United States 

Department sf 

Agriculture 

Southeastern Forest James W. McMinn 

Experiment Station 

General Technical 

Report SE-38 
July 1986 

Southeastern Forest Experiment Station 

P.0, BOX 2680 

Ashevit Fe, NC 28802 
Wood Energy Potential in 

Northwestern South Carolina 

James W. MklcMlnn, Research Forester 

Athens, Georgia 

The quantity of unused kood in dn 16-tou~tj area 

in oortkwestern South Garo:fna was prc:ected to be 

more than 16 million tons av~uaily, Wood that 1s 

unsurtable for products oehr tkac fbel amounts to 

nearly 9 rn11l:on tocs annuall), The most I~kely 

eqergy demand 3y inbbstrizl plsnts that are gow 

ca~a~dater for wood fuel systems >% 1,s rn~ 1 f ion tons 

annually, Maximum ~sojeeted devand for such piaots 

4s 3 rnillron tons per year, The area has a hsgh 

potential for development of wed energy at favorable 

costs, 

Keywords: Biomass, conp~ter program, forest res4- 

dues, fael wood, 

Introduction 

The Energy SeciarS~y Act af 1980 

directed the U,S, Department ~f 

Agrkultlrre to CO~~LIC% s'&uig";is"c; on tbe 

use of wood residues for energ2. In 

1981, a Ge-iesal Accaunti~g Office JGAO) 

report, ""Te Nation 'S 3nused i400d Offers 

Vast Potent-ial Enercjy and Presilucl 

Senef its ," rerecsmm~nr<ed that the Y,S, 

Department of Agriculture and US, 

Department of Energy cilriduct a series ef 

studies to assess the energy potent i a1 

of wocdy biomass i~1 jjr~~-~-is~~lg lilicales of 

tne hited States, In 1952, tnese two 

projects were me~yed tc nutiia! ~a3gieB'j.c 

when Energy Security kt funds were 

allocated for studies in Yew Enyl~~d, 

the Lake States, the ~orthwesc, and tne 

Southeast, This Keg;ost suw-i:nara" nes sPne 

stgdy conducted in the Sostheas: and 

presents the major resiil ts, 

Study Area and Objectives 

The Southeast study focused on an 

18-coussu yarea that covers approximately 

che northwestern third of South Carolina 

(fig, 1). This group of counties was 

selected on the basis of several Pactors, The area is to some degree 

geographical ly discrete becanse it is 

hounded on the southwest by the 

Chattoogs , Tugai oo, and Savannah Rivers 

(the Georgia State he) and on the 

north by the Southern Appalachian - Mountains, rhe counties form one af the 

[JSDA Forest Service Survey Units, wh-b"ch 

ensures that certai~ sumnary data are 

readily available, Also, aq earl ies 

study by Hapris (1982) had indicated a 

woad surplus in the six most northwesterly courlties, 

Figure "h-Map of South Carolina showing the 

4 8-county sstudy area, 

P ihe ared is enti rely Fiediqosrt 

Plateau except for a oarrow fringe at 

the edge of three count"es that lies iq 

the mountains along toe Hue Ridge 

escarpment, Out of a total of 6,717,387 
acres, 68 percent (4,566,/82 acres) is 

forested (Snyder 1978), A little less 

than 1 percent of the forested land is 

cf assified as ""reserved timberland," 

with the remainder considered timberland, In this context, 'timber1 andt"s 

forest land producing or capable of 

producing crops of industri al wood and 

not withdrawn f ram timber production. 

Forest industry owns on1 y 13.2 percent 

of the timberland, The largest 

ownerski p categories are farmers and 

mi scell aneous private individuals with 

34.7 and 32,J percent of the acreage, 

respect i vel y, &her ownership cl asses 

are mi scei 1 aneous corporations , 88,3 

percent ; Nat i onaf Forests, 7.5 percent ; 

other Federal , 0,9 percent ; and State 

and local governments, 1.1 percent. The 

"irest-type groups reprfssentecl are: 

Forest -type Percentage of 

roup -.-% -..- - timber1 and ---- 

Loblolly-shortledf 

pine 49,4 

Oak-hickory 32,5 

Oak-phne 14,9 

El m-ash-catt onwood 23 

Wki te pi ne-hem1 ock 0,3 

Longleaf-slash pine 0,) 

Vi rtxtbal ?y a1 l of the acreage hd~ beeri 

farmed except for the wettest areas agad 

steepest sl opes ; hence, the di stri bution of forest types has been strs~gly 

inf f ueqced hy the sequence of ayricul - 

tural abandonment, natural succession, 

and logging of the higher quality tirnber 

fsr wood products, The low quality of 

the remaining hardwoods makes a substantial proportion of them unsuitable for 

conventional wood products, On some of 

the oak-hickory and oak-pine acreage the 

value of the logs is less thaq the cost 

aF logging, and pines would be the preferred timber species, However, the 

projected return on investment precludes 

conversion to pine due to the high cost 

04" rerriisvi rrg noilrrrerchantable material . Utilization of triis material for fuel 

N~U! 4 therefme be desi rab7e La improve 

: ~era17 coi~rnescial forest productivity 

i MC;~"R aggi;). 

The primary objectives of this 

study were to 

Estimate the quantity and types 

of residues that could be supplied for 

future fuel use, 

EstimaLe potential demand For 

wood fuels, 

Analyze the potential impact of 

wood energy development on conventional 

wood products, 

Assess the general feasibility 

of wood energy devel opmenl , 

Although the assessment was for a speci - 

fic area, the approach and methods used 

were designed to be appf icable to other 

areas, particularly to the Piedmont of 

the Southeast, 

Quantity and Types of Residues 

There are six possible sources of 

residues in the Piedmonk logging residue and noncommercial stands, land conversion, precolnmerci al "i;i nni ng, 

salvage, prilnary m-i ia residues, and 

surplus growing stock, Preccrrmmescia'i 

thinning was judged unfeasible in the 

study area, hence that source was 

s~itted from the estimates, Residues 

from logy! ng and noncommerci a? stands, 

salvage, and surplus growing stock were 

estimated on the basis of a custom 

forecat information ret ri eval and standard published s%a&istics, both of which 

were generated by the Forest Inventory 

and Analysis Unit (f IA) of the 

Southeastern Forest Experiment Stat ion, 

Land conversion residues were estimated 

from additional data provided by the FHA 

UnSt. Primary mi5 1 residue estimates 

were obtained from a 198% Commodity 

2rai n Survey by the Coopcrratisde 

E>r"ens.r" on Service, Glervson Un9" versity, 

The i nf srrnat i on redri eval provi ded 

residue estirnztes hy forest type for 

three six-county gs~ups because statistically reliab!~ estimates cs~ld ndt be 

qenerated by FXA for smaller areas, 
Data for each six-county group were 

apportioned among the individual counties according to the ratio of forest 

acreage of the county to forest acreage 

of the group, The distribution of 

forest acreage in each county among 

major fsrest types sr broad management 

groups was based on Snyder (1978). FIA 

estimates of recent past treatment provided an estimate of the fraction of 

each forest type that would csntri bute 

to res i due product i on, 

A detailed breakdown of residue type 

by county was derived to serve as input 

For the assessment of general feasibility of wood energy development, which 

includes production costs, 

Overall estimates, in thousands oh 

green tons per year, for the study 

area were: 

togging residues & 

noncommerci a1 stands 6,770 

land conversi on 255 

Primary mi 11 residues 1,020 

Surpl us yrowi ng stock 7,164 

Two exploratory studies were conducted as we7 1, The fi rst deaf t with 

use of avai 1 able di gi t i zed el evat 1 on 

data in conjunction with satellite imagery do adjust recoverable residue 

estimates on the hasis of slope, The 

intent was to deduct from estimated 

residue that fraction in each forest 

type that occurs on slopes greater than 

a specified opwahility threshold, This 

effort was unsuccessful because the digital terrain made1 used was not sensitive enough to slope, and the satellite 

land-cover el assi f ication fai led do 

distinguish rrsnforested frurn forested 

I and, Land with fence rows, orchards, 

residential subdivisions, ete,, was frequently classified as mixed upland 

hardwood--the forest type of highest 

priority for energy use in the Piedmont. 

If a slope-related deduction from estf- 

~ated residue is to be made, the best 

data source is still slope estimates by 

FIA field crews, 

The second exploratory study dealt 

with development of biomass predict Fon 

equations for use with 1:24,000-scale 

GQ~ 0r iqf rared monossopic photo 

coverage. An overai 1 equation of low 

re1 iabi 9 ity (coeff ichent of variation of 

36 percent) was found to be just as 

reliable as equations for specific forest types in predicting total biomass. 

Residue estimates based on such an 

equation would entai l applying the 

equation to different forest types and 

condi ti ons , then us1 ng an appropri ate 

residue fraction for each set of conditions to derive residue from total 

bi omass, 

Potential Demand for Wood Fuels 

The object Has Lo establish a 

general framework by which potential 

regional demand for wood energy could be 

derived from public information, The 

strategy was to employ fi nancial 

equations for cash-flow analysis of 

a1 ternative energy systems in existing 

industries, The underlying rationale 

was that industry is by far the largest 

energy-consuming sector (about 65 percent of statewide use) and the financial, 

climate is the driving force behind 

system conversion, Major-size firms 

(those with 50 or more employees and at 

least one boiler with a capacity in 

excess of 20,000 pounds of steam per 

hour) were targeted. More than 100 

installations qualifying as major-size 

firms were identified in the study area 

fronf the Natjonal Emissions Data Systern 

and "ce South Carol ina Tndustriaf Directory, An additional constraint was that 

the financial analysis had to indicate a 

maximum payback period of 3 years, This 

approach identifies instal lations that 

are good conversion candidates, 

The cash-Flow analysis was based an 

the "Woad TVt>system pub1 ished by the 

N.C. Department of Commerce (1982). The 

model generates net eash-fl ow val ues 
from the engineering aspects of the 

system, fuel characteristics and prices, 

and f-inancl'af variables that include 

Federal and State taxes and credits, 

Regl'onaf averages were used for boiler 

capital and mairr"eenance costs, 

Payback periods were derived for a 

kange oh boiler system sizes under different sets of assumptions, The critical system sine fsr each set sf 

asslampf i ons was used in conjunction ~~'iitk 

reg4" anal energy co~ssunpt ion per enpf oyee 

to detesmiqe an upper limit for system 

sjze, A number of demand estimates were 

eevelsped by varying assumptions over a 

""reasonable range"" regards ng interest 

rates, 03 1 prices, wood prices, and 

hisher base prices for both fuels, The 

aggrega- eestirndted demand ranged from 

0,6 to 3 million tons in the intermediate term, with the most likely level 

approaching 13 million tons per year, 

Potential Impact sf Fuel Wood Use 

on C~nventi onal Wood Products 

The relationship between increased 

wood fuel use and the price and availability of wood for the more conventional 

products such as pulpwood, saw logs, 

veneer logs, etc, , was analyzed, 

She demand for conventional products 

was derived from the South Carol ina 

Coinnacidi ty Dra-E" n Survey conducted 

annual ly by the Cooperative Extension 

Service, Cl emson Uni versi ty, Data from 

1972 do 1981 were used to determine 

annual production for pine and hardwood 

products by county, and to determine 

convent isnal product mix, industrial 

capacity , de7 i vered val ue, and unit 

price, Industrial capacity for each 

prodl~ilt; was estimated by sumwing over 

the 18-county area the maximum annual 

productian in each county during the 

10-year period, Demand for wood fuel 

was from Harris (1982) and from the precedi ~g sect i on, 

Pi ne pul pwood , the largest conventional product category with an average 

annual production of over 23 million 

green tons, is approximately double the 

next largest category, pi ne sawt imber, 

Hardwood sawtimber and pulpwood eombi ned 

averaged nearly 1 million tons per yew, 

Overdl l production averaged more than 5 

mil lion tons anqua1 ly for the 10-year 

period, with a range from 4,212,340 do 

5,636,455 tons, The derived maximum 

capacity -is approximately 5 percent 

greater than the hi ghest si ngl e-year 

production, which occurred in 1976, 

Estimated postharvest residue ranges 

from 32 to 4,3 nillion tons an~ually, 

Bared on product output, for 1981 the 

~stimated acres narvested was 176,000, 

which is fairly consistent with the 

estimated 189,000 aonual acreage derived 

from FrA data for this cacegory of resi - 

dues, Since the potential demand ranged 

from ahout 0,6 to 3 million green tons 

annual ly, this residue source a1 one 

cou%d theoretically satisfy demand, 

A number of Sndustrial plants considering new solid-fuel combustion 

systems have found the cost of wood 

approximately equal to the cost oh coal, 

so the ceil ing price possible for wood 

fuel may depend on the price of coal 

rather than oil and natural gas, Recent 

(1982) delivered wood fuel prices range 

from about $14 to $18 per green ton. 

This approximates the del i vered price 

for hardwood yulpwosd, which is the only 

conventional product that wood fuel 

approaches in price, Greater fuel wood 

demand than anticipated in this study is 

judged to pose no problem for pulpwood 

supply, and the probable effect on the 

saw-log market is a simultaneous 

lowering OF costs and higher qua7 ity 

Isgs, The rationale for this expectation is that more acreage would be 

harvested and, because of their value, 

saw logs existiing on this acreage woiilii 

be marketed separately by the producer 

rather than included in the fuel mix, 

Feasibility of Woad Energy 

DeveS opment 

A common percept i an among managers, 

who might consider wood energy an 

option, is that it is uneconomical to 

transport residues more than a very 

short distance, A 20- to 30-mile maxi-. 

mum is frequently assumed, A recent 
study of mill residues in Georgia (Eza 

and others 1984a) showed that, from a 

pract ieal standpoi nt , transportat ion is 

not a limiting factor, To assess the 

general feasibility sf wosd Fuel development, thei r supply-demand a7 I &rcatl"on 

model called Wood Residue Distribution 

Sirnugator (WORDS) was used (Eza and 

others 5984b), 

As a measure of supply attractiveness, WORDS used purchase and dei ivery 

costs which include the cost of recovering res-idues and of transpostf~ag thern to 

the demand location, WORDS systernatical ly a1 '8sca"ces supply to dersra~d such 

that a least-ccst overall a1 location is 

approxiinated, The current versi oar a% 1o-- 

cates until efther a1 I supply is 

exhausted or all demand is satisfied, 

but id prevents allocalian of any supply 

with a higher cost than some specified 

alternate energy source, The total cost 

of acykll'riny and shipping wood fuel fro17 

each suppv location to each demand 

location is at so calculated, WORDS 

requires three basic inputs: quantity 

and cost of each type or combination of 

residues at each supply location, 

transporlata'on costs sped f i ed as 5ome 

function sf distances between supply and 

demand units, and alternate energy costs 

at each demand Iscation, For each 

source of demand, supply iscations are 

divided into up to nine preference categories according to the delivered costs 

of supply, The user specifies the cost 

houndari es on each preference cl ass, 

The model will handle several categories 

of material at each of 160 potential 

stiypl y and dernand locations, 

To evaluate tne rlddel for t9is 

appl i cat i on, sup91 y and denacd val ues 

were taken Prom our estipriates sf qudl;- 

tity and type of residues and potential 

denand. Four types of residues were 

cmsidered in this andlysis--surplus 

growing stock was not used, The types, 

quantitjes, and their estimated average 

costs at the supply locations were: 

Type A: residues recovered by 

integrating existi ~g 

roundwood operations to 

harvest tops, branches, 

and small trees; 6.77 

mi 1 l ion tons ; $27/ton, 

Type R: resl'clues recovered fsow 

noncommerci a7 forest-1 and 

conversion; 0,255 million 

tons ; 418!ton, 

Type 6: forest-land salvage aperations; 0,919 million 

tons ; $18/&sn. 

Type 11: prinary mi7 1 residues; 

1,02 million tons; 

$12/%;can, 

biodd energy valges were derived 

fl-om pu9l i shed jnfarliiiat ioq and transportd"t;on costs Prsn publi5hed informa~ion 

3nd a swvey of wood products transporters in Georgia, Twelve scenarios were 

developed, and eight were used to evaluate genera; mad eqersy Peasibil ity if? 

the stddy area, Table 1 sui~rnarizes the 

assun3tisns used in each scenario, An 

Table !..---.$~endri~?~ 9sed far W3ItDS sirnulator to evaluate wosd energy feasi b? l ity 

in northwestern South Carol i na --* ----- - - -- --a- ---..---*- ------- ------ - - Residue Alternate %Jo o (3 

Scenario typea e 

enerly - cost pr i ces - 

base 

base 

base 

base 

base 

base 

base 

base 

13 

Sase 

base 

Sase 

hdse 

"A = 7ncjyj~y residue, 9 = lanci conversion, 

C = salvage, D = mdll residue, 

9 First a? locate 6, then a1 locate O, 

"use $2 rather than 95 preference class. 

'~dd simulated power plant (410,000 tons/ 

year), 

"use linear prograinining. 
alternate energy price af $625 per 

millian B"cu"s is the equivalent of 

natural gas at 65 per thousand cubic 

feet and 80 percent eff S ci ency, 

The f 9" rst three scena ri os al l ocated 

all of the respective supplies at favorable cost (table 2), but none fully 

satisfied the overall demand of 1,s 

l~illion tons, The fourth scenario used 

the two least expensive residue types, 

which together would satisfy total 

demand; this scenario indicates that 

even at an upper boundary far demand 

estimates, wood energy could be s~~pliefi 

at a significantly lower cost than 

natural gas energy, Scenarios five and 

slx added successively more expensive 

residues to the a1 1 ocaL i on pool , 

Scenario seven used a natural gas price 

of $7.50 per thousand cubic feet, and 

scenario eight added 50 percent to wood 

residue purchase price with transportation cost the same, 

The last four scenarios were 

intended only to evaluate NORDS as a 

tool for this type aP general assessment, si nce precedi ng scenari os had 

a1 ready given strong indication of wood 

energy potential in the study area, 

Mote that the last scenario used a 

linear programming farnuladion to find 

the abso7 ute least-cost overaf l a1 l oca-- 

ti~n, but it is much more expensive than 

the simula$ion approach, Simulation 

expense and accuracy are both affected 

(in opposite directions) by the specified width of preference c% asses, 

WORDS can be used without mobif icaLion to evaluate supply-demand netwosks 

in other geographic regions, The model 

currently permits up to 160 demand locations, 160 supply locations, and eight 

types of material, which may be a71scated singly or in any user-specified 

combination, This particui ar assessment 

aggregated hoth supply and demand to the 

Table 2.--Sumnary of simulation assessment of wood energy feasibi l i ty showifiy 

those counties with minimum and maximum costs for each scenario, northwestern 

South Carol i na 

Mi nimum Average 

Scena ri o cost 

Oconee/Vork 

Edgef iel d 

Edgeti el d 

Sal uda 

Greenwood 

Newber ry 

Sal uda 

Newberry 

Oconee 

New~erry 

Sa 1 uda 

Newberry 

Lancaster $3.34 

Greenwood 4,17 

Cherokee 4.17 

Spartanbury 3.60 

Spartanburg 3.64 

Anderson 5,03 

Spart anburg 3,60 

Greenvi 1 l e 5.19 

Spartanburg 3.88 

Cherokee 3,Qf 

Power plant a 3.75 

Lancaster 3,69 

"~cenario 11 siinuiated the addition of one more demand source, a hypothetical 

power plant requi ring 410,000 tons of residue annually: this quantity was 

equal to the excess of supply over demand when mill residues and salvage 

residues were a1 located. The location of the plant was the approximate 

geographical center of the study area, Laurens County. This simulation 

resulted i~ the average cost of supply for the power plant itself being the 

highest of all demand sources, 
county level, but the level of aggregation is up to the user, The physical 

size and number of supply units and 

demand units need not be the same, and 

the user can subdivide a syslev in any 

way as long as supply units do not 

overlap and demand units do not overlap, 

This analysis indicates that there 

is a high potential for using wood as ai 

energy source in northwestern South 

Carolina. If fuel production were confined to the material not suitable for 

other forest products, the supply could 

easi ly accommodate a1 l industri al plants 

that are good candidates for conversion, 

plus a small power plant (about 50 

megawatts). If materi a1 is included 

that is suitable for other forest products, but not projected to be used, the 

potential supply is 16,1 million tons 

per year--over 10 times the projected 

demand of 1.5 mi 11 ion tons annually by 

industrial plants that would be good 

candidates for conversion. These esdi - 

mates are all at costs that compare 

favorably do the cost of currently used 

energy sources, These results raise a 

qrlestion asout the lack sf substantial 

wood energy developaenl in the study 

area, 

Although there are regianal differences, GAO has determined that 

several harriers appedr ts have a significa~t effe-t on residue use nationwide: 

1, Inadequate data on the volume, 

location, accessibility, and 

avai labil ity of forest residues, 

2, Lack of economical and effective equipment for harvesting 

and transport$ ng residues, 

3, Lack of investneqt eap-!taS 

needed for harvesting and using 

res -e" dues, 

4, L-irnited awareness an& acceptance 

of wood energy and product techno1 ogy among i~duftrial f-i rms, 

utilities, and State and local 

bodies, 

Additionaf obstacles were judged by GAO 

to discourage or prevent residue use in 

some areas of the country: 

5, Federal forest management 

pol i ci es and programs, 

6, Ut i 1 i ty practices and 

regul at i ons . 

7, Envi ronmental concerns re1 ated 

to greater use of residues, 

The Southeast study did not address the 

question of harriers explicitly. However, based on 5 years of wood-energy 

related work in the region, my impressions are as Pollaws, Of the seven 

items listed, only ite~s 4 and 6 have a 

significant influence on residue use in 

the study area, Lack of awareness and 

acceptance have been and remain a 

harrier, My perception is reinforced by 

the greater degree nP wood energy use in 

nei yhhoring Georgia and North Carol ina , both ef which have had aggressive wood 

energ prornot ion programs si nce 1978, 

In addi tisn, as of 1982 South Carol ina 

derived 36 pereent of its electrical 

energy from nuclear reactors, which are 

concentrated in the study area, Had the 

nuclear csmmidnent not been made before 

the OPEC scare of the early 5970is, wood 

mi gh't have been cons fdered inore seriously either as an electrical power 

source or, certaioly, as an industrial 

energy sou rce , 

Information nn wood raw material 

in the Southeast is probably as good as 

in any other region, if not better, 

However, fran my observdti O~S, potenti a1 

users wil I require more precise, 

locadjan-specific data to gain confidence about the use of this unfamiliar 

raw material, Remate sensing may provide a partial ss7utisn, The remotesensi~g applicatisns attempted in this 

study focused on readily available and 

inexpensive data, but were too "'quick 

and di rty""%o pr~vide estimates any more 

refined than the data available from 

standara FIA surveys, These tests by no 

rneacs el iininated remote seilsing as a 

pate~tia5 too7 for estimating fuel woad 

s;ip~%y* 
The quantf Gativf tool used in 

assess3 ng general feasi bi 1 i ky (WORDS) 

shrtcild provide a good general Framework 

for this type of study in other areas, 

If the analyst begins with WORD% as the 

fta~ework, a substantial proportion of 

the relevant data will he collected as 

i nput, 

Administrative representatives: 

H,K, Cordel 1 , USDA Forest Servi ce 

R,H, Becker, Clemson University 

Ouantitu and types sf residues: 

L,E. Nix and H,A, Shain 

GI emssn Uni versi ty 

Conclusions 

Potential demand far wood fue%s: 

Current residue types, va? unes , +nd roadside costs in the study area 

we :: 

0" DO: I ars 

qesiduetype greentons Ferton ----- P *- 

LA, Harris, Clemsen University 

Wax Schrei ber, 

Western Carol i na Uni versi ty 

inggi ng r2s i dues 

4 ncncommerci dl 

staqds 6. $70 27 

Land conversion 0,255 18 

Potential demand esthmates rdnged 

Prom 08,6 to 3 mil ?$on tr3~s ia% the Snterqediate term, with the most likely level 

approaching 1,s milllion eons per yrzr, 

Wood fdel deveboprnent :s j:~aged 

to have 1 jttle e-f*ect qq ~t3er indt~stri al wood except serbaps, f~r 3 

simultaneous decrease i~ prtcz md 

increase iq quality of 5aw Icys, 

Woad residue e.rerqj deuel~~rnenl 

should be generally fe2rik1le 47 t?e a.ed 

snd, in fact, wuld support 3 sna77 

power plant (50-megawatt range) ax 

favorable prices. 

Acknowledgment 

For major contributions in the 

respective aspects of this study, the 

author thanks the following: 

Feasibil it y of development : -- 

D,A, Eza and F,E, Dress 

University of Georgia 

Bioaass estimatiafi from aerial photos: 

J, Smith, "ai irgirja P01yteh;hn-i~ 

Ins"% tt~te and State tlni versity 

Literature CJ ted 

Eza, Dcdglas k.; kP:?n, James W,; Dress, Peter E, 

Cost-effective tru~krag d1:tances for woody 

bioqass fuels, Ees, Notp 5%-325, Ashevil le, NC: 

U,S, kpartme~t of Agr;cultare, Forest Service, 

Soutneastern Forest Experiment Statiori; 1984a, 

7 ppB 

Eza, 3cuglas A,; McYi~n, James K,; Dress, Peter E, 

Wood res%due di rtri bation srrn~" ator (WORDS), kc, 

Teen, yep, SE-28, Ashevi lle, KC: U,S, Departvent 

of Agrlcults~~e, Forest Service, Ssbtb~astern 

Forest Exper-went Station; 1964b, 6 DP, 

Harris, Robert P, Warket potential for wood f=~et --a Iim~tins factor in woo4 ewrgy development, Forest 

Products Journal 32~67-70; 19f2, 

McXi sn, 3mes W, Wio:e-tree hasvesting affects pine 

regeneration and hardwood conpetitfor, Soctherq 

Journal of Appl~ed "orestry %(2):E1-64; 1985, 

h,C, Department of Commerce, Woad IV, In: Pulaskl, 

E,C,; Jahn, L,G,; Elliott., R,h,, eds, Uosd energy 

Information gbide, Rale~gk, NC: Energy Division, 

N,C, kpartmnt of Comerce; 1982:26-42, 

Snyder, Nolan L, Forest statistics for the Piedmont of 

South Carolina, 1977, Resour, Bull, SE-95, 

Ashev" Se, NC: U,S, Department of Wgrf cul tore, 

Forest Service, Southeastern Forest Experiment 

Station; 1978, 33 pp, 
McMi nn, James W, 

Wood energy potential in northwestern South Carolina. Gen. Tech, 

Rep, SE-38. Asheville, NC: Department of Agriculture, Forest 

Service, Southeastern Forest Experiment Stat ion ; 1986. 8 pg. 

The quantity of unused wood in an 18-county area in northwestern Soul 

Carolina was projected to be more than 16 million tons annually, Wot 

that is unsuitable for products other than fuel amounts to nearly 9 

million tons annually. The most likely energy demand by industrial 

plants that are good candidates for wood fuel systems is 1.5 million 

tons annually. Maximum projected demand for such plants is 3 millioi 

tons per year. The area has a high potential for development of wooc 

energy at favorable costs. 

KEYWORDS: Biomass, computer programs, forest residues, fuelwood. 

McMi nn , James W. 

Wood energy potential in northwestern South Carolina. Gen. Tech. 

Rep, SE-38. Asheville, NC: Department of Agriculture, Forest 

Service, Southeastern Forest Experiment Stat ion; 1986. 8 pp, 

The quantity of unused wood in an 18-county area in northwestern South 

Carolina was projected to be more than 16 mi 11 ion tons annually. Wood 

that is unsuitable for products other than fuel amounts to nearly 9 

million tons annually. The most likely energy demand by industrial 

plants that are good candidates for wood fuel systems is 1.5 million 

tons annually. Maximum projected demand for such plants is 3 million 

tons per year. The area has a high potential for development of wood 

energy at favorable costs. 

KEYWORDS: Biomass, computer programs, forest residues, fuelwood. 

*U 5. GOVERNMENT PRINTING OFFICE: 1986-636-479 Qeqion 4. 
The Forest Service, U.S. Department of Agfiiculture, is dedicated to the p~ncipie of multiple 

use management of the Nation's 

forest resources for sustained 

yields of wd, water, forarge, 

wildlife , and recreation. Through 

forestry research, empration 

with the States and private forest 

owners, and management of the 

National Forests and National 

Grasslands, it strives-as directed by Congress-to provide 

increasingly greater service to a 

growing Nation. 

USDA policy does not permit dixrimination because of 

race, color, nation4 origin, sex or religion. Any person 

who believes he or she has ken discriminated wing in 

any USDA-related activity shoutd write immediately to 

the Secretary of Agniculture, Washington, B.C. 20250.