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-1 1

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 liter ature that discusses greenhouse gas (GHG) emissions leakage, which is an indirect source of b iogenic CO 2 emissions associated with the production, processing, and use of biogenic materia l 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 leakag e to consider when assessing biogenic CO 2 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 t he event that policy- or program-specific applications of the framework necessitate calculati ons of leakage, the analysis here could inform such a process. This appendix considers both intern ational 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, theNovember 2014 E-2

framework equation presented in this report include s a leakage term. 1 However, a specific quantification methodology recommendation is not pr ovided 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 fac tor ( 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 primar y factors that typically contribute to leakage, provides an overview of relevant literature, and of fers examples of different leakage analyses that have been conducted in different policy contexts an d with different goals, assumptions, and parameters.


## 2. Background

Policies and programs typically have limited sphere s 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 polic ies or behaviors that can and occur in many different contexts. Leakage effects could be positi ve (e.g., benefits of local tourism extending beyon d the region or technological innovation spreading fr om one firm to others) or negative (e.g., reduced deforestation in one region is at least partially o ffset by increased deforestation in other regions a s output prices rise). In the context of environmenta l policy, one of the key areas in which leakage has been examined in recent years is displacement of GH G-emitting activities to areas and/or sectors that are not covered by a policy, or program (Barke r et al., 2007; Weber and Peters, 2009; Chen, 2009). There are different definitions of carbon le akage in the literature, but the International Pane l on Climate Change (IPCC) Special Report on Land Use , Land-Use Change, and Forestry defines carbon leakage as “the indirect impact that a targe ted 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 bi ogenic carbon and CO 2 2 emissions fluxes, leakage represents any biogenic CO 2 flux changes outside of a biogenic feedstock produ ction assessment scope that can be attributed to the production acti vities (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 producti on for energy conversion).
If the assessment scope of the policy or program wa s 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 retr ospective 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 inc lude additional GHGs as appropriate for a particular policy application.November 2014 E-3

however, project assessment scopes are typically mo re limited. In some cases, especially where policies result in substantial price effects, there may be changes in activities and emissions that ta ke place outside the defined assessment scope. The rea son is that activities in regulated sectors/regions tend to shift to, or have influence s 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, emi ssions 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 affecti ng 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 o n 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 producti on can potentially be significant, the framework in may need to consider including leakage for certa in 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 feedstoc k production assessment scope.


## 3. Factors that Can Contribute to Leakage

In the general context of leakage related to GHG em issions globally, for example, as long as all emissions sources are governed by the same rules, s hifting emissions from one region to another is perfectly acceptable and indeed represents a more e conomically efficient outcome (Murray, 2008). If all regions are covered by the same policy or as sessment 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 diffic ult 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 l ess controlled entities. In general, leakage can erode net carbon reductions because “the spatial sc ale of intervention is inferior to the full scale o f the targeted problem” (Wunder, 2008, p.65).
The primary driver of leakage is economic—in global ly 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 pa tterns outside the assessment boundaries. This is because increased demand for a biogenic feedstoc k 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 increase s 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 pri ces for forest biomass could lead landowners to convert a large enough area of agricultural lands t o forests that regional carbon stocks are increased relative to baseline conditions.November 2014 E-4

Leakage effects, including ILUC, can also occur whe n lands and/or biogenic materials previously used for other purposes are instead diverted to bio genic feedstock production due to competition and resource scarcity. However, the market demand f or 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 dem and can be met through intensification of existing lands producing the original product mater ials 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 tran slated 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 shif ts 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 ot her biomass types may lead to production changes beyond the assessment area because of poten tial product substitution (Latta et al., 2013). Land can be used to produce a wide array of forestr y 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 t he land use allocation decision is made. In addition, many forestry and agricultural commoditie s have other commodities that are at least partially substitutable for them (e.g., livestock f eed 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 flu xes from biomass use appear larger or smaller than they actually are, thereby potentially undercu tting program objectives (Murray, 2008).

When these land-use transitions occur outside the a ssessment region, related GHG emissions fluxes may not be accounted for. Some of the literature in dicates that biogenic feedstock production projects reduce GHG emissions to the atmosphere onl y 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 wheth er an activity has resulted in GHG emission reducti ons or removals relative to what would have occurred in it s absence. This is generally a more complex criteri a for landbased mitigation activities than for point-source o r facility-based activities because of the inherent dispersed, heterogeneous, dynamic, and systems-based aspects o f agricultural and forestry production, but there are viable strategies for addressing additionality in these se ctors (Janzen et al., 2012).November 2014 E-5

the author argues they should be included in GHG ac counting 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, w hen considering leakage, estimates should reflect the following elements: connectedness of ou tput 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 prof ile 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 t he most important factors is the land supply elasticity. In cases where land supply is relativel y elastic, there will be relatively large increases in supply of a given land type when the returns to tha t 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 h and, then there will be a smaller response to changes in demand for individual commodities. The m ore competitive and integrated land markets are across regions, the larger the extent of leakag e expected when different regions face different incentives to mitigate emissions. However, when con sidering leakage across regions, even when considering only a single type of biomass, it shoul d be noted that there is a difference between shifts in production activity or area and net emiss ions. For example, even within the United States, shifting forestry area from the Southeast (SE) to P acific 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 acr oss carbon contained in aboveground biomass, belowground biomass, deadwood, forest floor, and so il organic carbon) (Heath et al., 2011).
The elasticity of demand for conventional commoditi es must also be considered. In cases where demand is inelastic, the quantity demanded will cha nge by a smaller percentage than prices rise. This inelastic behavior will result in a greater am ount 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 resul t in higher production costs and increased market prices will result in less production moving to oth er 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 d emand for substitutes and complements are also important to consider. Not only will increases in t he market prices of directly affected commodities potentially lead to increased production of those c ommodities 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 cr op varieties, and other yield enhancements) because hi gher output prices make it profitable to engage in more intensive production practices requiring gr eater input expenditures. Achieving higherNovember 2014 E-6

yields through intensification would limit direct a nd indirect land-use change (Searchinger, 2008) but may lead to other GHG fluxes (e.g., increased N 2 O emissions from higher levels of nitrogen fertilization, higher CO 2 associated with greater fossil fuel use for irriga tion). Thus, the net change in GHG emissions would depend on the relative chang es in emissions across all relevant pools and intensification could either increase or decrease t otal 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 s ectors has been more recent. There have been many studies of industrial carbon leakage ever sinc e the United Nations Framework Convention on Climate Change was established in 1992, identifyingdifferentiated responsibilities for reducing emissions, and the subsequent Kyoto Protocol was de veloped and agreed upon with emissions limits specified only for a set of developed countr ies. There is also extensive literature on the international trade and competitiveness under envir onmental 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 to tal emissions as much as distributional impacts of polluting industries’ potential relocation between states or countries.
Interest in leakage associated with land-using sect ors has grown considerably in the last decade with the development and implementation of bioenerg y policies and international policy interest in reducing emissions from deforestation and land degr adation (REDD+). In this section, an overview of the literature on leakage associated with land u se and potential relevance is provided, followed by brief sections summarizing some of the recent re levant 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, dynam ics, and numerous interactions. The literature offers an incomplete picture of leakage magnitude a nd what can be done to minimize negative leakage (Kim and Dale, 2011; Murray, 2008). Further more, the precise meaning of the term “leakage,” both in terms of scale and scope, can fl uctuate from study to study, making direct comparisons difficult. 4 Finally, few studies mirror the feedstock sub-deli neations used in the framework report, thereby complicating evaluations of feedstock-specific applicability.
Because the primary bioenergy, REDD, and other fore stry and agriculture policies of interest for leakage assessment have typically been implemented relatively recently, time series data for empirical analysis are limited. Typically, the poli cies being considered do not have direct historical precedent and would result in new markets being cre ated, 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 reg ions are accounted for in a consistent manner and r eflected under a regulatory framework, then there could be i ndirect land-use change without carbon leakage (bec ause 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 experien ce and limits the ability of statistical analyses o f existing data to explain future outcomes. Data limi tations, along with the complexity of adequately reflecting relevant factors influencing market outc omes 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 Greenho use Gases (FASOM-GHG), the Food and Agricultural Policy Research Institute (FAPRI) mode l, and the Global Trade Analysis Project (GTAP) general equilibrium model. These economic models va ry 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 a ll GHG emissions associated with the market activities being modeled. Therefore, in some applic ations, the changes in market activities and land use simulated using the models have been combined w ith 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 emiss ions.
Such models can lend insights as to the possible di rectionality and/or general magnitude of leakage. However, leakage affects multiple markets and regio ns simultaneously, thereby increasing the complexity of model projections and making it diffi cult to isolate the causes and effects of market and land-use shifts and related leakage. The result s are largely case-specific, and depend greatly on context and the assumptions of each particular stud y.
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 r epresent and understand the land use and emission dynamics. Many studies project multiple sc enarios using many different assumptions as sensitivity analyses to reflect parameter uncertain ty. 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 re search effort devoted to the effects of policies affecting the demand for agricultural commodities o n land use change, particularly in the context of increased demand for biofuels (e.g., ethanol and bi omass-based diesel). Notably, EPA includes estimates of land use change due to increased deman d of specific biofuels (e.g., corn ethanol) as part of the GHG accounting applied for Renewable Fu el 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 a nd within a study depending on underlyingNovember 2014 E-8

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 qua ntify 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 emissio n 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 bill ion 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, inclu ding 2.8 million ha in Brazil, 2.3 million ha in Ch ina 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 th e nature of the land-use change (i.e., what type of land is being converted). They assumed the conve rsion of forest to cropland releases 604 to 1,146 metric tons CO 2 e per ha, while the conversion of grassland/savanna h to cropland releases 75 to 305 metric tons CO 2 e per ha.
Regardless of the type of land converted, the payba ck period for these up-front emissions can be very long. Using an average of 351 metric tons CO 2 e per converted ha, Searchinger et al. (2008) estimated “carbon neutrality” for corn-based ethano l 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 r educed by half, and process efficiency gains of 40%. In that best-case scenario, net emissions appr oach those of gasoline after 30 years.
A key driver of the results in each of these studie s is that expanded corn ethanol production in the United States will substantially reduce U.S. corn e xports, leading to expanded foreign corn production. They assumed that foreign countries wil l convert natural vegetation to croplands, including tropical rainforests and other high carbo n density areas, which results in very large carbon emissions (Searchinger et al., 2008, 2009; F argione et al., 2008).
However, a number of subsequent studies have critiq ued 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 imp rovements over time, the substitution rate of dried distillers grains for corn, the types of land that are converted, GHG calculations, and long-ter m land dynamics in response to changing incentives, a mong others.
For instance, Hertel et al. (2010) employed an appr oach similar to Searchinger (2008), but used different assumptions about the land that will be c onverted to agriculture. Importantly, Hertel et al. (2010) relied on the GTAP-BIO version of the GTAP m odel, 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 Se archinger (2008). Whereas Searchinger (2008) estimated most conversion will be either for est to cropland or grassland to cropland, Hertel et al. (2010) estimated that much of the cropland t ransition will actually stem from degraded,November 2014 E-9

pasture, or other low-carbon lands, which substanti ally reduces emissions from indirect land use change. Studies such as Potter et al. (2007) and Ag uilar et al. (2012) have estimated significant quantities of marginal lands in the United States t hat they argue could be converted to biomass production, which would substantially reduce pressu re for conversion of higher carbon density forests and grasslands. However, it is also importa nt 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 m ore profitable to grow them on more productive croplands.
Drabik and de Gorter (2011) also measured carbon le akage 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 wil l 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 carb on 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. Conse rvation Reserve Program (CRP) and found that for every 100 acres of cropland retired under CRP i n the central United States, about 20 acres of non-cropland were converted to cropland. This leaka ge 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 affect ing forest use in one place are expected to impact forest management decisions elsewhere. In particula r, 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 h ave focused on the forestry sector.
Murray, McCarl, and Lee (2004) developed a conceptu al model for analyzing market adjustments and carbon leakage. They also applied FASOM to empi rically 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 t he policy is implemented. Alig et al. (1997) also applied FASOM and found that carbon benefits from e xpanded U.S. afforestation would largely be offset by converting existing forestland to agricul ture. Wear and Murray (2004) explored the effects of public forest conservation in the U.S. Pacific N orthwest on forest production and markets in the United States and Canada. They found that a total o f about 84% of reduced public harvest would be replaced by increased private harvest, and around 5 8% of reduced public harvest would be replaced within the United States and another 26% i n Canada.
Sohngen and Brown (2004) examined leakage associate d with a specific tropical forest conservation project in Bolivia. They developed a d ynamic timber market optimization model andNovember 2014 E-10

ran the model using differing assumptions about glo bal policies, capital constraints, demand elasticity, and deadwood decomposition rates. Overa ll, they found leakage rates of 5 to 42% for this project-level assessment. Leakage is lowest when de mand is more elastic and wood decomposition rates are faster.

There have also been a number of recent studies exa mining 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 fro m 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 el asticities of demand and supply of forestry products, lumber and wood products, and pulp and pa per 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 fr om 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 produc tion 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 emanatingfrom 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 aro und the globe. Their model showed significant GHG emissions from boreal and temperate forests, bu t 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 a re 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 la nd 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 prog rams that take leakage into account. However, the methodological approach of each program is care fully tailored to meet the program or policy’s requirements that the analysis is being designed to serve. As such, these methodologies may differNovember 2014 E-11

in several ways, including technical assumptions us ed, 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 (E ISA) specifies life-cycle GHG emissions reductions thresholds that renewable fuels must mee t to qualify in different categories and defines lifecycle GHG emissions to include “significant ind irect emissions such as significant emissions from land use change.” As a result, EPA’s analysis was c onducted 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 n ational aggregate demand for individual feedstocks. Partial equilibrium models of the globa l agricultural sector (FAPRI) and the domestic forest and agricultural sectors (FASOM-GHG) were us ed to simulate the effects of expanded bioenergy production consistent with RFS2 requireme nts 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 d ata 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 productio n.
Both direct and indirect land use changes are inclu ded in the calculation of the net lifecycle GHG reductions provided by individual biofuels feedstoc ks, including land use adjustments within the U.S. and internationally. Total net changes in acti vities are presented in that study; there is no separation of direct and indirect impacts. For a bi ofuel 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 fulfi lling statutory obligations as set forth in the EISA of 2 007. The goals, methodology, tools, and assumptions used for EPA’s RFS2 analysis may not ne cessarily be suited for another policy analysis. Whether and how to reflect leakage in the context o f a specific policy or program application of the biogenic assessment framework would need to be dete rmined for particular applications. Each analysis must design a framework that best suits th eir particular goals and analytical requirements. For more information on EPA’s RFS2 final rulemakingand analysis, refer to the Regulatory Impact Analysis (EPA, 2010).

5.1.2. CARB Analysis
The intent of the low carbon fuel standard (LFCS) i mplemented in California is to reduce the GHG emissions intensity of fuels used in the state usin g 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 sho uld capture indirect emissions within fuel emissions intensity estimates. Under this policy, t here is a GHG intensity target developed forNovember 2014 E-12

transportation fuels based on this lifecycle assess ment. 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). Chan ges in land use and activities estimated using this model are combined with emissions factors obta ined from the GREET model to generate estimates of the net changes in GHG emissions assoc iated with production of individual fuels. To calculate the carbon in baseline fossil fuels acros s their life cycle, CARB uses the Oil Production an d Greenhouse Gas Estimator to calculate a value for A nnual Crude Average Carbon Intensity.

5.1.3. EU RED Analysis
The EU Renewable Energy Directive (RED) mandates th at 20% of all energy usage in the EU, including at least 10% of all energy in road transp ort fuels, must be produced from renewable sources by 2020. In addition, an amended fuel quali ty directive was implemented requiring that the road transport fuel mix in the EU should be at leas t 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 bio fuels must achieve a minimum reduction in GHG of 35% relative to fossil fuels in order to be eligibl e for support under EU renewable energy policies. Beginning January 1, 2017, the threshold rises to 5 0% reduction in GHG. Beginning January 1, 2018, any facilities starting production on or after Janu ary 1, 2017, must meet a minimum GHG reduction of 60%.
A lifecycle methodology is defined to calculate emi ssions 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 re gulated entities can use for their calculations and reporting. Regulated entities also have an opti on to provide information about their specific production processes in order to calculate emission s that are specific to their process for use in place of the default values. The EU RED does not cu rrently account for indirect land use change, though it does restrict production of biofuels on l and that had high biodiversity status or high carbon content at any point on or after January 200 8. Information about biofuel sustainability must be tracked using a mass balance chain of custody sy stem. 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 account ing 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 leak age factors into their methodologies and are pragmatic attempts at best practices for use with e xisting carbon reduction projects. As with the literature discussion in Section 5.1, it is often d ifficult to determine the precise definition of lea kageNovember 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 sev en different protocols along a number of dimensions, including leakage. Those protocols incl uded the U.S. Department of Energy 1605(b) Technical Guidelines for Voluntary Reporting of Gre enhouse Gases (Office of Policy and International Affairs, 2007); Georgia Forestry Comm ission (GFC) Carbon Sequestration Registry Project Protocol (Georgia Forestry Commission, 2007 ); Chicago Climate Exchange (CCX) Sustainably Managed Forests/Long-Lived Wood Product s Protocols (Chicago Climate Exchange, 2007a, 2007b); California Climate Action Reserve (C AR) Forest Project Protocol (CAR, 2010); Voluntary Carbon Standard (VCS) Improved Forest Man agement Protocol (VCS, 2007a, 2007b); a protocol based on recommended concepts in Duke Univ ersity’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 Servic e 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 t hey conducted the assessment. Both VCS and HFF included all forest carbon pools assessed by Ga lik, Mobley, and deB. Richter (2009) and VCS generated values for leakage between 10 to 40% (wit h 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 develo ped 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 re ductions) that are attributable to the project:

• Shifts of pre-project activities, including decreas es in carbon stocks outside the area where the biomass is grown due to shifts in pre-project a ctivities;

• 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 ac tivities are relevant where the lands would be used for other purposes (e.g., agricultural product ion) 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 fore sts, cultivation of biomass on abandoned lands, and for biomass residues or wastes because they ass ume the use of the residue or waste is unlikely to affect the generation of the residue or waste. F or other types of biomass, the CDM guidance is to evaluate the potential displacement of activities o r 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 p roject boundary) due to the project; andNovember 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 renewab le 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 le akage 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 biomas s, the two categories of emission included are emissions from fertilizer application and project e missions from land clearing. It is assumed that all other emissions sources are likely to be smaller th an 10% individually and therefore do not need to be included. The guidance suggests that land use ch ange other than deforestation does not need to be included and the guidance indicates that the pro ject 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 suggest s evaluating if there is a surplus of biomass in the region of the project activity that is not curr ently utilized. If it is demonstrated (e.g., based on published literature, official reports) at the begi nning 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, th en 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) p rotocol (Version 3.2.) uses default leakage factors to account for changes in activities outsid e the project boundary. They define a decision tree for project developers to use to determine the appr opriate 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 fo rest management projects on actively managed forestland for projects that increase harvesting. I mproved 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 ac tively grazed, the leakage factor ranges from 10 to 50% as expected canopy cover under the projec t increases once canopy cover reaches 30% (canopy cover less than 30% is assumed to have 0% l eakage).
The updated VCS approach (Version 3.4, VCS, 2013) s tates that the potential for leakage should be identified and that projects are encouraged to incl ude 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 maintain ing 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 G HG sources do not have to be accounted for if the omitted decrease in carbon stocks or increase in GH G 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 wheth er changes in carbon stocks and emissionNovember 2014 E-15

meet this de minimus

level. In addition, there are specific sources def ined 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 ca lls 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 o r degradation moves to an area outside the project boundary and continues the deforestation or degrada tion activities), and ecological leakage (project causes changes in GHG emissions or fluxes of GHG em issions from ecosystems that are hydrologically connected to the project area). Inte rnational leakage does not need to be quantified under this protocol. In addition, projects cannot i nclude positive leakage where net GHG emissions outside the project area are reduced.


## 6. Summary

The manner in which leakage is calculated or incorp orated for a particular policy, program, or study will be highly dependent on the analytical requirem ents of the project, the assessment scope, the feedstock(s) under consideration, and the methodolo gy 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 pote ntial directionality and magnitude of leakage effects due to changes in biogenic feedstock use. H owever, application of the framework in this way may not be required for certain U.S. domestic polic y analyses. Therefore, because this framework is intended to be policy neutral, it does not prescrib e 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.



## 7. References

Aguilar, F.X., M.E. Goerndt, N. Song, and S. Shifle y. 2012. Internal, external, and location factors influencing cofiring of biomass with coal in the U. S. northern region. Energy Economics 34:1790- 1798. Alig, R., D. Adams, B. McCarl, J.M. Callaway, and S . Winnett. (1997). Assessing effects of mitigation strategies for global climate change with an intert emporal model of the U.S. forest and agriculture sectors. Environmental and Resource Eco nomics, 9(3), 259-274. Barker, T., I. Bashmakov, A. Alharthi, M. Amann, L. Cifuentes, J. Drexhage, M. Duan, O. Edenhofer, B. Flannery, M. Grubb, M. Hoogwijk, F. I. Ibitoye, C. J. Jepma, W. Pizer, and K. Yamaji. 2007. Chapter 11. Mitigation from a cross-sectoral perspective. I n B. Metz, O. Davidson, P. Bosch, R. Dave, and L. Meyer (eds.), Climate Change 2007—Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental P anel on Climate Change . New York, NY: Cambridge University Press. Climate Action Reserve (CAR). 2010. Forest Project Protocol . Los Angeles, CA: Climate Action Reserve.
California Air Resources Board. 2009. California Low Carbon Fuel Standard (LCFS). Sacramento.November 2014 E-16

Chen, Y. 2009. Does a regional greenhouse gas polic y make sense? A case study of carbon leakage and emissions spillover. Energy Economics, 31(5):667-675. Chicago Climate Exchange. 2007a. CCX Rulebook. 9.8.3. Long Lived Wood Products

. http://www.chicagoclimatex.com/docs/offsets/CCX_Rul ebook_Chapter09_OffsetsAndEarlyActi onCredits.pdf. Chicago Climate Exchange. 2007b. CCX Rulebook. 9.8.4. Managed Forest Projects

. http://www.chicagoclimatex.com/docs/offsets/CCX_Rul ebook_Chapter09_OffsetsAndEarlyActi onCredits.pdf. Daigneault, A., B. Sohngen, and R. Sedjo. 2012. An economic approach to assess the forest carbon implications of biomass energy. Environmental Science & Technology, 46(11):5664-5671. Drabik, D., and H. de Gorter. 2011. Biofuel policie s and carbon leakage. AgBioForum 14(3):104-110.
EPA. 2010. Renewable Fuel Standard2 Program (RFS2) Regulatory Impact Analysis . U.S. Environmental Protection Agency. http://www.epa.gov /otaq/renewablefuels/420r10006.pdf. Fargione, J., J. Hill, D. Tilman, S. Polasky, and P . Hawthorne. 2008. Land clearing and the biofuel carbon debt. Science, 319(5867):1235-1238. Galik, C.S., M.L. Mobley, and D. deB. Richter. 2009 . A virtual “field test” of forest management carbo n offset protocols: the influence of accounting. Mitigation and Adaptation Strategies for Global Change, 14:677-690. Gan, J., and B.A. McCarl. 2007. Measuring transnati onal leakage of forest conservation. Ecological Economics, 64:423-432. Georgia Forestry Commission. 2007. The Georgia Carbon Sequestration Registry – Project Protocol, Version 1.0 . Dry Branch. Harris, N., S. Grimland and S. Brown. (2008). GHG E mission Factors for Different Land-use Transitions in Selected Countries/Regions of the Wo rld. Report submitted to EPA, October 2008. Heath, L.S., J.E. Smith, C.W. Woodall, D.L. Azuma, and K.L. Waddell. 2011. Carbon stocks on forestland of the United States, with emphasis on U SDA Forest Service ownership. Ecosphere, 2(1), Article 6. Henders, S., and M. Ostwald. 2012. Forest carbon le akage quantification methods and their suitability for assessing leakage in REDD. Forests, 3:33-58. Hertel, T., A. Golub, A. Jones, M. O’Hare, et al. 2 010. Global land use and greenhouse gas emissions impacts of US maize ethanol: estimating market-medi ated responses. BioScience, 60(3):223- 231. Intergovernmental Panel on Climate Change (IPCC). 2 000. Land Use, Land-Use Change, and Forestry - A Special Report of the Intergovernmental Panel o n Climate Change (IPCC) . R.T. Watson, I.R. Noble, B. Bolin, N.H. Ravindranath, D.J. Verardo, &D.J. Dokken (eds.). Cambridge University Press, Cambridge, 377 pp. http://www.ipcc.ch/ipccre ports/sres/land_use/003.htmNovember 2014 E-17

Janzen, R., T. Baumann, L. Dyer, P. Hardy, and D. R eed. 2012. Additionality in Agricultural Offset Protocols . http://www.c-agg.org/cm_vault/files/docs/20120119 _- _Additionality_in_Agricultural_Offset_Protocols.pdf Khanna, M.; Crago, C. and Black, M. 2011. Can biofu els be a solution to climate change? The implications of land use change-related emissions f or policy. Interface Focus, 233-247. Khanna, Madhu; and Crago, C.L. 2012. Measuring Indi rect land Use Change with Biofuels: Implications for Policy. Annual Review of Resource Economics. DOI: 10.1146/annurevresource-110811-114523. Kim, S., and B.E. Dale. 2011. Indirect land use cha nge for biofuels: testing predictions and improving analytical methodologies. Biomass and Bioenergy, 35 (7):3235-3240. Latta, G.S., J.S. Baker, R.H. Beach, S.K. Rose, and B.A. McCarl. 2013. A multi-sector intertemporal optimization approach to assess the GHG implication s of U.S. forest and agricultural biomass electricity expansion. Journal of Forest Economics 19(4):361-383. Maine Forest Service, Environment Northeast, Manome t Center for Conservation Sciences, Maine Department of Environmental Protection. 2008. Recommendations to RGGI for Including New Forest Offset Categories: A Summary

. http://www.maine.gov/doc/mfs/mfs/topics/carbon/docs /pdf/recommendations_to_rggi_061 108.pdf. Murray, B.C. 2008. Leakage from an Avoided Deforestation Compnesation Policy: Concepts, Empirical Evidence, and Corrective Policy Options . Durham, NC: Duke University, Nicholas Insitute. Murray, B.C., B.A. McCarl, and H.-C. Lee. 2004. Est imating leakage from forest carbon sequestration
programs. Land Economics, 80(1):109-124.
OAL. 2010. Low Carbon Fuel Standard. California Off ice of Administrative Law. Sacramento: 63. http://www.arb.ca.gov/regact/2009/lcfs09/finalfro.p df. Office of Policy and International Affairs. 2007. Technical Guidelines — Voluntary Reporting of Greenhouse Gases (1605(b)) Program . U.S Department of Energy, Washington DC. Plevin, R.J., M. O'Hare, A.D. Jones, M.S. Torn, and H.K. Gibbs. 2010. Greenhouse gas emissions from biofuels' indirect land use change are uncertain bu t may be much greater than previously estimated. Environmental Science and Technology, 44(21):8015-8021. Potter, C., S. Klooster, S. Hiatt, M. Fladeland, V. Genovese and P. Gross. 2007. "Satellite-derived estimates of potential carbon sequestration through afforestation of agricultural lands in the United States." Climatic Change 80(3-4): 323-336. Searchinger, T. 2008. Response to New Fuel Alliance and DOE Analyst’s Criticisms of Science Studies of Greenhouse Gases and Biofuels. Searchinger, T., R. Heimlich, R.A. Houghton,F. Dong , A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.-H. Yu. 2008. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science, 319(5867):1238-1240. Searchinger, T., S.P. Hamburg, J. Melillo, W. Chame ides, P. Havlik, D.M. Kammen, G.E. Likens, R.N. Lubowski, M. Obersteiner, M. Oppenheimer, G.P. Robe rtson, W.H. Schlesinger, and G.D. Tilman. 2009. Fixing a critical climate accounting error. Science, 326(5952):527-528.November 2014 E-18

Sedjo, R. A., B. Sohngen and A. Riddle. 2013. "Wood Bioenergy and Land Use: A Challenge to the Searchinger Hypothesis." Industrial Biotechnology 9 (6): 319-327. Sedjo, R.A. and X. Tian. 2012. An investigation of the effects of wood bioenergy on forest carbon stocks. Journal of Environmental Protection , 3(9):989-1000. Sohngen, B., and S. Brown. 2004. Measuring leakage from carbon projects in open economies: A stop timber harvesting project in Bolivia as a case study. Canadian Journal of Forest Research, 34:829-839. Sohngen, B. and R. Sedjo. 2000. Potential carbon fl ux from timber harvests and management in the context of a global timber market. Climatic Change 44:151-172. Sun, B., and B. Sohngen. 2009. Set-asides for carbo n sequestration: Implications for permanence and leakage. Climate Change, 96:409-419. Taheripour F., T.W. Hertel, and W.E. Tyner, (2011). “Implications of biofuels mandates for the global livestock industry: a computable general equilibriu m analysis,” Agricultural Economics 42, pp. 325–342. UNFCCC. 2014. Indicative simplified baseline and mo nitoring methodologies for selected smallscale CDM project activity categories. http://cdm.unfccc.int/methodologies/SSCmethodologie s/approved/history/c_leak_biomass Voluntary Carbon Standard. 2007a. Voluntary Carbon Standard 2007

. http://www.vcs.org/docs/VCS% 202007.pdf. Voluntary Carbon Standard. 2007b. Voluntary Carbon Standard: Guidance for Agriculture , Forestry and Other Land Use Projects . http://www.vcs.org/docs/AFOLU%20Guidance%20Docume nt.pdf. VCS. 2013. Agriculture, Forestry, and Other Land Us e (AFOLU) Requirements. VCS Version 3 Requirements Document. October 8, 2013, v. 3.4. Wang, M. and Z. Haq. 2008. "Letter to Science." Was hington, DC: US Department of Energy. Wear, D.N. and B.C. Murray. 2004. Federal timber re strictions, interregional spillovers, and the impact on U.S. softwood markets. Journal of Environmental Economics and Management, 47(2):307-330

. Weber, C. L. and G. P. Peters. 2009. Climate change policy and international trade: policy considerations in the US. Energy Policy, 37(2):432-440. Wu, J. 2000. Slippage effects of the conservation r eserve programs. American Journal of Agricultural Economics, 82:979-992. Wunder, S. 2008. How do we deal with leakage? Chapt er 7 in Moving Ahead with REDD: Issues, Options and Implications, A. Angelsen (ed.). Bogor, Indonesia: CIFOR.