Optimizing biofuel production: An economic analysis for selected biofuel feedstock production in Hawaii

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

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Optimizing biofuel production: An economic analysis for selected biofuel feedstock production in Hawaii Nghia Tran a, Prabodh Illukpitiya b,*, John F. Yanagida c, Richard Ogoshi b a

College of Economics and Business Administration, Thai Nguyen University, Vietnam Department of Tropical Plant and Soil Sciences, University of Hawai’i at Manoa, 3190 Maile Way, Honolulu, HI 96822, USA c Department of Natural Resources and Environmental Management, University of Hawai’i at Manoa, 1910 East-West Road, Honolulu, HI 96822, USA b

article info

abstract

Article history:

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

Received 9 September 2010

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

Received in revised form

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

4 January 2011

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

Accepted 5 January 2011

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

Available online 2 February 2011

for Hawaii and compare their cost competitiveness. The selected feedstock consists of both ethanol and biodiesel producing crops. Ethanol feedstock includes sugar feedstock

Keywords:

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

Discount rate

feedstock consists of Jatropha and oil palm.

Feedstock

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

Breakeven price

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

Benefit:cost

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

Hawaii

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

Optimization

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.

* Corresponding author. Tel.: þ1 (808) 956 8902. E-mail address: [email protected] (P. Illukpitiya). 0961-9534/$ e see front matter Published by Elsevier Ltd. doi:10.1016/j.biombioe.2011.01.012

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1.

Introduction

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 sustainable 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 transportation 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 transportation sector [2]. Cellulosic biomass can also be converted 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 feedstock [3]. Biofuels offer alternative benefits on several fronts. These include energy benefits, environmental benefits [4], and industrial growth and employment opportunities. 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 warming 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 highenergy 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 additional 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

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to select economically feasible feedstock for biofuel production. Second, a mathematical optimization model 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 investment. 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 production 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 does not capture all local, regional and national impacts of a particular project hence accounting all economic impacts of a given project are needed for policy implementation. Data limitation is a major barrier to adequately analyze the overall impact of biofuel production at this stage. However, the potential regional impacts on a broader view were identified and briefly discussed.

2.1.1.

Private accounting stance

From a financial or private accounting stance, costs and returns are measured 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

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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 costs may 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: n X TCPij PVCij ¼ ð1 þ rÞn t¼1 PVBij ¼

n X GRij ð1 þ rÞn t¼1

where PVCij ¼ present value of production cost of ith crop in jth farm ($/acre); TCPij ¼ total cost of production of ith crop in jth farm ($/acre); PVBij ¼ present value of benefits of ith crop in jth farm ($/acre); GRij ¼ gross revenue of ith crop in jth farm ($/acre); r ¼ discount rate; and n ¼ project duration (years). In this analysis, n was assumed equal to 25 years and r was 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 corresponding crop’s total per acre energy production. 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 yield where yield is either in terms of feedstock or the appropriate conversion to energy.

2.2.

Optimization models

The second model developed for use in the economic analysis is the optimization model. Both static and dynamic optimization models were estimated and results applied to determine possible biofuel crops for production in Hawaii.

2.2.1.

Static optimization

The 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 process whereby 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 Hawaii was used as an illustration. The assumptions for this optimization problem are: (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 and moisture regime of “warm and moist” on the Big Island (iii) crop yields are the mean values of the crop yield intervals [10], (iv) bioenergy production should satisfy legislative mandated minimum of 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 optimization

The concept of risk often focuses on randomness or 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 was average yield for the specified bioenergy crop, grown on nonprime land given soil moisture and temperature classifications. The dynamic optimization model first relaxes the assumption of fixed yields and assumes that yield for a given crop has a specified range of values depending on soil and weather conditions. Using the dynamic optimization program, 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 producing candidate crops such as banagrass (Pennisetum purpureum), Eucalyptus (Eucalyptus spp.) and Leucaena (Leucaena leucocephala) and biodiesel crops such as Jatropha (Jatropha curcas) and oil palm (Elaeis guineensis). Various data sources and assumptions were used in estimating production costs. Cost of production data for sugarcane, banagrass, Eucalyptus and Leucaena are based on a University of Hawaii report [14]. Price per ton of sugarcane ($34.09) is obtained from the National Agricultural 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, Economic Development and Tourism [17]. Per gallon processing costs for Eucalyptus and Leucaena (wood based ethanol) were derived from Oregon state data [18] while the processing cost for banagrass was taken from report from National Renewable Energy Laboratory [19]. For oil palm, cost estimation was based on data from Malaysian oil palm board [20]. Average milling cost

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

Yield/ac/ year

Ethanol/biodiesel yield

Ethanol/biodiesel gallon/ac/year

Feedstock cost/gallon ($)

Feedstock cost/1000 Btu ($)

1. 2. 3. 4. 5. 6.

23.8 ton 21.5 ton 7.8 ton 8.8 ton 226 gallon 114 gallon

19.5 gallon/ton cane 67 gallon/ton 65 gallon/ton 65 gallon/ton 0.9 gallon biodiesel/gallon oil 0.9 gallon biodiesel/gallon oil

464.1 1440.5 507 572 203.4 102.6

2.01 0.88 1.56 0.83 11.25 18.73

0.026 0.012 0.021 0.012 0.090 0.154

Sugarcane Banagrass Eucalyptus Leucaena Oil palm Jatropha

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

for crude palm oil was obtained from oil palm cost of production survey [21]. Landed cost of palm oil feedstock in Hawaii is estimated 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. Operating and harvesting costs are based University of Hawaii Cooperative Extension Service [23]. Energy in Btu per gallon of ethanol and biodiesel were estimated using information from Jaeger et al. (2007) [18]. Prices were inflated to reflect current prices using appropriate inflation rates. Possible physical, environmental and socio-economic impacts associated with biofuel production were identified to highlight the importance of evaluating those impacts in future.

3.

Results and discussion

3.1.

Net returns model

Table 1 provides a comparison of crop yields, ethanol/biodiesel 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 and moisture regime of “warm and moist” for the

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). Leucaena and banagrass have feedstock costs less than $1.00/ gallon. Feedstock used for producing ethanol has lower feedstock 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 feedstock price data. Of the remaining bioenergy crops investigated, 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 chemical 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-

Table 2 e Feedstock and ethanol production, costs and revenues: sugar and cellulosic feedstock. Cost items

Unit

Sugarcane

Banagrass

Eucalyptus

Leucaena

934.69 94.47 841.22

1,264.24 56.08 1208.16

793.26 79.33 713.94

473.56 47.36 426.21

Total costs Fixed cost Total variable cost

$/acre $/acre $/acre

A. Feedstock production Primary production Gross revenue Net revenue

tons/acre $/acre $/year

47.60 815.08 119.61

21.50 1802.99 538.75

7.80

8.80

B. Production of ethanol Total processing cost Total production cost Gross revenue (ethanol) Net revenue (ethanol)

$/acre $/acre $/acre $/acre

495.35 1430.04 1114.67 315.38

1,959.08 3223.32 3465.16 241.83

821.34 1614.60 1219.60 395.00

926.64 1400.20 1375.96 24.24

Feedstock cost of ethanol Feedstock cost of ethanol Break- even price of feedstock Break- even price of ethanol

$/1000 Btu $/gallon $/ton $/gallon

0.026 2.01 39.27 3.08

0.012 0.88 58.80 2.24

0.021 1.56

0.012 0.83

3.18

2.45

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Table 3 e Feedstock and biodiesel production, costs and revenues: biodiesel feedstock. Cost items

Unit

Oil palm

Jatropha Scenario_1a Scenario_2b

Total variable costs Fixed cost Total cost

$/acre

1979.76

1710.93

1710.93

$/acre $/acre

175.90 2155.66

152.01 1862.93

152.01 1862.93

114.00

114.00

233.10 1629.84

396.30 1466.64

59.08

59.08

1922.02

1922.02

201.45 1720.57

342.49 1579.53

A. Feedstock production Primary Gallons/ 226.00 production acre Gross revenue $/acre 447.25 Net revenue $/acre 1708.41 B. Production of biodiesel Total processing $/acre 133.34 cost Total production $/acre 2289.00 cost Gross revenue $/acre 454.62 Net revenue from $/acre 1834.38 biodiesel Feedstock cost of $/1000 0.090 biodiesel Btu Feedstock cost of $/gallon 10.60 biodiesel 9.54 Breakeven price $/ton of feedstock Breakeven price $/gallon 11.25 of biodiesel

0.154

0.154

18.16

18.16

16.34

16.34

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.

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.

 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 biodiesel 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 is measured 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 Institute 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 ethanol ¼ 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 advancement 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

Fig. 1 e Ethanol production from burn and unburn sugarcane under different conversion ratios. Scenario 1: conversion rate of ethanol per ton of dry matter [ 67 gallons. Scenario 2: conversion rate of ethanol per ton of dry matter [ 80 gallons. Scenario 3: conversion rate of ethanol per ton of dry matter [ 100 gallons. Scenario 4: variable conversion rates of ethanol per ton of dry matter over time. For 1e5 years (67/65 gallons), for 6e10 years (80 gallons), for 11e15 years (90 gallons), for 16e20 years (100 gallons), for 21e25 years (110 gallons).

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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.

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 negative 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

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, ground water 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 domestically or internationally, and it has been argued that the indirect 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

Fig. 3 e Net revenue of ethanol production under varying ethanol conversion rates. Notes: varying ethanol conversion rates per ton of dry matter: For 1e5 years (67/65 gallons), for 6e10 years (80 gallons), for 11e15 years (90 gallons), for 16e20 years (100 gallons), for 21e25 years (110 gallons).

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

1. 2. 3. 4. 5. 6.

Eucalyptus Leucaena Banagrass Sugarcane Oil palm Jatropha

Net return ($/acre)

Yield (tons/acre)

Yield (gallon/acre)

Btu/acre

naa naa 538.75 125.58 1708.41 1629.84

7.8 8.8 21.5 23.8

507 572 1440.5 464.1 203.4 102.6

38,684,100 43,643,600 109,910,150 35,410,830 2,400,120 12,106,800

Net return solution ($) Energy production solution (Btu)

Maximizing net return

Maximizing production

Area (acre)

Production (Btu)

Area (acre)

Production (Btu)

74,973

8.2Eþ12

74,973

8.2(þ12)

40,391,703 8.24029Eþ12

40,391,703 8.24029(þ12)

a na ¼ Not 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

1. 2. 3. 4. 5. 6.

Eucalyptus Leucaena Banagrass Sugarcane Oil palm Jatropha

Net return ($/acre)

Yield (tons/acre)

Yield (gallon/acre)

Btu/acre

395.00 38.57 241.83 199.82 1834.38 1720.57

7.8 8.8 21.5 23.8

507 572 1440.5 464.1 203.4 102.6

3,868,4100 43,643,600 109,910,150 3,541,0830 2,400,120 12,106,800

Net return solution ($) Energy production solution (Btu)

Maximizing production

Area (acre)

Production (Btu)

Area (acre)

Production (Btu)

74,973

8.2Eþ12

74,973

8.2(þ12)

18,130,720 8.24029Eþ12

that would cause demographic and social changes in rural areas and fiscal impacts for the local governments.

3.2.

Maximizing net return

18,130,720 8.24029(þ12)

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

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 calculate net return, all 74,973 acres of non-prime land should be planted with banagrass. Among the candidate biofuel feedstock 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

1. 2. 3. 4. 5. 6.

Eucalyptus Leucaena Banagrass Sugarcane Oil Palm Jatropha

Net returns based on average yields ($/acre/yr) 395.00 38.57 241.83 199.82 1834.38 1720.57

Crop yield (gallons of ethanol or biodiesel/acre/yr) Average

Observed range

507 572 1440.5 464.1 203.4 102.6

436e696 78e1255 583e2546 378e552 107e268 26e343

Probability of negative net returns 1.00 0.607 0.237 1.00 1.00 1.00

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bioenergy feedstock considered for this study could guarantee a positive net return under the observed production conditions 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 positive 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 negative 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 agricultural 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 original version of this manuscript. The authors are fully

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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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