18.4.2 Feedstock Options

In China, the constraints of limited and decreasing arable land, as well as a huge population, are forcing the government to strike a balance between food security, energy security and environmental protection. Therefore, Medium — and Long-Term Development Plan for Renewable Energy in China clearly state that “Biofuel [production] must not compete with grain over land, it must not compete with food that customers demand, it must not compete with feed for livestock, [and] it must not inflict harm on the environment” [6]. To develop useful cellulosic energy crops, it is therefore important to have a basic understanding of available land and the competing needs in relation to crop production. Specifically, large scale cultivation of cellulosic energy crops is prohibited in China, unless it is done on marginal land. Therefore, because China is a large agricultural country, crop residues have been identified as the major cellulosic energy resource.

According to the survey and evaluation report on National crop straws, the theoretical annual yield of crop straws in China is 820 million tons (air-dry weight, 15% moisture) [7]. Corn, wheat and rice, which are the three most dominant grain crops, accounted for more than 75% of the total agricultural residue resources. Geographically, more than 50% of these straw resources are located in eight provinces (Sichuan, Henan, Shandong, Hebei, Jiangsu, Hunan, Hubei and Zhejiang) (Figure 19.1). The annual yield and percentage of straw contributed to the total resource is listed in Table 19.1.

The available amount of crop residue, estimated at 687 million tons, is less than the theoretical value because all harvest technologies will leave some stubble in the field. Furthermore, many of the collectable straw residues are already being used for fertilizer, feed, fuel, and industrial materials. Therefore, in addition to the 129 million tons of straw that is currently being used for fuel, it is estimated that an additional 215 million tons of straw could be utilized for biofuel production [7].

Eucalyptus, pine and poplar are three important, quick growing tree species that have been chosen for planting in China. This has given China the largest area of planted forest in the world, but the purpose of planting those lands is to provide environmental protection and fulfill industrial demand. Therefore, it is unacceptable to produce energy using these woods. However, forestry residues, including harvesting and wood processing wastes, forest management cuttings and small branches, shrub cuttings, economy forests, bamboo forests, shrubs growing under the primary trees and municipal green forest cuttings, can be used.

According to the seventh National Forestry Survey (2004-2008) [8], China processes 195 million ha of forest area with cover rate of 20.36% each year. Of this total, 64.2 million ha is for timber, 82.1 million ha is for protection forest, 20.4 million ha for economic forest,

1.8 million ha for firewood forest, 12.0 million ha for special-use forest and 14.6 million ha is for other uses. Based on these six forest categories, the total amount of available forest residues was estimated to be 368 million tons in 2008. Although it is expected that area of forest land in China will continue to increase, forestry residue resources are expected to remain stable for the next ten years due to many constraints, such as cutting regulations,

lack of industrial collection, processing capacity, environmental protection, and competing uses with other industrial production activities.

A national survey organized by Ministry of Land and Resources of People’s Republic of China on the national land resource indicated there are about 82.5 million ha of un­cultivated land of which 34 million ha, including waste land and winter-fallowed paddy land, could be made available for energy crop production. However, two critical points sought by the Chinese bioenergy market were that (1) non-food crops should be used for bioenergy production and, of even more importance, (2) neither yield nor the ability of cultivators to produce food should be threatened or reduced.

Sweet sorghum is an energy crop that can meet the requirements established for bioenergy in China. As a variant of conventional sorghum, sweet sorghum can be characterized as multiplatform crop capable of producing grain, sugar, and cellulosic straw. The production of sweet sorghum does not decrease the capability to produce grain from cultivated land since it can be rotated with conventional grain sorghum depending upon market forces. In addition, the high photosynthetic efficiency and excellent stress tolerance of sweet sorghum enables it to be grown on lower quality, drought prone or saline land. Potential grain and stalk yields for sweet sorghum in China range from 2.25 to 7.5 tonnes per ha and 40 to 120 tonnes per ha, respectively [9]. Sweet sorghum can produce one or two times more ethanol per unit land area than corn. Moreover, new sweet sorghum varieties (e. g., the Liaotian, Chuntian, or Nengsi series) with better geographic adaptability, high stalk yield, high brix, and tolerance to saline-alkali stresses have been developed by Chinese scientists. Currently, sweet sorghum is produced primarily in northern China, with the total production in 2008 being 2.5 million tonnes [10]. Therefore, as bioethanol production using advanced processing technologies improves, sweet sorghum would appear to be the most promising bioenergy crop for China.

China also has a very rich Miscanthus resource, which is being developed into a potential energy crop by Chinese research scientists and engineers. Based on average precipitation amounts, temperature ranges and other factors, areas in East, Central, South, and Southwest China are most suitable for Miscanthus production. It appears that production of Miscanthus can be an effective complement to sweet sorghum production, but due to the late start of energy crops research in China, Miscanthus studies are still in the initial stages of development.

Forestry residues could provide another cellulosic resource, but due to many existing obstacles including environmental protection policies, competing uses for industrial pro­cessing, high costs for collection, and natural conditions where forests exist, large scale use of these residues for energy production still faces many challenges. Therefore, wood energy crops will likely not be developed very fast for at least then next ten years.

The “Rack System Concept” envisions that round bales will be loaded into a rack in the field or at a Satellite Storage Location (SSL). This rack is off-loaded at the plant, emptied when the bales are needed to supply the plant, and then returned to be refilled. It is cycled multiple times each week within the closed logistics system operated for a specific plant.

When reading this specific example, the reader should understand that others are imple­menting the feedstock logistics principles in a different way because they are dealing with a different crop in a different region with a different harvest season. Also, the farming culture is different in different regions, and this can have an influence on logistics system design.

All the different options cannot be discussed here; however, a specific example is needed to help the reader “think through” an entire logistics system. The selection of the Rack System Concept for this example implies no criticism for the other ways of implementing the multibale handling unit concept currently being developed.

In this example, cost to grow, harvest, and store in an SSL is covered in the farmgate contact. The analysis begins with round bales in single-layer ambient storage in the SSL and ends with a stream of size-reduced material entering the bioenergy plant for 24/7 operation. To understand this example, it is appropriate to summarize the “baseline”constraints:

1. The rack and trailer design must conform to the standards for highway transport.

(The rackused for this example holds 16 5-ft diameter x 4-ft long (5 x 4) round bales. With two racks on a truck, a truckload is 32 round bales. Other rack designs are being investigated.)

2. The bales will be pre-loaded into the racks while the trailer is parked at the SSL. When a truck arrives, a trailer with two empty racks is dropped, and the trailer with two full racks is towed away. The goal is to exchange trailers in 10 minutes, or less.

3. At the bioenergy plant, the racks are unloaded from the truck and placed on a conveyor to be conveyed into the plant for immediate use, or stacked two high in at-plant storage. The goal to unload the trailer should be 10 minutes, or less.

4. The plant will operate with a minimum of 2.5 days of storage. All at-plant storage is bales in racks.

5. Hauling will be done 24 h/d. Truck drivers will work five 8-h shifts per week and operations will be continuous for a 6-day work week. The rack loading crew at an SSL will be organized such that each worker will work a 40-h week. SSL operations will proceed such that racks are loaded six 10-h workdays per week.

6. The example assumes that the plant processes one bale per minute. A bale at 15% moisture content weighs 900 lb = 0.45 ton. The dry ton per bale is:

0. 45 ton/bale x (1 — 0.15) = 0.3825 dry ton/bale

Assuming one bale is processed per minute, the processing rate is:

0.3825 dry ton/bale x 1 bale/min x 60 min/h = 23 dry ton/h

(For comparison, 23 dry ton/h = 552 dry ton/d. This size is in the 100-1000 dry ton/d range recommended for Regional Biomass Processing Depots by Eranki et al. [1].)

The processing of racks is:

3.75 rack/h x 24 h/d x 7 d/wk = 630 rack/wk = 315 truck loads/wk

Estimating woody biomass feedstock across a landscape consists of three basic steps: (1) quantifying estimates of forest characteristics, such as basal area, trees, and woody biomass tons per acre across a landscape; (2) using those estimates to help determine where to apply actual or hypothetical silvicultural prescriptions; and (3) combining estimates of woody biomass with prescriptions to calculate potential treatment residues that can be utilized for fuel or raw material. Quantifying existing forest characteristics can be a substantial endeavor. Generally, this process consists of sampling areas on the ground and recording tree measurements, such as species counts, diameter at breast height (1.37 m), total height, live crown ratio, age, and percentage cull and breakage [15]. From these tree measurements, estimates of standing volume and weight are calculated using allometric equations. These measurements and calculations are then summarized based on sampling design to describe multiple aspects of a forest on a per acre basis. A common classical approach to quantifying existing forest characteristics uses stratified random sampling to relate summarized values to polygons within groups (strata) of similar forest types, stockings, and canopy cover [16]. With this approach, polygons and strata are generally created and labeled through manually defining boundaries of similar forest cover types, percentage canopy cover, and topographic position derived from aerial and satellite imagery. For larger landscapes where manual interpretation is impractical, image classification techniques are used to develop appropriate strata. Once strata have been defined, a random sample of polygons within each stratum is selected, visited, and sampled to derive mean estimates of forest charac­teristics for that stratum. Mean strata estimates are then attributed to each polygon within each stratum.

While this basic approach is still used in many analyses, mean estimates relate to the stratum as a whole and do not account for spatial variations within a given stratum. Fur­thermore, the coarse grain nature of this type of estimate may not be suitable for fine scale projects that utilize only small portions of strata. To address this issue, recent anal­yses have developed spectral and textural relationships between remotely sensed data and field measurements [17-19]. Using these relationships, estimates of biomass can vary as spectral and textural values change, thereby maintaining the spatial heterogeneity of forest characteristics at fine spatial resolution across the landscape.

After forest characteristics have been quantified for polygons or cells, they can be used to help determine where silvicultural prescriptions are applied across a landscape. The process of allocating these prescriptions to forested areas can be done in a similar man­ner as allocating logistical cost. Specifically, rules can be developed and applied using the attributes of spatial objects to identify polygons, portions of polygons, or cells that meet defined thresholds. Once allocated, these prescriptions can be combined with quan­tified forest characteristics to provide spatially explicit estimates of potential total woody biomass that can be removed from a given location. Finally, depending on the efficacy of the harvesting system and the merchandizing of the trees, treatment residues can be calculated for a given location. These residues represent the amount of potentially avail­able woody biomass that can be utilized for energy and incorporated into potential woody biomass flows.

From the perspective of Civil Society Organizations (CSOs), the adoption of sustainability standards is a tool that can help promote social protection in an era of global free trade [9]. Examples of these standards in which CSO agendas are embedded include labor standards to prevent sweatshop and child labor and standards for social justice and equitable compensation of small-scale producers and indigenous communities. Some scholars have declared this proliferation of sustainability standards to be the rise of a new “NGO-Industrial Complex” [10].

Sustainability standards are based on the process of production, not necessarily observ­able qualities of the product [11]. Biomass for biofuels, such as corn stover, switchgrass, and wood, can be produced in a variety of ways, some very disruptive to social sustainability, particularly when there are changes in land use and land tenure. Such changes have impor­tant implications for vulnerable populations, particularly indigenous peoples. Indigenous communities holding land in common and small-holders find it difficult to gather together the resources necessary to certify small lots, and certification of cultural integrity is often not amenable to conventional measurement [12].

Tightly integrated value chains in biomass used for energy are highly visible. Thus, production processes of biomass for energy are likely to be monitored by social justice organizations [13]. Activists have found that in tightly integrated supply chains, it is easier to link abusive practices in the production process with their consequences for workers, producers and communities at the production end, versus open markets where “[fjragmented supply chains conceal the social relations and exploitative practices of production” [14: 5]. Exposes of social injustice in bioenergy crop production have had serious implications for sales and stock values of companies involved. And if the driver of the biomass energy value chain is a government, the legitimacy of that government program, and therefore its continued public support, is in jeopardy. Furthermore, if lenders require social sustainability standards to be in place, there is a much higher likelihood that they will be implemented throughout the value chain.

However, large-scale producers are advantaged over small and marginalized producer — groups in certification, even if they do not necessarily contribute more to social sustain­ability. Their size and scale increases their ability to pay high certification costs and deliver large and consistent volumes of products at a constant quality. Certification benefits large corporate downstream firms by allowing them to control and switch between certified, sub­stitutable suppliers. Suppliers unable to conform to the wishes of the buyer are ultimately excluded from the chain [15]. Thus, social sustainability requires standards that are not too burdensome for small scale producers to implement, as social equity is enhanced by multiple producers rather than a single supplier.

While governments once regulated working conditions and protected land tenure, the global sourcing of biomass for biofuels has shifted certification of all types of sustainability to third party certifiers [16]. The shift to market-driven regulation has created a fundamental paradox of globalization. On the one hand, major corporations have become increasingly powerful and have assumed greater market dominance. At the same time, many of these corporations are confronted with a growing assortment of stakeholder concerns about how their products are produced, their social impacts, and the overall sustainability of the system.

Governments, such as those in the United States, Australia, and the European Union, are major investors and eventually major users of biofuels for military transportation. They also set emission standards for all fuel users in their geographic jurisdictions. Attention to social sustainability can help avoid public pressure against biofuels being seen as socially detrimental.

Woody biomass from purpose-grown energy crops offers the opportunity to positively affect logistics costs in several ways. One of the most obvious is the opportunity to reduce transportation costs by geographically concentrating the source of the material, in the form of plantations, close to the consuming facility. Secondly, and even more impactful, would be the higher productivity of the energy plantations versus wood derived from natural stands. Producing more biomass per acre means less acres required to sustain operations, resulting in shorter haul distances for the woody biomass fuel or feedstock. Therefore, it can be seen that highly-productive energy plantations, grown in close proximity to the consuming bioenergy facility, offer an excellent opportunity to minimize the logistical complexity and cost of sourcing the woody fuel or feedstock.

Dedicated woody energy crops currently represent only a minor source of biomass for energy, although it is expected that energy plantations will become an increasingly impor­tant source in the future. Harvesting systems for woody biomass from energy plantations remain somewhat developmental and will need to be adapted to the specifics of the regime being considered. Specifically, the number of stems, spacing and tree size are important determinants of feasible harvesting solutions, production, and costs.

Short-rotation woody energy crops from genera such as the willows (Salix sp.), pines (Pinus sp.), poplar (Populus sp.) and Eucalyptus (Eucalyptus sp.) provide important SRWC crops. SWRC crops differ from pulp or sawlog stand thinnings and logging residues as biomass sources in that the sole purpose of intensive energy crop plantations is biomass production. By contrast, thinning materials and logging residues from silvicultural treat­ments in forestry are a secondary product, after sawlogs or pulp. Poplar energy wood crop rotations are short, from 7 to 15 years [1], and stands are established primarily through cuttings. Willow rotations may be even shorter (3-4 years). Because poplar and willows can also be regenerated well in coppice systems, coppice regeneration systems can also be deployed for both crops. Coppice systems are those in which stump sprouts or “suckers” re-sprout from stumps to establish the new stand of woody crop following harvest.

The systematic row-crop spatial location and small diameter of short rotation woody energy crops are conducive to agriculture-style harvesting with short-rotation woody har­vesters. These purpose-built machines are forage harvesters with harvesting heads that can handle woody stems, typically less than 5 inches (12.7 cm) in diameter at breast height (DBH). A major advantage of using short rotation woody harvesters is that the resulting material delivered to roadside is a chip that is ready for transport without further prepro­cessing, that is, a single pass system. A further advantage of short rotation woody crop harvesters over the equivalent, conventional timber harvesting equipment (e. g., small exca­vators with harvester heads), is that they are able to conduct continuous travel harvesting, rather than stop-and-go felling of individual stems [2].

Although dedicated SRWC harvesters are the most promising emerging modern equip­ment for woody energy crops, a variety of conventional logging equipment has been evalu­ated in the context of woody biomass. Feller-bunchers and single-grip harvesters designed for sawlog production have been evaluated, as have a variety of forwarding systems. Mobile harvester-chipper-forwarders with knuckleboom harvester arms, chipper-forwarders, slash forwarders, slash compactors, and slash bundlers all have potential use with short-rotation crops. However, these systems tend to have either lower overall hourly production or higher hourly logging costs compared to modified swath harvesters because they require multi­stage processing. The many harvester-chipper-forwarders now available for woody biomass tend to be designed for larger diameter stems than are achieved in short rotation crops, and are better designed for intermediate thinning treatments in stands being grown for pulp or sawlog production. Unlike SWRC harvesters that have evolved from forage harvesters, the harvester-chipper-forwarder style machines tend to be designed for single approach harvest. That is, they have a harvester head mounted on a knuckleboom arm that is used to fell one or more stems, and the stems or bunch of stems are fed into the conveyor-feed mouth of an internal chipper. They are not able to perform continuous travel harvesting, but instead must stop intermittently.

The production function discussed previously in this chapter was deterministic. Risk was not considered in that function. A stochastic production function is a more general presentation of the same concept. Rather than a single output quantity being associated with each input quantity or set of input quantities, the stochastic production function has a probability distribution of output quantity associated with each input quantity or set of input quantities. This specification of a stochastic production function allows input quantities to affect not only the mean yield but also the probability of various degrees of loss (or gain) relative to the mean. In other words, the quantity of inputs used can affect the mean, variance, skewness, and kurtosis of the yield probability distribution. The farmer’s decision problem may be more realistically portrayed by a stochastic production function. The additional complexity introduced by the stochastic production function may also be more representative of the decision challenges faced by farmers. Not only must they consider the effects of numerous inputs on expected yield but also their effects on the probability of various degrees of yield loss or gain relative to the average yield.

As an industry that relies upon the growth of plant material, the basic denominator of all supply chains is availability of suitable agricultural land. Land is also the foundation for most other agricultural industries, including forage and grain for beef, swine and poultry production as well as the commodity or row crops. Competition for land will be one of the primary challenges to overcome in achieving commercial scale biomass production [3]. However, developing a sustainable feedstock supply chain is much more complicated than simply finding available land. Commercializing dedicated energy crops requires not only finding land but also recruiting landowners to use their land for biomass production. Recruiting landowners is a difficult task that must address several factors, including out — of-pocket costs, return to land and labor, competing land uses and existing relationships on the land.

It is often said that “marginal” land will be used for production of energy crops. But what is marginal land? Is it abandoned agricultural land? Fallow land? Land used simply for grazing or pasturing of animals? The definitions are broad and challenging and no two people can agree on a sound definition. It is safer to say that the use of agricultural land will be determined by its owner, who is guided by several factors: financial return, existing relationships (lease holders), and their farm management skills. For any available land to be enrolled in energy production, it must provide the right value to the farmer, both financially and as it fits with the landowner’s management objectives. There is no argument that some land areas are more suitable for certain types of biomass production, while other areas are either not suitable or simply unavailable. In a perfect world, each acre of agricultural land would undergo a rigorous evaluation to determine its highest value and best use. In reality, though, there are many other factors that influence decisions made regarding land use.

When landowners make decisions regarding how to use their land, the current use, current and projected level of management, and several community-oriented issues all influence their final determination. However, the most influential factor and what usually finalizes the decision is whether or not a particular land use will be profitable to the landowner [4]. When commercializing energy crop production, landowners and farmers typically think about the cash flow of their operations in annual terms. Operating capital outlays offset by harvest season income is the common modus operandi for commodity row crops. However, when planning financial operations related to dedicated energy crops, the timeframe between outlays and income is much more extended and no longer fits with traditional agricultural operations. To willingly invest in in biomass production for cellulosic bioenergy operations, landowners and farmers must understand and be comfortable with this key difference and know how to plan accordingly. Simply comparing the current year’s return from a crop like soybeans to the possible return from the same acre with switchgrass would not be a valid or accurate comparison. Similarly, comparing the current year’s annual return on hay production to that from an energy crop on the same acre would not be accurate either. In both cases, many other factors can impact the annual return.

For row crops, annual returns are impacted by weather, global demand, and many other factors. Over the last three to four years, a variety of these factors have convened to make commodity prices for corn and soybean reach record levels [5]. These current high commodity prices make producing corn and soybeans on less productive acres more attractive. As a result, many agricultural areas have seen an expansion of these crops onto land where row crops have traditionally not been grown, as farmers can afford to take a lower yield per acre and still achieve a break-even or profitable return on investment with the current market. This expansion of row crops into less productive areas will impact land availability for energy crop production. Likewise, forage markets are significantly driven by annual weather patterns. During 2012, there was an intense drought throughout the central portion of the United States and, therefore, prices for hay moving out of the southeast reached near record levels. To compare these one-year returns to energy crop production on similar acres, the landowner and/or farmer must evaluate long-term benefits.

Energy crop production can bring a significant multiyear benefit to the landowner or farmer. In most cases, it is envisioned that long-term (5-10 year) contracts will be utilized to reduce risk for both the conversion facility and the producer. These long-term contracts will guarantee price stability over the contract period. Given that most dedicated energy crops’ drought tolerance is greater, yield variation due to weather will be minimal compared to traditional row crops, thus resulting in more consistent yields over time. Consistent yields lead to more consistent returns in long-term contracts. As landowners and farmers evaluate energy crop production, they should compare returns from the previous 10 years of competing land use against potential 10-year returns from the selected energy crop. Only then can a clear decision be made based on financial returns per acre of land.

Investors want to build a plant as large as possible because processing cost (per unit of product) typically goes down as plant size increases. Overend [4], Jenkins [5], and others more recently have shown how the average delivered cost of feedstock increases as plant size increases. The reason is that, as the feedstock consumption increases, the size of the production area increases, and hauling cost increases with average haul distance. Two factors are important:

1. Density of feedstock production — the percentage of total land area within a given radius of the plant that is attracted into feedstock production.

2. Feedstock yield — the tons of biomass harvested per unit of production land.

The two terms are sometimes lumped together into the term “feedstock density”. The influence of feedstock density is shown in Figure 13.4. Average hauling cost increases with plant size for all densities. Note, however, that the cost increase is much less for the higher density curves. A plant owner seeking to obtain an economy-of-scale benefit by building the largest plant possible will want to locate where the maximum number of surrounding land owners sign up to grow feedstock.

Figure 13.4 Influence of plant size on mean hauling cost. Production area percentage is the percentage of the total land area attracted into feedstock production.

In addition to chipper-forwarders, there are now commercially available machines capable of harvesting, self-feeding, chipping, and transporting woody biomass. Though not com­monly in use, these machines have the advantage of performing “single pass” utilization of thinned materials, when larger diameter (e. g., >5-inch DBH) must be processed.

14.7 Woody Biomass Transportation

Along with harvesting and processing costs, transportation costs are a major determinant of the delivered cost of woody biomass. Even after communition or compaction, woody biomass tends to be bulky and difficult to transport efficiently. The preferred approach, when possible, is to maximize net payload by using the largest trailer possible. For example, high — capacity chip tractor-semi-trailer combinations, also called chip vans, can exceed 19 m in length, 45 000 kg in gross vehicle weight, and 30 000 kg in net payload. Large payloads distribute the fixed costs of transportation over a larger amount of material and generally, though not always, result in greater input/output efficiency in variable costs, such as fuel consumption. Larger payloads also reduce operational delays associated with the loading and unloading of many small trucks compared to loading fewer large trucks. Though ideal from an operational standpoint, a number of factors constrain the use of these vehicles in woody biomass logistics.

The production function and profit function presented previously in this chapter make no explicit mention of resource, environmental, and social dimensions of sustainability. The profit maximization model implies reward for efficient use of resources. Similarly, rewards for efficient use of inputs provide disincentive for waste and related environmental emis­sions. The expected profit maximization and variance minimization model makes explicit the trade-offs between input use, expected profit, and variance of profit. Input use and implied waste may be increased or decreased to affect variability of profit. Various terms can be added to the expected profit maximization model to explicitly incorporate sustain­ability conditions and incentives. Explicit restrictions can be placed on the crop production function to limit the amount of nutrient loss to surface and groundwater. Such constraints raise the cost of production if they are binding. Emissions restrictions may represent regu­latory limits or sustainability conditions imposed by the processor or the farmer. Incentive payments can be added to the income portion of the function reflecting payments received for attaining sustainability criteria. Such terms could represent income from a watershed nutrient trading program or from credits from a carbon capture and retention program.

Outside of the expected profit maximization model, footprint and life cycle analysis based measures of performance can be included in contracts and farmers along with the processor and input providers can apply existing standards for sustainable production to set practices and procedures. Third-party certifying agencies can be employed to audit procedures, monitor progress, and suggest avenues for improvement. Such efforts can inform ongoing technology development efforts within the system.

Markets for traditionally non-market goods and services include markets for nutrient discharge permits or reduction credits, markets for carbon emission reduction credits, markets for renewable energy credits, and others. In addition, some manufacturers may sell branded or certified sustainable products at a premium to capture consumers’ willingness to pay for sustainability attributes. Some corporate retailers impose sustainability criteria on their suppliers and incorporate the increased cost of procurement into their product pricing. Such retailers may differentiate their products by highlighting the sustainability criteria in advertising campaigns.