Francisco X. Aguilar, Michael Goerndt, Stephen Shifley, Nianfu Song

Department of ForestryThe School of Natural Resources

University of Missouri

Logging residues Removal of excess biomass (fuel treatments) Fuelwood from forestlands Primary and secondary wood processing mill

residues and pulping liquors Urban wood residues Dedicated energy plantations

Climate change: Reduce CO2 emissions from fossil fuels.

Rules and regulations: E.g. Renewable Portfolio Standards.

Economics: Relatively low cost for conversion to co-firing compared to other renewable energy (e.g. wind, solar, liquid biofuels)

Forest stewardship: e.g. Promote forest health

Estimate potential for co-firing of biomass in existing coal-fired power plants for the U.S. Northern Region

Use results to establish a “coarse screen” for county-level potential of co-firing biomass for electricity based on physical factors

County-level is smallest practical scale for estimation, given restrictions on estimation of explanatory factors (e.g. infrastructure, waterways, biomass resource availability).

Potential for co-firing can be indicated by estimated presence (probability estimate>0.5).

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Important Issues:

◦ Possible spatial interdependence

◦ Dependence of county-level co-firing on presence of coal-fired power plant(s)

Theoretical Framework◦ Natural conditionality of co-firing on presence of

coal-fired power plants.◦ Probability of co-firing y within the ith county is

conditional on the expected probability of a coal power plant in the same county (E[ci]) & other location factors captured in an information factor matrix X.

◦ Prob(yi=1| E[ci], X) = F(E[ci|Lα], Xβ)

County-level probability for placement of coal-fired power plants was analyzed as a first stage (Model A)

Two models created for final stage (co-firing probability (potential))

1. Model B: Known coal power plant frequency included as independent variable

2. Model C: First stage (Model A) estimates included as independent variable

Standard probit regression◦ Assumes binary response (0,1)◦ Does not account for spatial dependencies

Bayesian spatial autoregressive probit◦ Assumes binary response (0,1)◦ Accounts for spatial dependencies

Preliminary Chi-squared tests conducted on dependent variables for spatial dependence prior to assessing Bayesian spatial autoregressive probit

Dependent◦ Location of coal-fired power plants & co-firing

status (EPA, DOE)

Independent◦ Electricity demand (EIA)◦ Infrastructure (EPA, US Census)◦ Coal availability and price◦ Renewable energy policy◦ Resource availability of biomass (TPO, NASS)◦ Sub-regional variation

Energy demand◦ Population◦ County area

Infrastructure◦ Rail presence◦ Road presence◦ River & stream presence

Renewable energy policy◦ Renewable energy portfolio standards (RPS) by 2001

Resource availability of biomass◦ Wood mill residues◦ Corn yield (stover)

Spatial autoregressive probit: no significant improvement over standard probit

Energy demand proxies such as county area & urban percentage of county area were highly significant

Infrastructural proxies of road presence & stream presence (namely road x stream interaction) were highly significant.

Known frequency of coal-fired power plant highly significant.

Significant proxies◦ Electricity Price◦ Rail Presence◦ Road presence x stream presence◦ Wood mill residues◦ RPS implementation◦ One sub-regional indicator

Component from Model A not significant Significant proxies◦ Rail Presence◦ Road presence x stream presence◦ Wood mill residues◦ RPS implementation◦ Two sub-regional indicators

5 counties with high potential but no current co-firing facilities

Indicated counties have high values for electricity demand, infrastructure & mill residues

Model success rate = 96%

3 counties with high potential but no current co-firing facilities

Indicated counties have high values for infrastructure & mill residues

Model success rate = 96%

Notable positive relationship between electricity price and probability of co-firing biomass

Adoption of RPS was significant for both final models, denoting a strong relationship between energy policy and co-firing

Counties identified by Models B & C had fairly high values for relevant infrastructure and biomass supply (mill residues)

Inclusion of known coal-fired power plant frequency in Model B did not decrease significance of infrastructural proxies

Infrastructure variables such as road presence are vital to co-firing operations with or without current presence of coal-fired power plants

Sub-regional variation has a greater effect on co-firing probability in the absence of known coal-fired power plant frequency

Physical potential of co-firing biomass is highly influenced by variables indicating Supply infrastructure Current availability of wood mill residues

Implementation of RPS has a significant positive effect on co-firing

Valuable county-level preliminary examination of co-firing potential across the Northern region.

Dr. Michael Goerndtgoerndtm@missouri.eduDepartment of ForestryUniversity of Missouri