November 2014 E-1 

Appendix E: Discussion of Leakage Literature 

Table of Contents 

1. Introduction ................................................................................................................................................................. E-1

2. Background .................................................................................................................................................................. E-2

3. Factors that Can Contribute to Leakage ........................................................................................................... E-3

4. Overview of Relevant Literature ......................................................................................................................... E-6

4.1. Literature Research on Leakage ................................................................................................................ E-6

4.2. Leakage Literature: Agriculture ................................................................................................................. E-7

4.3. Leakage Literature: Forestry Sector ......................................................................................................... E-9

5. Examples of Leakage Analysis ........................................................................................................................... E-10

5.1. Policy Analyses that Include Leakage ................................................................................................... E-10

5.1.1. Renewable Fuel Standard ................................................................................................................ E-11

5.1.2. CARB Analysis ....................................................................................................................................... E-11

5.1.3. EU RED Analysis ................................................................................................................................... E-12

5.2. Treatment of Leakage in Existing Carbon Accounting Protocols .............................................. E-12

6. Summary .................................................................................................................................................................... E-15

7. References .................................................................................................................................................................. E-15

1. Introduction 

The purpose of this appendix is to review the literature that discusses greenhouse gas (GHG) 

emissions leakage, which is an indirect source of biogenic CO2 emissions associated with the 

production, processing, and use of biogenic material at stationary sources. Specifically, this 

appendix examines a range of studies that, using a variety of modeling approaches and scenario 

designs, evaluate how and why leakage occurs. There is a particular focus on indirect land use 

change (ILUC), which is an important form of leakage to consider when assessing biogenic CO2

emissions from stationary sources. This examination is intended to help identify important factors 

that could be considered when assessing leakage and different methods that have been used to 

calculate emissions leakage in other contexts. In the event that policy- or program-specific 

applications of the framework necessitate calculations of leakage, the analysis here could inform 

such a process. This appendix considers both international and domestic (interregional) leakage for 

completeness. 

Recognizing that leakage associated with bioenergy feedstock production can occur due to market 

and land use change induced by displaced feedstock or feedstock substitute production, the 
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framework equation presented in this report includes a leakage term.1 However, a specific 

quantification methodology recommendation is not provided in this report: the determination to 

estimate and include leakage in applications of the framework, as well as the methods to calculate 

it, will be policy- or program-specific. 

The potential importance of leakage can vary across feedstocks and production circumstances. It is 

important to recognize that biogenic assessment factor (BAF) results may differ considerably for 

some feedstocks depending on treatment of leakage, but quantifying it is complex, as discussed in 

the literature review below. 

The remainder of this appendix discusses the primary factors that typically contribute to leakage, 

provides an overview of relevant literature, and offers examples of different leakage analyses that 

have been conducted in different policy contexts and with different goals, assumptions, and 

parameters. 

2. Background 

Policies and programs typically have limited spheres of direct influence or scope and therefore may 

result in changes in activities outside their scope that can contribute to the net impacts of the 

action. Leakage is an indirect consequence of policies or behaviors that can and occur in many 

different contexts. Leakage effects could be positive (e.g., benefits of local tourism extending beyond 

the region or technological innovation spreading from one firm to others) or negative (e.g., reduced 

deforestation in one region is at least partially offset by increased deforestation in other regions as 

output prices rise). In the context of environmental policy, one of the key areas in which leakage has

been examined in recent years is displacement of GHG-emitting activities to areas and/or sectors 

that are not covered by a policy, or program (Barker et al., 2007; Weber and Peters, 2009; Chen, 

2009). There are different definitions of carbon leakage in the literature, but the International Panel 

on Climate Change (IPCC) Special Report on Land Use, Land-Use Change, and Forestry defines 

carbon leakage as “the indirect impact that a targeted LULUCF activity in a certain place at a certain

time has on carbon storage at another place or time” (IPCC, 2000). 

In the context of the framework and its focus on biogenic carbon and CO2

2 emissions fluxes, leakage 

represents any biogenic CO2 flux changes outside of a biogenic feedstock production assessment 

scope that can be attributed to the production activities (e.g., replacement of diverted crop, 

livestock, or forest products on other lands due to a change in land use from conventional 

commodity production to biogenic feedstock production for energy conversion). 

If the assessment scope of the policy or program was global, then there would be no leakage 

because all emissions would be inherently captured within the assessment scope. In practice, 

 

1

 The LEAK term could be incorporated into the retrospective reference point and future anticipated baselines in

different ways. For further information on the retrospective reference point baseline, see Appendix H. For further 

information on the future anticipated baseline, see Appendix J. 

2

 The framework could potentially be expanded to include additional GHGs as appropriate for a particular policy 

application. 
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however, project assessment scopes are typically more limited. In some cases, especially where 

policies result in substantial price effects, there may be changes in activities and emissions that take 

place outside the defined assessment scope. The reason is that activities in regulated 

sectors/regions tend to shift to, or have influences on, sectors/regions outside the regulatory 

and/or assessment framework, particularly if there is reduced product availability from the 

regulated sectors/regions. In that case, there will tend to be price increases that will induce 

expanded production in other related sectors and in other regions. 

Although outside of a project’s direct control, emissions that are shifted to another location or 

sector may have an important effect on the project’s net GHG benefits. One of the more important 

sources of leakage for programs or policies affecting land use is the impact on carbon storage due to 

shifting land use. Where land is changing uses due to these indirect pressures, this specific leakage 

effect is commonly referred to as ILUC. Depending on the lands being converted and biogenic 

material being produced, ILUC can cause net changes in GHG emissions or sequestration. Because 

leakage from additional biogenic feedstock production can potentially be significant, the framework 

in may need to consider including leakage for certain policy or program applications. Inclusion of 

leakage estimates would account for changes in GHG emissions from ILUC or other sources of 

leakage that occur outside of the biogenic feedstock production assessment scope. 

3. Factors that Can Contribute to Leakage 

In the general context of leakage related to GHG emissions globally, for example, as long as all 

emissions sources are governed by the same rules, shifting emissions from one region to another is 

perfectly acceptable and indeed represents a more economically efficient outcome (Murray, 2008). 

If all regions are covered by the same policy or assessment system, no leakage would occur, because 

all emissions would be accounted for (assuming full enforcement) (Murray, 2008). However, few 

policies have a global scope, making leakage difficult to avoid. Whenever incentives for action differ

across potentially affected entities, there will be a tendency to shift activities that result in 

emissions from more highly controlled entities to less controlled entities. In general, leakage can 

erode net carbon reductions because “the spatial scale of intervention is inferior to the full scale of 

the targeted problem” (Wunder, 2008, p.65). 

The primary driver of leakage is economic—in globally integrated markets, increased demand for a 

biogenic feedstock for energy within the assessment area may lead to increased production of that 

type of biomass and/or other changes in land use patterns outside the assessment boundaries. This 

is because increased demand for a biogenic feedstock for energy production triggers higher overall 

demand for the biogenic feedstock, thereby leading to higher commodity prices for that feedstock 

and its substitutes. These commodity price increases can lead to a succession of land-use changes to 

produce more feedstock, including the conversion of forest and other high-carbon storage 

ecosystems to lower carbon storage systems and the release of carbon stored in soils and 

vegetation. However, depending on the feedstock and time frame considered, it is also possible for 

positive leakage to occur. For instance, higher prices for forest biomass could lead landowners to 

convert a large enough area of agricultural lands to forests that regional carbon stocks are 

increased relative to baseline conditions. 
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Leakage effects, including ILUC, can also occur when lands and/or biogenic materials previously 

used for other purposes are instead diverted to biogenic feedstock production due to competition 

and resource scarcity. However, the market demand for the original product still exists and with 

higher commodity prices there is still an incentive for supply of the original product to approach 

the original quantity demanded. This additional demand can be met through intensification of 

existing lands producing the original product materials elsewhere or extensification, which means 

bringing new lands into production. 

Agricultural and forest commodities are frequently traded in markets that operate at a local, 

regional, national, or global scale. As a result of this integration, changes in the supply and demand

of commodities in one part of the world may be translated into changes in market supply and 

demand of the same and related commodities in other parts of the world. Policies targeting land use 

for specific activities in one location can induce a broader reallocation of land use unless such shifts 

are specifically and effectively restricted by the policy (e.g., Wu, 2000; Wear and Murray, 2004; 

Murray, McCarl, and Lee, 2004). 

Similarly, substitutability and competition with other biomass types may lead to production 

changes beyond the assessment area because of potential product substitution (Latta et al., 2013). 

Land can be used to produce a wide array of forestry and agricultural products. Land cover and 

land use are expected to vary over time as land is allocated to activities that yield the highest net 

present value based on information available when the land use allocation decision is made. In 

addition, many forestry and agricultural commodities have other commodities that are at least 

partially substitutable for them (e.g., livestock feed can be made using a variety of grains and 

oilseeds, including corn, wheat, rye, barley, oats, soybeans, and others used in various combinations 

that meet livestock nutritional requirements). As a result, commodity prices are generally 

correlated due to adjustments taking place on both supply and demand sides as both buyers and 

sellers adjust to changing relative prices. Thus, there may be an associated emissions shift from 

assessed regions to unassessed regions due to land use change and other production-related 

activities. Ignoring leakage can make emissions fluxes from biomass use appear larger or smaller 

than they actually are, thereby potentially undercutting program objectives (Murray, 2008). 

When these land-use transitions occur outside the assessment region, related GHG emissions fluxes 

may not be accounted for. Some of the literature indicates that biogenic feedstock production 

projects reduce GHG emissions to the atmosphere only if the net growth of harvesting of the 

biomass for energy captures carbon above and beyond what would be sequestered anyway (i.e., if 

sequestration is additional).3 In one study, foregone sequestration is considered the equivalent of 

additional emissions, and when these emissions are associated with activities producing biomass, 

 

3

 “Additionality” is a criterion for assessing whether an activity has resulted in GHG emission reductions or 

removals relative to what would have occurred in its absence. This is generally a more complex criteria for landbased mitigation activities than for point-source or facility-based activities because of the inherent dispersed, 

heterogeneous, dynamic, and systems-based aspects of agricultural and forestry production, but there are viable 

strategies for addressing additionality in these sectors (Janzen et al., 2012). 
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the author argues they should be included in GHG accounting associated with the biomass 

production (Searchinger, 2008). 

“Leakage potential can be high if no counteracting provisions are put in place” (Murray, 2008, 

p. 10); and the economic forces driving leakage are interdependent and “difficult to restrain” 

(Murray, 2008, p. 18). According to some studies, when considering leakage, estimates should 

reflect the following elements: connectedness of output and land markets, mobility of labor and 

capital, consumer flexibility, producer flexibility, availability of alternative lands for production, and 

ability of producers to change their emissions profile without modifying production (Wunder, 

2008; Henders and Ostwald, 2012). 

The change in total cultivated land associated with a change in demand for bioenergy feedstocks 

will depend on a number of parameters, but one of the most important factors is the land supply 

elasticity. In cases where land supply is relatively elastic, there will be relatively large increases in 

supply of a given land type when the returns to that type of land increase. Landowners will shift 

their land cover and crop mixes to provide more of the commodities that are in greater demand. If 

land supply is relatively inelastic, on the other hand, then there will be a smaller response to 

changes in demand for individual commodities. The more competitive and integrated land markets 

are across regions, the larger the extent of leakage expected when different regions face different 

incentives to mitigate emissions. However, when considering leakage across regions, even when 

considering only a single type of biomass, it should be noted that there is a difference between 

shifts in production activity or area and net emissions. For example, even within the United States, 

shifting forestry area from the Southeast (SE) to Pacific Northwest (PNW) would likely reduce net 

carbon emissions, whereas the reverse would result in significant positive domestic leakage due to 

the lower carbon density of SE forests (summing across carbon contained in aboveground biomass, 

belowground biomass, deadwood, forest floor, and soil organic carbon) (Heath et al., 2011). 

The elasticity of demand for conventional commodities must also be considered. In cases where 

demand is inelastic, the quantity demanded will change by a smaller percentage than prices rise. 

This inelastic behavior will result in a greater amount of leakage than in markets with more elastic 

demand. Higher production costs resulting in lower production levels in regulated regions will 

result in a great deal of shifting of production to regions unaffected or less affected by policies 

because the overall market demand does not decline much in response to higher prices. When 

demand is highly elastic, policy impacts that result in higher production costs and increased market 

prices will result in less production moving to other regions because the equilibrium quantity 

demanded will decline by a greater percentage than price increases. In addition to the own-price 

elasticity of demand, cross-price elasticities of demand for substitutes and complements are also 

important to consider. Not only will increases in the market prices of directly affected commodities 

potentially lead to increased production of those commodities into less directly impacted regions, 

but they will also impact production of complement and substitute commodities in other regions. 

Another important point of consideration is that as demand for a commodity increases, producers 

may intensify production practices (e.g., increase fertilization rates, use of irrigation, improved crop 

varieties, and other yield enhancements) because higher output prices make it profitable to engage 

in more intensive production practices requiring greater input expenditures. Achieving higher 
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yields through intensification would limit direct and indirect land-use change (Searchinger, 2008) 

but may lead to other GHG fluxes (e.g., increased N2O emissions from higher levels of nitrogen 

fertilization, higher CO2 associated with greater fossil fuel use for irrigation). Thus, the net change 

in GHG emissions would depend on the relative changes in emissions across all relevant pools and 

intensification could either increase or decrease total emissions relative to extensification. 

4. Overview of Relevant Literature 

Although the concept of carbon emissions leakage in industrial sectors has been widely studied for 

over 20 years, the focus on leakage in land-using sectors has been more recent. There have been 

many studies of industrial carbon leakage ever since the United Nations Framework Convention on 

Climate Change was established in 1992, identifying differentiated responsibilities for reducing 

emissions, and the subsequent Kyoto Protocol was developed and agreed upon with emissions 

limits specified only for a set of developed countries. There is also extensive literature on the 

international trade and competitiveness under environmental policy going back to the 1980s, 

although this literature on “pollution havens” was not typically focused on global pollutants and 

often not explicitly focused on implications for total emissions as much as distributional impacts of 

polluting industries’ potential relocation between states or countries. 

Interest in leakage associated with land-using sectors has grown considerably in the last decade 

with the development and implementation of bioenergy policies and international policy interest in 

reducing emissions from deforestation and land degradation (REDD+). In this section, an overview 

of the literature on leakage associated with land use and potential relevance is provided, followed 

by brief sections summarizing some of the recent relevant literature focused on agriculture and 

forestry applications. 

4.1. Literature Research on Leakage 

A full accounting for leakage associated with land use and related GHGs is very complex because of 

the multiple affected markets, heterogeneity, dynamics, and numerous interactions. The literature 

offers an incomplete picture of leakage magnitude and what can be done to minimize negative 

leakage (Kim and Dale, 2011; Murray, 2008). Furthermore, the precise meaning of the term 

“leakage,” both in terms of scale and scope, can fluctuate from study to study, making direct 

comparisons difficult.4 Finally, few studies mirror the feedstock sub-delineations used in the 

framework report, thereby complicating evaluations of feedstock-specific applicability. 

Because the primary bioenergy, REDD, and other forestry and agriculture policies of interest for 

leakage assessment have typically been implemented relatively recently, time series data for 

empirical analysis are limited. Typically, the policies being considered do not have direct historical

precedent and would result in new markets being created, which results in changes in market and 

 

4

 “Leakage” sometimes refers simply to indirect land-use change but can also be used along with carbon debt or 

market price impacts. If GHG emissions from all regions are accounted for in a consistent manner and reflected 

under a regulatory framework, then there could be indirect land-use change without carbon leakage (because any 

changes in emissions associated with indirect land-use change have been reflected in GHG accounting). 
November 2014 E-7 

land use activities that fall outside past experience and limits the ability of statistical analyses of 

existing data to explain future outcomes. Data limitations, along with the complexity of adequately 

reflecting relevant factors influencing market outcomes and land dynamics, have resulted in there 

being a limited empirical literature on leakage in land-based sectors. Absent empirical data from 

representative case studies, leakage estimates have instead employed a variety of economic land 

use models covering the agricultural, forestry and other land use sectors, such as, the Forest and 

Agricultural Sector Optimization Model with Greenhouse Gases (FASOM-GHG), the Food and 

Agricultural Policy Research Institute (FAPRI) model, and the Global Trade Analysis Project (GTAP) 

general equilibrium model. These economic models vary widely in terms of model type, inputs, and 

assumptions, as well as scope and scale in terms of output. In addition, many of the existing 

economic land use models do not fully account for all GHG emissions associated with the market 

activities being modeled. Therefore, in some applications, the changes in market activities and land 

use simulated using the models have been combined with emissions factors available in models 

such as the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) 

model in order to estimate net changes in GHG emissions. 

Such models can lend insights as to the possible directionality and/or general magnitude of leakage. 

However, leakage affects multiple markets and regions simultaneously, thereby increasing the 

complexity of model projections and making it difficult to isolate the causes and effects of market 

and land-use shifts and related leakage. The results are largely case-specific, and depend greatly on 

context and the assumptions of each particular study. 

In countries where land use is highly regulated and controlled with few linkages to international 

markets, it may be easier to estimate (and control) leakage. When land use change occurs in an 

unplanned and unpredictable manner across numerous countries (e.g., indirect land-use change 

caused by bioenergy development), detecting leakage is particularly difficult and involves many 

different measurements and analyses to adequately represent and understand the land use and 

emission dynamics. Many studies project multiple scenarios using many different assumptions as 

sensitivity analyses to reflect parameter uncertainty. Because of this and the aforementioned 

variability in how leakage is defined and measured, there is considerable inconsistency 

surrounding leakage estimates (e.g., Plevin et al., 2010). 

4.2. Leakage Literature: Agriculture 

There has been a growing amount of attention and research effort devoted to the effects of policies 

affecting the demand for agricultural commodities on land use change, particularly in the context of 

increased demand for biofuels (e.g., ethanol and biomass-based diesel). Notably, EPA includes 

estimates of land use change due to increased demand of specific biofuels (e.g., corn ethanol) as 

part of the GHG accounting applied for Renewable Fuel Standard (RFS2) feedstock analyses (EPA, 

2010), as described in more detail in Section 5.1.1. Partly because of the implementation of RFS2 

and biofuels policies in the European Union, Brazil, and elsewhere in recent years, there has been a 

great deal of interest in indirect land use change and leakage associated with bioenergy policies. 

The existing literature assessing potential leakage magnitude from corn ethanol production shows 

that estimates differ considerably across studies and within a study depending on underlying 
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assumptions (Khanna et al., 2011; Khanna and Crago, 2012). Searchinger et al. (2008, 2009) and 

Fargione et al. (2008) are frequently cited studies among the first to challenge the net benefits 

associated with biofuels on the basis that indirect land use and GHG emissions from land use 

conversion were not being fully captured and to quantify the impacts. For instance, Searchinger et 

al. (2008) combines calculated changes in land use they estimate are necessary to meet required 

increases in U.S. corn ethanol volumes with emission factors from the GREET lifecycle model to 

project changes in net GHG emissions resulting from an increase in U.S. corn ethanol production. 

Searchinger et al. (2008) calculated that a 56 billion liter increase in ethanol production would 

divert corn from 12.8 million ha (hectares) of U.S. cropland. This would in turn bring 10.8 million ha

of additional land globally into cultivation, including 2.8 million ha in Brazil, 2.3 million ha in China 

and India, and 2.2 million ha in the United States. The emissions associated with converting this 

land represent leakage (aside from direct land use change for corn production in the United States), 

but the true magnitude of the leakage depends on the nature of the land-use change (i.e., what type 

of land is being converted). They assumed the conversion of forest to cropland releases 604 to 

1,146 metric tons CO2e per ha, while the conversion of grassland/savannah to cropland releases 75 

to 305 metric tons CO2e per ha. 

Regardless of the type of land converted, the payback period for these up-front emissions can be 

very long. Using an average of 351 metric tons CO2e per converted ha, Searchinger et al. (2008) 

estimated “carbon neutrality” for corn-based ethanol only after 167 years. Using the standard 30-

year time frame, corn-based ethanol results in a 93% increase in net emissions compared with 

business-as-usual gasoline consumption. This study also ran a sensitivity analysis that includes 

20% increases in grain yields, land-use emissions reduced by half, and process efficiency gains of 

40%. In that best-case scenario, net emissions approach those of gasoline after 30 years. 

A key driver of the results in each of these studies is that expanded corn ethanol production in the 

United States will substantially reduce U.S. corn exports, leading to expanded foreign corn 

production. They assumed that foreign countries will convert natural vegetation to croplands, 

including tropical rainforests and other high carbon density areas, which results in very large 

carbon emissions (Searchinger et al., 2008, 2009; Fargione et al., 2008). 

However, a number of subsequent studies have critiqued some of the assumptions used in 

Searchinger et al. (2008) (e.g., Wang and Haq, 2008) and many subsequent studies provide 

alternative estimates of ILUC and leakage. Some of the key considerations influencing ILUC include 

assumptions regarding price elasticities, yield improvements over time, the substitution rate of 

dried distillers grains for corn, the types of land that are converted, GHG calculations, and long-term 

land dynamics in response to changing incentives, among others. 

For instance, Hertel et al. (2010) employed an approach similar to Searchinger (2008), but used 

different assumptions about the land that will be converted to agriculture. Importantly, Hertel et al.

(2010) relied on the GTAP-BIO version of the GTAP model, a general equilibrium agricultural 

economic model that provides more land use data and economic details of land use than the more 

basic assumption about land requirements used by Searchinger (2008). Whereas Searchinger 

(2008) estimated most conversion will be either forest to cropland or grassland to cropland, Hertel 

et al. (2010) estimated that much of the cropland transition will actually stem from degraded, 
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pasture, or other low-carbon lands, which substantially reduces emissions from indirect land use 

change. Studies such as Potter et al. (2007) and Aguilar et al. (2012) have estimated significant 

quantities of marginal lands in the United States that they argue could be converted to biomass 

production, which would substantially reduce pressure for conversion of higher carbon density 

forests and grasslands. However, it is also important to consider the economic incentives facing 

landowners. The fact that it is physically possible to grow bioenergy crops on marginal lands does 

not mean landowners will choose to do so if it is more profitable to grow them on more productive 

croplands. 

Drabik and de Gorter (2011) also measured carbon leakage associated with biofuels (specifically, 

corn-based ethanol), but instead of land-use change they focused on the market response to 

corresponding changes in fuel prices. Specifically, if fuel prices decline as a result of increased 

ethanol production, then total fuel consumption will presumably increase (i.e., the rebound effect). 

As a result, 1 gallon of ethanol does not replace 1 gallon of gasoline. Instead, because of the price 

impact, Drabik and de Gorter (2011) showed 1 gallon of ethanol replacing only 0.35 to 0.5 gallons 

of gasoline. Thus, although average per gallon carbon intensity of fuel may decrease as a result of 

ethanol, overall fuel consumption may increase and could potentially overwhelm these reductions. 

In another application examining potential domestic leakage of environmental policy, Wu (2000) 

analyzed domestic leakage related to the U.S. Conservation Reserve Program (CRP) and found that 

for every 100 acres of cropland retired under CRP in the central United States, about 20 acres of 

non-cropland were converted to cropland. This leakage effect was estimated to offset about 9% of 

the CRP water erosion benefits and 14% of the wind erosion benefits. 

4.3. Leakage Literature: Forestry Sector 

Similar to the agricultural sector, policies affecting forest use in one place are expected to impact 

forest management decisions elsewhere. In particular, there has been a great deal of interest in 

quantifying the extent to which forest conversation measures in one location induce greater timber 

harvesting elsewhere. Many carbon leakage studies have focused on the forestry sector. 

Murray, McCarl, and Lee (2004) developed a conceptual model for analyzing market adjustments 

and carbon leakage. They also applied FASOM to empirically estimate leakage from different forest 

preservation strategies in the United States. They estimated leakage for U.S. carbon sequestration 

policies and found leakage rates varying from less than 10 to over 90% depending on policy 

specifications and region(s) of the country where the policy is implemented. Alig et al. (1997) also 

applied FASOM and found that carbon benefits from expanded U.S. afforestation would largely be 

offset by converting existing forestland to agriculture. Wear and Murray (2004) explored the effects 

of public forest conservation in the U.S. Pacific Northwest on forest production and markets in the 

United States and Canada. They found that a total of about 84% of reduced public harvest would be 

replaced by increased private harvest, and around 58% of reduced public harvest would be 

replaced within the United States and another 26% in Canada. 

Sohngen and Brown (2004) examined leakage associated with a specific tropical forest 

conservation project in Bolivia. They developed a dynamic timber market optimization model and 
November 2014 E-10 

ran the model using differing assumptions about global policies, capital constraints, demand 

elasticity, and deadwood decomposition rates. Overall, they found leakage rates of 5 to 42% for this 

project-level assessment. Leakage is lowest when demand is more elastic and wood decomposition 

rates are faster. 

There have also been a number of recent studies examining leakage associated with forestry 

policies at the global level. Gan and McCarl (2007) estimated international leakage with the GTAP 

general equilibrium model. They defined leakage in terms of forest production rather than 

emissions, but the changes in forest production from their model would clearly have implications 

for GHG emissions. They examined lumber, paper, and log markets and analytically derived the 

transnational leakage and applied GTAP to estimate leakage at a global level. The study concludes 

that leakage is related to assumptions about the elasticities of demand and supply of forestry 

products, lumber and wood products, and pulp and paper products across many countries. They 

also note that cooperation among countries tends to alleviate leakage. Under current global trade 

conditions, they estimated leakage rates ranging from 42 to 95% with leakage rates above 70% for 

the majority of regions. Because they are defining leakage in terms of forest products production, 

carbon leakage may be even greater if forest production is shifting to less efficient production 

regions and/or regions with higher carbon density. Sun and Sohngen (2009) use a global land-use 

forestry model to estimate global leakage emanating from three different forestry set-aside 

scenarios and find that leakage could be nearly 100% in the near term under one of the global 

policies examined. Sohngen and Sedjo (2000) showed the impact of increased demand on harvests 

and management of industrial forests in regions around the globe. Their model showed significant 

GHG emissions from boreal and temperate forests, but this impact was dampened by the rising 

influence of subtropical plantations. Overall, they found carbon leakage to be less than 16%. 

In addition, several recent studies have identified the potential for increased bioenergy demand to 

raise biomass prices sufficiently to induce greater levels of afforestation and more intensive forest 

management to the extent that total carbon stocks are actually increased (Daigneault, Sohngen, and 

Sedjo, 2012; Sedjo and Tian, 2012; Sedjo, Sohngen, and Riddle, 2013). There may be a short-term 

reduction in forest carbon as harvesting increases but greater sequestration in the long term as 

forest area and carbon densities increase. 

5. Examples of Leakage Analysis 

Although the development and implementation of a land use analysis that adequately reflects 

leakage is a very complex endeavor, there are cases where leakage has been estimated and used in 

calculations of net GHG emissions profiles for both policy analyses and carbon accounting 

protocols. Several examples are presented below. 

5.1. Policy Analyses that Include Leakage 

There are a number of examples of policies and programs that take leakage into account. However, 

the methodological approach of each program is carefully tailored to meet the program or policy’s 

requirements that the analysis is being designed to serve. As such, these methodologies may differ 
November 2014 E-11 

in several ways, including technical assumptions used, models (and types of models) used, scope 

(spatial and temporal), and many other factors. 

5.1.1. Renewable Fuel Standard 

The Energy Independence and Security Act of 2007 (EISA) specifies life-cycle GHG emissions 

reductions thresholds that renewable fuels must meet to qualify in different categories and defines 

lifecycle GHG emissions to include “significant indirect emissions such as significant emissions from 

land use change.” As a result, EPA’s analysis was conducted to capture emissions that may result 

from indirect land use changes in the United States and abroad. EPA’s analysis of the RFS2 program 

was conducted estimating the effects of shocks of national aggregate demand for individual 

feedstocks. Partial equilibrium models of the global agricultural sector (FAPRI) and the domestic 

forest and agricultural sectors (FASOM-GHG) were used to simulate the effects of expanded 

bioenergy production consistent with RFS2 requirements on land and commodity markets. The 

changes in market activities and land use generated using these models were combined with 

emissions factors from the GREET model, satellite data analysis of modeling of land use change 

emissions (Harris et al., 2008), and IPCC emissions factors to generate estimates of lifecycle 

emissions associated with renewable fuels production. 

Both direct and indirect land use changes are included in the calculation of the net lifecycle GHG 

reductions provided by individual biofuels feedstocks, including land use adjustments within the 

U.S. and internationally. Total net changes in activities are presented in that study; there is no 

separation of direct and indirect impacts. For a biofuel pathway to qualify under a given RFS2 

category, it must meet or exceed the GHG reduction threshold for that category based on the total 

net emissions associated with that pathway relative to the use of fossil fuels. Renewable fuels have 

a minimum target of 20% reduction; advanced fuels, including biomass-based diesel, must provide 

at least a 50% reduction; and cellulosic fuels must reduce emissions by at least 60%. 

It is also important to note that the inclusion of leakage in the RFS2 analysis is the result of fulfilling 

statutory obligations as set forth in the EISA of 2007. The goals, methodology, tools, and 

assumptions used for EPA’s RFS2 analysis may not necessarily be suited for another policy analysis. 

Whether and how to reflect leakage in the context of a specific policy or program application of the 

biogenic assessment framework would need to be determined for particular applications. Each 

analysis must design a framework that best suits their particular goals and analytical requirements. 

For more information on EPA’s RFS2 final rulemaking and analysis, refer to the Regulatory Impact 

Analysis (EPA, 2010). 

5.1.2. CARB Analysis 

The intent of the low carbon fuel standard (LFCS) implemented in California is to reduce the GHG 

emissions intensity of fuels used in the state using a performance-based standard. In its rulemaking 

implementing the LCFS, the California Air Resources Board (CARB) noted that incomplete policy 

coverage could result in little change in emissions at the global level (CARB, 2009). The LCFS 

implemented by CARB attempts to control leakage by adopting very similar language on including 

indirect emissions from EISA (OAL, 2010), which should capture indirect emissions within fuel 

emissions intensity estimates. Under this policy, there is a GHG intensity target developed for 
November 2014 E-12 

transportation fuels based on this lifecycle assessment. Regulated parties are the transportation 

energy suppliers, who are allowed to trade credits, providing incentives for using fuels with lower 

net carbon emissions and stimulating investment in continued development of low-carbon fuels. 

CARB has worked extensively with the GTAP model to assess international land use emissions 

within a global framework (e.g., Tyner, 2011). Changes in land use and activities estimated using 

this model are combined with emissions factors obtained from the GREET model to generate 

estimates of the net changes in GHG emissions associated with production of individual fuels. To 

calculate the carbon in baseline fossil fuels across their life cycle, CARB uses the Oil Production and 

Greenhouse Gas Estimator to calculate a value for Annual Crude Average Carbon Intensity. 

5.1.3. EU RED Analysis 

The EU Renewable Energy Directive (RED) mandates that 20% of all energy usage in the EU, 

including at least 10% of all energy in road transport fuels, must be produced from renewable 

sources by 2020. In addition, an amended fuel quality directive was implemented requiring that the 

road transport fuel mix in the EU should be at least 6% less carbon intensive than the diesel and 

gasoline baseline by 2020. The EU RED also states that under national biofuel support systems, “the 

contribution made by biofuels produced from wastes, residues, non-food cellulosic material, and 

ligno-cellulosic material shall be considered to be twice that made by other biofuels.” This policy 

also specifies sustainability criteria, whereby biofuels must achieve a minimum reduction in GHG of 

35% relative to fossil fuels in order to be eligible for support under EU renewable energy policies. 

Beginning January 1, 2017, the threshold rises to 50% reduction in GHG. Beginning January 1, 2018, 

any facilities starting production on or after January 1, 2017, must meet a minimum GHG reduction 

of 60%. 

A lifecycle methodology is defined to calculate emissions from biofuels production for the purposes 

of calculating the net GHG reductions. The European Commission has provided default emissions 

factors for each biofuel production pathway that regulated entities can use for their calculations 

and reporting. Regulated entities also have an option to provide information about their specific 

production processes in order to calculate emissions that are specific to their process for use in 

place of the default values. The EU RED does not currently account for indirect land use change, 

though it does restrict production of biofuels on land that had high biodiversity status or high 

carbon content at any point on or after January 2008. Information about biofuel sustainability must 

be tracked using a mass balance chain of custody system. There have been recent proposals and 

considerable debate about adding specific indirect land use change emissions factors to this policy, 

but agreement has not yet been reached. 

5.2. Treatment of Leakage in Existing Carbon Accounting Protocols 

Several organizations have developed carbon accounting protocols for companies and entities 

looking to measure the impact of carbon reduction (i.e., offset) projects. In creating these protocols, 

developers have devised methods to incorporate leakage factors into their methodologies and are 

pragmatic attempts at best practices for use with existing carbon reduction projects. As with the 

literature discussion in Section 5.1, it is often difficult to determine the precise definition of leakage 
November 2014 E-13 

used in each protocol and there are not necessarily mechanisms to adjust carbon credits on the 

basis of leakage. 

Galik, Mobley, and deB. Richter (2009) assessed seven different protocols along a number of 

dimensions, including leakage. Those protocols included the U.S. Department of Energy 1605(b) 

Technical Guidelines for Voluntary Reporting of Greenhouse Gases (Office of Policy and 

International Affairs, 2007); Georgia Forestry Commission (GFC) Carbon Sequestration Registry 

Project Protocol (Georgia Forestry Commission, 2007); Chicago Climate Exchange (CCX) 

Sustainably Managed Forests/Long-Lived Wood Products Protocols (Chicago Climate Exchange, 

2007a, 2007b); California Climate Action Reserve (CAR) Forest Project Protocol (CAR, 2010); 

Voluntary Carbon Standard (VCS) Improved Forest Management Protocol (VCS, 2007a, 2007b); a 

protocol based on recommended concepts in Duke University’s Harnessing Farms and Forests in the 

Low-Carbon Economy (HFF) publication; and a draft recommendation for active forest management 

offset projects proposed by the Maine Forest Service and others under RGGI (Maine Forest Service 

et al., 2008). They found that only the VCS and HFF accounting standards had quantified 

mechanisms for accounting for leakage at the time they conducted the assessment. Both VCS and 

HFF included all forest carbon pools assessed by Galik, Mobley, and deB. Richter (2009) and VCS 

generated values for leakage between 10 to 40% (with a base case of 10%) while HFF included 

leakage of 33.5 to 44.5% (base case of 43%). 

In addition, there are accounting procedures developed by the UNFCCC Clean Development 

Mechanism (CDM). The CDM methodology for simplified baseline and monitoring methodologies 

for small scale biomass project activities (UNFCCC, 2014) identifies three potentially significant 

sources of emissions (>10 % of project emissions reductions) that are attributable to the project: 

• Shifts of pre-project activities, including decreases in carbon stocks outside the area where 

the biomass is grown due to shifts in pre-project activities; 

• Emissions related to the production of the biomass; and 

• Competing uses for the biomass. 

Those emissions may be considered project emissions if they arise from lands under the control of 

the project owners or sources of leakage. 

CDM guidance suggests that shifts in pre-project activities are relevant where the lands would be 

used for other purposes (e.g., agricultural production) in the absence of the project. In cases where 

the land would not be used or where land use inside the project boundary does not change as a 

result of the project, the guidance is that leakage does not generally need to be included. That 

applies to extraction of biomass from existing forests, cultivation of biomass on abandoned lands, 

and for biomass residues or wastes because they assume the use of the residue or waste is unlikely 

to affect the generation of the residue or waste. For other types of biomass, the CDM guidance is to 

evaluate the potential displacement of activities or people using the following indicators: 

• Percentage of families/households of the community involved in or affected by the project 

activity displaced (from within to outside of the project boundary) due to the project; and 
November 2014 E-14 

• Percentage of total production of the main product (e.g., corn, beef) within the project 

boundary displaced due to the generation of renewable biomass. 

If the values of these two indictors are both less than 10%, then leakage is assumed to be 0%. If the 

value of either indicator is >10% but <50%, then leakage is assumed to be equal to 15% of the 

difference between baseline and project emissions. If the value of either is >50%, then this 

simplified methodology is not applicable and a new procedure must be submitted for approval. 

In terms of emissions from the production of biomass, the two categories of emission included are 

emissions from fertilizer application and project emissions from land clearing. It is assumed that all

other emissions sources are likely to be smaller than 10% individually and therefore do not need to 

be included. The guidance suggests that land use change other than deforestation does not need to 

be included and the guidance indicates that the project developers should demonstrate the area 

where the biomass is grown is not a forest and has not been deforested within the last 10 years. 

For competing uses of biomass, the guidance suggests evaluating if there is a surplus of biomass in 

the region of the project activity that is not currently utilized. If it is demonstrated (e.g., based on 

published literature, official reports) at the beginning of each crediting period that the quantity of

available biomass in the region (e.g., 50 km radius) is at least 25% greater than the quantity of 

biomass utilized including the project activity, then this source of leakage can be assumed to be 0%. 

Otherwise, leakage should be estimated and deducted from project emissions reductions, 

The recently updated Climate Action Reserve (CAR) protocol (Version 3.2.) uses default leakage 

factors to account for changes in activities outside the project boundary. They define a decision tree

for project developers to use to determine the appropriate leakage factor. A standard discount of 

24% is used for cropland converted to forest (CAR, 2010; Henders and Ostwald, 2012). For other 

land uses, leakage is defined as 0% for improved forest management projects on actively managed 

forestland for projects that increase harvesting. Improved forest management projects that result 

in reduced harvesting relative to the baseline are assumed to have a leakage rate of 20% of the 

difference in harvest volume. When land had been actively grazed, the leakage factor ranges from 

10 to 50% as expected canopy cover under the project increases once canopy cover reaches 30% 

(canopy cover less than 30% is assumed to have 0% leakage). 

The updated VCS approach (Version 3.4, VCS, 2013) states that the potential for leakage should be 

identified and that projects are encouraged to include leakage management zones as part of the 

project design. Leakage management zones should be used to minimize the displacement of land 

use activities outside the project area by maintaining the production of goods and services within 

areas under control of the project proponent or by addressing socio-economic factors that drive 

land use change. Activities to mitigate leakage and reduce deforestation and/or forest or wetland 

degradation are encouraged. 

In calculating leakage, specific carbon pools and GHG sources do not have to be accounted for if the 

omitted decrease in carbon stocks or increase in GHG emissions amounts to less than 5% of the 

project GHG reduction. Peer-reviewed literature or the CDM afforestation/reforestation 

methodological tools may be used to determine whether changes in carbon stocks and emission 
November 2014 E-15 

meet this de minimus level. In addition, there are specific sources defined as de minimus (e.g., GHG 

emissions from removal or burning of vegetation and collection of non-renewable wood sources for 

fencing off the project area). The protocol also calls for methodologies used for project accounting 

to adjust emissions for all significant sources of leakage using verifiable assumptions. VCS requires 

accounting for market leakage (production shifting elsewhere to make up for reduced supply), 

activity-shifting leakage (agent of deforestation or degradation moves to an area outside the project 

boundary and continues the deforestation or degradation activities), and ecological leakage (project 

causes changes in GHG emissions or fluxes of GHG emissions from ecosystems that are 

hydrologically connected to the project area). International leakage does not need to be quantified 

under this protocol. In addition, projects cannot include positive leakage where net GHG emissions 

outside the project area are reduced. 

6. Summary 

The manner in which leakage is calculated or incorporated for a particular policy, program, or study 

will be highly dependent on the analytical requirements of the project, the assessment scope, the 

feedstock(s) under consideration, and the methodology developed to carry out such an analysis. A 

national or global analysis of changes in feedstock demand and related commodity market and land 

use activities could generate estimates of the potential directionality and magnitude of leakage 

effects due to changes in biogenic feedstock use. However, application of the framework in this way 

may not be required for certain U.S. domestic policy analyses. Therefore, because this framework is 

intended to be policy neutral, it does not prescribe a particular leakage estimation method. For any 

potential application of the framework that aims to incorporate impacts from leakage, many 

important factors must be considered, as discussed in this appendix and shown in the examples 

above. 

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