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Optimizing biofuel production: An economic analysis forselected biofuel feedstock production in Hawaii

Nghia Tran a, Prabodh Illukpitiya b,*, John F. Yanagida c, Richard Ogoshi b

aCollege of Economics and Business Administration, Thai Nguyen University, VietnambDepartment of Tropical Plant and Soil Sciences, University of Hawai’i at Manoa, 3190 Maile Way, Honolulu, HI 96822, USAcDepartment of Natural Resources and Environmental Management, University of Hawai’i at Manoa, 1910 East-West Road,

Honolulu, HI 96822, USA

a r t i c l e i n f o

Article history:

Received 9 September 2010

Received in revised form

4 January 2011

Accepted 5 January 2011

Available online 2 February 2011

Keywords:

Discount rate

Feedstock

Breakeven price

Benefit:cost

Hawaii

Optimization

* Corresponding author. Tel.: þ1 (808) 956 89E-mail address: prabodh@hawaii.edu (P.

0961-9534/$ e see front matter Published bydoi:10.1016/j.biombioe.2011.01.012

a b s t r a c t

Hawaii’s agricultural sector has an immense supply of natural resources that can be

further developed and utilized to produce biofuel. Transformation of the renewable and

abundant biomass resources into a cost competitive, high performance biofuel could

reduce Hawaii’s dependence on fossil fuel importation and enhance energy security. The

objectives of the study are to evaluate the economic feasibility of selected bioenergy crops

for Hawaii and compare their cost competitiveness. The selected feedstock consists of both

ethanol and biodiesel producing crops. Ethanol feedstock includes sugar feedstock

(sugarcane) and lignocellulosic feedstock (banagrass, Eucalyptus, and Leucaena). Biodiesel

feedstock consists of Jatropha and oil palm.

The economic analysis is divided into two parts. First, a financial analysis was used to

select feasible feedstock for biofuel production. For each feedstock, net return, feedstock

cost per Btu, feedstock cost per gallon of ethanol/biodiesel, breakeven price of feedstock

and breakeven price of ethanol/biodiesel were calculated. Leucaena shows the lowest

feedstock cost per Btu while banagrass has the highest positive net returns in terms of both

feedstock price and energy price.

The second approach assumes an objective of maximizing net returns. Given this

assumption, biofuel producers will produce only banagrass. As an example, the production

of bioenergy on the island of Hawaii is illustrated where 74,793 acres of non-prime land

having a “warm and moist” soil temperature and moisture regime are available. Using

average yields (static optimization), banagrass production on this acreage can yield 8.24

trillion Btus of energy (ethanol). This satisfies the State’s 10% self-sufficiency energy goal of

3.9 trillion Btus by 2010. Incorporating risk through variability in crop yields and biofuel

prices separately shows banagrass as having the highest probability for receiving a positive

net return. Banagrass is the leading candidate crop for biofuel production in Hawaii and the

State of Hawaii ethanol goal can be achieved by allocating non-prime lands for banagrass

production without compromising prime lands currently allocated for agricultural food

production in Hawaii. Physical, environmental and socio-economic impacts should be

accounted for in evaluating future biofuel projects.

Published by Elsevier Ltd.

02.Illukpitiya).Elsevier Ltd.

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 4 1757

1. Introduction to select economically feasible feedstock for biofuel

The economic development of modern societies is crucially

dependent on energy. The way energy is produced,

supplied, and consumed strongly affects the local and

global environment and is therefore a key issue in sus-

tainable development. The World Energy Council (WEC)

study in 1993 [1] indicates that global energy would need to

come from renewable sources by the year 2020 in order to

stabilize global greenhouse gas emissions. Biofuels are

currently the only renewable sources of liquid trans-

portation fuels. At present, the primary sources of biofuels

are grain and sugar crops, from which over 17,000 million

gallons of ethanol are produced annually for the trans-

portation sector [2]. Cellulosic biomass can also be con-

verted into ethanol or other liquid biofuels. However,

ethanol producing nations like the USA continue to produce

ethanol from corn grain rather from cellulosic stalk because

of higher conversion costs associated with cellulosic feed-

stock [3]. Biofuels offer alternative benefits on several

fronts. These include energy benefits, environmental bene-

fits [4], and industrial growth and employment opportuni-

ties. In the short to medium term, renewable energy can

help diversify energy sources, thus improving the security

of energy supply necessary for sustainable economic

development.

The growing concern with rising oil prices, global warm-

ing and its consequences are the immediate justification for

lessening dependence on imported fossil fuels. Small islands

such as those that comprise Hawaii continue to face high-

energy costs and energy insecurity as the state is largely

dependent on imported petroleum products for energy.

Therefore, the high cost of imported fossil fuels, the addi-

tional benefits of increased energy security, and the creation

of new income and employment opportunities favor local

biofuel production in Hawaii.

The Hawaiian islands have varying agro-climatic regions

with a year-round growing season, relatively large arable

lands, and largely unexplored feedstock resources. In

addition, unlike food crops that require high production

standards for uniformity, appearance, and safety, energy

crops mainly need only to produce biomass, thus may be

grown on marginal lands with little input and protection

from pests. This research focuses on adding value to the

bioenergy knowledge base and enables growers and

processors to efficiently produce and convert biomass into

affordable biofuels. This enhances energy security and

generates income and new employment opportunities in

Hawaii without compromising prime lands allocated for

food production. Specifically, the study aims to determine

the economic competitiveness of producing ethanol and

biodiesel from first and second-generation biofuel feedstock

on non-prime lands in Hawaii.

2. Analytical framework and data sources

Two models are used to perform the economic analysis.

First, financial analysis, similar to crop budgeting, was used

production.

Second, amathematical optimizationmodel is constructed

to illustrate static and dynamic analysis with changes in

resource constraints.

2.1. Financial analysis

The economic analysis of projects is similar in form to

financial analysis since both appraise the profit of an invest-

ment. The financial analysis of a project estimates the profit

accruing to the project-operating entity or to the project,

whereas economic analysis measures the effect of the project

on the national economy. If a project is not financially

sustainable, economic benefits will not be realized [5].

Multiple accounts analysis which consists of various

categories of information on decision variables is widely used

for project assessment. This method recognizes the various

dimensions in economic and social assessment of alternative

management options. The major accounting stances are the

private, regional and provincial accounting stances. Private

accounting refers to inclusion of changes that accrue to the

decision-makers directly. The gains can be measured through

indicators such as net private benefits. The regional

accounting stances refer to estimation procedures where

changes occur within a specified region [6]. The development

of such complex accounts requires analyzing impacts of both

the social and ecological nature including environmental

values which do not have market values.

Given the wide range of feedstock available for the produc-

tion of biofuel under Hawaii’s tropical climatic conditions,

feedstock evaluation has become a priority. Therefore, the

financial analysis is mainly focused on the farmer’s point of

view concerning feedstock supply for biofuel production. This

information is also useful to biofuel producers interested in

identifying least cost feedstock options for future biofuel

production. Hence, a primary focus was given to the private

account stance in evaluating feedstock production for the

producers. Financial analysis doesnot capture all local, regional

andnational impactsofaparticularprojecthenceaccountingall

economic impacts of a given project are needed for policy

implementation.Data limitation isamajorbarrier toadequately

analyze the overall impact of biofuel production at this stage.

However, thepotential regional impacts on a broader viewwere

identified and briefly discussed.

2.1.1. Private accounting stanceFrom a financial or private accounting stance, costs and

returns aremeasured from the producers’ perspective:market

or administered prices are used; externalities are not usually

fully internalized; taxes are treated as a cost; and subsidies are

considered a benefit [7]. This can be measured through the

indicators such as net present value and private benefit cost

ratio etc.

In biofuel feedstock production, the cost of producing each

feedstock includes commonly used cost categories from land

preparation to harvesting. The analysis assumes that feedstock

production is on non-prime land under rainfed conditions. For

comparison purposes, analysis was extended to feedstock

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 41758

production on prime lands. Although the analysis concentrates

on the production of feedstock, energy conversion assumptions

are also utilized such that preliminary analysis involving the

processing of feedstock to biofuels can be conducted.

It should be noted that certain field operations are not

performed regularly and uniformly year after year, therefore,

annual costsmay differ over the crop’s life. From an economic

point of view, the overall approach is to estimate average

annual costs and returns over the entire economic life of the

crop, which allows for direct comparison among different

crops. To calculate costs and revenues in annual equivalent

terms, the present values of all costs and revenues over the

useful life of the crop were transformed into an equivalent

annuity. The following procedure was adopted in estimating

annual equivalent costs and revenues [8].

1. Present value of the total investment over a 25-year period

was estimated as:

PVCij ¼Xn

t¼1

TCPij

ð1þ rÞn

n

PVBij ¼X

t¼1

GRij

ð1þ rÞn

where PVCij¼ present value of production cost of ith crop in

jth farm ($/acre); TCPij¼ total cost of production of ith crop

in jth farm ($/acre); PVBij¼ present value of benefits of ith crop

in jth farm ($/acre); GRij¼ gross revenue of ith crop in jth farm

($/acre); r¼ discount rate; and n¼ project duration (years).

In this analysis, nwas assumed equal to 25 years and rwas

4.5% (average historical discount rate during 1986e2006 from

Federal Reserve System) [9].

Feedstock cost of ethanol per 1000 Btu was estimated by

dividing the cost per acre of producing each feedstock by the

correspondingcrop’s totalperacreenergyproduction.Feedstock

cost of either a gallon of ethanol or biodiesel was estimated by

dividing the per acre cost of producing the feedstock by the total

gallons (per acre) of ethanol/biodiesel produced for each crop.

Breakeven price of feedstock is that price of feedstock such

that net revenue equals zero. Breakeven price of ethanol or

biodiesel is the price of energy such that net returns in terms

of energy equals zero. The breakeven price is calculated as

cost divided by yieldwhere yield is either in terms of feedstock

or the appropriate conversion to energy.

2.2. Optimization models

The secondmodel developed for use in the economic analysis

is the optimization model. Both static and dynamic optimi-

zation models were estimated and results applied to deter-

mine possible biofuel crops for production in Hawaii.

2.2.1. Static optimizationThe biofuel producer or farm can be structured as having

interconnected activities called variables. Thus, changing

one variable (activity) may have effects on other variables

(activities). Variables interact with one another based on given

formulas and constraints. Typical constraints are resource

constraints such as land availability or labor availability. The

static optimization procedure is a simulation processwhereby

variables are given a single value (at a point in time) as

opposed to values randomly chosen from a given probability

distribution of values (over time).

The objective of the static optimization is to maximize net

returns from the production of biofuel crops given certain

constraints. Biofuel production on the Big Island of Hawaiiwas

used as an illustration. The assumptions for this optimization

problemare: (i) bioenergy crop producers are rational (produce

crops that are economically feasible or have a positive net

return), (ii) the maximum area available for all energy crops

should not exceed the amount of non-prime area having a soil

temperature andmoisture regime of “warm andmoist” on the

Big Island (iii) crop yields are themean values of the crop yield

intervals [10], (iv) bioenergy production should satisfy legisla-

tivemandatedminimumof at least 10%of energy requirement

for transportation by the year 2010, and (v) no radical changes

are assumed to occur in market conditions.

For assumption (ii), 74,973 acres of non-prime land is

available on the Big Island which has a soil temperature and

moisture regime of “warm and moist” [10]. For assumption

(iv), the 10% energy requirement is translated to be 3.9 trillion

Btus [11]. So the optimization problem is to maximize net

returns from bioenergy production with crop area� 74,973

acres and energy production� 3.9 trillion Btus.

2.2.2. Dynamic optimizationThe concept of risk often focuses on randomnessor variability of

outcomes [12]. Risk is a prevalent part of production agriculture

[13] through weather variability, fluctuations in input and

product prices, etc. In the static optimization model, each

parameter was assumed to have one value. Initially, yield

wasaverageyield for thespecifiedbioenergycrop,grownonnon-

prime land given soil moisture and temperature classifications.

The dynamic optimization model first relaxes the assump-

tion of fixed yields and assumes that yield for a given crop has

a specified range of values depending on soil and weather

conditions.Using thedynamicoptimizationprogram, risk levels

in terms of the probability of having a negative net return is

solved for the bioenergy crops.

2.3. Data sources

This research focused on first generation candidate crop

sugarcane, second-generation lignocellulosic feedstock produ-

cing candidate crops such as banagrass (Pennisetum purpureum),

Eucalyptus (Eucalyptus spp.) and Leucaena (Leucaena leucoce-

phala) and biodiesel crops such as Jatropha (Jatropha curcas) and

oil palm (Elaeis guineensis). Various data sources and assump-

tions were used in estimating production costs. Cost of

production data for sugarcane, banagrass, Eucalyptus and Leu-

caena are based on a University of Hawaii report [14]. Price per

ton of sugarcane ($34.09) is obtained from the National Agri-

cultural Statistics Service [15]. For sugarcane, ethanol yield per

ton was based on U.S Department of Agriculture [16]. Ethanol

yield for lignocellulossic feedstock were from Hawaii Business,

EconomicDevelopment and Tourism [17]. Per gallon processing

costs for Eucalyptus and Leucaena (wood based ethanol) were

derived fromOregonstatedata [18]while theprocessingcost for

banagrass was taken from report from National Renewable

Energy Laboratory [19]. For oil palm, cost estimation was based

ondata fromMalaysianoil palmboard [20]. Averagemilling cost

Table 1 e Comparison of biofuel yields, feedstock costs for biofuel crops.

Feedstock Yield/ac/year

Ethanol/biodieselyield

Ethanol/biodieselgallon/ac/year

Feedstockcost/gallon ($)

Feedstockcost/1000 Btu ($)

1. Sugarcane 23.8 ton 19.5 gallon/ton cane 464.1 2.01 0.026

2. Banagrass 21.5 ton 67 gallon/ton 1440.5 0.88 0.012

3. Eucalyptus 7.8 ton 65 gallon/ton 507 1.56 0.021

4. Leucaena 8.8 ton 65 gallon/ton 572 0.83 0.012

5. Oil palm 226 gallon 0.9 gallon biodiesel/gallon oil 203.4 11.25 0.090

6. Jatropha 114 gallon 0.9 gallon biodiesel/gallon oil 102.6 18.73 0.154

Note: energy conversion factor: for ethanol: 76,300 Btu/gallon, for biodiesel: 118,000 Btu/gallon (Jaeger et al., 2007).

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 4 1759

for crudepalmoilwasobtained fromoil palmcost of production

survey [21]. Landed cost of palm oil feedstock in Hawaii is esti-

mated based on Hawaii Department of Business, Economic

Development and Tourism [22]. In estimating harvesting costs

for Jatropha, macadamia nut was used as an analog crop for

Jatropha.OperatingandharvestingcostsarebasedUniversityof

Hawaii Cooperative Extension Service [23]. Energy in Btu per

gallon of ethanol and biodiesel were estimated using informa-

tion from Jaeger et al. (2007) [18]. Prices were inflated to reflect

current prices using appropriate inflation rates. Possible phys-

ical, environmental and socio-economic impacts associated

with biofuel production were identified to highlight the impor-

tance of evaluating those impacts in future.

3. Results and discussion

3.1. Net returns model

Table 1 provides a comparison of crop yields, ethanol/bio-

diesel yields, feedstock costs per gallon and feedstock costs

per 1000 Btu for the selected crops. Note that crop yields are

the average crops yields [10]. These yields assume a soil

temperature andmoisture regime of “warmandmoist” for the

Table 2 e Feedstock and ethanol production, costs and revenu

Cost items Unit Sugarcan

Total costs $/acre 934.69

Fixed cost $/acre 94.47

Total variable cost $/acre 841.22

A. Feedstock production

Primary production tons/acre 47.60

Gross revenue $/acre 815.08

Net revenue $/year �119.61

B. Production of ethanol

Total processing cost $/acre 495.35

Total production cost $/acre 1430.04

Gross revenue (ethanol) $/acre 1114.67

Net revenue (ethanol) $/acre �315.38

Feedstock cost of ethanol $/1000 Btu 0.026

Feedstock cost of ethanol $/gallon 2.01

Break- even price of feedstock $/ton 39.27

Break- even price of ethanol $/gallon 3.08

Big Island of Hawaii. Banagrass has the highest ethanol

production (1440.5 gallons/acre/year) and oil palm has the

highest biodiesel production (203.4 gallons/acre/year). Leu-

caena and banagrass have feedstock costs less than $1.00/

gallon. Feedstock used for producing ethanol has lower feed-

stock costs (per gallon and per 1000 Btu) than feedstock used

for the production of biodiesel.

Tables 2 and 3 summarize the major components of the

economic analysis including analysis involving the feedstock

and conversion of the feedstock to either ethanol or biodiesel.

The major findings are as follows.

� Net returns (based on feedstock price) are not available for

Eucalyptus and Leucaena because of the absence of feed-

stock price data. Of the remaining bioenergy crops investi-

gated, only banagrass shows a positive net return per acre.

For these biofuel crops, high production costs are primarily

due to field operation costs (fertilizer, pesticides and other che-

mical application) and harvesting costs. With improved yields,

the cost component can be reduced and net returns improved.

� Net returns after conversion to ethanol and biodiesel show

only banagrass production as having positive net returns

from ethanol production. This is due to the crop’s high-

es: sugar and cellulosic feedstock.

e Banagrass Eucalyptus Leucaena

1,264.24 793.26 473.56

56.08 79.33 47.36

1208.16 713.94 426.21

21.50 7.80 8.80

1802.99

538.75

1,959.08 821.34 926.64

3223.32 1614.60 1400.20

3465.16 1219.60 1375.96

241.83 �395.00 �24.24

0.012 0.021 0.012

0.88 1.56 0.83

58.80

2.24 3.18 2.45

Table 3 e Feedstock and biodiesel production, costs andrevenues: biodiesel feedstock.

Cost items Unit Oil palm Jatropha

Scenario_1a Scenario_2b

Total variable

costs

$/acre 1979.76 1710.93 1710.93

Fixed cost $/acre 175.90 152.01 152.01

Total cost $/acre 2155.66 1862.93 1862.93

A. Feedstock production

Primary

production

Gallons/

acre

226.00 114.00 114.00

Gross revenue $/acre 447.25 233.10 396.30

Net revenue $/acre �1708.41 �1629.84 �1466.64

B. Production of biodiesel

Total processing

cost

$/acre 133.34 59.08 59.08

Total production

cost

$/acre 2289.00 1922.02 1922.02

Gross revenue $/acre 454.62 201.45 342.49

Net revenue from

biodiesel

$/acre �1834.38 �1720.57 �1579.53

Feedstock cost of

biodiesel

$/1000

Btu

0.090 0.154 0.154

Feedstock cost of

biodiesel

$/gallon 10.60 18.16 18.16

Breakeven price

of feedstock

$/ton 9.54 16.34 16.34

Breakeven price

of biodiesel

$/gallon 11.25 18.73 18.73

a Scenario 1: palm oil price as a substitute for Jatropha oil price.

b Scenario 2: soybean oil price as a substitute for Jatropha oil price.

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 41760

energy yield (conversion to ethanol). However, it should be

noted that conversion costs to ethanol from cellulosic

feedstock is still under investigation and hence the results

should be interpreted with caution.

Fig. 1 e Ethanol production from burn and unburn sugarcane un

of ethanol per ton of dry matter[ 67 gallons. Scenario 2: conve

Scenario 3: conversion rate of ethanol per ton of dry matter[ 1

per ton of dry matter over time. For 1e5 years (67/65 gallons), f

16e20 years (100 gallons), for 21e25 years (110 gallons).

� Compared to ethanol, feedstock costs per gallon of biodiesel

crops are higher. Jatropha and oil palm research in Hawaii is

still in its infancy and yield improvements, development of

harvesting machinery, and improved production practices

could substantially reduce costs and improve net returns for

these oil-producing crops.

� Breakeven prices for ethanol producing crops (sugarcane,

banagrass, Eucalyptus and Leucaena) are lower than bio-

diesel producing crops (Jatropha and oil palm).

The net returns analysis shows that banagrass is the only

bioenergy crop that has a positive net return for either case

(i.e., when the price of banagrass ismeasured as a feedstock or

in terms of ethanol). Biofuel producers choosing to maximize

net returns will produce banagrass. Hawaii Act 240 mandates

energy self-sufficiency with goals of producing 10% of its

transportation fuel from renewable resources by 2010 and 20%

by 2020. About 74,793 acres of non-prime land is available on

the Big Island which has a soil temperature and moisture

regime of “warm and moist” [10]. The Rocky Mountain Insti-

tute report [11] states that a 10% energy requirement for the

state of Hawaii is 3.9 trillion Btus.

For banagrass, energy production from a yield of 21.5 tons

of dry matter per acre is 1440.5 gallons of ethanol per acre.

Using an ethanol to energy conversion of 1 gallon of etha-

nol¼ 76,300 Btus [18], the yield from one acre of banagrass is

109,910,150 Btus. Consequently, 74,793 acres of “warm and

moist”, non-prime land on the Big Island will yield 8.24 trillion

Btus from banagrass production. This more than satisfies the

10% self-sufficiency goal for 2010 (3.9 trillion Btus).

The above analysis was based on the current conversion

rate of cellulosic feedstock to ethanol on non-prime lands.

However, higher conversion rates are possible with advance-

ment of technology. Fig. 1 shows the changes of net return

over time in unburn and burn sugarcane. For this scenario,

both cellulose ethanol production and sugar ethanol

der different conversion ratios. Scenario 1: conversion rate

rsion rate of ethanol per ton of dry matter[ 80 gallons.

00 gallons. Scenario 4: variable conversion rates of ethanol

or 6e10 years (80 gallons), for 11e15 years (90 gallons), for

Fig. 2 e Net revenue of ethanol production in banagrass under varying ethanol conversion rates.

Notes: current conversion rate of ethanol per ton of dry matter[ 67 gallons.

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 4 1761

production from sugarcane was considered for prime lands.

When the different scenarios were introduced, the ethanol

production from sugarcane becomes profitable for Hawaii.

Fig. 2 shows changes of net return of ethanol production from

cellulosic feedstock under current conversion rates on prime

lands. Compared to non-prime lands, cellulose feedstocks on

prime lands provide higher net revenue from ethanol

production though Eucalyptus and Leucaena still yield nega-

tive net returns. However, when varying conversion rates of

cellulosic feedstock to ethanol is considered, even Leucaena

could yield positive net returns from ethanol production

(Fig. 3).

Large-scale feedstock development projects would cause

physical, environmental and socio-economic impacts which

are inter-related. Physical changes for example include

landscape changes including groundcover, soil and water

Fig. 3 e Net revenue of ethanol production under varying ethano

per ton of dry matter: For 1e5 years (67/65 gallons), for 6e10 yea

(100 gallons), for 21e25 years (110 gallons).

resources. Soil compaction due to daily running of truckloads

of bulky feedstocks from production sites and processing

plants would be included as regional impacts. Physical

changes may affect use of resources. Environmental changes

are partly due to physical changes which include biodiversity

loss, groundwater pollution as a result from year-round usage

of agrochemicals, issues of soil acidity and salinity and waste

of biofuel production. There will be on-site effects as well as

off-site effects. Due to the global nature of commodity

markets, environmental impacts can occur either domesti-

cally or internationally, and it has been argued that the indi-

rect impacts of U.S. feedstock production for ethanol may

threaten globally important ecosystems such as the Amazon

Forest [24]. Net energy balance and carbon balance are two

important areas to be considered in future research. The

socio-economic impacts are the adjustment of labor market

l conversion rates. Notes: varying ethanol conversion rates

rs (80 gallons), for 11e15 years (90 gallons), for 16e20 years

Table 4 e Maximizing net returns and feedstock production using feedstock price (crop yields evaluated at their means).

Feedstock Net return($/acre)

Yield(tons/acre)

Yield(gallon/acre)

Btu/acre Maximizing net return Maximizing production

Area(acre)

Production(Btu)

Area(acre)

Production(Btu)

1. Eucalyptus naa 7.8 507 38,684,100

2. Leucaena naa 8.8 572 43,643,600

3. Banagrass 538.75 21.5 1440.5 109,910,150 74,973 8.2Eþ12 74,973 8.2(þ12)

4. Sugarcane �125.58 23.8 464.1 35,410,830

5. Oil palm �1708.41 203.4 2,400,120

6. Jatropha �1629.84 102.6 12,106,800

Net return solution ($) 40,391,703 40,391,703

Energy production solution (Btu) 8.24029Eþ12 8.24029(þ12)

a na¼Not available. There are no feedstock costs available for Eucalyptus and Leucaena.

Table 5 e Maximizing net returns and energy production using energy price (crop yields evaluated at their means).

Feedstock Net return($/acre)

Yield(tons/acre)

Yield(gallon/acre)

Btu/acre Maximizing net return Maximizing production

Area(acre)

Production(Btu)

Area(acre)

Production(Btu)

1. Eucalyptus �395.00 7.8 507 3,868,4100

2. Leucaena �38.57 8.8 572 43,643,600

3. Banagrass 241.83 21.5 1440.5 109,910,150 74,973 8.2Eþ12 74,973 8.2(þ12)

4. Sugarcane �199.82 23.8 464.1 3,541,0830

5. Oil palm �1834.38 203.4 2,400,120

6. Jatropha �1720.57 102.6 12,106,800

Net return solution ($) 18,130,720 18,130,720

Energy production solution (Btu) 8.24029Eþ12 8.24029(þ12)

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 41762

that would cause demographic and social changes in rural

areas and fiscal impacts for the local governments.

3.2. Optimization models

Static optimization results, based on average yield of selected

feedstock are presented in Table 4. The results from the

optimization show that if feedstock prices are used to calcu-

late net return, all 74,973 acres of non-prime land should be

planted with banagrass. Among the candidate biofuel feed-

stock investigated, only banagrass has a positive net return

($538.75/acre). If the optimization problem were to maximize

energy production from these biofuel crops, the solution

would be to plant 74,973 acres in banagrass. Also, banagrass

Table 6 e Net returns analysis assuming yield risk.

Feedstock Net returns basedon average yields

($/acre/yr)

Cro

Average

1. Eucalyptus �395.00 507

2. Leucaena �38.57 572

3. Banagrass 241.83 1440.5

4. Sugarcane �199.82 464.1

5. Oil Palm �1834.38 203.4

6. Jatropha �1720.57 102.6

has the highest energy yield (109,910,150 Btu/acre) among the

candidate crops considered.

Table 5 shows the optimization results based on energy

price. Only banagrass yields a positive net return per acre

($241.83/acre). However, maximizing net returns result in all

74,793 acres being planted in banagrass. If the optimization

problem were to maximize energy production, the solution

would again be to plant 74,973 acres in banagrass. For both

solutions, energy production is 8.24 trillion Btus which

satisfies the 10% energy requirement.

The dynamic optimization results (with yields randomly

fluctuating over the given yield interval) in terms of the

probability of producing a negative net return are shown in

Table 6. The results suggest that none of the candidate

p yield (gallons of ethanol orbiodiesel/acre/yr)

Probability ofnegative net

returnsObserved range

436e696 1.00

78e1255 0.607

583e2546 0.237

378e552 1.00

107e268 1.00

26e343 1.00

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 7 5 6e1 7 6 4 1763

bioenergy feedstock considered for this study could guarantee

a positive net return under the observed production condi-

tions on the island of Hawaii. However, given ethanol and

biodiesel prices in 2007, the addition of yield risk shows that

sugarcane, Eucalyptus, oil palm and Jatropha will produce

negative net returns with certainty if these crops are used in

the production of bioenergy. Although banagrass has a posi-

tive net return ($241.83/acre) when valued at its average yield

(1440.5 gallons of ethanol/acre e fixed yield), the dynamic

optimization model shows that the probability of banagrass

having a negative net return with risk from variable yields is

0.24 (or probability of banagrass having a positive net return as

0.76).

4. Conclusion

All biofuel crops with the exception of banagrass show nega-

tive net returns due to low yields and high production costs on

non-prime land. However, caution should also be used for

banagrass since the technology for cellulosic feedstock

conversion to ethanol is still developmental. When different

scenarios were considered, a positive net return was observed

for sugarcane and Leucaena in producing ethanol. For

example, both sugar and cellulose ethanol is possible from

sugarcane. For prime lands, sugarcane production for ethanol

in Hawaii is economical. Also under higher conversion rates,

ethanol production from Leucaena is economical on prime

lands.

Compared to ethanol, feedstock costs per gallon (or per

1000 Btu) of biodiesel are considerably higher. Jatropha and oil

palm research in Hawaii is in its initial stage and yield

improvements and development of harvesting machinery

could substantially improve net returns for these biodiesel

producing crops.

The dynamic optimization results show that given a world

with risk, banagrass (although with calculated positive net

returns) has a 23.7% probability of receiving negative net

returns due to random yield fluctuations (yield risk) and

a 24.1% probability of having negative returns from random

price fluctuations (price risk). Planting banagrass on 74,793

acres of non-prime land on the island of Hawaii would be

sufficient to meet the 10% ethanol goal for the State of Hawaii

in 2010. It is noteworthy that satisfying Hawaii Act 240 is

achievable without compromising prime lands’ use in agri-

cultural production. The lack of accounting for all impacts of

a biofuel project is a deficit of the analysis. Hence, physical,

environmental and socio-economic impacts should be

accounted for in order to evaluate regional impacts of future

biofuel projects.

Acknowledgements

The authors gratefully acknowledge the Hawaii Department

of Business, Economic Development and Tourism and Black

and Veatch Inc. for funding support for this project. The

authors also wish to thank the anonymous reviewers for their

constructive comments and suggestions in revising the orig-

inal version of this manuscript. The authors are fully

responsible for the content of this manuscript and any

remaining errors.

Notes: financial and economic terms in this paper have

been used as generic terms.

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