 1

00

0 g

t/ye

ar)



Coos Bay

Roseburg

Siletz

Vernonia


0

100

200

300

400

500

600

700

800

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055

Bio

mas

s d

eliv

ere

d, 1

00

0gt

/5-y

r p

eri

od

Year

Siletz Depot Under Different Capital Costs

Cap Cost $0/gt

Cap Cost $5/gt


Benton 25%

Lincoln 1%

Linn 25%

Marion 24%

Polk 25%

Albany

Coos 95%

Douglas 5%

Coos Bay Benton

5%

Douglas 21%

Lane 60%

Linn 14%

Eugene

Douglas 18%

Jackson 31%

Josephine 51%

Grants Pass

Lincoln 100%

Polk 0%

Siletz Clatsop

2%

Columbia 71%

Tillamook 1%

Washington

26%

Vernonia


Next Steps

• Model the reduction in transport costs from depot to refinery available when rail is an option

• Develop the operating costs of the depots

• Develop the capital costs of the depots (vary by type of development)

• Introduce a penalty (depreciation) for idle depots

Thank You!

Mindy S. Crandall, Ph.D. Candidate

Applied Economics

Forest Engineering, Resources, and Management

Oregon State University

[email protected]

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