ORIGINAL ARTICLE

REDD+ and international leakage via food and timbermarkets: a CGE analysis

Onno Kuik

Received: 6 March 2013 /Accepted: 11 November 2013# Springer Science+Business Media Dordrecht 2013

Abstract This paper studies the effect of international trade in food and timber on land use andpotential carbon leakage in the context of actions to reduce emissions from deforestation andforest degradation (REDD+). First a simple analytical model of international leakage ispresented that focuses on international competition between firms that produce food and timber.A formula for the leakage rate in the model is derived. The results of the analytical model arethen tested with a large Computable General Equilibrium (CGE) model and it is verified that thequalitative results from the analytical model hold. Finally, a scenario of leakage rate trajectoriesis presented for a number of key tropical forest regions for the next two decades and a sensitivityanalysis is performed on key parameters. Computed leakage rates range between 0.5 % forBrazil and 11.3 % for Malaysia and are fairly stable over the projection period. Leakage ratesincrease with a higher supply elasticity of land and a higher trade elasticity, they decrease with ahigher elasticity of input substitution in production and appear to be independent of the rates offorest conservation and absolute prices of food and timber.

Keywords REDD+ . Carbon-leakage . Deforestation . Trade and the environment . iLUC

JEL Classification F18 . Q23 . Q27 . Q56

1 Introduction

Deforestation and forest degradation are possibly responsible for about 15 % of globalanthropogenic greenhouse gas emissions (van der Werf et al. 2009). A number of studieshave suggested that mitigating deforestation and forest degradation may be a relatively cost-effective measure to reduce greenhouse gas emissions as compared to measures that focus onenergy-related emissions (Stern 2006; Kindermann et al. 2011). Furthermore, it is not surpris-ing that the international community has embarked on a policy strategy that focuses onreducing emissions from deforestation and forest degradation (REDD+) given the severalvaluable ecosystem services provided by natural forests (Millennium Ecosystem Assessment

Mitig Adapt Strateg Glob ChangeDOI 10.1007/s11027-013-9527-2

O. Kuik (*)Institute for Environmental Studies (IVM), Faculty of Earth and Life Sciences, VU University Amsterdam,De Boeleaan 1085, 1081 HVAmsterdam, The Netherlandse-mail: onno.kuik@vu.nl

2005), including habitat provision for nearly 90 % of the world’s terrestrial biodiversity (WorldBank 2004).

REDD+ is an incentives-based mechanism to compensate countries for proven reductionsin greenhouse gas emissions from deforestation, forest degradation and enhancement ofterrestrial carbon stocks (Agrawal et al. 2011). The idea of this compensation mechanismwas developed in the context of a legally binding future climate agreement under the UnitedNations Framework Convention on Climate Change (UNFCCC). The debate on REDD+ hastriggered a large number of activities at all policy levels (Pistorius 2012). One of the aims ofthese activities is to examine how an international REDD+ compensation mechanism canprovide the best incentives for forest conservation at the local level (Gupta 2012; vanNoordwijk et al. 2013). This is of prime importance as the history of global forest governancehas seen many failures of ‘translating’ global initiatives into local actions (Humphreys 2006;Haug and Gupta 2013).

The implementation of REDD+ policies faces many challenges including limited availabil-ity of funds, lack of governmental capacity at various levels, corruption, impermanence ofstorage over time, information gaps, and failures to match compensation to performance(Agrawal et al. 2011). There are also concerns about potentially negative impacts ofREDD+ funding and projects on biodiversity and on the livelihoods, equity, poverty, andparticipatory resource governance of forest dwellers (Agrawal et al. 2011). In addition, ifREDD+ were to become a success in reducing emissions, it could potentially flood the marketof carbon credits. This might in the short to medium term undermine efforts to reduce energy-related emissions and the necessary transformation to a low-carbon energy system (Boselloet al. 2010). Finally, there are concerns about leakage—a phenomenon occurring whendeforestation mitigation measures and associated carbon emissions in one place (or point intime) are (partly) offset by increased deforestation and emissions in another place (or point intime). Leakage can occur within national borders or it can occur across borders.

Carbon leakage has been studied extensively in the context of industrial and energy relatedgreenhouse gas reduction policies, starting from Felder and Rutherford (1993). The majority ofthese studies rely on Computable General Equilibrium (CGE) models (e.g. Kuik and Gerlagh2003; Paltsev 2001; Burniaux and Oliveira Martins 2012; Babiker 2005). The rate of leakage,defined as the ratio of increased emissions abroad to reduced emissions at home, is typicallyestimated in the range of 5 to 25 % (Branger and Quirion 2013).

A number of studies have also addressed the issue of leakage arising from forest conser-vation policies. In this context, the term displacement effect is often used. Displacement refersto the geographical shifts in the supply of commodities from the policy region to other regions.Leakage is the effect on emissions that is associated with these shifts. Wear and Murray (2004)carried out an ex-post econometric study on the displacement effects of timber harvestingrestrictions on US federal lands in the Northwest Pacific since the late 1980s. These timberharvesting restrictions were in part driven by efforts to protect the habitat of the northernspotted owl as mandated by the US Endangered Species Act of 1973. Wear and Murrayestimated that over 40 % of the forgone harvests were shifted to private lands within theregion, as well as to other regions in the US and to Canada. Lang and Chang (2006) suggestthat the 1998 logging ban in China resulted in a substantial displacement of forest exploitationto Southeast Asian countries with weak regulatory regimes, such as Indonesia, Myanmar,Cambodia, and Papua New Guinea. Meyfroidt and Lambin (2009) suggest that Vietnam’sforest transition (increasing forest cover since 1992) has been facilitated by the displacement offorest exploitation to and legal and illegal imports from Cambodia and Laos in the early 1990s,as well as from Malaysia, Myanmar and Indonesia at a later stage. They use detailed materialflow analysis and estimate that 39 % of the regrowth of Vietnam’s forests over the period

Mitig Adapt Strateg Glob Change

1987-06 was enabled by the international shift in forest exploitation and imports. The authorsalso estimate that approximately half of the wood imports to Vietnam in this period wereillegal.

Gan and McCarl (2007) used a CGE model to simulate displacement effects of unilateraltimber supply restrictions internationally, distinguishing amongst ten countries/regions. Theyfound displacement rates between 42 % (Canada) and 95 % (Russia). Displacement rates forregions with tropical forests ranged from 70 % (East Asia) to 87 % (Sub-Saharan Africa).Studies on within-country leakage due to regional carbon sequestration from forestry projectshave been carried out for the USA (Murray et al. 2004) and for Bolivia (Sohngen and Brown2004). The estimated rates of displacement of various projects in these countries variedbetween 0 % and 100 %.

These ‘displacement’ studies might, though, provide limited insights into the question ofcarbon leakage. First, they do not quantify the extent of deforestation and its associatedemissions, and second, they are concerned with forestry only and do not take agriculture intoaccount. Studies on the drivers of deforestation have consistently found that agriculturalexpansion is one of the most frequent proximate drivers of deforestation (Angelsen 2007;Geist and Lambin 2002). The recent attention to the negative effects that the increased demandfor biofuels may have to food production and carbon emissions through direct and indirectland use changes (iLUC) has increased the focus on the role of agricultural expansion ondeforestation and associated greenhouse gas emissions (Searchinger et al. 2008; Chakravortyet al. 2011). Havlík et al. (2011) model global iLUC due to increased biofuel demand with aglobal dynamic partial equilibrium model that distinguishes between various spatially definedland uses, including cropland, grassland, short rotation tree plantations, managed and unman-aged forest. Their results suggest substantial ‘leakage’ through iLUC, especially for firstgeneration biofuels that compete with food production. Meyfroidt et al. (2013) review theliterature on iLUC and emphasize the need to study global commodity value chains in additionto the more traditional, place-centered supply-side view of land use change.

This paper builds on this literature by focusing on the role of agricultural markets as both adriver of deforestation, as well as a transmission channel of leakage. The approach of thispaper is as follows. First, abstracting from the real world REDD+ design and implementationproblems that were briefly sketched above.

2 An analytical model of leakage

In this section, a model is developed that represents a transmission channel for carbon leakagedue to forest conservation policies. It builds on and extends existing models of industrialcarbon leakage (Di Maria and Smulders 2004; Gerlagh and Kuik 2007). Let us consider twocountries, denoted by subscript i=A,B. The two countries both have an unmanaged reservoir ofnatural forest and they share an identical economic structure; they are only different in size andenvironmental protection policy. Let θ denote the share of country A in global production(GDP). We also assume that, contrary to country B, country A implements measures to limitdeforestation. To keep the model tractable, only the firms operating in the land-intensive sector(agriculture and forestry) are described. This sector is assumed competitive so that firms areprice takers. The model describes the output of this sector—an aggregate of crops, livestockand timber—the use of (forest) land as a factor of production, international trade patterns, andtakes prices of labor and other production factors as constant. In the model, any positivechange in the use of land as a factor of production is associated with a specific volume of CO2

emissions per unit of (converted) land, Ci.

Mitig Adapt Strateg Glob Change

The model assumes perfect substitution in the international markets for the aggregateland-intensive good (for which there is a single world market price). World output isequal to world demand, and demand depends on the market price through the elasticityof demand ε:

θYA þ 1 − θð ÞYB ¼ −εq; ð1Þ

where all variables are in log-differences, so that Yi is the relative change in output incountry i, and q is the relative change in the land-intensive good’s price. The modelassumes competitive producers, which implies that output prices change proportionallyto input prices, multiplied by their shares in the input bundle. All other input prices areassumed constant, so that output prices only depend on land prices.

q ¼ αpA ð2Þ

q ¼ αpB; ð3Þ

where α is the share of land in value added, and pi is the relative price change ofland in country i. Competitive production implies that land demand Li is propor-tional to output and proportional to the price difference between output and inputprices; i.e.

LA ¼ YA þ μ q − pAð Þ ð4Þ

LB ¼ YB þ μ q − pBð Þ; ð5Þ

where μ is the elasticity of substitution between land and other inputs (capital, labor). Finally,the model is closed by the land supply functions,

LA ¼ R* ð6Þ

LB ¼ ψpB ð7Þ

where ψ is the elasticity of supply and R* is an exogenous policy variable thatcaptures the extent of restrictions on the supply of new land from the reservoir ofunmanaged natural forest land in country A. R* can be thought of as the effect ofREDD+ policies on deforestation. Seven equations determine the seven variables, YA,YB, q, pA, pB, LA, LB as a function of R*. Since all equations are linear in all variables,all variables are hence also proportional to R*. The carbon leakage rate can be definedas the increase in absolute emissions in country B divided by the correspondingdecrease in country A:

LR ¼ −1 − θθ

LBLA

CB

CA; ð8Þ

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where CBCA

indicates the ratio of carbon emissions per hectare from forest clearing incountry B to those in country A. While regional and subregional differences in perhectare emissions most likely exist, in this paper it is assumed that CB

CA¼ 1 for all

regions due to lack of appropriate data.Through substitution and rearrangement, the leakage rate can be solved as

LR ¼ 1 − θð Þψ1 − θð Þψþ αεþ 1 − αð Þμ: ð9Þ

The leakage rate is negatively related to the size of country A, ∂LR/∂θ<0. When country Acomprises of the whole world, i.e. θ=1, then by definition the leakage rate becomes equal tozero. In a continuous model, the leakage rate decreases with the size of the country. Theleakage rate is negatively related to the elasticity of demand, i.e. ∂LR/∂ε<0. When country Areduces land conversion, the higher the elasticity of demand, the higher is the decrease indemand of land-intensive goods. The leakage rate is also negatively related to the elasticity ofsubstitution between land and other inputs (capital, labor), i.e. ∂LR/∂μ<0. The easier it is tosubstitute land for other inputs, the less effect the policy variable R* has on output prices andchanges in the production plans of country B.

The leakage rate is positively related to the supply elasticity of land, i.e. ∂LR/∂ψ>0. Whenland supply is fully elastic, i.e. ψ=∞, land prices in country B are constant, pB=0 (7),global output prices q are also constant, q=0 (3), there is no change in overalldemand, θYA+(1–θ)YB (1), and no input substitution in country B (5). Thus, anydecrease in productive land in country A is exactly offset by a proportional decreasein its share of output, an increase in output in country B, and a proportional increasein land endowment in country B. It is also interesting to note that the model’s leakage rate doesnot depend on the absolute size of the policy variable R*, nor on the output price of the land-intensive good, q.

3 Simulating leakage in a CGE model

3.1 The model

The study uses the dynamic GTAP (Global Trade Analysis Project) model “GDyn” tosimulate leakage in the REDD+ context. GDyn is a dynamic, multi-country, multi-sectorCGE model of the world economy (Ianchovichina and McDougall 2001). The model takesinto account the interactions of decisions of consumers and producers in all markets. Thesedecisions are uniquely determined by relative prices. Consumers have preferences over privateconsumption goods, a composite government good, and savings. Total demand for goods is thesum of final and intermediate demands. Producers maximize profits given a constant returns-to-scale production technology for all firms. The competitive equilibrium for each period ischaracterized by market clearance on all markets and by the zero-profit condition for all firms.The model does not optimize across time periods. The link between time periods is throughsavings and investments. The model allows for the international transfer of capital so that thereturns on investments across regions tend to become more equal over time.

The substitution between domestically-produced and imported goods is imperfect, follow-ing the approach suggested by Armington (1969) who treats goods of different origin asdistinct and non-homogeneous. The model also takes account of international trade margins,import and export tariffs and domestic (product) taxes.

Mitig Adapt Strateg Glob Change

As common in most CGE models, production and consumption flows are measured inmonetary values. While GDyn decomposes changes in values into price and volume changes,Baltzer and Kloverpris (2008) showed that these volume changes are not equivalent to physicalchanges (in hectares). The land volume changes in GDyn refer to ‘effective land’, where a largerweight is given to land of higher productivity. Baltzer and Kloverpris (2008) proposed a way totrack changes in physical land in themodel. Their proposal is implemented in the current model.

3.2 Data and parameters

The data underlying GDyn describe the world economy in the base year. For thesimulations in this paper, the GTAP database V6 was used aggregated to twelve regionsand six sectors. The regions include the tropical forest countries that hold the majority oftropical forests and that experience the highest rates of deforestation: Indonesia,Cambodia/Lao People's Democratic Republic (PDR), Malaysia, Other Southeast Asia,Brazil, Bolivia/Ecuador, Peru, Colombia, and tropical Africa, China and industrializedcountries as major timber consumers, and the rest of the world. Appendix A contains adetailed description of the country aggregation. Sectors include: crops, animal products,forestry, produced food and fiber, wood and paper products, and other sectors (includingmining, manufacturing and service sectors). Additional data on land use and forestry weretaken from FAO (2010), Lee et al. (2005), and the University of Wisconsin’s Center forSustainability and the Global Environment’s (SAGE) global database of agricultural landuse and land cover (Ramankutty et al. 2008; Lee et al. 2009).

In the model, four types of land use are distinguished: cropland, grazing land, timberland(production forest), and unmanaged forestland (‘old-growth forest’). Selected statistics on landuse, forest area and rates of deforestation are presented in Table 1 below.

Each type of land can be transformed into another type, but this transformation is not costless.At the start of the simulation, unmanaged forestland is not used for the production of goods. Ifland rents of the cultivated types of land rise, it may become attractive to transform some area ofunmanaged forestland into productive land, i.e. cropland, grazing or timberland. The sensitivityof aggregate land supply to land rents is governed by the elasticity of land supply. It is assumedthat any hectare increase in the supply of cultivated land in tropical rainforest countries is at theexpense of a hectare of unmanaged forestland, resulting in deforestation. This is obviously asomewhat simplistic representation of deforestation. It assumes that all additional forestryactivity is carried out in an unsustainable way. This representation is used for analytical clarity.

The GTAP version 6 database includes a set of region and sector-specific default param-eters, including most of the parameters used in Eq. 9. Because the basic specification of theGDyn model assumes a fixed aggregate land endowment, the elasticity of land supply, ψ, iszero. For the current study, the elasticities of land supply were calibrated on the actual rates ofdeforestation in the regions of interest by model simulations. That is, an iterative procedurewas used to manually adjust the supply elasticities of regions until the model’s forecast ofdeforestation over the period 2000-10 matches the actual rates as presented in Table 1. Thisprocedure only gives sensible results (i.e. a positive elasticity of supply) for regions with apositive rate of deforestation. Other SE Asia has a negative rate of deforestation (i.e. netreforestation). The supply elasticity of land in this region has been set at zero. Supplyelasticities of land in China and industrialized countries are also set at zero.

Table 2 summarizes country-specific parameters that appear in the leakage rate formula.They are derived from the GTAP V6 database and from the calibration described above.

Table 2 shows the relative sizes of the countries, θ, measured in production of land-intensive goods. Other things equal, we would expect smaller rates of leakage for larger

Mitig Adapt Strateg Glob Change

regions such as Brazil and Africa. In contrast to the simple model, there is regional variation inthe other parameters, such as the share of land in value added, α, which is higher for the SouthAsian countries than for other rainforest countries. There are also variations in the elasticity ofsubstitution, μ, and the price elasticity of demand for land-intensive goods, ε. Finally there isvariation in the supply elasticity of land, ψ, which is high in regions such as Cambodia/LaoPDR and Bolivia/Ecuador and low in Colombia and Africa.

Inserting the relative sizes of the regions and the global average of the other parameters forthe rainforest regions in Eq. 9 (last row of Table 2), the (unweighted) average of the leakagerate at 21.3 %, with the highest rates for Bolivia/Ecuador (23.1 %) and Malaysia (23.0 %) andthe lowest rates for Brazil (18.0 %) and Africa (18.1 %).

Table 1 Data on land use and deforestation in tropical rainforest regions

Country/region Crop areain 2001

Pasturearea in2001

Timberlandarea in 2001

Unmanagedforest area in2000

Deforestation2000–2010

Deforestation2000–2010

‘000 ha ‘000 ha ‘000 ha ‘000 ha ‘000 ha % per year

Indonesia 29368 2463 26702 72707 4977 0.51

Cambodia/Lao PDR 17144 3260 37757 25586 5345 0.88

Malaysia 6200 293 7018 14573 1135 0.54

Other Southeast Asia 43461 906 20894 16954 –a –a

Brazil 48495 181047 165606 380337 26421 0.50

Bolivia/Ecuador 4229 37623 24364 47568 4871 0.70

Peru 2111 17333 17258 51955 1221 0.18

Colombia 3314 35507 14206 47303 1010 0.17

Africa 107326 440813 229752 79112 7057 0.23

PDR People’s Democratic Republica The forest area in other Southeast' Asia increased by 2.6 million hectares in the aggregate

Sources: FAO 2010; SAGE database (Ramankutty et al. 2008); and own calculations

Table 2 Parameters in leakage rate formula

Country/region Share in worldproduction

Share of land invalue added

Elasticity of Inputsubstitution

Elasticity ofdemand

Elasticity ofland supply

θ α μ ε ψ

Indonesia 0.16 0.52 0.31 0.18 0.20

Cambodia/Lao PDR 0.06 0.52 0.35 0.21 0.15

Malaysia 0.04 0.56 0.32 0.33 0.22

Brazil 0.29 0.18 0.39 0.27 0.12

Bolivia/Ecuador 0.03 0.29 0.37 0.23 0.15

Peru 0.05 0.29 0.35 0.24 0.09

Colombia 0.08 0.29 0.40 0.26 0.04

Africa 0.29 0.22 0.31 0.18 0.01

Rainforest countries(weighted average)

0.31 0.35 0.24 0.10

PDR People’s Democratic Republic

Mitig Adapt Strateg Glob Change

We next turn to the GDyn model for deeper insights into the regional variation of theleakage rate, taking into account regional differences in the parameters and natural and man-made barriers to international trade.

4 Simulation experiments

To test the leakage equation against real data, a number of simulation experiments are carried outwith the full GDyn model, adjusted and parameterized as described above. A baseline scenario ofdeforestation was developed for the 2010-30 period. Eight policy scenarios impose certain restric-tions on the rate of deforestation in each of the eight tropical forest regions, with positive defores-tation as the baseline. As a hypothetical example it is assumed that REDD+ incentives effectivelyreduce the annual rate of deforestation by 50% as of 2013 for each of the regions.Wewill later showthat themagnitude of the conservation effort has no effect on the rate of leakage. The leakage rate isthen computed as the ratio of the increase in the supply of land, i.e. the conversion of unmanagedforestland, in the other tropical forest regions, to the decrease in the conversion of unmanagedforestland in the policy countries. Additional simulations are carried out to determine the sensitivityof the leakage rates to parameters and scenario assumptions in order to test the validity of Eq. 9.

The baseline scenario uses macroeconomic and population projections from Foure et al.(2010). See also the GTAP website with information on dynamic baselines for GDyn: https://www.gtap.agecon.purdue.edu/models/Dynamic/Baseline/default.asp. For the land-intensivesectors (crops, animal production, and forestry) region-specific price projections from Golubet al. (2009) were superimposed upon the macroeconomic scenario. Technically, this allowsfor GDyn to determine the rates of technical progress (total factor productivity) that generatethese price paths (given the underlying changes in demand for the corresponding commoditiesand changes in the supplies of land, labor and capital inputs as prescribed by the macroeco-nomic scenario). The resulting changes in total factor productivity correspond to the projec-tions by Ludena et al. (2006) for crop and animal production and of the Global Timber Model(Sohngen et al. 2009) for forestry.

The world price paths of agricultural and forestry outputs in the baseline scenario, as takenfromGolub et al. (2009) are rather flat.World market prices of crops increase by 0.4 % per year,with relatively higher price increases in Asia and Africa and relatively lower price increases inLatin America. World market prices of animal products remain stable, although decreasing inLatin America and increasing in Asia and Africa. World market prices of forestry productsincrease with 0.9 % per year, with the highest increases in Asia and Africa. The projection ofworld market prices does not exactly match that of Golub et al. (2009) due to regionalaggregation issues. The difference is very small, however. A sensitivity analysis also examinesa scenario with higher agricultural and forestry prices in the baseline.

The policy scenarios superimpose REDD+ type deforestation reduction policies in individ-ual regions on top of the baseline scenario. A number of assumptions and parameters arevaried in the sensitivity analysis.

5 Results

5.1 Baseline scenario

In the baseline scenario, deforestation in the tropical rainforest countries—defined as theencroachment of natural forestland by agriculture and production forestry—increases by

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124 million hectares over the 2010-30 period. This is in the middle of the range suggested byLambin and Meyfroidt (2011) and comparable to estimates of Havlík et al. (2011). More thanhalf of this deforestation is projected to occur in Brazil, about 20 % in Southeast Asia andnearly 10 % in Africa.

5.2 Policy simulations

In the policy simulations the rate of deforestation is reduced by 50 % as of 2013 in eachtropical forest country, one at a time. The resulting decrease in productive land (relative to thebaseline) increases the price of land, as well as the production costs and output prices of land-intensive goods (crops animal and forestry products). Through international trade, the produc-tion of land-intensive goods is stimulated in third countries. This results in additional demandfor land and thus deforestation in third countries. Figure 1 presents the ratio of deforestationabroad to reduced deforestation in the eight policy countries. If the carbon emissions attributedto one hectare of deforestation across all countries equalize, this ratio would effectivelymeasure carbon leakage—i.e. the share of additional carbon emissions abroad that offsetsthe reduction of carbon emissions due to forest conservation in the policy country.

The country specific leakage rates are relatively constant over time and vary between 0.5 %in Brazil and 11.3 % in Malaysia. That is, for every hundred hectares of forest conserved inMalaysia (Brazil), deforestation in other rainforest countries increases by 11.3 (0.5) hectares.

In comparison to the global assessment with the simple leakage rate formula in Section 3.2,three issues are worth noticing. The distribution of leakage rates, from relatively low in Braziland Africa to relatively high in Malaysia, is fairly equal. But the magnitudes of the leakagerates computed with GDyn are smaller than those of the simple formula. The unweightedaverage of the GDyn leakage rates is 4.0 % against 21.3 % from the simple formula. This is aconsequence of the more realistic treatment of international trade in GDyn that takes accountof natural and man-made barriers to trade. In GDyn international differences in commodityprices lead to profitable arbitrage opportunities in international trade, but this does notnecessarily lead to the complete equalization of commodity prices, q, across all countrymarkets as was assumed by the simple analytical model. Finally, the spread of the leakagerates from 0.5 % to 11.3 % is much larger than that computed by the simple leakage formula.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Malaysia

Indonesia

Colombia

Cambodia/Lao PDR

Peru

Bolivia/Ecuador

Africa

Brazil

Fig. 1 Country specific leakage rates in the policy simulation

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This emphasizes the importance of taking account of local differences in economic andtechnological conditions between regions.

While the analytical model assumed all regions to be identical except for their sizes, closerinspection of Table 1 reveals that in reality regions also differ in the relative ratios of naturalforestland and cultivated land, and particularly in the ratios of deforested to cultivated land.The highest ratios are in Asian regions and the lowest are in Africa. All else being equal, thehigher the ratio of deforested to cultivated land, the higher the price increase ofcultivated land due to a reduction in deforestation (i.e. a supply restriction of land)and the higher the expected rate of leakage. Recalling the analytical model and thecountry-specific parameters of Table 2, the difference between the leakage rates is alsoa function of the share of land in value added, α, the substitution elasticity between land andother inputs, μ, the elasticity of demand, ε, and the share of the region’s output on the worldmarket, θ. The leakage rate is also a function of the average supply elasticity of land in foreignregions, ψ. In comparison to the simple analytical model, the division of a singleland-intensive commodity into three sub-commodities (crops, animal products and timber),as well as the relative openness of countries to international trade, constitute further refinementsof the CGE model. Table 3 shows the changes of the key variables in the GDyn policysimulations relative to the baseline for 2013, i.e. the start year of the REDD+ policyimplementation.

Table 3 shows how restrictions on forestland conversion affect the area of land that isavailable for crops, grazing, and commercial forestry. The increased scarcity of land increasesits market price and this in turn increases the prices of land-intensive commodities. In mostregions, except for Cambodia/Lao PDR, the higher land prices mostly affect the price oftimber. The higher domestic market prices lead to higher export prices (not shown in Table 3)and eventually to higher world market prices. For most countries, the increase in world market

Table 3 Changes of selected variables due to forest conservation in 2013 (in % unless indicated otherwise)

Indonesia Cambodia/Lao PDR

Malaysia Brazil Bolivia/Ecuador

Peru Colombia Africa

Change in land endowment in policy region in comparison to baseline

Land (1000 ha) −280.1 −235.5 −69.4 −2218.6 −214.6 −78.3 −33.7 −275.3Land (%) −0.430 −0.360 −0.461 −0.509 −0.296 −0.204 −0.062 −0.035

Change in domestic prices relative to baseline

Land 0.645 0.723 0.344 1.025 0.354 0.452 0.093 0.069

Crops 0.307 0.464 0.167 0.181 0.093 0.120 0.028 0.012

Animalproducts

0.048 0.053 0.000 0.056 0.059 0.058 0.017 0.003

Timber 0.429 0.202 0.217 0.783 0.352 0.351 0.104 0.046

Change in world market prices relative to baseline

Crops 0.011 0.003 0.003 0.024 0.002 0.001 0.001 0.001

Animalproducts

0.002 0.001 0.001 0.008 0.000 0.000 0.000 0.000

Timber 0.045 0.016 0.033 0.020 0.002 0.001 0.001 0.011

Change in land endowment abroad relative to baseline

Land (1000 ha) 21.4 6.5 7.5 12.7 3.4 1.7 1.6 2.5

Leakage rate (– Δ Land endowment abroad/Δ land endowment policy region) × 100

Leakage rate 7.64 2.78 10.73 0.57 1.59 2.21 4.63 0.91

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prices in greatest for timber. This makes sense since tropical forest countries have a relativelylarge share in world timber trade.

The higher world market prices induce a supply response in third countries, with a corre-sponding rise in forestland conversions. In absolute numbers, forest conservation in Indonesialeads to the highest forestland conversion in third countries; in relative terms, the leakage rate ishighest for Malaysian forest conservation. Although third-country forestland conversion (becauseof forest conservation) in Brazil is the second-highest, the leakage rate, in relative terms, is lowest.

5.3 Model sensitivity to changes in key parameters

To further analyze the mechanisms of carbon leakage, it is instructive to examine thesensitivity of the leakage rate to key parameters. The analytical model suggested that this ratewould be independent of the price of the land-intensive good, q, as well as of the rate of forestconservation, R*. Hence, the model suggests zero correlations between q and the leakage rateand R* and the leakage rate, respectively. In addition, the analytical model predicted the signsof the correlation between the leakage rate and certain parameters, such as the supply elasticityof land, ψ, and the substitution elasticity between factors of production, μ. The analyticalmodel suggests a positive association between ψ and the leakage rate and a negative associ-ation between μ and the leakage rate.

Finally, a sharp distinction between the analytical model and GDyn is that the latter assumesnatural and man-made frictions in the international trade of goods. Specifically, the‘Armington’ assumption imposes that goods of different country origin are treated as (moreor less) imperfect substitutes in demand. The degree of substitutability is measured by thesubstitution elasticity between domestically-produced and imported goods, η. GDyn distin-guishes between two sets of imports substitution parameters: the first between imported goodsof different origin and the second between imported and domestically-produced goods. Theanalytical model is silent on the association between this elasticity and the leakage rate, butcommon sense suggests this to be positive. That is, the more domestically-produced andimported goods (and imported goods of different origin) are considered similar, thehigher the leakage rate will be.

BASE 1.5 x R* 1.5 x q 2 x ψ 2 x µ 2 x ηIndonesia 7.65 7.68 7.63 13.49 4.50 8.76

Brazil 0.72 0.61 0.77 1.21 0.31 1.03

Africa 1.18 0.97 1.26 2.05 0.48 1.42

0

2

4

6

8

10

12

14

16

Lea

kag

e ra

te (

%)

Fig. 2 Sensitivity of the leakage rate to key parameters and scenario assumptions

Mitig Adapt Strateg Glob Change

The sensitivity analysis is carried out for three important forest regions: Indonesia, Braziland Africa (see Table 1). As was shown above, in the GDyn policy simulations Indonesia has arelatively high leakage rate, while Brazil and Africa have relatively low rates. Figure 2 showsthe results of the sensitivity analysis.

Figure 2 shows that the changes in leakage rates in the sensitivity analyses broadlyconfirm the expectations based on the analytical model. Increasing the rate of con-servation (1.5 ×R*) as well as the baseline prices for agricultural and forestry products (1.5 × q)hardly affect the leakage rates of Indonesia, Brazil, and Africa. Increasing the supply elasticitiesof land (2 × ψ), increasing the substitution elasticity between land and other inputs (2 × μ), andincreasing the Armington elasticities (2 × η) lead to expected changes in the leakage rates of allthree regions.

6 Conclusion

This paper presented a simple analytical model of carbon leakage due to forest conservationpolicies, such as promoted by REDD+. The simple model gives a clear economic descriptionof the mechanisms at play and the key economic and technological parameters of the problem.The simple model was tested against a large CGE model (GDyn) with real data. While theCGE model did reveal that real-life imperfections in international trade and regional variationin natural, economic and technological conditions are of prime importance to the magnitudesof the rates of carbon leakage, it also validated the main qualitative insights from the simplemodel in terms of the significance and direction of influence of key variables and parameters.

For policy makers that have to address the issue of leakage it is important to understand thekey economic mechanisms and parameters that determine it. Knowledge of these mechanismsand parameters allows policy makers to look into the black box of large (CGE) models andhave a more focused dialogue with modelers. It may also allow them to choose the appropriatemeasures to mitigate leakage.

It is acknowledged that the concept of leakage that was employed in this paper is rathersimple. For analytical clarity and because of limitations of the CGE model, leakage isequated to the induced conversion of natural forestland, assuming globally equalgreenhouse gas emissions per unit of land converted, and not accounting for potentialadditional leakage due to induced management changes on timberlands and agricultural lands.In terms of REDD+ the analysis focuses on the deforestation part and not on thedegradation part.

Carbon leakage and the related concept of indirect land use change (iLUC) are complexphenomena. The analysis also points to the importance of regional variation in natural,technological and economic conditions. While the CGE model that was used in this paperwas able to account for some of this variation at national and regional scales, it is likely to bethe case that subnational and local scales are at least as important. A great challenge is tocombine and integrate global economic and land-use models that work at fine spatial resolu-tions to better take account of regional variation.

This paper treated forest conservation policy (the variable R* in the analytical model) asexogenous. In reality, forest conservation policy is likely to be a function of the size anddistribution of the REDD+ funds and the effectiveness of policy implementation and enforce-ment. An additional research challenge would be to determine some optimal R* based oncarbon prices, under different assumptions on the effectiveness of policy implementation andenforcement, and international carbon leakage.

Mitig Adapt Strateg Glob Change

Acknowledgments I gratefully acknowledge the financial support of the European Union through the researchproject “Reducing Emissions through Deforestation and Land Degradation through Alternative Land uses inRainforests in the Tropics” (REDD-ALERT), and the intellectual support of researchers in the REDD-ALERTproject, including Robin Matthews, George Dyer, Joyeeta Gupta, Patrick Meyfroidt, Meine van Noordwijk andmany others. I also thank two anonymous reviewers for valuable comments. All remaining errors are mine.

Appendix

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Table 4 Aggregation of regions

Region Countries and regions included

Indonesia Indonesia

Cambodia/LaoPDR

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Malaysia Malaysia

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Brazil Brazil

Bolivia/Ecuador Bolivia, Ecuador

Peru Peru

Colombia Colombia

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China China, Hong Kong

Industrializedcountries

Australia, Canada, Europe, New Zealand, Japan, Korea, US

Rest of world All other countries

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