introduction - department of agricultural...

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Contents1 Introduction........................................................................................................................3

2 Feedstock use, availability and cost..................................................................................4

2.1 Major types of feedstocks............................................................................................4

2.1.1 Conventional Crops..............................................................................................4

2.1.2 Crop Residues......................................................................................................6

2.1.3 Dedicated Energy Crops......................................................................................7

2.2 Logistics......................................................................................................................9

2.2.1 Harvest and Hauling.............................................................................................9

2.2.2 Storage................................................................................................................10

2.2.3 Transportation....................................................................................................11

3 Key Issues in Feedstock supply.......................................................................................12

3.1 Food Security.............................................................................................................12

3.2 Food versus fuel - land competition.........................................................................13

3.3 Greenhouse Gas implications....................................................................................15

3.4 Marginal Land for Energy Crop Production.............................................................15

3.5 Technological Progress.............................................................................................17

3.6 Water Supplies...........................................................................................................18

3.7 Uncertainty................................................................................................................18

3.7.1 Policy and Uncertainty.......................................................................................19

3.7.2 Weather and climate change..............................................................................21

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3.8 Asset fixity.................................................................................................................22

4 Economics of Fuel Replacement......................................................................................24

4.1 Energy Equivalency and Ethanol Value....................................................................24

4.2 Fuel Market Demand.................................................................................................24

4.3 Infrastructure and the Blend Wall.............................................................................25

4.4 Trade..........................................................................................................................26

4.4.1 Overview............................................................................................................26

4.4.2 Ethanol...............................................................................................................26

4.4.3 Biodiesel.............................................................................................................28

5 Market and Welfare implications of biofuel production..................................................29

5.1 Market implications...................................................................................................29

5.2 Welfare implications.................................................................................................30

5.3 Implications for Rural Economies.............................................................................31

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Chapter 23. Economic Assessment of Biofuel Production

1 Introduction

Biofuel today largely refers to ethanol and biodiesel. Ethanol is derived from different types

of agricultural feedstocks such as first-generation biomass (i.e. corn, sugarcane), or second-

generation crop residues (i.e. corn stover) and dedicated energy crops (i.e. switchgrass and

miscanthus). Biodiesel can be derived from agricultural vegetable oils such as soybean or

canola and from animal fats such as tallow. Availability and cost of feedstock production

varies significantly by region and feedstock. Moreover, the cost of handling and processing is

also highly variable.

Large-scale production of biomass creates competition for land. Corn and sugarcane, the two

currently leading feedstocks for ethanol production globally, are also important food or

animal feed crops. This food versus fuel competition leads to concerns regarding food

security and price volatility within agricultural markets (McPhail and Babcock 2012).

Besides, land currently in forest or grass may convert to crop production in response to

reduced food or feed production, commonly known as the indirect land use change or leakage

effects (Searchinger et al. 2008; Murray et al. 2004).

The economic competitiveness of biofuel depends on conventional fuel markets, the

substitution characteristics of the fuels and policy requirements. Fuel prices have been

volatile due to demand growth, increasing production cost, reserve discovery, and trade

disruptions. Current ethanol production, particularly from second-generation feedstock,

depends heavily on policy support. Technological progress in feedstock yield, energy

recovery and process cost is needed to increase second generation feedstock market

competitiveness.

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Finally, local community employment and income are affected by biofuel production.

The effects vary across communities and production systems, since the employment and

income effects depend on the biofuel plant, and the biomass supply along with community

socioeconomic status and the presence of other industries which may compete or cooperate

with biofuel production.

The economic literature on biofuels is vast and diverse and we review it in terms of three

aspects: 1) feedstock use, availability and cost, 2) issues regarding the ability to sustain

biofeedstock production, 3) relationship to conventional fuel and fuel markets, and 4) socio-

economic effects.

2 Feedstock use, availability and cost

The ability to use agricultural feedstocks depends in part on the type and on the cost of

getting them to the plant. Here we examine the major types then turn to logistics.

2.1 Major types of feedstocks

To date biofuels have largely arisen from starch and sugar based conventional crops (on the

ethanol side) along with oils and fats (on the biodiesel side). Each will be covered below.

2.1.1 Conventional Crops

In 2015, more than 95 billion litres of fuel ethanol was produced around the world, an

increase of 50% compared with 2008 levels1. Most of this ethanol is produced from

conventional “food” crops, such as corn grain in the U.S. and sugarcane in Brazil. These

conventional crops are rich in sugar or starch which are readily convertible into ethanol,

rendering processing relatively inexpensive (Crago et al. 2010). In the U.S., corn is the major

source, with more than 200 operating plants producing a fuel volume amounting to 10% of

1 Data from Renewable Fuel Association at: http://www.ethanolrfa.org/resources/industry/statistics/

http://www.ethanolrfa.org/resources/industry/statistics/

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the gasoline pool in 20152. The U.S. corn ethanol industry started its rapid growth in 2005

and production has more than tripled from 15 to 55 billion litres from 2005 to 2012 (Figure

23.1).

Place Figure 23.1 Here

Figure 23.1 US Ethanol Production from 1999 to 2013

Data source: Ethanol data from Renewable Fuel Association (2016) and gasoline data

from EIA (2016b)

Today the corn ethanol industry consumes a considerable volume of corn. Based on a

conversion assumption of 417 litres of ethanol per MT of corn3, about 119 million MT corn

was utilized for ethanol production in 2013, accounting for 34% of total US production. The

transportation, storage and handling of corn are significant and the majority of the currently-

operating ethanol plants are located in the Corn Belt close to the source.

Feedstocks have also been classified with respect to GHG emissions. McCarl and Schneider

(2000 and 2001) investigated the potential of bioenergy produced from biofeedstocks as a

way of creating GHG offsets and found that cellulosic ethanol was an integral part of

deriving a portable energy source from agricultural sources, particularly into the future.

Farrell et al. (2006) concluded that “large-scale use of ethanol for fuel will almost certainly

require cellulosic technology”.

2.1.1.1 Cost of Production for Conventional Ethanol

Production cost is largely determined by the cost of the feedstock and its movement to the

plant. As we can see in Figure 23.2, delivered feedstock cost can account for up to 80% of the

total production cost for corn ethanol, particularly in 2012, when corn prices reached their

2 Save as above.3 See at: http://www.eia.gov/biofuels/issuestrends/pdf/bit.pdf

http://www.eia.gov/biofuels/issuestrends/pdf/bit.pdf

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highest recorded levels (Iowa State University 2016). Similarly, feedstock cost also accounts

for about 60-70% of the total cost for sugarcane ethanol production in Brazil4.

First generation ethanol is produced through fermentation of sugars. The cost of this

processing technique is relatively low, especially compared with those for second generation

ethanol. According to the monthly report from Iowa State University (2016), processing cost

(feedstock cost excluded) for corn ethanol is about $0.16 per litre. Similarly, results can be

found in Kwiatkowski et al. (2006) with an estimation $0.11 per litre.


Figure 23.2 Corn Ethanol Price and Production Cost per Litre

Data source: Iowa State University (2016)

2.1.2 Crop Residues

Crop residues cellulosic feedstock source and have been argued to be abundant at a relevantly

cheap price (Sanchez et al. 2015). Estimates from Kim and Dale (2004) indicated that total

cellulosic ethanol production from crop residues and crop wastes could potentially as amount

to 491 billion litres per year. Such residues include stalks, stubble, leaves, and seed pods. The

most common crop residue in the U.S. is corn stover.

2.1.2.1 Cost of Production for Ethanol from Agricultural Residues

Ethanol production from agricultural residues faces several economic challenges. First,

agricultural residues can be bulky and geographically dispersed, leading to long hauling

distances and high transportation costs (Gold and Seuring 2011). Second, current cellulosic

ethanol processing cost is much higher than conventional ethanol. Tao et al. (2014) indicated

that the conversion cost (feedstock cost excluded) for corn stover was $0.35 per litre

4 Data from IRENA Bioethanol at: http://costing.irena.org/charts/bioethanol.aspx

http://costing.irena.org/charts/bioethanol.aspx

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compared with $0.16 per litre for a typical corn ethanol plant in Iowa (Iowa State University

2016).

Third technology improvements are likely needed for the commercialization of agricultural

residues, including improvements in the efficiencies of feedstock collection and

preprocessing systems (Hess et al. 2007) plus energy recovery and lowered processing costs.

There are several environmental concerns when using agricultural residues. First, residues

such as corn stover and wheat straw left on the ground reduce soil erosion, improve soil

quality, provide nutrients and sequester carbon, which in turn increases land productivity and

water quality, as well as reducing atmospheric GHG concentrations (Blanco-Canqui and Lal

2009). As a result, environmental impact needs to be carefully and comprehensively assessed

before large-scale removal of agricultural crop residues. Appropriate maximum levels of

removal need to be established to prevent significant adverse environmental impacts and

practices such as replacing lost fertilizer should be considered (Kim and Dale 2004).

2.1.3 Dedicated Energy Crops

Cellulosic feedstocks can also come from dedicated energy crops such as switchgrass,

miscanthus, hybrid poplar, willow, energy cane, and energy sorghum. The U.S. Department

of Energy (DOE) (2011) provides a thorough list of potential energy crops, including

information on production, associated costs, and environmental implications.

The economics of dedicated energy crops have been investigated in a number of studies.

Babcock et al. (20122011) indicate costs are high and that “emergence of a cellulosic ethanol

industry is unlikely without costly government subsidies”. Sheehan et al. (2000) argues that

although the possibility of cellulosic ethanol has been known since the 1880s costs have not

reached a level where production is competitive with either conventional fossil fuels or first

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generation ethanol (Wyman 2007). From an economic standpoint these crops require

minimal inputs for production, and can be produced on land unsuitable for traditional crops,

discussed in further detail in the following section.

Large estimates of cellulosic potential have been advanced. The DOE (2011) in the ‘Billion

Ton Update’, indicates that dedicated energy crops could make up 30% of the available

cellulosic biomass in the U.S. by 2020. Researchers have estimated that by 2030, there could

be 63 million acres of land in the U.S., and 800 million acres globally, producing energy

crops (IEA Bioenergy 2009). However progress has been slow. During the first years

following the implementation of the RFS2 there was limited production of cellulosic biofuel

(EPA 2009). By 2016, a time when the RFS2 was originally designed to mandate more than 4

billion gallons of cellulosic ethanol production, only about 100 million gallons are actually

being produced with about 200 million gallon of total plant capacity, and no plants under

construction5. Of the existing 100 million gallons being produced only between 2.5-27.5%

used dedicated energy crop feedstocks, with the majority utilizing corn stover. Researchers

have indicated that the cost component restricting commercialization is processing, where

Lau and Dale (2009) indicate that current efficiency needs to increase to be competitive.

Several additional economic issues limit commercialization of cellulosic feedstocks. The

main ones include logistical considerations and the use of irrigation in production. Many

cellulosic crops can be harvested and baled using existing forestry and haying equipment.

However significant logistical infrastructure is required to attain full commercialization.

Many of these crops are bulky with low energy density, high moisture produced in a highly

seasonal pattern requiring significant storage, large transportation needs, and highly variable

yields.

5 http://www.ethanolproducer.com/plants/listplants/US/Existing/Cellulosic

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2.2 Logistics

Biomass feedstocks are bulky with low energy density and in cases high water content,

potentially widespread across the landscape with seasonal production leaving the harvest

management, storage, preprocessing and seasonal supply as key elements affecting the

economic viability of the industry (An et al. 2011). This is particularly true for cellulosic

biofuel production. Research has indicated that feedstock logistics costs encompass 8% of

grain-based ethanol production costs (Hess et al. 2007) but 35-65% of the cellulosic ethanol

production costs (CAST 2007). Improving the unique logistics design for biofuel production

(Figure 23.3) can greatly lower the energy costs, making it more achievable to develop low

cost domestic supplies. Below we review aspects of logistical costs.


Figure 23.3 Schematic of Biofuel Logistics

Note: The color and line type represent feedstock type and transportation method

respectively.

2.2.1 Harvest and Hauling

Feedstock production can be dispersed or concentrated spatially depending on yields and

density of producing area (McCarl et al. 2000). Therefore, a key element in the supply chain

design of a biorefinery involves where to collect feedstocks and harvest timing. Storage is

also a key element to maintaining the seasonal supply of feedstock. Biomass yield and

moisture content are also dependent on both the timing of harvesting operations and the

weather. Therefore, biorefineries and growers must plan harvesting operations, not only to

increase biomass yield, but also to reduce moisture content and storage requirements while

considering the availability of harvesting equipment, the weather, and plant maturity (An et

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al. 2011; Kim 2011). It is also an option to preprocess feedstocks in the field in order to

reduce transportation cost per energy unit and storage losses.

2.2.2 Storage

One characteristic of most cellulosic supply chains is seasonal harvest. In the face of that a

year round feedstock supply makes storage an important component of the logistics system.

Storage can also remove points of peak supply and increase the efficiency of the

transportation system. However, fixed costs for storage solutions can be large, and there are

several economic considerations involved with evaluating storage facilities as discussed

below.

Firstly, the benefits and costs of alternative storage methods along with associated storage

losses are important. Storage costs and losses are also influenced by preprocessing method.

For example, woody biomass can be processed to pellets or chips of baled at different costs

and moisture contents. Furthermore, storage can occur in the field, in intermediate storage

locations, or at the biorefinery. Stored feedstock can be protected from the weather and fire

using a variety of options, ranging from tarps to buildings. Generally, low cost methods lead

to high storage losses while negligible losses result from high cost preprocessing and storage

methods. Moreover, storage methods may vary spatially and temporally. Studies on dry

matter loss have been done finding for example, large round bales may have advantages over

a large rectangular bales when stored outdoors (Cundiff and Grisso 2008). However, a large

rectangular bale system has harvest, handling, and storage economies of size advantages

(English et al. 2008; Thorsell et al. 2004). Therefore, simply choosing the storage method

with lowest cost or minimum dry matter losses may not be appropriate. One should also

consider the effects of storage on the costs and benefits of other logistics alternatives in order

to minimize overall logistical costs (Rentizelas et al. 2009).

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Storage scheduling is another important consideration as it impacts the time requires, the dry

matter loss rate and the resulting material flow. Additionally, uncertain supply from farmland

motivates a storage facility to have a safety stock margin allowing continuous support of

processing facilities. Rentizelas et al. (2009) assumed a 20-day full load margin.

Finally, given the fixed costs of facility establishment, key design elements also involve

storage facility capacity and location. Choice of a cost minimizing storage capacity should

consider all aspects of the logistics system as integrated factors. The basic criteria iscriterion

is that the benefit gained from having a storage facility should exceed operation plus fixed

cost. The facility location should coordinate with upstream and downstream facilities for

feedstock production, preprocessing, transport and conversion.

2.2.3 Transportation

Transportation unifies farm level supplies of feedstock, preprocessing, storage depots, and the

biorefinery into a complete supply chain. Transportation issues are highly reliant on other

elements in supply chain and two major factors influencing the transportation cost are the

feedstock characteristics and the location of facilities.

Feedstock bulk density has a major impact on the supply chain including transportation

(Sokhansanj et al. 2002)(Sokhansanj, Turhollow, Cushman, & Cundiff, 2002). For example,

truckloads of baled biomass are limited by volume rather than weight, resulting in high

delivery costs (transportation and handling) per unit energy. Transporting unwanted water in

feedstock is also uneconomical. In order to reduce delivered biomass costs, it is desirable to

preprocess biomass close to the point of harvest and/or depot into a higher density, drier,

stable, standardized, and easily transportable form.

Alternatives for feedstock transportation include roads, railways, waterways, and/or a

mixture. Both road and railway have advantages and disadvantages, for example, railway

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performs better than road when the travel distance is long but the rails must be convenient to

the feedstock production, storage, and use locations. Various transportation methods have

been examined, noting the importance of travel distance, speed slope of road, the access of

highway were all found to influence the hauling speed and the resulting costs (Kumar and

Sokhansanj 2007; Yu et al. 2011).

3 Key Issues in Feedstock supply

3.1 Food Security

A key concern with increased biofuel production is food security. Increasing biofuels requires

either diverting commodities that would ordinarily be part of the food supply chain (including

feed commodities for livestock) or diverting lands into production which in turn reduced

food/feed commodity production. Either action can lower food availability, which is a key

element of food security i.e. “sufficient quantities of food available on a consistent basis”

(WFP 2016). On a global basis both food and energy demand are growing but the land and

water resource bases are not (FAO 2013). The International Energy Agency (IEA 2013)

projects that biofuel consumption will increase from 1.3 million barrels of oil equivalent per

day (mboe/d) in 2011 to 2.1, and 4.1 in 2020 and 2035 an increase of almost three times. That

growth again raises the food versus fuel concern.

Many feedstocks such as corn, sorghum, and barley are feed grains for livestock a key food

source, or are direct sources of human consumption (see Table 23.1). Hence, producing

biofuel from these crops affects the human food supply. For example, in the U.S., corn

converted to ethanol lessens the availability of feeds for livestock, and also the expansion of

ethanol production reduces agricultural land devoted to other crops (primarily soybeans

according to Babcock 2015). Moreover, biofuel targets and policies increase demands for

diverting food or feed crops (Karp and Richter 2011). For example, US corn use for

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producing ethanol increased greatly during the past fifteen years (Figure 23.4) and in the UK,

the oilseed area will need to greatly increase to meet biodiesel requirements (Twining and

Clarke 2009).

Nevertheless, some argue that there large volumes of potential biofuel feedstocks that are

unrelated to human food supplies such as switchgrass, giant miscanthus, crop residues, and

forest products (e.g. the DOE Billion ton study (2011)) . But because of limited resource

availability, especially for land, increasing biofuel feedstocks could displace food crops

impacting the human food supply (i.e., the indirect land use change impact).


Figure 23.4 U.S. Domestic Corn Use

Data Source: USDA, ERS (2016b)

[3.2] Food versus fuel - land competition

A major concern arising with the rapid expansion of the ethanol industry was the food versus

fuel issue. Both of the major first generation crops, corn and sugarcane, are important food

and feed crops, and the additional demand from the ethanol industry reduces supplies

available for traditional uses causing accompanying commodity price rises and the potential

for diverted land. The U.S. food price index reached historical highs in 2008 and 2012 when

ethanol production rapidly expanded (FAO 2016). Several studies have argued ethanol

production had an important influence on corn prices (Ferris and Joshi 2004; Fortenbery and

Park 2008; McNew and Griffith 2005; Taylor et al. 2006).

The rise in commodity prices is also likely to induce changes in land use with less devoted to

traditional crops and a likely increase in production elsewhere with land moving out of other

crops, forests or grasslands (see Tyner et al. (1979) for early findings on this along with

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Murray et al., (2004) for a theoretical treatment and Searchinger et al. (20087) for a biofuel

centered discussion).

Competition for land between food and biofuel production has been the focus of many

scholars’ attention. During the 1970s and 1980s, researchers like Da Silva et al. (1978) and

Tyner et al. (1979) plus others as reviewed in Johansson and Azar (2007); and Rathmann et

al. , Szklo, and Schaeffer (2010) investigated the issue. The resultant findings can be divided

into two main groups. First, some indicate that there is significant competition between

production for food and biofuel, with some raising the issue of increasing food prices and

food shortage possibilities (Tyner et al. 1979; McCarl and Schneider 2001; Schneider and

McCarl 2003; Hill et al. 2006; Alexander and Hurt 2007; Mitchell 2008; Johansson and Azar

2007; Searchinger et al. 2008).

Second, others argue that the connection between increasing biofuel production and food

prices/food shortages is not strong. Goldemberg et al. , Coelho, and Guardabassi (2008) argue

that the increase of sugarcane production in Brazil only took place in an area previously

utilized for pasture, and therefore did not decrease food crop production. Lam et al. (2009)

argue use of Malaysian palm oil did not lower food supplies.

Overall the findings on food vs fuel are critically influenced by approach, data, location and

assumptions (HLPE 2013) and as of yet no clear consensus has yet emerged from this debate.

Nevertheless an ambitious and challenging question now is how to meet growing food and

energy global demands, while preserving environmental quality. The suggested solutions are

sound public policy, technological improvement, and global collaboration (Karp and Richter

2011; Tilman et al. 2009).

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3.2[3.3] Greenhouse Gas implications

A large environmental debate has been carried on regrading agricultural ethanol production

and its greenhouse gas (GHG) implications. A large number of studies discuss whether first

generation crop ethanol results in net GHG emission reductions (Farrell et al. 2006; Hill et al.

2006; Liska et al. 2009), especially when the food versus fuel - indirect land use change

effect is considered (Fargione et al. 2008; Hertel et al. 2010; Searchinger et al. 2008).

More generally GHG results from studies on feedstocks vary significantly depending on the

scale, and system boundary assumptions. Figure 23.5 shows life cycle emission estimates

from various feedstocks as estimated in the EPA’s Renewable Fuel Standard (RFS2)

Regulatory Impact Analysis. Corn and sugarcane ethanol were found to reduce GHG

emissions by 21% and 61%, respectively, compared with gasoline. However, corn stover was

found to potentially reduce emissions by 92-129% depending on the processing technology.

It is generally agreed that life-cycle GHG emissions for first generation ethanol crops are

much higher than those for cellulosic biofuel (McCarl 2008) with ethanol production from

cellulosic sources being promoted for their life-cycle GHG emission impacts compared with

first generation crop ethanol.

.


Figure 23.5 Lifecycle GHG Emissions from Biofuels, Petroleum as Baseline

Data Source: RFS2 Regulatory Impact Analysis (Chapter 2.6), EPA

3.3[3.4] Marginal Land for Energy Crop Production

The food versus fuel debate has stimulated considerable interest in producing cellulosic

feedstocks on non-crop lands also called marginal lands. Marginal lands, also named as idle,

generally refer to crop land suitable lands not currently used for growing crops. Marginal

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land is comprised of many specific types, such as abandoned croplands, cropland used as

pasture, barren lands, abandoned mine lands, degraded lands and lands in the Conservation

Reserve Program, but has no official definition. As a result, current studies examining the

bioenergy potential on marginal lands have generated considerably different results due to

dissimilarity in definition, model scale and data input (see Lewis and Kelly (20104) for a

detailed review).

According to a recent study by Milbrandt et al. (2014), there are 864,826 km2 of marginal

lands within the U.S., most of which were once used for crop production and have since

moved into grass or other vegetation. Most marginal lands are located in the West and

Northeastern U.S. while the Corn Belt has the least available.

Though not suitable for conventional crops, marginal land has been argued to provide great

potential for energy crops production. Gelfand et al. (2013) asserted that with proper

management practices, the marginal land in the Midwestern U.S. could provide about 5

gigajoules worth of ethanol per year per hectare. Milbrandt et al. (2014) concluded that the

total energy production could reach as high as 1,936 billion kWh - about half of total US

energy consumption in 20146.

The usage of marginal land for energy crop production can alleviate some of the pressure that

growing feedstocks for ethanol demand places on conventional cropland. However, both

economic and environmental consequences arise. Shiva (2014) examined the issue and found

that use of marginal land did alleviate the pressure on cropland, leading to a price reduction

in several agricultural commodities and increasing total producer and consumer welfare.

However, this was at the cost of increases in GHG emissions, erosion and chemical runoff,

because of the higher cultivated land use plus added fuel and fertilizer consumption and in

6 See EIA at: http://www.eia.gov/energyexplained/index.cfm?page=electricity_use

http://www.eia.gov/energyexplained/index.cfm?page=electricity_use

17

cases reduced sequestration. Moreover, when carbon payments were available, she found

switchgrass-based electricity production dominated as opposed to ethanol production.

3.4[3.5] Technological Progress

Future crop yields are of great concern to blenders, agricultural producers, and policy makers

regarding future biofuel production possibilities. With rapid technological improvements, the

agricultural sector will be able to devote resources to both food and biofuel (Alexander and

Hurt 2007; Ajanovic 2011). Between 1961 and 2014, global crop yields experienced

significant growth, with yields of corn (maize), rice, and soybeans more than doubling and

wheat yields tripling (Figure 23.6). But substantial needs are evident with Rosegrant et al. ,

Tokgoz, and Bhandary (2013) finding that in order to keep commodity prices at the 2010

level, corn yield growth rates need to increase by 100% by 2050.

There is reason for concern. Research has found that corn yield growth rates in the U.S. have

fallen from 1940-2009, from a rate of 3.67% to 1.75% (Feng 2012). More generally annual

crop yield growth rates for other crops have not exhibited strong upward trends (Figure 23.6

and Figure 23.7). Additionally the annual growth rates during 1961 to 1970 were

substantially larger than the rates during recent years, 2001 to 2014 and the world is now

facing a slowdown of agricultural technological progress.


Figure 23.6 Global Yield of Major Crops from 1961 to 2014

Data Source: FAOSTAT Database (2016)


Figure 23.7 Annual Yield Growth Rate of Major World Crop in Different 10-Year

Periods.

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Data Source: FAOSTAT Database (2016)

A number of sdies have analyzed technical progress historically. Some have found that

technical progress has decreased in the past two decades (Alston et al. , Beddow, and Pardey

2009; Baker et al. 2013; Villavicencio et al. 2013) while, others argue that there is no

statistical evidence of a slowdown (Aizen et al. 2008; Wang et al. 2015).

Climate change is an additional complicating factor. Many studies project altered mean and

increased yield variability under climate change (Olesen and Bindi 2002; Chen et al., McCarl,

and Schimmelpfennig 2004; Torriani et al. 2007; McCarl et al. , Villavicencio, and Wu 2008;

Xiong et al. 2009; Wang et al. , Wang, and Liu 2011) with some projecting future decreases

in yield for certain areas and crops (IPCC 2014). Furthermore, at the U.S. national level,

Villavicencio et al. (2013) find an overall negative effect of climate change on agricultural

productivity and compute that approximately an 18 % increase in annual public agricultural

research investment is needed to maintain preclimate-change technical progress rates. Hence,

agricultural technological progress is needed to meet the demand growth and overcome the

negative consequences of climate change.

3.5[3.6] Water Supplies

Another important economic concern involves irrigation. This is particularly important in

regions such as Texas, western Kansas and Nebraska where the depletion of the Ogallala

aquifer groundwater resource is accelerating and this limits long run production possibilities.

3.6[3.7] Uncertainty

An important facet of the relationship between current biofuel mandates and agricultural

market variability is uncertainty. Agricultural production yields exhibit substantial variability.

Policy uncertainty is also present with mandates relaxed, subsidies imposed then discontinued

and other changes occurring. Thus, actors in the renewable fuel sector make decisions based

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on uncertain persistence of ethanol mandates and subsidies, fluctuating crop yield production

levels and year to year variation in future commodity prices.

3.6.1[3.7.1] Policy and Uncertainty

Policy and uncertainty interact in several ways to influence biofuel production and market

effects. First, since biofuel refining involves large persistent investments, there is an element

of uncertainty introduced by alterations in biofuels policy. Second, there is the possibility that

biofuels policy can be modified to limit market fluctuations.

3.6.1.1[3.7.1.1] Policy Uncertainty

Biofuels policy has introduced uncertainty through alterations in its basic directions. In the

US biofuels were subsidized for a number of years with subsidies amounting to about $0.45

per gallon conventional ethanol, $1.01 biodiesel and $1.01 cellulosic ethanol. These were

discontinued in 2011. Second, the renewable fuel standard was passed in the 2007 energy

bill and mandated a schedule of future volumes of fuels of various forms including a

requirement for cellulosic ethanol. The 2016 requirement is 230 million gallons but the

legislation specified 4.25 billion gallons, creating a much less substantial market and causing

industry uncertainty about whether future mandates will support large capital investments.

Third, renewable fuel standards mandate production of larger volumes of ethanol than can be

absorbed under the current prevalent 10% blend and there have been conflicting policies

limiting penetration to 10% or below, although as of now the requirement has been moved to

a larger amount. Also, policy has introduced subsidies to increase higher ethanol volume

pumps allowing distribution of larger quantities put incidence of such pumps remains small.

Nevertheless, the blend wall has been a source of uncertainty and has caused ethanol to be

exported in recent years.

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3.6.1.2[3.7.1.2] Yield uncertainty and policy

Agricultural yields are subject to substantial yield uncertainty and this in interaction with

fixed mandates exacerbates price variability. In particular, biofuel policy in the U.S. is set a

priori, for the most part with levels that do not vary under fluctuations in crop availability. As

a consequence, commodity prices can sometimes rise to high levels, for example during the

2012 drought when national production was13% less than that in 2011, corn price rose to a

record high of $7.63 in August 2012 compared to $6.88 a year before and a pre-mandate level

of about $2 per bushel. A waiver for U.S. corn ethanol production is possible; however this

option has not yet been exercised, even in the instance of the 2012 drought.

High prices under the 2012 drought had impacts on non-ethanol users of commodities,

particularly livestock producers. An issue is how much could this volatility have been

reduced if the mandate was relaxed? Although there exist policy mechanisms in the U.S.

RFS2 mandates to accommodate minor production fluctuations, researchers at the time stated

the impact on corn prices in a drought year will continue to be a factor unless a waiver is

implemented (Babcock 2012cb). The RFS2 legislation indicates that a waiver can only be

issued if ‘economic harm’ is evident in the market (Tyner 2013), an outcome not explicitly

defined. The primary reason why there was not a waiver during the 2012 drought was that the

EPA assumed its use would cause unwanted agricultural market and ethanol blender actions

(Tyner 2013). More definate policy toward waivers could be formulated providing a less

volatile market (Jones 2014) as discussed below but this would reduce certainty for biofuel

producers in terms of the volume they could sell.

Several studies have addressed economic implications and policy in the face of yield

uncertainty. Tyner et al. (2012) examined multiple production shortfall scenarios, finding a

waiver on corn ethanol would have an impact on prices One scenario in their study assumed a

corn production reduction of 25% and found that reducing the 2013 blending requirements

21

from 13.8 billion gallons to 11.8 would reduce corn prices by xx7.9% or approximately $0.66

a bushel.

Jones (2014) examined the effect of making renewable fuel mandates conditional on total

production. Such mandates were found to decrease corn price variability reducing price

spikes while having a minimal impact on long-run commodity prices. Jones also found that

farmers would respond to such a policy change by reducing corn acreage as the policy caused

lower expected prices.

3.6.2[3.7.2] Weather and climate change

The weather component of crop yield variation is difficult to account for over time. Rainfall,

has been argued to account for over 50% of crop yield variation (Challinor et al. 2003).

Future crop yield projection models have been applied in short and long term settings.

Monthly short term crop prediction models use real-time on vegetation and weather coupled

with historical data to make projections (Prasad et al. 2006). These models employ remote

sensing techniques to gain real-time insight into water balances, and can bring news of an

upcoming drought and incidence across regions (Horie et al. , Yajima, and Nakagawa 1992).

Researchers have stressed the importance of small spatial scales to improve accuracy

(Challinor et al. 2003). Clearly regional analyses are important as they take into account

discrepancies in weather variability, resource endowments and economic factors.

Prior research has also found that climate change is affecting both the mean and variance of

crop yields (McCarl et al. , Villavicencio, and Wu 2008). This creates increased risk for

agents in the agricultural and biofuel sectors alike. The research also showed that using

historical yields to reflect future agricultural production under climate change was

unacceptable and regional and crop specific agricultural yields are changing. Research has

investigated how climate change induced uncertainty has impacted corn, soybeans, and

22

switchgrass prices from 1980-2006 (Mallory et al. 2011). The study found that the existence

of a small chance of prices experiencing an upward spike resulted in higher mean commodity

prices. Increasing the standard deviation for corn by 50% was found to increase corn prices

by $0.48. This finding was evident among the major crops used for traditional and advanced

biofuels.

3.7[3.8] Asset fixity

Biofuel production facilities are generally designed and built for a particular type of

feedstock and technology plus are fixed to a location and as built incur a large, sunk, fixed

cost. This fixes capacity, and characteristics in a location (Williamson 1979). This concept

has a direct impact on production cost, economics and mobility.

Previous research on ethanol production, say in Beach et al. (2009), exhibits feedstock mixes

and processing locations that vary widely between years with crop residues in one place in

one period and then switchgrass somewhere else in the next period. Such a result neglects

continuity of capacity and sunk fixed assets. Generally, built facilities will not be abandoned

and typically will be utilized for the length of the economic life unless they exit from the

market (e.g. due to unexpected regulatory or market price changes). This aspect creates

barriers to entry and exit from the market plus limits ability to react to technological and

feedstock developments.

Research has investigated the effects of asset fixity and sunk costs on biofuel investment.

Murto and Nese (2002) concluded that optimal investment decisions may be postponed, as

asserted by the theory of irreversible investment under uncertainty (Dixit and Pindyck 1994).

In particular it can be optimal to postpone an irreversible investment decision in order to

obtain more information or let markets develop or let technology advance.

23

Wolodarz (2013) found that asset fixity dramatically changed the results of sector studies like

Beach et al. 2011 asthose used in the EPA RFS2 analyses. In particularparticular,

considering fixity greatly affected where plants are located, the choice of feedstock and

substantially reduced the total number of plants constructed. Asset fixity renders investment

in existing plants is more attractive than new plants. Figure 23.8 shows this issue over time in

a two-period graphical framework from Wolodarz (2013), illustrating how existing facilities,

demand growth, and production capacity are related. Fixed capacity is restricted by region

and feedstock, whereas new facilities can be seen as mobile at the time of investment,

however at a greater cost. The figure depicts a flatter supply curve for new plants, reflecting a

larger capability to expand production through constructing. Existing facilities have steep

cost curves, as they are bound by their fixed capacity.


Figure 23.8 Visual Representation of Asset Fixity Concept Depicted Dynamically over

Two Time Periods

Data Source: Wlodarz (2013)

Retrofitting an existing plant is also an important concern. Singh and Eckhoff (1997)

investigated retrofits of a conventional dry-grind ethanol plant to add the quick germ process,

finding that such modifications increase the value of co-products from the facility. Rodríguez

Rodriguez et al. (2010) added a fiber component and estimated the retrofit decreased

processing costs by 13.5 ¢/gallon. Cuzens and Miller (1997) studied retrofitting sugar mills

with acid hydrolysis and found this reduced the heat and subsequent energy requirements for

the facility. Plevin and Mueller (2008) examined multiple retrofits of ethanol plants finding

that it decreased processing costs as well as impacted resulting GHG emissions by reducing

heat and energy losses.

24

4 Economics of Fuel Replacement

4.1 Energy Equivalency and Ethanol Value

When considering ethanol as a gasoline replacement, it important to consider equivalent

energy. On a BTU basis, it takes essentially 1.5 gallons of ethanol to go as far as one would

go on one gallon of gasoline (see Table 23.3). This implies the price of ethanol should be

2/3rds the price of gasoline on a pure energy basis although there is also value in meeting the

oxygenate requirement and the mandate levels. The later involves the renewable

identification number (RIN) credit system where RINs can be turned into EPA to indicate

fuel mandates have been met and adds differentially to the value of fuel depending on its

class under the EPA RFS classification.

4.2 Fuel Market Demand

The profitability and long run prospects for biofuel production depends closely on the crude

oil price. However, crude prices have been widely fluctuating since early 2000, bringing

uncertainty (Figure 23.9). In the fuel market, the demand for ethanol is pretty elastic. In other

words, the gasoline price largely determines the ethanol price after adjusting for energy

equivalency. In reality, there exists tax and policy supports for ethanol production plus RIN

prices but the gasoline price is a primary determinant (Pokrivcak and Rajcaniova 2011).


Figure 23.9 Rack Price7 for Ethanol and Gasoline

Data Source: USDA, ERS (2016a)

The relationship between ethanol and gasoline markets has been studied previously where

most test whether there is a substitution or complementary effect. Timilsina et al., Mevel, and

Shrestha (2011) analyzed the relationship between ethanol production and oil price

7 Wholesale truckload sales or smaller of gasoline where title transfers at a terminal

25

finding that the percent of biofuel in the total transportation fuel mix, would increase

from 2.4% to 5.4% with a 65% increase in oil price. Also, they indicated doubling the

oil price would increase biofuel market penetration to 12.6%. Anderson (2012) showed

that ethanol was a gasoline substitute finding that a $0.026/litre increase in ethanol (E85)

price relative to gasoline (E10) could lead to a 12–16% decline in the quantity of ethanol

demanded.

More generally, transport fuel consumption, especially from non-residential sectors,

is essentially a derived demand from demands for other goods and services (Berndt

and Wood 1975). There is no clear evidence that economic growth will lead to

increase in energy demand as studies analyzing this causal relation give varying

results (see Paul and Bhattacharya (2004) for a comprehensive review).

4.3 Infrastructure and the Blend Wall

Several recent studies, however, suggest that the relationship between ethanol and gasoline

the infrastructure incompatability of ethanol. In particular ethanol is a strong solvent, which

can cause corrosion of pipelines and degradation of seals and pump components. It also

dissolves residues and absorbs water. In turn this implies different transport and gas station

pumps. Currently most gasoline sold on market contains up to 10% of ethanol (E10) and it

looks like 15% is the limit for non-flex fuel cars. This imposes an upper bound on the

quantity of ethanol that can be blended that is called the "blend wall" (Tyner 2013; de Gorter

et al., Drabik, and Just 2013) and limits demand plus in recent times has stimulated ethanol

exports. Zhang et al., Qiu, and Wetzstein (2010) proposed a theoretical model and showed

that an increase in the percentage of ethanol that can be blended elicits an increase in the

price for ethanol and E85 which in turn might reduce the demand for blended ethanol and

increase regular gasoline consumption. Tenkorang et al. (2015) showed that although ethanol

26

(corn ethanol) is a substitute for conventional fuel/gasoline, it acts as a complement when

approaching the blend wall due to saturated demand.

4.4 Trade

Global biofuel production grew significantly from 2001 to 2013 (Figure 23.10) caused by

forces such as high fossil fuel prices, low biofuel feedstock prices, and policy mandates.

These factors also contributed to increased trade in biofuels. The U.S. (after 2010) and Brazil

exported large amounts of biofuel from corn and sugarcane. This met the requirements of

other countries mandates. This section will overview international biofuel markets and

driving forces.

4.4.1 Ethanol

The U.S. has been both an exporter and an importer. For the period before 2010, the rapid

growth in production and consumption driven by high gasoline prices, relatively low

feedstock prices and policies (e.g. blend mandates, tax credits). However, after 2010, the

maximum under the mandate was approached and feedstock prices took off since 2011 plus

the tax credits stopped all slowing down expansion. Therefore, pre 2010 the U.S. was a net

importer to fill the gap between domestic production and demand but became a major

exporter of ethanol after 2010 even though the domestic consumption still remains strong

(Figure 23.11). This U.S. exports is attributed to a decrease in Brazilian ethanol exports

(Yano et al., Blandford, and Surry 2012). In addition, the blend wall contributes to U.S.

exports as an outlet for additional production (EIA 2012). Moreover, in order to meet

mandates, the U.S. and Brazil engage in two-way trade of similar biofuel, commonly known

as intra-industry trade.


Figure 23.10 Global Ethanol Production (Million Litres)

27

Data Source: Renewable Fuels Association and EIA International Energy Statistics

(2016c)


Figure 23.11 U.S. Ethanol Production, Consumption and Trade (Million Litres)

Data Source: Renewable Fuels Association and EIA International Energy Statistics

(2016c)

Brazil is the second largest ethanol producer producing sugarcane based ethanol. Due to these

circumstances, Brazil has long been a net exporter of biofuel but the Brazilian government

eliminated the ethanol import tariff until the end of 2015 (FAS 2011), leading to the country’s

first imports of ethanol in 2010. Since 2010, Brazil imported biofuel from the U.S.

particularly following a poor crop yield induced decline in ethanol production occurred in

2011 and 2012 (Figure 23.12) although this rebounded in 2013 under low sugar prices.


Figure 23.12 Brazil Ethanol Production, Consumption and Trade (Million Litres)

Data Source: Foreign Agricultural Service (20145), USDA

The European Union (EU) ethanol production grew rapidly until 2010 but the EU is a net

imported due to large mandates despite being the third largest producer in the world (Figure

23.13). Until 2011 the imports mainly came from U.S. and Brazil but then the EU imposed

trade barriers in the form of antidumping duties. Subsequently, the EU began to import

ethanol from Central America and Southeast Asian countries. Since the EU blending mandate

continues to grow, it is expected that the imports will continue to be strong.


Figure 23.13 EU Ethanol Production, Consumption and Trade (Million Litres)

28


4.4.2 Biodiesel

Biodiesel related polices play an important role in production and trade. The RFS program

and rising fuel prices led to a surge in biodiesel production capacity and production during

2001-2008 and after 2011 (Figure 23.14, EIA 2009). There was not significant trade prior to

2007. The U.S. was a net exporter from 2007 to 2009 mainly when demand was small but

there was a biodiesel tax credit and EU subsidies (EIA 2009). In a process known as “splash

and dash”, biodiesel was blended with a small amount of diesel to satisfy the tax credit

requirement and then exported to the EU, where it received additional subsidies. “Splash and

dash” ended in 2009 when the U.S. Emergency Economic Stabilization Act of 2008 made the

reshipment of imported biodiesel illegal, and the World Trade Organization (WTO) ruled that

these exports were unfair to the EU. Additionally the EU imposed anti-dumping and anti-

subsidy duties on imports of biodiesel from the U.S. (Beckman 2015). Subsequently U.S.

trade was small until 2013 when U.S. became a net importer due to RFS mandate. U.S.

imports fell again in 2014 amid uncertainty surrounding future RFS targets and the

elimination of the biodiesel blender's tax credit. Higher RFS targets in 2015, stimulated

increased imports (EIA 2016a). Canada and Argentina have been the major import markets

for the U.S since 2013.


Figure 23.14 U.S. Biodiesel Production, Consumption and Trade (Million Litres)

Data Source: Renewable Fuels Association (2016)

In the biodiesel market Brazil has been both an importer and an exporter. Brazil did not trade

in 2012 and only exported biodiesel to the EU without any imports after 2013 (Figure 23.15,

FAS 2014). The EU is the largest biodiesel producer in the world, however the blending

29

mandates exceed the production capacity, thus the EU is an importer (Figure 23.16). In

March 2009, the EU imposed anti-dumping and anti-subsidy duties on imports from the U.S.

Furthermore, that trade barrier was extended to major biodiesel export countries including

Argentina and Indonesia.


Figure 23.15 Brazil Biodiesel Production, Consumption and Trade (Million Litres)



Figure 23.16 EU Biodiesel Production, Consumption and Trade (Million Litres)


5 Market and Welfare implications of biofuel production

5.1 Market implications

Biofuel production increases have influences commodity markets. Agricultural market

impacts range from altered commodity prices due to increased commodity demand,

impacting the producers, consumers, and secondary processors of such products. In 2005,

7.25 billion bushels of corn were consumed by non-ethanol sources, whereas that number in

2012 was slightly over 5 billion bushels, a 30% decrease (NASS Database 2014). This

Biofuel production creates a tighter supply environment and raises prices. The prices received

by farmers went up by 56% (17% in real terms) and hence land values for cropland also went

up from 2001-2015 by 273% (in real terms by 201%). Forestry markets are also impacted

mainly through land movements into agriculture. Therefore, iIt also stimulates additional

supplies with USDA showing in 2016 as compared with 2001 a 16% increase in planting, a

43% increase in total production, a 22% increase in yield per acre and an 85% increase in

30

nominal price (35% adjusted for inflation, see Table 23.5). The supply increase has also

come from intensification as manifest in yield per acre and extensification as manifest in

increased acreage. Note the increase in corn use for ethanol slowed dramatically in 2013

when the RFS ceiling on corn ethanol was essentially reached but production increases

continued and prices, and acreage declined but crop yield continued to increase. This

portends a lower corn price future.

The market implications of diverting commodities to biofuels are evident across the entire

economy. Agriculture, forestry, and traditional energy markets are the most affected.

Agricultural market impacts range from altered commodity prices due to increased

commodity demand, impacting the producers, consumers, and secondary processors of such

products where prices received by farmers went up by 56% (17% in real terms). Land values

for cropland also went up from 2001-2015 by 273% and in real terms by 201%. Similarly,

forestry markets are also impacted mainly through land movements into agriculture and land

values.

Several studies have examined the potential impact of U.S. biofuel policy on commodity

price and welfare. Babcock et al. (20112) compared the economic outcomes from three

ethanol policy scenarios; the full RFS2 mandate, the use of 2.4 billion gallons of RINs, and a

full waiver. The difference in corn price between having no biofuel mandate and the use of

RINs was an extra $0.28 per bushel. Additionally, the difference between the RIN policy and

the full mandate was an additional $0.91 more per bushel of corn with the total effect of the

mandate being a $1.19 increase in corn price..

5.2 Welfare implications

This section needs improvement it needs intro and gsome more findings

31

Besides cost-benefit consideration, policy makers also concern about the welfare changes

when introducing new industry and/or related policies into an economy. For example, as

traditional policy tools, subsidies and taxes usually twist the welfare distribution between

consumers and producers. There are also other aspects to assess welfare changes such as

considering externally and food security.

Another Ppolicy supporting biofuel was the $0.51 per gallon tax credit and the associated

$0.54 per gallon tariff on imported ethanol. Rajagopal et al. (2007) found that the ethanol tax

credit increased gasoline consumers and corn producers welfare, leading to an increase of

social welfare of number 17.4missiing $billion USD in total. Taheripour and Tyner (2008)

showed that the ethanol tax credit benefited corn producers. On the other hand, de Gorter and

Just (2009) indicated that the tax credit decreased total social welfare, mainly due to the

consumer welfare loss due to higher prices for commodities.

There are also studies that include estimates of environmental externalities. Vedenov and

Wetzstein (2008) estimate that producing ethanol generated a $0.04 per gallon environmental

benefit and added this to the market effects concluding that the $0.51 per gallon tax credit led

to a loss in total social welfare.

In addition, shifting market conditions, biofuels also impact welfare during periods of

reduced agricultural production. This circumstance instigates increased corn price volatility,

resulting in negative welfare effects both domestically and abroad. The findings by Babcock

(2012a) suggest that in the case where ethanol prices are below gasoline prices, a waiver

would have no impact. As a result, blenders would want to purchase as much ethanol as they

could up to the 10% blending limit; however the flexibility of both the refiners and the

blenders is an important issue.

Bruce A McCarl, 08/02/16,


already covered above - out of place and could just be deleted


missing from biblio


is this right

Guannan Zhao, 08/12/16,

Please see this paper ‘Ethanol Policy Analysis—What Have We Learned So Far?’, I edit this section but still not sure.

Numwaan, 08/02/16,

Do you refer to Tyner et al. (2012)? Please check the references.


not in blblio

32

Another important consideration are the impacts abroad. The RFS mandates can bestow

tradeoffs because consumer losses including some food security dimensions but producer

gains (Ewing and Msangi 2009; To and Grafton 2015).

5.3 Implications for Rural Economies

Rural community effects are another concern, especially in terms of employment and income.

In general, biofuel plants are generally located in rural areas due to feedstock availability and

byproduct sales to livestock producers (Lambert et al. 2008). In turn this has impacts on rural

employment (Domac et al., Richards, and Risovic 2005). Namely rural biofuel production

introduces both direct and indirect employment, with people hired to work in the plant or

supply chin plus those employed in supporting industries. Several studies have examined

such consequences.

Parcell and Westhoff (2006) reviewed studies on U.S. rural areas and synthesized total

effects. They estimated the industry employed approximately 3,500 workers and paid out

$132 million in salaries in 2006. Also they indicate that operating a 60 million gallon per year

(227 million litre per year) ethanol plant creates an estimated 54 direct and 210 indirect jobs.

Swenson (2005) estimates that a 40-employee ethanol plant will generate 155 indirect jobs

and about $1 million in direct wages that in turn will generate $2.8 million in economic

activity in the local economy. Brown et al. (2013) estimated that opening an ethanol plant

will increase employment by 0.9% within related industries, such as trucking and natural gas

distribution and found a corn based ethanol plant increased local employment by 254 jobs,

with 82 created within related industries.

Findings also show that employment and income effects varies dependent on the plant size

and feedstock, along with socioeconomic status and local industry characteristics (Table

33

23.2). Domac et.al (20056) found in developing countries, biofuel industry workers earn well

below an average wage and may prefer other opportunities. It has also been found that

industry expansion raises local wages. Finally, it is fair to say that, the interaction between

biofuels and related industries with respect to employment and income effects has not been

extensively studied.

Need a summary and perhaps research issues

With this section, we summarized several previous studies on assessing the impacts of biofuel

industry on employment and income. Most of these studies use empirical

approach or simulation approach such as input-output (IO) analytical

process. In general, the positive effects are generated from biofuel

production, but the magnitude is affected by many factors including

feedstock, socioeconomic status, other related industries and etc. When studying on

the impacts of bioenergy sector on employment, considerable effort should be

made to determine the extent and direction of effects both within the region under analysis

and also out of the specified region (Domac et al. 2005). If there exits some severe ‘leakage’,

then the predictions may not be reliable. Furthermore, additional efforts should also be made

to evaluate the duration of the impacts.

6 Conclusion

This chapter provides a comprehensive review and synthesis of the literature on the economic

assessment of biofuel production, and its corresponding social and environmental impacts.

The first generation biofuel has already become an important source of transportation fuel

with corn and sugarcane as the main feedstock. The second-generation biofuel based on crop

residues and dedicated energy crops, though with larger potential supply, have not enter


34

large-scale production yet. Feedstock processing cost is a major concern plus other factors

such as feedstock logistic and irrigation during production.

The expansion of biofuel has drawn great controversy. A key concern is food security

since feedstock like corn are also part of the food supply chain. Even feedstocks not related to

food supply competes with conventional crops for limited land sources (the leakage effect),

though current studies have not reached a general agreement on such connection. The food

versus fuel debate has stimulated considerable interests in producing feedstocks on marginal

lands, which is less linked to food production. However, the potential environmental impacts

such as increased fertilizer application and GHG flux change needs to be addressed carefully.

Another important environmental debate with biofuel production is its GHG offset

effect. Results from previous studies vary significantly depending on the scale, and system

boundary assumptions. Moreover, one major impact factor of the GHG results is the

aforementioned leakage effect on land use. From the life-cycle perspective, it is generally

agreed that first generation ethanol crops have much higher GHG emissions than the

cellulosic biofuel.

Technological progress is a key factor influencing the economic performance of

biofuel production. The slowdown of agricultural technological progress might worsen the

land competition between food and energy crops. Asset fixity is another complicating factor

considering that once built with a large sunk cost, biofuel production facilities are generally

fixed to a specific location, feedstock and technology type. Asset fixity concerns might

postpone the optimal investment decisions and make investment in existing plants more

attractive than new plants. Because of asset fixity, retrofitting an existing plant to new

process or feedstock has also drawn increasing attention. Moreover, biofuel production also

faces uncertainty from policy, market, technology process and climate change.

35

Market forces also affect biofuel production. Biofuel enters as a substitute for

gasoline and equals to 2/3 of gasoline on a BTU basis. The profitability and long run

prospects for biofuel production depends closely on the crude oil price, which have been

widely fluctuating since early 2000. Market demand for biofuel also depends on

infrastructure compatibility of ethanol. Market penetration is a necessity to break the blend

wall effect on ethanol demand. Increases in biofuel production and the blend wall effect have

led to increased trade in biofuels globally.

Finally, biofuel production have welfare and market implications. Extra demand for

biomass feedstock impacts directly affects land use and indirectly impacts commodity supply

and price plus, and alters the welfare distribution among agricultural producers and

consumers. Biofuel facilities can also help create jobs for local community and stimulate

rural development.

Future study of biofuel production needs to focus continuously on more cost-efficient

feedstock supply chain. On the market side, asset fixity and market penetration of ethanol

needs to be analyzed. On the environmental side, recently studies has been focusing on the

biogenic carbon plus the ecological effects of biofuel production.

36

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Table 23.1 Basic Energy Crops for Human Food Supply and Biofuel Production

Common Name Scientific Name Biofuel

Production

Human Food Supply

Corn, Maize Zea mays L. Bioethanol Human food and animal

feed

Rye Secale cereale Bioethanol Human food and animal

feed

Sorghum Sorghum bicolor Bioethanol Human food and animal

feed

Sugar beet Beta vulgaris Bioethanol Human food

Sugar cane Saccharum

officinarum

Bioethanol Human food and animal

feed

Wheat Triticum aestivum Bioethanol Human food and animal

feed

Sunflower Helianthus annuus L. Biodiesel Human food and

bird and pet feed

Soya, Soy Glycine max ( L.)

Merrill

Biodiesel Human food and animal

feed

Oil Palm Elaeis Guineensis Biodiesel Human food

Rapeseed,

Rape, Oilseed

Rape

Brassica napus

Linnaeus

Biodiesel Human food and animal

feed

Source: Paschalidou et al., Tsatiris, and Kitikidou (2016)

47

Table 23.2 Summary of Previous Studies on the Effects of Biofuel Industry on Employment and Income

EmploymentIncome ($,

million)

Source YearSize

(MGY) ScaleBiofuel type Direct

Indirect Direct Indirect

FAO 2003 Pakistan Woody 600,000FAO 2003 India Woody 3-4 millionFAO 2003 Brazil Ethanol 800,000Gallagher et al.

2000 376Multiple States,

US Ethanol900-1200 5499 194

BBI international Consulting 2003 10 HI, US Ethanol 22 154 5.1Petersan 2003 24 NE, US Ethanol 31 73 1.42 1.03Swensonm 2005 41 IA, US Ethanol 32 135 1.5 2.7Flanders et al. 2007 100 GA, US Ethanol 46 362 1.8 18.3Low and Isserman

20079 100Hamilton County, IL Ethanol 39 97

Guerrero et al.2011 40

Hockley County, TX Ethanol 35 18

Note: MGY=Million Gallon per Year, 1 gallon=3.785 litre


Yes

Numwaan, 08/02/16,

I saw only Low and Isserman (2007) in the references. Please check.


Numwaan, 08/02/16,

Not in the reference. FAO 2003

48

Table 23.3 Gasoline Gallon Equivalent (GGE) of Various Types of Fuels

Fuel Type Unit of Measure BTUs Per

Unit

kJ Per Unit Gallon Equivalent

Gasoline, regular unleaded, (typical) gallon 114,100 120,375.50 1.00 gallon

Gasoline, RFG, (10% MBTE) gallon 112,000 118,160.00 1.02 gallons

Diesel, (typical) gallon 129,800 136,939.00 0.88 gallons

Liquid natural gas (LNG), (typical) gallon 75,000 79,125.00 1.52 gallons

Liquefied petroleum gas (LPG or

propane)

gallon 84,300 88,936.50 1.35 gallons

Methanol (M-100) gallon 56,800 59,924.00 2.01 gallons

Methanol (M-85) gallon 65,400 68,997.00 1.74 gallons

Ethanol (M-100) gallon 76,100 80,285.50 1.50 gallons

Ethanol (E-85) gallon 81,800 86,299.00 1.40 gallons

Bio Diesel (B-20) gallon 129,500 136,622.50 0.88 gallons

49

Note: BTU value of gases is based on density at one atmosphere at 288.71 degrees Kelvin.Source: Energy Equivalents of Various Fuels, NAFA (2010)

50

Table 23.4 U.S. Domestic Corn Use (Billion Bushels) from 2001-2016

Year Other food, seed, and industrial uses Alcohol for fuel use Feed and residual use

2001 1.358 0.707 5.8452002 1.363 0.996 5.5452003 1.385 1.168 5.7782004 1.388 1.323 6.1322005 1.419 1.603 6.1112006 1.426 2.119 5.5352007 1.398 3.049 5.8532008 1.322 3.709 5.1282009 1.375 4.591 5.0962010 1.413 5.019 4.7702011 1.431 5.000 4.5122012 1.403 4.641 4.3092013 1.377 5.124 5.0332014 1.367 5.200 5.3162015 1.367 5.225 5.2002016 1.375 5.275 5.500

Source: USDA ERS web page: http://www.ers.usda.gov/topics/crops/corn/background.aspx

51

Table 23.5 Corn Production from 2001-2016

Year Planted acres in millions

Harvested acres in millions

Production in million bushels

Yield per acre

Price per Bushel

LandValue in $/acre

Price index all

ag1910-

14=100

Inflation cpi

1982-84=100

2001/02

75.702 68.768 9502.58 138.2 1.97 1,510 650 177.1

2002/03

78.894 69.33 8966.787 129.3 2.32 1,590 620 179.9

2003/04

78.603 70.944 10087.292 142.2 2.42 1,660 674 184

2004/05

80.929 73.631 11805.581 160.3 2.06 1,750 751 188.9

2005/06

81.779 75.117 11112.187 147.9 2 2,060 726 195.3

2006/07

78.327 70.638 10531.123 149.1 3.04 2,300 730 201.6

2007/08

93.527 86.52 13037.875 150.7 4.2 2,530 862 207.342

2008/09

85.982 78.57 12043.203 153.3 4.06 2,760 947 215.303

2009/10

86.382 79.49 13067.156 164.4 3.55 2,640 832 214.537

2010/11

88.192 81.446 12425.33 152.6 5.18 2,700 850 218.056

2011/12

91.936 83.879 12313.956 146.8 6.22 2,980 1032 224.939

2012/13

97.291 87.365 10755.111 123.1 6.89 3,350 1083 229.594

2013/ 95.365 87.451 13828.964 158.1 4.46 3,810 1104 232.957

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142014/

1590.597 83.136 14215.532 171 3.7 4,100 1112 236.736

2015/16

87.999 80.749 13601.198 168.438 3.65 4,130 1017 237.017

Source: USDA ERS web page: http://www.ers.usda.gov/topics/crops/corn/background.aspx

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