Francisco X. Aguilar, Michael Goerndt, Stephen Shifley, Nianfu Song
Department of Forestry
The School of Natural Resources
University of Missouri

Environment: Reduce fossil fuel emissions

Public Policy: Renewable Portfolio Standards

Economics: Relatively low cost

Forest stewardship: Promote forest health
1.
2.
Explore and parameterize factors
influencing cofiring
Establish a “coarse screen” for county-level
co-firing potential
Regional Science and Location Theory
 Internal: Plant specific conditions
(e.g. production technology)
 External: Public policy and
regulations
 Location (county)-specific: Location
characteristics (e.g. access to input
materials, distance to markets)
Study Area
Empirical Estimation
Econometric Analysis


County-level is smallest practical scale for
estimation. Restrictions on explanatory factors
data (e.g. biomass resource availability, U.S.
Census, AgCensus).
Potential for co-firing can be indicated by
estimated presence (probability >0.5).
7


Conditionality of co-firing on presence of
coal-fired power plants.
Probability of co-firing y within the ith county
is conditional on
◦ Expected probability of a coal power plant in the
same county &
◦ other internal, external and location specific factors


2-Stages:
1. County-level probability for
placement of coal-fired power plant
2. Coal-fired power plant probability
included as independent variable
Single-Stage: Known coal power plant
frequency included as independent
variable
Factors
Internal
Cofiring
Observed presence/Estimated probability of
coal-fired power plant
Technical cofiring feasibility
External
Electricity demand indicators
Coal price
Adoption of RPS
Location-specific
Land value
Transportation infrastructure
Resource availability of biomass
Wood mill operations
Sub-region-level conditions.
EIA regions also included
Internal factors
Proxy
Power
plant
presence
Description
Number of
coal-fired
power plants
per county
Units
Count (known
number of
coal-fired
power plants)
Source
U.S. Department of
Energy 2005
Technical
feasibility
Boiler type
indicator
Binary
U.S. Department of
Energy 2005
External factors
Proxy
Electricity
demand
indicators
Description
Units
Average electricity
price in (state)
US cents per
kilowatt-hour
County area
100 Square
kilometers
%
Percentage County
area urbanized
RPS
Adoption
RPS adopted by
2001 (state)
Binary
Land value
Median house
value (state)
Thousand US$
Source
U.S. Energy
Information
Administration
2008
ESRI
U.S. Census Bureau
2000
Database of State
Incentives for
Renewables and
Efficiency
U.S. Census Bureau
2000
County-specific factors
Proxy
Transportation
infrastructure
Coal availability
and price
Biomass
availability
Description
Units
Principal highways
(county)
Principal railways
(county)
Major rivers
(county)
Presence of coal
production (state)
Average coal price
(state)
Annual Corn Yield
Binary
Total annual mill
residues (county)
1000 cubic
meters
Binary
Binary
Binary
US$ per ton
100 metric
tons
Source
U.S. Census Bureau
2000
U.S. Census 2000
EPA, 1997
U.S. EIA 2008
U.S. EIA 2008
AgCensus 2008
FIA TPO 2007

Significant
 Electricity price
 County Area
 Urban Area
 Road x Rail x Stream presence
Factors (statistically significant)
2-stage
1-stage
Estimated probability coal /No. coal-fired
plants
X
X
Technical feasibility
X
X
Internal
External
Electricity price
X
County-specific
Urban Area
X
Road x Rail x River presence
Total mill residue
X
X
X
RPS not significant with inclusion of tech feasibility variable
East North Central Region binary variable significant
Internal

Technical feasibility identified as highly significant
External


Positive relationship between electricity price and
probability of co-firing biomass
Adoption of RPS only significant when technical
feasibility was removed
County-specific



Counties with high cofiring probability values
endowed with relevant infrastructure and biomass
supply (mill residues)
Higher urbanization drives greater energy demand
Greater probability of cofiring in EIA East North
Central Region (Great Lakes)



Biomass cofiring is correlated with
 Boiler technical feasibility
 Infrastructure factors: road, rail and river presence.
 No RPS effect
Cofiring operations are highly dependent on
residues from the wood products industry
Most counties with high potential for cofiring
reside in the Great Lakes region close to wood mills

USDA Forest Service Research & Development Woody
Biomass, Bioenergy, and Bioproducts. Assessment of
opportunities and long-term impacts on forest resources of
co-combustion of woody biomass with coal for electricity
generation in the U.S.

USDA Forest Service, Northern Research Station. Model
Development and Predictor Tools Using FIA and State Forest
Resource Assessments/Analysis of low diameter wood
processing facilities and biomass/fuel treatment areas in
support of Wood for Energy Program in the Northeast and
Midwestern U.S.
Francisco X. Aguilar
Department of Forestry
University of Missouri
aguilarf@missouri.edu