Francisco X. Aguilar, Michael Goerndt, Stephen Shifley, Nianfu Song                     Department of Forestry            ...
   Logging residues   Removal of excess biomass (fuel treatments)   Fuelwood from forestlands   Primary and secondary ...
   Climate change: Reduce CO2 emissions from    fossil fuels.   Rules and regulations: E.g. Renewable Portfolio    Stand...
   Estimate potential for co-firing of biomass in    existing coal-fired power plants for the U.S.    Northern Region   ...
   County-level is smallest practical scale for    estimation, given restrictions on estimation of    explanatory factors...
   Important Issues:    ◦ Possible spatial interdependence    ◦ Dependence of county-level co-firing on      presence of ...
   Theoretical Framework    ◦ Natural conditionality of co-firing on presence of      coal-fired power plants.    ◦ Proba...
   County-level probability for placement of coal-fired    power plants was analyzed as a first stage (Model A)   Two mo...
   Standard probit regression    ◦ Assumes binary response (0,1)    ◦ Does not account for spatial dependencies   Bayesi...
   Dependent    ◦ Location of coal-fired power plants & co-firing      status (EPA, DOE)   Independent    ◦   Electricit...
   Energy demand    ◦ Population    ◦ County area   Infrastructure    ◦ Rail presence    ◦ Road presence    ◦ River & st...
   Spatial autoregressive probit: no significant    improvement over standard probit   Energy demand proxies such as cou...
   Known frequency of coal-fired power plant    highly significant.   Significant proxies    ◦   Electricity Price    ◦ ...
   Component from Model A not significant   Significant proxies    ◦   Rail Presence    ◦   Road presence x stream prese...
   5 counties with    high potential    but no current    co-firing    facilities   Indicated    counties have    high v...
   3 counties with    high potential    but no current    co-firing    facilities   Indicated    counties have    high v...
   Notable positive relationship between electricity    price and probability of co-firing biomass   Adoption of RPS was...
   Inclusion of known coal-fired power plant    frequency in Model B did not decrease significance    of infrastructural ...
   Physical potential of co-firing biomass is highly    influenced by variables indicating     Supply infrastructure    ...
Dr. Michael Goerndtgoerndtm@missouri.eduDepartment of Forestry University of Missouri
Session 27 ic2011 goerndt
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Session 27 ic2011 goerndt

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Session 27 ic2011 goerndt

  1. 1. Francisco X. Aguilar, Michael Goerndt, Stephen Shifley, Nianfu Song Department of Forestry The School of Natural Resources University of Missouri
  2. 2.  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
  3. 3.  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
  4. 4.  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
  5. 5.  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). 5
  6. 6.  Important Issues: ◦ Possible spatial interdependence ◦ Dependence of county-level co-firing on presence of coal-fired power plant(s)
  7. 7.  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)
  8. 8.  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
  9. 9.  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
  10. 10.  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
  11. 11.  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)
  12. 12.  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.
  13. 13.  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
  14. 14.  Component from Model A not significant Significant proxies ◦ Rail Presence ◦ Road presence x stream presence ◦ Wood mill residues ◦ RPS implementation ◦ Two sub-regional indicators
  15. 15.  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%
  16. 16.  3 counties with high potential but no current co-firing facilities Indicated counties have high values for infrastructure & mill residues Model success rate = 96%
  17. 17.  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)
  18. 18.  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
  19. 19.  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.
  20. 20. Dr. Michael Goerndtgoerndtm@missouri.eduDepartment of Forestry University of Missouri

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